fumiyau/python_comments_analysys_500000_v2

f700405ef3096905f3ac4c83f8ec59ec74c727e1

1,339

py

Python

bugbounty/settings_utils.py

18F/tts-bug-bounty-dashboard

a9be81ff3eaf25db3192b64b5580764db9555dec

[ "CC0-1.0" ]

7

2017-07-15T13:48:00.000Z

2021-04-16T23:55:43.000Z

bugbounty/settings_utils.py

18F/tts-bug-bounty-dashboard

a9be81ff3eaf25db3192b64b5580764db9555dec

[ "CC0-1.0" ]

35

2017-06-19T16:15:20.000Z

2019-05-21T15:37:15.000Z

bugbounty/settings_utils.py

18F/tts-bug-bounty-dashboard

a9be81ff3eaf25db3192b64b5580764db9555dec

[ "CC0-1.0" ]

3

2017-07-14T02:37:40.000Z

2017-07-22T18:22:43.000Z

import os import json Environ = os._Environ def is_on_cloudfoundry(env: Environ=os.environ) -> bool: return 'VCAP_SERVICES' in env def load_cups_from_vcap_services(name: str, env: Environ=os.environ) -> None: ''' Detects if VCAP_SERVICES exists in the environment; if so, parses it and imports all the credentials from the given custom user-provided service (CUPS) as strings into the environment. For more details on CUPS, see: https://docs.cloudfoundry.org/devguide/services/user-provided.html ''' if not is_on_cloudfoundry(env): return vcap = json.loads(env['VCAP_SERVICES']) for entry in vcap.get('user-provided', []): if entry['name'] == name: for key, value in entry['credentials'].items(): env[key] = value def load_database_url_from_vcap_services(name: str, service: str, env: Environ=os.environ) -> str: """ Sets os.environ[DATABASE_URL] from a service entry in VCAP_SERVICES. """ if not is_on_cloudfoundry(env): return # FIXME: this'll break if there are multiple databases. Not an issue right # now, but could be in the future. Keep an eye on it. vcap = json.loads(env['VCAP_SERVICES']) env['DATABASE_URL'] = vcap[service][0]["credentials"]["uri"]

31.139535

78

0.65422

import os import json Environ = os._Environ def is_on_cloudfoundry(env: Environ=os.environ) -> bool: return 'VCAP_SERVICES' in env def load_cups_from_vcap_services(name: str, env: Environ=os.environ) -> None: if not is_on_cloudfoundry(env): return vcap = json.loads(env['VCAP_SERVICES']) for entry in vcap.get('user-provided', []): if entry['name'] == name: for key, value in entry['credentials'].items(): env[key] = value def load_database_url_from_vcap_services(name: str, service: str, env: Environ=os.environ) -> str: if not is_on_cloudfoundry(env): return # now, but could be in the future. Keep an eye on it. vcap = json.loads(env['VCAP_SERVICES']) env['DATABASE_URL'] = vcap[service][0]["credentials"]["uri"]

true

f70043a615333f2ac090006c4d192287d490deb1

2,723

py

Python

tvml/mongostats.py

pazlvbanke/tvmlopen

740afdb4982768cbcde7c7d5c8f3d09dbae81111

[ "MIT" ]

null

tvml/mongostats.py

pazlvbanke/tvmlopen

740afdb4982768cbcde7c7d5c8f3d09dbae81111

[ "MIT" ]

4

2020-03-20T10:55:56.000Z

2020-03-20T12:12:45.000Z

tvml/mongostats.py

pazlvbanke/tvmlopen

740afdb4982768cbcde7c7d5c8f3d09dbae81111

[ "MIT" ]

1

2020-03-20T10:21:55.000Z

from pymongo import MongoClient from pymongo import ReadPreference from datetime import datetime, timedelta class Mongo(MongoClient): def __init__(self, username, password, host, db='tags', collection='tweets_pipeline_v2'): uri = f"mongodb://{username}:{password}@{host}/{db}" super(Mongo, self).__init__(host=uri, authSource=db, authMechanism='SCRAM-SHA-256', port=27017, replicaset="rs0", read_preference=ReadPreference.SECONDARY, ) self.database = self.get_default_database() self.collection = collection def pipelined(self, count=True): query = {"status": "pipelined"} if count: return self.database[self.collection].count_documents(query) return self.database[self.collection].find(query) def feed(self, count=True): query = {"status": "graphicone_feed"} if count: return self.database[self.collection].count_documents(query) return self.database[self.collection].find(query) def search(self, count=True): query = {"status": "graphicone_search"} if count: return self.database[self.collection].count_documents(query) return self.database[self.collection].find(query) def left_for_analysts(self, count=True): query = {"in_app": {"$exists": False}, "status": "graphicone_feed"} if count: return self.database[self.collection].count_documents(query) return self.database[self.collection].find(query) def removed_validators(self, count=True): query = {"validator_username": {"$exists": True}, "status": "deleted"} if count: return self.database[self.collection].count_documents(query) return self.database[self.collection].find(query) def removed_analysts(self, count=True): query = {"status": "deleted_from_analytics"} if count: return self.database[self.collection].count_documents(query) return self.database[self.collection].find(query) # if __name__ == "__main__": # _username = "login" # _password = "passwd" # mongodb_host = "host address" # # mongo_client = Mongo(_username, _password, mongodb_host) # print(mongo_client.pipelined()) # print(mongo_client.search()) # print(mongo_client.feed()) # print(mongo_client.left_for_analysts()) # print(mongo_client.removed_validators()) # print(mongo_client.removed_analysts())

36.306667

93

0.607051

from pymongo import MongoClient from pymongo import ReadPreference from datetime import datetime, timedelta class Mongo(MongoClient): def __init__(self, username, password, host, db='tags', collection='tweets_pipeline_v2'): uri = f"mongodb://{username}:{password}@{host}/{db}" super(Mongo, self).__init__(host=uri, authSource=db, authMechanism='SCRAM-SHA-256', port=27017, replicaset="rs0", read_preference=ReadPreference.SECONDARY, ) self.database = self.get_default_database() self.collection = collection def pipelined(self, count=True): query = {"status": "pipelined"} if count: return self.database[self.collection].count_documents(query) return self.database[self.collection].find(query) def feed(self, count=True): query = {"status": "graphicone_feed"} if count: return self.database[self.collection].count_documents(query) return self.database[self.collection].find(query) def search(self, count=True): query = {"status": "graphicone_search"} if count: return self.database[self.collection].count_documents(query) return self.database[self.collection].find(query) def left_for_analysts(self, count=True): query = {"in_app": {"$exists": False}, "status": "graphicone_feed"} if count: return self.database[self.collection].count_documents(query) return self.database[self.collection].find(query) def removed_validators(self, count=True): query = {"validator_username": {"$exists": True}, "status": "deleted"} if count: return self.database[self.collection].count_documents(query) return self.database[self.collection].find(query) def removed_analysts(self, count=True): query = {"status": "deleted_from_analytics"} if count: return self.database[self.collection].count_documents(query) return self.database[self.collection].find(query)

true

f70044103ae2198905b5fb0cb3bb0f501d542759

984

py

Python

examples/_test_all.py

likianta/lk-lambdex

e9b5f96ea94c4b0fe25fd8adc7195e7366d26a6e

[ "MIT" ]

null

examples/_test_all.py

likianta/lk-lambdex

e9b5f96ea94c4b0fe25fd8adc7195e7366d26a6e

[ "MIT" ]

null

examples/_test_all.py

likianta/lk-lambdex

e9b5f96ea94c4b0fe25fd8adc7195e7366d26a6e

[ "MIT" ]

null

from lk_logger import lk from examples import t01_simple_examples as t01 from examples import t02_referencing as t02 from examples import t03_fibonacci as t03 from examples import t04_catch_exceptions as t04 from examples import t05_qt_button_click_event as t05 from examples import t06_lambdex_kwargs as t06 """ Rules.zh: 1. 所有模块的待测函数, 都必须以 test_ 开头 2. 所有模块的待测函数, 都必须是无参函数 """ if __name__ == '__main__': with lk.counting(6): for mod in [t01, t02, t03, t04, t05, t06]: lk.logdx(mod.__name__) with lk.counting(): for name in dir(mod): if name.startswith('test_'): func = getattr(mod, name) lk.logax('testing', func.__name__) try: func() except Exception as e: lk.logt('[I1117]', e) continue

30.75

58

0.539634

from lk_logger import lk from examples import t01_simple_examples as t01 from examples import t02_referencing as t02 from examples import t03_fibonacci as t03 from examples import t04_catch_exceptions as t04 from examples import t05_qt_button_click_event as t05 from examples import t06_lambdex_kwargs as t06 if __name__ == '__main__': with lk.counting(6): for mod in [t01, t02, t03, t04, t05, t06]: lk.logdx(mod.__name__) with lk.counting(): for name in dir(mod): if name.startswith('test_'): func = getattr(mod, name) lk.logax('testing', func.__name__) try: func() except Exception as e: lk.logt('[I1117]', e) continue

true

f700452101a3e4977fe2100fac23e05462b4fec1

2,279

py

Python

Machine Learning A-Z Template Folder/Part 2 - Regression/Section 5 - Multiple Linear Regression/data_preprocessing_template.py

ManjunathaPatkar/Machine-Learning

f1c6ec1a9f802f6e88ed67c0da6c1e9373790537

[ "MIT" ]

null

Machine Learning A-Z Template Folder/Part 2 - Regression/Section 5 - Multiple Linear Regression/data_preprocessing_template.py

ManjunathaPatkar/Machine-Learning

f1c6ec1a9f802f6e88ed67c0da6c1e9373790537

[ "MIT" ]

null

Machine Learning A-Z Template Folder/Part 2 - Regression/Section 5 - Multiple Linear Regression/data_preprocessing_template.py

ManjunathaPatkar/Machine-Learning

f1c6ec1a9f802f6e88ed67c0da6c1e9373790537

[ "MIT" ]

null

# Data Preprocessing Template # Importing the libraries import numpy as np import matplotlib.pyplot as plt import pandas as pd # Importing the dataset dataset = pd.read_csv('50_Startups.csv') X = dataset.iloc[:, :-1].values y = dataset.iloc[:, 4].values #encoding independent variable state #from sklearn.preprocessing import LabelEncoder, OneHotEncoder #labelencoder_X = LabelEncoder() #X[:, 3] = labelencoder_X.fit_transform(X[:, 3]) #onehotencoder = OneHotEncoder(categorical_features = [3]) #X = onehotencoder.fit_transform(X).toarray() from sklearn.preprocessing import LabelEncoder,OneHotEncoder from sklearn.compose import ColumnTransformer ct = ColumnTransformer([("State", OneHotEncoder(), [3])], remainder = 'passthrough') X= ct.fit_transform(X) #avoiding the dummy variable trap X=X[:,1:] # Splitting the dataset into the Training set and Test set from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0) # Feature Scaling """from sklearn.preprocessing import StandardScaler sc_X = StandardScaler() X_train = sc_X.fit_transform(X_train) X_test = sc_X.transform(X_test) sc_y = StandardScaler() y_train = sc_y.fit_transform(y_train)""" #fitting multiple linear regression to the training set from sklearn.linear_model import LinearRegression regressor=LinearRegression() regressor.fit(X_train,y_train) #Predicting the test set results y_pred=regressor.predict(X_test) #Building the optimal model using backward elimination import statsmodels.api as sm X=np.append(arr=np.ones((50,1)).astype(int),values=X,axis=1) #X_opt=X[:,[0,1,2,3,4,5]] X_opt = np.array(X[:, [0, 1, 2, 3, 4, 5]], dtype=float) regressor_OLS=sm.OLS(endog=y,exog=X_opt).fit() regressor_OLS.summary() X_opt = np.array(X[:, [0, 1, 3, 4, 5]], dtype=float) regressor_OLS=sm.OLS(endog=y,exog=X_opt).fit() regressor_OLS.summary() X_opt = np.array(X[:, [0, 3, 4, 5]], dtype=float) regressor_OLS=sm.OLS(endog=y,exog=X_opt).fit() regressor_OLS.summary() X_opt = np.array(X[:, [0, 3, 5]], dtype=float) regressor_OLS=sm.OLS(endog=y,exog=X_opt).fit() regressor_OLS.summary() X_opt = np.array(X[:, [0, 3]], dtype=float) regressor_OLS=sm.OLS(endog=y,exog=X_opt).fit() regressor_OLS.summary()

24.244681

92

0.746819

import numpy as np import matplotlib.pyplot as plt import pandas as pd dataset = pd.read_csv('50_Startups.csv') X = dataset.iloc[:, :-1].values y = dataset.iloc[:, 4].values from sklearn.preprocessing import LabelEncoder,OneHotEncoder from sklearn.compose import ColumnTransformer ct = ColumnTransformer([("State", OneHotEncoder(), [3])], remainder = 'passthrough') X= ct.fit_transform(X) X=X[:,1:] from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0) from sklearn.linear_model import LinearRegression regressor=LinearRegression() regressor.fit(X_train,y_train) y_pred=regressor.predict(X_test) import statsmodels.api as sm X=np.append(arr=np.ones((50,1)).astype(int),values=X,axis=1) X_opt = np.array(X[:, [0, 1, 2, 3, 4, 5]], dtype=float) regressor_OLS=sm.OLS(endog=y,exog=X_opt).fit() regressor_OLS.summary() X_opt = np.array(X[:, [0, 1, 3, 4, 5]], dtype=float) regressor_OLS=sm.OLS(endog=y,exog=X_opt).fit() regressor_OLS.summary() X_opt = np.array(X[:, [0, 3, 4, 5]], dtype=float) regressor_OLS=sm.OLS(endog=y,exog=X_opt).fit() regressor_OLS.summary() X_opt = np.array(X[:, [0, 3, 5]], dtype=float) regressor_OLS=sm.OLS(endog=y,exog=X_opt).fit() regressor_OLS.summary() X_opt = np.array(X[:, [0, 3]], dtype=float) regressor_OLS=sm.OLS(endog=y,exog=X_opt).fit() regressor_OLS.summary()

true

f70045418bc49a61f9a7a48205189a79ca91e491

85,068

py

Python

tools/ci_build/build.py

marijnfs/onnxruntime

6e1eb4b0efca9644c5f8979fbded9416fdd722dc

[ "MIT" ]

1

2019-01-15T18:10:37.000Z

tools/ci_build/build.py

marijnfs/onnxruntime

6e1eb4b0efca9644c5f8979fbded9416fdd722dc

[ "MIT" ]

null

tools/ci_build/build.py

marijnfs/onnxruntime

6e1eb4b0efca9644c5f8979fbded9416fdd722dc

[ "MIT" ]

null

#!/usr/bin/env python3 # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. import argparse import glob import multiprocessing import os import re import shutil import subprocess import sys import hashlib from logger import log class BaseError(Exception): """Base class for errors originating from build.py.""" pass class BuildError(BaseError): """Error from running build steps.""" def __init__(self, *messages): super().__init__("\n".join(messages)) class UsageError(BaseError): """Usage related error.""" def __init__(self, message): super().__init__(message) def _check_python_version(): # According to the BUILD.md, python 3.5+ is required: # Python 2 is definitely not supported and it should be safer to consider # it won't run with python 4: if sys.version_info[0] != 3: raise BuildError( "Bad python major version: expecting python 3, found version " "'{}'".format(sys.version)) if sys.version_info[1] < 5: raise BuildError( "Bad python minor version: expecting python 3.5+, found version " "'{}'".format(sys.version)) _check_python_version() def parse_arguments(): parser = argparse.ArgumentParser( description="ONNXRuntime CI build driver.", usage=""" # noqa Default behavior is --update --build --test for native architecture builds. Default behavior is --update --build for cross-compiled builds. The Update phase will update git submodules, and run cmake to generate makefiles. The Build phase will build all projects. The Test phase will run all unit tests, and optionally the ONNX tests. Use the individual flags to only run the specified stages. """) # Main arguments parser.add_argument( "--build_dir", required=True, help="Path to the build directory.") parser.add_argument( "--config", nargs="+", default=["Debug"], choices=["Debug", "MinSizeRel", "Release", "RelWithDebInfo"], help="Configuration(s) to build.") parser.add_argument( "--update", action='store_true', help="Update makefiles.") parser.add_argument("--build", action='store_true', help="Build.") parser.add_argument( "--clean", action='store_true', help="Run 'cmake --build --target clean' for the selected config/s.") parser.add_argument( "--parallel", action='store_true', help="""Use parallel build. The build setup doesn't get all dependencies right, so --parallel only works if you're just rebuilding ONNXRuntime code. If you've done an update that fetched external dependencies you have to build without --parallel the first time. Once that's done , run with "--build --parallel --test" to just build in parallel and run tests.""") parser.add_argument("--test", action='store_true', help="Run unit tests.") parser.add_argument( "--skip_tests", action='store_true', help="Skip all tests.") # Training options parser.add_argument( "--enable_nvtx_profile", action='store_true', help="Enable NVTX profile in ORT.") parser.add_argument( "--enable_training", action='store_true', help="Enable training in ORT.") parser.add_argument( "--enable_training_python_frontend_e2e_tests", action="store_true", help="Enable the pytorch frontend training tests.") parser.add_argument( "--enable_training_pipeline_e2e_tests", action="store_true", help="Enable the pipeline c++ e2e tests.") parser.add_argument( "--use_horovod", action='store_true', help="Enable Horovod.") parser.add_argument( "--mpi_home", help="Path to MPI installation dir") parser.add_argument( "--nccl_home", help="Path to NCCL installation dir") # enable ONNX tests parser.add_argument( "--enable_onnx_tests", action='store_true', help="""When running the Test phase, run onnx_test_running against available test data directories.""") parser.add_argument("--path_to_protoc_exe", help="Path to protoc exe.") parser.add_argument( "--fuzz_testing", action='store_true', help="Enable Fuzz testing of the onnxruntime.") parser.add_argument( "--enable_symbolic_shape_infer_tests", action='store_true', help="""When running the Test phase, run symbolic shape inference against available test data directories.""") # generate documentaiton parser.add_argument( "--gen_doc", action='store_true', help="Generate documentation on contrib ops") # CUDA related parser.add_argument("--use_cuda", action='store_true', help="Enable CUDA.") parser.add_argument( "--cuda_version", help="The version of CUDA toolkit to use. " "Auto-detect if not specified. e.g. 9.0") parser.add_argument( "--cuda_home", help="Path to CUDA home." "Read from CUDA_HOME environment variable if --use_cuda is true and " "--cuda_home is not specified.") parser.add_argument( "--cudnn_home", help="Path to CUDNN home. " "Read from CUDNN_HOME environment variable if --use_cuda is true and " "--cudnn_home is not specified.") # Python bindings parser.add_argument( "--enable_pybind", action='store_true', help="Enable Python Bindings.") parser.add_argument( "--build_wheel", action='store_true', help="Build Python Wheel.") parser.add_argument( "--wheel_name_suffix", help="Suffix to append to created wheel names. " "This value is currently only used for nightly builds.") parser.add_argument( "--numpy_version", help="Installs a specific version of numpy " "before building the python binding.") parser.add_argument( "--skip-keras-test", action='store_true', help="Skip tests with Keras if keras is installed") # C-Sharp bindings parser.add_argument( "--build_csharp", action='store_true', help="Build C#.Net DLL and NuGet package. This should be only used in CI pipelines. " "For building C# bindings and packaging them into nuget package use --build_nuget arg.") parser.add_argument( "--build_nuget", action='store_true', help="Build C#.Net DLL and NuGet package on the local machine. " "Currently only Windows and Linux platforms are supported.") # Java bindings parser.add_argument( "--build_java", action='store_true', help="Build Java bindings.") # Node.js binding parser.add_argument( "--build_nodejs", action='store_true', help="Build Node.js binding and NPM package.") # Build a shared lib parser.add_argument( "--build_shared_lib", action='store_true', help="Build a shared library for the ONNXRuntime.") # Build options parser.add_argument( "--cmake_extra_defines", nargs="+", help="Extra definitions to pass to CMake during build system " "generation. These are just CMake -D options without the leading -D.") parser.add_argument( "--target", help="Build a specific target, e.g. winml_dll") parser.add_argument( "--x86", action='store_true', help="Create x86 makefiles. Requires --update and no existing cache " "CMake setup. Delete CMakeCache.txt if needed") parser.add_argument( "--arm", action='store_true', help="Create ARM makefiles. Requires --update and no existing cache " "CMake setup. Delete CMakeCache.txt if needed") parser.add_argument( "--arm64", action='store_true', help="Create ARM64 makefiles. Requires --update and no existing cache " "CMake setup. Delete CMakeCache.txt if needed") parser.add_argument( "--msvc_toolset", help="MSVC toolset to use. e.g. 14.11") parser.add_argument("--android", action='store_true', help='Build for Android') parser.add_argument( "--android_abi", default="arm64-v8a", choices=["armeabi-v7a", "arm64-v8a", "x86", "x86_64"], help="Specify the target Android Application Binary Interface (ABI)") parser.add_argument("--android_api", type=int, default=27, help='Android API Level, e.g. 21') parser.add_argument("--android_sdk_path", type=str, help='Path to the Android SDK') parser.add_argument("--android_ndk_path", default="", help="Path to the Android NDK") parser.add_argument("--android_cpp_shared", action="store_true", help="Build with shared libc++ instead of the default static libc++.") parser.add_argument("--test_binary_size", action="store_true", help="If enabled, build will fail when the built binary size is larger than the threshold. " "This only applies to Android Minimal build for now.") parser.add_argument("--ios", action='store_true', help="build for ios") parser.add_argument( "--ios_sysroot", default="", help="Specify the location name of the macOS platform SDK to be used") parser.add_argument( "--ios_toolchain_dir", default="", help="Path to ios toolchain binaries") parser.add_argument( "--ios_toolchain_file", default="", help="Path to ios toolchain file, " "or cmake/onnxruntime_ios.toolchain.cmake will be used") parser.add_argument( "--xcode_code_signing_team_id", default="", help="The development team ID used for code signing in Xcode") parser.add_argument( "--use_xcode", action='store_true', help="Use Xcode as cmake generator, this is only supported on MacOS.") parser.add_argument( "--osx_arch", default="arm64", choices=["arm64", "x86_64"], help="Specify the Target specific architectures for macOS and iOS, This is only supported on MacOS") parser.add_argument( "--apple_deploy_target", type=str, help="Specify the minimum version of the target platform " "(e.g. macOS or iOS)" "This is only supported on MacOS") # Arguments needed by CI parser.add_argument( "--cmake_path", default="cmake", help="Path to the CMake program.") parser.add_argument( "--ctest_path", default="ctest", help="Path to the CTest program.") parser.add_argument( "--skip_submodule_sync", action='store_true', help="Don't do a " "'git submodule update'. Makes the Update phase faster.") parser.add_argument( "--use_vstest", action='store_true', help="Use use_vstest for running unitests.") parser.add_argument( "--use_jemalloc", action='store_true', help="Use jemalloc.") parser.add_argument( "--use_mimalloc", default=['none'], choices=['none', 'stl', 'arena', 'all'], help="Use mimalloc.") parser.add_argument( "--use_openblas", action='store_true', help="Build with OpenBLAS.") parser.add_argument( "--use_dnnl", action='store_true', help="Build with DNNL.") parser.add_argument( "--use_mklml", action='store_true', help="Build with MKLML.") parser.add_argument( "--use_featurizers", action='store_true', help="Build with ML Featurizer support.") parser.add_argument( "--use_ngraph", action='store_true', help="Build with nGraph.") parser.add_argument( "--use_openvino", nargs="?", const="CPU_FP32", choices=["CPU_FP32", "GPU_FP32", "GPU_FP16", "VAD-M_FP16", "MYRIAD_FP16", "VAD-F_FP32"], help="Build with OpenVINO for specific hardware.") parser.add_argument( "--use_nnapi", action='store_true', help="Build with NNAPI support.") parser.add_argument( "--use_rknpu", action='store_true', help="Build with RKNPU.") parser.add_argument( "--use_preinstalled_eigen", action='store_true', help="Use pre-installed Eigen.") parser.add_argument("--eigen_path", help="Path to pre-installed Eigen.") parser.add_argument( "--use_openmp", action='store_true', help="Build with OpenMP") parser.add_argument( "--enable_msinternal", action="store_true", help="Enable for Microsoft internal builds only.") parser.add_argument("--llvm_path", help="Path to llvm dir") parser.add_argument( "--use_vitisai", action='store_true', help="Build with Vitis-AI") parser.add_argument( "--use_nuphar", action='store_true', help="Build with nuphar") parser.add_argument( "--use_tensorrt", action='store_true', help="Build with TensorRT") parser.add_argument( "--tensorrt_home", help="Path to TensorRT installation dir") parser.add_argument( "--use_migraphx", action='store_true', help="Build with MIGraphX") parser.add_argument( "--migraphx_home", help="Path to MIGraphX installation dir") parser.add_argument( "--use_full_protobuf", action='store_true', help="Use the full protobuf library") parser.add_argument( "--skip_onnx_tests", action='store_true', help="Explicitly disable " "all onnx related tests. Note: Use --skip_tests to skip all tests.") parser.add_argument( "--skip_winml_tests", action='store_true', help="Explicitly disable all WinML related tests") parser.add_argument( "--skip_nodejs_tests", action='store_true', help="Explicitly disable all Node.js binding tests") parser.add_argument( "--enable_msvc_static_runtime", action='store_true', help="Enable static linking of MSVC runtimes.") parser.add_argument( "--enable_language_interop_ops", action='store_true', help="Enable operator implemented in language other than cpp") parser.add_argument( "--cmake_generator", choices=['Visual Studio 15 2017', 'Visual Studio 16 2019', 'Ninja'], default='Visual Studio 15 2017' if is_windows() else None, help="Specify the generator that CMake invokes. " "This is only supported on Windows") parser.add_argument( "--enable_multi_device_test", action='store_true', help="Test with multi-device. Mostly used for multi-device GPU") parser.add_argument( "--use_dml", action='store_true', help="Build with DirectML.") parser.add_argument( "--use_winml", action='store_true', help="Build with WinML.") parser.add_argument( "--winml_root_namespace_override", type=str, help="Specify the namespace that WinML builds into.") parser.add_argument( "--use_telemetry", action='store_true', help="Only official builds can set this flag to enable telemetry.") parser.add_argument( "--enable_wcos", action='store_true', help="Build for Windows Core OS.") parser.add_argument( "--enable_windows_store", action='store_true', help="Build for Windows Store") parser.add_argument( "--enable_lto", action='store_true', help="Enable Link Time Optimization") parser.add_argument( "--use_acl", nargs="?", const="ACL_1905", choices=["ACL_1902", "ACL_1905", "ACL_1908", "ACL_2002"], help="Build with ACL for ARM architectures.") parser.add_argument( "--use_armnn", action='store_true', help="Enable ArmNN Execution Provider.") parser.add_argument( "--armnn_relu", action='store_true', help="Use the Relu operator implementation from the ArmNN EP.") parser.add_argument( "--armnn_bn", action='store_true', help="Use the Batch Normalization operator implementation from the ArmNN EP.") parser.add_argument( "--build_micro_benchmarks", action='store_true', help="Build ONNXRuntime micro-benchmarks.") # options to reduce binary size parser.add_argument("--minimal_build", action='store_true', help="Create a build that only supports ORT format models. " "See /docs/ONNX_Runtime_Format_Model_Usage.md for more information. " "RTTI is automatically disabled in a minimal build.") parser.add_argument("--include_ops_by_model", type=str, help="include ops from model(s) under designated path.") parser.add_argument("--include_ops_by_config", type=str, help="include ops from config file. " "See /docs/Reduced_Operator_Kernel_build.md for more information.") parser.add_argument("--disable_contrib_ops", action='store_true', help="Disable contrib ops (reduces binary size)") parser.add_argument("--disable_ml_ops", action='store_true', help="Disable traditional ML ops (reduces binary size)") parser.add_argument("--disable_rtti", action='store_true', help="Disable RTTI (reduces binary size)") parser.add_argument("--disable_exceptions", action='store_true', help="Disable exceptions to reduce binary size. Requires --minimal_build.") parser.add_argument("--disable_ort_format_load", action='store_true', help='Disable support for loading ORT format models in a non-minimal build.') return parser.parse_args() def resolve_executable_path(command_or_path): """Returns the absolute path of an executable.""" executable_path = shutil.which(command_or_path) if executable_path is None: raise BuildError("Failed to resolve executable path for " "'{}'.".format(command_or_path)) return os.path.realpath(executable_path) def is_windows(): return sys.platform.startswith("win") def is_macOS(): return sys.platform.startswith("darwin") def is_linux(): return sys.platform.startswith("linux") def get_linux_distro(): try: with open('/etc/os-release', 'r') as f: dist_info = dict( line.strip().split('=', 1) for line in f.readlines()) return dist_info.get('NAME', '').strip('"'), dist_info.get( 'VERSION', '').strip('"') except (IOError, ValueError): return '', '' def is_ubuntu_1604(): dist, ver = get_linux_distro() return dist == 'Ubuntu' and ver.startswith('16.04') def get_config_build_dir(build_dir, config): # build directory per configuration return os.path.join(build_dir, config) def run_subprocess(args, cwd=None, capture=False, dll_path=None, shell=False, env={}): log.info("Running subprocess in '{0}'\n{1}".format( cwd or os.getcwd(), args)) my_env = os.environ.copy() if dll_path: if is_windows(): my_env["PATH"] = dll_path + os.pathsep + my_env["PATH"] else: if "LD_LIBRARY_PATH" in my_env: my_env["LD_LIBRARY_PATH"] += os.pathsep + dll_path else: my_env["LD_LIBRARY_PATH"] = dll_path stdout, stderr = (subprocess.PIPE, subprocess.STDOUT) if capture else ( None, None) my_env.update(env) completed_process = subprocess.run( args, cwd=cwd, check=True, stdout=stdout, stderr=stderr, env=my_env, shell=shell) log.debug("Subprocess completed. Return code=" + str(completed_process.returncode)) return completed_process def update_submodules(source_dir): run_subprocess(["git", "submodule", "sync", "--recursive"], cwd=source_dir) run_subprocess(["git", "submodule", "update", "--init", "--recursive"], cwd=source_dir) def is_docker(): path = '/proc/self/cgroup' return ( os.path.exists('/.dockerenv') or os.path.isfile(path) and any('docker' in line for line in open(path)) ) def is_sudo(): return 'SUDO_UID' in os.environ.keys() def install_apt_package(package): have = package in str(run_subprocess( ["apt", "list", "--installed", package], capture=True).stdout) if not have: if is_sudo(): run_subprocess(['apt-get', 'install', '-y', package]) else: raise BuildError(package + " APT package missing. Please re-run " "this script using sudo to install.") def install_ubuntu_deps(args): """Check if the necessary Ubuntu dependencies are installed. Not required on docker. Provide help output if missing.""" # check we need the packages first if not (args.enable_pybind or args.use_openblas): return # not needed on docker as packages are pre-installed if not is_docker(): try: if args.enable_pybind: install_apt_package("python3") if args.use_openblas: install_apt_package("libopenblas-dev") except Exception as e: raise BuildError("Error setting up required APT packages. " "{}".format(str(e))) def install_python_deps(numpy_version=""): dep_packages = ['setuptools', 'wheel', 'pytest'] dep_packages.append('numpy=={}'.format(numpy_version) if numpy_version else 'numpy>=1.16.6') dep_packages.append('sympy>=1.1') dep_packages.append('packaging') dep_packages.append('cerberus') run_subprocess([sys.executable, '-m', 'pip', 'install', '--trusted-host', 'files.pythonhosted.org'] + dep_packages) # We need to install Torch to test certain functionalities of the ORT Python package def install_torch(): # Command works for both Windows run_subprocess([sys.executable, '-m', 'pip', 'install', '--trusted-host', 'files.pythonhosted.org', 'torch===1.5.1+cu101', 'torchvision===0.6.1+cu101', '-f', 'https://download.pytorch.org/whl/torch_stable.html']) def check_md5(filename, expected_md5): if not os.path.exists(filename): return False hash_md5 = hashlib.md5() BLOCKSIZE = 1024*64 with open(filename, "rb") as f: buf = f.read(BLOCKSIZE) while len(buf) > 0: hash_md5.update(buf) buf = f.read(BLOCKSIZE) hex = hash_md5.hexdigest() if hex != expected_md5: log.info('md5 mismatch, expect %s, got %s' % (expected_md5, hex)) os.remove(filename) return False return True def setup_test_data(build_dir, configs): # create a shortcut for test models if there is a 'models' # folder in build_dir if is_windows(): src_model_dir = os.path.join(build_dir, 'models') if os.path.exists('C:\\local\\models') and not os.path.exists( src_model_dir): log.debug("creating shortcut %s -> %s" % ( 'C:\\local\\models', src_model_dir)) run_subprocess(['mklink', '/D', '/J', src_model_dir, 'C:\\local\\models'], shell=True) for config in configs: config_build_dir = get_config_build_dir(build_dir, config) os.makedirs(config_build_dir, exist_ok=True) dest_model_dir = os.path.join(config_build_dir, 'models') if os.path.exists('C:\\local\\models') and not os.path.exists( dest_model_dir): log.debug("creating shortcut %s -> %s" % ( 'C:\\local\\models', dest_model_dir)) run_subprocess(['mklink', '/D', '/J', dest_model_dir, 'C:\\local\\models'], shell=True) elif os.path.exists(src_model_dir) and not os.path.exists( dest_model_dir): log.debug("creating shortcut %s -> %s" % ( src_model_dir, dest_model_dir)) run_subprocess(['mklink', '/D', '/J', dest_model_dir, src_model_dir], shell=True) def use_dev_mode(args): if args.use_acl: return 'OFF' if args.use_armnn: return 'OFF' if args.ios and is_macOS(): return 'OFF' return 'ON' def generate_build_tree(cmake_path, source_dir, build_dir, cuda_home, cudnn_home, mpi_home, nccl_home, tensorrt_home, migraphx_home, path_to_protoc_exe, configs, cmake_extra_defines, args, cmake_extra_args): log.info("Generating CMake build tree") cmake_dir = os.path.join(source_dir, "cmake") # TODO: fix jemalloc build so it does not conflict with onnxruntime # shared lib builds. (e.g. onnxuntime_pybind) # for now, disable jemalloc if pybind is also enabled. cmake_args = [ cmake_path, cmake_dir, "-Donnxruntime_RUN_ONNX_TESTS=" + ( "ON" if args.enable_onnx_tests else "OFF"), "-Donnxruntime_BUILD_WINML_TESTS=" + ( "OFF" if args.skip_winml_tests else "ON"), "-Donnxruntime_GENERATE_TEST_REPORTS=ON", "-Donnxruntime_DEV_MODE=" + use_dev_mode(args), "-DPYTHON_EXECUTABLE=" + sys.executable, "-Donnxruntime_USE_CUDA=" + ("ON" if args.use_cuda else "OFF"), "-Donnxruntime_CUDNN_HOME=" + (cudnn_home if args.use_cuda else ""), "-Donnxruntime_USE_FEATURIZERS=" + ( "ON" if args.use_featurizers else "OFF"), "-Donnxruntime_CUDA_HOME=" + (cuda_home if args.use_cuda else ""), "-Donnxruntime_USE_JEMALLOC=" + ("ON" if args.use_jemalloc else "OFF"), "-Donnxruntime_USE_MIMALLOC_STL_ALLOCATOR=" + ( "ON" if args.use_mimalloc == "stl" or args.use_mimalloc == "all" else "OFF"), "-Donnxruntime_USE_MIMALLOC_ARENA_ALLOCATOR=" + ( "ON" if args.use_mimalloc == "arena" or args.use_mimalloc == "all" else "OFF"), "-Donnxruntime_ENABLE_PYTHON=" + ( "ON" if args.enable_pybind else "OFF"), "-Donnxruntime_BUILD_CSHARP=" + ("ON" if args.build_csharp else "OFF"), "-Donnxruntime_BUILD_JAVA=" + ("ON" if args.build_java else "OFF"), "-Donnxruntime_BUILD_NODEJS=" + ("ON" if args.build_nodejs else "OFF"), "-Donnxruntime_BUILD_SHARED_LIB=" + ( "ON" if args.build_shared_lib else "OFF"), "-Donnxruntime_USE_EIGEN_FOR_BLAS=" + ( "OFF" if args.use_openblas else "ON"), "-Donnxruntime_USE_OPENBLAS=" + ("ON" if args.use_openblas else "OFF"), "-Donnxruntime_USE_DNNL=" + ("ON" if args.use_dnnl else "OFF"), "-Donnxruntime_USE_MKLML=" + ("ON" if args.use_mklml else "OFF"), "-Donnxruntime_USE_NGRAPH=" + ("ON" if args.use_ngraph else "OFF"), "-Donnxruntime_USE_NNAPI_BUILTIN=" + ("ON" if args.use_nnapi else "OFF"), "-Donnxruntime_USE_RKNPU=" + ("ON" if args.use_rknpu else "OFF"), "-Donnxruntime_USE_OPENMP=" + ( "ON" if args.use_openmp and not ( args.use_nnapi or (args.use_mklml and (is_macOS() or is_windows())) or args.use_ngraph or args.android or (args.ios and is_macOS()) or args.use_rknpu) else "OFF"), "-Donnxruntime_USE_TVM=" + ("ON" if args.use_nuphar else "OFF"), "-Donnxruntime_USE_LLVM=" + ("ON" if args.use_nuphar else "OFF"), "-Donnxruntime_ENABLE_MICROSOFT_INTERNAL=" + ( "ON" if args.enable_msinternal else "OFF"), "-Donnxruntime_USE_VITISAI=" + ("ON" if args.use_vitisai else "OFF"), "-Donnxruntime_USE_NUPHAR=" + ("ON" if args.use_nuphar else "OFF"), "-Donnxruntime_USE_TENSORRT=" + ("ON" if args.use_tensorrt else "OFF"), "-Donnxruntime_TENSORRT_HOME=" + ( tensorrt_home if args.use_tensorrt else ""), # set vars for migraphx "-Donnxruntime_USE_MIGRAPHX=" + ("ON" if args.use_migraphx else "OFF"), "-Donnxruntime_MIGRAPHX_HOME=" + (migraphx_home if args.use_migraphx else ""), # By default - we currently support only cross compiling for # ARM/ARM64 (no native compilation supported through this # script). "-Donnxruntime_CROSS_COMPILING=" + ( "ON" if args.arm64 or args.arm else "OFF"), "-Donnxruntime_DISABLE_CONTRIB_OPS=" + ("ON" if args.disable_contrib_ops else "OFF"), "-Donnxruntime_DISABLE_ML_OPS=" + ("ON" if args.disable_ml_ops else "OFF"), "-Donnxruntime_DISABLE_RTTI=" + ("ON" if args.disable_rtti else "OFF"), "-Donnxruntime_DISABLE_EXCEPTIONS=" + ("ON" if args.disable_exceptions else "OFF"), "-Donnxruntime_DISABLE_ORT_FORMAT_LOAD=" + ("ON" if args.disable_ort_format_load else "OFF"), "-Donnxruntime_MINIMAL_BUILD=" + ("ON" if args.minimal_build else "OFF"), "-Donnxruntime_REDUCED_OPS_BUILD=" + ( "ON" if args.include_ops_by_config or args.include_ops_by_model else "OFF"), "-Donnxruntime_MSVC_STATIC_RUNTIME=" + ( "ON" if args.enable_msvc_static_runtime else "OFF"), # enable pyop if it is nightly build "-Donnxruntime_ENABLE_LANGUAGE_INTEROP_OPS=" + ( "ON" if args.enable_language_interop_ops else "OFF"), "-Donnxruntime_USE_DML=" + ("ON" if args.use_dml else "OFF"), "-Donnxruntime_USE_WINML=" + ("ON" if args.use_winml else "OFF"), "-Donnxruntime_USE_TELEMETRY=" + ( "ON" if args.use_telemetry else "OFF"), "-Donnxruntime_ENABLE_LTO=" + ("ON" if args.enable_lto else "OFF"), "-Donnxruntime_USE_ACL=" + ("ON" if args.use_acl else "OFF"), "-Donnxruntime_USE_ACL_1902=" + ( "ON" if args.use_acl == "ACL_1902" else "OFF"), "-Donnxruntime_USE_ACL_1905=" + ( "ON" if args.use_acl == "ACL_1905" else "OFF"), "-Donnxruntime_USE_ACL_1908=" + ( "ON" if args.use_acl == "ACL_1908" else "OFF"), "-Donnxruntime_USE_ACL_2002=" + ( "ON" if args.use_acl == "ACL_2002" else "OFF"), "-Donnxruntime_USE_ARMNN=" + ( "ON" if args.use_armnn else "OFF"), "-Donnxruntime_ARMNN_RELU_USE_CPU=" + ( "OFF" if args.armnn_relu else "ON"), "-Donnxruntime_ARMNN_BN_USE_CPU=" + ( "OFF" if args.armnn_bn else "ON"), # Training related flags "-Donnxruntime_ENABLE_NVTX_PROFILE=" + ( "ON" if args.enable_nvtx_profile else "OFF"), "-Donnxruntime_ENABLE_TRAINING=" + ( "ON" if args.enable_training else "OFF"), "-Donnxruntime_USE_HOROVOD=" + ( "ON" if args.use_horovod else "OFF"), "-Donnxruntime_BUILD_BENCHMARKS=" + ( "ON" if args.build_micro_benchmarks else "OFF") ] if mpi_home and os.path.exists(mpi_home): cmake_args += ["-Donnxruntime_MPI_HOME=" + mpi_home] if nccl_home and os.path.exists(nccl_home): cmake_args += ["-Donnxruntime_NCCL_HOME=" + nccl_home] if args.winml_root_namespace_override: cmake_args += ["-Donnxruntime_WINML_NAMESPACE_OVERRIDE=" + args.winml_root_namespace_override] if args.use_openvino: cmake_args += ["-Donnxruntime_USE_OPENVINO=ON", "-Donnxruntime_USE_OPENVINO_MYRIAD=" + ( "ON" if args.use_openvino == "MYRIAD_FP16" else "OFF"), "-Donnxruntime_USE_OPENVINO_GPU_FP32=" + ( "ON" if args.use_openvino == "GPU_FP32" else "OFF"), "-Donnxruntime_USE_OPENVINO_GPU_FP16=" + ( "ON" if args.use_openvino == "GPU_FP16" else "OFF"), "-Donnxruntime_USE_OPENVINO_CPU_FP32=" + ( "ON" if args.use_openvino == "CPU_FP32" else "OFF"), "-Donnxruntime_USE_OPENVINO_VAD_M=" + ( "ON" if args.use_openvino == "VAD-M_FP16" else "OFF"), "-Donnxruntime_USE_OPENVINO_VAD_F=" + ( "ON" if args.use_openvino == "VAD-F_FP32" else "OFF"), "-Donnxruntime_USE_OPENVINO_BINARY=" + ( "ON" if args.use_openvino else "OFF")] # temp turn on only for linux gpu build if not is_windows(): if args.use_cuda: cmake_args += [ "-Donnxruntime_USE_FULL_PROTOBUF=ON"] # nGraph, TensorRT and OpenVINO providers currently only supports # full_protobuf option. if (args.use_full_protobuf or args.use_ngraph or args.use_tensorrt or args.use_openvino or args.use_vitisai or args.gen_doc): cmake_args += [ "-Donnxruntime_USE_FULL_PROTOBUF=ON", "-DProtobuf_USE_STATIC_LIBS=ON" ] if args.use_nuphar and args.llvm_path is not None: cmake_args += ["-DLLVM_DIR=%s" % args.llvm_path] if args.use_cuda and not is_windows(): nvml_stub_path = cuda_home + "/lib64/stubs" cmake_args += ["-DCUDA_CUDA_LIBRARY=" + nvml_stub_path] if args.use_preinstalled_eigen: cmake_args += ["-Donnxruntime_USE_PREINSTALLED_EIGEN=ON", "-Deigen_SOURCE_PATH=" + args.eigen_path] if args.android: cmake_args += [ "-DCMAKE_TOOLCHAIN_FILE=" + args.android_ndk_path + "/build/cmake/android.toolchain.cmake", "-DANDROID_PLATFORM=android-" + str(args.android_api), "-DANDROID_ABI=" + str(args.android_abi) ] if args.android_cpp_shared: cmake_args += ["-DANDROID_STL=c++_shared"] if args.ios: if is_macOS(): needed_args = [ args.use_xcode, args.ios_sysroot, args.apple_deploy_target, ] arg_names = [ "--use_xcode " + "<need use xcode to cross build iOS on MacOS>", "--ios_sysroot " + "<the location or name of the macOS platform SDK>", "--apple_deploy_target " + "<the minimum version of the target platform>", ] if not all(needed_args): raise BuildError( "iOS build on MacOS canceled due to missing arguments: " + ', '.join( val for val, cond in zip(arg_names, needed_args) if not cond)) cmake_args += [ "-DCMAKE_SYSTEM_NAME=iOS", "-Donnxruntime_BUILD_SHARED_LIB=ON", "-DCMAKE_OSX_SYSROOT=" + args.ios_sysroot, "-DCMAKE_OSX_ARCHITECTURES=" + args.osx_arch, "-DCMAKE_OSX_DEPLOYMENT_TARGET=" + args.apple_deploy_target, # we do not need protoc binary for ios cross build "-Dprotobuf_BUILD_PROTOC_BINARIES=OFF", "-DCMAKE_TOOLCHAIN_FILE=" + ( args.ios_toolchain_file if args.ios_toolchain_file else "../cmake/onnxruntime_ios.toolchain.cmake") ] # Code sign the binaries, if the code signing development team id is provided if args.xcode_code_signing_team_id: cmake_args += ["-DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=" + args.xcode_code_signing_team_id] else: # TODO: the cross compiling on Linux is not officially supported by Apple # and is already broken with the latest codebase, so it should be removed. # We are cross compiling on Linux needed_args = [ args.ios_sysroot, args.arm64 or args.arm, args.ios_toolchain_dir ] arg_names = [ "--ios_sysroot <path to sysroot>", "--arm or --arm64", "--ios_toolchain_dir <path to toolchain>" ] if not all(needed_args): raise BuildError( "iOS build canceled due to missing arguments: " + ', '.join( val for val, cond in zip(arg_names, needed_args) if not cond)) compilers = sorted( glob.glob(args.ios_toolchain_dir + "/bin/*-clang*")) os.environ["PATH"] = os.path.join( args.ios_toolchain_dir, "bin") + os.pathsep + os.environ.get( "PATH", "") os.environ["LD_LIBRARY_PATH"] = os.path.join( args.ios_toolchain_dir, "/lib") + os.pathsep + os.environ.get( "LD_LIBRARY_PATH", "") if len(compilers) != 2: raise BuildError( "error identifying compilers in ios_toolchain_dir") cmake_args += [ "-DCMAKE_OSX_ARCHITECTURES=" + ("arm64" if args.arm64 else "arm"), "-DCMAKE_SYSTEM_NAME=iOSCross", "-Donnxruntime_BUILD_UNIT_TESTS=OFF", "-DCMAKE_OSX_SYSROOT=" + args.ios_sysroot, "-DCMAKE_C_COMPILER=" + compilers[0], "-DCMAKE_CXX_COMPILER=" + compilers[1] ] if path_to_protoc_exe: cmake_args += [ "-DONNX_CUSTOM_PROTOC_EXECUTABLE=%s" % path_to_protoc_exe] if args.fuzz_testing: if not (args.build_shared_lib and is_windows() and args.cmake_generator == 'Visual Studio 16 2019' and args.use_full_protobuf): raise BuildError( "Fuzz test has only be tested with build shared libs option using MSVC on windows") cmake_args += [ "-Donnxruntime_BUILD_UNIT_TESTS=ON", "-Donnxruntime_FUZZ_TEST=ON", "-Donnxruntime_USE_FULL_PROTOBUF=ON"] if args.gen_doc: cmake_args += ["-Donnxruntime_PYBIND_EXPORT_OPSCHEMA=ON"] else: cmake_args += ["-Donnxruntime_PYBIND_EXPORT_OPSCHEMA=OFF"] cmake_args += ["-D{}".format(define) for define in cmake_extra_defines] cmake_args += cmake_extra_args # ADO pipelines will store the pipeline build number # (e.g. 191101-2300.1.master) and source version in environment # variables. If present, use these values to define the # WinML/ORT DLL versions. build_number = os.getenv('Build_BuildNumber') source_version = os.getenv('Build_SourceVersion') if build_number and source_version: build_matches = re.fullmatch( r"(\d\d)(\d\d)(\d\d)(\d\d)\.(\d+)", build_number) if build_matches: YY = build_matches.group(2) MM = build_matches.group(3) DD = build_matches.group(4) # Get ORT major and minor number with open(os.path.join(source_dir, 'VERSION_NUMBER')) as f: first_line = f.readline() ort_version_matches = re.match(r"(\d+).(\d+)", first_line) if not ort_version_matches: raise BuildError("Couldn't read version from VERSION_FILE") ort_major = ort_version_matches.group(1) ort_minor = ort_version_matches.group(2) # Example (BuildNumber: 191101-2300.1.master, # SourceVersion: 0bce7ae6755c792eda558e5d27ded701707dc404) # MajorPart = 1 # MinorPart = 0 # BuildPart = 1911 # PrivatePart = 123 # String = 191101-2300.1.master.0bce7ae cmake_args += [ "-DVERSION_MAJOR_PART={}".format(ort_major), "-DVERSION_MINOR_PART={}".format(ort_minor), "-DVERSION_BUILD_PART={}".format(YY), "-DVERSION_PRIVATE_PART={}{}".format(MM, DD), "-DVERSION_STRING={}.{}.{}.{}".format( ort_major, ort_minor, build_number, source_version[0:7]) ] for config in configs: config_build_dir = get_config_build_dir(build_dir, config) os.makedirs(config_build_dir, exist_ok=True) if args.use_nuphar: os.environ["PATH"] = os.path.join( config_build_dir, "external", "tvm", config) + os.pathsep + os.path.dirname(sys.executable) + os.pathsep + os.environ["PATH"] run_subprocess( cmake_args + [ "-Donnxruntime_ENABLE_MEMLEAK_CHECKER=" + ("ON" if config.lower() == 'debug' and not args.use_nuphar and not args.use_ngraph and not args.use_openvino and not args.enable_msvc_static_runtime else "OFF"), "-DCMAKE_BUILD_TYPE={}".format(config)], cwd=config_build_dir) def clean_targets(cmake_path, build_dir, configs): for config in configs: log.info("Cleaning targets for %s configuration", config) build_dir2 = get_config_build_dir(build_dir, config) cmd_args = [cmake_path, "--build", build_dir2, "--config", config, "--target", "clean"] run_subprocess(cmd_args) def build_targets(args, cmake_path, build_dir, configs, parallel, target=None): for config in configs: log.info("Building targets for %s configuration", config) build_dir2 = get_config_build_dir(build_dir, config) cmd_args = [cmake_path, "--build", build_dir2, "--config", config] if target: cmd_args.extend(['--target', target]) build_tool_args = [] if parallel: num_cores = str(multiprocessing.cpu_count()) if is_windows() and args.cmake_generator != 'Ninja': build_tool_args += [ "/maxcpucount:" + num_cores, # if nodeReuse is true, msbuild processes will stay around for a bit after the build completes "/nodeReuse:False", ] elif (is_macOS() and args.use_xcode): # CMake will generate correct build tool args for Xcode cmd_args += ["--parallel", num_cores] elif args.cmake_generator != 'Ninja': build_tool_args += ["-j" + num_cores] if build_tool_args: cmd_args += ["--"] cmd_args += build_tool_args env = {} if args.android: env['ANDROID_SDK_ROOT'] = args.android_sdk_path run_subprocess(cmd_args, env=env) def add_dir_if_exists(directory, dir_list): if os.path.isdir(directory): dir_list.append(directory) def setup_cuda_vars(args): cuda_home = "" cudnn_home = "" if args.use_cuda: cuda_home = args.cuda_home if args.cuda_home else os.getenv( "CUDA_HOME") cudnn_home = args.cudnn_home if args.cudnn_home else os.getenv( "CUDNN_HOME") cuda_home_valid = (cuda_home is not None and os.path.exists(cuda_home)) cudnn_home_valid = (cudnn_home is not None and os.path.exists( cudnn_home)) if not cuda_home_valid or not cudnn_home_valid: raise BuildError( "cuda_home and cudnn_home paths must be specified and valid.", "cuda_home='{}' valid={}. cudnn_home='{}' valid={}" .format( cuda_home, cuda_home_valid, cudnn_home, cudnn_home_valid)) return cuda_home, cudnn_home def setup_tensorrt_vars(args): tensorrt_home = "" if args.use_tensorrt: tensorrt_home = (args.tensorrt_home if args.tensorrt_home else os.getenv("TENSORRT_HOME")) tensorrt_home_valid = (tensorrt_home is not None and os.path.exists(tensorrt_home)) if not tensorrt_home_valid: raise BuildError( "tensorrt_home paths must be specified and valid.", "tensorrt_home='{}' valid={}." .format(tensorrt_home, tensorrt_home_valid)) # Set maximum workspace size in byte for # TensorRT (1GB = 1073741824 bytes). os.environ["ORT_TENSORRT_MAX_WORKSPACE_SIZE"] = "1073741824" # Set maximum number of iterations to detect unsupported nodes # and partition the models for TensorRT. os.environ["ORT_TENSORRT_MAX_PARTITION_ITERATIONS"] = "1000" # Set minimum subgraph node size in graph partitioning # for TensorRT. os.environ["ORT_TENSORRT_MIN_SUBGRAPH_SIZE"] = "1" # Set FP16 flag os.environ["ORT_TENSORRT_FP16_ENABLE"] = "0" return tensorrt_home def setup_migraphx_vars(args): migraphx_home = None if (args.use_migraphx): print("migraphx_home = {}".format(args.migraphx_home)) migraphx_home = args.migraphx_home or os.getenv("MIGRAPHX_HOME") or None migraphx_home_not_valid = (migraphx_home and not os.path.exists(migraphx_home)) if (migraphx_home_not_valid): raise BuildError("migraphx_home paths must be specified and valid.", "migraphx_home='{}' valid={}." .format(migraphx_home, migraphx_home_not_valid)) return migraphx_home or '' def setup_dml_build(args, cmake_path, build_dir, configs): if args.use_dml: for config in configs: # Run the RESTORE_PACKAGES target to perform the initial # NuGet setup. cmd_args = [cmake_path, "--build", get_config_build_dir(build_dir, config), "--config", config, "--target", "RESTORE_PACKAGES"] run_subprocess(cmd_args) def adb_push(src, dest, **kwargs): return run_subprocess(['adb', 'push', src, dest], **kwargs) def adb_shell(*args, **kwargs): return run_subprocess(['adb', 'shell', *args], **kwargs) def run_android_tests(args, source_dir, config, cwd): if args.android_abi == 'x86_64': run_subprocess(os.path.join( source_dir, 'tools', 'ci_build', 'github', 'android', 'start_android_emulator.sh')) adb_push('testdata', '/data/local/tmp/', cwd=cwd) adb_push( os.path.join(source_dir, 'cmake', 'external', 'onnx', 'onnx', 'backend', 'test'), '/data/local/tmp/', cwd=cwd) adb_push('onnxruntime_test_all', '/data/local/tmp/', cwd=cwd) adb_push('onnx_test_runner', '/data/local/tmp/', cwd=cwd) adb_shell('cd /data/local/tmp && /data/local/tmp/onnxruntime_test_all') if args.use_nnapi: adb_shell('cd /data/local/tmp && /data/local/tmp/onnx_test_runner -e nnapi /data/local/tmp/test') else: adb_shell('cd /data/local/tmp && /data/local/tmp/onnx_test_runner /data/local/tmp/test') # run shared_lib_test if necessary if args.build_shared_lib: adb_push('libonnxruntime.so', '/data/local/tmp/', cwd=cwd) adb_push('onnxruntime_shared_lib_test', '/data/local/tmp/', cwd=cwd) adb_shell( 'cd /data/local/tmp && ' + 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/data/local/tmp && ' + '/data/local/tmp/onnxruntime_shared_lib_test') elif args.android_abi == 'arm64-v8a': # For Android arm64 abi we are only verify the size of the binary generated by minimal build config # Will fail the build if the shared_lib size is larger than the threshold if args.minimal_build and config == 'MinSizeRel' and args.build_shared_lib and args.test_binary_size: # set current size limit to 1100KB bin_size_threshold = 1100000 bin_actual_size = os.path.getsize(os.path.join(cwd, 'libonnxruntime.so')) log.info('Android arm64 minsizerel libonnxruntime.so size [' + str(bin_actual_size) + 'B]') # Write the binary size to a file for uploading later with open(os.path.join(cwd, 'binary_size_data.txt'), 'w') as file: file.writelines([ 'os,arch,build_config,size\n', 'android,arm64-v8a,minimal-baseline,' + str(bin_actual_size) + '\n' ]) if bin_actual_size > bin_size_threshold: raise BuildError('Android arm64 minsizerel libonnxruntime.so size [' + str(bin_actual_size) + 'B] is bigger than threshold [' + str(bin_size_threshold) + 'B]') def run_ios_tests(args, source_dir, config, cwd): cpr = run_subprocess(["xcodebuild", "test", "-project", "./onnxruntime.xcodeproj", "-configuration", config, "-scheme", "onnxruntime_test_all_xc", "-destination", "platform=iOS Simulator,OS=latest,name=iPhone SE (2nd generation)"], cwd=cwd) if cpr.returncode == 0: cpr = run_subprocess(["xcodebuild", "test", "-project", "./onnxruntime.xcodeproj", "-configuration", config, "-scheme", "onnxruntime_shared_lib_test_xc", "-destination", "platform=iOS Simulator,OS=latest,name=iPhone SE (2nd generation)"], cwd=cwd) cpr.check_returncode() def run_orttraining_test_orttrainer_frontend_separately(cwd): class TestNameCollecterPlugin: def __init__(self): self.collected = set() def pytest_collection_modifyitems(self, items): for item in items: print('item.name: ', item.name) test_name = item.name start = test_name.find('[') if start > 0: test_name = test_name[:start] self.collected.add(test_name) import pytest plugin = TestNameCollecterPlugin() test_script_filename = os.path.join(cwd, "orttraining_test_orttrainer_frontend.py") pytest.main(['--collect-only', test_script_filename], plugins=[plugin]) for test_name in plugin.collected: run_subprocess([ sys.executable, '-m', 'pytest', 'orttraining_test_orttrainer_frontend.py', '-v', '-k', test_name], cwd=cwd) def run_training_python_frontend_tests(cwd): run_subprocess([sys.executable, 'onnxruntime_test_ort_trainer.py'], cwd=cwd) run_subprocess([sys.executable, 'onnxruntime_test_training_unit_tests.py'], cwd=cwd) run_subprocess([ sys.executable, 'orttraining_test_transformers.py', 'BertModelTest.test_for_pretraining_full_precision_list_input'], cwd=cwd) run_subprocess([ sys.executable, 'orttraining_test_transformers.py', 'BertModelTest.test_for_pretraining_full_precision_dict_input'], cwd=cwd) run_subprocess([ sys.executable, 'orttraining_test_transformers.py', 'BertModelTest.test_for_pretraining_full_precision_list_and_dict_input'], cwd=cwd) # TODO: use run_orttraining_test_orttrainer_frontend_separately to work around a sporadic segfault. # shall revert to run_subprocess call once the segfault issue is resolved. run_orttraining_test_orttrainer_frontend_separately(cwd) # run_subprocess([sys.executable, '-m', 'pytest', '-sv', 'orttraining_test_orttrainer_frontend.py'], cwd=cwd) run_subprocess([sys.executable, '-m', 'pytest', '-sv', 'orttraining_test_orttrainer_bert_toy_onnx.py'], cwd=cwd) def run_training_python_frontend_e2e_tests(cwd): # frontend tests are to be added here: log.info("Running python frontend e2e tests.") import torch ngpus = torch.cuda.device_count() if ngpus > 1: bert_pretrain_script = 'orttraining_run_bert_pretrain.py' log.debug('RUN: mpirun -n {} ''-x' 'NCCL_DEBUG=INFO'' {} {} {}'.format( ngpus, sys.executable, bert_pretrain_script, 'ORTBertPretrainTest.test_pretrain_throughput')) run_subprocess([ 'mpirun', '-n', str(ngpus), '-x', 'NCCL_DEBUG=INFO', sys.executable, bert_pretrain_script, 'ORTBertPretrainTest.test_pretrain_throughput'], cwd=cwd) log.debug('RUN: mpirun -n {} ''-x' 'NCCL_DEBUG=INFO'' {} {} {}'.format( ngpus, sys.executable, bert_pretrain_script, 'ORTBertPretrainTest.test_pretrain_convergence')) run_subprocess([ 'mpirun', '-n', str(ngpus), '-x', 'NCCL_DEBUG=INFO', sys.executable, bert_pretrain_script, 'ORTBertPretrainTest.test_pretrain_convergence'], cwd=cwd) # a long run log.debug('RUN: mpirun -n {} ''-x' 'NCCL_DEBUG=INFO'' {} {}'.format( ngpus, sys.executable, bert_pretrain_script)) run_subprocess([ 'mpirun', '-n', str(ngpus), '-x', 'NCCL_DEBUG=INFO', sys.executable, bert_pretrain_script], cwd=cwd) log.debug('RUN: mpirun -n {} {} orttraining_run_glue.py'.format(ngpus, sys.executable)) run_subprocess([ 'mpirun', '-n', str(ngpus), '-x', 'NCCL_DEBUG=INFO', sys.executable, 'orttraining_run_glue.py'], cwd=cwd) # with orttraining_run_glue.py. # 1. we like to force to use single GPU (with CUDA_VISIBLE_DEVICES) # for fine-tune tests. # 2. need to run test separately (not to mix between fp16 # and full precision runs. this need to be investigated). run_subprocess( [sys.executable, 'orttraining_run_glue.py', 'ORTGlueTest.test_bert_with_mrpc', '-v'], cwd=cwd, env={'CUDA_VISIBLE_DEVICES': '0'}) run_subprocess( [sys.executable, 'orttraining_run_glue.py', 'ORTGlueTest.test_bert_fp16_with_mrpc', '-v'], cwd=cwd, env={'CUDA_VISIBLE_DEVICES': '0'}) run_subprocess( [sys.executable, 'orttraining_run_glue.py', 'ORTGlueTest.test_roberta_with_mrpc', '-v'], cwd=cwd, env={'CUDA_VISIBLE_DEVICES': '0'}) run_subprocess( [sys.executable, 'orttraining_run_glue.py', 'ORTGlueTest.test_roberta_fp16_with_mrpc', '-v'], cwd=cwd, env={'CUDA_VISIBLE_DEVICES': '0'}) run_subprocess( [sys.executable, 'orttraining_run_multiple_choice.py', 'ORTMultipleChoiceTest.test_bert_fp16_with_swag', '-v'], cwd=cwd, env={'CUDA_VISIBLE_DEVICES': '0'}) run_subprocess([sys.executable, 'onnxruntime_test_ort_trainer_with_mixed_precision.py'], cwd=cwd) run_subprocess([ sys.executable, 'orttraining_test_transformers.py', 'BertModelTest.test_for_pretraining_mixed_precision'], cwd=cwd) # this test is not stable. need to skip to unblock release # run_subprocess([ # sys.executable, 'orttraining_test_transformers.py', # 'BertModelTest.test_for_pretraining_mixed_precision_with_gradient_accumulation'], cwd=cwd) def run_training_pipeline_e2e_tests(cwd): # pipeline tests are to be added here: log.info("Running pipeline e2e tests.") import torch ngpus = torch.cuda.device_count() command = ['./onnxruntime_training_bert', '--ort_log_severity', '1', '--optimizer=Lamb', '--learning_rate=3e-3', '--max_seq_length=128', '--max_predictions_per_seq=20', '--warmup_ratio=0.2843', '--warmup_mode=Poly', '--model_name', '/bert_ort/bert_models/nv/bert-large/' + 'bert-large-uncased_L_24_H_1024_A_16_V_30528_S_512_Dp_0.1_optimized_layer_norm_opset12', '--train_data_dir', '/bert_data/128/books_wiki_en_corpus/train', '--test_data_dir', '/bert_data/128/books_wiki_en_corpus/test', '--display_loss_steps', '1', '--use_nccl', '--use_mixed_precision', '--allreduce_in_fp16', '--gradient_accumulation_steps', '48', '--num_train_steps', '96', '--train_batch_size', '50'] # TODO: currently the CI machine only has 4 GPUs for parallel tests. # Fill in more pipeline partition options when the machine has different GPUs counts. if ngpus != 4: return # Test 4-way pipeline parallel pp_command = ['mpirun', '-n', str(ngpus)] + command + ['--pipeline_parallel_size', '4', '--cut_group_info', '1149:407-1219/1341/1463/1585/1707/1829,' + '1881:407-1951/2073/2195/2317/2439/2561,' + '2613:407-2683/2805/2927/3049/3171/3293'] command_str = ', '.join(pp_command) log.debug('RUN: ' + command_str) run_subprocess(pp_command, cwd=cwd) # Test 2-way data parallel + 2-way pipeline parallel pp_dp_command = ['mpirun', '-n', str(ngpus)] pp_dp_command = pp_dp_command + command pp_dp_command = pp_dp_command + ['--data_parallel_size', '2', '--pipeline_parallel_size', '2', '--cut_group_info', '1881:407-1951/2073/2195/2317/2439/2561/2683/2805/2927/3049/3171/3293'] command_str = ', '.join(pp_dp_command) log.debug('RUN: ' + command_str) run_subprocess(pp_dp_command, cwd=cwd) def run_onnxruntime_tests(args, source_dir, ctest_path, build_dir, configs): for config in configs: log.info("Running tests for %s configuration", config) cwd = get_config_build_dir(build_dir, config) if args.enable_training and args.use_cuda and args.enable_training_python_frontend_e2e_tests: # run frontend tests for orttraining-linux-gpu-frontend_test-ci-pipeline. # this is not a PR merge test so skip other non-frontend tests. run_training_python_frontend_e2e_tests(cwd=cwd) run_training_python_frontend_tests(cwd=cwd) continue if args.enable_training and args.use_cuda and args.enable_training_pipeline_e2e_tests: # run distributed pipeline test on 4-GPU CI machine. run_training_pipeline_e2e_tests(cwd=cwd) continue if args.android: run_android_tests(args, source_dir, config, cwd) continue elif args.ios: run_ios_tests(args, source_dir, config, cwd) continue dll_path_list = [] if args.use_nuphar: dll_path_list.append(os.path.join( build_dir, config, "external", "tvm", config)) if args.use_tensorrt: dll_path_list.append(os.path.join(args.tensorrt_home, 'lib')) if args.use_mklml: dll_path_list.append(os.path.join(build_dir, config, "mklml", "src", "project_mklml", "lib")) if not is_windows(): # A workaround for making libonnxruntime_providers_shared.so loadable. dll_path_list.append(os.path.join(build_dir, config)) dll_path = None if len(dll_path_list) > 0: dll_path = os.pathsep.join(dll_path_list) if ctest_path is None: # Get the "Google Test Adapter" for vstest. if not os.path.exists(os.path.join(cwd, 'googletestadapter.0.17.1')): run_subprocess( ['nuget.exe', 'restore', os.path.join(source_dir, 'packages.config'), '-ConfigFile', os.path.join(source_dir, 'NuGet.config'), '-PackagesDirectory', cwd]) cwd2 = os.path.join(cwd, config) executables = ['onnxruntime_test_all.exe'] if args.build_shared_lib: executables.append('onnxruntime_shared_lib_test.exe') executables.append('onnxruntime_global_thread_pools_test.exe') run_subprocess( ['vstest.console.exe', '--parallel', '--TestAdapterPath:..\\googletestadapter.0.17.1\\build\\_common', # noqa '/Logger:trx', '/Enablecodecoverage', '/Platform:x64', "/Settings:%s" % os.path.join( source_dir, 'cmake\\codeconv.runsettings')] + executables, cwd=cwd2, dll_path=dll_path) else: ctest_cmd = [ctest_path, "--build-config", config, "--verbose", "--timeout", "3600"] run_subprocess(ctest_cmd, cwd=cwd, dll_path=dll_path) if args.enable_pybind: # Disable python tests for TensorRT because many tests are # not supported yet. if args.use_tensorrt: return # Disable python tests in a reduced build as we don't know which ops have been included and which # models can run if args.include_ops_by_model or args.include_ops_by_config or args.minimal_build: return if is_windows(): cwd = os.path.join(cwd, config) run_subprocess([sys.executable, 'onnxruntime_test_python.py'], cwd=cwd, dll_path=dll_path) if args.enable_symbolic_shape_infer_tests: run_subprocess([sys.executable, 'onnxruntime_test_python_symbolic_shape_infer.py'], cwd=cwd, dll_path=dll_path) # For CUDA enabled builds test IOBinding feature if args.use_cuda: # We need to have Torch installed to test the IOBinding feature # which currently uses Torch's allocator to allocate GPU memory for testing log.info("Testing IOBinding feature") run_subprocess([sys.executable, 'onnxruntime_test_python_iobinding.py'], cwd=cwd, dll_path=dll_path) if not args.disable_ml_ops: run_subprocess([sys.executable, 'onnxruntime_test_python_mlops.py'], cwd=cwd, dll_path=dll_path) if args.enable_training and args.use_cuda: # run basic frontend tests run_training_python_frontend_tests(cwd=cwd) try: import onnx # noqa onnx_test = True except ImportError as error: log.exception(error) log.warning("onnx is not installed. The ONNX tests will be skipped.") onnx_test = False if onnx_test: run_subprocess([sys.executable, 'onnxruntime_test_python_backend.py'], cwd=cwd, dll_path=dll_path) if not args.disable_ml_ops: run_subprocess([sys.executable, 'onnxruntime_test_python_backend_mlops.py'], cwd=cwd, dll_path=dll_path) run_subprocess([sys.executable, os.path.join(source_dir, 'onnxruntime', 'test', 'onnx', 'gen_test_models.py'), '--output_dir', 'test_models'], cwd=cwd) if not args.skip_onnx_tests: run_subprocess([os.path.join(cwd, 'onnx_test_runner'), 'test_models'], cwd=cwd) if config != 'Debug': run_subprocess([sys.executable, 'onnx_backend_test_series.py'], cwd=cwd, dll_path=dll_path) if not args.skip_keras_test: try: import onnxmltools # noqa import keras # noqa onnxml_test = True except ImportError: log.warning( "onnxmltools and keras are not installed. " "The keras tests will be skipped.") onnxml_test = False if onnxml_test: run_subprocess( [sys.executable, 'onnxruntime_test_python_keras.py'], cwd=cwd, dll_path=dll_path) def nuphar_run_python_tests(build_dir, configs): """nuphar temporary function for running python tests separately as it requires ONNX 1.5.0 """ for config in configs: if config == 'Debug': continue cwd = get_config_build_dir(build_dir, config) if is_windows(): cwd = os.path.join(cwd, config) dll_path = os.path.join(build_dir, config, "external", "tvm", config) # install onnx for shape inference in testing Nuphar scripts # this needs to happen after onnx_test_data preparation which # uses onnx 1.3.0 run_subprocess( [sys.executable, '-m', 'pip', 'install', '--user', 'onnx==1.5.0']) run_subprocess( [sys.executable, 'onnxruntime_test_python_nuphar.py'], cwd=cwd, dll_path=dll_path) def run_nodejs_tests(nodejs_binding_dir): args = ['npm', 'test', '--', '--timeout=2000'] if is_windows(): args = ['cmd', '/c'] + args run_subprocess(args, cwd=nodejs_binding_dir) def build_python_wheel( source_dir, build_dir, configs, use_cuda, use_ngraph, use_dnnl, use_tensorrt, use_openvino, use_nuphar, use_vitisai, use_acl, use_armnn, use_dml, wheel_name_suffix, enable_training, nightly_build=False, featurizers_build=False, use_ninja=False): for config in configs: cwd = get_config_build_dir(build_dir, config) if is_windows() and not use_ninja: cwd = os.path.join(cwd, config) args = [sys.executable, os.path.join(source_dir, 'setup.py'), 'bdist_wheel'] # We explicitly override the platform tag in the name of the generated build wheel # so that we can install the wheel on Mac OS X versions 10.12+. # Without this explicit override, we will something like this while building on MacOS 10.14 - # [WARNING] MACOSX_DEPLOYMENT_TARGET is set to a lower value (10.12) # than the version on which the Python interpreter was compiled (10.14) and will be ignored. # Since we need to support 10.12+, we explicitly override the platform tag. # See PR #3626 for more details if is_macOS(): args += ['-p', 'macosx_10_12_x86_64'] # Any combination of the following arguments can be applied if nightly_build: args.append('--nightly_build') if featurizers_build: args.append("--use_featurizers") if wheel_name_suffix: args.append('--wheel_name_suffix={}'.format(wheel_name_suffix)) if enable_training: args.append("--enable_training") # The following arguments are mutually exclusive if use_tensorrt: args.append('--use_tensorrt') elif use_cuda: args.append('--use_cuda') elif use_ngraph: args.append('--use_ngraph') elif use_openvino: args.append('--use_openvino') elif use_dnnl: args.append('--use_dnnl') elif use_nuphar: args.append('--use_nuphar') elif use_vitisai: args.append('--use_vitisai') elif use_acl: args.append('--use_acl') elif use_armnn: args.append('--use_armnn') elif use_dml: args.append('--use_dml') run_subprocess(args, cwd=cwd) def derive_linux_build_property(): if is_windows(): return "/p:IsLinuxBuild=\"false\"" else: return "/p:IsLinuxBuild=\"true\"" def build_nuget_package(source_dir, build_dir, configs, use_cuda, use_openvino, use_tensorrt, use_dnnl, use_mklml): if not (is_windows() or is_linux()): raise BuildError( 'Currently csharp builds and nuget package creation is only supportted ' 'on Windows and Linux platforms.') csharp_build_dir = os.path.join(source_dir, 'csharp') is_linux_build = derive_linux_build_property() # derive package name and execution provider based on the build args execution_provider = "/p:ExecutionProvider=\"None\"" package_name = "/p:OrtPackageId=\"Microsoft.ML.OnnxRuntime\"" if use_openvino: execution_provider = "/p:ExecutionProvider=\"openvino\"" package_name = "/p:OrtPackageId=\"Microsoft.ML.OnnxRuntime.OpenVino\"" elif use_tensorrt: execution_provider = "/p:ExecutionProvider=\"tensorrt\"" package_name = "/p:OrtPackageId=\"Microsoft.ML.OnnxRuntime.TensorRT\"" elif use_dnnl: execution_provider = "/p:ExecutionProvider=\"dnnl\"" package_name = "/p:OrtPackageId=\"Microsoft.ML.OnnxRuntime.DNNL\"" elif use_cuda: package_name = "/p:OrtPackageId=\"Microsoft.ML.OnnxRuntime.Gpu\"" elif use_mklml: package_name = "/p:OrtPackageId=\"Microsoft.ML.OnnxRuntime.MKLML\"" else: pass # set build directory based on build_dir arg native_dir = os.path.normpath(os.path.join(source_dir, build_dir)) ort_build_dir = "/p:OnnxRuntimeBuildDirectory=\"" + native_dir + "\"" # dotnet restore cmd_args = ["dotnet", "restore", "OnnxRuntime.CSharp.sln", "--configfile", "Nuget.CSharp.config"] run_subprocess(cmd_args, cwd=csharp_build_dir) # build csharp bindings and create nuget package for each config for config in configs: if is_linux(): native_build_dir = os.path.join(native_dir, config) cmd_args = ["make", "install", "DESTDIR=.//nuget-staging"] run_subprocess(cmd_args, cwd=native_build_dir) configuration = "/p:Configuration=\"" + config + "\"" cmd_args = ["dotnet", "msbuild", "OnnxRuntime.CSharp.sln", configuration, package_name, is_linux_build, ort_build_dir] run_subprocess(cmd_args, cwd=csharp_build_dir) cmd_args = [ "dotnet", "msbuild", "OnnxRuntime.CSharp.proj", "/t:CreatePackage", package_name, configuration, execution_provider, is_linux_build, ort_build_dir] run_subprocess(cmd_args, cwd=csharp_build_dir) def run_csharp_tests(source_dir, build_dir, use_cuda, use_openvino, use_tensorrt, use_dnnl): # Currently only running tests on windows. if not is_windows(): return csharp_source_dir = os.path.join(source_dir, 'csharp') is_linux_build = derive_linux_build_property() # define macros based on build args macros = "" if use_openvino: macros += "USE_OPENVINO;" if use_tensorrt: macros += "USE_TENSORRT;" if use_dnnl: macros += "USE_DNNL;" if use_cuda: macros += "USE_CUDA;" define_constants = "" if macros != "": define_constants = "/p:DefineConstants=\"" + macros + "\"" # set build directory based on build_dir arg native_build_dir = os.path.normpath(os.path.join(source_dir, build_dir)) ort_build_dir = "/p:OnnxRuntimeBuildDirectory=\"" + native_build_dir + "\"" # Skip pretrained models test. Only run unit tests as part of the build # add "--verbosity", "detailed" to this command if required cmd_args = ["dotnet", "test", "test\\Microsoft.ML.OnnxRuntime.Tests\\Microsoft.ML.OnnxRuntime.Tests.csproj", "--filter", "FullyQualifiedName!=Microsoft.ML.OnnxRuntime.Tests.InferenceTest.TestPreTrainedModels", is_linux_build, define_constants, ort_build_dir] run_subprocess(cmd_args, cwd=csharp_source_dir) def build_protoc_for_host(cmake_path, source_dir, build_dir, args): if (args.arm or args.arm64 or args.enable_windows_store) and (not is_windows() and not args.ios): raise BuildError( 'Currently only support building protoc for Windows host while ' 'cross-compiling for ARM/ARM64/Store and linux cross-compiling iOS') log.info( "Building protoc for host to be used in cross-compiled build process") protoc_build_dir = os.path.join(os.getcwd(), build_dir, 'host_protoc') os.makedirs(protoc_build_dir, exist_ok=True) # Generate step cmd_args = [ cmake_path, os.path.join(source_dir, 'cmake', 'external', 'protobuf', 'cmake'), '-Dprotobuf_BUILD_TESTS=OFF', '-Dprotobuf_WITH_ZLIB_DEFAULT=OFF', '-Dprotobuf_BUILD_SHARED_LIBS=OFF' ] is_ninja = args.cmake_generator == 'Ninja' if args.cmake_generator is not None and not (is_macOS() and args.use_xcode): cmd_args += ['-G', args.cmake_generator] if is_windows(): if not is_ninja: cmd_args += ['-T', 'host=x64'] elif is_macOS(): if args.use_xcode: cmd_args += ['-G', 'Xcode'] # CMake < 3.18 has a bug setting system arch to arm64 (if not specified) for Xcode 12, # protoc for host should be built using host architecture # Explicitly specify the CMAKE_OSX_ARCHITECTURES for x86_64 Mac. import platform if platform.machine() == 'x86_64': cmd_args += ['-DCMAKE_OSX_ARCHITECTURES=x86_64'] run_subprocess(cmd_args, cwd=protoc_build_dir) # Build step cmd_args = [cmake_path, "--build", protoc_build_dir, "--config", "Release", "--target", "protoc"] run_subprocess(cmd_args) # Absolute protoc path is needed for cmake config_dir = '' suffix = '' if (is_windows() and not is_ninja) or (is_macOS() and args.use_xcode): config_dir = 'Release' if is_windows(): suffix = '.exe' expected_protoc_path = os.path.join(protoc_build_dir, config_dir, 'protoc' + suffix) if not os.path.exists(expected_protoc_path): raise BuildError("Couldn't find {}. Host build of protoc failed.".format(expected_protoc_path)) return expected_protoc_path def generate_documentation(source_dir, build_dir, configs): operator_doc_path = os.path.join(source_dir, 'docs', 'ContribOperators.md') opkernel_doc_path = os.path.join(source_dir, 'docs', 'OperatorKernels.md') for config in configs: # Copy the gen_contrib_doc.py. shutil.copy( os.path.join(source_dir, 'tools', 'python', 'gen_contrib_doc.py'), os.path.join(build_dir, config)) shutil.copy( os.path.join(source_dir, 'tools', 'python', 'gen_opkernel_doc.py'), os.path.join(build_dir, config)) run_subprocess( [sys.executable, 'gen_contrib_doc.py', '--output_path', operator_doc_path], cwd=os.path.join(build_dir, config)) run_subprocess( [sys.executable, 'gen_opkernel_doc.py', '--output_path', opkernel_doc_path], cwd=os.path.join(build_dir, config)) docdiff = '' try: docdiff = subprocess.check_output(['git', 'diff', opkernel_doc_path]) except subprocess.CalledProcessError: print('git diff returned non-zero error code') if len(docdiff) > 0: # Show warning instead of throwing exception, because it is # dependent on build configuration for including # execution propviders log.warning( 'The updated opkernel document file ' + str(opkernel_doc_path) + ' is different from the checked in version. Consider ' 'regenerating the file with CPU, DNNL and CUDA providers enabled.') log.debug('diff:\n' + str(docdiff)) docdiff = '' try: docdiff = subprocess.check_output(['git', 'diff', operator_doc_path]) except subprocess.CalledProcessError: print('git diff returned non-zero error code') if len(docdiff) > 0: raise BuildError( 'The updated operator document file ' + str(operator_doc_path) + ' must be checked in.\n diff:\n' + str(docdiff)) def main(): args = parse_arguments() cmake_extra_defines = (args.cmake_extra_defines if args.cmake_extra_defines else []) cross_compiling = args.arm or args.arm64 or args.android # If there was no explicit argument saying what to do, default # to update, build and test (for native builds). if not (args.update or args.clean or args.build or args.test): log.debug( "Defaulting to running update, build " "[and test for native builds].") args.update = True args.build = True if cross_compiling: args.test = args.android_abi == 'x86_64' or args.android_abi == 'arm64-v8a' else: args.test = True if args.skip_tests: args.test = False if args.include_ops_by_model or args.include_ops_by_config: from exclude_unused_ops import exclude_unused_ops models_path = args.include_ops_by_model if args.include_ops_by_model else '' config_path = args.include_ops_by_config if args.include_ops_by_config else '' exclude_unused_ops(models_path, config_path, use_cuda=args.use_cuda) if args.use_tensorrt: args.use_cuda = True if args.build_wheel or args.gen_doc: args.enable_pybind = True if args.build_csharp or args.build_nuget or args.build_java or args.build_nodejs: args.build_shared_lib = True if args.build_nuget and cross_compiling: raise BuildError('Currently nuget package creation is not supported while cross-compiling') if args.enable_pybind and args.disable_exceptions: raise BuildError('Python bindings require exceptions to be enabled.') if args.minimal_build and args.disable_ort_format_load: raise BuildError('Minimal build requires loading ORT format models.') # Disabling unit tests for VAD-F as FPGA only supports # models with NCHW layout if args.use_openvino == "VAD-F_FP32": args.test = False configs = set(args.config) # setup paths and directories cmake_path = resolve_executable_path(args.cmake_path) ctest_path = None if args.use_vstest else resolve_executable_path( args.ctest_path) build_dir = args.build_dir script_dir = os.path.realpath(os.path.dirname(__file__)) source_dir = os.path.normpath(os.path.join(script_dir, "..", "..")) # if using cuda, setup cuda paths and env vars cuda_home, cudnn_home = setup_cuda_vars(args) mpi_home = args.mpi_home nccl_home = args.nccl_home # if using tensorrt, setup tensorrt paths tensorrt_home = setup_tensorrt_vars(args) # if using migraphx, setup migraphx paths migraphx_home = setup_migraphx_vars(args) os.makedirs(build_dir, exist_ok=True) log.info("Build started") if args.update: cmake_extra_args = [] path_to_protoc_exe = args.path_to_protoc_exe if not args.skip_submodule_sync: update_submodules(source_dir) if is_windows(): if args.cmake_generator == 'Ninja': if args.x86 or args.arm or args.arm64: raise BuildError( "To cross-compile with Ninja, load the toolset " "environment for the target processor (e.g. Cross " "Tools Command Prompt for VS)") cmake_extra_args = ['-G', args.cmake_generator] elif args.x86: cmake_extra_args = [ '-A', 'Win32', '-T', 'host=x64', '-G', args.cmake_generator ] elif args.arm or args.arm64: # Cross-compiling for ARM(64) architecture # First build protoc for host to use during cross-compilation if path_to_protoc_exe is None: path_to_protoc_exe = build_protoc_for_host( cmake_path, source_dir, build_dir, args) if args.arm: cmake_extra_args = ['-A', 'ARM'] else: cmake_extra_args = ['-A', 'ARM64'] cmake_extra_args += ['-G', args.cmake_generator] # Cannot test on host build machine for cross-compiled # builds (Override any user-defined behaviour for test if any) if args.test: log.info( "Cannot test on host build machine for cross-compiled " "ARM(64) builds. Will skip test running after build.") args.test = False else: if (args.msvc_toolset == '14.16' and args.cmake_generator == 'Visual Studio 16 2019'): # CUDA 10.0 requires _MSC_VER >= 1700 and # _MSC_VER < 1920, aka Visual Studio version # in [2012, 2019). In VS2019, we have to use # Side-by-side minor version MSVC toolsets from # Visual Studio 2017 14.16 is MSVC version # 141 is MSVC Toolset Version # Cuda VS extension should be installed to # C:\Program Files (x86)\Microsoft Visual # Studio\2019\Enterprise\MSBuild\Microsoft\VC\v160\BuildCustomizations # noqa toolset = 'v141,host=x64,version=' + args.msvc_toolset elif args.msvc_toolset: toolset = 'host=x64,version=' + args.msvc_toolset else: toolset = 'host=x64' if args.cuda_version: toolset += ',cuda=' + args.cuda_version cmake_extra_args = [ '-A', 'x64', '-T', toolset, '-G', args.cmake_generator ] if args.enable_windows_store: cmake_extra_args.append( '-DCMAKE_TOOLCHAIN_FILE=' + os.path.join( source_dir, 'cmake', 'store_toolchain.cmake')) if args.enable_wcos: cmake_extra_args.append('-DCMAKE_USER_MAKE_RULES_OVERRIDE=wcos_rules_override.cmake') elif args.cmake_generator is not None and not (is_macOS() and args.use_xcode): cmake_extra_args += ['-G', args.cmake_generator] elif is_macOS() and args.use_xcode: cmake_extra_args += ['-G', 'Xcode'] if (args.android or args.ios or args.enable_windows_store) and args.path_to_protoc_exe is None: # Cross-compiling for Android and iOS path_to_protoc_exe = build_protoc_for_host( cmake_path, source_dir, build_dir, args) if is_ubuntu_1604(): if (args.arm or args.arm64): raise BuildError( "Only Windows ARM(64) cross-compiled builds supported " "currently through this script") install_ubuntu_deps(args) if not is_docker() and not args.use_acl and not args.use_armnn: install_python_deps() if args.enable_pybind and is_windows(): install_python_deps(args.numpy_version) if args.enable_onnx_tests: setup_test_data(build_dir, configs) generate_build_tree( cmake_path, source_dir, build_dir, cuda_home, cudnn_home, mpi_home, nccl_home, tensorrt_home, migraphx_home, path_to_protoc_exe, configs, cmake_extra_defines, args, cmake_extra_args) if args.clean: clean_targets(cmake_path, build_dir, configs) # if using DML, perform initial nuget package restore setup_dml_build(args, cmake_path, build_dir, configs) if args.build: build_targets(args, cmake_path, build_dir, configs, args.parallel, args.target) if args.test: run_onnxruntime_tests(args, source_dir, ctest_path, build_dir, configs) # run nuphar python tests last, as it installs ONNX 1.5.0 if args.enable_pybind and not args.skip_onnx_tests and args.use_nuphar: nuphar_run_python_tests(build_dir, configs) # run node.js binding tests if args.build_nodejs and not args.skip_nodejs_tests: nodejs_binding_dir = os.path.normpath(os.path.join(source_dir, "nodejs")) run_nodejs_tests(nodejs_binding_dir) if args.build: if args.build_wheel: nightly_build = bool(os.getenv('NIGHTLY_BUILD') == '1') build_python_wheel( source_dir, build_dir, configs, args.use_cuda, args.use_ngraph, args.use_dnnl, args.use_tensorrt, args.use_openvino, args.use_nuphar, args.use_vitisai, args.use_acl, args.use_armnn, args.use_dml, args.wheel_name_suffix, args.enable_training, nightly_build=nightly_build, featurizers_build=args.use_featurizers, use_ninja=(args.cmake_generator == 'Ninja') ) if args.build_nuget: build_nuget_package( source_dir, build_dir, configs, args.use_cuda, args.use_openvino, args.use_tensorrt, args.use_dnnl, args.use_mklml ) if args.test and args.build_nuget: run_csharp_tests( source_dir, build_dir, args.use_cuda, args.use_openvino, args.use_tensorrt, args.use_dnnl) if args.gen_doc and (args.build or args.test): generate_documentation(source_dir, build_dir, configs) log.info("Build complete") if __name__ == "__main__": try: sys.exit(main()) except BaseError as e: log.error(str(e)) sys.exit(1)

43.557604

119

0.613627

import argparse import glob import multiprocessing import os import re import shutil import subprocess import sys import hashlib from logger import log class BaseError(Exception): pass class BuildError(BaseError): def __init__(self, *messages): super().__init__("\n".join(messages)) class UsageError(BaseError): def __init__(self, message): super().__init__(message) def _check_python_version(): if sys.version_info[0] != 3: raise BuildError( "Bad python major version: expecting python 3, found version " "'{}'".format(sys.version)) if sys.version_info[1] < 5: raise BuildError( "Bad python minor version: expecting python 3.5+, found version " "'{}'".format(sys.version)) _check_python_version() def parse_arguments(): parser = argparse.ArgumentParser( description="ONNXRuntime CI build driver.", usage=""" # noqa Default behavior is --update --build --test for native architecture builds. Default behavior is --update --build for cross-compiled builds. The Update phase will update git submodules, and run cmake to generate makefiles. The Build phase will build all projects. The Test phase will run all unit tests, and optionally the ONNX tests. Use the individual flags to only run the specified stages. """) # Main arguments parser.add_argument( "--build_dir", required=True, help="Path to the build directory.") parser.add_argument( "--config", nargs="+", default=["Debug"], choices=["Debug", "MinSizeRel", "Release", "RelWithDebInfo"], help="Configuration(s) to build.") parser.add_argument( "--update", action='store_true', help="Update makefiles.") parser.add_argument("--build", action='store_true', help="Build.") parser.add_argument( "--clean", action='store_true', help="Run 'cmake --build --target clean' for the selected config/s.") parser.add_argument( "--parallel", action='store_true', help="""Use parallel build. The build setup doesn't get all dependencies right, so --parallel only works if you're just rebuilding ONNXRuntime code. If you've done an update that fetched external dependencies you have to build without --parallel the first time. Once that's done , run with "--build --parallel --test" to just build in parallel and run tests.""") parser.add_argument("--test", action='store_true', help="Run unit tests.") parser.add_argument( "--skip_tests", action='store_true', help="Skip all tests.") # Training options parser.add_argument( "--enable_nvtx_profile", action='store_true', help="Enable NVTX profile in ORT.") parser.add_argument( "--enable_training", action='store_true', help="Enable training in ORT.") parser.add_argument( "--enable_training_python_frontend_e2e_tests", action="store_true", help="Enable the pytorch frontend training tests.") parser.add_argument( "--enable_training_pipeline_e2e_tests", action="store_true", help="Enable the pipeline c++ e2e tests.") parser.add_argument( "--use_horovod", action='store_true', help="Enable Horovod.") parser.add_argument( "--mpi_home", help="Path to MPI installation dir") parser.add_argument( "--nccl_home", help="Path to NCCL installation dir") # enable ONNX tests parser.add_argument( "--enable_onnx_tests", action='store_true', help="""When running the Test phase, run onnx_test_running against available test data directories.""") parser.add_argument("--path_to_protoc_exe", help="Path to protoc exe.") parser.add_argument( "--fuzz_testing", action='store_true', help="Enable Fuzz testing of the onnxruntime.") parser.add_argument( "--enable_symbolic_shape_infer_tests", action='store_true', help="""When running the Test phase, run symbolic shape inference against available test data directories.""") # generate documentaiton parser.add_argument( "--gen_doc", action='store_true', help="Generate documentation on contrib ops") # CUDA related parser.add_argument("--use_cuda", action='store_true', help="Enable CUDA.") parser.add_argument( "--cuda_version", help="The version of CUDA toolkit to use. " "Auto-detect if not specified. e.g. 9.0") parser.add_argument( "--cuda_home", help="Path to CUDA home." "Read from CUDA_HOME environment variable if --use_cuda is true and " "--cuda_home is not specified.") parser.add_argument( "--cudnn_home", help="Path to CUDNN home. " "Read from CUDNN_HOME environment variable if --use_cuda is true and " "--cudnn_home is not specified.") # Python bindings parser.add_argument( "--enable_pybind", action='store_true', help="Enable Python Bindings.") parser.add_argument( "--build_wheel", action='store_true', help="Build Python Wheel.") parser.add_argument( "--wheel_name_suffix", help="Suffix to append to created wheel names. " "This value is currently only used for nightly builds.") parser.add_argument( "--numpy_version", help="Installs a specific version of numpy " "before building the python binding.") parser.add_argument( "--skip-keras-test", action='store_true', help="Skip tests with Keras if keras is installed") # C-Sharp bindings parser.add_argument( "--build_csharp", action='store_true', help="Build C#.Net DLL and NuGet package. This should be only used in CI pipelines. " "For building C# bindings and packaging them into nuget package use --build_nuget arg.") parser.add_argument( "--build_nuget", action='store_true', help="Build C#.Net DLL and NuGet package on the local machine. " "Currently only Windows and Linux platforms are supported.") # Java bindings parser.add_argument( "--build_java", action='store_true', help="Build Java bindings.") # Node.js binding parser.add_argument( "--build_nodejs", action='store_true', help="Build Node.js binding and NPM package.") # Build a shared lib parser.add_argument( "--build_shared_lib", action='store_true', help="Build a shared library for the ONNXRuntime.") # Build options parser.add_argument( "--cmake_extra_defines", nargs="+", help="Extra definitions to pass to CMake during build system " "generation. These are just CMake -D options without the leading -D.") parser.add_argument( "--target", help="Build a specific target, e.g. winml_dll") parser.add_argument( "--x86", action='store_true', help="Create x86 makefiles. Requires --update and no existing cache " "CMake setup. Delete CMakeCache.txt if needed") parser.add_argument( "--arm", action='store_true', help="Create ARM makefiles. Requires --update and no existing cache " "CMake setup. Delete CMakeCache.txt if needed") parser.add_argument( "--arm64", action='store_true', help="Create ARM64 makefiles. Requires --update and no existing cache " "CMake setup. Delete CMakeCache.txt if needed") parser.add_argument( "--msvc_toolset", help="MSVC toolset to use. e.g. 14.11") parser.add_argument("--android", action='store_true', help='Build for Android') parser.add_argument( "--android_abi", default="arm64-v8a", choices=["armeabi-v7a", "arm64-v8a", "x86", "x86_64"], help="Specify the target Android Application Binary Interface (ABI)") parser.add_argument("--android_api", type=int, default=27, help='Android API Level, e.g. 21') parser.add_argument("--android_sdk_path", type=str, help='Path to the Android SDK') parser.add_argument("--android_ndk_path", default="", help="Path to the Android NDK") parser.add_argument("--android_cpp_shared", action="store_true", help="Build with shared libc++ instead of the default static libc++.") parser.add_argument("--test_binary_size", action="store_true", help="If enabled, build will fail when the built binary size is larger than the threshold. " "This only applies to Android Minimal build for now.") parser.add_argument("--ios", action='store_true', help="build for ios") parser.add_argument( "--ios_sysroot", default="", help="Specify the location name of the macOS platform SDK to be used") parser.add_argument( "--ios_toolchain_dir", default="", help="Path to ios toolchain binaries") parser.add_argument( "--ios_toolchain_file", default="", help="Path to ios toolchain file, " "or cmake/onnxruntime_ios.toolchain.cmake will be used") parser.add_argument( "--xcode_code_signing_team_id", default="", help="The development team ID used for code signing in Xcode") parser.add_argument( "--use_xcode", action='store_true', help="Use Xcode as cmake generator, this is only supported on MacOS.") parser.add_argument( "--osx_arch", default="arm64", choices=["arm64", "x86_64"], help="Specify the Target specific architectures for macOS and iOS, This is only supported on MacOS") parser.add_argument( "--apple_deploy_target", type=str, help="Specify the minimum version of the target platform " "(e.g. macOS or iOS)" "This is only supported on MacOS") # Arguments needed by CI parser.add_argument( "--cmake_path", default="cmake", help="Path to the CMake program.") parser.add_argument( "--ctest_path", default="ctest", help="Path to the CTest program.") parser.add_argument( "--skip_submodule_sync", action='store_true', help="Don't do a " "'git submodule update'. Makes the Update phase faster.") parser.add_argument( "--use_vstest", action='store_true', help="Use use_vstest for running unitests.") parser.add_argument( "--use_jemalloc", action='store_true', help="Use jemalloc.") parser.add_argument( "--use_mimalloc", default=['none'], choices=['none', 'stl', 'arena', 'all'], help="Use mimalloc.") parser.add_argument( "--use_openblas", action='store_true', help="Build with OpenBLAS.") parser.add_argument( "--use_dnnl", action='store_true', help="Build with DNNL.") parser.add_argument( "--use_mklml", action='store_true', help="Build with MKLML.") parser.add_argument( "--use_featurizers", action='store_true', help="Build with ML Featurizer support.") parser.add_argument( "--use_ngraph", action='store_true', help="Build with nGraph.") parser.add_argument( "--use_openvino", nargs="?", const="CPU_FP32", choices=["CPU_FP32", "GPU_FP32", "GPU_FP16", "VAD-M_FP16", "MYRIAD_FP16", "VAD-F_FP32"], help="Build with OpenVINO for specific hardware.") parser.add_argument( "--use_nnapi", action='store_true', help="Build with NNAPI support.") parser.add_argument( "--use_rknpu", action='store_true', help="Build with RKNPU.") parser.add_argument( "--use_preinstalled_eigen", action='store_true', help="Use pre-installed Eigen.") parser.add_argument("--eigen_path", help="Path to pre-installed Eigen.") parser.add_argument( "--use_openmp", action='store_true', help="Build with OpenMP") parser.add_argument( "--enable_msinternal", action="store_true", help="Enable for Microsoft internal builds only.") parser.add_argument("--llvm_path", help="Path to llvm dir") parser.add_argument( "--use_vitisai", action='store_true', help="Build with Vitis-AI") parser.add_argument( "--use_nuphar", action='store_true', help="Build with nuphar") parser.add_argument( "--use_tensorrt", action='store_true', help="Build with TensorRT") parser.add_argument( "--tensorrt_home", help="Path to TensorRT installation dir") parser.add_argument( "--use_migraphx", action='store_true', help="Build with MIGraphX") parser.add_argument( "--migraphx_home", help="Path to MIGraphX installation dir") parser.add_argument( "--use_full_protobuf", action='store_true', help="Use the full protobuf library") parser.add_argument( "--skip_onnx_tests", action='store_true', help="Explicitly disable " "all onnx related tests. Note: Use --skip_tests to skip all tests.") parser.add_argument( "--skip_winml_tests", action='store_true', help="Explicitly disable all WinML related tests") parser.add_argument( "--skip_nodejs_tests", action='store_true', help="Explicitly disable all Node.js binding tests") parser.add_argument( "--enable_msvc_static_runtime", action='store_true', help="Enable static linking of MSVC runtimes.") parser.add_argument( "--enable_language_interop_ops", action='store_true', help="Enable operator implemented in language other than cpp") parser.add_argument( "--cmake_generator", choices=['Visual Studio 15 2017', 'Visual Studio 16 2019', 'Ninja'], default='Visual Studio 15 2017' if is_windows() else None, help="Specify the generator that CMake invokes. " "This is only supported on Windows") parser.add_argument( "--enable_multi_device_test", action='store_true', help="Test with multi-device. Mostly used for multi-device GPU") parser.add_argument( "--use_dml", action='store_true', help="Build with DirectML.") parser.add_argument( "--use_winml", action='store_true', help="Build with WinML.") parser.add_argument( "--winml_root_namespace_override", type=str, help="Specify the namespace that WinML builds into.") parser.add_argument( "--use_telemetry", action='store_true', help="Only official builds can set this flag to enable telemetry.") parser.add_argument( "--enable_wcos", action='store_true', help="Build for Windows Core OS.") parser.add_argument( "--enable_windows_store", action='store_true', help="Build for Windows Store") parser.add_argument( "--enable_lto", action='store_true', help="Enable Link Time Optimization") parser.add_argument( "--use_acl", nargs="?", const="ACL_1905", choices=["ACL_1902", "ACL_1905", "ACL_1908", "ACL_2002"], help="Build with ACL for ARM architectures.") parser.add_argument( "--use_armnn", action='store_true', help="Enable ArmNN Execution Provider.") parser.add_argument( "--armnn_relu", action='store_true', help="Use the Relu operator implementation from the ArmNN EP.") parser.add_argument( "--armnn_bn", action='store_true', help="Use the Batch Normalization operator implementation from the ArmNN EP.") parser.add_argument( "--build_micro_benchmarks", action='store_true', help="Build ONNXRuntime micro-benchmarks.") parser.add_argument("--minimal_build", action='store_true', help="Create a build that only supports ORT format models. " "See /docs/ONNX_Runtime_Format_Model_Usage.md for more information. " "RTTI is automatically disabled in a minimal build.") parser.add_argument("--include_ops_by_model", type=str, help="include ops from model(s) under designated path.") parser.add_argument("--include_ops_by_config", type=str, help="include ops from config file. " "See /docs/Reduced_Operator_Kernel_build.md for more information.") parser.add_argument("--disable_contrib_ops", action='store_true', help="Disable contrib ops (reduces binary size)") parser.add_argument("--disable_ml_ops", action='store_true', help="Disable traditional ML ops (reduces binary size)") parser.add_argument("--disable_rtti", action='store_true', help="Disable RTTI (reduces binary size)") parser.add_argument("--disable_exceptions", action='store_true', help="Disable exceptions to reduce binary size. Requires --minimal_build.") parser.add_argument("--disable_ort_format_load", action='store_true', help='Disable support for loading ORT format models in a non-minimal build.') return parser.parse_args() def resolve_executable_path(command_or_path): executable_path = shutil.which(command_or_path) if executable_path is None: raise BuildError("Failed to resolve executable path for " "'{}'.".format(command_or_path)) return os.path.realpath(executable_path) def is_windows(): return sys.platform.startswith("win") def is_macOS(): return sys.platform.startswith("darwin") def is_linux(): return sys.platform.startswith("linux") def get_linux_distro(): try: with open('/etc/os-release', 'r') as f: dist_info = dict( line.strip().split('=', 1) for line in f.readlines()) return dist_info.get('NAME', '').strip('"'), dist_info.get( 'VERSION', '').strip('"') except (IOError, ValueError): return '', '' def is_ubuntu_1604(): dist, ver = get_linux_distro() return dist == 'Ubuntu' and ver.startswith('16.04') def get_config_build_dir(build_dir, config): return os.path.join(build_dir, config) def run_subprocess(args, cwd=None, capture=False, dll_path=None, shell=False, env={}): log.info("Running subprocess in '{0}'\n{1}".format( cwd or os.getcwd(), args)) my_env = os.environ.copy() if dll_path: if is_windows(): my_env["PATH"] = dll_path + os.pathsep + my_env["PATH"] else: if "LD_LIBRARY_PATH" in my_env: my_env["LD_LIBRARY_PATH"] += os.pathsep + dll_path else: my_env["LD_LIBRARY_PATH"] = dll_path stdout, stderr = (subprocess.PIPE, subprocess.STDOUT) if capture else ( None, None) my_env.update(env) completed_process = subprocess.run( args, cwd=cwd, check=True, stdout=stdout, stderr=stderr, env=my_env, shell=shell) log.debug("Subprocess completed. Return code=" + str(completed_process.returncode)) return completed_process def update_submodules(source_dir): run_subprocess(["git", "submodule", "sync", "--recursive"], cwd=source_dir) run_subprocess(["git", "submodule", "update", "--init", "--recursive"], cwd=source_dir) def is_docker(): path = '/proc/self/cgroup' return ( os.path.exists('/.dockerenv') or os.path.isfile(path) and any('docker' in line for line in open(path)) ) def is_sudo(): return 'SUDO_UID' in os.environ.keys() def install_apt_package(package): have = package in str(run_subprocess( ["apt", "list", "--installed", package], capture=True).stdout) if not have: if is_sudo(): run_subprocess(['apt-get', 'install', '-y', package]) else: raise BuildError(package + " APT package missing. Please re-run " "this script using sudo to install.") def install_ubuntu_deps(args): if not (args.enable_pybind or args.use_openblas): return if not is_docker(): try: if args.enable_pybind: install_apt_package("python3") if args.use_openblas: install_apt_package("libopenblas-dev") except Exception as e: raise BuildError("Error setting up required APT packages. " "{}".format(str(e))) def install_python_deps(numpy_version=""): dep_packages = ['setuptools', 'wheel', 'pytest'] dep_packages.append('numpy=={}'.format(numpy_version) if numpy_version else 'numpy>=1.16.6') dep_packages.append('sympy>=1.1') dep_packages.append('packaging') dep_packages.append('cerberus') run_subprocess([sys.executable, '-m', 'pip', 'install', '--trusted-host', 'files.pythonhosted.org'] + dep_packages) def install_torch(): run_subprocess([sys.executable, '-m', 'pip', 'install', '--trusted-host', 'files.pythonhosted.org', 'torch===1.5.1+cu101', 'torchvision===0.6.1+cu101', '-f', 'https://download.pytorch.org/whl/torch_stable.html']) def check_md5(filename, expected_md5): if not os.path.exists(filename): return False hash_md5 = hashlib.md5() BLOCKSIZE = 1024*64 with open(filename, "rb") as f: buf = f.read(BLOCKSIZE) while len(buf) > 0: hash_md5.update(buf) buf = f.read(BLOCKSIZE) hex = hash_md5.hexdigest() if hex != expected_md5: log.info('md5 mismatch, expect %s, got %s' % (expected_md5, hex)) os.remove(filename) return False return True def setup_test_data(build_dir, configs): if is_windows(): src_model_dir = os.path.join(build_dir, 'models') if os.path.exists('C:\\local\\models') and not os.path.exists( src_model_dir): log.debug("creating shortcut %s -> %s" % ( 'C:\\local\\models', src_model_dir)) run_subprocess(['mklink', '/D', '/J', src_model_dir, 'C:\\local\\models'], shell=True) for config in configs: config_build_dir = get_config_build_dir(build_dir, config) os.makedirs(config_build_dir, exist_ok=True) dest_model_dir = os.path.join(config_build_dir, 'models') if os.path.exists('C:\\local\\models') and not os.path.exists( dest_model_dir): log.debug("creating shortcut %s -> %s" % ( 'C:\\local\\models', dest_model_dir)) run_subprocess(['mklink', '/D', '/J', dest_model_dir, 'C:\\local\\models'], shell=True) elif os.path.exists(src_model_dir) and not os.path.exists( dest_model_dir): log.debug("creating shortcut %s -> %s" % ( src_model_dir, dest_model_dir)) run_subprocess(['mklink', '/D', '/J', dest_model_dir, src_model_dir], shell=True) def use_dev_mode(args): if args.use_acl: return 'OFF' if args.use_armnn: return 'OFF' if args.ios and is_macOS(): return 'OFF' return 'ON' def generate_build_tree(cmake_path, source_dir, build_dir, cuda_home, cudnn_home, mpi_home, nccl_home, tensorrt_home, migraphx_home, path_to_protoc_exe, configs, cmake_extra_defines, args, cmake_extra_args): log.info("Generating CMake build tree") cmake_dir = os.path.join(source_dir, "cmake") cmake_args = [ cmake_path, cmake_dir, "-Donnxruntime_RUN_ONNX_TESTS=" + ( "ON" if args.enable_onnx_tests else "OFF"), "-Donnxruntime_BUILD_WINML_TESTS=" + ( "OFF" if args.skip_winml_tests else "ON"), "-Donnxruntime_GENERATE_TEST_REPORTS=ON", "-Donnxruntime_DEV_MODE=" + use_dev_mode(args), "-DPYTHON_EXECUTABLE=" + sys.executable, "-Donnxruntime_USE_CUDA=" + ("ON" if args.use_cuda else "OFF"), "-Donnxruntime_CUDNN_HOME=" + (cudnn_home if args.use_cuda else ""), "-Donnxruntime_USE_FEATURIZERS=" + ( "ON" if args.use_featurizers else "OFF"), "-Donnxruntime_CUDA_HOME=" + (cuda_home if args.use_cuda else ""), "-Donnxruntime_USE_JEMALLOC=" + ("ON" if args.use_jemalloc else "OFF"), "-Donnxruntime_USE_MIMALLOC_STL_ALLOCATOR=" + ( "ON" if args.use_mimalloc == "stl" or args.use_mimalloc == "all" else "OFF"), "-Donnxruntime_USE_MIMALLOC_ARENA_ALLOCATOR=" + ( "ON" if args.use_mimalloc == "arena" or args.use_mimalloc == "all" else "OFF"), "-Donnxruntime_ENABLE_PYTHON=" + ( "ON" if args.enable_pybind else "OFF"), "-Donnxruntime_BUILD_CSHARP=" + ("ON" if args.build_csharp else "OFF"), "-Donnxruntime_BUILD_JAVA=" + ("ON" if args.build_java else "OFF"), "-Donnxruntime_BUILD_NODEJS=" + ("ON" if args.build_nodejs else "OFF"), "-Donnxruntime_BUILD_SHARED_LIB=" + ( "ON" if args.build_shared_lib else "OFF"), "-Donnxruntime_USE_EIGEN_FOR_BLAS=" + ( "OFF" if args.use_openblas else "ON"), "-Donnxruntime_USE_OPENBLAS=" + ("ON" if args.use_openblas else "OFF"), "-Donnxruntime_USE_DNNL=" + ("ON" if args.use_dnnl else "OFF"), "-Donnxruntime_USE_MKLML=" + ("ON" if args.use_mklml else "OFF"), "-Donnxruntime_USE_NGRAPH=" + ("ON" if args.use_ngraph else "OFF"), "-Donnxruntime_USE_NNAPI_BUILTIN=" + ("ON" if args.use_nnapi else "OFF"), "-Donnxruntime_USE_RKNPU=" + ("ON" if args.use_rknpu else "OFF"), "-Donnxruntime_USE_OPENMP=" + ( "ON" if args.use_openmp and not ( args.use_nnapi or (args.use_mklml and (is_macOS() or is_windows())) or args.use_ngraph or args.android or (args.ios and is_macOS()) or args.use_rknpu) else "OFF"), "-Donnxruntime_USE_TVM=" + ("ON" if args.use_nuphar else "OFF"), "-Donnxruntime_USE_LLVM=" + ("ON" if args.use_nuphar else "OFF"), "-Donnxruntime_ENABLE_MICROSOFT_INTERNAL=" + ( "ON" if args.enable_msinternal else "OFF"), "-Donnxruntime_USE_VITISAI=" + ("ON" if args.use_vitisai else "OFF"), "-Donnxruntime_USE_NUPHAR=" + ("ON" if args.use_nuphar else "OFF"), "-Donnxruntime_USE_TENSORRT=" + ("ON" if args.use_tensorrt else "OFF"), "-Donnxruntime_TENSORRT_HOME=" + ( tensorrt_home if args.use_tensorrt else ""), "-Donnxruntime_USE_MIGRAPHX=" + ("ON" if args.use_migraphx else "OFF"), "-Donnxruntime_MIGRAPHX_HOME=" + (migraphx_home if args.use_migraphx else ""), "-Donnxruntime_CROSS_COMPILING=" + ( "ON" if args.arm64 or args.arm else "OFF"), "-Donnxruntime_DISABLE_CONTRIB_OPS=" + ("ON" if args.disable_contrib_ops else "OFF"), "-Donnxruntime_DISABLE_ML_OPS=" + ("ON" if args.disable_ml_ops else "OFF"), "-Donnxruntime_DISABLE_RTTI=" + ("ON" if args.disable_rtti else "OFF"), "-Donnxruntime_DISABLE_EXCEPTIONS=" + ("ON" if args.disable_exceptions else "OFF"), "-Donnxruntime_DISABLE_ORT_FORMAT_LOAD=" + ("ON" if args.disable_ort_format_load else "OFF"), "-Donnxruntime_MINIMAL_BUILD=" + ("ON" if args.minimal_build else "OFF"), "-Donnxruntime_REDUCED_OPS_BUILD=" + ( "ON" if args.include_ops_by_config or args.include_ops_by_model else "OFF"), "-Donnxruntime_MSVC_STATIC_RUNTIME=" + ( "ON" if args.enable_msvc_static_runtime else "OFF"), "-Donnxruntime_ENABLE_LANGUAGE_INTEROP_OPS=" + ( "ON" if args.enable_language_interop_ops else "OFF"), "-Donnxruntime_USE_DML=" + ("ON" if args.use_dml else "OFF"), "-Donnxruntime_USE_WINML=" + ("ON" if args.use_winml else "OFF"), "-Donnxruntime_USE_TELEMETRY=" + ( "ON" if args.use_telemetry else "OFF"), "-Donnxruntime_ENABLE_LTO=" + ("ON" if args.enable_lto else "OFF"), "-Donnxruntime_USE_ACL=" + ("ON" if args.use_acl else "OFF"), "-Donnxruntime_USE_ACL_1902=" + ( "ON" if args.use_acl == "ACL_1902" else "OFF"), "-Donnxruntime_USE_ACL_1905=" + ( "ON" if args.use_acl == "ACL_1905" else "OFF"), "-Donnxruntime_USE_ACL_1908=" + ( "ON" if args.use_acl == "ACL_1908" else "OFF"), "-Donnxruntime_USE_ACL_2002=" + ( "ON" if args.use_acl == "ACL_2002" else "OFF"), "-Donnxruntime_USE_ARMNN=" + ( "ON" if args.use_armnn else "OFF"), "-Donnxruntime_ARMNN_RELU_USE_CPU=" + ( "OFF" if args.armnn_relu else "ON"), "-Donnxruntime_ARMNN_BN_USE_CPU=" + ( "OFF" if args.armnn_bn else "ON"), "-Donnxruntime_ENABLE_NVTX_PROFILE=" + ( "ON" if args.enable_nvtx_profile else "OFF"), "-Donnxruntime_ENABLE_TRAINING=" + ( "ON" if args.enable_training else "OFF"), "-Donnxruntime_USE_HOROVOD=" + ( "ON" if args.use_horovod else "OFF"), "-Donnxruntime_BUILD_BENCHMARKS=" + ( "ON" if args.build_micro_benchmarks else "OFF") ] if mpi_home and os.path.exists(mpi_home): cmake_args += ["-Donnxruntime_MPI_HOME=" + mpi_home] if nccl_home and os.path.exists(nccl_home): cmake_args += ["-Donnxruntime_NCCL_HOME=" + nccl_home] if args.winml_root_namespace_override: cmake_args += ["-Donnxruntime_WINML_NAMESPACE_OVERRIDE=" + args.winml_root_namespace_override] if args.use_openvino: cmake_args += ["-Donnxruntime_USE_OPENVINO=ON", "-Donnxruntime_USE_OPENVINO_MYRIAD=" + ( "ON" if args.use_openvino == "MYRIAD_FP16" else "OFF"), "-Donnxruntime_USE_OPENVINO_GPU_FP32=" + ( "ON" if args.use_openvino == "GPU_FP32" else "OFF"), "-Donnxruntime_USE_OPENVINO_GPU_FP16=" + ( "ON" if args.use_openvino == "GPU_FP16" else "OFF"), "-Donnxruntime_USE_OPENVINO_CPU_FP32=" + ( "ON" if args.use_openvino == "CPU_FP32" else "OFF"), "-Donnxruntime_USE_OPENVINO_VAD_M=" + ( "ON" if args.use_openvino == "VAD-M_FP16" else "OFF"), "-Donnxruntime_USE_OPENVINO_VAD_F=" + ( "ON" if args.use_openvino == "VAD-F_FP32" else "OFF"), "-Donnxruntime_USE_OPENVINO_BINARY=" + ( "ON" if args.use_openvino else "OFF")] if not is_windows(): if args.use_cuda: cmake_args += [ "-Donnxruntime_USE_FULL_PROTOBUF=ON"] if (args.use_full_protobuf or args.use_ngraph or args.use_tensorrt or args.use_openvino or args.use_vitisai or args.gen_doc): cmake_args += [ "-Donnxruntime_USE_FULL_PROTOBUF=ON", "-DProtobuf_USE_STATIC_LIBS=ON" ] if args.use_nuphar and args.llvm_path is not None: cmake_args += ["-DLLVM_DIR=%s" % args.llvm_path] if args.use_cuda and not is_windows(): nvml_stub_path = cuda_home + "/lib64/stubs" cmake_args += ["-DCUDA_CUDA_LIBRARY=" + nvml_stub_path] if args.use_preinstalled_eigen: cmake_args += ["-Donnxruntime_USE_PREINSTALLED_EIGEN=ON", "-Deigen_SOURCE_PATH=" + args.eigen_path] if args.android: cmake_args += [ "-DCMAKE_TOOLCHAIN_FILE=" + args.android_ndk_path + "/build/cmake/android.toolchain.cmake", "-DANDROID_PLATFORM=android-" + str(args.android_api), "-DANDROID_ABI=" + str(args.android_abi) ] if args.android_cpp_shared: cmake_args += ["-DANDROID_STL=c++_shared"] if args.ios: if is_macOS(): needed_args = [ args.use_xcode, args.ios_sysroot, args.apple_deploy_target, ] arg_names = [ "--use_xcode " + "<need use xcode to cross build iOS on MacOS>", "--ios_sysroot " + "<the location or name of the macOS platform SDK>", "--apple_deploy_target " + "<the minimum version of the target platform>", ] if not all(needed_args): raise BuildError( "iOS build on MacOS canceled due to missing arguments: " + ', '.join( val for val, cond in zip(arg_names, needed_args) if not cond)) cmake_args += [ "-DCMAKE_SYSTEM_NAME=iOS", "-Donnxruntime_BUILD_SHARED_LIB=ON", "-DCMAKE_OSX_SYSROOT=" + args.ios_sysroot, "-DCMAKE_OSX_ARCHITECTURES=" + args.osx_arch, "-DCMAKE_OSX_DEPLOYMENT_TARGET=" + args.apple_deploy_target, "-Dprotobuf_BUILD_PROTOC_BINARIES=OFF", "-DCMAKE_TOOLCHAIN_FILE=" + ( args.ios_toolchain_file if args.ios_toolchain_file else "../cmake/onnxruntime_ios.toolchain.cmake") ] if args.xcode_code_signing_team_id: cmake_args += ["-DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=" + args.xcode_code_signing_team_id] else: needed_args = [ args.ios_sysroot, args.arm64 or args.arm, args.ios_toolchain_dir ] arg_names = [ "--ios_sysroot <path to sysroot>", "--arm or --arm64", "--ios_toolchain_dir <path to toolchain>" ] if not all(needed_args): raise BuildError( "iOS build canceled due to missing arguments: " + ', '.join( val for val, cond in zip(arg_names, needed_args) if not cond)) compilers = sorted( glob.glob(args.ios_toolchain_dir + "/bin/*-clang*")) os.environ["PATH"] = os.path.join( args.ios_toolchain_dir, "bin") + os.pathsep + os.environ.get( "PATH", "") os.environ["LD_LIBRARY_PATH"] = os.path.join( args.ios_toolchain_dir, "/lib") + os.pathsep + os.environ.get( "LD_LIBRARY_PATH", "") if len(compilers) != 2: raise BuildError( "error identifying compilers in ios_toolchain_dir") cmake_args += [ "-DCMAKE_OSX_ARCHITECTURES=" + ("arm64" if args.arm64 else "arm"), "-DCMAKE_SYSTEM_NAME=iOSCross", "-Donnxruntime_BUILD_UNIT_TESTS=OFF", "-DCMAKE_OSX_SYSROOT=" + args.ios_sysroot, "-DCMAKE_C_COMPILER=" + compilers[0], "-DCMAKE_CXX_COMPILER=" + compilers[1] ] if path_to_protoc_exe: cmake_args += [ "-DONNX_CUSTOM_PROTOC_EXECUTABLE=%s" % path_to_protoc_exe] if args.fuzz_testing: if not (args.build_shared_lib and is_windows() and args.cmake_generator == 'Visual Studio 16 2019' and args.use_full_protobuf): raise BuildError( "Fuzz test has only be tested with build shared libs option using MSVC on windows") cmake_args += [ "-Donnxruntime_BUILD_UNIT_TESTS=ON", "-Donnxruntime_FUZZ_TEST=ON", "-Donnxruntime_USE_FULL_PROTOBUF=ON"] if args.gen_doc: cmake_args += ["-Donnxruntime_PYBIND_EXPORT_OPSCHEMA=ON"] else: cmake_args += ["-Donnxruntime_PYBIND_EXPORT_OPSCHEMA=OFF"] cmake_args += ["-D{}".format(define) for define in cmake_extra_defines] cmake_args += cmake_extra_args build_number = os.getenv('Build_BuildNumber') source_version = os.getenv('Build_SourceVersion') if build_number and source_version: build_matches = re.fullmatch( r"(\d\d)(\d\d)(\d\d)(\d\d)\.(\d+)", build_number) if build_matches: YY = build_matches.group(2) MM = build_matches.group(3) DD = build_matches.group(4) with open(os.path.join(source_dir, 'VERSION_NUMBER')) as f: first_line = f.readline() ort_version_matches = re.match(r"(\d+).(\d+)", first_line) if not ort_version_matches: raise BuildError("Couldn't read version from VERSION_FILE") ort_major = ort_version_matches.group(1) ort_minor = ort_version_matches.group(2) # Example (BuildNumber: 191101-2300.1.master, # SourceVersion: 0bce7ae6755c792eda558e5d27ded701707dc404) # MajorPart = 1 # MinorPart = 0 # BuildPart = 1911 # PrivatePart = 123 # String = 191101-2300.1.master.0bce7ae cmake_args += [ "-DVERSION_MAJOR_PART={}".format(ort_major), "-DVERSION_MINOR_PART={}".format(ort_minor), "-DVERSION_BUILD_PART={}".format(YY), "-DVERSION_PRIVATE_PART={}{}".format(MM, DD), "-DVERSION_STRING={}.{}.{}.{}".format( ort_major, ort_minor, build_number, source_version[0:7]) ] for config in configs: config_build_dir = get_config_build_dir(build_dir, config) os.makedirs(config_build_dir, exist_ok=True) if args.use_nuphar: os.environ["PATH"] = os.path.join( config_build_dir, "external", "tvm", config) + os.pathsep + os.path.dirname(sys.executable) + os.pathsep + os.environ["PATH"] run_subprocess( cmake_args + [ "-Donnxruntime_ENABLE_MEMLEAK_CHECKER=" + ("ON" if config.lower() == 'debug' and not args.use_nuphar and not args.use_ngraph and not args.use_openvino and not args.enable_msvc_static_runtime else "OFF"), "-DCMAKE_BUILD_TYPE={}".format(config)], cwd=config_build_dir) def clean_targets(cmake_path, build_dir, configs): for config in configs: log.info("Cleaning targets for %s configuration", config) build_dir2 = get_config_build_dir(build_dir, config) cmd_args = [cmake_path, "--build", build_dir2, "--config", config, "--target", "clean"] run_subprocess(cmd_args) def build_targets(args, cmake_path, build_dir, configs, parallel, target=None): for config in configs: log.info("Building targets for %s configuration", config) build_dir2 = get_config_build_dir(build_dir, config) cmd_args = [cmake_path, "--build", build_dir2, "--config", config] if target: cmd_args.extend(['--target', target]) build_tool_args = [] if parallel: num_cores = str(multiprocessing.cpu_count()) if is_windows() and args.cmake_generator != 'Ninja': build_tool_args += [ "/maxcpucount:" + num_cores, # if nodeReuse is true, msbuild processes will stay around for a bit after the build completes "/nodeReuse:False", ] elif (is_macOS() and args.use_xcode): # CMake will generate correct build tool args for Xcode cmd_args += ["--parallel", num_cores] elif args.cmake_generator != 'Ninja': build_tool_args += ["-j" + num_cores] if build_tool_args: cmd_args += ["--"] cmd_args += build_tool_args env = {} if args.android: env['ANDROID_SDK_ROOT'] = args.android_sdk_path run_subprocess(cmd_args, env=env) def add_dir_if_exists(directory, dir_list): if os.path.isdir(directory): dir_list.append(directory) def setup_cuda_vars(args): cuda_home = "" cudnn_home = "" if args.use_cuda: cuda_home = args.cuda_home if args.cuda_home else os.getenv( "CUDA_HOME") cudnn_home = args.cudnn_home if args.cudnn_home else os.getenv( "CUDNN_HOME") cuda_home_valid = (cuda_home is not None and os.path.exists(cuda_home)) cudnn_home_valid = (cudnn_home is not None and os.path.exists( cudnn_home)) if not cuda_home_valid or not cudnn_home_valid: raise BuildError( "cuda_home and cudnn_home paths must be specified and valid.", "cuda_home='{}' valid={}. cudnn_home='{}' valid={}" .format( cuda_home, cuda_home_valid, cudnn_home, cudnn_home_valid)) return cuda_home, cudnn_home def setup_tensorrt_vars(args): tensorrt_home = "" if args.use_tensorrt: tensorrt_home = (args.tensorrt_home if args.tensorrt_home else os.getenv("TENSORRT_HOME")) tensorrt_home_valid = (tensorrt_home is not None and os.path.exists(tensorrt_home)) if not tensorrt_home_valid: raise BuildError( "tensorrt_home paths must be specified and valid.", "tensorrt_home='{}' valid={}." .format(tensorrt_home, tensorrt_home_valid)) # Set maximum workspace size in byte for # TensorRT (1GB = 1073741824 bytes). os.environ["ORT_TENSORRT_MAX_WORKSPACE_SIZE"] = "1073741824" # Set maximum number of iterations to detect unsupported nodes # and partition the models for TensorRT. os.environ["ORT_TENSORRT_MAX_PARTITION_ITERATIONS"] = "1000" # Set minimum subgraph node size in graph partitioning # for TensorRT. os.environ["ORT_TENSORRT_MIN_SUBGRAPH_SIZE"] = "1" # Set FP16 flag os.environ["ORT_TENSORRT_FP16_ENABLE"] = "0" return tensorrt_home def setup_migraphx_vars(args): migraphx_home = None if (args.use_migraphx): print("migraphx_home = {}".format(args.migraphx_home)) migraphx_home = args.migraphx_home or os.getenv("MIGRAPHX_HOME") or None migraphx_home_not_valid = (migraphx_home and not os.path.exists(migraphx_home)) if (migraphx_home_not_valid): raise BuildError("migraphx_home paths must be specified and valid.", "migraphx_home='{}' valid={}." .format(migraphx_home, migraphx_home_not_valid)) return migraphx_home or '' def setup_dml_build(args, cmake_path, build_dir, configs): if args.use_dml: for config in configs: # Run the RESTORE_PACKAGES target to perform the initial # NuGet setup. cmd_args = [cmake_path, "--build", get_config_build_dir(build_dir, config), "--config", config, "--target", "RESTORE_PACKAGES"] run_subprocess(cmd_args) def adb_push(src, dest, **kwargs): return run_subprocess(['adb', 'push', src, dest], **kwargs) def adb_shell(*args, **kwargs): return run_subprocess(['adb', 'shell', *args], **kwargs) def run_android_tests(args, source_dir, config, cwd): if args.android_abi == 'x86_64': run_subprocess(os.path.join( source_dir, 'tools', 'ci_build', 'github', 'android', 'start_android_emulator.sh')) adb_push('testdata', '/data/local/tmp/', cwd=cwd) adb_push( os.path.join(source_dir, 'cmake', 'external', 'onnx', 'onnx', 'backend', 'test'), '/data/local/tmp/', cwd=cwd) adb_push('onnxruntime_test_all', '/data/local/tmp/', cwd=cwd) adb_push('onnx_test_runner', '/data/local/tmp/', cwd=cwd) adb_shell('cd /data/local/tmp && /data/local/tmp/onnxruntime_test_all') if args.use_nnapi: adb_shell('cd /data/local/tmp && /data/local/tmp/onnx_test_runner -e nnapi /data/local/tmp/test') else: adb_shell('cd /data/local/tmp && /data/local/tmp/onnx_test_runner /data/local/tmp/test') # run shared_lib_test if necessary if args.build_shared_lib: adb_push('libonnxruntime.so', '/data/local/tmp/', cwd=cwd) adb_push('onnxruntime_shared_lib_test', '/data/local/tmp/', cwd=cwd) adb_shell( 'cd /data/local/tmp && ' + 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/data/local/tmp && ' + '/data/local/tmp/onnxruntime_shared_lib_test') elif args.android_abi == 'arm64-v8a': # For Android arm64 abi we are only verify the size of the binary generated by minimal build config # Will fail the build if the shared_lib size is larger than the threshold if args.minimal_build and config == 'MinSizeRel' and args.build_shared_lib and args.test_binary_size: # set current size limit to 1100KB bin_size_threshold = 1100000 bin_actual_size = os.path.getsize(os.path.join(cwd, 'libonnxruntime.so')) log.info('Android arm64 minsizerel libonnxruntime.so size [' + str(bin_actual_size) + 'B]') # Write the binary size to a file for uploading later with open(os.path.join(cwd, 'binary_size_data.txt'), 'w') as file: file.writelines([ 'os,arch,build_config,size\n', 'android,arm64-v8a,minimal-baseline,' + str(bin_actual_size) + '\n' ]) if bin_actual_size > bin_size_threshold: raise BuildError('Android arm64 minsizerel libonnxruntime.so size [' + str(bin_actual_size) + 'B] is bigger than threshold [' + str(bin_size_threshold) + 'B]') def run_ios_tests(args, source_dir, config, cwd): cpr = run_subprocess(["xcodebuild", "test", "-project", "./onnxruntime.xcodeproj", "-configuration", config, "-scheme", "onnxruntime_test_all_xc", "-destination", "platform=iOS Simulator,OS=latest,name=iPhone SE (2nd generation)"], cwd=cwd) if cpr.returncode == 0: cpr = run_subprocess(["xcodebuild", "test", "-project", "./onnxruntime.xcodeproj", "-configuration", config, "-scheme", "onnxruntime_shared_lib_test_xc", "-destination", "platform=iOS Simulator,OS=latest,name=iPhone SE (2nd generation)"], cwd=cwd) cpr.check_returncode() def run_orttraining_test_orttrainer_frontend_separately(cwd): class TestNameCollecterPlugin: def __init__(self): self.collected = set() def pytest_collection_modifyitems(self, items): for item in items: print('item.name: ', item.name) test_name = item.name start = test_name.find('[') if start > 0: test_name = test_name[:start] self.collected.add(test_name) import pytest plugin = TestNameCollecterPlugin() test_script_filename = os.path.join(cwd, "orttraining_test_orttrainer_frontend.py") pytest.main(['--collect-only', test_script_filename], plugins=[plugin]) for test_name in plugin.collected: run_subprocess([ sys.executable, '-m', 'pytest', 'orttraining_test_orttrainer_frontend.py', '-v', '-k', test_name], cwd=cwd) def run_training_python_frontend_tests(cwd): run_subprocess([sys.executable, 'onnxruntime_test_ort_trainer.py'], cwd=cwd) run_subprocess([sys.executable, 'onnxruntime_test_training_unit_tests.py'], cwd=cwd) run_subprocess([ sys.executable, 'orttraining_test_transformers.py', 'BertModelTest.test_for_pretraining_full_precision_list_input'], cwd=cwd) run_subprocess([ sys.executable, 'orttraining_test_transformers.py', 'BertModelTest.test_for_pretraining_full_precision_dict_input'], cwd=cwd) run_subprocess([ sys.executable, 'orttraining_test_transformers.py', 'BertModelTest.test_for_pretraining_full_precision_list_and_dict_input'], cwd=cwd) # TODO: use run_orttraining_test_orttrainer_frontend_separately to work around a sporadic segfault. # shall revert to run_subprocess call once the segfault issue is resolved. run_orttraining_test_orttrainer_frontend_separately(cwd) # run_subprocess([sys.executable, '-m', 'pytest', '-sv', 'orttraining_test_orttrainer_frontend.py'], cwd=cwd) run_subprocess([sys.executable, '-m', 'pytest', '-sv', 'orttraining_test_orttrainer_bert_toy_onnx.py'], cwd=cwd) def run_training_python_frontend_e2e_tests(cwd): # frontend tests are to be added here: log.info("Running python frontend e2e tests.") import torch ngpus = torch.cuda.device_count() if ngpus > 1: bert_pretrain_script = 'orttraining_run_bert_pretrain.py' log.debug('RUN: mpirun -n {} ''-x' 'NCCL_DEBUG=INFO'' {} {} {}'.format( ngpus, sys.executable, bert_pretrain_script, 'ORTBertPretrainTest.test_pretrain_throughput')) run_subprocess([ 'mpirun', '-n', str(ngpus), '-x', 'NCCL_DEBUG=INFO', sys.executable, bert_pretrain_script, 'ORTBertPretrainTest.test_pretrain_throughput'], cwd=cwd) log.debug('RUN: mpirun -n {} ''-x' 'NCCL_DEBUG=INFO'' {} {} {}'.format( ngpus, sys.executable, bert_pretrain_script, 'ORTBertPretrainTest.test_pretrain_convergence')) run_subprocess([ 'mpirun', '-n', str(ngpus), '-x', 'NCCL_DEBUG=INFO', sys.executable, bert_pretrain_script, 'ORTBertPretrainTest.test_pretrain_convergence'], cwd=cwd) # a long run log.debug('RUN: mpirun -n {} ''-x' 'NCCL_DEBUG=INFO'' {} {}'.format( ngpus, sys.executable, bert_pretrain_script)) run_subprocess([ 'mpirun', '-n', str(ngpus), '-x', 'NCCL_DEBUG=INFO', sys.executable, bert_pretrain_script], cwd=cwd) log.debug('RUN: mpirun -n {} {} orttraining_run_glue.py'.format(ngpus, sys.executable)) run_subprocess([ 'mpirun', '-n', str(ngpus), '-x', 'NCCL_DEBUG=INFO', sys.executable, 'orttraining_run_glue.py'], cwd=cwd) # with orttraining_run_glue.py. # 1. we like to force to use single GPU (with CUDA_VISIBLE_DEVICES) # for fine-tune tests. # 2. need to run test separately (not to mix between fp16 # and full precision runs. this need to be investigated). run_subprocess( [sys.executable, 'orttraining_run_glue.py', 'ORTGlueTest.test_bert_with_mrpc', '-v'], cwd=cwd, env={'CUDA_VISIBLE_DEVICES': '0'}) run_subprocess( [sys.executable, 'orttraining_run_glue.py', 'ORTGlueTest.test_bert_fp16_with_mrpc', '-v'], cwd=cwd, env={'CUDA_VISIBLE_DEVICES': '0'}) run_subprocess( [sys.executable, 'orttraining_run_glue.py', 'ORTGlueTest.test_roberta_with_mrpc', '-v'], cwd=cwd, env={'CUDA_VISIBLE_DEVICES': '0'}) run_subprocess( [sys.executable, 'orttraining_run_glue.py', 'ORTGlueTest.test_roberta_fp16_with_mrpc', '-v'], cwd=cwd, env={'CUDA_VISIBLE_DEVICES': '0'}) run_subprocess( [sys.executable, 'orttraining_run_multiple_choice.py', 'ORTMultipleChoiceTest.test_bert_fp16_with_swag', '-v'], cwd=cwd, env={'CUDA_VISIBLE_DEVICES': '0'}) run_subprocess([sys.executable, 'onnxruntime_test_ort_trainer_with_mixed_precision.py'], cwd=cwd) run_subprocess([ sys.executable, 'orttraining_test_transformers.py', 'BertModelTest.test_for_pretraining_mixed_precision'], cwd=cwd) # this test is not stable. need to skip to unblock release # run_subprocess([ # sys.executable, 'orttraining_test_transformers.py', # 'BertModelTest.test_for_pretraining_mixed_precision_with_gradient_accumulation'], cwd=cwd) def run_training_pipeline_e2e_tests(cwd): # pipeline tests are to be added here: log.info("Running pipeline e2e tests.") import torch ngpus = torch.cuda.device_count() command = ['./onnxruntime_training_bert', '--ort_log_severity', '1', '--optimizer=Lamb', '--learning_rate=3e-3', '--max_seq_length=128', '--max_predictions_per_seq=20', '--warmup_ratio=0.2843', '--warmup_mode=Poly', '--model_name', '/bert_ort/bert_models/nv/bert-large/' + 'bert-large-uncased_L_24_H_1024_A_16_V_30528_S_512_Dp_0.1_optimized_layer_norm_opset12', '--train_data_dir', '/bert_data/128/books_wiki_en_corpus/train', '--test_data_dir', '/bert_data/128/books_wiki_en_corpus/test', '--display_loss_steps', '1', '--use_nccl', '--use_mixed_precision', '--allreduce_in_fp16', '--gradient_accumulation_steps', '48', '--num_train_steps', '96', '--train_batch_size', '50'] # TODO: currently the CI machine only has 4 GPUs for parallel tests. # Fill in more pipeline partition options when the machine has different GPUs counts. if ngpus != 4: return # Test 4-way pipeline parallel pp_command = ['mpirun', '-n', str(ngpus)] + command + ['--pipeline_parallel_size', '4', '--cut_group_info', '1149:407-1219/1341/1463/1585/1707/1829,' + '1881:407-1951/2073/2195/2317/2439/2561,' + '2613:407-2683/2805/2927/3049/3171/3293'] command_str = ', '.join(pp_command) log.debug('RUN: ' + command_str) run_subprocess(pp_command, cwd=cwd) # Test 2-way data parallel + 2-way pipeline parallel pp_dp_command = ['mpirun', '-n', str(ngpus)] pp_dp_command = pp_dp_command + command pp_dp_command = pp_dp_command + ['--data_parallel_size', '2', '--pipeline_parallel_size', '2', '--cut_group_info', '1881:407-1951/2073/2195/2317/2439/2561/2683/2805/2927/3049/3171/3293'] command_str = ', '.join(pp_dp_command) log.debug('RUN: ' + command_str) run_subprocess(pp_dp_command, cwd=cwd) def run_onnxruntime_tests(args, source_dir, ctest_path, build_dir, configs): for config in configs: log.info("Running tests for %s configuration", config) cwd = get_config_build_dir(build_dir, config) if args.enable_training and args.use_cuda and args.enable_training_python_frontend_e2e_tests: # run frontend tests for orttraining-linux-gpu-frontend_test-ci-pipeline. # this is not a PR merge test so skip other non-frontend tests. run_training_python_frontend_e2e_tests(cwd=cwd) run_training_python_frontend_tests(cwd=cwd) continue if args.enable_training and args.use_cuda and args.enable_training_pipeline_e2e_tests: # run distributed pipeline test on 4-GPU CI machine. run_training_pipeline_e2e_tests(cwd=cwd) continue if args.android: run_android_tests(args, source_dir, config, cwd) continue elif args.ios: run_ios_tests(args, source_dir, config, cwd) continue dll_path_list = [] if args.use_nuphar: dll_path_list.append(os.path.join( build_dir, config, "external", "tvm", config)) if args.use_tensorrt: dll_path_list.append(os.path.join(args.tensorrt_home, 'lib')) if args.use_mklml: dll_path_list.append(os.path.join(build_dir, config, "mklml", "src", "project_mklml", "lib")) if not is_windows(): # A workaround for making libonnxruntime_providers_shared.so loadable. dll_path_list.append(os.path.join(build_dir, config)) dll_path = None if len(dll_path_list) > 0: dll_path = os.pathsep.join(dll_path_list) if ctest_path is None: # Get the "Google Test Adapter" for vstest. if not os.path.exists(os.path.join(cwd, 'googletestadapter.0.17.1')): run_subprocess( ['nuget.exe', 'restore', os.path.join(source_dir, 'packages.config'), '-ConfigFile', os.path.join(source_dir, 'NuGet.config'), '-PackagesDirectory', cwd]) cwd2 = os.path.join(cwd, config) executables = ['onnxruntime_test_all.exe'] if args.build_shared_lib: executables.append('onnxruntime_shared_lib_test.exe') executables.append('onnxruntime_global_thread_pools_test.exe') run_subprocess( ['vstest.console.exe', '--parallel', '--TestAdapterPath:..\\googletestadapter.0.17.1\\build\\_common', # noqa '/Logger:trx', '/Enablecodecoverage', '/Platform:x64', "/Settings:%s" % os.path.join( source_dir, 'cmake\\codeconv.runsettings')] + executables, cwd=cwd2, dll_path=dll_path) else: ctest_cmd = [ctest_path, "--build-config", config, "--verbose", "--timeout", "3600"] run_subprocess(ctest_cmd, cwd=cwd, dll_path=dll_path) if args.enable_pybind: # Disable python tests for TensorRT because many tests are # not supported yet. if args.use_tensorrt: return # Disable python tests in a reduced build as we don't know which ops have been included and which if args.include_ops_by_model or args.include_ops_by_config or args.minimal_build: return if is_windows(): cwd = os.path.join(cwd, config) run_subprocess([sys.executable, 'onnxruntime_test_python.py'], cwd=cwd, dll_path=dll_path) if args.enable_symbolic_shape_infer_tests: run_subprocess([sys.executable, 'onnxruntime_test_python_symbolic_shape_infer.py'], cwd=cwd, dll_path=dll_path) if args.use_cuda: log.info("Testing IOBinding feature") run_subprocess([sys.executable, 'onnxruntime_test_python_iobinding.py'], cwd=cwd, dll_path=dll_path) if not args.disable_ml_ops: run_subprocess([sys.executable, 'onnxruntime_test_python_mlops.py'], cwd=cwd, dll_path=dll_path) if args.enable_training and args.use_cuda: # run basic frontend tests run_training_python_frontend_tests(cwd=cwd) try: import onnx # noqa onnx_test = True except ImportError as error: log.exception(error) log.warning("onnx is not installed. The ONNX tests will be skipped.") onnx_test = False if onnx_test: run_subprocess([sys.executable, 'onnxruntime_test_python_backend.py'], cwd=cwd, dll_path=dll_path) if not args.disable_ml_ops: run_subprocess([sys.executable, 'onnxruntime_test_python_backend_mlops.py'], cwd=cwd, dll_path=dll_path) run_subprocess([sys.executable, os.path.join(source_dir, 'onnxruntime', 'test', 'onnx', 'gen_test_models.py'), '--output_dir', 'test_models'], cwd=cwd) if not args.skip_onnx_tests: run_subprocess([os.path.join(cwd, 'onnx_test_runner'), 'test_models'], cwd=cwd) if config != 'Debug': run_subprocess([sys.executable, 'onnx_backend_test_series.py'], cwd=cwd, dll_path=dll_path) if not args.skip_keras_test: try: import onnxmltools # noqa import keras # noqa onnxml_test = True except ImportError: log.warning( "onnxmltools and keras are not installed. " "The keras tests will be skipped.") onnxml_test = False if onnxml_test: run_subprocess( [sys.executable, 'onnxruntime_test_python_keras.py'], cwd=cwd, dll_path=dll_path) def nuphar_run_python_tests(build_dir, configs): for config in configs: if config == 'Debug': continue cwd = get_config_build_dir(build_dir, config) if is_windows(): cwd = os.path.join(cwd, config) dll_path = os.path.join(build_dir, config, "external", "tvm", config) # install onnx for shape inference in testing Nuphar scripts # this needs to happen after onnx_test_data preparation which # uses onnx 1.3.0 run_subprocess( [sys.executable, '-m', 'pip', 'install', '--user', 'onnx==1.5.0']) run_subprocess( [sys.executable, 'onnxruntime_test_python_nuphar.py'], cwd=cwd, dll_path=dll_path) def run_nodejs_tests(nodejs_binding_dir): args = ['npm', 'test', '--', '--timeout=2000'] if is_windows(): args = ['cmd', '/c'] + args run_subprocess(args, cwd=nodejs_binding_dir) def build_python_wheel( source_dir, build_dir, configs, use_cuda, use_ngraph, use_dnnl, use_tensorrt, use_openvino, use_nuphar, use_vitisai, use_acl, use_armnn, use_dml, wheel_name_suffix, enable_training, nightly_build=False, featurizers_build=False, use_ninja=False): for config in configs: cwd = get_config_build_dir(build_dir, config) if is_windows() and not use_ninja: cwd = os.path.join(cwd, config) args = [sys.executable, os.path.join(source_dir, 'setup.py'), 'bdist_wheel'] # We explicitly override the platform tag in the name of the generated build wheel # so that we can install the wheel on Mac OS X versions 10.12+. # Without this explicit override, we will something like this while building on MacOS 10.14 - # [WARNING] MACOSX_DEPLOYMENT_TARGET is set to a lower value (10.12) # than the version on which the Python interpreter was compiled (10.14) and will be ignored. # Since we need to support 10.12+, we explicitly override the platform tag. # See PR #3626 for more details if is_macOS(): args += ['-p', 'macosx_10_12_x86_64'] # Any combination of the following arguments can be applied if nightly_build: args.append('--nightly_build') if featurizers_build: args.append("--use_featurizers") if wheel_name_suffix: args.append('--wheel_name_suffix={}'.format(wheel_name_suffix)) if enable_training: args.append("--enable_training") # The following arguments are mutually exclusive if use_tensorrt: args.append('--use_tensorrt') elif use_cuda: args.append('--use_cuda') elif use_ngraph: args.append('--use_ngraph') elif use_openvino: args.append('--use_openvino') elif use_dnnl: args.append('--use_dnnl') elif use_nuphar: args.append('--use_nuphar') elif use_vitisai: args.append('--use_vitisai') elif use_acl: args.append('--use_acl') elif use_armnn: args.append('--use_armnn') elif use_dml: args.append('--use_dml') run_subprocess(args, cwd=cwd) def derive_linux_build_property(): if is_windows(): return "/p:IsLinuxBuild=\"false\"" else: return "/p:IsLinuxBuild=\"true\"" def build_nuget_package(source_dir, build_dir, configs, use_cuda, use_openvino, use_tensorrt, use_dnnl, use_mklml): if not (is_windows() or is_linux()): raise BuildError( 'Currently csharp builds and nuget package creation is only supportted ' 'on Windows and Linux platforms.') csharp_build_dir = os.path.join(source_dir, 'csharp') is_linux_build = derive_linux_build_property() # derive package name and execution provider based on the build args execution_provider = "/p:ExecutionProvider=\"None\"" package_name = "/p:OrtPackageId=\"Microsoft.ML.OnnxRuntime\"" if use_openvino: execution_provider = "/p:ExecutionProvider=\"openvino\"" package_name = "/p:OrtPackageId=\"Microsoft.ML.OnnxRuntime.OpenVino\"" elif use_tensorrt: execution_provider = "/p:ExecutionProvider=\"tensorrt\"" package_name = "/p:OrtPackageId=\"Microsoft.ML.OnnxRuntime.TensorRT\"" elif use_dnnl: execution_provider = "/p:ExecutionProvider=\"dnnl\"" package_name = "/p:OrtPackageId=\"Microsoft.ML.OnnxRuntime.DNNL\"" elif use_cuda: package_name = "/p:OrtPackageId=\"Microsoft.ML.OnnxRuntime.Gpu\"" elif use_mklml: package_name = "/p:OrtPackageId=\"Microsoft.ML.OnnxRuntime.MKLML\"" else: pass # set build directory based on build_dir arg native_dir = os.path.normpath(os.path.join(source_dir, build_dir)) ort_build_dir = "/p:OnnxRuntimeBuildDirectory=\"" + native_dir + "\"" # dotnet restore cmd_args = ["dotnet", "restore", "OnnxRuntime.CSharp.sln", "--configfile", "Nuget.CSharp.config"] run_subprocess(cmd_args, cwd=csharp_build_dir) # build csharp bindings and create nuget package for each config for config in configs: if is_linux(): native_build_dir = os.path.join(native_dir, config) cmd_args = ["make", "install", "DESTDIR=.//nuget-staging"] run_subprocess(cmd_args, cwd=native_build_dir) configuration = "/p:Configuration=\"" + config + "\"" cmd_args = ["dotnet", "msbuild", "OnnxRuntime.CSharp.sln", configuration, package_name, is_linux_build, ort_build_dir] run_subprocess(cmd_args, cwd=csharp_build_dir) cmd_args = [ "dotnet", "msbuild", "OnnxRuntime.CSharp.proj", "/t:CreatePackage", package_name, configuration, execution_provider, is_linux_build, ort_build_dir] run_subprocess(cmd_args, cwd=csharp_build_dir) def run_csharp_tests(source_dir, build_dir, use_cuda, use_openvino, use_tensorrt, use_dnnl): # Currently only running tests on windows. if not is_windows(): return csharp_source_dir = os.path.join(source_dir, 'csharp') is_linux_build = derive_linux_build_property() # define macros based on build args macros = "" if use_openvino: macros += "USE_OPENVINO;" if use_tensorrt: macros += "USE_TENSORRT;" if use_dnnl: macros += "USE_DNNL;" if use_cuda: macros += "USE_CUDA;" define_constants = "" if macros != "": define_constants = "/p:DefineConstants=\"" + macros + "\"" # set build directory based on build_dir arg native_build_dir = os.path.normpath(os.path.join(source_dir, build_dir)) ort_build_dir = "/p:OnnxRuntimeBuildDirectory=\"" + native_build_dir + "\"" # Skip pretrained models test. Only run unit tests as part of the build # add "--verbosity", "detailed" to this command if required cmd_args = ["dotnet", "test", "test\\Microsoft.ML.OnnxRuntime.Tests\\Microsoft.ML.OnnxRuntime.Tests.csproj", "--filter", "FullyQualifiedName!=Microsoft.ML.OnnxRuntime.Tests.InferenceTest.TestPreTrainedModels", is_linux_build, define_constants, ort_build_dir] run_subprocess(cmd_args, cwd=csharp_source_dir) def build_protoc_for_host(cmake_path, source_dir, build_dir, args): if (args.arm or args.arm64 or args.enable_windows_store) and (not is_windows() and not args.ios): raise BuildError( 'Currently only support building protoc for Windows host while ' 'cross-compiling for ARM/ARM64/Store and linux cross-compiling iOS') log.info( "Building protoc for host to be used in cross-compiled build process") protoc_build_dir = os.path.join(os.getcwd(), build_dir, 'host_protoc') os.makedirs(protoc_build_dir, exist_ok=True) # Generate step cmd_args = [ cmake_path, os.path.join(source_dir, 'cmake', 'external', 'protobuf', 'cmake'), '-Dprotobuf_BUILD_TESTS=OFF', '-Dprotobuf_WITH_ZLIB_DEFAULT=OFF', '-Dprotobuf_BUILD_SHARED_LIBS=OFF' ] is_ninja = args.cmake_generator == 'Ninja' if args.cmake_generator is not None and not (is_macOS() and args.use_xcode): cmd_args += ['-G', args.cmake_generator] if is_windows(): if not is_ninja: cmd_args += ['-T', 'host=x64'] elif is_macOS(): if args.use_xcode: cmd_args += ['-G', 'Xcode'] # CMake < 3.18 has a bug setting system arch to arm64 (if not specified) for Xcode 12, # protoc for host should be built using host architecture # Explicitly specify the CMAKE_OSX_ARCHITECTURES for x86_64 Mac. import platform if platform.machine() == 'x86_64': cmd_args += ['-DCMAKE_OSX_ARCHITECTURES=x86_64'] run_subprocess(cmd_args, cwd=protoc_build_dir) # Build step cmd_args = [cmake_path, "--build", protoc_build_dir, "--config", "Release", "--target", "protoc"] run_subprocess(cmd_args) # Absolute protoc path is needed for cmake config_dir = '' suffix = '' if (is_windows() and not is_ninja) or (is_macOS() and args.use_xcode): config_dir = 'Release' if is_windows(): suffix = '.exe' expected_protoc_path = os.path.join(protoc_build_dir, config_dir, 'protoc' + suffix) if not os.path.exists(expected_protoc_path): raise BuildError("Couldn't find {}. Host build of protoc failed.".format(expected_protoc_path)) return expected_protoc_path def generate_documentation(source_dir, build_dir, configs): operator_doc_path = os.path.join(source_dir, 'docs', 'ContribOperators.md') opkernel_doc_path = os.path.join(source_dir, 'docs', 'OperatorKernels.md') for config in configs: shutil.copy( os.path.join(source_dir, 'tools', 'python', 'gen_contrib_doc.py'), os.path.join(build_dir, config)) shutil.copy( os.path.join(source_dir, 'tools', 'python', 'gen_opkernel_doc.py'), os.path.join(build_dir, config)) run_subprocess( [sys.executable, 'gen_contrib_doc.py', '--output_path', operator_doc_path], cwd=os.path.join(build_dir, config)) run_subprocess( [sys.executable, 'gen_opkernel_doc.py', '--output_path', opkernel_doc_path], cwd=os.path.join(build_dir, config)) docdiff = '' try: docdiff = subprocess.check_output(['git', 'diff', opkernel_doc_path]) except subprocess.CalledProcessError: print('git diff returned non-zero error code') if len(docdiff) > 0: log.warning( 'The updated opkernel document file ' + str(opkernel_doc_path) + ' is different from the checked in version. Consider ' 'regenerating the file with CPU, DNNL and CUDA providers enabled.') log.debug('diff:\n' + str(docdiff)) docdiff = '' try: docdiff = subprocess.check_output(['git', 'diff', operator_doc_path]) except subprocess.CalledProcessError: print('git diff returned non-zero error code') if len(docdiff) > 0: raise BuildError( 'The updated operator document file ' + str(operator_doc_path) + ' must be checked in.\n diff:\n' + str(docdiff)) def main(): args = parse_arguments() cmake_extra_defines = (args.cmake_extra_defines if args.cmake_extra_defines else []) cross_compiling = args.arm or args.arm64 or args.android if not (args.update or args.clean or args.build or args.test): log.debug( "Defaulting to running update, build " "[and test for native builds].") args.update = True args.build = True if cross_compiling: args.test = args.android_abi == 'x86_64' or args.android_abi == 'arm64-v8a' else: args.test = True if args.skip_tests: args.test = False if args.include_ops_by_model or args.include_ops_by_config: from exclude_unused_ops import exclude_unused_ops models_path = args.include_ops_by_model if args.include_ops_by_model else '' config_path = args.include_ops_by_config if args.include_ops_by_config else '' exclude_unused_ops(models_path, config_path, use_cuda=args.use_cuda) if args.use_tensorrt: args.use_cuda = True if args.build_wheel or args.gen_doc: args.enable_pybind = True if args.build_csharp or args.build_nuget or args.build_java or args.build_nodejs: args.build_shared_lib = True if args.build_nuget and cross_compiling: raise BuildError('Currently nuget package creation is not supported while cross-compiling') if args.enable_pybind and args.disable_exceptions: raise BuildError('Python bindings require exceptions to be enabled.') if args.minimal_build and args.disable_ort_format_load: raise BuildError('Minimal build requires loading ORT format models.') if args.use_openvino == "VAD-F_FP32": args.test = False configs = set(args.config) cmake_path = resolve_executable_path(args.cmake_path) ctest_path = None if args.use_vstest else resolve_executable_path( args.ctest_path) build_dir = args.build_dir script_dir = os.path.realpath(os.path.dirname(__file__)) source_dir = os.path.normpath(os.path.join(script_dir, "..", "..")) cuda_home, cudnn_home = setup_cuda_vars(args) mpi_home = args.mpi_home nccl_home = args.nccl_home tensorrt_home = setup_tensorrt_vars(args) migraphx_home = setup_migraphx_vars(args) os.makedirs(build_dir, exist_ok=True) log.info("Build started") if args.update: cmake_extra_args = [] path_to_protoc_exe = args.path_to_protoc_exe if not args.skip_submodule_sync: update_submodules(source_dir) if is_windows(): if args.cmake_generator == 'Ninja': if args.x86 or args.arm or args.arm64: raise BuildError( "To cross-compile with Ninja, load the toolset " "environment for the target processor (e.g. Cross " "Tools Command Prompt for VS)") cmake_extra_args = ['-G', args.cmake_generator] elif args.x86: cmake_extra_args = [ '-A', 'Win32', '-T', 'host=x64', '-G', args.cmake_generator ] elif args.arm or args.arm64: if path_to_protoc_exe is None: path_to_protoc_exe = build_protoc_for_host( cmake_path, source_dir, build_dir, args) if args.arm: cmake_extra_args = ['-A', 'ARM'] else: cmake_extra_args = ['-A', 'ARM64'] cmake_extra_args += ['-G', args.cmake_generator] if args.test: log.info( "Cannot test on host build machine for cross-compiled " "ARM(64) builds. Will skip test running after build.") args.test = False else: if (args.msvc_toolset == '14.16' and args.cmake_generator == 'Visual Studio 16 2019'): toolset = 'v141,host=x64,version=' + args.msvc_toolset elif args.msvc_toolset: toolset = 'host=x64,version=' + args.msvc_toolset else: toolset = 'host=x64' if args.cuda_version: toolset += ',cuda=' + args.cuda_version cmake_extra_args = [ '-A', 'x64', '-T', toolset, '-G', args.cmake_generator ] if args.enable_windows_store: cmake_extra_args.append( '-DCMAKE_TOOLCHAIN_FILE=' + os.path.join( source_dir, 'cmake', 'store_toolchain.cmake')) if args.enable_wcos: cmake_extra_args.append('-DCMAKE_USER_MAKE_RULES_OVERRIDE=wcos_rules_override.cmake') elif args.cmake_generator is not None and not (is_macOS() and args.use_xcode): cmake_extra_args += ['-G', args.cmake_generator] elif is_macOS() and args.use_xcode: cmake_extra_args += ['-G', 'Xcode'] if (args.android or args.ios or args.enable_windows_store) and args.path_to_protoc_exe is None: path_to_protoc_exe = build_protoc_for_host( cmake_path, source_dir, build_dir, args) if is_ubuntu_1604(): if (args.arm or args.arm64): raise BuildError( "Only Windows ARM(64) cross-compiled builds supported " "currently through this script") install_ubuntu_deps(args) if not is_docker() and not args.use_acl and not args.use_armnn: install_python_deps() if args.enable_pybind and is_windows(): install_python_deps(args.numpy_version) if args.enable_onnx_tests: setup_test_data(build_dir, configs) generate_build_tree( cmake_path, source_dir, build_dir, cuda_home, cudnn_home, mpi_home, nccl_home, tensorrt_home, migraphx_home, path_to_protoc_exe, configs, cmake_extra_defines, args, cmake_extra_args) if args.clean: clean_targets(cmake_path, build_dir, configs) setup_dml_build(args, cmake_path, build_dir, configs) if args.build: build_targets(args, cmake_path, build_dir, configs, args.parallel, args.target) if args.test: run_onnxruntime_tests(args, source_dir, ctest_path, build_dir, configs) if args.enable_pybind and not args.skip_onnx_tests and args.use_nuphar: nuphar_run_python_tests(build_dir, configs) if args.build_nodejs and not args.skip_nodejs_tests: nodejs_binding_dir = os.path.normpath(os.path.join(source_dir, "nodejs")) run_nodejs_tests(nodejs_binding_dir) if args.build: if args.build_wheel: nightly_build = bool(os.getenv('NIGHTLY_BUILD') == '1') build_python_wheel( source_dir, build_dir, configs, args.use_cuda, args.use_ngraph, args.use_dnnl, args.use_tensorrt, args.use_openvino, args.use_nuphar, args.use_vitisai, args.use_acl, args.use_armnn, args.use_dml, args.wheel_name_suffix, args.enable_training, nightly_build=nightly_build, featurizers_build=args.use_featurizers, use_ninja=(args.cmake_generator == 'Ninja') ) if args.build_nuget: build_nuget_package( source_dir, build_dir, configs, args.use_cuda, args.use_openvino, args.use_tensorrt, args.use_dnnl, args.use_mklml ) if args.test and args.build_nuget: run_csharp_tests( source_dir, build_dir, args.use_cuda, args.use_openvino, args.use_tensorrt, args.use_dnnl) if args.gen_doc and (args.build or args.test): generate_documentation(source_dir, build_dir, configs) log.info("Build complete") if __name__ == "__main__": try: sys.exit(main()) except BaseError as e: log.error(str(e)) sys.exit(1)

true

f70045804ab2dc1175a34896e20405f1777bd9de

3,755

py

Python

acme/agents/jax/dqn/config.py

nrocketmann/acme-intrinsic

ce90aa15ec785a8618a2505410ab6b9f1f9b5a32

[ "Apache-2.0" ]

null

acme/agents/jax/dqn/config.py

nrocketmann/acme-intrinsic

ce90aa15ec785a8618a2505410ab6b9f1f9b5a32

[ "Apache-2.0" ]

null

acme/agents/jax/dqn/config.py

nrocketmann/acme-intrinsic

ce90aa15ec785a8618a2505410ab6b9f1f9b5a32

[ "Apache-2.0" ]

null

# python3 # Copyright 2018 DeepMind Technologies Limited. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """DQN config.""" import dataclasses from acme.adders import reverb as adders_reverb import numpy as np @dataclasses.dataclass class DQNConfig: """Configuration options for DQN agent.""" epsilon: float = 0.05 # Action selection via epsilon-greedy policy. # TODO(b/191706065): update all clients and remove this field. seed: int = 1 # Random seed. # Learning rule learning_rate: float = 1e-3 # Learning rate for Adam optimizer. adam_eps: float = 1e-8 # Eps for Adam optimizer. discount: float = 0.99 # Discount rate applied to value per timestep. n_step: int = 5 # N-step TD learning. target_update_period: int = 100 # Update target network every period. max_gradient_norm: float = np.inf # For gradient clipping. # Replay options batch_size: int = 256 # Number of transitions per batch. min_replay_size: int = 1_000 # Minimum replay size. max_replay_size: int = 1_000_000 # Maximum replay size. replay_table_name: str = adders_reverb.DEFAULT_PRIORITY_TABLE importance_sampling_exponent: float = 0.2 # Importance sampling for replay. priority_exponent: float = 0.6 # Priority exponent for replay. prefetch_size: int = 4 # Prefetch size for reverb replay performance. samples_per_insert: float = 0.5 # Ratio of learning samples to insert. # Rate to be used for the SampleToInsertRatio rate limitter tolerance. # See a formula in make_replay_tables for more details. samples_per_insert_tolerance_rate: float = 0.1 # How many gradient updates to perform per learner step. num_sgd_steps_per_step: int = 1 @dataclasses.dataclass class DQNEmpowermentConfig: """Configuration options for DQN agent.""" epsilon: float = 0.05 # Action selection via epsilon-greedy policy. # TODO(b/191706065): update all clients and remove this field. seed: int = 1 # Random seed. # Learning rule learning_rate: float = 1e-3 # Learning rate for Adam optimizer. adam_eps: float = 1e-8 # Eps for Adam optimizer. discount: float = 0.99 # Discount rate applied to value per timestep. n_step: int = 5 # N-step TD learning. target_update_period: int = 100 # Update target network every period. max_gradient_norm: float = np.inf # For gradient clipping. # Replay options batch_size: int = 256 # Number of transitions per batch. min_replay_size: int = 1_000 # Minimum replay size. max_replay_size: int = 1_000_000 # Maximum replay size. replay_table_name: str = adders_reverb.DEFAULT_PRIORITY_TABLE importance_sampling_exponent: float = 0.2 # Importance sampling for replay. priority_exponent: float = 0.6 # Priority exponent for replay. prefetch_size: int = 4 # Prefetch size for reverb replay performance. samples_per_insert: float = 0.5 # Ratio of learning samples to insert. sequence_length: int = 10 prefetch_size: int = 4 sequence_period: int = 2 # Rate to be used for the SampleToInsertRatio rate limitter tolerance. # See a formula in make_replay_tables for more details. samples_per_insert_tolerance_rate: float = 0.1 # How many gradient updates to perform per learner step. num_sgd_steps_per_step: int = 1

42.191011

78

0.747537

import dataclasses from acme.adders import reverb as adders_reverb import numpy as np @dataclasses.dataclass class DQNConfig: epsilon: float = 0.05 seed: int = 1 learning_rate: float = 1e-3 adam_eps: float = 1e-8 discount: float = 0.99 n_step: int = 5 target_update_period: int = 100 max_gradient_norm: float = np.inf batch_size: int = 256 min_replay_size: int = 1_000 max_replay_size: int = 1_000_000 replay_table_name: str = adders_reverb.DEFAULT_PRIORITY_TABLE importance_sampling_exponent: float = 0.2 priority_exponent: float = 0.6 prefetch_size: int = 4 samples_per_insert: float = 0.5 samples_per_insert_tolerance_rate: float = 0.1 num_sgd_steps_per_step: int = 1 @dataclasses.dataclass class DQNEmpowermentConfig: epsilon: float = 0.05 seed: int = 1 learning_rate: float = 1e-3 adam_eps: float = 1e-8 discount: float = 0.99 n_step: int = 5 target_update_period: int = 100 max_gradient_norm: float = np.inf batch_size: int = 256 min_replay_size: int = 1_000 max_replay_size: int = 1_000_000 replay_table_name: str = adders_reverb.DEFAULT_PRIORITY_TABLE importance_sampling_exponent: float = 0.2 priority_exponent: float = 0.6 prefetch_size: int = 4 samples_per_insert: float = 0.5 sequence_length: int = 10 prefetch_size: int = 4 sequence_period: int = 2 samples_per_insert_tolerance_rate: float = 0.1 num_sgd_steps_per_step: int = 1

true

f7004704fef6b4c992ee9e18c045004750d66571

587

py

Python

core/learnable_priors/normal_prior.py

insilicomedicine/TRIP

5e7b9da298aa47a71c71e1144ff1d8e538dbccaa

[ "MIT" ]

25

2019-10-29T02:06:03.000Z

2021-04-12T03:14:21.000Z

core/learnable_priors/normal_prior.py

insilicomedicine/TRIP

5e7b9da298aa47a71c71e1144ff1d8e538dbccaa

[ "MIT" ]

1

2020-12-23T01:51:18.000Z

core/learnable_priors/normal_prior.py

insilicomedicine/TRIP

5e7b9da298aa47a71c71e1144ff1d8e538dbccaa

[ "MIT" ]

4

2020-01-05T17:32:54.000Z

2020-11-18T05:24:37.000Z

import torch from torch import nn from torch.distributions import MultivariateNormal class Normal(nn.Module): def __init__(self, num_vars=100): super(Normal, self).__init__() self.num_vars = num_vars self.means = nn.Parameter(torch.zeros(num_vars)) self.std = nn.Parameter(torch.eye(num_vars)) def log_prob(self, x): distr = MultivariateNormal(self.means, self.std) return distr.log_prob(x) def sample(self, num_samples): distr = MultivariateNormal(self.means, self.std) return distr.sample_n(num_samples)

27.952381

56

0.684838

import torch from torch import nn from torch.distributions import MultivariateNormal class Normal(nn.Module): def __init__(self, num_vars=100): super(Normal, self).__init__() self.num_vars = num_vars self.means = nn.Parameter(torch.zeros(num_vars)) self.std = nn.Parameter(torch.eye(num_vars)) def log_prob(self, x): distr = MultivariateNormal(self.means, self.std) return distr.log_prob(x) def sample(self, num_samples): distr = MultivariateNormal(self.means, self.std) return distr.sample_n(num_samples)

true

f70047cdafe4dcd083f47814ee7d17be097fee36

2,338

py

Python

algorithms/quicksort.py

not-sponsored/Guide-to-Data-Structures-and-Algorithms-Exercises

a905298c594a826e558cd1c94876b632db5d4d11

[ "Fair" ]

null

algorithms/quicksort.py

not-sponsored/Guide-to-Data-Structures-and-Algorithms-Exercises

a905298c594a826e558cd1c94876b632db5d4d11

[ "Fair" ]

null

algorithms/quicksort.py

not-sponsored/Guide-to-Data-Structures-and-Algorithms-Exercises

a905298c594a826e558cd1c94876b632db5d4d11

[ "Fair" ]

null

import sys class SortableArray(): def __init__(self, arr): self.arr = arr def partition(self, left, right): # choose right most as pivot pivot_index = right # get pivot value for compares pivot = self.arr[pivot_index] right -= 1 print(f'left orig: {left} right orig: {right}') while True: # move left pointer until we hit a value >= pivot while self.arr[left] < pivot: print(f'left: {left}') left += 1 print('left', left) # move right until hit a value <= pivot or hits 0 index while right > 0 and self.arr[right] > pivot: print(f'right: {right}') right -= 1 print('right', right) # if left >= right then we break and swap if left >= right: break # if not we swap right and left and continue else: self.arr[left], self.arr[right] = self.arr[right], self.arr[left] left += 1 # finally swap left and pivot self.arr[left], self.arr[pivot_index] = self.arr[pivot_index], self.arr[left] print(self.arr) return left def quicksort(self, left, right): # base case one element if right - left <= 0: return # partition and get pivot pivot_index = self.partition(left, right) # recursively call for left partition self.quicksort(left, pivot_index - 1) # recursively call for right partition self.quicksort(pivot_index + 1, right) def quickselect(self, kth_lowest_num, left, right): # base case one element if right - left <= 0: return self.arr[left] # partition and get pivot pivot_index = self.partition(left, right) # if kth is less than pivot, recursively call for left if kth_lowest_num < pivot_index: self.quickselect(kth_lowest_num, left, pivot_index - 1) # if kth is greater than pivot, recursively call for right elif kth_lowest_num > pivot_index: self.quickselect(kth_lowest_num, pivot_index + 1, right) # else we have the kth num bc kth = pivot_index else: # weird error returns a None print(f'kth {kth_lowest_num}: {self.arr[pivot_index]}') return self.arr[pivot_index] def main(arr, kth): sortable = SortableArray(arr) print(sortable.quickselect(kth, 0, len(arr) - 1)) sortable.quicksort(0, len(arr) - 1) print(f'final sorted array: {sortable.arr}') if __name__ == '__main__': main([int(x) for x in sys.argv[1].split(',')], int(sys.argv[2]))

28.168675

79

0.671514

import sys class SortableArray(): def __init__(self, arr): self.arr = arr def partition(self, left, right): pivot_index = right pivot = self.arr[pivot_index] right -= 1 print(f'left orig: {left} right orig: {right}') while True: while self.arr[left] < pivot: print(f'left: {left}') left += 1 print('left', left) while right > 0 and self.arr[right] > pivot: print(f'right: {right}') right -= 1 print('right', right) if left >= right: break else: self.arr[left], self.arr[right] = self.arr[right], self.arr[left] left += 1 self.arr[left], self.arr[pivot_index] = self.arr[pivot_index], self.arr[left] print(self.arr) return left def quicksort(self, left, right): if right - left <= 0: return pivot_index = self.partition(left, right) self.quicksort(left, pivot_index - 1) self.quicksort(pivot_index + 1, right) def quickselect(self, kth_lowest_num, left, right): if right - left <= 0: return self.arr[left] pivot_index = self.partition(left, right) if kth_lowest_num < pivot_index: self.quickselect(kth_lowest_num, left, pivot_index - 1) elif kth_lowest_num > pivot_index: self.quickselect(kth_lowest_num, pivot_index + 1, right) else: print(f'kth {kth_lowest_num}: {self.arr[pivot_index]}') return self.arr[pivot_index] def main(arr, kth): sortable = SortableArray(arr) print(sortable.quickselect(kth, 0, len(arr) - 1)) sortable.quicksort(0, len(arr) - 1) print(f'final sorted array: {sortable.arr}') if __name__ == '__main__': main([int(x) for x in sys.argv[1].split(',')], int(sys.argv[2]))

true

f70049a506cae229d8102cddf51c76a5b5acadbc

18,069

py

Python

dit/math/ops.py

Ejjaffe/dit

c9d206f03d1de5a0a298b1d0ea9d79ea5e789ee1

[ "BSD-3-Clause" ]

1

2020-03-13T10:30:11.000Z

dit/math/ops.py

Ejjaffe/dit

c9d206f03d1de5a0a298b1d0ea9d79ea5e789ee1

[ "BSD-3-Clause" ]

null

dit/math/ops.py

Ejjaffe/dit

c9d206f03d1de5a0a298b1d0ea9d79ea5e789ee1

[ "BSD-3-Clause" ]

null

""" Classes to contextualize math operations in log vs linear space. """ from types import MethodType import numpy as np from ..exceptions import InvalidBase __all__ = ( 'get_ops', 'LinearOperations', 'LogOperations', ) # For 2.x, these are ascii strings. For 3.x these are unicode strings. acceptable_base_strings = {'linear', 'e'} def get_ops(base): """ Returns an *Operations instance, depending on the base. Parameters ---------- base : float, 'linear', 'e' The base for the Operations instance. """ # Let's not initialize unless we have to. if base in cache: ops = cache[base] else: # This assumes that 'linear' is in cache. ops = LogOperations(base) cache[base] = ops return ops def exp_func(b): """ Returns a base-`b` exponential function. Parameters ---------- b : positive float or 'e' The base of the desired exponential function. Returns ------- exp : function The base-`b` exponential function. The returned function will operate elementwise on NumPy arrays, but note, it is not a ufunc. Examples -------- >>> exp2 = exp_func(2) >>> exp2(1) 2.0 >>> exp3 = exp_func(3) >>> exp3(1) 3.0 Raises ------ InvalidBase If the base is less than zero or equal to one. """ from dit.utils import is_string_like if is_string_like(b) and b not in acceptable_base_strings: raise InvalidBase(msg=b) if b == 'linear': exp = lambda x: x # pragma: no branch elif b == 2: exp = np.exp2 elif b == 10: exp = lambda x: 10**x elif b == 'e' or np.isclose(b, np.e): exp = np.exp else: if b <= 0 or b == 1: raise InvalidBase(b) def exp(x, base=b): """ Return `base`**`x` Parameters ---------- x : float The number to exponentiate base : float The base of the exponential Returns ------- p : float `base`**`x` """ return base**np.asarray(x) return exp def log_func(b): """ Returns a base-`b` logarithm function. Parameters ---------- b : positive float or 'e' The base of the desired logarithm function. Returns ------- log : function The base-`b` logarithm function. The returned function will operate elementwise on NumPy arrays, but note, it is not a ufunc. Examples -------- >>> log2 = log_func(2) >>> log2(2) 1.0 >>> log3 = log_func(3) >>> log3(3) 1.0 Raises ------ InvalidBase If the base is less than zero or equal to one. """ from dit.utils import is_string_like if is_string_like(b) and b not in acceptable_base_strings: raise InvalidBase(msg=b) if b == 'linear': log = lambda x: x # pragma: no branch elif b == 2: log = np.log2 elif b == 10: log = np.log10 elif b == 'e' or np.isclose(b, np.e): log = np.log else: if b <= 0 or b == 1: raise InvalidBase(b) Z = np.log(b) def log(x, func=np.log): """ Return the log of `x` Parameters ---------- x : float The value to take the log of func : function A logarithm function Returns ------- log : float The logarithm of `x` in base `b` (from outer scope) """ return func(x) / Z return log class Operations(object): """ Base class which implements certain math operations. For example, regular addition with log probabilities is handled specially. While we could implement many more operations, we do not. Their usage is uncommon and their implementation would be slower as well. For example, subtraction with log probabailities must go as: .. math:: log_2(x-y) = log_2(x) + log_2(1 - 2^[ log_2(y) - log_2(x) ]) Note that if :math:`y > x`, then :math:`log(y) > log(x)` and the inner term of the second logarithm will be less than 0, yielding NaN. """ ### Do we allow base == 'e' or should we convert to its numerical value? ### Ans: We store whatever was specified but provide get_base() with an ### option to return a numerical base. one = None zero = None base = None exp = None log = None def get_base(self, numerical=False): """ Returns the base in which operations take place. For linear-based operations, the result is 'linear'. Parameters ---------- numerical : bool If `True`, then if the base is 'e', it is returned as a float. """ if numerical and self.base == 'e': base = np.exp(1) else: base = self.base return base def is_null(self, p): """ Returns `True` if `p` is a null probability. Parameters ---------- p : float The probability to be tested. """ return np.isclose(self.zero, p) def is_null_exact(self, p): """ Returns `True` if `p` is exactly a null probability. Parameters ---------- p : float The probability to be tested. """ return self.zero == p def add(self, x, y): """ Abstract base class """ raise NotImplementedError def add_inplace(self, x, y): """ Abstract base class """ raise NotImplementedError def add_reduce(self, x): """ Abstract base class """ raise NotImplementedError def mult(self, x, y): """ Abstract base class """ raise NotImplementedError def mult_inplace(self, x, y): """ Abstract base class """ raise NotImplementedError def mult_reduce(self, x): """ Abstract base class """ raise NotImplementedError def invert(self, x): """ Abstract base class """ raise NotImplementedError def normalize(self, x): """ Abstract base class """ raise NotImplementedError class LinearOperations(Operations): """ The class of operations on linear values. """ one = 1 zero = 0 base = 'linear' # If the functions below are standard Python functions (as opposed to # NumPy ufuncs), then they will be treated as unbound methods for the class. # During instantiation, they are bound to the instance (since before # instantiation they are class methods) and thus, we are left with # bound methods (undesirably). If we had modified these attributes in the # __init__ function, then they would not be bound (or even unbound methods) # but functions instead (desirably). This is precisely what LogOperations # does, which is why it does not have this issue. An alternative approach # is to explicitly declare these functions to be static methods, as we # do below. # exp = staticmethod(exp_func(base)) log = staticmethod(log_func(base)) def add(self, x, y): """ Add the arrays element-wise. Neither x nor y will be modified. Assumption: :math:`y >= 0`. Operation: :math:`z[i] = x[i] + y[i]` Parameters ---------- x, y : NumPy arrays, shape (n,) The arrays to add. Returns ------- z : NumPy array, shape (n,) The resultant array. """ z = x + y return z def add_inplace(self, x, y): """ Adds `y` to `x`, in-place. `x` will be modified, but `y` will not. Assumption: :math:`y >= 0`. Operation: :math:`x[i] += y[i]` Parameters ---------- x, y : NumPy arrays, shape (n,) The arrays to add. Returns ------- x : NumPy array, shape (n,) The resultant array. """ x += y return x def add_reduce(self, x, axis=None): """ Performs an `addition' reduction on `x`. Assumption: :math:`y >= 0`. Operation: :math:`z = \\sum_i x[i]` Returns ------- z : float The summation of the elements in `x`. """ z = x.sum(axis=axis) return z def mult(self, x, y): """ Multiplies the arrays element-wise. Neither x nor y will be modified. Operation: :math:`z[i] = x[i] * y[i]` Parameters ---------- x, y : NumPy arrays, shape (n,) The arrays to multiply. Returns ------- z : NumPy array, shape (n,) The resultant array. """ z = x * y return z def mult_inplace(self, x, y): """ Multiplies `y` to `x`, in-place. `x` will be modified, but `y` will not. Operation: :math:`x[i] *= y[i]` Parameters ---------- x, y : NumPy arrays, shape (n,) The arrays to multiply. Returns ------- x : NumPy array, shape (n,) The resultant array. """ x *= y return x def mult_reduce(self, x, axis=None): """ Performs an `multiplication' reduction on `x`. Operation: :math:`z = \\prod_i x[i]` Returns ------- z : float The product of the elements in `x`. """ z = np.prod(x, axis=axis) return z def invert(self, x): """ Returns the element-wise multiplicative inverse of x. Operation: :math:`z[i] = 1/x[i]` Parameters ---------- x : NumPy array, shape (n,) The array to invert. Returns ------- z : NumPy array, shape (n,) The inverted array. """ z = 1 / x return z def normalize(self, x, axis=None): """ Returns a normalized version of x. Operation: :math:`z[i] = x[i] / sum(x)` If x is 2D and axis is None, then normalization is over all elements. Use axis=-1 to normalize each row of x. Parameters ---------- x : NumPy array, shape (n,) The array to normalize. Returns ------- z : NumPy array, shape (n,) The normalized array. """ z = x / x.sum(axis=None) return z def set_add(ops): """ Set the add method on the LogOperations instance. """ # To preserve numerical accuracy, we must make use of a logaddexp # function. These functions only exist in Numpy for base-e and base-2. # For all other bases, we must convert and then convert back. # In each case, we use default arguments to make the function that we # are calling 'local'. base = ops.base if base == 2: def add(self, x, y, func=np.logaddexp2): return func(x, y) elif base == 'e' or np.isclose(base, np.e): def add(self, x, y, func=np.logaddexp): return func(x, y) else: # No need to optimize this... def add(self, x, y): # Convert log_b probabilities to log_2 probabilities. x2 = x * np.log2(base) y2 = y * np.log2(base) z = np.logaddexp2(x2, y2) # Convert log_2 probabilities to log_b probabilities. z *= self.log(2) return z add.__doc__ = """ Add the arrays element-wise. Neither x nor y will be modified. Assumption: y <= 0. Parameters ---------- x, y : NumPy arrays, shape (n,) The arrays to add. Returns ------- z : NumPy array, shape (n,) The resultant array. """ ops.add = MethodType(add, ops) def set_add_inplace(ops): """ Set the add_inplace method on the LogOperations instance. """ base = ops.base if base == 2: def add_inplace(self, x, y, func=np.logaddexp2): return func(x, y, x) elif base == 'e' or np.isclose(base, np.e): def add_inplace(self, x, y, func=np.logaddexp): return func(x, y, x) else: def add_inplace(self, x, y): x *= np.log2(base) y2 = y * np.log2(base) np.logaddexp2(x, y2, x) x *= self.log(2) return x add_inplace.__doc__ = """ Adds `y` to `x`, in-place. `x` will be modified, but `y` will not. Assumption: :math:`y <= 0`. Parameters ---------- x, y : NumPy arrays, shape (n,) The arrays to add. Returns ------- x : NumPy array, shape (n,) The resultant array. """ ops.add_inplace = MethodType(add_inplace, ops) def set_add_reduce(ops): """ Set the add_reduce method on the LogOperations instance. """ # https://github.com/numpy/numpy/issues/4599 base = ops.base if base == 2: def add_reduce(self, x, axis=None, func=np.logaddexp2): if len(x) == 0: # Since logaddexp.identity is None, we handle it separately. z = self.zero else: # Note, we are converting to a NumPy array, if necessary. z = func.reduce(x, axis=axis, dtype=float) return z elif base == 'e' or np.isclose(base, np.e): def add_reduce(self, x, axis=None, func=np.logaddexp): if len(x) == 0: # Since logaddexp.identity is None, we handle it separately. z = self.zero else: # Note, we are converting to a NumPy array, if necessary. z = func.reduce(x, axis=axis, dtype=float) return z else: def add_reduce(self, x, axis=None): if len(x) == 0: # Since logaddexp.identity is None, we handle it separately. z = self.zero else: # Note, we are converting to a NumPy array, if necessary. # Change the base-2, add, and then convert back. x2 = x * np.log2(base) z = np.logaddexp2.reduce(x2, axis=axis, dtype=float) z /= np.log2(base) return z add_reduce.__doc__ = """ Performs an `addition' reduction on `x`. Assumption: :math:`y <= 0`. Returns ------- z : float The summation of the elements in `x`. """ ops.add_reduce = MethodType(add_reduce, ops) class LogOperations(Operations): one = None zero = None base = None exp = None log = None def __init__(self, base): """ Initialize the log operation manager. Parameters ---------- base : float The base of the logarithm. """ self.set_base(base) def set_base(self, base): """ Change the base of the logarithm. Parameters ---------- base : float The base of the logarithm. """ self.base = base self.exp = exp_func(base) self.log = log_func(base) # Note: When base < 1, zero == +inf. When base > 1, zero == -inf. self.one = self.log(1) self.zero = self.log(0) # Update the add methods. set_add(self) set_add_inplace(self) set_add_reduce(self) def mult(self, x, y): """ Multiplies the arrays element-wise. Neither `x` nor `y` will be modified. Parameters ---------- x, y : NumPy arrays, shape (n,) The arrays to multiply. Returns ------- z : NumPy array, shape (n,) The resultant array. """ z = x + y return z def mult_inplace(self, x, y): """ Multiplies `y` to `x`, in-place. `x` will be modified, but `y` will not. Parameters ---------- x, y : NumPy arrays, shape (n,) The arrays to multiply. Returns ------- x : NumPy array, shape (n,) The resultant array. """ x += y return x def mult_reduce(self, x, axis=None): """ Performs an `multiplication' reduction on `x`. Returns ------- z : float The product of the elements in `x`. """ # The identity for addition in NumPy is zero. # This corresponds to an identity of 1 for log operations, and this is # exactly the desired identity for multiplying probabilities. z = x.sum(axis=axis) return z def invert(self, x): """ Returns the element-wise multiplicative inverse of `x`: :math:`1/x`. Parameters ---------- x : NumPy array, shape (n,) The array to invert. Returns ------- z : NumPy array, shape (n,) The inverted array. """ z = -x return z def normalize(self, x, axis=None): """ Returns a normalized version of `x`. Non-log equivalent operation: :math:`z[i] = x[i] / sum(x)` If `x` is 2D and axis is None, then normalization is over all elements. Use axis=-1 to normalize each row of `x`. Parameters ---------- x : NumPy array, shape (n,) The array to normalize. Returns ------- z : NumPy array, shape (n,) The normalized array. """ # The API way would be: mult(x, invert( add_reduce(x) )) # We'll avoid some of those function calls. z = x - self.add_reduce(x, axis=axis) return z cache = { 'linear': LinearOperations(), 2: LogOperations(2), 'e': LogOperations('e') }

24.05992

82

0.517295

from types import MethodType import numpy as np from ..exceptions import InvalidBase __all__ = ( 'get_ops', 'LinearOperations', 'LogOperations', ) acceptable_base_strings = {'linear', 'e'} def get_ops(base): if base in cache: ops = cache[base] else: # This assumes that 'linear' is in cache. ops = LogOperations(base) cache[base] = ops return ops def exp_func(b): from dit.utils import is_string_like if is_string_like(b) and b not in acceptable_base_strings: raise InvalidBase(msg=b) if b == 'linear': exp = lambda x: x # pragma: no branch elif b == 2: exp = np.exp2 elif b == 10: exp = lambda x: 10**x elif b == 'e' or np.isclose(b, np.e): exp = np.exp else: if b <= 0 or b == 1: raise InvalidBase(b) def exp(x, base=b): """ Return `base`**`x` Parameters ---------- x : float The number to exponentiate base : float The base of the exponential Returns ------- p : float `base`**`x` """ return base**np.asarray(x) return exp def log_func(b): from dit.utils import is_string_like if is_string_like(b) and b not in acceptable_base_strings: raise InvalidBase(msg=b) if b == 'linear': log = lambda x: x # pragma: no branch elif b == 2: log = np.log2 elif b == 10: log = np.log10 elif b == 'e' or np.isclose(b, np.e): log = np.log else: if b <= 0 or b == 1: raise InvalidBase(b) Z = np.log(b) def log(x, func=np.log): """ Return the log of `x` Parameters ---------- x : float The value to take the log of func : function A logarithm function Returns ------- log : float The logarithm of `x` in base `b` (from outer scope) """ return func(x) / Z return log class Operations(object): ### Do we allow base == 'e' or should we convert to its numerical value? ### Ans: We store whatever was specified but provide get_base() with an ### option to return a numerical base. one = None zero = None base = None exp = None log = None def get_base(self, numerical=False): if numerical and self.base == 'e': base = np.exp(1) else: base = self.base return base def is_null(self, p): return np.isclose(self.zero, p) def is_null_exact(self, p): return self.zero == p def add(self, x, y): raise NotImplementedError def add_inplace(self, x, y): raise NotImplementedError def add_reduce(self, x): raise NotImplementedError def mult(self, x, y): raise NotImplementedError def mult_inplace(self, x, y): raise NotImplementedError def mult_reduce(self, x): raise NotImplementedError def invert(self, x): raise NotImplementedError def normalize(self, x): raise NotImplementedError class LinearOperations(Operations): one = 1 zero = 0 base = 'linear' # If the functions below are standard Python functions (as opposed to # NumPy ufuncs), then they will be treated as unbound methods for the class. # During instantiation, they are bound to the instance (since before # instantiation they are class methods) and thus, we are left with # bound methods (undesirably). If we had modified these attributes in the # __init__ function, then they would not be bound (or even unbound methods) # but functions instead (desirably). This is precisely what LogOperations # does, which is why it does not have this issue. An alternative approach # is to explicitly declare these functions to be static methods, as we # do below. # exp = staticmethod(exp_func(base)) log = staticmethod(log_func(base)) def add(self, x, y): z = x + y return z def add_inplace(self, x, y): x += y return x def add_reduce(self, x, axis=None): z = x.sum(axis=axis) return z def mult(self, x, y): z = x * y return z def mult_inplace(self, x, y): x *= y return x def mult_reduce(self, x, axis=None): z = np.prod(x, axis=axis) return z def invert(self, x): z = 1 / x return z def normalize(self, x, axis=None): z = x / x.sum(axis=None) return z def set_add(ops): # To preserve numerical accuracy, we must make use of a logaddexp # function. These functions only exist in Numpy for base-e and base-2. # For all other bases, we must convert and then convert back. # In each case, we use default arguments to make the function that we # are calling 'local'. base = ops.base if base == 2: def add(self, x, y, func=np.logaddexp2): return func(x, y) elif base == 'e' or np.isclose(base, np.e): def add(self, x, y, func=np.logaddexp): return func(x, y) else: # No need to optimize this... def add(self, x, y): # Convert log_b probabilities to log_2 probabilities. x2 = x * np.log2(base) y2 = y * np.log2(base) z = np.logaddexp2(x2, y2) # Convert log_2 probabilities to log_b probabilities. z *= self.log(2) return z add.__doc__ = """ Add the arrays element-wise. Neither x nor y will be modified. Assumption: y <= 0. Parameters ---------- x, y : NumPy arrays, shape (n,) The arrays to add. Returns ------- z : NumPy array, shape (n,) The resultant array. """ ops.add = MethodType(add, ops) def set_add_inplace(ops): base = ops.base if base == 2: def add_inplace(self, x, y, func=np.logaddexp2): return func(x, y, x) elif base == 'e' or np.isclose(base, np.e): def add_inplace(self, x, y, func=np.logaddexp): return func(x, y, x) else: def add_inplace(self, x, y): x *= np.log2(base) y2 = y * np.log2(base) np.logaddexp2(x, y2, x) x *= self.log(2) return x add_inplace.__doc__ = """ Adds `y` to `x`, in-place. `x` will be modified, but `y` will not. Assumption: :math:`y <= 0`. Parameters ---------- x, y : NumPy arrays, shape (n,) The arrays to add. Returns ------- x : NumPy array, shape (n,) The resultant array. """ ops.add_inplace = MethodType(add_inplace, ops) def set_add_reduce(ops): # https://github.com/numpy/numpy/issues/4599 base = ops.base if base == 2: def add_reduce(self, x, axis=None, func=np.logaddexp2): if len(x) == 0: # Since logaddexp.identity is None, we handle it separately. z = self.zero else: # Note, we are converting to a NumPy array, if necessary. z = func.reduce(x, axis=axis, dtype=float) return z elif base == 'e' or np.isclose(base, np.e): def add_reduce(self, x, axis=None, func=np.logaddexp): if len(x) == 0: # Since logaddexp.identity is None, we handle it separately. z = self.zero else: # Note, we are converting to a NumPy array, if necessary. z = func.reduce(x, axis=axis, dtype=float) return z else: def add_reduce(self, x, axis=None): if len(x) == 0: # Since logaddexp.identity is None, we handle it separately. z = self.zero else: # Note, we are converting to a NumPy array, if necessary. # Change the base-2, add, and then convert back. x2 = x * np.log2(base) z = np.logaddexp2.reduce(x2, axis=axis, dtype=float) z /= np.log2(base) return z add_reduce.__doc__ = """ Performs an `addition' reduction on `x`. Assumption: :math:`y <= 0`. Returns ------- z : float The summation of the elements in `x`. """ ops.add_reduce = MethodType(add_reduce, ops) class LogOperations(Operations): one = None zero = None base = None exp = None log = None def __init__(self, base): self.set_base(base) def set_base(self, base): self.base = base self.exp = exp_func(base) self.log = log_func(base) self.one = self.log(1) self.zero = self.log(0) set_add(self) set_add_inplace(self) set_add_reduce(self) def mult(self, x, y): z = x + y return z def mult_inplace(self, x, y): x += y return x def mult_reduce(self, x, axis=None): z = x.sum(axis=axis) return z def invert(self, x): z = -x return z def normalize(self, x, axis=None): z = x - self.add_reduce(x, axis=axis) return z cache = { 'linear': LinearOperations(), 2: LogOperations(2), 'e': LogOperations('e') }

true

f70049a62ff8108e599465f06904de5438b65282

19,426

py

Python

release/scripts/modules/bl_i18n_utils/utils_spell_check.py

lordloki/upbge

18d0f5419cc1338ecf739362afef56bd96b42cfb

[ "Naumen", "Condor-1.1", "MS-PL" ]

1

2022-01-11T10:02:21.000Z

release/scripts/modules/bl_i18n_utils/utils_spell_check.py

lordloki/upbge

18d0f5419cc1338ecf739362afef56bd96b42cfb

[ "Naumen", "Condor-1.1", "MS-PL" ]

null

release/scripts/modules/bl_i18n_utils/utils_spell_check.py

lordloki/upbge

18d0f5419cc1338ecf739362afef56bd96b42cfb

[ "Naumen", "Condor-1.1", "MS-PL" ]

null

# ##### BEGIN GPL LICENSE BLOCK ##### # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software Foundation, # Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. # # ##### END GPL LICENSE BLOCK ##### # <pep8 compliant> import enchant import os import pickle import re class SpellChecker: """ A basic spell checker. """ # These must be all lower case for comparisons uimsgs = { # OK words "adaptively", "adaptivity", "aren", # aren't "betweens", # yuck! in-betweens! "boolean", "booleans", "chamfer", "couldn", # couldn't "customizable", "decrement", "derivate", "deterministically", "doesn", # doesn't "duplications", "effector", "equi", # equi-angular, etc. "fader", "globbing", "hasn", # hasn't "hetero", "hoc", # ad-hoc "incompressible", "indices", "instantiation", "iridas", "isn", # isn't "iterable", "kyrgyz", "latin", "merchantability", "mplayer", "ons", # add-ons "pong", # ping pong "scalable", "shadeless", "shouldn", # shouldn't "smoothen", "spacings", "teleport", "teleporting", "vertices", "wasn", # wasn't # Merged words "antialiasing", "antialias", "arcsine", "arccosine", "arctangent", "autoclip", "autocomplete", "autoexec", "autoexecution", "autogenerated", "autolock", "automask", "automasking", "automerge", "autoname", "autopack", "autosave", "autoscale", "autosmooth", "autosplit", "backface", "backfacing", "backimage", "backscattered", "bandnoise", "bindcode", "bitdepth", "bitflag", "bitflags", "bitrate", "blackbody", "blendfile", "blendin", "bonesize", "boundbox", "boxpack", "buffersize", "builtin", "builtins", "bytecode", "chunksize", "customdata", "dataset", "datasets", "de", "deadzone", "deconstruct", "defocus", "denoise", "denoised", "denoising", "denoiser", "deselect", "deselecting", "deselection", "despill", "despilling", "dirtree", "editcurve", "editmesh", "filebrowser", "filelist", "filename", "filenames", "filepath", "filepaths", "forcefield", "forcefields", "fulldome", "fulldomes", "fullscreen", "gridline", "gridlines", "hardlight", "hemi", "hostname", "inbetween", "inscatter", "inscattering", "libdata", "lightcache", "lightprobe", "lightprobes", "lightless", "lineset", "linestyle", "linestyles", "localview", "lookup", "lookups", "mathutils", "micropolygon", "midlevel", "midground", "mixdown", "monospaced", "multi", "multifractal", "multiframe", "multilayer", "multipaint", "multires", "multiresolution", "multisampling", "multiscatter", "multitexture", "multithreaded", "multiuser", "multiview", "namespace", "nodetree", "nodetrees", "keyconfig", "offscreen", "online", "playhead", "popup", "popups", "pointcloud", "pre", "precache", "precaching", "precalculate", "precomputing", "prefetch", "premultiply", "premultiplied", "prepass", "prepend", "preprocess", "preprocessing", "preprocessor", "preseek", "promillage", "pushdown", "raytree", "readonly", "realtime", "reinject", "reinjected", "rekey", "remesh", "reprojection", "reproject", "reprojecting", "resize", "restpose", "resync", "retarget", "retargets", "retargeting", "retargeted", "retiming", "rigidbody", "ringnoise", "rolloff", "runtime", "scanline", "screenshot", "screenshots", "seekability", "selfcollision", "shadowbuffer", "shadowbuffers", "singletexture", "spellcheck", "spellchecking", "startup", "stateful", "starfield", "studiolight", "subflare", "subflares", "subframe", "subframes", "subclass", "subclasses", "subclassing", "subdirectory", "subdirectories", "subdir", "subdirs", "subitem", "submode", "submodule", "submodules", "subpath", "subsize", "substep", "substeps", "targetless", "textbox", "textboxes", "tilemode", "timestamp", "timestamps", "timestep", "timesteps", "todo", "tradeoff", "un", "unassociate", "unassociated", "unbake", "unclosed", "uncomment", "unculled", "undeformed", "undistort", "undistorted", "undistortion", "ungroup", "ungrouped", "unhide", "unindent", "unkeyed", "unlink", "unlinked", "unmute", "unphysical", "unpremultiply", "unprojected", "unprotect", "unreacted", "unreferenced", "unregister", "unselect", "unselected", "unselectable", "unsets", "unshadowed", "unspill", "unstitchable", "unstitch", "unsubdivided", "unsubdivide", "untrusted", "vectorscope", "whitespace", "whitespaces", "worldspace", "workflow", "workspace", "workspaces", # Neologisms, slangs "affectable", "animatable", "automagic", "automagically", "blobby", "blockiness", "blocky", "collider", "colliders", "deformer", "deformers", "determinator", "editability", "effectors", "expander", "instancer", "keyer", "lacunarity", "linkable", "numerics", "occluder", "occluders", "overridable", "passepartout", "perspectively", "pixelate", "pointiness", "polycount", "polygonization", "polygonalization", # yuck! "scalings", "selectable", "selectability", "shaper", "smoothen", "smoothening", "spherize", "spherized", "stitchable", "symmetrize", "trackability", "transmissivity", "rasterized", "rasterization", "rasterizer", "renderer", "renderers", "renderable", "renderability", # Really bad!!! "convertor", "fullscr", # Abbreviations "aero", "amb", "anim", "aov", "app", "bbox", "bboxes", "bksp", # Backspace "bool", "calc", "cfl", "config", "configs", "const", "coord", "coords", "degr", "diff", "dof", "dupli", "duplis", "eg", "esc", "expr", "fac", "fra", "fract", "frs", "grless", "http", "init", "irr", # Irradiance "kbit", "kb", "lang", "langs", "lclick", "rclick", "lensdist", "loc", "rot", "pos", "lorem", "luma", "mbs", # mouse button 'select'. "mem", "multicam", "num", "ok", "orco", "ortho", "pano", "persp", "pref", "prefs", "prev", "param", "premul", "quad", "quads", "quat", "quats", "recalc", "recalcs", "refl", "sce", "sel", "spec", "struct", "structs", "subdiv", "sys", "tex", "texcoord", "tmr", # timer "tri", "tris", "udim", "udims", "upres", # Upresolution "usd", "uv", "uvs", "uvw", "uw", "uvmap", "ve", "vec", "vel", # velocity! "vert", "verts", "vis", "vram", "xor", "xyz", "xzy", "yxz", "yzx", "zxy", "zyx", "xy", "xz", "yx", "yz", "zx", "zy", # General computer/science terms "affine", "albedo", "anamorphic", "anisotropic", "anisotropy", "bitangent", "boid", "boids", "ceil", "compressibility", "curvilinear", "equiangular", "equisolid", "euler", "eulers", "fribidi", "gettext", "hashable", "hotspot", "interocular", "intrinsics", "irradiance", "isosurface", "jitter", "jittering", "jittered", "keymap", "keymaps", "lambertian", "laplacian", "metadata", "msgfmt", "nand", "xnor", "normals", "numpad", "octahedral", "octree", "omnidirectional", "opengl", "openmp", "parametrization", "photoreceptor", "poly", "polyline", "polylines", "probabilistically", "pulldown", "pulldowns", "quantized", "quartic", "quaternion", "quaternions", "quintic", "samplerate", "sawtooth", "scrollback", "scrollbar", "scroller", "searchable", "spacebar", "subtractive", "superellipse", "tooltip", "tooltips", "trackpad", "tuple", "unicode", "viewport", "viewports", "viscoelastic", "vorticity", "waveform", "waveforms", "wildcard", "wildcards", "wintab", # Some Windows tablet API # General computer graphics terms "anaglyph", "bezier", "beziers", "bicubic", "bilinear", "bindpose", "binormal", "blackpoint", "whitepoint", "blinn", "bokeh", "catadioptric", "centroid", "chroma", "chrominance", "clearcoat", "codec", "codecs", "collada", "compositing", "crossfade", "cubemap", "cubemaps", "cuda", "deinterlace", "dropoff", "duotone", "dv", "eigenvectors", "emissive", "equirectangular", "filmlike", "fisheye", "framerate", "gimbal", "grayscale", "icosphere", "inpaint", "kerning", "lightmap", "linearlight", "lossless", "lossy", "luminance", "mantaflow", "matcap", "midtones", "mipmap", "mipmaps", "mip", "ngon", "ngons", "ntsc", "nurb", "nurbs", "perlin", "phong", "pinlight", "qi", "radiosity", "raycasting", "raytrace", "raytracing", "raytraced", "refractions", "remesher", "remeshing", "remesh", "renderfarm", "scanfill", "shader", "shaders", "shadowmap", "shadowmaps", "softlight", "specular", "specularity", "spillmap", "sobel", "stereoscopy", "texel", "timecode", "tonemap", "toon", "transmissive", "vividlight", "volumetrics", "voronoi", "voxel", "voxels", "vsync", "wireframe", "zmask", "ztransp", # Blender terms "audaspace", "azone", # action zone "backwire", "bbone", "bendy", # bones "bmesh", "breakdowner", "bspline", "bweight", "colorband", "datablock", "datablocks", "despeckle", "depsgraph", "dopesheet", "dupliface", "duplifaces", "dupliframe", "dupliframes", "dupliobject", "dupliob", "dupligroup", "duplivert", "dyntopo", "editbone", "editmode", "eevee", "fcurve", "fcurves", "fedge", "fedges", "filmic", "fluidsim", "freestyle", "enum", "enums", "gizmogroup", "gon", "gons", # N-Gon(s) "gpencil", "idcol", "keyframe", "keyframes", "keyframing", "keyframed", "lookdev", "luminocity", "mathvis", "metaball", "metaballs", "mball", "metaelement", "metaelements", "metastrip", "metastrips", "movieclip", "mpoly", "mtex", "nabla", "navmesh", "outliner", "overscan", "paintmap", "paintmaps", "polygroup", "polygroups", "poselib", "pushpull", "pyconstraint", "pyconstraints", "qe", # keys... "shaderfx", "shaderfxs", "shapekey", "shapekeys", "shrinkfatten", "shrinkwrap", "softbody", "stucci", "subdiv", "subtype", "sunsky", "tessface", "tessfaces", "texface", "timeline", "timelines", "tosphere", "uilist", "userpref", "vcol", "vcols", "vgroup", "vgroups", "vinterlace", "vse", "wasd", "wasdqe", # keys... "wetmap", "wetmaps", "wpaint", "uvwarp", # UOC (Ugly Operator Categories) "cachefile", "paintcurve", "ptcache", "dpaint", # Algorithm/library names "ashikhmin", # Ashikhmin-Shirley "arsloe", # Texel-Marsen-Arsloe "beckmann", "blackman", # Blackman-Harris "blosc", "burley", # Christensen-Burley "catmull", "catrom", "chebychev", "conrady", # Brown-Conrady "courant", "cryptomatte", "crypto", "embree", "gmp", "hosek", "kutta", "lennard", "marsen", # Texel-Marsen-Arsloe "mikktspace", "minkowski", "minnaert", "moskowitz", # Pierson-Moskowitz "musgrave", "nayar", "netravali", "nishita", "ogawa", "oren", "peucker", # Ramer-Douglas-Peucker "pierson", # Pierson-Moskowitz "preetham", "prewitt", "ramer", # Ramer-Douglas-Peucker "runge", "sobol", "verlet", "wilkie", "worley", # Acronyms "aa", "msaa", "ao", "api", "apic", # Affine Particle-In-Cell "asc", "cdl", "ascii", "atrac", "avx", "bsdf", "bssrdf", "bw", "ccd", "cmd", "cmos", "cpus", "ctrl", "cw", "ccw", "dev", "djv", "dpi", "dvar", "dx", "eo", "fh", "fk", "fov", "fft", "futura", "fx", "gfx", "ggx", "gl", "glsl", "gpl", "gpu", "gpus", "hc", "hdc", "hdr", "hdri", "hdris", "hh", "mm", "ss", "ff", # hh:mm:ss:ff timecode "hsv", "hsva", "hsl", "id", "ies", "ior", "itu", "jonswap", "lhs", "lmb", "mmb", "rmb", "kb", "mocap", "msgid", "msgids", "mux", "ndof", "ppc", "precisa", "px", "qmc", "rdp", "rgb", "rgba", "rhs", "rv", "sdl", "sl", "smpte", "ssao", "ssr", "svn", "tma", "ui", "unix", "vbo", "vbos", "vr", "wxyz", "xr", "ycc", "ycca", "yrgb", "yuv", "yuva", # Blender acronyms "bge", "bli", "bpy", "bvh", "dbvt", "dop", # BLI K-Dop BVH "ik", "nla", "py", "qbvh", "rna", "rvo", "simd", "sph", "svbvh", # Files types/formats "avi", "attrac", "autocad", "autodesk", "bmp", "btx", "cineon", "dpx", "dwaa", "dwab", "dxf", "eps", "exr", "fbx", "fbxnode", "ffmpeg", "flac", "gltf", "gzip", "ico", "jpg", "jpeg", "jpegs", "json", "matroska", "mdd", "mkv", "mpeg", "mjpeg", "mtl", "ogg", "openjpeg", "osl", "oso", "piz", "png", "pngs", "po", "quicktime", "rle", "sgi", "stl", "svg", "targa", "tga", "tiff", "theora", "vorbis", "vp9", "wav", "webm", "xiph", "xml", "xna", "xvid", } _valid_before = "(?<=[\\s*'\"`])|(?<=[a-zA-Z][/-])|(?<=^)" _valid_after = "(?=[\\s'\"`.!?,;:])|(?=[/-]\\s*[a-zA-Z])|(?=$)" _valid_words = "(?:{})(?:(?:[A-Z]+[a-z]*)|[A-Z]*|[a-z]*)(?:{})".format(_valid_before, _valid_after) _split_words = re.compile(_valid_words).findall @classmethod def split_words(cls, text): return [w for w in cls._split_words(text) if w] def __init__(self, settings, lang="en_US"): self.settings = settings self.dict_spelling = enchant.Dict(lang) self.cache = set(self.uimsgs) cache = self.settings.SPELL_CACHE if cache and os.path.exists(cache): with open(cache, 'rb') as f: self.cache |= set(pickle.load(f)) def __del__(self): cache = self.settings.SPELL_CACHE if cache and os.path.exists(cache): with open(cache, 'wb') as f: pickle.dump(self.cache, f) def check(self, txt): ret = [] if txt in self.cache: return ret for w in self.split_words(txt): w_lower = w.lower() if w_lower in self.cache: continue if not self.dict_spelling.check(w): ret.append((w, self.dict_spelling.suggest(w))) else: self.cache.add(w_lower) if not ret: self.cache.add(txt) return ret

23.20908

103

0.438279

import enchant import os import pickle import re class SpellChecker: uimsgs = { "adaptively", "adaptivity", "aren", "betweens", # yuck! in-betweens! "boolean", "booleans", "chamfer", "couldn", # couldn't "customizable", "decrement", "derivate", "deterministically", "doesn", "duplications", "effector", "equi", # equi-angular, etc. "fader", "globbing", "hasn", # hasn't "hetero", "hoc", "incompressible", "indices", "instantiation", "iridas", "isn", "iterable", "kyrgyz", "latin", "merchantability", "mplayer", "ons", # add-ons "pong", # ping pong "scalable", "shadeless", "shouldn", # shouldn't "smoothen", "spacings", "teleport", "teleporting", "vertices", "wasn", # Merged words "antialiasing", "antialias", "arcsine", "arccosine", "arctangent", "autoclip", "autocomplete", "autoexec", "autoexecution", "autogenerated", "autolock", "automask", "automasking", "automerge", "autoname", "autopack", "autosave", "autoscale", "autosmooth", "autosplit", "backface", "backfacing", "backimage", "backscattered", "bandnoise", "bindcode", "bitdepth", "bitflag", "bitflags", "bitrate", "blackbody", "blendfile", "blendin", "bonesize", "boundbox", "boxpack", "buffersize", "builtin", "builtins", "bytecode", "chunksize", "customdata", "dataset", "datasets", "de", "deadzone", "deconstruct", "defocus", "denoise", "denoised", "denoising", "denoiser", "deselect", "deselecting", "deselection", "despill", "despilling", "dirtree", "editcurve", "editmesh", "filebrowser", "filelist", "filename", "filenames", "filepath", "filepaths", "forcefield", "forcefields", "fulldome", "fulldomes", "fullscreen", "gridline", "gridlines", "hardlight", "hemi", "hostname", "inbetween", "inscatter", "inscattering", "libdata", "lightcache", "lightprobe", "lightprobes", "lightless", "lineset", "linestyle", "linestyles", "localview", "lookup", "lookups", "mathutils", "micropolygon", "midlevel", "midground", "mixdown", "monospaced", "multi", "multifractal", "multiframe", "multilayer", "multipaint", "multires", "multiresolution", "multisampling", "multiscatter", "multitexture", "multithreaded", "multiuser", "multiview", "namespace", "nodetree", "nodetrees", "keyconfig", "offscreen", "online", "playhead", "popup", "popups", "pointcloud", "pre", "precache", "precaching", "precalculate", "precomputing", "prefetch", "premultiply", "premultiplied", "prepass", "prepend", "preprocess", "preprocessing", "preprocessor", "preseek", "promillage", "pushdown", "raytree", "readonly", "realtime", "reinject", "reinjected", "rekey", "remesh", "reprojection", "reproject", "reprojecting", "resize", "restpose", "resync", "retarget", "retargets", "retargeting", "retargeted", "retiming", "rigidbody", "ringnoise", "rolloff", "runtime", "scanline", "screenshot", "screenshots", "seekability", "selfcollision", "shadowbuffer", "shadowbuffers", "singletexture", "spellcheck", "spellchecking", "startup", "stateful", "starfield", "studiolight", "subflare", "subflares", "subframe", "subframes", "subclass", "subclasses", "subclassing", "subdirectory", "subdirectories", "subdir", "subdirs", "subitem", "submode", "submodule", "submodules", "subpath", "subsize", "substep", "substeps", "targetless", "textbox", "textboxes", "tilemode", "timestamp", "timestamps", "timestep", "timesteps", "todo", "tradeoff", "un", "unassociate", "unassociated", "unbake", "unclosed", "uncomment", "unculled", "undeformed", "undistort", "undistorted", "undistortion", "ungroup", "ungrouped", "unhide", "unindent", "unkeyed", "unlink", "unlinked", "unmute", "unphysical", "unpremultiply", "unprojected", "unprotect", "unreacted", "unreferenced", "unregister", "unselect", "unselected", "unselectable", "unsets", "unshadowed", "unspill", "unstitchable", "unstitch", "unsubdivided", "unsubdivide", "untrusted", "vectorscope", "whitespace", "whitespaces", "worldspace", "workflow", "workspace", "workspaces", # Neologisms, slangs "affectable", "animatable", "automagic", "automagically", "blobby", "blockiness", "blocky", "collider", "colliders", "deformer", "deformers", "determinator", "editability", "effectors", "expander", "instancer", "keyer", "lacunarity", "linkable", "numerics", "occluder", "occluders", "overridable", "passepartout", "perspectively", "pixelate", "pointiness", "polycount", "polygonization", "polygonalization", # yuck! "scalings", "selectable", "selectability", "shaper", "smoothen", "smoothening", "spherize", "spherized", "stitchable", "symmetrize", "trackability", "transmissivity", "rasterized", "rasterization", "rasterizer", "renderer", "renderers", "renderable", "renderability", # Really bad!!! "convertor", "fullscr", # Abbreviations "aero", "amb", "anim", "aov", "app", "bbox", "bboxes", "bksp", # Backspace "bool", "calc", "cfl", "config", "configs", "const", "coord", "coords", "degr", "diff", "dof", "dupli", "duplis", "eg", "esc", "expr", "fac", "fra", "fract", "frs", "grless", "http", "init", "irr", # Irradiance "kbit", "kb", "lang", "langs", "lclick", "rclick", "lensdist", "loc", "rot", "pos", "lorem", "luma", "mbs", # mouse button 'select'. "mem", "multicam", "num", "ok", "orco", "ortho", "pano", "persp", "pref", "prefs", "prev", "param", "premul", "quad", "quads", "quat", "quats", "recalc", "recalcs", "refl", "sce", "sel", "spec", "struct", "structs", "subdiv", "sys", "tex", "texcoord", "tmr", # timer "tri", "tris", "udim", "udims", "upres", # Upresolution "usd", "uv", "uvs", "uvw", "uw", "uvmap", "ve", "vec", "vel", # velocity! "vert", "verts", "vis", "vram", "xor", "xyz", "xzy", "yxz", "yzx", "zxy", "zyx", "xy", "xz", "yx", "yz", "zx", "zy", # General computer/science terms "affine", "albedo", "anamorphic", "anisotropic", "anisotropy", "bitangent", "boid", "boids", "ceil", "compressibility", "curvilinear", "equiangular", "equisolid", "euler", "eulers", "fribidi", "gettext", "hashable", "hotspot", "interocular", "intrinsics", "irradiance", "isosurface", "jitter", "jittering", "jittered", "keymap", "keymaps", "lambertian", "laplacian", "metadata", "msgfmt", "nand", "xnor", "normals", "numpad", "octahedral", "octree", "omnidirectional", "opengl", "openmp", "parametrization", "photoreceptor", "poly", "polyline", "polylines", "probabilistically", "pulldown", "pulldowns", "quantized", "quartic", "quaternion", "quaternions", "quintic", "samplerate", "sawtooth", "scrollback", "scrollbar", "scroller", "searchable", "spacebar", "subtractive", "superellipse", "tooltip", "tooltips", "trackpad", "tuple", "unicode", "viewport", "viewports", "viscoelastic", "vorticity", "waveform", "waveforms", "wildcard", "wildcards", "wintab", # Some Windows tablet API # General computer graphics terms "anaglyph", "bezier", "beziers", "bicubic", "bilinear", "bindpose", "binormal", "blackpoint", "whitepoint", "blinn", "bokeh", "catadioptric", "centroid", "chroma", "chrominance", "clearcoat", "codec", "codecs", "collada", "compositing", "crossfade", "cubemap", "cubemaps", "cuda", "deinterlace", "dropoff", "duotone", "dv", "eigenvectors", "emissive", "equirectangular", "filmlike", "fisheye", "framerate", "gimbal", "grayscale", "icosphere", "inpaint", "kerning", "lightmap", "linearlight", "lossless", "lossy", "luminance", "mantaflow", "matcap", "midtones", "mipmap", "mipmaps", "mip", "ngon", "ngons", "ntsc", "nurb", "nurbs", "perlin", "phong", "pinlight", "qi", "radiosity", "raycasting", "raytrace", "raytracing", "raytraced", "refractions", "remesher", "remeshing", "remesh", "renderfarm", "scanfill", "shader", "shaders", "shadowmap", "shadowmaps", "softlight", "specular", "specularity", "spillmap", "sobel", "stereoscopy", "texel", "timecode", "tonemap", "toon", "transmissive", "vividlight", "volumetrics", "voronoi", "voxel", "voxels", "vsync", "wireframe", "zmask", "ztransp", # Blender terms "audaspace", "azone", # action zone "backwire", "bbone", "bendy", # bones "bmesh", "breakdowner", "bspline", "bweight", "colorband", "datablock", "datablocks", "despeckle", "depsgraph", "dopesheet", "dupliface", "duplifaces", "dupliframe", "dupliframes", "dupliobject", "dupliob", "dupligroup", "duplivert", "dyntopo", "editbone", "editmode", "eevee", "fcurve", "fcurves", "fedge", "fedges", "filmic", "fluidsim", "freestyle", "enum", "enums", "gizmogroup", "gon", "gons", # N-Gon(s) "gpencil", "idcol", "keyframe", "keyframes", "keyframing", "keyframed", "lookdev", "luminocity", "mathvis", "metaball", "metaballs", "mball", "metaelement", "metaelements", "metastrip", "metastrips", "movieclip", "mpoly", "mtex", "nabla", "navmesh", "outliner", "overscan", "paintmap", "paintmaps", "polygroup", "polygroups", "poselib", "pushpull", "pyconstraint", "pyconstraints", "qe", # keys... "shaderfx", "shaderfxs", "shapekey", "shapekeys", "shrinkfatten", "shrinkwrap", "softbody", "stucci", "subdiv", "subtype", "sunsky", "tessface", "tessfaces", "texface", "timeline", "timelines", "tosphere", "uilist", "userpref", "vcol", "vcols", "vgroup", "vgroups", "vinterlace", "vse", "wasd", "wasdqe", # keys... "wetmap", "wetmaps", "wpaint", "uvwarp", # UOC (Ugly Operator Categories) "cachefile", "paintcurve", "ptcache", "dpaint", # Algorithm/library names "ashikhmin", # Ashikhmin-Shirley "arsloe", # Texel-Marsen-Arsloe "beckmann", "blackman", # Blackman-Harris "blosc", "burley", # Christensen-Burley "catmull", "catrom", "chebychev", "conrady", # Brown-Conrady "courant", "cryptomatte", "crypto", "embree", "gmp", "hosek", "kutta", "lennard", "marsen", # Texel-Marsen-Arsloe "mikktspace", "minkowski", "minnaert", "moskowitz", # Pierson-Moskowitz "musgrave", "nayar", "netravali", "nishita", "ogawa", "oren", "peucker", # Ramer-Douglas-Peucker "pierson", # Pierson-Moskowitz "preetham", "prewitt", "ramer", # Ramer-Douglas-Peucker "runge", "sobol", "verlet", "wilkie", "worley", # Acronyms "aa", "msaa", "ao", "api", "apic", # Affine Particle-In-Cell "asc", "cdl", "ascii", "atrac", "avx", "bsdf", "bssrdf", "bw", "ccd", "cmd", "cmos", "cpus", "ctrl", "cw", "ccw", "dev", "djv", "dpi", "dvar", "dx", "eo", "fh", "fk", "fov", "fft", "futura", "fx", "gfx", "ggx", "gl", "glsl", "gpl", "gpu", "gpus", "hc", "hdc", "hdr", "hdri", "hdris", "hh", "mm", "ss", "ff", # hh:mm:ss:ff timecode "hsv", "hsva", "hsl", "id", "ies", "ior", "itu", "jonswap", "lhs", "lmb", "mmb", "rmb", "kb", "mocap", "msgid", "msgids", "mux", "ndof", "ppc", "precisa", "px", "qmc", "rdp", "rgb", "rgba", "rhs", "rv", "sdl", "sl", "smpte", "ssao", "ssr", "svn", "tma", "ui", "unix", "vbo", "vbos", "vr", "wxyz", "xr", "ycc", "ycca", "yrgb", "yuv", "yuva", # Blender acronyms "bge", "bli", "bpy", "bvh", "dbvt", "dop", # BLI K-Dop BVH "ik", "nla", "py", "qbvh", "rna", "rvo", "simd", "sph", "svbvh", # Files types/formats "avi", "attrac", "autocad", "autodesk", "bmp", "btx", "cineon", "dpx", "dwaa", "dwab", "dxf", "eps", "exr", "fbx", "fbxnode", "ffmpeg", "flac", "gltf", "gzip", "ico", "jpg", "jpeg", "jpegs", "json", "matroska", "mdd", "mkv", "mpeg", "mjpeg", "mtl", "ogg", "openjpeg", "osl", "oso", "piz", "png", "pngs", "po", "quicktime", "rle", "sgi", "stl", "svg", "targa", "tga", "tiff", "theora", "vorbis", "vp9", "wav", "webm", "xiph", "xml", "xna", "xvid", } _valid_before = "(?<=[\\s*'\"`])|(?<=[a-zA-Z][/-])|(?<=^)" _valid_after = "(?=[\\s'\"`.!?,;:])|(?=[/-]\\s*[a-zA-Z])|(?=$)" _valid_words = "(?:{})(?:(?:[A-Z]+[a-z]*)|[A-Z]*|[a-z]*)(?:{})".format(_valid_before, _valid_after) _split_words = re.compile(_valid_words).findall @classmethod def split_words(cls, text): return [w for w in cls._split_words(text) if w] def __init__(self, settings, lang="en_US"): self.settings = settings self.dict_spelling = enchant.Dict(lang) self.cache = set(self.uimsgs) cache = self.settings.SPELL_CACHE if cache and os.path.exists(cache): with open(cache, 'rb') as f: self.cache |= set(pickle.load(f)) def __del__(self): cache = self.settings.SPELL_CACHE if cache and os.path.exists(cache): with open(cache, 'wb') as f: pickle.dump(self.cache, f) def check(self, txt): ret = [] if txt in self.cache: return ret for w in self.split_words(txt): w_lower = w.lower() if w_lower in self.cache: continue if not self.dict_spelling.check(w): ret.append((w, self.dict_spelling.suggest(w))) else: self.cache.add(w_lower) if not ret: self.cache.add(txt) return ret

true

f7004a6a9da6e184c08cdd829e3bc4d6ac4c80b7

12,774

py

Python

scripts/sphere/register.py

NingAnMe/voxelmorph

3a1a4c2f456af2dba5552efc1b08c68af38e54dc

[ "Apache-2.0" ]

null

scripts/sphere/register.py

NingAnMe/voxelmorph

3a1a4c2f456af2dba5552efc1b08c68af38e54dc

[ "Apache-2.0" ]

null

scripts/sphere/register.py

NingAnMe/voxelmorph

3a1a4c2f456af2dba5552efc1b08c68af38e54dc

[ "Apache-2.0" ]

null

#!/usr/bin/env python """ Example script to register two volumes with VoxelMorph models. Please make sure to use trained models appropriately. Let's say we have a model trained to register a scan (moving) to an atlas (fixed). To register a scan to the atlas and save the warp field, run: register.py --moving moving.nii.gz --fixed fixed.nii.gz --model model.pt --moved moved.nii.gz --warp warp.nii.gz The source and target input images are expected to be affinely registered. If you use this code, please cite the following, and read function docs for further info/citations VoxelMorph: A Learning Framework for Deformable Medical Image Registration G. Balakrishnan, A. Zhao, M. R. Sabuncu, J. Guttag, A.V. Dalca. IEEE TMI: Transactions on Medical Imaging. 38(8). pp 1788-1800. 2019. or Unsupervised Learning for Probabilistic Diffeomorphic Registration for Images and Surfaces A.V. Dalca, G. Balakrishnan, J. Guttag, M.R. Sabuncu. MedIA: Medical Image Analysis. (57). pp 226-236, 2019 Copyright 2020 Adrian V. Dalca Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ import os import argparse import matplotlib.pyplot as plt # third party import numpy as np import nibabel as nib import torch from scipy.interpolate import RegularGridInterpolator from astropy.coordinates import cartesian_to_spherical, spherical_to_cartesian # import voxelmorph with sphere backend os.environ['VXM_BACKEND'] = 'sphere' import voxelmorph as vxm # nopep8 import math # parse commandline args parser = argparse.ArgumentParser() parser.add_argument('--moving', required=True, help='moving image (source) filename') parser.add_argument('--fixed', required=True, help='fixed image (target) filename') parser.add_argument('--moved', help='warped image output filename') parser.add_argument('--model', required=True, help='pytorch model for nonlinear registration') # parser.add_argument('--normalize_type', default='std', help='select the data normalization processing type') parser.add_argument('--warp', help='output warp deformation filename') parser.add_argument('--sphere_sub', help='sphere_sub image filename') parser.add_argument('--sphere_atlas', help='sphere_atlas image filename') parser.add_argument('--sphere_reg', help='sphere.reg image output filename') parser.add_argument('--sulc_sub', help='silc_sub image filename') parser.add_argument('--sulc_atlas', help='silc_atlas image filename') parser.add_argument('--sphere_freesurfer', help='sphere_freesurfer image filename') parser.add_argument('--plot_image', help='show time image output filename') parser.add_argument('--plot_image_dif_1', help='show dif image output filename') parser.add_argument('--plot_image_dif_2', help='show dif image output filename') parser.add_argument('-g', '--gpu', help='GPU number(s) - if not supplied, CPU is used') parser.add_argument('--multichannel', action='store_true', help='specify that data has multiple channels') args = parser.parse_args() def meannormalize(sub_data): mean = np.mean(sub_data) std = np.std(sub_data) norm = (sub_data - mean) / std return norm, mean, std def backmeannormalize(input, mean, std): output = input * std + mean return output def minmaxnormalize(sub_data): zeros = sub_data == 0 max = np.max(sub_data) min = np.min(sub_data) norm = (sub_data - min) / (max - min) norm[zeros] = 0 return norm def backminmaxnormalize(input, max, min): output = input * (max - min) + min return output def domainnorm(sub_data): domain = 33 norm = sub_data / domain return norm def backdomainnorm(sub_data): domain = 33 output = sub_data * domain return output # def normalize_forword(data, type="std"): # if type == "std": # return meannormalize(data) # elif type == "min_max": # return minmaxnormalize(data) # else: # raise KeyError("type is error") # # def normalize_backword(data, a, b, type="std"): # if type == "std": # return backmeannormalize(data, a, b) # elif type == "min_max": # return backminmaxnormalize(data, a, b) # else: # raise KeyError("type is error") def interpolate(warp_file, lh_sphere): x = np.linspace(-128, 128, 256) # phi ### y = np.linspace(0, 512, 512) # theta ### # print(warp_file.files) warp = warp_file.squeeze() warp = warp.permute(0, 2, 1) warp = warp.detach().numpy() # warp = warp_file['vol'] # warp = np.moveaxis(warp, 1, -1) interpolate_function_x = RegularGridInterpolator((x, y), -warp[0]) # x-axis interpolate_function_y = RegularGridInterpolator((x, y), -warp[1]) # y-axis coords, faces = nib.freesurfer.read_geometry(lh_sphere) r, phi, theta = cartesian_to_spherical(coords[:, 0], coords[:, 1], coords[:, 2]) p = phi.degree t = theta.degree theta_bins = 512 phi_bins = 256 theta_width = math.degrees(2 * np.pi) / theta_bins t /= theta_width phi_width = math.degrees(np.pi) / phi_bins p /= phi_width t = t.reshape(-1, 1) p = p.reshape(-1, 1) pts = np.concatenate((p, t), axis=1) new_pts_x = interpolate_function_x(pts) new_pts_y = interpolate_function_y(pts) x_prime = pts.T[0] + new_pts_x y_prime = pts.T[1] + new_pts_y x_prime *= phi_width y_prime *= theta_width y_prime = np.clip(y_prime, 0, 360) x_prime = np.clip(x_prime, -90, 90) t_prime = [math.radians(i) for i in y_prime] p_prime = [math.radians(i) for i in x_prime] t_prime = np.array(t_prime) p_prime = np.array(p_prime) return r, p_prime, t_prime # save 4 image def save4image(lh_sphere_sub, lh_sphere_atlas, lh_sulc_sub, lh_sulc_atlas, lh_sphere_freesurfer, phi_prime, theta_prime, imagesavefilename): lh_morph_sulc_sub = nib.freesurfer.read_morph_data(lh_sulc_sub) lh_morph_sulc_atlas = nib.freesurfer.read_morph_data(lh_sulc_atlas) coords_sub, faces_sub = nib.freesurfer.read_geometry(lh_sphere_sub) r_sub, phi_sub, theta_sub = cartesian_to_spherical(coords_sub[:, 0], coords_sub[:, 1], coords_sub[:, 2]) coords_atlas, faces_atlas = nib.freesurfer.read_geometry(lh_sphere_atlas) r_atlas, phi_atlas, theta_atlas = cartesian_to_spherical(coords_atlas[:, 0], coords_atlas[:, 1], coords_atlas[:, 2]) coords_freesurfer, faces_freesurfer = nib.freesurfer.read_geometry(lh_sphere_freesurfer) r_reg, phi_reg, theta_reg = cartesian_to_spherical(coords_freesurfer[:, 0], coords_freesurfer[:, 1], coords_freesurfer[:, 2]) fig = plt.figure(figsize=(14, 7)) ax = fig.add_subplot(141) ax.scatter(phi_sub.degree, theta_sub.degree, s=0.1, c=lh_morph_sulc_sub) # phi.degree: [-90, 90], theta.degree: [0, 360] plt.title('Moving') ax = fig.add_subplot(142) ax.scatter(phi_atlas.degree, theta_atlas.degree, s=0.1, c=lh_morph_sulc_atlas) plt.title('Fixed') ax = fig.add_subplot(143) phi_prime = [math.degrees(p) for p in phi_prime] thtea_prime = [math.degrees(t) for t in theta_prime] ax.scatter(phi_prime, thtea_prime, s=0.1, c=lh_morph_sulc_sub) # (256, 512) plt.title('Moved') ax = fig.add_subplot(144) ax.scatter(phi_reg.degree, theta_reg.degree, s=0.1, c=lh_morph_sulc_sub) # (256, 512) plt.title('Moved FreeSurfer') plt.savefig(imagesavefilename) def xyz2degree(lh_sphere, lh_sulc): # coords: return (x, y, z) coordinates # faces: defining mesh triangles coords, faces = nib.freesurfer.read_geometry(lh_sphere) # (r: radius, phi: latitude, theta: longitude) in radians r, phi, theta = cartesian_to_spherical(coords[:, 0], coords[:, 1], coords[:, 2]) lat = phi.degree + 90 lon = theta.degree # resize to (512, 256) y_bins = 512 x_bins = 256 y_width = math.degrees(2 * np.pi) / y_bins ys = lon // y_width x_width = math.degrees(np.pi) / x_bins xs = lat // x_width ys = np.clip(ys, 0, 511) xs = np.clip(xs, 0, 255) # load curv and sulc info lh_morph_sulc = nib.freesurfer.read_morph_data(lh_sulc) xs = xs.astype(np.int32) ys = ys.astype(np.int32) # values store [theta, phi, sulc value, curv value] values = np.zeros((512, 256)) values[ys, xs] = lh_morph_sulc # values[1, ys, xs] = lh_morph_curv return values def xyz2degree2(phi, theta, lh_sulc): lat = phi + 90 lon = theta # resize to (512, 256) y_bins = 512 x_bins = 256 y_width = math.degrees(2 * np.pi) / y_bins ys = lon // y_width x_width = math.degrees(np.pi) / x_bins xs = lat // x_width ys = np.clip(ys, 0, 511) xs = np.clip(xs, 0, 255) # load curv and sulc info lh_morph_sulc = nib.freesurfer.read_morph_data(lh_sulc) xs = xs.astype(np.int32) ys = ys.astype(np.int32) # values store [theta, phi, sulc value, curv value] values = np.zeros((512, 256)) values[ys, xs] = lh_morph_sulc # values[1, ys, xs] = lh_morph_curv return values # device handling if args.gpu and (args.gpu != '-1'): device = 'cuda' os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu else: device = 'cpu' os.environ['CUDA_VISIBLE_DEVICES'] = '-1' # load moving and fixed images add_feat_axis = not args.multichannel moving = vxm.py.utils.load_volfile(args.moving, add_batch_axis=True, add_feat_axis=add_feat_axis) fixed, fixed_affine = vxm.py.utils.load_volfile( args.fixed, add_batch_axis=True, add_feat_axis=add_feat_axis, ret_affine=True) # load and set up model model = vxm.networks.VxmDense.load(args.model, device) model.to(device) model.eval() # set up normalize type # normalize_type = args.normalize_type # normalize_type = "min_max" # set up tensors and permute # moving, a_moving, b_moving = normalize_forword(moving, type=normalize_type) # fixed, a_fixed, b_fixed = normalize_forword(fixed, type=normalize_type) # moving = domainnorm(moving) moving = minmaxnormalize(moving) fixed = minmaxnormalize(fixed) input_moving = torch.from_numpy(moving).to(device).float().permute(0, 3, 1, 2) input_fixed = torch.from_numpy(fixed).to(device).float().permute(0, 3, 1, 2) # predict moved, warp = model(input_moving, input_fixed, registration=True) # moved = normalize_backword(moved, a_moving, b_moving, type=normalize_type) # moved = backdomainnorm(moved) if args.sphere_sub: c, faces = nib.freesurfer.read_geometry(args.sphere_sub) coords = np.empty(shape=c.shape) r, phi_prime, theta_prime = interpolate(warp, args.sphere_sub) coords[:, 0], coords[:, 1], coords[:, 2] = spherical_to_cartesian(r, phi_prime, theta_prime) nib.freesurfer.io.write_geometry(args.sphere_reg, coords, faces) if args.plot_image: lh_sphere_sub = args.sphere_sub lh_sphere_atlas = args.sphere_atlas lh_sulc_sub = args.sulc_sub lh_sulc_atlas = args.sulc_atlas lh_sphere_freesurfer = args.sphere_freesurfer imagesavefilename = args.plot_image save4image(lh_sphere_sub, lh_sphere_atlas, lh_sulc_sub, lh_sulc_atlas, lh_sphere_freesurfer, phi_prime, theta_prime, imagesavefilename) if args.plot_image_dif_1 or args.plot_image_dif_2: imagesavefilenamedif_1 = args.plot_image_dif_1 imagesavefilenamedif_2 = args.plot_image_dif_2 dif_moving = xyz2degree(lh_sphere_sub, lh_sulc_sub) dif_moved = xyz2degree2(phi_prime, theta_prime, lh_sulc_sub) dif_freesurfer = xyz2degree(lh_sphere_freesurfer, lh_sulc_sub) dif_moved_moving = dif_moved - dif_moving print(np.nanmax(dif_moved_moving), np.nanmin(dif_moved_moving), np.nanmean(dif_moved_moving)) dif_freesurfer_moved = dif_freesurfer - dif_moved plt.figure(figsize=(14, 7)) plt.imshow(dif_moved_moving) plt.title('moved_moving') plt.colorbar() plt.savefig(imagesavefilenamedif_1) plt.figure(figsize=(14, 7)) plt.imshow(dif_freesurfer_moved) plt.title('freesurfer_moved') plt.colorbar() plt.savefig(imagesavefilenamedif_2) # save moved image if args.moved: moved = moved.detach().cpu().numpy().squeeze() vxm.py.utils.save_volfile(moved, args.moved, fixed_affine) # save warp if args.warp: warp = warp.detach().cpu().numpy().squeeze() vxm.py.utils.save_volfile(warp, args.warp, fixed_affine)

35.287293

120

0.702129

import os import argparse import matplotlib.pyplot as plt import numpy as np import nibabel as nib import torch from scipy.interpolate import RegularGridInterpolator from astropy.coordinates import cartesian_to_spherical, spherical_to_cartesian os.environ['VXM_BACKEND'] = 'sphere' import voxelmorph as vxm import math parser = argparse.ArgumentParser() parser.add_argument('--moving', required=True, help='moving image (source) filename') parser.add_argument('--fixed', required=True, help='fixed image (target) filename') parser.add_argument('--moved', help='warped image output filename') parser.add_argument('--model', required=True, help='pytorch model for nonlinear registration') parser.add_argument('--warp', help='output warp deformation filename') parser.add_argument('--sphere_sub', help='sphere_sub image filename') parser.add_argument('--sphere_atlas', help='sphere_atlas image filename') parser.add_argument('--sphere_reg', help='sphere.reg image output filename') parser.add_argument('--sulc_sub', help='silc_sub image filename') parser.add_argument('--sulc_atlas', help='silc_atlas image filename') parser.add_argument('--sphere_freesurfer', help='sphere_freesurfer image filename') parser.add_argument('--plot_image', help='show time image output filename') parser.add_argument('--plot_image_dif_1', help='show dif image output filename') parser.add_argument('--plot_image_dif_2', help='show dif image output filename') parser.add_argument('-g', '--gpu', help='GPU number(s) - if not supplied, CPU is used') parser.add_argument('--multichannel', action='store_true', help='specify that data has multiple channels') args = parser.parse_args() def meannormalize(sub_data): mean = np.mean(sub_data) std = np.std(sub_data) norm = (sub_data - mean) / std return norm, mean, std def backmeannormalize(input, mean, std): output = input * std + mean return output def minmaxnormalize(sub_data): zeros = sub_data == 0 max = np.max(sub_data) min = np.min(sub_data) norm = (sub_data - min) / (max - min) norm[zeros] = 0 return norm def backminmaxnormalize(input, max, min): output = input * (max - min) + min return output def domainnorm(sub_data): domain = 33 norm = sub_data / domain return norm def backdomainnorm(sub_data): domain = 33 output = sub_data * domain return output def interpolate(warp_file, lh_sphere): x = np.linspace(-128, 128, 256) y = np.linspace(0, 512, 512) warp = warp_file.squeeze() warp = warp.permute(0, 2, 1) warp = warp.detach().numpy() interpolate_function_x = RegularGridInterpolator((x, y), -warp[0]) interpolate_function_y = RegularGridInterpolator((x, y), -warp[1]) coords, faces = nib.freesurfer.read_geometry(lh_sphere) r, phi, theta = cartesian_to_spherical(coords[:, 0], coords[:, 1], coords[:, 2]) p = phi.degree t = theta.degree theta_bins = 512 phi_bins = 256 theta_width = math.degrees(2 * np.pi) / theta_bins t /= theta_width phi_width = math.degrees(np.pi) / phi_bins p /= phi_width t = t.reshape(-1, 1) p = p.reshape(-1, 1) pts = np.concatenate((p, t), axis=1) new_pts_x = interpolate_function_x(pts) new_pts_y = interpolate_function_y(pts) x_prime = pts.T[0] + new_pts_x y_prime = pts.T[1] + new_pts_y x_prime *= phi_width y_prime *= theta_width y_prime = np.clip(y_prime, 0, 360) x_prime = np.clip(x_prime, -90, 90) t_prime = [math.radians(i) for i in y_prime] p_prime = [math.radians(i) for i in x_prime] t_prime = np.array(t_prime) p_prime = np.array(p_prime) return r, p_prime, t_prime def save4image(lh_sphere_sub, lh_sphere_atlas, lh_sulc_sub, lh_sulc_atlas, lh_sphere_freesurfer, phi_prime, theta_prime, imagesavefilename): lh_morph_sulc_sub = nib.freesurfer.read_morph_data(lh_sulc_sub) lh_morph_sulc_atlas = nib.freesurfer.read_morph_data(lh_sulc_atlas) coords_sub, faces_sub = nib.freesurfer.read_geometry(lh_sphere_sub) r_sub, phi_sub, theta_sub = cartesian_to_spherical(coords_sub[:, 0], coords_sub[:, 1], coords_sub[:, 2]) coords_atlas, faces_atlas = nib.freesurfer.read_geometry(lh_sphere_atlas) r_atlas, phi_atlas, theta_atlas = cartesian_to_spherical(coords_atlas[:, 0], coords_atlas[:, 1], coords_atlas[:, 2]) coords_freesurfer, faces_freesurfer = nib.freesurfer.read_geometry(lh_sphere_freesurfer) r_reg, phi_reg, theta_reg = cartesian_to_spherical(coords_freesurfer[:, 0], coords_freesurfer[:, 1], coords_freesurfer[:, 2]) fig = plt.figure(figsize=(14, 7)) ax = fig.add_subplot(141) ax.scatter(phi_sub.degree, theta_sub.degree, s=0.1, c=lh_morph_sulc_sub) plt.title('Moving') ax = fig.add_subplot(142) ax.scatter(phi_atlas.degree, theta_atlas.degree, s=0.1, c=lh_morph_sulc_atlas) plt.title('Fixed') ax = fig.add_subplot(143) phi_prime = [math.degrees(p) for p in phi_prime] thtea_prime = [math.degrees(t) for t in theta_prime] ax.scatter(phi_prime, thtea_prime, s=0.1, c=lh_morph_sulc_sub) plt.title('Moved') ax = fig.add_subplot(144) ax.scatter(phi_reg.degree, theta_reg.degree, s=0.1, c=lh_morph_sulc_sub) plt.title('Moved FreeSurfer') plt.savefig(imagesavefilename) def xyz2degree(lh_sphere, lh_sulc): coords, faces = nib.freesurfer.read_geometry(lh_sphere) r, phi, theta = cartesian_to_spherical(coords[:, 0], coords[:, 1], coords[:, 2]) lat = phi.degree + 90 lon = theta.degree y_bins = 512 x_bins = 256 y_width = math.degrees(2 * np.pi) / y_bins ys = lon // y_width x_width = math.degrees(np.pi) / x_bins xs = lat // x_width ys = np.clip(ys, 0, 511) xs = np.clip(xs, 0, 255) lh_morph_sulc = nib.freesurfer.read_morph_data(lh_sulc) xs = xs.astype(np.int32) ys = ys.astype(np.int32) values = np.zeros((512, 256)) values[ys, xs] = lh_morph_sulc return values def xyz2degree2(phi, theta, lh_sulc): lat = phi + 90 lon = theta y_bins = 512 x_bins = 256 y_width = math.degrees(2 * np.pi) / y_bins ys = lon // y_width x_width = math.degrees(np.pi) / x_bins xs = lat // x_width ys = np.clip(ys, 0, 511) xs = np.clip(xs, 0, 255) lh_morph_sulc = nib.freesurfer.read_morph_data(lh_sulc) xs = xs.astype(np.int32) ys = ys.astype(np.int32) values = np.zeros((512, 256)) values[ys, xs] = lh_morph_sulc return values if args.gpu and (args.gpu != '-1'): device = 'cuda' os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu else: device = 'cpu' os.environ['CUDA_VISIBLE_DEVICES'] = '-1' add_feat_axis = not args.multichannel moving = vxm.py.utils.load_volfile(args.moving, add_batch_axis=True, add_feat_axis=add_feat_axis) fixed, fixed_affine = vxm.py.utils.load_volfile( args.fixed, add_batch_axis=True, add_feat_axis=add_feat_axis, ret_affine=True) model = vxm.networks.VxmDense.load(args.model, device) model.to(device) model.eval() moving = minmaxnormalize(moving) fixed = minmaxnormalize(fixed) input_moving = torch.from_numpy(moving).to(device).float().permute(0, 3, 1, 2) input_fixed = torch.from_numpy(fixed).to(device).float().permute(0, 3, 1, 2) moved, warp = model(input_moving, input_fixed, registration=True) if args.sphere_sub: c, faces = nib.freesurfer.read_geometry(args.sphere_sub) coords = np.empty(shape=c.shape) r, phi_prime, theta_prime = interpolate(warp, args.sphere_sub) coords[:, 0], coords[:, 1], coords[:, 2] = spherical_to_cartesian(r, phi_prime, theta_prime) nib.freesurfer.io.write_geometry(args.sphere_reg, coords, faces) if args.plot_image: lh_sphere_sub = args.sphere_sub lh_sphere_atlas = args.sphere_atlas lh_sulc_sub = args.sulc_sub lh_sulc_atlas = args.sulc_atlas lh_sphere_freesurfer = args.sphere_freesurfer imagesavefilename = args.plot_image save4image(lh_sphere_sub, lh_sphere_atlas, lh_sulc_sub, lh_sulc_atlas, lh_sphere_freesurfer, phi_prime, theta_prime, imagesavefilename) if args.plot_image_dif_1 or args.plot_image_dif_2: imagesavefilenamedif_1 = args.plot_image_dif_1 imagesavefilenamedif_2 = args.plot_image_dif_2 dif_moving = xyz2degree(lh_sphere_sub, lh_sulc_sub) dif_moved = xyz2degree2(phi_prime, theta_prime, lh_sulc_sub) dif_freesurfer = xyz2degree(lh_sphere_freesurfer, lh_sulc_sub) dif_moved_moving = dif_moved - dif_moving print(np.nanmax(dif_moved_moving), np.nanmin(dif_moved_moving), np.nanmean(dif_moved_moving)) dif_freesurfer_moved = dif_freesurfer - dif_moved plt.figure(figsize=(14, 7)) plt.imshow(dif_moved_moving) plt.title('moved_moving') plt.colorbar() plt.savefig(imagesavefilenamedif_1) plt.figure(figsize=(14, 7)) plt.imshow(dif_freesurfer_moved) plt.title('freesurfer_moved') plt.colorbar() plt.savefig(imagesavefilenamedif_2) if args.moved: moved = moved.detach().cpu().numpy().squeeze() vxm.py.utils.save_volfile(moved, args.moved, fixed_affine) if args.warp: warp = warp.detach().cpu().numpy().squeeze() vxm.py.utils.save_volfile(warp, args.warp, fixed_affine)

true

f7004b5c8eda4fe476b1e7d12428eec65275cd45

13,707

py

Python

src/pymor/reductors/residual.py

meretp/pymor

0965a5c3d0725466103efae5190493fceb2bf441

[ "Unlicense" ]

182

2015-01-06T16:12:45.000Z

2022-03-22T09:24:45.000Z

src/pymor/reductors/residual.py

meretp/pymor

0965a5c3d0725466103efae5190493fceb2bf441

[ "Unlicense" ]

1,305

2015-01-06T15:02:31.000Z

2022-03-29T14:58:28.000Z

src/pymor/reductors/residual.py

HenKlei/pymor

01876cd39e04bec6d4299f36b59663cd08beafd3

[ "Unlicense" ]

68

2015-01-06T11:13:20.000Z

2022-03-21T18:07:42.000Z

# This file is part of the pyMOR project (https://www.pymor.org). # Copyright 2013-2021 pyMOR developers and contributors. All rights reserved. # License: BSD 2-Clause License (https://opensource.org/licenses/BSD-2-Clause) import numpy as np from pymor.algorithms.image import estimate_image_hierarchical from pymor.algorithms.projection import project, project_to_subbasis from pymor.core.base import BasicObject from pymor.core.exceptions import ImageCollectionError from pymor.operators.constructions import ZeroOperator from pymor.operators.interface import Operator class ResidualReductor(BasicObject): """Generic reduced basis residual reductor. Given an operator and a right-hand side, the residual is given by:: residual.apply(U, mu) == operator.apply(U, mu) - rhs.as_range_array(mu) When operator maps to functionals instead of vectors, we are interested in the Riesz representative of the residual:: residual.apply(U, mu) == product.apply_inverse(operator.apply(U, mu) - rhs.as_range_array(mu)) Given a basis `RB` of a subspace of the source space of `operator`, this reductor uses :func:`~pymor.algorithms.image.estimate_image_hierarchical` to determine a low-dimensional subspace containing the image of the subspace under `residual` (resp. `riesz_residual`), computes an orthonormal basis `residual_range` for this range space and then returns the Petrov-Galerkin projection :: projected_residual == project(residual, range_basis=residual_range, source_basis=RB) of the residual operator. Given a reduced basis coefficient vector `u`, w.r.t. `RB`, the (dual) norm of the residual can then be computed as :: projected_residual.apply(u, mu).norm() Moreover, a `reconstruct` method is provided such that :: residual_reductor.reconstruct(projected_residual.apply(u, mu)) == residual.apply(RB.lincomb(u), mu) Parameters ---------- RB |VectorArray| containing a basis of the reduced space onto which to project. operator See definition of `residual`. rhs See definition of `residual`. If `None`, zero right-hand side is assumed. product Inner product |Operator| w.r.t. which to orthonormalize and w.r.t. which to compute the Riesz representatives in case `operator` maps to functionals. riesz_representatives If `True` compute the Riesz representative of the residual. """ def __init__(self, RB, operator, rhs=None, product=None, riesz_representatives=False): assert RB in operator.source assert rhs is None \ or (rhs.source.is_scalar and rhs.range == operator.range and rhs.linear) assert product is None or product.source == product.range == operator.range self.__auto_init(locals()) self.residual_range = operator.range.empty() self.residual_range_dims = [] def reduce(self): if self.residual_range is not False: with self.logger.block('Estimating residual range ...'): try: self.residual_range, self.residual_range_dims = \ estimate_image_hierarchical([self.operator], [self.rhs], self.RB, (self.residual_range, self.residual_range_dims), orthonormalize=True, product=self.product, riesz_representatives=self.riesz_representatives) except ImageCollectionError as e: self.logger.warning(f'Cannot compute range of {e.op}. Evaluation will be slow.') self.residual_range = False if self.residual_range is False: operator = project(self.operator, None, self.RB) return NonProjectedResidualOperator(operator, self.rhs, self.riesz_representatives, self.product) with self.logger.block('Projecting residual operator ...'): if self.riesz_representatives: operator = project(self.operator, self.residual_range, self.RB, product=None) # the product cancels out rhs = project(self.rhs, self.residual_range, None, product=None) else: operator = project(self.operator, self.residual_range, self.RB, product=self.product) rhs = project(self.rhs, self.residual_range, None, product=self.product) return ResidualOperator(operator, rhs) def reconstruct(self, u): """Reconstruct high-dimensional residual vector from reduced vector `u`.""" if self.residual_range is False: if self.product: return u * (u.norm() / u.norm(self.product))[0] else: return u else: return self.residual_range[:u.dim].lincomb(u.to_numpy()) class ResidualOperator(Operator): """Instantiated by :class:`ResidualReductor`.""" def __init__(self, operator, rhs, name=None): self.__auto_init(locals()) self.source = operator.source self.range = operator.range self.linear = operator.linear self.rhs_vector = rhs.as_range_array() if rhs and not rhs.parametric else None def apply(self, U, mu=None): V = self.operator.apply(U, mu=mu) if self.rhs: F = self.rhs_vector or self.rhs.as_range_array(mu) if len(V) > 1: V -= F[[0]*len(V)] else: V -= F return V def projected_to_subbasis(self, dim_range=None, dim_source=None, name=None): return ResidualOperator(project_to_subbasis(self.operator, dim_range, dim_source), project_to_subbasis(self.rhs, dim_range, None), name=name) class NonProjectedResidualOperator(ResidualOperator): """Instantiated by :class:`ResidualReductor`. Not to be used directly. """ def __init__(self, operator, rhs, riesz_representatives, product): super().__init__(operator, rhs) self.__auto_init(locals()) def apply(self, U, mu=None): R = super().apply(U, mu=mu) if self.product: if self.riesz_representatives: R_riesz = self.product.apply_inverse(R) # divide by norm, except when norm is zero: inversel2 = 1./R_riesz.norm() inversel2 = np.nan_to_num(inversel2) R_riesz.scal(np.sqrt(R_riesz.pairwise_inner(R)) * inversel2) return R_riesz else: # divide by norm, except when norm is zero: inversel2 = 1./R.norm() inversel2 = np.nan_to_num(inversel2) R.scal(np.sqrt(self.product.pairwise_apply2(R, R)) * inversel2) return R else: return R def projected_to_subbasis(self, dim_range=None, dim_source=None, name=None): return self.with_(operator=project_to_subbasis(self.operator, None, dim_source)) class ImplicitEulerResidualReductor(BasicObject): """Reduced basis residual reductor with mass operator for implicit Euler timestepping. Given an operator, mass and a functional, the concatenation of residual operator with the Riesz isomorphism is given by:: riesz_residual.apply(U, U_old, mu) == product.apply_inverse(operator.apply(U, mu) + 1/dt*mass.apply(U, mu) - 1/dt*mass.apply(U_old, mu) - rhs.as_vector(mu)) This reductor determines a low-dimensional subspace of the image of a reduced basis space under `riesz_residual` using :func:`~pymor.algorithms.image.estimate_image_hierarchical`, computes an orthonormal basis `residual_range` of this range space and then returns the Petrov-Galerkin projection :: projected_riesz_residual == riesz_residual.projected(range_basis=residual_range, source_basis=RB) of the `riesz_residual` operator. Given reduced basis coefficient vectors `u` and `u_old`, the dual norm of the residual can then be computed as :: projected_riesz_residual.apply(u, u_old, mu).norm() Moreover, a `reconstruct` method is provided such that :: residual_reductor.reconstruct(projected_riesz_residual.apply(u, u_old, mu)) == riesz_residual.apply(RB.lincomb(u), RB.lincomb(u_old), mu) Parameters ---------- operator See definition of `riesz_residual`. mass The mass operator. See definition of `riesz_residual`. dt The time step size. See definition of `riesz_residual`. rhs See definition of `riesz_residual`. If `None`, zero right-hand side is assumed. RB |VectorArray| containing a basis of the reduced space onto which to project. product Inner product |Operator| w.r.t. which to compute the Riesz representatives. """ def __init__(self, RB, operator, mass, dt, rhs=None, product=None): assert RB in operator.source assert rhs.source.is_scalar and rhs.range == operator.range and rhs.linear assert product is None or product.source == product.range == operator.range self.__auto_init(locals()) self.residual_range = operator.range.empty() self.residual_range_dims = [] def reduce(self): if self.residual_range is not False: with self.logger.block('Estimating residual range ...'): try: self.residual_range, self.residual_range_dims = \ estimate_image_hierarchical([self.operator, self.mass], [self.rhs], self.RB, (self.residual_range, self.residual_range_dims), orthonormalize=True, product=self.product, riesz_representatives=True) except ImageCollectionError as e: self.logger.warning(f'Cannot compute range of {e.op}. Evaluation will be slow.') self.residual_range = False if self.residual_range is False: operator = project(self.operator, None, self.RB) mass = project(self.mass, None, self.RB) return NonProjectedImplicitEulerResidualOperator(operator, mass, self.rhs, self.dt, self.product) with self.logger.block('Projecting residual operator ...'): # the product always cancels out operator = project(self.operator, self.residual_range, self.RB, product=None) mass = project(self.mass, self.residual_range, self.RB, product=None) rhs = project(self.rhs, self.residual_range, None, product=None) return ImplicitEulerResidualOperator(operator, mass, rhs, self.dt) def reconstruct(self, u): """Reconstruct high-dimensional residual vector from reduced vector `u`.""" if self.residual_range is False: if self.product: return u * (u.norm() / u.norm(self.product))[0] else: return u else: return self.residual_range[:u.dim].lincomb(u.to_numpy()) class ImplicitEulerResidualOperator(Operator): """Instantiated by :class:`ImplicitEulerResidualReductor`.""" def __init__(self, operator, mass, rhs, dt, name=None): self.__auto_init(locals()) self.source = operator.source self.range = operator.range self.linear = operator.linear self.rhs_vector = rhs.as_range_array() if not rhs.parametric else None def apply(self, U, U_old, mu=None): V = self.operator.apply(U, mu=mu) V.axpy(1./self.dt, self.mass.apply(U, mu=mu)) V.axpy(-1./self.dt, self.mass.apply(U_old, mu=mu)) if not isinstance(self.rhs, ZeroOperator): F = self.rhs_vector or self.rhs.as_range_array(mu) if len(V) > 1: V -= F[[0]*len(V)] else: V -= F return V def projected_to_subbasis(self, dim_range=None, dim_source=None, name=None): return ImplicitEulerResidualOperator(project_to_subbasis(self.operator, dim_range, dim_source), project_to_subbasis(self.mass, dim_range, dim_source), project_to_subbasis(self.rhs, dim_range, None), self.dt, name=name) class NonProjectedImplicitEulerResidualOperator(ImplicitEulerResidualOperator): """Instantiated by :class:`ImplicitEulerResidualReductor`. Not to be used directly. """ def __init__(self, operator, mass, rhs, dt, product): super().__init__(operator, mass, rhs, dt) self.product = product def apply(self, U, U_old, mu=None): R = super().apply(U, U_old, mu=mu) if self.product: R_riesz = self.product.apply_inverse(R) # divide by norm, except when norm is zero: inversel2 = 1./R_riesz.norm() inversel2 = np.nan_to_num(inversel2) R_riesz.scal(np.sqrt(R_riesz.pairwise_inner(R)) * inversel2) return R_riesz else: return R def projected_to_subbasis(self, dim_range=None, dim_source=None, name=None): return self.with_(operator=project_to_subbasis(self.operator, None, dim_source), mass=project_to_subbasis(self.mass, None, dim_source))

43.376582

120

0.625885

import numpy as np from pymor.algorithms.image import estimate_image_hierarchical from pymor.algorithms.projection import project, project_to_subbasis from pymor.core.base import BasicObject from pymor.core.exceptions import ImageCollectionError from pymor.operators.constructions import ZeroOperator from pymor.operators.interface import Operator class ResidualReductor(BasicObject): def __init__(self, RB, operator, rhs=None, product=None, riesz_representatives=False): assert RB in operator.source assert rhs is None \ or (rhs.source.is_scalar and rhs.range == operator.range and rhs.linear) assert product is None or product.source == product.range == operator.range self.__auto_init(locals()) self.residual_range = operator.range.empty() self.residual_range_dims = [] def reduce(self): if self.residual_range is not False: with self.logger.block('Estimating residual range ...'): try: self.residual_range, self.residual_range_dims = \ estimate_image_hierarchical([self.operator], [self.rhs], self.RB, (self.residual_range, self.residual_range_dims), orthonormalize=True, product=self.product, riesz_representatives=self.riesz_representatives) except ImageCollectionError as e: self.logger.warning(f'Cannot compute range of {e.op}. Evaluation will be slow.') self.residual_range = False if self.residual_range is False: operator = project(self.operator, None, self.RB) return NonProjectedResidualOperator(operator, self.rhs, self.riesz_representatives, self.product) with self.logger.block('Projecting residual operator ...'): if self.riesz_representatives: operator = project(self.operator, self.residual_range, self.RB, product=None) rhs = project(self.rhs, self.residual_range, None, product=None) else: operator = project(self.operator, self.residual_range, self.RB, product=self.product) rhs = project(self.rhs, self.residual_range, None, product=self.product) return ResidualOperator(operator, rhs) def reconstruct(self, u): if self.residual_range is False: if self.product: return u * (u.norm() / u.norm(self.product))[0] else: return u else: return self.residual_range[:u.dim].lincomb(u.to_numpy()) class ResidualOperator(Operator): def __init__(self, operator, rhs, name=None): self.__auto_init(locals()) self.source = operator.source self.range = operator.range self.linear = operator.linear self.rhs_vector = rhs.as_range_array() if rhs and not rhs.parametric else None def apply(self, U, mu=None): V = self.operator.apply(U, mu=mu) if self.rhs: F = self.rhs_vector or self.rhs.as_range_array(mu) if len(V) > 1: V -= F[[0]*len(V)] else: V -= F return V def projected_to_subbasis(self, dim_range=None, dim_source=None, name=None): return ResidualOperator(project_to_subbasis(self.operator, dim_range, dim_source), project_to_subbasis(self.rhs, dim_range, None), name=name) class NonProjectedResidualOperator(ResidualOperator): def __init__(self, operator, rhs, riesz_representatives, product): super().__init__(operator, rhs) self.__auto_init(locals()) def apply(self, U, mu=None): R = super().apply(U, mu=mu) if self.product: if self.riesz_representatives: R_riesz = self.product.apply_inverse(R) inversel2 = 1./R_riesz.norm() inversel2 = np.nan_to_num(inversel2) R_riesz.scal(np.sqrt(R_riesz.pairwise_inner(R)) * inversel2) return R_riesz else: inversel2 = 1./R.norm() inversel2 = np.nan_to_num(inversel2) R.scal(np.sqrt(self.product.pairwise_apply2(R, R)) * inversel2) return R else: return R def projected_to_subbasis(self, dim_range=None, dim_source=None, name=None): return self.with_(operator=project_to_subbasis(self.operator, None, dim_source)) class ImplicitEulerResidualReductor(BasicObject): def __init__(self, RB, operator, mass, dt, rhs=None, product=None): assert RB in operator.source assert rhs.source.is_scalar and rhs.range == operator.range and rhs.linear assert product is None or product.source == product.range == operator.range self.__auto_init(locals()) self.residual_range = operator.range.empty() self.residual_range_dims = [] def reduce(self): if self.residual_range is not False: with self.logger.block('Estimating residual range ...'): try: self.residual_range, self.residual_range_dims = \ estimate_image_hierarchical([self.operator, self.mass], [self.rhs], self.RB, (self.residual_range, self.residual_range_dims), orthonormalize=True, product=self.product, riesz_representatives=True) except ImageCollectionError as e: self.logger.warning(f'Cannot compute range of {e.op}. Evaluation will be slow.') self.residual_range = False if self.residual_range is False: operator = project(self.operator, None, self.RB) mass = project(self.mass, None, self.RB) return NonProjectedImplicitEulerResidualOperator(operator, mass, self.rhs, self.dt, self.product) with self.logger.block('Projecting residual operator ...'): operator = project(self.operator, self.residual_range, self.RB, product=None) mass = project(self.mass, self.residual_range, self.RB, product=None) rhs = project(self.rhs, self.residual_range, None, product=None) return ImplicitEulerResidualOperator(operator, mass, rhs, self.dt) def reconstruct(self, u): if self.residual_range is False: if self.product: return u * (u.norm() / u.norm(self.product))[0] else: return u else: return self.residual_range[:u.dim].lincomb(u.to_numpy()) class ImplicitEulerResidualOperator(Operator): def __init__(self, operator, mass, rhs, dt, name=None): self.__auto_init(locals()) self.source = operator.source self.range = operator.range self.linear = operator.linear self.rhs_vector = rhs.as_range_array() if not rhs.parametric else None def apply(self, U, U_old, mu=None): V = self.operator.apply(U, mu=mu) V.axpy(1./self.dt, self.mass.apply(U, mu=mu)) V.axpy(-1./self.dt, self.mass.apply(U_old, mu=mu)) if not isinstance(self.rhs, ZeroOperator): F = self.rhs_vector or self.rhs.as_range_array(mu) if len(V) > 1: V -= F[[0]*len(V)] else: V -= F return V def projected_to_subbasis(self, dim_range=None, dim_source=None, name=None): return ImplicitEulerResidualOperator(project_to_subbasis(self.operator, dim_range, dim_source), project_to_subbasis(self.mass, dim_range, dim_source), project_to_subbasis(self.rhs, dim_range, None), self.dt, name=name) class NonProjectedImplicitEulerResidualOperator(ImplicitEulerResidualOperator): def __init__(self, operator, mass, rhs, dt, product): super().__init__(operator, mass, rhs, dt) self.product = product def apply(self, U, U_old, mu=None): R = super().apply(U, U_old, mu=mu) if self.product: R_riesz = self.product.apply_inverse(R) inversel2 = 1./R_riesz.norm() inversel2 = np.nan_to_num(inversel2) R_riesz.scal(np.sqrt(R_riesz.pairwise_inner(R)) * inversel2) return R_riesz else: return R def projected_to_subbasis(self, dim_range=None, dim_source=None, name=None): return self.with_(operator=project_to_subbasis(self.operator, None, dim_source), mass=project_to_subbasis(self.mass, None, dim_source))

true

f7004c81dd586096d66ee40abfa3ddd193a494db

7,828

py

Python

rain_api_core/egress_util.py

asfadmin/rain-api-core

99985d4a346ab06449a42ed6b5b91f36d2bc760a

[ "Apache-2.0" ]

1

2020-05-06T22:01:22.000Z

rain_api_core/egress_util.py

asfadmin/rain-api-core

99985d4a346ab06449a42ed6b5b91f36d2bc760a

[ "Apache-2.0" ]

87

2019-09-16T20:45:59.000Z

2022-03-31T21:18:44.000Z

rain_api_core/egress_util.py

asfadmin/rain-api-core

99985d4a346ab06449a42ed6b5b91f36d2bc760a

[ "Apache-2.0" ]

2

2020-05-06T22:01:29.000Z

2021-03-23T18:22:52.000Z

import logging import hmac from hashlib import sha256 import os import urllib from datetime import datetime log = logging.getLogger(__name__) # This warning is stupid # pylint: disable=logging-fstring-interpolation def prepend_bucketname(name): prefix = os.getenv('BUCKETNAME_PREFIX', "gsfc-ngap-{}-".format(os.getenv('MATURITY', 'DEV')[0:1].lower())) return "{}{}".format(prefix, name) def hmacsha256(key, string): return hmac.new(key, string.encode('utf-8'), sha256) def get_presigned_url(session, bucket_name, object_name, region_name, expire_seconds, user_id, method='GET'): timez = datetime.utcnow().strftime('%Y%m%dT%H%M%SZ') datez = timez[:8] hostname = "{0}.s3{1}.amazonaws.com".format(bucket_name, "."+region_name if region_name != "us-east-1" else "") cred = session['Credentials']['AccessKeyId'] secret = session['Credentials']['SecretAccessKey'] token = session['Credentials']['SessionToken'] aws4_request = "/".join([datez, region_name, "s3", "aws4_request"]) cred_string = "{0}/{1}".format(cred, aws4_request) # Canonical Query String Parts parts = ["A-userid={0}".format(user_id), "X-Amz-Algorithm=AWS4-HMAC-SHA256", "X-Amz-Credential="+urllib.parse.quote_plus(cred_string), "X-Amz-Date="+timez, "X-Amz-Expires={0}".format(expire_seconds), "X-Amz-Security-Token="+urllib.parse.quote_plus(token), "X-Amz-SignedHeaders=host"] can_query_string = "&".join(parts) # Canonical Requst can_req = method + "\n/" + object_name + "\n" + can_query_string + "\nhost:" + hostname + "\n\nhost\nUNSIGNED-PAYLOAD" can_req_hash = sha256(can_req.encode('utf-8')).hexdigest() # String to Sign stringtosign = "\n".join(["AWS4-HMAC-SHA256", timez, aws4_request, can_req_hash]) # Signing Key StepOne = hmacsha256( "AWS4{0}".format(secret).encode('utf-8'), datez).digest() StepTwo = hmacsha256( StepOne, region_name ).digest() StepThree = hmacsha256( StepTwo, "s3").digest() SigningKey = hmacsha256( StepThree, "aws4_request").digest() # Final Signature Signature = hmacsha256(SigningKey, stringtosign).hexdigest() # Dump URL url = "https://" + hostname + "/" + object_name + "?" + can_query_string + "&X-Amz-Signature=" + Signature return url def get_bucket_dynamic_path(path_list, b_map): # Old and REVERSE format has no 'MAP'. In either case, we don't want it fouling our dict. if 'MAP' in b_map: map_dict = b_map['MAP'] else: map_dict = b_map mapping = [] log.debug("Pathparts is {0}".format(", ".join(path_list))) # walk the bucket map to see if this path is valid for path_part in path_list: # Check if we hit a leaf of the YAML tree if (mapping and isinstance(map_dict, str)) or 'bucket' in map_dict: # customheaders = {} if isinstance(map_dict, dict) and 'bucket' in map_dict: bucketname = map_dict['bucket'] if 'headers' in map_dict: customheaders = map_dict['headers'] else: bucketname = map_dict log.debug(f'mapping: {mapping}') # Pop mapping off path_list for _ in mapping: path_list.pop(0) # Join the remaining bits together to form object_name object_name = "/".join(path_list) bucket_path = "/".join(mapping) log.info("Bucket mapping was {0}, object was {1}".format(bucket_path, object_name)) return prepend_bucketname(bucketname), bucket_path, object_name, customheaders if path_part in map_dict: map_dict = map_dict[path_part] mapping.append(path_part) log.debug("Found {0}, Mapping is now {1}".format(path_part, "/".join(mapping))) else: log.warning("Could not find {0} in bucketmap".format(path_part)) log.debug('said bucketmap: {}'.format(map_dict)) return False, False, False, {} # what? No path? return False, False, False, {} def process_varargs(varargs: list, b_map: dict): """ wrapper around process_request that returns legacy values to preserve backward compatibility :param varargs: a list with the path to the file requested. :param b_map: bucket map :return: path, bucket, object_name """ log.warning('Deprecated process_varargs() called.') path, bucket, object_name, _ = process_request(varargs, b_map) return path, bucket, object_name def process_request(varargs, b_map): varargs = varargs.split("/") # Make sure we got at least 1 path, and 1 file name: if len(varargs) < 2: return "/".join(varargs), None, None, [] # Watch for ASF-ish reverse URL mapping formats: if len(varargs) == 3: if os.getenv('USE_REVERSE_BUCKET_MAP', 'FALSE').lower() == 'true': varargs[0], varargs[1] = varargs[1], varargs[0] # Look up the bucket from path parts bucket, path, object_name, headers = get_bucket_dynamic_path(varargs, b_map) # If we didn't figure out the bucket, we don't know the path/object_name if not bucket: object_name = varargs.pop(-1) path = "/".join(varargs) return path, bucket, object_name, headers def bucket_prefix_match(bucket_check, bucket_map, object_name=""): log.debug(f"bucket_prefix_match(): checking if {bucket_check} matches {bucket_map} w/ optional obj '{object_name}'") if bucket_check == bucket_map.split('/')[0] and object_name.startswith("/".join(bucket_map.split('/')[1:])): log.debug(f"Prefixed Bucket Map matched: s3://{bucket_check}/{object_name} => {bucket_map}") return True return False # Sort public/private buckets such that object-prefixes are processed FIRST def get_sorted_bucket_list(b_map, bucket_group): if bucket_group not in b_map: # But why?! log.warning(f"Bucket map does not contain bucket group '{bucket_group}'") return [] # b_map[bucket_group] SHOULD be a dict, but list actually works too. if isinstance(b_map[bucket_group], dict): return sorted(list(b_map[bucket_group].keys()), key=lambda e: e.count("/"), reverse=True ) if isinstance(b_map[bucket_group], list): return sorted(list(b_map[bucket_group]), key=lambda e: e.count("/"), reverse=True ) # Something went wrong. return [] def check_private_bucket(bucket, b_map, object_name=""): log.debug('check_private_buckets(): bucket: {}'.format(bucket)) # Check public bucket file: if 'PRIVATE_BUCKETS' in b_map: # Prioritize prefixed buckets first, the deeper the better! sorted_buckets = get_sorted_bucket_list(b_map, 'PRIVATE_BUCKETS') log.debug(f"Sorted PRIVATE buckets are {sorted_buckets}") for priv_bucket in sorted_buckets: if bucket_prefix_match(bucket, prepend_bucketname(priv_bucket), object_name): # This bucket is PRIVATE, return group! return b_map['PRIVATE_BUCKETS'][priv_bucket] return False def check_public_bucket(bucket, b_map, object_name=""): # Check for PUBLIC_BUCKETS in bucket map file if 'PUBLIC_BUCKETS' in b_map: sorted_buckets = get_sorted_bucket_list(b_map, 'PUBLIC_BUCKETS') log.debug(f"Sorted PUBLIC buckets are {sorted_buckets}") for pub_bucket in sorted_buckets: if bucket_prefix_match(bucket, prepend_bucketname(pub_bucket), object_name): # This bucket is public! log.debug("found a public, we'll take it") return True # Did not find this in public bucket list log.debug('we did not find a public bucket for {}'.format(bucket)) return False

37.454545

122

0.649974

import logging import hmac from hashlib import sha256 import os import urllib from datetime import datetime log = logging.getLogger(__name__) def prepend_bucketname(name): prefix = os.getenv('BUCKETNAME_PREFIX', "gsfc-ngap-{}-".format(os.getenv('MATURITY', 'DEV')[0:1].lower())) return "{}{}".format(prefix, name) def hmacsha256(key, string): return hmac.new(key, string.encode('utf-8'), sha256) def get_presigned_url(session, bucket_name, object_name, region_name, expire_seconds, user_id, method='GET'): timez = datetime.utcnow().strftime('%Y%m%dT%H%M%SZ') datez = timez[:8] hostname = "{0}.s3{1}.amazonaws.com".format(bucket_name, "."+region_name if region_name != "us-east-1" else "") cred = session['Credentials']['AccessKeyId'] secret = session['Credentials']['SecretAccessKey'] token = session['Credentials']['SessionToken'] aws4_request = "/".join([datez, region_name, "s3", "aws4_request"]) cred_string = "{0}/{1}".format(cred, aws4_request) parts = ["A-userid={0}".format(user_id), "X-Amz-Algorithm=AWS4-HMAC-SHA256", "X-Amz-Credential="+urllib.parse.quote_plus(cred_string), "X-Amz-Date="+timez, "X-Amz-Expires={0}".format(expire_seconds), "X-Amz-Security-Token="+urllib.parse.quote_plus(token), "X-Amz-SignedHeaders=host"] can_query_string = "&".join(parts) can_req = method + "\n/" + object_name + "\n" + can_query_string + "\nhost:" + hostname + "\n\nhost\nUNSIGNED-PAYLOAD" can_req_hash = sha256(can_req.encode('utf-8')).hexdigest() stringtosign = "\n".join(["AWS4-HMAC-SHA256", timez, aws4_request, can_req_hash]) StepOne = hmacsha256( "AWS4{0}".format(secret).encode('utf-8'), datez).digest() StepTwo = hmacsha256( StepOne, region_name ).digest() StepThree = hmacsha256( StepTwo, "s3").digest() SigningKey = hmacsha256( StepThree, "aws4_request").digest() Signature = hmacsha256(SigningKey, stringtosign).hexdigest() url = "https://" + hostname + "/" + object_name + "?" + can_query_string + "&X-Amz-Signature=" + Signature return url def get_bucket_dynamic_path(path_list, b_map): if 'MAP' in b_map: map_dict = b_map['MAP'] else: map_dict = b_map mapping = [] log.debug("Pathparts is {0}".format(", ".join(path_list))) # walk the bucket map to see if this path is valid for path_part in path_list: # Check if we hit a leaf of the YAML tree if (mapping and isinstance(map_dict, str)) or 'bucket' in map_dict: # customheaders = {} if isinstance(map_dict, dict) and 'bucket' in map_dict: bucketname = map_dict['bucket'] if 'headers' in map_dict: customheaders = map_dict['headers'] else: bucketname = map_dict log.debug(f'mapping: {mapping}') # Pop mapping off path_list for _ in mapping: path_list.pop(0) # Join the remaining bits together to form object_name object_name = "/".join(path_list) bucket_path = "/".join(mapping) log.info("Bucket mapping was {0}, object was {1}".format(bucket_path, object_name)) return prepend_bucketname(bucketname), bucket_path, object_name, customheaders if path_part in map_dict: map_dict = map_dict[path_part] mapping.append(path_part) log.debug("Found {0}, Mapping is now {1}".format(path_part, "/".join(mapping))) else: log.warning("Could not find {0} in bucketmap".format(path_part)) log.debug('said bucketmap: {}'.format(map_dict)) return False, False, False, {} # what? No path? return False, False, False, {} def process_varargs(varargs: list, b_map: dict): log.warning('Deprecated process_varargs() called.') path, bucket, object_name, _ = process_request(varargs, b_map) return path, bucket, object_name def process_request(varargs, b_map): varargs = varargs.split("/") # Make sure we got at least 1 path, and 1 file name: if len(varargs) < 2: return "/".join(varargs), None, None, [] # Watch for ASF-ish reverse URL mapping formats: if len(varargs) == 3: if os.getenv('USE_REVERSE_BUCKET_MAP', 'FALSE').lower() == 'true': varargs[0], varargs[1] = varargs[1], varargs[0] # Look up the bucket from path parts bucket, path, object_name, headers = get_bucket_dynamic_path(varargs, b_map) # If we didn't figure out the bucket, we don't know the path/object_name if not bucket: object_name = varargs.pop(-1) path = "/".join(varargs) return path, bucket, object_name, headers def bucket_prefix_match(bucket_check, bucket_map, object_name=""): log.debug(f"bucket_prefix_match(): checking if {bucket_check} matches {bucket_map} w/ optional obj '{object_name}'") if bucket_check == bucket_map.split('/')[0] and object_name.startswith("/".join(bucket_map.split('/')[1:])): log.debug(f"Prefixed Bucket Map matched: s3://{bucket_check}/{object_name} => {bucket_map}") return True return False # Sort public/private buckets such that object-prefixes are processed FIRST def get_sorted_bucket_list(b_map, bucket_group): if bucket_group not in b_map: # But why?! log.warning(f"Bucket map does not contain bucket group '{bucket_group}'") return [] # b_map[bucket_group] SHOULD be a dict, but list actually works too. if isinstance(b_map[bucket_group], dict): return sorted(list(b_map[bucket_group].keys()), key=lambda e: e.count("/"), reverse=True ) if isinstance(b_map[bucket_group], list): return sorted(list(b_map[bucket_group]), key=lambda e: e.count("/"), reverse=True ) # Something went wrong. return [] def check_private_bucket(bucket, b_map, object_name=""): log.debug('check_private_buckets(): bucket: {}'.format(bucket)) # Check public bucket file: if 'PRIVATE_BUCKETS' in b_map: # Prioritize prefixed buckets first, the deeper the better! sorted_buckets = get_sorted_bucket_list(b_map, 'PRIVATE_BUCKETS') log.debug(f"Sorted PRIVATE buckets are {sorted_buckets}") for priv_bucket in sorted_buckets: if bucket_prefix_match(bucket, prepend_bucketname(priv_bucket), object_name): # This bucket is PRIVATE, return group! return b_map['PRIVATE_BUCKETS'][priv_bucket] return False def check_public_bucket(bucket, b_map, object_name=""): # Check for PUBLIC_BUCKETS in bucket map file if 'PUBLIC_BUCKETS' in b_map: sorted_buckets = get_sorted_bucket_list(b_map, 'PUBLIC_BUCKETS') log.debug(f"Sorted PUBLIC buckets are {sorted_buckets}") for pub_bucket in sorted_buckets: if bucket_prefix_match(bucket, prepend_bucketname(pub_bucket), object_name): # This bucket is public! log.debug("found a public, we'll take it") return True log.debug('we did not find a public bucket for {}'.format(bucket)) return False

true

f7004e07d39bc89734abdba71f14d69e5287529e

871

py

Python

app/main/views.py

stephane-evrard/IP4_News

866855710cbdc51f02d0d10b09971121affeafe9

[ "Unlicense" ]

null

app/main/views.py

stephane-evrard/IP4_News

866855710cbdc51f02d0d10b09971121affeafe9

[ "Unlicense" ]

null

app/main/views.py

stephane-evrard/IP4_News

866855710cbdc51f02d0d10b09971121affeafe9

[ "Unlicense" ]

null

from flask import render_template,request,redirect,url_for from . import main from ..requests import get_sources,get_articles from ..models import Sources #views @main.route('/') def index(): ''' view root page function that returns the index the page and its data ''' sources = get_sources('business') sports_sources = get_sources('sports') technology_sources = get_sources('technology') entertainment_sources = get_sources('entertainment') title = "News Of The Day" return render_template('index.html',title = title, sources = sources,sports_sources = sports_sources,technology_sources = technology_sources,entertainment_sources = entertainment_sources) @main.route('/sources/<id>') def articles(id): ''' view articles page ''' articles = get_articles(id) title = f'NH | {id}' return render_template('articles.html',title= title,articles = articles)

31.107143

188

0.762342

from flask import render_template,request,redirect,url_for from . import main from ..requests import get_sources,get_articles from ..models import Sources @main.route('/') def index(): sources = get_sources('business') sports_sources = get_sources('sports') technology_sources = get_sources('technology') entertainment_sources = get_sources('entertainment') title = "News Of The Day" return render_template('index.html',title = title, sources = sources,sports_sources = sports_sources,technology_sources = technology_sources,entertainment_sources = entertainment_sources) @main.route('/sources/<id>') def articles(id): articles = get_articles(id) title = f'NH | {id}' return render_template('articles.html',title= title,articles = articles)

true

f7004e17940f340a8654a088ad8c1a3611a4bb3f

3,415

py

Python

polyaxon_schemas/ml/layers/wrappers.py

granularai/polyaxon-schemas

017ae74701f21f12f0b25e75379681ea5d8baa9e

[ "MIT" ]

null

polyaxon_schemas/ml/layers/wrappers.py

granularai/polyaxon-schemas

017ae74701f21f12f0b25e75379681ea5d8baa9e

[ "MIT" ]

null

polyaxon_schemas/ml/layers/wrappers.py

granularai/polyaxon-schemas

017ae74701f21f12f0b25e75379681ea5d8baa9e

[ "MIT" ]

null

# -*- coding: utf-8 -*- from __future__ import absolute_import, division, print_function from marshmallow import fields from polyaxon_schemas.ml.layers.base import BaseLayerConfig, BaseLayerSchema class WrapperSchema(BaseLayerSchema): layer = fields.Nested('LayerSchema') @staticmethod def schema_config(): return WrapperConfig class WrapperConfig(BaseLayerConfig): """Abstract wrapper base class. Wrappers take another layer and augment it in various ways. Do not use this class as a layer, it is only an abstract base class. Two usable wrappers are the `TimeDistributed` and `Bidirectional` wrappers. Args: layer: The layer to be wrapped. """ IDENTIFIER = 'Wrapper' SCHEMA = WrapperSchema def __init__(self, layer, **kwargs): super(WrapperConfig, self).__init__(**kwargs) self.layer = layer class TimeDistributedSchema(WrapperSchema): @staticmethod def schema_config(): return TimeDistributedConfig class TimeDistributedConfig(WrapperConfig): """This wrapper allows to apply a layer to every temporal slice of an input. The input should be at least 3D, and the dimension of index one will be considered to be the temporal dimension. Consider a batch of 32 samples, where each sample is a sequence of 10 vectors of 16 dimensions. The batch input shape of the layer is then `(32, 10, 16)`, and the `input_shape`, not including the samples dimension, is `(10, 16)`. You can then use `TimeDistributed` to apply a `Dense` layer to each of the 10 timesteps, independently: ```python # as the first layer in a model x = TimeDistributed(Dense(8))(x) # now x.output_shape == (None, 10, 8) ``` The output will then have shape `(32, 10, 8)`. In subsequent layers, there is no need for the `input_shape`: ```python x = TimeDistributed(Dense(32))(x) # now x.output_shape == (None, 10, 32) ``` The output will then have shape `(32, 10, 32)`. `TimeDistributed` can be used with arbitrary layers, not just `Dense`, for instance with a `Conv2D` layer: ```python x = TimeDistributed(Conv2D(64, (3, 3)))(x) ``` Args: layer: a layer instance. Polyaxonfile usage: ```yaml TimeDistributed: layer: Dense: units: 2 ``` """ IDENTIFIER = 'TimeDistributed' SCHEMA = TimeDistributedSchema class BidirectionalSchema(WrapperSchema): @staticmethod def schema_config(): return BidirectionalConfig class BidirectionalConfig(WrapperConfig): """Bidirectional wrapper for RNNs. Args: layer: `Recurrent` instance. merge_mode: Mode by which outputs of the forward and backward RNNs will be combined. One of {'sum', 'mul', 'concat', 'ave', None}. If None, the outputs will not be combined, they will be returned as a list. Raises: ValueError: In case of invalid `merge_mode` argument. Example: ```python x = Bidirectional(plx.layers.LSTM(units=128, dropout=0.2, recurrent_dropout=0.2))(x) ``` Polyaxonfile usage: ```yaml Bidirectional: layer: LSTM: units: 128 dropout: 0.2 recurrent_dropout: 0.2 ``` """ IDENTIFIER = 'Bidirectional' SCHEMA = BidirectionalSchema

25.296296

88

0.652709

from __future__ import absolute_import, division, print_function from marshmallow import fields from polyaxon_schemas.ml.layers.base import BaseLayerConfig, BaseLayerSchema class WrapperSchema(BaseLayerSchema): layer = fields.Nested('LayerSchema') @staticmethod def schema_config(): return WrapperConfig class WrapperConfig(BaseLayerConfig): IDENTIFIER = 'Wrapper' SCHEMA = WrapperSchema def __init__(self, layer, **kwargs): super(WrapperConfig, self).__init__(**kwargs) self.layer = layer class TimeDistributedSchema(WrapperSchema): @staticmethod def schema_config(): return TimeDistributedConfig class TimeDistributedConfig(WrapperConfig): IDENTIFIER = 'TimeDistributed' SCHEMA = TimeDistributedSchema class BidirectionalSchema(WrapperSchema): @staticmethod def schema_config(): return BidirectionalConfig class BidirectionalConfig(WrapperConfig): IDENTIFIER = 'Bidirectional' SCHEMA = BidirectionalSchema

true

f7004f189ed755c741a1e9adf7075481f0a47ecb

719

py

Python

conftest.py

DedMazzai/feedback-form

37d0b18a97af43469050be4a8351198521e7f445

[ "Apache-2.0" ]

null

conftest.py

DedMazzai/feedback-form

37d0b18a97af43469050be4a8351198521e7f445

[ "Apache-2.0" ]

null

conftest.py

DedMazzai/feedback-form

37d0b18a97af43469050be4a8351198521e7f445

[ "Apache-2.0" ]

null

import pytest from pages.aplication import Application def pytest_addoption(parser): parser.addoption('--browser_name', action='store', default="chrome", help="Choose browser: chrome or firefox") parser.addoption('--base_url', action='store', default='https://prodoctorov.ru/new/rate/doctor/12/' , help="Choose base_url") @pytest.fixture def app(request): browser_name = request.config.getoption("--browser_name") # для вызова из командной строки и выбора браузера base_url = request.config.getoption("--base_url") fixture = Application(browser_name=browser_name, base_url=base_url) yield fixture print("\nquit browser..") fixture.destroy() return fixture

34.238095

114

0.713491

import pytest from pages.aplication import Application def pytest_addoption(parser): parser.addoption('--browser_name', action='store', default="chrome", help="Choose browser: chrome or firefox") parser.addoption('--base_url', action='store', default='https://prodoctorov.ru/new/rate/doctor/12/' , help="Choose base_url") @pytest.fixture def app(request): browser_name = request.config.getoption("--browser_name") base_url = request.config.getoption("--base_url") fixture = Application(browser_name=browser_name, base_url=base_url) yield fixture print("\nquit browser..") fixture.destroy() return fixture

true

f7004f7c1cf7e6495b7823fbbf31d91eda9a6100

2,036

py

Python

src/optimModels/utils/constantes.py

BioSystemsUM/optimModels

41e8ec5e99a66052fc8b547b60f979d2a2fd669d

[ "Apache-2.0" ]

1

2020-01-09T08:58:05.000Z

src/optimModels/utils/constantes.py

BioSystemsUM/optimModels

41e8ec5e99a66052fc8b547b60f979d2a2fd669d

[ "Apache-2.0" ]

null

src/optimModels/utils/constantes.py

BioSystemsUM/optimModels

41e8ec5e99a66052fc8b547b60f979d2a2fd669d

[ "Apache-2.0" ]

2

2020-01-16T16:15:28.000Z

2020-01-21T16:31:20.000Z

class optimType: REACTION_KO = 1 REACTION_UO = 2 GENE_KO = 3 GENE_UO = 4 MEDIUM = 5 MEDIUM_LEVELS = 6 MEDIUM_REACTION_KO = 7 MEDIUM_REACTION_UO = 8 COMPOSITION = 9 PROTEIN_KO = 10 PROTEIN_UO = 11 types = {1:"Reaction Knockouts",2:"Reaction Under/Over expression", 3:"Gene Knockouts", 4:"Gene Under/Over expression", 5:"Medium compositions",6:"Medium compositions with levels", 7:"Medium with Reaction Knockouts",8: "Medium with Reaction Under/Over expression", 9:"Community Composition", 10:"Protein knockouts", 11:"Protein Under/Over expression"} def get_optim_type_name(self, id): return optimType.types.get(id) class solverMethod: LSODA = 1 LSODAR = 2 LSODE = 3 HEUN = 4 EULER = 5 RK4 = 6 DORMAN_PRINCE = 7 RKFehlberg = 8 Dopri5 = 9 Dop853 = 10 Vode = 11 Radau5 = 12 AdamsBashforth2=13 AdamsBashMoulton2=14 methods ={1:"LSODA",2:"LSODAR", 3: "LSODE", 4: "HEUN", 5: "EULER", 6: "Range Kutta 4", 7: "DORMAN PRINCE", 8: "RKFehlberg", 9: "Dopri5", 10: "Dop853", 11: "Vode", 12: "Radau5", 13: "AdamsBashforth2", 14: "AdamsBashMoulton2" } def get_solver_method_name(self, id): return solverMethod.methods.get(id) class solverStatus: ''' Enumeration of possible solution status. (FROM FRAMED ''' OPTIMAL = 1 UNKNOWN = 0 ERROR = 2 SUBOPTIMAL = -1 UNBOUNDED = -2 INFEASIBLE = -3 INF_OR_UNB = -4 @staticmethod def get_status_str(id): if solverStatus.ERROR == id : str="Error" elif solverStatus.OPTIMAL == id: str = "Optimal" elif solverStatus.SUBOPTIMAL == id: str = "Sub-Optimal" elif solverStatus.UNBOUNDED == id or solverStatus.INFEASIBLE == id or solverStatus.INF_OR_UNB == id: str = "Infeasible or unbounded problem." else: str = "Unknown" return str

27.146667

111

0.59332

class optimType: REACTION_KO = 1 REACTION_UO = 2 GENE_KO = 3 GENE_UO = 4 MEDIUM = 5 MEDIUM_LEVELS = 6 MEDIUM_REACTION_KO = 7 MEDIUM_REACTION_UO = 8 COMPOSITION = 9 PROTEIN_KO = 10 PROTEIN_UO = 11 types = {1:"Reaction Knockouts",2:"Reaction Under/Over expression", 3:"Gene Knockouts", 4:"Gene Under/Over expression", 5:"Medium compositions",6:"Medium compositions with levels", 7:"Medium with Reaction Knockouts",8: "Medium with Reaction Under/Over expression", 9:"Community Composition", 10:"Protein knockouts", 11:"Protein Under/Over expression"} def get_optim_type_name(self, id): return optimType.types.get(id) class solverMethod: LSODA = 1 LSODAR = 2 LSODE = 3 HEUN = 4 EULER = 5 RK4 = 6 DORMAN_PRINCE = 7 RKFehlberg = 8 Dopri5 = 9 Dop853 = 10 Vode = 11 Radau5 = 12 AdamsBashforth2=13 AdamsBashMoulton2=14 methods ={1:"LSODA",2:"LSODAR", 3: "LSODE", 4: "HEUN", 5: "EULER", 6: "Range Kutta 4", 7: "DORMAN PRINCE", 8: "RKFehlberg", 9: "Dopri5", 10: "Dop853", 11: "Vode", 12: "Radau5", 13: "AdamsBashforth2", 14: "AdamsBashMoulton2" } def get_solver_method_name(self, id): return solverMethod.methods.get(id) class solverStatus: OPTIMAL = 1 UNKNOWN = 0 ERROR = 2 SUBOPTIMAL = -1 UNBOUNDED = -2 INFEASIBLE = -3 INF_OR_UNB = -4 @staticmethod def get_status_str(id): if solverStatus.ERROR == id : str="Error" elif solverStatus.OPTIMAL == id: str = "Optimal" elif solverStatus.SUBOPTIMAL == id: str = "Sub-Optimal" elif solverStatus.UNBOUNDED == id or solverStatus.INFEASIBLE == id or solverStatus.INF_OR_UNB == id: str = "Infeasible or unbounded problem." else: str = "Unknown" return str

true

f70050160877a22962c4c5bcea97ccc0f2832657

997

py

Python

backend/app/app/schemas/dataroom.py

saschajullmann/sedotra

aaa38f6d533daa725a7037a8c446da978ffafa7d

[ "MIT" ]

null

backend/app/app/schemas/dataroom.py

saschajullmann/sedotra

aaa38f6d533daa725a7037a8c446da978ffafa7d

[ "MIT" ]

null

backend/app/app/schemas/dataroom.py

saschajullmann/sedotra

aaa38f6d533daa725a7037a8c446da978ffafa7d

[ "MIT" ]

null

from enum import Enum from typing import Optional from uuid import UUID from pydantic import BaseModel from app.models import User, Organization class DataRoomBase(BaseModel): name: Optional[str] = None description: Optional[str] = None class DataRoomCreateRequest(DataRoomBase): name: str class DataRoomCreate(DataRoomCreateRequest): creator: User organization: Organization class Config: arbitrary_types_allowed = True class DataRoomRole(str, Enum): OWNER = "OWNER" ADMIN = "ADMIN" MEMBER = "MEMBER" class DataRoomUserRoleRequest(BaseModel): user_id: UUID user_role: DataRoomRole class Config: use_enum_values = True class DataRoomTeamRoleRequest(BaseModel): team_id: UUID team_role: DataRoomRole class Config: use_enum_values = True class DataRoomUpdate(DataRoomBase): pass class DataRoomInDBBase(DataRoomBase): id: Optional[UUID] = None class Config: orm_mode = True

17.189655

44

0.716148

from enum import Enum from typing import Optional from uuid import UUID from pydantic import BaseModel from app.models import User, Organization class DataRoomBase(BaseModel): name: Optional[str] = None description: Optional[str] = None class DataRoomCreateRequest(DataRoomBase): name: str class DataRoomCreate(DataRoomCreateRequest): creator: User organization: Organization class Config: arbitrary_types_allowed = True class DataRoomRole(str, Enum): OWNER = "OWNER" ADMIN = "ADMIN" MEMBER = "MEMBER" class DataRoomUserRoleRequest(BaseModel): user_id: UUID user_role: DataRoomRole class Config: use_enum_values = True class DataRoomTeamRoleRequest(BaseModel): team_id: UUID team_role: DataRoomRole class Config: use_enum_values = True class DataRoomUpdate(DataRoomBase): pass class DataRoomInDBBase(DataRoomBase): id: Optional[UUID] = None class Config: orm_mode = True

true

f70052f70cc24783258d4832724f32f148a9ec92

1,470

py

Python

xhs_wechat_noteid_script.py

HhhuYu/xhs_simple_crawler

7612d3a7eff7d4b2ded8bbb54bf05f201c607e22

[ "MIT" ]

70

2019-04-12T09:17:53.000Z

2020-12-13T07:33:48.000Z

xhs_wechat_noteid_script.py

redoubtei/xhs_simple_crawler

7612d3a7eff7d4b2ded8bbb54bf05f201c607e22

[ "MIT" ]

1

2019-04-18T15:39:16.000Z

2019-05-17T04:09:36.000Z

xhs_wechat_noteid_script.py

redoubtei/xhs_simple_crawler

7612d3a7eff7d4b2ded8bbb54bf05f201c607e22

[ "MIT" ]

23

2019-05-14T09:36:00.000Z

2020-11-30T11:32:36.000Z

import json import pymongo from config import * def response(flow): global collection client = pymongo.MongoClient(MONGO_URL) db = client[WECHAT_XHS_MONGO_DB] collection = db[WECHAT_XHS_NOTE_MONGO_COLLECTION] url1 = 'https://www.xiaohongshu.com/sapi/wx_mp_api/sns/v1/search/notes?' url2 = 'https://www.xiaohongshu.com/fe_api/burdock/v1/page/' if flow.request.url.startswith(url1): # 数据的解析 print(flow.request.url) notes = json.loads(flow.response.text)["data"]["notes"] for note in notes: note_id = note["id"] img_list = note["images_list"] title = note["title"] user = note["user"] content = { "note_id": note_id, "img_list": img_list, "title": title, "user":user } collection.insert(content) elif flow.request.url.startswith(url2): print(flow.request.url) notes = json.loads(flow.response.text)["data"] for note in notes: note_id = note["id"] img_list = note["cover"] title = note["title"] user = note["user"] content = { "note_id": note_id, "img_list": img_list, "title": title, "user": user } collection.insert(content)

27.735849

77

0.512925

import json import pymongo from config import * def response(flow): global collection client = pymongo.MongoClient(MONGO_URL) db = client[WECHAT_XHS_MONGO_DB] collection = db[WECHAT_XHS_NOTE_MONGO_COLLECTION] url1 = 'https://www.xiaohongshu.com/sapi/wx_mp_api/sns/v1/search/notes?' url2 = 'https://www.xiaohongshu.com/fe_api/burdock/v1/page/' if flow.request.url.startswith(url1): print(flow.request.url) notes = json.loads(flow.response.text)["data"]["notes"] for note in notes: note_id = note["id"] img_list = note["images_list"] title = note["title"] user = note["user"] content = { "note_id": note_id, "img_list": img_list, "title": title, "user":user } collection.insert(content) elif flow.request.url.startswith(url2): print(flow.request.url) notes = json.loads(flow.response.text)["data"] for note in notes: note_id = note["id"] img_list = note["cover"] title = note["title"] user = note["user"] content = { "note_id": note_id, "img_list": img_list, "title": title, "user": user } collection.insert(content)

true

f7005399cea08f14e2a5c830358e28def1a4179c

2,362

py

Python

TradingAlgorythm.py

b00v1ne/TrBot11

01d156f6c01cfb167aa3a38186402d4c7c4ff818

[ "MIT" ]

null

TradingAlgorythm.py

b00v1ne/TrBot11

01d156f6c01cfb167aa3a38186402d4c7c4ff818

[ "MIT" ]

null

TradingAlgorythm.py

b00v1ne/TrBot11

01d156f6c01cfb167aa3a38186402d4c7c4ff818

[ "MIT" ]

null

import json from datetime import datetime, timedelta from bittrex.bittrex import Bittrex def TradingAlorythm(command, market, amount, coinname, step, stoploss, key, secret): TestTrading = Bittrex(key, secret) period = timedelta(seconds=20) next_tick = datetime.now() + period seconds = 20 firstCycle = True if command == "y": print("buying {0} of {1} coins".format(amount, coinname)) # раскомментировать для созадния ордера на покупку # TestTrading.buy_limit(market, amount, coinprice) while command == "y": # таймер каждые 20 секунд if next_tick <= datetime.now(): print("Connecting to Bittrex") seconds += 20 next_tick += period print("Timer ticked") print("Updating stock exchange...") # Считываем значения курса t = TestTrading.get_ticker(market) # Запрашиваем баланс balance = TestTrading.get_balance(coinname) # Запрашиваем текущие ордера orders = TestTrading.get_open_orders(market) a = json.dumps(t) # Печатаем значения курса print(t) # Печатаем баланс print("Balance is {} ".format(balance['result']['Available'])) # Печатаем ордера print(orders) # Раскладываем по переменным bid = t['result']['Bid'] ask = t['result']['Ask'] last = t['result']['Last'] if firstCycle: StartValue = bid firstCycle = False Stop_loss = StartValue - 0.00000007 print("*--------------------------") print("| Start Value | {: .8f} ".format(StartValue)) print("| Stop loss | {: .8f} ".format(Stop_loss)) print("|--------------------------") print("| Bid | {: .8f} ".format(bid)) print("| Ask | {: .8f} ".format(ask)) print("| Last | {: .8f} ".format(last)) print("*--------------------------") # Добавляем Bid в конец массива # A.append(float(bid)) if bid >= step + StartValue: print("MOVE STOP-LOSS") StartValue = bid if bid <= stoploss: print("Sell order sent")

38.721311

84

0.506351

import json from datetime import datetime, timedelta from bittrex.bittrex import Bittrex def TradingAlorythm(command, market, amount, coinname, step, stoploss, key, secret): TestTrading = Bittrex(key, secret) period = timedelta(seconds=20) next_tick = datetime.now() + period seconds = 20 firstCycle = True if command == "y": print("buying {0} of {1} coins".format(amount, coinname)) while command == "y": if next_tick <= datetime.now(): print("Connecting to Bittrex") seconds += 20 next_tick += period print("Timer ticked") print("Updating stock exchange...") t = TestTrading.get_ticker(market) balance = TestTrading.get_balance(coinname) orders = TestTrading.get_open_orders(market) a = json.dumps(t) print(t) print("Balance is {} ".format(balance['result']['Available'])) print(orders) bid = t['result']['Bid'] ask = t['result']['Ask'] last = t['result']['Last'] if firstCycle: StartValue = bid firstCycle = False Stop_loss = StartValue - 0.00000007 print("*--------------------------") print("| Start Value | {: .8f} ".format(StartValue)) print("| Stop loss | {: .8f} ".format(Stop_loss)) print("|--------------------------") print("| Bid | {: .8f} ".format(bid)) print("| Ask | {: .8f} ".format(ask)) print("| Last | {: .8f} ".format(last)) print("*--------------------------") if bid >= step + StartValue: print("MOVE STOP-LOSS") StartValue = bid if bid <= stoploss: print("Sell order sent")

true

f70053d83788a2e90b07b78a54f2c2a35ef9b934

2,044

py

Python

flash.py

segrids/arduino_due

f375020b81459eae9b325aa3646ff84efc2853e8

[ "MIT" ]

3

2021-08-20T16:03:37.000Z

2022-03-23T20:23:30.000Z

flash.py

segrids/testbench

f375020b81459eae9b325aa3646ff84efc2853e8

[ "MIT" ]

null

flash.py

segrids/testbench

f375020b81459eae9b325aa3646ff84efc2853e8

[ "MIT" ]

null

#!/usr/bin/env python3 """flash.py Usage: flash.py [<image>] [options] flash.py (-h | --help) Options: -h --help Show this screen. --target=<target> Select the target device [default: SAM3x8e]. --erase Erase the target before flashing. --port= Target device port [default: ttyACM0]. -v --verify Hash the flash and compare to binary. -r --reset Reset the CPU (after write). --bootloader=<bl> Specify a custom bootloader binary [default: sam3x8e/bootloader.bin]. --plane=<pl> Select flash plane 0 or 1 [default: 0]. --boot-rom Boot from ROM. """ from sys import exit, stdout import time from docopt import docopt from py.uart import Serial from py import * if __name__ == '__main__': args = docopt(__doc__) target = args['--target'] image = args['<image>'] port = '/dev/' + args['--port'] plane = int(args['--plane']) bootloader = args['--bootloader'] verify = args['--verify'] erase = args['--erase'] boot_rom = args['--boot-rom'] reset = args['--reset'] print('Selected port:', port) print('Selected image:', image) if target == 'HT32': from py.ht32.isp import ISP, isp image = args['<image>'] if image is None: if reset: isp(Serial(port)).reset() else: print('No image specified, not flashing.') else: with open(image, 'rb') as f: binary = f.read() isp = ISP(Serial(port)) isp.page_erase(start_addr=0x0, end_addr=0x1000) isp.flash(0x00, binary) isp.reset() elif target == 'SAM3x8e': from py.sam3x8e.programmer import program if bootloader is None: bootloader = 'sam3x8e/bootloader.bin' program(port, image=image, erase=erase, reset=True,\ verify=verify, bootloader_image=bootloader, plane=plane, boot_rom=boot_rom) else: print('Unknown target.')

30.507463

91

0.572407

from sys import exit, stdout import time from docopt import docopt from py.uart import Serial from py import * if __name__ == '__main__': args = docopt(__doc__) target = args['--target'] image = args['<image>'] port = '/dev/' + args['--port'] plane = int(args['--plane']) bootloader = args['--bootloader'] verify = args['--verify'] erase = args['--erase'] boot_rom = args['--boot-rom'] reset = args['--reset'] print('Selected port:', port) print('Selected image:', image) if target == 'HT32': from py.ht32.isp import ISP, isp image = args['<image>'] if image is None: if reset: isp(Serial(port)).reset() else: print('No image specified, not flashing.') else: with open(image, 'rb') as f: binary = f.read() isp = ISP(Serial(port)) isp.page_erase(start_addr=0x0, end_addr=0x1000) isp.flash(0x00, binary) isp.reset() elif target == 'SAM3x8e': from py.sam3x8e.programmer import program if bootloader is None: bootloader = 'sam3x8e/bootloader.bin' program(port, image=image, erase=erase, reset=True,\ verify=verify, bootloader_image=bootloader, plane=plane, boot_rom=boot_rom) else: print('Unknown target.')

true

f700541898a7e9b3e0e2a00fccab8f254ebb8886

28,007

py

Python

tests/functional/test_managesf.py

earlren1014/RedHat-Software-Factory

dd50eba4e353945886ebceb5dd608179d608b956

[ "Apache-2.0" ]

null

tests/functional/test_managesf.py

earlren1014/RedHat-Software-Factory

dd50eba4e353945886ebceb5dd608179d608b956

[ "Apache-2.0" ]

null

tests/functional/test_managesf.py

earlren1014/RedHat-Software-Factory

dd50eba4e353945886ebceb5dd608179d608b956

[ "Apache-2.0" ]

null

#!/bin/env python # # Copyright (C) 2014 eNovance SAS <[email protected]> # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. import os import config import shutil import requests from utils import Base from utils import ManageSfUtils from utils import GerritGitUtils from utils import create_random_str from utils import set_private_key from utils import is_present, skipIfServiceMissing, skipIfServicePresent from utils import skipIfIssueTrackerMissing from pysflib.sfredmine import RedmineUtils from pysflib.sfgerrit import GerritUtils class TestConditionalTesting(Base): """Functional tests validating the service decorators. If the tests are not skipped as expected, fail the tests. """ @skipIfServiceMissing('SomeLameFantasyServiceThatDoesNotExist') def test_skip_if_service_missing(self): self.fail('Failure to detect that a service is missing') # assuming gerrit will always be there ... @skipIfServicePresent('SFGerrit') def test_skip_if_service_present(self): self.fail('Failure to detect that a service is present') class TestManageSF(Base): """ Functional tests that validate managesf features. Here we do basic verifications about project creation with managesf. """ @classmethod def setUpClass(cls): cls.msu = ManageSfUtils(config.GATEWAY_URL) @classmethod def tearDownClass(cls): pass def setUp(self): self.projects = [] self.dirs_to_delete = [] self.rm = RedmineUtils( config.GATEWAY_URL + "/redmine/", auth_cookie=config.USERS[config.ADMIN_USER]['auth_cookie']) self.gu = GerritUtils( config.GATEWAY_URL, auth_cookie=config.USERS[config.ADMIN_USER]['auth_cookie']) def project_exists_ex(self, name, user): # Test here the project is "public" # ( Redmine API project detail does not return the private/public flag) rm = RedmineUtils( config.GATEWAY_URL + "/redmine/", auth_cookie=config.USERS[user]['auth_cookie']) try: return rm.project_exists(name) except Exception: return False def tearDown(self): for name in self.projects: self.msu.deleteProject(name, config.ADMIN_USER) for dirs in self.dirs_to_delete: shutil.rmtree(dirs) def create_project(self, name, user, options=None): self.msu.createProject(name, user, options) self.projects.append(name) def test_create_public_project_as_admin(self): """ Create public project on redmine and gerrit as admin """ pname = 'p_%s' % create_random_str() self.create_project(pname, config.ADMIN_USER) # Gerrit part self.assertTrue(self.gu.project_exists(pname)) self.assertTrue(self.gu.group_exists('%s-ptl' % pname)) self.assertTrue(self.gu.group_exists('%s-core' % pname)) # TODO(Project creator, as project owner, should only be in ptl group) self.assertTrue( self.gu.member_in_group(config.ADMIN_USER, '%s-ptl' % pname)) self.assertTrue( self.gu.member_in_group(config.ADMIN_USER, '%s-core' % pname)) # Redmine part if is_present("SFRedmine"): self.assertTrue(self.rm.project_exists(pname)) self.assertTrue( self.rm.check_user_role(pname, config.ADMIN_USER, 'Manager')) self.assertTrue( self.rm.check_user_role(pname, config.ADMIN_USER, 'Developer')) self.assertTrue(self.project_exists_ex(pname, config.USER_2)) def test_create_private_project_as_admin(self): """ Create private project on redmine and gerrit as admin """ pname = 'p_%s' % create_random_str() options = {"private": ""} self.create_project(pname, config.ADMIN_USER, options=options) # Gerrit part self.assertTrue(self.gu.project_exists(pname)) self.assertTrue(self.gu.group_exists('%s-ptl' % pname)) self.assertTrue(self.gu.group_exists('%s-core' % pname)) self.assertTrue(self.gu.group_exists('%s-dev' % pname)) # TODO(Project creator, as project owner, should only be in ptl group) self.assertTrue( self.gu.member_in_group(config.ADMIN_USER, '%s-ptl' % pname)) self.assertTrue( self.gu.member_in_group(config.ADMIN_USER, '%s-core' % pname)) self.assertTrue( self.gu.member_in_group(config.ADMIN_USER, '%s-dev' % pname)) # Redmine part if is_present("SFRedmine"): self.assertTrue(self.rm.project_exists(pname)) self.assertTrue( self.rm.check_user_role(pname, config.ADMIN_USER, 'Manager')) self.assertTrue( self.rm.check_user_role(pname, config.ADMIN_USER, 'Developer')) self.assertFalse(self.project_exists_ex(pname, config.USER_2)) def test_delete_public_project_as_admin(self): """ Delete public project on redmine and gerrit as admin """ pname = 'p_%s' % create_random_str() self.create_project(pname, config.ADMIN_USER) self.assertTrue(self.gu.project_exists(pname)) if is_present("SFRedmine"): self.assertTrue(self.rm.project_exists(pname)) self.msu.deleteProject(pname, config.ADMIN_USER) self.assertFalse(self.gu.project_exists(pname)) self.assertFalse(self.gu.group_exists('%s-ptl' % pname)) if is_present("SFRedmine"): self.assertFalse(self.rm.project_exists(pname)) self.assertFalse(self.gu.group_exists('%s-core' % pname)) self.projects.remove(pname) def test_create_public_project_as_user(self): """ Create public project on redmine and gerrit as user """ pname = 'p_%s' % create_random_str() self.create_project(pname, config.USER_2) # Gerrit part self.assertTrue(self.gu.project_exists(pname)) self.assertTrue(self.gu.group_exists('%s-ptl' % pname)) self.assertTrue(self.gu.group_exists('%s-core' % pname)) # TODO(Project creator, as project owner, should only be in ptl group) self.assertTrue( self.gu.member_in_group(config.ADMIN_USER, '%s-ptl' % pname)) self.assertTrue( self.gu.member_in_group(config.ADMIN_USER, '%s-core' % pname)) # Redmine part if is_present("SFRedmine"): self.assertTrue(self.rm.project_exists(pname)) self.assertTrue(self.project_exists_ex(pname, config.USER_2)) self.assertTrue( self.rm.check_user_role(pname, config.USER_2, 'Manager')) self.assertTrue( self.rm.check_user_role(pname, config.USER_2, 'Developer')) self.assertTrue(self.project_exists_ex(pname, config.USER_3)) def test_create_private_project_as_user(self): """ Create private project on redmine and gerrit as user """ pname = 'p_%s' % create_random_str() options = {"private": ""} self.create_project(pname, config.USER_2, options=options) # Gerrit part self.assertTrue(self.gu.project_exists(pname)) self.assertTrue(self.gu.group_exists('%s-ptl' % pname)) self.assertTrue(self.gu.group_exists('%s-core' % pname)) self.assertTrue(self.gu.group_exists('%s-dev' % pname)) # TODO(Project creator, as project owner, should only be in ptl group) self.assertTrue( self.gu.member_in_group(config.USER_2, '%s-ptl' % pname)) self.assertTrue( self.gu.member_in_group(config.USER_2, '%s-core' % pname)) self.assertTrue( self.gu.member_in_group(config.USER_2, '%s-dev' % pname)) # Redmine part if is_present("SFRedmine"): self.assertTrue(self.rm.project_exists(pname)) self.assertTrue(self.project_exists_ex(pname, config.USER_2)) self.assertTrue( self.rm.check_user_role(pname, config.USER_2, 'Manager')) self.assertTrue( self.rm.check_user_role(pname, config.USER_2, 'Developer')) self.assertFalse(self.project_exists_ex(pname, config.USER_3)) def test_create_public_project_with_users_in_group(self): """ Create public project on redmine and gerrit with users in groups """ pname = 'p_%s' % create_random_str() u2mail = config.USERS[config.USER_2]['email'] u3mail = config.USERS[config.USER_3]['email'] options = {"ptl-group": "", "core-group": "%s,%s" % (u2mail, u3mail), } self.create_project(pname, config.ADMIN_USER, options=options) # Gerrit part self.assertTrue(self.gu.project_exists(pname)) # TODO(Project creator, as project owner, should only be in ptl group) self.assertTrue( self.gu.member_in_group(config.ADMIN_USER, '%s-ptl' % pname)) for user in (config.ADMIN_USER, config.USER_2, config.USER_3): self.assertTrue(self.gu.member_in_group(user, '%s-core' % pname)) if is_present("SFRedmine"): self.assertTrue(self.rm.project_exists(pname)) self.assertTrue( self.rm.check_user_role(pname, config.ADMIN_USER, 'Manager')) for user in (config.ADMIN_USER, config.USER_2, config.USER_3): self.assertTrue(self.rm.check_user_role(pname, user, 'Developer')) def test_create_private_project_with_users_in_group(self): """ Create private project on redmine and gerrit with users in groups """ pname = 'p_%s' % create_random_str() u2mail = config.USERS[config.USER_2]['email'] u3mail = config.USERS[config.USER_3]['email'] u4mail = config.USERS[config.USER_4]['email'] options = {"private": "", "ptl-group": "", "core-group": "%s,%s" % (u2mail, u3mail), "dev-group": "%s" % u4mail, } self.create_project(pname, config.ADMIN_USER, options=options) # Gerrit part self.assertTrue(self.gu.project_exists(pname)) # TODO(Project creator, as project owner, should only be in ptl group) self.assertTrue( self.gu.member_in_group(config.ADMIN_USER, '%s-ptl' % pname)) for user in (config.ADMIN_USER, config.USER_2, config.USER_3): self.assertTrue(self.gu.member_in_group(user, '%s-core' % pname)) self.assertTrue( self.gu.member_in_group(config.USER_4, '%s-dev' % pname)) # Redmine part if is_present("SFRedmine"): # it should be visible to admin self.assertTrue(self.rm.project_exists(pname)) self.assertTrue( self.rm.check_user_role(pname, config.ADMIN_USER, 'Manager')) for user in (config.ADMIN_USER, config.USER_2, config.USER_3, config.USER_4): self.assertTrue(self.rm.check_user_role(pname, user, 'Developer')) def test_create_public_project_as_admin_clone_as_admin(self): """ Clone public project as admin and check content """ pname = 'a_%s' % create_random_str() self.create_project(pname, config.ADMIN_USER) ggu = GerritGitUtils(config.ADMIN_USER, config.ADMIN_PRIV_KEY_PATH, config.USERS[config.ADMIN_USER]['email']) url = "ssh://%s@%s:29418/%s" % (config.ADMIN_USER, config.GATEWAY_HOST, pname) clone_dir = ggu.clone(url, pname) self.dirs_to_delete.append(os.path.dirname(clone_dir)) # Test that the clone is a success self.assertTrue(os.path.isdir(clone_dir)) # Verify master own the .gitreview file self.assertTrue(os.path.isfile(os.path.join(clone_dir, '.gitreview'))) # Verify meta/config branch own both group and ACLs config file ggu.fetch_meta_config(clone_dir) self.assertTrue(os.path.isfile(os.path.join(clone_dir, 'project.config'))) self.assertTrue(os.path.isfile(os.path.join(clone_dir, 'groups'))) # There is no group dev for a public project content = file(os.path.join(clone_dir, 'project.config')).read() self.assertFalse('%s-dev' % pname in content) content = file(os.path.join(clone_dir, 'groups')).read() self.assertFalse('%s-dev' % pname in content) def test_create_private_project_as_admin_clone_as_admin(self): """ Clone private project as admin and check content """ pname = 'p_%s' % create_random_str() options = {"private": ""} self.create_project(pname, config.ADMIN_USER, options=options) ggu = GerritGitUtils(config.ADMIN_USER, config.ADMIN_PRIV_KEY_PATH, config.USERS[config.ADMIN_USER]['email']) url = "ssh://%s@%s:29418/%s" % (config.ADMIN_USER, config.GATEWAY_HOST, pname) clone_dir = ggu.clone(url, pname) self.dirs_to_delete.append(os.path.dirname(clone_dir)) # Test that the clone is a success self.assertTrue(os.path.isdir(clone_dir)) # Verify master own the .gitreview file self.assertTrue(os.path.isfile(os.path.join(clone_dir, '.gitreview'))) # Verify meta/config branch own both group and ACLs config file ggu.fetch_meta_config(clone_dir) self.assertTrue(os.path.isfile(os.path.join(clone_dir, 'project.config'))) self.assertTrue(os.path.isfile(os.path.join(clone_dir, 'groups'))) # There is a group dev for a private project content = file(os.path.join(clone_dir, 'project.config')).read() self.assertTrue('%s-dev' % pname in content) content = file(os.path.join(clone_dir, 'groups')).read() self.assertTrue('%s-dev' % pname in content) def test_create_public_project_as_admin_clone_as_user(self): """ Create public project as admin then clone as user """ pname = 'p_%s' % create_random_str() # create the project as admin self.create_project(pname, config.ADMIN_USER) # add user2 ssh pubkey to user2 gu = GerritUtils( config.GATEWAY_URL, auth_cookie=config.USERS[config.USER_2]['auth_cookie']) gu.add_pubkey(config.USER_2_PUB_KEY) # prepare to clone priv_key_path = set_private_key(config.USER_2_PRIV_KEY) self.dirs_to_delete.append(os.path.dirname(priv_key_path)) ggu = GerritGitUtils(config.USER_2, priv_key_path, config.USERS[config.USER_2]['email']) url = "ssh://%s@%s:29418/%s" % (config.USER_2, config.GATEWAY_HOST, pname) # clone clone_dir = ggu.clone(url, pname) self.dirs_to_delete.append(os.path.dirname(clone_dir)) # Test that the clone is a success self.assertTrue(os.path.isdir(clone_dir)) # Verify master own the .gitreview file self.assertTrue(os.path.isfile(os.path.join(clone_dir, '.gitreview'))) def test_create_public_project_as_user_clone_as_user(self): """ Create public project as user then clone as user """ pname = 'p_%s' % create_random_str() # create the project as admin self.create_project(pname, config.USER_2) # add user2 ssh pubkey to user2 gu = GerritUtils( config.GATEWAY_URL, auth_cookie=config.USERS[config.USER_2]['auth_cookie']) gu.add_pubkey(config.USER_2_PUB_KEY) # prepare to clone priv_key_path = set_private_key(config.USER_2_PRIV_KEY) self.dirs_to_delete.append(os.path.dirname(priv_key_path)) ggu = GerritGitUtils(config.USER_2, priv_key_path, config.USERS[config.USER_2]['email']) url = "ssh://%s@%s:29418/%s" % (config.USER_2, config.GATEWAY_HOST, pname) # clone clone_dir = ggu.clone(url, pname) self.dirs_to_delete.append(os.path.dirname(clone_dir)) # Test that the clone is a success self.assertTrue(os.path.isdir(clone_dir)) # Verify master own the .gitreview file self.assertTrue(os.path.isfile(os.path.join(clone_dir, '.gitreview'))) def test_upstream(self): """ Validate upstream feature of managesf """ # Create a test upstream project pname_us = 'p_upstream' self.create_project(pname_us, config.ADMIN_USER) ggu_us = GerritGitUtils(config.ADMIN_USER, config.ADMIN_PRIV_KEY_PATH, config.USERS[config.ADMIN_USER]['email']) url = "ssh://%s@%s:29418/%s" % (config.ADMIN_USER, config.GATEWAY_HOST, pname_us) # clone us_clone_dir = ggu_us.clone(url, pname_us) self.dirs_to_delete.append(os.path.dirname(us_clone_dir)) # Test that the clone is a success self.assertTrue(os.path.isdir(us_clone_dir)) # push some test files to the upstream project us_files = [str(x) for x in range(1, 10)] for f in us_files: file(os.path.join(us_clone_dir, f), 'w').write(f) os.chmod(os.path.join(us_clone_dir, f), 0755) ggu_us.add_commit_in_branch(us_clone_dir, "master", commit="Adding files 1-10", files=us_files) ggu_us.direct_push_branch(us_clone_dir, "master") ggu_us.add_commit_in_branch(us_clone_dir, "branch1") ggu_us.direct_push_branch(us_clone_dir, "branch1") # No create a test project with upstream pointing to the above upstream_url = "ssh://%s@%s:29418/%s" % ( config.ADMIN_USER, config.GATEWAY_HOST, pname_us) pname = 'p_%s' % create_random_str() # create the project as admin options = {"upstream": upstream_url, "upstream-ssh-key": config.ADMIN_PRIV_KEY_PATH} self.create_project(pname, config.ADMIN_USER, options=options) ggu = GerritGitUtils(config.ADMIN_USER, config.ADMIN_PRIV_KEY_PATH, config.USERS[config.ADMIN_USER]['email']) url = "ssh://%s@%s:29418/%s" % (config.ADMIN_USER, config.GATEWAY_HOST, pname) # clone clone_dir = ggu.clone(url, pname) # Check if the files pushed in upstream project is present files = [f for f in os.listdir(clone_dir) if not f.startswith('.')] self.assertEqual(set(files), set(us_files)) branches = ggu.get_branches(clone_dir, True) self.assertNotIn('gerrit/branch1', branches) # Test upstream with additional branches pname2 = 'p_%s' % create_random_str() options['add-branches'] = '' self.create_project(pname2, config.ADMIN_USER, options=options) url = "ssh://%s@%s:29418/%s" % (config.ADMIN_USER, config.GATEWAY_HOST, pname2) clone_dir = ggu.clone(url, pname2) branches = ggu.get_branches(clone_dir, True) self.assertIn('gerrit/branch1', branches) self.dirs_to_delete.append(os.path.dirname(clone_dir)) def test_delete_project_as_admin(self): """ Check if admin can delete projects that are not owned by admin """ pname = 'p_%s' % create_random_str() self.create_project(pname, config.USER_2) self.assertTrue(self.gu.project_exists(pname)) if is_present("SFRedmine"): self.assertTrue(self.rm.project_exists(pname)) self.msu.deleteProject(pname, config.ADMIN_USER) self.assertFalse(self.gu.project_exists(pname)) self.assertFalse(self.gu.group_exists('%s-ptl' % pname)) if is_present("SFRedmine"): self.assertFalse(self.rm.project_exists(pname)) self.assertFalse(self.gu.group_exists('%s-core' % pname)) self.projects.remove(pname) def test_basic_ops_project_namespace(self): """ Check if a project named with a / (namespace) is handled correctly on basic ops by managesf """ pname = 'skydive/%s' % create_random_str() self.create_project(pname, config.USER_2) self.assertTrue(self.gu.project_exists(pname)) self.assertTrue(self.gu.group_exists('%s-ptl' % pname)) if is_present("SFRedmine"): rname = '_'.join(pname.split('/')) self.assertTrue(self.rm.project_exists(rname)) # Try to clone ggu = GerritGitUtils(config.ADMIN_USER, config.ADMIN_PRIV_KEY_PATH, config.USERS[config.ADMIN_USER]['email']) url = "ssh://%s@%s:29418/%s" % (config.ADMIN_USER, config.GATEWAY_HOST, pname) clone_dir = ggu.clone(url, pname.split('/')[-1]) self.dirs_to_delete.append(os.path.dirname(clone_dir)) # Test that the clone is a success self.assertTrue(os.path.isdir(clone_dir)) # Verify master own the .gitreview file self.assertTrue(os.path.isfile(os.path.join(clone_dir, '.gitreview'))) # Delete the project from SF self.msu.deleteProject(pname, config.ADMIN_USER) self.assertFalse(self.gu.project_exists(pname)) self.assertFalse(self.gu.group_exists('%s-ptl' % pname)) if is_present("SFRedmine"): rname = '_'.join(pname.split('/')) self.assertFalse(self.rm.project_exists(rname)) self.assertFalse(self.gu.group_exists('%s-core' % pname)) # Clean local clone directory self.projects.remove(pname) # For now listing users comes from Redmine @skipIfIssueTrackerMissing() def test_list_active_members(self): """ Check the list of members as a list of tuples of emails and names """ self.assertTrue(self.msu.list_active_members(config.USER_2)) def test_init_user_tests(self): """ Check if a test init feature behave as expected """ project = 'p_%s' % create_random_str() self.create_project(project, config.USER_4) self.msu.create_init_tests(project, config.USER_4) ggu = GerritGitUtils(config.ADMIN_USER, config.ADMIN_PRIV_KEY_PATH, config.USERS[config.ADMIN_USER]['email']) open_reviews = ggu.list_open_reviews('config', config.GATEWAY_HOST) match = [True for review in open_reviews if review['commitMessage']. startswith("%s proposes initial test " "definition for project %s" % (config.USER_4, project))] self.assertEqual(len(match), 1) open_reviews = ggu.list_open_reviews(project, config.GATEWAY_HOST) match = [True for review in open_reviews if review['commitMessage']. startswith("%s proposes initial test " "scripts for project %s" % (config.USER_4, project))] self.assertEqual(len(match), 1) def test_rest_urls_accessible(self): """ Check if managesf URLs are all working """ project = 'p_%s' % create_random_str() self.create_project(project, config.ADMIN_USER) cookies = dict( auth_pubtkt=config.USERS[config.ADMIN_USER]['auth_cookie']) paths = [ "/manage/project/", "/manage/project/%s" % project, "/manage/project/membership/"] for path in paths: url = "http://%s%s" % (config.GATEWAY_HOST, path) resp = requests.get(url, cookies=cookies) self.assertEqual(200, resp.status_code) def test_validate_get_all_project_details(self): """ Check if managesf allow us to fetch projects details """ project = 'p_%s' % create_random_str() self.create_project(project, config.USER_2) admin_cookies = dict( auth_pubtkt=config.USERS[config.ADMIN_USER]['auth_cookie']) user2_cookies = dict( auth_pubtkt=config.USERS[config.USER_2]['auth_cookie']) url = "http://%s%s" % (config.GATEWAY_HOST, "/manage/project/") resp = requests.get(url, cookies=admin_cookies) self.assertEqual(200, resp.status_code) self.assertTrue(project in resp.json()) self.assertTrue('config' in resp.json()) resp = requests.get(url, cookies=user2_cookies) self.assertEqual(200, resp.status_code) self.assertTrue(project in resp.json()) self.assertTrue('config' in resp.json()) resp = requests.get(url, cookies=user2_cookies) # Validate the same behavior with project including a '/' project = 'p/%s' % create_random_str() self.create_project(project, config.USER_2) url = "http://%s%s" % (config.GATEWAY_HOST, "/manage/project/") # Wait 15 seconds for managesf cache invalidation import time time.sleep(15) resp = requests.get(url, cookies=user2_cookies) self.assertEqual(200, resp.status_code) self.assertTrue(project in resp.json()) def test_project_pages_config(self): """ Check if managesf allow us to configure pages for a project """ project = 'p_%s' % create_random_str() self.create_project(project, config.USER_2) self.assertTrue(self.gu.project_exists(project)) self.assertTrue(self.rm.project_exists(project)) self.msu.update_project_page(config.USER_2, project, "http://tests.com/") self.assertEqual(self.msu.get_project_page(config.USER_2, project).strip(), "\"http://tests.com/\"") self.msu.delete_project_page(config.USER_3, project) self.assertEqual(self.msu.get_project_page(config.USER_2, project).strip(), "\"http://tests.com/\"") self.msu.delete_project_page(config.USER_2, project) self.assertEqual(self.msu.get_project_page(config.USER_2, project).strip(), "")

46.523256

79

0.602956

import os import config import shutil import requests from utils import Base from utils import ManageSfUtils from utils import GerritGitUtils from utils import create_random_str from utils import set_private_key from utils import is_present, skipIfServiceMissing, skipIfServicePresent from utils import skipIfIssueTrackerMissing from pysflib.sfredmine import RedmineUtils from pysflib.sfgerrit import GerritUtils class TestConditionalTesting(Base): """Functional tests validating the service decorators. If the tests are not skipped as expected, fail the tests. """ @skipIfServiceMissing('SomeLameFantasyServiceThatDoesNotExist') def test_skip_if_service_missing(self): self.fail('Failure to detect that a service is missing') @skipIfServicePresent('SFGerrit') def test_skip_if_service_present(self): self.fail('Failure to detect that a service is present') class TestManageSF(Base): """ Functional tests that validate managesf features. Here we do basic verifications about project creation with managesf. """ @classmethod def setUpClass(cls): cls.msu = ManageSfUtils(config.GATEWAY_URL) @classmethod def tearDownClass(cls): pass def setUp(self): self.projects = [] self.dirs_to_delete = [] self.rm = RedmineUtils( config.GATEWAY_URL + "/redmine/", auth_cookie=config.USERS[config.ADMIN_USER]['auth_cookie']) self.gu = GerritUtils( config.GATEWAY_URL, auth_cookie=config.USERS[config.ADMIN_USER]['auth_cookie']) def project_exists_ex(self, name, user): rm = RedmineUtils( config.GATEWAY_URL + "/redmine/", auth_cookie=config.USERS[user]['auth_cookie']) try: return rm.project_exists(name) except Exception: return False def tearDown(self): for name in self.projects: self.msu.deleteProject(name, config.ADMIN_USER) for dirs in self.dirs_to_delete: shutil.rmtree(dirs) def create_project(self, name, user, options=None): self.msu.createProject(name, user, options) self.projects.append(name) def test_create_public_project_as_admin(self): """ Create public project on redmine and gerrit as admin """ pname = 'p_%s' % create_random_str() self.create_project(pname, config.ADMIN_USER) self.assertTrue(self.gu.project_exists(pname)) self.assertTrue(self.gu.group_exists('%s-ptl' % pname)) self.assertTrue(self.gu.group_exists('%s-core' % pname)) self.assertTrue( self.gu.member_in_group(config.ADMIN_USER, '%s-ptl' % pname)) self.assertTrue( self.gu.member_in_group(config.ADMIN_USER, '%s-core' % pname)) if is_present("SFRedmine"): self.assertTrue(self.rm.project_exists(pname)) self.assertTrue( self.rm.check_user_role(pname, config.ADMIN_USER, 'Manager')) self.assertTrue( self.rm.check_user_role(pname, config.ADMIN_USER, 'Developer')) self.assertTrue(self.project_exists_ex(pname, config.USER_2)) def test_create_private_project_as_admin(self): """ Create private project on redmine and gerrit as admin """ pname = 'p_%s' % create_random_str() options = {"private": ""} self.create_project(pname, config.ADMIN_USER, options=options) self.assertTrue(self.gu.project_exists(pname)) self.assertTrue(self.gu.group_exists('%s-ptl' % pname)) self.assertTrue(self.gu.group_exists('%s-core' % pname)) self.assertTrue(self.gu.group_exists('%s-dev' % pname)) self.assertTrue( self.gu.member_in_group(config.ADMIN_USER, '%s-ptl' % pname)) self.assertTrue( self.gu.member_in_group(config.ADMIN_USER, '%s-core' % pname)) self.assertTrue( self.gu.member_in_group(config.ADMIN_USER, '%s-dev' % pname)) if is_present("SFRedmine"): self.assertTrue(self.rm.project_exists(pname)) self.assertTrue( self.rm.check_user_role(pname, config.ADMIN_USER, 'Manager')) self.assertTrue( self.rm.check_user_role(pname, config.ADMIN_USER, 'Developer')) self.assertFalse(self.project_exists_ex(pname, config.USER_2)) def test_delete_public_project_as_admin(self): """ Delete public project on redmine and gerrit as admin """ pname = 'p_%s' % create_random_str() self.create_project(pname, config.ADMIN_USER) self.assertTrue(self.gu.project_exists(pname)) if is_present("SFRedmine"): self.assertTrue(self.rm.project_exists(pname)) self.msu.deleteProject(pname, config.ADMIN_USER) self.assertFalse(self.gu.project_exists(pname)) self.assertFalse(self.gu.group_exists('%s-ptl' % pname)) if is_present("SFRedmine"): self.assertFalse(self.rm.project_exists(pname)) self.assertFalse(self.gu.group_exists('%s-core' % pname)) self.projects.remove(pname) def test_create_public_project_as_user(self): """ Create public project on redmine and gerrit as user """ pname = 'p_%s' % create_random_str() self.create_project(pname, config.USER_2) self.assertTrue(self.gu.project_exists(pname)) self.assertTrue(self.gu.group_exists('%s-ptl' % pname)) self.assertTrue(self.gu.group_exists('%s-core' % pname)) self.assertTrue( self.gu.member_in_group(config.ADMIN_USER, '%s-ptl' % pname)) self.assertTrue( self.gu.member_in_group(config.ADMIN_USER, '%s-core' % pname)) if is_present("SFRedmine"): self.assertTrue(self.rm.project_exists(pname)) self.assertTrue(self.project_exists_ex(pname, config.USER_2)) self.assertTrue( self.rm.check_user_role(pname, config.USER_2, 'Manager')) self.assertTrue( self.rm.check_user_role(pname, config.USER_2, 'Developer')) self.assertTrue(self.project_exists_ex(pname, config.USER_3)) def test_create_private_project_as_user(self): """ Create private project on redmine and gerrit as user """ pname = 'p_%s' % create_random_str() options = {"private": ""} self.create_project(pname, config.USER_2, options=options) self.assertTrue(self.gu.project_exists(pname)) self.assertTrue(self.gu.group_exists('%s-ptl' % pname)) self.assertTrue(self.gu.group_exists('%s-core' % pname)) self.assertTrue(self.gu.group_exists('%s-dev' % pname)) self.assertTrue( self.gu.member_in_group(config.USER_2, '%s-ptl' % pname)) self.assertTrue( self.gu.member_in_group(config.USER_2, '%s-core' % pname)) self.assertTrue( self.gu.member_in_group(config.USER_2, '%s-dev' % pname)) if is_present("SFRedmine"): self.assertTrue(self.rm.project_exists(pname)) self.assertTrue(self.project_exists_ex(pname, config.USER_2)) self.assertTrue( self.rm.check_user_role(pname, config.USER_2, 'Manager')) self.assertTrue( self.rm.check_user_role(pname, config.USER_2, 'Developer')) self.assertFalse(self.project_exists_ex(pname, config.USER_3)) def test_create_public_project_with_users_in_group(self): """ Create public project on redmine and gerrit with users in groups """ pname = 'p_%s' % create_random_str() u2mail = config.USERS[config.USER_2]['email'] u3mail = config.USERS[config.USER_3]['email'] options = {"ptl-group": "", "core-group": "%s,%s" % (u2mail, u3mail), } self.create_project(pname, config.ADMIN_USER, options=options) self.assertTrue(self.gu.project_exists(pname)) self.assertTrue( self.gu.member_in_group(config.ADMIN_USER, '%s-ptl' % pname)) for user in (config.ADMIN_USER, config.USER_2, config.USER_3): self.assertTrue(self.gu.member_in_group(user, '%s-core' % pname)) if is_present("SFRedmine"): self.assertTrue(self.rm.project_exists(pname)) self.assertTrue( self.rm.check_user_role(pname, config.ADMIN_USER, 'Manager')) for user in (config.ADMIN_USER, config.USER_2, config.USER_3): self.assertTrue(self.rm.check_user_role(pname, user, 'Developer')) def test_create_private_project_with_users_in_group(self): """ Create private project on redmine and gerrit with users in groups """ pname = 'p_%s' % create_random_str() u2mail = config.USERS[config.USER_2]['email'] u3mail = config.USERS[config.USER_3]['email'] u4mail = config.USERS[config.USER_4]['email'] options = {"private": "", "ptl-group": "", "core-group": "%s,%s" % (u2mail, u3mail), "dev-group": "%s" % u4mail, } self.create_project(pname, config.ADMIN_USER, options=options) self.assertTrue(self.gu.project_exists(pname)) self.assertTrue( self.gu.member_in_group(config.ADMIN_USER, '%s-ptl' % pname)) for user in (config.ADMIN_USER, config.USER_2, config.USER_3): self.assertTrue(self.gu.member_in_group(user, '%s-core' % pname)) self.assertTrue( self.gu.member_in_group(config.USER_4, '%s-dev' % pname)) if is_present("SFRedmine"): self.assertTrue(self.rm.project_exists(pname)) self.assertTrue( self.rm.check_user_role(pname, config.ADMIN_USER, 'Manager')) for user in (config.ADMIN_USER, config.USER_2, config.USER_3, config.USER_4): self.assertTrue(self.rm.check_user_role(pname, user, 'Developer')) def test_create_public_project_as_admin_clone_as_admin(self): """ Clone public project as admin and check content """ pname = 'a_%s' % create_random_str() self.create_project(pname, config.ADMIN_USER) ggu = GerritGitUtils(config.ADMIN_USER, config.ADMIN_PRIV_KEY_PATH, config.USERS[config.ADMIN_USER]['email']) url = "ssh://%s@%s:29418/%s" % (config.ADMIN_USER, config.GATEWAY_HOST, pname) clone_dir = ggu.clone(url, pname) self.dirs_to_delete.append(os.path.dirname(clone_dir)) self.assertTrue(os.path.isdir(clone_dir)) self.assertTrue(os.path.isfile(os.path.join(clone_dir, '.gitreview'))) ggu.fetch_meta_config(clone_dir) self.assertTrue(os.path.isfile(os.path.join(clone_dir, 'project.config'))) self.assertTrue(os.path.isfile(os.path.join(clone_dir, 'groups'))) content = file(os.path.join(clone_dir, 'project.config')).read() self.assertFalse('%s-dev' % pname in content) content = file(os.path.join(clone_dir, 'groups')).read() self.assertFalse('%s-dev' % pname in content) def test_create_private_project_as_admin_clone_as_admin(self): """ Clone private project as admin and check content """ pname = 'p_%s' % create_random_str() options = {"private": ""} self.create_project(pname, config.ADMIN_USER, options=options) ggu = GerritGitUtils(config.ADMIN_USER, config.ADMIN_PRIV_KEY_PATH, config.USERS[config.ADMIN_USER]['email']) url = "ssh://%s@%s:29418/%s" % (config.ADMIN_USER, config.GATEWAY_HOST, pname) clone_dir = ggu.clone(url, pname) self.dirs_to_delete.append(os.path.dirname(clone_dir)) self.assertTrue(os.path.isdir(clone_dir)) self.assertTrue(os.path.isfile(os.path.join(clone_dir, '.gitreview'))) ggu.fetch_meta_config(clone_dir) self.assertTrue(os.path.isfile(os.path.join(clone_dir, 'project.config'))) self.assertTrue(os.path.isfile(os.path.join(clone_dir, 'groups'))) content = file(os.path.join(clone_dir, 'project.config')).read() self.assertTrue('%s-dev' % pname in content) content = file(os.path.join(clone_dir, 'groups')).read() self.assertTrue('%s-dev' % pname in content) def test_create_public_project_as_admin_clone_as_user(self): """ Create public project as admin then clone as user """ pname = 'p_%s' % create_random_str() self.create_project(pname, config.ADMIN_USER) gu = GerritUtils( config.GATEWAY_URL, auth_cookie=config.USERS[config.USER_2]['auth_cookie']) gu.add_pubkey(config.USER_2_PUB_KEY) priv_key_path = set_private_key(config.USER_2_PRIV_KEY) self.dirs_to_delete.append(os.path.dirname(priv_key_path)) ggu = GerritGitUtils(config.USER_2, priv_key_path, config.USERS[config.USER_2]['email']) url = "ssh://%s@%s:29418/%s" % (config.USER_2, config.GATEWAY_HOST, pname) clone_dir = ggu.clone(url, pname) self.dirs_to_delete.append(os.path.dirname(clone_dir)) self.assertTrue(os.path.isdir(clone_dir)) self.assertTrue(os.path.isfile(os.path.join(clone_dir, '.gitreview'))) def test_create_public_project_as_user_clone_as_user(self): """ Create public project as user then clone as user """ pname = 'p_%s' % create_random_str() self.create_project(pname, config.USER_2) gu = GerritUtils( config.GATEWAY_URL, auth_cookie=config.USERS[config.USER_2]['auth_cookie']) gu.add_pubkey(config.USER_2_PUB_KEY) priv_key_path = set_private_key(config.USER_2_PRIV_KEY) self.dirs_to_delete.append(os.path.dirname(priv_key_path)) ggu = GerritGitUtils(config.USER_2, priv_key_path, config.USERS[config.USER_2]['email']) url = "ssh://%s@%s:29418/%s" % (config.USER_2, config.GATEWAY_HOST, pname) clone_dir = ggu.clone(url, pname) self.dirs_to_delete.append(os.path.dirname(clone_dir)) self.assertTrue(os.path.isdir(clone_dir)) self.assertTrue(os.path.isfile(os.path.join(clone_dir, '.gitreview'))) def test_upstream(self): """ Validate upstream feature of managesf """ pname_us = 'p_upstream' self.create_project(pname_us, config.ADMIN_USER) ggu_us = GerritGitUtils(config.ADMIN_USER, config.ADMIN_PRIV_KEY_PATH, config.USERS[config.ADMIN_USER]['email']) url = "ssh://%s@%s:29418/%s" % (config.ADMIN_USER, config.GATEWAY_HOST, pname_us) us_clone_dir = ggu_us.clone(url, pname_us) self.dirs_to_delete.append(os.path.dirname(us_clone_dir)) self.assertTrue(os.path.isdir(us_clone_dir)) us_files = [str(x) for x in range(1, 10)] for f in us_files: file(os.path.join(us_clone_dir, f), 'w').write(f) os.chmod(os.path.join(us_clone_dir, f), 0755) ggu_us.add_commit_in_branch(us_clone_dir, "master", commit="Adding files 1-10", files=us_files) ggu_us.direct_push_branch(us_clone_dir, "master") ggu_us.add_commit_in_branch(us_clone_dir, "branch1") ggu_us.direct_push_branch(us_clone_dir, "branch1") upstream_url = "ssh://%s@%s:29418/%s" % ( config.ADMIN_USER, config.GATEWAY_HOST, pname_us) pname = 'p_%s' % create_random_str() options = {"upstream": upstream_url, "upstream-ssh-key": config.ADMIN_PRIV_KEY_PATH} self.create_project(pname, config.ADMIN_USER, options=options) ggu = GerritGitUtils(config.ADMIN_USER, config.ADMIN_PRIV_KEY_PATH, config.USERS[config.ADMIN_USER]['email']) url = "ssh://%s@%s:29418/%s" % (config.ADMIN_USER, config.GATEWAY_HOST, pname) clone_dir = ggu.clone(url, pname) files = [f for f in os.listdir(clone_dir) if not f.startswith('.')] self.assertEqual(set(files), set(us_files)) branches = ggu.get_branches(clone_dir, True) self.assertNotIn('gerrit/branch1', branches) pname2 = 'p_%s' % create_random_str() options['add-branches'] = '' self.create_project(pname2, config.ADMIN_USER, options=options) url = "ssh://%s@%s:29418/%s" % (config.ADMIN_USER, config.GATEWAY_HOST, pname2) clone_dir = ggu.clone(url, pname2) branches = ggu.get_branches(clone_dir, True) self.assertIn('gerrit/branch1', branches) self.dirs_to_delete.append(os.path.dirname(clone_dir)) def test_delete_project_as_admin(self): """ Check if admin can delete projects that are not owned by admin """ pname = 'p_%s' % create_random_str() self.create_project(pname, config.USER_2) self.assertTrue(self.gu.project_exists(pname)) if is_present("SFRedmine"): self.assertTrue(self.rm.project_exists(pname)) self.msu.deleteProject(pname, config.ADMIN_USER) self.assertFalse(self.gu.project_exists(pname)) self.assertFalse(self.gu.group_exists('%s-ptl' % pname)) if is_present("SFRedmine"): self.assertFalse(self.rm.project_exists(pname)) self.assertFalse(self.gu.group_exists('%s-core' % pname)) self.projects.remove(pname) def test_basic_ops_project_namespace(self): """ Check if a project named with a / (namespace) is handled correctly on basic ops by managesf """ pname = 'skydive/%s' % create_random_str() self.create_project(pname, config.USER_2) self.assertTrue(self.gu.project_exists(pname)) self.assertTrue(self.gu.group_exists('%s-ptl' % pname)) if is_present("SFRedmine"): rname = '_'.join(pname.split('/')) self.assertTrue(self.rm.project_exists(rname)) ggu = GerritGitUtils(config.ADMIN_USER, config.ADMIN_PRIV_KEY_PATH, config.USERS[config.ADMIN_USER]['email']) url = "ssh://%s@%s:29418/%s" % (config.ADMIN_USER, config.GATEWAY_HOST, pname) clone_dir = ggu.clone(url, pname.split('/')[-1]) self.dirs_to_delete.append(os.path.dirname(clone_dir)) self.assertTrue(os.path.isdir(clone_dir)) self.assertTrue(os.path.isfile(os.path.join(clone_dir, '.gitreview'))) self.msu.deleteProject(pname, config.ADMIN_USER) self.assertFalse(self.gu.project_exists(pname)) self.assertFalse(self.gu.group_exists('%s-ptl' % pname)) if is_present("SFRedmine"): rname = '_'.join(pname.split('/')) self.assertFalse(self.rm.project_exists(rname)) self.assertFalse(self.gu.group_exists('%s-core' % pname)) self.projects.remove(pname) @skipIfIssueTrackerMissing() def test_list_active_members(self): """ Check the list of members as a list of tuples of emails and names """ self.assertTrue(self.msu.list_active_members(config.USER_2)) def test_init_user_tests(self): """ Check if a test init feature behave as expected """ project = 'p_%s' % create_random_str() self.create_project(project, config.USER_4) self.msu.create_init_tests(project, config.USER_4) ggu = GerritGitUtils(config.ADMIN_USER, config.ADMIN_PRIV_KEY_PATH, config.USERS[config.ADMIN_USER]['email']) open_reviews = ggu.list_open_reviews('config', config.GATEWAY_HOST) match = [True for review in open_reviews if review['commitMessage']. startswith("%s proposes initial test " "definition for project %s" % (config.USER_4, project))] self.assertEqual(len(match), 1) open_reviews = ggu.list_open_reviews(project, config.GATEWAY_HOST) match = [True for review in open_reviews if review['commitMessage']. startswith("%s proposes initial test " "scripts for project %s" % (config.USER_4, project))] self.assertEqual(len(match), 1) def test_rest_urls_accessible(self): """ Check if managesf URLs are all working """ project = 'p_%s' % create_random_str() self.create_project(project, config.ADMIN_USER) cookies = dict( auth_pubtkt=config.USERS[config.ADMIN_USER]['auth_cookie']) paths = [ "/manage/project/", "/manage/project/%s" % project, "/manage/project/membership/"] for path in paths: url = "http://%s%s" % (config.GATEWAY_HOST, path) resp = requests.get(url, cookies=cookies) self.assertEqual(200, resp.status_code) def test_validate_get_all_project_details(self): """ Check if managesf allow us to fetch projects details """ project = 'p_%s' % create_random_str() self.create_project(project, config.USER_2) admin_cookies = dict( auth_pubtkt=config.USERS[config.ADMIN_USER]['auth_cookie']) user2_cookies = dict( auth_pubtkt=config.USERS[config.USER_2]['auth_cookie']) url = "http://%s%s" % (config.GATEWAY_HOST, "/manage/project/") resp = requests.get(url, cookies=admin_cookies) self.assertEqual(200, resp.status_code) self.assertTrue(project in resp.json()) self.assertTrue('config' in resp.json()) resp = requests.get(url, cookies=user2_cookies) self.assertEqual(200, resp.status_code) self.assertTrue(project in resp.json()) self.assertTrue('config' in resp.json()) resp = requests.get(url, cookies=user2_cookies) project = 'p/%s' % create_random_str() self.create_project(project, config.USER_2) url = "http://%s%s" % (config.GATEWAY_HOST, "/manage/project/") import time time.sleep(15) resp = requests.get(url, cookies=user2_cookies) self.assertEqual(200, resp.status_code) self.assertTrue(project in resp.json()) def test_project_pages_config(self): """ Check if managesf allow us to configure pages for a project """ project = 'p_%s' % create_random_str() self.create_project(project, config.USER_2) self.assertTrue(self.gu.project_exists(project)) self.assertTrue(self.rm.project_exists(project)) self.msu.update_project_page(config.USER_2, project, "http://tests.com/") self.assertEqual(self.msu.get_project_page(config.USER_2, project).strip(), "\"http://tests.com/\"") self.msu.delete_project_page(config.USER_3, project) self.assertEqual(self.msu.get_project_page(config.USER_2, project).strip(), "\"http://tests.com/\"") self.msu.delete_project_page(config.USER_2, project) self.assertEqual(self.msu.get_project_page(config.USER_2, project).strip(), "")

false

true

f70054db99d6dc4c33a51f803f6de4a176c6c5f4

6,215

py

Python

sdk/containerservice/azure-mgmt-containerservice/azure/mgmt/containerservice/v2022_04_01/operations/_private_link_resources_operations.py

dubiety/azure-sdk-for-python

62ffa839f5d753594cf0fe63668f454a9d87a346

[ "MIT" ]

1

2022-02-01T18:50:12.000Z

sdk/containerservice/azure-mgmt-containerservice/azure/mgmt/containerservice/v2022_04_01/operations/_private_link_resources_operations.py

ellhe-blaster/azure-sdk-for-python

82193ba5e81cc5e5e5a5239bba58abe62e86f469

[ "MIT" ]

null

sdk/containerservice/azure-mgmt-containerservice/azure/mgmt/containerservice/v2022_04_01/operations/_private_link_resources_operations.py

ellhe-blaster/azure-sdk-for-python

82193ba5e81cc5e5e5a5239bba58abe62e86f469

[ "MIT" ]

null

# pylint: disable=too-many-lines # coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------- from typing import Any, Callable, Dict, Optional, TypeVar from msrest import Serializer from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error from azure.core.pipeline import PipelineResponse from azure.core.pipeline.transport import HttpResponse from azure.core.rest import HttpRequest from azure.core.tracing.decorator import distributed_trace from azure.mgmt.core.exceptions import ARMErrorFormat from .. import models as _models from .._vendor import _convert_request, _format_url_section T = TypeVar('T') ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]] _SERIALIZER = Serializer() _SERIALIZER.client_side_validation = False def build_list_request( subscription_id: str, resource_group_name: str, resource_name: str, **kwargs: Any ) -> HttpRequest: api_version = kwargs.pop('api_version', "2022-04-01") # type: str accept = "application/json" # Construct URL _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.ContainerService/managedClusters/{resourceName}/privateLinkResources") # pylint: disable=line-too-long path_format_arguments = { "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1), "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1), "resourceName": _SERIALIZER.url("resource_name", resource_name, 'str', max_length=63, min_length=1, pattern=r'^[a-zA-Z0-9]$|^[a-zA-Z0-9][-_a-zA-Z0-9]{0,61}[a-zA-Z0-9]$'), } _url = _format_url_section(_url, **path_format_arguments) # Construct parameters _query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str') # Construct headers _header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') return HttpRequest( method="GET", url=_url, params=_query_parameters, headers=_header_parameters, **kwargs ) class PrivateLinkResourcesOperations(object): """PrivateLinkResourcesOperations operations. You should not instantiate this class directly. Instead, you should create a Client instance that instantiates it for you and attaches it as an attribute. :ivar models: Alias to model classes used in this operation group. :type models: ~azure.mgmt.containerservice.v2022_04_01.models :param client: Client for service requests. :param config: Configuration of service client. :param serializer: An object model serializer. :param deserializer: An object model deserializer. """ models = _models def __init__(self, client, config, serializer, deserializer): self._client = client self._serialize = serializer self._deserialize = deserializer self._config = config @distributed_trace def list( self, resource_group_name: str, resource_name: str, **kwargs: Any ) -> "_models.PrivateLinkResourcesListResult": """Gets a list of private link resources in the specified managed cluster. To learn more about private clusters, see: https://docs.microsoft.com/azure/aks/private-clusters. :param resource_group_name: The name of the resource group. The name is case insensitive. :type resource_group_name: str :param resource_name: The name of the managed cluster resource. :type resource_name: str :keyword callable cls: A custom type or function that will be passed the direct response :return: PrivateLinkResourcesListResult, or the result of cls(response) :rtype: ~azure.mgmt.containerservice.v2022_04_01.models.PrivateLinkResourcesListResult :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["_models.PrivateLinkResourcesListResult"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = kwargs.pop('api_version', "2022-04-01") # type: str request = build_list_request( subscription_id=self._config.subscription_id, resource_group_name=resource_group_name, resource_name=resource_name, api_version=api_version, template_url=self.list.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = self._client._pipeline.run( # pylint: disable=protected-access request, stream=False, **kwargs ) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) deserialized = self._deserialize('PrivateLinkResourcesListResult', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized list.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.ContainerService/managedClusters/{resourceName}/privateLinkResources"} # type: ignore

42.862069

229

0.696219

from typing import Any, Callable, Dict, Optional, TypeVar from msrest import Serializer from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error from azure.core.pipeline import PipelineResponse from azure.core.pipeline.transport import HttpResponse from azure.core.rest import HttpRequest from azure.core.tracing.decorator import distributed_trace from azure.mgmt.core.exceptions import ARMErrorFormat from .. import models as _models from .._vendor import _convert_request, _format_url_section T = TypeVar('T') ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]] _SERIALIZER = Serializer() _SERIALIZER.client_side_validation = False def build_list_request( subscription_id: str, resource_group_name: str, resource_name: str, **kwargs: Any ) -> HttpRequest: api_version = kwargs.pop('api_version', "2022-04-01") accept = "application/json" _url = kwargs.pop("template_url", "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.ContainerService/managedClusters/{resourceName}/privateLinkResources") path_format_arguments = { "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1), "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1), "resourceName": _SERIALIZER.url("resource_name", resource_name, 'str', max_length=63, min_length=1, pattern=r'^[a-zA-Z0-9]$|^[a-zA-Z0-9][-_a-zA-Z0-9]{0,61}[a-zA-Z0-9]$'), } _url = _format_url_section(_url, **path_format_arguments) _query_parameters = kwargs.pop("params", {}) _query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str') _header_parameters = kwargs.pop("headers", {}) _header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') return HttpRequest( method="GET", url=_url, params=_query_parameters, headers=_header_parameters, **kwargs ) class PrivateLinkResourcesOperations(object): models = _models def __init__(self, client, config, serializer, deserializer): self._client = client self._serialize = serializer self._deserialize = deserializer self._config = config @distributed_trace def list( self, resource_group_name: str, resource_name: str, **kwargs: Any ) -> "_models.PrivateLinkResourcesListResult": cls = kwargs.pop('cls', None) error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = kwargs.pop('api_version', "2022-04-01") request = build_list_request( subscription_id=self._config.subscription_id, resource_group_name=resource_group_name, resource_name=resource_name, api_version=api_version, template_url=self.list.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = self._client._pipeline.run( request, stream=False, **kwargs ) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) deserialized = self._deserialize('PrivateLinkResourcesListResult', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized list.metadata = {'url': "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.ContainerService/managedClusters/{resourceName}/privateLinkResources"}

true

f70054faef3359df19102559f92b4e27574b584c

451

py

Python

improve_django_v3/menu/migrations/0006_auto_20170930_1108.py

squadran2003/improve-a-django-project

89cff9f0b0e6534036e45565a17ae807b0e0a8b5

[ "MIT" ]

null

improve_django_v3/menu/migrations/0006_auto_20170930_1108.py

squadran2003/improve-a-django-project

89cff9f0b0e6534036e45565a17ae807b0e0a8b5

[ "MIT" ]

null

improve_django_v3/menu/migrations/0006_auto_20170930_1108.py

squadran2003/improve-a-django-project

89cff9f0b0e6534036e45565a17ae807b0e0a8b5

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Generated by Django 1.9.9 on 2017-09-30 18:08 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('menu', '0005_auto_20170930_1059'), ] operations = [ migrations.AlterField( model_name='item', name='description', field=models.CharField(max_length=255), ), ]

21.47619

51

0.616408

from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('menu', '0005_auto_20170930_1059'), ] operations = [ migrations.AlterField( model_name='item', name='description', field=models.CharField(max_length=255), ), ]

true

f700558e81a49cf9663da27bc6a4e2869c418a2f

704

py

Python

radio/apps.py

MaxwellDPS/trunk-player

19f116e64249823f3a12b35ed55252db60b1cf78

[ "MIT" ]

null

radio/apps.py

MaxwellDPS/trunk-player

19f116e64249823f3a12b35ed55252db60b1cf78

[ "MIT" ]

7

2021-06-10T23:24:05.000Z

2022-03-03T21:48:12.000Z

radio/apps.py

MaxwellDPS/trunk-player

19f116e64249823f3a12b35ed55252db60b1cf78

[ "MIT" ]

1

2022-03-26T07:04:21.000Z

from importlib import import_module from django.db.models.signals import post_migrate from django.apps import AppConfig def default_data_setup(sender, **kwargs): from django.contrib.auth.models import User try: anon = User.objects.get(username='ANONYMOUS_USER') except User.DoesNotExist: print('Adding ANONYMOUS_USER') anon = User.objects.create_user('ANONYMOUS_USER', '[email protected]') # Make the user un usable anon.set_unusable_password() anon.is_active = False anon.save() class RadioConfig(AppConfig): name = 'radio' def ready(self): post_migrate.connect(default_data_setup, sender=self)

27.076923

87

0.697443

from importlib import import_module from django.db.models.signals import post_migrate from django.apps import AppConfig def default_data_setup(sender, **kwargs): from django.contrib.auth.models import User try: anon = User.objects.get(username='ANONYMOUS_USER') except User.DoesNotExist: print('Adding ANONYMOUS_USER') anon = User.objects.create_user('ANONYMOUS_USER', '[email protected]') anon.set_unusable_password() anon.is_active = False anon.save() class RadioConfig(AppConfig): name = 'radio' def ready(self): post_migrate.connect(default_data_setup, sender=self)

true

f7005624326db8bd844029d49a4f69d03cd93970

3,257

py

Python

settings/ngrams.py

GU-DataLab/topic-modeling-textPrep

8138040308f33785cc5baa5f5982c2543e26ae0f

[ "MIT" ]

1

2022-03-19T11:33:49.000Z

settings/ngrams.py

GU-DataLab/topic-modeling-textPrep

8138040308f33785cc5baa5f5982c2543e26ae0f

[ "MIT" ]

null

settings/ngrams.py

GU-DataLab/topic-modeling-textPrep

8138040308f33785cc5baa5f5982c2543e26ae0f

[ "MIT" ]

null

from nltk.util import ngrams from nltk.corpus import stopwords from collections import Counter from .common import get_pp_pipeline def or_list(booleans): return True in booleans def get_ngrams(D): ''' Returns all ngrams (aka a token containing a dollar sign ($)) from a set of topics or documents :param topics: :return: ''' ngrams = set() for d in D: for w in d: if '$' in w: ngrams.add(w) return list(ngrams) def get_frequent_ngrams(text, n, stopword_list, threshold): bigrams = ngrams(text, n) bigram_freq = Counter(bigrams) frequent_bigrams = [] for bigram, freq in bigram_freq.most_common(): if not (or_list([i in stopword_list for i in bigram])): if freq > threshold: frequent_bigrams.append('{}${}'.format(bigram[0], bigram[1])) else: break return frequent_bigrams def ngrammize_text(text, ngrams): bigrammized_text = [] i = 0 while i < len(text): term = text[i] if i == len(text)-1: bigrammized_text.append(term) else: next_term = text[i+1] test_bigram = '{}${}'.format(term, next_term) if test_bigram in ngrams: bigrammized_text.append(test_bigram) i += 1 else: bigrammized_text.append(term) i += 1 return bigrammized_text def get_dataset_ngrams(docs, min_freq=1000, sw=None, extra_bigrams=None, extra_ngrams=None): if not sw: sw = stopwords.words('english') sw_pp = get_pp_pipeline(remove_stopwords=False) sw = sw_pp.clean_document(sw) full_text = [] for doc in docs: full_text.extend(doc) frequent_bigrams = get_frequent_ngrams(full_text, 2, sw, min_freq) if extra_bigrams: frequent_bigrams.extend(extra_bigrams) bigrammized_text = ngrammize_text(full_text, frequent_bigrams) frequent_ngrams = get_frequent_ngrams(bigrammized_text, 2, sw, min_freq) if extra_ngrams: frequent_ngrams.extend(extra_ngrams) return frequent_bigrams, frequent_ngrams def insert_ngrams_flat_from_lists(docs, frequent_bigrams, frequent_ngrams): for i in range(0, len(docs)): doc = docs[i] doc = ngrammize_text(doc, frequent_bigrams) doc = ngrammize_text(doc, frequent_ngrams) docs[i] = doc return docs def insert_ngrams_flat(docs, min_freq=1000, sw=None, extra_bigrams=None, extra_ngrams=None): fb, fn = get_dataset_ngrams(docs, min_freq, sw, extra_bigrams, extra_ngrams) return insert_ngrams_flat_from_lists(docs, fb, fn) def insert_ngrams_from_lists(date_doc_tuples, frequent_bigrams, frequent_ngrams): for i in range(0, len(date_doc_tuples)): date, doc = date_doc_tuples[i] doc = ngrammize_text(doc, frequent_bigrams) doc = ngrammize_text(doc, frequent_ngrams) date_doc_tuples[i] = (date, doc) return date_doc_tuples def insert_ngrams(date_docs, min_freq=1000, sw=None, extra_bigrams=None, extra_ngrams=None): fb, fn = get_dataset_ngrams([x[1] for x in date_docs], min_freq, sw, extra_bigrams, extra_ngrams) return insert_ngrams_from_lists(date_docs, fb, fn)

32.57

101

0.661652

from nltk.util import ngrams from nltk.corpus import stopwords from collections import Counter from .common import get_pp_pipeline def or_list(booleans): return True in booleans def get_ngrams(D): ngrams = set() for d in D: for w in d: if '$' in w: ngrams.add(w) return list(ngrams) def get_frequent_ngrams(text, n, stopword_list, threshold): bigrams = ngrams(text, n) bigram_freq = Counter(bigrams) frequent_bigrams = [] for bigram, freq in bigram_freq.most_common(): if not (or_list([i in stopword_list for i in bigram])): if freq > threshold: frequent_bigrams.append('{}${}'.format(bigram[0], bigram[1])) else: break return frequent_bigrams def ngrammize_text(text, ngrams): bigrammized_text = [] i = 0 while i < len(text): term = text[i] if i == len(text)-1: bigrammized_text.append(term) else: next_term = text[i+1] test_bigram = '{}${}'.format(term, next_term) if test_bigram in ngrams: bigrammized_text.append(test_bigram) i += 1 else: bigrammized_text.append(term) i += 1 return bigrammized_text def get_dataset_ngrams(docs, min_freq=1000, sw=None, extra_bigrams=None, extra_ngrams=None): if not sw: sw = stopwords.words('english') sw_pp = get_pp_pipeline(remove_stopwords=False) sw = sw_pp.clean_document(sw) full_text = [] for doc in docs: full_text.extend(doc) frequent_bigrams = get_frequent_ngrams(full_text, 2, sw, min_freq) if extra_bigrams: frequent_bigrams.extend(extra_bigrams) bigrammized_text = ngrammize_text(full_text, frequent_bigrams) frequent_ngrams = get_frequent_ngrams(bigrammized_text, 2, sw, min_freq) if extra_ngrams: frequent_ngrams.extend(extra_ngrams) return frequent_bigrams, frequent_ngrams def insert_ngrams_flat_from_lists(docs, frequent_bigrams, frequent_ngrams): for i in range(0, len(docs)): doc = docs[i] doc = ngrammize_text(doc, frequent_bigrams) doc = ngrammize_text(doc, frequent_ngrams) docs[i] = doc return docs def insert_ngrams_flat(docs, min_freq=1000, sw=None, extra_bigrams=None, extra_ngrams=None): fb, fn = get_dataset_ngrams(docs, min_freq, sw, extra_bigrams, extra_ngrams) return insert_ngrams_flat_from_lists(docs, fb, fn) def insert_ngrams_from_lists(date_doc_tuples, frequent_bigrams, frequent_ngrams): for i in range(0, len(date_doc_tuples)): date, doc = date_doc_tuples[i] doc = ngrammize_text(doc, frequent_bigrams) doc = ngrammize_text(doc, frequent_ngrams) date_doc_tuples[i] = (date, doc) return date_doc_tuples def insert_ngrams(date_docs, min_freq=1000, sw=None, extra_bigrams=None, extra_ngrams=None): fb, fn = get_dataset_ngrams([x[1] for x in date_docs], min_freq, sw, extra_bigrams, extra_ngrams) return insert_ngrams_from_lists(date_docs, fb, fn)

true

f700567345c8f7d640f885f9ec02c826d22cb2ad

9,755

py

Python

core/src/autogluon/core/task/base/base_task.py

zhiqiangdon/autogluon

71ee7ef0f05d8f0aad112d8c1719174aa33194d9

[ "Apache-2.0" ]

4,462

2019-12-09T17:41:07.000Z

2022-03-31T22:00:41.000Z

core/src/autogluon/core/task/base/base_task.py

zhiqiangdon/autogluon

71ee7ef0f05d8f0aad112d8c1719174aa33194d9

[ "Apache-2.0" ]

1,408

2019-12-09T17:48:59.000Z

2022-03-31T20:24:12.000Z

core/src/autogluon/core/task/base/base_task.py

zhiqiangdon/autogluon

71ee7ef0f05d8f0aad112d8c1719174aa33194d9

[ "Apache-2.0" ]

623

2019-12-10T02:04:18.000Z

2022-03-20T17:11:01.000Z

import collections import copy import logging import time from abc import abstractmethod from ...scheduler import HyperbandScheduler, RLScheduler, FIFOScheduler from ...scheduler.seq_scheduler import LocalSequentialScheduler from ...utils import in_ipynb, try_import_mxnet from ...utils.utils import setup_compute __all__ = [ 'BaseTask', 'compile_scheduler_options', 'compile_scheduler_options_v2', 'create_scheduler'] Results = collections.namedtuple('Results', 'model reward config time metadata') schedulers = { 'local': LocalSequentialScheduler, 'fifo': FIFOScheduler, 'rl': RLScheduler, 'hyperband_stopping': HyperbandScheduler, 'hyperband_promotion': HyperbandScheduler, } logger = logging.getLogger(__name__) logger.setLevel(logging.WARNING) def create_scheduler(train_fn, scheduler, scheduler_options): if isinstance(scheduler, str): scheduler_cls = schedulers[scheduler.lower()] else: assert callable(scheduler) scheduler_cls = scheduler scheduler_options = copy.copy(scheduler_options) return scheduler_cls(train_fn, **scheduler_options) class BaseTask(object): """BaseTask for AutoGluon applications """ @property @staticmethod def Dataset(): try_import_mxnet() from autogluon.mxnet.utils.dataset import BaseDataset return BaseDataset @classmethod def run_fit(cls, train_fn, search_strategy, scheduler_options, plot_results=False): start_time = time.time() # create scheduler and schedule tasks scheduler = create_scheduler(train_fn, search_strategy, scheduler_options) scheduler.run() scheduler.join_jobs() # gather the best configuration best_reward = scheduler.get_best_reward() best_config = scheduler.get_best_config() args = train_fn.args args.final_fit = True if hasattr(args, 'epochs') and hasattr(args, 'final_fit_epochs'): args.epochs = args.final_fit_epochs train_fn.args.update({'final_fit':True}) train_fn.kwvars.update({'final_fit':True}) scheduler_final = create_scheduler(train_fn, search_strategy, scheduler_options) results = scheduler_final.run_with_config(best_config) total_time = time.time() - start_time if plot_results or in_ipynb(): plot_training_curves = scheduler_options['checkpoint'].replace('exp1.ag', 'plot_training_curves.png') scheduler.get_training_curves(filename=plot_training_curves, plot=True, use_legend=False) record_args = copy.deepcopy(args) if results is None: logger.warning('No valid results obtained with best config, the result may not be useful...') results = {} results.update(best_reward=best_reward, best_config=best_config, total_time=total_time, metadata=scheduler.metadata, training_history=scheduler.training_history, config_history=scheduler.config_history, reward_attr=scheduler._reward_attr, args=record_args) return results @classmethod @abstractmethod def fit(cls, *args, **kwargs): pass # These search_strategies use HyperbandScheduler, along with certain # searchers. searcher_for_hyperband_strategy = { 'hyperband': 'random', 'bayesopt_hyperband': 'bayesopt'} def compile_scheduler_options( scheduler_options, search_strategy, search_options, nthreads_per_trial, ngpus_per_trial, checkpoint, num_trials, time_out, resume, visualizer, time_attr, reward_attr, dist_ip_addrs, epochs=None): """ Updates a copy of scheduler_options (scheduler-specific options, can be empty) with general options. The result can be passed to __init__ of the scheduler. Special role of epochs for HyperbandScheduler: If the search_strategy involves HyperbandScheduler and epochs is given, then this value is copied to scheduler_options['max_t']. Pass epochs for applications where the time_attr is epoch, and epochs is the maximum number of epochs. :param scheduler_options: :param search_strategy: :param search_options: :param nthreads_per_trial: :param ngpus_per_trial: :param checkpoint: :param num_trials: :param time_out: :param resume: :param visualizer: :param time_attr: :param reward_attr: :param dist_ip_addrs: :param kwargs: :param epochs: See above. Optional :return: Copy of scheduler_options with updates """ if scheduler_options is None: scheduler_options = dict() else: assert isinstance(scheduler_options, dict) assert isinstance(search_strategy, str) if search_options is None: search_options = dict() if visualizer is None: visualizer = 'none' if time_attr is None: time_attr = 'epoch' if reward_attr is None: reward_attr = 'accuracy' scheduler_options = copy.copy(scheduler_options) scheduler_options.update({ 'resource': { 'num_cpus': nthreads_per_trial, 'num_gpus': ngpus_per_trial}, 'searcher': search_strategy, 'search_options': search_options, 'checkpoint': checkpoint, 'resume': resume, 'num_trials': num_trials, 'time_out': time_out, 'reward_attr': reward_attr, 'time_attr': time_attr, 'visualizer': visualizer, 'dist_ip_addrs': dist_ip_addrs}) searcher = searcher_for_hyperband_strategy.get(search_strategy) if searcher is not None: scheduler_options['searcher'] = searcher if epochs is not None: scheduler_options['max_t'] = epochs return scheduler_options # TODO: Migrate TextPredictor to use this version, delete old version def compile_scheduler_options_v2( scheduler_options, nthreads_per_trial, ngpus_per_trial, num_trials, time_out, scheduler=None, search_strategy=None, search_options=None, checkpoint=None, resume=False, visualizer=None, time_attr=None, reward_attr=None, dist_ip_addrs=None, epochs=None): """ Updates a copy of scheduler_options (scheduler-specific options, can be empty) with general options. The result can be passed to __init__ of the scheduler. Special role of epochs for HyperbandScheduler: If the search_strategy involves HyperbandScheduler and epochs is given, then this value is copied to scheduler_options['max_t']. Pass epochs for applications where the time_attr is epoch, and epochs is the maximum number of epochs. :param scheduler_options: :param scheduler: :param search_strategy: :param search_options: :param nthreads_per_trial: :param ngpus_per_trial: :param checkpoint: :param num_trials: :param time_out: :param resume: :param visualizer: :param time_attr: :param reward_attr: :param dist_ip_addrs: :param kwargs: :param epochs: See above. Optional :return: Copy of scheduler_options with updates """ if scheduler_options is None: scheduler_options = dict() else: assert isinstance(scheduler_options, dict) scheduler_options = copy.copy(scheduler_options) if dist_ip_addrs is None: dist_ip_addrs = [] if search_strategy is None: search_strategy = 'random' if scheduler is None: scheduler = 'local' assert isinstance(search_strategy, str) if search_options is None: search_options = dict() if visualizer is None: visualizer = 'none' if time_attr is None: time_attr = 'epoch' if reward_attr is None: reward_attr = 'validation_performance' scheduler_params = { 'resource': { 'num_cpus': nthreads_per_trial, 'num_gpus': ngpus_per_trial}, 'scheduler': scheduler, 'searcher': search_strategy, 'search_options': search_options, 'checkpoint': checkpoint, 'resume': resume, 'num_trials': num_trials, 'time_out': time_out, 'reward_attr': reward_attr, 'time_attr': time_attr, 'visualizer': visualizer, 'dist_ip_addrs': dist_ip_addrs, } resource = None if 'resource' in scheduler_options: scheduler_params['resource'].update(scheduler_options['resource']) resource = scheduler_params['resource'].copy() scheduler_params.update(scheduler_options) if resource: scheduler_params['resource'] = resource scheduler_params['resource']['num_cpus'], scheduler_params['resource']['num_gpus'] = setup_compute( nthreads_per_trial=scheduler_params['resource']['num_cpus'], ngpus_per_trial=scheduler_params['resource']['num_gpus'], ) # TODO: use 'auto' downstream searcher = searcher_for_hyperband_strategy.get(scheduler_params['searcher']) if searcher is not None: scheduler_params['searcher'] = searcher if epochs is not None: scheduler_params['max_t'] = epochs required_options = [ 'resource', 'scheduler', 'searcher', 'search_options', 'checkpoint', 'resume', 'num_trials', 'time_out', 'reward_attr', 'time_attr', 'visualizer', 'dist_ip_addrs', ] missing_options = [] for option in required_options: if option not in scheduler_params: missing_options.append(option) if missing_options: raise AssertionError(f'Missing required keys in scheduler_options: {missing_options}') return scheduler_params

35.089928

153

0.679036

import collections import copy import logging import time from abc import abstractmethod from ...scheduler import HyperbandScheduler, RLScheduler, FIFOScheduler from ...scheduler.seq_scheduler import LocalSequentialScheduler from ...utils import in_ipynb, try_import_mxnet from ...utils.utils import setup_compute __all__ = [ 'BaseTask', 'compile_scheduler_options', 'compile_scheduler_options_v2', 'create_scheduler'] Results = collections.namedtuple('Results', 'model reward config time metadata') schedulers = { 'local': LocalSequentialScheduler, 'fifo': FIFOScheduler, 'rl': RLScheduler, 'hyperband_stopping': HyperbandScheduler, 'hyperband_promotion': HyperbandScheduler, } logger = logging.getLogger(__name__) logger.setLevel(logging.WARNING) def create_scheduler(train_fn, scheduler, scheduler_options): if isinstance(scheduler, str): scheduler_cls = schedulers[scheduler.lower()] else: assert callable(scheduler) scheduler_cls = scheduler scheduler_options = copy.copy(scheduler_options) return scheduler_cls(train_fn, **scheduler_options) class BaseTask(object): @property @staticmethod def Dataset(): try_import_mxnet() from autogluon.mxnet.utils.dataset import BaseDataset return BaseDataset @classmethod def run_fit(cls, train_fn, search_strategy, scheduler_options, plot_results=False): start_time = time.time() scheduler = create_scheduler(train_fn, search_strategy, scheduler_options) scheduler.run() scheduler.join_jobs() best_reward = scheduler.get_best_reward() best_config = scheduler.get_best_config() args = train_fn.args args.final_fit = True if hasattr(args, 'epochs') and hasattr(args, 'final_fit_epochs'): args.epochs = args.final_fit_epochs train_fn.args.update({'final_fit':True}) train_fn.kwvars.update({'final_fit':True}) scheduler_final = create_scheduler(train_fn, search_strategy, scheduler_options) results = scheduler_final.run_with_config(best_config) total_time = time.time() - start_time if plot_results or in_ipynb(): plot_training_curves = scheduler_options['checkpoint'].replace('exp1.ag', 'plot_training_curves.png') scheduler.get_training_curves(filename=plot_training_curves, plot=True, use_legend=False) record_args = copy.deepcopy(args) if results is None: logger.warning('No valid results obtained with best config, the result may not be useful...') results = {} results.update(best_reward=best_reward, best_config=best_config, total_time=total_time, metadata=scheduler.metadata, training_history=scheduler.training_history, config_history=scheduler.config_history, reward_attr=scheduler._reward_attr, args=record_args) return results @classmethod @abstractmethod def fit(cls, *args, **kwargs): pass searcher_for_hyperband_strategy = { 'hyperband': 'random', 'bayesopt_hyperband': 'bayesopt'} def compile_scheduler_options( scheduler_options, search_strategy, search_options, nthreads_per_trial, ngpus_per_trial, checkpoint, num_trials, time_out, resume, visualizer, time_attr, reward_attr, dist_ip_addrs, epochs=None): if scheduler_options is None: scheduler_options = dict() else: assert isinstance(scheduler_options, dict) assert isinstance(search_strategy, str) if search_options is None: search_options = dict() if visualizer is None: visualizer = 'none' if time_attr is None: time_attr = 'epoch' if reward_attr is None: reward_attr = 'accuracy' scheduler_options = copy.copy(scheduler_options) scheduler_options.update({ 'resource': { 'num_cpus': nthreads_per_trial, 'num_gpus': ngpus_per_trial}, 'searcher': search_strategy, 'search_options': search_options, 'checkpoint': checkpoint, 'resume': resume, 'num_trials': num_trials, 'time_out': time_out, 'reward_attr': reward_attr, 'time_attr': time_attr, 'visualizer': visualizer, 'dist_ip_addrs': dist_ip_addrs}) searcher = searcher_for_hyperband_strategy.get(search_strategy) if searcher is not None: scheduler_options['searcher'] = searcher if epochs is not None: scheduler_options['max_t'] = epochs return scheduler_options def compile_scheduler_options_v2( scheduler_options, nthreads_per_trial, ngpus_per_trial, num_trials, time_out, scheduler=None, search_strategy=None, search_options=None, checkpoint=None, resume=False, visualizer=None, time_attr=None, reward_attr=None, dist_ip_addrs=None, epochs=None): if scheduler_options is None: scheduler_options = dict() else: assert isinstance(scheduler_options, dict) scheduler_options = copy.copy(scheduler_options) if dist_ip_addrs is None: dist_ip_addrs = [] if search_strategy is None: search_strategy = 'random' if scheduler is None: scheduler = 'local' assert isinstance(search_strategy, str) if search_options is None: search_options = dict() if visualizer is None: visualizer = 'none' if time_attr is None: time_attr = 'epoch' if reward_attr is None: reward_attr = 'validation_performance' scheduler_params = { 'resource': { 'num_cpus': nthreads_per_trial, 'num_gpus': ngpus_per_trial}, 'scheduler': scheduler, 'searcher': search_strategy, 'search_options': search_options, 'checkpoint': checkpoint, 'resume': resume, 'num_trials': num_trials, 'time_out': time_out, 'reward_attr': reward_attr, 'time_attr': time_attr, 'visualizer': visualizer, 'dist_ip_addrs': dist_ip_addrs, } resource = None if 'resource' in scheduler_options: scheduler_params['resource'].update(scheduler_options['resource']) resource = scheduler_params['resource'].copy() scheduler_params.update(scheduler_options) if resource: scheduler_params['resource'] = resource scheduler_params['resource']['num_cpus'], scheduler_params['resource']['num_gpus'] = setup_compute( nthreads_per_trial=scheduler_params['resource']['num_cpus'], ngpus_per_trial=scheduler_params['resource']['num_gpus'], ) searcher = searcher_for_hyperband_strategy.get(scheduler_params['searcher']) if searcher is not None: scheduler_params['searcher'] = searcher if epochs is not None: scheduler_params['max_t'] = epochs required_options = [ 'resource', 'scheduler', 'searcher', 'search_options', 'checkpoint', 'resume', 'num_trials', 'time_out', 'reward_attr', 'time_attr', 'visualizer', 'dist_ip_addrs', ] missing_options = [] for option in required_options: if option not in scheduler_params: missing_options.append(option) if missing_options: raise AssertionError(f'Missing required keys in scheduler_options: {missing_options}') return scheduler_params

true

f700577848be1d3db591bde11b9367926ef378b4

57,165

py

Python

CPAC/utils/create_fsl_model.py

chrisfoulon/C-PAC

2746a90c39cea586aede98343c5927252bb8e81a

[ "BSD-3-Clause" ]

1

2021-08-02T23:23:39.000Z

CPAC/utils/create_fsl_model.py

chrisfoulon/C-PAC

2746a90c39cea586aede98343c5927252bb8e81a

[ "BSD-3-Clause" ]

null

CPAC/utils/create_fsl_model.py

chrisfoulon/C-PAC

2746a90c39cea586aede98343c5927252bb8e81a

[ "BSD-3-Clause" ]

2

2021-08-02T23:23:40.000Z

2022-02-26T12:39:30.000Z

def load_pheno_file(pheno_file): import os import pandas as pd if not os.path.isfile(pheno_file): err = "\n\n[!] CPAC says: The group-level analysis phenotype file "\ "provided does not exist!\nPath provided: %s\n\n" \ % pheno_file raise Exception(err) with open(os.path.abspath(pheno_file),"r") as f: pheno_dataframe = pd.read_csv(f) return pheno_dataframe def load_group_participant_list(group_participant_list_file): import os import pandas as pd if not os.path.isfile(group_participant_list_file): err = "\n\n[!] CPAC says: The group-level analysis subject list "\ "provided does not exist!\nPath provided: %s\n\n" \ % group_subject_list raise Exception(err) with open(group_participant_list_file,"r") as f: group_subs_dataframe = pd.read_csv(f) if "participant" not in ga_sublist.columns: err = "\n\n[!] CPAC says: Your group-level analysis subject "\ "list CSV is missing a 'participant' column.\n\n" raise Exception(err) return group_subs_dataframe def process_pheno_file(pheno_file_dataframe, group_subs_dataframe, \ participant_id_label): # drops participants from the phenotype file if they are not in the group # analysis participant list # also handles sessions and series appropriately for repeated measures # input # pheno_file_dataframe: Pandas dataframe of the phenotype file # group_subs_dataframe: Pandas dataframe of the group analysis # participant list # participant_id_label: string of the name of the participant column in # the phenotype file # output # pheno_file_rows: a list of dictionaries, with each dictionary being # one of the rows from the phenotype file, with the # format of {header: value, header: value, ..} import os import pandas as pd if not isinstance(pheno_file_dataframe, pd.DataFrame): err = "\n\n[!] CPAC says: The phenotype information input should " \ "be a Python Pandas dataframe object.\n\n" raise Exception(err) if not isinstance(group_subs_dataframe, pd.DataFrame): err = "\n\n[!] CPAC says: The group analysis participant list input "\ "should be a Python Pandas dataframe object.\n\n" raise Exception(err) if not isinstance(participant_id_label, str): err = "\n\n[!] CPAC says: The participant ID label input should be " \ "a string.\n\n" pheno = pheno_file_dataframe pheno_file_rows = [] df_rows = [] # convert from dataframe to list and make those strings just in case subjects = list(group_subs_dataframe.participant) subjects = [str(i) for i in subjects] sessions = None series = None if "session" in group_subs_dataframe.columns: sessions = list(group_subs_dataframe.session) sessions = [str(i) for i in sessions] if "series" in group_subs_dataframe.columns: series = list(group_subs_dataframe.series) series = [str(i) for i in series] # use an integer for iteration because we're not sure if there will be # sessions and/or series for i in range(0,len(subjects)): full_id = [] subject = subjects[i] full_id.append(subject) if sessions and series: session = sessions[i] scan = series[i] full_id.append(session) full_id.append(scan) try: row = pheno[(pheno[participant_id_label] == subject) & \ (pheno.session == session) & \ (pheno.series == scan)] except: row = pheno[(pheno[participant_id_label] == int(subject)) & \ (pheno.session == session) & \ (pheno.series == scan)] elif sessions: session = sessions[i] full_id.append(session) try: row = pheno[(pheno[participant_id_label] == subject) & \ (pheno.session == session)] except: row = pheno[(pheno[participant_id_label] == int(subject)) & \ (pheno.session == session)] elif series: scan = series[i] full_id.append(scan) try: row = pheno[(pheno[participant_id_label] == subject) & \ (pheno.series == scan)] except: row = pheno[(pheno[participant_id_label] == int(subject)) & \ (pheno.series == scan)] else: full_id.append(subject) try: row = pheno[(pheno[participant_id_label] == subject)] except: row = pheno[(pheno[participant_id_label] == int(subject))] if len(row) > 1: err = "\n\n[!] CPAC says: Multiple phenotype entries were " \ "found for these criteria:\n\n%s\n\nPlease ensure " \ "your group analysis participant list and phenotype " \ "file are configured correctly.\n\n" % str(full_id) raise Exception(err) elif len(row) == 1: df_rows.append(row) new_pheno_df = pd.concat(df_rows) pheno_file_rows = new_pheno_df.to_dict("records") return pheno_file_rows def create_pheno_dict(pheno_file_rows, ev_selections, participant_id_label): # creates the phenotype data dictionary in a format Patsy requires, # and also demeans the continuous EVs marked for demeaning # input # pheno_file_rows: a list of dictionaries, with each dictionary being # one of the rows from the phenotype file, with the # format of {header: value, header: value, ..} # ev_selections: a dictionary with keys for "categorical" and "demean", # with the entries being lists of phenotype EV names # participant_id_label: string of the name of the participant column in # the phenotype file # output # pheno_data_dict: a dictionary with each key being a phenotype column, # and each entry being a list of values, IN ORDER # data is also in Patsy-acceptable format import os import csv import numpy as np pheno_data_dict = {} for line in pheno_file_rows: for val in line.values(): # if there are any blank values in the pheno row, skip this # row. if not, continue on with the "else" clause if val == "": break else: for key in line.keys(): # if there are blank entries because of an empty row in # the CSV (such as ",,,,,"), move on to the next entry #if len(line[key]) == 0: # continue if key not in pheno_data_dict.keys(): pheno_data_dict[key] = [] # create a list within one of the dictionary values for # that EV if it is categorical; formats this list into a # form Patsy can understand regarding categoricals: # example: { ADHD: ['adhd1', 'adhd1', 'adhd0'] } # instead of just [1, 1, 0], etc. if 'categorical' in ev_selections.keys(): if key in ev_selections['categorical']: pheno_data_dict[key].append(key + str(line[key])) elif (key == subject_id_label) or (key == "session") or \ (key == "series"): pheno_data_dict[key].append(line[key]) else: pheno_data_dict[key].append(float(line[key])) elif (key == subject_id_label) or (key == "session") or \ (key == "series"): pheno_data_dict[key].append(line[key]) else: pheno_data_dict[key].append(float(line[key])) # this needs to run after each list in each key has been fully # populated above for key in pheno_data_dict.keys(): # demean the EVs marked for demeaning if 'demean' in ev_selections.keys(): if key in ev_selections['demean']: new_demeaned_evs = [] mean_evs = 0.0 # populate a dictionary, a key for each demeanable EV, with # the value being the sum of all the values (which need to be # converted to float first) for val in pheno_data_dict[key]: mean_evs += float(val) # calculate the mean of the current EV in this loop mean_evs = mean_evs / len(pheno_data_dict[key]) # remove the EV's mean from each value of this EV # (demean it!) for val in pheno_data_dict[key]: new_demeaned_evs.append(float(val) - mean_evs) # replace pheno_data_dict[key] = new_demeaned_evs # converts non-categorical EV lists into NumPy arrays # so that Patsy may read them in properly if 'categorical' in ev_selections.keys(): if key not in ev_selections['categorical']: pheno_data_dict[key] = np.array(pheno_data_dict[key]) return pheno_data_dict def get_measure_dict(param_file): # load the CPAC-generated power parameters file and parse it # input # param_file: a full path to the CPAC-generated power parameters CSV # output # measure_dict: a dictionary of dictionaries in the following format # {"MeanFD_Power": {"participant_01": 15.43, # "participant_02": 13.22}, # "MeanFD_Jenkinson": {"participant_01": 18.55, # "participant_02": 16.27}, # ...} import os import pandas as pd if not os.path.isfile(param_file): err = "\n\n[!] CPAC says: You've included a motion parameter in " \ "your group-level analysis model design formula, but " \ "there is no motion parameters file available.\n\n" raise Exception(err) with open(param_file,"r") as f: motion_params = pd.read_csv(f, index_col=False) measures = ['MeanFD_Power', 'MeanFD_Jenkinson', 'MeanDVARS'] measure_dict = {} for m in measures: measure_map = {} if m in motion_params.columns: part_ids = list(motion_params["Subject"]) part_ids = [str(i) for i in part_ids] scan_ids = list(motion_params["Scan"]) scan_ids = [str(i) for i in scan_ids] measure_vals = list(motion_params[m]) measure_vals = [float(i) for i in measure_vals] for part_id, scan_id, measure_val in \ zip(part_ids, scan_ids, measure_vals): measure_map[(part_id,scan_id)] = measure_val measure_dict[m] = measure_map return measure_dict def get_custom_roi_info(roi_means_dict): # check if roi_means_dict == None: err_string = "\n\n[!] CPAC says: The custom ROI means were not " \ "calculated properly during the group analysis " \ "model generation.\n\n" raise Exception(err_string) roi_num = len(roi_means_dict.values()[0]) # this will be a dictionary matching ROI regressor header labels with # the actual ROI dictionaries roi_dict_dict = {} # split the roi_means_dict from { subID: [mean1,mean2,mean3,..], ..} # to three dictionaries of { subID: mean1, .. }, { subID: mean2, .. }, # and so on for num in range(0,roi_num): label = "Custom_ROI_Mean_%d" % int(num+1) temp_roi_dict = {} for key in roi_means_dict.keys(): temp_roi_dict[key] = roi_means_dict[key][num-1] roi_dict_dict[label] = temp_roi_dict return roi_dict_dict def model_group_var_separately(grouping_var, formula, pheno_data_dict, \ ev_selections, coding_scheme): if grouping_var == None or grouping_var not in formula: print '\n\n[!] CPAC says: Model group variances separately is ' \ 'enabled, but the grouping variable set is either set to ' \ 'None, or was not included in the model as one of the ' \ 'EVs.\n' print 'Design formula: ', formula print 'Grouping variable: ', grouping_var, '\n\n' raise Exception # do this a little early for the grouping variable so that it doesn't # get in the way of doing this for the other EVs once they have the # grouping variable in their names if 'categorical' in ev_selections.keys(): for EV_name in ev_selections['categorical']: if EV_name == grouping_var: if coding_scheme == 'Treatment': formula = formula.replace(EV_name, 'C(' + EV_name + ')') elif coding_scheme == 'Sum': formula = formula.replace(EV_name, 'C(' + EV_name + \ ', Sum)') groupvar_levels = [] grouping_var_id_dict = {} idx = 0 for cat_ev_value in pheno_data_dict[grouping_var]: # here, each "cat_ev_value" will be one of the Patsy-format values # of the categorical EV that the user has selected as the grouping # variable, i.e. "sex1, sex1, sex0, sex1", etc.. # cat_ev_level is the level digit or label without the EV name # ex. sex1 becomes 1 cat_ev_level = str(cat_ev_value).replace(str(grouping_var), "") if cat_ev_level not in groupvar_levels: groupvar_levels.append(cat_ev_level) # groupvar_levels only keeps track of how many levels there are in # the grouping variable # populate this dict for creating the .grp file: try: grouping_var_id_dict[cat_ev_level].append(idx) except: grouping_var_id_dict[cat_ev_level] = [idx] idx += 1 split_EVs = {} for key in pheno_data_dict.keys(): # here, "key" is the name of each EV from the phenotype file, as # they are labeled in the phenotype file (not Patsy format) if (key in formula) and (key != grouping_var): # for the formula edit new_key_string = "" for level in groupvar_levels: # for the new split EV label groupvar_with_level = str(grouping_var) + str(level) new_key = key + "__" + groupvar_with_level # for the formula edit if new_key_string == "": new_key_string = new_key else: new_key_string = new_key_string + " + " + new_key split_EVs[new_key] = [] # for the formula as well if key in ev_selections["categorical"]: ev_selections["categorical"].append(new_key) for val, groupvar_val in zip(pheno_data_dict[key], \ pheno_data_dict[grouping_var]): if groupvar_with_level == groupvar_val: split_EVs[new_key].append(val) else: split_EVs[new_key].append(0) del pheno_data_dict[key] if key in ev_selections["categorical"]: ev_selections["categorical"].remove(key) # formula edit formula = formula.replace(key, new_key_string) # put split EVs into pheno data dict pheno_data_dict.update(split_EVs) # parse through ev_selections, find the categorical names within the # design formula and insert C(<name>, Sum) into the design formula # this is required for Patsy to process the categorical EVs # properly when generating the design matrix (this goes into the # .mat file) if 'categorical' in ev_selections.keys(): for EV_name in ev_selections['categorical']: if EV_name != grouping_var: if coding_scheme == 'Treatment': formula = formula.replace(EV_name, 'C(' + EV_name + ')') elif coding_scheme == 'Sum': formula = formula.replace(EV_name, 'C(' + EV_name + \ ', Sum)') # remove intercept when modeling group variances separately formula = formula + " - 1" return pheno_data_dict, formula, grouping_var_id_dict def check_multicollinearity(matrix): import numpy as np print "\nChecking for multicollinearity in the model.." U, s, V = np.linalg.svd(matrix) max_singular = np.max(s) min_singular = np.min(s) print "Max singular: ", max_singular print "Min singular: ", min_singular print "Rank: ", np.linalg.matrix_rank(matrix), "\n" if min_singular == 0: print '[!] CPAC warns: Detected multicollinearity in the ' \ 'computed group-level analysis model. Please double-' \ 'check your model design.\n\n' else: condition_number = float(max_singular)/float(min_singular) print "Condition number: %f\n\n" % condition_number if condition_number > 30: print '[!] CPAC warns: Detected multicollinearity in the ' \ 'computed group-level analysis model. Please double-' \ 'check your model design.\n\n' def write_mat_file(design_matrix, output_dir, model_name, \ depatsified_EV_names, current_output=None): import os import numpy as np dimx = None dimy = None if len(design_matrix.shape) == 1: dimy = 1 dimx = design_matrix.shape[0] else: dimx, dimy = design_matrix.shape ppstring = '/PPheights' for i in range(0, dimy): ppstring += '\t' + '%1.5e' %(1.0) ppstring += '\n' filename = model_name + ".mat" out_file = os.path.join(output_dir, filename) if not os.path.exists(output_dir): os.makedirs(output_dir) with open(out_file, 'wt') as f: print >>f, '/NumWaves\t%d' %dimy print >>f, '/NumPoints\t%d' %dimx print >>f, ppstring # print labels for the columns - mainly for double-checking your model col_string = '\n' for col in depatsified_EV_names: col_string = col_string + col + '\t' print >>f, col_string, '\n' print >>f, '/Matrix' np.savetxt(f, design_matrix, fmt='%1.5e', delimiter='\t') def create_grp_file(design_matrix, grouping_var_id_dict, output_dir, \ model_name, current_output=None): import os import numpy as np dimx = None dimy = None if len(design_matrix.shape) == 1: dimy = 1 dimx = design_matrix.shape[0] else: dimx, dimy = design_matrix.shape design_matrix_ones = np.ones(dimx) if not (grouping_var_id_dict == None): i = 1 for key in sorted(grouping_var_id_dict.keys()): for index in grouping_var_id_dict[key]: design_matrix_ones[index] = i i += 1 filename = model_name + ".grp" out_file = os.path.join(output_dir, filename) if not os.path.exists(output_dir): os.makedirs(output_dir) with open(out_file, "wt") as f: print >>f, '/NumWaves\t1' print >>f, '/NumPoints\t%d\n' %dimx print >>f, '/Matrix' np.savetxt(f, design_matrix_ones, fmt='%d', delimiter='\t') def create_design_matrix(pheno_file, ev_selections, formula, \ subject_id_label, sub_list=None, \ coding_scheme="Treatment", grouping_var=None, \ new_regressor_dict=None, roi_means_dict=None, \ output_dir=None, model_name="design", \ current_output=None): # this should allow the user to easily create a FLAMEO-formatted .mat file # and .grp file from the command line or from within CPAC # input # pheno_file: full path to a CSV file with the phenotypic data # # ev_selections: a Python dictionary of two lists denoting which EVs are # categorical, and which should be demeaned # format - {"categorical": ["dx_group", "sex"], # "demean": ["age"]} # # formula: a string with the Patsy-format design matrix formula # more info here: # http://patsy.readthedocs.org/en/latest/formulas.html # # subject_id_label: a string denoting the header label of the subject ID # column in the phenotype CSV file # # sub_list: (optional) full path to a CSV file containing the # participant IDs, and optionally session and/or series IDs # that you want included from the phenotypic file # NOTE: if not provided, all rows from phenotypic are included # in the model # # coding_scheme: (optional) which encoding scheme Patsy should use when # creating the design matrix - "Treatment" (default) or # "Sum" # # grouping_var: (optional) the grouping variable to use if modeling # group variances separately # # new_regressor_dict: (optional) a Python dictionary containing other # dictionaries of subject IDs matched to the values # of each new regressor # format - {"MeanFD": {"sub001": 0.493, # "sub002": 0.211,}, # "Measure_Mean": {"sub001": 0.193, # "sub002": 0.392}, # ..} # # roi_means_dict: (optional) a Python dictionary of lists containing the # mean values of user-defined ROIs of the derivative for # each subject # format - {"sub001": [3.23, 2.11], # "sub002": [1.79, 3.03]} # (with the length of the lists being the number of # ROIs specified) # # output_dir: (optional) where to write the .mat file # # model_name: (optional) name of the group analysis model # # current_output: (optional) name of the derivative in the analysis # # output # dmatrix: a Patsy object of the design matrix # depatsified_EV_names: a list of the column names of the design matrix import os import patsy import numpy as np # if running this script alone outside of CPAC if output_dir == None: output_dir = os.getcwd() # let's process the phenotype file data and drop rows (participants) if # they are not listed in the participant list pheno_file_df = load_pheno_file(pheno_file) participant_list_df = load_group_participant_list(sub_list) pheno_file_rows = process_pheno_file(pheno_file_df, participant_list_df, \ subject_id_label) # get number of subjects that have the derivative for this current model # (basically, the amount of time points, which must be greater than the # number of EVs) num_subjects = len(participant_list_df) # for repeated measures if "session" in participant_list_df.columns: ev_selections["categorical"].append("session") if "series" in participant_list_df.columns: ev_selections["categorical"].append("series") # start adding additionally created EVs if new_regressor_dict: for measure in new_regressor_dict.keys(): if (measure in formula): measure_dict = new_regressor_dict[measure] for pheno_row_dict in pheno_file_rows: participant_id = pheno_row_dict[subject_id_label] if ("session" in pheno_row_dict.keys()) and \ ("series" in pheno_row_dict.keys()): session_id = pheno_row_dict["session"] series_id = pheno_row_dict["series"] participant_tuple = \ (participant_id, session_id, series_id) elif "session" in pheno_row_dict.keys(): session_id = pheno_row_dict["session"] participant_tuple = (participant_id, session_id) elif "series" in pheno_row_dict.keys(): series_id = pheno_row_dict["series"] participant_tuple = (participant_id, series_id) else: participant_tuple = (participant_id) pheno_row_dict[measure] = measure_dict[participant_tuple] ev_selections["demean"].append(measure) if "Custom_ROI_Mean" in formula: # include the means of the specified ROIs as regressors if roi_means_dict == None: err = "\n\n[!] You included 'Custom_ROI_Mean' in your model " \ "design, but there are no mean of ROI values provided." \ "\n\n" raise Exception(err) # roi_dict_dict is a dictionary of dictionaries, with each dictionary # holding all of the means for one ROI, with each entry being a mean # for a participant (the keys are the participant IDs) # ex. {participant_01: 35.15, participant_02: 50.00} # with the float values being all of the means of one of # the ROIs specified # there will be a dictionary for each ROI specified roi_dict_dict = get_custom_roi_info(roi_means_dict) add_formula_string = "" for roi_column in roi_dict_dict.keys(): roi_dict = roi_dict_dict[roi_column] for pheno_row_dict in pheno_file_rows: participant_id = pheno_row_dict[subject_id_label] if ("session" in pheno_row_dict.keys()) and \ ("series" in pheno_row_dict.keys()): session_id = pheno_row_dict["session"] series_id = pheno_row_dict["series"] participant_tuple = \ (participant_id, session_id, series_id) elif "session" in pheno_row_dict.keys(): session_id = pheno_row_dict["session"] participant_tuple = (participant_id, session_id) elif "series" in pheno_row_dict.keys(): series_id = pheno_row_dict["series"] participant_tuple = (participant_id, series_id) else: participant_tuple = (participant_id) pheno_row_dict[roi_column] = roi_dict[participant_tuple] ev_selections["demean"].append(roi_column) # create a string of all the new custom ROI regressor column names # to be inserted into the design formula, so that Patsy will # accept the phenotypic data dictionary that now has these columns if add_formula_string == "": add_formula_string = add_formula_string + roi_column else: add_formula_string = add_formula_string + " + " + roi_column # a regressor column of ROI means for each custom-specified ROI has # now been added to the model with appropriate column labels formula = formula.replace("Custom_ROI_Mean",add_formula_string) # return the data from the phenotype file processed properly for Patsy # and load it into 'pheno_data_dict' # format: dictionary, each key is the name of an EV, and its value is # a LIST of values in order of the subjects # - categorical EVs are already renamed from '0,1,..' to # 'EV0,EV1,..' with EV being the EV name # - EVs to be demeaned are already demeaned # - numerical EVs (non-categorical) are in a list which # have been converted into a NumPy array pheno_data_dict = create_pheno_dict(pheno_file_rows, ev_selections, \ subject_id_label) # handle modeling group variances separately (if enabled), then edit the # formula to be in Patsy language if grouping_var != None: pheno_data_dict, formula, grouping_var_id_dict = \ model_group_var_separately(grouping_var, \ formula, pheno_data_dict, \ ev_selections, coding_scheme) else: grouping_var_id_dict = None if 'categorical' in ev_selections.keys(): for EV_name in ev_selections['categorical']: if coding_scheme == 'Treatment': formula = formula.replace(EV_name, 'C(' + EV_name + ')') elif coding_scheme == 'Sum': formula = formula.replace(EV_name, 'C(' + EV_name + \ ', Sum)') # create the Patsy design matrix! try: dmatrix = patsy.dmatrix(formula, pheno_data_dict, NA_action='raise') except: print '\n\n[!] CPAC says: Design matrix creation wasn\'t ' \ 'successful - do the terms in your formula correctly ' \ 'correspond to the EVs listed in your phenotype file?\n' print 'Phenotype file provided: ' print pheno_file, '\n' print "Phenotypic data columns (regressors): ", pheno_data_dict.keys() print "Formula: %s\n\n" % formula raise Exception # check the model for multicollinearity - Patsy takes care of this, but # just in case check_multicollinearity(np.array(dmatrix)) # prepare for final stages design_matrix = np.array(dmatrix, dtype=np.float16) column_names = dmatrix.design_info.column_names # check to make sure there are more time points than EVs! if len(column_names) >= num_subjects: err = "\n\n[!] CPAC says: There are more EVs than there are " \ "subjects currently included in the model for %s. There must " \ "be more subjects than EVs in the design.\n\nNumber of " \ "subjects: %d\nNumber of EVs: %d\n\nNote: An 'Intercept' " \ "column gets added to the design as an EV, so there will be " \ "one more EV than you may have specified in your design. In " \ "addition, if you specified to model group variances " \ "separately, an Intercept column will not be included, but " \ "the amount of EVs can nearly double once they are split " \ "along the grouping variable.\n\n" \ "If the number of subjects is lower than the number of " \ "subjects in your group analysis subject list, this may be " \ "because not every subject in the subject list has an output " \ "for %s in the individual-level analysis output directory.\n\n"\ % (current_output, num_subjects, len(column_names), \ current_output) raise Exception(err) # remove the header formatting Patsy creates for categorical variables # because we are going to use depatsified_EV_names in the "Available EVs # for Contrasts" list on the next page, and also to test user-made custom # contrast files depatsified_EV_names = [] for column in column_names: # if using Sum encoding, a column name may look like this: # C(adhd, Sum)[S.adhd0] # this loop leaves it with only "adhd0" in this case, for the # contrasts list for the next GUI page column_string = column string_for_removal = '' for char in column_string: string_for_removal = string_for_removal + char if char == '.': column_string = column_string.replace(string_for_removal, '') string_for_removal = '' column_string = column_string.replace(']', '') depatsified_EV_names.append(column_string) # write the .mat file finally write_mat_file(design_matrix, output_dir, model_name, \ depatsified_EV_names, current_output) # write the .grp file also create_grp_file(design_matrix, grouping_var_id_dict, output_dir, \ model_name, current_output) # return the PATSY OBJECT of dmatrix, not the Numpy array "design_matrix" return dmatrix, depatsified_EV_names def positive(dmat, a, coding, group_sep, grouping_var): import numpy as np # this is also where the "Intercept" column gets introduced into # the contrasts columns, for when the user uses the model builder's # contrast builder evs = dmat.design_info.column_name_indexes con = np.zeros(dmat.shape[1]) if group_sep == True: if "__" in a and grouping_var in a: ev_desc = a.split("__") for ev in evs: count = 0 for desc in ev_desc: if desc in ev: count += 1 if count == len(ev_desc): con[evs[ev]] = 1 break else: # it is a dropped term so make all other terms in that # category at -1 term = a.split('[')[0] for ev in evs: if ev.startswith(term): con[evs[ev]]= -1 elif len(a.split(grouping_var)) > 2: # this is if the current parsed contrast is the actual # grouping variable, as the Patsified name will have the # variable's name string in it twice for ev in evs: if a.split(".")[1] in ev: con[evs[ev]] = 1 break else: # it is a dropped term so make all other terms in that # category at -1 term = a.split('[')[0] for ev in evs: if ev.startswith(term): con[evs[ev]]= -1 # else not modeling group variances separately else: if a in evs: con[evs[a]] = 1 else: # it is a dropped term so make all other terms in that category # at -1 term = a.split('[')[0] for ev in evs: if ev.startswith(term): con[evs[ev]]= -1 if coding == "Treatment": # make Intercept 0 con[0] = 0 elif coding == "Sum": # make Intercept 1 con[1] = 1 return con def greater_than(dmat, a, b, coding, group_sep, grouping_var): c1 = positive(dmat, a, coding, group_sep, grouping_var) c2 = positive(dmat, b, coding, group_sep, grouping_var) return c1-c2 def negative(dmat, a, coding, group_sep, grouping_var): con = 0-positive(dmat, a, coding, group_sep, grouping_var) return con def create_dummy_string(length): ppstring = "" for i in range(0, length): ppstring += '\t' + '%1.5e' %(1.0) ppstring += '\n' return ppstring def create_con_file(con_dict, col_names, file_name, current_output, out_dir): import os print "col names: " print col_names with open(os.path.join(out_dir, file_name) + ".con",'w+') as f: # write header num = 1 for key in con_dict: f.write("/ContrastName%s\t%s\n" %(num,key)) num += 1 f.write("/NumWaves\t%d\n" %len(con_dict[key])) f.write("/NumContrasts\t%d\n" %len(con_dict)) f.write("/PPheights%s" %create_dummy_string(len(con_dict[key]))) f.write("/RequiredEffect%s" %create_dummy_string(len(con_dict[key]))) f.write("\n\n") # print labels for the columns - mainly for double-checking your # model col_string = '\n' for col in col_names: col_string = col_string + col + '\t' print >>f, col_string, '\n' # write data f.write("/Matrix\n") for key in con_dict: for v in con_dict[key]: f.write("%1.5e\t" %v) f.write("\n") def create_fts_file(ftest_list, con_dict, model_name, current_output, out_dir): import os import numpy as np try: print "\nFound f-tests in your model, writing f-tests file " \ "(.fts)..\n" with open(os.path.join(out_dir, model_name + '.fts'), 'w') as f: print >>f, '/NumWaves\t', len(con_dict) print >>f, '/NumContrasts\t', len(ftest_list) # process each f-test ftst = [] for ftest_string in ftest_list: ftest_vector = [] cons_in_ftest = ftest_string.split(",") for con in con_dict.keys(): if con in cons_in_ftest: ftest_vector.append(1) else: ftest_vector.append(0) ftst.append(ftest_vector) fts_n = np.array(ftst) # print labels for the columns - mainly for double-checking your # model col_string = '\n' for con in con_dict.keys(): col_string = col_string + con + '\t' print >>f, col_string, '\n' print >>f, '/Matrix' for i in range(fts_n.shape[0]): print >>f, ' '.join(fts_n[i].astype('str')) except Exception as e: filepath = os.path.join(out_dir, "model_files", current_output, \ model_name + '.fts') errmsg = "\n\n[!] CPAC says: Could not create .fts file for " \ "FLAMEO or write it to disk.\nAttempted filepath: %s\n" \ "Error details: %s\n\n" % (filepath, e) raise Exception(errmsg) def create_con_ftst_file(con_file, model_name, current_output, output_dir, column_names, coding_scheme, group_sep): """ Create the contrasts and fts file """ import os import numpy as np with open(con_file, "r") as f: evs = f.readline() evs = evs.rstrip('\r\n').split(',') count_ftests = 0 # TODO: this needs to be re-visited, but I think this was originally added # TODO: to counteract the fact that if someone was making a custom # TODO: contrasts matrix CSV, they wouldn't know that the design matrix # TODO: would have the Intercept added to it? but what if it wasn't? # TODO: comment out for now... but test # remove "Contrasts" label and replace it with "Intercept" #evs[0] = "Intercept" fTest = False print "evs: " print evs for ev in evs: if "f_test" in ev: count_ftests += 1 if count_ftests > 0: fTest = True try: data = np.genfromtxt(con_file, names=True, delimiter=',', dtype=None) except: print "Error: Could not successfully read in contrast file: ",con_file raise Exception lst = data.tolist() ftst = [] fts_columns = [] contrasts = [] contrast_names = [] length = None length = len(list(lst[0])) # lst = list of tuples, "tp" # tp = tuple in the format (contrast_name, 0, 0, 0, 0, ...) # with the zeroes being the vector of contrasts for that contrast for tp in lst: contrast_names.append(tp[0]) # create a list of integers that is the vector for the contrast # ex. [0, 1, 1, 0, ..] con_vector = list(tp)[1:(length-count_ftests)] fts_vector = list(tp)[(length-count_ftests):length] fts_columns.append(fts_vector) # TODO: see note about Intercept above # add Intercept column # if not group_sep: # if coding_scheme == "Treatment": # con_vector.insert(0, 0) # elif coding_scheme == "Sum": # con_vector.insert(0, 1) contrasts.append(con_vector) # contrast_names = list of the names of the contrasts (not regressors) # contrasts = list of lists with the contrast vectors num_EVs_in_con_file = len(contrasts[0]) contrasts = np.array(contrasts, dtype=np.float16) fts_columns = np.array(fts_columns) # if there are f-tests, create the array for them if fTest: if len(contrast_names) < 2: errmsg = "\n\n[!] CPAC says: Not enough contrasts for running " \ "f-tests.\nTip: Do you have only one contrast in your " \ "contrasts file? f-tests require more than one contrast.\n"\ "Either remove the f-tests or include more contrasts.\n\n" raise Exception(errmsg) fts_n = fts_columns.T if len(column_names) != (num_EVs_in_con_file): err_string = "\n\n[!] CPAC says: The number of EVs in your model " \ "design matrix (found in the %s.mat file) does not " \ "match the number of EVs (columns) in your custom " \ "contrasts matrix CSV file.\n\nCustom contrasts matrix "\ "file: %s\n\nNumber of EVs in design matrix: %d\n" \ "Number of EVs in contrasts file: %d\n\nThe column " \ "labels in the design matrix should match those in " \ "your contrasts .CSV file.\nColumn labels in design " \ "matrix:\n%s" % (model_name, con_file, \ len(column_names), num_EVs_in_con_file, \ str(column_names)) raise Exception(err_string) for design_mat_col, con_csv_col in zip(column_names, evs[1:]): if con_csv_col not in design_mat_col: errmsg = "\n\n[!] CPAC says: The names of the EVs in your " \ "custom contrasts .csv file do not match the names or " \ "order of the EVs in the design matrix. Please make " \ "sure these are consistent.\nDesign matrix EV columns: "\ "%s\nYour contrasts matrix columns: %s\n\n" \ % (column_names, evs[1:]) raise Exception(errmsg) out_dir = os.path.join(output_dir, model_name + '.con') with open(out_dir,"wt") as f: idx = 1 pp_str = '/PPheights' re_str = '/RequiredEffect' for name in contrast_names: print >>f, '/ContrastName%d' %idx, '\t', name pp_str += '\t%1.5e' %(1) re_str += '\t%1.5e' %(1) idx += 1 print >>f, '/NumWaves\t', (contrasts.shape)[1] print >>f, '/NumContrasts\t', (contrasts.shape)[0] print >>f, pp_str print >>f, re_str + '\n' # print labels for the columns - mainly for double-checking your model col_string = '\n' for ev in evs: col_string = col_string + ev + '\t' print >>f, col_string, '\n' print >>f, '/Matrix' np.savetxt(f, contrasts, fmt='%1.5e', delimiter='\t') if fTest: print "\nFound f-tests in your model, writing f-tests file (.fts)..\n" ftest_out_dir = os.path.join(output_dir, model_name + '.fts') with open(ftest_out_dir,"wt") as f: print >>f, '/NumWaves\t', (contrasts.shape)[0] print >>f, '/NumContrasts\t', count_ftests # print labels for the columns - mainly for double-checking your # model col_string = '\n' for con in contrast_names: col_string = col_string + con + '\t' print >>f, col_string, '\n' print >>f, '/Matrix' for i in range(fts_n.shape[0]): print >>f, ' '.join(fts_n[i].astype('str')) def process_contrast(parsed_contrast, operator, ev_selections, group_sep, \ grouping_var, coding_scheme): # take the contrast strings and process them appropriately # extract the two separate contrasts (if there are two), and then # identify which are categorical - adapting the string if so parsed_EVs_in_contrast = [] EVs_in_contrast = parsed_contrast.split(operator) if '' in EVs_in_contrast: EVs_in_contrast.remove('') for EV in EVs_in_contrast: skip = 0 # they need to be put back into Patsy formatted header titles # because the dmatrix gets passed into the function that writes # out the contrast matrix if 'categorical' in ev_selections.keys(): for cat_EV in ev_selections['categorical']: # second half of this if clause is in case group variances # are being modeled separately, and we don't want the EV # that is the grouping variable (which is now present in # other EV names) to confound this operation if group_sep == True: gpvar = grouping_var else: gpvar = "..." if (cat_EV in EV) and not (gpvar in EV and \ "__" in EV): # handle interactions if ":" in EV: temp_split_EV = EV.split(":") for interaction_EV in temp_split_EV: if cat_EV in interaction_EV: current_EV = interaction_EV else: current_EV = EV if coding_scheme == 'Treatment': cat_EV_contrast = EV.replace(EV, 'C(' + cat_EV + \ ')[T.' + current_EV+\ ']') elif coding_scheme == 'Sum': cat_EV_contrast = EV.replace(EV, 'C(' + cat_EV + \ ', Sum)[S.' + \ current_EV + ']') parsed_EVs_in_contrast.append(cat_EV_contrast) skip = 1 if skip == 0: parsed_EVs_in_contrast.append(EV) # handle interactions if ":" in EV and len(parsed_EVs_in_contrast) == 2: parsed_EVs_in_contrast = [parsed_EVs_in_contrast[0] + ":" + \ parsed_EVs_in_contrast[1]] if ":" in EV and len(parsed_EVs_in_contrast) == 3: parsed_EVs_in_contrast = [parsed_EVs_in_contrast[0], \ parsed_EVs_in_contrast[1] + ":" + \ parsed_EVs_in_contrast[2]] return parsed_EVs_in_contrast def run(group_config, current_output, param_file=None, \ derivative_means_dict=None, roi_means_dict=None, \ model_out_dir=None, CPAC_run=True): import os import csv import numpy as np # open the GROUP ANALYSIS FSL .YML CONFIG FILE, not the main pipeline # config .yml file! if CPAC_run: c = group_config else: try: c = Configuration(yaml.load(open(os.path.realpath(group_config), \ 'r'))) except: raise Exception("Error in reading %s configuration file" \ % group_config) # pull in the gpa settings! ph = c.pheno_file sublist = c.subject_list ev_selections = c.ev_selections subject_id_label = c.subject_id_label formula = c.design_formula coding_scheme = c.coding_scheme[0] group_sep = c.group_sep grouping_var = c.grouping_var contrasts = c.contrasts f_tests = c.f_tests custom_contrasts = c.custom_contrasts model_name = c.model_name output_dir = c.output_dir # make sure the group analysis output directory exists try: if not os.path.exists(output_dir): os.makedirs(output_dir) except: print '\n\n[!] CPAC says: Could not successfully create the group ' \ 'analysis output directory:\n', output_dir, '\n\nMake sure ' \ 'you have write access in this file structure.\n\n\n' raise Exception measure_dict = {} # extract motion measures from CPAC-generated power params file if param_file != None: measure_dict = get_measure_dict(param_file) # combine the motion measures dictionary with the measure_mean # dictionary (if it exists) if derivative_means_dict: measure_dict["Measure_Mean"] = derivative_means_dict # create the .mat and .grp files for FLAME design_matrix, regressor_names = create_design_matrix(ph, sublist, \ ev_selections, formula, \ subject_id_label, coding_scheme, \ grouping_var, measure_dict, \ roi_means_dict, model_out_dir, \ model_name, current_output) # Create contrasts_dict dictionary for the .con file generation later contrasts_list = contrasts contrasts_dict = {} for contrast in contrasts_list: # each 'contrast' is a string the user input of the desired contrast # remove all spaces parsed_contrast = contrast.replace(' ', '') EVs_in_contrast = [] parsed_EVs_in_contrast = [] if '>' in parsed_contrast: parsed_EVs_in_contrast = \ process_contrast(parsed_contrast, '>', ev_selections, \ group_sep, grouping_var, coding_scheme) contrasts_dict[parsed_contrast] = \ greater_than(design_matrix, parsed_EVs_in_contrast[0], \ parsed_EVs_in_contrast[1], coding_scheme, \ group_sep, grouping_var) elif '<' in parsed_contrast: parsed_EVs_in_contrast = \ process_contrast(parsed_contrast, '<', ev_selections, \ group_sep, grouping_var, coding_scheme) contrasts_dict[parsed_contrast] = \ greater_than(design_matrix, parsed_EVs_in_contrast[1], \ parsed_EVs_in_contrast[0], coding_scheme, \ group_sep, grouping_var) else: contrast_string = parsed_contrast.replace('+',',+,') contrast_string = contrast_string.replace('-',',-,') contrast_items = contrast_string.split(',') if '' in contrast_items: contrast_items.remove('') if '+' in contrast_items and len(contrast_items) == 2: parsed_EVs_in_contrast = \ process_contrast(parsed_contrast, '+', ev_selections, \ group_sep, grouping_var, coding_scheme) contrasts_dict[parsed_contrast] = \ positive(design_matrix, parsed_EVs_in_contrast[0], \ coding_scheme, group_sep, grouping_var) elif '-' in contrast_items and len(contrast_items) == 2: parsed_EVs_in_contrast = \ process_contrast(parsed_contrast, '-', ev_selections, \ group_sep, grouping_var, coding_scheme) contrasts_dict[parsed_contrast] = \ negative(design_matrix, parsed_EVs_in_contrast[0], \ coding_scheme, group_sep, grouping_var) if len(contrast_items) > 2: idx = 0 for item in contrast_items: # they need to be put back into Patsy formatted header # titles because the dmatrix gets passed into the function # that writes out the contrast matrix if 'categorical' in ev_selections.keys(): for cat_EV in ev_selections['categorical']: if cat_EV in item: if coding_scheme == 'Treatment': item = item.replace(item, \ 'C(' + cat_EV + ')[T.' + item + ']') elif coding_scheme == 'Sum': item = item.replace(item, \ 'C(' + cat_EV + ', Sum)[S.' + item + ']') if idx == 0: if item != '+' and item != '-': contrast_vector = positive(dmatrix, item) if parsed_contrast not in contrasts_dict.keys(): contrasts_dict[parsed_contrast] = contrast_vector else: contrasts_dict[parsed_contrast] += contrast_vector elif idx != 0: if item != '+' and item != '-': if contrast_items[idx-1] == '+': contrast_vector = positive(dmatrix, item, \ coding_scheme, group_sep,\ grouping_var) if parsed_contrast not in contrasts_dict.keys(): contrasts_dict[parsed_contrast] = contrast_vector else: contrasts_dict[parsed_contrast] += contrast_vector if contrast_items[idx-1] == '-': contrast_vector = negative(dmatrix, item, \ coding_scheme, group_sep,\ grouping_var) if parsed_contrast not in contrasts_dict.keys(): contrasts_dict[parsed_contrast] = contrast_vector else: contrasts_dict[parsed_contrast] += contrast_vector idx += 1 # finally write out the .con file and .fts file (if f-tests) if (custom_contrasts == None) or (custom_contrasts == '') or \ ("None" in custom_contrasts): print "Writing contrasts file (.con) based on contrasts provided " \ "using the group analysis model builder's contrasts editor.." create_con_file(contrasts_dict, regressor_names, model_name, \ current_output, model_out_dir) if f_tests: create_fts_file(f_tests, contrasts_dict, model_name, \ current_output, model_out_dir) else: print "\nWriting contrasts file (.con) based on contrasts provided " \ "with a custom contrasts matrix CSV file..\n" create_con_ftst_file(custom_contrasts, model_name, current_output, \ model_out_dir, regressor_names, \ coding_scheme, group_sep)

34.62447

86

0.544966

def load_pheno_file(pheno_file): import os import pandas as pd if not os.path.isfile(pheno_file): err = "\n\n[!] CPAC says: The group-level analysis phenotype file "\ "provided does not exist!\nPath provided: %s\n\n" \ % pheno_file raise Exception(err) with open(os.path.abspath(pheno_file),"r") as f: pheno_dataframe = pd.read_csv(f) return pheno_dataframe def load_group_participant_list(group_participant_list_file): import os import pandas as pd if not os.path.isfile(group_participant_list_file): err = "\n\n[!] CPAC says: The group-level analysis subject list "\ "provided does not exist!\nPath provided: %s\n\n" \ % group_subject_list raise Exception(err) with open(group_participant_list_file,"r") as f: group_subs_dataframe = pd.read_csv(f) if "participant" not in ga_sublist.columns: err = "\n\n[!] CPAC says: Your group-level analysis subject "\ "list CSV is missing a 'participant' column.\n\n" raise Exception(err) return group_subs_dataframe def process_pheno_file(pheno_file_dataframe, group_subs_dataframe, \ participant_id_label): import os import pandas as pd if not isinstance(pheno_file_dataframe, pd.DataFrame): err = "\n\n[!] CPAC says: The phenotype information input should " \ "be a Python Pandas dataframe object.\n\n" raise Exception(err) if not isinstance(group_subs_dataframe, pd.DataFrame): err = "\n\n[!] CPAC says: The group analysis participant list input "\ "should be a Python Pandas dataframe object.\n\n" raise Exception(err) if not isinstance(participant_id_label, str): err = "\n\n[!] CPAC says: The participant ID label input should be " \ "a string.\n\n" pheno = pheno_file_dataframe pheno_file_rows = [] df_rows = [] subjects = list(group_subs_dataframe.participant) subjects = [str(i) for i in subjects] sessions = None series = None if "session" in group_subs_dataframe.columns: sessions = list(group_subs_dataframe.session) sessions = [str(i) for i in sessions] if "series" in group_subs_dataframe.columns: series = list(group_subs_dataframe.series) series = [str(i) for i in series] # sessions and/or series for i in range(0,len(subjects)): full_id = [] subject = subjects[i] full_id.append(subject) if sessions and series: session = sessions[i] scan = series[i] full_id.append(session) full_id.append(scan) try: row = pheno[(pheno[participant_id_label] == subject) & \ (pheno.session == session) & \ (pheno.series == scan)] except: row = pheno[(pheno[participant_id_label] == int(subject)) & \ (pheno.session == session) & \ (pheno.series == scan)] elif sessions: session = sessions[i] full_id.append(session) try: row = pheno[(pheno[participant_id_label] == subject) & \ (pheno.session == session)] except: row = pheno[(pheno[participant_id_label] == int(subject)) & \ (pheno.session == session)] elif series: scan = series[i] full_id.append(scan) try: row = pheno[(pheno[participant_id_label] == subject) & \ (pheno.series == scan)] except: row = pheno[(pheno[participant_id_label] == int(subject)) & \ (pheno.series == scan)] else: full_id.append(subject) try: row = pheno[(pheno[participant_id_label] == subject)] except: row = pheno[(pheno[participant_id_label] == int(subject))] if len(row) > 1: err = "\n\n[!] CPAC says: Multiple phenotype entries were " \ "found for these criteria:\n\n%s\n\nPlease ensure " \ "your group analysis participant list and phenotype " \ "file are configured correctly.\n\n" % str(full_id) raise Exception(err) elif len(row) == 1: df_rows.append(row) new_pheno_df = pd.concat(df_rows) pheno_file_rows = new_pheno_df.to_dict("records") return pheno_file_rows def create_pheno_dict(pheno_file_rows, ev_selections, participant_id_label): # creates the phenotype data dictionary in a format Patsy requires, # and also demeans the continuous EVs marked for demeaning # input # pheno_file_rows: a list of dictionaries, with each dictionary being # one of the rows from the phenotype file, with the # format of {header: value, header: value, ..} # ev_selections: a dictionary with keys for "categorical" and "demean", # with the entries being lists of phenotype EV names # participant_id_label: string of the name of the participant column in # the phenotype file # output # pheno_data_dict: a dictionary with each key being a phenotype column, # and each entry being a list of values, IN ORDER # data is also in Patsy-acceptable format import os import csv import numpy as np pheno_data_dict = {} for line in pheno_file_rows: for val in line.values(): # if there are any blank values in the pheno row, skip this # row. if not, continue on with the "else" clause if val == "": break else: for key in line.keys(): # if there are blank entries because of an empty row in # the CSV (such as ",,,,,"), move on to the next entry #if len(line[key]) == 0: # continue if key not in pheno_data_dict.keys(): pheno_data_dict[key] = [] # create a list within one of the dictionary values for # that EV if it is categorical; formats this list into a # form Patsy can understand regarding categoricals: # example: { ADHD: ['adhd1', 'adhd1', 'adhd0'] } # instead of just [1, 1, 0], etc. if 'categorical' in ev_selections.keys(): if key in ev_selections['categorical']: pheno_data_dict[key].append(key + str(line[key])) elif (key == subject_id_label) or (key == "session") or \ (key == "series"): pheno_data_dict[key].append(line[key]) else: pheno_data_dict[key].append(float(line[key])) elif (key == subject_id_label) or (key == "session") or \ (key == "series"): pheno_data_dict[key].append(line[key]) else: pheno_data_dict[key].append(float(line[key])) # this needs to run after each list in each key has been fully # populated above for key in pheno_data_dict.keys(): # demean the EVs marked for demeaning if 'demean' in ev_selections.keys(): if key in ev_selections['demean']: new_demeaned_evs = [] mean_evs = 0.0 # populate a dictionary, a key for each demeanable EV, with # the value being the sum of all the values (which need to be # converted to float first) for val in pheno_data_dict[key]: mean_evs += float(val) # calculate the mean of the current EV in this loop mean_evs = mean_evs / len(pheno_data_dict[key]) # remove the EV's mean from each value of this EV for val in pheno_data_dict[key]: new_demeaned_evs.append(float(val) - mean_evs) pheno_data_dict[key] = new_demeaned_evs if 'categorical' in ev_selections.keys(): if key not in ev_selections['categorical']: pheno_data_dict[key] = np.array(pheno_data_dict[key]) return pheno_data_dict def get_measure_dict(param_file): import os import pandas as pd if not os.path.isfile(param_file): err = "\n\n[!] CPAC says: You've included a motion parameter in " \ "your group-level analysis model design formula, but " \ "there is no motion parameters file available.\n\n" raise Exception(err) with open(param_file,"r") as f: motion_params = pd.read_csv(f, index_col=False) measures = ['MeanFD_Power', 'MeanFD_Jenkinson', 'MeanDVARS'] measure_dict = {} for m in measures: measure_map = {} if m in motion_params.columns: part_ids = list(motion_params["Subject"]) part_ids = [str(i) for i in part_ids] scan_ids = list(motion_params["Scan"]) scan_ids = [str(i) for i in scan_ids] measure_vals = list(motion_params[m]) measure_vals = [float(i) for i in measure_vals] for part_id, scan_id, measure_val in \ zip(part_ids, scan_ids, measure_vals): measure_map[(part_id,scan_id)] = measure_val measure_dict[m] = measure_map return measure_dict def get_custom_roi_info(roi_means_dict): # check if roi_means_dict == None: err_string = "\n\n[!] CPAC says: The custom ROI means were not " \ "calculated properly during the group analysis " \ "model generation.\n\n" raise Exception(err_string) roi_num = len(roi_means_dict.values()[0]) # this will be a dictionary matching ROI regressor header labels with # the actual ROI dictionaries roi_dict_dict = {} # split the roi_means_dict from { subID: [mean1,mean2,mean3,..], ..} # to three dictionaries of { subID: mean1, .. }, { subID: mean2, .. }, # and so on for num in range(0,roi_num): label = "Custom_ROI_Mean_%d" % int(num+1) temp_roi_dict = {} for key in roi_means_dict.keys(): temp_roi_dict[key] = roi_means_dict[key][num-1] roi_dict_dict[label] = temp_roi_dict return roi_dict_dict def model_group_var_separately(grouping_var, formula, pheno_data_dict, \ ev_selections, coding_scheme): if grouping_var == None or grouping_var not in formula: print '\n\n[!] CPAC says: Model group variances separately is ' \ 'enabled, but the grouping variable set is either set to ' \ 'None, or was not included in the model as one of the ' \ 'EVs.\n' print 'Design formula: ', formula print 'Grouping variable: ', grouping_var, '\n\n' raise Exception # do this a little early for the grouping variable so that it doesn't if 'categorical' in ev_selections.keys(): for EV_name in ev_selections['categorical']: if EV_name == grouping_var: if coding_scheme == 'Treatment': formula = formula.replace(EV_name, 'C(' + EV_name + ')') elif coding_scheme == 'Sum': formula = formula.replace(EV_name, 'C(' + EV_name + \ ', Sum)') groupvar_levels = [] grouping_var_id_dict = {} idx = 0 for cat_ev_value in pheno_data_dict[grouping_var]: cat_ev_level = str(cat_ev_value).replace(str(grouping_var), "") if cat_ev_level not in groupvar_levels: groupvar_levels.append(cat_ev_level) try: grouping_var_id_dict[cat_ev_level].append(idx) except: grouping_var_id_dict[cat_ev_level] = [idx] idx += 1 split_EVs = {} for key in pheno_data_dict.keys(): if (key in formula) and (key != grouping_var): new_key_string = "" for level in groupvar_levels: groupvar_with_level = str(grouping_var) + str(level) new_key = key + "__" + groupvar_with_level if new_key_string == "": new_key_string = new_key else: new_key_string = new_key_string + " + " + new_key split_EVs[new_key] = [] if key in ev_selections["categorical"]: ev_selections["categorical"].append(new_key) for val, groupvar_val in zip(pheno_data_dict[key], \ pheno_data_dict[grouping_var]): if groupvar_with_level == groupvar_val: split_EVs[new_key].append(val) else: split_EVs[new_key].append(0) del pheno_data_dict[key] if key in ev_selections["categorical"]: ev_selections["categorical"].remove(key) formula = formula.replace(key, new_key_string) pheno_data_dict.update(split_EVs) if 'categorical' in ev_selections.keys(): for EV_name in ev_selections['categorical']: if EV_name != grouping_var: if coding_scheme == 'Treatment': formula = formula.replace(EV_name, 'C(' + EV_name + ')') elif coding_scheme == 'Sum': formula = formula.replace(EV_name, 'C(' + EV_name + \ ', Sum)') formula = formula + " - 1" return pheno_data_dict, formula, grouping_var_id_dict def check_multicollinearity(matrix): import numpy as np print "\nChecking for multicollinearity in the model.." U, s, V = np.linalg.svd(matrix) max_singular = np.max(s) min_singular = np.min(s) print "Max singular: ", max_singular print "Min singular: ", min_singular print "Rank: ", np.linalg.matrix_rank(matrix), "\n" if min_singular == 0: print '[!] CPAC warns: Detected multicollinearity in the ' \ 'computed group-level analysis model. Please double-' \ 'check your model design.\n\n' else: condition_number = float(max_singular)/float(min_singular) print "Condition number: %f\n\n" % condition_number if condition_number > 30: print '[!] CPAC warns: Detected multicollinearity in the ' \ 'computed group-level analysis model. Please double-' \ 'check your model design.\n\n' def write_mat_file(design_matrix, output_dir, model_name, \ depatsified_EV_names, current_output=None): import os import numpy as np dimx = None dimy = None if len(design_matrix.shape) == 1: dimy = 1 dimx = design_matrix.shape[0] else: dimx, dimy = design_matrix.shape ppstring = '/PPheights' for i in range(0, dimy): ppstring += '\t' + '%1.5e' %(1.0) ppstring += '\n' filename = model_name + ".mat" out_file = os.path.join(output_dir, filename) if not os.path.exists(output_dir): os.makedirs(output_dir) with open(out_file, 'wt') as f: print >>f, '/NumWaves\t%d' %dimy print >>f, '/NumPoints\t%d' %dimx print >>f, ppstring col_string = '\n' for col in depatsified_EV_names: col_string = col_string + col + '\t' print >>f, col_string, '\n' print >>f, '/Matrix' np.savetxt(f, design_matrix, fmt='%1.5e', delimiter='\t') def create_grp_file(design_matrix, grouping_var_id_dict, output_dir, \ model_name, current_output=None): import os import numpy as np dimx = None dimy = None if len(design_matrix.shape) == 1: dimy = 1 dimx = design_matrix.shape[0] else: dimx, dimy = design_matrix.shape design_matrix_ones = np.ones(dimx) if not (grouping_var_id_dict == None): i = 1 for key in sorted(grouping_var_id_dict.keys()): for index in grouping_var_id_dict[key]: design_matrix_ones[index] = i i += 1 filename = model_name + ".grp" out_file = os.path.join(output_dir, filename) if not os.path.exists(output_dir): os.makedirs(output_dir) with open(out_file, "wt") as f: print >>f, '/NumWaves\t1' print >>f, '/NumPoints\t%d\n' %dimx print >>f, '/Matrix' np.savetxt(f, design_matrix_ones, fmt='%d', delimiter='\t') def create_design_matrix(pheno_file, ev_selections, formula, \ subject_id_label, sub_list=None, \ coding_scheme="Treatment", grouping_var=None, \ new_regressor_dict=None, roi_means_dict=None, \ output_dir=None, model_name="design", \ current_output=None): import os import patsy import numpy as np if output_dir == None: output_dir = os.getcwd() # they are not listed in the participant list pheno_file_df = load_pheno_file(pheno_file) participant_list_df = load_group_participant_list(sub_list) pheno_file_rows = process_pheno_file(pheno_file_df, participant_list_df, \ subject_id_label) # get number of subjects that have the derivative for this current model # (basically, the amount of time points, which must be greater than the # number of EVs) num_subjects = len(participant_list_df) # for repeated measures if "session" in participant_list_df.columns: ev_selections["categorical"].append("session") if "series" in participant_list_df.columns: ev_selections["categorical"].append("series") # start adding additionally created EVs if new_regressor_dict: for measure in new_regressor_dict.keys(): if (measure in formula): measure_dict = new_regressor_dict[measure] for pheno_row_dict in pheno_file_rows: participant_id = pheno_row_dict[subject_id_label] if ("session" in pheno_row_dict.keys()) and \ ("series" in pheno_row_dict.keys()): session_id = pheno_row_dict["session"] series_id = pheno_row_dict["series"] participant_tuple = \ (participant_id, session_id, series_id) elif "session" in pheno_row_dict.keys(): session_id = pheno_row_dict["session"] participant_tuple = (participant_id, session_id) elif "series" in pheno_row_dict.keys(): series_id = pheno_row_dict["series"] participant_tuple = (participant_id, series_id) else: participant_tuple = (participant_id) pheno_row_dict[measure] = measure_dict[participant_tuple] ev_selections["demean"].append(measure) if "Custom_ROI_Mean" in formula: # include the means of the specified ROIs as regressors if roi_means_dict == None: err = "\n\n[!] You included 'Custom_ROI_Mean' in your model " \ "design, but there are no mean of ROI values provided." \ "\n\n" raise Exception(err) # roi_dict_dict is a dictionary of dictionaries, with each dictionary # holding all of the means for one ROI, with each entry being a mean # for a participant (the keys are the participant IDs) # ex. {participant_01: 35.15, participant_02: 50.00} # with the float values being all of the means of one of # the ROIs specified # there will be a dictionary for each ROI specified roi_dict_dict = get_custom_roi_info(roi_means_dict) add_formula_string = "" for roi_column in roi_dict_dict.keys(): roi_dict = roi_dict_dict[roi_column] for pheno_row_dict in pheno_file_rows: participant_id = pheno_row_dict[subject_id_label] if ("session" in pheno_row_dict.keys()) and \ ("series" in pheno_row_dict.keys()): session_id = pheno_row_dict["session"] series_id = pheno_row_dict["series"] participant_tuple = \ (participant_id, session_id, series_id) elif "session" in pheno_row_dict.keys(): session_id = pheno_row_dict["session"] participant_tuple = (participant_id, session_id) elif "series" in pheno_row_dict.keys(): series_id = pheno_row_dict["series"] participant_tuple = (participant_id, series_id) else: participant_tuple = (participant_id) pheno_row_dict[roi_column] = roi_dict[participant_tuple] ev_selections["demean"].append(roi_column) # create a string of all the new custom ROI regressor column names # to be inserted into the design formula, so that Patsy will # accept the phenotypic data dictionary that now has these columns if add_formula_string == "": add_formula_string = add_formula_string + roi_column else: add_formula_string = add_formula_string + " + " + roi_column # a regressor column of ROI means for each custom-specified ROI has # now been added to the model with appropriate column labels formula = formula.replace("Custom_ROI_Mean",add_formula_string) # return the data from the phenotype file processed properly for Patsy # and load it into 'pheno_data_dict' # format: dictionary, each key is the name of an EV, and its value is # a LIST of values in order of the subjects # - categorical EVs are already renamed from '0,1,..' to # 'EV0,EV1,..' with EV being the EV name # - EVs to be demeaned are already demeaned # - numerical EVs (non-categorical) are in a list which # have been converted into a NumPy array pheno_data_dict = create_pheno_dict(pheno_file_rows, ev_selections, \ subject_id_label) # handle modeling group variances separately (if enabled), then edit the # formula to be in Patsy language if grouping_var != None: pheno_data_dict, formula, grouping_var_id_dict = \ model_group_var_separately(grouping_var, \ formula, pheno_data_dict, \ ev_selections, coding_scheme) else: grouping_var_id_dict = None if 'categorical' in ev_selections.keys(): for EV_name in ev_selections['categorical']: if coding_scheme == 'Treatment': formula = formula.replace(EV_name, 'C(' + EV_name + ')') elif coding_scheme == 'Sum': formula = formula.replace(EV_name, 'C(' + EV_name + \ ', Sum)') # create the Patsy design matrix! try: dmatrix = patsy.dmatrix(formula, pheno_data_dict, NA_action='raise') except: print '\n\n[!] CPAC says: Design matrix creation wasn\'t ' \ 'successful - do the terms in your formula correctly ' \ 'correspond to the EVs listed in your phenotype file?\n' print 'Phenotype file provided: ' print pheno_file, '\n' print "Phenotypic data columns (regressors): ", pheno_data_dict.keys() print "Formula: %s\n\n" % formula raise Exception check_multicollinearity(np.array(dmatrix)) design_matrix = np.array(dmatrix, dtype=np.float16) column_names = dmatrix.design_info.column_names if len(column_names) >= num_subjects: err = "\n\n[!] CPAC says: There are more EVs than there are " \ "subjects currently included in the model for %s. There must " \ "be more subjects than EVs in the design.\n\nNumber of " \ "subjects: %d\nNumber of EVs: %d\n\nNote: An 'Intercept' " \ "column gets added to the design as an EV, so there will be " \ "one more EV than you may have specified in your design. In " \ "addition, if you specified to model group variances " \ "separately, an Intercept column will not be included, but " \ "the amount of EVs can nearly double once they are split " \ "along the grouping variable.\n\n" \ "If the number of subjects is lower than the number of " \ "subjects in your group analysis subject list, this may be " \ "because not every subject in the subject list has an output " \ "for %s in the individual-level analysis output directory.\n\n"\ % (current_output, num_subjects, len(column_names), \ current_output) raise Exception(err) # for Contrasts" list on the next page, and also to test user-made custom depatsified_EV_names = [] for column in column_names: column_string = column string_for_removal = '' for char in column_string: string_for_removal = string_for_removal + char if char == '.': column_string = column_string.replace(string_for_removal, '') string_for_removal = '' column_string = column_string.replace(']', '') depatsified_EV_names.append(column_string) write_mat_file(design_matrix, output_dir, model_name, \ depatsified_EV_names, current_output) create_grp_file(design_matrix, grouping_var_id_dict, output_dir, \ model_name, current_output) return dmatrix, depatsified_EV_names def positive(dmat, a, coding, group_sep, grouping_var): import numpy as np # contrast builder evs = dmat.design_info.column_name_indexes con = np.zeros(dmat.shape[1]) if group_sep == True: if "__" in a and grouping_var in a: ev_desc = a.split("__") for ev in evs: count = 0 for desc in ev_desc: if desc in ev: count += 1 if count == len(ev_desc): con[evs[ev]] = 1 break else: # it is a dropped term so make all other terms in that # category at -1 term = a.split('[')[0] for ev in evs: if ev.startswith(term): con[evs[ev]]= -1 elif len(a.split(grouping_var)) > 2: # this is if the current parsed contrast is the actual # grouping variable, as the Patsified name will have the # variable's name string in it twice for ev in evs: if a.split(".")[1] in ev: con[evs[ev]] = 1 break else: term = a.split('[')[0] for ev in evs: if ev.startswith(term): con[evs[ev]]= -1 else: if a in evs: con[evs[a]] = 1 else: term = a.split('[')[0] for ev in evs: if ev.startswith(term): con[evs[ev]]= -1 if coding == "Treatment": con[0] = 0 elif coding == "Sum": con[1] = 1 return con def greater_than(dmat, a, b, coding, group_sep, grouping_var): c1 = positive(dmat, a, coding, group_sep, grouping_var) c2 = positive(dmat, b, coding, group_sep, grouping_var) return c1-c2 def negative(dmat, a, coding, group_sep, grouping_var): con = 0-positive(dmat, a, coding, group_sep, grouping_var) return con def create_dummy_string(length): ppstring = "" for i in range(0, length): ppstring += '\t' + '%1.5e' %(1.0) ppstring += '\n' return ppstring def create_con_file(con_dict, col_names, file_name, current_output, out_dir): import os print "col names: " print col_names with open(os.path.join(out_dir, file_name) + ".con",'w+') as f: num = 1 for key in con_dict: f.write("/ContrastName%s\t%s\n" %(num,key)) num += 1 f.write("/NumWaves\t%d\n" %len(con_dict[key])) f.write("/NumContrasts\t%d\n" %len(con_dict)) f.write("/PPheights%s" %create_dummy_string(len(con_dict[key]))) f.write("/RequiredEffect%s" %create_dummy_string(len(con_dict[key]))) f.write("\n\n") col_string = '\n' for col in col_names: col_string = col_string + col + '\t' print >>f, col_string, '\n' f.write("/Matrix\n") for key in con_dict: for v in con_dict[key]: f.write("%1.5e\t" %v) f.write("\n") def create_fts_file(ftest_list, con_dict, model_name, current_output, out_dir): import os import numpy as np try: print "\nFound f-tests in your model, writing f-tests file " \ "(.fts)..\n" with open(os.path.join(out_dir, model_name + '.fts'), 'w') as f: print >>f, '/NumWaves\t', len(con_dict) print >>f, '/NumContrasts\t', len(ftest_list) ftst = [] for ftest_string in ftest_list: ftest_vector = [] cons_in_ftest = ftest_string.split(",") for con in con_dict.keys(): if con in cons_in_ftest: ftest_vector.append(1) else: ftest_vector.append(0) ftst.append(ftest_vector) fts_n = np.array(ftst) col_string = '\n' for con in con_dict.keys(): col_string = col_string + con + '\t' print >>f, col_string, '\n' print >>f, '/Matrix' for i in range(fts_n.shape[0]): print >>f, ' '.join(fts_n[i].astype('str')) except Exception as e: filepath = os.path.join(out_dir, "model_files", current_output, \ model_name + '.fts') errmsg = "\n\n[!] CPAC says: Could not create .fts file for " \ "FLAMEO or write it to disk.\nAttempted filepath: %s\n" \ "Error details: %s\n\n" % (filepath, e) raise Exception(errmsg) def create_con_ftst_file(con_file, model_name, current_output, output_dir, column_names, coding_scheme, group_sep): """ Create the contrasts and fts file """ import os import numpy as np with open(con_file, "r") as f: evs = f.readline() evs = evs.rstrip('\r\n').split(',') count_ftests = 0 # TODO: would have the Intercept added to it? but what if it wasn't? fTest = False print "evs: " print evs for ev in evs: if "f_test" in ev: count_ftests += 1 if count_ftests > 0: fTest = True try: data = np.genfromtxt(con_file, names=True, delimiter=',', dtype=None) except: print "Error: Could not successfully read in contrast file: ",con_file raise Exception lst = data.tolist() ftst = [] fts_columns = [] contrasts = [] contrast_names = [] length = None length = len(list(lst[0])) for tp in lst: contrast_names.append(tp[0]) con_vector = list(tp)[1:(length-count_ftests)] fts_vector = list(tp)[(length-count_ftests):length] fts_columns.append(fts_vector) contrasts.append(con_vector) num_EVs_in_con_file = len(contrasts[0]) contrasts = np.array(contrasts, dtype=np.float16) fts_columns = np.array(fts_columns) if fTest: if len(contrast_names) < 2: errmsg = "\n\n[!] CPAC says: Not enough contrasts for running " \ "f-tests.\nTip: Do you have only one contrast in your " \ "contrasts file? f-tests require more than one contrast.\n"\ "Either remove the f-tests or include more contrasts.\n\n" raise Exception(errmsg) fts_n = fts_columns.T if len(column_names) != (num_EVs_in_con_file): err_string = "\n\n[!] CPAC says: The number of EVs in your model " \ "design matrix (found in the %s.mat file) does not " \ "match the number of EVs (columns) in your custom " \ "contrasts matrix CSV file.\n\nCustom contrasts matrix "\ "file: %s\n\nNumber of EVs in design matrix: %d\n" \ "Number of EVs in contrasts file: %d\n\nThe column " \ "labels in the design matrix should match those in " \ "your contrasts .CSV file.\nColumn labels in design " \ "matrix:\n%s" % (model_name, con_file, \ len(column_names), num_EVs_in_con_file, \ str(column_names)) raise Exception(err_string) for design_mat_col, con_csv_col in zip(column_names, evs[1:]): if con_csv_col not in design_mat_col: errmsg = "\n\n[!] CPAC says: The names of the EVs in your " \ "custom contrasts .csv file do not match the names or " \ "order of the EVs in the design matrix. Please make " \ "sure these are consistent.\nDesign matrix EV columns: "\ "%s\nYour contrasts matrix columns: %s\n\n" \ % (column_names, evs[1:]) raise Exception(errmsg) out_dir = os.path.join(output_dir, model_name + '.con') with open(out_dir,"wt") as f: idx = 1 pp_str = '/PPheights' re_str = '/RequiredEffect' for name in contrast_names: print >>f, '/ContrastName%d' %idx, '\t', name pp_str += '\t%1.5e' %(1) re_str += '\t%1.5e' %(1) idx += 1 print >>f, '/NumWaves\t', (contrasts.shape)[1] print >>f, '/NumContrasts\t', (contrasts.shape)[0] print >>f, pp_str print >>f, re_str + '\n' col_string = '\n' for ev in evs: col_string = col_string + ev + '\t' print >>f, col_string, '\n' print >>f, '/Matrix' np.savetxt(f, contrasts, fmt='%1.5e', delimiter='\t') if fTest: print "\nFound f-tests in your model, writing f-tests file (.fts)..\n" ftest_out_dir = os.path.join(output_dir, model_name + '.fts') with open(ftest_out_dir,"wt") as f: print >>f, '/NumWaves\t', (contrasts.shape)[0] print >>f, '/NumContrasts\t', count_ftests col_string = '\n' for con in contrast_names: col_string = col_string + con + '\t' print >>f, col_string, '\n' print >>f, '/Matrix' for i in range(fts_n.shape[0]): print >>f, ' '.join(fts_n[i].astype('str')) def process_contrast(parsed_contrast, operator, ev_selections, group_sep, \ grouping_var, coding_scheme): parsed_EVs_in_contrast = [] EVs_in_contrast = parsed_contrast.split(operator) if '' in EVs_in_contrast: EVs_in_contrast.remove('') for EV in EVs_in_contrast: skip = 0 if 'categorical' in ev_selections.keys(): for cat_EV in ev_selections['categorical']: # that is the grouping variable (which is now present in # other EV names) to confound this operation if group_sep == True: gpvar = grouping_var else: gpvar = "..." if (cat_EV in EV) and not (gpvar in EV and \ "__" in EV): # handle interactions if ":" in EV: temp_split_EV = EV.split(":") for interaction_EV in temp_split_EV: if cat_EV in interaction_EV: current_EV = interaction_EV else: current_EV = EV if coding_scheme == 'Treatment': cat_EV_contrast = EV.replace(EV, 'C(' + cat_EV + \ ')[T.' + current_EV+\ ']') elif coding_scheme == 'Sum': cat_EV_contrast = EV.replace(EV, 'C(' + cat_EV + \ ', Sum)[S.' + \ current_EV + ']') parsed_EVs_in_contrast.append(cat_EV_contrast) skip = 1 if skip == 0: parsed_EVs_in_contrast.append(EV) # handle interactions if ":" in EV and len(parsed_EVs_in_contrast) == 2: parsed_EVs_in_contrast = [parsed_EVs_in_contrast[0] + ":" + \ parsed_EVs_in_contrast[1]] if ":" in EV and len(parsed_EVs_in_contrast) == 3: parsed_EVs_in_contrast = [parsed_EVs_in_contrast[0], \ parsed_EVs_in_contrast[1] + ":" + \ parsed_EVs_in_contrast[2]] return parsed_EVs_in_contrast def run(group_config, current_output, param_file=None, \ derivative_means_dict=None, roi_means_dict=None, \ model_out_dir=None, CPAC_run=True): import os import csv import numpy as np # open the GROUP ANALYSIS FSL .YML CONFIG FILE, not the main pipeline # config .yml file! if CPAC_run: c = group_config else: try: c = Configuration(yaml.load(open(os.path.realpath(group_config), \ 'r'))) except: raise Exception("Error in reading %s configuration file" \ % group_config) # pull in the gpa settings! ph = c.pheno_file sublist = c.subject_list ev_selections = c.ev_selections subject_id_label = c.subject_id_label formula = c.design_formula coding_scheme = c.coding_scheme[0] group_sep = c.group_sep grouping_var = c.grouping_var contrasts = c.contrasts f_tests = c.f_tests custom_contrasts = c.custom_contrasts model_name = c.model_name output_dir = c.output_dir # make sure the group analysis output directory exists try: if not os.path.exists(output_dir): os.makedirs(output_dir) except: print '\n\n[!] CPAC says: Could not successfully create the group ' \ 'analysis output directory:\n', output_dir, '\n\nMake sure ' \ 'you have write access in this file structure.\n\n\n' raise Exception measure_dict = {} # extract motion measures from CPAC-generated power params file if param_file != None: measure_dict = get_measure_dict(param_file) # combine the motion measures dictionary with the measure_mean # dictionary (if it exists) if derivative_means_dict: measure_dict["Measure_Mean"] = derivative_means_dict # create the .mat and .grp files for FLAME design_matrix, regressor_names = create_design_matrix(ph, sublist, \ ev_selections, formula, \ subject_id_label, coding_scheme, \ grouping_var, measure_dict, \ roi_means_dict, model_out_dir, \ model_name, current_output) # Create contrasts_dict dictionary for the .con file generation later contrasts_list = contrasts contrasts_dict = {} for contrast in contrasts_list: # each 'contrast' is a string the user input of the desired contrast # remove all spaces parsed_contrast = contrast.replace(' ', '') EVs_in_contrast = [] parsed_EVs_in_contrast = [] if '>' in parsed_contrast: parsed_EVs_in_contrast = \ process_contrast(parsed_contrast, '>', ev_selections, \ group_sep, grouping_var, coding_scheme) contrasts_dict[parsed_contrast] = \ greater_than(design_matrix, parsed_EVs_in_contrast[0], \ parsed_EVs_in_contrast[1], coding_scheme, \ group_sep, grouping_var) elif '<' in parsed_contrast: parsed_EVs_in_contrast = \ process_contrast(parsed_contrast, '<', ev_selections, \ group_sep, grouping_var, coding_scheme) contrasts_dict[parsed_contrast] = \ greater_than(design_matrix, parsed_EVs_in_contrast[1], \ parsed_EVs_in_contrast[0], coding_scheme, \ group_sep, grouping_var) else: contrast_string = parsed_contrast.replace('+',',+,') contrast_string = contrast_string.replace('-',',-,') contrast_items = contrast_string.split(',') if '' in contrast_items: contrast_items.remove('') if '+' in contrast_items and len(contrast_items) == 2: parsed_EVs_in_contrast = \ process_contrast(parsed_contrast, '+', ev_selections, \ group_sep, grouping_var, coding_scheme) contrasts_dict[parsed_contrast] = \ positive(design_matrix, parsed_EVs_in_contrast[0], \ coding_scheme, group_sep, grouping_var) elif '-' in contrast_items and len(contrast_items) == 2: parsed_EVs_in_contrast = \ process_contrast(parsed_contrast, '-', ev_selections, \ group_sep, grouping_var, coding_scheme) contrasts_dict[parsed_contrast] = \ negative(design_matrix, parsed_EVs_in_contrast[0], \ coding_scheme, group_sep, grouping_var) if len(contrast_items) > 2: idx = 0 for item in contrast_items: # they need to be put back into Patsy formatted header # titles because the dmatrix gets passed into the function # that writes out the contrast matrix if 'categorical' in ev_selections.keys(): for cat_EV in ev_selections['categorical']: if cat_EV in item: if coding_scheme == 'Treatment': item = item.replace(item, \ 'C(' + cat_EV + ')[T.' + item + ']') elif coding_scheme == 'Sum': item = item.replace(item, \ 'C(' + cat_EV + ', Sum)[S.' + item + ']') if idx == 0: if item != '+' and item != '-': contrast_vector = positive(dmatrix, item) if parsed_contrast not in contrasts_dict.keys(): contrasts_dict[parsed_contrast] = contrast_vector else: contrasts_dict[parsed_contrast] += contrast_vector elif idx != 0: if item != '+' and item != '-': if contrast_items[idx-1] == '+': contrast_vector = positive(dmatrix, item, \ coding_scheme, group_sep,\ grouping_var) if parsed_contrast not in contrasts_dict.keys(): contrasts_dict[parsed_contrast] = contrast_vector else: contrasts_dict[parsed_contrast] += contrast_vector if contrast_items[idx-1] == '-': contrast_vector = negative(dmatrix, item, \ coding_scheme, group_sep,\ grouping_var) if parsed_contrast not in contrasts_dict.keys(): contrasts_dict[parsed_contrast] = contrast_vector else: contrasts_dict[parsed_contrast] += contrast_vector idx += 1 # finally write out the .con file and .fts file (if f-tests) if (custom_contrasts == None) or (custom_contrasts == '') or \ ("None" in custom_contrasts): print "Writing contrasts file (.con) based on contrasts provided " \ "using the group analysis model builder's contrasts editor.." create_con_file(contrasts_dict, regressor_names, model_name, \ current_output, model_out_dir) if f_tests: create_fts_file(f_tests, contrasts_dict, model_name, \ current_output, model_out_dir) else: print "\nWriting contrasts file (.con) based on contrasts provided " \ "with a custom contrasts matrix CSV file..\n" create_con_ftst_file(custom_contrasts, model_name, current_output, \ model_out_dir, regressor_names, \ coding_scheme, group_sep)

false

true

f700578bbd38004bdff93a11c4a048931a765100

977

py

Python

office365/runtime/compat.py

rikeshtailor/Office365-REST-Python-Client

ca7bfa1b22212137bb4e984c0457632163e89a43

[ "MIT" ]

null

office365/runtime/compat.py

rikeshtailor/Office365-REST-Python-Client

ca7bfa1b22212137bb4e984c0457632163e89a43

[ "MIT" ]

null

office365/runtime/compat.py

rikeshtailor/Office365-REST-Python-Client

ca7bfa1b22212137bb4e984c0457632163e89a43

[ "MIT" ]

null

import sys # ------- # Pythons # ------- # Syntax sugar. _ver = sys.version_info #: Python 2.x? is_py2 = (_ver[0] == 2) #: Python 3.x? is_py3 = (_ver[0] == 3) if is_py2: from urlparse import urlparse from urllib import quote from urlparse import urljoin import pytz as timezone from email import message_from_string as message_from_bytes_or_string from __builtin__ import xrange as range_or_xrange elif is_py3: from urllib.parse import urlparse from urllib.parse import quote from urllib.parse import urljoin from datetime import timezone from email import message_from_bytes as message_from_bytes_or_string from builtins import range as range_or_xrange def message_as_bytes_or_string(message): if is_py2: return message.as_string() else: return message.as_bytes() def is_string_type(value): if is_py2: return isinstance(value, basestring) else: return type(value) is str

22.204545

73

0.708291

import sys _ver = sys.version_info is_py2 = (_ver[0] == 2) is_py3 = (_ver[0] == 3) if is_py2: from urlparse import urlparse from urllib import quote from urlparse import urljoin import pytz as timezone from email import message_from_string as message_from_bytes_or_string from __builtin__ import xrange as range_or_xrange elif is_py3: from urllib.parse import urlparse from urllib.parse import quote from urllib.parse import urljoin from datetime import timezone from email import message_from_bytes as message_from_bytes_or_string from builtins import range as range_or_xrange def message_as_bytes_or_string(message): if is_py2: return message.as_string() else: return message.as_bytes() def is_string_type(value): if is_py2: return isinstance(value, basestring) else: return type(value) is str

true

f70058779cdd3ba0324c41a9eaa37779092a6ad0

715

py

Python

motiv/sync/mixin.py

SaadTalaat/motiv

5d41fd100ece50b28b137447d33ed00a4050b3da

[ "Apache-2.0" ]

null

motiv/sync/mixin.py

SaadTalaat/motiv

5d41fd100ece50b28b137447d33ed00a4050b3da

[ "Apache-2.0" ]

9

2019-03-29T15:01:22.000Z

2020-09-18T12:52:45.000Z

motiv/sync/mixin.py

SaadTalaat/motiv

5d41fd100ece50b28b137447d33ed00a4050b3da

[ "Apache-2.0" ]

null

"""motiv synchronization primitives Module: Using a uniform interface to define synchronization primitives helps us use multiple execution frameworks without changing any of the code written. for example, multiprocessing vs threading. """ import abc class SystemEvent(abc.ABC): """Event abstract class""" @abc.abstractmethod def is_set(self): """checks if the event is set.""" @abc.abstractmethod def set(self): """sets the event""" @abc.abstractmethod def clear(self): """clears the event""" @abc.abstractmethod def wait(self, *args, **kwargs): """waits till event is set""" __all__ = [ 'SystemEvent', ]

19.861111

57

0.636364

import abc class SystemEvent(abc.ABC): @abc.abstractmethod def is_set(self): @abc.abstractmethod def set(self): @abc.abstractmethod def clear(self): @abc.abstractmethod def wait(self, *args, **kwargs): __all__ = [ 'SystemEvent', ]

true

f700596493274b2208efa335ed2a2ced29634349

1,595

py

Python

profiles_api/models.py

nafis-badar/profiles-rest-api

9188a8dbdad3b7fdf462d38d664f1756b788e2c3

[ "MIT" ]

null

profiles_api/models.py

nafis-badar/profiles-rest-api

9188a8dbdad3b7fdf462d38d664f1756b788e2c3

[ "MIT" ]

null

profiles_api/models.py

nafis-badar/profiles-rest-api

9188a8dbdad3b7fdf462d38d664f1756b788e2c3

[ "MIT" ]

null

from django.db import models from django.contrib.auth.models import AbstractBaseUser from django.contrib.auth.models import PermissionsMixin,BaseUserManager # Create your models here. class UserProfileManager(BaseUserManager): """Manager for user profiles """ def create_user(self,email,name,password=None): """ Create a New user profile""" if not email: raise ValueError("User must gave an email address") email=self.normalize_email(email) user=self.model(email=email,name=name) user.set_password(password) user.save(using=self._db) return user def create_superuser(self,email,name,password): """Create and save a new superuser with given details """ user=self._create_user(email,aname,password) user.is_superuser=True user.is_staff=True user.save(using=self._db) return user class UserProfile(AbstractBaseUser,PermissionsMixin): """ Database model for users in the system """ email=models.EmailField(max_length=100,unique=True) name=models.CharField(max_length=255) is_active=models.BooleanField(default=True) is_staff=models.BooleanField(default=False) objects=UserProfileManager() USERNAME_FIELD='email' REQUIRED_FIELDS=['name'] def get_full_name(self): """ Retrieve full name of user """ return self.name def get_short_name(self): """ Retrieve short name of User """ return self.name def __str__(self): """Return String representation """ return self.email

29.537037

71

0.684639

from django.db import models from django.contrib.auth.models import AbstractBaseUser from django.contrib.auth.models import PermissionsMixin,BaseUserManager class UserProfileManager(BaseUserManager): def create_user(self,email,name,password=None): if not email: raise ValueError("User must gave an email address") email=self.normalize_email(email) user=self.model(email=email,name=name) user.set_password(password) user.save(using=self._db) return user def create_superuser(self,email,name,password): user=self._create_user(email,aname,password) user.is_superuser=True user.is_staff=True user.save(using=self._db) return user class UserProfile(AbstractBaseUser,PermissionsMixin): email=models.EmailField(max_length=100,unique=True) name=models.CharField(max_length=255) is_active=models.BooleanField(default=True) is_staff=models.BooleanField(default=False) objects=UserProfileManager() USERNAME_FIELD='email' REQUIRED_FIELDS=['name'] def get_full_name(self): return self.name def get_short_name(self): return self.name def __str__(self): return self.email

true

f7005979eaa3f5c9c2b34b11e8e9c3da9e1602fc

2,997

bzl

Python

tensorflow_decision_forests/keras/wrapper/wrapper.bzl

xsSilva/decision-forests

5eb3524f4d86d1fa177dae7caba5c6e4516d7030

[ "Apache-2.0" ]

null

tensorflow_decision_forests/keras/wrapper/wrapper.bzl

xsSilva/decision-forests

5eb3524f4d86d1fa177dae7caba5c6e4516d7030

[ "Apache-2.0" ]

null

tensorflow_decision_forests/keras/wrapper/wrapper.bzl

xsSilva/decision-forests

5eb3524f4d86d1fa177dae7caba5c6e4516d7030

[ "Apache-2.0" ]

null

"""Rule generation utilities.""" load("@org_tensorflow//tensorflow:tensorflow.bzl", "if_not_windows", "tf_binary_additional_srcs", "tf_cc_binary", "tf_copts") load("//tensorflow_decision_forests/tensorflow:utils.bzl", "rpath_linkopts_to_tensorflow") def py_wrap_yggdrasil_learners( name = None, learner_deps = []): """Creates Keras wrappers around Yggdrasil Decision Forest (YDF) learners. Creates a py_library called "{name}" and containing the file "{name}.py". This library introduces a TensorFlow Decision Forests (TFDF) Keras class wrapping for each YDF learner defined in "learner_deps". The constructor of these classes contains a argument for the learner generic hyper-parameter. For example, if "learner_deps" contains a c++ dependency that register a learner with a key equal to "RANDOM_FOREST", the wrapper will create a python class called "RandomForestModel" deriving the base TFDF model class. Args: name: Name of the rule. learner_deps: List of dependencies linking Yggdrasil Decision Forest learners. """ # Absolute path to the wrapper generator directory. wrapper_package = "//tensorflow_decision_forests/keras/wrapper" # Filename of the wrapper generator source code in the user package. local_cc_main = name + "_wrapper_main.cc" # Target name of the wrapper generator binary. wrapper_name = name + "_wrapper_main" # Target name of the command running the wrapper generator. run_wrapper_name = name + "_run_wrapper" # Copy the wrapper main source code to the user package. native.genrule( name = name + "_copy_cc_main", outs = [local_cc_main], srcs = [wrapper_package + ":wrapper_main.cc"], cmd = "cp $< $@", ) # Compiles the wrapper binary. tf_cc_binary( name = wrapper_name, copts = tf_copts(), linkopts = if_not_windows(["-lm", "-Wl,-ldl"]) + rpath_linkopts_to_tensorflow(wrapper_name), srcs = [":" + local_cc_main], deps = [ wrapper_package + ":wrapper", ] + learner_deps, linkstatic = 1, ) # Runs the wrapper binary and generate the wrapper .py source code. native.genrule( name = run_wrapper_name, srcs = [], outs = [name + ".py"], cmd = "$(location " + wrapper_name + ") > \"$@\"", tools = [":" + wrapper_name] + tf_binary_additional_srcs(), ) # Python library around the generated .py source code. native.py_library( name = name, srcs = [name + ".py"], srcs_version = "PY3", deps = [ "//tensorflow_decision_forests/keras:core", "@org_tensorflow//tensorflow/python", "@ydf//yggdrasil_decision_forests/model:abstract_model_py_proto", "@ydf//yggdrasil_decision_forests/learner:abstract_learner_py_proto", ], data = [":" + run_wrapper_name, ":" + wrapper_name], )

37.4625

125

0.651652

load("@org_tensorflow//tensorflow:tensorflow.bzl", "if_not_windows", "tf_binary_additional_srcs", "tf_cc_binary", "tf_copts") load("//tensorflow_decision_forests/tensorflow:utils.bzl", "rpath_linkopts_to_tensorflow") def py_wrap_yggdrasil_learners( name = None, learner_deps = []): wrapper_package = "//tensorflow_decision_forests/keras/wrapper" local_cc_main = name + "_wrapper_main.cc" wrapper_name = name + "_wrapper_main" run_wrapper_name = name + "_run_wrapper" native.genrule( name = name + "_copy_cc_main", outs = [local_cc_main], srcs = [wrapper_package + ":wrapper_main.cc"], cmd = "cp $< $@", ) tf_cc_binary( name = wrapper_name, copts = tf_copts(), linkopts = if_not_windows(["-lm", "-Wl,-ldl"]) + rpath_linkopts_to_tensorflow(wrapper_name), srcs = [":" + local_cc_main], deps = [ wrapper_package + ":wrapper", ] + learner_deps, linkstatic = 1, ) native.genrule( name = run_wrapper_name, srcs = [], outs = [name + ".py"], cmd = "$(location " + wrapper_name + ") > \"$@\"", tools = [":" + wrapper_name] + tf_binary_additional_srcs(), ) native.py_library( name = name, srcs = [name + ".py"], srcs_version = "PY3", deps = [ "//tensorflow_decision_forests/keras:core", "@org_tensorflow//tensorflow/python", "@ydf//yggdrasil_decision_forests/model:abstract_model_py_proto", "@ydf//yggdrasil_decision_forests/learner:abstract_learner_py_proto", ], data = [":" + run_wrapper_name, ":" + wrapper_name], )

true

f7005b28d042d57735c533e59720388a5a80e44f

4,801

py

Python

tools/poor-mans-video-editor.py

PansoK/slp

e2c478b00f8f054b24eebb257e18a57451471c79

[ "MIT" ]

null

tools/poor-mans-video-editor.py

PansoK/slp

e2c478b00f8f054b24eebb257e18a57451471c79

[ "MIT" ]

null

tools/poor-mans-video-editor.py

PansoK/slp

e2c478b00f8f054b24eebb257e18a57451471c79

[ "MIT" ]

null

""" Input: tsv file in the form Input Video filename | topic | subtopic | title greek | title english | start time | end time | delete segments input.mp4 | 1 | 1 | έξοδος | output | 00:10:05 | 00:30:10 | 00:11:15-00:12:30,00:20:35-00:22:10 """ import os import subprocess import sys import yaml def run_cmd(command: str): """run_cmd Run given shell command Args: command (str): Shell command to run Returns: (int, str): Status code, stdout of shell command Examples: >>> run_cmd("ls /") (0, 'bin\nboot\ndev\netc\nhome\ninit\nlib\nlib32\nlib64\nlibx32\nlost+found\nmedia\nmnt\nopt\nproc\nroot\nrun\nsbin\nsnap\nsrv\nsys\ntmp\nusr\nvar\n') """ command = f'{os.getenv("SHELL")} -c "{command}"' pipe = subprocess.Popen( command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT ) stdout = "" if pipe.stdout is not None: stdout = "".join( [line.decode("utf-8") for line in iter(pipe.stdout.readline, b"")] ) pipe.stdout.close() returncode = pipe.wait() print(stdout) return returncode, stdout def out_video(segment, greek=True): title_idx = 3 if greek else 4 title, topic, subtopic = segment[title_idx], segment[1], segment[2] name = f"{title}_{topic}-{subtopic}.mp4" return name def input_video(segment): return segment[0] def manage_timestamps(segment): try: st, et = segment[5], segment[6] except: st = segment[5] return [st] try: delete_timestamps = segment[7] except: return [st, et] if not delete_timestamps: return [st, et] else: return ( [st] + [ t for s in delete_timestamps.split(",") for t in (s.split("-")[0], s.split("-")[1]) ] + [et] ) def to_cut_fmt(timestamp): out = "" labels = ["h", "m", "s"] lb_idx = 0 for c in timestamp: if c == ":": out += labels[lb_idx] lb_idx += 1 else: out += c return out def to_cut_yaml(inmp4, outmp4, ymlname, timestamps): def pairwise(iterable): "s -> (s0, s1), (s2, s3), (s4, s5), ..." a = iter(iterable) return list(zip(a, a)) timestamps = [to_cut_fmt(t) for t in timestamps] timeframe = [] if len(timestamps) == 1: timeframe = [{"from": "start", "to": timestamps[0]}] else: for s, e in pairwise(["start"] + timestamps + ["end"]): timeframe += [{"from": s, "to": e}] out = { "input": inmp4, "output": outmp4, "cut_method": "delete", "timeframe": timeframe, } with open(ymlname, "w") as fd: yaml.dump(out, fd, default_flow_style=False, sort_keys=False) def format_timestamp_args(timestamps): if len(timestamps) == 1: return [f"-ss {timestamps[0]} "] def pairwise(iterable): "s -> (s0, s1), (s2, s3), (s4, s5), ..." a = iter(iterable) return list(zip(a, a)) cmds = [f"-ss {s} -to {e}" for s, e in pairwise(timestamps)] return cmds def ffmpeg(inp, out, timestamps_args): if len(timestamps_args) == 1: run_cmd(f"ffmpeg -y -i '{inp}' " + timestamps_args[0] + f" -c:v h265_nvenc -crf 24 -preset fast -c:a copy '{out}'") return mp4s = [] for i, arg in enumerate(timestamps_args): mp4s.append(f"{i}.mp4") cmd = f"ffmpeg -i '{inp}' " + arg + f" -c:v h265_nvenc -crf 24 -preset fast -c:a copy '{i}.mp4'" print(cmd) run_cmd(cmd) tmp = ".tmp_files.txt" with open(tmp, "w") as fd: for f in mp4s: fd.write(f"file '{f}'\n") run_cmd(f"ffmpeg -y -f concat -i .tmp_files.txt '{out}'") run_cmd(f"rm {tmp} " + " ".join(mp4s)) def read_split_tsv(timestamp_file): with open(timestamp_file) as f: segments = [ln.strip().split("\t") for ln in f] return segments def main(): timestamp_file = sys.argv[1] segments = read_split_tsv(timestamp_file) for segment in segments: inmp4 = input_video(segment) outmp4 = "out/" + out_video(segment, greek=True) timestamps = manage_timestamps(segment) timestamp_args = format_timestamp_args(timestamps) ffmpeg(inmp4, outmp4, timestamp_args) def main1(): timestamp_file = sys.argv[1] segments = read_split_tsv(timestamp_file) for i, segment in enumerate(segments): inmp4 = input_video(segment) outmp4 = out_video(segment, greek=True) timestamps = manage_timestamps(segment) to_cut_yaml(inmp4, outmp4, f"{i}.yml", timestamps) if __name__ == "__main__": main()

25.005208

158

0.565715

import os import subprocess import sys import yaml def run_cmd(command: str): command = f'{os.getenv("SHELL")} -c "{command}"' pipe = subprocess.Popen( command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT ) stdout = "" if pipe.stdout is not None: stdout = "".join( [line.decode("utf-8") for line in iter(pipe.stdout.readline, b"")] ) pipe.stdout.close() returncode = pipe.wait() print(stdout) return returncode, stdout def out_video(segment, greek=True): title_idx = 3 if greek else 4 title, topic, subtopic = segment[title_idx], segment[1], segment[2] name = f"{title}_{topic}-{subtopic}.mp4" return name def input_video(segment): return segment[0] def manage_timestamps(segment): try: st, et = segment[5], segment[6] except: st = segment[5] return [st] try: delete_timestamps = segment[7] except: return [st, et] if not delete_timestamps: return [st, et] else: return ( [st] + [ t for s in delete_timestamps.split(",") for t in (s.split("-")[0], s.split("-")[1]) ] + [et] ) def to_cut_fmt(timestamp): out = "" labels = ["h", "m", "s"] lb_idx = 0 for c in timestamp: if c == ":": out += labels[lb_idx] lb_idx += 1 else: out += c return out def to_cut_yaml(inmp4, outmp4, ymlname, timestamps): def pairwise(iterable): a = iter(iterable) return list(zip(a, a)) timestamps = [to_cut_fmt(t) for t in timestamps] timeframe = [] if len(timestamps) == 1: timeframe = [{"from": "start", "to": timestamps[0]}] else: for s, e in pairwise(["start"] + timestamps + ["end"]): timeframe += [{"from": s, "to": e}] out = { "input": inmp4, "output": outmp4, "cut_method": "delete", "timeframe": timeframe, } with open(ymlname, "w") as fd: yaml.dump(out, fd, default_flow_style=False, sort_keys=False) def format_timestamp_args(timestamps): if len(timestamps) == 1: return [f"-ss {timestamps[0]} "] def pairwise(iterable): a = iter(iterable) return list(zip(a, a)) cmds = [f"-ss {s} -to {e}" for s, e in pairwise(timestamps)] return cmds def ffmpeg(inp, out, timestamps_args): if len(timestamps_args) == 1: run_cmd(f"ffmpeg -y -i '{inp}' " + timestamps_args[0] + f" -c:v h265_nvenc -crf 24 -preset fast -c:a copy '{out}'") return mp4s = [] for i, arg in enumerate(timestamps_args): mp4s.append(f"{i}.mp4") cmd = f"ffmpeg -i '{inp}' " + arg + f" -c:v h265_nvenc -crf 24 -preset fast -c:a copy '{i}.mp4'" print(cmd) run_cmd(cmd) tmp = ".tmp_files.txt" with open(tmp, "w") as fd: for f in mp4s: fd.write(f"file '{f}'\n") run_cmd(f"ffmpeg -y -f concat -i .tmp_files.txt '{out}'") run_cmd(f"rm {tmp} " + " ".join(mp4s)) def read_split_tsv(timestamp_file): with open(timestamp_file) as f: segments = [ln.strip().split("\t") for ln in f] return segments def main(): timestamp_file = sys.argv[1] segments = read_split_tsv(timestamp_file) for segment in segments: inmp4 = input_video(segment) outmp4 = "out/" + out_video(segment, greek=True) timestamps = manage_timestamps(segment) timestamp_args = format_timestamp_args(timestamps) ffmpeg(inmp4, outmp4, timestamp_args) def main1(): timestamp_file = sys.argv[1] segments = read_split_tsv(timestamp_file) for i, segment in enumerate(segments): inmp4 = input_video(segment) outmp4 = out_video(segment, greek=True) timestamps = manage_timestamps(segment) to_cut_yaml(inmp4, outmp4, f"{i}.yml", timestamps) if __name__ == "__main__": main()

true

f7005b4ebd505da9c9784f4e6d7f8617d47934f7

590

py

Python

pyqldb/__init__.py

awslabs/amazon-qldb-driver-python

05ed5463189a7126983052fe0e3bc3486b9136ff

[ "Apache-2.0" ]

39

2019-10-30T09:01:54.000Z

2022-02-06T18:30:27.000Z

pyqldb/__init__.py

awslabs/amazon-qldb-driver-python

05ed5463189a7126983052fe0e3bc3486b9136ff

[ "Apache-2.0" ]

30

2020-03-05T23:52:57.000Z

2022-03-24T16:04:06.000Z

pyqldb/__init__.py

awslabs/amazon-qldb-driver-python

05ed5463189a7126983052fe0e3bc3486b9136ff

[ "Apache-2.0" ]

28

2019-12-09T17:21:59.000Z

2022-01-25T11:57:51.000Z

# Copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file except in compliance with # the License. A copy of the License is located at # # http://www.apache.org/licenses/LICENSE-2.0 # # or in the "license" file accompanying this file. This file is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR # CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions # and limitations under the License. __version__ = '3.2.2'

45.384615

118

0.759322

__version__ = '3.2.2'

true

f7005b6cc167114b14a57d483e597b70ad7731ba

5,077

py

Python

init.py

shackspace/one_button

a8b23ffdd0bd39497d465c6d7af2af4464063a48

[ "WTFPL" ]

1

2015-04-03T21:19:30.000Z

init.py

shackspace/one_button

a8b23ffdd0bd39497d465c6d7af2af4464063a48

[ "WTFPL" ]

null

init.py

shackspace/one_button

a8b23ffdd0bd39497d465c6d7af2af4464063a48

[ "WTFPL" ]

null

#!/usr/bin/env python from threading import Timer,Thread import RPIO from RPIO import PWM import paramiko import json import sys from time import time, sleep from relaxxapi.relaxxapi import relaxx r = None sftp_base_path = "/home/shack/music" button = 4 loud1 = 21 loud2 = 22 state = 0 def init_state(): state = 0 RPIO.setup(loud1, RPIO.OUT) RPIO.setup(loud2, RPIO.OUT) t1_2 = 1 timer=None t2_4 = 1 t4_5 = 3 def time3_trans(): global state if state is 4: state = 5 stop_sirene1() stop_sirene2() disable_all_timers() delete_current_music() state = 0 else: print("State is not 4, will do nothing") def time2_trans(): global state global timer if state is 2: state = 4 start_sirene2() timer= Timer(t4_5,time3_trans).start() else: print("State is not 2, will do nothing") def time1_trans(): global state global timer if state is 1: state = 2 start_sirene1() timer=Timer(t2_4,time2_trans).start() else: print("State is not 1, will do nothing") def btn_trans(a,edge): global state global timer print("Button: %s , edge: %s, state: %d" % (str(a), str(edge),state)) if edge and state is 0: state = 1 timer=Timer(t1_2,time1_trans).start() # stopped pressing the button but the timeout is not over elif not edge and (state is 1 or state is 4 or state is 2): state = 0 disable_all_timers() stop_sirene1() stop_sirene2() try: play_next() except: tell_gobbelz("Cannot play next song. Sorry:(") tell_gobbelz("Bailing out") sys.exit(1) elif not edge and state is 5: print("button released while removing music, all fine") else: print("this should never happen") def disable_all_timers(): print("disabling all the timers") global timer try: timer.cancel() print("timer canceled") except: pass def start_sirene1(): print("start Sirene 1") RPIO.output(loud1, True) def start_sirene2(): print("starting Sirene 2") RPIO.output(loud2, True) def stop_sirene1(): print("stopping Sirene 1") RPIO.output(loud1, False) def stop_sirene2(): print("stopping Sirene 2") RPIO.output(loud2, False) def play_radio(): #TODO play radio if r.get_current().get("file", "") == "http://ice.somafm.com/groovesalad": print("will not skip own sender") return print("playing radio") tell_gobbelz("Starting Radio Stream") r.add_song("http://ice.somafm.com/groovesalad") r.play_last() def play_next(): print ("playing next song") try: #sanity if is_last_song(): raise Exception("Last song in playlist") r.next_song() except: print("no next song, starting radio") play_radio() def is_last_song(): return r.get_current()["Pos"] == r.get_last()["Pos"] def delete_current_music(): print("delete current music") current = r.get_current() if not current: print("Nothing is running, bailing out") return delete_remote_file(current) play_next() def delete_remote_file(current): try: sftp_delete_remote_file(current["file"]) say_song_killed(current.get("Title", "Unbekannter Title"), current.get("Artist", "Unbekannter Kuenstler")) except Exception as e: print("Cannot delete remote file! ( %s ) " %str(e)) def sftp_delete_remote_file(f): host = "mpd.shack" port = 22 transport = paramiko.Transport((host, port)) username = 'shack' passwd = 'shackit' transport.connect(username=username, password=passwd) sftp = paramiko.SFTPClient.from_transport(transport) #print(sftp.stat('%s/%s'%(base_path,f))) print(sftp.unlink('%s/%s' % (sftp_base_path, f))) sftp.close() transport.close() def say_song_killed(name, author): tell_gobbelz('%s von %s wurde vernichtet!' % (name, author) ) def tell_gobbelz(text): import requests headers = {'Content-type': 'application/json', 'Accept': 'text/plain'} data = {'text': text} # curl -i -H "content-type: application/json" # -X POST -d "{\"text\" : \"Hallo shackspace\"}" kiosk.shack:8080/say/ requests.post("http://kiosk.shack:8080/say/", data=json.dumps(data), headers=headers) if __name__ == "__main__": from time import sleep init_state() print("initializing relaxxapi") try: r = relaxx(relaxxurl="http://lounge.mpd.shack/") except: tell_gobbelz("EM PE DE unreachable!") tell_gobbelz("Bailing out") sys.exit(1) print("adding interrupt") RPIO.add_interrupt_callback(button,callback=btn_trans,pull_up_down=RPIO.PUD_DOWN) #,debounce_timeout_ms=1 print ("Start Interrupt handler") RPIO.wait_for_interrupts() #Thread(target=start_hal,args=(hal_speed,)).start()

24.887255

109

0.621233

from threading import Timer,Thread import RPIO from RPIO import PWM import paramiko import json import sys from time import time, sleep from relaxxapi.relaxxapi import relaxx r = None sftp_base_path = "/home/shack/music" button = 4 loud1 = 21 loud2 = 22 state = 0 def init_state(): state = 0 RPIO.setup(loud1, RPIO.OUT) RPIO.setup(loud2, RPIO.OUT) t1_2 = 1 timer=None t2_4 = 1 t4_5 = 3 def time3_trans(): global state if state is 4: state = 5 stop_sirene1() stop_sirene2() disable_all_timers() delete_current_music() state = 0 else: print("State is not 4, will do nothing") def time2_trans(): global state global timer if state is 2: state = 4 start_sirene2() timer= Timer(t4_5,time3_trans).start() else: print("State is not 2, will do nothing") def time1_trans(): global state global timer if state is 1: state = 2 start_sirene1() timer=Timer(t2_4,time2_trans).start() else: print("State is not 1, will do nothing") def btn_trans(a,edge): global state global timer print("Button: %s , edge: %s, state: %d" % (str(a), str(edge),state)) if edge and state is 0: state = 1 timer=Timer(t1_2,time1_trans).start() elif not edge and (state is 1 or state is 4 or state is 2): state = 0 disable_all_timers() stop_sirene1() stop_sirene2() try: play_next() except: tell_gobbelz("Cannot play next song. Sorry:(") tell_gobbelz("Bailing out") sys.exit(1) elif not edge and state is 5: print("button released while removing music, all fine") else: print("this should never happen") def disable_all_timers(): print("disabling all the timers") global timer try: timer.cancel() print("timer canceled") except: pass def start_sirene1(): print("start Sirene 1") RPIO.output(loud1, True) def start_sirene2(): print("starting Sirene 2") RPIO.output(loud2, True) def stop_sirene1(): print("stopping Sirene 1") RPIO.output(loud1, False) def stop_sirene2(): print("stopping Sirene 2") RPIO.output(loud2, False) def play_radio(): if r.get_current().get("file", "") == "http://ice.somafm.com/groovesalad": print("will not skip own sender") return print("playing radio") tell_gobbelz("Starting Radio Stream") r.add_song("http://ice.somafm.com/groovesalad") r.play_last() def play_next(): print ("playing next song") try: if is_last_song(): raise Exception("Last song in playlist") r.next_song() except: print("no next song, starting radio") play_radio() def is_last_song(): return r.get_current()["Pos"] == r.get_last()["Pos"] def delete_current_music(): print("delete current music") current = r.get_current() if not current: print("Nothing is running, bailing out") return delete_remote_file(current) play_next() def delete_remote_file(current): try: sftp_delete_remote_file(current["file"]) say_song_killed(current.get("Title", "Unbekannter Title"), current.get("Artist", "Unbekannter Kuenstler")) except Exception as e: print("Cannot delete remote file! ( %s ) " %str(e)) def sftp_delete_remote_file(f): host = "mpd.shack" port = 22 transport = paramiko.Transport((host, port)) username = 'shack' passwd = 'shackit' transport.connect(username=username, password=passwd) sftp = paramiko.SFTPClient.from_transport(transport) print(sftp.unlink('%s/%s' % (sftp_base_path, f))) sftp.close() transport.close() def say_song_killed(name, author): tell_gobbelz('%s von %s wurde vernichtet!' % (name, author) ) def tell_gobbelz(text): import requests headers = {'Content-type': 'application/json', 'Accept': 'text/plain'} data = {'text': text} requests.post("http://kiosk.shack:8080/say/", data=json.dumps(data), headers=headers) if __name__ == "__main__": from time import sleep init_state() print("initializing relaxxapi") try: r = relaxx(relaxxurl="http://lounge.mpd.shack/") except: tell_gobbelz("EM PE DE unreachable!") tell_gobbelz("Bailing out") sys.exit(1) print("adding interrupt") RPIO.add_interrupt_callback(button,callback=btn_trans,pull_up_down=RPIO.PUD_DOWN) print ("Start Interrupt handler") RPIO.wait_for_interrupts()

true

f7005bd1aad9ac2334d62b543f0e7ac8f6381776

10,551

py

Python

napari/_qt/layer_controls/qt_vectors_controls.py

Mishrasubha/napari

c4d1038fc3ed30dc228949cbdedf12826ec2efc2

[ "BSD-3-Clause" ]

null

napari/_qt/layer_controls/qt_vectors_controls.py

Mishrasubha/napari

c4d1038fc3ed30dc228949cbdedf12826ec2efc2

[ "BSD-3-Clause" ]

3

2020-11-14T08:35:18.000Z

2021-07-26T10:06:32.000Z

napari/_qt/layer_controls/qt_vectors_controls.py

Mishrasubha/napari

c4d1038fc3ed30dc228949cbdedf12826ec2efc2

[ "BSD-3-Clause" ]

null

import numpy as np from qtpy.QtCore import Qt from qtpy.QtWidgets import QComboBox, QDoubleSpinBox, QLabel from ...layers.utils._color_manager_constants import ColorMode from ...utils.translations import trans from ..utils import qt_signals_blocked from ..widgets.qt_color_swatch import QColorSwatchEdit from .qt_layer_controls_base import QtLayerControls class QtVectorsControls(QtLayerControls): """Qt view and controls for the napari Vectors layer. Parameters ---------- layer : napari.layers.Vectors An instance of a napari Vectors layer. Attributes ---------- edge_color_label : qtpy.QtWidgets.QLabel Label for edgeColorSwatch edgeColorSwatch : qtpy.QtWidgets.QFrame Color swatch showing display color of vectors. edgeComboBox : qtpy.QtWidgets.QComboBox Dropdown widget to select display color for vectors. color_mode_comboBox : qtpy.QtWidgets.QComboBox Dropdown widget to select edge_color_mode for the vectors. color_prop_box : qtpy.QtWidgets.QComboBox Dropdown widget to select _edge_color_property for the vectors. edge_prop_label : qtpy.QtWidgets.QLabel Label for color_prop_box grid_layout : qtpy.QtWidgets.QGridLayout Layout of Qt widget controls for the layer. layer : napari.layers.Vectors An instance of a napari Vectors layer. lengthSpinBox : qtpy.QtWidgets.QDoubleSpinBox Spin box widget controlling line length of vectors. Multiplicative factor on projections for length of all vectors. widthSpinBox : qtpy.QtWidgets.QDoubleSpinBox Spin box widget controlling edge line width of vectors. """ def __init__(self, layer): super().__init__(layer) self.layer.events.edge_width.connect(self._on_edge_width_change) self.layer.events.length.connect(self._on_length_change) self.layer.events.edge_color_mode.connect( self._on_edge_color_mode_change ) self.layer.events.edge_color.connect(self._on_edge_color_change) # dropdown to select the property for mapping edge_color color_properties = self._get_property_values() color_prop_box = QComboBox(self) color_prop_box.activated[str].connect(self.change_edge_color_property) color_prop_box.addItems(color_properties) self.color_prop_box = color_prop_box self.edge_prop_label = QLabel(trans._('edge property:')) # vector direct color mode adjustment and widget self.edgeColorEdit = QColorSwatchEdit( initial_color=self.layer.edge_color, tooltip=trans._( 'click to set current edge color', ), ) self.edgeColorEdit.color_changed.connect(self.change_edge_color_direct) self.edge_color_label = QLabel(trans._('edge color:')) self._on_edge_color_change() # dropdown to select the edge color mode colorModeComboBox = QComboBox(self) color_modes = [e.value for e in ColorMode] colorModeComboBox.addItems(color_modes) colorModeComboBox.activated[str].connect(self.change_edge_color_mode) self.color_mode_comboBox = colorModeComboBox self._on_edge_color_mode_change() # line width in pixels self.widthSpinBox = QDoubleSpinBox() self.widthSpinBox.setKeyboardTracking(False) self.widthSpinBox.setSingleStep(0.1) self.widthSpinBox.setMinimum(0.1) self.widthSpinBox.setMaximum(np.inf) self.widthSpinBox.setValue(self.layer.edge_width) self.widthSpinBox.valueChanged.connect(self.change_width) # line length self.lengthSpinBox = QDoubleSpinBox() self.lengthSpinBox.setKeyboardTracking(False) self.lengthSpinBox.setSingleStep(0.1) self.lengthSpinBox.setValue(self.layer.length) self.lengthSpinBox.setMinimum(0.1) self.lengthSpinBox.setMaximum(np.inf) self.lengthSpinBox.valueChanged.connect(self.change_length) # grid_layout created in QtLayerControls # addWidget(widget, row, column, [row_span, column_span]) self.grid_layout.addWidget(QLabel(trans._('opacity:')), 0, 0) self.grid_layout.addWidget(self.opacitySlider, 0, 1, 1, 2) self.grid_layout.addWidget(QLabel(trans._('width:')), 1, 0) self.grid_layout.addWidget(self.widthSpinBox, 1, 1, 1, 2) self.grid_layout.addWidget(QLabel(trans._('length:')), 2, 0) self.grid_layout.addWidget(self.lengthSpinBox, 2, 1, 1, 2) self.grid_layout.addWidget(QLabel(trans._('blending:')), 3, 0) self.grid_layout.addWidget(self.blendComboBox, 3, 1, 1, 2) self.grid_layout.addWidget(QLabel(trans._('edge color mode:')), 4, 0) self.grid_layout.addWidget(self.color_mode_comboBox, 4, 1, 1, 2) self.grid_layout.addWidget(self.edge_color_label, 5, 0) self.grid_layout.addWidget(self.edgeColorEdit, 5, 1, 1, 2) self.grid_layout.addWidget(self.edge_prop_label, 6, 0) self.grid_layout.addWidget(self.color_prop_box, 6, 1, 1, 2) self.grid_layout.setRowStretch(7, 1) self.grid_layout.setColumnStretch(1, 1) self.grid_layout.setSpacing(4) def change_edge_color_property(self, property: str): """Change edge_color_property of vectors on the layer model. This property is the property the edge color is mapped to. Parameters ---------- property : str property to map the edge color to """ mode = self.layer.edge_color_mode try: self.layer.edge_color = property self.layer.edge_color_mode = mode except TypeError: # if the selected property is the wrong type for the current color mode # the color mode will be changed to the appropriate type, so we must update self._on_edge_color_mode_change() raise def change_edge_color_mode(self, mode: str): """Change edge color mode of vectors on the layer model. Parameters ---------- mode : str Edge color for vectors. Must be: 'direct', 'cycle', or 'colormap' """ old_mode = self.layer.edge_color_mode with self.layer.events.edge_color_mode.blocker(): try: self.layer.edge_color_mode = mode self._update_edge_color_gui(mode) except ValueError: # if the color mode was invalid, revert to the old mode self.layer.edge_color_mode = old_mode raise def change_edge_color_direct(self, color: np.ndarray): """Change edge color of vectors on the layer model. Parameters ---------- color : np.ndarray Edge color for vectors, in an RGBA array """ self.layer.edge_color = color def change_width(self, value): """Change edge line width of vectors on the layer model. Parameters ---------- value : float Line width of vectors. """ self.layer.edge_width = value self.widthSpinBox.clearFocus() self.setFocus() def change_length(self, value): """Change length of vectors on the layer model. Multiplicative factor on projections for length of all vectors. Parameters ---------- value : float Length of vectors. """ self.layer.length = value self.lengthSpinBox.clearFocus() self.setFocus() def _update_edge_color_gui(self, mode: str): """Update the GUI element associated with edge_color. This is typically used when edge_color_mode changes Parameters ---------- mode : str The new edge_color mode the GUI needs to be updated for. Should be: 'direct', 'cycle', 'colormap' """ if mode in ('cycle', 'colormap'): self.edgeColorEdit.setHidden(True) self.edge_color_label.setHidden(True) self.color_prop_box.setHidden(False) self.edge_prop_label.setHidden(False) elif mode == 'direct': self.edgeColorEdit.setHidden(False) self.edge_color_label.setHidden(False) self.color_prop_box.setHidden(True) self.edge_prop_label.setHidden(True) def _get_property_values(self): """Get the current property values from the Vectors layer Returns ------- property_values : np.ndarray array of all of the union of the property names (keys) in Vectors.properties and Vectors._property_choices """ property_choices = [*self.layer._property_choices] properties = [*self.layer.properties] property_values = np.union1d(property_choices, properties) return property_values def _on_length_change(self): """Change length of vectors.""" with self.layer.events.length.blocker(): self.lengthSpinBox.setValue(self.layer.length) def _on_edge_width_change(self): """Receive layer model width change event and update width spinbox.""" with self.layer.events.edge_width.blocker(): self.widthSpinBox.setValue(self.layer.edge_width) def _on_edge_color_mode_change(self): """Receive layer model edge color mode change event & update dropdown.""" with qt_signals_blocked(self.color_mode_comboBox): mode = self.layer._edge.color_mode index = self.color_mode_comboBox.findText( mode, Qt.MatchFixedString ) self.color_mode_comboBox.setCurrentIndex(index) self._update_edge_color_gui(mode) def _on_edge_color_change(self): """Receive layer model edge color change event & update dropdown.""" if ( self.layer._edge.color_mode == ColorMode.DIRECT and len(self.layer.data) > 0 ): with qt_signals_blocked(self.edgeColorEdit): self.edgeColorEdit.setColor(self.layer.edge_color[0]) elif self.layer._edge.color_mode in ( ColorMode.CYCLE, ColorMode.COLORMAP, ): with qt_signals_blocked(self.color_prop_box): prop = self.layer._edge.color_properties.name index = self.color_prop_box.findText(prop, Qt.MatchFixedString) self.color_prop_box.setCurrentIndex(index)

39.223048

87

0.65444

import numpy as np from qtpy.QtCore import Qt from qtpy.QtWidgets import QComboBox, QDoubleSpinBox, QLabel from ...layers.utils._color_manager_constants import ColorMode from ...utils.translations import trans from ..utils import qt_signals_blocked from ..widgets.qt_color_swatch import QColorSwatchEdit from .qt_layer_controls_base import QtLayerControls class QtVectorsControls(QtLayerControls): def __init__(self, layer): super().__init__(layer) self.layer.events.edge_width.connect(self._on_edge_width_change) self.layer.events.length.connect(self._on_length_change) self.layer.events.edge_color_mode.connect( self._on_edge_color_mode_change ) self.layer.events.edge_color.connect(self._on_edge_color_change) color_properties = self._get_property_values() color_prop_box = QComboBox(self) color_prop_box.activated[str].connect(self.change_edge_color_property) color_prop_box.addItems(color_properties) self.color_prop_box = color_prop_box self.edge_prop_label = QLabel(trans._('edge property:')) self.edgeColorEdit = QColorSwatchEdit( initial_color=self.layer.edge_color, tooltip=trans._( 'click to set current edge color', ), ) self.edgeColorEdit.color_changed.connect(self.change_edge_color_direct) self.edge_color_label = QLabel(trans._('edge color:')) self._on_edge_color_change() colorModeComboBox = QComboBox(self) color_modes = [e.value for e in ColorMode] colorModeComboBox.addItems(color_modes) colorModeComboBox.activated[str].connect(self.change_edge_color_mode) self.color_mode_comboBox = colorModeComboBox self._on_edge_color_mode_change() self.widthSpinBox = QDoubleSpinBox() self.widthSpinBox.setKeyboardTracking(False) self.widthSpinBox.setSingleStep(0.1) self.widthSpinBox.setMinimum(0.1) self.widthSpinBox.setMaximum(np.inf) self.widthSpinBox.setValue(self.layer.edge_width) self.widthSpinBox.valueChanged.connect(self.change_width) self.lengthSpinBox = QDoubleSpinBox() self.lengthSpinBox.setKeyboardTracking(False) self.lengthSpinBox.setSingleStep(0.1) self.lengthSpinBox.setValue(self.layer.length) self.lengthSpinBox.setMinimum(0.1) self.lengthSpinBox.setMaximum(np.inf) self.lengthSpinBox.valueChanged.connect(self.change_length) self.grid_layout.addWidget(QLabel(trans._('opacity:')), 0, 0) self.grid_layout.addWidget(self.opacitySlider, 0, 1, 1, 2) self.grid_layout.addWidget(QLabel(trans._('width:')), 1, 0) self.grid_layout.addWidget(self.widthSpinBox, 1, 1, 1, 2) self.grid_layout.addWidget(QLabel(trans._('length:')), 2, 0) self.grid_layout.addWidget(self.lengthSpinBox, 2, 1, 1, 2) self.grid_layout.addWidget(QLabel(trans._('blending:')), 3, 0) self.grid_layout.addWidget(self.blendComboBox, 3, 1, 1, 2) self.grid_layout.addWidget(QLabel(trans._('edge color mode:')), 4, 0) self.grid_layout.addWidget(self.color_mode_comboBox, 4, 1, 1, 2) self.grid_layout.addWidget(self.edge_color_label, 5, 0) self.grid_layout.addWidget(self.edgeColorEdit, 5, 1, 1, 2) self.grid_layout.addWidget(self.edge_prop_label, 6, 0) self.grid_layout.addWidget(self.color_prop_box, 6, 1, 1, 2) self.grid_layout.setRowStretch(7, 1) self.grid_layout.setColumnStretch(1, 1) self.grid_layout.setSpacing(4) def change_edge_color_property(self, property: str): mode = self.layer.edge_color_mode try: self.layer.edge_color = property self.layer.edge_color_mode = mode except TypeError: self._on_edge_color_mode_change() raise def change_edge_color_mode(self, mode: str): old_mode = self.layer.edge_color_mode with self.layer.events.edge_color_mode.blocker(): try: self.layer.edge_color_mode = mode self._update_edge_color_gui(mode) except ValueError: self.layer.edge_color_mode = old_mode raise def change_edge_color_direct(self, color: np.ndarray): self.layer.edge_color = color def change_width(self, value): self.layer.edge_width = value self.widthSpinBox.clearFocus() self.setFocus() def change_length(self, value): self.layer.length = value self.lengthSpinBox.clearFocus() self.setFocus() def _update_edge_color_gui(self, mode: str): if mode in ('cycle', 'colormap'): self.edgeColorEdit.setHidden(True) self.edge_color_label.setHidden(True) self.color_prop_box.setHidden(False) self.edge_prop_label.setHidden(False) elif mode == 'direct': self.edgeColorEdit.setHidden(False) self.edge_color_label.setHidden(False) self.color_prop_box.setHidden(True) self.edge_prop_label.setHidden(True) def _get_property_values(self): property_choices = [*self.layer._property_choices] properties = [*self.layer.properties] property_values = np.union1d(property_choices, properties) return property_values def _on_length_change(self): with self.layer.events.length.blocker(): self.lengthSpinBox.setValue(self.layer.length) def _on_edge_width_change(self): with self.layer.events.edge_width.blocker(): self.widthSpinBox.setValue(self.layer.edge_width) def _on_edge_color_mode_change(self): with qt_signals_blocked(self.color_mode_comboBox): mode = self.layer._edge.color_mode index = self.color_mode_comboBox.findText( mode, Qt.MatchFixedString ) self.color_mode_comboBox.setCurrentIndex(index) self._update_edge_color_gui(mode) def _on_edge_color_change(self): if ( self.layer._edge.color_mode == ColorMode.DIRECT and len(self.layer.data) > 0 ): with qt_signals_blocked(self.edgeColorEdit): self.edgeColorEdit.setColor(self.layer.edge_color[0]) elif self.layer._edge.color_mode in ( ColorMode.CYCLE, ColorMode.COLORMAP, ): with qt_signals_blocked(self.color_prop_box): prop = self.layer._edge.color_properties.name index = self.color_prop_box.findText(prop, Qt.MatchFixedString) self.color_prop_box.setCurrentIndex(index)

true

f7005d9399a9f18e174ddc2c6c5409f9ec3370f1

944

py

Python

isi_sdk_8_0_1/test/test_hardware_tapes_devices.py

mohitjain97/isilon_sdk_python

a371f438f542568edb8cda35e929e6b300b1177c

[ "Unlicense" ]

24

2018-06-22T14:13:23.000Z

2022-03-23T01:21:26.000Z

isi_sdk_8_0_1/test/test_hardware_tapes_devices.py

mohitjain97/isilon_sdk_python

a371f438f542568edb8cda35e929e6b300b1177c

[ "Unlicense" ]

46

2018-04-30T13:28:22.000Z

2022-03-21T21:11:07.000Z

isi_sdk_8_0_1/test/test_hardware_tapes_devices.py

mohitjain97/isilon_sdk_python

a371f438f542568edb8cda35e929e6b300b1177c

[ "Unlicense" ]

29

2018-06-19T00:14:04.000Z

2022-02-08T17:51:19.000Z

# coding: utf-8 """ Isilon SDK Isilon SDK - Language bindings for the OneFS API # noqa: E501 OpenAPI spec version: 4 Contact: [email protected] Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import unittest import isi_sdk_8_0_1 from isi_sdk_8_0_1.models.hardware_tapes_devices import HardwareTapesDevices # noqa: E501 from isi_sdk_8_0_1.rest import ApiException class TestHardwareTapesDevices(unittest.TestCase): """HardwareTapesDevices unit test stubs""" def setUp(self): pass def tearDown(self): pass def testHardwareTapesDevices(self): """Test HardwareTapesDevices""" # FIXME: construct object with mandatory attributes with example values # model = isi_sdk_8_0_1.models.hardware_tapes_devices.HardwareTapesDevices() # noqa: E501 pass if __name__ == '__main__': unittest.main()

23.02439

98

0.71822

from __future__ import absolute_import import unittest import isi_sdk_8_0_1 from isi_sdk_8_0_1.models.hardware_tapes_devices import HardwareTapesDevices from isi_sdk_8_0_1.rest import ApiException class TestHardwareTapesDevices(unittest.TestCase): def setUp(self): pass def tearDown(self): pass def testHardwareTapesDevices(self): pass if __name__ == '__main__': unittest.main()

true

f7005e840555e823b72518c963e171385bf675ec

1,423

py

Python

run.py

xinetzone/self-driving-dash

66860074099c90e620e21bc30b26efd366cc8f1a

[ "Apache-2.0" ]

null

run.py

xinetzone/self-driving-dash

66860074099c90e620e21bc30b26efd366cc8f1a

[ "Apache-2.0" ]

null

run.py

xinetzone/self-driving-dash

66860074099c90e620e21bc30b26efd366cc8f1a

[ "Apache-2.0" ]

null

from dash import dcc, html from dash.dependencies import Input, Output from app import app from layouts import index, record, watch, replay, about # from examples.run import callback_example from callbacks.record import * from callbacks.watch import * from callbacks.replay import * layout = html.Article([ dcc.Location(id='url', refresh=False), # 定位地址栏 html.Section(id='page-content'), # 页面布局 ]) @app.callback(Output('page-content', 'children'), Input('url', 'pathname')) def display_page(pathname): if pathname == '/': return index.layout if pathname == '/record': return record.layout if pathname == '/watch': return watch.layout if pathname == '/replay': return replay.layout if pathname == '/about': return about.layout # elif pathname.startswith('/examples/'): # return callback_example(pathname) # else: # return '404' app.config.suppress_callback_exceptions = True # 用于支持多页应用 if __name__ == '__main__': import asyncio from dash_xinet.server import run_server port = 7777 # app.run_server(debug=True, port=5555, threaded=True) # app.run_server(app, debug=True, port=5555, threaded=True) run = run_server(app, layout, port=port, debug=True ) asyncio.run(run) else: app.layout = layout server = app.server # 用于 Dash 服务器部署

27.365385

63

0.647927

from dash import dcc, html from dash.dependencies import Input, Output from app import app from layouts import index, record, watch, replay, about from callbacks.record import * from callbacks.watch import * from callbacks.replay import * layout = html.Article([ dcc.Location(id='url', refresh=False), html.Section(id='page-content'), ]) @app.callback(Output('page-content', 'children'), Input('url', 'pathname')) def display_page(pathname): if pathname == '/': return index.layout if pathname == '/record': return record.layout if pathname == '/watch': return watch.layout if pathname == '/replay': return replay.layout if pathname == '/about': return about.layout app.config.suppress_callback_exceptions = True if __name__ == '__main__': import asyncio from dash_xinet.server import run_server port = 7777 run = run_server(app, layout, port=port, debug=True ) asyncio.run(run) else: app.layout = layout server = app.server

true

f700608f35098a7965b60e53b106f0704bc73300

28,769

py

Python

neorl/tune/runners/estune.py

XuboGU/neorl

066cdbd9e9cdbfe371278dba3ece116f25edab2d

[ "MIT" ]

1

2021-07-06T20:31:38.000Z

neorl/tune/runners/estune.py

XuboGU/neorl

066cdbd9e9cdbfe371278dba3ece116f25edab2d

[ "MIT" ]

null

neorl/tune/runners/estune.py

XuboGU/neorl

066cdbd9e9cdbfe371278dba3ece116f25edab2d

[ "MIT" ]

null

import numpy as np import pandas as pd import os import random import math from itertools import repeat import itertools import sys, copy, shutil import subprocess from multiprocessing.dummy import Pool from collections import defaultdict import copy import random import matplotlib.pyplot as plt try: from collections.abc import Sequence except ImportError: from collections import Sequence class ESTUNE: """ A class to parse neorl input template and construct cases for evolution strategy (ES) hyperparameter optimisation inputs: The template input file Class object from PARSER.py, featuring user input for TUNE neorl logo """ def __init__(self, tuneclass, inputfile, tuneblock, logo): self.logo=logo self.inputfile=inputfile self.tuneblock=tuneblock self.n_last_episodes=int(self.tuneblock["n_last_episodes"]) self.ncores=int(self.tuneblock["ncores"]) self.ncases=int(self.tuneblock["ncases"]) #--------------------------------------- # define genetic algorithm parameters #--------------------------------------- self.popsize=10 if self.ncases < self.popsize: self.ngens=1 else: self.ngens=int(self.ncases/self.popsize) self.MU=5 if tuneclass == 'gatune': # ES/GA tune print("Performing semi-GA Tune") self.INDPB=0.1 elif tuneclass == 'estune': # ES tune print("Performing ES Tune") self.INDPB=1.0 else: # default setting is ES tune print("Performing ES Tune") self.INDPB=1.0 self.CXPB=0.5 self.MUTPB=0.2 self.ETA=0.6 self.SMAX=0.5 self.paramvals=dict() self.paraminds=dict() self.datatypes=[] #------------------------------- # construct results directory #------------------------------- if os.path.exists('./tunecases/'): shutil.rmtree('./tunecases/') os.makedirs('./tunecases/', exist_ok=True) else: os.makedirs('./tunecases/', exist_ok=True) self.csvlogger='tune.csv' self.tunesummary='tunesummary.txt' #--------------------------------- # parse the input template #--------------------------------- with open (self.inputfile, 'r') as input_file_text: self.template=input_file_text.readlines() first=0; last=0 for i in range(len(self.template)): if ('READ TUNE' in self.template[i]): first=i if ('END TUNE' in self.template[i]): last=i if first == 0 and last ==0: raise ('TUNE card cannot be found') del self.template[first: last+1] self.template="".join(self.template) def tune_count(self): """ 1- This function uses self.tuneblock, parse it, infer all parameters to be tuned and thier distribution 2- This function creates GA engine and instantiates the initial population for evolution algorithm """ self.param_dict={} for item in self.tuneblock: if '{' in item and '}' in item and item[0] != '#': #----------------------------------------------------- # check the existence of the name in the template #----------------------------------------------------- if item not in self.template: raise ValueError('parameter {} in TUNE block cannot be found in any other block, e.g. DQN, GA, PPO, etc.'.format(item)) item_lst=self.tuneblock[item].split(",") item_lst=[item.strip() for item in item_lst] # get rid of white spaces in the splitted values #------------------------------------------------------- # check if a uniform distribution of floats is identified #------------------------------------------------------- try: if "float" in item_lst: item_lst[0]=float(item_lst[0]) item_lst[1]=float(item_lst[1]) self.datatypes.append("float") print ('-- debug: parameter {} has uniform distribution of type --float-- between {} and {}'.format(item,item_lst[0],item_lst[1])) elif "u" in item_lst: item_lst[0]=float(item_lst[0]) item_lst[1]=float(item_lst[1]) self.datatypes.append("float") print ('-- debug: parameter {} has uniform distribution of type --float-- between {} and {}'.format(item,item_lst[0],item_lst[1])) except: raise Exception ('--error: TUNE cannot construct the user-given uniform distribution of --floats-- for {} according to (low, high, u) syntax'.format(item)) #--------------------------------------------------- # check if a random integer distribution is identified #--------------------------------------------------- try: if "int" in item_lst: item_lst[0]=int(item_lst[0]) item_lst[1]=int(item_lst[1]) self.datatypes.append("int") print ('-- debug: parameter {} has uniform distribution of type --int-- between {} and {}'.format(item,item_lst[0],item_lst[1])) elif "randint" in item_lst: item_lst[0]=int(item_lst[0]) item_lst[1]=int(item_lst[1]) self.datatypes.append("int") print ('-- debug: parameter {} has uniform distribution of type --int-- between {} and {}'.format(item,item_lst[0],item_lst[1])) except: raise Exception ('--error: TUNE cannot construct the user-given uniform distribution of --int-- for {} according to (low, high, u) syntax'.format(item)) #----------------------------------------------------- # check if a grid is identified #----------------------------------------------------- try: if "grid" in item_lst: element_lst=[] for element in item_lst: # check if it is an integer not_int=0 try: element_lst.append(int(element.strip())) except Exception: not_int=1 # else check if the elment is float if not_int: try: element_lst.append(float(element.strip())) # else consider it a string except Exception: element_lst.append(str(element.strip())) item_lst=element_lst self.datatypes.append("grid") print ('-- debug: parameter {} has grid type with values {}'.format(item,item_lst)) except: raise Exception ('--error: TUNE cannot construct the user-given grid for {} according to the comma-seperated syntax'.format(item)) self.param_dict[item]=item_lst # Save the final parsed list for parameter {XXX} #----------------------------------------------------- # infer the bounds for strategy vector #----------------------------------------------------- if len(self.param_dict.keys()) <= 10: self.SMIN=0.1 else: self.SMIN=1/(len(self.param_dict.keys())) def gen_cases(self, x=0): """ This function infers neorl.py path """ self.tune_count() self.param_names=list(self.param_dict.keys()) #----------------------- # Infer neorl.py path #----------------------- # Find neorl path #self.here=os.path.dirname(os.path.abspath(__file__)) #self.neorl_path=self.here.replace('src/tune','neorl.py') #try to infer neorl.py internally to call neorl inside or neorl #self.python_path=self.here.replace('neorl/src/tune','anaconda3/bin/python3') #try to infer python3 path to call neorl inside or neorl self.neorl_path=sys.argv[0] self.python_path=sys.executable print('--debug: NEORLPATH=', self.neorl_path) print('--debug: PYTHONPATH=', self.python_path) def GenES(self): """ Individual generator: 1- This function uses self.param_dict to obtain bounds for individual parameters Returns: -ind (list): an individual vector with values samples from inferred distribution -strategy (list): the strategy vector with values between smin and smax """ size=len(self.param_dict.keys()) # size of individual content=[] self.LOW=[] # Lower bounds for the parameters to be tuned self.UP=[] # Upper bounds for parameters to be tuned for key in list(self.param_dict.keys()): if 'int' in self.param_dict[key]: content.append(random.randint(self.param_dict[key][0], self.param_dict[key][1])) elif 'randint' in self.param_dict[key]: content.append(random.randint(self.param_dict[key][0], self.param_dict[key][1])) elif 'float' in self.param_dict[key]: content.append(random.uniform(self.param_dict[key][0], self.param_dict[key][1])) elif 'u' in self.param_dict[key]: content.append(random.uniform(self.param_dict[key][0], self.param_dict[key][1])) elif 'grid' in self.param_dict[key]: self.real_grid=list(self.param_dict[key]) self.real_grid.remove('grid') # get rid of the 'grid' to avoid sampling it self.paramvals[key]=self.real_grid content.append(random.sample(self.real_grid, 1)[0]) self.paraminds[len(content)-1]=key else: raise Exception('unknown data type is given, either int/randint, float/u, or grid are allowed for parameter distribution types') self.LOW.append(self.param_dict[key][0]) self.UP.append(self.param_dict[key][1]) ind=list(content) size = len(list(self.param_dict.keys())) strategy= [random.uniform(self.SMIN, self.SMAX) for _ in range(size)] return ind, strategy def init_pop(self): """ Population initializer Returns: -pop (dict): initial population in a dictionary form """ # initialize the population and strategy and run them in parallel (these samples will be used to initialize the memory) pop=defaultdict(list) for i in range(self.popsize): #caseid='es_gen{}_ind{}'.format(0,i+1) data=self.GenES() pop[i].append(data[0]) pop[i].append(data[1]) if self.ncores > 1: # evaluate warmup in parallel core_list=[] for key in pop: caseid='ind{}'.format(key+1) core_list.append([pop[key][0], caseid]) p=Pool(self.ncores) fitness=p.map(self.gen_object, core_list) p.close(); p.join() [pop[ind].append(fitness[ind]) for ind in range(len(pop))] else: # evaluate warmup in series for key in pop: caseid='ind{}'.format(key+1) fitness=self.fit(pop[key][0], caseid) pop[key].append(fitness) return pop # return final pop dictionary with ind, strategy, and fitness def fit(self, ind, caseid): """ This function evaluates an individual's fitness Inputs: -ind (list): an individual whose fitness to evaluate -caseid (str): a string that specifies the given individual Returns: -mean_reward (float): fitness value """ try: #--------------------------------------------- # Prepares directories and files for one case # -------------------------------------------- self.param_names=list(self.param_dict.keys()) i = caseid[3:] os.makedirs('./tunecases/case{}'.format(i), exist_ok=True) self.new_template=copy.deepcopy(self.template) for j in range (len(self.param_names)): self.new_template=self.new_template.replace(str(self.param_names[j]), str(ind[j])) filename='./tunecases/case{}/case{}.inp'.format(i, i) with open (filename, 'w') as fout: fout.writelines(self.new_template) # copy external files into the new directory, if extfiles card exists if 'extfiles' in self.tuneblock.keys(): if self.tuneblock['extfiles']: print('--debug: external files are identified, copying them into each case directory') for item in self.tuneblock['extfiles']: os.system('cp -r {} ./tunecases/case{}/'.format(item, i)) casenum = caseid[3:] print('--------------------------------------------------') print('Running TUNE Case {}/{}: {}'.format(casenum, self.ncases, ind)) subprocess.call([self.python_path, self.neorl_path, '-i', 'case{}.inp'.format(casenum)], cwd='./tunecases/case{}/'.format(casenum)) # this exceutes neorl for this case.inp print('--------------------------------------------------') #-------------------------------------------------------------------------------------------------------------- # Try to infer the _out.csv file in the directory since only one method is allowed csvfile=[f for f in os.listdir('./tunecases/case{}/case{}_log/'.format(casenum, casenum)) if f.endswith('_out.csv')] if len(csvfile) > 1: raise Exception ('multiple *_out.csv files can be found in the logger of TUNE, only one is allowed') #-------------------------------------------------------------------------------------------------------------- reward_lst=pd.read_csv('./tunecases/case{}/case{}_log/{}'.format(casenum,casenum, csvfile[0]), usecols=['reward']).values mean_reward=np.mean(reward_lst[-self.n_last_episodes:]) max_reward=np.max(reward_lst) with open (self.csvlogger, 'a') as fout: fout.write(str(casenum) +',') [fout.write(str(item) + ',') for item in ind] fout.write(str(mean_reward) + ',' + str(max_reward) + '\n') return mean_reward except: print('--error: case{}.inp failed during execution'.format(casenum)) return 'case{}.inp:failed'.format(casenum) def gen_object(self, inp): """ This is a worker for the multiprocess Pool Inputs: -inp (list of lists): contains data for each core [[ind1, caseid1], ..., [indN, caseidN]] Returns: -fitness value (float) """ return self.fit(inp[0], inp[1]) def select(self, pop): """ Selection function sorts the population from max to min based on fitness and selects the k best Inputs: -pop (dict): population in dictionary structure -k (int): top k individuals are selected Returns: -best_dict (dict): the new orded dictionary with top k selected """ k=self.MU pop=list(pop.items()) pop.sort(key=lambda e: e[1][2], reverse=True) sorted_dict=dict(pop[:k]) # This block creates a new dict where keys are reset to 0 ... k in order to avoid unordered keys after sort best_dict=defaultdict(list) index=0 for key in sorted_dict: best_dict[index].append(sorted_dict[key][0]) best_dict[index].append(sorted_dict[key][1]) best_dict[index].append(sorted_dict[key][2]) index+=1 sorted_dict.clear() return best_dict def cx(self, ind1, ind2, strat1, strat2): """ Executes a classical two points crossover on both the individuals and their strategy. The individuals/strategies should be a list. The crossover points for the individual and the strategy are the same. Inputs: -ind1 (list): The first individual participating in the crossover. -ind2 (list): The second individual participating in the crossover. -strat1 (list): The first evolution strategy participating in the crossover. -strat2 (list): The second evolution strategy Returns: - The new ind1, ind2, strat1, strat2, after crossover in list form """ #for item in ind1: # print('individual 1', type(item)) #for item in ind2: # print('individual 2', type(item)) #for item in strat1: # print('strategy 1', type(item)) #for item in strat2: # print('strategy 2', type(item)) size = min(len(ind1), len(ind2)) pt1 = random.randint(1, size) pt2 = random.randint(1, size-1) if pt2 >= pt1: pt2 +=1 else: pt1, pt2 = pt2, pt1 ind1[pt1:pt2], ind2[pt1:pt2] = ind2[pt1:pt2], ind1[pt1:pt2] strat1[pt1:pt2], strat2[pt1:pt2] = strat2[pt1:pt2], strat1[pt1:pt2] return ind1, ind2, strat1, strat2 def mutES(self, ind, strat): """ Mutate an evolution strategy according to mixed Discrete/Continuous mutation rules Input: -ind (list): individual to be mutated -strat (list): individual strategy to be mutated Returns: -ind (list): new individual after mutation -strat (list): individual strategy after mutation """ size=len(ind) tau=1/np.sqrt(2*size) tau_prime=1/np.sqrt(2*np.sqrt(size)) for i in range(size): # Grid distribution received if self.datatypes[i] == "grid": #if i in self.paraminds.keys(): norm=random.gauss(0,1) # modify the ind strategy strat[i] = 1/(1+(1-strat[i])/strat[i]*np.exp(-tau*norm-tau_prime*random.gauss(0,1))) # make a transformation of strategy to ensure it is between smin, smax y=(strat[i]-self.SMIN)/(self.SMAX-self.SMIN) if np.floor(y) % 2 == 0: y_prime=np.abs(y-np.floor(y)) else: y_prime=1-np.abs(y-np.floor(y)) strat[i] = self.SMIN + (self.SMAX-self.SMIN)*y_prime # check if this attribute is mutated based on the updated strategy if random.random() < strat[i]: # make a list of possibilities after excluding the current value to enforce mutation paramname=self.paraminds[i] ind[i]=random.sample(self.paramvals[paramname], 1)[0] # Random integer distribution received elif self.datatypes[i] == "int": norm=random.gauss(0,1) # modify the ind strategy strat[i] = 1/(1+(1-strat[i])/strat[i]*np.exp(-tau*norm-tau_prime*random.gauss(0,1))) # make a transformation of strategy to ensure it is between smin, smax y=(strat[i]-self.SMIN)/(self.SMAX-self.SMIN) if np.floor(y) % 2 == 0: y_prime=np.abs(y-np.floor(y)) else: y_prime=1-np.abs(y-np.floor(y)) strat[i] = self.SMIN + (self.SMAX-self.SMIN)*y_prime # check if this attribute is mutated based on the updated strategy #if random.random() < strat[i]: # make a list of possibilities after excluding the current value to enforce mutation choices=list(range(self.LOW[i], self.UP[i]+1)) choices.remove(ind[i]) ind[i] = random.choice(choices) # Uniform float distribution received elif self.datatypes[i] == "float": norm=random.gauss(0,1) if random.random() < self.INDPB: # this indicates whether ind/strategy to be mutated or not for this float variable strat[i] *= np.exp(tau*norm + tau_prime * random.gauss(0,1)) # normal mutation strategy ind[i] += strat[i] * random.gauss(0,1) # update the individual position # check if the new individual falls within lower/uppder boundaries if ind[i] < self.LOW[i]: ind[i] = self.LOW[i] if ind[i] > self.UP[i]: ind[i] = self.UP[i] else: raise Exception('ES mutation strategy works with int, float, or grid distributions, the type provided cannot be interpreted') return ind, strat def GenOffspring(self, pop): """ This function generates the offspring by applying crossover, mutation, OR reproduction. Inputs: -pop (dict): population in dictionary structure Returns: -offspring (dict): new modified population in dictionary structure """ pop_indices=list(range(0,len(pop))) offspring=defaultdict(list) for i in range(self.popsize): alpha=random.random() #---------------------- # Crossover #---------------------- if alpha < self.CXPB: index1, index2=random.sample(pop_indices,2) ind1, ind2, strat1, strat2=self.cx(ind1=list(pop[index1][0]), ind2=list(pop[index2][0]), strat1=list(pop[index1][1]), strat2=list(pop[index2][1])) offspring[i].append(ind1) offspring[i].append(strat1) #print('crossover is done for sample {} between {} and {}'.format(i,index1,index2)) #---------------------- # Mutation #---------------------- elif alpha < self.CXPB + self.MUTPB: # Apply mutation index = random.choice(pop_indices) ind, strat=self.mutES(ind=list(pop[index][0]), strat=list(pop[index][1])) offspring[i].append(ind) offspring[i].append(strat) #print('mutation is done for sample {} based on {}'.format(i,index)) #------------------------------ # Reproduction from population #------------------------------ else: index=random.choice(pop_indices) offspring[i].append(pop[index][0]) offspring[i].append(pop[index][1]) #print('reproduction is done for sample {} based on {}'.format(i,index)) return offspring def run_cases(self): """ This function runs the evolutioanry algorithm over self.ngens generations. """ #------------------------------ # Begin the evolution process #------------------------------ with open (self.csvlogger, 'w') as fout: fout.write('caseid, ') [fout.write(item + ',') for item in self.param_names] fout.write('mean_reward,max_reward\n') #print('PARAM dict', self.param_dict) #print('PARAM types', self.datatypes) self.population=self.init_pop() case_idx=0 self.currentcase=self.popsize+1 for gen in range(1, self.ngens): case_idx=0 caseids=['ind{}'.format(ind) for ind in range(self.currentcase, self.currentcase+self.popsize+1)] # Vary the population and generate new offspring offspring=self.GenOffspring(pop=self.population) # Evaluate the individuals with invalid fitness using multiprocessing Pool if self.ncores > 1: core_list=[] for key in offspring: core_list.append([offspring[key][0], caseids[case_idx]]) case_idx+=1 # initialize a pool p=Pool(self.ncores) fitness=p.map(self.gen_object, core_list) p.close(); p.join() [offspring[ind].append(fitness[ind]) for ind in range(len(offspring))] else: for ind in range(len(offspring)): fitness=self.fit(offspring[ind][0], caseids[case_idx]) case_idx+=1 offspring[ind].append(fitness) self.currentcase+=self.popsize # Select the next generation population self.population = copy.deepcopy(self.select(pop=offspring)) csvdata=pd.read_csv('tune.csv') asc_data=csvdata.sort_values(by=['caseid'],ascending=True) des_data=csvdata.sort_values(by=['mean_reward'],ascending=False) des_data2=csvdata.sort_values(by=['max_reward'],ascending=False) asc_data.to_csv('tune.csv', index=False) mean = np.mean(des_data.iloc[:,4:5]) totalmean=mean.tolist()[0] try: failed_cases=len([print ('failed') for item in self.population if isinstance(item, str)]) except: failed_cases='NA' print ('%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%') print('Mean Rewards for all cases=', totalmean) print ('%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%') print ('All TUNE CASES ARE COMPLETED') print ('%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%') print('--debug: Check tunesummary.txt file for best hyperparameters found') print('--debug: Check tune.csv file for complete csv logger of all cases results') print('--debug: Check tunecases directory for case-by-case detailed results') with open ('tunesummary.txt', 'w') as fout: fout.write(self.logo) fout.write('*****************************************************\n') fout.write('Summary for the TUNE case \n') fout.write('*****************************************************\n') fout.write('Number of cases evaluated: {} \n'.format(self.ncases)) fout.write('Number of failed cases: {} \n'.format(failed_cases)) fout.write('Parameter names: {} \n'.format(self.param_names)) fout.write('Parameter values: {} \n '.format(self.param_dict)) fout.write ('--------------------------------------------------------------------------------------\n') if des_data.shape[0] < 20: top=des_data.shape[0] fout.write ('Top {} hyperparameter configurations ranked according to MEAN reward \n'.format(top)) fout.write(des_data.iloc[:top].to_string(index=False)) else: top=20 fout.write ('Top {} hyperparameter configurations ranked according to MEAN reward \n'.format(top)) fout.write(des_data.iloc[:top].to_string(index=False)) fout.write ('\n') fout.write ('--------------------------------------------------------------------------------------\n') if des_data2.shape[0] < 20: top=des_data2.shape[0] fout.write ('Top {} hyperparameter configurations ranked according to MAX reward \n'.format(top)) fout.write(des_data2.iloc[:top].to_string(index=False)) else: top=20 fout.write ('Top {} hyperparameter configurations ranked according to MAX reward \n'.format(top)) fout.write(des_data2.iloc[:top].to_string(index=False))

46.326892

184

0.509437

import numpy as np import pandas as pd import os import random import math from itertools import repeat import itertools import sys, copy, shutil import subprocess from multiprocessing.dummy import Pool from collections import defaultdict import copy import random import matplotlib.pyplot as plt try: from collections.abc import Sequence except ImportError: from collections import Sequence class ESTUNE: def __init__(self, tuneclass, inputfile, tuneblock, logo): self.logo=logo self.inputfile=inputfile self.tuneblock=tuneblock self.n_last_episodes=int(self.tuneblock["n_last_episodes"]) self.ncores=int(self.tuneblock["ncores"]) self.ncases=int(self.tuneblock["ncases"]) self.popsize=10 if self.ncases < self.popsize: self.ngens=1 else: self.ngens=int(self.ncases/self.popsize) self.MU=5 if tuneclass == 'gatune': print("Performing semi-GA Tune") self.INDPB=0.1 elif tuneclass == 'estune': print("Performing ES Tune") self.INDPB=1.0 else: print("Performing ES Tune") self.INDPB=1.0 self.CXPB=0.5 self.MUTPB=0.2 self.ETA=0.6 self.SMAX=0.5 self.paramvals=dict() self.paraminds=dict() self.datatypes=[] if os.path.exists('./tunecases/'): shutil.rmtree('./tunecases/') os.makedirs('./tunecases/', exist_ok=True) else: os.makedirs('./tunecases/', exist_ok=True) self.csvlogger='tune.csv' self.tunesummary='tunesummary.txt' with open (self.inputfile, 'r') as input_file_text: self.template=input_file_text.readlines() first=0; last=0 for i in range(len(self.template)): if ('READ TUNE' in self.template[i]): first=i if ('END TUNE' in self.template[i]): last=i if first == 0 and last ==0: raise ('TUNE card cannot be found') del self.template[first: last+1] self.template="".join(self.template) def tune_count(self): self.param_dict={} for item in self.tuneblock: if '{' in item and '}' in item and item[0] != '#': if item not in self.template: raise ValueError('parameter {} in TUNE block cannot be found in any other block, e.g. DQN, GA, PPO, etc.'.format(item)) item_lst=self.tuneblock[item].split(",") item_lst=[item.strip() for item in item_lst] try: if "float" in item_lst: item_lst[0]=float(item_lst[0]) item_lst[1]=float(item_lst[1]) self.datatypes.append("float") print ('-- debug: parameter {} has uniform distribution of type --float-- between {} and {}'.format(item,item_lst[0],item_lst[1])) elif "u" in item_lst: item_lst[0]=float(item_lst[0]) item_lst[1]=float(item_lst[1]) self.datatypes.append("float") print ('-- debug: parameter {} has uniform distribution of type --float-- between {} and {}'.format(item,item_lst[0],item_lst[1])) except: raise Exception ('--error: TUNE cannot construct the user-given uniform distribution of --floats-- for {} according to (low, high, u) syntax'.format(item)) try: if "int" in item_lst: item_lst[0]=int(item_lst[0]) item_lst[1]=int(item_lst[1]) self.datatypes.append("int") print ('-- debug: parameter {} has uniform distribution of type --int-- between {} and {}'.format(item,item_lst[0],item_lst[1])) elif "randint" in item_lst: item_lst[0]=int(item_lst[0]) item_lst[1]=int(item_lst[1]) self.datatypes.append("int") print ('-- debug: parameter {} has uniform distribution of type --int-- between {} and {}'.format(item,item_lst[0],item_lst[1])) except: raise Exception ('--error: TUNE cannot construct the user-given uniform distribution of --int-- for {} according to (low, high, u) syntax'.format(item)) try: if "grid" in item_lst: element_lst=[] for element in item_lst: not_int=0 try: element_lst.append(int(element.strip())) except Exception: not_int=1 if not_int: try: element_lst.append(float(element.strip())) except Exception: element_lst.append(str(element.strip())) item_lst=element_lst self.datatypes.append("grid") print ('-- debug: parameter {} has grid type with values {}'.format(item,item_lst)) except: raise Exception ('--error: TUNE cannot construct the user-given grid for {} according to the comma-seperated syntax'.format(item)) self.param_dict[item]=item_lst if len(self.param_dict.keys()) <= 10: self.SMIN=0.1 else: self.SMIN=1/(len(self.param_dict.keys())) def gen_cases(self, x=0): self.tune_count() self.param_names=list(self.param_dict.keys()) self.neorl_path=sys.argv[0] self.python_path=sys.executable print('--debug: NEORLPATH=', self.neorl_path) print('--debug: PYTHONPATH=', self.python_path) def GenES(self): size=len(self.param_dict.keys()) content=[] self.LOW=[] self.UP=[] for key in list(self.param_dict.keys()): if 'int' in self.param_dict[key]: content.append(random.randint(self.param_dict[key][0], self.param_dict[key][1])) elif 'randint' in self.param_dict[key]: content.append(random.randint(self.param_dict[key][0], self.param_dict[key][1])) elif 'float' in self.param_dict[key]: content.append(random.uniform(self.param_dict[key][0], self.param_dict[key][1])) elif 'u' in self.param_dict[key]: content.append(random.uniform(self.param_dict[key][0], self.param_dict[key][1])) elif 'grid' in self.param_dict[key]: self.real_grid=list(self.param_dict[key]) self.real_grid.remove('grid') self.paramvals[key]=self.real_grid content.append(random.sample(self.real_grid, 1)[0]) self.paraminds[len(content)-1]=key else: raise Exception('unknown data type is given, either int/randint, float/u, or grid are allowed for parameter distribution types') self.LOW.append(self.param_dict[key][0]) self.UP.append(self.param_dict[key][1]) ind=list(content) size = len(list(self.param_dict.keys())) strategy= [random.uniform(self.SMIN, self.SMAX) for _ in range(size)] return ind, strategy def init_pop(self): pop=defaultdict(list) for i in range(self.popsize): data=self.GenES() pop[i].append(data[0]) pop[i].append(data[1]) if self.ncores > 1: core_list=[] for key in pop: caseid='ind{}'.format(key+1) core_list.append([pop[key][0], caseid]) p=Pool(self.ncores) fitness=p.map(self.gen_object, core_list) p.close(); p.join() [pop[ind].append(fitness[ind]) for ind in range(len(pop))] else: for key in pop: caseid='ind{}'.format(key+1) fitness=self.fit(pop[key][0], caseid) pop[key].append(fitness) return pop def fit(self, ind, caseid): try: self.param_names=list(self.param_dict.keys()) i = caseid[3:] os.makedirs('./tunecases/case{}'.format(i), exist_ok=True) self.new_template=copy.deepcopy(self.template) for j in range (len(self.param_names)): self.new_template=self.new_template.replace(str(self.param_names[j]), str(ind[j])) filename='./tunecases/case{}/case{}.inp'.format(i, i) with open (filename, 'w') as fout: fout.writelines(self.new_template) if 'extfiles' in self.tuneblock.keys(): if self.tuneblock['extfiles']: print('--debug: external files are identified, copying them into each case directory') for item in self.tuneblock['extfiles']: os.system('cp -r {} ./tunecases/case{}/'.format(item, i)) casenum = caseid[3:] print('--------------------------------------------------') print('Running TUNE Case {}/{}: {}'.format(casenum, self.ncases, ind)) subprocess.call([self.python_path, self.neorl_path, '-i', 'case{}.inp'.format(casenum)], cwd='./tunecases/case{}/'.format(casenum)) print('--------------------------------------------------') csvfile=[f for f in os.listdir('./tunecases/case{}/case{}_log/'.format(casenum, casenum)) if f.endswith('_out.csv')] if len(csvfile) > 1: raise Exception ('multiple *_out.csv files can be found in the logger of TUNE, only one is allowed') reward_lst=pd.read_csv('./tunecases/case{}/case{}_log/{}'.format(casenum,casenum, csvfile[0]), usecols=['reward']).values mean_reward=np.mean(reward_lst[-self.n_last_episodes:]) max_reward=np.max(reward_lst) with open (self.csvlogger, 'a') as fout: fout.write(str(casenum) +',') [fout.write(str(item) + ',') for item in ind] fout.write(str(mean_reward) + ',' + str(max_reward) + '\n') return mean_reward except: print('--error: case{}.inp failed during execution'.format(casenum)) return 'case{}.inp:failed'.format(casenum) def gen_object(self, inp): return self.fit(inp[0], inp[1]) def select(self, pop): k=self.MU pop=list(pop.items()) pop.sort(key=lambda e: e[1][2], reverse=True) sorted_dict=dict(pop[:k]) best_dict=defaultdict(list) index=0 for key in sorted_dict: best_dict[index].append(sorted_dict[key][0]) best_dict[index].append(sorted_dict[key][1]) best_dict[index].append(sorted_dict[key][2]) index+=1 sorted_dict.clear() return best_dict def cx(self, ind1, ind2, strat1, strat2): size = min(len(ind1), len(ind2)) pt1 = random.randint(1, size) pt2 = random.randint(1, size-1) if pt2 >= pt1: pt2 +=1 else: pt1, pt2 = pt2, pt1 ind1[pt1:pt2], ind2[pt1:pt2] = ind2[pt1:pt2], ind1[pt1:pt2] strat1[pt1:pt2], strat2[pt1:pt2] = strat2[pt1:pt2], strat1[pt1:pt2] return ind1, ind2, strat1, strat2 def mutES(self, ind, strat): size=len(ind) tau=1/np.sqrt(2*size) tau_prime=1/np.sqrt(2*np.sqrt(size)) for i in range(size): if self.datatypes[i] == "grid": norm=random.gauss(0,1) strat[i] = 1/(1+(1-strat[i])/strat[i]*np.exp(-tau*norm-tau_prime*random.gauss(0,1))) y=(strat[i]-self.SMIN)/(self.SMAX-self.SMIN) if np.floor(y) % 2 == 0: y_prime=np.abs(y-np.floor(y)) else: y_prime=1-np.abs(y-np.floor(y)) strat[i] = self.SMIN + (self.SMAX-self.SMIN)*y_prime if random.random() < strat[i]: paramname=self.paraminds[i] ind[i]=random.sample(self.paramvals[paramname], 1)[0] elif self.datatypes[i] == "int": norm=random.gauss(0,1) strat[i] = 1/(1+(1-strat[i])/strat[i]*np.exp(-tau*norm-tau_prime*random.gauss(0,1))) y=(strat[i]-self.SMIN)/(self.SMAX-self.SMIN) if np.floor(y) % 2 == 0: y_prime=np.abs(y-np.floor(y)) else: y_prime=1-np.abs(y-np.floor(y)) strat[i] = self.SMIN + (self.SMAX-self.SMIN)*y_prime choices=list(range(self.LOW[i], self.UP[i]+1)) choices.remove(ind[i]) ind[i] = random.choice(choices) elif self.datatypes[i] == "float": norm=random.gauss(0,1) if random.random() < self.INDPB: strat[i] *= np.exp(tau*norm + tau_prime * random.gauss(0,1)) ind[i] += strat[i] * random.gauss(0,1) if ind[i] < self.LOW[i]: ind[i] = self.LOW[i] if ind[i] > self.UP[i]: ind[i] = self.UP[i] else: raise Exception('ES mutation strategy works with int, float, or grid distributions, the type provided cannot be interpreted') return ind, strat def GenOffspring(self, pop): pop_indices=list(range(0,len(pop))) offspring=defaultdict(list) for i in range(self.popsize): alpha=random.random() if alpha < self.CXPB: index1, index2=random.sample(pop_indices,2) ind1, ind2, strat1, strat2=self.cx(ind1=list(pop[index1][0]), ind2=list(pop[index2][0]), strat1=list(pop[index1][1]), strat2=list(pop[index2][1])) offspring[i].append(ind1) offspring[i].append(strat1) elif alpha < self.CXPB + self.MUTPB: index = random.choice(pop_indices) ind, strat=self.mutES(ind=list(pop[index][0]), strat=list(pop[index][1])) offspring[i].append(ind) offspring[i].append(strat) else: index=random.choice(pop_indices) offspring[i].append(pop[index][0]) offspring[i].append(pop[index][1]) return offspring def run_cases(self): with open (self.csvlogger, 'w') as fout: fout.write('caseid, ') [fout.write(item + ',') for item in self.param_names] fout.write('mean_reward,max_reward\n') self.population=self.init_pop() case_idx=0 self.currentcase=self.popsize+1 for gen in range(1, self.ngens): case_idx=0 caseids=['ind{}'.format(ind) for ind in range(self.currentcase, self.currentcase+self.popsize+1)] offspring=self.GenOffspring(pop=self.population) if self.ncores > 1: core_list=[] for key in offspring: core_list.append([offspring[key][0], caseids[case_idx]]) case_idx+=1 p=Pool(self.ncores) fitness=p.map(self.gen_object, core_list) p.close(); p.join() [offspring[ind].append(fitness[ind]) for ind in range(len(offspring))] else: for ind in range(len(offspring)): fitness=self.fit(offspring[ind][0], caseids[case_idx]) case_idx+=1 offspring[ind].append(fitness) self.currentcase+=self.popsize self.population = copy.deepcopy(self.select(pop=offspring)) csvdata=pd.read_csv('tune.csv') asc_data=csvdata.sort_values(by=['caseid'],ascending=True) des_data=csvdata.sort_values(by=['mean_reward'],ascending=False) des_data2=csvdata.sort_values(by=['max_reward'],ascending=False) asc_data.to_csv('tune.csv', index=False) mean = np.mean(des_data.iloc[:,4:5]) totalmean=mean.tolist()[0] try: failed_cases=len([print ('failed') for item in self.population if isinstance(item, str)]) except: failed_cases='NA' print ('%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%') print('Mean Rewards for all cases=', totalmean) print ('%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%') print ('All TUNE CASES ARE COMPLETED') print ('%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%') print('--debug: Check tunesummary.txt file for best hyperparameters found') print('--debug: Check tune.csv file for complete csv logger of all cases results') print('--debug: Check tunecases directory for case-by-case detailed results') with open ('tunesummary.txt', 'w') as fout: fout.write(self.logo) fout.write('*****************************************************\n') fout.write('Summary for the TUNE case \n') fout.write('*****************************************************\n') fout.write('Number of cases evaluated: {} \n'.format(self.ncases)) fout.write('Number of failed cases: {} \n'.format(failed_cases)) fout.write('Parameter names: {} \n'.format(self.param_names)) fout.write('Parameter values: {} \n '.format(self.param_dict)) fout.write ('--------------------------------------------------------------------------------------\n') if des_data.shape[0] < 20: top=des_data.shape[0] fout.write ('Top {} hyperparameter configurations ranked according to MEAN reward \n'.format(top)) fout.write(des_data.iloc[:top].to_string(index=False)) else: top=20 fout.write ('Top {} hyperparameter configurations ranked according to MEAN reward \n'.format(top)) fout.write(des_data.iloc[:top].to_string(index=False)) fout.write ('\n') fout.write ('--------------------------------------------------------------------------------------\n') if des_data2.shape[0] < 20: top=des_data2.shape[0] fout.write ('Top {} hyperparameter configurations ranked according to MAX reward \n'.format(top)) fout.write(des_data2.iloc[:top].to_string(index=False)) else: top=20 fout.write ('Top {} hyperparameter configurations ranked according to MAX reward \n'.format(top)) fout.write(des_data2.iloc[:top].to_string(index=False))

true

f70060aa3fd6b00edb6202ecf166cc9464082bba

12,370

py

Python

test_net.py

zhuriheng/faster-rcnn.pytorch

7536b0f5eee254350fb4dce5c4a077ac6d29db16

[ "MIT" ]

null

test_net.py

zhuriheng/faster-rcnn.pytorch

7536b0f5eee254350fb4dce5c4a077ac6d29db16

[ "MIT" ]

null

test_net.py

zhuriheng/faster-rcnn.pytorch

7536b0f5eee254350fb4dce5c4a077ac6d29db16

[ "MIT" ]

null

# -------------------------------------------------------- # Tensorflow Faster R-CNN # Licensed under The MIT License [see LICENSE for details] # Written by Jiasen Lu, Jianwei Yang, based on code from Ross Girshick # -------------------------------------------------------- from __future__ import absolute_import from __future__ import division from __future__ import print_function import _init_paths import os import sys import numpy as np import argparse import pprint import pdb import time import cv2 import torch from torch.autograd import Variable import torch.nn as nn import torch.optim as optim import pickle from roi_data_layer.roidb import combined_roidb from roi_data_layer.roibatchLoader import roibatchLoader from model.utils.config import cfg, cfg_from_file, cfg_from_list, get_output_dir from model.rpn.bbox_transform import clip_boxes from model.nms.nms_wrapper import nms from model.rpn.bbox_transform import bbox_transform_inv from model.utils.net_utils import save_net, load_net, vis_detections from model.faster_rcnn.vgg16 import vgg16 from model.faster_rcnn.resnet import resnet try: xrange # Python 2 except NameError: xrange = range # Python 3 def parse_args(): """ Parse input arguments """ parser = argparse.ArgumentParser(description='Train a Fast R-CNN network') parser.add_argument('--dataset', dest='dataset', help='training dataset', default='pascal_voc', type=str) parser.add_argument('--cfg', dest='cfg_file', help='optional config file', default='cfgs/vgg16.yml', type=str) parser.add_argument('--net', dest='net', help='vgg16, res50, res101, res152', default='res101', type=str) parser.add_argument('--set', dest='set_cfgs', help='set config keys', default=None, nargs=argparse.REMAINDER) parser.add_argument('--load_dir', dest='load_dir', help='directory to load models', default="models", type=str) parser.add_argument('--cuda', dest='cuda', help='whether use CUDA', action='store_true') parser.add_argument('--ls', dest='large_scale', help='whether use large imag scale', action='store_true') parser.add_argument('--mGPUs', dest='mGPUs', help='whether use multiple GPUs', action='store_true') parser.add_argument('--cag', dest='class_agnostic', help='whether perform class_agnostic bbox regression', action='store_true') parser.add_argument('--parallel_type', dest='parallel_type', help='which part of model to parallel, 0: all, 1: model before roi pooling', default=0, type=int) parser.add_argument('--checksession', dest='checksession', help='checksession to load model', default=1, type=int) parser.add_argument('--checkepoch', dest='checkepoch', help='checkepoch to load network', default=1, type=int) parser.add_argument('--checkpoint', dest='checkpoint', help='checkpoint to load network', default=10021, type=int) parser.add_argument('--vis', dest='vis', help='visualization mode', action='store_true') parser.add_argument('--input_dir', dest='input_dir', help='directory to save models', type=str) args = parser.parse_args() return args lr = cfg.TRAIN.LEARNING_RATE momentum = cfg.TRAIN.MOMENTUM weight_decay = cfg.TRAIN.WEIGHT_DECAY if __name__ == '__main__': args = parse_args() print('Called with args:') print(args) if torch.cuda.is_available() and not args.cuda: print("WARNING: You have a CUDA device, so you should probably run with --cuda") np.random.seed(cfg.RNG_SEED) if args.dataset == "pascal_voc": args.imdb_name = "voc_2007_trainval" args.imdbval_name = "voc_2007_test" args.set_cfgs = ['ANCHOR_SCALES', '[8, 16, 32]', 'ANCHOR_RATIOS', '[0.5,1,2]'] elif args.dataset == "pascal_voc_0712": args.imdb_name = "voc_2007_trainval+voc_2012_trainval" args.imdbval_name = "voc_2007_test" args.set_cfgs = ['ANCHOR_SCALES', '[8, 16, 32]', 'ANCHOR_RATIOS', '[0.5,1,2]'] elif args.dataset == "coco": args.imdb_name = "coco_2014_train+coco_2014_valminusminival" args.imdbval_name = "coco_2014_minival" args.set_cfgs = ['ANCHOR_SCALES', '[4, 8, 16, 32]', 'ANCHOR_RATIOS', '[0.5,1,2]'] elif args.dataset == "imagenet": args.imdb_name = "imagenet_train" args.imdbval_name = "imagenet_val" args.set_cfgs = ['ANCHOR_SCALES', '[8, 16, 32]', 'ANCHOR_RATIOS', '[0.5,1,2]'] elif args.dataset == "vg": args.imdb_name = "vg_150-50-50_minitrain" args.imdbval_name = "vg_150-50-50_minival" args.set_cfgs = ['ANCHOR_SCALES', '[4, 8, 16, 32]', 'ANCHOR_RATIOS', '[0.5,1,2]'] args.cfg_file = "cfgs/{}/{}_ls.yml".format(args.dataset, args.net) if args.large_scale else "cfgs/{}/{}.yml".format( args.dataset, args.net) if args.cfg_file is not None: cfg_from_file(args.cfg_file) if args.set_cfgs is not None: cfg_from_list(args.set_cfgs) print('Using config:') pprint.pprint(cfg) cfg.TRAIN.USE_FLIPPED = False imdb, roidb, ratio_list, ratio_index = combined_roidb(args.imdbval_name, False) imdb.competition_mode(on=True) print('{:d} roidb entries'.format(len(roidb))) input_dir = args.input_dir if not os.path.exists(input_dir): raise Exception('There is no input directory for loading network from ' + input_dir) load_name = os.path.join(input_dir, 'faster_rcnn_{}_{}_{}.pth'.format(args.checksession, args.checkepoch, args.checkpoint)) # initilize the network here. if args.net == 'vgg16': fasterRCNN = vgg16(imdb.classes, pretrained=False, class_agnostic=args.class_agnostic) elif args.net == 'res101': fasterRCNN = resnet(imdb.classes, 101, pretrained=False, class_agnostic=args.class_agnostic) elif args.net == 'res50': fasterRCNN = resnet(imdb.classes, 50, pretrained=False, class_agnostic=args.class_agnostic) elif args.net == 'res152': fasterRCNN = resnet(imdb.classes, 152, pretrained=False, class_agnostic=args.class_agnostic) else: print("network is not defined") pdb.set_trace() fasterRCNN.create_architecture() print("load checkpoint %s" % (load_name)) checkpoint = torch.load(load_name) fasterRCNN.load_state_dict(checkpoint['model']) if 'pooling_mode' in checkpoint.keys(): cfg.POOLING_MODE = checkpoint['pooling_mode'] print('load model successfully!') # initilize the tensor holder here. im_data = torch.FloatTensor(1) im_info = torch.FloatTensor(1) num_boxes = torch.LongTensor(1) gt_boxes = torch.FloatTensor(1) # ship to cuda if args.cuda: im_data = im_data.cuda() im_info = im_info.cuda() num_boxes = num_boxes.cuda() gt_boxes = gt_boxes.cuda() # make variable im_data = Variable(im_data) im_info = Variable(im_info) num_boxes = Variable(num_boxes) gt_boxes = Variable(gt_boxes) if args.cuda: cfg.CUDA = True if args.cuda: fasterRCNN.cuda() start = time.time() max_per_image = 100 vis = args.vis if vis: thresh = 0.05 else: thresh = 0.0 save_name = 'faster_rcnn_10' num_images = len(imdb.image_index) all_boxes = [[[] for _ in xrange(num_images)] for _ in xrange(imdb.num_classes)] output_dir = get_output_dir(imdb, save_name) dataset = roibatchLoader(roidb, ratio_list, ratio_index, 1, \ imdb.num_classes, training=False, normalize = False) dataloader = torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=False, num_workers=0, pin_memory=True) data_iter = iter(dataloader) _t = {'im_detect': time.time(), 'misc': time.time()} det_file = os.path.join(output_dir, 'detections.pkl') fasterRCNN.eval() empty_array = np.transpose(np.array([[],[],[],[],[]]), (1,0)) for i in range(num_images): data = next(data_iter) im_data.data.resize_(data[0].size()).copy_(data[0]) im_info.data.resize_(data[1].size()).copy_(data[1]) gt_boxes.data.resize_(data[2].size()).copy_(data[2]) num_boxes.data.resize_(data[3].size()).copy_(data[3]) det_tic = time.time() rois, cls_prob, bbox_pred, \ rpn_loss_cls, rpn_loss_box, \ RCNN_loss_cls, RCNN_loss_bbox, \ rois_label = fasterRCNN(im_data, im_info, gt_boxes, num_boxes) scores = cls_prob.data boxes = rois.data[:, :, 1:5] if cfg.TEST.BBOX_REG: # Apply bounding-box regression deltas box_deltas = bbox_pred.data if cfg.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED: # Optionally normalize targets by a precomputed mean and stdev if args.class_agnostic: box_deltas = box_deltas.view(-1, 4) * torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_STDS).cuda() \ + torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_MEANS).cuda() box_deltas = box_deltas.view(1, -1, 4) else: box_deltas = box_deltas.view(-1, 4) * torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_STDS).cuda() \ + torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_MEANS).cuda() box_deltas = box_deltas.view(1, -1, 4 * len(imdb.classes)) pred_boxes = bbox_transform_inv(boxes, box_deltas, 1) pred_boxes = clip_boxes(pred_boxes, im_info.data, 1) else: # Simply repeat the boxes, once for each class _ = torch.from_numpy(np.tile(boxes, (1, scores.shape[1]))) pred_boxes = _.cuda() if args.cuda > 0 else _ pred_boxes /= data[1][0][2].item() scores = scores.squeeze() pred_boxes = pred_boxes.squeeze() det_toc = time.time() detect_time = det_toc - det_tic misc_tic = time.time() if vis: im = cv2.imread(imdb.image_path_at(i)) im2show = np.copy(im) for j in xrange(1, imdb.num_classes): inds = torch.nonzero(scores[:,j]>thresh).view(-1) # if there is det if inds.numel() > 0: cls_scores = scores[:,j][inds] _, order = torch.sort(cls_scores, 0, True) if args.class_agnostic: cls_boxes = pred_boxes[inds, :] else: cls_boxes = pred_boxes[inds][:, j * 4:(j + 1) * 4] cls_dets = torch.cat((cls_boxes, cls_scores.unsqueeze(1)), 1) # cls_dets = torch.cat((cls_boxes, cls_scores), 1) cls_dets = cls_dets[order] keep = nms(cls_dets, cfg.TEST.NMS) cls_dets = cls_dets[keep.view(-1).long()] if vis: im2show = vis_detections(im2show, imdb.classes[j], cls_dets.cpu().numpy(), 0.3) all_boxes[j][i] = cls_dets.cpu().numpy() else: all_boxes[j][i] = empty_array # Limit to max_per_image detections *over all classes* if max_per_image > 0: image_scores = np.hstack([all_boxes[j][i][:, -1] for j in xrange(1, imdb.num_classes)]) if len(image_scores) > max_per_image: image_thresh = np.sort(image_scores)[-max_per_image] for j in xrange(1, imdb.num_classes): keep = np.where(all_boxes[j][i][:, -1] >= image_thresh)[0] all_boxes[j][i] = all_boxes[j][i][keep, :] misc_toc = time.time() nms_time = misc_toc - misc_tic sys.stdout.write('im_detect: {:d}/{:d} {:.3f}s {:.3f}s \r' \ .format(i + 1, num_images, detect_time, nms_time)) sys.stdout.flush() if vis: cv2.imwrite('result.png', im2show) pdb.set_trace() #cv2.imshow('test', im2show) #cv2.waitKey(0) with open(det_file, 'wb') as f: pickle.dump(all_boxes, f, pickle.HIGHEST_PROTOCOL) print('Evaluating detections') imdb.evaluate_detections(all_boxes, output_dir) end = time.time() print("test time: %0.4fs" % (end - start))

37.147147

118

0.621746

from __future__ import absolute_import from __future__ import division from __future__ import print_function import _init_paths import os import sys import numpy as np import argparse import pprint import pdb import time import cv2 import torch from torch.autograd import Variable import torch.nn as nn import torch.optim as optim import pickle from roi_data_layer.roidb import combined_roidb from roi_data_layer.roibatchLoader import roibatchLoader from model.utils.config import cfg, cfg_from_file, cfg_from_list, get_output_dir from model.rpn.bbox_transform import clip_boxes from model.nms.nms_wrapper import nms from model.rpn.bbox_transform import bbox_transform_inv from model.utils.net_utils import save_net, load_net, vis_detections from model.faster_rcnn.vgg16 import vgg16 from model.faster_rcnn.resnet import resnet try: xrange except NameError: xrange = range def parse_args(): parser = argparse.ArgumentParser(description='Train a Fast R-CNN network') parser.add_argument('--dataset', dest='dataset', help='training dataset', default='pascal_voc', type=str) parser.add_argument('--cfg', dest='cfg_file', help='optional config file', default='cfgs/vgg16.yml', type=str) parser.add_argument('--net', dest='net', help='vgg16, res50, res101, res152', default='res101', type=str) parser.add_argument('--set', dest='set_cfgs', help='set config keys', default=None, nargs=argparse.REMAINDER) parser.add_argument('--load_dir', dest='load_dir', help='directory to load models', default="models", type=str) parser.add_argument('--cuda', dest='cuda', help='whether use CUDA', action='store_true') parser.add_argument('--ls', dest='large_scale', help='whether use large imag scale', action='store_true') parser.add_argument('--mGPUs', dest='mGPUs', help='whether use multiple GPUs', action='store_true') parser.add_argument('--cag', dest='class_agnostic', help='whether perform class_agnostic bbox regression', action='store_true') parser.add_argument('--parallel_type', dest='parallel_type', help='which part of model to parallel, 0: all, 1: model before roi pooling', default=0, type=int) parser.add_argument('--checksession', dest='checksession', help='checksession to load model', default=1, type=int) parser.add_argument('--checkepoch', dest='checkepoch', help='checkepoch to load network', default=1, type=int) parser.add_argument('--checkpoint', dest='checkpoint', help='checkpoint to load network', default=10021, type=int) parser.add_argument('--vis', dest='vis', help='visualization mode', action='store_true') parser.add_argument('--input_dir', dest='input_dir', help='directory to save models', type=str) args = parser.parse_args() return args lr = cfg.TRAIN.LEARNING_RATE momentum = cfg.TRAIN.MOMENTUM weight_decay = cfg.TRAIN.WEIGHT_DECAY if __name__ == '__main__': args = parse_args() print('Called with args:') print(args) if torch.cuda.is_available() and not args.cuda: print("WARNING: You have a CUDA device, so you should probably run with --cuda") np.random.seed(cfg.RNG_SEED) if args.dataset == "pascal_voc": args.imdb_name = "voc_2007_trainval" args.imdbval_name = "voc_2007_test" args.set_cfgs = ['ANCHOR_SCALES', '[8, 16, 32]', 'ANCHOR_RATIOS', '[0.5,1,2]'] elif args.dataset == "pascal_voc_0712": args.imdb_name = "voc_2007_trainval+voc_2012_trainval" args.imdbval_name = "voc_2007_test" args.set_cfgs = ['ANCHOR_SCALES', '[8, 16, 32]', 'ANCHOR_RATIOS', '[0.5,1,2]'] elif args.dataset == "coco": args.imdb_name = "coco_2014_train+coco_2014_valminusminival" args.imdbval_name = "coco_2014_minival" args.set_cfgs = ['ANCHOR_SCALES', '[4, 8, 16, 32]', 'ANCHOR_RATIOS', '[0.5,1,2]'] elif args.dataset == "imagenet": args.imdb_name = "imagenet_train" args.imdbval_name = "imagenet_val" args.set_cfgs = ['ANCHOR_SCALES', '[8, 16, 32]', 'ANCHOR_RATIOS', '[0.5,1,2]'] elif args.dataset == "vg": args.imdb_name = "vg_150-50-50_minitrain" args.imdbval_name = "vg_150-50-50_minival" args.set_cfgs = ['ANCHOR_SCALES', '[4, 8, 16, 32]', 'ANCHOR_RATIOS', '[0.5,1,2]'] args.cfg_file = "cfgs/{}/{}_ls.yml".format(args.dataset, args.net) if args.large_scale else "cfgs/{}/{}.yml".format( args.dataset, args.net) if args.cfg_file is not None: cfg_from_file(args.cfg_file) if args.set_cfgs is not None: cfg_from_list(args.set_cfgs) print('Using config:') pprint.pprint(cfg) cfg.TRAIN.USE_FLIPPED = False imdb, roidb, ratio_list, ratio_index = combined_roidb(args.imdbval_name, False) imdb.competition_mode(on=True) print('{:d} roidb entries'.format(len(roidb))) input_dir = args.input_dir if not os.path.exists(input_dir): raise Exception('There is no input directory for loading network from ' + input_dir) load_name = os.path.join(input_dir, 'faster_rcnn_{}_{}_{}.pth'.format(args.checksession, args.checkepoch, args.checkpoint)) if args.net == 'vgg16': fasterRCNN = vgg16(imdb.classes, pretrained=False, class_agnostic=args.class_agnostic) elif args.net == 'res101': fasterRCNN = resnet(imdb.classes, 101, pretrained=False, class_agnostic=args.class_agnostic) elif args.net == 'res50': fasterRCNN = resnet(imdb.classes, 50, pretrained=False, class_agnostic=args.class_agnostic) elif args.net == 'res152': fasterRCNN = resnet(imdb.classes, 152, pretrained=False, class_agnostic=args.class_agnostic) else: print("network is not defined") pdb.set_trace() fasterRCNN.create_architecture() print("load checkpoint %s" % (load_name)) checkpoint = torch.load(load_name) fasterRCNN.load_state_dict(checkpoint['model']) if 'pooling_mode' in checkpoint.keys(): cfg.POOLING_MODE = checkpoint['pooling_mode'] print('load model successfully!') im_data = torch.FloatTensor(1) im_info = torch.FloatTensor(1) num_boxes = torch.LongTensor(1) gt_boxes = torch.FloatTensor(1) if args.cuda: im_data = im_data.cuda() im_info = im_info.cuda() num_boxes = num_boxes.cuda() gt_boxes = gt_boxes.cuda() im_data = Variable(im_data) im_info = Variable(im_info) num_boxes = Variable(num_boxes) gt_boxes = Variable(gt_boxes) if args.cuda: cfg.CUDA = True if args.cuda: fasterRCNN.cuda() start = time.time() max_per_image = 100 vis = args.vis if vis: thresh = 0.05 else: thresh = 0.0 save_name = 'faster_rcnn_10' num_images = len(imdb.image_index) all_boxes = [[[] for _ in xrange(num_images)] for _ in xrange(imdb.num_classes)] output_dir = get_output_dir(imdb, save_name) dataset = roibatchLoader(roidb, ratio_list, ratio_index, 1, \ imdb.num_classes, training=False, normalize = False) dataloader = torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=False, num_workers=0, pin_memory=True) data_iter = iter(dataloader) _t = {'im_detect': time.time(), 'misc': time.time()} det_file = os.path.join(output_dir, 'detections.pkl') fasterRCNN.eval() empty_array = np.transpose(np.array([[],[],[],[],[]]), (1,0)) for i in range(num_images): data = next(data_iter) im_data.data.resize_(data[0].size()).copy_(data[0]) im_info.data.resize_(data[1].size()).copy_(data[1]) gt_boxes.data.resize_(data[2].size()).copy_(data[2]) num_boxes.data.resize_(data[3].size()).copy_(data[3]) det_tic = time.time() rois, cls_prob, bbox_pred, \ rpn_loss_cls, rpn_loss_box, \ RCNN_loss_cls, RCNN_loss_bbox, \ rois_label = fasterRCNN(im_data, im_info, gt_boxes, num_boxes) scores = cls_prob.data boxes = rois.data[:, :, 1:5] if cfg.TEST.BBOX_REG: box_deltas = bbox_pred.data if cfg.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED: if args.class_agnostic: box_deltas = box_deltas.view(-1, 4) * torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_STDS).cuda() \ + torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_MEANS).cuda() box_deltas = box_deltas.view(1, -1, 4) else: box_deltas = box_deltas.view(-1, 4) * torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_STDS).cuda() \ + torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_MEANS).cuda() box_deltas = box_deltas.view(1, -1, 4 * len(imdb.classes)) pred_boxes = bbox_transform_inv(boxes, box_deltas, 1) pred_boxes = clip_boxes(pred_boxes, im_info.data, 1) else: _ = torch.from_numpy(np.tile(boxes, (1, scores.shape[1]))) pred_boxes = _.cuda() if args.cuda > 0 else _ pred_boxes /= data[1][0][2].item() scores = scores.squeeze() pred_boxes = pred_boxes.squeeze() det_toc = time.time() detect_time = det_toc - det_tic misc_tic = time.time() if vis: im = cv2.imread(imdb.image_path_at(i)) im2show = np.copy(im) for j in xrange(1, imdb.num_classes): inds = torch.nonzero(scores[:,j]>thresh).view(-1) if inds.numel() > 0: cls_scores = scores[:,j][inds] _, order = torch.sort(cls_scores, 0, True) if args.class_agnostic: cls_boxes = pred_boxes[inds, :] else: cls_boxes = pred_boxes[inds][:, j * 4:(j + 1) * 4] cls_dets = torch.cat((cls_boxes, cls_scores.unsqueeze(1)), 1) cls_dets = cls_dets[order] keep = nms(cls_dets, cfg.TEST.NMS) cls_dets = cls_dets[keep.view(-1).long()] if vis: im2show = vis_detections(im2show, imdb.classes[j], cls_dets.cpu().numpy(), 0.3) all_boxes[j][i] = cls_dets.cpu().numpy() else: all_boxes[j][i] = empty_array if max_per_image > 0: image_scores = np.hstack([all_boxes[j][i][:, -1] for j in xrange(1, imdb.num_classes)]) if len(image_scores) > max_per_image: image_thresh = np.sort(image_scores)[-max_per_image] for j in xrange(1, imdb.num_classes): keep = np.where(all_boxes[j][i][:, -1] >= image_thresh)[0] all_boxes[j][i] = all_boxes[j][i][keep, :] misc_toc = time.time() nms_time = misc_toc - misc_tic sys.stdout.write('im_detect: {:d}/{:d} {:.3f}s {:.3f}s \r' \ .format(i + 1, num_images, detect_time, nms_time)) sys.stdout.flush() if vis: cv2.imwrite('result.png', im2show) pdb.set_trace() with open(det_file, 'wb') as f: pickle.dump(all_boxes, f, pickle.HIGHEST_PROTOCOL) print('Evaluating detections') imdb.evaluate_detections(all_boxes, output_dir) end = time.time() print("test time: %0.4fs" % (end - start))

true

f7006109577a06b46b567649a683de1311a12be8

270

py

Python

.github/templates/flask/module/__main__.py

mondbev1/python-project-template

74e8c903643cca2971c09cb3fff08287dfa74bcc

[ "Unlicense" ]

null

.github/templates/flask/module/__main__.py

mondbev1/python-project-template

74e8c903643cca2971c09cb3fff08287dfa74bcc

[ "Unlicense" ]

null

.github/templates/flask/module/__main__.py

mondbev1/python-project-template

74e8c903643cca2971c09cb3fff08287dfa74bcc

[ "Unlicense" ]

null

import click from flask.cli import FlaskGroup from . import create_app @click.group(cls=FlaskGroup, create_app=create_app) def main(): """Management script for the python_project_template application.""" if __name__ == "__main__": # pragma: no cover main()

20.769231

72

0.740741

import click from flask.cli import FlaskGroup from . import create_app @click.group(cls=FlaskGroup, create_app=create_app) def main(): if __name__ == "__main__": main()

true

f700615e2a905b6e5d941c75f337b6670c36b49b

3,149

py

Python

system/system.py

hirune924/kaggle-HuBMAP

e4c2008378eb773db551cee52380bfccdf3a10fa

[ "Apache-2.0" ]

null

system/system.py

hirune924/kaggle-HuBMAP

e4c2008378eb773db551cee52380bfccdf3a10fa

[ "Apache-2.0" ]

null

system/system.py

hirune924/kaggle-HuBMAP

e4c2008378eb773db551cee52380bfccdf3a10fa

[ "Apache-2.0" ]

null

import pytorch_lightning as pl from loss.loss import get_loss from optimizer.optimizer import get_optimizer from scheduler.scheduler import get_scheduler import torch import numpy as np from pytorch_lightning.metrics import Accuracy import segmentation_models_pytorch as smp from utils.utils import load_obj import albumentations as A from utils.preprocessing import * import shutil class LitClassifier(pl.LightningModule): def __init__(self, hparams, model): super().__init__() self.save_hyperparameters(hparams) self.model = model self.criteria = get_loss(hparams.training.loss) #self.accuracy = Accuracy() self.dice = smp.utils.losses.DiceLoss(activation='sigmoid') def forward(self, x): # use forward for inference/predictions return self.model(x) def configure_optimizers(self): optimizer = get_optimizer(self.model.parameters(), self.hparams.training.optimizer) scheduler = get_scheduler(optimizer, self.hparams.training.scheduler) return [optimizer], [scheduler] def training_step(self, batch, batch_idx): x, y = batch if self.hparams.dataset.mixup: num_batch = self.hparams.dataset.batch_size alpha = 0.2 #rnd = torch.from_numpy(np.random.beta(alpha,alpha,int(num_batch/2))).type_as(x) #rnd = rnd.reshape(int(num_batch/2), 1, 1, 1) #x = x[:int(num_batch/2)]*rnd + x[int(num_batch/2):]*(1-rnd) #y = y[:int(num_batch/2)]*rnd + y[int(num_batch/2):]*(1-rnd) rnd = torch.from_numpy(np.random.beta(alpha,alpha,1)).type_as(x) x = x[:int(num_batch/2)]*rnd + x[int(num_batch/2):]*(1-rnd) y_hat = self.model(x) if self.hparams.dataset.mixup: loss = self.criteria(y_hat, y[:int(num_batch/2)])*rnd + self.criteria(y_hat, y[int(num_batch/2):])*(1-rnd) else: loss = self.criteria(y_hat, y) self.log('train_loss', loss, on_epoch=True) return loss def validation_step(self, batch, batch_idx): x, y = batch y_hat = self.model(x) loss = self.criteria(y_hat, y) dice = 1-self.dice(y_hat, y) #self.log('val_loss', loss) #self.log('val_dice', dice) return { "val_loss": loss, "val_dice": dice } def validation_epoch_end(self, outputs): avg_val_loss = torch.stack([x["val_loss"] for x in outputs]).mean() avg_val_dice = torch.stack([x["val_dice"] for x in outputs]).mean() self.log('val_loss', avg_val_loss) self.log('val_dice', avg_val_dice) #y = torch.cat([x["y"] for x in outputs]).cpu() #y_hat = torch.cat([x["y_hat"] for x in outputs]).cpu() #preds = np.argmax(y_hat, axis=1) #val_accuracy = self.accuracy(y, preds) #self.log('avg_val_loss', avg_val_loss) #self.log('val_acc', val_accuracy) def test_step(self, batch, batch_idx): x, y = batch y_hat = self.model(x) loss = self.criteria(y_hat, y) self.log('test_loss', loss)

33.5

118

0.621467

import pytorch_lightning as pl from loss.loss import get_loss from optimizer.optimizer import get_optimizer from scheduler.scheduler import get_scheduler import torch import numpy as np from pytorch_lightning.metrics import Accuracy import segmentation_models_pytorch as smp from utils.utils import load_obj import albumentations as A from utils.preprocessing import * import shutil class LitClassifier(pl.LightningModule): def __init__(self, hparams, model): super().__init__() self.save_hyperparameters(hparams) self.model = model self.criteria = get_loss(hparams.training.loss) self.dice = smp.utils.losses.DiceLoss(activation='sigmoid') def forward(self, x): return self.model(x) def configure_optimizers(self): optimizer = get_optimizer(self.model.parameters(), self.hparams.training.optimizer) scheduler = get_scheduler(optimizer, self.hparams.training.scheduler) return [optimizer], [scheduler] def training_step(self, batch, batch_idx): x, y = batch if self.hparams.dataset.mixup: num_batch = self.hparams.dataset.batch_size alpha = 0.2 rnd = torch.from_numpy(np.random.beta(alpha,alpha,1)).type_as(x) x = x[:int(num_batch/2)]*rnd + x[int(num_batch/2):]*(1-rnd) y_hat = self.model(x) if self.hparams.dataset.mixup: loss = self.criteria(y_hat, y[:int(num_batch/2)])*rnd + self.criteria(y_hat, y[int(num_batch/2):])*(1-rnd) else: loss = self.criteria(y_hat, y) self.log('train_loss', loss, on_epoch=True) return loss def validation_step(self, batch, batch_idx): x, y = batch y_hat = self.model(x) loss = self.criteria(y_hat, y) dice = 1-self.dice(y_hat, y) return { "val_loss": loss, "val_dice": dice } def validation_epoch_end(self, outputs): avg_val_loss = torch.stack([x["val_loss"] for x in outputs]).mean() avg_val_dice = torch.stack([x["val_dice"] for x in outputs]).mean() self.log('val_loss', avg_val_loss) self.log('val_dice', avg_val_dice) def test_step(self, batch, batch_idx): x, y = batch y_hat = self.model(x) loss = self.criteria(y_hat, y) self.log('test_loss', loss)

true

f70061697e12c46d39594f2bf2f9bb8e344f31c7

9,929

py

Python

coronatest_analyze_csv.py

han-kwang/coronatest-scandata

98fd49f4fdcda10561bce41e769bbbb70ecfe94e

[ "MIT" ]

null

coronatest_analyze_csv.py

han-kwang/coronatest-scandata

98fd49f4fdcda10561bce41e769bbbb70ecfe94e

[ "MIT" ]

null

coronatest_analyze_csv.py

han-kwang/coronatest-scandata

98fd49f4fdcda10561bce41e769bbbb70ecfe94e

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """Analyze CSV file into scores. Created on Sat Feb 12 22:15:29 2022 // @hk_nien """ from pathlib import Path import os import re import sys import pandas as pd import numpy as np PCODES = dict([ # Regio Noord (1011, 'Amsterdam'), (1625, 'Hoorn|Zwaag'), (1811, 'Alkmaar'), (7471, 'Goor'), (7556, 'Hengelo'), (7903, 'Hoogeveen'), (7942, 'Meppel'), (8011, 'Zwolle'), (8232, 'Lelystad'), (8442, 'Heerenveen'), (8911, 'Leeuwarden'), (9291, 'Kollum'), (9501, 'Stadskanaal'), (9726, 'Groningen'), # Regio Midden (2406, 'Alphen a/d Rijn'), (2515, 'Den Haag'), (3013, 'Rotterdam'), (3511, 'Utrecht'), (3901, 'Veenendaal'), ((7137, 7131), 'Lichtenvoorde|Groenlo'), (7311, 'Apeldoorn'), # Regio Zuid (4325, 'Renesse'), (4462, 'Goes'), (4701, 'Roosendaal'), (5038, 'Tilburg'), (5401, 'Uden'), (5611, 'Eindhoven'), (5801, 'Oostrum'), (6101, 'Echt'), (6229, 'Maastricht'), (6541, 'Nijmegen'), ]) def get_bad_scan_times(): """Return list of Timestamps with bad scan times, from CSV data.""" df = pd.read_csv('data-ggd/ggd_bad_scans.txt', comment='#') tstamps = pd.to_datetime(df['Timestamp']).to_list() return tstamps def _mean_time(ts_list): """Return mean timestamp value from list of timestamps.""" ts0 = ts_list[0] delta_sum = pd.Timedelta(0) for ts in ts_list: delta_sum += (ts -ts0) ts_mean = ts0 + delta_sum / len(ts_list) return ts_mean def _delta_time_hhmm(hm): """Convert 'hh:mm' string to TimeDelta.""" return pd.Timedelta(f'{hm}:00') def _summary_to_scores(summary): """Convert summary from _read_log to scores dict and effective timestamp. Parameters: - summary: dict with int(pc4) -> [(query_time, appt_time), ...] Return: - scores dict: int(pc4) -> score (int or float or '?') - timestamp: middle query timestamp of this run. """ # Convert to number codes. scores = {k: '?' for k in PCODES} multi_pcs = {} # pc4 -> (pc4[0], pc4[1], ...) for pc in PCODES: if isinstance(pc, tuple): for pc1 in pc: multi_pcs[pc1] = pc qtms = [] dhm = _delta_time_hhmm for pc4, vlist in summary.items(): pc4 = int(pc4) if pc4 not in scores: if pc4 in multi_pcs: pc4_key = multi_pcs[pc4] else: print(f'{pc4} not in list...') continue else: pc4_key = pc4 if len(vlist) == 0: scores[pc4_key] = 7 continue qtm = _mean_time([v[0] for v in vlist]) # query time qtms.append(qtm) atm = min(v[1] for v in vlist) # earliest appointment time qtm_00 = pd.Timestamp(qtm.strftime('%Y-%m-%dT00:00')) thresholds = [ (3, qtm_00 + dhm('23:59')), (4, qtm + dhm('24:00')), (5, qtm_00 + dhm('48:00')), (6, qtm + dhm('48:00')), (6.3, qtm_00 + dhm('72:00')), (6.7, qtm + dhm('72:00')), (7, atm) ] if qtm.hour < 9: thresholds.insert(0, (1, qtm_00 + dhm('13:00'))) elif qtm.hour < 13: thresholds.insert(0, (1, qtm + dhm('4:00'))) elif qtm.hour < 17: thresholds.insert(0, (1, qtm_00 + dhm('24:00'))) thresholds.insert(1, (2, qtm + dhm('20:00'))) else: thresholds.insert(0, (1, qtm_00 + dhm('24:00'))) thresholds.insert(1, (2, qtm_00 + dhm('37:00'))) for s, tm in thresholds: if atm < tm: scores[pc4_key] = s break if len(qtms) == 0: qtm_mid = pd.Timestamp(None) else: qtm_min = min(qtms) qtm_mid = qtm_min + (max(qtms) - qtm_min)/2 return scores, qtm_mid def _get_min_wait(summary): """Return minimum and median wait Timedelta between scan time and appointment. summary is dict of pc4 -> list of timestamps No data -> 999 h. For the median, NaT is counted as infinite. """ wtimes = [] for _, vlist in summary.items(): wtimes_this = [atm - qtm for qtm, atm in vlist] wtimes.append( min(wtimes_this) if wtimes_this else pd.Timedelta(99, 'h') ) minwait = min(wtimes) if wtimes else 999 medwait = pd.Timedelta(np.median(wtimes)) return minwait, medwait def load_csv(csv_fname): """Return DataFrame and list of start times (+1).""" df = pd.read_csv(csv_fname, comment='#') df['req_pc4'] = df['req_pc4'].astype(int) for c in df.columns: if c.endswith('_time') or c.endswith('_date'): df[c] = pd.to_datetime(df[c]) else: df.loc[df[c].isna(), c] = None # start_tms: list of scan start times (plus one extra at the end) start_tms = df.loc[df['scan_time'].diff() > pd.Timedelta('10 min'), 'scan_time'] start_tms = [df.iloc[0]['scan_time']] + list(start_tms) start_tms += [df.iloc[-1]['scan_time'] + pd.Timedelta('1 min')] return df, start_tms def load_multi_csvs(csv_fnames): """Return DataFrame and list of start times (+1)""" dfs = [] start_tms = [] for f in csv_fnames: df, st = load_csv(f) dfs.append(df) start_tms.extend(st[:-1]) df = pd.concat(dfs).reset_index() start_tms.append(df.iloc[-1]['scan_time'] + pd.Timedelta('1 min')) return df, start_tms def get_scan_scores(df, tm_range): """Get scan scores as pc4 -> score dict. Parameters: - df: DataFrame with scan_time, req_date, req_pc4, opt0_short_addr, opt0_time, opt0_loc_id, etc. - tm_range: (tm_start, tm_stop) timestamps. Return: - tstamp: timestamp of the scan (mid-point) - scores: dict of pc4->score - min_wait: Timedelta of minimum wait time from scan to appointment """ mask = (df['scan_time'] >= tm_range[0]) & (df['scan_time'] < tm_range[1]) df1 = df.loc[mask] summary = {} for pc4, city_re in PCODES.items(): pc4_tup = (pc4,) if isinstance(pc4, int) else pc4 options = [] req_pc4 = None for _, row in df1.loc[df1['req_pc4'].isin(pc4_tup)].iterrows(): req_pc4 = int(row['req_pc4']) for i in range(3): addr = row[f'opt{i}_short_addr'] if addr and re.match(f'{city_re}$', addr[5:]): options.append((row['scan_time'], row[f'opt{i}_time'])) if req_pc4 is not None: summary[req_pc4] = options scores, tstamp = _summary_to_scores(summary) if pd.isna(tstamp): tstamp = df1.iloc[len(df1)//2]['scan_time'] minwait, medwait = _get_min_wait(summary) if medwait == 999: medwait = pd.Timedelta(None) return tstamp, scores, minwait, medwait def get_scan_scores_df(df, tm_ranges, decimal_comma=True): """Get scan scores as dataframe, from csv dataframe. Blacklisted scan times are dropped. Parameters: - df: DataFrame with scan_time, req_date, req_pc4, opt0_short_addr, opt0_time, opt0_loc_id, etc. - tm_ranges: list of timestamps (+one at the end) with boundaries of timestamp ranges. - decimal_comma: True to have string values 6,3 rather than float 6.3. Return: - Dataframe with scores, date_str, time_str, pc4, min_wait, med_wait as columns. """ n = len(tm_ranges) records = [] index = [] minwait_hs = [] medwait_hs = [] bad_stimes = get_bad_scan_times() for i in range(n-1): tm_ra = tm_ranges[i:i+2] is_ok = True for tm in bad_stimes: if tm_ra[0] <= tm < tm_ra[1]: is_ok = False break if not is_ok: print(f'Dropped scan at {tm_ra[0].strftime("%Y-%m-%d %H:%M")}') continue tm, scores, minwait, medwait = get_scan_scores(df, tm_ra) records.append(scores) index.append(tm) minwait_hs.append(minwait.total_seconds() / 3600) medwait_hs.append(medwait.total_seconds() / 3600) dates = [t.strftime('%Y-%m-%d') for t in index] times = [t.strftime('%H:%M') for t in index] sdf = pd.DataFrame.from_records(records) sdf.insert(0, 'Time', times) sdf.insert(0, 'Date', dates) sdf['min_wait_h'] = np.around(minwait_hs, 2) sdf['med_wait_h'] = np.around(medwait_hs, 2) sdf.loc[sdf['min_wait_h'].isna(), 'min_wait_h'] = 999 sdf.columns = [ ('/'.join([str(x) for x in c]) if isinstance(c, tuple) else c) for c in sdf.columns ] if decimal_comma: for c in sdf.columns[2:]: sdf[c] = sdf[c].astype(str) sdf[c] = sdf[c].str.replace('.', ',', regex=False) sdf[c] = sdf[c].str.replace(',0$', '', regex=False) sdf[c] = sdf[c].str.replace('?', '', regex=False) return sdf if __name__ == '__main__': in_spyder = ('SPYDER_ARGS' in os.environ) csv_fnames = sorted(Path('data-ggd').glob('ggd_scan-????-W??.csv')) do_all = ('--all' in sys.argv) do_all = do_all or in_spyder and input('(A)ll or latest?').lower() == 'a' if do_all: df, start_tms = load_multi_csvs(csv_fnames) sdf = get_scan_scores_df(df, start_tms).iloc[::-1] else: df, start_tms = load_csv(csv_fnames[-1]) sdf = get_scan_scores_df(df, start_tms[-2:]) print(sdf) if len(sdf) > 1: sdf.to_clipboard(index=False) print('Copied to clipboard including headers') elif len(sdf) == 1: sdf.iloc[[0], 2:].to_clipboard(header=False, index=False) print('Copied to clipboard, scores only.') else: print('No output.') if not in_spyder: # Note: in Spyder, copy/paste will stall while input is blocked. input('Press Enter to quit and clear clipboard.')

30.835404

84

0.570551

from pathlib import Path import os import re import sys import pandas as pd import numpy as np PCODES = dict([ (1011, 'Amsterdam'), (1625, 'Hoorn|Zwaag'), (1811, 'Alkmaar'), (7471, 'Goor'), (7556, 'Hengelo'), (7903, 'Hoogeveen'), (7942, 'Meppel'), (8011, 'Zwolle'), (8232, 'Lelystad'), (8442, 'Heerenveen'), (8911, 'Leeuwarden'), (9291, 'Kollum'), (9501, 'Stadskanaal'), (9726, 'Groningen'), (2406, 'Alphen a/d Rijn'), (2515, 'Den Haag'), (3013, 'Rotterdam'), (3511, 'Utrecht'), (3901, 'Veenendaal'), ((7137, 7131), 'Lichtenvoorde|Groenlo'), (7311, 'Apeldoorn'), (4325, 'Renesse'), (4462, 'Goes'), (4701, 'Roosendaal'), (5038, 'Tilburg'), (5401, 'Uden'), (5611, 'Eindhoven'), (5801, 'Oostrum'), (6101, 'Echt'), (6229, 'Maastricht'), (6541, 'Nijmegen'), ]) def get_bad_scan_times(): df = pd.read_csv('data-ggd/ggd_bad_scans.txt', comment='#') tstamps = pd.to_datetime(df['Timestamp']).to_list() return tstamps def _mean_time(ts_list): ts0 = ts_list[0] delta_sum = pd.Timedelta(0) for ts in ts_list: delta_sum += (ts -ts0) ts_mean = ts0 + delta_sum / len(ts_list) return ts_mean def _delta_time_hhmm(hm): return pd.Timedelta(f'{hm}:00') def _summary_to_scores(summary): scores = {k: '?' for k in PCODES} multi_pcs = {} for pc in PCODES: if isinstance(pc, tuple): for pc1 in pc: multi_pcs[pc1] = pc qtms = [] dhm = _delta_time_hhmm for pc4, vlist in summary.items(): pc4 = int(pc4) if pc4 not in scores: if pc4 in multi_pcs: pc4_key = multi_pcs[pc4] else: print(f'{pc4} not in list...') continue else: pc4_key = pc4 if len(vlist) == 0: scores[pc4_key] = 7 continue qtm = _mean_time([v[0] for v in vlist]) qtms.append(qtm) atm = min(v[1] for v in vlist) qtm_00 = pd.Timestamp(qtm.strftime('%Y-%m-%dT00:00')) thresholds = [ (3, qtm_00 + dhm('23:59')), (4, qtm + dhm('24:00')), (5, qtm_00 + dhm('48:00')), (6, qtm + dhm('48:00')), (6.3, qtm_00 + dhm('72:00')), (6.7, qtm + dhm('72:00')), (7, atm) ] if qtm.hour < 9: thresholds.insert(0, (1, qtm_00 + dhm('13:00'))) elif qtm.hour < 13: thresholds.insert(0, (1, qtm + dhm('4:00'))) elif qtm.hour < 17: thresholds.insert(0, (1, qtm_00 + dhm('24:00'))) thresholds.insert(1, (2, qtm + dhm('20:00'))) else: thresholds.insert(0, (1, qtm_00 + dhm('24:00'))) thresholds.insert(1, (2, qtm_00 + dhm('37:00'))) for s, tm in thresholds: if atm < tm: scores[pc4_key] = s break if len(qtms) == 0: qtm_mid = pd.Timestamp(None) else: qtm_min = min(qtms) qtm_mid = qtm_min + (max(qtms) - qtm_min)/2 return scores, qtm_mid def _get_min_wait(summary): wtimes = [] for _, vlist in summary.items(): wtimes_this = [atm - qtm for qtm, atm in vlist] wtimes.append( min(wtimes_this) if wtimes_this else pd.Timedelta(99, 'h') ) minwait = min(wtimes) if wtimes else 999 medwait = pd.Timedelta(np.median(wtimes)) return minwait, medwait def load_csv(csv_fname): df = pd.read_csv(csv_fname, comment='#') df['req_pc4'] = df['req_pc4'].astype(int) for c in df.columns: if c.endswith('_time') or c.endswith('_date'): df[c] = pd.to_datetime(df[c]) else: df.loc[df[c].isna(), c] = None start_tms = df.loc[df['scan_time'].diff() > pd.Timedelta('10 min'), 'scan_time'] start_tms = [df.iloc[0]['scan_time']] + list(start_tms) start_tms += [df.iloc[-1]['scan_time'] + pd.Timedelta('1 min')] return df, start_tms def load_multi_csvs(csv_fnames): dfs = [] start_tms = [] for f in csv_fnames: df, st = load_csv(f) dfs.append(df) start_tms.extend(st[:-1]) df = pd.concat(dfs).reset_index() start_tms.append(df.iloc[-1]['scan_time'] + pd.Timedelta('1 min')) return df, start_tms def get_scan_scores(df, tm_range): mask = (df['scan_time'] >= tm_range[0]) & (df['scan_time'] < tm_range[1]) df1 = df.loc[mask] summary = {} for pc4, city_re in PCODES.items(): pc4_tup = (pc4,) if isinstance(pc4, int) else pc4 options = [] req_pc4 = None for _, row in df1.loc[df1['req_pc4'].isin(pc4_tup)].iterrows(): req_pc4 = int(row['req_pc4']) for i in range(3): addr = row[f'opt{i}_short_addr'] if addr and re.match(f'{city_re}$', addr[5:]): options.append((row['scan_time'], row[f'opt{i}_time'])) if req_pc4 is not None: summary[req_pc4] = options scores, tstamp = _summary_to_scores(summary) if pd.isna(tstamp): tstamp = df1.iloc[len(df1)//2]['scan_time'] minwait, medwait = _get_min_wait(summary) if medwait == 999: medwait = pd.Timedelta(None) return tstamp, scores, minwait, medwait def get_scan_scores_df(df, tm_ranges, decimal_comma=True): n = len(tm_ranges) records = [] index = [] minwait_hs = [] medwait_hs = [] bad_stimes = get_bad_scan_times() for i in range(n-1): tm_ra = tm_ranges[i:i+2] is_ok = True for tm in bad_stimes: if tm_ra[0] <= tm < tm_ra[1]: is_ok = False break if not is_ok: print(f'Dropped scan at {tm_ra[0].strftime("%Y-%m-%d %H:%M")}') continue tm, scores, minwait, medwait = get_scan_scores(df, tm_ra) records.append(scores) index.append(tm) minwait_hs.append(minwait.total_seconds() / 3600) medwait_hs.append(medwait.total_seconds() / 3600) dates = [t.strftime('%Y-%m-%d') for t in index] times = [t.strftime('%H:%M') for t in index] sdf = pd.DataFrame.from_records(records) sdf.insert(0, 'Time', times) sdf.insert(0, 'Date', dates) sdf['min_wait_h'] = np.around(minwait_hs, 2) sdf['med_wait_h'] = np.around(medwait_hs, 2) sdf.loc[sdf['min_wait_h'].isna(), 'min_wait_h'] = 999 sdf.columns = [ ('/'.join([str(x) for x in c]) if isinstance(c, tuple) else c) for c in sdf.columns ] if decimal_comma: for c in sdf.columns[2:]: sdf[c] = sdf[c].astype(str) sdf[c] = sdf[c].str.replace('.', ',', regex=False) sdf[c] = sdf[c].str.replace(',0$', '', regex=False) sdf[c] = sdf[c].str.replace('?', '', regex=False) return sdf if __name__ == '__main__': in_spyder = ('SPYDER_ARGS' in os.environ) csv_fnames = sorted(Path('data-ggd').glob('ggd_scan-????-W??.csv')) do_all = ('--all' in sys.argv) do_all = do_all or in_spyder and input('(A)ll or latest?').lower() == 'a' if do_all: df, start_tms = load_multi_csvs(csv_fnames) sdf = get_scan_scores_df(df, start_tms).iloc[::-1] else: df, start_tms = load_csv(csv_fnames[-1]) sdf = get_scan_scores_df(df, start_tms[-2:]) print(sdf) if len(sdf) > 1: sdf.to_clipboard(index=False) print('Copied to clipboard including headers') elif len(sdf) == 1: sdf.iloc[[0], 2:].to_clipboard(header=False, index=False) print('Copied to clipboard, scores only.') else: print('No output.') if not in_spyder: input('Press Enter to quit and clear clipboard.')

true

f700617c620c0aa66f529697d1f5e651108eedf8

4,485

py

Python

Starter_Code 2/qualifier/app.py

RonakAgarwal/FinTech_M2_CH

78bcd41bf1944106106bb6e31bd4a8d92d974565

[ "MIT" ]

null

Starter_Code 2/qualifier/app.py

RonakAgarwal/FinTech_M2_CH

78bcd41bf1944106106bb6e31bd4a8d92d974565

[ "MIT" ]

null

Starter_Code 2/qualifier/app.py

RonakAgarwal/FinTech_M2_CH

78bcd41bf1944106106bb6e31bd4a8d92d974565

[ "MIT" ]

null

# -*- coding: utf-8 -*- """Loan Qualifier Application. This is a command line application to match applicants with qualifying loans. Example: $ python app.py """ from re import T import sys import fire import questionary from pathlib import Path import csv from qualifier.utils.fileio import ( load_csv, save_csv, ) from qualifier.utils.calculators import ( calculate_monthly_debt_ratio, calculate_loan_to_value_ratio, ) from qualifier.filters.max_loan_size import filter_max_loan_size from qualifier.filters.credit_score import filter_credit_score from qualifier.filters.debt_to_income import filter_debt_to_income from qualifier.filters.loan_to_value import filter_loan_to_value def load_bank_data(): """Ask for the file path to the latest banking data and load the CSV file. Returns: The bank data from the data rate sheet CSV file. """ csvpath = questionary.text("Enter a file path to a rate-sheet (.csv):").ask() csvpath = Path(csvpath) if not csvpath.exists(): sys.exit(f"Oops! Can't find this path: {csvpath}") return load_csv(csvpath) def get_applicant_info(): """Prompt dialog to get the applicant's financial information. Returns: Returns the applicant's financial information. """ credit_score = questionary.text("What's your credit score?").ask() debt = questionary.text("What's your current amount of monthly debt?").ask() income = questionary.text("What's your total monthly income?").ask() loan_amount = questionary.text("What's your desired loan amount?").ask() home_value = questionary.text("What's your home value?").ask() credit_score = int(credit_score) debt = float(debt) income = float(income) loan_amount = float(loan_amount) home_value = float(home_value) return credit_score, debt, income, loan_amount, home_value def find_qualifying_loans(bank_data, credit_score, debt, income, loan, home_value): """Determine which loans the user qualifies for. Loan qualification criteria is based on: - Credit Score - Loan Size - Debit to Income ratio (calculated) - Loan to Value ratio (calculated) Args: bank_data (list): A list of bank data. credit_score (int): The applicant's current credit score. debt (float): The applicant's total monthly debt payments. income (float): The applicant's total monthly income. loan (float): The total loan amount applied for. home_value (float): The estimated home value. Returns: A list of the banks willing to underwrite the loan. """ # Calculate the monthly debt ratio monthly_debt_ratio = calculate_monthly_debt_ratio(debt, income) print(f"The monthly debt to income ratio is {monthly_debt_ratio:.02f}") # Calculate loan to value ratio loan_to_value_ratio = calculate_loan_to_value_ratio(loan, home_value) print(f"The loan to value ratio is {loan_to_value_ratio:.02f}.") # Run qualification filters bank_data_filtered = filter_max_loan_size(loan, bank_data) bank_data_filtered = filter_credit_score(credit_score, bank_data_filtered) bank_data_filtered = filter_debt_to_income(monthly_debt_ratio, bank_data_filtered) bank_data_filtered = filter_loan_to_value(loan_to_value_ratio, bank_data_filtered) print(f"Found {len(bank_data_filtered)} qualifying loans") return bank_data_filtered def save_qualifying_loans(qualifying_loans): """Saves the qualifying loans to a CSV file. Args: qualifying_loans (list of lists): The qualifying bank loans. """ # @TODO: Complete the usability dialog for savings the CSV Files. # YOUR CODE HERE! choice = questionary.confirm ("Would you like to save the qualifying loans?").ask() if (choice == T) : filepath = questionary.text ("Please enter the file path").ask() save_csv(qualifying_loans, filepath) def run(): """The main function for running the script.""" # Load the latest Bank data bank_data = load_bank_data() # Get the applicant's information credit_score, debt, income, loan_amount, home_value = get_applicant_info() # Find qualifying loans qualifying_loans = find_qualifying_loans( bank_data, credit_score, debt, income, loan_amount, home_value ) # Save qualifying loans save_qualifying_loans(qualifying_loans) if __name__ == "__main__": fire.Fire(run)

30.719178

87

0.716611

from re import T import sys import fire import questionary from pathlib import Path import csv from qualifier.utils.fileio import ( load_csv, save_csv, ) from qualifier.utils.calculators import ( calculate_monthly_debt_ratio, calculate_loan_to_value_ratio, ) from qualifier.filters.max_loan_size import filter_max_loan_size from qualifier.filters.credit_score import filter_credit_score from qualifier.filters.debt_to_income import filter_debt_to_income from qualifier.filters.loan_to_value import filter_loan_to_value def load_bank_data(): csvpath = questionary.text("Enter a file path to a rate-sheet (.csv):").ask() csvpath = Path(csvpath) if not csvpath.exists(): sys.exit(f"Oops! Can't find this path: {csvpath}") return load_csv(csvpath) def get_applicant_info(): credit_score = questionary.text("What's your credit score?").ask() debt = questionary.text("What's your current amount of monthly debt?").ask() income = questionary.text("What's your total monthly income?").ask() loan_amount = questionary.text("What's your desired loan amount?").ask() home_value = questionary.text("What's your home value?").ask() credit_score = int(credit_score) debt = float(debt) income = float(income) loan_amount = float(loan_amount) home_value = float(home_value) return credit_score, debt, income, loan_amount, home_value def find_qualifying_loans(bank_data, credit_score, debt, income, loan, home_value): monthly_debt_ratio = calculate_monthly_debt_ratio(debt, income) print(f"The monthly debt to income ratio is {monthly_debt_ratio:.02f}") loan_to_value_ratio = calculate_loan_to_value_ratio(loan, home_value) print(f"The loan to value ratio is {loan_to_value_ratio:.02f}.") bank_data_filtered = filter_max_loan_size(loan, bank_data) bank_data_filtered = filter_credit_score(credit_score, bank_data_filtered) bank_data_filtered = filter_debt_to_income(monthly_debt_ratio, bank_data_filtered) bank_data_filtered = filter_loan_to_value(loan_to_value_ratio, bank_data_filtered) print(f"Found {len(bank_data_filtered)} qualifying loans") return bank_data_filtered def save_qualifying_loans(qualifying_loans): choice = questionary.confirm ("Would you like to save the qualifying loans?").ask() if (choice == T) : filepath = questionary.text ("Please enter the file path").ask() save_csv(qualifying_loans, filepath) def run(): bank_data = load_bank_data() credit_score, debt, income, loan_amount, home_value = get_applicant_info() # Find qualifying loans qualifying_loans = find_qualifying_loans( bank_data, credit_score, debt, income, loan_amount, home_value ) # Save qualifying loans save_qualifying_loans(qualifying_loans) if __name__ == "__main__": fire.Fire(run)

true

f70061f5cb80071c33ec1a33d265c8b68682e42c

26,852

py

Python

vnpy/app/portfolio_strategy/backtesting.py

hardywu/vnpy

81ab73dc57d12a3ff7c74c73665513b46fc0f668

[ "MIT" ]

1

2021-05-14T12:57:08.000Z

vnpy/app/portfolio_strategy/backtesting.py

hardywu/vnpy

81ab73dc57d12a3ff7c74c73665513b46fc0f668

[ "MIT" ]

null

vnpy/app/portfolio_strategy/backtesting.py

hardywu/vnpy

81ab73dc57d12a3ff7c74c73665513b46fc0f668

[ "MIT" ]

1

2021-06-14T13:26:41.000Z

from collections import defaultdict from datetime import date, datetime, timedelta from typing import Dict, List, Set, Tuple from functools import lru_cache from copy import copy import traceback import numpy as np import plotly.graph_objects as go from plotly.subplots import make_subplots from pandas import DataFrame from vnpy.trader.constant import Direction, Offset, Interval, Status from vnpy.trader.database import database_manager from vnpy.trader.object import OrderData, TradeData, BarData from vnpy.trader.utility import round_to, extract_vt_symbol from .template import StrategyTemplate INTERVAL_DELTA_MAP = { Interval.MINUTE: timedelta(minutes=1), Interval.HOUR: timedelta(hours=1), Interval.DAILY: timedelta(days=1), } class BacktestingEngine: """""" gateway_name = "BACKTESTING" def __init__(self): """""" self.vt_symbols: List[str] = [] self.start: datetime = None self.end: datetime = None self.rates: Dict[str, float] = 0 self.slippages: Dict[str, float] = 0 self.sizes: Dict[str, float] = 1 self.priceticks: Dict[str, float] = 0 self.capital: float = 1_000_000 self.risk_free: float = 0.02 self.strategy: StrategyTemplate = None self.bars: Dict[str, BarData] = {} self.datetime: datetime = None self.interval: Interval = None self.days: int = 0 self.history_data: Dict[Tuple, BarData] = {} self.dts: Set[datetime] = set() self.limit_order_count = 0 self.limit_orders = {} self.active_limit_orders = {} self.trade_count = 0 self.trades = {} self.logs = [] self.daily_results = {} self.daily_df = None def clear_data(self) -> None: """ Clear all data of last backtesting. """ self.strategy = None self.bars = {} self.datetime = None self.limit_order_count = 0 self.limit_orders.clear() self.active_limit_orders.clear() self.trade_count = 0 self.trades.clear() self.logs.clear() self.daily_results.clear() self.daily_df = None def set_parameters( self, vt_symbols: List[str], interval: Interval, start: datetime, rates: Dict[str, float], slippages: Dict[str, float], sizes: Dict[str, float], priceticks: Dict[str, float], capital: int = 0, end: datetime = None, risk_free: float = 0 ) -> None: """""" self.vt_symbols = vt_symbols self.interval = interval self.rates = rates self.slippages = slippages self.sizes = sizes self.priceticks = priceticks self.start = start self.end = end self.capital = capital self.risk_free = risk_free def add_strategy(self, strategy_class: type, setting: dict) -> None: """""" self.strategy = strategy_class( self, strategy_class.__name__, copy(self.vt_symbols), setting ) def load_data(self) -> None: """""" self.output("开始加载历史数据") if not self.end: self.end = datetime.now() if self.start >= self.end: self.output("起始日期必须小于结束日期") return # Clear previously loaded history data self.history_data.clear() self.dts.clear() # Load 30 days of data each time and allow for progress update progress_delta = timedelta(days=30) total_delta = self.end - self.start interval_delta = INTERVAL_DELTA_MAP[self.interval] for vt_symbol in self.vt_symbols: start = self.start end = self.start + progress_delta progress = 0 data_count = 0 while start < self.end: end = min(end, self.end) # Make sure end time stays within set range data = load_bar_data( vt_symbol, self.interval, start, end ) for bar in data: self.dts.add(bar.datetime) self.history_data[(bar.datetime, vt_symbol)] = bar data_count += 1 progress += progress_delta / total_delta progress = min(progress, 1) progress_bar = "#" * int(progress * 10) self.output(f"{vt_symbol}加载进度：{progress_bar} [{progress:.0%}]") start = end + interval_delta end += (progress_delta + interval_delta) self.output(f"{vt_symbol}历史数据加载完成，数据量：{data_count}") self.output("所有历史数据加载完成") def run_backtesting(self) -> None: """""" self.strategy.on_init() # Generate sorted datetime list dts = list(self.dts) dts.sort() # Use the first [days] of history data for initializing strategy day_count = 0 ix = 0 for ix, dt in enumerate(dts): if self.datetime and dt.day != self.datetime.day: day_count += 1 if day_count >= self.days: break try: self.new_bars(dt) except Exception: self.output("触发异常，回测终止") self.output(traceback.format_exc()) return self.strategy.inited = True self.output("策略初始化完成") self.strategy.on_start() self.strategy.trading = True self.output("开始回放历史数据") # Use the rest of history data for running backtesting for dt in dts[ix:]: try: self.new_bars(dt) except Exception: self.output("触发异常，回测终止") self.output(traceback.format_exc()) return self.output("历史数据回放结束") def calculate_result(self) -> None: """""" self.output("开始计算逐日盯市盈亏") if not self.trades: self.output("成交记录为空，无法计算") return # Add trade data into daily reuslt. for trade in self.trades.values(): d = trade.datetime.date() daily_result = self.daily_results[d] daily_result.add_trade(trade) # Calculate daily result by iteration. pre_closes = {} start_poses = {} for daily_result in self.daily_results.values(): daily_result.calculate_pnl( pre_closes, start_poses, self.sizes, self.rates, self.slippages, ) pre_closes = daily_result.close_prices start_poses = daily_result.end_poses # Generate dataframe results = defaultdict(list) for daily_result in self.daily_results.values(): fields = [ "date", "trade_count", "turnover", "commission", "slippage", "trading_pnl", "holding_pnl", "total_pnl", "net_pnl" ] for key in fields: value = getattr(daily_result, key) results[key].append(value) self.daily_df = DataFrame.from_dict(results).set_index("date") self.output("逐日盯市盈亏计算完成") return self.daily_df def calculate_statistics(self, df: DataFrame = None, output=True) -> None: """""" self.output("开始计算策略统计指标") # Check DataFrame input exterior if df is None: df = self.daily_df # Check for init DataFrame if df is None: # Set all statistics to 0 if no trade. start_date = "" end_date = "" total_days = 0 profit_days = 0 loss_days = 0 end_balance = 0 max_drawdown = 0 max_ddpercent = 0 max_drawdown_duration = 0 total_net_pnl = 0 daily_net_pnl = 0 total_commission = 0 daily_commission = 0 total_slippage = 0 daily_slippage = 0 total_turnover = 0 daily_turnover = 0 total_trade_count = 0 daily_trade_count = 0 total_return = 0 annual_return = 0 daily_return = 0 return_std = 0 sharpe_ratio = 0 return_drawdown_ratio = 0 else: # Calculate balance related time series data df["balance"] = df["net_pnl"].cumsum() + self.capital df["return"] = np.log(df["balance"] / df["balance"].shift(1)).fillna(0) df["highlevel"] = ( df["balance"].rolling( min_periods=1, window=len(df), center=False).max() ) df["drawdown"] = df["balance"] - df["highlevel"] df["ddpercent"] = df["drawdown"] / df["highlevel"] * 100 # Calculate statistics value start_date = df.index[0] end_date = df.index[-1] total_days = len(df) profit_days = len(df[df["net_pnl"] > 0]) loss_days = len(df[df["net_pnl"] < 0]) end_balance = df["balance"].iloc[-1] max_drawdown = df["drawdown"].min() max_ddpercent = df["ddpercent"].min() max_drawdown_end = df["drawdown"].idxmin() if isinstance(max_drawdown_end, date): max_drawdown_start = df["balance"][:max_drawdown_end].idxmax() max_drawdown_duration = (max_drawdown_end - max_drawdown_start).days else: max_drawdown_duration = 0 total_net_pnl = df["net_pnl"].sum() daily_net_pnl = total_net_pnl / total_days total_commission = df["commission"].sum() daily_commission = total_commission / total_days total_slippage = df["slippage"].sum() daily_slippage = total_slippage / total_days total_turnover = df["turnover"].sum() daily_turnover = total_turnover / total_days total_trade_count = df["trade_count"].sum() daily_trade_count = total_trade_count / total_days total_return = (end_balance / self.capital - 1) * 100 annual_return = total_return / total_days * 240 daily_return = df["return"].mean() * 100 return_std = df["return"].std() * 100 if return_std: daily_risk_free = self.risk_free / np.sqrt(240) sharpe_ratio = (daily_return - daily_risk_free) / return_std * np.sqrt(240) else: sharpe_ratio = 0 return_drawdown_ratio = -total_net_pnl / max_drawdown # Output if output: self.output("-" * 30) self.output(f"首个交易日：\t{start_date}") self.output(f"最后交易日：\t{end_date}") self.output(f"总交易日：\t{total_days}") self.output(f"盈利交易日：\t{profit_days}") self.output(f"亏损交易日：\t{loss_days}") self.output(f"起始资金：\t{self.capital:,.2f}") self.output(f"结束资金：\t{end_balance:,.2f}") self.output(f"总收益率：\t{total_return:,.2f}%") self.output(f"年化收益：\t{annual_return:,.2f}%") self.output(f"最大回撤: \t{max_drawdown:,.2f}") self.output(f"百分比最大回撤: {max_ddpercent:,.2f}%") self.output(f"最长回撤天数: \t{max_drawdown_duration}") self.output(f"总盈亏：\t{total_net_pnl:,.2f}") self.output(f"总手续费：\t{total_commission:,.2f}") self.output(f"总滑点：\t{total_slippage:,.2f}") self.output(f"总成交金额：\t{total_turnover:,.2f}") self.output(f"总成交笔数：\t{total_trade_count}") self.output(f"日均盈亏：\t{daily_net_pnl:,.2f}") self.output(f"日均手续费：\t{daily_commission:,.2f}") self.output(f"日均滑点：\t{daily_slippage:,.2f}") self.output(f"日均成交金额：\t{daily_turnover:,.2f}") self.output(f"日均成交笔数：\t{daily_trade_count}") self.output(f"日均收益率：\t{daily_return:,.2f}%") self.output(f"收益标准差：\t{return_std:,.2f}%") self.output(f"Sharpe Ratio：\t{sharpe_ratio:,.2f}") self.output(f"收益回撤比：\t{return_drawdown_ratio:,.2f}") statistics = { "start_date": start_date, "end_date": end_date, "total_days": total_days, "profit_days": profit_days, "loss_days": loss_days, "capital": self.capital, "end_balance": end_balance, "max_drawdown": max_drawdown, "max_ddpercent": max_ddpercent, "max_drawdown_duration": max_drawdown_duration, "total_net_pnl": total_net_pnl, "daily_net_pnl": daily_net_pnl, "total_commission": total_commission, "daily_commission": daily_commission, "total_slippage": total_slippage, "daily_slippage": daily_slippage, "total_turnover": total_turnover, "daily_turnover": daily_turnover, "total_trade_count": total_trade_count, "daily_trade_count": daily_trade_count, "total_return": total_return, "annual_return": annual_return, "daily_return": daily_return, "return_std": return_std, "sharpe_ratio": sharpe_ratio, "return_drawdown_ratio": return_drawdown_ratio, } # Filter potential error infinite value for key, value in statistics.items(): if value in (np.inf, -np.inf): value = 0 statistics[key] = np.nan_to_num(value) self.output("策略统计指标计算完成") return statistics def show_chart(self, df: DataFrame = None) -> None: """""" # Check DataFrame input exterior if df is None: df = self.daily_df # Check for init DataFrame if df is None: return fig = make_subplots( rows=4, cols=1, subplot_titles=["Balance", "Drawdown", "Daily Pnl", "Pnl Distribution"], vertical_spacing=0.06 ) balance_line = go.Scatter( x=df.index, y=df["balance"], mode="lines", name="Balance" ) drawdown_scatter = go.Scatter( x=df.index, y=df["drawdown"], fillcolor="red", fill='tozeroy', mode="lines", name="Drawdown" ) pnl_bar = go.Bar(y=df["net_pnl"], name="Daily Pnl") pnl_histogram = go.Histogram(x=df["net_pnl"], nbinsx=100, name="Days") fig.add_trace(balance_line, row=1, col=1) fig.add_trace(drawdown_scatter, row=2, col=1) fig.add_trace(pnl_bar, row=3, col=1) fig.add_trace(pnl_histogram, row=4, col=1) fig.update_layout(height=1000, width=1000) fig.show() def update_daily_close(self, bars: Dict[str, BarData], dt: datetime) -> None: """""" d = dt.date() close_prices = {} for bar in bars.values(): close_prices[bar.vt_symbol] = bar.close_price daily_result = self.daily_results.get(d, None) if daily_result: daily_result.update_close_prices(close_prices) else: self.daily_results[d] = PortfolioDailyResult(d, close_prices) def new_bars(self, dt: datetime) -> None: """""" self.datetime = dt bars: Dict[str, BarData] = {} for vt_symbol in self.vt_symbols: bar = self.history_data.get((dt, vt_symbol), None) # If bar data of vt_symbol at dt exists if bar: # Update bar data for crossing order self.bars[vt_symbol] = bar # Put bar into dict for strategy.on_bars update bars[vt_symbol] = bar # Otherwise, use previous close to backfill elif vt_symbol in self.bars: old_bar = self.bars[vt_symbol] bar = BarData( symbol=old_bar.symbol, exchange=old_bar.exchange, datetime=dt, open_price=old_bar.close_price, high_price=old_bar.close_price, low_price=old_bar.close_price, close_price=old_bar.close_price, gateway_name=old_bar.gateway_name ) self.bars[vt_symbol] = bar self.cross_limit_order() self.strategy.on_bars(bars) self.update_daily_close(self.bars, dt) def cross_limit_order(self) -> None: """ Cross limit order with last bar/tick data. """ for order in list(self.active_limit_orders.values()): bar = self.bars[order.vt_symbol] long_cross_price = bar.low_price short_cross_price = bar.high_price long_best_price = bar.open_price short_best_price = bar.open_price # Push order update with status "not traded" (pending). if order.status == Status.SUBMITTING: order.status = Status.NOTTRADED self.strategy.update_order(order) # Check whether limit orders can be filled. long_cross = ( order.direction == Direction.LONG and order.price >= long_cross_price and long_cross_price > 0 ) short_cross = ( order.direction == Direction.SHORT and order.price <= short_cross_price and short_cross_price > 0 ) if not long_cross and not short_cross: continue # Push order update with status "all traded" (filled). order.traded = order.volume order.status = Status.ALLTRADED self.strategy.update_order(order) self.active_limit_orders.pop(order.vt_orderid) # Push trade update self.trade_count += 1 if long_cross: trade_price = min(order.price, long_best_price) else: trade_price = max(order.price, short_best_price) trade = TradeData( symbol=order.symbol, exchange=order.exchange, orderid=order.orderid, tradeid=str(self.trade_count), direction=order.direction, offset=order.offset, price=trade_price, volume=order.volume, datetime=self.datetime, gateway_name=self.gateway_name, ) self.strategy.update_trade(trade) self.trades[trade.vt_tradeid] = trade def load_bars( self, strategy: StrategyTemplate, days: int, interval: Interval ) -> None: """""" self.days = days def send_order( self, strategy: StrategyTemplate, vt_symbol: str, direction: Direction, offset: Offset, price: float, volume: float, lock: bool, net: bool ) -> List[str]: """""" price = round_to(price, self.priceticks[vt_symbol]) symbol, exchange = extract_vt_symbol(vt_symbol) self.limit_order_count += 1 order = OrderData( symbol=symbol, exchange=exchange, orderid=str(self.limit_order_count), direction=direction, offset=offset, price=price, volume=volume, status=Status.SUBMITTING, datetime=self.datetime, gateway_name=self.gateway_name, ) self.active_limit_orders[order.vt_orderid] = order self.limit_orders[order.vt_orderid] = order return [order.vt_orderid] def cancel_order(self, strategy: StrategyTemplate, vt_orderid: str) -> None: """ Cancel order by vt_orderid. """ if vt_orderid not in self.active_limit_orders: return order = self.active_limit_orders.pop(vt_orderid) order.status = Status.CANCELLED self.strategy.update_order(order) def write_log(self, msg: str, strategy: StrategyTemplate = None) -> None: """ Write log message. """ msg = f"{self.datetime}\t{msg}" self.logs.append(msg) def send_email(self, msg: str, strategy: StrategyTemplate = None) -> None: """ Send email to default receiver. """ pass def sync_strategy_data(self, strategy: StrategyTemplate) -> None: """ Sync strategy data into json file. """ pass def put_strategy_event(self, strategy: StrategyTemplate) -> None: """ Put an event to update strategy status. """ pass def output(self, msg) -> None: """ Output message of backtesting engine. """ print(f"{datetime.now()}\t{msg}") def get_all_trades(self) -> List[TradeData]: """ Return all trade data of current backtesting result. """ return list(self.trades.values()) def get_all_orders(self) -> List[OrderData]: """ Return all limit order data of current backtesting result. """ return list(self.limit_orders.values()) def get_all_daily_results(self) -> List["PortfolioDailyResult"]: """ Return all daily result data. """ return list(self.daily_results.values()) class ContractDailyResult: """""" def __init__(self, result_date: date, close_price: float): """""" self.date: date = result_date self.close_price: float = close_price self.pre_close: float = 0 self.trades: List[TradeData] = [] self.trade_count: int = 0 self.start_pos: float = 0 self.end_pos: float = 0 self.turnover: float = 0 self.commission: float = 0 self.slippage: float = 0 self.trading_pnl: float = 0 self.holding_pnl: float = 0 self.total_pnl: float = 0 self.net_pnl: float = 0 def add_trade(self, trade: TradeData) -> None: """""" self.trades.append(trade) def calculate_pnl( self, pre_close: float, start_pos: float, size: int, rate: float, slippage: float ) -> None: """""" # If no pre_close provided on the first day, # use value 1 to avoid zero division error if pre_close: self.pre_close = pre_close else: self.pre_close = 1 # Holding pnl is the pnl from holding position at day start self.start_pos = start_pos self.end_pos = start_pos self.holding_pnl = self.start_pos * (self.close_price - self.pre_close) * size # Trading pnl is the pnl from new trade during the day self.trade_count = len(self.trades) for trade in self.trades: if trade.direction == Direction.LONG: pos_change = trade.volume else: pos_change = -trade.volume self.end_pos += pos_change turnover = trade.volume * size * trade.price self.trading_pnl += pos_change * (self.close_price - trade.price) * size self.slippage += trade.volume * size * slippage self.turnover += turnover self.commission += turnover * rate # Net pnl takes account of commission and slippage cost self.total_pnl = self.trading_pnl + self.holding_pnl self.net_pnl = self.total_pnl - self.commission - self.slippage def update_close_price(self, close_price: float) -> None: """""" self.close_price = close_price class PortfolioDailyResult: """""" def __init__(self, result_date: date, close_prices: Dict[str, float]): """""" self.date: date = result_date self.close_prices: Dict[str, float] = close_prices self.pre_closes: Dict[str, float] = {} self.start_poses: Dict[str, float] = {} self.end_poses: Dict[str, float] = {} self.contract_results: Dict[str, ContractDailyResult] = {} for vt_symbol, close_price in close_prices.items(): self.contract_results[vt_symbol] = ContractDailyResult(result_date, close_price) self.trade_count: int = 0 self.turnover: float = 0 self.commission: float = 0 self.slippage: float = 0 self.trading_pnl: float = 0 self.holding_pnl: float = 0 self.total_pnl: float = 0 self.net_pnl: float = 0 def add_trade(self, trade: TradeData) -> None: """""" contract_result = self.contract_results[trade.vt_symbol] contract_result.add_trade(trade) def calculate_pnl( self, pre_closes: Dict[str, float], start_poses: Dict[str, float], sizes: Dict[str, float], rates: Dict[str, float], slippages: Dict[str, float], ) -> None: """""" self.pre_closes = pre_closes for vt_symbol, contract_result in self.contract_results.items(): contract_result.calculate_pnl( pre_closes.get(vt_symbol, 0), start_poses.get(vt_symbol, 0), sizes[vt_symbol], rates[vt_symbol], slippages[vt_symbol] ) self.trade_count += contract_result.trade_count self.turnover += contract_result.turnover self.commission += contract_result.commission self.slippage += contract_result.slippage self.trading_pnl += contract_result.trading_pnl self.holding_pnl += contract_result.holding_pnl self.total_pnl += contract_result.total_pnl self.net_pnl += contract_result.net_pnl self.end_poses[vt_symbol] = contract_result.end_pos def update_close_prices(self, close_prices: Dict[str, float]) -> None: """""" self.close_prices = close_prices for vt_symbol, close_price in close_prices.items(): contract_result = self.contract_results.get(vt_symbol, None) if contract_result: contract_result.update_close_price(close_price) @lru_cache(maxsize=999) def load_bar_data( vt_symbol: str, interval: Interval, start: datetime, end: datetime ): """""" symbol, exchange = extract_vt_symbol(vt_symbol) return database_manager.load_bar_data( symbol, exchange, interval, start, end )

31.405848

92

0.559437

from collections import defaultdict from datetime import date, datetime, timedelta from typing import Dict, List, Set, Tuple from functools import lru_cache from copy import copy import traceback import numpy as np import plotly.graph_objects as go from plotly.subplots import make_subplots from pandas import DataFrame from vnpy.trader.constant import Direction, Offset, Interval, Status from vnpy.trader.database import database_manager from vnpy.trader.object import OrderData, TradeData, BarData from vnpy.trader.utility import round_to, extract_vt_symbol from .template import StrategyTemplate INTERVAL_DELTA_MAP = { Interval.MINUTE: timedelta(minutes=1), Interval.HOUR: timedelta(hours=1), Interval.DAILY: timedelta(days=1), } class BacktestingEngine: gateway_name = "BACKTESTING" def __init__(self): self.vt_symbols: List[str] = [] self.start: datetime = None self.end: datetime = None self.rates: Dict[str, float] = 0 self.slippages: Dict[str, float] = 0 self.sizes: Dict[str, float] = 1 self.priceticks: Dict[str, float] = 0 self.capital: float = 1_000_000 self.risk_free: float = 0.02 self.strategy: StrategyTemplate = None self.bars: Dict[str, BarData] = {} self.datetime: datetime = None self.interval: Interval = None self.days: int = 0 self.history_data: Dict[Tuple, BarData] = {} self.dts: Set[datetime] = set() self.limit_order_count = 0 self.limit_orders = {} self.active_limit_orders = {} self.trade_count = 0 self.trades = {} self.logs = [] self.daily_results = {} self.daily_df = None def clear_data(self) -> None: self.strategy = None self.bars = {} self.datetime = None self.limit_order_count = 0 self.limit_orders.clear() self.active_limit_orders.clear() self.trade_count = 0 self.trades.clear() self.logs.clear() self.daily_results.clear() self.daily_df = None def set_parameters( self, vt_symbols: List[str], interval: Interval, start: datetime, rates: Dict[str, float], slippages: Dict[str, float], sizes: Dict[str, float], priceticks: Dict[str, float], capital: int = 0, end: datetime = None, risk_free: float = 0 ) -> None: self.vt_symbols = vt_symbols self.interval = interval self.rates = rates self.slippages = slippages self.sizes = sizes self.priceticks = priceticks self.start = start self.end = end self.capital = capital self.risk_free = risk_free def add_strategy(self, strategy_class: type, setting: dict) -> None: self.strategy = strategy_class( self, strategy_class.__name__, copy(self.vt_symbols), setting ) def load_data(self) -> None: self.output("开始加载历史数据") if not self.end: self.end = datetime.now() if self.start >= self.end: self.output("起始日期必须小于结束日期") return self.history_data.clear() self.dts.clear() progress_delta = timedelta(days=30) total_delta = self.end - self.start interval_delta = INTERVAL_DELTA_MAP[self.interval] for vt_symbol in self.vt_symbols: start = self.start end = self.start + progress_delta progress = 0 data_count = 0 while start < self.end: end = min(end, self.end) data = load_bar_data( vt_symbol, self.interval, start, end ) for bar in data: self.dts.add(bar.datetime) self.history_data[(bar.datetime, vt_symbol)] = bar data_count += 1 progress += progress_delta / total_delta progress = min(progress, 1) progress_bar = "#" * int(progress * 10) self.output(f"{vt_symbol}加载进度：{progress_bar} [{progress:.0%}]") start = end + interval_delta end += (progress_delta + interval_delta) self.output(f"{vt_symbol}历史数据加载完成，数据量：{data_count}") self.output("所有历史数据加载完成") def run_backtesting(self) -> None: self.strategy.on_init() dts = list(self.dts) dts.sort() day_count = 0 ix = 0 for ix, dt in enumerate(dts): if self.datetime and dt.day != self.datetime.day: day_count += 1 if day_count >= self.days: break try: self.new_bars(dt) except Exception: self.output("触发异常，回测终止") self.output(traceback.format_exc()) return self.strategy.inited = True self.output("策略初始化完成") self.strategy.on_start() self.strategy.trading = True self.output("开始回放历史数据") for dt in dts[ix:]: try: self.new_bars(dt) except Exception: self.output("触发异常，回测终止") self.output(traceback.format_exc()) return self.output("历史数据回放结束") def calculate_result(self) -> None: self.output("开始计算逐日盯市盈亏") if not self.trades: self.output("成交记录为空，无法计算") return for trade in self.trades.values(): d = trade.datetime.date() daily_result = self.daily_results[d] daily_result.add_trade(trade) pre_closes = {} start_poses = {} for daily_result in self.daily_results.values(): daily_result.calculate_pnl( pre_closes, start_poses, self.sizes, self.rates, self.slippages, ) pre_closes = daily_result.close_prices start_poses = daily_result.end_poses results = defaultdict(list) for daily_result in self.daily_results.values(): fields = [ "date", "trade_count", "turnover", "commission", "slippage", "trading_pnl", "holding_pnl", "total_pnl", "net_pnl" ] for key in fields: value = getattr(daily_result, key) results[key].append(value) self.daily_df = DataFrame.from_dict(results).set_index("date") self.output("逐日盯市盈亏计算完成") return self.daily_df def calculate_statistics(self, df: DataFrame = None, output=True) -> None: self.output("开始计算策略统计指标") if df is None: df = self.daily_df if df is None: start_date = "" end_date = "" total_days = 0 profit_days = 0 loss_days = 0 end_balance = 0 max_drawdown = 0 max_ddpercent = 0 max_drawdown_duration = 0 total_net_pnl = 0 daily_net_pnl = 0 total_commission = 0 daily_commission = 0 total_slippage = 0 daily_slippage = 0 total_turnover = 0 daily_turnover = 0 total_trade_count = 0 daily_trade_count = 0 total_return = 0 annual_return = 0 daily_return = 0 return_std = 0 sharpe_ratio = 0 return_drawdown_ratio = 0 else: df["balance"] = df["net_pnl"].cumsum() + self.capital df["return"] = np.log(df["balance"] / df["balance"].shift(1)).fillna(0) df["highlevel"] = ( df["balance"].rolling( min_periods=1, window=len(df), center=False).max() ) df["drawdown"] = df["balance"] - df["highlevel"] df["ddpercent"] = df["drawdown"] / df["highlevel"] * 100 start_date = df.index[0] end_date = df.index[-1] total_days = len(df) profit_days = len(df[df["net_pnl"] > 0]) loss_days = len(df[df["net_pnl"] < 0]) end_balance = df["balance"].iloc[-1] max_drawdown = df["drawdown"].min() max_ddpercent = df["ddpercent"].min() max_drawdown_end = df["drawdown"].idxmin() if isinstance(max_drawdown_end, date): max_drawdown_start = df["balance"][:max_drawdown_end].idxmax() max_drawdown_duration = (max_drawdown_end - max_drawdown_start).days else: max_drawdown_duration = 0 total_net_pnl = df["net_pnl"].sum() daily_net_pnl = total_net_pnl / total_days total_commission = df["commission"].sum() daily_commission = total_commission / total_days total_slippage = df["slippage"].sum() daily_slippage = total_slippage / total_days total_turnover = df["turnover"].sum() daily_turnover = total_turnover / total_days total_trade_count = df["trade_count"].sum() daily_trade_count = total_trade_count / total_days total_return = (end_balance / self.capital - 1) * 100 annual_return = total_return / total_days * 240 daily_return = df["return"].mean() * 100 return_std = df["return"].std() * 100 if return_std: daily_risk_free = self.risk_free / np.sqrt(240) sharpe_ratio = (daily_return - daily_risk_free) / return_std * np.sqrt(240) else: sharpe_ratio = 0 return_drawdown_ratio = -total_net_pnl / max_drawdown if output: self.output("-" * 30) self.output(f"首个交易日：\t{start_date}") self.output(f"最后交易日：\t{end_date}") self.output(f"总交易日：\t{total_days}") self.output(f"盈利交易日：\t{profit_days}") self.output(f"亏损交易日：\t{loss_days}") self.output(f"起始资金：\t{self.capital:,.2f}") self.output(f"结束资金：\t{end_balance:,.2f}") self.output(f"总收益率：\t{total_return:,.2f}%") self.output(f"年化收益：\t{annual_return:,.2f}%") self.output(f"最大回撤: \t{max_drawdown:,.2f}") self.output(f"百分比最大回撤: {max_ddpercent:,.2f}%") self.output(f"最长回撤天数: \t{max_drawdown_duration}") self.output(f"总盈亏：\t{total_net_pnl:,.2f}") self.output(f"总手续费：\t{total_commission:,.2f}") self.output(f"总滑点：\t{total_slippage:,.2f}") self.output(f"总成交金额：\t{total_turnover:,.2f}") self.output(f"总成交笔数：\t{total_trade_count}") self.output(f"日均盈亏：\t{daily_net_pnl:,.2f}") self.output(f"日均手续费：\t{daily_commission:,.2f}") self.output(f"日均滑点：\t{daily_slippage:,.2f}") self.output(f"日均成交金额：\t{daily_turnover:,.2f}") self.output(f"日均成交笔数：\t{daily_trade_count}") self.output(f"日均收益率：\t{daily_return:,.2f}%") self.output(f"收益标准差：\t{return_std:,.2f}%") self.output(f"Sharpe Ratio：\t{sharpe_ratio:,.2f}") self.output(f"收益回撤比：\t{return_drawdown_ratio:,.2f}") statistics = { "start_date": start_date, "end_date": end_date, "total_days": total_days, "profit_days": profit_days, "loss_days": loss_days, "capital": self.capital, "end_balance": end_balance, "max_drawdown": max_drawdown, "max_ddpercent": max_ddpercent, "max_drawdown_duration": max_drawdown_duration, "total_net_pnl": total_net_pnl, "daily_net_pnl": daily_net_pnl, "total_commission": total_commission, "daily_commission": daily_commission, "total_slippage": total_slippage, "daily_slippage": daily_slippage, "total_turnover": total_turnover, "daily_turnover": daily_turnover, "total_trade_count": total_trade_count, "daily_trade_count": daily_trade_count, "total_return": total_return, "annual_return": annual_return, "daily_return": daily_return, "return_std": return_std, "sharpe_ratio": sharpe_ratio, "return_drawdown_ratio": return_drawdown_ratio, } for key, value in statistics.items(): if value in (np.inf, -np.inf): value = 0 statistics[key] = np.nan_to_num(value) self.output("策略统计指标计算完成") return statistics def show_chart(self, df: DataFrame = None) -> None: if df is None: df = self.daily_df if df is None: return fig = make_subplots( rows=4, cols=1, subplot_titles=["Balance", "Drawdown", "Daily Pnl", "Pnl Distribution"], vertical_spacing=0.06 ) balance_line = go.Scatter( x=df.index, y=df["balance"], mode="lines", name="Balance" ) drawdown_scatter = go.Scatter( x=df.index, y=df["drawdown"], fillcolor="red", fill='tozeroy', mode="lines", name="Drawdown" ) pnl_bar = go.Bar(y=df["net_pnl"], name="Daily Pnl") pnl_histogram = go.Histogram(x=df["net_pnl"], nbinsx=100, name="Days") fig.add_trace(balance_line, row=1, col=1) fig.add_trace(drawdown_scatter, row=2, col=1) fig.add_trace(pnl_bar, row=3, col=1) fig.add_trace(pnl_histogram, row=4, col=1) fig.update_layout(height=1000, width=1000) fig.show() def update_daily_close(self, bars: Dict[str, BarData], dt: datetime) -> None: d = dt.date() close_prices = {} for bar in bars.values(): close_prices[bar.vt_symbol] = bar.close_price daily_result = self.daily_results.get(d, None) if daily_result: daily_result.update_close_prices(close_prices) else: self.daily_results[d] = PortfolioDailyResult(d, close_prices) def new_bars(self, dt: datetime) -> None: self.datetime = dt bars: Dict[str, BarData] = {} for vt_symbol in self.vt_symbols: bar = self.history_data.get((dt, vt_symbol), None) if bar: self.bars[vt_symbol] = bar bars[vt_symbol] = bar elif vt_symbol in self.bars: old_bar = self.bars[vt_symbol] bar = BarData( symbol=old_bar.symbol, exchange=old_bar.exchange, datetime=dt, open_price=old_bar.close_price, high_price=old_bar.close_price, low_price=old_bar.close_price, close_price=old_bar.close_price, gateway_name=old_bar.gateway_name ) self.bars[vt_symbol] = bar self.cross_limit_order() self.strategy.on_bars(bars) self.update_daily_close(self.bars, dt) def cross_limit_order(self) -> None: for order in list(self.active_limit_orders.values()): bar = self.bars[order.vt_symbol] long_cross_price = bar.low_price short_cross_price = bar.high_price long_best_price = bar.open_price short_best_price = bar.open_price if order.status == Status.SUBMITTING: order.status = Status.NOTTRADED self.strategy.update_order(order) long_cross = ( order.direction == Direction.LONG and order.price >= long_cross_price and long_cross_price > 0 ) short_cross = ( order.direction == Direction.SHORT and order.price <= short_cross_price and short_cross_price > 0 ) if not long_cross and not short_cross: continue order.traded = order.volume order.status = Status.ALLTRADED self.strategy.update_order(order) self.active_limit_orders.pop(order.vt_orderid) self.trade_count += 1 if long_cross: trade_price = min(order.price, long_best_price) else: trade_price = max(order.price, short_best_price) trade = TradeData( symbol=order.symbol, exchange=order.exchange, orderid=order.orderid, tradeid=str(self.trade_count), direction=order.direction, offset=order.offset, price=trade_price, volume=order.volume, datetime=self.datetime, gateway_name=self.gateway_name, ) self.strategy.update_trade(trade) self.trades[trade.vt_tradeid] = trade def load_bars( self, strategy: StrategyTemplate, days: int, interval: Interval ) -> None: self.days = days def send_order( self, strategy: StrategyTemplate, vt_symbol: str, direction: Direction, offset: Offset, price: float, volume: float, lock: bool, net: bool ) -> List[str]: price = round_to(price, self.priceticks[vt_symbol]) symbol, exchange = extract_vt_symbol(vt_symbol) self.limit_order_count += 1 order = OrderData( symbol=symbol, exchange=exchange, orderid=str(self.limit_order_count), direction=direction, offset=offset, price=price, volume=volume, status=Status.SUBMITTING, datetime=self.datetime, gateway_name=self.gateway_name, ) self.active_limit_orders[order.vt_orderid] = order self.limit_orders[order.vt_orderid] = order return [order.vt_orderid] def cancel_order(self, strategy: StrategyTemplate, vt_orderid: str) -> None: if vt_orderid not in self.active_limit_orders: return order = self.active_limit_orders.pop(vt_orderid) order.status = Status.CANCELLED self.strategy.update_order(order) def write_log(self, msg: str, strategy: StrategyTemplate = None) -> None: msg = f"{self.datetime}\t{msg}" self.logs.append(msg) def send_email(self, msg: str, strategy: StrategyTemplate = None) -> None: pass def sync_strategy_data(self, strategy: StrategyTemplate) -> None: pass def put_strategy_event(self, strategy: StrategyTemplate) -> None: pass def output(self, msg) -> None: print(f"{datetime.now()}\t{msg}") def get_all_trades(self) -> List[TradeData]: return list(self.trades.values()) def get_all_orders(self) -> List[OrderData]: return list(self.limit_orders.values()) def get_all_daily_results(self) -> List["PortfolioDailyResult"]: return list(self.daily_results.values()) class ContractDailyResult: def __init__(self, result_date: date, close_price: float): self.date: date = result_date self.close_price: float = close_price self.pre_close: float = 0 self.trades: List[TradeData] = [] self.trade_count: int = 0 self.start_pos: float = 0 self.end_pos: float = 0 self.turnover: float = 0 self.commission: float = 0 self.slippage: float = 0 self.trading_pnl: float = 0 self.holding_pnl: float = 0 self.total_pnl: float = 0 self.net_pnl: float = 0 def add_trade(self, trade: TradeData) -> None: self.trades.append(trade) def calculate_pnl( self, pre_close: float, start_pos: float, size: int, rate: float, slippage: float ) -> None: if pre_close: self.pre_close = pre_close else: self.pre_close = 1 self.start_pos = start_pos self.end_pos = start_pos self.holding_pnl = self.start_pos * (self.close_price - self.pre_close) * size self.trade_count = len(self.trades) for trade in self.trades: if trade.direction == Direction.LONG: pos_change = trade.volume else: pos_change = -trade.volume self.end_pos += pos_change turnover = trade.volume * size * trade.price self.trading_pnl += pos_change * (self.close_price - trade.price) * size self.slippage += trade.volume * size * slippage self.turnover += turnover self.commission += turnover * rate self.total_pnl = self.trading_pnl + self.holding_pnl self.net_pnl = self.total_pnl - self.commission - self.slippage def update_close_price(self, close_price: float) -> None: self.close_price = close_price class PortfolioDailyResult: def __init__(self, result_date: date, close_prices: Dict[str, float]): self.date: date = result_date self.close_prices: Dict[str, float] = close_prices self.pre_closes: Dict[str, float] = {} self.start_poses: Dict[str, float] = {} self.end_poses: Dict[str, float] = {} self.contract_results: Dict[str, ContractDailyResult] = {} for vt_symbol, close_price in close_prices.items(): self.contract_results[vt_symbol] = ContractDailyResult(result_date, close_price) self.trade_count: int = 0 self.turnover: float = 0 self.commission: float = 0 self.slippage: float = 0 self.trading_pnl: float = 0 self.holding_pnl: float = 0 self.total_pnl: float = 0 self.net_pnl: float = 0 def add_trade(self, trade: TradeData) -> None: contract_result = self.contract_results[trade.vt_symbol] contract_result.add_trade(trade) def calculate_pnl( self, pre_closes: Dict[str, float], start_poses: Dict[str, float], sizes: Dict[str, float], rates: Dict[str, float], slippages: Dict[str, float], ) -> None: self.pre_closes = pre_closes for vt_symbol, contract_result in self.contract_results.items(): contract_result.calculate_pnl( pre_closes.get(vt_symbol, 0), start_poses.get(vt_symbol, 0), sizes[vt_symbol], rates[vt_symbol], slippages[vt_symbol] ) self.trade_count += contract_result.trade_count self.turnover += contract_result.turnover self.commission += contract_result.commission self.slippage += contract_result.slippage self.trading_pnl += contract_result.trading_pnl self.holding_pnl += contract_result.holding_pnl self.total_pnl += contract_result.total_pnl self.net_pnl += contract_result.net_pnl self.end_poses[vt_symbol] = contract_result.end_pos def update_close_prices(self, close_prices: Dict[str, float]) -> None: self.close_prices = close_prices for vt_symbol, close_price in close_prices.items(): contract_result = self.contract_results.get(vt_symbol, None) if contract_result: contract_result.update_close_price(close_price) @lru_cache(maxsize=999) def load_bar_data( vt_symbol: str, interval: Interval, start: datetime, end: datetime ): symbol, exchange = extract_vt_symbol(vt_symbol) return database_manager.load_bar_data( symbol, exchange, interval, start, end )

true

f700642b6d0a80da9a02e1d4c273cd2930c77980

2,659

py

Python

src/python/pants/backend/codegen/export_codegen_goal_test.py

rcuza/pants

0429258b181986eed856ae45af93b776727774a0

[ "Apache-2.0" ]

1

2021-02-22T18:11:26.000Z

src/python/pants/backend/codegen/export_codegen_goal_test.py

rcuza/pants

0429258b181986eed856ae45af93b776727774a0

[ "Apache-2.0" ]

null

src/python/pants/backend/codegen/export_codegen_goal_test.py

rcuza/pants

0429258b181986eed856ae45af93b776727774a0

[ "Apache-2.0" ]

2

2021-05-11T07:51:26.000Z

2021-05-19T10:14:46.000Z

# Copyright 2020 Pants project contributors (see CONTRIBUTORS.md). # Licensed under the Apache License, Version 2.0 (see LICENSE). from pathlib import Path import pytest from pants.backend.codegen.export_codegen_goal import ExportCodegen from pants.backend.codegen.export_codegen_goal import rules as write_codegen_rules from pants.core.target_types import FilesSources, ResourcesSources from pants.core.util_rules import distdir from pants.engine.fs import CreateDigest, FileContent, Snapshot from pants.engine.rules import Get, rule from pants.engine.target import GeneratedSources, GenerateSourcesRequest, Sources, Target from pants.engine.unions import UnionRule from pants.testutil.rule_runner import RuleRunner class Gen1Sources(Sources): pass class Gen2Sources(Sources): pass class Gen1Target(Target): alias = "gen1" core_fields = (Gen1Sources,) class Gen2Target(Target): alias = "gen2" core_fields = (Gen2Sources,) class Gen1Request(GenerateSourcesRequest): input = Gen1Sources output = FilesSources class Gen2Request(GenerateSourcesRequest): input = Gen2Sources output = ResourcesSources @rule async def gen1(_: Gen1Request) -> GeneratedSources: result = await Get(Snapshot, CreateDigest([FileContent("assets/README.md", b"Hello!")])) return GeneratedSources(result) @rule async def gen2(_: Gen2Request) -> GeneratedSources: result = await Get(Snapshot, CreateDigest([FileContent("src/haskell/app.hs", b"10 * 4")])) return GeneratedSources(result) @pytest.fixture def rule_runner() -> RuleRunner: return RuleRunner( rules=[ *write_codegen_rules(), gen1, gen2, UnionRule(GenerateSourcesRequest, Gen1Request), UnionRule(GenerateSourcesRequest, Gen2Request), *distdir.rules(), ], target_types=[Gen1Target, Gen2Target], ) def test_no_codegen_targets(rule_runner: RuleRunner, caplog) -> None: result = rule_runner.run_goal_rule(ExportCodegen) assert result.exit_code == 0 assert len(caplog.records) == 1 assert "No codegen files/targets matched. All codegen target types: gen1, gen2" in caplog.text def test_export_codegen(rule_runner: RuleRunner) -> None: rule_runner.add_to_build_file("", "gen1(name='gen1')\ngen2(name='gen2')\n") result = rule_runner.run_goal_rule(ExportCodegen, args=["::"]) assert result.exit_code == 0 parent_dir = Path(rule_runner.build_root, "dist", "codegen") assert (parent_dir / "assets" / "README.md").read_text() == "Hello!" assert (parent_dir / "src" / "haskell" / "app.hs").read_text() == "10 * 4"

30.215909

98

0.723956

from pathlib import Path import pytest from pants.backend.codegen.export_codegen_goal import ExportCodegen from pants.backend.codegen.export_codegen_goal import rules as write_codegen_rules from pants.core.target_types import FilesSources, ResourcesSources from pants.core.util_rules import distdir from pants.engine.fs import CreateDigest, FileContent, Snapshot from pants.engine.rules import Get, rule from pants.engine.target import GeneratedSources, GenerateSourcesRequest, Sources, Target from pants.engine.unions import UnionRule from pants.testutil.rule_runner import RuleRunner class Gen1Sources(Sources): pass class Gen2Sources(Sources): pass class Gen1Target(Target): alias = "gen1" core_fields = (Gen1Sources,) class Gen2Target(Target): alias = "gen2" core_fields = (Gen2Sources,) class Gen1Request(GenerateSourcesRequest): input = Gen1Sources output = FilesSources class Gen2Request(GenerateSourcesRequest): input = Gen2Sources output = ResourcesSources @rule async def gen1(_: Gen1Request) -> GeneratedSources: result = await Get(Snapshot, CreateDigest([FileContent("assets/README.md", b"Hello!")])) return GeneratedSources(result) @rule async def gen2(_: Gen2Request) -> GeneratedSources: result = await Get(Snapshot, CreateDigest([FileContent("src/haskell/app.hs", b"10 * 4")])) return GeneratedSources(result) @pytest.fixture def rule_runner() -> RuleRunner: return RuleRunner( rules=[ *write_codegen_rules(), gen1, gen2, UnionRule(GenerateSourcesRequest, Gen1Request), UnionRule(GenerateSourcesRequest, Gen2Request), *distdir.rules(), ], target_types=[Gen1Target, Gen2Target], ) def test_no_codegen_targets(rule_runner: RuleRunner, caplog) -> None: result = rule_runner.run_goal_rule(ExportCodegen) assert result.exit_code == 0 assert len(caplog.records) == 1 assert "No codegen files/targets matched. All codegen target types: gen1, gen2" in caplog.text def test_export_codegen(rule_runner: RuleRunner) -> None: rule_runner.add_to_build_file("", "gen1(name='gen1')\ngen2(name='gen2')\n") result = rule_runner.run_goal_rule(ExportCodegen, args=["::"]) assert result.exit_code == 0 parent_dir = Path(rule_runner.build_root, "dist", "codegen") assert (parent_dir / "assets" / "README.md").read_text() == "Hello!" assert (parent_dir / "src" / "haskell" / "app.hs").read_text() == "10 * 4"

true

f7006506115787b6ab648322e288f899a2ea56b5

3,002

py

Python

movies_recommender/RecommenderSVD.py

mateuszrusin/filmweb-rekomendacje

06bdff3825f4c9e7b80fb5778d1a40388d2313d9

[ "MIT" ]

3

2019-12-10T10:20:07.000Z

2020-12-03T17:37:24.000Z

movies_recommender/RecommenderSVD.py

mateuszrusin/filmweb-rekomendacje

06bdff3825f4c9e7b80fb5778d1a40388d2313d9

[ "MIT" ]

2

2021-03-31T19:31:04.000Z

2021-12-13T20:32:18.000Z

movies_recommender/RecommenderSVD.py

mateuszrusin/filmweb-rekomendacje

06bdff3825f4c9e7b80fb5778d1a40388d2313d9

[ "MIT" ]

4

2019-11-21T23:49:39.000Z

2020-12-03T17:37:26.000Z

from collections import defaultdict from operator import itemgetter # python -m movies_recommender.RecommenderSVD from movies_analyzer.Movies import Movies from movies_analyzer.RecommendationDataset import RecommendationDataSet from movies_recommender.Recommender import Recommender from surprise import SVD, KNNBasic from movies_recommender.utils import get_top_n class RecommenderSVD(Recommender): def __init__(self, recommendation_dataset: RecommendationDataSet): super(RecommenderSVD, self).__init__(recommendation_dataset.movies) self.algorithm = SVD() self.recommendation_dataset = recommendation_dataset def fit(self, dataset): return self.algorithm.fit(dataset) def test(self, test_set): return self.algorithm.test(test_set) def get_recommendation(self, watched, k=20): # get dataset new_user_id, full_dataset = self.recommendation_dataset.get_dataset_with_extended_user(watched) inner_user_id = full_dataset.to_inner_uid(new_user_id) # after new dataset we need again train our model with the new user for the whole # dataset with the new user. self.algorithm.fit(full_dataset) # watched movies watched = {full_dataset.to_inner_iid(key): value for key,value in watched.items()} # Calculate for all similar user, predictions test_items = [ self.algorithm.predict(new_user_id, full_dataset.to_raw_iid(i)) for i in range(0, full_dataset.n_items) if i not in watched ] topn_items = [i[0] for i in get_top_n(test_items, n=k, minimum_rating=1.0)[new_user_id]] return self.movies.get_movie_by_movie_ids(topn_items) if __name__ == '__main__': from movies_recommender.Recommender import test_recommendation from movies_recommender.RecommenderSVD import RecommenderSVD from movies_analyzer.RecommendationDataset import RecommendationDataSet from movies_analyzer.Movies import Movies movies = Movies() recommendation_dataset = RecommendationDataSet(movies=movies) recommender = RecommenderSVD(recommendation_dataset) assert recommender.__module__[:len('movies_recommender.')] == 'movies_recommender.' test_recommendation(recommender, recommendation_dataset, example_items=['arek','mateusz'], anti_test=True) """ For test only %load_ext autoreload %autoreload 2 from filmweb_integrator.fwimdbmerge.filmweb import Filmweb from filmweb_integrator.fwimdbmerge.merger import Merger, get_json_df from movies_recommender.Recommender import get_moviescore_df, get_watched recommender.fit(recommendation_dataset.full_dataset) self = recommender # get recommendation for one user merger = Merger(filmweb=Filmweb(), imdb=movies.imdb) watched = get_watched(get_moviescore_df(merger, recommender.movies,'arek')) k = 20 k_inner_item = 20 self.get_recommendation(watched) """

37.061728

103

0.741506

from collections import defaultdict from operator import itemgetter from movies_analyzer.Movies import Movies from movies_analyzer.RecommendationDataset import RecommendationDataSet from movies_recommender.Recommender import Recommender from surprise import SVD, KNNBasic from movies_recommender.utils import get_top_n class RecommenderSVD(Recommender): def __init__(self, recommendation_dataset: RecommendationDataSet): super(RecommenderSVD, self).__init__(recommendation_dataset.movies) self.algorithm = SVD() self.recommendation_dataset = recommendation_dataset def fit(self, dataset): return self.algorithm.fit(dataset) def test(self, test_set): return self.algorithm.test(test_set) def get_recommendation(self, watched, k=20): new_user_id, full_dataset = self.recommendation_dataset.get_dataset_with_extended_user(watched) inner_user_id = full_dataset.to_inner_uid(new_user_id) self.algorithm.fit(full_dataset) watched = {full_dataset.to_inner_iid(key): value for key,value in watched.items()} test_items = [ self.algorithm.predict(new_user_id, full_dataset.to_raw_iid(i)) for i in range(0, full_dataset.n_items) if i not in watched ] topn_items = [i[0] for i in get_top_n(test_items, n=k, minimum_rating=1.0)[new_user_id]] return self.movies.get_movie_by_movie_ids(topn_items) if __name__ == '__main__': from movies_recommender.Recommender import test_recommendation from movies_recommender.RecommenderSVD import RecommenderSVD from movies_analyzer.RecommendationDataset import RecommendationDataSet from movies_analyzer.Movies import Movies movies = Movies() recommendation_dataset = RecommendationDataSet(movies=movies) recommender = RecommenderSVD(recommendation_dataset) assert recommender.__module__[:len('movies_recommender.')] == 'movies_recommender.' test_recommendation(recommender, recommendation_dataset, example_items=['arek','mateusz'], anti_test=True)

true

f700658fdb8362dbb88d94c948cdb708d1fac1ec

1,320

py

Python

lib/iana/ripe/objects/mntner.py

sschwetz/network_tech

fc65166e71bfdb5a0e99ca7e7ce9f7814b92869b

[ "Apache-2.0" ]

73

2017-05-04T06:35:20.000Z

2022-02-03T13:57:00.000Z

lib/iana/ripe/objects/mntner.py

sschwetz/network_tech

fc65166e71bfdb5a0e99ca7e7ce9f7814b92869b

[ "Apache-2.0" ]

35

2017-11-09T16:28:48.000Z

2022-01-12T08:15:48.000Z

lib/iana/ripe/objects/mntner.py

sschwetz/network_tech

fc65166e71bfdb5a0e99ca7e7ce9f7814b92869b

[ "Apache-2.0" ]

20

2017-11-08T05:07:59.000Z

2021-12-09T17:41:06.000Z

""" Copyright 2019 Glen Harmon MNTNER Object Description https://www.ripe.net/manage-ips-and-asns/db/support/documentation/ripe-database-documentation/rpsl-object-types/4-3-descriptions-of-secondary-objects/4-3-4-description-of-the-mntner-object """ from .rpsl import Rpsl class Maintainer(Rpsl): def __init__(self): self.handle = None self.description = list() self.update_to = list() self.maintainer_notify = list() self.authentication = list() super().__init__() def html(self, heading_level=1): return super().html( title='Maintainer', attributes=[ (None, self.handle), ('Description', self.description), ('Update To', self.update_to), ('Maintainer Notify', self.maintainer_notify), ('Authentication', self.authentication), ('Organisation', self.organisation), ('Admin Contact', self.admin_contact), ('Technical Contact', self.technical_contact), ('Remarks', self.remarks), ('Notify', self.notify), ('Maintained By', self.maintained_by), ('Modified', self.modified), ('Type', self.type_), ] )

32.195122

188

0.567424

from .rpsl import Rpsl class Maintainer(Rpsl): def __init__(self): self.handle = None self.description = list() self.update_to = list() self.maintainer_notify = list() self.authentication = list() super().__init__() def html(self, heading_level=1): return super().html( title='Maintainer', attributes=[ (None, self.handle), ('Description', self.description), ('Update To', self.update_to), ('Maintainer Notify', self.maintainer_notify), ('Authentication', self.authentication), ('Organisation', self.organisation), ('Admin Contact', self.admin_contact), ('Technical Contact', self.technical_contact), ('Remarks', self.remarks), ('Notify', self.notify), ('Maintained By', self.maintained_by), ('Modified', self.modified), ('Type', self.type_), ] )

true

f700666f3ddc127976f134add98c0159870b03e1

8,280

py

Python

safedelete/queryset.py

MarcelForArt/django-safedelete

0902e3db957bee0fa9bf9058e8f0a0d2e0e027d7

[ "BSD-3-Clause" ]

null

safedelete/queryset.py

MarcelForArt/django-safedelete

0902e3db957bee0fa9bf9058e8f0a0d2e0e027d7

[ "BSD-3-Clause" ]

null

safedelete/queryset.py

MarcelForArt/django-safedelete

0902e3db957bee0fa9bf9058e8f0a0d2e0e027d7

[ "BSD-3-Clause" ]

null

from django.db.models import query from .query import SafeDeleteQuery from functools import partial, reduce from django.db.models.constants import LOOKUP_SEP from django.db.models import Max, Min, F from django.utils.module_loading import import_string def get_lookup_value(obj, field): return reduce(lambda i, f: getattr(i, f), field.split(LOOKUP_SEP), obj) class SafeDeleteQueryset(query.QuerySet): """Default queryset for the SafeDeleteManager. Takes care of "lazily evaluating" safedelete QuerySets. QuerySets passed within the ``SafeDeleteQueryset`` will have all of the models available. The deleted policy is evaluated at the very end of the chain when the QuerySet itself is evaluated. """ def __init__(self, model=None, query=None, using=None, hints=None): super(SafeDeleteQueryset, self).__init__(model=model, query=query, using=using, hints=hints) self.query = query or SafeDeleteQuery(self.model) def delete(self, force_policy=None): """Overrides bulk delete behaviour. .. note:: The current implementation loses performance on bulk deletes in order to safely delete objects according to the deletion policies set. .. seealso:: :py:func:`safedelete.models.SafeDeleteModel.delete` """ assert self.query.can_filter(), "Cannot use 'limit' or 'offset' with delete." # TODO: Replace this by bulk update if we can for obj in self.all(): obj.delete(force_policy=force_policy) self._result_cache = None delete.alters_data = True def undelete(self, force_policy=None): """Undelete all soft deleted models. .. note:: The current implementation loses performance on bulk undeletes in order to call the pre/post-save signals. .. seealso:: :py:func:`safedelete.models.SafeDeleteModel.undelete` """ assert self.query.can_filter(), "Cannot use 'limit' or 'offset' with undelete." # TODO: Replace this by bulk update if we can (need to call pre/post-save signal) for obj in self.all(): obj.undelete(force_policy=force_policy) self._result_cache = None undelete.alters_data = True def all(self, force_visibility=None): """Override so related managers can also see the deleted models. A model's m2m field does not easily have access to `all_objects` and so setting `force_visibility` to True is a way of getting all of the models. It is not recommended to use `force_visibility` outside of related models because it will create a new queryset. Args: force_visibility: Force a deletion visibility. (default: {None}) """ if force_visibility is not None: self.query._safedelete_force_visibility = force_visibility return super(SafeDeleteQueryset, self).all() def filter(self, *args, **kwargs): # Return a copy, see #131 queryset = self._clone() queryset.query.check_field_filter(**kwargs) return super(SafeDeleteQueryset, queryset).filter(*args, **kwargs) class OrderedSafeDeleteQueryset(SafeDeleteQueryset): """ # ADDED BY LEE This extends SafeDeleteQueryset with methods from OrderedModelQuerySet of the django-ordered-model package, so that we can have both proper ordering and safe-deletion """ def _get_order_field_name(self): return self.model.order_field_name def _get_order_field_lookup(self, lookup): order_field_name = self._get_order_field_name() return LOOKUP_SEP.join([order_field_name, lookup]) def _get_order_with_respect_to(self): model = self.model order_with_respect_to = model.order_with_respect_to if isinstance(order_with_respect_to, str): order_with_respect_to = (order_with_respect_to,) if order_with_respect_to is None: raise AssertionError( ( 'ordered model admin "{0}" has not specified "order_with_respect_to"; note that this ' "should go in the model body, and is not to be confused with the Meta property of the same name, " "which is independent Django functionality" ).format(model) ) return order_with_respect_to def get_max_order(self): order_field_name = self._get_order_field_name() return self.aggregate(Max(order_field_name)).get( self._get_order_field_lookup("max") ) def get_min_order(self): order_field_name = self._get_order_field_name() return self.aggregate(Min(order_field_name)).get( self._get_order_field_lookup("min") ) def get_next_order(self): order = self.get_max_order() return order + 1 if order is not None else 0 def above(self, order, inclusive=False): """Filter items above order.""" lookup = "gte" if inclusive else "gt" return self.filter(**{self._get_order_field_lookup(lookup): order}) def above_instance(self, ref, inclusive=False): """Filter items above ref's order.""" order_field_name = self._get_order_field_name() order = getattr(ref, order_field_name) return self.above(order, inclusive=inclusive) def below(self, order, inclusive=False): """Filter items below order.""" lookup = "lte" if inclusive else "lt" return self.filter(**{self._get_order_field_lookup(lookup): order}) def below_instance(self, ref, inclusive=False): """Filter items below ref's order.""" order_field_name = self._get_order_field_name() order = getattr(ref, order_field_name) return self.below(order, inclusive=inclusive) def decrease_order(self, **extra_kwargs): """Decrease `order_field_name` value by 1.""" order_field_name = self._get_order_field_name() update_kwargs = {order_field_name: F(order_field_name) - 1} if extra_kwargs: update_kwargs.update(extra_kwargs) return self.update(**update_kwargs) def increase_order(self, **extra_kwargs): """Increase `order_field_name` value by 1.""" order_field_name = self._get_order_field_name() update_kwargs = {order_field_name: F(order_field_name) + 1} if extra_kwargs: update_kwargs.update(extra_kwargs) return self.update(**update_kwargs) def bulk_create(self, objs, batch_size=None): order_field_name = self._get_order_field_name() order_with_respect_to = self.model.order_with_respect_to objs = list(objs) if order_with_respect_to: order_with_respect_to_mapping = {} order_with_respect_to = self._get_order_with_respect_to() for obj in objs: key = tuple( get_lookup_value(obj, field) for field in order_with_respect_to ) if key in order_with_respect_to_mapping: order_with_respect_to_mapping[key] += 1 else: order_with_respect_to_mapping[ key ] = self.filter_by_order_with_respect_to(obj).get_next_order() setattr(obj, order_field_name, order_with_respect_to_mapping[key]) else: for order, obj in enumerate(objs, self.get_next_order()): setattr(obj, order_field_name, order) return super().bulk_create(objs, batch_size=batch_size) def _get_order_with_respect_to_filter_kwargs(self, ref): order_with_respect_to = self._get_order_with_respect_to() _get_lookup_value = partial(get_lookup_value, ref) return {field: _get_lookup_value(field) for field in order_with_respect_to} _get_order_with_respect_to_filter_kwargs.queryset_only = False def filter_by_order_with_respect_to(self, ref): order_with_respect_to = self.model.order_with_respect_to if order_with_respect_to: filter_kwargs = self._get_order_with_respect_to_filter_kwargs(ref) return self.filter(**filter_kwargs) return self

40.390244

118

0.666304

from django.db.models import query from .query import SafeDeleteQuery from functools import partial, reduce from django.db.models.constants import LOOKUP_SEP from django.db.models import Max, Min, F from django.utils.module_loading import import_string def get_lookup_value(obj, field): return reduce(lambda i, f: getattr(i, f), field.split(LOOKUP_SEP), obj) class SafeDeleteQueryset(query.QuerySet): def __init__(self, model=None, query=None, using=None, hints=None): super(SafeDeleteQueryset, self).__init__(model=model, query=query, using=using, hints=hints) self.query = query or SafeDeleteQuery(self.model) def delete(self, force_policy=None): assert self.query.can_filter(), "Cannot use 'limit' or 'offset' with delete." for obj in self.all(): obj.delete(force_policy=force_policy) self._result_cache = None delete.alters_data = True def undelete(self, force_policy=None): assert self.query.can_filter(), "Cannot use 'limit' or 'offset' with undelete." for obj in self.all(): obj.undelete(force_policy=force_policy) self._result_cache = None undelete.alters_data = True def all(self, force_visibility=None): if force_visibility is not None: self.query._safedelete_force_visibility = force_visibility return super(SafeDeleteQueryset, self).all() def filter(self, *args, **kwargs): queryset = self._clone() queryset.query.check_field_filter(**kwargs) return super(SafeDeleteQueryset, queryset).filter(*args, **kwargs) class OrderedSafeDeleteQueryset(SafeDeleteQueryset): def _get_order_field_name(self): return self.model.order_field_name def _get_order_field_lookup(self, lookup): order_field_name = self._get_order_field_name() return LOOKUP_SEP.join([order_field_name, lookup]) def _get_order_with_respect_to(self): model = self.model order_with_respect_to = model.order_with_respect_to if isinstance(order_with_respect_to, str): order_with_respect_to = (order_with_respect_to,) if order_with_respect_to is None: raise AssertionError( ( 'ordered model admin "{0}" has not specified "order_with_respect_to"; note that this ' "should go in the model body, and is not to be confused with the Meta property of the same name, " "which is independent Django functionality" ).format(model) ) return order_with_respect_to def get_max_order(self): order_field_name = self._get_order_field_name() return self.aggregate(Max(order_field_name)).get( self._get_order_field_lookup("max") ) def get_min_order(self): order_field_name = self._get_order_field_name() return self.aggregate(Min(order_field_name)).get( self._get_order_field_lookup("min") ) def get_next_order(self): order = self.get_max_order() return order + 1 if order is not None else 0 def above(self, order, inclusive=False): lookup = "gte" if inclusive else "gt" return self.filter(**{self._get_order_field_lookup(lookup): order}) def above_instance(self, ref, inclusive=False): order_field_name = self._get_order_field_name() order = getattr(ref, order_field_name) return self.above(order, inclusive=inclusive) def below(self, order, inclusive=False): lookup = "lte" if inclusive else "lt" return self.filter(**{self._get_order_field_lookup(lookup): order}) def below_instance(self, ref, inclusive=False): order_field_name = self._get_order_field_name() order = getattr(ref, order_field_name) return self.below(order, inclusive=inclusive) def decrease_order(self, **extra_kwargs): order_field_name = self._get_order_field_name() update_kwargs = {order_field_name: F(order_field_name) - 1} if extra_kwargs: update_kwargs.update(extra_kwargs) return self.update(**update_kwargs) def increase_order(self, **extra_kwargs): order_field_name = self._get_order_field_name() update_kwargs = {order_field_name: F(order_field_name) + 1} if extra_kwargs: update_kwargs.update(extra_kwargs) return self.update(**update_kwargs) def bulk_create(self, objs, batch_size=None): order_field_name = self._get_order_field_name() order_with_respect_to = self.model.order_with_respect_to objs = list(objs) if order_with_respect_to: order_with_respect_to_mapping = {} order_with_respect_to = self._get_order_with_respect_to() for obj in objs: key = tuple( get_lookup_value(obj, field) for field in order_with_respect_to ) if key in order_with_respect_to_mapping: order_with_respect_to_mapping[key] += 1 else: order_with_respect_to_mapping[ key ] = self.filter_by_order_with_respect_to(obj).get_next_order() setattr(obj, order_field_name, order_with_respect_to_mapping[key]) else: for order, obj in enumerate(objs, self.get_next_order()): setattr(obj, order_field_name, order) return super().bulk_create(objs, batch_size=batch_size) def _get_order_with_respect_to_filter_kwargs(self, ref): order_with_respect_to = self._get_order_with_respect_to() _get_lookup_value = partial(get_lookup_value, ref) return {field: _get_lookup_value(field) for field in order_with_respect_to} _get_order_with_respect_to_filter_kwargs.queryset_only = False def filter_by_order_with_respect_to(self, ref): order_with_respect_to = self.model.order_with_respect_to if order_with_respect_to: filter_kwargs = self._get_order_with_respect_to_filter_kwargs(ref) return self.filter(**filter_kwargs) return self

true

f70067377c11a437d33b5c947e65a2cb6d20e8e6

2,056

py

Python

markovGames/learning/bruteSearch.py

rohit-konda/markovGames

d6dd1b8a11f1c95658a468f9e471aecfcf0e6839

[ "MIT" ]

null

markovGames/learning/bruteSearch.py

rohit-konda/markovGames

d6dd1b8a11f1c95658a468f9e471aecfcf0e6839

[ "MIT" ]

null

markovGames/learning/bruteSearch.py

rohit-konda/markovGames

d6dd1b8a11f1c95658a468f9e471aecfcf0e6839

[ "MIT" ]

null

import numpy as np from itertools import product from markovGames.gameDefs.mdpDefs import Policy def getAllDetPol(numStates, numActions): detProbs = [np.array([1 if j == i else 0 for j in range(numActions)]) for i in range(numActions)] return product(detProbs, repeat=numStates) def getPolList(states, acSet): # list of possible deterministic policies numStates = len(states) numActions = len(acSet) detPol = getAllDetPol(numStates, numActions) return [Policy(states, pol, acSet) for pol in detPol] def prodPolList(states, listActions): # get policies for each action Set polList = [getPolList(states, ac) for ac in listActions] return polList def getPayoff(utilMap, listAcSet): # utilMap: maps list of agent policies to real numbers, # allPolicyList: list of agent i (list of possible policies) def utilInd(index): jointAc = [listAcSet[j][ind] for j, ind in enumerate(index)] val = utilMap(jointAc) return val numPL = [len(pL) for pL in listAcSet] payoff = np.zeros(numPL) for ind in product(*[range(nI) for nI in numPL]): payoff[ind] = utilInd(ind) return payoff def getArgOpt(tensor): return np.unravel_index(np.argmax(tensor), tensor.shape) def bruteFindNash(payoffList): TOLERANCE = 1e-7 cpnes = list(np.argwhere(payoffList[0] > np.amax(payoffList[0], 0) - TOLERANCE)) cpnes = [tuple(cpne) for cpne in cpnes] N = len(payoffList) for i in range(1, N): pMat = payoffList[i] for cpne in cpnes[:]: ind = cpne[:i] + (slice(None),) + cpne[i + 1:] if pMat[cpne] < np.max(pMat[ind]) - TOLERANCE: cpnes.pop(cpnes.index(cpne)) return cpnes def getEfficiency(cpnes, welfareMat): # welfareMat - matrix form of welfare pneWelf = [welfareMat[cpne] for cpne in cpnes] opt = np.max(welfareMat) priceRatios = [float(pne) / opt for pne in pneWelf] return priceRatios def getPoA(cpnes, welfareMat): return min(getEfficiency(cpnes, welfareMat))

29.797101

101

0.668774

import numpy as np from itertools import product from markovGames.gameDefs.mdpDefs import Policy def getAllDetPol(numStates, numActions): detProbs = [np.array([1 if j == i else 0 for j in range(numActions)]) for i in range(numActions)] return product(detProbs, repeat=numStates) def getPolList(states, acSet): numStates = len(states) numActions = len(acSet) detPol = getAllDetPol(numStates, numActions) return [Policy(states, pol, acSet) for pol in detPol] def prodPolList(states, listActions): polList = [getPolList(states, ac) for ac in listActions] return polList def getPayoff(utilMap, listAcSet): def utilInd(index): jointAc = [listAcSet[j][ind] for j, ind in enumerate(index)] val = utilMap(jointAc) return val numPL = [len(pL) for pL in listAcSet] payoff = np.zeros(numPL) for ind in product(*[range(nI) for nI in numPL]): payoff[ind] = utilInd(ind) return payoff def getArgOpt(tensor): return np.unravel_index(np.argmax(tensor), tensor.shape) def bruteFindNash(payoffList): TOLERANCE = 1e-7 cpnes = list(np.argwhere(payoffList[0] > np.amax(payoffList[0], 0) - TOLERANCE)) cpnes = [tuple(cpne) for cpne in cpnes] N = len(payoffList) for i in range(1, N): pMat = payoffList[i] for cpne in cpnes[:]: ind = cpne[:i] + (slice(None),) + cpne[i + 1:] if pMat[cpne] < np.max(pMat[ind]) - TOLERANCE: cpnes.pop(cpnes.index(cpne)) return cpnes def getEfficiency(cpnes, welfareMat): pneWelf = [welfareMat[cpne] for cpne in cpnes] opt = np.max(welfareMat) priceRatios = [float(pne) / opt for pne in pneWelf] return priceRatios def getPoA(cpnes, welfareMat): return min(getEfficiency(cpnes, welfareMat))

true

f70068b2a70ca8e179b30626bfb6b8c33328a45e

2,694

py

Python

cloudmesh-john/setup.py

cybertraining-dsc/fa19-516-163

d6ba96bd08c8f37c50ac86bf83df7a01d1f31b98

[ "Apache-2.0" ]

null

cloudmesh-john/setup.py

cybertraining-dsc/fa19-516-163

d6ba96bd08c8f37c50ac86bf83df7a01d1f31b98

[ "Apache-2.0" ]

1

2019-09-24T13:59:25.000Z

2019-09-27T00:16:20.000Z

cloudmesh-john/setup.py

cybertraining-dsc/fa19-516-163

d6ba96bd08c8f37c50ac86bf83df7a01d1f31b98

[ "Apache-2.0" ]

1

2019-09-25T00:21:42.000Z

#!/usr/bin/env python # ----------------------------------------------------------------------- # # Copyright 2017, Gregor von Laszewski, Indiana University # # # # Licensed under the Apache License, Version 2.0 (the "License"); you may # # not use this file except in compliance with the License. You may obtain # # a copy of the License at # # # # http://www.apache.org/licenses/LICENSE-2.0 # # # # Unless required by applicable law or agreed to in writing, software # # distributed under the License is distributed on an "AS IS" BASIS, # # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.# # See the License for the specific language governing permissions and # # limitations under the License. # # ------------------------------------------------------------------------# from setuptools import find_packages, setup import io def readfile(filename): with io.open(filename, encoding="utf-8") as stream: return stream.read().split() #requiers = readfile ('requirements.txt') # # add minimum requirements here # requiers = """ psutil pygments """.split("\n") # dependency_links = ['http://github.com/nicolaiarocci/eve.git@develop'] version = readfile("VERSION")[0].strip() with open('README.md') as f: long_description = f.read() NAME = "cloudmesh-john" DESCRIPTION = "A command called john and foo for the cloudmesh shell" AUTHOR = "Gregor von Laszewski" AUTHOR_EMAIL = "[email protected]" URL = "https://github.com/cloudmesh/cloudmesh-john" setup( name=NAME, author=AUTHOR, author_email=AUTHOR_EMAIL, description=DESCRIPTION, long_description=long_description, long_description_content_type="text/markdown", version=version, license="Apache 2.0", url=URL, packages=find_packages(), classifiers=[ "Development Status :: 5 - Production/Stable", "Environment :: Web Environment", "Intended Audience :: Developers", "License :: OSI Approved :: BSD License", "Operating System :: OS Independent", "Programming Language :: Python", "Programming Language :: Python :: 2", "Programming Language :: Python :: 3", ], install_requires=requiers, tests_require=[ "flake8", "coverage", ], zip_safe=False, namespace_packages=['cloudmesh'], )

33.675

75

0.554937

from setuptools import find_packages, setup import io def readfile(filename): with io.open(filename, encoding="utf-8") as stream: return stream.read().split() requiers = """ psutil pygments """.split("\n") version = readfile("VERSION")[0].strip() with open('README.md') as f: long_description = f.read() NAME = "cloudmesh-john" DESCRIPTION = "A command called john and foo for the cloudmesh shell" AUTHOR = "Gregor von Laszewski" AUTHOR_EMAIL = "[email protected]" URL = "https://github.com/cloudmesh/cloudmesh-john" setup( name=NAME, author=AUTHOR, author_email=AUTHOR_EMAIL, description=DESCRIPTION, long_description=long_description, long_description_content_type="text/markdown", version=version, license="Apache 2.0", url=URL, packages=find_packages(), classifiers=[ "Development Status :: 5 - Production/Stable", "Environment :: Web Environment", "Intended Audience :: Developers", "License :: OSI Approved :: BSD License", "Operating System :: OS Independent", "Programming Language :: Python", "Programming Language :: Python :: 2", "Programming Language :: Python :: 3", ], install_requires=requiers, tests_require=[ "flake8", "coverage", ], zip_safe=False, namespace_packages=['cloudmesh'], )

true

f7006aafd3946f2f78f841d508a5f60194382134

6,057

py

Python

ctd_processing/_old_dialog.py

sharksmhi/ctd_processing

616df4cd7ed626b678622448a08a0356086a8a3f

[ "MIT" ]

null

ctd_processing/_old_dialog.py

sharksmhi/ctd_processing

616df4cd7ed626b678622448a08a0356086a8a3f

[ "MIT" ]

null

ctd_processing/_old_dialog.py

sharksmhi/ctd_processing

616df4cd7ed626b678622448a08a0356086a8a3f

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Mon Feb 06 11:19:04 2012 Program to open a dialog box and get a file, check the file name and send it along with the serienummer @author: a001109 Updated: 191010 MHan, Updated for Svea profiles and new CTD. """ def checkCtdFileName(ctd=None, confile='.XMLCON'): import Tkinter, tkFileDialog import os import time import sys import codecs root = Tkinter.Tk() root.withdraw() #hiding tkinter window if ctd == None: sys.exit("No CTD given, when calling checkCtdFileName() add a CTD number like checkCtdFileName(ctd='1044')") file_path = tkFileDialog.askopenfilename(title="Open file", initialdir="C:\\ctd\\temp", filetypes=[("hdr files",".hdr")]) # if '/' in file_path: # file_path = file_path.replace('/','\\') fname = os.path.basename(file_path).upper() print fname path = os.path.dirname(file_path) #Ctd from Dana if fname[:4] == '26DA': #Dana filenames usually look lite this: 26DA.2016.10.1.hdr #Merge Cruisenr and activity number 1001. Add zeros. #If serienummer is over 100 add this to next cruise nr 1000 if int(fname.split('.')[3].zfill(2)) > 99: serienummer = str(int(fname.split('.')[2])+1).zfill(2) + fname.split('.')[3][1:] else: serienummer = fname.split('.')[2].zfill(2) + fname.split('.')[3].zfill(2) #CTD from Aranda or Meri or Aura elif fname[:2] == 'AR' or fname[:2] == 'ME' or fname[:2] == 'AU': #sv19d0003.hex #serienummer = fname[3:7] if len(fname) == 12: serienummer = fname[5:8] # justerar till 4 siffror serienummer = serienummer.zfill(4) else: # if series is 4 digits serienummer = fname[5:8] serienummer = serienummer.zfill(4) elif fname[:2] == 'SV': # Svea serienummer = fname[5:9] #CTD in processed format: 'SBE09_0745_20161010_1139_34_01_0544.hdr' #to be reprocessed elif fname[:5] == 'SBE09': serienummer = fname.split('_')[-1][:4] else: sys.exit('could not get "serienummer" from file name %s, stops!' % fname) print 'serienummer: ',serienummer, # Open the header file: with codecs.open(file_path,'r', encoding = 'cp1252') as f: allHeaderInfo = f.readlines() f.closed stationname = '' for rows in allHeaderInfo: print rows if '* System UpLoad Time' in rows: datestring = rows[23:40] if '** Station:' in rows: stationname = rows[13:].strip('\r\n') print 'Stationsnamn:',stationname #Feb 28 2012 16:13 -> 20120228_1613 c = time.strptime(datestring,"%b %d %Y %H:%M") datum = time.strftime("%Y%m%d_%H%M",c) if fname[:2] == 'AR': new_fname = 'SBE09_' + ctd + '_' + datum + '_34_01_' + serienummer elif fname[:2] == 'SV': new_fname = 'SBE09_' + ctd + '_' + datum + '_77_10_' + serienummer elif fname[:2] == 'ME': new_fname = 'SBE09_' + ctd + '_' + datum + '_34_02_' + serienummer elif fname[:2] == 'AU': new_fname = 'SBE09_' + ctd + '_' + datum + '_34_07_' + serienummer elif fname[:4] == '26DA': new_fname = 'SBE09_' + ctd + '_' + datum + '_26_01_' + serienummer elif fname[:5] == 'SBE09': new_fname = fname.split('.')[0] else: sys.exit('Fel format serienummer!') print new_fname #fname = 'SBE19_0745_20110409_0522_77_28_0053.hex' #fname = os.path.basename(file_path) sub_str = new_fname.split('_') #sub_str = fname.split('_') #Kontrollera filnames längd: #SBE19_6164_20110801_1550_77_01_0053.hex counter = 0 for part in sub_str: counter = counter + 1 if counter == 1: #kontrollera filnamn, ev behöver vi lägga in en bättre kontroll print counter if part != 'SBE09': sys.exit('Fel instrumentnamn!') if counter == 2: print counter if part not in ['0745','1044','0817','0403','0827','1387']: sys.exit('Fel intrument serienummer!') if counter == 3: print counter if len(part) != 8: sys.exit('Fel datumformat!') if counter == 4: print counter if len(part) != 4: sys.exit('Fel tidsformat!') if counter == 5: print counter if part == 34: sys.exit('Fel landkod!') if counter == 6: print counter if part == 01: sys.exit('Fel fartygskod!') if counter == 7: print counter serieNo = part.split('.')[0] print serieNo if len(part.split('.')[0]) != 4: sys.exit('Fel format serienummer!') # if part.split('.')[1] != 'hex': # sys.exit('Fel filformat skall vara *.hex!') #Ändrar namnet på filerna men enbart om de är olika. if fname.split('.')[0] != new_fname: os.rename(file_path, path + '\\' + new_fname + '.hdr') print path + '\\' + fname.rsplit('.',1)[0] + confile print path + '\\' + new_fname + confile os.rename(path + '\\' + fname.rsplit('.',1)[0] + confile, path + '\\' + new_fname + confile) os.rename(path + '\\' + fname.rsplit('.',1)[0] + '.hex', path + '\\' + new_fname + '.hex') os.rename(path + '\\' + fname.rsplit('.',1)[0] + '.bl', path + '\\' + new_fname + '.bl') return new_fname, serieNo, stationname #Korrekt namn format: Kontrollera namnet #SBE19plus_01906164_2011_08_03_0001.hex #SBE19_6164_20110801_1550_77_01_0053

37.85625

116

0.527819

""" Created on Mon Feb 06 11:19:04 2012 Program to open a dialog box and get a file, check the file name and send it along with the serienummer @author: a001109 Updated: 191010 MHan, Updated for Svea profiles and new CTD. """ def checkCtdFileName(ctd=None, confile='.XMLCON'): import Tkinter, tkFileDialog import os import time import sys import codecs root = Tkinter.Tk() root.withdraw() if ctd == None: sys.exit("No CTD given, when calling checkCtdFileName() add a CTD number like checkCtdFileName(ctd='1044')") file_path = tkFileDialog.askopenfilename(title="Open file", initialdir="C:\\ctd\\temp", filetypes=[("hdr files",".hdr")]) fname = os.path.basename(file_path).upper() print fname path = os.path.dirname(file_path) if fname[:4] == '26DA': if int(fname.split('.')[3].zfill(2)) > 99: serienummer = str(int(fname.split('.')[2])+1).zfill(2) + fname.split('.')[3][1:] else: serienummer = fname.split('.')[2].zfill(2) + fname.split('.')[3].zfill(2) elif fname[:2] == 'AR' or fname[:2] == 'ME' or fname[:2] == 'AU': if len(fname) == 12: serienummer = fname[5:8] serienummer = serienummer.zfill(4) else: serienummer = fname[5:8] serienummer = serienummer.zfill(4) elif fname[:2] == 'SV': serienummer = fname[5:9] elif fname[:5] == 'SBE09': serienummer = fname.split('_')[-1][:4] else: sys.exit('could not get "serienummer" from file name %s, stops!' % fname) print 'serienummer: ',serienummer, with codecs.open(file_path,'r', encoding = 'cp1252') as f: allHeaderInfo = f.readlines() f.closed stationname = '' for rows in allHeaderInfo: print rows if '* System UpLoad Time' in rows: datestring = rows[23:40] if '** Station:' in rows: stationname = rows[13:].strip('\r\n') print 'Stationsnamn:',stationname c = time.strptime(datestring,"%b %d %Y %H:%M") datum = time.strftime("%Y%m%d_%H%M",c) if fname[:2] == 'AR': new_fname = 'SBE09_' + ctd + '_' + datum + '_34_01_' + serienummer elif fname[:2] == 'SV': new_fname = 'SBE09_' + ctd + '_' + datum + '_77_10_' + serienummer elif fname[:2] == 'ME': new_fname = 'SBE09_' + ctd + '_' + datum + '_34_02_' + serienummer elif fname[:2] == 'AU': new_fname = 'SBE09_' + ctd + '_' + datum + '_34_07_' + serienummer elif fname[:4] == '26DA': new_fname = 'SBE09_' + ctd + '_' + datum + '_26_01_' + serienummer elif fname[:5] == 'SBE09': new_fname = fname.split('.')[0] else: sys.exit('Fel format serienummer!') print new_fname sub_str = new_fname.split('_') counter = 0 for part in sub_str: counter = counter + 1 if counter == 1: print counter if part != 'SBE09': sys.exit('Fel instrumentnamn!') if counter == 2: print counter if part not in ['0745','1044','0817','0403','0827','1387']: sys.exit('Fel intrument serienummer!') if counter == 3: print counter if len(part) != 8: sys.exit('Fel datumformat!') if counter == 4: print counter if len(part) != 4: sys.exit('Fel tidsformat!') if counter == 5: print counter if part == 34: sys.exit('Fel landkod!') if counter == 6: print counter if part == 01: sys.exit('Fel fartygskod!') if counter == 7: print counter serieNo = part.split('.')[0] print serieNo if len(part.split('.')[0]) != 4: sys.exit('Fel format serienummer!') if fname.split('.')[0] != new_fname: os.rename(file_path, path + '\\' + new_fname + '.hdr') print path + '\\' + fname.rsplit('.',1)[0] + confile print path + '\\' + new_fname + confile os.rename(path + '\\' + fname.rsplit('.',1)[0] + confile, path + '\\' + new_fname + confile) os.rename(path + '\\' + fname.rsplit('.',1)[0] + '.hex', path + '\\' + new_fname + '.hex') os.rename(path + '\\' + fname.rsplit('.',1)[0] + '.bl', path + '\\' + new_fname + '.bl') return new_fname, serieNo, stationname

false

true

f7006be989e72bdce78c68bad744121539b63e0a

8,142

py

Python

framework/TSA/Wavelet.py

FlanFlanagan/raven

bd7fca18af94376a28e2144ba1da72c01c8d343c

[ "Apache-2.0" ]

159

2017-03-24T21:07:06.000Z

2022-03-20T13:44:40.000Z

framework/TSA/Wavelet.py

FlanFlanagan/raven

bd7fca18af94376a28e2144ba1da72c01c8d343c

[ "Apache-2.0" ]

1,667

2017-03-27T14:41:22.000Z

2022-03-31T19:50:06.000Z

framework/TSA/Wavelet.py

wanghy-anl/raven

ef1372364a2776385931763f2b28fdf2930c77b9

[ "Apache-2.0" ]

95

2017-03-24T21:05:03.000Z

2022-03-08T17:30:22.000Z

# Copyright 2017 Battelle Energy Alliance, LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Discrete Wavelet Transformation for Characterization of Time-Series Data. """ import numpy as np from utils import InputData, InputTypes, xmlUtils from .TimeSeriesAnalyzer import TimeSeriesGenerator, TimeSeriesCharacterizer # utility methods class Wavelet(TimeSeriesGenerator, TimeSeriesCharacterizer): """ Perform Discrete Wavelet Transformation on time-dependent data. """ @classmethod def getInputSpecification(cls): """ Method to get a reference to a class that specifies the input data for class cls. @ Out, specs, InputData.ParameterInput, class to use for specifying input of cls. """ specs = super(Wavelet, cls).getInputSpecification() specs.name = 'wavelet' specs.description = r"""Discrete Wavelet TimeSeriesAnalysis algorithm. Performs a discrete wavelet transform on time-dependent data. Note: This TSA module requires pywavelets to be installed within your python environment.""" specs.addSub(InputData.parameterInputFactory( 'family', contentType=InputTypes.StringType, descr=r"""The type of wavelet to use for the transformation. There are several possible families to choose from, and most families contain more than one variation. For more information regarding the wavelet families, refer to the Pywavelets documentation located at: https://pywavelets.readthedocs.io/en/latest/ref/wavelets.html (wavelet-families) \\ Possible values are: \begin{itemize} \item \textbf{haar family}: haar \item \textbf{db family}: db1, db2, db3, db4, db5, db6, db7, db8, db9, db10, db11, db12, db13, db14, db15, db16, db17, db18, db19, db20, db21, db22, db23, db24, db25, db26, db27, db28, db29, db30, db31, db32, db33, db34, db35, db36, db37, db38 \item \textbf{sym family}: sym2, sym3, sym4, sym5, sym6, sym7, sym8, sym9, sym10, sym11, sym12, sym13, sym14, sym15, sym16, sym17, sym18, sym19, sym20 \item \textbf{coif family}: coif1, coif2, coif3, coif4, coif5, coif6, coif7, coif8, coif9, coif10, coif11, coif12, coif13, coif14, coif15, coif16, coif17 \item \textbf{bior family}: bior1.1, bior1.3, bior1.5, bior2.2, bior2.4, bior2.6, bior2.8, bior3.1, bior3.3, bior3.5, bior3.7, bior3.9, bior4.4, bior5.5, bior6.8 \item \textbf{rbio family}: rbio1.1, rbio1.3, rbio1.5, rbio2.2, rbio2.4, rbio2.6, rbio2.8, rbio3.1, rbio3.3, rbio3.5, rbio3.7, rbio3.9, rbio4.4, rbio5.5, rbio6.8 \item \textbf{dmey family}: dmey \item \textbf{gaus family}: gaus1, gaus2, gaus3, gaus4, gaus5, gaus6, gaus7, gaus8 \item \textbf{mexh family}: mexh \item \textbf{morl family}: morl \item \textbf{cgau family}: cgau1, cgau2, cgau3, cgau4, cgau5, cgau6, cgau7, cgau8 \item \textbf{shan family}: shan \item \textbf{fbsp family}: fbsp \item \textbf{cmor family}: cmor \end{itemize}""")) return specs def __init__(self, *args, **kwargs): """ A constructor that will appropriately intialize a time-series analysis object @ In, args, list, an arbitrary list of positional values @ In, kwargs, dict, an arbitrary dictionary of keywords and values @ Out, None """ # general infrastructure super().__init__(*args, **kwargs) def handleInput(self, spec): """ Reads user inputs into this object. @ In, spec, InputData.InputParams, input specifications @ Out, settings, dict, initialization settings for this algorithm """ settings = super().handleInput(spec) settings['family'] = spec.findFirst('family').value return settings def characterize(self, signal, pivot, targets, settings): """ This function utilizes the Discrete Wavelet Transform to characterize a time-dependent series of data. @ In, signal, np.ndarray, time series with dims [time, target] @ In, pivot, np.1darray, time-like parameter values @ In, targets, list(str), names of targets in same order as signal @ In, settings, dict, additional settings specific to this algorithm @ Out, params, dict, characteristic parameters """ # TODO extend to continuous wavelet transform try: import pywt except ModuleNotFoundError: print("This RAVEN TSA Module requires the PYWAVELETS library to be installed in the current python environment") raise ModuleNotFoundError ## The pivot input parameter isn't used explicity in the ## transformation as it assumed/required that each element in the ## time-dependent series is independent, uniquely indexed and ## sorted in time. family = settings['family'] params = {target: {'results': {}} for target in targets} for i, target in enumerate(targets): results = params[target]['results'] results['coeff_a'], results['coeff_d'] = pywt.dwt(signal[:, i], family) return params def getParamNames(self, settings): """ Return list of expected variable names based on the parameters @ In, settings, dict, training parameters for this algorithm @ Out, names, list, string list of names """ # FIXME we don't know a priori how many entries will be in the decomp, so we can't register it yet! raise NotImplementedError('Cannot predict variables for Wavelet!') names = [] for target in settings['target']: base = f'{self.name}__{target}' def getParamsAsVars(self, params): """ Map characterization parameters into flattened variable format @ In, params, dict, trained parameters (as from characterize) @ Out, rlz, dict, realization-style response """ # FIXME we don't know a priori how many entries will be in the decomp, so we can't register it yet! raise NotImplementedError('Cannot predict variables for Wavelet!') rlz = {} for target, info in params.items(): base = f'{self.name}__{target}' for name, values in info['results'].items(): for v, val in enumerate(values): rlz[f'{base}__{name}__{v}'] = val return rlz def generate(self, params, pivot, settings): """ Generates a synthetic history from fitted parameters. @ In, params, dict, characterization such as otained from self.characterize() @ In, pivot, np.array(float), pivot parameter values @ In, settings, dict, additional settings specific to algorithm @ Out, synthetic, np.array(float), synthetic ARMA signal """ try: import pywt except ModuleNotFoundError: print("This RAVEN TSA Module requires the PYWAVELETS library to be installed in the current python environment") raise ModuleNotFoundError synthetic = np.zeros((len(pivot), len(params))) family = settings['family'] for t, (target, _) in enumerate(params.items()): results = params[target]['results'] cA = results['coeff_a'] cD = results['coeff_d'] synthetic[:, t] = pywt.idwt(cA, cD, family) return synthetic def writeXML(self, writeTo, params): """ Allows the engine to put whatever it wants into an XML to print to file. @ In, writeTo, xmlUtils.StaticXmlElement, entity to write to @ In, params, dict, trained parameters as from self.characterize @ Out, None """ for target, info in params.items(): base = xmlUtils.newNode(target) writeTo.append(base) for name, value in info['results'].items(): base.append(xmlUtils.newNode(name, text=','.join([str(v) for v in value])))

41.753846

118

0.681774

import numpy as np from utils import InputData, InputTypes, xmlUtils from .TimeSeriesAnalyzer import TimeSeriesGenerator, TimeSeriesCharacterizer class Wavelet(TimeSeriesGenerator, TimeSeriesCharacterizer): @classmethod def getInputSpecification(cls): specs = super(Wavelet, cls).getInputSpecification() specs.name = 'wavelet' specs.description = r"""Discrete Wavelet TimeSeriesAnalysis algorithm. Performs a discrete wavelet transform on time-dependent data. Note: This TSA module requires pywavelets to be installed within your python environment.""" specs.addSub(InputData.parameterInputFactory( 'family', contentType=InputTypes.StringType, descr=r"""The type of wavelet to use for the transformation. There are several possible families to choose from, and most families contain more than one variation. For more information regarding the wavelet families, refer to the Pywavelets documentation located at: https://pywavelets.readthedocs.io/en/latest/ref/wavelets.html (wavelet-families) \\ Possible values are: \begin{itemize} \item \textbf{haar family}: haar \item \textbf{db family}: db1, db2, db3, db4, db5, db6, db7, db8, db9, db10, db11, db12, db13, db14, db15, db16, db17, db18, db19, db20, db21, db22, db23, db24, db25, db26, db27, db28, db29, db30, db31, db32, db33, db34, db35, db36, db37, db38 \item \textbf{sym family}: sym2, sym3, sym4, sym5, sym6, sym7, sym8, sym9, sym10, sym11, sym12, sym13, sym14, sym15, sym16, sym17, sym18, sym19, sym20 \item \textbf{coif family}: coif1, coif2, coif3, coif4, coif5, coif6, coif7, coif8, coif9, coif10, coif11, coif12, coif13, coif14, coif15, coif16, coif17 \item \textbf{bior family}: bior1.1, bior1.3, bior1.5, bior2.2, bior2.4, bior2.6, bior2.8, bior3.1, bior3.3, bior3.5, bior3.7, bior3.9, bior4.4, bior5.5, bior6.8 \item \textbf{rbio family}: rbio1.1, rbio1.3, rbio1.5, rbio2.2, rbio2.4, rbio2.6, rbio2.8, rbio3.1, rbio3.3, rbio3.5, rbio3.7, rbio3.9, rbio4.4, rbio5.5, rbio6.8 \item \textbf{dmey family}: dmey \item \textbf{gaus family}: gaus1, gaus2, gaus3, gaus4, gaus5, gaus6, gaus7, gaus8 \item \textbf{mexh family}: mexh \item \textbf{morl family}: morl \item \textbf{cgau family}: cgau1, cgau2, cgau3, cgau4, cgau5, cgau6, cgau7, cgau8 \item \textbf{shan family}: shan \item \textbf{fbsp family}: fbsp \item \textbf{cmor family}: cmor \end{itemize}""")) return specs def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) def handleInput(self, spec): settings = super().handleInput(spec) settings['family'] = spec.findFirst('family').value return settings def characterize(self, signal, pivot, targets, settings): try: import pywt except ModuleNotFoundError: print("This RAVEN TSA Module requires the PYWAVELETS library to be installed in the current python environment") raise ModuleNotFoundError ## transformation as it assumed/required that each element in the ## time-dependent series is independent, uniquely indexed and ## sorted in time. family = settings['family'] params = {target: {'results': {}} for target in targets} for i, target in enumerate(targets): results = params[target]['results'] results['coeff_a'], results['coeff_d'] = pywt.dwt(signal[:, i], family) return params def getParamNames(self, settings): # FIXME we don't know a priori how many entries will be in the decomp, so we can't register it yet! raise NotImplementedError('Cannot predict variables for Wavelet!') names = [] for target in settings['target']: base = f'{self.name}__{target}' def getParamsAsVars(self, params): # FIXME we don't know a priori how many entries will be in the decomp, so we can't register it yet! raise NotImplementedError('Cannot predict variables for Wavelet!') rlz = {} for target, info in params.items(): base = f'{self.name}__{target}' for name, values in info['results'].items(): for v, val in enumerate(values): rlz[f'{base}__{name}__{v}'] = val return rlz def generate(self, params, pivot, settings): try: import pywt except ModuleNotFoundError: print("This RAVEN TSA Module requires the PYWAVELETS library to be installed in the current python environment") raise ModuleNotFoundError synthetic = np.zeros((len(pivot), len(params))) family = settings['family'] for t, (target, _) in enumerate(params.items()): results = params[target]['results'] cA = results['coeff_a'] cD = results['coeff_d'] synthetic[:, t] = pywt.idwt(cA, cD, family) return synthetic def writeXML(self, writeTo, params): for target, info in params.items(): base = xmlUtils.newNode(target) writeTo.append(base) for name, value in info['results'].items(): base.append(xmlUtils.newNode(name, text=','.join([str(v) for v in value])))

true

f7006c01dfe517f7ba15beb6a2ddbc1643eb7d09

12,557

py

Python

openstack_dashboard/test/integration_tests/pages/project/network/networkspage.py

Mirantis/mos-horizon

d2444220d959c8b921436bd75459c2face0e71d2

[ "Apache-2.0" ]

9

2016-06-03T03:53:24.000Z

2017-05-20T16:53:23.000Z

openstack_dashboard/test/integration_tests/pages/project/network/networkspage.py

Mirantis/mos-horizon

d2444220d959c8b921436bd75459c2face0e71d2

[ "Apache-2.0" ]

1

2016-09-08T10:57:46.000Z

2016-09-08T10:59:06.000Z

openstack_dashboard/test/integration_tests/pages/project/network/networkspage.py

Mirantis/mos-horizon

d2444220d959c8b921436bd75459c2face0e71d2

[ "Apache-2.0" ]

4

2016-08-01T10:50:15.000Z

2017-02-22T12:11:19.000Z

# Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. from openstack_dashboard.test.integration_tests.pages import basepage from openstack_dashboard.test.integration_tests.regions import forms from openstack_dashboard.test.integration_tests.regions import tables from selenium.common import exceptions from selenium.webdriver.common import by class ElementTable(tables.TableRegion): name = "element" CREATE_FORM_FIELDS = () EDIT_FORM_FIELDS = () @tables.bind_table_action('create') def create(self, create_button): create_button.click() return forms.FormRegion(self.driver, self.conf, field_mappings=self.CREATE_FORM_FIELDS) @tables.bind_table_action('delete') def delete(self, delete_button): delete_button.click() return forms.BaseFormRegion(self.driver, self.conf) @tables.bind_row_action('edit', primary=True) def edit(self, edit_button, row): edit_button.click() return forms.FormRegion(self.driver, self.conf, field_mappings=self.EDIT_FORM_FIELDS) class SubnetsTable(ElementTable): name = "subnets" CREATE_FORM_FIELDS = (("subnet_name", "cidr", "ip_version", "gateway_ip", "no_gateway"), ("enable_dhcp", "allocation_pools", "dns_nameservers", "host_routes")) EDIT_FORM_FIELDS = CREATE_FORM_FIELDS @tables.bind_table_action('create') def create(self, create_button): create_button.click() return forms.TabbedFormRegion(self.driver, self.conf, self.CREATE_FORM_FIELDS) @tables.bind_row_action('edit') def edit(self, edit_button): edit_button.click() return forms.TabbedFormRegion(self.driver, self.conf, self.EDIT_FORM_FIELDS) class NetworksTable(ElementTable): name = "networks" CREATE_FORM_FIELDS = (("net_name", "admin_state", "shared", "with_subnet"), ("subnet_name", "cidr", "ip_version", "gateway_ip", "no_gateway"), ("enable_dhcp", "allocation_pools", "dns_nameservers", "host_routes")) EDIT_FORM_FIELDS = ("name", "network_id", "admin_state", "shared") ADD_SUBNET_FORM_FIELDS = (("subnet_name", "cidr", "ip_version", "gateway_ip", "no_gateway"), ("enable_dhcp", "allocation_pools", "dns_nameservers", "host_routes")) @tables.bind_table_action('create') def create(self, create_button): create_button.click() return forms.TabbedFormRegion(self.driver, self.conf, self.CREATE_FORM_FIELDS) @tables.bind_row_action('subnet') def edit_add_subnet(self, edit_button, row): edit_button.click() return forms.TabbedFormRegion(self.driver, self.conf, self.ADD_SUBNET_FORM_FIELDS) @tables.bind_row_action('delete') def edit_delete_network(self, delete_button, row): delete_button.click() return forms.BaseFormRegion(self.driver, self.conf) class NetworksPage(basepage.BaseNavigationPage): DEFAULT_ADMIN_STATE = 'True' DEFAULT_CREATE_SUBNET = True DEFAULT_IP_VERSION = '4' DEFAULT_DISABLE_GATEWAY = False DEFAULT_ENABLE_DHCP = True NETWORKS_TABLE_NAME_COLUMN = 'name' NETWORKS_TABLE_STATUS_COLUMN = 'status' SUBNET_TAB_INDEX = 1 DETAILS_TAB_INDEX = 2 def __init__(self, driver, conf): super(NetworksPage, self).__init__(driver, conf) self._page_title = "Networks" def _get_row_with_network_name(self, name): return self.networks_table.get_row( self.NETWORKS_TABLE_NAME_COLUMN, name) @property def networks_table(self): return NetworksTable(self.driver, self.conf) def create_network(self, network_name, subnet_name, admin_state=DEFAULT_ADMIN_STATE, create_subnet=DEFAULT_CREATE_SUBNET, network_address=None, ip_version=DEFAULT_IP_VERSION, gateway_ip=None, disable_gateway=DEFAULT_DISABLE_GATEWAY, enable_dhcp=DEFAULT_ENABLE_DHCP, allocation_pools=None, dns_name_servers=None, host_routes=None): create_network_form = self.networks_table.create() create_network_form.net_name.text = network_name create_network_form.admin_state.value = admin_state if not create_subnet: create_network_form.with_subnet.unmark() else: create_network_form.switch_to(self.SUBNET_TAB_INDEX) create_network_form.subnet_name.text = subnet_name if network_address is None: network_address = self.conf.network.network_cidr create_network_form.cidr.text = network_address create_network_form.ip_version.value = ip_version if gateway_ip is not None: create_network_form.gateway_ip.text = gateway_ip if disable_gateway: create_network_form.disable_gateway.mark() create_network_form.switch_to(self.DETAILS_TAB_INDEX) if not enable_dhcp: create_network_form.enable_dhcp.unmark() if allocation_pools is not None: create_network_form.allocation_pools.text = allocation_pools if dns_name_servers is not None: create_network_form.dns_nameservers.text = dns_name_servers if host_routes is not None: create_network_form.host_routes.text = host_routes create_network_form.submit() def delete_network(self, name): row = self._get_row_with_network_name(name) confirm_delete_networks_form = \ self.networks_table.edit_delete_network(row) confirm_delete_networks_form.submit() def is_network_present(self, name): return bool(self._get_row_with_network_name(name)) def is_network_active(self, name): def cell_getter(): row = self._get_row_with_network_name(name) return row and row.cells[self.NETWORKS_TABLE_STATUS_COLUMN] return bool(self.networks_table.wait_cell_status(cell_getter, 'Active')) def add_subnet(self, net_name, subnet_name, network_address=None, ip_version=DEFAULT_IP_VERSION, gateway_ip=None, disable_gateway=DEFAULT_DISABLE_GATEWAY, enable_dhcp=DEFAULT_ENABLE_DHCP, allocation_pools=None, dns_name_servers=None, host_routes=None): row = self._get_row_with_network_name(net_name) add_subnet_form = self.networks_table.edit_add_subnet(row) add_subnet_form.subnet_name.text = subnet_name if network_address is None: network_address = self.conf.network.network_cidr add_subnet_form.cidr.text = network_address add_subnet_form.ip_version.value = ip_version if gateway_ip is not None: add_subnet_form.gateway_ip.text = gateway_ip if disable_gateway: add_subnet_form.disable_gateway.mark() add_subnet_form.switch_to(self.SUBNET_TAB_INDEX) if not enable_dhcp: add_subnet_form.enable_dhcp.unmark() if allocation_pools is not None: add_subnet_form.allocation_pools.text = allocation_pools if dns_name_servers is not None: add_subnet_form.dns_nameservers.text = dns_name_servers if host_routes is not None: add_subnet_form.host_routes.text = host_routes add_subnet_form.submit() return NetworkOverviewPage(self.driver, self.conf, net_name) def go_to_overview(self, name): _network_items_locator = (by.By.CSS_SELECTOR, 'a[href$="/detail"]') net_items = self._get_elements(*_network_items_locator) for item in net_items: if item.text == name: item.click() break else: raise exceptions.NoSuchElementException( "Not found element with text: %s" % name) return NetworkOverviewPage(self.driver, self.conf, name) class NetworkOverviewPage(basepage.BaseNavigationPage): DEFAULT_ADMIN_STATE = 'True' DEFAULT_IP_VERSION = '4' DEFAULT_DISABLE_GATEWAY = False DEFAULT_ENABLE_DHCP = True DETAILS_TAB_INDEX = 1 TABLE_NAME_COLUMN = 'name' _edit_network_locator = ( by.By.CSS_SELECTOR, 'form.actions_column > .btn-group > a.btn:nth-child(1)') _dropdown_open_locator = ( by.By.CSS_SELECTOR, 'form.actions_column > .btn-group > a.btn:nth-child(2)') _dropdown_menu_locator = ( by.By.CSS_SELECTOR, 'form.actions_column > .btn-group > ul.row_actions > li > *') def __init__(self, driver, conf, network_name): super(NetworkOverviewPage, self).__init__(driver, conf) self._page_title = "Network Details: {}".format(network_name) @property def subnets_table(self): return SubnetsTable(self.driver, self.conf) def _get_row_with_name(self, name, table): return table.get_row(self.TABLE_NAME_COLUMN, name) def _get_row_action(self, action_name): open_dropdown_elem = self._get_element(*self._dropdown_open_locator) open_dropdown_elem.click() for action in self._get_elements(*self._dropdown_menu_locator): pattern = "__action_%s" % action_name if action.get_attribute('id').endswith(pattern): action_element = action break return action_element def delete_network(self): delete_elem = self._get_row_action('delete') delete_elem.click() confirm_delete_network_form = forms.BaseFormRegion(self.driver, self.conf) confirm_delete_network_form.submit() return NetworksPage(self.driver, self.conf) def create_subnet(self, subnet_name, network_address=None, ip_version=DEFAULT_IP_VERSION, gateway_ip=None, disable_gateway=DEFAULT_DISABLE_GATEWAY, enable_dhcp=DEFAULT_ENABLE_DHCP, allocation_pools=None, dns_name_servers=None, host_routes=None): create_subnet_form = self.subnets_table.create() create_subnet_form.subnet_name.text = subnet_name if network_address is None: network_address = self.conf.network.network_cidr create_subnet_form.cidr.text = network_address create_subnet_form.ip_version.value = ip_version if gateway_ip is not None: create_subnet_form.gateway_ip.text = gateway_ip if disable_gateway: create_subnet_form.disable_gateway.mark() create_subnet_form.tabs.switch_to(self.DETAILS_TAB_INDEX) if not enable_dhcp: create_subnet_form.enable_dhcp.unmark() if allocation_pools is not None: create_subnet_form.allocation_pools.text = allocation_pools if dns_name_servers is not None: create_subnet_form.dns_nameservers.text = dns_name_servers if host_routes is not None: create_subnet_form.host_routes.text = host_routes create_subnet_form.submit() def delete_subnet(self, name): row = self._get_row_with_name(name, self.subnets_table) row.mark() confirm_delete_subnet_form = self.subnets_table.delete() confirm_delete_subnet_form.submit() def is_subnet_present(self, name): return bool(self._get_row_with_name(name, self.subnets_table))

41.305921

78

0.649359

from openstack_dashboard.test.integration_tests.pages import basepage from openstack_dashboard.test.integration_tests.regions import forms from openstack_dashboard.test.integration_tests.regions import tables from selenium.common import exceptions from selenium.webdriver.common import by class ElementTable(tables.TableRegion): name = "element" CREATE_FORM_FIELDS = () EDIT_FORM_FIELDS = () @tables.bind_table_action('create') def create(self, create_button): create_button.click() return forms.FormRegion(self.driver, self.conf, field_mappings=self.CREATE_FORM_FIELDS) @tables.bind_table_action('delete') def delete(self, delete_button): delete_button.click() return forms.BaseFormRegion(self.driver, self.conf) @tables.bind_row_action('edit', primary=True) def edit(self, edit_button, row): edit_button.click() return forms.FormRegion(self.driver, self.conf, field_mappings=self.EDIT_FORM_FIELDS) class SubnetsTable(ElementTable): name = "subnets" CREATE_FORM_FIELDS = (("subnet_name", "cidr", "ip_version", "gateway_ip", "no_gateway"), ("enable_dhcp", "allocation_pools", "dns_nameservers", "host_routes")) EDIT_FORM_FIELDS = CREATE_FORM_FIELDS @tables.bind_table_action('create') def create(self, create_button): create_button.click() return forms.TabbedFormRegion(self.driver, self.conf, self.CREATE_FORM_FIELDS) @tables.bind_row_action('edit') def edit(self, edit_button): edit_button.click() return forms.TabbedFormRegion(self.driver, self.conf, self.EDIT_FORM_FIELDS) class NetworksTable(ElementTable): name = "networks" CREATE_FORM_FIELDS = (("net_name", "admin_state", "shared", "with_subnet"), ("subnet_name", "cidr", "ip_version", "gateway_ip", "no_gateway"), ("enable_dhcp", "allocation_pools", "dns_nameservers", "host_routes")) EDIT_FORM_FIELDS = ("name", "network_id", "admin_state", "shared") ADD_SUBNET_FORM_FIELDS = (("subnet_name", "cidr", "ip_version", "gateway_ip", "no_gateway"), ("enable_dhcp", "allocation_pools", "dns_nameservers", "host_routes")) @tables.bind_table_action('create') def create(self, create_button): create_button.click() return forms.TabbedFormRegion(self.driver, self.conf, self.CREATE_FORM_FIELDS) @tables.bind_row_action('subnet') def edit_add_subnet(self, edit_button, row): edit_button.click() return forms.TabbedFormRegion(self.driver, self.conf, self.ADD_SUBNET_FORM_FIELDS) @tables.bind_row_action('delete') def edit_delete_network(self, delete_button, row): delete_button.click() return forms.BaseFormRegion(self.driver, self.conf) class NetworksPage(basepage.BaseNavigationPage): DEFAULT_ADMIN_STATE = 'True' DEFAULT_CREATE_SUBNET = True DEFAULT_IP_VERSION = '4' DEFAULT_DISABLE_GATEWAY = False DEFAULT_ENABLE_DHCP = True NETWORKS_TABLE_NAME_COLUMN = 'name' NETWORKS_TABLE_STATUS_COLUMN = 'status' SUBNET_TAB_INDEX = 1 DETAILS_TAB_INDEX = 2 def __init__(self, driver, conf): super(NetworksPage, self).__init__(driver, conf) self._page_title = "Networks" def _get_row_with_network_name(self, name): return self.networks_table.get_row( self.NETWORKS_TABLE_NAME_COLUMN, name) @property def networks_table(self): return NetworksTable(self.driver, self.conf) def create_network(self, network_name, subnet_name, admin_state=DEFAULT_ADMIN_STATE, create_subnet=DEFAULT_CREATE_SUBNET, network_address=None, ip_version=DEFAULT_IP_VERSION, gateway_ip=None, disable_gateway=DEFAULT_DISABLE_GATEWAY, enable_dhcp=DEFAULT_ENABLE_DHCP, allocation_pools=None, dns_name_servers=None, host_routes=None): create_network_form = self.networks_table.create() create_network_form.net_name.text = network_name create_network_form.admin_state.value = admin_state if not create_subnet: create_network_form.with_subnet.unmark() else: create_network_form.switch_to(self.SUBNET_TAB_INDEX) create_network_form.subnet_name.text = subnet_name if network_address is None: network_address = self.conf.network.network_cidr create_network_form.cidr.text = network_address create_network_form.ip_version.value = ip_version if gateway_ip is not None: create_network_form.gateway_ip.text = gateway_ip if disable_gateway: create_network_form.disable_gateway.mark() create_network_form.switch_to(self.DETAILS_TAB_INDEX) if not enable_dhcp: create_network_form.enable_dhcp.unmark() if allocation_pools is not None: create_network_form.allocation_pools.text = allocation_pools if dns_name_servers is not None: create_network_form.dns_nameservers.text = dns_name_servers if host_routes is not None: create_network_form.host_routes.text = host_routes create_network_form.submit() def delete_network(self, name): row = self._get_row_with_network_name(name) confirm_delete_networks_form = \ self.networks_table.edit_delete_network(row) confirm_delete_networks_form.submit() def is_network_present(self, name): return bool(self._get_row_with_network_name(name)) def is_network_active(self, name): def cell_getter(): row = self._get_row_with_network_name(name) return row and row.cells[self.NETWORKS_TABLE_STATUS_COLUMN] return bool(self.networks_table.wait_cell_status(cell_getter, 'Active')) def add_subnet(self, net_name, subnet_name, network_address=None, ip_version=DEFAULT_IP_VERSION, gateway_ip=None, disable_gateway=DEFAULT_DISABLE_GATEWAY, enable_dhcp=DEFAULT_ENABLE_DHCP, allocation_pools=None, dns_name_servers=None, host_routes=None): row = self._get_row_with_network_name(net_name) add_subnet_form = self.networks_table.edit_add_subnet(row) add_subnet_form.subnet_name.text = subnet_name if network_address is None: network_address = self.conf.network.network_cidr add_subnet_form.cidr.text = network_address add_subnet_form.ip_version.value = ip_version if gateway_ip is not None: add_subnet_form.gateway_ip.text = gateway_ip if disable_gateway: add_subnet_form.disable_gateway.mark() add_subnet_form.switch_to(self.SUBNET_TAB_INDEX) if not enable_dhcp: add_subnet_form.enable_dhcp.unmark() if allocation_pools is not None: add_subnet_form.allocation_pools.text = allocation_pools if dns_name_servers is not None: add_subnet_form.dns_nameservers.text = dns_name_servers if host_routes is not None: add_subnet_form.host_routes.text = host_routes add_subnet_form.submit() return NetworkOverviewPage(self.driver, self.conf, net_name) def go_to_overview(self, name): _network_items_locator = (by.By.CSS_SELECTOR, 'a[href$="/detail"]') net_items = self._get_elements(*_network_items_locator) for item in net_items: if item.text == name: item.click() break else: raise exceptions.NoSuchElementException( "Not found element with text: %s" % name) return NetworkOverviewPage(self.driver, self.conf, name) class NetworkOverviewPage(basepage.BaseNavigationPage): DEFAULT_ADMIN_STATE = 'True' DEFAULT_IP_VERSION = '4' DEFAULT_DISABLE_GATEWAY = False DEFAULT_ENABLE_DHCP = True DETAILS_TAB_INDEX = 1 TABLE_NAME_COLUMN = 'name' _edit_network_locator = ( by.By.CSS_SELECTOR, 'form.actions_column > .btn-group > a.btn:nth-child(1)') _dropdown_open_locator = ( by.By.CSS_SELECTOR, 'form.actions_column > .btn-group > a.btn:nth-child(2)') _dropdown_menu_locator = ( by.By.CSS_SELECTOR, 'form.actions_column > .btn-group > ul.row_actions > li > *') def __init__(self, driver, conf, network_name): super(NetworkOverviewPage, self).__init__(driver, conf) self._page_title = "Network Details: {}".format(network_name) @property def subnets_table(self): return SubnetsTable(self.driver, self.conf) def _get_row_with_name(self, name, table): return table.get_row(self.TABLE_NAME_COLUMN, name) def _get_row_action(self, action_name): open_dropdown_elem = self._get_element(*self._dropdown_open_locator) open_dropdown_elem.click() for action in self._get_elements(*self._dropdown_menu_locator): pattern = "__action_%s" % action_name if action.get_attribute('id').endswith(pattern): action_element = action break return action_element def delete_network(self): delete_elem = self._get_row_action('delete') delete_elem.click() confirm_delete_network_form = forms.BaseFormRegion(self.driver, self.conf) confirm_delete_network_form.submit() return NetworksPage(self.driver, self.conf) def create_subnet(self, subnet_name, network_address=None, ip_version=DEFAULT_IP_VERSION, gateway_ip=None, disable_gateway=DEFAULT_DISABLE_GATEWAY, enable_dhcp=DEFAULT_ENABLE_DHCP, allocation_pools=None, dns_name_servers=None, host_routes=None): create_subnet_form = self.subnets_table.create() create_subnet_form.subnet_name.text = subnet_name if network_address is None: network_address = self.conf.network.network_cidr create_subnet_form.cidr.text = network_address create_subnet_form.ip_version.value = ip_version if gateway_ip is not None: create_subnet_form.gateway_ip.text = gateway_ip if disable_gateway: create_subnet_form.disable_gateway.mark() create_subnet_form.tabs.switch_to(self.DETAILS_TAB_INDEX) if not enable_dhcp: create_subnet_form.enable_dhcp.unmark() if allocation_pools is not None: create_subnet_form.allocation_pools.text = allocation_pools if dns_name_servers is not None: create_subnet_form.dns_nameservers.text = dns_name_servers if host_routes is not None: create_subnet_form.host_routes.text = host_routes create_subnet_form.submit() def delete_subnet(self, name): row = self._get_row_with_name(name, self.subnets_table) row.mark() confirm_delete_subnet_form = self.subnets_table.delete() confirm_delete_subnet_form.submit() def is_subnet_present(self, name): return bool(self._get_row_with_name(name, self.subnets_table))

true

f7006e06b03a87904485aac3fcf8e274655056ff

226

py

Python

part/pg/fixed_percent/part.py

fasiondog/hikyuu_house

0f0b609f17d94ba60da193de9c753eae54693817

[ "MIT" ]

null

part/pg/fixed_percent/part.py

fasiondog/hikyuu_house

0f0b609f17d94ba60da193de9c753eae54693817

[ "MIT" ]

null

part/pg/fixed_percent/part.py

fasiondog/hikyuu_house

0f0b609f17d94ba60da193de9c753eae54693817

[ "MIT" ]

1

2021-12-22T06:31:26.000Z

from hikyuu import PG_FixedPercent # 部件作者 author = "fasiondog" # 版本 version = '20200825' def part(p=0.2): return PG_FixedPercent(p) part.__doc__ = PG_FixedPercent.__doc__ if __name__ == '__main__': print(part())

13.294118

38

0.70354

from hikyuu import PG_FixedPercent author = "fasiondog" version = '20200825' def part(p=0.2): return PG_FixedPercent(p) part.__doc__ = PG_FixedPercent.__doc__ if __name__ == '__main__': print(part())

true

f7006fac7a55e33300dd99dba56d7fa113cff7fb

980

py

Python

kubernetes/test/test_v1_flex_volume_source.py

reymont/python

02a3a31c630c305527b328af49724f348fbdae15

[ "Apache-2.0" ]

1

2018-10-20T19:37:57.000Z

kubernetes/test/test_v1_flex_volume_source.py

reymont/python

02a3a31c630c305527b328af49724f348fbdae15

[ "Apache-2.0" ]

null

kubernetes/test/test_v1_flex_volume_source.py

reymont/python

02a3a31c630c305527b328af49724f348fbdae15

[ "Apache-2.0" ]

2

2018-07-27T19:39:34.000Z

2020-12-25T02:48:27.000Z

# coding: utf-8 """ Kubernetes No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen) OpenAPI spec version: v1.11.1 Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import os import sys import unittest import kubernetes.client from kubernetes.client.rest import ApiException from kubernetes.client.models.v1_flex_volume_source import V1FlexVolumeSource class TestV1FlexVolumeSource(unittest.TestCase): """ V1FlexVolumeSource unit test stubs """ def setUp(self): pass def tearDown(self): pass def testV1FlexVolumeSource(self): """ Test V1FlexVolumeSource """ # FIXME: construct object with mandatory attributes with example values #model = kubernetes.client.models.v1_flex_volume_source.V1FlexVolumeSource() pass if __name__ == '__main__': unittest.main()

21.777778

105

0.714286

from __future__ import absolute_import import os import sys import unittest import kubernetes.client from kubernetes.client.rest import ApiException from kubernetes.client.models.v1_flex_volume_source import V1FlexVolumeSource class TestV1FlexVolumeSource(unittest.TestCase): def setUp(self): pass def tearDown(self): pass def testV1FlexVolumeSource(self): pass if __name__ == '__main__': unittest.main()

true

f700701e51582a6f314450ea9547949094b4db62

3,429

py

Python

fineract/objects/group.py

mobidevke/py-fineract

712b0c20686accd7d7e0a2356ccaf59c5fe4f7dd

[ "Apache-2.0" ]

7

2019-03-11T16:17:33.000Z

2020-10-22T21:57:51.000Z

fineract/objects/group.py

mobidevke/py-fineract

712b0c20686accd7d7e0a2356ccaf59c5fe4f7dd

[ "Apache-2.0" ]

3

2019-11-05T20:22:16.000Z

2019-12-11T17:09:04.000Z

fineract/objects/group.py

mobidevke/py-fineract

712b0c20686accd7d7e0a2356ccaf59c5fe4f7dd

[ "Apache-2.0" ]

2

2020-11-19T16:00:36.000Z

2021-11-19T09:36:13.000Z

from fineract.objects.fineract_object import DataFineractObject from fineract.objects.types import Type class Group(DataFineractObject): """ This class represents a Group. """ def __repr__(self): return self.get__repr__({'group_id': self.id}) def _init_attributes(self): self.id = None self.account_no = None self.external_id = None self.name = None self.status = None self.active = None self.activation_date = None self.office_id = None self.office_name = None self.hierarchy = None def _use_attributes(self, attributes): self.id = attributes.get('id', None) self.account_no = attributes.get('accountNo', None) self.external_id = attributes.get('externalId', None) self.name = attributes.get('name', None) self.status = self._make_fineract_object(GroupStatus, attributes.get('status', None)) self.active = attributes.get('active', None) self.activation_date = self._make_date_object(attributes.get('activationDate', None)) self.office_id = attributes.get('officeId', None) self.office_name = attributes.get('officeName', None) self.hierarchy = attributes.get('hierarchy', None) def add_members(self, members_list): params = { 'clientMembers': members_list } data = self.request_handler.make_request( 'POST', '/groups/{}?command=associateClients'.format(self.id), json=params ) return data['groupId'] == self.id def remove_members(self, members_list): params = { 'clientMembers': members_list } data = self.request_handler.make_request( 'POST', '/groups/{}?command=disassociateClients'.format(self.id), json=params ) return data['groupId'] == self.id @classmethod def create(cls, request_handler, name, office_id, active=True, activation_date=None): """Create a group :param request_handler: :param name: :param office_id: :param active: :param activation_date: :rtype: :class:`fineract.objects.group.Group` """ data = { 'name': name, 'officeId': office_id, 'active': active, 'activationDate': activation_date or cls._get_current_date() } res = request_handler.make_request( 'POST', '/groups', json=data ) group_id = res['groupId'] return cls(request_handler, request_handler.make_request( 'GET', '/groups/{}'.format(group_id) ), False) @classmethod def get_group_by_name(cls, request_handler, name): """Get a group by name :param request_handler: :param name: :rtype: :class:`fineract.objects.group.Group` """ data = request_handler.make_request( 'GET', '/groups' ) if data: for item in data: if item['name'] == name: print(item) return cls(request_handler, item, False) return None class GroupStatus(Type): """ This class represents a Group status. """ pass

29.307692

93

0.567221

from fineract.objects.fineract_object import DataFineractObject from fineract.objects.types import Type class Group(DataFineractObject): def __repr__(self): return self.get__repr__({'group_id': self.id}) def _init_attributes(self): self.id = None self.account_no = None self.external_id = None self.name = None self.status = None self.active = None self.activation_date = None self.office_id = None self.office_name = None self.hierarchy = None def _use_attributes(self, attributes): self.id = attributes.get('id', None) self.account_no = attributes.get('accountNo', None) self.external_id = attributes.get('externalId', None) self.name = attributes.get('name', None) self.status = self._make_fineract_object(GroupStatus, attributes.get('status', None)) self.active = attributes.get('active', None) self.activation_date = self._make_date_object(attributes.get('activationDate', None)) self.office_id = attributes.get('officeId', None) self.office_name = attributes.get('officeName', None) self.hierarchy = attributes.get('hierarchy', None) def add_members(self, members_list): params = { 'clientMembers': members_list } data = self.request_handler.make_request( 'POST', '/groups/{}?command=associateClients'.format(self.id), json=params ) return data['groupId'] == self.id def remove_members(self, members_list): params = { 'clientMembers': members_list } data = self.request_handler.make_request( 'POST', '/groups/{}?command=disassociateClients'.format(self.id), json=params ) return data['groupId'] == self.id @classmethod def create(cls, request_handler, name, office_id, active=True, activation_date=None): data = { 'name': name, 'officeId': office_id, 'active': active, 'activationDate': activation_date or cls._get_current_date() } res = request_handler.make_request( 'POST', '/groups', json=data ) group_id = res['groupId'] return cls(request_handler, request_handler.make_request( 'GET', '/groups/{}'.format(group_id) ), False) @classmethod def get_group_by_name(cls, request_handler, name): data = request_handler.make_request( 'GET', '/groups' ) if data: for item in data: if item['name'] == name: print(item) return cls(request_handler, item, False) return None class GroupStatus(Type): pass

true

f700704654c1bdfa793688460f5e7faa103312e6

734

py

Python

magic_notifier/sms_clients/cgsms_client.py

jefcolbi/django-magic-notifier

f0b035027a165c7dbbd166cd0dfbf52d9b0a11c6

[ "MIT" ]

13

2021-07-25T19:03:43.000Z

2022-01-30T23:53:03.000Z

magic_notifier/sms_clients/cgsms_client.py

jefcolbi/django-magic-notifier

f0b035027a165c7dbbd166cd0dfbf52d9b0a11c6

[ "MIT" ]

null

magic_notifier/sms_clients/cgsms_client.py

jefcolbi/django-magic-notifier

f0b035027a165c7dbbd166cd0dfbf52d9b0a11c6

[ "MIT" ]

null

import logging import requests from django.conf import settings from .base import BaseSmsClient logger = logging.getLogger("notifier") class CGSmsClient(BaseSmsClient): @classmethod def send(cls, number: str, text: str, **kwargs): sub_account = settings.NOTIFIER["SMS"]["GATEWAYS"]["CGS"]["SUB_ACCOUNT"] sub_account_pass = settings.NOTIFIER["SMS"]["GATEWAYS"]["CGS"]["SUB_ACCOUNT_PASSWORD"] params = { "sub_account": sub_account, "sub_account_pass": sub_account_pass, "action": "send_sms", "message": text, "recipients": number, } res = requests.get("http://cheapglobalsms.com/api_v1", params=params) return res

28.230769

94

0.633515

import logging import requests from django.conf import settings from .base import BaseSmsClient logger = logging.getLogger("notifier") class CGSmsClient(BaseSmsClient): @classmethod def send(cls, number: str, text: str, **kwargs): sub_account = settings.NOTIFIER["SMS"]["GATEWAYS"]["CGS"]["SUB_ACCOUNT"] sub_account_pass = settings.NOTIFIER["SMS"]["GATEWAYS"]["CGS"]["SUB_ACCOUNT_PASSWORD"] params = { "sub_account": sub_account, "sub_account_pass": sub_account_pass, "action": "send_sms", "message": text, "recipients": number, } res = requests.get("http://cheapglobalsms.com/api_v1", params=params) return res

true

f700708d0b9048b76b77d12bc9318376b5a488e6

114,661

py

Python

CppHeaderParser-2.7/CppHeaderParser/CppHeaderParser.py

lamondlab/sipify

0e2f345ddc9514666d067fc32851882ffb22944a

[ "Apache-2.0" ]

4

2018-03-02T10:53:04.000Z

2019-01-25T07:56:13.000Z

CppHeaderParser-2.7/CppHeaderParser/CppHeaderParser.py

lamondlab/sipify

0e2f345ddc9514666d067fc32851882ffb22944a

[ "Apache-2.0" ]

1

2018-05-08T11:12:14.000Z

2021-07-13T09:29:12.000Z

CppHeaderParser-2.7/CppHeaderParser/CppHeaderParser.py

lamondlab/sipify

0e2f345ddc9514666d067fc32851882ffb22944a

[ "Apache-2.0" ]

1

2018-05-11T05:14:00.000Z

#!/usr/bin/python # # Author: Jashua R. Cloutier (contact via https://bitbucket.org/senex) # Project: http://senexcanis.com/open-source/cppheaderparser/ # # Copyright (C) 2011, Jashua R. Cloutier # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions # are met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in # the documentation and/or other materials provided with the # distribution. # # * Neither the name of Jashua R. Cloutier nor the names of its # contributors may be used to endorse or promote products derived from # this software without specific prior written permission. Stories, # blog entries etc making reference to this project may mention the # name Jashua R. Cloutier in terms of project originator/creator etc. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS # "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT # LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS # FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE # COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, # INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, # BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; # LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER # CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT # LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN # ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. # # # The CppHeaderParser.py script is written in Python 2.4 and released to # the open source community for continuous improvements under the BSD # 2.0 new license, which can be found at: # # http://www.opensource.org/licenses/bsd-license.php # """Parse C++ header files and generate a data structure representing the class """ import ply.lex as lex import os import sys import re import inspect def lineno(): """Returns the current line number in our program.""" return inspect.currentframe().f_back.f_lineno version = __version__ = "2.7" tokens = [ 'NUMBER', 'FLOAT_NUMBER', 'TEMPLATE_NAME', 'NAME', 'OPEN_PAREN', 'CLOSE_PAREN', 'OPEN_BRACE', 'CLOSE_BRACE', 'OPEN_SQUARE_BRACKET', 'CLOSE_SQUARE_BRACKET', 'COLON', 'SEMI_COLON', 'COMMA', 'TAB', 'BACKSLASH', 'PIPE', 'PERCENT', 'EXCLAMATION', 'CARET', 'COMMENT_SINGLELINE', 'COMMENT_MULTILINE', 'PRECOMP_MACRO', 'PRECOMP_MACRO_CONT', 'ASTERISK', 'AMPERSTAND', 'EQUALS', 'MINUS', 'PLUS', 'DIVIDE', 'CHAR_LITERAL', 'STRING_LITERAL', 'NEW_LINE', 'SQUOTE', ] t_ignore = " \r.?@\f" t_NUMBER = r'[0-9][0-9XxA-Fa-f]*' t_FLOAT_NUMBER = r'[-+]?[0-9]*\.[0-9]+([eE][-+]?[0-9]+)?' t_TEMPLATE_NAME = r'CppHeaderParser_template_[0-9]+' t_NAME = r'[<>A-Za-z_~][A-Za-z0-9_]*' t_OPEN_PAREN = r'$' t_CLOSE_PAREN = r'$' t_OPEN_BRACE = r'{' t_CLOSE_BRACE = r'}' t_OPEN_SQUARE_BRACKET = r'\[' t_CLOSE_SQUARE_BRACKET = r'\]' t_SEMI_COLON = r';' t_COLON = r':' t_COMMA = r',' t_TAB = r'\t' t_BACKSLASH = r'\\' t_PIPE = r'\|' t_PERCENT = r'%' t_CARET = r'\^' t_EXCLAMATION = r'!' t_PRECOMP_MACRO = r'\#.*' t_PRECOMP_MACRO_CONT = r'.*\\\n' def t_COMMENT_SINGLELINE(t): r'\/\/.*\n' global doxygenCommentCache if t.value.startswith("///") or t.value.startswith("//!"): if doxygenCommentCache: doxygenCommentCache += "\n" if t.value.endswith("\n"): doxygenCommentCache += t.value[:-1] else: doxygenCommentCache += t.value t.lexer.lineno += len([a for a in t.value if a=="\n"]) t_ASTERISK = r'\*' t_MINUS = r'\-' t_PLUS = r'\+' t_DIVIDE = r'/(?!/)' t_AMPERSTAND = r'&' t_EQUALS = r'=' t_CHAR_LITERAL = "'.'" t_SQUOTE = "'" #found at http://wordaligned.org/articles/string-literals-and-regular-expressions #TODO: This does not work with the string "bla \" bla" t_STRING_LITERAL = r'"([^"\\]|\\.)*"' #Found at http://ostermiller.org/findcomment.html def t_COMMENT_MULTILINE(t): r'/\*([^*]|[\r\n]|(\*+([^*/]|[\r\n])))*\*+/' global doxygenCommentCache if t.value.startswith("/**") or t.value.startswith("/*!"): #not sure why, but get double new lines v = t.value.replace("\n\n", "\n") #strip prefixing whitespace v = re.sub("\n[\s]+\*", "\n*", v) doxygenCommentCache += v t.lexer.lineno += len([a for a in t.value if a=="\n"]) def t_NEWLINE(t): r'\n+' t.lexer.lineno += len(t.value) def t_error(v): print(( "Lex error: ", v )) lex.lex() # Controls error_print print_errors = 1 # Controls warning_print print_warnings = 1 # Controls debug_print debug = 0 # Controls trace_print debug_trace = 0 def error_print(arg): if print_errors: print(("[%4d] %s"%(inspect.currentframe().f_back.f_lineno, arg))) def warning_print(arg): if print_warnings: print(("[%4d] %s"%(inspect.currentframe().f_back.f_lineno, arg))) def debug_print(arg): global debug if debug: print(("[%4d] %s"%(inspect.currentframe().f_back.f_lineno, arg))) def trace_print(*arg): global debug_trace if debug_trace: sys.stdout.write("[%s] "%(inspect.currentframe().f_back.f_lineno)) for a in arg: sys.stdout.write("%s "%a) sys.stdout.write("\n") supportedAccessSpecifier = [ 'public', 'protected', 'private', 'public slots', 'protected slots', 'private slots', 'public Q_SLOTS', 'protected Q_SLOTS', 'private Q_SLOTS', 'signals', 'Q_SIGNALS', ] #Symbols to ignore, usually special macros ignoreSymbols = [ 'Q_OBJECT', 'Q_PROPERTY()', 'Q_DECLARE_FLAGS()', 'Q_INVOKABLE', ] doxygenCommentCache = "" #Track what was added in what order and at what depth parseHistory = [] def is_namespace(nameStack): """Determines if a namespace is being specified""" if len(nameStack) == 0: return False if nameStack[0] == "namespace": return True return False def is_enum_namestack(nameStack): """Determines if a namestack is an enum namestack""" if len(nameStack) == 0: return False if nameStack[0] == "enum": return True if len(nameStack) > 1 and nameStack[0] == "typedef" and nameStack[1] == "enum": return True return False def is_fundamental(s): for a in s.split(): if a not in ["size_t", "struct", "union", "unsigned", "signed", "bool", "char", "short", "int", "float", "double", "long", "void", "*"]: return False return True def is_function_pointer_stack(stack): """Count how many non-nested paranthesis are in the stack. Useful for determining if a stack is a function pointer""" paren_depth = 0 paren_count = 0 star_after_first_paren = False last_e = None for e in stack: if e == "(": paren_depth += 1 elif e == ")" and paren_depth > 0: paren_depth -= 1 if paren_depth == 0: paren_count += 1 elif e == "*" and last_e == "(" and paren_count == 0 and paren_depth == 1: star_after_first_paren = True last_e = e if star_after_first_paren and paren_count == 2: return True else: return False def is_method_namestack(stack): r = False if '(' not in stack: r = False elif stack[0] == 'typedef': r = False # TODO deal with typedef function prototypes #elif '=' in stack and stack.index('=') < stack.index('(') and stack[stack.index('=')-1] != 'operator': r = False #disabled July6th - allow all operators elif 'operator' in stack: r = True # allow all operators elif '{' in stack and stack.index('{') < stack.index('('): r = False # struct that looks like a method/class elif '(' in stack and ')' in stack: if '{' in stack and '}' in stack: r = True elif stack[-1] == ';': if is_function_pointer_stack(stack): r = False else: r = True elif '{' in stack: r = True # ideally we catch both braces... TODO else: r = False #Test for case of property set to something with parens such as "static const int CONST_A = (1 << 7) - 1;" if r and "(" in stack and "=" in stack and 'operator' not in stack: if stack.index("=") < stack.index("("): r = False return r def is_property_namestack(nameStack): r = False if '(' not in nameStack and ')' not in nameStack: r = True elif "(" in nameStack and "=" in nameStack and nameStack.index("=") < nameStack.index("("): r = True #See if we are a function pointer if not r and is_function_pointer_stack(nameStack): r = True return r def detect_lineno(s): """Detect the line number for a given token string""" try: rtn = s.lineno() if rtn != -1: return rtn except: pass global curLine return curLine def filter_out_attribute_keyword(stack): """Strips __attribute__ and its parenthetical expression from the stack""" if "__attribute__" not in stack: return stack try: debug_print("Stripping __attribute__ from %s"% stack) attr_index = stack.index("__attribute__") attr_end = attr_index + 1 #Assuming not followed by parenthetical expression which wont happen #Find final paren if stack[attr_index + 1] == '(': paren_count = 1 for i in range(attr_index + 2, len(stack)): elm = stack[i] if elm == '(': paren_count += 1 elif elm == ')': paren_count -= 1 if paren_count == 0: attr_end = i + 1 break new_stack = stack[0:attr_index] + stack[attr_end:] debug_print("stripped stack is %s"% new_stack) return new_stack except: return stack class TagStr(str): """Wrapper for a string that allows us to store the line number associated with it""" lineno_reg = {} def __new__(cls,*args,**kw): new_obj = str.__new__(cls,*args) if "lineno" in kw: TagStr.lineno_reg[id(new_obj)] = kw["lineno"] return new_obj def __del__(self): try: del TagStr.lineno_reg[id(self)] except: pass def lineno(self): return TagStr.lineno_reg.get(id(self), -1) class CppParseError(Exception): pass class CppClass(dict): """Takes a name stack and turns it into a class Contains the following Keys: self['name'] - Name of the class self['doxygen'] - Doxygen comments associated with the class if they exist self['inherits'] - List of Classes that this one inherits where the values are of the form {"access": Anything in supportedAccessSpecifier "class": Name of the class self['methods'] - Dictionary where keys are from supportedAccessSpecifier and values are a lists of CppMethod's self['properties'] - Dictionary where keys are from supportedAccessSpecifier and values are lists of CppVariable's self['enums'] - Dictionary where keys are from supportedAccessSpecifier and values are lists of CppEnum's self['structs'] - Dictionary where keys are from supportedAccessSpecifier and values are lists of nested Struct's An example of how this could look is as follows: #self = { 'name': "" 'inherits':[] 'methods': { 'public':[], 'protected':[], 'private':[] }, 'properties': { 'public':[], 'protected':[], 'private':[] }, 'enums': { 'public':[], 'protected':[], 'private':[] } } """ def get_all_methods(self): r = [] for typ in supportedAccessSpecifier: r += self['methods'][typ] return r def get_all_method_names( self ): r = [] for typ in supportedAccessSpecifier: r += self.get_method_names(typ) # returns list return r def get_all_pure_virtual_methods( self ): r = {} for typ in supportedAccessSpecifier: r.update(self.get_pure_virtual_methods(typ)) # returns dict return r def get_method_names( self, type='public' ): return [ meth['name'] for meth in self['methods'][ type ] ] def get_pure_virtual_methods( self, type='public' ): r = {} for meth in self['methods'][ type ]: if meth['pure_virtual']: r[ meth['name'] ] = meth return r def __init__(self, nameStack, curTemplate): self['nested_classes'] = [] self['parent'] = None self['abstract'] = False self._public_enums = {} self._public_structs = {} self._public_typedefs = {} self._public_forward_declares = [] self['namespace'] = "" debug_print( "Class: %s"%nameStack ) debug_print( "Template: %s"%curTemplate) if (len(nameStack) < 2): nameStack.insert(1, "")#anonymous struct global doxygenCommentCache if len(doxygenCommentCache): self["doxygen"] = doxygenCommentCache doxygenCommentCache = "" if "::" in "".join(nameStack): #Re-Join class paths (ex ['class', 'Bar', ':', ':', 'Foo'] -> ['class', 'Bar::Foo'] try: new_nameStack = [] for name in nameStack: if len(new_nameStack) == 0: new_nameStack.append(name) elif name == ":" and new_nameStack[-1].endswith(":"): new_nameStack[-1] += name elif new_nameStack[-1].endswith("::"): new_nameStack[-2] += new_nameStack[-1] + name del new_nameStack[-1] else: new_nameStack.append(name) trace_print("Convert from namestack\n %s\nto\n%s"%(nameStack, new_nameStack)) nameStack = new_nameStack except: pass # Handle final specifier self["final"] = False try: final_index = nameStack.index("final") # Dont trip up the rest of the logic del nameStack[final_index] self["final"] = True trace_print("final") except: pass self["name"] = nameStack[1] self["line_number"] = detect_lineno(nameStack[0]) #Handle template classes if len(nameStack) > 3 and nameStack[2].startswith("<"): open_template_count = 0 param_separator = 0 found_first = False i = 0 for elm in nameStack: if '<' in elm : open_template_count += 1 found_first = True elif '>' in elm: open_template_count -= 1 if found_first and open_template_count == 0: self["name"] = "".join(nameStack[1:i + 1]) break; i += 1 elif ":" in nameStack: self['name'] = nameStack[ nameStack.index(':') - 1 ] inheritList = [] if nameStack.count(':') == 1: nameStack = nameStack[nameStack.index(":") + 1:] while len(nameStack): tmpStack = [] tmpInheritClass = {"access":"private", "virtual": False} if "," in nameStack: tmpStack = nameStack[:nameStack.index(",")] nameStack = nameStack[nameStack.index(",") + 1:] else: tmpStack = nameStack nameStack = [] # Convert template classes to one name in the last index for i in range(0, len(tmpStack)): if '<' in tmpStack[i]: tmpStack2 = tmpStack[:i-1] tmpStack2.append("".join(tmpStack[i-1:])) tmpStack = tmpStack2 break if len(tmpStack) == 0: break; elif len(tmpStack) == 1: tmpInheritClass["class"] = tmpStack[0] elif len(tmpStack) == 2: tmpInheritClass["access"] = tmpStack[0] tmpInheritClass["class"] = tmpStack[1] elif len(tmpStack) == 3 and "virtual" in tmpStack: tmpInheritClass["access"] = tmpStack[1] if tmpStack[1] != "virtual" else tmpStack[0] tmpInheritClass["class"] = tmpStack[2] tmpInheritClass["virtual"] = True else: warning_print( "Warning: can not parse inheriting class %s"%(" ".join(tmpStack))) if '>' in tmpStack: pass # allow skip templates for now else: raise NotImplemented if 'class' in tmpInheritClass: inheritList.append(tmpInheritClass) elif nameStack.count(':') == 2: self['parent'] = self['name']; self['name'] = nameStack[-1] elif nameStack.count(':') > 2 and nameStack[0] in ("class", "struct"): tmpStack = nameStack[nameStack.index(":") + 1:] superTmpStack = [[]] for tok in tmpStack: if tok == ',': superTmpStack.append([]) else: superTmpStack[-1].append(tok) for tmpStack in superTmpStack: tmpInheritClass = {"access":"private"} if len(tmpStack) and tmpStack[0] in supportedAccessSpecifier: tmpInheritClass["access"] = tmpStack[0] tmpStack = tmpStack[1:] inheritNSStack = [] while len(tmpStack) > 3: if tmpStack[0] == ':': break; if tmpStack[1] != ':': break; if tmpStack[2] != ':': break; inheritNSStack.append(tmpStack[0]) tmpStack = tmpStack[3:] if len(tmpStack) == 1 and tmpStack[0] != ':': inheritNSStack.append(tmpStack[0]) tmpInheritClass["class"] = "::".join(inheritNSStack) inheritList.append(tmpInheritClass) self['inherits'] = inheritList if curTemplate: self["template"] = curTemplate trace_print("Setting template to '%s'"%self["template"]) methodAccessSpecificList = {} propertyAccessSpecificList = {} enumAccessSpecificList = {} structAccessSpecificList = {} typedefAccessSpecificList = {} forwardAccessSpecificList = {} for accessSpecifier in supportedAccessSpecifier: methodAccessSpecificList[accessSpecifier] = [] propertyAccessSpecificList[accessSpecifier] = [] enumAccessSpecificList[accessSpecifier] = [] structAccessSpecificList[accessSpecifier] = [] typedefAccessSpecificList[accessSpecifier] = [] forwardAccessSpecificList[accessSpecifier] = [] self['methods'] = methodAccessSpecificList self['properties'] = propertyAccessSpecificList self['enums'] = enumAccessSpecificList self['structs'] = structAccessSpecificList self['typedefs'] = typedefAccessSpecificList self['forward_declares'] = forwardAccessSpecificList def show(self): """Convert class to a string""" namespace_prefix = "" if self["namespace"]: namespace_prefix = self["namespace"] + "::" rtn = "%s %s"%(self["declaration_method"], namespace_prefix + self["name"]) if self["final"]: rtn += " final" if self['abstract']: rtn += ' (abstract)\n' else: rtn += '\n' if 'doxygen' in list(self.keys()): rtn += self["doxygen"] + '\n' if 'parent' in list(self.keys()) and self['parent']: rtn += 'parent class: ' + self['parent'] + '\n' if "inherits" in list(self.keys()): rtn += " Inherits: " for inheritClass in self["inherits"]: if inheritClass["virtual"]: rtn += "virtual " rtn += "%s %s, "%(inheritClass["access"], inheritClass["class"]) rtn += "\n" rtn += " {\n" for accessSpecifier in supportedAccessSpecifier: rtn += " %s\n"%(accessSpecifier) #Enums if (len(self["enums"][accessSpecifier])): rtn += " <Enums>\n" for enum in self["enums"][accessSpecifier]: rtn += " %s\n"%(repr(enum)) #Properties if (len(self["properties"][accessSpecifier])): rtn += " <Properties>\n" for property in self["properties"][accessSpecifier]: rtn += " %s\n"%(repr(property)) #Methods if (len(self["methods"][accessSpecifier])): rtn += " <Methods>\n" for method in self["methods"][accessSpecifier]: rtn += "\t\t" + method.show() + '\n' rtn += " }\n" print(rtn) def __str__(self): """Convert class to a string""" namespace_prefix = "" if self["namespace"]: namespace_prefix = self["namespace"] + "::" rtn = "%s %s"%(self["declaration_method"], namespace_prefix + self["name"]) if self["final"]: rtn += " final" if self['abstract']: rtn += ' (abstract)\n' else: rtn += '\n' if 'doxygen' in list(self.keys()): rtn += self["doxygen"] + '\n' if 'parent' in list(self.keys()) and self['parent']: rtn += 'parent class: ' + self['parent'] + '\n' if "inherits" in list(self.keys()) and len(self["inherits"]): rtn += "Inherits: " for inheritClass in self["inherits"]: if inheritClass.get("virtual", False): rtn += "virtual " rtn += "%s %s, "%(inheritClass["access"], inheritClass["class"]) rtn += "\n" rtn += "{\n" for accessSpecifier in supportedAccessSpecifier: rtn += "%s\n"%(accessSpecifier) #Enums if (len(self["enums"][accessSpecifier])): rtn += " // Enums\n" for enum in self["enums"][accessSpecifier]: rtn += " %s\n"%(repr(enum)) #Properties if (len(self["properties"][accessSpecifier])): rtn += " // Properties\n" for property in self["properties"][accessSpecifier]: rtn += " %s\n"%(repr(property)) #Methods if (len(self["methods"][accessSpecifier])): rtn += " // Methods\n" for method in self["methods"][accessSpecifier]: rtn += " %s\n"%(repr(method)) rtn += "}\n" return rtn class CppUnion( CppClass ): """Takes a name stack and turns it into a union Contains the following Keys: self['name'] - Name of the union self['doxygen'] - Doxygen comments associated with the union if they exist self['members'] - List of members the union has An example of how this could look is as follows: #self = { 'name': "" 'members': [] } """ def __init__(self, nameStack): CppClass.__init__(self, nameStack, None) self["name"] = "union " + self["name"] self["members"] = self["properties"]["public"] def transform_to_union_keys(self): print("union keys: %s"%list(self.keys())) for key in ['inherits', 'parent', 'abstract', 'namespace', 'typedefs', 'methods']: del self[key] def show(self): """Convert class to a string""" print(self) def __str__(self): """Convert class to a string""" namespace_prefix = "" if self["namespace"]: namespace_prefix = self["namespace"] + "::" rtn = "%s %s"%(self["declaration_method"], namespace_prefix + self["name"]) if self['abstract']: rtn += ' (abstract)\n' else: rtn += '\n' if 'doxygen' in list(self.keys()): rtn += self["doxygen"] + '\n' if 'parent' in list(self.keys()) and self['parent']: rtn += 'parent class: ' + self['parent'] + '\n' rtn += "{\n" for member in self["members"]: rtn += " %s\n"%(repr(member)) rtn += "}\n" return rtn class _CppMethod( dict ): def _params_helper1( self, stack ): # deal with "throw" keyword if 'throw' in stack: stack = stack[ : stack.index('throw') ] ## remove GCC keyword __attribute__(...) and preserve returns ## cleaned = [] hit = False; hitOpen = 0; hitClose = 0 for a in stack: if a == '__attribute__': hit = True if hit: if a == '(': hitOpen += 1 elif a == ')': hitClose += 1 if a==')' and hitOpen == hitClose: hit = False else: cleaned.append( a ) stack = cleaned # also deal with attribute((const)) function prefix # # TODO this needs to be better # if len(stack) > 5: a = ''.join(stack) if a.startswith('((__const__))'): stack = stack[ 5 : ] elif a.startswith('__attribute__((__const__))'): stack = stack[ 6 : ] stack = stack[stack.index('(') + 1: ] if not stack: return [] if len(stack)>=3 and stack[0]==')' and stack[1]==':': # is this always a constructor? self['constructor'] = True return [] stack.reverse(); _end_ = stack.index(')'); stack.reverse() stack = stack[ : len(stack)-(_end_+1) ] if '(' not in stack: return stack # safe to return, no defaults that init a class # transforms ['someclass', '(', '0', '0', '0', ')'] into "someclass(0,0,0)'" r = []; hit=False for a in stack: if a == '(': hit=True elif a == ')': hit=False if hit or a == ')': r[-1] = r[-1] + a else: r.append( a ) return r def _params_helper2( self, params ): for p in params: p['method'] = self # save reference in variable to parent method if '::' in p['type']: ns = p['type'].split('::')[0] if ns not in Resolver.NAMESPACES and ns in Resolver.CLASSES: p['type'] = self['namespace'] + p['type'] else: p['namespace'] = self[ 'namespace' ] class CppMethod( _CppMethod ): """Takes a name stack and turns it into a method Contains the following Keys: self['rtnType'] - Return type of the method (ex. "int") self['name'] - Name of the method (ex. "getSize") self['doxygen'] - Doxygen comments associated with the method if they exist self['parameters'] - List of CppVariables """ def show(self): r = ['method name: %s (%s)' %(self['name'],self['debug']) ] if self['returns']: r.append( 'returns: %s'%self['returns'] ) if self['parameters']: r.append( 'number arguments: %s' %len(self['parameters'])) if self['pure_virtual']: r.append( 'pure virtual: %s'%self['pure_virtual'] ) if self['constructor']: r.append( 'constructor' ) if self['destructor']: r.append( 'destructor' ) return '\n\t\t '.join( r ) def __init__(self, nameStack, curClass, methinfo, curTemplate): debug_print( "Method: %s"%nameStack ) debug_print( "Template: %s"%curTemplate ) global doxygenCommentCache if len(doxygenCommentCache): self["doxygen"] = doxygenCommentCache doxygenCommentCache = "" if "operator" in nameStack: self["rtnType"] = " ".join(nameStack[:nameStack.index('operator')]) self["name"] = "".join(nameStack[nameStack.index('operator'):nameStack.index('(')]) else: self["rtnType"] = " ".join(nameStack[:nameStack.index('(') - 1]) self["name"] = " ".join(nameStack[nameStack.index('(') - 1:nameStack.index('(')]) if self["rtnType"].startswith("virtual"): self["rtnType"] = self["rtnType"][len("virtual"):].strip() if len(self["rtnType"]) == 0 or self["name"] == curClass: self["rtnType"] = "void" self["rtnType"] = self["rtnType"].replace(' : : ', '::' ) self["rtnType"] = self["rtnType"].replace(" <","<") self["rtnType"] = self["rtnType"].replace(" >",">").replace(">>", "> >").replace(">>", "> >") self["rtnType"] = self["rtnType"].replace(" ,",",") for spec in ["const", "final", "override"]: self[spec] = False for i in reversed(nameStack): if i == spec: self[spec] = True break elif i == ")": break self.update( methinfo ) self["line_number"] = detect_lineno(nameStack[0]) #Filter out initializer lists used in constructors try: paren_depth_counter = 0 for i in range(0, len(nameStack)): elm = nameStack[i] if elm == "(": paren_depth_counter += 1 if elm == ")": paren_depth_counter -=1 if paren_depth_counter == 0 and nameStack[i+1] == ':': debug_print("Stripping out initializer list") nameStack = nameStack[:i+1] break except: pass paramsStack = self._params_helper1( nameStack ) debug_print( "curTemplate: %s"%curTemplate) if curTemplate: self["template"] = curTemplate debug_print( "SET self['template'] to `%s`"%self["template"]) params = [] #See if there is a doxygen comment for the variable doxyVarDesc = {} if "doxygen" in self: doxyLines = self["doxygen"].split("\n") lastParamDesc = "" for doxyLine in doxyLines: if " @param " in doxyLine or " \param " in doxyLine: try: #Strip out the param doxyLine = doxyLine[doxyLine.find("param ") + 6:] (var, desc) = doxyLine.split(" ", 1) doxyVarDesc[var] = desc.strip() lastParamDesc = var except: pass elif " @return " in doxyLine or " \return " in doxyLine: lastParamDesc = "" # not handled for now elif lastParamDesc: try: doxyLine = doxyLine.strip() if " " not in doxyLine: lastParamDesc = "" continue doxyLine = doxyLine[doxyLine.find(" ") + 1:] doxyVarDesc[lastParamDesc] += " " + doxyLine except: pass #Create the variable now while (len(paramsStack)): # Find commas that are not nexted in <>'s like template types open_template_count = 0 param_separator = 0 i = 0 for elm in paramsStack: if '<' in elm : open_template_count += 1 elif '>' in elm: open_template_count -= 1 elif elm == ',' and open_template_count == 0: param_separator = i break i += 1 if param_separator: param = CppVariable(paramsStack[0:param_separator], doxyVarDesc=doxyVarDesc) if len(list(param.keys())): params.append(param) paramsStack = paramsStack[param_separator + 1:] else: param = CppVariable(paramsStack, doxyVarDesc=doxyVarDesc) if len(list(param.keys())): params.append(param) break self["parameters"] = params #self._params_helper2( params ) # mods params inplace def __str__(self): filter_keys = ("parent", "defined", "operator", "returns_reference") cpy = dict((k,v) for (k,v) in list(self.items()) if k not in filter_keys) return "%s"%cpy class _CppVariable(dict): def _name_stack_helper( self, stack ): stack = list(stack) if '=' not in stack: # TODO refactor me # check for array[n] and deal with funny array syntax: "int myvar:99" array = [] while stack and stack[-1].isdigit(): array.append( stack.pop() ) if array: array.reverse(); self['array'] = int(''.join(array)) if stack and stack[-1].endswith(':'): stack[-1] = stack[-1][:-1] while stack and not stack[-1]: stack.pop() # can be empty return stack def init(self): #assert self['name'] # allow unnamed variables, methods like this: "void func(void);" a = [] self['aliases'] = []; self['parent'] = None; self['typedef'] = None for key in 'constant reference pointer static typedefs class fundamental unresolved'.split(): self[ key ] = 0 for b in self['type'].split(): if b == '__const__': b = 'const' a.append( b ) self['type'] = ' '.join( a ) class CppVariable( _CppVariable ): """Takes a name stack and turns it into a method Contains the following Keys: self['type'] - Type for the variable (ex. "const string &") self['name'] - Name of the variable (ex. "numItems") self['namespace'] - Namespace containing the enum self['desc'] - Description of the variable if part of a method (optional) self['doxygen'] - Doxygen comments associated with the method if they exist self['defaultValue'] - Default value of the variable, this key will only exist if there is a default value self['extern'] - True if its an extern, false if not """ Vars = [] def __init__(self, nameStack, **kwargs): debug_print("trace %s"%nameStack) if len(nameStack) and nameStack[0] == "extern": self['extern'] = True del nameStack[0] else: self['extern'] = False _stack_ = nameStack if "[" in nameStack: #strip off array informatin arrayStack = nameStack[nameStack.index("["):] if nameStack.count("[") > 1: debug_print("Multi dimensional array") debug_print("arrayStack=%s"%arrayStack) nums = filter(lambda x: x.isdigit(), arrayStack) # Calculate size by multiplying all dimensions p = 1 for n in nums: p *= int(n) #Multi dimensional array self["array_size"] = p self["multi_dimensional_array"] = 1 self["multi_dimensional_array_size"] = "x".join(nums) else: debug_print("Array") if len(arrayStack) == 3: self["array_size"] = arrayStack[1] nameStack = nameStack[:nameStack.index("[")] self["array"] = 1 else: self["array"] = 0 nameStack = self._name_stack_helper( nameStack ) global doxygenCommentCache if len(doxygenCommentCache): self["doxygen"] = doxygenCommentCache doxygenCommentCache = "" debug_print( "Variable: %s"%nameStack ) self["line_number"] = detect_lineno(nameStack[0]) self["function_pointer"] = 0 if (len(nameStack) < 2): # +++ if len(nameStack) == 1: self['type'] = nameStack[0]; self['name'] = '' else: error_print(_stack_); assert 0 elif is_function_pointer_stack(nameStack): #function pointer self["type"] = " ".join(nameStack[:nameStack.index("(") + 2] + nameStack[nameStack.index(")") :]) self["name"] = " ".join(nameStack[nameStack.index("(") + 2 : nameStack.index(")")]) self["function_pointer"] = 1 elif ("=" in nameStack): self["type"] = " ".join(nameStack[:nameStack.index("=") - 1]) self["name"] = nameStack[nameStack.index("=") - 1] self["defaultValue"] = " ".join(nameStack[nameStack.index("=") + 1:]) # deprecate camelCase in dicts self['default'] = " ".join(nameStack[nameStack.index("=") + 1:]) elif is_fundamental(nameStack[-1]) or nameStack[-1] in ['>', '<' , ':', '.']: #Un named parameter self["type"] = " ".join(nameStack) self["name"] = "" else: # common case self["type"] = " ".join(nameStack[:-1]) self["name"] = nameStack[-1] self["type"] = self["type"].replace(" :",":") self["type"] = self["type"].replace(": ",":") self["type"] = self["type"].replace(" <","<") self["type"] = self["type"].replace(" >",">").replace(">>", "> >").replace(">>", "> >") self["type"] = self["type"].replace(" ,",",") #Optional doxygen description try: self["desc"] = kwargs["doxyVarDesc"][self["name"]] except: pass self.init() CppVariable.Vars.append( self ) # save and resolve later def __str__(self): keys_white_list = ['constant','name','reference','type','static','pointer','desc', 'line_number', 'extern'] cpy = dict((k,v) for (k,v) in list(self.items()) if k in keys_white_list) if "array_size" in self: cpy["array_size"] = self["array_size"] return "%s"%cpy class _CppEnum(dict): def resolve_enum_values( self, values ): """Evaluates the values list of dictionaries passed in and figures out what the enum value for each enum is editing in place: Example: From: [{'name': 'ORANGE'}, {'name': 'RED'}, {'name': 'GREEN', 'value': '8'}] To: [{'name': 'ORANGE', 'value': 0}, {'name': 'RED', 'value': 1}, {'name': 'GREEN', 'value': 8}] """ t = int; i = 0 names = [ v['name'] for v in values ] for v in values: if 'value' in v: a = v['value'].strip() # Remove single quotes from single quoted chars (unless part of some expression if len(a) == 3 and a[0] == "'" and a[2] == "'": a = v['value'] = a[1] if a.lower().startswith("0x"): try: i = a = int(a , 16) except:pass elif a.isdigit(): i = a = int( a ) elif a in names: for other in values: if other['name'] == a: v['value'] = other['value'] break elif '"' in a or "'" in a: t = str # only if there are quotes it this a string enum else: try: a = i = ord(a) except: pass #Allow access of what is in the file pre-convert if converted if v['value'] != str(a): v['raw_value'] = v['value'] v['value'] = a else: v['value'] = i try: v['value'] = v['value'].replace(" < < ", " << ").replace(" >> ", " >> ") except: pass i += 1 return t class CppEnum(_CppEnum): """Takes a name stack and turns it into an Enum Contains the following Keys: self['name'] - Name of the enum (ex. "ItemState") self['namespace'] - Namespace containing the enum self['values'] - List of values where the values are a dictionary of the form {"name": name of the key (ex. "PARSING_HEADER"), "value": Specified value of the enum, this key will only exist if a value for a given enum value was defined } """ def __init__(self, nameStack): global doxygenCommentCache if len(doxygenCommentCache): self["doxygen"] = doxygenCommentCache doxygenCommentCache = "" if len(nameStack) == 3 and nameStack[0] == "enum": debug_print("Created enum as just name/value") self["name"] = nameStack[1] self["instances"]=[nameStack[2]] if len(nameStack) < 4 or "{" not in nameStack or "}" not in nameStack: #Not enough stuff for an enum debug_print("Bad enum") return valueList = [] self["line_number"] = detect_lineno(nameStack[0]) #Figure out what values it has valueStack = nameStack[nameStack.index('{') + 1: nameStack.index('}')] while len(valueStack): tmpStack = [] if "," in valueStack: tmpStack = valueStack[:valueStack.index(",")] valueStack = valueStack[valueStack.index(",") + 1:] else: tmpStack = valueStack valueStack = [] d = {} if len(tmpStack) == 1: d["name"] = tmpStack[0] elif len(tmpStack) >= 3 and tmpStack[1] == "=": d["name"] = tmpStack[0]; d["value"] = " ".join(tmpStack[2:]) elif len(tmpStack) == 2 and tmpStack[1] == "=": debug_print( "WARN-enum: parser missed value for %s"%tmpStack[0] ) d["name"] = tmpStack[0] if d: valueList.append( d ) if len(valueList): self['type'] = self.resolve_enum_values( valueList ) # returns int for standard enum self["values"] = valueList else: warning_print( 'WARN-enum: empty enum %s'%nameStack ) return #Figure out if it has a name preBraceStack = nameStack[:nameStack.index("{")] postBraceStack = nameStack[nameStack.index("}") + 1:] self["typedef"] = False if (len(preBraceStack) == 2 and "typedef" not in nameStack): self["name"] = preBraceStack[1] elif len(postBraceStack) and "typedef" in nameStack: self["name"] = " ".join(postBraceStack) self["typedef"] = True else: warning_print( 'WARN-enum: nameless enum %s'%nameStack ) #See if there are instances of this if "typedef" not in nameStack and len(postBraceStack): self["instances"] = [] for var in postBraceStack: if "," in var: continue self["instances"].append(var) self["namespace"] = "" class CppStruct(dict): Structs = [] def __init__(self, nameStack): if len(nameStack) >= 2: self['type'] = nameStack[1] else: self['type'] = None self['fields'] = [] self.Structs.append( self ) global curLine self["line_number"] = curLine C99_NONSTANDARD = { 'int8' : 'signed char', 'int16' : 'short int', 'int32' : 'int', 'int64' : 'int64_t', # this can be: long int (64bit), or long long int (32bit) 'uint' : 'unsigned int', 'uint8' : 'unsigned char', 'uint16' : 'unsigned short int', 'uint32' : 'unsigned int', 'uint64' : 'uint64_t', # depends on host bits } def standardize_fundamental( s ): if s in C99_NONSTANDARD: return C99_NONSTANDARD[ s ] else: return s class Resolver(object): C_FUNDAMENTAL = 'size_t unsigned signed bool char wchar short int float double long void'.split() C_FUNDAMENTAL += 'struct union enum'.split() SubTypedefs = {} # TODO deprecate? NAMESPACES = [] CLASSES = {} STRUCTS = {} def initextra(self): self.typedefs = {} self.typedefs_order = [] self.classes_order = [] self.structs = Resolver.STRUCTS self.structs_order = [] self.namespaces = Resolver.NAMESPACES # save all namespaces self.curStruct = None self.stack = [] # full name stack, good idea to keep both stacks? (simple stack and full stack) self._classes_brace_level = {} # class name : level self._structs_brace_level = {} # struct type : level self._method_body = None self._forward_decls = [] self._template_typenames = [] # template<typename XXX> def current_namespace(self): return self.cur_namespace(True) def cur_namespace(self, add_double_colon=False): rtn = "" i = 0 while i < len(self.nameSpaces): rtn += self.nameSpaces[i] if add_double_colon or i < len(self.nameSpaces) - 1: rtn += "::" i+=1 return rtn def guess_ctypes_type( self, string ): pointers = string.count('*') string = string.replace('*','') a = string.split() if 'unsigned' in a: u = 'u' else: u = '' if 'long' in a and 'double' in a: b = 'longdouble' # there is no ctypes.c_ulongdouble (this is a 64bit float?) elif a.count('long') == 2 and 'int' in a: b = '%sint64' %u elif a.count('long') == 2: b = '%slonglong' %u elif 'long' in a: b = '%slong' %u elif 'double' in a: b = 'double' # no udouble in ctypes elif 'short' in a: b = '%sshort' %u elif 'char' in a: b = '%schar' %u elif 'wchar' in a: b = 'wchar' elif 'bool' in a: b = 'bool' elif 'float' in a: b = 'float' elif 'int' in a: b = '%sint' %u elif 'int8' in a: b = 'int8' elif 'int16' in a: b = 'int16' elif 'int32' in a: b = 'int32' elif 'int64' in a: b = 'int64' elif 'uint' in a: b = 'uint' elif 'uint8' in a: b = 'uint8' elif 'uint16' in a: b = 'uint16' elif 'uint32' in a: b = 'uint32' elif 'uint64' in a: b = 'uint64' elif 'size_t' in a: b = 'size_t' elif 'void' in a: b = 'void_p' elif string in 'struct union'.split(): b = 'void_p' # what should be done here? don't trust struct, it could be a class, no need to expose via ctypes else: b = 'void_p' if not pointers: return 'ctypes.c_%s' %b else: x = '' for i in range(pointers): x += 'ctypes.POINTER(' x += 'ctypes.c_%s' %b x += ')' * pointers return x def resolve_type( self, string, result ): # recursive ''' keeps track of useful things like: how many pointers, number of typedefs, is fundamental or a class, etc... ''' ## be careful with templates, what is inside <something*> can be a pointer but the overall type is not a pointer ## these come before a template s = string.split('<')[0] result[ 'constant' ] += s.split().count('const') result[ 'static' ] += s.split().count('static') result[ 'mutable' ] = 'mutable' in s.split() ## these come after a template s = string.split('>')[-1] result[ 'pointer' ] += s.count('*') result[ 'reference' ] += s.count('&') x = string; alias = False for a in '* & const static mutable'.split(): x = x.replace(a,'') for y in x.split(): if y not in self.C_FUNDAMENTAL: alias = y; break #if alias == 'class': # result['class'] = result['name'] # forward decl of class # result['forward_decl'] = True if alias == '__extension__': result['fundamental_extension'] = True elif alias: result['aliases'].append( alias ) if alias in C99_NONSTANDARD: result['type'] = C99_NONSTANDARD[ alias ] result['typedef'] = alias result['typedefs'] += 1 elif alias in self.typedefs: result['typedefs'] += 1 result['typedef'] = alias self.resolve_type( self.typedefs[alias], result ) elif alias in self.classes: klass = self.classes[alias]; result['fundamental'] = False result['class'] = klass result['unresolved'] = False else: result['unresolved'] = True else: result['fundamental'] = True result['unresolved'] = False def finalize_vars(self): for s in CppStruct.Structs: # vars within structs can be ignored if they do not resolve for var in s['fields']: var['parent'] = s['type'] #for c in self.classes.values(): # for var in c.get_all_properties(): var['parent'] = c['name'] ## RESOLVE ## for var in CppVariable.Vars: self.resolve_type( var['type'], var ) #if 'method' in var and var['method']['name'] == '_notifyCurrentCamera': print(var); assert 0 # then find concrete type and best guess ctypes type # for var in CppVariable.Vars: if not var['aliases']: #var['fundamental']: var['ctypes_type'] = self.guess_ctypes_type( var['type'] ) else: var['unresolved'] = False # below may test to True if var['class']: var['ctypes_type'] = 'ctypes.c_void_p' else: assert var['aliases'] tag = var['aliases'][0] klass = None nestedEnum = None nestedStruct = None nestedTypedef = None if 'method' in var and 'parent' in list(var['method'].keys()): klass = var['method']['parent'] if tag in var['method']['parent']._public_enums: nestedEnum = var['method']['parent']._public_enums[ tag ] elif tag in var['method']['parent']._public_structs: nestedStruct = var['method']['parent']._public_structs[ tag ] elif tag in var['method']['parent']._public_typedefs: nestedTypedef = var['method']['parent']._public_typedefs[ tag ] if '<' in tag: # should also contain '>' var['template'] = tag # do not resolve templates var['ctypes_type'] = 'ctypes.c_void_p' var['unresolved'] = True elif nestedEnum: enum = nestedEnum if enum['type'] is int: var['ctypes_type'] = 'ctypes.c_int' var['raw_type'] = 'int' elif enum['type'] is str: var['ctypes_type'] = 'ctypes.c_char_p' var['raw_type'] = 'char*' var['enum'] = var['method']['path'] + '::' + enum['name'] var['fundamental'] = True elif nestedStruct: var['ctypes_type'] = 'ctypes.c_void_p' var['raw_type'] = var['method']['path'] + '::' + nestedStruct['type'] var['fundamental'] = False elif nestedTypedef: var['fundamental'] = is_fundamental( nestedTypedef ) if not var['fundamental']: var['raw_type'] = var['method']['path'] + '::' + tag else: _tag = tag if '::' in tag and tag.split('::')[0] in self.namespaces: tag = tag.split('::')[-1] con = self.concrete_typedef( _tag ) if con: var['concrete_type'] = con var['ctypes_type'] = self.guess_ctypes_type( var['concrete_type'] ) elif tag in self.structs: trace_print( 'STRUCT', var ) var['struct'] = tag var['ctypes_type'] = 'ctypes.c_void_p' var['raw_type'] = self.structs[tag]['namespace'] + '::' + tag elif tag in self._forward_decls: var['forward_declared'] = tag var['ctypes_type'] = 'ctypes.c_void_p' elif tag in self.global_enums: enum = self.global_enums[ tag ] if enum['type'] is int: var['ctypes_type'] = 'ctypes.c_int' var['raw_type'] = 'int' elif enum['type'] is str: var['ctypes_type'] = 'ctypes.c_char_p' var['raw_type'] = 'char*' var['enum'] = enum['namespace'] + enum['name'] var['fundamental'] = True elif var['parent']: warning_print( 'WARN unresolved %s'%_tag) var['ctypes_type'] = 'ctypes.c_void_p' var['unresolved'] = True elif tag.count('::')==1: trace_print( 'trying to find nested something in', tag ) a = tag.split('::')[0] b = tag.split('::')[-1] if a in self.classes: # a::b is most likely something nested in a class klass = self.classes[ a ] if b in klass._public_enums: trace_print( '...found nested enum', b ) enum = klass._public_enums[ b ] if enum['type'] is int: var['ctypes_type'] = 'ctypes.c_int' var['raw_type'] = 'int' elif enum['type'] is str: var['ctypes_type'] = 'ctypes.c_char_p' var['raw_type'] = 'char*' try: if 'method' in var: var['enum'] = var['method']['path'] + '::' + enum['name'] else: # class property var['unresolved'] = True except: var['unresolved'] = True var['fundamental'] = True else: var['unresolved'] = True # TODO klass._public_xxx elif a in self.namespaces: # a::b can also be a nested namespace if b in self.global_enums: enum = self.global_enums[ b ] trace_print(enum) trace_print(var) assert 0 elif b in self.global_enums: # falling back, this is a big ugly enum = self.global_enums[ b ] assert a in enum['namespace'].split('::') if enum['type'] is int: var['ctypes_type'] = 'ctypes.c_int' var['raw_type'] = 'int' elif enum['type'] is str: var['ctypes_type'] = 'ctypes.c_char_p' var['raw_type'] = 'char*' var['fundamental'] = True else: # boost::gets::crazy trace_print('NAMESPACES', self.namespaces) trace_print( a, b ) trace_print( '---- boost gets crazy ----' ) var['ctypes_type'] = 'ctypes.c_void_p' var['unresolved'] = True elif 'namespace' in var and self.concrete_typedef(var['namespace']+tag): #print( 'TRYING WITH NS', var['namespace'] ) con = self.concrete_typedef( var['namespace']+tag ) if con: var['typedef'] = var['namespace']+tag var['type'] = con if 'struct' in con.split(): var['raw_type'] = var['typedef'] var['ctypes_type'] = 'ctypes.c_void_p' else: self.resolve_type( var['type'], var ) var['ctypes_type'] = self.guess_ctypes_type( var['type'] ) elif '::' in var: var['ctypes_type'] = 'ctypes.c_void_p' var['unresolved'] = True elif tag in self.SubTypedefs: # TODO remove SubTypedefs if 'property_of_class' in var or 'property_of_struct' in var: trace_print( 'class:', self.SubTypedefs[ tag ], 'tag:', tag ) var['typedef'] = self.SubTypedefs[ tag ] # class name var['ctypes_type'] = 'ctypes.c_void_p' else: trace_print( "WARN-this should almost never happen!" ) trace_print( var ); trace_print('-'*80) var['unresolved'] = True elif tag in self._template_typenames: var['typename'] = tag var['ctypes_type'] = 'ctypes.c_void_p' var['unresolved'] = True # TODO, how to deal with templates? elif tag.startswith('_'): # assume starting with underscore is not important for wrapping warning_print( 'WARN unresolved %s'%_tag) var['ctypes_type'] = 'ctypes.c_void_p' var['unresolved'] = True else: trace_print( 'WARN: unknown type', var ) assert 'property_of_class' in var or 'property_of_struct' # only allow this case var['unresolved'] = True ## if not resolved and is a method param, not going to wrap these methods ## if var['unresolved'] and 'method' in var: var['method']['unresolved_parameters'] = True # create stripped raw_type # p = '* & const static mutable'.split() # +++ new July7: "mutable" for var in CppVariable.Vars: if 'raw_type' not in var: raw = [] for x in var['type'].split(): if x not in p: raw.append( x ) var['raw_type'] = ' '.join( raw ) #if 'AutoConstantEntry' in var['raw_type']: print(var); assert 0 if var['class']: if '::' not in var['raw_type']: if not var['class']['parent']: var['raw_type'] = var['class']['namespace'] + '::' + var['raw_type'] elif var['class']['parent'] in self.classes: parent = self.classes[ var['class']['parent'] ] var['raw_type'] = parent['namespace'] + '::' + var['class']['name'] + '::' + var['raw_type'] else: var['unresolved'] = True elif '::' in var['raw_type'] and var['raw_type'].split('::')[0] not in self.namespaces: var['raw_type'] = var['class']['namespace'] + '::' + var['raw_type'] else: var['unresolved'] = True elif 'forward_declared' in var and 'namespace' in var: if '::' not in var['raw_type']: var['raw_type'] = var['namespace'] + var['raw_type'] elif '::' in var['raw_type'] and var['raw_type'].split('::')[0] in self.namespaces: pass else: trace_print('-'*80); trace_print(var); raise NotImplemented ## need full name space for classes in raw type ## if var['raw_type'].startswith( '::' ): #print(var) #print('NAMESPACE', var['class']['namespace']) #print( 'PARENT NS', var['class']['parent']['namespace'] ) #assert 0 var['unresolved'] = True if 'method' in var: var['method']['unresolved_parameters'] = True #var['raw_type'] = var['raw_type'][2:] # Take care of #defines and #pragmas etc trace_print("Processing precomp_macro_buf: %s"%self._precomp_macro_buf) for m in self._precomp_macro_buf: macro = m.replace("<CppHeaderParser_newline_temp_replacement>\\n", "\n") try: if macro.lower().startswith("#define"): trace_print("Adding #define %s"%macro) self.defines.append(macro.split(" ", 1)[1].strip()) elif macro.lower().startswith("#if") or macro.lower().startswith("#endif") or macro.lower().startswith("#else"): self.conditionals.append(macro) elif macro.lower().startswith("#pragma"): trace_print("Adding #pragma %s"%macro) self.pragmas.append(macro.split(" ", 1)[1].strip()) elif macro.lower().startswith("#include"): trace_print("Adding #include %s"%macro) self.includes.append(macro.split(" ", 1)[1].strip()) else: debug_print("Cant detect what to do with precomp macro '%s'"%macro) except: pass self._precomp_macro_buf = None def concrete_typedef( self, key ): if key not in self.typedefs: #print( 'FAILED typedef', key ) return None while key in self.typedefs: prev = key key = self.typedefs[ key ] if '<' in key or '>' in key: return prev # stop at template if key.startswith('std::'): return key # stop at std lib return key class _CppHeader( Resolver ): def finalize(self): self.finalize_vars() # finalize classes and method returns types for cls in list(self.classes.values()): for meth in cls.get_all_methods(): if meth['pure_virtual']: cls['abstract'] = True if not meth['returns_fundamental'] and meth['returns'] in C99_NONSTANDARD: meth['returns'] = C99_NONSTANDARD[meth['returns']] meth['returns_fundamental'] = True elif not meth['returns_fundamental']: # describe the return type con = None if cls['namespace'] and '::' not in meth['returns']: con = self.concrete_typedef( cls['namespace'] + '::' + meth['returns'] ) else: con = self.concrete_typedef( meth['returns'] ) if con: meth['returns_concrete'] = con meth['returns_fundamental'] = is_fundamental( con ) elif meth['returns'] in self.classes: trace_print( 'meth returns class:', meth['returns'] ) meth['returns_class'] = True elif meth['returns'] in self.SubTypedefs: meth['returns_class'] = True meth['returns_nested'] = self.SubTypedefs[ meth['returns'] ] elif meth['returns'] in cls._public_enums: enum = cls._public_enums[ meth['returns'] ] meth['returns_enum'] = enum['type'] meth['returns_fundamental'] = True if enum['type'] == int: meth['returns'] = 'int' else: meth['returns'] = 'char*' elif meth['returns'] in self.global_enums: enum = self.global_enums[ meth['returns'] ] meth['returns_enum'] = enum['type'] meth['returns_fundamental'] = True if enum['type'] == int: meth['returns'] = 'int' else: meth['returns'] = 'char*' elif meth['returns'].count('::')==1: trace_print( meth ) a,b = meth['returns'].split('::') if a in self.namespaces: if b in self.classes: klass = self.classes[ b ] meth['returns_class'] = a + '::' + b elif '<' in b and '>' in b: warning_print( 'WARN-can not return template: %s'%b ) meth['returns_unknown'] = True elif b in self.global_enums: enum = self.global_enums[ b ] meth['returns_enum'] = enum['type'] meth['returns_fundamental'] = True if enum['type'] == int: meth['returns'] = 'int' else: meth['returns'] = 'char*' else: trace_print( a, b); trace_print( meth); meth['returns_unknown'] = True # +++ elif a in self.classes: klass = self.classes[ a ] if b in klass._public_enums: trace_print( '...found nested enum', b ) enum = klass._public_enums[ b ] meth['returns_enum'] = enum['type'] meth['returns_fundamental'] = True if enum['type'] == int: meth['returns'] = 'int' else: meth['returns'] = 'char*' elif b in klass._public_forward_declares: meth['returns_class'] = True elif b in klass._public_typedefs: typedef = klass._public_typedefs[ b ] meth['returns_fundamental'] = is_fundamental( typedef ) else: trace_print( meth ) # should be a nested class, TODO fix me. meth['returns_unknown'] = True elif '::' in meth['returns']: trace_print('TODO namespace or extra nested return:', meth) meth['returns_unknown'] = True else: trace_print( 'WARN: UNKNOWN RETURN', meth['name'], meth['returns']) meth['returns_unknown'] = True if meth["returns"].startswith(": : "): meth["returns"] = meth["returns"].replace(": : ", "::") for cls in list(self.classes.values()): methnames = cls.get_all_method_names() pvm = cls.get_all_pure_virtual_methods() for d in cls['inherits']: c = d['class'] a = d['access'] # do not depend on this to be 'public' trace_print( 'PARENT CLASS:', c ) if c not in self.classes: trace_print('WARN: parent class not found') if c in self.classes and self.classes[c]['abstract']: p = self.classes[ c ] for meth in p.get_all_methods(): #p["methods"]["public"]: trace_print( '\t\tmeth', meth['name'], 'pure virtual', meth['pure_virtual'] ) if meth['pure_virtual'] and meth['name'] not in methnames: cls['abstract'] = True; break def evaluate_struct_stack(self): """Create a Struct out of the name stack (but not its parts)""" #print( 'eval struct stack', self.nameStack ) #if self.braceDepth != len(self.nameSpaces): return struct = CppStruct(self.nameStack) struct["namespace"] = self.cur_namespace() self.structs[ struct['type'] ] = struct self.structs_order.append( struct ) if self.curClass: struct['parent'] = self.curClass klass = self.classes[ self.curClass ] klass['structs'][self.curAccessSpecifier].append( struct ) if self.curAccessSpecifier == 'public': klass._public_structs[ struct['type'] ] = struct self.curStruct = struct self._structs_brace_level[ struct['type'] ] = self.braceDepth def parse_method_type( self, stack ): trace_print( 'meth type info', stack ) if stack[0] in ':;' and stack[1] != ':': stack = stack[1:] info = { 'debug': ' '.join(stack).replace(' : : ', '::' ).replace(' < ', '<' ).replace(' > ', '> ' ).replace(" >",">").replace(">>", "> >").replace(">>", "> >"), 'class':None, 'namespace':self.cur_namespace(add_double_colon=True), } for tag in 'defined pure_virtual operator constructor destructor extern template virtual static explicit inline friend returns returns_pointer returns_fundamental returns_class'.split(): info[tag]=False header = stack[ : stack.index('(') ] header = ' '.join( header ) header = header.replace(' : : ', '::' ) header = header.replace(' < ', '<' ) header = header.replace(' > ', '> ' ) header = header.strip() if '{' in stack: info['defined'] = True self._method_body = self.braceDepth + 1 trace_print( 'NEW METHOD WITH BODY', self.braceDepth ) elif stack[-1] == ';': info['defined'] = False self._method_body = None # not a great idea to be clearing here else: assert 0 if len(stack) > 3 and stack[-1] == ';' and stack[-2] == '0' and stack[-3] == '=': info['pure_virtual'] = True r = header.split() name = None if 'operator' in stack: # rare case op overload defined outside of class op = stack[ stack.index('operator')+1 : stack.index('(') ] op = ''.join(op) if not op: if " ".join(['operator', '(', ')', '(']) in " ".join(stack): op = "()" else: trace_print( 'Error parsing operator') return None info['operator'] = op name = 'operator' + op a = stack[ : stack.index('operator') ] elif r: name = r[-1] a = r[ : -1 ] # strip name if name is None: return None #if name.startswith('~'): name = name[1:] while a and a[0] == '}': # strip - can have multiple } } a = a[1:] if '::' in name: #klass,name = name.split('::') # methods can be defined outside of class klass = name[ : name.rindex('::') ] name = name.split('::')[-1] info['class'] = klass if klass in self.classes and not self.curClass: #Class function defined outside the class return None # info['name'] = name #else: info['name'] = name if name.startswith('~'): info['destructor'] = True name = name[1:] elif not a or (name == self.curClass and len(self.curClass)): info['constructor'] = True info['name'] = name for tag in 'extern virtual static explicit inline friend'.split(): if tag in a: info[ tag ] = True; a.remove( tag ) # inplace if 'template' in a: a.remove('template') b = ' '.join( a ) if '>' in b: info['template'] = b[ : b.index('>')+1 ] info['returns'] = b[ b.index('>')+1 : ] # find return type, could be incorrect... TODO if '<typename' in info['template'].split(): typname = info['template'].split()[-1] typname = typname[ : -1 ] # strip '>' if typname not in self._template_typenames: self._template_typenames.append( typname ) else: info['returns'] = ' '.join( a ) else: info['returns'] = ' '.join( a ) info['returns'] = info['returns'].replace(' <', '<').strip() ## be careful with templates, do not count pointers inside template info['returns_pointer'] = info['returns'].split('>')[-1].count('*') if info['returns_pointer']: info['returns'] = info['returns'].replace('*','').strip() info['returns_reference'] = '&' in info['returns'] if info['returns']: info['returns'] = info['returns'].replace('&','').strip() a = [] for b in info['returns'].split(): if b == '__const__': info['returns_const'] = True elif b == 'const': info['returns_const'] = True else: a.append( b ) info['returns'] = ' '.join( a ) info['returns_fundamental'] = is_fundamental( info['returns'] ) return info def evaluate_method_stack(self): """Create a method out of the name stack""" if self.curStruct: trace_print( 'WARN - struct contains methods - skipping' ) trace_print( self.stack ) assert 0 info = self.parse_method_type( self.stack ) if info: if info[ 'class' ] and info['class'] in self.classes: # case where methods are defined outside of class newMethod = CppMethod(self.nameStack, info['name'], info, self.curTemplate) klass = self.classes[ info['class'] ] klass[ 'methods' ][ 'public' ].append( newMethod ) newMethod['parent'] = klass if klass['namespace']: newMethod['path'] = klass['namespace'] + '::' + klass['name'] else: newMethod['path'] = klass['name'] elif self.curClass: # normal case newMethod = CppMethod(self.nameStack, self.curClass, info, self.curTemplate) klass = self.classes[self.curClass] klass['methods'][self.curAccessSpecifier].append(newMethod) newMethod['parent'] = klass if klass['namespace']: newMethod['path'] = klass['namespace'] + '::' + klass['name'] else: newMethod['path'] = klass['name'] else: #non class functions debug_print("FREE FUNCTION") newMethod = CppMethod(self.nameStack, None, info, self.curTemplate) self.functions.append(newMethod) global parseHistory parseHistory.append({"braceDepth": self.braceDepth, "item_type": "method", "item": newMethod}) else: trace_print( 'free function?', self.nameStack ) self.stack = [] def _parse_typedef( self, stack, namespace='' ): if not stack or 'typedef' not in stack: return stack = list( stack ) # copy just to be safe if stack[-1] == ';': stack.pop() while stack and stack[-1].isdigit(): stack.pop() # throw away array size for now idx = stack.index('typedef') if stack[-1] == "]": try: name = namespace + "".join(stack[-4:]) # Strip off the array part so the rest of the parsing is better stack = stack[:-3] except: name = namespace + stack[-1] else: name = namespace + stack[-1] s = '' for a in stack[idx+1:-1]: if a == '{': break if not s or s[-1] in ':<>' or a in ':<>': s += a # keep compact else: s += ' ' + a # spacing r = {'name':name, 'raw':s, 'type':s} if not is_fundamental(s): if 'struct' in s.split(): pass # TODO is this right? "struct ns::something" elif '::' not in s: s = namespace + s # only add the current name space if no namespace given r['type'] = s if s: return r def evaluate_typedef(self): ns = self.cur_namespace(add_double_colon=True) res = self._parse_typedef( self.stack, ns ) if res: name = res['name'] self.typedefs[ name ] = res['type'] if name not in self.typedefs_order: self.typedefs_order.append( name ) def evaluate_property_stack(self): """Create a Property out of the name stack""" global parseHistory assert self.stack[-1] == ';' debug_print( "trace" ) if self.nameStack[0] == 'typedef': if self.curClass: typedef = self._parse_typedef( self.stack ) name = typedef['name'] klass = self.classes[ self.curClass ] klass[ 'typedefs' ][ self.curAccessSpecifier ].append( name ) if self.curAccessSpecifier == 'public': klass._public_typedefs[ name ] = typedef['type'] Resolver.SubTypedefs[ name ] = self.curClass else: assert 0 elif self.curStruct or self.curClass: if len(self.nameStack) == 1: #See if we can de anonymize the type filteredParseHistory = [h for h in parseHistory if h["braceDepth"] == self.braceDepth] if len(filteredParseHistory) and filteredParseHistory[-1]["item_type"] == "class": self.nameStack.insert(0, filteredParseHistory[-1]["item"]["name"]) debug_print("DEANONYMOIZING %s to type '%s'"%(self.nameStack[1], self.nameStack[0])) if "," in self.nameStack: #Maybe we have a variable list #Figure out what part is the variable separator but remember templates of function pointer #First find left most comma outside of a > and ) leftMostComma = 0; for i in range(0, len(self.nameStack)): name = self.nameStack[i] if name in (">", ")"): leftMostComma = 0 if leftMostComma == 0 and name == ",": leftMostComma = i # Is it really a list of variables? if leftMostComma != 0: trace_print("Multiple variables for namestack in %s. Separating processing"%self.nameStack) orig_nameStack = self.nameStack[:] orig_stack = self.stack[:] type_nameStack = orig_nameStack[:leftMostComma-1] for name in orig_nameStack[leftMostComma - 1::2]: self.nameStack = type_nameStack + [name] self.stack = orig_stack[:] # Not maintained for mucking, but this path it doesnt matter self.evaluate_property_stack() return newVar = CppVariable(self.nameStack) newVar['namespace'] = self.current_namespace() if self.curStruct: self.curStruct[ 'fields' ].append( newVar ) newVar['property_of_struct'] = self.curStruct elif self.curClass: klass = self.classes[self.curClass] klass["properties"][self.curAccessSpecifier].append(newVar) newVar['property_of_class'] = klass['name'] parseHistory.append({"braceDepth": self.braceDepth, "item_type": "variable", "item": newVar}) else: debug_print( "Found Global variable" ) newVar = CppVariable(self.nameStack) self.variables.append(newVar) self.stack = [] # CLEAR STACK def evaluate_class_stack(self): """Create a Class out of the name stack (but not its parts)""" #dont support sub classes today #print( 'eval class stack', self.nameStack ) parent = self.curClass if self.braceDepth > len( self.nameSpaces) and parent: trace_print( 'HIT NESTED SUBCLASS' ) self.accessSpecifierStack.append(self.curAccessSpecifier) elif self.braceDepth != len(self.nameSpaces): error_print( 'ERROR: WRONG BRACE DEPTH' ) return # When dealing with typedefed structs, get rid of typedef keyword to handle later on if self.nameStack[0] == "typedef": del self.nameStack[0] if len(self.nameStack) == 1: self.anon_struct_counter += 1 # We cant handle more than 1 anonymous struct, so name them uniquely self.nameStack.append("<anon-struct-%d>"%self.anon_struct_counter) if self.nameStack[0] == "class": self.curAccessSpecifier = 'private' else:#struct self.curAccessSpecifier = 'public' debug_print("curAccessSpecifier changed/defaulted to %s"%self.curAccessSpecifier) if self.nameStack[0] == "union": newClass = CppUnion(self.nameStack) self.anon_union_counter = [self.braceDepth, 2] trace_print( 'NEW UNION', newClass['name'] ) else: newClass = CppClass(self.nameStack, self.curTemplate) trace_print( 'NEW CLASS', newClass['name'] ) newClass["declaration_method"] = self.nameStack[0] self.classes_order.append( newClass ) # good idea to save ordering self.stack = [] # fixes if class declared with ';' in closing brace if parent: newClass["namespace"] = self.classes[ parent ]['namespace'] + '::' + parent newClass['parent'] = parent self.classes[ parent ]['nested_classes'].append( newClass ) ## supports nested classes with the same name ## self.curClass = key = parent+'::'+newClass['name'] self._classes_brace_level[ key ] = self.braceDepth elif newClass['parent']: # nested class defined outside of parent. A::B {...} parent = newClass['parent'] newClass["namespace"] = self.classes[ parent ]['namespace'] + '::' + parent self.classes[ parent ]['nested_classes'].append( newClass ) ## supports nested classes with the same name ## self.curClass = key = parent+'::'+newClass['name'] self._classes_brace_level[ key ] = self.braceDepth else: newClass["namespace"] = self.cur_namespace() key = newClass['name'] self.curClass = newClass["name"] self._classes_brace_level[ newClass['name'] ] = self.braceDepth if not key.endswith("::") and not key.endswith(" ") and len(key) != 0: if key in self.classes: trace_print( 'ERROR name collision:', key ) self.classes[key].show() trace_print('-'*80) newClass.show() assert key not in self.classes # namespace collision self.classes[ key ] = newClass global parseHistory parseHistory.append({"braceDepth": self.braceDepth, "item_type": "class", "item": newClass}) def evalute_forward_decl(self): trace_print( 'FORWARD DECL', self.nameStack ) assert self.nameStack[0] in ('class', 'struct') name = self.nameStack[-1] if self.curClass: klass = self.classes[ self.curClass ] klass['forward_declares'][self.curAccessSpecifier].append( name ) if self.curAccessSpecifier == 'public': klass._public_forward_declares.append( name ) else: self._forward_decls.append( name ) class CppHeader( _CppHeader ): """Parsed C++ class header Variables produced: self.classes - Dictionary of classes found in a given header file where the key is the name of the class """ IGNORE_NAMES = '__extension__'.split() def show(self): for className in list(self.classes.keys()):self.classes[className].show() def __init__(self, headerFileName, argType="file", **kwargs): """Create the parsed C++ header file parse tree headerFileName - Name of the file to parse OR actual file contents (depends on argType) argType - Indicates how to interpret headerFileName as a file string or file name kwargs - Supports the following keywords """ ## reset global state ## global doxygenCommentCache doxygenCommentCache = "" CppVariable.Vars = [] CppStruct.Structs = [] if (argType == "file"): self.headerFileName = os.path.expandvars(headerFileName) self.mainClass = os.path.split(self.headerFileName)[1][:-2] headerFileStr = "" elif argType == "string": self.headerFileName = "" self.mainClass = "???" headerFileStr = headerFileName else: raise Exception("Arg type must be either file or string") self.curClass = "" # nested classes have parent::nested, but no extra namespace, # this keeps the API compatible, TODO proper namespace for everything. Resolver.CLASSES = {} self.classes = Resolver.CLASSES #Functions that are not part of a class self.functions = [] self.pragmas = [] self.defines = [] self.includes = [] self.conditionals = [] self._precomp_macro_buf = [] #for internal purposes, will end up filling out pragmras and defines at the end self.enums = [] self.variables = [] self.global_enums = {} self.nameStack = [] self.nameSpaces = [] self.curAccessSpecifier = 'private' # private is default self.curTemplate = None self.accessSpecifierStack = [] self.accessSpecifierScratch = [] debug_print("curAccessSpecifier changed/defaulted to %s"%self.curAccessSpecifier) self.initextra() # Old namestacks for a given level self.nameStackHistory = [] self.anon_struct_counter = 0 self.anon_union_counter = [-1, 0] self.templateRegistry = [] if (len(self.headerFileName)): fd = open(self.headerFileName) headerFileStr = "".join(fd.readlines()) fd.close() # Make sure supportedAccessSpecifier are sane for i in range(0, len(supportedAccessSpecifier)): if " " not in supportedAccessSpecifier[i]: continue supportedAccessSpecifier[i] = re.sub("[ ]+", " ", supportedAccessSpecifier[i]).strip() # Strip out template declarations templateSectionsToSliceOut = [] try: for m in re.finditer("template[\t ]*<[^>]*>", headerFileStr): start = m.start() # Search for the final '>' which may or may not be caught in the case of nexted <>'s for i in range(start, len(headerFileStr)): if headerFileStr[i] == '<': firstBracket = i break ltgtStackCount = 1 #Now look for fianl '>' for i in range(firstBracket + 1, len(headerFileStr)): if headerFileStr[i] == '<': ltgtStackCount += 1 elif headerFileStr[i] == '>': ltgtStackCount -= 1 if ltgtStackCount == 0: end = i break templateSectionsToSliceOut.append((start, end)) # Now strip out all instances of the template templateSectionsToSliceOut.reverse() for tslice in templateSectionsToSliceOut: # Replace the template symbol with a single symbol template_symbol="CppHeaderParser_template_%d"%len(self.templateRegistry) self.templateRegistry.append(headerFileStr[tslice[0]: tslice[1]+1]) newlines = headerFileStr[tslice[0]: tslice[1]].count("\n") * "\n" #Keep line numbers the same headerFileStr = headerFileStr[:tslice[0]] + newlines + " " + template_symbol + " " + headerFileStr[tslice[1] + 1:] except: pass # Change multi line #defines and expressions to single lines maintaining line nubmers # Based from http://stackoverflow.com/questions/2424458/regular-expression-to-match-cs-multiline-preprocessor-statements matches = re.findall(r'(?m)^(?:.*\\\r?\n)+.*$', headerFileStr) is_define = re.compile(r'[ \t\v]*#[Dd][Ee][Ff][Ii][Nn][Ee]') for m in matches: #Keep the newlines so that linecount doesnt break num_newlines = len([a for a in m if a=="\n"]) if is_define.match(m): new_m = m.replace("\n", "<CppHeaderParser_newline_temp_replacement>\\n") else: # Just expression taking up multiple lines, make it take 1 line for easier parsing new_m = m.replace("\\\n", " ") if (num_newlines > 0): new_m += "\n"*(num_newlines) headerFileStr = headerFileStr.replace(m, new_m) #Filter out Extern "C" statements. These are order dependent matches = re.findall(re.compile(r'extern[\t ]+"[Cc]"[\t \n\r]*{', re.DOTALL), headerFileStr) for m in matches: #Keep the newlines so that linecount doesnt break num_newlines = len([a for a in m if a=="\n"]) headerFileStr = headerFileStr.replace(m, "\n" * num_newlines) headerFileStr = re.sub(r'extern[ ]+"[Cc]"[ ]*', "", headerFileStr) #Filter out any ignore symbols that end with "()" to account for #define magic functions for ignore in ignoreSymbols: if not ignore.endswith("()"): continue while True: locStart = headerFileStr.find(ignore[:-1]) if locStart == -1: break; locEnd = None #Now walk till we find the last paren and account for sub parens parenCount = 1 inQuotes = False for i in range(locStart + len(ignore) - 1, len(headerFileStr)): c = headerFileStr[i] if not inQuotes: if c == "(": parenCount += 1 elif c == ")": parenCount -= 1 elif c == '"': inQuotes = True if parenCount == 0: locEnd = i + 1 break; else: if c == '"' and headerFileStr[i-1] != '\\': inQuotes = False if locEnd: #Strip it out but keep the linecount the same so line numbers are right match_str = headerFileStr[locStart:locEnd] debug_print("Striping out '%s'"%match_str) num_newlines = len([a for a in match_str if a=="\n"]) headerFileStr = headerFileStr.replace(headerFileStr[locStart:locEnd], "\n"*num_newlines) self.braceDepth = 0 lex.lex() lex.input(headerFileStr) global curLine global curChar curLine = 0 curChar = 0 try: while True: tok = lex.token() if not tok: break if self.anon_union_counter[0] == self.braceDepth and self.anon_union_counter[1]: self.anon_union_counter[1] -= 1 tok.value = TagStr(tok.value, lineno=tok.lineno) #debug_print("TOK: %s"%tok) if tok.type == 'NAME' and tok.value in self.IGNORE_NAMES: continue if tok.type != 'TEMPLATE_NAME': self.stack.append( tok.value ) curLine = tok.lineno curChar = tok.lexpos if (tok.type in ('PRECOMP_MACRO', 'PRECOMP_MACRO_CONT')): debug_print("PRECOMP: %s"%tok) self._precomp_macro_buf.append(tok.value) self.stack = [] self.nameStack = [] continue if tok.type == 'TEMPLATE_NAME': try: templateId = int(tok.value.replace("CppHeaderParser_template_","")) self.curTemplate = self.templateRegistry[templateId] except: pass if (tok.type == 'OPEN_BRACE'): if len(self.nameStack) >= 2 and is_namespace(self.nameStack): # namespace {} with no name used in boost, this sets default? if self.nameStack[1] == "__IGNORED_NAMESPACE__CppHeaderParser__":#Used in filtering extern "C" self.nameStack[1] = "" self.nameSpaces.append(self.nameStack[1]) ns = self.cur_namespace(); self.stack = [] if ns not in self.namespaces: self.namespaces.append( ns ) # Detect special condition of macro magic before class declaration so we # can filter it out if 'class' in self.nameStack and self.nameStack[0] != 'class': classLocationNS = self.nameStack.index("class") classLocationS = self.stack.index("class") if "(" not in self.nameStack[classLocationNS:]: debug_print("keyword 'class' found in unexpected location in nameStack, must be following #define magic. Process that before moving on") origNameStack = self.nameStack origStack = self.stack #Process first part of stack which is probably #define macro magic and may cause issues self.nameStack = self.nameStack[:classLocationNS] self.stack = self.stack[:classLocationS] try: self.evaluate_stack() except: debug_print("Error processing #define magic... Oh well") #Process rest of stack self.nameStack = origNameStack[classLocationNS:] self.stack = origStack[classLocationS:] if len(self.nameStack) and not is_enum_namestack(self.nameStack): self.evaluate_stack() else: self.nameStack.append(tok.value) if self.stack and self.stack[0] == 'class': self.stack = [] self.braceDepth += 1 elif (tok.type == 'CLOSE_BRACE'): if self.braceDepth == 0: continue if (self.braceDepth == len(self.nameSpaces)): tmp = self.nameSpaces.pop() self.stack = [] # clear stack when namespace ends? if len(self.nameStack) and is_enum_namestack(self.nameStack): self.nameStack.append(tok.value) elif self.braceDepth < 10: self.evaluate_stack() else: self.nameStack = [] self.braceDepth -= 1 #self.stack = []; print 'BRACE DEPTH', self.braceDepth, 'NS', len(self.nameSpaces) if self.curClass: debug_print( 'CURBD %s'%self._classes_brace_level[ self.curClass ] ) if (self.braceDepth == 0) or (self.curClass and self._classes_brace_level[self.curClass]==self.braceDepth): trace_print( 'END OF CLASS DEF' ) if self.accessSpecifierStack: self.curAccessSpecifier = self.accessSpecifierStack[-1] self.accessSpecifierStack = self.accessSpecifierStack[:-1] if self.curClass and self.classes[ self.curClass ]['parent']: self.curClass = self.classes[ self.curClass ]['parent'] else: self.curClass = ""; #self.curStruct = None self.stack = [] #if self.curStruct: self.curStruct = None if self.braceDepth == 0 or (self.curStruct and self._structs_brace_level[self.curStruct['type']]==self.braceDepth): trace_print( 'END OF STRUCT DEF' ) self.curStruct = None if self._method_body and (self.braceDepth + 1) <= self._method_body: self._method_body = None; self.stack = []; self.nameStack = []; trace_print( 'FORCE CLEAR METHBODY' ) if (tok.type == 'OPEN_PAREN'): self.nameStack.append(tok.value) elif (tok.type == 'CLOSE_PAREN'): self.nameStack.append(tok.value) elif (tok.type == 'OPEN_SQUARE_BRACKET'): self.nameStack.append(tok.value) elif (tok.type == 'CLOSE_SQUARE_BRACKET'): self.nameStack.append(tok.value) elif (tok.type == 'TAB'): pass elif (tok.type == 'EQUALS'): self.nameStack.append(tok.value) elif (tok.type == 'COMMA'): self.nameStack.append(tok.value) elif (tok.type == 'BACKSLASH'): self.nameStack.append(tok.value) elif (tok.type == 'DIVIDE'): self.nameStack.append(tok.value) elif (tok.type == 'PIPE'): self.nameStack.append(tok.value) elif (tok.type == 'PERCENT'): self.nameStack.append(tok.value) elif (tok.type == 'CARET'): self.nameStack.append(tok.value) elif (tok.type == 'EXCLAMATION'): self.nameStack.append(tok.value) elif (tok.type == 'SQUOTE'): pass elif (tok.type == 'NUMBER' or tok.type == 'FLOAT_NUMBER'): self.nameStack.append(tok.value) elif (tok.type == 'MINUS'): self.nameStack.append(tok.value) elif (tok.type == 'PLUS'): self.nameStack.append(tok.value) elif (tok.type == 'STRING_LITERAL'): self.nameStack.append(tok.value) elif (tok.type == 'NAME' or tok.type == 'AMPERSTAND' or tok.type == 'ASTERISK' or tok.type == 'CHAR_LITERAL'): if tok.value in ignoreSymbols: debug_print("Ignore symbol %s"%tok.value) elif (tok.value == 'class'): self.nameStack.append(tok.value) elif tok.value in supportedAccessSpecifier: if len(self.nameStack) and self.nameStack[0] in ("class", "struct", "union"): self.nameStack.append(tok.value) elif self.braceDepth == len(self.nameSpaces) + 1 or self.braceDepth == (len(self.nameSpaces) + len(self.curClass.split("::"))): self.curAccessSpecifier = tok.value; self.accessSpecifierScratch.append(tok.value) debug_print("curAccessSpecifier updated to %s"%self.curAccessSpecifier) self.stack = [] else: self.nameStack.append(tok.value) if self.anon_union_counter[0] == self.braceDepth: self.anon_union_counter = [-1, 0] elif (tok.type == 'COLON'): #Dont want colon to be first in stack if len(self.nameStack) == 0: self.accessSpecifierScratch = [] continue # Handle situation where access specifiers can be multi words such as "public slots" jns = " ".join(self.accessSpecifierScratch + self.nameStack) if jns in supportedAccessSpecifier: self.curAccessSpecifier = jns; debug_print("curAccessSpecifier updated to %s"%self.curAccessSpecifier) self.stack = [] self.nameStack = [] else: self.nameStack.append(tok.value) self.accessSpecifierScratch = [] elif (tok.type == 'SEMI_COLON'): if self.anon_union_counter[0] == self.braceDepth and self.anon_union_counter[1]: debug_print("Creating anonymous union") #Force the processing of an anonymous union saved_namestack = self.nameStack[:] saved_stack = self.stack[:] self.nameStack = [""] self.stack = self.nameStack + [";"] self.nameStack = self.nameStack[0:1] debug_print("pre eval anon stack") self.evaluate_stack( tok.type ) debug_print("post eval anon stack") self.nameStack = saved_namestack self.stack = saved_stack self.anon_union_counter = [-1, 0]; if (self.braceDepth < 10): self.evaluate_stack( tok.type ) self.stack = [] self.nameStack = [] except: if (debug): raise raise CppParseError("Not able to parse %s on line %d evaluating \"%s\"\nError around: %s" % (self.headerFileName, tok.lineno, tok.value, " ".join(self.nameStack))) self.finalize() global parseHistory parseHistory = [] # Delete some temporary variables for key in ["_precomp_macro_buf", "nameStack", "nameSpaces", "curAccessSpecifier", "accessSpecifierStack", "accessSpecifierScratch", "nameStackHistory", "anon_struct_counter", "anon_union_counter", "_classes_brace_level", "_forward_decls", "stack", "mainClass", "curStruct", "_template_typenames", "_method_body", "braceDepth", "_structs_brace_level", "typedefs_order", "curTemplate", "templateRegistry"]: del self.__dict__[key] def evaluate_stack(self, token=None): """Evaluates the current name stack""" global doxygenCommentCache self.nameStack = filter_out_attribute_keyword(self.nameStack) self.stack = filter_out_attribute_keyword(self.stack) nameStackCopy = self.nameStack[:] debug_print( "Evaluating stack %s\n BraceDepth: %s (called from %d)" %(self.nameStack,self.braceDepth, inspect.currentframe().f_back.f_lineno)) #Handle special case of overloading operator () if "operator()(" in "".join(self.nameStack): operator_index = self.nameStack.index("operator") self.nameStack.pop(operator_index + 2) self.nameStack.pop(operator_index + 1) self.nameStack[operator_index] = "operator()" if (len(self.curClass)): debug_print( "%s (%s) "%(self.curClass, self.curAccessSpecifier)) else: debug_print( "<anonymous> (%s) "%self.curAccessSpecifier) #Filter special case of array with casting in it try: bracePos = self.nameStack.index("[") parenPos = self.nameStack.index("(") if bracePos == parenPos - 1: endParen = self.nameStack.index(")") self.nameStack = self.nameStack[:bracePos + 1] + self.nameStack[endParen + 1:] debug_print("Filtered namestack to=%s"%self.nameStack) except: pass #if 'typedef' in self.nameStack: self.evaluate_typedef() # allows nested typedefs, probably a bad idea if (not self.curClass and 'typedef' in self.nameStack and (('struct' not in self.nameStack and 'union' not in self.nameStack) or self.stack[-1] == ";") and not is_enum_namestack(self.nameStack)): trace_print('STACK', self.stack) self.evaluate_typedef() return elif (len(self.nameStack) == 0): debug_print( "trace" ) debug_print( "(Empty Stack)" ) return elif (self.nameStack[0] == "namespace"): #Taken care of outside of here pass elif len(self.nameStack) == 2 and self.nameStack[0] == "friend":#friend class declaration pass elif len(self.nameStack) >= 2 and self.nameStack[0] == 'using' and self.nameStack[1] == 'namespace': pass # TODO elif is_enum_namestack(self.nameStack): debug_print( "trace" ) self.evaluate_enum_stack() elif self._method_body and (self.braceDepth + 1) > self._method_body: trace_print( 'INSIDE METHOD DEF' ) elif is_method_namestack(self.stack) and not self.curStruct and '(' in self.nameStack: debug_print( "trace" ) if self.braceDepth > 0: if "{" in self.stack and self.stack[0] != '{' and self.stack[-1] == ';' and self.braceDepth == 1: #Special case of a method defined outside a class that has a body pass else: self.evaluate_method_stack() else: #Free function self.evaluate_method_stack() elif (len(self.nameStack) == 1 and len(self.nameStackHistory) > self.braceDepth and (self.nameStackHistory[self.braceDepth][0][0:2] == ["typedef", "struct"] or self.nameStackHistory[self.braceDepth][0][0:2] == ["typedef", "union"])): # Look for the name of a typedef struct: struct typedef {...] StructName; or unions to get renamed debug_print("found the naming of a union") type_name_to_rename = self.nameStackHistory[self.braceDepth][1] new_name = self.nameStack[0] type_to_rename = self.classes[type_name_to_rename] type_to_rename["name"] = self.nameStack[0] #Now re install it in its new location self.classes[new_name] = type_to_rename del self.classes[type_name_to_rename] elif is_property_namestack(self.nameStack) and self.stack[-1] == ';': debug_print( "trace" ) if self.nameStack[0] in ('class', 'struct') and len(self.stack) == 3: self.evalute_forward_decl() elif len(self.nameStack) >= 2 and (self.nameStack[0]=='friend' and self.nameStack[1]=='class'): pass else: self.evaluate_property_stack() # catches class props and structs in a namespace elif self.nameStack[0] in ("class", "struct", "union") or self.nameStack[0] == 'typedef' and self.nameStack[1] in ('struct', 'union'): #Parsing a union can reuse much of the class parsing debug_print( "trace" ) self.evaluate_class_stack() elif not self.curClass: debug_print( "trace" ) if is_enum_namestack(self.nameStack): self.evaluate_enum_stack() elif self.curStruct and self.stack[-1] == ';': self.evaluate_property_stack() # this catches fields of global structs self.nameStack = [] doxygenCommentCache = "" elif (self.braceDepth < 1): debug_print( "trace" ) #Ignore global stuff for now debug_print( "Global stuff: %s"%self.nameStack ) self.nameStack = [] doxygenCommentCache = "" elif (self.braceDepth > len(self.nameSpaces) + 1): debug_print( "trace" ) self.nameStack = [] doxygenCommentCache = "" try: self.nameStackHistory[self.braceDepth] = (nameStackCopy, self.curClass) except: self.nameStackHistory.append((nameStackCopy, self.curClass)) self.nameStack = [] # its a little confusing to have some if/else above return and others not, and then clearning the nameStack down here doxygenCommentCache = "" self.curTemplate = None def evaluate_enum_stack(self): """Create an Enum out of the name stack""" debug_print( "evaluating enum" ) newEnum = CppEnum(self.nameStack) if len(list(newEnum.keys())): if len(self.curClass): newEnum["namespace"] = self.cur_namespace(False) klass = self.classes[self.curClass] klass["enums"][self.curAccessSpecifier].append(newEnum) if self.curAccessSpecifier == 'public' and 'name' in newEnum: klass._public_enums[ newEnum['name'] ] = newEnum else: newEnum["namespace"] = self.cur_namespace(True) self.enums.append(newEnum) if 'name' in newEnum and newEnum['name']: self.global_enums[ newEnum['name'] ] = newEnum #This enum has instances, turn them into properties if "instances" in newEnum: instanceType = "enum" if "name" in newEnum: instanceType = newEnum["name"] for instance in newEnum["instances"]: self.nameStack = [instanceType, instance] self.evaluate_property_stack() del newEnum["instances"] def strip_parent_keys(self): """Strip all parent keys to prevent loops""" obj_queue = [self] while len(obj_queue): obj = obj_queue.pop() trace_print("pop %s type %s"%(obj, type(obj))) try: if "parent" in obj.keys(): del obj["parent"] trace_print("Stripped parent from %s"%obj.keys()) except: pass # Figure out what sub types are one of ours try: if not hasattr(obj, 'keys'): obj = obj.__dict__ for k in obj.keys(): trace_print("-Try key %s"%(k)) trace_print("-type %s"%(type(obj[k]))) if k in ["nameStackHistory", "parent", "_public_typedefs"]: continue if type(obj[k]) == list: for i in obj[k]: trace_print("push l %s"%i) obj_queue.append(i) elif type(obj[k]) == dict: if len(obj): trace_print("push d %s"%obj[k]) obj_queue.append(obj[k]) elif type(obj[k]) == type(type(0)): if type(obj[k]) == int: obj[k] = "int" elif type(obj[k]) == str: obj[k] = "string" else: obj[k] = "???" trace_print("next key\n") except: trace_print("Exception") def toJSON(self, indent=4): """Converts a parsed structure to JSON""" import json self.strip_parent_keys() try: del self.__dict__["classes_order"] except: pass return json.dumps(self.__dict__, indent=indent) def __repr__(self): rtn = { "classes": self.classes, "functions": self.functions, "enums": self.enums, "variables": self.variables, } return repr(rtn) def __str__(self): rtn = "" for className in list(self.classes.keys()): rtn += "%s\n"%self.classes[className] if self.functions: rtn += "// functions\n" for f in self.functions: rtn += "%s\n"%f if self.variables: rtn += "// variables\n" for f in self.variables: rtn += "%s\n"%f if self.enums: rtn += "// enums\n" for f in self.enums: rtn += "%s\n"%f return rtn

43.697027

210

0.51306

import ply.lex as lex import os import sys import re import inspect def lineno(): return inspect.currentframe().f_back.f_lineno version = __version__ = "2.7" tokens = [ 'NUMBER', 'FLOAT_NUMBER', 'TEMPLATE_NAME', 'NAME', 'OPEN_PAREN', 'CLOSE_PAREN', 'OPEN_BRACE', 'CLOSE_BRACE', 'OPEN_SQUARE_BRACKET', 'CLOSE_SQUARE_BRACKET', 'COLON', 'SEMI_COLON', 'COMMA', 'TAB', 'BACKSLASH', 'PIPE', 'PERCENT', 'EXCLAMATION', 'CARET', 'COMMENT_SINGLELINE', 'COMMENT_MULTILINE', 'PRECOMP_MACRO', 'PRECOMP_MACRO_CONT', 'ASTERISK', 'AMPERSTAND', 'EQUALS', 'MINUS', 'PLUS', 'DIVIDE', 'CHAR_LITERAL', 'STRING_LITERAL', 'NEW_LINE', 'SQUOTE', ] t_ignore = " \r.?@\f" t_NUMBER = r'[0-9][0-9XxA-Fa-f]*' t_FLOAT_NUMBER = r'[-+]?[0-9]*\.[0-9]+([eE][-+]?[0-9]+)?' t_TEMPLATE_NAME = r'CppHeaderParser_template_[0-9]+' t_NAME = r'[<>A-Za-z_~][A-Za-z0-9_]*' t_OPEN_PAREN = r'$' t_CLOSE_PAREN = r'$' t_OPEN_BRACE = r'{' t_CLOSE_BRACE = r'}' t_OPEN_SQUARE_BRACKET = r'\[' t_CLOSE_SQUARE_BRACKET = r'\]' t_SEMI_COLON = r';' t_COLON = r':' t_COMMA = r',' t_TAB = r'\t' t_BACKSLASH = r'\\' t_PIPE = r'\|' t_PERCENT = r'%' t_CARET = r'\^' t_EXCLAMATION = r'!' t_PRECOMP_MACRO = r'\#.*' t_PRECOMP_MACRO_CONT = r'.*\\\n' def t_COMMENT_SINGLELINE(t): global doxygenCommentCache if t.value.startswith("///") or t.value.startswith("//!"): if doxygenCommentCache: doxygenCommentCache += "\n" if t.value.endswith("\n"): doxygenCommentCache += t.value[:-1] else: doxygenCommentCache += t.value t.lexer.lineno += len([a for a in t.value if a=="\n"]) t_ASTERISK = r'\*' t_MINUS = r'\-' t_PLUS = r'\+' t_DIVIDE = r'/(?!/)' t_AMPERSTAND = r'&' t_EQUALS = r'=' t_CHAR_LITERAL = "'.'" t_SQUOTE = "'" #found at http://wordaligned.org/articles/string-literals-and-regular-expressions #TODO: This does not work with the string "bla \" bla" t_STRING_LITERAL = r'"([^"\\]|\\.)*"' #Found at http://ostermiller.org/findcomment.html def t_COMMENT_MULTILINE(t): global doxygenCommentCache if t.value.startswith("/**") or t.value.startswith("/*!"): #not sure why, but get double new lines v = t.value.replace("\n\n", "\n") #strip prefixing whitespace v = re.sub("\n[\s]+\*", "\n*", v) doxygenCommentCache += v t.lexer.lineno += len([a for a in t.value if a=="\n"]) def t_NEWLINE(t): t.lexer.lineno += len(t.value) def t_error(v): print(( "Lex error: ", v )) lex.lex() # Controls error_print print_errors = 1 # Controls warning_print print_warnings = 1 # Controls debug_print debug = 0 # Controls trace_print debug_trace = 0 def error_print(arg): if print_errors: print(("[%4d] %s"%(inspect.currentframe().f_back.f_lineno, arg))) def warning_print(arg): if print_warnings: print(("[%4d] %s"%(inspect.currentframe().f_back.f_lineno, arg))) def debug_print(arg): global debug if debug: print(("[%4d] %s"%(inspect.currentframe().f_back.f_lineno, arg))) def trace_print(*arg): global debug_trace if debug_trace: sys.stdout.write("[%s] "%(inspect.currentframe().f_back.f_lineno)) for a in arg: sys.stdout.write("%s "%a) sys.stdout.write("\n") supportedAccessSpecifier = [ 'public', 'protected', 'private', 'public slots', 'protected slots', 'private slots', 'public Q_SLOTS', 'protected Q_SLOTS', 'private Q_SLOTS', 'signals', 'Q_SIGNALS', ] #Symbols to ignore, usually special macros ignoreSymbols = [ 'Q_OBJECT', 'Q_PROPERTY()', 'Q_DECLARE_FLAGS()', 'Q_INVOKABLE', ] doxygenCommentCache = "" #Track what was added in what order and at what depth parseHistory = [] def is_namespace(nameStack): if len(nameStack) == 0: return False if nameStack[0] == "namespace": return True return False def is_enum_namestack(nameStack): if len(nameStack) == 0: return False if nameStack[0] == "enum": return True if len(nameStack) > 1 and nameStack[0] == "typedef" and nameStack[1] == "enum": return True return False def is_fundamental(s): for a in s.split(): if a not in ["size_t", "struct", "union", "unsigned", "signed", "bool", "char", "short", "int", "float", "double", "long", "void", "*"]: return False return True def is_function_pointer_stack(stack): paren_depth = 0 paren_count = 0 star_after_first_paren = False last_e = None for e in stack: if e == "(": paren_depth += 1 elif e == ")" and paren_depth > 0: paren_depth -= 1 if paren_depth == 0: paren_count += 1 elif e == "*" and last_e == "(" and paren_count == 0 and paren_depth == 1: star_after_first_paren = True last_e = e if star_after_first_paren and paren_count == 2: return True else: return False def is_method_namestack(stack): r = False if '(' not in stack: r = False elif stack[0] == 'typedef': r = False # TODO deal with typedef function prototypes #elif '=' in stack and stack.index('=') < stack.index('(') and stack[stack.index('=')-1] != 'operator': r = False #disabled July6th - allow all operators elif 'operator' in stack: r = True # allow all operators elif '{' in stack and stack.index('{') < stack.index('('): r = False # struct that looks like a method/class elif '(' in stack and ')' in stack: if '{' in stack and '}' in stack: r = True elif stack[-1] == ';': if is_function_pointer_stack(stack): r = False else: r = True elif '{' in stack: r = True # ideally we catch both braces... TODO else: r = False #Test for case of property set to something with parens such as "static const int CONST_A = (1 << 7) - 1;" if r and "(" in stack and "=" in stack and 'operator' not in stack: if stack.index("=") < stack.index("("): r = False return r def is_property_namestack(nameStack): r = False if '(' not in nameStack and ')' not in nameStack: r = True elif "(" in nameStack and "=" in nameStack and nameStack.index("=") < nameStack.index("("): r = True #See if we are a function pointer if not r and is_function_pointer_stack(nameStack): r = True return r def detect_lineno(s): try: rtn = s.lineno() if rtn != -1: return rtn except: pass global curLine return curLine def filter_out_attribute_keyword(stack): if "__attribute__" not in stack: return stack try: debug_print("Stripping __attribute__ from %s"% stack) attr_index = stack.index("__attribute__") attr_end = attr_index + 1 #Assuming not followed by parenthetical expression which wont happen #Find final paren if stack[attr_index + 1] == '(': paren_count = 1 for i in range(attr_index + 2, len(stack)): elm = stack[i] if elm == '(': paren_count += 1 elif elm == ')': paren_count -= 1 if paren_count == 0: attr_end = i + 1 break new_stack = stack[0:attr_index] + stack[attr_end:] debug_print("stripped stack is %s"% new_stack) return new_stack except: return stack class TagStr(str): lineno_reg = {} def __new__(cls,*args,**kw): new_obj = str.__new__(cls,*args) if "lineno" in kw: TagStr.lineno_reg[id(new_obj)] = kw["lineno"] return new_obj def __del__(self): try: del TagStr.lineno_reg[id(self)] except: pass def lineno(self): return TagStr.lineno_reg.get(id(self), -1) class CppParseError(Exception): pass class CppClass(dict): def get_all_methods(self): r = [] for typ in supportedAccessSpecifier: r += self['methods'][typ] return r def get_all_method_names( self ): r = [] for typ in supportedAccessSpecifier: r += self.get_method_names(typ) # returns list return r def get_all_pure_virtual_methods( self ): r = {} for typ in supportedAccessSpecifier: r.update(self.get_pure_virtual_methods(typ)) # returns dict return r def get_method_names( self, type='public' ): return [ meth['name'] for meth in self['methods'][ type ] ] def get_pure_virtual_methods( self, type='public' ): r = {} for meth in self['methods'][ type ]: if meth['pure_virtual']: r[ meth['name'] ] = meth return r def __init__(self, nameStack, curTemplate): self['nested_classes'] = [] self['parent'] = None self['abstract'] = False self._public_enums = {} self._public_structs = {} self._public_typedefs = {} self._public_forward_declares = [] self['namespace'] = "" debug_print( "Class: %s"%nameStack ) debug_print( "Template: %s"%curTemplate) if (len(nameStack) < 2): nameStack.insert(1, "")#anonymous struct global doxygenCommentCache if len(doxygenCommentCache): self["doxygen"] = doxygenCommentCache doxygenCommentCache = "" if "::" in "".join(nameStack): #Re-Join class paths (ex ['class', 'Bar', ':', ':', 'Foo'] -> ['class', 'Bar::Foo'] try: new_nameStack = [] for name in nameStack: if len(new_nameStack) == 0: new_nameStack.append(name) elif name == ":" and new_nameStack[-1].endswith(":"): new_nameStack[-1] += name elif new_nameStack[-1].endswith("::"): new_nameStack[-2] += new_nameStack[-1] + name del new_nameStack[-1] else: new_nameStack.append(name) trace_print("Convert from namestack\n %s\nto\n%s"%(nameStack, new_nameStack)) nameStack = new_nameStack except: pass # Handle final specifier self["final"] = False try: final_index = nameStack.index("final") # Dont trip up the rest of the logic del nameStack[final_index] self["final"] = True trace_print("final") except: pass self["name"] = nameStack[1] self["line_number"] = detect_lineno(nameStack[0]) #Handle template classes if len(nameStack) > 3 and nameStack[2].startswith("<"): open_template_count = 0 param_separator = 0 found_first = False i = 0 for elm in nameStack: if '<' in elm : open_template_count += 1 found_first = True elif '>' in elm: open_template_count -= 1 if found_first and open_template_count == 0: self["name"] = "".join(nameStack[1:i + 1]) break; i += 1 elif ":" in nameStack: self['name'] = nameStack[ nameStack.index(':') - 1 ] inheritList = [] if nameStack.count(':') == 1: nameStack = nameStack[nameStack.index(":") + 1:] while len(nameStack): tmpStack = [] tmpInheritClass = {"access":"private", "virtual": False} if "," in nameStack: tmpStack = nameStack[:nameStack.index(",")] nameStack = nameStack[nameStack.index(",") + 1:] else: tmpStack = nameStack nameStack = [] # Convert template classes to one name in the last index for i in range(0, len(tmpStack)): if '<' in tmpStack[i]: tmpStack2 = tmpStack[:i-1] tmpStack2.append("".join(tmpStack[i-1:])) tmpStack = tmpStack2 break if len(tmpStack) == 0: break; elif len(tmpStack) == 1: tmpInheritClass["class"] = tmpStack[0] elif len(tmpStack) == 2: tmpInheritClass["access"] = tmpStack[0] tmpInheritClass["class"] = tmpStack[1] elif len(tmpStack) == 3 and "virtual" in tmpStack: tmpInheritClass["access"] = tmpStack[1] if tmpStack[1] != "virtual" else tmpStack[0] tmpInheritClass["class"] = tmpStack[2] tmpInheritClass["virtual"] = True else: warning_print( "Warning: can not parse inheriting class %s"%(" ".join(tmpStack))) if '>' in tmpStack: pass # allow skip templates for now else: raise NotImplemented if 'class' in tmpInheritClass: inheritList.append(tmpInheritClass) elif nameStack.count(':') == 2: self['parent'] = self['name']; self['name'] = nameStack[-1] elif nameStack.count(':') > 2 and nameStack[0] in ("class", "struct"): tmpStack = nameStack[nameStack.index(":") + 1:] superTmpStack = [[]] for tok in tmpStack: if tok == ',': superTmpStack.append([]) else: superTmpStack[-1].append(tok) for tmpStack in superTmpStack: tmpInheritClass = {"access":"private"} if len(tmpStack) and tmpStack[0] in supportedAccessSpecifier: tmpInheritClass["access"] = tmpStack[0] tmpStack = tmpStack[1:] inheritNSStack = [] while len(tmpStack) > 3: if tmpStack[0] == ':': break; if tmpStack[1] != ':': break; if tmpStack[2] != ':': break; inheritNSStack.append(tmpStack[0]) tmpStack = tmpStack[3:] if len(tmpStack) == 1 and tmpStack[0] != ':': inheritNSStack.append(tmpStack[0]) tmpInheritClass["class"] = "::".join(inheritNSStack) inheritList.append(tmpInheritClass) self['inherits'] = inheritList if curTemplate: self["template"] = curTemplate trace_print("Setting template to '%s'"%self["template"]) methodAccessSpecificList = {} propertyAccessSpecificList = {} enumAccessSpecificList = {} structAccessSpecificList = {} typedefAccessSpecificList = {} forwardAccessSpecificList = {} for accessSpecifier in supportedAccessSpecifier: methodAccessSpecificList[accessSpecifier] = [] propertyAccessSpecificList[accessSpecifier] = [] enumAccessSpecificList[accessSpecifier] = [] structAccessSpecificList[accessSpecifier] = [] typedefAccessSpecificList[accessSpecifier] = [] forwardAccessSpecificList[accessSpecifier] = [] self['methods'] = methodAccessSpecificList self['properties'] = propertyAccessSpecificList self['enums'] = enumAccessSpecificList self['structs'] = structAccessSpecificList self['typedefs'] = typedefAccessSpecificList self['forward_declares'] = forwardAccessSpecificList def show(self): namespace_prefix = "" if self["namespace"]: namespace_prefix = self["namespace"] + "::" rtn = "%s %s"%(self["declaration_method"], namespace_prefix + self["name"]) if self["final"]: rtn += " final" if self['abstract']: rtn += ' (abstract)\n' else: rtn += '\n' if 'doxygen' in list(self.keys()): rtn += self["doxygen"] + '\n' if 'parent' in list(self.keys()) and self['parent']: rtn += 'parent class: ' + self['parent'] + '\n' if "inherits" in list(self.keys()): rtn += " Inherits: " for inheritClass in self["inherits"]: if inheritClass["virtual"]: rtn += "virtual " rtn += "%s %s, "%(inheritClass["access"], inheritClass["class"]) rtn += "\n" rtn += " {\n" for accessSpecifier in supportedAccessSpecifier: rtn += " %s\n"%(accessSpecifier) #Enums if (len(self["enums"][accessSpecifier])): rtn += " <Enums>\n" for enum in self["enums"][accessSpecifier]: rtn += " %s\n"%(repr(enum)) #Properties if (len(self["properties"][accessSpecifier])): rtn += " <Properties>\n" for property in self["properties"][accessSpecifier]: rtn += " %s\n"%(repr(property)) #Methods if (len(self["methods"][accessSpecifier])): rtn += " <Methods>\n" for method in self["methods"][accessSpecifier]: rtn += "\t\t" + method.show() + '\n' rtn += " }\n" print(rtn) def __str__(self): namespace_prefix = "" if self["namespace"]: namespace_prefix = self["namespace"] + "::" rtn = "%s %s"%(self["declaration_method"], namespace_prefix + self["name"]) if self["final"]: rtn += " final" if self['abstract']: rtn += ' (abstract)\n' else: rtn += '\n' if 'doxygen' in list(self.keys()): rtn += self["doxygen"] + '\n' if 'parent' in list(self.keys()) and self['parent']: rtn += 'parent class: ' + self['parent'] + '\n' if "inherits" in list(self.keys()) and len(self["inherits"]): rtn += "Inherits: " for inheritClass in self["inherits"]: if inheritClass.get("virtual", False): rtn += "virtual " rtn += "%s %s, "%(inheritClass["access"], inheritClass["class"]) rtn += "\n" rtn += "{\n" for accessSpecifier in supportedAccessSpecifier: rtn += "%s\n"%(accessSpecifier) #Enums if (len(self["enums"][accessSpecifier])): rtn += " // Enums\n" for enum in self["enums"][accessSpecifier]: rtn += " %s\n"%(repr(enum)) #Properties if (len(self["properties"][accessSpecifier])): rtn += " // Properties\n" for property in self["properties"][accessSpecifier]: rtn += " %s\n"%(repr(property)) #Methods if (len(self["methods"][accessSpecifier])): rtn += " // Methods\n" for method in self["methods"][accessSpecifier]: rtn += " %s\n"%(repr(method)) rtn += "}\n" return rtn class CppUnion( CppClass ): def __init__(self, nameStack): CppClass.__init__(self, nameStack, None) self["name"] = "union " + self["name"] self["members"] = self["properties"]["public"] def transform_to_union_keys(self): print("union keys: %s"%list(self.keys())) for key in ['inherits', 'parent', 'abstract', 'namespace', 'typedefs', 'methods']: del self[key] def show(self): print(self) def __str__(self): namespace_prefix = "" if self["namespace"]: namespace_prefix = self["namespace"] + "::" rtn = "%s %s"%(self["declaration_method"], namespace_prefix + self["name"]) if self['abstract']: rtn += ' (abstract)\n' else: rtn += '\n' if 'doxygen' in list(self.keys()): rtn += self["doxygen"] + '\n' if 'parent' in list(self.keys()) and self['parent']: rtn += 'parent class: ' + self['parent'] + '\n' rtn += "{\n" for member in self["members"]: rtn += " %s\n"%(repr(member)) rtn += "}\n" return rtn class _CppMethod( dict ): def _params_helper1( self, stack ): # deal with "throw" keyword if 'throw' in stack: stack = stack[ : stack.index('throw') ] ## remove GCC keyword __attribute__(...) and preserve returns ## cleaned = [] hit = False; hitOpen = 0; hitClose = 0 for a in stack: if a == '__attribute__': hit = True if hit: if a == '(': hitOpen += 1 elif a == ')': hitClose += 1 if a==')' and hitOpen == hitClose: hit = False else: cleaned.append( a ) stack = cleaned # also deal with attribute((const)) function prefix # # TODO this needs to be better # if len(stack) > 5: a = ''.join(stack) if a.startswith('((__const__))'): stack = stack[ 5 : ] elif a.startswith('__attribute__((__const__))'): stack = stack[ 6 : ] stack = stack[stack.index('(') + 1: ] if not stack: return [] if len(stack)>=3 and stack[0]==')' and stack[1]==':': # is this always a constructor? self['constructor'] = True return [] stack.reverse(); _end_ = stack.index(')'); stack.reverse() stack = stack[ : len(stack)-(_end_+1) ] if '(' not in stack: return stack # safe to return, no defaults that init a class # transforms ['someclass', '(', '0', '0', '0', ')'] into "someclass(0,0,0)'" r = []; hit=False for a in stack: if a == '(': hit=True elif a == ')': hit=False if hit or a == ')': r[-1] = r[-1] + a else: r.append( a ) return r def _params_helper2( self, params ): for p in params: p['method'] = self if '::' in p['type']: ns = p['type'].split('::')[0] if ns not in Resolver.NAMESPACES and ns in Resolver.CLASSES: p['type'] = self['namespace'] + p['type'] else: p['namespace'] = self[ 'namespace' ] class CppMethod( _CppMethod ): def show(self): r = ['method name: %s (%s)' %(self['name'],self['debug']) ] if self['returns']: r.append( 'returns: %s'%self['returns'] ) if self['parameters']: r.append( 'number arguments: %s' %len(self['parameters'])) if self['pure_virtual']: r.append( 'pure virtual: %s'%self['pure_virtual'] ) if self['constructor']: r.append( 'constructor' ) if self['destructor']: r.append( 'destructor' ) return '\n\t\t '.join( r ) def __init__(self, nameStack, curClass, methinfo, curTemplate): debug_print( "Method: %s"%nameStack ) debug_print( "Template: %s"%curTemplate ) global doxygenCommentCache if len(doxygenCommentCache): self["doxygen"] = doxygenCommentCache doxygenCommentCache = "" if "operator" in nameStack: self["rtnType"] = " ".join(nameStack[:nameStack.index('operator')]) self["name"] = "".join(nameStack[nameStack.index('operator'):nameStack.index('(')]) else: self["rtnType"] = " ".join(nameStack[:nameStack.index('(') - 1]) self["name"] = " ".join(nameStack[nameStack.index('(') - 1:nameStack.index('(')]) if self["rtnType"].startswith("virtual"): self["rtnType"] = self["rtnType"][len("virtual"):].strip() if len(self["rtnType"]) == 0 or self["name"] == curClass: self["rtnType"] = "void" self["rtnType"] = self["rtnType"].replace(' : : ', '::' ) self["rtnType"] = self["rtnType"].replace(" <","<") self["rtnType"] = self["rtnType"].replace(" >",">").replace(">>", "> >").replace(">>", "> >") self["rtnType"] = self["rtnType"].replace(" ,",",") for spec in ["const", "final", "override"]: self[spec] = False for i in reversed(nameStack): if i == spec: self[spec] = True break elif i == ")": break self.update( methinfo ) self["line_number"] = detect_lineno(nameStack[0]) try: paren_depth_counter = 0 for i in range(0, len(nameStack)): elm = nameStack[i] if elm == "(": paren_depth_counter += 1 if elm == ")": paren_depth_counter -=1 if paren_depth_counter == 0 and nameStack[i+1] == ':': debug_print("Stripping out initializer list") nameStack = nameStack[:i+1] break except: pass paramsStack = self._params_helper1( nameStack ) debug_print( "curTemplate: %s"%curTemplate) if curTemplate: self["template"] = curTemplate debug_print( "SET self['template'] to `%s`"%self["template"]) params = [] doxyVarDesc = {} if "doxygen" in self: doxyLines = self["doxygen"].split("\n") lastParamDesc = "" for doxyLine in doxyLines: if " @param " in doxyLine or " \param " in doxyLine: try: doxyLine = doxyLine[doxyLine.find("param ") + 6:] (var, desc) = doxyLine.split(" ", 1) doxyVarDesc[var] = desc.strip() lastParamDesc = var except: pass elif " @return " in doxyLine or " \return " in doxyLine: lastParamDesc = "" elif lastParamDesc: try: doxyLine = doxyLine.strip() if " " not in doxyLine: lastParamDesc = "" continue doxyLine = doxyLine[doxyLine.find(" ") + 1:] doxyVarDesc[lastParamDesc] += " " + doxyLine except: pass while (len(paramsStack)): open_template_count = 0 param_separator = 0 i = 0 for elm in paramsStack: if '<' in elm : open_template_count += 1 elif '>' in elm: open_template_count -= 1 elif elm == ',' and open_template_count == 0: param_separator = i break i += 1 if param_separator: param = CppVariable(paramsStack[0:param_separator], doxyVarDesc=doxyVarDesc) if len(list(param.keys())): params.append(param) paramsStack = paramsStack[param_separator + 1:] else: param = CppVariable(paramsStack, doxyVarDesc=doxyVarDesc) if len(list(param.keys())): params.append(param) break self["parameters"] = params #self._params_helper2( params ) # mods params inplace def __str__(self): filter_keys = ("parent", "defined", "operator", "returns_reference") cpy = dict((k,v) for (k,v) in list(self.items()) if k not in filter_keys) return "%s"%cpy class _CppVariable(dict): def _name_stack_helper( self, stack ): stack = list(stack) if '=' not in stack: # TODO refactor me # check for array[n] and deal with funny array syntax: "int myvar:99" array = [] while stack and stack[-1].isdigit(): array.append( stack.pop() ) if array: array.reverse(); self['array'] = int(''.join(array)) if stack and stack[-1].endswith(':'): stack[-1] = stack[-1][:-1] while stack and not stack[-1]: stack.pop() # can be empty return stack def init(self): #assert self['name'] # allow unnamed variables, methods like this: "void func(void);" a = [] self['aliases'] = []; self['parent'] = None; self['typedef'] = None for key in 'constant reference pointer static typedefs class fundamental unresolved'.split(): self[ key ] = 0 for b in self['type'].split(): if b == '__const__': b = 'const' a.append( b ) self['type'] = ' '.join( a ) class CppVariable( _CppVariable ): Vars = [] def __init__(self, nameStack, **kwargs): debug_print("trace %s"%nameStack) if len(nameStack) and nameStack[0] == "extern": self['extern'] = True del nameStack[0] else: self['extern'] = False _stack_ = nameStack if "[" in nameStack: #strip off array informatin arrayStack = nameStack[nameStack.index("["):] if nameStack.count("[") > 1: debug_print("Multi dimensional array") debug_print("arrayStack=%s"%arrayStack) nums = filter(lambda x: x.isdigit(), arrayStack) # Calculate size by multiplying all dimensions p = 1 for n in nums: p *= int(n) #Multi dimensional array self["array_size"] = p self["multi_dimensional_array"] = 1 self["multi_dimensional_array_size"] = "x".join(nums) else: debug_print("Array") if len(arrayStack) == 3: self["array_size"] = arrayStack[1] nameStack = nameStack[:nameStack.index("[")] self["array"] = 1 else: self["array"] = 0 nameStack = self._name_stack_helper( nameStack ) global doxygenCommentCache if len(doxygenCommentCache): self["doxygen"] = doxygenCommentCache doxygenCommentCache = "" debug_print( "Variable: %s"%nameStack ) self["line_number"] = detect_lineno(nameStack[0]) self["function_pointer"] = 0 if (len(nameStack) < 2): # +++ if len(nameStack) == 1: self['type'] = nameStack[0]; self['name'] = '' else: error_print(_stack_); assert 0 elif is_function_pointer_stack(nameStack): #function pointer self["type"] = " ".join(nameStack[:nameStack.index("(") + 2] + nameStack[nameStack.index(")") :]) self["name"] = " ".join(nameStack[nameStack.index("(") + 2 : nameStack.index(")")]) self["function_pointer"] = 1 elif ("=" in nameStack): self["type"] = " ".join(nameStack[:nameStack.index("=") - 1]) self["name"] = nameStack[nameStack.index("=") - 1] self["defaultValue"] = " ".join(nameStack[nameStack.index("=") + 1:]) # deprecate camelCase in dicts self['default'] = " ".join(nameStack[nameStack.index("=") + 1:]) elif is_fundamental(nameStack[-1]) or nameStack[-1] in ['>', '<' , ':', '.']: #Un named parameter self["type"] = " ".join(nameStack) self["name"] = "" else: # common case self["type"] = " ".join(nameStack[:-1]) self["name"] = nameStack[-1] self["type"] = self["type"].replace(" :",":") self["type"] = self["type"].replace(": ",":") self["type"] = self["type"].replace(" <","<") self["type"] = self["type"].replace(" >",">").replace(">>", "> >").replace(">>", "> >") self["type"] = self["type"].replace(" ,",",") #Optional doxygen description try: self["desc"] = kwargs["doxyVarDesc"][self["name"]] except: pass self.init() CppVariable.Vars.append( self ) # save and resolve later def __str__(self): keys_white_list = ['constant','name','reference','type','static','pointer','desc', 'line_number', 'extern'] cpy = dict((k,v) for (k,v) in list(self.items()) if k in keys_white_list) if "array_size" in self: cpy["array_size"] = self["array_size"] return "%s"%cpy class _CppEnum(dict): def resolve_enum_values( self, values ): t = int; i = 0 names = [ v['name'] for v in values ] for v in values: if 'value' in v: a = v['value'].strip() # Remove single quotes from single quoted chars (unless part of some expression if len(a) == 3 and a[0] == "'" and a[2] == "'": a = v['value'] = a[1] if a.lower().startswith("0x"): try: i = a = int(a , 16) except:pass elif a.isdigit(): i = a = int( a ) elif a in names: for other in values: if other['name'] == a: v['value'] = other['value'] break elif '"' in a or "'" in a: t = str # only if there are quotes it this a string enum else: try: a = i = ord(a) except: pass #Allow access of what is in the file pre-convert if converted if v['value'] != str(a): v['raw_value'] = v['value'] v['value'] = a else: v['value'] = i try: v['value'] = v['value'].replace(" < < ", " << ").replace(" >> ", " >> ") except: pass i += 1 return t class CppEnum(_CppEnum): def __init__(self, nameStack): global doxygenCommentCache if len(doxygenCommentCache): self["doxygen"] = doxygenCommentCache doxygenCommentCache = "" if len(nameStack) == 3 and nameStack[0] == "enum": debug_print("Created enum as just name/value") self["name"] = nameStack[1] self["instances"]=[nameStack[2]] if len(nameStack) < 4 or "{" not in nameStack or "}" not in nameStack: #Not enough stuff for an enum debug_print("Bad enum") return valueList = [] self["line_number"] = detect_lineno(nameStack[0]) #Figure out what values it has valueStack = nameStack[nameStack.index('{') + 1: nameStack.index('}')] while len(valueStack): tmpStack = [] if "," in valueStack: tmpStack = valueStack[:valueStack.index(",")] valueStack = valueStack[valueStack.index(",") + 1:] else: tmpStack = valueStack valueStack = [] d = {} if len(tmpStack) == 1: d["name"] = tmpStack[0] elif len(tmpStack) >= 3 and tmpStack[1] == "=": d["name"] = tmpStack[0]; d["value"] = " ".join(tmpStack[2:]) elif len(tmpStack) == 2 and tmpStack[1] == "=": debug_print( "WARN-enum: parser missed value for %s"%tmpStack[0] ) d["name"] = tmpStack[0] if d: valueList.append( d ) if len(valueList): self['type'] = self.resolve_enum_values( valueList ) # returns int for standard enum self["values"] = valueList else: warning_print( 'WARN-enum: empty enum %s'%nameStack ) return #Figure out if it has a name preBraceStack = nameStack[:nameStack.index("{")] postBraceStack = nameStack[nameStack.index("}") + 1:] self["typedef"] = False if (len(preBraceStack) == 2 and "typedef" not in nameStack): self["name"] = preBraceStack[1] elif len(postBraceStack) and "typedef" in nameStack: self["name"] = " ".join(postBraceStack) self["typedef"] = True else: warning_print( 'WARN-enum: nameless enum %s'%nameStack ) #See if there are instances of this if "typedef" not in nameStack and len(postBraceStack): self["instances"] = [] for var in postBraceStack: if "," in var: continue self["instances"].append(var) self["namespace"] = "" class CppStruct(dict): Structs = [] def __init__(self, nameStack): if len(nameStack) >= 2: self['type'] = nameStack[1] else: self['type'] = None self['fields'] = [] self.Structs.append( self ) global curLine self["line_number"] = curLine C99_NONSTANDARD = { 'int8' : 'signed char', 'int16' : 'short int', 'int32' : 'int', 'int64' : 'int64_t', # this can be: long int (64bit), or long long int (32bit) 'uint' : 'unsigned int', 'uint8' : 'unsigned char', 'uint16' : 'unsigned short int', 'uint32' : 'unsigned int', 'uint64' : 'uint64_t', # depends on host bits } def standardize_fundamental( s ): if s in C99_NONSTANDARD: return C99_NONSTANDARD[ s ] else: return s class Resolver(object): C_FUNDAMENTAL = 'size_t unsigned signed bool char wchar short int float double long void'.split() C_FUNDAMENTAL += 'struct union enum'.split() SubTypedefs = {} # TODO deprecate? NAMESPACES = [] CLASSES = {} STRUCTS = {} def initextra(self): self.typedefs = {} self.typedefs_order = [] self.classes_order = [] self.structs = Resolver.STRUCTS self.structs_order = [] self.namespaces = Resolver.NAMESPACES # save all namespaces self.curStruct = None self.stack = [] # full name stack, good idea to keep both stacks? (simple stack and full stack) self._classes_brace_level = {} # class name : level self._structs_brace_level = {} # struct type : level self._method_body = None self._forward_decls = [] self._template_typenames = [] # template<typename XXX> def current_namespace(self): return self.cur_namespace(True) def cur_namespace(self, add_double_colon=False): rtn = "" i = 0 while i < len(self.nameSpaces): rtn += self.nameSpaces[i] if add_double_colon or i < len(self.nameSpaces) - 1: rtn += "::" i+=1 return rtn def guess_ctypes_type( self, string ): pointers = string.count('*') string = string.replace('*','') a = string.split() if 'unsigned' in a: u = 'u' else: u = '' if 'long' in a and 'double' in a: b = 'longdouble' # there is no ctypes.c_ulongdouble (this is a 64bit float?) elif a.count('long') == 2 and 'int' in a: b = '%sint64' %u elif a.count('long') == 2: b = '%slonglong' %u elif 'long' in a: b = '%slong' %u elif 'double' in a: b = 'double' # no udouble in ctypes elif 'short' in a: b = '%sshort' %u elif 'char' in a: b = '%schar' %u elif 'wchar' in a: b = 'wchar' elif 'bool' in a: b = 'bool' elif 'float' in a: b = 'float' elif 'int' in a: b = '%sint' %u elif 'int8' in a: b = 'int8' elif 'int16' in a: b = 'int16' elif 'int32' in a: b = 'int32' elif 'int64' in a: b = 'int64' elif 'uint' in a: b = 'uint' elif 'uint8' in a: b = 'uint8' elif 'uint16' in a: b = 'uint16' elif 'uint32' in a: b = 'uint32' elif 'uint64' in a: b = 'uint64' elif 'size_t' in a: b = 'size_t' elif 'void' in a: b = 'void_p' elif string in 'struct union'.split(): b = 'void_p' # what should be done here? don't trust struct, it could be a class, no need to expose via ctypes else: b = 'void_p' if not pointers: return 'ctypes.c_%s' %b else: x = '' for i in range(pointers): x += 'ctypes.POINTER(' x += 'ctypes.c_%s' %b x += ')' * pointers return x def resolve_type( self, string, result ): # recursive ## be careful with templates, what is inside <something*> can be a pointer but the overall type is not a pointer ## these come before a template s = string.split('<')[0] result[ 'constant' ] += s.split().count('const') result[ 'static' ] += s.split().count('static') result[ 'mutable' ] = 'mutable' in s.split() ## these come after a template s = string.split('>')[-1] result[ 'pointer' ] += s.count('*') result[ 'reference' ] += s.count('&') x = string; alias = False for a in '* & const static mutable'.split(): x = x.replace(a,'') for y in x.split(): if y not in self.C_FUNDAMENTAL: alias = y; break #if alias == 'class': # result['class'] = result['name'] # forward decl of class # result['forward_decl'] = True if alias == '__extension__': result['fundamental_extension'] = True elif alias: result['aliases'].append( alias ) if alias in C99_NONSTANDARD: result['type'] = C99_NONSTANDARD[ alias ] result['typedef'] = alias result['typedefs'] += 1 elif alias in self.typedefs: result['typedefs'] += 1 result['typedef'] = alias self.resolve_type( self.typedefs[alias], result ) elif alias in self.classes: klass = self.classes[alias]; result['fundamental'] = False result['class'] = klass result['unresolved'] = False else: result['unresolved'] = True else: result['fundamental'] = True result['unresolved'] = False def finalize_vars(self): for s in CppStruct.Structs: # vars within structs can be ignored if they do not resolve for var in s['fields']: var['parent'] = s['type'] #for c in self.classes.values(): # for var in c.get_all_properties(): var['parent'] = c['name'] ## RESOLVE ## for var in CppVariable.Vars: self.resolve_type( var['type'], var ) #if 'method' in var and var['method']['name'] == '_notifyCurrentCamera': print(var); assert 0 # then find concrete type and best guess ctypes type # for var in CppVariable.Vars: if not var['aliases']: #var['fundamental']: var['ctypes_type'] = self.guess_ctypes_type( var['type'] ) else: var['unresolved'] = False # below may test to True if var['class']: var['ctypes_type'] = 'ctypes.c_void_p' else: assert var['aliases'] tag = var['aliases'][0] klass = None nestedEnum = None nestedStruct = None nestedTypedef = None if 'method' in var and 'parent' in list(var['method'].keys()): klass = var['method']['parent'] if tag in var['method']['parent']._public_enums: nestedEnum = var['method']['parent']._public_enums[ tag ] elif tag in var['method']['parent']._public_structs: nestedStruct = var['method']['parent']._public_structs[ tag ] elif tag in var['method']['parent']._public_typedefs: nestedTypedef = var['method']['parent']._public_typedefs[ tag ] if '<' in tag: # should also contain '>' var['template'] = tag # do not resolve templates var['ctypes_type'] = 'ctypes.c_void_p' var['unresolved'] = True elif nestedEnum: enum = nestedEnum if enum['type'] is int: var['ctypes_type'] = 'ctypes.c_int' var['raw_type'] = 'int' elif enum['type'] is str: var['ctypes_type'] = 'ctypes.c_char_p' var['raw_type'] = 'char*' var['enum'] = var['method']['path'] + '::' + enum['name'] var['fundamental'] = True elif nestedStruct: var['ctypes_type'] = 'ctypes.c_void_p' var['raw_type'] = var['method']['path'] + '::' + nestedStruct['type'] var['fundamental'] = False elif nestedTypedef: var['fundamental'] = is_fundamental( nestedTypedef ) if not var['fundamental']: var['raw_type'] = var['method']['path'] + '::' + tag else: _tag = tag if '::' in tag and tag.split('::')[0] in self.namespaces: tag = tag.split('::')[-1] con = self.concrete_typedef( _tag ) if con: var['concrete_type'] = con var['ctypes_type'] = self.guess_ctypes_type( var['concrete_type'] ) elif tag in self.structs: trace_print( 'STRUCT', var ) var['struct'] = tag var['ctypes_type'] = 'ctypes.c_void_p' var['raw_type'] = self.structs[tag]['namespace'] + '::' + tag elif tag in self._forward_decls: var['forward_declared'] = tag var['ctypes_type'] = 'ctypes.c_void_p' elif tag in self.global_enums: enum = self.global_enums[ tag ] if enum['type'] is int: var['ctypes_type'] = 'ctypes.c_int' var['raw_type'] = 'int' elif enum['type'] is str: var['ctypes_type'] = 'ctypes.c_char_p' var['raw_type'] = 'char*' var['enum'] = enum['namespace'] + enum['name'] var['fundamental'] = True elif var['parent']: warning_print( 'WARN unresolved %s'%_tag) var['ctypes_type'] = 'ctypes.c_void_p' var['unresolved'] = True elif tag.count('::')==1: trace_print( 'trying to find nested something in', tag ) a = tag.split('::')[0] b = tag.split('::')[-1] if a in self.classes: # a::b is most likely something nested in a class klass = self.classes[ a ] if b in klass._public_enums: trace_print( '...found nested enum', b ) enum = klass._public_enums[ b ] if enum['type'] is int: var['ctypes_type'] = 'ctypes.c_int' var['raw_type'] = 'int' elif enum['type'] is str: var['ctypes_type'] = 'ctypes.c_char_p' var['raw_type'] = 'char*' try: if 'method' in var: var['enum'] = var['method']['path'] + '::' + enum['name'] else: # class property var['unresolved'] = True except: var['unresolved'] = True var['fundamental'] = True else: var['unresolved'] = True # TODO klass._public_xxx elif a in self.namespaces: # a::b can also be a nested namespace if b in self.global_enums: enum = self.global_enums[ b ] trace_print(enum) trace_print(var) assert 0 elif b in self.global_enums: # falling back, this is a big ugly enum = self.global_enums[ b ] assert a in enum['namespace'].split('::') if enum['type'] is int: var['ctypes_type'] = 'ctypes.c_int' var['raw_type'] = 'int' elif enum['type'] is str: var['ctypes_type'] = 'ctypes.c_char_p' var['raw_type'] = 'char*' var['fundamental'] = True else: # boost::gets::crazy trace_print('NAMESPACES', self.namespaces) trace_print( a, b ) trace_print( '---- boost gets crazy ----' ) var['ctypes_type'] = 'ctypes.c_void_p' var['unresolved'] = True elif 'namespace' in var and self.concrete_typedef(var['namespace']+tag): #print( 'TRYING WITH NS', var['namespace'] ) con = self.concrete_typedef( var['namespace']+tag ) if con: var['typedef'] = var['namespace']+tag var['type'] = con if 'struct' in con.split(): var['raw_type'] = var['typedef'] var['ctypes_type'] = 'ctypes.c_void_p' else: self.resolve_type( var['type'], var ) var['ctypes_type'] = self.guess_ctypes_type( var['type'] ) elif '::' in var: var['ctypes_type'] = 'ctypes.c_void_p' var['unresolved'] = True elif tag in self.SubTypedefs: # TODO remove SubTypedefs if 'property_of_class' in var or 'property_of_struct' in var: trace_print( 'class:', self.SubTypedefs[ tag ], 'tag:', tag ) var['typedef'] = self.SubTypedefs[ tag ] # class name var['ctypes_type'] = 'ctypes.c_void_p' else: trace_print( "WARN-this should almost never happen!" ) trace_print( var ); trace_print('-'*80) var['unresolved'] = True elif tag in self._template_typenames: var['typename'] = tag var['ctypes_type'] = 'ctypes.c_void_p' var['unresolved'] = True # TODO, how to deal with templates? elif tag.startswith('_'): # assume starting with underscore is not important for wrapping warning_print( 'WARN unresolved %s'%_tag) var['ctypes_type'] = 'ctypes.c_void_p' var['unresolved'] = True else: trace_print( 'WARN: unknown type', var ) assert 'property_of_class' in var or 'property_of_struct' # only allow this case var['unresolved'] = True ## if not resolved and is a method param, not going to wrap these methods ## if var['unresolved'] and 'method' in var: var['method']['unresolved_parameters'] = True # create stripped raw_type # p = '* & const static mutable'.split() # +++ new July7: "mutable" for var in CppVariable.Vars: if 'raw_type' not in var: raw = [] for x in var['type'].split(): if x not in p: raw.append( x ) var['raw_type'] = ' '.join( raw ) #if 'AutoConstantEntry' in var['raw_type']: print(var); assert 0 if var['class']: if '::' not in var['raw_type']: if not var['class']['parent']: var['raw_type'] = var['class']['namespace'] + '::' + var['raw_type'] elif var['class']['parent'] in self.classes: parent = self.classes[ var['class']['parent'] ] var['raw_type'] = parent['namespace'] + '::' + var['class']['name'] + '::' + var['raw_type'] else: var['unresolved'] = True elif '::' in var['raw_type'] and var['raw_type'].split('::')[0] not in self.namespaces: var['raw_type'] = var['class']['namespace'] + '::' + var['raw_type'] else: var['unresolved'] = True elif 'forward_declared' in var and 'namespace' in var: if '::' not in var['raw_type']: var['raw_type'] = var['namespace'] + var['raw_type'] elif '::' in var['raw_type'] and var['raw_type'].split('::')[0] in self.namespaces: pass else: trace_print('-'*80); trace_print(var); raise NotImplemented ## need full name space for classes in raw type ## if var['raw_type'].startswith( '::' ): #print(var) #print('NAMESPACE', var['class']['namespace']) #print( 'PARENT NS', var['class']['parent']['namespace'] ) #assert 0 var['unresolved'] = True if 'method' in var: var['method']['unresolved_parameters'] = True #var['raw_type'] = var['raw_type'][2:] # Take care of #defines and #pragmas etc trace_print("Processing precomp_macro_buf: %s"%self._precomp_macro_buf) for m in self._precomp_macro_buf: macro = m.replace("<CppHeaderParser_newline_temp_replacement>\\n", "\n") try: if macro.lower().startswith("#define"): trace_print("Adding #define %s"%macro) self.defines.append(macro.split(" ", 1)[1].strip()) elif macro.lower().startswith("#if") or macro.lower().startswith("#endif") or macro.lower().startswith("#else"): self.conditionals.append(macro) elif macro.lower().startswith("#pragma"): trace_print("Adding #pragma %s"%macro) self.pragmas.append(macro.split(" ", 1)[1].strip()) elif macro.lower().startswith("#include"): trace_print("Adding #include %s"%macro) self.includes.append(macro.split(" ", 1)[1].strip()) else: debug_print("Cant detect what to do with precomp macro '%s'"%macro) except: pass self._precomp_macro_buf = None def concrete_typedef( self, key ): if key not in self.typedefs: #print( 'FAILED typedef', key ) return None while key in self.typedefs: prev = key key = self.typedefs[ key ] if '<' in key or '>' in key: return prev # stop at template if key.startswith('std::'): return key # stop at std lib return key class _CppHeader( Resolver ): def finalize(self): self.finalize_vars() # finalize classes and method returns types for cls in list(self.classes.values()): for meth in cls.get_all_methods(): if meth['pure_virtual']: cls['abstract'] = True if not meth['returns_fundamental'] and meth['returns'] in C99_NONSTANDARD: meth['returns'] = C99_NONSTANDARD[meth['returns']] meth['returns_fundamental'] = True elif not meth['returns_fundamental']: # describe the return type con = None if cls['namespace'] and '::' not in meth['returns']: con = self.concrete_typedef( cls['namespace'] + '::' + meth['returns'] ) else: con = self.concrete_typedef( meth['returns'] ) if con: meth['returns_concrete'] = con meth['returns_fundamental'] = is_fundamental( con ) elif meth['returns'] in self.classes: trace_print( 'meth returns class:', meth['returns'] ) meth['returns_class'] = True elif meth['returns'] in self.SubTypedefs: meth['returns_class'] = True meth['returns_nested'] = self.SubTypedefs[ meth['returns'] ] elif meth['returns'] in cls._public_enums: enum = cls._public_enums[ meth['returns'] ] meth['returns_enum'] = enum['type'] meth['returns_fundamental'] = True if enum['type'] == int: meth['returns'] = 'int' else: meth['returns'] = 'char*' elif meth['returns'] in self.global_enums: enum = self.global_enums[ meth['returns'] ] meth['returns_enum'] = enum['type'] meth['returns_fundamental'] = True if enum['type'] == int: meth['returns'] = 'int' else: meth['returns'] = 'char*' elif meth['returns'].count('::')==1: trace_print( meth ) a,b = meth['returns'].split('::') if a in self.namespaces: if b in self.classes: klass = self.classes[ b ] meth['returns_class'] = a + '::' + b elif '<' in b and '>' in b: warning_print( 'WARN-can not return template: %s'%b ) meth['returns_unknown'] = True elif b in self.global_enums: enum = self.global_enums[ b ] meth['returns_enum'] = enum['type'] meth['returns_fundamental'] = True if enum['type'] == int: meth['returns'] = 'int' else: meth['returns'] = 'char*' else: trace_print( a, b); trace_print( meth); meth['returns_unknown'] = True # +++ elif a in self.classes: klass = self.classes[ a ] if b in klass._public_enums: trace_print( '...found nested enum', b ) enum = klass._public_enums[ b ] meth['returns_enum'] = enum['type'] meth['returns_fundamental'] = True if enum['type'] == int: meth['returns'] = 'int' else: meth['returns'] = 'char*' elif b in klass._public_forward_declares: meth['returns_class'] = True elif b in klass._public_typedefs: typedef = klass._public_typedefs[ b ] meth['returns_fundamental'] = is_fundamental( typedef ) else: trace_print( meth ) # should be a nested class, TODO fix me. meth['returns_unknown'] = True elif '::' in meth['returns']: trace_print('TODO namespace or extra nested return:', meth) meth['returns_unknown'] = True else: trace_print( 'WARN: UNKNOWN RETURN', meth['name'], meth['returns']) meth['returns_unknown'] = True if meth["returns"].startswith(": : "): meth["returns"] = meth["returns"].replace(": : ", "::") for cls in list(self.classes.values()): methnames = cls.get_all_method_names() pvm = cls.get_all_pure_virtual_methods() for d in cls['inherits']: c = d['class'] a = d['access'] # do not depend on this to be 'public' trace_print( 'PARENT CLASS:', c ) if c not in self.classes: trace_print('WARN: parent class not found') if c in self.classes and self.classes[c]['abstract']: p = self.classes[ c ] for meth in p.get_all_methods(): #p["methods"]["public"]: trace_print( '\t\tmeth', meth['name'], 'pure virtual', meth['pure_virtual'] ) if meth['pure_virtual'] and meth['name'] not in methnames: cls['abstract'] = True; break def evaluate_struct_stack(self): #print( 'eval struct stack', self.nameStack ) #if self.braceDepth != len(self.nameSpaces): return struct = CppStruct(self.nameStack) struct["namespace"] = self.cur_namespace() self.structs[ struct['type'] ] = struct self.structs_order.append( struct ) if self.curClass: struct['parent'] = self.curClass klass = self.classes[ self.curClass ] klass['structs'][self.curAccessSpecifier].append( struct ) if self.curAccessSpecifier == 'public': klass._public_structs[ struct['type'] ] = struct self.curStruct = struct self._structs_brace_level[ struct['type'] ] = self.braceDepth def parse_method_type( self, stack ): trace_print( 'meth type info', stack ) if stack[0] in ':;' and stack[1] != ':': stack = stack[1:] info = { 'debug': ' '.join(stack).replace(' : : ', '::' ).replace(' < ', '<' ).replace(' > ', '> ' ).replace(" >",">").replace(">>", "> >").replace(">>", "> >"), 'class':None, 'namespace':self.cur_namespace(add_double_colon=True), } for tag in 'defined pure_virtual operator constructor destructor extern template virtual static explicit inline friend returns returns_pointer returns_fundamental returns_class'.split(): info[tag]=False header = stack[ : stack.index('(') ] header = ' '.join( header ) header = header.replace(' : : ', '::' ) header = header.replace(' < ', '<' ) header = header.replace(' > ', '> ' ) header = header.strip() if '{' in stack: info['defined'] = True self._method_body = self.braceDepth + 1 trace_print( 'NEW METHOD WITH BODY', self.braceDepth ) elif stack[-1] == ';': info['defined'] = False self._method_body = None # not a great idea to be clearing here else: assert 0 if len(stack) > 3 and stack[-1] == ';' and stack[-2] == '0' and stack[-3] == '=': info['pure_virtual'] = True r = header.split() name = None if 'operator' in stack: # rare case op overload defined outside of class op = stack[ stack.index('operator')+1 : stack.index('(') ] op = ''.join(op) if not op: if " ".join(['operator', '(', ')', '(']) in " ".join(stack): op = "()" else: trace_print( 'Error parsing operator') return None info['operator'] = op name = 'operator' + op a = stack[ : stack.index('operator') ] elif r: name = r[-1] a = r[ : -1 ] # strip name if name is None: return None #if name.startswith('~'): name = name[1:] while a and a[0] == '}': # strip - can have multiple } } a = a[1:] if '::' in name: #klass,name = name.split('::') # methods can be defined outside of class klass = name[ : name.rindex('::') ] name = name.split('::')[-1] info['class'] = klass if klass in self.classes and not self.curClass: #Class function defined outside the class return None # info['name'] = name #else: info['name'] = name if name.startswith('~'): info['destructor'] = True name = name[1:] elif not a or (name == self.curClass and len(self.curClass)): info['constructor'] = True info['name'] = name for tag in 'extern virtual static explicit inline friend'.split(): if tag in a: info[ tag ] = True; a.remove( tag ) # inplace if 'template' in a: a.remove('template') b = ' '.join( a ) if '>' in b: info['template'] = b[ : b.index('>')+1 ] info['returns'] = b[ b.index('>')+1 : ] # find return type, could be incorrect... TODO if '<typename' in info['template'].split(): typname = info['template'].split()[-1] typname = typname[ : -1 ] # strip '>' if typname not in self._template_typenames: self._template_typenames.append( typname ) else: info['returns'] = ' '.join( a ) else: info['returns'] = ' '.join( a ) info['returns'] = info['returns'].replace(' <', '<').strip() ## be careful with templates, do not count pointers inside template info['returns_pointer'] = info['returns'].split('>')[-1].count('*') if info['returns_pointer']: info['returns'] = info['returns'].replace('*','').strip() info['returns_reference'] = '&' in info['returns'] if info['returns']: info['returns'] = info['returns'].replace('&','').strip() a = [] for b in info['returns'].split(): if b == '__const__': info['returns_const'] = True elif b == 'const': info['returns_const'] = True else: a.append( b ) info['returns'] = ' '.join( a ) info['returns_fundamental'] = is_fundamental( info['returns'] ) return info def evaluate_method_stack(self): if self.curStruct: trace_print( 'WARN - struct contains methods - skipping' ) trace_print( self.stack ) assert 0 info = self.parse_method_type( self.stack ) if info: if info[ 'class' ] and info['class'] in self.classes: # case where methods are defined outside of class newMethod = CppMethod(self.nameStack, info['name'], info, self.curTemplate) klass = self.classes[ info['class'] ] klass[ 'methods' ][ 'public' ].append( newMethod ) newMethod['parent'] = klass if klass['namespace']: newMethod['path'] = klass['namespace'] + '::' + klass['name'] else: newMethod['path'] = klass['name'] elif self.curClass: # normal case newMethod = CppMethod(self.nameStack, self.curClass, info, self.curTemplate) klass = self.classes[self.curClass] klass['methods'][self.curAccessSpecifier].append(newMethod) newMethod['parent'] = klass if klass['namespace']: newMethod['path'] = klass['namespace'] + '::' + klass['name'] else: newMethod['path'] = klass['name'] else: #non class functions debug_print("FREE FUNCTION") newMethod = CppMethod(self.nameStack, None, info, self.curTemplate) self.functions.append(newMethod) global parseHistory parseHistory.append({"braceDepth": self.braceDepth, "item_type": "method", "item": newMethod}) else: trace_print( 'free function?', self.nameStack ) self.stack = [] def _parse_typedef( self, stack, namespace='' ): if not stack or 'typedef' not in stack: return stack = list( stack ) # copy just to be safe if stack[-1] == ';': stack.pop() while stack and stack[-1].isdigit(): stack.pop() # throw away array size for now idx = stack.index('typedef') if stack[-1] == "]": try: name = namespace + "".join(stack[-4:]) # Strip off the array part so the rest of the parsing is better stack = stack[:-3] except: name = namespace + stack[-1] else: name = namespace + stack[-1] s = '' for a in stack[idx+1:-1]: if a == '{': break if not s or s[-1] in ':<>' or a in ':<>': s += a # keep compact else: s += ' ' + a # spacing r = {'name':name, 'raw':s, 'type':s} if not is_fundamental(s): if 'struct' in s.split(): pass # TODO is this right? "struct ns::something" elif '::' not in s: s = namespace + s # only add the current name space if no namespace given r['type'] = s if s: return r def evaluate_typedef(self): ns = self.cur_namespace(add_double_colon=True) res = self._parse_typedef( self.stack, ns ) if res: name = res['name'] self.typedefs[ name ] = res['type'] if name not in self.typedefs_order: self.typedefs_order.append( name ) def evaluate_property_stack(self): global parseHistory assert self.stack[-1] == ';' debug_print( "trace" ) if self.nameStack[0] == 'typedef': if self.curClass: typedef = self._parse_typedef( self.stack ) name = typedef['name'] klass = self.classes[ self.curClass ] klass[ 'typedefs' ][ self.curAccessSpecifier ].append( name ) if self.curAccessSpecifier == 'public': klass._public_typedefs[ name ] = typedef['type'] Resolver.SubTypedefs[ name ] = self.curClass else: assert 0 elif self.curStruct or self.curClass: if len(self.nameStack) == 1: #See if we can de anonymize the type filteredParseHistory = [h for h in parseHistory if h["braceDepth"] == self.braceDepth] if len(filteredParseHistory) and filteredParseHistory[-1]["item_type"] == "class": self.nameStack.insert(0, filteredParseHistory[-1]["item"]["name"]) debug_print("DEANONYMOIZING %s to type '%s'"%(self.nameStack[1], self.nameStack[0])) if "," in self.nameStack: #Maybe we have a variable list #Figure out what part is the variable separator but remember templates of function pointer #First find left most comma outside of a > and ) leftMostComma = 0; for i in range(0, len(self.nameStack)): name = self.nameStack[i] if name in (">", ")"): leftMostComma = 0 if leftMostComma == 0 and name == ",": leftMostComma = i # Is it really a list of variables? if leftMostComma != 0: trace_print("Multiple variables for namestack in %s. Separating processing"%self.nameStack) orig_nameStack = self.nameStack[:] orig_stack = self.stack[:] type_nameStack = orig_nameStack[:leftMostComma-1] for name in orig_nameStack[leftMostComma - 1::2]: self.nameStack = type_nameStack + [name] self.stack = orig_stack[:] # Not maintained for mucking, but this path it doesnt matter self.evaluate_property_stack() return newVar = CppVariable(self.nameStack) newVar['namespace'] = self.current_namespace() if self.curStruct: self.curStruct[ 'fields' ].append( newVar ) newVar['property_of_struct'] = self.curStruct elif self.curClass: klass = self.classes[self.curClass] klass["properties"][self.curAccessSpecifier].append(newVar) newVar['property_of_class'] = klass['name'] parseHistory.append({"braceDepth": self.braceDepth, "item_type": "variable", "item": newVar}) else: debug_print( "Found Global variable" ) newVar = CppVariable(self.nameStack) self.variables.append(newVar) self.stack = [] # CLEAR STACK def evaluate_class_stack(self): #dont support sub classes today #print( 'eval class stack', self.nameStack ) parent = self.curClass if self.braceDepth > len( self.nameSpaces) and parent: trace_print( 'HIT NESTED SUBCLASS' ) self.accessSpecifierStack.append(self.curAccessSpecifier) elif self.braceDepth != len(self.nameSpaces): error_print( 'ERROR: WRONG BRACE DEPTH' ) return # When dealing with typedefed structs, get rid of typedef keyword to handle later on if self.nameStack[0] == "typedef": del self.nameStack[0] if len(self.nameStack) == 1: self.anon_struct_counter += 1 # We cant handle more than 1 anonymous struct, so name them uniquely self.nameStack.append("<anon-struct-%d>"%self.anon_struct_counter) if self.nameStack[0] == "class": self.curAccessSpecifier = 'private' else:#struct self.curAccessSpecifier = 'public' debug_print("curAccessSpecifier changed/defaulted to %s"%self.curAccessSpecifier) if self.nameStack[0] == "union": newClass = CppUnion(self.nameStack) self.anon_union_counter = [self.braceDepth, 2] trace_print( 'NEW UNION', newClass['name'] ) else: newClass = CppClass(self.nameStack, self.curTemplate) trace_print( 'NEW CLASS', newClass['name'] ) newClass["declaration_method"] = self.nameStack[0] self.classes_order.append( newClass ) # good idea to save ordering self.stack = [] # fixes if class declared with ';' in closing brace if parent: newClass["namespace"] = self.classes[ parent ]['namespace'] + '::' + parent newClass['parent'] = parent self.classes[ parent ]['nested_classes'].append( newClass ) ## supports nested classes with the same name ## self.curClass = key = parent+'::'+newClass['name'] self._classes_brace_level[ key ] = self.braceDepth elif newClass['parent']: # nested class defined outside of parent. A::B {...} parent = newClass['parent'] newClass["namespace"] = self.classes[ parent ]['namespace'] + '::' + parent self.classes[ parent ]['nested_classes'].append( newClass ) ## supports nested classes with the same name ## self.curClass = key = parent+'::'+newClass['name'] self._classes_brace_level[ key ] = self.braceDepth else: newClass["namespace"] = self.cur_namespace() key = newClass['name'] self.curClass = newClass["name"] self._classes_brace_level[ newClass['name'] ] = self.braceDepth if not key.endswith("::") and not key.endswith(" ") and len(key) != 0: if key in self.classes: trace_print( 'ERROR name collision:', key ) self.classes[key].show() trace_print('-'*80) newClass.show() assert key not in self.classes # namespace collision self.classes[ key ] = newClass global parseHistory parseHistory.append({"braceDepth": self.braceDepth, "item_type": "class", "item": newClass}) def evalute_forward_decl(self): trace_print( 'FORWARD DECL', self.nameStack ) assert self.nameStack[0] in ('class', 'struct') name = self.nameStack[-1] if self.curClass: klass = self.classes[ self.curClass ] klass['forward_declares'][self.curAccessSpecifier].append( name ) if self.curAccessSpecifier == 'public': klass._public_forward_declares.append( name ) else: self._forward_decls.append( name ) class CppHeader( _CppHeader ): IGNORE_NAMES = '__extension__'.split() def show(self): for className in list(self.classes.keys()):self.classes[className].show() def __init__(self, headerFileName, argType="file", **kwargs): ## reset global state ## global doxygenCommentCache doxygenCommentCache = "" CppVariable.Vars = [] CppStruct.Structs = [] if (argType == "file"): self.headerFileName = os.path.expandvars(headerFileName) self.mainClass = os.path.split(self.headerFileName)[1][:-2] headerFileStr = "" elif argType == "string": self.headerFileName = "" self.mainClass = "???" headerFileStr = headerFileName else: raise Exception("Arg type must be either file or string") self.curClass = "" # nested classes have parent::nested, but no extra namespace, # this keeps the API compatible, TODO proper namespace for everything. Resolver.CLASSES = {} self.classes = Resolver.CLASSES #Functions that are not part of a class self.functions = [] self.pragmas = [] self.defines = [] self.includes = [] self.conditionals = [] self._precomp_macro_buf = [] #for internal purposes, will end up filling out pragmras and defines at the end self.enums = [] self.variables = [] self.global_enums = {} self.nameStack = [] self.nameSpaces = [] self.curAccessSpecifier = 'private' # private is default self.curTemplate = None self.accessSpecifierStack = [] self.accessSpecifierScratch = [] debug_print("curAccessSpecifier changed/defaulted to %s"%self.curAccessSpecifier) self.initextra() # Old namestacks for a given level self.nameStackHistory = [] self.anon_struct_counter = 0 self.anon_union_counter = [-1, 0] self.templateRegistry = [] if (len(self.headerFileName)): fd = open(self.headerFileName) headerFileStr = "".join(fd.readlines()) fd.close() # Make sure supportedAccessSpecifier are sane for i in range(0, len(supportedAccessSpecifier)): if " " not in supportedAccessSpecifier[i]: continue supportedAccessSpecifier[i] = re.sub("[ ]+", " ", supportedAccessSpecifier[i]).strip() # Strip out template declarations templateSectionsToSliceOut = [] try: for m in re.finditer("template[\t ]*<[^>]*>", headerFileStr): start = m.start() # Search for the final '>' which may or may not be caught in the case of nexted <>'s for i in range(start, len(headerFileStr)): if headerFileStr[i] == '<': firstBracket = i break ltgtStackCount = 1 #Now look for fianl '>' for i in range(firstBracket + 1, len(headerFileStr)): if headerFileStr[i] == '<': ltgtStackCount += 1 elif headerFileStr[i] == '>': ltgtStackCount -= 1 if ltgtStackCount == 0: end = i break templateSectionsToSliceOut.append((start, end)) # Now strip out all instances of the template templateSectionsToSliceOut.reverse() for tslice in templateSectionsToSliceOut: # Replace the template symbol with a single symbol template_symbol="CppHeaderParser_template_%d"%len(self.templateRegistry) self.templateRegistry.append(headerFileStr[tslice[0]: tslice[1]+1]) newlines = headerFileStr[tslice[0]: tslice[1]].count("\n") * "\n" #Keep line numbers the same headerFileStr = headerFileStr[:tslice[0]] + newlines + " " + template_symbol + " " + headerFileStr[tslice[1] + 1:] except: pass # Change multi line #defines and expressions to single lines maintaining line nubmers # Based from http://stackoverflow.com/questions/2424458/regular-expression-to-match-cs-multiline-preprocessor-statements matches = re.findall(r'(?m)^(?:.*\\\r?\n)+.*$', headerFileStr) is_define = re.compile(r'[ \t\v]*#[Dd][Ee][Ff][Ii][Nn][Ee]') for m in matches: #Keep the newlines so that linecount doesnt break num_newlines = len([a for a in m if a=="\n"]) if is_define.match(m): new_m = m.replace("\n", "<CppHeaderParser_newline_temp_replacement>\\n") else: # Just expression taking up multiple lines, make it take 1 line for easier parsing new_m = m.replace("\\\n", " ") if (num_newlines > 0): new_m += "\n"*(num_newlines) headerFileStr = headerFileStr.replace(m, new_m) #Filter out Extern "C" statements. These are order dependent matches = re.findall(re.compile(r'extern[\t ]+"[Cc]"[\t \n\r]*{', re.DOTALL), headerFileStr) for m in matches: #Keep the newlines so that linecount doesnt break num_newlines = len([a for a in m if a=="\n"]) headerFileStr = headerFileStr.replace(m, "\n" * num_newlines) headerFileStr = re.sub(r'extern[ ]+"[Cc]"[ ]*', "", headerFileStr) #Filter out any ignore symbols that end with "()" to account for #define magic functions for ignore in ignoreSymbols: if not ignore.endswith("()"): continue while True: locStart = headerFileStr.find(ignore[:-1]) if locStart == -1: break; locEnd = None #Now walk till we find the last paren and account for sub parens parenCount = 1 inQuotes = False for i in range(locStart + len(ignore) - 1, len(headerFileStr)): c = headerFileStr[i] if not inQuotes: if c == "(": parenCount += 1 elif c == ")": parenCount -= 1 elif c == '"': inQuotes = True if parenCount == 0: locEnd = i + 1 break; else: if c == '"' and headerFileStr[i-1] != '\\': inQuotes = False if locEnd: #Strip it out but keep the linecount the same so line numbers are right match_str = headerFileStr[locStart:locEnd] debug_print("Striping out '%s'"%match_str) num_newlines = len([a for a in match_str if a=="\n"]) headerFileStr = headerFileStr.replace(headerFileStr[locStart:locEnd], "\n"*num_newlines) self.braceDepth = 0 lex.lex() lex.input(headerFileStr) global curLine global curChar curLine = 0 curChar = 0 try: while True: tok = lex.token() if not tok: break if self.anon_union_counter[0] == self.braceDepth and self.anon_union_counter[1]: self.anon_union_counter[1] -= 1 tok.value = TagStr(tok.value, lineno=tok.lineno) #debug_print("TOK: %s"%tok) if tok.type == 'NAME' and tok.value in self.IGNORE_NAMES: continue if tok.type != 'TEMPLATE_NAME': self.stack.append( tok.value ) curLine = tok.lineno curChar = tok.lexpos if (tok.type in ('PRECOMP_MACRO', 'PRECOMP_MACRO_CONT')): debug_print("PRECOMP: %s"%tok) self._precomp_macro_buf.append(tok.value) self.stack = [] self.nameStack = [] continue if tok.type == 'TEMPLATE_NAME': try: templateId = int(tok.value.replace("CppHeaderParser_template_","")) self.curTemplate = self.templateRegistry[templateId] except: pass if (tok.type == 'OPEN_BRACE'): if len(self.nameStack) >= 2 and is_namespace(self.nameStack): # namespace {} with no name used in boost, this sets default? if self.nameStack[1] == "__IGNORED_NAMESPACE__CppHeaderParser__":#Used in filtering extern "C" self.nameStack[1] = "" self.nameSpaces.append(self.nameStack[1]) ns = self.cur_namespace(); self.stack = [] if ns not in self.namespaces: self.namespaces.append( ns ) # Detect special condition of macro magic before class declaration so we # can filter it out if 'class' in self.nameStack and self.nameStack[0] != 'class': classLocationNS = self.nameStack.index("class") classLocationS = self.stack.index("class") if "(" not in self.nameStack[classLocationNS:]: debug_print("keyword 'class' found in unexpected location in nameStack, must be following origNameStack = self.nameStack origStack = self.stack #Process first part of stack which is probably #define macro magic and may cause issues self.nameStack = self.nameStack[:classLocationNS] self.stack = self.stack[:classLocationS] try: self.evaluate_stack() except: debug_print("Error processing #Process rest of stack self.nameStack = origNameStack[classLocationNS:] self.stack = origStack[classLocationS:] if len(self.nameStack) and not is_enum_namestack(self.nameStack): self.evaluate_stack() else: self.nameStack.append(tok.value) if self.stack and self.stack[0] == 'class': self.stack = [] self.braceDepth += 1 elif (tok.type == 'CLOSE_BRACE'): if self.braceDepth == 0: continue if (self.braceDepth == len(self.nameSpaces)): tmp = self.nameSpaces.pop() self.stack = [] # clear stack when namespace ends? if len(self.nameStack) and is_enum_namestack(self.nameStack): self.nameStack.append(tok.value) elif self.braceDepth < 10: self.evaluate_stack() else: self.nameStack = [] self.braceDepth -= 1 #self.stack = []; print 'BRACE DEPTH', self.braceDepth, 'NS', len(self.nameSpaces) if self.curClass: debug_print( 'CURBD %s'%self._classes_brace_level[ self.curClass ] ) if (self.braceDepth == 0) or (self.curClass and self._classes_brace_level[self.curClass]==self.braceDepth): trace_print( 'END OF CLASS DEF' ) if self.accessSpecifierStack: self.curAccessSpecifier = self.accessSpecifierStack[-1] self.accessSpecifierStack = self.accessSpecifierStack[:-1] if self.curClass and self.classes[ self.curClass ]['parent']: self.curClass = self.classes[ self.curClass ]['parent'] else: self.curClass = ""; #self.curStruct = None self.stack = [] #if self.curStruct: self.curStruct = None if self.braceDepth == 0 or (self.curStruct and self._structs_brace_level[self.curStruct['type']]==self.braceDepth): trace_print( 'END OF STRUCT DEF' ) self.curStruct = None if self._method_body and (self.braceDepth + 1) <= self._method_body: self._method_body = None; self.stack = []; self.nameStack = []; trace_print( 'FORCE CLEAR METHBODY' ) if (tok.type == 'OPEN_PAREN'): self.nameStack.append(tok.value) elif (tok.type == 'CLOSE_PAREN'): self.nameStack.append(tok.value) elif (tok.type == 'OPEN_SQUARE_BRACKET'): self.nameStack.append(tok.value) elif (tok.type == 'CLOSE_SQUARE_BRACKET'): self.nameStack.append(tok.value) elif (tok.type == 'TAB'): pass elif (tok.type == 'EQUALS'): self.nameStack.append(tok.value) elif (tok.type == 'COMMA'): self.nameStack.append(tok.value) elif (tok.type == 'BACKSLASH'): self.nameStack.append(tok.value) elif (tok.type == 'DIVIDE'): self.nameStack.append(tok.value) elif (tok.type == 'PIPE'): self.nameStack.append(tok.value) elif (tok.type == 'PERCENT'): self.nameStack.append(tok.value) elif (tok.type == 'CARET'): self.nameStack.append(tok.value) elif (tok.type == 'EXCLAMATION'): self.nameStack.append(tok.value) elif (tok.type == 'SQUOTE'): pass elif (tok.type == 'NUMBER' or tok.type == 'FLOAT_NUMBER'): self.nameStack.append(tok.value) elif (tok.type == 'MINUS'): self.nameStack.append(tok.value) elif (tok.type == 'PLUS'): self.nameStack.append(tok.value) elif (tok.type == 'STRING_LITERAL'): self.nameStack.append(tok.value) elif (tok.type == 'NAME' or tok.type == 'AMPERSTAND' or tok.type == 'ASTERISK' or tok.type == 'CHAR_LITERAL'): if tok.value in ignoreSymbols: debug_print("Ignore symbol %s"%tok.value) elif (tok.value == 'class'): self.nameStack.append(tok.value) elif tok.value in supportedAccessSpecifier: if len(self.nameStack) and self.nameStack[0] in ("class", "struct", "union"): self.nameStack.append(tok.value) elif self.braceDepth == len(self.nameSpaces) + 1 or self.braceDepth == (len(self.nameSpaces) + len(self.curClass.split("::"))): self.curAccessSpecifier = tok.value; self.accessSpecifierScratch.append(tok.value) debug_print("curAccessSpecifier updated to %s"%self.curAccessSpecifier) self.stack = [] else: self.nameStack.append(tok.value) if self.anon_union_counter[0] == self.braceDepth: self.anon_union_counter = [-1, 0] elif (tok.type == 'COLON'): #Dont want colon to be first in stack if len(self.nameStack) == 0: self.accessSpecifierScratch = [] continue # Handle situation where access specifiers can be multi words such as "public slots" jns = " ".join(self.accessSpecifierScratch + self.nameStack) if jns in supportedAccessSpecifier: self.curAccessSpecifier = jns; debug_print("curAccessSpecifier updated to %s"%self.curAccessSpecifier) self.stack = [] self.nameStack = [] else: self.nameStack.append(tok.value) self.accessSpecifierScratch = [] elif (tok.type == 'SEMI_COLON'): if self.anon_union_counter[0] == self.braceDepth and self.anon_union_counter[1]: debug_print("Creating anonymous union") #Force the processing of an anonymous union saved_namestack = self.nameStack[:] saved_stack = self.stack[:] self.nameStack = [""] self.stack = self.nameStack + [";"] self.nameStack = self.nameStack[0:1] debug_print("pre eval anon stack") self.evaluate_stack( tok.type ) debug_print("post eval anon stack") self.nameStack = saved_namestack self.stack = saved_stack self.anon_union_counter = [-1, 0]; if (self.braceDepth < 10): self.evaluate_stack( tok.type ) self.stack = [] self.nameStack = [] except: if (debug): raise raise CppParseError("Not able to parse %s on line %d evaluating \"%s\"\nError around: %s" % (self.headerFileName, tok.lineno, tok.value, " ".join(self.nameStack))) self.finalize() global parseHistory parseHistory = [] # Delete some temporary variables for key in ["_precomp_macro_buf", "nameStack", "nameSpaces", "curAccessSpecifier", "accessSpecifierStack", "accessSpecifierScratch", "nameStackHistory", "anon_struct_counter", "anon_union_counter", "_classes_brace_level", "_forward_decls", "stack", "mainClass", "curStruct", "_template_typenames", "_method_body", "braceDepth", "_structs_brace_level", "typedefs_order", "curTemplate", "templateRegistry"]: del self.__dict__[key] def evaluate_stack(self, token=None): global doxygenCommentCache self.nameStack = filter_out_attribute_keyword(self.nameStack) self.stack = filter_out_attribute_keyword(self.stack) nameStackCopy = self.nameStack[:] debug_print( "Evaluating stack %s\n BraceDepth: %s (called from %d)" %(self.nameStack,self.braceDepth, inspect.currentframe().f_back.f_lineno)) #Handle special case of overloading operator () if "operator()(" in "".join(self.nameStack): operator_index = self.nameStack.index("operator") self.nameStack.pop(operator_index + 2) self.nameStack.pop(operator_index + 1) self.nameStack[operator_index] = "operator()" if (len(self.curClass)): debug_print( "%s (%s) "%(self.curClass, self.curAccessSpecifier)) else: debug_print( "<anonymous> (%s) "%self.curAccessSpecifier) #Filter special case of array with casting in it try: bracePos = self.nameStack.index("[") parenPos = self.nameStack.index("(") if bracePos == parenPos - 1: endParen = self.nameStack.index(")") self.nameStack = self.nameStack[:bracePos + 1] + self.nameStack[endParen + 1:] debug_print("Filtered namestack to=%s"%self.nameStack) except: pass #if 'typedef' in self.nameStack: self.evaluate_typedef() # allows nested typedefs, probably a bad idea if (not self.curClass and 'typedef' in self.nameStack and (('struct' not in self.nameStack and 'union' not in self.nameStack) or self.stack[-1] == ";") and not is_enum_namestack(self.nameStack)): trace_print('STACK', self.stack) self.evaluate_typedef() return elif (len(self.nameStack) == 0): debug_print( "trace" ) debug_print( "(Empty Stack)" ) return elif (self.nameStack[0] == "namespace"): #Taken care of outside of here pass elif len(self.nameStack) == 2 and self.nameStack[0] == "friend":#friend class declaration pass elif len(self.nameStack) >= 2 and self.nameStack[0] == 'using' and self.nameStack[1] == 'namespace': pass # TODO elif is_enum_namestack(self.nameStack): debug_print( "trace" ) self.evaluate_enum_stack() elif self._method_body and (self.braceDepth + 1) > self._method_body: trace_print( 'INSIDE METHOD DEF' ) elif is_method_namestack(self.stack) and not self.curStruct and '(' in self.nameStack: debug_print( "trace" ) if self.braceDepth > 0: if "{" in self.stack and self.stack[0] != '{' and self.stack[-1] == ';' and self.braceDepth == 1: #Special case of a method defined outside a class that has a body pass else: self.evaluate_method_stack() else: #Free function self.evaluate_method_stack() elif (len(self.nameStack) == 1 and len(self.nameStackHistory) > self.braceDepth and (self.nameStackHistory[self.braceDepth][0][0:2] == ["typedef", "struct"] or self.nameStackHistory[self.braceDepth][0][0:2] == ["typedef", "union"])): # Look for the name of a typedef struct: struct typedef {...] StructName; or unions to get renamed debug_print("found the naming of a union") type_name_to_rename = self.nameStackHistory[self.braceDepth][1] new_name = self.nameStack[0] type_to_rename = self.classes[type_name_to_rename] type_to_rename["name"] = self.nameStack[0] #Now re install it in its new location self.classes[new_name] = type_to_rename del self.classes[type_name_to_rename] elif is_property_namestack(self.nameStack) and self.stack[-1] == ';': debug_print( "trace" ) if self.nameStack[0] in ('class', 'struct') and len(self.stack) == 3: self.evalute_forward_decl() elif len(self.nameStack) >= 2 and (self.nameStack[0]=='friend' and self.nameStack[1]=='class'): pass else: self.evaluate_property_stack() # catches class props and structs in a namespace elif self.nameStack[0] in ("class", "struct", "union") or self.nameStack[0] == 'typedef' and self.nameStack[1] in ('struct', 'union'): #Parsing a union can reuse much of the class parsing debug_print( "trace" ) self.evaluate_class_stack() elif not self.curClass: debug_print( "trace" ) if is_enum_namestack(self.nameStack): self.evaluate_enum_stack() elif self.curStruct and self.stack[-1] == ';': self.evaluate_property_stack() # this catches fields of global structs self.nameStack = [] doxygenCommentCache = "" elif (self.braceDepth < 1): debug_print( "trace" ) #Ignore global stuff for now debug_print( "Global stuff: %s"%self.nameStack ) self.nameStack = [] doxygenCommentCache = "" elif (self.braceDepth > len(self.nameSpaces) + 1): debug_print( "trace" ) self.nameStack = [] doxygenCommentCache = "" try: self.nameStackHistory[self.braceDepth] = (nameStackCopy, self.curClass) except: self.nameStackHistory.append((nameStackCopy, self.curClass)) self.nameStack = [] # its a little confusing to have some if/else above return and others not, and then clearning the nameStack down here doxygenCommentCache = "" self.curTemplate = None def evaluate_enum_stack(self): debug_print( "evaluating enum" ) newEnum = CppEnum(self.nameStack) if len(list(newEnum.keys())): if len(self.curClass): newEnum["namespace"] = self.cur_namespace(False) klass = self.classes[self.curClass] klass["enums"][self.curAccessSpecifier].append(newEnum) if self.curAccessSpecifier == 'public' and 'name' in newEnum: klass._public_enums[ newEnum['name'] ] = newEnum else: newEnum["namespace"] = self.cur_namespace(True) self.enums.append(newEnum) if 'name' in newEnum and newEnum['name']: self.global_enums[ newEnum['name'] ] = newEnum #This enum has instances, turn them into properties if "instances" in newEnum: instanceType = "enum" if "name" in newEnum: instanceType = newEnum["name"] for instance in newEnum["instances"]: self.nameStack = [instanceType, instance] self.evaluate_property_stack() del newEnum["instances"] def strip_parent_keys(self): obj_queue = [self] while len(obj_queue): obj = obj_queue.pop() trace_print("pop %s type %s"%(obj, type(obj))) try: if "parent" in obj.keys(): del obj["parent"] trace_print("Stripped parent from %s"%obj.keys()) except: pass # Figure out what sub types are one of ours try: if not hasattr(obj, 'keys'): obj = obj.__dict__ for k in obj.keys(): trace_print("-Try key %s"%(k)) trace_print("-type %s"%(type(obj[k]))) if k in ["nameStackHistory", "parent", "_public_typedefs"]: continue if type(obj[k]) == list: for i in obj[k]: trace_print("push l %s"%i) obj_queue.append(i) elif type(obj[k]) == dict: if len(obj): trace_print("push d %s"%obj[k]) obj_queue.append(obj[k]) elif type(obj[k]) == type(type(0)): if type(obj[k]) == int: obj[k] = "int" elif type(obj[k]) == str: obj[k] = "string" else: obj[k] = "???" trace_print("next key\n") except: trace_print("Exception") def toJSON(self, indent=4): import json self.strip_parent_keys() try: del self.__dict__["classes_order"] except: pass return json.dumps(self.__dict__, indent=indent) def __repr__(self): rtn = { "classes": self.classes, "functions": self.functions, "enums": self.enums, "variables": self.variables, } return repr(rtn) def __str__(self): rtn = "" for className in list(self.classes.keys()): rtn += "%s\n"%self.classes[className] if self.functions: rtn += "// functions\n" for f in self.functions: rtn += "%s\n"%f if self.variables: rtn += "// variables\n" for f in self.variables: rtn += "%s\n"%f if self.enums: rtn += "// enums\n" for f in self.enums: rtn += "%s\n"%f return rtn

true

f7007173943c99d08791c125b906d4befe6387ea

24,923

py

Python

tensorflow/contrib/rnn/python/ops/lstm_ops.py

M155K4R4/Tensorflow

e5e03ef3148303b3dfed89a1492dedf92b45be25

[ "Apache-2.0" ]

5

2019-05-23T02:59:21.000Z

2020-02-05T08:20:23.000Z

tensorflow/contrib/rnn/python/ops/lstm_ops.py

M155K4R4/Tensorflow

e5e03ef3148303b3dfed89a1492dedf92b45be25

[ "Apache-2.0" ]

null

tensorflow/contrib/rnn/python/ops/lstm_ops.py

M155K4R4/Tensorflow

e5e03ef3148303b3dfed89a1492dedf92b45be25

[ "Apache-2.0" ]

2

2019-07-04T00:47:02.000Z

2019-07-08T08:47:05.000Z

# Copyright 2016 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """LSTM Block Cell ops.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import abc from tensorflow.contrib.rnn.ops import gen_lstm_ops from tensorflow.contrib.util import loader from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.layers import base as base_layer from tensorflow.python.ops import array_ops from tensorflow.python.ops import init_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import nn_ops from tensorflow.python.ops import rnn_cell_impl from tensorflow.python.platform import resource_loader _lstm_ops_so = loader.load_op_library( resource_loader.get_path_to_datafile("_lstm_ops.so")) LayerRNNCell = rnn_cell_impl.LayerRNNCell # pylint: disable=invalid-name # pylint: disable=invalid-name def _lstm_block_cell(x, cs_prev, h_prev, w, b, wci=None, wcf=None, wco=None, forget_bias=None, cell_clip=None, use_peephole=None, name=None): r"""Computes the LSTM cell forward propagation for 1 time step. This implementation uses 1 weight matrix and 1 bias vector, and there's an optional peephole connection. This kernel op implements the following mathematical equations: ```python xh = [x, h_prev] [i, ci, f, o] = xh * w + b f = f + forget_bias if not use_peephole: wci = wcf = wco = 0 i = sigmoid(cs_prev * wci + i) f = sigmoid(cs_prev * wcf + f) ci = tanh(ci) cs = ci .* i + cs_prev .* f cs = clip(cs, cell_clip) o = sigmoid(cs * wco + o) co = tanh(cs) h = co .* o ``` Args: x: A `Tensor`. Must be one of the following types: `float32`. The input to the LSTM cell, shape (batch_size, num_inputs). cs_prev: A `Tensor`. Must have the same type as `x`. Value of the cell state at previous time step. h_prev: A `Tensor`. Must have the same type as `x`. Output of the previous cell at previous time step. w: A `Tensor`. Must have the same type as `x`. The weight matrix. b: A `Tensor`. Must have the same type as `x`. The bias vector. wci: A `Tensor`. Must have the same type as `x`. The weight matrix for input gate peephole connection. wcf: A `Tensor`. Must have the same type as `x`. The weight matrix for forget gate peephole connection. wco: A `Tensor`. Must have the same type as `x`. The weight matrix for output gate peephole connection. forget_bias: An optional `float`. Defaults to `1`. The forget gate bias. cell_clip: An optional `float`. Defaults to `-1` (no clipping). Value to clip the 'cs' value to. Disable by setting to negative value. use_peephole: An optional `bool`. Defaults to `False`. Whether to use peephole weights. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (i, cs, f, o, ci, co, h). i: A `Tensor`. Has the same type as `x`. The input gate. cs: A `Tensor`. Has the same type as `x`. The cell state before the tanh. f: A `Tensor`. Has the same type as `x`. The forget gate. o: A `Tensor`. Has the same type as `x`. The output gate. ci: A `Tensor`. Has the same type as `x`. The cell input. co: A `Tensor`. Has the same type as `x`. The cell after the tanh. h: A `Tensor`. Has the same type as `x`. The output h vector. Raises: ValueError: If cell_size is None. """ if wci is None: cell_size = cs_prev.get_shape().with_rank(2)[1].value if cell_size is None: raise ValueError("cell_size from `cs_prev` should not be None.") wci = array_ops.constant(0, dtype=dtypes.float32, shape=[cell_size]) wcf = wci wco = wci # pylint: disable=protected-access return gen_lstm_ops.lstm_block_cell( x=x, cs_prev=cs_prev, h_prev=h_prev, w=w, wci=wci, wcf=wcf, wco=wco, b=b, forget_bias=forget_bias, cell_clip=cell_clip if cell_clip is not None else -1, use_peephole=use_peephole, name=name) # pylint: enable=protected-access def _block_lstm(seq_len_max, x, w, b, cs_prev=None, h_prev=None, wci=None, wcf=None, wco=None, forget_bias=None, cell_clip=None, use_peephole=None, name=None): r"""TODO(williamchan): add doc. Args: seq_len_max: A `Tensor` of type `int64`. x: A list of at least 1 `Tensor` objects of the same type in: `float32`. w: A `Tensor`. Must have the same type as `x`. b: A `Tensor`. Must have the same type as `x`. cs_prev: A `Tensor`. Must have the same type as `x`. h_prev: A `Tensor`. Must have the same type as `x`. wci: A `Tensor`. Must have the same type as `x`. wcf: A `Tensor`. Must have the same type as `x`. wco: A `Tensor`. Must have the same type as `x`. forget_bias: An optional `float`. Defaults to `1`. cell_clip: An optional `float`. Defaults to `-1` (no clipping). use_peephole: An optional `bool`. Defaults to `False`. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (i, cs, f, o, ci, co, h). i: A list with the same number of `Tensor` objects as `x` of `Tensor` objects of the same type as x. cs: A list with the same number of `Tensor` objects as `x` of `Tensor` objects of the same type as x. f: A list with the same number of `Tensor` objects as `x` of `Tensor` objects of the same type as x. o: A list with the same number of `Tensor` objects as `x` of `Tensor` objects of the same type as x. ci: A list with the same number of `Tensor` objects as `x` of `Tensor` objects of the same type as x. co: A list with the same number of `Tensor` objects as `x` of `Tensor` objects of the same type as x. h: A list with the same number of `Tensor` objects as `x` of `Tensor` objects of the same type as x. Raises: ValueError: If `b` does not have a valid shape. """ batch_size = x[0].get_shape().with_rank(2)[0].value cell_size4 = b.get_shape().with_rank(1)[0].value if cell_size4 is None: raise ValueError("`b` shape must not be None.") cell_size = cell_size4 / 4 zero_state = None if cs_prev is None or h_prev is None: zero_state = array_ops.constant( 0, dtype=dtypes.float32, shape=[batch_size, cell_size]) if cs_prev is None: cs_prev = zero_state if h_prev is None: h_prev = zero_state if wci is None: wci = array_ops.constant(0, dtype=dtypes.float32, shape=[cell_size]) wcf = wci wco = wci # pylint: disable=protected-access i, cs, f, o, ci, co, h = gen_lstm_ops.block_lstm( seq_len_max=seq_len_max, x=array_ops.stack(x), cs_prev=cs_prev, h_prev=h_prev, w=w, wci=wci, wcf=wcf, wco=wco, b=b, forget_bias=forget_bias, cell_clip=cell_clip if cell_clip is not None else -1, name=name, use_peephole=use_peephole) return array_ops.unstack(i), array_ops.unstack(cs), array_ops.unstack( f), array_ops.unstack(o), array_ops.unstack(ci), array_ops.unstack( co), array_ops.unstack(h) # pylint: enable=protected-access # pylint: enable=invalid-name _lstm_block_cell_grad_outputs = ["cs_prev_grad", "dicfo"] @ops.RegisterGradient("LSTMBlockCell") def _LSTMBlockCellGrad(op, *grad): """Gradient for LSTMBlockCell.""" (x, cs_prev, h_prev, w, wci, wcf, wco, b) = op.inputs (i, cs, f, o, ci, co, _) = op.outputs (_, cs_grad, _, _, _, _, h_grad) = grad batch_size = x.get_shape().with_rank(2)[0].value if batch_size is None: batch_size = -1 input_size = x.get_shape().with_rank(2)[1].value if input_size is None: raise ValueError("input_size from `x` should not be None.") cell_size = cs_prev.get_shape().with_rank(2)[1].value if cell_size is None: raise ValueError("cell_size from `cs_prev` should not be None.") (cs_prev_grad, dicfo, wci_grad, wcf_grad, wco_grad) = gen_lstm_ops.lstm_block_cell_grad( x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, cs_grad, h_grad, use_peephole=op.get_attr("use_peephole")) # Backprop from dicfo to xh. xh_grad = math_ops.matmul(dicfo, w, transpose_b=True) x_grad = array_ops.slice(xh_grad, (0, 0), (batch_size, input_size)) x_grad.get_shape().merge_with(x.get_shape()) h_prev_grad = array_ops.slice(xh_grad, (0, input_size), (batch_size, cell_size)) h_prev_grad.get_shape().merge_with(h_prev.get_shape()) # Backprop from dicfo to w. xh = array_ops.concat([x, h_prev], 1) w_grad = math_ops.matmul(xh, dicfo, transpose_a=True) w_grad.get_shape().merge_with(w.get_shape()) # Backprop from dicfo to b. b_grad = nn_ops.bias_add_grad(dicfo) b_grad.get_shape().merge_with(b.get_shape()) return (x_grad, cs_prev_grad, h_prev_grad, w_grad, wci_grad, wcf_grad, wco_grad, b_grad) @ops.RegisterGradient("BlockLSTM") def _BlockLSTMGrad(op, *grad): """Gradient for BlockLSTM.""" seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b = op.inputs i, cs, f, o, ci, co, h = op.outputs cs_grad = grad[1] h_grad = grad[6] (x_grad, cs_prev_grad, h_prev_grad, w_grad, wci_grad, wcf_grad, wco_grad, b_grad) = gen_lstm_ops.block_lstm_grad( seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, h, cs_grad, h_grad, use_peephole=op.get_attr("use_peephole")) return [ None, x_grad, cs_prev_grad, h_prev_grad, w_grad, wci_grad, wcf_grad, wco_grad, b_grad ] class LSTMBlockCell(LayerRNNCell): """Basic LSTM recurrent network cell. The implementation is based on: http://arxiv.org/abs/1409.2329. We add `forget_bias` (default: 1) to the biases of the forget gate in order to reduce the scale of forgetting in the beginning of the training. Unlike `rnn_cell_impl.LSTMCell`, this is a monolithic op and should be much faster. The weight and bias matrices should be compatible as long as the variable scope matches. """ def __init__(self, num_units, forget_bias=1.0, cell_clip=None, use_peephole=False, reuse=None, name="lstm_cell"): """Initialize the basic LSTM cell. Args: num_units: int, The number of units in the LSTM cell. forget_bias: float, The bias added to forget gates (see above). cell_clip: An optional `float`. Defaults to `-1` (no clipping). use_peephole: Whether to use peephole connections or not. reuse: (optional) boolean describing whether to reuse variables in an existing scope. If not `True`, and the existing scope already has the given variables, an error is raised. name: String, the name of the layer. Layers with the same name will share weights, but to avoid mistakes we require reuse=True in such cases. By default this is "lstm_cell", for variable-name compatibility with `tf.nn.rnn_cell.LSTMCell`. When restoring from CudnnLSTM-trained checkpoints, must use CudnnCompatibleLSTMBlockCell instead. """ super(LSTMBlockCell, self).__init__(_reuse=reuse, name=name) self._num_units = num_units self._forget_bias = forget_bias self._use_peephole = use_peephole self._cell_clip = cell_clip if cell_clip is not None else -1 self._names = { "W": "kernel", "b": "bias", "wci": "w_i_diag", "wcf": "w_f_diag", "wco": "w_o_diag", "scope": "lstm_cell" } # Inputs must be 2-dimensional. self.input_spec = base_layer.InputSpec(ndim=2) @property def state_size(self): return rnn_cell_impl.LSTMStateTuple(self._num_units, self._num_units) @property def output_size(self): return self._num_units def build(self, inputs_shape): if not inputs_shape[1].value: raise ValueError( "Expecting inputs_shape[1] to be set: %s" % str(inputs_shape)) input_size = inputs_shape[1].value self._kernel = self.add_variable( self._names["W"], [input_size + self._num_units, self._num_units * 4]) self._bias = self.add_variable( self._names["b"], [self._num_units * 4], initializer=init_ops.constant_initializer(0.0)) if self._use_peephole: self._w_i_diag = self.add_variable(self._names["wci"], [self._num_units]) self._w_f_diag = self.add_variable(self._names["wcf"], [self._num_units]) self._w_o_diag = self.add_variable(self._names["wco"], [self._num_units]) self.built = True def call(self, inputs, state): """Long short-term memory cell (LSTM).""" if len(state) != 2: raise ValueError("Expecting state to be a tuple with length 2.") if self._use_peephole: wci = self._w_i_diag wcf = self._w_f_diag wco = self._w_o_diag else: wci = wcf = wco = array_ops.zeros([self._num_units]) (cs_prev, h_prev) = state (_, cs, _, _, _, _, h) = _lstm_block_cell( inputs, cs_prev, h_prev, self._kernel, self._bias, wci=wci, wcf=wcf, wco=wco, forget_bias=self._forget_bias, cell_clip=self._cell_clip, use_peephole=self._use_peephole) new_state = rnn_cell_impl.LSTMStateTuple(cs, h) return h, new_state class LSTMBlockWrapper(base_layer.Layer): """This is a helper class that provides housekeeping for LSTM cells. This may be useful for alternative LSTM and similar type of cells. The subclasses must implement `_call_cell` method and `num_units` property. """ @abc.abstractproperty def num_units(self): """Number of units in this cell (output dimension).""" pass @abc.abstractmethod def _call_cell(self, inputs, initial_cell_state, initial_output, dtype, sequence_length): """Run this LSTM on inputs, starting from the given state. This method must be implemented by subclasses and does the actual work of calling the cell. Args: inputs: `3-D` tensor with shape `[time_len, batch_size, input_size]` initial_cell_state: initial value for cell state, shape `[batch_size, self._num_units]` initial_output: initial value of cell output, shape `[batch_size, self._num_units]` dtype: The data type for the initial state and expected output. sequence_length: Specifies the length of each sequence in inputs. An int32 or int64 vector (tensor) size [batch_size], values in [0, time_len) or None. Returns: A pair containing: - State: A `3-D` tensor of shape `[time_len, batch_size, output_size]` - Output: A `3-D` tensor of shape `[time_len, batch_size, output_size]` """ pass def call(self, inputs, initial_state=None, dtype=None, sequence_length=None): """Run this LSTM on inputs, starting from the given state. Args: inputs: `3-D` tensor with shape `[time_len, batch_size, input_size]` or a list of `time_len` tensors of shape `[batch_size, input_size]`. initial_state: a tuple `(initial_cell_state, initial_output)` with tensors of shape `[batch_size, self._num_units]`. If this is not provided, the cell is expected to create a zero initial state of type `dtype`. dtype: The data type for the initial state and expected output. Required if `initial_state` is not provided or RNN state has a heterogeneous dtype. sequence_length: Specifies the length of each sequence in inputs. An `int32` or `int64` vector (tensor) size `[batch_size]`, values in `[0, time_len).` Defaults to `time_len` for each element. Returns: A pair containing: - Output: A `3-D` tensor of shape `[time_len, batch_size, output_size]` or a list of time_len tensors of shape `[batch_size, output_size]`, to match the type of the `inputs`. - Final state: a tuple `(cell_state, output)` matching `initial_state`. Raises: ValueError: in case of shape mismatches """ is_list = isinstance(inputs, list) if is_list: inputs = array_ops.stack(inputs) inputs_shape = inputs.get_shape().with_rank(3) if not inputs_shape[2]: raise ValueError("Expecting inputs_shape[2] to be set: %s" % inputs_shape) batch_size = inputs_shape[1].value if batch_size is None: batch_size = array_ops.shape(inputs)[1] time_len = inputs_shape[0].value if time_len is None: time_len = array_ops.shape(inputs)[0] # Provide default values for initial_state and dtype if initial_state is None: if dtype is None: raise ValueError("Either initial_state or dtype needs to be specified") z = array_ops.zeros( array_ops.stack([batch_size, self.num_units]), dtype=dtype) initial_state = z, z else: if len(initial_state) != 2: raise ValueError( "Expecting initial_state to be a tuple with length 2 or None") if dtype is None: dtype = initial_state[0].dtype # create the actual cell if sequence_length is not None: sequence_length = ops.convert_to_tensor(sequence_length) initial_cell_state, initial_output = initial_state # pylint: disable=unpacking-non-sequence cell_states, outputs = self._call_cell( inputs, initial_cell_state, initial_output, dtype, sequence_length) if sequence_length is not None: # Mask out the part beyond sequence_length mask = array_ops.transpose( array_ops.sequence_mask(sequence_length, time_len, dtype=dtype), [1, 0]) mask = array_ops.tile( array_ops.expand_dims(mask, [-1]), [1, 1, self.num_units]) outputs *= mask # Prepend initial states to cell_states and outputs for indexing to work # correctly,since we want to access the last valid state at # sequence_length - 1, which can even be -1, corresponding to the # initial state. mod_cell_states = array_ops.concat( [array_ops.expand_dims(initial_cell_state, [0]), cell_states], 0) mod_outputs = array_ops.concat( [array_ops.expand_dims(initial_output, [0]), outputs], 0) final_cell_state = self._gather_states(mod_cell_states, sequence_length, batch_size) final_output = self._gather_states(mod_outputs, sequence_length, batch_size) else: # No sequence_lengths used: final state is the last state final_cell_state = cell_states[-1] final_output = outputs[-1] if is_list: # Input was a list, so return a list outputs = array_ops.unstack(outputs) final_state = rnn_cell_impl.LSTMStateTuple(final_cell_state, final_output) return outputs, final_state def _gather_states(self, data, indices, batch_size): """Produce `out`, s.t. out(i, j) = data(indices(i), i, j).""" mod_indices = indices * batch_size + math_ops.range(batch_size) return array_ops.gather( array_ops.reshape(data, [-1, self.num_units]), mod_indices) class LSTMBlockFusedCell(LSTMBlockWrapper): """FusedRNNCell implementation of LSTM. This is an extremely efficient LSTM implementation, that uses a single TF op for the entire LSTM. It should be both faster and more memory-efficient than LSTMBlockCell defined above. The implementation is based on: http://arxiv.org/abs/1409.2329. We add forget_bias (default: 1) to the biases of the forget gate in order to reduce the scale of forgetting in the beginning of the training. The variable naming is consistent with `rnn_cell_impl.LSTMCell`. """ def __init__(self, num_units, forget_bias=1.0, cell_clip=None, use_peephole=False, reuse=None, name="lstm_fused_cell"): """Initialize the LSTM cell. Args: num_units: int, The number of units in the LSTM cell. forget_bias: float, The bias added to forget gates (see above). cell_clip: clip the cell to this value. Default is no cell clipping. use_peephole: Whether to use peephole connections or not. reuse: (optional) boolean describing whether to reuse variables in an existing scope. If not `True`, and the existing scope already has the given variables, an error is raised. name: String, the name of the layer. Layers with the same name will share weights, but to avoid mistakes we require reuse=True in such cases. By default this is "lstm_cell", for variable-name compatibility with `tf.nn.rnn_cell.LSTMCell`. """ super(LSTMBlockFusedCell, self).__init__(_reuse=reuse, name=name) self._num_units = num_units self._forget_bias = forget_bias self._cell_clip = cell_clip if cell_clip is not None else -1 self._use_peephole = use_peephole # Inputs must be 3-dimensional. self.input_spec = base_layer.InputSpec(ndim=3) @property def num_units(self): """Number of units in this cell (output dimension).""" return self._num_units def build(self, input_shape): input_size = input_shape[2].value self._kernel = self.add_variable( "kernel", [input_size + self._num_units, self._num_units * 4]) self._bias = self.add_variable( "bias", [self._num_units * 4], initializer=init_ops.constant_initializer(0.0)) if self._use_peephole: self._w_i_diag = self.add_variable("w_i_diag", [self._num_units]) self._w_f_diag = self.add_variable("w_f_diag", [self._num_units]) self._w_o_diag = self.add_variable("w_o_diag", [self._num_units]) self.built = True def _call_cell(self, inputs, initial_cell_state=None, initial_output=None, dtype=None, sequence_length=None): """Run this LSTM on inputs, starting from the given state. Args: inputs: `3-D` tensor with shape `[time_len, batch_size, input_size]` initial_cell_state: initial value for cell state, shape `[batch_size, self._num_units]` initial_output: initial value of cell output, shape `[batch_size, self._num_units]` dtype: The data type for the initial state and expected output. sequence_length: Specifies the length of each sequence in inputs. An `int32` or `int64` vector (tensor) size `[batch_size]`, values in `[0, time_len)` or None. Returns: A pair containing: - Cell state (cs): A `3-D` tensor of shape `[time_len, batch_size, output_size]` - Output (h): A `3-D` tensor of shape `[time_len, batch_size, output_size]` """ inputs_shape = inputs.get_shape().with_rank(3) time_len = inputs_shape[0].value if time_len is None: time_len = array_ops.shape(inputs)[0] if self._use_peephole: wci = self._w_i_diag wco = self._w_o_diag wcf = self._w_f_diag else: wci = wcf = wco = array_ops.zeros([self._num_units], dtype=dtype) if sequence_length is None: max_seq_len = math_ops.to_int64(time_len) else: max_seq_len = math_ops.to_int64(math_ops.reduce_max(sequence_length)) _, cs, _, _, _, _, h = gen_lstm_ops.block_lstm( seq_len_max=max_seq_len, x=inputs, cs_prev=initial_cell_state, h_prev=initial_output, w=self._kernel, wci=wci, wcf=wcf, wco=wco, b=self._bias, forget_bias=self._forget_bias, cell_clip=self._cell_clip, use_peephole=self._use_peephole) return cs, h

35.401989

96

0.650564

from __future__ import absolute_import from __future__ import division from __future__ import print_function import abc from tensorflow.contrib.rnn.ops import gen_lstm_ops from tensorflow.contrib.util import loader from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.layers import base as base_layer from tensorflow.python.ops import array_ops from tensorflow.python.ops import init_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import nn_ops from tensorflow.python.ops import rnn_cell_impl from tensorflow.python.platform import resource_loader _lstm_ops_so = loader.load_op_library( resource_loader.get_path_to_datafile("_lstm_ops.so")) LayerRNNCell = rnn_cell_impl.LayerRNNCell def _lstm_block_cell(x, cs_prev, h_prev, w, b, wci=None, wcf=None, wco=None, forget_bias=None, cell_clip=None, use_peephole=None, name=None): if wci is None: cell_size = cs_prev.get_shape().with_rank(2)[1].value if cell_size is None: raise ValueError("cell_size from `cs_prev` should not be None.") wci = array_ops.constant(0, dtype=dtypes.float32, shape=[cell_size]) wcf = wci wco = wci return gen_lstm_ops.lstm_block_cell( x=x, cs_prev=cs_prev, h_prev=h_prev, w=w, wci=wci, wcf=wcf, wco=wco, b=b, forget_bias=forget_bias, cell_clip=cell_clip if cell_clip is not None else -1, use_peephole=use_peephole, name=name) def _block_lstm(seq_len_max, x, w, b, cs_prev=None, h_prev=None, wci=None, wcf=None, wco=None, forget_bias=None, cell_clip=None, use_peephole=None, name=None): batch_size = x[0].get_shape().with_rank(2)[0].value cell_size4 = b.get_shape().with_rank(1)[0].value if cell_size4 is None: raise ValueError("`b` shape must not be None.") cell_size = cell_size4 / 4 zero_state = None if cs_prev is None or h_prev is None: zero_state = array_ops.constant( 0, dtype=dtypes.float32, shape=[batch_size, cell_size]) if cs_prev is None: cs_prev = zero_state if h_prev is None: h_prev = zero_state if wci is None: wci = array_ops.constant(0, dtype=dtypes.float32, shape=[cell_size]) wcf = wci wco = wci i, cs, f, o, ci, co, h = gen_lstm_ops.block_lstm( seq_len_max=seq_len_max, x=array_ops.stack(x), cs_prev=cs_prev, h_prev=h_prev, w=w, wci=wci, wcf=wcf, wco=wco, b=b, forget_bias=forget_bias, cell_clip=cell_clip if cell_clip is not None else -1, name=name, use_peephole=use_peephole) return array_ops.unstack(i), array_ops.unstack(cs), array_ops.unstack( f), array_ops.unstack(o), array_ops.unstack(ci), array_ops.unstack( co), array_ops.unstack(h) _lstm_block_cell_grad_outputs = ["cs_prev_grad", "dicfo"] @ops.RegisterGradient("LSTMBlockCell") def _LSTMBlockCellGrad(op, *grad): (x, cs_prev, h_prev, w, wci, wcf, wco, b) = op.inputs (i, cs, f, o, ci, co, _) = op.outputs (_, cs_grad, _, _, _, _, h_grad) = grad batch_size = x.get_shape().with_rank(2)[0].value if batch_size is None: batch_size = -1 input_size = x.get_shape().with_rank(2)[1].value if input_size is None: raise ValueError("input_size from `x` should not be None.") cell_size = cs_prev.get_shape().with_rank(2)[1].value if cell_size is None: raise ValueError("cell_size from `cs_prev` should not be None.") (cs_prev_grad, dicfo, wci_grad, wcf_grad, wco_grad) = gen_lstm_ops.lstm_block_cell_grad( x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, cs_grad, h_grad, use_peephole=op.get_attr("use_peephole")) xh_grad = math_ops.matmul(dicfo, w, transpose_b=True) x_grad = array_ops.slice(xh_grad, (0, 0), (batch_size, input_size)) x_grad.get_shape().merge_with(x.get_shape()) h_prev_grad = array_ops.slice(xh_grad, (0, input_size), (batch_size, cell_size)) h_prev_grad.get_shape().merge_with(h_prev.get_shape()) xh = array_ops.concat([x, h_prev], 1) w_grad = math_ops.matmul(xh, dicfo, transpose_a=True) w_grad.get_shape().merge_with(w.get_shape()) b_grad = nn_ops.bias_add_grad(dicfo) b_grad.get_shape().merge_with(b.get_shape()) return (x_grad, cs_prev_grad, h_prev_grad, w_grad, wci_grad, wcf_grad, wco_grad, b_grad) @ops.RegisterGradient("BlockLSTM") def _BlockLSTMGrad(op, *grad): seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b = op.inputs i, cs, f, o, ci, co, h = op.outputs cs_grad = grad[1] h_grad = grad[6] (x_grad, cs_prev_grad, h_prev_grad, w_grad, wci_grad, wcf_grad, wco_grad, b_grad) = gen_lstm_ops.block_lstm_grad( seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, h, cs_grad, h_grad, use_peephole=op.get_attr("use_peephole")) return [ None, x_grad, cs_prev_grad, h_prev_grad, w_grad, wci_grad, wcf_grad, wco_grad, b_grad ] class LSTMBlockCell(LayerRNNCell): def __init__(self, num_units, forget_bias=1.0, cell_clip=None, use_peephole=False, reuse=None, name="lstm_cell"): super(LSTMBlockCell, self).__init__(_reuse=reuse, name=name) self._num_units = num_units self._forget_bias = forget_bias self._use_peephole = use_peephole self._cell_clip = cell_clip if cell_clip is not None else -1 self._names = { "W": "kernel", "b": "bias", "wci": "w_i_diag", "wcf": "w_f_diag", "wco": "w_o_diag", "scope": "lstm_cell" } self.input_spec = base_layer.InputSpec(ndim=2) @property def state_size(self): return rnn_cell_impl.LSTMStateTuple(self._num_units, self._num_units) @property def output_size(self): return self._num_units def build(self, inputs_shape): if not inputs_shape[1].value: raise ValueError( "Expecting inputs_shape[1] to be set: %s" % str(inputs_shape)) input_size = inputs_shape[1].value self._kernel = self.add_variable( self._names["W"], [input_size + self._num_units, self._num_units * 4]) self._bias = self.add_variable( self._names["b"], [self._num_units * 4], initializer=init_ops.constant_initializer(0.0)) if self._use_peephole: self._w_i_diag = self.add_variable(self._names["wci"], [self._num_units]) self._w_f_diag = self.add_variable(self._names["wcf"], [self._num_units]) self._w_o_diag = self.add_variable(self._names["wco"], [self._num_units]) self.built = True def call(self, inputs, state): if len(state) != 2: raise ValueError("Expecting state to be a tuple with length 2.") if self._use_peephole: wci = self._w_i_diag wcf = self._w_f_diag wco = self._w_o_diag else: wci = wcf = wco = array_ops.zeros([self._num_units]) (cs_prev, h_prev) = state (_, cs, _, _, _, _, h) = _lstm_block_cell( inputs, cs_prev, h_prev, self._kernel, self._bias, wci=wci, wcf=wcf, wco=wco, forget_bias=self._forget_bias, cell_clip=self._cell_clip, use_peephole=self._use_peephole) new_state = rnn_cell_impl.LSTMStateTuple(cs, h) return h, new_state class LSTMBlockWrapper(base_layer.Layer): @abc.abstractproperty def num_units(self): pass @abc.abstractmethod def _call_cell(self, inputs, initial_cell_state, initial_output, dtype, sequence_length): pass def call(self, inputs, initial_state=None, dtype=None, sequence_length=None): is_list = isinstance(inputs, list) if is_list: inputs = array_ops.stack(inputs) inputs_shape = inputs.get_shape().with_rank(3) if not inputs_shape[2]: raise ValueError("Expecting inputs_shape[2] to be set: %s" % inputs_shape) batch_size = inputs_shape[1].value if batch_size is None: batch_size = array_ops.shape(inputs)[1] time_len = inputs_shape[0].value if time_len is None: time_len = array_ops.shape(inputs)[0] if initial_state is None: if dtype is None: raise ValueError("Either initial_state or dtype needs to be specified") z = array_ops.zeros( array_ops.stack([batch_size, self.num_units]), dtype=dtype) initial_state = z, z else: if len(initial_state) != 2: raise ValueError( "Expecting initial_state to be a tuple with length 2 or None") if dtype is None: dtype = initial_state[0].dtype if sequence_length is not None: sequence_length = ops.convert_to_tensor(sequence_length) initial_cell_state, initial_output = initial_state cell_states, outputs = self._call_cell( inputs, initial_cell_state, initial_output, dtype, sequence_length) if sequence_length is not None: mask = array_ops.transpose( array_ops.sequence_mask(sequence_length, time_len, dtype=dtype), [1, 0]) mask = array_ops.tile( array_ops.expand_dims(mask, [-1]), [1, 1, self.num_units]) outputs *= mask mod_cell_states = array_ops.concat( [array_ops.expand_dims(initial_cell_state, [0]), cell_states], 0) mod_outputs = array_ops.concat( [array_ops.expand_dims(initial_output, [0]), outputs], 0) final_cell_state = self._gather_states(mod_cell_states, sequence_length, batch_size) final_output = self._gather_states(mod_outputs, sequence_length, batch_size) else: final_cell_state = cell_states[-1] final_output = outputs[-1] if is_list: outputs = array_ops.unstack(outputs) final_state = rnn_cell_impl.LSTMStateTuple(final_cell_state, final_output) return outputs, final_state def _gather_states(self, data, indices, batch_size): mod_indices = indices * batch_size + math_ops.range(batch_size) return array_ops.gather( array_ops.reshape(data, [-1, self.num_units]), mod_indices) class LSTMBlockFusedCell(LSTMBlockWrapper): def __init__(self, num_units, forget_bias=1.0, cell_clip=None, use_peephole=False, reuse=None, name="lstm_fused_cell"): super(LSTMBlockFusedCell, self).__init__(_reuse=reuse, name=name) self._num_units = num_units self._forget_bias = forget_bias self._cell_clip = cell_clip if cell_clip is not None else -1 self._use_peephole = use_peephole self.input_spec = base_layer.InputSpec(ndim=3) @property def num_units(self): return self._num_units def build(self, input_shape): input_size = input_shape[2].value self._kernel = self.add_variable( "kernel", [input_size + self._num_units, self._num_units * 4]) self._bias = self.add_variable( "bias", [self._num_units * 4], initializer=init_ops.constant_initializer(0.0)) if self._use_peephole: self._w_i_diag = self.add_variable("w_i_diag", [self._num_units]) self._w_f_diag = self.add_variable("w_f_diag", [self._num_units]) self._w_o_diag = self.add_variable("w_o_diag", [self._num_units]) self.built = True def _call_cell(self, inputs, initial_cell_state=None, initial_output=None, dtype=None, sequence_length=None): inputs_shape = inputs.get_shape().with_rank(3) time_len = inputs_shape[0].value if time_len is None: time_len = array_ops.shape(inputs)[0] if self._use_peephole: wci = self._w_i_diag wco = self._w_o_diag wcf = self._w_f_diag else: wci = wcf = wco = array_ops.zeros([self._num_units], dtype=dtype) if sequence_length is None: max_seq_len = math_ops.to_int64(time_len) else: max_seq_len = math_ops.to_int64(math_ops.reduce_max(sequence_length)) _, cs, _, _, _, _, h = gen_lstm_ops.block_lstm( seq_len_max=max_seq_len, x=inputs, cs_prev=initial_cell_state, h_prev=initial_output, w=self._kernel, wci=wci, wcf=wcf, wco=wco, b=self._bias, forget_bias=self._forget_bias, cell_clip=self._cell_clip, use_peephole=self._use_peephole) return cs, h

true

f7007281123ef0b43ad24fd2ed13f4b9a7f55812

867

py

Python

common/clean_svt_transcript.py

phsmit/kaldi-recipes

b2e2e098cc344767a9ac6cc15942afea5685a7e8

[ "Apache-2.0" ]

null

common/clean_svt_transcript.py

phsmit/kaldi-recipes

b2e2e098cc344767a9ac6cc15942afea5685a7e8

[ "Apache-2.0" ]

null

common/clean_svt_transcript.py

phsmit/kaldi-recipes

b2e2e098cc344767a9ac6cc15942afea5685a7e8

[ "Apache-2.0" ]

null

#!/usr/bin/env python3 import argparse import codecs import sys def transform(i,o): for line in i: if len(line.strip()) == 0: continue key, trans = line.strip().split(None, 1) ntrans = [] for t in trans.split(): if t.startswith("<"): continue ntrans.append(t.lower()) print("{} {}".format(key, " ".join(ntrans)), file=o) if __name__ == "__main__": parser = argparse.ArgumentParser(description='') parser.add_argument('infile', nargs='?', type=argparse.FileType('r', encoding='utf-8'), default=codecs.getreader('utf-8')(sys.stdin.buffer)) parser.add_argument('outfile', nargs='?', type=argparse.FileType('w', encoding='utf-8'), default=codecs.getwriter('utf-8')(sys.stdout.buffer)) args = parser.parse_args() transform(args.infile, args.outfile)

30.964286

146

0.608997

import argparse import codecs import sys def transform(i,o): for line in i: if len(line.strip()) == 0: continue key, trans = line.strip().split(None, 1) ntrans = [] for t in trans.split(): if t.startswith("<"): continue ntrans.append(t.lower()) print("{} {}".format(key, " ".join(ntrans)), file=o) if __name__ == "__main__": parser = argparse.ArgumentParser(description='') parser.add_argument('infile', nargs='?', type=argparse.FileType('r', encoding='utf-8'), default=codecs.getreader('utf-8')(sys.stdin.buffer)) parser.add_argument('outfile', nargs='?', type=argparse.FileType('w', encoding='utf-8'), default=codecs.getwriter('utf-8')(sys.stdout.buffer)) args = parser.parse_args() transform(args.infile, args.outfile)

true

f70072e664f2a4c0803afed709115398c03808ab

11,745

py

Python

PSN.py

jeevangelista/PSN

b47e95e8e3c205c0aa29438a025644ce1d25f93e

[ "MIT" ]

null

PSN.py

jeevangelista/PSN

b47e95e8e3c205c0aa29438a025644ce1d25f93e

[ "MIT" ]

null

PSN.py

jeevangelista/PSN

b47e95e8e3c205c0aa29438a025644ce1d25f93e

[ "MIT" ]

null

#!/Users/erol/Code/2.7/bin/python #Change this before running import sys, getopt, glob, csv from math import sqrt from Bio import Struct from Bio.Struct.Geometry import center_of_mass from Bio.PDB import * import numpy as np def string_to_float(val): try: return float(val) except ValueError as e: raise # Returns the number of atoms per residue pair that have a # euclidean distance below a threshold # Inputs: # model: Structure object # threshold: Threshold # excluded: Excluded atoms # Returns: |R| x |R| matrix |R| = # of residues, Rij = # of connected atom bet Ri and Rj def compute_atom_contact(model, threshold, excluded=[]): res_count = 0 # Get residue counts on all chain for chain in model: res_count += len(chain) # generate mod contact matrix mod_contact_matrix = np.zeros((res_count,res_count)) last_i = 1 for chain in model: i = last_i chaindex_i = 0 for res_i in chain.get_residues(): index_i = chaindex_i + i last_i = index_i # we note the last value of i for multiple chain chaindex_j = 0 for res_j in chain.get_residues(): index_j = chaindex_j + index_i # don't recompute if index_j <= len(chain): if index_j > index_i: for at_i in res_i: if at_i.get_name() not in excluded: for at_j in res_j: if at_j.get_name() not in excluded: euclidean = np.linalg.norm(at_i.get_coord()-at_j.get_coord()) if euclidean <= threshold: # add one interacting node to both residues mod_contact_matrix[index_i-1][index_j-1] += 1 mod_contact_matrix[index_j-1][index_i-1] += 1 chaindex_j += 1 chaindex_i += 1 # # Assume for now na single chain lang # for res_i in range(len(chain)): # index_i = res_i + i # add i to consider multiple chains # last_i = index_i # we note the last value of i for multiple chain # for res_j in range(len(chain)-res_i): # index_j = res_j + index_i # if index_i < index_j: # Wag na magrecompute # # for ptm # try: # for at_i in chain[index_i]: # if at_i not in excluded: # for at_j in chain[index_j]: # if at_j not in excluded: # euclidean = np.linalg.norm(at_i.get_coord()-at_j.get_coord()) # if euclidean <= threshold: # # add one interacting node to both residues # mod_contact_matrix[index_i-1][index_j-1] += 1 # mod_contact_matrix[index_j-1][index_i-1] += 1 # except KeyError as e: # break return mod_contact_matrix # Returns the ave number of atoms per residue pair that have a # euclidean distance below a threshold # Inputs: # structure: Structure object # threshold: Threshold # excluded: Excluded atoms # Returns: |R| x |R| matrix |R| = # of residues def compute_ave_atom_contact(structure, threshold, excluded=[]): res_count = 0 # Get residue counts on all chain for chain in structure[0]: res_count += len(chain) struct_contact_matrix = np.zeros((res_count,res_count)) for model in structure: mod_contact_matrix = compute_atom_contact(model, threshold, excluded) struct_contact_matrix += mod_contact_matrix ave_struct_contact_matrix = struct_contact_matrix / len(structure) return ave_struct_contact_matrix # returns a dictionary that contains the max contact per residue def compute_max_residue(structure, ave_struct_contact_matrix): res_dict = {} i = 0 for chain in structure[0]: for res in chain: contact_atoms = np.sum(ave_struct_contact_matrix[i]) i+=1 if res.get_resname() not in res_dict: res_dict[res.get_resname()] = contact_atoms elif res_dict[res.get_resname()] < contact_atoms: res_dict[res.get_resname()] = contact_atoms return res_dict # compute normalization factor per residue def compute_normalization_factor(pdb_dir, threshold, output): parser = PDBParser() tot_files = 0.0 res_dir_dict = {} for filename in glob.iglob(pdb_dir + "/*.pdb"): tot_files += 1 label = filename.split('/')[-1].split('.')[0] print label structure = parser.get_structure(label,filename) ave_struct_contact_matrix = compute_ave_atom_contact(structure, 2) res_dict = compute_max_residue(structure, ave_struct_contact_matrix) for key in res_dict: if key not in res_dir_dict: res_dir_dict[key] = res_dict[key] else: res_dir_dict[key] += res_dict[key] for key in res_dir_dict: res_dir_dict[key] = res_dir_dict[key]/tot_files with open(output, "w") as outfile: csvfile = csv.writer(outfile) for key in res_dir_dict: csvfile.writerow([key,res_dir_dict[key]]) print res_dir_dict # read normalization file and returns it in a dictionary def read_normalization(norm_fact): norm_dict = {} with open(norm_fact) as normfile: csvfile = csv.reader(normfile) for row in csvfile: norm_dict[row[0]] = float(row[1]) return norm_dict def compute_interaction(res_1, res_2, inter_atoms, norm_dict): # for amber res_1 = 'HIS' if res_1 in ['HIE', 'HID', 'HIP'] else res_1 res_2 = 'HIS' if res_2 in ['HIE', 'HID', 'HIP'] else res_2 norm_1 = norm_dict[res_1] norm_2 = norm_dict[res_2] # remove tom if norm_1 == 0 or norm_2 == 0: return 0 return (inter_atoms*100.0)/sqrt(norm_1*norm_2) # Returns the number of atoms per residue pair that have a # euclidean distance below a threshold # Inputs: # model: Structure object # threshold: Threshold # excluded: Excluded atoms # Returns: |R| x |R| matrix |R| = # of residues, Rij = interaction strength def compute_atom_interaction(model, contact_thresh, interaction_thresh, norm_dict, residues, excluded=[]): model_contact = compute_atom_contact(model, contact_thresh, excluded) model_interact = np.zeros(model_contact.shape) for i in range(len(residues)): for j in range(len(residues)-i): index_j = j + i index_i = i # if equal zero lang if index_i < index_j: int_str = compute_interaction(residues[index_i], residues[index_j], model_contact[index_i][index_j], norm_dict) #rint int_str if int_str >= interaction_thresh: model_interact[index_i][index_j] = 1 model_interact[index_j][index_i] = 1 return model_interact def compute_interaction_matrix(structure, contact_thresh, interaction_thresh, edge_prct, norm_dict, outdir, excluded=[]): residues = [] for res in structure[0].get_residues(): residues.append(res.get_resname()) struct_interact = np.zeros((len(residues),len(residues))) PSN = np.zeros((len(residues),len(residues))) for model in structure: model_interact = compute_atom_interaction(model, contact_thresh, interaction_thresh, norm_dict, residues, excluded=[]) struct_interact = struct_interact + model_interact edge_thresh = edge_prct * len(structure) for i in range(len(residues)): for j in range(len(residues)-i): index_j = j + i index_i = i # if equal zero lang if index_i < index_j: if struct_interact[index_i][index_j]>= edge_thresh: PSN[index_i][index_j] = 1 PSN[index_j][index_i] = 1 filename = "%s/%s_%.2f_%.2f_%.2f" % (outdir,structure.get_id(),contact_thresh,interaction_thresh,edge_prct) np.savetxt(filename,PSN,fmt='%d') def get_graph_rep(pdb_dir, contact_thresh, interaction_thresh, edge_prct, norm_dict, outdir, excluded=[]): parser = PDBParser() for filename in glob.iglob(pdb_dir + "/*.pdb"): label = filename.split('/')[-1].split('.')[0] print label structure = parser.get_structure(label,filename) for int_thresh in interaction_thresh: compute_interaction_matrix(structure, contact_thresh, int_thresh, edge_prct, norm_dict, outdir, excluded=[]) def main(argv): pdb_dir = '' norm_fact = '' outdir = '' norm = 1 interaction_thresh = 1 contact_thresh = 4.5 normalize = False edge_prct = 0.9 bb = ['N','CA','C','O'] try: opts, args = getopt.getopt(argv,"hnp:f:c:i:e:o:",["pdb_dir=", "norm_fact=", "interaction_thresh=", "contact_thresh=", "edge_prct=", "normalize", "outdir="]) except getopt.GetoptError: print 'blast_parser.py -i <input fasta> -x <blast xml> -o <output>' sys.exit(2) for opt, arg in opts: if opt == '-h': print 'Computes a network given a threshold or computes normalization factor' print '''PSN.py -p --pdb_dir\t\tpdb directory -f --norm_fact\t\tnormalization factor file -i --interaction_thresh\tcomma separated interaction strengths -c --contact_thresh\tcontact threshold -n --normalize\t\tComputes normalization factor of input pdbs -o --output\t\tName of output''' sys.exit() elif opt in ("-p", "--pdb_dir"): pdb_dir = arg elif opt in ("-f", "--norm_fact"): norm_fact = arg elif opt in ("-e", "--edge_prct"): edge_prct = string_to_float(arg) elif opt in ("-c", "--contact_thresh"): contact_thresh = string_to_float(arg) elif opt in ("-i", "--interaction_thresh"): interaction_thresh = [] for i in arg.split(","): interaction_thresh.append(string_to_float(i)) elif opt in ("-o", "--output"): outdir = arg elif opt in ("-n","--normalize"): normalize = True # parser = PDBParser() # structure = parser.get_structure('1akg','../native/1acw.pdb') # model = structure[0] # #compute_normalization_factor(pdb_dir, contact_thresh, output) # compute_interaction_matrix(structure, # contact_thresh, # interaction_thresh, # edge_prct, # norm_dict) if normalize: compute_normalization_factor(pdb_dir, contact_thresh, outdir) else: norm_dict = read_normalization(norm_fact) get_graph_rep(pdb_dir, contact_thresh, interaction_thresh, edge_prct, norm_dict, outdir, bb) if __name__ == "__main__": main(sys.argv[1:])

35.27027

109

0.572073

import sys, getopt, glob, csv from math import sqrt from Bio import Struct from Bio.Struct.Geometry import center_of_mass from Bio.PDB import * import numpy as np def string_to_float(val): try: return float(val) except ValueError as e: raise def compute_atom_contact(model, threshold, excluded=[]): res_count = 0 for chain in model: res_count += len(chain) mod_contact_matrix = np.zeros((res_count,res_count)) last_i = 1 for chain in model: i = last_i chaindex_i = 0 for res_i in chain.get_residues(): index_i = chaindex_i + i last_i = index_i chaindex_j = 0 for res_j in chain.get_residues(): index_j = chaindex_j + index_i if index_j <= len(chain): if index_j > index_i: for at_i in res_i: if at_i.get_name() not in excluded: for at_j in res_j: if at_j.get_name() not in excluded: euclidean = np.linalg.norm(at_i.get_coord()-at_j.get_coord()) if euclidean <= threshold: # add one interacting node to both residues mod_contact_matrix[index_i-1][index_j-1] += 1 mod_contact_matrix[index_j-1][index_i-1] += 1 chaindex_j += 1 chaindex_i += 1 # # Assume for now na single chain lang # for res_i in range(len(chain)): # index_i = res_i + i # add i to consider multiple chains # last_i = index_i # we note the last value of i for multiple chain # for res_j in range(len(chain)-res_i): # index_j = res_j + index_i # if index_i < index_j: # Wag na magrecompute # # for ptm # try: # for at_i in chain[index_i]: # if at_i not in excluded: # for at_j in chain[index_j]: # if at_j not in excluded: # euclidean = np.linalg.norm(at_i.get_coord()-at_j.get_coord()) # if euclidean <= threshold: # # add one interacting node to both residues # mod_contact_matrix[index_i-1][index_j-1] += 1 # mod_contact_matrix[index_j-1][index_i-1] += 1 # except KeyError as e: # break return mod_contact_matrix # Returns the ave number of atoms per residue pair that have a # euclidean distance below a threshold # Inputs: # structure: Structure object # threshold: Threshold # excluded: Excluded atoms # Returns: |R| x |R| matrix |R| = # of residues def compute_ave_atom_contact(structure, threshold, excluded=[]): res_count = 0 # Get residue counts on all chain for chain in structure[0]: res_count += len(chain) struct_contact_matrix = np.zeros((res_count,res_count)) for model in structure: mod_contact_matrix = compute_atom_contact(model, threshold, excluded) struct_contact_matrix += mod_contact_matrix ave_struct_contact_matrix = struct_contact_matrix / len(structure) return ave_struct_contact_matrix # returns a dictionary that contains the max contact per residue def compute_max_residue(structure, ave_struct_contact_matrix): res_dict = {} i = 0 for chain in structure[0]: for res in chain: contact_atoms = np.sum(ave_struct_contact_matrix[i]) i+=1 if res.get_resname() not in res_dict: res_dict[res.get_resname()] = contact_atoms elif res_dict[res.get_resname()] < contact_atoms: res_dict[res.get_resname()] = contact_atoms return res_dict # compute normalization factor per residue def compute_normalization_factor(pdb_dir, threshold, output): parser = PDBParser() tot_files = 0.0 res_dir_dict = {} for filename in glob.iglob(pdb_dir + "/*.pdb"): tot_files += 1 label = filename.split('/')[-1].split('.')[0] print label structure = parser.get_structure(label,filename) ave_struct_contact_matrix = compute_ave_atom_contact(structure, 2) res_dict = compute_max_residue(structure, ave_struct_contact_matrix) for key in res_dict: if key not in res_dir_dict: res_dir_dict[key] = res_dict[key] else: res_dir_dict[key] += res_dict[key] for key in res_dir_dict: res_dir_dict[key] = res_dir_dict[key]/tot_files with open(output, "w") as outfile: csvfile = csv.writer(outfile) for key in res_dir_dict: csvfile.writerow([key,res_dir_dict[key]]) print res_dir_dict # read normalization file and returns it in a dictionary def read_normalization(norm_fact): norm_dict = {} with open(norm_fact) as normfile: csvfile = csv.reader(normfile) for row in csvfile: norm_dict[row[0]] = float(row[1]) return norm_dict def compute_interaction(res_1, res_2, inter_atoms, norm_dict): # for amber res_1 = 'HIS' if res_1 in ['HIE', 'HID', 'HIP'] else res_1 res_2 = 'HIS' if res_2 in ['HIE', 'HID', 'HIP'] else res_2 norm_1 = norm_dict[res_1] norm_2 = norm_dict[res_2] # remove tom if norm_1 == 0 or norm_2 == 0: return 0 return (inter_atoms*100.0)/sqrt(norm_1*norm_2) # Returns the number of atoms per residue pair that have a # euclidean distance below a threshold # Inputs: # model: Structure object # threshold: Threshold # excluded: Excluded atoms # Returns: |R| x |R| matrix |R| = # of residues, Rij = interaction strength def compute_atom_interaction(model, contact_thresh, interaction_thresh, norm_dict, residues, excluded=[]): model_contact = compute_atom_contact(model, contact_thresh, excluded) model_interact = np.zeros(model_contact.shape) for i in range(len(residues)): for j in range(len(residues)-i): index_j = j + i index_i = i # if equal zero lang if index_i < index_j: int_str = compute_interaction(residues[index_i], residues[index_j], model_contact[index_i][index_j], norm_dict) #rint int_str if int_str >= interaction_thresh: model_interact[index_i][index_j] = 1 model_interact[index_j][index_i] = 1 return model_interact def compute_interaction_matrix(structure, contact_thresh, interaction_thresh, edge_prct, norm_dict, outdir, excluded=[]): residues = [] for res in structure[0].get_residues(): residues.append(res.get_resname()) struct_interact = np.zeros((len(residues),len(residues))) PSN = np.zeros((len(residues),len(residues))) for model in structure: model_interact = compute_atom_interaction(model, contact_thresh, interaction_thresh, norm_dict, residues, excluded=[]) struct_interact = struct_interact + model_interact edge_thresh = edge_prct * len(structure) for i in range(len(residues)): for j in range(len(residues)-i): index_j = j + i index_i = i # if equal zero lang if index_i < index_j: if struct_interact[index_i][index_j]>= edge_thresh: PSN[index_i][index_j] = 1 PSN[index_j][index_i] = 1 filename = "%s/%s_%.2f_%.2f_%.2f" % (outdir,structure.get_id(),contact_thresh,interaction_thresh,edge_prct) np.savetxt(filename,PSN,fmt='%d') def get_graph_rep(pdb_dir, contact_thresh, interaction_thresh, edge_prct, norm_dict, outdir, excluded=[]): parser = PDBParser() for filename in glob.iglob(pdb_dir + "/*.pdb"): label = filename.split('/')[-1].split('.')[0] print label structure = parser.get_structure(label,filename) for int_thresh in interaction_thresh: compute_interaction_matrix(structure, contact_thresh, int_thresh, edge_prct, norm_dict, outdir, excluded=[]) def main(argv): pdb_dir = '' norm_fact = '' outdir = '' norm = 1 interaction_thresh = 1 contact_thresh = 4.5 normalize = False edge_prct = 0.9 bb = ['N','CA','C','O'] try: opts, args = getopt.getopt(argv,"hnp:f:c:i:e:o:",["pdb_dir=", "norm_fact=", "interaction_thresh=", "contact_thresh=", "edge_prct=", "normalize", "outdir="]) except getopt.GetoptError: print 'blast_parser.py -i <input fasta> -x <blast xml> -o <output>' sys.exit(2) for opt, arg in opts: if opt == '-h': print 'Computes a network given a threshold or computes normalization factor' print '''PSN.py -p --pdb_dir\t\tpdb directory -f --norm_fact\t\tnormalization factor file -i --interaction_thresh\tcomma separated interaction strengths -c --contact_thresh\tcontact threshold -n --normalize\t\tComputes normalization factor of input pdbs -o --output\t\tName of output''' sys.exit() elif opt in ("-p", "--pdb_dir"): pdb_dir = arg elif opt in ("-f", "--norm_fact"): norm_fact = arg elif opt in ("-e", "--edge_prct"): edge_prct = string_to_float(arg) elif opt in ("-c", "--contact_thresh"): contact_thresh = string_to_float(arg) elif opt in ("-i", "--interaction_thresh"): interaction_thresh = [] for i in arg.split(","): interaction_thresh.append(string_to_float(i)) elif opt in ("-o", "--output"): outdir = arg elif opt in ("-n","--normalize"): normalize = True # parser = PDBParser() # structure = parser.get_structure('1akg','../native/1acw.pdb') # model = structure[0] # #compute_normalization_factor(pdb_dir, contact_thresh, output) # compute_interaction_matrix(structure, # contact_thresh, # interaction_thresh, # edge_prct, # norm_dict) if normalize: compute_normalization_factor(pdb_dir, contact_thresh, outdir) else: norm_dict = read_normalization(norm_fact) get_graph_rep(pdb_dir, contact_thresh, interaction_thresh, edge_prct, norm_dict, outdir, bb) if __name__ == "__main__": main(sys.argv[1:])

false

true

f70073d75e19b01388a36182b6eeb036bacce511

2,604

py

Python

ExeProc/red/pythoncode/addressbook_pb2.py

vitorCamargo/distributed-systems

23a294a21b9d148a415429dcd326a978780a50d0

[ "MIT" ]

null

ExeProc/red/pythoncode/addressbook_pb2.py

vitorCamargo/distributed-systems

23a294a21b9d148a415429dcd326a978780a50d0

[ "MIT" ]

null

ExeProc/red/pythoncode/addressbook_pb2.py

vitorCamargo/distributed-systems

23a294a21b9d148a415429dcd326a978780a50d0

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Generated by the protocol buffer compiler. DO NOT EDIT! # source: addressbook.proto import sys _b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1')) from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from google.protobuf import reflection as _reflection from google.protobuf import symbol_database as _symbol_database # @@protoc_insertion_point(imports) _sym_db = _symbol_database.Default() DESCRIPTOR = _descriptor.FileDescriptor( name='addressbook.proto', package='', syntax='proto3', serialized_options=None, serialized_pb=_b('\n\x11\x61\x64\x64ressbook.proto\"1\n\x06Person\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\n\n\x02id\x18\x02 \x01(\x05\x12\r\n\x05\x65mail\x18\x03 \x01(\tb\x06proto3') ) _PERSON = _descriptor.Descriptor( name='Person', full_name='Person', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='name', full_name='Person.name', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='id', full_name='Person.id', index=1, number=2, type=5, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='email', full_name='Person.email', index=2, number=3, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=21, serialized_end=70, ) DESCRIPTOR.message_types_by_name['Person'] = _PERSON _sym_db.RegisterFileDescriptor(DESCRIPTOR) Person = _reflection.GeneratedProtocolMessageType('Person', (_message.Message,), dict( DESCRIPTOR = _PERSON, __module__ = 'addressbook_pb2' # @@protoc_insertion_point(class_scope:Person) )) _sym_db.RegisterMessage(Person) # @@protoc_insertion_point(module_scope)

30.635294

182

0.731183

import sys _b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1')) from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from google.protobuf import reflection as _reflection from google.protobuf import symbol_database as _symbol_database _sym_db = _symbol_database.Default() DESCRIPTOR = _descriptor.FileDescriptor( name='addressbook.proto', package='', syntax='proto3', serialized_options=None, serialized_pb=_b('\n\x11\x61\x64\x64ressbook.proto\"1\n\x06Person\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\n\n\x02id\x18\x02 \x01(\x05\x12\r\n\x05\x65mail\x18\x03 \x01(\tb\x06proto3') ) _PERSON = _descriptor.Descriptor( name='Person', full_name='Person', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='name', full_name='Person.name', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='id', full_name='Person.id', index=1, number=2, type=5, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='email', full_name='Person.email', index=2, number=3, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=21, serialized_end=70, ) DESCRIPTOR.message_types_by_name['Person'] = _PERSON _sym_db.RegisterFileDescriptor(DESCRIPTOR) Person = _reflection.GeneratedProtocolMessageType('Person', (_message.Message,), dict( DESCRIPTOR = _PERSON, __module__ = 'addressbook_pb2' # @@protoc_insertion_point(class_scope:Person) )) _sym_db.RegisterMessage(Person) # @@protoc_insertion_point(module_scope)

true

f7007426c0895ef71449b89c57011d35fd1a9bea

2,298

py

Python

tutorial/helpers.py

DEVESHTARASIA/big-data-tutorial

74e2aa1241c30913c5f12b9667f9d626002b98a2

[ "CC-BY-3.0" ]

56

2015-01-01T01:43:31.000Z

2021-10-04T17:47:40.000Z

tutorial/helpers.py

DEVESHTARASIA/big-data-tutorial

74e2aa1241c30913c5f12b9667f9d626002b98a2

[ "CC-BY-3.0" ]

null

tutorial/helpers.py

DEVESHTARASIA/big-data-tutorial

74e2aa1241c30913c5f12b9667f9d626002b98a2

[ "CC-BY-3.0" ]

51

2015-01-16T15:43:06.000Z

2021-10-06T18:29:05.000Z

""" Small helpers for code that is not shown in the notebooks """ from sklearn import neighbors, datasets, linear_model import pylab as pl import numpy as np from matplotlib.colors import ListedColormap # Create color maps for 3-class classification problem, as with iris cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF']) cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF']) def plot_iris_knn(): iris = datasets.load_iris() X = iris.data[:, :2] # we only take the first two features. We could # avoid this ugly slicing by using a two-dim dataset y = iris.target knn = neighbors.KNeighborsClassifier(n_neighbors=3) knn.fit(X, y) x_min, x_max = X[:, 0].min() - .1, X[:, 0].max() + .1 y_min, y_max = X[:, 1].min() - .1, X[:, 1].max() + .1 xx, yy = np.meshgrid(np.linspace(x_min, x_max, 100), np.linspace(y_min, y_max, 100)) Z = knn.predict(np.c_[xx.ravel(), yy.ravel()]) # Put the result into a color plot Z = Z.reshape(xx.shape) pl.figure() pl.pcolormesh(xx, yy, Z, cmap=cmap_light) # Plot also the training points pl.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold) pl.xlabel('sepal length (cm)') pl.ylabel('sepal width (cm)') pl.axis('tight') def plot_polynomial_regression(): rng = np.random.RandomState(0) x = 2*rng.rand(100) - 1 f = lambda t: 1.2 * t**2 + .1 * t**3 - .4 * t **5 - .5 * t ** 9 y = f(x) + .4 * rng.normal(size=100) x_test = np.linspace(-1, 1, 100) pl.figure() pl.scatter(x, y, s=4) X = np.array([x**i for i in range(5)]).T X_test = np.array([x_test**i for i in range(5)]).T regr = linear_model.LinearRegression() regr.fit(X, y) pl.plot(x_test, regr.predict(X_test), label='4th order') X = np.array([x**i for i in range(10)]).T X_test = np.array([x_test**i for i in range(10)]).T regr = linear_model.LinearRegression() regr.fit(X, y) pl.plot(x_test, regr.predict(X_test), label='9th order') pl.legend(loc='best') pl.axis('tight') pl.title('Fitting a 4th and a 9th order polynomial') pl.figure() pl.scatter(x, y, s=4) pl.plot(x_test, f(x_test), label="truth") pl.axis('tight') pl.title('Ground truth (9th order polynomial)')

31.479452

76

0.608355

from sklearn import neighbors, datasets, linear_model import pylab as pl import numpy as np from matplotlib.colors import ListedColormap cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF']) cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF']) def plot_iris_knn(): iris = datasets.load_iris() X = iris.data[:, :2] y = iris.target knn = neighbors.KNeighborsClassifier(n_neighbors=3) knn.fit(X, y) x_min, x_max = X[:, 0].min() - .1, X[:, 0].max() + .1 y_min, y_max = X[:, 1].min() - .1, X[:, 1].max() + .1 xx, yy = np.meshgrid(np.linspace(x_min, x_max, 100), np.linspace(y_min, y_max, 100)) Z = knn.predict(np.c_[xx.ravel(), yy.ravel()]) Z = Z.reshape(xx.shape) pl.figure() pl.pcolormesh(xx, yy, Z, cmap=cmap_light) pl.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold) pl.xlabel('sepal length (cm)') pl.ylabel('sepal width (cm)') pl.axis('tight') def plot_polynomial_regression(): rng = np.random.RandomState(0) x = 2*rng.rand(100) - 1 f = lambda t: 1.2 * t**2 + .1 * t**3 - .4 * t **5 - .5 * t ** 9 y = f(x) + .4 * rng.normal(size=100) x_test = np.linspace(-1, 1, 100) pl.figure() pl.scatter(x, y, s=4) X = np.array([x**i for i in range(5)]).T X_test = np.array([x_test**i for i in range(5)]).T regr = linear_model.LinearRegression() regr.fit(X, y) pl.plot(x_test, regr.predict(X_test), label='4th order') X = np.array([x**i for i in range(10)]).T X_test = np.array([x_test**i for i in range(10)]).T regr = linear_model.LinearRegression() regr.fit(X, y) pl.plot(x_test, regr.predict(X_test), label='9th order') pl.legend(loc='best') pl.axis('tight') pl.title('Fitting a 4th and a 9th order polynomial') pl.figure() pl.scatter(x, y, s=4) pl.plot(x_test, f(x_test), label="truth") pl.axis('tight') pl.title('Ground truth (9th order polynomial)')

true

f7007438100c6e954cb5fc874048d000c8b4c1f5

3,253

py

Python

iwata/settings.py

gingerbeardman/iwata-asks-downloader

8b60b24bd7ad4c5a8b72e2cf9ab7557df65015d9

[ "MIT" ]

16

2020-01-05T14:55:01.000Z

2021-06-06T09:41:08.000Z

iwata/settings.py

gingerbeardman/iwata-asks-downloader

8b60b24bd7ad4c5a8b72e2cf9ab7557df65015d9

[ "MIT" ]

9

2020-01-05T16:42:36.000Z

2020-01-06T15:03:56.000Z

iwata/settings.py

gingerbeardman/iwata-asks-downloader

8b60b24bd7ad4c5a8b72e2cf9ab7557df65015d9

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Scrapy settings for iwata project # # For simplicity, this file contains only settings considered important or # commonly used. You can find more settings consulting the documentation: # # https://doc.scrapy.org/en/latest/topics/settings.html # https://doc.scrapy.org/en/latest/topics/downloader-middleware.html # https://doc.scrapy.org/en/latest/topics/spider-middleware.html BOT_NAME = 'iwata' SPIDER_MODULES = ['iwata.spiders'] NEWSPIDER_MODULE = 'iwata.spiders' # Crawl responsibly by identifying yourself (and your website) on the user-agent #USER_AGENT = 'iwata (+http://www.yourdomain.com)' # Obey robots.txt rules ROBOTSTXT_OBEY = False # Configure maximum concurrent requests performed by Scrapy (default: 16) #CONCURRENT_REQUESTS = 32 # Configure a delay for requests for the same website (default: 0) # See https://doc.scrapy.org/en/latest/topics/settings.html#download-delay # See also autothrottle settings and docs #DOWNLOAD_DELAY = 3 # The download delay setting will honor only one of: #CONCURRENT_REQUESTS_PER_DOMAIN = 16 #CONCURRENT_REQUESTS_PER_IP = 16 # Disable cookies (enabled by default) #COOKIES_ENABLED = False # Disable Telnet Console (enabled by default) #TELNETCONSOLE_ENABLED = False # Override the default request headers: #DEFAULT_REQUEST_HEADERS = { # 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8', # 'Accept-Language': 'en', #} # Enable or disable spider middlewares # See https://doc.scrapy.org/en/latest/topics/spider-middleware.html #SPIDER_MIDDLEWARES = { # 'iwata.middlewares.IwataSpiderMiddleware': 543, #} # Enable or disable downloader middlewares # See https://doc.scrapy.org/en/latest/topics/downloader-middleware.html #DOWNLOADER_MIDDLEWARES = { # 'iwata.middlewares.IwataDownloaderMiddleware': 543, #} # Enable or disable extensions # See https://doc.scrapy.org/en/latest/topics/extensions.html #EXTENSIONS = { # 'scrapy.extensions.telnet.TelnetConsole': None, #} # Configure item pipelines # See https://doc.scrapy.org/en/latest/topics/item-pipeline.html ITEM_PIPELINES = { # 'iwata.pipelines.IwataPipeline': 100, # 'iwata.pipelines.JsonWriterPipeline': 200, 'iwata.pipelines.MarkdownWriterPipeline': 300, 'scrapy.pipelines.images.ImagesPipeline': 400, } IMAGES_STORE = '_images' # Enable and configure the AutoThrottle extension (disabled by default) # See https://doc.scrapy.org/en/latest/topics/autothrottle.html #AUTOTHROTTLE_ENABLED = True # The initial download delay #AUTOTHROTTLE_START_DELAY = 2 # The maximum download delay to be set in case of high latencies #AUTOTHROTTLE_MAX_DELAY = 10 # The average number of requests Scrapy should be sending in parallel to # each remote server #AUTOTHROTTLE_TARGET_CONCURRENCY = 1.0 # Enable showing throttling stats for every response received: #AUTOTHROTTLE_DEBUG = False # Enable and configure HTTP caching (disabled by default) # See https://doc.scrapy.org/en/latest/topics/downloader-middleware.html#httpcache-middleware-settings HTTPCACHE_ENABLED = True #HTTPCACHE_EXPIRATION_SECS = 0 #HTTPCACHE_DIR = 'httpcache' #HTTPCACHE_IGNORE_HTTP_CODES = [] #HTTPCACHE_STORAGE = 'scrapy.extensions.httpcache.FilesystemCacheStorage' FEED_EXPORT_ENCODING = 'utf-8'

33.885417

102

0.774055

BOT_NAME = 'iwata' SPIDER_MODULES = ['iwata.spiders'] NEWSPIDER_MODULE = 'iwata.spiders' ROBOTSTXT_OBEY = False ITEM_PIPELINES = { 'iwata.pipelines.MarkdownWriterPipeline': 300, 'scrapy.pipelines.images.ImagesPipeline': 400, } IMAGES_STORE = '_images' HTTPCACHE_ENABLED = True FEED_EXPORT_ENCODING = 'utf-8'

true

f70074f951aabc6cf1fa5cd4f6fc099ca53429b3

4,050

py

Python

Examples/Host Record/update_host_record/update_host_record_form.py

kemori-bc/gateway-workflows

5aa1e3492b0c0b4ec23a6247ca92861cc77f2187

[ "Apache-2.0" ]

null

Examples/Host Record/update_host_record/update_host_record_form.py

kemori-bc/gateway-workflows

5aa1e3492b0c0b4ec23a6247ca92861cc77f2187

[ "Apache-2.0" ]

null

Examples/Host Record/update_host_record/update_host_record_form.py

kemori-bc/gateway-workflows

5aa1e3492b0c0b4ec23a6247ca92861cc77f2187

[ "Apache-2.0" ]

null

# Copyright 2020 BlueCat Networks (USA) Inc. and its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # By: BlueCat Networks # Date: 2021-08-23 # Gateway Version: 20.12.1 # Description: Example Gateway workflow """ Update host record form """ from wtforms import SubmitField from bluecat.wtform_fields import ( Configuration, View, Zone, HostRecord, CustomStringField, PlainHTML, CustomBooleanField, ) from bluecat.server_endpoints import get_host_records_endpoint from bluecat.wtform_extensions import GatewayForm class GenericFormTemplate(GatewayForm): """Form to generate HTML and Javascript for the update_host_record workflow Note: When updating the form, remember to make the corresponding changes to the workflow pages """ workflow_name = "update_host_record" workflow_permission = "update_host_record_page" configuration = Configuration( workflow_name=workflow_name, permissions=workflow_permission, label="Configuration", required=True, coerce=int, clear_below_on_change=False, is_disabled_on_start=False, on_complete=["call_view"], enable_dependencies={"on_complete": ["view"]}, disable_dependencies={"on_change": ["view"]}, clear_dependencies={"on_change": ["view"]}, ) view = View( workflow_name=workflow_name, permissions=workflow_permission, label="View", required=True, one_off=True, clear_below_on_change=False, enable_dependencies={"on_complete": ["parent_zone"]}, disable_dependencies={"on_change": ["parent_zone"]}, clear_dependencies={"on_change": ["parent_zone"]}, should_cascade_disable_on_change=True, should_cascade_clear_on_change=True, ) parent_zone = Zone( workflow_name=workflow_name, permissions=workflow_permission, label="Zone", required=True, start_initialized=True, inputs={"zone": "parent_zone", "configuration": "configuration", "view": "view"}, clear_below_on_change=False, enable_dependencies={"on_complete": ["host_record"]}, disable_dependencies={"on_change": ["host_record"]}, clear_dependencies={"on_change": ["host_record", "name", "ip4_address"]}, should_cascade_disable_on_change=True, should_cascade_clear_on_change=True, ) host_record = HostRecord( workflow_name=workflow_name, permissions=workflow_permission, label="Host Record", required=True, inputs={ "configuration": "configuration", "view": "view", "parent_zone": "parent_zone", "host_record": "host_record", }, server_outputs={"on_complete": {"name": "name", "addresses": "ip4_address"}}, server_side_output_method=get_host_records_endpoint, clear_below_on_change=False, enable_dependencies={"on_complete": ["submit", "name", "ip4_address", "deploy_now"]}, disable_dependencies={"on_change": ["submit", "name", "ip4_address", "deploy_now"]}, should_cascade_disable_on_change=True, ) separator = PlainHTML("<hr>") name = CustomStringField(label="New Host Name", required=True) ip4_address = CustomStringField( label="IPv4 Address (multiple IPv4 addresses must be separated by a comma)", required=True ) deploy_now = CustomBooleanField(label="Deploy Now") submit = SubmitField(label="Update")

34.033613

98

0.682469

from wtforms import SubmitField from bluecat.wtform_fields import ( Configuration, View, Zone, HostRecord, CustomStringField, PlainHTML, CustomBooleanField, ) from bluecat.server_endpoints import get_host_records_endpoint from bluecat.wtform_extensions import GatewayForm class GenericFormTemplate(GatewayForm): workflow_name = "update_host_record" workflow_permission = "update_host_record_page" configuration = Configuration( workflow_name=workflow_name, permissions=workflow_permission, label="Configuration", required=True, coerce=int, clear_below_on_change=False, is_disabled_on_start=False, on_complete=["call_view"], enable_dependencies={"on_complete": ["view"]}, disable_dependencies={"on_change": ["view"]}, clear_dependencies={"on_change": ["view"]}, ) view = View( workflow_name=workflow_name, permissions=workflow_permission, label="View", required=True, one_off=True, clear_below_on_change=False, enable_dependencies={"on_complete": ["parent_zone"]}, disable_dependencies={"on_change": ["parent_zone"]}, clear_dependencies={"on_change": ["parent_zone"]}, should_cascade_disable_on_change=True, should_cascade_clear_on_change=True, ) parent_zone = Zone( workflow_name=workflow_name, permissions=workflow_permission, label="Zone", required=True, start_initialized=True, inputs={"zone": "parent_zone", "configuration": "configuration", "view": "view"}, clear_below_on_change=False, enable_dependencies={"on_complete": ["host_record"]}, disable_dependencies={"on_change": ["host_record"]}, clear_dependencies={"on_change": ["host_record", "name", "ip4_address"]}, should_cascade_disable_on_change=True, should_cascade_clear_on_change=True, ) host_record = HostRecord( workflow_name=workflow_name, permissions=workflow_permission, label="Host Record", required=True, inputs={ "configuration": "configuration", "view": "view", "parent_zone": "parent_zone", "host_record": "host_record", }, server_outputs={"on_complete": {"name": "name", "addresses": "ip4_address"}}, server_side_output_method=get_host_records_endpoint, clear_below_on_change=False, enable_dependencies={"on_complete": ["submit", "name", "ip4_address", "deploy_now"]}, disable_dependencies={"on_change": ["submit", "name", "ip4_address", "deploy_now"]}, should_cascade_disable_on_change=True, ) separator = PlainHTML("<hr>") name = CustomStringField(label="New Host Name", required=True) ip4_address = CustomStringField( label="IPv4 Address (multiple IPv4 addresses must be separated by a comma)", required=True ) deploy_now = CustomBooleanField(label="Deploy Now") submit = SubmitField(label="Update")

true

f7007599d5407174b0644c00cd4a735463bb5b0e

12,011

py

Python

plugins/modules/oci_data_catalog_namespace_facts.py

LaudateCorpus1/oci-ansible-collection

2b1cd87b4d652a97c1ca752cfc4fdc4bdb37a7e7

[ "Apache-2.0" ]

null

plugins/modules/oci_data_catalog_namespace_facts.py

LaudateCorpus1/oci-ansible-collection

2b1cd87b4d652a97c1ca752cfc4fdc4bdb37a7e7

[ "Apache-2.0" ]

null

plugins/modules/oci_data_catalog_namespace_facts.py

LaudateCorpus1/oci-ansible-collection

2b1cd87b4d652a97c1ca752cfc4fdc4bdb37a7e7

[ "Apache-2.0" ]

null

#!/usr/bin/python # Copyright (c) 2020, 2022 Oracle and/or its affiliates. # This software is made available to you under the terms of the GPL 3.0 license or the Apache 2.0 license. # GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt) # Apache License v2.0 # See LICENSE.TXT for details. # GENERATED FILE - DO NOT EDIT - MANUAL CHANGES WILL BE OVERWRITTEN from __future__ import absolute_import, division, print_function __metaclass__ = type ANSIBLE_METADATA = { "metadata_version": "1.1", "status": ["preview"], "supported_by": "community", } DOCUMENTATION = """ --- module: oci_data_catalog_namespace_facts short_description: Fetches details about one or multiple Namespace resources in Oracle Cloud Infrastructure description: - Fetches details about one or multiple Namespace resources in Oracle Cloud Infrastructure - Returns a list of namespaces within a data catalog. - If I(namespace_id) is specified, the details of a single Namespace will be returned. version_added: "2.9.0" author: Oracle (@oracle) options: catalog_id: description: - Unique catalog identifier. type: str required: true namespace_id: description: - Unique namespace identifier. - Required to get a specific namespace. type: str aliases: ["id"] fields: description: - Specifies the fields to return in a namespace response. type: list elements: str choices: - "key" - "displayName" - "description" - "lifecycleState" - "timeCreated" - "timeUpdated" - "createdById" - "updatedById" - "properties" display_name: description: - A filter to return only resources that match the entire display name given. The match is not case sensitive. type: str aliases: ["name"] display_name_contains: description: - "A filter to return only resources that match display name pattern given. The match is not case sensitive. For Example : /folders?displayNameContains=Cu.* The above would match all folders with display name that starts with \\"Cu\\" or has the pattern \\"Cu\\" anywhere in between." type: str lifecycle_state: description: - A filter to return only resources that match the specified lifecycle state. The value is case insensitive. type: str choices: - "CREATING" - "ACTIVE" - "INACTIVE" - "UPDATING" - "DELETING" - "DELETED" - "FAILED" - "MOVING" time_created: description: - Time that the resource was created. An L(RFC3339,https://tools.ietf.org/html/rfc3339) formatted datetime string. type: str time_updated: description: - Time that the resource was updated. An L(RFC3339,https://tools.ietf.org/html/rfc3339) formatted datetime string. type: str created_by_id: description: - OCID of the user who created the resource. type: str updated_by_id: description: - OCID of the user who updated the resource. type: str sort_by: description: - The field to sort by. Only one sort order may be provided. Default order for TIMECREATED is descending. Default order for DISPLAYNAME is ascending. If no value is specified TIMECREATED is default. type: str choices: - "TIMECREATED" - "DISPLAYNAME" sort_order: description: - The sort order to use, either 'asc' or 'desc'. type: str choices: - "ASC" - "DESC" extends_documentation_fragment: [ oracle.oci.oracle ] """ EXAMPLES = """ - name: Get a specific namespace oci_data_catalog_namespace_facts: # required catalog_id: "ocid1.catalog.oc1..xxxxxxEXAMPLExxxxxx" namespace_id: "ocid1.namespace.oc1..xxxxxxEXAMPLExxxxxx" # optional fields: [ "key" ] - name: List namespaces oci_data_catalog_namespace_facts: # required catalog_id: "ocid1.catalog.oc1..xxxxxxEXAMPLExxxxxx" # optional fields: [ "key" ] display_name: display_name_example display_name_contains: display_name_contains_example lifecycle_state: CREATING time_created: 2013-10-20T19:20:30+01:00 time_updated: 2013-10-20T19:20:30+01:00 created_by_id: "ocid1.createdby.oc1..xxxxxxEXAMPLExxxxxx" updated_by_id: "ocid1.updatedby.oc1..xxxxxxEXAMPLExxxxxx" sort_by: TIMECREATED sort_order: ASC """ RETURN = """ namespaces: description: - List of Namespace resources returned: on success type: complex contains: key: description: - Unique namespace key that is immutable. returned: on success type: str sample: key_example display_name: description: - Name of the Namespace returned: on success type: str sample: display_name_example description: description: - Description for the namespace returned: on success type: str sample: description_example is_service_defined: description: - If this field is defined by service or by a user returned: on success type: bool sample: true lifecycle_state: description: - The current state of the namespace. returned: on success type: str sample: CREATING time_created: description: - "The date and time the namespace was created, in the format defined by L(RFC3339,https://tools.ietf.org/html/rfc3339). Example: `2019-03-25T21:10:29.600Z`" returned: on success type: str sample: "2013-10-20T19:20:30+01:00" time_updated: description: - The last time that any change was made to the namespace. An L(RFC3339,https://tools.ietf.org/html/rfc3339) formatted datetime string. - Returned for get operation returned: on success type: str sample: "2013-10-20T19:20:30+01:00" created_by_id: description: - OCID of the user who created the namespace. - Returned for get operation returned: on success type: str sample: "ocid1.createdby.oc1..xxxxxxEXAMPLExxxxxx" updated_by_id: description: - OCID of the user who last modified the namespace. - Returned for get operation returned: on success type: str sample: "ocid1.updatedby.oc1..xxxxxxEXAMPLExxxxxx" sample: [{ "key": "key_example", "display_name": "display_name_example", "description": "description_example", "is_service_defined": true, "lifecycle_state": "CREATING", "time_created": "2013-10-20T19:20:30+01:00", "time_updated": "2013-10-20T19:20:30+01:00", "created_by_id": "ocid1.createdby.oc1..xxxxxxEXAMPLExxxxxx", "updated_by_id": "ocid1.updatedby.oc1..xxxxxxEXAMPLExxxxxx" }] """ from ansible.module_utils.basic import AnsibleModule from ansible_collections.oracle.oci.plugins.module_utils import oci_common_utils from ansible_collections.oracle.oci.plugins.module_utils.oci_resource_utils import ( OCIResourceFactsHelperBase, get_custom_class, ) try: from oci.data_catalog import DataCatalogClient HAS_OCI_PY_SDK = True except ImportError: HAS_OCI_PY_SDK = False class DataCatalogNamespaceFactsHelperGen(OCIResourceFactsHelperBase): """Supported operations: get, list""" def get_required_params_for_get(self): return [ "catalog_id", "namespace_id", ] def get_required_params_for_list(self): return [ "catalog_id", ] def get_resource(self): optional_get_method_params = [ "fields", ] optional_kwargs = dict( (param, self.module.params[param]) for param in optional_get_method_params if self.module.params.get(param) is not None ) return oci_common_utils.call_with_backoff( self.client.get_namespace, catalog_id=self.module.params.get("catalog_id"), namespace_id=self.module.params.get("namespace_id"), **optional_kwargs ) def list_resources(self): optional_list_method_params = [ "display_name", "display_name_contains", "lifecycle_state", "time_created", "time_updated", "created_by_id", "updated_by_id", "sort_by", "sort_order", "fields", ] optional_kwargs = dict( (param, self.module.params[param]) for param in optional_list_method_params if self.module.params.get(param) is not None ) return oci_common_utils.list_all_resources( self.client.list_namespaces, catalog_id=self.module.params.get("catalog_id"), **optional_kwargs ) DataCatalogNamespaceFactsHelperCustom = get_custom_class( "DataCatalogNamespaceFactsHelperCustom" ) class ResourceFactsHelper( DataCatalogNamespaceFactsHelperCustom, DataCatalogNamespaceFactsHelperGen ): pass def main(): module_args = oci_common_utils.get_common_arg_spec() module_args.update( dict( catalog_id=dict(type="str", required=True), namespace_id=dict(aliases=["id"], type="str"), fields=dict( type="list", elements="str", choices=[ "key", "displayName", "description", "lifecycleState", "timeCreated", "timeUpdated", "createdById", "updatedById", "properties", ], ), display_name=dict(aliases=["name"], type="str"), display_name_contains=dict(type="str"), lifecycle_state=dict( type="str", choices=[ "CREATING", "ACTIVE", "INACTIVE", "UPDATING", "DELETING", "DELETED", "FAILED", "MOVING", ], ), time_created=dict(type="str"), time_updated=dict(type="str"), created_by_id=dict(type="str"), updated_by_id=dict(type="str"), sort_by=dict(type="str", choices=["TIMECREATED", "DISPLAYNAME"]), sort_order=dict(type="str", choices=["ASC", "DESC"]), ) ) module = AnsibleModule(argument_spec=module_args) if not HAS_OCI_PY_SDK: module.fail_json(msg="oci python sdk required for this module.") resource_facts_helper = ResourceFactsHelper( module=module, resource_type="namespace", service_client_class=DataCatalogClient, namespace="data_catalog", ) result = [] if resource_facts_helper.is_get(): result = [resource_facts_helper.get()] elif resource_facts_helper.is_list(): result = resource_facts_helper.list() else: resource_facts_helper.fail() module.exit_json(namespaces=result) if __name__ == "__main__": main()

32.114973

151

0.589709

from __future__ import absolute_import, division, print_function __metaclass__ = type ANSIBLE_METADATA = { "metadata_version": "1.1", "status": ["preview"], "supported_by": "community", } DOCUMENTATION = """ --- module: oci_data_catalog_namespace_facts short_description: Fetches details about one or multiple Namespace resources in Oracle Cloud Infrastructure description: - Fetches details about one or multiple Namespace resources in Oracle Cloud Infrastructure - Returns a list of namespaces within a data catalog. - If I(namespace_id) is specified, the details of a single Namespace will be returned. version_added: "2.9.0" author: Oracle (@oracle) options: catalog_id: description: - Unique catalog identifier. type: str required: true namespace_id: description: - Unique namespace identifier. - Required to get a specific namespace. type: str aliases: ["id"] fields: description: - Specifies the fields to return in a namespace response. type: list elements: str choices: - "key" - "displayName" - "description" - "lifecycleState" - "timeCreated" - "timeUpdated" - "createdById" - "updatedById" - "properties" display_name: description: - A filter to return only resources that match the entire display name given. The match is not case sensitive. type: str aliases: ["name"] display_name_contains: description: - "A filter to return only resources that match display name pattern given. The match is not case sensitive. For Example : /folders?displayNameContains=Cu.* The above would match all folders with display name that starts with \\"Cu\\" or has the pattern \\"Cu\\" anywhere in between." type: str lifecycle_state: description: - A filter to return only resources that match the specified lifecycle state. The value is case insensitive. type: str choices: - "CREATING" - "ACTIVE" - "INACTIVE" - "UPDATING" - "DELETING" - "DELETED" - "FAILED" - "MOVING" time_created: description: - Time that the resource was created. An L(RFC3339,https://tools.ietf.org/html/rfc3339) formatted datetime string. type: str time_updated: description: - Time that the resource was updated. An L(RFC3339,https://tools.ietf.org/html/rfc3339) formatted datetime string. type: str created_by_id: description: - OCID of the user who created the resource. type: str updated_by_id: description: - OCID of the user who updated the resource. type: str sort_by: description: - The field to sort by. Only one sort order may be provided. Default order for TIMECREATED is descending. Default order for DISPLAYNAME is ascending. If no value is specified TIMECREATED is default. type: str choices: - "TIMECREATED" - "DISPLAYNAME" sort_order: description: - The sort order to use, either 'asc' or 'desc'. type: str choices: - "ASC" - "DESC" extends_documentation_fragment: [ oracle.oci.oracle ] """ EXAMPLES = """ - name: Get a specific namespace oci_data_catalog_namespace_facts: # required catalog_id: "ocid1.catalog.oc1..xxxxxxEXAMPLExxxxxx" namespace_id: "ocid1.namespace.oc1..xxxxxxEXAMPLExxxxxx" # optional fields: [ "key" ] - name: List namespaces oci_data_catalog_namespace_facts: # required catalog_id: "ocid1.catalog.oc1..xxxxxxEXAMPLExxxxxx" # optional fields: [ "key" ] display_name: display_name_example display_name_contains: display_name_contains_example lifecycle_state: CREATING time_created: 2013-10-20T19:20:30+01:00 time_updated: 2013-10-20T19:20:30+01:00 created_by_id: "ocid1.createdby.oc1..xxxxxxEXAMPLExxxxxx" updated_by_id: "ocid1.updatedby.oc1..xxxxxxEXAMPLExxxxxx" sort_by: TIMECREATED sort_order: ASC """ RETURN = """ namespaces: description: - List of Namespace resources returned: on success type: complex contains: key: description: - Unique namespace key that is immutable. returned: on success type: str sample: key_example display_name: description: - Name of the Namespace returned: on success type: str sample: display_name_example description: description: - Description for the namespace returned: on success type: str sample: description_example is_service_defined: description: - If this field is defined by service or by a user returned: on success type: bool sample: true lifecycle_state: description: - The current state of the namespace. returned: on success type: str sample: CREATING time_created: description: - "The date and time the namespace was created, in the format defined by L(RFC3339,https://tools.ietf.org/html/rfc3339). Example: `2019-03-25T21:10:29.600Z`" returned: on success type: str sample: "2013-10-20T19:20:30+01:00" time_updated: description: - The last time that any change was made to the namespace. An L(RFC3339,https://tools.ietf.org/html/rfc3339) formatted datetime string. - Returned for get operation returned: on success type: str sample: "2013-10-20T19:20:30+01:00" created_by_id: description: - OCID of the user who created the namespace. - Returned for get operation returned: on success type: str sample: "ocid1.createdby.oc1..xxxxxxEXAMPLExxxxxx" updated_by_id: description: - OCID of the user who last modified the namespace. - Returned for get operation returned: on success type: str sample: "ocid1.updatedby.oc1..xxxxxxEXAMPLExxxxxx" sample: [{ "key": "key_example", "display_name": "display_name_example", "description": "description_example", "is_service_defined": true, "lifecycle_state": "CREATING", "time_created": "2013-10-20T19:20:30+01:00", "time_updated": "2013-10-20T19:20:30+01:00", "created_by_id": "ocid1.createdby.oc1..xxxxxxEXAMPLExxxxxx", "updated_by_id": "ocid1.updatedby.oc1..xxxxxxEXAMPLExxxxxx" }] """ from ansible.module_utils.basic import AnsibleModule from ansible_collections.oracle.oci.plugins.module_utils import oci_common_utils from ansible_collections.oracle.oci.plugins.module_utils.oci_resource_utils import ( OCIResourceFactsHelperBase, get_custom_class, ) try: from oci.data_catalog import DataCatalogClient HAS_OCI_PY_SDK = True except ImportError: HAS_OCI_PY_SDK = False class DataCatalogNamespaceFactsHelperGen(OCIResourceFactsHelperBase): def get_required_params_for_get(self): return [ "catalog_id", "namespace_id", ] def get_required_params_for_list(self): return [ "catalog_id", ] def get_resource(self): optional_get_method_params = [ "fields", ] optional_kwargs = dict( (param, self.module.params[param]) for param in optional_get_method_params if self.module.params.get(param) is not None ) return oci_common_utils.call_with_backoff( self.client.get_namespace, catalog_id=self.module.params.get("catalog_id"), namespace_id=self.module.params.get("namespace_id"), **optional_kwargs ) def list_resources(self): optional_list_method_params = [ "display_name", "display_name_contains", "lifecycle_state", "time_created", "time_updated", "created_by_id", "updated_by_id", "sort_by", "sort_order", "fields", ] optional_kwargs = dict( (param, self.module.params[param]) for param in optional_list_method_params if self.module.params.get(param) is not None ) return oci_common_utils.list_all_resources( self.client.list_namespaces, catalog_id=self.module.params.get("catalog_id"), **optional_kwargs ) DataCatalogNamespaceFactsHelperCustom = get_custom_class( "DataCatalogNamespaceFactsHelperCustom" ) class ResourceFactsHelper( DataCatalogNamespaceFactsHelperCustom, DataCatalogNamespaceFactsHelperGen ): pass def main(): module_args = oci_common_utils.get_common_arg_spec() module_args.update( dict( catalog_id=dict(type="str", required=True), namespace_id=dict(aliases=["id"], type="str"), fields=dict( type="list", elements="str", choices=[ "key", "displayName", "description", "lifecycleState", "timeCreated", "timeUpdated", "createdById", "updatedById", "properties", ], ), display_name=dict(aliases=["name"], type="str"), display_name_contains=dict(type="str"), lifecycle_state=dict( type="str", choices=[ "CREATING", "ACTIVE", "INACTIVE", "UPDATING", "DELETING", "DELETED", "FAILED", "MOVING", ], ), time_created=dict(type="str"), time_updated=dict(type="str"), created_by_id=dict(type="str"), updated_by_id=dict(type="str"), sort_by=dict(type="str", choices=["TIMECREATED", "DISPLAYNAME"]), sort_order=dict(type="str", choices=["ASC", "DESC"]), ) ) module = AnsibleModule(argument_spec=module_args) if not HAS_OCI_PY_SDK: module.fail_json(msg="oci python sdk required for this module.") resource_facts_helper = ResourceFactsHelper( module=module, resource_type="namespace", service_client_class=DataCatalogClient, namespace="data_catalog", ) result = [] if resource_facts_helper.is_get(): result = [resource_facts_helper.get()] elif resource_facts_helper.is_list(): result = resource_facts_helper.list() else: resource_facts_helper.fail() module.exit_json(namespaces=result) if __name__ == "__main__": main()

true

f700763f7e7484369e4aaaf60e64c92af4f1f9e9

89

py

Python

api/run.py

clodonil/mytasks

0355c9a37139d3cd5aeef24e804a2e3f513136b6

[ "Apache-2.0" ]

null

api/run.py

clodonil/mytasks

0355c9a37139d3cd5aeef24e804a2e3f513136b6

[ "Apache-2.0" ]

null

api/run.py

clodonil/mytasks

0355c9a37139d3cd5aeef24e804a2e3f513136b6

[ "Apache-2.0" ]

null

from app import app app.run(app.config['HOST'], app.config['PORT'], app.config['DEBUG'])

29.666667

68

0.696629

from app import app app.run(app.config['HOST'], app.config['PORT'], app.config['DEBUG'])

true

f70076a50a1ec77000417afebe6c4b53d762ed40

81

py

Python

project/api/__init__.py

Shiqan/fortnite-replay-api

0ef42287a559b55864244961c3ccf7a697be740f

[ "MIT" ]

4

2019-05-16T12:52:40.000Z

2020-04-21T19:24:36.000Z

project/api/__init__.py

Shiqan/fortnite-replay-api

0ef42287a559b55864244961c3ccf7a697be740f

[ "MIT" ]

null

project/api/__init__.py

Shiqan/fortnite-replay-api

0ef42287a559b55864244961c3ccf7a697be740f

[ "MIT" ]

2

2021-03-23T13:27:02.000Z

2022-03-06T21:30:08.000Z

#!/usr/bin/python # -*- coding: utf-8 -*- """ Fortnite replay api blueprint """

11.571429

29

0.592593

true

f70078f9bc488153bc2bdd1da52425628d5aa4e9

1,968

py

Python

tests/unit_tests/modules/s3/s3gis/KMLLayer.py

PeterDaveHello/eden

26174a9dde2f19cd3bc879694f373ad5f765b6ed

[ "MIT" ]

1

2017-07-22T18:49:34.000Z

tests/unit_tests/modules/s3/s3gis/KMLLayer.py

PeterDaveHello/eden

26174a9dde2f19cd3bc879694f373ad5f765b6ed

[ "MIT" ]

null

tests/unit_tests/modules/s3/s3gis/KMLLayer.py

PeterDaveHello/eden

26174a9dde2f19cd3bc879694f373ad5f765b6ed

[ "MIT" ]

1

2019-12-16T15:14:46.000Z

s3gis_tests = load_module("tests.unit_tests.modules.s3.s3gis") s3gis = s3gis_tests.s3gis def test_KMLLayer(): current.session.s3.debug = True current.request.utcnow = datetime.datetime.now() s3gis_tests.layer_test( db, db.gis_layer_kml, dict( name = "Test KML", description = "Test KML layer", enabled = True, created_on = datetime.datetime.now(), modified_on = datetime.datetime.now(), url = "test://test_KML", ), "S3.gis.layers_kml", [ { "marker_height": 34, "marker_image": u"gis_marker.image.marker_red.png", "marker_width": 20, "name": u"Test KML", # this shows that caching is OK: "url": u"/eden/default/download/gis_cache2.file.Test_20KML.kml" } ], session = session, request = request, ) def test_KMLCaching_not_possible(): import os.path import sys class Mock(object): pass mock_stderr = Mock() buffer = [] def mock_write(error_message): buffer.append(error_message) mock_stderr.write = mock_write with s3gis_tests.Change( os.path, { "exists": lambda *a, **kw: False } ): with s3gis_tests.Change( sys, { "stderr": mock_stderr } ): with s3gis_tests.Change( current.session.s3, { "debug": False } ): kml_layer = s3gis.KMLLayer(s3gis.GIS()) js = kml_layer.as_javascript() assert session.error.startswith( "GIS: KML layers cannot be cached: " ) assert "GIS: KML layers cannot be cached:" in buffer[0]

27.71831

79

0.490346

s3gis_tests = load_module("tests.unit_tests.modules.s3.s3gis") s3gis = s3gis_tests.s3gis def test_KMLLayer(): current.session.s3.debug = True current.request.utcnow = datetime.datetime.now() s3gis_tests.layer_test( db, db.gis_layer_kml, dict( name = "Test KML", description = "Test KML layer", enabled = True, created_on = datetime.datetime.now(), modified_on = datetime.datetime.now(), url = "test://test_KML", ), "S3.gis.layers_kml", [ { "marker_height": 34, "marker_image": u"gis_marker.image.marker_red.png", "marker_width": 20, "name": u"Test KML", "url": u"/eden/default/download/gis_cache2.file.Test_20KML.kml" } ], session = session, request = request, ) def test_KMLCaching_not_possible(): import os.path import sys class Mock(object): pass mock_stderr = Mock() buffer = [] def mock_write(error_message): buffer.append(error_message) mock_stderr.write = mock_write with s3gis_tests.Change( os.path, { "exists": lambda *a, **kw: False } ): with s3gis_tests.Change( sys, { "stderr": mock_stderr } ): with s3gis_tests.Change( current.session.s3, { "debug": False } ): kml_layer = s3gis.KMLLayer(s3gis.GIS()) js = kml_layer.as_javascript() assert session.error.startswith( "GIS: KML layers cannot be cached: " ) assert "GIS: KML layers cannot be cached:" in buffer[0]

true

f70079abc23e2a4161a080f73867e8d2f28c4762

1,338

py

Python

examples/starwars/data.py

mdornseif/graphene-gae

a223d10b7558c7e8e6d190cd1297eba54878c4c8

[ "BSD-3-Clause" ]

128

2016-05-17T05:48:13.000Z

2021-11-08T12:45:59.000Z

examples/starwars/data.py

mdornseif/graphene-gae

a223d10b7558c7e8e6d190cd1297eba54878c4c8

[ "BSD-3-Clause" ]

42

2016-05-26T04:35:45.000Z

2021-03-25T21:28:05.000Z

examples/starwars/data.py

mdornseif/graphene-gae

a223d10b7558c7e8e6d190cd1297eba54878c4c8

[ "BSD-3-Clause" ]

13

2016-06-04T14:03:10.000Z

2020-10-01T06:18:43.000Z

from .models import Character, Faction, Ship __author__ = 'ekampf' def initialize(): human = Character(name='Human') human.put() droid = Character(name='Droid') droid.put() rebels = Faction(id="rebels", name='Alliance to Restore the Republic', hero_key=human.key) rebels.put() empire = Faction(id="empire", name='Galactic Empire', hero_key=droid.key) empire.put() xwing = Ship(name='X-Wing', faction_key=rebels.key) xwing.put() ywing = Ship(name='Y-Wing', faction_key=rebels.key) ywing.put() awing = Ship(name='A-Wing', faction_key=rebels.key) awing.put() # Yeah, technically it's Corellian. But it flew in the service of the rebels, # so for the purposes of this demo it's a rebel ship. falcon = Ship(name='Millenium Falcon', faction_key=rebels.key) falcon.put() homeOne = Ship(name='Home One', faction_key=rebels.key) homeOne.put() tieFighter = Ship(name='TIE Fighter', faction_key=empire.key) tieFighter.put() tieInterceptor = Ship(name='TIE Interceptor', faction_key=empire.key) tieInterceptor.put() executor = Ship(name='Executor', faction_key=empire.key) executor.put() def create_ship(ship_name, faction_key): new_ship = Ship(name=ship_name, faction_key=faction_key) new_ship.put() return new_ship

26.235294

94

0.68012

from .models import Character, Faction, Ship __author__ = 'ekampf' def initialize(): human = Character(name='Human') human.put() droid = Character(name='Droid') droid.put() rebels = Faction(id="rebels", name='Alliance to Restore the Republic', hero_key=human.key) rebels.put() empire = Faction(id="empire", name='Galactic Empire', hero_key=droid.key) empire.put() xwing = Ship(name='X-Wing', faction_key=rebels.key) xwing.put() ywing = Ship(name='Y-Wing', faction_key=rebels.key) ywing.put() awing = Ship(name='A-Wing', faction_key=rebels.key) awing.put() # so for the purposes of this demo it's a rebel ship. falcon = Ship(name='Millenium Falcon', faction_key=rebels.key) falcon.put() homeOne = Ship(name='Home One', faction_key=rebels.key) homeOne.put() tieFighter = Ship(name='TIE Fighter', faction_key=empire.key) tieFighter.put() tieInterceptor = Ship(name='TIE Interceptor', faction_key=empire.key) tieInterceptor.put() executor = Ship(name='Executor', faction_key=empire.key) executor.put() def create_ship(ship_name, faction_key): new_ship = Ship(name=ship_name, faction_key=faction_key) new_ship.put() return new_ship

true

f70079cc4279ef48d761d342b154ee50b32ac6d2

14,748

py

Python

rssant_cli/rss.py

dumpmemory/rssant

8bacf91bedc2d2fa35f3e8b0f6a8b8c2a712c1a6

[ "BSD-3-Clause" ]

null

rssant_cli/rss.py

dumpmemory/rssant

8bacf91bedc2d2fa35f3e8b0f6a8b8c2a712c1a6

[ "BSD-3-Clause" ]

null

rssant_cli/rss.py

dumpmemory/rssant

8bacf91bedc2d2fa35f3e8b0f6a8b8c2a712c1a6

[ "BSD-3-Clause" ]

null

import logging import time import json from collections import defaultdict import tqdm import click from django.utils import timezone from django.db import transaction, connection from django.db.models import Q from django.contrib.auth import get_user_model import rssant_common.django_setup # noqa:F401 from rssant_api.models import Feed, Story, UnionFeed, UserStory, UserFeed from rssant_api.helper import reverse_url from rssant_common import _proxy_helper from rssant_common.helper import format_table, pretty_format_json from rssant_feedlib.reader import FeedResponseStatus, FeedReader from rssant_common import unionid from rssant_feedlib import processor from rssant_common.actor_client import scheduler from rssant_config import CONFIG LOG = logging.getLogger(__name__) @click.group() def main(): """RSS Commands""" def _decode_feed_ids(option_feeds): """ >>> _decode_feed_ids('123,456') [123, 456] """ return [int(x) for x in option_feeds.strip().split(',')] def _decode_union_feed_ids(option_feeds): """ >>> _decode_union_feed_ids('014064,0140be') [196, 366] """ return [unionid.decode(x)[1] for x in option_feeds.strip().split(',')] def _get_all_feed_ids(): feed_ids = [feed.id for feed in Feed.objects.only('id').all()] return feed_ids def _get_feed_ids(option_feeds): if option_feeds and option_feeds != 'all': feed_ids = _decode_feed_ids(option_feeds) else: feed_ids = _get_all_feed_ids() return feed_ids def _get_story_ids(option_storys): if option_storys: story_ids = option_storys.strip().split(',') else: story_ids = [story.id for story in Story.objects.only('id').all()] return story_ids @main.command() @click.option('--dry-run', is_flag=True) def fix_feed_total_storys(dry_run=False): incorrect_feeds = Story.query_feed_incorrect_total_storys() LOG.info('total %s incorrect feeds', len(incorrect_feeds)) header = ['feed_id', 'total_storys', 'correct_total_storys'] click.echo(format_table(incorrect_feeds, header=header)) if dry_run: return with transaction.atomic(): num_corrected = 0 for feed_id, *__ in tqdm.tqdm(incorrect_feeds, ncols=80, ascii=True): fixed = Story.fix_feed_total_storys(feed_id) if fixed: num_corrected += 1 LOG.info('correct %s feeds', num_corrected) @main.command() @click.option('--feeds', help="feed ids, separate by ','") def update_feed_monthly_story_count(feeds=None): feed_ids = _get_feed_ids(feeds) LOG.info('total %s feeds', len(feed_ids)) for feed_id in tqdm.tqdm(feed_ids, ncols=80, ascii=True): with transaction.atomic(): Story.refresh_feed_monthly_story_count(feed_id) @main.command() @click.option('--feeds', help="feed ids, separate by ','") def update_feed_dryness(feeds=None): feed_ids = _get_feed_ids(feeds) LOG.info('total %s feeds', len(feed_ids)) for feed_id in tqdm.tqdm(feed_ids, ncols=80, ascii=True): with transaction.atomic(): feed = Feed.get_by_pk(feed_id) if feed.total_storys <= 0: continue cnt = feed.monthly_story_count if not cnt: Story.refresh_feed_monthly_story_count(feed_id) feed.refresh_from_db() feed.dryness = feed.monthly_story_count.dryness() feed.save() @main.command() @click.option('--feeds', help="feed ids, separate by ','") def update_feed_dt_first_story_published(feeds=None): feed_ids = _get_feed_ids(feeds) LOG.info('total %s feeds', len(feed_ids)) for feed_id in tqdm.tqdm(feed_ids, ncols=80, ascii=True): with transaction.atomic(): feed = Feed.get_by_pk(feed_id) if feed.dt_first_story_published: continue if feed.total_storys <= 0: continue try: story = Story.get_by_offset(feed_id, 0, detail=True) except Story.DoesNotExist: LOG.warning(f'story feed_id={feed_id} offset=0 not exists') continue feed.dt_first_story_published = story.dt_published feed.save() @main.command() @click.option('--storys', help="story ids, separate by ','") def update_story_has_mathjax(storys=None): story_ids = _get_story_ids(storys) LOG.info('total %s storys', len(story_ids)) for story_id in tqdm.tqdm(story_ids, ncols=80, ascii=True): with transaction.atomic(): story = Story.objects.only('id', 'content', '_version').get(pk=story_id) if processor.story_has_mathjax(story.content): story.has_mathjax = True story.save() @main.command() def update_story_is_user_marked(): user_storys = list( UserStory.objects .exclude(is_watched=False, is_favorited=False) .all() ) LOG.info('total %s user marked storys', len(user_storys)) if not user_storys: return for user_story in tqdm.tqdm(user_storys, ncols=80, ascii=True): Story.set_user_marked_by_id(user_story.story_id) @main.command() @click.option('--storys', help="story ids, separate by ','") def process_story_links(storys=None): story_ids = _get_story_ids(storys) LOG.info('total %s storys', len(story_ids)) for story_id in tqdm.tqdm(story_ids, ncols=80, ascii=True): with transaction.atomic(): story = Story.objects.only('id', 'content', '_version').get(pk=story_id) content = processor.process_story_links(story.content, story.link) if story.content != content: story.content = content story.save() @main.command() @click.option('--storys', help="story ids, separate by ','") def update_story_images(storys=None): story_ids = _get_story_ids(storys) LOG.info('total %s storys', len(story_ids)) for story_id in tqdm.tqdm(story_ids, ncols=80, ascii=True): story = Story.objects.get(pk=story_id) scheduler.tell('harbor_rss.update_story_images', dict( story_id=story_id, story_url=story.link, images=[], )) @main.command() @click.argument('unionid_text') def decode_unionid(unionid_text): numbers = unionid.decode(unionid_text) if len(numbers) == 3: click.echo('user_id={} feed_id={} offset={}'.format(*numbers)) elif len(numbers) == 2: click.echo('user_id={} feed_id={}'.format(*numbers)) else: click.echo(numbers) @main.command() @click.option('--days', type=int, default=1) @click.option('--limit', type=int, default=100) @click.option('--threshold', type=int, default=99) def delete_invalid_feeds(days=1, limit=100, threshold=99): sql = """ SELECT feed_id, title, link, url, status_code, count FROM ( SELECT feed_id, status_code, count(1) as count FROM rssant_api_rawfeed WHERE dt_created >= %s and (status_code < 200 or status_code >= 400) group by feed_id, status_code having count(1) > 3 order by count desc limit %s ) error_feed join rssant_api_feed on error_feed.feed_id = rssant_api_feed.id order by feed_id, status_code, count; """ sql_ok_count = """ SELECT feed_id, count(1) as count FROM rssant_api_rawfeed WHERE dt_created >= %s and (status_code >= 200 and status_code < 400) AND feed_id=ANY(%s) group by feed_id """ t_begin = timezone.now() - timezone.timedelta(days=days) error_feeds = defaultdict(dict) with connection.cursor() as cursor: cursor.execute(sql, [t_begin, limit]) for feed_id, title, link, url, status_code, count in cursor.fetchall(): error_feeds[feed_id].update(feed_id=feed_id, title=title, link=link, url=url) error = error_feeds[feed_id].setdefault('error', {}) error_name = FeedResponseStatus.name_of(status_code) error[error_name] = count error_feeds[feed_id]['error_count'] = sum(error.values()) error_feeds[feed_id].update(ok_count=0, error_percent=100) cursor.execute(sql_ok_count, [t_begin, list(error_feeds)]) for feed_id, ok_count in cursor.fetchall(): feed = error_feeds[feed_id] total = feed['error_count'] + ok_count error_percent = round((feed['error_count'] / total) * 100) feed.update(ok_count=ok_count, error_percent=error_percent) error_feeds = list(sorted(error_feeds.values(), key=lambda x: x['error_percent'], reverse=True)) delete_feed_ids = [] for feed in error_feeds: if feed['error_percent'] >= threshold: delete_feed_ids.append(feed['feed_id']) click.echo(pretty_format_json(feed)) if delete_feed_ids: confirm_delete = click.confirm(f'Delete {len(delete_feed_ids)} feeds?') if not confirm_delete: click.echo('Abort!') else: UnionFeed.bulk_delete(delete_feed_ids) click.echo('Done!') return error_feeds @main.command() def fix_user_story_offset(): sql = """ SELECT us.id, us."offset", story."offset" FROM rssant_api_userstory AS us LEFT OUTER JOIN rssant_api_story AS story ON us.story_id=story.id WHERE us."offset" != story."offset" """ items = [] with connection.cursor() as cursor: cursor.execute(sql) for us_id, us_offset, story_offset in cursor.fetchall(): items.append((us_id, us_offset, story_offset)) click.echo(f'total {len(items)} mismatch user story offset') if not items: return with transaction.atomic(): for us_id, us_offset, story_offset in tqdm.tqdm(items, ncols=80, ascii=True): UserStory.objects.filter(pk=us_id).update(offset=-us_offset) for us_id, us_offset, story_offset in tqdm.tqdm(items, ncols=80, ascii=True): UserStory.objects.filter(pk=us_id).update(offset=story_offset) @main.command() def subscribe_changelog(): changelog_url = CONFIG.root_url.rstrip('/') + '/changelog.atom' feed = Feed.objects.get(url=changelog_url) if not feed: click.echo(f'not found changelog feed url={changelog_url}') return click.echo(f'changelog feed {feed}') User = get_user_model() users = list(User.objects.all()) click.echo(f'total {len(users)} users') for user in tqdm.tqdm(users, ncols=80, ascii=True): with transaction.atomic(): user_feed = UserFeed.objects\ .filter(user_id=user.id, feed_id=feed.id).first() if not user_feed: user_feed = UserFeed( user_id=user.id, feed_id=feed.id, is_from_bookmark=False, ) user_feed.save() @main.command() def update_feed_use_proxy(): if not CONFIG.rss_proxy_enable: click.echo('rss proxy not enable!') return blacklist = [ '%博客园%', '%微信%', '%新浪%', '%的评论%', '%Comments on%', ] sql = """ select * from rssant_api_feed where (NOT title LIKE ANY(%s)) AND ( dt_created >= '2020-04-01' or (total_storys <= 5 and dt_updated <= '2019-12-01') ) """ feeds = list(Feed.objects.raw(sql, [blacklist])) click.echo(f'{len(feeds)} feeds need check') reader = FeedReader(**_proxy_helper.get_proxy_options()) proxy_feeds = [] with reader: for i, feed in enumerate(feeds): click.echo(f'#{i} {feed}') status = reader.read(feed.url).status click.echo(f' #{i} status={FeedResponseStatus.name_of(status)}') if FeedResponseStatus.is_need_proxy(status): proxy_status = reader.read(feed.url, use_proxy=True).status click.echo(f' #{i} proxy_status={FeedResponseStatus.name_of(proxy_status)}') if proxy_status == 200: proxy_feeds.append(feed) click.echo(f'{len(proxy_feeds)} feeds need use proxy') if proxy_feeds: with transaction.atomic(): for feed in tqdm.tqdm(proxy_feeds, ncols=80, ascii=True): feed.refresh_from_db() feed.use_proxy = True feed.save() @main.command() @click.argument('key') def delete_feed(key): try: key = int(key) except ValueError: pass # ignore if isinstance(key, int): feed = Feed.get_by_pk(key) else: feed = Feed.objects.filter( Q(url__contains=key) | Q(title__contains=key) ).first() if not feed: print(f'not found feed like {key}') return if click.confirm(f'delete {feed} ?'): feed.delete() @main.command() @click.option('--feeds', help="feed ids, separate by ','") @click.option('--union-feeds', help="union feed ids, separate by ','") @click.option('--key', help="feed url or title keyword") @click.option('--expire', type=int, default=1, help="expire hours") def refresh_feed(feeds, union_feeds, key, expire=None): feed_ids = [] if feeds: feed_ids.extend(_get_feed_ids(feeds)) if union_feeds: feed_ids.extend(_decode_union_feed_ids(union_feeds)) if key: cond = Q(url__contains=key) | Q(title__contains=key) feed_objs = Feed.objects.filter(cond).only('id').all() feed_ids.extend(x.id for x in feed_objs) feed_ids = list(sorted(set(feed_ids))) expire_at = time.time() + expire * 60 * 60 for feed_id in tqdm.tqdm(feed_ids, ncols=80, ascii=True): feed = Feed.objects.only('id', 'url', 'use_proxy').get(pk=feed_id) scheduler.tell('worker_rss.sync_feed', dict( feed_id=feed.id, url=feed.url, use_proxy=feed.use_proxy, is_refresh=True, ), expire_at=expire_at) @main.command() @click.option('--feeds', required=True, help="feed ids, separate by ','") def update_feed_reverse_url(feeds): feed_ids = _get_feed_ids(feeds) for feed_id in tqdm.tqdm(feed_ids, ncols=80, ascii=True): feed = Feed.objects.get(pk=feed_id) feed.reverse_url = reverse_url(feed.url) feed.save() @main.command() @click.option('--dst', required=True, help='actor dst') @click.option('--content', help='message content') @click.option('--expire-seconds', type=int, help='expire time in seconds') def tell(dst, content, expire_seconds): if content: content = json.loads(content) expire_at = None if expire_seconds: expire_at = int(time.time()) + expire_seconds scheduler.tell(dst, content=content, expire_at=expire_at) if __name__ == "__main__": main()

34.865248

100

0.641782

import logging import time import json from collections import defaultdict import tqdm import click from django.utils import timezone from django.db import transaction, connection from django.db.models import Q from django.contrib.auth import get_user_model import rssant_common.django_setup from rssant_api.models import Feed, Story, UnionFeed, UserStory, UserFeed from rssant_api.helper import reverse_url from rssant_common import _proxy_helper from rssant_common.helper import format_table, pretty_format_json from rssant_feedlib.reader import FeedResponseStatus, FeedReader from rssant_common import unionid from rssant_feedlib import processor from rssant_common.actor_client import scheduler from rssant_config import CONFIG LOG = logging.getLogger(__name__) @click.group() def main(): def _decode_feed_ids(option_feeds): return [int(x) for x in option_feeds.strip().split(',')] def _decode_union_feed_ids(option_feeds): return [unionid.decode(x)[1] for x in option_feeds.strip().split(',')] def _get_all_feed_ids(): feed_ids = [feed.id for feed in Feed.objects.only('id').all()] return feed_ids def _get_feed_ids(option_feeds): if option_feeds and option_feeds != 'all': feed_ids = _decode_feed_ids(option_feeds) else: feed_ids = _get_all_feed_ids() return feed_ids def _get_story_ids(option_storys): if option_storys: story_ids = option_storys.strip().split(',') else: story_ids = [story.id for story in Story.objects.only('id').all()] return story_ids @main.command() @click.option('--dry-run', is_flag=True) def fix_feed_total_storys(dry_run=False): incorrect_feeds = Story.query_feed_incorrect_total_storys() LOG.info('total %s incorrect feeds', len(incorrect_feeds)) header = ['feed_id', 'total_storys', 'correct_total_storys'] click.echo(format_table(incorrect_feeds, header=header)) if dry_run: return with transaction.atomic(): num_corrected = 0 for feed_id, *__ in tqdm.tqdm(incorrect_feeds, ncols=80, ascii=True): fixed = Story.fix_feed_total_storys(feed_id) if fixed: num_corrected += 1 LOG.info('correct %s feeds', num_corrected) @main.command() @click.option('--feeds', help="feed ids, separate by ','") def update_feed_monthly_story_count(feeds=None): feed_ids = _get_feed_ids(feeds) LOG.info('total %s feeds', len(feed_ids)) for feed_id in tqdm.tqdm(feed_ids, ncols=80, ascii=True): with transaction.atomic(): Story.refresh_feed_monthly_story_count(feed_id) @main.command() @click.option('--feeds', help="feed ids, separate by ','") def update_feed_dryness(feeds=None): feed_ids = _get_feed_ids(feeds) LOG.info('total %s feeds', len(feed_ids)) for feed_id in tqdm.tqdm(feed_ids, ncols=80, ascii=True): with transaction.atomic(): feed = Feed.get_by_pk(feed_id) if feed.total_storys <= 0: continue cnt = feed.monthly_story_count if not cnt: Story.refresh_feed_monthly_story_count(feed_id) feed.refresh_from_db() feed.dryness = feed.monthly_story_count.dryness() feed.save() @main.command() @click.option('--feeds', help="feed ids, separate by ','") def update_feed_dt_first_story_published(feeds=None): feed_ids = _get_feed_ids(feeds) LOG.info('total %s feeds', len(feed_ids)) for feed_id in tqdm.tqdm(feed_ids, ncols=80, ascii=True): with transaction.atomic(): feed = Feed.get_by_pk(feed_id) if feed.dt_first_story_published: continue if feed.total_storys <= 0: continue try: story = Story.get_by_offset(feed_id, 0, detail=True) except Story.DoesNotExist: LOG.warning(f'story feed_id={feed_id} offset=0 not exists') continue feed.dt_first_story_published = story.dt_published feed.save() @main.command() @click.option('--storys', help="story ids, separate by ','") def update_story_has_mathjax(storys=None): story_ids = _get_story_ids(storys) LOG.info('total %s storys', len(story_ids)) for story_id in tqdm.tqdm(story_ids, ncols=80, ascii=True): with transaction.atomic(): story = Story.objects.only('id', 'content', '_version').get(pk=story_id) if processor.story_has_mathjax(story.content): story.has_mathjax = True story.save() @main.command() def update_story_is_user_marked(): user_storys = list( UserStory.objects .exclude(is_watched=False, is_favorited=False) .all() ) LOG.info('total %s user marked storys', len(user_storys)) if not user_storys: return for user_story in tqdm.tqdm(user_storys, ncols=80, ascii=True): Story.set_user_marked_by_id(user_story.story_id) @main.command() @click.option('--storys', help="story ids, separate by ','") def process_story_links(storys=None): story_ids = _get_story_ids(storys) LOG.info('total %s storys', len(story_ids)) for story_id in tqdm.tqdm(story_ids, ncols=80, ascii=True): with transaction.atomic(): story = Story.objects.only('id', 'content', '_version').get(pk=story_id) content = processor.process_story_links(story.content, story.link) if story.content != content: story.content = content story.save() @main.command() @click.option('--storys', help="story ids, separate by ','") def update_story_images(storys=None): story_ids = _get_story_ids(storys) LOG.info('total %s storys', len(story_ids)) for story_id in tqdm.tqdm(story_ids, ncols=80, ascii=True): story = Story.objects.get(pk=story_id) scheduler.tell('harbor_rss.update_story_images', dict( story_id=story_id, story_url=story.link, images=[], )) @main.command() @click.argument('unionid_text') def decode_unionid(unionid_text): numbers = unionid.decode(unionid_text) if len(numbers) == 3: click.echo('user_id={} feed_id={} offset={}'.format(*numbers)) elif len(numbers) == 2: click.echo('user_id={} feed_id={}'.format(*numbers)) else: click.echo(numbers) @main.command() @click.option('--days', type=int, default=1) @click.option('--limit', type=int, default=100) @click.option('--threshold', type=int, default=99) def delete_invalid_feeds(days=1, limit=100, threshold=99): sql = """ SELECT feed_id, title, link, url, status_code, count FROM ( SELECT feed_id, status_code, count(1) as count FROM rssant_api_rawfeed WHERE dt_created >= %s and (status_code < 200 or status_code >= 400) group by feed_id, status_code having count(1) > 3 order by count desc limit %s ) error_feed join rssant_api_feed on error_feed.feed_id = rssant_api_feed.id order by feed_id, status_code, count; """ sql_ok_count = """ SELECT feed_id, count(1) as count FROM rssant_api_rawfeed WHERE dt_created >= %s and (status_code >= 200 and status_code < 400) AND feed_id=ANY(%s) group by feed_id """ t_begin = timezone.now() - timezone.timedelta(days=days) error_feeds = defaultdict(dict) with connection.cursor() as cursor: cursor.execute(sql, [t_begin, limit]) for feed_id, title, link, url, status_code, count in cursor.fetchall(): error_feeds[feed_id].update(feed_id=feed_id, title=title, link=link, url=url) error = error_feeds[feed_id].setdefault('error', {}) error_name = FeedResponseStatus.name_of(status_code) error[error_name] = count error_feeds[feed_id]['error_count'] = sum(error.values()) error_feeds[feed_id].update(ok_count=0, error_percent=100) cursor.execute(sql_ok_count, [t_begin, list(error_feeds)]) for feed_id, ok_count in cursor.fetchall(): feed = error_feeds[feed_id] total = feed['error_count'] + ok_count error_percent = round((feed['error_count'] / total) * 100) feed.update(ok_count=ok_count, error_percent=error_percent) error_feeds = list(sorted(error_feeds.values(), key=lambda x: x['error_percent'], reverse=True)) delete_feed_ids = [] for feed in error_feeds: if feed['error_percent'] >= threshold: delete_feed_ids.append(feed['feed_id']) click.echo(pretty_format_json(feed)) if delete_feed_ids: confirm_delete = click.confirm(f'Delete {len(delete_feed_ids)} feeds?') if not confirm_delete: click.echo('Abort!') else: UnionFeed.bulk_delete(delete_feed_ids) click.echo('Done!') return error_feeds @main.command() def fix_user_story_offset(): sql = """ SELECT us.id, us."offset", story."offset" FROM rssant_api_userstory AS us LEFT OUTER JOIN rssant_api_story AS story ON us.story_id=story.id WHERE us."offset" != story."offset" """ items = [] with connection.cursor() as cursor: cursor.execute(sql) for us_id, us_offset, story_offset in cursor.fetchall(): items.append((us_id, us_offset, story_offset)) click.echo(f'total {len(items)} mismatch user story offset') if not items: return with transaction.atomic(): for us_id, us_offset, story_offset in tqdm.tqdm(items, ncols=80, ascii=True): UserStory.objects.filter(pk=us_id).update(offset=-us_offset) for us_id, us_offset, story_offset in tqdm.tqdm(items, ncols=80, ascii=True): UserStory.objects.filter(pk=us_id).update(offset=story_offset) @main.command() def subscribe_changelog(): changelog_url = CONFIG.root_url.rstrip('/') + '/changelog.atom' feed = Feed.objects.get(url=changelog_url) if not feed: click.echo(f'not found changelog feed url={changelog_url}') return click.echo(f'changelog feed {feed}') User = get_user_model() users = list(User.objects.all()) click.echo(f'total {len(users)} users') for user in tqdm.tqdm(users, ncols=80, ascii=True): with transaction.atomic(): user_feed = UserFeed.objects\ .filter(user_id=user.id, feed_id=feed.id).first() if not user_feed: user_feed = UserFeed( user_id=user.id, feed_id=feed.id, is_from_bookmark=False, ) user_feed.save() @main.command() def update_feed_use_proxy(): if not CONFIG.rss_proxy_enable: click.echo('rss proxy not enable!') return blacklist = [ '%博客园%', '%微信%', '%新浪%', '%的评论%', '%Comments on%', ] sql = """ select * from rssant_api_feed where (NOT title LIKE ANY(%s)) AND ( dt_created >= '2020-04-01' or (total_storys <= 5 and dt_updated <= '2019-12-01') ) """ feeds = list(Feed.objects.raw(sql, [blacklist])) click.echo(f'{len(feeds)} feeds need check') reader = FeedReader(**_proxy_helper.get_proxy_options()) proxy_feeds = [] with reader: for i, feed in enumerate(feeds): click.echo(f'#{i} {feed}') status = reader.read(feed.url).status click.echo(f' #{i} status={FeedResponseStatus.name_of(status)}') if FeedResponseStatus.is_need_proxy(status): proxy_status = reader.read(feed.url, use_proxy=True).status click.echo(f' #{i} proxy_status={FeedResponseStatus.name_of(proxy_status)}') if proxy_status == 200: proxy_feeds.append(feed) click.echo(f'{len(proxy_feeds)} feeds need use proxy') if proxy_feeds: with transaction.atomic(): for feed in tqdm.tqdm(proxy_feeds, ncols=80, ascii=True): feed.refresh_from_db() feed.use_proxy = True feed.save() @main.command() @click.argument('key') def delete_feed(key): try: key = int(key) except ValueError: pass if isinstance(key, int): feed = Feed.get_by_pk(key) else: feed = Feed.objects.filter( Q(url__contains=key) | Q(title__contains=key) ).first() if not feed: print(f'not found feed like {key}') return if click.confirm(f'delete {feed} ?'): feed.delete() @main.command() @click.option('--feeds', help="feed ids, separate by ','") @click.option('--union-feeds', help="union feed ids, separate by ','") @click.option('--key', help="feed url or title keyword") @click.option('--expire', type=int, default=1, help="expire hours") def refresh_feed(feeds, union_feeds, key, expire=None): feed_ids = [] if feeds: feed_ids.extend(_get_feed_ids(feeds)) if union_feeds: feed_ids.extend(_decode_union_feed_ids(union_feeds)) if key: cond = Q(url__contains=key) | Q(title__contains=key) feed_objs = Feed.objects.filter(cond).only('id').all() feed_ids.extend(x.id for x in feed_objs) feed_ids = list(sorted(set(feed_ids))) expire_at = time.time() + expire * 60 * 60 for feed_id in tqdm.tqdm(feed_ids, ncols=80, ascii=True): feed = Feed.objects.only('id', 'url', 'use_proxy').get(pk=feed_id) scheduler.tell('worker_rss.sync_feed', dict( feed_id=feed.id, url=feed.url, use_proxy=feed.use_proxy, is_refresh=True, ), expire_at=expire_at) @main.command() @click.option('--feeds', required=True, help="feed ids, separate by ','") def update_feed_reverse_url(feeds): feed_ids = _get_feed_ids(feeds) for feed_id in tqdm.tqdm(feed_ids, ncols=80, ascii=True): feed = Feed.objects.get(pk=feed_id) feed.reverse_url = reverse_url(feed.url) feed.save() @main.command() @click.option('--dst', required=True, help='actor dst') @click.option('--content', help='message content') @click.option('--expire-seconds', type=int, help='expire time in seconds') def tell(dst, content, expire_seconds): if content: content = json.loads(content) expire_at = None if expire_seconds: expire_at = int(time.time()) + expire_seconds scheduler.tell(dst, content=content, expire_at=expire_at) if __name__ == "__main__": main()

true

f7007aa840274af20197e60983ca3a3b63feaf8b

1,554

py

Python

solutions/REF_11_main.py

ManoloBrn/gcloudtraining17

8b72f16b3868239743c7a207fb44082d055da4e3

[ "Apache-2.0" ]

null

solutions/REF_11_main.py

ManoloBrn/gcloudtraining17

8b72f16b3868239743c7a207fb44082d055da4e3

[ "Apache-2.0" ]

null

solutions/REF_11_main.py

ManoloBrn/gcloudtraining17

8b72f16b3868239743c7a207fb44082d055da4e3

[ "Apache-2.0" ]

2

2016-11-28T05:36:44.000Z

2017-02-13T01:31:38.000Z

#!/usr/bin/env python import webapp2 from google.appengine.api import app_identity from google.appengine.api import mail from conference import ConferenceApi class SetAnnouncementHandler(webapp2.RequestHandler): def get(self): """Set Announcement in Memcache.""" header = self.request.headers.get('X-AppEngine-Cron', None) if not header: raise ValueError('attempt to access cron handler directly, ' 'missing custom App Engine header') ConferenceApi._cacheAnnouncement() self.response.set_status(204) class SendConfirmationEmailHandler(webapp2.RequestHandler): def post(self): """Send email confirming Conference creation.""" header = self.request.headers.get('X-AppEngine-QueueName', None) if not header: raise ValueError('attempt to access task handler directly, ' 'missing custom App Engine header') mail.send_mail( 'noreply@%s.appspotmail.com' % ( app_identity.get_application_id()), # from self.request.get('email'), # to 'You created a new Conference!', # subj 'Hi, you have created a following ' # body 'conference:\r\n\r\n%s' % self.request.get( 'conferenceInfo') ) app = webapp2.WSGIApplication([ ('/crons/set_announcement', SetAnnouncementHandler), ('/tasks/send_confirmation_email', SendConfirmationEmailHandler) ], debug=True)

37.902439

72

0.623552

import webapp2 from google.appengine.api import app_identity from google.appengine.api import mail from conference import ConferenceApi class SetAnnouncementHandler(webapp2.RequestHandler): def get(self): header = self.request.headers.get('X-AppEngine-Cron', None) if not header: raise ValueError('attempt to access cron handler directly, ' 'missing custom App Engine header') ConferenceApi._cacheAnnouncement() self.response.set_status(204) class SendConfirmationEmailHandler(webapp2.RequestHandler): def post(self): header = self.request.headers.get('X-AppEngine-QueueName', None) if not header: raise ValueError('attempt to access task handler directly, ' 'missing custom App Engine header') mail.send_mail( 'noreply@%s.appspotmail.com' % ( app_identity.get_application_id()), self.request.get('email'), 'You created a new Conference!', 'Hi, you have created a following ' 'conference:\r\n\r\n%s' % self.request.get( 'conferenceInfo') ) app = webapp2.WSGIApplication([ ('/crons/set_announcement', SetAnnouncementHandler), ('/tasks/send_confirmation_email', SendConfirmationEmailHandler) ], debug=True)

true

f7007aeadf7f3003bdb12b053d54a12891257329

5,750

py

Python

torchkit/models/vision/segmentation/unet.py

cosmic-cortex/torchkit

9f44c8a500a4345d81feac14b6b200c5d190283a

[ "MIT" ]

null

torchkit/models/vision/segmentation/unet.py

cosmic-cortex/torchkit

9f44c8a500a4345d81feac14b6b200c5d190283a

[ "MIT" ]

null

torchkit/models/vision/segmentation/unet.py

cosmic-cortex/torchkit

9f44c8a500a4345d81feac14b6b200c5d190283a

[ "MIT" ]

null

import torch import torch.nn as nn import torch.nn.functional as F from torch.autograd import Variable def pad_to_shape(this, shp): """ Not a very safe function. """ return F.pad(this, (0, shp[3] - this.shape[3], 0, shp[2] - this.shape[2])) class First(nn.Module): def __init__(self, in_channels, middle_channels, out_channels, dropout=False): super(First, self).__init__() layers = [ nn.Conv2d(in_channels, middle_channels, kernel_size=3, padding=1), nn.BatchNorm2d(middle_channels), nn.ReLU(inplace=True), nn.Conv2d(middle_channels, out_channels, kernel_size=3, padding=1), nn.BatchNorm2d(out_channels), nn.ReLU(inplace=True) ] if dropout: assert 0 <= dropout <= 1, 'dropout must be between 0 and 1' layers.append(nn.Dropout2d(p=dropout)) self.first = nn.Sequential(*layers) def forward(self, x): return self.first(x) class Encoder(nn.Module): def __init__( self, in_channels, middle_channels, out_channels, dropout=False, downsample_kernel=2 ): super(Encoder, self).__init__() layers = [ nn.MaxPool2d(kernel_size=downsample_kernel), nn.Conv2d(in_channels, middle_channels, kernel_size=3, padding=1), nn.BatchNorm2d(middle_channels), nn.ReLU(inplace=True), nn.Conv2d(middle_channels, out_channels, kernel_size=3, padding=1), nn.BatchNorm2d(out_channels), nn.ReLU(inplace=True) ] if dropout: assert 0 <= dropout <= 1, 'dropout must be between 0 and 1' layers.append(nn.Dropout2d(p=dropout)) self.encoder = nn.Sequential(*layers) def forward(self, x): return self.encoder(x) class Center(nn.Module): def __init__(self, in_channels, middle_channels, out_channels, deconv_channels, dropout=False): super(Center, self).__init__() layers = [ nn.MaxPool2d(kernel_size=2), nn.Conv2d(in_channels, middle_channels, kernel_size=3, padding=1), nn.BatchNorm2d(middle_channels), nn.ReLU(inplace=True), nn.Conv2d(middle_channels, out_channels, kernel_size=3, padding=1), nn.BatchNorm2d(out_channels), nn.ReLU(inplace=True), nn.ConvTranspose2d(out_channels, deconv_channels, kernel_size=2, stride=2) ] if dropout: assert 0 <= dropout <= 1, 'dropout must be between 0 and 1' layers.append(nn.Dropout2d(p=dropout)) self.center = nn.Sequential(*layers) def forward(self, x): return self.center(x) class Decoder(nn.Module): def __init__(self, in_channels, middle_channels, out_channels, deconv_channels, dropout=False): super(Decoder, self).__init__() layers = [ nn.Conv2d(in_channels, middle_channels, kernel_size=3, padding=1), nn.BatchNorm2d(middle_channels), nn.ReLU(inplace=True), nn.Conv2d(middle_channels, out_channels, kernel_size=3, padding=1), nn.BatchNorm2d(out_channels), nn.ReLU(inplace=True), nn.ConvTranspose2d(out_channels, deconv_channels, kernel_size=2, stride=2) ] if dropout: assert 0 <= dropout <= 1, 'dropout must be between 0 and 1' layers.append(nn.Dropout2d(p=dropout)) self.decoder = nn.Sequential(*layers) def forward(self, x): return self.decoder(x) class Last(nn.Module): def __init__(self, in_channels, middle_channels, out_channels, softmax=False): super(Last, self).__init__() layers = [ nn.Conv2d(in_channels, middle_channels, kernel_size=3, padding=1), nn.BatchNorm2d(middle_channels), nn.ReLU(inplace=True), nn.Conv2d(middle_channels, middle_channels, kernel_size=3, padding=1), nn.BatchNorm2d(middle_channels), nn.ReLU(inplace=True), nn.Conv2d(middle_channels, out_channels, kernel_size=1), nn.Sigmoid() ] if softmax: layers.append(nn.Softmax2d()) self.first = nn.Sequential(*layers) def forward(self, x): return self.first(x) class UNet(nn.Module): def __init__(self, in_channels, out_channels, softmax=False): super(UNet, self).__init__() self.first = First(in_channels, 64, 64) self.encoder_1 = Encoder(64, 128, 128) self.encoder_2 = Encoder(128, 256, 256) self.encoder_3 = Encoder(256, 512, 512) self.center = Center(512, 1024, 1024, 512) self.decoder_3 = Decoder(1024, 512, 512, 256) self.decoder_2 = Decoder(512, 256, 256, 128) self.decoder_1 = Decoder(256, 128, 128, 64) self.last = Last(128, 64, out_channels, softmax=softmax) def forward(self, x): x_first = self.first(x) x_enc_1 = self.encoder_1(x_first) x_enc_2 = self.encoder_2(x_enc_1) x_enc_3 = self.encoder_3(x_enc_2) x_cent = self.center(x_enc_3) x_dec_3 = self.decoder_3(torch.cat([pad_to_shape(x_cent, x_enc_3.shape), x_enc_3], dim=1)) x_dec_2 = self.decoder_2(torch.cat([pad_to_shape(x_dec_3, x_enc_2.shape), x_enc_2], dim=1)) x_dec_1 = self.decoder_1(torch.cat([pad_to_shape(x_dec_2, x_enc_1.shape), x_enc_1], dim=1)) return self.last(torch.cat([pad_to_shape(x_dec_1, x_first.shape), x_first], dim=1)) if __name__ == '__main__': pass

35.060976

100

0.602957

import torch import torch.nn as nn import torch.nn.functional as F from torch.autograd import Variable def pad_to_shape(this, shp): return F.pad(this, (0, shp[3] - this.shape[3], 0, shp[2] - this.shape[2])) class First(nn.Module): def __init__(self, in_channels, middle_channels, out_channels, dropout=False): super(First, self).__init__() layers = [ nn.Conv2d(in_channels, middle_channels, kernel_size=3, padding=1), nn.BatchNorm2d(middle_channels), nn.ReLU(inplace=True), nn.Conv2d(middle_channels, out_channels, kernel_size=3, padding=1), nn.BatchNorm2d(out_channels), nn.ReLU(inplace=True) ] if dropout: assert 0 <= dropout <= 1, 'dropout must be between 0 and 1' layers.append(nn.Dropout2d(p=dropout)) self.first = nn.Sequential(*layers) def forward(self, x): return self.first(x) class Encoder(nn.Module): def __init__( self, in_channels, middle_channels, out_channels, dropout=False, downsample_kernel=2 ): super(Encoder, self).__init__() layers = [ nn.MaxPool2d(kernel_size=downsample_kernel), nn.Conv2d(in_channels, middle_channels, kernel_size=3, padding=1), nn.BatchNorm2d(middle_channels), nn.ReLU(inplace=True), nn.Conv2d(middle_channels, out_channels, kernel_size=3, padding=1), nn.BatchNorm2d(out_channels), nn.ReLU(inplace=True) ] if dropout: assert 0 <= dropout <= 1, 'dropout must be between 0 and 1' layers.append(nn.Dropout2d(p=dropout)) self.encoder = nn.Sequential(*layers) def forward(self, x): return self.encoder(x) class Center(nn.Module): def __init__(self, in_channels, middle_channels, out_channels, deconv_channels, dropout=False): super(Center, self).__init__() layers = [ nn.MaxPool2d(kernel_size=2), nn.Conv2d(in_channels, middle_channels, kernel_size=3, padding=1), nn.BatchNorm2d(middle_channels), nn.ReLU(inplace=True), nn.Conv2d(middle_channels, out_channels, kernel_size=3, padding=1), nn.BatchNorm2d(out_channels), nn.ReLU(inplace=True), nn.ConvTranspose2d(out_channels, deconv_channels, kernel_size=2, stride=2) ] if dropout: assert 0 <= dropout <= 1, 'dropout must be between 0 and 1' layers.append(nn.Dropout2d(p=dropout)) self.center = nn.Sequential(*layers) def forward(self, x): return self.center(x) class Decoder(nn.Module): def __init__(self, in_channels, middle_channels, out_channels, deconv_channels, dropout=False): super(Decoder, self).__init__() layers = [ nn.Conv2d(in_channels, middle_channels, kernel_size=3, padding=1), nn.BatchNorm2d(middle_channels), nn.ReLU(inplace=True), nn.Conv2d(middle_channels, out_channels, kernel_size=3, padding=1), nn.BatchNorm2d(out_channels), nn.ReLU(inplace=True), nn.ConvTranspose2d(out_channels, deconv_channels, kernel_size=2, stride=2) ] if dropout: assert 0 <= dropout <= 1, 'dropout must be between 0 and 1' layers.append(nn.Dropout2d(p=dropout)) self.decoder = nn.Sequential(*layers) def forward(self, x): return self.decoder(x) class Last(nn.Module): def __init__(self, in_channels, middle_channels, out_channels, softmax=False): super(Last, self).__init__() layers = [ nn.Conv2d(in_channels, middle_channels, kernel_size=3, padding=1), nn.BatchNorm2d(middle_channels), nn.ReLU(inplace=True), nn.Conv2d(middle_channels, middle_channels, kernel_size=3, padding=1), nn.BatchNorm2d(middle_channels), nn.ReLU(inplace=True), nn.Conv2d(middle_channels, out_channels, kernel_size=1), nn.Sigmoid() ] if softmax: layers.append(nn.Softmax2d()) self.first = nn.Sequential(*layers) def forward(self, x): return self.first(x) class UNet(nn.Module): def __init__(self, in_channels, out_channels, softmax=False): super(UNet, self).__init__() self.first = First(in_channels, 64, 64) self.encoder_1 = Encoder(64, 128, 128) self.encoder_2 = Encoder(128, 256, 256) self.encoder_3 = Encoder(256, 512, 512) self.center = Center(512, 1024, 1024, 512) self.decoder_3 = Decoder(1024, 512, 512, 256) self.decoder_2 = Decoder(512, 256, 256, 128) self.decoder_1 = Decoder(256, 128, 128, 64) self.last = Last(128, 64, out_channels, softmax=softmax) def forward(self, x): x_first = self.first(x) x_enc_1 = self.encoder_1(x_first) x_enc_2 = self.encoder_2(x_enc_1) x_enc_3 = self.encoder_3(x_enc_2) x_cent = self.center(x_enc_3) x_dec_3 = self.decoder_3(torch.cat([pad_to_shape(x_cent, x_enc_3.shape), x_enc_3], dim=1)) x_dec_2 = self.decoder_2(torch.cat([pad_to_shape(x_dec_3, x_enc_2.shape), x_enc_2], dim=1)) x_dec_1 = self.decoder_1(torch.cat([pad_to_shape(x_dec_2, x_enc_1.shape), x_enc_1], dim=1)) return self.last(torch.cat([pad_to_shape(x_dec_1, x_first.shape), x_first], dim=1)) if __name__ == '__main__': pass

true

f7007b869cca1c346ea889953e37448001e40b10

5,930

py

Python

run_decoding/run_decoding_WM_across_epochs_and_conditions.py

romquentin/decod_WM_Selection_and_maintenance

fc1bf2f21959795fbea731f642cc750c2b61bce2

[ "BSD-3-Clause" ]

7

2018-07-16T01:59:03.000Z

2021-07-28T09:48:13.000Z

run_decoding/run_decoding_WM_across_epochs_and_conditions.py

romquentin/decod_WM_Selection_and_maintenance

fc1bf2f21959795fbea731f642cc750c2b61bce2

[ "BSD-3-Clause" ]

1

2020-03-15T00:35:45.000Z

2020-04-17T09:54:38.000Z

run_decoding/run_decoding_WM_across_epochs_and_conditions.py

romquentin/decod_WM_Selection_and_maintenance

fc1bf2f21959795fbea731f642cc750c2b61bce2

[ "BSD-3-Clause" ]

4

2018-08-02T08:52:59.000Z

2021-12-17T11:43:47.000Z

"""Run decoding analyses in sensors space accross memory content and visual perception for the working memory task and save decoding performance""" # Authors: Romain Quentin <[email protected]> # Jean-Remi King <[email protected]> # # License: BSD (3-clause) import os import os.path as op import numpy as np import mne from h5io import read_hdf5 from mne.decoding import GeneralizingEstimator, LinearModel from sklearn.pipeline import make_pipeline from sklearn.preprocessing import StandardScaler from sklearn.linear_model import Ridge from sklearn.metrics import make_scorer from sklearn.model_selection import StratifiedKFold from jr.gat import (AngularRegression, scorer_spearman, scorer_angle) from base import (complete_behavior, get_events_interactions) from config import path_data import sys subject = sys.argv[1] # read a swarm file for parralel computing on biowulf output_folder = '/sensors_accross_epochs_and_conditions/' # Create result folder results_folder = op.join(path_data + 'results/' + subject + output_folder) if not os.path.exists(results_folder): os.makedirs(results_folder) # read behavior fname = op.join(path_data, subject, 'behavior_Target.hdf5') events = read_hdf5(fname) events = complete_behavior(events) events = get_events_interactions(events) # read stimulus epochs fname = op.join(path_data, subject, 'epochs_Target.fif') epochs_target = mne.read_epochs(fname) epochs_target.pick_types(meg=True, ref_meg=False) epochs_target.crop(-0.2, 0.9) # read cue epochs fname = op.join(path_data, subject, 'epochs_Cue.fif') epochs_cue = mne.read_epochs(fname) epochs_cue.pick_types(meg=True, ref_meg=False) epochs_cue.crop(0, 1.5) # read probe epochs fname = op.join(path_data, subject, 'epochs_Probe.fif') epochs_probe = mne.read_epochs(fname) epochs_probe.pick_types(meg=True, ref_meg=False) epochs_probe.crop(0, 0.9) # Concatenate the data of the three epochs X0 = epochs_target._data X1 = epochs_cue._data X2 = epochs_probe._data X = np.concatenate((X0, X1, X2), axis=2) # Define pair of analyses (train on the 2nd and test on the 1st ) paired_analyses = [['target_sfreq_cue_left_sfreq', 'left_sfreq'], ['target_sfreq_cue_right_sfreq', 'right_sfreq'], ['left_sfreq', 'target_sfreq_cue_left_sfreq'], ['right_sfreq', 'target_sfreq_cue_right_sfreq'], ['target_angle_cue_left_angle', 'left_angle'], ['target_angle_cue_right_angle', 'right_angle'], ['left_angle', 'target_angle_cue_left_angle'], ['right_angle', 'target_angle_cue_right_angle']] # Loop across each pair of analyses for paired_analysis in paired_analyses: y_test = np.array(events[paired_analysis[0]]) y_train = np.array(events[paired_analysis[1]]) # Define estimators depending on the analysis if 'angle' in paired_analysis[0][:14]: clf = make_pipeline(StandardScaler(), LinearModel(AngularRegression(Ridge(), independent=False))) scorer = scorer_angle kwargs = dict() gat = GeneralizingEstimator(clf, scoring=make_scorer(scorer), n_jobs=24, **kwargs) y_test = np.array(y_test, dtype=float) y_train = np.array(y_train, dtype=float) elif 'sfreq' in paired_analysis[0][:14]: clf = make_pipeline(StandardScaler(), LinearModel(Ridge())) scorer = scorer_spearman kwargs = dict() gat = GeneralizingEstimator(clf, scoring=make_scorer(scorer), n_jobs=24, **kwargs) y_test = np.array(y_test, dtype=float) y_train = np.array(y_train, dtype=float) # only consider trials with correct fixation sel = np.where(events['is_eye_fixed'] == 1)[0] y_train = y_train[sel] y_test = y_test[sel] X = np.concatenate((X0, X1, X2), axis=2) X = X[sel] # only consider non NaN values # Run decoding accross condition cv = StratifiedKFold(7) scores = list() scs = list() if np.isnan(y_train).any(): sel = np.where(~np.isnan(y_train))[0] for train, test in cv.split(X[sel], y_train[sel]): gat.fit(X[sel][train], y_train[sel][train]) score = gat.score(X[sel][test], y_test[sel][test]) sc = gat.score(X[sel][test], y_train[sel][test]) # test on same scores.append(score) scs.append(sc) scores = np.mean(scores, axis=0) scs = np.mean(scs, axis=0) else: for train, test in cv.split(X, y_train): y_te = y_test[test] X_te = X[test] y_te = y_te[np.where(~np.isnan(y_te))[0]] X_te = X_te[np.where(~np.isnan(y_te))[0]] y_tr = y_train[train] X_tr = X[train] y_tr = y_tr[np.where(~np.isnan(y_tr))[0]] X_tr = X_tr[np.where(~np.isnan(y_tr))[0]] y_tr_te = y_train[test] X_tr_te = X[test] y_tr_te = y_tr_te[np.where(~np.isnan(y_tr_te))[0]] X_tr_te = X_tr_te[np.where(~np.isnan(y_tr_te))[0]] gat.fit(X_tr, y_tr) score = gat.score(X_te, y_te) sc = gat.score(X_tr_te, y_tr_te) # test on same scores.append(score) scs.append(sc) scores = np.mean(scores, axis=0) scs = np.mean(scs, axis=0) # save cross-validated scores fname = results_folder +\ '%s_scores_%s_cross_%s.npy' % (subject, paired_analysis[0], paired_analysis[1]) np.save(fname, np.array(scores)) # save accross condition scores fname = results_folder +\ '%s_scores_%s.npy' % (subject, paired_analysis[1]) np.save(fname, np.array(scs)) # save scores test/train on same condition

41.468531

78

0.641147

import os import os.path as op import numpy as np import mne from h5io import read_hdf5 from mne.decoding import GeneralizingEstimator, LinearModel from sklearn.pipeline import make_pipeline from sklearn.preprocessing import StandardScaler from sklearn.linear_model import Ridge from sklearn.metrics import make_scorer from sklearn.model_selection import StratifiedKFold from jr.gat import (AngularRegression, scorer_spearman, scorer_angle) from base import (complete_behavior, get_events_interactions) from config import path_data import sys subject = sys.argv[1] output_folder = '/sensors_accross_epochs_and_conditions/' results_folder = op.join(path_data + 'results/' + subject + output_folder) if not os.path.exists(results_folder): os.makedirs(results_folder) fname = op.join(path_data, subject, 'behavior_Target.hdf5') events = read_hdf5(fname) events = complete_behavior(events) events = get_events_interactions(events) fname = op.join(path_data, subject, 'epochs_Target.fif') epochs_target = mne.read_epochs(fname) epochs_target.pick_types(meg=True, ref_meg=False) epochs_target.crop(-0.2, 0.9) fname = op.join(path_data, subject, 'epochs_Cue.fif') epochs_cue = mne.read_epochs(fname) epochs_cue.pick_types(meg=True, ref_meg=False) epochs_cue.crop(0, 1.5) fname = op.join(path_data, subject, 'epochs_Probe.fif') epochs_probe = mne.read_epochs(fname) epochs_probe.pick_types(meg=True, ref_meg=False) epochs_probe.crop(0, 0.9) X0 = epochs_target._data X1 = epochs_cue._data X2 = epochs_probe._data X = np.concatenate((X0, X1, X2), axis=2) paired_analyses = [['target_sfreq_cue_left_sfreq', 'left_sfreq'], ['target_sfreq_cue_right_sfreq', 'right_sfreq'], ['left_sfreq', 'target_sfreq_cue_left_sfreq'], ['right_sfreq', 'target_sfreq_cue_right_sfreq'], ['target_angle_cue_left_angle', 'left_angle'], ['target_angle_cue_right_angle', 'right_angle'], ['left_angle', 'target_angle_cue_left_angle'], ['right_angle', 'target_angle_cue_right_angle']] for paired_analysis in paired_analyses: y_test = np.array(events[paired_analysis[0]]) y_train = np.array(events[paired_analysis[1]]) if 'angle' in paired_analysis[0][:14]: clf = make_pipeline(StandardScaler(), LinearModel(AngularRegression(Ridge(), independent=False))) scorer = scorer_angle kwargs = dict() gat = GeneralizingEstimator(clf, scoring=make_scorer(scorer), n_jobs=24, **kwargs) y_test = np.array(y_test, dtype=float) y_train = np.array(y_train, dtype=float) elif 'sfreq' in paired_analysis[0][:14]: clf = make_pipeline(StandardScaler(), LinearModel(Ridge())) scorer = scorer_spearman kwargs = dict() gat = GeneralizingEstimator(clf, scoring=make_scorer(scorer), n_jobs=24, **kwargs) y_test = np.array(y_test, dtype=float) y_train = np.array(y_train, dtype=float) sel = np.where(events['is_eye_fixed'] == 1)[0] y_train = y_train[sel] y_test = y_test[sel] X = np.concatenate((X0, X1, X2), axis=2) X = X[sel] cv = StratifiedKFold(7) scores = list() scs = list() if np.isnan(y_train).any(): sel = np.where(~np.isnan(y_train))[0] for train, test in cv.split(X[sel], y_train[sel]): gat.fit(X[sel][train], y_train[sel][train]) score = gat.score(X[sel][test], y_test[sel][test]) sc = gat.score(X[sel][test], y_train[sel][test]) scores.append(score) scs.append(sc) scores = np.mean(scores, axis=0) scs = np.mean(scs, axis=0) else: for train, test in cv.split(X, y_train): y_te = y_test[test] X_te = X[test] y_te = y_te[np.where(~np.isnan(y_te))[0]] X_te = X_te[np.where(~np.isnan(y_te))[0]] y_tr = y_train[train] X_tr = X[train] y_tr = y_tr[np.where(~np.isnan(y_tr))[0]] X_tr = X_tr[np.where(~np.isnan(y_tr))[0]] y_tr_te = y_train[test] X_tr_te = X[test] y_tr_te = y_tr_te[np.where(~np.isnan(y_tr_te))[0]] X_tr_te = X_tr_te[np.where(~np.isnan(y_tr_te))[0]] gat.fit(X_tr, y_tr) score = gat.score(X_te, y_te) sc = gat.score(X_tr_te, y_tr_te) scores.append(score) scs.append(sc) scores = np.mean(scores, axis=0) scs = np.mean(scs, axis=0) fname = results_folder +\ '%s_scores_%s_cross_%s.npy' % (subject, paired_analysis[0], paired_analysis[1]) np.save(fname, np.array(scores)) fname = results_folder +\ '%s_scores_%s.npy' % (subject, paired_analysis[1]) np.save(fname, np.array(scs))

true

f7007b8f63065271ad10c75bc6357fbca374b88c

8,674

py

Python

test/sagemaker_tests/pytorch/inference/conftest.py

leezu/deep-learning-containers

52591228240ad88d1eb39f419ade93d3ca5ec695

[ "Apache-2.0" ]

null

test/sagemaker_tests/pytorch/inference/conftest.py

leezu/deep-learning-containers

52591228240ad88d1eb39f419ade93d3ca5ec695

[ "Apache-2.0" ]

null

test/sagemaker_tests/pytorch/inference/conftest.py

leezu/deep-learning-containers

52591228240ad88d1eb39f419ade93d3ca5ec695

[ "Apache-2.0" ]

null

# Copyright 2019-2020 Amazon.com, Inc. or its affiliates. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"). You # may not use this file except in compliance with the License. A copy of # the License is located at # # http://aws.amazon.com/apache2.0/ # # or in the "license" file accompanying this file. This file is # distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF # ANY KIND, either express or implied. See the License for the specific # language governing permissions and limitations under the License. from __future__ import absolute_import import boto3 import os import logging import platform import pytest import shutil import sys import tempfile from sagemaker import LocalSession, Session from sagemaker.pytorch import PyTorch from .utils import image_utils logger = logging.getLogger(__name__) logging.getLogger('boto').setLevel(logging.INFO) logging.getLogger('boto3').setLevel(logging.INFO) logging.getLogger('botocore').setLevel(logging.INFO) logging.getLogger('factory.py').setLevel(logging.INFO) logging.getLogger('auth.py').setLevel(logging.INFO) logging.getLogger('connectionpool.py').setLevel(logging.INFO) dir_path = os.path.dirname(os.path.realpath(__file__)) NO_P2_REGIONS = ['ap-east-1', 'ap-northeast-3', 'ap-southeast-2', 'ca-central-1', 'eu-central-1', 'eu-north-1', 'eu-west-2', 'eu-west-3', 'us-west-1', 'sa-east-1', 'me-south-1'] NO_P3_REGIONS = ['ap-east-1', 'ap-northeast-3', 'ap-southeast-1', 'ap-southeast-2', 'ap-south-1', 'ca-central-1', 'eu-central-1', 'eu-north-1', 'eu-west-2', 'eu-west-3', 'sa-east-1', 'us-west-1', 'me-south-1'] def pytest_addoption(parser): parser.addoption('--build-image', '-D', action='store_true') parser.addoption('--build-base-image', '-B', action='store_true') parser.addoption('--aws-id') parser.addoption('--instance-type') parser.addoption('--accelerator-type', default=None) parser.addoption('--docker-base-name', default='pytorch') parser.addoption('--region', default='us-west-2') parser.addoption('--framework-version', default=PyTorch.LATEST_VERSION) parser.addoption('--py-version', choices=['2', '3'], default=str(sys.version_info.major)) # Processor is still "cpu" for EIA tests parser.addoption('--processor', choices=['gpu', 'cpu', 'eia'], default='cpu') # If not specified, will default to {framework-version}-{processor}-py{py-version} parser.addoption('--tag', default=None) parser.addoption('--generate-coverage-doc', default=False, action='store_true', help='use this option to generate test coverage doc') def pytest_collection_modifyitems(session, config, items): if config.getoption("--generate-coverage-doc"): from test.test_utils.test_reporting import TestReportGenerator report_generator = TestReportGenerator(items, is_sagemaker=True) report_generator.generate_coverage_doc(framework="pytorch", job_type="inference") @pytest.fixture(scope='session', name='docker_base_name') def fixture_docker_base_name(request): return request.config.getoption('--docker-base-name') @pytest.fixture(scope='session', name='region') def fixture_region(request): return request.config.getoption('--region') @pytest.fixture(scope='session', name='framework_version') def fixture_framework_version(request): return request.config.getoption('--framework-version') @pytest.fixture(scope='session', name='py_version') def fixture_py_version(request): return 'py{}'.format(int(request.config.getoption('--py-version'))) @pytest.fixture(scope='session', name='processor') def fixture_processor(request): return request.config.getoption('--processor') @pytest.fixture(scope='session', name='tag') def fixture_tag(request, framework_version, processor, py_version): provided_tag = request.config.getoption('--tag') default_tag = '{}-{}-{}'.format(framework_version, processor, py_version) return provided_tag if provided_tag else default_tag @pytest.fixture(scope='session', name='docker_image') def fixture_docker_image(docker_base_name, tag): return '{}:{}'.format(docker_base_name, tag) @pytest.fixture def opt_ml(): tmp = tempfile.mkdtemp() os.mkdir(os.path.join(tmp, 'output')) # Docker cannot mount Mac OS /var folder properly see # https://forums.docker.com/t/var-folders-isnt-mounted-properly/9600 opt_ml_dir = '/private{}'.format(tmp) if platform.system() == 'Darwin' else tmp yield opt_ml_dir shutil.rmtree(tmp, True) @pytest.fixture(scope='session', name='use_gpu') def fixture_use_gpu(processor): return processor == 'gpu' @pytest.fixture(scope='session', name='build_base_image', autouse=True) def fixture_build_base_image(request, framework_version, py_version, processor, tag, docker_base_name): build_base_image = request.config.getoption('--build-base-image') if build_base_image: return image_utils.build_base_image(framework_name=docker_base_name, framework_version=framework_version, py_version=py_version, base_image_tag=tag, processor=processor, cwd=os.path.join(dir_path, '..')) return tag @pytest.fixture(scope='session', name='sagemaker_session') def fixture_sagemaker_session(region): return Session(boto_session=boto3.Session(region_name=region)) @pytest.fixture(scope='session', name='sagemaker_local_session') def fixture_sagemaker_local_session(region): return LocalSession(boto_session=boto3.Session(region_name=region)) @pytest.fixture(name='aws_id', scope='session') def fixture_aws_id(request): return request.config.getoption('--aws-id') @pytest.fixture(name='instance_type', scope='session') def fixture_instance_type(request, processor): provided_instance_type = request.config.getoption('--instance-type') default_instance_type = 'local' if processor == 'cpu' else 'local_gpu' return provided_instance_type or default_instance_type @pytest.fixture(name='accelerator_type', scope='session') def fixture_accelerator_type(request): return request.config.getoption('--accelerator-type') @pytest.fixture(name='docker_registry', scope='session') def fixture_docker_registry(aws_id, region): return '{}.dkr.ecr.{}.amazonaws.com'.format(aws_id, region) @pytest.fixture(name='ecr_image', scope='session') def fixture_ecr_image(docker_registry, docker_base_name, tag): return '{}/{}:{}'.format(docker_registry, docker_base_name, tag) @pytest.fixture(autouse=True) def skip_by_device_type(request, use_gpu, instance_type, accelerator_type): is_gpu = use_gpu or instance_type[3] in ['g', 'p'] is_eia = accelerator_type is not None # Separate out cases for clearer logic. # When running GPU test, skip CPU test. When running CPU test, skip GPU test. if (request.node.get_closest_marker('gpu_test') and not is_gpu) or \ (request.node.get_closest_marker('cpu_test') and is_gpu): pytest.skip('Skipping because running on \'{}\' instance'.format(instance_type)) # When running EIA test, skip the CPU and GPU functions elif (request.node.get_closest_marker('gpu_test') or request.node.get_closest_marker('cpu_test')) and is_eia: pytest.skip('Skipping because running on \'{}\' instance'.format(instance_type)) # When running CPU or GPU test, skip EIA test. elif request.node.get_closest_marker('eia_test') and not is_eia: pytest.skip('Skipping because running on \'{}\' instance'.format(instance_type)) @pytest.fixture(autouse=True) def skip_by_py_version(request, py_version): if request.node.get_closest_marker('skip_py2') and py_version != 'py3': pytest.skip('Skipping the test because Python 2 is not supported.') @pytest.fixture(autouse=True) def skip_gpu_instance_restricted_regions(region, instance_type): if (region in NO_P2_REGIONS and instance_type.startswith('ml.p2')) \ or (region in NO_P3_REGIONS and instance_type.startswith('ml.p3')): pytest.skip('Skipping GPU test in region {}'.format(region)) @pytest.fixture(autouse=True) def skip_gpu_py2(request, use_gpu, instance_type, py_version, framework_version): is_gpu = use_gpu or instance_type[3] in ['g', 'p'] if request.node.get_closest_marker('skip_gpu_py2') and is_gpu and py_version != 'py3' \ and framework_version == '1.4.0': pytest.skip('Skipping the test until mms issue resolved.')

40.157407

113

0.715241

from __future__ import absolute_import import boto3 import os import logging import platform import pytest import shutil import sys import tempfile from sagemaker import LocalSession, Session from sagemaker.pytorch import PyTorch from .utils import image_utils logger = logging.getLogger(__name__) logging.getLogger('boto').setLevel(logging.INFO) logging.getLogger('boto3').setLevel(logging.INFO) logging.getLogger('botocore').setLevel(logging.INFO) logging.getLogger('factory.py').setLevel(logging.INFO) logging.getLogger('auth.py').setLevel(logging.INFO) logging.getLogger('connectionpool.py').setLevel(logging.INFO) dir_path = os.path.dirname(os.path.realpath(__file__)) NO_P2_REGIONS = ['ap-east-1', 'ap-northeast-3', 'ap-southeast-2', 'ca-central-1', 'eu-central-1', 'eu-north-1', 'eu-west-2', 'eu-west-3', 'us-west-1', 'sa-east-1', 'me-south-1'] NO_P3_REGIONS = ['ap-east-1', 'ap-northeast-3', 'ap-southeast-1', 'ap-southeast-2', 'ap-south-1', 'ca-central-1', 'eu-central-1', 'eu-north-1', 'eu-west-2', 'eu-west-3', 'sa-east-1', 'us-west-1', 'me-south-1'] def pytest_addoption(parser): parser.addoption('--build-image', '-D', action='store_true') parser.addoption('--build-base-image', '-B', action='store_true') parser.addoption('--aws-id') parser.addoption('--instance-type') parser.addoption('--accelerator-type', default=None) parser.addoption('--docker-base-name', default='pytorch') parser.addoption('--region', default='us-west-2') parser.addoption('--framework-version', default=PyTorch.LATEST_VERSION) parser.addoption('--py-version', choices=['2', '3'], default=str(sys.version_info.major)) parser.addoption('--processor', choices=['gpu', 'cpu', 'eia'], default='cpu') parser.addoption('--tag', default=None) parser.addoption('--generate-coverage-doc', default=False, action='store_true', help='use this option to generate test coverage doc') def pytest_collection_modifyitems(session, config, items): if config.getoption("--generate-coverage-doc"): from test.test_utils.test_reporting import TestReportGenerator report_generator = TestReportGenerator(items, is_sagemaker=True) report_generator.generate_coverage_doc(framework="pytorch", job_type="inference") @pytest.fixture(scope='session', name='docker_base_name') def fixture_docker_base_name(request): return request.config.getoption('--docker-base-name') @pytest.fixture(scope='session', name='region') def fixture_region(request): return request.config.getoption('--region') @pytest.fixture(scope='session', name='framework_version') def fixture_framework_version(request): return request.config.getoption('--framework-version') @pytest.fixture(scope='session', name='py_version') def fixture_py_version(request): return 'py{}'.format(int(request.config.getoption('--py-version'))) @pytest.fixture(scope='session', name='processor') def fixture_processor(request): return request.config.getoption('--processor') @pytest.fixture(scope='session', name='tag') def fixture_tag(request, framework_version, processor, py_version): provided_tag = request.config.getoption('--tag') default_tag = '{}-{}-{}'.format(framework_version, processor, py_version) return provided_tag if provided_tag else default_tag @pytest.fixture(scope='session', name='docker_image') def fixture_docker_image(docker_base_name, tag): return '{}:{}'.format(docker_base_name, tag) @pytest.fixture def opt_ml(): tmp = tempfile.mkdtemp() os.mkdir(os.path.join(tmp, 'output')) opt_ml_dir = '/private{}'.format(tmp) if platform.system() == 'Darwin' else tmp yield opt_ml_dir shutil.rmtree(tmp, True) @pytest.fixture(scope='session', name='use_gpu') def fixture_use_gpu(processor): return processor == 'gpu' @pytest.fixture(scope='session', name='build_base_image', autouse=True) def fixture_build_base_image(request, framework_version, py_version, processor, tag, docker_base_name): build_base_image = request.config.getoption('--build-base-image') if build_base_image: return image_utils.build_base_image(framework_name=docker_base_name, framework_version=framework_version, py_version=py_version, base_image_tag=tag, processor=processor, cwd=os.path.join(dir_path, '..')) return tag @pytest.fixture(scope='session', name='sagemaker_session') def fixture_sagemaker_session(region): return Session(boto_session=boto3.Session(region_name=region)) @pytest.fixture(scope='session', name='sagemaker_local_session') def fixture_sagemaker_local_session(region): return LocalSession(boto_session=boto3.Session(region_name=region)) @pytest.fixture(name='aws_id', scope='session') def fixture_aws_id(request): return request.config.getoption('--aws-id') @pytest.fixture(name='instance_type', scope='session') def fixture_instance_type(request, processor): provided_instance_type = request.config.getoption('--instance-type') default_instance_type = 'local' if processor == 'cpu' else 'local_gpu' return provided_instance_type or default_instance_type @pytest.fixture(name='accelerator_type', scope='session') def fixture_accelerator_type(request): return request.config.getoption('--accelerator-type') @pytest.fixture(name='docker_registry', scope='session') def fixture_docker_registry(aws_id, region): return '{}.dkr.ecr.{}.amazonaws.com'.format(aws_id, region) @pytest.fixture(name='ecr_image', scope='session') def fixture_ecr_image(docker_registry, docker_base_name, tag): return '{}/{}:{}'.format(docker_registry, docker_base_name, tag) @pytest.fixture(autouse=True) def skip_by_device_type(request, use_gpu, instance_type, accelerator_type): is_gpu = use_gpu or instance_type[3] in ['g', 'p'] is_eia = accelerator_type is not None if (request.node.get_closest_marker('gpu_test') and not is_gpu) or \ (request.node.get_closest_marker('cpu_test') and is_gpu): pytest.skip('Skipping because running on \'{}\' instance'.format(instance_type)) elif (request.node.get_closest_marker('gpu_test') or request.node.get_closest_marker('cpu_test')) and is_eia: pytest.skip('Skipping because running on \'{}\' instance'.format(instance_type)) elif request.node.get_closest_marker('eia_test') and not is_eia: pytest.skip('Skipping because running on \'{}\' instance'.format(instance_type)) @pytest.fixture(autouse=True) def skip_by_py_version(request, py_version): if request.node.get_closest_marker('skip_py2') and py_version != 'py3': pytest.skip('Skipping the test because Python 2 is not supported.') @pytest.fixture(autouse=True) def skip_gpu_instance_restricted_regions(region, instance_type): if (region in NO_P2_REGIONS and instance_type.startswith('ml.p2')) \ or (region in NO_P3_REGIONS and instance_type.startswith('ml.p3')): pytest.skip('Skipping GPU test in region {}'.format(region)) @pytest.fixture(autouse=True) def skip_gpu_py2(request, use_gpu, instance_type, py_version, framework_version): is_gpu = use_gpu or instance_type[3] in ['g', 'p'] if request.node.get_closest_marker('skip_gpu_py2') and is_gpu and py_version != 'py3' \ and framework_version == '1.4.0': pytest.skip('Skipping the test until mms issue resolved.')

true

f7007ce6b17b426ac80e33b7b3f19e6da1a19280

4,146

py

Python

dsbox/ml/visualization/metrics.py

Pandinosaurus/dsbox

aea56049025ed7e6e66427f8636286f8be1b6e03

[ "Apache-2.0" ]

16

2020-05-11T09:10:15.000Z

2021-04-13T08:43:28.000Z

dsbox/ml/visualization/metrics.py

Pandinosaurus/dsbox

aea56049025ed7e6e66427f8636286f8be1b6e03

[ "Apache-2.0" ]

1

2020-12-03T20:02:32.000Z

dsbox/ml/visualization/metrics.py

Pandinosaurus/dsbox

aea56049025ed7e6e66427f8636286f8be1b6e03

[ "Apache-2.0" ]

1

2020-05-11T17:22:20.000Z

import matplotlib.pyplot as plt import numpy as np import pandas as pd from sklearn.metrics import roc_curve, auc __author__ = "Aurélien Massiot" __credits__ = "https://github.com/octo-technology/bdacore" __license__ = "Apache 2.0" def plot_confusion_matrix(confusion_matrix, classes_list, normalize=True, figsize=(10, 7), fontsize=14, cmap="Blues"): """ Display a pretty confusion matrix. Parameters ---------- confusion_matrix : array-like classes_list : list, classes list of the confusion matrix normalize : boolean, normalize confusion matrix figsize : tuple, optional (default=(10,7)) set the figure size fontsize : int, optional (default=14) set the font size cmap : str, optional (default="Blues") set the colormap Returns ------- Confusion matrix figure Examples -------- >>> from dsbox.ml.visualization.metrics import plot_confusion_matrix >>> array = [[ 8458, 227, 1730], \ [ 1073, 37590, 1613], \ [ 2390, 1159, 17540]] >>> classes_list = ["A", "B", "C"] >>> plot_confusion_matrix(array, classes_list) """ confusion_matrix = np.array(confusion_matrix) fig, ax = plt.subplots(figsize=figsize) if normalize: normalized_cm = np.array(confusion_matrix).astype('float') / np.array(confusion_matrix).sum(axis=1)[:, np.newaxis] df_cm = pd.DataFrame( normalized_cm, index=classes_list, columns=classes_list, ) plt.matshow(df_cm, fignum=0, cmap=cmap) else: df_cm = pd.DataFrame( confusion_matrix, index=classes_list, columns=classes_list, ) plt.matshow(df_cm, fignum=0, cmap=cmap) ax.set_xticks(np.arange(len(classes_list))) ax.set_yticks(np.arange(len(classes_list))) ax.set_xticklabels(classes_list) ax.set_yticklabels(classes_list) plt.setp(ax.get_xticklabels(), rotation=45, ha="right", rotation_mode="anchor") for i in range(len(classes_list)): for j in range(len(classes_list)): ax.text(j, i, confusion_matrix[i, j], ha="center", va="center", color="grey", fontsize=fontsize) plt.ylabel('True labels') plt.xlabel('Predicted labels') plt.show() def plot_roc_curve(y_test, y_pred_probas, proba_step=None): """ Plot ROC curve with probabilities thresholds. Parameters ---------- y_test : array-like true labels y_pred_probas : array-like predicted labels proba_step : int (optional) (default=None) if set, give the step for each probability display. If None, nothing is displayed. Examples -------- >>> from dsbox.ml.visualization.metrics import plot_roc_curve >>> from sklearn import datasets >>> from sklearn.model_selection import train_test_split >>> from sklearn.ensemble import RandomForestClassifier >>> X, y = datasets.make_moons(noise=0.3, random_state=0) >>> X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.5, random_state=0) >>> clf = RandomForestClassifier(n_estimators=10, random_state=42) >>> _ = clf.fit(X_train, y_train) >>> y_pred_probas = clf.predict_proba(X_test) >>> plot_roc_curve(y_test, y_pred_probas, proba_step=2) """ fpr, tpr, thresholds = roc_curve(y_test, y_pred_probas[:, 1]) auc_score = auc(fpr, tpr) plt.figure() lw = 1 plt.plot(fpr, tpr, color='darkorange', lw=lw, marker='.') plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--') if proba_step is not None: i = 0 for x, y, txt in zip(fpr, tpr, thresholds): if i % proba_step == 0: plt.annotate(np.round(txt, 2), (x, y - 0.04), color='darkgray', fontsize=8) i += 1 plt.xlim([0.0, 1.0]) plt.ylim([0.0, 1.05]) plt.xlabel('False Positive Rate') plt.ylabel('True Positive Rate') plt.title('Receiver operating characteristic (ROC) - AUC score: {}'.format(str(np.round(auc_score,3)))) plt.show()

31.409091

118

0.623493

import matplotlib.pyplot as plt import numpy as np import pandas as pd from sklearn.metrics import roc_curve, auc __author__ = "Aurélien Massiot" __credits__ = "https://github.com/octo-technology/bdacore" __license__ = "Apache 2.0" def plot_confusion_matrix(confusion_matrix, classes_list, normalize=True, figsize=(10, 7), fontsize=14, cmap="Blues"): confusion_matrix = np.array(confusion_matrix) fig, ax = plt.subplots(figsize=figsize) if normalize: normalized_cm = np.array(confusion_matrix).astype('float') / np.array(confusion_matrix).sum(axis=1)[:, np.newaxis] df_cm = pd.DataFrame( normalized_cm, index=classes_list, columns=classes_list, ) plt.matshow(df_cm, fignum=0, cmap=cmap) else: df_cm = pd.DataFrame( confusion_matrix, index=classes_list, columns=classes_list, ) plt.matshow(df_cm, fignum=0, cmap=cmap) ax.set_xticks(np.arange(len(classes_list))) ax.set_yticks(np.arange(len(classes_list))) ax.set_xticklabels(classes_list) ax.set_yticklabels(classes_list) plt.setp(ax.get_xticklabels(), rotation=45, ha="right", rotation_mode="anchor") for i in range(len(classes_list)): for j in range(len(classes_list)): ax.text(j, i, confusion_matrix[i, j], ha="center", va="center", color="grey", fontsize=fontsize) plt.ylabel('True labels') plt.xlabel('Predicted labels') plt.show() def plot_roc_curve(y_test, y_pred_probas, proba_step=None): fpr, tpr, thresholds = roc_curve(y_test, y_pred_probas[:, 1]) auc_score = auc(fpr, tpr) plt.figure() lw = 1 plt.plot(fpr, tpr, color='darkorange', lw=lw, marker='.') plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--') if proba_step is not None: i = 0 for x, y, txt in zip(fpr, tpr, thresholds): if i % proba_step == 0: plt.annotate(np.round(txt, 2), (x, y - 0.04), color='darkgray', fontsize=8) i += 1 plt.xlim([0.0, 1.0]) plt.ylim([0.0, 1.05]) plt.xlabel('False Positive Rate') plt.ylabel('True Positive Rate') plt.title('Receiver operating characteristic (ROC) - AUC score: {}'.format(str(np.round(auc_score,3)))) plt.show()

true

f7007d740bcaf7a6890bc4f3746a71b835476624

75

py

Python

tests/csrf_tests/csrf_token_error_handler_urls.py

jpmallarino/django

659d2421c7adbbcd205604002d521d82d6b0b465

[ "BSD-3-Clause", "0BSD" ]

16

2019-08-10T12:24:06.000Z

2020-05-21T09:11:14.000Z

tests/csrf_tests/csrf_token_error_handler_urls.py

jpmallarino/django

659d2421c7adbbcd205604002d521d82d6b0b465

[ "BSD-3-Clause", "0BSD" ]

12

2019-08-10T11:55:29.000Z

2020-05-21T04:46:30.000Z

tests/csrf_tests/csrf_token_error_handler_urls.py

jpmallarino/django

659d2421c7adbbcd205604002d521d82d6b0b465

[ "BSD-3-Clause", "0BSD" ]

3

2019-08-20T13:29:34.000Z

2020-01-30T22:05:10.000Z

urlpatterns = [] handler404 = "csrf_tests.views.csrf_token_error_handler"

18.75

56

0.8

urlpatterns = [] handler404 = "csrf_tests.views.csrf_token_error_handler"

true

f7007dd29ef8b16e106db064eeeb8545c8e209c3

1,031

py

Python

clients/python/sust/api/climate_explorer/clientgen/test/test_physical_risk_summary_indicators.py

sustglobal/dev-center

817947b2a91500c634c4cac9063e5084efb2f8a6

[ "Apache-2.0" ]

null

clients/python/sust/api/climate_explorer/clientgen/test/test_physical_risk_summary_indicators.py

sustglobal/dev-center

817947b2a91500c634c4cac9063e5084efb2f8a6

[ "Apache-2.0" ]

6

2021-12-06T19:25:57.000Z

2022-03-01T20:58:55.000Z

clients/python/sust/api/climate_explorer/clientgen/test/test_physical_risk_summary_indicators.py

sustglobal/dev-center

817947b2a91500c634c4cac9063e5084efb2f8a6

[ "Apache-2.0" ]

1

2021-12-03T22:39:06.000Z

""" Sust Global Climate Explorer API This API provides programmatic access to physical risk exposure data. For more guidance on using this API, please visit the Sust Global Dev Center: https://developers.sustglobal.com. # noqa: E501 The version of the OpenAPI document: beta Generated by: https://openapi-generator.tech """ import sys import unittest import sust.api.climate_explorer.clientgen from sust.api.climate_explorer.clientgen.model.physical_risk_summary_indicators import PhysicalRiskSummaryIndicators class TestPhysicalRiskSummaryIndicators(unittest.TestCase): """PhysicalRiskSummaryIndicators unit test stubs""" def setUp(self): pass def tearDown(self): pass def testPhysicalRiskSummaryIndicators(self): """Test PhysicalRiskSummaryIndicators""" # FIXME: construct object with mandatory attributes with example values # model = PhysicalRiskSummaryIndicators() # noqa: E501 pass if __name__ == '__main__': unittest.main()

28.638889

202

0.741028

import sys import unittest import sust.api.climate_explorer.clientgen from sust.api.climate_explorer.clientgen.model.physical_risk_summary_indicators import PhysicalRiskSummaryIndicators class TestPhysicalRiskSummaryIndicators(unittest.TestCase): def setUp(self): pass def tearDown(self): pass def testPhysicalRiskSummaryIndicators(self): pass if __name__ == '__main__': unittest.main()

true

f7007e7d6cadbb4707818ec05e6fcbc50ba52dfb

2,656

py

Python

sysinv/sysinv/sysinv/sysinv/common/service.py

starlingx-staging/stx-config

ccbf0392d1941e7cad6673f6351bd905a5a5d419

[ "Apache-2.0" ]

null

sysinv/sysinv/sysinv/sysinv/common/service.py

starlingx-staging/stx-config

ccbf0392d1941e7cad6673f6351bd905a5a5d419

[ "Apache-2.0" ]

null

sysinv/sysinv/sysinv/sysinv/common/service.py

starlingx-staging/stx-config

ccbf0392d1941e7cad6673f6351bd905a5a5d419

[ "Apache-2.0" ]

null

#!/usr/bin/env python # -*- encoding: utf-8 -*- # # Copyright © 2012 eNovance <[email protected]> # # Author: Julien Danjou <[email protected]> # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. import socket from oslo_config import cfg from sysinv.openstack.common import context from sysinv.openstack.common import log from sysinv.openstack.common import periodic_task from sysinv.openstack.common import rpc from sysinv.openstack.common.rpc import service as rpc_service from oslo_service import service cfg.CONF.register_opts([ cfg.IntOpt('periodic_interval', default=60, help='seconds between running periodic tasks'), cfg.StrOpt('host', default=socket.getfqdn(), help='Name of this node. This can be an opaque identifier. ' 'It is not necessarily a hostname, FQDN, or IP address. ' 'However, the node name must be valid within ' 'an AMQP key, and if using ZeroMQ, a valid ' 'hostname, FQDN, or IP address'), ]) CONF = cfg.CONF class PeriodicService(rpc_service.Service, periodic_task.PeriodicTasks): def start(self): super(PeriodicService, self).start() admin_context = context.RequestContext('admin', 'admin', is_admin=True) self.tg.add_timer(cfg.CONF.periodic_interval, self.manager.periodic_tasks, context=admin_context) def prepare_service(argv=None): if argv is None: argv = [] rpc.set_defaults(control_exchange='sysinv') cfg.set_defaults(log.log_opts, default_log_levels=['amqplib=WARN', 'qpid.messaging=INFO', 'sqlalchemy=WARN', 'keystoneclient=INFO', 'stevedore=INFO', 'eventlet.wsgi.server=WARN' ]) cfg.CONF(argv[1:], project='sysinv') log.setup('sysinv') def process_launcher(): return service.ProcessLauncher(CONF)

34.947368

79

0.622364

import socket from oslo_config import cfg from sysinv.openstack.common import context from sysinv.openstack.common import log from sysinv.openstack.common import periodic_task from sysinv.openstack.common import rpc from sysinv.openstack.common.rpc import service as rpc_service from oslo_service import service cfg.CONF.register_opts([ cfg.IntOpt('periodic_interval', default=60, help='seconds between running periodic tasks'), cfg.StrOpt('host', default=socket.getfqdn(), help='Name of this node. This can be an opaque identifier. ' 'It is not necessarily a hostname, FQDN, or IP address. ' 'However, the node name must be valid within ' 'an AMQP key, and if using ZeroMQ, a valid ' 'hostname, FQDN, or IP address'), ]) CONF = cfg.CONF class PeriodicService(rpc_service.Service, periodic_task.PeriodicTasks): def start(self): super(PeriodicService, self).start() admin_context = context.RequestContext('admin', 'admin', is_admin=True) self.tg.add_timer(cfg.CONF.periodic_interval, self.manager.periodic_tasks, context=admin_context) def prepare_service(argv=None): if argv is None: argv = [] rpc.set_defaults(control_exchange='sysinv') cfg.set_defaults(log.log_opts, default_log_levels=['amqplib=WARN', 'qpid.messaging=INFO', 'sqlalchemy=WARN', 'keystoneclient=INFO', 'stevedore=INFO', 'eventlet.wsgi.server=WARN' ]) cfg.CONF(argv[1:], project='sysinv') log.setup('sysinv') def process_launcher(): return service.ProcessLauncher(CONF)

true

f7007ecebe6e595cb0fbec944c1292dd13f2083b

1,680

py

Python

setup.py

sbrisard/rebin

a0abc9b6e6f82f3c80fe30129f139f1d54f78471

[ "BSD-3-Clause" ]

null

setup.py

sbrisard/rebin

a0abc9b6e6f82f3c80fe30129f139f1d54f78471

[ "BSD-3-Clause" ]

null

setup.py

sbrisard/rebin

a0abc9b6e6f82f3c80fe30129f139f1d54f78471

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- import re import unittest from setuptools import setup def my_test_suite(): """From http://stackoverflow.com/questions/17001010/. """ test_loader = unittest.TestLoader() test_suite = test_loader.discover('tests', pattern='test_*.py') return test_suite with open('rebin.py', 'r') as f: lines = f.read() version = re.search(r'^__version__\s*=\s*[\'"]([^\'"]*)[\'"]', lines, re.MULTILINE).group(1) description = re.search(r'^u\"\"\"(.*)', lines, re.MULTILINE).group(1) long_description = re.search('^u\"\"\"(.*)^\"\"\"', lines, re.MULTILINE | re.DOTALL).group(1) author = re.search(r'^__author__\s*=\s*[\'"]([^\'"]*)[\'"]', lines, re.MULTILINE).group(1) print(long_description) setup( name='rebin', version=version, description=description, long_description=long_description, url='https://github.com/sbrisard/rebin', author=author, author_email='', py_modules=['rebin'], license='BSD-3', classifiers=['Development Status :: 4 - Beta', 'Intended Audience :: Developers', 'Intended Audience :: Science/Research', 'License :: OSI Approved :: BSD License', 'Operating System :: OS Independent', 'Topic :: Software Development :: Build Tools', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3', 'Topic :: Scientific/Engineering'], test_suite='setup.my_test_suite', install_requires=['numpy'], )

32.307692

74

0.550595

import re import unittest from setuptools import setup def my_test_suite(): test_loader = unittest.TestLoader() test_suite = test_loader.discover('tests', pattern='test_*.py') return test_suite with open('rebin.py', 'r') as f: lines = f.read() version = re.search(r'^__version__\s*=\s*[\'"]([^\'"]*)[\'"]', lines, re.MULTILINE).group(1) description = re.search(r'^u\"\"\"(.*)', lines, re.MULTILINE).group(1) long_description = re.search('^u\"\"\"(.*)^\"\"\"', lines, re.MULTILINE | re.DOTALL).group(1) author = re.search(r'^__author__\s*=\s*[\'"]([^\'"]*)[\'"]', lines, re.MULTILINE).group(1) print(long_description) setup( name='rebin', version=version, description=description, long_description=long_description, url='https://github.com/sbrisard/rebin', author=author, author_email='', py_modules=['rebin'], license='BSD-3', classifiers=['Development Status :: 4 - Beta', 'Intended Audience :: Developers', 'Intended Audience :: Science/Research', 'License :: OSI Approved :: BSD License', 'Operating System :: OS Independent', 'Topic :: Software Development :: Build Tools', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3', 'Topic :: Scientific/Engineering'], test_suite='setup.my_test_suite', install_requires=['numpy'], )

true

f7007ee1209e9e3b464ffcab4ffdfa1412233c4b

91,000

py

Python

src/metpy/calc/thermo.py

aschueth/MetPy

5e906c0fcfadccdc8514011d15d911243130d405

[ "BSD-3-Clause" ]

1

2021-08-16T13:07:33.000Z

src/metpy/calc/thermo.py

aschueth/MetPy

5e906c0fcfadccdc8514011d15d911243130d405

[ "BSD-3-Clause" ]

null

src/metpy/calc/thermo.py

aschueth/MetPy

5e906c0fcfadccdc8514011d15d911243130d405

[ "BSD-3-Clause" ]

null

# Copyright (c) 2008,2015,2016,2017,2018,2019 MetPy Developers. # Distributed under the terms of the BSD 3-Clause License. # SPDX-License-Identifier: BSD-3-Clause """Contains a collection of thermodynamic calculations.""" import warnings import numpy as np import scipy.integrate as si import scipy.optimize as so from .tools import (_greater_or_close, _less_or_close, _remove_nans, find_bounding_indices, find_intersections, first_derivative, get_layer) from .. import constants as mpconsts from ..cbook import broadcast_indices from ..interpolate.one_dimension import interpolate_1d from ..package_tools import Exporter from ..units import check_units, concatenate, units from ..xarray import preprocess_xarray exporter = Exporter(globals()) sat_pressure_0c = 6.112 * units.millibar @exporter.export @preprocess_xarray @check_units('[temperature]', '[temperature]') def relative_humidity_from_dewpoint(temperature, dewpoint): r"""Calculate the relative humidity. Uses temperature and dewpoint in celsius to calculate relative humidity using the ratio of vapor pressure to saturation vapor pressures. Parameters ---------- temperature : `pint.Quantity` air temperature dewpoint : `pint.Quantity` dewpoint temperature Returns ------- `pint.Quantity` relative humidity See Also -------- saturation_vapor_pressure """ e = saturation_vapor_pressure(dewpoint) e_s = saturation_vapor_pressure(temperature) return (e / e_s) @exporter.export @preprocess_xarray @check_units('[pressure]', '[pressure]') def exner_function(pressure, reference_pressure=mpconsts.P0): r"""Calculate the Exner function. .. math:: \Pi = \left( \frac{p}{p_0} \right)^\kappa This can be used to calculate potential temperature from temperature (and visa-versa), since .. math:: \Pi = \frac{T}{\theta} Parameters ---------- pressure : `pint.Quantity` total atmospheric pressure reference_pressure : `pint.Quantity`, optional The reference pressure against which to calculate the Exner function, defaults to metpy.constants.P0 Returns ------- `pint.Quantity` The value of the Exner function at the given pressure See Also -------- potential_temperature temperature_from_potential_temperature """ return (pressure / reference_pressure).to('dimensionless')**mpconsts.kappa @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]') def potential_temperature(pressure, temperature): r"""Calculate the potential temperature. Uses the Poisson equation to calculation the potential temperature given `pressure` and `temperature`. Parameters ---------- pressure : `pint.Quantity` total atmospheric pressure temperature : `pint.Quantity` air temperature Returns ------- `pint.Quantity` The potential temperature corresponding to the temperature and pressure. See Also -------- dry_lapse Notes ----- Formula: .. math:: \Theta = T (P_0 / P)^\kappa Examples -------- >>> from metpy.units import units >>> metpy.calc.potential_temperature(800. * units.mbar, 273. * units.kelvin) <Quantity(290.9665329591884, 'kelvin')> """ return temperature / exner_function(pressure) @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]') def temperature_from_potential_temperature(pressure, potential_temperature): r"""Calculate the temperature from a given potential temperature. Uses the inverse of the Poisson equation to calculate the temperature from a given potential temperature at a specific pressure level. Parameters ---------- pressure : `pint.Quantity` total atmospheric pressure potential_temperature : `pint.Quantity` potential temperature Returns ------- `pint.Quantity` The temperature corresponding to the potential temperature and pressure. See Also -------- dry_lapse potential_temperature Notes ----- Formula: .. math:: T = \Theta (P / P_0)^\kappa Examples -------- >>> from metpy.units import units >>> from metpy.calc import temperature_from_potential_temperature >>> # potential temperature >>> theta = np.array([ 286.12859679, 288.22362587]) * units.kelvin >>> p = 850 * units.mbar >>> T = temperature_from_potential_temperature(p, theta) """ return potential_temperature * exner_function(pressure) @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]', '[pressure]') def dry_lapse(pressure, temperature, reference_pressure=None): r"""Calculate the temperature at a level assuming only dry processes. This function lifts a parcel starting at `temperature`, conserving potential temperature. The starting pressure can be given by `reference_pressure`. Parameters ---------- pressure : `pint.Quantity` The atmospheric pressure level(s) of interest temperature : `pint.Quantity` The starting temperature reference_pressure : `pint.Quantity`, optional The reference pressure. If not given, it defaults to the first element of the pressure array. Returns ------- `pint.Quantity` The resulting parcel temperature at levels given by `pressure` See Also -------- moist_lapse : Calculate parcel temperature assuming liquid saturation processes parcel_profile : Calculate complete parcel profile potential_temperature """ if reference_pressure is None: reference_pressure = pressure[0] return temperature * (pressure / reference_pressure)**mpconsts.kappa @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]', '[pressure]') def moist_lapse(pressure, temperature, reference_pressure=None): r"""Calculate the temperature at a level assuming liquid saturation processes. This function lifts a parcel starting at `temperature`. The starting pressure can be given by `reference_pressure`. Essentially, this function is calculating moist pseudo-adiabats. Parameters ---------- pressure : `pint.Quantity` The atmospheric pressure level(s) of interest temperature : `pint.Quantity` The starting temperature reference_pressure : `pint.Quantity`, optional The reference pressure. If not given, it defaults to the first element of the pressure array. Returns ------- `pint.Quantity` The temperature corresponding to the starting temperature and pressure levels. See Also -------- dry_lapse : Calculate parcel temperature assuming dry adiabatic processes parcel_profile : Calculate complete parcel profile Notes ----- This function is implemented by integrating the following differential equation: .. math:: \frac{dT}{dP} = \frac{1}{P} \frac{R_d T + L_v r_s} {C_{pd} + \frac{L_v^2 r_s \epsilon}{R_d T^2}} This equation comes from [Bakhshaii2013]_. """ def dt(t, p): t = units.Quantity(t, temperature.units) p = units.Quantity(p, pressure.units) rs = saturation_mixing_ratio(p, t) frac = ((mpconsts.Rd * t + mpconsts.Lv * rs) / (mpconsts.Cp_d + (mpconsts.Lv * mpconsts.Lv * rs * mpconsts.epsilon / (mpconsts.Rd * t * t)))).to('kelvin') return (frac / p).magnitude if reference_pressure is None: reference_pressure = pressure[0] pressure = pressure.to('mbar') reference_pressure = reference_pressure.to('mbar') temperature = np.atleast_1d(temperature) side = 'left' pres_decreasing = (pressure[0] > pressure[-1]) if pres_decreasing: # Everything is easier if pressures are in increasing order pressure = pressure[::-1] side = 'right' ref_pres_idx = np.searchsorted(pressure.m, reference_pressure.m, side=side) ret_temperatures = np.empty((0, temperature.shape[0])) if reference_pressure > pressure.min(): # Integrate downward in pressure pres_down = np.append(reference_pressure.m, pressure[(ref_pres_idx - 1)::-1].m) trace_down = si.odeint(dt, temperature.m.squeeze(), pres_down.squeeze()) ret_temperatures = np.concatenate((ret_temperatures, trace_down[:0:-1])) if reference_pressure < pressure.max(): # Integrate upward in pressure pres_up = np.append(reference_pressure.m, pressure[ref_pres_idx:].m) trace_up = si.odeint(dt, temperature.m.squeeze(), pres_up.squeeze()) ret_temperatures = np.concatenate((ret_temperatures, trace_up[1:])) if pres_decreasing: ret_temperatures = ret_temperatures[::-1] return units.Quantity(ret_temperatures.T.squeeze(), temperature.units) @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]', '[temperature]') def lcl(pressure, temperature, dewpoint, max_iters=50, eps=1e-5): r"""Calculate the lifted condensation level (LCL) using from the starting point. The starting state for the parcel is defined by `temperature`, `dewpoint`, and `pressure`. If these are arrays, this function will return a LCL for every index. This function does work with surface grids as a result. Parameters ---------- pressure : `pint.Quantity` The starting atmospheric pressure temperature : `pint.Quantity` The starting temperature dewpoint : `pint.Quantity` The starting dewpoint Returns ------- `pint.Quantity` The LCL pressure `pint.Quantity` The LCL temperature Other Parameters ---------------- max_iters : int, optional The maximum number of iterations to use in calculation, defaults to 50. eps : float, optional The desired relative error in the calculated value, defaults to 1e-5. See Also -------- parcel_profile Notes ----- This function is implemented using an iterative approach to solve for the LCL. The basic algorithm is: 1. Find the dewpoint from the LCL pressure and starting mixing ratio 2. Find the LCL pressure from the starting temperature and dewpoint 3. Iterate until convergence The function is guaranteed to finish by virtue of the `max_iters` counter. """ def _lcl_iter(p, p0, w, t): td = globals()['dewpoint'](vapor_pressure(units.Quantity(p, pressure.units), w)) return (p0 * (td / t) ** (1. / mpconsts.kappa)).m w = mixing_ratio(saturation_vapor_pressure(dewpoint), pressure) lcl_p = so.fixed_point(_lcl_iter, pressure.m, args=(pressure.m, w, temperature), xtol=eps, maxiter=max_iters) # np.isclose needed if surface is LCL due to precision error with np.log in dewpoint. # Causes issues with parcel_profile_with_lcl if removed. Issue #1187 lcl_p = np.where(np.isclose(lcl_p, pressure.m), pressure.m, lcl_p) * pressure.units return lcl_p, globals()['dewpoint'](vapor_pressure(lcl_p, w)).to(temperature.units) @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]', '[temperature]', '[temperature]') def lfc(pressure, temperature, dewpoint, parcel_temperature_profile=None, dewpoint_start=None, which='top'): r"""Calculate the level of free convection (LFC). This works by finding the first intersection of the ideal parcel path and the measured parcel temperature. If this intersection occurs below the LCL, the LFC is determined to be the same as the LCL, based upon the conditions set forth in [USAF1990]_, pg 4-14, where a parcel must be lifted dry adiabatically to saturation before it can freely rise. Parameters ---------- pressure : `pint.Quantity` The atmospheric pressure temperature : `pint.Quantity` The temperature at the levels given by `pressure` dewpoint : `pint.Quantity` The dewpoint at the levels given by `pressure` parcel_temperature_profile: `pint.Quantity`, optional The parcel temperature profile from which to calculate the LFC. Defaults to the surface parcel profile. dewpoint_start: `pint.Quantity`, optional The dewpoint of the parcel for which to calculate the LFC. Defaults to the surface dewpoint. which: str, optional Pick which LFC to return. Options are 'top', 'bottom', 'wide', 'most_cape', and 'all'. 'top' returns the lowest-pressure LFC, default. 'bottom' returns the highest-pressure LFC. 'wide' returns the LFC whose corresponding EL is farthest away. 'most_cape' returns the LFC that results in the most CAPE in the profile. Returns ------- `pint.Quantity` The LFC pressure, or array of same if which='all' `pint.Quantity` The LFC temperature, or array of same if which='all' See Also -------- parcel_profile """ pressure, temperature, dewpoint = _remove_nans(pressure, temperature, dewpoint) # Default to surface parcel if no profile or starting pressure level is given if parcel_temperature_profile is None: new_stuff = parcel_profile_with_lcl(pressure, temperature, dewpoint) pressure, temperature, dewpoint, parcel_temperature_profile = new_stuff parcel_temperature_profile = parcel_temperature_profile.to(temperature.units) if dewpoint_start is None: dewpoint_start = dewpoint[0] # The parcel profile and data may have the same first data point. # If that is the case, ignore that point to get the real first # intersection for the LFC calculation. Use logarithmic interpolation. if np.isclose(parcel_temperature_profile[0].to(temperature.units).m, temperature[0].m): x, y = find_intersections(pressure[1:], parcel_temperature_profile[1:], temperature[1:], direction='increasing', log_x=True) else: x, y = find_intersections(pressure, parcel_temperature_profile, temperature, direction='increasing', log_x=True) # Compute LCL for this parcel for future comparisons this_lcl = lcl(pressure[0], parcel_temperature_profile[0], dewpoint_start) # The LFC could: # 1) Not exist # 2) Exist but be equal to the LCL # 3) Exist and be above the LCL # LFC does not exist or is LCL if len(x) == 0: # Is there any positive area above the LCL? mask = pressure < this_lcl[0] if np.all(_less_or_close(parcel_temperature_profile[mask], temperature[mask])): # LFC doesn't exist x, y = np.nan * pressure.units, np.nan * temperature.units else: # LFC = LCL x, y = this_lcl return x, y # LFC exists. Make sure it is no lower than the LCL else: idx = x < this_lcl[0] # LFC height < LCL height, so set LFC = LCL if not any(idx): el_pres, _ = find_intersections(pressure[1:], parcel_temperature_profile[1:], temperature[1:], direction='decreasing', log_x=True) if np.min(el_pres) > this_lcl[0]: x, y = np.nan * pressure.units, np.nan * temperature.units else: x, y = this_lcl return x, y # Otherwise, find all LFCs that exist above the LCL # What is returned depends on which flag as described in the docstring else: return _multiple_el_lfc_options(x, y, idx, which, pressure, parcel_temperature_profile, temperature, dewpoint, intersect_type='LFC') def _multiple_el_lfc_options(intersect_pressures, intersect_temperatures, valid_x, which, pressure, parcel_temperature_profile, temperature, dewpoint, intersect_type): """Choose which ELs and LFCs to return from a sounding.""" p_list, t_list = intersect_pressures[valid_x], intersect_temperatures[valid_x] if which == 'all': x, y = p_list, t_list elif which == 'bottom': x, y = p_list[0], t_list[0] elif which == 'top': x, y = p_list[-1], t_list[-1] elif which == 'wide': x, y = _wide_option(intersect_type, p_list, t_list, pressure, parcel_temperature_profile, temperature) elif which == 'most_cape': x, y = _most_cape_option(intersect_type, p_list, t_list, pressure, temperature, dewpoint, parcel_temperature_profile) else: raise ValueError('Invalid option for "which". Valid options are "top", "bottom", ' '"wide", "most_cape", and "all".') return x, y def _wide_option(intersect_type, p_list, t_list, pressure, parcel_temperature_profile, temperature): """Calculate the LFC or EL that produces the greatest distance between these points.""" # zip the LFC and EL lists together and find greatest difference if intersect_type == 'LFC': # Find EL intersection pressure values lfc_p_list = p_list el_p_list, _ = find_intersections(pressure[1:], parcel_temperature_profile[1:], temperature[1:], direction='decreasing', log_x=True) else: # intersect_type == 'EL' el_p_list = p_list # Find LFC intersection pressure values lfc_p_list, _ = find_intersections(pressure, parcel_temperature_profile, temperature, direction='increasing', log_x=True) diff = [lfc_p.m - el_p.m for lfc_p, el_p in zip(lfc_p_list, el_p_list)] return (p_list[np.where(diff == np.max(diff))][0], t_list[np.where(diff == np.max(diff))][0]) def _most_cape_option(intersect_type, p_list, t_list, pressure, temperature, dewpoint, parcel_temperature_profile): """Calculate the LFC or EL that produces the most CAPE in the profile.""" # Need to loop through all possible combinations of cape, find greatest cape profile cape_list, pair_list = [], [] for which_lfc in ['top', 'bottom']: for which_el in ['top', 'bottom']: cape, _ = cape_cin(pressure, temperature, dewpoint, parcel_temperature_profile, which_lfc=which_lfc, which_el=which_el) cape_list.append(cape.m) pair_list.append([which_lfc, which_el]) (lfc_chosen, el_chosen) = pair_list[np.where(cape_list == np.max(cape_list))[0][0]] if intersect_type == 'LFC': if lfc_chosen == 'top': x, y = p_list[-1], t_list[-1] else: # 'bottom' is returned x, y = p_list[0], t_list[0] else: # EL is returned if el_chosen == 'top': x, y = p_list[-1], t_list[-1] else: x, y = p_list[0], t_list[0] return x, y @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]', '[temperature]', '[temperature]') def el(pressure, temperature, dewpoint, parcel_temperature_profile=None, which='top'): r"""Calculate the equilibrium level. This works by finding the last intersection of the ideal parcel path and the measured environmental temperature. If there is one or fewer intersections, there is no equilibrium level. Parameters ---------- pressure : `pint.Quantity` The atmospheric pressure profile temperature : `pint.Quantity` The temperature at the levels given by `pressure` dewpoint : `pint.Quantity` The dewpoint at the levels given by `pressure` parcel_temperature_profile: `pint.Quantity`, optional The parcel temperature profile from which to calculate the EL. Defaults to the surface parcel profile. which: str, optional Pick which LFC to return. Options are 'top', 'bottom', 'wide', 'most_cape', and 'all'. 'top' returns the lowest-pressure EL, default. 'bottom' returns the highest-pressure EL. 'wide' returns the EL whose corresponding LFC is farthest away. 'most_cape' returns the EL that results in the most CAPE in the profile. Returns ------- `pint.Quantity` The EL pressure, or array of same if which='all' `pint.Quantity` The EL temperature, or array of same if which='all' See Also -------- parcel_profile """ pressure, temperature, dewpoint = _remove_nans(pressure, temperature, dewpoint) # Default to surface parcel if no profile or starting pressure level is given if parcel_temperature_profile is None: new_stuff = parcel_profile_with_lcl(pressure, temperature, dewpoint) pressure, temperature, dewpoint, parcel_temperature_profile = new_stuff parcel_temperature_profile = parcel_temperature_profile.to(temperature.units) # If the top of the sounding parcel is warmer than the environment, there is no EL if parcel_temperature_profile[-1] > temperature[-1]: return np.nan * pressure.units, np.nan * temperature.units # Interpolate in log space to find the appropriate pressure - units have to be stripped # and reassigned to allow np.log() to function properly. x, y = find_intersections(pressure[1:], parcel_temperature_profile[1:], temperature[1:], direction='decreasing', log_x=True) lcl_p, _ = lcl(pressure[0], temperature[0], dewpoint[0]) idx = x < lcl_p if len(x) > 0 and x[-1] < lcl_p: return _multiple_el_lfc_options(x, y, idx, which, pressure, parcel_temperature_profile, temperature, dewpoint, intersect_type='EL') else: return np.nan * pressure.units, np.nan * temperature.units @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]', '[temperature]') def parcel_profile(pressure, temperature, dewpoint): r"""Calculate the profile a parcel takes through the atmosphere. The parcel starts at `temperature`, and `dewpoint`, lifted up dry adiabatically to the LCL, and then moist adiabatically from there. `pressure` specifies the pressure levels for the profile. Parameters ---------- pressure : `pint.Quantity` The atmospheric pressure level(s) of interest. This array must be from high to low pressure. temperature : `pint.Quantity` The starting temperature dewpoint : `pint.Quantity` The starting dewpoint Returns ------- `pint.Quantity` The parcel temperatures at the specified pressure levels. See Also -------- lcl, moist_lapse, dry_lapse """ _, _, _, t_l, _, t_u = _parcel_profile_helper(pressure, temperature, dewpoint) return concatenate((t_l, t_u)) @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]', '[temperature]') def parcel_profile_with_lcl(pressure, temperature, dewpoint): r"""Calculate the profile a parcel takes through the atmosphere. The parcel starts at `temperature`, and `dewpoint`, lifted up dry adiabatically to the LCL, and then moist adiabatically from there. `pressure` specifies the pressure levels for the profile. This function returns a profile that includes the LCL. Parameters ---------- pressure : `pint.Quantity` The atmospheric pressure level(s) of interest. This array must be from high to low pressure. temperature : `pint.Quantity` The atmospheric temperature at the levels in `pressure`. The first entry should be at the same level as the first `pressure` data point. dewpoint : `pint.Quantity` The atmospheric dewpoint at the levels in `pressure`. The first entry should be at the same level as the first `pressure` data point. Returns ------- pressure : `pint.Quantity` The parcel profile pressures, which includes the specified levels and the LCL ambient_temperature : `pint.Quantity` The atmospheric temperature values, including the value interpolated to the LCL level ambient_dew_point : `pint.Quantity` The atmospheric dewpoint values, including the value interpolated to the LCL level profile_temperature : `pint.Quantity` The parcel profile temperatures at all of the levels in the returned pressures array, including the LCL. See Also -------- lcl, moist_lapse, dry_lapse, parcel_profile """ p_l, p_lcl, p_u, t_l, t_lcl, t_u = _parcel_profile_helper(pressure, temperature[0], dewpoint[0]) new_press = concatenate((p_l, p_lcl, p_u)) prof_temp = concatenate((t_l, t_lcl, t_u)) new_temp = _insert_lcl_level(pressure, temperature, p_lcl) new_dewp = _insert_lcl_level(pressure, dewpoint, p_lcl) return new_press, new_temp, new_dewp, prof_temp def _parcel_profile_helper(pressure, temperature, dewpoint): """Help calculate parcel profiles. Returns the temperature and pressure, above, below, and including the LCL. The other calculation functions decide what to do with the pieces. """ # Find the LCL press_lcl, temp_lcl = lcl(pressure[0], temperature, dewpoint) press_lcl = press_lcl.to(pressure.units) # Find the dry adiabatic profile, *including* the LCL. We need >= the LCL in case the # LCL is included in the levels. It's slightly redundant in that case, but simplifies # the logic for removing it later. press_lower = concatenate((pressure[pressure >= press_lcl], press_lcl)) temp_lower = dry_lapse(press_lower, temperature) # If the pressure profile doesn't make it to the lcl, we can stop here if _greater_or_close(np.nanmin(pressure.m), press_lcl.m): return (press_lower[:-1], press_lcl, units.Quantity(np.array([]), press_lower.units), temp_lower[:-1], temp_lcl, units.Quantity(np.array([]), temp_lower.units)) # Find moist pseudo-adiabatic profile starting at the LCL press_upper = concatenate((press_lcl, pressure[pressure < press_lcl])) temp_upper = moist_lapse(press_upper, temp_lower[-1]).to(temp_lower.units) # Return profile pieces return (press_lower[:-1], press_lcl, press_upper[1:], temp_lower[:-1], temp_lcl, temp_upper[1:]) def _insert_lcl_level(pressure, temperature, lcl_pressure): """Insert the LCL pressure into the profile.""" interp_temp = interpolate_1d(lcl_pressure, pressure, temperature) # Pressure needs to be increasing for searchsorted, so flip it and then convert # the index back to the original array loc = pressure.size - pressure[::-1].searchsorted(lcl_pressure) return np.insert(temperature.m, loc, interp_temp.m) * temperature.units @exporter.export @preprocess_xarray @check_units('[pressure]', '[dimensionless]') def vapor_pressure(pressure, mixing_ratio): r"""Calculate water vapor (partial) pressure. Given total `pressure` and water vapor `mixing_ratio`, calculates the partial pressure of water vapor. Parameters ---------- pressure : `pint.Quantity` total atmospheric pressure mixing_ratio : `pint.Quantity` dimensionless mass mixing ratio Returns ------- `pint.Quantity` The ambient water vapor (partial) pressure in the same units as `pressure`. Notes ----- This function is a straightforward implementation of the equation given in many places, such as [Hobbs1977]_ pg.71: .. math:: e = p \frac{r}{r + \epsilon} See Also -------- saturation_vapor_pressure, dewpoint """ return pressure * mixing_ratio / (mpconsts.epsilon + mixing_ratio) @exporter.export @preprocess_xarray @check_units('[temperature]') def saturation_vapor_pressure(temperature): r"""Calculate the saturation water vapor (partial) pressure. Parameters ---------- temperature : `pint.Quantity` air temperature Returns ------- `pint.Quantity` The saturation water vapor (partial) pressure See Also -------- vapor_pressure, dewpoint Notes ----- Instead of temperature, dewpoint may be used in order to calculate the actual (ambient) water vapor (partial) pressure. The formula used is that from [Bolton1980]_ for T in degrees Celsius: .. math:: 6.112 e^\frac{17.67T}{T + 243.5} """ # Converted from original in terms of C to use kelvin. Using raw absolute values of C in # a formula plays havoc with units support. return sat_pressure_0c * np.exp(17.67 * (temperature - 273.15 * units.kelvin) / (temperature - 29.65 * units.kelvin)) @exporter.export @preprocess_xarray @check_units('[temperature]', '[dimensionless]') def dewpoint_from_relative_humidity(temperature, relative_humidity): r"""Calculate the ambient dewpoint given air temperature and relative humidity. Parameters ---------- temperature : `pint.Quantity` air temperature relative_humidity : `pint.Quantity` relative humidity expressed as a ratio in the range 0 < relative_humidity <= 1 Returns ------- `pint.Quantity` The dewpoint temperature See Also -------- dewpoint, saturation_vapor_pressure """ if np.any(relative_humidity > 1.2): warnings.warn('Relative humidity >120%, ensure proper units.') return dewpoint(relative_humidity * saturation_vapor_pressure(temperature)) @exporter.export @preprocess_xarray @check_units('[pressure]') def dewpoint(vapor_pressure): r"""Calculate the ambient dewpoint given the vapor pressure. Parameters ---------- e : `pint.Quantity` Water vapor partial pressure Returns ------- `pint.Quantity` dewpoint temperature See Also -------- dewpoint_from_relative_humidity, saturation_vapor_pressure, vapor_pressure Notes ----- This function inverts the [Bolton1980]_ formula for saturation vapor pressure to instead calculate the temperature. This yield the following formula for dewpoint in degrees Celsius: .. math:: T = \frac{243.5 log(e / 6.112)}{17.67 - log(e / 6.112)} """ val = np.log(vapor_pressure / sat_pressure_0c) return 0. * units.degC + 243.5 * units.delta_degC * val / (17.67 - val) @exporter.export @preprocess_xarray @check_units('[pressure]', '[pressure]', '[dimensionless]') def mixing_ratio(partial_press, total_press, molecular_weight_ratio=mpconsts.epsilon): r"""Calculate the mixing ratio of a gas. This calculates mixing ratio given its partial pressure and the total pressure of the air. There are no required units for the input arrays, other than that they have the same units. Parameters ---------- partial_press : `pint.Quantity` Partial pressure of the constituent gas total_press : `pint.Quantity` Total air pressure molecular_weight_ratio : `pint.Quantity` or float, optional The ratio of the molecular weight of the constituent gas to that assumed for air. Defaults to the ratio for water vapor to dry air (:math:`\epsilon\approx0.622`). Returns ------- `pint.Quantity` The (mass) mixing ratio, dimensionless (e.g. Kg/Kg or g/g) Notes ----- This function is a straightforward implementation of the equation given in many places, such as [Hobbs1977]_ pg.73: .. math:: r = \epsilon \frac{e}{p - e} See Also -------- saturation_mixing_ratio, vapor_pressure """ return (molecular_weight_ratio * partial_press / (total_press - partial_press)).to('dimensionless') @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]') def saturation_mixing_ratio(total_press, temperature): r"""Calculate the saturation mixing ratio of water vapor. This calculation is given total pressure and the temperature. The implementation uses the formula outlined in [Hobbs1977]_ pg.73. Parameters ---------- total_press: `pint.Quantity` Total atmospheric pressure temperature: `pint.Quantity` air temperature Returns ------- `pint.Quantity` The saturation mixing ratio, dimensionless """ return mixing_ratio(saturation_vapor_pressure(temperature), total_press) @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]', '[temperature]') def equivalent_potential_temperature(pressure, temperature, dewpoint): r"""Calculate equivalent potential temperature. This calculation must be given an air parcel's pressure, temperature, and dewpoint. The implementation uses the formula outlined in [Bolton1980]_: First, the LCL temperature is calculated: .. math:: T_{L}=\frac{1}{\frac{1}{T_{D}-56}+\frac{ln(T_{K}/T_{D})}{800}}+56 Which is then used to calculate the potential temperature at the LCL: .. math:: \theta_{DL}=T_{K}\left(\frac{1000}{p-e}\right)^k \left(\frac{T_{K}}{T_{L}}\right)^{.28r} Both of these are used to calculate the final equivalent potential temperature: .. math:: \theta_{E}=\theta_{DL}\exp\left[\left(\frac{3036.}{T_{L}} -1.78\right)*r(1+.448r)\right] Parameters ---------- pressure: `pint.Quantity` Total atmospheric pressure temperature: `pint.Quantity` Temperature of parcel dewpoint: `pint.Quantity` Dewpoint of parcel Returns ------- `pint.Quantity` The equivalent potential temperature of the parcel Notes ----- [Bolton1980]_ formula for Theta-e is used, since according to [DaviesJones2009]_ it is the most accurate non-iterative formulation available. """ t = temperature.to('kelvin').magnitude td = dewpoint.to('kelvin').magnitude p = pressure.to('hPa').magnitude e = saturation_vapor_pressure(dewpoint).to('hPa').magnitude r = saturation_mixing_ratio(pressure, dewpoint).magnitude t_l = 56 + 1. / (1. / (td - 56) + np.log(t / td) / 800.) th_l = t * (1000 / (p - e)) ** mpconsts.kappa * (t / t_l) ** (0.28 * r) th_e = th_l * np.exp((3036. / t_l - 1.78) * r * (1 + 0.448 * r)) return th_e * units.kelvin @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]') def saturation_equivalent_potential_temperature(pressure, temperature): r"""Calculate saturation equivalent potential temperature. This calculation must be given an air parcel's pressure and temperature. The implementation uses the formula outlined in [Bolton1980]_ for the equivalent potential temperature, and assumes a saturated process. First, because we assume a saturated process, the temperature at the LCL is equivalent to the current temperature. Therefore the following equation .. math:: T_{L}=\frac{1}{\frac{1}{T_{D}-56}+\frac{ln(T_{K}/T_{D})}{800}}+56 reduces to .. math:: T_{L} = T_{K} Then the potential temperature at the temperature/LCL is calculated: .. math:: \theta_{DL}=T_{K}\left(\frac{1000}{p-e}\right)^k \left(\frac{T_{K}}{T_{L}}\right)^{.28r} However, because .. math:: T_{L} = T_{K} it follows that .. math:: \theta_{DL}=T_{K}\left(\frac{1000}{p-e}\right)^k Both of these are used to calculate the final equivalent potential temperature: .. math:: \theta_{E}=\theta_{DL}\exp\left[\left(\frac{3036.}{T_{K}} -1.78\right)*r(1+.448r)\right] Parameters ---------- pressure: `pint.Quantity` Total atmospheric pressure temperature: `pint.Quantity` Temperature of parcel Returns ------- `pint.Quantity` The saturation equivalent potential temperature of the parcel Notes ----- [Bolton1980]_ formula for Theta-e is used (for saturated case), since according to [DaviesJones2009]_ it is the most accurate non-iterative formulation available. """ t = temperature.to('kelvin').magnitude p = pressure.to('hPa').magnitude e = saturation_vapor_pressure(temperature).to('hPa').magnitude r = saturation_mixing_ratio(pressure, temperature).magnitude th_l = t * (1000 / (p - e)) ** mpconsts.kappa th_es = th_l * np.exp((3036. / t - 1.78) * r * (1 + 0.448 * r)) return th_es * units.kelvin @exporter.export @preprocess_xarray @check_units('[temperature]', '[dimensionless]', '[dimensionless]') def virtual_temperature(temperature, mixing_ratio, molecular_weight_ratio=mpconsts.epsilon): r"""Calculate virtual temperature. This calculation must be given an air parcel's temperature and mixing ratio. The implementation uses the formula outlined in [Hobbs2006]_ pg.80. Parameters ---------- temperature: `pint.Quantity` air temperature mixing_ratio : `pint.Quantity` dimensionless mass mixing ratio molecular_weight_ratio : `pint.Quantity` or float, optional The ratio of the molecular weight of the constituent gas to that assumed for air. Defaults to the ratio for water vapor to dry air. (:math:`\epsilon\approx0.622`). Returns ------- `pint.Quantity` The corresponding virtual temperature of the parcel Notes ----- .. math:: T_v = T \frac{\text{w} + \epsilon}{\epsilon\,(1 + \text{w})} """ return temperature * ((mixing_ratio + molecular_weight_ratio) / (molecular_weight_ratio * (1 + mixing_ratio))) @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]', '[dimensionless]', '[dimensionless]') def virtual_potential_temperature(pressure, temperature, mixing_ratio, molecular_weight_ratio=mpconsts.epsilon): r"""Calculate virtual potential temperature. This calculation must be given an air parcel's pressure, temperature, and mixing ratio. The implementation uses the formula outlined in [Markowski2010]_ pg.13. Parameters ---------- pressure: `pint.Quantity` Total atmospheric pressure temperature: `pint.Quantity` air temperature mixing_ratio : `pint.Quantity` dimensionless mass mixing ratio molecular_weight_ratio : `pint.Quantity` or float, optional The ratio of the molecular weight of the constituent gas to that assumed for air. Defaults to the ratio for water vapor to dry air. (:math:`\epsilon\approx0.622`). Returns ------- `pint.Quantity` The corresponding virtual potential temperature of the parcel Notes ----- .. math:: \Theta_v = \Theta \frac{\text{w} + \epsilon}{\epsilon\,(1 + \text{w})} """ pottemp = potential_temperature(pressure, temperature) return virtual_temperature(pottemp, mixing_ratio, molecular_weight_ratio) @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]', '[dimensionless]', '[dimensionless]') def density(pressure, temperature, mixing_ratio, molecular_weight_ratio=mpconsts.epsilon): r"""Calculate density. This calculation must be given an air parcel's pressure, temperature, and mixing ratio. The implementation uses the formula outlined in [Hobbs2006]_ pg.67. Parameters ---------- pressure: `pint.Quantity` Total atmospheric pressure temperature: `pint.Quantity` air temperature mixing_ratio : `pint.Quantity` dimensionless mass mixing ratio molecular_weight_ratio : `pint.Quantity` or float, optional The ratio of the molecular weight of the constituent gas to that assumed for air. Defaults to the ratio for water vapor to dry air. (:math:`\epsilon\approx0.622`). Returns ------- `pint.Quantity` The corresponding density of the parcel Notes ----- .. math:: \rho = \frac{p}{R_dT_v} """ virttemp = virtual_temperature(temperature, mixing_ratio, molecular_weight_ratio) return (pressure / (mpconsts.Rd * virttemp)).to(units.kilogram / units.meter ** 3) @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]', '[temperature]') def relative_humidity_wet_psychrometric(pressure, dry_bulb_temperature, web_bulb_temperature, **kwargs): r"""Calculate the relative humidity with wet bulb and dry bulb temperatures. This uses a psychrometric relationship as outlined in [WMO8-2014]_, with coefficients from [Fan1987]_. Parameters ---------- pressure: `pint.Quantity` Total atmospheric pressure dry_bulb_temperature: `pint.Quantity` Dry bulb temperature web_bulb_temperature: `pint.Quantity` Wet bulb temperature Returns ------- `pint.Quantity` Relative humidity Notes ----- .. math:: relative_humidity = \frac{e}{e_s} * :math:`relative_humidity` is relative humidity as a unitless ratio * :math:`e` is vapor pressure from the wet psychrometric calculation * :math:`e_s` is the saturation vapor pressure See Also -------- psychrometric_vapor_pressure_wet, saturation_vapor_pressure """ return (psychrometric_vapor_pressure_wet(pressure, dry_bulb_temperature, web_bulb_temperature, **kwargs) / saturation_vapor_pressure(dry_bulb_temperature)) @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]', '[temperature]') def psychrometric_vapor_pressure_wet(pressure, dry_bulb_temperature, wet_bulb_temperature, psychrometer_coefficient=6.21e-4 / units.kelvin): r"""Calculate the vapor pressure with wet bulb and dry bulb temperatures. This uses a psychrometric relationship as outlined in [WMO8-2014]_, with coefficients from [Fan1987]_. Parameters ---------- pressure: `pint.Quantity` Total atmospheric pressure dry_bulb_temperature: `pint.Quantity` Dry bulb temperature wet_bulb_temperature: `pint.Quantity` Wet bulb temperature psychrometer_coefficient: `pint.Quantity`, optional Psychrometer coefficient. Defaults to 6.21e-4 K^-1. Returns ------- `pint.Quantity` Vapor pressure Notes ----- .. math:: e' = e'_w(T_w) - A p (T - T_w) * :math:`e'` is vapor pressure * :math:`e'_w(T_w)` is the saturation vapor pressure with respect to water at temperature :math:`T_w` * :math:`p` is the pressure of the wet bulb * :math:`T` is the temperature of the dry bulb * :math:`T_w` is the temperature of the wet bulb * :math:`A` is the psychrometer coefficient Psychrometer coefficient depends on the specific instrument being used and the ventilation of the instrument. See Also -------- saturation_vapor_pressure """ return (saturation_vapor_pressure(wet_bulb_temperature) - psychrometer_coefficient * pressure * (dry_bulb_temperature - wet_bulb_temperature).to('kelvin')) @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]', '[dimensionless]') def mixing_ratio_from_relative_humidity(pressure, temperature, relative_humidity): r"""Calculate the mixing ratio from relative humidity, temperature, and pressure. Parameters ---------- pressure: `pint.Quantity` Total atmospheric pressure temperature: `pint.Quantity` Air temperature relative_humidity: array_like The relative humidity expressed as a unitless ratio in the range [0, 1]. Can also pass a percentage if proper units are attached. Returns ------- `pint.Quantity` Dimensionless mixing ratio Notes ----- Formula adapted from [Hobbs1977]_ pg. 74. .. math:: w = (relative_humidity)(w_s) * :math:`w` is mixing ratio * :math:`relative_humidity` is relative humidity as a unitless ratio * :math:`w_s` is the saturation mixing ratio See Also -------- relative_humidity_from_mixing_ratio, saturation_mixing_ratio """ return (relative_humidity * saturation_mixing_ratio(pressure, temperature)).to('dimensionless') @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]', '[dimensionless]') def relative_humidity_from_mixing_ratio(pressure, temperature, mixing_ratio): r"""Calculate the relative humidity from mixing ratio, temperature, and pressure. Parameters ---------- pressure: `pint.Quantity` Total atmospheric pressure temperature: `pint.Quantity` Air temperature mixing_ratio: `pint.Quantity` Dimensionless mass mixing ratio Returns ------- `pint.Quantity` Relative humidity Notes ----- Formula based on that from [Hobbs1977]_ pg. 74. .. math:: relative_humidity = \frac{w}{w_s} * :math:`relative_humidity` is relative humidity as a unitless ratio * :math:`w` is mixing ratio * :math:`w_s` is the saturation mixing ratio See Also -------- mixing_ratio_from_relative_humidity, saturation_mixing_ratio """ return mixing_ratio / saturation_mixing_ratio(pressure, temperature) @exporter.export @preprocess_xarray @check_units('[dimensionless]') def mixing_ratio_from_specific_humidity(specific_humidity): r"""Calculate the mixing ratio from specific humidity. Parameters ---------- specific_humidity: `pint.Quantity` Specific humidity of air Returns ------- `pint.Quantity` Mixing ratio Notes ----- Formula from [Salby1996]_ pg. 118. .. math:: w = \frac{q}{1-q} * :math:`w` is mixing ratio * :math:`q` is the specific humidity See Also -------- mixing_ratio, specific_humidity_from_mixing_ratio """ try: specific_humidity = specific_humidity.to('dimensionless') except AttributeError: pass return specific_humidity / (1 - specific_humidity) @exporter.export @preprocess_xarray @check_units('[dimensionless]') def specific_humidity_from_mixing_ratio(mixing_ratio): r"""Calculate the specific humidity from the mixing ratio. Parameters ---------- mixing_ratio: `pint.Quantity` mixing ratio Returns ------- `pint.Quantity` Specific humidity Notes ----- Formula from [Salby1996]_ pg. 118. .. math:: q = \frac{w}{1+w} * :math:`w` is mixing ratio * :math:`q` is the specific humidity See Also -------- mixing_ratio, mixing_ratio_from_specific_humidity """ try: mixing_ratio = mixing_ratio.to('dimensionless') except AttributeError: pass return mixing_ratio / (1 + mixing_ratio) @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]', '[dimensionless]') def relative_humidity_from_specific_humidity(pressure, temperature, specific_humidity): r"""Calculate the relative humidity from specific humidity, temperature, and pressure. Parameters ---------- pressure: `pint.Quantity` Total atmospheric pressure temperature: `pint.Quantity` Air temperature specific_humidity: `pint.Quantity` Specific humidity of air Returns ------- `pint.Quantity` Relative humidity Notes ----- Formula based on that from [Hobbs1977]_ pg. 74. and [Salby1996]_ pg. 118. .. math:: relative_humidity = \frac{q}{(1-q)w_s} * :math:`relative_humidity` is relative humidity as a unitless ratio * :math:`q` is specific humidity * :math:`w_s` is the saturation mixing ratio See Also -------- relative_humidity_from_mixing_ratio """ return (mixing_ratio_from_specific_humidity(specific_humidity) / saturation_mixing_ratio(pressure, temperature)) @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]', '[temperature]', '[temperature]') def cape_cin(pressure, temperature, dewpoint, parcel_profile, which_lfc='bottom', which_el='top'): r"""Calculate CAPE and CIN. Calculate the convective available potential energy (CAPE) and convective inhibition (CIN) of a given upper air profile and parcel path. CIN is integrated between the surface and LFC, CAPE is integrated between the LFC and EL (or top of sounding). Intersection points of the measured temperature profile and parcel profile are logarithmically interpolated. Parameters ---------- pressure : `pint.Quantity` The atmospheric pressure level(s) of interest, in order from highest to lowest pressure. temperature : `pint.Quantity` The atmospheric temperature corresponding to pressure. dewpoint : `pint.Quantity` The atmospheric dewpoint corresponding to pressure. parcel_profile : `pint.Quantity` The temperature profile of the parcel. which_lfc : str Choose which LFC to integrate from. Valid options are 'top', 'bottom', 'wide', and 'most_cape'. Default is 'bottom'. which_el : str Choose which EL to integrate to. Valid options are 'top', 'bottom', 'wide', and 'most_cape'. Default is 'top'. Returns ------- `pint.Quantity` Convective Available Potential Energy (CAPE). `pint.Quantity` Convective INhibition (CIN). Notes ----- Formula adopted from [Hobbs1977]_. .. math:: \text{CAPE} = -R_d \int_{LFC}^{EL} (T_{parcel} - T_{env}) d\text{ln}(p) .. math:: \text{CIN} = -R_d \int_{SFC}^{LFC} (T_{parcel} - T_{env}) d\text{ln}(p) * :math:`CAPE` Convective available potential energy * :math:`CIN` Convective inhibition * :math:`LFC` Pressure of the level of free convection * :math:`EL` Pressure of the equilibrium level * :math:`SFC` Level of the surface or beginning of parcel path * :math:`R_d` Gas constant * :math:`g` Gravitational acceleration * :math:`T_{parcel}` Parcel temperature * :math:`T_{env}` Environment temperature * :math:`p` Atmospheric pressure See Also -------- lfc, el """ pressure, temperature, dewpoint, parcel_profile = _remove_nans(pressure, temperature, dewpoint, parcel_profile) # Calculate LFC limit of integration lfc_pressure, _ = lfc(pressure, temperature, dewpoint, parcel_temperature_profile=parcel_profile, which=which_lfc) # If there is no LFC, no need to proceed. if np.isnan(lfc_pressure): return 0 * units('J/kg'), 0 * units('J/kg') else: lfc_pressure = lfc_pressure.magnitude # Calculate the EL limit of integration el_pressure, _ = el(pressure, temperature, dewpoint, parcel_temperature_profile=parcel_profile, which=which_el) # No EL and we use the top reading of the sounding. if np.isnan(el_pressure): el_pressure = pressure[-1].magnitude else: el_pressure = el_pressure.magnitude # Difference between the parcel path and measured temperature profiles y = (parcel_profile - temperature).to(units.degK) # Estimate zero crossings x, y = _find_append_zero_crossings(np.copy(pressure), y) # CAPE # Only use data between the LFC and EL for calculation p_mask = _less_or_close(x.m, lfc_pressure) & _greater_or_close(x.m, el_pressure) x_clipped = x[p_mask].magnitude y_clipped = y[p_mask].magnitude cape = (mpconsts.Rd * (np.trapz(y_clipped, np.log(x_clipped)) * units.degK)).to(units('J/kg')) # CIN # Only use data between the surface and LFC for calculation p_mask = _greater_or_close(x.m, lfc_pressure) x_clipped = x[p_mask].magnitude y_clipped = y[p_mask].magnitude cin = (mpconsts.Rd * (np.trapz(y_clipped, np.log(x_clipped)) * units.degK)).to(units('J/kg')) # Set CIN to 0 if it's returned as a positive value (#1190) if cin > 0 * units('J/kg'): cin = 0 * units('J/kg') return cape, cin def _find_append_zero_crossings(x, y): r""" Find and interpolate zero crossings. Estimate the zero crossings of an x,y series and add estimated crossings to series, returning a sorted array with no duplicate values. Parameters ---------- x : `pint.Quantity` x values of data y : `pint.Quantity` y values of data Returns ------- x : `pint.Quantity` x values of data y : `pint.Quantity` y values of data """ crossings = find_intersections(x[1:], y[1:], np.zeros_like(y[1:]) * y.units, log_x=True) x = concatenate((x, crossings[0])) y = concatenate((y, crossings[1])) # Resort so that data are in order sort_idx = np.argsort(x) x = x[sort_idx] y = y[sort_idx] # Remove duplicate data points if there are any keep_idx = np.ediff1d(x.magnitude, to_end=[1]) > 1e-6 x = x[keep_idx] y = y[keep_idx] return x, y @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]', '[temperature]') def most_unstable_parcel(pressure, temperature, dewpoint, height=None, bottom=None, depth=300 * units.hPa): """ Determine the most unstable parcel in a layer. Determines the most unstable parcel of air by calculating the equivalent potential temperature and finding its maximum in the specified layer. Parameters ---------- pressure: `pint.Quantity` Atmospheric pressure profile temperature: `pint.Quantity` Atmospheric temperature profile dewpoint: `pint.Quantity` Atmospheric dewpoint profile height: `pint.Quantity`, optional Atmospheric height profile. Standard atmosphere assumed when None (the default). bottom: `pint.Quantity`, optional Bottom of the layer to consider for the calculation in pressure or height. Defaults to using the bottom pressure or height. depth: `pint.Quantity`, optional Depth of the layer to consider for the calculation in pressure or height. Defaults to 300 hPa. Returns ------- `pint.Quantity` Pressure, temperature, and dewpoint of most unstable parcel in the profile. integer Index of the most unstable parcel in the given profile See Also -------- get_layer """ p_layer, t_layer, td_layer = get_layer(pressure, temperature, dewpoint, bottom=bottom, depth=depth, height=height, interpolate=False) theta_e = equivalent_potential_temperature(p_layer, t_layer, td_layer) max_idx = np.argmax(theta_e) return p_layer[max_idx], t_layer[max_idx], td_layer[max_idx], max_idx @exporter.export @preprocess_xarray @check_units('[temperature]', '[pressure]', '[temperature]') def isentropic_interpolation(levels, pressure, temperature, *args, axis=0, temperature_out=False, max_iters=50, eps=1e-6, bottom_up_search=True, **kwargs): r"""Interpolate data in isobaric coordinates to isentropic coordinates. Parameters ---------- levels : array One-dimensional array of desired potential temperature surfaces pressure : array One-dimensional array of pressure levels temperature : array Array of temperature axis : int, optional The axis corresponding to the vertical in the temperature array, defaults to 0. temperature_out : bool, optional If true, will calculate temperature and output as the last item in the output list. Defaults to False. max_iters : int, optional The maximum number of iterations to use in calculation, defaults to 50. eps : float, optional The desired absolute error in the calculated value, defaults to 1e-6. bottom_up_search : bool, optional Controls whether to search for levels bottom-up, or top-down. Defaults to True, which is bottom-up search. args : array, optional Any additional variables will be interpolated to each isentropic level. Returns ------- list List with pressure at each isentropic level, followed by each additional argument interpolated to isentropic coordinates. Notes ----- Input variable arrays must have the same number of vertical levels as the pressure levels array. Pressure is calculated on isentropic surfaces by assuming that temperature varies linearly with the natural log of pressure. Linear interpolation is then used in the vertical to find the pressure at each isentropic level. Interpolation method from [Ziv1994]_. Any additional arguments are assumed to vary linearly with temperature and will be linearly interpolated to the new isentropic levels. See Also -------- potential_temperature """ # iteration function to be used later # Calculates theta from linearly interpolated temperature and solves for pressure def _isen_iter(iter_log_p, isentlevs_nd, ka, a, b, pok): exner = pok * np.exp(-ka * iter_log_p) t = a * iter_log_p + b # Newton-Raphson iteration f = isentlevs_nd - t * exner fp = exner * (ka * t - a) return iter_log_p - (f / fp) # Get dimensions in temperature ndim = temperature.ndim # Convert units pres = pressure.to('hPa') temperature = temperature.to('kelvin') slices = [np.newaxis] * ndim slices[axis] = slice(None) slices = tuple(slices) pres = np.broadcast_to(pres[slices].magnitude, temperature.shape) * pres.units # Sort input data sort_pres = np.argsort(pres.m, axis=axis) sort_pres = np.swapaxes(np.swapaxes(sort_pres, 0, axis)[::-1], 0, axis) sorter = broadcast_indices(pres, sort_pres, ndim, axis) levs = pres[sorter] tmpk = temperature[sorter] levels = np.asarray(levels.m_as('kelvin')).reshape(-1) isentlevels = levels[np.argsort(levels)] # Make the desired isentropic levels the same shape as temperature shape = list(temperature.shape) shape[axis] = isentlevels.size isentlevs_nd = np.broadcast_to(isentlevels[slices], shape) # exponent to Poisson's Equation, which is imported above ka = mpconsts.kappa.m_as('dimensionless') # calculate theta for each point pres_theta = potential_temperature(levs, tmpk) # Raise error if input theta level is larger than pres_theta max if np.max(pres_theta.m) < np.max(levels): raise ValueError('Input theta level out of data bounds') # Find log of pressure to implement assumption of linear temperature dependence on # ln(p) log_p = np.log(levs.m) # Calculations for interpolation routine pok = mpconsts.P0 ** ka # index values for each point for the pressure level nearest to the desired theta level above, below, good = find_bounding_indices(pres_theta.m, levels, axis, from_below=bottom_up_search) # calculate constants for the interpolation a = (tmpk.m[above] - tmpk.m[below]) / (log_p[above] - log_p[below]) b = tmpk.m[above] - a * log_p[above] # calculate first guess for interpolation isentprs = 0.5 * (log_p[above] + log_p[below]) # Make sure we ignore any nans in the data for solving; checking a is enough since it # combines log_p and tmpk. good &= ~np.isnan(a) # iterative interpolation using scipy.optimize.fixed_point and _isen_iter defined above log_p_solved = so.fixed_point(_isen_iter, isentprs[good], args=(isentlevs_nd[good], ka, a[good], b[good], pok.m), xtol=eps, maxiter=max_iters) # get back pressure from log p isentprs[good] = np.exp(log_p_solved) # Mask out points we know are bad as well as points that are beyond the max pressure isentprs[~(good & _less_or_close(isentprs, np.max(pres.m)))] = np.nan # create list for storing output data ret = [isentprs * units.hPa] # if temperature_out = true, calculate temperature and output as last item in list if temperature_out: ret.append((isentlevs_nd / ((mpconsts.P0.m / isentprs) ** ka)) * units.kelvin) # do an interpolation for each additional argument if args: others = interpolate_1d(isentlevels, pres_theta.m, *(arr[sorter] for arr in args), axis=axis, return_list_always=True) ret.extend(others) return ret @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]', '[temperature]') def surface_based_cape_cin(pressure, temperature, dewpoint): r"""Calculate surface-based CAPE and CIN. Calculate the convective available potential energy (CAPE) and convective inhibition (CIN) of a given upper air profile for a surface-based parcel. CIN is integrated between the surface and LFC, CAPE is integrated between the LFC and EL (or top of sounding). Intersection points of the measured temperature profile and parcel profile are logarithmically interpolated. Parameters ---------- pressure : `pint.Quantity` Atmospheric pressure profile. The first entry should be the starting (surface) observation, with the array going from high to low pressure. temperature : `pint.Quantity` Temperature profile corresponding to the `pressure` profile. dewpoint : `pint.Quantity` Dewpoint profile corresponding to the `pressure` profile. Returns ------- `pint.Quantity` Surface based Convective Available Potential Energy (CAPE). `pint.Quantity` Surface based Convective INhibition (CIN). See Also -------- cape_cin, parcel_profile """ pressure, temperature, dewpoint = _remove_nans(pressure, temperature, dewpoint) p, t, td, profile = parcel_profile_with_lcl(pressure, temperature, dewpoint) return cape_cin(p, t, td, profile) @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]', '[temperature]') def most_unstable_cape_cin(pressure, temperature, dewpoint, **kwargs): r"""Calculate most unstable CAPE/CIN. Calculate the convective available potential energy (CAPE) and convective inhibition (CIN) of a given upper air profile and most unstable parcel path. CIN is integrated between the surface and LFC, CAPE is integrated between the LFC and EL (or top of sounding). Intersection points of the measured temperature profile and parcel profile are logarithmically interpolated. Parameters ---------- pressure : `pint.Quantity` Pressure profile temperature : `pint.Quantity` Temperature profile dewpoint : `pint.Quantity` Dew point profile kwargs Additional keyword arguments to pass to `most_unstable_parcel` Returns ------- `pint.Quantity` Most unstable Convective Available Potential Energy (CAPE). `pint.Quantity` Most unstable Convective INhibition (CIN). See Also -------- cape_cin, most_unstable_parcel, parcel_profile """ pressure, temperature, dewpoint = _remove_nans(pressure, temperature, dewpoint) _, _, _, parcel_idx = most_unstable_parcel(pressure, temperature, dewpoint, **kwargs) p, t, td, mu_profile = parcel_profile_with_lcl(pressure[parcel_idx:], temperature[parcel_idx:], dewpoint[parcel_idx:]) return cape_cin(p, t, td, mu_profile) @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]', '[temperature]') def mixed_layer_cape_cin(pressure, temperature, dewpoint, **kwargs): r"""Calculate mixed-layer CAPE and CIN. Calculate the convective available potential energy (CAPE) and convective inhibition (CIN) of a given upper air profile and mixed-layer parcel path. CIN is integrated between the surface and LFC, CAPE is integrated between the LFC and EL (or top of sounding). Intersection points of the measured temperature profile and parcel profile are logarithmically interpolated. Kwargs for `mixed_parcel` can be provided, such as `depth`. Default mixed-layer depth is 100 hPa. Parameters ---------- pressure : `pint.Quantity` Pressure profile temperature : `pint.Quantity` Temperature profile dewpoint : `pint.Quantity` Dewpoint profile kwargs Additional keyword arguments to pass to `mixed_parcel` Returns ------- `pint.Quantity` Mixed-layer Convective Available Potential Energy (CAPE). `pint.Quantity` Mixed-layer Convective INhibition (CIN). See Also -------- cape_cin, mixed_parcel, parcel_profile """ depth = kwargs.get('depth', 100 * units.hPa) parcel_pressure, parcel_temp, parcel_dewpoint = mixed_parcel(pressure, temperature, dewpoint, **kwargs) # Remove values below top of mixed layer and add in the mixed layer values pressure_prof = pressure[pressure < (pressure[0] - depth)] temp_prof = temperature[pressure < (pressure[0] - depth)] dew_prof = dewpoint[pressure < (pressure[0] - depth)] pressure_prof = concatenate([parcel_pressure, pressure_prof]) temp_prof = concatenate([parcel_temp, temp_prof]) dew_prof = concatenate([parcel_dewpoint, dew_prof]) p, t, td, ml_profile = parcel_profile_with_lcl(pressure_prof, temp_prof, dew_prof) return cape_cin(p, t, td, ml_profile) @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]', '[temperature]') def mixed_parcel(pressure, temperature, dewpoint, parcel_start_pressure=None, height=None, bottom=None, depth=100 * units.hPa, interpolate=True): r"""Calculate the properties of a parcel mixed from a layer. Determines the properties of an air parcel that is the result of complete mixing of a given atmospheric layer. Parameters ---------- pressure : `pint.Quantity` Atmospheric pressure profile temperature : `pint.Quantity` Atmospheric temperature profile dewpoint : `pint.Quantity` Atmospheric dewpoint profile parcel_start_pressure : `pint.Quantity`, optional Pressure at which the mixed parcel should begin (default None) height: `pint.Quantity`, optional Atmospheric heights corresponding to the given pressures (default None) bottom : `pint.Quantity`, optional The bottom of the layer as a pressure or height above the surface pressure (default None) depth : `pint.Quantity`, optional The thickness of the layer as a pressure or height above the bottom of the layer (default 100 hPa) interpolate : bool, optional Interpolate the top and bottom points if they are not in the given data Returns ------- `pint.Quantity` The pressure of the mixed parcel `pint.Quantity` The temperature of the mixed parcel `pint.Quantity` The dewpoint of the mixed parcel """ # If a parcel starting pressure is not provided, use the surface if not parcel_start_pressure: parcel_start_pressure = pressure[0] # Calculate the potential temperature and mixing ratio over the layer theta = potential_temperature(pressure, temperature) mixing_ratio = saturation_mixing_ratio(pressure, dewpoint) # Mix the variables over the layer mean_theta, mean_mixing_ratio = mixed_layer(pressure, theta, mixing_ratio, bottom=bottom, height=height, depth=depth, interpolate=interpolate) # Convert back to temperature mean_temperature = mean_theta * exner_function(parcel_start_pressure) # Convert back to dewpoint mean_vapor_pressure = vapor_pressure(parcel_start_pressure, mean_mixing_ratio) # Using globals() here allows us to keep the dewpoint parameter but still call the # function of the same name. mean_dewpoint = globals()['dewpoint'](mean_vapor_pressure) return (parcel_start_pressure, mean_temperature.to(temperature.units), mean_dewpoint.to(dewpoint.units)) @exporter.export @preprocess_xarray @check_units('[pressure]') def mixed_layer(pressure, *args, height=None, bottom=None, depth=100 * units.hPa, interpolate=True): r"""Mix variable(s) over a layer, yielding a mass-weighted average. This function will integrate a data variable with respect to pressure and determine the average value using the mean value theorem. Parameters ---------- pressure : array-like Atmospheric pressure profile datavar : array-like Atmospheric variable measured at the given pressures height: array-like, optional Atmospheric heights corresponding to the given pressures (default None) bottom : `pint.Quantity`, optional The bottom of the layer as a pressure or height above the surface pressure (default None) depth : `pint.Quantity`, optional The thickness of the layer as a pressure or height above the bottom of the layer (default 100 hPa) interpolate : bool, optional Interpolate the top and bottom points if they are not in the given data (default True) Returns ------- `pint.Quantity` The mixed value of the data variable. """ layer = get_layer(pressure, *args, height=height, bottom=bottom, depth=depth, interpolate=interpolate) p_layer = layer[0] datavars_layer = layer[1:] ret = [] for datavar_layer in datavars_layer: actual_depth = abs(p_layer[0] - p_layer[-1]) ret.append((-1. / actual_depth.m) * np.trapz(datavar_layer.m, p_layer.m) * datavar_layer.units) return ret @exporter.export @preprocess_xarray @check_units('[length]', '[temperature]') def dry_static_energy(height, temperature): r"""Calculate the dry static energy of parcels. This function will calculate the dry static energy following the first two terms of equation 3.72 in [Hobbs2006]_. Notes ----- .. math::\text{dry static energy} = c_{pd} * T + gz * :math:`T` is temperature * :math:`z` is height Parameters ---------- height : `pint.Quantity` Atmospheric height temperature : `pint.Quantity` Air temperature Returns ------- `pint.Quantity` The dry static energy """ return (mpconsts.g * height + mpconsts.Cp_d * temperature).to('kJ/kg') @exporter.export @preprocess_xarray @check_units('[length]', '[temperature]', '[dimensionless]') def moist_static_energy(height, temperature, specific_humidity): r"""Calculate the moist static energy of parcels. This function will calculate the moist static energy following equation 3.72 in [Hobbs2006]_. Notes ----- .. math::\text{moist static energy} = c_{pd} * T + gz + L_v q * :math:`T` is temperature * :math:`z` is height * :math:`q` is specific humidity Parameters ---------- height : `pint.Quantity` Atmospheric height temperature : `pint.Quantity` Air temperature specific_humidity : `pint.Quantity` Atmospheric specific humidity Returns ------- `pint.Quantity` The moist static energy """ return (dry_static_energy(height, temperature) + mpconsts.Lv * specific_humidity.to('dimensionless')).to('kJ/kg') @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]') def thickness_hydrostatic(pressure, temperature, mixing_ratio=None, molecular_weight_ratio=mpconsts.epsilon, bottom=None, depth=None): r"""Calculate the thickness of a layer via the hypsometric equation. This thickness calculation uses the pressure and temperature profiles (and optionally mixing ratio) via the hypsometric equation with virtual temperature adjustment .. math:: Z_2 - Z_1 = -\frac{R_d}{g} \int_{p_1}^{p_2} T_v d\ln p, which is based off of Equation 3.24 in [Hobbs2006]_. This assumes a hydrostatic atmosphere. Layer bottom and depth specified in pressure. Parameters ---------- pressure : `pint.Quantity` Atmospheric pressure profile temperature : `pint.Quantity` Atmospheric temperature profile mixing_ratio : `pint.Quantity`, optional Profile of dimensionless mass mixing ratio. If none is given, virtual temperature is simply set to be the given temperature. molecular_weight_ratio : `pint.Quantity` or float, optional The ratio of the molecular weight of the constituent gas to that assumed for air. Defaults to the ratio for water vapor to dry air. (:math:`\epsilon\approx0.622`). bottom : `pint.Quantity`, optional The bottom of the layer in pressure. Defaults to the first observation. depth : `pint.Quantity`, optional The depth of the layer in hPa. Defaults to the full profile if bottom is not given, and 100 hPa if bottom is given. Returns ------- `pint.Quantity` The thickness of the layer in meters. See Also -------- thickness_hydrostatic_from_relative_humidity, pressure_to_height_std, virtual_temperature """ # Get the data for the layer, conditional upon bottom/depth being specified and mixing # ratio being given if bottom is None and depth is None: if mixing_ratio is None: layer_p, layer_virttemp = pressure, temperature else: layer_p = pressure layer_virttemp = virtual_temperature(temperature, mixing_ratio, molecular_weight_ratio) else: if mixing_ratio is None: layer_p, layer_virttemp = get_layer(pressure, temperature, bottom=bottom, depth=depth) else: layer_p, layer_temp, layer_w = get_layer(pressure, temperature, mixing_ratio, bottom=bottom, depth=depth) layer_virttemp = virtual_temperature(layer_temp, layer_w, molecular_weight_ratio) # Take the integral (with unit handling) and return the result in meters return (- mpconsts.Rd / mpconsts.g * np.trapz( layer_virttemp.m_as('K'), x=np.log(layer_p.m_as('hPa'))) * units.K).to('m') @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]') def thickness_hydrostatic_from_relative_humidity(pressure, temperature, relative_humidity, bottom=None, depth=None): r"""Calculate the thickness of a layer given pressure, temperature and relative humidity. Similar to ``thickness_hydrostatic``, this thickness calculation uses the pressure, temperature, and relative humidity profiles via the hypsometric equation with virtual temperature adjustment. .. math:: Z_2 - Z_1 = -\frac{R_d}{g} \int_{p_1}^{p_2} T_v d\ln p, which is based off of Equation 3.24 in [Hobbs2006]_. Virtual temperature is calculated from the profiles of temperature and relative humidity. This assumes a hydrostatic atmosphere. Layer bottom and depth specified in pressure. Parameters ---------- pressure : `pint.Quantity` Atmospheric pressure profile temperature : `pint.Quantity` Atmospheric temperature profile relative_humidity : `pint.Quantity` Atmospheric relative humidity profile. The relative humidity is expressed as a unitless ratio in the range [0, 1]. Can also pass a percentage if proper units are attached. bottom : `pint.Quantity`, optional The bottom of the layer in pressure. Defaults to the first observation. depth : `pint.Quantity`, optional The depth of the layer in hPa. Defaults to the full profile if bottom is not given, and 100 hPa if bottom is given. Returns ------- `pint.Quantity` The thickness of the layer in meters. See Also -------- thickness_hydrostatic, pressure_to_height_std, virtual_temperature, mixing_ratio_from_relative_humidity """ mixing = mixing_ratio_from_relative_humidity(pressure, temperature, relative_humidity) return thickness_hydrostatic(pressure, temperature, mixing_ratio=mixing, bottom=bottom, depth=depth) @exporter.export @preprocess_xarray @check_units('[length]', '[temperature]') def brunt_vaisala_frequency_squared(height, potential_temperature, axis=0): r"""Calculate the square of the Brunt-Vaisala frequency. Brunt-Vaisala frequency squared (a measure of atmospheric stability) is given by the formula: .. math:: N^2 = \frac{g}{\theta} \frac{d\theta}{dz} This formula is based off of Equations 3.75 and 3.77 in [Hobbs2006]_. Parameters ---------- height : `pint.Quantity` One-dimensional profile of atmospheric height potential_temperature : `pint.Quantity` Atmospheric potential temperature axis : int, optional The axis corresponding to vertical in the potential temperature array, defaults to 0. Returns ------- `pint.Quantity` The square of the Brunt-Vaisala frequency. See Also -------- brunt_vaisala_frequency, brunt_vaisala_period, potential_temperature """ # Ensure validity of temperature units potential_temperature = potential_temperature.to('K') # Calculate and return the square of Brunt-Vaisala frequency return mpconsts.g / potential_temperature * first_derivative(potential_temperature, x=height, axis=axis) @exporter.export @preprocess_xarray @check_units('[length]', '[temperature]') def brunt_vaisala_frequency(height, potential_temperature, axis=0): r"""Calculate the Brunt-Vaisala frequency. This function will calculate the Brunt-Vaisala frequency as follows: .. math:: N = \left( \frac{g}{\theta} \frac{d\theta}{dz} \right)^\frac{1}{2} This formula based off of Equations 3.75 and 3.77 in [Hobbs2006]_. This function is a wrapper for `brunt_vaisala_frequency_squared` that filters out negative (unstable) quantities and takes the square root. Parameters ---------- height : `pint.Quantity` One-dimensional profile of atmospheric height potential_temperature : `pint.Quantity` Atmospheric potential temperature axis : int, optional The axis corresponding to vertical in the potential temperature array, defaults to 0. Returns ------- `pint.Quantity` Brunt-Vaisala frequency. See Also -------- brunt_vaisala_frequency_squared, brunt_vaisala_period, potential_temperature """ bv_freq_squared = brunt_vaisala_frequency_squared(height, potential_temperature, axis=axis) bv_freq_squared[bv_freq_squared.magnitude < 0] = np.nan return np.sqrt(bv_freq_squared) @exporter.export @preprocess_xarray @check_units('[length]', '[temperature]') def brunt_vaisala_period(height, potential_temperature, axis=0): r"""Calculate the Brunt-Vaisala period. This function is a helper function for `brunt_vaisala_frequency` that calculates the period of oscillation as in Exercise 3.13 of [Hobbs2006]_: .. math:: \tau = \frac{2\pi}{N} Returns `NaN` when :math:`N^2 > 0`. Parameters ---------- height : `pint.Quantity` One-dimensional profile of atmospheric height potential_temperature : pint.Quantity` Atmospheric potential temperature axis : int, optional The axis corresponding to vertical in the potential temperature array, defaults to 0. Returns ------- `pint.Quantity` Brunt-Vaisala period. See Also -------- brunt_vaisala_frequency, brunt_vaisala_frequency_squared, potential_temperature """ bv_freq_squared = brunt_vaisala_frequency_squared(height, potential_temperature, axis=axis) bv_freq_squared[bv_freq_squared.magnitude <= 0] = np.nan return 2 * np.pi / np.sqrt(bv_freq_squared) @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]', '[temperature]') def wet_bulb_temperature(pressure, temperature, dewpoint): """Calculate the wet-bulb temperature using Normand's rule. This function calculates the wet-bulb temperature using the Normand method. The LCL is computed, and that parcel brought down to the starting pressure along a moist adiabat. The Normand method (and others) are described and compared by [Knox2017]_. Parameters ---------- pressure : `pint.Quantity` Initial atmospheric pressure temperature : `pint.Quantity` Initial atmospheric temperature dewpoint : `pint.Quantity` Initial atmospheric dewpoint Returns ------- `pint.Quantity` Wet-bulb temperature See Also -------- lcl, moist_lapse """ if not hasattr(pressure, 'shape'): pressure = np.atleast_1d(pressure) temperature = np.atleast_1d(temperature) dewpoint = np.atleast_1d(dewpoint) it = np.nditer([pressure, temperature, dewpoint, None], op_dtypes=['float', 'float', 'float', 'float'], flags=['buffered']) for press, temp, dewp, ret in it: press = press * pressure.units temp = temp * temperature.units dewp = dewp * dewpoint.units lcl_pressure, lcl_temperature = lcl(press, temp, dewp) moist_adiabat_temperatures = moist_lapse(concatenate([lcl_pressure, press]), lcl_temperature) ret[...] = moist_adiabat_temperatures[-1].magnitude # If we started with a scalar, return a scalar if it.operands[3].size == 1: return it.operands[3][0] * moist_adiabat_temperatures.units return it.operands[3] * moist_adiabat_temperatures.units @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]') def static_stability(pressure, temperature, axis=0): r"""Calculate the static stability within a vertical profile. .. math:: \sigma = -\frac{RT}{p} \frac{\partial \ln \theta}{\partial p} This formula is based on equation 4.3.6 in [Bluestein1992]_. Parameters ---------- pressure : `pint.Quantity` Profile of atmospheric pressure temperature : `pint.Quantity` Profile of temperature axis : int, optional The axis corresponding to vertical in the pressure and temperature arrays, defaults to 0. Returns ------- `pint.Quantity` The profile of static stability. """ theta = potential_temperature(pressure, temperature) return - mpconsts.Rd * temperature / pressure * first_derivative(np.log(theta.m_as('K')), x=pressure, axis=axis) @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]', '[dimensionless]') def dewpoint_from_specific_humidity(pressure, temperature, specific_humidity): r"""Calculate the dewpoint from specific humidity, temperature, and pressure. Parameters ---------- pressure: `pint.Quantity` Total atmospheric pressure temperature: `pint.Quantity` Air temperature specific_humidity: `pint.Quantity` Specific humidity of air Returns ------- `pint.Quantity` Dew point temperature See Also -------- relative_humidity_from_mixing_ratio, dewpoint_from_relative_humidity """ return dewpoint_from_relative_humidity(temperature, relative_humidity_from_specific_humidity( pressure, temperature, specific_humidity)) @exporter.export @preprocess_xarray @check_units('[length]/[time]', '[pressure]', '[temperature]') def vertical_velocity_pressure(w, pressure, temperature, mixing_ratio=0): r"""Calculate omega from w assuming hydrostatic conditions. This function converts vertical velocity with respect to height :math:`\left(w = \frac{Dz}{Dt}\right)` to that with respect to pressure :math:`\left(\omega = \frac{Dp}{Dt}\right)` assuming hydrostatic conditions on the synoptic scale. By Equation 7.33 in [Hobbs2006]_, .. math:: \omega \simeq -\rho g w Density (:math:`\rho`) is calculated using the :func:`density` function, from the given pressure and temperature. If `mixing_ratio` is given, the virtual temperature correction is used, otherwise, dry air is assumed. Parameters ---------- w: `pint.Quantity` Vertical velocity in terms of height pressure: `pint.Quantity` Total atmospheric pressure temperature: `pint.Quantity` Air temperature mixing_ratio: `pint.Quantity`, optional Mixing_ratio ratio of air Returns ------- `pint.Quantity` Vertical velocity in terms of pressure (in Pascals / second) See Also -------- density, vertical_velocity """ rho = density(pressure, temperature, mixing_ratio) return (-mpconsts.g * rho * w).to('Pa/s') @exporter.export @preprocess_xarray @check_units('[pressure]/[time]', '[pressure]', '[temperature]') def vertical_velocity(omega, pressure, temperature, mixing_ratio=0): r"""Calculate w from omega assuming hydrostatic conditions. This function converts vertical velocity with respect to pressure :math:`\left(\omega = \frac{Dp}{Dt}\right)` to that with respect to height :math:`\left(w = \frac{Dz}{Dt}\right)` assuming hydrostatic conditions on the synoptic scale. By Equation 7.33 in [Hobbs2006]_, .. math:: \omega \simeq -\rho g w so that .. math:: w \simeq \frac{- \omega}{\rho g} Density (:math:`\rho`) is calculated using the :func:`density` function, from the given pressure and temperature. If `mixing_ratio` is given, the virtual temperature correction is used, otherwise, dry air is assumed. Parameters ---------- omega: `pint.Quantity` Vertical velocity in terms of pressure pressure: `pint.Quantity` Total atmospheric pressure temperature: `pint.Quantity` Air temperature mixing_ratio: `pint.Quantity`, optional Mixing ratio of air Returns ------- `pint.Quantity` Vertical velocity in terms of height (in meters / second) See Also -------- density, vertical_velocity_pressure """ rho = density(pressure, temperature, mixing_ratio) return (omega / (- mpconsts.g * rho)).to('m/s') @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]') def specific_humidity_from_dewpoint(pressure, dewpoint): r"""Calculate the specific humidity from the dewpoint temperature and pressure. Parameters ---------- dewpoint: `pint.Quantity` dewpoint temperature pressure: `pint.Quantity` pressure Returns ------- `pint.Quantity` Specific humidity See Also -------- mixing_ratio, saturation_mixing_ratio """ mixing_ratio = saturation_mixing_ratio(pressure, dewpoint) return specific_humidity_from_mixing_ratio(mixing_ratio) @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]', '[temperature]') def lifted_index(pressure, temperature, parcel_profile): """Calculate Lifted Index from the pressure temperature and parcel profile. Lifted index formula derived from [Galway1956]_ and referenced by [Doswell-Schultz2006]_: LI = T500 - Tp500 where: T500 is the measured temperature at 500 hPa. Tp500 is the temperature of the lifted parcel at 500 hPa. Calculation of the lifted index is defined as the temperature difference between the observed 500 hPa temperature and the temperature of a parcel lifted from the surface to 500 hPa. Parameters ---------- pressure : `pint.Quantity` The atmospheric pressure level(s) of interest, in order from highest to lowest pressure. temperature : `pint.Quantity` The atmospheric temperature corresponding to pressure. parcel_profile : `pint.Quantity` The temperature profile of the parcel. Returns ------- `pint.Quantity` Lifted Index. """ # find the index for the 500 hPa pressure level. idx = np.where(pressure == 500 * units.hPa) # find the measured temperature at 500 hPa. T500 = temperature[idx] # find the parcel profile temperature at 500 hPa. Tp500 = parcel_profile[idx] # calculate the lifted index. lifted_index = T500 - Tp500.to(units.degC) return lifted_index @exporter.export @preprocess_xarray @check_units('[length]', '[temperature]', '[speed]', '[speed]') def gradient_richardson_number(height, potential_temperature, u, v, axis=0): r"""Calculate the gradient (or flux) Richardson number. .. math:: Ri = (g/\theta) * \frac{\left(\partial \theta/\partial z\)} {[\left(\partial u / \partial z\right)^2 + \left(\partial v / \partial z\right)^2} See [Holton2004]_ pg. 121-122. As noted by [Holton2004]_, flux Richardson number values below 0.25 indicate turbulence. Parameters ---------- height : `pint.Quantity` Atmospheric height potential_temperature : `pint.Quantity` Atmospheric potential temperature u : `pint.Quantity` x component of the wind v : `pint.Quantity` y component of the wind axis : int, optional The axis corresponding to vertical, defaults to 0. Returns ------- `pint.Quantity` Gradient Richardson number """ dthetadz = first_derivative(potential_temperature, x=height, axis=axis) dudz = first_derivative(u, x=height, axis=axis) dvdz = first_derivative(v, x=height, axis=axis) return (mpconsts.g / potential_temperature) * (dthetadz / (dudz ** 2 + dvdz ** 2))

34.249153

95

0.664571

import warnings import numpy as np import scipy.integrate as si import scipy.optimize as so from .tools import (_greater_or_close, _less_or_close, _remove_nans, find_bounding_indices, find_intersections, first_derivative, get_layer) from .. import constants as mpconsts from ..cbook import broadcast_indices from ..interpolate.one_dimension import interpolate_1d from ..package_tools import Exporter from ..units import check_units, concatenate, units from ..xarray import preprocess_xarray exporter = Exporter(globals()) sat_pressure_0c = 6.112 * units.millibar @exporter.export @preprocess_xarray @check_units('[temperature]', '[temperature]') def relative_humidity_from_dewpoint(temperature, dewpoint): e = saturation_vapor_pressure(dewpoint) e_s = saturation_vapor_pressure(temperature) return (e / e_s) @exporter.export @preprocess_xarray @check_units('[pressure]', '[pressure]') def exner_function(pressure, reference_pressure=mpconsts.P0): return (pressure / reference_pressure).to('dimensionless')**mpconsts.kappa @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]') def potential_temperature(pressure, temperature): return temperature / exner_function(pressure) @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]') def temperature_from_potential_temperature(pressure, potential_temperature): return potential_temperature * exner_function(pressure) @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]', '[pressure]') def dry_lapse(pressure, temperature, reference_pressure=None): if reference_pressure is None: reference_pressure = pressure[0] return temperature * (pressure / reference_pressure)**mpconsts.kappa @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]', '[pressure]') def moist_lapse(pressure, temperature, reference_pressure=None): def dt(t, p): t = units.Quantity(t, temperature.units) p = units.Quantity(p, pressure.units) rs = saturation_mixing_ratio(p, t) frac = ((mpconsts.Rd * t + mpconsts.Lv * rs) / (mpconsts.Cp_d + (mpconsts.Lv * mpconsts.Lv * rs * mpconsts.epsilon / (mpconsts.Rd * t * t)))).to('kelvin') return (frac / p).magnitude if reference_pressure is None: reference_pressure = pressure[0] pressure = pressure.to('mbar') reference_pressure = reference_pressure.to('mbar') temperature = np.atleast_1d(temperature) side = 'left' pres_decreasing = (pressure[0] > pressure[-1]) if pres_decreasing: pressure = pressure[::-1] side = 'right' ref_pres_idx = np.searchsorted(pressure.m, reference_pressure.m, side=side) ret_temperatures = np.empty((0, temperature.shape[0])) if reference_pressure > pressure.min(): pres_down = np.append(reference_pressure.m, pressure[(ref_pres_idx - 1)::-1].m) trace_down = si.odeint(dt, temperature.m.squeeze(), pres_down.squeeze()) ret_temperatures = np.concatenate((ret_temperatures, trace_down[:0:-1])) if reference_pressure < pressure.max(): pres_up = np.append(reference_pressure.m, pressure[ref_pres_idx:].m) trace_up = si.odeint(dt, temperature.m.squeeze(), pres_up.squeeze()) ret_temperatures = np.concatenate((ret_temperatures, trace_up[1:])) if pres_decreasing: ret_temperatures = ret_temperatures[::-1] return units.Quantity(ret_temperatures.T.squeeze(), temperature.units) @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]', '[temperature]') def lcl(pressure, temperature, dewpoint, max_iters=50, eps=1e-5): def _lcl_iter(p, p0, w, t): td = globals()['dewpoint'](vapor_pressure(units.Quantity(p, pressure.units), w)) return (p0 * (td / t) ** (1. / mpconsts.kappa)).m w = mixing_ratio(saturation_vapor_pressure(dewpoint), pressure) lcl_p = so.fixed_point(_lcl_iter, pressure.m, args=(pressure.m, w, temperature), xtol=eps, maxiter=max_iters) lcl_p = np.where(np.isclose(lcl_p, pressure.m), pressure.m, lcl_p) * pressure.units return lcl_p, globals()['dewpoint'](vapor_pressure(lcl_p, w)).to(temperature.units) @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]', '[temperature]', '[temperature]') def lfc(pressure, temperature, dewpoint, parcel_temperature_profile=None, dewpoint_start=None, which='top'): pressure, temperature, dewpoint = _remove_nans(pressure, temperature, dewpoint) if parcel_temperature_profile is None: new_stuff = parcel_profile_with_lcl(pressure, temperature, dewpoint) pressure, temperature, dewpoint, parcel_temperature_profile = new_stuff parcel_temperature_profile = parcel_temperature_profile.to(temperature.units) if dewpoint_start is None: dewpoint_start = dewpoint[0] if np.isclose(parcel_temperature_profile[0].to(temperature.units).m, temperature[0].m): x, y = find_intersections(pressure[1:], parcel_temperature_profile[1:], temperature[1:], direction='increasing', log_x=True) else: x, y = find_intersections(pressure, parcel_temperature_profile, temperature, direction='increasing', log_x=True) this_lcl = lcl(pressure[0], parcel_temperature_profile[0], dewpoint_start) if len(x) == 0: mask = pressure < this_lcl[0] if np.all(_less_or_close(parcel_temperature_profile[mask], temperature[mask])): x, y = np.nan * pressure.units, np.nan * temperature.units else: # LFC = LCL x, y = this_lcl return x, y # LFC exists. Make sure it is no lower than the LCL else: idx = x < this_lcl[0] # LFC height < LCL height, so set LFC = LCL if not any(idx): el_pres, _ = find_intersections(pressure[1:], parcel_temperature_profile[1:], temperature[1:], direction='decreasing', log_x=True) if np.min(el_pres) > this_lcl[0]: x, y = np.nan * pressure.units, np.nan * temperature.units else: x, y = this_lcl return x, y # Otherwise, find all LFCs that exist above the LCL # What is returned depends on which flag as described in the docstring else: return _multiple_el_lfc_options(x, y, idx, which, pressure, parcel_temperature_profile, temperature, dewpoint, intersect_type='LFC') def _multiple_el_lfc_options(intersect_pressures, intersect_temperatures, valid_x, which, pressure, parcel_temperature_profile, temperature, dewpoint, intersect_type): p_list, t_list = intersect_pressures[valid_x], intersect_temperatures[valid_x] if which == 'all': x, y = p_list, t_list elif which == 'bottom': x, y = p_list[0], t_list[0] elif which == 'top': x, y = p_list[-1], t_list[-1] elif which == 'wide': x, y = _wide_option(intersect_type, p_list, t_list, pressure, parcel_temperature_profile, temperature) elif which == 'most_cape': x, y = _most_cape_option(intersect_type, p_list, t_list, pressure, temperature, dewpoint, parcel_temperature_profile) else: raise ValueError('Invalid option for "which". Valid options are "top", "bottom", ' '"wide", "most_cape", and "all".') return x, y def _wide_option(intersect_type, p_list, t_list, pressure, parcel_temperature_profile, temperature): # zip the LFC and EL lists together and find greatest difference if intersect_type == 'LFC': # Find EL intersection pressure values lfc_p_list = p_list el_p_list, _ = find_intersections(pressure[1:], parcel_temperature_profile[1:], temperature[1:], direction='decreasing', log_x=True) else: # intersect_type == 'EL' el_p_list = p_list # Find LFC intersection pressure values lfc_p_list, _ = find_intersections(pressure, parcel_temperature_profile, temperature, direction='increasing', log_x=True) diff = [lfc_p.m - el_p.m for lfc_p, el_p in zip(lfc_p_list, el_p_list)] return (p_list[np.where(diff == np.max(diff))][0], t_list[np.where(diff == np.max(diff))][0]) def _most_cape_option(intersect_type, p_list, t_list, pressure, temperature, dewpoint, parcel_temperature_profile): # Need to loop through all possible combinations of cape, find greatest cape profile cape_list, pair_list = [], [] for which_lfc in ['top', 'bottom']: for which_el in ['top', 'bottom']: cape, _ = cape_cin(pressure, temperature, dewpoint, parcel_temperature_profile, which_lfc=which_lfc, which_el=which_el) cape_list.append(cape.m) pair_list.append([which_lfc, which_el]) (lfc_chosen, el_chosen) = pair_list[np.where(cape_list == np.max(cape_list))[0][0]] if intersect_type == 'LFC': if lfc_chosen == 'top': x, y = p_list[-1], t_list[-1] else: # 'bottom' is returned x, y = p_list[0], t_list[0] else: # EL is returned if el_chosen == 'top': x, y = p_list[-1], t_list[-1] else: x, y = p_list[0], t_list[0] return x, y @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]', '[temperature]', '[temperature]') def el(pressure, temperature, dewpoint, parcel_temperature_profile=None, which='top'): pressure, temperature, dewpoint = _remove_nans(pressure, temperature, dewpoint) # Default to surface parcel if no profile or starting pressure level is given if parcel_temperature_profile is None: new_stuff = parcel_profile_with_lcl(pressure, temperature, dewpoint) pressure, temperature, dewpoint, parcel_temperature_profile = new_stuff parcel_temperature_profile = parcel_temperature_profile.to(temperature.units) # If the top of the sounding parcel is warmer than the environment, there is no EL if parcel_temperature_profile[-1] > temperature[-1]: return np.nan * pressure.units, np.nan * temperature.units # Interpolate in log space to find the appropriate pressure - units have to be stripped # and reassigned to allow np.log() to function properly. x, y = find_intersections(pressure[1:], parcel_temperature_profile[1:], temperature[1:], direction='decreasing', log_x=True) lcl_p, _ = lcl(pressure[0], temperature[0], dewpoint[0]) idx = x < lcl_p if len(x) > 0 and x[-1] < lcl_p: return _multiple_el_lfc_options(x, y, idx, which, pressure, parcel_temperature_profile, temperature, dewpoint, intersect_type='EL') else: return np.nan * pressure.units, np.nan * temperature.units @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]', '[temperature]') def parcel_profile(pressure, temperature, dewpoint): _, _, _, t_l, _, t_u = _parcel_profile_helper(pressure, temperature, dewpoint) return concatenate((t_l, t_u)) @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]', '[temperature]') def parcel_profile_with_lcl(pressure, temperature, dewpoint): p_l, p_lcl, p_u, t_l, t_lcl, t_u = _parcel_profile_helper(pressure, temperature[0], dewpoint[0]) new_press = concatenate((p_l, p_lcl, p_u)) prof_temp = concatenate((t_l, t_lcl, t_u)) new_temp = _insert_lcl_level(pressure, temperature, p_lcl) new_dewp = _insert_lcl_level(pressure, dewpoint, p_lcl) return new_press, new_temp, new_dewp, prof_temp def _parcel_profile_helper(pressure, temperature, dewpoint): # Find the LCL press_lcl, temp_lcl = lcl(pressure[0], temperature, dewpoint) press_lcl = press_lcl.to(pressure.units) # Find the dry adiabatic profile, *including* the LCL. We need >= the LCL in case the # LCL is included in the levels. It's slightly redundant in that case, but simplifies press_lower = concatenate((pressure[pressure >= press_lcl], press_lcl)) temp_lower = dry_lapse(press_lower, temperature) if _greater_or_close(np.nanmin(pressure.m), press_lcl.m): return (press_lower[:-1], press_lcl, units.Quantity(np.array([]), press_lower.units), temp_lower[:-1], temp_lcl, units.Quantity(np.array([]), temp_lower.units)) # Find moist pseudo-adiabatic profile starting at the LCL press_upper = concatenate((press_lcl, pressure[pressure < press_lcl])) temp_upper = moist_lapse(press_upper, temp_lower[-1]).to(temp_lower.units) # Return profile pieces return (press_lower[:-1], press_lcl, press_upper[1:], temp_lower[:-1], temp_lcl, temp_upper[1:]) def _insert_lcl_level(pressure, temperature, lcl_pressure): interp_temp = interpolate_1d(lcl_pressure, pressure, temperature) # Pressure needs to be increasing for searchsorted, so flip it and then convert # the index back to the original array loc = pressure.size - pressure[::-1].searchsorted(lcl_pressure) return np.insert(temperature.m, loc, interp_temp.m) * temperature.units @exporter.export @preprocess_xarray @check_units('[pressure]', '[dimensionless]') def vapor_pressure(pressure, mixing_ratio): return pressure * mixing_ratio / (mpconsts.epsilon + mixing_ratio) @exporter.export @preprocess_xarray @check_units('[temperature]') def saturation_vapor_pressure(temperature): # Converted from original in terms of C to use kelvin. Using raw absolute values of C in # a formula plays havoc with units support. return sat_pressure_0c * np.exp(17.67 * (temperature - 273.15 * units.kelvin) / (temperature - 29.65 * units.kelvin)) @exporter.export @preprocess_xarray @check_units('[temperature]', '[dimensionless]') def dewpoint_from_relative_humidity(temperature, relative_humidity): if np.any(relative_humidity > 1.2): warnings.warn('Relative humidity >120%, ensure proper units.') return dewpoint(relative_humidity * saturation_vapor_pressure(temperature)) @exporter.export @preprocess_xarray @check_units('[pressure]') def dewpoint(vapor_pressure): val = np.log(vapor_pressure / sat_pressure_0c) return 0. * units.degC + 243.5 * units.delta_degC * val / (17.67 - val) @exporter.export @preprocess_xarray @check_units('[pressure]', '[pressure]', '[dimensionless]') def mixing_ratio(partial_press, total_press, molecular_weight_ratio=mpconsts.epsilon): return (molecular_weight_ratio * partial_press / (total_press - partial_press)).to('dimensionless') @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]') def saturation_mixing_ratio(total_press, temperature): return mixing_ratio(saturation_vapor_pressure(temperature), total_press) @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]', '[temperature]') def equivalent_potential_temperature(pressure, temperature, dewpoint): t = temperature.to('kelvin').magnitude td = dewpoint.to('kelvin').magnitude p = pressure.to('hPa').magnitude e = saturation_vapor_pressure(dewpoint).to('hPa').magnitude r = saturation_mixing_ratio(pressure, dewpoint).magnitude t_l = 56 + 1. / (1. / (td - 56) + np.log(t / td) / 800.) th_l = t * (1000 / (p - e)) ** mpconsts.kappa * (t / t_l) ** (0.28 * r) th_e = th_l * np.exp((3036. / t_l - 1.78) * r * (1 + 0.448 * r)) return th_e * units.kelvin @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]') def saturation_equivalent_potential_temperature(pressure, temperature): t = temperature.to('kelvin').magnitude p = pressure.to('hPa').magnitude e = saturation_vapor_pressure(temperature).to('hPa').magnitude r = saturation_mixing_ratio(pressure, temperature).magnitude th_l = t * (1000 / (p - e)) ** mpconsts.kappa th_es = th_l * np.exp((3036. / t - 1.78) * r * (1 + 0.448 * r)) return th_es * units.kelvin @exporter.export @preprocess_xarray @check_units('[temperature]', '[dimensionless]', '[dimensionless]') def virtual_temperature(temperature, mixing_ratio, molecular_weight_ratio=mpconsts.epsilon): return temperature * ((mixing_ratio + molecular_weight_ratio) / (molecular_weight_ratio * (1 + mixing_ratio))) @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]', '[dimensionless]', '[dimensionless]') def virtual_potential_temperature(pressure, temperature, mixing_ratio, molecular_weight_ratio=mpconsts.epsilon): pottemp = potential_temperature(pressure, temperature) return virtual_temperature(pottemp, mixing_ratio, molecular_weight_ratio) @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]', '[dimensionless]', '[dimensionless]') def density(pressure, temperature, mixing_ratio, molecular_weight_ratio=mpconsts.epsilon): virttemp = virtual_temperature(temperature, mixing_ratio, molecular_weight_ratio) return (pressure / (mpconsts.Rd * virttemp)).to(units.kilogram / units.meter ** 3) @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]', '[temperature]') def relative_humidity_wet_psychrometric(pressure, dry_bulb_temperature, web_bulb_temperature, **kwargs): return (psychrometric_vapor_pressure_wet(pressure, dry_bulb_temperature, web_bulb_temperature, **kwargs) / saturation_vapor_pressure(dry_bulb_temperature)) @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]', '[temperature]') def psychrometric_vapor_pressure_wet(pressure, dry_bulb_temperature, wet_bulb_temperature, psychrometer_coefficient=6.21e-4 / units.kelvin): return (saturation_vapor_pressure(wet_bulb_temperature) - psychrometer_coefficient * pressure * (dry_bulb_temperature - wet_bulb_temperature).to('kelvin')) @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]', '[dimensionless]') def mixing_ratio_from_relative_humidity(pressure, temperature, relative_humidity): return (relative_humidity * saturation_mixing_ratio(pressure, temperature)).to('dimensionless') @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]', '[dimensionless]') def relative_humidity_from_mixing_ratio(pressure, temperature, mixing_ratio): return mixing_ratio / saturation_mixing_ratio(pressure, temperature) @exporter.export @preprocess_xarray @check_units('[dimensionless]') def mixing_ratio_from_specific_humidity(specific_humidity): try: specific_humidity = specific_humidity.to('dimensionless') except AttributeError: pass return specific_humidity / (1 - specific_humidity) @exporter.export @preprocess_xarray @check_units('[dimensionless]') def specific_humidity_from_mixing_ratio(mixing_ratio): try: mixing_ratio = mixing_ratio.to('dimensionless') except AttributeError: pass return mixing_ratio / (1 + mixing_ratio) @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]', '[dimensionless]') def relative_humidity_from_specific_humidity(pressure, temperature, specific_humidity): return (mixing_ratio_from_specific_humidity(specific_humidity) / saturation_mixing_ratio(pressure, temperature)) @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]', '[temperature]', '[temperature]') def cape_cin(pressure, temperature, dewpoint, parcel_profile, which_lfc='bottom', which_el='top'): pressure, temperature, dewpoint, parcel_profile = _remove_nans(pressure, temperature, dewpoint, parcel_profile) # Calculate LFC limit of integration lfc_pressure, _ = lfc(pressure, temperature, dewpoint, parcel_temperature_profile=parcel_profile, which=which_lfc) # If there is no LFC, no need to proceed. if np.isnan(lfc_pressure): return 0 * units('J/kg'), 0 * units('J/kg') else: lfc_pressure = lfc_pressure.magnitude # Calculate the EL limit of integration el_pressure, _ = el(pressure, temperature, dewpoint, parcel_temperature_profile=parcel_profile, which=which_el) # No EL and we use the top reading of the sounding. if np.isnan(el_pressure): el_pressure = pressure[-1].magnitude else: el_pressure = el_pressure.magnitude # Difference between the parcel path and measured temperature profiles y = (parcel_profile - temperature).to(units.degK) # Estimate zero crossings x, y = _find_append_zero_crossings(np.copy(pressure), y) # CAPE # Only use data between the LFC and EL for calculation p_mask = _less_or_close(x.m, lfc_pressure) & _greater_or_close(x.m, el_pressure) x_clipped = x[p_mask].magnitude y_clipped = y[p_mask].magnitude cape = (mpconsts.Rd * (np.trapz(y_clipped, np.log(x_clipped)) * units.degK)).to(units('J/kg')) # CIN # Only use data between the surface and LFC for calculation p_mask = _greater_or_close(x.m, lfc_pressure) x_clipped = x[p_mask].magnitude y_clipped = y[p_mask].magnitude cin = (mpconsts.Rd * (np.trapz(y_clipped, np.log(x_clipped)) * units.degK)).to(units('J/kg')) # Set CIN to 0 if it's returned as a positive value (#1190) if cin > 0 * units('J/kg'): cin = 0 * units('J/kg') return cape, cin def _find_append_zero_crossings(x, y): crossings = find_intersections(x[1:], y[1:], np.zeros_like(y[1:]) * y.units, log_x=True) x = concatenate((x, crossings[0])) y = concatenate((y, crossings[1])) sort_idx = np.argsort(x) x = x[sort_idx] y = y[sort_idx] keep_idx = np.ediff1d(x.magnitude, to_end=[1]) > 1e-6 x = x[keep_idx] y = y[keep_idx] return x, y @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]', '[temperature]') def most_unstable_parcel(pressure, temperature, dewpoint, height=None, bottom=None, depth=300 * units.hPa): p_layer, t_layer, td_layer = get_layer(pressure, temperature, dewpoint, bottom=bottom, depth=depth, height=height, interpolate=False) theta_e = equivalent_potential_temperature(p_layer, t_layer, td_layer) max_idx = np.argmax(theta_e) return p_layer[max_idx], t_layer[max_idx], td_layer[max_idx], max_idx @exporter.export @preprocess_xarray @check_units('[temperature]', '[pressure]', '[temperature]') def isentropic_interpolation(levels, pressure, temperature, *args, axis=0, temperature_out=False, max_iters=50, eps=1e-6, bottom_up_search=True, **kwargs): def _isen_iter(iter_log_p, isentlevs_nd, ka, a, b, pok): exner = pok * np.exp(-ka * iter_log_p) t = a * iter_log_p + b f = isentlevs_nd - t * exner fp = exner * (ka * t - a) return iter_log_p - (f / fp) ndim = temperature.ndim pres = pressure.to('hPa') temperature = temperature.to('kelvin') slices = [np.newaxis] * ndim slices[axis] = slice(None) slices = tuple(slices) pres = np.broadcast_to(pres[slices].magnitude, temperature.shape) * pres.units sort_pres = np.argsort(pres.m, axis=axis) sort_pres = np.swapaxes(np.swapaxes(sort_pres, 0, axis)[::-1], 0, axis) sorter = broadcast_indices(pres, sort_pres, ndim, axis) levs = pres[sorter] tmpk = temperature[sorter] levels = np.asarray(levels.m_as('kelvin')).reshape(-1) isentlevels = levels[np.argsort(levels)] shape = list(temperature.shape) shape[axis] = isentlevels.size isentlevs_nd = np.broadcast_to(isentlevels[slices], shape) ka = mpconsts.kappa.m_as('dimensionless') # calculate theta for each point pres_theta = potential_temperature(levs, tmpk) # Raise error if input theta level is larger than pres_theta max if np.max(pres_theta.m) < np.max(levels): raise ValueError('Input theta level out of data bounds') # Find log of pressure to implement assumption of linear temperature dependence on # ln(p) log_p = np.log(levs.m) # Calculations for interpolation routine pok = mpconsts.P0 ** ka # index values for each point for the pressure level nearest to the desired theta level above, below, good = find_bounding_indices(pres_theta.m, levels, axis, from_below=bottom_up_search) # calculate constants for the interpolation a = (tmpk.m[above] - tmpk.m[below]) / (log_p[above] - log_p[below]) b = tmpk.m[above] - a * log_p[above] # calculate first guess for interpolation isentprs = 0.5 * (log_p[above] + log_p[below]) # Make sure we ignore any nans in the data for solving; checking a is enough since it # combines log_p and tmpk. good &= ~np.isnan(a) # iterative interpolation using scipy.optimize.fixed_point and _isen_iter defined above log_p_solved = so.fixed_point(_isen_iter, isentprs[good], args=(isentlevs_nd[good], ka, a[good], b[good], pok.m), xtol=eps, maxiter=max_iters) # get back pressure from log p isentprs[good] = np.exp(log_p_solved) # Mask out points we know are bad as well as points that are beyond the max pressure isentprs[~(good & _less_or_close(isentprs, np.max(pres.m)))] = np.nan # create list for storing output data ret = [isentprs * units.hPa] # if temperature_out = true, calculate temperature and output as last item in list if temperature_out: ret.append((isentlevs_nd / ((mpconsts.P0.m / isentprs) ** ka)) * units.kelvin) # do an interpolation for each additional argument if args: others = interpolate_1d(isentlevels, pres_theta.m, *(arr[sorter] for arr in args), axis=axis, return_list_always=True) ret.extend(others) return ret @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]', '[temperature]') def surface_based_cape_cin(pressure, temperature, dewpoint): pressure, temperature, dewpoint = _remove_nans(pressure, temperature, dewpoint) p, t, td, profile = parcel_profile_with_lcl(pressure, temperature, dewpoint) return cape_cin(p, t, td, profile) @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]', '[temperature]') def most_unstable_cape_cin(pressure, temperature, dewpoint, **kwargs): pressure, temperature, dewpoint = _remove_nans(pressure, temperature, dewpoint) _, _, _, parcel_idx = most_unstable_parcel(pressure, temperature, dewpoint, **kwargs) p, t, td, mu_profile = parcel_profile_with_lcl(pressure[parcel_idx:], temperature[parcel_idx:], dewpoint[parcel_idx:]) return cape_cin(p, t, td, mu_profile) @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]', '[temperature]') def mixed_layer_cape_cin(pressure, temperature, dewpoint, **kwargs): depth = kwargs.get('depth', 100 * units.hPa) parcel_pressure, parcel_temp, parcel_dewpoint = mixed_parcel(pressure, temperature, dewpoint, **kwargs) # Remove values below top of mixed layer and add in the mixed layer values pressure_prof = pressure[pressure < (pressure[0] - depth)] temp_prof = temperature[pressure < (pressure[0] - depth)] dew_prof = dewpoint[pressure < (pressure[0] - depth)] pressure_prof = concatenate([parcel_pressure, pressure_prof]) temp_prof = concatenate([parcel_temp, temp_prof]) dew_prof = concatenate([parcel_dewpoint, dew_prof]) p, t, td, ml_profile = parcel_profile_with_lcl(pressure_prof, temp_prof, dew_prof) return cape_cin(p, t, td, ml_profile) @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]', '[temperature]') def mixed_parcel(pressure, temperature, dewpoint, parcel_start_pressure=None, height=None, bottom=None, depth=100 * units.hPa, interpolate=True): # If a parcel starting pressure is not provided, use the surface if not parcel_start_pressure: parcel_start_pressure = pressure[0] # Calculate the potential temperature and mixing ratio over the layer theta = potential_temperature(pressure, temperature) mixing_ratio = saturation_mixing_ratio(pressure, dewpoint) # Mix the variables over the layer mean_theta, mean_mixing_ratio = mixed_layer(pressure, theta, mixing_ratio, bottom=bottom, height=height, depth=depth, interpolate=interpolate) # Convert back to temperature mean_temperature = mean_theta * exner_function(parcel_start_pressure) # Convert back to dewpoint mean_vapor_pressure = vapor_pressure(parcel_start_pressure, mean_mixing_ratio) # Using globals() here allows us to keep the dewpoint parameter but still call the # function of the same name. mean_dewpoint = globals()['dewpoint'](mean_vapor_pressure) return (parcel_start_pressure, mean_temperature.to(temperature.units), mean_dewpoint.to(dewpoint.units)) @exporter.export @preprocess_xarray @check_units('[pressure]') def mixed_layer(pressure, *args, height=None, bottom=None, depth=100 * units.hPa, interpolate=True): layer = get_layer(pressure, *args, height=height, bottom=bottom, depth=depth, interpolate=interpolate) p_layer = layer[0] datavars_layer = layer[1:] ret = [] for datavar_layer in datavars_layer: actual_depth = abs(p_layer[0] - p_layer[-1]) ret.append((-1. / actual_depth.m) * np.trapz(datavar_layer.m, p_layer.m) * datavar_layer.units) return ret @exporter.export @preprocess_xarray @check_units('[length]', '[temperature]') def dry_static_energy(height, temperature): return (mpconsts.g * height + mpconsts.Cp_d * temperature).to('kJ/kg') @exporter.export @preprocess_xarray @check_units('[length]', '[temperature]', '[dimensionless]') def moist_static_energy(height, temperature, specific_humidity): return (dry_static_energy(height, temperature) + mpconsts.Lv * specific_humidity.to('dimensionless')).to('kJ/kg') @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]') def thickness_hydrostatic(pressure, temperature, mixing_ratio=None, molecular_weight_ratio=mpconsts.epsilon, bottom=None, depth=None): # Get the data for the layer, conditional upon bottom/depth being specified and mixing # ratio being given if bottom is None and depth is None: if mixing_ratio is None: layer_p, layer_virttemp = pressure, temperature else: layer_p = pressure layer_virttemp = virtual_temperature(temperature, mixing_ratio, molecular_weight_ratio) else: if mixing_ratio is None: layer_p, layer_virttemp = get_layer(pressure, temperature, bottom=bottom, depth=depth) else: layer_p, layer_temp, layer_w = get_layer(pressure, temperature, mixing_ratio, bottom=bottom, depth=depth) layer_virttemp = virtual_temperature(layer_temp, layer_w, molecular_weight_ratio) # Take the integral (with unit handling) and return the result in meters return (- mpconsts.Rd / mpconsts.g * np.trapz( layer_virttemp.m_as('K'), x=np.log(layer_p.m_as('hPa'))) * units.K).to('m') @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]') def thickness_hydrostatic_from_relative_humidity(pressure, temperature, relative_humidity, bottom=None, depth=None): mixing = mixing_ratio_from_relative_humidity(pressure, temperature, relative_humidity) return thickness_hydrostatic(pressure, temperature, mixing_ratio=mixing, bottom=bottom, depth=depth) @exporter.export @preprocess_xarray @check_units('[length]', '[temperature]') def brunt_vaisala_frequency_squared(height, potential_temperature, axis=0): # Ensure validity of temperature units potential_temperature = potential_temperature.to('K') # Calculate and return the square of Brunt-Vaisala frequency return mpconsts.g / potential_temperature * first_derivative(potential_temperature, x=height, axis=axis) @exporter.export @preprocess_xarray @check_units('[length]', '[temperature]') def brunt_vaisala_frequency(height, potential_temperature, axis=0): bv_freq_squared = brunt_vaisala_frequency_squared(height, potential_temperature, axis=axis) bv_freq_squared[bv_freq_squared.magnitude < 0] = np.nan return np.sqrt(bv_freq_squared) @exporter.export @preprocess_xarray @check_units('[length]', '[temperature]') def brunt_vaisala_period(height, potential_temperature, axis=0): bv_freq_squared = brunt_vaisala_frequency_squared(height, potential_temperature, axis=axis) bv_freq_squared[bv_freq_squared.magnitude <= 0] = np.nan return 2 * np.pi / np.sqrt(bv_freq_squared) @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]', '[temperature]') def wet_bulb_temperature(pressure, temperature, dewpoint): if not hasattr(pressure, 'shape'): pressure = np.atleast_1d(pressure) temperature = np.atleast_1d(temperature) dewpoint = np.atleast_1d(dewpoint) it = np.nditer([pressure, temperature, dewpoint, None], op_dtypes=['float', 'float', 'float', 'float'], flags=['buffered']) for press, temp, dewp, ret in it: press = press * pressure.units temp = temp * temperature.units dewp = dewp * dewpoint.units lcl_pressure, lcl_temperature = lcl(press, temp, dewp) moist_adiabat_temperatures = moist_lapse(concatenate([lcl_pressure, press]), lcl_temperature) ret[...] = moist_adiabat_temperatures[-1].magnitude # If we started with a scalar, return a scalar if it.operands[3].size == 1: return it.operands[3][0] * moist_adiabat_temperatures.units return it.operands[3] * moist_adiabat_temperatures.units @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]') def static_stability(pressure, temperature, axis=0): theta = potential_temperature(pressure, temperature) return - mpconsts.Rd * temperature / pressure * first_derivative(np.log(theta.m_as('K')), x=pressure, axis=axis) @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]', '[dimensionless]') def dewpoint_from_specific_humidity(pressure, temperature, specific_humidity): return dewpoint_from_relative_humidity(temperature, relative_humidity_from_specific_humidity( pressure, temperature, specific_humidity)) @exporter.export @preprocess_xarray @check_units('[length]/[time]', '[pressure]', '[temperature]') def vertical_velocity_pressure(w, pressure, temperature, mixing_ratio=0): rho = density(pressure, temperature, mixing_ratio) return (-mpconsts.g * rho * w).to('Pa/s') @exporter.export @preprocess_xarray @check_units('[pressure]/[time]', '[pressure]', '[temperature]') def vertical_velocity(omega, pressure, temperature, mixing_ratio=0): rho = density(pressure, temperature, mixing_ratio) return (omega / (- mpconsts.g * rho)).to('m/s') @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]') def specific_humidity_from_dewpoint(pressure, dewpoint): mixing_ratio = saturation_mixing_ratio(pressure, dewpoint) return specific_humidity_from_mixing_ratio(mixing_ratio) @exporter.export @preprocess_xarray @check_units('[pressure]', '[temperature]', '[temperature]') def lifted_index(pressure, temperature, parcel_profile): # find the index for the 500 hPa pressure level. idx = np.where(pressure == 500 * units.hPa) # find the measured temperature at 500 hPa. T500 = temperature[idx] # find the parcel profile temperature at 500 hPa. Tp500 = parcel_profile[idx] # calculate the lifted index. lifted_index = T500 - Tp500.to(units.degC) return lifted_index @exporter.export @preprocess_xarray @check_units('[length]', '[temperature]', '[speed]', '[speed]') def gradient_richardson_number(height, potential_temperature, u, v, axis=0): dthetadz = first_derivative(potential_temperature, x=height, axis=axis) dudz = first_derivative(u, x=height, axis=axis) dvdz = first_derivative(v, x=height, axis=axis) return (mpconsts.g / potential_temperature) * (dthetadz / (dudz ** 2 + dvdz ** 2))

true

f70080a8c5b6f93830a0ccc665937affad0153c6

33

py

Python

privacy/util/webhook.py

Justasic/Privacy.py

471e6669a1e22e539459bbeb24de4efd3b7a11e0

[ "BSD-3-Clause" ]

3

2019-11-04T16:11:36.000Z

2021-05-24T18:56:40.000Z

privacy/util/webhook.py

Justasic/Privacy.py

471e6669a1e22e539459bbeb24de4efd3b7a11e0

[ "BSD-3-Clause" ]

9

2019-11-09T01:47:58.000Z

2022-03-30T00:19:20.000Z

privacy/util/webhook.py

Justasic/Privacy.py

471e6669a1e22e539459bbeb24de4efd3b7a11e0

[ "BSD-3-Clause" ]

2

2020-08-12T03:22:29.000Z

2020-12-10T00:08:46.000Z

# TODO: maybe make this flexible

16.5

32

0.757576

true

f7008119dbfc3df53830c38b0b4d874106e6e295

1,207

py

Python

examples/progress-bar/custom-key-bindings.py

gousaiyang/python-prompt-toolkit

6237764658214af4c24633795d2571d2bd03375d

[ "BSD-3-Clause" ]

4,028

2015-01-02T16:31:38.000Z

2018-10-25T14:51:02.000Z

examples/progress-bar/custom-key-bindings.py

gousaiyang/python-prompt-toolkit

6237764658214af4c24633795d2571d2bd03375d

[ "BSD-3-Clause" ]

709

2018-10-25T22:36:52.000Z

2022-03-28T18:34:35.000Z

examples/progress-bar/custom-key-bindings.py

gousaiyang/python-prompt-toolkit

6237764658214af4c24633795d2571d2bd03375d

[ "BSD-3-Clause" ]

399

2018-10-26T18:08:46.000Z

2022-03-28T16:09:41.000Z

#!/usr/bin/env python """ A very simple progress bar which keep track of the progress as we consume an iterator. """ import os import signal import time from prompt_toolkit import HTML from prompt_toolkit.key_binding import KeyBindings from prompt_toolkit.patch_stdout import patch_stdout from prompt_toolkit.shortcuts import ProgressBar def main(): bottom_toolbar = HTML( ' [f] Print "f" [q] Abort [x] Send Control-C.' ) # Create custom key bindings first. kb = KeyBindings() cancel = [False] @kb.add("f") def _(event): print("You pressed `f`.") @kb.add("q") def _(event): "Quit by setting cancel flag." cancel[0] = True @kb.add("x") def _(event): "Quit by sending SIGINT to the main thread." os.kill(os.getpid(), signal.SIGINT) # Use `patch_stdout`, to make sure that prints go above the # application. with patch_stdout(): with ProgressBar(key_bindings=kb, bottom_toolbar=bottom_toolbar) as pb: for i in pb(range(800)): time.sleep(0.01) if cancel[0]: break if __name__ == "__main__": main()

23.211538

79

0.614747

import os import signal import time from prompt_toolkit import HTML from prompt_toolkit.key_binding import KeyBindings from prompt_toolkit.patch_stdout import patch_stdout from prompt_toolkit.shortcuts import ProgressBar def main(): bottom_toolbar = HTML( ' [f] Print "f" [q] Abort [x] Send Control-C.' ) kb = KeyBindings() cancel = [False] @kb.add("f") def _(event): print("You pressed `f`.") @kb.add("q") def _(event): cancel[0] = True @kb.add("x") def _(event): os.kill(os.getpid(), signal.SIGINT) with patch_stdout(): with ProgressBar(key_bindings=kb, bottom_toolbar=bottom_toolbar) as pb: for i in pb(range(800)): time.sleep(0.01) if cancel[0]: break if __name__ == "__main__": main()

true

f700817b9394d6608b7a517ea91f3f68d7dd7ca4

15,460

py

Python

TwitterMiner_UserAllTweets.py

BrainRift/twitterminer

eda652dbf8f69c429707fd14969ba9e70ff97351

[ "MIT" ]

4

2019-06-12T20:03:47.000Z

2021-12-14T17:05:45.000Z

TwitterMiner_UserAllTweets.py

BrainRift/twitterminer

eda652dbf8f69c429707fd14969ba9e70ff97351

[ "MIT" ]

1

2019-06-20T01:32:06.000Z

2019-06-20T01:33:13.000Z

TwitterMiner_UserAllTweets.py

BrainRift/twitterminer

eda652dbf8f69c429707fd14969ba9e70ff97351

[ "MIT" ]

2

2020-01-03T03:43:03.000Z

2021-01-06T18:45:49.000Z

#!/usr/bin/env python # encoding: utf-8 import os import sqlite3 as lite import sys import json import time import urllib.request import tweepy from TwitterMiner_Keys import * from tweepy import OAuthHandler from TwitterMiner_settings import * import hashlib #from Twitter_validate import validate_image def dump_hash(twitter_dump): data_hash = None # Ensure the value starts with nothing dump = hashlib.sha1() dump.update(twitter_dump) data_hash = dump.hexdigest() return data_hash def file_hash(point_to_file): hash_sha1 = hashlib.sha1() with open(point_to_file, "rb") as f: for chunk in iter(lambda: f.read(4096), b""): hash_sha1.update(chunk) print(hash_sha1.hexdigest()) return hash_sha1.hexdigest() def extract_image_blob(posted_image_dest): with open("test.jpg", "wb") as image_file: c.execute("SELECT tweeted_image FROM T_Tweets WHERE Tweet_id = " + str(tweet_id)) ablob = c.fetchone() image_file.write(ablob[0]) def create_db(table_name): c.execute("PRAGMA journal_mode = WAL") c.execute("CREATE TABLE IF NOT EXISTS " + table_name + "(tweet_id INTEGER NOT NULL PRIMARY KEY, date_mined TEXT, screen_name TEXT, \ user_id INTEGER, users_name TEXT, created_at_UTC TEXT, is_retweet TEXT, \ retweeted_times TEXT, text TEXT, place_name TEXT, country_code TEXT, country TEXT, \ bounding_box TEXT, source_tweeted TEXT, geo TEXT, in_reply_to_user TEXT, \ inreply_statusid TEXT, posted_image_dest TEXT, tweeted_image BLOB, image_hash TEXT, \ media_type TEXT, media_url TEXT, media_id TEXT, posted_video_dest TEXT, \ tweeted_video BLOB, video_hash TEXT, video_type TEXT, video_url TEXT, \ url_in_tweet TEXT, status BLOB, status_hash TEXT, bookmark TEXT)") conn.commit() def get_all_tweets(screen_name): #Twitter only allows access to a users most recent 3240 tweets with this method #initialize a list to hold all the tweepy Tweets alltweets = [] #make initial request for most recent tweets (200 is the maximum allowed count) try: new_tweets = api.user_timeline(screen_name = screen_name, count=200) except tweepy.TweepError: print("Failed to pull tweets from %s" % screen_name) print("User may be protected/private.") print("Exiting...") sys.exit() except tweepy.RateLimitError: # I want to add code here to switch creds if a Rate limit occurs print("Failed to pull the tweets due to a Twitter Rate Limit error.") print("Please wait 15 min and try again...") sys.exit() #save most recent tweets alltweets.extend(new_tweets) #save the id of the oldest tweet less one oldest = alltweets[-1].id - 1 #keep grabbing tweets until there are no tweets left to grab while len(new_tweets) > 0: print("getting tweets before %s" % (oldest)) #all subsiquent requests use the max_id param to prevent duplicates new_tweets = api.user_timeline(screen_name = screen_name,count=200,max_id=oldest) #save most recent tweets alltweets.extend(new_tweets) #update the id of the oldest tweet less one oldest = alltweets[-1].id - 1 print("...%s tweets downloaded so far" % (len(alltweets))) #transform the tweepy tweets into a 2D array that will populate the csv for status in alltweets: # Pull the pieces of the tweet and put them in a variable Tweetid = status.id screenname = status.user.screen_name userid = status.user.id usersname = status.user.name tweettime = status.created_at # Checks to see if status has the attribute of status.retweeted_status, then assigns is_retweet a value if hasattr(status, 'retweeted_status'): is_retweet = True #Added this section on 6-19-19 due to truncated ReTweets #This checks for populated data in the extended_tweet #If data is populated, it pulls the entire full_text #Thanks to Fraser Phillips for finding this issue if hasattr(status.retweeted_status, 'extended_tweet'): Amp_text = str(status.retweeted_status.extended_tweet['full_text']) tweet = "RT: " + Amp_text.replace('&','&') else: Amp_text = status.retweeted_status.text tweet = "RT: " + Amp_text.replace('&','&') else: is_retweet = False Amp_text = status.text tweet = Amp_text.replace('&','&') retweeted_times = status.retweet_count if status.place is not None: placename = status.place.full_name countrycode = status.place.country_code country = status.place.country boundingbox = str(status.place.bounding_box.coordinates) else: placename = None countrycode = None country = None boundingbox = None Tweet_source = status.source geo = status.geo if geo is not None: geo = json.dumps(geo) inreplytouser = status.in_reply_to_screen_name inreply_tostatus = status.in_reply_to_status_id_str #Checks for Media in the Tweet and downloads it if 'media' in status.entities: image_posted = status.entities['media'][0]['media_url'] remove_tweet_url = image_posted.split('/')[-1] posted_image_dest = os.path.join("Case_Attachments/" + casename + "/tweets/" + screenname + "/tweeted_image/" + remove_tweet_url) image_path = "Case_Attachments/" + casename + "/tweets/" + screenname + "/tweeted_image/" if not os.path.exists(image_path): os.makedirs(image_path) try: print("Downloading... %s" % posted_image_dest) urllib.request.urlretrieve(image_posted, filename = posted_image_dest) tweeted_image = open(posted_image_dest, "rb").read() image_hash = dump_hash(tweeted_image) except urllib.error.URLError as e: print("Error downloading file... %s ... from TweetID: %s" % (posted_image_dest, str(Tweetid))) posted_image_dest = "ERROR DOWNLOADING FILE" tweeted_image = None image_hash = None pass except: print("Error downloading file... %s ... from TweetID: %s" % (posted_image_dest, str(Tweetid))) posted_image_dest = "ERROR DOWNLOADING FILE - Unknown Error" tweeted_image = None image_hash = None pass mediatype = status.entities['media'][0]['type'] mediaurl = status.entities['media'][0]['media_url'] mediaid = status.entities['media'][0]['id'] else: posted_image_dest = None mediatype = None mediaurl = None mediaid = None tweeted_image = None image_hash = None # New video Code #Checks for Video in the tweets and downloads it if hasattr(status, 'extended_entities'): if 'video_info' in status.extended_entities['media'][0]: # This section checks the number of dictionaries are in the variants # It then looks at the bitrate of the variants and determines the highest value # Once the highest value is determined, it extracts that video. variant_times = len(status.extended_entities['media'][0]['video_info']['variants']) # Gets the number of variants bit_rate = -1 for variant_count in range(0, variant_times): #iterates through all the variants in that tweets if 'bitrate' in status.extended_entities['media'][0]['video_info']['variants'][variant_count] and \ bit_rate < status.extended_entities['media'][0]['video_info']['variants'][variant_count]['bitrate']: bit_rate = status.extended_entities['media'][0]['video_info']['variants'][variant_count]['bitrate'] videourl = status.extended_entities['media'][0]['video_info']['variants'][variant_count]['url'] videotype = status.extended_entities['media'][0]['video_info']['variants'][variant_count]['content_type'] remove_video_url = videourl.split('/')[-1] posted_video_dest = os.path.join("Case_Attachments/" + casename + "/tweets/" + screenname + "/tweeted_video/" + remove_video_url) video_path = "Case_Attachments/" + casename + "/tweets/" + screenname + "/tweeted_video/" if not os.path.exists(video_path): os.makedirs(video_path) try: print("Downloading... %s" % posted_video_dest) urllib.request.urlretrieve(videourl, filename = posted_video_dest) tweeted_video = open(posted_video_dest, "rb").read() video_hash = dump_hash(tweeted_video) except urllib.error.URLError as e: print("Error downloading file... %s ... from TweetID: %s" % (posted_video_dest, str(Tweetid))) posted_image_dest = "ERROR DOWNLOADING FILE" tweeted_video = None video_hash = None pass except: print("Error downloading file... %s ... from TweetID: %s" % (posted_video_dest, str(Tweetid))) posted_image_dest = "ERROR DOWNLOADING FILE" tweeted_video = None video_hash = None pass else: posted_video_dest = None videotype= None videourl= None tweeted_video = None video_hash = None else: posted_video_dest = None videotype= None videourl= None tweeted_video = None video_hash = None # End Video Check # End new video Code if not status.entities['urls']: url_in_tweet = None else: url_in_tweet = str(status.entities['urls'][0]['url']) #Grab the current date and time now = time.strftime("%c") # Starts the raw hash process status_dump = str(status).encode('utf-8') status_hash = dump_hash(status_dump) bookmark = None # Writes the data collected in the variables to the database try: c.execute("INSERT INTO " + table_name + "(tweet_id, date_mined, screen_name, user_id, users_name, \ created_at_UTC, is_retweet, retweeted_times,text, place_name, \ country_code, country, bounding_box, source_tweeted, geo, \ in_reply_to_user, inreply_statusid, posted_image_dest, \ tweeted_image, image_hash, media_type, media_url, media_id, \ posted_video_dest, tweeted_video, video_hash, video_type, \ video_url, url_in_tweet, status, status_hash, bookmark) \ VALUES(?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)" , \ (Tweetid, now, screenname, userid, usersname, tweettime, is_retweet, retweeted_times, tweet, placename, countrycode, country, boundingbox, Tweet_source, geo, inreplytouser, inreply_tostatus, posted_image_dest, tweeted_image, image_hash, mediatype, mediaurl, mediaid, posted_video_dest, tweeted_video, video_hash, videotype, videourl, url_in_tweet, str(status), status_hash, bookmark)) conn.commit() print(str(Tweetid), "--- Successfully added to the Database") except lite.IntegrityError: print(str(Tweetid), "--- Record already Exists") if __name__ == '__main__': auth = OAuthHandler(consumer_key, consumer_secret) auth.set_access_token(access_token, access_token_secret) api = tweepy.API(auth) #--------- #--------- #--------- Be sure to enter a unique case name -- This is handled in TwitterMiner_settings now #--------- #--------- casename = CASE_NAME dbname = casename + ".db" conn = lite.connect(dbname) c = conn.cursor() screenname = USER_NAME table_name = USER_NAME + "_Tweets" create_db(table_name) get_all_tweets(screenname) print("\n Finished collecting Tweets from user --- %s" % screenname) print("Results are stored in " + casename) #validate_image('T_Tweets')

42.944444

153

0.504657

import os import sqlite3 as lite import sys import json import time import urllib.request import tweepy from TwitterMiner_Keys import * from tweepy import OAuthHandler from TwitterMiner_settings import * import hashlib def dump_hash(twitter_dump): data_hash = None dump = hashlib.sha1() dump.update(twitter_dump) data_hash = dump.hexdigest() return data_hash def file_hash(point_to_file): hash_sha1 = hashlib.sha1() with open(point_to_file, "rb") as f: for chunk in iter(lambda: f.read(4096), b""): hash_sha1.update(chunk) print(hash_sha1.hexdigest()) return hash_sha1.hexdigest() def extract_image_blob(posted_image_dest): with open("test.jpg", "wb") as image_file: c.execute("SELECT tweeted_image FROM T_Tweets WHERE Tweet_id = " + str(tweet_id)) ablob = c.fetchone() image_file.write(ablob[0]) def create_db(table_name): c.execute("PRAGMA journal_mode = WAL") c.execute("CREATE TABLE IF NOT EXISTS " + table_name + "(tweet_id INTEGER NOT NULL PRIMARY KEY, date_mined TEXT, screen_name TEXT, \ user_id INTEGER, users_name TEXT, created_at_UTC TEXT, is_retweet TEXT, \ retweeted_times TEXT, text TEXT, place_name TEXT, country_code TEXT, country TEXT, \ bounding_box TEXT, source_tweeted TEXT, geo TEXT, in_reply_to_user TEXT, \ inreply_statusid TEXT, posted_image_dest TEXT, tweeted_image BLOB, image_hash TEXT, \ media_type TEXT, media_url TEXT, media_id TEXT, posted_video_dest TEXT, \ tweeted_video BLOB, video_hash TEXT, video_type TEXT, video_url TEXT, \ url_in_tweet TEXT, status BLOB, status_hash TEXT, bookmark TEXT)") conn.commit() def get_all_tweets(screen_name): alltweets = [] try: new_tweets = api.user_timeline(screen_name = screen_name, count=200) except tweepy.TweepError: print("Failed to pull tweets from %s" % screen_name) print("User may be protected/private.") print("Exiting...") sys.exit() except tweepy.RateLimitError: print("Failed to pull the tweets due to a Twitter Rate Limit error.") print("Please wait 15 min and try again...") sys.exit() alltweets.extend(new_tweets) oldest = alltweets[-1].id - 1 while len(new_tweets) > 0: print("getting tweets before %s" % (oldest)) new_tweets = api.user_timeline(screen_name = screen_name,count=200,max_id=oldest) alltweets.extend(new_tweets) oldest = alltweets[-1].id - 1 print("...%s tweets downloaded so far" % (len(alltweets))) for status in alltweets: Tweetid = status.id screenname = status.user.screen_name userid = status.user.id usersname = status.user.name tweettime = status.created_at if hasattr(status, 'retweeted_status'): is_retweet = True if hasattr(status.retweeted_status, 'extended_tweet'): Amp_text = str(status.retweeted_status.extended_tweet['full_text']) tweet = "RT: " + Amp_text.replace('&','&') else: Amp_text = status.retweeted_status.text tweet = "RT: " + Amp_text.replace('&','&') else: is_retweet = False Amp_text = status.text tweet = Amp_text.replace('&','&') retweeted_times = status.retweet_count if status.place is not None: placename = status.place.full_name countrycode = status.place.country_code country = status.place.country boundingbox = str(status.place.bounding_box.coordinates) else: placename = None countrycode = None country = None boundingbox = None Tweet_source = status.source geo = status.geo if geo is not None: geo = json.dumps(geo) inreplytouser = status.in_reply_to_screen_name inreply_tostatus = status.in_reply_to_status_id_str if 'media' in status.entities: image_posted = status.entities['media'][0]['media_url'] remove_tweet_url = image_posted.split('/')[-1] posted_image_dest = os.path.join("Case_Attachments/" + casename + "/tweets/" + screenname + "/tweeted_image/" + remove_tweet_url) image_path = "Case_Attachments/" + casename + "/tweets/" + screenname + "/tweeted_image/" if not os.path.exists(image_path): os.makedirs(image_path) try: print("Downloading... %s" % posted_image_dest) urllib.request.urlretrieve(image_posted, filename = posted_image_dest) tweeted_image = open(posted_image_dest, "rb").read() image_hash = dump_hash(tweeted_image) except urllib.error.URLError as e: print("Error downloading file... %s ... from TweetID: %s" % (posted_image_dest, str(Tweetid))) posted_image_dest = "ERROR DOWNLOADING FILE" tweeted_image = None image_hash = None pass except: print("Error downloading file... %s ... from TweetID: %s" % (posted_image_dest, str(Tweetid))) posted_image_dest = "ERROR DOWNLOADING FILE - Unknown Error" tweeted_image = None image_hash = None pass mediatype = status.entities['media'][0]['type'] mediaurl = status.entities['media'][0]['media_url'] mediaid = status.entities['media'][0]['id'] else: posted_image_dest = None mediatype = None mediaurl = None mediaid = None tweeted_image = None image_hash = None if hasattr(status, 'extended_entities'): if 'video_info' in status.extended_entities['media'][0]: variant_times = len(status.extended_entities['media'][0]['video_info']['variants']) bit_rate = -1 for variant_count in range(0, variant_times): if 'bitrate' in status.extended_entities['media'][0]['video_info']['variants'][variant_count] and \ bit_rate < status.extended_entities['media'][0]['video_info']['variants'][variant_count]['bitrate']: bit_rate = status.extended_entities['media'][0]['video_info']['variants'][variant_count]['bitrate'] videourl = status.extended_entities['media'][0]['video_info']['variants'][variant_count]['url'] videotype = status.extended_entities['media'][0]['video_info']['variants'][variant_count]['content_type'] remove_video_url = videourl.split('/')[-1] posted_video_dest = os.path.join("Case_Attachments/" + casename + "/tweets/" + screenname + "/tweeted_video/" + remove_video_url) video_path = "Case_Attachments/" + casename + "/tweets/" + screenname + "/tweeted_video/" if not os.path.exists(video_path): os.makedirs(video_path) try: print("Downloading... %s" % posted_video_dest) urllib.request.urlretrieve(videourl, filename = posted_video_dest) tweeted_video = open(posted_video_dest, "rb").read() video_hash = dump_hash(tweeted_video) except urllib.error.URLError as e: print("Error downloading file... %s ... from TweetID: %s" % (posted_video_dest, str(Tweetid))) posted_image_dest = "ERROR DOWNLOADING FILE" tweeted_video = None video_hash = None pass except: print("Error downloading file... %s ... from TweetID: %s" % (posted_video_dest, str(Tweetid))) posted_image_dest = "ERROR DOWNLOADING FILE" tweeted_video = None video_hash = None pass else: posted_video_dest = None videotype= None videourl= None tweeted_video = None video_hash = None else: posted_video_dest = None videotype= None videourl= None tweeted_video = None video_hash = None if not status.entities['urls']: url_in_tweet = None else: url_in_tweet = str(status.entities['urls'][0]['url']) now = time.strftime("%c") status_dump = str(status).encode('utf-8') status_hash = dump_hash(status_dump) bookmark = None try: c.execute("INSERT INTO " + table_name + "(tweet_id, date_mined, screen_name, user_id, users_name, \ created_at_UTC, is_retweet, retweeted_times,text, place_name, \ country_code, country, bounding_box, source_tweeted, geo, \ in_reply_to_user, inreply_statusid, posted_image_dest, \ tweeted_image, image_hash, media_type, media_url, media_id, \ posted_video_dest, tweeted_video, video_hash, video_type, \ video_url, url_in_tweet, status, status_hash, bookmark) \ VALUES(?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)" , \ (Tweetid, now, screenname, userid, usersname, tweettime, is_retweet, retweeted_times, tweet, placename, countrycode, country, boundingbox, Tweet_source, geo, inreplytouser, inreply_tostatus, posted_image_dest, tweeted_image, image_hash, mediatype, mediaurl, mediaid, posted_video_dest, tweeted_video, video_hash, videotype, videourl, url_in_tweet, str(status), status_hash, bookmark)) conn.commit() print(str(Tweetid), "--- Successfully added to the Database") except lite.IntegrityError: print(str(Tweetid), "--- Record already Exists") if __name__ == '__main__': auth = OAuthHandler(consumer_key, consumer_secret) auth.set_access_token(access_token, access_token_secret) api = tweepy.API(auth) casename = CASE_NAME dbname = casename + ".db" conn = lite.connect(dbname) c = conn.cursor() screenname = USER_NAME table_name = USER_NAME + "_Tweets" create_db(table_name) get_all_tweets(screenname) print("\n Finished collecting Tweets from user --- %s" % screenname) print("Results are stored in " + casename)

true

f70082354fbf968017c0cf3ef52caa845a6bb7ca

5,156

py

Python

onlinecourse/migrations/0001_initial.py

Safia2202/onlinecourseibm

1818bb81b0c22cc237dcf5ee24381783b8d3f8fc

[ "Apache-2.0" ]

null

onlinecourse/migrations/0001_initial.py

Safia2202/onlinecourseibm

1818bb81b0c22cc237dcf5ee24381783b8d3f8fc

[ "Apache-2.0" ]

null

onlinecourse/migrations/0001_initial.py

Safia2202/onlinecourseibm

1818bb81b0c22cc237dcf5ee24381783b8d3f8fc

[ "Apache-2.0" ]

1

2021-03-19T09:38:51.000Z

# Generated by Django 3.1.3 on 2021-03-13 11:57 from django.conf import settings from django.db import migrations, models import django.db.models.deletion import django.utils.timezone class Migration(migrations.Migration): initial = True dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ] operations = [ migrations.CreateModel( name='Choice', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('choice_text', models.CharField(max_length=1000)), ('is_correct', models.BooleanField(default=False)), ], ), migrations.CreateModel( name='Course', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(default='online course', max_length=30)), ('image', models.ImageField(upload_to='course_images/')), ('description', models.CharField(max_length=1000)), ('pub_date', models.DateField(null=True)), ('total_enrollment', models.IntegerField(default=0)), ], ), migrations.CreateModel( name='Enrollment', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('date_enrolled', models.DateField(default=django.utils.timezone.now)), ('mode', models.CharField(choices=[('audit', 'Audit'), ('honor', 'Honor'), ('BETA', 'BETA')], default='audit', max_length=5)), ('rating', models.FloatField(default=5.0)), ('course', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='onlinecourse.course')), ('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)), ], ), migrations.CreateModel( name='Submission', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('choices', models.ManyToManyField(to='onlinecourse.Choice')), ('enrollment', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='onlinecourse.enrollment')), ], ), migrations.CreateModel( name='Question', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('question_text', models.CharField(max_length=1000)), ('grade', models.IntegerField(default=1)), ('course', models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to='onlinecourse.course')), ], ), migrations.CreateModel( name='Lesson', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('title', models.CharField(default='title', max_length=200)), ('order', models.IntegerField(default=0)), ('content', models.TextField()), ('course', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='onlinecourse.course')), ], ), migrations.CreateModel( name='Learner', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('occupation', models.CharField(choices=[('student', 'Student'), ('developer', 'Developer'), ('data_scientist', 'Data Scientist'), ('dba', 'Database Admin')], default='student', max_length=20)), ('social_link', models.URLField()), ('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)), ], ), migrations.CreateModel( name='Instructor', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('full_time', models.BooleanField(default=True)), ('total_learners', models.IntegerField()), ('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)), ], ), migrations.AddField( model_name='course', name='instructors', field=models.ManyToManyField(to='onlinecourse.Instructor'), ), migrations.AddField( model_name='course', name='users', field=models.ManyToManyField(through='onlinecourse.Enrollment', to=settings.AUTH_USER_MODEL), ), migrations.AddField( model_name='choice', name='question', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='onlinecourse.question'), ), ]

47.302752

210

0.585531

from django.conf import settings from django.db import migrations, models import django.db.models.deletion import django.utils.timezone class Migration(migrations.Migration): initial = True dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ] operations = [ migrations.CreateModel( name='Choice', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('choice_text', models.CharField(max_length=1000)), ('is_correct', models.BooleanField(default=False)), ], ), migrations.CreateModel( name='Course', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(default='online course', max_length=30)), ('image', models.ImageField(upload_to='course_images/')), ('description', models.CharField(max_length=1000)), ('pub_date', models.DateField(null=True)), ('total_enrollment', models.IntegerField(default=0)), ], ), migrations.CreateModel( name='Enrollment', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('date_enrolled', models.DateField(default=django.utils.timezone.now)), ('mode', models.CharField(choices=[('audit', 'Audit'), ('honor', 'Honor'), ('BETA', 'BETA')], default='audit', max_length=5)), ('rating', models.FloatField(default=5.0)), ('course', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='onlinecourse.course')), ('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)), ], ), migrations.CreateModel( name='Submission', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('choices', models.ManyToManyField(to='onlinecourse.Choice')), ('enrollment', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='onlinecourse.enrollment')), ], ), migrations.CreateModel( name='Question', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('question_text', models.CharField(max_length=1000)), ('grade', models.IntegerField(default=1)), ('course', models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to='onlinecourse.course')), ], ), migrations.CreateModel( name='Lesson', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('title', models.CharField(default='title', max_length=200)), ('order', models.IntegerField(default=0)), ('content', models.TextField()), ('course', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='onlinecourse.course')), ], ), migrations.CreateModel( name='Learner', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('occupation', models.CharField(choices=[('student', 'Student'), ('developer', 'Developer'), ('data_scientist', 'Data Scientist'), ('dba', 'Database Admin')], default='student', max_length=20)), ('social_link', models.URLField()), ('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)), ], ), migrations.CreateModel( name='Instructor', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('full_time', models.BooleanField(default=True)), ('total_learners', models.IntegerField()), ('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)), ], ), migrations.AddField( model_name='course', name='instructors', field=models.ManyToManyField(to='onlinecourse.Instructor'), ), migrations.AddField( model_name='course', name='users', field=models.ManyToManyField(through='onlinecourse.Enrollment', to=settings.AUTH_USER_MODEL), ), migrations.AddField( model_name='choice', name='question', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='onlinecourse.question'), ), ]

true

f700828dd6b4498dce5f1f79ff761916bbd8bed5

33,203

py

Python

indico/modules/categories/controllers/display.py

bnavigator/indico

2881b6ea69ef992396a3a147f61d8598c11f41c9

[ "MIT" ]

1

2019-11-03T11:34:16.000Z

indico/modules/categories/controllers/display.py

NP-compete/indico

80db7ca0ef9d1f3240a16b9ff2d84bf0bf26c549

[ "MIT" ]

null

indico/modules/categories/controllers/display.py

NP-compete/indico

80db7ca0ef9d1f3240a16b9ff2d84bf0bf26c549

[ "MIT" ]

null

# This file is part of Indico. # Copyright (C) 2002 - 2019 CERN # # Indico is free software; you can redistribute it and/or # modify it under the terms of the MIT License; see the # LICENSE file for more details. from __future__ import unicode_literals from datetime import date, datetime, time, timedelta from functools import partial from io import BytesIO from itertools import chain, groupby, imap from math import ceil from operator import attrgetter, itemgetter from time import mktime import dateutil from dateutil.relativedelta import relativedelta from flask import Response, flash, jsonify, redirect, request, session from pytz import utc from sqlalchemy.orm import joinedload, load_only, subqueryload, undefer, undefer_group from werkzeug.exceptions import BadRequest, NotFound from indico.core.db import db from indico.core.db.sqlalchemy.colors import ColorTuple from indico.core.db.sqlalchemy.util.queries import get_n_matching from indico.modules.categories.controllers.base import RHDisplayCategoryBase from indico.modules.categories.legacy import XMLCategorySerializer from indico.modules.categories.models.categories import Category from indico.modules.categories.serialize import (serialize_categories_ical, serialize_category, serialize_category_atom, serialize_category_chain) from indico.modules.categories.util import get_category_stats, get_upcoming_events, serialize_event_for_json_ld from indico.modules.categories.views import WPCategory, WPCategoryCalendar, WPCategoryStatistics from indico.modules.events.models.events import Event from indico.modules.events.timetable.util import get_category_timetable from indico.modules.events.util import get_base_ical_parameters from indico.modules.news.util import get_recent_news from indico.modules.users import User from indico.modules.users.models.favorites import favorite_category_table from indico.util.date_time import format_date, format_number, now_utc from indico.util.decorators import classproperty from indico.util.fs import secure_filename from indico.util.i18n import _ from indico.util.string import to_unicode from indico.web.flask.templating import get_template_module from indico.web.flask.util import send_file, url_for from indico.web.rh import RH from indico.web.util import jsonify_data CALENDAR_COLOR_PALETTE = [ ColorTuple('#1F1100', '#ECC495'), ColorTuple('#0F0202', '#B9CBCA'), ColorTuple('#0D1E1F', '#C2ECEF'), ColorTuple('#000000', '#D0C296'), ColorTuple('#202020', '#EFEBC2') ] def _flat_map(func, list_): return chain.from_iterable(imap(func, list_)) class RHCategoryIcon(RHDisplayCategoryBase): _category_query_options = undefer('icon'), def _check_access(self): # Category icons are always public pass def _process(self): if not self.category.has_icon: raise NotFound metadata = self.category.icon_metadata return send_file(metadata['filename'], BytesIO(self.category.icon), mimetype=metadata['content_type'], conditional=True) class RHCategoryLogo(RHDisplayCategoryBase): _category_query_options = undefer('logo'), def _process(self): if not self.category.has_logo: raise NotFound metadata = self.category.logo_metadata return send_file(metadata['filename'], BytesIO(self.category.logo), mimetype=metadata['content_type'], conditional=True) class RHCategoryStatistics(RHDisplayCategoryBase): def _get_stats_json(self, stats): data = {'events': stats['events_by_year'], 'contributions': stats['contribs_by_year'], 'files': stats['attachments'], 'updated': stats['updated'].isoformat()} if self.category.is_root: data['users'] = self._count_users() return jsonify(data) def _get_stats_html(self, stats): plots, values, updated = self._process_stats(stats, root=self.category.is_root) return WPCategoryStatistics.render_template('category_statistics.html', self.category, plots=plots, values=values, updated=updated, has_stats=True) def _process(self): stats = get_category_stats(self.category.id) if request.accept_mimetypes.best_match(('application/json', 'text/html')) == 'application/json': return self._get_stats_json(stats) else: return self._get_stats_html(stats) def _plot_data(self, stats, tooltip=''): years = sorted(stats.iterkeys()) min_year = now_utc().year max_year = min_year if years: min_year = min(min_year, years[0]) - 1 max_year = max(max_year, years[-1]) data = {year: stats.get(year, 0) for year in xrange(min_year, max_year + 1)} max_y = ceil(max(data.itervalues()) * 1.1) # 1.1 for padding in the graph else: data = {} max_y = 0 return {'min_x': min_year, 'max_x': max_year, 'min_y': 0, 'max_y': max_y, 'values': data, 'total': sum(data.itervalues()), 'label_x': _("Years"), 'label_y': '', 'tooltip': tooltip} def _process_stats(self, stats, root=False): # tooltip formatting is for ease of translation plots = [(_('Number of events'), _('The year is the one of the start date of the event.'), self._plot_data(stats.get('events_by_year', {}), tooltip=_('{value} events in {year}').format(value='', year=''))), (_('Number of contributions'), _('The year is the one of the start date of the contribution.'), self._plot_data(stats.get('contribs_by_year', {}), tooltip=_('{value} contributions in {year}').format(value='', year='')))] values = [(_('Number of attachments'), stats['attachments'])] if root: values.append((_('Number of users'), self._count_users())) return plots, values, stats['updated'] def _count_users(self): return User.find(is_deleted=False, is_pending=False).count() class RHCategoryInfo(RHDisplayCategoryBase): @classproperty @classmethod def _category_query_options(cls): children_strategy = subqueryload('children') children_strategy.load_only('id', 'parent_id', 'title', 'protection_mode', 'event_creation_restricted') children_strategy.subqueryload('acl_entries') children_strategy.undefer('deep_children_count') children_strategy.undefer('deep_events_count') children_strategy.undefer('has_events') return (children_strategy, load_only('id', 'parent_id', 'title', 'protection_mode'), subqueryload('acl_entries'), undefer('deep_children_count'), undefer('deep_events_count'), undefer('has_events'), undefer('chain')) def _process(self): return jsonify_data(flash=False, **serialize_category_chain(self.category, include_children=True, include_parents=True)) class RHReachableCategoriesInfo(RH): def _get_reachable_categories(self, id_, excluded_ids): cat = Category.query.filter_by(id=id_).options(joinedload('children').load_only('id')).one() ids = ({c.id for c in cat.children} | {c.id for c in cat.parent_chain_query}) - excluded_ids if not ids: return [] return (Category.query .filter(Category.id.in_(ids)) .options(*RHCategoryInfo._category_query_options) .all()) def _process(self): excluded_ids = set(request.json.get('exclude', set())) if request.json else set() categories = self._get_reachable_categories(request.view_args['category_id'], excluded_ids=excluded_ids) return jsonify_data(categories=[serialize_category_chain(c, include_children=True) for c in categories], flash=False) class RHCategorySearch(RH): def _process(self): q = request.args['q'].lower() query = (Category.query .filter(Category.title_matches(q)) .options(undefer('deep_children_count'), undefer('deep_events_count'), undefer('has_events'), joinedload('acl_entries'))) if session.user: # Prefer favorite categories query = query.order_by(Category.favorite_of.any(favorite_category_table.c.user_id == session.user.id) .desc()) # Prefer exact matches and matches at the beginning, then order by category title and if # those are identical by the chain titles query = (query .order_by((db.func.lower(Category.title) == q).desc(), db.func.lower(Category.title).startswith(q).desc(), db.func.lower(Category.title), Category.chain_titles)) total_count = query.count() query = query.limit(10) return jsonify_data(categories=[serialize_category(c, with_favorite=True, with_path=True) for c in query], total_count=total_count, flash=False) class RHSubcatInfo(RHDisplayCategoryBase): """Get basic information about subcategories. This is intended to return information shown on a category display page that is not needed immediately and is somewhat expensive to retrieve. """ @classproperty @classmethod def _category_query_options(cls): children_strategy = joinedload('children') children_strategy.load_only('id') children_strategy.undefer('deep_events_count') return children_strategy, load_only('id', 'parent_id', 'protection_mode') def _process(self): event_counts = {c.id: {'value': c.deep_events_count, 'pretty': format_number(c.deep_events_count)} for c in self.category.children} return jsonify_data(flash=False, event_counts=event_counts) class RHDisplayCategoryEventsBase(RHDisplayCategoryBase): """Base class for display pages displaying an event list""" _category_query_options = (joinedload('children').load_only('id', 'title', 'protection_mode'), undefer('attachment_count'), undefer('has_events')) _event_query_options = (joinedload('person_links'), joinedload('series'), undefer_group('series'), load_only('id', 'category_id', 'created_dt', 'start_dt', 'end_dt', 'timezone', 'protection_mode', 'title', 'type_', 'series_pos', 'series_count', 'own_address', 'own_venue_id', 'own_venue_name')) def _process_args(self): RHDisplayCategoryBase._process_args(self) self.now = now_utc(exact=False).astimezone(self.category.display_tzinfo) def format_event_date(self, event): day_month = 'dd MMM' tzinfo = self.category.display_tzinfo start_dt = event.start_dt.astimezone(tzinfo) end_dt = event.end_dt.astimezone(tzinfo) if start_dt.year != end_dt.year: return '{} - {}'.format(to_unicode(format_date(start_dt, timezone=tzinfo)), to_unicode(format_date(end_dt, timezone=tzinfo))) elif (start_dt.month != end_dt.month) or (start_dt.day != end_dt.day): return '{} - {}'.format(to_unicode(format_date(start_dt, day_month, timezone=tzinfo)), to_unicode(format_date(end_dt, day_month, timezone=tzinfo))) else: return to_unicode(format_date(start_dt, day_month, timezone=tzinfo)) def group_by_month(self, events): def _format_tuple(x): (year, month), events = x return {'name': format_date(date(year, month, 1), format='MMMM yyyy'), 'events': list(events), 'is_current': year == self.now.year and month == self.now.month} def _key(event): start_dt = event.start_dt.astimezone(self.category.tzinfo) return start_dt.year, start_dt.month months = groupby(events, key=_key) return map(_format_tuple, months) def happening_now(self, event): return event.start_dt <= self.now < event.end_dt def is_recent(self, dt): return dt > self.now - relativedelta(weeks=1) class RHDisplayCategory(RHDisplayCategoryEventsBase): """Show the contents of a category (events/subcategories)""" def _process(self): # Current events, which are always shown by default are events of this month and of the previous month. # If there are no events in this range, it will include the last and next month containing events. past_threshold = self.now - relativedelta(months=1, day=1, hour=0, minute=0) future_threshold = self.now + relativedelta(months=1, day=1, hour=0, minute=0) next_event_start_dt = (db.session.query(Event.start_dt) .filter(Event.start_dt >= self.now, Event.category_id == self.category.id) .order_by(Event.start_dt.asc(), Event.id.asc()) .first() or (None,))[0] previous_event_start_dt = (db.session.query(Event.start_dt) .filter(Event.start_dt < self.now, Event.category_id == self.category.id) .order_by(Event.start_dt.desc(), Event.id.desc()) .first() or (None,))[0] if next_event_start_dt is not None and next_event_start_dt > future_threshold: future_threshold = next_event_start_dt + relativedelta(months=1, day=1, hour=0, minute=0) if previous_event_start_dt is not None and previous_event_start_dt < past_threshold: past_threshold = previous_event_start_dt.replace(day=1, hour=0, minute=0) event_query = (Event.query.with_parent(self.category) .options(*self._event_query_options) .order_by(Event.start_dt.desc(), Event.id.desc())) past_event_query = event_query.filter(Event.start_dt < past_threshold) future_event_query = event_query.filter(Event.start_dt >= future_threshold) current_event_query = event_query.filter(Event.start_dt >= past_threshold, Event.start_dt < future_threshold) json_ld_events = events = current_event_query.filter(Event.start_dt < future_threshold).all() events_by_month = self.group_by_month(events) future_event_count = future_event_query.count() past_event_count = past_event_query.count() if not session.user and future_event_count: json_ld_events = json_ld_events + future_event_query.all() show_future_events = bool(self.category.id in session.get('fetch_future_events_in', set()) or (session.user and session.user.settings.get('show_future_events', False))) show_past_events = bool(self.category.id in session.get('fetch_past_events_in', set()) or (session.user and session.user.settings.get('show_past_events', False))) managers = sorted(self.category.get_manager_list(), key=attrgetter('principal_type.name', 'name')) threshold_format = '%Y-%m' params = {'event_count': len(events), 'events_by_month': events_by_month, 'format_event_date': self.format_event_date, 'future_event_count': future_event_count, 'show_future_events': show_future_events, 'future_threshold': future_threshold.strftime(threshold_format), 'happening_now': self.happening_now, 'is_recent': self.is_recent, 'managers': managers, 'past_event_count': past_event_count, 'show_past_events': show_past_events, 'past_threshold': past_threshold.strftime(threshold_format), 'json_ld': map(serialize_event_for_json_ld, json_ld_events), 'atom_feed_url': url_for('.export_atom', self.category), 'atom_feed_title': _('Events of "{}"').format(self.category.title)} params.update(get_base_ical_parameters(session.user, 'category', '/export/categ/{0}.ics'.format(self.category.id), {'from': '-31d'})) if not self.category.is_root: return WPCategory.render_template('display/category.html', self.category, **params) news = get_recent_news() upcoming_events = get_upcoming_events() return WPCategory.render_template('display/root_category.html', self.category, news=news, upcoming_events=upcoming_events, **params) class RHEventList(RHDisplayCategoryEventsBase): """Return the HTML for the event list before/after a specific month""" def _parse_year_month(self, string): try: dt = datetime.strptime(string, '%Y-%m') except (TypeError, ValueError): return None return self.category.display_tzinfo.localize(dt) def _process_args(self): RHDisplayCategoryEventsBase._process_args(self) before = self._parse_year_month(request.args.get('before')) after = self._parse_year_month(request.args.get('after')) if before is None and after is None: raise BadRequest('"before" or "after" parameter must be specified') event_query = (Event.query.with_parent(self.category) .options(*self._event_query_options) .order_by(Event.start_dt.desc(), Event.id.desc())) if before: event_query = event_query.filter(Event.start_dt < before) if after: event_query = event_query.filter(Event.start_dt >= after) self.events = event_query.all() def _process(self): events_by_month = self.group_by_month(self.events) tpl = get_template_module('categories/display/event_list.html') html = tpl.event_list_block(events_by_month=events_by_month, format_event_date=self.format_event_date, is_recent=self.is_recent, happening_now=self.happening_now) return jsonify_data(flash=False, html=html) class RHShowEventsInCategoryBase(RHDisplayCategoryBase): """Set whether the events in a category are automatically displayed or not""" session_field = '' def _show_events(self, show_events): category_ids = session.setdefault(self.session_field, set()) if show_events: category_ids.add(self.category.id) else: category_ids.discard(self.category.id) session.modified = True def _process_DELETE(self): self._show_events(False) def _process_PUT(self): self._show_events(True) class RHShowFutureEventsInCategory(RHShowEventsInCategoryBase): """Set whether the past events in a category are automatically displayed or not""" session_field = 'fetch_future_events_in' class RHShowPastEventsInCategory(RHShowEventsInCategoryBase): """Set whether the past events in a category are automatically displayed or not""" session_field = 'fetch_past_events_in' class RHExportCategoryICAL(RHDisplayCategoryBase): def _process(self): filename = '{}-category.ics'.format(secure_filename(self.category.title, str(self.category.id))) buf = serialize_categories_ical([self.category.id], session.user, Event.end_dt >= (now_utc() - timedelta(weeks=4))) return send_file(filename, buf, 'text/calendar') class RHExportCategoryAtom(RHDisplayCategoryBase): def _process(self): filename = '{}-category.atom'.format(secure_filename(self.category.title, str(self.category.id))) buf = serialize_category_atom(self.category, url_for(request.endpoint, self.category, _external=True), session.user, Event.end_dt >= now_utc()) return send_file(filename, buf, 'application/atom+xml') class RHXMLExportCategoryInfo(RH): def _process_args(self): try: id_ = int(request.args['id']) except ValueError: raise BadRequest('Invalid Category ID') self.category = Category.get_one(id_, is_deleted=False) def _process(self): category_xml_info = XMLCategorySerializer(self.category).serialize_category() return Response(category_xml_info, mimetype='text/xml') class RHCategoryOverview(RHDisplayCategoryBase): """Display the events for a particular day, week or month""" def _process_args(self): RHDisplayCategoryBase._process_args(self) self.detail = request.args.get('detail', 'event') if self.detail not in ('event', 'session', 'contribution'): raise BadRequest('Invalid detail argument') self.period = request.args.get('period', 'day') if self.period not in ('day', 'month', 'week'): raise BadRequest('Invalid period argument') if 'date' in request.args: try: date = datetime.strptime(request.args['date'], '%Y-%m-%d') except ValueError: raise BadRequest('Invalid date argument') else: date = datetime.now() date = self.category.display_tzinfo.localize(date) date = date.replace(hour=0, minute=0, second=0, microsecond=0) if self.period == 'day': self.start_dt = date self.end_dt = self.start_dt + relativedelta(days=1) elif self.period == 'week': self.start_dt = date - relativedelta(days=date.weekday()) self.end_dt = self.start_dt + relativedelta(days=7) elif self.period == 'month': self.start_dt = date + relativedelta(day=1) self.end_dt = self.start_dt + relativedelta(months=1) def _process(self): info = get_category_timetable([self.category.id], self.start_dt, self.end_dt, detail_level=self.detail, tz=self.category.display_tzinfo, from_categ=self.category, grouped=False) events = info['events'] # Only categories with icons are listed in the sidebar subcategory_ids = {event.category.effective_icon_data['source_id'] for event in events if event.category.has_effective_icon} subcategories = Category.query.filter(Category.id.in_(subcategory_ids)) # Events spanning multiple days must appear on all days events = _flat_map(partial(self._process_multiday_events, info), events) def _event_sort_key(event): # Ongoing events are shown after all other events on the same day and are sorted by start_date ongoing = getattr(event, 'ongoing', False) return (event.start_dt.date(), ongoing, -mktime(event.first_occurence_start_dt.timetuple()) if ongoing else event.start_dt.time()) events = sorted(events, key=_event_sort_key) params = { 'detail': self.detail, 'period': self.period, 'subcategories': subcategories, 'start_dt': self.start_dt, 'end_dt': self.end_dt - relativedelta(days=1), # Display a close-ended interval 'previous_day_url': self._other_day_url(self.start_dt - relativedelta(days=1)), 'next_day_url': self._other_day_url(self.start_dt + relativedelta(days=1)), 'previous_month_url': self._other_day_url(self.start_dt - relativedelta(months=1)), 'next_month_url': self._other_day_url(self.start_dt + relativedelta(months=1)), 'previous_year_url': self._other_day_url(self.start_dt - relativedelta(years=1)), 'next_year_url': self._other_day_url(self.start_dt + relativedelta(years=1)), 'mathjax': True } if self.detail != 'event': cte = self.category.get_protection_parent_cte() params['accessible_categories'] = {cat_id for cat_id, prot_parent_id in db.session.query(cte) if prot_parent_id == self.category.id} if self.period == 'day': return WPCategory.render_template('display/overview/day.html', self.category, events=events, **params) elif self.period == 'week': days = self._get_week_days() template = 'display/overview/week.html' params['previous_week_url'] = self._other_day_url(self.start_dt - relativedelta(days=7)) params['next_week_url'] = self._other_day_url(self.start_dt + relativedelta(days=7)) elif self.period == 'month': days = self._get_calendar_days() template = 'display/overview/month.html' events_by_day = [] for day in days: events_by_day.append((day, self._pop_head_while(lambda x: x.start_dt.date() <= day.date(), events))) # Check whether all weekends are empty hide_weekend = (not any(map(itemgetter(1), events_by_day[5::7])) and not any(map(itemgetter(1), events_by_day[6::7]))) if hide_weekend: events_by_day = [x for x in events_by_day if x[0].weekday() not in (5, 6)] return WPCategory.render_template(template, self.category, events_by_day=events_by_day, hide_weekend=hide_weekend, **params) def _get_week_days(self): # Return the days shown in the weekly overview return self._get_days(self.start_dt, self.end_dt) def _get_calendar_days(self): # Return the days shown in the monthly overview start_dt = self.start_dt - relativedelta(days=self.start_dt.weekday()) end_dt = self.end_dt + relativedelta(days=(7 - self.end_dt.weekday()) % 7) return self._get_days(start_dt, end_dt) @staticmethod def _get_days(start_dt, end_dt): # Return all days in the open-ended interval current_dt = start_dt tz = current_dt.tzinfo next_day = current_dt.date() + timedelta(1) beginning_of_next_day = tz.localize(datetime.combine(next_day, time())) while current_dt < end_dt: yield current_dt current_dt = beginning_of_next_day beginning_of_next_day = current_dt + relativedelta(days=1) @staticmethod def _pop_head_while(predicate, list_): # Pop the head of the list while the predicate is true and return the popped elements res = [] while len(list_) and predicate(list_[0]): res.append(list_[0]) list_.pop(0) return res def _other_day_url(self, date): return url_for('.overview', self.category, detail=self.detail, period=self.period, date=format_date(date, 'yyyy-MM-dd')) def _process_multiday_events(self, info, event): # Add "fake" proxy events for events spanning multiple days such that there is one event per day # Function type: Event -> List[Event] tzinfo = self.category.display_tzinfo # Breaks, contributions and sessions grouped by start_dt. Each EventProxy will return the relevant ones only timetable_objects = sorted(chain(*info[event.id].values()), key=attrgetter('timetable_entry.start_dt')) timetable_objects_by_date = {x[0]: list(x[1]) for x in groupby(timetable_objects, key=lambda x: x.start_dt.astimezone(tzinfo).date())} # All the days of the event shown in the overview event_days = self._get_days(max(self.start_dt, event.start_dt.astimezone(tzinfo)), min(self.end_dt, event.end_dt.astimezone(tzinfo))) # Generate a proxy object with adjusted start_dt and timetable_objects for each day return [_EventProxy(event, day, tzinfo, timetable_objects_by_date.get(day.date(), [])) for day in event_days] class _EventProxy(object): def __init__(self, event, date, tzinfo, timetable_objects): start_dt = datetime.combine(date, event.start_dt.astimezone(tzinfo).timetz()) assert date >= event.start_dt assert date <= event.end_dt object.__setattr__(self, '_start_dt', start_dt) object.__setattr__(self, '_real_event', event) object.__setattr__(self, '_event_tz_start_date', event.start_dt.astimezone(tzinfo).date()) object.__setattr__(self, '_timetable_objects', timetable_objects) def __getattribute__(self, name): if name == 'start_dt': return object.__getattribute__(self, '_start_dt') event = object.__getattribute__(self, '_real_event') if name == 'timetable_objects': return object.__getattribute__(self, '_timetable_objects') if name == 'ongoing': # the event is "ongoing" if the dates (in the tz of the category) # of the event and the proxy (calendar entry) don't match event_start_date = object.__getattribute__(self, '_event_tz_start_date') return event_start_date != self.start_dt.date() if name == 'first_occurence_start_dt': return event.start_dt return getattr(event, name) def __setattr__(self, name, value): raise AttributeError('This instance is read-only') def __repr__(self): return '<_EventProxy({}, {})>'.format(self.start_dt, object.__getattribute__(self, '_real_event')) class RHCategoryCalendarView(RHDisplayCategoryBase): def _process(self): if not request.is_xhr: return WPCategoryCalendar.render_template('display/calendar.html', self.category, start_dt=request.args.get('start_dt')) tz = self.category.display_tzinfo start = tz.localize(dateutil.parser.parse(request.args['start'])).astimezone(utc) end = tz.localize(dateutil.parser.parse(request.args['end'])).astimezone(utc) query = (Event.query .filter(Event.starts_between(start, end), Event.is_visible_in(self.category.id), ~Event.is_deleted) .options(load_only('id', 'title', 'start_dt', 'end_dt', 'category_id'))) events = self._get_event_data(query) ongoing_events = (Event.query .filter(Event.is_visible_in(self.category.id), Event.start_dt < start, Event.end_dt > end) .options(load_only('id', 'title', 'start_dt', 'end_dt', 'timezone')) .order_by(Event.title) .all()) return jsonify_data(flash=False, events=events, ongoing_event_count=len(ongoing_events), ongoing_events_html=self._render_ongoing_events(ongoing_events)) def _get_event_data(self, event_query): data = [] tz = self.category.display_tzinfo for event in event_query: category_id = event.category_id event_data = {'title': event.title, 'start': event.start_dt.astimezone(tz).replace(tzinfo=None).isoformat(), 'end': event.end_dt.astimezone(tz).replace(tzinfo=None).isoformat(), 'url': event.url} colors = CALENDAR_COLOR_PALETTE[category_id % len(CALENDAR_COLOR_PALETTE)] event_data.update({'textColor': '#' + colors.text, 'color': '#' + colors.background}) data.append(event_data) return data def _render_ongoing_events(self, ongoing_events): template = get_template_module('categories/display/_calendar_ongoing_events.html') return template.render_ongoing_events(ongoing_events, self.category.display_tzinfo) class RHCategoryUpcomingEvent(RHDisplayCategoryBase): """Redirect to the upcoming event of a category.""" def _process(self): event = self._get_upcoming_event() if event: return redirect(event.url) else: flash(_('There are no upcoming events for this category')) return redirect(self.category.url) def _get_upcoming_event(self): query = (Event.query .filter(Event.is_visible_in(self.category.id), Event.start_dt > now_utc(), ~Event.is_deleted) .options(subqueryload('acl_entries')) .order_by(Event.start_dt, Event.id)) res = get_n_matching(query, 1, lambda event: event.can_access(session.user)) if res: return res[0]

47.981214

120

0.640033

from __future__ import unicode_literals from datetime import date, datetime, time, timedelta from functools import partial from io import BytesIO from itertools import chain, groupby, imap from math import ceil from operator import attrgetter, itemgetter from time import mktime import dateutil from dateutil.relativedelta import relativedelta from flask import Response, flash, jsonify, redirect, request, session from pytz import utc from sqlalchemy.orm import joinedload, load_only, subqueryload, undefer, undefer_group from werkzeug.exceptions import BadRequest, NotFound from indico.core.db import db from indico.core.db.sqlalchemy.colors import ColorTuple from indico.core.db.sqlalchemy.util.queries import get_n_matching from indico.modules.categories.controllers.base import RHDisplayCategoryBase from indico.modules.categories.legacy import XMLCategorySerializer from indico.modules.categories.models.categories import Category from indico.modules.categories.serialize import (serialize_categories_ical, serialize_category, serialize_category_atom, serialize_category_chain) from indico.modules.categories.util import get_category_stats, get_upcoming_events, serialize_event_for_json_ld from indico.modules.categories.views import WPCategory, WPCategoryCalendar, WPCategoryStatistics from indico.modules.events.models.events import Event from indico.modules.events.timetable.util import get_category_timetable from indico.modules.events.util import get_base_ical_parameters from indico.modules.news.util import get_recent_news from indico.modules.users import User from indico.modules.users.models.favorites import favorite_category_table from indico.util.date_time import format_date, format_number, now_utc from indico.util.decorators import classproperty from indico.util.fs import secure_filename from indico.util.i18n import _ from indico.util.string import to_unicode from indico.web.flask.templating import get_template_module from indico.web.flask.util import send_file, url_for from indico.web.rh import RH from indico.web.util import jsonify_data CALENDAR_COLOR_PALETTE = [ ColorTuple('#1F1100', '#ECC495'), ColorTuple('#0F0202', '#B9CBCA'), ColorTuple('#0D1E1F', '#C2ECEF'), ColorTuple('#000000', '#D0C296'), ColorTuple('#202020', '#EFEBC2') ] def _flat_map(func, list_): return chain.from_iterable(imap(func, list_)) class RHCategoryIcon(RHDisplayCategoryBase): _category_query_options = undefer('icon'), def _check_access(self): pass def _process(self): if not self.category.has_icon: raise NotFound metadata = self.category.icon_metadata return send_file(metadata['filename'], BytesIO(self.category.icon), mimetype=metadata['content_type'], conditional=True) class RHCategoryLogo(RHDisplayCategoryBase): _category_query_options = undefer('logo'), def _process(self): if not self.category.has_logo: raise NotFound metadata = self.category.logo_metadata return send_file(metadata['filename'], BytesIO(self.category.logo), mimetype=metadata['content_type'], conditional=True) class RHCategoryStatistics(RHDisplayCategoryBase): def _get_stats_json(self, stats): data = {'events': stats['events_by_year'], 'contributions': stats['contribs_by_year'], 'files': stats['attachments'], 'updated': stats['updated'].isoformat()} if self.category.is_root: data['users'] = self._count_users() return jsonify(data) def _get_stats_html(self, stats): plots, values, updated = self._process_stats(stats, root=self.category.is_root) return WPCategoryStatistics.render_template('category_statistics.html', self.category, plots=plots, values=values, updated=updated, has_stats=True) def _process(self): stats = get_category_stats(self.category.id) if request.accept_mimetypes.best_match(('application/json', 'text/html')) == 'application/json': return self._get_stats_json(stats) else: return self._get_stats_html(stats) def _plot_data(self, stats, tooltip=''): years = sorted(stats.iterkeys()) min_year = now_utc().year max_year = min_year if years: min_year = min(min_year, years[0]) - 1 max_year = max(max_year, years[-1]) data = {year: stats.get(year, 0) for year in xrange(min_year, max_year + 1)} max_y = ceil(max(data.itervalues()) * 1.1) else: data = {} max_y = 0 return {'min_x': min_year, 'max_x': max_year, 'min_y': 0, 'max_y': max_y, 'values': data, 'total': sum(data.itervalues()), 'label_x': _("Years"), 'label_y': '', 'tooltip': tooltip} def _process_stats(self, stats, root=False): plots = [(_('Number of events'), _('The year is the one of the start date of the event.'), self._plot_data(stats.get('events_by_year', {}), tooltip=_('{value} events in {year}').format(value='', year=''))), (_('Number of contributions'), _('The year is the one of the start date of the contribution.'), self._plot_data(stats.get('contribs_by_year', {}), tooltip=_('{value} contributions in {year}').format(value='', year='')))] values = [(_('Number of attachments'), stats['attachments'])] if root: values.append((_('Number of users'), self._count_users())) return plots, values, stats['updated'] def _count_users(self): return User.find(is_deleted=False, is_pending=False).count() class RHCategoryInfo(RHDisplayCategoryBase): @classproperty @classmethod def _category_query_options(cls): children_strategy = subqueryload('children') children_strategy.load_only('id', 'parent_id', 'title', 'protection_mode', 'event_creation_restricted') children_strategy.subqueryload('acl_entries') children_strategy.undefer('deep_children_count') children_strategy.undefer('deep_events_count') children_strategy.undefer('has_events') return (children_strategy, load_only('id', 'parent_id', 'title', 'protection_mode'), subqueryload('acl_entries'), undefer('deep_children_count'), undefer('deep_events_count'), undefer('has_events'), undefer('chain')) def _process(self): return jsonify_data(flash=False, **serialize_category_chain(self.category, include_children=True, include_parents=True)) class RHReachableCategoriesInfo(RH): def _get_reachable_categories(self, id_, excluded_ids): cat = Category.query.filter_by(id=id_).options(joinedload('children').load_only('id')).one() ids = ({c.id for c in cat.children} | {c.id for c in cat.parent_chain_query}) - excluded_ids if not ids: return [] return (Category.query .filter(Category.id.in_(ids)) .options(*RHCategoryInfo._category_query_options) .all()) def _process(self): excluded_ids = set(request.json.get('exclude', set())) if request.json else set() categories = self._get_reachable_categories(request.view_args['category_id'], excluded_ids=excluded_ids) return jsonify_data(categories=[serialize_category_chain(c, include_children=True) for c in categories], flash=False) class RHCategorySearch(RH): def _process(self): q = request.args['q'].lower() query = (Category.query .filter(Category.title_matches(q)) .options(undefer('deep_children_count'), undefer('deep_events_count'), undefer('has_events'), joinedload('acl_entries'))) if session.user: query = query.order_by(Category.favorite_of.any(favorite_category_table.c.user_id == session.user.id) .desc()) query = (query .order_by((db.func.lower(Category.title) == q).desc(), db.func.lower(Category.title).startswith(q).desc(), db.func.lower(Category.title), Category.chain_titles)) total_count = query.count() query = query.limit(10) return jsonify_data(categories=[serialize_category(c, with_favorite=True, with_path=True) for c in query], total_count=total_count, flash=False) class RHSubcatInfo(RHDisplayCategoryBase): @classproperty @classmethod def _category_query_options(cls): children_strategy = joinedload('children') children_strategy.load_only('id') children_strategy.undefer('deep_events_count') return children_strategy, load_only('id', 'parent_id', 'protection_mode') def _process(self): event_counts = {c.id: {'value': c.deep_events_count, 'pretty': format_number(c.deep_events_count)} for c in self.category.children} return jsonify_data(flash=False, event_counts=event_counts) class RHDisplayCategoryEventsBase(RHDisplayCategoryBase): _category_query_options = (joinedload('children').load_only('id', 'title', 'protection_mode'), undefer('attachment_count'), undefer('has_events')) _event_query_options = (joinedload('person_links'), joinedload('series'), undefer_group('series'), load_only('id', 'category_id', 'created_dt', 'start_dt', 'end_dt', 'timezone', 'protection_mode', 'title', 'type_', 'series_pos', 'series_count', 'own_address', 'own_venue_id', 'own_venue_name')) def _process_args(self): RHDisplayCategoryBase._process_args(self) self.now = now_utc(exact=False).astimezone(self.category.display_tzinfo) def format_event_date(self, event): day_month = 'dd MMM' tzinfo = self.category.display_tzinfo start_dt = event.start_dt.astimezone(tzinfo) end_dt = event.end_dt.astimezone(tzinfo) if start_dt.year != end_dt.year: return '{} - {}'.format(to_unicode(format_date(start_dt, timezone=tzinfo)), to_unicode(format_date(end_dt, timezone=tzinfo))) elif (start_dt.month != end_dt.month) or (start_dt.day != end_dt.day): return '{} - {}'.format(to_unicode(format_date(start_dt, day_month, timezone=tzinfo)), to_unicode(format_date(end_dt, day_month, timezone=tzinfo))) else: return to_unicode(format_date(start_dt, day_month, timezone=tzinfo)) def group_by_month(self, events): def _format_tuple(x): (year, month), events = x return {'name': format_date(date(year, month, 1), format='MMMM yyyy'), 'events': list(events), 'is_current': year == self.now.year and month == self.now.month} def _key(event): start_dt = event.start_dt.astimezone(self.category.tzinfo) return start_dt.year, start_dt.month months = groupby(events, key=_key) return map(_format_tuple, months) def happening_now(self, event): return event.start_dt <= self.now < event.end_dt def is_recent(self, dt): return dt > self.now - relativedelta(weeks=1) class RHDisplayCategory(RHDisplayCategoryEventsBase): def _process(self): past_threshold = self.now - relativedelta(months=1, day=1, hour=0, minute=0) future_threshold = self.now + relativedelta(months=1, day=1, hour=0, minute=0) next_event_start_dt = (db.session.query(Event.start_dt) .filter(Event.start_dt >= self.now, Event.category_id == self.category.id) .order_by(Event.start_dt.asc(), Event.id.asc()) .first() or (None,))[0] previous_event_start_dt = (db.session.query(Event.start_dt) .filter(Event.start_dt < self.now, Event.category_id == self.category.id) .order_by(Event.start_dt.desc(), Event.id.desc()) .first() or (None,))[0] if next_event_start_dt is not None and next_event_start_dt > future_threshold: future_threshold = next_event_start_dt + relativedelta(months=1, day=1, hour=0, minute=0) if previous_event_start_dt is not None and previous_event_start_dt < past_threshold: past_threshold = previous_event_start_dt.replace(day=1, hour=0, minute=0) event_query = (Event.query.with_parent(self.category) .options(*self._event_query_options) .order_by(Event.start_dt.desc(), Event.id.desc())) past_event_query = event_query.filter(Event.start_dt < past_threshold) future_event_query = event_query.filter(Event.start_dt >= future_threshold) current_event_query = event_query.filter(Event.start_dt >= past_threshold, Event.start_dt < future_threshold) json_ld_events = events = current_event_query.filter(Event.start_dt < future_threshold).all() events_by_month = self.group_by_month(events) future_event_count = future_event_query.count() past_event_count = past_event_query.count() if not session.user and future_event_count: json_ld_events = json_ld_events + future_event_query.all() show_future_events = bool(self.category.id in session.get('fetch_future_events_in', set()) or (session.user and session.user.settings.get('show_future_events', False))) show_past_events = bool(self.category.id in session.get('fetch_past_events_in', set()) or (session.user and session.user.settings.get('show_past_events', False))) managers = sorted(self.category.get_manager_list(), key=attrgetter('principal_type.name', 'name')) threshold_format = '%Y-%m' params = {'event_count': len(events), 'events_by_month': events_by_month, 'format_event_date': self.format_event_date, 'future_event_count': future_event_count, 'show_future_events': show_future_events, 'future_threshold': future_threshold.strftime(threshold_format), 'happening_now': self.happening_now, 'is_recent': self.is_recent, 'managers': managers, 'past_event_count': past_event_count, 'show_past_events': show_past_events, 'past_threshold': past_threshold.strftime(threshold_format), 'json_ld': map(serialize_event_for_json_ld, json_ld_events), 'atom_feed_url': url_for('.export_atom', self.category), 'atom_feed_title': _('Events of "{}"').format(self.category.title)} params.update(get_base_ical_parameters(session.user, 'category', '/export/categ/{0}.ics'.format(self.category.id), {'from': '-31d'})) if not self.category.is_root: return WPCategory.render_template('display/category.html', self.category, **params) news = get_recent_news() upcoming_events = get_upcoming_events() return WPCategory.render_template('display/root_category.html', self.category, news=news, upcoming_events=upcoming_events, **params) class RHEventList(RHDisplayCategoryEventsBase): def _parse_year_month(self, string): try: dt = datetime.strptime(string, '%Y-%m') except (TypeError, ValueError): return None return self.category.display_tzinfo.localize(dt) def _process_args(self): RHDisplayCategoryEventsBase._process_args(self) before = self._parse_year_month(request.args.get('before')) after = self._parse_year_month(request.args.get('after')) if before is None and after is None: raise BadRequest('"before" or "after" parameter must be specified') event_query = (Event.query.with_parent(self.category) .options(*self._event_query_options) .order_by(Event.start_dt.desc(), Event.id.desc())) if before: event_query = event_query.filter(Event.start_dt < before) if after: event_query = event_query.filter(Event.start_dt >= after) self.events = event_query.all() def _process(self): events_by_month = self.group_by_month(self.events) tpl = get_template_module('categories/display/event_list.html') html = tpl.event_list_block(events_by_month=events_by_month, format_event_date=self.format_event_date, is_recent=self.is_recent, happening_now=self.happening_now) return jsonify_data(flash=False, html=html) class RHShowEventsInCategoryBase(RHDisplayCategoryBase): session_field = '' def _show_events(self, show_events): category_ids = session.setdefault(self.session_field, set()) if show_events: category_ids.add(self.category.id) else: category_ids.discard(self.category.id) session.modified = True def _process_DELETE(self): self._show_events(False) def _process_PUT(self): self._show_events(True) class RHShowFutureEventsInCategory(RHShowEventsInCategoryBase): session_field = 'fetch_future_events_in' class RHShowPastEventsInCategory(RHShowEventsInCategoryBase): session_field = 'fetch_past_events_in' class RHExportCategoryICAL(RHDisplayCategoryBase): def _process(self): filename = '{}-category.ics'.format(secure_filename(self.category.title, str(self.category.id))) buf = serialize_categories_ical([self.category.id], session.user, Event.end_dt >= (now_utc() - timedelta(weeks=4))) return send_file(filename, buf, 'text/calendar') class RHExportCategoryAtom(RHDisplayCategoryBase): def _process(self): filename = '{}-category.atom'.format(secure_filename(self.category.title, str(self.category.id))) buf = serialize_category_atom(self.category, url_for(request.endpoint, self.category, _external=True), session.user, Event.end_dt >= now_utc()) return send_file(filename, buf, 'application/atom+xml') class RHXMLExportCategoryInfo(RH): def _process_args(self): try: id_ = int(request.args['id']) except ValueError: raise BadRequest('Invalid Category ID') self.category = Category.get_one(id_, is_deleted=False) def _process(self): category_xml_info = XMLCategorySerializer(self.category).serialize_category() return Response(category_xml_info, mimetype='text/xml') class RHCategoryOverview(RHDisplayCategoryBase): def _process_args(self): RHDisplayCategoryBase._process_args(self) self.detail = request.args.get('detail', 'event') if self.detail not in ('event', 'session', 'contribution'): raise BadRequest('Invalid detail argument') self.period = request.args.get('period', 'day') if self.period not in ('day', 'month', 'week'): raise BadRequest('Invalid period argument') if 'date' in request.args: try: date = datetime.strptime(request.args['date'], '%Y-%m-%d') except ValueError: raise BadRequest('Invalid date argument') else: date = datetime.now() date = self.category.display_tzinfo.localize(date) date = date.replace(hour=0, minute=0, second=0, microsecond=0) if self.period == 'day': self.start_dt = date self.end_dt = self.start_dt + relativedelta(days=1) elif self.period == 'week': self.start_dt = date - relativedelta(days=date.weekday()) self.end_dt = self.start_dt + relativedelta(days=7) elif self.period == 'month': self.start_dt = date + relativedelta(day=1) self.end_dt = self.start_dt + relativedelta(months=1) def _process(self): info = get_category_timetable([self.category.id], self.start_dt, self.end_dt, detail_level=self.detail, tz=self.category.display_tzinfo, from_categ=self.category, grouped=False) events = info['events'] subcategory_ids = {event.category.effective_icon_data['source_id'] for event in events if event.category.has_effective_icon} subcategories = Category.query.filter(Category.id.in_(subcategory_ids)) events = _flat_map(partial(self._process_multiday_events, info), events) def _event_sort_key(event): ongoing = getattr(event, 'ongoing', False) return (event.start_dt.date(), ongoing, -mktime(event.first_occurence_start_dt.timetuple()) if ongoing else event.start_dt.time()) events = sorted(events, key=_event_sort_key) params = { 'detail': self.detail, 'period': self.period, 'subcategories': subcategories, 'start_dt': self.start_dt, 'end_dt': self.end_dt - relativedelta(days=1), 'previous_day_url': self._other_day_url(self.start_dt - relativedelta(days=1)), 'next_day_url': self._other_day_url(self.start_dt + relativedelta(days=1)), 'previous_month_url': self._other_day_url(self.start_dt - relativedelta(months=1)), 'next_month_url': self._other_day_url(self.start_dt + relativedelta(months=1)), 'previous_year_url': self._other_day_url(self.start_dt - relativedelta(years=1)), 'next_year_url': self._other_day_url(self.start_dt + relativedelta(years=1)), 'mathjax': True } if self.detail != 'event': cte = self.category.get_protection_parent_cte() params['accessible_categories'] = {cat_id for cat_id, prot_parent_id in db.session.query(cte) if prot_parent_id == self.category.id} if self.period == 'day': return WPCategory.render_template('display/overview/day.html', self.category, events=events, **params) elif self.period == 'week': days = self._get_week_days() template = 'display/overview/week.html' params['previous_week_url'] = self._other_day_url(self.start_dt - relativedelta(days=7)) params['next_week_url'] = self._other_day_url(self.start_dt + relativedelta(days=7)) elif self.period == 'month': days = self._get_calendar_days() template = 'display/overview/month.html' events_by_day = [] for day in days: events_by_day.append((day, self._pop_head_while(lambda x: x.start_dt.date() <= day.date(), events))) hide_weekend = (not any(map(itemgetter(1), events_by_day[5::7])) and not any(map(itemgetter(1), events_by_day[6::7]))) if hide_weekend: events_by_day = [x for x in events_by_day if x[0].weekday() not in (5, 6)] return WPCategory.render_template(template, self.category, events_by_day=events_by_day, hide_weekend=hide_weekend, **params) def _get_week_days(self): return self._get_days(self.start_dt, self.end_dt) def _get_calendar_days(self): start_dt = self.start_dt - relativedelta(days=self.start_dt.weekday()) end_dt = self.end_dt + relativedelta(days=(7 - self.end_dt.weekday()) % 7) return self._get_days(start_dt, end_dt) @staticmethod def _get_days(start_dt, end_dt): current_dt = start_dt tz = current_dt.tzinfo next_day = current_dt.date() + timedelta(1) beginning_of_next_day = tz.localize(datetime.combine(next_day, time())) while current_dt < end_dt: yield current_dt current_dt = beginning_of_next_day beginning_of_next_day = current_dt + relativedelta(days=1) @staticmethod def _pop_head_while(predicate, list_): res = [] while len(list_) and predicate(list_[0]): res.append(list_[0]) list_.pop(0) return res def _other_day_url(self, date): return url_for('.overview', self.category, detail=self.detail, period=self.period, date=format_date(date, 'yyyy-MM-dd')) def _process_multiday_events(self, info, event): tzinfo = self.category.display_tzinfo timetable_objects = sorted(chain(*info[event.id].values()), key=attrgetter('timetable_entry.start_dt')) timetable_objects_by_date = {x[0]: list(x[1]) for x in groupby(timetable_objects, key=lambda x: x.start_dt.astimezone(tzinfo).date())} event_days = self._get_days(max(self.start_dt, event.start_dt.astimezone(tzinfo)), min(self.end_dt, event.end_dt.astimezone(tzinfo))) return [_EventProxy(event, day, tzinfo, timetable_objects_by_date.get(day.date(), [])) for day in event_days] class _EventProxy(object): def __init__(self, event, date, tzinfo, timetable_objects): start_dt = datetime.combine(date, event.start_dt.astimezone(tzinfo).timetz()) assert date >= event.start_dt assert date <= event.end_dt object.__setattr__(self, '_start_dt', start_dt) object.__setattr__(self, '_real_event', event) object.__setattr__(self, '_event_tz_start_date', event.start_dt.astimezone(tzinfo).date()) object.__setattr__(self, '_timetable_objects', timetable_objects) def __getattribute__(self, name): if name == 'start_dt': return object.__getattribute__(self, '_start_dt') event = object.__getattribute__(self, '_real_event') if name == 'timetable_objects': return object.__getattribute__(self, '_timetable_objects') if name == 'ongoing': event_start_date = object.__getattribute__(self, '_event_tz_start_date') return event_start_date != self.start_dt.date() if name == 'first_occurence_start_dt': return event.start_dt return getattr(event, name) def __setattr__(self, name, value): raise AttributeError('This instance is read-only') def __repr__(self): return '<_EventProxy({}, {})>'.format(self.start_dt, object.__getattribute__(self, '_real_event')) class RHCategoryCalendarView(RHDisplayCategoryBase): def _process(self): if not request.is_xhr: return WPCategoryCalendar.render_template('display/calendar.html', self.category, start_dt=request.args.get('start_dt')) tz = self.category.display_tzinfo start = tz.localize(dateutil.parser.parse(request.args['start'])).astimezone(utc) end = tz.localize(dateutil.parser.parse(request.args['end'])).astimezone(utc) query = (Event.query .filter(Event.starts_between(start, end), Event.is_visible_in(self.category.id), ~Event.is_deleted) .options(load_only('id', 'title', 'start_dt', 'end_dt', 'category_id'))) events = self._get_event_data(query) ongoing_events = (Event.query .filter(Event.is_visible_in(self.category.id), Event.start_dt < start, Event.end_dt > end) .options(load_only('id', 'title', 'start_dt', 'end_dt', 'timezone')) .order_by(Event.title) .all()) return jsonify_data(flash=False, events=events, ongoing_event_count=len(ongoing_events), ongoing_events_html=self._render_ongoing_events(ongoing_events)) def _get_event_data(self, event_query): data = [] tz = self.category.display_tzinfo for event in event_query: category_id = event.category_id event_data = {'title': event.title, 'start': event.start_dt.astimezone(tz).replace(tzinfo=None).isoformat(), 'end': event.end_dt.astimezone(tz).replace(tzinfo=None).isoformat(), 'url': event.url} colors = CALENDAR_COLOR_PALETTE[category_id % len(CALENDAR_COLOR_PALETTE)] event_data.update({'textColor': ' data.append(event_data) return data def _render_ongoing_events(self, ongoing_events): template = get_template_module('categories/display/_calendar_ongoing_events.html') return template.render_ongoing_events(ongoing_events, self.category.display_tzinfo) class RHCategoryUpcomingEvent(RHDisplayCategoryBase): def _process(self): event = self._get_upcoming_event() if event: return redirect(event.url) else: flash(_('There are no upcoming events for this category')) return redirect(self.category.url) def _get_upcoming_event(self): query = (Event.query .filter(Event.is_visible_in(self.category.id), Event.start_dt > now_utc(), ~Event.is_deleted) .options(subqueryload('acl_entries')) .order_by(Event.start_dt, Event.id)) res = get_n_matching(query, 1, lambda event: event.can_access(session.user)) if res: return res[0]

true

f70082df53201fc2c56287cc36465ce81430d5e2

43,776

py

Python

python/tvm/relay/op/strategy/generic.py

xndcn/tvm

7a20b4a490acbdcefa92c9b7c68188139f4916a3

[ "Zlib", "Unlicense", "Apache-2.0", "BSD-2-Clause", "MIT", "ECL-2.0" ]

null

python/tvm/relay/op/strategy/generic.py

xndcn/tvm

7a20b4a490acbdcefa92c9b7c68188139f4916a3

[ "Zlib", "Unlicense", "Apache-2.0", "BSD-2-Clause", "MIT", "ECL-2.0" ]

null

python/tvm/relay/op/strategy/generic.py

xndcn/tvm

7a20b4a490acbdcefa92c9b7c68188139f4916a3

[ "Zlib", "Unlicense", "Apache-2.0", "BSD-2-Clause", "MIT", "ECL-2.0" ]

null

# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. """Definition of generic operator strategy.""" # pylint: disable=invalid-name,unused-argument import logging import re from tvm import topi, _ffi, te, ir from tvm.topi.utils import get_const_int, get_const_float, get_const_tuple, get_float_tuple from tvm.target import generic_func, override_native_generic_func from .. import op as _op logger = logging.getLogger("strategy") def naive_schedule(_, outs, target): """Return the naive default schedule""" if "gpu" in target.keys: # For GPU, we at least need thread binding to make a valid schedule. # So the naive schedule cannot be compiled. raise RuntimeError( "Cannot compile for GPU targets if no tuned schedule is found. " "Please see the warning messages above for more information about the failed workloads." ) return te.create_schedule(outs[-1].op) def wrap_topi_schedule(topi_schedule): """Wrap TOPI schedule which doesn't use attrs""" def wrapper(attrs, outs, target): with target: return topi_schedule(outs) return wrapper def get_conv2d_in_channels(data_shape, data_layout): """Get conv2d input channels""" data_shape = get_const_tuple(data_shape) if len(data_shape) == 4: idx = data_layout.find("C") assert idx >= 0, "Invalid conv2d data layout {}".format(data_layout) return data_shape[idx] if re.match(r"NCHW\d*c", data_layout): # NCHW[8]c return data_shape[1] * data_shape[4] raise ValueError("Unknown conv2d data layout {}".format(data_layout)) def get_conv2d_out_channels(kernel_shape, kernel_layout): """Get conv2d output channels""" kernel_shape = get_const_tuple(kernel_shape) if len(kernel_shape) == 4: idx = kernel_layout.find("O") assert idx >= 0, "Invalid conv2d kernel layout {}".format(kernel_layout) return kernel_shape[idx] if re.match(r"OIHW\d*i\d*o", kernel_layout): return kernel_shape[0] * kernel_shape[5] if re.match(r"OIHW\d*o", kernel_layout): return kernel_shape[0] * kernel_shape[4] raise ValueError("Unknown conv2d kernel layout {}".format(kernel_layout)) def is_depthwise_conv2d(data_shape, data_layout, kernel_shape, kernel_layout, groups): ic = get_conv2d_in_channels(data_shape, data_layout) oc = get_conv2d_out_channels(kernel_shape, kernel_layout) return ic == oc == groups @generic_func def schedule_injective(attrs, outs, target): """Schedule injective ops""" with target: return topi.generic.schedule_injective(outs) @generic_func def schedule_reduce(attrs, outs, target): """Schedule reduction ops""" with target: return topi.generic.schedule_reduce(outs) _op._schedule_injective = schedule_injective _op._schedule_reduce = schedule_reduce # concatenate @generic_func def schedule_concatenate(attrs, outs, target): """Schedule concatenate op""" with target: return topi.generic.schedule_injective(outs) # pool @generic_func def schedule_pool(attrs, outs, target): """Schedule pooling ops""" with target: return topi.generic.schedule_pool(outs, attrs.layout) # pool_grad @generic_func def schedule_pool_grad(attrs, outs, target): """Schedule pooling gradient ops""" with target: return topi.generic.schedule_pool_grad(outs) # adaptive pool @generic_func def schedule_adaptive_pool(attrs, outs, target): """Schedule adaptive pooling ops""" with target: return topi.generic.schedule_adaptive_pool(outs) # softmax def wrap_compute_softmax(topi_compute): """Wrap softmax topi compute""" def _compute_softmax(attrs, inputs, out_type): axis = attrs.get_int("axis") return [topi_compute(inputs[0], axis)] return _compute_softmax @override_native_generic_func("softmax_strategy") def softmax_strategy(attrs, inputs, out_type, target): """softmax generic strategy""" strategy = _op.OpStrategy() strategy.add_implementation( wrap_compute_softmax(topi.nn.softmax), wrap_topi_schedule(topi.generic.schedule_softmax), name="softmax.generic", ) return strategy # log_softmax @generic_func def schedule_log_softmax(attrs, outs, target): """Schedule log_softmax op""" with target: return topi.generic.schedule_softmax(outs) # lrn @generic_func def schedule_lrn(attrs, outs, target): """Schedule LRN op""" with target: return topi.generic.schedule_lrn(outs) # bitpack @generic_func def schedule_bitpack(attrs, outs, target): """Schedule bitpack""" with target: return topi.generic.schedule_bitpack(outs) get_auto_scheduler_rewritten_layout = _ffi.get_global_func( "relay.attrs.get_auto_scheduler_rewritten_layout" ) # conv2d def wrap_compute_conv2d( topi_compute, need_data_layout=False, need_out_layout=False, has_groups=False, need_auto_scheduler_layout=False, ): """Wrap conv2d topi compute""" def _compute_conv2d(attrs, inputs, out_type): padding = get_const_tuple(attrs.padding) strides = get_const_tuple(attrs.strides) dilation = get_const_tuple(attrs.dilation) data_layout = attrs.get_str("data_layout") out_layout = attrs.get_str("out_layout") out_dtype = attrs.out_dtype auto_scheduler_rewritten_layout = get_auto_scheduler_rewritten_layout(attrs) out_dtype = inputs[0].dtype if out_dtype in ("same", "") else out_dtype args = [inputs[0], inputs[1], strides, padding, dilation] if has_groups: args.append(attrs.groups) if need_data_layout: args.append(data_layout) if need_out_layout: args.append(out_layout) args.append(out_dtype) if need_auto_scheduler_layout: args.append(auto_scheduler_rewritten_layout) return [topi_compute(*args)] return _compute_conv2d @override_native_generic_func("conv2d_strategy") def conv2d_strategy(attrs, inputs, out_type, target): """conv2d generic strategy""" logger.warning("conv2d is not optimized for this platform.") strategy = _op.OpStrategy() data, kernel = inputs dilation = get_const_tuple(attrs.dilation) groups = attrs.groups layout = attrs.data_layout kernel_layout = attrs.kernel_layout (dilation_h, dilation_w) = dilation if dilation_h < 1 or dilation_w < 1: raise ValueError("dilation should be positive value") if groups == 1: if layout == "NCHW": assert kernel_layout == "OIHW" strategy.add_implementation( wrap_compute_conv2d(topi.nn.conv2d_nchw), wrap_topi_schedule(topi.generic.schedule_conv2d_nchw), name="conv2d_nchw.generic", ) elif layout == "NHWC": assert kernel_layout == "HWIO" strategy.add_implementation( wrap_compute_conv2d(topi.nn.conv2d_nhwc), wrap_topi_schedule(topi.generic.schedule_conv2d_nhwc), name="conv2d_nhwc.generic", ) elif layout == "HWCN": assert kernel_layout == "HWIO" strategy.add_implementation( wrap_compute_conv2d(topi.nn.conv2d_hwcn), wrap_topi_schedule(topi.generic.schedule_conv2d_hwcn), name="conv2d_hwcn.generic", ) else: raise RuntimeError("Unsupported conv2d layout {}".format(layout)) elif is_depthwise_conv2d(data.shape, layout, kernel.shape, kernel_layout, groups): if layout == "NCHW": assert kernel_layout == "OIHW" strategy.add_implementation( wrap_compute_conv2d(topi.nn.depthwise_conv2d_nchw), wrap_topi_schedule(topi.generic.schedule_depthwise_conv2d_nchw), name="depthwise_conv2d_nchw.generic", ) elif layout == "NHWC": assert kernel_layout == "HWOI" strategy.add_implementation( wrap_compute_conv2d(topi.nn.depthwise_conv2d_nhwc), wrap_topi_schedule(topi.generic.schedule_depthwise_conv2d_nhwc), name="depthwise_conv2d_nhwc.generic", ) else: raise RuntimeError("Unsupported depthwise_conv2d layout {}".format(layout)) else: # group_conv2d if layout == "NCHW": assert kernel_layout == "OIHW" strategy.add_implementation( wrap_compute_conv2d(topi.nn.group_conv2d_nchw, has_groups=True), wrap_topi_schedule(topi.generic.schedule_group_conv2d_nchw), name="group_conv2d_nchw.generic", ) elif layout == "NHWC": assert kernel_layout == "HWIO" strategy.add_implementation( wrap_compute_conv2d(topi.nn.group_conv2d_nhwc, has_groups=True), wrap_topi_schedule(topi.generic.schedule_group_conv2d_nhwc), name="group_conv2d_nhwc.generic", ) else: raise RuntimeError("Unsupported group_conv2d layout {}".format(layout)) return strategy # conv2d_NCHWc @override_native_generic_func("conv2d_NCHWc_strategy") def conv2d_NCHWc_strategy(attrs, inputs, out_type, target): """conv2d_NCHWc generic strategy""" logger.warning("conv2d_NCHWc is not optimized for this platform.") strategy = _op.OpStrategy() if inputs[0].dtype == "int8" or inputs[0].dtype == "uint8": strategy.add_implementation( wrap_compute_conv2d(topi.nn.conv2d_NCHWc_int8, True, True), wrap_topi_schedule(topi.generic.schedule_conv2d_NCHWc_int8), name="conv2d_NCHWc_int8.generic", ) else: strategy.add_implementation( wrap_compute_conv2d(topi.nn.conv2d_NCHWc, True, True), wrap_topi_schedule(topi.generic.schedule_conv2d_NCHWc), name="conv2d_NCHWc.generic", ) return strategy # depthwise_conv2d_NCHWc @override_native_generic_func("depthwise_conv2d_NCHWc_strategy") def depthwise_conv2d_NCHWc_strategy(attrs, inputs, out_type, target): """depthwise_conv2d generic strategy""" logger.warning("depthwise_conv2d_NCHWc is not optimized for this platform.") strategy = _op.OpStrategy() strategy.add_implementation( wrap_compute_conv2d(topi.nn.depthwise_conv2d_NCHWc, True, True), wrap_topi_schedule(topi.generic.schedule_depthwise_conv2d_NCHWc), name="depthwise_conv2d_NCHWc.generic", ) return strategy # conv2d_winograd_without_weight_transform @override_native_generic_func("conv2d_winograd_without_weight_transform_strategy") def conv2d_winograd_without_weight_transfrom_strategy(attrs, inputs, out_type, target): """conv2d_winograd_without_weight_transfrom generic strategy""" raise ValueError("No generic implemenation for conv2d_winograd_without_weight_transform") # conv2d_gemm_without_weight_transform @override_native_generic_func("conv2d_gemm_without_weight_transform_strategy") def conv2d_gemm_without_weight_transform_strategy(attrs, inputs, out_type, target): """conv2d_gemm_without_weight_transfrom generic strategy""" raise ValueError("No generic implemenation for conv2d_gemm_without_weight_transform") # conv2d_winograd_weight_transform @generic_func def schedule_conv2d_winograd_weight_transform(attrs, outs, target): """Schedule conv2d_winograd_weight_transform""" with target: return topi.generic.schedule_conv2d_winograd_weight_transform(outs) # conv2d_winograd_nnpack_weight_transform @generic_func def schedule_conv2d_winograd_nnpack_weight_transform(attrs, outs, target): """Schedule conv2d_winograd_nnpack_weight_transform""" with target: return topi.generic.schedule_conv2d_winograd_nnpack_weight_transform(outs) # conv2d_gemm_weight_transform @generic_func def schedule_conv2d_gemm_weight_transform(attrs, outs, target): """Schedule conv2d_gemm_weight_transform""" with target: return topi.generic.schedule_conv2d_gemm_weight_transform(outs) # deformable_conv2d def wrap_compute_deformable_conv2d(topi_compute): """wrap deformable_conv2d topi compute""" def _compute_deformable_conv2d(attrs, inputs, out_dtype): padding = get_const_tuple(attrs.padding) strides = get_const_tuple(attrs.strides) dilation = get_const_tuple(attrs.dilation) deformable_groups = attrs.deformable_groups groups = attrs.groups out_dtype = attrs.out_dtype out_dtype = inputs[0].dtype if out_dtype in ("same", "") else out_dtype out = topi_compute( inputs[0], inputs[1], inputs[2], strides, padding, dilation, deformable_groups, groups, out_dtype, ) return [out] return _compute_deformable_conv2d @override_native_generic_func("deformable_conv2d_strategy") def deformable_conv2d_strategy(attrs, inputs, out_type, target): """deformable_conv2d generic strategy""" layout = attrs.data_layout strategy = _op.OpStrategy() if layout == "NCHW": strategy.add_implementation( wrap_compute_deformable_conv2d(topi.nn.deformable_conv2d_nchw), wrap_topi_schedule(topi.generic.schedule_deformable_conv2d_nchw), name="deformable_conv2d_nchw.generic", ) elif layout == "NHWC": # This implementation should never be picked by autotvm strategy.add_implementation( wrap_compute_deformable_conv2d(topi.nn.deformable_conv2d_nhwc), naive_schedule, name="deformable_conv2d_nhwc.generic", ) else: raise RuntimeError("Layout %s is not supported in deformable conv2d" % layout) return strategy # conv2d_transpose def wrap_compute_conv2d_transpose(topi_compute): """wrap conv2d_transpose topi compute""" def compute_conv2d_transpose(attrs, inputs, out_dtype): """Compute definition of conv2d_transpose""" padding = get_const_tuple(attrs.padding) strides = get_const_tuple(attrs.strides) out_dtype = attrs.out_dtype out_dtype = inputs[0].dtype if out_dtype in ("same", "") else out_dtype output_padding = get_const_tuple(attrs.output_padding) out = topi_compute(inputs[0], inputs[1], strides, padding, out_dtype, output_padding) return [out] return compute_conv2d_transpose @override_native_generic_func("conv2d_transpose_strategy") def conv2d_transpose_strategy(attrs, inputs, out_type, target): """conv2d_transpose generic strategy""" logger.warning("conv2d_transpose is not optimized for this platform.") layout = attrs.data_layout dilation = get_const_tuple(attrs.dilation) groups = attrs.groups assert layout == "NCHW", "only support nchw for now" assert dilation == (1, 1), "not support dilate now" assert groups == 1, "only support groups == 1 for now" strategy = _op.OpStrategy() strategy.add_implementation( wrap_compute_conv2d_transpose(topi.nn.conv2d_transpose_nchw), wrap_topi_schedule(topi.generic.schedule_conv2d_transpose_nchw), name="conv2d_transpose_nchw.generic", ) return strategy # conv3d_transpose def wrap_compute_conv3d_transpose(topi_compute): """wrap conv3d_transpose topi compute""" def compute_conv3d_transpose(attrs, inputs, out_dtype): """Compute definition of conv3d_transpose""" padding = get_const_tuple(attrs.padding) strides = get_const_tuple(attrs.strides) output_padding = get_const_tuple(attrs.output_padding) out_dtype = attrs.out_dtype out_dtype = inputs[0].dtype if out_dtype in ("same", "") else out_dtype out = topi_compute(inputs[0], inputs[1], strides, padding, out_dtype, output_padding) return [out] return compute_conv3d_transpose @override_native_generic_func("conv3d_transpose_strategy") def conv3d_transpose_strategy(attrs, inputs, out_type, target): """conv3d_transpose generic strategy""" logger.warning("conv3d_transpose is not optimized for this platform.") layout = attrs.data_layout dilation = get_const_tuple(attrs.dilation) groups = attrs.groups assert layout == "NCDHW", "only support ncdhw for now" assert dilation == (1, 1, 1), "not support dilate now" assert groups == 1, "only support groups == 1 for now" strategy = _op.OpStrategy() strategy.add_implementation( wrap_compute_conv3d_transpose(topi.nn.conv3d_transpose_ncdhw), wrap_topi_schedule(topi.generic.schedule_conv3d_transpose_ncdhw), name="conv3d_transpose_ncdhw.generic", ) return strategy # conv3d def wrap_compute_conv3d(topi_compute, need_layout=False): """wrap conv3d topi compute""" def _compute_conv3d(attrs, inputs, out_type): padding = get_const_tuple(attrs.padding) strides = get_const_tuple(attrs.strides) dilation = get_const_tuple(attrs.dilation) groups = attrs.groups layout = attrs.data_layout out_dtype = attrs.out_dtype out_dtype = inputs[0].dtype if out_dtype in ("same", "") else out_dtype (dilation_d, dilation_h, dilation_w) = dilation if dilation_d < 1 or dilation_h < 1 or dilation_w < 1: raise ValueError("Dilation should be positive value") if groups != 1: raise ValueError("Not support arbitrary group number for conv3d") if need_layout: out = topi_compute(inputs[0], inputs[1], strides, padding, dilation, layout, out_dtype) else: out = topi_compute(inputs[0], inputs[1], strides, padding, dilation, out_dtype) return [out] return _compute_conv3d @override_native_generic_func("conv3d_strategy") def conv3d_strategy(attrs, inputs, out_type, target): """conv3d generic strategy""" logger.warning("conv3d is not optimized for this platform.") strategy = _op.OpStrategy() layout = attrs.data_layout if layout == "NCDHW": strategy.add_implementation( wrap_compute_conv3d(topi.nn.conv3d_ncdhw), wrap_topi_schedule(topi.generic.schedule_conv3d_ncdhw), name="conv3d_ncdhw.generic", ) elif layout == "NDHWC": strategy.add_implementation( wrap_compute_conv3d(topi.nn.conv3d_ndhwc), wrap_topi_schedule(topi.generic.schedule_conv3d_ndhwc), name="conv3d_ndhwc.generic", ) else: raise ValueError("Not support this layout {} yet".format(layout)) return strategy # conv3d_winograd_without_weight_transform @override_native_generic_func("conv3d_winograd_without_weight_transform_strategy") def conv3d_winograd_without_weight_transfrom_strategy(attrs, inputs, out_type, target): """conv3d_winograd_without_weight_transfrom generic strategy""" raise ValueError("No generic implemenation for conv3d_winograd_without_weight_transform") # conv3d_winograd_weight_transform @generic_func def schedule_conv3d_winograd_weight_transform(attrs, outs, target): """Schedule conv3d_winograd_weight_transform""" with target: return topi.generic.schedule_conv3d_winograd_weight_transform(outs) # conv1d def wrap_compute_conv1d(topi_compute): """wrap conv1d topi compute""" def _compute_conv1d(attrs, inputs, out_type): """Compute definition of conv1d""" strides = get_const_tuple(attrs.strides) padding = get_const_tuple(attrs.padding) dilation = get_const_tuple(attrs.dilation) out_dtype = attrs.out_dtype out_dtype = inputs[0].dtype if out_dtype in ("same", "") else out_dtype return [topi_compute(inputs[0], inputs[1], strides, padding, dilation, out_dtype)] return _compute_conv1d @override_native_generic_func("conv1d_strategy") def conv1d_strategy(attrs, inputs, out_type, target): """conv1d generic strategy""" logger.warning("conv1d is not optimized for this platform.") layout = attrs.data_layout dilation = get_const_tuple(attrs.dilation) if dilation[0] < 1: raise ValueError("dilation should be a positive value") strategy = _op.OpStrategy() if layout == "NCW": strategy.add_implementation( wrap_compute_conv1d(topi.nn.conv1d_ncw), wrap_topi_schedule(topi.generic.schedule_conv1d_ncw), name="conv1d_ncw.generic", ) elif layout == "NWC": strategy.add_implementation( wrap_compute_conv1d(topi.nn.conv1d_nwc), wrap_topi_schedule(topi.generic.schedule_conv1d_nwc), name="conv1d_nwc.generic", ) else: raise ValueError("Unsupported conv1d layout {}".format(layout)) return strategy # conv1d_transpose def wrap_compute_conv1d_transpose(topi_compute): """wrap conv1d_transpose topi compute""" def _compute_conv1d_tranpsoe(attrs, inputs, out_type): padding = get_const_tuple(attrs.padding) strides = get_const_tuple(attrs.strides) out_dtype = attrs.out_dtype out_dtype = inputs[0].dtype if out_dtype in ("same", "") else out_dtype output_padding = get_const_tuple(attrs.output_padding) out = topi_compute(inputs[0], inputs[1], strides, padding, out_dtype, output_padding) return [out] return _compute_conv1d_tranpsoe @override_native_generic_func("conv1d_transpose_strategy") def conv1d_transpose_strategy(attrs, inputs, out_type, target): """conv1d_transpose generic strategy""" logger.warning("conv1d_transpose is not optimized for this platform.") strategy = _op.OpStrategy() layout = attrs.data_layout dilation = get_const_tuple(attrs.dilation) groups = attrs.groups assert layout == "NCW", "conv1d_transpose ncw only supported" assert dilation == (1,), "conv1d_transpose dilation is not supported" assert groups == 1, "conv1d_transpose groups == 1 only supported" strategy.add_implementation( wrap_compute_conv1d_transpose(topi.nn.conv1d_transpose_ncw), wrap_topi_schedule(topi.generic.schedule_conv1d_transpose_ncw), name="conv1d_transpose_ncw.generic", ) return strategy # dilation2d def wrap_compute_dilation2d(topi_compute, need_data_layout=False): """Wrap dilation2d topi compute""" def _compute_dilation2d(attrs, inputs, out_type): padding = get_const_tuple(attrs.padding) strides = get_const_tuple(attrs.strides) dilations = get_const_tuple(attrs.dilations) data_layout = attrs.get_str("data_layout") out_dtype = attrs.out_dtype out_dtype = inputs[0].dtype if out_dtype in ("same", "") else out_dtype args = [inputs[0], inputs[1], strides, padding, dilations] if need_data_layout: args.append(data_layout) args.append(out_dtype) return [topi_compute(*args)] return _compute_dilation2d @override_native_generic_func("dilation2d_strategy") def dilation2d_strategy(attrs, inputs, out_type, target): """dilation2d_strategy generic strategy""" logger.warning("dilation2d_strategy is not optimized for this platform.") strategy = _op.OpStrategy() dilations = get_const_tuple(attrs.dilations) layout = attrs.data_layout kernel_layout = attrs.kernel_layout assert layout in ["NCHW", "NHWC"] (dilation_h, dilation_w) = dilations if dilation_h < 1 or dilation_w < 1: raise ValueError("dilation should be positive value") if layout == "NCHW": assert kernel_layout == "IHW" strategy.add_implementation( wrap_compute_dilation2d(topi.image.dilation2d_nchw), wrap_topi_schedule(topi.generic.schedule_dilation2d_nchw), name="dilation2d_nchw.generic", ) elif layout == "NHWC": assert kernel_layout == "HWI" strategy.add_implementation( wrap_compute_dilation2d(topi.image.dilation2d_nhwc), wrap_topi_schedule(topi.generic.schedule_dilation2d_nhwc), name="dilation2d_nhwc.generic", ) else: raise RuntimeError("Unsupported dilation2d layout {}".format(layout)) return strategy # dense def wrap_compute_dense(topi_compute): """wrap dense topi compute""" def _compute_dense(attrs, inputs, out_type): """Compute definition of dense""" out_dtype = attrs.out_dtype out_dtype = inputs[0].dtype if out_dtype == "" else out_dtype return [topi_compute(inputs[0], inputs[1], None, out_dtype)] return _compute_dense @override_native_generic_func("dense_strategy") def dense_strategy(attrs, inputs, out_type, target): """dense generic strategy""" logger.warning("dense is not optimized for this platform.") strategy = _op.OpStrategy() strategy.add_implementation( wrap_compute_dense(topi.nn.dense), wrap_topi_schedule(topi.generic.schedule_dense), name="dense.generic", ) return strategy # batch_matmul def wrap_compute_batch_matmul(topi_compute): """wrap batch_matmul topi compute""" def _compute_batch_matmul(attrs, inputs, out_type): return [topi_compute(inputs[0], inputs[1], out_type.shape)] return _compute_batch_matmul @override_native_generic_func("batch_matmul_strategy") def batch_matmul_strategy(attrs, inputs, out_type, target): """batch_matmul generic strategy""" logger.warning("batch_matmul is not optimized for this platform.") strategy = _op.OpStrategy() strategy.add_implementation( wrap_compute_batch_matmul(topi.nn.batch_matmul), wrap_topi_schedule(topi.generic.schedule_batch_matmul), name="batch_matmul.generic", ) return strategy # sparse dense def wrap_compute_sparse_dense(topi_compute): """wrap sparse dense topi compute""" def _compute_sparse_dense(attrs, inputs, out_type): return [topi_compute(inputs[0], inputs[1], inputs[2], inputs[3], attrs["sparse_lhs"])] return _compute_sparse_dense @override_native_generic_func("sparse_dense_strategy") def sparse_dense_strategy(attrs, inputs, out_type, target): """sparse dense generic strategy""" logger.warning("sparse dense is not optimized for this platform.") strategy = _op.OpStrategy() strategy.add_implementation( wrap_compute_sparse_dense(topi.nn.sparse_dense), wrap_topi_schedule(topi.generic.schedule_sparse_dense), name="sparse_dense.generic", ) return strategy @override_native_generic_func("sparse_dense_padded_strategy") def sparse_dense_padded_strategy(attrs, inputs, out_type, target): """sparse dense padded generic strategy""" raise NotImplementedError("sparse_dense_padded is only implemented for cuda") # sparse_transpose @generic_func def schedule_sparse_transpose(attrs, outs, target): """schedule sparse_transpose""" with target: return topi.generic.schedule_sparse_transpose(outs) # argsort def wrap_compute_argsort(topi_compute): """Wrap argsort topi compute""" def _compute_argsort(attrs, inputs, _): axis = get_const_int(attrs.axis) is_ascend = bool(get_const_int(attrs.is_ascend)) dtype = attrs.dtype return [topi_compute(inputs[0], axis=axis, is_ascend=is_ascend, dtype=dtype)] return _compute_argsort @override_native_generic_func("argsort_strategy") def argsort_strategy(attrs, inputs, out_type, target): """argsort generic strategy""" strategy = _op.OpStrategy() strategy.add_implementation( wrap_compute_argsort(topi.argsort), wrap_topi_schedule(topi.generic.schedule_argsort), name="argsort.generic", ) return strategy # topk def wrap_compute_topk(topi_compute): """Wrap topk compute""" def _compute_topk(attrs, inputs, out_type): if attrs.k is not None: k = attrs.k else: k = inputs[1] axis = get_const_int(attrs.axis) ret_type = attrs.ret_type is_ascend = bool(get_const_int(attrs.is_ascend)) dtype = attrs.dtype out = topi_compute(inputs[0], k, axis, ret_type, is_ascend, dtype) out = out if isinstance(out, list) else [out] return out return _compute_topk @override_native_generic_func("topk_strategy") def topk_strategy(attrs, inputs, out_type, target): """topk generic strategy""" strategy = _op.OpStrategy() strategy.add_implementation( wrap_compute_topk(topi.topk), wrap_topi_schedule(topi.generic.schedule_topk), name="topk.generic", ) return strategy # multibox_prior def wrap_compute_multibox_prior(topi_compute): """Wrap multibox_prior compute""" def _compute_multibox_prior(attrs, inputs, _): """Compute definition of multibox_prior""" sizes = get_float_tuple(attrs.sizes) ratios = get_float_tuple(attrs.ratios) steps = get_float_tuple(attrs.steps) offsets = get_float_tuple(attrs.offsets) clip = bool(get_const_int(attrs.clip)) return [topi_compute(inputs[0], sizes, ratios, steps, offsets, clip)] return _compute_multibox_prior @override_native_generic_func("multibox_prior_strategy") def multibox_prior_strategy(attrs, inputs, out_type, target): """multibox_prior generic strategy""" strategy = _op.OpStrategy() strategy.add_implementation( wrap_compute_multibox_prior(topi.vision.ssd.multibox_prior), wrap_topi_schedule(topi.generic.schedule_multibox_prior), name="multibox_prior.generic", ) return strategy # multibox_transform_loc def wrap_compute_multibox_transform_loc(topi_compute): """Wrap multibox_transform_loc compute""" def _compute_multibox_transform_loc(attrs, inputs, _): """Compute definition of multibox_detection""" clip = bool(get_const_int(attrs.clip)) threshold = get_const_float(attrs.threshold) variances = get_float_tuple(attrs.variances) return topi_compute(inputs[0], inputs[1], inputs[2], clip, threshold, variances) return _compute_multibox_transform_loc @override_native_generic_func("multibox_transform_loc_strategy") def multibox_transform_loc_strategy(attrs, inputs, out_type, target): """schedule multibox_transform_loc""" strategy = _op.OpStrategy() strategy.add_implementation( wrap_compute_multibox_transform_loc(topi.vision.ssd.multibox_transform_loc), wrap_topi_schedule(topi.generic.schedule_multibox_transform_loc), name="multibox_transform_loc.generic", ) return strategy # get_valid_counts def wrap_compute_get_valid_counts(topi_compute): """wrap get_valid_counts topi compute""" def _compute_get_valid_counts(attrs, inputs, out_type): score_threshold = inputs[1] id_index = get_const_int(attrs.id_index) score_index = get_const_int(attrs.score_index) if attrs.score_threshold is not None: score_threshold = get_const_float(attrs.score_threshold) return topi_compute(inputs[0], score_threshold, id_index, score_index) return _compute_get_valid_counts @override_native_generic_func("get_valid_counts_strategy") def get_valid_counts_strategy(attrs, inputs, out_type, target): """get_valid_counts generic strategy""" strategy = _op.OpStrategy() strategy.add_implementation( wrap_compute_get_valid_counts(topi.vision.get_valid_counts), wrap_topi_schedule(topi.generic.schedule_get_valid_counts), name="get_valid_counts.generic", ) return strategy # non-maximum suppression def wrap_compute_nms(topi_compute): """wrap nms topi compute""" def _compute_nms(attrs, inputs, out_type): max_output_size = inputs[3] iou_threshold = inputs[4] if attrs.max_output_size is not None: max_output_size = attrs.max_output_size if attrs.iou_threshold is not None: iou_threshold = get_const_float(attrs.iou_threshold) return_indices = bool(get_const_int(attrs.return_indices)) force_suppress = bool(get_const_int(attrs.force_suppress)) top_k = get_const_int(attrs.top_k) coord_start = get_const_int(attrs.coord_start) score_index = get_const_int(attrs.score_index) id_index = get_const_int(attrs.id_index) invalid_to_bottom = bool(get_const_int(attrs.invalid_to_bottom)) if return_indices: return topi_compute( inputs[0], inputs[1], inputs[2], max_output_size, iou_threshold, force_suppress, top_k, coord_start, score_index, id_index, return_indices, invalid_to_bottom, ) return [ topi_compute( inputs[0], inputs[1], inputs[2], max_output_size, iou_threshold, force_suppress, top_k, coord_start, score_index, id_index, return_indices, invalid_to_bottom, ) ] return _compute_nms @override_native_generic_func("non_max_suppression_strategy") def nms_strategy(attrs, inputs, out_type, target): """nms generic strategy""" strategy = _op.OpStrategy() strategy.add_implementation( wrap_compute_nms(topi.vision.non_max_suppression), wrap_topi_schedule(topi.generic.schedule_nms), name="nms.generic", ) return strategy # roi_align def wrap_compute_roi_align(topi_compute): """wrap roi_align topi compute""" def _compute_roi_align(attrs, inputs, out_type): assert attrs.layout == "NCHW" pooled_size = get_const_tuple(attrs.pooled_size) return [ topi_compute( inputs[0], inputs[1], pooled_size=pooled_size, spatial_scale=attrs.spatial_scale, sample_ratio=attrs.sample_ratio, ) ] return _compute_roi_align @override_native_generic_func("roi_align_strategy") def roi_align_strategy(attrs, inputs, out_type, target): """roi_align generic strategy""" strategy = _op.OpStrategy() layout = attrs.layout assert layout == "NCHW", "only support nchw for now" strategy.add_implementation( wrap_compute_roi_align(topi.vision.rcnn.roi_align_nchw), wrap_topi_schedule(topi.generic.schedule_roi_align), name="roi_align.generic", ) return strategy # roi_pool @generic_func def schedule_roi_pool(attrs, outs, target): """schedule roi_pool""" with target: return topi.generic.schedule_roi_pool(outs) # proposal def wrap_compute_proposal(topi_compute): """wrap proposal topi compute""" def _compute_proposal(attrs, inputs, out_type): scales = get_float_tuple(attrs.scales) ratios = get_float_tuple(attrs.ratios) feature_stride = attrs.feature_stride threshold = attrs.threshold rpn_pre_nms_top_n = attrs.rpn_pre_nms_top_n rpn_post_nms_top_n = attrs.rpn_post_nms_top_n rpn_min_size = attrs.rpn_min_size iou_loss = bool(get_const_int(attrs.iou_loss)) return [ topi_compute( inputs[0], inputs[1], inputs[2], scales, ratios, feature_stride, threshold, rpn_pre_nms_top_n, rpn_post_nms_top_n, rpn_min_size, iou_loss, ) ] return _compute_proposal @override_native_generic_func("proposal_strategy") def proposal_strategy(attrs, inputs, out_type, target): """proposal generic strategy""" strategy = _op.OpStrategy() strategy.add_implementation( wrap_compute_proposal(topi.vision.rcnn.proposal), wrap_topi_schedule(topi.generic.schedule_proposal), name="proposal.generic", ) return strategy # scatter @override_native_generic_func("scatter_strategy") def scatter_strategy(attrs, outs, out_type, target): strategy = _op.OpStrategy() strategy.add_implementation( wrap_compute_scatter(topi.scatter), wrap_topi_schedule(topi.generic.schedule_scatter), name="scatter.generic", ) return strategy def wrap_compute_scatter(topi_compute): """Wrap scatter topi compute""" def _compute_scatter(attrs, inputs, _): return [topi_compute(inputs[0], inputs[1], inputs[2], axis=attrs.axis)] return _compute_scatter @override_native_generic_func("scatter_add_strategy") def scatter_add_strategy(attrs, outs, out_type, target): strategy = _op.OpStrategy() strategy.add_implementation( wrap_compute_scatter(topi.scatter_add), wrap_topi_schedule(topi.generic.schedule_scatter), name="scatter_add.generic", ) return strategy # scatter_nd @override_native_generic_func("scatter_nd_strategy") def scatter_nd_strategy(attrs, inputs, out_type, target): """scatter_nd generic strategy""" strategy = _op.OpStrategy() strategy.add_implementation( wrap_compute_scatter_nd(topi.scatter_nd), wrap_topi_schedule(topi.generic.schedule_extern), name="scatter_nd.generic", ) return strategy def wrap_compute_scatter_nd(topi_compute): """Wrap scatter_nd topi compute""" def _compute_scatter_nd(attrs, inputs, _): return [topi_compute(inputs[0], inputs[1], attrs.out_shape)] return _compute_scatter_nd # bitserial_conv2d def wrap_compute_bitserial_conv2d(topi_compute): """wrap bitserial_conv2d topi compute""" def compute_bitserial_conv2d(attrs, inputs, out_dtype): """Compute definition for bitserial conv2d.""" padding = get_const_tuple(attrs.padding) strides = get_const_tuple(attrs.strides) activation_bits = attrs.activation_bits weight_bits = attrs.weight_bits pack_dtype = attrs.pack_dtype out_dtype = attrs.out_dtype unipolar = attrs.unipolar return [ topi_compute( inputs[0], inputs[1], strides, padding, activation_bits, weight_bits, pack_dtype, out_dtype, unipolar, ) ] return compute_bitserial_conv2d @override_native_generic_func("bitserial_conv2d_strategy") def bitserial_conv2d_strategy(attrs, inputs, out_type, target): """bitserial_conv2d generic strategy""" logger.warning("bitserial_conv2d is not optimized for this platform.") strategy = _op.OpStrategy() layout = attrs.data_layout if layout == "NCHW": strategy.add_implementation( wrap_compute_bitserial_conv2d(topi.nn.bitserial_conv2d_nchw), wrap_topi_schedule(topi.generic.schedule_bitserial_conv2d_nchw), name="bitserial_conv2d_nchw.generic", ) elif layout == "NHWC": strategy.add_implementation( wrap_compute_bitserial_conv2d(topi.nn.bitserial_conv2d_nhwc), wrap_topi_schedule(topi.generic.schedule_bitserial_conv2d_nhwc), name="bitserial_conv2d_nhwc.generic", ) else: raise ValueError("Data layout {} not supported.".format(layout)) return strategy # bitserial_dense def wrap_compute_bitserial_dense(topi_compute): """wrap bitserial_dense topi compute""" def compute_bitserial_dense(attrs, inputs, out_type): """Compute definition of bitserial dense""" data_bits = attrs.data_bits weight_bits = attrs.weight_bits pack_dtype = attrs.pack_dtype out_dtype = attrs.out_dtype out_dtype = inputs[0].dtype if out_dtype == "" else out_dtype unipolar = attrs.unipolar return [ topi_compute( inputs[0], inputs[1], data_bits, weight_bits, pack_dtype, out_dtype, unipolar ) ] return compute_bitserial_dense @override_native_generic_func("bitserial_dense_strategy") def bitserial_dense_strategy(attrs, inputs, out_type, target): """bitserial_dense generic strategy""" logger.warning("bitserial_dense is not optimized for this platform.") strategy = _op.OpStrategy() strategy.add_implementation( wrap_compute_bitserial_dense(topi.nn.bitserial_dense), wrap_topi_schedule(topi.generic.schedule_bitserial_dense), name="bitserial_dense.generic", ) return strategy # correlation def wrap_compute_correlation(topi_compute): """wrap correlation topi compute""" def _compute_correlation(attrs, inputs, out_type): kernel_size = attrs.kernel_size max_displacement = attrs.max_displacement stride1 = attrs.stride1 stride2 = attrs.stride2 padding = get_const_tuple(attrs.padding) is_multiply = attrs.is_multiply return [ topi_compute( inputs[0], inputs[1], kernel_size, max_displacement, stride1, stride2, padding, is_multiply, ) ] return _compute_correlation @override_native_generic_func("correlation_strategy") def correlation_strategy(attrs, inputs, out_type, target): """correlation generic strategy""" logger.warning("correlation is not optimized for this platform.") layout = attrs.layout assert layout == "NCHW", "Only support NCHW layout" strategy = _op.OpStrategy() strategy.add_implementation( wrap_compute_correlation(topi.nn.correlation_nchw), wrap_topi_schedule(topi.generic.schedule_correlation_nchw), name="correlation.generic", ) return strategy # argwhere def wrap_compute_argwhere(topi_compute): """wrap argwhere topi compute""" def _compute_argwhere(attrs, inputs, out_type): output_shape = [] for s in out_type.shape: if hasattr(s, "value"): output_shape.append(s) else: output_shape.append(te.var("any_dim", "int32")) new_output_type = ir.TensorType(output_shape, "int32") return [topi_compute(new_output_type, inputs[0])] return _compute_argwhere @override_native_generic_func("argwhere_strategy") def argwhere_strategy(attrs, inputs, out_type, target): """argwhere generic strategy""" strategy = _op.OpStrategy() strategy.add_implementation( wrap_compute_argwhere(topi.argwhere), wrap_topi_schedule(topi.generic.schedule_argwhere), name="argwhere.generic", ) return strategy

34.442172

100

0.690927

import logging import re from tvm import topi, _ffi, te, ir from tvm.topi.utils import get_const_int, get_const_float, get_const_tuple, get_float_tuple from tvm.target import generic_func, override_native_generic_func from .. import op as _op logger = logging.getLogger("strategy") def naive_schedule(_, outs, target): if "gpu" in target.keys: raise RuntimeError( "Cannot compile for GPU targets if no tuned schedule is found. " "Please see the warning messages above for more information about the failed workloads." ) return te.create_schedule(outs[-1].op) def wrap_topi_schedule(topi_schedule): def wrapper(attrs, outs, target): with target: return topi_schedule(outs) return wrapper def get_conv2d_in_channels(data_shape, data_layout): data_shape = get_const_tuple(data_shape) if len(data_shape) == 4: idx = data_layout.find("C") assert idx >= 0, "Invalid conv2d data layout {}".format(data_layout) return data_shape[idx] if re.match(r"NCHW\d*c", data_layout): return data_shape[1] * data_shape[4] raise ValueError("Unknown conv2d data layout {}".format(data_layout)) def get_conv2d_out_channels(kernel_shape, kernel_layout): kernel_shape = get_const_tuple(kernel_shape) if len(kernel_shape) == 4: idx = kernel_layout.find("O") assert idx >= 0, "Invalid conv2d kernel layout {}".format(kernel_layout) return kernel_shape[idx] if re.match(r"OIHW\d*i\d*o", kernel_layout): return kernel_shape[0] * kernel_shape[5] if re.match(r"OIHW\d*o", kernel_layout): return kernel_shape[0] * kernel_shape[4] raise ValueError("Unknown conv2d kernel layout {}".format(kernel_layout)) def is_depthwise_conv2d(data_shape, data_layout, kernel_shape, kernel_layout, groups): ic = get_conv2d_in_channels(data_shape, data_layout) oc = get_conv2d_out_channels(kernel_shape, kernel_layout) return ic == oc == groups @generic_func def schedule_injective(attrs, outs, target): with target: return topi.generic.schedule_injective(outs) @generic_func def schedule_reduce(attrs, outs, target): with target: return topi.generic.schedule_reduce(outs) _op._schedule_injective = schedule_injective _op._schedule_reduce = schedule_reduce @generic_func def schedule_concatenate(attrs, outs, target): with target: return topi.generic.schedule_injective(outs) @generic_func def schedule_pool(attrs, outs, target): with target: return topi.generic.schedule_pool(outs, attrs.layout) @generic_func def schedule_pool_grad(attrs, outs, target): with target: return topi.generic.schedule_pool_grad(outs) @generic_func def schedule_adaptive_pool(attrs, outs, target): with target: return topi.generic.schedule_adaptive_pool(outs) def wrap_compute_softmax(topi_compute): def _compute_softmax(attrs, inputs, out_type): axis = attrs.get_int("axis") return [topi_compute(inputs[0], axis)] return _compute_softmax @override_native_generic_func("softmax_strategy") def softmax_strategy(attrs, inputs, out_type, target): strategy = _op.OpStrategy() strategy.add_implementation( wrap_compute_softmax(topi.nn.softmax), wrap_topi_schedule(topi.generic.schedule_softmax), name="softmax.generic", ) return strategy @generic_func def schedule_log_softmax(attrs, outs, target): with target: return topi.generic.schedule_softmax(outs) @generic_func def schedule_lrn(attrs, outs, target): with target: return topi.generic.schedule_lrn(outs) @generic_func def schedule_bitpack(attrs, outs, target): with target: return topi.generic.schedule_bitpack(outs) get_auto_scheduler_rewritten_layout = _ffi.get_global_func( "relay.attrs.get_auto_scheduler_rewritten_layout" ) def wrap_compute_conv2d( topi_compute, need_data_layout=False, need_out_layout=False, has_groups=False, need_auto_scheduler_layout=False, ): def _compute_conv2d(attrs, inputs, out_type): padding = get_const_tuple(attrs.padding) strides = get_const_tuple(attrs.strides) dilation = get_const_tuple(attrs.dilation) data_layout = attrs.get_str("data_layout") out_layout = attrs.get_str("out_layout") out_dtype = attrs.out_dtype auto_scheduler_rewritten_layout = get_auto_scheduler_rewritten_layout(attrs) out_dtype = inputs[0].dtype if out_dtype in ("same", "") else out_dtype args = [inputs[0], inputs[1], strides, padding, dilation] if has_groups: args.append(attrs.groups) if need_data_layout: args.append(data_layout) if need_out_layout: args.append(out_layout) args.append(out_dtype) if need_auto_scheduler_layout: args.append(auto_scheduler_rewritten_layout) return [topi_compute(*args)] return _compute_conv2d @override_native_generic_func("conv2d_strategy") def conv2d_strategy(attrs, inputs, out_type, target): logger.warning("conv2d is not optimized for this platform.") strategy = _op.OpStrategy() data, kernel = inputs dilation = get_const_tuple(attrs.dilation) groups = attrs.groups layout = attrs.data_layout kernel_layout = attrs.kernel_layout (dilation_h, dilation_w) = dilation if dilation_h < 1 or dilation_w < 1: raise ValueError("dilation should be positive value") if groups == 1: if layout == "NCHW": assert kernel_layout == "OIHW" strategy.add_implementation( wrap_compute_conv2d(topi.nn.conv2d_nchw), wrap_topi_schedule(topi.generic.schedule_conv2d_nchw), name="conv2d_nchw.generic", ) elif layout == "NHWC": assert kernel_layout == "HWIO" strategy.add_implementation( wrap_compute_conv2d(topi.nn.conv2d_nhwc), wrap_topi_schedule(topi.generic.schedule_conv2d_nhwc), name="conv2d_nhwc.generic", ) elif layout == "HWCN": assert kernel_layout == "HWIO" strategy.add_implementation( wrap_compute_conv2d(topi.nn.conv2d_hwcn), wrap_topi_schedule(topi.generic.schedule_conv2d_hwcn), name="conv2d_hwcn.generic", ) else: raise RuntimeError("Unsupported conv2d layout {}".format(layout)) elif is_depthwise_conv2d(data.shape, layout, kernel.shape, kernel_layout, groups): if layout == "NCHW": assert kernel_layout == "OIHW" strategy.add_implementation( wrap_compute_conv2d(topi.nn.depthwise_conv2d_nchw), wrap_topi_schedule(topi.generic.schedule_depthwise_conv2d_nchw), name="depthwise_conv2d_nchw.generic", ) elif layout == "NHWC": assert kernel_layout == "HWOI" strategy.add_implementation( wrap_compute_conv2d(topi.nn.depthwise_conv2d_nhwc), wrap_topi_schedule(topi.generic.schedule_depthwise_conv2d_nhwc), name="depthwise_conv2d_nhwc.generic", ) else: raise RuntimeError("Unsupported depthwise_conv2d layout {}".format(layout)) else: if layout == "NCHW": assert kernel_layout == "OIHW" strategy.add_implementation( wrap_compute_conv2d(topi.nn.group_conv2d_nchw, has_groups=True), wrap_topi_schedule(topi.generic.schedule_group_conv2d_nchw), name="group_conv2d_nchw.generic", ) elif layout == "NHWC": assert kernel_layout == "HWIO" strategy.add_implementation( wrap_compute_conv2d(topi.nn.group_conv2d_nhwc, has_groups=True), wrap_topi_schedule(topi.generic.schedule_group_conv2d_nhwc), name="group_conv2d_nhwc.generic", ) else: raise RuntimeError("Unsupported group_conv2d layout {}".format(layout)) return strategy @override_native_generic_func("conv2d_NCHWc_strategy") def conv2d_NCHWc_strategy(attrs, inputs, out_type, target): logger.warning("conv2d_NCHWc is not optimized for this platform.") strategy = _op.OpStrategy() if inputs[0].dtype == "int8" or inputs[0].dtype == "uint8": strategy.add_implementation( wrap_compute_conv2d(topi.nn.conv2d_NCHWc_int8, True, True), wrap_topi_schedule(topi.generic.schedule_conv2d_NCHWc_int8), name="conv2d_NCHWc_int8.generic", ) else: strategy.add_implementation( wrap_compute_conv2d(topi.nn.conv2d_NCHWc, True, True), wrap_topi_schedule(topi.generic.schedule_conv2d_NCHWc), name="conv2d_NCHWc.generic", ) return strategy @override_native_generic_func("depthwise_conv2d_NCHWc_strategy") def depthwise_conv2d_NCHWc_strategy(attrs, inputs, out_type, target): logger.warning("depthwise_conv2d_NCHWc is not optimized for this platform.") strategy = _op.OpStrategy() strategy.add_implementation( wrap_compute_conv2d(topi.nn.depthwise_conv2d_NCHWc, True, True), wrap_topi_schedule(topi.generic.schedule_depthwise_conv2d_NCHWc), name="depthwise_conv2d_NCHWc.generic", ) return strategy @override_native_generic_func("conv2d_winograd_without_weight_transform_strategy") def conv2d_winograd_without_weight_transfrom_strategy(attrs, inputs, out_type, target): raise ValueError("No generic implemenation for conv2d_winograd_without_weight_transform") @override_native_generic_func("conv2d_gemm_without_weight_transform_strategy") def conv2d_gemm_without_weight_transform_strategy(attrs, inputs, out_type, target): raise ValueError("No generic implemenation for conv2d_gemm_without_weight_transform") @generic_func def schedule_conv2d_winograd_weight_transform(attrs, outs, target): with target: return topi.generic.schedule_conv2d_winograd_weight_transform(outs) @generic_func def schedule_conv2d_winograd_nnpack_weight_transform(attrs, outs, target): with target: return topi.generic.schedule_conv2d_winograd_nnpack_weight_transform(outs) @generic_func def schedule_conv2d_gemm_weight_transform(attrs, outs, target): with target: return topi.generic.schedule_conv2d_gemm_weight_transform(outs) def wrap_compute_deformable_conv2d(topi_compute): def _compute_deformable_conv2d(attrs, inputs, out_dtype): padding = get_const_tuple(attrs.padding) strides = get_const_tuple(attrs.strides) dilation = get_const_tuple(attrs.dilation) deformable_groups = attrs.deformable_groups groups = attrs.groups out_dtype = attrs.out_dtype out_dtype = inputs[0].dtype if out_dtype in ("same", "") else out_dtype out = topi_compute( inputs[0], inputs[1], inputs[2], strides, padding, dilation, deformable_groups, groups, out_dtype, ) return [out] return _compute_deformable_conv2d @override_native_generic_func("deformable_conv2d_strategy") def deformable_conv2d_strategy(attrs, inputs, out_type, target): layout = attrs.data_layout strategy = _op.OpStrategy() if layout == "NCHW": strategy.add_implementation( wrap_compute_deformable_conv2d(topi.nn.deformable_conv2d_nchw), wrap_topi_schedule(topi.generic.schedule_deformable_conv2d_nchw), name="deformable_conv2d_nchw.generic", ) elif layout == "NHWC": strategy.add_implementation( wrap_compute_deformable_conv2d(topi.nn.deformable_conv2d_nhwc), naive_schedule, name="deformable_conv2d_nhwc.generic", ) else: raise RuntimeError("Layout %s is not supported in deformable conv2d" % layout) return strategy def wrap_compute_conv2d_transpose(topi_compute): def compute_conv2d_transpose(attrs, inputs, out_dtype): padding = get_const_tuple(attrs.padding) strides = get_const_tuple(attrs.strides) out_dtype = attrs.out_dtype out_dtype = inputs[0].dtype if out_dtype in ("same", "") else out_dtype output_padding = get_const_tuple(attrs.output_padding) out = topi_compute(inputs[0], inputs[1], strides, padding, out_dtype, output_padding) return [out] return compute_conv2d_transpose @override_native_generic_func("conv2d_transpose_strategy") def conv2d_transpose_strategy(attrs, inputs, out_type, target): logger.warning("conv2d_transpose is not optimized for this platform.") layout = attrs.data_layout dilation = get_const_tuple(attrs.dilation) groups = attrs.groups assert layout == "NCHW", "only support nchw for now" assert dilation == (1, 1), "not support dilate now" assert groups == 1, "only support groups == 1 for now" strategy = _op.OpStrategy() strategy.add_implementation( wrap_compute_conv2d_transpose(topi.nn.conv2d_transpose_nchw), wrap_topi_schedule(topi.generic.schedule_conv2d_transpose_nchw), name="conv2d_transpose_nchw.generic", ) return strategy def wrap_compute_conv3d_transpose(topi_compute): def compute_conv3d_transpose(attrs, inputs, out_dtype): padding = get_const_tuple(attrs.padding) strides = get_const_tuple(attrs.strides) output_padding = get_const_tuple(attrs.output_padding) out_dtype = attrs.out_dtype out_dtype = inputs[0].dtype if out_dtype in ("same", "") else out_dtype out = topi_compute(inputs[0], inputs[1], strides, padding, out_dtype, output_padding) return [out] return compute_conv3d_transpose @override_native_generic_func("conv3d_transpose_strategy") def conv3d_transpose_strategy(attrs, inputs, out_type, target): logger.warning("conv3d_transpose is not optimized for this platform.") layout = attrs.data_layout dilation = get_const_tuple(attrs.dilation) groups = attrs.groups assert layout == "NCDHW", "only support ncdhw for now" assert dilation == (1, 1, 1), "not support dilate now" assert groups == 1, "only support groups == 1 for now" strategy = _op.OpStrategy() strategy.add_implementation( wrap_compute_conv3d_transpose(topi.nn.conv3d_transpose_ncdhw), wrap_topi_schedule(topi.generic.schedule_conv3d_transpose_ncdhw), name="conv3d_transpose_ncdhw.generic", ) return strategy def wrap_compute_conv3d(topi_compute, need_layout=False): def _compute_conv3d(attrs, inputs, out_type): padding = get_const_tuple(attrs.padding) strides = get_const_tuple(attrs.strides) dilation = get_const_tuple(attrs.dilation) groups = attrs.groups layout = attrs.data_layout out_dtype = attrs.out_dtype out_dtype = inputs[0].dtype if out_dtype in ("same", "") else out_dtype (dilation_d, dilation_h, dilation_w) = dilation if dilation_d < 1 or dilation_h < 1 or dilation_w < 1: raise ValueError("Dilation should be positive value") if groups != 1: raise ValueError("Not support arbitrary group number for conv3d") if need_layout: out = topi_compute(inputs[0], inputs[1], strides, padding, dilation, layout, out_dtype) else: out = topi_compute(inputs[0], inputs[1], strides, padding, dilation, out_dtype) return [out] return _compute_conv3d @override_native_generic_func("conv3d_strategy") def conv3d_strategy(attrs, inputs, out_type, target): logger.warning("conv3d is not optimized for this platform.") strategy = _op.OpStrategy() layout = attrs.data_layout if layout == "NCDHW": strategy.add_implementation( wrap_compute_conv3d(topi.nn.conv3d_ncdhw), wrap_topi_schedule(topi.generic.schedule_conv3d_ncdhw), name="conv3d_ncdhw.generic", ) elif layout == "NDHWC": strategy.add_implementation( wrap_compute_conv3d(topi.nn.conv3d_ndhwc), wrap_topi_schedule(topi.generic.schedule_conv3d_ndhwc), name="conv3d_ndhwc.generic", ) else: raise ValueError("Not support this layout {} yet".format(layout)) return strategy @override_native_generic_func("conv3d_winograd_without_weight_transform_strategy") def conv3d_winograd_without_weight_transfrom_strategy(attrs, inputs, out_type, target): raise ValueError("No generic implemenation for conv3d_winograd_without_weight_transform") @generic_func def schedule_conv3d_winograd_weight_transform(attrs, outs, target): with target: return topi.generic.schedule_conv3d_winograd_weight_transform(outs) def wrap_compute_conv1d(topi_compute): def _compute_conv1d(attrs, inputs, out_type): strides = get_const_tuple(attrs.strides) padding = get_const_tuple(attrs.padding) dilation = get_const_tuple(attrs.dilation) out_dtype = attrs.out_dtype out_dtype = inputs[0].dtype if out_dtype in ("same", "") else out_dtype return [topi_compute(inputs[0], inputs[1], strides, padding, dilation, out_dtype)] return _compute_conv1d @override_native_generic_func("conv1d_strategy") def conv1d_strategy(attrs, inputs, out_type, target): logger.warning("conv1d is not optimized for this platform.") layout = attrs.data_layout dilation = get_const_tuple(attrs.dilation) if dilation[0] < 1: raise ValueError("dilation should be a positive value") strategy = _op.OpStrategy() if layout == "NCW": strategy.add_implementation( wrap_compute_conv1d(topi.nn.conv1d_ncw), wrap_topi_schedule(topi.generic.schedule_conv1d_ncw), name="conv1d_ncw.generic", ) elif layout == "NWC": strategy.add_implementation( wrap_compute_conv1d(topi.nn.conv1d_nwc), wrap_topi_schedule(topi.generic.schedule_conv1d_nwc), name="conv1d_nwc.generic", ) else: raise ValueError("Unsupported conv1d layout {}".format(layout)) return strategy def wrap_compute_conv1d_transpose(topi_compute): def _compute_conv1d_tranpsoe(attrs, inputs, out_type): padding = get_const_tuple(attrs.padding) strides = get_const_tuple(attrs.strides) out_dtype = attrs.out_dtype out_dtype = inputs[0].dtype if out_dtype in ("same", "") else out_dtype output_padding = get_const_tuple(attrs.output_padding) out = topi_compute(inputs[0], inputs[1], strides, padding, out_dtype, output_padding) return [out] return _compute_conv1d_tranpsoe @override_native_generic_func("conv1d_transpose_strategy") def conv1d_transpose_strategy(attrs, inputs, out_type, target): logger.warning("conv1d_transpose is not optimized for this platform.") strategy = _op.OpStrategy() layout = attrs.data_layout dilation = get_const_tuple(attrs.dilation) groups = attrs.groups assert layout == "NCW", "conv1d_transpose ncw only supported" assert dilation == (1,), "conv1d_transpose dilation is not supported" assert groups == 1, "conv1d_transpose groups == 1 only supported" strategy.add_implementation( wrap_compute_conv1d_transpose(topi.nn.conv1d_transpose_ncw), wrap_topi_schedule(topi.generic.schedule_conv1d_transpose_ncw), name="conv1d_transpose_ncw.generic", ) return strategy def wrap_compute_dilation2d(topi_compute, need_data_layout=False): def _compute_dilation2d(attrs, inputs, out_type): padding = get_const_tuple(attrs.padding) strides = get_const_tuple(attrs.strides) dilations = get_const_tuple(attrs.dilations) data_layout = attrs.get_str("data_layout") out_dtype = attrs.out_dtype out_dtype = inputs[0].dtype if out_dtype in ("same", "") else out_dtype args = [inputs[0], inputs[1], strides, padding, dilations] if need_data_layout: args.append(data_layout) args.append(out_dtype) return [topi_compute(*args)] return _compute_dilation2d @override_native_generic_func("dilation2d_strategy") def dilation2d_strategy(attrs, inputs, out_type, target): logger.warning("dilation2d_strategy is not optimized for this platform.") strategy = _op.OpStrategy() dilations = get_const_tuple(attrs.dilations) layout = attrs.data_layout kernel_layout = attrs.kernel_layout assert layout in ["NCHW", "NHWC"] (dilation_h, dilation_w) = dilations if dilation_h < 1 or dilation_w < 1: raise ValueError("dilation should be positive value") if layout == "NCHW": assert kernel_layout == "IHW" strategy.add_implementation( wrap_compute_dilation2d(topi.image.dilation2d_nchw), wrap_topi_schedule(topi.generic.schedule_dilation2d_nchw), name="dilation2d_nchw.generic", ) elif layout == "NHWC": assert kernel_layout == "HWI" strategy.add_implementation( wrap_compute_dilation2d(topi.image.dilation2d_nhwc), wrap_topi_schedule(topi.generic.schedule_dilation2d_nhwc), name="dilation2d_nhwc.generic", ) else: raise RuntimeError("Unsupported dilation2d layout {}".format(layout)) return strategy def wrap_compute_dense(topi_compute): def _compute_dense(attrs, inputs, out_type): out_dtype = attrs.out_dtype out_dtype = inputs[0].dtype if out_dtype == "" else out_dtype return [topi_compute(inputs[0], inputs[1], None, out_dtype)] return _compute_dense @override_native_generic_func("dense_strategy") def dense_strategy(attrs, inputs, out_type, target): logger.warning("dense is not optimized for this platform.") strategy = _op.OpStrategy() strategy.add_implementation( wrap_compute_dense(topi.nn.dense), wrap_topi_schedule(topi.generic.schedule_dense), name="dense.generic", ) return strategy def wrap_compute_batch_matmul(topi_compute): def _compute_batch_matmul(attrs, inputs, out_type): return [topi_compute(inputs[0], inputs[1], out_type.shape)] return _compute_batch_matmul @override_native_generic_func("batch_matmul_strategy") def batch_matmul_strategy(attrs, inputs, out_type, target): logger.warning("batch_matmul is not optimized for this platform.") strategy = _op.OpStrategy() strategy.add_implementation( wrap_compute_batch_matmul(topi.nn.batch_matmul), wrap_topi_schedule(topi.generic.schedule_batch_matmul), name="batch_matmul.generic", ) return strategy def wrap_compute_sparse_dense(topi_compute): def _compute_sparse_dense(attrs, inputs, out_type): return [topi_compute(inputs[0], inputs[1], inputs[2], inputs[3], attrs["sparse_lhs"])] return _compute_sparse_dense @override_native_generic_func("sparse_dense_strategy") def sparse_dense_strategy(attrs, inputs, out_type, target): logger.warning("sparse dense is not optimized for this platform.") strategy = _op.OpStrategy() strategy.add_implementation( wrap_compute_sparse_dense(topi.nn.sparse_dense), wrap_topi_schedule(topi.generic.schedule_sparse_dense), name="sparse_dense.generic", ) return strategy @override_native_generic_func("sparse_dense_padded_strategy") def sparse_dense_padded_strategy(attrs, inputs, out_type, target): raise NotImplementedError("sparse_dense_padded is only implemented for cuda") @generic_func def schedule_sparse_transpose(attrs, outs, target): with target: return topi.generic.schedule_sparse_transpose(outs) def wrap_compute_argsort(topi_compute): def _compute_argsort(attrs, inputs, _): axis = get_const_int(attrs.axis) is_ascend = bool(get_const_int(attrs.is_ascend)) dtype = attrs.dtype return [topi_compute(inputs[0], axis=axis, is_ascend=is_ascend, dtype=dtype)] return _compute_argsort @override_native_generic_func("argsort_strategy") def argsort_strategy(attrs, inputs, out_type, target): strategy = _op.OpStrategy() strategy.add_implementation( wrap_compute_argsort(topi.argsort), wrap_topi_schedule(topi.generic.schedule_argsort), name="argsort.generic", ) return strategy def wrap_compute_topk(topi_compute): def _compute_topk(attrs, inputs, out_type): if attrs.k is not None: k = attrs.k else: k = inputs[1] axis = get_const_int(attrs.axis) ret_type = attrs.ret_type is_ascend = bool(get_const_int(attrs.is_ascend)) dtype = attrs.dtype out = topi_compute(inputs[0], k, axis, ret_type, is_ascend, dtype) out = out if isinstance(out, list) else [out] return out return _compute_topk @override_native_generic_func("topk_strategy") def topk_strategy(attrs, inputs, out_type, target): strategy = _op.OpStrategy() strategy.add_implementation( wrap_compute_topk(topi.topk), wrap_topi_schedule(topi.generic.schedule_topk), name="topk.generic", ) return strategy def wrap_compute_multibox_prior(topi_compute): def _compute_multibox_prior(attrs, inputs, _): sizes = get_float_tuple(attrs.sizes) ratios = get_float_tuple(attrs.ratios) steps = get_float_tuple(attrs.steps) offsets = get_float_tuple(attrs.offsets) clip = bool(get_const_int(attrs.clip)) return [topi_compute(inputs[0], sizes, ratios, steps, offsets, clip)] return _compute_multibox_prior @override_native_generic_func("multibox_prior_strategy") def multibox_prior_strategy(attrs, inputs, out_type, target): strategy = _op.OpStrategy() strategy.add_implementation( wrap_compute_multibox_prior(topi.vision.ssd.multibox_prior), wrap_topi_schedule(topi.generic.schedule_multibox_prior), name="multibox_prior.generic", ) return strategy def wrap_compute_multibox_transform_loc(topi_compute): def _compute_multibox_transform_loc(attrs, inputs, _): clip = bool(get_const_int(attrs.clip)) threshold = get_const_float(attrs.threshold) variances = get_float_tuple(attrs.variances) return topi_compute(inputs[0], inputs[1], inputs[2], clip, threshold, variances) return _compute_multibox_transform_loc @override_native_generic_func("multibox_transform_loc_strategy") def multibox_transform_loc_strategy(attrs, inputs, out_type, target): strategy = _op.OpStrategy() strategy.add_implementation( wrap_compute_multibox_transform_loc(topi.vision.ssd.multibox_transform_loc), wrap_topi_schedule(topi.generic.schedule_multibox_transform_loc), name="multibox_transform_loc.generic", ) return strategy def wrap_compute_get_valid_counts(topi_compute): def _compute_get_valid_counts(attrs, inputs, out_type): score_threshold = inputs[1] id_index = get_const_int(attrs.id_index) score_index = get_const_int(attrs.score_index) if attrs.score_threshold is not None: score_threshold = get_const_float(attrs.score_threshold) return topi_compute(inputs[0], score_threshold, id_index, score_index) return _compute_get_valid_counts @override_native_generic_func("get_valid_counts_strategy") def get_valid_counts_strategy(attrs, inputs, out_type, target): strategy = _op.OpStrategy() strategy.add_implementation( wrap_compute_get_valid_counts(topi.vision.get_valid_counts), wrap_topi_schedule(topi.generic.schedule_get_valid_counts), name="get_valid_counts.generic", ) return strategy def wrap_compute_nms(topi_compute): def _compute_nms(attrs, inputs, out_type): max_output_size = inputs[3] iou_threshold = inputs[4] if attrs.max_output_size is not None: max_output_size = attrs.max_output_size if attrs.iou_threshold is not None: iou_threshold = get_const_float(attrs.iou_threshold) return_indices = bool(get_const_int(attrs.return_indices)) force_suppress = bool(get_const_int(attrs.force_suppress)) top_k = get_const_int(attrs.top_k) coord_start = get_const_int(attrs.coord_start) score_index = get_const_int(attrs.score_index) id_index = get_const_int(attrs.id_index) invalid_to_bottom = bool(get_const_int(attrs.invalid_to_bottom)) if return_indices: return topi_compute( inputs[0], inputs[1], inputs[2], max_output_size, iou_threshold, force_suppress, top_k, coord_start, score_index, id_index, return_indices, invalid_to_bottom, ) return [ topi_compute( inputs[0], inputs[1], inputs[2], max_output_size, iou_threshold, force_suppress, top_k, coord_start, score_index, id_index, return_indices, invalid_to_bottom, ) ] return _compute_nms @override_native_generic_func("non_max_suppression_strategy") def nms_strategy(attrs, inputs, out_type, target): strategy = _op.OpStrategy() strategy.add_implementation( wrap_compute_nms(topi.vision.non_max_suppression), wrap_topi_schedule(topi.generic.schedule_nms), name="nms.generic", ) return strategy def wrap_compute_roi_align(topi_compute): def _compute_roi_align(attrs, inputs, out_type): assert attrs.layout == "NCHW" pooled_size = get_const_tuple(attrs.pooled_size) return [ topi_compute( inputs[0], inputs[1], pooled_size=pooled_size, spatial_scale=attrs.spatial_scale, sample_ratio=attrs.sample_ratio, ) ] return _compute_roi_align @override_native_generic_func("roi_align_strategy") def roi_align_strategy(attrs, inputs, out_type, target): strategy = _op.OpStrategy() layout = attrs.layout assert layout == "NCHW", "only support nchw for now" strategy.add_implementation( wrap_compute_roi_align(topi.vision.rcnn.roi_align_nchw), wrap_topi_schedule(topi.generic.schedule_roi_align), name="roi_align.generic", ) return strategy @generic_func def schedule_roi_pool(attrs, outs, target): with target: return topi.generic.schedule_roi_pool(outs) def wrap_compute_proposal(topi_compute): def _compute_proposal(attrs, inputs, out_type): scales = get_float_tuple(attrs.scales) ratios = get_float_tuple(attrs.ratios) feature_stride = attrs.feature_stride threshold = attrs.threshold rpn_pre_nms_top_n = attrs.rpn_pre_nms_top_n rpn_post_nms_top_n = attrs.rpn_post_nms_top_n rpn_min_size = attrs.rpn_min_size iou_loss = bool(get_const_int(attrs.iou_loss)) return [ topi_compute( inputs[0], inputs[1], inputs[2], scales, ratios, feature_stride, threshold, rpn_pre_nms_top_n, rpn_post_nms_top_n, rpn_min_size, iou_loss, ) ] return _compute_proposal @override_native_generic_func("proposal_strategy") def proposal_strategy(attrs, inputs, out_type, target): strategy = _op.OpStrategy() strategy.add_implementation( wrap_compute_proposal(topi.vision.rcnn.proposal), wrap_topi_schedule(topi.generic.schedule_proposal), name="proposal.generic", ) return strategy @override_native_generic_func("scatter_strategy") def scatter_strategy(attrs, outs, out_type, target): strategy = _op.OpStrategy() strategy.add_implementation( wrap_compute_scatter(topi.scatter), wrap_topi_schedule(topi.generic.schedule_scatter), name="scatter.generic", ) return strategy def wrap_compute_scatter(topi_compute): def _compute_scatter(attrs, inputs, _): return [topi_compute(inputs[0], inputs[1], inputs[2], axis=attrs.axis)] return _compute_scatter @override_native_generic_func("scatter_add_strategy") def scatter_add_strategy(attrs, outs, out_type, target): strategy = _op.OpStrategy() strategy.add_implementation( wrap_compute_scatter(topi.scatter_add), wrap_topi_schedule(topi.generic.schedule_scatter), name="scatter_add.generic", ) return strategy @override_native_generic_func("scatter_nd_strategy") def scatter_nd_strategy(attrs, inputs, out_type, target): strategy = _op.OpStrategy() strategy.add_implementation( wrap_compute_scatter_nd(topi.scatter_nd), wrap_topi_schedule(topi.generic.schedule_extern), name="scatter_nd.generic", ) return strategy def wrap_compute_scatter_nd(topi_compute): def _compute_scatter_nd(attrs, inputs, _): return [topi_compute(inputs[0], inputs[1], attrs.out_shape)] return _compute_scatter_nd def wrap_compute_bitserial_conv2d(topi_compute): def compute_bitserial_conv2d(attrs, inputs, out_dtype): padding = get_const_tuple(attrs.padding) strides = get_const_tuple(attrs.strides) activation_bits = attrs.activation_bits weight_bits = attrs.weight_bits pack_dtype = attrs.pack_dtype out_dtype = attrs.out_dtype unipolar = attrs.unipolar return [ topi_compute( inputs[0], inputs[1], strides, padding, activation_bits, weight_bits, pack_dtype, out_dtype, unipolar, ) ] return compute_bitserial_conv2d @override_native_generic_func("bitserial_conv2d_strategy") def bitserial_conv2d_strategy(attrs, inputs, out_type, target): logger.warning("bitserial_conv2d is not optimized for this platform.") strategy = _op.OpStrategy() layout = attrs.data_layout if layout == "NCHW": strategy.add_implementation( wrap_compute_bitserial_conv2d(topi.nn.bitserial_conv2d_nchw), wrap_topi_schedule(topi.generic.schedule_bitserial_conv2d_nchw), name="bitserial_conv2d_nchw.generic", ) elif layout == "NHWC": strategy.add_implementation( wrap_compute_bitserial_conv2d(topi.nn.bitserial_conv2d_nhwc), wrap_topi_schedule(topi.generic.schedule_bitserial_conv2d_nhwc), name="bitserial_conv2d_nhwc.generic", ) else: raise ValueError("Data layout {} not supported.".format(layout)) return strategy def wrap_compute_bitserial_dense(topi_compute): def compute_bitserial_dense(attrs, inputs, out_type): data_bits = attrs.data_bits weight_bits = attrs.weight_bits pack_dtype = attrs.pack_dtype out_dtype = attrs.out_dtype out_dtype = inputs[0].dtype if out_dtype == "" else out_dtype unipolar = attrs.unipolar return [ topi_compute( inputs[0], inputs[1], data_bits, weight_bits, pack_dtype, out_dtype, unipolar ) ] return compute_bitserial_dense @override_native_generic_func("bitserial_dense_strategy") def bitserial_dense_strategy(attrs, inputs, out_type, target): logger.warning("bitserial_dense is not optimized for this platform.") strategy = _op.OpStrategy() strategy.add_implementation( wrap_compute_bitserial_dense(topi.nn.bitserial_dense), wrap_topi_schedule(topi.generic.schedule_bitserial_dense), name="bitserial_dense.generic", ) return strategy def wrap_compute_correlation(topi_compute): def _compute_correlation(attrs, inputs, out_type): kernel_size = attrs.kernel_size max_displacement = attrs.max_displacement stride1 = attrs.stride1 stride2 = attrs.stride2 padding = get_const_tuple(attrs.padding) is_multiply = attrs.is_multiply return [ topi_compute( inputs[0], inputs[1], kernel_size, max_displacement, stride1, stride2, padding, is_multiply, ) ] return _compute_correlation @override_native_generic_func("correlation_strategy") def correlation_strategy(attrs, inputs, out_type, target): logger.warning("correlation is not optimized for this platform.") layout = attrs.layout assert layout == "NCHW", "Only support NCHW layout" strategy = _op.OpStrategy() strategy.add_implementation( wrap_compute_correlation(topi.nn.correlation_nchw), wrap_topi_schedule(topi.generic.schedule_correlation_nchw), name="correlation.generic", ) return strategy def wrap_compute_argwhere(topi_compute): def _compute_argwhere(attrs, inputs, out_type): output_shape = [] for s in out_type.shape: if hasattr(s, "value"): output_shape.append(s) else: output_shape.append(te.var("any_dim", "int32")) new_output_type = ir.TensorType(output_shape, "int32") return [topi_compute(new_output_type, inputs[0])] return _compute_argwhere @override_native_generic_func("argwhere_strategy") def argwhere_strategy(attrs, inputs, out_type, target): strategy = _op.OpStrategy() strategy.add_implementation( wrap_compute_argwhere(topi.argwhere), wrap_topi_schedule(topi.generic.schedule_argwhere), name="argwhere.generic", ) return strategy

true

f700831c65772de4a694995aa5bd1a37f57f8a1c

11,955

py

Python

googlemaps_helpers/main.py

mrcagney/googlemaps_helpers

75dfcc3e5e788d04c3af3e7608909b349ac83e8d

[ "MIT" ]

1

2017-06-25T17:58:37.000Z

googlemaps_helpers/main.py

araichev/googlemaps_helpers

75dfcc3e5e788d04c3af3e7608909b349ac83e8d

[ "MIT" ]

null

googlemaps_helpers/main.py

araichev/googlemaps_helpers

75dfcc3e5e788d04c3af3e7608909b349ac83e8d

[ "MIT" ]

null

from itertools import product import math from collections import OrderedDict from pathlib import Path import logging import pandas as pd import numpy as np import geopandas as gpd import shapely.geometry as sg import googlemaps # Configure logging logger = logging.getLogger() handler = logging.StreamHandler() formatter = logging.Formatter( '%(asctime)s %(name)-12s %(levelname)-8s \n%(message)s') handler.setFormatter(formatter) logger.addHandler(handler) logger.setLevel(logging.INFO) WGS84 = {'init': 'epsg:4326'} # Maximum number of elements in a Google Maps Distance Matrix API query MAX_ELEMENTS = 100 def flip_coords(xy_list): """ Given a list of coordinate pairs, swap the first and second coordinates and return the resulting list. """ return [(y, x) for (x, y) in xy_list] def make_ids(n, prefix='row_'): """ Return a list of ``n`` (integer) unique strings of the form ``prefix``<number>. """ k = int(math.log10(n)) + 1 # Number of digits for padding return [prefix + '{num:0{pad}d}'.format(num=i, pad=k) for i in range(n)] def to_df(distance_matrix_response, origin_ids=None, destination_ids=None): """ Given a (decoded) JSON response to a Google Maps Distance Matrix API call, convert it into a DataFrame with the following columns. - ``'origin_address'`` - ``'origin_id'``: ID of origin; defaults to an element of :func:`make_ids` - ``'destination_address'`` - ``'destination_id'``: ID of destination; defaluts to an element of :func:`make_ids` - ``'duration'``: time from origin to destination; includes time in traffic if that's available in the response - ``'distance'``: distance from origin to destination The origin and destination addresses in the response can optionally be assigned IDs by setting ``origin_ids`` (list of strings) and ``destination_ids`` (list of strings). """ # Initialize r = distance_matrix_response columns = ['origin_address', 'destination_address', 'origin_id', 'destination_id', 'duration', 'distance'] f = pd.DataFrame([], columns=columns) # Append addresses if not r['rows']: return f f['origin_address'], f['destination_address'] = zip( *product(r['origin_addresses'], r['destination_addresses'])) # Append IDs if origin_ids is None: origin_ids = make_ids(len(r['origin_addresses'])) if destination_ids is None: destination_ids = make_ids(len(r['destination_addresses'])) f['origin_id'], f['destination_id'] = zip( *product(origin_ids, destination_ids)) # Append durations and distances durs = [] dists = [] for row in r['rows']: for e in row['elements']: if e['status'] == 'OK': if 'duration_in_traffic' in e: dur_key = 'duration_in_traffic' else: dur_key = 'duration' durs.append(e[dur_key]['value']) dists.append(e['distance']['value']) else: durs.append(np.nan) dists.append(np.nan) f['duration'] = durs f['distance'] = dists return f def point_df_to_gdf(f, x_col='lon', y_col='lat', from_crs=WGS84): """ Given a DataFrame of points with x coordinates in the column ``x_col`` and y coordinates in the column ``y_col``, with respect to the GeoPandas coordinate reference system ``from_crs`` (dictionary), convert the DataFrame into a GeoDataFrame with that coordinate reference system and with a ``'geometry'`` column that corresponds to the points. Delete the original x and y columns, and return the result. """ f = f.copy() f['geometry'] = f[[x_col, y_col]].apply(lambda p: sg.Point(p), axis=1) f = f.drop([x_col, y_col], axis=1) f = gpd.GeoDataFrame(f) f.crs = from_crs return f def point_gdf_to_df(f, x_col='lon', y_col='lat', to_crs=WGS84): """ The inverse of :func:`point_df_to_gdf`. Given a GeoDataFrame of points, convert to the coordinate reference system ``to_crs`` (dictionary), then split its ``'geometry'`` column into x coordinates in the column ``x_col`` and y coordinates in the columns ``y_col``, deleting the ``'geometry'`` column afterwards. Coerce the result into a DataFrame and return it. """ f = f.copy() if f.crs is None: raise ValueError('GeoDataFrame needs a crs attribute') if f.crs != to_crs: f = f.to_crs(to_crs) f[x_col], f[y_col] = zip(*f['geometry'].map(lambda p: p.coords[0])) del f['geometry'] return pd.DataFrame(f) def build_distance_matrix_df(client, origins_gdf, destinations_gdf, origin_id_col=None, destination_id_col=None, max_elements=MAX_ELEMENTS, **distance_matrix_kwargs): """ Compute the duration-distance matrix between the given origins and destinations, assuming that the number of origins multiplied by the number of destinations is at most ``max_elements``. To do this, call the Google Maps Distance Matrix API once. INPUT: - ``client``: google-maps-services-python Client instance - ``origins_gdf``: GeoDataFrame of point; the origins - ``destinations_gdf``: GeoDataFrame of points; the destinations - ``origin_id_col``: string; name of ID column in ``origins_gdf`` - ``destination_id_col``: string; name of ID column in ``destinations_gdf`` - ``max_elements``: integer; max number of elements allowable in one Google Maps Distance Matrix API call - ``distance_matrix_kwargs``: dictionary; keyword arguments for Google Maps Distance Matrix API OUTPUT: A DataFrame of the form output by :func:`to_df` where the origins come from ``origins_gdf`` and the destinations come from ``destinations_gdf``. Return an empty DataFrame with the expected column names if an HTTPError on Timeout exception occurs. """ # Initialize origin and destinations GeoDataFrames o_gdf = origins_gdf.copy() d_gdf = destinations_gdf.copy() n = o_gdf.shape[0]*d_gdf.shape[0] if n > max_elements: raise ValueError('Number of origins times number of destinations ' 'is {}, which exceeds threshold of {} elements'.format( n, max_elements)) # Prepare origin data if o_gdf.crs != WGS84: o_gdf = o_gdf.to_crs(WGS84) if origin_id_col is None: origin_id_col = 'temp_id' o_gdf[origin_id_col] = make_ids(o_gdf.shape[0]) o_locs = [geo.coords[0] for geo in o_gdf['geometry']] o_ids = o_gdf[origin_id_col].values # Prepare destination data if d_gdf.crs != WGS84: d_gdf = d_gdf.to_crs(WGS84) if destination_id_col is None: destination_id_col = 'temp_id' d_gdf[destination_id_col] = make_ids(d_gdf.shape[0]) d_locs = [geo.coords[0] for geo in d_gdf['geometry']] d_ids = d_gdf[destination_id_col].values # Get matrix info try: r = client.distance_matrix(flip_coords(o_locs), flip_coords(d_locs), **distance_matrix_kwargs) f = to_df(r, o_ids, d_ids) except (googlemaps.exceptions.HTTPError, googlemaps.exceptions.Timeout): # Empty DataFrame f = pd.DataFrame(columns=[ 'origin_address', 'origin_id', 'destination_address', 'destination_id', 'duration', 'distance', ]) return f def run_distance_matrix_job(client, origins_gdf, destinations_gdf, out_dir, origin_id_col=None, destination_id_col=None, max_elements=MAX_ELEMENTS, **distance_matrix_kwargs): """ Compute the duration-distance matrix between the given origins and destinations. To do this, call the Google Maps Distance Matrix API repeatedly, ensuring that each call uses no more than ``max_elements`` elements. INPUT: - ``client``: google-maps-services-python Client instance - ``origins_gdf``: GeoDataFrame of points; the origins - ``destinations_gdf``: GeoDataFrame of points; the destinations - ``out_dir``: string or Path object of a directory at which to store the output files; create the directory if it does not exist - ``origin_id_col``: string; name of ID column in ``origins_gdf`` - ``destination_id_col``: string; name of ID column in ``destinations_gdf`` - ``max_elements``: integer; max number of elements allowable in one Google Maps Distance Matrix API call - ``distance_matrix_kwargs``: dictionary; keyword arguments for Google Maps Distance Matrix API OUTPUT: A collection of CSV files located at ``out_dir`` of the form output by :func:`to_df`, where the origins comes from ``origins_gdf`` and the destinations come from ``destinations_gdf``. Each file will contains one origin points and at most ``max_elements`` destination points, for a total of at most ``max_elements`` rows. An empty DataFrame with the expected column names will be saved to file if an HTTPError on Timeout exception occurs. This can happen if, for example, the daily query limit is exceeded. """ o_gdf = origins_gdf.copy() d_gdf = destinations_gdf.copy() n_o = o_gdf.shape[0] n_d = d_gdf.shape[0] # Create IDs if necessary if origin_id_col is None: origin_id_col = 'ersatz_origin_id' o_gdf[origin_id_col] = make_ids(n_o, 'orig_row_') if destination_id_col is None: destination_id_col = 'ersatz_destination_id' d_gdf[destination_id_col] = make_ids(n_d, 'dest_row_') # Get mode for logging mode = distance_matrix_kwargs.get('mode', 'driving') # Make output directory if it does not exist out_dir = Path(out_dir) if not out_dir.exists(): out_dir.mkdir(parents=True) # Iterate through origins. # For each origin segment all destinations into chunks of size # at most ``max_elements``. # For each destination chunk, build a one-to-many matrix from the # origin to all the destinations in the chunk and save it to file. for ix, orig_id in o_gdf[[origin_id_col]].itertuples(): logger.info('Working on origin {} of {} (id {})'.format( ix + 1, n_o, orig_id)) # Chunk destinations and build one-to-many matrices from origin # to destination chunks. # A failed attempt (e.g. through API usage over limit) # will build an empty matrix for j in range(math.ceil(n_d/max_elements)): n1 = max_elements*j n2 = min(max_elements*(j + 1), n_d) dest_id1, dest_id2 = ( d_gdf[destination_id_col].iat[n1], d_gdf[destination_id_col].iat[n2 - 1] ) path = Path(out_dir)/'{}_from_{}_to_{}--{}.csv'.format( mode, orig_id, dest_id1, dest_id2) f = build_distance_matrix_df(client, o_gdf.loc[ix:ix], d_gdf.iloc[n1:n2], origin_id_col=origin_id_col, destination_id_col=destination_id_col, **distance_matrix_kwargs) f.to_csv(path, index=False) if f.empty: logger.info('* Failed to get data for ' + path.stem) def compute_cost(n, cost=0.5/1000, num_freebies=0, daily_limit=100000, chunk_size=MAX_ELEMENTS): """ Estimate the cost of a sequence of Google Maps Distance Matrix queries comprising a total of n elements at ``cost`` USD per element, where the first ``num_freebies`` (integer) elements are free. Return a Series that includes the cost and some other metadata. """ d = OrderedDict() d['#elements'] = n d['exceeds {!s}-element daily limit?'.format(daily_limit)] = ( n > daily_limit) d['estimated cost for job in USD'] = max(0, n - num_freebies)*cost d['estimated duration for job in minutes'] = n/chunk_size/60 return pd.Series(d)

36.448171

76

0.658302

from itertools import product import math from collections import OrderedDict from pathlib import Path import logging import pandas as pd import numpy as np import geopandas as gpd import shapely.geometry as sg import googlemaps logger = logging.getLogger() handler = logging.StreamHandler() formatter = logging.Formatter( '%(asctime)s %(name)-12s %(levelname)-8s \n%(message)s') handler.setFormatter(formatter) logger.addHandler(handler) logger.setLevel(logging.INFO) WGS84 = {'init': 'epsg:4326'} MAX_ELEMENTS = 100 def flip_coords(xy_list): return [(y, x) for (x, y) in xy_list] def make_ids(n, prefix='row_'): k = int(math.log10(n)) + 1 return [prefix + '{num:0{pad}d}'.format(num=i, pad=k) for i in range(n)] def to_df(distance_matrix_response, origin_ids=None, destination_ids=None): r = distance_matrix_response columns = ['origin_address', 'destination_address', 'origin_id', 'destination_id', 'duration', 'distance'] f = pd.DataFrame([], columns=columns) if not r['rows']: return f f['origin_address'], f['destination_address'] = zip( *product(r['origin_addresses'], r['destination_addresses'])) if origin_ids is None: origin_ids = make_ids(len(r['origin_addresses'])) if destination_ids is None: destination_ids = make_ids(len(r['destination_addresses'])) f['origin_id'], f['destination_id'] = zip( *product(origin_ids, destination_ids)) durs = [] dists = [] for row in r['rows']: for e in row['elements']: if e['status'] == 'OK': if 'duration_in_traffic' in e: dur_key = 'duration_in_traffic' else: dur_key = 'duration' durs.append(e[dur_key]['value']) dists.append(e['distance']['value']) else: durs.append(np.nan) dists.append(np.nan) f['duration'] = durs f['distance'] = dists return f def point_df_to_gdf(f, x_col='lon', y_col='lat', from_crs=WGS84): f = f.copy() f['geometry'] = f[[x_col, y_col]].apply(lambda p: sg.Point(p), axis=1) f = f.drop([x_col, y_col], axis=1) f = gpd.GeoDataFrame(f) f.crs = from_crs return f def point_gdf_to_df(f, x_col='lon', y_col='lat', to_crs=WGS84): f = f.copy() if f.crs is None: raise ValueError('GeoDataFrame needs a crs attribute') if f.crs != to_crs: f = f.to_crs(to_crs) f[x_col], f[y_col] = zip(*f['geometry'].map(lambda p: p.coords[0])) del f['geometry'] return pd.DataFrame(f) def build_distance_matrix_df(client, origins_gdf, destinations_gdf, origin_id_col=None, destination_id_col=None, max_elements=MAX_ELEMENTS, **distance_matrix_kwargs): o_gdf = origins_gdf.copy() d_gdf = destinations_gdf.copy() n = o_gdf.shape[0]*d_gdf.shape[0] if n > max_elements: raise ValueError('Number of origins times number of destinations ' 'is {}, which exceeds threshold of {} elements'.format( n, max_elements)) if o_gdf.crs != WGS84: o_gdf = o_gdf.to_crs(WGS84) if origin_id_col is None: origin_id_col = 'temp_id' o_gdf[origin_id_col] = make_ids(o_gdf.shape[0]) o_locs = [geo.coords[0] for geo in o_gdf['geometry']] o_ids = o_gdf[origin_id_col].values if d_gdf.crs != WGS84: d_gdf = d_gdf.to_crs(WGS84) if destination_id_col is None: destination_id_col = 'temp_id' d_gdf[destination_id_col] = make_ids(d_gdf.shape[0]) d_locs = [geo.coords[0] for geo in d_gdf['geometry']] d_ids = d_gdf[destination_id_col].values try: r = client.distance_matrix(flip_coords(o_locs), flip_coords(d_locs), **distance_matrix_kwargs) f = to_df(r, o_ids, d_ids) except (googlemaps.exceptions.HTTPError, googlemaps.exceptions.Timeout): f = pd.DataFrame(columns=[ 'origin_address', 'origin_id', 'destination_address', 'destination_id', 'duration', 'distance', ]) return f def run_distance_matrix_job(client, origins_gdf, destinations_gdf, out_dir, origin_id_col=None, destination_id_col=None, max_elements=MAX_ELEMENTS, **distance_matrix_kwargs): o_gdf = origins_gdf.copy() d_gdf = destinations_gdf.copy() n_o = o_gdf.shape[0] n_d = d_gdf.shape[0] if origin_id_col is None: origin_id_col = 'ersatz_origin_id' o_gdf[origin_id_col] = make_ids(n_o, 'orig_row_') if destination_id_col is None: destination_id_col = 'ersatz_destination_id' d_gdf[destination_id_col] = make_ids(n_d, 'dest_row_') mode = distance_matrix_kwargs.get('mode', 'driving') out_dir = Path(out_dir) if not out_dir.exists(): out_dir.mkdir(parents=True) for ix, orig_id in o_gdf[[origin_id_col]].itertuples(): logger.info('Working on origin {} of {} (id {})'.format( ix + 1, n_o, orig_id)) for j in range(math.ceil(n_d/max_elements)): n1 = max_elements*j n2 = min(max_elements*(j + 1), n_d) dest_id1, dest_id2 = ( d_gdf[destination_id_col].iat[n1], d_gdf[destination_id_col].iat[n2 - 1] ) path = Path(out_dir)/'{}_from_{}_to_{}--{}.csv'.format( mode, orig_id, dest_id1, dest_id2) f = build_distance_matrix_df(client, o_gdf.loc[ix:ix], d_gdf.iloc[n1:n2], origin_id_col=origin_id_col, destination_id_col=destination_id_col, **distance_matrix_kwargs) f.to_csv(path, index=False) if f.empty: logger.info('* Failed to get data for ' + path.stem) def compute_cost(n, cost=0.5/1000, num_freebies=0, daily_limit=100000, chunk_size=MAX_ELEMENTS): d = OrderedDict() d['#elements'] = n d['exceeds {!s}-element daily limit?'.format(daily_limit)] = ( n > daily_limit) d['estimated cost for job in USD'] = max(0, n - num_freebies)*cost d['estimated duration for job in minutes'] = n/chunk_size/60 return pd.Series(d)

true

f700833e6a88443d785e41ccd0504c32a9ae21bb

12,489

py

Python

locust/test/test_runners.py

wenlongyan/locust

777d1cc9c8dd93fc8693f06780cbf8166fe137df

[ "MIT" ]

1

2019-10-18T10:18:20.000Z

locust/test/test_runners.py

wenlongyan/locust

777d1cc9c8dd93fc8693f06780cbf8166fe137df

[ "MIT" ]

null

locust/test/test_runners.py

wenlongyan/locust

777d1cc9c8dd93fc8693f06780cbf8166fe137df

[ "MIT" ]

null

import unittest import gevent from gevent import sleep from gevent.queue import Queue import mock from locust import events from locust.core import Locust, TaskSet, task from locust.exception import LocustError from locust.main import parse_options from locust.rpc import Message from locust.runners import LocalLocustRunner, MasterLocustRunner from locust.stats import global_stats, RequestStats from locust.test.testcases import LocustTestCase def mocked_rpc_server(): class MockedRpcServer(object): queue = Queue() outbox = [] def __init__(self, host, port): pass @classmethod def mocked_send(cls, message): cls.queue.put(message.serialize()) sleep(0) def recv(self): results = self.queue.get() return Message.unserialize(results) def send(self, message): self.outbox.append(message.serialize()) return MockedRpcServer class TestMasterRunner(LocustTestCase): def setUp(self): global_stats.reset_all() self._slave_report_event_handlers = [h for h in events.slave_report._handlers] parser, _, _ = parse_options() args = [ "--clients", "10", "--hatch-rate", "10" ] opts, _ = parser.parse_args(args) self.options = opts def tearDown(self): events.slave_report._handlers = self._slave_report_event_handlers def test_slave_connect(self): class MyTestLocust(Locust): pass with mock.patch("locust.rpc.rpc.Server", mocked_rpc_server()) as server: master = MasterLocustRunner(MyTestLocust, self.options) server.mocked_send(Message("client_ready", None, "zeh_fake_client1")) self.assertEqual(1, len(master.clients)) self.assertTrue("zeh_fake_client1" in master.clients, "Could not find fake client in master instance's clients dict") server.mocked_send(Message("client_ready", None, "zeh_fake_client2")) server.mocked_send(Message("client_ready", None, "zeh_fake_client3")) server.mocked_send(Message("client_ready", None, "zeh_fake_client4")) self.assertEqual(4, len(master.clients)) server.mocked_send(Message("quit", None, "zeh_fake_client3")) self.assertEqual(3, len(master.clients)) def test_slave_stats_report_median(self): class MyTestLocust(Locust): pass with mock.patch("locust.rpc.rpc.Server", mocked_rpc_server()) as server: master = MasterLocustRunner(MyTestLocust, self.options) server.mocked_send(Message("client_ready", None, "fake_client")) master.stats.get("/", "GET").log(100, 23455) master.stats.get("/", "GET").log(800, 23455) master.stats.get("/", "GET").log(700, 23455) data = {"user_count":1} events.report_to_master.fire(client_id="fake_client", data=data) master.stats.clear_all() server.mocked_send(Message("stats", data, "fake_client")) s = master.stats.get("/", "GET") self.assertEqual(700, s.median_response_time) def test_master_total_stats(self): class MyTestLocust(Locust): pass with mock.patch("locust.rpc.rpc.Server", mocked_rpc_server()) as server: master = MasterLocustRunner(MyTestLocust, self.options) server.mocked_send(Message("client_ready", None, "fake_client")) stats = RequestStats() stats.log_request("GET", "/1", 100, 3546) stats.log_request("GET", "/1", 800, 56743) stats2 = RequestStats() stats2.log_request("GET", "/2", 700, 2201) server.mocked_send(Message("stats", { "stats":stats.serialize_stats(), "stats_total": stats.total.serialize(), "errors":stats.serialize_errors(), "user_count": 1, }, "fake_client")) server.mocked_send(Message("stats", { "stats":stats2.serialize_stats(), "stats_total": stats2.total.serialize(), "errors":stats2.serialize_errors(), "user_count": 2, }, "fake_client")) self.assertEqual(700, master.stats.total.median_response_time) def test_master_current_response_times(self): class MyTestLocust(Locust): pass start_time = 1 with mock.patch("time.time") as mocked_time: mocked_time.return_value = start_time global_stats.reset_all() with mock.patch("locust.rpc.rpc.Server", mocked_rpc_server()) as server: master = MasterLocustRunner(MyTestLocust, self.options) mocked_time.return_value += 1 server.mocked_send(Message("client_ready", None, "fake_client")) stats = RequestStats() stats.log_request("GET", "/1", 100, 3546) stats.log_request("GET", "/1", 800, 56743) server.mocked_send(Message("stats", { "stats":stats.serialize_stats(), "stats_total": stats.total.get_stripped_report(), "errors":stats.serialize_errors(), "user_count": 1, }, "fake_client")) mocked_time.return_value += 1 stats2 = RequestStats() stats2.log_request("GET", "/2", 400, 2201) server.mocked_send(Message("stats", { "stats":stats2.serialize_stats(), "stats_total": stats2.total.get_stripped_report(), "errors":stats2.serialize_errors(), "user_count": 2, }, "fake_client")) mocked_time.return_value += 4 self.assertEqual(400, master.stats.total.get_current_response_time_percentile(0.5)) self.assertEqual(800, master.stats.total.get_current_response_time_percentile(0.95)) # let 10 second pass, do some more requests, send it to the master and make # sure the current response time percentiles only accounts for these new requests mocked_time.return_value += 10 stats.log_request("GET", "/1", 20, 1) stats.log_request("GET", "/1", 30, 1) stats.log_request("GET", "/1", 3000, 1) server.mocked_send(Message("stats", { "stats":stats.serialize_stats(), "stats_total": stats.total.get_stripped_report(), "errors":stats.serialize_errors(), "user_count": 2, }, "fake_client")) self.assertEqual(30, master.stats.total.get_current_response_time_percentile(0.5)) self.assertEqual(3000, master.stats.total.get_current_response_time_percentile(0.95)) def test_spawn_zero_locusts(self): class MyTaskSet(TaskSet): @task def my_task(self): pass class MyTestLocust(Locust): task_set = MyTaskSet min_wait = 100 max_wait = 100 runner = LocalLocustRunner([MyTestLocust], self.options) timeout = gevent.Timeout(2.0) timeout.start() try: runner.start_hatching(0, 1, wait=True) runner.greenlet.join() except gevent.Timeout: self.fail("Got Timeout exception. A locust seems to have been spawned, even though 0 was specified.") finally: timeout.cancel() def test_spawn_uneven_locusts(self): """ Tests that we can accurately spawn a certain number of locusts, even if it's not an even number of the connected slaves """ class MyTestLocust(Locust): pass with mock.patch("locust.rpc.rpc.Server", mocked_rpc_server()) as server: master = MasterLocustRunner(MyTestLocust, self.options) for i in range(5): server.mocked_send(Message("client_ready", None, "fake_client%i" % i)) master.start_hatching(7, 7) self.assertEqual(5, len(server.outbox)) num_clients = 0 for msg in server.outbox: num_clients += Message.unserialize(msg).data["num_clients"] self.assertEqual(7, num_clients, "Total number of locusts that would have been spawned is not 7") def test_spawn_fewer_locusts_than_slaves(self): class MyTestLocust(Locust): pass with mock.patch("locust.rpc.rpc.Server", mocked_rpc_server()) as server: master = MasterLocustRunner(MyTestLocust, self.options) for i in range(5): server.mocked_send(Message("client_ready", None, "fake_client%i" % i)) master.start_hatching(2, 2) self.assertEqual(5, len(server.outbox)) num_clients = 0 for msg in server.outbox: num_clients += Message.unserialize(msg).data["num_clients"] self.assertEqual(2, num_clients, "Total number of locusts that would have been spawned is not 2") def test_exception_in_task(self): class HeyAnException(Exception): pass class MyLocust(Locust): class task_set(TaskSet): @task def will_error(self): raise HeyAnException(":(") runner = LocalLocustRunner([MyLocust], self.options) l = MyLocust() l._catch_exceptions = False self.assertRaises(HeyAnException, l.run) self.assertRaises(HeyAnException, l.run) self.assertEqual(1, len(runner.exceptions)) hash_key, exception = runner.exceptions.popitem() self.assertTrue("traceback" in exception) self.assertTrue("HeyAnException" in exception["traceback"]) self.assertEqual(2, exception["count"]) def test_exception_is_catched(self): """ Test that exceptions are stored, and execution continues """ class HeyAnException(Exception): pass class MyTaskSet(TaskSet): def __init__(self, *a, **kw): super(MyTaskSet, self).__init__(*a, **kw) self._task_queue = [ {"callable":self.will_error, "args":[], "kwargs":{}}, {"callable":self.will_stop, "args":[], "kwargs":{}}, ] @task(1) def will_error(self): raise HeyAnException(":(") @task(1) def will_stop(self): self.interrupt() class MyLocust(Locust): min_wait = 10 max_wait = 10 task_set = MyTaskSet runner = LocalLocustRunner([MyLocust], self.options) l = MyLocust() # supress stderr with mock.patch("sys.stderr") as mocked: l.task_set._task_queue = [l.task_set.will_error, l.task_set.will_stop] self.assertRaises(LocustError, l.run) # make sure HeyAnException isn't raised l.task_set._task_queue = [l.task_set.will_error, l.task_set.will_stop] self.assertRaises(LocustError, l.run) # make sure HeyAnException isn't raised self.assertEqual(2, len(mocked.method_calls)) # make sure exception was stored self.assertEqual(1, len(runner.exceptions)) hash_key, exception = runner.exceptions.popitem() self.assertTrue("traceback" in exception) self.assertTrue("HeyAnException" in exception["traceback"]) self.assertEqual(2, exception["count"]) class TestMessageSerializing(unittest.TestCase): def test_message_serialize(self): msg = Message("client_ready", None, "my_id") rebuilt = Message.unserialize(msg.serialize()) self.assertEqual(msg.type, rebuilt.type) self.assertEqual(msg.data, rebuilt.data) self.assertEqual(msg.node_id, rebuilt.node_id)

40.417476

129

0.57899

import unittest import gevent from gevent import sleep from gevent.queue import Queue import mock from locust import events from locust.core import Locust, TaskSet, task from locust.exception import LocustError from locust.main import parse_options from locust.rpc import Message from locust.runners import LocalLocustRunner, MasterLocustRunner from locust.stats import global_stats, RequestStats from locust.test.testcases import LocustTestCase def mocked_rpc_server(): class MockedRpcServer(object): queue = Queue() outbox = [] def __init__(self, host, port): pass @classmethod def mocked_send(cls, message): cls.queue.put(message.serialize()) sleep(0) def recv(self): results = self.queue.get() return Message.unserialize(results) def send(self, message): self.outbox.append(message.serialize()) return MockedRpcServer class TestMasterRunner(LocustTestCase): def setUp(self): global_stats.reset_all() self._slave_report_event_handlers = [h for h in events.slave_report._handlers] parser, _, _ = parse_options() args = [ "--clients", "10", "--hatch-rate", "10" ] opts, _ = parser.parse_args(args) self.options = opts def tearDown(self): events.slave_report._handlers = self._slave_report_event_handlers def test_slave_connect(self): class MyTestLocust(Locust): pass with mock.patch("locust.rpc.rpc.Server", mocked_rpc_server()) as server: master = MasterLocustRunner(MyTestLocust, self.options) server.mocked_send(Message("client_ready", None, "zeh_fake_client1")) self.assertEqual(1, len(master.clients)) self.assertTrue("zeh_fake_client1" in master.clients, "Could not find fake client in master instance's clients dict") server.mocked_send(Message("client_ready", None, "zeh_fake_client2")) server.mocked_send(Message("client_ready", None, "zeh_fake_client3")) server.mocked_send(Message("client_ready", None, "zeh_fake_client4")) self.assertEqual(4, len(master.clients)) server.mocked_send(Message("quit", None, "zeh_fake_client3")) self.assertEqual(3, len(master.clients)) def test_slave_stats_report_median(self): class MyTestLocust(Locust): pass with mock.patch("locust.rpc.rpc.Server", mocked_rpc_server()) as server: master = MasterLocustRunner(MyTestLocust, self.options) server.mocked_send(Message("client_ready", None, "fake_client")) master.stats.get("/", "GET").log(100, 23455) master.stats.get("/", "GET").log(800, 23455) master.stats.get("/", "GET").log(700, 23455) data = {"user_count":1} events.report_to_master.fire(client_id="fake_client", data=data) master.stats.clear_all() server.mocked_send(Message("stats", data, "fake_client")) s = master.stats.get("/", "GET") self.assertEqual(700, s.median_response_time) def test_master_total_stats(self): class MyTestLocust(Locust): pass with mock.patch("locust.rpc.rpc.Server", mocked_rpc_server()) as server: master = MasterLocustRunner(MyTestLocust, self.options) server.mocked_send(Message("client_ready", None, "fake_client")) stats = RequestStats() stats.log_request("GET", "/1", 100, 3546) stats.log_request("GET", "/1", 800, 56743) stats2 = RequestStats() stats2.log_request("GET", "/2", 700, 2201) server.mocked_send(Message("stats", { "stats":stats.serialize_stats(), "stats_total": stats.total.serialize(), "errors":stats.serialize_errors(), "user_count": 1, }, "fake_client")) server.mocked_send(Message("stats", { "stats":stats2.serialize_stats(), "stats_total": stats2.total.serialize(), "errors":stats2.serialize_errors(), "user_count": 2, }, "fake_client")) self.assertEqual(700, master.stats.total.median_response_time) def test_master_current_response_times(self): class MyTestLocust(Locust): pass start_time = 1 with mock.patch("time.time") as mocked_time: mocked_time.return_value = start_time global_stats.reset_all() with mock.patch("locust.rpc.rpc.Server", mocked_rpc_server()) as server: master = MasterLocustRunner(MyTestLocust, self.options) mocked_time.return_value += 1 server.mocked_send(Message("client_ready", None, "fake_client")) stats = RequestStats() stats.log_request("GET", "/1", 100, 3546) stats.log_request("GET", "/1", 800, 56743) server.mocked_send(Message("stats", { "stats":stats.serialize_stats(), "stats_total": stats.total.get_stripped_report(), "errors":stats.serialize_errors(), "user_count": 1, }, "fake_client")) mocked_time.return_value += 1 stats2 = RequestStats() stats2.log_request("GET", "/2", 400, 2201) server.mocked_send(Message("stats", { "stats":stats2.serialize_stats(), "stats_total": stats2.total.get_stripped_report(), "errors":stats2.serialize_errors(), "user_count": 2, }, "fake_client")) mocked_time.return_value += 4 self.assertEqual(400, master.stats.total.get_current_response_time_percentile(0.5)) self.assertEqual(800, master.stats.total.get_current_response_time_percentile(0.95)) # let 10 second pass, do some more requests, send it to the master and make # sure the current response time percentiles only accounts for these new requests mocked_time.return_value += 10 stats.log_request("GET", "/1", 20, 1) stats.log_request("GET", "/1", 30, 1) stats.log_request("GET", "/1", 3000, 1) server.mocked_send(Message("stats", { "stats":stats.serialize_stats(), "stats_total": stats.total.get_stripped_report(), "errors":stats.serialize_errors(), "user_count": 2, }, "fake_client")) self.assertEqual(30, master.stats.total.get_current_response_time_percentile(0.5)) self.assertEqual(3000, master.stats.total.get_current_response_time_percentile(0.95)) def test_spawn_zero_locusts(self): class MyTaskSet(TaskSet): @task def my_task(self): pass class MyTestLocust(Locust): task_set = MyTaskSet min_wait = 100 max_wait = 100 runner = LocalLocustRunner([MyTestLocust], self.options) timeout = gevent.Timeout(2.0) timeout.start() try: runner.start_hatching(0, 1, wait=True) runner.greenlet.join() except gevent.Timeout: self.fail("Got Timeout exception. A locust seems to have been spawned, even though 0 was specified.") finally: timeout.cancel() def test_spawn_uneven_locusts(self): class MyTestLocust(Locust): pass with mock.patch("locust.rpc.rpc.Server", mocked_rpc_server()) as server: master = MasterLocustRunner(MyTestLocust, self.options) for i in range(5): server.mocked_send(Message("client_ready", None, "fake_client%i" % i)) master.start_hatching(7, 7) self.assertEqual(5, len(server.outbox)) num_clients = 0 for msg in server.outbox: num_clients += Message.unserialize(msg).data["num_clients"] self.assertEqual(7, num_clients, "Total number of locusts that would have been spawned is not 7") def test_spawn_fewer_locusts_than_slaves(self): class MyTestLocust(Locust): pass with mock.patch("locust.rpc.rpc.Server", mocked_rpc_server()) as server: master = MasterLocustRunner(MyTestLocust, self.options) for i in range(5): server.mocked_send(Message("client_ready", None, "fake_client%i" % i)) master.start_hatching(2, 2) self.assertEqual(5, len(server.outbox)) num_clients = 0 for msg in server.outbox: num_clients += Message.unserialize(msg).data["num_clients"] self.assertEqual(2, num_clients, "Total number of locusts that would have been spawned is not 2") def test_exception_in_task(self): class HeyAnException(Exception): pass class MyLocust(Locust): class task_set(TaskSet): @task def will_error(self): raise HeyAnException(":(") runner = LocalLocustRunner([MyLocust], self.options) l = MyLocust() l._catch_exceptions = False self.assertRaises(HeyAnException, l.run) self.assertRaises(HeyAnException, l.run) self.assertEqual(1, len(runner.exceptions)) hash_key, exception = runner.exceptions.popitem() self.assertTrue("traceback" in exception) self.assertTrue("HeyAnException" in exception["traceback"]) self.assertEqual(2, exception["count"]) def test_exception_is_catched(self): class HeyAnException(Exception): pass class MyTaskSet(TaskSet): def __init__(self, *a, **kw): super(MyTaskSet, self).__init__(*a, **kw) self._task_queue = [ {"callable":self.will_error, "args":[], "kwargs":{}}, {"callable":self.will_stop, "args":[], "kwargs":{}}, ] @task(1) def will_error(self): raise HeyAnException(":(") @task(1) def will_stop(self): self.interrupt() class MyLocust(Locust): min_wait = 10 max_wait = 10 task_set = MyTaskSet runner = LocalLocustRunner([MyLocust], self.options) l = MyLocust() # supress stderr with mock.patch("sys.stderr") as mocked: l.task_set._task_queue = [l.task_set.will_error, l.task_set.will_stop] self.assertRaises(LocustError, l.run) # make sure HeyAnException isn't raised l.task_set._task_queue = [l.task_set.will_error, l.task_set.will_stop] self.assertRaises(LocustError, l.run) self.assertEqual(2, len(mocked.method_calls)) # make sure exception was stored self.assertEqual(1, len(runner.exceptions)) hash_key, exception = runner.exceptions.popitem() self.assertTrue("traceback" in exception) self.assertTrue("HeyAnException" in exception["traceback"]) self.assertEqual(2, exception["count"]) class TestMessageSerializing(unittest.TestCase): def test_message_serialize(self): msg = Message("client_ready", None, "my_id") rebuilt = Message.unserialize(msg.serialize()) self.assertEqual(msg.type, rebuilt.type) self.assertEqual(msg.data, rebuilt.data) self.assertEqual(msg.node_id, rebuilt.node_id)

true

f70083f0a986985862c8523f29ef60425bd92fd6

1,690

py

Python

revoke.py

jarviswwong/openssl-ca-server

7e966d5c1e9be28466553cb7b8e9a94cdf5d22ee

[ "MIT" ]

3

2021-07-29T09:49:08.000Z

2021-11-25T11:42:34.000Z

revoke.py

Damoclesword/openssl-ca-server

7e966d5c1e9be28466553cb7b8e9a94cdf5d22ee

[ "MIT" ]

null

revoke.py

Damoclesword/openssl-ca-server

7e966d5c1e9be28466553cb7b8e9a94cdf5d22ee

[ "MIT" ]

2

2020-04-28T23:34:44.000Z

2021-06-10T05:34:36.000Z

import os import datetime import hashlib import pexpect from config import * from common import openssl, jsonMessage, gencrl from OpenSSL import crypto # 通过证书文件吊销证书 def revokeFromCert(cert): # 读取证书数据 try: x509_obj = crypto.load_certificate(crypto.FILETYPE_PEM, cert) # get_serial_number返回10进制的serial，需转为16进制 serial = hex(x509_obj.get_serial_number())[2:] except crypto.Error: return jsonMessage(status=-1, msg="[ERROR]: Wrong certificate (X509) format!") # 存到临时文件夹里 path = os.path.join( '/tmp', hashlib.md5(str(datetime.datetime.now()).encode('utf-8')).hexdigest() + "_revokecert.crt") with open(path, "w") as f: f.write(cert.decode('utf8')) return revoking(path, serial) # 通过serial吊销证书，方法是去CA/newcerts文件夹下寻找相应证书的备份 # @serial：必须为16进制格式 def revokeFromSerial(serial): path = os.path.join(CA_NEWCERTS, serial + ".pem") if not os.path.exists(path): msg = "[ERROR]: This may be an invalid serial number!" return jsonMessage(-1, msg) return revoking(path, serial) def revoking(certfile, serial): child = openssl('ca', '-revoke', certfile) ret = child.expect( ['Already revoked', 'Revoking Certificate', pexpect.EOF]) if ret == 0: msg = "[ERROR]: This certificate is revoked!" return jsonMessage(-1, msg) elif ret == 1: msg = "Revoke Certificate success! Serial number is " + serial # 重新生成一遍证书文件 gencrl() return jsonMessage(0, msg, {"Serial Number": serial}) elif ret == 2: msg = "[ERROR]: Revoke failed, unknown error!" return jsonMessage(-1, msg)

29.137931

79

0.634911

import os import datetime import hashlib import pexpect from config import * from common import openssl, jsonMessage, gencrl from OpenSSL import crypto def revokeFromCert(cert): try: x509_obj = crypto.load_certificate(crypto.FILETYPE_PEM, cert) serial = hex(x509_obj.get_serial_number())[2:] except crypto.Error: return jsonMessage(status=-1, msg="[ERROR]: Wrong certificate (X509) format!") path = os.path.join( '/tmp', hashlib.md5(str(datetime.datetime.now()).encode('utf-8')).hexdigest() + "_revokecert.crt") with open(path, "w") as f: f.write(cert.decode('utf8')) return revoking(path, serial) def revokeFromSerial(serial): path = os.path.join(CA_NEWCERTS, serial + ".pem") if not os.path.exists(path): msg = "[ERROR]: This may be an invalid serial number!" return jsonMessage(-1, msg) return revoking(path, serial) def revoking(certfile, serial): child = openssl('ca', '-revoke', certfile) ret = child.expect( ['Already revoked', 'Revoking Certificate', pexpect.EOF]) if ret == 0: msg = "[ERROR]: This certificate is revoked!" return jsonMessage(-1, msg) elif ret == 1: msg = "Revoke Certificate success! Serial number is " + serial gencrl() return jsonMessage(0, msg, {"Serial Number": serial}) elif ret == 2: msg = "[ERROR]: Revoke failed, unknown error!" return jsonMessage(-1, msg)

true

f700848773d400c4ada7594725b4d25b98b7c121

1,536

py

Python

dev/circuitpython/examples/neotrellis_simpletest.py

scripsi/picodeebee

0ec77e92f09fa8711705623482e57a5e0b702696

[ "MIT" ]

7

2021-03-15T10:06:20.000Z

2022-03-23T02:53:15.000Z

Lights/adafruit-circuitpython-bundle-6.x-mpy-20210310/examples/neotrellis_simpletest.py

IanSMoyes/SpiderPi

cc3469980ae87b92d0dc43c05dbd579f0fa8c4b1

[ "Apache-2.0" ]

5

2021-04-27T18:21:11.000Z

2021-05-02T14:17:14.000Z

Lights/adafruit-circuitpython-bundle-6.x-mpy-20210310/examples/neotrellis_simpletest.py

IanSMoyes/SpiderPi

cc3469980ae87b92d0dc43c05dbd579f0fa8c4b1

[ "Apache-2.0" ]

null

# SPDX-FileCopyrightText: 2021 ladyada for Adafruit Industries # SPDX-License-Identifier: MIT import time from board import SCL, SDA import busio from adafruit_neotrellis.neotrellis import NeoTrellis # create the i2c object for the trellis i2c_bus = busio.I2C(SCL, SDA) # create the trellis trellis = NeoTrellis(i2c_bus) # some color definitions OFF = (0, 0, 0) RED = (255, 0, 0) YELLOW = (255, 150, 0) GREEN = (0, 255, 0) CYAN = (0, 255, 255) BLUE = (0, 0, 255) PURPLE = (180, 0, 255) # this will be called when button events are received def blink(event): # turn the LED on when a rising edge is detected if event.edge == NeoTrellis.EDGE_RISING: trellis.pixels[event.number] = CYAN # turn the LED off when a rising edge is detected elif event.edge == NeoTrellis.EDGE_FALLING: trellis.pixels[event.number] = OFF for i in range(16): # activate rising edge events on all keys trellis.activate_key(i, NeoTrellis.EDGE_RISING) # activate falling edge events on all keys trellis.activate_key(i, NeoTrellis.EDGE_FALLING) # set all keys to trigger the blink callback trellis.callbacks[i] = blink # cycle the LEDs on startup trellis.pixels[i] = PURPLE time.sleep(0.05) for i in range(16): trellis.pixels[i] = OFF time.sleep(0.05) while True: # call the sync function call any triggered callbacks trellis.sync() # the trellis can only be read every 17 millisecons or so time.sleep(0.02)

27.428571

63

0.676432

import time from board import SCL, SDA import busio from adafruit_neotrellis.neotrellis import NeoTrellis i2c_bus = busio.I2C(SCL, SDA) trellis = NeoTrellis(i2c_bus) OFF = (0, 0, 0) RED = (255, 0, 0) YELLOW = (255, 150, 0) GREEN = (0, 255, 0) CYAN = (0, 255, 255) BLUE = (0, 0, 255) PURPLE = (180, 0, 255) def blink(event): if event.edge == NeoTrellis.EDGE_RISING: trellis.pixels[event.number] = CYAN elif event.edge == NeoTrellis.EDGE_FALLING: trellis.pixels[event.number] = OFF for i in range(16): trellis.activate_key(i, NeoTrellis.EDGE_RISING) trellis.activate_key(i, NeoTrellis.EDGE_FALLING) trellis.callbacks[i] = blink trellis.pixels[i] = PURPLE time.sleep(0.05) for i in range(16): trellis.pixels[i] = OFF time.sleep(0.05) while True: trellis.sync() time.sleep(0.02)

true

f70085721ab1f27e1f288841c761b393cb3b5ed0

2,068

py

Python

MuPythonLibrary/Uefi/EdkII/VariableFormat_Test.py

matthewfcarlson/mu_pip_python_library

659538b80fd5c060e053e14a828d9d41161682a1

[ "BSD-2-Clause" ]

8

2019-10-05T09:06:39.000Z

2022-03-11T10:45:12.000Z

MuPythonLibrary/Uefi/EdkII/VariableFormat_Test.py

matthewfcarlson/mu_pip_python_library

659538b80fd5c060e053e14a828d9d41161682a1

[ "BSD-2-Clause" ]

3

2018-12-14T21:14:17.000Z

2019-04-18T20:26:55.000Z

MuPythonLibrary/Uefi/EdkII/VariableFormat_Test.py

matthewfcarlson/mu_pip_python_library

659538b80fd5c060e053e14a828d9d41161682a1

[ "BSD-2-Clause" ]

8

2019-05-10T19:18:39.000Z

2022-03-11T10:45:09.000Z

# @file VariableFormat_Test.py # Unit test harness for the VariableFormat module/classes. # ## # Copyright (c) 2017, Microsoft Corporation # # All rights reserved. # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # 1. Redistributions of source code must retain the above copyright notice, # this list of conditions and the following disclaimer. # 2. Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND # ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED # WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. # IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, # INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, # BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, # DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF # LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE # OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF # ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ## import unittest import MuPythonLibrary.Uefi.EdkII.VariableFormat as VF class TestVariableHeader(unittest.TestCase): def test_set_name(self): var = VF.VariableHeader() test_name = "MyNewName" var.set_name(test_name) self.assertEqual(var.Name, test_name) def test_get_packed_name(self): var = VF.VariableHeader() test_name = "MyNewName" var.set_name(test_name) test_name_packed = bytes.fromhex('4D0079004E00650077004E0061006D0065000000') self.assertEqual(var.get_packed_name(), test_name_packed) if __name__ == '__main__': unittest.main()

38.296296

84

0.762089

import unittest import MuPythonLibrary.Uefi.EdkII.VariableFormat as VF class TestVariableHeader(unittest.TestCase): def test_set_name(self): var = VF.VariableHeader() test_name = "MyNewName" var.set_name(test_name) self.assertEqual(var.Name, test_name) def test_get_packed_name(self): var = VF.VariableHeader() test_name = "MyNewName" var.set_name(test_name) test_name_packed = bytes.fromhex('4D0079004E00650077004E0061006D0065000000') self.assertEqual(var.get_packed_name(), test_name_packed) if __name__ == '__main__': unittest.main()

true

f7008588a2462d13b0cabd06be14dc705036a2fd

3,068

py

Python

yaql/tests/test_common.py

nzlosh/yaql

bb65fc64026d431ffb866d02825deb3a0e4b5943

[ "Apache-2.0" ]

112

2015-10-18T02:57:41.000Z

2022-03-28T18:26:36.000Z

yaql/tests/test_common.py

nzlosh/yaql

bb65fc64026d431ffb866d02825deb3a0e4b5943

[ "Apache-2.0" ]

3

2020-06-09T11:54:38.000Z

2021-04-30T06:12:37.000Z

yaql/tests/test_common.py

nzlosh/yaql

bb65fc64026d431ffb866d02825deb3a0e4b5943

[ "Apache-2.0" ]

27

2015-12-10T00:10:02.000Z

2022-03-20T21:51:01.000Z

# Copyright (c) 2015 Mirantis, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. import yaql.tests class TestCommon(yaql.tests.TestCase): def test_null(self): self.assertIsNone(self.eval('null')) def test_true(self): res = self.eval('true') self.assertTrue(res) self.assertIsInstance(res, bool) def test_false(self): res = self.eval('false') self.assertFalse(res) self.assertIsInstance(res, bool) def test_string(self): self.assertEqual('True', self.eval('True')) self.assertEqual('some string', self.eval("'some string'")) def test_null_to_null(self): self.assertTrue(self.eval('null = null')) self.assertFalse(self.eval('null != null')) self.assertTrue(self.eval('null <= null')) self.assertTrue(self.eval('null >= null')) self.assertFalse(self.eval('null < null')) self.assertFalse(self.eval('null > null')) def test_ordering(self): self.assertTrue(self.eval('null < 0')) self.assertTrue(self.eval('null < true')) self.assertTrue(self.eval('null < false')) self.assertTrue(self.eval('null < a')) self.assertTrue(self.eval('null <= 0')) self.assertFalse(self.eval('null > 0')) self.assertFalse(self.eval('null >= 0')) self.assertTrue(self.eval('null != 0')) self.assertTrue(self.eval('null != false')) self.assertFalse(self.eval('null = false')) self.assertFalse(self.eval('null = 0')) self.assertFalse(self.eval('0 < null')) self.assertFalse(self.eval('0 <= null')) self.assertTrue(self.eval('0 >= null')) self.assertTrue(self.eval('0 > null')) def test_max(self): self.assertEqual(5, self.eval('max(1, 5)')) self.assertEqual(-1, self.eval('max(null, -1)')) self.assertIsNone(self.eval('max(null, null)')) def test_min(self): self.assertEqual(1, self.eval('min(1, 5)')) self.assertIsNone(self.eval('min(null, -1)')) self.assertIsNone(self.eval('min(null, null)')) def test_comparision_of_incomparable(self): self.assertFalse(self.eval('a = 1')) self.assertFalse(self.eval('a = false')) self.assertFalse(self.eval('a = null')) self.assertFalse(self.eval('[a] = [false]')) self.assertTrue(self.eval('a != 1')) self.assertTrue(self.eval('a != false')) self.assertTrue(self.eval('[a] != [false]')) self.assertTrue(self.eval('a != null'))

38.35

78

0.622229

import yaql.tests class TestCommon(yaql.tests.TestCase): def test_null(self): self.assertIsNone(self.eval('null')) def test_true(self): res = self.eval('true') self.assertTrue(res) self.assertIsInstance(res, bool) def test_false(self): res = self.eval('false') self.assertFalse(res) self.assertIsInstance(res, bool) def test_string(self): self.assertEqual('True', self.eval('True')) self.assertEqual('some string', self.eval("'some string'")) def test_null_to_null(self): self.assertTrue(self.eval('null = null')) self.assertFalse(self.eval('null != null')) self.assertTrue(self.eval('null <= null')) self.assertTrue(self.eval('null >= null')) self.assertFalse(self.eval('null < null')) self.assertFalse(self.eval('null > null')) def test_ordering(self): self.assertTrue(self.eval('null < 0')) self.assertTrue(self.eval('null < true')) self.assertTrue(self.eval('null < false')) self.assertTrue(self.eval('null < a')) self.assertTrue(self.eval('null <= 0')) self.assertFalse(self.eval('null > 0')) self.assertFalse(self.eval('null >= 0')) self.assertTrue(self.eval('null != 0')) self.assertTrue(self.eval('null != false')) self.assertFalse(self.eval('null = false')) self.assertFalse(self.eval('null = 0')) self.assertFalse(self.eval('0 < null')) self.assertFalse(self.eval('0 <= null')) self.assertTrue(self.eval('0 >= null')) self.assertTrue(self.eval('0 > null')) def test_max(self): self.assertEqual(5, self.eval('max(1, 5)')) self.assertEqual(-1, self.eval('max(null, -1)')) self.assertIsNone(self.eval('max(null, null)')) def test_min(self): self.assertEqual(1, self.eval('min(1, 5)')) self.assertIsNone(self.eval('min(null, -1)')) self.assertIsNone(self.eval('min(null, null)')) def test_comparision_of_incomparable(self): self.assertFalse(self.eval('a = 1')) self.assertFalse(self.eval('a = false')) self.assertFalse(self.eval('a = null')) self.assertFalse(self.eval('[a] = [false]')) self.assertTrue(self.eval('a != 1')) self.assertTrue(self.eval('a != false')) self.assertTrue(self.eval('[a] != [false]')) self.assertTrue(self.eval('a != null'))

true

f70086097b2870bddac591ff695bf96e16de715d

7,800

py

Python

keras/saving/saved_model/layer_serialization.py

quantumalaviya/keras

8d874de12ed2e199d9528bfff891f4f60ee2a636

[ "Apache-2.0" ]

1

2021-09-11T21:25:20.000Z

keras/saving/saved_model/layer_serialization.py

quantumalaviya/keras

8d874de12ed2e199d9528bfff891f4f60ee2a636

[ "Apache-2.0" ]

null

keras/saving/saved_model/layer_serialization.py

quantumalaviya/keras

8d874de12ed2e199d9528bfff891f4f60ee2a636

[ "Apache-2.0" ]

null

# Copyright 2019 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Classes and functions implementing Layer SavedModel serialization.""" from keras.mixed_precision import policy from keras.saving.saved_model import base_serialization from keras.saving.saved_model import constants from keras.saving.saved_model import save_impl from keras.saving.saved_model import serialized_attributes from keras.utils import generic_utils import tensorflow.compat.v2 as tf class LayerSavedModelSaver(base_serialization.SavedModelSaver): """Implements Layer SavedModel serialization.""" @property def object_identifier(self): return constants.LAYER_IDENTIFIER @property def python_properties(self): # TODO(kathywu): Add python property validator return self._python_properties_internal() def _python_properties_internal(self): """Returns dictionary of all python properties.""" # TODO(kathywu): Add support for metrics serialization. # TODO(kathywu): Synchronize with the keras spec (go/keras-json-spec) once # the python config serialization has caught up. metadata = dict( name=self.obj.name, trainable=self.obj.trainable, expects_training_arg=self.obj._expects_training_arg, # pylint: disable=protected-access dtype=policy.serialize(self.obj._dtype_policy), # pylint: disable=protected-access batch_input_shape=getattr(self.obj, '_batch_input_shape', None), stateful=self.obj.stateful, must_restore_from_config=self.obj._must_restore_from_config, # pylint: disable=protected-access ) metadata.update(get_serialized(self.obj)) if self.obj.input_spec is not None: # Layer's input_spec has already been type-checked in the property setter. metadata['input_spec'] = tf.nest.map_structure( lambda x: generic_utils.serialize_keras_object(x) if x else None, self.obj.input_spec) if (self.obj.activity_regularizer is not None and hasattr(self.obj.activity_regularizer, 'get_config')): metadata['activity_regularizer'] = generic_utils.serialize_keras_object( self.obj.activity_regularizer) if self.obj._build_input_shape is not None: # pylint: disable=protected-access metadata['build_input_shape'] = self.obj._build_input_shape # pylint: disable=protected-access return metadata def objects_to_serialize(self, serialization_cache): return (self._get_serialized_attributes( serialization_cache).objects_to_serialize) def functions_to_serialize(self, serialization_cache): return (self._get_serialized_attributes( serialization_cache).functions_to_serialize) def _get_serialized_attributes(self, serialization_cache): """Generates or retrieves serialized attributes from cache.""" keras_cache = serialization_cache.setdefault(constants.KERAS_CACHE_KEY, {}) if self.obj in keras_cache: return keras_cache[self.obj] serialized_attr = keras_cache[self.obj] = ( serialized_attributes.SerializedAttributes.new(self.obj)) if (save_impl.should_skip_serialization(self.obj) or self.obj._must_restore_from_config): # pylint: disable=protected-access return serialized_attr object_dict, function_dict = self._get_serialized_attributes_internal( serialization_cache) serialized_attr.set_and_validate_objects(object_dict) serialized_attr.set_and_validate_functions(function_dict) return serialized_attr def _get_serialized_attributes_internal(self, serialization_cache): """Returns dictionary of serialized attributes.""" objects = save_impl.wrap_layer_objects(self.obj, serialization_cache) functions = save_impl.wrap_layer_functions(self.obj, serialization_cache) # Attribute validator requires that the default save signature is added to # function dict, even if the value is None. functions['_default_save_signature'] = None return objects, functions # TODO(kathywu): Move serialization utils (and related utils from # generic_utils.py) to a separate file. def get_serialized(obj): with generic_utils.skip_failed_serialization(): # Store the config dictionary, which may be used when reviving the object. # When loading, the program will attempt to revive the object from config, # and if that fails, the object will be revived from the SavedModel. return generic_utils.serialize_keras_object(obj) class InputLayerSavedModelSaver(base_serialization.SavedModelSaver): """InputLayer serialization.""" @property def object_identifier(self): return constants.INPUT_LAYER_IDENTIFIER @property def python_properties(self): return dict( class_name=type(self.obj).__name__, name=self.obj.name, dtype=self.obj.dtype, sparse=self.obj.sparse, ragged=self.obj.ragged, batch_input_shape=self.obj._batch_input_shape, # pylint: disable=protected-access config=self.obj.get_config()) def objects_to_serialize(self, serialization_cache): return {} def functions_to_serialize(self, serialization_cache): return {} class RNNSavedModelSaver(LayerSavedModelSaver): """RNN layer serialization.""" @property def object_identifier(self): return constants.RNN_LAYER_IDENTIFIER def _get_serialized_attributes_internal(self, serialization_cache): objects, functions = ( super(RNNSavedModelSaver, self)._get_serialized_attributes_internal( serialization_cache)) states = tf.__internal__.tracking.wrap(self.obj.states) # SaveModel require all the objects to be Trackable when saving. # If the states is still a tuple after wrap_or_unwrap, it means it doesn't # contain any trackable item within it, eg empty tuple or (None, None) for # stateless ConvLSTM2D. We convert them to list so that wrap_or_unwrap can # make it a Trackable again for saving. When loaded, ConvLSTM2D is # able to handle the tuple/list conversion. if isinstance(states, tuple): states = tf.__internal__.tracking.wrap(list(states)) objects['states'] = states return objects, functions class VocabularySavedModelSaver(LayerSavedModelSaver): """Handles vocabulary layer serialization. This class is needed for StringLookup, IntegerLookup, and TextVectorization, which all have a vocabulary as part of the config. Currently, we keep this vocab as part of the config until saving, when we need to clear it to avoid initializing a StaticHashTable twice (once when restoring the config and once when restoring restoring module resources). After clearing the vocab, we presist a property to the layer indicating it was constructed with a vocab. """ @property def python_properties(self): # TODO(kathywu): Add python property validator metadata = self._python_properties_internal() # Clear the vocabulary from the config during saving. metadata['config']['vocabulary'] = None # Persist a property to track that a vocabulary was passed on construction. metadata['config']['has_input_vocabulary'] = self.obj._has_input_vocabulary # pylint: disable=protected-access return metadata

42.162162

115

0.751154

from keras.mixed_precision import policy from keras.saving.saved_model import base_serialization from keras.saving.saved_model import constants from keras.saving.saved_model import save_impl from keras.saving.saved_model import serialized_attributes from keras.utils import generic_utils import tensorflow.compat.v2 as tf class LayerSavedModelSaver(base_serialization.SavedModelSaver): @property def object_identifier(self): return constants.LAYER_IDENTIFIER @property def python_properties(self): return self._python_properties_internal() def _python_properties_internal(self): metadata = dict( name=self.obj.name, trainable=self.obj.trainable, expects_training_arg=self.obj._expects_training_arg, dtype=policy.serialize(self.obj._dtype_policy), batch_input_shape=getattr(self.obj, '_batch_input_shape', None), stateful=self.obj.stateful, must_restore_from_config=self.obj._must_restore_from_config, ) metadata.update(get_serialized(self.obj)) if self.obj.input_spec is not None: metadata['input_spec'] = tf.nest.map_structure( lambda x: generic_utils.serialize_keras_object(x) if x else None, self.obj.input_spec) if (self.obj.activity_regularizer is not None and hasattr(self.obj.activity_regularizer, 'get_config')): metadata['activity_regularizer'] = generic_utils.serialize_keras_object( self.obj.activity_regularizer) if self.obj._build_input_shape is not None: # pylint: disable=protected-access metadata['build_input_shape'] = self.obj._build_input_shape # pylint: disable=protected-access return metadata def objects_to_serialize(self, serialization_cache): return (self._get_serialized_attributes( serialization_cache).objects_to_serialize) def functions_to_serialize(self, serialization_cache): return (self._get_serialized_attributes( serialization_cache).functions_to_serialize) def _get_serialized_attributes(self, serialization_cache): keras_cache = serialization_cache.setdefault(constants.KERAS_CACHE_KEY, {}) if self.obj in keras_cache: return keras_cache[self.obj] serialized_attr = keras_cache[self.obj] = ( serialized_attributes.SerializedAttributes.new(self.obj)) if (save_impl.should_skip_serialization(self.obj) or self.obj._must_restore_from_config): # pylint: disable=protected-access return serialized_attr object_dict, function_dict = self._get_serialized_attributes_internal( serialization_cache) serialized_attr.set_and_validate_objects(object_dict) serialized_attr.set_and_validate_functions(function_dict) return serialized_attr def _get_serialized_attributes_internal(self, serialization_cache): objects = save_impl.wrap_layer_objects(self.obj, serialization_cache) functions = save_impl.wrap_layer_functions(self.obj, serialization_cache) # Attribute validator requires that the default save signature is added to # function dict, even if the value is None. functions['_default_save_signature'] = None return objects, functions # TODO(kathywu): Move serialization utils (and related utils from # generic_utils.py) to a separate file. def get_serialized(obj): with generic_utils.skip_failed_serialization(): # Store the config dictionary, which may be used when reviving the object. # When loading, the program will attempt to revive the object from config, # and if that fails, the object will be revived from the SavedModel. return generic_utils.serialize_keras_object(obj) class InputLayerSavedModelSaver(base_serialization.SavedModelSaver): @property def object_identifier(self): return constants.INPUT_LAYER_IDENTIFIER @property def python_properties(self): return dict( class_name=type(self.obj).__name__, name=self.obj.name, dtype=self.obj.dtype, sparse=self.obj.sparse, ragged=self.obj.ragged, batch_input_shape=self.obj._batch_input_shape, # pylint: disable=protected-access config=self.obj.get_config()) def objects_to_serialize(self, serialization_cache): return {} def functions_to_serialize(self, serialization_cache): return {} class RNNSavedModelSaver(LayerSavedModelSaver): @property def object_identifier(self): return constants.RNN_LAYER_IDENTIFIER def _get_serialized_attributes_internal(self, serialization_cache): objects, functions = ( super(RNNSavedModelSaver, self)._get_serialized_attributes_internal( serialization_cache)) states = tf.__internal__.tracking.wrap(self.obj.states) # SaveModel require all the objects to be Trackable when saving. # If the states is still a tuple after wrap_or_unwrap, it means it doesn't if isinstance(states, tuple): states = tf.__internal__.tracking.wrap(list(states)) objects['states'] = states return objects, functions class VocabularySavedModelSaver(LayerSavedModelSaver): @property def python_properties(self): metadata = self._python_properties_internal() metadata['config']['vocabulary'] = None metadata['config']['has_input_vocabulary'] = self.obj._has_input_vocabulary return metadata

true

f700864adcc91b720f4dd4a166b39644acc7d0b2

76

py

Python

random number generation.py

Ratheshprabakar/Python-Programs

fca9d4f0b5f5f5693b3d7e25c6d890f4973dc19e

[ "MIT" ]

2

2019-07-10T06:32:05.000Z

2019-11-13T07:52:53.000Z

random number generation.py

Ratheshprabakar/Python-Programs

fca9d4f0b5f5f5693b3d7e25c6d890f4973dc19e

[ "MIT" ]

null

random number generation.py

Ratheshprabakar/Python-Programs

fca9d4f0b5f5f5693b3d7e25c6d890f4973dc19e

[ "MIT" ]

1

2019-10-12T06:56:13.000Z

import random x=random.random() print("The Random number is",round(x,3))

19

41

0.710526

import random x=random.random() print("The Random number is",round(x,3))

true

f700867ec71ff7fa38349f5f83924777079c446f

1,627

py

Python

cmframework/src/cmframework/utils/cmpluginmanager.py

akraino-edge-stack/ta-config-manager

8a3f88d0dbf6afdb0130b9d35e563f8a54d15d44

[ "Apache-2.0" ]

null

cmframework/src/cmframework/utils/cmpluginmanager.py

akraino-edge-stack/ta-config-manager

8a3f88d0dbf6afdb0130b9d35e563f8a54d15d44

[ "Apache-2.0" ]

null

cmframework/src/cmframework/utils/cmpluginmanager.py

akraino-edge-stack/ta-config-manager

8a3f88d0dbf6afdb0130b9d35e563f8a54d15d44

[ "Apache-2.0" ]

null

# Copyright 2019 Nokia # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # vim: tabstop=8 expandtab shiftwidth=4 softtabstop=4 import re import logging from cmframework.apis import cmerror class CMPluginManager(object): def __init__(self, plugins_path): self.pluginlist = {} self.filterdict = {} self.plugins_path = plugins_path # pylint: disable=no-self-use def load_plugin(self): raise cmerror.CMError('Not implemented') # pylint: disable=no-self-use def build_input(self, indata, filtername): search_re = re.compile(filtername) if isinstance(indata, dict): filter_data = {} for key, value in indata.iteritems(): logging.debug('Matching %s against %s', key, filtername) if search_re.match(key): filter_data[key] = value else: filter_data = [] for key in indata: logging.debug('Matching %s against %s', key, filtername) if search_re.match(key): filter_data.append(key) return filter_data

33.204082

74

0.653964

import re import logging from cmframework.apis import cmerror class CMPluginManager(object): def __init__(self, plugins_path): self.pluginlist = {} self.filterdict = {} self.plugins_path = plugins_path def load_plugin(self): raise cmerror.CMError('Not implemented') def build_input(self, indata, filtername): search_re = re.compile(filtername) if isinstance(indata, dict): filter_data = {} for key, value in indata.iteritems(): logging.debug('Matching %s against %s', key, filtername) if search_re.match(key): filter_data[key] = value else: filter_data = [] for key in indata: logging.debug('Matching %s against %s', key, filtername) if search_re.match(key): filter_data.append(key) return filter_data

true

f70086a12cb7a805133ce4cd015dbd84c54c0b65

28

py

Python

ent2id/__init__.py

skojaku/ent2id

1483cc9430999db7a6598dfdf0afa7302ada4893

[ "CC0-1.0" ]

null

ent2id/__init__.py

skojaku/ent2id

1483cc9430999db7a6598dfdf0afa7302ada4893

[ "CC0-1.0" ]

null

ent2id/__init__.py

skojaku/ent2id

1483cc9430999db7a6598dfdf0afa7302ada4893

[ "CC0-1.0" ]

null

from ent2id.Ent2Id import *

14

27

0.785714

from ent2id.Ent2Id import *

true

f70086a2b3578a8b051c307075e29ccb0a1db0f5

5,775

py

Python

venv/Lib/site-packages/tencentcloud/hcm/v20181106/models.py

Lparksi/bot

8a38953d09436b60e8edff4ebe86bf19fe3b7046

[ "MIT" ]

3

2020-03-31T10:36:31.000Z

2020-04-23T12:01:10.000Z

venv/Lib/site-packages/tencentcloud/hcm/v20181106/models.py

Lparksi/bot

8a38953d09436b60e8edff4ebe86bf19fe3b7046

[ "MIT" ]

1

2020-07-16T14:51:26.000Z

2020-07-30T12:46:55.000Z

venv/Lib/site-packages/tencentcloud/hcm/v20181106/models.py

Lparksi/bot

8a38953d09436b60e8edff4ebe86bf19fe3b7046

[ "MIT" ]

null

# -*- coding: utf8 -*- # Copyright (c) 2017-2018 THL A29 Limited, a Tencent company. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from tencentcloud.common.abstract_model import AbstractModel class EvaluationRequest(AbstractModel): """Evaluation请求参数结构体 """ def __init__(self): """ :param SessionId: 图片唯一标识，一张图片一个SessionId； :type SessionId: str :param Image: 图片数据，需要使用base64对图片的二进制数据进行编码，与url参数二者填一即可； :type Image: str :param HcmAppid: 业务应用ID，与账号应用APPID无关，是用来方便客户管理服务的参数，新的 HcmAppid 可以在[控制台](https://console.cloud.tencent.com/hcm)【应用管理】下新建。 :type HcmAppid: str :param Url: 图片url，与Image参数二者填一即可； :type Url: str :param SupportHorizontalImage: 横屏拍摄开关，若开启则支持传输横屏拍摄的图片； :type SupportHorizontalImage: bool :param RejectNonArithmeticImage: 拒绝非速算图（如风景图、人物图）开关，若开启，则遇到非速算图会快速返回拒绝的结果，但极端情况下可能会影响评估结果（比如算式截图贴到风景画里可能被判为非速算图直接返回了）。 :type RejectNonArithmeticImage: bool :param IsAsync: 异步模式标识，0：同步模式，1：异步模式。默认为同步模式 :type IsAsync: int :param EnableDispRelatedVertical: 是否展开耦合算式中的竖式计算 :type EnableDispRelatedVertical: bool :param EnableDispMidresult: 是否展示竖式算式的中间结果和格式控制字符 :type EnableDispMidresult: bool :param EnablePdfRecognize: 是否开启pdf识别，默认开启 :type EnablePdfRecognize: bool :param PdfPageIndex: pdf页码，从0开始，默认为0 :type PdfPageIndex: int """ self.SessionId = None self.Image = None self.HcmAppid = None self.Url = None self.SupportHorizontalImage = None self.RejectNonArithmeticImage = None self.IsAsync = None self.EnableDispRelatedVertical = None self.EnableDispMidresult = None self.EnablePdfRecognize = None self.PdfPageIndex = None def _deserialize(self, params): self.SessionId = params.get("SessionId") self.Image = params.get("Image") self.HcmAppid = params.get("HcmAppid") self.Url = params.get("Url") self.SupportHorizontalImage = params.get("SupportHorizontalImage") self.RejectNonArithmeticImage = params.get("RejectNonArithmeticImage") self.IsAsync = params.get("IsAsync") self.EnableDispRelatedVertical = params.get("EnableDispRelatedVertical") self.EnableDispMidresult = params.get("EnableDispMidresult") self.EnablePdfRecognize = params.get("EnablePdfRecognize") self.PdfPageIndex = params.get("PdfPageIndex") class EvaluationResponse(AbstractModel): """Evaluation返回参数结构体 """ def __init__(self): """ :param SessionId: 图片唯一标识，一张图片一个SessionId； :type SessionId: str :param Items: 识别出的算式信息；注意：此字段可能返回 null，表示取不到有效值。 :type Items: list of Item :param TaskId: 任务 id，用于查询接口 :type TaskId: str :param RequestId: 唯一请求 ID，每次请求都会返回。定位问题时需要提供该次请求的 RequestId。 :type RequestId: str """ self.SessionId = None self.Items = None self.TaskId = None self.RequestId = None def _deserialize(self, params): self.SessionId = params.get("SessionId") if params.get("Items") is not None: self.Items = [] for item in params.get("Items"): obj = Item() obj._deserialize(item) self.Items.append(obj) self.TaskId = params.get("TaskId") self.RequestId = params.get("RequestId") class Item(AbstractModel): """识别出的算术式信息及评估结果 """ def __init__(self): """ :param Item: 识别的算式是否正确 :type Item: str :param ItemString: 识别的算式 :type ItemString: str :param ItemCoord: 识别的算式在图片上的位置信息 :type ItemCoord: :class:`tencentcloud.hcm.v20181106.models.ItemCoord` :param Answer: 推荐的答案，暂不支持多个关系运算符、无关系运算符、单位换算错题的推荐答案返回。 :type Answer: str :param ExpressionType: 算式题型编号，如加减乘除四则题型，具体题型及编号如下：1 加减乘除四则 2 加减乘除已知结果求运算因子3 判断大小 4 约等于估算 5 带余数除法 6 分数四则运算 7 单位换算 8 竖式加减法 9 竖式乘除法 10 脱式计算 11 解方程注意：此字段可能返回 null，表示取不到有效值。 :type ExpressionType: str """ self.Item = None self.ItemString = None self.ItemCoord = None self.Answer = None self.ExpressionType = None def _deserialize(self, params): self.Item = params.get("Item") self.ItemString = params.get("ItemString") if params.get("ItemCoord") is not None: self.ItemCoord = ItemCoord() self.ItemCoord._deserialize(params.get("ItemCoord")) self.Answer = params.get("Answer") self.ExpressionType = params.get("ExpressionType") class ItemCoord(AbstractModel): """目标算式在图片上的坐标信息 """ def __init__(self): """ :param Height: 算式高度 :type Height: int :param Width: 算式宽度 :type Width: int :param X: 算式图的左上角横坐标 :type X: int :param Y: 算式图的左上角纵坐标 :type Y: int """ self.Height = None self.Width = None self.X = None self.Y = None def _deserialize(self, params): self.Height = params.get("Height") self.Width = params.get("Width") self.X = params.get("X") self.Y = params.get("Y")

33.381503

151

0.641732

from tencentcloud.common.abstract_model import AbstractModel class EvaluationRequest(AbstractModel): def __init__(self): self.SessionId = None self.Image = None self.HcmAppid = None self.Url = None self.SupportHorizontalImage = None self.RejectNonArithmeticImage = None self.IsAsync = None self.EnableDispRelatedVertical = None self.EnableDispMidresult = None self.EnablePdfRecognize = None self.PdfPageIndex = None def _deserialize(self, params): self.SessionId = params.get("SessionId") self.Image = params.get("Image") self.HcmAppid = params.get("HcmAppid") self.Url = params.get("Url") self.SupportHorizontalImage = params.get("SupportHorizontalImage") self.RejectNonArithmeticImage = params.get("RejectNonArithmeticImage") self.IsAsync = params.get("IsAsync") self.EnableDispRelatedVertical = params.get("EnableDispRelatedVertical") self.EnableDispMidresult = params.get("EnableDispMidresult") self.EnablePdfRecognize = params.get("EnablePdfRecognize") self.PdfPageIndex = params.get("PdfPageIndex") class EvaluationResponse(AbstractModel): def __init__(self): self.SessionId = None self.Items = None self.TaskId = None self.RequestId = None def _deserialize(self, params): self.SessionId = params.get("SessionId") if params.get("Items") is not None: self.Items = [] for item in params.get("Items"): obj = Item() obj._deserialize(item) self.Items.append(obj) self.TaskId = params.get("TaskId") self.RequestId = params.get("RequestId") class Item(AbstractModel): def __init__(self): self.Item = None self.ItemString = None self.ItemCoord = None self.Answer = None self.ExpressionType = None def _deserialize(self, params): self.Item = params.get("Item") self.ItemString = params.get("ItemString") if params.get("ItemCoord") is not None: self.ItemCoord = ItemCoord() self.ItemCoord._deserialize(params.get("ItemCoord")) self.Answer = params.get("Answer") self.ExpressionType = params.get("ExpressionType") class ItemCoord(AbstractModel): def __init__(self): self.Height = None self.Width = None self.X = None self.Y = None def _deserialize(self, params): self.Height = params.get("Height") self.Width = params.get("Width") self.X = params.get("X") self.Y = params.get("Y")

true

f70087c4c75215da4de26e95ab7e5bd813b1cd4c

9,438

py

Python

tests/utils/git_test.py

breml/rally

ea43beb6b60481ee77508da84605add571fc021d

[ "Apache-2.0" ]

null

tests/utils/git_test.py

breml/rally

ea43beb6b60481ee77508da84605add571fc021d

[ "Apache-2.0" ]

null

tests/utils/git_test.py

breml/rally

ea43beb6b60481ee77508da84605add571fc021d

[ "Apache-2.0" ]

null

# Licensed to Elasticsearch B.V. under one or more contributor # license agreements. See the NOTICE file distributed with # this work for additional information regarding copyright # ownership. Elasticsearch B.V. licenses this file to you under # the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. import os import logging import unittest.mock as mock from unittest import TestCase from esrally import exceptions from esrally.utils import git class GitTests(TestCase): def test_is_git_working_copy(self): test_dir = os.path.dirname(os.path.dirname(__file__)) # this test is assuming that nobody stripped the git repo info in their Rally working copy self.assertFalse(git.is_working_copy(test_dir)) self.assertTrue(git.is_working_copy(os.path.dirname(test_dir))) @mock.patch("esrally.utils.process.run_subprocess_with_output") @mock.patch("esrally.utils.process.run_subprocess_with_logging") def test_git_version_too_old(self, run_subprocess_with_logging, run_subprocess): # any non-zero return value will do run_subprocess_with_logging.return_value = 64 run_subprocess.return_value = "1.0.0" with self.assertRaises(exceptions.SystemSetupError) as ctx: git.head_revision("/src") self.assertEqual("Your git version is [1.0.0] but Rally requires at least git 1.9. Please update git.", ctx.exception.args[0]) run_subprocess_with_logging.assert_called_with("git -C /src --version", level=logging.DEBUG) @mock.patch("esrally.utils.io.ensure_dir") @mock.patch("esrally.utils.process.run_subprocess_with_logging") def test_clone_successful(self, run_subprocess_with_logging, ensure_dir): run_subprocess_with_logging.return_value = 0 src = "/src" remote = "http://github.com/some/project" git.clone(src, remote) ensure_dir.assert_called_with(src) run_subprocess_with_logging.assert_called_with("git clone http://github.com/some/project /src") @mock.patch("esrally.utils.io.ensure_dir") @mock.patch("esrally.utils.process.run_subprocess_with_logging") def test_clone_with_error(self, run_subprocess_with_logging, ensure_dir): run_subprocess_with_logging.return_value = 128 src = "/src" remote = "http://github.com/some/project" with self.assertRaises(exceptions.SupplyError) as ctx: git.clone(src, remote) self.assertEqual("Could not clone from [http://github.com/some/project] to [/src]", ctx.exception.args[0]) ensure_dir.assert_called_with(src) run_subprocess_with_logging.assert_called_with("git clone http://github.com/some/project /src") @mock.patch("esrally.utils.process.run_subprocess") @mock.patch("esrally.utils.process.run_subprocess_with_logging") def test_fetch_successful(self, run_subprocess_with_logging, run_subprocess): run_subprocess_with_logging.return_value = 0 run_subprocess.return_value = False git.fetch("/src", remote="my-origin") run_subprocess.assert_called_with("git -C /src fetch --prune --quiet my-origin") @mock.patch("esrally.utils.process.run_subprocess") @mock.patch("esrally.utils.process.run_subprocess_with_logging") def test_fetch_with_error(self, run_subprocess_with_logging, run_subprocess): run_subprocess_with_logging.return_value = 0 run_subprocess.return_value = True with self.assertRaises(exceptions.SupplyError) as ctx: git.fetch("/src", remote="my-origin") self.assertEqual("Could not fetch source tree from [my-origin]", ctx.exception.args[0]) run_subprocess.assert_called_with("git -C /src fetch --prune --quiet my-origin") @mock.patch("esrally.utils.process.run_subprocess") @mock.patch("esrally.utils.process.run_subprocess_with_logging") def test_checkout_successful(self, run_subprocess_with_logging, run_subprocess): run_subprocess_with_logging.return_value = 0 run_subprocess.return_value = False git.checkout("/src", "feature-branch") run_subprocess.assert_called_with("git -C /src checkout --quiet feature-branch") @mock.patch("esrally.utils.process.run_subprocess") @mock.patch("esrally.utils.process.run_subprocess_with_logging") def test_checkout_with_error(self, run_subprocess_with_logging, run_subprocess): run_subprocess_with_logging.return_value = 0 run_subprocess.return_value = True with self.assertRaises(exceptions.SupplyError) as ctx: git.checkout("/src", "feature-branch") self.assertEqual("Could not checkout branch [feature-branch]. Do you have uncommitted changes?", ctx.exception.args[0]) run_subprocess.assert_called_with("git -C /src checkout --quiet feature-branch") @mock.patch("esrally.utils.process.run_subprocess") @mock.patch("esrally.utils.process.run_subprocess_with_logging") def test_rebase(self, run_subprocess_with_logging, run_subprocess): run_subprocess_with_logging.return_value = 0 run_subprocess.return_value = False git.rebase("/src", remote="my-origin", branch="feature-branch") calls = [ mock.call("git -C /src checkout --quiet feature-branch"), mock.call("git -C /src rebase --quiet my-origin/feature-branch") ] run_subprocess.assert_has_calls(calls) @mock.patch("esrally.utils.process.run_subprocess") @mock.patch("esrally.utils.process.run_subprocess_with_logging") def test_pull(self, run_subprocess_with_logging, run_subprocess): run_subprocess_with_logging.return_value = 0 run_subprocess.return_value = False git.pull("/src", remote="my-origin", branch="feature-branch") calls = [ mock.call("git -C /src fetch --prune --quiet my-origin"), mock.call("git -C /src checkout --quiet feature-branch"), mock.call("git -C /src rebase --quiet my-origin/feature-branch") ] run_subprocess.assert_has_calls(calls) @mock.patch("esrally.utils.process.run_subprocess") @mock.patch("esrally.utils.process.run_subprocess_with_logging") def test_pull_ts(self, run_subprocess_with_logging, run_subprocess): run_subprocess_with_logging.return_value = 0 run_subprocess.return_value = False git.pull_ts("/src", "20160101T110000Z") run_subprocess.assert_called_with( "git -C /src fetch --quiet origin && git -C /src checkout " "--quiet `git -C /src rev-list -n 1 --before=\"20160101T110000Z\" --date=iso8601 origin/master`") @mock.patch("esrally.utils.process.run_subprocess") @mock.patch("esrally.utils.process.run_subprocess_with_logging") def test_pull_revision(self, run_subprocess_with_logging, run_subprocess): run_subprocess_with_logging.return_value = 0 run_subprocess.return_value = False git.pull_revision("/src", "3694a07") run_subprocess.assert_called_with("git -C /src fetch --quiet origin && git -C /src checkout --quiet 3694a07") @mock.patch("esrally.utils.process.run_subprocess_with_output") @mock.patch("esrally.utils.process.run_subprocess_with_logging") def test_head_revision(self, run_subprocess_with_logging, run_subprocess): run_subprocess_with_logging.return_value = 0 run_subprocess.return_value = ["3694a07"] self.assertEqual("3694a07", git.head_revision("/src")) run_subprocess.assert_called_with("git -C /src rev-parse --short HEAD") @mock.patch("esrally.utils.process.run_subprocess_with_output") @mock.patch("esrally.utils.process.run_subprocess_with_logging") def test_list_remote_branches(self, run_subprocess_with_logging, run_subprocess): run_subprocess_with_logging.return_value = 0 run_subprocess.return_value = [" origin/HEAD", " origin/master", " origin/5.0.0-alpha1", " origin/5"] self.assertEqual(["master", "5.0.0-alpha1", "5"], git.branches("/src", remote=True)) run_subprocess.assert_called_with("git -C /src for-each-ref refs/remotes/ --format='%(refname:short)'") @mock.patch("esrally.utils.process.run_subprocess_with_output") @mock.patch("esrally.utils.process.run_subprocess_with_logging") def test_list_local_branches(self, run_subprocess_with_logging, run_subprocess): run_subprocess_with_logging.return_value = 0 run_subprocess.return_value = [" HEAD", " master", " 5.0.0-alpha1", " 5"] self.assertEqual(["master", "5.0.0-alpha1", "5"], git.branches("/src", remote=False)) run_subprocess.assert_called_with("git -C /src for-each-ref refs/heads/ --format='%(refname:short)'")

52.433333

134

0.703963

import os import logging import unittest.mock as mock from unittest import TestCase from esrally import exceptions from esrally.utils import git class GitTests(TestCase): def test_is_git_working_copy(self): test_dir = os.path.dirname(os.path.dirname(__file__)) self.assertFalse(git.is_working_copy(test_dir)) self.assertTrue(git.is_working_copy(os.path.dirname(test_dir))) @mock.patch("esrally.utils.process.run_subprocess_with_output") @mock.patch("esrally.utils.process.run_subprocess_with_logging") def test_git_version_too_old(self, run_subprocess_with_logging, run_subprocess): run_subprocess_with_logging.return_value = 64 run_subprocess.return_value = "1.0.0" with self.assertRaises(exceptions.SystemSetupError) as ctx: git.head_revision("/src") self.assertEqual("Your git version is [1.0.0] but Rally requires at least git 1.9. Please update git.", ctx.exception.args[0]) run_subprocess_with_logging.assert_called_with("git -C /src --version", level=logging.DEBUG) @mock.patch("esrally.utils.io.ensure_dir") @mock.patch("esrally.utils.process.run_subprocess_with_logging") def test_clone_successful(self, run_subprocess_with_logging, ensure_dir): run_subprocess_with_logging.return_value = 0 src = "/src" remote = "http://github.com/some/project" git.clone(src, remote) ensure_dir.assert_called_with(src) run_subprocess_with_logging.assert_called_with("git clone http://github.com/some/project /src") @mock.patch("esrally.utils.io.ensure_dir") @mock.patch("esrally.utils.process.run_subprocess_with_logging") def test_clone_with_error(self, run_subprocess_with_logging, ensure_dir): run_subprocess_with_logging.return_value = 128 src = "/src" remote = "http://github.com/some/project" with self.assertRaises(exceptions.SupplyError) as ctx: git.clone(src, remote) self.assertEqual("Could not clone from [http://github.com/some/project] to [/src]", ctx.exception.args[0]) ensure_dir.assert_called_with(src) run_subprocess_with_logging.assert_called_with("git clone http://github.com/some/project /src") @mock.patch("esrally.utils.process.run_subprocess") @mock.patch("esrally.utils.process.run_subprocess_with_logging") def test_fetch_successful(self, run_subprocess_with_logging, run_subprocess): run_subprocess_with_logging.return_value = 0 run_subprocess.return_value = False git.fetch("/src", remote="my-origin") run_subprocess.assert_called_with("git -C /src fetch --prune --quiet my-origin") @mock.patch("esrally.utils.process.run_subprocess") @mock.patch("esrally.utils.process.run_subprocess_with_logging") def test_fetch_with_error(self, run_subprocess_with_logging, run_subprocess): run_subprocess_with_logging.return_value = 0 run_subprocess.return_value = True with self.assertRaises(exceptions.SupplyError) as ctx: git.fetch("/src", remote="my-origin") self.assertEqual("Could not fetch source tree from [my-origin]", ctx.exception.args[0]) run_subprocess.assert_called_with("git -C /src fetch --prune --quiet my-origin") @mock.patch("esrally.utils.process.run_subprocess") @mock.patch("esrally.utils.process.run_subprocess_with_logging") def test_checkout_successful(self, run_subprocess_with_logging, run_subprocess): run_subprocess_with_logging.return_value = 0 run_subprocess.return_value = False git.checkout("/src", "feature-branch") run_subprocess.assert_called_with("git -C /src checkout --quiet feature-branch") @mock.patch("esrally.utils.process.run_subprocess") @mock.patch("esrally.utils.process.run_subprocess_with_logging") def test_checkout_with_error(self, run_subprocess_with_logging, run_subprocess): run_subprocess_with_logging.return_value = 0 run_subprocess.return_value = True with self.assertRaises(exceptions.SupplyError) as ctx: git.checkout("/src", "feature-branch") self.assertEqual("Could not checkout branch [feature-branch]. Do you have uncommitted changes?", ctx.exception.args[0]) run_subprocess.assert_called_with("git -C /src checkout --quiet feature-branch") @mock.patch("esrally.utils.process.run_subprocess") @mock.patch("esrally.utils.process.run_subprocess_with_logging") def test_rebase(self, run_subprocess_with_logging, run_subprocess): run_subprocess_with_logging.return_value = 0 run_subprocess.return_value = False git.rebase("/src", remote="my-origin", branch="feature-branch") calls = [ mock.call("git -C /src checkout --quiet feature-branch"), mock.call("git -C /src rebase --quiet my-origin/feature-branch") ] run_subprocess.assert_has_calls(calls) @mock.patch("esrally.utils.process.run_subprocess") @mock.patch("esrally.utils.process.run_subprocess_with_logging") def test_pull(self, run_subprocess_with_logging, run_subprocess): run_subprocess_with_logging.return_value = 0 run_subprocess.return_value = False git.pull("/src", remote="my-origin", branch="feature-branch") calls = [ mock.call("git -C /src fetch --prune --quiet my-origin"), mock.call("git -C /src checkout --quiet feature-branch"), mock.call("git -C /src rebase --quiet my-origin/feature-branch") ] run_subprocess.assert_has_calls(calls) @mock.patch("esrally.utils.process.run_subprocess") @mock.patch("esrally.utils.process.run_subprocess_with_logging") def test_pull_ts(self, run_subprocess_with_logging, run_subprocess): run_subprocess_with_logging.return_value = 0 run_subprocess.return_value = False git.pull_ts("/src", "20160101T110000Z") run_subprocess.assert_called_with( "git -C /src fetch --quiet origin && git -C /src checkout " "--quiet `git -C /src rev-list -n 1 --before=\"20160101T110000Z\" --date=iso8601 origin/master`") @mock.patch("esrally.utils.process.run_subprocess") @mock.patch("esrally.utils.process.run_subprocess_with_logging") def test_pull_revision(self, run_subprocess_with_logging, run_subprocess): run_subprocess_with_logging.return_value = 0 run_subprocess.return_value = False git.pull_revision("/src", "3694a07") run_subprocess.assert_called_with("git -C /src fetch --quiet origin && git -C /src checkout --quiet 3694a07") @mock.patch("esrally.utils.process.run_subprocess_with_output") @mock.patch("esrally.utils.process.run_subprocess_with_logging") def test_head_revision(self, run_subprocess_with_logging, run_subprocess): run_subprocess_with_logging.return_value = 0 run_subprocess.return_value = ["3694a07"] self.assertEqual("3694a07", git.head_revision("/src")) run_subprocess.assert_called_with("git -C /src rev-parse --short HEAD") @mock.patch("esrally.utils.process.run_subprocess_with_output") @mock.patch("esrally.utils.process.run_subprocess_with_logging") def test_list_remote_branches(self, run_subprocess_with_logging, run_subprocess): run_subprocess_with_logging.return_value = 0 run_subprocess.return_value = [" origin/HEAD", " origin/master", " origin/5.0.0-alpha1", " origin/5"] self.assertEqual(["master", "5.0.0-alpha1", "5"], git.branches("/src", remote=True)) run_subprocess.assert_called_with("git -C /src for-each-ref refs/remotes/ --format='%(refname:short)'") @mock.patch("esrally.utils.process.run_subprocess_with_output") @mock.patch("esrally.utils.process.run_subprocess_with_logging") def test_list_local_branches(self, run_subprocess_with_logging, run_subprocess): run_subprocess_with_logging.return_value = 0 run_subprocess.return_value = [" HEAD", " master", " 5.0.0-alpha1", " 5"] self.assertEqual(["master", "5.0.0-alpha1", "5"], git.branches("/src", remote=False)) run_subprocess.assert_called_with("git -C /src for-each-ref refs/heads/ --format='%(refname:short)'")

true