xszheng2020/the_stack_dedup_python_hits_0 · Datasets at Hugging Face

3227a055c835557ad7f0f841ab6676069d791695

10,965

py

Python

verify/imagenet.py

CAS-LRJ/DeepPAC

75059572c23474d32a762aca5640f4d799fd992a

[ "Apache-2.0" ]

null

verify/imagenet.py

CAS-LRJ/DeepPAC

75059572c23474d32a762aca5640f4d799fd992a

[ "Apache-2.0" ]

null

verify/imagenet.py

CAS-LRJ/DeepPAC

75059572c23474d32a762aca5640f4d799fd992a

[ "Apache-2.0" ]

null

import torch from torchvision import transforms from PIL import Image import numpy as np import math from sklearn.linear_model import LinearRegression from .grid import Grid, grid_split import torch.backends.cudnn as cudnn ''' Global Constants: TASK_NAME: Name of the verification task (deprecated) PATH: The path of the model file. (Initialized in imagenet_verify) mean, stdvar: The normalization parameters of the data. (Initialized in imagenet_verify, default mean=(0.4914,0.4822,0.4465) stdvar=(0.2023,0.1994,0.2010)) delta: The radius of the L-inf Ball. (Initialized in imagenet_verify, default 4/255) significance, error: The significance and the error rate of the PAC-Model. (Initialized in imagenet_verify, default 0.01 and 0.001) final_samples: The number of samples needed to calculate the final margin. (Initialized in imagenet_verify, default 1600, according to defualt error rate and significance) Batchsize: The batchsize of sampling procedure. (Initialized in imagenet_verify, defualt 200) device: Which device to be utilised by Pytorch. (Initialized in imagenet_verify, default 'cuda') model: The Pytorch Network to be verified. (Initialized in imagenet_verify) pretrans: The torchvision transform to process the image. (Resize and Tensorize) normalization_trans: The normalization transform to normalize the data. (Initialized in imagenet_verify) sampling_budget: The sampling limit for each stepwise splitting. (Initialized in imagenet_verify) init_grid: The Grid for Imagenet Data (224*224) Functions: grid_batch_sample: Grid-based Sampling for Scenario Optimization (Untargetted) scenario_optimization: Main Verification Function (Focused Learning, Stepwise-Splitting) imagenet_verify: Entry Function ''' pretrans = transforms.Compose([transforms.Resize((224, 224)), transforms.ToTensor(), ]) mean = (0.485, 0.456, 0.406) stdvar = (0.229, 0.224, 0.225) normalization_trans = transforms.Normalize(mean, stdvar) sampling_budget = 20000 delta = 4/255 error = 1e-2 significance = 1e-3 Batchsize = 200 device = 'cuda' init_grid = [Grid(0, 0, 224, 224)] PATH = './models/imagenet_linf_4.pth' def grid_batch_sample(grid_list, n_sample, batch_num, lower, upper, model, fixed_coeff=None, label=0): global normalization_trans, device feature_final = [] result_final = [] fixed_features = [] # Calculate the Iteration Number n_iter = math.ceil(n_sample/batch_num) model.eval() for iter in range(n_iter): samples = np.random.uniform(lower, upper, (batch_num,)+lower.shape) samples_ = normalization_trans( torch.tensor(samples)).float().to(device) with torch.no_grad(): results_ = model(samples_).cpu().detach().numpy() # Calculate the Untargeted Score Difference results_ = np.max(np.delete(results_, label, 1), 1) - results_[:, label] results_ = results_.reshape(batch_num, -1) result_final.append(results_) # Calculate the Fixed Constant fixed_result_i = (samples.reshape(batch_num, -1) @ fixed_coeff.reshape(-1)).reshape((batch_num, -1)) fixed_features.append(fixed_result_i) # Calculate the Grid Sum feature_iter_i = [] for grid in grid_list: for channel in range(3): grid_data = samples[:, channel, grid.leftup_x:grid.rightdown_x, grid.leftup_y:grid.rightdown_y] grid_sum = np.sum(grid_data, axis=1, keepdims=True) grid_sum = np.sum(grid_sum, axis=2, keepdims=True) grid_sum = grid_sum.reshape(-1, 1) feature_iter_i.append(grid_sum) # Merge the Grid Sums feature_iter_i = np.hstack(feature_iter_i) feature_final.append(feature_iter_i) # Merge the Batch Results feature_final = np.vstack(feature_final) result_final = np.vstack(result_final) fixed_features = np.vstack(fixed_features) return feature_final, result_final, fixed_features def scenario_optimization(image, label): global significance, error, init_grid, model, sampling_budget, delta global pretrans, normalization_trans, Batchsize, final_samples # Split into 7x7 small grids (32x32 split) grid_list = grid_split(init_grid, 32, 32) img = pretrans(image) img_np = img.detach().numpy() # Calculate the Lower and Upper Bounds img_upper = np.clip(img_np+delta, 0., 1.) img_lower = np.clip(img_np-delta, 0., 1.) fixed_coeff = np.zeros((3, 224, 224)) # Grid Refinement Procedure n_refine = 5 for refine_step in range(n_refine): print('Stepwise Spliting #', refine_step, 'Start') print('Sampling... (%d samples)' % sampling_budget) features, scores, fixed_constant = grid_batch_sample( grid_list, sampling_budget, Batchsize, img_lower, img_upper, model, fixed_coeff, label) print('Constructing Template...') # Linear Regression to construct the Coarse Model for Stepwise Splitting reg = LinearRegression(fit_intercept=True).fit( features, scores-fixed_constant) coeff = np.array(reg.coef_).reshape(-1, 3) # Use the L2 Norm to Identify the Important Grids coeff_l2 = np.sqrt(np.sum(coeff*coeff, axis=1)) coeff_l2_index = np.argsort(coeff_l2) coeff_l2_index_low = coeff_l2_index[:math.ceil( len(coeff_l2_index)*0.75)] coeff_l2_index_high = coeff_l2_index[math.ceil( len(coeff_l2_index)*0.75):] # Fix the Less Important Grids for index in coeff_l2_index_low: grid = grid_list[index] fixed_coeff[0, grid.leftup_x:grid.rightdown_x, grid.leftup_y:grid.rightdown_y] = coeff[index, 0] fixed_coeff[1, grid.leftup_x:grid.rightdown_x, grid.leftup_y:grid.rightdown_y] = coeff[index, 1] fixed_coeff[2, grid.leftup_x:grid.rightdown_x, grid.leftup_y:grid.rightdown_y] = coeff[index, 2] # Split the Rest Grids print('Spliting...') grid_list_i = [] for index in coeff_l2_index_high: grid_list_i.append(grid_list[index]) grid_list = grid_split(grid_list_i, 2, 2) del features, scores, fixed_constant # Last Step, To Fix the Rest Grids print('Last Step...') features, scores, fixed_constant = grid_batch_sample( grid_list, sampling_budget, Batchsize, img_lower, img_upper, model, fixed_coeff, label) reg = LinearRegression(fit_intercept=True).fit( features, scores-fixed_constant) intercept = reg.intercept_ coeff = np.array(reg.coef_).reshape(-1, 3) for index in range(len(coeff)): grid = grid_list[index] fixed_coeff[0, grid.leftup_x:grid.rightdown_x, grid.leftup_y:grid.rightdown_y] = coeff[index, 0] fixed_coeff[1, grid.leftup_x:grid.rightdown_x, grid.leftup_y:grid.rightdown_y] = coeff[index, 1] fixed_coeff[2, grid.leftup_x:grid.rightdown_x, grid.leftup_y:grid.rightdown_y] = coeff[index, 2] del features, scores, fixed_constant # Calculate the Margin features, scores, fixed_constant = grid_batch_sample( init_grid, final_samples, Batchsize, img_lower, img_upper, model, fixed_coeff, label) eps_max = np.max(np.abs(scores-fixed_constant-intercept)) print('Margin: ', eps_max) del features, scores, fixed_constant safe = True unsafe = False # Calculate the Maximum of the Learned Model, Find The Potential Counter-Example val_max = fixed_coeff[fixed_coeff < 0]@img_lower[fixed_coeff < 0] + \ fixed_coeff[fixed_coeff > 0]@img_upper[fixed_coeff > 0] + \ intercept+eps_max print('Evaluated Delta Max Value: ', val_max) if val_max > 0: print('Potential Counter-example Found') safe = False # Examine the Potential Counter-Example ce = np.zeros_like(img_lower) ce[fixed_coeff <= 0] = img_lower[fixed_coeff <= 0] ce[fixed_coeff > 0] = img_upper[fixed_coeff > 0] with torch.no_grad(): ce = normalization_trans(torch.tensor(ce).unsqueeze(0)).to(device) scores = model(ce)[0] print('True Label: ', torch.argmax(scores), 'Score: ', torch.max( scores), 'Original Label:', label, 'Scores: ', scores[label]) if torch.argmax(scores) != label: unsafe = True print('Conter-example Confirmed') if safe: print('Network is PAC-model robust with error rate', error, 'and confidence level', 1-significance) return 1 elif unsafe: print('Unsafe. Adversarial Example Found.') return 0 print('Unknown. Potential Counter-Example exists.') return 2 def imagenet_verify(model_class, args): global delta, PATH, error, significance, final_samples, normalization_trans, mean, stdvar, dataset, device, model, Batchsize, sampling_budget PATH = args.model delta = args.radius/255. error = args.epsilon significance = args.eta Batchsize = args.batchsize image_path = args.image final_samples = math.ceil(2/error*(math.log(1/significance)+1)) final_samples = math.ceil(final_samples/Batchsize)*Batchsize model = model_class() model.load_state_dict(torch.load(PATH)) if getattr(args, 'mean') != None: mean = args.mean if getattr(args, 'std') != None: stdvar = args.std if getattr(args, 'budget') != None: sampling_budget = args.budget normalization_trans = transforms.Normalize(mean, stdvar) if args.gpu == False: device = 'cpu' np.random.seed(0) if device == 'cuda': cudnn.deterministic = True cudnn.benchmark = False model = model.to(device) model.eval() image = Image.open(image_path).convert('RGB') if getattr(args, 'label') != None: label = args.label else: label = int(torch.argmax(model(normalization_trans( pretrans(image)).unsqueeze(0).to(device))[0]).cpu()) print('True Label: ', label) try: print('Verification Radius(L-inf): ', args.radius) print('Mean: ', mean) print('Std: ', stdvar) return scenario_optimization(image, label) except Exception as err: print('Error: Verification Failed') print(err)

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3228d6088055f54b7b82121a3d3e109e936942b3

1,623

py

Python

setup.py

cakebread/musubi

5b5f1bdf65fe07c14ff7bb2252c278f6ca0c903c

[ "BSD-2-Clause" ]

5

2015-05-18T13:18:26.000Z

2020-01-14T08:24:08.000Z

setup.py

cakebread/musubi

5b5f1bdf65fe07c14ff7bb2252c278f6ca0c903c

[ "BSD-2-Clause" ]

null

setup.py

cakebread/musubi

5b5f1bdf65fe07c14ff7bb2252c278f6ca0c903c

[ "BSD-2-Clause" ]

null

#!/usr/bin/env python PROJECT = 'musubi' VERSION = '0.2' import distribute_setup distribute_setup.use_setuptools() from setuptools import setup, find_packages try: long_description = open('README.rst', 'rt').read() except IOError: long_description = 'Uh oh, we may need a new hard drive.' setup( name=PROJECT, version=VERSION, description='Musubi is a command-line DNSBL checker and MX toolkit.', long_description=long_description, author='Rob Cakebread', author_email='cakebread@gmail.com', url='https://github.com/cakebread/musubi', download_url='https://github.com/cakebread/musubi/tarball/master', classifiers=['Development Status :: 3 - Alpha', 'License :: OSI Approved :: BSD License', 'Programming Language :: Python', 'Programming Language :: Python :: 2', 'Programming Language :: Python :: 2.7', 'Intended Audience :: Developers', 'Environment :: Console', ], platforms=['Any'], scripts=[], provides=[], install_requires=['requests', 'dnspython', 'IPy', 'distribute', 'cliff', 'cliff-tablib', 'gevent', 'greenlet'], namespace_packages=[], packages=find_packages(), include_package_data=True, entry_points={ 'console_scripts': [ 'musubi = musubi.main:main' ], 'musubi.cli': [ 'ips = musubi.ips:GetIPs', 'mx = musubi.mx:GetMX', 'spf = musubi.spf:GetSPF', 'scan = musubi.scan:Scan', ], }, zip_safe=False, )

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null

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null

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0

3229164df79c432f6f7ad72e86350bc6d3ce6e18

1,048

py

Python

airflow_ml_dags/images/airflow-preprocess/preprocess.py

made-ml-in-prod-2021/holyketzer

f693f2d5fce8cced03873e2b89cbe10617996c64

[ "MIT" ]

null

airflow_ml_dags/images/airflow-preprocess/preprocess.py

made-ml-in-prod-2021/holyketzer

f693f2d5fce8cced03873e2b89cbe10617996c64

[ "MIT" ]

2

2021-05-21T09:09:23.000Z

2021-06-05T08:13:40.000Z

airflow_ml_dags/images/airflow-preprocess/preprocess.py

made-ml-in-prod-2021/holyketzer

f693f2d5fce8cced03873e2b89cbe10617996c64

[ "MIT" ]

null

import os import pandas as pd import click from datetime import date @click.command("preprocess") @click.option("--input-dir") @click.option("--output-dir") @click.option("--mode") def preprocess(input_dir: str, output_dir, mode): if mode == "data": data = pd.read_csv(os.path.join(input_dir, "data.csv")) data["FirstLength"] = data["First"].apply(len) data["LastLength"] = data["Last"].apply(len) file = "data.csv" elif mode == "target": data = pd.read_csv(os.path.join(input_dir, "target.csv")) today = date.today() data["Age"] = pd.to_datetime(data["Birthdate"]).apply( lambda born: today.year - born.year - ((today.month, today.day) < (born.month, born.day)) ) data.drop(columns=["Birthdate"], inplace=True) file = "target.csv" else: raise ValueError(f"unknown mode: '{mode}'") os.makedirs(output_dir, exist_ok=True) data.to_csv(os.path.join(output_dir, file), index=False) if __name__ == '__main__': preprocess()

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null

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null

0

1

0

32298c15e29bc9b924d33fac9a984d4c8170430a

581

py

Python

estrutura_while/barra-de-progresso.py

BEp0/Estudos_de_Python

da32a01d3f4462b3e6b1b6035106895afe9c7627

[ "MIT" ]

1

2021-02-15T19:14:44.000Z

estrutura_while/barra-de-progresso.py

BEp0/Estudos_de_Python

da32a01d3f4462b3e6b1b6035106895afe9c7627

[ "MIT" ]

null

estrutura_while/barra-de-progresso.py

BEp0/Estudos_de_Python

da32a01d3f4462b3e6b1b6035106895afe9c7627

[ "MIT" ]

null

from time import sleep from sys import stdout def barra(v): v = int(v) print('[ ', end='') for v in range(0, v): print(f'-', end='', flush=True) sleep(0.1) print(' ]', end='\n') def calcularNotas(): soma = 0 v = 0 for i in range(0, 2): nota = float(input(f'\n{i + 1}º nota : ')) soma += nota v = soma // 2 print('\nCALCULANDO: ', end='\b') barra(v) return print(f'MÉDIA FOI: {soma / 2}') def main(): calcularNotas() sleep(1) print('\n__FIM__\n') if __name__ == "__main__": main()

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0

322c0212f8148c0b38508aaf2672d99f9c4007b4

8,524

py

Python

src/apodeixi/text_layout/tests_unit/test_column_layout.py

ChateauClaudia-Labs/apodeixi

dd668e210e92cabc2682ad3049781c06e58e3101

[ "MIT" ]

null

src/apodeixi/text_layout/tests_unit/test_column_layout.py

ChateauClaudia-Labs/apodeixi

dd668e210e92cabc2682ad3049781c06e58e3101

[ "MIT" ]

null

src/apodeixi/text_layout/tests_unit/test_column_layout.py

ChateauClaudia-Labs/apodeixi

dd668e210e92cabc2682ad3049781c06e58e3101

[ "MIT" ]

null

import sys as _sys import pandas as _pd from apodeixi.testing_framework.a6i_unit_test import ApodeixiUnitTest from apodeixi.util.formatting_utils import DictionaryFormatter from apodeixi.util.a6i_error import ApodeixiError, FunctionalTrace from apodeixi.text_layout.column_layout import ColumnWidthCalculator class Test_ColumnWidthCalculator(ApodeixiUnitTest): def setUp(self): super().setUp() def test_sparse_layout(self): self._shell_test_case('test_sparse_layout', viewport_width=50, viewport_height=40, max_word_length=20) def test_thick_layout(self): self._shell_test_case('test_thick_layout', viewport_width=100, viewport_height=40, max_word_length=20) def _shell_test_case(self, name, viewport_width, viewport_height, max_word_length): INPUT_FOLDER = self.input_data INPUT_FILE = name + '_INPUT.csv' OUTPUT_FOLDER = self.output_data OUTPUT_FILE = name + '_OUTPUT.csv' EXPECTED_FOLDER = self.expected_data EXPECTED_FILE = name + '_EXPECTED.csv' OUTPUT_COMPARISON_FILE = name + '_comparison_OUTPUT.txt' EXPECTED_COMPARISON_FILE = name + '_comparison_EXPECTED.txt' OUTPUT_EXPLAIN_FILE = name + '_explain_OUTPUT.txt' EXPECTED_EXPLAIN_FILE = name + '_explain_EXPECTED.txt' OUTPUT_RESULTS_FILE = name + '_results_OUTPUT.txt' EXPECTED_RESULTS_FILE = name + '_results_EXPECTED.txt' try: root_trace = FunctionalTrace(parent_trace=None, path_mask=self._path_mask).doing("Testing computation of column widths") data_df = self.load_csv(root_trace, INPUT_FOLDER + '/' + INPUT_FILE) calc = ColumnWidthCalculator( data_df = data_df, viewport_width = viewport_width, viewport_height = viewport_height, max_word_length = max_word_length) result_dict = calc.calc(root_trace) output_df = calc.analysis_df output_explain = '\n'.join(calc.explanations) # Save DataFrame, explain and results in case the assertion below fails, so that we can do # a visual comparison of OUTPUT vs EXPECTED csv files output_df.to_csv(OUTPUT_FOLDER + '/' + OUTPUT_FILE) with open(OUTPUT_FOLDER + '/' + OUTPUT_EXPLAIN_FILE, 'w') as file: file .write(output_explain) # Make results readable by creating a pretty result_nice = DictionaryFormatter().dict_2_nice(parent_trace = root_trace, a_dict = result_dict) with open(OUTPUT_FOLDER + '/' + OUTPUT_RESULTS_FILE, 'w') as file: file .write(result_nice) # Load the output we just saved, which we'll use for regression comparison since in Pandas the act of loading will # slightly change formats and we want to apply the same such changes as were applied to the expected output, # to avoid frivolous differences that don't deserve to cause this test to fail loaded_output_df = self.load_csv(root_trace, OUTPUT_FOLDER + '/' + OUTPUT_FILE) # Now load the expected output. expected_df = self.load_csv(root_trace, EXPECTED_FOLDER + '/' + EXPECTED_FILE) check, comparison_dict = self._compare_dataframes( df1 = loaded_output_df, df1_name = "output", df2 = expected_df, df2_name = "expected") df_comparison_nice = DictionaryFormatter().dict_2_nice(parent_trace = root_trace, a_dict = comparison_dict, flatten=True) with open(OUTPUT_FOLDER + '/' + OUTPUT_COMPARISON_FILE, 'w') as file: file .write(df_comparison_nice) with open(EXPECTED_FOLDER + '/' + EXPECTED_COMPARISON_FILE, 'r') as file: expected_df_comparison = file.read() with open(EXPECTED_FOLDER + '/' + EXPECTED_EXPLAIN_FILE, 'r') as file: expected_explain = file.read() with open(EXPECTED_FOLDER + '/' + EXPECTED_RESULTS_FILE, 'r') as file: expected_result = file.read() except ApodeixiError as ex: print(ex.trace_message()) self.assertTrue(1==2) self.assertEqual(df_comparison_nice, expected_df_comparison) self.assertTrue(check) self.assertEqual(output_explain, expected_explain) self.assertEqual(result_nice, expected_result) def _compare_dataframes(self, df1, df2, df1_name, df2_name): ''' Helper method used in lieu of dataframe.equals, which fails for spurious reasons. Under this method's policy, two dataframes are equal if they have the same columns, indices, and are point-wise equal. Method returns two things: a boolean result of the comparison, and a dictionary to pin point where there are differences, if any ''' # Prepare an explanation of where the dataframes differ, if they do differ. This visibility helps with debugging comparison_dict = {} cols_1 = set(df1.columns) cols_2 = set(df2.columns) # Ensure determinism with sort common_cols = list(cols_1.intersection(cols_2)) common_cols.sort() missing_in_1 = list(cols_2.difference(cols_1)) missing_in_1.sort() missing_in_2 = list(cols_1.difference(cols_2)) missing_in_2.sort() comparison_dict[df1_name + ' shape'] = str(df1.shape) comparison_dict[df2_name + ' shape'] = str(df2.shape) if len(missing_in_1) > 0: comparison_dict[df1_name + ' missing columns'] = '\n'.join(missing_in_1) if len(missing_in_2) > 0: comparison_dict[df2_name + ' missing columns'] = '\n'.join(missing_in_2) # Initialize true until profen false check = True if not df1.index.equals(df2.index): check = False else: # Compare element by element for the common_cols cell_dict = {} for row in df1.iterrows(): row1_nb = row[0] row1_data = row[1] for col in common_cols: # use common_cols that is a deterministic list val1 = row1_data[col] val2 = df2.iloc[row1_nb][col] if val1 != val2: check = False coords = col + '.row' + str(row1_nb) cell_dict[coords] = "values differ" cell_dict[coords + '.' + df1_name] = str(val1) cell_dict[coords + '.' + df2_name] = str(val2) comparison_dict['elt-by-elt comparison'] = cell_dict if check: comparison_dict['Result of elt-by-elt comparison'] = "Everything matches" return check, comparison_dict if __name__ == "__main__": # execute only if run as a script def main(args): T = Test_ColumnWidthCalculator() T.setUp() what_to_do = args[1] if what_to_do=='sparse_layout': T.test_small_text() main(_sys.argv)

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null

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null

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1

0

322e21d79121fc682dbbeaf19bfb0822ed607a7a

4,236

py

Python

pru/db/geo/geo_admin.py

euctrl-pru/rt-python

da5d0040e250bd159845a0d43bf0b73eab368863

[ "MIT" ]

null

pru/db/geo/geo_admin.py

euctrl-pru/rt-python

da5d0040e250bd159845a0d43bf0b73eab368863

[ "MIT" ]

null

pru/db/geo/geo_admin.py

euctrl-pru/rt-python

da5d0040e250bd159845a0d43bf0b73eab368863

[ "MIT" ]

null

# # Copyright (c) 2018 Via Technology Ltd. All Rights Reserved. # Consult your license regarding permissions and restrictions. # """ Administration operations for the geo db. """ import os import socket import time from pru.db.geo.geo_init import load_airspace, remove_all_sectors, tear_down from pru.db.geo.geo_init import load_airports, remove_all_airports from pru.db.geo.geo_init import load_user_airspace, remove_all_user_defined_sectors from pru.db.common_init import create as create_db, DB_TYPE_GEO from pru.db.geo.geo_init import create as create_geo_db from pru.logger import logger import pru.db.context as ctx log = logger(__name__) def remove_geo_db(): """ Remove the db """ remove_all_sectors() remove_all_airports() remove_all_user_defined_sectors() tear_down() def create_geo_database(): """ Create a geo db. """ log.info("Starting to create the geo db") log.info("Waiting for the database to be ready") log.info(f"Testing connection on host: {ctx.geo_db_hostname} and port {ctx.geo_db_port}") # We need to sleep and retry ubtil the db wakes up s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) while True: try: s.connect((ctx.geo_db_hostname, int(ctx.geo_db_port))) s.close() break except socket.error as ex: log.debug("Database not ready..") time.sleep(5) # 5 seconds between tests log.info("Geo database is now ready.") if create_db(DB_TYPE_GEO): if create_geo_db(): log.info("Geo database creation is complete.") return True else: log.info("Failed to make the airspace db, could not create the tables.") else: log.info("Failed to make the airspace db, could not create the database.") def initialise_airspace(sector_file_path, reset=False): """ Uses the provided file path to load the sectors file, may be csv or geojson. If no sectors file is found we return false. Reset=True Remove all and replace with this file. Reset=False Add these sectors to the sectors table. Note, this is not an update. return True if we succeeded A tuple of (False, message) if we fail """ connection = ctx.get_connection(ctx.CONTEXT, ctx.DB_USER) context = ctx.CONTEXT if os.path.exists(sector_file_path): if reset: remove_all_sectors() load_airspace(sector_file_path, context, connection) return True else: return (False, "Path not found " + sector_file_path) def initialise_airports(airports_file_path, reset=False): """ Uses the provided file path to load an airports file, must be csv. If no airports file is found we return false. Reset=True Remove all and replace with this file. Reset=False Add these airports to the sectors table. Note, this is not an update. return True if we succeeded A tuple of (False, message) if we fail """ connection = ctx.get_connection(ctx.CONTEXT, ctx.DB_USER) context = ctx.CONTEXT if os.path.exists(airports_file_path): if reset: remove_all_airports() load_airports(airports_file_path, context, connection) return True else: return (False, "Path not found " + airports_file_path) def initialise_user_airspace(user_sector_file_path, reset=False): """ Uses the provided file path to load the users sectors file, may be csv or geojson. If no sectors file is found we return false. Reset=True Remove all and replace with this file. Reset=False Add these sectors to the user sectors table. Note, this is not an update. return True if we succeeded A tuple of (False, message) if we fail """ connection = ctx.get_connection(ctx.CONTEXT, ctx.DB_USER) context = ctx.CONTEXT if os.path.exists(user_sector_file_path): if reset: remove_all_user_defined_sectors() load_user_airspace(user_sector_file_path, context, connection) return True else: return (False, "Path not found " + user_sector_file_path)

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null

0

null

0

1

0

322f9af92fcd6688ac16683be314d7931fa1f2eb

4,040

py

Python

tests/test_autogeometry.py

fabiommendes/easymunk

420dfc4a006997c47887f6876876249674feb3cd

[ "MIT" ]

1

2021-07-02T11:59:07.000Z

tests/test_autogeometry.py

fabiommendes/easymunk

420dfc4a006997c47887f6876876249674feb3cd

[ "MIT" ]

null

tests/test_autogeometry.py

fabiommendes/easymunk

420dfc4a006997c47887f6876876249674feb3cd

[ "MIT" ]

1

2022-01-14T20:18:35.000Z

from typing import List, Tuple import easymunk as a from easymunk import BB, Vec2d class TestAutoGeometry: def test_is_closed(self) -> None: not_closed: List[Tuple[float, float]] = [(0, 0), (1, 1), (0, 1)] closed: List[Tuple[float, float]] = [(0, 0), (1, 1), (0, 1), (0, 0)] assert not a.is_closed(not_closed) assert a.is_closed(closed) def test_simplify_curves(self) -> None: p1: List[Tuple[float, float]] = [(0, 0), (0, 10), (5, 11), (10, 10), (0, 10)] expected = [(0, 0), (0, 10), (10, 10), (0, 10)] actual = a.simplify_curves(p1, 1) assert actual == expected def test_simplify_vertexes(self) -> None: p1: List[Tuple[float, float]] = [(0, 0), (0, 10), (5, 11), (10, 10), (0, 10)] expected = [(0, 0), (0, 10), (10, 10), (0, 10)] actual = a.simplify_vertexes(p1, 1) assert actual == expected def test_to_convex_hull(self) -> None: p1: List[Tuple[float, float]] = [(0, 0), (0, 10), (5, 5), (10, 10), (10, 0)] expected = [(0, 0), (10, 0), (10, 10), (0, 10), (0, 0)] actual = a.to_convex_hull(p1, 1) assert actual == expected def test_convex_decomposition(self) -> None: # TODO: Use a more complicated polygon as test case p1: List[Tuple[float, float]] = [ (0, 0), (5, 0), (10, 10), (20, 20), (5, 5), (0, 10), (0, 0), ] expected = [ [(5.0, 5.0), (6.25, 2.5), (20.0, 20.0), (5.0, 5.0)], [(0.0, 0.0), (5.0, 0.0), (6.25, 2.5), (5.0, 5.0), (0.0, 10.0), (0.0, 0.0)], ] actual = a.convex_decomposition(p1, 0.1) actual.sort(key=len) # TODO: The result of convex_decomposition is not stable between # environments, so we cant have this assert here. # assert actual == expected def test_march_soft(self) -> None: img = [ " xx ", " xx ", " xx ", " xx ", " xx ", " xxxxx", " xxxxx", ] def sample_func(point: Tuple[float, float]) -> float: x = int(point[0]) y = int(point[1]) if img[y][x] == "x": return 1 return 0 pl_set = a.march_soft(BB(0, 0, 6, 6), 7, 7, 0.5, sample_func) expected = [ [ (1.5, 6.0), (1.5, 5.0), (1.5, 4.0), (1.5, 3.0), (1.5, 2.0), (1.5, 1.0), (1.5, 0.0), ], [ (3.5, 0.0), (3.5, 1.0), (3.5, 2.0), (3.5, 3.0), (3.5, 4.0), (4.0, 4.5), (5.0, 4.5), (6.0, 4.5), ], ] assert list(pl_set) == expected def test_march_hard(self) -> None: img = [ " xx ", " xx ", " xx ", " xx ", " xx ", " xxxxx", " xxxxx", ] def sample_func(point: Tuple[float, float]) -> float: x = int(point[0]) y = int(point[1]) if img[y][x] == "x": return 1 return 0 actual = list(a.march_hard(BB(0, 0, 6, 6), 7, 7, 0.5, sample_func)) expected = [ [ (1.5, 6.0), (1.5, 5.0), (1.5, 4.0), (1.5, 3.0), (1.5, 2.0), (1.5, 1.0), (1.5, 0.0), ], [ (3.5, 0.0), (3.5, 1.0), (3.5, 2.0), (3.5, 3.0), (3.5, 4.0), (3.5, 4.5), (4.0, 4.5), (5.0, 4.5), (6.0, 4.5), ], ] assert actual == expected

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0.142857

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null

0

null

0

1

0

32306c14bb390e41af15482d3244081bad57ece0

13,144

py

Python

darshan-util/pydarshan/darshan/backend/cffi_backend.py

gaocegege/darshan

2d54cd8ec96d26db23e9ca421df48d2031a4c55e

[ "mpich2" ]

null

darshan-util/pydarshan/darshan/backend/cffi_backend.py

gaocegege/darshan

2d54cd8ec96d26db23e9ca421df48d2031a4c55e

[ "mpich2" ]

null

darshan-util/pydarshan/darshan/backend/cffi_backend.py

gaocegege/darshan

2d54cd8ec96d26db23e9ca421df48d2031a4c55e

[ "mpich2" ]

null

# -*- coding: utf-8 -*- import cffi import ctypes import numpy as np import pandas as pd from darshan.api_def_c import load_darshan_header from darshan.discover_darshan import find_utils from darshan.discover_darshan import check_version API_def_c = load_darshan_header() ffi = cffi.FFI() ffi.cdef(API_def_c) libdutil = None libdutil = find_utils(ffi, libdutil) def log_open(filename): """ Opens a darshan logfile. Args: filename (str): Path to a darshan log file Return: log handle """ b_fname = filename.encode() handle = libdutil.darshan_log_open(b_fname) log = {"handle": handle, 'modules': None, 'name_records': None} return log def log_close(log): """ Closes the logfile and releases allocated memory. """ libdutil.darshan_log_close(log['handle']) #modules = {} return def log_get_job(log): """ Returns a dictionary with information about the current job. """ job = {} jobrec = ffi.new("struct darshan_job *") libdutil.darshan_log_get_job(log['handle'], jobrec) job['uid'] = jobrec[0].uid job['start_time'] = jobrec[0].start_time job['end_time'] = jobrec[0].end_time job['nprocs'] = jobrec[0].nprocs job['jobid'] = jobrec[0].jobid mstr = ffi.string(jobrec[0].metadata).decode("utf-8") md = {} for kv in mstr.split('\n')[:-1]: k,v = kv.split('=', maxsplit=1) md[k] = v job['metadata'] = md return job def log_get_exe(log): """ Get details about the executable (path and arguments) Args: log: handle returned by darshan.open Return: string: executeable path and arguments """ exestr = ffi.new("char[]", 4096) libdutil.darshan_log_get_exe(log['handle'], exestr) return ffi.string(exestr).decode("utf-8") def log_get_mounts(log): """ Returns a list of available mounts recorded for the log. Args: log: handle returned by darshan.open """ mntlst = [] mnts = ffi.new("struct darshan_mnt_info **") cnt = ffi.new("int *") libdutil.darshan_log_get_mounts(log['handle'], mnts, cnt) for i in range(0, cnt[0]): mntlst.append((ffi.string(mnts[0][i].mnt_path).decode("utf-8"), ffi.string(mnts[0][i].mnt_type).decode("utf-8"))) return mntlst def log_get_modules(log): """ Return a dictionary containing available modules including information about the contents available for each module in the current log. Args: log: handle returned by darshan.open Return: dict: Modules with additional info for current log. """ # use cached module index if already present if log['modules'] != None: return log['modules'] modules = {} mods = ffi.new("struct darshan_mod_info **") cnt = ffi.new("int *") libdutil.darshan_log_get_modules(log['handle'], mods, cnt) for i in range(0, cnt[0]): modules[ffi.string(mods[0][i].name).decode("utf-8")] = \ {'len': mods[0][i].len, 'ver': mods[0][i].ver, 'idx': mods[0][i].idx} # add to cache log['modules'] = modules return modules def log_get_name_records(log): """ Return a dictionary resovling hash to string (typically a filepath). Args: log: handle returned by darshan.open hash: hash-value (a number) Return: dict: the name records """ # used cached name_records if already present if log['name_records'] != None: return log['name_records'] name_records = {} nrecs = ffi.new("struct darshan_name_record **") cnt = ffi.new("int *") libdutil.darshan_log_get_name_records(log['handle'], nrecs, cnt) for i in range(0, cnt[0]): name_records[nrecs[0][i].id] = ffi.string(nrecs[0][i].name).decode("utf-8") # add to cache log['name_records'] = name_records return name_records def log_lookup_name_records(log, ids=[]): """ Resolve a single hash to it's name record string (typically a filepath). Args: log: handle returned by darshan.open hash: hash-value (a number) Return: dict: the name records """ name_records = {} #cids = ffi.new("darshan_record_id *") * len(ids) whitelist = (ctypes.c_ulonglong * len(ids))(*ids) whitelist_cnt = len(ids) whitelistp = ffi.from_buffer(whitelist) nrecs = ffi.new("struct darshan_name_record **") cnt = ffi.new("int *") libdutil.darshan_log_get_filtered_name_records(log['handle'], nrecs, cnt, ffi.cast("darshan_record_id *", whitelistp), whitelist_cnt) for i in range(0, cnt[0]): name_records[nrecs[0][i].id] = ffi.string(nrecs[0][i].name).decode("utf-8") # add to cache log['name_records'] = name_records return name_records def log_get_dxt_record(log, mod_name, mod_type, reads=True, writes=True, mode='dict'): """ Returns a dictionary holding a dxt darshan log record. Args: log: Handle returned by darshan.open mod_name (str): Name of the Darshan module mod_type (str): String containing the C type Return: dict: generic log record Example: The typical darshan log record provides two arrays, on for integer counters and one for floating point counters: >>> darshan.log_get_dxt_record(log, "DXT_POSIX", "struct dxt_file_record **") {'rank': 0, 'read_count': 11, 'read_segments': array([...]), ...} """ modules = log_get_modules(log) #name_records = log_get_name_records(log) rec = {} buf = ffi.new("void **") r = libdutil.darshan_log_get_record(log['handle'], modules[mod_name]['idx'], buf) if r < 1: return None filerec = ffi.cast(mod_type, buf) clst = [] rec['id'] = filerec[0].base_rec.id rec['rank'] = filerec[0].base_rec.rank rec['hostname'] = ffi.string(filerec[0].hostname).decode("utf-8") #rec['filename'] = name_records[rec['id']] wcnt = filerec[0].write_count rcnt = filerec[0].read_count rec['write_count'] = wcnt rec['read_count'] = rcnt rec['write_segments'] = [] rec['read_segments'] = [] size_of = ffi.sizeof("struct dxt_file_record") segments = ffi.cast("struct segment_info *", buf[0] + size_of ) for i in range(wcnt): seg = { "offset": segments[i].offset, "length": segments[i].length, "start_time": segments[i].start_time, "end_time": segments[i].end_time } rec['write_segments'].append(seg) for i in range(rcnt): i = i + wcnt seg = { "offset": segments[i].offset, "length": segments[i].length, "start_time": segments[i].start_time, "end_time": segments[i].end_time } rec['read_segments'].append(seg) if mode == "pandas": rec['read_segments'] = pd.DataFrame(rec['read_segments']) rec['write_segments'] = pd.DataFrame(rec['write_segments']) return rec def log_get_generic_record(log, mod_name, mod_type, mode='numpy'): """ Returns a dictionary holding a generic darshan log record. Args: log: Handle returned by darshan.open mod_name (str): Name of the Darshan module mod_type (str): String containing the C type Return: dict: generic log record Example: The typical darshan log record provides two arrays, on for integer counters and one for floating point counters: >>> darshan.log_get_generic_record(log, "POSIX", "struct darshan_posix_file **") {'counters': array([...], dtype=int64), 'fcounters': array([...])} """ modules = log_get_modules(log) rec = {} buf = ffi.new("void **") r = libdutil.darshan_log_get_record(log['handle'], modules[mod_name]['idx'], buf) if r < 1: return None rbuf = ffi.cast(mod_type, buf) rec['id'] = rbuf[0].base_rec.id rec['rank'] = rbuf[0].base_rec.rank clst = [] for i in range(0, len(rbuf[0].counters)): clst.append(rbuf[0].counters[i]) rec['counters'] = np.array(clst, dtype=np.int64) cdict = dict(zip(counter_names(mod_name), rec['counters'])) flst = [] for i in range(0, len(rbuf[0].fcounters)): flst.append(rbuf[0].fcounters[i]) rec['fcounters'] = np.array(flst, dtype=np.float64) fcdict = dict(zip(fcounter_names(mod_name), rec['fcounters'])) if mode == "dict": rec = {'counters': cdict, 'fcounter': fcdict} if mode == "pandas": rec = { 'counters': pd.DataFrame(cdict, index=[0]), 'fcounters': pd.DataFrame(fcdict, index=[0]) } return rec def counter_names(mod_name, fcnts=False): """ Returns a list of available counter names for the module. By default only integer counter names are listed, unless fcnts is set to true in which case only the floating point counter names are listed. Args: mod_name (str): Name of the module to return counter names. fcnts (bool): Switch to request floating point counters instead of integer. (Default: False) Return: list: Counter names as strings. """ if mod_name == 'MPI-IO': mod_name = 'MPIIO' names = [] i = 0 if fcnts: F = "f_" else: F = "" end = "{0}_{1}NUM_INDICES".format(mod_name.upper(), F.upper()) var_name = "{0}_{1}counter_names".format(mod_name.lower(), F.lower()) while True: try: var = getattr(libdutil, var_name) except: var = None if not var: return None name = ffi.string(var[i]).decode("utf-8") if name == end: break names.append(name) i += 1 return names def fcounter_names(mod_name): """ Returns a list of available floating point counter names for the module. Args: mod_name (str): Name of the module to return counter names. Return: list: Available floiting point counter names as strings. """ return counter_names(mod_name, fcnts=True) def log_get_bgq_record(log): """ Returns a darshan log record for BG/Q. Args: log: handle returned by darshan.open """ return log_get_generic_record(log, "BG/Q", "struct darshan_bgq_record **") def log_get_hdf5_file_record(log): """ Returns a darshan log record for an HDF5 file. Args: log: handle returned by darshan.open """ return log_get_generic_record(log, "H5F", "struct darshan_hdf5_file **") def log_get_hdf5_dataset_record(log): """ Returns a darshan log record for an HDF5 dataset. Args: log: handle returned by darshan.open """ return log_get_generic_record(log, "H5D", "struct darshan_hdf5_dataset **") def log_get_lustre_record(log): """ Returns a darshan log record for Lustre. Args: log: handle returned by darshan.open """ modules = log_get_modules(log) rec = {} buf = ffi.new("void **") r = libdutil.darshan_log_get_record(log['handle'], modules['LUSTRE']['idx'], buf) if r < 1: return None rbuf = ffi.cast("struct darshan_lustre_record **", buf) rec['id'] = rbuf[0].base_rec.id rec['rank'] = rbuf[0].base_rec.rank clst = [] for i in range(0, len(rbuf[0].counters)): clst.append(rbuf[0].counters[i]) rec['counters'] = np.array(clst, dtype=np.int64) cdict = dict(zip(counter_names('LUSTRE'), rec['counters'])) # FIXME ostlst = [] for i in range(0, cdict['LUSTRE_STRIPE_WIDTH']): print(rbuf[0].ost_ids[i]) rec['ost_ids'] = np.array(ostlst, dtype=np.int64) print(rec['ost_ids']) sys.exit() if mode == "dict": rec = {'counters': cdict, 'fcounter': fcdict} if mode == "pandas": rec = { 'counters': pd.DataFrame(cdict, index=[0]), 'fcounters': pd.DataFrame(fcdict, index=[0]) } return rec def log_get_mpiio_record(log): """ Returns a darshan log record for MPI-IO. Args: log: handle returned by darshan.open Returns: dict: log record """ return log_get_generic_record(log, "MPI-IO", "struct darshan_mpiio_file **") def log_get_pnetcdf_record(log): """ Returns a darshan log record for PnetCDF. Args: log: handle returned by darshan.open Returns: dict: log record """ return log_get_generic_record(log, "PNETCDF", "struct darshan_pnetcdf_file **") def log_get_posix_record(log): """ Returns a darshan log record for Args: log: handle returned by darshan.open Returns: dict: log record """ return log_get_generic_record(log, "POSIX", "struct darshan_posix_file **") def log_get_stdio_record(log): """ Returns a darshan log record for STDIO. Args: log: handle returned by darshan.open Returns: dict: log record """ return log_get_generic_record(log, "STDIO", "struct darshan_stdio_file **")

24.295749

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1,785

13,144

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0.445284

0.440928

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0

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0.253652

13,144

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0.785831

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0

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0.009009

0

null

0

null

0

1

0

32363a369f2abd8123a3c352cf5267f2cd8f6e3e

882

py

Python

pluggklockan.py

Vforsh03/Pluggklockan

845dbe82476ad3ecd8664b7cd99ce74311b92830

[ "MIT" ]

null

pluggklockan.py

Vforsh03/Pluggklockan

845dbe82476ad3ecd8664b7cd99ce74311b92830

[ "MIT" ]

null

pluggklockan.py

Vforsh03/Pluggklockan

845dbe82476ad3ecd8664b7cd99ce74311b92830

[ "MIT" ]

null

import time def countdown(time_sec, to_do): while time_sec: mins, secs = divmod(time_sec, 60) timeformat = '{:02d}:{:02d}'.format(mins, secs) print(timeformat, end='\r') time.sleep(1) time_sec -= 1 if time_sec == 0: print("Det här har du att göra: ") for sak in to_do: print(sak) def main(): to_do = [] saker = int(input("Hur många saker ska du lägga till på listan?: ")) for _ in range(saker): to_do.append(input("Sak: ")) while len(to_do) > 0: tid = int(input("Hur många sekunder vill du tima: ")) countdown(tid, to_do) to_do.remove(input("Vilken sak vill du ta bort? ")) print(to_do) if len(to_do) == 0: print("Du har inget att göra, gör vad fan du vill") if __name__ == "__main__": main()

25.941176

73

0.538549

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882

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0.079646

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0

0.018644

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882

33

74

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0.747458

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0.076923

false

0

0.038462

0

0.115385

0.192308

0

null

0

null

0

1

0

32364b003eb60db5ffb76e4251c347561207ed8b

1,397

py

Python

gallery/views.py

mkbeh/Site-Nordic-Walking-

ba98f41db09ed448ecc4db175f65ef4fa2d64979

[ "MIT" ]

null

gallery/views.py

mkbeh/Site-Nordic-Walking-

ba98f41db09ed448ecc4db175f65ef4fa2d64979

[ "MIT" ]

8

2021-04-08T21:57:55.000Z

2022-03-12T00:50:38.000Z

gallery/views.py

mkbeh/Site-Nordic-Walking-

ba98f41db09ed448ecc4db175f65ef4fa2d64979

[ "MIT" ]

null

from django.shortcuts import render, get_object_or_404 from django.views.decorators.cache import cache_page from .models import PhotoAlbum, VideoAlbum from blog.utils import get_pagination_page def albums_list(request): album_specific_data = {'photo': (PhotoAlbum, 'Фото альбомы'), 'video': (VideoAlbum, 'Видео альбомы')} album_type = request.path.split('/')[2] album_obj, album_type = album_specific_data.get(album_type) albums = album_obj.objects.all().order_by('-created') page = get_pagination_page(request, albums) return render( request, 'gallery/album.html', {'albums': page.object_list, 'page': page, 'album_type': album_type} ) @cache_page(10*60) def album_detail(request, album_type, album_name): album_specific_data = {'photo': (PhotoAlbum, 50), 'video': (VideoAlbum, 4)} album_obj, num_pages = album_specific_data.get(album_type) obj = get_object_or_404(album_obj, name=album_name) if album_type == 'photo': files = obj.images_set.all() template = 'gallery/photo_detail.html' else: files = obj.videos_set.all() template = 'gallery/video_detail.html' page = get_pagination_page(request, files, num_pages) return render( request, template, {'album_name': album_name, 'files': page.object_list, 'page': page, 'total_files': len(files)} )

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null

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null

0

1

0

3236d1e8e71e93e12b492398d92736947474b9fb

2,134

py

Python

test/test_post.py

enjoy233/zhihu-py3

bcb4aa8325f8b54d3b44bd0bdc959edd9761fcfc

[ "MIT" ]

1,321

2015-02-16T13:19:42.000Z

2022-03-25T15:03:58.000Z

test/test_post.py

fru1tw4ter/zhihu-py3

bcb4aa8325f8b54d3b44bd0bdc959edd9761fcfc

[ "MIT" ]

64

2015-07-03T12:30:08.000Z

2022-03-01T00:55:50.000Z

test/test_post.py

fru1tw4ter/zhihu-py3

bcb4aa8325f8b54d3b44bd0bdc959edd9761fcfc

[ "MIT" ]

551

2015-02-22T11:21:40.000Z

2022-03-25T13:22:13.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- from __future__ import print_function, division, unicode_literals import unittest import os import json from zhihu import Post from test_utils import TEST_DATA_PATH class ColumnTest(unittest.TestCase): @classmethod def setUpClass(cls): url = 'http://zhuanlan.zhihu.com/xiepanda/20202275' post_path = os.path.join(TEST_DATA_PATH, 'column_post.json') with open(post_path, 'r') as f: post_json = json.load(f) post_saved_path = os.path.join(TEST_DATA_PATH, 'post.md') with open(post_saved_path, 'rb') as f: cls.post_saved = f.read() cls.post = Post(url) cls.post.soup = post_json cls.expected = {'column_in_name': 'xiepanda', 'slug': 20202275, 'column_name': '谢熊猫出没注意', 'author_name': '谢熊猫君', 'author_id': 'xiepanda', 'title': '为了做一个称职的吃货，他决定连着吃一百天转基因食物', 'upvote_num': 963, 'comment_num': 199} def test_column_in_name(self): self.assertEqual(self.expected['column_in_name'], self.post.column_in_name) def test_slug(self): self.assertEqual(self.expected['slug'], self.post.slug) def test_author(self): self.assertEqual(self.expected['author_name'], self.post.author.name) self.assertEqual(self.expected['author_id'], self.post.author.id) def test_title(self): self.assertEqual(self.expected['title'], self.post.title) def test_upvote_num(self): self.assertEqual(self.expected['upvote_num'], self.post.upvote_num) def test_comment_num(self): self.assertEqual(self.expected['comment_num'], self.post.comment_num) def test_save(self): save_name = 'post_save' self.post.save(filepath=TEST_DATA_PATH, filename=save_name) post_saved_path = os.path.join(TEST_DATA_PATH, save_name + '.md') with open(post_saved_path, 'rb') as f: post_saved = f.read() os.remove(post_saved_path) self.assertEqual(self.post_saved, post_saved)

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null

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null

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323ae527f5aea6328f8ca830f729b3e6114a8c51

503

py

Python

algorithm implement (python)/mergesort.py

yedkk/algorithm-design

433b70e8302ec91b74542e9144dd93fdb5b0f8d3

[ "MIT" ]

2

2021-06-01T02:31:06.000Z

2021-06-01T02:39:45.000Z

algorithm implement (python)/mergesort.py

yedkk/algorithm-design

433b70e8302ec91b74542e9144dd93fdb5b0f8d3

[ "MIT" ]

null

algorithm implement (python)/mergesort.py

yedkk/algorithm-design

433b70e8302ec91b74542e9144dd93fdb5b0f8d3

[ "MIT" ]

null

def getArray(): line = input() line = line.strip().split(' ')[1:] s = [] for x in line: s.append(int(x)) return s def merge(s1, s2): n1 = len(s1) n2 = len(s2) p1 = 0 p2 = 0 s = [] while(p1 < n1 or p2 < n2): if(p1 < n1 and (p2 >= n2 or s1[p1] < s2[p2])): s.append(s1[p1]) p1 += 1 else: s.append(s2[p2]) p2 += 1 return s def output(s): print (len(s), end = ' ') print (' '.join(map(str, s)), end = '') s1 = getArray() s2 = getArray() s = merge(s1, s2) output(s)

13.236842

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null

0

null

0

1

0

323b7d2cb5ec3fee745d90ccfecbe50bdd67fcc2

1,276

py

Python

src/CSVtoJSON.py

CloudSevenConsulting/DustyDynamo

335e9a2efc71ccf42cf9dfc7c13fcf62cd5d9453

[ "MIT" ]

null

src/CSVtoJSON.py

CloudSevenConsulting/DustyDynamo

335e9a2efc71ccf42cf9dfc7c13fcf62cd5d9453

[ "MIT" ]

null

src/CSVtoJSON.py

CloudSevenConsulting/DustyDynamo

335e9a2efc71ccf42cf9dfc7c13fcf62cd5d9453

[ "MIT" ]

null

import csv import json from pprint import pprint import os stockData = ['RIO'] for i in range(0,len(stockData)): csvfile = open(stockData[i]+'.csv', 'r') fieldnames = ("NetworkTime","StockID","Open","High", "Low", "Close", "Adj Close", "Volume") reader = csv.DictReader( csvfile, fieldnames) data = open(stockData[i]+'.json', 'w') data.write('[\n') for row in reader: data.write('{ \n' \ + '"MoteTimestamp": "%s",' %row['NetworkTime'] \ + '\n"MoteID": %s,' %row['StockID'] \ + '\n "StockData":{' \ + '\n "OpenPrice": %s,' %row['Open'] \ + '\n "HighPrice": %s,' %row['High'] \ + '\n "LowPrice": %s,' %row['Low'] \ + '\n "ClosePrice": %s,' %row['Close'] \ + '\n "Adj Close": %s,' %row['Adj Close'] \ + '\n "VolumeNumber": %s' %row['Volume'] \ + '\n }' \ + '\n},\n' ) data.close() with open(stockData[i]+'.json', 'rb+') as filehandle: filehandle.seek(-3, os.SEEK_END) filehandle.truncate() filehandle.close() with open(stockData[i]+'.json', 'a') as filehandle: filehandle.write("\n]")

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null

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null

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323d0642bd0b2e71b6ea4028021ab212c0e0889f

700

py

Python

core/api.py

rastos/Mi-Fit-and-Zepp-workout-exporter

e05dd7321b71dff6a4e2f4794d0d66d4eee2cbfa

[ "MIT" ]

13

2021-04-13T14:27:58.000Z

2022-02-09T18:32:37.000Z

core/api.py

rastos/Mi-Fit-and-Zepp-workout-exporter

e05dd7321b71dff6a4e2f4794d0d66d4eee2cbfa

[ "MIT" ]

3

2021-06-03T20:27:34.000Z

2021-06-04T06:24:18.000Z

core/api.py

rastos/Mi-Fit-and-Zepp-workout-exporter

e05dd7321b71dff6a4e2f4794d0d66d4eee2cbfa

[ "MIT" ]

2

2021-06-03T20:29:54.000Z

2021-08-13T22:28:59.000Z

import requests class Api: def __init__(self, token): self.token = token def get_history(self): r = requests.get('https://api-mifit-de2.huami.com/v1/sport/run/history.json', headers={ 'apptoken': self.token }, params={ 'source': 'run.mifit.huami.com', }) r.raise_for_status() return r.json() def get_detail(self, track_id, source): r = requests.get('https://api-mifit-de2.huami.com/v1/sport/run/detail.json', headers={ 'apptoken': self.token }, params={ 'trackid': track_id, 'source': source, }) r.raise_for_status() return r.json()

24.137931

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0.304286

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0

0.333333

0

null

0

null

0

1

0

323de0cd069365ae5cc57c4534ae993e3a17cc39

7,616

py

Python

Server/Python/tests/dbsserver_t/unittests/web_t/DBSMigrateModel_t.py

vkuznet/DBS

14df8bbe8ee8f874fe423399b18afef911fe78c7

[ "Apache-2.0" ]

8

2015-08-14T04:01:32.000Z

2021-06-03T00:56:42.000Z

Server/Python/tests/dbsserver_t/unittests/web_t/DBSMigrateModel_t.py

yuyiguo/DBS

14df8bbe8ee8f874fe423399b18afef911fe78c7

[ "Apache-2.0" ]

162

2015-01-07T21:34:47.000Z

2021-10-13T09:42:41.000Z

Server/Python/tests/dbsserver_t/unittests/web_t/DBSMigrateModel_t.py

yuyiguo/DBS

14df8bbe8ee8f874fe423399b18afef911fe78c7

[ "Apache-2.0" ]

16

2015-01-22T15:27:29.000Z

2021-04-28T09:23:28.000Z

#!/usr/bin/env python """ DBS 3 Migrate REST model unittests The DBS3 Migration Service must be stopped before executing the unittest. In addition, take care that no instance is running on the same DB. Else the single unittests can happen to fail due to race conditions with DBS3 Migration Service. """ from dbsserver_t.utils.DBSRestApi import DBSRestApi from dbsserver_t.utils.DBSDataProvider import DBSBlockDataProvider, create_child_data_provider from dbsserver_t.utils.TestTools import expectedFailure from itertools import chain import os import socket import unittest class DBSMigrateModel_t(unittest.TestCase): _data_provider = None _saved_data = {} def __init__(self, methodName='runTest'): super(DBSMigrateModel_t, self).__init__(methodName) if not self._data_provider: self.setUpClass() @classmethod def setUpClass(cls): cls._data_provider = DBSBlockDataProvider(num_of_blocks=1, num_of_files=10, num_of_runs=10, num_of_lumis=10) ### According to https://svnweb.cern.ch/trac/CMSDMWM/ticket/4068, blocks and dataset migration should use ### separate input data. _independent(_child)_data_provider will provide them. cls._independent_data_provider = DBSBlockDataProvider(num_of_blocks=5, num_of_files=10, num_of_runs=10, num_of_lumis=10) cls._parent_data_provider = DBSBlockDataProvider(num_of_blocks=1, num_of_files=10, num_of_runs=10, num_of_lumis=10) cls._child_data_provider = create_child_data_provider(cls._parent_data_provider) cls._independent_child_data_provider = create_child_data_provider(cls._independent_data_provider) config = os.environ['DBS_TEST_CONFIG'] service = os.environ.get("DBS_TEST_SERVICE", "DBSMigrate") #Use one specific database cms_dbs3_local3@int2r for migration unittests when migration_test=True cls._migrate_api = DBSRestApi(config, service, migration_test=True) cls._migration_url = 'https://%s/dbs/dev/global/DBSWriter' % (socket.getfqdn()) cls._writer_api = DBSRestApi(config, 'DBSWriter') def setUp(self): pass @expectedFailure def test_01_migration_removal(self): """test01: Clean-up old migration requests. Test to remove migration requests between different DBS instances\n""" for status in sorted(self._migrate_api.list('status'), key=lambda status: status['migration_request_id']): data = {'migration_rqst_id': status['migration_request_id']} if status['migration_status'] in (0, 3, 9) and status['create_by'] == os.getlogin(): self._migrate_api.insert('remove', data) else: self.assertRaises(Exception, self._migrate_api.insert, 'remove', data) def test_02_migration_request(self): """test02: Negative test to request a migration between different DBS instances before injecting data. This is a negative test because the block was not inserted into the source DB.\n""" for block_name in (block['block']['block_name'] for block in self._child_data_provider.block_dump()): toMigrate = {'migration_url' : self._migration_url, 'migration_input' : block_name} self.assertRaises(Exception, self._migrate_api.insert, 'submit', toMigrate) def test_03_insert_data_to_migrate(self): """test03: Insert data to migrate into source DBS instance. This is has to be done for the next several tests.\n""" for block in chain(self._data_provider.block_dump(), self._independent_data_provider.block_dump(), self._parent_data_provider.block_dump(), self._child_data_provider.block_dump(), self._independent_child_data_provider.block_dump()): self._writer_api.insert('bulkblocks', block) def test_04_migration_request(self): """test04: Test to request a migration between different DBS instances by block.\n""" for block_name in (block['block']['block_name'] for block in self._child_data_provider.block_dump()): toMigrate = {'migration_url' : self._migration_url, 'migration_input' : block_name} result = self._migrate_api.insert('submit', toMigrate) self._saved_data.setdefault('migration_rqst_ids', []).append(result['migration_details']['migration_request_id']) self._saved_data.setdefault('migration_inputs', []).append(block_name) def test_05_migration_request(self): """test05: Test to request a migration between different DBS instances by dataset.\n""" datasets = set((block['dataset']['dataset'] for block in chain(self._child_data_provider.block_dump(), self._independent_child_data_provider.block_dump()))) for dataset in datasets: toMigrate = {'migration_url' : self._migration_url, 'migration_input' : dataset} result = self._migrate_api.insert('submit', toMigrate) self._saved_data.setdefault('migration_rqst_ids', []).append(result['migration_details']['migration_request_id']) def test_06_migration_status(self): """test06: Test to check the status of an ongoing migration between different DBS instances by id. \n""" status = self._migrate_api.list('status') self.assertTrue(isinstance(status, list)) for migration_rqst_id in self._saved_data['migration_rqst_ids']: status = self._migrate_api.list('status', migration_rqst_id) self.assertEqual(len(status), 1) def test_07_migration_status(self): """test07: Test to check the status of an ongoing migration between different DBS instances by block. \n""" for migration_input in self._saved_data['migration_inputs']: status = self._migrate_api.list('status', block_name=migration_input) self.assertEqual(len(status), 1) def test_08_migration_status(self): """test08: Test to check the status of an ongoing migration between different DBS instances by dataset. \n""" datasets = set((block_name.split('#', 1)[0] for block_name in self._saved_data['migration_inputs'])) for dataset in datasets: status = self._migrate_api.list('status', dataset=dataset) self.assertTrue(len(status)>=1) def test_09_migration_removal(self): "test09: Test to remove a pending migration request between different DBS instances. \n" for migration_rqst_id in self._saved_data['migration_rqst_ids']: data = {'migration_rqst_id': migration_rqst_id} self._migrate_api.insert('remove', data) def test_99_save_data_to_disk(self): """test99: Save data to disk to re-use data for migration server unittests. \n""" self._data_provider.save('migration_unittest_data.pkl') self._independent_data_provider.save('migration_unittest_independent_data.pkl') self._parent_data_provider.save('migration_unittest_parent_data.pkl') self._independent_child_data_provider.save('migration_unittest_independent_child_data.pkl') self._child_data_provider.save('migration_unittest_child_data.pkl') if __name__ == "__main__": SUITE = unittest.TestLoader().loadTestsFromTestCase(DBSMigrateModel_t) unittest.TextTestRunner(verbosity=2).run(SUITE)

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0

null

0

null

0

1

0

323e018247ff04ecd6fd2937c2a4145cd45afc55

844

py

Python

setup.py

sgang007/audio_chat_client

e2c1caf6ec1a781be0d22f516e55434099514da1

[ "MIT" ]

null

setup.py

sgang007/audio_chat_client

e2c1caf6ec1a781be0d22f516e55434099514da1

[ "MIT" ]

null

setup.py

sgang007/audio_chat_client

e2c1caf6ec1a781be0d22f516e55434099514da1

[ "MIT" ]

null

from setuptools import setup, find_packages # from distutils.core import setup # import py2exe # import sys import os del os.link # sys.setrecursionlimit(5000) with open('requirements.txt') as f: required = f.read().splitlines() def readme(): with open('README.md') as f: return f.read() setup(name='varta-chat', version='1.0', description='Audio Chat framework', long_description=readme(), url='https://github.com/sgang007/audio_chat_client', author='Shubhojyoti Ganguly', author_email='shubho.important@gmail.com', license='MIT', packages=find_packages(), install_requires=required, entry_points={ 'console_scripts': [ 'varta = client.__main__:key_listener', ] }, include_package_data=True, zip_safe=True)

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323e28eb5aa06c996913613c2bfc7c17a0e85d7c

2,334

py

Python

kglib/tests/end_to_end/kgcn/diagnosis_debug.py

graknlabs/research

ae3ee07106739efd10f0627058210038ab5956d3

[ "Apache-2.0" ]

13

2018-09-25T13:29:08.000Z

2018-12-10T11:04:38.000Z

kglib/tests/end_to_end/kgcn/diagnosis_debug.py

graknlabs/research

ae3ee07106739efd10f0627058210038ab5956d3

[ "Apache-2.0" ]

23

2018-09-17T20:31:44.000Z

2018-12-14T11:21:52.000Z

kglib/tests/end_to_end/kgcn/diagnosis_debug.py

graknlabs/research

ae3ee07106739efd10f0627058210038ab5956d3

[ "Apache-2.0" ]

1

2018-09-25T15:56:32.000Z

# # Copyright (C) 2021 Vaticle # # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. # import os import sys import unittest from kglib.kgcn_tensorflow.examples.diagnosis.diagnosis import diagnosis_example class TestDiagnosisExampleDebug(unittest.TestCase): """ A copy of the end-to-end test for local debugging. Requires a TypeDB server to be started in the background manually. Run with: bazel test //kglib/tests/end_to_end:diagnosis --test_output=streamed --spawn_strategy=standalone --action_env=PATH --test_arg=--<path/to/your/typedb/directory> """ def setUp(self): self._typedb_binary_location = sys.argv.pop() base_dir = os.getenv("TEST_SRCDIR") + "/" + os.getenv("TEST_WORKSPACE") self._data_file_location = base_dir + sys.argv.pop() self._schema_file_location = base_dir + sys.argv.pop() def test_learning_is_done(self): solveds_tr, solveds_ge = diagnosis_example(self._typedb_binary_location, schema_file_path=self._schema_file_location, seed_data_file_path=self._data_file_location) self.assertGreaterEqual(solveds_tr[-1], 0.7) self.assertLessEqual(solveds_tr[-1], 0.99) self.assertGreaterEqual(solveds_ge[-1], 0.7) self.assertLessEqual(solveds_ge[-1], 0.99) if __name__ == "__main__": # This handles the fact that additional arguments that are supplied by our py_test definition # https://stackoverflow.com/a/38012249 unittest.main(argv=['ignored-arg'])

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324140adbf8ce6a27b7f51c371562021ff506dae

1,668

py

Python

python/math_utils.py

PROrock/codin-game-puzzles

a0444719f9a629fc97b1da6f175ecd462a9ff59b

[ "MIT" ]

1

2021-06-16T02:33:57.000Z

python/math_utils.py

PROrock/codin-game-puzzles

a0444719f9a629fc97b1da6f175ecd462a9ff59b

[ "MIT" ]

null

python/math_utils.py

PROrock/codin-game-puzzles

a0444719f9a629fc97b1da6f175ecd462a9ff59b

[ "MIT" ]

null

def signum(x): if x > 0: return 1 if x < 0: return -1 return 0 # copy of Python 3.5 implementation - probably not needed def isclose(a, b, rel_tol=1e-09, abs_tol=0.0): return abs(a - b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol) def gcd(a, b): """Greatest common divisor""" return _gcd_internal(abs(a), abs(b)) def _gcd_internal(a, b): """Greatest common divisor internal""" # Impl. notes: Euler algorithm, both a and b are not negative # There exists faster algorithm (which uses division by 2, which is faster) # -> Stein's algorithm https://en.wikipedia.org/wiki/Binary_GCD_algorithm # print a, b if a == b: return a if b == 1: return 1 if a == 0 or b == 0: return max(a, b) return gcd(b, a % b) def combinations_generator(n, k): """Generates all combinations of list of length n with k ones (lexicographically sorted). Storing only one indices and creating the combination list might be more performant. """ combination = [1 if i >= n - k else 0 for i in xrange(n)] while True: yield combination combination = copy(combination) # get first one with zero before it one_indices = [idx for idx, value in enumerate(combination) if value] for one_idx_idx, one_idx in enumerate(one_indices): combination[one_idx] = 0 if one_idx > 0 and one_idx - 1 != one_indices[one_idx_idx - 1]: for i in xrange(one_idx_idx + 1): combination[one_idx - i - 1] = 1 break else: # all combinations generated, breaking break

32.076923

93

0.607914

253

1,668

3.909091

0.391304

0.016178

0.0273

0.020222

0.068756

0

0.02209

0.294365

1,668

51

94

32.705882

0.818182

0.343525

0

0.066667

0

1

0.166667

false

0

0.033333

0.4

0

null

0

null

0

1

0

3247a207cdb1e57a605f9bb8949d6c37632fda73

3,707

py

Python

pymt/grids/map.py

mwtoews/pymt

81a8469b0d0d115d21186ec1d1c9575690d51850

[ "MIT" ]

null

pymt/grids/map.py

mwtoews/pymt

81a8469b0d0d115d21186ec1d1c9575690d51850

[ "MIT" ]

null

pymt/grids/map.py

mwtoews/pymt

81a8469b0d0d115d21186ec1d1c9575690d51850

[ "MIT" ]

null

#! /bin/env python """ Examples ======== **Rectilinear** Create a rectilinear grid that is 2x3:: (0) --- (1) --- (2) | | | | | | | [0] | [1] | | | | | | | (3) --- (4) --- (5) Numbers in parens are node IDs, and numbers in square brackets are cell IDs. >>> g = RectilinearMap ([0, 2], [0, 1, 2]) >>> g.get_x () array([ 0., 1., 2., 0., 1., 2.]) >>> g.get_y () array([ 0., 0., 0., 2., 2., 2.]) Node 1 is shared by both cell 0, and 1; node 5 only is part of cell 1. >>> g.get_shared_cells (1) [0, 1] >>> g.get_shared_cells (5) [1] Point (.5, 1.) is contained only within cell 0. >>> g.is_in_cell (.5, 1., 0) True >>> g.is_in_cell (.5, 1., 1) False Point (1., 1.) is on a border and so is contained by both cells. >>> g.is_in_cell (1, 1., 0) True >>> g.is_in_cell (1, 1., 1) True """ from shapely.geometry import Point, asLineString, asPoint, asPolygon from pymt.grids import ( Rectilinear, Structured, UniformRectilinear, Unstructured, UnstructuredPoints, ) class UnstructuredMap(Unstructured): name = "Unstructured" def __init__(self, *args, **kwargs): super(UnstructuredMap, self).__init__(*args, **kwargs) self._point = {} last_offset = 0 for (cell_id, offset) in enumerate(self._offset): cell = self._connectivity[last_offset:offset] last_offset = offset for point_id in cell: try: self._point[point_id].append(cell_id) except KeyError: self._point[point_id] = [cell_id] (point_x, point_y) = (self.get_x(), self.get_y()) self._polys = [] last_offset = 0 for (cell_id, offset) in enumerate(self._offset): cell = self._connectivity[last_offset:offset] last_offset = offset (x, y) = (point_x.take(cell), point_y.take(cell)) if len(x) > 2: self._polys.append(asPolygon(zip(x, y))) elif len(x) == 2: self._polys.append(asLineString(zip(x, y))) else: self._polys.append(asPoint(zip(x, y))) def get_shared_cells(self, point_id): """ Parameters ---------- point_id: int ID of a point in the grid. Returns ------- ndarray of int Indices to cells that share a given node. """ return self._point[point_id] def is_in_cell(self, x, y, cell_id): """Check if a point is in a cell. Parameters ---------- x: float x-coordinate of point to check. y: float y-coordinate of point to check. cell_id: int ID of the cell in the grid. Returns ------- bool True if the point (x, y) is contained in the cell. """ pt = Point((x, y)) return self._polys[cell_id].contains(pt) or self._polys[cell_id].touches(pt) class UnstructuredPointsMap(UnstructuredPoints): name = "UnstructuredPoints" def get_shared_cells(self, point_id): # pylint: disable=no-self-use return [] def is_in_cell(self, x, y, cell_id): # pylint: disable=no-self-use return False class StructuredMap(Structured, UnstructuredMap): name = "Structured" class RectilinearMap(Rectilinear, UnstructuredMap): name = "Rectilinear" class UniformRectilinearMap(UniformRectilinear, UnstructuredMap): name = "UniformRectilinear" if __name__ == "__main__": import doctest doctest.testmod(optionflags=doctest.NORMALIZE_WHITESPACE)

23.916129

84

0.555705

468

3,707

4.228632

0.252137

0.027287

0.024255

0.018191

0.279434

0.235472

0.195048

0.123295

0.100051

0

0.022683

0.310224

3,707

154

85

24.071429

0.751271

0.362018

0

0.218182

0

0.035566

0

1

0.090909

false

0

0.054545

0.036364

0.4

0

null

0

null

0

1

0

3247e08ee12a7d9414679491f0b3e5ad060be2e8

27,447

py

Python

jogo.py

AdamastorLinsFrancaNetto/jogo-academic-journey

ad312d255abe23e243ba39732e972cf45f092b08

[ "MIT" ]

null

jogo.py

AdamastorLinsFrancaNetto/jogo-academic-journey

ad312d255abe23e243ba39732e972cf45f092b08

[ "MIT" ]

null

jogo.py

AdamastorLinsFrancaNetto/jogo-academic-journey

ad312d255abe23e243ba39732e972cf45f092b08

[ "MIT" ]

null

import pygame from conteudo import Conteudo, Nave, Tiro import random class Jogo: def __init__(self): self.fundo1 = Conteudo("arquivos/espaco.png", 0, 0) self.fundo2 = Conteudo("arquivos/espaco.png", 0, -960) self.nave = Nave("arquivos/nave1.png",630,750) self.comando = Conteudo("arquivos/comando1.png", 40, 960) self.comandoo = Conteudo("arquivos/comandoo1.png", 40, 960) self.comandooo = Conteudo("arquivos/comandooo1.png", 40, 900) self.comandoooo = Conteudo("arquivos/comandoooo1.png", 40, 960) self.dialogo1 = Conteudo("arquivos/dialogo1.png", 330, 120) self.dialogo2 = Conteudo("arquivos/dialogo4.png", 330, 120) self.dialogo3 = Conteudo("arquivos/dialogo6.png", 330, 120) self.dialogo4 = Conteudo("arquivos/dialogo8.png", 330, 120) self.dialogo5 = Conteudo("arquivos/dialogo9.png", 330, 120) self.armadura5 = Conteudo("arquivos/armadura5.png", 10, 10) self.armadura4 = Conteudo("arquivos/armadura4.png", 10, 10) self.armadura3 = Conteudo("arquivos/armadura3.png", 10, 10) self.armadura2 = Conteudo("arquivos/armadura2.png", 10, 10) self.armadura1 = Conteudo("arquivos/armadura1.png", 10, 10) self.discernimento0 = Conteudo("arquivos/disc0.png", 820, 10) self.discernimento1 = Conteudo("arquivos/disc1.png", 820, 10) self.discernimento2 = Conteudo("arquivos/disc2.png", 820, 10) self.discernimento3 = Conteudo("arquivos/disc3.png", 820, 10) self.discernimento4 = Conteudo("arquivos/disc4.png", 820, 10) self.discernimento5 = Conteudo("arquivos/disc5.png", 820, 10) self.planetainimigo = Conteudo("arquivos/pr1.png", 910, -320) self.planetaaliado = Conteudo("arquivos/pb1.png", 10, -600) self.resiliencia0 = Conteudo("arquivos/resili0.png", 824, 56) self.resiliencia1 = Conteudo("arquivos/resili1.png", 824, 56) self.resiliencia2 = Conteudo("arquivos/resili2.png", 824, 56) self.resiliencia3 = Conteudo("arquivos/resili3.png", 824, 56) self.resiliencia4 = Conteudo("arquivos/resili4.png", 824, 56) self.resiliencia5 = Conteudo("arquivos/resili5.png", 824, 56) self.condecoracoes = Conteudo("arquivos/condecoracoes.png", 1010, 755) self.condecoracao1 = Conteudo("arquivos/condecoracao1.png", 1010, 790) self.condecoracao2 = Conteudo("arquivos/condecoracao2.png", 1100, 790) self.condecoracao3 = Conteudo("arquivos/condecoracao3.png", 1190, 790) self.destreza0 = Conteudo("arquivos/destreza0.png", 821, 104) self.destreza1 = Conteudo("arquivos/destreza1.png", 821, 104) self.destreza2 = Conteudo("arquivos/destreza2.png", 821, 104) self.destreza3 = Conteudo("arquivos/destreza3.png", 821, 104) self.destreza4 = Conteudo("arquivos/destreza4.png", 821, 104) self.destreza5 = Conteudo("arquivos/destreza5.png", 821, 104) self.gggg = Conteudo("arquivos/gggg1.png", 1000, -230) self.ggg = Conteudo("arquivos/ggg1.png", 700, -180) self.gg = Conteudo("arquivos/gg1.png", 400, -130) self.g = Conteudo("arquivos/g1.png", 100, -100) self.r = Conteudo("arquivos/r.png", 600, -50) self.tiro = Tiro("arquivos/x1.png", -100,-100) self.aste1 = Conteudo("arquivos/aste1.png", 840, -50) self.aste2 = Conteudo("arquivos/aste2.png", 640, -120) self.aste3 = Conteudo("arquivos/aste3.png", 440, -190) self.aste11 = Conteudo("arquivos/aste11.png", 840, -50) self.aste22 = Conteudo("arquivos/aste22.png", 640, -120) self.aste33 = Conteudo("arquivos/aste33.png", 440, -190) self.p1 = Conteudo("arquivos/p1.png", 0, -40) self.p2 = Conteudo("arquivos/p2.png", 427, -40) self.p3 = Conteudo("arquivos/p3.png", 854, -40) self.i1 = Conteudo("arquivos/i1.png", 0, -40) self.i2 = Conteudo("arquivos/i2.png", 427, -40) self.i3 = Conteudo("arquivos/i3.png", 854, -40) self.w1 = Conteudo("arquivos/w1.png", 0, -40) self.w2 = Conteudo("arquivos/w2.png", 427, -40) self.w3 = Conteudo("arquivos/w3.png", 854, -40) self.f1 = Conteudo("arquivos/f1.png", 0, -40) self.f2 = Conteudo("arquivos/f2.png", 427, -40) self.f3 = Conteudo("arquivos/f3.png", 854, -40) self.d1 = Conteudo("arquivos/d1.png", 0, -40) self.d2 = Conteudo("arquivos/d2.png", 427, -40) self.d3 = Conteudo("arquivos/d3.png", 854, -40) self.fim = Conteudo("arquivos/fim.png", 0, 0) self.boleana_dialogo = False self.mudar_cena = False self.foi = False self.contagem_resili = 0 self.contagem_destre = 0 self.contagem_dialogo1 = 1 self.contagem_paliados = 1 self.contagem_pinimigos = 1 self.inicio_asteroides = 0 self.contagem_gggg = 1 self.contagem_ggg = 1 self.contagem_gg = 1 self.contagem_g = 1 self.contagem_r = 1 self.contagem_barreira = 0 self.inicio = 0 self.inicio1 = 0 self.final = 0 def draw(self, tela): self.fundo1.draw(tela) self.fundo2.draw(tela) self.tiro.draw(tela) self.nave.draw(tela) self.comando.draw(tela) if self.nave.contagem_discernimento == 5 and self.contagem_paliados == 6: self.comandoo.draw(tela) if self.nave.contagem_resiliencia == 5 and self.contagem_gggg == 6: self.comandooo.draw(tela) if self.inicio == 1: self.comandoooo.draw(tela) if self.comandoooo.personagens.rect[1] == 370: self.dialogo4.draw(tela) if self.inicio1 == 1: self.condecoracao3.draw(tela) if self.contagem_dialogo1 == 9: self.dialogo5.draw(tela) if self.final == 1: self.fim.draw(tela) self.armadura5.draw(tela) self.armadura4.draw(tela) self.armadura3.draw(tela) self.armadura2.draw(tela) self.armadura1.draw(tela) if self.contagem_dialogo1 == 3: self.planetaaliado.draw(tela) if self.contagem_dialogo1 == 3: self.planetainimigo.draw(tela) self.aste1.draw(tela) self.aste2.draw(tela) self.aste3.draw(tela) if self.contagem_dialogo1 == 3: self.discernimento0.draw(tela) if self.nave.contagem_discernimento == 1: self.discernimento1.draw(tela) if self.nave.contagem_discernimento == 2: self.discernimento2.draw(tela) if self.nave.contagem_discernimento == 3: self.discernimento3.draw(tela) if self.nave.contagem_discernimento == 4: self.discernimento4.draw(tela) if self.nave.contagem_discernimento == 5: self.discernimento5.draw(tela) if self.nave.contagem_resiliencia == 1: self.resiliencia1.draw(tela) if self.nave.contagem_resiliencia == 2: self.resiliencia2.draw(tela) if self.nave.contagem_resiliencia == 3: self.resiliencia3.draw(tela) if self.nave.contagem_resiliencia == 4: self.resiliencia4.draw(tela) if self.nave.contagem_resiliencia == 5: self.resiliencia5.draw(tela) if self.nave.contagem_destreza == 1: self.destreza1.draw(tela) if self.nave.contagem_destreza == 2: self.destreza2.draw(tela) if self.nave.contagem_destreza == 3: self.destreza3.draw(tela) if self.nave.contagem_destreza == 4: self.destreza4.draw(tela) if self.nave.contagem_destreza == 5: self.destreza5.draw(tela) if self.comando.personagens.rect[1] == 370: self.dialogo1.draw(tela) if self.comandoo.personagens.rect[1] == 370: self.dialogo2.draw(tela) if self.comandooo.personagens.rect[1] == 370: self.dialogo3.draw(tela) if self.contagem_resili == 1: self.condecoracoes.draw(tela) self.condecoracao1.draw(tela) self.resiliencia0.draw(tela) if self.contagem_destre == 1: self.condecoracao2.draw(tela) self.destreza0.draw(tela) if self.inicio_asteroides == 1: self.gggg.draw(tela) self.ggg.draw(tela) self.gg.draw(tela) self.g.draw(tela) self.r.draw(tela) self.aste11.draw(tela) self.aste22.draw(tela) self.aste33.draw(tela) if self.nave.contagem_resiliencia == 6: self.comandooo.draw(tela) if self.contagem_dialogo1 == 7: self.p1.draw(tela) self.p2.draw(tela) self.p3.draw(tela) if self.contagem_barreira == 1: self.i1.draw(tela) self.i2.draw(tela) self.i3.draw(tela) if self.contagem_barreira == 2: self.w1.draw(tela) self.w2.draw(tela) self.w3.draw(tela) if self.contagem_barreira == 3: self.f1.draw(tela) self.f2.draw(tela) self.f3.draw(tela) if self.contagem_barreira == 4: self.d1.draw(tela) self.d2.draw(tela) self.d3.draw(tela) def atualizacoes(self): self.movimento_fundo() self.nave.animacoes("nave", 2, 2) self.comando.animacoes("comando", 2, 2) self.comandoo.animacoes("comandoo", 2, 2) self.comandooo.animacoes("comandooo", 2, 2) self.comandoooo.animacoes("comandoooo", 2, 2) self.tiro.animacoes("x",2,2) self.planetas_inimigos() self.planetas_aliados() self.nave.colisao_planetas(self.planetainimigo.group, "planetainimigos") self.nave.colisao_planetas(self.aste1.group, "aste1") self.nave.colisao_planetas(self.aste2.group, "aste2") self.nave.colisao_planetas(self.aste3.group, "aste3") self.nave.colisao_planetas(self.planetaaliado.group, "planetaaliados") self.tiro.colisao_tiro(self.planetainimigo.group, "planetainimigos") self.tiro.colisao_tiro(self.planetaaliado.group, "planetaaliados") self.tiro.colisao_tiroast1(self.aste1.group, "aste1") self.tiro.colisao_tiroast1(self.aste2.group, "aste2") self.tiro.colisao_tiroast1(self.aste3.group, "aste3") self.nave.colisao_asteroides(self.gggg.group, "gggg") self.nave.colisao_asteroides(self.ggg.group, "ggg") self.nave.colisao_asteroides(self.gg.group, "gg") self.nave.colisao_asteroides(self.g.group, "g") self.nave.colisao_asteroides(self.r.group, "r") self.nave.colisao_asteroides(self.aste11.group, "aste11") self.nave.colisao_asteroides(self.aste22.group, "aste22") self.nave.colisao_asteroides(self.aste33.group, "aste33") self.tiro.colisao_tiroo(self.gggg.group, "gggg") self.tiro.colisao_tiroo(self.ggg.group, "ggg") self.tiro.colisao_tiroo(self.gg.group, "gg") self.tiro.colisao_tiroo(self.g.group, "g") self.tiro.colisao_tiroast2(self.aste11.group, "aste11") self.tiro.colisao_tiroast2(self.aste22.group, "aste22") self.tiro.colisao_tiroast2(self.aste33.group, "aste33") self.nave.colisao_barreira(self.p1.group, "p1") self.nave.colisao_barreira(self.p2.group, "p2") self.nave.colisao_barreira(self.p3.group, "p3") self.nave.colisao_barreira(self.i1.group, "i1") self.nave.colisao_barreira(self.i2.group, "i2") self.nave.colisao_barreira(self.i3.group, "i3") self.nave.colisao_barreira(self.w1.group, "w1") self.nave.colisao_barreira(self.w2.group, "w2") self.nave.colisao_barreira(self.w3.group, "w3") self.nave.colisao_barreira(self.f1.group, "f1") self.nave.colisao_barreira(self.f2.group, "f2") self.nave.colisao_barreira(self.f3.group, "f3") self.nave.colisao_barreira(self.d1.group, "d1") self.nave.colisao_barreira(self.d2.group, "d2") self.nave.colisao_barreira(self.d3.group, "d3") self.tiro.colisao_barreirat(self.p1.group, "p1") self.tiro.colisao_barreirat(self.p2.group, "p2") self.tiro.colisao_barreirat(self.p3.group, "p3") self.tiro.colisao_barreirat(self.i1.group, "i1") self.tiro.colisao_barreirat(self.i2.group, "i2") self.tiro.colisao_barreirat(self.i3.group, "i3") self.tiro.colisao_barreirat(self.w1.group, "w1") self.tiro.colisao_barreirat(self.w2.group, "w2") self.tiro.colisao_barreirat(self.w3.group, "w3") self.tiro.colisao_barreirat(self.f1.group, "f1") self.tiro.colisao_barreirat(self.f2.group, "f2") self.tiro.colisao_barreirat(self.f3.group, "f3") self.tiro.colisao_barreirat(self.d1.group, "d1") self.tiro.colisao_barreirat(self.d2.group, "d2") self.tiro.colisao_barreirat(self.d3.group, "d3") self.quantidade_armadura() self.quantidade_disernimento() self.quantidade_resiliencia() self.quantidade_destreza() self.movimento_primeira() self.movimento_segunda() self.movimento_terceira() self.asteroides() self.disparado() self.barreira() self.movimento_quarta() def movimento_primeira(self): if self.nave.contagem_enter == 1: self.comando.personagens.rect[1] -= 3 if self.comando.personagens.rect[1] <= 370: self.comando.personagens.rect[1] = 370 self.nave.contagem_enter += 1 pygame.mixer.init() self.som_dialogo = pygame.mixer.Sound("arquivos/gravacao1.mpeg") self.som_dialogo.play() if self.contagem_dialogo1 == 3: self.comando.personagens.rect[1] += 6 if self.comando.personagens.rect[1] >= 960: self.comando.personagens.rect[1] = 960 self.comando.personagens.kill() def movimento_segunda(self): if self.nave.contagem_discernimento == 5 and self.contagem_paliados == 6 and self.contagem_dialogo1 == 3: self.comandoo.personagens.rect[1] -= 3 if self.comandoo.personagens.rect[1] <= 370: self.comandoo.personagens.rect[1] = 370 self.contagem_resili += 1 self.contagem_dialogo1 += 1 pygame.mixer.init() self.som_dialogo = pygame.mixer.Sound("arquivos/gravacao3.mpeg") self.som_dialogo.play() if self.contagem_dialogo1 == 5: self.comandoo.personagens.rect[1] += 6 if self.comandoo.personagens.rect[1] >= 960: self.comandoo.personagens.rect[1] = 960 self.comandoo.personagens.kill() def movimento_terceira(self): if self.nave.contagem_resiliencia == 5 and self.contagem_gggg == 6 and self.contagem_dialogo1 == 5: self.comandooo.personagens.rect[1] -= 3 if self.comandooo.personagens.rect[1] <= 370: self.comandooo.personagens.rect[1] = 370 pygame.mixer.init() self.som_dialogo = pygame.mixer.Sound("arquivos/gravacao4.mpeg") self.som_dialogo.play() self.contagem_destre += 1 self.contagem_dialogo1 += 1 if self.contagem_dialogo1 == 7: self.comandooo.personagens.rect[1] += 6 if self.comandooo.personagens.rect[1] >= 960: self.comandooo.personagens.rect[1] = 960 self.comandooo.personagens.kill() def movimento_fundo(self): self.fundo1.personagens.rect[1] += 4 self.fundo2.personagens.rect[1] += 4 if self.fundo1.personagens.rect[1] >= 960: self.fundo1.personagens.rect[1] = 0 if self.fundo2.personagens.rect[1] >= 0: self.fundo2.personagens.rect[1] = -960 def quantidade_armadura(self): if self.nave.contagem_armadura == 4: self.armadura5.personagens.kill() if self.nave.contagem_armadura == 3: self.armadura4.personagens.kill() if self.nave.contagem_armadura == 2: self.armadura3.personagens.kill() if self.nave.contagem_armadura == 1: self.armadura2.personagens.kill() if self.nave.contagem_armadura == 0: self.mudar_cena = True def quantidade_disernimento(self): if self.nave.contagem_discernimento == 1: self.discernimento0.personagens.kill() if self.nave.contagem_discernimento == 2: self.discernimento1.personagens.kill() if self.nave.contagem_discernimento == 3: self.discernimento2.personagens.kill() if self.nave.contagem_discernimento == 4: self.discernimento3.personagens.kill() if self.nave.contagem_discernimento == 5: self.discernimento4.personagens.kill() def quantidade_resiliencia(self): if self.nave.contagem_resiliencia == 1: self.resiliencia0.personagens.kill() if self.nave.contagem_resiliencia == 2: self.resiliencia1.personagens.kill() if self.nave.contagem_resiliencia == 3: self.resiliencia2.personagens.kill() if self.nave.contagem_resiliencia == 4: self.resiliencia3.personagens.kill() if self.nave.contagem_resiliencia == 5: self.resiliencia4.personagens.kill() def quantidade_destreza(self): if self.nave.contagem_destreza == 1: self.destreza0.personagens.kill() if self.nave.contagem_destreza == 2: self.destreza1.personagens.kill() if self.nave.contagem_destreza == 3: self.destreza2.personagens.kill() if self.nave.contagem_destreza == 4: self.destreza3.personagens.kill() if self.nave.contagem_destreza == 5: self.destreza4.personagens.kill() def dialogo(self, event): if event.type == pygame.KEYDOWN: if event.key == pygame.K_KP_ENTER: self.boleana_dialogo = True if event.type == pygame.KEYUP: if event.key == pygame.K_KP_ENTER: self.boleana_dialogo = False if self.boleana_dialogo: self.dialogo1.personagens.kill() self.contagem_dialogo1 +=1 self.dialogo1 = Conteudo("arquivos/dialogo" + str(self.contagem_dialogo1) + ".png", 330, 120) if self.contagem_dialogo1 <= 2: pygame.mixer.init() self.som_dialogo = pygame.mixer.Sound("arquivos/gravacao2.mpeg") self.som_dialogo.play() print("Nº dialogo:", self.contagem_dialogo1) if self.contagem_dialogo1 == 5: self.dialogo2.personagens.kill() self.inicio_asteroides = 1 def planetas_inimigos(self): if self.comando.personagens.rect[1] == 960 and self.contagem_pinimigos <= 5: self.planetainimigo.personagens.rect[1] += 6 self.aste1.personagens.rect[1] += 7 self.aste2.personagens.rect[1] += 7 self.aste3.personagens.rect[1] += 7 if self.aste1.personagens.rect[1] >= 960 and self.contagem_pinimigos <= 5: self.aste1.personagens.kill() if self.contagem_pinimigos <= 4: self.aste1 = Conteudo("arquivos/aste1.png", random.randrange(50, 1000), -50) if self.aste2.personagens.rect[1] >= 960 and self.contagem_pinimigos <= 5: self.aste2.personagens.kill() if self.contagem_pinimigos <= 4: self.aste2 = Conteudo("arquivos/aste2.png", random.randrange(50, 1000), -120) if self.aste3.personagens.rect[1] >= 960 and self.contagem_pinimigos <= 5: self.aste3.personagens.kill() if self.contagem_pinimigos <= 4: self.aste3 = Conteudo("arquivos/aste3.png", random.randrange(50, 1000), -190) if self.planetainimigo.personagens.rect[1] >= 960 and self.contagem_pinimigos <= 5: self.planetainimigo.personagens.kill() self.contagem_pinimigos += 1 if self.contagem_pinimigos <= 5: self.planetainimigo = Conteudo("arquivos/pr" + str(self.contagem_pinimigos) + ".png", random.randrange(50, 900), -320) def planetas_aliados(self): if self.comando.personagens.rect[1] == 960 and self.contagem_paliados <= 5: self.planetaaliado.personagens.rect[1] += 6 if self.planetaaliado.personagens.rect[1] >= 960 and self.contagem_paliados <= 5: self.planetaaliado.personagens.kill() self.contagem_paliados += 1 if self.contagem_paliados <= 5: self.planetaaliado = Conteudo("arquivos/pb" + str(self.contagem_paliados) + ".png", random.randrange(50, 900), -440) def asteroides(self): if self.inicio_asteroides == 1: self.gggg.personagens.rect[1] += 4 self.ggg.personagens.rect[1] += 4 self.gg.personagens.rect[1] += 4 self.g.personagens.rect[1] += 4 self.r.personagens.rect[1] += 4 self.aste11.personagens.rect[1] += 7 self.aste22.personagens.rect[1] += 7 self.aste33.personagens.rect[1] += 7 if self.aste11.personagens.rect[1] >= 960 and self.contagem_gggg <= 4: self.aste11.personagens.kill() if self.contagem_gggg <= 4: self.aste11 = Conteudo("arquivos/aste11.png", random.randrange(50, 1000), -50) if self.aste22.personagens.rect[1] >= 960 and self.contagem_gggg <= 4: self.aste22.personagens.kill() if self.contagem_gggg <= 4: self.aste22 = Conteudo("arquivos/aste22.png", random.randrange(50, 1000), -120) if self.aste33.personagens.rect[1] >= 960 and self.contagem_gggg <= 4: self.aste33.personagens.kill() if self.contagem_gggg <= 4: self.aste33 = Conteudo("arquivos/aste33.png", random.randrange(50, 1000), -190) if self.gggg.personagens.rect[1] >= 960 and self.contagem_gggg <= 5: self.gggg.personagens.kill() self.contagem_gggg += 1 if self.contagem_gggg <= 5: self.gggg = Conteudo("arquivos/gggg" + str(self.contagem_gggg) + ".png", random.randrange(50, 1000), -230) if self.ggg.personagens.rect[1] >= 960 and self.contagem_ggg <= 5: self.ggg.personagens.kill() self.contagem_ggg += 1 if self.contagem_ggg <= 5: self.ggg = Conteudo("arquivos/ggg" + str(self.contagem_ggg) + ".png", random.randrange(50, 1000), -180) if self.gg.personagens.rect[1] >= 960 and self.contagem_gg <= 5: self.gg.personagens.kill() self.contagem_gg += 1 if self.contagem_gg <= 5: self.gg = Conteudo("arquivos/gg" + str(self.contagem_gg) + ".png", random.randrange(50, 1000), -130) if self.g.personagens.rect[1] >= 960 and self.contagem_g <= 5: self.g.personagens.kill() self.contagem_g += 1 if self.contagem_g <= 5: self.g = Conteudo("arquivos/g" + str(self.contagem_g) + ".png", random.randrange(50, 1000), -100) if self.r.personagens.rect[1] >= 960 and self.contagem_r <= 5: self.r.personagens.kill() self.contagem_r += 1 if self.contagem_r <= 5: self.r = Conteudo("arquivos/r.png", random.randrange(50, 1000), -50) def disparado(self): if self.tiro.tiro: self.tiro.personagens.rect[1] = (self.nave.personagens.rect[1] + 30) self.tiro.personagens.rect[0] = (self.nave.personagens.rect[0] + 62) self.foi = True if self.foi: if self.tiro.personagens.rect[1] >= 0: self.tiro.personagens.rect[1] -= 15 if self.tiro.personagens.rect[1] == 0: self.tiro.personagens.kill() self.tiro.personagens.rect[1] = -200 self.tiro.personagens.rect[0] = -200 self.foi = False if self.tiro.tiro: self.tiro = Tiro("arquivos/x1.png", self.nave.personagens.rect[0] + 62, self.nave.personagens.rect[1] + 30) def barreira(self): if self.contagem_dialogo1 == 7: self.p1.personagens.rect[1] += 3 self.p2.personagens.rect[1] += 3 self.p3.personagens.rect[1] += 3 if self.p1.personagens.rect[1] >= 960: self.p1.personagens.kill() self.p2.personagens.kill() self.p3.personagens.kill() self.contagem_barreira = 1 if self.contagem_barreira == 1: self.i1.personagens.rect[1] += 3 self.i2.personagens.rect[1] += 3 self.i3.personagens.rect[1] += 3 if self.i1.personagens.rect[1] >= 960: self.i1.personagens.kill() self.i2.personagens.kill() self.i3.personagens.kill() self.contagem_barreira = 2 if self.contagem_barreira == 2: self.w1.personagens.rect[1] += 3 self.w2.personagens.rect[1] += 3 self.w3.personagens.rect[1] += 3 if self.w1.personagens.rect[1] >= 960: self.w1.personagens.kill() self.w2.personagens.kill() self.w3.personagens.kill() self.contagem_barreira = 3 if self.contagem_barreira == 3: self.f1.personagens.rect[1] += 3 self.f2.personagens.rect[1] += 3 self.f3.personagens.rect[1] += 3 if self.f2.personagens.rect[1] >= 960: self.f1.personagens.kill() self.f2.personagens.kill() self.f3.personagens.kill() self.contagem_barreira = 4 if self.contagem_barreira == 4: self.d1.personagens.rect[1] += 3 self.d2.personagens.rect[1] += 3 self.d3.personagens.rect[1] += 3 if self.d1.personagens.rect[1] >= 960: self.contagem_barreira = 5 self.d1.personagens.kill() self.d2.personagens.kill() self.d3.personagens.kill() self.inicio = 1 def movimento_quarta(self): if self.inicio == 1: self.comandoooo.personagens.rect[1] -= 3 if self.comandoooo.personagens.rect[1] <= 370: self.comandoooo.personagens.rect[1] = 370 pygame.mixer.init() self.som_dialogo = pygame.mixer.Sound("arquivos/gravacao5.mpeg") self.som_dialogo.play() self.inicio1 = 1 if self.contagem_dialogo1 == 8: self.dialogo4.personagens.kill() if self.contagem_dialogo1 == 9: pygame.mixer.init() self.som_dialogo = pygame.mixer.Sound("arquivos/gravacao6.mpeg") self.som_dialogo.play() self.dialogo5.personagens.kill() if self.contagem_dialogo1 == 10: self.comandoooo.personagens.rect[1] += 6 if self.comandoooo.personagens.rect[1] >= 960: self.comandoooo.personagens.rect[1] = 960 self.comandoooo.personagens.kill() self.final = 1

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0.001812

0

null

0

null

0

1

0

32485d3d2f97d8719c9ad7891c585aced9f9c6ac

1,308

py

Python

xpresso/binders/dependants.py

adriangb/xpresso

43fcc360f7b19c00e0b78480f96390bcb4d28053

[ "MIT" ]

75

2022-01-18T02:17:57.000Z

2022-03-24T02:30:04.000Z

xpresso/binders/dependants.py

adriangb/xpresso

43fcc360f7b19c00e0b78480f96390bcb4d28053

[ "MIT" ]

73

2022-01-18T03:01:27.000Z

2022-03-27T16:41:38.000Z

xpresso/binders/dependants.py

adriangb/xpresso

43fcc360f7b19c00e0b78480f96390bcb4d28053

[ "MIT" ]

3

2022-01-18T22:47:06.000Z

2022-01-25T02:03:53.000Z

import inspect import typing from di.api.dependencies import CacheKey from di.dependant import Dependant, Marker from xpresso._utils.typing import Protocol from xpresso.binders.api import SupportsExtractor, SupportsOpenAPI T = typing.TypeVar("T", covariant=True) class SupportsMarker(Protocol[T]): def register_parameter(self, param: inspect.Parameter) -> T: ... class Binder(Dependant[typing.Any]): def __init__( self, *, openapi: SupportsOpenAPI, extractor: SupportsExtractor, ) -> None: super().__init__(call=extractor.extract, scope="connection") self.openapi = openapi self.extractor = extractor @property def cache_key(self) -> CacheKey: return self.extractor class BinderMarker(Marker): def __init__( self, *, extractor_marker: SupportsMarker[SupportsExtractor], openapi_marker: SupportsMarker[SupportsOpenAPI], ) -> None: self.extractor_marker = extractor_marker self.openapi_marker = openapi_marker def register_parameter(self, param: inspect.Parameter) -> Binder: return Binder( openapi=self.openapi_marker.register_parameter(param), extractor=self.extractor_marker.register_parameter(param), )

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1,308

48

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0.432432

0

null

0

null

0

1

0

3249b98ec0603abf9f97a5033a897bd1e2965b76

440

py

Python

Cisco/Python/Modulo_3/for/exercicio1.py

ThiagoKS-7/Python_Essencials_1_cisco

a417747e873f69bb307c4d36205797b191b5b45a

[ "MIT" ]

null

Cisco/Python/Modulo_3/for/exercicio1.py

ThiagoKS-7/Python_Essencials_1_cisco

a417747e873f69bb307c4d36205797b191b5b45a

[ "MIT" ]

null

Cisco/Python/Modulo_3/for/exercicio1.py

ThiagoKS-7/Python_Essencials_1_cisco

a417747e873f69bb307c4d36205797b191b5b45a

[ "MIT" ]

null

def main(): import time # Write a for loop that counts to five. # Body of the loop - print the loop iteration number and the word "Mississippi". # Body of the loop - use: time.sleep(1) # Write a print function with the final message. for i in range(5): print(f'{i + 1} Mississipi') time.sleep(1) print("Ready or not, here i come!") if __name__ == '__main__': main()

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null

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null

0

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0

3252c61f7a71dbc22f9e4a1f7ba0cf98c90f9ea0

8,931

py

Python

pytorch-transformers-extensions/examples/run_inference.py

deepchatterjeevns/nlp_projects

8ea4a846138da0bcee2970907ea3340b1cdc74cb

[ "MIT" ]

21

2019-07-25T08:39:56.000Z

2020-12-14T09:59:06.000Z

pytorch-transformers-extensions/examples/run_inference.py

deepchatterjeevns/nlp_projects

8ea4a846138da0bcee2970907ea3340b1cdc74cb

[ "MIT" ]

1

2019-08-05T03:23:54.000Z

2019-08-05T03:24:39.000Z

pytorch-transformers-extensions/examples/run_inference.py

deepchatterjeevns/nlp_projects

8ea4a846138da0bcee2970907ea3340b1cdc74cb

[ "MIT" ]

15

2019-07-31T13:37:14.000Z

2021-09-28T19:01:27.000Z

# coding=utf-8 # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Running inference for sequence classification on various datasets (Bert, XLM, XLNet).""" from __future__ import absolute_import, division, print_function import argparse import logging import os import numpy as np from scipy.special import softmax import torch from torch.utils.data import (DataLoader, SequentialSampler, TensorDataset) from tqdm import tqdm, trange from pytorch_transformers import (WEIGHTS_NAME, BertConfig, BertForSequenceClassification, BertTokenizer, XLMConfig, XLMForSequenceClassification, XLMTokenizer, XLNetConfig, XLNetForSequenceClassification, XLNetTokenizer) from utils_dataset import (compute_metrics, convert_examples_to_features, output_modes, processors, InputExample) logger = logging.getLogger(__name__) ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) for conf in (BertConfig, XLNetConfig, XLMConfig)), ()) MODEL_CLASSES = { 'bert': (BertConfig, BertForSequenceClassification, BertTokenizer), 'xlnet': (XLNetConfig, XLNetForSequenceClassification, XLNetTokenizer), 'xlm': (XLMConfig, XLMForSequenceClassification, XLMTokenizer), } def inference(args, model, tokenizer, prefix=""): inf_task = args.task_name inf_dataset = load_example(args, inf_task, tokenizer) inf_sampler = SequentialSampler(inf_dataset) inf_dataloader = DataLoader(inf_dataset, sampler=inf_sampler, batch_size=1) # Inference! logger.info("***** Running inference {} *****".format(prefix)) preds = None out_label_ids = None for batch in tqdm(inf_dataloader, desc="Inferencing"): model.eval() batch = tuple(t.to(args.device) for t in batch) with torch.no_grad(): inputs = {'input_ids': batch[0], 'attention_mask': batch[1], 'token_type_ids': batch[2] if args.model_type in ['bert', 'xlnet'] else None, # XLM don't use segment_ids 'labels': batch[3]} outputs = model(**inputs) inf_loss, logits = outputs[:2] pred_arr = logits.detach().cpu().numpy() out_label_ids = inputs['labels'].detach().cpu().numpy() logger.info("pred_arr: %s", pred_arr) pred_prob = np.squeeze(softmax(pred_arr, axis=1)) logger.info("[0]: %s, [1]: %s", pred_prob[0], pred_prob[1]) if args.output_mode == "classification": pred = np.argmax(pred_arr, axis=1) elif args.output_mode == "regression": pred = np.squeeze(pred_arr) if pred == 0: logger.info("Text is negative with confidence: %d ", pred_prob[0]*100) else: logger.info("Text is positive with confidence: %d ", pred_prob[1]*100) def load_example(args, task, tokenizer): processor = processors[task]() output_mode = output_modes[task] logger.info("Creating features from input") label_list = processor.get_labels() examples = [InputExample(guid=0, text_a=args.text, text_b=None, label='1')] features = convert_examples_to_features(examples, label_list, args.max_seq_length, tokenizer, output_mode, cls_token_at_end=bool(args.model_type in ['xlnet']), # xlnet has a cls token at the end cls_token=tokenizer.cls_token, sep_token=tokenizer.sep_token, cls_token_segment_id=2 if args.model_type in ['xlnet'] else 1, pad_on_left=bool(args.model_type in ['xlnet']), # pad on the left for xlnet pad_token_segment_id=4 if args.model_type in ['xlnet'] else 0) # Convert to Tensors and build dataset all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long) all_input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long) all_segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long) if output_mode == "classification": all_label_ids = torch.tensor([f.label_id for f in features], dtype=torch.long) elif output_mode == "regression": all_label_ids = torch.tensor([f.label_id for f in features], dtype=torch.float) dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids) return dataset def main(): parser = argparse.ArgumentParser() ## Required parameters parser.add_argument("--model_type", default=None, type=str, required=True, help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys())) parser.add_argument("--model_name_or_path", default=None, type=str, required=True, help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS)) parser.add_argument("--task_name", default=None, type=str, required=True, help="The name of the task to train selected in the list: " + ", ".join(processors.keys())) parser.add_argument("--text", default="None", type=str, required=True, help="text to analyze") ## Other parameters parser.add_argument("--config_name", default="", type=str, help="Pretrained config name or path if not the same as model_name") parser.add_argument("--tokenizer_name", default="", type=str, help="Pretrained tokenizer name or path if not the same as model_name") parser.add_argument("--max_seq_length", default=128, type=int, help="The maximum total input sequence length after tokenization. Sequences longer " "than this will be truncated, sequences shorter will be padded.") parser.add_argument("--do_lower_case", action='store_true', help="Set this flag if you are using an uncased model.") parser.add_argument("--no_cuda", action='store_true', help="Avoid using CUDA when available") parser.add_argument('--server_ip', type=str, default='', help="For distant debugging.") parser.add_argument('--server_port', type=str, default='', help="For distant debugging.") args = parser.parse_args() # Setup distant debugging if needed if args.server_ip and args.server_port: # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script import ptvsd print("Waiting for debugger attach") ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True) ptvsd.wait_for_attach() # Setup CUDA, GPU & distributed training args.device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu") # Setup logging logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt = '%m/%d/%Y %H:%M:%S', level = logging.INFO) logger.warning("device: %s, ", args.device) # Prepare task args.task_name = args.task_name.lower() if args.task_name not in processors: raise ValueError("Task not found: %s" % (args.task_name)) processor = processors[args.task_name]() args.output_mode = output_modes[args.task_name] label_list = processor.get_labels() num_labels = len(label_list) args.model_type = args.model_type.lower() config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type] config = config_class.from_pretrained(args.config_name if args.config_name else args.model_name_or_path, num_labels=num_labels, finetuning_task=args.task_name) tokenizer = tokenizer_class.from_pretrained(args.tokenizer_name if args.tokenizer_name else args.model_name_or_path, do_lower_case=args.do_lower_case) model = model_class.from_pretrained(args.model_name_or_path, from_tf=bool('.ckpt' in args.model_name_or_path), config=config) model.to(args.device) logger.info("Inference parameters %s", args) # Inference inference(args, model, tokenizer) if __name__ == "__main__": main()

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3256173ee4e9a424745cf36c9f1ac6cf9bf2bc08

7,872

py

Python

tools/table.py

asterick/minimon.js

4876544525eb1bfef1b81a12807e7ba37cdd4949

[ "0BSD" ]

5

2019-04-25T00:19:56.000Z

2020-09-02T01:24:40.000Z

tools/table.py

asterick/minimon.js

4876544525eb1bfef1b81a12807e7ba37cdd4949

[ "0BSD" ]

6

2020-05-23T23:17:59.000Z

2022-02-17T21:50:46.000Z

tools/table.py

asterick/minimon.js

4876544525eb1bfef1b81a12807e7ba37cdd4949

[ "0BSD" ]

null

#!/usr/bin/env python3 # ISC License # # Copyright (c) 2019, Bryon Vandiver # # Permission to use, copy, modify, and/or distribute this software for any # purpose with or without fee is hereby granted, provided that the above # copyright notice and this permission notice appear in all copies. # # THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES # WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF # MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR # ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES # WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN # ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF # OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. from json import dumps import os import csv CSV_LOCATION = os.path.join(os.path.abspath(os.path.dirname(__file__)), 's1c88.csv') op0s, op1s, op2s = [None] * 0x100, [None] * 0x100, [None] * 0x100 CONDITIONS = { 'C': 'cpu.reg.flag.c', 'NC': '!cpu.reg.flag.c', 'Z': 'cpu.reg.flag.z', 'NZ': '!cpu.reg.flag.z', 'V': 'cpu.reg.flag.v', 'NV': '!cpu.reg.flag.v', 'M': 'cpu.reg.flag.n', 'P': '!cpu.reg.flag.n', 'LT': 'cpu.reg.flag.n != cpu.reg.flag.v', 'LE': '(cpu.reg.flag.n != cpu.reg.flag.v) || cpu.reg.flag.z', 'GT': '(cpu.reg.flag.n == cpu.reg.flag.v) && !cpu.reg.flag.z', 'GE': 'cpu.reg.flag.n == cpu.reg.flag.v', 'F0': 'cpu.reg.flag.f0', 'F1': 'cpu.reg.flag.f1', 'F2': 'cpu.reg.flag.f2', 'F3': 'cpu.reg.flag.f3', 'NF0': '!cpu.reg.flag.f0', 'NF1': '!cpu.reg.flag.f1', 'NF2': '!cpu.reg.flag.f2', 'NF3': '!cpu.reg.flag.f3', } ARGUMENTS = { 'A': (8, False, False, 'a'), 'B': (8, False, False, 'b'), 'L': (8, False, False, 'l'), 'H': (8, False, False, 'h'), 'BR': (8, False, False, 'br'), 'SC': (8, False, False, 'sc'), 'EP': (8, False, False, 'ep'), 'XP': (8, False, False, 'xp'), 'YP': (8, False, False, 'yp'), 'NB': (8, False, False, 'nb'), 'BA': (16, False, False, 'ba'), 'HL': (16, False, False, 'hl'), 'IX': (16, False, False, 'ix'), 'IY': (16, False, False, 'iy'), 'SP': (16, False, False, 'sp'), 'PC': (16, False, False, 'pc'), '#nn': (8, True, False, 'imm8'), 'rr': (8, True, False, 'imm8'), '#mmnn': (16, True, False, 'imm16'), 'qqrr': (16, True, False, 'imm16'), '[kk]': (16, True, True, 'vect'), # Special '[hhll]': (-1, True, True, 'ind16'), '[HL]': (-1, True, True, 'absHL'), '[IX]': (-1, True, True, 'absIX'), '[IY]': (-1, True, True, 'absIY'), '[BR:ll]': (-1, True, True, 'absBR'), '[SP+dd]': (-1, True, True, 'indDSP'), '[IX+dd]': (-1, True, True, 'indDIX'), '[IY+dd]': (-1, True, True, 'indDIY'), '[IX+L]': (-1, True, True, 'indIIX'), '[IY+L]': (-1, True, True, 'indIIY'), } OPERATIONS = { 'INC': (8, 'ReadWrite'), 'DEC': (8, 'ReadWrite'), 'SLA': (8, 'ReadWrite'), 'SLL': (8, 'ReadWrite'), 'SRA': (8, 'ReadWrite'), 'SRL': (8, 'ReadWrite'), 'RL': (8, 'ReadWrite'), 'RLC': (8, 'ReadWrite'), 'RR': (8, 'ReadWrite'), 'RRC': (8, 'ReadWrite'), 'CPL': (8, 'ReadWrite'), 'NEG': (8, 'ReadWrite'), 'LD': (8, 'Write', 'Read'), 'ADD': (8, 'ReadWrite', 'Read'), 'ADC': (8, 'ReadWrite', 'Read'), 'SUB': (8, 'ReadWrite', 'Read'), 'SBC': (8, 'ReadWrite', 'Read'), 'AND': (8, 'ReadWrite', 'Read'), 'OR': (8, 'ReadWrite', 'Read'), 'XOR': (8, 'ReadWrite', 'Read'), 'CP': (8, 'Read', 'Read'), 'BIT': (8, 'Read', 'Read'), 'CALL': (16, 'Read'), 'CARS': (8, 'Read'), 'CARL': (16, 'Read'), 'JRS': (8, 'Read'), 'JRL': (16, 'Read'), 'JP': (8, 'Read'), 'INT': (8, 'Read'), 'RETE': (8,), 'PUSH': (-1, 'Read'), 'POP': (-1, 'Write'), 'EX': (-1, 'ReadWrite', 'ReadWrite'), 'SWAP': (8, 'ReadWrite') } def get_name(*args): return "inst_%s" % '_'.join([arg.lower() for arg in args if arg]) def format_arg(i, siz, mem, ind, nam): if mem: return "data%i" % i else: return "cpu.reg.%s" % nam def format(cycles, op, *args): condition = None cycles, skipped = [int(c) for c in cycles.split(",") * 2][:2] if len(args) > 0 and args[0] in CONDITIONS: condition, args = args[0], args[1:] try: ops = OPERATIONS[op] args = [ARGUMENTS[arg] for arg in args if arg] default_size, directions = ops[0], ops[1:] if len(args) >= 1: size = max(default_size, *[s for s, i, m, n in args]) else: size = default_size name = get_name(op, condition, *[n for s, i, m, n in args]) print ("static int %s(Machine::State& cpu) {" % name) for i, (siz, mem, ind, nam) in enumerate(args): if ind: print ("\tconst auto addr%i = calc_%s(cpu);" % (i, nam)) safety = "" if "Write" in directions[i] else "const " if "Read" in directions[i]: print ("\t%suint%i_t data%i = cpu_read%s(cpu, addr%i);" % (safety, size, i, size, i)) else: print ("\tuint%i_t data%i;" % (size, i)) elif mem: print ("\tconst uint%i_t data%i = cpu_imm%i(cpu);" % (size, i, siz)) if condition: print ("\tif (!(%s)) {" % CONDITIONS[condition]) print ("\t\tcpu.reg.cb = cpu.reg.nb;") print ("\t\treturn %i;" % skipped) print ("\t}") print ("\top_%s%i(%s);" % (op.lower(), size, ', '.join(['cpu']+[format_arg(i, *a) for i, a in enumerate(args)]))); block = False for i, (siz, mem, ind, nam) in enumerate(args): if ind and "Write" in directions[i]: print ("\tcpu_write%s(cpu, data%i, addr%i);" % (size, i, i)) if nam in ['sc', 'nb'] and "Write" in directions[i]: block = True if block or op == 'RETE': print ("\treturn %i + inst_advance(cpu); // Block IRQs" % cycles) else: print ("\treturn %i;" % cycles) print ("}\n") return name except: name = get_name(op, condition, *args) print ("int clock_%s(Machine::State& cpu) {" % name) print ("\t%s(cpu);" % name) print ("\treturn %i;" % cycles) print ("}\n") return "clock_%s" % name # Generate switch table def dump_table(instructions, indent): for i, t in enumerate(instructions): if not t: continue print ("%scase 0x%02X: return %s(cpu);" % (indent, i, t)) #print (i, t) print ("%sdefault: return inst_undefined(cpu);" % indent) with open(CSV_LOCATION, 'r') as csvfile: spamreader = csv.reader(csvfile) next(spamreader) for row in spamreader: code, cycles0, op0, arg0_1, arg0_2, cycles1, op1, arg1_1, arg1_2, cycles2, op2, arg2_1, arg2_2 = row code = int(code, 16) if op0 != 'undefined': op0s[code] = format(cycles0, op0, arg0_1, arg0_2) if op1 != 'undefined': op1s[code] = format(cycles1, op1, arg1_1, arg1_2) if op2 != 'undefined': op2s[code] = format(cycles2, op2, arg2_1, arg2_2) print ("int inst_advance(Machine::State& cpu) {") print ("\tswitch (cpu_imm8(cpu)) {") dump_table(op0s, '\t') print ("\tcase 0xCE:") print ("\t\tswitch (cpu_imm8(cpu)) {") dump_table(op1s, '\t\t') print ("\t\t}") print ("\tcase 0xCF:") print ("\t\tswitch (cpu_imm8(cpu)) {") dump_table(op2s, '\t\t') print ("\t\t}") print ("\t}") print ("}")

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123

0.506225

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7,872

3.689784

0.268978

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0.06604

0.016764

0.178054

0.137668

0.084836

0.062484

0.035052

0

0.03328

0.282393

7,872

241

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0.663657

0.102769

0

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0

0.01087

0.264001

0.007644

0

0.003381

0

1

0.021739

false

0

0.016304

0.005435

0.065217

0.163043

0

null

0

null

0

1

0

325927f14aed5b03fe28e7161da22ac9db1b0f2b

15,364

py

Python

test_log.py

erkooi/desp_tools

2bea2e44591ceeeb62cbfe163b4635a3157f6582

[ "Apache-2.0" ]

null

test_log.py

erkooi/desp_tools

2bea2e44591ceeeb62cbfe163b4635a3157f6582

[ "Apache-2.0" ]

null

test_log.py

erkooi/desp_tools

2bea2e44591ceeeb62cbfe163b4635a3157f6582

[ "Apache-2.0" ]

null

############################################################################### # # Copyright (C) 2012 # ASTRON (Netherlands Institute for Radio Astronomy) <http://www.astron.nl/> # P.O.Box 2, 7990 AA Dwingeloo, The Netherlands # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program. If not, see <http://www.gnu.org/licenses/>. # ############################################################################### """Test logging utilities * Provide logging with standardized prefixes: . time : self, if notime = 0 . verbosity level : self, if noVLevel = 0 . test case ID : self, if noTestId = 0 . message text : argument msgString, the actual text to log * All append_log statements that have verbosity level equal or lower than the test case verbosity level will get logged. * The logging gets output to the stdio and to a file if a file name is provided. * It is also possible to append other files to the test logging file. * Best practise is to use the following verbosity levels for the append_log argument: -v 0 Log test result -v 1 Log test title -v 2 Log errors -v 3 Log info -v 4 Log error details -v 5 Log info details -v 6 Log debug -v 7 Log debug details """ ################################################################################ # System imports import sys import time import common as cm ################################################################################ # Functions class Testlog: V_RESULT = 0 V_TITLE = 1 V_ERRORS = 2 V_INFO = 3 V_ERROR_DETAILS = 4 V_INFO_DETAILS = 5 V_DEBUG = 6 V_DEBUG_DETAILS = 7 _logName=None def __init__(self, verbosity=11, testId='', sectionId='', logName=None): self.verbosity = verbosity # Verbosity threshold used by append_log() to decide whether to log the input string or not self._testId = testId # Test ID that optionally gets used as prefix in append_log line self._sectionId = sectionId # Section ID that optionally gets used as prefix in append_log line self._logName = logName # Name for the file that will contain the append_log if self._logName != None: try: self._logFile = open(self._logName,'w') except IOError: print('ERROR : Can not open log file %s' % self._logName) def __del__(self): if self._logName != None: self.close_log() def close_log(self): if self._logName != None: self._logFile.close() # The testId can should remain fixed at __init__, but the user can change the sectionId during the execution def set_section_id(self, sectionId): self._sectionId = sectionId def verbose_levels(self): return "0=result; 1=title; 2=errors; 3=info; 4=error details; 5=info details; 6=debug; 7=debug details" # Print the message string and append it to the test log file in the Testlog style def append_log(self, vLevel, msgString, noTime=0, noVLevel=0, noTestId=0, noSectionId=0): if vLevel <= self.verbosity: txt = '' if noTime == 0: t = time.localtime() txt = txt + '[%d:%02d:%02d %02d:%02d:%02d]' % (t.tm_year, t.tm_mon, t.tm_mday, t.tm_hour, t.tm_min, t.tm_sec) if noVLevel == 0: txt = txt + ' - (%d) ' % vLevel if noTestId == 0: txt = txt + self._testId if noSectionId == 0: txt = txt + self._sectionId txt = txt + msgString print(txt) #sys.stdout.flush() if self._logName != None: self._logFile.write(txt + '\n') # Print the repeat message string at regular intervals and append it to the test log file in the Testlog style def append_log_rep(self, vLevel, rep, nofRep, nofLog=5, noTime=0, noVLevel=0, noTestId=0, noSectionId=0): if nofRep < nofLog: logInterval = 1 else: logInterval = nofRep//nofLog if rep%logInterval==0 or rep==nofRep-1: self.append_log(3, 'Rep-%d' % rep) # Print the contents of an array to the test log file def append_log_data(self, vLevel, prefixStr, data, radix='dec', dataWidth=8, nofColumns=16, rulers=False, noTime=0, noVLevel=0, noTestId=0, noSectionId=0): if vLevel <= self.verbosity: r = 0 columnWidth = dataWidth + 1 # use 1 space between columns if rulers: rowStr = 'Col:' for i in range(nofColumns): rowStr += '%*d' % (columnWidth, i) self.append_log(vLevel, prefixStr + rowStr, noTime, noVLevel, noTestId, noSectionId) self.append_log(vLevel, prefixStr + 'Row:', noTime, noVLevel, noTestId, noSectionId) rowStr = prefixStr + ('%-4d' % r) else: rowStr = prefixStr k = 0 # Make sure data is a list, otherwise the following fails if cm.depth(data)==0: data=cm.listify(data) n = len(data) for i in range(n): if radix=='uns': rowStr += ' %*d' % (dataWidth, data[i]) if radix=='dec': rowStr += ' %*d' % (dataWidth, data[i]) if radix=='hex': rowStr += ' %0*x' % (dataWidth, data[i]) if k < nofColumns-1: k = k + 1 else: self.append_log(vLevel, prefixStr + rowStr, noTime, noVLevel, noTestId, noSectionId) rowStr = prefixStr r = r + 1 if rulers: rowStr += ('%-4d' % r) k = 0 if k!=0: self.append_log(vLevel, prefixStr + rowStr, noTime, noVLevel, noTestId, noSectionId) def data_to_string(self, data, dataWidth=4, dataLeft=False, fractionWidth=2, fractionExponent=False): """Print data to string with length dataWidth + 1 white space Default print the data as %s string to support any type If the data is float or complex then print it using fraction notation when fractionExponent=False or using exponent notation when fractionExponent=True. The fractionWidth specifies the width of the floating point value. The data is printed left or right aligned dependent on dataLeft. For all data types the returned data string has length dataWidth + 1 for a white space such that it can be used as a fixed size element string when printing a row of data on a line. . data = the data, can be float complex or other e.g. int, string, tuple . dataWidth = width of the printed data string . dataLeft = when True then left align the data in the printed data string, else right align . fractionWidth = width of the fraction in case of float data . fractionExponent = when True print exponent in case of float data, else only print fraction """ if isinstance(data, float): # Log in float format if fractionExponent: dataStr = '%.*e' % (fractionWidth, data) # log data as float with exponent else: dataStr = '%.*f' % (fractionWidth, data) # log data as float elif isinstance(data, complex): # Log in complex float format if fractionExponent: dataStr = '%.*e,' % (fractionWidth, data.real) # log data real part as float with exponent dataStr += '%.*ej' % (fractionWidth, data.imag) # log data imag part as float with exponent else: dataStr = '%.*f,' % (fractionWidth, data.real) # log data real part as float dataStr += '%.*fj' % (fractionWidth, data.imag) # log data imag part as float else: # Default log data as string dataStr = '%s' % str(data) # the data can be any type that fits %s e.g. int, string, tuple # the explicite conversion by str() is needed for tuple # Left or right align the dataStr within dataWidth if dataLeft: dataStr = '%-*s ' % (dataWidth, dataStr) else: dataStr = '%*s ' % (dataWidth, dataStr) return dataStr def append_log_one_dimensional_list(self, vLevel, name, L, prefixStr='', dataWidth=4, dataLeft=False, fractionWidth=0, fractionExponent=False, colIndices=None): """Log list L[col] in one row with index labels . vLevel = verbosity level . name = name, title of the list . L = the one dimensional list . prefixStr = prefix string that is printed before every line, can e.g. be used for grep . dataWidth = of data in column, see self.data_to_string . dataLeft = of data in column, see self.data_to_string . fractionWidth = of data in column, see self.data_to_string . fractionExponent = of data in column, see self.data_to_string . colIndices = when None then log counter index, else use index from list Remarks: . This append_log_one_dimensional_list is similar to using append_log_data with nofColumns=len(L) . This append_log_one_dimensional_list is similar to append_log_two_dimensional_list with 1 row. """ if vLevel <= self.verbosity: self.append_log(vLevel, '') # start with newline self.append_log(vLevel, prefixStr + '%s:' % name) nof_cols = len(L) # Print row with column indices if colIndices == None: colIndices = list(range(nof_cols)) col_index_str = '. index : ' for col in colIndices: col_index_str += '%*d ' % (dataWidth, col) self.append_log(vLevel, prefixStr + col_index_str) # Print row with data line_str = '. value : ' uniqueL = cm.unique(L) if len(uniqueL)==1: line_str += 'all ' + self.data_to_string(uniqueL[0], dataWidth, dataLeft, fractionWidth, fractionExponent) else: for col in range(nof_cols): line_str += self.data_to_string(L[col], dataWidth, dataLeft, fractionWidth, fractionExponent) self.append_log(vLevel, prefixStr + '%s' % line_str) self.append_log(vLevel, '') # end with newline def append_log_two_dimensional_list(self, vLevel, name, A, prefixStr='', transpose=False, reverseCols=False, reverseRows=False, dataWidth=4, dataLeft=False, fractionWidth=0, fractionExponent=False, colIndices=None, rowIndices=None): """ Log two dimensional list A[row][col] per row with index labels . vLevel = verbosity level . name = name, title of the list . A = the two dimensional list . prefixStr = prefix string that is printed before every line, can e.g. be used for grep . transpose = when true transpose(A) to log rows as columns and columns as rows . reverseCols = when true reverse the order of the columns . reverseRows = when true reverse the order of the rows . dataWidth = of data in column, see self.data_to_string . dataLeft = of data in column, see self.data_to_string . fractionWidth = of data in column, see self.data_to_string . fractionExponent = of data in column, see self.data_to_string . colIndices = when None then log counter index, else use index from list . rowIndices = when None then log counter index, else use index from list (can be text index) Remarks: . The example recipy for making a two dimensional list of the form A[rows][cols] is: A = [], row=[], row.append(element) for all cols, A.append(row) for all rows or use cm.create_multidimensional_list([Number of rows][Number of cols]) """ if vLevel <= self.verbosity: self.append_log(vLevel, '') # start with newline self.append_log(vLevel, prefixStr + '%s:' % name) if transpose: #print name, transpose A = cm.transpose(A) if reverseRows: A = cm.reverse_rows_ud(A) if reverseCols: A = cm.reverse_cols_lr(A) nof_rows = len(A) nof_cols = len(A[0]) self.append_log(vLevel, prefixStr + 'col :') # Print row with column indices if colIndices == None: colIndices = list(range(nof_cols)) if rowIndices == None: rowIndices = list(range(nof_rows)) rowIndexLength = 6 # default row_str prefix length else: rowIndexLength = 3 + len(str(rowIndices[-1])) # use last row index string for row_str prefix length col_index_str = ' ' * rowIndexLength for col in colIndices: col_index_str += '%*d ' % (dataWidth, col) self.append_log(vLevel, prefixStr + col_index_str) self.append_log(vLevel, prefixStr + 'row :') # For each row print row index and row with data for ri,row in enumerate(rowIndices): row_str = '%3s : ' % row # row index, log index as string to support also text index uniqueRow = cm.unique(A[ri]) if len(uniqueRow)==1: row_str += 'all ' + self.data_to_string(uniqueRow[0], dataWidth, dataLeft, fractionWidth, fractionExponent) else: for col in range(nof_cols): row_str += self.data_to_string(A[ri][col], dataWidth, dataLeft, fractionWidth, fractionExponent) self.append_log(vLevel, prefixStr + '%s' % row_str) self.append_log(vLevel, '') # end with newline # Read the contents of a file and append that to the test log file def append_log_file(self, vLevel, fileName): try: appFile = open(fileName,'r') self.append_log(vLevel,appFile.read(),1,1,1,1) appFile.close() except IOError: self.append_log(vLevel,'ERROR : Can not open file %s' % fileName)

48.466877

164

0.570034

1,894

15,364

4.536431

0.187434

0.036662

0.028748

0.039804

0.42237

0.385242

0.342877

0.329842

0.304353

0.261639

0

0.010481

0.329341

15,364

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48.620253

0.82337

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false

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0.011696

0

null

0

null

0

1

0

325b56ca169aa22d3b3e5e502acb535b1e7a8a46

868

py

Python

subaudible/subparse.py

RobbieClarken/subaudible

f22bdec90693727b36eff426e96d6960387fb94d

[ "MIT" ]

null

subaudible/subparse.py

RobbieClarken/subaudible

f22bdec90693727b36eff426e96d6960387fb94d

[ "MIT" ]

null

subaudible/subparse.py

RobbieClarken/subaudible

f22bdec90693727b36eff426e96d6960387fb94d

[ "MIT" ]

null

import re def parse_srt(line_iter): """ Parses SubRip text into caption dicts. Args: line_iter: An iterator that yields lines of a SubRip file. Yields: dict: Caption dicts with `start`, `end` and `text` keys. """ line_iter = iter(line.rstrip('\r\n') for line in line_iter) while True: next(line_iter) # Skip counter start, end = parse_time_line(next(line_iter)) text = '\n'.join(iter(line_iter.__next__, '')) yield {'start': start, 'end': end, 'text': text} def parse_time_line(line): return (parse_time(time_str) for time_str in line.split('-->')) def parse_time(time_str): time_str = time_str.replace(',', '.') match = re.search('(\d\d):(\d\d):(\d\d).(\d\d\d)', time_str) h, m, s, ms = (int(s) for s in match.groups()) return 3600 * h + 60 * m + s + 1e-3 * ms

27.125

67

0.59447

136

868

3.617647

0.426471

0.03252

0.042683

0.04878

0.018293

0

0.01214

0.240783

868

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68

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0.080871

0.045101

0

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0.2

false

0

0.066667

0.4

0

null

0

null

0

1

0

325b89ab7374be326978f10a334f001191bd3ead

1,971

py

Python

application/models/basemodel.py

ahmedsadman/festive

e0e739f126de2e8368014398f5c928c410098da5

[ "MIT" ]

2

2020-10-19T23:26:23.000Z

2020-10-20T02:14:10.000Z

application/models/basemodel.py

ahmedsadman/fest-management-api

e0e739f126de2e8368014398f5c928c410098da5

[ "MIT" ]

null

application/models/basemodel.py

ahmedsadman/fest-management-api

e0e739f126de2e8368014398f5c928c410098da5

[ "MIT" ]

1

2021-08-04T15:45:29.000Z

from sqlalchemy import func from application import db from application.helpers.error_handlers import ServerError class BaseModel(db.Model): __abstract__ = True def save(self): """save the item to database""" try: db.session.add(self) db.session.commit() except Exception as e: raise ServerError(message="Failed to save the item", error=e) def delete(self): """delete the item from database""" try: db.session.delete(self) db.session.commit() except Exception as e: raise ServerError(message="Deletion failed", error=e) @classmethod def find_by_id(cls, id): return cls.query.filter_by(id=id).first() @classmethod def find_query(cls, _filter): """Build the query with the given level one filters (filters that has direct match with entity attributes, not any nested relationship). Returns 'query' object""" query = cls.query exclude_lower = [int, bool] for attr, value in _filter.items(): # func.lower doesn't work for INT/BOOL types in some production # databases, so this should be properly handled # ex: lower(event.id) won't work because event.id is INT type # So the logic is, whenever the passed 'value' in this scope is # INT, it means # we don't need to lower anything. Just compare the vanilla value _attr = getattr(cls, attr) _attr = ( _attr if (type(value) in exclude_lower) else func.lower(_attr) ) _value = ( value if (type(value) in exclude_lower) else func.lower(value) ) query = query.filter(_attr == _value) return query @classmethod def find(cls, _filter): """find all entities by given filter""" return cls.find_query(_filter).all()

33.982759

78

0.597666

247

1,971

4.672065

0.437247

0.031196

0.046794

0.034662

0.169844

0.103986

0

0.316591

1,971

57

79

34.578947

0.856719

0.281583

0

0.243243

0

0.027656

0

1

0.135135

false

0

0.081081

0.027027

0.351351

0

null

0

null

0

1

0

325fc49ee449fcf77d594c853f23436486f7b300

2,711

py

Python

tests/io/s3/test_s3_fetcher.py

ToucanToco/PeaKina

afaeec65d9b136d42331f140c3048d27bcddb6b1

[ "BSD-3-Clause" ]

null

tests/io/s3/test_s3_fetcher.py

ToucanToco/PeaKina

afaeec65d9b136d42331f140c3048d27bcddb6b1

[ "BSD-3-Clause" ]

null

tests/io/s3/test_s3_fetcher.py

ToucanToco/PeaKina

afaeec65d9b136d42331f140c3048d27bcddb6b1

[ "BSD-3-Clause" ]

null

from typing import Any, Dict import boto3 import pytest from s3fs import S3FileSystem from peakina.io.s3.s3_fetcher import S3Fetcher @pytest.fixture def s3_fetcher(s3_endpoint_url): return S3Fetcher(client_kwargs={"endpoint_url": s3_endpoint_url}) def test_s3_fetcher_open(s3_fetcher): dirpath = "s3://accessKey1:verySecretKey1@mybucket" filepath = f"{dirpath}/0_0.csv" with s3_fetcher.open(filepath) as f: assert f.read() == b"a,b\n0,0\n0,1" def test_s3_fetcher_listdir(s3_fetcher, mocker): s3_mtime_mock = mocker.patch("peakina.io.s3.s3_fetcher.s3_mtime") dirpath = "s3://accessKey1:verySecretKey1@mybucket" assert s3_fetcher.listdir(dirpath) == [ "0_0.csv", "0_1.csv", "mydir", ] assert s3_fetcher.mtime(f"{dirpath}/0_0.csv") > 0 assert s3_fetcher.mtime(f"{dirpath}/mydir") is None s3_mtime_mock.assert_not_called() def test_s3_fetcher_mtime(s3_fetcher): dirpath = "s3://accessKey1:verySecretKey1@mybucket" filepath = f"{dirpath}/0_0.csv" assert s3_fetcher.mtime(filepath) > 0 def test_s3_fetcher_open_retry(s3_fetcher, s3_endpoint_url, mocker): session = boto3.session.Session() s3_client = session.client( service_name="s3", aws_access_key_id="accessKey1", aws_secret_access_key="verySecretKey1", endpoint_url=s3_endpoint_url, ) dirpath = "s3://accessKey1:verySecretKey1@mybucket" filepath = f"{dirpath}/for_retry_0_0.csv" s3_client.upload_file("tests/fixtures/for_retry_0_0.csv", "mybucket", "for_retry_0_0.csv") class S3FileSystemThatFailsOpen(S3FileSystem): # type:ignore[misc] def __init__(self, key: str, secret: str, client_kwargs: Dict[str, Any]) -> None: super().__init__(key=key, secret=secret, client_kwargs=client_kwargs) self.invalidated_cache = False def open(self, path, mode="rb", block_size=None, cache_options=None, **kwargs): if not self.invalidated_cache: raise Exception("argh!") return super().open(path, mode, block_size, cache_options, **kwargs) def invalidate_cache(self, path=None): self.invalidated_cache = True mocker.patch("peakina.io.s3.s3_utils.s3fs.S3FileSystem", S3FileSystemThatFailsOpen) logger_mock = mocker.patch("peakina.io.s3.s3_utils.logger") with s3_fetcher.open(filepath) as f: # ensure logger doesn't log credentials logger_mock.warning.assert_called_once_with( "could not open mybucket/for_retry_0_0.csv: argh!" ) assert f.read() == b"a,b\n0,0\n0,1" s3_client.delete_object(Bucket="mybucket", Key="tests/fixtures/for_retry_0_0.csv")

33.8875

94

0.693471

375

2,711

4.741333

0.266667

0.086052

0.025309

0.028121

0.431384

0.3009

0.245782

0.134421

0.102362

0

0.040326

0.185909

2,711

79

95

34.316456

0.765292

0.020288

0

0.175439

0

0.220882

0.141726

0

0.140351

1

0.140351

false

0

0.087719

0.017544

0.280702

0

null

0

null

0

1

0

32638416d54a115fde42bba19086c99e40948e61

802

py

Python

backend/events/tests/test_views.py

trfoss/parrot

2f120ee1ab82368f85b2b5a7f1c45afc26aa8963

[ "BSD-2-Clause" ]

5

2019-02-25T02:24:51.000Z

2019-04-21T00:56:43.000Z

backend/events/tests/test_views.py

trfoss/parrot

2f120ee1ab82368f85b2b5a7f1c45afc26aa8963

[ "BSD-2-Clause" ]

51

2019-02-06T03:36:27.000Z

2021-06-10T21:11:24.000Z

backend/events/tests/test_views.py

trfoss/parrot

2f120ee1ab82368f85b2b5a7f1c45afc26aa8963

[ "BSD-2-Clause" ]

7

2019-02-06T04:37:10.000Z

2019-03-28T07:52:26.000Z

""" backend/events/tests/test_views.py Tests for the events page views. We use the test client. Read more at https://docs.djangoproject.com/en/2.1/topics/testing/tools/ """ import json from django.test import TestCase class EventsPageViewTests(TestCase): """Events page view tests for route /events/data """ fixtures = [ 'event.json', 'team.json', 'teammember.json', ] def test_events_data(self): """Test route /events/data - it returns status code 200 - it returns a non-empty list """ response = self.client.get('/events/data') self.assertEqual(response.status_code, 200) obj = json.loads(response.content) self.assertTrue(isinstance(obj, list)) self.assertTrue(len(obj) > 0)

26.733333

69

0.63591

102

802

4.960784

0.588235

0.079051

0.059289

0

0.014851

0.244389

802

29

70

27.655172

0.820132

0.377805

0

0.100877

0

0.214286

1

0.071429

false

0

0.142857

0

0.357143

0

null

0

null

0

1

0

3265c12d40cc56aa2b76c483dff904dc52c43391

11,333

py

Python

myfunds/web/views/crypto/views.py

anzodev/myfunds

9f6cda99f443cec064d15d7ff7780f297cbdfe10

[ "MIT" ]

null

myfunds/web/views/crypto/views.py

anzodev/myfunds

9f6cda99f443cec064d15d7ff7780f297cbdfe10

[ "MIT" ]

null

myfunds/web/views/crypto/views.py

anzodev/myfunds

9f6cda99f443cec064d15d7ff7780f297cbdfe10

[ "MIT" ]

null

import csv import io from datetime import datetime import peewee as pw from flask import Blueprint from flask import g from flask import make_response from flask import redirect from flask import render_template from flask import request from flask import url_for from wtforms import Form from wtforms import IntegerField from wtforms import validators as vals from myfunds.core.constants import CryptoDirection from myfunds.core.models import CryptoActionLog from myfunds.core.models import CryptoBalance from myfunds.core.models import CryptoCurrency from myfunds.core.models import CryptoTransaction from myfunds.core.models import db_proxy from myfunds.modules import cmc from myfunds.web import ajax from myfunds.web import auth from myfunds.web import notify from myfunds.web import utils from myfunds.web.constants import DATETIME_FORMAT from myfunds.web.forms import AddCryptoBalanceForm from myfunds.web.forms import AddCyptoTransactionForm from myfunds.web.forms import DeleteCryptoBalanceForm from myfunds.web.forms import UpdateCryptoBalanceQuantityForm USD_CODE = "USD" USD_PRECISION = 2 CRYPTO_PRECISION = 8 bp = Blueprint("crypto", __name__, template_folder="templates") @bp.route("/crypto") @auth.login_required def index(): currencies = CryptoCurrency.select().order_by(CryptoCurrency.symbol) balances = ( CryptoBalance.select() .join(CryptoCurrency) .where(CryptoBalance.account == g.authorized_account) .order_by(CryptoBalance.name, CryptoCurrency.symbol) ) investments = ( CryptoTransaction.select( pw.fn.COUNT(CryptoTransaction.id), pw.fn.SUM(CryptoTransaction.amount), ) .where( (CryptoTransaction.account == g.authorized_account) & (CryptoTransaction.direction == CryptoDirection.INVESTMENT) ) .scalar(as_tuple=True) ) if investments[1] is None: investments = None fixed_profit = ( CryptoTransaction.select( pw.fn.COUNT(CryptoTransaction.id), pw.fn.SUM(CryptoTransaction.amount), ) .where( (CryptoTransaction.account == g.authorized_account) & (CryptoTransaction.direction == CryptoDirection.FIXED_PROFIT) ) .scalar(as_tuple=True) ) if fixed_profit[1] is None: fixed_profit = None amount_pattern = utils.make_amount_pattern(8) return render_template( "crypto/view.html", currencies=currencies, investments=investments, fixed_profit=fixed_profit, balances=balances, amount_pattern=amount_pattern, ) @bp.route("/crypto/balances/new", methods=["POST"]) @auth.login_required def new_balance(): redirect_url = url_for("crypto.index") form = AddCryptoBalanceForm(request.form) utils.validate_form(form, redirect_url) name = form.name.data currency_id = form.currency_id.data currency = CryptoCurrency.get_or_none(id=currency_id) if currency is None: notify.error("Currency not found.") return redirect(redirect_url) balance = CryptoBalance.create( account=g.authorized_account, currency=currency, name=name, quantity=0, ) notify.info(f"New balance '{balance.name}' was created.") return redirect(redirect_url) @bp.route("/crypto/balances/delete", methods=["POST"]) @auth.login_required def delete_balance(): redirect_url = url_for("crypto.index") form = DeleteCryptoBalanceForm(request.form) utils.validate_form(form, redirect_url) balance_id = form.balance_id.data balance = CryptoBalance.get_or_none(id=balance_id, account=g.authorized_account) if balance is None: notify.error("Balance not found.") return redirect(redirect_url) balance.delete_instance() notify.info(f"Balance '{balance.name}' was deleted.") return redirect(redirect_url) @bp.route("/crypto/balances/update-quantity", methods=["POST"]) @auth.login_required def update_quantity(): redirect_url = url_for("crypto.index") form = UpdateCryptoBalanceQuantityForm(request.form) form.quantity.validators.append( vals.Regexp(utils.make_amount_pattern(CRYPTO_PRECISION)) ) utils.validate_form(form, redirect_url) action = form.action.data balance_id = form.balance_id.data quantity = utils.amount_to_subunits(form.quantity.data, CRYPTO_PRECISION) balance = CryptoBalance.get_or_none(id=balance_id, account=g.authorized_account) if balance is None: notify.error("Balance not found.") return redirect(redirect_url) quantity_before = balance.quantity if action == "set": balance.quantity = quantity elif action == "add": balance.quantity += quantity else: balance.quantity -= quantity if balance.quantity < 0: notify.error("Balance quantity can't be less then zero.") return redirect(redirect_url) with db_proxy.atomic(): CryptoActionLog.create( account=g.authorized_account, message=( f"{action.capitalize()} {form.quantity.data} {balance.currency.symbol} " f"for {balance.name} ({balance.id}), " f"before: {utils.make_hrf_amount(quantity_before, CRYPTO_PRECISION)}, " f"after: {utils.make_hrf_amount(balance.quantity, CRYPTO_PRECISION)}." ), created_at=datetime.now(), ) balance.save() notify.info("Balance quantity was updated.") return redirect(redirect_url) @bp.route("/crypto/invest", methods=["POST"]) @auth.login_required def invest(): redirect_url = url_for("crypto.index") quantity_validator = vals.Regexp(utils.make_amount_pattern(CRYPTO_PRECISION)) price_validator = vals.Regexp(utils.make_amount_pattern(USD_PRECISION)) form = AddCyptoTransactionForm(request.form) form.quantity.validators.append(quantity_validator) form.price.validators.append(price_validator) utils.validate_form(form, redirect_url) currency_id = form.currency_id.data quantity = form.quantity.data price = form.price.data amount = round(float(quantity) * float(price), USD_PRECISION) currency = CryptoCurrency.get_or_none(id=currency_id) if currency is None: notify.error("Currency not found.") return redirect(redirect_url) with db_proxy.atomic(): creation_time = datetime.now() CryptoTransaction.create( account=g.authorized_account, direction=CryptoDirection.INVESTMENT, symbol=currency.symbol, quantity=utils.amount_to_subunits(quantity, CRYPTO_PRECISION), price=utils.amount_to_subunits(price, USD_PRECISION), amount=utils.amount_to_subunits(amount, USD_PRECISION), created_at=creation_time, ) CryptoActionLog.create( account=g.authorized_account, message=( f"Invest ${amount}, bought {quantity} {currency.symbol} by ${price}." ), created_at=creation_time, ) notify.info("New investment was added.") return redirect(redirect_url) @bp.route("/crypto/fix-profit", methods=["POST"]) @auth.login_required def fix_profit(): redirect_url = url_for("crypto.index") quantity_validator = vals.Regexp(utils.make_amount_pattern(CRYPTO_PRECISION)) price_validator = vals.Regexp(utils.make_amount_pattern(USD_PRECISION)) form = AddCyptoTransactionForm(request.form) form.quantity.validators.append(quantity_validator) form.price.validators.append(price_validator) utils.validate_form(form, redirect_url) currency_id = form.currency_id.data quantity = form.quantity.data price = form.price.data amount = round(float(quantity) * float(price), USD_PRECISION) currency = CryptoCurrency.get_or_none(id=currency_id) if currency is None: notify.error("Currency not found.") return redirect(redirect_url) with db_proxy.atomic(): creation_time = datetime.now() CryptoTransaction.create( account=g.authorized_account, direction=CryptoDirection.FIXED_PROFIT, symbol=currency.symbol, quantity=utils.amount_to_subunits(quantity, CRYPTO_PRECISION), price=utils.amount_to_subunits(price, USD_PRECISION), amount=utils.amount_to_subunits(amount, USD_PRECISION), created_at=creation_time, ) CryptoActionLog.create( account=g.authorized_account, message=( f"Fix profit ${amount}, sell {quantity} {currency.symbol} by ${price}." ), created_at=creation_time, ) notify.info("New profit fix was added.") return redirect(redirect_url) @bp.route("/ajax/balances-values") @ajax.ajax_endpoint @auth.login_required def ajax_balances_values(): balances = ( CryptoBalance.select() .join(CryptoCurrency) .where(CryptoBalance.account == g.authorized_account) ) currencies_ids = [i.currency.cmc_id for i in balances] prices = cmc.fetch_prices(currencies_ids, USD_CODE) data = {} for b in balances: price, amount = prices.get(b.currency.cmc_id), None if price is not None: amount = round( float(utils.make_hrf_amount(b.quantity, CRYPTO_PRECISION)) * price, USD_PRECISION, ) data[int(b.id)] = {"price": price, "amount": amount} return data class ActionsFilterForm(Form): offset = IntegerField(validators=[vals.Optional()]) limit = IntegerField(validators=[vals.Optional()]) @bp.route("/crypto/actions") @auth.login_required def actions(): filter_form = ActionsFilterForm(request.args) utils.validate_form(filter_form, url_for("crypto.actions"), error_notify=None) offset = filter_form.offset.data or 0 limit = filter_form.limit.data or 10 filters = {"offset": offset, "limit": limit} limit_plus_one = limit + 1 query = ( CryptoActionLog.select() .where(CryptoActionLog.account == g.authorized_account) .order_by(CryptoActionLog.created_at.desc()) .offset(offset) .limit(limit_plus_one) ) actions = list(query)[:limit] has_prev = offset > 0 has_next = len(query) == limit_plus_one return render_template( "crypto/actions.html", filters=filters, actions=actions, has_prev=has_prev, has_next=has_next, ) @bp.route("/crypto/actions/export") @auth.login_required def export_actions(): actions = ( CryptoActionLog.select() .where(CryptoActionLog.account == g.authorized_account) .order_by(CryptoActionLog.created_at.desc()) ) buffer = io.StringIO() csvwriter = csv.writer(buffer, delimiter=";", quoting=csv.QUOTE_ALL) csvwriter.writerow(["Time", "Message"]) for i in actions.iterator(): csvwriter.writerow([i.created_at.strftime(DATETIME_FORMAT), i.message]) res = make_response(buffer.getvalue()) res.headers["Content-Disposition"] = "attachment; filename=actions.csv" res.headers["Content-type"] = "text/csv" return res

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11,333

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null

0

null

0

1

0

32665f5e99814a1ca419ee599a7bb327ba8ffbf0

9,115

py

Python

src/modeci_mdf/interfaces/pytorch/mod_torch_builtins.py

29riyasaxena/MDF

476e6950d0f14f29463eb4f6e3be518dfb2160a5

[ "Apache-2.0" ]

12

2021-01-18T20:38:21.000Z

2022-03-29T15:01:10.000Z

src/modeci_mdf/interfaces/pytorch/mod_torch_builtins.py

29riyasaxena/MDF

476e6950d0f14f29463eb4f6e3be518dfb2160a5

[ "Apache-2.0" ]

101

2020-12-14T15:23:07.000Z

2022-03-31T17:06:19.000Z

src/modeci_mdf/interfaces/pytorch/mod_torch_builtins.py

29riyasaxena/MDF

476e6950d0f14f29463eb4f6e3be518dfb2160a5

[ "Apache-2.0" ]

15

2020-12-04T22:37:14.000Z

2022-03-31T09:48:03.000Z

""" Wrap commonly-used torch builtins in nn.Module subclass for easier automatic construction of script """ import torch import torch.nn as nn import torch.nn.functional as F class argmax(torch.nn.Module): def __init__(self): super().__init__() def forward(self, A): return torch.argmax(A) class argmin(torch.nn.Module): def __init__(self): super().__init__() def forward(self, A): return torch.argmin(A) class matmul(torch.nn.Module): def __init__(self): super().__init__() def forward(self, A, B): return torch.matmul(A, B.T) class add(torch.nn.Module): def __init__(self): super().__init__() def forward(self, A, B): return torch.add(A, B) class sin(torch.nn.Module): def __init__(self): super().__init__() def forward(self, A): return torch.sin(A) class cos(torch.nn.Module): def __init__(self): super().__init__() def forward(self, A): return torch.cos(A) class abs(torch.nn.Module): def __init__(self): super().__init__() def forward(self, A): return torch.abs(A) class flatten(torch.nn.Module): def __init__(self): super().__init__() def forward(self, A): return torch.reshape(A, (1, -1)) class clip(torch.nn.Module): def __init__(self): super().__init__() def forward(self, A, min_val, max_val): return torch.clamp(A, min_val, max_val) class shape(torch.nn.Module): def __init__(self): super().__init__() def forward(self, A): return torch.tensor(A.size()).to(torch.int64) class det(torch.nn.Module): def __init__(self): super().__init__() def forward(self, A): return torch.det(A) class And(torch.nn.Module): def __init__(self): super().__init__() def forward(self, A, B): return torch.logical_and(A > 0, B > 0) class Or(torch.nn.Module): def __init__(self): super().__init__() def forward(self, A, B): return torch.logical_or(A > 0, B > 0) class Xor(torch.nn.Module): def __init__(self): super().__init__() def forward(self, A, B): return torch.logical_xor(A > 0, B > 0) class concat(torch.nn.Module): def __init__(self): super().__init__() def forward(self, A, axis=0): return torch.cat(A, axis) class ceil(torch.nn.Module): def __init__(self): super().__init__() def forward(self, A): return torch.ceil(A) class floor(torch.nn.Module): def __init__(self): super().__init__() def forward(self, A): return torch.floor(A) class bitshift(torch.nn.Module): def __init__(self, DIR): super().__init__() self.dir = DIR def forward(self, A, B): if self.dir == "RIGHT": return A.to(torch.int64) >> B.to(torch.int64) else: return A.to(torch.int64) << B.to(torch.int64) class conv(torch.nn.Module): def __init__( self, auto_pad="NOTSET", kernel_shape=None, group=1, strides=[1, 1], dilations=[1, 1], pads=[0, 0, 0, 0], ): super().__init__() self.group = group self.auto_pad = auto_pad self.strides = tuple(strides) self.dilations = tuple(dilations) self.kernel_shape = kernel_shape def forward(self, A, W, B=None): if self.auto_pad == "NOTSET": self.pads = tuple(pads) elif self.auto_pad == "VALID": self.pads = (0, 0, 0, 0) elif self.auto_pad == "SAME_UPPER": pad_dim1 = ( torch.ceil(torch.tensor(A.shape[2]).to(torch.float32) / strides[0]) .to(torch.int64) .item() ) pad_dim2 = ( torch.ceil(torch.tensor(A.shape[3]).to(torch.float32) / strides[1]) .to(torch.int64) .item() ) if pad_dim1 % 2 == 0 and pad_dim2 % 2 == 0: self.pads = (pad_dim1 // 2, pad_dim1 // 2, pad_dim2 // 2, pad_dim2 // 2) elif pad_dim1 % 2 == 0 and pad_dim2 % 2 != 0: self.pads = ( pad_dim1 // 2, pad_dim1 // 2, pad_dim2 // 2, pad_dim2 // 2 + 1, ) elif pad_dim1 % 2 != 0 and pad_dim2 % 2 == 0: self.pads = ( pad_dim1 // 2, pad_dim1 // 2 + 1, pad_dim2 // 2, pad_dim2 // 2, ) elif pad_dim1 % 2 != 0 and pad_dim2 % 2 != 0: self.pads = ( pad_dim1 // 2, pad_dim1 // 2 + 1, pad_dim2 // 2, pad_dim2 // 2 + 1, ) elif self.auto_pad == "SAME_LOWER": pad_dim1 = ( torch.ceil(torch.tensor(A.shape[2]).to(torch.float32) / strides[0]) .to(torch.int64) .item() ) pad_dim2 = ( torch.ceil(torch.tensor(A.shape[3]).to(torch.float32) / strides[1]) .to(torch.int64) .item() ) if pad_dim1 % 2 == 0 and pad_dim2 % 2 == 0: self.pads = (pad_dim1 // 2, pad_dim1 // 2, pad_dim2 // 2, pad_dim2 // 2) elif pad_dim1 % 2 == 0 and pad_dim2 % 2 != 0: self.pads = ( pad_dim1 // 2, pad_dim1 // 2, pad_dim2 // 2 + 1, pad_dim2 // 2, ) elif pad_dim1 % 2 != 0 and pad_dim2 % 2 == 0: self.pads = ( pad_dim1 // 2 + 1, pad_dim1 // 2, pad_dim2 // 2, pad_dim2 / 2, ) elif pad_dim1 % 2 != 0 and pad_dim2 % 2 != 0: self.pads = ( pad_dim1 // 2 + 1, pad_dim1 // 2, pad_dim2 // 2 + 1, pad_dim2 // 2, ) A = F.pad(A, self.pads) return F.conv2d( A, W, bias=B, stride=self.strides, padding=self.pads, dilation=self.dilations, groups=self.group, ) class elu(torch.nn.Module): def __init__(self, alpha=1.0): super().__init__() self.alpha = alpha def forward(self, A): return nn.ELU(alpha=self.alpha)(A.to(torch.float32)) class hardsigmoid(torch.nn.Module): def __init__(self, alpha=0.2, beta=0.5): super().__init__() self.alpha = alpha self.beta = beta def forward(self, A): return torch.clamp(self.alpha * (A.to(torch.float32)) + self.beta, 0, 1) class hardswish(torch.nn.Module): def __init__(self): super().__init__() self.alpha = 1.0 / 6 self.beta = 0.5 def forward(self, A): return A * torch.clamp(self.alpha * (A.to(torch.float32)) + self.beta, 0, 1) class hardmax(torch.nn.Module): def __init__(self, axis=-1): super().__init__() self.axis = axis def forward(self, A): A = A.to(torch.float32) rank = A.shape if self.axis < 0: self.axis += len(rank) tensor = torch.arange(rank[self.axis]) repeats = [] repeats.append(1) for i, idx in enumerate(reversed(rank[: self.axis])): repeats.append(1) tensor = torch.stack([tensor] * idx) for i, idx in enumerate(rank[self.axis + 1 :]): repeats.append(idx) tensor = tensor.unsqueeze(-1).repeat(repeats) repeats[-1] = 1 # b = torch.stack([torch.stack([torch.arange(4)] * 3)] *2) # print(tensor.shape) max_values, _ = torch.max(A, dim=self.axis) # print(max_values, max_values.shape) # tensor = torch.reshape(tensor, tuple(rank)) tensor[A != torch.unsqueeze(max_values, dim=self.axis)] = rank[self.axis] # print(b) first_max, _ = torch.min(tensor, dim=self.axis) one_hot = torch.nn.functional.one_hot(first_max, rank[self.axis]) return one_hot class compress(torch.nn.Module): def __init__(self, axis=None): self.axis = axis super().__init__() def forward(self, A, B): idx = (B.to(torch.bool) != 0).nonzero().reshape(-1) if self.axis != None: return torch.index_select(A, self.axis, idx) else: return torch.index_select(A.reshape(-1), 0, idx) # TODO: Many more to be implemented __all__ = [ "argmax", "argmin", "matmul", "add", "sin", "cos", "abs", "flatten", "clip", "shape", "det", "And", "Or", "Xor", "concat", "ceil", "floor", "bitshift", "conv", "elu", "hardsigmoid", "hardswish", "compress", ]

23.798956

88

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1,139

9,115

3.763828

0.115891

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0.072778

0.089573

0.595055

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0.470725

0.461628

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0

0.037545

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0.697411

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false

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0.072727

0.367273

0

null

0

null

0

1

0

326698864c4df87b158debf66bd86b994c325aa0

8,068

py

Python

taf/testlib/snmphelpers.py

stepanandr/taf

75cb85861f8e9703bab7dc6195f3926b8394e3d0

[ "Apache-2.0" ]

10

2016-12-16T00:05:58.000Z

2018-10-30T17:48:25.000Z

taf/testlib/snmphelpers.py

stepanandr/taf

75cb85861f8e9703bab7dc6195f3926b8394e3d0

[ "Apache-2.0" ]

40

2017-01-04T23:07:05.000Z

2018-04-16T19:52:02.000Z

taf/testlib/snmphelpers.py

stepanandr/taf

75cb85861f8e9703bab7dc6195f3926b8394e3d0

[ "Apache-2.0" ]

23

2016-12-30T05:03:53.000Z

2020-04-01T08:40:24.000Z

# Copyright (c) 2011 - 2017, Intel Corporation. # # 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. """``snmphelpers.py`` `SNMP specific helpers functions` """ import sys import os import shutil import tarfile from subprocess import Popen, PIPE import pytest import paramiko as paramiko from . import helpers from . import loggers # create logger for module def is_mibs_folder_empty(path): """Checks is MIBs folder empty of not. Args: path(str): path to MIBs folder Returns: bool: True if empty and False if not Examples:: is_mibs_folder_empty() """ empty = True if os.path.exists(path): for file_n in os.listdir(path): if 'ONS' in file_n or "ons" in file_n: empty = False return empty def clear_mibs_folder(path): """Removes all ONS mibs from MIBS folder. Args: path(str): path to MIBs folder Examples:: clear_mibs_folder() """ if os.path.exists(path): shutil.rmtree(path) def get_remote_file(hostname, port, username, password, remotepath, localpath): """Get remote file to local machine. Args: hostname(str): Remote IP-address port(int): Remote SSH port username(str): Remote host username for authentication password(str): Remote host password for authentication remotepath(str): Remote file to download location path localpath(str): Local path to save remote file Examples:: get_remote_file(host, port, username, password, tar_remotepath, tar_localpath) """ transport = paramiko.Transport((hostname, port)) transport.connect(username=username, password=password) sftp = paramiko.SFTPClient.from_transport(transport) try: sftp.get(remotepath=remotepath, localpath=localpath) finally: sftp.close() transport.close() def untar_file(tar_path, untar_path): """Unpack tar file. Args: tar_path(str): Path to tar file untar_path(str): Path where to unpack Examples:: untar_file(tar_localpath, mib_path_txt) """ old_folder = os.path.join(untar_path, 'mibs') if os.path.isfile(old_folder): os.remove(old_folder) tar = tarfile.open(tar_path) tar.extractall(untar_path) tar.close() os.remove(tar_path) def file_convert(mib_txt_path, mib_py_path): """Convert .txt MIB to .py. Args: mib_txt_path(str): Full path to .txt MIB. mib_py_path(str): Full path to .py MIB Examples:: file_convert(mib_txt_path, mib_py_path) """ mod_logger_snmp = loggers.module_logger(name=__name__) # translate .txt mib into python format using 3rd party tools 'smidump' smidump = Popen(['smidump', '-k', '-f', 'python', mib_txt_path], stdout=PIPE) list_stdout = smidump.communicate()[0] if len(list_stdout) == 0: return "Fail" # create tmp directory for filling MIBs dictionary mib_path_tmp = os.path.join(mib_py_path, 'tmp') if not os.path.exists(mib_path_tmp): os.makedirs(mib_path_tmp) # added tmp path into sys.path for imports converted MIB's sys.path.append(mib_path_tmp) # get file without extension file_name = os.path.splitext(os.path.basename(mib_txt_path))[0] # create .py name temp_file_name = "{0}.py".format(file_name) # create .tmp file path for imports temp_file_path = os.path.join(mib_path_tmp, temp_file_name) # save and import converted MIB's with open(temp_file_path, "ab") as a: a.write(list_stdout) temp_module = __import__(os.path.splitext(os.path.basename(mib_txt_path))[0]) # update helpers.MIBS_DICT with MIB data if "moduleName" in list(temp_module.MIB.keys()) and "nodes" in list(temp_module.MIB.keys()): helpers.MIBS_DICT.update({temp_module.MIB["moduleName"]: list(temp_module.MIB["nodes"].keys())}) # clear tmp file path sys.path.remove(mib_path_tmp) os.remove(temp_file_path) # translate MIB from .py into pysnmp format using 3rd party tools 'libsmi2pysnmp' pipe = Popen(['libsmi2pysnmp', '--no-text'], stdout=PIPE, stdin=PIPE) stdout = pipe.communicate(input=list_stdout) # get MIB name from itself, add .py and save it. mib_name = "{0}.py".format(temp_module.MIB["moduleName"]) mib_py_path = os.path.join(mib_py_path, mib_name) mod_logger_snmp.debug("Convert %s to %s" % (file_name, temp_file_name)) with open(mib_py_path, 'a') as py_file: for string in stdout: if string is not None: str_dict = string.decode('utf-8').split('\n') for each_str in str_dict: if "ModuleCompliance" in each_str: if "ObjectGroup" in each_str: py_file.write(each_str + '\n') elif "Compliance)" in each_str: pass else: py_file.write(each_str + '\n') return mib_name def convert_to_py(txt_dir_path, py_dir_path): """Converts .txt MIB's to .py. Args: txt_dir_path(str): Path to dir with .txt MIB's. py_dir_path(str): Path to dir with .py MIB's Examples:: convert_to_py(mib_path_tmp, mib_path) """ mod_logger_snmp = loggers.module_logger(name=__name__) txt_dir_path = os.path.join(txt_dir_path, "MIB") mod_logger_snmp.debug("Converts .txt MIB's to .py") os.environ['SMIPATH'] = txt_dir_path for mib in os.listdir(txt_dir_path): mib_txt_path = os.path.join(txt_dir_path, mib) retry_count = 3 retry = 1 while retry <= retry_count: mib_py = file_convert(mib_txt_path, py_dir_path) if mib_py not in os.listdir(py_dir_path): mod_logger_snmp.debug("Converted MIB %s is not present at %s" % (mib, py_dir_path)) retry += 1 if retry > retry_count: mod_logger_snmp.debug("Can not convert %s" % (mib, )) else: mod_logger_snmp.debug("Converted MIB %s is present at %s" % (mib, py_dir_path)) retry = retry_count + 1 shutil.rmtree(txt_dir_path) shutil.rmtree(os.path.join(py_dir_path, "tmp")) def create_mib_folder(config, path, env): """Creates MIB folder. Args: config(dict): Configuration dictionary. path(str): Path to MIB folder. env(Environment): Environment object. Examples:: create_mib_folder() """ if config is None: pytest.fail("UI settings not fount in environment configuration.") host = config['host'] port = int(config['port']) username = config['username'] password = config['password'] tar_folder = config['tar_remotepath'] tar_file = os.path.split(tar_folder)[1] branch = env.env_prop['switchppVersion'] platform = getattr(getattr(env.switch[1], 'hw', None), 'snmp_path', None) tar_remotepath = tar_folder.format(**locals()) if not os.path.exists(path): os.makedirs(path) tar_localpath = os.path.join(path, tar_file) mib_path_tmp = os.path.join(path, 'tmp') if not os.path.exists(mib_path_tmp): os.makedirs(mib_path_tmp) mib_path_txt = os.path.join(path, 'txt') if not os.path.exists(mib_path_txt): os.makedirs(mib_path_txt) get_remote_file(host, port, username, password, tar_remotepath, tar_localpath) untar_file(tar_localpath, mib_path_txt) convert_to_py(mib_path_txt, path)

29.992565

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null

0

null

0

1

0

32675e661c420861aca3a72ce984ac5043cdeab4

2,868

py

Python

elexon_api/utils.py

GiorgioBalestrieri/elexon_api_tool

5b271e9d4a52dec5585a232833a699b8392ee6b0

[ "MIT" ]

4

2019-06-07T11:14:46.000Z

2021-04-01T14:15:14.000Z

elexon_api/utils.py

GiorgioBalestrieri/elexon_api_tool

5b271e9d4a52dec5585a232833a699b8392ee6b0

[ "MIT" ]

null

elexon_api/utils.py

GiorgioBalestrieri/elexon_api_tool

5b271e9d4a52dec5585a232833a699b8392ee6b0

[ "MIT" ]

6

2019-02-28T20:24:26.000Z

2021-03-30T18:08:23.000Z

import os from pathlib import Path import pandas as pd from collections import defaultdict from typing import Dict, List from .config import REQUIRED_D, API_KEY_FILENAME import logging logger = logging.getLogger(__name__) logger.addHandler(logging.NullHandler()) def get_required_parameters(service_code: str) -> List[str]: """Get list of required parameters for service.""" return REQUIRED_D[service_code] def _get_path_to_module() -> Path: """Get path to this module.""" return Path(os.path.realpath(__file__)).parent def get_api_key_path(filename=API_KEY_FILENAME) -> Path: """Load api key.""" path_to_dir = _get_path_to_module() return path_to_dir / filename class ElexonAPIException(Exception): pass def extract_df(r_dict: dict) -> pd.DataFrame: """Extract DataFrame from dictionary. Parameters ---------- r_dict Obtained from response through xmltodict. """ r_body = r_dict['responseBody'] r_items_list = r_body['responseList']['item'] try: df_items = pd.DataFrame(r_items_list) except Exception as e: logger.warning(f"Failed to create DataFrame.", exc_info=True) try: df_items = pd.DataFrame(r_items_list, index=[0]) except Exception as e: logger.error("Failed to create DataFrame.") raise e return df_items def extract_df_by_record_type(r_dict: dict) -> Dict[str,pd.DataFrame]: content: List[dict] = r_dict['responseBody']['responseList']['item'] records_d = split_list_of_dicts(content, 'recordType') return {k: pd.DataFrame(l) for k,l in records_d.items()} def split_list_of_dicts(dict_list: List[dict], key: str) -> Dict[str,List[dict]]: """Split list of dictionaries into multiples lists based on a specific key. Output lists are stored in a dicionary with the value used as key. Example: >>> dict_list = [ { "recordType": "a", "foo": 1, "bar": 1, }, { "recordType": "b", "foo": 2, "bar": 2, }, { "recordType": "b", "foo": 3, "bar": 3, } ] >>> split_list_of_dicts(dict_list, 'recordType') { "a": [ { "recordType": "a", "foo": 1, "bar": 1, }, ], "b": [ { "recordType": "b", "foo": 2, "bar": 2, }, { "recordType": "b", "foo": 3, "bar": 3, } ] } ] """ result = defaultdict(list) for d in dict_list: result[d[key]].append(d) return result

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null

0

null

0

1

0

326881582afe0e7d4f36578fa52df6c3b487641d

1,608

py

Python

relative_connectivity_of_subgraphs.py

doberse/RRI

e2fdc085d8040efc230a25eec670dd6839cbf1f7

[ "MIT" ]

null

relative_connectivity_of_subgraphs.py

doberse/RRI

e2fdc085d8040efc230a25eec670dd6839cbf1f7

[ "MIT" ]

null

relative_connectivity_of_subgraphs.py

doberse/RRI

e2fdc085d8040efc230a25eec670dd6839cbf1f7

[ "MIT" ]

null

# -*- coding: utf-8 -*- import networkx as nx import pandas as pd #Other nodes connected by one node r=open('input_data/BC-related_RRI_network.txt') ll=r.readlines() r.close() rna_pairs=[] node_to_nodes={} for l in ll: ws=l.strip().split('\t') qx=sorted(ws[0:2]) rna_pairs.append((qx[0],qx[1])) for i in [0,1]: if i==0: j=1 else: j=0 if qx[i] not in node_to_nodes: node_to_nodes[qx[i]]=[qx[j]] else: node_to_nodes[qx[i]].append(qx[j]) #Dictionary of Node No. r=open('input_data/RRI_node.csv') r.readline() no2node={} for l in r: ws=l.strip().split(',') no2node[ws[0]]='~'.join(ws[1:7]) r.close() #Sort nodes by node degree node_degree={} for k in node_to_nodes: node_degree[k]=len(node_to_nodes[k]) df=pd.DataFrame(node_degree,index=['Degree']) df=df.sort_values(by='Degree',axis=1,ascending=False) nodes=df.columns.values #Compute the relative conectivity of subgraphs G=nx.Graph() node_G=[] w=open('RC_in_BC-related_RRI_network.csv','w') w.write('Node,No.,Relative connectivity\n') k=0 lim=len(nodes) while k<lim: node_key=nodes[k] node_G.append(node_key) G.add_node(node_key)#Add the node in subgraphs for node in node_G: if node in set(node_to_nodes[node_key]): G.add_edge(node_key,node)#Add the edge in subgraphs largest_components=max(nx.connected_components(G),key=len) k+=1 w.write(no2node[node_key]+','+str(k)+','+str(len(largest_components)/float(len(node_G)))+'\n') w.close()

26.360656

99

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1,608

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null

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1

0

326dd27e7ff223645c2d0bf5d397fdea5ed20af2

2,632

py

Python

src/piotr/cmdline/fs.py

orangecms/piotr

f892ce6eaaa08ea81eb01943a388b64fbf3ccc44

[ "MIT" ]

47

2021-07-02T08:39:02.000Z

2021-11-08T22:21:39.000Z

src/piotr/cmdline/fs.py

orangecms/piotr

f892ce6eaaa08ea81eb01943a388b64fbf3ccc44

[ "MIT" ]

2

2021-07-08T09:25:30.000Z

2021-07-12T10:06:51.000Z

src/piotr/cmdline/fs.py

orangecms/piotr

f892ce6eaaa08ea81eb01943a388b64fbf3ccc44

[ "MIT" ]

5

2021-07-08T08:29:17.000Z

2021-10-18T13:35:11.000Z

""" FS commandline module. Allows to: - list host filesystems - remove a specific host filesystem - add a specific host filesystem """ from os.path import basename from piotr.cmdline import CmdlineModule, module, command from piotr.user import UserDirectory as ud from piotr.util import confirm @module('fs', 'List, add, remove Piotr host filesystems') class FsModule(CmdlineModule): def __init__(self): super().__init__() @command('List available host filesystems') def list(self, options): """ List available FSs. """ self.title(' Installed host filesystems:') print('') count = 0 for fs in ud.get().getHostFilesystems(): fs_line = (self.term.bold + '{fs:<40}' + self.term.normal + \ '{extra:<40}').format( fs=' > %s' % fs['file'], extra='(version {version}, platform: {platform}, cpu: {cpu} ({endian}), type: {fstype})'.format( version=fs['version'], platform=fs['platform'], cpu=fs['cpu'], fstype=fs['type'], endian='little-endian' if fs['endian']=='little' else 'big-endian' ) ) print(fs_line) count += 1 print('') print(' %d filesystem(s) available' % count) print('') @command('Remove a specific filesystem', ['fs name']) def remove(self, options): """ Remove filesystem from our repository. Expects options[0] to be the name of the target filesystem to remove. """ if len(options) >= 1: # Ask for confirm if confirm('Are you sure to remove this filesystem'): # Remove kernel by name if ud.get().removeHostFs(options[0]): print('Filesystem %s successfully removed.' % options[0]) else: self.error('An error occurred while removing host filesystem.') else: self.important(' You must provide a host filesystem name to remove.') @command('Add a specific host filesystem', ['path']) def add(self, options): """ Add kernel to our kernel repository. """ if len(options) >= 1: if ud.get().addHostFs(options[0]): print('Host filesystem successfully added to our registry.') else: self.error('An error occurred while importing host filesystem.') else: self.important(' You must provide a filesystem file to add.')

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112

0.549392

287

2,632

5.003484

0.341463

0.068245

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0.110028

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0

0.00681

0.330547

2,632

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0

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0

0.020408

0.307414

0

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false

0

0.142857

0

0.244898

0.142857

0

null

0

null

0

1

0

327639bba2a2aa36c47d30fbf67b64ee714db74b

2,975

py

Python

RNAstructure_Source/RNAstructure_python_interface/Error_handling.py

mayc2/PseudoKnot_research

33e94b84435d87aff3d89dbad970c438ac173331

[ "MIT" ]

null

RNAstructure_Source/RNAstructure_python_interface/Error_handling.py

mayc2/PseudoKnot_research

33e94b84435d87aff3d89dbad970c438ac173331

[ "MIT" ]

null

RNAstructure_Source/RNAstructure_python_interface/Error_handling.py

mayc2/PseudoKnot_research

33e94b84435d87aff3d89dbad970c438ac173331

[ "MIT" ]

null

#automated error checking for RNAstructure python interface from __future__ import print_function import inspect from functools import wraps from collections import defaultdict debug = False class StructureError(Exception): pass class RNAstructureInternalError(Exception):pass lookup_exceptions = defaultdict(lambda:RuntimeError, { 1:IOError, 2:IOError, 3:IndexError, 4:IndexError, 5:EnvironmentError, 6:StructureError, 7:StructureError, 8:StructureError, 9:StructureError, 10:ValueError, 11:ValueError, 12:ValueError, 13:IOError, 14:RNAstructureInternalError, 15:ValueError, 16:ValueError, 17:ValueError, 18:ValueError, 19:ValueError, 20:ValueError, 21:RNAstructureInternalError, 22:RNAstructureInternalError, 23:ValueError, 24:ValueError, 25:ValueError, 26:ValueError }) def check_for_errors(method): @wraps(method) def RNAstructure_error_checker(self,*args,**kwargs): if debug: print ("checking for errors in %s" % method.__name__) ret = method(self,*args,**kwargs) error = self.GetErrorCode() self.ResetError() if error != 0: raise lookup_exceptions[error]("Error in %s: " % method.__name__ + self.GetErrorMessage(error)) return ret return RNAstructure_error_checker def check_for_init_errors(method): @wraps(method) def RNAstructure_error_checker(self,*args): if debug: print ("checking for errors in %s" % method.__name__) ret = method(self,*args) error = self.GetErrorCode() if error != 0: raise RuntimeError("Error in call to %s.%s: " % (self.__name__,method.__name__) + self.GetErrorMessage(error)) return ret return RNAstructure_error_checker def is_init(method): result = inspect.ismethod(method) and method.__name__=="__init__" if inspect.ismethod(method): pass return result def not_excluded(method): excluded = ["__repr__","__setattr__","__getattr__","__str__","__init__","<lambda>","swig_repr", "GetErrorCode","GetErrorMessage","GetErrorMessageString","ResetError","fromFile","fromString"] result = inspect.ismethod(method) and method.__name__ not in excluded if inspect.ismethod(method): if debug: print ("checking if", method.__name__ , "should be excluded: ",result) return result def decorate_methods(decorator,methodtype): def decorate(cls): for attr in inspect.getmembers(cls, methodtype): if debug: print ("decorating %s!" % attr[0]) setattr(cls, attr[0], decorator(getattr(cls, attr[0]))) return cls return decorate

35.416667

110

0.621176

299

2,975

5.913043

0.347826

0.039593

0.054299

0.033937

0.263575

0.218326

0

0.022482

0.282353

2,975

83

111

35.843373

0.805621

0.019496

0

0.233766

0

0.095336

0.007202

0

1

0.103896

false

0.038961

0.051948

0

0.285714

0.064935

0

null

0

null

0

1

0

3276b79a61cf27161c545de376944d5851538c10

52,691

py

Python

Src/si_figs.py

jomimc/FoldAsymCode

1896e5768e738bb5d1921a3f4c8eaf7f66c06be9

[ "MIT" ]

1

2020-10-07T14:24:06.000Z

Src/si_figs.py

jomimc/FoldAsymCode

1896e5768e738bb5d1921a3f4c8eaf7f66c06be9

[ "MIT" ]

null

Src/si_figs.py

jomimc/FoldAsymCode

1896e5768e738bb5d1921a3f4c8eaf7f66c06be9

[ "MIT" ]

null

from collections import defaultdict, Counter from itertools import product, permutations from glob import glob import json import os from pathlib import Path import pickle import sqlite3 import string import sys import time import matplotlib as mpl from matplotlib import colors from matplotlib import pyplot as plt from matplotlib.gridspec import GridSpec from matplotlib.lines import Line2D import matplotlib.patches as mpatches from multiprocessing import Pool import numpy as np import pandas as pd from palettable.colorbrewer.qualitative import Paired_12 from palettable.colorbrewer.diverging import PuOr_5, RdYlGn_6, PuOr_10, RdBu_10 from palettable.scientific.diverging import Cork_10 from scipy.spatial import distance_matrix, ConvexHull, convex_hull_plot_2d from scipy.stats import linregress, pearsonr, lognorm import seaborn as sns import svgutils.compose as sc import asym_io from asym_io import PATH_BASE, PATH_ASYM, PATH_ASYM_DATA import asym_utils as utils import folding_rate import paper_figs import structure PATH_FIG = PATH_ASYM.joinpath("Figures") PATH_FIG_DATA = PATH_FIG.joinpath("Data") custom_cmap = sns.diverging_palette(230, 22, s=100, l=47, n=13) c_helix = custom_cmap[2] c_sheet = custom_cmap[10] col = [c_helix, c_sheet, "#CB7CE6", "#79C726"] #################################################################### ### SI Figures #################################################################### ### FIG 1 def fig1(df, nx=3, ny=3, N=50): fig, ax = plt.subplots(nx,ny, figsize=(12,12)) ax = ax.reshape(ax.size) fig.subplots_adjust(hspace=.5) lbls = ['Helix', 'Sheet', 'Coil', 'Disorder'] cat = 'HS.D' scop_desc = {row[1]:row[2] for row in pd.read_csv(PATH_BASE.joinpath('SCOP/scop-des-latest.txt')).itertuples()} CF_count = sorted(df.CF.value_counts().items(), key=lambda x:x[1], reverse=True)[1:] bold_idx = [0, 1, 2, 6, 8] for i in range(nx*ny): cf_id, count = CF_count[i] countN, countC = utils.pdb_end_stats_disorder_N_C(df.loc[df.CF==cf_id], N=N, s1='SEQ_PDB2', s2='SS_PDB2') base = np.zeros(len(countN['S']), dtype=float) Yt = np.array([[sum(p.values()) for p in countN[s]] for s in cat]).sum(axis=0) X = np.arange(base.size) for j, s in enumerate(cat): YN = np.array([sum(p.values()) for p in countN[s]]) YC = np.array([sum(p.values()) for p in countC[s]]) ax[i].plot(YN/Yt, '-', c=col[j], label=f"{s} N") ax[i].plot(YC/Yt, ':', c=col[j], label=f"{s} C") if i in bold_idx: ax[i].set_title(f"{scop_desc[int(cf_id)][:40]}\nTotal sequences: {count}", fontweight='bold') else: ax[i].set_title(f"{scop_desc[int(cf_id)][:40]}\nTotal sequences: {count}") ax[i].set_xlabel('Sequence distance from ends') if not i%3: ax[i].set_ylabel('Secondary\nstructure\nprobability') handles = [Line2D([0], [0], ls=ls, c=c, label=l) for ls, c, l in zip(['-', '--'], ['k']*2, ['N', 'C'])] + \ [Line2D([0], [0], ls='-', c=c, label=l) for l, c in zip(lbls, col)] ax[1].legend(handles=handles, bbox_to_anchor=(1.40, 1.45), frameon=False, ncol=6, columnspacing=1.5, handlelength=2.0) fig.savefig(PATH_FIG.joinpath("si1.pdf"), bbox_inches='tight') #################################################################### ### FIG 2 def fig2(): pfdb = asym_io.load_pfdb() fig, ax = plt.subplots(1,2, figsize=(10,5)) fig.subplots_adjust(wspace=0.3) X1 = np.log10(pfdb.loc[pfdb.use, 'L']) X2 = np.log10(pfdb.loc[pfdb.use, 'CO']) Y = pfdb.loc[pfdb.use, 'log_kf'] sns.regplot(X1, Y, ax=ax[0]) sns.regplot(X2, Y, ax=ax[1]) print(pearsonr(X1, Y)) print(pearsonr(X2, Y)) ax[0].set_ylabel(r'$\log_{10} k_f$') ax[1].set_ylabel(r'$\log_{10} k_f$') ax[0].set_xlabel(r'$\log_{10}$ Sequence Length') ax[1].set_xlabel(r'$\log_{10}$ Contact Order') fs = 14 for i, b in zip([0,1], list('ABCDEFGHI')): ax[i].text( -0.10, 1.05, b, transform=ax[i].transAxes, fontsize=fs) fig.savefig(PATH_FIG.joinpath("si2.pdf"), bbox_inches='tight') #################################################################### ### FIG 3 def fig3(pdb, Y='S_ASYM'): LO = folding_rate.get_folding_translation_rates(pdb.copy(), which='lo') HI = folding_rate.get_folding_translation_rates(pdb.copy(), which='hi') fig, ax = plt.subplots() lbls = ['Fit', r"$95\% CI$", r"$95\% CI$"] for i, d in enumerate([pdb, LO, HI]): print(f"{i}: frac R less than 0 = {utils.R_frac_1(d)}") print(f"{i}: Euk frac (.1 < R < 10) = {utils.R_frac_2(d, k=5)}") print(f"{i}: Prok frac (.1 < R < 10) = {utils.R_frac_2(d, k=10)}") print(f"{i}: frac R faster than 'speed-limit' = {utils.R_frac_3(d)}") print(f"{i}: frac R slower than 20 minutes = {utils.R_frac_4(d)}") print() sns.distplot(d['REL_RATE'], label=lbls[i], color=col[i]) ax.legend(loc='best', frameon=False) ax.set_xlim(-6, 6) ax.set_xlabel(r'$\log_{10}R$') ax.set_ylabel('Density') fig.savefig(PATH_FIG.joinpath("si3.pdf"), bbox_inches='tight') #################################################################### ### FIG 4 def fig4(pdb, Y='S_ASYM'): LO = folding_rate.get_folding_translation_rates(pdb.copy(), which='lo') HI = folding_rate.get_folding_translation_rates(pdb.copy(), which='hi') # For the results using only 2-state proteins... # HI = folding_rate.get_folding_translation_rates(pdb.copy(), which='best', only2s=True) fig = plt.figure(figsize=(8,10.5)) gs = GridSpec(5,12, wspace=0.5, hspace=0.0, height_ratios=[1,0.5,1,0.5,1.5]) ax = [fig.add_subplot(gs[i*2,j*4:(j+1)*4]) for i in [0,1] for j in [0,1,2]] + \ [fig.add_subplot(gs[4,:5]), fig.add_subplot(gs[4,7:])] X = np.arange(10) width = .35 ttls = [r'$\alpha$ Helix', r'$\beta$ Sheet'] lbls = [r'$E_{\alpha}$', r'$E_{\beta}$'] custom_cmap = sns.diverging_palette(230, 22, s=100, l=47, n=13) c_helix = custom_cmap[0] c_sheet = custom_cmap[12] col = [c_helix, c_sheet] bins = np.linspace(-0.20, 0.20, 80) width = np.diff(bins[:2]) X = bins[:-1] + width * 0.5 mid = 39 sep = 0.05 for k, pdb in enumerate([LO, HI]): quantiles = pdb['REL_RATE'].quantile(np.arange(0,1.1,.1)).values pdb['quant'] = pdb['REL_RATE'].apply(lambda x: utils.assign_quantile(x, quantiles)) enrich_data = pickle.load(open(PATH_FIG_DATA.joinpath("fig3_enrich.pickle"), 'rb')) for i, Y in enumerate(['H_ASYM', 'S_ASYM']): for j in range(len(quantiles)-1): hist, bins = np.histogram(pdb.loc[pdb.quant==j, Y], bins=bins) hist = hist / hist.sum() if i: ax[k*3+i].bar(X[:mid], (hist/hist.sum())[:mid], width, bottom=[sep*j]*mid, color='grey', alpha=.5) ax[k*3+i].bar(X[-mid:], (hist/hist.sum())[-mid:], width, bottom=[sep*j]*mid, color=col[i], alpha=.5) else: ax[k*3+i].bar(X[:mid], (hist/hist.sum())[:mid], width, bottom=[sep*j]*mid, color=col[i], alpha=.5) ax[k*3+i].bar(X[-mid:], (hist/hist.sum())[-mid:], width, bottom=[sep*j]*mid, color='grey', alpha=.5) ax[k*3+i].plot(X[:mid], (hist/hist.sum()+sep*j)[:mid], '-', c='k', alpha=.5) ax[k*3+i].plot(X[-mid:], (hist/hist.sum()+sep*j)[-mid:], '-', c='k', alpha=.5) mean = np.mean(enrich_data[Y[0]], axis=0) lo = np.abs(mean - np.quantile(enrich_data[Y[0]], 0.025, axis=0)) hi = np.abs(mean - np.quantile(enrich_data[Y[0]], 0.975, axis=0)) ax[k*3+2].barh([sep*j+(i+.7)*sep/3 for j in range(10)], mean, sep/3, xerr=(lo, hi), color=col[i], ec='k', alpha=.5, label=lbls[i], error_kw={'lw':.8}) ax[k*3+2].plot([0,0], [-0.05, 0.5], '-', c='k', lw=.1) for i in [0,2]: ax[k*3+i].set_yticks(np.arange(len(quantiles))*sep) ax[k*3+i].set_yticklabels([round(x,1) for x in quantiles]) for i in range(2): ax[k*3+i].spines['top'].set_visible(False) ax[k*3+i].spines['right'].set_visible(False) for i in range(1,3): ax[k*3+i].spines['left'].set_visible(False) ax[k*3+i].spines['top'].set_visible(False) for i in range(3): ax[k*3+i].set_ylim(0-sep/4, (0.5+sep/4)*1.05) ax[k*3+1].set_yticks([]) ax[k*3+2].yaxis.set_label_position('right') ax[k*3+2].yaxis.tick_right() ax[k*3+0].set_xlabel(r"asym$_{\alpha}$") ax[k*3+1].set_xlabel(r"asym$_{\beta}$") ax[k*3+0].set_ylabel(r'$\log_{10}R$') ax[k*3+2].set_xlabel('N terminal\nEnrichment') plot_metric_space(fig, ax[6:]) fs = 14 for i, b in zip([0,3,6], list('ABCDEFGHI')): ax[i].text( -0.20, 1.05, b, transform=ax[i].transAxes, fontsize=fs) fig.savefig(PATH_FIG.joinpath("si4.pdf"), bbox_inches='tight') def get_ci_index(X, Y): xlo = np.quantile(X, 0.025) xhi = np.quantile(X, 0.975) ylo = np.quantile(Y, 0.025) yhi = np.quantile(Y, 0.975) return np.where((X>=xlo)&(X<=xhi)&(Y>=ylo)&(Y<=yhi))[0] def plot_hull(boot_fit, patt, ax='', c='k', lw=1): idx = get_ci_index(*boot_fit[:,:2].T) tmp = boot_fit[idx].copy() hull = ConvexHull(np.array([boot_fit[idx,1], boot_fit[idx, 0]]).T) for simplex in hull.simplices: if not isinstance(ax, str): ax.plot(tmp[simplex, 1], tmp[simplex, 0], patt, c=c, lw=lw) else: plt.plot(tmp[simplex, 1], tmp[simplex, 0], patt, c=c, lw=lw) def plot_metric_space(fig, ax): fit = pickle.load(open(PATH_FIG_DATA.joinpath("boot_fit_met.pickle"), 'rb'))['AA'] boot_fit = pickle.load(open(PATH_FIG_DATA.joinpath("boot_fit_param.pickle"), 'rb')) boot_fit_0 = pickle.load(open(PATH_FIG_DATA.joinpath("boot_fit_param_useall.pickle"), 'rb')) X, Y = np.meshgrid(fit["c1"], fit["c2"]) cmap = colors.ListedColormap(sns.diverging_palette(230, 22, s=100, l=47, n=8)) bounds = np.linspace(-2, 2, 9) norm = colors.BoundaryNorm(bounds, cmap.N) im = [] ttls = ['Helices', 'Sheets'] for i in range(2): im = ax[i].contourf(X, Y, fit['met'][:,:,i], bounds, cmap=cmap, vmin=-2, vmax=2, norm=norm) cbar = fig.colorbar(im, ax=ax[i], fraction=0.046, pad=0.04, norm=norm, boundaries=bounds, ticks=bounds) cbar.set_label(r"$R_{\mathregular{max}}$", labelpad=-5) ax[i].set_xlabel('A') ax[i].set_xlim(X.min(), X.max()) ax[i].set_ylabel('B') ax[i].set_ylim(Y.max(), Y.min()) ax[i].invert_yaxis() ax[i].set_aspect((np.max(X)-np.min(X))/(np.max(Y)-np.min(Y))) ax[i].set_title(ttls[i]) col = ['k', '#79C726'] for i, boofi in enumerate([boot_fit, boot_fit_0]): for j in range(2): for bf, p in zip(boofi, ['-', ':']): plot_hull(bf, p, ax[j], c=col[i]) c1 = [13.77, -6.07] c1a = [11.36553036, -4.87716477] c1b = [16.17819934, -7.27168306] patt = ['*', 'o', 'o'] lbls = ['Fit', r"$95\% CI$", r"$95\% CI$"] col = "#CB7CE6" for i in range(2): for coef, p, l in zip([c1, c1a, c1b], patt, lbls): ax[i].plot([coef[0]], [coef[1]], p, label=l, fillstyle='none', ms=10, c=col, mew=2) ax[i].legend(loc='best', frameon=False) #################################################################### ### FIG 5 def fig5(): fig, ax = plt.subplots(2,1) fig.subplots_adjust(hspace=0.3) bins = np.arange(0,620,20) X = [bins[:-1] + np.diff(bins[:2])] bins = np.arange(0,61,2.0) X.append(bins[:-1] + np.diff(bins[:2])) yellows = sns.diverging_palette(5, 55, s=95, l=77, n=13) pinks = sns.diverging_palette(5, 55, s=70, l=52, n=13) col = [yellows[12], pinks[0]] col2 = [yellows[10], pinks[3]] data = [pickle.load(open(PATH_FIG_DATA.joinpath(f"dom_{x}_dist_boot.pickle"), 'rb')) for x in ['aa', 'smco']] for j in range(2): for i in [1,2]: MEAN, LO, HI = [np.array(x) for x in data[j][f"pos{i}"]] ax[j].plot(X[j], MEAN, '--', c=col[i-1], label=f'position {i}') ax[j].fill_between(X[j], LO, HI, color=col2[i-1], alpha=0.5) ax[0].set_xlabel('Sequence Length') ax[1].set_xlabel('Contact Order') ax[0].set_ylabel('Density') ax[1].set_ylabel('Density') ax[0].legend(loc='upper right', frameon=False) fig.savefig(PATH_FIG.joinpath("si5.pdf"), bbox_inches='tight') #################################################################### ### FIG 6 def fig6(X='REL_RATE', Y='S_ASYM'): fig, ax = plt.subplots(1,2, figsize=(10,4)) fig.subplots_adjust(hspace=0.7, wspace=0.3) sep = 0.40 col = Paired_12.hex_colors[5] ttls = [f"Position {i}" for i in range(1,3)] dom_pos_boot = pickle.load(open(PATH_FIG_DATA.joinpath("dom_pos_boot.pickle"), 'rb')) custom_cmap = sns.diverging_palette(230, 22, s=100, l=47, n=13) c_helix = custom_cmap[2] c_sheet = custom_cmap[11] col = [c_helix, c_sheet, "#CB7CE6", "#79C726"] # ttls = ["Two-domain", "Three-domain"] xlbls = [r'$E_{\alpha}$', r'$E_{\beta}$'] for i in range(2): for j, (pos, dat) in enumerate(dom_pos_boot[2].items()): quantiles = dat[0].mean(axis=0) mean = dat[1][:,i,:].mean(axis=0) lo = np.abs(np.quantile(dat[1][:,i,:], 0.025, axis=0) - mean) hi = np.abs(np.quantile(dat[1][:,i,:], 0.975, axis=0) - mean) ax[j].bar(np.arange(10)+(i+1)*sep, mean, sep, yerr=(lo, hi), color=col[i], label=xlbls[i], alpha=0.7, error_kw={'lw':.8}) ax[j].set_xticks(np.arange(len(quantiles))) ax[j].set_xticklabels(np.round(quantiles, 1), rotation=90) ax[i].spines['top'].set_visible(False) ax[i].spines['right'].set_visible(False) ax[i].set_title(ttls[i], loc='left') ax[i].set_xlabel(r'$\log_{10}R$') # ax[i,k].set_ylabel('N terminal\nEnrichment') ax[i].set_ylabel("N Terminal Enrichment") ax[0].legend(bbox_to_anchor=(1.17, 1.12), frameon=False, ncol=3) fig.savefig(PATH_FIG.joinpath("si6.pdf"), bbox_inches='tight') #################################################################### ### FIG 7 def fig7(pdb, Y='D_ASYM'): fig, ax = plt.subplots(3,3, figsize=(12,8)) fig.subplots_adjust(hspace=0.5, wspace=0.5) sep = 0.05 col = Paired_12.hex_colors[7] xlbls = [r'$\log_{10} R$', 'Sequence Length', 'Contact Order'] ttls = ['Full sample', 'Eukaryotes', 'Prokaryotes'] for k, df in enumerate([pdb, pdb.loc[pdb.k_trans==5], pdb.loc[pdb.k_trans==10]]): for i, X in enumerate(['REL_RATE', 'AA_PDB', 'CO']): quantiles = df[X].quantile(np.arange(0,1.1,.1)).values df['quant'] = df[X].apply(lambda x: utils.assign_quantile(x, quantiles)) ratio = [] for j in range(len(quantiles)-1): left = len(df.loc[(df.quant==j)&(df[Y]<0)]) / max(1, len(df.loc[(df.quant==j)])) right = len(df.loc[(df.quant==j)&(df[Y]>0)]) / max(1, len(df.loc[(df.quant==j)])) ratio.append((right - left)) # print(ratio) ax[i,k].bar([sep*j+sep/2 for j in range(10)], ratio, sep/2, color=[col if r > 0 else 'grey' for r in ratio], alpha=.5) ax[i,k].set_xticks(np.arange(len(quantiles))*sep) if i == 1: ax[i,k].set_xticklabels([int(x) for x in quantiles], rotation=90) else: ax[i,k].set_xticklabels([round(x,1) for x in quantiles], rotation=90) ax[i,k].set_xlabel(xlbls[i]) ax[i,k].set_ylabel('N terminal\nEnrichment') ax[0,k].set_title(ttls[k]) fig.savefig(PATH_FIG.joinpath("si7.pdf"), bbox_inches='tight') #################################################################### ### FIG 8 def fig8(df_pdb): fig = plt.figure() gs = GridSpec(2,1, wspace=0.0, height_ratios=[.5,1]) ax = [fig.add_subplot(gs[1,0]), fig.add_subplot(gs[0,0])] X = np.arange(-3, 3, 0.01) Y = np.array([(10**x + 1)/max(10**x, 1) for x in X]) Y2 = (1+10**X) / np.array([max(1, 10**x+30./100.) for x in X]) ax[0].plot(X, Y, '-', label=r"$\tau_{ribo}=0$") ax[0].plot(X, Y2, ':', label=r"$\tau_{ribo}=0.3\tau_{trans}$") lbls = ['1ILO', '2OT2', '3BID'] patt = ['o', 's', '^'] for l, p in zip(lbls, patt): X, Y = np.load(PATH_FIG_DATA.joinpath(f"{l}.npy")) ax[0].plot(X, Y, p, label=l, alpha=0.5, mec='k', ms=7) ax[0].set_xlim(-2.3, 2.3) ax[0].set_ylim(1, 2.05) ax[0].set_xlabel(r'$\log_{10} R$') ax[0].set_ylabel("Speed-up") ax[0].spines['top'].set_visible(False) ax[0].spines['right'].set_visible(False) ax[0].legend(loc='upper right', frameon=False, bbox_to_anchor=(1.05, 1.00), ncol=1, labelspacing=.1) fig8a(df_pdb, ax[1]) fig.savefig(PATH_FIG.joinpath("si8.pdf"), bbox_inches='tight') def fig8a(df_pdb, ax): lbls = ['2OT2', '1ILO', '3BID'] idx = [98212, 19922, 127370] SS = df_pdb.loc[idx, 'SS_PDB2'].values custom_cmap = sns.diverging_palette(230, 22, s=100, l=47, n=13) col_key = {'.':'grey', 'D':'grey', 'H':custom_cmap[3], 'S':custom_cmap[9]} ec_key = {'.':'grey', 'D':'grey', 'H':custom_cmap[1], 'S':custom_cmap[11]} wid_key = {'.':0.1, 'D':0.1, 'H':0.3, 'S':0.3} lw_key = {'.':0.7, 'D':0.7, 'H':1.5, 'S':1.5} for i, ss in enumerate(SS): left = 0. for j, strand in enumerate(new_figs.generate_strand(ss)): s = strand[0] ax.barh([i], [len(strand)], wid_key[s], left=[left], color=col_key[s], ec=ec_key[s], linewidth=lw_key[s]) left += len(strand) + 0.20 ax.annotate("N", xy=(-0.01, 1.0), xycoords='axes fraction') ax.annotate("C", xy=(0.59, 1.0), xycoords='axes fraction') for pos in ['left', 'right', 'top', 'bottom']: ax.spines[pos].set_visible(False) col = np.array(custom_cmap)[[3,9,1,11]] ax.legend(handles=[mpatches.Patch(fc=c1, ec=c2, label=l) for c1, c2, l in zip(col[:2], col[2:], ['Helix', 'Sheet'])], loc='upper right', frameon=False, ncol=1, bbox_to_anchor=(0.95, 1.10)) ax.set_xticks([]) ax.set_yticks(range(3)) ax.set_yticklabels(lbls) ax.tick_params(axis='y', which='major', length=0, pad=10) #################################################################### ### FIG 9 def fig9(pdb, s='S'): pdb = pdb.loc[(pdb.USE_RSA)] pdb = pdb.loc[(pdb.SS_PDB2.str.len()==pdb.RSA.apply(len))] path = PATH_FIG_DATA.joinpath("RSA_quantiles.pickle") if path.exists(): quantiles, euk_quantiles, prok_quantiles = pickle.load(open(path, 'rb')) else: quantiles = [np.quantile([x for y in pdb['RSA'] for x in y if np.isfinite(x)], x/3) for x in range(1,4)] euk_quantiles = [np.quantile([x for y in pdb.loc[pdb.k_trans==5, 'RSA'] for x in y if np.isfinite(x)], x/3) for x in range(1,4)] prok_quantiles = [np.quantile([x for y in pdb.loc[pdb.k_trans==10, 'RSA'] for x in y if np.isfinite(x)], x/3) for x in range(1,4)] pickle.dump([quantiles, euk_quantiles, prok_quantiles], open(path, 'wb')) print(quantiles) # fig, ax = plt.subplots(4,3, figsize=(8,8)) # fig.subplots_adjust(wspace=0.5) fig = plt.figure(figsize=(12,9)) gs = GridSpec(5,3, wspace=0.3, height_ratios=[1,1,1,1,1]) ax = [fig.add_subplot(gs[j,i]) for i in range(3) for j in [0,1]] + \ [fig.add_subplot(gs[j,i]) for i in range(3) for j in [3,4]] print("All proteins, all SS") fig9a(pdb['RSA'], pdb['SS_PDB2'], quantiles, ax[:2], s='SH.D') print("euk proteins, all ss") fig9a(pdb.loc[pdb.k_trans==5, 'RSA'], pdb.loc[pdb.k_trans==5, 'SS_PDB2'], euk_quantiles, ax[2:4], s='SH.D') print("Prok proteins, all SS") fig9a(pdb.loc[pdb.k_trans==10, 'RSA'], pdb.loc[pdb.k_trans==10, 'SS_PDB2'], prok_quantiles, ax[4:6], s='SH.D') print("Euk proteins, only SHC") fig9a(pdb.loc[pdb.k_trans==5, 'RSA'], pdb.loc[pdb.k_trans==5, 'SS_PDB2'], euk_quantiles, ax[6:8], s='SH.') print("Euk proteins, only S") fig9a(pdb.loc[pdb.k_trans==5, 'RSA'], pdb.loc[pdb.k_trans==5, 'SS_PDB2'], euk_quantiles, ax[8:10], s='S') print("Prok proteins, only S") fig9a(pdb.loc[pdb.k_trans==10, 'RSA'], pdb.loc[pdb.k_trans==10, 'SS_PDB2'], prok_quantiles, ax[10:12], s='S') ttls = ['All proteins\nAll residues', 'Eukaryotic proteins\nAll residues', 'Prokaryotic proteins\nAll residues', 'Eukaryotic proteins\nHelix, sheet and coil', 'Eukaryotic proteins\nOnly Sheets', 'Prokaryotic proteins\nOnly Sheets'] col = np.array(list(Paired_12.hex_colors))[[0,2,4,6]] lbls = ['Buried', 'Middle', 'Exposed'] ax[0].set_ylabel('Solvent accessibility\nprobability') ax[1].set_ylabel('Solvent accessibility\nasymmetry\n$\\log_2 (N / C)$') ax[6].set_ylabel('Solvent accessibility\nprobability') ax[7].set_ylabel('Solvent accessibility\nasymmetry\n$\\log_2 (N / C)$') handles = [Line2D([0], [0], ls=ls, c=c, label=l) for ls, c, l in zip(['-', '--'], ['k']*2, ['N', 'C'])] + \ [Line2D([0], [0], ls='-', c=c, label=l) for l, c in zip(lbls, col)] ax[8].legend(handles=handles, bbox_to_anchor=(1.30, 1.85), frameon=False, ncol=5, columnspacing=1.5, handlelength=2.0, labelspacing=2.0) for i, a in enumerate(ax): if i % 2: ax[i].set_xticks(range(0, 60, 10)) ax[i].set_xlabel('Sequence distance from ends') else: ax[i].set_xticks([]) ax[i].set_title(ttls[i//2]) ax[i].set_xlim(0, 50) fig.savefig(PATH_FIG.joinpath("si9.pdf"), bbox_inches='tight') def fig9a(rsa_list, ss_list, quantiles, ax, s='S'): cat = 'BME' countN, countC = utils.sheets_rsa_seq_dist(rsa_list, ss_list, quantiles, ss_key=s) col = np.array(list(Paired_12.hex_colors))[[0,2,4,6]] base = np.zeros(len(countN[cat[0]]), dtype=float) YtN = np.array(list(countN.values())).sum(axis=0) YtC = np.array(list(countC.values())).sum(axis=0) X = np.arange(base.size) for i, s in enumerate(cat): YN = countN[s] YC = countC[s] ax[0].plot(YN/YtN, '-', c=col[i], label=f"{s} N") ax[0].plot(YC/YtC, ':', c=col[i], label=f"{s} C") ax[1].plot(np.log2(YN/YC*YtC/YtN), '-', c=col[i], label=f"{s}") print(s, np.round((np.sum(YN[:20]) / np.sum(YtN[:20])) / (np.sum(YC[:20]) / np.sum(YtC[:20])), 2)) ax[1].plot([0]*base.size, ':', c='k') ax[0].set_ylim(0,1) ax[1].set_ylim(-1,1) for a in ax: a.set_xlim(X[0], X[-1]) #################################################################### ### FIG 10 def fig10(pdb): pfdb = asym_io.load_pfdb() acpro = asym_io.load_acpro() fig = plt.figure(figsize=(12,9)) gs = GridSpec(3,7, wspace=0.0, width_ratios=[5,0.2,5,0.4,3,1.0,6], height_ratios=[1,.3,1]) ax = [fig.add_subplot(gs[2,i*2]) for i in range(4)] + \ [fig.add_subplot(gs[0,0:3]), fig.add_subplot(gs[0,5:])] # sns.distplot(pdb.ln_kf, ax=ax[5], label='PDB - PFDB fit', hist=False) pdb = pdb.copy() coef = folding_rate.linear_fit(np.log10(acpro['L']), acpro['log_kf']).params pdb['ln_kf'] = folding_rate.pred_fold(np.log10(pdb.AA_PDB), coef) pdb = utils.get_rel_rate(pdb) fig10a(fig, ax[4]) fig10b(fig, ax[:4], pdb) # sns.distplot(pdb.ln_kf, ax=ax[5], label='PDB - ACPro fit', hist=False) # sns.distplot(pfdb.log_kf, ax=ax[5], label='PFDB data', kde=False, norm_hist=True) # sns.distplot(acpro["ln kf"], ax=ax[5], label='KDB data', kde=False, norm_hist=True) sns.regplot(np.log10(acpro['L']), acpro['log_kf'], label='ACPro data', scatter_kws={"alpha":0.5}) sns.regplot(np.log10(pfdb.loc[pfdb.use, 'L']), pfdb.loc[pfdb.use, 'log_kf'], label='PFDB data', scatter_kws={"alpha":0.5}) ax[5].legend(loc='best', frameon=False) ax[5].set_xlabel(r"$\log_{10}L$") ax[5].set_ylabel(r"$\log_{10}k_f$") fs = 14 for i, b in zip([4,5,0,2,3], list('ABCDEFGHI')): ax[i].text( -0.20, 1.16, b, transform=ax[i].transAxes, fontsize=fs) fig.savefig(PATH_FIG.joinpath("si10.pdf"), bbox_inches='tight') def fig10a(fig, ax): Rdist_data = pickle.load(open(PATH_FIG_DATA.joinpath("R_dist_acpro.pickle"), 'rb')) custom_cmap = sns.diverging_palette(230, 22, s=100, l=47, n=13) c_helix = custom_cmap[2] c_sheet = custom_cmap[10] col = [c_helix, c_sheet, "#CB7CE6", "#79C726"] lbls = ['All', 'Prokaryotes', 'Eukaryotes'] for i, k in enumerate(['All', 'Prok', 'Euk']): ax.plot(Rdist_data['grid'], Rdist_data[k][0], '-', c=col[i], label=lbls[i]) ax.fill_between(Rdist_data['grid'], Rdist_data[k][1], Rdist_data[k][2], color=col[i], alpha=0.5) ax.plot([0,0], [0, 0.60], ':', c='k', alpha=0.7) ax.set_xlabel(r'$\log_{10} R$') ax.set_ylabel('Density') ax.set_xticks(np.arange(-6, 5, 2)) ax.set_xlim(-7, 2) ax.set_ylim(0, 0.60) ax.legend(loc='upper center', bbox_to_anchor=(0.55, 1.17), frameon=False, ncol=3, columnspacing=2) ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False) def fig10b(fig, ax, pdb, Y='S_ASYM'): ft = 12 X = np.arange(10) width = .35 ttls = [r'$\alpha$ Helix', r'$\beta$ Sheet'] lbls = [r'$E_{\alpha}$', r'$E_{\beta}$'] # col = np.array(Paired_12.hex_colors)[[1,5]] custom_cmap = sns.diverging_palette(230, 22, s=100, l=47, n=13) c_helix = custom_cmap[0] c_sheet = custom_cmap[12] col = [c_helix, c_sheet] bins = np.linspace(-0.20, 0.20, 80) width = np.diff(bins[:2]) X = bins[:-1] + width * 0.5 mid = 39 sep = 0.05 enrich_data = pickle.load(open(PATH_FIG_DATA.joinpath("fig3_enrich_acpro.pickle"), 'rb')) quantiles = enrich_data['edges'].mean(axis=0) for i, Y in enumerate(['H_ASYM', 'S_ASYM']): for j in range(len(quantiles)-1): hist, bins = np.histogram(pdb.loc[pdb.quant==j, Y], bins=bins) hist = hist / hist.sum() # total = len(pdb)/10 # left = len(pdb.loc[(pdb.quant==j)&(pdb[Y]<0)]) / total # right = len(pdb.loc[(pdb.quant==j)&(pdb[Y]>0)]) / total # print(Y, j, ''.join([f"{x:6.3f}" for x in [left, right, left/right, right / left]])) if i: ax[i].bar(X[:mid], (hist/hist.sum())[:mid], width, bottom=[sep*j]*mid, color='grey', alpha=.5) ax[i].bar(X[-mid:], (hist/hist.sum())[-mid:], width, bottom=[sep*j]*mid, color=col[i], alpha=.5) else: ax[i].bar(X[:mid], (hist/hist.sum())[:mid], width, bottom=[sep*j]*mid, color=col[i], alpha=.5) ax[i].bar(X[-mid:], (hist/hist.sum())[-mid:], width, bottom=[sep*j]*mid, color='grey', alpha=.5) ax[i].plot(X[:mid], (hist/hist.sum()+sep*j)[:mid], '-', c='k', alpha=.5) ax[i].plot(X[-mid:], (hist/hist.sum()+sep*j)[-mid:], '-', c='k', alpha=.5) mean = np.mean(enrich_data[Y[0]], axis=0) lo = np.abs(mean - np.quantile(enrich_data[Y[0]], 0.025, axis=0)) hi = np.abs(mean - np.quantile(enrich_data[Y[0]], 0.975, axis=0)) ax[2].barh([sep*j+(i+.7)*sep/3 for j in range(10)], mean, sep/3, xerr=(lo, hi), color=col[i], ec='k', alpha=.5, label=lbls[i], error_kw={'lw':.8}) ax[2].plot([0,0], [-0.05, 0.5], '-', c='k', lw=.1) ax[0].set_yticks(np.arange(len(quantiles))*sep) ax[0].set_yticklabels([round(x,1) for x in quantiles]) ax[2].legend(loc='upper center', ncol=2, columnspacing=1.5, frameon=False, bbox_to_anchor=(0.52, 1.15)) for i, t in zip([0,1], ttls): ax[i].set_title(t) ax[i].set_xlim(-.15, .15) ax[i].set_xticks([-.1, 0, .1]) for i in range(3): ax[i].spines['top'].set_visible(False) ax[i].spines['right'].set_visible(False) ax[i].set_ylim(0-sep/4, 0.5+sep) for i in [1,2]: ax[i].spines['left'].set_visible(False) ax[i].set_yticks([]) ax[0].set_xlabel(r"asym$_{\alpha}$") ax[1].set_xlabel(r"asym$_{\beta}$") ax[0].set_ylabel(r'$\log_{10}R$') ax[2].set_xlabel('N terminal\nEnrichment') pdb = pdb.loc[pdb.OC!='Viruses'] X = np.arange(10) X = np.array([sep*j+(i+.7)*sep/3 for j in range(10)]) width = .175 ttls = ['Eukaryote ', 'Prokaryote '] lbls = [r'$E_{\alpha}$', r'$E_{\beta}$'] custom_cmap = sns.diverging_palette(230, 22, s=100, l=47, n=13) col = [custom_cmap[i] for i in [3, 9, 0, 12]] paths = [f"fig3_enrich_{a}_acpro.pickle" for a in ['eukaryote', 'prokaryote']] for i, path in enumerate(paths): enrich_data = pickle.load(open(PATH_FIG_DATA.joinpath(path), 'rb')) for j, Y in enumerate(['H_ASYM', 'S_ASYM']): # adjust = (j - 1 + i*2)*width adjust = (j*2 - 4.0 + i)*(sep/5) mean = np.mean(enrich_data[Y[0]], axis=0) lo = np.abs(mean - np.quantile(enrich_data[Y[0]], 0.025, axis=0)) hi = np.abs(mean - np.quantile(enrich_data[Y[0]], 0.975, axis=0)) print(i, Y, max(np.abs(mean))) ax[3].barh(X+adjust, mean, sep/5.0, ec='k', xerr=(lo, hi), color=col[i*2+j], label=ttls[i]+lbls[j], lw=0.001, error_kw={'lw':.2}) ax[3].plot([0,0], [-0.05, 0.5], '-', c='k', lw=.1) ax[3].set_yticks(np.arange(len(quantiles))*sep) ax[3].set_ylabel(r'$\log_{10} R$') ax[3].set_yticklabels([round(x,1) for x in quantiles]) ax[3].set_xlabel('N terminal\nEnrichment') ax[3].set_xlim(-.42, .42) ax[3].set_ylim(0-sep/4, 0.5+sep) ax[3].spines['top'].set_visible(False) ax[3].spines['left'].set_visible(False) handles = [mpatches.Patch([], [], color=col[j*2+i], label=ttls[j]+lbls[i]) for i in [0,1] for j in [1,0]] ax[3].legend(handles=handles, bbox_to_anchor=(1.05, 1.25), frameon=False, loc='upper right', ncol=2, columnspacing=1.0, handlelength=1.5) ax[3].yaxis.set_label_position('right') ax[3].yaxis.tick_right() #################################################################### ### FIG 11 def fig11(pdb, X='AA_PDB', Y='CO', w=.1, ax='', fig=''): if isinstance(ax, str): fig, ax = plt.subplots(4,2, figsize=(9,12)) fig.subplots_adjust(wspace=0.0, hspace=0.65) # ax = ax.reshape(ax.size) pdb_CO = np.load(PATH_FIG_DATA.joinpath("pdb_config_CO.npy"))[:,:,0] df = pdb.copy() q = np.arange(w,1+w,w) lbls = ['Helix', 'Sheet'] # cb_lbl = [r"$E_{\alpha}$", r"$E_{\beta}$"] cb_lbl = [r"$asym_{\alpha}$", r"$asym_{\beta}$"] vmax = 0.03 vmin = -vmax for j, co in enumerate(pdb_CO.T): df['CO'] = co quant1 = [df[X].min()] + list(df[X].quantile(q).values) quant2 = [df[Y].min()] + list(df[Y].quantile(q).values) for i, Z in enumerate(['H_ASYM', 'S_ASYM']): mean = [] for l1, h1 in zip(quant1[:-1], quant1[1:]): for l2, h2 in zip(quant2[:-1], quant2[1:]): samp = df.loc[(df[X]>=l1)&(df[X]<h1)&(df[Y]>=l2)&(df[Y]<h2), Z] mean.append(samp.mean()) # left = len(df.loc[(df[X]>=l1)&(df[X]<h1)&(df[Y]>=l2)&(df[Y]<h2)&(df[Z]<0)]) # right = len(df.loc[(df[X]>=l1)&(df[X]<h1)&(df[Y]>=l2)&(df[Y]<h2)&(df[Z]>0)]) # tot = max(len(df.loc[(df[X]>=l1)&(df[X]<h1)&(df[Y]>=l2)&(df[Y]<h2)]), 1) # mean.append((right - left)/tot) cmap = sns.diverging_palette(230, 22, s=100, l=47, as_cmap=True) norm = colors.BoundaryNorm([vmin, vmax], cmap.N) bounds = np.linspace(vmin, vmax, 3) im = ax[j,i].imshow(np.array(mean).reshape(q.size, q.size).T, cmap=cmap, vmin=vmin, vmax=vmax) cbar = fig.colorbar(im, cmap=cmap, ticks=bounds, ax=ax[j,i], fraction=0.046, pad=0.04) cbar.set_label(cb_lbl[i], labelpad=-5) ax[j,i].set_title(lbls[i]) ax[j,i].set_xticks(np.arange(q.size+1)-0.5) ax[j,i].set_yticks(np.arange(q.size+1)-0.5) ax[j,i].set_xticklabels([int(x) for x in quant1], rotation=90) ax[j,i].set_yticklabels([int(round(x,0)) for x in quant2]) for a in ax.ravel(): a.invert_yaxis() a.set_xlabel('Sequence Length') a.set_ylabel('Contact Order') a.tick_params(axis='both', which='major', direction='in') fs = 14 for i, b in zip(range(4), list('ABCDEFGHI')): ax[i,0].text( -0.20, 1.16, b, transform=ax[i,0].transAxes, fontsize=fs) fig.savefig(PATH_FIG.joinpath("si11.pdf"), bbox_inches='tight') def fig12(pdb, X='REL_RATE', Y='S_ASYM', w=0.1): fig = plt.figure(figsize=(8,12)) gs = GridSpec(3,2, wspace=0.4, hspace=0.5, width_ratios=[1,1]) ax_all = [[fig.add_subplot(gs[j,i]) for i in [0,1]] for j in range(3)] custom_cmap = sns.diverging_palette(230, 22, s=100, l=47, n=13) c_helix = custom_cmap[2] c_sheet = custom_cmap[10] col = [c_helix, c_sheet] bins = np.linspace(-0.20, 0.20, 80) width = np.diff(bins[:2]) mid = 39 sep = 0.05 lbls = ['Sheet', 'Helix'] quantiles = pdb[X].quantile(np.arange(0,1+w,w)).values # print(np.round(quantiles, 2)) pdb['quant'] = pdb[X].apply(lambda x: utils.assign_quantile(x, quantiles)) # pdb['quant'] = np.random.choice(pdb['quant'], len(pdb), replace=False) for ax, threshold in zip(ax_all, [0, 0.025, 0.05]): print(f"threshold = {threshold}") for i, Y in enumerate(['S_ASYM', 'H_ASYM']): ratio1 = [] ratio2 = [] lefts = [] rights = [] for j in range(len(quantiles)-1): hist, bins = np.histogram(pdb.loc[pdb.quant==j, Y], bins=bins) hist = hist / hist.sum() left = len(pdb.loc[(pdb.quant==j)&(pdb[Y]<-threshold)]) / max(len(pdb.loc[(pdb.quant==j)]), 1) right = len(pdb.loc[(pdb.quant==j)&(pdb[Y]>threshold)]) / max(len(pdb.loc[(pdb.quant==j)]), 1) lefts.append(left) rights.append(right) ratio1.append((right - left)) ratio2.append(np.log2(right / left)) print(Y, j, left, right) xgrid = [sep*j+(i+1.0)*sep/3 for j in range(len(quantiles)-1)] ax[0].barh(xgrid, ratio1, sep/3, color=col[i], alpha=.5) ax[1].barh(xgrid, ratio2, sep/3, color=col[i], alpha=.5) ax[0].set_xticks(np.arange(-0.3, 0.4, 0.1)) for a in ax: a.set_yticks(np.arange(len(quantiles))*sep) a.set_yticklabels([round(x,1) for x in quantiles]) a.plot([0,0], [-0.05, 0.5], '-', c='k', lw=.1) a.spines['top'].set_visible(False) a.spines['right'].set_visible(False) a.set_ylim(0, 0.5) a.set_ylabel(r'$\log_{10}R$') ax[0].set_xlim(-0.35, 0.35) ax[1].set_xlim(-1.50, 1.50) ax[0].set_xlabel(r'$P(\mathregular{{asym}} \geq {0}) - P(\mathregular{{asym}} \leq -{0})$'.format(*[threshold]*2)) ax[1].set_xlabel(r'$\log_{{2}} \frac{{P(\mathregular{{asym}} \geq {0})}}{{P(\mathregular{{asym}} \leq -{0})}} $'.format(*[threshold]*2)) fig.savefig(PATH_FIG.joinpath("si12.pdf"), bbox_inches='tight') def fig13(df, X='AA_PDB', Y='CO', w=.1, ax='', fig=''): if isinstance(ax, str): fig, ax = plt.subplots(1,3, figsize=(15,4)) fig.subplots_adjust(wspace=0.5) q = np.arange(w,1+w,w) quant1 = [df[X].min()] + list(df[X].quantile(q).values) quant2 = [df[Y].min()] + list(df[Y].quantile(q).values) lbls = ['Helix', 'Sheet'] cb_lbl = [r"$asym_{\alpha}$", r"$asym_{\beta}$"] vmax = 0.03 vmin = -vmax count = [] for i, Z in enumerate(['H_ASYM', 'S_ASYM']): mean = [] for l1, h1 in zip(quant1[:-1], quant1[1:]): for l2, h2 in zip(quant2[:-1], quant2[1:]): samp = df.loc[(df[X]>=l1)&(df[X]<h1)&(df[Y]>=l2)&(df[Y]<h2), Z] mean.append(samp.mean()) # left = len(df.loc[(df[X]>=l1)&(df[X]<h1)&(df[Y]>=l2)&(df[Y]<h2)&(df[Z]<0)]) # right = len(df.loc[(df[X]>=l1)&(df[X]<h1)&(df[Y]>=l2)&(df[Y]<h2)&(df[Z]>0)]) # tot = max(len(df.loc[(df[X]>=l1)&(df[X]<h1)&(df[Y]>=l2)&(df[Y]<h2)]), 1) # mean.append((right - left)/tot) if not i: count.append(len(samp)) # print(len(samp)) mean = np.array(mean).reshape(q.size, q.size) count = np.array(count).reshape(q.size, q.size) cmap = sns.diverging_palette(230, 22, s=100, l=47, as_cmap=True) norm = colors.BoundaryNorm([vmin, vmax], cmap.N) bounds = np.linspace(vmin, vmax, 3) im = ax[i].imshow(mean.T, cmap=cmap, vmin=vmin, vmax=vmax) cbar = fig.colorbar(im, cmap=cmap, ticks=bounds, ax=ax[i], fraction=0.046, pad=0.04) cbar.set_label(cb_lbl[i], labelpad=-5) ax[i].set_title(lbls[i]) ax[i].set_xticks(np.arange(q.size+1)-0.5) ax[i].set_yticks(np.arange(q.size+1)-0.5) ax[i].set_xticklabels([int(x) for x in quant1], rotation=90) ax[i].set_yticklabels([int(round(x,0)) for x in quant2]) for i in [2]: cmap = plt.cm.Greys # norm = colors.BoundaryNorm([-.04, .04], cmap.N) # bounds = np.linspace(-.04, .04, 5) im = ax[i].imshow(np.array(count).reshape(q.size, q.size).T, cmap=cmap, vmin=0) cbar = fig.colorbar(im, cmap=cmap, ax=ax[i], fraction=0.046, pad=0.04) cbar.set_label('Count') ax[i].set_title('Distribution') ax[i].set_xticks(np.arange(q.size+1)-0.5) ax[i].set_yticks(np.arange(q.size+1)-0.5) ax[i].set_xticklabels([int(x) for x in quant1], rotation=90) ax[i].set_yticklabels([int(round(x,0)) for x in quant2]) for a in ax: a.invert_yaxis() a.set_xlabel('Sequence Length') a.set_ylabel('Contact Order') a.tick_params(axis='both', which='major', direction='in') fs = 14 for i, b in zip([0,1,2], list('ABCDEFGHI')): ax[i].text( -0.20, 1.05, b, transform=ax[i].transAxes, fontsize=fs) fig.savefig(PATH_FIG.joinpath("si13.pdf"), bbox_inches='tight') def scop_ss(): fig, ax = plt.subplots(2,1) cat = 'HS.D' N = 50 X = np.arange(50) Nboot, Cboot, asym, enrich_edges, enrich_vals = pickle.load(open(PATH_FIG_DATA.joinpath(f"pdb_scop_indep.pickle"), 'rb')) data = [Nboot, Cboot, asym] custom_cmap = sns.diverging_palette(230, 22, s=100, l=47, n=13) c_helix = custom_cmap[2] c_sheet = custom_cmap[10] col = [c_helix, c_sheet, "#CB7CE6", "#79C726"] lbls = ['Helix', 'Sheet', 'Coil', 'Disorder'] for j, s in enumerate(cat): ax[0].plot(X, data[0][s]['mean']/4, '-', c=col[j], label=f"{s} N") ax[0].fill_between(X, data[0][s]['hi']/4, data[0][s]['lo']/4, color="grey", label=f"{s} N", alpha=0.5) ax[0].plot(X, data[1][s]['mean']/4, '--', c=col[j], label=f"{s} N") ax[0].fill_between(X, data[1][s]['hi']/4, data[1][s]['lo']/4, color="grey", label=f"{s} N", alpha=0.2) print(s, round(np.mean(data[2][s]['mean']), 2), round(np.mean(data[2][s]['mean'][:20]), 2), round(np.mean(data[2][s]['mean'][20:]), 2)) ax[1].plot(X, np.log2(data[2][s]['mean']), '-', c=col[j], label=lbls[j]) ax[1].fill_between(X, np.log2(data[2][s]['hi']), np.log2(data[2][s]['lo']), color="grey", label=f"{s} N", alpha=0.2) ax[1].set_ylim(-1, 1.3) ax[1].plot([0]*50, '-', c='k') ax[1].set_yticks(np.arange(-1,1.5,0.5)) ax[0].set_ylim(0, 0.6) ax[1].set_xlabel('Sequence distance from ends') ax[0].set_ylabel('Secondary structure\nprobability') ax[1].set_ylabel('Structural asymmetry\n$\\log_2 (N / C)$') fs = 14 for i, b in zip([0,1], list('ABCDEFGHI')): ax[i].text( -0.10, 1.05, b, transform=ax[i].transAxes, fontsize=fs) fig.savefig(PATH_FIG.joinpath("si14.pdf"), bbox_inches='tight') def percentage_asym(x): return np.sign(x) * 100*2**(abs(x)) - np.sign(x) * 100 def fig15(): fig, ax = plt.subplots(3,1, figsize=(10,10)) cat = 'HS.D' N = 100 X = np.arange(N) Nboot, Cboot, asym, = pickle.load(open(PATH_FIG_DATA.joinpath(f"pdb_ss_max_asym.pickle"), 'rb')) data = [Nboot, Cboot, asym] custom_cmap = sns.diverging_palette(230, 22, s=100, l=47, n=13) c_helix = custom_cmap[2] c_sheet = custom_cmap[10] col = [c_helix, c_sheet, "#CB7CE6", "#79C726"] lbls = ['Helix', 'Sheet', 'Coil', 'Disorder'] X2 = np.arange(5) for j, s in enumerate(cat): ax[0].plot(X, data[0][s]['mean']/2, '-', c=col[j], label=f"{s} N") ax[0].fill_between(X, data[0][s]['hi']/2, data[0][s]['lo']/2, color="grey", label=f"{s} N", alpha=0.5) ax[0].plot(X, data[1][s]['mean']/2, '--', c=col[j], label=f"{s} N") ax[0].fill_between(X, data[1][s]['hi']/2, data[1][s]['lo']/2, color="grey", label=f"{s} N", alpha=0.2) for k in range(5): print(s, round(np.mean(data[2][s]['mean']), 2), round(np.mean(data[2][s]['mean'][k*20:(k+1)*20]), 2)) ax[1].plot(X, np.log2(data[2][s]['mean']), '-', c=col[j], label=lbls[j]) ax[1].fill_between(X, np.log2(data[2][s]['hi']), np.log2(data[2][s]['lo']), color="grey", label=f"{s} N", alpha=0.2) if s in 'HS': Y2 = [percentage_asym(np.log2(data[2][s]['mean'])[k*20:(k+1)*20].mean()) for k in range(5)] ax[2].bar(X2, Y2, 0.5, color=col[j], label=lbls[j], ec='k') ax[1].set_ylim(-1.5, 2.0) ax[1].plot([0]*100, '-', c='k') ax[2].plot([0]*5, '-', c='k') ax[1].set_yticks(np.arange(-1,2.5,0.5)) ax[0].set_ylim(0, 0.6) ax[2].set_xticks(np.arange(5)) ax[2].set_xticklabels([f"{i*20} - {(i+1)*20}" for i in range(5)]) ax[0].set_xlabel('Sequence distance from ends') ax[1].set_xlabel('Sequence distance from ends') ax[2].set_xlabel('Sequence distance from ends') ax[0].set_ylabel('Secondary structure\nprobability') ax[1].set_ylabel('Structural asymmetry\n$\\log_2 (N / C)$') ax[2].set_ylabel('Percentage asymmetry') fs = 14 for i, b in zip([0,1,2], list('ABCDEFGHI')): ax[i].text( -0.10, 1.05, b, transform=ax[i].transAxes, fontsize=fs) fig.savefig(PATH_FIG.joinpath("si15.pdf"), bbox_inches='tight') def oligomer(pdb, X='REL_RATE', Y='S_ASYM', w=0.1): pdb = pdb.copy() fig = plt.figure(figsize=(8,8)) gs = GridSpec(2,2, wspace=0.4, hspace=0.5, width_ratios=[1,1]) ax_all = [[fig.add_subplot(gs[j,i]) for i in [0,1]] for j in range(2)] custom_cmap = sns.diverging_palette(230, 22, s=100, l=47, n=13) c_helix = custom_cmap[2] c_sheet = custom_cmap[10] col = [c_helix, c_sheet] bins = np.linspace(-0.20, 0.20, 80) width = np.diff(bins[:2]) mid = 39 sep = 0.05 threshold = 0 lbls = [r'$E_{\beta}$', r'$E_{\alpha}$'] ttls = ['Monomers', 'Oligomers'] for ax, idx, ttl in zip(ax_all, [pdb.NPROT==1, pdb.NPROT>1], ttls): quantiles = pdb.loc[idx, X].quantile(np.arange(0,1+w,w)).values pdb['quant'] = pdb.loc[idx, X].apply(lambda x: utils.assign_quantile(x, quantiles)) for i, Y in enumerate(['S_ASYM', 'H_ASYM']): ratio1 = [] ratio2 = [] lefts = [] rights = [] for j in range(len(quantiles)-1): hist, bins = np.histogram(pdb.loc[(idx)&(pdb.quant==j), Y], bins=bins) hist = hist / hist.sum() left = len(pdb.loc[(idx)&(pdb.quant==j)&(pdb[Y]<-threshold)]) / max(len(pdb.loc[(idx)&(pdb.quant==j)]), 1) right = len(pdb.loc[(idx)&(pdb.quant==j)&(pdb[Y]>threshold)]) / max(len(pdb.loc[(idx)&(pdb.quant==j)]), 1) lefts.append(left) rights.append(right) ratio1.append((right - left)) ratio2.append(np.log2(right / left)) xgrid = [sep*j+(i+1.0)*sep/3 for j in range(len(quantiles)-1)] ax[0].barh(xgrid, ratio1, sep/3, color=col[i], alpha=.5, label=lbls[i]) ax[1].barh(xgrid, ratio2, sep/3, color=col[i], alpha=.5) ax[0].set_xticks(np.arange(-0.3, 0.4, 0.1)) for a in ax: a.set_yticks(np.arange(len(quantiles))*sep) a.set_yticklabels([round(x,1) for x in quantiles]) a.plot([0,0], [-0.05, 0.5], '-', c='k', lw=.1) a.spines['top'].set_visible(False) a.spines['right'].set_visible(False) a.set_ylim(0, 0.5) a.set_ylabel(r'$\log_{10}R$') a.set_title(f"{ttl}, N={np.sum(idx)}") ax[0].set_xlim(-0.35, 0.35) ax[1].set_xlim(-1.50, 1.50) ax[0].set_xlabel(r'$P(\mathregular{{asym}} \geq {0}) - P(\mathregular{{asym}} \leq -{0})$'.format(*[threshold]*2)) ax[1].set_xlabel(r'$\log_{{2}} \frac{{P(\mathregular{{asym}} \geq {0})}}{{P(\mathregular{{asym}} \leq -{0})}} $'.format(*[threshold]*2)) ax[0].legend(loc='upper center', ncol=2, columnspacing=3, frameon=False, bbox_to_anchor=(1.20, 1.20)) fig.savefig(PATH_FIG.joinpath("si16.pdf"), bbox_inches='tight') fig.savefig(PATH_FIG.joinpath("oligomers.png"), bbox_inches='tight') def scop2(X='REL_RATE', Y='S_ASYM', w=0.1): fig, ax = plt.subplots(figsize=(10,6)) edges, data = pickle.load(open(PATH_FIG_DATA.joinpath("pdb_scop_indep.pickle"), 'rb'))[3:] edges = edges[0] sep = 0.05 lbls = [r'$E_{\alpha}$', r'$E_{\beta}$'] for i, Y in enumerate(['H_ASYM', 'S_ASYM']): mean = np.mean(data[:,i], axis=0) lo = np.abs(mean - np.quantile(data[:,i], 0.025, axis=0)) hi = np.abs(mean - np.quantile(data[:,i], 0.975, axis=0)) ax.barh([sep*j+(i+.7)*sep/3 for j in range(10)], mean, sep/3, xerr=(lo, hi), color=col[i], ec='k', alpha=.5, label=lbls[i], error_kw={'lw':.8}) ax.plot([0,0], [-0.05, 0.5], '-', c='k', lw=.1) ax.set_yticks(np.arange(len(edges))*sep) ax.set_yticklabels([round(x,1) for x in edges]) ax.legend(loc='upper center', ncol=2, columnspacing=3, frameon=False, bbox_to_anchor=(0.52, 1.06)) ax.set_xlim(-.38, .38) ax.set_xticks(np.arange(-.3, .4, .1)) # To create this figure, you need to download the complete # Human and E. coli proteomes at: # https://alphafold.ebi.ac.uk/download # and then change the code so that "base" points to the # folder that contains the downloaded ".pdb" files def disorder_proteome(N=100): fig, ax = plt.subplots(1,2, figsize=(12,4)) lbls = ["Human", "Ecoli"] ttls = ["Human", "E. coli"] for i, l in enumerate(lbls): path = PATH_FIG_DATA.joinpath(f"alphafold_{l}.npy") if not path.exists(): base = PATH_BASE.joinpath(f"AlphaFold/{l}") countN = np.zeros(N, float) countC = np.zeros(N, float) tot = np.zeros(N, float) with Pool(50) as pool: dis = list(pool.imap_unordered(utils.get_disorder_from_conf, base.glob("*pdb"), 10)) for d in dis: n = min(int(len(d)/2), N) countN[:n] = countN[:n] + d[:n] countC[:n] = countC[:n] + d[-n:][::-1] tot[:n] = tot[:n] + 1 fracN = countN / tot fracC = countC / tot np.save(path, np.array([fracN, fracC])) else: fracN, fracC = np.load(path) ax[i].plot(np.arange(N)+1, fracN, '-', label='N') ax[i].plot(np.arange(N)+1, fracC, '--', label='C') ax[i].set_title(ttls[i]) ax[i].set_xlabel("Sequence distance from ends") ax[i].set_ylabel("Disorder probability") ax[i].set_ylim(0, 1) ax[i].legend(loc='best', frameon=False) fig.savefig(PATH_FIG.joinpath("si17.pdf"), bbox_inches='tight') def kfold_vs_ss(): pfdb = asym_io.load_pfdb() fig, ax = plt.subplots(figsize=(8,8)) for c in pfdb.Class.unique(): X = np.log10(pfdb.loc[pfdb.Class==c, 'L']) Y = pfdb.loc[pfdb.Class==c, 'log_kf'] sns.regplot(X, Y, label=c) ax.set_xlabel(r"$\log_{10}$ Sequence Length") ax.set_ylabel(r"$\log_{10} k_f$") ax.legend(loc='best', frameon=False) fig.savefig(PATH_FIG.joinpath("si18.pdf"), bbox_inches='tight') def hbond_asym(pdb, Xl='REL_RATE', Y='hb_asym', w=0.1): fig = plt.figure(figsize=(9,6)) gs = GridSpec(1,2, wspace=0.2, hspace=0.0, width_ratios=[1,.3]) ax = [fig.add_subplot(gs[i]) for i in [0,1]] col = np.array(Paired_12.hex_colors)[[1,3]] bins = np.linspace(-0.20, 0.20, 80) width = np.diff(bins[:2]) X = bins[:-1] + width * 0.5 mid = 39 sep = 0.05 quantiles = pdb[Xl].quantile(np.arange(0,1+w,w)).values ratio = [] lefts = [] rights = [] threshold = 0.00 for j in range(len(quantiles)-1): hist, bins = np.histogram(pdb.loc[pdb.quant==j, Y], bins=bins) hist = hist / hist.sum() left = len(pdb.loc[(pdb.quant==j)&(pdb[Y]<-threshold)]) / max(len(pdb.loc[(pdb.quant==j)]), 1) right = len(pdb.loc[(pdb.quant==j)&(pdb[Y]>threshold)]) / max(len(pdb.loc[(pdb.quant==j)]), 1) lefts.append(left) rights.append(right) ratio.append((right - left)) ax[0].bar(X[:mid], (hist/hist.sum())[:mid], width, bottom=[sep*j]*mid, color='grey', alpha=.5) ax[0].bar(X[-mid:], (hist/hist.sum())[-mid:], width, bottom=[sep*j]*mid, color=col[0], alpha=.5) ax[0].plot(X[:mid], (hist/hist.sum()+sep*j)[:mid], '-', c='k', alpha=.5) ax[0].plot(X[-mid:], (hist/hist.sum()+sep*j)[-mid:], '-', c='k', alpha=.5) ax[0].set_yticks(np.arange(len(quantiles))*sep) ax[0].set_yticklabels([round(x,1) for x in quantiles]) ax[1].barh([sep*j+sep/2 for j in range(len(quantiles)-1)], ratio, sep/2, color=[col[0] if r > 0 else 'grey' for r in ratio], alpha=.5) ax[1].plot([0,0], [-0.05, 0.5], '-', c='k', lw=.1) ax[0].spines['top'].set_visible(False) ax[0].spines['right'].set_visible(False) ax[1].spines['top'].set_visible(False) ax[1].spines['right'].set_visible(False) ax[1].spines['left'].set_visible(False) ax[1].set_yticks([]) for a in ax: a.set_ylim(0, 0.60) ax[0].set_xlabel('Asymmetry in mean hydrogen bond length') ax[0].set_ylabel(r'$\log_{10}R$') ax[1].set_xlabel('N terminal enrichment') fig.savefig(PATH_FIG.joinpath("si19.pdf"), bbox_inches='tight') def hyd_asym(pdb, Xl='REL_RATE', Y='hyd_asym', w=0.1): fig = plt.figure(figsize=(9,6)) gs = GridSpec(1,2, wspace=0.2, hspace=0.0, width_ratios=[1,.3]) ax = [fig.add_subplot(gs[i]) for i in [0,1]] col = np.array(Paired_12.hex_colors)[[1,3]] bins = np.linspace(-4.5, 4.5, 80) width = np.diff(bins[:2]) X = bins[:-1] + width * 0.5 mid = 39 sep = 0.05 quantiles = pdb[Xl].quantile(np.arange(0,1+w,w)).values ratio = [] lefts = [] rights = [] threshold = 0.00 for j in range(len(quantiles)-1): hist, bins = np.histogram(pdb.loc[pdb.quant==j, Y], bins=bins) hist = hist / hist.sum() left = len(pdb.loc[(pdb.quant==j)&(pdb[Y]<-threshold)]) / max(len(pdb.loc[(pdb.quant==j)]), 1) right = len(pdb.loc[(pdb.quant==j)&(pdb[Y]>threshold)]) / max(len(pdb.loc[(pdb.quant==j)]), 1) lefts.append(left) rights.append(right) ratio.append((right - left)) ax[0].bar(X[:mid], (hist/hist.sum())[:mid], width, bottom=[sep*j]*mid, color='grey', alpha=.5) ax[0].bar(X[-mid:], (hist/hist.sum())[-mid:], width, bottom=[sep*j]*mid, color=col[0], alpha=.5) ax[0].plot(X[:mid], (hist/hist.sum()+sep*j)[:mid], '-', c='k', alpha=.5) ax[0].plot(X[-mid:], (hist/hist.sum()+sep*j)[-mid:], '-', c='k', alpha=.5) ax[0].set_yticks(np.arange(len(quantiles))*sep) ax[0].set_yticklabels([round(x,1) for x in quantiles]) ax[1].barh([sep*j+sep/2 for j in range(len(quantiles)-1)], ratio, sep/2, color=[col[0] if r > 0 else 'grey' for r in ratio], alpha=.5) ax[1].plot([0,0], [-0.05, 0.5], '-', c='k', lw=.1) ax[0].spines['top'].set_visible(False) ax[0].spines['right'].set_visible(False) ax[1].spines['top'].set_visible(False) ax[1].spines['right'].set_visible(False) ax[1].spines['left'].set_visible(False) ax[1].set_yticks([]) for a in ax: a.set_ylim(0, 0.60) ax[0].set_xlabel('Asymmetry in mean hydrophobicity') ax[0].set_ylabel(r'$\log_{10}R$') ax[1].set_xlabel('N terminal enrichment') fig.savefig(PATH_FIG.joinpath("si20.pdf"), bbox_inches='tight')

42.085463

162

0.557723

8,901

52,691

3.21065

0.073475

0.008713

0.007768

0.012492

0.698194

0.622227

0.570929

0.537756

0.493317

0.460004

0

0.055077

0.209523

52,691

1,251

163

42.119105

0.631053

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0.397089

0

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0.014935

0

1

0.030146

false

0

0.034304

0.00104

0.066528

0.02183

0

null

0

null

0

1

0

327857254668f20b13612c825f93043e95b1c5c9

3,449

py

Python

test_beam_search.py

slegroux/slgBeam

733049ad4a97f582bc169623941cfbdf3efea207

[ "Apache-2.0" ]

null

test_beam_search.py

slegroux/slgBeam

733049ad4a97f582bc169623941cfbdf3efea207

[ "Apache-2.0" ]

null

test_beam_search.py

slegroux/slgBeam

733049ad4a97f582bc169623941cfbdf3efea207

[ "Apache-2.0" ]

null

#!/usr/bin/env python # (c) 2020 Sylvain Le Groux <slegroux@ccrma.stanford.edu> import pytest from pytest import approx import numpy as np import torch from IPython import embed from beam_search import Tokenizer, Score, BeamSearch @pytest.fixture(scope='module') def data(): mat = torch.Tensor(np.genfromtxt('data/rnnOutput.csv',delimiter=';')[:,: -1]) # mat = mat.unsqueeze(0) classes = ' !"#&\'()*+,-./0123456789:;?ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz_' mat_prob = np.array([[0.2, 0.0, 0.8], [0.4, 0.0, 0.6]]) syms = 'ab-' bs = BeamSearch(syms, mat_prob) bs2 = BeamSearch(classes, mat) data = {'probs': mat_prob, 'syms': syms, 'bs': bs, 'mat': mat, 'classes': classes, 'bs2': bs2} return(data) def test_data(data): assert data['probs'].shape == (2,3) assert data['mat'].shape == (100, 80) assert len(data['classes']) == 79 def test_tokenizer(data): tok = Tokenizer(data['syms']) assert(tok.char2int('b') == 1) assert(tok.int2char(1) == 'b') tok2 = Tokenizer(data['classes']) assert(tok2.char2int('Y') == 51) assert(tok2.int2char(51) == 'Y') def test_score(data): tok = Tokenizer(data['syms']) score = Score(tok, data['probs']) assert score(1,'-') == 0.6 tok2 = Tokenizer(data['classes']) score = Score(tok2, data['mat']) assert float(score(0,' ')) == approx(float(0.946499)) def test_init(data): beam_search = data['bs'] b, nb, s_b, s_nb = beam_search.init_paths() assert b == {''} assert nb == {'a', 'b'} assert s_b == {'-': 0.8} assert s_nb == {'b': 0.0, 'a': 0.2} bs2 = data['bs2'] b, nb, s_b, s_nb = bs2.init_paths() assert b == {''} # assert s_b == {'-': 0.8} def test_prune(data): bs = data['bs'] path_b, path_nb = bs.prune_paths({''}, {'a','b'}, {'-':0.2}, {'a': 0.1,'b': 0.3}, 2) assert path_b == {''} assert path_nb == {'b'} print(bs.score_b, bs.score_nb) def test_extend_blank(data): bs = data['bs'] init_b, init_nb, init_s_b, init_s_nb = bs.init_paths() print("init:", init_b, init_nb, init_s_b, init_s_nb) # incidentally init global b & nb paths path_b, path_nb = bs.prune_paths(init_b, init_nb,init_s_b, init_s_nb, 2) print("Pruned: ", path_b, path_nb) print(bs.score_b, bs.score_nb) new_path_b, new_score_b = bs.extend_with_blank(path_b, path_nb, 1) print(new_path_b, new_score_b) def test_extend_syms(data): bs = data['bs'] init_b, init_nb, init_s_b, init_s_nb = bs.init_paths() print("init:", init_b, init_nb, init_s_b, init_s_nb) # incidentally init global b & nb paths path_b, path_nb = bs.prune_paths(init_b, init_nb,init_s_b, init_s_nb, 2) print("Pruned: ", path_b, path_nb) print(bs.score_b, bs.score_nb) new_path_nb, new_score_nb = bs.extend_with_symbol(path_b, path_nb, 1) print(new_path_nb, new_score_nb) def test_merge(data): bs = data['bs'] init_b, init_nb, init_s_b, init_s_nb = bs.init_paths() path_b, path_nb = bs.prune_paths(init_b, init_nb,init_s_b, init_s_nb, 2) new_path_b, new_score_b = bs.extend_with_blank(path_b, path_nb, 1) new_path_nb, new_score_nb = bs.extend_with_symbol(path_b, path_nb, 1) bs.merge_paths(new_path_b, new_path_nb, new_score_b, new_score_nb) def test_decode(data): bs = data['bs'] print("decoded: ", bs.decode(2)) bs2 = data['bs2'] print("decoded: ", bs2.decode(1))

33.813725

98

0.632647

573

3,449

3.544503

0.162304

0.039389

0.044313

0.054161

0.464796

0.402757

0.373215

0.351059

0.339242

0

0.034446

0.19194

3,449

101

99

34.148515

0.694295

0.057988

0

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0

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0

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1

0.123457

false

0

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0.197531

0.135802

0

null

0

null

0

1

0

327872875221fcfb18f1db81613c4a83884de390

3,404

py

Python

src/main/python/hydra/kafkatest/maxrate_test.py

bopopescu/hydra

ec0793f8c1f49ceb93bf1f1a9789085b68d55f08

[ "Apache-2.0" ]

10

2016-05-28T15:56:43.000Z

2018-01-03T21:30:58.000Z

src/main/python/hydra/kafkatest/maxrate_test.py

bopopescu/hydra

ec0793f8c1f49ceb93bf1f1a9789085b68d55f08

[ "Apache-2.0" ]

17

2016-06-06T22:15:28.000Z

2020-07-22T20:28:12.000Z

src/main/python/hydra/kafkatest/maxrate_test.py

bopopescu/hydra

ec0793f8c1f49ceb93bf1f1a9789085b68d55f08

[ "Apache-2.0" ]

5

2016-06-01T22:01:44.000Z

2020-07-22T20:12:49.000Z

__author__ = 'annyz' from pprint import pprint, pformat # NOQA import logging import os import sys from datetime import datetime from hydra.lib import util from hydra.kafkatest.runtest import RunTestKAFKA from hydra.lib.boundary import Scanner from optparse import OptionParser l = util.createlogger('runSuitMaxRate', logging.INFO) class RunSuitMaxRate(object): def __init__(self, options): l.info(" Starting Max Rate ....") pwd = os.getcwd() fname = 'kafkasuit.test.log' ofile = open(pwd + '/' + fname, 'w') ofile.truncate() ofile.write('Starting at :' + datetime.now().strftime("%Y-%m-%d %H:%M:%S") + '\n') # setattr(options, 'test_duration', 15) setattr(options, 'msg_batch', 100) setattr(options, 'msg_rate', 10000) setattr(options, 'keep_running', False) setattr(options, 'acks', 0) setattr(options, 'linger_ms', 0) setattr(options, 'consumer_max_buffer_size', 0) self.first_test = None # Parameters client_set = [30, 60, 120, 240, 480, 960, 1920, 3840, 7680, 10000] for client_count in client_set: setattr(options, 'total_sub_apps', int(client_count / 10)) if not self.first_test: runner = RunTestKAFKA(options, None) self.first_test = runner self.first_test.start_appserver() else: # Keep the old runner # But rescale the app runner.set_options(options) runner.scale_sub_app() if client_count < 50: scanner = Scanner(runner.run, 30000) elif client_count < 200: scanner = Scanner(runner.run, 10000) else: scanner = Scanner(runner.run, 500) (status, rate, drop) = scanner.find_max_rate() l.info("Found for Client Count %d Max message Rate %d with drop %f" % (client_count, rate, drop)) maxrate_drop = drop maxrate_rate = rate if True and maxrate_drop != 0: l.info("Searching for no-drop rate") scanner_drop = Scanner(runner.run, maxrate_rate / 2) (status, step_cnt, nodrop, nodrop_rate) = scanner_drop.search(0.5, 0.01) l.info("Found for Client Count %d Max message Rate %d with no drop (%f)" % (client_count, nodrop_rate, nodrop)) else: nodrop_rate = rate # Delete all launched apps once the required drop is achieved for this set runner.delete_all_launched_apps() self.first_test.stop_appserver() l.info("TestSuite Completed.") sys.exit(0) def Run(argv): # NOQA usage = ('python %prog --c_pub --c_sub' ' --test_duration=<time to run test> --msg_batch=<msg burst batch before sleep>') parser = OptionParser(description='kafka scale maxrate test master', version="0.1", usage=usage) parser.add_option("--test_duration", dest='test_duration', type='int', default=15) parser.add_option("--msg_batch", dest='msg_batch', type='int', default=100) parser.add_option("--config_file", dest='config_file', type='string', default='hydra.ini') (options, args) = parser.parse_args() RunSuitMaxRate(options) return True

38.247191

94

0.595476

414

3,404

4.73913

0.413043

0.057085

0.033129

0.035168

0.044852

0

0.033638

0.292597

3,404

88

95

38.681818

0.781146

0.050235

0

0.042857

0

0.177985

0.013953

0

1

0.028571

false

0

0.128571

0

0.185714

0.014286

0

null

0

null

0

1

0

327a4fc033970cf2fec138ab6d2ea6fa9e580d97

1,574

py

Python

map_report.py

porcpine1967/aoe2stats

52965e437b8471753186ba1fc34cb773807eb496

[ "MIT" ]

null

map_report.py

porcpine1967/aoe2stats

52965e437b8471753186ba1fc34cb773807eb496

[ "MIT" ]

null

map_report.py

porcpine1967/aoe2stats

52965e437b8471753186ba1fc34cb773807eb496

[ "MIT" ]

null

#!/usr/bin/env python """ Writes out map popularity of last two pools.""" from datetime import datetime, timedelta from utils.map_pools import map_type_filter, pools from utils.tools import execute_sql, last_time_breakpoint, map_name_lookup SQL = """SELECT map_type, COUNT(*) as cnt FROM matches WHERE started BETWEEN {:0.0f} AND {:0.0f} {} AND team_size = {} GROUP BY map_type ORDER BY cnt DESC""" def run(): """ Run the report.""" map_names = map_name_lookup() weeks = pools()[-2:] for size in (1, 2): print("TEAM" if size > 1 else "1v1") week_infos = [] for idx, week in enumerate(weeks): week_info = [] year = int(week[:4]) month = int(week[4:6]) day = int(week[6:]) start = last_time_breakpoint(datetime(year, month, day)) end = start + timedelta(days=14) sql = SQL.format( start.timestamp(), end.timestamp(), map_type_filter(week, size), size ) total = 0 for map_type, count in execute_sql(sql): week_info.append((map_names[map_type], count,)) total += count hold = [] for name, count in week_info: hold.append("{:17}: {:4.1f}%".format(name, 100.0 * count / total)) week_infos.append(hold) print("{:^24} {:^24}".format(*weeks)) for idx in range(len(week_infos[0])): print("{} {}".format(week_infos[0][idx], week_infos[1][idx])) if __name__ == "__main__": run()

32.122449

85

0.560991

209

1,574

4.043062

0.401914

0.049704

0.042604

0

0.028907

0.296696

1,574

48

86

32.791667

0.734417

0.051461

0

0.136486

0

1

0.025641

false

0

0.076923

0

0.102564

0.076923

0

null

0

null

0

1

0

327cb6d4121abb0fa5a0265759fdf829da140dce

6,303

py

Python

tempdb/postgres.py

runfalk/tempdb

a19f7568db1795025c9ec8adfd84a9544f9a6966

[ "MIT" ]

2

2021-01-17T00:01:14.000Z

2021-01-18T09:26:56.000Z

tempdb/postgres.py

runfalk/tempdb

a19f7568db1795025c9ec8adfd84a9544f9a6966

[ "MIT" ]

null

tempdb/postgres.py

runfalk/tempdb

a19f7568db1795025c9ec8adfd84a9544f9a6966

[ "MIT" ]

null

import getpass import os import platform import psycopg2 import sys import tempfile from glob import glob from psycopg2.extensions import ISOLATION_LEVEL_AUTOCOMMIT, quote_ident from subprocess import check_output, PIPE, Popen from time import sleep from ._compat import ustr from .utils import is_executable, Uri, Version __all__ = [ "PostgresFactory", "PostgresCluster", ] class PostgresFactory(object): def __init__(self, pg_bin_dir, superuser=None): # Temporary value until the first time we request it self._version = None self.initdb = os.path.join(pg_bin_dir, "initdb") if not is_executable(self.initdb): raise ValueError( "Unable to find initdb command in {}".format(pg_bin_dir) ) self.postgres = os.path.join(pg_bin_dir, "postgres") if not is_executable(self.postgres): raise ValueError( "Unable to find postgres command in {}".format(pg_bin_dir) ) if superuser is None: superuser = getpass.getuser() self.superuser = superuser @property def version(self): if self._version is None: self._version = get_version(self.postgres) return self._version def init_cluster(self, data_dir=None): """ Create a postgres cluster that trusts all incoming connections. This is great for testing, but a horrible idea for production usage. :param data_dir: Directory to create cluster in. This directory will be automatically created if necessary. :return: Path to the created cluster that can be used by load_cluster() """ if data_dir is None: data_dir = tempfile.mkdtemp() # If the target directory is not empty we don't want to risk wiping it if os.listdir(data_dir): raise ValueError(( "The given data directory {} is not empty. A new cluster will " "not be created." ).format(data_dir)) check_output([ self.initdb, "-U", self.superuser, "-A", "trust", data_dir ]) return data_dir def create_temporary_cluster(self): data_dir = self.init_cluster() # Since we know this database should never be loaded again we disable # safe guards Postgres has to prevent data corruption return self.load_cluster( data_dir, is_temporary=True, fsync=False, full_page_writes=False, ) def load_cluster(self, data_dir, is_temporary=False, **params): uri = Uri( scheme="postgresql", user=self.superuser, host=data_dir, params=params, ) return PostgresCluster(self.postgres, uri, is_temporary) class PostgresCluster(object): def __init__(self, postgres_bin, uri, is_temporary=False): if uri.host is None or not uri.host.startswith("/"): msg = "{!r} doesn't point to a UNIX socket directory" raise ValueError(msg.format(uri)) self.uri = uri self.is_temporary = is_temporary self.returncode = None cmd = [ postgres_bin, "-D", uri.host, "-k", uri.host, "-c", "listen_addresses=", ] # Add additional configuration from kwargs for k, v in uri.params.items(): if isinstance(v, bool): v = "on" if v else "off" cmd.extend(["-c", "{}={}".format(k, v)]) # Start cluster self.process = Popen( cmd, stdout=PIPE, stderr=PIPE, ) # Wait for a ".s.PGSQL.<id>" file to appear before continuing while not glob(os.path.join(uri.host, ".s.PGSQL.*")): sleep(0.1) # Superuser connection self.conn = psycopg2.connect( ustr(self.uri.replace(database="postgres")) ) self.conn.set_isolation_level(ISOLATION_LEVEL_AUTOCOMMIT) def __del__(self): self.close() def iter_databases(self): with self.conn.cursor() as c: default_databases = {"postgres", "template0", "template1"} c.execute("SELECT datname FROM pg_database") for name, in c: if name not in default_databases: yield name def create_database(self, name, template=None): if name in self.iter_databases(): raise KeyError("The database {!r} already exists".format(name)) with self.conn.cursor() as c: sql = "CREATE DATABASE {}".format(quote_ident(name, c)) if template is not None: sql += " TEMPLATE {}".format(quote_ident(template, c)) c.execute(sql) return PostgresDatabase(self, self.uri.replace(database=name)) def get_database(self, name): if name not in self.iter_databases(): raise KeyError("The database {!r} doesn't exist".format(name)) return PostgresDatabase(self, self.uri.replace(database=name)) def close(self): if self.process is None: return # Kill all connections but this control connection. This prevents # the server waiting for connections to close indefinately with self.conn.cursor() as c: c.execute(""" SELECT pg_terminate_backend(pid) FROM pg_stat_activity WHERE pid != pg_backend_pid() """) self.conn.close() self.process.terminate() self.returncode = self.process.wait() # Remove temporary clusters when closing if self.is_temporary: for path, dirs, files in os.walk(self.uri.host, topdown=False): for f in files: os.remove(os.path.join(path, f)) for d in dirs: os.rmdir(os.path.join(path, d)) os.rmdir(self.uri.host) self.process = None class PostgresDatabase(object): def __init__(self, cluster, uri): self.cluster = cluster self.uri = uri @property def dsn(self): return ustr(self.uri)

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0.58369

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6,303

4.778667

0.3

0.023438

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0.095703

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0.001645

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6,303

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0.004656

0

1

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0.013699

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0.006849

0.246575

0

null

0

null

0

1

0

327ee9780e46ebbfd9024596b22934ad7011175f

426

py

Python

nymph/modules/tool.py

smilelight/nymph

c8da2211f7a8f58d1c6d327b243e419ed9e64ead

[ "Apache-2.0" ]

1

2020-08-10T00:58:14.000Z

nymph/modules/tool.py

smilelight/nymph

c8da2211f7a8f58d1c6d327b243e419ed9e64ead

[ "Apache-2.0" ]

null

nymph/modules/tool.py

smilelight/nymph

c8da2211f7a8f58d1c6d327b243e419ed9e64ead

[ "Apache-2.0" ]

1

2021-07-03T07:06:41.000Z

# -*- coding: utf-8 -*- import pandas as pd def save_dict_to_csv(dict_data: dict, csv_path: str): indexes = list(dict_data.keys()) columns = list(list(dict_data.values())[0].keys()) data = [] for row in dict_data: data.append([item for item in dict_data[row].values()]) pd_data = pd.DataFrame(data, index=indexes, columns=columns) pd_data.to_csv(csv_path, encoding='utf8') return pd_data

30.428571

64

0.666667

67

426

4.029851

0.462687

0.148148

0.088889

0

0.008646

0.185446

426

13

65

32.769231

0.769452

0.049296

0

0.009926

0

1

0.1

false

0

0.1

0

0.3

0

null

0

null

0

1

0

328065cc7a0c80c52a732c0213b03b1281db7d57

1,035

py

Python

Python/rockpaperscissors/rockpaperscissors.py

rvrheenen/OpenKattis

7fd59fcb54e86cdf10f56c580c218c62e584f391

[ "MIT" ]

12

2016-10-03T20:43:43.000Z

2021-06-12T17:18:42.000Z

Python/rockpaperscissors/rockpaperscissors.py

rvrheenen/OpenKattis

7fd59fcb54e86cdf10f56c580c218c62e584f391

[ "MIT" ]

null

Python/rockpaperscissors/rockpaperscissors.py

rvrheenen/OpenKattis

7fd59fcb54e86cdf10f56c580c218c62e584f391

[ "MIT" ]

10

2017-11-14T19:56:37.000Z

2021-02-02T07:39:57.000Z

# WORKS BUT ISN'T FAST ENOUGH first_run = True while(True): inp = input().split() if len(inp) == 1: break if first_run: first_run = False else: print() nPlayers, nGames = [int(x) for x in inp] resultsW = [0] * nPlayers resultsL = [0] * nPlayers for i in range( int( ((nGames*nPlayers)*(nPlayers - 1)) / 2 ) ): p1, p1move, p2, p2move = [int(x) if x.isdigit() else x for x in input().split()] if p1move == p2move: continue if (p1move == "scissors" and p2move == "paper") or (p1move == "paper" and p2move == "rock") or (p1move == "rock" and p2move == "scissors"): resultsW[p1-1] += 1 resultsL[p2-1] += 1 else: resultsW[p2-1] += 1 resultsL[p1-1] += 1 for i in range(nPlayers): w_plus_l = resultsL[i] + resultsW[i] if w_plus_l == 0: print("-") else: print("%.3f" % (resultsL[i] / w_plus_l)) print("\n\n\n\n\n\n\n") print(resultsW)

32.34375

147

0.510145

142

1,035

3.65493

0.338028

0.023121

0.028902

0.030829

0.013487

0

0.044733

0.330435

1,035

32

148

32.34375

0.704185

0.026087

0

0.1

0

0.052632

0

1

0

false

0

0.166667

0

null

0

null

0

1

0

3280c700cb467b6fd44a96a8f003a083cb2e0a5f

9,460

py

Python

monitorcontrol/monitor_control.py

klwlau/monitorcontrol

92d07c7a93585de14551ba1f1dd8bb3a009c4842

[ "MIT" ]

null

monitorcontrol/monitor_control.py

klwlau/monitorcontrol

92d07c7a93585de14551ba1f1dd8bb3a009c4842

[ "MIT" ]

null

monitorcontrol/monitor_control.py

klwlau/monitorcontrol

92d07c7a93585de14551ba1f1dd8bb3a009c4842

[ "MIT" ]

null

############################################################################### # Copyright 2019 Alex M. # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. ############################################################################### from . import vcp import sys from typing import Type, List, Union, Iterable class Monitor: """ A physical monitor attached to a Virtual Control Panel (VCP). Generated with :py:meth:`get_monitors()` or :py:meth:`iterate_monitors()`. Args: vcp: virtual control panel for the monitor """ #: Power modes and their integer values. POWER_MODES = { "on": 0x01, "standby": 0x02, "suspend": 0x03, "off_soft": 0x04, "off_hard": 0x05, } def __init__(self, vcp: Type[vcp.VCP]): self.vcp = vcp self.code_maximum = {} def __enter__(self): self.open() return self def __exit__(self, exc_type, exc_val, exc_tb): self.close() def open(self): """ Opens the connection to the VCP. Raises: VCPError: failed to open VCP """ self.vcp.open() def close(self): """ Closes the connection to the VCP. Raises: VCPError: failed to close VCP """ self.vcp.close() def _get_code_maximum(self, code: Type[vcp.VCPCode]) -> int: """ Gets the maximum values for a given code, and caches in the class dictionary if not already found. Args: code: feature code definition class Returns: maximum value for the given code Raises: TypeError: code is write only """ if not code.readable: raise TypeError(f"code is not readable: {code.name}") if code.value in self.code_maximum: return self.code_maximum[code.value] else: _, maximum = self.vcp.get_vcp_feature(code.value) self.code_maximum[code.value] = maximum return maximum def _set_vcp_feature(self, code: Type[vcp.VCPCode], value: int): """ Sets the value of a feature on the virtual control panel. Args: code: feature code definition class value: feature value Raises: TypeError: code is ready only ValueError: value is greater than the maximum allowable VCPError: failed to get VCP feature """ if code.type == "ro": raise TypeError(f"cannot write read-only code: {code.name}") elif code.type == "rw": maximum = self._get_code_maximum(code) if value > maximum: raise ValueError( f"value of {value} exceeds code maximum of {maximum}" ) self.vcp.set_vcp_feature(code.value, value) def _get_vcp_feature(self, code: Type[vcp.VCPCode]) -> int: """ Gets the value of a feature from the virtual control panel. Args: code: feature code definition class Returns: current feature value Raises: TypeError: code is write only VCPError: failed to get VCP feature """ if code.type == "wo": raise TypeError(f"cannot read write-only code: {code.name}") current, maximum = self.vcp.get_vcp_feature(code.value) return current @property def luminance(self) -> int: """ Gets the monitors back-light luminance. Returns: current luminance value Raises: VCPError: failed to get luminance from the VCP """ code = vcp.get_vcp_code_definition("image_luminance") return self._get_vcp_feature(code) @luminance.setter def luminance(self, value: int): """ Sets the monitors back-light luminance. Args: value: new luminance value (typically 0-100) Raises:##### have not implemented or checked ValueError: luminance outside of valid range VCPError: failed to set luminance in the VCP """ code = vcp.get_vcp_code_definition("image_luminance") self._set_vcp_feature(code, value) @property def contrast(self) -> int: """ Gets the monitors contrast. Returns: current contrast value Raises: VCPError: failed to get contrast from the VCP """ code = vcp.get_vcp_code_definition("image_contrast") return self._get_vcp_feature(code) @contrast.setter def contrast(self, value: int): """ Sets the monitors back-light contrast. Args: value: new contrast value (typically 0-100) Raises: ValueError: contrast outside of valid range VCPError: failed to set contrast in the VCP """ code = vcp.get_vcp_code_definition("image_contrast") self._set_vcp_feature(code, value) @property def power_mode(self) -> int: """ The monitor power mode. When used as a getter this returns the integer value of the monitor power mode. When used as a setter an integer value or a power mode string from :py:attr:`Monitor.POWER_MODES` may be used. Raises: VCPError: failed to get or set the power mode ValueError: set power state outside of valid range KeyError: set power mode string is invalid """ code = vcp.get_vcp_code_definition("display_power_mode") return self._get_vcp_feature(code) @power_mode.setter def power_mode(self, value: Union[int, str]): if isinstance(value, str): mode_value = Monitor.POWER_MODES[value] elif isinstance(value, int): mode_value = value else: raise TypeError("unsupported mode type: " + repr(type(value))) if mode_value not in Monitor.POWER_MODES.values(): raise ValueError(f"cannot set reserved mode value: {mode_value}") code = vcp.get_vcp_code_definition("display_power_mode") self._set_vcp_feature(code, mode_value) def get_vcps() -> List[Type[vcp.VCP]]: """ Discovers virtual control panels. This function should not be used directly in most cases, use :py:meth:`get_monitors()` or :py:meth:`iterate_monitors()` to get monitors with VCPs. Returns: List of VCPs in a closed state. Raises: NotImplementedError: not implemented for your operating system VCPError: failed to list VCPs """ if sys.platform == "win32" or sys.platform.startswith("linux"): return vcp.get_vcps() else: raise NotImplementedError(f"not implemented for {sys.platform}") def get_monitors() -> List[Monitor]: """ Creates a list of all monitors. Returns: List of monitors in a closed state. Raises: NotImplementedError: not implemented for your operating system VCPError: failed to list VCPs Example: Setting the power mode of all monitors to standby:: for monitor in get_monitors(): try: monitor.open() # put monitor in standby mode monitor.power_mode = "standby" except VCPError: print("uh-oh") raise finally: monitor.close() Setting all monitors to the maximum brightness using the context manager:: for monitor in get_monitors(): with monitor as m: # set back-light luminance to 100% m.luminance = 100 """ return [Monitor(v) for v in get_vcps()] def iterate_monitors() -> Iterable[Monitor]: """ Iterates through all monitors, opening and closing the VCP for each monitor. Yields: Monitor in an open state. Raises: NotImplementedError: not implemented for this platform VCPError: failed to list VCPs Example: Setting all monitors to the maximum brightness:: for monitor in iterate_monitors(): monitor.luminance = 100 """ for v in get_vcps(): monitor = Monitor(v) with monitor: yield monitor

30.031746

79

0.595455

1,137

9,460

4.85752

0.226913

0.015209

0.034764

0.014123

0.380952

0.319392

0.266341

0.227413

0.151729

0.091979

0

0.005852

0.313531

9,460

314

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30.127389

0.844626

0.502537

0

0.186813

0

0.111233

0

0.005479

0

1

0.186813

false

0

0.032967

0

0.340659

0

null

0

null

0

1

0

328135201e01cdb2208c77c5703c4b619db0d327

6,201

py

Python

algorithms/vae.py

ENSP-AI-Mentoring/machine-learning-algorithms

d53d5342f79d08066e158228cab6240872f61f72

[ "Apache-2.0" ]

1

2021-11-14T19:46:46.000Z

algorithms/vae.py

ENSP-AI-Mentoring/machine-learning-algorithms

d53d5342f79d08066e158228cab6240872f61f72

[ "Apache-2.0" ]

null

algorithms/vae.py

ENSP-AI-Mentoring/machine-learning-algorithms

d53d5342f79d08066e158228cab6240872f61f72

[ "Apache-2.0" ]

null

import numpy as np import torch from torch.optim import Adam from torch.utils.data import DataLoader from tqdm import tqdm class VAE: def __init__( self, train_data, test_data, in_dim, encoder_width, decoder_width, latent_dim, device=None, ): # device self.name = "VAE" if device is None: device = torch.device("cuda") if device else torch.device("cpu") self.device = device self.latent_dim = latent_dim self.encoder_width = encoder_width self.decoder_width = decoder_width self.in_dim = in_dim # initialize encoder/decoder weights and biases self.weights, self.biases = self.init_vae_params( in_dim, encoder_width, decoder_width, latent_dim ) # config dataset self.train_data = train_data data = next(iter(train_data)) self.example_size = data.size() self.test_data = test_data def train(self, batch_size, max_epoch, lr, weight_decay): optimizer = self._get_optimizer(lr, weight_decay) hist_loss = [] train_dataloader = DataLoader( self.train_data, batch_size, shuffle=True, drop_last=True, num_workers=0 ) # print initial loss data = next(iter(train_dataloader)) Xground = data.view((batch_size, -1)).to(self.device) loss = self._vae_loss(Xground) tk = tqdm(range(max_epoch)) for epoch in tk: for ii, data in enumerate(train_dataloader): Xground = data.view((batch_size, -1)).to(self.device) optimizer.zero_grad() loss = self._vae_loss(Xground) # backward propagate loss.backward() optimizer.step() hist_loss.append(loss.item()) tk.set_postfix({"val_loss": hist_loss[-1], "epoch": epoch}) return np.array(hist_loss) def test1(self, batch_size): """data reconstruction test""" test_dataloader = DataLoader( self.test_data, batch_size, shuffle=True, drop_last=True, num_workers=0 ) data = next(iter(test_dataloader)) Xground = data.view((batch_size, -1)).to(self.device) z_mean, z_logstd = self._encoding(Xground) epsi = torch.randn(z_logstd.size()).to(self.device) z_star = z_mean + torch.exp(0.5 * z_logstd) * epsi Xstar = self._decoding(z_star) Xstar = torch.sigmoid(Xstar) Xstar = Xstar.view(data.size()) return data, Xstar def test2(self, batch_size): """distribution transformation test(generate artificial dataset from random noises)""" Z = torch.randn((batch_size, self.latent_dim)).to(self.device) Xstar = self._decoding(Z).view((-1, *self.example_size)) return Xstar def _vae_loss(self, Xground): """compute VAE loss = kl_loss + likelihood_loss""" # KL loss z_mean, z_logstd = self._encoding(Xground) kl_loss = 0.5 * torch.sum( 1 + z_logstd - z_mean ** 2 - torch.exp(z_logstd), dim=1 ) # likelihood loss epsi = torch.randn(z_logstd.size()).to(self.device) z_star = z_mean + torch.exp(0.5 * z_logstd) * epsi # reparameterize trick Xstar = self._decoding(z_star) llh_loss = Xground * torch.log(1e-12 + Xstar) + (1 - Xground) * torch.log( 1e-12 + 1 - Xstar ) llh_loss = torch.sum(llh_loss, dim=1) var_loss = -torch.mean(kl_loss + llh_loss) return var_loss def _get_optimizer(self, lr, weight_decay): opt_params = [] # adding weights to optimization paramters list for k, v in self.weights.items(): opt_params.append({"params": v, "lr": lr}) # adding biases to optimization parameters list for k, v in self.biases.items(): opt_params.append({"params": v, "lr": lr}) return Adam(opt_params, lr=lr, weight_decay=weight_decay) def _encoding(self, X): # Kingma Supplemtary C.2 output = ( torch.matmul(X, self.weights["encoder_hidden_w"]) + self.biases["encoder_hidden_b"] ) output = torch.tanh(output) mean_output = ( torch.matmul(output, self.weights["latent_mean_w"]) + self.biases["latent_mean_b"] ) logstd_output = ( torch.matmul(output, self.weights["latent_std_w"]) + self.biases["latent_std_b"] ) return mean_output, logstd_output def _decoding(self, Z): output = ( torch.matmul(Z, self.weights["decoder_hidden_w"]) + self.biases["decoder_hidden_b"] ) output = torch.tanh(output) Xstar = ( torch.matmul(output, self.weights["decoder_out_w"]) + self.biases["decoder_out_b"] ) Xstar = torch.sigmoid(Xstar) return Xstar def init_vae_params(self, in_dim, encoder_width, decoder_width, latent_dim): weights = { "encoder_hidden_w": self.xavier_init(in_dim, encoder_width), "latent_mean_w": self.xavier_init(encoder_width, latent_dim), "latent_std_w": self.xavier_init(encoder_width, latent_dim), "decoder_hidden_w": self.xavier_init(latent_dim, decoder_width), "decoder_out_w": self.xavier_init(decoder_width, in_dim), } biases = { "encoder_hidden_b": self.xavier_init(1, encoder_width), "latent_mean_b": self.xavier_init(1, latent_dim), "latent_std_b": self.xavier_init(1, latent_dim), "decoder_hidden_b": self.xavier_init(1, decoder_width), "decoder_out_b": self.xavier_init(1, in_dim), } return weights, biases def xavier_init(self, in_d, out_d): xavier_stddev = np.sqrt(2.0 / (in_d + out_d)) W = torch.normal( size=(in_d, out_d), mean=0.0, std=xavier_stddev, requires_grad=True, device=self.device, ) return W

31.8

94

0.588776

777

6,201

4.442728

0.185328

0.028679

0.040556

0.021727

0.348204

0.275492

0.237833

0.182213

0.110371

0

0.008555

0.302532

6,201

194

95

31.963918

0.789595

0.067247

0

0.161972

0

0.055401

0

1

0.070423

false

0

0.035211

0

0.176056

0

null

0

null

0

1

0

32837c01862960b0796752083e66eefb2afb0c24

1,244

py

Python

qfig.py

mth1haha/BlockchainQueueingNetwork

611dc84b857efbec22edfe5f3a1bb8f7052a39aa

[ "Apache-2.0" ]

1

2021-11-30T08:22:43.000Z

qfig.py

mth1haha/BlockchainQueueingNetwork

611dc84b857efbec22edfe5f3a1bb8f7052a39aa

[ "Apache-2.0" ]

null

qfig.py

mth1haha/BlockchainQueueingNetwork

611dc84b857efbec22edfe5f3a1bb8f7052a39aa

[ "Apache-2.0" ]

1

2020-11-25T08:48:25.000Z

import simpy as sp import numpy as np import seaborn as sns import matplotlib.pyplot as plt from scipy import stats, integrate def client(env, lamda, q, tic): meant = 1/lamda while True: t = np.random.exponential(meant) yield env.timeout(t) q.put('job') tic.append(env.now) def server(env, alpha, mu1, mu2, q, toc): mean1 = 1/mu1 mean2 = 1/mu2 while True: yield q.get() p = np.random.uniform() if p < alpha: t = np.random.exponential(mean1) else: t = np.random.exponential(mean2) yield env.timeout(t) toc.append(env.now) lamda = 75 alpha = 0.333 mu1 = 370 mu2 = 370*(0.666) num_bins = 50 runtime = 1000 #运行多长时间 tic = [] #每个任务进系统的时间点 toc = [] #每个任务出系统的时间点 env = sp.Environment() q = sp.Store(env) env.process(client(env, lamda, q, tic)) env.process(server(env, alpha, mu1, mu2, q, toc)) env.run(until=runtime) l = len(tic) a = toc b = toc #b = toc[0:l:40] histdata = [b[i] - b[i-1] for i in range(1, len(b))] sns.distplot(histdata, kde=False, fit=stats.expon) plt.xlabel("inter departure time (s)") plt.xlim(0,0.15) #plt.ylim(0,100) plt.savefig('dist1.png') plt.show() #plt.hist(histdata, num_bins) #plt.show()

20.393443

52

0.619775

203

1,244

3.788177

0.458128

0.041612

0.035111

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null

0

null

0

1

0

328b211073d9f2b0d84385aebf512b9639d8569d

1,133

py

Python

application/utils/data_transfer_objects.py

charles-crawford/sentiment

38cfd6af1cc81ad1858621a182cd76dc3e5f04db

[ "MIT" ]

null

application/utils/data_transfer_objects.py

charles-crawford/sentiment

38cfd6af1cc81ad1858621a182cd76dc3e5f04db

[ "MIT" ]

null

application/utils/data_transfer_objects.py

charles-crawford/sentiment

38cfd6af1cc81ad1858621a182cd76dc3e5f04db

[ "MIT" ]

null

from flask_restx.fields import String, Boolean, Raw, List, Float, Nested class DataTransferObjects: def __init__(self, ns): self.ns = ns self.general_responses = {200: 'OK', 404: "Resource not found", 400: "Bad Request", 500: "Internal Server Error"} self.plain_text = self.ns.model('plain_text', { 'plain_text': String(example='some sample text') }) self.text_list = self.ns.model('text_list', { 'text_list': List(String(), example=['This is the first sentence.', 'This is the second sentence.']) }) self.label = self.ns.model('label', { 'value': String(example='POSITIVE'), 'confidence': Float(example=.9) }) self.prediction = self.ns.model('prediction', { 'text': String(example='some sample text'), 'labels': List(Nested(self.label)) }) self.predictions = self.ns.model('predictions', { 'predictions': List(Nested(self.prediction)) })

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0

null

0

null

0

1

0

329003760fc6877a5fb340f8c2de344d9c2c4d3e

13,284

py

Python

grover.py

raulillo82/TFG-Fisica-2021

8acfd748c7f49ea294606a9c185227927ec2e256

[ "MIT" ]

null

grover.py

raulillo82/TFG-Fisica-2021

8acfd748c7f49ea294606a9c185227927ec2e256

[ "MIT" ]

null

grover.py

raulillo82/TFG-Fisica-2021

8acfd748c7f49ea294606a9c185227927ec2e256

[ "MIT" ]

null

#!/usr/bin/python3 ''' * Copyright (C) 2021 Raúl Osuna Sánchez-Infante * * This software may be modified and distributed under the terms * of the MIT license. See the LICENSE.txt file for details. ''' ################## #Needed libraries# ################## import matplotlib as mpl mpl.use('TkAgg') import matplotlib.pyplot as plt import qiskit as q import sys from qiskit.visualization import plot_histogram from qiskit.providers.ibmq import least_busy from random import getrandbits ''' Grover's algorithim. Intro ''' ####################### #Functions definitions# ####################### ''' Usage function calling the program with "-h" or "--help" will display the help without returning an error (help was intended) calling the progam with no options or wrong ones, will display the same help but returning an error Please bear in mind that some combination of options are simply ignored, see the text of this function itself ''' def usage(): print("Usage: " + str((sys.argv)[0]) + " i j k l") print("i: Number of qubits (2 or 3, will yield error if different)") print("j: Number of solutions (only taken into account if i=3, otherwise ignored). Can only be 1 or 2, will yield error otherwise") print("k: Number of iterations (only taken into account for i=3 and j=1, othwerise ignored). Can only be 1 or 2, will yield error otherwise") print("l: Perform computations in real quantum hardware, can only be 0 (no) or 1 (yes), will yield error otherwise") if len(sys.argv) == 2 and (str((sys.argv)[1]) == "-h" or str((sys.argv)[1]) == "--help"): exit(0) else: exit(1) ''' Check whether parameter is an integer ''' def is_intstring(s): try: int(s) return True except ValueError: return False ''' Initialization: Simply apply an H gate to every qubit ''' def initialize(): if len(sys.argv) == 1: print ("No arguments given") usage() elif len(sys.argv) > 5 or str((sys.argv)[1]) == "-h" or str((sys.argv)[1]) == "--help" or (not (is_intstring(sys.argv[1]))) or (int((sys.argv)[1]) != 2 and (int((sys.argv)[1]) != 3)): #elif (int((sys.argv)[1]) != 2 and (int((sys.argv)[1]) != 3)): usage() else: #print ("Rest of cases") for arg in sys.argv[2:]: if not is_intstring(arg): sys.exit("All arguments must be integers. Exit.") qc = q.QuantumCircuit((sys.argv)[1]) #Apply a H-gate to all qubits in qc for i in range(qc.num_qubits): qc.h(i) qc.barrier() return qc ''' Implement multi controlled Z-gate, easy to reutilize ''' def mctz(qc): qc.h(2) qc.mct(list(range(2)), 2) qc.h(2) ''' Oracle metaimplementation This function will simply call one of the possibles oracles functions ''' def oracle (qc): #Generate some random bits and implement the oracle accordingly with the result bits=getrandbits(qc.num_qubits) #2 qubits if int((sys.argv)[1]) == 2: print("Random bits to search for are (decimal representation): " + str(bits)) oracle_2_qubits(qc,bits) #3 qubits elif int((sys.argv)[1]) == 3: #Single solution if int((sys.argv)[2]) == 1: ''' Explanation: less than sqrt(N) iterations will be needed (so will need to "floor" (truncate) the result) As 2 < sqrt(8) < 3 --> n=2 for 100% prob. With n=1, p=0.78125=78,125% In the classical case, p=1/4=25% (single query followed by a random guess: 1/8 + 7/8 · 1/7 = 1/4 = 25%) Classical results with two runs, p=1/8+7/8·1/7+6/8·1/6= 1/4 + 1/8 = 3/8 = 0.375 = 37,5% ''' print("Random bits to search for are (decimal representation): " + str(bits)) #Check whether 1 or 2 iterations were requested if (int((sys.argv)[3]) == 1) or (int((sys.argv)[3]) == 2): iterations = int((sys.argv)[3]) for i in range(iterations): oracle_3_qubits_single_solution(qc,bits) diffusion(grover_circuit) #For any other case, wrong arguments were used, exit else: usage() #2 possible solutions elif int((sys.argv)[2]) == 2: ''' Explanation: less than sqrt(N/M) times (M=2 different results to look for) will be needed (so will need to "floor" (truncate) the result) As sqrt(8/2) = 2 --> n=1 for a theoretical 100% prob. In the classical case, 13/28 = 46,4% ''' #A list instead of a single element will be used, initialize it with the previous value as first element bits=[bits] #Generate the second element, also randomly bits.append(getrandbits(qc.num_qubits)) #Elements have to be different, regenerate as many times as needed till different while bits[0] == bits[1]: bits[1]=getrandbits(3) #When done, sort the list of random bits. Order does not matter for our upcoming permutations bits.sort() print("Random bits to search for are (decimal representation): " + str(bits[0]) + " and " + str(bits[1])) oracle_3_qubits_2_solutions(qc,bits) #Algorithm only implemented for 1 or 2 possible solution(s), exit if something different requested else: usage() #Algorithm only implemented for 1 or 2 qubits, exit if something different requested else: usage() ''' Oracle implementation for 2 qubits. Simply a controlled-Z gate (cz in qiskit). For qubits different to 1, an x-gate is needed before and after the cz-gate ''' def oracle_2_qubits(qc,bits): if bits == 0: #00 qc.x(0) qc.x(1) qc.cz(0, 1) qc.x(0) qc.x(1) elif bits == 1: #01 qc.x(1) qc.cz(0,1) qc.x(1) elif bits == 2: #10 qc.x(0) qc.cz(0,1) qc.x(0) elif bits == 3: #11 qc.cz(0,1) qc.barrier() ''' Oracle implementation for 3 qubits and single solution. Reference for oracles: https://www.nature.com/articles/s41467-017-01904-7 (table 1) ''' def oracle_3_qubits_single_solution(qc,bits): if bits == 0: for i in range(3): qc.x(i) mctz(qc) for i in range(3): qc.x(i) elif bits == 1: for i in range(1, 3): qc.x(i) mctz(qc) for i in range(1, 3): qc.x(i) elif bits == 2: for i in range(0, 3, 2): qc.x(i) mctz(qc) for i in range(0, 3, 2): qc.x(i) elif bits == 3: qc.x(2) mctz(qc) qc.x(2) elif bits == 4: for i in range(2): qc.x(i) mctz(qc) for i in range(2): qc.x(i) elif bits == 5: qc.x(1) mctz(qc) qc.x(1) elif bits == 6: qc.x(0) mctz(qc) qc.x(0) elif bits == 7: mctz(qc) qc.barrier() ''' Oracle implementation for 3 qubits and two possible solutions. Reference for oracles: https://www.nature.com/articles/s41467-017-01904-7 (table 2) ''' def oracle_3_qubits_2_solutions(qc,bits): if (bits[0] == 0 and bits[1] == 1): for i in range(1,3): qc.z(i) qc.cz(1, 2) elif (bits[0] == 0 and bits[1] == 2): for i in range(0, 3, 2): qc.z(i) qc.cz(0, 2) elif (bits[0] == 0 and bits[1] == 3): for i in range(3): qc.z(i) qc.cz(1, 2) qc.cz(0, 2) elif (bits[0] == 0 and bits[1] == 4): for i in range(2): qc.z(i) qc.cz(0, 1) elif (bits[0] == 0 and bits[1] == 5): for i in range(3): qc.z(i) qc.cz(1, 2) qc.cz(0, 1) elif (bits[0] == 0 and bits[1] == 6): for i in range(3): qc.z(i) qc.cz(0, 2) qc.cz(0, 1) elif (bits[0] == 0 and bits[1] == 7): for i in range(3): qc.z(i) qc.cz(1, 2) qc.cz(0, 2) qc.cz(0, 1) elif (bits[0] == 1 and bits[1] == 2): for i in range(2): qc.z(i) qc.cz(1, 2) qc.cz(0, 2) elif (bits[0] == 1 and bits[1] == 3): qc.z(0) qc.cz(0, 2) elif (bits[0] == 1 and bits[1] == 4): for i in range(0, 3, 2): qc.z(i) qc.cz(1, 2) qc.cz(0, 1) elif (bits[0] == 1 and bits[1] == 5): qc.z(0) qc.cz(0, 1) elif (bits[0] == 1 and bits[1] == 6): qc.z(0) qc.cz(1, 2) qc.cz(0, 2) qc.cz(0, 1) elif (bits[0] == 1 and bits[1] == 7): qc.z(0) qc.cz(0, 2) qc.cz(0, 1) elif (bits[0] == 2 and bits[1] == 3): qc.z(1) qc.cz(1, 2) elif (bits[0] == 2 and bits[1] == 4): for i in range(1,3): qc.z(i) qc.cz(0, 2) qc.cz(0, 1) elif (bits[0] == 2 and bits[1] == 5): qc.z(1) qc.cz(1, 2) qc.cz(0, 2) qc.cz(0, 1) elif (bits[0] == 2 and bits[1] == 6): qc.z(1) qc.cz(0, 1) elif (bits[0] == 2 and bits[1] == 7): qc.z(1) qc.cz(1, 2) qc.cz(0, 1) elif (bits[0] == 3 and bits[1] == 4): qc.z(2) qc.cz(1, 2) qc.cz(0, 2) qc.cz(0, 1) elif (bits[0] == 3 and bits[1] == 5): qc.cz(0, 2) qc.cz(0, 1) elif (bits[0] == 3 and bits[1] == 6): qc.cz(1, 2) qc.cz(0, 1) elif (bits[0] == 3 and bits[1] == 7): qc.cz(0, 1) elif (bits[0] == 4 and bits[1] == 5): qc.z(2) qc.cz(1, 2) elif (bits[0] == 4 and bits[1] == 6): qc.z(2) qc.cz(0, 2) elif (bits[0] == 4 and bits[1] == 7): qc.z(2) qc.cz(1, 2) qc.cz(0, 2) elif (bits[0] == 5 and bits[1] == 6): qc.cz(1, 2) qc.cz(0, 2) elif (bits[0] == 5 and bits[1] == 7): qc.cz(0, 2) elif (bits[0] == 6 and bits[1] == 7): qc.cz(1, 2) qc.barrier() ''' Diffusion operator: Flip sign and amplify For 2 qubits, simply apply H and Z to each qubit, then cz, and then apply H again to each qubit: ''' def diffusion(qc): if qc.num_qubits == 2: qc.h(0) qc.h(1) qc.z(0) qc.z(1) qc.cz(0,1) qc.h(0) qc.h(1) elif qc.num_qubits == 3: #Apply diffusion operator for i in range(3): qc.h(i) qc.x(i) # multi-controlled-toffoli mctz(qc) qc.barrier() for i in range(3): qc.x(i) qc.h(i) #qc.barrier() ''' Add measurements and plot the quantum circuit: ''' def measure(qc): qc.measure_all() qc.draw('mpl') plt.draw() plt.title("Quantum Circuit") ''' Generate results from quantum simulator (no plotting) ''' def results_qsim(qc): backend = q.Aer.get_backend('qasm_simulator') job = q.execute(qc, backend, shots = 1024) return job ''' Generate results from real quantum hardware (no plotting) ''' def results_qhw(qc): ''' #Only needed if credentials are not stored (e.g., deleted and regeneration is needed token='XXXXXXXX' #Use token from ibm quantum portal if needed to enable again, should be stored under ~/.qiskit directory q.IBMQ.save_account(token) ''' provider = q.IBMQ.load_account() provider = q.IBMQ.get_provider() device = q.providers.ibmq.least_busy(provider.backends(filters=lambda x: x.configuration().n_qubits >= 3 and not x.configuration().simulator and x.status().operational==True)) print("Running on current least busy device: ", device) transpiled_grover_circuit = q.transpile(qc, device, optimization_level=3) qobj = q.assemble(transpiled_grover_circuit) job = device.run(qobj) q.tools.monitor.job_monitor(job, interval=2) return job ''' Plot results ''' def draw_job (job,title): result = job.result() counts = result.get_counts() plot_histogram(counts) plt.draw() plt.title(title) ############################## #End of functions definitions# ############################## ################################ #Program actually starts here!!# ################################ #Initialization grover_circuit = initialize() #Generate the oracle randomly according to the command line arguments oracle(grover_circuit) #Diffusion if (not(int(sys.argv[1]) == 3 and int(sys.argv[2]) == 1)): diffusion(grover_circuit) #Add measurements measure(grover_circuit) #Generate results in simulator job_sim = results_qsim(grover_circuit) #Plot these results draw_job(job_sim, "Quantum simulator output") #Generate results in quantum hw if requested if int(sys.argv[4]) == 1: plt.show(block=False) plt.draw() #Next line needed for keeping computations in background while still seeing the previous plots plt.pause(0.001) #Generate results in real quantum hardware job_qhw = results_qhw(grover_circuit) #Plot these results as well draw_job(job_qhw, "Quantum hardware output") #Keep plots active when done till they're closed, used for explanations during presentations plt.show()

29.851685

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2,078

13,284

3.464389

0.179981

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0.025698

0.033616

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0.301014

0.247118

0.217947

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13,284

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0.720034

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null

0

null

0

1

0

3294741b0f8e1bf0eeabf4019d19a68a63e99c23

1,419

py

Python

tests/bind_tests/diagram_tests/strategies.py

lycantropos/voronoi

977e0b3e5eff2dd294e2e6ce1a8030c763e86233

[ "MIT" ]

null

tests/bind_tests/diagram_tests/strategies.py

lycantropos/voronoi

977e0b3e5eff2dd294e2e6ce1a8030c763e86233

[ "MIT" ]

null

tests/bind_tests/diagram_tests/strategies.py

lycantropos/voronoi

977e0b3e5eff2dd294e2e6ce1a8030c763e86233

[ "MIT" ]

null

from hypothesis import strategies from hypothesis_geometry import planar from tests.bind_tests.hints import (BoundCell, BoundDiagram, BoundEdge, BoundVertex) from tests.bind_tests.utils import (bound_source_categories, to_bound_multipoint, to_bound_multisegment) from tests.strategies import (doubles, integers_32, sizes) from tests.utils import to_maybe booleans = strategies.booleans() coordinates = doubles empty_diagrams = strategies.builds(BoundDiagram) source_categories = strategies.sampled_from(bound_source_categories) cells = strategies.builds(BoundCell, sizes, source_categories) vertices = strategies.builds(BoundVertex, coordinates, coordinates) edges = strategies.builds(BoundEdge, to_maybe(vertices), cells, booleans, booleans) cells_lists = strategies.lists(cells) edges_lists = strategies.lists(edges) vertices_lists = strategies.lists(vertices) diagrams = strategies.builds(BoundDiagram, cells_lists, edges_lists, vertices_lists) multipoints = planar.multipoints(integers_32).map(to_bound_multipoint) multisegments = planar.multisegments(integers_32).map(to_bound_multisegment)

44.34375

76

0.653982

132

1,419

6.80303

0.287879

0.089087

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0.040089

0.044543

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0.284708

1,419

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0.206897

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null

0

null

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0

32955f3ecdc5ec46e6e7127a3ed57f1411af2c54

2,381

py

Python

apps/blog/serializers.py

yc19890920/dble_fastapi_blog

dd9b8984d849df893d4fea270e8b75ac12d01241

[ "Apache-2.0" ]

null

apps/blog/serializers.py

yc19890920/dble_fastapi_blog

dd9b8984d849df893d4fea270e8b75ac12d01241

[ "Apache-2.0" ]

2

2021-03-31T19:56:46.000Z

2021-04-30T21:19:15.000Z

apps/blog/serializers.py

yc19890920/dble_fastapi_blog

dd9b8984d849df893d4fea270e8b75ac12d01241

[ "Apache-2.0" ]

null

""" @Author: YangCheng @contact: 1248644045@qq.com @Software: Y.C @Time: 2020/7/21 15:22 """ from typing import List from pydantic import BaseModel, Field from tortoise import Tortoise from tortoise.contrib.pydantic import pydantic_model_creator, pydantic_queryset_creator from lib.tortoise.pydantic import json_encoders from .models import Tag, Category, Article Tortoise.init_models(["apps.blog.models"], "models") class PydanticResponse(BaseModel): index: int limit: int total: int # -*- tag -*- # Tag create/update TagCreateRequest = pydantic_model_creator( Tag, name="TagCreateRequest", exclude_readonly=True ) TagCreateResponse = pydantic_model_creator( Category, name="TagCreateResponse", exclude=["articles"] ) TagCreateResponse.Config.json_encoders = json_encoders # Tag List TagListSerializer = pydantic_queryset_creator( Tag, name="TagListSerializer", exclude=["articles"] ) class TagListResponse(PydanticResponse): results: List[TagListSerializer] class TagResponse(BaseModel): id: int name: str # -*- Category -*- # Category create/update CategoryCreateRequest = pydantic_model_creator( Category, name="CategoryCreateRequest", exclude_readonly=True ) CategoryCreateResponse = pydantic_model_creator( Category, name="CategoryCreateResponse", exclude=("articles",) ) CategoryCreateResponse.Config.json_encoders = json_encoders # Category List CategoryListSerializer = pydantic_queryset_creator( Category, name="CategoryListSerializer", exclude=("articles",) ) class CategoryListResponse(PydanticResponse): results: List[CategoryListSerializer] # -*- Article -*- # Article create/update class ArticleCreateRequest(BaseModel): title: str = Field(..., description="Title") content: str = Field(..., description="Content") abstract: str = None status: str = Field(default="publish", description="Content") category_id: int = Field(..., description="category_id") tags: List[int] = Field(..., description="tag_id list") ArticleCreateResponse = pydantic_model_creator( Article, name="ArticleCreateResponse" ) ArticleCreateResponse.Config.json_encoders = json_encoders ArticleListSerializer = pydantic_queryset_creator( Article, name="ArticleListSerializer" ) # Article List class ArticleListResponse(PydanticResponse): results: List[ArticleCreateResponse]

25.063158

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2,381

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0.481481

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null

0

null

0

1

0

329a1a34027b83c6621340af222a98c0d43067e0

1,102

py

Python

Python/image_analysis_centerlines/analysis_example.py

fromenlab/guides

ac9831265f8219d5b5a8ee3a441fc77c7ae4fe3b

[ "MIT" ]

null

Python/image_analysis_centerlines/analysis_example.py

fromenlab/guides

ac9831265f8219d5b5a8ee3a441fc77c7ae4fe3b

[ "MIT" ]

null

Python/image_analysis_centerlines/analysis_example.py

fromenlab/guides

ac9831265f8219d5b5a8ee3a441fc77c7ae4fe3b

[ "MIT" ]

null

from skimage import img_as_bool, io, color, morphology import matplotlib.pyplot as plt import numpy as np import pandas as pd # Testing process # Import images one = img_as_bool(color.rgb2gray(io.imread('1.jpg'))) cross = img_as_bool(color.rgb2gray(io.imread('cross.jpg'))) grid = img_as_bool(color.rgb2gray(io.imread('grid.jpg'))) # Get skeleton one_skel = morphology.skeletonize(one) cross_skel = morphology.skeletonize(cross) grid_skel = morphology.skeletonize(grid) # Get medial axis one_med, one_med_distance = morphology.medial_axis(one, return_distance=True) cross_med, cross_med_distance = morphology.medial_axis(cross, return_distance=True) grid_med, grid_med_distance = morphology.medial_axis(grid, return_distance=True) # Get skeleton distance one_skel_distance = one_med_distance*one_skel # Data processing for "1.jpg" one_skel_nonzero = one_skel_distance.nonzero() trans = np.transpose(one_skel_nonzero) df_coords = pd.DataFrame(data = trans, columns = ["y", "x"]) df_dist = pd.DataFrame(data = one_skel_distance[one_skel_nonzero]) combined = pd.concat([df_coords, df_dist], axis=1)

34.4375

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170

1,102

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1

0

329a5ba2f15a3280c3c7c2b2a6a0114abcec0cf9

485

py

Python

resources/settings.py

Miriel-py/Room-Wizard

83d86fe8e8fed8bb073b38465cd0e97b1a6113b8

[ "MIT" ]

null

resources/settings.py

Miriel-py/Room-Wizard

83d86fe8e8fed8bb073b38465cd0e97b1a6113b8

[ "MIT" ]

null

resources/settings.py

Miriel-py/Room-Wizard

83d86fe8e8fed8bb073b38465cd0e97b1a6113b8

[ "MIT" ]

null

# global_data.py import os from dotenv import load_dotenv # Read the bot token from the .env file load_dotenv() TOKEN = os.getenv('DISCORD_TOKEN') DEBUG_MODE = os.getenv('DEBUG_MODE') BOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) DB_FILE = os.path.join(BOT_DIR, 'database/room_wizard_db.db') LOG_FILE = os.path.join(BOT_DIR, 'logs/discord.log') DEV_GUILDS = [730115558766411857] # Embed color EMBED_COLOR = 0x6C48A7 DEFAULT_FOOTER = 'Just pinning things.'

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78

485

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0.08547

0.074074

0.08547

0.205128

0.11396

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0.053364

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485

20

70

24.25

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0.181818

0

null

0

null

0

1

0

329eec6934c9b0ff2824d0ffd01a1902dae80850

1,767

py

Python

detection_algorithms/temporal_anomaly_detection/model_def.py

hanahs-deepfake-detection/detection-algorithms

6d7ec53eaf333adb10a1aba448f80fceaf7722be

[ "MIT" ]

null

detection_algorithms/temporal_anomaly_detection/model_def.py

hanahs-deepfake-detection/detection-algorithms

6d7ec53eaf333adb10a1aba448f80fceaf7722be

[ "MIT" ]

null

detection_algorithms/temporal_anomaly_detection/model_def.py

hanahs-deepfake-detection/detection-algorithms

6d7ec53eaf333adb10a1aba448f80fceaf7722be

[ "MIT" ]

null

""" Model Definition """ from tensorflow import keras from tensorflow.keras.applications import ResNet101V2 from tensorflow.keras.layers import ( BatchNormalization, Conv2D, Dense, Dropout, Flatten, LSTM, MaxPool2D, TimeDistributed, Lambda ) import tensorflow as tf from .spatial_transformer.bilinear_sampler import BilinearSampler def gen_model(batch_size, video_frames): inputs = keras.Input((video_frames, 384, 512, 3), batch_size=batch_size) x = TimeDistributed(Conv2D(32, kernel_size=(3, 3), activation='relu'))(inputs) x = TimeDistributed(MaxPool2D())(x) x = TimeDistributed(Conv2D(32, kernel_size=(3, 3), activation='relu'))(x) x = TimeDistributed(MaxPool2D())(x) x = TimeDistributed(Flatten())(x) x = TimeDistributed(Dense(64, activation='tanh', kernel_initializer='zeros'))(x) x = TimeDistributed(Dropout(0.5))(x) x = TimeDistributed(Dense(6, activation='tanh', kernel_initializer='zeros', bias_initializer=lambda shape, dtype=None: tf.constant( [1, 0, 0, 0, 1, 0], tf.float32 )))(x) x = Lambda(lambda ls: tf.concat([ls[0], tf.reshape(ls[1], (batch_size, video_frames, -1))], -1))([x, inputs]) x = TimeDistributed(BilinearSampler(input_shape=(batch_size, 384, 512, 3), output_shape=(batch_size, 224, 224, 3)))(x) resnet = ResNet101V2(include_top=False, weights=None) x = TimeDistributed(resnet)(x) x = TimeDistributed(Flatten())(x) x = LSTM(32, return_sequences=True)(x) x = LSTM(32)(x) x = Dense(10, activation='relu')(x) x = BatchNormalization()(x) x = Dense(1, activation='sigmoid')(x) model = keras.Model(inputs=inputs, outputs=x) return model

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0.654782

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1,767

5.163636

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1,767

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0

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0

0.2

0

null

0

null

0

1

0

329f8f1e2538fb2f56b719613eee2ed54216347d

4,884

py

Python

osspeak/platforms/windows.py

OSSpeak/OSSpeak

327c38a37684165f87bf8d76ab2ca135b43b8ab7

[ "MIT" ]

1

2020-03-17T10:24:41.000Z

osspeak/platforms/windows.py

OSSpeak/OSSpeak

327c38a37684165f87bf8d76ab2ca135b43b8ab7

[ "MIT" ]

12

2016-09-28T05:16:00.000Z

2020-11-27T22:32:40.000Z

osspeak/platforms/windows.py

OSSpeak/OSSpeak

327c38a37684165f87bf8d76ab2ca135b43b8ab7

[ "MIT" ]

null

''' Collection of Windows-specific I/O functions ''' import msvcrt import time import ctypes from platforms import winconstants, winclipboard EnumWindows = ctypes.windll.user32.EnumWindows EnumWindowsProc = ctypes.WINFUNCTYPE(ctypes.c_bool, ctypes.POINTER(ctypes.c_int), ctypes.POINTER(ctypes.c_int)) GetWindowText = ctypes.windll.user32.GetWindowTextW GetWindowTextLength = ctypes.windll.user32.GetWindowTextLengthW IsWindowVisible = ctypes.windll.user32.IsWindowVisible def flush_io_buffer(): while msvcrt.kbhit(): print(msvcrt.getch().decode('utf8'), end='') def close_active_window(): hwnd = ctypes.windll.user32.GetForegroundWindow() ctypes.windll.user32.PostMessageA(hwnd, winconstants.WM_CLOSE, 0, 0) def get_active_window_name(): hwnd = ctypes.windll.user32.GetForegroundWindow() return get_window_title(hwnd) def maximize_active_window(): hwnd = ctypes.windll.user32.GetForegroundWindow() ctypes.windll.user32.ShowWindow(hwnd, 3) def minimize_active_window(): hwnd = ctypes.windll.user32.GetForegroundWindow() ctypes.windll.user32.ShowWindow(hwnd, 6) def get_window_title(hwnd): length = GetWindowTextLength(hwnd) buff = ctypes.create_unicode_buffer(length + 1) GetWindowText(hwnd, buff, length + 1) return buff.value def get_matching_windows(title_list): matches = {} def window_enum_callback(hwnd, lParam): if IsWindowVisible(hwnd): window_name = get_window_title(hwnd).lower() for name in title_list: if name not in window_name: return True matches[window_name] = hwnd return True EnumWindows(EnumWindowsProc(window_enum_callback), 0) return matches def activate_window(title, position=1): if position > 0: position -= 1 matches = get_matching_windows(title) sorted_keys = list(sorted(matches.keys(), key=len)) key = sorted_keys[position] hwnd = matches[key] # magic incantations to activate window consistently IsIconic = ctypes.windll.user32.IsIconic ShowWindow = ctypes.windll.user32.ShowWindow GetForegroundWindow = ctypes.windll.user32.GetForegroundWindow GetWindowThreadProcessId = ctypes.windll.user32.GetWindowThreadProcessId BringWindowToTop = ctypes.windll.user32.BringWindowToTop AttachThreadInput = ctypes.windll.user32.AttachThreadInput SetForegroundWindow = ctypes.windll.user32.SetForegroundWindow SystemParametersInfo = ctypes.windll.user32.SystemParametersInfoA if IsIconic(hwnd): ShowWindow(hwnd, winconstants.SW_RESTORE) if GetForegroundWindow() == hwnd: return True ForegroundThreadID = GetWindowThreadProcessId(GetForegroundWindow(), None) ThisThreadID = GetWindowThreadProcessId(hwnd, None) if AttachThreadInput(ThisThreadID, ForegroundThreadID, True): BringWindowToTop(hwnd) SetForegroundWindow(hwnd) AttachThreadInput(ThisThreadID, ForegroundThreadID, False) if GetForegroundWindow() == hwnd: return True timeout = ctypes.c_int() zero = ctypes.c_int(0) SystemParametersInfo(winconstants.SPI_GETFOREGROUNDLOCKTIMEOUT, 0, ctypes.byref(timeout), 0) (winconstants.SPI_SETFOREGROUNDLOCKTIMEOUT, 0, ctypes.byref(zero), winconstants.SPIF_SENDCHANGE) BringWindowToTop(hwnd) SetForegroundWindow(hwnd) SystemParametersInfo(winconstants.SPI_SETFOREGROUNDLOCKTIMEOUT, 0, ctypes.byref(timeout), winconstants.SPIF_SENDCHANGE) if GetForegroundWindow() == hwnd: return True return False def get_mouse_location(): pt = winconstants.POINT() ctypes.windll.user32.GetCursorPos(ctypes.byref(pt)) return pt.x, pt.y def mouse_click(button, direction, number): event_nums = get_mouse_event_nums(button, direction) for i in range(number): for num in event_nums: ctypes.windll.user32.mouse_event(num, 0, 0, 0, 0) def mouse_move(x=None, y=None, relative=False): startx, starty = get_mouse_location() if not relative: if x is None: x = startx if y is None: y = starty ctypes.windll.user32.SetCursorPos(x, y) return if x is None: x = 0 if y is None: y = 0 ctypes.windll.user32.SetCursorPos(startx + x, starty + y) def get_clipboard_contents(): return winclipboard.init_windows_clipboard()[1]() def set_clipboard_contents(text): return winclipboard.init_windows_clipboard()[0](str(text)) def get_mouse_event_nums(button, direction): if button == 'left' and direction == 'down': return [2] if button == 'left' and direction == 'up': return [4] if button == 'left' and direction == 'both': return [2, 4] if button == 'right' and direction == 'down': return [8] if button == 'right' and direction == 'up': return [16] if button == 'right' and direction == 'both': return [8, 16]

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559

4,884

6.159213

0.255814

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0.053732

0.250363

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4,884

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124

37.282443

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0

0.037037

0.018519

0.296296

0.009259

0

null

0

null

0

1

0

32a0d30f56c4a1916c5ad0aef5a7b50495e1860b

715

py

Python

sudokusolver/common/messenger.py

Blondberg/SudokuSolver

4a6f1f927d41f7a39a953b9784b28d570edf1f09

[ "MIT" ]

null

sudokusolver/common/messenger.py

Blondberg/SudokuSolver

4a6f1f927d41f7a39a953b9784b28d570edf1f09

[ "MIT" ]

null

sudokusolver/common/messenger.py

Blondberg/SudokuSolver

4a6f1f927d41f7a39a953b9784b28d570edf1f09

[ "MIT" ]

null

# messenger.py - contains functions to create different kinds of messages like info or error # color the text, usage: print bcolors.WARNING + "Warning: No active frommets remain. Continue?" + bcolors.ENDC BCOLORS = { 'HEADER': '\033[95m', 'OKBLUE': '\033[94m', 'OKGREEN': '\033[92m', 'WARNING': '\033[93m', 'FAIL': '\033[91m', 'ENDC': '\033[0m', 'BOLD': '\033[1m', 'UNDERLINE': '\033[4m' } # Information message def info(message): print(BCOLORS['OKBLUE'] + message + BCOLORS['ENDC']) # Action message def action(message): print(BCOLORS['OKGREEN'] + message + BCOLORS['ENDC']) # Error message def error(message): print(BCOLORS['FAIL'] + message + BCOLORS['ENDC'])

23.833333

111

0.633566

87

715

5.206897

0.528736

0.10596

0.125828

0

0.063574

0.186014

715

29

112

24.655172

0.714777

0.348252

0

0.297826

0

1

0.1875

false

0

0.1875

0

null

0

null

0

1

0

32a23291b7486cbc9a87ce5a914dd735071b20e4

554

py

Python

test.py

w0w/miniPFC

63b1bf608de03efada2a1b57c0370b6a7c2bf1ad

[ "MIT" ]

null

test.py

w0w/miniPFC

63b1bf608de03efada2a1b57c0370b6a7c2bf1ad

[ "MIT" ]

null

test.py

w0w/miniPFC

63b1bf608de03efada2a1b57c0370b6a7c2bf1ad

[ "MIT" ]

null

import json import RPi.GPIO as GPIO from modules.sensor import getTempC, getHumidity def loadConfig(): with open('./config/pin.json') as data_file: data = json.load(data_file) return data currentPins = loadConfig().values() def bootActuators(): '''Assumes that pi is booting and set off all the relays''' GPIO.setmode(GPIO.BOARD) for i, p in enumerate(currentPins): GPIO.setup(p, GPIO.OUT) GPIO.output(p, GPIO.HIGH) print(p, GPIO.input(p)) print('Actuators turned off') bootActuators()

25.181818

63

0.66426

76

554

4.815789

0.644737

0.040984

0

0.220217

554

22

64

25.181818

0.847222

0.095668

0

0.074597

0

1

0.125

false

0

0.1875

0

0.375

0.125

0

null

0

null

0

1

0

32a426fd1c9efac97183a6c708ae91ac77c14062

1,170

py

Python

example.py

clagraff/habu

28d05c2fa2204b26177bbaed969648b92b89c735

[ "MIT" ]

null

example.py

clagraff/habu

28d05c2fa2204b26177bbaed969648b92b89c735

[ "MIT" ]

null

example.py

clagraff/habu

28d05c2fa2204b26177bbaed969648b92b89c735

[ "MIT" ]

null

import json import habu def do_req(uri, *args, **kwargs): route_data = { "/": { "_links": { "people": { "href": "/people" }, "animals": { "href": "/animals" } } }, "/people": { "_links": { "self": { "href": "/products" } }, "_embedded": { "people": [ { "_links": { "self": { "href": "/people/clagraff" } }, "name": "Curtis", "age": 22 } ] }, "total": 1 }, "/people/clagraff": { "_links": { "self": { "href": "/people/clagraff" } }, "name": "Curtis", "age": 22 } } return route_data[uri] def main(): habu.set_request_func(do_req) api = habu.enter("/") people = api.people() print("There are %i people" % people.total) for person in people.embedded.people: print("Hi! I am %s and I am %i years old" % (person.name, person.age)) curtis = habu.enter("/people/clagraff") print(curtis) if __name__ == "__main__": main()

23.4

105

0.417949

105

1,170

4.47619

0.438095

0.119149

0.082979

0.080851

0.178723

0

0.007194

0.405983

1,170

49

106

23.877551

0.669065

0

0.073171

0

0.232479

0

1

0.04878

false

0

0.04878

0

0.121951

0.073171

0

null

0

null

0

1

0

32a62b611ae086d7c010dc8106960f0f8f3738b2

1,162

py

Python

notify_tweet.py

mkaraki/WatchTweets

9b0a4ef66e38311453fff99d02091758b1bd0df5

[ "MIT" ]

null

notify_tweet.py

mkaraki/WatchTweets

9b0a4ef66e38311453fff99d02091758b1bd0df5

[ "MIT" ]

1

2022-01-26T18:03:15.000Z

2022-01-26T18:03:35.000Z

notify_tweet.py

mkaraki/WatchTweets

9b0a4ef66e38311453fff99d02091758b1bd0df5

[ "MIT" ]

null

import json import os import requests from dotenv import load_dotenv # You have to configure in this file to notify other services def notifyHandler(tweet): notifyDiscord(tweet) return def notifyDiscord(tweet, find_user_info=False): msg = tweet['text'] if ('entities' in tweet and 'urls' in tweet['entities']): for (i, url) in enumerate(tweet['entities']['urls']): msg = msg.replace(url['url'], url['expanded_url']) c = { 'embeds': [{ 'description': msg, 'author': { 'name': tweet['author_id'], 'url': 'https://twitter.com/intent/user?user_id=' + tweet['author_id'], }, 'title': 'Tweet', 'url': 'https://twitter.com/intent/like?tweet_id=' + tweet['id'], 'footer': { 'text': 'Twitter', 'icon_url': 'http://github.com/twitter.png', }, 'timestamp': tweet['created_at'], }] } requests.post(os.getenv('DISCORD_WEBHOOK_URL'), json.dumps( c), headers={'Content-Type': 'application/json'}) return load_dotenv(override=True)

26.409091

87

0.553356

129

1,162

4.883721

0.534884

0.031746

0.04127

0.057143

0.07619

0

0.288296

1,162

43

88

27.023256

0.76179

0.050775

0

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0

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0.0625

false

0

0.125

0

0.25

0

null

0

null

0

1

0

32ac15da27e5771cb19e9b355fd09244b1a2fee3

561

py

Python

misprogs/sensor_Luz_LCD.py

dacocube/CursoGalileo

1dac903031d9ff61174cb0c5e00e3f3795ea60de

[ "Apache-2.0" ]

null

misprogs/sensor_Luz_LCD.py

dacocube/CursoGalileo

1dac903031d9ff61174cb0c5e00e3f3795ea60de

[ "Apache-2.0" ]

null

misprogs/sensor_Luz_LCD.py

dacocube/CursoGalileo

1dac903031d9ff61174cb0c5e00e3f3795ea60de

[ "Apache-2.0" ]

null

import signal import sys import time import pyupm_grove as grove import pyupm_i2clcd as lcd def interruptHandler(signal, frame): sys.exit(0) if __name__=='__main__': signal.signal(signal.SIGINT, interruptHandler) myLcd = lcd.Jhd1313m1(0, 0x3E,0x62) sensorluz=grove.GroveLight(0) coloR=255 colorG=200 colorB=100 myLcd.setColor(coloR,colorG,colorB) #read the input and print, waiting 1/2 seconds between reading while True: valorSensor=sensorluz.value() myLcd.setCursor(0,0) myLcd.write('%6d'% valorSensor) time.sleep(0.5) del sensorluz

20.777778

63

0.761141

82

561

5.085366

0.634146

0.052758

0

0.061856

0.135472

561

26

64

21.576923

0.797938

0.108734

0

0.022044

0

0.016032

0

1

0.047619

false

0

0.238095

0

0.285714

0

null

0

null

0

1

0

32b26100558c8d0079fd4f055056d994cd62c099

9,553

py

Python

clustviz/clarans.py

barbarametzler/ClustViz

a460e1ffb5195dfe1e12bca106366901d169a690

[ "MIT" ]

6

2019-11-14T11:22:54.000Z

2020-03-01T09:14:21.000Z

clustviz/clarans.py

barbarametzler/ClustViz

a460e1ffb5195dfe1e12bca106366901d169a690

[ "MIT" ]

2

2020-07-21T07:49:07.000Z

2021-04-06T16:16:09.000Z

clustviz/clarans.py

barbarametzler/ClustViz

a460e1ffb5195dfe1e12bca106366901d169a690

[ "MIT" ]

5

2020-07-14T15:22:00.000Z

2022-03-19T19:45:32.000Z

import random from typing import Tuple, Dict, Any import scipy import itertools import graphviz import numpy as np import pandas as pd from clustviz.pam import plot_pam from pyclustering.utils import euclidean_distance_square from pyclustering.cluster.clarans import clarans as clarans_pyclustering class clarans(clarans_pyclustering): def process(self, plotting: bool = False): """! @brief Performs cluster analysis in line with rules of CLARANS algorithm. @return (clarans) Returns itself (CLARANS instance). @see get_clusters() @see get_medoids() """ random.seed() # loop for a numlocal number of times for _ in range(0, self.__numlocal): print("numlocal: ", _) # set (current) random medoids self.__current = random.sample( range(0, len(self.__pointer_data)), self.__number_clusters ) # update clusters in line with random allocated medoids self.__update_clusters(self.__current) # optimize configuration self.__optimize_configuration() # obtain cost of current cluster configuration and compare it with the best obtained estimation = self.__calculate_estimation() if estimation < self.__optimal_estimation: print( "Better configuration found with medoids: {0} and cost: {1}".format( self.__current[:], estimation ) ) self.__optimal_medoids = self.__current[:] self.__optimal_estimation = estimation if plotting is True: self.__update_clusters(self.__optimal_medoids) plot_pam( self.__pointer_data, dict(zip(self.__optimal_medoids, self.__clusters)), ) else: print( "Configuration found does not improve current best one because its cost is {0}".format( estimation ) ) if plotting is True: self.__update_clusters(self.__current[:]) plot_pam( self.__pointer_data, dict(zip(self.__current[:], self.__clusters)), ) self.__update_clusters(self.__optimal_medoids) if plotting is True: print("FINAL RESULT:") plot_pam( self.__pointer_data, dict(zip(self.__optimal_medoids, self.__clusters)), ) return self def __optimize_configuration(self): """! @brief Finds quasi-optimal medoids and updates in line with them clusters in line with algorithm's rules. """ index_neighbor = 0 counter = 0 while index_neighbor < self.__maxneighbor: # get random current medoid that is to be replaced current_medoid_index = self.__current[ random.randint(0, self.__number_clusters - 1) ] current_medoid_cluster_index = self.__belong[current_medoid_index] # get new candidate to be medoid candidate_medoid_index = random.randint( 0, len(self.__pointer_data) - 1 ) while candidate_medoid_index in self.__current: candidate_medoid_index = random.randint( 0, len(self.__pointer_data) - 1 ) candidate_cost = 0.0 for point_index in range(0, len(self.__pointer_data)): if point_index not in self.__current: # get non-medoid point and its medoid point_cluster_index = self.__belong[point_index] point_medoid_index = self.__current[point_cluster_index] # get other medoid that is nearest to the point (except current and candidate) other_medoid_index = self.__find_another_nearest_medoid( point_index, current_medoid_index ) other_medoid_cluster_index = self.__belong[ other_medoid_index ] # for optimization calculate all required distances # from the point to current medoid distance_current = euclidean_distance_square( self.__pointer_data[point_index], self.__pointer_data[current_medoid_index], ) # from the point to candidate median distance_candidate = euclidean_distance_square( self.__pointer_data[point_index], self.__pointer_data[candidate_medoid_index], ) # from the point to nearest (own) medoid distance_nearest = float("inf") if (point_medoid_index != candidate_medoid_index) and ( point_medoid_index != current_medoid_cluster_index ): distance_nearest = euclidean_distance_square( self.__pointer_data[point_index], self.__pointer_data[point_medoid_index], ) # apply rules for cost calculation if point_cluster_index == current_medoid_cluster_index: # case 1: if distance_candidate >= distance_nearest: candidate_cost += ( distance_nearest - distance_current ) # case 2: else: candidate_cost += ( distance_candidate - distance_current ) elif point_cluster_index == other_medoid_cluster_index: # case 3 ('nearest medoid' is the representative object of that cluster and object is more # similar to 'nearest' than to 'candidate'): if distance_candidate > distance_nearest: pass # case 4: else: candidate_cost += ( distance_candidate - distance_nearest ) if candidate_cost < 0: counter += 1 # set candidate that has won self.__current[ current_medoid_cluster_index ] = candidate_medoid_index # recalculate clusters self.__update_clusters(self.__current) # reset iterations and starts investigation from the begining index_neighbor = 0 else: index_neighbor += 1 print("Medoid set changed {0} times".format(counter)) def compute_cost_clarans(data: pd.DataFrame, _cur_choice: list) -> Tuple[float, Dict[Any, list]]: """ A function to compute the configuration cost. (modified from that of CLARA) :param data: The input dataframe. :param _cur_choice: The current set of medoid choices. :return: The total configuration cost, the medoids. """ total_cost = 0.0 medoids = {} for idx in _cur_choice: medoids[idx] = [] for i in list(data.index): choice = -1 min_cost = np.inf for m in medoids: # fast_euclidean from CLARA tmp = np.linalg.norm(data.loc[m] - data.loc[i]) if tmp < min_cost: choice = m min_cost = tmp medoids[choice].append(i) total_cost += min_cost # print("total_cost: ", total_cost) return total_cost, medoids def plot_tree_clarans(data: pd.DataFrame, k: int) -> None: """ plot G_{k,n} as in the paper of CLARANS; only to use with small input data. :param data: input DataFrame. :param k: number of points in each combination (possible set of medoids). """ n = len(data) num_points = int(scipy.special.binom(n, k)) num_neigh = k * (n - k) if (num_points > 50) or (num_neigh > 10): print( "Either graph nodes are more than 50 or neighbors are more than 10, the graph would be too big" ) return # all possibile combinations of k elements from input data name_nodes = list(itertools.combinations(list(data.index), k)) dot = graphviz.Digraph(comment="Clustering") # draw nodes, also adding the configuration cost for i in range(num_points): tot_cost, meds = compute_cost_clarans(data, list(name_nodes[i])) tc = round(tot_cost, 3) dot.node(str(name_nodes[i]), str(name_nodes[i]) + ": " + str(tc)) # only connect nodes if they have k-1 common elements for i in range(num_points): for j in range(num_points): if i != j: if ( len(set(list(name_nodes[i])) & set(list(name_nodes[j]))) == k - 1 ): dot.edge(str(name_nodes[i]), str(name_nodes[j])) graph = graphviz.Source(dot) # .view() display(graph)

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0.539098

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9,553

4.968367

0.234694

0.036147

0.040049

0.022592

0.240501

0.177449

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0.111316

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36.323194

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0.036585

0

null

0

null

0

1

0

32b489e63deb6a7323ecb9996f33d06edac172bd

1,507

py

Python

bin/demo_findit_backup_url.py

cariaso/metapub

bfa361dd6e5de8ee0859e596d490fb478f7dcfba

[ "Apache-2.0" ]

28

2019-09-09T08:12:31.000Z

2021-12-17T00:09:14.000Z

bin/demo_findit_backup_url.py

cariaso/metapub

bfa361dd6e5de8ee0859e596d490fb478f7dcfba

[ "Apache-2.0" ]

33

2019-11-07T05:36:04.000Z

2022-01-29T01:14:57.000Z

bin/demo_findit_backup_url.py

cariaso/metapub

bfa361dd6e5de8ee0859e596d490fb478f7dcfba

[ "Apache-2.0" ]

10

2019-09-09T10:04:05.000Z

2021-06-08T16:00:14.000Z

from __future__ import absolute_import, print_function, unicode_literals import os import requests from metapub.findit import FindIt from metapub.exceptions import * from requests.packages import urllib3 urllib3.disable_warnings() OUTPUT_DIR = 'findit' CURL_TIMEOUT = 4000 def try_request(url): # verify=False means it ignores bad SSL certs OK_STATUS_CODES = [200, 301, 302, 307] response = requests.get(url, stream=True, timeout=CURL_TIMEOUT, verify=False) if response.status_code in OK_STATUS_CODES: if response.headers.get('content-type').find('pdf') > -1: return True return False def try_backup_url(pmid): source = FindIt(pmid=pmid) if not source.pma: return if source.url: print(pmid, source.pma.journal, source.url, try_request(source.url)) else: print(pmid, source.pma.journal, source.reason) try: if source.backup_url is not None: print(pmid, source.pma.journal, source.backup_url, try_request(source.backup_url)) else: print(pmid, source.pma.journal, "no backup url") except Exception as err: print(pmid, '%r' % err) if __name__=='__main__': import sys try: start_pmid = int(sys.argv[1]) except (IndexError, TypeError) as err: print("Supply a pubmed ID as the starting point for this script.") sys.exit() for pmid in range(start_pmid, start_pmid+1000): try_backup_url(pmid)

28.433962

98

0.666224

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4.717073

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0.055843

0.062048

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0.136505

0.066184

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0.020924

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1,507

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0.1

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0.3

0.175

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null

0

null

0

1

0

32b5c206b4bd2dca61a6557018af529be9b8ba2f

3,939

py

Python

kgcnn/layers/conv/dmpnn_conv.py

the16thpythonist/gcnn_keras

27d794095b684333d93149c825d84b85df8c30ff

[ "MIT" ]

47

2021-03-10T10:15:42.000Z

2022-03-14T00:53:40.000Z

kgcnn/layers/conv/dmpnn_conv.py

the16thpythonist/gcnn_keras

27d794095b684333d93149c825d84b85df8c30ff

[ "MIT" ]

36

2021-05-06T15:06:51.000Z

2022-03-02T13:06:16.000Z

kgcnn/layers/conv/dmpnn_conv.py

the16thpythonist/gcnn_keras

27d794095b684333d93149c825d84b85df8c30ff

[ "MIT" ]

11

2021-04-05T02:14:27.000Z

2022-03-02T03:25:52.000Z

import tensorflow as tf from kgcnn.layers.base import GraphBaseLayer from kgcnn.layers.gather import GatherNodesOutgoing, GatherNodesIngoing from kgcnn.layers.pooling import PoolingLocalEdges from kgcnn.layers.modules import LazySubtract @tf.keras.utils.register_keras_serializable(package='kgcnn', name='DMPNNGatherEdgesPairs') class DMPNNGatherEdgesPairs(GraphBaseLayer): """Gather edge pairs that also works for invalid indices given a certain pair, i.e. if a edge does not have its reverse counterpart in the edge indices list. This class is used in `DMPNN <https://pubs.acs.org/doi/full/10.1021/acs.jcim.9b00237>`_ . """ def __init__(self, **kwargs): """Initialize layer.""" super(DMPNNGatherEdgesPairs, self).__init__(**kwargs) self.gather_layer = GatherNodesIngoing() def build(self, input_shape): """Build layer.""" super(DMPNNGatherEdgesPairs, self).build(input_shape) def call(self, inputs, **kwargs): """Forward pass. Args: inputs (list): [edges, pair_index] - edges (tf.RaggedTensor): Node embeddings of shape (batch, [M], F) - pair_index (tf.RaggedTensor): Edge indices referring to edges of shape (batch, [M], 1) Returns: list: Gathered edge embeddings that match the reverse edges of shape (batch, [M], F) for selection_index. """ self.assert_ragged_input_rank(inputs) edges, pair_index = inputs index_corrected = tf.RaggedTensor.from_row_splits( tf.where(pair_index.values >= 0, pair_index.values, tf.zeros_like(pair_index.values)), pair_index.row_splits, validate=self.ragged_validate) edges_paired = self.gather_layer([edges, index_corrected], **kwargs) edges_corrected = tf.RaggedTensor.from_row_splits( tf.where(pair_index.values >= 0, edges_paired.values, tf.zeros_like(edges_paired.values)), edges_paired.row_splits, validate=self.ragged_validate) return edges_corrected @tf.keras.utils.register_keras_serializable(package='kgcnn', name='DMPNNPPoolingEdgesDirected') class DMPNNPPoolingEdgesDirected(GraphBaseLayer): """Pooling of edges for around a target node as defined by `DMPNN <https://pubs.acs.org/doi/full/10.1021/acs.jcim.9b00237>`_ . This slightly different than the normal node aggregation from message passing like networks. Requires edge pairs for this implementation. """ def __init__(self, **kwargs): """Initialize layer.""" super(DMPNNPPoolingEdgesDirected, self).__init__(**kwargs) self.pool_edge_1 = PoolingLocalEdges(pooling_method="sum") self.gather_edges = GatherNodesOutgoing() self.gather_pairs = DMPNNGatherEdgesPairs() self.subtract_layer = LazySubtract() def build(self, input_shape): """Build layer.""" super(DMPNNPPoolingEdgesDirected, self).build(input_shape) def call(self, inputs, **kwargs): """Forward pass. Args: inputs: [nodes, edges, edge_index, edge_reverse_pair] - nodes (tf.RaggedTensor): Node embeddings of shape (batch, [N], F) - edges (tf.RaggedTensor): Edge or message embeddings of shape (batch, [M], F) - edge_index (tf.RaggedTensor): Edge indices referring to nodes of shape (batch, [M], 2) - edge_reverse_pair (tf.RaggedTensor): Pair mappings for reverse edges (batch, [M], 1) Returns: tf.RaggedTensor: Edge embeddings of shape (batch, [M], F) """ n, ed, edi, edp = inputs pool_edge_receive = self.pool_edge_1([n, ed, edi], **kwargs) # Sum pooling of all edges ed_new = self.gather_edges([pool_edge_receive, edi], **kwargs) ed_not = self.gather_pairs([ed, edp], **kwargs) out = self.subtract_layer([ed_new, ed_not], **kwargs) return out

43.766667

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0.67276

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3,939

5.381551

0.289308

0.049085

0.032723

0.030386

0.354889

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0.293728

0.201792

0.172965

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0.219345

3,939

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false

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0.375

0

null

0

null

0

1

0

32b877d4916dd5d40bd6976997b7ef7d01823785

349

py

Python

api/admin.py

jchmura/suchary-django

af2e8a62d222fd6eb18f29af95c23ab098ccc2a6

[ "MIT" ]

null

api/admin.py

jchmura/suchary-django

af2e8a62d222fd6eb18f29af95c23ab098ccc2a6

[ "MIT" ]

2

2021-03-19T21:54:17.000Z

2021-06-10T19:20:12.000Z

api/admin.py

jchmura/suchary-django

af2e8a62d222fd6eb18f29af95c23ab098ccc2a6

[ "MIT" ]

null

from django.contrib import admin from api.models import Device class DeviceAdmin(admin.ModelAdmin): list_display = ['android_id', 'alias', 'model', 'os_version', 'version', 'created', 'last_seen', 'active'] list_filter = ['active'] search_fields = ['registration_id', 'android_id', 'alias'] admin.site.register(Device, DeviceAdmin)

26.846154

110

0.713467

42

349

5.738095

0.690476

0.074689

0.116183

0

0.13467

349

12

111

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0.798013

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0

1

0

false

0

0.285714

0

0.857143

0

null

0

null

0

1

0

32bd83533b8a10d702670e0618e12d21f2714992

712

py

Python

f8a_jobs/handlers/flow.py

sawood14012/fabric8-analytics-jobs

a7d850dfef5785144676b9a3b4e29942161e5347

[ "Apache-2.0" ]

5

2017-05-04T11:22:31.000Z

2018-08-24T16:12:30.000Z

f8a_jobs/handlers/flow.py

sawood14012/fabric8-analytics-jobs

a7d850dfef5785144676b9a3b4e29942161e5347

[ "Apache-2.0" ]

325

2017-05-03T08:44:03.000Z

2021-12-13T21:03:49.000Z

f8a_jobs/handlers/flow.py

sawood14012/fabric8-analytics-jobs

a7d850dfef5785144676b9a3b4e29942161e5347

[ "Apache-2.0" ]

28

2017-05-02T05:09:32.000Z

2021-03-11T09:42:34.000Z

"""Schedule multiple flows of a type.""" from .base import BaseHandler class FlowScheduling(BaseHandler): """Schedule multiple flows of a type.""" def execute(self, flow_name, flow_arguments): """Schedule multiple flows of a type, do filter expansion if needed. :param flow_name: flow name that should be scheduled :param flow_arguments: a list of flow arguments per flow """ for node_args in flow_arguments: if self.is_filter_query(node_args): for args in self.expand_filter_query(node_args): self.run_selinon_flow(flow_name, args) else: self.run_selinon_flow(flow_name, node_args)

33.904762

76

0.651685

94

712

4.734043

0.425532

0.089888

0.141573

0.155056

0.305618

0

0.273876

712

20

77

35.6

0.860735

0.345506

0

1

0.111111

false

0

0.111111

0

0.333333

0

null

0

null

0

1

0

32bdf6c9f66952e90bfd46bcfa58f2ec034c3c0d

1,032

py

Python

mako/stats/notifier.py

zer0tonin/mako

12420056e13e1acd333e686537d5ebc909450620

[ "MIT" ]

null

mako/stats/notifier.py

zer0tonin/mako

12420056e13e1acd333e686537d5ebc909450620

[ "MIT" ]

1

2021-06-02T04:22:46.000Z

mako/stats/notifier.py

zer0tonin/mako

12420056e13e1acd333e686537d5ebc909450620

[ "MIT" ]

null

import logging logger = logging.getLogger(__name__) class Notifier: def __init__(self, redis): self.redis = redis async def notify_guilds(self): guilds_set = "guilds" logger.debug("Scanning {}".format(guilds_set)) result = [] async for guild_id in self.redis.isscan(guilds_set): result.extend(await self.notify_guild(guild_id)) return result async def notify_guild(self, guild_id): notify_list = "guilds:{}:notify".format(guild_id) level_zset = "guilds:{}:levels".format(guild_id) result = [] logger.debug("Popping {} queue".format(notify_list)) user_id = await self.redis.lpop(notify_list) while user_id is not None: logger.debug("Accessing {} zset for user: {}".format(level_zset, user_id)) level = await self.redis.zscore(level_zset, user_id) result.append((guild_id, user_id, level)) user_id = await self.redis.lpop(notify_list) return result

30.352941

86

0.631783

130

1,032

4.761538

0.323077

0.087237

0.067851

0.048465

0.109855

0

0.255814

1,032

33

87

31.272727

0.80599

0

0.25

0

0.092054

0

1

0.041667

false

0

0.041667

0

0.208333

0

null

0

null

0

1

0

32c304191982cf35da8aed8e53fd875c3bef3ba2

1,505

py

Python

PageObjectModel/Test/addAndEditionData.py

lblaszkowski/Arena

61f924bc7c3994ec7714fe68f60b02b35ccd286b

[ "Apache-2.0" ]

null

PageObjectModel/Test/addAndEditionData.py

lblaszkowski/Arena

61f924bc7c3994ec7714fe68f60b02b35ccd286b

[ "Apache-2.0" ]

null

PageObjectModel/Test/addAndEditionData.py

lblaszkowski/Arena

61f924bc7c3994ec7714fe68f60b02b35ccd286b

[ "Apache-2.0" ]

null

import unittest from selenium import webdriver from PageObjectModel.Pages.addAndEditionDataPage import AddAndEditionData_Page from time import sleep url = 'https://buggy-testingcup.pgs-soft.com/' class AddAndEditionDataPage(unittest.TestCase): def setUp(self, browser="mozilla", task="task_3"): if browser == "chrome" or browser == "ch": self.driver = webdriver.Chrome(executable_path=r'../Drivers/ChromeDrive_74/chromedriver.exe') self.driver.maximize_window() self.driver.get(url + task) elif browser == "mozilla" or browser == "ff": self.driver = webdriver.Firefox(executable_path=r'../Drivers/FirefoxDrive_24/geckodriver.exe') self.driver.maximize_window() self.driver.get(url + task) else: print("Brak przeglądarki") raise Exception("Brak przeglądarki") return self.driver def tearDown(self): self.driver.close() self.driver.quit() def test_AddAndEditionData(self): AddandEditionData = AddAndEditionData_Page(self.driver) AddandEditionData.menuButtonClick() AddandEditionData.dropdownMenuClick() AddandEditionData.editFile() AddandEditionData.fieldName("Jan") AddandEditionData.fieldSurname("Nowak") AddandEditionData.fieldNotes("Testowy napis") AddandEditionData.fieldPhone("10981234098") AddandEditionData.fieldImage() AddandEditionData.saveButton()

32.717391

106

0.67907

142

1,505

7.126761

0.528169

0.098814

0.037549

0.043478

0.092885

0

0.013594

0.21794

1,505

45

107

33.444444

0.846219

0

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0

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0.056

0

1

0.090909

false

0

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0

0.272727

0.030303

0

null

0

null

0

1

0

32c57ec480ef32335403cba14fba78c713f0eb97

741

py

Python

azext_script/compilers/az/handlers/HDInsight.py

yorek/adl

d9da1b7d46c71415e38a6efe5b1c8d45b02b3704

[ "MIT" ]

null

azext_script/compilers/az/handlers/HDInsight.py

yorek/adl

d9da1b7d46c71415e38a6efe5b1c8d45b02b3704

[ "MIT" ]

1

2018-10-15T05:51:38.000Z

azext_script/compilers/az/handlers/HDInsight.py

yorek/adl

d9da1b7d46c71415e38a6efe5b1c8d45b02b3704

[ "MIT" ]

1

2018-10-18T18:41:02.000Z

from .Generic import GenericHandler class HDInsightHandler(GenericHandler): azure_object = "hdinsight" def execute(self): fqn = self.get_full_resource_name() self.add_context_parameter("resource-group", "group") if fqn == "hdinsight" and self.action == "create": self.add_context_parameter("location", "location") if 'storage account' in self.context: storage_account = self.context["storage account"] storage_account += ".blob.core.windows.net" self.add_parameter("storage-account", storage_account) cmd = super(HDInsightHandler, self).execute() self.save_to_context() return cmd

30.875

70

0.618084

75

741

5.92

0.506667

0.189189

0.063063

0.103604

0

0.279352

741

23

71

32.217391

0.831461

0

0.17027

0.02973

0

1

0.066667

false

0

0.066667

0

0.333333

0

null

0

null

0

1

0

32c6c31592e8107e78ef2bb52771dcffacd50781

393

py

Python

html_mining/twitter.py

sourceperl/sandbox

bbe1be52c3e51906a8ec94411c4df6a95dcbb39c

[ "MIT" ]

null

html_mining/twitter.py

sourceperl/sandbox

bbe1be52c3e51906a8ec94411c4df6a95dcbb39c

[ "MIT" ]

null

html_mining/twitter.py

sourceperl/sandbox

bbe1be52c3e51906a8ec94411c4df6a95dcbb39c

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- import requests from bs4 import BeautifulSoup r = requests.get("https://twitter.com/ThePSF", headers={"User-Agent": ""}) if r.status_code == 200: s = BeautifulSoup(r.content, "html.parser") # extract tweets l_tw = [] for p in s.find_all("p", attrs={"class": "tweet-text"}): l_tw.append(p.text.strip()) print(l_tw)

23.117647

74

0.62341

58

393

4.137931

0.775862

0.0375

0

0.018692

0.183206

393

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75

24.5625

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0.222222

0.111111

0

null

0

null

0

1

0

32c80a80f478110db9183291633d248502cd65ad

590

py

Python

warehouse_labeling_machines/libs/utils.py

sdg97/warehouse_labeling_machines

3650b9fb2d3fef85ee01925acf0a9266dafe746a

[ "Apache-2.0" ]

null

warehouse_labeling_machines/libs/utils.py

sdg97/warehouse_labeling_machines

3650b9fb2d3fef85ee01925acf0a9266dafe746a

[ "Apache-2.0" ]

null

warehouse_labeling_machines/libs/utils.py

sdg97/warehouse_labeling_machines

3650b9fb2d3fef85ee01925acf0a9266dafe746a

[ "Apache-2.0" ]

null

#!/usr/bin/python # -*- coding: utf-8 -*- import decimal import multiprocessing import random def roundDecimal(v): ''' Sembra che l'arrotondamento di un decimal sia più complicato del previsto ''' return v.quantize(decimal.Decimal('0.01'), rounding=decimal.ROUND_HALF_UP) def maybeStart(startCb, debug): ''' Ogni tanto esegue questa callback... Ad ogni restart di un worker in maniera casuale esegue la callback ''' if debug: return workers = multiprocessing.cpu_count() * 2 + 1 if random.randrange(workers) == 0: startCb()

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78

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590

5.2

0.72

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0.225424

590

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79

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0

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0

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0

0.636364

0

null

0

null

0

1

0

086749fe086bfe8b53982e2dc76e87c1e91b6cc7

1,596

py

Python

code/p3.py

OscarFlores-IFi/CDINP19

7fb0cb6ff36b9a10bcfa0772b172c5e49996df48

[ "MIT" ]

null

code/p3.py

OscarFlores-IFi/CDINP19

7fb0cb6ff36b9a10bcfa0772b172c5e49996df48

[ "MIT" ]

null

code/p3.py

OscarFlores-IFi/CDINP19

7fb0cb6ff36b9a10bcfa0772b172c5e49996df48

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Mon Feb 11 09:18:37 2019 @author: if715029 """ import pandas as pd import numpy as np import sklearn.metrics as skm import scipy.spatial.distance as sc #%% Leer datos data = pd.read_excel('../data/Test de películas(1-16).xlsx', encoding='latin_1') #%% Seleccionar datos (a mi estilo) pel = pd.DataFrame() for i in range((len(data.T)-5)//3): pel = pel.append(data.iloc[:,6+i*3]) pel = pel.T print(pel) #%% Seleccionar datos (estilo Riemann) csel = np.arange(6,243,3) cnames = list(data.columns.values[csel]) datan = data[cnames] #%% Promedios movie_prom = datan.mean(axis=0) user_prom = datan.mean(axis=1) #%% Calificaciones a binarios (>= 3) datan = datan.copy() datan[datan<3] = 0 datan[datan>=3] = 1 #%% Calcular distancias de indices de similitud #D1 = sc.pdist(datan,'hamming') # hamming == matching D1 = sc.pdist(datan,'jaccard') D1 = sc.squareform(D1) #D2 = sc.pdist(data_b,'jaccard') # hamming == matching #D2 = sc.squareform(D2) Isim1 = 1-D1 #%% Seleccionar usuario y determinar sus parecidos user = 1 Isim_user = Isim1[user] Isim_user_sort = np.sort(Isim_user) indx_user = np.argsort(Isim_user) #%% Recomendación de películas p1. USER = datan.loc[user] USER_sim = datan.loc[indx_user[-2]] indx_recomend1 = (USER_sim==1)&(USER==0) recomend1 = list(USER.index[indx_recomend1]) #%% Recomendación peliculas p2. USER = datan.loc[user] USER_sim = np.mean(datan.loc[indx_user[-6:-1]],axis = 0) USER_sim[USER_sim<=.5]=0 USER_sim[USER_sim>.5]=1 indx_recomend2 = (USER_sim==1)&(USER==0) recomend2 = list(USER.index[indx_recomend2])

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0

0.051338

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1,596

72

81

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0.028571

0

null

0

null

0

1

0

0867a27f2b0a9d65b0fbacf348d77dfbc3427264

1,187

py

Python

itao/utils/qt_logger.py

MaxChangInnodisk/itao

b0745eb48bf67718ef00db566c4cc19896d903a7

[ "MIT" ]

null

itao/utils/qt_logger.py

MaxChangInnodisk/itao

b0745eb48bf67718ef00db566c4cc19896d903a7

[ "MIT" ]

null

itao/utils/qt_logger.py

MaxChangInnodisk/itao

b0745eb48bf67718ef00db566c4cc19896d903a7

[ "MIT" ]

null

import logging class CustomLogger: def __init__(self): pass """ Create logger which name is 'dev' """ def create_logger(self, name='dev', log_file='itao.log', write_mode='w'): logger = logging.getLogger(name) # setup LEVEL logger.setLevel(logging.DEBUG) # setup formatter formatter = logging.Formatter( "%(asctime)s %(levelname)-.4s %(message)s", "%m-%d %H:%M:%S") # setup handler stream_handler = logging.StreamHandler() file_handler = logging.FileHandler(log_file, write_mode, 'utf-8') # add formatter into handler stream_handler.setFormatter(formatter) file_handler.setFormatter(formatter) # add handler into logger logger.addHandler(stream_handler) logger.addHandler(file_handler) logger.info('Create Logger: {}'.format(name)) return logger """ get logger """ def get_logger(self, name='dev', log_file='itao.log', write_mode='w'): logger = logging.getLogger(name) return logger if logger.hasHandlers() else self.create_logger(name, log_file, write_mode)

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0.333333

0

null

0

null

0

1

0

08687783aacc944c351fc37618c9c87ef69b3d6b

2,296

py

Python

scripts/ndvi_diff.py

hkfrei/pythonRemoteSensing

c8681d859313ee5ad01e5b9753f8c43462268624

[ "MIT" ]

1

2019-12-18T21:54:22.000Z

scripts/ndvi_diff.py

hkfrei/pythonRemoteSensing

c8681d859313ee5ad01e5b9753f8c43462268624

[ "MIT" ]

null

scripts/ndvi_diff.py

hkfrei/pythonRemoteSensing

c8681d859313ee5ad01e5b9753f8c43462268624

[ "MIT" ]

1

2020-07-01T16:44:21.000Z

import numpy import rasterio import gdal print('all modules imported') # path to the folder with the ndvi rasters base_path = "/Users/hk/Downloads/gaga/" # shapefile with forest mask forest_mask = base_path + "waldmaske_wgs84.shp" # initialize the necessary rasters for the ndvi calculation. ndvi_2017 = rasterio.open(base_path + "ndvi_17.tiff", driver="GTiff") ndvi_2018 = rasterio.open(base_path + "ndvi_18.tiff", driver="GTiff") # print out metadata about the ndvi's print(ndvi_2018.count) # number of raster bands print(ndvi_2017.count) # number of raster bands print(ndvi_2018.height) # column count print(ndvi_2018.dtypes) # data type of the raster e.g. ('float64',) print(ndvi_2018.crs) # projection of the raster e.g. EPSG:32632 print("calculate ndvi difference") # this is will give us an array of values, not an actual raster image. ndvi_diff_array = numpy.subtract(ndvi_2018.read(1), ndvi_2017.read(1)) print("reclassify") # reclassify ndvi_diff_reclass_array = numpy.where( ndvi_diff_array <= -0.05, 1, 9999.0 ) # create a new (empty) raster for the "original" diff ndvi_diff_image = rasterio.open(base_path + "ndvi_diff.tif", "w", driver="Gtiff", width=ndvi_2018.width, height=ndvi_2018.height, count=1, crs=ndvi_2018.crs, transform=ndvi_2018.transform, dtype='float64') # create a new (empty) raster for the reclassified diff ndvi_diff_reclass_image = rasterio.open(base_path + "ndvi_reclass_diff.tif", "w", driver="Gtiff", width=ndvi_2018.width, height=ndvi_2018.height, count=1, crs=ndvi_2018.crs, transform=ndvi_2018.transform, dtype='float64') # write the ndvi's to raster ndvi_diff_image.write(ndvi_diff_array.astype("float64"), 1) ndvi_diff_reclass_image.write(ndvi_diff_reclass_array.astype("float64"), 1) ndvi_diff_image.close() ndvi_diff_reclass_image.close() # extract forest areas # Make sure to add correct Nodata and Alpha values. They have to match the reclassified values. warp_options = gdal.WarpOptions(cutlineDSName=forest_mask, cropToCutline=True, dstNodata=9999, dstAlpha=9999) gdal.Warp(base_path + "change_masked.tif", base_path + "ndvi_reclass_diff.tif", options=warp_options) print("finished")

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0.125

0.28125

0

null

0

null

0

1

0

0869fc3b1af3273cc468fc0da2d162910f894bff

3,610

py

Python

studio/model.py

NunoEdgarGFlowHub/studio

42b221892a81535842ff25cbbcc434d6422a19e5

[ "Apache-2.0" ]

null

studio/model.py

NunoEdgarGFlowHub/studio

42b221892a81535842ff25cbbcc434d6422a19e5

[ "Apache-2.0" ]

null

studio/model.py

NunoEdgarGFlowHub/studio

42b221892a81535842ff25cbbcc434d6422a19e5

[ "Apache-2.0" ]

null

"""Data providers.""" import os try: # try-except statement needed because # pip module is not available in google app engine import pip except ImportError: pip = None import yaml import six from .artifact_store import get_artifact_store from .http_provider import HTTPProvider from .firebase_provider import FirebaseProvider from .s3_provider import S3Provider from .gs_provider import GSProvider from . import logs def get_config(config_file=None): config_paths = [] if config_file: if not os.path.exists(config_file): raise ValueError('User config file {} not found' .format(config_file)) config_paths.append(os.path.expanduser(config_file)) config_paths.append(os.path.expanduser('~/.studioml/config.yaml')) config_paths.append( os.path.join( os.path.dirname(os.path.realpath(__file__)), "default_config.yaml")) for path in config_paths: if not os.path.exists(path): continue with(open(path)) as f: config = yaml.load(f.read()) def replace_with_env(config): for key, value in six.iteritems(config): if isinstance(value, six.string_types): config[key] = os.path.expandvars(value) elif isinstance(value, dict): replace_with_env(value) replace_with_env(config) return config raise ValueError('None of the config paths {} exits!' .format(config_paths)) def get_db_provider(config=None, blocking_auth=True): if not config: config = get_config() verbose = parse_verbosity(config.get('verbose')) logger = logs.getLogger("get_db_provider") logger.setLevel(verbose) logger.debug('Choosing db provider with config:') logger.debug(config) if 'storage' in config.keys(): artifact_store = get_artifact_store( config['storage'], blocking_auth=blocking_auth, verbose=verbose) else: artifact_store = None assert 'database' in config.keys() db_config = config['database'] if db_config['type'].lower() == 'firebase': return FirebaseProvider( db_config, blocking_auth, verbose=verbose, store=artifact_store) elif db_config['type'].lower() == 'http': return HTTPProvider(db_config, verbose=verbose, blocking_auth=blocking_auth) elif db_config['type'].lower() == 's3': return S3Provider(db_config, verbose=verbose, store=artifact_store, blocking_auth=blocking_auth) elif db_config['type'].lower() == 'gs': return GSProvider(db_config, verbose=verbose, store=artifact_store, blocking_auth=blocking_auth) else: raise ValueError('Unknown type of the database ' + db_config['type']) def parse_verbosity(verbosity=None): if verbosity is None: return parse_verbosity('info') if verbosity == 'True': return parse_verbosity('info') logger_levels = { 'debug': 10, 'info': 20, 'warn': 30, 'error': 40, 'crit': 50 } if isinstance(verbosity, six.string_types) and \ verbosity in logger_levels.keys(): return logger_levels[verbosity] else: return int(verbosity)

28.88

77

0.591967

396

3,610

5.217172

0.277778

0.058083

0.029042

0.046467

0.188771

0.135528

0.093901

0.061955

0

0.005645

0.313019

3,610

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0.827419

0.027978

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0

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0.25

0

null

0

null

0

1

0

086b6939a15a14e2ba2c7a9bf78818444b385782

7,310

py

Python

extendPlugins/minecraft.py

f88af65a/XyzB0ts

21a557288877b24f337f16002d8bb72b155f2551

[ "MIT" ]

4

2021-10-17T11:54:07.000Z

2022-03-18T13:10:11.000Z

extendPlugins/minecraft.py

f88af65a/XyzB0ts

21a557288877b24f337f16002d8bb72b155f2551

[ "MIT" ]

null

extendPlugins/minecraft.py

f88af65a/XyzB0ts

21a557288877b24f337f16002d8bb72b155f2551

[ "MIT" ]

1

2021-10-16T09:51:25.000Z

import asyncio import json import socket import time from botsdk.util.BotPlugin import BotPlugin from botsdk.util.Error import printTraceBack def getMcRequestData(ip, port): data = (b"\x00\xff\xff\xff\xff\x0f" + bytes([len(ip.encode("utf8"))]) + ip.encode("utf8") + int.to_bytes(port, 2, byteorder="big") + b"\x01\x01\x00") return bytes([len(data) - 2]) + data def getVarInt(b): b = list(b) b.reverse() ans = 0 for i in b: ans <<= 7 ans |= (i & 127) return ans class plugin(BotPlugin): "/[mcbe/mcpe] ip [端口]" def onLoad(self): self.name = "minecraft" self.addTarget("GroupMessage", "mc", self.getMc) self.addTarget("GroupMessage", "mcbe", self.getBe) self.addTarget("GROUP:1", "mc", self.getMc) self.addTarget("GROUP:1", "mcbe", self.getBe) self.addBotType("Mirai") self.addBotType("Kaiheila") self.canDetach = True async def getMc(self, request): "/mc ip [端口]不写默认25565" data = request.getFirstTextSplit() serverIp = None serverPort = 25565 if len(data) < 2: await request.sendMessage("缺少参数\n/mc ip [端口]不写默认25565") return if len(data) >= 2: serverIp = data[1] if len(data) >= 3: if not (data[2].isnumeric() and int(data[2]) >= 0 and int(data[2]) <= 65535): request.sendMessage("端口有误") return serverPort = int(data[2]) # 初始化socket with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock: sock.settimeout(0) loop = asyncio.get_event_loop() # 连接 try: await loop.sock_connect(sock, (serverIp, serverPort)) except Exception: await request.sendMessage("连接失败") return requestData = getMcRequestData(serverIp, serverPort) # 发送 try: await loop.sock_sendall(sock, requestData) except Exception: await request.sendMessage("请求发送失败") return # 接受 responseData = bytes() breakFlag = True dataSize = 10000000 stime = time.time() while time.time() - stime <= 2 and breakFlag: for i in range(0, len(responseData)): if int(responseData[i]) & 128 == 0: dataSize = getVarInt(responseData[0:i + 1]) + i + 1 break if len(responseData) == dataSize: breakFlag = False break rdata = await loop.sock_recv(sock, 10240) if len(rdata) == 0: await request.sendMessage("接受请求时连接断开") return -1 responseData += rdata await asyncio.sleep(0) for i in range(0, len(responseData)): if int(responseData[i]) & 128 == 0: responseData = responseData[i + 2:] break for i in range(0, len(responseData)): if int(responseData[i]) & 128 == 0: responseData = responseData[i + 1:] break responseData = json.loads(responseData) description = "" if "text" in responseData["description"]: description = responseData["description"]["text"] if "extra" in responseData["description"]: for i in responseData["description"]["extra"]: if "text" in i: description += i["text"] try: printData = "信息:{0}\n版本:{1}\n人数:{2}/{3}".format( description, responseData["version"]["name"], responseData["players"]["online"], responseData["players"]["max"]) if "playerlist" in data: printData += "\n在线玩家:\n" for i in range(0, len(responseData["players"]["sample"])): printData += (responseData ["players"]["sample"][i]["name"]) if i != len(responseData["players"]["sample"]) - 1: printData += "\n" await request.sendMessage(printData) except Exception: await request.sendMessage("解析过程中出错") printTraceBack() async def getBe(self, request): "/mcbe ip [端口]不写默认19132" data = request.getFirstTextSplit() serverIp = None serverPort = 19132 if len(data) < 2: await request.sendMessage("缺少参数\n/mcbe ip [端口]不写默认19132") return if len(data) >= 2: serverIp = data[1] if len(data) == 3: if not (data[2].isnumeric() and int(data[2]) >= 0 and int(data[2]) <= 65535): request.sendMessage("端口有误") return serverPort = int(data[2]) # 初始化socket with socket.socket(socket.AF_INET, socket.SOCK_DGRAM) as sock: sock.settimeout(0) loop = asyncio.get_event_loop() # 连接 try: await loop.sock_connect(sock, (serverIp, serverPort)) except Exception: await request.sendMessage("连接失败") return requestData = (b"\x01" + b"\x00" * 8 + b"\x00\xff\xff\x00\xfe\xfe\xfe" + b"\xfe\xfd\xfd\xfd\xfd\x12\x34\x56\x78" + b"\x00" * 8) # 发送 try: await loop.sock_sendall(sock, requestData) except Exception: await request.sendMessage("请求发送失败") return # 接受 responseData = bytes() breakFlag = True stime = time.time() while time.time() - stime <= 2 and breakFlag: try: responseData = await loop.sock_recv(sock, 10240) except Exception: responseData = b"" if len(responseData) == 0: sock.close() await request.sendMessage("接收过程中连接断开") return breakFlag = False await asyncio.sleep(0) responseData = responseData[35:].decode() responseData = responseData.split(";") printData = "" try: printData += f"服务器名:{responseData[1]}\n" printData += f"人数:{responseData[4]}/{responseData[5]}\n" printData += f"游戏模式:{responseData[8]}\n" printData += ( f"版本:{responseData[0]} {responseData[2]} {responseData[3]}" ) await request.sendMessage(printData) except Exception: await request.sendMessage("解析过程中出错") printTraceBack() def handle(*args, **kwargs): return plugin(*args, **kwargs)

36.733668

79

0.477291

687

7,310

5.056769

0.232897

0.072539

0.079447

0.046632

0.48129

0.467473

0.423719

0.415947

0.39407

0

0.037037

0.409029

7,310

198

80

36.919192

0.76713

0.008071

0

0.466292

0

0.097742

0.027808

0

1

0.022472

false

0

0.033708

0.005618

0.134831

0.078652

0

null

0

null

0

1

0

086ccdd01316fbb3c32c9928ed64ba2001cd4f5d

2,583

py

Python

main.py

brpaz/ulauncher-dockerhub

22e646bda40328373a4d90fa0aece2cac0187a42

[ "MIT" ]

3

2020-09-04T07:56:47.000Z

2022-01-05T13:19:25.000Z

main.py

brpaz/ulauncher-dockerhub

22e646bda40328373a4d90fa0aece2cac0187a42

[ "MIT" ]

null

main.py

brpaz/ulauncher-dockerhub

22e646bda40328373a4d90fa0aece2cac0187a42

[ "MIT" ]

null

""" Main Module """ import logging from ulauncher.api.client.Extension import Extension from ulauncher.api.client.EventListener import EventListener from ulauncher.api.shared.event import KeywordQueryEvent from ulauncher.api.shared.item.ExtensionResultItem import ExtensionResultItem from ulauncher.api.shared.action.RenderResultListAction import RenderResultListAction from ulauncher.api.shared.action.DoNothingAction import DoNothingAction from ulauncher.api.shared.action.HideWindowAction import HideWindowAction from ulauncher.api.shared.action.OpenUrlAction import OpenUrlAction from dockerhub.client import Client logger = logging.getLogger(__name__) class DockerHubExtension(Extension): """ Main Extension Class """ def __init__(self): """ Initializes the extension """ super(DockerHubExtension, self).__init__() self.dockerhub = Client() self.subscribe(KeywordQueryEvent, KeywordQueryEventListener()) def search_repositories(self, query): """ Shows the a list of DockerHub repositories """ if len(query) < 3: return RenderResultListAction([ ExtensionResultItem( icon='images/icon.png', name='Keep typing to search on Docker Hub ...', highlightable=False, on_enter=DoNothingAction()) ]) repos = self.dockerhub.search_repos(query) items = [] if not repos: return RenderResultListAction([ ExtensionResultItem( icon="images/icon.png", name="No results found matching your criteria", highlightable=False, on_enter=HideWindowAction()) ]) for repo in repos[:8]: items.append( ExtensionResultItem(icon='images/icon.png', name="%s 🟊 %s" % (repo["name"], repo["stars"]), description=repo["description"], on_enter=OpenUrlAction(repo["url"]))) return RenderResultListAction(items) class KeywordQueryEventListener(EventListener): """ Listener that handles the user input """ # pylint: disable=unused-argument,no-self-use def on_event(self, event, extension): """ Handles the event """ query = event.get_argument() or "" return extension.search_repositories(query) if __name__ == '__main__': DockerHubExtension().run()

34.905405

85

0.622145

233

2,583

6.781116

0.381974

0.065823

0.081013

0.083544

0.182278

0.111392

0.086076

0

0.001085

0.286101

2,583

73

86

35.383562

0.855206

0.080527

0

0.163265

0

0.068862

0

1

0.061224

false

0

0.204082

0

0.387755

0

null

0

null

0

1

0

0871266d4d435da659b3d90a1e0729b53c28c39c

2,448

py

Python

game/gamesrc/objects/character.py

ranka47/battle-of-hogwarts

e7b2265ebe5661249dd28e472c49b74c1bbcdf23

[ "BSD-3-Clause" ]

2

2019-02-24T00:20:47.000Z

2020-04-24T15:50:31.000Z

game/gamesrc/objects/character.py

ranka47/battle-of-hogwarts

e7b2265ebe5661249dd28e472c49b74c1bbcdf23

[ "BSD-3-Clause" ]

null

game/gamesrc/objects/character.py

ranka47/battle-of-hogwarts

e7b2265ebe5661249dd28e472c49b74c1bbcdf23

[ "BSD-3-Clause" ]

1

2019-01-05T15:51:37.000Z

""" Template for Characters Copy this module up one level and name it as you like, then use it as a template to create your own Character class. To make new logins default to creating characters of your new type, change settings.BASE_CHARACTER_TYPECLASS to point to your new class, e.g. settings.BASE_CHARACTER_TYPECLASS = "game.gamesrc.objects.mychar.MyChar" Note that objects already created in the database will not notice this change, you have to convert them manually e.g. with the @typeclass command. """ from ev import Character as DefaultCharacter from ev import Script import random class Character(DefaultCharacter): """ The Character is like any normal Object (see example/object.py for a list of properties and methods), except it actually implements some of its hook methods to do some work: at_basetype_setup - always assigns the default_cmdset to this object type (important!)sets locks so character cannot be picked up and its commands only be called by itself, not anyone else. (to change things, use at_object_creation() instead) at_after_move - launches the "look" command at_post_puppet(player) - when Player disconnects from the Character, we store the current location, so the "unconnected" character object does not need to stay on grid but can be given a None-location while offline. at_pre_puppet - just before Player re-connects, retrieves the character's old location and puts it back on the grid with a "charname has connected" message echoed to the room """ def at_object_creation(self): self.db.score = 0 self.db.health_max = 100 self.db.health = self.db.health_max self.db.will = 100 self.db.respawns = 0 houses = ["Gryffindor","Hufflepuff","Slytherin","Ravenclaw"] self.db.house = houses[random.randint(0, len(houses) - 1)] self.db.dementors = 0 self.db.spiders = 0 self.db.willow = 0 self.db.rodents = 0 self.db.boggart = 0 self.db.parallax = 0 self.db.dragon = 0 def respawn(self): self.msg("You lost a life and respawn with all your default powers") self.db.health = self.db.health_max self.db.score -= 50 self.db.will = 100 self.db.respawns += 1

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0873053669c5a9be614101baec79eda2eb276cb9

3,170

py

Python

lesson5/lesson5_task4.py

nekdfl/GB-python-developer

ca3f34bac2a92a930779f89357941bfa9634b3d4

[ "MIT" ]

null

lesson5/lesson5_task4.py

nekdfl/GB-python-developer

ca3f34bac2a92a930779f89357941bfa9634b3d4

[ "MIT" ]

null

lesson5/lesson5_task4.py

nekdfl/GB-python-developer

ca3f34bac2a92a930779f89357941bfa9634b3d4

[ "MIT" ]

null

""" Создать (не программно) текстовый файл со следующим содержимым: One — 1 Two — 2 Three — 3 Four — 4 Необходимо написать программу, открывающую файл на чтение и считывающую построчно данные. При этом английские числительные должны заменяться на русские. Новый блок строк должен записываться в новый текстовый файл. """ def readfile(filepath): res = "" with open(filepath, 'r') as f: res = f.read() return res def make_dict(task2_data, delimiter=" - "): # print(task2_data) res_dict = {} for lnum, line in enumerate(task2_data.split("\n")): lnum += 1 # номер строки начинается с 0 if line != "": try: strelemcnt = len(line.split(delimiter)) if strelemcnt == 2: # print(f"Обработка строки {lnum} ok") word, nn = line.split(delimiter) res_dict[nn] = word else: raise RuntimeError(f"Ошибка ввода данных. Неверное количество аргументов в строке {lnum}.") except ValueError as e: raise ValueError(f"Неверный формат числа в строке {lnum}. Ошибка {e}") return res_dict def translate(en_dict, ru_dict): pass resdict = {} for key in en_dict.keys(): resdict[key] = ru_dict[key] return resdict def write_dict(filepath, dict, delimeter): pass lines = [] for key in dict.keys(): line = dict[key] + delimeter + key lines.append(line) with open(filepath, 'w+') as f: f.writelines("\n".join(lines)) f.seek(0) print(f"содержимое выходного файла {filepath}\n{f.read()}") def full_variant(): infile_name = "task4_data_in.txt" outfile_name = "task4_data_out.txt" ru_dict = {'1': 'Один', '2': 'Два', '3': 'Три', '4': 'Четыре'} try: task2_data = readfile(infile_name) except IOError as e: print(f"Ошибка работы с файлом: {e}") try: file_data_dict = make_dict(task2_data) except ValueError as e: print(f"{e}") exit(1) except RuntimeError as e: print(f"{e}") exit(2) try: resdict = translate(file_data_dict, ru_dict) # print(resdict) except KeyError as e: print(f"В словаре переводчика нет значения для {e}") exit(3) write_dict(outfile_name, resdict, " - ") print("Программа завершена") def short_variant(): infile_name = "task4_data_in.txt" outfile_name = "task4_data_out.txt" ru_dict = {'1': 'Один', '2': 'Два', '3': 'Три', '4': 'Четыре'} en_dict = {'1': 'one', '2': 'Two', '3': 'Three', '4': 'Four'} delimeter = " - " res_lines = [] with open(infile_name, "r") as ifile: for line in ifile: for kword in en_dict.keys(): if line.count(kword): res_lines.append(ru_dict[kword] + delimeter + kword) with open(outfile_name, "w+") as ofile: ofile.writelines("\n".join(res_lines)) ofile.seek(0) print(f"содержимое выходного файла {outfile_name}\n{ofile.read()}") if __name__ == "__main__": # main() short_variant()

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0

null

0

null

0

1

0

0876136eb46ef1d30f09dbd0eff572dd1e4a0144

28,812

py

Python

generator.py

jimstorch/DGGen

cdecbc4bfa491a634aac370de05b21bb6f6cf8e1

[ "Apache-2.0" ]

19

2016-12-04T12:43:43.000Z

2022-01-25T01:00:24.000Z

generator.py

jimstorch/DGGen

cdecbc4bfa491a634aac370de05b21bb6f6cf8e1

[ "Apache-2.0" ]

9

2017-01-04T16:33:00.000Z

2021-11-16T06:02:16.000Z

generator.py

jimstorch/DGGen

cdecbc4bfa491a634aac370de05b21bb6f6cf8e1

[ "Apache-2.0" ]

7

2016-12-04T12:43:47.000Z

2022-02-04T13:10:58.000Z

#!/usr/bin/env python3 import argparse import csv import datetime import json import logging import os import sys import warnings from collections import defaultdict from copy import copy from dataclasses import dataclass from itertools import islice, cycle, chain from random import randint, shuffle, choice, sample from textwrap import shorten, wrap from typing import List, Any, Dict, Tuple from reportlab.pdfbase import pdfmetrics from reportlab.pdfbase.ttfonts import TTFont from reportlab.pdfgen import canvas script_name = os.path.basename(sys.argv[0]) description = """ Generate characters for the Delta Green pen-and-paper roleplaying game from Arc Dream Publishing. """ __version__ = "1.4" logger = logging.getLogger(script_name) TEXT_COLOR = (0, 0.1, 0.5) DEFAULT_FONT = "Special Elite" MONTHS = ("JAN", "FEB", "MAR", "APR", "MAY", "JUN", "JUL", "AUG", "SEP", "OCT", "NOV", "DEC") SUGGESTED_BONUS_CHANCE = 75 def main(): options = get_options() init_logger(options.verbosity) logger.debug(options) data = load_data(options) pages_per_sheet = 2 if options.equip else 1 professions = [data.professions[options.type]] if options.type else data.professions.values() p = Need2KnowPDF(options.output, professions, pages_per_sheet=pages_per_sheet) for profession in professions: label = generate_label(profession) p.bookmark(label) for sex in islice( cycle(["female", "male"]), options.count or profession["number_to_generate"] ): c = Need2KnowCharacter( data=data, sex=sex, profession=profession, label_override=options.label, employer_override=options.employer, ) if options.equip: c.equip(profession.get("equipment-kit", None)) c.print_footnotes() p.add_page(c.d) if pages_per_sheet >= 2: p.add_page_2(c.e) p.save_pdf() logger.info("Wrote %s", options.output) class Need2KnowCharacter(object): statpools = [ [13, 13, 12, 12, 11, 11], [15, 14, 12, 11, 10, 10], [17, 14, 13, 10, 10, 8], ] DEFAULT_SKILLS = { "accounting": 10, "alertness": 20, "athletics": 30, "bureaucracy": 10, "criminology": 10, "disguise": 10, "dodge": 30, "drive": 20, "firearms": 20, "first aid": 10, "heavy machinery": 10, "history": 10, "humint": 10, "melee weapons": 30, "navigate": 10, "occult": 10, "persuade": 20, "psychotherapy": 10, "ride": 10, "search": 20, "stealth": 10, "survival": 10, "swim": 20, "unarmed combat": 40, } BONUS = [ "accounting", "alertness", "anthropology", "archeology", "art1", "artillery", "athletics", "bureaucracy", "computer science", "craft1value", "criminology", "demolitions", "disguise", "dodge", "drive", "firearms", "first aid", "forensics", "heavy machinery", "heavy weapons", "history", "humint", "law", "medicine", "melee weapons", "militaryscience1value", "navigate", "occult", "persuade", "pharmacy", "pilot1value", "psychotherapy", "ride", "science1value", "search", "sigint", "stealth", "surgery", "survival", "swim", "unarmed combat", "language1", ] def __init__(self, data, sex, profession, label_override=None, employer_override=None): self.data = data self.profession = profession self.sex = sex # Hold all dictionaries self.d = {} self.e = {} self.footnotes = defaultdict( iter( ["*", "†", "‡", "§", "¶", "**", "††", "‡‡", "§§", "¶¶", "***", "†††", "‡‡‡", "§§§"] ).__next__ ) self.generate_demographics(label_override, employer_override) self.generate_stats() self.generate_derived_attributes() self.generate_skills() def generate_demographics(self, label_override, employer_override): if self.sex == "male": self.d["male"] = "X" self.d["name"] = ( choice(self.data.family_names).upper() + ", " + choice(self.data.male_given_names) ) else: self.d["female"] = "X" self.d["name"] = ( choice(self.data.family_names).upper() + ", " + choice(self.data.female_given_names) ) self.d["profession"] = label_override or self.profession["label"] self.d["employer"] = employer_override or ", ".join( e for e in [self.profession.get("employer", ""), self.profession.get("division", "")] if e ) self.d["nationality"] = "(U.S.A.) " + choice(self.data.towns) self.d["age"] = "%d (%s %d)" % (randint(24, 55), choice(MONTHS), (randint(1, 28))) def generate_stats(self): rolled = [[sum(sorted([randint(1, 6) for _ in range(4)])[1:]) for _ in range(6)]] pool = choice(self.statpools + rolled) shuffle(pool) for score, stat in zip( pool, ["strength", "constitution", "dexterity", "intelligence", "power", "charisma"] ): self.d[stat] = score self.d[f"{stat}_x5"] = score * 5 self.d[f"{stat}_distinguishing"] = self.distinguishing(stat, score) def generate_derived_attributes(self): self.d["hitpoints"] = int(round((self.d["strength"] + self.d["constitution"]) / 2.0)) self.d["willpower"] = self.d["power"] self.d["sanity"] = self.d["power"] * 5 self.d["breaking point"] = self.d["power"] * 4 self.damage_bonus = ((self.d["strength"] - 1) >> 2) - 2 self.d["damage bonus"] = "DB=%d" % self.damage_bonus def generate_skills(self): # Default skills self.d.update(self.DEFAULT_SKILLS) # Professional skills self.d.update(self.profession["skills"]["fixed"]) for skill, score in sample( self.profession["skills"].get("possible", {}).items(), self.profession["skills"].get("possible-count", 0), ): self.d[skill] = score for i in range(self.profession["bonds"]): self.d[f"bond{i}"] = self.d["charisma"] # Bonus skills self.generate_bonus_skills(self.profession) def generate_bonus_skills(self, profession): bonus_skills = [ s for s in profession["skills"].get("bonus", []) if randint(1, 100) <= SUGGESTED_BONUS_CHANCE ] + sample(self.BONUS, len(self.BONUS)) bonuses_applied = 0 while bonuses_applied < 8: skill = bonus_skills.pop(0) boosted = self.d.get(skill, 0) + 20 if boosted <= 80: self.d[skill] = boosted bonuses_applied += 1 logger.debug("%s, boosted %s to %s", self, skill, boosted) else: logger.info( "%s, Skipped boost - %s already at %s", self, skill, self.d.get(skill, 0) ) def __str__(self): return ", ".join( [ self.d.get(i) for i in ("name", "profession", "employer", "department") if self.d.get(i) ] ) def distinguishing(self, field, value): return choice(self.data.distinguishing.get((field, value), [""])) def equip(self, kit_name=None): weapons = [self.data.weapons["unarmed"]] if kit_name: kit = self.data.kits[kit_name] weapons += self.build_weapon_list(kit["weapons"]) gear = [] for item in kit["armour"] + kit["gear"]: notes = ( (" ".join(self.store_footnote(n) for n in item["notes"]) + " ") if "notes" in item else "" ) text = notes + (self.data.armour[item["type"]] if "type" in item else item["text"]) gear.append(text) wrapped_gear = list(chain(*[wrap(item, 55, subsequent_indent=" ") for item in gear])) if len(wrapped_gear) > 22: logger.warning("Too much gear - truncated.") for i, line in enumerate(wrapped_gear): self.e[f"gear{i}"] = line if len(weapons) > 7: logger.warning("Too many weapons %s - truncated.", weapons) for i, weapon in enumerate(weapons[:7]): self.equip_weapon(i, weapon) def build_weapon_list(self, weapons_to_add): result = [] for weapon_to_add in weapons_to_add: if "type" in weapon_to_add: weapon = copy(self.data.weapons.get(weapon_to_add["type"], None)) if weapon: if "notes" in weapon_to_add: weapon["notes"] = weapon_to_add["notes"] result += ( [weapon] if "chance" not in weapon_to_add or weapon_to_add["chance"] >= randint(1, 100) else [] ) else: logger.error("Unknown weapon type %s", weapon_to_add["type"]) elif "one-of" in weapon_to_add: result += self.build_weapon_list([choice(weapon_to_add["one-of"])]) elif "both" in weapon_to_add: result += self.build_weapon_list(w for w in weapon_to_add["both"]) else: logger.error("Don't understand weapon %r", weapon_to_add) return result def equip_weapon(self, slot, weapon): self.e[f"weapon{slot}"] = shorten(weapon["name"], 15, placeholder="…") roll = int(self.d.get(weapon["skill"], 0) + (weapon["bonus"] if "bonus" in weapon else 0)) self.e[f"weapon{slot}_roll"] = f"{roll}%" if "base-range" in weapon: self.e[f"weapon{slot}_range"] = weapon["base-range"] if "ap" in weapon: self.e[f"weapon{slot}_ap"] = f"{weapon['ap']}" if "lethality" in weapon: lethality = weapon["lethality"] lethality_note_indicator = ( self.store_footnote(lethality["special"]) if "special" in lethality else None ) self.e[f"weapon{slot}_lethality"] = ( f"{lethality['rating']}%" if lethality["rating"] else "" ) + (f" {lethality_note_indicator}" if lethality_note_indicator else "") if "ammo" in weapon: self.e[f"weapon{slot}_ammo"] = f"{weapon['ammo']}" if "kill-radius" in weapon: self.e[f"weapon{slot}_kill_radius"] = f"{weapon['kill-radius']}" if "notes" in weapon: self.e[f"weapon{slot}_note"] = " ".join(self.store_footnote(n) for n in weapon["notes"]) if "damage" in weapon: damage = weapon["damage"] damage_note_indicator = ( self.store_footnote(damage["special"]) if "special" in damage else None ) if "dice" in damage: damage_modifier = (damage["modifier"] if "modifier" in damage else 0) + ( self.damage_bonus if "db-applies" in damage and damage["db-applies"] else 0 ) damage_roll = f"{damage['dice']}D{damage['die-type']}" + ( f"{damage_modifier:+d}" if damage_modifier else "" ) else: damage_roll = "" self.e[f"weapon{slot}_damage"] = damage_roll + ( f" {damage_note_indicator}" if damage_note_indicator else "" ) def print_footnotes(self): notes = list( chain( *[ wrap(f"{pointer} {note}", 40, subsequent_indent=" ") for (note, pointer) in list(self.footnotes.items()) ] ) ) if len(notes) > 12: logger.warning("Too many footnotes - truncated.") for i, note in enumerate(notes[:12]): self.e[f"note{i}"] = note def store_footnote(self, note): """Returns indicator character""" return self.footnotes[note] if note else None class Need2KnowPDF(object): # Location of form fields in Points (1/72 inch) - 0,0 is bottom-left - and font size field_xys = { # Personal Data "name": (75, 693, 11), "profession": (343, 693, 11), "employer": (75, 665, 11), "nationality": (343, 665, 11), "age": (185, 640, 11), "birthday": (200, 640, 11), "male": (98, 639, 11), "female": (76, 639, 11), # Statistical Data "strength": (136, 604, 11), "constitution": (136, 586, 11), "dexterity": (136, 568, 11), "intelligence": (136, 550, 11), "power": (136, 532, 11), "charisma": (136, 514, 11), "strength_x5": (172, 604, 11), "constitution_x5": (172, 586, 11), "dexterity_x5": (172, 568, 11), "intelligence_x5": (172, 550, 11), "power_x5": (172, 532, 11), "charisma_x5": (172, 514, 11), "strength_distinguishing": (208, 604, 11), "constitution_distinguishing": (208, 586, 11), "dexterity_distinguishing": (208, 568, 11), "intelligence_distinguishing": (208, 550, 11), "power_distinguishing": (208, 532, 11), "charisma_distinguishing": (208, 514, 11), "damage bonus": (555, 200, 11), "hitpoints": (195, 482, 11), "willpower": (195, 464, 11), "sanity": (195, 446, 11), "breaking point": (195, 428, 11), "bond0": (512, 604, 11), "bond1": (512, 586, 11), "bond2": (512, 568, 11), "bond3": (512, 550, 11), # Applicable Skill Sets "accounting": (200, 361, 11), "alertness": (200, 343, 11), "anthropology": (200, 325, 11), "archeology": (200, 307, 11), "art1": (200, 289, 11), "art2": (200, 281, 11), "artillery": (200, 253, 11), "athletics": (200, 235, 11), "bureaucracy": (200, 217, 11), "computer science": (200, 200, 11), "craft1label": (90, 185, 9), "craft1value": (200, 185, 9), "craft2label": (90, 177, 9), "craft2value": (200, 177, 9), "craft3label": (90, 169, 9), "craft3value": (200, 169, 9), "craft4label": (90, 161, 9), "craft4value": (200, 161, 9), "criminology": (200, 145, 11), "demolitions": (200, 127, 11), "disguise": (200, 109, 11), "dodge": (200, 91, 11), "drive": (200, 73, 11), "firearms": (200, 54, 11), "first aid": (361, 361, 11), "forensics": (361, 343, 11), "heavy machinery": (361, 325, 11), "heavy weapons": (361, 307, 11), "history": (361, 289, 11), "humint": (361, 270, 11), "law": (361, 253, 11), "medicine": (361, 235, 11), "melee weapons": (361, 217, 11), "militaryscience1value": (361, 199, 11), "militaryscience1label": (327, 199, 11), "militaryscience2value": (361, 186, 11), "militaryscience2label": (327, 186, 11), "navigate": (361, 163, 11), "occult": (361, 145, 11), "persuade": (361, 127, 11), "pharmacy": (361, 109, 11), "pilot1value": (361, 91, 9), "pilot1label": (290, 91, 9), "pilot2value": (361, 83, 9), "pilot2label": (290, 83, 9), "psychotherapy": (361, 54, 11), "ride": (521, 361, 11), "science1label": (442, 347, 9), "science1value": (521, 347, 9), "science2label": (442, 340, 9), "science2value": (521, 340, 9), "science3label": (442, 333, 9), "science3value": (521, 333, 9), "science4label": (442, 326, 9), "science4value": (521, 326, 9), "search": (521, 307, 11), "sigint": (521, 289, 11), "stealth": (521, 270, 11), "surgery": (521, 253, 11), "survival": (521, 235, 11), "swim": (521, 217, 11), "unarmed combat": (521, 200, 11), "unnatural": (521, 181, 11), "language1": (521, 145, 11), "language2": (521, 127, 11), "language3": (521, 109, 11), "skill1": (521, 91, 11), "skill2": (521, 73, 11), "skill3": (521, 54, 11), # 2nd page "weapon0": (85, 480, 11), "weapon0_roll": (175, 480, 11), "weapon0_range": (215, 480, 11), "weapon0_damage": (270, 480, 11), "weapon0_ap": (345, 480, 11), "weapon0_lethality": (410, 480, 11), "weapon0_kill_radius": (462, 480, 11), "weapon0_ammo": (525, 480, 11), "weapon0_note": (560, 480, 11), "weapon1": (85, 461, 11), "weapon1_roll": (175, 461, 11), "weapon1_range": (215, 461, 11), "weapon1_damage": (270, 461, 11), "weapon1_ap": (345, 461, 11), "weapon1_lethality": (410, 461, 11), "weapon1_kill_radius": (462, 461, 11), "weapon1_ammo": (525, 461, 11), "weapon1_note": (560, 461, 11), "weapon2": (85, 442, 11), "weapon2_roll": (175, 442, 11), "weapon2_range": (215, 442, 11), "weapon2_damage": (270, 442, 11), "weapon2_ap": (345, 442, 11), "weapon2_lethality": (410, 442, 11), "weapon2_kill_radius": (462, 442, 11), "weapon2_ammo": (525, 442, 11), "weapon2_note": (560, 442, 11), "weapon3": (85, 423, 11), "weapon3_roll": (175, 423, 11), "weapon3_range": (215, 423, 11), "weapon3_damage": (270, 423, 11), "weapon3_ap": (345, 423, 11), "weapon3_lethality": (410, 423, 11), "weapon3_kill_radius": (462, 423, 11), "weapon3_ammo": (525, 423, 11), "weapon3_note": (560, 423, 11), "weapon4": (85, 404, 11), "weapon4_roll": (175, 404, 11), "weapon4_range": (215, 404, 11), "weapon4_damage": (270, 404, 11), "weapon4_ap": (345, 404, 11), "weapon4_lethality": (410, 404, 11), "weapon4_kill_radius": (462, 404, 11), "weapon4_ammo": (525, 404, 11), "weapon4_note": (560, 404, 11), "weapon5": (85, 385, 11), "weapon5_roll": (175, 385, 11), "weapon5_range": (215, 385, 11), "weapon5_damage": (270, 385, 11), "weapon5_ap": (345, 385, 11), "weapon5_lethality": (410, 385, 11), "weapon5_kill_radius": (462, 385, 11), "weapon5_ammo": (525, 385, 11), "weapon5_note": (560, 385, 11), "weapon6": (85, 366, 11), "weapon6_roll": (175, 366, 11), "weapon6_range": (215, 366, 11), "weapon6_damage": (270, 366, 11), "weapon6_ap": (345, 366, 11), "weapon6_lethality": (410, 366, 11), "weapon6_kill_radius": (465, 366, 11), "weapon6_ammo": (525, 366, 11), "weapon6_note": (560, 366, 11), "gear0": (75, 628, 8), "gear1": (75, 618, 8), "gear2": (75, 608, 8), "gear3": (75, 598, 8), "gear4": (75, 588, 8), "gear5": (75, 578, 8), "gear6": (75, 568, 8), "gear7": (75, 558, 8), "gear8": (75, 548, 8), "gear9": (75, 538, 8), "gear10": (75, 528, 8), "gear11": (323, 628, 8), "gear12": (323, 618, 8), "gear13": (323, 608, 8), "gear14": (323, 598, 8), "gear15": (323, 588, 8), "gear16": (323, 578, 8), "gear17": (323, 568, 8), "gear18": (323, 558, 8), "gear19": (323, 548, 8), "gear20": (323, 538, 8), "gear21": (323, 528, 8), "note0": (50, 40, 8), "note1": (50, 30, 8), "note2": (50, 20, 8), "note3": (50, 10, 8), "note4": (240, 40, 8), "note5": (240, 30, 8), "note6": (240, 20, 8), "note7": (240, 10, 8), "note8": (410, 40, 8), "note9": (410, 30, 8), "note10": (410, 20, 8), "note11": (410, 10, 8), } # Fields that also get a multiplier x5_stats = ["strength", "constitution", "dexterity", "intelligence", "power", "charisma"] def __init__(self, filename, professions, pages_per_sheet=1): self.filename = filename self.pages_per_sheet = pages_per_sheet self.c = canvas.Canvas(self.filename) # Set US Letter in points self.c.setPageSize((612, 792)) self.c.setAuthor("https://github.com/jimstorch/DGGen") self.c.setTitle("Delta Green Agent Roster") self.c.setSubject("Pre-generated characters for the Delta Green RPG") # Register Custom Fonts pdfmetrics.registerFont(TTFont("Special Elite", "data/SpecialElite.ttf")) pdfmetrics.registerFont(TTFont("OCRA", "data/OCRA.ttf")) if len(professions) > 1: self.generate_toc(professions, pages_per_sheet) def generate_toc(self, professions, pages_per_sheet): """Build a clickable Table of Contents on page 1""" self.bookmark("Table of Contents") self.c.setFillColorRGB(0, 0, 0) self.c.setFont("OCRA", 10) now = datetime.datetime.utcnow().isoformat() + "Z" self.c.drawString(150, 712, "DGGEN DTG " + now) self.c.drawString(150, 700, "CLASSIFIED/DG/NTK//") self.c.drawString(150, 688, "SUBJ ROSTER/ACTIVE/NOCELL/CONUS//") top = 650 pagenum = 2 for count, profession in enumerate(professions): label = generate_label(profession) chapter = "{:.<40}".format(shorten(label, 37, placeholder="")) + "{:.>4}".format( pagenum ) self.c.drawString(150, top - self.line_drop(count), chapter) self.c.linkAbsolute( label, label, (145, (top - 6) - self.line_drop(count), 470, (top + 18) - self.line_drop(count)), ) pagenum += profession["number_to_generate"] * pages_per_sheet if pages_per_sheet == 1: chapter = "{:.<40}".format("Blank Character Sheet Second Page") + "{:.>4}".format( pagenum + profession["number_to_generate"] ) self.c.drawString(150, top - self.line_drop(pagenum), chapter) self.c.linkAbsolute( "Back Page", "Back Page", ( 145, (top - 6) - self.line_drop(pagenum), 470, (top + 18) - self.line_drop(pagenum), ), ) self.c.showPage() @staticmethod def line_drop(count, linesize=22): return count * linesize def bookmark(self, text): self.c.bookmarkPage(text) self.c.addOutlineEntry(text, text) def draw_string(self, x, y, size, text): self.c.setFont(DEFAULT_FONT, size) self.c.setFillColorRGB(*TEXT_COLOR) self.c.drawString(x, y, str(text)) def fill_field(self, field, value): try: x, y, s = self.field_xys[field] self.draw_string(x, y, s, str(value)) except KeyError: logger.error("Unknown field %s", field) def add_page(self, d): # Add background. ReportLab will cache it for repeat self.c.drawImage("data/Character Sheet NO BACKGROUND FRONT.jpg", 0, 0, 612, 792) for key in d: self.fill_field(key, d[key]) # Tell ReportLab we're done with current page self.c.showPage() def add_page_2(self, e): # Add background. ReportLab will cache it for repeat self.c.drawImage("data/Character Sheet NO BACKGROUND BACK.jpg", 0, 0, 612, 792) for key in e: self.fill_field(key, e[key]) # Tell ReportLab we're done with current page self.c.showPage() def save_pdf(self): if self.pages_per_sheet == 1: self.bookmark("Back Page") self.c.drawImage("data/Character Sheet NO BACKGROUND BACK.jpg", 0, 0, 612, 792) self.c.showPage() self.c.save() def generate_label(profession): return ", ".join( e for e in [ profession.get("label", ""), profession.get("employer", ""), profession.get("division", ""), ] if e ) def get_options(): """Get options and arguments from argv string.""" parser = argparse.ArgumentParser(description=description) parser.add_argument( "-v", "--verbosity", action="count", default=0, help="specify up to three times to increase verbosity, " "i.e. -v to see warnings, -vv for information messages, or -vvv for debug messages.", ) parser.add_argument("-V", "--version", action="version", version=__version__) parser.add_argument( "-o", "--output", action="store", default=f"DeltaGreenPregen-{datetime.datetime.now() :%Y-%m-%d-%H-%M}.pdf", help="Output PDF file. Defaults to %(default)s.", ) parser.add_argument( "-t", "--type", action="store", help=f"Select single profession to generate." ) parser.add_argument("-l", "--label", action="store", help="Override profession label.") parser.add_argument( "-c", "--count", type=int, action="store", help="Generate this many characters of each profession.", ) parser.add_argument( "-e", "--employer", action="store", help="Set employer for all generated characters." ) parser.add_argument( "-u", "--unequipped", action="store_false", dest="equip", help="Don't generate equipment.", default=True, ) data = parser.add_argument_group(title="Data", description="Data file locations") data.add_argument( "--professions", action="store", default="data/professions.json", help="Data file for professions - defaults to %(default)s", ) return parser.parse_args() @dataclass class Data: male_given_names: List[str] female_given_names: List[str] family_names: List[str] towns: List[str] professions: Dict[str, Any] kits: Dict[str, Any] weapons: Dict[str, Any] armour: Dict[str, Any] distinguishing: Dict[Tuple[str, int], List[str]] def load_data(options): with open("data/boys1986.txt") as f: male_given_names = f.read().splitlines() with open("data/girls1986.txt") as f: female_given_names = f.read().splitlines() with open("data/surnames.txt") as f: family_names = f.read().splitlines() with open("data/towns.txt") as f: towns = f.read().splitlines() with open(options.professions) as f: professions = json.load(f) with open("data/equipment.json") as f: equipment = json.load(f) kits = equipment["kits"] weapons = equipment["weapons"] armour = equipment["armour"] distinguishing = {} with open("data/distinguishing-features.csv") as f: for row in csv.DictReader(f): for value in range(int(row["from"]), int(row["to"]) + 1): distinguishing.setdefault((row["statistic"], value), []).append( row["distinguishing"] ) data = Data( male_given_names=male_given_names, female_given_names=female_given_names, family_names=family_names, towns=towns, professions=professions, kits=kits, weapons=weapons, armour=armour, distinguishing=distinguishing, ) return data def init_logger(verbosity, stream=sys.stdout): """Initialize logger and warnings according to verbosity argument. Verbosity levels of 0-3 supported.""" is_not_debug = verbosity <= 2 level = ( [logging.ERROR, logging.WARNING, logging.INFO][verbosity] if is_not_debug else logging.DEBUG ) log_format = ( "%(message)s" if is_not_debug else "%(asctime)s %(levelname)-8s %(name)s %(module)s.py:%(funcName)s():%(lineno)d %(message)s" ) logging.basicConfig(level=level, format=log_format, stream=stream) if is_not_debug: warnings.filterwarnings("ignore") if __name__ == "__main__": sys.exit(main())

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null

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null

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1

0

087670710e46b9499b04f22d8a01fa0767bf4b47

9,093

py

Python

tests/test_remote.py

bcyran/philipstv

6037724d5fab0b72265c2de2c0441a64f6e00c00

[ "MIT" ]

null

tests/test_remote.py

bcyran/philipstv

6037724d5fab0b72265c2de2c0441a64f6e00c00

[ "MIT" ]

null

tests/test_remote.py

bcyran/philipstv

6037724d5fab0b72265c2de2c0441a64f6e00c00

[ "MIT" ]

null

from typing import Union from unittest.mock import Mock, create_autospec import pytest from pytest import MonkeyPatch from philipstv import PhilipsTVAPI, PhilipsTVPairer, PhilipsTVRemote, PhilipsTVRemoteError from philipstv.model import ( AllChannels, AmbilightColor, AmbilightColors, AmbilightLayer, AmbilightPower, AmbilightPowerValue, AmbilightTopology, Application, ApplicationComponent, ApplicationIntent, Applications, Channel, ChannelID, ChannelList, ChannelShort, CurrentChannel, CurrentVolume, DeviceInfo, InputKey, InputKeyValue, PowerState, PowerStateValue, SetChannel, Volume, ) CHANNELS = AllChannels( version=1, id="all", list_type="MixedSources", medium="mixed", operator="OPER", install_country="Poland", channel=[ Channel( ccid=35, preset="1", name="Polsat HD", onid=1537, tsid=24, sid=2403, service_type="audio_video", type="DVB_C", logo_version=33, ), Channel( ccid=40, preset="3", name="TVN HD", onid=666, tsid=24, sid=2403, service_type="audio_video", type="DVB_C", logo_version=33, ), ], ) APPLICATION_SPOTIFY = Application( intent=ApplicationIntent( component=ApplicationComponent( package_name="com.spotify.tv.android", class_name="com.spotify.tv.android.SpotifyTVActivity", ), action="android.intent.action.MAIN", ), label="Spotify", order=0, id="com.spotify.tv.android.SpotifyTVActivity-com.spotify.tv.android", type="app", ) APPLICATION_NETFLIX = Application( intent=ApplicationIntent( component=ApplicationComponent( package_name="com.netflix.ninja", class_name="com.netflix.ninja.MainActivity", ), action="android.intent.action.MAIN", ), label="Netflix", order=0, id="com.netflix.ninja.MainActivity-com.netflix.ninja", type="app", ) APPLICATIONS = Applications( version=0, applications=[APPLICATION_SPOTIFY, APPLICATION_NETFLIX], ) @pytest.fixture def api_mock() -> Mock: return create_autospec(PhilipsTVAPI, spec_set=True, instance=True) # type: ignore def test_host(api_mock: Mock) -> None: expected_host = "192.168.0.66" api_mock.host = expected_host result = PhilipsTVRemote(api_mock).host assert result == expected_host def test_auth(api_mock: PhilipsTVAPI) -> None: expected_credentials = ("<key>", "<secret>") remote = PhilipsTVRemote(api_mock) remote.auth = expected_credentials assert remote.auth == expected_credentials assert api_mock.auth == expected_credentials def test_pair(api_mock: Mock, monkeypatch: MonkeyPatch) -> None: given_id = "<id>" pairer_mock = create_autospec(PhilipsTVPairer) pairer_mock.return_value = pairer_mock monkeypatch.setattr("philipstv.remote.PhilipsTVPairer", pairer_mock) def fake_callback() -> str: return "str" PhilipsTVRemote(api_mock).pair(fake_callback, given_id) pairer_mock.pair.assert_called_once_with(fake_callback) device_info = pairer_mock.call_args.args[1] assert isinstance(device_info, DeviceInfo) assert device_info.id == given_id def test_pair_no_id(api_mock: Mock, monkeypatch: MonkeyPatch) -> None: pairer_mock = create_autospec(PhilipsTVPairer) pairer_mock.return_value = pairer_mock monkeypatch.setattr("philipstv.remote.PhilipsTVPairer", pairer_mock) PhilipsTVRemote(api_mock).pair(lambda: "str") device_info = pairer_mock.call_args.args[1] assert isinstance(device_info, DeviceInfo) assert device_info.id.isalnum() assert len(device_info.id) == 16 def test_get_power(api_mock: Mock) -> None: api_mock.get_powerstate.return_value = PowerState(powerstate=PowerStateValue.STANDBY) result = PhilipsTVRemote(api_mock).get_power() assert result is False def test_set_power(api_mock: Mock) -> None: PhilipsTVRemote(api_mock).set_power(True) api_mock.set_powerstate.assert_called_once_with(PowerState(powerstate=PowerStateValue.ON)) def test_get_volume(api_mock: Mock) -> None: api_mock.get_volume.return_value = CurrentVolume(muted=False, current=15, min=0, max=60) result = PhilipsTVRemote(api_mock).get_volume() assert result == 15 def test_set_volume(api_mock: Mock) -> None: PhilipsTVRemote(api_mock).set_volume(20) api_mock.set_volume.assert_called_once_with(Volume(current=20, muted=False)) def test_get_current_channel(api_mock: Mock) -> None: api_mock.get_current_channel.return_value = CurrentChannel( channel=ChannelShort(ccid=5, preset="10", name="TVN HD"), channel_list=ChannelList(id="allcab", version="1"), ) result = PhilipsTVRemote(api_mock).get_current_channel() assert result == "TVN HD" @pytest.mark.parametrize( "input, expected", [ (1, SetChannel(channel=ChannelID(ccid=35))), ("Polsat HD", SetChannel(channel=ChannelID(ccid=35))), (3, SetChannel(channel=ChannelID(ccid=40))), ("TVN HD", SetChannel(channel=ChannelID(ccid=40))), ], ) def test_set_channel(api_mock: Mock, input: Union[int, str], expected: SetChannel) -> None: api_mock.get_all_channels.return_value = CHANNELS remote = PhilipsTVRemote(api_mock) remote.set_channel(input) api_mock.set_channel.assert_called_once_with(expected) remote.set_channel(input) api_mock.get_all_channels.assert_called_once() def test_set_channel_error(api_mock: Mock) -> None: api_mock.get_current_channel.return_value = CHANNELS with pytest.raises(PhilipsTVRemoteError): PhilipsTVRemote(api_mock).set_channel("random channel") def test_get_all_channels(api_mock: Mock) -> None: api_mock.get_all_channels.return_value = CHANNELS result = PhilipsTVRemote(api_mock).get_all_channels() assert result == {1: "Polsat HD", 3: "TVN HD"} def test_input_key(api_mock: Mock) -> None: PhilipsTVRemote(api_mock).input_key(InputKeyValue.STANDBY) api_mock.input_key.assert_called_once_with(InputKey(key=InputKeyValue.STANDBY)) def test_get_ambilight_power(api_mock: Mock) -> None: api_mock.get_ambilight_power.return_value = AmbilightPower(power=AmbilightPowerValue.OFF) result = PhilipsTVRemote(api_mock).get_ambilight_power() assert result is False def test_set_ambilight_power(api_mock: Mock) -> None: PhilipsTVRemote(api_mock).set_ambilight_power(True) api_mock.set_ambilight_power.assert_called_once_with( AmbilightPower(power=AmbilightPowerValue.ON) ) def test_set_ambilight_color(api_mock: Mock) -> None: PhilipsTVRemote(api_mock).set_ambilight_color(AmbilightColor(r=0, g=69, b=255)) api_mock.set_ambilight_cached.assert_called_once_with(AmbilightColor(r=0, g=69, b=255)) def test_set_ambilight_color_sides(api_mock: Mock) -> None: left_color = AmbilightColor(r=255, g=0, b=0) top_color = AmbilightColor(r=0, g=255, b=0) right_color = AmbilightColor(r=0, g=0, b=255) bottom_color = AmbilightColor(r=125, g=0, b=125) topology = AmbilightTopology(layers=1, left=2, top=3, right=2, bottom=3) api_mock.get_ambilight_topology.return_value = topology PhilipsTVRemote(api_mock).set_ambilight_color( left=left_color, top=top_color, right=right_color, bottom=bottom_color ) api_mock.set_ambilight_cached.assert_called_once_with( AmbilightColors( __root__={ "layer1": AmbilightLayer( left={str(point): left_color for point in range(topology.left)}, top={str(point): top_color for point in range(topology.top)}, right={str(point): right_color for point in range(topology.right)}, bottom={str(point): bottom_color for point in range(topology.bottom)}, ) } ) ) def test_get_applications(api_mock: Mock) -> None: api_mock.get_applications.return_value = APPLICATIONS result = PhilipsTVRemote(api_mock).get_applications() assert result == ["Spotify", "Netflix"] @pytest.mark.parametrize( "app, expected", [ ("Spotify", APPLICATION_SPOTIFY), ("Netflix", APPLICATION_NETFLIX), ], ) def test_launch_application(api_mock: Mock, app: str, expected: ApplicationIntent) -> None: api_mock.get_applications.return_value = APPLICATIONS remote = PhilipsTVRemote(api_mock) remote.launch_application(app) api_mock.launch_application.assert_called_once_with(expected) remote.launch_application(app) api_mock.get_applications.assert_called_once() def test_launch_application_error(api_mock: Mock) -> None: api_mock.get_applications.return_value = APPLICATIONS with pytest.raises(PhilipsTVRemoteError): PhilipsTVRemote(api_mock).launch_application("whatever")

29.144231

94

0.698119

1,083

9,093

5.598338

0.174515

0.073891

0.036286

0.03711

0.505195

0.369949

0.280719

0.241959

0.179119

0.12535

0

0.015448

0.195535

9,093

311

95

29.237942

0.813397

0.00132

0

0.25431

0

0.066637

0.035136

0

0.107759

1

0.094828

false

0

0.025862

0.008621

0.12931

0

null

0

null

0

1

0

0879ba08e89fa5f242f50ddb01acf847e7896d29

9,612

py

Python

a2t/src/test_runner.py

syeda-khurrath/fabric8-analytics-common

421f7e27869c5695ed73b51e6422e097aba00108

[ "Apache-2.0" ]

null

a2t/src/test_runner.py

syeda-khurrath/fabric8-analytics-common

421f7e27869c5695ed73b51e6422e097aba00108

[ "Apache-2.0" ]

4

2019-05-20T08:27:47.000Z

2019-05-20T08:29:57.000Z

a2t/src/test_runner.py

codeready-analytics/fabric8-analytics-common

a763c5534d601f2f40a0f02c02914c49ea23669d

[ "Apache-2.0" ]

1

2020-10-05T21:12:44.000Z

"""Implementation of benchmarks. Copyright (c) 2019 Red Hat Inc. This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see <http://www.gnu.org/licenses/>. """ import sys from random import randint from fastlog import log from time import time from queue import Queue from threading import Thread from report_generator import generate_csv_report from component_generator import ComponentGenerator from setup import parse_tags # directory containing test results RESULT_DIRECTORY = "test_results" def check_number_of_results(queue_size, component_analysis_count, stack_analysis_count): """Check if we really got the same number of results as expected. When the server respond by any HTTP error code (4xx, 5xx), the results are NOT stored in the queue. This means that number of results stored in the queue might be less than number of threads set up by user via CLI parameters in certain situations. This function check this situation. """ log.info("queue size: {size}".format(size=queue_size)) expected = component_analysis_count + 2 * stack_analysis_count if queue_size != expected: log.warning("Warning: {expected} results expected, but only {got} is presented".format( expected=expected, got=queue_size)) log.warning("This means that {n} analysis ends with error or exception".format( n=expected - queue_size)) def prepare_component_generators(python_payload, maven_payload, npm_payload): """Prepare all required component generators for selected payload types.""" component_generator = ComponentGenerator() g_python = component_generator.generator_for_ecosystem("pypi") g_maven = component_generator.generator_for_ecosystem("maven") g_npm = component_generator.generator_for_ecosystem("npm") generators = [] if python_payload: generators.append(g_python) if maven_payload: generators.append(g_maven) if npm_payload: generators.append(g_npm) return generators def initialize_generators(generators): """Initialize the generators randomly so we don't start from the 1st item.""" for i in range(randint(10, 100)): for g in generators: next(g) def component_analysis_benchmark(queue, threads, component_analysis, thread_count, python_payload, maven_payload, npm_payload): """Component analysis benchmark.""" generators = prepare_component_generators(python_payload, maven_payload, npm_payload) initialize_generators(generators) for t in range(thread_count): g = generators[randint(0, len(generators) - 1)] ecosystem, component, version = next(g) with log.indent(): log.info("Component analysis for E/P/V {} {} {}".format(ecosystem, component, version)) t = Thread(target=component_analysis.start, args=(t, ecosystem, component, version, queue)) t.start() threads.append(t) # skip some items for i in range(randint(5, 25)): next(g) def stack_analysis_benchmark(queue, threads, stack_analysis, thread_count, python_payload, maven_payload, npm_payload): """Stack analysis benchmark.""" # TODO: read automagically from the filelist manifests = ( ("maven", "clojure_1_6_0.xml"), ("maven", "clojure_1_7_0.xml"), ("maven", "clojure_1_8_0.xml"), ("maven", "clojure_junit.xml"), ("pypi", "click_6_star.txt"), ("pypi", "array_split.txt"), ("pypi", "fastlog_urllib_requests.txt"), ("pypi", "requests_latest.txt"), ("pypi", "numpy_latest.txt"), ("pypi", "flask_latest.txt"), ("pypi", "scipy_latest.txt"), ("pypi", "pygame_latest.txt"), ("pypi", "pyglet_latest.txt"), ("pypi", "dash_latest.txt"), ("pypi", "pudb_latest.txt"), ("pypi", "pytest_latest.txt"), ("pypi", "numpy_1_11_0.txt"), ("pypi", "numpy_1_12_0.txt"), ("pypi", "numpy_1_16_2.txt"), ("pypi", "numpy_1_16_3.txt"), ("pypi", "numpy_scipy.txt"), ("pypi", "pytest_2_0_0.txt"), ("pypi", "pytest_2_0_1.txt"), ("pypi", "pytest_3_2_2.txt"), ("pypi", "requests_2_20_0.txt"), ("pypi", "requests_2_20_1.txt"), ("pypi", "requests_2_21_0.txt"), ("pypi", "scipy_1_1_0.txt"), ("pypi", "scipy_1_2_0.txt"), ("pypi", "scipy_1_2_1.txt"), ("npm", "array.json"), ("npm", "dependency_array.json"), ("npm", "dependency_emitter_component.json"), ("npm", "dependency_jquery.json"), ("npm", "dependency_jquery_react.json"), ("npm", "dependency_lodash.json"), ("npm", "dependency_lodash_react_jquery.json"), ("npm", "dependency_react.json"), ("npm", "dependency_to_function.json"), ("npm", "dependency_to_function_vue_array.json"), ("npm", "dependency_underscore.json"), ("npm", "dependency_underscore_react_jquery.json"), ("npm", "dependency_vue.json"), ("npm", "dependency_vue_to_function.json"), ("npm", "empty.json"), ("npm", "jquery.json"), ("npm", "lodash.json"), ("npm", "mocha.json"), ("npm", "no_requirements.json"), ("npm", "underscore.json"), ("npm", "wisp.json"), ) for t in range(thread_count): manifest_idx = randint(0, len(manifests) - 1) manifest = manifests[manifest_idx] with log.indent(): log.info("Stack analysis") ecosystem = manifest[0] manifest_file = manifest[1] t = Thread(target=stack_analysis.start, args=(t, ecosystem, manifest_file, queue)) t.start() threads.append(t) def wait_for_all_threads(threads): """Wait for all threads to finish.""" log.info("Waiting for all threads to finish") for t in threads: t.join() log.success("Done") def run_test(cfg, test, i, component_analysis, stack_analysis): """Run one selected test.""" test_name = test["Name"] log.info("Starting test #{n} with name '{desc}'".format(n=i, desc=test_name)) with log.indent(): start = time() threads = [] queue = Queue() with log.indent(): component_analysis_count = int(test["Component analysis"]) stack_analysis_count = int(test["Stack analysis"]) python_payload = test["Python payload"] in ("Yes", "yes") maven_payload = test["Maven payload"] in ("Yes", "yes") npm_payload = test["NPM payload"] in ("Yes", "yes") component_analysis_benchmark(queue, threads, component_analysis, component_analysis_count, python_payload, maven_payload, npm_payload) stack_analysis_benchmark(queue, threads, stack_analysis, stack_analysis_count, python_payload, maven_payload, npm_payload) wait_for_all_threads(threads) queue_size = queue.qsize() check_number_of_results(queue_size, component_analysis_count, stack_analysis_count) end = time() # TODO: use better approach to join paths filename = RESULT_DIRECTORY + "/" + test_name.replace(" ", "_") + ".csv" log.info("Generating test report into file '{filename}'".format(filename=filename)) generate_csv_report(queue, test, start, end, end - start, filename) def run_all_loaded_tests(cfg, tests, component_analysis, stack_analysis): """Run all tests read from CSV file.""" i = 1 for test in tests: run_test(cfg, test, i, component_analysis, stack_analysis) i += 1 def run_tests_with_tags(cfg, tests, tags, component_analysis, stack_analysis): """Run tests read from CSV file that are marged by any of tags provided in tags parameter.""" i = 1 for test in tests: test_tags = parse_tags(test["Tags"]) test_name = test["Name"] if tags <= test_tags: run_test(cfg, test, i, component_analysis, stack_analysis) i += 1 else: log.info("Skipping test #{n} with name '{desc}'".format(n=i, desc=test_name)) def no_tests(tests): """Predicate for number of tests.""" return not tests or len(tests) == 0 def start_tests(cfg, tests, tags, component_analysis, stack_analysis): """Start all tests using the already loaded configuration.""" log.info("Run tests") with log.indent(): if no_tests(tests): log.error("No tests loaded!") sys.exit(-1) if len(tests) == 1: log.success("Loaded 1 test") else: log.success("Loaded {n} tests".format(n=len(tests))) if not tags: run_all_loaded_tests(cfg, tests, component_analysis, stack_analysis) else: run_tests_with_tags(cfg, tests, tags, component_analysis, stack_analysis)

37.546875

99

0.634415

1,209

9,612

4.837055

0.224152

0.029925

0.037791

0.04617

0.350205

0.233755

0.197503

0.161936

0.15236

0.112175

0

0.011998

0.24563

9,612

255

100

37.694118

0.794511

0.168435

0

0.168539

0

0.212343

0.046668

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0.003922

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1

0.061798

false

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0.050562

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0.123596

0

null

0

null

0

1

0

087e86827c6cc73f03d6554fcf8f36b2777a11b4

1,221

py

Python

win/python/CAO/calcClient.py

kioto/ORiN2Sample

a7a9007b696fdd3ab29f1ec5cededc59b232fae2

[ "MIT" ]

null

win/python/CAO/calcClient.py

kioto/ORiN2Sample

a7a9007b696fdd3ab29f1ec5cededc59b232fae2

[ "MIT" ]

null

win/python/CAO/calcClient.py

kioto/ORiN2Sample

a7a9007b696fdd3ab29f1ec5cededc59b232fae2

[ "MIT" ]

null

import win32com.client import time class CalcClient(object): def __init__(self): # CAOエンジンの作成 self._eng = win32com.client.Dispatch('CAO.CaoEngine') self._ws = self._eng.Workspaces(0) self._ctrl = self._ws.AddController('bb1', 'CaoProv.Blackboard') # 変数の追加 self._var_cmd = self._ctrl.AddVariable('cmd') self._var_val1 = self._ctrl.AddVariable('val1') self._var_val2 = self._ctrl.AddVariable('val2') self._var_res = self._ctrl.AddVariable('res') self._var_ack = self._ctrl.AddVariable('ack') def calc(self, cmd_str, val1, val2): print(f'calc({cmd_str}, {val1}, {val2})') self._var_val1.Value = val1 self._var_val2.Value = val2 self._var_cmd.Value = cmd_str # ここで計算が実行 # 計算の終了待ち while True: if self._var_ack.Value is True: break time.sleep(0.1) res = self._var_res.Value print(' = ', res) time.sleep(1) if __name__ == '__main__': cc = CalcClient() cc.calc('ADD', 123, 567) cc.calc('SUB', 123, 567) cc.calc('MUL', 123, 567) cc.calc('DIV', 123, 567)

29.780488

73

0.564292

150

1,221

4.293333

0.36

0.108696

0.147516

0.055901

0

0.052817

0.302211

1,221

41

74

29.780488

0.703052

0.027027

0

0.091783

0

1

0.066667

false

0

0.066667

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0.166667

0.066667

0

null

0

null

0

1

0

08804de9d3324b167c6447b69cc226552d4b7bbe

282

py

Python

Mundo-1/exercicio-05.py

FRafaelPA/Praticando-Python

d8a46beceeae2ac20acf4c63f86a32cba537c896

[ "MIT" ]

null

Mundo-1/exercicio-05.py

FRafaelPA/Praticando-Python

d8a46beceeae2ac20acf4c63f86a32cba537c896

[ "MIT" ]

null

Mundo-1/exercicio-05.py

FRafaelPA/Praticando-Python

d8a46beceeae2ac20acf4c63f86a32cba537c896

[ "MIT" ]

null

''' Faça um programa que leia um número inteiro e mostre na tela o seu sucessor e seu antecessor. ''' n = int(input('Entre com um valor: ')) antecessor = n - 1 sucessor = n + 1 msg = 'o antecessor do número {} é {} e seu sucessor é {}'.format(n, antecessor, sucessor) print(msg)

23.5

93

0.673759

47

282

4.042553

0.574468

0.115789

0

0.008889

0.202128

282

12

94

23.5

0.835556

0.329787

0

0.38674

0

1

0

false

0

0.2

0

null

0

null

0

1

0

0886c3adb37d4bb2d284b34954bef308daf23bd3

508

py

Python

001-Python-basico/008-desafio-pratico.py

clebertonf/Python-course

a57f405cbd27f96e0cb61128df31e9249c79a962

[ "MIT" ]

null

001-Python-basico/008-desafio-pratico.py

clebertonf/Python-course

a57f405cbd27f96e0cb61128df31e9249c79a962

[ "MIT" ]

null

001-Python-basico/008-desafio-pratico.py

clebertonf/Python-course

a57f405cbd27f96e0cb61128df31e9249c79a962

[ "MIT" ]

null

from datetime import date year_current_date = date.today().year def get_info(name, age, height, weight): year_birth = year_current_date - age imc = round(weight / (height ** 2), 2) print(f"{name} tem {age} anos, {height} de altura e pesa {weight} KG.") print(f"O IMC do {name} é: {imc}") print(f"{name} nasceu em {year_birth}") get_info("Cleberton", 28, 1.69, 75) # Função recebe algumas informaçoes por parametro, e retorna ano de nascimento, imc # com algumas frases customizadas

28.222222

83

0.687008

80

508

4.2625

0.6125

0.052786

0.087977

0

0.021739

0.185039

508

17

84

29.882353

0.801932

0.222441

0

0.111111

0.313776

0

1

0.111111

false

0

0.111111

0

0.222222

0.333333

0

null

0

null

0

1

0

08873554c1a8d8174ca6425485bfe2a0d0880e6a

2,306

py

Python

tests/components/speedtestdotnet/test_init.py

pcaston/core

e74d946cef7a9d4e232ae9e0ba150d18018cfe33

[ "Apache-2.0" ]

1

2021-07-08T20:09:55.000Z

tests/components/speedtestdotnet/test_init.py

pcaston/core

e74d946cef7a9d4e232ae9e0ba150d18018cfe33

[ "Apache-2.0" ]

47

2021-02-21T23:43:07.000Z

2022-03-31T06:07:10.000Z

tests/components/speedtestdotnet/test_init.py

OpenPeerPower/core

f673dfac9f2d0c48fa30af37b0a99df9dd6640ee

[ "Apache-2.0" ]

null

"""Tests for SpeedTest integration.""" from unittest.mock import patch import speedtest from openpeerpower import config_entries from openpeerpower.components import speedtestdotnet from openpeerpower.setup import async_setup_component from tests.common import MockConfigEntry async def test_setup_with_config(opp): """Test that we import the config and setup the integration.""" config = { speedtestdotnet.DOMAIN: { speedtestdotnet.CONF_SERVER_ID: "1", speedtestdotnet.CONF_MANUAL: True, speedtestdotnet.CONF_SCAN_INTERVAL: "00:01:00", } } with patch("speedtest.Speedtest"): assert await async_setup_component(opp, speedtestdotnet.DOMAIN, config) async def test_successful_config_entry(opp): """Test that SpeedTestDotNet is configured successfully.""" entry = MockConfigEntry( domain=speedtestdotnet.DOMAIN, data={}, ) entry.add_to_opp(opp) with patch("speedtest.Speedtest"), patch( "openpeerpower.config_entries.ConfigEntries.async_forward_entry_setup", return_value=True, ) as forward_entry_setup: await opp.config_entries.async_setup(entry.entry_id) assert entry.state is config_entries.ConfigEntryState.LOADED assert forward_entry_setup.mock_calls[0][1] == ( entry, "sensor", ) async def test_setup_failed(opp): """Test SpeedTestDotNet failed due to an error.""" entry = MockConfigEntry( domain=speedtestdotnet.DOMAIN, data={}, ) entry.add_to_opp(opp) with patch("speedtest.Speedtest", side_effect=speedtest.ConfigRetrievalError): await opp.config_entries.async_setup(entry.entry_id) assert entry.state is config_entries.ConfigEntryState.SETUP_RETRY async def test_unload_entry(opp): """Test removing SpeedTestDotNet.""" entry = MockConfigEntry( domain=speedtestdotnet.DOMAIN, data={}, ) entry.add_to_opp(opp) with patch("speedtest.Speedtest"): await opp.config_entries.async_setup(entry.entry_id) assert await opp.config_entries.async_unload(entry.entry_id) await opp.async_block_till_done() assert entry.state is config_entries.ConfigEntryState.NOT_LOADED assert speedtestdotnet.DOMAIN not in opp.data

28.825

82

0.717259

268

2,306

5.958955

0.276119

0.073262

0.030056

0.067627

0.365686

0.349405

0.319975

0

0.004849

0.195143

2,306

79

83

29.189873

0.855603

0.013877

0

0.320755

0

0.077184

0.03301

0

0.132075

1

0

false

0

0.113208

0

0.113208

0

null

0

null

0

1

0

0888b580bb9eb1968da656fe5efb329d6602a748

616

py

Python

case/xpath.py

xierensong/learnPython

33f9891d8a8ed39772ff9bcbeb1e5cff6f3b5455

[ "MIT" ]

null

case/xpath.py

xierensong/learnPython

33f9891d8a8ed39772ff9bcbeb1e5cff6f3b5455

[ "MIT" ]

null

case/xpath.py

xierensong/learnPython

33f9891d8a8ed39772ff9bcbeb1e5cff6f3b5455

[ "MIT" ]

1

2018-10-11T08:20:44.000Z

import requests from lxml import etree if __name__ == '__main__': headers = {"User-Agent":'Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/67.0.3396.99 Safari/537.36'} url = 'https://www.apache.org/dist/ant/' sourceHTML = requests.get(url, headers = headers) selector = etree.HTML(sourceHTML.text) folder_list = selector.xpath('//pre[position()=1]/a[@href]') for elmt in folder_list: # href_TT = elmt.get('href') print('href_TT ', href_TT) if href_TT[len(href_TT)-1] == '/': print('folder_list', elmt.attrib)

41.066667

144

0.63474

89

616

4.213483

0.640449

0.08

0

0.058943

0.201299

616

15

145

41.066667

0.703252

0

0.076923

0.349026

0.045455

0

1

0

false

0

0.153846

0

0.153846

0

null

0

null

0

1

0

088ddea79b72540b919336ee600c90b0505ded86

5,132

py

Python

jelm/tests/unit/test_jelm_class.py

endremborza/jelm

6916bbd4ceb909ad3350c56d3a149bdb97671489

[ "MIT" ]

null

jelm/tests/unit/test_jelm_class.py

endremborza/jelm

6916bbd4ceb909ad3350c56d3a149bdb97671489

[ "MIT" ]

null

jelm/tests/unit/test_jelm_class.py

endremborza/jelm

6916bbd4ceb909ad3350c56d3a149bdb97671489

[ "MIT" ]

null

import pytest from jelm import Jelm, Node, Edge from jelm.tests.network_case_set_class import NetwokCaseTemplate def test_eq(jelm_pair_case: NetwokCaseTemplate): jelm_pair_case.evaluate_fun(non_altering_function=lambda x: x) assert not (10 == Jelm()) assert not ("fing" == Jelm()) def test_jelm_repr(jelm_pair_case: NetwokCaseTemplate): def repr_check(el: Jelm): repr_string = el.__repr__() assert "jelm" in repr_string assert str(len(el.nodes.keys())) in repr_string return el jelm_pair_case.evaluate_fun(non_altering_function=repr_check) def test_neighbors(jelm_pair_case: NetwokCaseTemplate): def neighbor_check(el: Jelm): for nid, n in el.nodes.items(): for nid2 in n.neighbors.keys(): assert nid in el.get_node(nid2).neighbors.keys() for nid3 in n.target_neighbors.keys(): assert nid in el.get_node(nid3).source_neighbors.keys() return el jelm_pair_case.evaluate_fun(non_altering_function=neighbor_check) def test_add_node_as_object_w_cases(jelm_pair_case: NetwokCaseTemplate): def add_node_as_obj(el: Jelm): el.add_object({"type": "node", "id": "n10"}) return el def assert_node_as_obj_added(el: Jelm): assert isinstance(el.get_node("n10"), Node) def catch_node_as_obj_add(el: Jelm, e): assert isinstance(e, ValueError) assert isinstance(el.get_node("n10"), Node) jelm_pair_case.evaluate_fun( altering_function=add_node_as_obj, assert_alteration=assert_node_as_obj_added, catch_alteration_exception=catch_node_as_obj_add, ) def test_add_edge_as_object_w_cases(jelm_pair_case: NetwokCaseTemplate): def add_edge_as_obj(el: Jelm): el.add_object({"type": "edge", "source": "n1", "target": "n2"}) return el def assert_edge_as_obj_added(el: Jelm): n = el.get_node("n1") assert "n2" in n.neighbors.keys() assert "n1" in el.get_node("n2").neighbors assert "n2" in n.target_neighbors.keys() def catch_edge_as_obj_add(el: Jelm, e): assert isinstance(e, KeyError) assert ("n1" not in el.nodes.keys()) or ("n2" not in el.nodes.keys()) jelm_pair_case.evaluate_fun( altering_function=add_edge_as_obj, assert_alteration=assert_edge_as_obj_added, catch_alteration_exception=catch_edge_as_obj_add, ) def test_add_edge_jelm_object_w_cases(jelm_pair_case: NetwokCaseTemplate): def add_edge_jelm_obj(el: Jelm): el.add_object(Edge(source="n1", target="n2", id="fing")) return el def assert_edge_jelm_obj_added(el: Jelm): n = el.get_node("n1") assert "n2" in n.neighbors.keys() assert "n1" in el.get_node("n2").neighbors assert "n2" in n.target_neighbors.keys() edge_ids = [e.id for e in n.neighbors["n2"]] assert "fing" in edge_ids def catch_edge_jelm_obj_add(el: Jelm, e): assert isinstance(e, KeyError) assert ("n1" not in el.nodes.keys()) or ("n2" not in el.nodes.keys()) jelm_pair_case.evaluate_fun( altering_function=add_edge_jelm_obj, assert_alteration=assert_edge_jelm_obj_added, catch_alteration_exception=catch_edge_jelm_obj_add, ) def test_init(): el = Jelm(metadata={"author": "John Doe"}, objects=[]) assert isinstance(el.objects, list) assert isinstance(el.metadata, dict) el2 = Jelm(metadata={"author": "John Doe"}, nodes={}) assert el == el2 el3 = Jelm() assert not (el == el3) el4_1 = Jelm(nodes={"id1": Node(id="n1")}) el4_2 = Jelm(objects=[{"type": "node", "id": "n1"}]) assert el4_1 == el4_2 def test_init_w_cases(jelm_pair_case: NetwokCaseTemplate): def transform_init(el): el_from_nodes = Jelm(metadata=el.metadata, nodes=el.nodes) assert el_from_nodes == el return el_from_nodes jelm_pair_case.evaluate_fun(non_altering_function=transform_init) def test_add_object(): el = Jelm() el.add_object({"type": "node", "id": "n1"}) el.add_object(Node(id="n2")) el.add_object({"type": "edge", "source": "n1", "target": "n2"}) el.add_object(Node(id="n3", attributes={"priority": "low"})) with pytest.raises(ValueError): el.add_object({"no": "type"}) with pytest.raises(ValueError): el.add_object({"type": "wrong"}) with pytest.raises(ValueError): el.add_object(10) el.add_edge("n3", "n2") el.add_node("n4", {"order": "latest"}) assert len(set([type(o) for o in el.objects])) > 1 assert isinstance(el.objects[0], Node) assert isinstance(el.objects[2], Edge) def test_iter(): el = Jelm( metadata={"author": "John Doe"}, objects=[ {"type": "node", "id": "n1"}, {"type": "node", "id": "n2"}, {"type": "edge", "source": "n1", "target": "n2"}, ], ) for idx, o in enumerate(el): if idx < 2: assert isinstance(o, Node) else: assert isinstance(o, Edge)

25.034146

77

0.639517

726

5,132

4.242424

0.140496

0.031169

0.054545

0.068182

0.647078

0.516558

0.502273

0.356494

0.271429

0.237662

0

0.016153

0.227981

5,132

204

78

25.156863

0.761232

0

0.223141

0

0.055144

0

0.305785

1

0.181818

false

0

0.024793

0

0.256198

0

null

0

null

0

1

0

088f0e150b58a95dbcc3bacf169c6bdc57e4eedc

6,582

py

Python

trajectory_prediction/evaluation.py

libai2019/dataset-api

2f793821864f32bd210c17060a70682488bb74e0

[ "Apache-2.0" ]

385

2018-07-02T22:21:25.000Z

2022-03-28T13:12:47.000Z

trajectory_prediction/evaluation.py

libai2019/dataset-api

2f793821864f32bd210c17060a70682488bb74e0

[ "Apache-2.0" ]

102

2018-08-01T10:40:40.000Z

2022-03-16T10:32:44.000Z

trajectory_prediction/evaluation.py

libai2019/dataset-api

2f793821864f32bd210c17060a70682488bb74e0

[ "Apache-2.0" ]

98

2018-07-12T18:36:42.000Z

2022-03-20T04:38:03.000Z

''' Evaluation code for trajectory prediction. We record the objects in the last frame of every sequence in test dataset as considered objects, which is stored in considered_objects.txt. We compare the error between your predicted locations in the next 3s(six positions) and the ground truth for these considered objects. To run this script, make sure that your results are in required format. ''' import os import argparse import numpy as np def evaluation(frame_data_result, frame_data_gt, consider_peds): # defined length of predicted trajectory predict_len = 6 # the counter for testing sequences sequence_count = 0 # weighted coefficient for vehicles, pedestrians, bicyclists respectively vehicle_coe = 0.2 pedestrian_coe = 0.58 bicycle_coe = 0.22 # error for missing considered objects miss_error = 100 # record displacement error for three types of objects vehicle_error = [] pedestrian_error = [] bicycle_error = [] # record final displacement error for three types of objects vehicle_final_error = [] pedestrian_final_error = [] bicycle_final_error = [] for i in range(0, len(frame_data_result) - predict_len + 1, predict_len): current_consider_ped = consider_peds[sequence_count] sequence_count = sequence_count + 1 for j in range(i, i + predict_len): for ped_gt in frame_data_gt[j]: if current_consider_ped.count(int(ped_gt[0])): # ignore unknown objects if ped_gt[1] == 5: continue # error will be large if missing considered objects error = miss_error for ped_res in frame_data_result[j]: if int(ped_res[0]) == int(ped_gt[0]): error = distance([ped_gt[2], ped_gt[3]], [ped_res[2], ped_res[3]]) break # distribute the error to different types of objects if ped_gt[1] == 1 or ped_gt[1] == 2: vehicle_error.append(error) if j == i + predict_len - 1: vehicle_final_error.append(error) elif ped_gt[1] == 3: pedestrian_error.append(error) if j == i + predict_len - 1: pedestrian_final_error.append(error) elif ped_gt[1] == 4: bicycle_error.append(error) if j == i + predict_len - 1: bicycle_final_error.append(error) # the mean error for objects vehicle_mean_error = sum(vehicle_error) / len(vehicle_error) pedestrian_mean_error = sum(pedestrian_error) / len(pedestrian_error) bicycle_mean_error = sum(bicycle_error) / len(bicycle_error) # the final error for objects vehicle_final_error = sum(vehicle_final_error) / len(vehicle_final_error) pedestrian_final_error = sum(pedestrian_final_error) / len(pedestrian_final_error) bicycle_final_error = sum(bicycle_final_error) / len(bicycle_final_error) # weighted sum of mean error WSADE = vehicle_mean_error * vehicle_coe + pedestrian_mean_error * pedestrian_coe + bicycle_mean_error * bicycle_coe # weighted sum of final error WSFDE = vehicle_final_error * vehicle_coe + pedestrian_final_error * pedestrian_coe + bicycle_final_error * bicycle_coe print('WSADE:', WSADE) print('ADEv, ADEp, ADEb:', vehicle_mean_error, pedestrian_mean_error, bicycle_mean_error) print('WSFDE:', WSFDE) print('FDEv, FDEp, FDEb:',vehicle_final_error, pedestrian_final_error, bicycle_final_error) return (WSADE, vehicle_mean_error, pedestrian_mean_error, bicycle_mean_error, WSFDE, vehicle_final_error, pedestrian_final_error, bicycle_final_error) def readConsiderObjects(filename): print('Load file: ', filename) # load considered objects of each sequence consider_peds = [] with open(filename, 'r') as file_to_read: while True: lines = file_to_read.readline() if not lines: break curLine = lines.strip().split(" ") intLine = map(int, curLine) consider_peds.append(intLine) return consider_peds def readTrajectory(filename): print('Load file: ',filename) raw_data = [] # load all the data in the file with open(filename, 'r') as file_to_read: while True: lines = file_to_read.readline() if not lines: break timestamp, id, type, x, y = [float(i) for i in lines.split()] raw_data.append((timestamp, id, type, x, y)) # get frame list frameList = [] for i in range(len(raw_data)): if frameList.count(raw_data[i][0]) == 0: frameList.append(raw_data[i][0]) counter = 0 frame_data = [] for ind, frame in enumerate(frameList): pedsInFrame = [] # Extract all pedestrians in current frame for r in range(counter, len(raw_data)): row = raw_data[r] if raw_data[r][0] == frame: pedsInFrame.append([row[1], row[2], row[3], row[4]]) counter += 1 else: break frame_data.append(pedsInFrame) return frame_data def distance(pos1, pos2): # Euclidean distance return np.sqrt(pow(pos1[0]-pos2[0], 2) + pow(pos1[1]-pos2[1], 2)) def main(): parser = argparse.ArgumentParser( description='Evaluation self localization.') parser.add_argument('--gt_dir', default='./test_eval_data/prediction_gt.txt', help='the dir of ground truth') parser.add_argument('--object_file', default='./test_eval_data/considered_objects.txt', help='the dir of considered objects') parser.add_argument('--res_file', default='./test_eval_data/prediction_result.txt', help='the dir of results') args = parser.parse_args() # load results file_result = args.res_file frame_data_result = readTrajectory(file_result) # load ground truth file_gt = args.gt_dir frame_data_gt = readTrajectory(file_gt) # load considered objects file_consider_objects = args.object_file consider_peds = readConsiderObjects(file_consider_objects) # Do evaluation evaluation(frame_data_result, frame_data_gt, consider_peds) if __name__ == '__main__': main()

36.364641

139

0.628836

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6,582

4.758162

0.2237

0.066074

0.034562

0.027446

0.282084

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0.191105

0.153748

0.038628

0

0.012553

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6,582

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36.566667

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0.104348

0.052174

0

null

0

null

0

1

0

0891695cf058c07ea805662895cf40325fd7ce37

2,561

py

Python

shellfoundry/commands/install_command.py

p-sherratt/shellfoundry

d1f35a31123b9e701c801345fb633b6fda5420b7

[ "Apache-2.0" ]

null

shellfoundry/commands/install_command.py

p-sherratt/shellfoundry

d1f35a31123b9e701c801345fb633b6fda5420b7

[ "Apache-2.0" ]

1

2021-03-25T23:21:02.000Z

shellfoundry/commands/install_command.py

p-sherratt/shellfoundry

d1f35a31123b9e701c801345fb633b6fda5420b7

[ "Apache-2.0" ]

null

# !/usr/bin/python # -*- coding: utf-8 -*- import click import os try: # Python 2.x version from urllib2 import HTTPError, URLError except: # Python 3.x version from urllib.error import HTTPError, URLError from shellfoundry.exceptions import FatalError from shellfoundry.utilities.config_reader import Configuration, CloudShellConfigReader from shellfoundry.utilities.installer import ShellInstaller from shellfoundry.utilities.shell_config_reader import ShellConfigReader from shellfoundry.utilities.shell_package import ShellPackage from shellfoundry.utilities.shell_package_installer import ShellPackageInstaller class InstallCommandExecutor(object): def __init__(self, cloudshell_config_reader=None, installer=None, shell_config_reader=None, shell_package_installer=None): self.cloudshell_config_reader = cloudshell_config_reader or Configuration(CloudShellConfigReader()) self.installer = installer or ShellInstaller() self.shell_config_reader = shell_config_reader or ShellConfigReader() self.shell_package_installer = shell_package_installer or ShellPackageInstaller() def install(self): current_path = os.getcwd() shell_package = ShellPackage(current_path) if shell_package.is_layer_one(): click.secho("Installing a L1 shell directly via shellfoundry is not supported. " "Please follow the L1 shell import procedure described in help.quali.com.", fg="yellow") else: if shell_package.is_tosca(): self.shell_package_installer.install(current_path) else: self._install_old_school_shell() click.secho('Successfully installed shell', fg='green') def _install_old_school_shell(self): error = None try: cloudshell_config = self.cloudshell_config_reader.read() shell_config = self.shell_config_reader.read() self.installer.install(shell_config.name, cloudshell_config) except HTTPError as e: if e.code == 401: raise FatalError('Login to CloudShell failed. Please verify the credentials in the config') error = str(e) except URLError: raise FatalError('Connection to CloudShell Server failed. Please make sure it is up and running properly.') except Exception as e: error = str(e) if error: raise FatalError("Failed to install shell. CloudShell responded with: '{}'".format(error))

43.40678

119

0.705193

289

2,561

6.058824

0.363322

0.068532

0.071388

0.051399

0.042262

0

0.004539

0.225693

2,561

58

120

44.155172

0.878467

0.029676

0

0.12766

0

0.157661

0

1

0.06383

false

0

0.234043

0

0.319149

0

null

0

null

0

1

0

0896e29401ea1989cb26ef01107f5729035c11a7

4,405

py

Python

app/__main__.py

pablohawz/tfg-Scan-Paint-clone

056cd50d9e4274620cf085a41ed9d326e16dd47b

[ "MIT" ]

null

app/__main__.py

pablohawz/tfg-Scan-Paint-clone

056cd50d9e4274620cf085a41ed9d326e16dd47b

[ "MIT" ]

null

app/__main__.py

pablohawz/tfg-Scan-Paint-clone

056cd50d9e4274620cf085a41ed9d326e16dd47b

[ "MIT" ]

null

# This Python file uses the following encoding: utf-8 from app.package.views.Calibrate_view import CalibrateView from app.package.controllers.Calibrate_controller import CalibrateController from app.package.models.Calibrate_model import CalibrateModel import sys import matplotlib from PySide2.QtWidgets import QApplication from PySide2 import QtCore from .package.models.NewProjectModel import NewProjectModel from .package.models.DataAcquisitionModel import DataAcquisitionModel from .package.models.DisplayResultsModel import DisplayResultsModel from .package.controllers.Navigator import Navigator from .package.controllers.NewProjectController import NewProjectController from .package.controllers.DataAcquisitionController import ( DataAcquisitionController) from .package.controllers.DisplayResultsController import ( DisplayResultsController) from .package.views.MainWindow import MainWindow from .package.views.NewProjectView import NewProjectView from .package.views.DataAcquisitionView import DataAcquisitionView from .package.views.DisplayResultsView import DisplayResultsView class App(QApplication): # Diccionario que mapea nombres con Vistas views = {} @staticmethod def log(msg: str) -> None: print(f'[App] {msg}') def __init__(self, args): super(App, self).__init__(args) self.navigator = Navigator() self.navigator.navigator.connect(self.change_view) # MODELS self.new_project_model = NewProjectModel() self.data_acquisition_model = DataAcquisitionModel() self.display_results_model = DisplayResultsModel() self.calibrate_model = CalibrateModel() # CONTROLLERS self.new_project_controller = NewProjectController( self.new_project_model, self.navigator) self.data_acquisition_controller = DataAcquisitionController( self.data_acquisition_model, self.navigator) self.display_results_controller = DisplayResultsController( self.display_results_model, self.navigator) self.calibrate_controller = CalibrateController( self.calibrate_model, self.navigator) # VIEWS self.main_view = MainWindow(None, self.navigator) self.new_project_view = NewProjectView( self.new_project_model, self.new_project_controller) self.data_acquisition_view = DataAcquisitionView( self.data_acquisition_model, self.data_acquisition_controller) self.display_results_view = DisplayResultsView( self.display_results_model, self.display_results_controller) self.calibrate_view = CalibrateView( self.calibrate_model, self.calibrate_controller) self.views['main_view'] = self.main_view self.views['new_project'] = self.new_project_view self.views['data_acquisition'] = self.data_acquisition_view self.views['display_results'] = self.display_results_view self.views['calibrate'] = self.calibrate_view self.change_view('new_project') @QtCore.Slot(str) def change_view(self, name_view, closeOthers=True): self.log(f'Navigating to {name_view}') _view = self.views.get(name_view) if _view is None: raise Exception(f'{name_view} is not part of Views dictionary.') if closeOthers: self.log('closing other views...') for view in self.views: if view != name_view: self.views.get(view).close() _view.open() sys._excepthook = sys.excepthook def exception_hook(exctype, value, traceback): print(exctype, value, traceback) sys._excepthook(exctype, value, traceback) sys.exit(1) sys.excepthook = exception_hook def main(): QtCore.QCoreApplication.setAttribute(QtCore.Qt.AA_ShareOpenGLContexts) app = App([]) sys.exit(app.exec_()) matplotlib.use('tkagg') if __name__ == "__main__": main() # if __name__ == "__main__": # import cProfile # cProfile.run('main()', 'output.dat') # import pstats # from pstats import SortKey # with open("output_time.dat", "w") as f: # p = pstats.Stats("output.dat", stream=f) # p.sort_stats("time").print_stats() # with open("output_calls.dat", "w") as f: # p = pstats.Stats("output.dat", stream=f) # p.sort_stats("calls").print_stats()

33.120301

76

0.711691

481

4,405

6.297297

0.247401

0.039947

0.032354

0.018818

0.081215

0.029713

0

0.001132

0.197503

4,405

132

77

33.371212

0.855728

0.119864

0

0.048187

0

1

0.061728

false

0

0.222222

0

0.308642

0.024691

0

null

0

null

0

1

0

089a04fda175104b7a74e5689381760d2e0c8310

1,513

py

Python

PyEEA/analysis/SimulationAnalysisEngine.py

ThomasJFR/PyEEA

7927ee5ff1de8d3cf9e9654899ea4c2c0284519c

[ "MIT" ]

1

2020-06-15T03:16:06.000Z

PyEEA/analysis/SimulationAnalysisEngine.py

ThomasJFR/PyEEA

7927ee5ff1de8d3cf9e9654899ea4c2c0284519c

[ "MIT" ]

1

2020-06-19T04:56:21.000Z

PyEEA/analysis/SimulationAnalysisEngine.py

ThomasJFR/PyEEA

7927ee5ff1de8d3cf9e9654899ea4c2c0284519c

[ "MIT" ]

null

from numpy.random import standard_normal from numbers import Number def simulation_analysis(project, sim_dict, iterations=250, valuator=None): """ Purpose: Analyses the effects of uncertainty of a system by performing a Monte Carlo simulation. Args: project: An instance of Project to perform the simulation on sim_dict: A dict where the key is the name of the cashflow to simulate and the value is either a number defining the standard deviation for the cashflow as a percentage, or a function defining some way to modify the cashflow by an amount """ # Make every sim_fun value a callable, converting numbers to stdev functions for key in sim_dict: if isinstance(sim_dict[key], Number): stdev = sim_dict[key] def std_dist(amt): return amt * stdev * standard_normal() sim_dict[key] = std_dist valuator = valuator or project.npw if not callable(valuator): return TypeError("Valuator must be a callable construct!") # Perform the simulation valuations = [] for _ in range(iterations): with project as p: for key in sim_dict: sim_fun = sim_dict[key] n_cashflows = len(p[key]) for n in range(n_cashflows): cf = p[key][n] cf.amount += sim_fun(cf.amount) valuations.append(valuator()) return valuations

35.186047

110

0.61996

197

1,513

4.664975

0.431472

0.060936

0.043526

0.023939

0.032644

0

0.002921

0.321216

1,513

42

111

36.02381

0.891918

0.364177

0

0.086957

0

0.04126

0

1

0.086957

false

0

0.086957

0.043478

0.304348

0

null

0

null

0

1

0

08a37f1f4c2faa26bde495db95f37f4816d7caf0

12,652

py

Python

dh/network/__init__.py

dhaase-de/dh-python-dh

40b04407e5f67ec261f559263718ec2b2588dabb

[ "MIT" ]

null

dh/network/__init__.py

dhaase-de/dh-python-dh

40b04407e5f67ec261f559263718ec2b2588dabb

[ "MIT" ]

null

dh/network/__init__.py

dhaase-de/dh-python-dh

40b04407e5f67ec261f559263718ec2b2588dabb

[ "MIT" ]

null

""" Tools for network communication. """ import abc import io import json import socket import struct import sys import time import zlib import dh.ejson import dh.utils # NumPy is only needed for some parts and is optional try: import numpy as np except ImportError as e: _NUMPY_ERROR = e else: _NUMPY_ERROR = None ### #%% socket message types ### class SocketMessageType(abc.ABC): """ Base class providing `send()` and `recv()` methods for sending and receiving (higher-level) messages via the socket `socket`. """ @abc.abstractmethod def send(self, socket, x): pass @abc.abstractmethod def recv(self, socket): pass class ByteSocketMessageType(SocketMessageType): """ Class providing methods for sending and receiving byte *messages* of up to 4 GiB in size via a given socket. Each message has a fixed-length (four byte) header, specifying the length of the message content. Thus, calls to `send()` and `recv()` always ensure that the entire message is being sent/received. If `compress` is `True`, messages are compressed before sending and decompressed after receiving. This reduces the network load but costs more time. The value for `compress` must be the same for both the server and the client. """ def __init__(self, compress=False): self._compress = compress def _recvn(self, socket, byteCount): """ Receive and return a fixed number of `byteCount` bytes from the socket. """ b = io.BytesIO() while True: currentByteCount = b.getbuffer().nbytes if currentByteCount >= byteCount: break packet = socket.recv(byteCount - currentByteCount) if len(packet) == 0: return None b.write(packet) return b.getvalue() def send(self, socket, b): if self._compress: b = zlib.compress(b) header = struct.pack(">I", int(len(b))) socket.sendall(header + b) def recv(self, socket): header = self._recvn(socket, 4) if header is None: return None length = struct.unpack(">I", header)[0] b = self._recvn(socket, length) if self._compress: b = zlib.decompress(b) return b class NumpySocketMessageType(ByteSocketMessageType): """ Class providing `send()` and `recv()` methods for sending and receiving NumPy ndarray objects via the given socket. """ def __init__(self, *args, **kwargs): if _NUMPY_ERROR is not None: raise _NUMPY_ERROR super().__init__(*args, **kwargs) def send(self, socket, x): b = io.BytesIO() np.save(file=b, arr=x, allow_pickle=False, fix_imports=False) super().send(socket, b.getvalue()) def recv(self, socket): b = io.BytesIO(super().recv(socket)) return np.load(file=b, allow_pickle=False, fix_imports=False) class JsonSocketMessageType(ByteSocketMessageType): """ Class providing `send()` and `recv()` methods for sending and receiving JSON-serializable objects via the given socket. """ def send(self, socket, x): j = json.dumps(x, ensure_ascii=True) b = bytes(j, "ascii") super().send(socket, b) def recv(self, socket): b = super().recv(socket) j = b.decode("ascii") x = json.loads(j) return x class ExtendedJsonSocketMessageType(ByteSocketMessageType): """ Class providing `send()` and `recv()` methods for sending and receiving JSON-serializable (with extended range of supported types, see `dh.ejson`) objects via the given socket. .. seealso:: `dh.ejson`. """ def send(self, socket, x): j = dh.ejson.dumps(x) b = bytes(j, "ascii") super().send(socket, b) def recv(self, socket): b = super().recv(socket) j = b.decode("ascii") x = dh.ejson.loads(j) return x ### #%% extended socket with support for multiple message types ### class MessageSocket(): """ This is a wrapper class for `socket.socket` which supports the methods `msend()` and `mrecv()`, which send/receive entire (higher-level) messages. For both methods, the `messageType` argument must be an instance of the class `SocketMessageType`. Note: in this context, 'message' means a high-level, user-defined object, not the 'message' used in the context of `socket.socket.recvmsg` and `socket.socket.sendmsg`. """ def __init__(self, socket): self._socket = socket def msend(self, messageType, x): messageType.send(self._socket, x) def mrecv(self, messageType): return messageType.recv(self._socket) ### #%% socket servers/clients ### class SocketServer(abc.ABC): """ Simple socket server which accepts connections on the specified `host` and `port` and communicates with the client as specified in `communicate()`. See http://stackoverflow.com/a/19742674/1913780 for an explanation of `nodelay`. """ def __init__(self, host="", port=7214, backlog=5, nodelay=True): print("Creating socket...") self._socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) self._socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) if nodelay: self._socket.setsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1) print("Binding socket to {}:{}...".format(host if len(host) > 0 else "*", port)) self._socket.bind((host, port)) self._backlog = backlog self._nodelay = nodelay def _print(self, text): print("[{}] {}".format(dh.utils.dtstr(compact=False), text)) def run(self): self._socket.listen(self._backlog) while True: self._print("Waiting for connection...") sys.stdout.flush() (connectionSocket, connectionAddress) = self._socket.accept() self._print("Accepted connection from {}:{}".format(connectionAddress[0], connectionAddress[1])) t0 = time.time() try: self.communicate(MessageSocket(connectionSocket)) connectionSocket.close() except Exception as e: self._print("** {}: {}".format(type(e).__name__, e)) self._print("Finished request from {}:{} after {} ms".format(connectionAddress[0], connectionAddress[1], dh.utils.around((time.time() - t0) * 1000.0))) @abc.abstractmethod def communicate(self, socket): """ Implements the entire communication happening for one connection with a client via high-level socket messages (see `SocketMessageType`). Counterpart of `SocketClient.communicate`. See specific client/server implementations for examples. """ pass class SocketClient(abc.ABC): """ Simple socket client which connects to the server on the specified `host` and `port` each time `query()` is called. The communication with the server is specified in `communicate()`. See http://stackoverflow.com/a/19742674/1913780 for an explanation of `nodelay`. """ def __init__(self, host, port=7214, nodelay=True): self._host = host self._port = port self._nodelay = nodelay def query(self, *args, **kwargs): # establish connection with the server self._socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) if self._nodelay: self._socket.setsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1) self._socket.connect((self._host, self._port)) # actual communication, keep result result = self.communicate(MessageSocket(self._socket), *args, **kwargs) # close connection self._socket.shutdown(socket.SHUT_RDWR) self._socket.close() return result @abc.abstractmethod def communicate(self, socket, *args, **kwargs): """ Implements the entire communication happening for one connection with a server via high-level socket messages (see `SocketMessageType`). Counterpart of `SocketServer.communicate`. See specific client/server implementations for examples. """ pass class ImageProcessingServer(SocketServer): """ Special case of `SocketServer` which accepts a NumPy array and JSON-encoded parameters and returns a NumPy array. The counterpart is the `ImageProcessingClient` class. To specify the processing behavior, sub-class this class and implement the static method `process(data, params)`. """ def communicate(self, socket): # receive input image and parameters data = socket.mrecv(NumpySocketMessageType()) params = socket.mrecv(JsonSocketMessageType()) # process try: result = self.process(data=data, params=params) except Exception as e: self._print("** {}: {}".format(type(e).__name__, e)) result = np.zeros(shape=(0, 0), dtype="uint8") # send result image socket.msend(NumpySocketMessageType(), result) @staticmethod @abc.abstractmethod def process(data, params): """ This function specifies the processing behavior of this server and must be implemeted by the user. """ pass class ImageProcessingClient(SocketClient): """ Special case of `SocketClient` which sends a NumPy array and JSON-encoded parameters and receives a NumPy array. The counterpart is the `ImageProcessingServer` class. The processing behavior is specified by sub-classing `ImageProcessingServer` and implementing the static method `process(data, params)`. """ def communicate(self, socket, data, params): # send input image and parameters socket.msend(NumpySocketMessageType(), data) socket.msend(JsonSocketMessageType(), params) # receive result image return socket.mrecv(NumpySocketMessageType()) def process(self, data, params): """ Just another name for the `query` method (to better show the connection to the server's `process` method). """ return self.query(data=data, params=params) class ImageProcessingServer2(SocketServer): """ Special case of `SocketServer` which accepts a NumPy array and JSON-encoded parameters and returns a NumPy array plus a JSON-encodable object. The counterpart is the `ImageProcessingClient2` class. To specify the processing behavior, sub-class this class and implement the static method `process(data, params)`. """ def communicate(self, socket): # receive input image and parameters data = socket.mrecv(NumpySocketMessageType()) params = socket.mrecv(JsonSocketMessageType()) # process try: (result, info) = self.process(data=data, params=params) except Exception as e: self._print("** {}: {}".format(type(e).__name__, e)) result = np.zeros(shape=(0, 0), dtype="uint8") info = None # send result image and info socket.msend(NumpySocketMessageType(), result) socket.msend(JsonSocketMessageType(), info) @staticmethod @abc.abstractmethod def process(data, params): """ This function specifies the processing behavior of this server and must be implemeted by the user. """ pass class ImageProcessingClient2(SocketClient): """ Special case of `SocketClient` which sends a NumPy array and JSON-encoded parameters and receives a NumPy array and a JSON-encoded object. The counterpart is the `ImageProcessingServer2` class. The processing behavior is specified by sub-classing `ImageProcessingServer` and implementing the static method `process(data, params)`. """ def communicate(self, socket, data, params): # send input image and parameters socket.msend(NumpySocketMessageType(), data) socket.msend(JsonSocketMessageType(), params) # receive result image result = socket.mrecv(NumpySocketMessageType()) info = socket.mrecv(JsonSocketMessageType()) return (result, info) def process(self, data, params): """ Just another name for the `query` method (to better show the connection to the server's `process` method). """ return self.query(data=data, params=params)

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163

0.641084

1,467

12,652

5.462849

0.207226

0.041178

0.010981

0.017969

0.518343

0.481283

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0

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12,652

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false

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0.054645

0

null

0

null

0

1

0

08a7afeb8a1abc10ec91968f8b8eddea6a7e071a

16,361

py

Python

qtable/engine.py

ihgazni2/qtable

269bb1052d7c7aeeae4d0b1024746fae38870c40

[ "MIT" ]

null

qtable/engine.py

ihgazni2/qtable

269bb1052d7c7aeeae4d0b1024746fae38870c40

[ "MIT" ]

null

qtable/engine.py

ihgazni2/qtable

269bb1052d7c7aeeae4d0b1024746fae38870c40

[ "MIT" ]

null

import pandas as pd import numpy as np import elist.elist as elel import edict.edict as eded import tlist.tlist as tltl import copy __all__ = [ '_append_col', '_append_cols', '_append_row', '_append_rows', '_cn2clocs', '_col', '_cols', '_columns_map', '_crop', '_get_clocs', '_get_rlocs', '_getitem', '_index_map', '_insert_col', '_insert_cols', '_insert_row', '_insert_rows', '_ltd_index_first', '_ltd_index_last', '_name2ilocs', '_prepend_col', '_prepend_cols', '_prepend_row', '_prepend_rows', '_reindex_cols', '_reindex_rows', '_rename_cols', '_rename_rows', '_repl_col', '_repl_cols', '_repl_row', '_repl_rows', '_rmcol', '_rmcols', '_rmrow', '_rmrows', '_rn2rlocs', '_row', '_rows', '_setitem', '_subtb', '_swapcol', '_swaprow', '_transpose', '_fliplr', '_flipud' ] #all operations will generate a new Qtable(copy.deepcopy), and will not change the original Qtable #columns col-names-list no-duplicate-names-permitted #index rowname-names-list no-duplicate-names-permitted #df pd.DataFrame def _index_map(df): d = elel.ivdict(list(df.index)) return(d) def _columns_map(df): d = elel.ivdict(list(df.columns)) return(d) def _name2ilocs(rowname,colname,**kwargs): if('index_map' in kwargs): index_map = kwargs['index_map'] else: df = kwargs['DF'] index_map = _index_map(df) if('columns_map' in kwargs): columns_map = kwargs['columns_map'] else: df = kwargs['DF'] columns_map = _columns_map(df) kl,vl = eded.d2kvlist(index_map) rlocs = elel.indexes_all(vl,rowname) kl,vl = eded.d2kvlist(columns_map) clocs = elel.indexes_all(vl,colname) return((rlocs,clocs)) # index_map = _index_map(df) # columns_map = _columns_map(df) # _getitem(df,rowname,colname,rloc=0,cloc=0) # rloc relative-row-position # cloc relative-col-position def _getitem(df,rowname,colname,*args,**kwargs): rlocs,clocs = _name2ilocs(rowname,colname,index_map=kwargs['index_map'],columns_map=kwargs['columns_map']) rslt = df.iloc[rlocs,clocs] args = list(args) if(args.__len__()==0): pass else: rloc = args[0] cloc = args[1] rslt = rslt.iloc[rloc,cloc] return(rslt) def _setitem(df,rowname,colname,value,*args,**kwargs): rlocs,clocs = _name2ilocs(rowname,colname,index_map=kwargs['index_map'],columns_map=kwargs['columns_map']) rslt = df.iloc[rlocs,clocs] args = list(args) if(args.__len__()==0): rslt = value else: rloc = args[0] cloc = args[1] rslt.iloc[rloc,cloc] = value df.iloc[rlocs,clocs] = rslt #rn ---------------------rowname def _rn2rlocs(rowname,**kwargs): if('index_map' in kwargs): index_map = kwargs['index_map'] else: df = kwargs['DF'] index_map = _index_map(df) kl,vl = eded.d2kvlist(index_map) rlocs = elel.indexes_all(vl,rowname) rlocs.sort() return(rlocs) def _row(df,rowname,*args,**kwargs): rlocs = _rn2rlocs(rowname,**kwargs) args = list(args) if(args.__len__()==0): pass else: rlocs = elel.select_seqs(rlocs,args) return(df.iloc[rlocs]) #cn ---------------------colname def _cn2clocs(colname,**kwargs): if('columns_map' in kwargs): columns_map = kwargs['columns_map'] else: df = kwargs['DF'] columns_map = _columns_map(df) kl,vl = eded.d2kvlist(columns_map) clocs = elel.indexes_all(vl,colname) clocs.sort() return(clocs) def _col(df,colname,*args,**kwargs): clocs = _cn2clocs(colname,**kwargs) args = list(args) if(args.__len__()==0): pass else: clocs = elel.select_seqs(clocs,args) return(df.iloc[:,clocs]) def _get_rlocs(rownames,**kwargs): rlocs = [] for i in range(rownames.__len__()): rowname = rownames[i] tmp = _rn2rlocs(rowname,**kwargs) rlocs = elel.concat(rlocs,tmp) rlocs.sort() return(rlocs) def _get_clocs(colnames,**kwargs): clocs = [] for i in range(colnames.__len__()): colname = colnames[i] tmp = _cn2clocs(colname,**kwargs) clocs = elel.concat(clocs,tmp) clocs.sort() return(clocs) def _rows(df,*rownames,**kwargs): rownames = list(rownames) if(isinstance(rownames[0],list)): rownames = rownames[0] else: pass rlocs = _get_rlocs(rownames,**kwargs) return(df.iloc[rlocs]) def _cols(df,*colnames,**kwargs): colnames = list(colnames) if(isinstance(colnames[0],list)): colnames = colnames[0] else: pass clocs = _get_clocs(colnames,**kwargs) return(df.iloc[:,clocs]) def _subtb(df,rownames,colnames,**kwargs): rownames = elel.uniqualize(rownames) colnames = elel.uniqualize(colnames) rlocs = _get_rlocs(rownames,**kwargs) clocs = _get_clocs(colnames,**kwargs) return(df.iloc[rlocs,clocs]) def _ltd_index_first(ltd,value): for i in range(ltd.__len__()): if(ltd[i] == value): return(i) else: pass raise ValueError("value not exist") def _ltd_index_last(ltd,value): for i in range(ltd.__len__()-1,-1,-1): if(ltd[i] == value): return(i) else: pass raise ValueError("value not exist") def _crop(df,top,left,bot,right,**kwargs): imd = kwargs['index_map'] top = _ltd_index_first(imd,top) bot = _ltd_index_last(imd,bot) cmd = kwargs['columns_map'] left = _ltd_index_first(cmd,left) right = _ltd_index_last(cmd,right) rownames = list(df.index[top:bot+1]) colnames = list(df.columns[left:right+1]) return(_subtb(df,rownames,colnames,**kwargs)) def _swapcol(df,colname1,colname2,*args,**kwargs): df = copy.deepcopy(df) clocs1 = _cn2clocs(colname1,**kwargs) clocs2 = _cn2clocs(colname2,**kwargs) args = list(args) if(args.__len__()==0): which1 = 0 which2 = 0 elif(args.__len__()==1): which1 = args[0] which2 = 0 else: which1 = args[0] which2 = args[1] cloc1 = clocs1[which1] cloc2 = clocs2[which2] clocs = elel.init_range(0,df.columns.__len__(),1) clocs = elel.iswap(clocs,cloc1,cloc2) return(df.iloc[:,clocs]) def _reindex_cols(df,*columns,**kwargs): df = copy.deepcopy(df) columns = list(columns) if(isinstance(columns[0],list)): columns = columns[0] else: pass clocs_array = [] for i in range(columns.__len__()): clocs = _cn2clocs(columns[i],**kwargs) clocs_array.append(clocs) if("whiches" in kwargs): whiches = kwargs['whiches'] else: whiches = elel.init(clocs_array.__len__(),0) clocs = elel.batexec(lambda clocs,which:clocs[which],clocs_array,whiches) return(df.iloc[:,clocs]) def _swaprow(df,rowname1,rowname2,*args,**kwargs): df = copy.deepcopy(df) rlocs1 = _rn2rlocs(rowname1,**kwargs) rlocs2 = _rn2rlocs(rowname2,**kwargs) args = list(args) if(args.__len__()==0): which1 = 0 which2 = 0 elif(args.__len__()==1): which1 = args[0] which2 = 0 else: which1 = args[0] which2 = args[1] rloc1 = rlocs1[which1] rloc2 = rlocs2[which2] rlocs = elel.init_range(0,df.columns.__len__(),1) rlocs = elel.iswap(rlocs,rloc1,rloc2) return(df.iloc[rlocs]) def _reindex_rows(df,*index,**kwargs): df = copy.deepcopy(df) index = list(index) if(isinstance(index[0],list)): index = index[0] else: pass rlocs_array = [] for i in range(index.__len__()): rlocs = _rn2rlocs(index[i],**kwargs) rlocs_array.append(rlocs) if("whiches" in kwargs): whiches = kwargs['whiches'] else: whiches = elel.init(rlocs_array.__len__(),0) rlocs = elel.batexec(lambda rlocs,which:rlocs[which],rlocs_array,whiches) return(df.iloc[rlocs]) def _rmcol(df,colname,*args,**kwargs): df = copy.deepcopy(df) clocs = _cn2clocs(colname,**kwargs) if(args.__len__()==0): whiches = elel.init_range(0,clocs.__len__(),1) else: whiches = list(args) clocs = elel.select_seqs(clocs,whiches) all_clocs = elel.init_range(0,df.columns.__len__(),1) lefted_clocs = elel.select_seqs_not(all_clocs,clocs) return(df.iloc[:,lefted_clocs]) def _rmcols(df,*colnames,**kwargs): df = copy.deepcopy(df) colnames = list(colnames) if(isinstance(colnames[0],list)): colnames = colnames[0] else: pass clocs_array = [] for i in range(colnames.__len__()): clocs = _cn2clocs(colnames[i],**kwargs) clocs_array.append(clocs) if("whiches" in kwargs): whiches = kwargs['whiches'] clocs = elel.batexec(lambda clocs,which:clocs[which],clocs_array,whiches) else: #by default remove all clocs = elel.concat(*clocs_array) all_clocs = elel.init_range(0,df.columns.__len__(),1) lefted_clocs = elel.select_seqs_not(all_clocs,clocs) return(df.iloc[:,lefted_clocs]) def _rmrow(df,rowname,*args,**kwargs): df = copy.deepcopy(df) rlocs = _rn2rlocs(rowname,**kwargs) if(args.__len__()==0): whiches = elel.init_range(0,rlocs.__len__(),1) else: whiches = list(args) rlocs = elel.select_seqs(rlocs,whiches) all_rlocs = elel.init_range(0,df.index.__len__(),1) lefted_rlocs = elel.select_seqs_not(all_rlocs,rlocs) return(df.iloc[lefted_rlocs]) def _rmrows(df,*rownames,**kwargs): df = copy.deepcopy(df) rownames = list(rownames) if(isinstance(rownames[0],list)): rownames = rownames[0] else: pass rlocs_array = [] for i in range(rownames.__len__()): rlocs = _rn2rlocs(rownames[i],**kwargs) rlocs_array.append(rlocs) if("whiches" in kwargs): whiches = kwargs['whiches'] rlocs = elel.batexec(lambda rlocs,which:rlocs[which],rlocs_array,whiches) else: #by default remove all rlocs = elel.concat(*rlocs_array) all_rlocs = elel.init_range(0,df.index.__len__(),1) lefted_rlocs = elel.select_seqs_not(all_rlocs,rlocs) return(df.iloc[lefted_rlocs]) def _insert_col(df,pos,*args,**kwargs): df = copy.deepcopy(df) if(isinstance(pos,int)): pass else: clocs = _cn2clocs(pos,**kwargs) if('which' in kwargs): which = kwargs['which'] else: which = 0 pos = clocs[which] + 1 args = list(args) if(args.__len__() == 1): colname = list(args[0].keys())[0] values = list(args[0].values())[0] else: colname = args[0] if(isinstance(args[1],list)): values = args[1] else: values = args[1:] #### #### df.insert(pos,colname,values,kwargs['allow_duplicates']) return(df) def _insert_cols(df,pos,*args,**kwargs): df = copy.deepcopy(df) if(isinstance(pos,int)): pass else: clocs = _cn2clocs(pos,**kwargs) if('which' in kwargs): which = kwargs['which'] else: which = 0 pos = clocs[which] + 1 args = list(args) if(isinstance(args[0],dict)): kl,vl = eded.d2kvlist(args[0]) else: if(isinstance(args[1],list)): kl = elel.select_evens(args) vl = elel.select_odds(args) else: kl,vl = elel.brkl2kvlist(args,df.index.__len__()+1) for i in range(kl.__len__()): colname = kl[i] values = vl[i] df.insert(pos+i,colname,values,kwargs['allow_duplicates']) return(df) def _insert_row(df,pos,*args,**kwargs): df = df.T df = _insert_col(df,pos,*args,**kwargs) df = df.T return(df) def _insert_rows(df,pos,*args,**kwargs): df = df.T df = _insert_cols(df,pos,*args,**kwargs) df = df.T return(df) def _append_col(df,*args,**kwargs): pos = df.columns.__len__() return(_insert_col(df,pos,*args,**kwargs)) def _append_cols(df,*args,**kwargs): pos = df.columns.__len__() return(_insert_cols(df,pos,*args,**kwargs)) def _append_row(df,*args,**kwargs): pos = df.index.__len__() return(_insert_row(df,pos,*args,**kwargs)) def _append_rows(df,*args,**kwargs): pos = df.index.__len__() return(_insert_rows(df,pos,*args,**kwargs)) def _prepend_col(df,*args,**kwargs): return(_insert_col(df,0,*args,**kwargs)) def _prepend_cols(df,*args,**kwargs): return(_insert_cols(df,0,*args,**kwargs)) def _prepend_row(df,*args,**kwargs): return(_insert_row(df,0,*args,**kwargs)) def _prepend_rows(df,*args,**kwargs): return(_insert_rows(df,0,*args,**kwargs)) def _rename_cols(df,*colnames): df = copy.deepcopy(df) colnames = list(colnames) if(isinstance(colnames[0],list)): colnames = colnames[0] else: pass df.columns = colnames return(df) def _rename_rows(df,*rownames): df = copy.deepcopy(df) rownames = list(rownames) if(isinstance(rownames[0],list)): rownames = rownames[0] else: pass df.index = rownames return(df) def _repl_col(df,pos,*args,**kwargs): df = copy.deepcopy(df) if(isinstance(pos,int)): pos = pos + 1 else: clocs = _cn2clocs(pos,**kwargs) if('which' in kwargs): which = kwargs['which'] else: which = 0 pos = clocs[which] + 1 args = list(args) if(args.__len__() == 1): colname = list(args[0].keys())[0] values = list(args[0].values())[0] else: colname = args[0] if(isinstance(args[1],list)): values = args[1] else: values = args[1:] df.insert(pos,colname,values,kwargs['allow_duplicates']) pos = pos -1 all_clocs = elel.init_range(0,df.columns.__len__(),1) all_clocs.remove(pos) return(df.iloc[:,all_clocs]) def _repl_cols(df,poses,*args,**kwargs): df = copy.deepcopy(df) args = list(args) if(isinstance(args[0],dict)): kl,vl = eded.d2kvlist(args[0]) else: if(isinstance(args[1],list)): kl = elel.select_evens(args) vl = elel.select_odds(args) else: kl,vl = elel.brkl2kvlist(args,df.index.__len__()+1) if(isinstance(poses[0],int)): pass else: colnames = poses clocs_array = [] for i in range(colnames.__len__()): clocs = _cn2clocs(colnames[i],**kwargs) clocs_array.append((clocs,i)) if("whiches" in kwargs): whiches = kwargs['whiches'] clocs_array = elel.mapv(clocs_array,lambda ele:ele[0]) clocs = elel.batexec(lambda clocs,which:clocs[which],clocs_array,whiches) poses = clocs else: #by default replace all nkl = [] nvl = [] nclocs = [] for i in range(clocs_array.__len__()): clocs = clocs_array[i][0] index = clocs_array[i][1] tmpkl = elel.init(clocs.__len__(),kl[i]) tmpvl = elel.init(clocs.__len__(),vl[i]) nkl = elel.concat(nkl,tmpkl) nvl = elel.concat(nvl,tmpvl) nclocs = elel.concat(nclocs,clocs) #batsort poses = nclocs kl,vl = elel.batsorted(nclocs,nkl,nvl) poses = elel.mapv(poses,lambda pos:pos+1) poses.sort() for i in range(0,poses.__len__()): pos = poses[i] df.insert(pos,kl[i],vl[i],kwargs['allow_duplicates']) pos = pos -1 all_clocs = elel.init_range(0,df.columns.__len__(),1) all_clocs.remove(pos) df = df.iloc[:,all_clocs] return(df) def _repl_row(df,pos,*args,**kwargs): df = df.T df = _repl_col(df,pos,*args,**kwargs) df = df.T return(df) def _repl_rows(df,poses,*args,**kwargs): df = df.T df = _repl_cols(df,poses,*args,**kwargs) df = df.T return(df) def _transpose(df): df = copy.deepcopy(df) df = df.T return(df) def _fliplr(df,**kwargs): columns = list(df.columns) columns.reverse() df = _reindex_cols(df,columns,**kwargs) return(df) def _flipud(df,**kwargs): index = list(df.index) index.reverse() df = _reindex_rows(df,index,**kwargs) return(df)

27.40536

110

0.603753

2,150

16,361

4.351628

0.077209

0.038478

0.020522

0.025652

0.670051

0.594912

0.540188

0.521377

0.492946

0.448055

0

0.015329

0.242406

16,361

596

111

27.451342

0.739492

0.032883

0

0.591954

0

0.049693

0

1

0.088123

false

0.030651

0.011494

0.007663

0.099617

0

null

0

null

0

1

0

08ab2c42e46cc085323887951f27802509bc2c01

1,131

py

Python

pythonDesafios/desafio058.py

mateusdev7/desafios-python

6160ddc84548c7af7f5775f9acabe58238f83008

[ "MIT" ]

null

pythonDesafios/desafio058.py

mateusdev7/desafios-python

6160ddc84548c7af7f5775f9acabe58238f83008

[ "MIT" ]

null

pythonDesafios/desafio058.py

mateusdev7/desafios-python

6160ddc84548c7af7f5775f9acabe58238f83008

[ "MIT" ]

null

from random import randint from time import sleep opcao = 123 cont = 0 while opcao != 0: print('-=-' * 20) print('Vou pensar em um número entre 0 e 10, quer tentar adivinhar?') print('-=-' * 20) print('\n[ 1 ] Sim [ 0 ] Não') opcao = int(input('Escolha uma das opções acima\n>')) if opcao == 1: computador = randint(0, 10) # O computador sorteia um número de 0 a 10 usuario = int(input('\nEscolha um número entre 0 e 10: ').strip()) cont += 1 while usuario != computador: if usuario < computador: print('Mais... Tente novamente') else: print('Menos... Tente novamente') usuario = int(input('Insira outro número: ')) cont += 1 if usuario == computador: print('\nPARABÉNS. Você ACERTOU!!!') print('Calculando a quantide de tentivas necessárias...') sleep(1) print('-=-' * 15) print(f'Você precisou de {cont} tentativa(s) para acertar.') print('-=-'* 15) elif opcao == 0: print('Você saiu do jogo.')

35.34375

78

0.535809

137

1,131

4.423358

0.50365

0.039604

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0.046205

0.056106

0

0.042272

0.330681

1,131

32

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35.34375

0.758256

0.035367

0

0.2

0

0.338532

0

1

0

false

0

0.066667

0

0.066667

0.4

0

null

0

null

0

1

0

08ab8c8ec2777c51be6f0455ab77ed9f159c8995

1,896

py

Python

FeatureEngineeringPy_DataScience/demo153_rarecategories.py

mahnooranjum/Programming_DataScience

f7a4215d4615b3f8460c3a1944a585628cf6930d

[ "MIT" ]

null

FeatureEngineeringPy_DataScience/demo153_rarecategories.py

mahnooranjum/Programming_DataScience

f7a4215d4615b3f8460c3a1944a585628cf6930d

[ "MIT" ]

null

FeatureEngineeringPy_DataScience/demo153_rarecategories.py

mahnooranjum/Programming_DataScience

f7a4215d4615b3f8460c3a1944a585628cf6930d

[ "MIT" ]

null

# -*- coding: utf-8 -*- """Demo153_RareCategories.ipynb ## Rare Categories - Labels - The number of labels in the dataset are different - __high cardinality__ refers to uniqueness of data values - The lower the cardinality, the more duplicated elements in a column - A column with the lowest possible cardinality would have the same value for every row - Highly cardinal variables dominate tree based algorithms - Labels may only be present in the training data set, but not in the test data set - Labels may appear in the test set that were not present in the training set __Tree methods are biased towards variables with many labels__ """ import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import numpy as np from google.colab import drive drive.mount('/content/gdrive') data = pd.read_csv("gdrive/My Drive/Colab Notebooks/FeatureEngineering/train.csv") cat_cols = ['Name', 'Sex', 'Ticket', 'Cabin', 'Embarked'] for i in cat_cols: print('Number of categories in the variable {}: {}'.format(i,len(data[i].unique()))) print('Total rows: {}'.format(len(data))) data['Sex'].value_counts() data['Cabin_processed'] = data['Cabin'].astype(str).str[0] data['Cabin_processed_X'] = data['Cabin'].astype(str).str[1] cat_cols = [ 'Sex', 'Embarked', 'Cabin_processed'] for i in cat_cols: sns.catplot(x=i, kind='count', data=data) data['Cabin_processed'].value_counts() / len(data) for i in cat_cols: sns.catplot(x=i,data=data, hue='Survived', kind='count', palette="ch:.25") """### Transform Rare Labels""" _temp = pd.Series(data['Cabin_processed'].value_counts() / len(data)) _temp.sort_values(ascending=False) _temp _temp = pd.Series(data['Cabin_processed'].value_counts() / len(data)) _temp for i in _labels: data['Cabin_processed'].replace(i, 'rare', inplace=True) _temp = pd.Series(data['Cabin_processed'].value_counts() / len(data)) _temp

26.704225

88

0.728903

289

1,896

4.650519

0.432526

0.060268

0.09375

0.068452

0.220982

0.18006

0.153274

0.116071

0

0.004884

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1,896

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26.704225

0.815629

0.341245

0

0.310345

0

0.273927

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0.068966

0

null

0

null

0

1

0

08af904e7f82a923beed7c2fa65793eb9bf02793

878

py

Python

popbl_servicesapp/flask_app/order/application/api_client.py

xetxezarreta/master-popbl1

253880b9ba358f63f666893cdbbffe7391fcd096

[ "MIT" ]

null

popbl_servicesapp/flask_app/order/application/api_client.py

xetxezarreta/master-popbl1

253880b9ba358f63f666893cdbbffe7391fcd096

[ "MIT" ]

1

2021-06-02T00:57:11.000Z

popbl_servicesapp/flask_app/order/application/api_client.py

xetxezarreta/master-popbl1

253880b9ba358f63f666893cdbbffe7391fcd096

[ "MIT" ]

null

import requests import json from os import environ from .models import Order, Piece from .BLConsul import BLConsul GATEWAY_PORT = environ.get("HAPROXY_PORT") GATEWAY_ADDRESS = environ.get("HAPROXY_IP") MACHINE_SERVICE = "machine" PAYMENT_SERVICE = "payment" DELIVERY_SERVICE = "delivery" AUTH_SERVICE = "auth" CA_CERT = environ.get("RABBITMQ_CA_CERT") consul = BLConsul.get_instance() class ApiClient: @staticmethod def auth_get_pubkey(): consul_dict = consul.get_service(AUTH_SERVICE) print("CONSUL RESPONSE {}".format(consul_dict)) address = consul_dict['Address'] port = str(consul_dict['Port']) r = requests.get("http://{}:{}/{}/pubkey".format(address, port, AUTH_SERVICE), verify=False) if r.status_code == 200: content = json.loads(r.content) return content["publicKey"].encode("utf-8")

29.266667

100

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878

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878

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0

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0.041667

0

null

0

null

0

1

0

08b08e4c091db6970d8bd9b3e8f858f92dfeb9ac

2,569

py

Python

polya/modules/congruence_closure_module.py

holtzermann17/polya

6d611bf47185249a96f4cf7ee9b3884bc70a15ac

[ "Apache-2.0" ]

24

2015-01-01T18:21:40.000Z

2021-08-29T01:56:14.000Z

polya/modules/congruence_closure_module.py

holtzermann17/polya

6d611bf47185249a96f4cf7ee9b3884bc70a15ac

[ "Apache-2.0" ]

1

2018-09-06T17:53:13.000Z

2018-09-07T13:57:39.000Z

polya/modules/congruence_closure_module.py

holtzermann17/polya

6d611bf47185249a96f4cf7ee9b3884bc70a15ac

[ "Apache-2.0" ]

4

2017-02-08T15:04:09.000Z

2021-05-02T15:13:05.000Z

#################################################################################################### # # congruence_closure_module.py # # Authors: # Jeremy Avigad # Rob Lewis # # This module maintains a union-find structure for terms in Blackboard, which is currently only used # for congruence closure. It should perhaps be integrated differently into Blackboard. # # Contains a set for each equality class (up to constant multiples) of terms, and tracks which terms # appear as arguments to which function terms. # #################################################################################################### import polya.main.terms as terms import polya.main.messages as messages import polya.util.timer as timer import fractions import itertools class CongClosureModule: def __init__(self): pass def update_blackboard(self, B): """ Checks the blackboard B for function terms with equal arguments, and asserts that the function terms are equal. """ def eq_func_terms(f1, f2): """ Returns true if f1 and f2 have the same name and arity, and all args are equal. """ if f1.func_name != f2.func_name or len(f1.args) != len(f2.args): return False for i in range(len(f1.args)): arg1, arg2 = f1.args[i], f2.args[i] if arg1.coeff == 0: eq = B.implies(arg2.term.index, terms.EQ, 0, 0) or arg2.coeff == 0 else: eq = B.implies(arg1.term.index, terms.EQ, fractions.Fraction(arg2.coeff, arg1.coeff), arg2.term.index) if not eq: return False return True timer.start(timer.CCM) messages.announce_module('congruence closure module') func_classes = {} for i in (d for d in range(B.num_terms) if isinstance(B.term_defs[d], terms.FuncTerm)): name = B.term_defs[i].func_name func_classes[name] = func_classes.get(name, []) + [i] for name in func_classes: tinds = func_classes[name] for (i, j) in itertools.combinations(tinds, 2): # ti and tj are function terms with the same symbols. check if they're equal. f1, f2 = B.term_defs[i], B.term_defs[j] if eq_func_terms(f1, f2): B.assert_comparison(terms.IVar(i) == terms.IVar(j)) timer.stop(timer.CCM) def get_split_weight(self, B): return None

36.7

100

0.54963

323

2,569

4.28483

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0.028902

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70

101

36.7

0.742873

0.256131

0

0.054054

0

0.015281

0

0.027027

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0.108108

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0.027027

0.135135

0.027027

0.378378

0

null

0

null

0

1

0

08b3ea49c776eba1ca9a6e036f7a93721ad3e46b

3,280

py

Python

build.py

Jackcava/mappingToFHIR

3189b55121a50ee1c4734227cde6da58ed6cb576

[ "MIT" ]

null

build.py

Jackcava/mappingToFHIR

3189b55121a50ee1c4734227cde6da58ed6cb576

[ "MIT" ]

null

build.py

Jackcava/mappingToFHIR

3189b55121a50ee1c4734227cde6da58ed6cb576

[ "MIT" ]

null

import pandas as pd import numpy as np import csv def buildPat(row,key): if key == "extension.valueAddress.city": return row.A01_DESC_LUOGO_NASCITA elif key == "identifier.value": return row.A01_ID_PERSONA elif key == "name.family": return row.A01_COGNOME elif key == "name.given": return row.A01_NOME elif key == "gender": if row.A01_SESSO=='M': return 'male' elif row.A01_SESSO=='F': return 'female' else: return 'unknown' elif key == "birthDate": if isinstance(row.A01_DATA_NASCITA,str): return row.A01_DATA_NASCITA[:10] else: return row.A01_DATA_NASCITA.strftime("%Y-%m-%d") elif key == "contact.relationship.coding.code": if row.A02_DESC_TELEFONO1 in ("MAMMA","PAPA'","MADRE","PADRE"): return 'PRN' elif row.A02_DESC_TELEFONO1 == "ZIA": return 'AUNT' elif row.A02_DESC_TELEFONO1 == "ZIO": return 'UNCLE' else: return '' elif key == "contact.relationship.coding.display": if row.A02_DESC_TELEFONO1 in ("MAMMA","PAPA'","MADRE","PADRE"): return 'parent' elif row.A02_DESC_TELEFONO1 == "ZIA": return 'aunt' elif row.A02_DESC_TELEFONO1 == "ZIO": return 'uncle' else: return '' elif key == "contact.telecom.emailvalue": return row.A02_EMAIL elif key == "contact.telecom.phonevalue": return row.A02_NUM_TELEFONO1 elif key == "contact.relationship.coding.code2": if row.A02_DESC_TELEFONO2 in ("MAMMA","PAPA'","PAPA","MADRE","PADRE"): return 'PRN' elif row.A02_DESC_TELEFONO2 == "ZIA": return 'AUNT' elif row.A02_DESC_TELEFONO2 == "ZIO": return 'UNCLE' else: return '' elif key == "contact.relationship.coding.display2": if row.A02_DESC_TELEFONO2 in ("MAMMA","PAPA'","PAPA","MADRE","PADRE"): return 'parent' elif row.A02_DESC_TELEFONO2 == "ZIA": return 'aunt' elif row.A02_DESC_TELEFONO2 == "ZIO": return 'uncle' else: return '' elif key == "contact.telecom.phonevalue2": return row.A02_NUM_TELEFONO2 def buildCond(row,key): if key == "extension.valueDateTime": if isinstance(row.DT_REGISTRAZIONE,str): return row.DT_REGISTRAZIONE[:10] else: return row.DT_REGISTRAZIONE.strftime("%Y-%m-%d") elif key == "bodySite.coding.code": if row.TITOLO_LIV2 == "Sottosede": return row.CODICE_LIV2 elif key == "bodySite.text": if row.TITOLO_LIV2 == "Sottosede": return row.DESC_LIV2 elif key == "stage.summary.text": if row.TITOLO_LIV2 == "Stadio": stadio = row.CODICE_LIV2.split()[1] return stadio elif key == "subject.reference": return "Patient/"+row.ID_PAZIENTE elif key == "recordedDate": if isinstance(row.DT_REGISTRAZIONE,str): return row.DT_REGISTRAZIONE[:10] else: return row.DT_REGISTRAZIONE.strftime("%Y-%m-%d") elif key == "description": return row.DESC_LIV2

34.893617

78

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383

3,280

4.785901

0.245431

0.06874

0.065466

0.061102

0.608838

0.518276

0.508456

0.47245

0.454992

0

0.034273

0.297256

3,280

93

79

35.268817

0.760954

0

0.505495

0

0.197866

0.080793

0

1

0.021978

false

0

0.032967

0

0.461538

0

null

0

null

0

1

0

08b53ae263a1ae583483ba9e1d84efca2906ad4a

2,109

py

Python

sources-filter-list.py

kerberizer/wikimedia-scripts

18b78d5cc0042d5efcb355a65f4309fb4ae97eaf

[ "CC0-1.0" ]

null

sources-filter-list.py

kerberizer/wikimedia-scripts

18b78d5cc0042d5efcb355a65f4309fb4ae97eaf

[ "CC0-1.0" ]

null

sources-filter-list.py

kerberizer/wikimedia-scripts

18b78d5cc0042d5efcb355a65f4309fb4ae97eaf

[ "CC0-1.0" ]

1

2016-07-31T07:26:33.000Z

#!/usr/bin/env python3 import locale import sys from datetime import datetime as dt import pywikibot as pwb def main(argv): dump_only = False if len(argv) > 1: if argv.pop() == '--dump': dump_only = True else: print('Error: Unrecognized option.', file=sys.stderr) sys.exit(1) wik = pwb.Site(code='bg', fam='wikipedia') params = { 'action': 'query', 'format': 'json', 'list': 'abusefilters', 'formatversion': '2', 'abfstartid': '12', 'abfendid': '12', 'abfprop': 'pattern', } pattern = pwb.data.api.Request( site=wik, parameters=params ).submit()['query']['abusefilters'][0]['pattern'] site_list = [_[5:][:-4].replace('\\.', '.') for _ in pattern.splitlines() if _[2:5] == "'\\b"] site_list.sort() if dump_only: for site in site_list: print('* {}'.format(site)) else: list_page_name = 'Уикипедия:Патрульори/СФИН' list_page = pwb.Page(wik, list_page_name) lnum_page = pwb.Page(wik, list_page_name + '/N') lupd_page = pwb.Page(wik, list_page_name + '/U') list_page.text = '{{' + list_page_name + '/H}}\n' site_index = '' for site in site_list: if site[0] != site_index: list_page.text += '\n<h3> {} </h3>\n'.format(site[0].capitalize()) site_index = site[0] list_page.text += '* {}\n'.format(site) list_page.text += '\n{{' + list_page_name + '/F}}' lnum_page.text = str(len(site_list)) locale.setlocale(locale.LC_TIME, 'bg_BG.UTF-8') lupd_page.text = dt.now().strftime('%H:%M на %e %B %Y').lower() locale.resetlocale(locale.LC_TIME) list_page.save(summary='Бот: актуализация', quiet=True) lnum_page.save(summary='Бот: актуализация', quiet=True) lupd_page.save(summary='Бот: актуализация', quiet=True) if __name__ == '__main__': main(sys.argv) # vim: set ts=4 sts=4 sw=4 tw=100 et:

31.477612

98

0.543385

266

2,109

4.12406

0.406015

0.087511

0.065634

0.038286

0.208751

0.177758

0

0.016678

0.289237

2,109

66

99

31.954545

0.715143

0.027027

0

0.075472

0

0.157073

0.012195

0

1

0.018868

false

0

0.075472

0

0.09434

0.037736

0

null

0

null

0

1

0

08c039eccfb3500006401f61d37873f932777364

1,120

py

Python

douyin/hot/trend.py

miaotiaotech/DouYin

e996ad99ce27e0d13f2856c497fd4b4f05f95b56

[ "MIT" ]

657

2018-10-24T16:58:04.000Z

2022-03-15T03:58:04.000Z

douyin/hot/trend.py

1997lw/DouYin

5859f4db5258ad10926fddaa2b4074c85581d419

[ "MIT" ]

15

2018-10-30T09:40:11.000Z

2020-08-09T13:58:31.000Z

douyin/hot/trend.py

1997lw/DouYin

5859f4db5258ad10926fddaa2b4074c85581d419

[ "MIT" ]

249

2018-10-25T07:12:14.000Z

2022-02-21T07:49:58.000Z

from douyin.utils import fetch from douyin.config import hot_trend_url, common_headers from douyin.utils.tranform import data_to_music, data_to_topic from douyin.structures.hot import HotTrend from douyin.utils.common import parse_datetime # define trend query params query = { 'version_code': '2.9.1', 'count': '10', } def trend(): """ get trend result :return: """ offset = 0 while True: query['cursor'] = str(offset) result = fetch(hot_trend_url, headers=common_headers, params=query, verify=False) category_list = result.get('category_list') datetime = parse_datetime(result.get('extra', {}).get('now')) final = [] for item in category_list: # process per category if item.get('desc') == '热门话题': final.append(data_to_topic(item.get('challenge_info', {}))) if item.get('desc') == '热门音乐': final.append(data_to_music(item.get('music_info', {}))) yield HotTrend(datetime=datetime, data=final, offset=offset, count=int(query.get('count'))) offset += 10

32

99

0.633036

142

1,120

4.838028

0.429577

0.07278

0.065502

0.037846

0

0.009335

0.234821

1,120

34

100

32.941176

0.792299

0.065179

0

0.093567

0

1

0.041667

false

0

0.208333

0

0.25

0

null

0

null

0

1

0

08c1a85992031481a6829f933c45c2206c709fa4

288

py

Python

hashing/hashing.py

subhamsagar524/Learn-Blockchain

316f30ed9d43f6ab806ca87b9b83c0237ef69828

[ "MIT" ]

null

hashing/hashing.py

subhamsagar524/Learn-Blockchain

316f30ed9d43f6ab806ca87b9b83c0237ef69828

[ "MIT" ]

null

hashing/hashing.py

subhamsagar524/Learn-Blockchain

316f30ed9d43f6ab806ca87b9b83c0237ef69828

[ "MIT" ]

1

2020-03-13T06:32:46.000Z

# Import the hashing Library import hashlib # Get the string as input word = input("Enter the word for Hashing: ") # Get the hashing hashed_code = hashlib.sha256(word.encode()) final = hashed_code.hexdigest() # Print the result print("Hashed with 256 bit: ") print(final)

20.571429

45

0.704861

41

288

4.902439

0.560976

0.099502

0

0.025974

0.197917

288

13

46

22.153846

0.844156

0.288194

0

0.262032

0

1

0

false

0

0.166667

0

0.166667

0.333333

0

null

0

null

0

1

0