xszheng2020/the_stack_dedup_python_hits_0 · Datasets at Hugging Face

c173709b35ae18fdb3b908927a799df5ea385960

1,353

py

Python

backend/app/__init__.py

mushcatshiro/flask-template

27a1558e831ef2c14622c320a5e6fd6991b94bcf

[ "MIT" ]

null

backend/app/__init__.py

mushcatshiro/flask-template

27a1558e831ef2c14622c320a5e6fd6991b94bcf

[ "MIT" ]

null

backend/app/__init__.py

mushcatshiro/flask-template

27a1558e831ef2c14622c320a5e6fd6991b94bcf

[ "MIT" ]

null

from flask import Flask, jsonify from flask_marshmallow import Marshmallow from flask_sqlalchemy import SQLAlchemy from flask_track_usage import TrackUsage from backend.config import config, Config from celery import Celery db = SQLAlchemy() ma = Marshmallow() celery = Celery( __name__, broker=Config.CELERY_B...

24.160714

63

0.702882

182

1,353

5.010989

0.296703

0.039474

0.04386

0.041667

0

0.017304

0.18847

1,353

55

64

24.6

0.813297

0.050998

0

0.046058

0

1

0.128205

false

0

0.205128

0.051282

0.435897

0.102564

0

null

0

null

0

1

0

c1743e483c31f6d867722bc46b72b5ece150db84

1,561

py

Python

pineboolib/fllegacy/tests/test_settings.py

juanjosepablos/pineboo

f6ce515aec6e0139821bb9c1d62536d9fb50dae4

[ "MIT" ]

null

pineboolib/fllegacy/tests/test_settings.py

juanjosepablos/pineboo

f6ce515aec6e0139821bb9c1d62536d9fb50dae4

[ "MIT" ]

1

2017-10-30T22:00:48.000Z

2017-11-11T19:34:32.000Z

pineboolib/fllegacy/tests/test_settings.py

juanjosepablos/pineboo

f6ce515aec6e0139821bb9c1d62536d9fb50dae4

[ "MIT" ]

1

2017-10-30T20:16:38.000Z

"""Test_flutil module.""" import unittest from pineboolib.fllegacy import flsettings class TestSettings(unittest.TestCase): def test_settings(self) -> None: """Test read functions.""" setting = flsettings.FLSettings() setting.writeEntryList("test_uno", [""]) setting.writeEntryLi...

43.361111

85

0.675208

171

1,561

5.953216

0.28655

0.133595

0.165029

0.103143

0.486248

0.037328

0

0.023256

0.173607

1,561

35

86

44.6

0.765891

0.025625

0

0.188742

0

0.37037

1

0.037037

false

0

0.074074

0

0.148148

0

null

0

null

0

1

0

c174bae4ae48802d0df61029ad46df721459ecfb

1,109

py

Python

setup.py

nat236919/pyhighstakes

dbe7dcf81febd361b6b0084788d9f176e1008ee7

[ "MIT" ]

null

setup.py

nat236919/pyhighstakes

dbe7dcf81febd361b6b0084788d9f176e1008ee7

[ "MIT" ]

2

2020-01-29T02:03:55.000Z

2020-01-29T02:05:17.000Z

setup.py

nat236919/pyhighstakes

dbe7dcf81febd361b6b0084788d9f176e1008ee7

[ "MIT" ]

1

2020-01-28T14:14:45.000Z

""" FILE: setup.py DESCRIPTION: Set up PyPI as a Python Library DATE: 27-Jan-2020 """ import setuptools with open('README.md') as f: README = f.read() setuptools.setup( author='Nuttaphat Arunoprayoch', author_email='nat236919@gmail.com', name='pyhighstakes', license='MIT', description='PyHigh...

30.805556

74

0.660956

121

1,109

5.983471

0.669421

0.041436

0.066298

0.09116

0

0.028377

0.205591

1,109

36

75

30.805556

0.793417

0.138864

0

0.493136

0

1

0

false

0

0.038462

0

0.038462

0

null

0

null

0

1

0

c1771dbe757ef772ec9d522997c65b45e5bb1019

14,552

py

Python

kernel/rnn.py

diwu1990/UnaryComputingSim

1a4746b7bcc2fcde5144a8b42fc94e6f5cd82b8e

[ "MIT" ]

1

2020-06-24T09:54:06.000Z

kernel/rnn.py

RuokaiYin/UnarySim

343ff9abf356a63d526b1df8eb946ad528690a27

[ "MIT" ]

null

kernel/rnn.py

RuokaiYin/UnarySim

343ff9abf356a63d526b1df8eb946ad528690a27

[ "MIT" ]

null

import math import torch import copy from torch import Tensor import torch.nn as nn import torch.nn.functional as F from UnarySim.kernel import FSUAdd from UnarySim.kernel import FSUMul from UnarySim.kernel import FSULinear from torch.cuda.amp import autocast from typing import List, Tuple, Optional, overload, Union fr...

42.17971

169

0.604178

1,945

14,552

4.3491

0.117738

0.04043

0.023052

0.023643

0.72148

0.661662

0.619459

0.593924

0.556803

0.520156

0

0.005937

0.270753

14,552

344

170

42.302326

0.79118

0.12122

0

0.494253

0

0.065945

0

0.019157

1

0.045977

false

0

0.057471

0

0.1341

0

null

0

null

0

1

0

c17afdf4c22417a1397dcd7e27fcec9b2af92696

3,505

py

Python

bentosign/bentosign_object.py

BentoSign/python_bentosign

f1f00b1c84af353c3945e1ed09c8460ee836d1be

[ "MIT" ]

null

bentosign/bentosign_object.py

BentoSign/python_bentosign

f1f00b1c84af353c3945e1ed09c8460ee836d1be

[ "MIT" ]

null

bentosign/bentosign_object.py

BentoSign/python_bentosign

f1f00b1c84af353c3945e1ed09c8460ee836d1be

[ "MIT" ]

null

import json import requests from .bentosign_errors import BentoSignError # An BentoSignObject is a dictionary where ``object.key=value`` is a shortcut for ``object[key]=value`` class BentoSignObject(dict): def __init__(self): super(BentoSignObject, self).__init__() # Define __getattr__, __setattr__...

31.294643

103

0.607133

405

3,505

5.012346

0.212346

0.023645

0.027094

0.027586

0.538424

0.415764

0.344828

0.256158

0.180296

0.133005

0

0.004821

0.289872

3,505

111

104

31.576577

0.810767

0.136662

0

0.461538

0

0.056754

0

1

0.153846

false

0

0.038462

0.012821

0.294872

0

null

0

null

0

1

0

c17d194b756e7fa590502bf845583e715519cb01

460

py

Python

app/career/serializers/weekly_business.py

TIHLDE/Lepton

60ec0793381f1c1b222f305586e8c2d4345fb566

[ "MIT" ]

7

2021-03-04T18:49:12.000Z

2021-03-08T18:25:51.000Z

app/career/serializers/weekly_business.py

TIHLDE/Lepton

60ec0793381f1c1b222f305586e8c2d4345fb566

[ "MIT" ]

251

2021-03-04T19:19:14.000Z

2022-03-31T14:47:53.000Z

app/career/serializers/weekly_business.py

tihlde/Lepton

5cab3522c421b76373a5c25f49267cfaef7b826a

[ "MIT" ]

3

2021-10-05T19:03:04.000Z

2022-02-25T13:32:09.000Z

from app.career.models import WeeklyBusiness from app.common.serializers import BaseModelSerializer class WeeklyBusinessSerializer(BaseModelSerializer): class Meta: model = WeeklyBusiness fields = ( "id", "created_at", "updated_at", "image", ...

23

54

0.523913

34

460

6.970588

0.764706

0.059072

0

0.384783

460

19

55

24.210526

0.837456

0

0.132609

0

1

0

false

0

0.117647

0

0.235294

0

null

0

null

0

1

0

c17d66b90ac3507c48eac335d807b7e734538bbd

3,401

py

Python

emovix-twitter-detectlang.py

eMOVIX/emovix-twitter-detectlang

d4ad5a9845b9b98fec70490c81481390a24a9af1

[ "Apache-2.0" ]

null

emovix-twitter-detectlang.py

eMOVIX/emovix-twitter-detectlang

d4ad5a9845b9b98fec70490c81481390a24a9af1

[ "Apache-2.0" ]

null

emovix-twitter-detectlang.py

eMOVIX/emovix-twitter-detectlang

d4ad5a9845b9b98fec70490c81481390a24a9af1

[ "Apache-2.0" ]

null

__author__ = 'Jordi Vilaplana' from pymongo import MongoClient import detectlanguage import json import logging import time logging.basicConfig( filename='emovix_twitter_detectlang.log', level=logging.WARNING, format='%(asctime)s %(name)-12s %(levelname)-8s %(message)s', datefmt='%d-%m-%y %H:%M') # C...

36.569892

128

0.527786

292

3,401

5.931507

0.390411

0.057159

0.055427

0.166282

0.12933

0.095843

0

0.011671

0.370185

3,401

92

129

36.967391

0.796919

0.019994

0

0.180556

0

0.167318

0.032142

0

1

0

false

0

0.069444

0

0.069444

0

null

0

null

0

1

0

c17d75beb39c3d0157047d3515d20edb8d4e90a4

1,178

py

Python

src/azure-cli/azure/cli/command_modules/storage/operations/queue.py

YuanyuanNi/azure-cli

63844964374858bfacd209bfe1b69eb456bd64ca

[ "MIT" ]

3,287

2016-07-26T17:34:33.000Z

2022-03-31T09:52:13.000Z

src/azure-cli/azure/cli/command_modules/storage/operations/queue.py

YuanyuanNi/azure-cli

63844964374858bfacd209bfe1b69eb456bd64ca

[ "MIT" ]

19,206

2016-07-26T07:04:42.000Z

2022-03-31T23:57:09.000Z

src/azure-cli/azure/cli/command_modules/storage/operations/queue.py

YuanyuanNi/azure-cli

63844964374858bfacd209bfe1b69eb456bd64ca

[ "MIT" ]

2,575

2016-07-26T06:44:40.000Z

2022-03-31T22:56:06.000Z

# -------------------------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # --------------------------------------------------------------------...

42.071429

94

0.60017

121

1,178

5.570248

0.487603

0.074184

0.097923

0

0.00103

0.175722

1,178

27

95

43.62963

0.6931

0.285229

0

0.026284

0

1

0.058824

false

0

0.117647

0

0.235294

0

null

0

null

0

1

0

c17f0a03fa3b18a4b271f3edc88481c6be2c623b

745

py

Python

arrays/sorting/selection_sort.py

AyoubEssrifi/Data-Structures-Algorithms

d903bccba46cd8f2a35728f47cdfc836d5dfdf1a

[ "MIT" ]

null

arrays/sorting/selection_sort.py

AyoubEssrifi/Data-Structures-Algorithms

d903bccba46cd8f2a35728f47cdfc836d5dfdf1a

[ "MIT" ]

null

arrays/sorting/selection_sort.py

AyoubEssrifi/Data-Structures-Algorithms

d903bccba46cd8f2a35728f47cdfc836d5dfdf1a

[ "MIT" ]

null

import numpy as np import math def selection_sort(arr): """ Performs a selection sort algorithm - Time complexity: O(n²) Args: arr (list): List to sort Returns: (list): Sorted list """ i = 0 while i < len(arr): j = i min = math.inf while j < len...

20.135135

44

0.507383

102

745

3.617647

0.421569

0.105691

0.086721

0

0.030435

0.38255

745

36

45

20.694444

0.771739

0.186577

0

0.008772

0

1

0.043478

false

0

0.086957

0

0.173913

0.043478

0

null

0

null

0

1

0

c17f1a7c03757d8295e2618ab5615740ad1e5ea5

6,819

py

Python

dumb_cup/dc.py

Ladvien/ezsp32_upython_env

5bdec9e1d2eabb117941a5ce0e74570757e87740

[ "MIT" ]

null

dumb_cup/dc.py

Ladvien/ezsp32_upython_env

5bdec9e1d2eabb117941a5ce0e74570757e87740

[ "MIT" ]

null

dumb_cup/dc.py

Ladvien/ezsp32_upython_env

5bdec9e1d2eabb117941a5ce0e74570757e87740

[ "MIT" ]

1

2020-05-27T13:18:31.000Z

import time from machine import I2C, Pin, Timer from dumb_cup.v53l0x import VL53L0X from dumb_cup.adxl345 import ADXL345 from dumb_cup.spirit_level import SpiritLevel ############### # Constants ############### OZ_FULL = const(16) INIT_SAMPES = const(50) NUM_SAMPLES = const(15) RND_PLCS ...

25.349442

90

0.592022

964

6,819

3.950207

0.267635

0.015756

0.023109

0.014706

0.200105

0.162553

0.13813

0.127101

0.110819

0.059874

0

0.033986

0.266461

6,819

268

91

25.44403

0.727309

0.171579

0

0.210191

0

0.046664

0

1

0.076433

false

0.006369

0.057325

0.006369

0.165605

0.063694

0

null

0

null

0

1

0

c17f98ab5a946504638ab093524b788e3de36f90

2,042

py

Python

nsh/asteroids/views.py

kowabunga314/NSH

01dfc56d88e9b3b96e5bed61be24dd1e5080abbc

[ "MIT" ]

null

nsh/asteroids/views.py

kowabunga314/NSH

01dfc56d88e9b3b96e5bed61be24dd1e5080abbc

[ "MIT" ]

1

2021-08-31T23:10:01.000Z

nsh/asteroids/views.py

kowabunga314/NSH

01dfc56d88e9b3b96e5bed61be24dd1e5080abbc

[ "MIT" ]

null

from requests import api from rest_framework.decorators import api_view from rest_framework.response import Response from asteroids.api import AsteroidApi from asteroids.serializers import ApproachSerializer from clients.neows import NeoWs from django.shortcuts import render @api_view(['GET']) def get_closest_approac...

31.415385

92

0.760039

243

2,042

6

0.144033

0.106996

0.057613

0.035665

0.660494

0.558985

0.464335

0.423182

0.335391

0

0.15475

2,042

64

93

31.90625

0.844728

0.134672

0

0.297297

0

0.034208

0

1

0.108108

false

0

0.189189

0

0.405405

0

null

0

null

0

1

0

c1814a99cc81b34203c9bfed3dc84d50f6f96bd3

1,313

py

Python

shell_test.py

zignig/cqparts_bucket

9707b0948a9dd1ed514e03c291a3b96fddc4a22d

[ "Apache-2.0" ]

10

2018-09-18T08:09:02.000Z

2022-03-18T06:24:22.000Z

shell_test.py

zignig/cqparts-bucket

9707b0948a9dd1ed514e03c291a3b96fddc4a22d

[ "Apache-2.0" ]

1

2018-08-09T01:57:32.000Z

shell_test.py

zignig/cqparts-bucket

9707b0948a9dd1ed514e03c291a3b96fddc4a22d

[ "Apache-2.0" ]

1

2018-12-07T20:14:04.000Z

""" Shell test # 2019 Simon Kirkby obeygiantrobot@gmail.com """ import cadquery as cq import cqparts from cqparts.params import PositiveFloat, Int from cqparts.display import render_props from cqparts.constraint import Mate from cqparts.utils.geometry import CoordSystem from cqparts.search import register # base s...

29.840909

81

0.611577

187

1,313

4.245989

0.40107

0.02267

0.020151

0.235516

0.187657

0.085642

0

0.038268

0.243717

1,313

43

82

30.534884

0.761329

0.055598

0

0.037338

0

1

0.033333

false

0

0.266667

0

0.466667

0

null

0

null

0

1

0

c187d975a2a0abd411797bab631c97d079d986a0

3,250

py

Python

SCOTUS/SVE/infer_sco50case.py

JeffT13/LegalUISRNN

a5efdba091746a0e04da9faccdad74b3b4cec74f

[ "Apache-2.0" ]

1

2020-11-17T02:33:28.000Z

SCOTUS/SVE/infer_sco50case.py

JeffT13/LegalUISRNN

a5efdba091746a0e04da9faccdad74b3b4cec74f

[ "Apache-2.0" ]

null

SCOTUS/SVE/infer_sco50case.py

JeffT13/LegalUISRNN

a5efdba091746a0e04da9faccdad74b3b4cec74f

[ "Apache-2.0" ]

null

''' SCOTUS d-vec UISRNN inference''' import sys sys.path.append("./LegalUISRNN") import numpy as np import os, csv import torch import uisrnn case_path = '/scratch/jt2565/sco50/sco50wav_proc_case/' total_cases = (len(os.listdir(case_path))/2) train_cases = (total_cases//10)*9 print("# of training:", train_cases) prin...

28.761062

74

0.626462

448

3,250

4.339286

0.330357

0.041667

0.057613

0.024691

0.092593

0.030864

0

0.020098

0.249846

3,250

113

75

28.761062

0.777276

0.134154

0

0.106667

0

0.114725

0.030379

0

1

0

false

0

0.066667

0

0.066667

0.186667

0

null

0

null

0

1

0

c1883470a70c6ef2fb0240606651d61fa3e8412b

4,143

py

Python

evaluation/evaluation_template.py

MaviccPRP/Anonymizer

3d75ed3e97e260b6ded7e188eb3d58d749844e36

[ "MIT" ]

null

evaluation/evaluation_template.py

MaviccPRP/Anonymizer

3d75ed3e97e260b6ded7e188eb3d58d749844e36

[ "MIT" ]

2

2019-06-14T19:55:39.000Z

2019-06-14T20:16:11.000Z

evaluation/evaluation_template.py

MaviccPRP/Anonymizer

3d75ed3e97e260b6ded7e188eb3d58d749844e36

[ "MIT" ]

1

2020-03-13T14:32:31.000Z

# Evaluation script template for de-identification tool https://github.com/dieterich-lab/Anonymize from sklearn.metrics import classification_report from sklearn.metrics import confusion_matrix from sklearn.metrics import roc_curve, auc from sklearn.metrics import matthews_corrcoef from sklearn.metrics import fb...

30.688889

99

0.668839

670

4,143

3.995522

0.2

0.042959

0.040344

0.03362

0.673889

0.649981

0.631304

0.612626

0.581248

0

0.02329

0.18127

4,143

135

100

30.688889

0.76592

0.129858

0

0.571429

0

0.106678

0

1

0

false

0

0.132653

0

0.132653

0.22449

0

null

0

null

0

1

0

c1893382322e8407dce4b1d084d353bc65babc08

880

py

Python

tests/utils.py

CrisKP/gyst

c3a86564bcf278d2b6a177b20840d50f71dd63b1

[ "MIT" ]

12

2017-09-10T01:43:42.000Z

2020-09-20T01:17:20.000Z

functional_tests/utils.py

HelloMelanieC/FiveUp

ab97d311f163b09146fe330e4360d8e75d769f95

[ "MIT" ]

22

2016-12-26T21:46:10.000Z

2022-02-10T08:01:52.000Z

tests/utils.py

CrisKP/gyst

c3a86564bcf278d2b6a177b20840d50f71dd63b1

[ "MIT" ]

4

2017-08-24T16:01:37.000Z

2019-02-14T23:50:17.000Z

from contextlib import contextmanager from django.contrib.staticfiles.testing import StaticLiveServerTestCase from selenium.webdriver.firefox.webdriver import WebDriver from selenium.webdriver.support.expected_conditions import staleness_of from selenium.webdriver.support.ui import WebDriverWait class SeleniumTestC...

32.592593

74

0.761364

93

880

7.064516

0.494624

0.054795

0.09589

0.085236

0

0.005413

0.160227

880

26

75

33.846154

0.883627

0

0.1

0

0.004545

0

1

0.15

false

0

0.25

0

0.45

0

null

0

null

0

1

0

c18b1f9e7702a88aea07ec02c274a084bf5ac802

411

py

Python

dictsort.py

robertbyers1111/python

e87558f2432f0a4a86f17c47c6b19e1345625b83

[ "MIT" ]

null

dictsort.py

robertbyers1111/python

e87558f2432f0a4a86f17c47c6b19e1345625b83

[ "MIT" ]

null

dictsort.py

robertbyers1111/python

e87558f2432f0a4a86f17c47c6b19e1345625b83

[ "MIT" ]

null

#!/usr/bin/python3 # Iterate through a list of dictionaries, sorted by a field from the dictionaries response = [ {'a':1, 'b':2222, 'LastModified':1320, 'c':33}, {'a':11, 'LastModified':1229, 'b':222, 'c':3}, {'LastModified':1400,'a':111, 'b':2, 'c':3333}, {'a':1111, 'b':22, 'LastModified':180, 'c':333} ] respon...

25.6875

81

0.627737

64

411

4.03125

0.640625

0

0.129577

0.136253

411

15

82

27.4

0.597183

0.23601

0

0.233974

0

1

0

false

0

0.111111

0

null

0

null

0

1

0

c18c4a2ad72355e5338af8e668969552070e526d

617

py

Python

setup.py

urbangrammarai/graphics

69bf5976a11c783fc8a27f59ef57efefbbee6aa8

[ "BSD-3-Clause" ]

1

2021-05-30T07:41:23.000Z

setup.py

urbangrammarai/graphics

69bf5976a11c783fc8a27f59ef57efefbbee6aa8

[ "BSD-3-Clause" ]

2

2021-02-19T09:00:03.000Z

2021-10-16T18:59:05.000Z

setup.py

urbangrammarai/graphics

69bf5976a11c783fc8a27f59ef57efefbbee6aa8

[ "BSD-3-Clause" ]

null

import setuptools with open("README.md", "r", encoding="utf8") as fh: long_description = fh.read() setuptools.setup( name="urbangrammar_graphics", version="1.2.3", author="Martin Fleischmann", author_email="martin@martinfleischmann.net", python_requires=">=3.6", install_requires=["matplotl...

32.473684

70

0.719611

71

617

6.098592

0.774648

0.138568

0.08776

0.138568

0

0.011278

0.137763

617

18

71

34.277778

0.802632

0

0.36953

0.079417

0

1

0

false

0

0.0625

0

0.0625

0

null

0

null

0

1

0

c18d7f1c745b04cc0b216ca0de4f5614635c60a7

701

py

Python

Supervised Learning/Tilecoder.py

panchyo0/Reinforcement-Learning

b823d7900f020db154d0a61f3683e0cce29e3797

[ "MIT" ]

null

Supervised Learning/Tilecoder.py

panchyo0/Reinforcement-Learning

b823d7900f020db154d0a61f3683e0cce29e3797

[ "MIT" ]

null

Supervised Learning/Tilecoder.py

panchyo0/Reinforcement-Learning

b823d7900f020db154d0a61f3683e0cce29e3797

[ "MIT" ]

null

numTilings = 8 def tilecode(in1, in2, tileIndices): # write your tilecoder here (5 lines or so) for i in range(0,numTilings): col = (in1 + i*(0.6/numTilings)) // 0.6 row = (in2 + i*(0.6/numTilings)) // 0.6 tile = (i*121) + (11*col) + row tileIndices[i] = in...

26.961538

77

0.584879

89

701

4.606742

0.41573

0.058537

0.12439

0.121951

0.073171

0

0.087379

0.265335

701

25

78

28.04

0.708738

0.238231

0

0.065606

0

1

0.166667

false

0

0.25

0.166667

0

null

0

null

0

1

0

c18ed66e182091851798f0c36bc54305d626d02e

868

py

Python

diary/models.py

yheiblog/base_diary

01c2914019d6581c67071c7823a677e3c3e70950

[ "MIT" ]

null

diary/models.py

yheiblog/base_diary

01c2914019d6581c67071c7823a677e3c3e70950

[ "MIT" ]

16

2021-06-07T13:23:10.000Z

2022-02-05T23:18:43.000Z

diary/models.py

yheiblog/base_diary

01c2914019d6581c67071c7823a677e3c3e70950

[ "MIT" ]

1

2021-08-18T23:40:21.000Z

from django.db import models from django.db.models.deletion import CASCADE from django.contrib.auth.models import AbstractUser # Create your models here. class User(AbstractUser): pass class Post(models.Model): id = models.AutoField(primary_key=True) user = models.ForeignKey(User, on_delete=CASCADE) title =...

27.125

54

0.756912

116

868

5.491379

0.465517

0.050235

0.131868

0.156986

0.342229

0.254317

0

0.016

0.135945

868

31

55

28

0.833333

0.02765

0

0.166667

0

0.028504

0

1

0.041667

false

0.041667

0.125

0.041667

0.791667

0

null

0

null

0

1

0

c191569683e9fab2fbe1691bb0fddbc792e5b431

1,586

py

Python

main-bme280-thread.py

obviateio/micropython-esp32

9b10e1fd96a0ef80ddd6ced79d0ab1a941da4870

[ "MIT" ]

null

main-bme280-thread.py

obviateio/micropython-esp32

9b10e1fd96a0ef80ddd6ced79d0ab1a941da4870

[ "MIT" ]

null

main-bme280-thread.py

obviateio/micropython-esp32

9b10e1fd96a0ef80ddd6ced79d0ab1a941da4870

[ "MIT" ]

null

# Originally from: # https://github.com/loboris/MicroPython_ESP32_psRAM_LoBo/blob/master/MicroPython_BUILD/components/micropython/esp32/modules_examples/bme280.py import machine, _thread, time import micropython, gc import bme280 i2c=machine.I2C(scl=machine.Pin(22),sda=machine.Pin(21),speed=400000) bme=bme280.BME280(i2...

36.883721

153

0.594578

194

1,586

4.721649

0.443299

0.043668

0.062227

0.091703

0.133188

0

0.082051

0.262295

1,586

42

154

37.761905

0.700855

0.099622

0

0.166667

0

0.05614

0

1

0.055556

false

0

0.083333

0

0.166667

0

null

0

null

0

1

0

c191d5b851aa17930934dfcfc06e7f9a47195619

1,644

py

Python

preprocess_package/test_pdf2crops.py

Hupengyu/Paddle_learning

0ac1e2ad32e41ac87bbb19e4535a4bc253ca9b0f

[ "Apache-2.0" ]

1

2021-08-02T01:51:35.000Z

preprocess_package/test_pdf2crops.py

Hupengyu/Paddle_learning

0ac1e2ad32e41ac87bbb19e4535a4bc253ca9b0f

[ "Apache-2.0" ]

1

2021-11-03T08:58:30.000Z

preprocess_package/test_pdf2crops.py

Hupengyu/Paddle_learning

0ac1e2ad32e41ac87bbb19e4535a4bc253ca9b0f

[ "Apache-2.0" ]

null

from preprocess_package.pdf2pages import pdf2pages from preprocess_package.cut_images import cut_images_save import os import cv2 pwd = os.getcwd() image_index = 1 if __name__ == '__main__': imgs_file_path = './pictures/发票扫描图片' failed_imgs_file_path = './pictures/failed_images' single_img_file_path = './...

33.55102

113

0.631995

223

1,644

4.264574

0.255605

0.058885

0.068349

0.063091

0.256572

0.212408

0

0.014073

0.222019

1,644

48

114

34.25

0.729476

0.327859

0

0.086957

0

0.173469

0.102041

0

1

0

false

0

0.173913

0

0.173913

0

null

0

null

0

1

0

c192bf77330b23f5e695e3815dc856ee252d5e96

5,305

py

Python

geeker/mylog/log_config.py

4379711/functools_lyl

61b6cdbf304d3eacbffcbf85a27ecf72d3d275d8

[ "MIT" ]

1

2019-07-23T09:35:35.000Z

geeker/mylog/log_config.py

4379711/functools_lyl

61b6cdbf304d3eacbffcbf85a27ecf72d3d275d8

[ "MIT" ]

null

geeker/mylog/log_config.py

4379711/functools_lyl

61b6cdbf304d3eacbffcbf85a27ecf72d3d275d8

[ "MIT" ]

null

# -*- coding: utf-8 -*- import logging import logging.handlers from logging.handlers import TimedRotatingFileHandler import gzip import os import time from geeker.functions import Singleton class GzTimedRotatingFileHandler(TimedRotatingFileHandler): def __init__(self, filename, when, interval, **kwar...

37.359155

114

0.532328

561

5,305

4.818182

0.276292

0.023677

0.033666

0.019238

0.072512

0.031077

0.010359

0

0.009925

0.373233

5,305

141

115

37.624113

0.803008

0.055231

0

0.086957

0

0.008696

0.045672

0

1

0.06087

false

0

0.06087

0

0.165217

0

null

0

null

0

1

0

c198314254036559bc40906b82bd6eec9b2cf13a

4,150

py

Python

ohos/ndk/archive_ndk.py

ShadowCCY/build

5c88ebad21093ef816087c9160bda8e5e9035008

[ "Apache-2.0" ]

null

ohos/ndk/archive_ndk.py

ShadowCCY/build

5c88ebad21093ef816087c9160bda8e5e9035008

[ "Apache-2.0" ]

14

2021-09-07T08:39:43.000Z

2021-09-17T08:50:23.000Z

ohos/ndk/archive_ndk.py

ShadowCCY/build

5c88ebad21093ef816087c9160bda8e5e9035008

[ "Apache-2.0" ]

1

2021-09-07T06:19:48.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright (c) 2021 Huawei Device Co., Ltd. # 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 # #...

39.150943

78

0.604819

510

4,150

4.709804

0.319608

0.022481

0.043714

0.021649

0.267277

0.201499

0.126561

0.04746

0

0.004871

0.30747

4,150

105

79

39.52381

0.830898

0.161687

0

0.109589

0

0.077101

0

1

0.054795

false

0.013699

0.068493

0

0.136986

0

null

0

null

0

1

0

c1983d2c626217431ca80bafdd345d71048cc22d

587

py

Python

lib/utils/utils.py

PaperCodeSubmission/ICML2020-697

00f7732c236b9c6234e76a47dfebe5de314d5c01

[ "MIT" ]

12

2019-09-26T01:55:25.000Z

2020-01-21T06:53:04.000Z

lib/utils/utils.py

PaperCodeSubmission/ICML2020-697

00f7732c236b9c6234e76a47dfebe5de314d5c01

[ "MIT" ]

2

2021-08-09T03:53:26.000Z

2021-08-18T10:16:25.000Z

lib/utils/utils.py

PaperCodeSubmission/ICML2020-697

00f7732c236b9c6234e76a47dfebe5de314d5c01

[ "MIT" ]

4

2021-06-09T06:02:15.000Z

2021-10-05T13:33:15.000Z

import numpy as np """ some really utils functions """ def get_score_label_array_from_dict(score_dict, label_dict): """ :param score_dict: defaultdict(list) :param label_dict: defaultdict(list) :return: np array with score and label """ assert len(score_dict) == len(label_dict), "The score_di...

27.952381

90

0.678024

88

587

4.306818

0.409091

0.14248

0.073879

0

0.201022

587

21

91

27.952381

0.808102

0.190801

0

0.098321

0

0.111111

1

0.111111

false

0

0.111111

0

0.333333

0

null

0

null

0

1

0

c19b573ce46ab404e883f0fd6d1da1c94abd41bc

512

py

Python

algorithms/Python/implementation/bon appetit.py

Kumbong/hackerrank

36125f3a17c3e0f1fa889495e8ad33b0aa424552

[ "MIT" ]

8

2019-09-19T19:38:09.000Z

2022-02-14T13:59:37.000Z

algorithms/Python/implementation/bon appetit.py

Kumbong/hacker-rank

36125f3a17c3e0f1fa889495e8ad33b0aa424552

[ "MIT" ]

null

algorithms/Python/implementation/bon appetit.py

Kumbong/hacker-rank

36125f3a17c3e0f1fa889495e8ad33b0aa424552

[ "MIT" ]

7

2019-09-23T13:17:27.000Z

2022-01-27T18:02:16.000Z

#!/bin/python3 import math import os import random import re import sys # Complete the bonAppetit function below. def bonAppetit(bill, k, b): sum = 0 for i in bill: sum+=i sum = sum - bill[k] if sum//2 == b: print('Bon Appetit') else: print(b-(sum//2)) if __name__ == '__ma...

16

51

0.556641

76

512

3.644737

0.526316

0.054152

0.108303

0.115523

0

0.016173

0.275391

512

31

52

16.516129

0.730458

0.103516

0

0.041575

0

1

0.047619

false

0

0.238095

0

0.285714

0.095238

0

null

0

null

0

1

0

c19c3f1d1283d225db3d7d7ddef5d1c3d3346d48

393

py

Python

plugins/amazon/__init__.py

nim4/pantea

29bee731157b1a643bcfeed37133c7575bc9340f

[ "MIT" ]

3

2016-09-26T06:47:57.000Z

2017-10-03T17:05:16.000Z

plugins/amazon/__init__.py

nim4/pantea

29bee731157b1a643bcfeed37133c7575bc9340f

[ "MIT" ]

null

plugins/amazon/__init__.py

nim4/pantea

29bee731157b1a643bcfeed37133c7575bc9340f

[ "MIT" ]

null

from lib.util import get_info_by_url, insert_to_db __host = ".amazon." __sess = "x-main=" def parse(headers): name = get_info_by_url('http://' + headers["Host"] + '/gp/history/', headers, [("id='nav-signin-text' class='nav-button-em'>", "<")])[0] if name is None: return False insert_to_db("Am...

28.071429

76

0.613232

56

393

4.053571

0.660714

0.061674

0.079295

0.105727

0

0.003106

0.180662

393

13

77

30.230769

0.701863

0

0.254453

0.05598

0

1

0.1

false

0

0.1

0

0.4

0

null

0

null

0

1

0

c1a0698046ea5c15fed9403075b6dadc726dcd15

1,562

py

Python

ax/exceptions/data_provider.py

sparks-baird/Ax

57ba8714902ac218eb87dc2f90090678aa307a43

[ "MIT" ]

null

ax/exceptions/data_provider.py

sparks-baird/Ax

57ba8714902ac218eb87dc2f90090678aa307a43

[ "MIT" ]

null

ax/exceptions/data_provider.py

sparks-baird/Ax

57ba8714902ac218eb87dc2f90090678aa307a43

[ "MIT" ]

null

#!/usr/bin/env python3 # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. from typing import Any, Iterable class DataProviderError(Exception): """Base Exception for Ax DataProviders. ...

33.234043

86

0.65493

200

1,562

4.91

0.435

0.14664

0.10387

0.04277

0.038697

0

0.000867

0.261204

1,562

46

87

33.956522

0.850087

0.258643

0

0.074074

0

0.222812

0

1

0.148148

false

0

0.037037

0.074074

0.333333

0

null

0

null

0

1

0

c1a36f901ada2ecff8f592caff250af3e4962c6a

6,713

py

Python

gui/backend/tests/unit/test_Sqleditor.py

mike-lischke/mysql-shell-plugins

d7d15591dd8e70f7f5ef8ea579e0797eff30fa0a

[ "Apache-2.0", "CC0-1.0" ]

11

2022-03-02T11:04:16.000Z

2022-03-29T05:28:23.000Z

gui/backend/tests/unit/test_Sqleditor.py

mike-lischke/mysql-shell-plugins

d7d15591dd8e70f7f5ef8ea579e0797eff30fa0a

[ "Apache-2.0", "CC0-1.0" ]

1

2022-03-25T15:12:16.000Z

2022-03-31T18:59:22.000Z

gui/backend/tests/unit/test_Sqleditor.py

mike-lischke/mysql-shell-plugins

d7d15591dd8e70f7f5ef8ea579e0797eff30fa0a

[ "Apache-2.0", "CC0-1.0" ]

3

2022-03-24T11:32:12.000Z

2022-03-25T20:40:14.000Z

# Copyright (c) 2020, 2022, Oracle and/or its affiliates. # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License, version 2.0, # as published by the Free Software Foundation. # # This program is also distributed with certain software (including # ...

39.958333

120

0.714584

839

6,713

5.444577

0.261025

0.039405

0.035026

0.022329

0.359676

0.321804

0.258538

0.243214

0.175569

0.113398

0

0.008457

0.207359

6,713

167

121

40.197605

0.850028

0.202443

0

0.212963

0

0.104898

0

0.092593

1

0.101852

false

0

0.101852

0

0.268519

0

null

0

null

0

1

0

c1a639306302ef557c6752a6a01fb763ef49ebd4

4,846

py

Python

examples/poly_model/poly_dimension_adaptive.py

wedeling/FabUQCampaign

f89ee1a7b72ec1c41d6bf662f1b42acd8065cb32

[ "BSD-3-Clause" ]

1

2020-06-26T10:37:56.000Z

examples/poly_model/poly_dimension_adaptive.py

wedeling/FabUQCampaign

f89ee1a7b72ec1c41d6bf662f1b42acd8065cb32

[ "BSD-3-Clause" ]

null

examples/poly_model/poly_dimension_adaptive.py

wedeling/FabUQCampaign

f89ee1a7b72ec1c41d6bf662f1b42acd8065cb32

[ "BSD-3-Clause" ]

2

2020-04-20T12:50:11.000Z

2020-04-24T10:35:13.000Z

import chaospy as cp import numpy as np import easyvvuq as uq import os # import fabsim3_cmd_api as fab import matplotlib.pyplot as plt plt.close('all') # author: Wouter Edeling __license__ = "LGPL" HOME = os.path.abspath(os.path.dirname(__file__)) # Set up a fresh campaign called "sc" my_campaign = uq.Campaign(nam...

35.372263

88

0.639909

626

4,846

4.757189

0.333866

0.067159

0.028543

0.014775

0.246474

0.198791

0.173942

0.143049

0

0.009422

0.189641

4,846

137

89

35.372263

0.748918

0.291993

0

0.225

0

0.185458

0.055932

0

1

0

false

0

0.0625

0

0.0625

0.15

0

null

0

null

0

1

0

c1a87ca84277a3dd76ad935ca0c45a7628e65f14

3,996

py

Python

preprocess_data/preprocess_data.py

s-diaco/DRL4Trading

8f6042d7ce0381dc1fc809558e529a512c4c4e01

[ "MIT" ]

4

2021-07-05T12:18:34.000Z

2022-02-07T19:41:45.000Z

preprocess_data/preprocess_data.py

s-diaco/DRL4Trading

8f6042d7ce0381dc1fc809558e529a512c4c4e01

[ "MIT" ]

5

2021-04-04T09:44:59.000Z

2021-06-26T09:38:53.000Z

preprocess_data/preprocess_data.py

s-diaco/DRL4Trading

8f6042d7ce0381dc1fc809558e529a512c4c4e01

[ "MIT" ]

2

2021-07-05T12:18:34.000Z

2021-08-04T08:01:11.000Z

"""preprocess data before using it""" import logging import pandas as pd from preprocess_data import csv_data, custom_col_base, custom_columns def col_from_cls(client_class, data): ''' Create "series" from a given function and dataframe Parameters: client_func (callable): fu...

35.362832

78

0.651401

512

3,996

4.732422

0.230469

0.031779

0.049938

0.026414

0.288898

0.258357

0.231944

0.160132

0

0.265766

3,996

112

79

35.678571

0.825153

0.214965

0

0.205882

0

0.182508

0.047195

0

0.008929

0

1

0.058824

false

0

0.044118

0

0.161765

0

null

0

null

0

1

0

c1ab4ab02c0597d4e6630967716526281f28d594

17,930

py

Python

constants.py

datarobot-community/soccer_match_prediction

80aea8e7e14d772178909656496e05122ebff0a8

[ "Apache-2.0" ]

2

2020-06-16T14:34:13.000Z

2020-07-15T18:13:41.000Z

constants.py

datarobot-community/soccer_match_prediction

80aea8e7e14d772178909656496e05122ebff0a8

[ "Apache-2.0" ]

null

constants.py

datarobot-community/soccer_match_prediction

80aea8e7e14d772178909656496e05122ebff0a8

[ "Apache-2.0" ]

2

2021-07-23T06:40:49.000Z

2022-02-11T17:52:26.000Z

LEAGUES = [ 'Scottish Premiership', 'Italy Serie A', 'French Ligue 1', 'Spanish Segunda Division', 'Australian A-League', 'Italy Serie B', 'Dutch Eredivisie', 'Mexican Primera Division Torneo Clausura', 'Russian Premier Liga', 'Spanish Primera Division', 'English League One',...

26.562963

47

0.56068

2,533

17,930

3.968812

0.409791

0.005968

0.004377

0.00557

0

0.186672

0.255159

17,930

674

48

26.602374

0.56608

0

0.456497

0

1

0

false

0

null

0

null

0

1

0

c1ae4861c8eb6db57eabd223609f74170cb8381d

474

py

Python

string-list.py

leaen/Codeeval-solutions

fa83cb4fba3e56f79c0a6b00361c18cd3092c3f0

[ "MIT" ]

null

string-list.py

leaen/Codeeval-solutions

fa83cb4fba3e56f79c0a6b00361c18cd3092c3f0

[ "MIT" ]

null

string-list.py

leaen/Codeeval-solutions

fa83cb4fba3e56f79c0a6b00361c18cd3092c3f0

[ "MIT" ]

null

import sys import itertools def main(): with open(sys.argv[1]) as input_file: for line in input_file: length, letters = line.strip().split(',') length = int(length) letters = letters.strip() result = set(''.join(e) for e in itertools.product(let...

24.947368

87

0.525316

52

474

4.596154

0.576923

0.075314

0

0.003247

0.350211

474

18

88

26.333333

0.772727

0

0.021097

0

1

0.076923

false

0

0.153846

0

0.230769

0.076923

0

null

0

null

0

1

0

c1aeb6b648385e5dd2afb1d49db366eb4bba7f45

1,295

py

Python

tests/compiler_2.py

louisyoungx/zpycli

87deb188d1fd7782c60912edf1eeedb719b649a6

[ "MIT" ]

null

tests/compiler_2.py

louisyoungx/zpycli

87deb188d1fd7782c60912edf1eeedb719b649a6

[ "MIT" ]

null

tests/compiler_2.py

louisyoungx/zpycli

87deb188d1fd7782c60912edf1eeedb719b649a6

[ "MIT" ]

null

import sys if ".." not in sys.path: sys.path.insert(0,"..") import zpylib.ast.lexer as lex from zpylib.grammar import * # Test it out data = """ # TEST Apple = 3 + 4 * 10 + -20 *2 def Print(what): if True: go(what + 10) 如果错: x = 'yoo what's up' '''what fuck''' {:.2f}.format(name) """ class Compi...

24.433962

79

0.550579

166

1,295

4.26506

0.421687

0.079096

0.04661

0.048023

0

0.013514

0.314286

1,295

52

80

24.903846

0.783784

0.097297

0

0.05

0

0.143718

0

1

0.1

false

0

0.075

0

0.2

0.05

0

null

0

null

0

1

0

c1aeb773120f6e8b25cc706f47a68a22cbbc7e59

989

py

Python

setup.py

wasilak/yamllint-junit

0861484dc38220f772a4563974eebeae71ee6fb2

[ "MIT" ]

4

2017-11-28T22:04:01.000Z

2021-08-18T16:09:02.000Z

setup.py

wasilak/yamllint-junit

0861484dc38220f772a4563974eebeae71ee6fb2

[ "MIT" ]

5

2020-08-03T15:44:28.000Z

2021-11-05T10:47:51.000Z

setup.py

wasilak/yamllint-junit

0861484dc38220f772a4563974eebeae71ee6fb2

[ "MIT" ]

1

2020-08-28T15:05:51.000Z

#! /usr/bin/env python # -*- coding: utf-8 -*- from setuptools import setup from yamllint_junit_bootstrap import bootstrap version = bootstrap.__version__ with open("README.md", "r") as fh: long_description = fh.read() setup( name='yamllint-junit', packages=['yamllint_junit_bootstrap'], version=ve...

25.358974

89

0.65723

109

989

5.761468

0.605505

0.144904

0.105096

0.095541

0.117834

0

0.002509

0.194135

989

38

90

26.026316

0.785445

0.044489

0

0.064516

0

0.348887

0.091198

0

1

0

false

0

0.064516

0

0.064516

0

null

0

null

0

1

0

c1affedd9b9ad2746552cd2f0960a1b9537db67c

6,151

py

Python

pyalfred/server/database.py

tingiskhan/pyalfred

c9fac2672af92906bcc4294e14844e904423c2e6

[ "MIT" ]

null

pyalfred/server/database.py

tingiskhan/pyalfred

c9fac2672af92906bcc4294e14844e904423c2e6

[ "MIT" ]

null

pyalfred/server/database.py

tingiskhan/pyalfred

c9fac2672af92906bcc4294e14844e904423c2e6

[ "MIT" ]

null

from typing import Union, List, Type from sqlalchemy.orm import scoped_session, sessionmaker from logging import Logger from starlette.endpoints import HTTPEndpoint from starlette.requests import Request from starlette.responses import JSONResponse from starlette.status import HTTP_500_INTERNAL_SERVER_ERROR, HTTP_200_O...

34.172222

113

0.634368

734

6,151

5.076294

0.220708

0.048846

0.038647

0.024155

0.458132

0.405797

0.391304

0.359635

0.322598

0.272142

0

0.006733

0.275565

6,151

179

114

34.363128

0.829443

0.058202

0

0.418605

0

0.094781

0.021993

0

1

0.031008

false

0

0.100775

0.015504

0.232558

0

null

0

null

0

1

0

c1b0a13dd61f692f08ec2294ecce99fedfad9c35

4,628

py

Python

hdv/redfish/http_handler.py

opnfv/cirv-hdv

2d145d4f1fd231def2c9d52a71267031b938c0ac

[ "Apache-2.0" ]

null

hdv/redfish/http_handler.py

opnfv/cirv-hdv

2d145d4f1fd231def2c9d52a71267031b938c0ac

[ "Apache-2.0" ]

null

hdv/redfish/http_handler.py

opnfv/cirv-hdv

2d145d4f1fd231def2c9d52a71267031b938c0ac

[ "Apache-2.0" ]

null

############################################################################## # Copyright (c) 2020 China Mobile Co.,Ltd and others. # # All rights reserved. This program and the accompanying materials # are made available under the terms of the Apache License, Version 2.0 # which accompanies this distribution, and is ...

35.6

79

0.590104

574

4,628

4.641115

0.259582

0.086712

0.054054

0.058559

0.588213

0.576201

0.556682

0.478604

0.446321

0

0.035975

0.261236

4,628

129

80

35.875969

0.7432

0.144555

0

0.593023

0

0.080416

0

1

0.069767

false

0

0.081395

0

0.22093

0

null

0

null

0

1

0

c1b2c130162bc0b7988dfc259c05389cac64465c

3,006

py

Python

appserver/migrations/versions/a6ca510027a5_seed_annotationstyle_and_domainurlsmap_.py

SBRG/lifelike

a7b715f38b389a585c10e6d0d067345937455c13

[ "MIT" ]

8

2022-01-28T08:43:07.000Z

2022-03-23T11:18:10.000Z

appserver/migrations/versions/a6ca510027a5_seed_annotationstyle_and_domainurlsmap_.py

SBRG/lifelike

a7b715f38b389a585c10e6d0d067345937455c13

[ "MIT" ]

23

2022-02-14T15:25:00.000Z

2022-03-28T15:30:45.000Z

appserver/migrations/versions/a6ca510027a5_seed_annotationstyle_and_domainurlsmap_.py

SBRG/lifelike

a7b715f38b389a585c10e6d0d067345937455c13

[ "MIT" ]

5

2022-01-28T15:45:44.000Z

2022-03-14T11:36:49.000Z

"""Seed AnnotationStyle and DomainURLsMap tables Revision ID: a6ca510027a5 Revises: fb1654973fbd Create Date: 2020-08-19 23:27:53.132930 """ import json from alembic import context from alembic import op import sqlalchemy as sa from sqlalchemy.orm.session import Session from sqlalchemy.sql import table, column from...

28.093458

76

0.641717

361

3,006

5.152355

0.365651

0.072581

0.062903

0.030645

0.052688

0

0.023348

0.244844

3,006

106

77

28.358491

0.796035

0.192947

0

0.085714

0

0.161519

0.025042

0

1

0.057143

false

0.028571

0.1

0

0.157143

0

null

0

null

0

1

0

c1b62424e13951dc15b2f1d8f1ebe1749e5c2d11

3,334

py

Python

prepare_dataset.py

TheElderMindseeker/pytorch-domain-adaptation

70ca862708bd6e59b5eee5d7c8bd808ef3457dc8

[ "MIT" ]

null

prepare_dataset.py

TheElderMindseeker/pytorch-domain-adaptation

70ca862708bd6e59b5eee5d7c8bd808ef3457dc8

[ "MIT" ]

null

prepare_dataset.py

TheElderMindseeker/pytorch-domain-adaptation

70ca862708bd6e59b5eee5d7c8bd808ef3457dc8

[ "MIT" ]

null

# pylint: disable=invalid-name,missing-docstring import os import subprocess import urllib.parse import uuid def read_temporal_data(temporal_path: str): temporal_data = dict() for line in open(temporal_path, 'r'): name, v_cls, s_frm_1, f_frm_1, s_frm_2, f_frm_2 = line.strip().split() temporal_...

38.767442

79

0.596581

442

3,334

4.255656

0.278281

0.051037

0.047847

0.037214

0.178628

0.117491

0.068581

0.04891

0

0.013344

0.280744

3,334

85

80

39.223529

0.771059

0.013797

0

0.028169

0

0.126293

0.053256

0

1

0.014085

false

0

0.056338

0

0.084507

0.028169

0

null

0

null

0

1

0

c1b8b6e21410622622aa640ba1d082574eb412ec

8,201

py

Python

lib/providers/theaudiodb.py

DaVukovic/script.artwork.beef

cb6e54cd2b3bccc82660ad277c13618746f36e08

[ "MIT" ]

40

2017-10-29T22:43:43.000Z

2022-03-12T05:59:05.000Z

lib/providers/theaudiodb.py

supmagc/script.artwork.beef

afd76b290d13b2e9ea00d7f9772961353f4640b8

[ "MIT" ]

47

2017-01-31T21:28:20.000Z

2021-03-23T06:53:51.000Z

lib/providers/theaudiodb.py

supmagc/script.artwork.beef

afd76b290d13b2e9ea00d7f9772961353f4640b8

[ "MIT" ]

31

2017-10-16T05:28:53.000Z

2022-02-24T19:50:24.000Z

import xbmc from lib.libs import mediatypes from lib.libs.addonsettings import settings from lib.libs.pykodi import json, UTF8JSONDecoder from lib.libs.utils import SortedDisplay from lib.providers.base import AbstractProvider, AbstractImageProvider, cache, build_key_error class TheAudioDBAbstractProvider(AbstractIm...

44.32973

118

0.631386

864

8,201

5.900463

0.204861

0.010592

0.015104

0.013731

0.37603

0.36583

0.354649

0.321302

0.300314

0.280306

0

0.017363

0.241556

8,201

184

119

44.570652

0.802251

0.007804

0

0.374194

0

0.019355

0.177158

0.007868

0

1

0.103226

false

0

0.03871

0.019355

0.432258

0

null

0

null

0

1

0

c1bb44a49533620993e5fb48385ae2720ad43392

2,786

py

Python

code/attr2vec_func.py

xuliang09/JAPE

961a0cf9d10abf81cc24a71ac33274b42bc2fbc1

[ "MIT" ]

91

2018-03-13T03:56:15.000Z

2022-03-26T13:47:22.000Z

code/attr2vec_func.py

codeinging/JAPE

b4b3617a7c61df5f7093921553dd3b0a7497506d

[ "MIT" ]

10

2018-04-02T15:47:08.000Z

2022-03-01T09:28:10.000Z

code/attr2vec_func.py

codeinging/JAPE

b4b3617a7c61df5f7093921553dd3b0a7497506d

[ "MIT" ]

23

2018-05-30T07:18:38.000Z

2021-08-15T06:13:29.000Z

import math import collections import random import numpy as np import tensorflow as tf import itertools import time def sum_rows(x): """Returns a vector summing up each row of the matrix x.""" cols = tf.shape(x)[1] ones_shape = tf.stack([cols, 1]) ones = tf.ones(ones_shape, x.dtype) return tf.res...

35.265823

119

0.645011

389

2,786

4.329049

0.249357

0.037411

0.038599

0.045131

0.192993

0.179335

0.11639

0

0.011021

0.250897

2,786

78

120

35.717949

0.795879

0.019024

0

0.179104

0

1

0.044776

false

0

0.104478

0

0.19403

0

null

0

null

0

1

0

c1bb5795f5cf4b1f0f656850cbb021970ffc7c82

5,218

py

Python

src/installer/src/tortuga/db/componentDbApi.py

sutasu/tortuga

48d7cde4fa652346600b217043b4a734fa2ba455

[ "Apache-2.0" ]

33

2018-03-02T17:07:39.000Z

2021-05-21T18:02:51.000Z

src/installer/src/tortuga/db/componentDbApi.py

sutasu/tortuga

48d7cde4fa652346600b217043b4a734fa2ba455

[ "Apache-2.0" ]

201

2018-03-05T14:28:24.000Z

2020-11-23T19:58:27.000Z

src/installer/src/tortuga/db/componentDbApi.py

sutasu/tortuga

48d7cde4fa652346600b217043b4a734fa2ba455

[ "Apache-2.0" ]

23

2018-03-02T17:21:59.000Z

2020-11-18T14:52:38.000Z

# Copyright 2008-2018 Univa Corporation # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in...

31.624242

76

0.590264

413

5,218

7.380145

0.336562

0.019685

0.029528

0.031168

0.352362

0.265748

0

0.003531

0.348793

5,218

164

77

31.817073

0.893467

0.232656

0

0.47619

0

0.018423

0

1

0.071429

false

0

0.095238

0

0.22619

0

null

0

null

0

1

0

c1c255b1f03413b3ba902b4ef59492fdc92fec91

2,667

py

Python

network/embedding.py

cherry979988/OpenNRE

740e6abd3b8b30625cb88a643a2acb65d5e923dd

[ "MIT" ]

1

2019-10-08T02:53:28.000Z

network/embedding.py

flyounger/OpenNRE

1f30a0b3109e9b3c3284fa72a1562f81cdb70fdf

[ "MIT" ]

null

network/embedding.py

flyounger/OpenNRE

1f30a0b3109e9b3c3284fa72a1562f81cdb70fdf

[ "MIT" ]

1

2020-07-22T08:39:09.000Z

import tensorflow as tf import numpy as np FLAGS = tf.app.flags.FLAGS class Embedding(object): def __init__(self, is_training, word_vec, word, pos1, pos2): temp_word_embedding = tf.get_variable(initializer=word_vec, name = 'temp_word_embedding', dtype=tf.float32) unk_word_embedding = tf.get_varia...

56.744681

179

0.656543

359

2,667

4.612813

0.167131

0.057971

0.061594

0.399758

0.336957

0.253623

0.234903

0

0.026316

0.230596

2,667

46

180

57.978261

0.780702

0

0.102564

0

0.03637

0

1

0.102564

false

0

0.051282

0

0.282051

0

null

0

null

0

1

0

c1c3368fb90c0a4c1c5f3d9670d74ff773fbc850

768

py

Python

experiments/mapModel.py

PerceptronV/denoising-text-autoencoders

8ef59e2531a4fc7531002cffd3546f27eadb8ec9

[ "Apache-2.0" ]

null

experiments/mapModel.py

PerceptronV/denoising-text-autoencoders

8ef59e2531a4fc7531002cffd3546f27eadb8ec9

[ "Apache-2.0" ]

null

experiments/mapModel.py

PerceptronV/denoising-text-autoencoders

8ef59e2531a4fc7531002cffd3546f27eadb8ec9

[ "Apache-2.0" ]

1

2022-03-04T05:57:52.000Z

import torch.nn as nn class MappingModel(nn.Module): def __init__(self, dims, nlayers=1, units=128, activation=nn.ReLU): super(MappingModel, self).__init__() print(nlayers) if nlayers == 1: self.linmap = nn.Linear(dims, dims) elif nlayers == 2: self.linmap =...

30.72

76

0.518229

84

768

4.642857

0.357143

0.123077

0.092308

0.112821

0.205128

0

0.014028

0.35026

768

24

77

32

0.767535

0

1

0.095238

false

0

0.047619

0

0.238095

0.047619

0

null

0

null

0

1

0

c1c4c548cee3344d6a0288f1062e34cef764d45e

1,166

py

Python

clafiyy/new/sum.py

cy486/DomRead

852289588b902dedba5fd3f2a57b39d2a2b027ba

[ "Apache-2.0" ]

null

clafiyy/new/sum.py

cy486/DomRead

852289588b902dedba5fd3f2a57b39d2a2b027ba

[ "Apache-2.0" ]

null

clafiyy/new/sum.py

cy486/DomRead

852289588b902dedba5fd3f2a57b39d2a2b027ba

[ "Apache-2.0" ]

null

# @Time : 2019/5/22 11:26 # @Author : shakespere # @FileName: sum.py import pandas as pd submission_1 = pd.read_csv("./data/merge_0.8550913438849271_predictions.csv") submission_2 = pd.read_csv("./data/merge_0.8551243481873769_predictions.csv") submission_3 = pd.read_csv("./data/merge_0.8571411176454415_pre...

58.3

302

0.747856

169

1,166

4.905325

0.278107

0.075995

0.086852

0.125452

0.414958

0.183353

0

0.184484

0.093482

1,166

20

303

58.3

0.599811

0.056604

0

0.374768

0.367347

0

1

0

false

0

0.071429

0

0.071429

0

null

0

null

0

1

0

c1c554676d59b1bbb3e5980c3277d57ab792911e

4,216

py

Python

lexos/receivers/matrix_receiver.py

WheatonCS/Lexos

994be4e403053ebbef18e5758a100af616195706

[ "MIT" ]

107

2015-03-19T09:10:31.000Z

2022-01-29T01:33:48.000Z

lexos/receivers/matrix_receiver.py

WheatonCS/Lexos

994be4e403053ebbef18e5758a100af616195706

[ "MIT" ]

864

2015-05-19T19:27:00.000Z

2022-01-28T18:48:52.000Z

lexos/receivers/matrix_receiver.py

WheatonCS/Lexos

994be4e403053ebbef18e5758a100af616195706

[ "MIT" ]

25

2015-06-02T23:03:06.000Z

2020-08-06T04:27:49.000Z

"""This is the receiver for the matrix model.""" from typing import NamedTuple, Optional, Dict from lexos.receivers.base_receiver import BaseReceiver DocumentLabelMap = Dict[int, str] class TokenOption(NamedTuple): """A typed tuple to represent token option.""" # the size of each token n_gram_size: in...

31.699248

79

0.671252

561

4,216

4.743316

0.221034

0.066141

0.049605

0.054115

0.361894

0.278091

0.074408

0.045847

0

0.000952

0.252609

4,216

132

80

31.939394

0.843542

0.306689

0

0.052632

0

0.087675

0.018685

0

1

0.087719

false

0

0.035088

0

0.45614

0

null

0

null

0

1

0

c1c5a2754fda11c89b443f7e02da5e10a6ab2f74

3,791

py

Python

src/train_process/build_cnn_model/Train_model.py

rober5566a/NTUT_109-2_MVA_Final-Project

d18494760750efae1ff0810dcaa281a03d0827c0

[ "MIT" ]

null

src/train_process/build_cnn_model/Train_model.py

rober5566a/NTUT_109-2_MVA_Final-Project

d18494760750efae1ff0810dcaa281a03d0827c0

[ "MIT" ]

null

src/train_process/build_cnn_model/Train_model.py

rober5566a/NTUT_109-2_MVA_Final-Project

d18494760750efae1ff0810dcaa281a03d0827c0

[ "MIT" ]

null

import torch import torch.nn as nn import torch.nn.functional as F from torch.autograd import Variable from Model.CNN_1 import CNN def test_performance(model, device, test_loader, loss, pred_train_labels, train_labels, printHistory=True): test_num_right = 0 for step, (datas, labels) in enumerate(test_loader):...

37.534653

118

0.613558

502

3,791

4.318725

0.161355

0.086255

0.055351

0.0369

0.430812

0.350554

0.278598

0.158672

0.077491

0.049816

0

0.01454

0.274334

3,791

100

119

37.91

0.773537

0.152994

0

0.032787

0

0.016393

0.037547

0

1

0.032787

false

0

0.081967

0

0.147541

0.114754

0

null

0

null

0

1

0

c1c892c83086b1d88c5c729a393446e22f1c34cf

1,653

py

Python

edgedb/lang/schema/basetypes/uuid.py

jonathanslenders/edgedb

35ad66c4bd525cd9966f8029e5b385e888323f82

[ "Apache-2.0" ]

1

2021-12-15T09:34:48.000Z

edgedb/lang/schema/basetypes/uuid.py

jonathanslenders/edgedb

35ad66c4bd525cd9966f8029e5b385e888323f82

[ "Apache-2.0" ]

null

edgedb/lang/schema/basetypes/uuid.py

jonathanslenders/edgedb

35ad66c4bd525cd9966f8029e5b385e888323f82

[ "Apache-2.0" ]

null

# # This source file is part of the EdgeDB open source project. # # Copyright 2008-present MagicStack Inc. and the EdgeDB authors. # # 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...

30.611111

76

0.696915

235

1,653

4.748936

0.47234

0.053763

0.023297

0.028674

0.071685

0

0.006944

0.215971

1,653

53

77

31.188679

0.854167

0.389595

0

0.086957

0

0.027245

0

1

0.086957

false

0

0.173913

0.043478

0.347826

0

null

0

null

0

1

0

c1cbab332513a23ee2e04a851cca5c5b17c6cd1c

9,348

py

Python

GraphOfDocs_Representation/create.py

imis-lab/book-chapter

8260a60ec91dd29616eeed80f34bdea00fb73cd7

[ "MIT" ]

2

2020-09-29T11:40:56.000Z

2020-09-29T11:41:04.000Z

GraphOfDocs_Representation/create.py

imis-lab/personnel-selection

f04d78c8211f21ab53db4ecdddfc9c78ffc26a36

[ "MIT" ]

null

GraphOfDocs_Representation/create.py

imis-lab/personnel-selection

f04d78c8211f21ab53db4ecdddfc9c78ffc26a36

[ "MIT" ]

null

""" This script contains functions that create data in the Neo4j database. """ import json import platform from pathlib import Path from gensim.models import Word2Vec from GraphOfDocs_Representation.utils import ( clear_screen, generate_words ) # Initialize an empty set of edges. edges = {} # Initia...

39.443038

109

0.586543

1,187

9,348

4.56866

0.240101

0.01844

0.01549

0.023234

0.194726

0.144938

0.106214

0.089618

0

0.007151

0.311831

9,348

236

110

39.610169

0.835846

0.350877

0

0.170543

0

0.007752

0.246756

0.045247

0

0.023256

1

0.03876

false

0

0.03876

0

0.108527

0.023256

0

null

0

null

0

1

0

c1cf6fd8b07f9923a803e8e52a473b551c59d7c1

4,653

py

Python

bin/star_imports.py

HansPinckaers/impsort.vim

56e367e3e0ce5d7ea5c800492270282c3a53eda2

[ "MIT" ]

40

2016-05-31T22:19:42.000Z

2022-01-08T15:24:23.000Z

bin/star_imports.py

HansPinckaers/impsort.vim

56e367e3e0ce5d7ea5c800492270282c3a53eda2

[ "MIT" ]

35

2016-06-06T16:41:49.000Z

2022-03-16T13:40:56.000Z

bin/star_imports.py

HansPinckaers/impsort.vim

56e367e3e0ce5d7ea5c800492270282c3a53eda2

[ "MIT" ]

10

2017-04-14T07:42:06.000Z

2022-02-24T08:45:39.000Z

#!/usr/bin/env python """Very simple AST parser to get star imports. Nothing more. """ import os import ast import imp import sys from importlib import import_module try: str_ = unicode # noqa F821 except: str_ = str modules_seen = set() import_names = set() class NodeVisitor(ast.NodeVisitor): using_...

28.2

76

0.537073

564

4,653

4.269504

0.257092

0.026578

0.027409

0.016196

0.145349

0.13289

0.104651

0.04402

0

0.003695

0.360198

4,653

164

77

28.371951

0.805173

0.043413

0

0.215385

0

0.016441

0

1

0.076923

false

0.007692

0.161538

0

0.323077

0.007692

0

null

0

null

0

1

0

c1d364f1b1140a439139d1301d9e7d4e7d3275ad

482

py

Python

loader.py

afsara-rahman/Quadruple

5cc5e0007f16eb75b0368427652f671cbf78f15f

[ "MIT" ]

null

loader.py

afsara-rahman/Quadruple

5cc5e0007f16eb75b0368427652f671cbf78f15f

[ "MIT" ]

null

loader.py

afsara-rahman/Quadruple

5cc5e0007f16eb75b0368427652f671cbf78f15f

[ "MIT" ]

null

#Media loader class. #Loads images. import os, sys, pygame from pygame.locals import * #Load an image. :) def load_image(file, transparent = True): print("Loading " + file + " ..") fullname = os.path.join('media', file) image = pygame.image.load(fullname) if transparent == True: image = image...

24.1

46

0.634855

60

482

5.033333

0.566667

0.099338

0.112583

0

0.00542

0.23444

482

19

47

25.368421

0.813008

0.10166

0

0.037296

0

1

0.076923

false

0

0.153846

0

0.307692

0.076923

0

null

0

null

0

1

0

c1d522ea9886ba08db71b2edc7bf38ab91008a5b

26,793

py

Python

vespa/common/pulse_funcs/bloch_multi.py

vespa-mrs/vespa

6d3e84a206ec427ac1304e70c7fadf817432956b

[ "BSD-3-Clause" ]

null

vespa/common/pulse_funcs/bloch_multi.py

vespa-mrs/vespa

6d3e84a206ec427ac1304e70c7fadf817432956b

[ "BSD-3-Clause" ]

4

2021-04-17T13:58:31.000Z

2022-01-20T14:19:57.000Z

vespa/common/pulse_funcs/bloch_multi.py

vespa-mrs/vespa

6d3e84a206ec427ac1304e70c7fadf817432956b

[ "BSD-3-Clause" ]

3

2021-06-05T16:34:57.000Z

2022-01-19T16:13:22.000Z

# Python modules import multiprocessing import math # 3rd party modules import numpy as np # Our modules from pylab import * # GAMMA = 26753.0 - replaced with user defined value TWOPI = 6.283185 def isiterable(p_object): try: it = iter(p_object) except TypeError: return False return ...

33.324627

134

0.556265

3,653

26,793

4.053928

0.197646

0.005942

0.008508

0.017287

0.166655

0.156054

0.127287

0.109325

0.101762

0

0.030462

0.317546

26,793

803

135

33.366127

0.779437

0.374389

0

0.183824

0

0.051914

0.004926

0

1

0.034314

false

0.004902

0.009804

0.002451

0.073529

0.034314

0

null

0

null

0

1

0

c1d995325cdcad39ba73751aa9937c73ab171ea8

2,763

py

Python

lib/rucio/client/accountlimitclient.py

balrampariyarath/rucio

8a68017af6b44485a9620566f1afc013838413c1

[ "Apache-2.0" ]

1

2017-08-07T13:34:55.000Z

lib/rucio/client/accountlimitclient.py

balrampariyarath/rucio

8a68017af6b44485a9620566f1afc013838413c1

[ "Apache-2.0" ]

null

lib/rucio/client/accountlimitclient.py

balrampariyarath/rucio

8a68017af6b44485a9620566f1afc013838413c1

[ "Apache-2.0" ]

null

# Copyright European Organization for Nuclear Research (CERN) # # 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 # # Authors: # - Mario Lassnig, <mario...

37.337838

154

0.674267

377

2,763

4.801061

0.360743

0.033149

0.024309

0.026519

0.390055

0.285083

0

0.013029

0.222222

2,763

73

155

37.849315

0.829223

0.386898

0

0.428571

0

0.025526

0

1

0.107143

false

0

0.178571

0

0.428571

0

null

0

null

0

1

0

c1dae57fa4e3464ff7fae0220eed527133197673

5,146

py

Python

functions.py

Project3-Group10/stocker-eyes

73fd410091348a4a358681a554b80e7c1c47b1ab

[ "FTL" ]

1

2021-04-17T07:13:39.000Z

functions.py

Project3-Group10/stocker-eyes

73fd410091348a4a358681a554b80e7c1c47b1ab

[ "FTL" ]

33

2021-04-14T13:56:07.000Z

2021-05-05T04:43:10.000Z

functions.py

Project3-Group10/stocker-eyes

73fd410091348a4a358681a554b80e7c1c47b1ab

[ "FTL" ]

1

2021-10-14T00:44:00.000Z

import os from dotenv import load_dotenv, find_dotenv import requests import smtplib, ssl from email.mime.text import MIMEText from email.mime.multipart import MIMEMultipart import requests_cache load_dotenv(find_dotenv()) ALPHA_API_KEY = os.getenv('ALPHA_API_KEY') NEWS_API = os.getenv('GET_NEWS_KEY') EMAIL_PASSWORD ...

37.838235

136

0.706374

671

5,146

5.187779

0.233979

0.028727

0.045964

0.057455

0.493536

0.449296

0.425165

0.367136

0.342718

0

0.028189

0.186553

5,146

136

137

37.838235

0.803392

0.110766

0

0.257426

0

0.208297

0.019096

0

1

0.059406

false

0.049505

0.069307

0

0.168317

0.059406

0

null

0

null

0

1

0

c1dd6874deb6cbc28514ef2ec2c7c714f60a5adc

1,282

py

Python

poetry/apps/corpus/spiders/strofa.py

IlyaGusev/PoetryCorpus

7a5f70e6a46b4717f7c903671f9a6a917aee6162

[ "Apache-2.0" ]

45

2016-10-24T13:13:55.000Z

2022-01-21T05:39:06.000Z

poetry/apps/corpus/spiders/strofa.py

IlyaGusev/PoetryCorpus

7a5f70e6a46b4717f7c903671f9a6a917aee6162

[ "Apache-2.0" ]

23

2017-01-18T17:34:25.000Z

2017-11-01T17:39:02.000Z

poetry/apps/corpus/spiders/strofa.py

IlyaGusev/PoetryCorpus

7a5f70e6a46b4717f7c903671f9a6a917aee6162

[ "Apache-2.0" ]

7

2017-08-25T03:08:08.000Z

2020-05-22T22:55:58.000Z

import scrapy import re class StrofaSpider(scrapy.Spider): name = 'poems_strofa' start_urls = ['http://strofa.su/vse-poety/'] custom_settings = {} def parse(self, response): for href in response.css('.poemlinks a::attr(href)'): poet_url = response.urljoin(href.extract()) ...

33.736842

80

0.569423

158

1,282

4.525316

0.392405

0.076923

0.062937

0.053147

0.271329

0.13986

0

0.01164

0.262871

1,282

37

81

34.648649

0.744974

0

0.066667

0

0.149219

0

1

0.1

false

0

0.066667

0

0.3

0

null

0

null

0

1

0

c1e580496376c92672392fa607ae6aa0fdbdf110

2,446

py

Python

data/local_news_data/crawler/spmiddlewares/fake404.py

SSK-14/Covid19-Search-Engine

2a9e0066e766d8a356a2c4a1ebd51c0aeb3cd4b6

[ "Apache-2.0" ]

1

2020-06-14T16:52:55.000Z

data/local_news_data/crawler/spmiddlewares/fake404.py

SSK-14/Covid19-Search-Engine

2a9e0066e766d8a356a2c4a1ebd51c0aeb3cd4b6

[ "Apache-2.0" ]

1

2020-05-06T14:28:10.000Z

data/local_news_data/crawler/spmiddlewares/fake404.py

SSK-14/Covid19-Search-Engine

2a9e0066e766d8a356a2c4a1ebd51c0aeb3cd4b6

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- """ This script was borrowed from the RISJbot repository (https://github.com/pmyteh/RISJbot) All credit goes to original author """ # Define here the models for your spider middleware # # See documentation in: # http://doc.scrapy.org/en/latest/topics/spider-middleware.html # from scrapy_deltaf...

34.450704

88

0.647588

284

2,446

5.482394

0.517606

0.030829

0.055234

0

0.018931

0.26574

2,446

70

89

34.942857

0.847996

0.343009

0

0.143974

0.033225

0

1

0.147059

false

0

0.088235

0.029412

0.382353

0

null

0

null

0

1

0

c1e80041ebc0d75ee3fd1571a9a3316026d87a13

4,405

py

Python

app/views/recommend.py

HunterLC/FARSystem

a8b91fcd1914e84dd2ec2b8321c51627779bb89b

[ "Apache-2.0" ]

null

app/views/recommend.py

HunterLC/FARSystem

a8b91fcd1914e84dd2ec2b8321c51627779bb89b

[ "Apache-2.0" ]

null

app/views/recommend.py

HunterLC/FARSystem

a8b91fcd1914e84dd2ec2b8321c51627779bb89b

[ "Apache-2.0" ]

null

from flask import Blueprint, redirect, url_for from flask import request from flask import render_template from flask import session from sqlalchemy import and_ from .. import db from app.models import Actors, Users, Likes from ..recommend import Recommend recommend_blue = Blueprint('recommend', __name__) @recommen...

40.787037

115

0.638138

535

4,405

4.938318

0.241122

0.06056

0.063588

0.028766

0.293717

0.260787

0.174868

0.135503

0

0.009023

0.245176

4,405

107

116

41.168224

0.785564

0.019296

0

0.202381

0

0.118494

0.02974

0

1

0.035714

false

0

0.095238

0

0.166667

0.047619

0

null

0

null

0

1

0

c1ea027732ae7e7ee9cb06e19892037f93c8cab4

1,946

py

Python

neutron/tests/unit/agent/ovsdb/native/test_connection.py

SUSE-Cloud/neutron

879665d3041e74df4b287b4c18b88288850cf11c

[ "Apache-2.0" ]

null

neutron/tests/unit/agent/ovsdb/native/test_connection.py

SUSE-Cloud/neutron

879665d3041e74df4b287b4c18b88288850cf11c

[ "Apache-2.0" ]

null

neutron/tests/unit/agent/ovsdb/native/test_connection.py

SUSE-Cloud/neutron

879665d3041e74df4b287b4c18b88288850cf11c

[ "Apache-2.0" ]

null

# Copyright 2015, Red Hat, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agr...

43.244444

78

0.658787

239

1,946

5.1841

0.493724

0.048426

0.060533

0.046005

0.140436

0.103309

0

0.007018

0.267729

1,946

44

79

44.227273

0.862456

0.31295

0

0.049168

0.015885

0

0.08

1

0.04

false

0

0.24

0

0.32

0

null

0

null

0

1

0

c1ed087977b1a3a244c379b67768158ed7b31bba

3,031

py

Python

setup.py

satvidh/batch-scoring

13da21e813da3e757526b9c51f7dd1fe2b224603

[ "BSD-3-Clause" ]

null

setup.py

satvidh/batch-scoring

13da21e813da3e757526b9c51f7dd1fe2b224603

[ "BSD-3-Clause" ]

null

setup.py

satvidh/batch-scoring

13da21e813da3e757526b9c51f7dd1fe2b224603

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python import sys import codecs import os.path import re from setuptools import setup, find_packages extra = {} def read_requirements_file(file): fname = os.path.join(os.path.abspath(os.path.dirname(__file__)), file) with open(fname, 'r') as r: return r.readlines() install_requires = ...

34.83908

109

0.709997

412

3,031

5.072816

0.468447

0.051675

0.050239

0.021531

0.097129

0.055502

0.028708

0

0.026432

0.176179

3,031

86

110

35.244186

0.810573

0.325965

0

0.358379

0.141869

0

1

0.018868

false

0

0.09434

0

0.132075

0

null

0

null

0

1

0

c1eef54b8313d4e6d9e3bc6391a17c5d907262af

2,513

py

Python

Backend/home/home.py

davematias/PortfolioBackendV2

8fedb0cae038dda4d5c06cd2b24e17dcfee614ce

[ "MIT" ]

null

Backend/home/home.py

davematias/PortfolioBackendV2

8fedb0cae038dda4d5c06cd2b24e17dcfee614ce

[ "MIT" ]

9

2020-09-07T07:14:00.000Z

2022-02-18T09:55:16.000Z

Backend/home/home.py

davematias/PortfolioV2

8fedb0cae038dda4d5c06cd2b24e17dcfee614ce

[ "MIT" ]

null

import os import smtplib, ssl from email.message import EmailMessage from typing import Tuple from flask import Blueprint, jsonify, request from flask_jwt_extended import jwt_required from utils import dynamo site_blueprint = Blueprint('site', __name__,) @site_blueprint.route('/profile', methods=['GET']) def profile(...

28.556818

150

0.653004

296

2,513

5.412162

0.375

0.048689

0.040574

0.044944

0.072409

0

0.008682

0.220852

2,513

88

150

28.556818

0.809499

0

0.163087

0.008353

0

1

0.081967

false

0.032787

0.114754

0.016393

0.409836

0.081967

0

null

0

null

0

1

0

c1eff3a6449550c9247cbee5b8aedcd969ea87e9

1,845

py

Python

src/nmrezman/phase01/train/train_findings.py

mozzilab/NM_Radiology_AI

8df83c14e88534142f43411e33913682eab26582

[ "MIT" ]

1

2022-03-17T12:28:12.000Z

src/nmrezman/phase01/train/train_findings.py

mozzilab/NM_Radiology_AI

8df83c14e88534142f43411e33913682eab26582

[ "MIT" ]

null

src/nmrezman/phase01/train/train_findings.py

mozzilab/NM_Radiology_AI

8df83c14e88534142f43411e33913682eab26582

[ "MIT" ]

null

# %% # nmrezman from .general import train_findings_model # Misc import argparse # %% desc_str = "Train Phase 01 Findings vs No Findings Model" def get_args_parser(): parser = argparse.ArgumentParser(description=desc_str, add_help=False) # Paths parser.add_argument( "--data_path", typ...

26.73913

135

0.645528

228

1,845

4.995614

0.307018

0.036874

0.074627

0.083407

0.355575

0.311677

0.283582

0.257243

0.199298

0.125549

0

0.012006

0.23252

1,845

68

136

27.132353

0.792373

0.055827

0

0.326087

0

0.220554

0.025982

0

1

0.021739

false

0

0.043478

0

0.086957

0

null

0

null

0

1

0

c1f0856e892c8089aea911b5d6e0ad351d8e20d8

1,474

py

Python

setup.py

olricson/remotefreebox

16e2a42ed7cfcfd1ab303184280564eeace77919

[ "BSD-2-Clause" ]

14

2015-01-04T22:14:07.000Z

2020-11-11T18:53:20.000Z

setup.py

olricson/remotefreebox

16e2a42ed7cfcfd1ab303184280564eeace77919

[ "BSD-2-Clause" ]

3

2017-11-08T14:28:32.000Z

2021-08-30T21:58:04.000Z

setup.py

olricson/remotefreebox

16e2a42ed7cfcfd1ab303184280564eeace77919

[ "BSD-2-Clause" ]

7

2015-03-17T12:43:09.000Z

2020-05-10T23:47:35.000Z

# Always prefer setuptools over distutils from setuptools import setup, find_packages # To use a consistent encoding from codecs import open from os import path here = path.abspath(path.dirname(__file__)) # Get the long description from the README file with open(path.join(here, 'README.md'), encoding='utf-8') as f: ...

29.48

72

0.677069

185

1,474

5.324324

0.589189

0.060914

0.101523

0.105584

0.054822

0

0.014617

0.210991

1,474

49

73

30.081633

0.83233

0.257123

0

0.440959

0.02214

0

1

0

false

0

0.107143

0

0.107143

0

null

0

null

0

1

0

c1f1080c356b5d730e19c3e67bf76fb079c62c16

2,237

py

Python

model_main.py

yuxuan1995liu/darkflowyolo_detection

a7807e9b85833e3f877d46bb60e8fa7d0596a10b

[ "MIT" ]

null

model_main.py

yuxuan1995liu/darkflowyolo_detection

a7807e9b85833e3f877d46bb60e8fa7d0596a10b

[ "MIT" ]

null

model_main.py

yuxuan1995liu/darkflowyolo_detection

a7807e9b85833e3f877d46bb60e8fa7d0596a10b

[ "MIT" ]

null

import matplotlib.pyplot as plt import numpy as np from darkflow.net.build import TFNet import cv2 import pprint as pp import os # options = {"model": "cfg/custum_yolo.cfg", # "batch": 8, # "load": "bin/yolo.weights", # "epoch": 3, # "trainer":"adam", # "gpu": 1...

28.679487

136

0.599911

292

2,237

4.417808

0.342466

0.093798

0.065116

0.018605

0.382946

0.32093

0.272868

0

0.034911

0.244524

2,237

78

137

28.679487

0.728402

0.281627

0

0.114052

0

1

0.022222

false

0

0.133333

0

0.177778

0.155556

0

null

0

null

0

1

0

c1f245590fbe23acc3fd6f32e6491551e7d43e6a

7,275

py

Python

train.py

angelorodem/tensorflow2-char-rnn

f28503c61de62eade9b477bf13573988fb3807de

[ "MIT" ]

null

train.py

angelorodem/tensorflow2-char-rnn

f28503c61de62eade9b477bf13573988fb3807de

[ "MIT" ]

null

train.py

angelorodem/tensorflow2-char-rnn

f28503c61de62eade9b477bf13573988fb3807de

[ "MIT" ]

null

from __future__ import absolute_import, division, print_function, unicode_literals import argparse import pickle from colorama import init, Fore init(autoreset=True) def str2bool(v): if isinstance(v, bool): return v if v.lower() in ('yes', 'true', 't', 'y', '1'): return True elif v.lower() ...

33.680556

101

0.677938

992

7,275

4.836694

0.272177

0.024385

0.031888

0.020008

0.189037

0.164652

0.12005

0.093372

0.073364

0

0.010859

0.189828

7,275

215

102

33.837209

0.80319

0.076014

0

0.160256

0

0.143858

0

1

0.038462

false

0

0.051282

0

0.134615

0.096154

0

null

0

null

0

1

0

c1f2c8a367fb5a9ac50a001ff98f24d08a3663e9

1,023

py

Python

spark-ml-workshop/SparkCustomMLExample/src/main/python/train.py

Code360In/spark-code-examples

181c9906d32571ba6138e63040edfcb4c74ef4bf

[ "MIT" ]

null

spark-ml-workshop/SparkCustomMLExample/src/main/python/train.py

Code360In/spark-code-examples

181c9906d32571ba6138e63040edfcb4c74ef4bf

[ "MIT" ]

null

spark-ml-workshop/SparkCustomMLExample/src/main/python/train.py

Code360In/spark-code-examples

181c9906d32571ba6138e63040edfcb4c74ef4bf

[ "MIT" ]

null

#!/usr/bin/python3 import pandas as pd import pickle import sys import base64 import re from sklearn.linear_model import LinearRegression # Here we keep input data to Dataframe constructor rows = [] for line in sys.stdin: line = line.replace('[', '') line = line.replace(']', '') line = line.replace('\n'...

23.790698

72

0.654936

143

1,023

4.594406

0.48951

0.048706

0.068493

0.057839

0.068493

0

0.01358

0.208211

1,023

42

73

24.357143

0.797531

0.170088

0

0.04497

0

1

0

false

0

0.206897

0

0.206897

0.034483

0

null

0

null

0

1

0

c1f4b67816e30d74cbc32d55dadcfb14a56793f0

1,043

py

Python

concurrent/futures/config.py

mikhtonyuk/rxpython

cfdd38225a3b7960bd475c6a4e380f3dd6a6a0fe

[ "MIT" ]

2

2015-11-25T15:56:04.000Z

2018-11-19T13:31:49.000Z

concurrent/futures/config.py

sergiimk/rxpython

cfdd38225a3b7960bd475c6a4e380f3dd6a6a0fe

[ "MIT" ]

null

concurrent/futures/config.py

sergiimk/rxpython

cfdd38225a3b7960bd475c6a4e380f3dd6a6a0fe

[ "MIT" ]

null

import traceback import logging logger = logging.getLogger(__package__) def log_error_handler(cls, tb): try: logger.error('Future/Task exception was never retrieved:\n%s', ''.join(tb)) except: pass class Default(object): # Called when failure of the future was not h...

28.972222

71

0.684564

115

1,043

5.895652

0.504348

0.117994

0.10177

0

0.255034

1,043

35

72

29.8

0.872587

0.160115

0

0.086957

0

0.052752

0

1

0.130435

false

0.043478

0.130435

0

0.434783

0

null

0

null

0

1

0

c1f564366e13f51ec035c82bf8d6c69e97064976

292

py

Python

mytest.py

chalendony/duden

bf455452ed68b4a7f39b45fec05c7236afef36e1

[ "MIT" ]

null

mytest.py

chalendony/duden

bf455452ed68b4a7f39b45fec05c7236afef36e1

[ "MIT" ]

null

mytest.py

chalendony/duden

bf455452ed68b4a7f39b45fec05c7236afef36e1

[ "MIT" ]

null

import duden def main(): # find the correct url # get definition and examples w1 = duden.get('einfach_einmal_simpel') # remove beispiel code to get the meanings??? print(w1.meaning_example) # change the depth, include code if __name__ == '__main__': main()

16.222222

49

0.660959

38

292

4.789474

0.763158

0

0.009091

0.246575

292

17

50

17.176471

0.818182

0.421233

0

0.176829

0.128049

0

1

0.166667

false

0

0.166667

0

0.333333

0.166667

0

null

0

null

0

1

0

c1f5fa22f45bcd7c67412cd5116711d5de10540b

1,275

py

Python

setup.py

gocept/gocept.download

205fc9d2f6c9dabc3081897ebb7cbaac31737f29

[ "ZPL-2.1" ]

1

2020-07-17T10:05:23.000Z

setup.py

gocept/gocept.download

205fc9d2f6c9dabc3081897ebb7cbaac31737f29

[ "ZPL-2.1" ]

null

setup.py

gocept/gocept.download

205fc9d2f6c9dabc3081897ebb7cbaac31737f29

[ "ZPL-2.1" ]

null

"""zc.buildout recipe for downloading and extracting an archive.""" from setuptools import setup, find_packages name = "gocept.download" classifiers = [ "Environment :: Console", "Environment :: Plugins", "Framework :: Buildout", "Intended Audience :: Developers", "Intended Audience :: System Ad...

31.875

70

0.644706

130

1,275

6.207692

0.669231

0.049566

0.039653

0

0.003937

0.203137

1,275

39

71

32.692308

0.790354

0.047843

0

0.465232

0

1

0

false

0

0.030303

0

0.030303

0

null

0

null

0

1

0

c1f7f53e370043972065de6da7367e0c0230a78e

489

py

Python

pyleecan/Methods/Slot/Slot/comp_radius_mid_active.py

IrakozeFD/pyleecan

5a93bd98755d880176c1ce8ac90f36ca1b907055

[ "Apache-2.0" ]

95

2019-01-23T04:19:45.000Z

2022-03-17T18:22:10.000Z

pyleecan/Methods/Slot/Slot/comp_radius_mid_active.py

IrakozeFD/pyleecan

5a93bd98755d880176c1ce8ac90f36ca1b907055

[ "Apache-2.0" ]

366

2019-02-20T07:15:08.000Z

2022-03-31T13:37:23.000Z

pyleecan/Methods/Slot/Slot/comp_radius_mid_active.py

IrakozeFD/pyleecan

5a93bd98755d880176c1ce8ac90f36ca1b907055

[ "Apache-2.0" ]

74

2019-01-24T01:47:31.000Z

2022-02-25T05:44:42.000Z

# -*- coding: utf-8 -*- def comp_radius_mid_active(self): """Compute the radius at the middle of the active part of the slot Parameters ---------- self : Slot A Slot object Returns ------- Rmw: float Mid active radius [m] """ Rbo = self.get_Rbo() Hslot = sel...

18.807692

70

0.556237

63

489

4.190476

0.539683

0.090909

0.106061

0.143939

0.151515

0

0.008982

0.316973

489

25

71

19.56

0.781437

0.394683

0

1

0.125

false

0

0.375

0

null

0

null

0

1

0

c1f7fea5aff1bf06616d7084eb6453de5655b0d4

9,044

py

Python

lib/roi_data_rel/minibatch_rel.py

champon1020/TRACE

8ed0aed87e153af66f02502887a4de0d39867209

[ "MIT" ]

34

2021-08-19T05:59:58.000Z

2022-03-26T09:26:54.000Z

lib/roi_data_rel/minibatch_rel.py

champon1020/TRACE

8ed0aed87e153af66f02502887a4de0d39867209

[ "MIT" ]

8

2021-09-15T05:27:23.000Z

2022-02-27T12:38:03.000Z

lib/roi_data_rel/minibatch_rel.py

champon1020/TRACE

8ed0aed87e153af66f02502887a4de0d39867209

[ "MIT" ]

6

2021-09-16T10:51:38.000Z

2022-03-05T22:48:54.000Z

# Adapted by Ji Zhang in 2019 # # Based on Detectron.pytorch/lib/roi_data/minibatch.py written by Roy Tseng import numpy as np import cv2 import os from core.config import cfg import utils.blob as blob_utils import roi_data.rpn def get_minibatch_blob_names(is_training=True): """Return blob names in the order in...

39.321739

148

0.604821

1,322

9,044

3.844175

0.186082

0.047226

0.01889

0.026564

0.389807

0.341401

0.286501

0.224715

0.200708

0.194805

0

0.0151

0.282397

9,044

230

149

39.321739

0.767951

0.194162

0

0.20915

0

0.056002

0.007485

0

0.013072

1

0.03268

false

0

0.039216

0

0.104575

0

null

0

null

0

1

0

c1f8caf0b7bccc8a2071b61868b5c37f051d87b7

1,825

py

Python

src/enamlnative/android/android_adapter.py

codelv/enaml-native

04c3a015bcd649f374c5ecd98fcddba5e4fbdbdc

[ "MIT" ]

237

2017-09-15T19:31:45.000Z

2022-03-17T04:22:20.000Z

src/enamlnative/android/android_adapter.py

codelv/enaml-native

04c3a015bcd649f374c5ecd98fcddba5e4fbdbdc

[ "MIT" ]

74

2017-09-06T20:16:41.000Z

2022-03-05T13:34:35.000Z

src/enamlnative/android/android_adapter.py

codelv/enaml-native

04c3a015bcd649f374c5ecd98fcddba5e4fbdbdc

[ "MIT" ]

22

2017-09-15T19:32:11.000Z

2022-03-17T18:33:39.000Z

""" Copyright (c) 2017, Jairus Martin. Distributed under the terms of the MIT License. The full license is in the file LICENSE, distributed with this software. Created on May 20, 2017 @author: jrm """ from atom.api import Typed, set_default from .android_view_group import AndroidViewGroup, ViewGroup from .bridge i...

34.433962

73

0.708493

167

1,825

7.634731

0.461078

0.091765

0.150588

0.112941

0.189804

0.136471

0

0.006734

0.186301

1,825

52

74

35.096154

0.851852

0.118356

0

0.1

0

0.3025

0.203125

0

1

0

false

0

0.1

0

0.8

0

null

0

null

0

1

0

c1fa13883070fa0500e4ad32b4f9fe914e19917d

21,559

py

Python

tola/views.py

meetdatastory/Activity-CE

2692e591f08cea7c869c045577b3d9e20d3ed335

[ "Apache-2.0" ]

null

tola/views.py

meetdatastory/Activity-CE

2692e591f08cea7c869c045577b3d9e20d3ed335

[ "Apache-2.0" ]

null

tola/views.py

meetdatastory/Activity-CE

2692e591f08cea7c869c045577b3d9e20d3ed335

[ "Apache-2.0" ]

null

from django.views.generic.edit import CreateView, UpdateView, DeleteView from django.views.generic.list import ListView from tola.forms import RegistrationForm, NewUserRegistrationForm, NewTolaUserRegistrationForm, BookmarkForm from django.contrib import messages from django.contrib.auth import logout from django.http ...

54.304786

353

0.689271

2,276

21,559

6.224956

0.11775

0.036702

0.04856

0.058724

0.592603

0.544184

0.514963

0.491248

0.451369

0.424831

0

0.003672

0.204091

21,559

396

354

54.441919

0.822018

0.046431

0

0.25

0

0.115535

0.040895

0

1

0.060714

false

0

0.057143

0.003571

0.242857

0

null

0

null

0

1

0

c1fac333c6571737c3ec2918f3ba6fbd833a009a

1,163

py

Python

ct8_refresh/loggers.py

jakubkazimierczak/ct8_refresh

252f1a80a64a6a249cf4d014d19b96291e4e5ad1

[ "Apache-2.0" ]

null

ct8_refresh/loggers.py

jakubkazimierczak/ct8_refresh

252f1a80a64a6a249cf4d014d19b96291e4e5ad1

[ "Apache-2.0" ]

3

2021-01-27T20:46:37.000Z

2021-04-05T23:03:15.000Z

ct8_refresh/loggers.py

jakubkazimierczak/ct8_refresh

252f1a80a64a6a249cf4d014d19b96291e4e5ad1

[ "Apache-2.0" ]

null

import sys from loguru import logger from . import LOG_PATH class Loggers: def __init__(self, debug_switch=None): self.file_handler_id = None self.stderr_handler_id = None # Initialise loggers logger.remove() # Disable default logger if debug_switch: self.ad...

29.820513

107

0.576096

141

1,163

4.539007

0.333333

0.084375

0.070313

0.079688

0.065625

0

0.002418

0.288908

1,163

38

108

30.605263

0.771463

0.035254

0

0.066667

0

0.1

0.240393

0.117069

0

1

0.1

false

0

0.1

0

0.3

0

null

0

null

0

1

0

c1fce5796c28ca9136dea1e831dd68b25a314665

2,525

py

Python

examples/low_rank_psd_matrix_approximation.py

captain-pool/pymanopt

df94ab9e03b5fa3041668defe995d93b8715a6d7

[ "BSD-3-Clause" ]

null

examples/low_rank_psd_matrix_approximation.py

captain-pool/pymanopt

df94ab9e03b5fa3041668defe995d93b8715a6d7

[ "BSD-3-Clause" ]

null

examples/low_rank_psd_matrix_approximation.py

captain-pool/pymanopt

df94ab9e03b5fa3041668defe995d93b8715a6d7

[ "BSD-3-Clause" ]

null

import autograd.numpy as np import tensorflow as tf import torch from numpy import linalg as la from numpy import random as rnd import pymanopt from examples._tools import ExampleRunner from pymanopt.manifolds import PSDFixedRank from pymanopt.solvers import TrustRegions SUPPORTED_BACKENDS = ("Autograd", "Callable",...

27.747253

77

0.640396

320

2,525

4.921875

0.26875

0.035556

0.049524

0.040635

0.214603

0.073651

0

0.010537

0.248317

2,525

90

78

28.055556

0.819283

0

0.138462

0

0.082376

0

1

0.123077

false

0

0.138462

0.061538

0.384615

0.030769

0

null

0

null

0

1

0

de03ad9d5c100d9cf2fd590826d8994ef9cf5a4e

3,730

py

Python

src/experiments/sparse_experiments_bipartite_auc.py

ChristianDjurhuus/RAA

b2eb1db527bcb09f35598c2bbf8dff2689ad599b

[ "MIT" ]

1

2022-03-16T16:09:22.000Z

src/experiments/sparse_experiments_bipartite_auc.py

ChristianDjurhuus/RAA

b2eb1db527bcb09f35598c2bbf8dff2689ad599b

[ "MIT" ]

null

src/experiments/sparse_experiments_bipartite_auc.py

ChristianDjurhuus/RAA

b2eb1db527bcb09f35598c2bbf8dff2689ad599b

[ "MIT" ]

1

2022-02-18T17:10:27.000Z

from src.models.train_DRRAA_module import DRRAA from src.models.train_LSM_module import LSM from src.models.train_BDRRAA_module import BDRRAA import torch import matplotlib.pyplot as plt import numpy as np import json import scipy.stats as st import matplotlib as mpl def sparse_experiments(datasets, ks, sample_size, i...

41.910112

176

0.617158

519

3,730

4.22158

0.246628

0.049293

0.038339

0.043816

0.343679

0.251027

0.221817

0.207211

0.180739

0

0.010561

0.263807

3,730

89

177

41.910112

0.787327

0.078016

0

0.153846

0

0.015385

0.131057

0.017221

0

1

0.015385

false

0

0.138462

0

0.153846

0.107692

0

null

0

null

0

1

0

de03ce4debb8154861870509c00893c2a07d55d2

1,531

py

Python

Tensorflow/XOR_multilayer_nn.py

ram1993/neuralnetwork

ef98557b07d2c08bbfb8610bb622d58ac5100ad1

[ "MIT" ]

null

Tensorflow/XOR_multilayer_nn.py

ram1993/neuralnetwork

ef98557b07d2c08bbfb8610bb622d58ac5100ad1

[ "MIT" ]

null

Tensorflow/XOR_multilayer_nn.py

ram1993/neuralnetwork

ef98557b07d2c08bbfb8610bb622d58ac5100ad1

[ "MIT" ]

null

import tensorflow as tf import numpy as np tf.set_random_seed(234) x_data = np.array([[0, 0], [0, 1], [1, 0], [1, 1]], dtype=np.float32) y_data = np.array([[0], [1], [1], [0]], dtype=np.float32) x = tf.placeholder(tf.float32, shape = [None,2]) y = tf.placeholder(tf.float32, shape=[None, 1]) w1 = tf.Variable(tf.rando...

28.351852

92

0.662312

266

1,531

3.718045

0.278195

0.08089

0.097068

0.145602

0.357937

0.309403

0.220425

0.076845

0

0.066017

0.129327

1,531

53

93

28.886792

0.675919

0.007185

0

0.026333

0

1

0

false

0

0.0625

0

0.0625

0.125

0

null

0

null

0

1

0

de077e70e5610936c78771d56c4e95d42be457e1

1,055

py

Python

locust/test/test_zmqrpc.py

ioneyed/locust

a8c0d7d8c588f3980303358298870f2ea394ab93

[ "MIT" ]

51

2019-02-01T19:43:37.000Z

2022-03-16T09:07:03.000Z

locust/test/test_zmqrpc.py

ioneyed/locust

a8c0d7d8c588f3980303358298870f2ea394ab93

[ "MIT" ]

2

2019-02-23T18:54:22.000Z

2019-11-09T01:30:32.000Z

locust/test/test_zmqrpc.py

ioneyed/locust

a8c0d7d8c588f3980303358298870f2ea394ab93

[ "MIT" ]

35

2019-02-08T02:00:31.000Z

2022-03-01T23:17:00.000Z

import unittest from time import sleep import zmq from locust.rpc import zmqrpc, Message PORT = 5557 class ZMQRPC_tests(unittest.TestCase): def setUp(self): self.server = zmqrpc.Server('*', PORT) self.client = zmqrpc.Client('localhost', PORT, 'identity') def tearDown(self): self.serve...

31.969697

74

0.645498

134

1,055

5.007463

0.373134

0.134128

0.077496

0.163934

0

0.008578

0.22654

1,055

32

75

32.96875

0.813725

0.075829

0

0.16

0

0.096708

0

0.24

1

0.16

false

0

0.16

0

0.36

0

null

0

null

0

1

0

de0b24691296a4bf0f78e47b9d82fcf62408dc87

20,854

py

Python

ingest_cmorph.py

monocongo/ingest_cmorph

4af25f3626184cfb9a89efa4da6729b9b46ebbbf

[ "BSD-3-Clause" ]

5

2018-02-14T00:39:35.000Z

2022-01-14T15:09:13.000Z

ingest_cmorph.py

monocongo/ingest_cmorph

4af25f3626184cfb9a89efa4da6729b9b46ebbbf

[ "BSD-3-Clause" ]

13

2018-01-25T23:19:04.000Z

2019-04-12T18:15:39.000Z

ingest_cmorph.py

monocongo/ingest_cmorph

4af25f3626184cfb9a89efa4da6729b9b46ebbbf

[ "BSD-3-Clause" ]

1

2020-04-29T23:30:32.000Z

import argparse import bz2 import calendar from datetime import datetime # import ftplib import gzip import logging import netCDF4 import numpy as np import os import shutil import urllib.error import urllib.request import warnings #-----------------------------------------------------------------------...

47.180995

197

0.545363

2,359

20,854

4.644765

0.208139

0.024824

0.004381

0.005476

0.186182

0.11463

0.08077

0.066898

0.057497

0.045268

0

0.020553

0.304738

20,854

442

198

47.180995

0.735154

0.37494

0

0.087963

0

0.00463

0.129749

0.026499

0

0.006787

0

1

0.041667

false

0

0.060185

0.00463

0.134259

0.027778

0

null

0

null

0

1

0

de11b84d64c7ee5e90a81ea6db93a10f72c41fe6

8,823

py

Python

ray_shuffling_data_loader/dataset.py

richardliaw/ray_shuffling_data_loader

055a0d47f823d878087a2a2d3fe50483b525f685

[ "Apache-2.0" ]

null

ray_shuffling_data_loader/dataset.py

richardliaw/ray_shuffling_data_loader

055a0d47f823d878087a2a2d3fe50483b525f685

[ "Apache-2.0" ]

null

ray_shuffling_data_loader/dataset.py

richardliaw/ray_shuffling_data_loader

055a0d47f823d878087a2a2d3fe50483b525f685

[ "Apache-2.0" ]

null

import functools from typing import List, Iterable import pandas as pd import ray from ray._private.utils import get_num_cpus from ray_shuffling_data_loader.shuffle import shuffle from ray_shuffling_data_loader.multiqueue import MultiQueue MULTIQUEUE_ACTOR_NAME = "MultiQueue" REDUCER_CLUSTER_CORE_SHARE = 0.6 class ...

39.923077

79

0.607163

1,121

8,823

4.554862

0.231936

0.031727

0.017822

0.018801

0.140423

0.084215

0.065022

0.053271

0.039561

0

0.006864

0.323019

8,823

220

80

40.104545

0.847983

0.28539

0

0.15942

0

0.133749

0.023603

0

0.009091

0

1

0.036232

false

0

0.065217

0

0.108696

0.057971

0

null

0

null

0

1

0

de148a2bd0c9ed523e7886ae8641709d4250a936

693

py

Python

dash_docs/chapters/dash_vtk/other/examples/t05_reader.py

wesleyacheng/dash-docs

c8510bf50d0ae55a56ae30f3057f2bfc6d3ca8d6

[ "MIT" ]

379

2017-06-21T14:35:52.000Z

2022-03-20T01:47:14.000Z

dash_docs/chapters/dash_vtk/other/examples/t05_reader.py

wesleyacheng/dash-docs

c8510bf50d0ae55a56ae30f3057f2bfc6d3ca8d6

[ "MIT" ]

746

2017-06-21T19:58:17.000Z

2022-03-23T14:51:24.000Z

dash_docs/chapters/dash_vtk/other/examples/t05_reader.py

wesleyacheng/dash-docs

c8510bf50d0ae55a56ae30f3057f2bfc6d3ca8d6

[ "MIT" ]

201

2017-06-21T21:53:19.000Z

2022-03-17T13:23:55.000Z

import os import dash import dash_html_components as html import dash_vtk # Get it here: https://github.com/plotly/dash-vtk/blob/master/demos/data/cow-nonormals.obj obj_file = "datasets/cow-nonormals.obj" txt_content = None with open(obj_file, 'r') as file: txt_content = file.read() content = dash_vtk.View([ ...

19.8

90

0.670996

92

693

4.793478

0.576087

0.079365

0.068027

0

0.010695

0.190476

693

34

91

20.382353

0.775401

0.142857

0

0.113367

0.043993

0

1

0

false

0

0.166667

0

0.166667

0

null

0

null

0

1

0

a9a5e57bf265778860430aa86749885c36d7995e

7,281

py

Python

traffic/build_dataset.py

daniele-canavese/fingerprinting

180cffd8e3edda0fe99fac003832027e1388687a

[ "Apache-2.0" ]

null

traffic/build_dataset.py

daniele-canavese/fingerprinting

180cffd8e3edda0fe99fac003832027e1388687a

[ "Apache-2.0" ]

null

traffic/build_dataset.py

daniele-canavese/fingerprinting

180cffd8e3edda0fe99fac003832027e1388687a

[ "Apache-2.0" ]

null

from argparse import ArgumentParser from glob import glob from os import listdir from os import system from os import unlink from os.path import isdir from numpy import linspace # Parses the input arguments. from numpy import split from pandas import DataFrame from pandas import read_csv parser = ArgumentParser(descr...

41.605714

117

0.513391

932

7,281

3.859442

0.222103

0.038921

0.020017

0.025299

0.190714

0.071726

0.05171

0.037809

0.023909

0

0.051897

0.341025

7,281

174

118

41.844828

0.697791

0.041615

0

0.028571

0

0.241091

0.009955

0

1

0.014286

false

0

0.071429

0

0.085714

0.092857

0

null

0

null

0

1

0

a9a67980e424c4f01050fbfb15319a06062ec61b

3,792

py

Python

dfm-i.py

gmlunesa/detectfacemask

92add56de32ab28bf9179986876407d681a5d6b6

[ "MIT" ]

6

2020-11-20T13:28:43.000Z

2021-12-16T06:42:46.000Z

dfm-i.py

gmlunesa/detectfacemask

92add56de32ab28bf9179986876407d681a5d6b6

[ "MIT" ]

null

dfm-i.py

gmlunesa/detectfacemask

92add56de32ab28bf9179986876407d681a5d6b6

[ "MIT" ]

null

# Import the necessary packages and libraries import numpy as np import argparse import cv2 import os from tensorflow.keras.models import load_model from tensorflow.keras.preprocessing.image import img_to_array from tensorflow.keras.applications.mobilenet_v2 import preprocess_input # Arguments are expected to be the ...

31.338843

79

0.709652

522

3,792

5.111111

0.415709

0.026987

0.021364

0.009745

0.058471

0.036732

0

0.039146

0.184863

3,792

121

80

31.338843

0.824005

0.314346

0

0.112193

0.015582

0

1

0

false

0

0.112903

0

0.112903

0.032258

0

null

0

null

0

1

0

a9a7bb5db5244b6878098fc53823b91f4fc0297e

439

py

Python

ExerciciosdePython/ex065.py

aleksandromelo/Exercicios

782ff539efa1286180eaf8df8c25c4eca7a5e669

[ "MIT" ]

null

ExerciciosdePython/ex065.py

aleksandromelo/Exercicios

782ff539efa1286180eaf8df8c25c4eca7a5e669

[ "MIT" ]

null

ExerciciosdePython/ex065.py

aleksandromelo/Exercicios

782ff539efa1286180eaf8df8c25c4eca7a5e669

[ "MIT" ]

null

n = 0 op = '' cont = 0 soma = 0 maior = 0 menor = 0 while op in 'Ss': n = int(input('Digite um número: ')) op = str(input('Quer continuar? [S/N] ')).upper().strip()[0] cont += 1 soma += n media = soma / cont if cont == 1: maior = n menor = n else: if n > maior: ...

16.259259

64

0.457859

64

439

3.140625

0.421875

0.049751

0

0.029091

0.373576

439

26

65

16.884615

0.701818

0

0.181818

0

0.166287

0

1

0

false

0

0.090909

0

null

0

null

0

1

0

a9ab13c622bacfe72e062806000e550232c0a0df

9,672

py

Python

scanner/vuln-creator/vulns_creator.py

mercadolibre/afip-grails

d38daa57a66d3ccc71c481a35148c1136a2d867f

[ "Apache-2.0" ]

22

2019-01-28T17:47:08.000Z

2020-05-14T21:49:49.000Z

scanner/vuln-creator/vulns_creator.py

joaquinlpereyra-ml/afip-grails

607211959394b6f05c064732398bde58cc009489

[ "Apache-2.0" ]

null

scanner/vuln-creator/vulns_creator.py

joaquinlpereyra-ml/afip-grails

607211959394b6f05c064732398bde58cc009489

[ "Apache-2.0" ]

1

2021-05-19T15:18:11.000Z

import requests import code import json import os.path from pathlib import Path AFIP_GROOVY_PATH = '{}/scanner'.format(Path('.')) class Problem: def __init__(self, name, problem_dict): self.name = name for key in problem_dict: setattr(self, key, problem_dict[key]) def __str__(self...

41.689655

127

0.580438

1,162

9,672

4.60327

0.191911

0.047112

0.053468

0.039073

0.378202

0.316508

0.285848

0.260423

0.242475

0

0.002337

0.292184

9,672

231

128

41.87013

0.778995

0.014061

0

0.323383

0

0.004975

0.228652

0.03487

0

0.004975

1

0.104478

false

0

0.044776

0.024876

0.268657

0.099502

0

null

0

null

0

1

0

a9aefe76b63591df69e485f31359a5e8e36a478c

1,301

py

Python

15/tests.py

remihuguet/aoc2020

c313c5b425dda92d949fd9ca4f18ff66f452794f

[ "MIT" ]

null

15/tests.py

remihuguet/aoc2020

c313c5b425dda92d949fd9ca4f18ff66f452794f

[ "MIT" ]

null

15/tests.py

remihuguet/aoc2020

c313c5b425dda92d949fd9ca4f18ff66f452794f

[ "MIT" ]

null

import pytest import memory inputs = [ (0, 3, 6, 436), (1, 3, 2, 1), (2, 1, 3, 10), (1, 2, 3, 27), (2, 3, 1, 78), (3, 2, 1, 438), (3, 1, 2, 1836) ] @pytest.fixture(params=inputs) def starting(request): return list(request.param[:3]), request.param[-1] def test_apply_rule_for_one_tur...

23.654545

82

0.601845

205

1,301

3.712195

0.239024

0.021025

0.165572

0.149803

0.47569

0.474376

0.344284

0.207622

0.131406

0

0.142012

0.2206

1,301

54

83

24.092593

0.608481

0

0.04878

0

0.219512

1

0.121951

false

0

0.04878

0.219512

0

null

0

null

0

1

0

a9b0a0692415b86e04e3728ac107f178159ea369

4,883

py

Python

Tools/Asynchronous_and_Constant_Input_API_call_checker/constant_input_parse_outfile.py

mlapistudy/ICSE2021_421

c7fca3ba7d53917b3b6de28a8cb1d343b5959118

[ "BSD-2-Clause" ]

9

2021-04-06T15:02:58.000Z

2022-03-07T03:36:22.000Z

Tools/Asynchronous_and_Constant_Input_API_call_checker/constant_input_parse_outfile.py

mlapistudy/ICSE2021_421

c7fca3ba7d53917b3b6de28a8cb1d343b5959118

[ "BSD-2-Clause" ]

null

Tools/Asynchronous_and_Constant_Input_API_call_checker/constant_input_parse_outfile.py

mlapistudy/ICSE2021_421

c7fca3ba7d53917b3b6de28a8cb1d343b5959118

[ "BSD-2-Clause" ]

1

2022-02-22T16:21:30.000Z

import sys import re from utils.utils import print_writeofd # First argument is whether or not to proceed with manual checking: if sys.argv[1] == '-m': MANUAL_CHECKING = True elif sys.argv[1] == '-a': MANUAL_CHECKING = False else: print("The first argument must be either -m or -a, see README.md for details...

37.561538

166

0.589392

634

4,883

4.471609

0.276025

0.163668

0.137919

0.026455

0.270547

0.221869

0.162963

0.132628

0

0.021481

0.275445

4,883

130

167

37.561538

0.779819

0.12267

0

0.264706

0

0.009804

0.350105

0.01685

0

1

0.009804

false

0

0.029412

0

0.068627

0.245098

0

null

0

null

0

1

0

a9b112d0bd56a41caca4c0915a3d8b20397d3e01

10,319

py

Python

seaice/tools/reader.py

andypbarrett/nsidc-seaice

167a16309f7eaadd5c613b54a7df26eb1f48c2f3

[ "MIT" ]

2

2020-08-27T08:40:22.000Z

2021-04-14T15:42:09.000Z

seaice/tools/reader.py

andypbarrett/nsidc-seaice

167a16309f7eaadd5c613b54a7df26eb1f48c2f3

[ "MIT" ]

null

seaice/tools/reader.py

andypbarrett/nsidc-seaice

167a16309f7eaadd5c613b54a7df26eb1f48c2f3

[ "MIT" ]

null

import calendar import os import re import pandas as pd REGIONAL_SHEETS = ['Baffin-Area-km^2', 'Baffin-Extent-km^2', 'Barents-Area-km^2', 'Barents-Extent-km^2', 'Beaufort-Area-km^2', 'Beaufort-Extent-km^2', ...

36.72242

98

0.515069

1,341

10,319

3.802386

0.132737

0.017651

0.019219

0.02981

0.610512

0.587958

0.534615

0.49343

0.448519

0.403412

0

0.032928

0.346642

10,319

280

99

36.853571

0.723376

0.070743

0

0.342391

0

0.005435

0.232156

0.114139

0

1

0.005435

false

0

0.021739

0

0.081522

0

null

0

null

0

1

0

a9b19a34b450c33dfddc6881e62d1f7147ec860d

599

py

Python

examples/accelerate/numba.py

Jie-Yuan/1_DataMining

f5338388b4f883233f350d4fb9c5903180883430

[ "Apache-2.0" ]

14

2019-06-25T13:46:32.000Z

2020-10-27T02:04:59.000Z

examples/accelerate/numba.py

Jie-Yuan/2_DataMining

f5338388b4f883233f350d4fb9c5903180883430

[ "Apache-2.0" ]

null

examples/accelerate/numba.py

Jie-Yuan/2_DataMining

f5338388b4f883233f350d4fb9c5903180883430

[ "Apache-2.0" ]

7

2019-06-25T13:26:16.000Z

2020-10-27T02:05:03.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- # @Project : tql-Python. # @File : numba # @Time : 2019-12-26 14:52 # @Author : yuanjie # @Email : yuanjie@xiaomi.com # @Software : PyCharm # @Description : from numba import jit, njit, vectorize import numpy as np x = np.arange(10...

20.655172

64

0.597663

84

599

4.25

0.714286

0.084034

0

0.052392

0.267112

599

28

65

21.392857

0.76082

0.567613

0

1

0.111111

false

0

0.222222

0

0.444444

0

null

0

null

0

1

0

a9b2fc7d45738b3e7655e81adf3bd08e96111434

2,119

py

Python

scripts/dataset_processing/ljspeech/create_token2idx_dict.py

PatrykNeubauer/NeMo

3ada744b884dba5f233f22c6991fc6092c6ca8d0

[ "Apache-2.0" ]

null

scripts/dataset_processing/ljspeech/create_token2idx_dict.py

PatrykNeubauer/NeMo

3ada744b884dba5f233f22c6991fc6092c6ca8d0

[ "Apache-2.0" ]

null

scripts/dataset_processing/ljspeech/create_token2idx_dict.py

PatrykNeubauer/NeMo

3ada744b884dba5f233f22c6991fc6092c6ca8d0

[ "Apache-2.0" ]

null

# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by appli...

30.710145

84

0.641812

258

2,119

5.217054

0.546512

0.114413

0.122585

0.130758

0.101783

0

0.020036

0.222747

2,119

68

85

31.161765

0.797207

0.337895

0

0.125453

0

1

0.025

false

0

0.075

0

0.1

0.025

0

null

0

null

0

1

0

a9b56b0379c4118f82605a6de23ecfa9e150dac1

3,454

py

Python

mt_dnn/inference.py

anlewy/mt-dnn

eeb6f01ce0630e61a52b8c9c6f7537cd34978e45

[ "MIT" ]

2,075

2019-02-25T08:54:38.000Z

2022-03-31T10:44:50.000Z

mt_dnn/inference.py

anlewy/mt-dnn

eeb6f01ce0630e61a52b8c9c6f7537cd34978e45

[ "MIT" ]

176

2019-03-12T02:58:42.000Z

2022-03-22T20:17:23.000Z

mt_dnn/inference.py

anlewy/mt-dnn

eeb6f01ce0630e61a52b8c9c6f7537cd34978e45

[ "MIT" ]

437

2019-03-11T21:36:21.000Z

2022-03-29T02:40:53.000Z

# coding=utf-8 # Copyright (c) Microsoft. All rights reserved. import enum from numpy.lib.arraysetops import isin from numpy.lib.function_base import insert from data_utils.metrics import calc_metrics from mt_dnn.batcher import Collater from data_utils.task_def import TaskType from data_utils.utils_qa import postproces...

36.357895

120

0.657209

453

3,454

4.796909

0.269316

0.07087

0.051542

0.066268

0.169351

0.127013

0.101243

0.065347

0

0.004605

0.245512

3,454

95

121

36.357895

0.82924

0.016792

0

0.095238

0

0.001768

0

0.035714

1

0.059524

false

0

0.142857

0

0.27381

0

null

0

null

0

1

0

a9b5d1f91dcab6368915c512048ca47cccd01d6f

2,694

py

Python

gffpal/scripts/rnammer2gff.py

darcyabjones/gffpal

9e195d94f5c9e259fa26f4840fbd440e3b37f777

[ "Apache-2.0" ]

1

2019-12-09T13:57:42.000Z

gffpal/scripts/rnammer2gff.py

darcyabjones/gffpal

9e195d94f5c9e259fa26f4840fbd440e3b37f777

[ "Apache-2.0" ]

1

2020-02-25T04:24:52.000Z

2020-02-27T08:02:14.000Z

gffpal/scripts/rnammer2gff.py

darcyabjones/gffpal

9e195d94f5c9e259fa26f4840fbd440e3b37f777

[ "Apache-2.0" ]

1

2019-12-09T13:57:44.000Z

import sys import argparse from copy import deepcopy from typing import cast from typing import List from gffpal.gff import GFF from gffpal.gff import GFF3Record from gffpal.attributes import GFF3Attributes import logging logger = logging.getLogger(__name__) TYPE_MAP = { "euk": { "5s_rrna": "rRNA_5S", ...

23.840708

73

0.544172

310

2,694

4.574194

0.358065

0.056417

0.028209

0.025388

0.056417

0

0.02601

0.32925

2,694

112

74

24.053571

0.758716

0

0.16129

0

0.155902

0

1

0.021505

false

0

0.096774

0

0.139785

0.010753

0

null

0

null

0

1

0

a9b847c118bf2fb5a2a27bd55f6e9604914a2fac

1,767

py

Python

9_Monitor_Data_Drift/1_Creating_data_drift_monitor.py

aditagrawal/Azure_ML

30d7021c19aef8f56b05580cba25d38a5bc0b24e

[ "Unlicense" ]

null

9_Monitor_Data_Drift/1_Creating_data_drift_monitor.py

aditagrawal/Azure_ML

30d7021c19aef8f56b05580cba25d38a5bc0b24e

[ "Unlicense" ]

null

9_Monitor_Data_Drift/1_Creating_data_drift_monitor.py

aditagrawal/Azure_ML

30d7021c19aef8f56b05580cba25d38a5bc0b24e

[ "Unlicense" ]

null

### Creating data drift monitor from azureml.datadrift import DataDriftDetector monitor = DataDriftDetector.create_from_datasets(workspace=ws, name='dataset-drift-detector', baseline_data_set=train_ds, ...

57

263

0.534805

171

1,767

5.385965

0.538012

0.030402

0.034745

0.073833

0.16721

0.076004

0

0.006667

0.405773

1,767

30

264

58.9

0.870476

0.258065

0

0.078522

0.054657

0

1

0

false

0

0.111111

0

0.111111

0

null

0

null

0

1

0

a9bc2c278dd9ef86fef7b968518e3ca025ab6ba8

850

py

Python

hardhat/recipes/python/hg_zipdoc.py

stangelandcl/hardhat

1ad0c5dec16728c0243023acb9594f435ef18f9c

[ "MIT" ]

null

hardhat/recipes/python/hg_zipdoc.py

stangelandcl/hardhat

1ad0c5dec16728c0243023acb9594f435ef18f9c

[ "MIT" ]

null

hardhat/recipes/python/hg_zipdoc.py

stangelandcl/hardhat

1ad0c5dec16728c0243023acb9594f435ef18f9c

[ "MIT" ]

null

import os import shutil from ..base import Downloader, Extractor, Recipe class HgZipDocRecipe(Downloader, Extractor, Recipe): def __init__(self, *args, **kwargs): super(HgZipDocRecipe, self).__init__(*args, **kwargs) self.sha256 = '0de7075c9be80856f3a1c8968f42cfa0' \ '9c44d09...

34

76

0.614118

92

850

5.576087

0.554348

0.046784

0.097466

0.054581

0

0.081761

0.251765

850

24

77

35.416667

0.724843

0

0.217647

0.075294

0

1

0.1

false

0

0.15

0

0.3

0

null

0

null

0

1

0

a9bf620f1f16041d166e420a5fdf6a5cba0d0ebe

3,130

py

Python

tps/problems/forms/generic.py

akmohtashami/tps-web

9dab3ffe97c21f658be30ce2f2711dd93e4ba60f

[ "MIT" ]

5

2019-02-26T06:10:43.000Z

2021-07-24T17:11:45.000Z

tps/problems/forms/generic.py

akmohtashami/tps-web

9dab3ffe97c21f658be30ce2f2711dd93e4ba60f

[ "MIT" ]

3

2019-08-15T13:56:03.000Z

2021-06-10T18:43:16.000Z

tps/problems/forms/generic.py

jonathanirvings/tps-web

46519347d4fc8bdced9b5bceb6cdee5ea4e508f2

[ "MIT" ]

2

2018-12-28T13:12:59.000Z

2020-12-25T18:42:13.000Z

from django import forms from django.core.exceptions import ValidationError from django.forms.models import construct_instance, InlineForeignKeyField from problems.forms.fields import AutoFilledField class ProblemObjectForm(forms.Form): def __init__(self, *args, **kwargs): self.problem = kwargs.pop("prob...

37.710843

89

0.667093

348

3,130

5.83046

0.327586

0.039921

0.0276

0.022178

0.249877

0.209463

0.169049

0

0.004587

0.233866

3,130

82

90

38.170732

0.841535

0.142173

0

0.350877

0

0.043381

0

1

0.122807

false

0

0.070175

0

0.298246

0

null

0

null

0

1

0

a9c0eb9661089aec6b023e06a45ffc338669fd5e

762

py

Python

examples/plot_regressor.py

pavelkomarov/projection-pursuit-regression

d8d4d0a72a0ba7f64d240bc3b14129ccd9c7cf9e

[ "BSD-3-Clause" ]

29

2018-02-08T21:24:01.000Z

2022-03-29T03:05:26.000Z

examples/plot_regressor.py

imanpalsingh/projection-pursuit

307ad765d447e81dce909dfa9778db1610704315

[ "BSD-3-Clause" ]

1

2021-12-20T00:22:05.000Z

2022-02-09T19:59:57.000Z

examples/plot_regressor.py

imanpalsingh/projection-pursuit

307ad765d447e81dce909dfa9778db1610704315

[ "BSD-3-Clause" ]

11

2019-02-24T00:28:53.000Z

2022-02-10T01:48:03.000Z

""" Plotting ProjectionPursuitRegressor This example trains a regressor to fit data in R1 against only a single output, so it can be visualized in 2D. """ import numpy as np import sys sys.path.append("..") from skpp import ProjectionPursuitRegressor from matplotlib import pyplot as plt X = np.arange(100).reshape(100...

28.222222

71

0.74147

118

762

4.788136

0.610169

0.00708

0

0.032984

0.124672

762

26

72

29.307692

0.814093

0.192913

0

0.276771

0

1

0

false

0

0.235294

0

0.235294

0

null

0

null

0

1

0

a9c12d24b96133b1aff1f7db06bdcc87c693d8f2

3,323

py

Python

web/http_server.py

telesoho/htmlcapture

ef78708fc79c6001a6fbaa1616a104075b6fb811

[ "MIT" ]

null

web/http_server.py

telesoho/htmlcapture

ef78708fc79c6001a6fbaa1616a104075b6fb811

[ "MIT" ]

null

web/http_server.py

telesoho/htmlcapture

ef78708fc79c6001a6fbaa1616a104075b6fb811

[ "MIT" ]

null

import os from flask import Flask, send_from_directory, request, redirect, url_for, flash from flask_cors import CORS from flask import render_template from flask import url_for from werkzeug.utils import secure_filename import re import subprocess import json web_dir = os.path.dirname(os.path.realpath(__file__)) roo...

31.056075

90

0.592838

433

3,323

4.413395

0.311778

0.047096

0.03663

0.040816

0.119309

0.088959

0.056515

0.029304

0

0.002895

0.272344

3,323

106

91

31.349057

0.787428

0.092988

0

0.192308

0

0.111111

0

1

0.076923

false

0

0.115385

0.025641

0.358974

0.012821

0

null

0

null

0

1

0

a9c14c4a8d16221e6534d0917e8a6861680f3692

483

py

Python

sm64r/Level.py

andrelikesdogs/sm64-randomizer

8c8d2f3e3b9f6b69d3595d78669aa120056b6f98

[ "MIT" ]

49

2019-07-01T16:19:32.000Z

2022-03-28T16:15:36.000Z

sm64r/Level.py

andrelikesdogs/sm64-randomizer

8c8d2f3e3b9f6b69d3595d78669aa120056b6f98

[ "MIT" ]

44

2019-10-19T18:19:56.000Z

2022-03-25T22:53:20.000Z

sm64r/Level.py

andrelikesdogs/sm64-randomizer

8c8d2f3e3b9f6b69d3595d78669aa120056b6f98

[ "MIT" ]

5

2020-03-13T22:56:56.000Z

2021-12-05T03:37:30.000Z

from typing import List, Dict from .Area import Area class Level: def __init__(self, course_id : int, name : str, properties : Dict = None, areas : List[Area] = None, offset : int = None): self.course_id = course_id self.name = name self.properties = properties or {} self.offset = offset self.a...

32.2

124

0.660455

70

483

4.385714

0.385714

0.104235

0.117264

0.104235

0

0.200828

483

15

125

32.2

0.795337

0.132505

0

0.100239

0.052506

0

1

0.181818

false

0

0.181818

0.090909

0.545455

0

null

0

null

0

1

0

a9c2e54eed94a614ce5ca1ba244a83aa4d8e44cc

3,182

py

Python

env/place_env.py

laiyao1/PPO-PyTorch

ce3d9ede43a8d1018bf66b2fc952b17e9fa6c844

[ "MIT" ]

null

env/place_env.py

laiyao1/PPO-PyTorch

ce3d9ede43a8d1018bf66b2fc952b17e9fa6c844

[ "MIT" ]

null

env/place_env.py

laiyao1/PPO-PyTorch

ce3d9ede43a8d1018bf66b2fc952b17e9fa6c844

[ "MIT" ]

null

import math from typing import Optional import gym from gym import spaces, logger from gym.utils import seeding import numpy as np import sys sys.path.append("..") from place_db import PlaceDB from build_graph import build_graph_from_placedb class PlaceEnv(gym.Env): def __init__(self, placedb, grid = 32): ...

33.851064

104

0.574796

437

3,182

3.933638

0.228833

0.061082

0.05701

0.052356

0.1879

0.175102

0.107621

0.068645

0

0.010614

0.318982

3,182

94

105

33.851064

0.782649

0.110622

0

0.140845

0

0.018794

0

0.028169

1

0.098592

false

0.014085

0.126761

0.028169

0.309859

0.014085

0

null

0

null

0

1

0

a9c311cfd77a2a5711f4d923436b80dcc24a6e7e

1,045

py

Python

arjuna/engine/unitee/markup/mrules.py

test-mile/arjuna

21880b41e061e11bac2e600a3614684f8af75b2f

[ "Apache-2.0" ]

9

2018-11-15T10:09:17.000Z

2021-01-12T05:59:19.000Z

arjuna/engine/unitee/markup/mrules.py

test-mile/arjuna

21880b41e061e11bac2e600a3614684f8af75b2f

[ "Apache-2.0" ]

2

2019-07-01T15:33:46.000Z

2019-07-12T13:04:08.000Z

arjuna/engine/unitee/markup/mrules.py

test-mile/arjuna

21880b41e061e11bac2e600a3614684f8af75b2f

[ "Apache-2.0" ]

4

2018-12-02T15:14:04.000Z

2020-05-28T12:57:24.000Z

from arjuna.engine.unitee.enums import * built_in_prop_type = { BuiltInProp.ID : str, BuiltInProp.PRIORITY : int, BuiltInProp.THREADS : int, BuiltInProp.NAME : str, BuiltInProp.AUTHOR : str, BuiltInProp.IDEA : str, BuiltInProp.UNSTABLE : bool, BuiltInProp.COMPONENT : str, BuiltInPro...

32.65625

101

0.625837

129

1,045

4.79845

0.395349

0.07916

0.088853

0.072698

0.12601

0

0.28134

1,045

32

102

32.65625

0.824234

0

0.056405

0

1

0.107143

false

0

0.035714

0.071429

0.214286

0

null

0

null

0

1

0

a9c4814c52edf43dbd36cb0343ece943f7363b17

487

py

Python

solution/greedy/14916/main.py

jungyoonoh/baekjoon-1

2b4437a4b5e06244fa47fae6c7b7be0157d0f94f

[ "MIT" ]

2,236

2019-08-05T00:36:59.000Z

2022-03-31T16:03:53.000Z

solution/greedy/14916/main.py

jungyoonoh/baekjoon-1

2b4437a4b5e06244fa47fae6c7b7be0157d0f94f

[ "MIT" ]

225

2020-12-17T10:20:45.000Z

2022-01-05T17:44:16.000Z

solution/greedy/14916/main.py

jungyoonoh/baekjoon-1

2b4437a4b5e06244fa47fae6c7b7be0157d0f94f

[ "MIT" ]

602

2019-08-05T00:46:25.000Z

2022-03-31T13:38:23.000Z

# Authored by : gusdn3477 # Co-authored by : - # Link : http://boj.kr/f274a6ac753440deb8c47de3ee127244 import sys def input(): return sys.stdin.readline().rstrip() N = int(input()) if N < 5: if N % 2 != 0: ans = -1 else: ans = N // 2 else: ct, N = divmod(N, 5) if N == 0: a...

18.037037

55

0.445585

67

487

3.238806

0.432836

0.0553

0.036866

0.046083

0

0.128028

0.406571

487

27

56

18.037037

0.622837

0.197125

0

0.333333

0

1

0.047619

false

0

0.047619

0.142857

0.047619

0

null

0

null

0

1

0

a9c796d46fb219ea35769e65d84e8f2b0db9cb46

675

py

Python

config.py

fernando-freires/Tank-Pong

0fd3cf52d467c00d5b52013da7ee2feb8451382d

[ "MIT" ]

null

config.py

fernando-freires/Tank-Pong

0fd3cf52d467c00d5b52013da7ee2feb8451382d

[ "MIT" ]

null

config.py

fernando-freires/Tank-Pong

0fd3cf52d467c00d5b52013da7ee2feb8451382d

[ "MIT" ]

null

import pygame pygame.font.init() # Screen screen_width = 800 screen_height = 550 # Colors RED = (134, 28, 9) YELLOW = (212, 169, 65) WHITE = (255, 255, 255) GREEN = (0, 127, 33) BLUE = (0, 97, 148) # Rectangles constants RECT_1 = (20, 20) RECT_2 = (60, 20) RECT_3 = (20, 60) RECT_4 = (30, 30) RECT_5 = (20, 108) RECT...

15.697674

48

0.653333

117

675

3.615385

0.529915

0.028369

0.080378

0.104019

0.122931

0

0.187726

0.179259

675

42

49

16.071429

0.575812

0.106667

0

0.060504

0

1

0

false

0

0.038462

0

0.038462

0

null

0

null

0

1

0