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

9d364f1bcfe20f4a4dd1ba1db0e841b93b086bab

1,672

py

Python

yagoMoviesDown.py

afshinsadeghi/DBPediaDownloder

f9c98cab6fc7ce1d4f1c707ce1491c5dacbaf2cc

[ "Apache-2.0" ]

1

2019-04-02T11:12:52.000Z

yagoMoviesDown.py

afshinsadeghi/DBPediaDownloder

f9c98cab6fc7ce1d4f1c707ce1491c5dacbaf2cc

[ "Apache-2.0" ]

null

yagoMoviesDown.py

afshinsadeghi/DBPediaDownloder

f9c98cab6fc7ce1d4f1c707ce1491c5dacbaf2cc

[ "Apache-2.0" ]

null

import os from time import sleep import requests querysub0 = 'https://linkeddata1.calcul.u-psud.fr/sparql?default-graph-uri=&query=construct%7B+%3Fs+%3Fp+%3Fo%7D+where+%7B+%0D%0Aselect+distinct+%3Fs+%3Fp+%3Fo+where+%7B%0D%0A%7B%0D%0A%3Fs1+++%3Chttp%3A%2F%2Fyago-knowledge.org%2Fresource%2FactedIn%3E+++%3Fs+.%0D%0A%3Fs2+++%3Chttp%3A%2F%2Fyago-knowledge.org%2Fresource%2Fdirected%3E+++%3Fs+.%0D%0A%3Fs+++%3Fp+++%3Fo.%0D%0A%7D+Union%7B%0D%0A%3Fs+++%3Chttp%3A%2F%2Fyago-knowledge.org%2Fresource%2FactedIn%3E+++%3Fs3+.%0D%0A%3Fs4+++%3Chttp%3A%2F%2Fyago-knowledge.org%2Fresource%2Fdirected%3E+++%3Fs3+.%0D%0A%3Fs+++%3Fp+++%3Fo.%0D%0A%7D+Union%7B%0D%0A%3Fs7+++%3Chttp%3A%2F%2Fyago-knowledge.org%2Fresource%2FactedIn%3E+++%3Fs5+.%0D%0A%3Fs+++%3Chttp%3A%2F%2Fyago-knowledge.org%2Fresource%2Fdirected%3E+++%3Fs5+.%0D%0A%3Fs+++%3Fp+++%3Fo.%7D+%0D%0A%7D%0D%0ALimit+10000+offset+' querysub1 = '+%7D%0D%0A&format=text%2Fplain&timeout=0' for counter in range(1, 700, 1): sleep(10) # sleep so to let the server breath download_big_file(counter) print "making yagoMovies.nt ..." os.system('find . -name "*.nt" -size -15 -delete') os.system("cat *.nt > a.ntt") os.system("rm *.nt") os.system("mv a.ntt yagoMovies.nt") print "yagoMovies.nt is created. have fun!"

59.714286

819

0.692584

9d3825f401efd886ac45bc856e7732ecfff783b3

1,418

py

Python

src/TestValues/TestProtractor3D.py

SvenKratz/Protractor3D

39b6c877cc88cae028ca938e994034b83fcccb68

[ "MIT" ]

4

2018-02-06T14:41:26.000Z

2020-03-19T14:16:05.000Z

src/TestValues/TestProtractor3D.py

SvenKratz/Protractor3D

39b6c877cc88cae028ca938e994034b83fcccb68

[ "MIT" ]

null

src/TestValues/TestProtractor3D.py

SvenKratz/Protractor3D

39b6c877cc88cae028ca938e994034b83fcccb68

[ "MIT" ]

null

''' Created on Apr 12, 2011 @author: svenkratz ''' import TestGestures import Protractor3D.Protractor3D from Protractor3D.Protractor3D import * triangle = triplify(TestGestures.Triangle) print triangle circle1 = triplify(TestGestures.Circle_1) circle2 = triplify(TestGestures.Circle_2) rectangle = triplify(TestGestures.Rectangle) p_triangle = Protractor3D(triangle) p_circle1 = Protractor3D(circle1) p_circle2 = Protractor3D(circle2) p_rectangle = Protractor3D(rectangle) #print p_circle1.trace # #print "Trace", p_triangle.trace #print "Resampled", p_triangle.resampled #print "Scaled", p_triangle.scaled #print "Centered", p_triangle.centered #print "Template", p_triangle.template print "========== Evaluations ==============" Protractor3D.DEBUG = 5 gesturesAndNames = [(p_triangle,"Triangle"), (p_circle1,"Circle1"), ( p_circle2, "Circle2") , (p_rectangle, "Rectangle")] while gesturesAndNames != []: gesture = gesturesAndNames[0] templates = gesturesAndNames[1:] gesturesAndNames = templates if len(templates) != 0: for t in templates: print "=======================================" print "Results for", gesture[1]," <---> ", t[1] gesture[0].protractor3D_classify(gesture[0].template, t[0].template)

22.507937

121

0.693935

9d3874299d6c36b60cba6fdb324222e4353364ea

481

py

Python

tests/test_actor.py

sdss/HAL

c7a2111f8737a498a124f5571d6f0e6b46e5c371

[ "BSD-3-Clause" ]

null

tests/test_actor.py

sdss/HAL

c7a2111f8737a498a124f5571d6f0e6b46e5c371

[ "BSD-3-Clause" ]

2

2022-01-14T04:50:58.000Z

2022-02-28T22:31:06.000Z

tests/test_actor.py

sdss/HAL

c7a2111f8737a498a124f5571d6f0e6b46e5c371

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- # # @Author: Jos Snchez-Gallego (gallegoj@uw.edu) # @Date: 2021-03-24 # @Filename: test_hal.py # @License: BSD 3-clause (http://www.opensource.org/licenses/BSD-3-Clause) import pytest from hal import __version__ pytestmark = [pytest.mark.asyncio]

20.913043

74

0.706861

9d3a4036188d6088bc1ce4cfe8dfff01c0a9fdb1

490

py

Python

day_07/puzzles.py

electronsandstuff/Advent-of-Code-2021

9c23872640e8d092088dcb6d5cb845cd11d98994

[ "BSD-3-Clause" ]

null

day_07/puzzles.py

electronsandstuff/Advent-of-Code-2021

9c23872640e8d092088dcb6d5cb845cd11d98994

[ "BSD-3-Clause" ]

null

day_07/puzzles.py

electronsandstuff/Advent-of-Code-2021

9c23872640e8d092088dcb6d5cb845cd11d98994

[ "BSD-3-Clause" ]

null

import numpy as np if __name__ == '__main__': with open('input.txt') as f: pin = np.array([int(x) for x in f.read().split(',')]) distances = np.abs(pin[None, :] - np.arange(pin.max() + 1)[:, None]) total_fuel = np.sum(distances, axis=1) print(f'Solution 1: {total_fuel.min()}') distances_v2 = crab_fuel(distances) total_fuel_v2 = np.sum(distances_v2, axis=1) print(f'Solution 2: {total_fuel_v2.min()}')

25.789474

72

0.608163

9d3ac14e7019eef027448de09382a14cd8c888c7

352

py

Python

radical_translations/core/migrations/0033_delete_work.py

kingsdigitallab/radical_translations

c18ca1ccc0ab2d88ae472dc2eda58e2ff9dcc76a

[ "MIT" ]

3

2022-02-08T18:03:44.000Z

2022-03-18T18:10:43.000Z

radical_translations/core/migrations/0033_delete_work.py

kingsdigitallab/radical_translations

c18ca1ccc0ab2d88ae472dc2eda58e2ff9dcc76a

[ "MIT" ]

19

2020-05-11T15:36:35.000Z

2022-02-08T11:26:40.000Z

radical_translations/core/migrations/0033_delete_work.py

kingsdigitallab/radical_translations

c18ca1ccc0ab2d88ae472dc2eda58e2ff9dcc76a

[ "MIT" ]

null

# Generated by Django 2.2.10 on 2020-05-18 10:49 from django.db import migrations

19.555556

63

0.616477

9d3b2ee3ee8d1f5868d497f89b1766382405982d

16,114

py

Python

sampling.py

bigdata-inha/FedDC

c90c48fc7e35b6cb80890194c8cdfb0d412a0819

[ "MIT" ]

null

sampling.py

bigdata-inha/FedDC

c90c48fc7e35b6cb80890194c8cdfb0d412a0819

[ "MIT" ]

null

sampling.py

bigdata-inha/FedDC

c90c48fc7e35b6cb80890194c8cdfb0d412a0819

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- # Python version: 3.6 import numpy as np from torchvision import datasets, transforms import logging import random import torch # Settings for a multiplicative linear congruential generator (aka Lehmer # generator) suggested in 'Random Number Generators: Good # Ones are Hard to Find' by Park and Miller. MLCG_MODULUS = 2**(31) - 1 MLCG_MULTIPLIER = 16807 # Default quantiles for federated evaluations. DEFAULT_QUANTILES = (0.0, 0.25, 0.5, 0.75, 1.0) def mnist_iid(dataset, num_users): """ Sample I.I.D. client data from MNIST dataset :param dataset: :param num_users: :return: dict of image index """ num_items = int(len(dataset) / num_users) dict_users, all_idxs = {}, [i for i in range(len(dataset))] for i in range(num_users): dict_users[i] = set(np.random.choice(all_idxs, num_items, replace=False)) all_idxs = list(set(all_idxs) - dict_users[i]) return dict_users def mnist_noniid(dataset, num_users): """ Sample non-I.I.D client data from MNIST dataset :param dataset: :param num_users: :return: """ # 60,000 training imgs --> 200 imgs/shard X 300 shards num_shards, num_imgs = 200, 300 idx_shard = [i for i in range(num_shards)] dict_users = {i: np.array([]) for i in range(num_users)} idxs = np.arange(num_shards * num_imgs) labels = dataset.train_labels.numpy() # sort labels idxs_labels = np.vstack((idxs, labels)) idxs_labels = idxs_labels[:, idxs_labels[1, :].argsort()] idxs = idxs_labels[0, :] # divide and assign 2 shards/client for i in range(num_users): rand_set = set(np.random.choice(idx_shard, 2, replace=False)) idx_shard = list(set(idx_shard) - rand_set) for rand in rand_set: dict_users[i] = np.concatenate( (dict_users[i], idxs[rand * num_imgs:(rand + 1) * num_imgs]), axis=0) return dict_users def mnist_noniid_unequal(dataset, num_users): """ Sample non-I.I.D client data from MNIST dataset s.t clients have unequal amount of data :param dataset: :param num_users: :returns a dict of clients with each clients assigned certain number of training imgs """ # 60,000 training imgs --> 50 imgs/shard X 1200 shards num_shards, num_imgs = 1200, 50 idx_shard = [i for i in range(num_shards)] dict_users = {i: np.array([]) for i in range(num_users)} idxs = np.arange(num_shards * num_imgs) labels = dataset.train_labels.numpy() # sort labels idxs_labels = np.vstack((idxs, labels)) idxs_labels = idxs_labels[:, idxs_labels[1, :].argsort()] idxs = idxs_labels[0, :] # Minimum and maximum shards assigned per client: min_shard = 1 max_shard = 30 # Divide the shards into random chunks for every client # s.t the sum of these chunks = num_shards random_shard_size = np.random.randint(min_shard, max_shard + 1, size=num_users) random_shard_size = np.around(random_shard_size / sum(random_shard_size) * num_shards) random_shard_size = random_shard_size.astype(int) # Assign the shards randomly to each client if sum(random_shard_size) > num_shards: for i in range(num_users): # First assign each client 1 shard to ensure every client has # atleast one shard of data rand_set = set(np.random.choice(idx_shard, 1, replace=False)) idx_shard = list(set(idx_shard) - rand_set) for rand in rand_set: dict_users[i] = np.concatenate( (dict_users[i], idxs[rand * num_imgs:(rand + 1) * num_imgs]), axis=0) random_shard_size = random_shard_size - 1 # Next, randomly assign the remaining shards for i in range(num_users): if len(idx_shard) == 0: continue shard_size = random_shard_size[i] if shard_size > len(idx_shard): shard_size = len(idx_shard) rand_set = set(np.random.choice(idx_shard, shard_size, replace=False)) idx_shard = list(set(idx_shard) - rand_set) for rand in rand_set: dict_users[i] = np.concatenate( (dict_users[i], idxs[rand * num_imgs:(rand + 1) * num_imgs]), axis=0) else: for i in range(num_users): shard_size = random_shard_size[i] rand_set = set(np.random.choice(idx_shard, shard_size, replace=False)) idx_shard = list(set(idx_shard) - rand_set) for rand in rand_set: dict_users[i] = np.concatenate( (dict_users[i], idxs[rand * num_imgs:(rand + 1) * num_imgs]), axis=0) if len(idx_shard) > 0: # Add the leftover shards to the client with minimum images: shard_size = len(idx_shard) # Add the remaining shard to the client with lowest data k = min(dict_users, key=lambda x: len(dict_users.get(x))) rand_set = set(np.random.choice(idx_shard, shard_size, replace=False)) idx_shard = list(set(idx_shard) - rand_set) for rand in rand_set: dict_users[k] = np.concatenate( (dict_users[k], idxs[rand * num_imgs:(rand + 1) * num_imgs]), axis=0) return dict_users def cifar_iid(dataset, num_users, args): """ Sample I.I.D. client data from CIFAR10 dataset :param dataset: :param num_users: :return: dict of image index """ torch.backends.cudnn.benchmark = False torch.backends.cudnn.deterministic = True torch.manual_seed(args.seed) torch.cuda.manual_seed(args.seed) random.seed(args.seed) np.random.seed(args.seed) num_items = int(len(dataset) / num_users) # dict_users? 0~100 50000 100 . indx list dict_users, all_idxs = {}, [i for i in range(len(dataset))] for i in range(num_users): dict_users[i] = set(np.random.choice(all_idxs, num_items, replace=False)) all_idxs = list(set(all_idxs) - dict_users[i]) return dict_users def imagenet_noniid(dataset, num_users, args, class_num=2): """ Sample non-I.I.D client data from CIFAR10 dataset :param dataset: :param num_users: :return: """ #num_shards -> / num_imgs -> .but imagenet . / # idxs torch.backends.cudnn.benchmark = False torch.backends.cudnn.deterministic = True torch.manual_seed(args.seed) torch.cuda.manual_seed(args.seed) random.seed(args.seed) np.random.seed(args.seed) # . #idx_shards -> n(n .) -> 2 x 100 = 200 #num_imgs -> . 5/100 =500, 2 500 num_shards, num_imgs = num_users*class_num, int(len(dataset)/num_users/class_num) idx_shard = [i for i in range(num_shards)] dict_users = {i: np.array([]) for i in range(num_users)} idxs = np.arange(num_shards * num_imgs) # labels = dataset.train_labels.numpy() labels = np.array(dataset.targets) # sort labels idxs = np.argsort(labels) class_count = [0 for i in range(num_shards)] for i in labels: class_count[i] += 1 accumulate_class_count = [0 for i in range(num_shards)] for c in range(num_shards): if c==0: accumulate_class_count[c] = class_count[0] else: accumulate_class_count[c] = accumulate_class_count[c-1] + class_count[c] idx_shuffle = np.random.permutation(idx_shard) client_class_set = [] for i in range(num_users): user_class_set = idx_shuffle[i*class_num:(i+1)*class_num] client_class_set.append(user_class_set) for class_seed in user_class_set: dict_users[i] = np.concatenate( (dict_users[i], idxs[accumulate_class_count[class_seed] -class_count[class_seed] :accumulate_class_count[class_seed]]), axis=0) return dict_users,client_class_set def cifar_noniid(dataset, num_users, args, class_num=2): """ Sample non-I.I.D client data from CIFAR10 dataset :param dataset: :param num_users: :return: """ torch.backends.cudnn.benchmark = False torch.backends.cudnn.deterministic = True torch.manual_seed(args.seed) torch.cuda.manual_seed(args.seed) random.seed(args.seed) np.random.seed(args.seed) # . #idx_shards -> n(n .) -> 2 x 100 = 200 #num_imgs -> . 5/100 =500, 2 500 num_shards, num_imgs = num_users*class_num, int(len(dataset)/num_users/class_num) idx_shard = [i for i in range(num_shards)] dict_users = {i: np.array([]) for i in range(num_users)} idxs = np.arange(num_shards * num_imgs) # labels = dataset.train_labels.numpy() labels = np.array(dataset.targets) #sort_index = np.argsort(labels) # sort labels idxs_labels = np.vstack((idxs, labels)) idxs_labels = idxs_labels[:, idxs_labels[1, :].argsort()] idxs = idxs_labels[0, :] user_classs_dict = [] # divide and assign for i in range(num_users): # 200 2 . rand_set = set(np.random.choice(idx_shard, class_num, replace=False)) if class_num > 1 and i != num_users-1: while dataset.targets[idxs[list(rand_set)[1] * num_imgs]] == dataset.targets[idxs[list(rand_set)[0] *num_imgs]]: rand_set = set(np.random.choice(idx_shard, class_num, replace=False)) #print(dataset.targets[idxs[list(rand_set)[1] * num_imgs]]) #print(dataset.targets[idxs[list(rand_set)[0] * num_imgs]]) #print('\t') user_classs_dict.append(rand_set) idx_shard = list(set(idx_shard) - rand_set) for rand in rand_set: dict_users[i] = np.concatenate( (dict_users[i], idxs[rand * num_imgs:(rand + 1) * num_imgs]), axis=0) # for data_idx, j in enumerate(dict_users[i]): # print(i, data_idx, dataset.targets[int(j)]) return dict_users, user_classs_dict if __name__ == '__main__': dataset_train = datasets.MNIST('./data/mnist/', train=True, download=True, transform=transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)) ])) num = 100 d = mnist_noniid(dataset_train, num)

37.561772

151

0.608353

9d3ca477c6b29581c9b909f6a0a67fb1fa79ccca

2,502

py

Python

codeforcesRating/codeforcesRating.py

gaurav512/Python-Scripts

46483ab09cccef380c8425d6924507e029745479

[ "MIT" ]

3

2020-05-23T14:31:35.000Z

2020-11-12T12:56:08.000Z

codeforcesRating/codeforcesRating.py

gaurav512/Python-Scripts

46483ab09cccef380c8425d6924507e029745479

[ "MIT" ]

null

codeforcesRating/codeforcesRating.py

gaurav512/Python-Scripts

46483ab09cccef380c8425d6924507e029745479

[ "MIT" ]

null

#! /usr/bin/python3 # Author: gaurav512 ''' Script written to scrape basic information about a Codeforces profile given the user id Usage: Enter the userid as command line argument OR as the input after running the following in terminal- python3 codeforces.py [userid] ''' import requests, bs4, sys if __name__ == '__main__': main()

30.144578

136

0.691847

9d3e62b9c9792273ad0f8b50076e62ff7aa9fb5b

566

py

Python

tests/test_combinator.py

BrunoSanchez/capsule_N1

a5ee3b74afc27de1a954ae2f9f96c278a4723226

[ "BSD-3-Clause" ]

12

2017-04-13T06:49:42.000Z

2019-11-19T09:27:43.000Z

tests/test_combinator.py

BrunoSanchez/capsule_N1

a5ee3b74afc27de1a954ae2f9f96c278a4723226

[ "BSD-3-Clause" ]

56

2017-09-05T16:00:57.000Z

2020-11-20T18:02:58.000Z

tests/test_combinator.py

BrunoSanchez/capsule_N1

a5ee3b74afc27de1a954ae2f9f96c278a4723226

[ "BSD-3-Clause" ]

5

2017-10-08T16:55:40.000Z

2020-09-22T14:04:53.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- # # test_combinator.py # # Copyright 2020 QuatroPe # # This file is part of ProperImage (https://github.com/quatrope/ProperImage) # License: BSD-3-Clause # Full Text: https://github.com/quatrope/ProperImage/blob/master/LICENSE.txt # """ test_combinator module from ProperImage for analysis of astronomical images Written by Bruno SANCHEZ PhD of Astromoy - UNC bruno@oac.unc.edu.ar Instituto de Astronomia Teorica y Experimental (IATE) UNC Cordoba - Argentina Of 301 """

17.151515

76

0.740283

9d3f7a7d27e1b7136efc12dc236457c627b3164e

1,025

py

Python

ch09-linear_model/src/score_card.py

ahitboyZBW/book-ml-sem

73208e7e492c9cbe82c4aaa6459a41e3ac1317be

[ "MIT" ]

137

2020-10-26T11:11:46.000Z

2022-03-29T01:21:22.000Z

ch09-linear_model/src/score_card.py

zengzhongjie/book-ml-sem

5d452a427db5ee65538d968ba5b938af013bb87c

[ "MIT" ]

4

2021-01-18T08:57:04.000Z

2021-07-29T02:39:00.000Z

ch09-linear_model/src/score_card.py

zengzhongjie/book-ml-sem

5d452a427db5ee65538d968ba5b938af013bb87c

[ "MIT" ]

46

2020-10-26T11:11:57.000Z

2022-03-08T00:15:32.000Z

''' parameter --------- df:woe,logit logitsklearn, return ------ Score example: df= cal_score(df,logit) '''

21.808511

77

0.559024

9d41431a104dca3b80f9642ad172c2f1314cf033

3,790

py

Python

Tools/ecl_ekf/batch_process_logdata_ekf.py

lgarciaos/Firmware

26dba1407bd1fbc65c23870a22fed904afba6347

[ "BSD-3-Clause" ]

4,224

2015-01-02T11:51:02.000Z

2020-10-27T23:42:28.000Z

Tools/ecl_ekf/batch_process_logdata_ekf.py

choudhary0parivesh/Firmware

02f4ad61ec8eb4f7906dd06b4eb1fd6abb994244

[ "BSD-3-Clause" ]

11,736

2015-01-01T11:59:16.000Z

2020-10-28T17:13:38.000Z

Tools/ecl_ekf/batch_process_logdata_ekf.py

choudhary0parivesh/Firmware

02f4ad61ec8eb4f7906dd06b4eb1fd6abb994244

[ "BSD-3-Clause" ]

11,850

2015-01-02T14:54:47.000Z

2020-10-28T16:42:47.000Z

#! /usr/bin/env python3 """ Runs process_logdata_ekf.py on the .ulg files in the supplied directory. ulog files are skipped from the analysis, if a corresponding .pdf file already exists (unless the overwrite flag was set). """ # -*- coding: utf-8 -*- import argparse import os, glob from process_logdata_ekf import process_logdata_ekf if __name__ == '__main__': main()

43.563218

125

0.656201

9d429d9ff49854612f73350299d50ebaeb16c00a

1,468

py

Python

goodok_mlu/trackers/neptune.py

roma-goodok/ml_utils

c1d6630021a519102b5c4e029cecccdd8a0da946

[ "MIT" ]

null

goodok_mlu/trackers/neptune.py

roma-goodok/ml_utils

c1d6630021a519102b5c4e029cecccdd8a0da946

[ "MIT" ]

null

goodok_mlu/trackers/neptune.py

roma-goodok/ml_utils

c1d6630021a519102b5c4e029cecccdd8a0da946

[ "MIT" ]

1

2021-03-29T13:15:02.000Z

import inspect import warnings from pathlib import Path

30.583333

92

0.632834

9d438aadf58244488ff98e5078d8104573590578

3,099

py

Python

pkgs/sdk-pkg/src/genie/libs/sdk/libs/abstracted_libs/iosxr/subsection.py

jbronikowski/genielibs

200a34e5fe4838a27b5a80d5973651b2e34ccafb

[ "Apache-2.0" ]

94

2018-04-30T20:29:15.000Z

2022-03-29T13:40:31.000Z

pkgs/sdk-pkg/src/genie/libs/sdk/libs/abstracted_libs/iosxr/subsection.py

jbronikowski/genielibs

200a34e5fe4838a27b5a80d5973651b2e34ccafb

[ "Apache-2.0" ]

67

2018-12-06T21:08:09.000Z

2022-03-29T18:00:46.000Z

pkgs/sdk-pkg/src/genie/libs/sdk/libs/abstracted_libs/iosxr/subsection.py

jbronikowski/genielibs

200a34e5fe4838a27b5a80d5973651b2e34ccafb

[ "Apache-2.0" ]

49

2018-06-29T18:59:03.000Z

2022-03-10T02:07:59.000Z

# Python import logging from os import path # Abstract from genie.abstract import Lookup # Parser from genie.libs import parser from genie.metaparser.util.exceptions import SchemaEmptyParserError # unicon from unicon.eal.dialogs import Statement, Dialog log = logging.getLogger(__name__) def save_device_information(device, **kwargs): """Install the commit packages. This is for IOSXR devices. Args: Mandatory: device (`obj`) : Device object. Returns: True: Result is PASSED False: Result is PASSX Raises: None Example: >>> save_device_information(device=Device()) """ # Checking the config-register has 0x2 # if not configure 0x2 # RP/0/RSP1/CPU0:PE1#admin config-register 0x2 if device.is_ha: conn = device.active else: conn = device # Install commit ( when thre are package to bring up features) # from admin prompt conn.admin_execute('install commit') def get_default_dir(device): """ Get the default directory of this device Args: Mandatory: device (`obj`) : Device object. Returns: default_dir (`str`): Default directory of the system Raises: Exception Example: >>> get_default_dir(device=device) """ try: lookup = Lookup.from_device(device) parsed_dict = lookup.parser.show_platform.Dir(device=device).parse() if ":" in parsed_dict['dir']['dir_name']: default_dir = parsed_dict['dir']['dir_name'] else: default_dir = '' except SchemaEmptyParserError as e: raise Exception("No output when executing 'dir' command") from e except Exception as e: raise Exception("Unable to execute 'dir' command") from e # Return default_dir to caller log.info("Default directory on '{d}' is '{dir}'".format(d=device.name, dir=default_dir)) return default_dir def configure_replace(device, file_location, timeout=60, file_name=None): """Configure replace on device Args: device (`obj`): Device object file_location (`str`): File location timeout (`int`): Timeout value in seconds file_name (`str`): File name Returns: None Raises: pyATS Results """ if file_name: file_location = '{}{}'.format( file_location, file_name) try: # check if file exist device.execute.error_pattern.append('.*Path does not exist.*') device.execute("dir {}".format(file_location)) except Exception: raise Exception("File {} does not exist".format(file_location)) dialog = Dialog([ Statement(pattern=r'\[no\]', action='sendline(y)', loop_continue=True, continue_timer=False)]) device.configure("load {}\ncommit replace".format(file_location), timeout=timeout, reply=dialog)

26.042017

77

0.601162

9d4487b1ae1496a3f2089388dee11fd461798de0

2,933

py

Python

whisper_scalability/plot.py

Evalir/research

0128cdc7c3cecaad4cc057886fd84e79b78f6b9c

[ "MIT" ]

42

2019-08-03T18:04:47.000Z

2022-02-28T14:24:56.000Z

whisper_scalability/plot.py

Evalir/research

0128cdc7c3cecaad4cc057886fd84e79b78f6b9c

[ "MIT" ]

88

2019-10-03T23:11:12.000Z

2022-03-30T05:28:44.000Z

whisper_scalability/plot.py

Evalir/research

0128cdc7c3cecaad4cc057886fd84e79b78f6b9c

[ "MIT" ]

3

2019-09-03T17:19:39.000Z

2021-12-27T16:53:44.000Z

import matplotlib.pyplot as plt import numpy as np from labellines import labelLines # # Trying to get interpolation to work but getting error: # # ValueError: The number of derivatives at boundaries does not match: expected 1, got 0+0 # from scipy.interpolate import make_interp_spline, BSpline # n_users = np.array([100, 10000, 1000000]) # bw_case8 = np.array([1, 1.5, 98.1]) # # 300 represents number of points to make between T.min and T.max # n_users_new = np.linspace(n_users.min(), n_users.max(), 300) # spl8 = make_interp_spline(n_users, bw_case8, k=3) # type: BSpline # bw_case8_smooth = spl8(n_users_new) # plt.plot(n_users_new, bw_case8_smooth, label='case 8', linewidth=2) n_users = [100, 10000, 1000000] bw_case1 = [1, 1, 1] bw_case2 = [97.7, 9.5*1000, 935.7*1000] bw_case3 = [49.3, 4.*10008, 476.8*1000] bw_case4 = [1, 1.5, 98.1] bw_case5 = [10.7, 978, 95.5*1000] bw_case6 = [21.5, 1.9*1000, 190.9*1000] bw_case7 = [3.9, 284.8, 27.8*1000] bw_case8 = [1, 1.5, 98.1] plt.xlim(100, 10**6) plt.ylim(1, 10**6) plt.plot(n_users, bw_case1, label='case 1', linewidth=4, linestyle='dashed') plt.plot(n_users, bw_case2, label='case 2', linewidth=4, linestyle='dashed') plt.plot(n_users, bw_case3, label='case 3', linewidth=4, linestyle='dashed') plt.plot(n_users, bw_case4, label='case 4', linewidth=4, linestyle='dashed') plt.plot(n_users, bw_case5, label='case 5', linewidth=4) plt.plot(n_users, bw_case6, label='case 6', linewidth=4) plt.plot(n_users, bw_case7, label='case 7', linewidth=4) plt.plot(n_users, bw_case8, label='case 8', linewidth=4) #labelLines(plt.gca().get_lines(),zorder=0) case1 = "Case 1. Only receiving messages meant for you [naive case]" case2 = "Case 2. Receiving messages for everyone [naive case]" case3 = "Case 3. All private messages go over one discovery topic [naive case]" case4 = "Case 4. All private messages partitioned into shards [naive case]" case5 = "Case 5. Case 4 + All messages passed through bloom filter" case6 = "Case 6. Case 5 + Benign duplicate receives" case7 = "Case 7. Case 6 + Mailserver case under good conditions with small bloom fp and mostly offline" case8 = "Case 8. Waku - No metadata protection with bloom filter and one node connected; static shard" plt.xlabel('number of users (log)') plt.ylabel('mb/day (log)') plt.legend([case1, case2, case3, case4, case5, case6, case7, case8], loc='upper left') plt.xscale('log') plt.yscale('log') plt.axhspan(0, 10, facecolor='0.2', alpha=0.2, color='blue') plt.axhspan(10, 30, facecolor='0.2', alpha=0.2, color='green') plt.axhspan(30, 100, facecolor='0.2', alpha=0.2, color='orange') plt.axhspan(100, 10**6, facecolor='0.2', alpha=0.2, color='red') #plt.axvspan(0, 10**2+3, facecolor='0.2', alpha=0.5) #plt.axvspan(10**4, 10**4+10**2, facecolor='0.2', alpha=0.5) #plt.axvspan(10**6, 10**6+10**4, facecolor='0.2', alpha=0.5) #for i in range(0, 5): # plt.axhspan(i, i+.2, facecolor='0.2', alpha=0.5) plt.show()

41.309859

103

0.703034

9d44910e8c82debe9ba07f0a00ed736a65d972a9

2,000

py

Python

polydomino/search.py

PsiACE/polydomino

ade7cdb303cb4073d8c075659a5494392d31f8b4

[ "MIT" ]

null

polydomino/search.py

PsiACE/polydomino

ade7cdb303cb4073d8c075659a5494392d31f8b4

[ "MIT" ]

null

polydomino/search.py

PsiACE/polydomino

ade7cdb303cb4073d8c075659a5494392d31f8b4

[ "MIT" ]

null

# import the necessary packages import argparse import cv2 import numpy as np from polydomino.colordescriptor import ColorDescriptor from polydomino.searcher import Searcher # construct the argument parser and parse the arguments ap = argparse.ArgumentParser() ap.add_argument( "-i", "--index", required=True, help="Path to where the computed index will be stored", ) ap.add_argument("-q", "--query", required=True, help="Path to the query image") ap.add_argument( "-fm", "--features", required=True, help="Method to get features of pics", ) ap.add_argument( "-sm", "--searcher", required=True, help="Method to search pics", ) # ap.add_argument("-r", "--result-path", required=True, help="Path to the result path") args = vars(ap.parse_args()) # initialize the image descriptor cd = ColorDescriptor((8, 12, 3)) # load the query image and describe it query = cv2.imread(args["query"]) if args["features"] == "color-moments": features = cd.color_moments(query) elif args["features"] == "hsv-describe": features = cd.hsv_describe(query) elif args["features"] == "gray-matrix": features = cd.gray_matrix(query) elif args["features"] == "humoments": features = cd.humoments(query) elif args["features"] == "ahash": features = cd.ahash(query) elif args["features"] == "phash": features = cd.phash(query) elif args["features"] == "dhash": features = cd.dhash(query) elif args["features"] == "mse": features = cd.mse(query) elif args["features"] == "hog": features = cd.hog(query) else: print("Sorry, we don't support this method.") exit(1) # perform the search method = args["searcher"] searcher = Searcher(args["index"]) results = searcher.search(features, method) print(results) # display the query cv2.namedWindow("Query", 0) cv2.resizeWindow("Query", 640, 480) cv2.imshow("Query", query) # loop over the results for (score, resultID) in results: result = cv2.imread(resultID) cv2.imshow("Result", result) cv2.waitKey(0)

30.30303

87

0.6935

9d44ee135064ae4d96e5b84d0ccf61816f50cfb1

1,253

py

Python

irekua_dev_tools/cli.py

CONABIO-audio/irekua-dev-tools

87485592b7d4793c5e1f6eda2e00247810bfc99c

[ "BSD-4-Clause" ]

null

irekua_dev_tools/cli.py

CONABIO-audio/irekua-dev-tools

87485592b7d4793c5e1f6eda2e00247810bfc99c

[ "BSD-4-Clause" ]

null

irekua_dev_tools/cli.py

CONABIO-audio/irekua-dev-tools

87485592b7d4793c5e1f6eda2e00247810bfc99c

[ "BSD-4-Clause" ]

null

import click from irekua_dev_tools.utils import load_config from irekua_dev_tools.utils import get_working_directory from irekua_dev_tools.utils import load_environment_variables from irekua_dev_tools.utils import load_repository_info from . import git from . import dev from . import config from . import db from .extra import clean cli.add_command(dev.cli) cli.add_command(git.cli) cli.add_command(config.cli) cli.add_command(db.cli) cli.add_command(clean)

29.833333

73

0.751796

9d451d7664d2140e40043248faa30a6b327e59ee

2,880

py

Python

optimism/test/testMinimizeScalar.py

btalamini/optimism

023e1b2a0b137900a7517e4c7ac5056255cf7bbe

[ "MIT" ]

null

optimism/test/testMinimizeScalar.py

btalamini/optimism

023e1b2a0b137900a7517e4c7ac5056255cf7bbe

[ "MIT" ]

1

2022-03-12T00:01:12.000Z

optimism/test/testMinimizeScalar.py

btalamini/optimism

023e1b2a0b137900a7517e4c7ac5056255cf7bbe

[ "MIT" ]

3

2021-12-23T19:53:31.000Z

2022-03-27T23:12:03.000Z

from optimism.JaxConfig import * from optimism import MinimizeScalar from optimism.test import TestFixture from optimism.material import J2Plastic df = jacfwd(f) if __name__ == '__main__': TestFixture.unittest.main()

34.698795

95

0.515278

9d46c2badf319d174f35513f77f2237bac4308e9

2,709

py

Python

anima/ui/review_dialog.py

MehmetErer/anima

f92ae599b5a4c181fc8e131a9ccdde537e635303

[ "MIT" ]

101

2015-02-08T22:20:11.000Z

2022-03-21T18:56:42.000Z

anima/ui/review_dialog.py

MehmetErer/anima

f92ae599b5a4c181fc8e131a9ccdde537e635303

[ "MIT" ]

23

2016-11-30T08:33:21.000Z

2021-01-26T12:11:12.000Z

anima/ui/review_dialog.py

MehmetErer/anima

f92ae599b5a4c181fc8e131a9ccdde537e635303

[ "MIT" ]

27

2015-01-03T06:49:45.000Z

2021-12-28T03:30:54.000Z

# -*- coding: utf-8 -*- """ import datetime from anima import defaults defaults.timing_resolution = datetime.timedelta(minutes=10) from anima.ui import SET_PYSIDE2 SET_PYSIDE2() from anima.ui.widgets.review import APPROVE, REQUEST_REVISION from anima.ui import review_dialog review_dialog.UI(review_type=REQUEST_REVISION) """ from anima.ui.lib import QtCore, QtWidgets from anima.ui.base import ui_caller, AnimaDialogBase def UI(app_in=None, executor=None, **kwargs): """ :param app_in: A Qt Application instance, which you can pass to let the UI be attached to the given applications event process. :param executor: Instead of calling app.exec_ the UI will call this given function. It also passes the created app instance to this executor. """ return ui_caller(app_in, executor, ReviewDialog, **kwargs)

29.769231

80

0.655592

9d47cbe33f2156eddf7fcd553e506425ed8d1607

12,737

py

Python

squares/dsl/interpreter.py

Vivokas20/SKEL

d8766ceaa8aa766ea3580bbb61b747572ebfe77c

[ "Apache-2.0" ]

1

2022-01-20T14:57:30.000Z

squares/dsl/interpreter.py

Vivokas20/SKEL

d8766ceaa8aa766ea3580bbb61b747572ebfe77c

[ "Apache-2.0" ]

null

squares/dsl/interpreter.py

Vivokas20/SKEL

d8766ceaa8aa766ea3580bbb61b747572ebfe77c

[ "Apache-2.0" ]

null

import math import re from itertools import permutations from logging import getLogger from typing import Tuple, Union from rpy2 import robjects from rpy2.rinterface_lib.embedded import RRuntimeError from z3 import BitVecVal from .. import util, results from ..decider import RowNumberInfo from ..program import LineInterpreter from ..tyrell.interpreter import InterpreterError logger = getLogger('squares.interpreter')

42.885522

176

0.544241

9d4857e094a5401228d6f2b6484e13982abb69b9

7,869

py

Python

src/data_preparation/process_airbnb_data.py

ejgenc/Data-Analysis_Istanbul-Health-Tourism

34b9838690ca640c6a7a60f63eb2f51983ec46ef

[ "MIT" ]

1

2020-11-18T15:27:53.000Z

src/data_preparation/process_airbnb_data.py

ejgenc/Data-Analysis_Istanbul-Health-Tourism

34b9838690ca640c6a7a60f63eb2f51983ec46ef

[ "MIT" ]

null

src/data_preparation/process_airbnb_data.py

ejgenc/Data-Analysis_Istanbul-Health-Tourism

34b9838690ca640c6a7a60f63eb2f51983ec46ef

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ ------ What is this file? ------ This script targets the istanbul_airbnb_raw.csv file. It cleans the .csv file in order to prepare it for further analysis """ #%% --- Import Required Packages --- import os import pathlib from pathlib import Path # To wrap around filepaths import numpy as np import pandas as pd from matplotlib import pyplot as plt from scipy.stats import iqr from src.helper_functions.data_preparation_helper_functions import sample_and_read_from_df from src.helper_functions.data_preparation_helper_functions import report_null_values #%% --- Set proper directory to assure integration with doit --- abspath = os.path.abspath(__file__) dname = os.path.dirname(abspath) os.chdir(dname) #%% --- Import Data --- import_fp = Path("../../data/raw/istanbul_airbnb_raw.csv") airbnb = pd.read_csv(import_fp, encoding='utf-8-sig') #%% --- Get a general sense of the datasets --- # Shape of the data print(airbnb.shape) # 16251 rows, 16 cols # First few lines print(airbnb.head()) #Not much info, let's print the columns airbnb_columns = airbnb.columns #%% --- Clean the dataset: Relevant - Irrelevant Columns --- airbnb_unwanted_columns = ["neighbourhood_group", "last_review", "number_of_reviews", "minimum_nights", "reviews_per_month", "calculated_host_listings_count", "availability_365"] #Drop unwanted columns airbnb.drop(columns = airbnb_unwanted_columns, axis = 1, inplace = True) # Check shape now print(airbnb.shape) # 16251 rows, 9 cols #%% --- Clean the dataset: Further Troubleshooting --- #I want to be able to randomly take n samples from each dataset and then print them #on a clean format to see the potential problems #If i had something to test for, i'd strive for somewhat of a representative sample size #while sampling. However, i think the best to do here would be to print what i can read #because i don't have any computational measure to test for something: sample_and_read_from_df(airbnb, 20) #SPOTTED PROBLEMS: # dataframe airbnb column neigborhood is not properly formatted: # Formatting fixes # should actually be called "district_tr" # There should be an accompanying "district_eng" column. #%% --- Fix column naming --- #I can use either dataframe.columns attribute to assign new columns #or i can pass a dictionary with old names/new names into dataframe.rename() airbnb_columns_in_english = ["listing_id", "name", "host_id", "host_name", "district_eng", "latitude", "longitude", "room_type", "price"] airbnb.columns = airbnb_columns_in_english #%% --- One-off fix for districts named "Eyup" --- eyup_mask = airbnb.loc[:,"district_eng"] == "Eyup" airbnb.loc[eyup_mask, "district_eng"] = "Eyupsultan" #%% --- Add a new "district_tr" column airbnb.loc[:,"district_tr"] = airbnb.loc[:,"district_eng"].str.lower().str.capitalize() #I will be using df.map() method, so i'll need two dataframes: one for existing values - tr values #and one for exixsting values - eng values unique_districts_tr_corrected = ["Kadky", "Fatih", "Tuzla", "Gaziosmanpaa", "skdar", "Adalar", "Saryer", "Arnavutky", "Silivri", "atalca", "Kkekmece", "Beyolu", "ile", "Kartal", "ili", "Beikta", "Kathane", "Esenyurt", "Bahelievler", "Avclar", "Baakehir", "Sultangazi", "Maltepe", "Sancaktepe", "Beykoz", "Bykekmece", "Bakrky", "Pendik", "Baclar", "Esenler", "Beylikdz", "mraniye", "Eypsultan", "ekmeky", "Ataehir", "Sultanbeyli", "Zeytinburnu", "Gngren", "Bayrampaa"] unique_districts_eng_corrected = ["Kadikoy", "Fatih", "Tuzla", "Gaziosmanpasa", "Uskudar", "Adalar", "Sariyer", "Arnavutkoy", "Silivri", "Catalca", "Kucukcekmece", "Beyoglu", "Sile", "Kartal", "Sisli", "Besiktas", "Kagithane", "Esenyurt", "Bahcelievler", "Avcilar", "Basaksehir", "Sultangazi", "Maltepe", "Sancaktepe", "Beykoz", "Buyukcekmece", "Bakirkoy", "Pendik", "Bagcilar", "Esenler", "Beylikduzu", "Umraniye", "Eyupsultan", "Cekmekoy", "Atasehir", "Sultanbeyli", "Zeytinburnu", "Gungoren", "Bayrampasa"] airbnb_unique_districts_dict_tr = dict(zip(unique_districts_eng_corrected, unique_districts_tr_corrected)) airbnb.loc[:,"district_tr"] = airbnb.loc[:,"district_tr"].map(airbnb_unique_districts_dict_tr) #%% --- EDA: Explore Missing Values --- #Let's check null values first null_report = report_null_values(airbnb) #We have so few missing values, dropping them won't affect our quality at all. # Let's do exactly that. airbnb.dropna(axis = 0, inplace = True) #%% --- EDA: Explore Datatype agreement --- #Now, let's check data type agreement for each column. data_types = airbnb.dtypes # The data types with "object" warrant further investigation #They could just be strings, but mixed data types also show as "object" # Let's select "object" data types and query once again. airbnb_dtype_object_only = airbnb.select_dtypes(include = ["object"]) print(airbnb_dtype_object_only.columns) #As all the column names seem to accomodate only strings, we can be #pretty sure that showing up as object is correct behavior. #%% --- EDA - Explore Outliers in price --- fig = plt.figure(figsize = (19.20, 10.80)) ax = fig.add_subplot(1,1,1) ax.hist(x = airbnb.loc[:,"price"], bins = 20) #Our histogram is very wonky. It's obvious that there are some issues. Let's see: # It doesn't make sense for a airbnb room to cost 0 liras. That's for sure. print(airbnb.loc[:,"price"].sort_values().head(20)) #What about maxes? print(airbnb.loc[:,"price"].sort_values(ascending = False).head(30)) #There are some very high maxes, that's for sure. Let's try to make heads and tails of #what these houses are: possible_outliers = airbnb.sort_values(by = "price", axis = 0, ascending = False).head(30) # A qualitative analysis of such houses show that there really aappears to be a problem #with pricing. Let's calculate the IQR to drop the outliers: #Calculate the iqr price_iqr = iqr(airbnb.loc[:,"price"], axis = 0) #Calculate q3 and q1 q1 = airbnb["price"].quantile(0.25) q3 = airbnb["price"].quantile(0.75) #Create min and max mask min_mask = airbnb.loc[:,"price"] >= q1 - (1.5 * price_iqr) max_mask = airbnb.loc[:,"price"] <= q3 + (1.5 * price_iqr) #Combine masks combined_mask = min_mask & max_mask #Create subset airbnb_within_iqr = airbnb.loc[combined_mask] fig = plt.figure(figsize = (19.20, 10.80)) ax = fig.add_subplot(1,1,1) ax.hist(x = airbnb_within_iqr.loc[:,"price"], bins = 20) #Alright, limiting our data to an IQR appears to omit a whole lot of data. #I am sure that some of the outliers we have are errors of entry. #However, the only ones that we can conclusively prove are the entries that are rated at 0. #We'll drop these #Create a mask for zeros zero_mask = (airbnb.loc[:,"price"] > 0) #Filter using the mask airbnb = airbnb.loc[zero_mask,:] # #%% --- Export Data --- export_fp = Path("../../data/processed/istanbul_airbnb_processed.csv") airbnb.to_csv(export_fp, encoding='utf-8-sig', index = False)

38.199029

106

0.641632

9d4ac45e3a86ef95dc9b84f578aa4f83f679c9b6

3,695

py

Python

py/shure.py

dman776/micboard

166987dfad529dc35654f402fdbbde7f16b60f77

[ "MIT" ]

44

2019-08-30T02:51:59.000Z

2022-03-15T13:47:18.000Z

py/shure.py

dman776/micboard

166987dfad529dc35654f402fdbbde7f16b60f77

[ "MIT" ]

21

2019-09-01T16:17:22.000Z

2022-02-01T15:47:55.000Z

py/shure.py

dman776/micboard

166987dfad529dc35654f402fdbbde7f16b60f77

[ "MIT" ]

16

2019-09-01T01:40:09.000Z

2022-03-15T17:12:28.000Z

import time import select import queue import atexit import sys import logging from networkdevice import ShureNetworkDevice from channel import chart_update_list, data_update_list # from mic import WirelessMic # from iem import IEM NetworkDevices = [] DeviceMessageQueue = queue.Queue() # @atexit.register # atexit.register(on_exit) # signal.signal(signal.SIGTERM, on_exit) # signal.signal(signal.SIGINT, on_exit)

29.56

97

0.603518

9d4cf41e0ad2b23397f4ee9bbfa792895ce345d0

277

py

Python

alviscorpus/status.py

Bibliome/alviscorpus

ec7bf45efbc6a3cd864fda48e0066090cfb93313

[ "MIT" ]

null

alviscorpus/status.py

Bibliome/alviscorpus

ec7bf45efbc6a3cd864fda48e0066090cfb93313

[ "MIT" ]

null

alviscorpus/status.py

Bibliome/alviscorpus

ec7bf45efbc6a3cd864fda48e0066090cfb93313

[ "MIT" ]

null

import enum QUEUED = Status.QUEUED STARTED = Status.STARTED FINISHED = Status.FINISHED ERROR = Status.ERROR

17.3125

26

0.67148

9d4d51c8583e8a9e583bab2100d07d40d0fad696

977

py

Python

01_simulate_competition_experiment.py

stevenshave/competition-label-affinity

2383309e852954a1bd88c6364087c3d57e7acec0

[ "MIT" ]

null

01_simulate_competition_experiment.py

stevenshave/competition-label-affinity

2383309e852954a1bd88c6364087c3d57e7acec0

[ "MIT" ]

null

01_simulate_competition_experiment.py

stevenshave/competition-label-affinity

2383309e852954a1bd88c6364087c3d57e7acec0

[ "MIT" ]

null

"""Simulation 1:1:1 comptition binding""" import numpy as np from high_accuracy_binding_equations import * # We can choose to work in a common unit, typically nM, or uM, as long as all # numbers are in the same unit, the result is valid. We assume uM for all # concentrations bellow. # First, lets simulate a few single points from three different systems # p, l and i are protrin, ligand and inhibitor concentrations respectively # kdpl is the dissociation constant (KD) of the protein-ligand interaction # kdpi is the dissociation constant (KD) of the protein-inhibitor interaction # We can either expand the dictionary with ** as shown in the example with # system1, or we can pass arguments to competition_pl with the following # singature: competition_pl(p, l , i, kdpl, kdpi) system1={"p":1, "l":2, "i":10, "kdpl":0.1, "kdpi":10} pl_conc=competition_pl(**system1) print(f"pl_conc = {round(pl_conc,4)}, fraction ligand bound = {round(pl_conc/system1['l'],4)}")

36.185185

95

0.745138

9d50b18aa63e6f3b4b6406ced31f91d878b8ae26

773

py

Python

e_vae_proj/qualitative/mnist/btcvae/gen_train.py

kuangdai/disentangling-vae

9a5f9da44a82a2c643b7289c4945320621b86247

[ "MIT" ]

1

2021-06-30T08:58:49.000Z

e_vae_proj/qualitative/mnist/btcvae/gen_train.py

kuangdai/disentangling-vae

9a5f9da44a82a2c643b7289c4945320621b86247

[ "MIT" ]

null

e_vae_proj/qualitative/mnist/btcvae/gen_train.py

kuangdai/disentangling-vae

9a5f9da44a82a2c643b7289c4945320621b86247

[ "MIT" ]

null

import numpy as np from pathlib import Path import sys if __name__ == '__main__': # absolute path my_path = Path(__file__).parent.resolve().expanduser() main_path = my_path.parent.parent seed = 0 nlat = 10 alpha = 1.0 beta = 6.0 gamma = 1.0 epochs = 100 # cmd template cmd = f'python main.py btcvae_mnist_{epochs}ep/z{nlat}_a{alpha}_b{beta}_g{gamma}_s{seed} -s {seed} ' \ f'--checkpoint-every 25 -d mnist -e {epochs} -b 64 --lr 0.0005 ' \ f'-z {nlat} -l btcvae --btcvae-A {alpha} --btcvae-B {beta} --btcvae-G {gamma} ' \ f'--no-test\n' with open(my_path / f'train_beta{beta}.sh', 'w') as f: unnormalized_beta = beta * nlat f.write(cmd)

28.62963

107

0.564036

9d5197f8d1796538860fe2f3fb98a1af46c8ef38

3,331

py

Python

tests/test_load.py

tom3131/simfin

8ef5a2b0dd67ddcd3f8b92b5cd45c1a483eeada1

[ "MIT" ]

231

2019-09-25T13:30:00.000Z

2022-03-26T08:00:47.000Z

tests/test_load.py

tom3131/simfin

8ef5a2b0dd67ddcd3f8b92b5cd45c1a483eeada1

[ "MIT" ]

11

2019-10-01T14:50:15.000Z

2022-02-23T10:35:47.000Z

tests/test_load.py

tom3131/simfin

8ef5a2b0dd67ddcd3f8b92b5cd45c1a483eeada1

[ "MIT" ]

36

2019-09-30T16:14:48.000Z

2022-03-19T19:59:30.000Z

########################################################################## # # Unit tests (pytest) for load.py # ########################################################################## # SimFin - Simple financial data for Python. # www.simfin.com - www.github.com/simfin/simfin # See README.md for instructions and LICENSE.txt for license details. ########################################################################## import simfin as sf from simfin.datasets import iter_all_datasets ########################################################################## # Test configuration. # Set data directory. sf.set_data_dir(data_dir='~/simfin_data/') # Load API key or use default 'free' if key-file doesn't exist. sf.load_api_key(path='~/simfin_api_key.txt', default_key='free') # Set number of days before refreshing data from SimFin server. refresh_days = 30 ########################################################################## # Helper functions. def _create_kwargs(variant, market): """ Create a dict with keyword args for sf.load() functions that take variant, market and refresh_days as kwargs. """ kwargs = \ { 'variant': variant, 'market': market, 'refresh_days': refresh_days, } return kwargs ########################################################################## # Test functions. def test_load(): """Test simfin.bulk.load()""" for dataset, variant, market in iter_all_datasets(): sf.load(dataset=dataset, variant=variant, market=market, refresh_days=refresh_days) def test_load_income(): """Test simfin.bulk.load_income()""" for dataset, variant, market in iter_all_datasets(datasets='income'): kwargs = _create_kwargs(variant=variant, market=market) sf.load_income(**kwargs) sf.load_income_banks(**kwargs) sf.load_income_insurance(**kwargs) def test_load_balance(): """Test simfin.bulk.load_balance()""" for dataset, variant, market in iter_all_datasets(datasets='balance'): kwargs = _create_kwargs(variant=variant, market=market) sf.load_balance(**kwargs) sf.load_balance_banks(**kwargs) sf.load_balance_insurance(**kwargs) def test_load_cashflow(): """Test simfin.bulk.load_cashflow()""" for dataset, variant, market in iter_all_datasets(datasets='cashflow'): kwargs = _create_kwargs(variant=variant, market=market) sf.load_cashflow(**kwargs) sf.load_cashflow_banks(**kwargs) sf.load_cashflow_insurance(**kwargs) def test_load_shareprices(): """Test simfin.bulk.load_shareprices()""" for dataset, variant, market in iter_all_datasets(datasets='shareprices'): kwargs = _create_kwargs(variant=variant, market=market) sf.load_shareprices(**kwargs) def test_load_companies(): """Test simfin.bulk.load_companies()""" for dataset, variant, market in iter_all_datasets(datasets='companies'): kwargs = _create_kwargs(variant=variant, market=market) sf.load_companies(**kwargs) def test_load_industries(): """Test simfin.bulk.load_industries()""" sf.load_industries(refresh_days=refresh_days) ##########################################################################

31.424528

78

0.576403

9d5477ef2956d3615e64662b0ab23440b2cbff69

447

py

Python

lab/__init__.py

patel-zeel/lab

cc0df2c03196863041e78fa4179445341e86958c

[ "MIT" ]

36

2018-05-08T20:54:21.000Z

2022-02-24T09:15:58.000Z

lab/__init__.py

patel-zeel/lab

cc0df2c03196863041e78fa4179445341e86958c

[ "MIT" ]

4

2021-06-24T11:59:29.000Z

2022-02-01T15:51:30.000Z

lab/__init__.py

patel-zeel/lab

cc0df2c03196863041e78fa4179445341e86958c

[ "MIT" ]

3

2021-02-14T13:00:26.000Z

2021-12-10T08:55:17.000Z

import sys from plum import Dispatcher B = sys.modules[__name__] # Allow both import styles. dispatch = Dispatcher() # This dispatch namespace will be used everywhere. from .generic import * from .shaping import * from .linear_algebra import * from .random import * from .numpy import * from .types import * from .control_flow import * # Fix namespace issues with `B.bvn_cdf` simply by setting it explicitly. B.bvn_cdf = B.generic.bvn_cdf

22.35

75

0.756152

9d556827bb836c6e6f6530ec156f0777935a5dea

1,514

py

Python

async_nbgrader/apps/exportapp.py

IllumiDesk/async_nbgrader

427e1b634277c043a1ed9f00bf7e417e0f611aca

[ "Apache-2.0" ]

2

2021-06-23T17:58:22.000Z

2021-09-27T10:00:01.000Z

async_nbgrader/apps/exportapp.py

IllumiDesk/async-nbgrader

427e1b634277c043a1ed9f00bf7e417e0f611aca

[ "Apache-2.0" ]

6

2021-06-17T21:40:24.000Z

2021-11-11T17:48:15.000Z

async_nbgrader/apps/exportapp.py

IllumiDesk/async-nbgrader

427e1b634277c043a1ed9f00bf7e417e0f611aca

[ "Apache-2.0" ]

2

2021-06-10T18:16:22.000Z

2021-06-17T02:52:45.000Z

# coding: utf-8 from nbgrader.api import Gradebook from nbgrader.apps import ExportApp as BaseExportApp from traitlets import Instance from traitlets import Type from traitlets import default from ..plugins import CanvasCsvExportPlugin from ..plugins import CustomExportPlugin aliases = { "log-level": "Application.log_level", "db": "CourseDirectory.db_url", "to": "CanvasCsvExportPlugin.to", "canvas_import": "CanvasCsvExportPlugin.canvas_import", "exporter": "ExportApp.plugin_class", "assignment": "CanvasCsvExportPlugin.assignment", "student": "CanvasCsvExportPlugin.student", "course": "CourseDirectory.course_id", } flags = {}

27.527273

78

0.703435

9d55833e8ac84841e916071829ab4546156cae04

2,810

py

Python

django_ocr_server/conf.py

shmakovpn/django_ocr_server

4d694629c39c18a6c13bcdfafdb8258b78e5a859

[ "Apache-2.0" ]

17

2019-12-04T03:14:56.000Z

2022-03-27T07:05:19.000Z

django_ocr_server/conf.py

shmakovpn/django_ocr_server

4d694629c39c18a6c13bcdfafdb8258b78e5a859

[ "Apache-2.0" ]

1

2020-04-17T07:32:30.000Z

django_ocr_server/conf.py

shmakovpn/django_ocr_server

4d694629c39c18a6c13bcdfafdb8258b78e5a859

[ "Apache-2.0" ]

5

2020-03-16T10:43:03.000Z

2021-07-14T14:43:49.000Z

""" django_ocr_server/conf.py +++++++++++++++++++++++++ The settings manager for **django_ocr_server**. Usage: .. code-block:: python from django_ocr_server.conf import ocr_settings # Next line will print a value of **OCR_TESSERACT_LANG** # using the variable from the Django's *settings.py* file # if the variable is set there. # Or the default value of **OCR_TESSERACT_LANG** from # *django_ocr_server/default_settings.py* otherwise. print(ocr_settings.OCR_TESSERACT_LANG) | Author: shmakovpn <shmakovpn@yandex.ru> | Date: 2021-01-20 """ from typing import List from datetime import timedelta from django.conf import settings as _s import django_ocr_server.default_settings as _ds ocr_settings: DjangoOcrSettings = DjangoOcrSettings() """The instance of settings manager of **django_ocr_server**"""

29.893617

79

0.687544

9d56f0959997626e16345a92ca50c1b01d2ed5e6

105

py

Python

ibms_project/sfm/apps.py

mohdbakhrayba/ibms

029e1f3bf108586289c65bb1d547f86851f9494f

[ "Apache-2.0" ]

null

ibms_project/sfm/apps.py

mohdbakhrayba/ibms

029e1f3bf108586289c65bb1d547f86851f9494f

[ "Apache-2.0" ]

null

ibms_project/sfm/apps.py

mohdbakhrayba/ibms

029e1f3bf108586289c65bb1d547f86851f9494f

[ "Apache-2.0" ]

null

from django.apps import AppConfig

17.5

33

0.704762

9d5735cba5c6faf4bc0915b6d346541d85cbb4ac

15,960

py

Python

torsion/model/symmetry_function.py

hnlab/TorsionNet

e81ab624f1340765345b34240a049a8cc5f4d581

[ "MIT" ]

15

2021-01-15T01:54:26.000Z

2022-03-31T16:00:52.000Z

torsion/model/symmetry_function.py

hnlab/TorsionNet

e81ab624f1340765345b34240a049a8cc5f4d581

[ "MIT" ]

2

2021-07-21T22:42:09.000Z

2021-11-22T06:39:20.000Z

torsion/model/symmetry_function.py

hnlab/TorsionNet

e81ab624f1340765345b34240a049a8cc5f4d581

[ "MIT" ]

6

2021-01-16T04:07:17.000Z

2022-02-23T02:11:49.000Z

import math import numpy as np from openeye import oechem from torsion.inchi_keys import get_torsion_oeatom_list, get_torsion_oebond def GetPairwiseDistanceMatrix(icoords, jcoords): ''' input: two sets of coordinates, icoords, jcoords; each of which are a list of OEDoubleArray(3) containing x, y, and z component output: xij - the x component of the distance matrix yij - the y component of the distance matrix zij - the z component of the distance matrix rij - the distance matrix rij2 - square of the distance matrix ''' nullRet = [None, None, None, None, None] ni = len(icoords) nj = len(jcoords) try: iArrayX = np.array([c[0] for c in icoords]) iArrayY = np.array([c[1] for c in icoords]) iArrayZ = np.array([c[2] for c in icoords]) iArrayX = np.repeat(iArrayX, nj) iArrayY = np.repeat(iArrayY, nj) iArrayZ = np.repeat(iArrayZ, nj) iArrayX = iArrayX.reshape(ni, nj) iArrayY = iArrayY.reshape(ni, nj) iArrayZ = iArrayZ.reshape(ni, nj) jArrayX = np.array([c[0] for c in jcoords]) jArrayY = np.array([c[1] for c in jcoords]) jArrayZ = np.array([c[2] for c in jcoords]) jArrayX = np.repeat(jArrayX, ni) jArrayY = np.repeat(jArrayY, ni) jArrayZ = np.repeat(jArrayZ, ni) jArrayX = jArrayX.reshape(nj, ni) jArrayY = jArrayY.reshape(nj, ni) jArrayZ = jArrayZ.reshape(nj, ni) jArrayX = np.transpose(jArrayX) jArrayY = np.transpose(jArrayY) jArrayZ = np.transpose(jArrayZ) ijArrayX = jArrayX - iArrayX ijArrayY = jArrayY - iArrayY ijArrayZ = jArrayZ - iArrayZ rijArraySq = (ijArrayX * ijArrayX) + (ijArrayY * ijArrayY) + (ijArrayZ * ijArrayZ) rijArray = np.sqrt(rijArraySq) return ijArrayX, ijArrayY, ijArrayZ, rijArray, rijArraySq except: return nullRet def GetThetaIJKMatrix(iCoords, jCoords, kCoords): ''' Using the given input, calculates a matrix of angles ijk iCoords -> OEDoubleArray containing x, y, and z component of the reference coordinate jCoordsList -> list of N OEDoubleArrays, each OEDoubleArray is of size 3 kCoordsList -> list of M OEDoubleArrays, each OEDoubleArray is of size 3 return a N-by-M matrix of angle theta_ijk ''' jiArrayX, jiArrayY, jiArrayZ, rjiArray, rjiArraySq \ = GetPairwiseDistanceMatrix(jCoords, iCoords) jkArrayX, jkArrayY, jkArrayZ, rjkArray, rjkArraySq \ = GetPairwiseDistanceMatrix(jCoords, kCoords) if jCoords == kCoords: rjkArray = np.eye(len(jCoords)) + np.sqrt(rjkArraySq) else: rjkArray = np.sqrt(rjkArraySq) if jCoords == iCoords: rjiArray = np.eye(len(jCoords)) + np.sqrt(rjiArraySq) else: rjiArray = np.sqrt(rjiArraySq) jiArrayX = jiArrayX / rjiArray jiArrayY = jiArrayY / rjiArray jiArrayZ = jiArrayZ / rjiArray jkArrayX = jkArrayX / rjkArray jkArrayY = jkArrayY / rjkArray jkArrayZ = jkArrayZ / rjkArray dotProduct = (jiArrayX * jkArrayX) + (jiArrayY * jkArrayY) + (jiArrayZ * jkArrayZ) dotProduct = np.select([dotProduct <= -1.0, dotProduct >= 1.0, np.abs(dotProduct) < 1.0], [-0.999, 0.999, dotProduct]) theta_ijk = np.arccos(dotProduct) return theta_ijk def GetThetaIJKLMatrix(mol, iAtoms, jAtom, kAtom, lAtoms, transform=True): ''' Using the given input, calculates a matrix of torsion angles around jk jAtom, kAtom -> OEAtombase, middle two atoms of the torsion iAtoms -> list of N OEAtombase lAtoms -> list of M OEAtombase return a N-by-M matrix of angle theta_ijkl ''' torsions = [] for iAtom in iAtoms: for lAtom in lAtoms: tor_angle = oechem.OEGetTorsion(mol, iAtom, jAtom, kAtom, lAtom) if not transform: torsions.append(tor_angle) else: torsions.append((math.pi + tor_angle) / 4.0) theta_ijkl = np.array(torsions) theta_ijkl = theta_ijkl.reshape(len(iAtoms), len(lAtoms)) return theta_ijkl

40.507614

111

0.553446

9d5757c4a8bf60547e9dd883852158e386888c4b

6,785

py

Python

recommendation/recommendation.py

Jackson-Y/Machine-Learning

ea0a8c65ce93501d51fad2d73300dc0a37e2c1d8

[ "MIT" ]

4

2017-08-17T02:11:45.000Z

2017-09-25T00:46:13.000Z

recommendation/recommendation.py

Jackson-Y/Machine-Learning

ea0a8c65ce93501d51fad2d73300dc0a37e2c1d8

[ "MIT" ]

null

recommendation/recommendation.py

Jackson-Y/Machine-Learning

ea0a8c65ce93501d51fad2d73300dc0a37e2c1d8

[ "MIT" ]

null

""" Candidate generation & LTR Learning to Ranking""" # -*- coding: utf-8 -*- from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import sys import argparse from operator import itemgetter from math import sqrt import pandas as pd import pymysql from sklearn.model_selection import train_test_split # from sklearn.metrics.pairwise import pairwise_distances # from sklearn.metrics import mean_squared_error FLAGS = None def main(_): """main function""" user_cf = UserBasedCF(20, 10) user_cf.load_data() user_cf.calc_user_similarity() recommended_articled = user_cf.recommendation(FLAGS.uid) print(recommended_articled[0:10]) out = PrintArticles(recommended_articled[0:10]) out.output() # user_cf.evaluate() if __name__ == '__main__': parser = argparse.ArgumentParser() parser.register("type", "bool", lambda v: v.lower() == "true") parser.add_argument( "--uid", type=int, default=80871, help="The user who is going to be recommended articles." ) parser.add_argument( "--n", type=int, default=10, help="Number of recommended articles." ) FLAGS, unparsed = parser.parse_known_args() print("{} {}".format(sys.argv[0], unparsed)) print(FLAGS) main(FLAGS)

36.875

115

0.592336

9d59344dd6f980db538f0cd26f71a979f4b914e4

1,592

py

Python

orchestration/dags/twitter_streaming.py

amommendes/tweetstream

ef09928a4f3344210c597388332d18a53149bb41

[ "Apache-2.0" ]

null

orchestration/dags/twitter_streaming.py

amommendes/tweetstream

ef09928a4f3344210c597388332d18a53149bb41

[ "Apache-2.0" ]

null

orchestration/dags/twitter_streaming.py

amommendes/tweetstream

ef09928a4f3344210c597388332d18a53149bb41

[ "Apache-2.0" ]

null

from datetime import timedelta from airflow import DAG from airflow.utils.dates import days_ago from airflow.operators.python_operator import PythonOperator from tweetstream.consumers.twitter_streaming import TwitterStreamingConsumer from tweetstream.clients.spark import SparkClient default_args = { "owner": "tweeetstream", "depends_on_past": False, "start_date": days_ago(1), "email": ["tweetstream@team.com"], "email_on_failure": False, "email_on_retry": False, "retries": 1, "retry_delay": timedelta(minutes=5), } dag = DAG( dag_id="twitter_streaming", default_args=default_args, description="Tweets Streaming Consumer", schedule_interval=timedelta(days=1), ) start_job_task = PythonOperator( dag=dag, task_id="start_streaming", python_callable=main, execution_timeout=None, )

30.615385

105

0.692839

9d59ba6b91ae4d068be41f7bfb3634b177f8ade2

217

py

Python

tests/expr/expr09.py

ktok07b6/polyphony

657c5c7440520db6b4985970bd50547407693ac4

[ "MIT" ]

83

2015-11-30T09:59:13.000Z

2021-08-03T09:12:28.000Z

tests/expr/expr09.py

jesseclin/polyphony

657c5c7440520db6b4985970bd50547407693ac4

[ "MIT" ]

4

2017-02-10T01:43:11.000Z

2020-07-14T03:52:25.000Z

tests/expr/expr09.py

jesseclin/polyphony

657c5c7440520db6b4985970bd50547407693ac4

[ "MIT" ]

11

2016-11-18T14:39:15.000Z

2021-02-23T10:05:20.000Z

from polyphony import testbench test()

16.692308

38

0.654378

9d5d5a4039dbeb89722961536cacebbce65b4ec3

1,059

py

Python

setup.py

fg1/ipynb_format

58dc276fca4f1fbb179d7e84ce41d59663d011c2

[ "BSD-3-Clause" ]

null

setup.py

fg1/ipynb_format

58dc276fca4f1fbb179d7e84ce41d59663d011c2

[ "BSD-3-Clause" ]

null

setup.py

fg1/ipynb_format

58dc276fca4f1fbb179d7e84ce41d59663d011c2

[ "BSD-3-Clause" ]

null

#!/usr/bin/python from setuptools import setup, find_packages from codecs import open with open('README.rst', 'r', 'utf-8') as fd: long_description = fd.read() setup(name='ipynb_format', version='0.1.1', description='A code formatter for python code in ipython notebooks', long_description=long_description, url='https://github.com/fg1/ipynb_format', author='fg1', license='BSD', classifiers=[ 'Development Status :: 3 - Alpha', 'Environment :: Console', 'Intended Audience :: Science/Research', 'License :: OSI Approved :: BSD License', 'Programming Language :: Python :: 2', 'Programming Language :: Python :: 2.6', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3', ], keywords='ipython notebook', packages=find_packages(), install_requires=['yapf'], entry_points={ 'console_scripts': [ 'ipynb_format=ipynb_format:cli', ], }, )

31.147059

74

0.588291

9d5e11c9180f5fb664452c5f269722fdf9e6a4db

140

py

Python

Homework/Homework2/2_3.py

404nofound/CS521-Info-Str-Python

2ead691c519f29713419e79d600a2d2a1c87d1c1

[ "Apache-2.0" ]

null

Homework/Homework2/2_3.py

404nofound/CS521-Info-Str-Python

2ead691c519f29713419e79d600a2d2a1c87d1c1

[ "Apache-2.0" ]

null

Homework/Homework2/2_3.py

404nofound/CS521-Info-Str-Python

2ead691c519f29713419e79d600a2d2a1c87d1c1

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- feet = eval(input("Enter a value for feet: ")) meter = feet * 0.305 print (feet, "feet is %.4f meters" %(meter))

17.5

46

0.585714

9d5e75f8cb60c04470b0412ae613020592c5aad7

16,072

py

Python

nn/units/ceecnet.py

feevos/ceecnet

9dc76f8cd16d44b264cae8c5846eefb8fcf6162d

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

45

2020-09-07T01:19:44.000Z

2022-03-15T14:44:20.000Z

nn/units/ceecnet.py

xautdestiny/ceecnet

897cd5c128ffd85369732d1cac82a6ddc7643afa

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

10

2020-10-02T10:14:47.000Z

2021-10-19T09:34:14.000Z

nn/units/ceecnet.py

xautdestiny/ceecnet

897cd5c128ffd85369732d1cac82a6ddc7643afa

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

14

2020-09-29T02:46:18.000Z

2021-09-27T07:13:47.000Z

from mxnet import gluon from mxnet.gluon import HybridBlock from ceecnet.nn.layers.conv2Dnormed import * from ceecnet.utils.get_norm import * from ceecnet.nn.layers.attention import * # ======= Definitions for CEEC unit v2 (replace concatenations with Fusion ========================= # -------------------------------------- helper functions ------------------------------------------- # -------------------------------------------------------------------------------------------------------------------

45.659091

186

0.66059

9d6069e2ba0263497aa9f814cd08018989e4473f

1,928

py

Python

reviewboard/reviews/search_indexes.py

znick/reviewboard

f32320b267efcdf2feff1661eabe57f99ef490a7

[ "MIT" ]

1

2018-08-23T09:19:02.000Z

reviewboard/reviews/search_indexes.py

klpyang/reviewboard

d7dabf36e5b492f18048dd7084026bf99d6933c5

[ "MIT" ]

null

reviewboard/reviews/search_indexes.py

klpyang/reviewboard

d7dabf36e5b492f18048dd7084026bf99d6933c5

[ "MIT" ]

1

2021-11-23T15:25:44.000Z

from django.db.models import Q from haystack import indexes from reviewboard.reviews.models import ReviewRequest

39.346939

76

0.705394

19b2caec75b18b0aa3e0597b5caa0b0c55ce8cad

7,365

py

Python

gpss/transaction.py

martendo/gpss.py

52c6781bd8a65b651381ed11da9e31ddfae6e313

[ "MIT" ]

2

2021-11-28T08:48:02.000Z

2022-03-09T16:19:06.000Z

gpss/transaction.py

martendo/gpss.py

52c6781bd8a65b651381ed11da9e31ddfae6e313

[ "MIT" ]

null

gpss/transaction.py

martendo/gpss.py

52c6781bd8a65b651381ed11da9e31ddfae6e313

[ "MIT" ]

null

from .statement import Statement, StatementType from .event import Event from ._helpers import debugmsg, simulation_error

41.610169

81

0.509029

19b32c34ea299311dabdf3d678344f668cb1f1a4

234

py

Python

stone/config.py

ichengplus/mpmatrix

41cf1ac48abe9aef1b92f1174157608a60e30da0

[ "Apache-2.0" ]

null

stone/config.py

ichengplus/mpmatrix

41cf1ac48abe9aef1b92f1174157608a60e30da0

[ "Apache-2.0" ]

3

2021-03-10T16:23:59.000Z

2022-02-13T12:02:00.000Z

stone/config.py

ichengplus/mpmatrix

41cf1ac48abe9aef1b92f1174157608a60e30da0

[ "Apache-2.0" ]

null

REDIS_URL = "redis://redis:6379/0" DEBUG = True TESTING = False JOBS = [ { 'id': 'actoken_refresh', 'func': 'actoken:refresh', 'args': None, 'trigger': 'interval', 'seconds': 7000 } ]

15.6

34

0.504274

19b3b12f916bfa71763e1f5555965f2dffc3a223

727

py

Python

utils/update.py

adavila0703/warehouse-hub

29778d605e372d6d6b41d05a3637edefb047f0bc

[ "MIT" ]

null

utils/update.py

adavila0703/warehouse-hub

29778d605e372d6d6b41d05a3637edefb047f0bc

[ "MIT" ]

null

utils/update.py

adavila0703/warehouse-hub

29778d605e372d6d6b41d05a3637edefb047f0bc

[ "MIT" ]

null

from shutil import copy, copytree, rmtree import pathlib import os import time def update(): """Update is a script to auto update all the files that the user is using""" print('Warehouse Hub is updating, do not close this window...') time.sleep(3) print('Applying patch...') time.sleep(1) copy('C:/warehousehub/warehousehub.exe', pathlib.Path().absolute()) rmtree(f'{pathlib.Path().absolute()}/templates') copytree('C:/warehousehub/templates', f'{pathlib.Path().absolute()}/templates') print('Patch Completed!') print('Warehouse Hub is restarting, please wait...') os.system(f'cmd /c "{pathlib.Path().absolute()}/warehousehub.exe"') if __name__ == '__main__': update()

25.068966

83

0.672627

19b3f6aeb28dd07d2770e4ea600d2a99c0c06e65

3,134

py

Python

train_video.py

jacke121/MBMD

2daf5edb4fb40ee652baead4f9332ca00fa111a5

[ "MIT" ]

220

2018-09-17T15:42:54.000Z

2021-09-13T13:14:22.000Z

train_video.py

jacke121/MBMD

2daf5edb4fb40ee652baead4f9332ca00fa111a5

[ "MIT" ]

12

2018-09-19T09:30:42.000Z

2019-07-01T04:03:51.000Z

train_video.py

jacke121/MBMD

2daf5edb4fb40ee652baead4f9332ca00fa111a5

[ "MIT" ]

60

2018-09-18T00:29:50.000Z

2021-02-22T03:55:19.000Z

import functools import tensorflow as tf from core import trainer_video, input_reader from core.model_builder import build_man_model from google.protobuf import text_format from object_detection.builders import input_reader_builder from object_detection.protos import input_reader_pb2 from object_detection.protos import model_pb2 from object_detection.protos import pipeline_pb2 from object_detection.protos import train_pb2 import os ''' lijun's code modify bb to conv1*2 conv3*2 l2 normalization to match ''' os.environ["CUDA_VISIBLE_DEVICES"]="0" #os.environ["CUDA_VISIBLE_DEVICES"]="0" tf.logging.set_verbosity(tf.logging.INFO) flags = tf.app.flags flags.DEFINE_string('train_dir', 'model/dump', 'Directory to save the checkpoints and training summaries.') flags.DEFINE_string('pipeline_config_path', 'model/ssd_mobilenet_video.config', 'Path to a pipeline_pb2.TrainEvalPipelineConfig config ' 'file. If provided, other configs are ignored') flags.DEFINE_string('train_config_path', '', 'Path to a train_pb2.TrainConfig config file.') flags.DEFINE_string('input_config_path', '', 'Path to an input_reader_pb2.InputReader config file.') flags.DEFINE_string('model_config_path', '', 'Path to a model_pb2.DetectionModel config file.') flags.DEFINE_string('image_root', '/home/xiaobai/Documents/ILSVRC2014_DET_train/image/ILSVRC2014_DET_train', 'Root path to input images') flags.DEFINE_string('video_root', '/home/xiaobai/Documents/ILSVRC2015/', 'Root path to input videos') flags.DEFINE_string('image_tfrecord', './train_seq.record', 'Path to image tfrecord.') flags.DEFINE_string('video_tfrecord', './train_vid.record', 'Path to video tfrecord') FLAGS = flags.FLAGS def get_configs_from_pipeline_file(): """Reads training configuration from a pipeline_pb2.TrainEvalPipelineConfig. Reads training config from file specified by pipeline_config_path flag. Returns: model_config: model_pb2.DetectionModel train_config: train_pb2.TrainConfig input_config: input_reader_pb2.InputReader """ pipeline_config = pipeline_pb2.TrainEvalPipelineConfig() with tf.gfile.GFile(FLAGS.pipeline_config_path, 'r') as f: text_format.Merge(f.read(), pipeline_config) model_config = pipeline_config.model.ssd train_config = pipeline_config.train_config input_config = pipeline_config.train_input_reader return model_config, train_config, input_config if __name__ == '__main__': # update moving average tf.app.run()

35.613636

128

0.744735

19b4fbf622ea3b5c2b94266b63984fdd1ea1e133

460

py

Python

config.py

icewing1996/biaffine-parser

f5a4ece7ba9a087d81b76dd6a8ea6aa7d90c6c82

[ "MIT" ]

1

2019-04-02T14:42:20.000Z

config.py

icewing1996/biaffine-parser

f5a4ece7ba9a087d81b76dd6a8ea6aa7d90c6c82

[ "MIT" ]

null

config.py

icewing1996/biaffine-parser

f5a4ece7ba9a087d81b76dd6a8ea6aa7d90c6c82

[ "MIT" ]

null

# -*- coding: utf-8 -*-

15.862069

24

0.558696

19b7ef31e8ac32e464e2b7f9641c6ad98cd6de46

3,301

py

Python

conf_dblp.py

AmiraKetfi/ScientificProductScraper

c700fb579ac47266e76ec834ccbd8674abeaff50

[ "MIT" ]

4

2018-04-04T12:10:59.000Z

2020-02-22T17:26:14.000Z

conf_dblp.py

AmiraKetfi/ScientificProductScraper

c700fb579ac47266e76ec834ccbd8674abeaff50

[ "MIT" ]

null

conf_dblp.py

AmiraKetfi/ScientificProductScraper

c700fb579ac47266e76ec834ccbd8674abeaff50

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Sat Mar 17 23:01:40 2018 @author: pc """ import scholarly,re,urllib.request,nltk import bs4 as bs # ============================================================================= # #Problme les derniere conf ne se rajoute pas # ============================================================================= # pass url_deb='https://dblp.uni-trier.de/db/conf/' url_deb2='http://dblp.uni-trier.de/db/conf/3dim/3dimpvt2012.html' url_deb3='http://dblp.uni-trier.de/db/conf/3dpvt/' #Timeline_of_conferences(url_deb2) publication_conf_dblp(url_deb3) #find_ComputerScienceConferences_Workshops_names_DBLP(url_deb)

38.383721

124

0.499546

19b8ce0aa97bf71df30c5a8e086263306534c4c7

4,540

py

Python

src/robot.py

FROG3160/FRC2018-ARWING

6635274d79839ea92d8591af2c8e51f8e1112ec1

[ "MIT" ]

1

2019-01-15T00:47:16.000Z

src/robot.py

FROG3160/FRC2018-ARWING

6635274d79839ea92d8591af2c8e51f8e1112ec1

[ "MIT" ]

18

2018-02-15T01:07:03.000Z

2018-04-10T00:25:59.000Z

src/robot.py

FROG3160/FRC2018-ARWING

6635274d79839ea92d8591af2c8e51f8e1112ec1

[ "MIT" ]

4

2018-01-31T01:53:44.000Z

2018-02-16T00:30:14.000Z

#!/usr/bin/env python3 """ Main code for Robot """ import wpilib import robotmap from wpilib import Joystick from subsystems.drivetrain import DriveTrain as Drive from subsystems.grabber import cubeGrabber from subsystems.elevator import Elevator from subsystems.climber import Climber from subsystems.autonomous import Autonomous from wpilib.sendablechooser import SendableChooser # from robotpy_ext.common_drivers.navx import AHRS if __name__ == "__main__": wpilib.run(Robot)

32.661871

83

0.652643

19b94d7c9d394f09ecf7228b67004f998dd55522

1,764

py

Python

api/attomized_avm.py

johncoleman83/attom_python_client

2fad572162f481a71cccf6003da4cbd8ec4477d4

[ "MIT" ]

null

api/attomized_avm.py

johncoleman83/attom_python_client

2fad572162f481a71cccf6003da4cbd8ec4477d4

[ "MIT" ]

null

api/attomized_avm.py

johncoleman83/attom_python_client

2fad572162f481a71cccf6003da4cbd8ec4477d4

[ "MIT" ]

1

2020-11-20T19:28:36.000Z

#!/usr/bin/env python3 """ ATTOM API https://api.developer.attomdata.com """ import requests from urllib.parse import quote, urlencode from api import api PATH = "attomavm/detail" def get_avm_by_address(number_street, city_state): """ API request to get attomavm/detail """ params = urlencode( { "address1": number_street, "address2": city_state, } ) url = "{}/{}?{}".format(api.ATTOM_URL, PATH, params) r = requests.get(url, headers=api.headers) return r.json()

27.138462

77

0.620181

19b9c7cf12ec5b8b173b1bc2764d7bfc2577385f

7,064

py

Python

idmap/models.py

tkhyn/django-idmap

383124fc4bd537d053f9d4c0d02a498f66831baa

[ "BSD-2-Clause" ]

1

2021-04-24T16:35:15.000Z

idmap/models.py

tkhyn/django-idmap

383124fc4bd537d053f9d4c0d02a498f66831baa

[ "BSD-2-Clause" ]

null

idmap/models.py

tkhyn/django-idmap

383124fc4bd537d053f9d4c0d02a498f66831baa

[ "BSD-2-Clause" ]

1

2021-02-27T14:45:48.000Z

import django from django.db import models from django.db.models.base import ModelBase from django.utils import six from .manager import IdMapManager from . import tls # thread local storage META_VALUES = { 'use_strong_refs': False, 'multi_db': False }

33.799043

80

0.58876

19bbd9ee5d1a69e647b6029452a9fd29e645da59

1,345

py

Python

test_search_in_rotated_sorted_array.py

jaebradley/leetcode.py

64634cc7d0e975ddd163f35acb18cc92960b8eb5

[ "MIT" ]

null

test_search_in_rotated_sorted_array.py

jaebradley/leetcode.py

64634cc7d0e975ddd163f35acb18cc92960b8eb5

[ "MIT" ]

2

2019-11-13T19:55:49.000Z

2019-11-13T19:55:57.000Z

test_search_in_rotated_sorted_array.py

jaebradley/leetcode.py

64634cc7d0e975ddd163f35acb18cc92960b8eb5

[ "MIT" ]

null

from unittest import TestCase from search_in_rotated_sorted_array import Solution

37.361111

72

0.684758

19bd0b651a92c3989a6dcd3e14655ea86b1f4a83

2,501

py

Python

pyrfu/pyrf/ts_skymap.py

ablotekar/irfu-python

740cb51ca9ce2ab0d62cb6fef3a7a722d430d79e

[ "MIT" ]

2

2020-11-27T11:35:42.000Z

2021-07-17T11:08:10.000Z

pyrfu/pyrf/ts_skymap.py

ablotekar/irfu-python

740cb51ca9ce2ab0d62cb6fef3a7a722d430d79e

[ "MIT" ]

1

2021-12-04T07:55:48.000Z

2021-12-10T12:45:27.000Z

pyrfu/pyrf/ts_skymap.py

ablotekar/irfu-python

740cb51ca9ce2ab0d62cb6fef3a7a722d430d79e

[ "MIT" ]

2

2021-07-17T11:08:12.000Z

2021-07-18T18:41:42.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- # 3rd party imports import numpy as np import xarray as xr __author__ = "Louis Richard" __email__ = "louisr@irfu.se" __copyright__ = "Copyright 2020-2021" __license__ = "MIT" __version__ = "2.3.7" __status__ = "Prototype" def ts_skymap(time, data, energy, phi, theta, **kwargs): r"""Creates a skymap of the distribution function. Parameters ---------- time : ndarray List of times. data : ndarray Values of the distribution function. energy : ndarray Energy levels. phi : ndarray Azimuthal angles. theta : ndarray Elevation angles. Other Parameters --------------- **kwargs Hash table of keyword arguments with : * energy0 : ndarray Energy table 0 (odd time indices). * energy1 : ndarray Energy table 1 (even time indices). * esteptable : ndarray Time series of the stepping table between energies (burst). Returns ------- out : xarray.Dataset Skymap of the distribution function. """ energy0, energy1, esteptable = [None] * 3 energy0_ok, energy1_ok, esteptable_ok = [False] * 3 if energy is None: if "energy0" in kwargs: energy0, energy0_ok = [kwargs["energy0"], True] if "energy1" in kwargs: energy1, energy1_ok = [kwargs["energy1"], True] if "esteptable" in kwargs: esteptable, esteptable_ok = [kwargs["esteptable"], True] if not energy0_ok and not energy1_ok and not esteptable_ok: raise ValueError("Energy input required") energy = np.tile(energy0, (len(esteptable), 1)) energy[esteptable == 1] = np.tile(energy1, (int(np.sum(esteptable)), 1)) if phi.ndim == 1: phi = np.tile(phi, (len(time), 1)) out_dict = {"data": (["time", "idx0", "idx1", "idx2"], data), "phi": (["time", "idx1"], phi), "theta": (["idx2"], theta), "energy": (["time", "idx0"], energy), "time": time, "idx0": np.arange(energy.shape[1]), "idx1": np.arange(phi.shape[1]), "idx2": np.arange(len(theta))} out = xr.Dataset(out_dict) if energy0_ok: out.attrs["energy0"] = energy0 if energy1_ok: out.attrs["energy1"] = energy1 if energy0_ok: out.attrs["esteptable"] = esteptable return out

26.892473

79

0.562575

19be0f2de874f8b441c89b5d8fd8cac69393789a

2,037

py

Python

src/log_utils.py

alexklwong/calibrated-backprojection-network

57dbec03c6da94ee0cd020b6de5f02e7e8ee726e

[ "Intel" ]

38

2021-08-28T06:01:25.000Z

2022-03-03T03:23:23.000Z

src/log_utils.py

alexklwong/calibrated-backprojection-network

57dbec03c6da94ee0cd020b6de5f02e7e8ee726e

[ "Intel" ]

14

2021-11-15T12:30:34.000Z

2022-03-30T14:03:16.000Z

src/log_utils.py

alexklwong/calibrated-backprojection-network

57dbec03c6da94ee0cd020b6de5f02e7e8ee726e

[ "Intel" ]

9

2021-10-19T23:45:07.000Z

2021-12-20T07:45:37.000Z

''' Author: Alex Wong <alexw@cs.ucla.edu> If you use this code, please cite the following paper: A. Wong, and S. Soatto. Unsupervised Depth Completion with Calibrated Backprojection Layers. https://arxiv.org/pdf/2108.10531.pdf @inproceedings{wong2021unsupervised, title={Unsupervised Depth Completion with Calibrated Backprojection Layers}, author={Wong, Alex and Soatto, Stefano}, booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision}, pages={12747--12756}, year={2021} } ''' import os import torch import numpy as np from matplotlib import pyplot as plt def log(s, filepath=None, to_console=True): ''' Logs a string to either file or console Arg(s): s : str string to log filepath output filepath for logging to_console : bool log to console ''' if to_console: print(s) if filepath is not None: if not os.path.isdir(os.path.dirname(filepath)): os.makedirs(os.path.dirname(filepath)) with open(filepath, 'w+') as o: o.write(s + '\n') else: with open(filepath, 'a+') as o: o.write(s + '\n') def colorize(T, colormap='magma'): ''' Colorizes a 1-channel tensor with matplotlib colormaps Arg(s): T : torch.Tensor[float32] 1-channel tensor colormap : str matplotlib colormap ''' cm = plt.cm.get_cmap(colormap) shape = T.shape # Convert to numpy array and transpose if shape[0] > 1: T = np.squeeze(np.transpose(T.cpu().numpy(), (0, 2, 3, 1))) else: T = np.squeeze(np.transpose(T.cpu().numpy(), (0, 2, 3, 1)), axis=-1) # Colorize using colormap and transpose back color = np.concatenate([ np.expand_dims(cm(T[n, ...])[..., 0:3], 0) for n in range(T.shape[0])], axis=0) color = np.transpose(color, (0, 3, 1, 2)) # Convert back to tensor return torch.from_numpy(color.astype(np.float32))

26.802632

92

0.60972

19c214d222aa500c556609e883b1ff02ba286869

788

py

Python

add-two-numbers/add-two-numbers.py

shaurya-src/code-leet

f642b81eb7bead46c66404bd48ca74bdfeb2abbb

[ "MIT" ]

null

add-two-numbers/add-two-numbers.py

shaurya-src/code-leet

f642b81eb7bead46c66404bd48ca74bdfeb2abbb

[ "MIT" ]

null

add-two-numbers/add-two-numbers.py

shaurya-src/code-leet

f642b81eb7bead46c66404bd48ca74bdfeb2abbb

[ "MIT" ]

null

# Definition for singly-linked list. # class ListNode: # def __init__(self, val=0, next=None): # self.val = val # self.next = next

28.142857

98

0.497462

19c251bd8c7eb79b25c470c6951dca0f932a8918

2,834

py

Python

likedtweets.py

PoliTwit1984/Politwitverse

837dd2d05b3977aa24a70f52a3b951ef22c51dc6

[ "MIT" ]

3

2022-01-05T07:12:14.000Z

2022-02-19T20:58:25.000Z

likedtweets.py

PoliTwit1984/Politwitverse

837dd2d05b3977aa24a70f52a3b951ef22c51dc6

[ "MIT" ]

25

2022-01-05T08:23:59.000Z

2022-02-07T01:25:39.000Z

likedtweets.py

PoliTwit1984/Politwitverse

837dd2d05b3977aa24a70f52a3b951ef22c51dc6

[ "MIT" ]

1

2022-02-01T22:39:57.000Z

import time import re import tweepy import preprocessor as p import config import string consumer_key = config.consumer_key consumer_secret = config.consumer_secret access_token = config.access_token access_token_secret = config.access_token_secret bearer_token = config.bearer_token username = config.username password = config.password def clean_text(text): """ Function to clean the text. Parameters: text: the raw text as a string value that needs to be cleaned Returns: cleaned_text: the cleaned text as string """ # convert to lower case cleaned_text = text.lower() # remove HTML tags html_pattern = re.compile('<.*?>') cleaned_text = re.sub(html_pattern, '', cleaned_text) # remove punctuations cleaned_text = cleaned_text.translate( str.maketrans('', '', string.punctuation)) return cleaned_text.strip() client = tweepy.Client(bearer_token=bearer_token) list_id = "1467207384011526144" # all missouri legislators response = client.get_list_members(list_id, max_results = 100) users = response.data metadata = response.meta next_token = metadata.get("next_token") print(next_token) while next_token is not None: for user in users: string = str(user.name)+","+str(user.id)+","+str(user.username)+"\n" with open('moleglistmembership.txt', 'a') as f: f.write(string) response = client.get_list_members(list_id, pagination_token = next_token, max_results = 100) users = response.data metadata = response.meta next_token = metadata.get("next_token") print(next_token) # tweet_text = tweet.text # tweet_clean_text = clean_tweets(tweet.text) # tweet_created_at = tweet.created_at # tweet_clean_text = clean_text(tweet_clean_text) # print(tweet_clean_text) # print('\n') # print(tweet_created_at) # print('\n') # print('-----------------------------------------------------------------') # with open('molegmembership.txt', 'a') as f: # f.write(tweet_clean_text) # f.write('\n') # response = client.get_list_tweets(list_id, max_results=100)

26.485981

97

0.677135

19c32bbd1169664ffd8d06d663183110a2d5e53c

391

py

Python

src/app/migrations/0004_history_missed.py

deadlock-delegate/arkdelegates

8a5262f51b519ba3bc10094756c8866fc550df65

[ "MIT" ]

2

2018-05-22T13:47:09.000Z

2018-05-23T12:45:05.000Z

src/app/migrations/0004_history_missed.py

deadlock-delegate/arkdelegates

8a5262f51b519ba3bc10094756c8866fc550df65

[ "MIT" ]

21

2018-05-08T12:56:46.000Z

2020-06-05T18:59:38.000Z

src/app/migrations/0004_history_missed.py

deadlock-delegate/arkdelegates

8a5262f51b519ba3bc10094756c8866fc550df65

[ "MIT" ]

4

2018-05-04T15:00:59.000Z

2019-02-13T02:39:07.000Z

# Generated by Django 2.0.3 on 2018-03-14 09:59 from django.db import migrations, models

20.578947

59

0.595908

19c43d42b7108f348940b9fd8fc9fb33a8830e2c

2,112

py

Python

audclass.py

theunafraid/audiofeedback-prevention

0dd3e8ab7b5a65aff214e74b7bd7869366b1b7b5

[ "Apache-2.0" ]

1

2022-01-20T08:30:20.000Z

audclass.py

theunafraid/audiofeedback-prevention

0dd3e8ab7b5a65aff214e74b7bd7869366b1b7b5

[ "Apache-2.0" ]

null

audclass.py

theunafraid/audiofeedback-prevention

0dd3e8ab7b5a65aff214e74b7bd7869366b1b7b5

[ "Apache-2.0" ]

null

import tensorflow as tf import numpy as np from tensorflow.python.ops.gen_batch_ops import batch from model import AudioClass from qrnn import QRNN from numpy.random import seed from numpy.random import randn from random import randint from lstmfcn import LSTM_FCN import librosa import os if __name__ == "__main__": main()

26.734177

78

0.588542

19c4cd4bbbc8cea75d64211787db92e8b023d09a

3,135

py

Python

pytanga/components/OpenConfig/routing/ospfv2/ospfv2Interface.py

renatoalmeidaoliveira/Pytanga

aa02f1c0f2573da1330d1d246ab780fa3be336a5

[ "MIT" ]

null

pytanga/components/OpenConfig/routing/ospfv2/ospfv2Interface.py

renatoalmeidaoliveira/Pytanga

aa02f1c0f2573da1330d1d246ab780fa3be336a5

[ "MIT" ]

null

pytanga/components/OpenConfig/routing/ospfv2/ospfv2Interface.py

renatoalmeidaoliveira/Pytanga

aa02f1c0f2573da1330d1d246ab780fa3be336a5

[ "MIT" ]

null

from pytanga.components import AbstractComponent

32.319588

94

0.500797

19c79aebe6cccec71cf534b0497f44d1a8496883

4,127

py

Python

python_implementation/matriz/quadrada.py

SousaPedro11/algoritmos

86a3601912778d120b9ec8094267c26a7eb6d153

[ "MIT" ]

null

python_implementation/matriz/quadrada.py

SousaPedro11/algoritmos

86a3601912778d120b9ec8094267c26a7eb6d153

[ "MIT" ]

null

python_implementation/matriz/quadrada.py

SousaPedro11/algoritmos

86a3601912778d120b9ec8094267c26a7eb6d153

[ "MIT" ]

null

import math from typing import List, Tuple if __name__ == '__main__': solucao_problema()

33.282258

100

0.628544

19c7ee6d99159a70af01f16f7e183bb9ec3972a5

7,017

py

Python

app_mongo.py

emmpets/MongoProject

cbef19b590503825158909703125b34c1bf536ec

[ "Apache-2.0" ]

null

app_mongo.py

emmpets/MongoProject

cbef19b590503825158909703125b34c1bf536ec

[ "Apache-2.0" ]

null

app_mongo.py

emmpets/MongoProject

cbef19b590503825158909703125b34c1bf536ec

[ "Apache-2.0" ]

null

from pymongo import MongoClient import pymongo from datetime import datetime,time import time from bson.code import Code mongo_client=MongoClient('mongodb://localhost:27017/') db=mongo_client.mydb db_col=db.things dbc = mongo_client.mydb.things print mongo_client print(db) print("connected") data = dbc.find() ans=True while ans: print(""" 1.How many unique users are there? 2.How many tweets (%) did the top 10 users (measured by the number of messages) publish? 3.What was the earliest and latest date (YYYY-MM-DD HH:MM:SS) that a message was published? 4.What is the mean time delta between all messages? 5.What is the mean length of a message? 6.What are the 10 most common unigram within the messages? 7.What are the 10 most common bigram within the messages? 8.What is the average number of hashtags (#) used within a message? 10.Exit/Quit """) ans = raw_input("What would you like to do? ") if ans == "1": print "The summary of all unique users is: ", first_querry() elif ans == "2": print("The percentage of the ALL messages of top ten user"), second_querry(), "%", elif ans == "3": print"The last message published on:", third_querry()[0] print"The earliest message published on:", third_querry()[1] elif ans == "4": print"The mean time delta between all messages is :", fourth_querry() elif ans == "5": print"The mean length of the messages is :", fifth_querry(data) elif ans == "6": print"The 10 most common unigrams within the messages are:", sixth_querry() elif ans == "7": print"The 10 most common bigrams within the messages are:", seventh_querry() elif ans == "8": print"The average number of hashtags (#) used within a message is:", eight_querry(data) elif ans == "9": ninth_querry() elif ans == "10": print("\n Goodbye") ans = None else: print("\n Not Valid Choice Try again")

30.11588

110

0.584865

19c9e0f683fb12bcf45633873b78ecba612bb09f

7,399

py

Python

theseus/util/serialize.py

shiplift/theseus

9324d67e6e0c6b93a7734a5531838c5a909a1424

[ "0BSD" ]

null

theseus/util/serialize.py

shiplift/theseus

9324d67e6e0c6b93a7734a5531838c5a909a1424

[ "0BSD" ]

null

theseus/util/serialize.py

shiplift/theseus

9324d67e6e0c6b93a7734a5531838c5a909a1424

[ "0BSD" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- """ serialize provide means to persist and recreate the currently known set of W_Tags and all shapes and transformations reachable from there. The rmarshal modules is used for serialization; the format is marshal_proto = ( int, # number of shapes [ # shape list ( # a shape int, # id (str, int), # tag [int], # structure: list of id's { # _hist (int, int) : # index, id int # count }, { # transformation_rules (int, int) : # index, id int # id } ) ], { (str, int) : # name arity int #id } ) The serialized tree is written to a '.docked' files """ import os.path from rpython.rlib.streamio import open_file_as_stream from rpython.rlib.rmarshal import get_marshaller, get_unmarshaller from rpython.rlib.debug import debug_start, debug_stop, debug_print from theseus.model import W_Tag from theseus.shape import in_storage_shape, CompoundShape marshal_proto = ( int, # number of shapes [ # shape list ( # a shape int, # id (str, int), # tag [int], # structure: list of id's { # _hist (int, int) : # index, id int # count }, { # transformation_rules (int, int) : # index, id int # id } ) ], { (str, int) : # name arity int #id } ) marshaller = get_marshaller(marshal_proto) unmarshaller = get_unmarshaller(marshal_proto) def punch_shape(s, registry): """ Punch a shape to a tuple for marshalling. See slurp_shapes, configure_shapes for inverse. Format is ( # a shape int, # id (str, int), # tag [int], # structure: list of id's { # _hist (int, int) : # index, id int # count }, { # transformation_rules (int, int) : # index, id int # id } ) """ if s == in_storage_shape: return (0, ('', 0), [], {}, {}) else: assert isinstance(s, CompoundShape) my_index = registry.index(s) hist = {} for (index, shape), count in s._hist.items(): shape_id = registry.index(shape) hist[(index, shape_id)] = count trans = {} for (index, shape), to_shape in s.transformation_rules.items(): shape_id = registry.index(shape) to_shape_id = registry.index(to_shape) trans[(index, registry.index(shape))] = registry.index(to_shape) punchee = ( registry.index(s), (s._tag.name, s._tag.arity()), [registry.index(subshape) for subshape in s._structure], hist, trans ) return punchee def recreate_shape(shape_desc, tags, registry): """ Recreate a shape from its punched format; see punch_shape. Does not handle history and transformations. See configure_shape(s). """ id, tag, structure_ids = shape_desc structure = [None] * len(structure_ids) for structure_index, sub_id in enumerate(structure_ids): assert sub_id < id subshape = registry[sub_id] assert subshape is not None structure[structure_index] = subshape return CompoundShape(tags[tag], structure) def configure_shape(shape, hist, trans, registry): """ Reconfigure a shape from its punched format; see punch_shape. Does _only_ handle history and transformations. See configure_shapes. """ assert isinstance(shape, CompoundShape) shape._hist = {} for (index, s_id), count in hist.items(): k = (index, registry[s_id]) shape._hist[k] = count shape.transformation_rules = {} for (index, s_id), to_s_id in trans.items(): k = (index, registry[s_id]) shape.transformation_rules[k] = registry[to_s_id] def configure_shapes(shapes, registry): """ Reconfigure all shapes. Does _only_ handle history and transformations. See configure_shapes. """ for id, _tag, _structure_ids, hist, trans in shapes: if id == 0: continue # in_storage_shape, no configure configure_shape(registry[id], hist, trans, registry) def slurp_registry(shapes, registry, tags_slurp, tags): """ Slurp all shapes from their punched format (see punch_shape) not including history or transformation """ known_ids = [0] for default_id in tags_slurp.values(): known_ids.append(default_id) for id, tag, structure_ids, _hist, _trans in shapes: if id in known_ids: continue assert registry[id] is None registry[id] = recreate_shape((id, tag, structure_ids), tags, registry) def punch_tags(tags): """ Punch all tags into marshallable format: ( int, # number of shapes [ # shape list ], { (str, int) : # name arity int #id } ) """ reg = [in_storage_shape] + CompoundShape._shapes punch_reg = [punch_shape(s, reg) for s in reg] res = {} for key, value in tags.items(): res[key] = reg.index(value.default_shape) return (len(punch_reg), punch_reg, res) def slurp_tags(un_tags): """ Slurp all tags from their punched format (see punch_tag). Recursively slurps shapes and then configures them. """ num_shapes, shapes_slurp, tags_slurp = un_tags registry = [None] * num_shapes registry[0] = in_storage_shape tags = {} for (name, arity), default_id in tags_slurp.items(): tag = W_Tag(name, arity) tags[(name, arity)] = tag registry[default_id] = tag.default_shape slurp_registry(shapes_slurp, registry, tags_slurp, tags) configure_shapes(shapes_slurp, registry) return tags def come_up(basename): """ Bring up previously marshalled Tags, shapes and transformations from '.docked' file un-marshalling, slurping and replacement of current Tags. """ from theseus.shape import CompoundShape # later # from os import stat # statres = stat(path) debug_start("theseus-come-up") path = basename + '.docked' if not os.path.exists(path): return try: f = open_file_as_stream(path, buffering=0) except OSError as e: os.write(2, "Error(come_up)%s -- %s\n" % (os.strerror(e.errno), path)) return try: res = unmarshaller(f.readall()) finally: f.close() del CompoundShape._shapes[:] W_Tag.tags.clear() new_tags = slurp_tags(res) for key, value in new_tags.items(): W_Tag.tags[key] = value debug_stop("theseus-come-up") def settle(basename): """ Settle Tags, shapes and transformations to a '.docked' file punching and marshalling all current Tags. """ debug_start("theseus-settle") path = basename + '.docked' buf = [] marshaller(buf, punch_tags(W_Tag.tags)) try: f = open_file_as_stream(path, mode="w", buffering=0) except OSError as e: os.write(2, "Error(settle)%s -- %s\n" % (os.strerror(e.errno), path)) return try: f.write(''.join(buf)) finally: f.close() debug_stop("theseus-settle")

27.403704

79

0.592783

19cc7f391c49230cd25af4f7949e261ca27ffe2b

1,359

py

Python

external_scripts/run2.py

AAS97/tokenizRE

0186a2b533edaa0045b16b0b111b9637248e5046

[ "MIT" ]

null

external_scripts/run2.py

AAS97/tokenizRE

0186a2b533edaa0045b16b0b111b9637248e5046

[ "MIT" ]

null

external_scripts/run2.py

AAS97/tokenizRE

0186a2b533edaa0045b16b0b111b9637248e5046

[ "MIT" ]

null

from web3 import Web3, HTTPProvider import json import os w3 = Web3(HTTPProvider("http://127.0.0.1:7545", request_kwargs={'timeout': 60})) print(f"Web3 is connected : {w3.isConnected()}") accounts = w3.eth.accounts # ------------------------------- get contract ------------------------------- # abi_path = "./vapp/src/contracts/" with open(os.path.join(abi_path, 'TokenHolderPayer.json'), "r") as file: property_contract_compiled = json.load(file) property_contract_abi = property_contract_compiled['abi'] contract_address = "0xE5972821D1218120C4E98986A3eEc997931690b4" property_contract = w3.eth.contract(address=contract_address, abi=property_contract_abi) # ------------------- buy some token from realestate agent ------------------- # amount = 500 # Allow token to be sent property_contract.functions.increaseAllowance(accounts[1], amount).transact({'from':accounts[0], 'gas': 420000, 'gasPrice': 21000}) balance = property_contract.functions.balanceOf(accounts[1]).call() print(f"initial balance {balance}") tx_hash = property_contract.functions.transferFrom(accounts[0], accounts[1], 500).transact({'from':accounts[1], 'gas': 420000, 'gasPrice': 21000}) receipt = w3.eth.waitForTransactionReceipt(tx_hash) balance = property_contract.functions.balanceOf(accounts[1]).call() print(f"final balance {balance}")

37.75

146

0.693893

19cc949ad53b4cdcbc1b975b94608f7737a43f64

825

py

Python

main.py

cltl-students/hamersma-agression-causes

11cbfd94031a0a3c84a27afa20d8a539acdab609

[ "MIT" ]

null

main.py

cltl-students/hamersma-agression-causes

11cbfd94031a0a3c84a27afa20d8a539acdab609

[ "MIT" ]

null

main.py

cltl-students/hamersma-agression-causes

11cbfd94031a0a3c84a27afa20d8a539acdab609

[ "MIT" ]

null

from preprocessing import preprocess from approach1_rulebased import get_predictions_rulebased from approach2_machine_learning import get_predictions_ml from bertopic_clustering import cluster_precursors def main(): '''Main function to use from commandline, preprocess input to generate embeddings, detect agression clauses using provided approach, extract features and labels from training and features from input data, trains a model and classifies test data using the trained model, evaluates predictions and goldlabels from input''' inputfile = 'sample_input.xls' preprocess(inputfile) get_predictions_rulebased() get_predictions_ml() ### only clusters with enough data, else everything in outlier cluster cluster_precursors() if __name__ == '__main__': main()

43.421053

118

0.778182

19cd9765a8c1a72e36854304f427fda7349e31d0

8,985

pyw

Python

12th project cs/Gui pages.pyw

Jatin-Ya/ChatApp-12th-project-

77ced9b18205728334f4370fbce8d74687bc5373

[ "Apache-2.0" ]

null

12th project cs/Gui pages.pyw

Jatin-Ya/ChatApp-12th-project-

77ced9b18205728334f4370fbce8d74687bc5373

[ "Apache-2.0" ]

null

12th project cs/Gui pages.pyw

Jatin-Ya/ChatApp-12th-project-

77ced9b18205728334f4370fbce8d74687bc5373

[ "Apache-2.0" ]

null

from tkinter import * import threading import sql_manager as ss g=GUI()

35.654762

111

0.575292

19cea24c1060f2d6ff7113c23c57266d177697db

1,528

py

Python

19/19a.py

jamOne-/adventofcode2018

d51c01578ae7e4f30824c4f6ace66958491c1ed4

[ "MIT" ]

null

19/19a.py

jamOne-/adventofcode2018

d51c01578ae7e4f30824c4f6ace66958491c1ed4

[ "MIT" ]

null

19/19a.py

jamOne-/adventofcode2018

d51c01578ae7e4f30824c4f6ace66958491c1ed4

[ "MIT" ]

null

import sys OPERATIONS = { 'addr': lambda a, b, c, registers: registers[a] + registers[b], 'addi': lambda a, b, c, registers: registers[a] + b, 'mulr': lambda a, b, c, registers: registers[a] * registers[b], 'muli': lambda a, b, c, registers: registers[a] * b, 'banr': lambda a, b, c, registers: registers[a] & registers[b], 'bani': lambda a, b, c, registers: registers[a] & b, 'borr': lambda a, b, c, registers: registers[a] | registers[b], 'bori': lambda a, b, c, registers: registers[a] | b, 'setr': lambda a, b, c, registers: registers[a], 'seti': lambda a, b, c, registers: a, 'grir': lambda a, b, c, registers: 1 if a > registers[b] else 0, 'gtri': lambda a, b, c, registers: 1 if registers[a] > b else 0, 'gtrr': lambda a, b, c, registers: 1 if registers[a] > registers[b] else 0, 'eqir': lambda a, b, c, registers: 1 if a == registers[b] else 0, 'eqri': lambda a, b, c, registers: 1 if registers[a] == b else 0, 'eqrr': lambda a, b, c, registers: 1 if registers[a] == registers[b] else 0 } print(solve(sys.stdin))

36.380952

77

0.633508

19d1e57d2a97ef66002ffa2d6966b97f5f533bee

1,878

py

Python

examples/websocket_echo_server.py

HMXHIU/VeryPowerfulAgents

06abd52776aeaf701637533f760176459c9c361c

[ "MIT" ]

2

2021-11-30T16:14:01.000Z

2022-03-04T09:20:51.000Z

examples/websocket_echo_server.py

HMXHIU/VeryPowerfulAgents

06abd52776aeaf701637533f760176459c9c361c

[ "MIT" ]

8

2021-02-10T15:43:49.000Z

2021-02-10T16:00:16.000Z

examples/websocket_echo_server.py

HMXHIU/VeryPowerfulAgents

06abd52776aeaf701637533f760176459c9c361c

[ "MIT" ]

1

2021-01-01T12:31:48.000Z

from aiohttp import web from agents import Agent if __name__ == "__main__": webserver = WebServer("127.0.0.1", 8080, "/ws")

30.786885

86

0.510117

19d3c2532fdc242dd0fdaf80342fa01cfdf2a61d

5,401

py

Python

janaganana/tables.py

deshetti/janaganana

f29ced95fc9f8b98f77560d9afdbd999510dd497

[ "MIT" ]

11

2017-02-16T20:45:54.000Z

2021-12-31T01:08:40.000Z

janaganana/tables.py

deshetti/janaganana

f29ced95fc9f8b98f77560d9afdbd999510dd497

[ "MIT" ]

18

2017-02-15T20:24:29.000Z

2022-03-29T21:54:36.000Z

janaganana/tables.py

deshetti/janaganana

f29ced95fc9f8b98f77560d9afdbd999510dd497

[ "MIT" ]

13

2017-02-16T20:45:25.000Z

2020-09-23T21:40:57.000Z

from wazimap.data.tables import FieldTable # Define our tables so the data API can discover them. # Household tables FieldTable(['rural population'], universe='Population', table_per_level=False) FieldTable(['area', 'sex'], universe='Population', table_per_level=False) FieldTable(['census_year', 'measure'], universe='A2-Decadal Variation', table_per_level=False) FieldTable(['census_year', 'sex_vis'], universe='VISUAL', table_per_level=False) FieldTable(['area', 'sex', 'literacy'], universe='Population', table_per_level=False) FieldTable(['area','village_town_comparison'], universe='A3APPENDIX', table_per_level=False) FieldTable(['religion', 'area', 'sex'], universe='Religion', table_per_level=False) FieldTable(['age', 'area', 'sex'], universe='Age', table_per_level=False) FieldTable(['village_town_measures','area'], universe='A1-', table_per_level=False) FieldTable(['education', 'area', 'sex'], universe='Education', table_per_level=False) FieldTable(['houseless_population','area', 'sex'], universe='A7-Houseless', table_per_level=False) FieldTable(['sc_houseless_population','area', 'sex'], universe='A8-SC_Houseless', table_per_level=False) FieldTable(['st_houseless_population','area', 'sex'], universe='A9-ST_Houseless', table_per_level=False) FieldTable(['village_measures','population_range'], universe='A3-Inhabited Villages', table_per_level=False) FieldTable(['maritalstatus', 'area', 'sex'], universe='Relation', table_per_level=False) FieldTable(['workertype','age_group','area','sex'], universe='B1-Workerstype', table_per_level=False) FieldTable(['sc_workertype','age_group','area','sex'], universe='B1SC-Workerstype', table_per_level=False) FieldTable(['st_workertype','age_group','area','sex'], universe='B1ST-Workerstype', table_per_level=False) FieldTable(['workers', 'area', 'workerssex'], universe='Workers', table_per_level=False) FieldTable(['workertype','education_level', 'area', 'sex'], universe='B3', table_per_level=False) FieldTable(['education_level', 'area', 'sex_vis'], universe='VISUAL', table_per_level=False) FieldTable(['sc_workertype','education_level', 'area', 'sex'], universe='B3SC', table_per_level=False) FieldTable(['st_workertype','education_level', 'area', 'sex'], universe='B3ST', table_per_level=False) FieldTable(['nonworkertype', 'age_group','area','sex'], universe='B13', table_per_level=False) FieldTable(['nonworkertype_vis', 'age_group','area','sex'], universe='VISUAL', table_per_level=False) FieldTable(['sc_nonworkertype', 'age_group','area','sex'], universe='B13SC', table_per_level=False) FieldTable(['st_nonworkertype', 'age_group','area','sex'], universe='B13ST', table_per_level=False) FieldTable(['religion','nonworkertype','age_group','area', 'sex'], universe='B14', table_per_level=False) FieldTable(['religion','area', 'sex'], universe='C1', table_per_level=False) FieldTable(['religious_community','area', 'sex'], universe='C1APPENDIX', table_per_level=False) FieldTable(['age_group','marital_status','area', 'sex'], universe='C2', table_per_level=False) FieldTable(['religion','marital_status','area', 'sex'], universe='C3', table_per_level=False) FieldTable(['mother_tongue_vis','area', 'sex'], universe='VISUAL', table_per_level=False) FieldTable(['disability','age_group','area', 'sex'], universe='c20', table_per_level=False) FieldTable(['mother_tongue','area', 'sex'], universe='c16', table_per_level=False) FieldTable(['educational_institution','age','area', 'sex'], universe='c10', table_per_level=False) FieldTable(['sc_educational_institution','age','area', 'sex'], universe='c10sc', table_per_level=False) FieldTable(['st_educational_institution','age','area', 'sex'], universe='c10st', table_per_level=False) FieldTable(['economic_activity','age','area', 'sex'], universe='c12', table_per_level=False) FieldTable(['marriage_duration','age','area', 'sex'], universe='c4', table_per_level=False) FieldTable(['parity','age','area'], universe='F1', table_per_level=False) FieldTable(['sc_parity','age','area'], universe='F1sc', table_per_level=False) FieldTable(['st_parity','age','area'], universe='F1st', table_per_level=False) FieldTable(['parity_vis','age','area'], universe='VISUAL', table_per_level=False) FieldTable(['surviving_children','age','area'], universe='F5', table_per_level=False) FieldTable(['sc_surviving_children','age','area'], universe='F5SC', table_per_level=False) FieldTable(['st_surviving_children','age','area'], universe='F5ST', table_per_level=False) FieldTable(['household_size','area'], universe='HH1', table_per_level=False) FieldTable(['household_size_vis','area'], universe='VISUAL', table_per_level=False) FieldTable(['sc_household_size','area'], universe='HH1SC', table_per_level=False) FieldTable(['st_household_size','area'], universe='HH1ST', table_per_level=False) FieldTable(['household_workers','workers_in_household','area'], universe='HH11', table_per_level=False) FieldTable(['household_size','available_for_work','area'], universe='HH12', table_per_level=False) FieldTable(['sevenyearsandabove','literates_in_household','area'], universe='HH08', table_per_level=False) FieldTable(['age','area', 'head','household_marital_status'], universe='HH06', table_per_level=False) FieldTable(['houseless_households','area'], universe='HH02', table_per_level=False) FieldTable(['households_size','aged_persons','area'], universe='HH05', table_per_level=False)

66.679012

108

0.755601

19d4df790639614b567c8829dbce219210c26642

585

py

Python

src/weekly-reset.py

SlimeeGameS/VirginityBot

a1745893f21a16112bbf775fb2aff199c14dbbbb

[ "CC0-1.0" ]

null

src/weekly-reset.py

SlimeeGameS/VirginityBot

a1745893f21a16112bbf775fb2aff199c14dbbbb

[ "CC0-1.0" ]

14

2020-03-26T01:02:31.000Z

2021-03-24T23:48:44.000Z

src/weekly-reset.py

SlimeeGameS/VirginityBot

a1745893f21a16112bbf775fb2aff199c14dbbbb

[ "CC0-1.0" ]

2

2020-08-09T19:08:41.000Z

2021-05-12T17:44:28.000Z

import os import asyncio import logging from pony.orm import * import logger from database import start_orm, get_biggest_virgin, Guild, Virgin logger = logging.getLogger('virginity-bot') if __name__ == '__main__': loop = asyncio.get_event_loop() loop.run_until_complete(main())

18.870968

65

0.729915

19d525875da360fb20fb2929a08fff78176398d0

1,165

py

Python

hardhat/recipes/racket.py

stangelandcl/hardhat

1ad0c5dec16728c0243023acb9594f435ef18f9c

[ "MIT" ]

null

hardhat/recipes/racket.py

stangelandcl/hardhat

1ad0c5dec16728c0243023acb9594f435ef18f9c

[ "MIT" ]

null

hardhat/recipes/racket.py

stangelandcl/hardhat

1ad0c5dec16728c0243023acb9594f435ef18f9c

[ "MIT" ]

null

import os import shutil from .base import GnuRecipe

31.486486

73

0.572532

19d5619a8ce652fe7933c1843f9585227eb325de

3,257

py

Python

lichess-gist.py

swimmy4days/lichess-gist

b70e605345f789e032291253df506384ccbaa270

[ "MIT" ]

null

lichess-gist.py

swimmy4days/lichess-gist

b70e605345f789e032291253df506384ccbaa270

[ "MIT" ]

null

lichess-gist.py

swimmy4days/lichess-gist

b70e605345f789e032291253df506384ccbaa270

[ "MIT" ]

null

import os import sys import berserk from github import Github, InputFileContent, Gist SEPARATOR = "." PADDING = {"puzzle": 0, "crazyhouse": 0, "chess960": 0, "kingOfTheHill": 0, "threeCheck": 2, "antichess": 0, "atomic": 0, "horde": 0, "racingKings": 0, "ultraBullet": 0, "blitz": 1, "classical": 1, "rapid": 0, "bullet": 0, "correspondence": 3} emojis = {"puzzle": "", "crazyhouse": "", "chess960": "960", "kingOfTheHill": "", "threeCheck": "3", "antichess": "", "atomic": "", "horde": "", "racingKings": "", "ultraBullet": "", "blitz": "", "classical": "", "rapid": "", "bullet": "", "correspondence": ""} ENV_VAR_GIST_ID = "GIST_ID" ENV_VAR_GITHUB_TOKEN = "GH_TOKEN" ENV_VAR_LICHESS_USERNAME = "LICHESS_USERNAME" REQUIRED_ENVS = [ ENV_VAR_GIST_ID, ENV_VAR_GITHUB_TOKEN, ENV_VAR_LICHESS_USERNAME ] if __name__ == "__main__": # test with python lichess-gist.py test <gist> <github-token> <user> if len(sys.argv) > 1: os.environ[ENV_VAR_GIST_ID] = sys.argv[2] os.environ[ENV_VAR_GITHUB_TOKEN] = sys.argv[3] os.environ[ENV_VAR_LICHESS_USERNAME] = sys.argv[4] main() # %%

31.317308

118

0.612834

19d5e02630a84a1866bbfe9f9deb571cc98a96cc

951

py

Python

alembic/versions/60c735df8d2f_.py

brouberol/grand-cedre

05f18d1f8b7253ffa7fb5b33b30ceadcc93c4e93

[ "BSD-3-Clause" ]

null

alembic/versions/60c735df8d2f_.py

brouberol/grand-cedre

05f18d1f8b7253ffa7fb5b33b30ceadcc93c4e93

[ "BSD-3-Clause" ]

22

2019-09-03T20:08:42.000Z

2022-03-11T23:58:02.000Z

alembic/versions/60c735df8d2f_.py

brouberol/grand-cedre

05f18d1f8b7253ffa7fb5b33b30ceadcc93c4e93

[ "BSD-3-Clause" ]

null

"""empty message Revision ID: 60c735df8d2f Revises: 88bb7e12da60 Create Date: 2019-09-06 08:27:03.082097 """ from alembic import op import sqlalchemy as sa # revision identifiers, used by Alembic. revision = "60c735df8d2f" down_revision = "88bb7e12da60" branch_labels = None depends_on = None

27.171429

84

0.690852

19d5e29e652c7abc55afdd0fed0c5112571018a1

3,640

py

Python

python/genre_classifier.py

nscharrenberg/Aliran

628de0476b8f8b413a6fdddf5392c590e8b27654

[ "MIT" ]

null

python/genre_classifier.py

nscharrenberg/Aliran

628de0476b8f8b413a6fdddf5392c590e8b27654

[ "MIT" ]

null

python/genre_classifier.py

nscharrenberg/Aliran

628de0476b8f8b413a6fdddf5392c590e8b27654

[ "MIT" ]

null

import scipy.io.wavfile as wav import numpy as np import os import pickle import random import operator from python_speech_features import mfcc dataset = [] training_set = [] test_set = [] # Get the distance between feature vectors # Find Neighbors # Identify the Nearest Neighbor (Genres) # Model Evaluation to get the accuracy # Extract features from the audio files and store them in a model file # Load in the Dataset if __name__ == '__main__': print('Starting....') local_filename = "dataset.aliran" extracting = False if extracting: print('Extracting Features...') print('Building Model...') extract_features(local_filename) print('Loading Dataset...') load_dataset(local_filename, 0.66, training_set, test_set) print('Making a prediction...') print('(This may take a few minutes)') predictions = [] for x in range(len(test_set)): predictions.append(nearest_genre(get_neighbors(training_set, test_set[x], 5))) accuracy = get_accuracy(test_set, predictions) print('Prediction Accuracy is:') print(accuracy)

26.376812

102

0.613462

19d94ed3daa7c3c452d53a4b890d6a26c3139991

1,653

py

Python

run.py

dkosilov/reconciler_anchor_salesforce

5cf6a8ccaedce84e7dab6c32955c644ede0c6e07

[ "Xnet", "X11" ]

1

2020-09-22T11:49:07.000Z

run.py

dkosilov/reconciler_anchor_salesforce

5cf6a8ccaedce84e7dab6c32955c644ede0c6e07

[ "Xnet", "X11" ]

null

run.py

dkosilov/reconciler_anchor_salesforce

5cf6a8ccaedce84e7dab6c32955c644ede0c6e07

[ "Xnet", "X11" ]

null

import argparse from libs.data_model import AnchorNorthstarDataframe, SalesForceDataframe, \ AnchorSalesforceAccountsDataframe, AnchorSalesforceContactsDataframe from libs.utils import save_dataframes_to_excel parser = argparse.ArgumentParser(description='Reconcile accounts and contacts between Anchor and Salesforce') parser.add_argument('-a', '--anchor-file', help='Path to Anchor Excel workbook', required=True) parser.add_argument('-n', '--northstar-file', help='Path to Northstar Excel workbook', required=True) parser.add_argument('-s', '--salesforce-file', help='Path to Salesforce Excel workbook', required=True) parser.add_argument('-t', '--account-name-match-ratio-threshold', type=int, help='Account names with specified (or above) similarity ratio will be used for joining Anchor and ' 'Salesforce account data. Number between 0 and 100.', default=75) parser.add_argument('-r', '--result-file', help='Path to result Excel workbook. The file will have 2 spreadsheets for accounts and ' 'contacts reconciliation', required=True) args = parser.parse_args() anchor_ns = AnchorNorthstarDataframe(args.anchor_file, args.northstar_file) salesforce = SalesForceDataframe(args.salesforce_file) anchor_sf_accounts = AnchorSalesforceAccountsDataframe(anchor_ns, salesforce, args.account_name_match_ratio_threshold) anchor_sf_contacts = AnchorSalesforceContactsDataframe(anchor_ns, salesforce) save_dataframes_to_excel(args.result_file, {'Accounts': anchor_sf_accounts.df, 'Contacts': anchor_sf_contacts.df}, wrap_text=False)

57

120

0.754991

19db3143b0967735343ec7fb40012d028a989ea5

1,650

py

Python

billrelease.py

arby36/BillAi

e5c10c35279a1669d218439671e03bc17acb7fdc

[ "MIT" ]

null

billrelease.py

arby36/BillAi

e5c10c35279a1669d218439671e03bc17acb7fdc

[ "MIT" ]

null

billrelease.py

arby36/BillAi

e5c10c35279a1669d218439671e03bc17acb7fdc

[ "MIT" ]

null

bill()

27.966102

139

0.527273

19ddf831a5a3b46c86717f74ec094bf9d7bcc0cd

757

py

Python

homeworks/hw05/tests/q3b1.py

cwakamiya/ieor135

084490380f265225927d11b43d948c1206b0aab8

[ "Apache-2.0" ]

28

2020-06-15T23:53:36.000Z

2022-03-19T09:27:02.000Z

homeworks/hw05/tests/q3b1.py

cwakamiya/ieor135

084490380f265225927d11b43d948c1206b0aab8

[ "Apache-2.0" ]

4

2020-06-24T22:20:31.000Z

2022-02-28T01:37:36.000Z

homeworks/hw05/tests/q3b1.py

cwakamiya/ieor135

084490380f265225927d11b43d948c1206b0aab8

[ "Apache-2.0" ]

78

2020-06-19T09:41:01.000Z

2022-02-05T00:13:29.000Z

test = { 'name': 'q3b1', 'points': 2, 'suites': [ { 'cases': [ { 'code': '>>> 4 <= ' "sum(list(X1.describe().loc['mean'])) " '<= 9\n' 'True', 'hidden': False, 'locked': False}, { 'code': '>>> len(X1) == 768\nTrue', 'hidden': False, 'locked': False}], 'scored': True, 'setup': '', 'teardown': '', 'type': 'doctest'}]}

47.3125

86

0.211361

19e32a5576ac8d30a109ed4090fee43e0912beb9

3,050

py

Python

scanpy/api/__init__.py

gioelelm/scanpy

97391a0e7908b9644b2d6640c8e26d37bdc7811e

[ "BSD-3-Clause" ]

null

scanpy/api/__init__.py

gioelelm/scanpy

97391a0e7908b9644b2d6640c8e26d37bdc7811e

[ "BSD-3-Clause" ]

null

scanpy/api/__init__.py

gioelelm/scanpy

97391a0e7908b9644b2d6640c8e26d37bdc7811e

[ "BSD-3-Clause" ]

1

2019-02-18T07:39:59.000Z

"""Scanpy's high-level API provides an overview of all features relevant to pratical use:: import scanpy.api as sc .. raw:: html <h3>Preprocessing tools</h3> Filtering of highly-variable genes, batch-effect correction, per-cell (UMI) normalization, preprocessing recipes. .. raw:: html <h4>Basic Preprocessing</h4> .. autosummary:: :toctree: . pp.filter_cells pp.filter_genes pp.filter_genes_dispersion pp.log1p pp.pca pp.normalize_per_cell pp.regress_out pp.scale pp.subsample .. raw:: html <h4>Recipes</h4> .. autosummary:: :toctree: . pp.recipe_zheng17 pp.recipe_weinreb16 .. raw:: html <h3>Machine Learning and Statistics tools<h3> .. raw:: html <h4>Visualization</h4> .. autosummary:: :toctree: . tl.pca tl.tsne tl.diffmap tl.draw_graph .. raw:: html <h4>Branching trajectories and pseudotime, clustering, differential expression</h4> .. autosummary:: :toctree: . tl.aga tl.louvain tl.dpt tl.rank_genes_groups .. raw:: html <h4>Simulations</h4> .. autosummary:: :toctree: . tl.sim .. raw:: html <h3>Generic methods</h3> .. raw:: html <h4>Reading and Writing</h4> .. autosummary:: :toctree: . read write read_10x_h5 .. raw:: html <h4>Data Structures</h4> .. autosummary:: :toctree: . AnnData DataGraph .. raw:: html <h3>Plotting</h3> .. raw:: html <h4>Generic plotting with AnnData</h4> .. autosummary:: :toctree: . pl.scatter pl.violin pl.ranking .. raw:: html <h4>Plotting tool results</h4> Methods that extract and visualize tool-specific annotation in an AnnData object. .. raw:: html <h5>Visualization</h5> .. autosummary:: :toctree: . pl.pca pl.pca_loadings pl.pca_scatter pl.pca_variance_ratio pl.tsne pl.diffmap pl.draw_graph .. raw:: html <h5>Branching trajectories and pseudotime, clustering, differential expression</h5> .. autosummary:: :toctree: . pl.aga pl.aga_graph pl.aga_path pl.louvain pl.dpt pl.dpt_scatter pl.dpt_groups_pseudotime pl.dpt_timeseries pl.rank_genes_groups pl.rank_genes_groups_violin .. raw:: html <h5>Simulations</h5> .. autosummary:: :toctree: . pl.sim .. raw:: html <h4>Builtin datasets</h4> Simple functions that provide annotated datasets for benchmarking. See `here <https://scanpy.readthedocs.io/en/latest/examples.html>`_ for extensive documented tutorials and use cases. All of these functions return an Annotated Data object. .. autosummary:: :toctree: . datasets.paul15 datasets.toggleswitch datasets.krumsiek11 datasets.blobs datasets.moignard15 """ from .. import __version__ from .. import settings from .. import logging from . import tl tools = tl from . import pl plotting = pl from . import pp preprocessing = pp from ..readwrite import read, read_10x_h5, write, read_params, write_params from . import datasets from ..data_structs import AnnData, DataGraph from .. import utils

14.95098

113

0.679016

19e36b29ee592d089dc07f0b81f9a1312e103cce

34,894

py

Python

sw/EdgeBERT/transformers/src/transformers/modeling_highway_albert.py

yihuajack/EdgeBERT

a51ae7557187e3251f4b11bc13ef9cbd336019ff

[ "Apache-2.0" ]

8

2021-11-01T01:38:04.000Z

2022-03-20T16:03:39.000Z

sw/EdgeBERT/transformers/src/transformers/modeling_highway_albert.py

yihuajack/EdgeBERT

a51ae7557187e3251f4b11bc13ef9cbd336019ff

[ "Apache-2.0" ]

1

2021-11-19T08:04:02.000Z

2021-12-19T07:21:48.000Z

sw/EdgeBERT/transformers/src/transformers/modeling_highway_albert.py

yihuajack/EdgeBERT

a51ae7557187e3251f4b11bc13ef9cbd336019ff

[ "Apache-2.0" ]

5

2021-11-19T07:52:44.000Z

2022-02-10T08:23:19.000Z

import torch from torch import nn from torch.nn import CrossEntropyLoss, MSELoss from .modeling_albert import AlbertPreTrainedModel, AlbertLayerNorm, AlbertLayerGroup from .modeling_bert import BertEmbeddings from .modeling_highway_bert import BertPooler import numpy as np

46.963661

148

0.611366

19e3c7e8cb0d8e13048dc4a21c8f8d2b1867724a

1,809

py

Python

tests/test_sar.py

chris-angeli-rft/cloud-custodian

5ff331b114a591dbaf6d672e30ceefb7ae64a5dd

[ "Apache-2.0" ]

8

2021-05-18T02:22:03.000Z

2021-09-11T02:49:04.000Z

tests/test_sar.py

chris-angeli-rft/cloud-custodian

5ff331b114a591dbaf6d672e30ceefb7ae64a5dd

[ "Apache-2.0" ]

1

2021-04-26T04:38:35.000Z

tests/test_sar.py

chris-angeli-rft/cloud-custodian

5ff331b114a591dbaf6d672e30ceefb7ae64a5dd

[ "Apache-2.0" ]

1

2021-11-10T02:28:47.000Z

# Copyright 2020 Kapil Thangavelu # # 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 .common import BaseTest

35.470588

74

0.616363

19e3cc99b66e2939b99c81e570efb9afd33fa23d

5,773

py

Python

rovina.py

Pandoro/tools

631c6036cb74dc845668fd912588fd31aae46f8b

[ "MIT" ]

1

2019-04-22T16:38:03.000Z

rovina.py

afcarl/tools-Pandoro

631c6036cb74dc845668fd912588fd31aae46f8b

[ "MIT" ]

2

2018-03-13T10:49:48.000Z

2018-03-13T10:54:01.000Z

rovina.py

afcarl/tools-Pandoro

631c6036cb74dc845668fd912588fd31aae46f8b

[ "MIT" ]

2

2018-03-08T19:40:10.000Z

2018-06-11T14:43:49.000Z

import json import os import sys sys.path.append('/usr/lib/python2.7/dist-packages') import cv2 import numpy as np from tqdm import * import dataset_utils

37.245161

161

0.625498

19e85b96640382129fd31d8131a6692e41afddf9

4,952

py

Python

gpgLabs/GPR/GPRlab1.py

victortocantins/gpgLabs

310b69c681dd1ebf91ba8be2b5ac27adf5fc0f12

[ "MIT" ]

null

gpgLabs/GPR/GPRlab1.py

victortocantins/gpgLabs

310b69c681dd1ebf91ba8be2b5ac27adf5fc0f12

[ "MIT" ]

null

gpgLabs/GPR/GPRlab1.py

victortocantins/gpgLabs

310b69c681dd1ebf91ba8be2b5ac27adf5fc0f12

[ "MIT" ]

null

import numpy as np from scipy.constants import mu_0, epsilon_0 import matplotlib.pyplot as plt from PIL import Image import warnings warnings.filterwarnings('ignore') from ipywidgets import interact, interactive, IntSlider, widget, FloatText, FloatSlider, fixed from .Wiggle import wiggle, PrimaryWave, ReflectedWave import requests from io import BytesIO ######################################## # DOWNLOAD FUNCTIONS ######################################## ######################################## # WIDGETS ######################################## ######################################## # FUNCTIONS ########################################

24.636816

94

0.544628

19ebb2a3f5203d8e575a8e0bab417177a0a48924

5,010

py

Python

third_party/unidecode/x0bd.py

asysc2020/contentbox

5c155976e0ce7ea308d62293ab89624d97b21d09

[ "Apache-2.0" ]

39

2015-06-10T23:18:07.000Z

2021-10-21T04:29:06.000Z

third_party/unidecode/x0bd.py

asysc2020/contentbox

5c155976e0ce7ea308d62293ab89624d97b21d09

[ "Apache-2.0" ]

2

2016-08-22T12:38:10.000Z

2017-01-26T18:37:33.000Z

third_party/unidecode/x0bd.py

asysc2020/contentbox

5c155976e0ce7ea308d62293ab89624d97b21d09

[ "Apache-2.0" ]

26

2015-06-10T22:09:15.000Z

2021-06-27T15:45:15.000Z

data = ( 'bols', # 0x00 'bolt', # 0x01 'bolp', # 0x02 'bolh', # 0x03 'bom', # 0x04 'bob', # 0x05 'bobs', # 0x06 'bos', # 0x07 'boss', # 0x08 'bong', # 0x09 'boj', # 0x0a 'boc', # 0x0b 'bok', # 0x0c 'bot', # 0x0d 'bop', # 0x0e 'boh', # 0x0f 'bwa', # 0x10 'bwag', # 0x11 'bwagg', # 0x12 'bwags', # 0x13 'bwan', # 0x14 'bwanj', # 0x15 'bwanh', # 0x16 'bwad', # 0x17 'bwal', # 0x18 'bwalg', # 0x19 'bwalm', # 0x1a 'bwalb', # 0x1b 'bwals', # 0x1c 'bwalt', # 0x1d 'bwalp', # 0x1e 'bwalh', # 0x1f 'bwam', # 0x20 'bwab', # 0x21 'bwabs', # 0x22 'bwas', # 0x23 'bwass', # 0x24 'bwang', # 0x25 'bwaj', # 0x26 'bwac', # 0x27 'bwak', # 0x28 'bwat', # 0x29 'bwap', # 0x2a 'bwah', # 0x2b 'bwae', # 0x2c 'bwaeg', # 0x2d 'bwaegg', # 0x2e 'bwaegs', # 0x2f 'bwaen', # 0x30 'bwaenj', # 0x31 'bwaenh', # 0x32 'bwaed', # 0x33 'bwael', # 0x34 'bwaelg', # 0x35 'bwaelm', # 0x36 'bwaelb', # 0x37 'bwaels', # 0x38 'bwaelt', # 0x39 'bwaelp', # 0x3a 'bwaelh', # 0x3b 'bwaem', # 0x3c 'bwaeb', # 0x3d 'bwaebs', # 0x3e 'bwaes', # 0x3f 'bwaess', # 0x40 'bwaeng', # 0x41 'bwaej', # 0x42 'bwaec', # 0x43 'bwaek', # 0x44 'bwaet', # 0x45 'bwaep', # 0x46 'bwaeh', # 0x47 'boe', # 0x48 'boeg', # 0x49 'boegg', # 0x4a 'boegs', # 0x4b 'boen', # 0x4c 'boenj', # 0x4d 'boenh', # 0x4e 'boed', # 0x4f 'boel', # 0x50 'boelg', # 0x51 'boelm', # 0x52 'boelb', # 0x53 'boels', # 0x54 'boelt', # 0x55 'boelp', # 0x56 'boelh', # 0x57 'boem', # 0x58 'boeb', # 0x59 'boebs', # 0x5a 'boes', # 0x5b 'boess', # 0x5c 'boeng', # 0x5d 'boej', # 0x5e 'boec', # 0x5f 'boek', # 0x60 'boet', # 0x61 'boep', # 0x62 'boeh', # 0x63 'byo', # 0x64 'byog', # 0x65 'byogg', # 0x66 'byogs', # 0x67 'byon', # 0x68 'byonj', # 0x69 'byonh', # 0x6a 'byod', # 0x6b 'byol', # 0x6c 'byolg', # 0x6d 'byolm', # 0x6e 'byolb', # 0x6f 'byols', # 0x70 'byolt', # 0x71 'byolp', # 0x72 'byolh', # 0x73 'byom', # 0x74 'byob', # 0x75 'byobs', # 0x76 'byos', # 0x77 'byoss', # 0x78 'byong', # 0x79 'byoj', # 0x7a 'byoc', # 0x7b 'byok', # 0x7c 'byot', # 0x7d 'byop', # 0x7e 'byoh', # 0x7f 'bu', # 0x80 'bug', # 0x81 'bugg', # 0x82 'bugs', # 0x83 'bun', # 0x84 'bunj', # 0x85 'bunh', # 0x86 'bud', # 0x87 'bul', # 0x88 'bulg', # 0x89 'bulm', # 0x8a 'bulb', # 0x8b 'buls', # 0x8c 'bult', # 0x8d 'bulp', # 0x8e 'bulh', # 0x8f 'bum', # 0x90 'bub', # 0x91 'bubs', # 0x92 'bus', # 0x93 'buss', # 0x94 'bung', # 0x95 'buj', # 0x96 'buc', # 0x97 'buk', # 0x98 'but', # 0x99 'bup', # 0x9a 'buh', # 0x9b 'bweo', # 0x9c 'bweog', # 0x9d 'bweogg', # 0x9e 'bweogs', # 0x9f 'bweon', # 0xa0 'bweonj', # 0xa1 'bweonh', # 0xa2 'bweod', # 0xa3 'bweol', # 0xa4 'bweolg', # 0xa5 'bweolm', # 0xa6 'bweolb', # 0xa7 'bweols', # 0xa8 'bweolt', # 0xa9 'bweolp', # 0xaa 'bweolh', # 0xab 'bweom', # 0xac 'bweob', # 0xad 'bweobs', # 0xae 'bweos', # 0xaf 'bweoss', # 0xb0 'bweong', # 0xb1 'bweoj', # 0xb2 'bweoc', # 0xb3 'bweok', # 0xb4 'bweot', # 0xb5 'bweop', # 0xb6 'bweoh', # 0xb7 'bwe', # 0xb8 'bweg', # 0xb9 'bwegg', # 0xba 'bwegs', # 0xbb 'bwen', # 0xbc 'bwenj', # 0xbd 'bwenh', # 0xbe 'bwed', # 0xbf 'bwel', # 0xc0 'bwelg', # 0xc1 'bwelm', # 0xc2 'bwelb', # 0xc3 'bwels', # 0xc4 'bwelt', # 0xc5 'bwelp', # 0xc6 'bwelh', # 0xc7 'bwem', # 0xc8 'bweb', # 0xc9 'bwebs', # 0xca 'bwes', # 0xcb 'bwess', # 0xcc 'bweng', # 0xcd 'bwej', # 0xce 'bwec', # 0xcf 'bwek', # 0xd0 'bwet', # 0xd1 'bwep', # 0xd2 'bweh', # 0xd3 'bwi', # 0xd4 'bwig', # 0xd5 'bwigg', # 0xd6 'bwigs', # 0xd7 'bwin', # 0xd8 'bwinj', # 0xd9 'bwinh', # 0xda 'bwid', # 0xdb 'bwil', # 0xdc 'bwilg', # 0xdd 'bwilm', # 0xde 'bwilb', # 0xdf 'bwils', # 0xe0 'bwilt', # 0xe1 'bwilp', # 0xe2 'bwilh', # 0xe3 'bwim', # 0xe4 'bwib', # 0xe5 'bwibs', # 0xe6 'bwis', # 0xe7 'bwiss', # 0xe8 'bwing', # 0xe9 'bwij', # 0xea 'bwic', # 0xeb 'bwik', # 0xec 'bwit', # 0xed 'bwip', # 0xee 'bwih', # 0xef 'byu', # 0xf0 'byug', # 0xf1 'byugg', # 0xf2 'byugs', # 0xf3 'byun', # 0xf4 'byunj', # 0xf5 'byunh', # 0xf6 'byud', # 0xf7 'byul', # 0xf8 'byulg', # 0xf9 'byulm', # 0xfa 'byulb', # 0xfb 'byuls', # 0xfc 'byult', # 0xfd 'byulp', # 0xfe 'byulh', # 0xff )

19.343629

20

0.436128

19ecf7e3e7e37b889b168edf93836870ccf82afb

55

py

Python

social/backends/azuread.py

raccoongang/python-social-auth

81c0a542d158772bd3486d31834c10af5d5f08b0

[ "BSD-3-Clause" ]

1,987

2015-01-01T16:12:45.000Z

2022-03-29T14:24:25.000Z

social/backends/azuread.py

raccoongang/python-social-auth

81c0a542d158772bd3486d31834c10af5d5f08b0

[ "BSD-3-Clause" ]

731

2015-01-01T22:55:25.000Z

2022-03-10T15:07:51.000Z

virtual/lib/python3.6/site-packages/social/backends/azuread.py

dennismwaniki67/awards

80ed10541f5f751aee5f8285ab1ad54cfecba95f

[ "MIT" ]

1,082

2015-01-01T16:27:26.000Z

2022-03-22T21:18:33.000Z

from social_core.backends.azuread import AzureADOAuth2

27.5

54

0.890909

19ed8ee16410261911df594fb0af9ff20f20ca7e

6,556

py

Python

pystitchy/grid.py

iht/Stitchy-Studio

f7faf846d7ce498ef5945caaff2b09f9108e2919

[ "MIT" ]

1

2021-02-28T17:27:16.000Z

pystitchy/grid.py

iht/Stitchy-Studio

f7faf846d7ce498ef5945caaff2b09f9108e2919

[ "MIT" ]

null

pystitchy/grid.py

iht/Stitchy-Studio

f7faf846d7ce498ef5945caaff2b09f9108e2919

[ "MIT" ]

null

# Copyright (c) 2012 Israel Herraiz <isra@herraiz.org> # Permission is hereby granted, free of charge, to any person obtaining # a copy of this software and associated documentation files (the # "Software"), to deal in the Software without restriction, including # without limitation the rights to use, copy, modify, merge, publish, # distribute, sublicense, and/or sell copies of the Software, and to # permit persons to whom the Software is furnished to do so, subject to # the following conditions: # The above copyright notice and this permission notice shall be # included in all copies or substantial portions of the Software. # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, # EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF # MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND # NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE # LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION # OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION # WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. import wx import numpy from numpy import zeros

33.968912

107

0.585265

19eeab362b9fb0e2d6b801d0a756ec8fc09dd20a

2,837

py

Python

usps_tools/exceptions.py

pedrovagner/usps-tools

6a241fda35db6590684a534b9c3cf78a589ea09d

[ "MIT" ]

null

usps_tools/exceptions.py

pedrovagner/usps-tools

6a241fda35db6590684a534b9c3cf78a589ea09d

[ "MIT" ]

null

usps_tools/exceptions.py

pedrovagner/usps-tools

6a241fda35db6590684a534b9c3cf78a589ea09d

[ "MIT" ]

null

import traceback from typing import Optional from .i18n import _

27.813725

114

0.601692

19effa59bdd92c4854c56be758df2693cacdcb3d

1,158

py

Python

scraper/engine.py

pesya/scraper

c088dc3dc613fec94e297ac71302d2305b44b14c

[ "BSD-3-Clause" ]

null

scraper/engine.py

pesya/scraper

c088dc3dc613fec94e297ac71302d2305b44b14c

[ "BSD-3-Clause" ]

null

scraper/engine.py

pesya/scraper

c088dc3dc613fec94e297ac71302d2305b44b14c

[ "BSD-3-Clause" ]

null

import sys import csv import requests from parsel import Selector from scraper.parser import get_features_from_item start_url = 'http://www.world-art.ru/animation/rating_top.php' SIGN_STDOUT = '-' FORMAT_CSV = 'csv' FORMAT_JL = 'jl' def parse(url: str, out_path: str, out_format: str): """ gets link and returns the response """ response = requests.get(url) assert response.status_code == 200, f'bad status code: {response.status_code}' response_html = Selector(response.text) links_to_films = response_html.xpath('//td[@class="review"]/a[@class="review"]/@href').getall() out_file = sys.stdout if out_path == SIGN_STDOUT else open(out_path, 'w', buffering=1, newline='') for link in links_to_films: item_response = requests.get(link) assert response.status_code == 200, f'bad status code: {item_response.status_code}' item = get_features_from_item(item_response) if out_format == FORMAT_CSV: item_writer = csv.writer(out_file, delimiter=' ', quotechar=',', quoting=csv.QUOTE_MINIMAL) item_writer.writerow(item.values()) out_file.close() return

28.243902

103

0.69171

19f24b3bd880d9e6bed48acb8886bc868d4be2dd

14,592

py

Python

gui/python/photogate/photogate/photogate_app.py

iorodeo/photogate_software

c9a97dc3da644fe093397dd11024825ba0d79519

[ "Apache-2.0" ]

1

2020-07-23T19:02:50.000Z

gui/python/photogate/photogate/photogate_app.py

iorodeo/photogate_software

c9a97dc3da644fe093397dd11024825ba0d79519

[ "Apache-2.0" ]

null

gui/python/photogate/photogate/photogate_app.py

iorodeo/photogate_software

c9a97dc3da644fe093397dd11024825ba0d79519

[ "Apache-2.0" ]

null

from __future__ import print_function import os import sys import serial.tools.list_ports from PyQt4 import QtCore from PyQt4 import QtGui from photogate_ui import Ui_PhotogateMainWindow from photogate_serial import PhotogateDevice from photogate_serial import getListOfPorts import dependency_hack try: import scipy.io HAVE_SCIPY_IO = True except ImportError: HAVE_SCIPY_IO = False # Utility functions # ----------------------------------------------------------------------------- def getPhotogateTimes(photogateDict): entryTime = uSecToSec(float(photogateDict['entryTime'])) exitTime = uSecToSec(float(photogateDict['exitTime'])) timeInGate = exitTime - entryTime return entryTime, exitTime, timeInGate def getTimeBetweenGates(photogateList): entryTime0 = uSecToSec(float(photogateList[0]['entryTime'])) entryTime1 = uSecToSec(float(photogateList[1]['entryTime'])) timeBetween = entryTime1 - entryTime0 return timeBetween def autoAddFileExtension(fileName,autoExt): fileNameBase, fileNameExt = os.path.splitext(fileName) if not fileNameExt: # Only add extension if there isn't one already fileName = '{0}{1}'.format(fileNameBase,autoExt) return fileName def uSecToSec(value): return (1.0e-6)*value def runPhotogateApp(): app = QtGui.QApplication(sys.argv) mainWindow = PhotogateMainWindow() mainWindow.main() app.exec_() # ----------------------------------------------------------------------------- if __name__ == '__main__': runPhotogateApp()

37.22449

94

0.614172

19f364dac17ba32accfedb9fef8b6459dc8369f0

114

py

Python

playground/step2/test1.py

jhson989/jhML

eb8b76d3b47df858e82cd971bb32794e12de4747

[ "Apache-2.0" ]

null

playground/step2/test1.py

jhson989/jhML

eb8b76d3b47df858e82cd971bb32794e12de4747

[ "Apache-2.0" ]

null

playground/step2/test1.py

jhson989/jhML

eb8b76d3b47df858e82cd971bb32794e12de4747

[ "Apache-2.0" ]

null

from core import Variable from operation import * a = Variable(2) b = square(a) c = square(b) print(c.data)

8.769231

25

0.675439

19f3a16361bc6bc5804201c9d2a4c2d8f966a4a0

106

py

Python

sharepoint/__init__.py

nessalc/python-sharepoint

d264cf5be56c8f9f619a4f72fd039c167cd01ba8

[ "MIT" ]

1

2019-06-03T03:16:44.000Z

sharepoint/__init__.py

nessalc/python-sharepoint

d264cf5be56c8f9f619a4f72fd039c167cd01ba8

[ "MIT" ]

null

sharepoint/__init__.py

nessalc/python-sharepoint

d264cf5be56c8f9f619a4f72fd039c167cd01ba8

[ "MIT" ]

null

name = 'sharepoint' from .sharepoint import SharePointSite __author__='James Classen' __version__='0.0.2'

21.2

38

0.792453

19f6250f9d15cae4fb338cfbac1c36e435b2c1ca

3,188

py

Python

third_party/nkata/tests/transformvideo_test.py

google/offline-content-packager

5a023eeeed4973e452309b434a59ce745487fdd6

[ "Apache-2.0" ]

32

2016-05-31T13:01:46.000Z

2022-03-18T11:17:36.000Z

third_party/nkata/tests/transformvideo_test.py

google/offline-content-packager

5a023eeeed4973e452309b434a59ce745487fdd6

[ "Apache-2.0" ]

null

third_party/nkata/tests/transformvideo_test.py

google/offline-content-packager

5a023eeeed4973e452309b434a59ce745487fdd6

[ "Apache-2.0" ]

29

2016-06-08T18:11:00.000Z

2021-09-28T04:14:34.000Z

# Copyright 2015 The Offline Content Packager Authors. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from os import makedirs from os.path import dirname from os.path import isdir from os.path import join import shutil import tempfile import unittest import jinja2 from scripts.transformations import VideoTransformation import yaml if __name__ == "__main__": unittest.main()

32.20202

78

0.69542

19f6287b8eec32e7e9ec1cec1c39636f68949b75

2,308

py

Python

src/E_get_mpns_v8_analyses_answers/query_mpns_v8_name_relationships.py

feiphoon/mpns-pipeline

d34a8609dc4cb04ccc3f5c9b79a52bfeecdb38f6

[ "MIT" ]

1

2022-03-28T10:46:58.000Z

src/E_get_mpns_v8_analyses_answers/query_mpns_v8_name_relationships.py

feiphoon/mpns-pipeline

d34a8609dc4cb04ccc3f5c9b79a52bfeecdb38f6

[ "MIT" ]

null

src/E_get_mpns_v8_analyses_answers/query_mpns_v8_name_relationships.py

feiphoon/mpns-pipeline

d34a8609dc4cb04ccc3f5c9b79a52bfeecdb38f6

[ "MIT" ]

null

from pathlib import Path from pyspark.sql import SparkSession, functions as f from pyspark.sql.dataframe import DataFrame # Monkeypatch in case I don't use Spark 3.0 DataFrame.transform = transform # Sample/demo purposes mpns_v8_name_relationships_filepath: str = ( "data/analysis/mpns/sample_mpns_v8/name_relationships/" ) output_filepath: str = "data/analysis/mpns/sample_mpns_v8/name_relationships/query/" query_mpns_v8_name_relationships( input_filepath=mpns_v8_name_relationships_filepath, output_filepath=output_filepath, sample_run=True, ) # # Real data # mpns_v8_name_relationships_filepath: str = ( # "data/analysis/mpns/mpns_v8/name_relationships/" # ) # output_filepath: str = "data/analysis/mpns/mpns_v8/name_relationships/query/" # query_mpns_v8_name_relationships( # input_filepath=mpns_v8_name_relationships_filepath, # output_filepath=output_filepath, # sample_run=False, # )

30.368421

86

0.747834

19f803a96ec5d364efd732c5edf09bf82c3dfe31

124

py

Python

library_homework/my_project/my_program.py

Tommy3121173/tommy

429aefb377f84a1d49e85f825a32ac2c160ebc85

[ "MIT" ]

null

library_homework/my_project/my_program.py

Tommy3121173/tommy

429aefb377f84a1d49e85f825a32ac2c160ebc85

[ "MIT" ]

null

library_homework/my_project/my_program.py

Tommy3121173/tommy

429aefb377f84a1d49e85f825a32ac2c160ebc85

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Wed May 23 16:56:57 2018 @author: tommy_mizuki """ import my_library my_func(1,2)

11.272727

35

0.645161

19f8e4fcaecd9a3968eed26a324bf80026d1583f

246

py

Python

algorithm/python/BAEKJOON_1436.py

cjsrhd94/TIL

b91bab7d99d10c63f91af0790cb28ec3d228b68b

[ "MIT" ]

1

2021-08-19T06:23:00.000Z

algorithm/python/BAEKJOON_1436.py

cjsrhd94/TIL

b91bab7d99d10c63f91af0790cb28ec3d228b68b

[ "MIT" ]

null

algorithm/python/BAEKJOON_1436.py

cjsrhd94/TIL

b91bab7d99d10c63f91af0790cb28ec3d228b68b

[ "MIT" ]

null

n = int(input()) count = 0 number = 0 while True: if '666' in str(number): # '666' count . count += 1 if count == n: #count print -> n print(number) break number += 1

24.6

68

0.565041

19f91845aaff11955f6b430aa3684474c464bf80

3,599

py

Python

cacheTraceAnalysis/plot/reqRate.py

Thesys-lab/cacheWorkloadAnalysisOSDI20

cfc5bbb5c8d909571546c78c247561c9db449469

[ "Apache-2.0" ]

6

2020-11-12T07:51:02.000Z

2022-03-27T20:20:01.000Z

cacheTraceAnalysis/plot/reqRate.py

Thesys-lab/InMemoryCachingWorkloadAnalysis

5f6f9f7e29a164478f3fc28eb64c170bbbafdec7

[ "Apache-2.0" ]

null

cacheTraceAnalysis/plot/reqRate.py

Thesys-lab/InMemoryCachingWorkloadAnalysis

5f6f9f7e29a164478f3fc28eb64c170bbbafdec7

[ "Apache-2.0" ]

1

2021-12-31T01:16:09.000Z

""" plot request rate """ import os, sys sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), "../")) from utils.common import * if __name__ == "__main__": import argparse ap = argparse.ArgumentParser() ap.add_argument("--trace", type=str, help="trace path") ap.add_argument("--type", type=str, default="cnt", help="plot type") ap.add_argument("--window", type=int, default=60, help="the size of window in sec") p = ap.parse_args() plot_req_rate(TwrShortBinTraceReader(p.trace), p.window, plot_type=(p.type, ))

38.698925

159

0.689358

19f928aecd4ff7011c0373aab909dea8913438c6

1,373

py

Python

config.py

ShallweJohn/MonsterBlog

f3bd0bdab99af6ba06b7b8fb0eaa6770115fc9c5

[ "MIT" ]

null

config.py

ShallweJohn/MonsterBlog

f3bd0bdab99af6ba06b7b8fb0eaa6770115fc9c5

[ "MIT" ]

3

2021-03-18T20:36:25.000Z

2021-09-07T23:54:49.000Z

config.py

ShallweJohn/MonsterBlog

f3bd0bdab99af6ba06b7b8fb0eaa6770115fc9c5

[ "MIT" ]

null

import os import redis import logging config = { 'development': DevelopmentConfig, 'production': ProductionConfig, 'default': DevelopmentConfig }

19.614286

81

0.699927

19fac7af0c83f21b636a9b1fa9c53ac1705d1cfb

5,097

py

Python

utils.py

sjenni/DeepBilevel

9db6c9d81188e891104677a7ffc4b045421fb097

[ "MIT" ]

8

2019-10-23T12:16:13.000Z

2020-11-16T02:20:28.000Z

utils.py

sjenni/DeepBilevel

9db6c9d81188e891104677a7ffc4b045421fb097

[ "MIT" ]

null

utils.py

sjenni/DeepBilevel

9db6c9d81188e891104677a7ffc4b045421fb097

[ "MIT" ]

4

2020-02-06T14:54:47.000Z

2020-10-25T03:03:04.000Z

import tensorflow as tf from tensorflow.python import pywrap_tensorflow from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training import saver as tf_saver def average_gradients(tower_grads): """Calculate the average gradient for each shared variable across all towers. Note that this function provides a synchronization point across all towers. Args: tower_grads: List of lists of (gradient, variable) tuples. The outer list is over individual gradients. The inner list is over the gradient calculation for each tower. Returns: List of pairs of (gradient, variable) where the gradient has been averaged across all towers. """ average_grads = [] for grad_and_vars in zip(*tower_grads): # Note that each grad_and_vars looks like the following: # ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN)) grads = [] for g, _ in grad_and_vars: # Add 0 dimension to the gradients to represent the tower. expanded_g = tf.expand_dims(g, 0) # Append on a 'tower' dimension which we will average over below. grads.append(expanded_g) # Average over the 'tower' dimension. grad = tf.concat(axis=0, values=grads) grad = tf.reduce_mean(grad, 0) # Keep in mind that the Variables are redundant because they are shared # across towers. So .. we will just return the first tower's pointer to # the Variable. v = grad_and_vars[0][1] grad_and_var = (grad, v) average_grads.append(grad_and_var) return average_grads def montage_tf(imgs, num_h, num_w): """Makes a montage of imgs that can be used in image_summaries. Args: imgs: Tensor of images num_h: Number of images per column num_w: Number of images per row Returns: A montage of num_h*num_w images """ imgs = tf.unstack(imgs) img_rows = [None] * num_h for r in range(num_h): img_rows[r] = tf.concat(axis=1, values=imgs[r * num_w:(r + 1) * num_w]) montage = tf.concat(axis=0, values=img_rows) return tf.expand_dims(montage, 0) def assign_from_checkpoint_fn(model_path, var_list, ignore_missing_vars=False, reshape_variables=False): """Returns a function that assigns specific variables from a checkpoint. Args: model_path: The full path to the model checkpoint. To get latest checkpoint use `model_path = tf.train.latest_checkpoint(checkpoint_dir)` var_list: A list of `Variable` objects or a dictionary mapping names in the checkpoint to the correspoing variables to initialize. If empty or None, it would return no_op(), None. ignore_missing_vars: Boolean, if True it would ignore variables missing in the checkpoint with a warning instead of failing. reshape_variables: Boolean, if True it would automatically reshape variables which are of different shape then the ones stored in the checkpoint but which have the same number of elements. Returns: A function that takes a single argument, a `tf.Session`, that applies the assignment operation. Raises: ValueError: If the checkpoint specified at `model_path` is missing one of the variables in `var_list`. """ if ignore_missing_vars: var_list = remove_missing(var_list, model_path) saver = tf_saver.Saver(var_list, reshape=reshape_variables) return callback def get_variables_to_train(trainable_scopes=None): """Returns a list of variables to train. Returns: A list of variables to train by the optimizer. """ if trainable_scopes is None: variables_to_train = tf.trainable_variables() else: scopes = [scope.strip() for scope in trainable_scopes.split(',')] variables_to_train = [] for scope in scopes: variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope) variables_to_train.extend(variables) print('Variables to train: {}'.format([v.op.name for v in variables_to_train])) return variables_to_train

36.148936

84

0.673141

19fb9cf0c33a5df90f7ff935997f2b53b4510673

1,234

py

Python

paralleldomain/model/annotation/polygon_2d.py

parallel-domain/pd-sdk

20e3d052a5cb612a2dd84bda7b1b5487a6a60edc

[ "Apache-2.0" ]

10

2021-11-17T17:23:49.000Z

2022-03-18T09:51:23.000Z

paralleldomain/model/annotation/polygon_2d.py

parallel-domain/pd-sdk

20e3d052a5cb612a2dd84bda7b1b5487a6a60edc

[ "Apache-2.0" ]

3

2021-12-02T17:16:20.000Z

2022-01-07T12:47:13.000Z

paralleldomain/model/annotation/polygon_2d.py

parallel-domain/pd-sdk

20e3d052a5cb612a2dd84bda7b1b5487a6a60edc

[ "Apache-2.0" ]

2

2022-03-09T07:03:54.000Z

2022-03-23T15:53:48.000Z

from dataclasses import dataclass from typing import List from paralleldomain.model.annotation.common import Annotation from paralleldomain.model.annotation.polyline_2d import Polyline2D

29.380952

114

0.691248

19fd46480858b4a1d5b5836cc3a46a14d32272f9

828

py

Python

tests/backup_bsps.py

LaudateCorpus1/bsp_tool

e8c2489ac3bda5a4467f1dce220a76bbf4ce5b19

[ "MIT" ]

null

tests/backup_bsps.py

LaudateCorpus1/bsp_tool

e8c2489ac3bda5a4467f1dce220a76bbf4ce5b19

[ "MIT" ]

null

tests/backup_bsps.py

LaudateCorpus1/bsp_tool

e8c2489ac3bda5a4467f1dce220a76bbf4ce5b19

[ "MIT" ]

null

import os import shutil import sys from maplist import installed_games backup_dir = "F:/bsps" if len(sys.argv) == 2: backup_dir = sys.argv[1] print(f"Making backups in '{backup_dir}'") i = 0 for base_dir, game_dir in installed_games: i += 1 print(f"Backing up ({i}/{len(installed_games)}) {game_dir}...") for map_dir in installed_games[(base_dir, game_dir)]: src_dir = os.path.join(base_dir, game_dir, map_dir) dest_dir = os.path.join(backup_dir, game_dir, map_dir) os.makedirs(dest_dir, exist_ok=True) try: # note the missed file(s) and continue shutil.copytree(src_dir, dest_dir, dirs_exist_ok=True) except shutil.Error as err: print(f"*** ERROR *** {err}") except FileNotFoundError as err: print(f"*** ERROR *** {err}")

30.666667

67

0.642512

19fe235467f017d20a959660a872441f0b170a74

770

py

Python

infobip_channels/whatsapp/models/response/get_templates.py

infobip-community/infobip-api-python-sdk

5ffc5ab877ee1748aa29391f991c8c5324387487

[ "MIT" ]

null

infobip_channels/whatsapp/models/response/get_templates.py

infobip-community/infobip-api-python-sdk

5ffc5ab877ee1748aa29391f991c8c5324387487

[ "MIT" ]

null

infobip_channels/whatsapp/models/response/get_templates.py

infobip-community/infobip-api-python-sdk

5ffc5ab877ee1748aa29391f991c8c5324387487

[ "MIT" ]

null

from typing import List, Optional from pydantic import AnyHttpUrl from infobip_channels.core.models import CamelCaseModel, ResponseBase

19.25

69

0.711688

19ff517f6d368213182e5f5031c40842eae17a49

1,391

py

Python

examples/server.py

fhamborg/Giveme5W

b5f49712654ab466e605716b4cd9f8dce9bcdd88

[ "Apache-2.0" ]

16

2018-03-28T11:20:11.000Z

2020-09-17T19:39:25.000Z

examples/server.py

fhamborg/Giveme5W

b5f49712654ab466e605716b4cd9f8dce9bcdd88

[ "Apache-2.0" ]

3

2018-03-15T10:17:29.000Z

2018-05-16T13:14:28.000Z

examples/server.py

fhamborg/Giveme5W

b5f49712654ab466e605716b4cd9f8dce9bcdd88

[ "Apache-2.0" ]

6

2018-05-08T12:53:51.000Z

2021-09-25T03:21:02.000Z

import logging from flask import Flask, request, jsonify from extractor.document import Document from extractor.five_w_extractor import FiveWExtractor app = Flask(__name__) log = logging.getLogger(__name__) host = None port = 5000 debug = False options = None extractor = FiveWExtractor() ch = logging.StreamHandler() ch.setLevel(logging.DEBUG) log.addHandler(ch) log.setLevel(logging.DEBUG) if __name__ == "__main__": run()

26.245283

82

0.675054

19ffa347e490ab19819ef9b329ffa153417391c5

826

py

Python

CPAC/utils/tests/test_symlinks.py

Lawreros/C-PAC

ce26ba9a38cbd401cd405150eeed23b805007724

[ "BSD-3-Clause" ]

125

2015-03-04T09:14:46.000Z

2022-03-29T07:46:12.000Z

CPAC/utils/tests/test_symlinks.py

Lawreros/C-PAC

ce26ba9a38cbd401cd405150eeed23b805007724

[ "BSD-3-Clause" ]

1,018

2015-01-04T16:01:29.000Z

2022-03-31T19:23:09.000Z

CPAC/utils/tests/test_symlinks.py

Lawreros/C-PAC

ce26ba9a38cbd401cd405150eeed23b805007724

[ "BSD-3-Clause" ]

117

2015-01-10T08:05:52.000Z

2022-01-18T05:16:51.000Z

import os import tempfile import pkg_resources as p from CPAC.utils.symlinks import create_symlinks mocked_outputs = \ p.resource_filename( "CPAC", os.path.join( 'utils', 'tests', 'test_symlinks-outputs.txt' ) )

19.666667

55

0.579903

c200bfcfb3506f7b5d5aa61e676f674b8d4fef20

14,488

py

Python

web/migrations/0001_initial.py

jmason-ebi/pdx

aec38d74a78c907041332f4623c01047f45f3f0a

[ "Apache-2.0" ]

null

web/migrations/0001_initial.py

jmason-ebi/pdx

aec38d74a78c907041332f4623c01047f45f3f0a

[ "Apache-2.0" ]

null

web/migrations/0001_initial.py

jmason-ebi/pdx

aec38d74a78c907041332f4623c01047f45f3f0a

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # Generated by Django 1.10.1 on 2016-09-09 15:33 from __future__ import unicode_literals from django.db import migrations, models import django.db.models.deletion import django_extensions.db.fields

50.835088

143

0.574752

c202c2c6ef86a127b7a659f1ab70e457fb054b54

4,799

py

Python

dserve/__init__.py

JIC-CSB/dserve

5f20d9de8ffb52f98ef9c68b327fe1ca9fcee17e

[ "MIT" ]

null

dserve/__init__.py

JIC-CSB/dserve

5f20d9de8ffb52f98ef9c68b327fe1ca9fcee17e

[ "MIT" ]

null

dserve/__init__.py

JIC-CSB/dserve

5f20d9de8ffb52f98ef9c68b327fe1ca9fcee17e

[ "MIT" ]

null

"""Script for running the dserve server.""" import os from flask import ( Flask, jsonify, send_file, abort, request, ) from flask_cors import CORS, cross_origin app = Flask(__name__) cors = CORS(app) def overlay_root(): overlays = app._dataset.access_overlays() content = { "_links": { "self": {"href": "/overlays"}}, } for overlay_name in overlays.keys(): value = {"href": "/overlays/{}".format(overlay_name)} content["_links"][overlay_name] = value return jsonify(content) def specific_overlay(overlay_name): overlays = app._dataset.access_overlays() try: overlay = overlays[overlay_name] except KeyError: abort(404) return jsonify(overlay) def creaate_new_overlay(overlay_name): empty_overlay = app._dataset.empty_overlay() try: app._dataset.persist_overlay(overlay_name, empty_overlay) except IOError: abort(409) return "", 201

25.526596

77

0.600542

c203136ec3038930bc5926aaf959f30e095e46a5

1,610

py

Python

kkutil/security.py

kaka19ace/kkutils

1ac449488d85ba2c6b18c5dc9cf77a0bc36579b1

[ "MIT" ]

1

2015-12-13T18:42:52.000Z

kkutil/security.py

kaka19ace/kkutil

1ac449488d85ba2c6b18c5dc9cf77a0bc36579b1

[ "MIT" ]

null

kkutil/security.py

kaka19ace/kkutil

1ac449488d85ba2c6b18c5dc9cf77a0bc36579b1

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- # """ util regex tool refs: http://www.symantec.com/connect/articles/detection-sql-injection-and-cross-site-scripting-attacks """ import re INJECTION_REGEX = re.compile( r"(%27)|(\')|(\-\-)|(%23)|(#)|" # Regex for detection of SQL meta-characters r"\w*((%27)|(\'))\s+((%6F)|o|(%4F))((%72)|r|(%52))\s*|" # Modified regex for detection of SQL meta-characters eg: ' or 1 = 1' detect word 'or', r"((%3D)|(=))[^\n]*((%27)|(\')|(\-\-)|(%3B)|(;))" # Regex for typical SQL Injection attack eg: '= 1 --' r"((%27)|(\'))union|" # Regex for detecting SQL Injection with the UNION keyword r"((%27)|(\'))select|" # Regex for detecting SQL Injection with the UNION keyword r"((%27)|(\'))insert|" # Regex for detecting SQL Injection with the UNION keyword r"((%27)|(\'))update|" # Regex for detecting SQL Injection with the UNION keyword r"((%27)|(\'))drop", # Regex for detecting SQL Injection with the UNION keyword re.IGNORECASE ) CSS_ATTACK_REGREX = re.compile(r"((%3C)|<)((%2F)|/)*[a-z0-9%]+((%3E)|>)", re.IGNORECASE) CSS_IMG_SRC_ATTACK_REGEX = re.compile( r"((%3C)|<)((%69)|i|(%49))((%6D)|m|(%4D))((%67)|g|(%47))[^\n]+((%3E)|>)", re.IGNORECASE ) CSS_PARANOID_ATTACK_REGEX = re.compile("((%3C)|<)[^\n]+((%3E)|>)", re.IGNORECASE)

35

148

0.608075

c204bfd19101390dbf534e7049d9b49aef3685e3

1,520

py

Python

update_eeprom_rc.py

rkojedzinszky/thermo-sensor

f0b5aa6dbf231b566e00a683c5bb1551569d2463

[ "BSD-3-Clause" ]

2

2019-04-25T17:38:02.000Z

2020-03-03T22:50:04.000Z

update_eeprom_rc.py

rkojedzinszky/thermo-sensor

f0b5aa6dbf231b566e00a683c5bb1551569d2463

[ "BSD-3-Clause" ]

null

update_eeprom_rc.py

rkojedzinszky/thermo-sensor

f0b5aa6dbf231b566e00a683c5bb1551569d2463

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python REGISTERS = { 'IOCFG2': 0x00, 'IOCFG1': 0x01, 'IOCFG0': 0x02, 'FIFOTHR': 0x03, 'SYNC1': 0x04, 'SYNC0': 0x05, 'PKTLEN': 0x06, 'PKTCTRL1': 0x07, 'PKTCTRL0': 0x08, 'ADDR': 0x09, 'CHANNR': 0x0A, 'FSCTRL1': 0x0B, 'FSCTRL0': 0x0C, 'FREQ2': 0x0D, 'FREQ1': 0x0E, 'FREQ0': 0x0F, 'MDMCFG4': 0x10, 'MDMCFG3': 0x11, 'MDMCFG2': 0x12, 'MDMCFG1': 0x13, 'MDMCFG0': 0x14, 'DEVIATN': 0x15, 'MCSM2': 0x16, 'MCSM1': 0x17, 'MCSM0': 0x18, 'FOCCFG': 0x19, 'BSCFG': 0x1A, 'AGCCTRL2': 0x1B, 'AGCCTRL1': 0x1C, 'AGCCTRL0': 0x1D, 'WOREVT1': 0x1E, 'WOREVT0': 0x1F, 'WORCTRL': 0x20, 'FREND1': 0x21, 'FREND0': 0x22, 'FSCAL3': 0x23, 'FSCAL2': 0x24, 'FSCAL1': 0x25, 'FSCAL0': 0x26, 'RCCTRL1': 0x27, 'RCCTRL0': 0x28, 'FSTEST': 0x29, 'PTEST': 0x2A, 'AGCTEST': 0x2B, 'TEST2': 0x2C, 'TEST1': 0x2D, 'TEST0': 0x2E, 'PATABLE': 0x3E, } if __name__ == '__main__': import sys import re with open('eeprom', 'r+b') as fh: fh.seek(20) for line in sys.stdin: if re.match('^\s*#', line): continue m = re.match('(?P<reg>\w+)\s+(?P<value>[0-9a-fA-F]+)', line) if not m: continue m = m.groupdict() fh.write(chr(REGISTERS[m['reg']])) fh.write(chr(int(m['value'], 16))) fh.write(b"\xff" * (512 - fh.tell()))

20.540541

72

0.484211

c205b5f889cdcc188c5b89c3efa9505bfb938fe3

384

py

Python

UsefulLink.py

qyu6/TAILab

6c3e7a7e2e49f7c673ab46b90c1568a96cce75b7

[ "Apache-2.0" ]

1

2022-01-10T15:14:55.000Z

UsefulLink.py

qyu6/TAILab

6c3e7a7e2e49f7c673ab46b90c1568a96cce75b7

[ "Apache-2.0" ]

null

UsefulLink.py

qyu6/TAILab

6c3e7a7e2e49f7c673ab46b90c1568a96cce75b7

[ "Apache-2.0" ]

null

''' @func:to store useful links module @create:2021.10.20 '''

25.6

74

0.65625