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def count_char(char, word): """Counts the characters in word""" return word.count(char) # If you want to do it manually try a for loop

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def get_sos_model(sample_narratives): """Return sample sos_model """ return { 'name': 'energy', 'description': "A system of systems model which encapsulates " "the future supply and demand of energy for the UK", 'scenarios': [ 'population' ], 'narratives': sample_narratives, 'sector_models': [ 'energy_demand', 'energy_supply' ], 'scenario_dependencies': [ { 'source': 'population', 'source_output': 'population_count', 'sink': 'energy_demand', 'sink_input': 'population' } ], 'model_dependencies': [ { 'source': 'energy_demand', 'source_output': 'gas_demand', 'sink': 'energy_supply', 'sink_input': 'natural_gas_demand' } ] }

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def check_context(model, sentence, company_name): """ Check if the company name in the sentence is actually a company name. :param model: the spacy model. :param sentence: the sentence to be analysed. :param company_name: the name of the company. :return: True if the company name means a company/product. """ doc = model(sentence) for t in doc.ents: if t.lower_ == company_name: #if company name is called if t.label_ == "ORG" or t.label_ == "PRODUCT": #check they actually mean the company return True return False

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def getItemSize(dataType): """ Gets the size of an object depending on its data type name Args: dataType (String): Data type of the object Returns: (Integer): Size of the object """ # If it's a vector 6, its size is 6 if dataType.startswith("VECTOR6"): return 6 # If it,s a vector 3, its size is 6 elif dataType.startswith("VECTOR3"): return 3 # Else its size is only 1 return 1

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import time def FloatDateTime(): """Returns datetime stamp in Miro's REV_DATETIME format as a float, e.g. 20110731.123456""" return float(time.strftime('%Y%m%d.%H%M%S', time.localtime()))

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import json def read_prediction_dependencies(pred_file): """ Reads in the predictions from the parser's output file. Returns: two String list with the predicted heads and dependency names, respectively. """ heads = [] deps = [] with open(pred_file, encoding="utf-8") as f: for line in f: j = json.loads(line) heads.extend(j["predicted_heads"]) deps.extend(j["predicted_dependencies"]) heads = list(map(str, heads)) return heads, deps

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def CleanGrant(grant): """Returns a "cleaned" grant by rounding properly the internal data. This insures that 2 grants coming from 2 different sources are actually identical, irrespective of the logging/storage precision used. """ return grant._replace(latitude=round(grant.latitude, 6), longitude=round(grant.longitude, 6), height_agl=round(grant.height_agl, 2), max_eirp=round(grant.max_eirp, 3))

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def trim(str): """Remove multiple spaces""" return ' '.join(str.strip().split())

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import torch def get_optimizer(lr): """ Specify an optimizer and its parameters. Returns ------- tuple(torch.optim.Optimizer, dict) The optimizer class and the dictionary of kwargs that should be passed in to the optimizer constructor. """ return (torch.optim.SGD, {"lr": lr, "weight_decay": 1e-6, "momentum": 0.9})

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import codecs import csv def open_csv(path): """open_csv.""" _lines = [] with codecs.open(path, encoding='utf8') as fs: for line in csv.reader(fs): if len(line) == 3: _lines.append(line) return _lines

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def merge(intervals: list[list[int]]) -> list[list[int]]: """Generate a new schedule with non-overlapping intervals by merging intervals which overlap Complexity: n = len(intervals) Time: O(nlogn) for the initial sort Space: O(n) for the worst case of no overlapping intervals Examples: >>> merge(intervals=[[1,3],[2,6],[8,10],[15,18]]) [[1, 6], [8, 10], [15, 18]] >>> merge(intervals=[[1,4],[4,5]]) [[1, 5]] >>> merge(intervals=[[1,4]]) [[1, 4]] """ ## EDGE CASES ## if len(intervals) <= 1: return intervals """ALGORITHM""" ## INITIALIZE VARS ## intervals.sort(key=lambda k: k[0]) # sort on start times # DS's/res merged_intervals = [] # MERGE INTERVALS prev_interval, remaining_intervals = intervals[0], intervals[1:] for curr_interval in remaining_intervals: # if prev interval end >= curr interval start if prev_interval[1] >= curr_interval[0]: # adjust new prev interval prev_interval[1] = max(prev_interval[1], curr_interval[1]) else: merged_intervals.append(prev_interval) prev_interval = curr_interval merged_intervals.append(prev_interval) return merged_intervals

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import math def distance(a, b): """ Computes a :param a: :param b: :return: """ x = a[0] - b[0] y = a[1] - b[1] return math.sqrt(x ** 2 + y ** 2)

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def rsquared_adj(r, nobs, df_res, has_constant=True): """ Compute the adjusted R^2, coefficient of determination. Args: r (float): rsquared value nobs (int): number of observations the model was fit on df_res (int): degrees of freedom of the residuals (nobs - number of model params) has_constant (bool): whether the fitted model included a constant (intercept) Returns: float: adjusted coefficient of determination """ if has_constant: return 1.0 - (nobs - 1) / df_res * (1.0 - r) else: return 1.0 - nobs / df_res * (1.0 - r)

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def _compile_unit(i): """Append gas to unit and update CO2e for pint/iam-unit compatibility""" if " equivalent" in i["unit"]: return i["unit"].replace("CO2 equivalent", "CO2e") if i["unit"] in ["kt", "t"]: return " ".join([i["unit"], i["gas"]]) else: return i["unit"]

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import math def Calculo_por_etapas(Diccionario): """Calculo de la hornilla por etapas""" Lista_Contenido=[] Lista_columnas=[] #Normalización de la capacidad de la hornilla #Mem_dias=float(Diccionario['¿Cada cuantos días quiere moler? (días)']) #Mem_Temp=Normalizar_Capacidad(float(Diccionario['Capacidad estimada de la hornilla']),Mem_dias) #print(float(Diccionario['Capacidad estimada de la hornilla'])) #print(Mem_Temp) Etapas=int(float(Diccionario['Etapas']))#Mem_Temp[1] #Etapas=12 #Saturador "minimo son dos etapas" if (Etapas>2): Factor_Division=Etapas-2 else: Factor_Division=2 Etapas=2 #Caracteristicas de las celdas de cada columna (Lista_columnas) #Fila 0 concentración de solidos inicial #Fila 1 Concentración de solidos final #Fila 2 Concentración promedio #Fila 3 Masa de jugo de entrada #Fila 4 Calor Especifico P Cte jugo #Fila 5 Densidad del Jugo #Fila 6 Volumen de jugo kg #Fila 7 Volumen de jugo en L #Fila 8 Temperatura de Entrada #Fila 9 Temperatura de Salida #Fila 10 Entalpia de Vaporización #Fila 11 Masa de Agua a Evaporar #Fila 12 Calor Nece Calc por Etapa for i in range(13): for j in range (Etapas): Lista_columnas.append(float(i+j)) Lista_Contenido.append(Lista_columnas) Lista_columnas=[] Lista_Contenido[0][0]=float(Diccionario['CSS del jugo pos-evaporación']) #Concentracion_solidos_inicial (CSS02) Lista_Contenido[1][0]=float(Diccionario['CSS panela']) #Concentracion_solidos_final (CSSF1) Lista_Contenido[0][Etapas-1]=float(Diccionario['CSS del jugo de Caña']) #Concentracion_solidos_inicial (CSS01) Lista_Contenido[1][Etapas-1]=float(Diccionario['CSS del jugo clarificado']) #Concentracion_solidos_final (CSSF1) if(Etapas>2): ite=0 for i in range(Etapas-2,0,-1): Lista_Contenido[0][i]=Lista_Contenido[1][i+1] if(ite==0): Lista_Contenido[1][i]=((Lista_Contenido[0][0]-Lista_Contenido[0][i])/Factor_Division)+Lista_Contenido[0][i] ite=ite+1 else: Lista_Contenido[1][i]=((Lista_Contenido[0][0]-Lista_Contenido[0][Etapas-2])/Factor_Division)+Lista_Contenido[0][i] for i in range(Etapas-1,-1,-1): #Concentración promedio=(Concentracion_solidos_inicial+Concentracion_solidos_final)/2 Lista_Contenido[2][i]=(Lista_Contenido[0][i]+Lista_Contenido[1][i])/2 if(i==Etapas-1): #Masa de jugo de entrada Lista_Contenido[3][i]=float(Diccionario['A clarificación']) else: #Masa de jugo de entrada=(Masa de jugo etapa anterior*CCS inicial etapa anterior)/CCS Final etapa anterior Lista_Contenido[3][i]=Lista_Contenido[3][i+1]*Lista_Contenido[0][i+1]/Lista_Contenido[1][i+1] #Calor_Especifico_P_Cte_jugo=4.18*(1-(0.006*Concetracion_promedio)) Lista_Contenido[4][i]=4.18*(1-(0.006*Lista_Contenido[2][i])) #Densidad_del_Jugo=997.39+(4.46*Concetracion_promedio) Lista_Contenido[5][i]=997.39+(4.46*Lista_Contenido[2][i]) #Volumen_jugo=Masa_jugo_de_entrada/Densidad_del_Jugo Lista_Contenido[6][i]=Lista_Contenido[3][i]/Lista_Contenido[5][i] #Volumen_jugo_L=Volumen_jugo*1000 Lista_Contenido[7][i]=Lista_Contenido[6][i]*1000.0 if(i==Etapas-1): #Temperatura_Entrada=Temperatura ambiente Lista_Contenido[8][i]=float(Diccionario['Temperatura del ambiente']) else: #Temperatura_Entrada=Temperatura_ebullición_agua+0.2209*math.exp(0.0557*Concentracion_solidos_inicial) Lista_Contenido[8][i]=Lista_Contenido[9][i+1] #Temperatura_Salida=G37+0.2209*math.exp(0.0557*Concentracion_solidos_final) Lista_Contenido[9][i]=float(Diccionario['Temperatura de ebullición del agua'])+0.2209*math.exp(0.0557*Lista_Contenido[1][i]) #Entalpia_Vaporizacion=(2492.9-(2.0523*Temperatura_Entrada))-(0.0030752*(Temperatura_Entrada**2)) Lista_Contenido[10][i]=(2492.9-(2.0523*Lista_Contenido[8][i]))-(0.0030752*(Lista_Contenido[8][i]**2)) #Masa_Agua_Evaporar=Masa_jugo_de_entrada-(Masa_jugo_de_entrada*Concentracion_solidos_inicial/Concentracion_solidos_final) Lista_Contenido[11][i]=Lista_Contenido[3][i]-(Lista_Contenido[3][i]*Lista_Contenido[0][i]/Lista_Contenido[1][i]) #Calor_por_Etapa=(Masa_jugo_de_entrada*Calor_Especifico_P_Cte_jugo*(Temperatura_Salida-Temperatura_Entrada)+Masa_Agua_Evaporar*Entalpia_Vaporizacion)/3600 Lista_Contenido[12][i]=(Lista_Contenido[3][i]*Lista_Contenido[4][i]*(Lista_Contenido[9][i]-Lista_Contenido[8][i])+Lista_Contenido[11][i]*Lista_Contenido[10][i])/3600.0 #Fijar decimales en 3 for j in range (13): for i in range (Etapas): Lista_Contenido[j][i]=round(Lista_Contenido[j][i],3) #Cambiar la salida o posicion de la paila de punteo a la paila 3 o 4 Lista_contenido_2=[] L_aux=[] for i in Lista_Contenido: inio=3 if (Etapas!=7): L_aux.append(i[2]) L_aux.append(i[1]) L_aux.append(i[0]) inio=3 else: L_aux.append(i[3]) L_aux.append(i[2]) L_aux.append(i[1]) L_aux.append(i[0]) inio=4 for t in range(inio,len(i)): L_aux.append(i[t]) Lista_contenido_2.append(L_aux) L_aux=[] Lista_Contenido=Lista_contenido_2 Etiquetas=[ 'Concentracion de Solidos Inicial [ºBrix]', 'Concentracion de Solidos Final [ºBrix]', 'Concentracion de Solidos Promedio [ºBrix]', 'Masa de Jugo Entrada [Kg]', 'Calor Especifico P Cte jugo [kJ/Kg °C]', 'Densidad del Jugo [kg/m3]', 'Volumen de jugo [m^3/kg]', 'Volumen de jugo [L]', 'Temperatura de Entrada [ºC]', 'Temperatura de Salida [ºC]', 'Entalpia de Vaporización [kJ/kg]', 'Masa de Agua a Evaporar [kg]', 'Calor Nece Calc por Etapa [kW]' ] Dict_aux=dict(zip(Etiquetas,Lista_Contenido)) Dict_aux_2=dict(zip(['Etapas'],[Etapas])) Dict_aux.update(Dict_aux_2) return Dict_aux

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from collections import defaultdict def concline_generator(matches, idxs, df, metadata, add_meta, category, fname, preserve_case=False): """ Get all conclines :param matches: a list of formatted matches :param idxs: their (sent, word) idx """ conc_res = [] # potential speedup: turn idxs into dict mdict = defaultdict(list) # if remaking idxs here, don't need to do it earlier idxs = list(matches.index) for mid, (s, i) in zip(matches, idxs): #for s, i in matches: mdict[s].append((i, mid)) # shorten df to just relevant sents to save lookup time df = df.loc[list(mdict.keys())] # don't look up the same sentence multiple times for s, tup in sorted(mdict.items()): sent = df.loc[s] if not preserve_case: sent = sent.str.lower() meta = metadata[s] sname = meta.get('speaker', 'none') for i, mid in tup: if not preserve_case: mid = mid.lower() ix = '%d,%d' % (s, i) start = ' '.join(sent.loc[:i-1].values) end = ' '.join(sent.loc[i+1:].values) lin = [ix, category, fname, sname, start, mid, end] if add_meta: for k, v in sorted(meta.items()): if k in ['speaker', 'parse', 'sent_id']: continue if isinstance(add_meta, list): if k in add_meta: lin.append(v) elif add_meta is True: lin.append(v) conc_res.append(lin) return conc_res

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def map_string(affix_string: str, punctuation: str, whitespace_only: bool = False) -> str: """Turn affix string into type char representation. Types are 'w' for non-whitespace char, and 's' for whitespace char. :param affix_string: a string :type: str :param punctuation: the set of characters to treat as punctuation :type punctuation: str :param whitespace_only: whether to treat only whitespace as word boundary or also include (some) punctuation :type whitespace_only: bool :return: the type char representation :rtype: str """ if whitespace_only: return "".join(["s" if char == " " else "w" for char in affix_string]) else: return "".join(["s" if char == " " or char in punctuation else "w" for char in affix_string])

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def modularity(modules, G, L): """ calculate modularity modularity = [list of nx.Graph objects] G = graph L = num of links """ N_m = len(modules) M = 0.0 for s in range(N_m): l_s = 0.0 d_s = 0 for i in modules[s]: l_s += float(modules[s].degree(i)) d_s += float(G.degree(i)) M += (l_s / L) - (d_s / (2.0 * L))**2 return M

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import re def extract_digits_from_end_of_string(input_string): """ Gets digits at the end of a string :param input_string: str :return: int """ result = re.search(r'(\d+)$', input_string) if result is not None: return int(result.group(0))

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import torch def move_bdim_to_front(x, result_ndim=None): """ Returns a tensor with a batch dimension at the front. If a batch dimension already exists, move it. Otherwise, create a new batch dimension at the front. If `result_ndim` is not None, ensure that the resulting tensor has rank equal to `result_ndim`. """ x_dim = len(x.shape) x_bdim = x.bdim if x_bdim is None: x = torch.unsqueeze(x, 0) else: x = torch.movedim(x, x_bdim, 0) if result_ndim is None: return x diff = result_ndim - x_dim - (x_bdim is None) for _ in range(diff): x = torch.unsqueeze(x, 1) return x

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import os def rawmap(k2i, file): """ Map index to raw data from file Arguments k2i: key-to-index map file: file containing raw data map Returns raw: index-to-raw map if file exists else identity map """ raw = {0: ''} if os.path.isfile(file): with open(file, "r") as f: for line in f.readlines(): line = line.split("\t") k, rw = line[0].strip(), line[1].strip() raw[k2i[k]] = rw else: for k in k2i: raw[k2i[k]] = k2i[k] return raw

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import random def out_flag(): """Either -o or --outfile""" return '-o' if random.randint(0, 1) else '--outfile'

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def replace_unwanted_xml_attrs(body): """ Method to return transformed string after removing all the unwanted characters from given xml body :param body: :return: """ return body.replace('&', '&amp;').replace('<', '&lt;').replace('>', '&gt;')

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def valid_template(template): """Is this a template that returns a valid URL?""" if template.name.lower() == "google books" and ( template.has("plainurl") or template.has("plain-url") ): return True if template.name.lower() == "billboardurlbyname": return True return False

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def get_choice(): """ Gets and returns choice for mode to use when running minimax """ choice = input( "Please enter a number (1 - 4)\n 1. Both players use minimax correctly at every turn\n 2. The starting player (X) is an expert and the opponent (0) only has a 50% chance to use minimax\n\t at each turn\n 3. The starting player (X) only has a 50% chance to use minimax at each turn and the opponent (0)\n\t is an expert.\n 4. Both players only have a 50% chance to use minimax at each turn.\n" ) while (choice != '1' and choice != '2' and choice != '3' and choice != '4'): choice = input("Not a choice. Go agane: (1 - 4)\n") return choice

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import argparse def validate_esc(esc): """Validate esc options\n Give an error if the characters aren't '*?[]' """ esc = esc.replace("]", "[") argset = set(esc) charset = {"*", "?", "["} if argset.difference(charset): err = "input character is not '*?[]'" raise argparse.ArgumentTypeError(err) return "".join(argset)

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import math def tanD(angle): """ angle est la mesure d'un angle en degrés ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Retourne la tangente de angle. """ return math.tan(math.radians(angle))

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def offset_zero_by_one(feature): """Sets the start coordinate to 1 if it is actually 0. Required for the flanking to work properly in those cases. """ if feature.start == 0: feature.start += 1 return feature

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from typing import OrderedDict def build_pathmatcher(name, defaultServiceUrl): """ This builds and returns a full pathMatcher entry, for appending to an existing URL map. Parameters: name: The name of the pathMatcher. defaultServiceUrl: Denotes the URL requests should go to if none of the path patterns match. """ matcher = OrderedDict() matcher['defaultService'] = defaultServiceUrl matcher['name'] = name return matcher

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import re def add_whitespace(c_fn): """ Add two spaces between all tokens of a C function """ tok = re.compile(r'[a-zA-Z0-9_]+|\*|\(|\)|\,|\[|\]') return ' ' + ' '.join(tok.findall(c_fn)) + ' '

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def mask_array(array, idx, n_behind, n_ahead): """[summary] Args: array ([type]): [description] idx ([type]): [description] n_behind ([type]): [description] n_ahead ([type]): [description] Returns: [type]: [description] """ first = max(0, idx - n_behind) last = min(idx + n_ahead + 1, len(array)) array_masked = array[first:last].copy() return array_masked

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def check_min_sample_periods(X, time_column, min_sample_periods): """ Check if all periods contained in a dataframe for a certain time_column contain at least min_sample_periods examples. """ return (X[time_column].value_counts() >= min_sample_periods).prod()

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def quote_ident(val): """ This method returns a new string replacing " with "", and adding a " at the start and end of the string. """ return '"' + val.replace('"', '""') + '"'

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def webpage_attribute_getter(attr): """ Helper function for defining getters for web_page attributes, e.g. ``get_foo_enabled = webpage_attribute_getter("foo")`` returns a value of ``webpage.foo`` attribute. """ def _getter(self): return getattr(self.web_page, attr) return _getter

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def party_name_from_key(party_key): """returns the relevant party name""" relevant_parties = {0: 'Alternativet', 1: 'Dansk Folkeparti', 2: 'Det Konservative Folkeparti', 3: 'Enhedslisten - De Rød-Grønne', 4: 'Liberal Alliance', 5: 'Nye Borgerlige', 6: 'Radikale Venstre', 7: 'SF - Socialistisk Folkeparti', 8: 'Socialdemokratiet', 9: 'Venstre, Danmarks Liberale Parti'} return relevant_parties[party_key]

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def assembleR(X, W, fct): """ """ M = W * fct(X) return M

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import uuid def make_uuid(value): """Converts a value into a python uuid object.""" if isinstance(value, uuid.UUID): return value return uuid.UUID(value)

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import re from pathlib import Path import json def parse_json_with_comments(pathlike): """ Parse a JSON file after removing any comments. Comments can use either ``//`` for single-line comments or or ``/* ... */`` for multi-line comments. The input filepath can be a string or ``pathlib.Path``. Parameters ---------- filename : str or os.PathLike Path to the input JSON file either as a string or as a ``pathlib.Path`` object. Returns ------- obj : dict JSON object representing the input file. Note ---- This code was adapted from: https://web.archive.org/web/20150520154859/http://www.lifl.fr/~riquetd/parse-a-json-file-with-comments.html """ # Regular expression to identify comments comment_re = re.compile(r'(^)?[^\S\n]*/(?:\*(.*?)\*/[^\S\n]*|/[^\n]*)($)?', re.DOTALL | re.MULTILINE) # if we passed in a string, convert it to a Path if isinstance(pathlike, str): pathlike = Path(pathlike) with open(pathlike, 'r') as file_buff: content = ''.join(file_buff.readlines()) # Looking for comments match = comment_re.search(content) while match: # single line comment content = content[:match.start()] + content[match.end():] match = comment_re.search(content) # Return JSON object config = json.loads(content) return config

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def write_velocity_files(U_25_RHS_str, U_50_RHS_str, U_100_RHS_str, U_125_RHS_str, U_150_RHS_str, U_25_LHS_str, U_50_LHS_str, U_100_LHS_str, U_125_LHS_str, U_150_LHS_str, path_0_100, path_0_125, path_0_150, path_0_25, path_0_50): """Create the details file for the surrounding cases, and write the velocities in line two""" fname = "details" # Filename file_25_path = path_0_25 file_50_path = path_0_50 file_100_path = path_0_100 file_125_path = path_0_125 file_150_path = path_0_150 details_file_25 = file_25_path + fname details_file_50 = file_50_path + fname details_file_100 = file_100_path + fname details_file_125 = file_125_path + fname details_file_150 = file_150_path + fname with open(details_file_25, 'w+') as f: f.write('Velocity' +'\n') f.write(U_25_RHS_str) with open(details_file_50, 'w+') as f: f.write('Velocity' +'\n') f.write(U_50_RHS_str) with open(details_file_100, 'w+') as f: f.write('Velocity' +'\n') f.write(U_100_RHS_str) with open(details_file_125, 'w+') as f: f.write('Velocity' +'\n') f.write(U_125_RHS_str) with open(details_file_150, 'w+') as f: f.write('Velocity' +'\n') f.write(U_150_RHS_str) return details_file_25, details_file_50, details_file_100, details_file_125, details_file_150

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def get_school_total_students(school_id, aug_school_info): """ Gets total number of students associated with a school. Args: district_id (str): NCES ID of target district (e.g. '0100005'). aug_school_info (pandas.DataFrame): Target augmented school information (as formatted by `auxiliary.data_handler.DataHandler`). Returns: int: Single number comprising school-level data. """ return int(aug_school_info.loc[school_id]["total_students"])

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def get_version(): # noqa: E501 """API version The API version # noqa: E501 :rtype: str """ return '1.0.0'

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import uuid def get_uuid_from_str(input_id: str) -> str: """ Returns an uuid3 string representation generated from an input string. :param input_id: :return: uuid3 string representation """ return str(uuid.uuid3(uuid.NAMESPACE_DNS, input_id))

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def createList(value, n): """ @param value: value to initialize the list @param n: list size to be created @return: size n list initialized to value """ return [value for i in range (n)]

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def heur(puzzle, item_total_calc, total_calc): """ Heuristic template that provides the current and target position for each number and the total function. Parameters: puzzle - the puzzle item_total_calc - takes 4 parameters: current row, target row, current col, target col. Returns int. total_calc - takes 1 parameter, the sum of item_total_calc over all entries, and returns int. This is the value of the heuristic function """ t = 0 for row in range(3): for col in range(3): val = puzzle.peek(row, col) - 1 target_col = val % 3 target_row = val / 3 # account for 0 as blank if target_row < 0: target_row = 2 t += item_total_calc(row, target_row, col, target_col) return total_calc(t)

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def make_argparse_help_safe(s): """Make strings safe for argparse's help. Argparse supports %{} - templates. This is sometimes not needed. Make user supplied strings safe for this. """ return s.replace('%', '%%').replace('%%%', '%%')

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def create_pysm_commands( mapfile, nside, bandcenter_ghz, bandwidth_ghz, beam_arcmin, coord, mpi_launch, mpi_procs, mpi_nodes, ): """ Return lines of shell code to generate the precomputed input sky map. """ mpistr = "{}".format(mpi_launch) if mpi_procs != "": mpistr = "{} {} 1".format(mpistr, mpi_procs) if mpi_nodes != "": mpistr = "{} {} 1".format(mpistr, mpi_nodes) outstr = "# Create sky model\n" outstr = '{}if [ ! -e "{}" ]; then\n'.format(outstr, mapfile) outstr = '{} echo "Creating sky model {} ..."\n'.format(outstr, mapfile) outstr = '{} {} ./pysm_sky.py --output "{}" --nside {} --bandcenter_ghz {} --bandwidth_ghz {} --beam_arcmin {} --coord {}\n'.format( outstr, mpistr, mapfile, nside, bandcenter_ghz, bandwidth_ghz, beam_arcmin, coord, ) outstr = "{}fi\n".format(outstr) outstr = "{}\n".format(outstr) return outstr

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import math def total_elastic_cross_section_browning1994_cm2(atomic_number, energy_keV): """ From browning1994 Valid in the range 100 eV to 30 keV for elements 1 to 92. """ Z = atomic_number E = energy_keV factor = 3.0e-18 power_z = math.pow(Z, 1.7) power_e = math.pow(E, 0.5) nominator = factor*power_z denominator = E + 0.005 * power_z * power_e + 0.0007 * Z * Z / power_e cross_section_cm2 = nominator/denominator return cross_section_cm2

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from typing import Tuple def decimal_to_boolean_list(num: int, padding: int = 0) -> Tuple[bool, ...]: """ Convert a decimal number into a tuple of booleans, representing its binary value. """ # Convert the decimal into binary binary = bin(num).replace('0b', '').zfill(padding) # Return a tuple of booleans, one for each element of the binary number (it's either '0' or '1' so we can convert # directly to boolean) return tuple(char == '1' for char in binary)

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def tpack(text, width=100): """Pack a list of words into lines, so long as each line (including intervening spaces) is no longer than _width_""" lines = [text[0]] for word in text[1:]: if len(lines[-1]) + 1 + len(word) <= width: lines[-1] += (' ' + word) else: lines += [word] return lines

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import os def _get_embedding_filename(base_dir, split_name, step): """Create the filename for embeddings.""" return os.path.join(base_dir, str(step), f'{split_name}-embeddings.tfrecord')

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import argparse def parse_args(): """Build file label list""" parser = argparse.ArgumentParser(description='Build file label list') parser.add_argument('data_path', type=str, help='root directory for the dataset') parser.add_argument('dataset', type=str, choices=[ 'ucf101', 'hmdb51', 'kinetics400', 'kinetics600', 'kinetics700', 'sthv1', 'sthv2'], help='name of the dataset') parser.add_argument('--ann_root', type=str, default='annotation') parser.add_argument('--out_root', type=str, default='../datalist') parser.add_argument('--phase', type=str, default='train', choices=['train', 'val']) parser.add_argument('--level', type=int, default=2, choices=[1, 2]) parser.add_argument('--source', type=str, default='rgb', choices=['rgb', 'flow', 'video']) parser.add_argument('--split', type=int, default=1, choices=[1, 2, 3]) args = parser.parse_args() return args

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import os def is_directory(dir_path): """Validates that the argument passed into 'argparse' is a directory.""" if not os.path.isdir(dir_path): raise ValueError('Path is not a directory: %s' % dir_path) return dir_path

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def is_palindrome(s: str) -> bool: """Return whether a string is a palindrome This is as efficient as you can get when computing whether a string is a palindrome. It runs in O(n) time and O(1) space. """ if len(s) <= 1: return True i = 0 j = len(s) - 1 while i < j: if s[i] != s[j]: return False i += 1 j -= 1 return True

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def my_hostogram(gray, bins): """ pixel values has to be within bins range, otherwise index out of range, for example if pixel 400th has value 70, but bins are -> [0...40], then histogram[70] yields IOR """ histogram = [0 for i in bins] for i in range(gray.shape[0]): for j in range(gray.shape[1]): histogram[gray[i][j]] = histogram[gray[i][j]] + 1 return histogram

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def reverse(password, position_x, position_y): """Reverse from position_x to position_y in password.""" password_slice = password[position_x:position_y + 1] password[position_x:position_y + 1] = password_slice[::-1] return password

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def GetBoolValueFromString(s): """Returns True for true/1 strings, and False for false/0, None otherwise.""" if s and s.lower() == 'true' or s == '1': return True elif s and s.lower() == 'false' or s == '0': return False else: return None

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def get_typical_qualifications(cfg): """ create qualification list to filter just workers with: - + 98% approval rate - + 500 or more accepted HIT - Location USA :param cfg: :return: """ if not cfg['hit_type'].getboolean('apply_qualification'): return [] qualification_requirements=[ { # Worker_​NumberHITsApproved 'QualificationTypeId': '00000000000000000040', 'Comparator': 'GreaterThanOrEqualTo', 'IntegerValues': [ 500, ], 'RequiredToPreview': False, 'ActionsGuarded': 'Accept' }, { # Worker_​PercentAssignmentsApproved 'QualificationTypeId': '000000000000000000L0', 'Comparator': 'GreaterThanOrEqualTo', 'IntegerValues': [ 98, ], 'RequiredToPreview': False, 'ActionsGuarded': 'Accept' }, { # Worker_Locale 'QualificationTypeId': '00000000000000000071', 'Comparator': 'EqualTo', 'LocaleValues': [ { 'Country':"US" } ], 'RequiredToPreview': False, 'ActionsGuarded': 'Accept' }, ] return qualification_requirements

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def search_sorted(array, value): """ Searches the given sorted array for the given value using a BinarySearch which should execute in O(log N). array a 1D sorted numerical array value the numerical value to search for returns index of array closest to value returns None if value is outside variable bounds """ def index_to_check(rmin, rmax): return (rmin + rmax) / 2 range_min = 0 range_max_0 = len(array) range_max = range_max_0 numloops = 0 while numloops < 100: numloops += 1 if (range_max - range_min) == 1: if (range_max == range_max_0) or (range_min == 0): raise LookupError("For some reason, range_max-" +\ "range_min reached 1 before " +\ "the element was found. The " +\ "element being searched for " +\ ("was %s. (min,max)" % (value,) +\ ("=%s" % ((range_min, range_max),)))) else: high_index = range_max else: high_index = index_to_check(range_min, range_max) high_val = array[high_index] low_val = array[high_index - 1] if value < low_val: range_max = high_index elif value > high_val: range_min = high_index else: # low_val <= value <= high_val if (2 * (high_val - value)) < (high_val - low_val): return high_index else: return high_index - 1 raise NotImplementedError("Something went wrong! I " +\ "caught a pseudo-infinite loop!")

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import os import subprocess def call_port(command, arguments): """ This function calls the port executable with the specified parameters, printing the output to stdout. """ command = ["port", command] + arguments if (os.getuid != 0): print("Using sudo to execute port.") return subprocess.call(["sudo"] + command) else: return subprocess.call(command)

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import argparse import os def parse_command_line_arguments(): """ Parse the command-line arguments being passed to RMG Py. This uses the :mod:`argparse` module, which ensures that the command-line arguments are sensible, parses them, and returns them. """ parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument('-i', '--input', metavar='FILE', help='a predictor training input file') parser.add_argument('-w', '--weights', metavar='H5', help='Saved model weights to continue training on (typically for transfer learning)') parser.add_argument('-d', '--data', metavar='FILE', help='A file specifying which datasets to train on. Alternatively, a space-separated .csv file' ' with SMILES/InChI and output(s) in the first and subsequent columns, respectively.') parser.add_argument('-o', '--out_dir', metavar='DIR', default=os.getcwd(), help='Output directory') parser.add_argument('-n', '--normalize', action='store_true', help='Normalize output based on training set mean and standard deviation') parser.add_argument('--save_tensors_dir', metavar='DIR', help='Location to save tensors on disk (frees up memory)') parser.add_argument('--keep_tensors', action='store_true', help='Do not delete directory containing tensors at end of job') parser.add_argument('-f', '--folds', type=int, default=5, help='number of folds for training') parser.add_argument('-tr', '--train_ratio', type=float, default=0.9, help='Fraction of training data to use for actual training, rest is early-stopping validation') parser.add_argument('-te', '--test_ratio', type=float, default=0.0, help='Fraction of data to use for testing. If loading data from database,' ' test ratios are specified in datasets file') parser.add_argument('-t', '--train_mode', default='full_train', help='train mode: currently support in_house and keras for k-fold cross-validation,' ' and full_train for full training') parser.add_argument('-bs', '--batch_size', type=int, default=1, help='batch training size') parser.add_argument('-lr', '--learning_rate', default='0.0007_30.0', help='two parameters for learning rate') parser.add_argument('-ep', '--nb_epoch', type=int, default=150, help='number of epochs for training') parser.add_argument('-pc', '--patience', type=int, default=10, help='Number of consecutive epochs allowed for loss increase before stopping early.' ' Note: A value of -1 indicates that the best model will NOT be saved!') parser.add_argument('-s', '--seed', type=int, default=0, help='Numpy random seed') return parser.parse_args()

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def _tear_down_response(data): """Helper function to extract header, payload and end from received response data.""" response_header = data[2:17] # Below is actually not used response_payload_size = data[18] response_payload = data[19:-2] response_end = data[-2:] return response_header, response_payload, response_end

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def stack_atomic_call_middleware(q_dict, q_queryset, logger, middleware): """ Calls the middleware function atomically. * Returns cached queue on error or None """ cached_q_dict = q_dict[:] cached_q_query = q_queryset.all() try: middleware(q_dict, q_queryset, logger) except: logger.error('MM_STACK: Middleware exception occurred in %s' % middleware.__name__) return [cached_q_dict, cached_q_query] return None

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def isText(node): """ Returns True if the supplied node is free text. """ return node.nodeType == node.TEXT_NODE

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def enumerate_changes(levels): """Assign a unique integer to each run of identical values. Repeated but non-consecutive values will be assigned different integers. """ return levels.diff().fillna(0).abs().cumsum().astype(int)

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def get_steps(x, shape): """ Convert a (vocab_size, steps * batch_size) array into a [(vocab_size, batch_size)] * steps list of views """ steps = shape[1] if x is None: return [None for step in range(steps)] xs = x.reshape(shape + (-1,)) return [xs[:, step, :] for step in range(steps)]

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import math def _validate(api_indicator_matype, option, parameters:dict, **kwargs): # -> dict """Validates kwargs and attaches them to parameters.""" # APO, PPO, BBANDS matype = int(math.fabs(kwargs["matype"])) if "matype" in kwargs else None if option == "matype" and matype is not None and matype in api_indicator_matype: parameters["matype"] = matype # BBANDS nbdevup = math.fabs(kwargs["nbdevup"]) if "nbdevup" in kwargs else None nbdevdn = math.fabs(kwargs["nbdevdn"]) if "nbdevdn" in kwargs else None if option == "nbdevup" and nbdevup is not None: parameters["nbdevup"] = nbdevup if option == "nbdevdn" and nbdevdn is not None: parameters["nbdevdn"] = nbdevdn # ULTOSC timeperiod1 = int(math.fabs(kwargs["timeperiod1"])) if "timeperiod1" in kwargs else None timeperiod2 = int(math.fabs(kwargs["timeperiod2"])) if "timeperiod2" in kwargs else None timeperiod3 = int(math.fabs(kwargs["timeperiod3"])) if "timeperiod3" in kwargs else None if option == "timeperiod1" and timeperiod1 is not None: parameters["timeperiod1"] = timeperiod1 if option == "timeperiod2" and timeperiod2 is not None: parameters["timeperiod2"] = timeperiod2 if option == "timeperiod3" and timeperiod3 is not None: parameters["timeperiod3"] = timeperiod3 # SAR acceleration = math.fabs(float(kwargs["acceleration"])) if "acceleration" in kwargs else None maximum = math.fabs(float(kwargs["maximum"])) if "maximum" in kwargs else None if option == "acceleration" and acceleration is not None: parameters["acceleration"] = acceleration if option == "maximum" and maximum is not None: parameters["maximum"] = maximum # MAMA fastlimit = math.fabs(float(kwargs["fastlimit"])) if "fastlimit" in kwargs else None slowlimit = math.fabs(float(kwargs["slowlimit"])) if "slowlimit" in kwargs else None if option == "fastlimit" and fastlimit is not None and fastlimit > 0 and fastlimit < 1: parameters["fastlimit"] = fastlimit if option == "slowlimit" and slowlimit is not None and slowlimit > 0 and slowlimit < 1: parameters["slowlimit"] = slowlimit # MACD, APO, PPO, ADOSC fastperiod = int(math.fabs(kwargs["fastperiod"])) if "fastperiod" in kwargs else None slowperiod = int(math.fabs(kwargs["slowperiod"])) if "slowperiod" in kwargs else None signalperiod = int(math.fabs(kwargs["signalperiod"])) if "signalperiod" in kwargs else None if option == "fastperiod" and fastperiod is not None: parameters["fastperiod"] = fastperiod if option == "slowperiod" and slowperiod is not None: parameters["slowperiod"] = slowperiod if option == "signalperiod" and signalperiod is not None: parameters["signalperiod"] = signalperiod # MACDEXT fastmatype = int(math.fabs(kwargs["fastmatype"])) if "fastmatype" in kwargs else None slowmatype = int(math.fabs(kwargs["slowmatype"])) if "slowmatype" in kwargs else None signalmatype = int(math.fabs(kwargs["signalmatype"])) if "signalmatype" in kwargs else None if option == "fastmatype" and fastmatype is not None and fastmatype in api_indicator_matype: parameters["fastmatype"] = fastmatype if option == "slowmatype" and slowmatype is not None and slowmatype in api_indicator_matype: parameters["slowmatype"] = slowmatype if option == "signalmatype" and signalmatype is not None and signalmatype in api_indicator_matype: parameters["signalmatype"] = signalmatype # STOCH(F), STOCHRSI fastkperiod = int(math.fabs(kwargs["fastkperiod"])) if "fastkperiod" in kwargs else None fastdperiod = int(math.fabs(kwargs["fastdperiod"])) if "fastdperiod" in kwargs else None fastdmatype = int(math.fabs(kwargs["fastdmatype"])) if "fastdmatype" in kwargs else None if option == "fastkperiod" and fastkperiod is not None: parameters["fastkperiod"] = fastkperiod if option == "fastdperiod" and fastdperiod is not None: parameters["fastdperiod"] = fastdperiod if option == "fastdmatype" and fastdmatype is not None and fastdmatype in api_indicator_matype: parameters["fastdmatype"] = fastdmatype # STOCH(F), STOCHRSI slowkperiod = int(math.fabs(kwargs["slowkperiod"])) if "slowkperiod" in kwargs else None slowdperiod = int(math.fabs(kwargs["slowdperiod"])) if "slowdperiod" in kwargs else None slowkmatype = int(math.fabs(kwargs["slowkmatype"])) if "slowkmatype" in kwargs else None slowdmatype = int(math.fabs(kwargs["slowdmatype"])) if "slowdmatype" in kwargs else None if option == "slowkperiod" and slowkperiod is not None: parameters["slowkperiod"] = slowkperiod if option == "slowdperiod" and slowdperiod is not None: parameters["slowdperiod"] = slowdperiod if option == "slowkmatype" and slowkmatype is not None and slowkmatype in api_indicator_matype: parameters["slowkmatype"] = slowkmatype if option == "slowdmatype" and slowdmatype is not None and slowdmatype in api_indicator_matype: parameters["slowdmatype"] = slowdmatype return parameters

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def scale_y_values(y_data, y_reference, y_max): """ Scale the plot in y direction, to prevent extreme values. :param y_data: the y data of the plot :param y_reference: the maximum value of the plot series (e.g. Normal force), which will be scaled to y_max :param y_max: the maximum y value for the plot (e.g. if y_max=1, no y value in the plot will be greater than 1) """ multipl_factor = y_max / y_reference for i in range(len(y_data)): y_data[i] = y_data[i] * multipl_factor return y_data, multipl_factor

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def set_heating_contribution(agent, pv_power): """ If the water tank is currently in use, compute and return the part of the pv_power used for heating the water""" pv_power_to_heating = 0 if agent.water_tank.is_active(): pv_power_to_heating = pv_power * agent.pv_panel.heating_contribution return pv_power_to_heating

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def sunlight_duration(hour_angle_sunrise): """Returns the duration of Sunlight, in minutes, with Hour Angle in degrees, hour_angle.""" sunlight_durration = 8 * hour_angle_sunrise # this seems like the wrong output return sunlight_durration

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import re def wrapper_handle_attrs(func): """转化html的标签属性为字典""" # 这是一个装饰Parsing.handle_attrs_tmp、Parsing.handle_attrs_tag的装饰器 def handle_attrs(self, attrs_str): attrs = dict() if attrs_str == '/': return attrs attrs_list = re.findall(self.attr_reg, attrs_str) for attr in attrs_list: attrs[attr[0]] = func(self, attr) return attrs return handle_attrs

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def match_term(term, dictionary, case_sensitive, lemmatize=True): """ Parameters ---------- term dictionary case_sensitive lemmatize Including lemmas improves performance slightly Returns ------- """ if (not case_sensitive and term.lower() in dictionary) or term in dictionary: return True if (case_sensitive and lemmatize) and term.rstrip('s').lower() in dictionary: return True elif (not case_sensitive and lemmatize) and term.rstrip('s') in dictionary: return True return False

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def deep_len(lnk): """ Returns the deep length of a possibly deep linked list. >>> deep_len(Link(1, Link(2, Link(3)))) 3 >>> deep_len(Link(Link(1, Link(2)), Link(3, Link(4)))) 4 >>> levels = Link(Link(Link(1, Link(2)), \ Link(3)), Link(Link(4), Link(5))) >>> print(levels) <<<1 2> 3> <4> 5> >>> deep_len(levels) 5 """ if not lnk: return 0 if type(lnk.first) == int: return 1 + deep_len(lnk.rest) return deep_len(lnk.first) + deep_len(lnk.rest)

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import os import glob def get_files_path(file_path: str) -> list: """Get all files path Args: file_path: root folder path Returns: list: list of string containing all files paths """ filepath='data' all_files = [] for root, dirs, files in os.walk(filepath): files = glob.glob(os.path.join(root,'*.json')) for f in files: all_files.append(f) return all_files

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import math def pixel_distance(A, B): """ In 9th grade I sat in geometry class wondering "when then hell am I ever going to use this?"...today is that day. Return the distance between two pixels """ (col_A, row_A) = A (col_B, row_B) = B return math.sqrt(math.pow(col_B - col_A, 2) + math.pow(row_B - row_A, 2))

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from pathlib import Path def get_archive(): """Ensure that the archive file exists and return its path. This is a function so the path can be made configurable in the future. Returns: :obj:`str`: The full local path to the archive file. """ filename = '/config/archive.txt' archfile = Path(filename) if not archfile.exists(): archfile.touch() return filename

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def convert_units(str): """ Convert some string with binary prefix to int bytes""" unit = ''.join(ele for ele in str if not ele.isdigit()).strip().lower() return int(''.join(ele for ele in str if ele.isdigit()))*{ "b": 1, "B": 1, "k": 2**10, "kb": 2**10, "m": 2**20, "mb": 2**20, "g": 2**30, "gb": 2**30, "t": 2**40, "tb": 2**40 }.get(unit, 1)

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def match_piecewise(candidates: set, symbol: str, sep: str='::') -> set: """ Match the requested symbol reverse piecewise (split on ``::``) against the candidates. This allows you to under-specify the base namespace so that ``"MyClass"`` can match ``my_namespace::MyClass`` Args: candidates: set of possible matches for symbol symbol: the symbol to match against sep: the separator between identifier elements Returns: set of matches """ piecewise_list = set() for item in candidates: split_symbol = symbol.split(sep) split_item = item.split(sep) split_symbol.reverse() split_item.reverse() min_length = len(split_symbol) split_item = split_item[:min_length] if split_symbol == split_item: piecewise_list.add(item) return piecewise_list

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def task_6_list_all_supplier_countries(cur) -> list: """ List all supplier countries Args: cur: psycopg cursor Returns: 29 records """ cur.execute("""SELECT country FROM suppliers""") return cur.fetchall()

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def kev_to_wavelength(kev): """Calculate the wavelength from kev""" lamda = 12.3984 / kev #keV to Angstrom return lamda

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def CalculateOSNames(os_name, os_variants): """Calculates all the names an OS can be called, according to its variants. @type os_name: string @param os_name: base name of the os @type os_variants: list or None @param os_variants: list of supported variants @rtype: list @return: list of valid names """ if os_variants: return ["%s+%s" % (os_name, v) for v in os_variants] else: return [os_name]

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def pad(data, pad_id): """ Pad all lists in data to the same length. """ width = max(len(d) for d in data) return [d + [pad_id] * (width - len(d)) for d in data]

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def dropannotation(annotation_list): """ Drop out the annotation contained in annotation_list """ target = "" for c in annotation_list: if not c == "#": target += c else: return target return target

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def EntryToSlaveName(entry): """Produces slave name from the slaves config dict.""" name = entry.get('slavename') or entry.get('hostname') if 'subdir' in entry: return '%s#%s' % (name, entry['subdir']) return name

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def _parallel_predict_proba(ensemble, X, idx, results): """ Compute predictions of SCM estimators """ for k in idx: res = ensemble.estimators[k].predict(X[:, ensemble.estim_features[k]]) results = results + res return results

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def read_test_case(file_path): """ reads one test case from file. returns contents of test case Parameters ---------- file_path : str the path of the test case file to read. Returns ------- list a list of contents of the test case. """ file = open(file_path, "r") number = int(file.readline().strip()) case = list() for i in range(number): case.append(file.readline().strip()) file.close() return case

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import torch def accuracy4batch(model, testloader, criterion): """save a model checkpoint INPUT: model: pytorch nn model. testloader: DataLoader. test data set criterion: criterion. loss criterion device: torch.device. device on which model/data is based OUTPUT: accuracy: float in [0:1]. percenct proportion of correct classifications in testloader test_loss: float. absolute error """ test_loss = 0 accuracy = 0 model.eval() with torch.no_grad(): for inputs, labels in testloader: inputs, labels = inputs.to(model.device), labels.to(model.device) logps = model.forward(inputs) batch_loss = criterion(logps, labels) test_loss += batch_loss.item() # Calculate accuracy ps = torch.exp(logps) top_p, top_class = ps.topk(1, dim=1) equals = top_class == labels.view(*top_class.shape) accuracy += torch.mean(equals.type(torch.FloatTensor)).item() accuracy = accuracy/len(testloader) return accuracy, test_loss

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import os def pickup_path(start_path, filename, default=None): """pickupping the config file path start path = "/foo/bar/boo", filename = "config.ini" finding candidates are ["/foo/bar/boo/config.ini", "/foo/bar/config.ini", "/foo/config.ini", "/config.ini"] """ start_point = os.path.normpath(os.path.abspath(start_path)) current = start_point candidates = [] while True: candidates.append(os.path.join(current, filename)) if current == "/": break current, dropped = os.path.split(current) for path in candidates: if os.path.exists(path): return path return default

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def add_matrices(matrix_a, matrix_b): """Add two n x n matrices """ return [[x + y for x, y in zip(matrix_a[i], matrix_b[i])] for i in range(len(matrix_a))]

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def matrix_multiply(A, B): """ Multiply two matrices A and B. :param A: the right matrix :param B: the left matrix :return: A * B """ # define m and n for the matrix as well as l, the connecting dimension between A and B m, l, n = len(A), len(A[0]), len(B[0]) # initialize an all zeros matrix C = [[0.0 for _ in range(len(B[0]))] for _ in range(len(A))] # iterative over the rows of C for i in range(m): # iterative over the columns of C for j in range(n): # set C[i][j] to the dot product of ith row of A and the jth column of B C[i][j] = sum(A[i][k] * B[k][j] for k in range(l)) # return the matrix C = A @ B return C

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def get_cd(wcs, n=1): """ Get the value of the change in world coordinate per pixel across a linear axis. Defaults to wcs.wcs.cd if present. Does not support rotated headers (e.g., with nonzero CDm_n where m!=n) """ if hasattr(wcs.wcs,'cd'): if wcs.wcs.cd[n-1,n-1] != 0: return wcs.wcs.cd[n-1,n-1] else: return wcs.wcs.get_cdelt()[n-1]

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def duplicate_detector(gate_orders: list[tuple[str]]) -> int: """Detects any schematics that have an identical combination of gates.""" difference = len(gate_orders) - len(list(set(gate_orders))) # List - list with no duplicates return difference

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def permutations(x): """Return all permutations of x""" def fn(i): if i == len(x): ans.append(x.copy()) for k in range(i, len(x)): x[i], x[k] = x[k], x[i] fn(i+1) x[i], x[k] = x[k], x[i] ans = [] fn(0) return ans

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def clean_string(s: str) -> str: """Cleans and returns an input string >>> clean_string(" xYz ") 'XYZ' """ return str(s).strip().upper()

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def XOR(v1, v2): """ XOR operation element by element from 2 lists :param v1: [1, 0, 1, 0, 0, 1] :param v2: [1, 1, 0, 0, 1, 1] :return: [0, 1, 1, 0, 1, 0] """ return [a ^ b for a, b in zip(v1, v2)]

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def get_dayofweek(date): """ Returns day of week in string format from date parameter (in datetime format). """ return date.strftime("%A")

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import torch from typing import Optional def iou( predict: torch.Tensor, target: torch.Tensor, mask: Optional[torch.Tensor] = None, ) -> torch.Tensor: """ This is a great loss because it emphasizes on the active regions of the predict and targets """ dims = tuple(range(predict.dim())[1:]) if mask is not None: predict = predict * mask target = target * mask intersect = (predict * target).sum(dims) union = (predict + target - predict * target).sum(dims) + 1e-4 return (intersect / union).sum() / intersect.numel()

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def getCondVisibility(condition): """ Returns ``True`` (``1``) or ``False`` (``0``) as a ``bool``. :param condition: string - condition to check. List of Conditions: http://wiki.xbmc.org/?title=List_of_Boolean_Conditions .. note:: You can combine two (or more) of the above settings by using "+" as an ``AND`` operator, "|" as an ``OR`` operator, "!" as a ``NOT`` operator, and "[" and "]" to bracket expressions. example:: visible = xbmc.getCondVisibility('[Control.IsVisible(41) + !Control.IsVisible(12)]') """ return bool(1)

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import re def sortRules(ruleList): """Return sorted list of rules. Rules should be in a tab-delimited format: 'rule\t\t[four letter negation tag]' Sorts list of rules descending based on length of the rule, splits each rule into components, converts pattern to regular expression, and appends it to the end of the rule. """ ruleList.sort(key = len, reverse = True) sortedList = [] for rule in ruleList: s = rule.strip().split('\t') splitTrig = s[0].split() trig = r'\s+'.join(splitTrig) pattern = r'\b(' + trig + r')\b' s.append(re.compile(pattern, re.IGNORECASE)) sortedList.append(s) return sortedList

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def to_array(string): """Converts a string to an array relative to its spaces. Args: string (str): The string to convert into array Returns: str: New array """ try: new_array = string.split(" ") # Convert the string into array while "" in new_array: # Check if the array contains empty strings new_array.remove("") return new_array except: print("The parameter string is not a str") return string

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def civic_methods(method001, method002, method003): """Create test fixture for methods.""" return [method001, method002, method003]

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