ytzi/the-stack-dedup-python-filtered · Datasets at Hugging Face

573671e14e06512a6056d7ef96ce655d220e4a19

2,857

py

Python

Run_exphydro_distributed_type1_pso.py

sopanpatil/exp-hydro

7295dddc4df1028f669a223e1b631a4a91669515

[ "MIT" ]

11

2016-11-25T13:05:26.000Z

2022-03-25T03:24:16.000Z

Run_exphydro_distributed_type1_pso.py

sopanpatil/exp-hydro

7295dddc4df1028f669a223e1b631a4a91669515

[ "MIT" ]

null

Run_exphydro_distributed_type1_pso.py

sopanpatil/exp-hydro

7295dddc4df1028f669a223e1b631a4a91669515

[ "MIT" ]

6

2017-03-28T12:06:00.000Z

2021-09-16T17:50:34.000Z

#!/usr/bin/env python # Programmer(s): Sopan Patil. """ MAIN PROGRAM FILE Run this file to optimise the model parameters of the spatially distributed version of EXP-HYDRO model using Particle Swarm Optimisation (PSO) algorithm. Type 1 Model: - This type of distributed model is pixel based (i.e., all sub-components have the same drainage area). - All pixels receive the same meteorological inputs. - Channel routing is ignored and it is assumed that streamflow generated from each pixel reaches the catchment outlet on same day. """ import numpy import os import time import matplotlib.pyplot as plt from exphydro.distributed import ExphydroDistrParameters from exphydro.distributed.type1 import ExphydroDistrModel from hydroutils import Calibration, ObjectiveFunction start_time = time.time() ###################################################################### # SET WORKING DIRECTORY # Getting current directory, i.e., directory containing this file dir1 = os.path.dirname(os.path.abspath('__file__')) # Setting to current directory os.chdir(dir1) ###################################################################### # MAIN PROGRAM # Load meteorological and observed flow data P = numpy.genfromtxt('SampleData/P_test.txt') # Observed rainfall (mm/day) T = numpy.genfromtxt('SampleData/T_test.txt') # Observed air temperature (deg C) PET = numpy.genfromtxt('SampleData/PET_test.txt') # Potential evapotranspiration (mm/day) Qobs = numpy.genfromtxt('SampleData/Q_test.txt') # Observed streamflow (mm/day) # Specify the number of pixels in the catchment npixels = 5 # Specify the no. of parameter sets (particles) in a PSO swarm npart = 10 # Generate 'npart' initial EXP-HYDRO model parameters params = [ExphydroDistrParameters(npixels) for j in range(npart)] # Initialise the model by loading its climate inputs model = ExphydroDistrModel(P, PET, T, npixels) # Specify the start and end day numbers of the calibration period. # This is done separately for the observed and simulated data # because they might not be of the same length in some cases. calperiods_obs = [365, 2557] calperiods_sim = [365, 2557] # Calibrate the model to identify optimal parameter set paramsmax = Calibration.pso_maximise(model, params, Qobs, ObjectiveFunction.klinggupta, calperiods_obs, calperiods_sim) print ('Calibration run KGE value = ', paramsmax.objval) # Run the optimised model for validation period Qsim = model.simulate(paramsmax) kge = ObjectiveFunction.klinggupta(Qobs[calperiods_obs[1]:], Qsim[calperiods_sim[1]:]) print ('Independent run KGE value = ', kge) print("Total runtime: %s seconds" % (time.time() - start_time)) # Plot the observed and simulated hydrographs plt.plot(Qobs[calperiods_obs[0]:], 'b-') plt.plot(Qsim[calperiods_sim[0]:], 'r-') plt.show() ######################################################################

35.7125

119

0.716136

5738d01ad1ed866e8e47c9a1f5dadbf2cfce3611

11,104

py

Python

multi_input_multi_output/train.py

alt113/CS591-Multimodal-Spring2021

f28bade729818aa51fd131e86f1ba2271cca8947

[ "MIT" ]

null

multi_input_multi_output/train.py

alt113/CS591-Multimodal-Spring2021

f28bade729818aa51fd131e86f1ba2271cca8947

[ "MIT" ]

1

2021-05-03T18:59:43.000Z

2021-05-03T19:04:19.000Z

multi_input_multi_output/train.py

alt113/CS591-Multimodal-Spring2021

f28bade729818aa51fd131e86f1ba2271cca8947

[ "MIT" ]

null

import os from multi_input_multi_output.models import MultiNet from shared_weights.helpers import config, utils from shared_weights.helpers.siamese_network import create_encoder from data.data_tf import fat_dataset import tensorflow as tf from tensorflow import keras # ---------------------- """ Data augmentation""" augmentation_input = keras.layers.Input(shape=config.IMG_SHAPE) data_augmentation = keras.layers.experimental.preprocessing.RandomTranslation( height_factor=(-0.2, 0.2), width_factor=(-0.2, 0.2), fill_mode="constant" )(augmentation_input) data_augmentation = keras.layers.experimental.preprocessing.RandomFlip(mode="horizontal")(data_augmentation) data_augmentation = keras.layers.experimental.preprocessing.RandomRotation(factor=0.15, fill_mode="constant")(data_augmentation) augmentation_output = keras.layers.experimental.preprocessing.RandomZoom(height_factor=(-0.3, 0.1), width_factor=(-0.3, 0.1), fill_mode="constant")(data_augmentation) data_augmentation = keras.Model(augmentation_input, augmentation_output) """ Unsupervised contrastive loss""" """ Train the model""" network_input = keras.layers.Input(shape=config.IMG_SHAPE) # Load RGB vision encoder. r_encoder = create_encoder(base='resnet50', pretrained=True)(network_input) encoder_output = keras.layers.Dense(config.HIDDEN_UNITS)(r_encoder) r_encoder = keras.Model(network_input, encoder_output) # Create representation learner. r_representation_learner = RepresentationLearner( r_encoder, config.PROJECTION_UNITS, num_augmentations=2, temperature=0.1 ) r_representation_learner.build((None, 128, 128, 3)) # base_path = os.environ['PYTHONPATH'].split(os.pathsep)[1] # representation_learner.load_weights(base_path + '/multi_input_multi_output/simclr/weights/simclr_resnet50_rgb_scratch_weights.h5') r_representation_learner.load_weights(config.RGB_MODALITY_WEIGHT_PATH) functional_model = flatten_model(r_representation_learner.layers[0]) rgb_encoder = functional_model.layers[1] # Load Depth vision encoder. d_encoder = create_encoder(base='resnet50', pretrained=True)(network_input) encoder_output = keras.layers.Dense(config.HIDDEN_UNITS)(d_encoder) d_encoder = keras.Model(network_input, encoder_output) # Create representation learner. d_representation_learner = RepresentationLearner( d_encoder, config.PROJECTION_UNITS, num_augmentations=2, temperature=0.1 ) d_representation_learner.build((None, 128, 128, 3)) # base_path = os.environ['PYTHONPATH'].split(os.pathsep)[1] # representation_learner.load_weights(base_path + '/multi_input_multi_output/simclr/weights/simclr_resnet50_rgb_scratch_weights.h5') d_representation_learner.load_weights(config.DEPTH_MODALITY_WEIGHT_PATH) functional_model = flatten_model(d_representation_learner.layers[0]) depth_encoder = functional_model.layers[1] # ---------------------- # RGB rgb_input = keras.layers.Input(shape=config.IMG_SHAPE) # rgb_encoder = keras.applications.ResNet50V2(include_top=False, # weights=None, # input_shape=config.IMG_SHAPE, # pooling="avg") rgb = rgb_encoder(rgb_input) rgb = keras.layers.Dropout(config.DROPOUT_RATE)(rgb) rgb = keras.layers.Dense(config.HIDDEN_UNITS, activation="relu")(rgb) rgb = keras.layers.Dropout(config.DROPOUT_RATE)(rgb) rgb = keras.layers.Flatten()(rgb) rgb = keras.layers.Dense(config.NUM_OF_CLASSES, activation="softmax")(rgb) rgb_classifier = keras.models.Model(inputs=rgb_input, outputs=rgb, name='rgb_classifier') for layer in rgb_classifier.layers: layer._name += '_rgb' layer.trainable = True print('[INFO] built rgb classifier') print(rgb_classifier.summary()) # Depth depth_input = keras.layers.Input(shape=config.IMG_SHAPE) # depth_encoder = keras.applications.ResNet50V2(include_top=False, # weights=None, # input_shape=config.IMG_SHAPE, # pooling="avg") depth = depth_encoder(depth_input) depth = keras.layers.Dropout(config.DROPOUT_RATE)(depth) depth = keras.layers.Dense(config.HIDDEN_UNITS, activation="relu")(depth) depth = keras.layers.Dropout(config.DROPOUT_RATE)(depth) depth = keras.layers.Flatten()(depth) depth = keras.layers.Dense(config.NUM_OF_CLASSES, activation="softmax")(depth) depth_classifier = keras.models.Model(inputs=depth_input, outputs=depth, name='depth_classifier') for layer in depth_classifier.layers: layer._name += '_depth' layer.trainable = True print('[INFO] built depth classifier') print(depth_classifier.summary()) # Build and compile MultiNet multinet_class = MultiNet(rgb_classifier=rgb_classifier, rgb_output_branch=rgb, depth_classifier=depth_classifier, depth_output_branch=depth) multinet_class.compile() multinet_model = multinet_class.model print('[INFO] built MultiNet classifier') # train the network to perform multi-output classification train_ds = fat_dataset(split='train', data_type='all', batch_size=config.BATCH_SIZE, shuffle=True, pairs=False) val_ds = fat_dataset(split='validation', data_type='all', batch_size=config.BATCH_SIZE, shuffle=True, pairs=False) print("[INFO] training MultiNet...") counter = 0 history = None toCSV = [] while counter <= config.EPOCHS: counter += 1 print(f'* Epoch: {counter}') data_batch = 0 for imgs, labels in train_ds: data_batch += 1 history = multinet_model.train_on_batch(x=[imgs[:, 0], imgs[:, 1]], y={'dense_5_rgb': labels[:], 'dense_7_depth': labels[:]}, reset_metrics=False, return_dict=True) print(f'* Data Batch: {data_batch}') print(f'\t{history}') break if counter % 10 == 0: print("[VALUE] Testing model on batch") for val_data, val_labels in val_ds: val_results = multinet_model.test_on_batch(x=[val_data[:, 0], val_data[:, 1]], y={'dense_5_rgb': val_labels[:], 'dense_7_depth': val_labels[:]}) print(val_results) toCSV.append(val_results) print('Saving MultiNet validation results as CSV file') utils.save_model_history(H=toCSV, path_to_csv=config.FROZEN_SIAMESE_TRAINING_HISTORY_CSV_PATH) rgb_classifier.save_weights(config.MIMO_RGB_WEIGHTS) print("Saved RGB model weights to disk") # serialize weights to HDF5 depth_classifier.save_weights(config.MIMO_DEPTH_WEIGHTS) print("Saved Depth model weights to disk")

40.525547

132

0.665616

573a1fa313f96c01ab6df0ada017abeca301701e

856

py

Python

tools/rebuild_caches.py

newbdoc/lookyloo

53a8952fccaf9ae42fa582d3475283babd55d08a

[ "BSD-3-Clause" ]

148

2020-06-14T06:55:42.000Z

2022-03-19T05:37:02.000Z

tools/rebuild_caches.py

newbdoc/lookyloo

53a8952fccaf9ae42fa582d3475283babd55d08a

[ "BSD-3-Clause" ]

261

2020-06-16T22:29:27.000Z

2022-03-31T10:40:52.000Z

tools/rebuild_caches.py

newbdoc/lookyloo

53a8952fccaf9ae42fa582d3475283babd55d08a

[ "BSD-3-Clause" ]

27

2020-06-08T12:28:33.000Z

2022-02-15T18:50:50.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- import argparse import logging from lookyloo.lookyloo import Indexing, Lookyloo logging.basicConfig(format='%(asctime)s %(name)s %(levelname)s:%(message)s', level=logging.INFO) if __name__ == '__main__': main()

25.939394

168

0.684579

573ad54818708562c075e93c746dc4448d743b12

740

py

Python

save_restore_model/tf1/restore1_1.py

zlpmichelle/crackingtensorflow

66c3517b60c3793ef06f904e5d58e4d044628182

[ "Apache-2.0" ]

3

2017-10-19T23:41:26.000Z

2019-10-22T08:59:35.000Z

save_restore_model/tf1/restore1_1.py

zlpmichelle/crackingtensorflow

66c3517b60c3793ef06f904e5d58e4d044628182

[ "Apache-2.0" ]

null

save_restore_model/tf1/restore1_1.py

zlpmichelle/crackingtensorflow

66c3517b60c3793ef06f904e5d58e4d044628182

[ "Apache-2.0" ]

null

import tensorflow as tf sess=tf.Session() #First let's load meta graph and restore weights saver = tf.train.import_meta_graph('/Users/lipingzhang/Downloads/model/my_tf_model-1000.meta') saver.restore(sess,tf.train.latest_checkpoint('/Users/lipingzhang/Downloads/model/')) # Now, let's access and create placeholders variables and # create feed-dict to feed new data graph = tf.get_default_graph() w1 = graph.get_tensor_by_name("w1:0") w2 = graph.get_tensor_by_name("w2:0") feed_dict ={w1:13.0,w2:17.0} #Now, access the op that you want to run. op_to_restore = graph.get_tensor_by_name("op_to_restore:0") #Add more to the current graph add_on_op = tf.multiply(op_to_restore,2) print sess.run(add_on_op,feed_dict) #This will print 120.

30.833333

94

0.777027

573b50d93fdcd613c5e4eb9cd5d3608413327c07

633

py

Python

src/game.py

LuisMarques99/Number-Guesser-Terminal

6abfac23268022f7ce3776a20d1d6f550955d6c8

[ "MIT" ]

null

src/game.py

LuisMarques99/Number-Guesser-Terminal

6abfac23268022f7ce3776a20d1d6f550955d6c8

[ "MIT" ]

null

src/game.py

LuisMarques99/Number-Guesser-Terminal

6abfac23268022f7ce3776a20d1d6f550955d6c8

[ "MIT" ]

null

from random import randrange if __name__ == "__main__": main()

21.827586

90

0.624013

573b7032640a85abec559a72d8a9edcb24834621

378

py

Python

Data Structures and Algorithms/HackerRank Algo Solutions/EASY PROBLEMS/Arrays.py

akkik04/Python-DataStructures-and-Algorithms

8db63173218e5a9205dbb325935c71fec93b695c

[ "MIT" ]

1

2022-01-22T18:19:07.000Z

Data Structures and Algorithms/HackerRank Algo Solutions/EASY PROBLEMS/Arrays.py

akkik04/Python-DataStructures-and-Algorithms

8db63173218e5a9205dbb325935c71fec93b695c

[ "MIT" ]

null

Data Structures and Algorithms/HackerRank Algo Solutions/EASY PROBLEMS/Arrays.py

akkik04/Python-DataStructures-and-Algorithms

8db63173218e5a9205dbb325935c71fec93b695c

[ "MIT" ]

null

# ARRAYS-DS HACKERANK SOLUTION: # creating a function to reverse the array. # receiving input. arr_count = int(input().strip()) arr = list(map(int, input().rstrip().split())) # printing the output. print(reverseArray(arr))

22.235294

47

0.653439

573eb0d44cfa9120f4cdede91149047e20c421a4

1,456

py

Python

bmds_server/analysis/admin.py

shapiromatron/bmds-server

0b2b79b521728582fa66100621e9ea03e251f9f1

[ "MIT" ]

1

2019-07-09T16:42:15.000Z

bmds_server/analysis/admin.py

shapiromatron/bmds-server

0b2b79b521728582fa66100621e9ea03e251f9f1

[ "MIT" ]

103

2016-11-14T15:58:53.000Z

2022-03-07T21:01:03.000Z

bmds_server/analysis/admin.py

shapiromatron/bmds-server

0b2b79b521728582fa66100621e9ea03e251f9f1

[ "MIT" ]

2

2017-03-17T20:43:22.000Z

2018-01-04T19:15:18.000Z

from django.contrib import admin from django.contrib.admin import SimpleListFilter from django.db.models import TextChoices from django.utils.html import format_html from . import models

28.54902

97

0.691621

573ec927838cc2f17f74c48d89acf3a9486bfe1d

96

py

Python

venv/lib/python3.8/site-packages/numpy/distutils/fcompiler/pg.py

Retraces/UkraineBot

3d5d7f8aaa58fa0cb8b98733b8808e5dfbdb8b71

[ "MIT" ]

2

2022-03-13T01:58:52.000Z

2022-03-31T06:07:54.000Z

venv/lib/python3.8/site-packages/numpy/distutils/fcompiler/pg.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

19

2021-11-20T04:09:18.000Z

2022-03-23T15:05:55.000Z

venv/lib/python3.8/site-packages/numpy/distutils/fcompiler/pg.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

null

/home/runner/.cache/pip/pool/34/e0/75/b2dceb8ef40c652edb20f4e059370015eddc8cdbde039f92ced519a83d

96

0.895833

5742d249cea7cefa19d4a4ea9010a2450f58aa8b

552

py

Python

03/00/0.py

pylangstudy/201801

eee9cfd2b370153359183d3c8f7fe117f4392142

[ "CC0-1.0" ]

null

03/00/0.py

pylangstudy/201801

eee9cfd2b370153359183d3c8f7fe117f4392142

[ "CC0-1.0" ]

null

03/00/0.py

pylangstudy/201801

eee9cfd2b370153359183d3c8f7fe117f4392142

[ "CC0-1.0" ]

null

#https://qiita.com/stkdev/items/a44976fb81ae90a66381 #import imaplib, re, email, six, dateutil.parser import imaplib, re, email email_default_encoding = 'iso-2022-jp' if __name__ == '__main__': main()

32.470588

161

0.71558

57452ea96aff7c8f3e31ad97f424bdd254f5bb63

5,468

py

Python

sql/filewalk.py

kylef-lab41/Redwood

c4e1c8284444b91246e52c165ea150eea23b26b9

[ "Apache-2.0" ]

null

sql/filewalk.py

kylef-lab41/Redwood

c4e1c8284444b91246e52c165ea150eea23b26b9

[ "Apache-2.0" ]

null

sql/filewalk.py

kylef-lab41/Redwood

c4e1c8284444b91246e52c165ea150eea23b26b9

[ "Apache-2.0" ]

null

import binascii import datetime import hashlib import mimetypes import os import re import struct import subprocess import sys import time import urllib import csv from Queue import Queue # 8 byte unique ID generator give a path. # - first five bytes are first five from sha1 of path name # - last 3 are the first three from the current time # Returns a long BUFFER = 4096 omitted_dirs = ['/dev', '/proc', '/sys', '/Volumes', '/mnt', '/net'] if __name__=="__main__": main(sys.argv)

32.939759

211

0.602597

574587d505f7c19dabd0452d40b6544e75b9a682

10,136

py

Python

processing_scripts/database_update/pokedex_entry.py

CorentG/Pokecube-Issues-and-Wiki

690af5d8499561f65f761fd49fbf5fc2bc85c4c3

[ "MIT" ]

24

2019-02-02T20:37:53.000Z

2022-02-09T13:51:41.000Z

processing_scripts/database_update/pokedex_entry.py

CorentG/Pokecube-Issues-and-Wiki

690af5d8499561f65f761fd49fbf5fc2bc85c4c3

[ "MIT" ]

671

2018-08-20T08:46:35.000Z

2022-03-26T00:11:43.000Z

processing_scripts/database_update/pokedex_entry.py

CorentG/Pokecube-Issues-and-Wiki

690af5d8499561f65f761fd49fbf5fc2bc85c4c3

[ "MIT" ]

68

2018-09-25T21:03:40.000Z

2022-02-25T19:59:51.000Z

import csv_loader import moves_names

37.128205

120

0.530979

5746c4fc2776ee414b40d5372100f22e8a3258f4

25,539

py

Python

tests/test_add.py

open-contracting/kingfisher-views

7887610a144493f2ccd0d9a22cf43157dc180479

[ "BSD-3-Clause" ]

2

2019-02-19T16:15:19.000Z

2020-07-25T04:05:45.000Z

tests/test_add.py

open-contracting/kingfisher-views

7887610a144493f2ccd0d9a22cf43157dc180479

[ "BSD-3-Clause" ]

142

2019-03-11T15:14:22.000Z

2020-11-11T19:26:09.000Z

tests/test_add.py

open-contracting/kingfisher-views

7887610a144493f2ccd0d9a22cf43157dc180479

[ "BSD-3-Clause" ]

5

2019-04-11T14:11:10.000Z

2020-07-30T22:45:59.000Z

import datetime import decimal from unittest.mock import patch import pytest from click.testing import CliRunner from psycopg2 import sql from manage import SUMMARIES, cli, construct_where_fragment from tests import assert_bad_argument, assert_log_records, assert_log_running, fixture, noop command = 'add' TABLES = { 'note', } SUMMARY_TABLES = set() SUMMARY_VIEWS = set() FIELD_LIST_TABLES = set() NO_FIELD_LIST_TABLES = set() NO_FIELD_LIST_VIEWS = set() for table_name, table in SUMMARIES.items(): FIELD_LIST_TABLES.add(f'{table_name}_field_list') if table.is_table: SUMMARY_TABLES.add(table_name) NO_FIELD_LIST_TABLES.add(f'{table_name}_no_field_list') else: SUMMARY_VIEWS.add(table_name) NO_FIELD_LIST_VIEWS.add(f'{table_name}_no_field_list') TABLES.add(f'{table_name}_no_data')

40.092622

118

0.539958

57476587984e17ece720d64d289aa21890dba64a

3,520

py

Python

ReportGenerator.py

taarruunnnn/VAPT-Report-Generator-Vulnerability

8d618c7ddac4f6fe0cedd9fa39ff61805e06fa38

[ "MIT" ]

1

2020-11-30T18:09:40.000Z

ReportGenerator.py

taarruunnnn/VAPT-Report-Generator-Vulnerability

8d618c7ddac4f6fe0cedd9fa39ff61805e06fa38

[ "MIT" ]

null

ReportGenerator.py

taarruunnnn/VAPT-Report-Generator-Vulnerability

8d618c7ddac4f6fe0cedd9fa39ff61805e06fa38

[ "MIT" ]

1

2020-09-16T20:51:18.000Z

import os from docx import Document from docx.shared import Inches from docx import section from docx.enum.text import WD_ALIGN_PARAGRAPH from docx.shared import Pt from docx.shared import Cm from docx.shared import RGBColor import docx

31.711712

92

0.609091

5750825ae1de9236544f8dff0657979e541dfed6

764

py

Python

Season 06 - Files in Python/Episode 02 - Copying Files.py/Episode 02 - Copying Files.py

Pythobit/Python-tutorial

b0743eaa9c237c3578131ead1b3f2c295f11b7ee

[ "MIT" ]

3

2021-02-19T18:33:00.000Z

2021-08-03T14:56:50.000Z

Season 06 - Files in Python/Episode 02 - Copying Files.py/Episode 02 - Copying Files.py

barawalojas/Python-tutorial

3f4b2b073e421888b3d62ff634658317d9abcb9b

[ "MIT" ]

1

2021-07-10T14:37:57.000Z

2021-07-20T09:51:39.000Z

Season 06 - Files in Python/Episode 02 - Copying Files.py/Episode 02 - Copying Files.py

barawalojas/Python-tutorial

3f4b2b073e421888b3d62ff634658317d9abcb9b

[ "MIT" ]

1

2021-08-02T05:39:38.000Z

# Copying files # Ask user for a list of 3 friends. # for each friend, we'll tell user whether they're nearby. # for each nearby friend, we'll save their name to `nearby_friends.txt`. friends = input('Enter three friends name(separated by commas): ').split(',') people = open('people.txt', 'r') people_nearby = [line.strip() for line in people.readlines()] people.close() # Making set of friends and peoples friends_set = set(friends) people_nearby_set = set(people_nearby) friends_nearby_set = friends_set.intersection(people_nearby_set) nearby_friends_file = open('nearby_friends.txt', 'w') for friend in friends_nearby_set: print(f'{friend} is nearby.! Meet up with them.') nearby_friends_file.write(f'{friend}\n') nearby_friends_file.close()

27.285714

77

0.743455

5750d5afb4b68c06b08670b53610fc887297a148

722

py

Python

beginner_contest/167/C.py

FGtatsuro/myatcoder

25a3123be6a6311e7d1c25394987de3e35575ff4

[ "MIT" ]

null

beginner_contest/167/C.py

FGtatsuro/myatcoder

25a3123be6a6311e7d1c25394987de3e35575ff4

[ "MIT" ]

null

beginner_contest/167/C.py

FGtatsuro/myatcoder

25a3123be6a6311e7d1c25394987de3e35575ff4

[ "MIT" ]

null

import sys input = sys.stdin.readline sys.setrecursionlimit(10 ** 7) n, m, x = map(int, input().split()) ca = [0] * n ca_sum = [0] * (m+1) for i in range(n): ca[i] = list(map(int, input().split())) for j in range(m+1): ca_sum[j] += ca[i][j] ans = 10 ** 10 for i in range(2 ** n): tmp = 0 tmp_ca_sum = ca_sum.copy() for j, v in enumerate(format(i, r'0{}b'.format(n))): if v == '0': continue for k in range(m+1): tmp_ca_sum[k] -= ca[j][k] flag = True for v2 in tmp_ca_sum[1:]: if v2 < x: flag = False break if flag: ans = min(ans, tmp_ca_sum[0]) if ans == 10 ** 10: print(-1) else: print(ans)

21.235294

56

0.49723

5752dc5277d06864407fc67287bd73391b57e2b0

923

py

Python

src/ProjectHeart/forms.py

LokotamaTheMastermind/secret-password-saver

e97f139b2cad9e1b0e9283079252d9a76764e3c1

[ "Unlicense" ]

null

src/ProjectHeart/forms.py

LokotamaTheMastermind/secret-password-saver

e97f139b2cad9e1b0e9283079252d9a76764e3c1

[ "Unlicense" ]

null

src/ProjectHeart/forms.py

LokotamaTheMastermind/secret-password-saver

e97f139b2cad9e1b0e9283079252d9a76764e3c1

[ "Unlicense" ]

null

from django import forms from .models import Passwords

43.952381

89

0.67714

57550dfdc85fef1e9e1bc0066478d7d691371d64

184

py

Python

data_relay/src/plugins/AzureBlob.py

phil-d-wilson/connectorV2

7077aa1c74276e8e334a8046793e942eec8d9975

[ "Apache-2.0" ]

null

data_relay/src/plugins/AzureBlob.py

phil-d-wilson/connectorV2

7077aa1c74276e8e334a8046793e942eec8d9975

[ "Apache-2.0" ]

49

2021-04-09T14:41:50.000Z

2021-07-28T10:54:48.000Z

data_relay/src/plugins/AzureBlob.py

phil-d-wilson/connectorV2

7077aa1c74276e8e334a8046793e942eec8d9975

[ "Apache-2.0" ]

2

2021-04-24T10:47:57.000Z

2021-07-17T07:13:00.000Z

NAME = "Azure BLOB storage" TYPE = "remote" FILE = "AzureBlob.yaml" VARS = [ "AZURE_BLOB_STORAGE_ACCOUNT", "AZURE_BLOB_STORAGE_ACCOUNT_KEY", "AZURE_BLOB_CONTAINER_NAME", ]

20.444444

37

0.717391

5755b24aeb6ff531368ac2aba89c8fd019b3b452

8,965

py

Python

tests/application/test_helpers.py

alphagov-mirror/performanceplatform-admin

b63ae42b1276699623ef208b7d6edd3e0ce4ca59

[ "MIT" ]

1

2017-05-14T21:31:33.000Z

tests/application/test_helpers.py

alphagov-mirror/performanceplatform-admin

b63ae42b1276699623ef208b7d6edd3e0ce4ca59

[ "MIT" ]

33

2015-01-05T12:23:45.000Z

2021-03-24T10:59:47.000Z

tests/application/test_helpers.py

alphagov-mirror/performanceplatform-admin

b63ae42b1276699623ef208b7d6edd3e0ce4ca59

[ "MIT" ]

4

2017-03-16T15:52:33.000Z

2021-04-10T20:14:53.000Z

import unittest from application import app from application.helpers import( requires_authentication, requires_feature, signed_in, group_by_group, signed_in_no_access, no_access, has_user_with_token, view_helpers, user_has_feature, ) from hamcrest import assert_that, equal_to, is_ from mock import patch def test_group_by_group_groups_datasets_by_group(self): data_sets = [ { 'data_group': "group_1", 'data_type': "type1" }, { 'data_group': "group_1", 'data_type': "type2" }, { 'data_group': "group_2", 'data_type': "type3" } ] grouped_data_sets = { "group_1": [ { 'data_group': "group_1", 'data_type': "type1" }, { 'data_group': "group_1", 'data_type': "type2" } ], "group_2": [ { 'data_group': "group_2", 'data_type': "type3" } ] } assert_that(group_by_group(data_sets), equal_to(grouped_data_sets)) def test_admin_user_has_bigedit_feature(self): user = {'permissions': ['admin']} assert_that(user_has_feature('big-edit', user), equal_to(True)) def test_dashboard_editor_user_does_not_have_bigedit_feature(self): user = {'permissions': ['dashboard-editor']} assert_that(user_has_feature('big-edit', user), equal_to(False)) def test_dashboard_editor_and_admin_user_does_have_bigedit_feature(self): user = {'permissions': ['dashboard-editor', 'admin']} assert_that(user_has_feature('big-edit', user), equal_to(True)) def test_user_with_permissions_not_in_list_features(self): user = {'permissions': ['signin']} assert_that(user_has_feature('big-edit', user), equal_to(False))

37.19917

78

0.636587

575730cc1be427336b55d40ef3a3e2821b465a72

1,210

py

Python

Unit 7/Ai bot/test bots/SlightlySmartSue.py

KevinBoxuGao/ICS3UI

2091a7c0276b888dd88f2063e6acd6e7ff7fb6fa

[ "MIT" ]

null

Unit 7/Ai bot/test bots/SlightlySmartSue.py

KevinBoxuGao/ICS3UI

2091a7c0276b888dd88f2063e6acd6e7ff7fb6fa

[ "MIT" ]

null

Unit 7/Ai bot/test bots/SlightlySmartSue.py

KevinBoxuGao/ICS3UI

2091a7c0276b888dd88f2063e6acd6e7ff7fb6fa

[ "MIT" ]

1

2020-03-09T16:22:33.000Z

from random import * #STRATEGY SUMMARY: DON'T DUCK IF THE OPPONENT HAS NO SNOWBALLS. OTHERWISE, PICK RANDOMLY.

31.842105

99

0.565289

5757a45f92b96ddd746ba5a5bd686085a734073c

298

py

Python

customers/views/customers.py

chorna/taxi24

09e174a0cb3b9543ca4987e60cd0d37ecda6ac3c

[ "MIT" ]

null

customers/views/customers.py

chorna/taxi24

09e174a0cb3b9543ca4987e60cd0d37ecda6ac3c

[ "MIT" ]

null

customers/views/customers.py

chorna/taxi24

09e174a0cb3b9543ca4987e60cd0d37ecda6ac3c

[ "MIT" ]

null

from rest_framework import viewsets from customers.models import Customer from customers.serializers.customers import CustomerSerializer # Create your views here.

24.833333

62

0.825503

57580cabba2c7dce9e5d8666af96b5e694af9738

5,370

py

Python

pysoa/test/plan/grammar/directives/expects_values.py

zetahernandez/pysoa

006e55ba877196a42c64f2ff453583d366082d55

[ "Apache-2.0" ]

91

2017-05-08T22:41:33.000Z

2022-02-09T11:37:07.000Z

pysoa/test/plan/grammar/directives/expects_values.py

zetahernandez/pysoa

006e55ba877196a42c64f2ff453583d366082d55

[ "Apache-2.0" ]

63

2017-06-14T20:08:49.000Z

2021-06-16T23:08:25.000Z

pysoa/test/plan/grammar/directives/expects_values.py

zetahernandez/pysoa

006e55ba877196a42c64f2ff453583d366082d55

[ "Apache-2.0" ]

26

2017-10-13T23:23:13.000Z

2022-01-11T16:58:17.000Z

""" Expect action directives """ from __future__ import ( absolute_import, unicode_literals, ) from pyparsing import ( CaselessLiteral, LineEnd, Literal, Optional, Suppress, ) from pysoa.test.plan.grammar.assertions import ( assert_not_expected, assert_not_present, assert_subset_structure, ) from pysoa.test.plan.grammar.data_types import ( DataTypeGrammar, get_parsed_data_type_value, ) from pysoa.test.plan.grammar.directive import ( ActionDirective, VarNameGrammar, VarValueGrammar, register_directive, ) from pysoa.test.plan.grammar.tools import path_put class ActionExpectsAnyDirective(ActionExpectsFieldValueDirective): """ Set expectations for values to be in the service call response where any value for the given data type will be accepted. """ def assert_test_case_action_results( self, action_name, action_case, test_case, test_fixture, action_response, job_response, msg=None, **kwargs ): if 'expects_not_present' in action_case: assert_not_present( action_case['expects_not_present'], action_response.body, msg, ) register_directive(ActionExpectsFieldValueDirective) register_directive(ActionExpectsAnyDirective) register_directive(ActionExpectsNoneDirective) register_directive(ActionExpectsNotPresentDirective)

26.716418

115

0.60298

575871e8030b4782c2b2ff33f329031a54131855

454

py

Python

src/manual/add_uuid_col.py

lshtm-gis/WHO_PHSM_Cleaning

5892673922fc555fb86d6e0be548b48c7dc66814

[ "MIT" ]

null

src/manual/add_uuid_col.py

lshtm-gis/WHO_PHSM_Cleaning

5892673922fc555fb86d6e0be548b48c7dc66814

[ "MIT" ]

123

2020-10-12T11:06:27.000Z

2021-04-28T15:32:29.000Z

src/manual/add_uuid_col.py

lshtm-gis/WHO_PHSM_Cleaning

5892673922fc555fb86d6e0be548b48c7dc66814

[ "MIT" ]

null

''' Script to add uuid to existing records Also shifts who_code values to original_who_code ''' import uuid import pandas as pd manually_cleaned = pd.read_csv('data/cleansed/mistress_latest_old.csv', low_memory=False) manually_cleaned['uuid'] = [str(uuid.uuid4()) for x in manually_cleaned.iloc[:, 1]] manually_cleaned['original_who_code'] = manually_cleaned['who_code'] manually_cleaned.to_csv('data/cleansed/mistress_latest.csv', index = False)

25.222222

89

0.779736

575a4a3127b8298acd5fe22aa043d391fe755667

1,821

py

Python

tests/test_qml.py

phil65/PrettyQt

26327670c46caa039c9bd15cb17a35ef5ad72e6c

[ "MIT" ]

7

2019-05-01T01:34:36.000Z

2022-03-08T02:24:14.000Z

tests/test_qml.py

phil65/PrettyQt

26327670c46caa039c9bd15cb17a35ef5ad72e6c

[ "MIT" ]

141

2019-04-16T11:22:01.000Z

2021-04-14T15:12:36.000Z

tests/test_qml.py

phil65/PrettyQt

26327670c46caa039c9bd15cb17a35ef5ad72e6c

[ "MIT" ]

5

2019-04-17T11:48:19.000Z

2021-11-21T10:30:19.000Z

"""Tests for `prettyqt` package.""" import pathlib import pytest from prettyqt import core, qml from prettyqt.utils import InvalidParamError # def test_jsvalue(): # val = qml.JSValue(2) # val["test"] = 1 # assert val["test"].toInt() == 1 # assert "test" in val # assert val.get_value() == 2

24.945205

59

0.641406

9386838c937de37405273fac5771d31ccf1a0479

2,550

py

Python

demo.py

HsienYu/tree_demo

aa2fa6c016b3ea5c1e768baa8ce4ea319c727bfc

[ "Artistic-2.0" ]

null

demo.py

HsienYu/tree_demo

aa2fa6c016b3ea5c1e768baa8ce4ea319c727bfc

[ "Artistic-2.0" ]

null

demo.py

HsienYu/tree_demo

aa2fa6c016b3ea5c1e768baa8ce4ea319c727bfc

[ "Artistic-2.0" ]

null

# Simple test for NeoPixels on Raspberry Pi import time import board import neopixel # Choose an open pin connected to the Data In of the NeoPixel strip, i.e. board.D18 # NeoPixels must be connected to D10, D12, D18 or D21 to work. pixel_pin = board.D18 # The number of NeoPixels num_pixels = 30 # The order of the pixel colors - RGB or GRB. Some NeoPixels have red and green reversed! # For RGBW NeoPixels, simply change the ORDER to RGBW or GRBW. ORDER = neopixel.GRB pixels = neopixel.NeoPixel(pixel_pin, num_pixels, brightness=0.2, auto_write=False, pixel_order=ORDER) try: while True: print("light start") repeat_fun(5, white_breath) # rainbow cycle with 1ms delay per step repeat_fun(3, rainbow_cycle, 0.01) # white_breath() # for i in range(num_pixels): # for r in range(255): # pixels[i] = (r, 0, 0) # pixels.show() # time.sleep(0.001) # j = i - 1 # for y in range(255): # pixels[j] = (y, y, y) # pixels.show() # time.sleep(0.001) # time.sleep(0.01) except KeyboardInterrupt: print("KeyboardInterrupt has been caught.")

25.757576

92

0.533333

93880a88a41dae3cf1a05e55925780f80609dbdb

1,774

py

Python

fsm.py

yusun1997/Chatbot

ee49d4a64857889ce1d1a8659a1de15cf062bd77

[ "MIT" ]

null

fsm.py

yusun1997/Chatbot

ee49d4a64857889ce1d1a8659a1de15cf062bd77

[ "MIT" ]

null

fsm.py

yusun1997/Chatbot

ee49d4a64857889ce1d1a8659a1de15cf062bd77

[ "MIT" ]

null

from transitions.extensions import GraphMachine

27.71875

89

0.638106

93881978c162edde4ca5dd970ae7fc5d1d4dfecc

1,861

py

Python

rptk/query/__init__.py

wolcomm/rptk

fe6c1b597741ff14e4c89519458bb0950f0aa955

[ "Apache-2.0" ]

15

2017-11-30T01:28:11.000Z

2021-08-12T09:17:36.000Z

rptk/query/__init__.py

wolcomm/rptk

fe6c1b597741ff14e4c89519458bb0950f0aa955

[ "Apache-2.0" ]

71

2018-06-22T09:54:50.000Z

2020-10-21T07:10:54.000Z

rptk/query/__init__.py

wolcomm/rptk

fe6c1b597741ff14e4c89519458bb0950f0aa955

[ "Apache-2.0" ]

2

2019-08-31T20:45:19.000Z

2019-10-02T18:26:58.000Z

# Copyright (c) 2018 Workonline Communications (Pty) Ltd. All rights reserved. # # The contents of this file are licensed under the Apache License version 2.0 # (the "License"); you may not use this file except in compliance with the # License. # # 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. """rptk module.query module.""" from __future__ import print_function from __future__ import unicode_literals from rptk.base import BaseObject try: basestring except NameError: basestring = str try: unicode except NameError: unicode = str

27.776119

79

0.667383

93888830e4d4bc95cb50e37baa9660d706afdc8a

1,697

py

Python

test/__main__.py

harisekhon/pylib

1d8fcfc0a26251a832536a5ff6bf0ef618b8508e

[ "MIT" ]

1

2015-12-17T21:08:22.000Z

test/__main__.py

harisekhon/pylib

1d8fcfc0a26251a832536a5ff6bf0ef618b8508e

[ "MIT" ]

null

test/__main__.py

harisekhon/pylib

1d8fcfc0a26251a832536a5ff6bf0ef618b8508e

[ "MIT" ]

null

#!/usr/bin/env python # vim:ts=4:sts=4:sw=4:et # # Author: Hari Sekhon # Date: 2015-11-14 12:21:54 +0000 (Sat, 14 Nov 2015) # # https://github.com/HariSekhon/pylib # # License: see accompanying Hari Sekhon LICENSE file # # If you're using my code you're welcome to connect with me on LinkedIn # and optionally send me feedback to help improve or steer this or other code I publish # # http://www.linkedin.com/in/harisekhon # from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals import os #import glob #import inspect #import subprocess #import sys ## using optparse rather than argparse for servers still on Python 2.6 #from optparse import OptionParser # libdir = os.path.join(os.path.dirname(inspect.getfile(inspect.currentframe())), '..') libdir = os.path.join(os.path.dirname(__file__), '..') # sys.path.append(libdir) # try: # from harisekhon.utils import * # except ImportError, e: # print('module import failed: %s' % e) # sys.exit(4) __author__ = 'Hari Sekhon' __version__ = '0.1' if __name__ == '__main__': main()

28.283333

88

0.700648

9389cb7a39d34434b205d05068e576faba98ddc7

1,639

py

Python

legacy/tests/test_complete_tdf.py

solar464/TDF_deterministic_encryption

ff9dceacb37ce7727a8205cc72a4d928d37cce6f

[ "MIT" ]

null

legacy/tests/test_complete_tdf.py

solar464/TDF_deterministic_encryption

ff9dceacb37ce7727a8205cc72a4d928d37cce6f

[ "MIT" ]

null

legacy/tests/test_complete_tdf.py

solar464/TDF_deterministic_encryption

ff9dceacb37ce7727a8205cc72a4d928d37cce6f

[ "MIT" ]

null

import unittest import pickle from array import array import complete_tdf from floodberry.floodberry_ed25519 import GE25519 from tdf_strucs import TDFMatrix, TDFError from complete_tdf import CTDFCodec as Codec, CTDFCipherText as CipherText from utils import int_lst_to_bitarr TEST_DIR = "legacy/tests/" PACK_TEST_KEY_FILE = TEST_DIR + "ctdf_pack_test_keys.p" PACK_TEST_CT_FILE = TEST_DIR + "ctdf_pack_test_ct.p" TDF_KEY_FILE = TEST_DIR + "ctdf_test_keys.p" TDF_CT_FILE = TEST_DIR + "ctdf_test_ct.p" """ x = [0,1,2] ctdf = CTDFCodec(len(x)*8) u = ctdf.encode(x) result = ctdf.decode(u) """ TDF = Codec.deserialize(TDF_KEY_FILE) CT = CipherText.deserialize(TDF_CT_FILE) X = int_lst_to_bitarr([0,1,2], 3)

29.267857

73

0.700427

938c81e0713358c52bf4da54926facac71c9eb0c

431

py

Python

wherethefuck/celery.py

luismasuelli/wherethefuck

6e68543a804c299be4362836c518e34f10029b48

[ "MIT" ]

1

2019-11-18T15:02:16.000Z

wherethefuck/celery.py

luismasuelli/wherethefuck

6e68543a804c299be4362836c518e34f10029b48

[ "MIT" ]

null

wherethefuck/celery.py

luismasuelli/wherethefuck

6e68543a804c299be4362836c518e34f10029b48

[ "MIT" ]

null

import os from celery import Celery from django.conf import settings # set the default Django settings module for the 'celery' program. os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'wherethefuck.settings') # create and run celery workers. app = Celery(broker=settings.CELERY_BROKER_URL) app.config_from_object('django.conf:settings') app.autodiscover_tasks(settings.INSTALLED_APPS) if __name__ == '__main__': app.start()

28.733333

72

0.798144

938caebc89dfb3ff5ef96ed29916b89a93439b97

395

py

Python

plot_main.py

Alexhaoge/MLSR

1397176ea4c71533f3995ff476727217125c9f83

[ "MIT" ]

1

2020-12-27T15:45:09.000Z

plot_main.py

Alexhaoge/MLSR

1397176ea4c71533f3995ff476727217125c9f83

[ "MIT" ]

null

plot_main.py

Alexhaoge/MLSR

1397176ea4c71533f3995ff476727217125c9f83

[ "MIT" ]

1

2021-04-08T17:03:36.000Z

from MLSR.data import DataSet from MLSR.plot import * x = DataSet('data/rand_select_400_avg.csv') x.generate_feature() y = DataSet('data/not_selected_avg.csv') y.generate_feature() z = DataSet.static_merge(x, y) #plot_tsne(z, 'log/tsne.png') z = z.convert_to_ssl() z0, z1 = z.split_by_weak_label() plot_tsne_ssl(z0, 'log/0_tsne.png', n_iter=300) plot_tsne_ssl(z1, 'log/1_tsne.png', n_iter=500)

28.214286

47

0.749367

939056f893dc7a63b3b4c5c9d0f8b92f4cb9205c

7,652

py

Python

utils/utils_convert2hdf5.py

jiyeonkim127/PSI

5c525d5304fb756c9314ea3e225bbb180e521b9a

[ "Xnet", "X11" ]

138

2020-04-18T19:32:12.000Z

2022-03-31T06:58:33.000Z

utils/utils_convert2hdf5.py

jiyeonkim127/PSI

5c525d5304fb756c9314ea3e225bbb180e521b9a

[ "Xnet", "X11" ]

19

2020-04-21T18:24:20.000Z

2022-03-12T00:25:11.000Z

utils/utils_convert2hdf5.py

jiyeonkim127/PSI

5c525d5304fb756c9314ea3e225bbb180e521b9a

[ "Xnet", "X11" ]

19

2020-04-22T01:32:25.000Z

2022-03-24T02:52:01.000Z

import numpy as np import scipy.io as sio import os, glob, sys import h5py_cache as h5c sys.path.append('/home/yzhang/workspaces/smpl-env-gen-3d-internal') sys.path.append('/home/yzhang/workspaces/smpl-env-gen-3d-internal/source') from batch_gen_hdf5 import BatchGeneratorWithSceneMeshMatfile import torch ''' In this script, we put all mat files into a hdf5 file, so as to speed up the data loading process. ''' dataset_path = '/mnt/hdd/PROX/snapshot_realcams_v3' outfilename = 'realcams.hdf5' h5file_path = os.path.join('/home/yzhang/Videos/PROXE', outfilename) batch_gen = BatchGeneratorWithSceneMeshMatfile(dataset_path=dataset_path, scene_verts_path = '/home/yzhang/Videos/PROXE/scenes_downsampled', scene_sdf_path = '/home/yzhang/Videos/PROXE/scenes_sdf', device=torch.device('cuda')) ### create the dataset used in the hdf5 file with h5c.File(h5file_path, mode='w',chunk_cache_mem_size=1024**2*128) as hdf5_file: while batch_gen.has_next_batch(): train_data = batch_gen.next_batch(1) if train_data is None: continue train_data_np = [x.detach().cpu().numpy() for x in train_data[:-1]] break [depth_batch, seg_batch, body_batch, cam_ext_batch, cam_int_batch, max_d_batch, s_verts_batch, s_faces_batch, s_grid_min_batch, s_grid_max_batch, s_grid_dim_batch, s_grid_sdf_batch] = train_data_np n_samples = batch_gen.n_samples print('-- n_samples={:d}'.format(n_samples)) hdf5_file.create_dataset("sceneid", shape=(1,), chunks=True, dtype=np.float32, maxshape=(None,) ) hdf5_file.create_dataset("depth", shape=(1,)+tuple(depth_batch.shape[1:]) ,chunks = True, dtype=np.float32, maxshape=(None,)+tuple(depth_batch.shape[1:]) ) hdf5_file.create_dataset("seg", shape=(1,)+tuple(seg_batch.shape[1:]) ,chunks = True, dtype=np.float32, maxshape=(None,)+tuple(seg_batch.shape[1:]) ) hdf5_file.create_dataset("body", shape=(1,)+tuple(body_batch.shape[1:]) ,chunks = True, dtype=np.float32, maxshape=(None,)+tuple(body_batch.shape[1:]) ) hdf5_file.create_dataset("cam_ext", shape=(1,)+tuple(cam_ext_batch.shape[1:]) ,chunks = True, dtype=np.float32, maxshape=(None,)+tuple(cam_ext_batch.shape[1:]) ) hdf5_file.create_dataset("cam_int", shape=(1,)+tuple(cam_int_batch.shape[1:]) ,chunks = True, dtype=np.float32, maxshape=(None,)+tuple(cam_int_batch.shape[1:]) ) hdf5_file.create_dataset("max_d", shape=(1,)+tuple(max_d_batch.shape[1:]) ,chunks = True, dtype=np.float32, maxshape=(None,)+tuple(max_d_batch.shape[1:]) ) # hdf5_file.create_dataset("s_verts", shape=(1,)+tuple(s_verts_batch.shape[1:]) ,chunks = True, dtype=np.float32, maxshape=(None,)+tuple(s_verts_batch.shape[1:]) ) # hdf5_file.create_dataset("s_faces", shape=(1,)+tuple(s_faces_batch.shape[1:]) ,chunks = True, dtype=np.float32, maxshape=(None,)+tuple(s_faces_batch.shape[1:]) ) # hdf5_file.create_dataset("s_grid_min", shape=(1,)+tuple(s_grid_min_batch.shape[1:]) ,chunks = True, dtype=np.float32, maxshape=(None,)+tuple(s_grid_min_batch.shape[1:])) # hdf5_file.create_dataset("s_grid_max", shape=(1,)+tuple(s_grid_max_batch.shape[1:]) ,chunks = True, dtype=np.float32, maxshape=(None,)+tuple(s_grid_max_batch.shape[1:])) # hdf5_file.create_dataset("s_grid_dim", shape=(1,)+tuple(s_grid_dim_batch.shape[1:]) ,chunks = True, dtype=np.float32, maxshape=(None,)+tuple(s_grid_dim_batch.shape[1:])) # hdf5_file.create_dataset("s_grid_sdf", shape=(1,)+tuple(s_grid_sdf_batch.shape[1:]) ,chunks = True, dtype=np.float32, maxshape=(None,)+tuple(s_grid_sdf_batch.shape[1:])) batch_gen.reset() scene_list = ['BasementSittingBooth','MPH1Library', 'MPH8', 'MPH11', 'MPH16', 'MPH112', 'N0SittingBooth', 'N0Sofa', 'N3Library', 'N3Office', 'N3OpenArea', 'Werkraum'] # !!!! important!cat ### create the dataset used in the hdf5 file idx = -1 while batch_gen.has_next_batch(): train_data = batch_gen.next_batch(1) if train_data is None: continue [depth_batch, seg_batch, body_batch, cam_ext_batch, cam_int_batch, max_d_batch, s_verts_batch, s_faces_batch, s_grid_min_batch, s_grid_max_batch, s_grid_dim_batch, s_grid_sdf_batch, filename_list] = train_data ## check unavaliable prox fitting body_z_batch = body_batch[:,2] if body_z_batch.abs().max() >= max_d_batch.abs().max(): print('-- encountered bad prox fitting. Skip it') continue if body_z_batch.min() <=0: print('-- encountered bad prox fitting. Skip it') continue idx = idx+1 print('-- processing batch idx {:d}'.format(idx)) filename = filename_list[0] scenename = filename.split('/')[-2].split('_')[0] sid = [scene_list.index(scenename)] hdf5_file["sceneid"].resize((hdf5_file["sceneid"].shape[0]+1, )) hdf5_file["sceneid"][-1,...] = sid[0] hdf5_file["depth"].resize((hdf5_file["depth"].shape[0]+1, )+hdf5_file["depth"].shape[1:]) hdf5_file["depth"][-1,...] = depth_batch[0].detach().cpu().numpy() hdf5_file["seg"].resize((hdf5_file["seg"].shape[0]+1, )+hdf5_file["seg"].shape[1:]) hdf5_file["seg"][-1,...] = seg_batch[0].detach().cpu().numpy() hdf5_file["body"].resize((hdf5_file["body"].shape[0]+1, )+hdf5_file["body"].shape[1:]) hdf5_file["body"][-1,...] = body_batch[0].detach().cpu().numpy() hdf5_file["cam_ext"].resize((hdf5_file["cam_ext"].shape[0]+1, )+hdf5_file["cam_ext"].shape[1:]) hdf5_file["cam_ext"][-1,...] = cam_ext_batch[0].detach().cpu().numpy() hdf5_file["cam_int"].resize((hdf5_file["cam_int"].shape[0]+1, )+hdf5_file["cam_int"].shape[1:]) hdf5_file["cam_int"][-1,...] = cam_int_batch[0].detach().cpu().numpy() hdf5_file["max_d"].resize((hdf5_file["max_d"].shape[0]+1, )+hdf5_file["max_d"].shape[1:]) hdf5_file["max_d"][-1,...] = max_d_batch[0].detach().cpu().numpy() # hdf5_file["s_verts"].resize((hdf5_file["s_verts"].shape[0]+1, )+hdf5_file["s_verts"].shape[1:]) # hdf5_file["s_verts"][-1,...] = s_verts_batch[0].detach().cpu().numpy() # hdf5_file["s_faces"].resize((hdf5_file["s_faces"].shape[0]+1, )+hdf5_file["s_faces"].shape[1:]) # hdf5_file["s_faces"][-1,...] = s_faces_batch[0].detach().cpu().numpy() # hdf5_file["s_grid_min"].resize((hdf5_file["s_grid_min"].shape[0]+1, )+hdf5_file["s_grid_min"].shape[1:]) # hdf5_file["s_grid_min"][-1,...] = s_grid_min_batch[0].detach().cpu().numpy() # hdf5_file["s_grid_max"].resize((hdf5_file["s_grid_max"].shape[0]+1, )+hdf5_file["s_grid_max"].shape[1:]) # hdf5_file["s_grid_max"][-1,...] = s_grid_max_batch[0].detach().cpu().numpy() # hdf5_file["s_grid_dim"].resize((hdf5_file["s_grid_dim"].shape[0]+1, )+hdf5_file["s_grid_dim"].shape[1:]) # hdf5_file["s_grid_dim"][-1,...] = s_grid_dim_batch[0].detach().cpu().numpy() # hdf5_file["s_grid_sdf"].resize((hdf5_file["s_grid_sdf"].shape[0]+1, )+hdf5_file["s_grid_sdf"].shape[1:]) # hdf5_file["s_grid_sdf"][-1,...] = s_grid_sdf_batch[0].detach().cpu().numpy() print('--file converting finish')

49.688312

176

0.627418

939077a570d47a79177487efee3028816d6b91da

421

py

Python

trains/models.py

Seshathri-saravanan/quest

397c92e36167b9554fd72f55bdac0a2cbcdfea6f

[ "MIT" ]

null

trains/models.py

Seshathri-saravanan/quest

397c92e36167b9554fd72f55bdac0a2cbcdfea6f

[ "MIT" ]

null

trains/models.py

Seshathri-saravanan/quest

397c92e36167b9554fd72f55bdac0a2cbcdfea6f

[ "MIT" ]

1

2021-11-09T15:58:33.000Z

from django.db import models

32.384615

49

0.724466

93938181b040ac3ac5f94151cbff662943eef747

3,324

py

Python

tests/test_names.py

fabiocaccamo/python-fontbro

2ed7ef0d3d1ed4d91387278cfb5f7fd63324451b

[ "MIT" ]

11

2021-11-17T23:51:55.000Z

2022-03-17T20:38:14.000Z

tests/test_names.py

fabiocaccamo/python-fontbro

2ed7ef0d3d1ed4d91387278cfb5f7fd63324451b

[ "MIT" ]

4

2022-02-21T02:16:06.000Z

2022-03-28T02:18:16.000Z

tests/test_names.py

fabiocaccamo/python-fontbro

2ed7ef0d3d1ed4d91387278cfb5f7fd63324451b

[ "MIT" ]

null

# -*- coding: utf-8 -*- from fontbro import Font from tests import AbstractTestCase

38.206897

85

0.65343

93946c005b5692a8ad70e09207171d1d003f400a

3,901

py

Python

parse_ecosim_files.py

lmorillas/ecosim_to_we

2bd158146fa72ec7cbc9bcd1aaa57f3c6715cb56

[ "Apache-2.0" ]

null

parse_ecosim_files.py

lmorillas/ecosim_to_we

2bd158146fa72ec7cbc9bcd1aaa57f3c6715cb56

[ "Apache-2.0" ]

null

parse_ecosim_files.py

lmorillas/ecosim_to_we

2bd158146fa72ec7cbc9bcd1aaa57f3c6715cb56

[ "Apache-2.0" ]

null

from amara.bindery import html from amara.lib import U import urllib import urlparse import time ''' This script extract data from html pages and write the data into a .json file ready to create the mediawiki / wikieducator pages ''' BASE = 'http://academics.smcvt.edu/dmccabe/teaching/Community/' def parse_notes_file(f): ''' Parse file like stream and returns title & content. Title is the first line of the file Content is delimited by 'beginnotes' and 'endnotes' words ''' title = f.readline().strip() content = [] for line in f: if line.startswith('beginnotes'): break for line in f: if line.startswith('endnotes'): break else: line = line.strip() or '\n\n' content.append(line) content = ' '.join(content) content = content.decode('utf-8', 'replace') return {'title': title, 'content': content} def parse_anchor_ecosim(anchor): ''' It returns text and href url from an html anchor for ecosim ''' name = U(anchor).lower().strip() url = urlparse.urljoin(BASE, anchor.href) return name, url def parse_anchor_notes(anchor): ''' It returns the text and href url from an html anchor for ecosim notes. Removes the 'Notes adn data from:' words from teh text. ''' name = U(anchor).replace('Notes and data from:', '').lower().strip() url = urlparse.urljoin(BASE, anchor.href) return name, url def parse_ecosim_file(url): ''' Parse the url from an ecosim data file. It returns the data, species, files ''' f = urllib.urlopen(url) lines = [l for l in f.readlines()] species = len(lines) -1 sites = len(lines[0].split()) -1 return ''.join(lines), species, sites def change_titles(pages): ''' Adds numbres to repeated titles ''' titles = {} for p in pages: title = p.get('title') n = titles.get(title, 0) if n == 0: titles[title] = 1 else: titles[title] = n + 1 title = numbertit(title, n) p['title'] = title if __name__ == '__main__': import json # Main index file f = 'http://academics.smcvt.edu/dmccabe/teaching/Community/NullModelData.html' doc = html.parse(f) #ecosim data links ecosim_files = doc.xml_select(u'//a[contains(@href, "matrices_ecosim")]') # ecosim notes links notes_files = doc.xml_select(u'//a[contains(@href, "matrices_notes")]') # name -> url ecodict = dict([parse_anchor_ecosim(e) for e in ecosim_files]) notesdict = dict([parse_anchor_notes(e) for e in notes_files]) # names sorted ecokeys = ecodict.keys() allnotes = parse_all_notes(notesdict) # json.dump(allnotes, open('allnotes.json', 'w')) # if you want to create a dump pages = [] for x in ecokeys: print 'parsing data', x k = str(x) # element to process Shelve keys must be Str eco_url = ecodict.get(k) data, species, sites = parse_ecosim_file(eco_url) d = allnotes.get(k) if not d: print 'Not found', k continue d['data'] = data #create_page(d, eco_url) d['species'] = species d['sites'] = sites d['source'] = eco_url d['name'] = k pages.append(d) time.sleep(0.2) # no want DOS change_titles(pages) json.dump(pages, open('pages_to_create.json', 'w'))

25.664474

93

0.605229

9394ac8b332dbc27f6671e32b2abfcd0890092b3

117

py

Python

web_scraping/ec2files/ec2file78.py

nikibhatt/Groa

fc2d4ae87cb825e6d54a0831c72be16541eebe61

[ "MIT" ]

1

2020-04-08T20:11:48.000Z

web_scraping/ec2files/ec2file78.py

cmgospod/Groa

31b3624bfe61e772b55f8175b4e95d63c9e67966

[ "MIT" ]

null

web_scraping/ec2files/ec2file78.py

cmgospod/Groa

31b3624bfe61e772b55f8175b4e95d63c9e67966

[ "MIT" ]

1

2020-09-12T07:07:41.000Z

from scraper import * s = Scraper(start=138996, end=140777, max_iter=30, scraper_instance=78) s.scrape_letterboxd()

39

72

0.777778

9396f021d37c0bb0196896103dbb10d80bb60437

20,826

py

Python

backend/tests/usecases/test_control_resource.py

crosspower/naruko

4c524e2ef955610a711830bc86d730ffe4fc2bd8

[ "MIT" ]

17

2019-01-23T04:37:43.000Z

2019-10-15T01:42:31.000Z

backend/tests/usecases/test_control_resource.py

snickerjp/naruko

4c524e2ef955610a711830bc86d730ffe4fc2bd8

[ "MIT" ]

1

2019-01-23T08:04:44.000Z

2019-01-23T08:44:33.000Z

backend/tests/usecases/test_control_resource.py

snickerjp/naruko

4c524e2ef955610a711830bc86d730ffe4fc2bd8

[ "MIT" ]

6

2019-01-23T09:10:59.000Z

2020-12-02T04:15:41.000Z

from django.core.exceptions import PermissionDenied from django.test import TestCase from backend.models import UserModel, AwsEnvironmentModel from unittest import mock # mock with mock.patch('backend.models.OperationLogModel.operation_log', lambda executor_index=None, target_method=None, target_arg_index_list=None: lambda func: func): from backend.usecases.control_resource import ControlResourceUseCase # : def test_start_resource_not_belong_to_tenant(self): mock_user = mock.Mock(spec=UserModel) mock_user.is_belong_to_tenant.return_value = False mock_aws = mock.Mock(spec=AwsEnvironmentModel) mock_resource = mock.Mock() # with self.assertRaises(PermissionDenied): ControlResourceUseCase(mock.Mock()).start_resource(mock_user, mock_aws, mock_resource) # mock_user.is_belong_to_tenant.assert_called_once() mock_user.has_aws_env.assert_not_called() mock_resource.start.assert_not_called() # :AWS # # : # :AWS # # : # :AWS # : # : # :AWS # # : # :AWS # # : # :AWS # # # # AWS # # : # :AWS

39.146617

162

0.687554

93972dbe0dbe487735de9457da88b6e093fc9d7c

371

py

Python

cool.py

divine-coder/CODECHEF-PYTHON

a1e34d6f9f75cf7b9497f1ef2f937cb4f64f1543

[ "MIT" ]

null

cool.py

divine-coder/CODECHEF-PYTHON

a1e34d6f9f75cf7b9497f1ef2f937cb4f64f1543

[ "MIT" ]

4

2020-10-04T07:49:30.000Z

2021-10-02T05:24:40.000Z

cool.py

divine-coder/CODECHEF-PYTHON

a1e34d6f9f75cf7b9497f1ef2f937cb4f64f1543

[ "MIT" ]

7

2020-10-04T07:46:55.000Z

2021-11-05T14:30:00.000Z

a=raw_input() b=raw_input() i=0 index=0 co=0 if a[0]==b[0]: co+=1 i+=1 while i<len(a): index1=check(b,index+1,a[i]) print a[i],index1 if index1!=-1: index=index1 co+=1 #print index i+=1 print co

13.740741

32

0.471698

939786f9e786e13e34a09c07c33b9d33a5fb6c2c

1,273

py

Python

core-python/Core_Python/file/RemoveTempDirs.py

theumang100/tutorials-1

497f54c2adb022c316530319a168fca1c007d4b1

[ "MIT" ]

9

2020-04-23T05:24:19.000Z

2022-02-17T16:37:51.000Z

core-python/Core_Python/file/RemoveTempDirs.py

theumang100/tutorials-1

497f54c2adb022c316530319a168fca1c007d4b1

[ "MIT" ]

5

2020-10-01T05:08:37.000Z

2020-10-12T03:18:10.000Z

core-python/Core_Python/file/RemoveTempDirs.py

theumang100/tutorials-1

497f54c2adb022c316530319a168fca1c007d4b1

[ "MIT" ]

9

2020-04-28T14:06:41.000Z

2021-10-19T18:32:28.000Z

import os from pathlib import Path from shutil import rmtree # change your parent dir accordingly try: directory = "TempDir" parent_dir = "E:/GitHub/1) Git_Tutorials_Repo_Projects/core-python/Core_Python/" td1, td2 = "TempA", "TempA" path = os.path.join(parent_dir, directory) temp_mul_dirs = os.path.join(path + os.sep + os.sep, td1 + os.sep + os.sep + td2) ''' This methods used to remove single file. all three methods used to delete symlink too''' os.remove(path +os.sep+os.sep+"TempFile.txt") os.unlink(path +os.sep+os.sep+td1+os.sep+os.sep+"TempFilea.txt") ''' we can also use this syntax pathlib.Path(path +os.sep+os.sep+"TempFile.txt").unlink() ''' f_path = Path(temp_mul_dirs +os.sep+os.sep+"TempFileb.txt") f_path.unlink(); ''' both methods for delete empty dir if single dir we can use rmdir if nested the removedirs''' # os.remove(path) # os.removedirs(path+os.sep+os.sep+td1) print("List of dirs before remove : ",os.listdir(path)) ''' For remove non empty directory we have to use shutil.rmtree and pathlib.Path(path),rmdir()''' rmtree(path+os.sep+os.sep+td1) Path(path).rmdir() print("List of dirs after remove : ",os.listdir(parent_dir)) except Exception as e: print(e)

47.148148

101

0.683425

939b63bdfc91f71662536be6efe59324a01bcaa9

587

py

Python

code/python/echomesh/color/WheelColor_test.py

silky/echomesh

2fe5a00a79c215b4aca4083e5252fcdcbd0507aa

[ "MIT" ]

1

2019-06-27T11:34:13.000Z

code/python/echomesh/color/WheelColor_test.py

silky/echomesh

2fe5a00a79c215b4aca4083e5252fcdcbd0507aa

[ "MIT" ]

null

code/python/echomesh/color/WheelColor_test.py

silky/echomesh

2fe5a00a79c215b4aca4083e5252fcdcbd0507aa

[ "MIT" ]

null

from __future__ import absolute_import, division, print_function, unicode_literals from echomesh.color import WheelColor from echomesh.util.TestCase import TestCase EXPECTED = [ [ 0., 1., 0.], [ 0.3, 0.7, 0. ], [ 0.6, 0.4, 0. ], [ 0.9, 0.1, 0. ], [ 0. , 0.2, 0.8], [ 0. , 0.5, 0.5], [ 0. , 0.8, 0.2], [ 0.9, 0. , 0.1], [ 0.6, 0. , 0.4], [ 0.3, 0. , 0.7], [ 0., 1., 0.]]

25.521739

82

0.558773

939de781cdf5e974811b59296ad87e9307743d04

1,000

py

Python

modules/IPlugin.py

SRottgardt/data_pipeline

8adcc886870f49bf0d544952be689d16825fe38e

[ "Apache-2.0" ]

3

2021-02-14T16:28:50.000Z

2021-02-16T23:23:49.000Z

modules/IPlugin.py

SRottgardt/data_pipeline

8adcc886870f49bf0d544952be689d16825fe38e

[ "Apache-2.0" ]

null

modules/IPlugin.py

SRottgardt/data_pipeline

8adcc886870f49bf0d544952be689d16825fe38e

[ "Apache-2.0" ]

null

from modules.CommandData import CommandData

26.315789

90

0.586

939df3af89d3fd8f0de44f63d3d42fe43872956f

4,890

py

Python

PySatImageAnalysis/sample_generator.py

danaja/sat_image_building_extraction

3d6cc26854666b566af0930a213a6f907733eaf7

[ "MIT" ]

2

2017-03-30T16:21:45.000Z

2019-01-09T03:01:01.000Z

PySatImageAnalysis/sample_generator.py

danaja/sat_image_building_extraction

3d6cc26854666b566af0930a213a6f907733eaf7

[ "MIT" ]

null

PySatImageAnalysis/sample_generator.py

danaja/sat_image_building_extraction

3d6cc26854666b566af0930a213a6f907733eaf7

[ "MIT" ]

1

2018-12-18T08:49:55.000Z

# -*- coding: utf-8 -*- #Used to generate positive building samples from google satellite images #based on OSM building polygons in geojson format # #Note 1: Accuracy of OSM building polygons may vary #Note 2: Requires downloaded google satellite images(tiles) to # have the following file name structure # part_latitude_of_center_longitude_of_center.png # This code was tested with tiles downloaded using # https://github.com/tdeo/maps-hd #Note 3: OSM building data downloaded from # mapzen.com/data/metro-extracts/ #@Author Danaja Maldeniya from osgeo import ogr import os import geoutils import image_utils as imu import cv2 import json import numpy as np map_zoom = 19 tile_size = 600 driver = ogr.GetDriverByName('ESRI Shapefile') shp = driver.Open(r'/home/danaja/Downloads/colombo_sri-lanka.imposm-shapefiles (2)/colombo_sri-lanka_osm_buildings.shp') layer = shp.GetLayer() spatialRef = layer.GetSpatialRef() print spatialRef #Loop through the image files to get their ref location(center) latitude and longitude tile_dir="/home/danaja/installations/maps-hd-master/bk3/images-st" tiles = os.listdir(tile_dir) #positive sample generation #============================================================================== # for tile in tiles: # tile_name = tile.replace(".png","") # print(tile) # center_lat = float(tile_name.split("_")[1]) # center_lon = float(tile_name.split("_")[2]) # extent = geoutils.get_tile_extent(center_lat,center_lon,map_zoom,tile_size) # layer.SetSpatialFilterRect(extent[2][1],extent[2][0],extent[1][1],extent[1][0]) # print("feature count: "+str(layer.GetFeatureCount())) # print(tile_dir+"/"+tile) # image = cv2.imread(tile_dir+"/"+tile) # b_channel, g_channel, r_channel = cv2.split(image) # alpha_channel = np.array(np.ones((tile_size,tile_size )) * 255,dtype=np.uint8) #creating a dummy alpha channel image. # image= cv2.merge((b_channel, g_channel, r_channel, alpha_channel)) # i = 0 # for feature in layer: # coordinates = [] # geom = feature.GetGeometryRef() # geom = json.loads(geom.ExportToJson()) # # for coordinate in geom['coordinates'][0]: # pixel = geoutils.get_pixel_location_in_tile_for_lat_lon( \ # coordinate[1],coordinate[0],center_lat,center_lon,map_zoom,tile_size) # if len(coordinates) == 0: # minx = pixel[0] # miny = pixel[1] # maxx = pixel[0] # maxy = pixel[1] # minx = min(minx,pixel[0]) # maxx = max(maxx,pixel[0]) # miny = min(miny,pixel[1]) # maxy = max(maxy,pixel[1]) # coordinates.append(tuple(reversed(pixel))) # # mask = np.zeros(image.shape, dtype=np.uint8) # roi_corners = np.array([coordinates], dtype=np.int32) # channel_count = image.shape[2] # i.e. 3 or 4 depending on your image # ignore_mask_color = (255,)*channel_count # cv2.fillPoly(mask, roi_corners, ignore_mask_color) # masked_image = cv2.bitwise_and(image, mask) # masked_image = masked_image[minx:maxx,miny:maxy] # cv2.imwrite("positive/"+tile_name+"_"+str(i)+".png",masked_image) # i=i+1 # layer.SetSpatialFilter(None) # #============================================================================== #negative sample generation min_size = 80 max_size = 100 for tile in tiles: tile_name = tile.replace(".png","") print(tile) center_lat = float(tile_name.split("_")[1]) center_lon = float(tile_name.split("_")[2]) extent = geoutils.get_tile_extent(center_lat,center_lon,map_zoom,tile_size) layer.SetSpatialFilterRect(extent[2][1],extent[2][0],extent[1][1],extent[1][0]) if layer.GetFeatureCount() > 0: layer.SetSpatialFilter(None) attempt = 0 success = 0 while (attempt <100 and success <20): box =imu.generate_random_box(tile_size,min_size,max_size) nw_corner = geoutils.get_lat_lon_of_point_in_tile(box[0],box[1],center_lat,center_lon,map_zoom,tile_size) se_corner = geoutils.get_lat_lon_of_point_in_tile(box[2],box[3],center_lat,center_lon,map_zoom,tile_size) layer.SetSpatialFilterRect(nw_corner[1],se_corner[0],se_corner[1],nw_corner[0]) fCount = layer.GetFeatureCount() if fCount >0: continue else: image = cv2.imread(tile_dir+"/"+tile) bld = image[int(box[1]):int(box[3]), \ int(box[0]):int(box[2])] cv2.imwrite("negative/"+tile_name+"_"+str(success)+".png",bld) success = success+1 layer.SetSpatialFilter(None) attempt = attempt +1

38.203125

124

0.616769

939e7757a3e174c6114642e42e77179f804882a6

779

py

Python

notebook/demo/src/multifuns.py

marketmodelbrokendown/1

587283fd972d0060815dde82a57667e74765c9ae

[ "MIT" ]

2

2019-03-13T15:34:42.000Z

2019-03-13T15:34:47.000Z

notebook/demo/src/multifuns.py

hervey-su/home

655b9e7b8180592742a132832795170a00debb47

[ "MIT" ]

1

2020-11-18T21:55:20.000Z

notebook/demo/src/multifuns.py

marketmodelbrokendown/1

587283fd972d0060815dde82a57667e74765c9ae

[ "MIT" ]

null

from ctypes import cdll,c_int,c_double,POINTER _lib = cdll.LoadLibrary('./demo/bin/libmultifuns.dll') # double dprod(double *x, int n) # int factorial(int n) # int isum(int array[], int size);

26.862069

59

0.658537

93a10bd2227db590b05aec0efe907cfefee1e40e

843

py

Python

api/nivo_api/cli/database.py

RemiDesgrange/nivo

e13dcd7c00d1fbc41c23d51c9004901d7704b498

[ "MIT" ]

2

2019-05-07T20:23:59.000Z

2020-04-26T11:18:38.000Z

api/nivo_api/cli/database.py

RemiDesgrange/nivo

e13dcd7c00d1fbc41c23d51c9004901d7704b498

[ "MIT" ]

89

2019-08-06T12:47:50.000Z

2022-03-28T04:03:25.000Z

api/nivo_api/cli/database.py

RemiDesgrange/nivo

e13dcd7c00d1fbc41c23d51c9004901d7704b498

[ "MIT" ]

1

2020-06-23T10:07:38.000Z

from nivo_api.core.db.connection import metadata, create_database_connections from sqlalchemy.engine import Engine from sqlalchemy.exc import ProgrammingError

32.423077

85

0.720047

93a2c7906ab4851fa8921bb2fef6ee5531e13056

357

py

Python

start_spiders.py

pluto-junzeng/baiduSpider

ea591920cd0994e83e36f033f98c6cc6859141d6

[ "Apache-2.0" ]

13

2020-12-07T03:19:12.000Z

2022-01-19T13:02:41.000Z

start_spiders.py

zengjunjun/baiduSpider

ea591920cd0994e83e36f033f98c6cc6859141d6

[ "Apache-2.0" ]

null

start_spiders.py

zengjunjun/baiduSpider

ea591920cd0994e83e36f033f98c6cc6859141d6

[ "Apache-2.0" ]

3

2021-07-10T08:24:55.000Z

2022-01-19T13:02:43.000Z

""" @Author:lichunhui @Time: @Description: """ from scrapy import cmdline # cmdline.execute("scrapy crawl baidu_spider".split()) # cmdline.execute("scrapy crawl baike_spider".split()) # cmdline.execute("scrapy crawl wiki_zh_spider".split()) # cmdline.execute("scrapy crawl wiki_en_spider".split()) cmdline.execute("scrapy crawlall".split())

25.5

56

0.7507

93a32ef6fddce5cbc92f060b72225c59adf371f7

515

py

Python

sources/pysimplegui/simpleeventloop.py

kantel/pythoncuriosa

4dfb92b443cbe0acf8d8efa5c54efbf13e834620

[ "MIT" ]

null

sources/pysimplegui/simpleeventloop.py

kantel/pythoncuriosa

4dfb92b443cbe0acf8d8efa5c54efbf13e834620

[ "MIT" ]

null

sources/pysimplegui/simpleeventloop.py

kantel/pythoncuriosa

4dfb92b443cbe0acf8d8efa5c54efbf13e834620

[ "MIT" ]

null

import PySimpleGUI as sg layout = [ [sg.Text("Wie heit Du?")], [sg.Input(key = "-INPUT-")], [sg.Text(size = (40, 1), key = "-OUTPUT-")], [sg.Button("Okay"), sg.Button("Quit")] ] window = sg.Window("Hallo PySimpleGUI", layout) keep_going = True while keep_going: event, values = window.read() if event == sg.WINDOW_CLOSED or event == "Quit": keep_going = False window["-OUTPUT-"].update("Hallchen " + values["-INPUT-"] + "!") window.close()

24.52381

69

0.557282

93a5a387bf24ca83ae37f5241ea161f3010ef4cf

3,247

py

Python

datasets/fusiongallery.py

weshoke/UV-Net

9e833df6868695a2cea5c5b79a0b613b224eacf2

[ "MIT" ]

null

datasets/fusiongallery.py

weshoke/UV-Net

9e833df6868695a2cea5c5b79a0b613b224eacf2

[ "MIT" ]

null

datasets/fusiongallery.py

weshoke/UV-Net

9e833df6868695a2cea5c5b79a0b613b224eacf2

[ "MIT" ]

null

import numpy as np import pathlib from torch.utils.data import Dataset, DataLoader import dgl import torch from dgl.data.utils import load_graphs import json from datasets import util from tqdm import tqdm

32.47

128

0.57838

93a73833278709acd49bb46a9f2c8ae73acf367a

3,690

py

Python

mpa/modules/models/heads/custom_ssd_head.py

openvinotoolkit/model_preparation_algorithm

8d36bf5944837b7a3d22fc2c3a4cb93423619fc2

[ "Apache-2.0" ]

null

mpa/modules/models/heads/custom_ssd_head.py

openvinotoolkit/model_preparation_algorithm

8d36bf5944837b7a3d22fc2c3a4cb93423619fc2

[ "Apache-2.0" ]

null

mpa/modules/models/heads/custom_ssd_head.py

openvinotoolkit/model_preparation_algorithm

8d36bf5944837b7a3d22fc2c3a4cb93423619fc2

[ "Apache-2.0" ]

null

# Copyright (C) 2022 Intel Corporation # SPDX-License-Identifier: Apache-2.0 # from mmdet.models.builder import HEADS, build_loss from mmdet.models.losses import smooth_l1_loss from mmdet.models.dense_heads.ssd_head import SSDHead

40.108696

79

0.622493

93a84d645ccedf01c50e4963b06e5f5cf6720d08

2,918

py

Python

Python/ml_converter.py

daduz11/ios-facenet-id

0ec634cf7f4f12c2bfa6334a72d5f2ab0a4afde4

[ "Apache-2.0" ]

2

2021-07-22T07:35:48.000Z

2022-03-03T05:48:08.000Z

Python/ml_converter.py

daduz11/ios-facenet-id

0ec634cf7f4f12c2bfa6334a72d5f2ab0a4afde4

[ "Apache-2.0" ]

null

Python/ml_converter.py

daduz11/ios-facenet-id

0ec634cf7f4f12c2bfa6334a72d5f2ab0a4afde4

[ "Apache-2.0" ]

2

2021-03-11T14:50:05.000Z

2021-04-18T14:58:24.000Z

""" Copyright 2020 daduz11 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. """ """ Firstly this script is used for the conversion of the freezed inference graph (pb format) into a CoreML model. Moreover the same script takes the CoreML model at 32bit precision to carries out the quantization from 16 to 1 bit. """ import argparse import sys import tfcoreml import coremltools from coremltools.models.neural_network import quantization_utils if __name__ == '__main__': main(parse_arguments(sys.argv[1:]))

38.906667

160

0.660384

93a90aa96a7060708343be286a46a3cbad16b9b8

628

py

Python

pizza_utils/stringutils.py

ILikePizza555/py-pizza-utils

f336fc2c391430f5d901d85dfda50974d9f8aba7

[ "MIT" ]

null

pizza_utils/stringutils.py

ILikePizza555/py-pizza-utils

f336fc2c391430f5d901d85dfda50974d9f8aba7

[ "MIT" ]

null

pizza_utils/stringutils.py

ILikePizza555/py-pizza-utils

f336fc2c391430f5d901d85dfda50974d9f8aba7

[ "MIT" ]

null

def find_from(string, subs, start = None, end = None): """ Returns a tuple of the lowest index where a substring in the iterable "subs" was found, and the substring. If multiple substrings are found, it will return the first one. If nothing is found, it will return (-1, None) """ string = string[start:end] last_index = len(string) substring = None for s in subs: i = string.find(s) if i != -1 and i < last_index: last_index = i substring = s if last_index == len(string): return (-1, None) return (last_index, substring)

27.304348

110

0.598726

93aa7bc7eef6be2b816f51dac8d5aa561ac4c490

4,844

py

Python

lab/experiment_futures.py

ajmal017/ta_scanner

21f12bfd8b5936d1d1977a32c756715539b0d97c

[ "BSD-3-Clause" ]

16

2020-06-22T05:24:20.000Z

2022-02-15T11:41:14.000Z

lab/experiment_futures.py

ajmal017/ta_scanner

21f12bfd8b5936d1d1977a32c756715539b0d97c

[ "BSD-3-Clause" ]

24

2020-07-07T04:22:03.000Z

2021-01-03T07:21:02.000Z

lab/experiment_futures.py

ajmal017/ta_scanner

21f12bfd8b5936d1d1977a32c756715539b0d97c

[ "BSD-3-Clause" ]

3

2020-06-21T12:12:14.000Z

2021-09-01T04:46:59.000Z

# todos # - [ ] all dates and date deltas are in time, not integers from loguru import logger from typing import Dict import sys import datetime from datetime import timedelta import numpy as np from ta_scanner.data.data import load_and_cache, db_data_fetch_between, aggregate_bars from ta_scanner.data.ib import IbDataFetcher from ta_scanner.experiments.simple_experiment import SimpleExperiment from ta_scanner.indicators import ( IndicatorSmaCrossover, IndicatorEmaCrossover, IndicatorParams, ) from ta_scanner.signals import Signal from ta_scanner.filters import FilterCumsum, FilterOptions, FilterNames from ta_scanner.reports import BasicReport from ta_scanner.models import gen_engine ib_data_fetcher = IbDataFetcher() instrument_symbol = "/NQ" rth = False interval = 1 field_name = "ema_cross" slow_sma = 25 fast_sma_min = 5 fast_sma_max = 20 filter_inverse = True win_pts = 75 loss_pts = 30 trade_interval = 12 test_total_pnl = 0.0 test_total_count = 0 all_test_results = [] engine = gen_engine() logger.remove() logger.add(sys.stderr, level="INFO") # fetch_data() for i in range(0, 33): initial = datetime.date(2020, 7, 10) + timedelta(days=i) test_start, test_end = initial, initial if initial.weekday() in [5, 6]: continue # fetch training data train_sd = initial - timedelta(days=5) train_ed = initial - timedelta(days=1) df_train = query_data(engine, instrument_symbol, train_sd, train_ed, interval) # for training data, let's find results for a range of SMA results = run_cross_range( df_train, slow_sma=slow_sma, fast_sma_min=fast_sma_min, fast_sma_max=fast_sma_max, ) fast_sma_pnl = [] for resultindex in range(2, len(results) - 3): fast_sma = results[resultindex][0] pnl = results[resultindex][1] result_set = results[resultindex - 2 : resultindex + 3] total_pnl = sum([x[1] for x in result_set]) fast_sma_pnl.append([fast_sma, total_pnl, pnl]) arr = np.array(fast_sma_pnl, dtype=float) max_tuple = np.unravel_index(np.argmax(arr, axis=None), arr.shape) optimal_fast_sma = int(arr[(max_tuple[0], 0)]) optimal_fast_sma_pnl = [x[2] for x in fast_sma_pnl if x[0] == optimal_fast_sma][0] # logger.info(f"Selected fast_sma={optimal_fast_sma}. PnL={optimal_fast_sma_pnl}") test_sd = initial test_ed = initial + timedelta(days=1) df_test = query_data(engine, instrument_symbol, test_sd, test_ed, interval) test_results = run_cross(df_test, optimal_fast_sma, slow_sma) all_test_results.append([initial] + list(test_results)) logger.info( f"Test Results. pnl={test_results[0]}, count={test_results[1]}, avg={test_results[2]}, median={test_results[3]}" ) test_total_pnl += test_results[0] test_total_count += test_results[1] logger.info( f"--- CumulativePnL={test_total_pnl}. Trades Count={test_total_count}. After={initial}" ) import csv with open("simple_results.csv", "w") as csvfile: spamwriter = csv.writer(csvfile) for row in all_test_results: spamwriter.writerow(row)

28

120

0.706441

93ade385d6ee900f8bf10af83edfd79ce2a15da9

841

py

Python

01.Hello_tkinter.py

amitdev101/learning-tkinter

1f7eabe1ac958c83c8bbe70e15682ecd4f7b5de5

[ "MIT" ]

null

01.Hello_tkinter.py

amitdev101/learning-tkinter

1f7eabe1ac958c83c8bbe70e15682ecd4f7b5de5

[ "MIT" ]

1

2020-11-15T15:43:03.000Z

2020-11-15T15:43:16.000Z

01.Hello_tkinter.py

amitdev101/learning-tkinter

1f7eabe1ac958c83c8bbe70e15682ecd4f7b5de5

[ "MIT" ]

null

import tkinter as tk import os print(tk) print(dir(tk)) print(tk.TkVersion) print(os.getcwd()) '''To initialize tkinter, we have to create a Tk root widget, which is a window with a title bar and other decoration provided by the window manager. The root widget has to be created before any other widgets and there can only be one root widget.''' root = tk.Tk() '''The next line of code contains the Label widget. The first parameter of the Label call is the name of the parent window, in our case "root". So our Label widget is a child of the root widget. The keyword parameter "text" specifies the text to be shown: ''' w = tk.Label(root,text='Hello world') '''The pack method tells Tk to fit the size of the window to the given text. ''' w.pack() '''The window won't appear until we enter the Tkinter event loop''' root.mainloop()

36.565217

115

0.737218

93aee3614d8d0959902e63d0a0a8aa33c102d4fd

14,700

py

Python

myscrumy/remiljscrumy/views.py

mikkeyiv/Django-App

b1114e9e53bd673119a38a1acfefb7a9fd9f172e

[ "MIT" ]

null

myscrumy/remiljscrumy/views.py

mikkeyiv/Django-App

b1114e9e53bd673119a38a1acfefb7a9fd9f172e

[ "MIT" ]

null

myscrumy/remiljscrumy/views.py

mikkeyiv/Django-App

b1114e9e53bd673119a38a1acfefb7a9fd9f172e

[ "MIT" ]

null

from django.shortcuts import render,redirect,get_object_or_404 from remiljscrumy.models import ScrumyGoals,GoalStatus,ScrumyHistory,User from django.http import HttpResponse,Http404,HttpResponseRedirect from .forms import SignupForm,CreateGoalForm,MoveGoalForm,DevMoveGoalForm,AdminChangeGoalForm,QAChangeGoalForm,QAChangegoal from django.contrib.auth import authenticate,login from django.contrib.auth.models import User,Group from django.conf import settings from django.core.exceptions import ObjectDoesNotExist from django.urls import reverse #from django.core.exceptions import ObjectDoesNotExist # Create your views here. # def move_goal(request, goal_id): # #response = ScrumyGoals.objects.get(goal_id=goal_id) # # try: # #goal = ScrumyGoals.objects.get(goal_id=goal_id) # # except ScrumyGoals.DoesNotExist: # # raise Http404 ('A record with that goal id does not exist') # instance = get_object_or_404(ScrumyGoals,goal_id=goal_id) # form = MoveGoalForm(request.POST or None, instance=instance) # if form. is_valid(): # instance = form.save(commit=False) # instance.save() # return redirect('home') # context={ # 'goal_id': instance.goal_id, # 'user': instance.user, # 'goal_status': instance.goal_status, # 'form':form, # } # return render(request, 'remiljscrumy/exception.html', context) #move_goal = form.save(commit=False) # move_goal = # form.save() # # goal_name = form.cleaned_data.get('goal_name') # # ScrumyGoals.objects.get(goal_name) # return redirect('home') # def form_valid(self, form): # form.instance.goal_status = self.request.user # return super(addgoalForm, self).form_valid(form) # } # return render(request, 'remiljscrumy/exception.html', context=gdict) #return HttpResponse(response) # return HttpResponse('%s is the response at the record of goal_id %s' % (response, goal_id))''' from random import randint def home(request): '''# all=','.join([eachgoal.goal_name for eachgoal in ScrumyGoals.objects.all()]) # home = ScrumyGoals.objects.filter(goal_name='keep learning django') # return HttpResponse(all) #homedict = {'goal_name':ScrumyGoals.objects.get(pk=3).goal_name,'goal_id':ScrumyGoals.objects.get(pk=3).goal_id, 'user': ScrumyGoals.objects.get(pk=3).user,} user = User.objects.get(email="louisoma@linuxjobber.com") name = user.scrumygoal.all() homedict={'goal_name':ScrumyGoals.objects.get(pk=1).goal_name,'goal_id':ScrumyGoals.objects.get(pk=1).goal_id,'user':ScrumyGoals.objects.get(pk=1).user, 'goal_name1':ScrumyGoals.objects.get(pk=2).goal_name,'goal_id1':ScrumyGoals.objects.get(pk=2).goal_id,'user':ScrumyGoals.objects.get(pk=2).user, 'goal_name2':ScrumyGoals.objects.get(pk=3).goal_name,'goal_id2':ScrumyGoals.objects.get(pk=3).goal_id,'user2':ScrumyGoals.objects.get(pk=3).user}''' # form = CreateGoalForm # if request.method == 'POST': # form = CreateGoalForm(request.POST) # if form .is_valid(): # add_goal = form.save(commit=True) # add_goal = form.save() # # #form.save() # return redirect('home') current = request.user week = GoalStatus.objects.get(pk=1) day = GoalStatus.objects.get(pk=2) verify = GoalStatus.objects.get(pk=3) done = GoalStatus.objects.get(pk=4) user = User.objects.all() weeklygoal = ScrumyGoals.objects.filter(goal_status=week) dailygoal = ScrumyGoals.objects.filter(goal_status=day) verifygoal = ScrumyGoals.objects.filter(goal_status=verify) donegoal = ScrumyGoals.objects.filter(goal_status=done) groups = current.groups.all() dev = Group.objects.get(name='Developer') owner = Group.objects.get(name='Owner') admin = Group.objects.get(name='Admin') qa = Group.objects.get(name='Quality Assurance') if not request.user.is_authenticated: return redirect('%s?next=%s' % (settings.LOGIN_URL, request.path)) if current.is_authenticated: if dev in groups or qa in groups or owner in groups: # if request.method == 'GET': # return render(request, 'remiljscrumy/home.html', context) form = CreateGoalForm() context = {'user': user, 'weeklygoal': weeklygoal, 'dailygoal': dailygoal, 'verifygoal': verifygoal, 'donegoal': donegoal, 'form': form, 'current': current, 'groups': groups,'dev': dev,'owner':owner,'admin':admin,'qa':qa} if request.method == 'POST': form = CreateGoalForm(request.POST) if form.is_valid(): post = form.save(commit=False) status_name = GoalStatus(id=1) post.goal_status = status_name post.user = current post = form.save() elif admin in groups: context = {'user': user, 'weeklygoal': weeklygoal, 'dailygoal': dailygoal, 'verifygoal': verifygoal, 'donegoal': donegoal,'current': current, 'groups': groups,'dev': dev,'owner':owner,'admin':admin,'qa':qa} return render(request, 'remiljscrumy/home.html', context) # else: # form = WeekOnlyAddGoalForm() # return HttpResponseRedirect(reverse('ayooluwaoyewoscrumy:homepage')) # if group == 'Admin': # context ={ # 'user':User.objects.all(), # 'weeklygoal':ScrumyGoals.objects.filter(goal_status=week), # 'dailygoal':ScrumyGoals.objects.filter(goal_status=day), # 'verifiedgoals':ScrumyGoals.objects.filter(goal_status=verify), # 'donegoal':ScrumyGoals.objects.filter(goal_status=done), # 'current':request.user, # 'groups':request.user.groups.all(), # 'admin': Group.objects.get(name="Admin"), # 'owner': Group.objects.get(name='Owner'), # 'dev': Group.objects.get(name='Developer'), # 'qa': Group.objects.get(name='Quality Assurance'),} # return render(request,'remiljscrumy/home.html',context=homedict) # if request.method == 'GET': # return render(request, 'remiljscrumy/home.html', context) #

49.328859

206

0.606531

93b17847a4ea4d1f1c0c385ce9727ab17aed5c27

3,088

py

Python

examples/ex03_oscillator_classes.py

icemtel/carpet

5905e02ab0e44822829a672955dccad3e09eea07

[ "MIT" ]

null

examples/ex03_oscillator_classes.py

icemtel/carpet

5905e02ab0e44822829a672955dccad3e09eea07

[ "MIT" ]

null

examples/ex03_oscillator_classes.py

icemtel/carpet

5905e02ab0e44822829a672955dccad3e09eea07

[ "MIT" ]

null

''' Cilia classes are used to compute fixed points faster. - Assume symmetry like in an m-twist (make a plot to see it) - Assume that symmetries is not broken in time -> define classes of symmetry and interactions between them. Done: - Create a ring of cilia. - Define symmetry classes - Use classes to solve ODE - Map back to cilia ''' import numpy as np import carpet import carpet.lattice.ring1d as lattice import carpet.physics.friction_pairwise as physics import carpet.classes as cc import carpet.visualize as vis import matplotlib.pyplot as plt ## Parameters # Physics set_name = 'machemer_1' # hydrodynamic friction coefficients data set period = 31.25 # [ms] period freq = 2 * np.pi / period # [rad/ms] angular frequency order_g11 = (4, 0) # order of Fourier expansion of friction coefficients order_g12 = (4, 4) # Geometry N = 6 # number of cilia a = 18 # [um] lattice spacing e1 = (1, 0) # direction of the chain ## Initialize # Geometry L1 = lattice.get_domain_size(N, a) coords, lattice_ids = lattice.get_nodes_and_ids(N, a, e1) # get cilia (nodes) coordinates NN, TT = lattice.get_neighbours_list(N, a, e1) # get list of neighbours and relative positions e1, e2 = lattice.get_basis(e1) get_k = lattice.define_get_k(N, a, e1) get_mtwist = lattice.define_get_mtwist(coords, N, a, e1) # Physics gmat_glob, q_glob = physics.define_gmat_glob_and_q_glob(set_name, e1, e2, a, NN, TT, order_g11, order_g12, period) right_side_of_ODE = physics.define_right_side_of_ODE(gmat_glob, q_glob) solve_cycle = carpet.define_solve_cycle(right_side_of_ODE, 2 * period, phi_global_func=carpet.get_mean_phase) # k-twist k1 = 2 phi0 = get_mtwist(k1) vis.plot_nodes(coords, phi=phi0) # visualize! plt.ylim([-L1 / 10, L1 / 10]) plt.show() ## Solve regularly tol = 1e-4 sol = solve_cycle(phi0, tol) phi1 = sol.y.T[-1] - 2 * np.pi # after one cycle ## Now solve with classes # Map to classes ix_to_class, class_to_ix = cc.get_classes(phi0) nclass = len(class_to_ix) # Get classes representatives # Get one oscillator from each of cilia classes unique_cilia_ids = cc.get_unique_cilia_ix( class_to_ix) # equivalent to sp.array([class_to_ix[iclass][0] for iclass in range(nclass)], dtype=sp.int64) # Get connections N1_class, T1_class = cc.get_neighbours_list_class(unique_cilia_ids, ix_to_class, NN, TT) # Define physics gmat_glob_class, q_glob_class = physics.define_gmat_glob_and_q_glob(set_name, e1, e2, a, N1_class, T1_class, order_g11, order_g12, period) right_side_of_ODE_class = physics.define_right_side_of_ODE(gmat_glob_class, q_glob_class) solve_cycle_class = carpet.define_solve_cycle(right_side_of_ODE_class, 2 * period, carpet.get_mean_phase) # Solve ODE phi0_class = phi0[unique_cilia_ids] sol = solve_cycle_class(phi0_class, tol) phi1_class = sol.y.T[-1] - 2 * np.pi # Map from classes back to cilia phi1_mapped_from_class = phi1_class[ix_to_class] ## Print how much phase changed print(phi1_mapped_from_class - phi1) # difference between two - should be on the order of tolerance or smaller

36.761905

114

0.748381

93b1f4ae1de1aaae99760a70f835707158943004

749

py

Python

cars/donkeycar/sim/Adafruit_PCA9685-pkg/Adafruit_PCA9685/__init__.py

kuaikai/kuaikai

ca7e7b2d2f6f16b892a21c819ba43201beadf370

[ "Apache-2.0" ]

6

2018-03-27T15:46:28.000Z

2018-06-23T21:56:15.000Z

cars/donkeycar/sim/Adafruit_PCA9685-pkg/Adafruit_PCA9685/__init__.py

kuaikai/kuaikai

ca7e7b2d2f6f16b892a21c819ba43201beadf370

[ "Apache-2.0" ]

3

2018-03-30T15:54:34.000Z

2018-07-11T19:44:59.000Z

cars/donkeycar/sim/Adafruit_PCA9685-pkg/Adafruit_PCA9685/__init__.py

kuaikai/kuaikai

ca7e7b2d2f6f16b892a21c819ba43201beadf370

[ "Apache-2.0" ]

null

""" SCL <scott@rerobots.net> 2018 """ import json import os import tempfile import time

22.69697

97

0.534045

93b2760677f1d106e80a9cb1e7a2b2ab58fbe987

2,851

py

Python

bayesian-belief/sequential_bayes.py

ichko/interactive

6659f81c11c0f180295b758b457343d32323eb35

[ "MIT" ]

null

bayesian-belief/sequential_bayes.py

ichko/interactive

6659f81c11c0f180295b758b457343d32323eb35

[ "MIT" ]

null

bayesian-belief/sequential_bayes.py

ichko/interactive

6659f81c11c0f180295b758b457343d32323eb35

[ "MIT" ]

1

2019-02-05T20:22:08.000Z

import numpy as np import matplotlib.pyplot as plt import scipy.stats FIG, (LEFT_AX, MIDDLE_AX, RIGHT_AX) = plt.subplots(1, 3, figsize=(10, 3)) X_RANGE = (-2, 2) Y_RANGE = (-2, 2) X_DATA = np.array([]) Y_DATA = np.array([]) BELIEF = SequentialBayes(np.array([0, 0]), np.diag([1, 1])) set_ax_range() plot_belief(RIGHT_AX) plot_belief_sample() FIG.canvas.mpl_connect('button_press_event', on_click) plt.show()

24.791304

88

0.618029

93b33aae2d1691aa0b0588d3a8ea2f43f4819a38

9,255

py

Python

cgc/legacy/kmeans.py

cffbots/cgc

1ea8b6bb6e4e9e728aff493744d8646b4953eaa4

[ "Apache-2.0" ]

11

2020-09-04T10:28:48.000Z

2022-03-10T13:56:43.000Z

cgc/legacy/kmeans.py

cffbots/cgc

1ea8b6bb6e4e9e728aff493744d8646b4953eaa4

[ "Apache-2.0" ]

40

2020-08-19T09:23:15.000Z

2022-03-01T16:16:30.000Z

cgc/legacy/kmeans.py

phenology/geoclustering

9b9b6ab8e64cdb62dbed6bdcfe63612e99665fd1

[ "Apache-2.0" ]

4

2020-10-03T21:17:18.000Z

2022-03-09T14:32:56.000Z

import numpy as np import logging import matplotlib.pyplot as plt from sklearn.cluster import KMeans from ..results import Results logger = logging.getLogger(__name__)

38.723849

79

0.581415

93b3d6b31717b3ff24e2cbf4724891aa06fd3451

5,175

py

Python

BAF2LOH.py

Xiaohuaniu0032/HLALOH

24587c75fad08e7f1821866fb72f9b7e756689bb

[ "MIT" ]

null

BAF2LOH.py

Xiaohuaniu0032/HLALOH

24587c75fad08e7f1821866fb72f9b7e756689bb

[ "MIT" ]

2

2020-10-26T01:39:33.000Z

2020-12-04T02:41:11.000Z

BAF2LOH.py

Xiaohuaniu0032/HLALOH

24587c75fad08e7f1821866fb72f9b7e756689bb

[ "MIT" ]

null

import sys import os import configparser import argparse import glob if __name__ == "__main__": main()

33.823529

183

0.592464

93b68bf304e52b47592144b9352709027d4393ab

3,221

py

Python

src/tests/benchmarks/tools/bench/Vellamo3.py

VirtualVFix/AndroidTestFramework

1feb769c6aca39a78e6daefd6face0a1e4d62cd4

[ "MIT" ]

null

src/tests/benchmarks/tools/bench/Vellamo3.py

VirtualVFix/AndroidTestFramework

1feb769c6aca39a78e6daefd6face0a1e4d62cd4

[ "MIT" ]

null

src/tests/benchmarks/tools/bench/Vellamo3.py

VirtualVFix/AndroidTestFramework

1feb769c6aca39a78e6daefd6face0a1e4d62cd4

[ "MIT" ]

null

# All rights reserved by forest fairy. # You cannot modify or share anything without sacrifice. # If you don't agree, keep calm and don't look at code bellow! __author__ = "VirtualV <https://github.com/virtualvfix>" __date__ = "$Apr 13, 2014 8:47:25 PM$" import re from config import CONFIG from tests.exceptions import ResultsNotFoundError from tests.benchmarks.tools.base import OnlineBenchmark

46.681159

118

0.533064

93b6e5c40e7caecbcb7b62ae060f41d6eac3c44d

3,879

py

Python

tests/commands/test_template_image_apply_overlays.py

dsoprea/image_template_overlay_apply

ce54429e07ac140b33add685d39221b1fb5cadb2

[ "MIT" ]

1

2020-05-07T00:24:21.000Z

tests/commands/test_template_image_apply_overlays.py

dsoprea/image_template_overlay_apply

ce54429e07ac140b33add685d39221b1fb5cadb2

[ "MIT" ]

null

tests/commands/test_template_image_apply_overlays.py

dsoprea/image_template_overlay_apply

ce54429e07ac140b33add685d39221b1fb5cadb2

[ "MIT" ]

null

import sys import unittest import os import tempfile import shutil import contextlib import json import subprocess import PIL import templatelayer.testing_common _APP_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..')) _SCRIPT_PATH = os.path.join(_APP_PATH, 'templatelayer', 'resources', 'scripts') _TOOL_FILEPATH = os.path.join(_SCRIPT_PATH, 'template_image_apply_overlays') sys.path.insert(0, _APP_PATH)

28.733333

80

0.467389

93b7d3ab9113fe2fed663ad41fb0b7d4b95f018e

3,993

py

Python

src/gpt2/evaluate_model.py

alexgQQ/GPT2

b2d78965f7cdcfe7dcf475969f4d4cce2b3ee82a

[ "Apache-2.0" ]

94

2020-05-05T04:27:05.000Z

2022-03-31T01:08:20.000Z

src/gpt2/evaluate_model.py

seeodm/GPT2

366d8517ac0bdf85e45e46adbef10cbe55740ee1

[ "Apache-2.0" ]

7

2020-09-11T02:25:30.000Z

2021-11-23T16:03:01.000Z

src/gpt2/evaluate_model.py

seeodm/GPT2

366d8517ac0bdf85e45e46adbef10cbe55740ee1

[ "Apache-2.0" ]

24

2020-07-14T19:15:39.000Z

2022-02-18T05:57:31.000Z

import argparse import torch import torch.nn as nn from gpt2.modeling import Transformer from gpt2.data import Dataset, Vocab, TokenizedCorpus from gpt2.evaluation import EvaluationSpec, EvaluateConfig, Evaluator from typing import Dict

42.478723

76

0.630604

93b7f2e32bcec2e7242f5985332622842d33261b

571

py

Python

alerter/src/alerter/alert_data/chainlink_contract_alert_data.py

SimplyVC/panic

2f5c327ea0d14b6a49dc8f4599a255048bc2ff6d

[ "Apache-2.0" ]

41

2019-08-23T12:40:42.000Z

2022-03-28T11:06:02.000Z

alerter/src/alerter/alert_data/chainlink_contract_alert_data.py

SimplyVC/panic

2f5c327ea0d14b6a49dc8f4599a255048bc2ff6d

[ "Apache-2.0" ]

147

2019-08-30T22:09:48.000Z

2022-03-30T08:46:26.000Z

alerter/src/alerter/alert_data/chainlink_contract_alert_data.py

SimplyVC/panic

2f5c327ea0d14b6a49dc8f4599a255048bc2ff6d

[ "Apache-2.0" ]

3

2019-09-03T21:12:28.000Z

2021-08-18T14:27:56.000Z

from typing import Dict from src.alerter.alert_data.alert_data import AlertData

24.826087

74

0.698774

93ba2653ba488171fc0c6a50b7e6cee03b9a572c

1,332

py

Python

mytrain/my_unpack.py

JinkelaCrops/t2t-learning

5d9b5a5164af763c24f1cbce9d97561e9f2b772c

[ "Apache-2.0" ]

5

2019-03-28T03:52:32.000Z

2021-02-24T07:09:26.000Z

mytrain/my_unpack.py

JinkelaCrops/t2t-learning

5d9b5a5164af763c24f1cbce9d97561e9f2b772c

[ "Apache-2.0" ]

null

mytrain/my_unpack.py

JinkelaCrops/t2t-learning

5d9b5a5164af763c24f1cbce9d97561e9f2b772c

[ "Apache-2.0" ]

2

2018-08-07T03:43:09.000Z

2019-12-09T06:41:40.000Z

from processutils.textfilter import Unpack from utils.simplelog import Logger import argparse parser = argparse.ArgumentParser(description="my_unpack") parser.add_argument('-f', "--file_prefix", required=True) parser.add_argument('-sep', "--separator", required=True) # args = parser.parse_args([ # "-f", "../test/medicine.sample.data/data.test", # "-sep", ' ||| ' # ]) args = parser.parse_args() args.output_src = args.file_prefix + ".src" args.output_tgt = args.file_prefix + ".tgt" log = Logger("my_filter", "my_filter.log").log() if __name__ == '__main__': with open(args.file_prefix, "r", encoding="utf8") as f: data = f.readlines() src_lines, tgt_lines = main(data) with open(args.output_src, "w", encoding="utf8") as f: f.writelines(src_lines) with open(args.output_tgt, "w", encoding="utf8") as f: f.writelines(tgt_lines)

29.6

90

0.638889

93baf5e4d83867b7e987a8bdfa95d1e350aa7b07

10,173

py

Python

source/api/dataplane/runtime/chalicelib/common.py

awslabs/aws-media-replay-engine

2c217eff42f8e2c56b43e2ecf593f5aaa92c5451

[ "Apache-2.0" ]

22

2021-11-24T01:23:07.000Z

2022-03-26T23:24:46.000Z

source/api/dataplane/runtime/chalicelib/common.py

awslabs/aws-media-replay-engine

2c217eff42f8e2c56b43e2ecf593f5aaa92c5451

[ "Apache-2.0" ]

null

source/api/dataplane/runtime/chalicelib/common.py

awslabs/aws-media-replay-engine

2c217eff42f8e2c56b43e2ecf593f5aaa92c5451

[ "Apache-2.0" ]

3

2021-12-10T09:42:51.000Z

2022-02-16T02:22:50.000Z

# Copyright 2021 Amazon.com, Inc. or its affiliates. All Rights Reserved. # SPDX-License-Identifier: Apache-2.0 import os import json import urllib.parse import boto3 import decimal from decimal import Decimal from datetime import datetime from chalice import Chalice from chalice import IAMAuthorizer from chalice import ChaliceViewError, BadRequestError, NotFoundError from botocore.config import Config from botocore.client import ClientError from boto3.dynamodb.conditions import Key, Attr, In from jsonschema import validate, ValidationError from chalicelib import replace_decimals s3_client = boto3.client("s3") ddb_resource = boto3.resource("dynamodb") PLUGIN_RESULT_TABLE_NAME = os.environ['PLUGIN_RESULT_TABLE_NAME'] def get_event_segment_metadata(name, program, classifier, tracknumber): """ Gets the Segment Metadata based on the segments found during Segmentation/Optimization process. """ name = urllib.parse.unquote(name) program = urllib.parse.unquote(program) classifier = urllib.parse.unquote(classifier) tracknumber = urllib.parse.unquote(tracknumber) try: # Get Event Segment Details # From the PluginResult Table, get the Clips Info plugin_table = ddb_resource.Table(PLUGIN_RESULT_TABLE_NAME) response = plugin_table.query( KeyConditionExpression=Key("PK").eq(f"{program}#{name}#{classifier}"), ScanIndexForward=False ) plugin_responses = response['Items'] while "LastEvaluatedKey" in response: response = plugin_table.query( ExclusiveStartKey=response["LastEvaluatedKey"], KeyConditionExpression=Key("PK").eq(f"{program}#{name}#{classifier}"), ScanIndexForward=False ) plugin_responses.extend(response["Items"]) # if "Items" not in plugin_response or len(plugin_response["Items"]) == 0: # print(f"No Plugin Responses found for event '{name}' in Program '{program}' for Classifier {classifier}") # raise NotFoundError(f"No Plugin Responses found for event '{name}' in Program '{program}' for Classifier {classifier}") clip_info = [] for res in plugin_responses: optoLength = 0 if 'OptoEnd' in res and 'OptoStart' in res: # By default OptoEnd and OptoStart are maps and have no Keys. Only when they do, we check for TrackNumber's if len(res['OptoEnd'].keys()) > 0 and len(res['OptoStart'].keys()) > 0: try: optoLength = res['OptoEnd'][tracknumber] - res['OptoStart'][tracknumber] except Exception as e: pass # Error if the TrackNumber does not exist. Simply Ignore since its a problem with Clip Gen # Calculate Opto Clip Duration for each Audio Track optoDurationsPerTrack = [] if 'OptoEnd' in res and 'OptoStart' in res: for k in res['OptoStart'].keys(): try: optoDur = {} optoDur[k] = res['OptoEnd'][k] - res['OptoStart'][k] optoDurationsPerTrack.append(optoDur) except Exception as e: pass # Error if the TrackNumber does not exist. Simply Ignore since its a problem with Clip Gen optoClipLocation = '' if 'OptimizedClipLocation' in res: # This is not ideal. We need to check of there exists a OptimizedClipLocation with the requested TrackNumber. # If not, likely a problem with Clip Gen. Instead of failing, we send an empty value for optoClipLocation back. for trackNo in res['OptimizedClipLocation'].keys(): if str(trackNo) == str(tracknumber): optoClipLocation = create_signed_url(res['OptimizedClipLocation'][tracknumber]) break origClipLocation = '' if 'OriginalClipLocation' in res: for trackNo in res['OriginalClipLocation'].keys(): if str(trackNo) == str(tracknumber): origClipLocation = create_signed_url(res['OriginalClipLocation'][tracknumber]) break label = '' if 'Label' in res: label = res['Label'] if str(label) == "": label = '<no label plugin configured>' clip_info.append({ 'OriginalClipLocation': origClipLocation, 'OriginalThumbnailLocation': create_signed_url( res['OriginalThumbnailLocation']) if 'OriginalThumbnailLocation' in res else '', 'OptimizedClipLocation': optoClipLocation, 'OptimizedThumbnailLocation': create_signed_url( res['OptimizedThumbnailLocation']) if 'OptimizedThumbnailLocation' in res else '', 'StartTime': res['Start'], 'Label': label, 'FeatureCount': 'TBD', 'OrigLength': 0 if 'Start' not in res else res['End'] - res['Start'], 'OptoLength': optoLength, 'OptimizedDurationPerTrack': optoDurationsPerTrack, 'OptoStartCode': '' if 'OptoStartCode' not in res else res['OptoStartCode'], 'OptoEndCode': '' if 'OptoEndCode' not in res else res['OptoEndCode'] }) final_response = {} final_response['Segments'] = clip_info except NotFoundError as e: print(e) print(f"Got chalice NotFoundError: {str(e)}") raise except Exception as e: print(e) print(f"Unable to get the Event '{name}' in Program '{program}': {str(e)}") raise ChaliceViewError(f"Unable to get the Event '{name}' in Program '{program}': {str(e)}") else: return replace_decimals(final_response)

45.013274

132

0.626954

93bc932331d06fe620b9dc241c2d48eeb8fdbdb8

9,559

py

Python

oec_sync/sync/oec.py

SamnnyWong/OECSynchronizer

9b28c96988158f5717bacd47f59cbabb1ce072cd

[ "Unlicense", "MIT" ]

null

oec_sync/sync/oec.py

SamnnyWong/OECSynchronizer

9b28c96988158f5717bacd47f59cbabb1ce072cd

[ "Unlicense", "MIT" ]

null

oec_sync/sync/oec.py

SamnnyWong/OECSynchronizer

9b28c96988158f5717bacd47f59cbabb1ce072cd

[ "Unlicense", "MIT" ]

null

from xml.etree import ElementTree as Etree from model import * from astro_unit import * from io import StringIO import logging # Maps field name to tuple of (type, unit) # Only the following columns will be understood PLANET_FIELDS = { "semimajoraxis": FieldMeta("number", 'AU'), "eccentricity": FieldMeta("number"), # unit not needed "periastron": FieldMeta("number", 'deg'), "longitude": FieldMeta("number", 'deg'), "ascendingnode": FieldMeta("number", 'deg'), "inclination": FieldMeta("number", 'deg'), "impactparameter": FieldMeta("number"), # unit not needed "meananomaly": FieldMeta("number", 'deg'), "period": FieldMeta("number", 'days'), "transittime": FieldMeta("number", 'BJD'), "periastrontime": FieldMeta("number", 'BJD'), "maximumrvtime": FieldMeta("number", 'BJD'), "separation": FieldMeta("number", 'arcsec'), # unit on xml element "mass": FieldMeta("number", 'M_j'), "radius": FieldMeta("number", 'R_j'), "temperature": FieldMeta("number", 'K'), "age": FieldMeta("number", 'Gyr'), # "discoverymethod": FieldMeta("discoverymethodtype"), # "istransiting": FieldMeta("boolean"), # "description": "xs:string", "discoveryyear": FieldMeta("number", None), # "lastupdate": FieldMeta("lastupdatedef", None), # "image", # "imagedescription", "spinorbitalignment": FieldMeta("number", 'deg'), "positionangle": FieldMeta("number", 'deg'), # "metallicity": FieldMeta("number"), # unit not needed # "spectraltype": FieldMeta("spectraltypedef"), # "magB": FieldMeta("number", None), "magH": FieldMeta("number", None), "magI": FieldMeta("number", None), "magJ": FieldMeta("number", None), "magK": FieldMeta("number", None), # "magR": FieldMeta("number", None), # "magU": FieldMeta("number", None), "magV": FieldMeta("number", None) } # def validate(self, file: str) -> None: # Validates an xml using schema defined by OEC. # Raises an exception if file does not follow the schema. # :param file: File name. # """ # return # skip for now, because OEC itself isn't following the schema # # tree = etree.parse(file) # # self._schema.assertValid(tree) def update_str(self, xml_string: str, update: PlanetarySysUpdate) \ -> Tuple[str, bool]: """ Apply a system update to an xml string. Also performs a check afterwards to determine if the action succeeded. :param xml_string: containing the xml representation of a system :param update: Update to be applied to the system :return: A tuple (content, succeeded) where: - content is the file content modified - succeeded indicates whether the update was successful. """ tree = Etree.parse(StringIO(xml_string)) ok = Adapter._write_system_update(tree, update) serialized = Etree.tostring(tree.getroot(), 'unicode', 'xml') return serialized, ok def update_file(self, filename: str, update: PlanetarySysUpdate) -> bool: """ Apply a system update to an xml file. :param filename: The system xml file :param update: Update to be applied to the system :return: Whether the update was successful """ tree = Etree.parse(filename) succeeded = Adapter._write_system_update(tree, update) tree.write(filename) return succeeded

37.194553

79

0.574746

93bd3505c0bee8de6a5685c5e02ee9cbc78b0fdd

9,072

py

Python

pyAnVIL/anvil/util/ingest_helper.py

anvilproject/client-apis

cbd892042e092b0a1dede4c561bcfdde15e9a3ad

[ "Apache-2.0" ]

8

2019-07-02T20:41:24.000Z

2022-01-12T21:50:21.000Z

pyAnVIL/anvil/util/ingest_helper.py

mmtmn/client-apis

215adae0b7f401b4bf62e7bd79b6a8adfe69cf4f

[ "Apache-2.0" ]

37

2019-01-16T17:48:02.000Z

2021-08-13T21:35:54.000Z

pyAnVIL/anvil/util/ingest_helper.py

mmtmn/client-apis

215adae0b7f401b4bf62e7bd79b6a8adfe69cf4f

[ "Apache-2.0" ]

7

2019-05-13T14:59:27.000Z

2022-01-12T21:50:22.000Z

"""Validate AnVIL workspace(s).""" import os from google.cloud.storage import Client from google.cloud.storage.blob import Blob from collections import defaultdict import ipywidgets as widgets from ipywidgets import interact from IPython.display import display import pandas as pd import firecloud.api as FAPI from types import SimpleNamespace import numpy as np

53.680473

270

0.61541

93bd854e0f319cd263a25841957dc223b7ca22bf

1,513

py

Python

acsl-pydev/acsl/lect03p2/c1_inter_stigid_sol1.py

odys-z/hello

39ca67cae34eb4bc4cbd848a06b3c0d65a995954

[ "MIT" ]

null

acsl-pydev/acsl/lect03p2/c1_inter_stigid_sol1.py

odys-z/hello

39ca67cae34eb4bc4cbd848a06b3c0d65a995954

[ "MIT" ]

3

2021-04-17T18:36:24.000Z

2022-03-04T20:30:09.000Z

acsl-pydev/acsl/lect03p2/c1_inter_stigid_sol1.py

odys-z/hello

39ca67cae34eb4bc4cbd848a06b3c0d65a995954

[ "MIT" ]

null

''' Intermediate C#1, stigid PROBLEM: Given a number less than 10^50 and length n, find the sum of all the n -digit numbers (starting on the left) that are formed such that, after the first n -digit number is formed all others are formed by deleting the leading digit and taking the next n -digits. ''' from unittest import TestCase # return a list t = TestCase() reslt = c1_inter_1([ ['1325678905', 2], ['54981230845791', 5], ['4837261529387456', 3], ['385018427388713440', 4], ['623387770165388734652209', 11]]) t.assertEqual(455, reslt[0]) t.assertEqual(489210, reslt[1]) t.assertEqual(7668, reslt[2]) t.assertEqual(75610, reslt[3]) t.assertEqual(736111971668, reslt[4]) reslt = c1_inter_1([[ '834127903876541', 3 ], [ '2424424442442420', 1 ], [ '12345678909876543210123456789', 12 ], [ '349216', 6 ], [ '11235813245590081487340005429', 2 ]]) t = TestCase() t.assertEqual(6947, reslt[0]) t.assertEqual(48, reslt[1]) t.assertEqual(9886419753191, reslt[2]) t.assertEqual(349216, reslt[3]) t.assertEqual(11 + 12 + 23 + 35 + 58 + 81 + 13 + 32 + 24 + 45 + 55 + 59 + 90 + 00 + 8 + 81 + 14 + 48 + 87 + 73 + 34 + 40 + 00 + 00 + 5 + 54 + 42 + 29, reslt[4]) print('OK!')

29.096154

150

0.643754

93be43a153e5c582c6fc97ea9f7ab11f70cb7196

1,857

py

Python

otcextensions/tests/functional/sdk/vlb/v3/test_certificate.py

artem-lifshits/python-otcextensions

2021da124f393e0429dd5913a3bc635e6143ba1e

[ "Apache-2.0" ]

10

2018-03-03T17:59:59.000Z

2020-01-08T10:03:00.000Z

otcextensions/tests/functional/sdk/vlb/v3/test_certificate.py

artem-lifshits/python-otcextensions

2021da124f393e0429dd5913a3bc635e6143ba1e

[ "Apache-2.0" ]

208

2020-02-10T08:27:46.000Z

2022-03-29T15:24:21.000Z

otcextensions/tests/functional/sdk/vlb/v3/test_certificate.py

artem-lifshits/python-otcextensions

2021da124f393e0429dd5913a3bc635e6143ba1e

[ "Apache-2.0" ]

15

2020-04-01T20:45:54.000Z

2022-03-23T12:45:43.000Z

# Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. from otcextensions.tests.functional.sdk.vlb import TestVlb

41.266667

76

0.736672

93c35e82e3070b5dcaa7b5ce0646c0a3d9c9b51e

5,760

py

Python

hasy.py

MartinThoma/cv-datasets

f0566839bc2e625274bd2d439114c6665ba1b37e

[ "MIT" ]

1

2017-03-11T14:14:12.000Z

hasy.py

MartinThoma/cv-datasets

f0566839bc2e625274bd2d439114c6665ba1b37e

[ "MIT" ]

null

hasy.py

MartinThoma/cv-datasets

f0566839bc2e625274bd2d439114c6665ba1b37e

[ "MIT" ]

null

# -*- coding: utf-8 -*- """Utility file for the HASYv2 dataset. See https://arxiv.org/abs/1701.08380 for details. """ from __future__ import absolute_import from keras.utils.data_utils import get_file from keras import backend as K import numpy as np import scipy.ndimage import os import tarfile import shutil import csv from six.moves import cPickle as pickle n_classes = 369 labels = [] def _load_csv(filepath, delimiter=',', quotechar="'"): """ Load a CSV file. Parameters ---------- filepath : str Path to a CSV file delimiter : str, optional quotechar : str, optional Returns ------- list of dicts : Each line of the CSV file is one element of the list. """ data = [] csv_dir = os.path.dirname(filepath) with open(filepath, 'rb') as csvfile: reader = csv.DictReader(csvfile, delimiter=delimiter, quotechar=quotechar) for row in reader: for el in ['path', 'path1', 'path2']: if el in row: row[el] = os.path.abspath(os.path.join(csv_dir, row[el])) data.append(row) return data def _generate_index(csv_filepath): """ Generate an index 0...k for the k labels. Parameters ---------- csv_filepath : str Path to 'test.csv' or 'train.csv' Returns ------- dict : Maps a symbol_id as in test.csv and train.csv to an integer in 0...k, where k is the total number of unique labels. """ symbol_id2index = {} data = _load_csv(csv_filepath) i = 0 labels = [] for item in data: if item['symbol_id'] not in symbol_id2index: symbol_id2index[item['symbol_id']] = i labels.append(item['latex']) i += 1 return symbol_id2index, labels def load_data(): """ Load HASYv2 dataset. # Returns Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`. """ # Download if not already done fname = 'HASYv2.tar.bz2' origin = 'https://zenodo.org/record/259444/files/HASYv2.tar.bz2' fpath = get_file(fname, origin=origin, untar=False, md5_hash='fddf23f36e24b5236f6b3a0880c778e3') path = os.path.dirname(fpath) # Extract content if not already done untar_fpath = os.path.join(path, "HASYv2") if not os.path.exists(untar_fpath): print('Untaring file...') tfile = tarfile.open(fpath, 'r:bz2') try: tfile.extractall(path=untar_fpath) except (Exception, KeyboardInterrupt) as e: if os.path.exists(untar_fpath): if os.path.isfile(untar_fpath): os.remove(untar_fpath) else: shutil.rmtree(untar_fpath) raise tfile.close() # Create pickle if not already done pickle_fpath = os.path.join(untar_fpath, "fold1.pickle") if not os.path.exists(pickle_fpath): # Load mapping from symbol names to indices symbol_csv_fpath = os.path.join(untar_fpath, "symbols.csv") symbol_id2index, labels = _generate_index(symbol_csv_fpath) globals()["labels"] = labels # Load first fold fold_dir = os.path.join(untar_fpath, "classification-task/fold-1") train_csv_fpath = os.path.join(fold_dir, "train.csv") test_csv_fpath = os.path.join(fold_dir, "test.csv") train_csv = _load_csv(train_csv_fpath) test_csv = _load_csv(test_csv_fpath) WIDTH = 32 HEIGHT = 32 x_train = np.zeros((len(train_csv), 1, WIDTH, HEIGHT), dtype=np.uint8) x_test = np.zeros((len(test_csv), 1, WIDTH, HEIGHT), dtype=np.uint8) y_train, s_train = [], [] y_test, s_test = [], [] # Load training data for i, data_item in enumerate(train_csv): fname = os.path.join(untar_fpath, data_item['path']) s_train.append(fname) x_train[i, 0, :, :] = scipy.ndimage.imread(fname, flatten=False, mode='L') label = symbol_id2index[data_item['symbol_id']] y_train.append(label) y_train = np.array(y_train, dtype=np.int64) # Load test data for i, data_item in enumerate(test_csv): fname = os.path.join(untar_fpath, data_item['path']) s_test.append(fname) x_train[i, 0, :, :] = scipy.ndimage.imread(fname, flatten=False, mode='L') label = symbol_id2index[data_item['symbol_id']] y_test.append(label) y_test = np.array(y_test, dtype=np.int64) data = {'x_train': x_train, 'y_train': y_train, 'x_test': x_test, 'y_test': y_test, 'labels': labels } # Store data as pickle to speed up later calls with open(pickle_fpath, 'wb') as f: pickle.dump(data, f, protocol=pickle.HIGHEST_PROTOCOL) else: with open(pickle_fpath, 'rb') as f: data = pickle.load(f) x_train = data['x_train'] y_train = data['y_train'] x_test = data['x_test'] y_test = data['y_test'] globals()["labels"] = data['labels'] y_train = np.reshape(y_train, (len(y_train), 1)) y_test = np.reshape(y_test, (len(y_test), 1)) if K.image_dim_ordering() == 'tf': x_train = x_train.transpose(0, 2, 3, 1) x_test = x_test.transpose(0, 2, 3, 1) return (x_train, y_train), (x_test, y_test)

31.823204

78

0.562326

93c6042870f0e48cc7e28c2ead79abb162bef666

1,201

py

Python

pyvisdk/do/quiesce_datastore_io_for_ha_failed.py

Infinidat/pyvisdk

f2f4e5f50da16f659ccc1d84b6a00f397fa997f8

[ "MIT" ]

null

pyvisdk/do/quiesce_datastore_io_for_ha_failed.py

Infinidat/pyvisdk

f2f4e5f50da16f659ccc1d84b6a00f397fa997f8

[ "MIT" ]

null

pyvisdk/do/quiesce_datastore_io_for_ha_failed.py

Infinidat/pyvisdk

f2f4e5f50da16f659ccc1d84b6a00f397fa997f8

[ "MIT" ]

null

import logging from pyvisdk.exceptions import InvalidArgumentError ######################################## # Automatically generated, do not edit. ######################################## log = logging.getLogger(__name__) def QuiesceDatastoreIOForHAFailed(vim, *args, **kwargs): '''A QuiesceDatastoreIOForHAFailed fault occurs when the HA agent on a host cannot quiesce file activity on a datastore to be unmouonted or removed.''' obj = vim.client.factory.create('{urn:vim25}QuiesceDatastoreIOForHAFailed') # do some validation checking... if (len(args) + len(kwargs)) < 10: raise IndexError('Expected at least 11 arguments got: %d' % len(args)) required = [ 'ds', 'dsName', 'host', 'hostName', 'name', 'type', 'dynamicProperty', 'dynamicType', 'faultCause', 'faultMessage' ] optional = [ ] for name, arg in zip(required+optional, args): setattr(obj, name, arg) for name, value in kwargs.items(): if name in required + optional: setattr(obj, name, value) else: raise InvalidArgumentError("Invalid argument: %s. Expected one of %s" % (name, ", ".join(required + optional))) return obj

34.314286

124

0.621982

93c65b7f60b1d4ed3df0c1dfda29fa877d20e341

8,071

py

Python

control/tracking.py

oholsen/hagedag

4e2881fa1f636228e5cbe76e61fb4b224f0b1e4a

[ "Apache-2.0" ]

null

control/tracking.py

oholsen/hagedag

4e2881fa1f636228e5cbe76e61fb4b224f0b1e4a

[ "Apache-2.0" ]

null

control/tracking.py

oholsen/hagedag

4e2881fa1f636228e5cbe76e61fb4b224f0b1e4a

[ "Apache-2.0" ]

null

""" Based on Extended kalman filter (EKF) localization sample in PythonRobotics by Atsushi Sakai (@Atsushi_twi) """ import math import matplotlib.pyplot as plt import numpy as np # Simulation parameter INPUT_NOISE = np.diag([0.1, np.deg2rad(30.0)]) ** 2 GPS_NOISE = np.diag([0.1, 0.1]) ** 2 # Covariance for EKF simulation Q = np.diag([ 0.02, # variance of location on x-axis 0.02, # variance of location on y-axis np.deg2rad(10.0), # variance of yaw angle 0.1 # variance of velocity ]) ** 2 # predict state covariance # Observation x,y position covariance, now dynamic from receiver (input stream) # R = np.diag([0.02, 0.02]) ** 2 def jacob_f(x, u, DT: float): """ Jacobian of Motion Model motion model x_{t+1} = x_t+v*dt*cos(yaw) y_{t+1} = y_t+v*dt*sin(yaw) yaw_{t+1} = yaw_t+omega*dt v_{t+1} = v{t} so dx/dyaw = -v*dt*sin(yaw) dx/dv = dt*cos(yaw) dy/dyaw = v*dt*cos(yaw) dy/dv = dt*sin(yaw) """ yaw = x[2, 0] v = u[0, 0] jF = np.array([ [1.0, 0.0, -DT * v * math.sin(yaw), DT * math.cos(yaw)], [0.0, 1.0, DT * v * math.cos(yaw), DT * math.sin(yaw)], [0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 1.0]]) return jF if __name__ == '__main__': import asyncio asyncio.run(main())

25.143302

107

0.525585

93c6c2347f6844b6a0ab7634a4e1c68474fa2859

337

py

Python

tests/test_plugin_setup.py

ldb385/q2-winnowing

f9c1dc7ecedbd3d204b3a26974f29a164de3eaf1

[ "BSD-3-Clause" ]

1

2020-07-24T21:58:38.000Z

tests/test_plugin_setup.py

ldb385/q2-winnowing

f9c1dc7ecedbd3d204b3a26974f29a164de3eaf1

[ "BSD-3-Clause" ]

1

2020-07-21T16:45:03.000Z

tests/test_plugin_setup.py

ldb385/q2-winnowing

f9c1dc7ecedbd3d204b3a26974f29a164de3eaf1

[ "BSD-3-Clause" ]

null

from unittest import TestCase, main as unittest_main from q2_winnowing.plugin_setup import plugin as winnowing_plugin if __name__ == '__main__': unittest_main()

22.466667

64

0.756677

93c70f97a9fcc20d868e2f05ea3a698a7c994530

974

py

Python

Lab11/BacktrackingIterative.py

alexnaiman/Fundamentals-Of-Programming---Lab-assignments

ef066e6036e20b9c686799f507f10e15e50e3285

[ "MIT" ]

4

2018-02-19T13:57:38.000Z

2022-01-08T04:10:54.000Z

Lab11/BacktrackingIterative.py

alexnaiman/Fundamentals-Of-Programming---Lab-assignments

ef066e6036e20b9c686799f507f10e15e50e3285

[ "MIT" ]

null

Lab11/BacktrackingIterative.py

alexnaiman/Fundamentals-Of-Programming---Lab-assignments

ef066e6036e20b9c686799f507f10e15e50e3285

[ "MIT" ]

null

l = [0, "-", "+"] n = int(input("Give number")) list2 = [] for i in range(n): list2.append(int(input(str(i) + ":"))) backIter() print("test")

21.173913

73

0.464066

93c86f77e89802184faaf894ae457e773562fb59

31,674

py

Python

dreadlord_counter_strike.py

lorenypsum/dreadlord_counter_strike

5f63c97ab28d84f8d7d9ff2f481c5111f0bc2ef1

[ "MIT" ]

null

dreadlord_counter_strike.py

lorenypsum/dreadlord_counter_strike

5f63c97ab28d84f8d7d9ff2f481c5111f0bc2ef1

[ "MIT" ]

null

dreadlord_counter_strike.py

lorenypsum/dreadlord_counter_strike

5f63c97ab28d84f8d7d9ff2f481c5111f0bc2ef1

[ "MIT" ]

null

from datetime import datetime from enum import Enum, auto from random import randint from time import sleep from typing import Optional, Tuple def ask_if_yes(input_text: str) -> bool: """ This function asks the player a question, and returns True if they typed yes, or False if they typed anything else. """ return input(input_text).lower() in ["y", "yes", "s", "sim"] def ask_if_wanna_continue(player_name: str) -> bool: """ This function asks the player if they want to continue the game, and returns the answer. """ print("You reached one possible end!!!") if ask_if_yes("Wanna change your fate? "): sleep(2) print("Very well then...") sleep(2) return True else: if ask_if_yes(f"{player_name} did you find the treasure I prepared for you? "): print("I hope you are not lying, you may leave now!!!") sleep(1) else: print("What a shame! you broke my heart :'(") sleep(1) return False def roll_for_item(player_name: str) -> Tuple[Optional[GameItem], GameStatus]: """ This function rolls the dice for the player. It returns the item that the player gained (if any), and the status of the player after the roll. """ roll = randint(1, 20) if player_name.lower() == "lurin": print(f"You rolled {roll}!") sleep(2) if ask_if_yes("Since you are inspired... wanna roll again? "): sleep(2) roll = randint(1, 20) print(f"Now your roll was {roll}") if roll == 1: print(f"HAHAHAHAHA, tragic! You got {roll}") sleep(2) if player_name.lower() != "lurin": print( f"Unfortunalety {player_name}, you are not Lurin, so you do not have another chance!!!" ) sleep(4) else: print( f"Unfortunalety fake {player_name}, even inspired you got it? You are a joke!!!" ) sleep(4) return None, GameStatus.DEAD if player_name.lower() == "snow": print(f"... you may have this *WONDERFUL DEATH* to help you kill STRAHD...") sleep(3) print("...the perfect item for you, huh?") sleep(2) print("...no, it is not a typo or some faulty logic!") sleep(2) print( "It is indeed the perfect item for you... you will play dead (you are used to it)... STRAHD flew away..." ) sleep(4) return GameItem.DEATH, GameStatus.ALIVE else: print( f"Well {player_name}, you may have this *DEATH* to help you kill STRAHD..." ) sleep(3) print("...since you are not SNOW....") sleep(2) print("...no, it is not a typo or some faulty logic!") sleep(2) print("...you are DEAD!") sleep(2) print("***Bad end!***") sleep(1) return None, GameStatus.DEAD elif roll <= 5: print(f"You got {roll}") if player_name.lower() != "kaede": print( f"Well {player_name}, you may have this *VIOLIN* to help you kill STRAHD..." ) sleep(3) print("...since you are not KAEDE.... gooood luck!") sleep(2) return GameItem.VIOLIN, GameStatus.ALIVE else: print(f"Well {player_name}, you may have this ***WONDERFUL VIOLIN***") sleep(3) print("the perfect item for you, huh?") sleep(2) return GameItem.VIOLIN, GameStatus.ALIVE elif roll <= 10: print(f"You got {roll}") if player_name.lower() != "soren": print( f"Well {player_name}, you may have this *SIMPLE BOW* to help you kill STRAHD..." ) sleep(3) print("...since you are not Soren... gooood luck!") sleep(2) return GameItem.SIMPLE_BOW, GameStatus.ALIVE else: print(f"Well {player_name}, you may have this ***WONDERFUl SIMPLE BOW***") sleep(3) print("the perfect item for you, huh?") sleep(2) print("just.. do not kill any cats with this, moron!!!") sleep(2) return GameItem.SIMPLE_BOW, GameStatus.ALIVE elif roll <= 15: print(f"You got {roll}") if player_name.lower() != "vis": print( f"Well {player_name}, you may have this *ORDINARY SWORD* to help you kill STRAHD..." ) sleep(3) print("...since you are not Vis... gooood luck!") sleep(2) print("and pray it won't fly...") sleep(2) return GameItem.ORDINARY_SWORD, GameStatus.ALIVE else: print( f"Well {player_name}, you may have this ***FANTASTIC ORDINARY SWORD*** to help you kill STRAHD" ) sleep(3) print("the perfect item for you, huh?") sleep(2) print("if it doesn't fly...") sleep(2) return GameItem.ORDINARY_SWORD, GameStatus.ALIVE elif roll < 20: print(f"You got {roll}") sleep(2) print( f"Well {player_name}, you may have ****STRAHD SLAYER SWORD***, go kill STRAHD, " ) sleep(3) print("...the legendary item!!!") sleep(2) print("...but hope it won't fly!!!") sleep(2) return GameItem.STRAHD_SLAYER_SWORD, GameStatus.ALIVE elif roll == 20: if player_name.lower() != "snow": print( f"Well {player_name}, you may have **** STRAHD SLAYER BOW***, go kill STRAHD, special treasures awaits you!!!" ) sleep(3) print("...the legendary perfect item!!!") sleep(2) print("...it doesn't even matter if it will fly!!!") sleep(2) return GameItem.STRAHD_SLAYER_BOW, GameStatus.ALIVE else: print( f"Well {player_name}, you seduced STRAHD, now you can claim your treasures" ) sleep(2) print(f"STRAHD licks you!!!") sleep(4) return GameItem.STRAHD_SLAYER_BOW, GameStatus.ALIVE return None, GameStatus.ALIVE def flee(player_name: str) -> GameStatus: """ This function asks the player if they want to flee. It returns the status of the player after their decision to flee. """ if ask_if_yes("Wanna flee now? "): sleep(2) print("...") sleep(1) print("We will see if flee you can... *** MUST ROLL THE DICE ***: ") sleep(2) print("Careful!!!") sleep(1) roll_the_dice = input( "*** Roll stealth *** (if you type it wrong it means you were not stealth) type: 'roll stealth' " ) sleep(4) if roll_the_dice == "roll stealth": roll = randint(1, 20) if roll <= 10: print(f"you rolled {roll}!") sleep(2) print("It means STRAHD noticed you!") sleep(2) print("...") sleep(2) print(" You are dead!!! ") sleep(2) print(" ***Bad end...*** ") sleep(1) return GameStatus.DEAD else: print(f"you rolled {roll}!!!") sleep(2) print("Congratulations, you managed to be stealth!!!") sleep(2) print("...") sleep(2) print("You may flee but you will continue being poor and weak...") sleep(2) print("...") sleep(2) print( "And remember there are real treasures waiting for you over there..." ) sleep(4) print("***Bad end...***") sleep(1) return GameStatus.ARREGAO else: if player_name.lower() in ["soren", "kaede", "leandro", "snow", "lurin"]: print("...") sleep(1) print("......") sleep(2) print("...........") sleep(2) print("I told you to be careful!") sleep(2) print(f"...{player_name} you are such a DOJI!!!") sleep(2) print("It means the STRAHD noticed you!") sleep(2) print("...") sleep(2) print(" You are dead!!! ") sleep(2) print(" ***Bad end...*** ") sleep(1) else: print("I told you to be careful!") sleep(2) print("...........") sleep(2) print(f"...{player_name} you are such a klutz!!!") sleep(2) print("It means STRAHD noticed you!") sleep(2) print("...") sleep(2) print(" You are dead!!! ") sleep(2) print(" ***Bad end...*** ") sleep(1) return GameStatus.DEAD else: return GameStatus.ALIVE def attack(player_name: str) -> Tuple[Optional[GameItem], GameStatus]: """ This function asks the player if they want to attack STRAHD. If the player answers yes, the player rolls for an item. This function returns the item obtained by a roll (if any), and the status of the player. """ print("You shall not pass!!!") if ask_if_yes(f"{player_name}, will you attack STRAHD? "): sleep(1) print("I honor your courage!") sleep(2) print("therefore...") sleep(1) print("I will help you...") sleep(1) print("I am giving you a chance to kill STRAHD and reclaim your treasures...") sleep(2) print( "Roll the dice and have a chance to win the perfect item for you... or even some STRAHD Slayer Shit!!!" ) sleep(3) print("It will increase your chances...") sleep(2) print( "....or kill you right away if you are as unlucky as Soren using his Sharp Shooting!!!" ) sleep(2) if ask_if_yes("Wanna roll the dice? "): return roll_for_item(player_name) else: if ask_if_yes("Are you sure? "): sleep(2) print("So you have chosen... Death!") sleep(2) return GameItem.DEATH, GameStatus.DEAD else: sleep(2) print("Glad you changed your mind...") sleep(2) print("Good... very good indeed...") sleep(2) return roll_for_item(player_name) else: print("If you won't attack STRAHD... then...") sleep(2) return None, flee(player_name) def decide_if_strahd_flies(player_name: str) -> bool: """ This function asks if the player wants to roll for stealth, which can give a chance for STRAHD not to fly. It returns whether STRAHD flies. """ print( "This is your chance... STRAHD has his attention captived by his 'vampirish's business'..." ) sleep(3) print("You are approaching him...") sleep(2) print("Careful...") sleep(2) print("Because vampires... can fly...") sleep(2) print("Roll stealth (if you type it wrong it means you were not stealth)...") roll_the_dice = input("type: 'roll stealth' ") sleep(2) if roll_the_dice == "roll stealth": roll = randint(1, 20) if roll <= 10: print("...") sleep(1) print("Unlucky") sleep(2) print(f"You rolled {roll}") sleep(2) print("STRAHD...") sleep(2) print("...flew up") sleep(2) print("Now, you have a huge disavantage") sleep(2) return True else: print(f"You rolled {roll}") sleep(2) print("Congratulations, you managed to be in stealth!") sleep(2) return False else: if player_name.lower() in ["soren", "kaede", "leandro", "snow"]: print("...") sleep(1) print("......") sleep(2) print("...........") sleep(2) print("I told you to be careful!") sleep(2) print(f"...{player_name} you are such a DOJI, STRAHD flew up...") sleep(2) print("Now, you have a huge disavantage") sleep(2) else: print("...") sleep(1) print("......") sleep(2) print("...........") sleep(2) print("I told you to be careful!") sleep(2) print(f"...{player_name} you are such a KLUTZ, STRAHD flew...") sleep(2) print("...STRAHD flew up...") sleep(2) print("Now, you have a huge disavantage") sleep(2) return True def calculate_win_probability( player_race: str, player_name: str, item: Optional[GameItem],strahd_flying: bool ) -> int: """ This function returns the probability that the player defeats STRAHD. The probability depends on the item the player is holding, and whether STRAHD is flying. """ if item == GameItem.DEATH: if player_name.lower() == "snow" and player_race.lower() == "kalashatar": return 90 else: return 0 elif item == GameItem.WOODEN_SWORD: if strahd_flying: return 5 else: return 10 elif item == GameItem.SIMPLE_BOW: if player_name.lower() == "soren" and player_race.lower() in [ "human", "humano", "elf", "elfo", ]: return 70 else: return 30 elif item == GameItem.VIOLIN: if player_name.lower() == "kaede" and player_race.lower() == "tiefling": return 70 else: return 30 elif item == GameItem.ORDINARY_SWORD: if strahd_flying: return 10 elif player_name.lower() == "vis" and player_race.lower() == "draconato": return 80 else: return 40 elif item == GameItem.STRAHD_SLAYER_SWORD: if strahd_flying: return 20 else: return 100 elif item == GameItem.STRAHD_SLAYER_BOW: return 100 else: return -1 def roll_for_win(probability: int) -> bool: """ This function returns whether the player defeats STRAHD, given a probability. """ return randint(1, 100) <= probability def after_battle(player_race: str, player_name: str, did_win: bool) -> GameStatus: """ This function conducts the scenario after the player has defeated, or not, STRAHD. It returns the status depending on whether the player won. """ if did_win: now = datetime.now() print("A day may come when the courage of men fails") sleep(2) print("but it is not THIS day, SATAN...") sleep(2) print("Because... you approached STRAHD...") sleep(2) print("Almost invisible to his senses...") sleep(2) print( "Somehow your weapon hit the weak point of STRAHD's... revealing his true identity" ) sleep(4) print( "He was just a bat... who looked like a DREADLORD..." ) sleep(4) print("It was a huge battle...") sleep(2) print( f"And it was the most awkward {now.strftime('%A')} you will ever remember." ) sleep(2) if ( player_race.lower() in ["master", "mestre"] and player_name.lower() == "zordnael" ): print("...") sleep(1) print( "***************************************************************************************************************************************" ) sleep(1) print( f"Congratulations {player_name}!!! You are the WINNER of this week's challenge, you shall receive 5000 dullas in Anastasia Dungeons Hills Cosmetics!" ) sleep(4) print("link") sleep(5) print("***CHEATER GOOD END***") sleep(2) return GameStatus.WINNER elif player_race.lower() == "racist" and player_name.lower() == "lili": print("...") sleep(1) print( "***************************************************************************************************************************************" ) sleep(1) print( f"Congratulations {player_name}!!! You are the WINNER of this week's challenge, you shall receive the prizes specially prepared for everybody in dullas from Anastasia Dungeons Hills Cosmetics!" ) sleep(4) print("https://drive.google.com/drive/folders/1Jn8YYdixNNRqCQgIClBmGLiFFxuSCQdc?usp=sharing") sleep(5) print("***BEST END***") sleep(2) return GameStatus.WINNER if did_win: print("...") sleep(1) print( "***************************************************************************************************************************************" ) sleep(1) if player_name.lower() == "soren": print( f"Congratulations {player_name}!!! you are the WINNER of this week's challenge, you received a cash prize of five thousand dullas from Anastasia Dungeons Hills Cosmetics!" ) sleep(4) print(f"And a prize... prepared specially for you {player_name}") sleep(2) print("... I know you doubted me... but here it is:") sleep(2) print("...") sleep(1) print("https://drive.google.com/drive/folders/1FerRt3mmaOm0ohSUXTkO-CmGIAluavXi?usp=sharing") sleep(5) print("...Your motherfuger cat killer !!!") sleep(2) print("***SOREN'S GOOD END***") sleep(2) elif player_name.lower() == "snow": print( f"Congratulations {player_name}!!! you are the WINNER of this week's challenge, you received a cash prize of five thousand dullas from Anastasia Dungeons Hills Cosmetics!" ) sleep(4) print(f"And a prize... prepared specially for you {player_name}") sleep(2) print("... I know you doubted me... but here it is:") sleep(2) print("...") sleep(1) print("https://drive.google.com/drive/folders/16STFQ-_0N_54oNNsVQnMjwjcBgubxgk7?usp=sharing") sleep(5) print("...Your motherfuger snow flake !!!") sleep(2) print("***SNOW'S GOOD END***") sleep(2) elif player_name.lower() == "kaede": print( f"Congratulations {player_name}!!! you are the WINNER of this week's challenge, you received a cash prize of five thousand dullas from Anastasia Dungeons Hills Cosmetics!" ) sleep(4) print(f"And a prize... prepared specially for you {player_name}") sleep(2) print("... I know you doubted me... but here it is:") sleep(2) print("...") sleep(1) print("https://drive.google.com/drive/folders/1XN9sItRxYR4Si4gWFeJtI0HGF39zC29a?usp=sharing") sleep(5) print("...Your motherfuger idol !!!") sleep(2) print("***KAEDE'S GOOD END***") sleep(2) elif player_name.lower() == "leandro": print( f"Congratulations {player_name}!!! you are the WINNER of this week's challenge, you received a cash prize of five thousand dullas from Anastasia Dungeons Hills Cosmetics!" ) sleep(4) print(f"And a prize... prepared specially for you {player_name}") sleep(2) print("... I know you doubted me... but here it is:") sleep(2) print("...") sleep(1) print("https://drive.google.com/drive/folders/1eP552hYwUXImmJ-DIX5o-wlp5VA96Sa0?usp=sharing") sleep(5) print("...Your motherfuger only roll 20 !!!") sleep(2) print("***LEANDRO'S GOOD END***") sleep(2) elif player_name.lower() == "vis": print( f"Congratulations {player_name}!!! you are the WINNER of this week's challenge, you received a cash prize of five thousand dullas from Anastasia Dungeons Hills Cosmetics!" ) sleep(4) print(f"And a prize... prepared specially for you {player_name}") sleep(2) print("... I know you doubted me... but here it is:") sleep(2) print("...") sleep(1) print("https://drive.google.com/drive/folders/19GRJJdlB8NbNl3QDXQM1-0ctXSX3mbwS?usp=sharing") sleep(5) print("...Your motherfuger iron wall !!!") sleep(2) print("***VIS'S GOOD END***") sleep(2) elif player_name.lower() == "lurin": print("CONGRATULATIONS!!!!! ") sleep(2) print("Bitch! ... ") sleep(2) print(" ... you stole my name...") sleep(2) print("You are arrested for identity theft!!!") sleep(2) print("...") sleep(1) print("del C://LeagueOfLegends") sleep(2) print("...") sleep(0.5) print(".....") sleep(0.5) print("......") sleep(0.5) print(".............") sleep(2) print("deletion completed") sleep(2) print("***PHONY'S GOOD END***") sleep(2) else: print( f"Congratulations {player_name}!!! you are the WINNER of this week's challenge, you shall receive this link from Anastasia Dungeons Hills Cosmetics!" ) sleep(4) print("https://drive.google.com/drive/folders/0B_sxkSE6-TfETlZoOHF1bTRGTXM?usp=sharing") sleep(5) print("***GOOD END***") sleep(2) sleep(1) return GameStatus.WINNER if not did_win: print("You tried to approach the devil carefully...") sleep(2) print("... but your hands were trembling...") sleep(2) print("...your weapon was not what you expected...") sleep(2) print("... It was a shit battle... but") sleep(2) print("The journey doesn't end here...") sleep(2) print("Death is just another way we have to choose...") sleep(2) print("...") sleep(1) if player_name.lower() == "vis": print("I really believed in you...") sleep(2) print("...but I guess...") sleep(1) print("you shoud have stayed in your bathroom...") sleep(2) print("eating lemon pies...") sleep(2) print("...") sleep(1) print(f"YOU DIED {player_name}") sleep(2) print("***VIS'S BAD END***") sleep(2) elif player_name.lower() == "soren": print("I really believed in you..") sleep(2) print("...but I guess...") sleep(1) print("Did you think it was a cat? ") sleep(2) print("Not today Satan!!!") sleep(2) print("...") sleep(1) print(f"You died! {player_name}") sleep(2) print("***SOREN'S BAD END***") sleep(2) elif player_name.lower() == "kaede": print("I really believed in you..") sleep(2) print("...but I guess...") sleep(1) print("") sleep(2) print("") sleep(2) print("") sleep(2) print("go play you Violin in Hell...") sleep(2) print("...") sleep(1) print(f"You died! {player_name}") sleep(2) print("***KAEDES'S BAD END***") sleep(2) elif player_name.lower() == "snow": print("I really believed in you..") sleep(2) print("...but I guess...") sleep(1) print("HAHAHAAHHAHAHA") sleep(2) print("It is cute you even tried!") sleep(2) print("but I will call you Nori!") sleep(2) print("...") sleep(1) print("You died! Nori!!!") sleep(2) print("***SNOW'S BAD END***") sleep(2) elif player_name.lower() == "lurin": print("I really believed in you..") sleep(2) print("...but I guess...") sleep(2) print("Bitch! ... ") sleep(2) print(" ... you stole my name...") sleep(2) print("You are arrested for identity theft!!!") sleep(2) print("...") sleep(1) print("del C://LeagueOfLegends") sleep(2) print("...") sleep(0.5) print(".....") sleep(0.5) print("......") sleep(0.5) print(".............") sleep(2) print("deletion completed") sleep(2) print("***PHONY'S GOOD END***") sleep(2) elif player_name.lower() == "leandro": print("nice try") sleep(2) print("...but I guess...") sleep(2) print("Try harder next time...") sleep(2) print("...Nicolas Cage Face...") sleep(2) print("***LEANDRO'S BAD END***") sleep(2) elif player_name.lower() == "buiu": print("nice try") sleep(2) print("...but I guess...") sleep(2) print("Try harder next time...") sleep(2) print(f"Did you really think this would work? Clown!") sleep(2) print("***RIDICULOUS BUIU'S END***") sleep(2) return GameStatus.HAHA elif player_name.lower() in ["strahd", "dreadlord"]: print("good try") sleep(2) print("...but I guess...") sleep(2) print("I never said you were in a cave...") sleep(2) print("There is sunlight now...") sleep(2) print("You are burning...") sleep(2) print("Till Death...") sleep(2) print("***RIDICULOUS STRAHD'S END***") sleep(2) else: print("I really believed in you..") sleep(2) print("...but I guess...") sleep(2) print("This is a shit meta game...") sleep(2) print( "Designed for players from a certain 16:20 tabletop Ravenloft campaign" ) sleep(2) print(f"Sorry, {player_name}...") sleep(2) print("You are dead!!!") sleep(2) print("***BAD END***") sleep(2) sleep(1) return GameStatus.DEAD def main(): """ This function conducts the entire game. """ wanna_continue = True while wanna_continue: player_race = input("Your race? ") player_name = input("Your name? ") status = flee(player_name) if status == GameStatus.ALIVE: item, status = attack(player_name) if status == GameStatus.ALIVE: strahd_flight = decide_if_strahd_flies(player_name) probability = calculate_win_probability( player_race, player_name, item, strahd_flight ) did_win = roll_for_win(probability) status = after_battle(player_race, player_name, did_win) if status == GameStatus.WINNER: sleep(5) print( "You are a winner, baby. But there are other possibilities over there..." ) wanna_continue = ask_if_wanna_continue(player_name) elif status == GameStatus.HAHA: wanna_continue = False else: wanna_continue = ask_if_wanna_continue(player_name) else: wanna_continue = ask_if_wanna_continue(player_name) elif status == GameStatus.DEAD: wanna_continue = ask_if_wanna_continue(player_name) else: print("...") wanna_continue = ask_if_wanna_continue(player_name) input() main()

36.281787

210

0.46224

93c8ba0b9839234f94247033001b32b0fa66bf75

193

py

Python

redacoes/models/vesibulares.py

VictorGM01/controle_de_questoes

658e81b2e2fe78fb1e6bb7ff3f537c8a28e7c9e8

[ "MIT" ]

1

2022-03-23T12:32:20.000Z

redacoes/models/vesibulares.py

VictorGM01/controle_de_questoes

658e81b2e2fe78fb1e6bb7ff3f537c8a28e7c9e8

[ "MIT" ]

null

redacoes/models/vesibulares.py

VictorGM01/controle_de_questoes

658e81b2e2fe78fb1e6bb7ff3f537c8a28e7c9e8

[ "MIT" ]

null

from django.db import models

17.545455

37

0.621762

93c9a643270a43403d7d70db7f672d353ef62da2

635

py

Python

backend/helper/mds.py

marinaevers/regional-correlations

8ca91a5283a92e75f3d99f870c295ca580edb949

[ "MIT" ]

null

backend/helper/mds.py

marinaevers/regional-correlations

8ca91a5283a92e75f3d99f870c295ca580edb949

[ "MIT" ]

null

backend/helper/mds.py

marinaevers/regional-correlations

8ca91a5283a92e75f3d99f870c295ca580edb949

[ "MIT" ]

null

import numpy as np def mds(d, dimensions=3): """ Multidimensional Scaling - Given a matrix of interpoint distances, find a set of low dimensional points that have similar interpoint distances. """ (n, n) = d.shape E = (-0.5 * d ** 2) # Use mat to get column and row means to act as column and row means. Er = np.mat(np.mean(E, 1)) Es = np.mat(np.mean(E, 0)) # From Principles of Multivariate Analysis: A User's Perspective (page 107). F = np.array(E - np.transpose(Er) - Es + np.mean(E)) [U, S, V] = np.linalg.svd(F) Y = U * np.sqrt(S) return (Y[:, 0:dimensions], S)

24.423077

80

0.601575

93c9ac724fdd806412549f0dec59d52778127c89

492

py

Python

sm3.py

matthewmuccio/InterviewPrepKit

13dabeddc3c83866c88bef1c80498c313e4c233e

[ "MIT" ]

2

2018-09-19T00:59:09.000Z

2022-01-09T18:38:01.000Z

sm3.py

matthewmuccio/InterviewPrepKit

13dabeddc3c83866c88bef1c80498c313e4c233e

[ "MIT" ]

null

sm3.py

matthewmuccio/InterviewPrepKit

13dabeddc3c83866c88bef1c80498c313e4c233e

[ "MIT" ]

null

#!/usr/bin/env python3 from collections import Counter # Complete the isValid function below. if __name__ == "__main__": s = input() result = isValid(s) print(result)

25.894737

217

0.630081

93caeae8160e612312e97b73a71f33bfdd865b27

10,238

py

Python

etiquette/constants.py

voussoir/etiquette

e982858c28335b11528c52af181abd1bbc71673f

[ "BSD-3-Clause" ]

20

2018-03-20T01:40:13.000Z

2022-02-11T20:23:41.000Z

etiquette/constants.py

voussoir/etiquette

e982858c28335b11528c52af181abd1bbc71673f

[ "BSD-3-Clause" ]

null

etiquette/constants.py

voussoir/etiquette

e982858c28335b11528c52af181abd1bbc71673f

[ "BSD-3-Clause" ]

1

2018-03-20T13:10:31.000Z

''' This file provides data and objects that do not change throughout the runtime. ''' import converter import string import traceback import warnings from voussoirkit import sqlhelpers from voussoirkit import winwhich # FFmpeg ########################################################################################### FFMPEG_NOT_FOUND = ''' ffmpeg or ffprobe not found. Add them to your PATH or use symlinks such that they appear in: Linux: which ffmpeg ; which ffprobe Windows: where ffmpeg & where ffprobe ''' ffmpeg = _load_ffmpeg() # Database ######################################################################################### DATABASE_VERSION = 20 DB_VERSION_PRAGMA = f''' PRAGMA user_version = {DATABASE_VERSION}; ''' DB_PRAGMAS = f''' PRAGMA cache_size = 10000; PRAGMA count_changes = OFF; PRAGMA foreign_keys = ON; ''' DB_INIT = f''' BEGIN; {DB_PRAGMAS} {DB_VERSION_PRAGMA} ---------------------------------------------------------------------------------------------------- CREATE TABLE IF NOT EXISTS albums( id TEXT PRIMARY KEY NOT NULL, title TEXT, description TEXT, created INT, thumbnail_photo TEXT, author_id TEXT, FOREIGN KEY(author_id) REFERENCES users(id), FOREIGN KEY(thumbnail_photo) REFERENCES photos(id) ); CREATE INDEX IF NOT EXISTS index_albums_id on albums(id); CREATE INDEX IF NOT EXISTS index_albums_author_id on albums(author_id); ---------------------------------------------------------------------------------------------------- CREATE TABLE IF NOT EXISTS bookmarks( id TEXT PRIMARY KEY NOT NULL, title TEXT, url TEXT, created INT, author_id TEXT, FOREIGN KEY(author_id) REFERENCES users(id) ); CREATE INDEX IF NOT EXISTS index_bookmarks_id on bookmarks(id); CREATE INDEX IF NOT EXISTS index_bookmarks_author_id on bookmarks(author_id); ---------------------------------------------------------------------------------------------------- CREATE TABLE IF NOT EXISTS photos( id TEXT PRIMARY KEY NOT NULL, filepath TEXT COLLATE NOCASE, basename TEXT COLLATE NOCASE, override_filename TEXT COLLATE NOCASE, extension TEXT COLLATE NOCASE, mtime INT, sha256 TEXT, width INT, height INT, ratio REAL, area INT, duration INT, bytes INT, created INT, thumbnail TEXT, tagged_at INT, author_id TEXT, searchhidden INT, FOREIGN KEY(author_id) REFERENCES users(id) ); CREATE INDEX IF NOT EXISTS index_photos_id on photos(id); CREATE INDEX IF NOT EXISTS index_photos_filepath on photos(filepath COLLATE NOCASE); CREATE INDEX IF NOT EXISTS index_photos_override_filename on photos(override_filename COLLATE NOCASE); CREATE INDEX IF NOT EXISTS index_photos_created on photos(created); CREATE INDEX IF NOT EXISTS index_photos_extension on photos(extension); CREATE INDEX IF NOT EXISTS index_photos_author_id on photos(author_id); CREATE INDEX IF NOT EXISTS index_photos_searchhidden on photos(searchhidden); ---------------------------------------------------------------------------------------------------- CREATE TABLE IF NOT EXISTS tags( id TEXT PRIMARY KEY NOT NULL, name TEXT NOT NULL, description TEXT, created INT, author_id TEXT, FOREIGN KEY(author_id) REFERENCES users(id) ); CREATE INDEX IF NOT EXISTS index_tags_id on tags(id); CREATE INDEX IF NOT EXISTS index_tags_name on tags(name); CREATE INDEX IF NOT EXISTS index_tags_author_id on tags(author_id); ---------------------------------------------------------------------------------------------------- CREATE TABLE IF NOT EXISTS users( id TEXT PRIMARY KEY NOT NULL, username TEXT NOT NULL COLLATE NOCASE, password BLOB NOT NULL, display_name TEXT, created INT ); CREATE INDEX IF NOT EXISTS index_users_id on users(id); CREATE INDEX IF NOT EXISTS index_users_username on users(username COLLATE NOCASE); ---------------------------------------------------------------------------------------------------- ---------------------------------------------------------------------------------------------------- CREATE TABLE IF NOT EXISTS album_associated_directories( albumid TEXT NOT NULL, directory TEXT NOT NULL COLLATE NOCASE, FOREIGN KEY(albumid) REFERENCES albums(id) ); CREATE INDEX IF NOT EXISTS index_album_associated_directories_albumid on album_associated_directories(albumid); CREATE INDEX IF NOT EXISTS index_album_associated_directories_directory on album_associated_directories(directory); ---------------------------------------------------------------------------------------------------- CREATE TABLE IF NOT EXISTS album_group_rel( parentid TEXT NOT NULL, memberid TEXT NOT NULL, FOREIGN KEY(parentid) REFERENCES albums(id), FOREIGN KEY(memberid) REFERENCES albums(id) ); CREATE INDEX IF NOT EXISTS index_album_group_rel_parentid on album_group_rel(parentid); CREATE INDEX IF NOT EXISTS index_album_group_rel_memberid on album_group_rel(memberid); ---------------------------------------------------------------------------------------------------- CREATE TABLE IF NOT EXISTS album_photo_rel( albumid TEXT NOT NULL, photoid TEXT NOT NULL, FOREIGN KEY(albumid) REFERENCES albums(id), FOREIGN KEY(photoid) REFERENCES photos(id) ); CREATE INDEX IF NOT EXISTS index_album_photo_rel_albumid on album_photo_rel(albumid); CREATE INDEX IF NOT EXISTS index_album_photo_rel_photoid on album_photo_rel(photoid); ---------------------------------------------------------------------------------------------------- CREATE TABLE IF NOT EXISTS id_numbers( tab TEXT NOT NULL, last_id TEXT NOT NULL ); ---------------------------------------------------------------------------------------------------- CREATE TABLE IF NOT EXISTS photo_tag_rel( photoid TEXT NOT NULL, tagid TEXT NOT NULL, FOREIGN KEY(photoid) REFERENCES photos(id), FOREIGN KEY(tagid) REFERENCES tags(id) ); CREATE INDEX IF NOT EXISTS index_photo_tag_rel_photoid on photo_tag_rel(photoid); CREATE INDEX IF NOT EXISTS index_photo_tag_rel_tagid on photo_tag_rel(tagid); CREATE INDEX IF NOT EXISTS index_photo_tag_rel_photoid_tagid on photo_tag_rel(photoid, tagid); ---------------------------------------------------------------------------------------------------- CREATE TABLE IF NOT EXISTS tag_group_rel( parentid TEXT NOT NULL, memberid TEXT NOT NULL, FOREIGN KEY(parentid) REFERENCES tags(id), FOREIGN KEY(memberid) REFERENCES tags(id) ); CREATE INDEX IF NOT EXISTS index_tag_group_rel_parentid on tag_group_rel(parentid); CREATE INDEX IF NOT EXISTS index_tag_group_rel_memberid on tag_group_rel(memberid); ---------------------------------------------------------------------------------------------------- CREATE TABLE IF NOT EXISTS tag_synonyms( name TEXT NOT NULL, mastername TEXT NOT NULL ); CREATE INDEX IF NOT EXISTS index_tag_synonyms_name on tag_synonyms(name); ---------------------------------------------------------------------------------------------------- COMMIT; ''' SQL_COLUMNS = sqlhelpers.extract_table_column_map(DB_INIT) SQL_INDEX = sqlhelpers.reverse_table_column_map(SQL_COLUMNS) ALLOWED_ORDERBY_COLUMNS = { 'area', 'basename', 'bitrate', 'bytes', 'created', 'duration', 'extension', 'height', 'random', 'ratio', 'tagged_at', 'width', } # Janitorial stuff ################################################################################# FILENAME_BADCHARS = '\\/:*?<>|"' USER_ID_CHARACTERS = string.digits + string.ascii_uppercase ADDITIONAL_MIMETYPES = { '7z': 'archive', 'gz': 'archive', 'rar': 'archive', 'aac': 'audio/aac', 'ac3': 'audio/ac3', 'dts': 'audio/dts', 'm4a': 'audio/mp4', 'opus': 'audio/ogg', 'mkv': 'video/x-matroska', 'ass': 'text/plain', 'md': 'text/plain', 'nfo': 'text/plain', 'rst': 'text/plain', 'srt': 'text/plain', } # Photodb ########################################################################################## DEFAULT_DATADIR = '_etiquette' DEFAULT_DBNAME = 'phototagger.db' DEFAULT_CONFIGNAME = 'config.json' DEFAULT_THUMBDIR = 'thumbnails' DEFAULT_CONFIGURATION = { 'cache_size': { 'album': 1000, 'bookmark': 100, 'photo': 100000, 'tag': 10000, 'user': 200, }, 'enable_feature': { 'album': { 'edit': True, 'new': True, }, 'bookmark': { 'edit': True, 'new': True, }, 'photo': { 'add_remove_tag': True, 'new': True, 'edit': True, 'generate_thumbnail': True, 'reload_metadata': True, }, 'tag': { 'edit': True, 'new': True, }, 'user': { 'edit': True, 'login': True, 'new': True, }, }, 'tag': { 'min_length': 1, 'max_length': 32, # 'valid_chars': string.ascii_lowercase + string.digits + '_()', }, 'user': { 'min_username_length': 2, 'min_password_length': 6, 'max_display_name_length': 24, 'max_username_length': 24, 'valid_chars': string.ascii_letters + string.digits + '_-', }, 'digest_exclude_files': [ 'phototagger.db', 'desktop.ini', 'thumbs.db', ], 'digest_exclude_dirs': [ '_etiquette', '_site_thumbnails', 'site_thumbnails', 'thumbnails', ], 'file_read_chunk': 2 ** 20, 'id_length': 12, 'thumbnail_width': 400, 'thumbnail_height': 400, 'recycle_instead_of_delete': True, 'motd_strings': [ 'Good morning, Paul. What will your first sequence of the day be?', ], }

31.696594

100

0.567298

93cb4419d9691b2ed3418c709e86de6b48657ce2

122

py

Python

Day_2_Software_engineering_best_practices/solutions/06_07_08_full_package/spectra_analysis/__init__.py

Morisset/python-workshop

ec8b0c4f08a24833e53a22f6b52566a08715c9d0

[ "BSD-3-Clause" ]

null

Day_2_Software_engineering_best_practices/solutions/06_07_08_full_package/spectra_analysis/__init__.py

Morisset/python-workshop

ec8b0c4f08a24833e53a22f6b52566a08715c9d0

[ "BSD-3-Clause" ]

null

Day_2_Software_engineering_best_practices/solutions/06_07_08_full_package/spectra_analysis/__init__.py

Morisset/python-workshop

ec8b0c4f08a24833e53a22f6b52566a08715c9d0

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- """ Spectra analysis utilities """ from ._version import __version__ __all__ = ['__version__']

12.2

33

0.663934

93cc27f8724fb44128386ebba57195949fa0feb9

88,309

py

Python

tacker/tests/unit/vnfm/infra_drivers/kubernetes/test_kubernetes_driver.py

takahashi-tsc/tacker

a0ae01a13dcc51bb374060adcbb4fd484ab37156

[ "Apache-2.0" ]

null

tacker/tests/unit/vnfm/infra_drivers/kubernetes/test_kubernetes_driver.py

takahashi-tsc/tacker

a0ae01a13dcc51bb374060adcbb4fd484ab37156

[ "Apache-2.0" ]

null

tacker/tests/unit/vnfm/infra_drivers/kubernetes/test_kubernetes_driver.py

takahashi-tsc/tacker

a0ae01a13dcc51bb374060adcbb4fd484ab37156

[ "Apache-2.0" ]

1

2020-11-16T02:14:35.000Z

# Copyright (C) 2020 FUJITSU # 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. import ddt import os from kubernetes import client from tacker.common import exceptions from tacker import context from tacker.db.db_sqlalchemy import models from tacker.extensions import vnfm from tacker import objects from tacker.objects import fields from tacker.objects.vnf_instance import VnfInstance from tacker.objects import vnf_package from tacker.objects import vnf_package_vnfd from tacker.objects import vnf_resources as vnf_resource_obj from tacker.tests.unit import base from tacker.tests.unit.db import utils from tacker.tests.unit.vnfm.infra_drivers.kubernetes import fakes from tacker.tests.unit.vnfm.infra_drivers.openstack.fixture_data import \ fixture_data_utils as fd_utils from tacker.vnfm.infra_drivers.kubernetes import kubernetes_driver from unittest import mock

51.372309

79

0.691651

93cd3692a60479202468f2712c8bb24c8cc1672a

841

py

Python

src/codplayer/__init__.py

petli/codplayer

172187b91662affd8e89f572c0db9be1c4257627

[ "MIT" ]

14

2015-04-27T20:40:46.000Z

2019-02-01T09:22:02.000Z

src/codplayer/__init__.py

petli/codplayer

172187b91662affd8e89f572c0db9be1c4257627

[ "MIT" ]

10

2015-01-05T18:11:28.000Z

2018-09-03T08:42:50.000Z

src/codplayer/__init__.py

petli/codplayer

172187b91662affd8e89f572c0db9be1c4257627

[ "MIT" ]

4

2017-03-03T16:59:39.000Z

2019-11-08T11:15:06.000Z

# codplayer supporting package # # Copyright 2013-2014 Peter Liljenberg <peter.liljenberg@gmail.com> # # Distributed under an MIT license, please see LICENSE in the top dir. # Don't include the audio device modules in the list of modules, # as they may not be available on all systems from pkg_resources import get_distribution import os import time version = get_distribution('codplayer').version # Check what file we are loaded from try: date = time.ctime(os.stat(__file__).st_mtime) except OSError as e: date = 'unknown ({})'.format(e) __all__ = [ 'audio', 'command', 'config', 'db', 'model', 'player', 'rest', 'rip', 'serialize', 'sink', 'source', 'state', 'toc', 'version' ]

19.55814

70

0.65874

93cf143d7b69f8a96f36f23910ce3b0b601f20d1

436

py

Python

lib/googlecloudsdk/third_party/apis/bigtableclusteradmin/v1/__init__.py

bopopescu/SDK

e6d9aaee2456f706d1d86e8ec2a41d146e33550d

[ "Apache-2.0" ]

null

lib/googlecloudsdk/third_party/apis/bigtableclusteradmin/v1/__init__.py

bopopescu/SDK

e6d9aaee2456f706d1d86e8ec2a41d146e33550d

[ "Apache-2.0" ]

null

lib/googlecloudsdk/third_party/apis/bigtableclusteradmin/v1/__init__.py

bopopescu/SDK

e6d9aaee2456f706d1d86e8ec2a41d146e33550d

[ "Apache-2.0" ]

2

2020-11-04T03:08:21.000Z

2020-11-05T08:14:41.000Z

"""Common imports for generated bigtableclusteradmin client library.""" # pylint:disable=wildcard-import import pkgutil from googlecloudsdk.third_party.apitools.base.py import * from googlecloudsdk.third_party.apis.bigtableclusteradmin.v1.bigtableclusteradmin_v1_client import * from googlecloudsdk.third_party.apis.bigtableclusteradmin.v1.bigtableclusteradmin_v1_messages import * __path__ = pkgutil.extend_path(__path__, __name__)

39.636364

102

0.855505

93d04402f33cb3c06a7016fef8b0328a457f038a

4,229

py

Python

elementalcms/management/pages.py

paranoid-software/elemental-cms

7f09f9cd5498577d23fa70d1a51497b9de232598

[ "MIT" ]

3

2022-01-12T09:11:54.000Z

2022-02-24T22:39:11.000Z

elementalcms/management/pages.py

paranoid-software/elemental-cms

7f09f9cd5498577d23fa70d1a51497b9de232598

[ "MIT" ]

null

elementalcms/management/pages.py

paranoid-software/elemental-cms

7f09f9cd5498577d23fa70d1a51497b9de232598

[ "MIT" ]

1

2022-01-12T09:11:56.000Z

from typing import Tuple, Optional import click from cloup import constraint, option, command, pass_context from cloup.constraints import RequireExactly from .pagescommands import Create, Remove, Push, Pull, List, Publish, Unpublish

34.663934

119

0.581698

93d13c525fccba1c9782ed2b28a9ab8aac0b37da

339

py

Python

shapesimage.py

riddhigupta1318/menu_driven

1a3e4a8d3ff3dbcd9cffaa87ab9fbc66868d9eb6

[ "Apache-2.0" ]

null

shapesimage.py

riddhigupta1318/menu_driven

1a3e4a8d3ff3dbcd9cffaa87ab9fbc66868d9eb6

[ "Apache-2.0" ]

null

shapesimage.py

riddhigupta1318/menu_driven

1a3e4a8d3ff3dbcd9cffaa87ab9fbc66868d9eb6

[ "Apache-2.0" ]

null

#!/user/bin/python3 import cv2 #loading image img=cv2.imread("dog.jpeg") img1=cv2.line(img,(0,0),(200,114),(110,176,123),2) #print height and width print(img.shape) #to display that image cv2.imshow("dogg",img1) #image window holder activate #wait key will destroy by pressing q button cv2.waitKey(0) cv2.destroyAllWindows()

16.142857

50

0.719764

93d37d046fccd50496fe96e2714742d3c5e3222c

2,139

py

Python

RNNS/utils/wrdembdGen.py

CenIII/Text-style-transfer-DeleteRetrieve

2b7aa017765dcae65b42fc94d3ccaddc57ac8661

[ "MIT" ]

null

RNNS/utils/wrdembdGen.py

CenIII/Text-style-transfer-DeleteRetrieve

2b7aa017765dcae65b42fc94d3ccaddc57ac8661

[ "MIT" ]

null

RNNS/utils/wrdembdGen.py

CenIII/Text-style-transfer-DeleteRetrieve

2b7aa017765dcae65b42fc94d3ccaddc57ac8661

[ "MIT" ]

null

import gensim import fnmatch import os import pickle import numpy as np # from symspellpy.symspellpy import SymSpell, Verbosity # import the module # initial_capacity = 83000 # # maximum edit distance per dictionary precalculation # max_edit_distance_dictionary = 2 # prefix_length = 7 # sym_spell = SymSpell(initial_capacity, max_edit_distance_dictionary, # prefix_length) # # load dictionary # dictionary_path = os.path.join(os.path.dirname(__file__), # "frequency_dictionary_en_82_765.txt") # term_index = 0 # column of the term in the dictionary text file # count_index = 1 # column of the term frequency in the dictionary text file # if not sym_spell.load_dictionary(dictionary_path, term_index, count_index): # print("Dictionary file not found") # max_edit_distance_lookup = 2 model = gensim.models.KeyedVectors.load_word2vec_format('~/Downloads/GoogleNews-vectors-negative300.bin', binary=True) wordlist = [] for dataset in ['yelp/']: filelist = os.listdir('../../Data/'+dataset) for file in filelist: with open('../../Data/'+dataset+file,'r') as f: line = f.readline() while line: # suggestions = sym_spell.lookup_compound(line, max_edit_distance_lookup) wordlist += line.split(' ') line = f.readline() wordlist.append('<unk>') wordlist.append('<m_end>') wordlist.append('@@START@@') wordlist.append('@@END@@') vocabs = set(wordlist) print(len(vocabs)) wordDict = {} word2vec = [] wastewords = [] word2vec.append(np.zeros(300)) wordDict['<PAD>']=0 cnt=1 for word in vocabs: if word in model.wv: word2vec.append(model.wv[word]) wordDict[word] = cnt cnt += 1 else: # wastewords.append(word) word2vec.append(np.random.uniform(-1,1,300)) wordDict[word] = cnt cnt += 1 word2vec = np.array(word2vec) # with open('./word2vec', "wb") as fp: #Pickling np.save('word2vec.npy',word2vec) with open('./wordDict', "wb") as fp: #Pickling pickle.dump(wordDict, fp) # with open('./word2vec', "rb") as fp: #Pickling # word2vec = pickle.load(fp) # with open('./wordDict', "rb") as fp: #Pickling # wordDict = pickle.load(fp) # pass

27.423077

118

0.694717

93d43839068d5fe40ab642bf29baf0d261531656

8,611

py

Python

cls_utils/job.py

prmurali1leo/Engineering_challenge

d73dcba265587c22f0869880bf372cfaa045bfa6

[ "MIT" ]

null

cls_utils/job.py

prmurali1leo/Engineering_challenge

d73dcba265587c22f0869880bf372cfaa045bfa6

[ "MIT" ]

null

cls_utils/job.py

prmurali1leo/Engineering_challenge

d73dcba265587c22f0869880bf372cfaa045bfa6

[ "MIT" ]

null

import pandas as pd import numpy as np from hashlib import md5 import datetime import pyarrow.parquet as pq import pyarrow as pa from src.dimension_surrogate_resolver import DimensionSurrogateResolver

42.004878

120

0.648124

93d52227fd91adf6e2131607d2e901a6c4913898

3,294

py

Python

busy_home.py

jerr0328/HAP-python

87199a1fb7ffc451961948c634e46439cbace370

[ "Apache-2.0" ]

462

2017-10-14T16:58:36.000Z

2022-03-24T01:40:23.000Z

busy_home.py

jerr0328/HAP-python

87199a1fb7ffc451961948c634e46439cbace370

[ "Apache-2.0" ]

371

2017-11-28T14:00:02.000Z

2022-03-31T21:44:07.000Z

busy_home.py

jerr0328/HAP-python

87199a1fb7ffc451961948c634e46439cbace370

[ "Apache-2.0" ]

129

2017-11-23T20:50:28.000Z

2022-03-17T01:26:53.000Z

"""Starts a fake fan, lightbulb, garage door and a TemperatureSensor """ import logging import signal import random from pyhap.accessory import Accessory, Bridge from pyhap.accessory_driver import AccessoryDriver from pyhap.const import (CATEGORY_FAN, CATEGORY_LIGHTBULB, CATEGORY_GARAGE_DOOR_OPENER, CATEGORY_SENSOR) logging.basicConfig(level=logging.INFO, format="[%(module)s] %(message)s") def get_bridge(driver): bridge = Bridge(driver, 'Bridge') bridge.add_accessory(LightBulb(driver, 'Lightbulb')) bridge.add_accessory(FakeFan(driver, 'Big Fan')) bridge.add_accessory(GarageDoor(driver, 'Garage')) bridge.add_accessory(TemperatureSensor(driver, 'Sensor')) return bridge driver = AccessoryDriver(port=51826, persist_file='busy_home.state') driver.add_accessory(accessory=get_bridge(driver)) signal.signal(signal.SIGTERM, driver.signal_handler) driver.start()

31.371429

77

0.683667

93d680ecf48e6dbb1495bab46f68ebdbe3aea08b

574

py

Python

Backend/src/commercial/urls.py

ChristianTaborda/Energycorp

2447b5af211501450177b0b60852dcb31d6ca12d

[ "MIT" ]

1

2020-12-31T00:07:40.000Z

Backend/src/commercial/urls.py

ChristianTaborda/Energycorp

2447b5af211501450177b0b60852dcb31d6ca12d

[ "MIT" ]

null

Backend/src/commercial/urls.py

ChristianTaborda/Energycorp

2447b5af211501450177b0b60852dcb31d6ca12d

[ "MIT" ]

null

from django.urls import path from .views import ( # CRUDS CommercialList, CommercialDelete, CommercialDetail, CommercialCreate, CommercialUpdate, CommercialDelete, CommercialInactivate, # QUERY ) urlpatterns = [ #CRUD path('', CommercialList.as_view()), path('create/', CommercialCreate.as_view()), path('<pk>/', CommercialDetail.as_view()), path('update/<pk>/', CommercialUpdate.as_view()), path('inactivate/<pk>/', CommercialInactivate.as_view()), path('delete/<pk>', CommercialDelete.as_view()) #QUERY ]

22.076923

61

0.667247

93d7075c75f515ae6f7dbc9fddf988695545df0c

2,715

py

Python

src/traquitanas/geo/layers.py

traquitanas/traquitanas

788a536de4c762b050e9d09c55b15e4d0bee3434

[ "MIT" ]

null

src/traquitanas/geo/layers.py

traquitanas/traquitanas

788a536de4c762b050e9d09c55b15e4d0bee3434

[ "MIT" ]

null

src/traquitanas/geo/layers.py

traquitanas/traquitanas

788a536de4c762b050e9d09c55b15e4d0bee3434

[ "MIT" ]

1

2021-10-07T20:58:56.000Z

import folium if __name__ == '__main__': pass

27.15

92

0.537385

93d7e71a979233c8c73b2a4018aacf592bc1a08e

1,277

py

Python

migrations/versions/6e5e2b4c2433_add_hometasks_for_students.py

AnvarGaliullin/LSP

ed1f00ddc6346c5c141b421c7a3305e4c9e1b0d1

[ "MIT" ]

null

migrations/versions/6e5e2b4c2433_add_hometasks_for_students.py

AnvarGaliullin/LSP

ed1f00ddc6346c5c141b421c7a3305e4c9e1b0d1

[ "MIT" ]

null

migrations/versions/6e5e2b4c2433_add_hometasks_for_students.py

AnvarGaliullin/LSP

ed1f00ddc6346c5c141b421c7a3305e4c9e1b0d1

[ "MIT" ]

null

"""Add Hometasks for Students Revision ID: 6e5e2b4c2433 Revises: b9acba47fd53 Create Date: 2020-01-10 20:52:40.063133 """ from alembic import op import sqlalchemy as sa # revision identifiers, used by Alembic. revision = '6e5e2b4c2433' down_revision = 'b9acba47fd53' branch_labels = None depends_on = None

31.925

103

0.702428

93d903dc4a4d4fc536ec37d420b4604d14554d90

1,759

py

Python

scripts/plotting.py

intelligent-soft-robots/o80_roboball2d

094d36f870b9c20ef5e05baf92ed8ed5b9a5277c

[ "BSD-3-Clause" ]

null

scripts/plotting.py

intelligent-soft-robots/o80_roboball2d

094d36f870b9c20ef5e05baf92ed8ed5b9a5277c

[ "BSD-3-Clause" ]

null

scripts/plotting.py

intelligent-soft-robots/o80_roboball2d

094d36f870b9c20ef5e05baf92ed8ed5b9a5277c

[ "BSD-3-Clause" ]

null

import time import math import fyplot import o80_roboball2d from functools import partial if __name__ == "__main__": run()

30.327586

84

0.651507

93d96a3758d5ca27cf2434f779255814b61dd0c7

10,099

py

Python

kvm_pirate/elf/structs.py

Mic92/kvm-pirate

26626db320b385f51ccb88dad76209a812c40ca6

[ "MIT" ]

6

2020-12-15T04:26:43.000Z

2020-12-15T13:26:09.000Z

kvm_pirate/elf/structs.py

Mic92/kvm-pirate

26626db320b385f51ccb88dad76209a812c40ca6

[ "MIT" ]

null

kvm_pirate/elf/structs.py

Mic92/kvm-pirate

26626db320b385f51ccb88dad76209a812c40ca6

[ "MIT" ]

null

# # Copyright (C) 2018 The Android Open Source Project # # 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. # """This file contains ELF C structs and data types.""" import ctypes from typing import Any from . import consts # ELF data types. Elf32_Addr = ctypes.c_uint32 Elf32_Off = ctypes.c_uint32 Elf32_Half = ctypes.c_uint16 Elf32_Word = ctypes.c_uint32 Elf32_Sword = ctypes.c_int32 Elf64_Addr = ctypes.c_uint64 Elf64_Off = ctypes.c_uint64 Elf64_Half = ctypes.c_uint16 Elf64_Word = ctypes.c_uint32 Elf64_Sword = ctypes.c_int32 Elf64_Xword = ctypes.c_uint64 Elf64_Sxword = ctypes.c_int64 # ELF C structs.

26.231169

77

0.600951

93db2131f51a021bb76ace2f9993a86a1d6b0e6b

469

py

Python

connect-2018/exercises/2018/lr-automation/cbr.py

cbcommunity/cb-connect

3ccfd1ed51e808f567f9f0fc4e8fe2688ef9ee76

[ "MIT" ]

5

2019-06-03T21:02:32.000Z

2020-12-01T08:59:50.000Z

connect-2018/exercises/2018/lr-automation/cbr.py

cbcommunity/cb-connect-2018

3ccfd1ed51e808f567f9f0fc4e8fe2688ef9ee76

[ "MIT" ]

null

connect-2018/exercises/2018/lr-automation/cbr.py

cbcommunity/cb-connect-2018

3ccfd1ed51e808f567f9f0fc4e8fe2688ef9ee76

[ "MIT" ]

1

2019-07-09T20:09:14.000Z

from cbapi.response import * from lrjob import run_liveresponse from cbapi.example_helpers import get_cb_response_object, build_cli_parser if __name__ == '__main__': main()

26.055556

74

0.742004

93db88634b9a24a07909d849964c5b879194e57a

6,655

py

Python

sqlite_to_stasis.py

mrozekma/Sprint

0bf531d2f16a7bc5b56dbc8c6eae5dc9e251b2f1

[ "MIT" ]

2

2015-03-18T13:58:46.000Z

2020-04-10T14:54:56.000Z

sqlite_to_stasis.py

mrozekma/Sprint

0bf531d2f16a7bc5b56dbc8c6eae5dc9e251b2f1

[ "MIT" ]

20

2015-01-16T18:46:53.000Z

2016-02-18T22:01:00.000Z

sqlite_to_stasis.py

mrozekma/Sprint

0bf531d2f16a7bc5b56dbc8c6eae5dc9e251b2f1

[ "MIT" ]

2

2015-08-24T15:39:20.000Z

2016-01-03T06:03:13.000Z

from os import rename from os.path import isfile import pickle import sqlite3 from stasis.DiskMap import DiskMap from utils import tsToDate, dateToTs from datetime import timedelta source = sqlite3.connect('db') source.row_factory = sqlite3.Row dest = DiskMap('db-new', create = True, cache = False) # Some cleanup, because sqlite apparently doesn't cascade deletes # This probably isn't comprehensive, but most databases shouldn't really need it anyway queries = [ "DELETE FROM availability WHERE NOT EXISTS (SELECT * FROM users WHERE availability.userid = users.id)", "DELETE FROM availability WHERE NOT EXISTS (SELECT * FROM sprints WHERE availability.sprintid = sprints.id)", "DELETE FROM grants WHERE NOT EXISTS (SELECT * FROM users WHERE grants.userid = users.id)", "DELETE FROM members WHERE NOT EXISTS (SELECT * FROM sprints WHERE members.sprintid = sprints.id)", "DELETE FROM tasks WHERE NOT EXISTS (SELECT * FROM sprints WHERE tasks.sprintid = sprints.id)", "DELETE FROM assigned WHERE NOT EXISTS (SELECT * FROM tasks WHERE assigned.taskid = tasks.id AND assigned.revision = tasks.revision)", ] for query in queries: cur = source.cursor() cur.execute(query) cur.close() # Some tables get converted directly: for table in ['users', 'sprints', 'groups', 'goals', 'log', 'projects', 'notes', 'messages', 'searches', 'retrospective_categories', 'retrospective_entries', 'changelog_views']: cur = source.cursor() cur.execute("SELECT * FROM %s" % table) for row in cur: data = {k: row[k] for k in row.keys()} print "%-20s %d" % (table, data['id']) dest[table][data['id']] = data cur.close() # Settings are converted to a straight key/value store; no IDs cur = source.cursor() cur.execute("SELECT * FROM settings WHERE name != 'gitURL'") for row in cur: data = {k: row[k] for k in row.keys()} print "%-20s %d" % ('settings', row['id']) dest['settings'][row['name']] = row['value'] cur.close() # Tasks have multiple revisions; they're stored as a list cur = source.cursor() cur.execute("SELECT * FROM tasks ORDER BY id, revision") for row in cur: rev = {k: row[k] for k in row.keys()} print "%-20s %d (revision %d)" % ('tasks', row['id'], row['revision']) if int(rev['revision']) == 1: dest['tasks'][rev['id']] = [rev] else: with dest['tasks'].change(rev['id']) as data: assert len(data) + 1 == rev['revision'] data.append(rev) cur.close() # Linking tables no longer exist # Instead, add the lists directly to the appropriate parent class # grants -> users.privileges # (the privileges table is gone entirely) for userid in dest['users']: with dest['users'].change(userid) as data: data['privileges'] = set() cur = source.cursor() cur.execute("SELECT g.userid, p.name FROM grants AS g, privileges AS p WHERE g.privid = p.id") for row in cur: print "%-20s %d (%s)" % ('grants', row['userid'], row['name']) with dest['users'].change(int(row['userid'])) as data: data['privileges'].add(row['name']) cur.close() # members -> sprints.members if 'sprints' in dest: for sprintid in dest['sprints']: with dest['sprints'].change(sprintid) as data: data['memberids'] = set() cur = source.cursor() cur.execute("SELECT * FROM members") for row in cur: print "%-20s %d (%d)" % ('members', row['sprintid'], row['userid']) with dest['sprints'].change(int(row['sprintid'])) as data: data['memberids'].add(row['userid']) cur.close() # assigned -> tasks.assigned if 'tasks' in dest: for taskid in dest['tasks']: with dest['tasks'].change(taskid) as data: for rev in data: rev['assignedids'] = set() cur = source.cursor() cur.execute("SELECT * FROM assigned") for row in cur: print "%-20s %d (revision %d) %s" % ('assigned', row['taskid'], row['revision'], row['userid']) with dest['tasks'].change(int(row['taskid'])) as data: data[int(row['revision']) - 1]['assignedids'].add(row['userid']) cur.close() # search_uses -> searches.followers if 'searches' in dest: for searchid in dest['searches']: with dest['searches'].change(searchid) as data: data['followerids'] = set() cur = source.cursor() cur.execute("SELECT * FROM search_uses") for row in cur: print "%-20s %d (%d)" % ('search_uses', row['searchid'], row['userid']) with dest['searches'].change(int(row['searchid'])) as data: data['followerids'].add(row['userid']) cur.close() # prefs is converted normally, except the id is now set to the userid # prefs_backlog_styles -> prefs.backlogStyles # prefs_messages -> prefs.messages cur = source.cursor() cur.execute("SELECT * FROM prefs") for row in cur: print "%-20s %d" % ('prefs', row['userid']) dest['prefs'][int(row['userid'])] = {} with dest['prefs'].change(int(row['userid'])) as data: data['id'] = int(row['userid']) data['defaultSprintTab'] = row['defaultSprintTab'] data['backlogStyles'] = {} cur2 = source.cursor() cur2.execute("SELECT * FROM prefs_backlog_styles WHERE userid = %d" % int(row['userid'])) for row2 in cur2: data['backlogStyles'][row2['status']] = row2['style'] cur2.close() data['messages'] = {} cur2 = source.cursor() cur2.execute("SELECT * FROM prefs_messages WHERE userid = %d" % int(row['userid'])) for row2 in cur2: data['messages'][row2['type']] = not not row2['enabled'] cur2.close() cur.close() # Anyone who doesn't have prefs gets a default record for userid in dest['users']: if userid not in dest['prefs']: dest['prefs'][userid] = {'id': userid, 'defaultSprintTab': 'backlog', 'backlogStyles': {status: 'show' for status in ['not started', 'in progress', 'complete', 'blocked', 'deferred', 'canceled', 'split']}, 'messages': {'sprintMembership': False, 'taskAssigned': False, 'noteRelated': True, 'noteMention': True, 'priv': True}} # Availability is now stored by sprint id # The contents are {user_id: {timestamp: hours}} if 'sprints' in dest: oneday = timedelta(1) for sprintid, data in dest['sprints'].iteritems(): m = {} for userid in data['memberids']: m[userid] = {} print "%-20s %d %d" % ('availability', sprintid, userid) cur = source.cursor() cur.execute("SELECT hours, timestamp FROM availability WHERE sprintid = %d AND userid = %d AND timestamp != 0" % (sprintid, userid)) for row in cur: m[userid][int(row['timestamp'])] = int(row['hours']) cur.close() dest['availability'][sprintid] = m # Make search.public a bool instead of an int if 'searches' in dest: for searchid, data in dest['searches'].iteritems(): with dest['searches'].change(searchid) as data: data['public'] = bool(data['public']) # Bump the DB version dest['settings']['dbVersion'] = 20 source.close() # Rename rename('db', 'db-old.sqlite') rename('db-new', 'db')

37.59887

327

0.677536

93db9daeaca176a0d9639c9a8adf4162b78f5785

52

py

Python

list_ebs.py

willfong/aws-helper

21708044fbf95b76393e9b5f0e86c5e74ff11c77

[ "MIT" ]

null

list_ebs.py

willfong/aws-helper

21708044fbf95b76393e9b5f0e86c5e74ff11c77

[ "MIT" ]

null

list_ebs.py

willfong/aws-helper

21708044fbf95b76393e9b5f0e86c5e74ff11c77

[ "MIT" ]

null

import boto3 aws_ebs_client = boto3.client('ebs')

10.4

36

0.75

93dfe7ab2f36df70ba6de51ccd1196139a54d7d0

1,211

py

Python

MakeSlides/MakeSlides.py

bobm123/BeeWareTalk

d6df32320f59bcd0f71a181c3d67ce4cbe5eb1b3

[ "MIT" ]

null

MakeSlides/MakeSlides.py

bobm123/BeeWareTalk

d6df32320f59bcd0f71a181c3d67ce4cbe5eb1b3

[ "MIT" ]

null

MakeSlides/MakeSlides.py

bobm123/BeeWareTalk

d6df32320f59bcd0f71a181c3d67ce4cbe5eb1b3

[ "MIT" ]

null

''' Generate slideshows from markdown that use the remark.js script details here: https://github.com/gnab/remark Run it like this: python MakeSlides.py <source_text.md> <Slidestack Title> index.html ''' import sys import os template = ''' <!DOCTYPE html> <html> <head> <title>{title_string}</title> <meta charset="utf-8"> <style>{css_string}</style> </head> <body> <textarea id="source">{markdown_string}</textarea> <script src="https://remarkjs.com/downloads/remark-latest.min.js"> </script> <script> var slideshow = remark.create(); </script> </body> </html> '''

20.525424

71

0.625929

93e030c92ac6f8fce1b888c7a5422a8bac82faba

144

py

Python

makesolid/__init__.py

aarchiba/makesolid

121fca121a838fa4d62ae96ce1fc81dba64c2198

[ "MIT" ]

null

makesolid/__init__.py

aarchiba/makesolid

121fca121a838fa4d62ae96ce1fc81dba64c2198

[ "MIT" ]

null

makesolid/__init__.py

aarchiba/makesolid

121fca121a838fa4d62ae96ce1fc81dba64c2198

[ "MIT" ]

null

# -*- coding: utf-8 -*- from __future__ import division, print_function from ._mesh import * from ._openscad import * from ._threads import *

18

47

0.722222

93e13a546c607eee62ff4605caebeeafa51bfb7a

6,805

py

Python

pricePrediction/preprocessData/prepareDataMol2Price.py

rsanchezgarc/CoPriNet

33708a82746278270fd1aa600d4b562ea0f62c1c

[ "MIT" ]

null

pricePrediction/preprocessData/prepareDataMol2Price.py

rsanchezgarc/CoPriNet

33708a82746278270fd1aa600d4b562ea0f62c1c

[ "MIT" ]

null

pricePrediction/preprocessData/prepareDataMol2Price.py

rsanchezgarc/CoPriNet

33708a82746278270fd1aa600d4b562ea0f62c1c

[ "MIT" ]

1

2022-03-02T16:21:16.000Z

import gzip import os import re import sys import time from functools import reduce from itertools import chain from multiprocessing import cpu_count import lmdb import psutil import joblib from joblib import Parallel, delayed import numpy as np from pricePrediction import config from pricePrediction.config import USE_MMOL_INSTEAD_GRAM from pricePrediction.preprocessData.serializeDatapoints import getExampleId, serializeExample from pricePrediction.utils import tryMakedir, getBucketRanges, search_buckedId, EncodedDirNamesAndTemplates from .smilesToGraph import smiles_to_graph, compute_nodes_degree, fromPerGramToPerMMolPrice PER_WORKER_MEMORY_GB = 2 if __name__ == "__main__": print( " ".join(sys.argv)) import argparse parser = argparse.ArgumentParser() parser.add_argument("-i", "--inputDir", type=str, default=config.DATASET_DIRNAME, help="Directory where smiles-price pairs are located") parser.add_argument("-o", "--encodedDir", type=str, default=config.ENCODED_DIR) parser.add_argument("-n", "--ncpus", type=int, default=config.N_CPUS) args = vars( parser.parse_args()) config.N_CPUS = args.get("ncpus", config.N_CPUS) dataBuilder = DataBuilder(n_cpus=config.N_CPUS) dataBuilder.prepareDataset(datasetSplit="train", **args) dataBuilder.prepareDataset(datasetSplit="val", **args) dataBuilder.prepareDataset(datasetSplit="test", **args) ''' python -m pricePrediction.preprocessData.prepareDataMol2Price '''

42.006173

140

0.627039

93e251e9378f58f91368189ca0f98d7e9d184630

173

py

Python

Demos/Demo-4.2 Modules/script_3.py

Josverl/MicroPython-Bootcamp

29f5ccc9768fbea621029dcf6eea9c91ff84c1d5

[ "MIT" ]

4

2018-04-28T13:43:20.000Z

2021-03-11T16:10:35.000Z

Demos/Demo-4.2 Modules/script_3.py

Josverl/MicroPython-Bootcamp

29f5ccc9768fbea621029dcf6eea9c91ff84c1d5

[ "MIT" ]

null

Demos/Demo-4.2 Modules/script_3.py

Josverl/MicroPython-Bootcamp

29f5ccc9768fbea621029dcf6eea9c91ff84c1d5

[ "MIT" ]

null

# import just one function from a module # to save memory from module import dowork #now we can us a different name to get to the imported function # dowork(13,45) dir()

19.222222

63

0.745665

93e2b831da7ddd82cdee3f6c7c6866a56f385beb

2,894

py

Python

lanzou/gui/workers/more.py

WaterLemons2k/lanzou-gui

f5c57f980ee9a6d47164a39b90d82eb0391ede8b

[ "MIT" ]

1,093

2019-12-25T10:42:34.000Z

2022-03-28T22:35:32.000Z

lanzou/gui/workers/more.py

Enrontime/lanzou-gui

8e89438d938ee4994a4118502c3f14d467b55acc

[ "MIT" ]

116

2019-12-24T04:01:43.000Z

2022-03-26T16:12:41.000Z

lanzou/gui/workers/more.py

Enrontime/lanzou-gui

8e89438d938ee4994a4118502c3f14d467b55acc

[ "MIT" ]

188

2020-01-11T14:17:13.000Z

2022-03-29T09:18:34.000Z

from PyQt5.QtCore import QThread, pyqtSignal, QMutex from lanzou.api import LanZouCloud from lanzou.gui.models import Infos from lanzou.debug import logger

34.86747

106

0.516586

93e32bae11b86f9998eb40958c5a33c52acf9800

2,728

py

Python

src/ipu/source/folder_monitor.py

feagi/feagi-core

d83c51480fcbe153fa14b2360b4d61f6ae4e2811

[ "Apache-2.0" ]

11

2020-02-18T16:03:10.000Z

2021-12-06T19:53:06.000Z

src/ipu/source/folder_monitor.py

feagi/feagi-core

d83c51480fcbe153fa14b2360b4d61f6ae4e2811

[ "Apache-2.0" ]

34

2019-12-17T04:59:42.000Z

2022-01-18T20:58:46.000Z

src/ipu/source/folder_monitor.py

feagi/feagi-core

d83c51480fcbe153fa14b2360b4d61f6ae4e2811

[ "Apache-2.0" ]

3

2019-12-16T06:09:56.000Z

2020-10-18T12:01:31.000Z

""" Source: https://camcairns.github.io/python/2017/09/06/python_watchdog_jobs_queue.html This class inherits from the Watchdog PatternMatchingEventHandler class. In this code our watchdog will only be triggered if a file is moved to have a .trigger extension. Once triggered the watchdog places the event object on the queue, ready to be picked up by the worker thread """ import string import time from queue import Queue from threading import Thread from watchdog.observers import Observer from watchdog.events import PatternMatchingEventHandler from inf import runtime_data from ipu.processor import utf # todo: combine all of this module into a single class

36.864865

117

0.66129