xszheng2020/the_stack_dedup_python_hits_0 · Datasets at Hugging Face

de9373d0df66278e0b02dc262104db37303b9a61

3,806

py

Python

server-program/clientApplication.py

ezequias2d/projeto-so

993f3dd12135946fe5b4351e8488b7aa8a18f37e

[ "MIT" ]

null

server-program/clientApplication.py

ezequias2d/projeto-so

993f3dd12135946fe5b4351e8488b7aa8a18f37e

[ "MIT" ]

null

server-program/clientApplication.py

ezequias2d/projeto-so

993f3dd12135946fe5b4351e8488b7aa8a18f37e

[ "MIT" ]

null

import socket import tokens import connection import io import os from PIL import Image from message.literalMessage import LiteralMessage from baseApplication import BaseApplication class ClientApplication(BaseApplication): def __init__(self, host, port): super().__init__(host, port, token...

34.288288

121

0.547031

423

3,806

4.739953

0.224586

0.04788

0.037406

0.042394

0.223441

0.095761

0

0.020639

0.350762

3,806

111

122

34.288288

0.790773

0

0.126316

0

0.094942

0

1

0.094737

false

0

0.084211

0

0.189474

0.210526

0

null

0

null

0

1

0

de95cb380efb4a5351375e80063db451dd2899b5

3,803

py

Python

TkPy/module.py

tbor8080/pyprog

3642b9af2a92f7369d9b6fa138e47ba22df3271c

[ "MIT" ]

null

TkPy/module.py

tbor8080/pyprog

3642b9af2a92f7369d9b6fa138e47ba22df3271c

[ "MIT" ]

null

TkPy/module.py

tbor8080/pyprog

3642b9af2a92f7369d9b6fa138e47ba22df3271c

[ "MIT" ]

null

import sys import os import tkinter.filedialog as fd from time import sleep import datetime import tkinter import tkinter as tk from tkinter import ttk from tkinter import scrolledtext import threading # New File & Duplicate File Save def saveasFilePath( filetype=[ ("",".txt"), ("CSV",".csv") ] ): return fd.asksa...

34.261261

106

0.616618

464

3,803

4.838362

0.273707

0.09265

0.071269

0.080178

0.385301

0.334967

0.253007

0.181292

0.148775

0.109577

0

0.024466

0.236918

3,803

111

107

34.261261

0.749139

0.104391

0

0.105263

0

0.05839

0

1

0.144737

false

0

0.131579

0.052632

0.355263

0.026316

0

null

0

null

0

1

0

de974a6af213636bff804abc1abfb40a31e4354d

8,810

py

Python

judge/base/__init__.py

fanzeyi/Vulpix

9448e968973073c98231b22663bbebb2a452dcd7

[ "BSD-3-Clause" ]

13

2015-03-08T11:59:28.000Z

2021-07-11T11:58:01.000Z

src/tornado/demos/Vulpix-master/judge/base/__init__.py

ptphp/PyLib

07ac99cf2deb725475f5771b123b9ea1375f5e65

[ "Apache-2.0" ]

null

src/tornado/demos/Vulpix-master/judge/base/__init__.py

ptphp/PyLib

07ac99cf2deb725475f5771b123b9ea1375f5e65

[ "Apache-2.0" ]

3

2015-05-29T16:14:08.000Z

2016-04-29T07:25:26.000Z

# -*- coding: utf-8 -*- # AUTHOR: Zeray Rice <fanzeyi1994@gmail.com> # FILE: judge/base/__init__.py # CREATED: 01:49:33 08/03/2012 # MODIFIED: 15:42:49 19/04/2012 # DESCRIPTION: Base handler import re import time import urllib import hashlib import httplib import datetime import functools import traceback import simp...

40.787037

147

0.559932

1,035

8,810

4.66087

0.29372

0.016584

0.018657

0.013474

0.16791

0.151119

0.100332

0.064677

0.045605

0

0.015778

0.316572

8,810

215

148

40.976744

0.785418

0.062089

0

0.177083

0

0.015625

0.092163

0.029053

0

1

0.088542

false

0.026042

0.119792

0.015625

0.333333

0

null

0

null

0

1

0

de9773cffe9839ef07dd2219fd1b0246be382284

1,839

py

Python

src/blog/migrations/0001_initial.py

triump0870/rohan

3bd56ccdc35cb67823117e78dc02becbfbd0b329

[ "MIT" ]

null

src/blog/migrations/0001_initial.py

triump0870/rohan

3bd56ccdc35cb67823117e78dc02becbfbd0b329

[ "MIT" ]

null

src/blog/migrations/0001_initial.py

triump0870/rohan

3bd56ccdc35cb67823117e78dc02becbfbd0b329

[ "MIT" ]

null

# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import models, migrations import markdownx.models import myblog.filename from django.conf import settings class Migration(migrations.Migration): dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ...

39.12766

152

0.582926

187

1,839

5.572193

0.465241

0.034549

0.03071

0.040307

0.278311

0.224568

0.151631

0

0.008191

0.269712

1,839

46

153

39.978261

0.767684

0.011419

0

0.275

0

0.088656

0

1

0

false

0

0.125

0

0.2

0

null

0

null

0

1

0

de9bc65cbfa30de1a8294fb16fd3712d1ce427db

3,566

py

Python

#17.py

Domino2357/daily-coding-problem

95ddef9db53c8b895f2c085ba6399a3144a4f8e6

[ "MIT" ]

null

#17.py

Domino2357/daily-coding-problem

95ddef9db53c8b895f2c085ba6399a3144a4f8e6

[ "MIT" ]

null

#17.py

Domino2357/daily-coding-problem

95ddef9db53c8b895f2c085ba6399a3144a4f8e6

[ "MIT" ]

null

""" This problem was asked by Google. Suppose we represent our file system by a string in the following manner: The string "dir\n\tsubdir1\n\tsubdir2\n\t\tfile.ext" represents: dir subdir1 subdir2 file.ext The directory dir contains an empty sub-directory subdir1 and a sub-directory subdir2 containin...

31.280702

124

0.666854

538

3,566

4.29368

0.299257

0.072727

0.109091

0.036797

0.183117

0.080519

0.022511

0

0.017372

0.257431

3,566

113

125

31.557522

0.854985

0.551598

0

0.288462

0

0.027628

0

1

0.096154

false

0

0.019231

0.192308

0.038462

0

null

0

null

0

1

0

de9bd50729808fda9f77f7ae5831c5d7b432a027

1,315

py

Python

turbot/db.py

emre/turbot

7bc49a8b79bce7f2490036d9255e5b3df8fff4b1

[ "MIT" ]

3

2017-10-17T22:02:06.000Z

2018-05-07T10:29:31.000Z

turbot/db.py

emre/turbot

7bc49a8b79bce7f2490036d9255e5b3df8fff4b1

[ "MIT" ]

null

turbot/db.py

emre/turbot

7bc49a8b79bce7f2490036d9255e5b3df8fff4b1

[ "MIT" ]

3

2018-10-16T13:28:57.000Z

2021-02-24T13:23:29.000Z

from os.path import expanduser, exists from os import makedirs TURBOT_PATH = expanduser('~/.turbot') UPVOTE_LOGS = expanduser("%s/upvote_logs" % TURBOT_PATH) CHECKPOINT = expanduser("%s/checkpoint" % TURBOT_PATH) REFUND_LOG = expanduser("%s/refunds" % TURBOT_PATH) def load_checkpoint(fallback_block_num=None): tr...

24.351852

56

0.650951

173

1,315

4.768786

0.300578

0.09697

0.087273

0.054545

0.233939

0.157576

0

0.222814

1,315

53

57

24.811321

0.807241

0

0.289474

0

0.047148

0

1

0.157895

false

0

0.052632

0.026316

0.342105

0

null

0

null

0

1

0

dea196647fceafaeec0ee9058ac3907d2c76082c

3,752

py

Python

pys3crypto.py

elitest/pys3crypto

9dfef5935ff1c663b8641eaa052e778cdf34a565

[ "MIT" ]

null

pys3crypto.py

elitest/pys3crypto

9dfef5935ff1c663b8641eaa052e778cdf34a565

[ "MIT" ]

null

pys3crypto.py

elitest/pys3crypto

9dfef5935ff1c663b8641eaa052e778cdf34a565

[ "MIT" ]

null

#!/usr/bin/env python3 # Original Author @elitest # This script uses boto3 to perform client side decryption # of data encryption keys and associated files # and encryption in ways compatible with the AWS SDKs # This support is not available in boto3 at this time # Wishlist: # Currently only tested with KMS managed s...

36.427184

125

0.709488

499

3,752

5.296593

0.386774

0.020431

0.027242

0.030269

0.121831

0.090049

0.062807

0

0.010475

0.185768

3,752

102

126

36.784314

0.854664

0.29371

0

0.169492

0

0.193659

0.012223

0

1

0.033898

false

0

0.067797

0

0.152542

0.033898

0

null

0

null

0

1

0

dea3d4b6a9500edd440cd83df9ceb44f4b4e36eb

1,777

py

Python

openTEL_11_19/presentation_figures/tm112_utils.py

psychemedia/presentations

a4d7058b1f716c59a89d0bcd1390ead75d769d43

[ "Apache-2.0" ]

null

openTEL_11_19/presentation_figures/tm112_utils.py

psychemedia/presentations

a4d7058b1f716c59a89d0bcd1390ead75d769d43

[ "Apache-2.0" ]

null

openTEL_11_19/presentation_figures/tm112_utils.py

psychemedia/presentations

a4d7058b1f716c59a89d0bcd1390ead75d769d43

[ "Apache-2.0" ]

1

2019-11-05T10:35:40.000Z

from IPython.display import HTML #TO DO - the nested table does not display? #Also, the nested execution seems to take a long time to run? #Profile it to see where I'm going wrong! def obj_display(v, nest=False, style=True): def nested(v): if nest: return obj_display(v, style=False) re...

38.630435

133

0.563309

269

1,777

3.598513

0.30855

0.018595

0.022727

0.051653

0.46281

0.363636

0

0.00297

0.241981

1,777

45

134

39.488889

0.715664

0.07991

0

0.363636

0

0.444799

0.207403

0

1

0.060606

false

0

0.151515

0

0.272727

0

null

0

null

0

1

0

dea6f4a43ec33dab31441d90f5221fa29eeb9456

8,191

py

Python

analysis_guis/code_test.py

Sepidak/spikeGUI

25ae60160308c0a34e7180f3e39a1c4dc6aad708

[ "MIT" ]

null

analysis_guis/code_test.py

Sepidak/spikeGUI

25ae60160308c0a34e7180f3e39a1c4dc6aad708

[ "MIT" ]

3

2021-08-09T21:51:41.000Z

2021-08-09T21:51:45.000Z

analysis_guis/code_test.py

Sepidak/spikeGUI

25ae60160308c0a34e7180f3e39a1c4dc6aad708

[ "MIT" ]

3

2021-10-16T14:07:59.000Z

2021-10-16T17:09:03.000Z

# -*- coding: utf-8 -*- """ Simple example using BarGraphItem """ # import initExample ## Add path to library (just for examples; you do not need this) import numpy as np import pickle as p import pandas as pd from analysis_guis.dialogs.rotation_filter import RotationFilter from analysis_guis.dialogs impor...

37.401826

129

0.605421

1,301

8,191

3.586472

0.256726

0.015002

0.015431

0.010716

0.087655

0.04715

0.036434

0.015002

0

0.021917

0.270297

8,191

218

130

37.573395

0.758742

0.43792

0

0.025316

0

0.02526

0.008735

0

1

0.012658

false

0

0.227848

0

0.253165

0

null

0

null

0

1

0

dea9df41450058a28e28c535ce8960f8b770dc38

1,147

py

Python

pex/pip/download_observer.py

sthagen/pantsbuild-pex

bffe6c3641b809cd3b20adbc7fdb2cf7e5f54309

[ "Apache-2.0" ]

null

pex/pip/download_observer.py

sthagen/pantsbuild-pex

bffe6c3641b809cd3b20adbc7fdb2cf7e5f54309

[ "Apache-2.0" ]

null

pex/pip/download_observer.py

sthagen/pantsbuild-pex

bffe6c3641b809cd3b20adbc7fdb2cf7e5f54309

[ "Apache-2.0" ]

null

# Copyright 2022 Pants project contributors (see CONTRIBUTORS.md). # Licensed under the Apache License, Version 2.0 (see LICENSE). from __future__ import absolute_import from pex.pip.log_analyzer import LogAnalyzer from pex.typing import TYPE_CHECKING, Generic if TYPE_CHECKING: from typing import Iterable, Mappi...

23.408163

66

0.646905

141

1,147

5.099291

0.446809

0.029207

0.038943

0.05007

0.083449

0

0.006928

0.244987

1,147

48

67

23.895833

0.823326

0.2415

0

0.133333

0

0.004662

0

1

0.1

false

0

0.233333

0.066667

0.566667

0

null

0

null

0

1

0

deabe0363fc1143c6a3fe5cc62b534d0a3e480ca

2,096

py

Python

pbpstats/data_loader/nba_possession_loader.py

pauldevos/pbpstats

71c0b5e2bd45d0ca031646c70cd1c1f30c6a7152

[ "MIT" ]

null

pbpstats/data_loader/nba_possession_loader.py

pauldevos/pbpstats

71c0b5e2bd45d0ca031646c70cd1c1f30c6a7152

[ "MIT" ]

null

pbpstats/data_loader/nba_possession_loader.py

pauldevos/pbpstats

71c0b5e2bd45d0ca031646c70cd1c1f30c6a7152

[ "MIT" ]

null

from pbpstats.resources.enhanced_pbp import StartOfPeriod class NbaPossessionLoader(object): """ Class for shared methods between :obj:`~pbpstats.data_loader.data_nba.possessions_loader.DataNbaPossessionLoader` and :obj:`~pbpstats.data_loader.stats_nba.possessions_loader.StatsNbaPossessionLoader` Bot...

39.54717

121

0.624046

220

2,096

5.772727

0.327273

0.056693

0.03937

0.043307

0.456693

0.434646

0.426772

0.32126

0

0.008826

0.297233

2,096

52

122

40.307692

0.85336

0.28292

0

0.375

0

1

0.0625

false

0

0.03125

0

0.15625

0

null

0

null

0

1

0

dead01ec590550c2d98b328ed72222f137d3778b

7,033

py

Python

vmware_nsx_tempest/tests/nsxv/api/base_provider.py

gravity-tak/vmware-nsx-tempest

3a1007d401c471d989345bb5a3f9769f84bd4ac6

[ "Apache-2.0" ]

null

vmware_nsx_tempest/tests/nsxv/api/base_provider.py

gravity-tak/vmware-nsx-tempest

3a1007d401c471d989345bb5a3f9769f84bd4ac6

[ "Apache-2.0" ]

null

vmware_nsx_tempest/tests/nsxv/api/base_provider.py

gravity-tak/vmware-nsx-tempest

3a1007d401c471d989345bb5a3f9769f84bd4ac6

[ "Apache-2.0" ]

null

# Copyright 2015 OpenStack Foundation # 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 requ...

39.072222

78

0.65278

879

7,033

4.994312

0.216155

0.047153

0.034169

0.038724

0.351708

0.328474

0.26492

0.248519

0.230296

0.207517

0

0.005272

0.271861

7,033

179

79

39.290503

0.851982

0.177023

0

0.259843

0

0.028233

0

1

0.110236

false

0.007874

0.047244

0

0.275591

0

null

0

null

0

1

0

deafcfc518bad5ab9572431f7de653f846580238

1,050

py

Python

python/5.concurrent/ZCoroutine/z_new_ipc/8.condition.py

lotapp/BaseCode

0255f498e1fe67ed2b3f66c84c96e44ef1f7d320

[ "Apache-2.0" ]

25

2018-06-13T08:13:44.000Z

2020-11-19T14:02:11.000Z

python/5.concurrent/ZCoroutine/z_new_ipc/8.condition.py

lotapp/BaseCode

0255f498e1fe67ed2b3f66c84c96e44ef1f7d320

[ "Apache-2.0" ]

null

python/5.concurrent/ZCoroutine/z_new_ipc/8.condition.py

lotapp/BaseCode

0255f498e1fe67ed2b3f66c84c96e44ef1f7d320

[ "Apache-2.0" ]

13

2018-06-13T08:13:38.000Z

2022-01-06T06:45:07.000Z

import asyncio cond = None p_list = [] # 生产者 async def producer(n): for i in range(5): async with cond: p_list.append(f"{n}-{i}") print(f"[生产者{n}]生产商品{n}-{i}") # 通知任意一个消费者 cond.notify() # 通知全部消费者：cond.notify_all() # 摸拟一个耗时操作 await asyncio.s...

23.333333

75

0.526667

137

1,050

3.883212

0.40146

0.056391

0.033835

0.06203

0.174812

0.105263

0

0.012987

0.34

1,050

44

76

23.863636

0.75469

0.086667

0

0.137931

0

0.082278

0

1

0

false

0

0.034483

0

0.034483

0.137931

0

null

0

null

0

1

0

deb039b791ed71607787c0d4ffc9f5bb4edef521

930

py

Python

Q846_Hand-of-Straights.py

xiaosean/leetcode_python

844ece02d699bfc620519bd94828ed0e18597f3e

[ "MIT" ]

null

Q846_Hand-of-Straights.py

xiaosean/leetcode_python

844ece02d699bfc620519bd94828ed0e18597f3e

[ "MIT" ]

null

Q846_Hand-of-Straights.py

xiaosean/leetcode_python

844ece02d699bfc620519bd94828ed0e18597f3e

[ "MIT" ]

null

from collections import Counter class Solution: def isNStraightHand(self, hand: List[int], W: int) -> bool: n = len(hand) groups = 0 if n == 0 or n % W != 0: return False groups_num = n // W c = Counter(hand) keys = list(c.keys()) keys.sort() ...

31

63

0.410753

109

930

3.440367

0.357798

0.085333

0.090667

0.064

0.085333

0

0.019231

0.496774

930

30

64

31

0.782051

0

0.103448

0

1

0.034483

false

0

0.034483

0

0.241379

0

null

0

null

0

1

0

deba0ac91a90f7d9408ab094dc6d137f7476170c

4,495

py

Python

smart_contract/__init__.py

publicqi/CTFd-Fox

b1d0169db884cdf3cb665faa8987443e7630d108

[ "MIT" ]

1

2021-01-09T15:20:14.000Z

smart_contract/__init__.py

publicqi/CTFd-Fox

b1d0169db884cdf3cb665faa8987443e7630d108

[ "MIT" ]

null

smart_contract/__init__.py

publicqi/CTFd-Fox

b1d0169db884cdf3cb665faa8987443e7630d108

[ "MIT" ]

null

from __future__ import division # Use floating point for math calculations from flask import Blueprint from CTFd.models import ( ChallengeFiles, Challenges, Fails, Flags, Hints, Solves, Tags, db, ) from CTFd.plugins import register_plugin_assets_directory from CTFd.plugins.challenges...

32.338129

87

0.629588

486

4,495

5.676955

0.218107

0.087713

0.056542

0.079739

0.460674

0.333092

0.300471

0.183762

0.137006

0

0.261624

4,495

138

88

32.572464

0.831274

0.008899

0

0.262295

0

0.120144

0.068269

0

1

0.065574

false

0

0.065574

0

0.229508

0.016393

0

null

0

null

0

1

0

debcd3fde3c56a4f5ccca0c23d8a57a7d2afd960

588

py

Python

Numbers/PrimeFac.py

Arjuna197/the100

2963b4fe1b1b8e673a23b2cf97f4bcb263af9781

[ "MIT" ]

1

2022-02-20T18:49:49.000Z

Numbers/PrimeFac.py

dan-garvey/the100

2963b4fe1b1b8e673a23b2cf97f4bcb263af9781

[ "MIT" ]

13

2017-12-13T02:31:54.000Z

2017-12-13T02:37:45.000Z

Numbers/PrimeFac.py

dan-garvey/the100

2963b4fe1b1b8e673a23b2cf97f4bcb263af9781

[ "MIT" ]

null

import math from math import* def isPrime(num): if num%2==0 or num%3==0: return False for n in range(5, int(num**(1/2))): if num%n==0: return False return True print('enter a positive integer') FacMe=int(input()) primefacts=[1] if not isPrime(FacMe): if FacMe...

21.777778

40

0.547619

85

588

3.788235

0.388235

0.198758

0.074534

0

0.042821

0.32483

588

26

41

22.615385

0.768262

0

0.083333

0

0.042705

0

1

0.041667

false

0

0.083333

0

0.25

0.083333

0

null

0

null

0

1

0

debe6ce18f853e6b1e54abf97ade00987edf8450

1,270

py

Python

runner/run_descriptions/runs/curious_vs_vanilla.py

alex-petrenko/curious-rl

6cd0eb78ab409c68f8dad1a8542d625f0dd39114

[ "MIT" ]

18

2018-12-29T01:52:25.000Z

2021-11-08T06:48:20.000Z

runner/run_descriptions/runs/curious_vs_vanilla.py

alex-petrenko/curious-rl

6cd0eb78ab409c68f8dad1a8542d625f0dd39114

[ "MIT" ]

2

2019-06-13T12:52:55.000Z

2019-10-30T03:27:11.000Z

runner/run_descriptions/runs/curious_vs_vanilla.py

alex-petrenko/curious-rl

6cd0eb78ab409c68f8dad1a8542d625f0dd39114

[ "MIT" ]

3

2019-05-11T07:50:53.000Z

2021-11-18T08:15:56.000Z

from runner.run_descriptions.run_description import RunDescription, Experiment, ParamGrid _params = ParamGrid([ ('prediction_bonus_coeff', [0.00, 0.05]), ]) _experiments = [ Experiment( 'doom_maze_very_sparse', 'python -m algorithms.curious_a2c.train_curious_a2c --env=doom_maze_very_sparse --g...

40.967742

145

0.711024

156

1,270

5.352564

0.301282

0.095808

0.081437

0.11497

0.653892

0.613174

0.555689

0.431138

0.354491

0

0.05482

0.166929

1,270

30

146

42.333333

0.734405

0.499213

0

0.083333

0.322581

0.301613

0

1

0

false

0

0.083333

0

0.083333

0

null

0

null

0

1

0

dec0b14005ec6feafc62d8f18253556640fa35db

145,150

py

Python

py/countdowntourney.py

elocemearg/atropine

894010bcc89d4e6962cf3fc15ef526068c38898d

[ "CC-BY-4.0" ]

null

py/countdowntourney.py

elocemearg/atropine

894010bcc89d4e6962cf3fc15ef526068c38898d

[ "CC-BY-4.0" ]

null

py/countdowntourney.py

elocemearg/atropine

894010bcc89d4e6962cf3fc15ef526068c38898d

[ "CC-BY-4.0" ]

null

#!/usr/bin/python3 import sys import sqlite3; import re; import os; import random import qualification from cttable import CandidateTable, TableVotingGroup, PhantomTableVotingGroup import cttable SW_VERSION_SPLIT = (1, 1, 4) SW_VERSION = ".".join([str(x) for x in SW_VERSION_SPLIT]) EARLIEST_COMPATIBLE_DB_VERSION = (...

39.691004

388

0.586035

19,416

145,150

4.19556

0.052431

0.019506

0.010385

0.017125

0.462724

0.381336

0.305508

0.254527

0.222659

0.197506

0

0.01518

0.316962

145,150

3,656

389

39.70186

0.806471

0.082962

0

0.358162

0

0.015315

0.283277

0.023378

0.000348

0

0.000152

0

1

0.091194

false

0.007309

0.002785

0.032718

0.204316

0.005569

0

null

0

null

0

1

0

dec0da50ce4a56fc78832aa67c6d71d1a1a1c437

995

py

Python

t/plugin/plugin_020deploy_test.py

jrmsdev/pysadm

0d6b3f0c8d870d83ab499c8d9487ec8e3a89fc37

[ "BSD-3-Clause" ]

1

2019-10-15T08:37:56.000Z

t/plugin/plugin_020deploy_test.py

jrmsdev/pysadm

0d6b3f0c8d870d83ab499c8d9487ec8e3a89fc37

[ "BSD-3-Clause" ]

null

t/plugin/plugin_020deploy_test.py

jrmsdev/pysadm

0d6b3f0c8d870d83ab499c8d9487ec8e3a89fc37

[ "BSD-3-Clause" ]

null

# Copyright (c) Jeremías Casteglione <jrmsdev@gmail.com> # See LICENSE file. from glob import glob from os import path, makedirs def test_deploy_testing(testing_plugin): makedirs(path.join('tdata', 'deploy', 'plugin'), exist_ok = True) p = testing_plugin('testing', ns = '_sadmtest', deploy = True) print('-- deploy...

36.851852

80

0.676382

134

995

4.91791

0.425373

0.098634

0.059181

0.075873

0.172989

0

0.001183

0.150754

995

26

81

38.269231

0.778698

0.072362

0

0.095238

0

0.155435

0

1

0.095238

false

0

0.095238

0

0.190476

0.095238

0

null

0

null

0

1

0

dec30d56b6d0887d305f33e490a67d25b3dd39cd

4,189

py

Python

jsonReadWrite.py

nsobczak/ActivityWatchToCSV

cefb67e9f1c834008f2b39c0baf6c7c506327a4d

[ "Apache-2.0" ]

null

jsonReadWrite.py

nsobczak/ActivityWatchToCSV

cefb67e9f1c834008f2b39c0baf6c7c506327a4d

[ "Apache-2.0" ]

null

jsonReadWrite.py

nsobczak/ActivityWatchToCSV

cefb67e9f1c834008f2b39c0baf6c7c506327a4d

[ "Apache-2.0" ]

null

""" ############## # jsonReader # ############## """ # Import import json from platform import system from enum import Enum from datetime import timedelta # %% ____________________________________________________________________________________________________ # ____________________________________________________...

30.136691

121

0.545476

403

4,189

5.168734

0.362283

0.053769

0.015362

0.016323

0.048968

0.035526

0

0.045263

0.319647

4,189

138

122

30.355072

0.685614

0.351635

0

0.035088

0

0.128381

0

1

0.035088

false

0

0.070175

0

0.192982

0.157895

0

null

0

null

0

1

0

dec3efd877d3ce87cbe9fc53530bf43be70d8149

306

py

Python

2021-12-23/1.py

xiaozhiyuqwq/seniorschool

7375038b00a6d2deaec5d70bfac25ddbf4d2558e

[ "Apache-2.0" ]

null

2021-12-23/1.py

xiaozhiyuqwq/seniorschool

7375038b00a6d2deaec5d70bfac25ddbf4d2558e

[ "Apache-2.0" ]

null

2021-12-23/1.py

xiaozhiyuqwq/seniorschool

7375038b00a6d2deaec5d70bfac25ddbf4d2558e

[ "Apache-2.0" ]

null

#初始化 t=0 #运算 for x in range(1,9): for y in range(1,11): for z in range(1,13): if 6*x+5*y+4*z==50: print("计算出x值为 ",x," y值为 ",y," z值为 ",z," 。") t=t+1 print("计算出一共有 {} 个结果。".format(t)) #by xiaozhiyuqwq #https://www.rainyat.work #2021-12-23

21.857143

60

0.46732

54

306

2.648148

0.648148

0.146853

0.167832

0

0.112195

0.330065

306

13

61

23.538462

0.585366

0.176471

0

0.141026

0

1

0

false

0

0.25

0

null

0

null

0

1

0

dec771d07fef05c3b6f9bec75d34bca56cffa1b5

3,648

py

Python

data_augmentor/multidimension.py

ZhiangChen/tornado_ML

d8bded61a6a234ca67e31776bc8576c6c18f5621

[ "MIT" ]

2

2018-12-09T20:08:51.000Z

2021-02-01T17:49:14.000Z

data_augmentor/multidimension.py

ZhiangChen/tornado_ML

d8bded61a6a234ca67e31776bc8576c6c18f5621

[ "MIT" ]

1

2019-11-15T06:15:03.000Z

multidimension.py

DREAMS-lab/data_augmentor

f204ee3af805b17d9946d3d5c6e7ca62398f09e5

[ "MIT" ]

null

""" multispectrum Zhiang Chen, Feb, 2020 """ import gdal import cv2 import numpy as np import math import os class MultDim(object): def __init__(self): pass def readTiff(self, tif_file, channel=3): self.ds = gdal.Open(tif_file) B = self.ds.GetRasterBand(1).ReadAsArray() G = se...

35.076923

105

0.569353

508

3,648

4.001969

0.214567

0.106247

0.070831

0.086572

0.185932

0.123955

0.079685

0

0.027819

0.260965

3,648

104

106

35.076923

0.726261

0.072643

0

0.092308

0

0.089357

0.070832

0

1

0.092308

false

0.015385

0.076923

0.015385

0.215385

0

null

0

null

0

1

0

deca8e26bb6a2a9ae53903a22809984f7a74b454

26,490

py

Python

project.py

PetruSicoe/Python101-GameProject

82121a8e110ee484acdf85843725882d60957b25

[ "CC-BY-4.0" ]

null

project.py

PetruSicoe/Python101-GameProject

82121a8e110ee484acdf85843725882d60957b25

[ "CC-BY-4.0" ]

null

project.py

PetruSicoe/Python101-GameProject

82121a8e110ee484acdf85843725882d60957b25

[ "CC-BY-4.0" ]

null

#!/usr/bin/env python3 from random import randrange import random import pygame, sys from pygame.locals import * import string pygame.font.init() MENU_WIDTH = 1000 MENU_HEIGHT = 1000 GUESS_WIDTH = 1000 GUESS_HEIGHT = 650 HANGMAN_WIDTH = 1300 HANGMAN_HEIGHT = 720 BLACK = (0,0,0) WHITE = (25...

40.197269

177

0.555795

3,379

26,490

4.150636

0.102397

0.042781

0.029947

0.034652

0.597433

0.525419

0.42574

0.366346

0.334118

0.305098

0

0.033925

0.339034

26,490

659

178

40.197269

0.767091

0.048622

0

0.380952

0

0.102041

0.019766

0

1

0.034014

false

0.004535

0.011338

0

0.054422

0.002268

0

null

0

null

0

1

0

decaa14b52fa5524baf2d5d190931296e44de823

2,018

py

Python

Modules/CrossMapLRN.py

EmilPi/PuzzleLib

31aa0fab3b5e9472b9b9871ca52e4d94ea683fa9

[ "Apache-2.0" ]

52

2020-02-28T20:40:15.000Z

2021-08-25T05:35:17.000Z

Modules/CrossMapLRN.py

EmilPi/PuzzleLib

31aa0fab3b5e9472b9b9871ca52e4d94ea683fa9

[ "Apache-2.0" ]

2

2021-02-14T15:57:03.000Z

2021-10-05T12:21:34.000Z

Modules/CrossMapLRN.py

EmilPi/PuzzleLib

31aa0fab3b5e9472b9b9871ca52e4d94ea683fa9

[ "Apache-2.0" ]

8

2020-02-28T20:40:11.000Z

2020-07-09T13:27:23.000Z

import numpy as np from PuzzleLib.Backend import gpuarray from PuzzleLib.Backend.Dnn import crossMapLRN, crossMapLRNBackward from PuzzleLib.Modules.LRN import LRN class CrossMapLRN(LRN): def __init__(self, N=5, alpha=1e-4, beta=0.75, K=2.0, name=None): super().__init__(N, alpha, beta, K, name) self.gradUsesOut...

32.031746

101

0.700198

296

2,018

4.712838

0.260135

0.051613

0.064516

0.057348

0.316846

0.295341

0.200717

0

0.024277

0.142716

2,018

62

102

32.548387

0.782081

0

0.095238

0

0.003964

0

0.047619

1

0.095238

false

0

0.095238

0

0.214286

0

null

0

null

0

1

0

decc19f50e9a41be1bc95cb6e0bf5f4f77162b78

4,802

py

Python

src/metrics.py

enryH/specpride

1bedd87dc8f31a6b86426c6e03dc0c27706bc9aa

[ "Apache-2.0" ]

2

2020-01-14T12:02:52.000Z

2020-01-14T14:03:30.000Z

src/metrics.py

enryH/specpride

1bedd87dc8f31a6b86426c6e03dc0c27706bc9aa

[ "Apache-2.0" ]

5

2019-12-09T10:59:10.000Z

2020-01-16T14:32:00.000Z

src/metrics.py

enryH/specpride

1bedd87dc8f31a6b86426c6e03dc0c27706bc9aa

[ "Apache-2.0" ]

9

2020-01-14T12:26:54.000Z

2020-01-16T08:26:06.000Z

import copy from typing import Iterable import numba as nb import numpy as np import spectrum_utils.spectrum as sus def dot(spectrum1: sus.MsmsSpectrum, spectrum2: sus.MsmsSpectrum, fragment_mz_tolerance: float) -> float: """ Compute the dot product between the given spectra. Parameters ----...

31.592105

79

0.660975

563

4,802

5.488455

0.197158

0.045307

0.061489

0.046602

0.469579

0.414887

0.370874

0.350162

0.308738

0

0.004499

0.259475

4,802

151

80

31.801325

0.864454

0.465223

0

0.209302

0

0.001808

0

1

0.116279

false

0

0.116279

0

0.372093

0

null

0

null

0

1

0

deccbee42c5be781692fc226272ac89e27a4e7a6

797

py

Python

examples/multi-class_neural_network.py

sun1638650145/classicML

7e0c2155bccb6e491a150ee689d3786526b74565

[ "Apache-2.0" ]

12

2020-05-10T12:11:06.000Z

2021-10-31T13:23:55.000Z

examples/multi-class_neural_network.py

sun1638650145/classicML

7e0c2155bccb6e491a150ee689d3786526b74565

[ "Apache-2.0" ]

null

examples/multi-class_neural_network.py

sun1638650145/classicML

7e0c2155bccb6e491a150ee689d3786526b74565

[ "Apache-2.0" ]

2

2021-01-17T06:22:05.000Z

2021-01-18T14:32:51.000Z

""" 这个例子将展示如何使用BP神经网络构建多分类的神经网络. """ import sys import classicML as cml DATASET_PATH = './datasets/iris_dataset.csv' CALLBACKS = [cml.callbacks.History(loss_name='categorical_crossentropy', metric_name='accuracy')] # 读取数据 ds = cml.data.Dataset(label_mode='one-hot', ...

28.464286

72

0.644918

89

797

5.651685

0.662921

0.043738

0.115308

0

0.019417

0.224592

797

27

73

29.518519

0.794498

0.117942

0

0.160405

0.108382

0

1

0

false

0

0.117647

0

0.117647

0

null

0

null

0

1

0

dece77460bb0515a4dff433a0f6f8e80d7adc76c

3,735

py

Python

yiffscraper/downloader.py

ScraperT/yiffscraper

49482a544fc7f11e6ea5db2626dbc2404529d656

[ "MIT" ]

42

2019-12-23T23:55:12.000Z

2022-02-07T04:12:59.000Z

yiffscraper/downloader.py

arin17bishwa/yiffscraper

49482a544fc7f11e6ea5db2626dbc2404529d656

[ "MIT" ]

7

2020-01-12T13:04:56.000Z

2020-05-18T07:11:51.000Z

yiffscraper/downloader.py

arin17bishwa/yiffscraper

49482a544fc7f11e6ea5db2626dbc2404529d656

[ "MIT" ]

7

2020-03-12T03:47:53.000Z

2020-07-26T08:05:55.000Z

import os import platform from datetime import datetime import time from pathlib import Path import asyncio from dateutil.parser import parse as parsedate from dateutil import tz import aiohttp def longpath(p): if p is None or platform.system() != "Windows": return Path(p) return Path("\\\\?\\" + str...

31.923077

122

0.626774

443

3,735

5.216704

0.309255

0.038944

0.025963

0.029857

0.234531

0.211164

0.163566

0.135872

0

0.003398

0.290763

3,735

116

123

32.198276

0.869007

0.055957

0

0.21978

0

0.018734

0

1

0.087912

false

0

0.098901

0.010989

0.406593

0

null

0

null

0

1

0

ded4491d8cef57cccb094e0f83641638968be15a

3,066

py

Python

src/tests/attention_test.py

feperessim/attention_keras

322a16ee147122026b63305aaa5e899d9e5de883

[ "MIT" ]

422

2019-03-17T13:08:59.000Z

2022-03-31T12:08:29.000Z

src/tests/attention_test.py

JKhodadadi/attention_keras

322a16ee147122026b63305aaa5e899d9e5de883

[ "MIT" ]

51

2019-03-17T20:08:11.000Z

2022-03-18T03:51:42.000Z

src/tests/attention_test.py

JKhodadadi/attention_keras

322a16ee147122026b63305aaa5e899d9e5de883

[ "MIT" ]

285

2019-03-17T19:06:22.000Z

2022-03-31T02:29:17.000Z

import pytest from layers.attention import AttentionLayer from tensorflow.keras.layers import Input, GRU, Dense, Concatenate, TimeDistributed from tensorflow.keras.models import Model import tensorflow as tf def test_attention_layer_standalone_fixed_b_fixed_t(): """ Tests fixed batch size and time steps E...

37.390244

101

0.7182

427

3,066

4.915691

0.238876

0.046689

0.060029

0.336827

0.311577

0.307766

0.216293

0.19676

0

0.032571

0.158839

3,066

82

102

37.390244

0.781311

0.031311

0

0.09375

0

0.1875

1

0.125

false

0

0.15625

0

0.3125

0.03125

0

null

0

null

0

1

0

ded5e7681d684ad45f836b0b523b89035ed45f16

1,572

py

Python

Python/9248_Suffix_Array/9248_suffix_array_lcp_array.py

ire4564/Baekjoon_Solutions

3e6689efa30d6b850cdc29570c76408a1e1b2b49

[ "Apache-2.0" ]

4

2020-11-17T09:52:29.000Z

2020-12-13T11:36:14.000Z

Python/9248_Suffix_Array/9248_suffix_array_lcp_array.py

ire4564/Baekjoon_Solutions

3e6689efa30d6b850cdc29570c76408a1e1b2b49

[ "Apache-2.0" ]

2

2020-11-19T11:21:02.000Z

2020-11-19T22:07:15.000Z

Python/9248_Suffix_Array/9248_suffix_array_lcp_array.py

ire4564/Baekjoon_Solutions

3e6689efa30d6b850cdc29570c76408a1e1b2b49

[ "Apache-2.0" ]

12

2020-11-17T06:55:13.000Z

2021-05-16T14:39:37.000Z

from itertools import zip_longest, islice def to_int_keys_best(l): seen = set() ls = [] for e in l: if not e in seen: ls.append(e) seen.add(e) ls.sort() index = {v: i for i, v in enumerate(ls)} return [index[v] for v in l] def suffix_array_best(...

20.684211

64

0.448473

235

1,572

2.855319

0.259574

0.114754

0.040238

0.058122

0.113264

0.09538

0.041729

0

0.018785

0.4243

1,572

75

65

20.96

0.722652

0

0.118644

0

0.007353

0

1

0.067797

false

0

0.016949

0

0.152542

0.050847

0

null

0

null

0

1

0

ded667020b68f181edc8b21f22dbb71557c2c7cc

1,329

py

Python

lgr/tools/compare/utils.py

ron813c/lgr-core

68ba730bf7f9e61cb97c9c08f61bc58b8ea24e7b

[ "BSD-3-Clause" ]

7

2017-07-10T22:39:52.000Z

2021-06-25T20:19:28.000Z

lgr/tools/compare/utils.py

ron813c/lgr-core

68ba730bf7f9e61cb97c9c08f61bc58b8ea24e7b

[ "BSD-3-Clause" ]

13

2016-10-26T19:42:00.000Z

2021-12-13T19:43:42.000Z

lgr/tools/compare/utils.py

ron813c/lgr-core

68ba730bf7f9e61cb97c9c08f61bc58b8ea24e7b

[ "BSD-3-Clause" ]

8

2016-11-07T15:40:27.000Z

2020-09-22T13:48:52.000Z

# -*- coding: utf-8 -*- """ utils.py - Definition of utility functions. """ from collections import namedtuple from lgr.utils import format_cp VariantProperties = namedtuple('VariantProperties', ['cp', 'type', 'when', 'not_when', ...

24.611111

77

0.574116

152

1,329

4.921053

0.355263

0.112299

0.06016

0.040107

0.117647

0

0.011931

0.306245

1,329

53

78

25.075472

0.799349

0.413845

0

0.144756

0

1

0.125

false

0

0.125

0

0.5

0

null

0

null

0

1

0

ded78378f0da72d7d6e0a021bbb1b4a6004db8f0

2,386

py

Python

tests/test__file_object.py

StateArchivesOfNorthCarolina/tomes_metadata

8b73096c1b16e0db2895a6c01d4fc4fd9621cf55

[ "MIT" ]

null

tests/test__file_object.py

StateArchivesOfNorthCarolina/tomes_metadata

8b73096c1b16e0db2895a6c01d4fc4fd9621cf55

[ "MIT" ]

2

2018-09-12T20:36:22.000Z

2018-09-13T20:14:50.000Z

tests/test__file_object.py

StateArchivesOfNorthCarolina/tomes-packager

8b73096c1b16e0db2895a6c01d4fc4fd9621cf55

[ "MIT" ]

null

#!/usr/bin/env python3 # import modules. import sys; sys.path.append("..") import hashlib import json import logging import os import plac import unittest import warnings from tomes_packager.lib.directory_object import * from tomes_packager.lib.file_object import * # enable logging. logging.basicConfig(level=logging....

27.744186

84

0.642079

311

2,386

4.733119

0.376206

0.047554

0.057065

0.027174

0.092391

0.065217

0

0.01068

0.254401

2,386

86

85

27.744186

0.816751

0.266555

0

0.017572

0

0.042553

1

0.085106

false

0

0.212766

0

0.319149

0.042553

0

null

0

null

0

1

0

deda4206dc73f8dbe4b33d7d756e79510962b4d8

10,829

py

Python

game.py

IliketoTranslate/Pickaxe-clicker

e74ebd66842bd47c4ed1c4460e9f45e30a2ad1d7

[ "MIT" ]

null

game.py

IliketoTranslate/Pickaxe-clicker

e74ebd66842bd47c4ed1c4460e9f45e30a2ad1d7

[ "MIT" ]

null

game.py

IliketoTranslate/Pickaxe-clicker

e74ebd66842bd47c4ed1c4460e9f45e30a2ad1d7

[ "MIT" ]

null

import pygame icon = pygame.image.load("diamond_pickaxe.png") screen_weight = 1750 screen_height = 980 pygame.init() window = pygame.display.set_mode((screen_weight, screen_height)) pygame.display.set_caption('Pickaxe clicker') pygame.display.set_icon(icon) # zmienne wytrzymałość_kilofa = 50 max_wytrzymałość_kilof...

49.447489

296

0.576138

1,196

10,829

4.982441

0.159699

0.040275

0.030206

0.036919

0.568216

0.513845

0.464004

0.411982

0.383118

0.298204

0

0.064236

0.341583

10,829

219

297

49.447489

0.771529

0.100286

0

0.494118

0

0.080367

0.007634

0

1

0.017647

false

0

0.005882

0

0.023529

0

null

0

null

0

1

0

dedba85b4c2428f8778fd3f7f0d4d19fee14a759

4,383

py

Python

tests/test_predictor.py

WeijieChen2017/pytorch-3dunet

15c782481cb7bc3e2083a80bcc8b114cc8697c20

[ "MIT" ]

1

2021-08-04T04:03:37.000Z

tests/test_predictor.py

LalithShiyam/pytorch-3dunet

f6b6c13cb0bb6194e95976b0245b76aaa9e9a496

[ "MIT" ]

null

tests/test_predictor.py

LalithShiyam/pytorch-3dunet

f6b6c13cb0bb6194e95976b0245b76aaa9e9a496

[ "MIT" ]

1

2022-03-14T04:43:24.000Z

import os from tempfile import NamedTemporaryFile import h5py import numpy as np import torch from skimage.metrics import adapted_rand_error from torch.utils.data import DataLoader from pytorch3dunet.datasets.hdf5 import StandardHDF5Dataset from pytorch3dunet.datasets.utils import prediction_collate, get_test_loaders...

35.346774

114

0.62423

536

4,383

4.897388

0.287313

0.038095

0.024381

0.018286

0.16

0.136762

0.113524

0.07619

0.053333

0

0.049643

0.264659

4,383

123

115

35.634146

0.764815

0.045631

0

0.04878

0

0.060105

0.016523

0

0.060976

1

0.085366

false

0.012195

0.158537

0.02439

0.304878

0

null

0

null

0

1

0

dedbd6180bc5f6b44a69dd4d23b7983f144a3239

2,560

py

Python

catalog/views.py

DigimundoTesca/Tv-Mundo

09904759d1f4f9bf2d5c7c31b97af82c3c963bfd

[ "MIT" ]

null

catalog/views.py

DigimundoTesca/Tv-Mundo

09904759d1f4f9bf2d5c7c31b97af82c3c963bfd

[ "MIT" ]

6

2017-09-19T07:26:14.000Z

2017-09-27T10:06:49.000Z

catalog/views.py

DigimundoTesca/Tv-Mundo

09904759d1f4f9bf2d5c7c31b97af82c3c963bfd

[ "MIT" ]

null

from django.shortcuts import render, get_object_or_404 from django.contrib.auth.decorators import login_required from catalog.models import Videos, Category, Docs, Subscriber from django.contrib.auth.decorators import login_required @login_required def home(request): template = 'home.html' category = Category....

24.380952

76

0.622656

288

2,560

5.447917

0.1875

0.101976

0.061185

0.10325

0.564054

0.516252

0.489484

0.441045

0.313576

0.247291

0

0.003642

0.249219

2,560

104

77

24.615385

0.812695

0

0.551724

0

0.083984

0

1

0.068966

false

0

0.045977

0

0.183908

0

null

0

null

0

1

0

dedc38f09d494832d839db3e999852609e6a45ac

519

py

Python

python/database/get_twitter_predict_by_order.py

visdata/DeepClue

8d80ecd783919c97ba225db67664a0dfe5f3fb37

[ "Apache-2.0" ]

1

2020-12-06T08:04:32.000Z

python/database/get_twitter_predict_by_order.py

visdata/DeepClue

8d80ecd783919c97ba225db67664a0dfe5f3fb37

[ "Apache-2.0" ]

null

python/database/get_twitter_predict_by_order.py

visdata/DeepClue

8d80ecd783919c97ba225db67664a0dfe5f3fb37

[ "Apache-2.0" ]

null

import MySQLdb db = MySQLdb.connect('localhost', 'root', 'vis_2014', 'FinanceVis') cursor = db.cursor() sql = 'select predict_news_word from all_twitter where symbol=%s order by predict_news_word+0 desc' cursor.execute(sql, ('AAPL', )) results = cursor.fetchall() file_twitter_predict = open('twitter_predict_AAPL.csv...

25.95

99

0.714836

75

519

4.76

0.573333

0.156863

0.151261

0

0.015766

0.144509

519

20

100

25.95

0.788288

0

0.303846

0.046154

0

1

0

false

0

0.066667

0

0.066667

0

null

0

null

0

1

0

dedeaccf1b8d4bb294ba8b9e2278d86179d43f0e

405

py

Python

kattis/solutions/alphabetspam.py

yifeng-pan/competitive_programming

c59edb1e08aa2db2158a814e3d34f4302658d98e

[ "Unlicense" ]

null

kattis/solutions/alphabetspam.py

yifeng-pan/competitive_programming

c59edb1e08aa2db2158a814e3d34f4302658d98e

[ "Unlicense" ]

null

kattis/solutions/alphabetspam.py

yifeng-pan/competitive_programming

c59edb1e08aa2db2158a814e3d34f4302658d98e

[ "Unlicense" ]

null

# https://open.kattis.com/problems/alphabetspam import sys import math xs = input() white = 0 lower = 0 higher =0 other = 0 for i in xs: if i == '_': white += 1 elif ('a' <= i) & (i <= 'z'): lower += 1 elif ('A' <= i) & (i <= "Z"): higher += 1 else: other += 1 print(...

15.576923

47

0.511111

61

405

3.377049

0.442623

0.097087

0.145631

0.067961

0.087379

0

0.02807

0.296296

405

26

48

15.576923

0.694737

0.111111

0

0.013928

0

1

0

false

0

0.1

0

0.1

0.2

0

null

0

null

0

1

0

dee0061d48e6e49cac68657f95ed5ac4927eaa8e

3,813

py

Python

src/chain_orientation_three_vars_symbolic.py

Scriddie/Varsortability

357213d5ceefb6362060c56e12c18b41dc689306

[ "MIT" ]

4

2021-12-08T07:54:00.000Z

2022-03-09T07:55:21.000Z

src/chain_orientation_three_vars_symbolic.py

Scriddie/Varsortability

357213d5ceefb6362060c56e12c18b41dc689306

[ "MIT" ]

null

src/chain_orientation_three_vars_symbolic.py

Scriddie/Varsortability

357213d5ceefb6362060c56e12c18b41dc689306

[ "MIT" ]

1

2022-03-09T07:55:43.000Z

import numpy as np from sympy import simplify, sqrt, symbols from sympy.stats import Normal, covariance as cov, variance as var def regcoeffs(x, y, z): covxy = cov(x, y) covyz = cov(y, z) varx = var(x) vary = var(y) varz = var(z) # forward f1 = simplify(covxy / varx) f2 = simplify(covy...

28.455224

69

0.441385

529

3,813

3.117202

0.189036

0.116434

0.061856

0.082474

0.366283

0.311704

0.304427

0.29715

0

0.069379

0.387621

3,813

133

70

28.669173

0.636831

0.073171

0

0.122449

0

0.096334

0

1

0.010204

false

0

0.030612

0

0.05102

0

null

0

null

0

1

0

dee0dfeab71167aee2a17e14945c71c0e31e66be

1,762

py

Python

jaffalearn/logging.py

tqbl/jaffalearn

a5bb79fcb3e84fd6e17b6356429e5885386a5a58

[ "0BSD" ]

null

jaffalearn/logging.py

tqbl/jaffalearn

a5bb79fcb3e84fd6e17b6356429e5885386a5a58

[ "0BSD" ]

null

jaffalearn/logging.py

tqbl/jaffalearn

a5bb79fcb3e84fd6e17b6356429e5885386a5a58

[ "0BSD" ]

null

from pathlib import Path import pandas as pd from torch.utils.tensorboard import SummaryWriter class Logger: def __init__(self, system, log_dir, overwrite=False): self.log_path = Path(log_dir) / 'history.csv' self.system = system self.tb_writer = None # Remove any previous Tens...

30.37931

70

0.605562

227

1,762

4.555066

0.356828

0.106383

0.074468

0.040619

0.083172

0.052224

0

0.0016

0.290579

1,762

57

71

30.912281

0.8256

0.097049

0

0.052632

0

0.033438

0

1

0.131579

false

0

0.078947

0

0.236842

0.052632

0

null

0

null

0

1

0

dee0ea830b4e14533eb75ccbf58b75a95766df8d

3,369

py

Python

python/soma_workflow/constants.py

denisri/soma-workflow

bc6f2f50d34437e86e850cb0d05ff26b041d560d

[ "CECILL-B" ]

null

python/soma_workflow/constants.py

denisri/soma-workflow

bc6f2f50d34437e86e850cb0d05ff26b041d560d

[ "CECILL-B" ]

44

2018-10-30T16:57:10.000Z

2022-03-15T10:54:57.000Z

python/soma_workflow/constants.py

populse/soma-workflow

e6d3e3c33ad41107ee3c959adc4832e6edd047f4

[ "CECILL-B" ]

null

# -*- coding: utf-8 -*- ''' author: Soizic Laguitton organization: I2BM, Neurospin, Gif-sur-Yvette, France organization: CATI, France organization: IFR 49 License: `CeCILL version 2 <http://www.cecill.info/licences/Licence_CeCILL_V2-en.html>`_ ''' # # Soma-workflow constants # # ''' Job status: ''' NOT_SUBMITTED ...

28.310924

88

0.655091

407

3,369

4.95086

0.235872

0.021836

0.035732

0.027792

0.493797

0.319107

0.129032

0.031762

0

0.002442

0.270703

3,369

118

89

28.550847

0.817664

0.078362

0

0.05

0

0.23395

0.038319

0

1

0

false

0

null

0

null

0

1

0

dee46fc1a2825aedf140afa6a83cd03a303bce36

1,980

py

Python

lab4_2/helpers/scanner.py

cinnamonbreakfast/flcd

f9168c1965976e9ae9477ee6b163a026f61acb1b

[ "MIT" ]

null

lab4_2/helpers/scanner.py

cinnamonbreakfast/flcd

f9168c1965976e9ae9477ee6b163a026f61acb1b

[ "MIT" ]

null

lab4_2/helpers/scanner.py

cinnamonbreakfast/flcd

f9168c1965976e9ae9477ee6b163a026f61acb1b

[ "MIT" ]

null

res_words = [] seps = [] ops = [] def load_dom(): with open('data/tokens', 'r') as f: for i in range(7): separator = f.readline().strip() if separator == "_": # Special case [SPACE] separator = " " seps.append(separator) for i ...

22.5

74

0.491414

209

1,980

4.636364

0.263158

0.139319

0.122807

0.136223

0.423117

0.285862

0.250774

0

0.011657

0.393434

1,980

88

75

22.5

0.795171

0.010101

0

0.385714

0

0.008172

0

1

0.071429

false

0.014286

0

0.157143

0

null

0

null

0

1

0

dee8b0a49fcef498a3468a8ea4df153befa037f5

26,370

py

Python

src/third_party/wiredtiger/test/suite/run.py

benety/mongo

203430ac9559f82ca01e3cbb3b0e09149fec0835

[ "Apache-2.0" ]

null

src/third_party/wiredtiger/test/suite/run.py

benety/mongo

203430ac9559f82ca01e3cbb3b0e09149fec0835

[ "Apache-2.0" ]

null

src/third_party/wiredtiger/test/suite/run.py

benety/mongo

203430ac9559f82ca01e3cbb3b0e09149fec0835

[ "Apache-2.0" ]

null

#!/usr/bin/env python # # Public Domain 2014-present MongoDB, Inc. # Public Domain 2008-2014 WiredTiger, Inc. # # This is free and unencumbered software released into the public domain. # # Anyone is free to copy, modify, publish, use, compile, sell, or # distribute this software, either in source code form or as a com...

40.631741

105

0.573834

3,387

26,370

4.41364

0.213463

0.01164

0.022476

0.013914

0.106228

0.080674

0.070306

0.059068

0.044886

0.035454

0

0.010474

0.333826

26,370

648

106

40.694444

0.840496

0.237619

0

0.234177

0

0.010549

0.095704

0.001305

0

0.001004

0

1

0.035865

false

0

0.016878

0.00211

0.090717

0.025316

0

null

0

null

0

1

0

deeb28c75145a6bebc3771235fab7a32732db4c0

684

py

Python

models/t_complex_gateway.py

THM-MA/XSDATA-waypoint

dd94442f9d6677c525bf3ebb03c15fec52fa1079

[ "MIT" ]

null

models/t_complex_gateway.py

THM-MA/XSDATA-waypoint

dd94442f9d6677c525bf3ebb03c15fec52fa1079

[ "MIT" ]

null

models/t_complex_gateway.py

THM-MA/XSDATA-waypoint

dd94442f9d6677c525bf3ebb03c15fec52fa1079

[ "MIT" ]

null

from dataclasses import dataclass, field from typing import Optional from .t_expression import TExpression from .t_gateway import TGateway __NAMESPACE__ = "http://www.omg.org/spec/BPMN/20100524/MODEL" @dataclass class TComplexGateway(TGateway): class Meta: name = "tComplexGateway" activation_conditi...

24.428571

71

0.622807

66

684

6.348485

0.530303

0.023866

0.076372

0.090692

0.205251

0

0.031746

0.263158

684

27

72

25.333333

0.799603

0

0.173913

0

0.229532

0

1

0

false

0

0.173913

0

0.347826

0

null

0

null

0

1

0

deedff750596df4bfdfcd2656752ec59911b5e80

2,713

py

Python

crawler/page_fetcher.py

AssisRaphael/PageColector

6753376996f12ee1cced96b89a3e34d6fdf66529

[ "MIT" ]

null

crawler/page_fetcher.py

AssisRaphael/PageColector

6753376996f12ee1cced96b89a3e34d6fdf66529

[ "MIT" ]

null

crawler/page_fetcher.py

AssisRaphael/PageColector

6753376996f12ee1cced96b89a3e34d6fdf66529

[ "MIT" ]

null

from bs4 import BeautifulSoup from threading import Thread import requests from urllib.parse import urlparse,urljoin from urllib import parse class PageFetcher(Thread): def __init__(self, obj_scheduler): self.obj_scheduler = obj_scheduler def request_url(self,obj_url): """ Faz ...

33.085366

109

0.570586

325

2,713

4.504615

0.338462

0.053279

0.067623

0.028689

0.074454

0.036885

0

0.00227

0.350534

2,713

81

110

33.493827

0.828604

0.150018

0

0.081633

0

0.024234

0

1

0.102041

false

0

0.102041

0

0.265306

0.020408

0

null

0

null

0

1

0

def0d455f3332a2d6ded90d585855fcbfa88a92a

2,098

py

Python

simublocks/dialog/importCodeDialog.py

bentoavb/simublocks

9d4a5600b8aecd2d188e9191d78789a1bd725ab8

[ "MIT" ]

2

2020-05-14T12:34:43.000Z

2020-06-11T23:48:09.000Z

simublocks/dialog/importCodeDialog.py

bentoavb/simublocks

9d4a5600b8aecd2d188e9191d78789a1bd725ab8

[ "MIT" ]

null

simublocks/dialog/importCodeDialog.py

bentoavb/simublocks

9d4a5600b8aecd2d188e9191d78789a1bd725ab8

[ "MIT" ]

1

2020-05-12T07:01:28.000Z

# MIT License # # Copyright (c) 2020 Anderson Vitor Bento # # 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...

38.851852

98

0.704957

296

2,098

4.969595

0.47973

0.059823

0.017675

0.023114

0.058464

0.031271

0

0.013213

0.206387

2,098

54

99

38.851852

0.87027

0.510963

0

0.16

0

0.057711

0

1

0.12

false

0

0.2

0

0.36

0

null

0

null

0

1

0

def2f40bc3a8f54d1a406e95811076ed0688d708

658

py

Python

delete_unuse_callkit.py

eyolo2021/ios-ui-sdk-set

a8897320c356ddd6dbfe964ef68eb76701759f03

[ "MIT" ]

14

2021-03-06T08:47:30.000Z

2022-02-11T09:42:24.000Z

delete_unuse_callkit.py

eyolo2021/ios-ui-sdk-set

a8897320c356ddd6dbfe964ef68eb76701759f03

[ "MIT" ]

3

2021-03-19T11:12:42.000Z

2021-11-29T14:56:33.000Z

delete_unuse_callkit.py

Zuzi007/ios-ui-sdk-set

2e51added5d697b4d1ab1ba2887ad297b408e7b0

[ "MIT" ]

12

2021-07-02T02:44:52.000Z

2022-03-01T05:15:22.000Z

#coding=utf-8 import os delete_files=["RCCall.mm","RCCXCall.m"] start_key = "RCCallKit_Delete_Start" end_key = "RCCallKit_Delete_end" def delete_used(file_path): print(file_path) f = open(file_path,"r") lines = f.readlines() f.close() # print(lines) result = [] flag = False for l in lines: if start_key...

15.666667

44

0.674772

109

658

3.926606

0.431193

0.074766

0.084112

0.060748

0

0.001873

0.18845

658

41

45

16.04878

0.799625

0.037994

0

0.148148

0

0.136508

0.034921

0

1

0.037037

false

0

0.037037

0

0.074074

0

null

0

null

0

1

0

def7ae196a0259e7e64d4dfd6522b1ee72138646

16,178

py

Python

api/yolo_minimal/utils.py

simonsmh/www

1741545e636540b9eb250840347f091082fe301a

[ "MIT" ]

5

2015-12-19T11:18:54.000Z

2016-08-27T02:21:59.000Z

api/yolo_minimal/utils.py

simonsmh/www

1741545e636540b9eb250840347f091082fe301a

[ "MIT" ]

null

api/yolo_minimal/utils.py

simonsmh/www

1741545e636540b9eb250840347f091082fe301a

[ "MIT" ]

1

2020-10-30T13:25:33.000Z

import math import os import random import cv2 import numpy as np import torch import torch.nn as nn import torch.nn.functional as F import torchvision def xyxy2xywh(x): # Transform box coordinates from [x1, y1, x2, y2] (where xy1=top-left, xy2=bottom-right) to [x, y, w, h] y = torch.zeros_like(x) if isinsta...

34.49467

117

0.52627

2,299

16,178

3.616355

0.207916

0.00866

0.006495

0.005773

0.148545

0.095261

0.082752

0.067116

0.052201

0.042098

0

0.055911

0.323402

16,178

468

118

34.568376

0.703636

0.23532

0

0.093023

0

0.002907

0.051162

0.001725

0

0.002907

1

0.043605

false

0

0.02907

0.002907

0.122093

0.008721

0

null

0

null

0

1

0

def8727d101b934efb5715bc01f3842eeeee3ee3

4,934

py

Python

ec2stack/__init__.py

sureshanaparti/cloudstack-ec2stack

8e07435d3d04357995f2a5d337adef62ecbfdd8d

[ "Apache-2.0" ]

13

2015-05-06T13:38:13.000Z

2021-11-09T21:39:01.000Z

ec2stack/__init__.py

sureshanaparti/cloudstack-ec2stack

8e07435d3d04357995f2a5d337adef62ecbfdd8d

[ "Apache-2.0" ]

3

2015-08-21T17:31:20.000Z

2021-07-07T08:39:11.000Z

ec2stack/__init__.py

sureshanaparti/cloudstack-ec2stack

8e07435d3d04357995f2a5d337adef62ecbfdd8d

[ "Apache-2.0" ]

17

2015-07-24T06:00:59.000Z

2021-11-09T21:38:52.000Z

#!/usr/bin/env python # encoding: utf-8 # # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache Licens...

28.356322

88

0.676125

598

4,934

5.428094

0.316054

0.033888

0.009858

0.011091

0.152495

0.075786

0.054837

0.028343

0

0.004737

0.229834

4,934

173

89

28.520231

0.849474

0.286583

0

0.065934

0

0.141846

0.027131

0

1

0.054945

false

0

0.120879

0

0.21978

0.010989

0

null

0

null

0

1

0

def98cf0f4126cdcda2bee2e5c8d96a01bc4937b

1,351

py

Python

solutions/5/guillaume/LookAhead.py

larsbratholm/champs_kaggle

fda4f213d02fd5e0138a86c52b4140c9f94fec6e

[ "MIT" ]

9

2020-08-14T23:11:16.000Z

2021-08-09T16:23:43.000Z

solutions/5/guillaume/LookAhead.py

larsbratholm/champs_kaggle

fda4f213d02fd5e0138a86c52b4140c9f94fec6e

[ "MIT" ]

1

2020-11-19T09:29:14.000Z

solutions/5/guillaume/LookAhead.py

larsbratholm/champs_kaggle

fda4f213d02fd5e0138a86c52b4140c9f94fec6e

[ "MIT" ]

2

2020-09-09T02:53:57.000Z

2020-12-06T08:20:52.000Z

import itertools as it from torch.optim import Optimizer class LookAhead(Optimizer): def __init__(self, base_optimizer,alpha=0.5, k=6): if not 0.0 <= alpha <= 1.0: raise ValueError(f'Invalid slow update rate: {alpha}') if not 1 <= k: raise ValueError(f'Invalid lookahead steps...

37.527778

75

0.559585

177

1,351

4.146893

0.327684

0.074932

0.081744

0.06267

0.06812

0

0.012263

0.336047

1,351

35

76

38.6

0.80602

0

0.060606

0

0.080681

0

1

0.060606

false

0

0.060606

0

0.181818

0

null

0

null

0

1

0

defcc91baa71d0c94f476ef6cc3d35765b3516a0

2,263

py

Python

addexp.py

Shajm44n/Expense

db3355d4d81d5dd57ceea81b1170724b8893e523

[ "MIT" ]

null

addexp.py

Shajm44n/Expense

db3355d4d81d5dd57ceea81b1170724b8893e523

[ "MIT" ]

null

addexp.py

Shajm44n/Expense

db3355d4d81d5dd57ceea81b1170724b8893e523

[ "MIT" ]

null

from tkinter import * # import expdate import mysql.connector db_connect=mysql.connector.connect(host="localhost",user="root",password="maan",database="expense") db_cursor=db_connect.cursor() def add_expense(day,month,year): print("add exp") window=Tk() window.title("Expense list") l_message=Label(w...

32.797101

236

0.643836

363

2,263

3.867769

0.242424

0.068376

0.051282

0.05698

0.061254

0.042735

0

0.049973

0.17764

2,263

68

237

33.279412

0.70446

0.006186

0

0.015873

0.15984

0.086376

0

1

0.031746

false

0.015873

0.031746

0

0.063492

0.079365

0

null

0

null

0

1

0

defde4b16a7fe68a1c0b7ba26a303a5bb6a695bc

12,389

py

Python

cma-evolve.py

simondlevy/CMA-Gym

ce0056873d42eae2b6769fe22fcf872459694f30

[ "Apache-2.0" ]

null

cma-evolve.py

simondlevy/CMA-Gym

ce0056873d42eae2b6769fe22fcf872459694f30

[ "Apache-2.0" ]

null

cma-evolve.py

simondlevy/CMA-Gym

ce0056873d42eae2b6769fe22fcf872459694f30

[ "Apache-2.0" ]

null

#!/usr/bin/env python3 import gym import torch import numpy as np import multiprocessing as mp import os import pickle import sys import time import logging import cma import argparse from torchmodel import StandardFCNet def _makedir(name): if not os.path.exists(name): os.makedirs(name) def get_logger():...

33.574526

123

0.600291

1,669

12,389

4.281606

0.158778

0.014694

0.02379

0.008396

0.172544

0.117128

0.074447

0.065211

0.015953

0

0.009484

0.26806

12,389

368

124

33.665761

0.778562

0.013964

0

0.102564

0

0.003205

0.050778

0

0.003205

1

0.092949

false

0

0.038462

0.00641

0.195513

0

null

0

null

0

1

0

defeff29d76d14fa0aceaad7cd54a55164f7136c

2,386

py

Python

rastervision/data/label_store/default.py

carderne/raster-vision

915fbcd3263d8f2193e65c2cd0eb53e050a47a01

[ "Apache-2.0" ]

4

2019-03-11T12:38:15.000Z

2021-04-06T14:57:52.000Z

rastervision/data/label_store/default.py

carderne/raster-vision

915fbcd3263d8f2193e65c2cd0eb53e050a47a01

[ "Apache-2.0" ]

null

rastervision/data/label_store/default.py

carderne/raster-vision

915fbcd3263d8f2193e65c2cd0eb53e050a47a01

[ "Apache-2.0" ]

1

2019-10-29T09:22:09.000Z

from abc import (ABC, abstractmethod) import os import rastervision as rv class LabelStoreDefaultProvider(ABC): @staticmethod @abstractmethod def is_default_for(task_type): """Returns True if this label store is the default for this tasks_type""" pass @staticmethod @abstractmetho...

27.425287

86

0.65088

267

2,386

5.670412

0.250936

0.07926

0.03963

0.624835

0.554822

0.53963

0

0.0017

0.260268

2,386

86

87

27.744186

0.856091

0.084241

0

0.6875

0

0.016136

0

1

0.1875

false

0.046875

0.484375

0

null

0

null

0

1

0

7202ced44b536e7785d48d42a3fe09355e98fc12

448

py

Python

guestbook/models.py

Bespolezniy/geek-world

8fbaf451b4e87e48e73eb289035ec0ea68ea0e68

[ "MIT" ]

null

guestbook/models.py

Bespolezniy/geek-world

8fbaf451b4e87e48e73eb289035ec0ea68ea0e68

[ "MIT" ]

null

guestbook/models.py

Bespolezniy/geek-world

8fbaf451b4e87e48e73eb289035ec0ea68ea0e68

[ "MIT" ]

null

from django.db import models # Create your models here. class GuestBook(models.Model): user = models.CharField(max_length=15, verbose_name="User") date = models.DateTimeField(db_index=True, auto_now_add=True, verbose_name="Published") content = models.TextField(verbose_name="Content") class Me...

37.333333

92

0.694196

56

448

5.375

0.625

0.182724

0

0.005587

0.200893

448

12

93

37.333333

0.835196

0.053571

0

0.143204

0

1

0

false

0

0.111111

0

0.666667

0

null

0

null

0

1

0

72043f3633eddba64964dbbdb6f17d84cf1d6267

34,859

py

Python

PA1/PA1_Q2/P21CS007_VGG16.py

aryachiranjeev/Dependable-AI

750570572c1baaa2590a89c0982e2f71b15b48b9

[ "MIT" ]

null

PA1/PA1_Q2/P21CS007_VGG16.py

aryachiranjeev/Dependable-AI

750570572c1baaa2590a89c0982e2f71b15b48b9

[ "MIT" ]

null

PA1/PA1_Q2/P21CS007_VGG16.py

aryachiranjeev/Dependable-AI

750570572c1baaa2590a89c0982e2f71b15b48b9

[ "MIT" ]

null

#!/usr/bin/env python # coding: utf-8 # In[2]: import numpy as np import pandas as pd import random import tensorflow as tf import matplotlib.pyplot as plt from tensorflow.keras.layers import Dense,Flatten,GlobalAveragePooling2D,Input,Lambda from tensorflow.keras.models import Model,load_model import tensorflow.kera...

33.16746

235

0.685304

5,083

34,859

4.336612

0.084399

0.017466

0.02536

0.021095

0.694642

0.612802

0.579867

0.572744

0.55047

0.493445

0

0.053156

0.200207

34,859

1,050

236

33.199048

0.737482

0.016495

0

0.437107

0

0.085928

0.044191

0

1

0.033019

false

0

0.033019

0

0.08805

0.125786

0

null

0

null

0

1

0

72045094280bf8b19ef8956f47fe38ea87d738b3

1,027

py

Python

notebooks/general.py

transientlunatic/grasshopper

1d3822427970d200341ff9d2823949fb4b27e001

[ "0BSD" ]

3

2020-09-26T01:27:13.000Z

2020-09-30T05:47:42.000Z

notebooks/general.py

transientlunatic/gravpy

1d3822427970d200341ff9d2823949fb4b27e001

[ "0BSD" ]

null

notebooks/general.py

transientlunatic/gravpy

1d3822427970d200341ff9d2823949fb4b27e001

[ "0BSD" ]

null

import numpy as np import astropy.units as u def snr(signal, detector): """ Calculate the SNR of a signal in a given detector, assuming that it has been detected with an optimal filter. See e.g. arxiv.org/abs/1408.0740 Parameters ---------- signal : Source A Source object which ...

30.205882

79

0.635833

138

1,027

4.724638

0.528986

0.116564

0

0.021108

0.261928

1,027

33

80

31.121212

0.83905

0.461538

0

1

0.083333

false

0

0.166667

0

0.333333

0

null

0

null

0

1

0

72082ffdc0eb8ab81095d7d094328792a40cbcea

6,898

py

Python

dlfairness/original_code/FairALM/Experiments-CelebA/results/quantitative_results/plot_celeba.py

lin-tan/fairness-variance

7f6aee23160707ffe78f429e5d960022ea1c9fe4

[ "BSD-3-Clause" ]

null

dlfairness/original_code/FairALM/Experiments-CelebA/results/quantitative_results/plot_celeba.py

lin-tan/fairness-variance

7f6aee23160707ffe78f429e5d960022ea1c9fe4

[ "BSD-3-Clause" ]

null

dlfairness/original_code/FairALM/Experiments-CelebA/results/quantitative_results/plot_celeba.py

lin-tan/fairness-variance

7f6aee23160707ffe78f429e5d960022ea1c9fe4

[ "BSD-3-Clause" ]

null

''' Script to plot the accuracy and the fairness measures for different algorithms from the log files ''' import matplotlib matplotlib.use('agg') from matplotlib import pyplot as plt import os print(os.getcwd()) import numpy as np plt.style.use('ggplot') def create_acc_lists(filepath): train_acc = [] train_d...

36.691489

81

0.621919

938

6,898

4.259062

0.216418

0.029787

0.035044

0.038298

0.471089

0.39975

0.363955

0.338423

0.287359

0.193242

0

0.044768

0.232531

6,898

187

82

36.887701

0.70986

0.065671

0

0.144928

0

0.146216

0.057147

0

1

0.07971

false

0

0.028986

0.021739

0.137681

0.014493

0

null

0

null

0

1

0

72085eb6f35c638ad1743b5ae7bd6a8de18fc6f3

682

py

Python

conqueror/scraper/base_yandex.py

piotrmaslanka/yandex-conqueror

cd0b50a43e25551f91150e0bee4f9cd307e4adce

[ "MIT" ]

12

2022-03-01T22:45:05.000Z

2022-03-16T05:46:24.000Z

conqueror/scraper/base_yandex.py

piotrmaslanka/yandex-conqueror

cd0b50a43e25551f91150e0bee4f9cd307e4adce

[ "MIT" ]

1

2022-03-02T10:18:05.000Z

2022-03-02T11:03:52.000Z

conqueror/scraper/base_yandex.py

piotrmaslanka/yandex-conqueror

cd0b50a43e25551f91150e0bee4f9cd307e4adce

[ "MIT" ]

1

2022-03-02T10:18:35.000Z

import requests from satella.coding.decorators import retry @retry(3, exc_classes=requests.RequestException) def get_yandex_request(url, arguments) -> dict: """ Return a JSON object querying Yandex at provided parameters. Handling CSRF will be done automatically. :param url: URL to ask :param ar...

28.416667

64

0.692082

84

682

5.535714

0.619048

0.03871

0.068817

0.08172

0.12043

0

0.001859

0.211144

682

23

65

29.652174

0.862454

0.313783

0

0.061927

0

1

0.083333

false

0

0.166667

0

0.416667

0

null

0

null

0

1

0

72097fdf43f5937088d329748fec0dc61447255f

6,142

py

Python

engine/azbatchengine.py

asedighi/azure_realtime_batch

c2cf4c8edc2bbded8377842fcad6370fd35af44e

[ "MIT" ]

3

2020-05-08T16:20:07.000Z

2021-10-06T11:16:10.000Z

engine/azbatchengine.py

asedighi/azure_realtime_batch

c2cf4c8edc2bbded8377842fcad6370fd35af44e

[ "MIT" ]

null

engine/azbatchengine.py

asedighi/azure_realtime_batch

c2cf4c8edc2bbded8377842fcad6370fd35af44e

[ "MIT" ]

null

# Copyright (c) Microsoft Corporation # # All rights reserved. # # MIT License # # 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...

29.38756

137

0.667209

765

6,142

5.206536

0.309804

0.032639

0.042681

0.031634

0.139844

0.12478

0.079337

0.062265

0

0.00272

0.221915

6,142

208

138

29.528846

0.830718

0.26506

0

0.103402

0.051925

0

1

0.104167

false

0.010417

0.145833

0.010417

0.291667

0.072917

0

null

0

null

0

1

0

720a41d918f83d5bbf26dfd204b04b9dc1b4ac43

1,090

py

Python

j.py

chirag127/Language-Translator-Using-Tkinter-in-Python

c790a0672c770cf703559d99c74ad581643f4d2f

[ "MIT" ]

null

j.py

chirag127/Language-Translator-Using-Tkinter-in-Python

c790a0672c770cf703559d99c74ad581643f4d2f

[ "MIT" ]

null

j.py

chirag127/Language-Translator-Using-Tkinter-in-Python

c790a0672c770cf703559d99c74ad581643f4d2f

[ "MIT" ]

null

import tkinter as tk import sys class PrintLogger(): # create file like object def __init__(self, textbox): # pass reference to text widget self.textbox = textbox # keep ref def write(self, text): self.textbox.insert(tk.END, text) # write text to textbox # could also scroll to end ...

24.772727

82

0.542202

127

1,090

4.543307

0.543307

0.041594

0.07279

0

0.0059

0.377982

1,090

43

83

25.348837

0.845133

0.291743

0

0.08

0

0.091984

0

1

0.16

false

0.04

0.08

0

0.28

0.2

0

null

0

null

0

1

0

720b01f5be1444386ad583c605e2465546f819c4

2,695

py

Python

byteweiser.py

urbanware-org/byteweiser

fc90d17b51ead44af53401dc9c8ca5f0efc5e72e

[ "MIT" ]

3

2017-11-27T00:35:04.000Z

2017-12-13T22:41:31.000Z

byteweiser.py

urbanware-org/byteweiser

fc90d17b51ead44af53401dc9c8ca5f0efc5e72e

[ "MIT" ]

1

2017-03-08T19:04:49.000Z

byteweiser.py

urbanware-org/byteweiser

fc90d17b51ead44af53401dc9c8ca5f0efc5e72e

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- # ============================================================================ # ByteWeiser - Byte comparison and replacement tool # Main script # Copyright (C) 2021 by Ralf Kilian # Distributed under the MIT License (https://opensource.org/licenses/MIT) # # GitHub: https...

34.551282

78

0.562152

330

2,695

4.490909

0.424242

0.021592

0.030364

0.050607

0.0722

0.046559

0

0.007493

0.257143

2,695

77

79

35

0.732767

0.18961

0

0.12

0

0.289862

0

1

0.02

false

0

0.1

0

0.12

0.08

0

null

0

null

0

1

0

720b83b3d481df1e875ae4b17eade77f3a7f0679

9,798

py

Python

scripts/st_dashboard.py

rsmith49/simple-budget-pld

1bee5a26f53aa4a5b0aab49ee4c158b5ecb7c743

[ "Apache-2.0" ]

1

2022-01-01T14:44:40.000Z

scripts/st_dashboard.py

rsmith49/simple-budget-pld

1bee5a26f53aa4a5b0aab49ee4c158b5ecb7c743

[ "Apache-2.0" ]

null

scripts/st_dashboard.py

rsmith49/simple-budget-pld

1bee5a26f53aa4a5b0aab49ee4c158b5ecb7c743

[ "Apache-2.0" ]

null

import altair as alt import os import pandas as pd import streamlit as st import sys from datetime import datetime from dateutil.relativedelta import relativedelta from dotenv import load_dotenv from plaid.api_client import ApiClient from plaid.exceptions import ApiException from pathlib import Path from traceback imp...

33.101351

119

0.610431

1,244

9,798

4.618971

0.262862

0.024365

0.019144

0.008354

0.17386

0.110338

0.096763

0.060912

0

0.007199

0.276995

9,798

295

120

33.213559

0.803924

0.122576

0

0.091346

0

0.004808

0.168805

0.016275

0

0.00678

0

1

0.024038

false

0

0.081731

0

0.120192

0

null

0

null

0

1

0

720ee96617fe84100cbf9c9517c56d368835bd2c

16,818

py

Python

scripts/devvnet_manager.py

spmckenney/Devv-Core

eb30ae3a092e3fe0f9f756f5f31bdce4f6215b98

[ "MIT" ]

null

scripts/devvnet_manager.py

spmckenney/Devv-Core

eb30ae3a092e3fe0f9f756f5f31bdce4f6215b98

[ "MIT" ]

null

scripts/devvnet_manager.py

spmckenney/Devv-Core

eb30ae3a092e3fe0f9f756f5f31bdce4f6215b98

[ "MIT" ]

null

import yaml import argparse import sys import os import subprocess import time def get_devvnet(filename): with open(filename, "r") as f: buf = ''.join(f.readlines()) conf = yaml.load(buf, Loader=yaml.Loader) # Set bind_port values port = conf['devvnet']['base_port'] for a in conf['devv...

33.171598

276

0.576347

2,131

16,818

4.278742

0.102299

0.044966

0.030708

0.011516

0.317833

0.232397

0.174929

0.149704

0.13753

0.12097

0

0.006098

0.278392

16,818

506

277

33.237154

0.745221

0.070401

0

0.26943

0

0.104802

0.007234

0

1

0.147668

false

0.041451

0.015544

0.056995

0.297927

0.054404

0

null

0

null

0

1

0

72103568b2899de2bb48ee1f49834b293ab3bb81

5,896

py

Python

run_qasm.py

t-imamichi/qiskit-utility

2e71d0457bba0e6eb91daa9dbb32f52d87fe9f0b

[ "Apache-2.0" ]

6

2019-02-27T11:53:18.000Z

2022-03-02T21:28:05.000Z

run_qasm.py

t-imamichi/qiskit-utility

2e71d0457bba0e6eb91daa9dbb32f52d87fe9f0b

[ "Apache-2.0" ]

null

run_qasm.py

t-imamichi/qiskit-utility

2e71d0457bba0e6eb91daa9dbb32f52d87fe9f0b

[ "Apache-2.0" ]

2

2019-05-03T23:52:03.000Z

2020-12-22T12:12:38.000Z

#!/usr/bin/env python # coding: utf-8 # Copyright 2018, IBM. # # This source code is licensed under the Apache License, Version 2.0 found in # the LICENSE.txt file in the root directory of this source tree. ''' This tool submits a QASM file to any backend and show the result. It requires 'Qconfig.py' to set a token o...

39.046358

116

0.608887

770

5,896

4.54026

0.280519

0.028318

0.05349

0.030034

0.100687

0.067506

0.05492

0.029748

0

0.013643

0.254071

5,896

150

117

39.306667

0.781264

0.176221

0

0.100917

0

0.172998

0.009083

0

1

0.082569

false

0

0.055046

0

0.183486

0.091743

0

null

0

null

0

1

0

7211ad9fb739bb9a8cf35bb0752773293df5ab6b

2,356

py

Python

api/teams/models.py

wepickheroes/wepickheroes.github.io

032c2a75ef058aaceb795ce552c52fbcc4cdbba3

[ "MIT" ]

3

2018-02-15T20:04:23.000Z

2018-09-29T18:13:55.000Z

api/teams/models.py

wepickheroes/wepickheroes.github.io

032c2a75ef058aaceb795ce552c52fbcc4cdbba3

[ "MIT" ]

5

2018-01-31T02:01:15.000Z

2018-05-11T04:07:32.000Z

api/teams/models.py

prattl/wepickheroes

032c2a75ef058aaceb795ce552c52fbcc4cdbba3

[ "MIT" ]

null

from django.conf import settings from django.contrib.auth import get_user_model from django.db import models from nucleus.models import ( AbstractBaseModel, EmailRecord, TeamMember, ) User = get_user_model() class Team(AbstractBaseModel): name = models.CharField(max_length=255) logo_url = models...

29.45

94

0.639219

283

2,356

5.159011

0.34629

0.021918

0.035616

0.046575

0.358219

0.269178

0.187671

0

0.003454

0.262733

2,356

79

95

29.822785

0.837075

0

0.1

0

0.016667

0.112054

0

1

0.066667

false

0

0.066667

0.016667

0.316667

0

null

0

null

0

1

0

721392272e51a8013f6d83d05f9c457dc8ce2f53

4,811

py

Python

print_results.py

MicImbriani/Keras-PRBX

ab9dd8196e6f184336f5b30715635670d3586136

[ "CC0-1.0" ]

1

2021-09-18T12:42:28.000Z

print_results.py

MicImbriani/SkinLesion-Segm-Classif-UNet-FocusNet-ResNet50

ab9dd8196e6f184336f5b30715635670d3586136

[ "CC0-1.0" ]

null

print_results.py

MicImbriani/SkinLesion-Segm-Classif-UNet-FocusNet-ResNet50

ab9dd8196e6f184336f5b30715635670d3586136

[ "CC0-1.0" ]

null

import numpy as np from keras.optimizers import Adam, SGD from tensorflow.keras.metrics import AUC import metrics from networks.unet_nn import unet from networks.unet_res_se_nn import unet_res_se from networks.focus import get_focusnetAlpha from networks.resnet import get_res from data_processing.generate_new_dataset...

29.335366

129

0.675327

651

4,811

4.75576

0.239631

0.031008

0.056848

0.054264

0.329457

0.283269

0.225775

0.158592

0.119832

0

0.047354

0.179173

4,811

163

130

29.515337

0.736642

0.649137

0

0.199338

0.096026

0

1

0

false

0

0.263158

0

0.263158

0.026316

0

null

0

null

0

1

0

72140b20f916fb997edbec8a00bb1402df3614ca

9,466

py

Python

game.py

distortedsignal/bohnanza

dfbcfafbdd07cb924cbbc2adc36db7e51673e546

[ "Apache-2.0" ]

null

game.py

distortedsignal/bohnanza

dfbcfafbdd07cb924cbbc2adc36db7e51673e546

[ "Apache-2.0" ]

null

game.py

distortedsignal/bohnanza

dfbcfafbdd07cb924cbbc2adc36db7e51673e546

[ "Apache-2.0" ]

null

""" An implementation of Bohnanza @author: David Kelley, 2018 """ import random from collections import defaultdict class Card: """Card Object Name and point thresholds are the only properties. The point thresholds are organized the way they are on the card - to get 1 point, you need th number of...

34.421818

88

0.555145

1,189

9,466

4.238015

0.184188

0.023219

0.023814

0.028577

0.261361

0.202818

0.124628

0.106767

0.071046

0.057154

0

0.016433

0.337841

9,466

275

89

34.421818

0.787492

0.169132

0

0.198864

0

0.036444

0

0.011364

1

0.147727

false

0.017045

0.011364

0.022727

0.295455

0.005682

0

null

0

null

0

1

0

72155749ca290c85d0fa365110369fcce2862271

1,872

py

Python

pytype/tests/test_calls.py

JelleZijlstra/pytype

962a0ebc05bd24dea172381b2bedcc547ba53dd5

[ "Apache-2.0" ]

11

2017-02-12T12:19:50.000Z

2022-03-06T08:56:48.000Z

pytype/tests/test_calls.py

JelleZijlstra/pytype

962a0ebc05bd24dea172381b2bedcc547ba53dd5

[ "Apache-2.0" ]

null

pytype/tests/test_calls.py

JelleZijlstra/pytype

962a0ebc05bd24dea172381b2bedcc547ba53dd5

[ "Apache-2.0" ]

2

2017-06-27T14:41:57.000Z

2021-12-05T11:27:33.000Z

"""Tests for calling other functions, and the corresponding checks.""" from pytype import utils from pytype.tests import test_inference class CallsTest(test_inference.InferenceTest): """Tests for checking function calls.""" def testOptional(self): with utils.Tempdir() as d: d.create_file("mod.pyi", "...

26.742857

73

0.553953

228

1,872

4.460526

0.276316

0.041298

0.048181

0.098328

0.650934

0.635202

0.611603

0.403147

0

0.013838

0.26656

1,872

69

74

27.130435

0.726875

0.052885

0

0.678571

0

0.354711

0

0.089286

1

0.089286

false

0

0.125

0

0.232143

0

null

0

null

0

1

0

7216c0aa91d2cb7e990847e2823233ead4e36ab3

724

py

Python

test/test_learning_00.py

autodrive/NAIST_DeepLearning

ac2c0512c43f71ea7df68567c5e24e689ac18aea

[ "Apache-2.0" ]

1

2018-09-26T01:52:35.000Z

test/test_learning_00.py

autodrive/NAIST_DeepLearning

ac2c0512c43f71ea7df68567c5e24e689ac18aea

[ "Apache-2.0" ]

5

2015-12-31T10:56:43.000Z

2018-11-16T08:57:12.000Z

test/test_learning_00.py

autodrive/NAIST_DeepLearning

ac2c0512c43f71ea7df68567c5e24e689ac18aea

[ "Apache-2.0" ]

1

2018-09-26T01:52:37.000Z

import unittest import lecture1_code00 as dl from sklearn.datasets.samples_generator import make_blobs class TestDeepLearning(unittest.TestCase): def setUp(self): self.X, self.Y = make_blobs(n_samples=50, centers=2, random_state=0, cluster_std=0.60) def tearDown(self): del self.X del ...

27.846154

97

0.585635

122

724

3.377049

0.377049

0.038835

0.021845

0.029126

0

0.107407

0.254144

724

25

98

28.96

0.655556

0

0.111111

1

0.166667

false

0

0.166667

0

0.388889

0

null

0

null

0

1

0

7217f6133fa71477eb286daa69250fadb04142e7

2,389

py

Python

edumediaitem/views_manage.py

shagun30/djambala-2

06f14e3dd237d7ebf535c62172cfe238c3934f4d

[ "BSD-3-Clause" ]

null

edumediaitem/views_manage.py

shagun30/djambala-2

06f14e3dd237d7ebf535c62172cfe238c3934f4d

[ "BSD-3-Clause" ]

null

edumediaitem/views_manage.py

shagun30/djambala-2

06f14e3dd237d7ebf535c62172cfe238c3934f4d

[ "BSD-3-Clause" ]

null

#-*-coding: utf-8 -*- """ /dms/edumediaitem/views_manage.py .. enthaelt den View fuer die Management-Ansicht des Medienpaketes Django content Management System Hans Rauch hans.rauch@gmx.net Die Programme des dms-Systems koennen frei genutzt und den spezifischen Beduerfnissen entsprechend angepasst werden. ...

38.532258

95

0.577648

260

2,389

5.073077

0.488462

0.078848

0.053071

0.059136

0.172858

0

0.012317

0.286312

2,389

61

96

39.163934

0.76129

0.194224

0

0.055556

0

0.235726

0.095338

0

1

0.027778

false

0

0.166667

0

0.222222

0

null

0

null

0

1

0

721a5ce052e7d21ea063652b0a161c21042f7f06

1,089

py

Python

tests/test_muduapiclient.py

hanqingliu/mudu-api-python-client

92541df27a518dad5312b39749dfbb8bd471a6b8

[ "Apache-2.0" ]

null

tests/test_muduapiclient.py

hanqingliu/mudu-api-python-client

92541df27a518dad5312b39749dfbb8bd471a6b8

[ "Apache-2.0" ]

null

tests/test_muduapiclient.py

hanqingliu/mudu-api-python-client

92541df27a518dad5312b39749dfbb8bd471a6b8

[ "Apache-2.0" ]

null

import ddt import mock from unittest import TestCase from muduapiclient.client import MuduApiClient, gen_signed_params import time @ddt.ddt class MuduApiClientTests(TestCase): @ddt.unpack @ddt.data( ('ACCESS_KEY', 'SECRET_KEY', {'page':1, 'live_status':2}), ) def test_gen_signed_params(self, a...

33

91

0.662994

129

1,089

5.403101

0.364341

0.120517

0.064562

0.060258

0.177905

0.143472

0

0.068886

0.200184

1,089

32

92

34.03125

0.731343

0

0.214286

0

0.193756

0.066116

0

0.142857

1

0.071429

false

0

0.178571

0

0.285714

0

null

0

null

0

1

0

721e9bba1e7ea66054b20c27b7571b65855aeaa1

5,970

py

Python

ttt.py

YukkuriC/PyTicTacToe

c38b330faeb956d82b401e5863c4982f725e5dab

[ "MIT" ]

null

ttt.py

YukkuriC/PyTicTacToe

c38b330faeb956d82b401e5863c4982f725e5dab

[ "MIT" ]

null

ttt.py

YukkuriC/PyTicTacToe

c38b330faeb956d82b401e5863c4982f725e5dab

[ "MIT" ]

null

__doc__ = ''' 井字棋基础设施包含棋盘类与单局游戏运行内核 ''' from threading import Thread from time import process_time if 'enums': OK = 0 # 游戏继续 ENDGAME = 1 # 形成三连 DRAW = 2 # 棋盘已满平局 INVALID = -1 # 非法返回值（类型错误/出界） CONFILCT = -2 # 冲突落子（下于已有棋子位置） ERROR = -3 # 代码报错 TIMEOUT = -4 # 代码超时 class Board: ""...

25.512821

78

0.469514

640

5,970

4.228125

0.317188

0.026608

0.020695

0.035107

0.114191

0.105322

0.083518

0.06541

0.04102

0

0.019722

0.422446

5,970

233

79

25.622318

0.765081

0.148409

0

0.158273

0

0.031041

0

1

0.079137

false

0

0.021583

0.007194

0.280576

0.007194

0

null

0

null

0

1

0

72207e110b7ba0434449b56ad831fee21813b6dc

1,015

py

Python

Minor Project/Weather GUI/pyowm_helper.py

ComputerScientist-01/Technocolabs-Internship-Project

3675cc6b9a40a885a29b105ec9b29945a1e4620c

[ "MIT" ]

4

2020-07-08T11:32:29.000Z

2021-08-05T02:54:02.000Z

Minor Project/Weather GUI/pyowm_helper.py

ComputerScientist-01/Technocolabs-Internship-Project

3675cc6b9a40a885a29b105ec9b29945a1e4620c

[ "MIT" ]

null

Minor Project/Weather GUI/pyowm_helper.py

ComputerScientist-01/Technocolabs-Internship-Project

3675cc6b9a40a885a29b105ec9b29945a1e4620c

[ "MIT" ]

null

import os import pyowm from datetime import datetime from timezone_conversion import gmt_to_eastern #API_KEY = os.environ['API_KEY'] owm=pyowm.OWM('0833f103dc7c2924da06db624f74565c') mgr=owm.weather_manager() def get_temperature(): days = [] dates = [] temp_min = [] temp_max = [] forecaster = mgr...

28.194444

63

0.639409

127

1,015

4.850394

0.393701

0.068182

0.038961

0.045455

0

0.038058

0.249261

1,015

35

64

29

0.770341

0.030542

0

0.069176

0.032553

0

1

0.034483

false

0

0.137931

0

0.172414

0

null

0

null

0

1

0

7222707469c1717bc369a16b35dc8703f4ba96c7

4,692

py

Python

SUAVE/SUAVE-2.5.0/trunk/SUAVE/Components/Energy/Storages/Batteries/Constant_Mass/Lithium_Ion_LiFePO4_18650.py

Vinicius-Tanigawa/Undergraduate-Research-Project

e92372f07882484b127d7affe305eeec2238b8a9

[ "MIT" ]

null

SUAVE/SUAVE-2.5.0/trunk/SUAVE/Components/Energy/Storages/Batteries/Constant_Mass/Lithium_Ion_LiFePO4_18650.py

Vinicius-Tanigawa/Undergraduate-Research-Project

e92372f07882484b127d7affe305eeec2238b8a9

[ "MIT" ]

null

SUAVE/SUAVE-2.5.0/trunk/SUAVE/Components/Energy/Storages/Batteries/Constant_Mass/Lithium_Ion_LiFePO4_18650.py

Vinicius-Tanigawa/Undergraduate-Research-Project

e92372f07882484b127d7affe305eeec2238b8a9

[ "MIT" ]

null

## @ingroup Components-Energy-Storages-Batteries-Constant_Mass # Lithium_Ion_LiFePO4_18650.py # # Created: Feb 2020, M. Clarke # Modified: Sep 2021, R. Erhard # ---------------------------------------------------------------------- # Imports # ---------------------------------------------------------------------- ...

53.931034

136

0.449915

441

4,692

4.684807

0.37415

0.100678

0.043562

0.036302

0.25847

0.249758

0.2091

0.158761

0.123911

0

0.061397

0.444587

4,692

87

137

53.931034

0.730622

0.339088

0

0.008661

0

1

0.033333

false

0

0.1

0

0.2

0

null

0

null

0

1

0

72230a4712ff2722d5fd895c22c3d235aabfdf44

3,544

py

Python

del_dupli_in_fasta.py

ba1/BioParsing

8a0257d4765a7bc86fef7688762abbeaaf3cef07

[ "MIT" ]

1

2017-06-19T15:15:26.000Z

del_dupli_in_fasta.py

ba1/BioParsing

8a0257d4765a7bc86fef7688762abbeaaf3cef07

[ "MIT" ]

null

del_dupli_in_fasta.py

ba1/BioParsing

8a0257d4765a7bc86fef7688762abbeaaf3cef07

[ "MIT" ]

null

''' Created on Oct 20, 2015 @author: bardya ''' import os import argparse from Bio import SeqIO def parse_args(): parser = argparse.ArgumentParser(description='Delete all duplicate entries (header+sequence) in fasta. If only sequence identical, add "| duplicate" to header.') parser.add_argument('-i', des...

35.79798

165

0.615406

425

3,544

5.032941

0.345882

0.023375

0.047686

0.021038

0.226741

0.202431

0.17064

0.13324

0

0.00681

0.254233

3,544

99

166

35.79798

0.802497

0.093115

0

0.179104

0

0.029851

0.265293

0.014045

0

1

0.059701

false

0

0.059701

0

0.149254

0.059701

0

null

0

null

0

1

0

72279efb6ba56531335b2f093691a4196e8f4923

2,531

py

Python

ardupilot/Tools/autotest/param_metadata/wikiemit.py

quadrotor-IITKgp/emulate_GPS

3c888d5b27b81fb17e74d995370f64bdb110fb65

[ "MIT" ]

1

2021-07-17T11:37:16.000Z

ardupilot/Tools/autotest/param_metadata/wikiemit.py

arl-kgp/emulate_GPS

3c888d5b27b81fb17e74d995370f64bdb110fb65

[ "MIT" ]

null

ardupilot/Tools/autotest/param_metadata/wikiemit.py

arl-kgp/emulate_GPS

3c888d5b27b81fb17e74d995370f64bdb110fb65

[ "MIT" ]

null

#!/usr/bin/env python import re from param import * from emit import Emit # Emit docs in a form acceptable to the APM wiki site class WikiEmit(Emit): def __init__(self): wiki_fname = 'Parameters.wiki' self.f = open(wiki_fname, mode='w') preamble = '''#summary Dynamically generated lis...

34.671233

121

0.468589

282

2,531

4.046099

0.322695

0.092025

0.099912

0.014023

0.078878

0.007011

0

0.00187

0.366258

2,531

72

122

35.152778

0.709476

0.031213

0

0.078431

0

0.201471

0.025746

0

1

0.137255

false

0

0.058824

0

0.27451

0

null

0

null

0

1

0

722ad974ef9283199399d93bbd17a334c7d31249

1,038

py

Python

master.py

iAzurel/thepicturesorter

21a3aee26adcfca0838db63be1434f7c49cd9548

[ "MIT" ]

null

master.py

iAzurel/thepicturesorter

21a3aee26adcfca0838db63be1434f7c49cd9548

[ "MIT" ]

null

master.py

iAzurel/thepicturesorter

21a3aee26adcfca0838db63be1434f7c49cd9548

[ "MIT" ]

null

#!/usr/bin/env python from PIL import Image import os, os.path import cv2 import sys # Detect faces, then returns number of faces. def detect_face(image_path, face_cascade): img = cv2.imread(image_path) gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # Change the values based on needs. faces = face_cascade.detect...

23.590909

76

0.716763

144

1,038

4.986111

0.506944

0.061281

0.027855

0.100279

0

0.018391

0.16185

1,038

44

77

23.590909

0.806897

0.136802

0

0.160134

0.150056

0

1

0.107143

false

0

0.142857

0

0.285714

0

null

0

null

0

1

0

7230fd2e2774f3460096d023d321613a2a314e63

2,850

py

Python

webscripts/plotlygraphs.py

KathrynDH/DataDashboard

1bf61497480f778a1c7cc9ce9fc7fb48b3067606

[ "MIT" ]

null

webscripts/plotlygraphs.py

KathrynDH/DataDashboard

1bf61497480f778a1c7cc9ce9fc7fb48b3067606

[ "MIT" ]

null

webscripts/plotlygraphs.py

KathrynDH/DataDashboard

1bf61497480f778a1c7cc9ce9fc7fb48b3067606

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Wed Jun 23 15:56:55 2021 @author: Kathryn Haske Create plotly graphs for webpage """ import pandas as pd import plotly.graph_objs as go def line_graph(x_list, df, name_col, y_cols, chart_title, x_label, y_label): """ Function to create plotly line graph Args: ...

27.403846

76

0.567018

376

2,850

4.140957

0.236702

0.057803

0.042389

0.073218

0.574181

0.558767

0

0.006948

0.343509

2,850

103

77

27.669903

0.825227

0.43193

0

0.380952

0

0.013158

0

1

0.071429

false

0

0.047619

0

0.190476

0

null

0

null

0

1

0

72314feeba462045a5c4c66db5b70dc7ce89e3a1

2,505

py

Python

jsl/experimental/seql/agents/bfgs_agent.py

AdrienCorenflos/JSL

8a3ba27179a2bd90207214fccb81df884b05c3d0

[ "MIT" ]

null

jsl/experimental/seql/agents/bfgs_agent.py

AdrienCorenflos/JSL

8a3ba27179a2bd90207214fccb81df884b05c3d0

[ "MIT" ]

null

jsl/experimental/seql/agents/bfgs_agent.py

AdrienCorenflos/JSL

8a3ba27179a2bd90207214fccb81df884b05c3d0

[ "MIT" ]

null

import jax.numpy as jnp from jax import vmap from jax.scipy.optimize import minimize import chex import typing_extensions from typing import Any, NamedTuple import warnings from jsl.experimental.seql.agents.agent_utils import Memory from jsl.experimental.seql.agents.base import Agent from jsl.experimental.seql.util...

26.09375

68

0.578842

282

2,505

5.003546

0.382979

0.057406

0.040397

0.048901

0.17151

0.092133

0

0.006635

0.338124

2,505

96

69

26.09375

0.844391

0.020758

0

0.098361

0

0.013152

0

0.032787

1

0.098361

false

0

0.163934

0.016393

0.47541

0

null

0

null

0

1

0

72320fd783db7905693b184e50b586992cf4d02b

2,379

py

Python

abusech/urlhaus.py

threatlead/abusech

6c62f51f773cb17ac6943d87fb697ce1e9dae049

[ "MIT" ]

null

abusech/urlhaus.py

threatlead/abusech

6c62f51f773cb17ac6943d87fb697ce1e9dae049

[ "MIT" ]

null

abusech/urlhaus.py

threatlead/abusech

6c62f51f773cb17ac6943d87fb697ce1e9dae049

[ "MIT" ]

null

from .abusech import AbuseCh from collections import namedtuple from datetime import datetime class UrlHaus(AbuseCh): base_url = 'https://urlhaus.abuse.ch' urls = namedtuple('UrlHaus', ['id', 'date_added', 'url', 'url_status', 'threat', 'tags', 'urlhaus_link', 'reporter']) payloads = namedtuple('Payload',...

43.254545

121

0.584279

294

2,379

4.585034

0.231293

0.046736

0.032641

0.05638

0.494807

0.457715

0.382789

0.354599

0

0.020624

0.245902

2,379

54

122

44.055556

0.730769

0

0.297872

0

0.100462

0.030685

0

1

0.106383

false

0

0.06383

0

0.361702

0

null

0

null

0

1

0

7233678cd98a3bf61296f7c1aa2006b01024a6ac

5,894

py

Python

thorbanks/checks.py

Jyrno42/django-thorbanks

a8e2daf20b981aecb0c8ee76b0474b6c8e2baad1

[ "BSD-3-Clause" ]

6

2015-06-15T12:47:05.000Z

2019-04-24T01:32:12.000Z

thorbanks/checks.py

Jyrno42/django-thorbanks

a8e2daf20b981aecb0c8ee76b0474b6c8e2baad1

[ "BSD-3-Clause" ]

13

2015-12-23T14:29:26.000Z

2021-02-18T18:35:56.000Z

thorbanks/checks.py

Jyrno42/django-thorbanks

a8e2daf20b981aecb0c8ee76b0474b6c8e2baad1

[ "BSD-3-Clause" ]

3

2016-08-08T10:35:39.000Z

2020-12-29T23:10:55.000Z

import os from django.conf import settings from django.core.checks import Error, register from thorbanks.settings import configure, parse_banklinks @register def check_model_settings(app_configs, **kwargs): issues = [] manual_models = getattr(settings, "THORBANKS_MANUAL_MODELS", None) if manual_models...

35.293413

119

0.449779

490

5,894

5.267347

0.244898

0.051143

0.072453

0.077489

0.496707

0.428516

0.354901

0.328555

0.323131

0.221232

0

0.011821

0.468951

5,894

166

120

35.506024

0.81278

0.023244

0

0.345324

0

0.280028

0.103772

0

1

0.014388

false

0

0.028777

0

0.057554

0

null

0

null

0

1

0

723547959ebc4a91f17440d870c4a23f152e86d1

4,705

py

Python

rm_protection/rm_p.py

https-waldoww90-wadewilson-com/rm-protection

4dcc678fa687373fb4439c5c4409f7649e653084

[ "MIT" ]

490

2017-02-03T14:15:50.000Z

2022-03-31T02:57:20.000Z

rm_protection/rm_p.py

https-waldoww90-wadewilson-com/rm-protection

4dcc678fa687373fb4439c5c4409f7649e653084

[ "MIT" ]

8

2017-02-03T16:13:53.000Z

2017-05-28T05:20:45.000Z

rm_protection/rm_p.py

alanzchen/rm-protection

4dcc678fa687373fb4439c5c4409f7649e653084

[ "MIT" ]

41

2017-02-04T15:13:26.000Z

2021-12-19T08:58:38.000Z

from sys import argv, exit from os.path import expanduser as expu, expandvars as expv from os.path import basename, dirname, abspath, isdir, exists from subprocess import Popen, PIPE from builtins import input from rm_protection.config import Config c = Config() evaledpaths = [] def pprint(msg): global c pr...

30.953947

120

0.530287

515

4,705

4.741748

0.264078

0.044226

0.04095

0.019656

0.126945

0.059787

0.038493

0

0.000998

0.361105

4,705

151

121

31.15894

0.811377

0

0.263566

0

0.086716

0

0.007752

0

1

0.069767

false

0.007752

0.046512

0.007752

0.217054

0.100775

0

null

0

null

0

1

0

72381b6de058125b33932e8f4cd988e19b104ff7

6,856

py

Python

src/text_normalizer/tokenization/_tokenize.py

arkataev/text_normalizer

a99326e31012157980d014c9730ac94bd1d18c1d

[ "MIT" ]

null

src/text_normalizer/tokenization/_tokenize.py

arkataev/text_normalizer

a99326e31012157980d014c9730ac94bd1d18c1d

[ "MIT" ]

null

src/text_normalizer/tokenization/_tokenize.py

arkataev/text_normalizer

a99326e31012157980d014c9730ac94bd1d18c1d

[ "MIT" ]

null

"""Модуль для создания и работы с токенами""" import logging import re import string from enum import IntEnum from functools import lru_cache from typing import Tuple, Iterator from nltk.corpus import stopwords from nltk.tokenize import ToktokTokenizer from nltk.tokenize.api import TokenizerI from ..config import Reg...

27.534137

107

0.641774

833

6,856

5.158463

0.337335

0.030719

0.018618

0.02653

0.099837

0.09495

0.038632

0.013963

0

0.010092

0.219516

6,856

248

108

27.645161

0.788077

0.305134

0

0.046875

0

0.007813

0.107348

0.035857

0

1

0.09375

false

0

0.078125

0.015625

0.429688

0

null

0

null

0

1

0

7239365caa1436583482800c75a7cb1d2a4fbe35

18,942

py

Python

pi/los.py

Coding-Badly/Little-Oven

3d1178f495aea1180e25bddbb4f139d8e37e6a65

[ "Apache-2.0" ]

null

pi/los.py

Coding-Badly/Little-Oven

3d1178f495aea1180e25bddbb4f139d8e37e6a65

[ "Apache-2.0" ]

null

pi/los.py

Coding-Badly/Little-Oven

3d1178f495aea1180e25bddbb4f139d8e37e6a65

[ "Apache-2.0" ]

null

#!/usr/bin/env python3 """============================================================================= los for Little-Oven. los (Little Oven Setup) prepares a Raspberry Pi for Little-Oven development. This module does the actual work. los (no extension) is a bash script that creates a service that runs this...

50.244032

184

0.658853

2,508

18,942

4.775917

0.192982

0.071631

0.053765

0.062448

0.415929

0.344882

0.309401

0.279095

0.230172

0.197278

0

0.011435

0.196706

18,942

376

185

50.37766

0.775762

0.268821

0

0.227106

0

0.003663

0.221746

0.064963

0

1

0.040293

false

0.03663

0.040293

0

0.091575

0.087912

0

null

0

null

0

1

0

723b9095a8d15e2c9c1b3f5d5be4c81a6f6e858e

2,304

py

Python

streamlit_app.py

fhebal/nlp-medical-notes

f1fed9e34ba47da14220b5719f28c1e720302f45

[ "MIT" ]

null

streamlit_app.py

fhebal/nlp-medical-notes

f1fed9e34ba47da14220b5719f28c1e720302f45

[ "MIT" ]

null

streamlit_app.py

fhebal/nlp-medical-notes

f1fed9e34ba47da14220b5719f28c1e720302f45

[ "MIT" ]

null

import streamlit as st import yaml from load_css import local_css import tensorflow as tf import tensorflow_hub as hub import tensorflow_text as text import numpy as np from random import sample import os local_css("style.css") prediction_key = { 0:'Gastroenterology', 1:'Neurology', 2:'Orthope...

29.538462

126

0.647569

300

2,304

4.816667

0.36

0.055363

0.033218

0.041522

0.057439

0.038754

0

0.005501

0.210938

2,304

77

127

29.922078

0.789329

0.039931

0

0.161758

0.040779

0

1

0.068966

false

0

0.155172

0

0.275862

0.017241

0

null

0

null

0

1

0

723e3c60c657572c4703c5d71bdcbccb656fe914

18,265

py

Python

src/elora/elora.py

morelandjs/elora

e902c40d66b0bf95a8d2374afa0cc165b87c9b82

[ "MIT" ]

1

2021-07-26T20:36:32.000Z

src/elora/elora.py

morelandjs/elora

e902c40d66b0bf95a8d2374afa0cc165b87c9b82

[ "MIT" ]

null

src/elora/elora.py

morelandjs/elora

e902c40d66b0bf95a8d2374afa0cc165b87c9b82

[ "MIT" ]

null

from operator import add, sub import numpy as np from scipy.stats import norm class Elora: def __init__(self, times, labels1, labels2, values, biases=0): """ Elo regressor algorithm for paired comparison time series prediction Author: J. Scott Moreland Args: times (a...

36.750503

80

0.594854

2,170

18,265

4.957604

0.136866

0.033835

0.030117

0.022123

0.556051

0.52045

0.496375

0.470998

0.466629

0.451292

0

0.02295

0.312948

18,265

496

81

36.824597

0.83417

0.373446

0

0.42233

0

0.043626

0

1

0.072816

false

0

0.014563

0

0.160194

0

null

0

null

0

1

0

723fcadfa719088f86b59d8093c6f9655d115794

48,147

py

Python

steady_cell_phenotype/poly.py

knappa/steadycellphenotype

b033f01ebc1fa062d310296f19f2f11b484cb557

[ "MIT" ]

1

2021-12-13T22:20:19.000Z

steady_cell_phenotype/poly.py

knappa/steadycellphenotype

b033f01ebc1fa062d310296f19f2f11b484cb557

[ "MIT" ]

5

2021-04-07T01:47:19.000Z

2021-11-17T01:46:19.000Z

steady_cell_phenotype/poly.py

knappa/steadycellphenotype

b033f01ebc1fa062d310296f19f2f11b484cb557

[ "MIT" ]

null

from __future__ import annotations import operator from enum import Enum from itertools import product from typing import Dict, Union import numpy as np class Operation(Enum): PLUS = 'PLUS' MINUS = 'MINUS' TIMES = 'TIMES' EXP = 'EXP' MAX = 'MAX' MIN = 'MIN' CONT = 'CONT' NOT = 'NOT' ...

38.985425

117

0.577897

5,664

48,147

4.648305

0.076977

0.026322

0.025182

0.013978

0.62933

0.549681

0.498899

0.417768

0.374202

0.350122

0

0.010594

0.302033

48,147

1,234

118

39.017018

0.772861

0.172202

0

0.49441

0

0.001242

0.051735

0.003637

0

0.013665

1

0.122981

false

0

0.007453

0.034783

0.367702

0

null

0

null

0

1

0

72404d3d39210b175e825c5b94b9e21a7e2698f1

421

py

Python

src/combine_npy.py

hongli-ma/RNANetMotif

34b4de443ec7edb59f4e4e06b17686543c438366

[ "MIT" ]

null

src/combine_npy.py

hongli-ma/RNANetMotif

34b4de443ec7edb59f4e4e06b17686543c438366

[ "MIT" ]

null

src/combine_npy.py

hongli-ma/RNANetMotif

34b4de443ec7edb59f4e4e06b17686543c438366

[ "MIT" ]

null

import numpy as np import sys import glob rbp=sys.argv[1] kmer=sys.argv[2] pfile_list=glob.glob("result_VDM3_"+rbp+"_positive_"+kmer+"_*.npy") pfile1=np.load(pfile_list[0]) psha=np.shape(pfile1) pmatrix=np.zeros(psha) for pfile in pfile_list: file=np.load(pfile) # file=np.fromfile(pfile,dtype=np.float32) p...

23.388889

86

0.750594

70

421

4.314286

0.5

0.089404

0.072848

0

0.023438

0.087886

421

17

87

24.764706

0.763021

0.095012

0

0.21164

0.095238

0

1

0

false

0

0.230769

0

0.230769

0

null

0

null

0

1

0

7242536c3707c16822eadee50c71c7b05cdd3796

7,768

py

Python

concourse/steps/scan_container_images.py

jia-jerry/cc-utils

01322d2acb7343c92138dcf0b6ac913b276525bc

[ "Apache-2.0" ]

null

concourse/steps/scan_container_images.py

jia-jerry/cc-utils

01322d2acb7343c92138dcf0b6ac913b276525bc

[ "Apache-2.0" ]

null

concourse/steps/scan_container_images.py

jia-jerry/cc-utils

01322d2acb7343c92138dcf0b6ac913b276525bc

[ "Apache-2.0" ]

null

# Copyright (c) 2019 SAP SE or an SAP affiliate company. All rights reserved. This file is licensed # under the Apache Software License, v. 2 except as noted otherwise in the LICENSE file # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the Licens...

36.299065

99

0.660788

899

7,768

5.458287

0.269188

0.042388

0.024251

0.012839

0.110047

0.072142

0.031384

0

0.001911

0.259011

7,768

213

100

36.469484

0.850591

0.121395

0

0.2

0

0.204115

0.045849

0

1

0.090909

false

0

0.048485

0.006061

0.230303

0.018182

0

null

0

null

0

1

0

72430bcb51d12558e07e88c7e1a6d221c05d6f85

647

py

Python

py/cv/video.py

YodaEmbedding/experiments

567c6a1c18fac2d951fe2af54aaa4917b7d529d2

[ "MIT" ]

null

py/cv/video.py

YodaEmbedding/experiments

567c6a1c18fac2d951fe2af54aaa4917b7d529d2

[ "MIT" ]

null

py/cv/video.py

YodaEmbedding/experiments

567c6a1c18fac2d951fe2af54aaa4917b7d529d2

[ "MIT" ]

null

import cv2 import numpy as np height = 500 width = 700 gray = np.zeros((height, width), dtype=np.uint8) # fourcc = cv2.VideoWriter_fourcc(*"MJPG") # filename = "output.avi" fourcc = cv2.VideoWriter_fourcc(*"MP4V") filename = "output.mp4" writer = cv2.VideoWriter( filename, fourcc, fps=30, frameSize=(width, height...

24.884615

70

0.689335

97

647

4.56701

0.56701

0.094808

0.090293

0.117381

0

0.047794

0.159196

647

25

71

25.88

0.766544

0.251932

0

0.029228

0

1

0

false

0

0.117647

0

0.117647

0

null

0

null

0

1

0

724b92184d8f2e9819e55008805cce856be796bd

4,012

py

Python

learnware/algorithm/anomaly_detect/iforest.py

marvinren/aiops_gaussian_learnware

47683546d6648a38bb71988c33f959cf7308376f

[ "Apache-2.0" ]

null

learnware/algorithm/anomaly_detect/iforest.py

marvinren/aiops_gaussian_learnware

47683546d6648a38bb71988c33f959cf7308376f

[ "Apache-2.0" ]

null

learnware/algorithm/anomaly_detect/iforest.py

marvinren/aiops_gaussian_learnware

47683546d6648a38bb71988c33f959cf7308376f

[ "Apache-2.0" ]

null

import numpy as np from scipy.stats import binom from sklearn.ensemble import IsolationForest from sklearn.preprocessing import MinMaxScaler from scipy.special import erf from learnware.algorithm.anomaly_detect.base import BaseAnomalyDetect class iForest(BaseAnomalyDetect): def __init__(self, n_estimators=100, ...

37.148148

79

0.598704

438

4,012

5.242009

0.257991

0.067944

0.070557

0.027439

0.110192

0.061847

0

0.007348

0.321535

4,012

107

80

37.495327

0.83615

0.03016

0

0.094118

0

0.027013

0

1

0.082353

false

0

0.070588

0

0.258824

0

null

0

null

0

1

0

7252008c26b1662083a1400694c806c34e33ed67

910

py

Python

graviteeio_cli/lint/functions/length.py

gravitee-io/gravitee-cli

8e3bf9f2c0c2873e0f6e67f8fcaf0d3b6c44b3ca

[ "Apache-2.0" ]

12

2019-05-29T20:06:01.000Z

2020-10-07T07:40:27.000Z

graviteeio_cli/lint/functions/length.py

gravitee-io/graviteeio-cli

0e0069b00ce40813efc7d40142a6dc4b4ec7a261

[ "Apache-2.0" ]

41

2019-11-04T18:18:18.000Z

2021-04-22T16:12:51.000Z

graviteeio_cli/lint/functions/length.py

gravitee-io/gravitee-cli

8e3bf9f2c0c2873e0f6e67f8fcaf0d3b6c44b3ca

[ "Apache-2.0" ]

6

2019-06-18T04:27:49.000Z

2021-06-02T17:52:24.000Z

from graviteeio_cli.lint.types.function_result import FunctionResult def length(value, **kwargs): """Count the length of a string an or array, the number of properties in an object, or a numeric value, and define minimum and/or maximum values.""" min = None max = None if "min" in kwargs and type(kwar...

26

152

0.597802

121

910

4.438017

0.380165

0.102421

0.040968

0.055866

0.078212

0

0.00157

0.3

910

34

153

26.764706

0.841444

0.156044

0

0.083333

0

0.065617

0

1

0.041667

false

0

0.041667

0

0.125

0

null

0

null

0

1

0

a0c69fd6e11617fc5f9eb586f7c2029856d0877b

2,399

py

Python

Technical_Indicators/rainbow_charts.py

vhn0912/Finance

39cf49d4d778d322537531cee4ce3981cc9951f9

[ "MIT" ]

441

2020-04-22T02:21:19.000Z

2022-03-29T15:00:24.000Z

Technical_Indicators/rainbow_charts.py

happydasch/Finance

4f6c5ea8f60fb0dc3b965ffb9628df83c2ecef35

[ "MIT" ]

5

2020-07-06T15:19:58.000Z

2021-07-23T18:32:29.000Z

Technical_Indicators/rainbow_charts.py

happydasch/Finance

4f6c5ea8f60fb0dc3b965ffb9628df83c2ecef35

[ "MIT" ]

111

2020-04-21T11:40:39.000Z

2022-03-20T07:26:17.000Z

import numpy as np import pandas as pd import matplotlib.pyplot as plt import warnings warnings.filterwarnings("ignore") import yfinance as yf yf.pdr_override() import datetime as dt # input symbol = 'AAPL' start = dt.date.today() - dt.timedelta(days = 365*2) end = dt.date.today() # Read data df = yf.download(symbol...

36.348485

111

0.667361

370

2,399

4.286486

0.383784

0.035309

0.052963

0.061791

0.090794

0

0.021179

0.094623

2,399

66

112

36.348485

0.709024

0.114631

0

0.037037

0

0.172577

0

1

0

false

0

0.148148

0

0.148148

0

null

0

null

0

1

0

a0c8d55fb37c691da19d42d22717e7769ad0fbbf

1,670

py

Python

UpWork_Projects/pdf_downloader.py

SurendraTamang/Web-Scrapping

2bb60cce9010b4b68f5c11bf295940832bb5df50

[ "MIT" ]

null

UpWork_Projects/pdf_downloader.py

SurendraTamang/Web-Scrapping

2bb60cce9010b4b68f5c11bf295940832bb5df50

[ "MIT" ]

null

UpWork_Projects/pdf_downloader.py

SurendraTamang/Web-Scrapping

2bb60cce9010b4b68f5c11bf295940832bb5df50

[ "MIT" ]

1

2022-01-18T17:15:51.000Z

import requests from urllib.request import urlopen from urllib.request import urlretrieve import cgi import os.path def retrive_file_name(url): #url = 'https://material.ibear.pt/BTHorarios2019/FileGet.aspx?FileId=5601' remotefile = urlopen(url) blah = remotefile.info()['Content-Disposition'] _, params ...

33.4

108

0.552096

190

1,670

4.7

0.394737

0.06271

0.038074

0.051512

0.385218

0.297872

0.192609

0.078387

0

0.042611

0.339521

1,670

50

109

33.4

0.766999

0.059281

0

0.121951

0

0.172611

0.103822

0

1

0.04878

false

0.02439

0.121951

0

0.195122

0.073171

0

null

0

null

0

1

0

a0cab7a3ae269edaac7fa1a7d902a54bd96a752d

13,282

py

Python

backend/app/vta/texdf/tex_df.py

megagonlabs/leam

f19830d4d6935bece7d163abbc533cfb4bc2e729

[ "Apache-2.0" ]

7

2020-09-14T07:03:51.000Z

2022-01-13T10:11:53.000Z

backend/app/vta/texdf/tex_df.py

megagonlabs/leam

f19830d4d6935bece7d163abbc533cfb4bc2e729

[ "Apache-2.0" ]

null

backend/app/vta/texdf/tex_df.py

megagonlabs/leam

f19830d4d6935bece7d163abbc533cfb4bc2e729

[ "Apache-2.0" ]

1

2020-09-07T22:26:27.000Z

import spacy import json, os import dill as pickle import numpy as np import pandas as pd from sklearn.feature_extraction.text import TfidfVectorizer from sqlalchemy import create_engine, select, MetaData, Table, Column, Integer, String from sqlalchemy.ext.declarative import declarative_base from sqlalchemy.orm import ...

37.840456

100

0.595844

1,732

13,282

4.331409

0.155889

0.019595

0.032925

0.032258

0.302319

0.213676

0.157691

0.141296

0.132765

0.11317

0

0.00302

0.301837

13,282

350

101

37.948571

0.805996

0.097651

0

0.22807

0

0.045994

0

0.002857

0.003509

1

0.119298

false

0.003509

0.05614

0.038596

0.266667

0.02807

0

null

0

null

0

1

0

a0ceec8ec85ef44ddb9d9cd56199a36790b171fc

4,171

py

Python

tests/contour_classifiers/test_randomforest.py

yamathcy/motif

3f43568e59f0879fbab5ef278e9e687b7cac3dd6

[ "MIT" ]

21

2016-08-22T22:00:49.000Z

2020-03-29T04:15:19.000Z

tests/contour_classifiers/test_randomforest.py

yamathcy/motif

3f43568e59f0879fbab5ef278e9e687b7cac3dd6

[ "MIT" ]

22

2016-08-28T01:07:08.000Z

2018-02-07T14:38:26.000Z

tests/contour_classifiers/test_randomforest.py

yamathcy/motif

3f43568e59f0879fbab5ef278e9e687b7cac3dd6

[ "MIT" ]

3

2017-01-12T10:04:27.000Z

2022-01-06T13:25:48.000Z

"""Test for motif.classify.mvgaussian """ from __future__ import print_function import unittest import numpy as np from motif.contour_classifiers import random_forest def array_equal(array1, array2): return np.all(np.isclose(array1, array2)) class TestRandomForest(unittest.TestCase): def setUp(self): ...

32.585938

76

0.529369

610

4,171

3.52623

0.142623

0.041841

0.034868

0.024175

0.470944

0.379823

0.27894

0.208275

0.166434

0

0.083786

0.293215

4,171

127

77

32.84252

0.645862

0.008152

0

0.327103

0

0.048668

0

0.17757

1

0.130841

false

0

0.037383

0.009346

0.186916

0.009346

0

null

0

null

0

1

0

a0cf8257e1729da63a070f7fb21ed2b3279418e3

7,365

py

Python

awsenv/profile.py

KensoDev/awsenv

4bf759106d2e0d79221d0ca9188ed7686e119b2c

[ "Apache-2.0" ]

6

2016-09-11T08:39:50.000Z

2018-10-22T13:41:34.000Z

awsenv/profile.py

KensoDev/awsenv

4bf759106d2e0d79221d0ca9188ed7686e119b2c

[ "Apache-2.0" ]

1

2017-01-09T23:58:20.000Z

awsenv/profile.py

KensoDev/awsenv

4bf759106d2e0d79221d0ca9188ed7686e119b2c

[ "Apache-2.0" ]

5

2017-01-09T23:26:12.000Z

2021-09-08T09:35:59.000Z

""" Profile-aware session wrapper. """ from os import environ from botocore.exceptions import ProfileNotFound from botocore.session import Session from awsenv.cache import CachedSession def get_default_profile_name(): """ Get the default profile name from the environment. """ return environ.get("AWS...

31.075949

91

0.60611

824

7,365

5.154126

0.167476

0.065693

0.031081

0.024723

0.310572

0.267954

0.183188

0.155875

0.110431

0.078879

0

0.001583

0.313917

7,365

236

92

31.207627

0.838908

0.166056

0

0.223684

0

0.07171

0.014718

0

1

0.138158

false

0

0.026316

0.072368

0.309211

0

null

0

null

0

1

0

a0d0d288568d1ad31c787944a756b68fdcfc394c

13,358

py

Python

cail/algo/twoiwil.py

Stanford-ILIAD/Confidence-Aware-Imitation-Learning

1d8af0e4ab87a025885133a2384d5a937329b2f5

[ "MIT" ]

16

2021-10-30T15:19:37.000Z

2022-03-23T12:57:49.000Z

cail/algo/twoiwil.py

syzhang092218-source/Confidence-Aware-Imitation-Learning

1d8af0e4ab87a025885133a2384d5a937329b2f5

[ "MIT" ]

null

cail/algo/twoiwil.py

syzhang092218-source/Confidence-Aware-Imitation-Learning

1d8af0e4ab87a025885133a2384d5a937329b2f5

[ "MIT" ]

2

2021-11-29T11:28:16.000Z

2022-03-06T14:12:47.000Z

import torch import os import torch.nn.functional as F import numpy as np import copy from torch import nn from torch.optim import Adam from torch.autograd import Variable from torch.utils.tensorboard import SummaryWriter from tqdm import tqdm from typing import Tuple from .ppo import PPO, PPOExpert from ...

34.786458

105

0.586166

1,593

13,358

4.700565

0.170747

0.041132

0.019097

0.012019

0.246528

0.148237

0.092815

0.076255

0.051549

0

0.012369

0.328193

13,358

383

106

34.877285

0.82193

0.269651

0

0.104712

0

0.023275

0.005819

0

1

0.036649

false

0

0.078534

0

0.13089

0.010471

0

null

0

null

0

1

0

a0d0f0826bf05af84c68e2d12e3788dc07ebfcd6

7,327

py

Python

data/generation_scripts/MantaFlow/scripts3D/compactifyData.py

tum-pbs/VOLSIM

795a31c813bf072eb88289126d7abd9fba8b0e54

[ "MIT" ]

7

2022-01-28T09:40:15.000Z

2022-03-07T01:52:00.000Z

data/generation_scripts/MantaFlow/scripts3D/compactifyData.py

tum-pbs/VOLSIM

795a31c813bf072eb88289126d7abd9fba8b0e54

[ "MIT" ]

null

data/generation_scripts/MantaFlow/scripts3D/compactifyData.py

tum-pbs/VOLSIM

795a31c813bf072eb88289126d7abd9fba8b0e54

[ "MIT" ]

1

2022-03-14T22:08:47.000Z

import numpy as np import os, shutil import imageio baseDir = "data/train_verbose" outDir = "data/train" #baseDir = "data/test_verbose" #outDir = "data/test" outDirVidCopy = "data/videos" combineVidsAll = {"smoke" : ["densMean", "densSlice", "velMean", "velSlice", "presMean", "presSlice"], "liquid": ["...

43.613095

153

0.512079

744

7,327

5.017473

0.240591

0.051433

0.050897

0.026788

0.301902

0.235735

0.128583

0.069113

0.047683

0

0.020792

0.376416

7,327

167

154

43.874252

0.796236

0.123106

0

0.125

0

0.071607

0

0.026786

1

0

false

0

0.026786

0

0.026786

0

null

0

null

0

1

0

a0d159678318f4de46108d8e3c19f4a355d8744f

14,238

py

Python

qiskit/aqua/operators/base_operator.py

Sahar2/qiskit-aqua

a228fbe6b9613cff43e47796a7e4843deba2b051

[ "Apache-2.0" ]

null

qiskit/aqua/operators/base_operator.py

Sahar2/qiskit-aqua

a228fbe6b9613cff43e47796a7e4843deba2b051

[ "Apache-2.0" ]

null

qiskit/aqua/operators/base_operator.py

Sahar2/qiskit-aqua

a228fbe6b9613cff43e47796a7e4843deba2b051

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # This code is part of Qiskit. # # (C) Copyright IBM 2019. # # This code is licensed under the Apache License, Version 2.0. You may # obtain a copy of this license in the LICENSE.txt file in the root directory # of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. # # Any modif...

44.633229

122

0.666877

1,626

14,238

5.613161

0.181427

0.041306

0.066725

0.058398

0.494467

0.426646

0.401227

0.391585

0.331872

0.318396

0

0.007989

0.270333

14,238

318

123

44.773585

0.870536

0.069883

0

0.409283

0

0.287783

0.052892

0

0.003145

0

1

0.172996

false

0

0.097046

0.012658

0.379747

0.021097

0

null

0

null

0

1

0

a0d37d7e9574c755f53a5c193de3f30cb81ee61a

4,447

py

Python

DataAnalysis/utils.py

Timlo512/AnomalyStockDetection

29f9aaef14f1d9823980d8022cdce1f7f6310813

[ "MIT" ]

2

2020-12-19T05:24:29.000Z

2021-05-15T19:35:40.000Z

DataAnalysis/utils.py

Timlo512/AnomalyStockDetection

29f9aaef14f1d9823980d8022cdce1f7f6310813

[ "MIT" ]

null

DataAnalysis/utils.py

Timlo512/AnomalyStockDetection

29f9aaef14f1d9823980d8022cdce1f7f6310813

[ "MIT" ]

5

2020-11-21T02:25:13.000Z

2022-01-31T12:46:02.000Z

import pandas as pd import numpy as np from sklearn.metrics import confusion_matrix import re def convert_data_sparse_matrix(df, row_label = 'stock_code', col_label = 'name_of_ccass_participant', value_label = 'shareholding'): """ Pivot table """ try: # Prepare zero matrix row_dim =...

32.698529

132

0.614796

641

4,447

4.098284

0.268331

0.027408

0.015226

0.020556

0.098211

0.075371

0.062429

0.03426

0

0.011448

0.273218

4,447

135

133

32.940741

0.801361

0.190241

0

0.192308

0

0.084583

0.007072

0

1

0.102564

false

0

0.051282

0.025641

0.307692

0.089744

0

null

0

null

0

1

0

a0d5155e320c1b2b6704a06d42d9b58088cb485b

1,429

py

Python

scripts/prepare_upload_files.py

MaayanLab/scAVI

7f3f83657d749520243535581db1080075e48aa5

[ "Apache-2.0" ]

3

2020-01-23T08:48:33.000Z

2021-07-21T02:42:28.000Z

scripts/prepare_upload_files.py

MaayanLab/scAVI

7f3f83657d749520243535581db1080075e48aa5

[ "Apache-2.0" ]

21

2019-10-25T15:38:37.000Z

2022-01-27T16:04:04.000Z

scripts/prepare_upload_files.py

MaayanLab/scAVI

7f3f83657d749520243535581db1080075e48aa5

[ "Apache-2.0" ]

1

2019-10-24T18:15:26.000Z

''' Prepare some files to test the upload functionality. ''' import sys sys.path.append('../') from database import * from pymongo import MongoClient mongo = MongoClient(MONGOURI) db = mongo['SCV'] coll = db['dataset'] from gene_expression import * expr_df, meta_doc = load_read_counts_and_meta(organism='mouse', gse...

30.404255

105

0.751575

233

1,429

4.313305

0.399142

0.095522

0.119403

0.107463

0.271642

0.157214

0.081592

0

0.014019

0.10147

1,429

46

106

31.065217

0.768692

0.131561

0

0.295844

0.220864

0

1

0

false

0

0.16

0

0.16

0

null

0

null

0

1

0