xszheng2020/the_stack_dedup_python_hits_0 · Datasets at Hugging Face

37b92a6bf223f5761c53562b6e0bd7327e57e2bf

2,373

py

Python

uiworld.py

touilleMan/trimps

603335009a1768f104e4ed317d24a75579f1aeb1

[ "WTFPL" ]

2

2021-11-08T02:46:09.000Z

2021-11-08T09:41:00.000Z

uiworld.py

touilleMan/trimps

603335009a1768f104e4ed317d24a75579f1aeb1

[ "WTFPL" ]

null

uiworld.py

touilleMan/trimps

603335009a1768f104e4ed317d24a75579f1aeb1

[ "WTFPL" ]

null

from PyQt4 import QtCore, QtGui class UiWorld(QtGui.QWidget): """Qt widget representing the world """ def __init__(self, parent): super(UiWorld, self).__init__(parent) self.__last_point = None self.image = QtGui.QImage(800, 600, QtGui.QImage.Format_ARGB32) self.image.fill(Qt...

35.954545

73

0.581542

309

2,373

4.333333

0.313916

0.100822

0.038835

0.033607

0.348021

0.31068

0.271845

0.231516

0

0.01135

0.294564

2,373

65

74

36.507692

0.78853

0.094395

0

0.196078

0

1

0.117647

false

0

0.019608

0

0.156863

0

null

0

null

0

1

0

37b9584c00e84d083226a073ff4103362f7643d1

7,215

py

Python

src/kblue/rfcomm.py

tulare/kblue

731aa3c4600f3b7c0e53efb51075335ca266b665

[ "MIT" ]

null

src/kblue/rfcomm.py

tulare/kblue

731aa3c4600f3b7c0e53efb51075335ca266b665

[ "MIT" ]

null

src/kblue/rfcomm.py

tulare/kblue

731aa3c4600f3b7c0e53efb51075335ca266b665

[ "MIT" ]

null

# -*- encoding: utf8 -*- import logging import re import socket import subprocess __all__ = [ 'RFComm' ] class RFCommNotConnected(BaseException) : pass class RFCommError(BaseException) : pass class RFComm : def __init__(self, bdaddr, port, timeout=5, encoding='utf-8') : self.logger = logging.g...

27.643678

151

0.537076

1,028

7,215

3.705253

0.131323

0.022053

0.070885

0.133893

0.551851

0.503807

0.501969

0.465739

0.446836

0.39958

0

0.024845

0.285932

7,215

260

152

27.75

0.71448

0.003049

0

0.475936

0

0.010695

0.107115

0.026856

0

1

0.106952

false

0.010695

0.02139

0.026738

0.203209

0.160428

0

null

0

null

0

1

0

37bb688f46ffd73cd913e78b3be6c48784ad0dc1

10,517

py

Python

SplineMeasurement/engine/vtk_widgets/ro_psi_spline_widget.py

TiNezlobinsky/SplineLV

7281bca555f8eda802091cfbe3687b8ab59bfa4b

[ "MIT" ]

null

SplineMeasurement/engine/vtk_widgets/ro_psi_spline_widget.py

TiNezlobinsky/SplineLV

7281bca555f8eda802091cfbe3687b8ab59bfa4b

[ "MIT" ]

null

SplineMeasurement/engine/vtk_widgets/ro_psi_spline_widget.py

TiNezlobinsky/SplineLV

7281bca555f8eda802091cfbe3687b8ab59bfa4b

[ "MIT" ]

null

from vtk import vtkSplineWidget, vtkLineSource, vtkActor, vtkPolyDataMapper from numpy import linspace from math import pi, asin, sqrt, sin from scipy import interpolate import numpy as np # CODE REGIONS: # 1) Spline computing # 2) Spline redrawing # 3) Setters # 4) Getters # 5) Coordinates transformation # 6) Handle...

32.865625

102

0.625464

1,312

10,517

4.745427

0.166159

0.04176

0.039672

0.04433

0.379859

0.274655

0.146482

0.089945

0.082236

0

0.010617

0.274603

10,517

319

103

32.968652

0.805479

0.127698

0

0.207729

0

0.00628

0

1

0.188406

false

0.004831

0.024155

0.062802

0.285024

0

null

0

null

0

1

0

37c5b7a57373382792f04fb19c487676bd6c5d39

12,537

py

Python

csaws_creation/train_val_creation/generate_patches.py

ChrisMats/seemingly_uninformative_labels

bcbe060f8be89d731626e3f37752d5906c0a6752

[ "MIT" ]

4

2020-10-14T03:57:52.000Z

2021-09-23T13:34:03.000Z

csaws_creation/train_val_creation/generate_patches.py

ChrisMats/seemingly_uninformative_labels

bcbe060f8be89d731626e3f37752d5906c0a6752

[ "MIT" ]

1

2021-06-04T10:34:32.000Z

2021-06-07T04:54:35.000Z

csaws_creation/train_val_creation/generate_patches.py

ChrisMats/seemingly_uninformative_labels

bcbe060f8be89d731626e3f37752d5906c0a6752

[ "MIT" ]

4

2021-02-23T07:05:31.000Z

2021-09-08T19:48:57.000Z

"""This script creates the patched dataset""" import sys import glob import json from tqdm import tqdm import numpy as np from PIL import Image import multiprocessing from datetime import datetime from joblib import Parallel, delayed from scipy.interpolate import interp1d from scipy.ndimage import generic_filter from ...

38.457055

126

0.582755

1,510

12,537

4.587417

0.170199

0.043886

0.03176

0.030027

0.325393

0.265483

0.228815

0.187383

0.156633

0.133824

0

0.013045

0.321289

12,537

325

127

38.575385

0.801034

0.127223

0

0.116505

0

0.0575

0.00196

0

1

0.009709

false

0

0.082524

0

0.097087

0.024272

0

null

0

null

0

1

0

80615ede9944c60d7f347d9b93800d3c39e08d0f

1,258

py

Python

tests/test_value.py

DanielTOsborne/repgen5

a13e0005dc2a471bb9c112b53ab5e2e0d2596f72

[ "MIT" ]

null

tests/test_value.py

DanielTOsborne/repgen5

a13e0005dc2a471bb9c112b53ab5e2e0d2596f72

[ "MIT" ]

1

2021-12-17T16:45:56.000Z

2022-02-02T20:40:57.000Z

tests/test_value.py

DanielTOsborne/repgen5

a13e0005dc2a471bb9c112b53ab5e2e0d2596f72

[ "MIT" ]

1

2021-03-31T21:38:55.000Z

import unittest from nose2.tools import params import sys import datetime sys.path.append("../") from repgen.data import Value from repgen.util import TZ def test_gents_scalar(): t_end = datetime.datetime.now().replace(minute=0,second=0,microsecond=0,tzinfo=TZ("UTC")) t_start = t_end-datetime.timedelta(hours...

34

139

0.645469

202

1,258

3.950495

0.277228

0.078947

0.114035

0.050125

0.575188

0.546366

0

0.053502

0.18283

1,258

36

140

34.944444

0.722763

0

0.193548

0

0.04213

0

0.354839

1

0.096774

false

0

0.193548

0

0.322581

0

null

0

null

0

1

0

8061f8f83585386d7f3cee51d2a8ec30b9f44859

9,096

py

Python

Data Science Project/Mall Customer Segmentation & Analysis/Mall Customer Segmentation & Analysis.py

jrderek/Data-science-master-resources

95adab02dccbf5fbe6333389324a1f8d032d3165

[ "MIT" ]

14

2020-09-17T17:04:04.000Z

2021-08-19T05:08:49.000Z

Data Science Project/Mall Customer Segmentation & Analysis/Mall Customer Segmentation & Analysis.py

jrderek/Data-science-master-resources

95adab02dccbf5fbe6333389324a1f8d032d3165

[ "MIT" ]

85

2020-10-01T16:53:21.000Z

2021-07-08T17:44:17.000Z

Data Science Project/Mall Customer Segmentation & Analysis/Mall Customer Segmentation & Analysis.py

jrderek/Data-science-master-resources

95adab02dccbf5fbe6333389324a1f8d032d3165

[ "MIT" ]

5

2020-09-18T08:53:01.000Z

2021-08-19T05:12:52.000Z

#!/usr/bin/env python # coding: utf-8 # ### Author : Sanjoy Biswas # ### Topic : Mall Customer Segmentation & Analysis # ### Email : sanjoy.eee32@gmail.com # # **Data Import And Preprocessing** # In[1]: import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns from plotly.offlin...

30.626263

498

0.744173

1,493

9,096

4.487609

0.316142

0.029104

0.018806

0.022836

0.131493

0.102687

0.085373

0.07194

0.067761

0.035224

0

0.024277

0.171284

9,096

296

499

30.72973

0.864553

0.610158

0

0.206897

0

0.142898

0.01963

0

1

0.011494

false

0

0.172414

0

0.206897

0.103448

0

null

0

null

0

1

0

80624346f155364d7ebf81125c59e583fc168c60

4,319

py

Python

colorlight-5a-75b/uart-probe/colorlight-uart-probe.py

TomKeddie/prj-litex

cc79c041d22ad552a12b49f531d007491b536521

[ "MIT" ]

2

2019-08-26T13:49:22.000Z

2019-11-11T18:43:29.000Z

colorlight-5a-75b/uart-probe/colorlight-uart-probe.py

TomKeddie/prj-litex

cc79c041d22ad552a12b49f531d007491b536521

[ "MIT" ]

null

colorlight-5a-75b/uart-probe/colorlight-uart-probe.py

TomKeddie/prj-litex

cc79c041d22ad552a12b49f531d007491b536521

[ "MIT" ]

null

#!/usr/bin/env python3 # This file is Copyright (c) 2020 Florent Kermarrec <florent@enjoy-digital.fr> # License: BSD # Disclaimer: This SoC is still a Proof of Concept with large timings violations on the IP/UDP and # Etherbone stack that need to be optimized. It was initially just used to validate the reversed # pin...

34.277778

115

0.552674

478

4,319

4.828452

0.453975

0.027296

0.025997

0.023397

0.05286

0

0.036909

0.197036

4,319

125

116

34.552

0.628604

0.274138

0

0.050633

0

0.06254

0

1

0.050633

false

0

0.139241

0

0.227848

0.012658

0

null

0

null

0

1

0

8063b17195dc598c722e984e0438f0d945c3c21d

4,109

py

Python

src/jig/commands/tests/test_sticky.py

robmadole/jig

6596e15afb0bb7f69850a71d9071440ba101f539

[ "BSD-2-Clause" ]

16

2015-04-07T19:26:01.000Z

2020-03-05T21:09:07.000Z

src/jig/commands/tests/test_sticky.py

robmadole/jig

6596e15afb0bb7f69850a71d9071440ba101f539

[ "BSD-2-Clause" ]

2

2015-02-11T13:29:35.000Z

2015-03-02T21:03:08.000Z

src/jig/commands/tests/test_sticky.py

robmadole/jig

6596e15afb0bb7f69850a71d9071440ba101f539

[ "BSD-2-Clause" ]

2

2020-05-29T06:48:16.000Z

2020-05-29T06:54:36.000Z

# coding=utf-8 import git from os.path import expanduser from mock import patch, MagicMock from jig.tests.testcase import CommandTestCase, result_with_hint from jig.commands import sticky from jig.exc import ( ForcedExit, JigUserDirectoryError, GitConfigError, InitTemplateDirAlreadySet, GitTemplatesMissing, Gi...

28.534722

76

0.607204

423

4,109

5.695035

0.293144

0.033209

0.040681

0.044832

0.290577

0.269822

0.243254

0.210046

0.157742

0

0.001395

0.302263

4,109

143

77

28.734266

0.838856

0.094427

0

0.344828

0

0.128422

0.05532

0

0.137931

1

0.114943

false

0

0.08046

0

0.229885

0

null

0

null

0

1

0

8063da22cb97bc1092b0a5274319e07381b8faeb

5,624

py

Python

model/utils.py

yhygao/CBIM-Medical-Image-Segmentation

5586f705156ef3c442393276d184e4d51d2a2408

[ "Apache-2.0" ]

20

2022-03-02T08:47:25.000Z

2022-03-30T11:18:26.000Z

model/utils.py

yhygao/CBIM-Medical-Image-Segmentation

5586f705156ef3c442393276d184e4d51d2a2408

[ "Apache-2.0" ]

3

2022-03-04T04:23:10.000Z

2022-03-05T17:29:52.000Z

model/utils.py

yhygao/CBIM-Medical-Image-Segmentation

5586f705156ef3c442393276d184e4d51d2a2408

[ "Apache-2.0" ]

5

2022-03-02T08:47:32.000Z

2022-03-30T11:18:53.000Z

import numpy as np import torch import torch.nn as nn import pdb def get_model(args, pretrain=False): if args.dimension == '2d': if args.model == 'unet': from .dim2 import UNet if pretrain: raise ValueError('No pretrain model available') return UNet(...

48.068376

477

0.641536

733

5,624

4.742156

0.150068

0.053797

0.037399

0.052359

0.657652

0.610472

0.585443

0.555524

0.520426

0.418872

0

0.013025

0.262802

5,624

116

478

48.482759

0.825374

0

0.365591

0

0.070959

0

1

0.010753

false

0

0.236559

0

0.419355

0

null

0

null

0

1

0

80647f5099199c99b0e0a984c775048c1fbf6fda

8,731

py

Python

parser.py

envlh/henry

53a1097a8650b99a8145b16853dbfece13922cb4

[ "CC0-1.0" ]

2

2022-01-10T12:36:21.000Z

2022-01-18T11:13:40.000Z

parser.py

envlh/henry

53a1097a8650b99a8145b16853dbfece13922cb4

[ "CC0-1.0" ]

null

parser.py

envlh/henry

53a1097a8650b99a8145b16853dbfece13922cb4

[ "CC0-1.0" ]

1

2022-01-10T13:15:43.000Z

import json import re import requests import unidecode import urllib.parse def normalize_lemma(lemma): return re.sub(r'[^a-z]', '', unidecode.unidecode(lemma)) def get_existing_entries(user_agent): url = 'https://query.wikidata.org/sparql?{}'.format(urllib.parse.urlencode({'query': 'SELECT DISTINCT (REPLACE...

45.712042

285

0.569465

948

8,731

5.113924

0.232068

0.055693

0.034653

0.017327

0.290635

0.262995

0.246493

0.169348

0.158416

0.135726

0

0.029348

0.262398

8,731

190

286

45.952632

0.723447

0.049021

0

0.036496

0

0.233824

0.030541

0

1

0.043796

false

0

0.036496

0.014599

0.116788

0.007299

0

null

0

null

0

1

0

8064b62c6658077a658035b75bf939d6a102f7cb

1,591

py

Python

tests/test_solidarity_tax_credit.py

RogerEMO/srd

40eb8bb02cfd3b1f60ed9eb3e361877fea744cb5

[ "MIT" ]

1

2021-11-22T18:15:09.000Z

tests/test_solidarity_tax_credit.py

RogerEMO/srd

40eb8bb02cfd3b1f60ed9eb3e361877fea744cb5

[ "MIT" ]

3

2021-05-10T18:46:16.000Z

2021-06-01T16:51:48.000Z

tests/test_solidarity_tax_credit.py

RogerEMO/srd

40eb8bb02cfd3b1f60ed9eb3e361877fea744cb5

[ "MIT" ]

1

2021-05-05T17:20:06.000Z

import pytest from math import isclose import sys sys.path.append('/Users/pyann/Dropbox (CEDIA)/srd/Model') import srd from srd import quebec qc_form = quebec.form(2016) @pytest.mark.parametrize('income, amount', [(0, 966), (33e3, 966), (51e3, 0), (34e3+(51e3-34...

32.469388

80

0.529855

208

1,591

3.980769

0.298077

0.050725

0.057971

0.067633

0.576087

0.530193

0.48913

0.423913

0.332126

0

0.124654

0.319296

1,591

48

81

33.145833

0.639889

0

0.342857

0

0.055772

0

0.085714

1

0.085714

false

0

0.142857

0

0.228571

0

null

0

null

0

1

0

806566fc4d9daeabfef5f2000f79ccd69c7d32a1

49,754

py

Python

2021/BondwireProfileEditor_Win_Linux.py

zhangjq933/HowtoSim_Script

d958cc6cc743106e8f6ddf58dead6551a8ac7784

[ "MIT" ]

79

2019-04-01T04:35:01.000Z

2022-03-30T10:59:32.000Z

2021/BondwireProfileEditor_Win_Linux.py

raflzhang/HowtoSim_Script

90fb8cca87d47d2c45b8ff5d07a35e8a6c846685

[ "MIT" ]

1

2020-03-29T20:52:06.000Z

2020-03-30T05:35:30.000Z

2021/BondwireProfileEditor_Win_Linux.py

raflzhang/HowtoSim_Script

90fb8cca87d47d2c45b8ff5d07a35e8a6c846685

[ "MIT" ]

73

2019-05-07T10:26:53.000Z

2022-03-24T02:25:08.000Z

# coding=utf-8 import os, re, sys, clr, json, math, logging, random, time from itertools import combinations os.chdir(os.path.dirname(__file__)) logging.basicConfig(filename='gui.log', filemode='w', encoding='utf-8', level=logging.DEBUG) clr.AddReference('System.Drawing') clr.AddReference('System.Windows.Forms') ...

39.8032

116

0.552116

5,292

49,754

5.088624

0.091837

0.010918

0.022838

0.03045

0.458242

0.344424

0.287757

0.258346

0.240039

0.225927

0

0.034494

0.328757

49,754

1,249

117

39.835068

0.771836

0.015215

0

0.228332

0

0.057783

0.002139

0

1

0.04287

false

0.00466

0.011184

0.001864

0.075489

0

null

0

null

0

1

0

806640663332b26791d299631d7a07702f2f99ab

1,738

py

Python

nodedge/blocks/custom/input_block.py

Nodedge/nodedge

5658269a1841f33b3c42d6f79b8b50411e105787

[ "MIT" ]

7

2020-03-25T19:54:56.000Z

2021-06-09T04:43:58.000Z

nodedge/blocks/custom/input_block.py

Nodedge/nodedge

5658269a1841f33b3c42d6f79b8b50411e105787

[ "MIT" ]

9

2020-01-17T10:47:54.000Z

2021-05-30T12:40:28.000Z

nodedge/blocks/custom/input_block.py

nodedge/nodedge

5658269a1841f33b3c42d6f79b8b50411e105787

[ "MIT" ]

null

# -*- coding: utf-8 -*- from typing import List from nodedge.blocks.block import Block from nodedge.blocks.block_config import BLOCKS_ICONS_PATH, OP_NODE_INPUT, registerNode from nodedge.blocks.graphics_block import GraphicsBlock from nodedge.blocks.graphics_input_block_content import GraphicsInputBlockContent from no...

30.491228

86

0.705984

169

1,738

7.065089

0.443787

0.046064

0.056951

0.036851

0

0.000725

0.206559

1,738

56

87

31.035714

0.86512

0.032796

0

0.067938

0.032777

0

1

0.097561

false

0

0.146341

0

0.512195

0

null

0

null

0

1

0

8066624d5dffeae87c4031c186ee89c3a0ab8dcd

5,890

py

Python

app/core.py

JulienPetit-1/DataTools_Project

60dc787e219e3a00a4a0b14808e8ad32a7e0f878

[ "MIT" ]

null

app/core.py

JulienPetit-1/DataTools_Project

60dc787e219e3a00a4a0b14808e8ad32a7e0f878

[ "MIT" ]

null

app/core.py

JulienPetit-1/DataTools_Project

60dc787e219e3a00a4a0b14808e8ad32a7e0f878

[ "MIT" ]

null

import pandas as pd class Core: def __init__(self, players): index_with_nan = players.index[players.isnull().any(axis=1)] players.drop(index_with_nan,0, inplace=True) self.Players = players def roi_top_players(self): ''' Sorted the player list by ROI from the top ...

38.496732

168

0.543633

682

5,890

4.593842

0.180352

0.052665

0.034472

0.04309

0.479094

0.442387

0.412384

0.361315

0.3045

0.291095

0

0.011917

0.344652

5,890

153

169

38.496732

0.799741

0.261969

0

0.16129

0

0.095119

0

1

0.177419

false

0

0.016129

0

0.370968

0.032258

0

null

0

null

0

1

0

80676fec9d54f2f82f75da34b314f6afd4212486

3,799

py

Python

main/classify_program.py

Abel-Huang/simple-image-classifier

89d2822c2b06cdec728f734d43d9638f4b601348

[ "MIT" ]

4

2017-05-17T08:01:38.000Z

2018-07-22T11:13:55.000Z

main/classify_program.py

Abel-Huang/ImageClassifier

89d2822c2b06cdec728f734d43d9638f4b601348

[ "MIT" ]

null

main/classify_program.py

Abel-Huang/ImageClassifier

89d2822c2b06cdec728f734d43d9638f4b601348

[ "MIT" ]

null

import cv2 import numpy as np from sklearn import svm from sklearn.externals import joblib from main import data_set as ds from main import feature_program as fp from util import save_2_db as db from util import file_manage as fm # 训练分类器 def train_classifier(feature_type): train_data = np.float32([]).reshape(0, 50...

37.245098

90

0.61253

521

3,799

4.297505

0.272553

0.073694

0.059402

0.04243

0.405092

0.363555

0.310853

0.251898

0.232247

0.192943

0

0.014612

0.261385

3,799

101

91

37.613861

0.783321

0.063701

0

0.25641

0

0.082366

0

1

0.025641

false

0

0.102564

0

0.128205

0.166667

0

null

0

null

0

1

0

8069c80b4ba47527c4c176a2e51cb7a78d306b86

4,616

py

Python

momoichigo/app/views/resource_queue_view.py

nothink/momoichigo

85710c31a4dddb85fc1597ceb31c80d97779502b

[ "MIT" ]

null

momoichigo/app/views/resource_queue_view.py

nothink/momoichigo

85710c31a4dddb85fc1597ceb31c80d97779502b

[ "MIT" ]

174

2021-06-21T08:19:03.000Z

2022-03-30T23:44:55.000Z

momoichigo/app/views/resource_queue_view.py

nothink/momoichigo

85710c31a4dddb85fc1597ceb31c80d97779502b

[ "MIT" ]

1

2021-09-24T13:40:53.000Z

"""momoichigo views.""" from __future__ import annotations import io import logging from typing import Any, List, Tuple from urllib.parse import urlparse import pendulum import requests from django.core.exceptions import ValidationError from django.db import transaction from rest_framework import mixins, status, view...

34.192593

85

0.620234

471

4,616

5.951168

0.388535

0.011416

0.02426

0.011416

0.039957

0.030681

0

0.007773

0.275347

4,616

134

86

34.447761

0.830194

0.129983

0

0.057471

0

0.024235

0

1

0.057471

false

0

0.195402

0

0.356322

0

null

0

null

0

1

0

806ddac5cfe116c67e0d9529de64b5b850440192

347

py

Python

Algoritmo(Python)/Alg_S7_4.py

Daniel-Conte/Exercicios-de-Algoritmo

5a42722516097d0aec14d80549e18501b182eebd

[ "MIT" ]

null

Algoritmo(Python)/Alg_S7_4.py

Daniel-Conte/Exercicios-de-Algoritmo

5a42722516097d0aec14d80549e18501b182eebd

[ "MIT" ]

null

Algoritmo(Python)/Alg_S7_4.py

Daniel-Conte/Exercicios-de-Algoritmo

5a42722516097d0aec14d80549e18501b182eebd

[ "MIT" ]

null

#variables media = 0 maior = 0 menor = 9999 #input + process for(i) in range(1, 11): N = int(input("Digite um número: ")) if(N > maior): maior = N if(N < menor): menor = N media = (media + (N / 10)) print("Maior número: {0}".format(maior)) print("Menor número: {0}".format(menor)) print(...

23.133333

40

0.570605

52

347

3.807692

0.442308

0.106061

0.131313

0

0.05283

0.236311

347

15

41

23.133333

0.69434

0.069164

0

0.192547

0

1

0

false

0

0.230769

0

null

0

null

0

1

0

806f5dbad8693bef1f42f7424619c01bd49c62cd

2,464

py

Python

test_depth_cityscapes.py

sanweiliti/Segmentation-MonoDepth-Pytorch

d1a3de8d10c60fe9d3b86b585e0f0089555fc8a6

[ "MIT" ]

25

2019-02-09T21:19:15.000Z

2022-01-24T22:11:20.000Z

test_depth_cityscapes.py

sanweiliti/Segmentation-MonoDepth-Pytorch

d1a3de8d10c60fe9d3b86b585e0f0089555fc8a6

[ "MIT" ]

null

test_depth_cityscapes.py

sanweiliti/Segmentation-MonoDepth-Pytorch

d1a3de8d10c60fe9d3b86b585e0f0089555fc8a6

[ "MIT" ]

4

2019-02-21T07:08:06.000Z

2022-01-25T12:43:24.000Z

import yaml import torch import argparse from torch.utils import data from tqdm import tqdm from ptsemseg.models import get_model from ptsemseg.loader import get_loader from ptsemseg.metrics import runningScoreDepth, averageMeter def count_parameters(model): return sum(p.numel() for p in model.parameters() if p...

28.988235

100

0.616477

308

2,464

4.753247

0.399351

0.03347

0.020492

0.025956

0.051913

0

0.00269

0.245536

2,464

84

101

29.333333

0.784831

0.053571

0

0.110108

0.013763

0

1

0.034483

false

0

0.137931

0.017241

0.189655

0.017241

0

null

0

null

0

1

0

806fe60353ed1a2e39330d425617b5fb47e04792

1,951

py

Python

tests/test_lambda.py

ZhukovAlexander/lambdify

e291c15bacffc871cd1c10aefe9f132420259dfd

[ "Apache-2.0" ]

51

2016-04-07T12:50:08.000Z

2020-05-19T14:56:47.000Z

tests/test_lambda.py

ZhukovAlexander/easy-lambda

e291c15bacffc871cd1c10aefe9f132420259dfd

[ "Apache-2.0" ]

null

tests/test_lambda.py

ZhukovAlexander/easy-lambda

e291c15bacffc871cd1c10aefe9f132420259dfd

[ "Apache-2.0" ]

8

2016-04-08T10:05:30.000Z

2020-01-20T14:01:05.000Z

import unittest import zipfile from StringIO import StringIO import tempfile import shutil import boto3 import dill import moto import mock import pip from easy_lambda.deployment import Lambda, DeploymentPackage @moto.mock_lambda class Test(unittest.TestCase): def setUp(self): super(Test, self).setUp() ...

28.691176

86

0.671963

240

1,951

5.370833

0.454167

0.037238

0.031032

0.037238

0.043445

0

0.01361

0.209124

1,951

67

87

29.119403

0.821776

0.136853

0

0.047619

0

0.118068

0.045915

0

0.071429

1

0.142857

false

0

0.261905

0.02381

0.47619

0

null

0

null

0

1

0

807024c63669049ff37fa7c1466e2b39243f3485

2,397

py

Python

command_executor/command.py

stephrdev/python-command-executor

87b43da25e86cd60ca29b31fe5d0202e7be53cf9

[ "MIT" ]

null

command_executor/command.py

stephrdev/python-command-executor

87b43da25e86cd60ca29b31fe5d0202e7be53cf9

[ "MIT" ]

2

2021-06-01T22:31:14.000Z

2021-06-01T22:32:14.000Z

command_executor/command.py

stephrdev/python-command-executor

87b43da25e86cd60ca29b31fe5d0202e7be53cf9

[ "MIT" ]

null

import shlex import subprocess from .exceptions import CommandExecutionError, CommandParameterError class Command(object): process = None command = 'true' ignore_output = True fail_silently = False required_parameters = None stdout = subprocess.PIPE stderr = subprocess.PIPE def __ini...

34.242857

90

0.637046

269

2,397

5.565056

0.315985

0.056112

0.029392

0.017368

0.022712

0

0.001741

0.281185

2,397

69

91

34.73913

0.867092

0.047977

0

0.038613

0

1

0.132075

false

0

0.056604

0.075472

0.45283

0

null

0

null

0

1

0

80727ce2ec20ae44ac4f84444e1d4ed99b47a36d

2,926

py

Python

tests/test_types.py

tonysimpson/pointbreak

04e59cdda19a797b926b9541607077ad77522503

[ "MIT" ]

6

2018-07-13T09:52:14.000Z

2019-11-27T12:39:27.000Z

tests/test_types.py

tonysimpson/pointbreak

04e59cdda19a797b926b9541607077ad77522503

[ "MIT" ]

10

2018-07-12T14:44:44.000Z

2019-02-07T18:59:02.000Z

tests/test_types.py

tonysimpson/pointbreak

04e59cdda19a797b926b9541607077ad77522503

[ "MIT" ]

null

import struct import pointbreak import pointbreak.types as types from pointbreak.types import TestAccessor as Accessor def test_type_get_simple_value(): accessor = Accessor(b"\x01\x00\x00\x00") ref = types.reference(types.int32, 0, accessor) assert ref.value == 1 def test_type_set_simple_value(): ...

32.153846

75

0.679084

445

2,926

4.289888

0.14382

0.092195

0.095338

0.080671

0.53955

0.393924

0.357255

0.321111

0.24044

0.203248

0

0.089084

0.182843

2,926

90

76

32.511111

0.709327

0

0.189189

0

0.062564

0.008205

0

0.202703

1

0.148649

false

0

0.054054

0

0.202703

0

null

0

null

0

1

0

8072dab55c7898746ef42113226d03eadb2ebafb

9,853

py

Python

data/bonecell.py

edocoh87/ssd.pytorch

09fe21af84976dd6ab09ff0c5649db2793e47468

[ "MIT" ]

null

data/bonecell.py

edocoh87/ssd.pytorch

09fe21af84976dd6ab09ff0c5649db2793e47468

[ "MIT" ]

null

data/bonecell.py

edocoh87/ssd.pytorch

09fe21af84976dd6ab09ff0c5649db2793e47468

[ "MIT" ]

null

""" Author: Edo Cohen-Karlik """ from __future__ import division # import os.path as osp import json # import sys import os import torch import torch.utils.data as data import cv2 import numpy as np #from augmentations import SSDAugmentation, SSDBoneCellAugmentation # ignore classes with label value -1. BONE_CELL_CL...

36.492593

99

0.603978

1,309

9,853

4.362872

0.194041

0.014708

0.01681

0.376642

0.366661

0.349501

0.316757

0.303449

0.291893

0

0.017027

0.266822

9,853

270

100

36.492593

0.773533

0.32792

0

0.136054

0

0.026084

0

1

0.115646

false

0

0.047619

0.006803

0.29932

0.006803

0

null

0

null

0

1

0

80757abfd788a13520ba7245a84a078943b84c38

2,556

py

Python

ABONO/__init__.py

SalahEddineLahniche/MLC-Kaggle-2017

489b76182227cbf51812c051381da4e58098d338

[ "MIT" ]

null

ABONO/__init__.py

SalahEddineLahniche/MLC-Kaggle-2017

489b76182227cbf51812c051381da4e58098d338

[ "MIT" ]

1

2018-04-25T20:48:35.000Z

2020-06-19T00:48:49.000Z

ABONO/__init__.py

SalahEddineLahniche/MLC-Kaggle-2017

489b76182227cbf51812c051381da4e58098d338

[ "MIT" ]

null

import functools import pandas as pd from ABONO.Regressor import Regressor from ABONO.Processer import Processer from ABONO.Session import Session TRAIN_PATH = 'data/train.csv' TEST_PATH = 'data/test.csv' def timed(session): def innertimed(f): import time @functools.wraps(f) def wrapped(*args): ...

38.149254

100

0.621283

338

2,556

4.550296

0.242604

0.071521

0.046814

0.057217

0.236671

0.213264

0.157347

0

0.001082

0.276604

2,556

66

101

38.727273

0.830719

0.039906

0

0.068966

0

0.071807

0

1

0.086207

false

0

0.103448

0

0.293103

0

null

0

null

0

1

0

8075ab5a27998d20f0817eccb49607a1460552d7

29,946

py

Python

neon/layers/recurrent.py

sjuvekar/neon

abe5d30a68663c739a97a9e657516d530c66dbd9

[ "Apache-2.0" ]

null

neon/layers/recurrent.py

sjuvekar/neon

abe5d30a68663c739a97a9e657516d530c66dbd9

[ "Apache-2.0" ]

4

2021-03-26T00:21:20.000Z

2022-03-12T00:46:11.000Z

neon/layers/recurrent.py

huamichaelchen/neon

abe5d30a68663c739a97a9e657516d530c66dbd9

[ "Apache-2.0" ]

1

2016-08-12T09:05:04.000Z

# ---------------------------------------------------------------------------- # Copyright 2015 Nervana Systems 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.o...

40.522327

98

0.601783

4,063

29,946

4.280581

0.108048

0.021159

0.017709

0.021504

0.589696

0.538466

0.486718

0.448655

0.391157

0.361373

0

0.012879

0.29994

29,946

738

99

40.577236

0.816733

0.367996

0

0.284431

0

0.006089

0

0.005988

1

0.086826

false

0

0.002994

0.161677

0

null

0

null

0

1

0

8075ab5b7644cd6b940830cbdac14017e16f9d27

439

py

Python

Exercise_7_9.py

kushrami/Python-Crash-Course-book-Excersice

7093181940a90d9f4bab5775ef56f57963450393

[ "Apache-2.0" ]

null

Exercise_7_9.py

kushrami/Python-Crash-Course-book-Excersice

7093181940a90d9f4bab5775ef56f57963450393

[ "Apache-2.0" ]

null

Exercise_7_9.py

kushrami/Python-Crash-Course-book-Excersice

7093181940a90d9f4bab5775ef56f57963450393

[ "Apache-2.0" ]

null

#No pastrami: sandwich_orders = ['maxican','pastrami','aloo','pastrami','spicypoteto''pastrami','lulu'] finished_sandwich = [] while sandwich_orders: sandwich = sandwich_orders.pop() if sandwich == 'pastrami': print("We are out of pastrami.") continue print("I made your",sandwich,"sandwich...

27.4375

89

0.697039

50

439

6

0.52

0.14

0

0.166287

439

15

90

29.266667

0.819672

0.027335

0

0.276995

0

1

0

false

0

0.272727

0

null

0

null

0

1

0

80785349bd512737005eabd1247ba002964d3d8f

6,811

py

Python

keyhandler.py

egriffith/AWSKeyHandler

9dbe1068440f801a7c522f7fd212bebef1af2a65

[ "MIT" ]

null

keyhandler.py

egriffith/AWSKeyHandler

9dbe1068440f801a7c522f7fd212bebef1af2a65

[ "MIT" ]

null

keyhandler.py

egriffith/AWSKeyHandler

9dbe1068440f801a7c522f7fd212bebef1af2a65

[ "MIT" ]

null

#! /usr/bin/env python3 import sys from os.path import expanduser import argparse import boto3 import botocore def printDebug(action, publicKeyName, publicKeyText, regionList, debug, dryRun, credProfile): return 0 def buildArgParser(argv): parser = argparse.ArgumentParser(description="Upload, delete, or lis...

38.050279

145

0.550286

645

6,811

5.75969

0.268217

0.01319

0.015074

0.035532

0.401346

0.394886

0.386272

0.331898

0.30821

0

0.005117

0.340038

6,811

178

146

38.264045

0.821357

0.00323

0

0.445255

0

0.007299

0.182823

0.009281

0

1

0.058394

false

0

0.051095

0.007299

0.160584

0.175182

0

null

0

null

0

1

0

807ac64b53208d8bf2363b570ae4aa35ea88e5a3

8,868

py

Python

scripts/cros_list_modified_packages.py

khromiumos/chromiumos-chromite

a42a85481cdd9d635dc40a04585e427f89f3bb3f

[ "BSD-3-Clause" ]

null

scripts/cros_list_modified_packages.py

khromiumos/chromiumos-chromite

a42a85481cdd9d635dc40a04585e427f89f3bb3f

[ "BSD-3-Clause" ]

2

2021-03-26T00:29:32.000Z

2021-04-30T21:29:33.000Z

scripts/cros_list_modified_packages.py

khromiumos/chromiumos-chromite

a42a85481cdd9d635dc40a04585e427f89f3bb3f

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- # Copyright (c) 2012 The Chromium OS Authors. All rights reserved. # Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. """Calculate what workon packages have changed since the last build. A workon package is treated as changed if any of the bel...

36.195918

80

0.726545

1,206

8,868

5.230514

0.310945

0.019023

0.023779

0.033291

0.09591

0.047559

0.015219

0

0.003621

0.190347

8,868

244

81

36.344262

0.87493

0.400992

0

0.05

0

0.008333

0.045779

0

0.004098

0.008333

1

0.1

false

0

0.133333

0

0.325

0.033333

0

null

0

null

0

1

0

807e9b1b026b86213f993cb57eef6c26141e77e2

3,222

py

Python

share/ttkwidgets/debugwindow.py

Marusoftware/Marutools

2b462ea02abaf957eb037c281b62d7efe053840e

[ "MIT" ]

null

share/ttkwidgets/debugwindow.py

Marusoftware/Marutools

2b462ea02abaf957eb037c281b62d7efe053840e

[ "MIT" ]

5

2021-01-21T09:46:12.000Z

2022-02-14T13:54:44.000Z

share/ttkwidgets/debugwindow.py

Marusoftware/Marutools

2b462ea02abaf957eb037c281b62d7efe053840e

[ "MIT" ]

2

2021-11-02T11:01:53.000Z

2022-02-14T10:11:21.000Z

""" Author: RedFantom License: GNU GPLv3 Source: This repository """ try: import Tkinter as tk import ttk import tkFileDialog as fd except ImportError: import tkinter as tk from tkinter import ttk import tkinter.filedialog as fd import sys from ttkwidgets import AutoHideScrollbar class DebugWi...

33.915789

118

0.619491

404

3,222

4.873762

0.331683

0.032504

0.019807

0.017268

0.068055

0.04063

0

0.005978

0.273122

3,222

94

119

34.276596

0.834757

0.251397

0

0.029372

0

1

0.107143

false

0.017857

0.160714

0

0.303571

0

null

0

null

0

1

0

807f29911fba7b1a336c50a170090123fe9e9f0c

967

py

Python

migrations/versions/e9596ed3a618_add_release_date_uk_field_and_director_.py

jimmybutton/moviedb

61028ac4db7f58a671ab3a1c2afd3bfb53372773

[ "MIT" ]

null

migrations/versions/e9596ed3a618_add_release_date_uk_field_and_director_.py

jimmybutton/moviedb

61028ac4db7f58a671ab3a1c2afd3bfb53372773

[ "MIT" ]

null

migrations/versions/e9596ed3a618_add_release_date_uk_field_and_director_.py

jimmybutton/moviedb

61028ac4db7f58a671ab3a1c2afd3bfb53372773

[ "MIT" ]

null

"""add release_date_uk field and director_id to movie model Revision ID: e9596ed3a618 Revises: affd804a37d8 Create Date: 2020-08-05 17:34:58.197456 """ from alembic import op import sqlalchemy as sa # revision identifiers, used by Alembic. revision = 'e9596ed3a618' down_revision = 'affd804a37d8' branch_labels = Non...

29.30303

82

0.698035

128

967

5.109375

0.460938

0.061162

0.059633

0.070336

0.204893

0.134557

0

0.058752

0.155119

967

32

83

30.21875

0.741738

0.349535

0

0.228041

0

1

0.142857

false

0

0.142857

0

0.285714

0

null

0

null

0

1

0

808194511d3bb385bc1da7eb37a9fb429a3efa5a

26,267

py

Python

retro/tables/generate_tdi_table.py

ellohfin/retro

58ec8f5b698e6140acd215717f051d99e407c4e5

[ "Apache-2.0" ]

1

2018-03-02T01:05:52.000Z

retro/tables/generate_tdi_table.py

ellohfin/retro

58ec8f5b698e6140acd215717f051d99e407c4e5

[ "Apache-2.0" ]

30

2018-01-30T21:03:28.000Z

2019-11-07T16:42:07.000Z

retro/tables/generate_tdi_table.py

ellohfin/retro

58ec8f5b698e6140acd215717f051d99e407c4e5

[ "Apache-2.0" ]

6

2017-07-27T19:49:13.000Z

2019-11-19T13:38:27.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- # pylint: disable=wrong-import-position, too-many-locals """ Create time- and DOM-independent (TDI) whole-detector Cartesian-binned Retro table. The generated table is useful for computing the total charge expected to be deposited by a hypothesis across the entire detecto...

37.685796

86

0.619751

3,555

26,267

4.374684

0.167651

0.014918

0.019097

0.010867

0.363812

0.277456

0.234311

0.207819

0.160108

0.14982

0

0.012624

0.273195

26,267

696

87

37.739943

0.802001

0.227015

0

0.156118

0

0.006329

0.241334

0.002408

0

0.001437

0.008439

1

0.006329

false

0

0.033755

0

0.048523

0.061181

0

null

0

null

0

1

0

8083cb6483b18e6dd4299dd81d56acefd37473b1

28,929

py

Python

exipicrename/exipicrename.py

unixhex/exipicrename2

b2a2f5af224c4a2c93f81e48c2622c7522d76489

[ "MIT" ]

1

2020-02-14T13:41:28.000Z

exipicrename/exipicrename.py

unixhex/exipicrename2

b2a2f5af224c4a2c93f81e48c2622c7522d76489

[ "MIT" ]

3

2021-06-08T19:46:29.000Z

2022-03-11T23:44:57.000Z

exipicrename/exipicrename.py

unixhex/exipicrename2

b2a2f5af224c4a2c93f81e48c2622c7522d76489

[ "MIT" ]

null

#!/usr/bin/env python3 """ exipicrename beta of python3 version. Reads exif data from pictures and rename them. Used exif tags are: * DateTimeOriginal * FNumber * ExposureTime * FocalLength * Model * ISOSpeedRatings """ # Copyright (c) 2019 Hella Breitkopf, https://www.unixwitch.de # MIT License -> see LICENSE fil...

35.023002

108

0.624564

3,649

28,929

4.67635

0.155385

0.021331

0.016409

0.009845

0.305556

0.243436

0.197082

0.165436

0.157876

0.14135

0

0.006009

0.257907

28,929

825

109

35.065455

0.788849

0.255246

0

0.202677

0

0.192381

0.027469

0

0.001212

0

1

0.116635

false

0.005736

0.024857

0

0.216061

0.030593

0

null

0

null

0

1

0

80855b801b71f76158fe03a357cb9349f1c0a767

4,324

py

Python

api/urls.py

deka108/meas_deka

9646b04b878f325ade0a59e41bfcb10ab962d753

[ "Apache-2.0" ]

null

api/urls.py

deka108/meas_deka

9646b04b878f325ade0a59e41bfcb10ab962d753

[ "Apache-2.0" ]

1

2018-06-19T16:27:31.000Z

2018-06-21T02:57:03.000Z

api/urls.py

deka108/mathqa-server

9646b04b878f325ade0a59e41bfcb10ab962d753

[ "Apache-2.0" ]

null

""" # Name: cms/urls.py # Description: # Created by: Phuc Le-Sanh # Date Created: Nov 23, 2016 # Last Modified: Nov 23, 2016 # Modified by: Phuc Le-Sanh """ from django.conf.urls import url, include # from rest_framework import routers from rest_framework.authtoken import views as rest_views from re...

37.275862

85

0.640148

556

4,324

4.859712

0.197842

0.057735

0.103627

0.014804

0.338268

0.280533

0.137306

0.125833

0.11695

0

0.01252

0.150324

4,324

115

86

37.6

0.722918

0.128816

0

0.028571

0

0.363005

0.187918

0

1

0

false

0

0.057143

0

0.057143

0

null

0

null

0

1

0

8088b3c5ba94e3f16d523776ee4f502d91b3b6b5

1,243

py

Python

44.wildcard-matching.py

leonhx/leetcode-practice

35fabe5a1b98c05a5dd5d6a62201e9cb54be69ec

[ "MIT" ]

null

44.wildcard-matching.py

leonhx/leetcode-practice

35fabe5a1b98c05a5dd5d6a62201e9cb54be69ec

[ "MIT" ]

null

44.wildcard-matching.py

leonhx/leetcode-practice

35fabe5a1b98c05a5dd5d6a62201e9cb54be69ec

[ "MIT" ]

null

# # @lc app=leetcode id=44 lang=python3 # # [44] Wildcard Matching # class Solution: def _consume_seq(self, s: str, p: str, s_i: int, p_i: int): while p_i < len(p) and p[p_i] != '*': if s_i >= len(s) or (p[p_i] != s[s_i] and p[p_i] != '?'): return -1, -1 s_i, p_i = s_...

34.527778

69

0.436846

221

1,243

2.171946

0.167421

0.104167

0.06875

0.083333

0.504167

0.25

0.1625

0

0.027586

0.416734

1,243

35

70

35.514286

0.634483

0.046661

0

0.137931

0

0.005089

0

1

0.068966

false

0

0.241379

0

null

0

null

0

1

0

8089824c1db000c6c79126935002dddc0b661de7

664

py

Python

Scripts/Utilities/linear_regg.py

aryanmangal769/UGV-DTU_ROS_Stack

6a00c83d076361bdf171c1ad4ef383ad262da4e6

[ "BSD-3-Clause" ]

null

Scripts/Utilities/linear_regg.py

aryanmangal769/UGV-DTU_ROS_Stack

6a00c83d076361bdf171c1ad4ef383ad262da4e6

[ "BSD-3-Clause" ]

null

Scripts/Utilities/linear_regg.py

aryanmangal769/UGV-DTU_ROS_Stack

6a00c83d076361bdf171c1ad4ef383ad262da4e6

[ "BSD-3-Clause" ]

null

import numpy as np from fractions import Fraction if __name__ == '__main__': #enter coordinates vectors Y = np.array([[-420,-330]]).T X = np.array([[300,0]]).T # y =mx +c O = np.ones(X.shape) A = np.append(X,O,axis=1) A_t = A.T A_t_dot_A = A_t.dot(A) A_t_dot_A_inv = np.linalg.inv(A_t_dot_A) ...

16.6

48

0.643072

133

664

2.87218

0.323308

0.062827

0.104712

0.109948

0.185864

0.180628

0.078534

0

0.026022

0.189759

664

39

49

17.025641

0.684015

0.120482

0

0.052083

0

1

0

false

0

0.111111

0

0.111111

0

null

0

null

0

1

0

808a844aeabff3fdc0f7f9b10b9c6a241b07b945

2,159

py

Python

pythia/pyre/inventory/FacilityArrayFacility.py

willic3/pythia

2657b95a0c07fd3c914ab6b5f7ec89a8edba004c

[ "BSD-3-Clause" ]

1

2015-11-30T08:01:39.000Z

pythia/pyre/inventory/FacilityArrayFacility.py

willic3/pythia

2657b95a0c07fd3c914ab6b5f7ec89a8edba004c

[ "BSD-3-Clause" ]

27

2018-05-24T18:31:25.000Z

2021-10-16T03:57:52.000Z

pythia/pyre/inventory/FacilityArrayFacility.py

willic3/pythia

2657b95a0c07fd3c914ab6b5f7ec89a8edba004c

[ "BSD-3-Clause" ]

7

2019-07-19T02:30:56.000Z

2021-06-02T22:00:01.000Z

#!/usr/bin/env python # # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # # California Institute of Technology # (C) 2008 All Rights Reserved # # {LicenseText} # # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~...

29.986111

94

0.544697

198

2,159

5.878788

0.419192

0.034364

0.024055

0.042955

0

0.007124

0.284854

2,159

71

95

30.408451

0.746762

0.181102

0

0.061003

0.013113

0

1

0.076923

false

0

0.102564

0

0.282051

0

null

0

null

0

1

0

808ba59db073ed00f5b7a13b6e51d1825bca7ae9

2,052

py

Python

psearch/scripts/split.py

meddwl/psearch

58c374bdf6550ab43a8832aeaf9b18d5969640b5

[ "BSD-3-Clause" ]

24

2018-11-05T10:07:26.000Z

2022-03-28T06:26:23.000Z

psearch/scripts/split.py

meddwl/psearch

58c374bdf6550ab43a8832aeaf9b18d5969640b5

[ "BSD-3-Clause" ]

4

2020-01-03T21:10:16.000Z

2021-11-04T16:47:55.000Z

psearch/scripts/split.py

meddwl/psearch

58c374bdf6550ab43a8832aeaf9b18d5969640b5

[ "BSD-3-Clause" ]

10

2019-11-21T18:48:28.000Z

2021-08-22T12:19:01.000Z

#!/usr/bin/env python3 # author : Alina Kutlushina # date : 01.05.2018 # license : BSD-3 #============================================================================== import sys import argparse import pandas as pd def main(in_fname, out_act_fname, out_inact_fname): """ split a d...

41.04

113

0.60039

278

2,052

4.226619

0.381295

0.040851

0.05617

0.040851

0.201702

0.181277

0.168511

0.071489

0

0.009038

0.245127

2,052

49

114

41.877551

0.749516

0.209064

0

0.276623

0

1

0.035714

false

0

0.107143

0

0.142857

0

null

0

null

0

1

0

808f76f97cbe057b74c0a81a931896d5d9eb9b7d

2,084

py

Python

1024/cl/spiders/grass.py

wkias/1024

501e9cb2563e8dc6cad84e99db2128f2a447af91

[ "MIT" ]

2

2020-12-02T12:25:52.000Z

2021-01-08T02:51:54.000Z

1024/cl/spiders/grass.py

wkias/1024

501e9cb2563e8dc6cad84e99db2128f2a447af91

[ "MIT" ]

null

1024/cl/spiders/grass.py

wkias/1024

501e9cb2563e8dc6cad84e99db2128f2a447af91

[ "MIT" ]

null

# -*- coding: utf-8 -*- import scrapy from ..items import ClItem from ..settings import META_URL from ..settings import SELECT from ..settings import TYPE from ..settings import DOWNLOAD_HISTORY class GrassSpider(scrapy.Spider): name = 'grass' # allowed_domains = [] start_urls = [META_URL + 'thread.php?fi...

41.68

88

0.490883

240

2,084

4.1625

0.329167

0.088088

0.03003

0.054054

0.211211

0.144144

0.102102

0

0.011494

0.332054

2,084

49

89

42.530612

0.706178

0.056142

0

0.209302

0

0.137035

0

1

0.023256

false

0

0.139535

0

0.232558

0

null

0

null

0

1

0

8091f803a97bac3ff576f5dd377c3775b3de1ebd

740

py

Python

Twitoff-01/twitoff/app.py

ivan-mihailov/LS-Unit-3-Sprint-3-Module-1

964029740d8db34121f19e5dec4c76c23c256c01

[ "Apache-2.0" ]

null

Twitoff-01/twitoff/app.py

ivan-mihailov/LS-Unit-3-Sprint-3-Module-1

964029740d8db34121f19e5dec4c76c23c256c01

[ "Apache-2.0" ]

null

Twitoff-01/twitoff/app.py

ivan-mihailov/LS-Unit-3-Sprint-3-Module-1

964029740d8db34121f19e5dec4c76c23c256c01

[ "Apache-2.0" ]

null

import os from flask import Flask, render_template, request from .models import db, User def create_app(): """Create and configure an instance of the Flask appication.""" app_dir = os.path.dirname(os.path.abspath(__file__)) database = "sqlite:///{}".format(os.path.join(app_dir, "twitoff.sqlite3")) ...

29.6

83

0.644595

94

740

4.882979

0.56383

0.039216

0.082789

0.074074

0.100218

0

0.001712

0.210811

740

24

84

30.833333

0.784247

0.133784

0

0.159306

0.083596

0

1

0.133333

false

0

0.2

0

0.466667

0

null

0

null

0

1

0

809321ce7f4ed89b3d9c2cee1b729e5803693f21

1,664

py

Python

locations/spiders/costacoffee_pl.py

cmecklenborg/alltheplaces

e62b59fb0071b6e289c4622d368fdb203a28347e

[ "MIT" ]

null

locations/spiders/costacoffee_pl.py

cmecklenborg/alltheplaces

e62b59fb0071b6e289c4622d368fdb203a28347e

[ "MIT" ]

null

locations/spiders/costacoffee_pl.py

cmecklenborg/alltheplaces

e62b59fb0071b6e289c4622d368fdb203a28347e

[ "MIT" ]

null

import scrapy from locations.items import GeojsonPointItem class CostaCoffeePLSpider(scrapy.Spider): name = "costacoffee_pl" item_attributes = {"brand": "Costa Coffee", "brand_wikidata": "Q608845"} allowed_domains = ["api.costacoffee.pl"] start_urls = ["https://api.costacoffee.pl/api/storelocator/lis...

32.627451

86

0.412861

121

1,664

5.61157

0.578512

0.057437

0.047128

0

0.006704

0.462139

1,664

50

87

33.28

0.751955

0.043269

0

0.069767

0

0.183019

0

1

0.023256

false

0

0.046512

0

0.186047

0

null

0

null

0

1

0

809358564886b7b38cfb4df0981ded339161c3b7

7,530

py

Python

Recurrent Neural Network.py

Sayansree/Recurrent-Neural-Network-from-scrach

16daa7a203b4558fecbd783d9218929561485bb3

[ "MIT" ]

null

Recurrent Neural Network.py

Sayansree/Recurrent-Neural-Network-from-scrach

16daa7a203b4558fecbd783d9218929561485bb3

[ "MIT" ]

null

Recurrent Neural Network.py

Sayansree/Recurrent-Neural-Network-from-scrach

16daa7a203b4558fecbd783d9218929561485bb3

[ "MIT" ]

null

import numpy as np """ basic implementation of Recurrent Neural Networks from scrach to train model to learn to add any number pair when given in binary arrayed format devloper-->sayaneree paria """ class RecurrentNeuralNetwork: def __init__(self,hidden_size=10): """hidden_size is number of...

41.147541

165

0.619389

1,012

7,530

4.501976

0.238142

0.026339

0.027656

0.005268

0.098771

0.065189

0.04741

0.032924

0.015803

0

0.024004

0.286321

7,530

182

166

41.373626

0.823781

0.290704

0

0.023529

0

0.01052

0

1

0.117647

false

0

0.011765

0

0.223529

0.035294

0

null

0

null

0

1

0

8093a2725077b2b49d9c6858f567993bef3daea9

572

py

Python

Asyncio/asyncio_ensure_future.py

xlui/PythonExamples

0389efb84e01dc1310bb2bab7aa2433c0e1b45c4

[ "MIT" ]

null

Asyncio/asyncio_ensure_future.py

xlui/PythonExamples

0389efb84e01dc1310bb2bab7aa2433c0e1b45c4

[ "MIT" ]

null

Asyncio/asyncio_ensure_future.py

xlui/PythonExamples

0389efb84e01dc1310bb2bab7aa2433c0e1b45c4

[ "MIT" ]

null

# asyncio_ensure_future.py import asyncio async def wrapped(): print('now in function wrapped') return 'result' async def inner(task): print('now in function inner') print('inner: waiting for {!r}'.format(task)) ret = await task print('inner: task return: {}'.format(ret)) async def outer()...

20.428571

49

0.685315

77

572

4.961039

0.415584

0.094241

0.099476

0.094241

0

0.181818

572

27

50

21.185185

0.816239

0.041958

0

0.254579

0

1

0

false

0

0.055556

0

0.111111

0.388889

0

null

0

null

0

1

0

80940e10972b61cf1900104bd927a2163e4fae1d

1,737

py

Python

tests/test_db_operations.py

antoniodimariano/metrics_consumer

5c485f3b6c2b6788f947c02b49083ce237424bfc

[ "Apache-2.0" ]

null

tests/test_db_operations.py

antoniodimariano/metrics_consumer

5c485f3b6c2b6788f947c02b49083ce237424bfc

[ "Apache-2.0" ]

null

tests/test_db_operations.py

antoniodimariano/metrics_consumer

5c485f3b6c2b6788f947c02b49083ce237424bfc

[ "Apache-2.0" ]

null

from psycopg2 import pool import psycopg2 import psycopg2.extras import unittest class TestDB(unittest.TestCase): def setUp(self): self.connection_pool = pool.ThreadedConnectionPool(1, 2, database='test', user='postgresql', password='test123', hos...

39.477273

185

0.666091

209

1,737

5.315789

0.416268

0.113411

0.018002

0.191719

0.10261

0

0.038971

0.217041

1,737

43

186

40.395349

0.777941

0

0.028571

0.157743

0.027058

0

0.2

1

0.142857

false

0.028571

0.114286

0

0.285714

0

null

0

null

0

1

0

8095fed737853a16f266e29c70aa1c6f509f7dd8

967

py

Python

test-framework/test-suites/integration/tests/report/test_report_discovery.py

khanfluence/stacki-cumulus-switch

df54afb20f6ea6a3a136b3c09b30df54ea79ffcc

[ "BSD-3-Clause" ]

null

test-framework/test-suites/integration/tests/report/test_report_discovery.py

khanfluence/stacki-cumulus-switch

df54afb20f6ea6a3a136b3c09b30df54ea79ffcc

[ "BSD-3-Clause" ]

null

test-framework/test-suites/integration/tests/report/test_report_discovery.py

khanfluence/stacki-cumulus-switch

df54afb20f6ea6a3a136b3c09b30df54ea79ffcc

[ "BSD-3-Clause" ]

null

import os import subprocess import pytest @pytest.mark.usefixtures("revert_discovery") class TestReportDiscovery: def test_report_daemon_not_running(self, host): "Test the output when the discovery daemon is not running" # Make sure discovery isn't running result = host.run("stack disable discovery") asser...

28.441176

60

0.741468

137

967

5.175182

0.335766

0.135402

0.09591

0.101551

0.561354

0

0.004988

0.170631

967

33

61

29.30303

0.879052

0.219235

0

0.285714

0

0.384437

0

0.380952

1

0.095238

false

0

0.142857

0

0.285714

0

null

0

null

0

1

0

809668ea6678e6fc0ac8190a7a64ddbf086a2f6c

862

py

Python

Python/magic_8_ball.py

rockchipgh/Hacktoberfest2020-1

1d1e28614aa16c1bac2560b0250ce0014e48241d

[ "MIT" ]

null

Python/magic_8_ball.py

rockchipgh/Hacktoberfest2020-1

1d1e28614aa16c1bac2560b0250ce0014e48241d

[ "MIT" ]

null

Python/magic_8_ball.py

rockchipgh/Hacktoberfest2020-1

1d1e28614aa16c1bac2560b0250ce0014e48241d

[ "MIT" ]

null

#*****MAGIC 8 BALL CODE***** import sys import random ans = True while ans: question = input("ask the magic 8 ball a question: (press enter to quit) ") answers = random.randint(1,8) if question == "": sys.exit() elif answers == 1: print ("Good:)") elif answ...

22.684211

88

0.512761

109

862

4.055046

0.513761

0.199095

0.045249

0.049774

0.167421

0

0.021467

0.351508

862

37

89

23.297297

0.769231

0.031323

0

0.041667

0.332134

0

1

0

false

0

0.083333

0

0.083333

0.333333

0

null

0

null

0

1

0

8098871e0930a689062b0ccaa88626806d0cc195

3,372

py

Python

venus/venus/test_venus.py

FrederichRiver/neutrino2

65e158f0d64046628cf2d1d52bdb3161489c7595

[ "BSD-3-Clause" ]

null

venus/venus/test_venus.py

FrederichRiver/neutrino2

65e158f0d64046628cf2d1d52bdb3161489c7595

[ "BSD-3-Clause" ]

null

venus/venus/test_venus.py

FrederichRiver/neutrino2

65e158f0d64046628cf2d1d52bdb3161489c7595

[ "BSD-3-Clause" ]

null

from stock_base import StockEventBase, dataLine def unit_test_NoneHeaderError(): try: raise NoneHeaderError('Test!') except NoneHeaderError as e: print(e) def unit_test_stockEventBase(): from dev_global.env import GLOBAL_HEADER import pandas as pd event = StockEventBase(GLOBAL_HE...

28.576271

68

0.673191

430

3,372

4.990698

0.3

0.067102

0.051258

0.022367

0.173346

0.102516

0.069897

0.035415

0

0.026417

0.225386

3,372

117

69

28.820513

0.795176

0.051008

0

0.179775

0

0.076441

0

1

0.11236

false

0

0.191011

0

0.303371

0.168539

0

null

0

null

0

1

0

8099edc75a8e1289de2d8bd7684b17513889d966

7,592

py

Python

io_mesh_urho/utils.py

practicing01/Urho3D-Blender

820f03c34adda7594aa8ebc3f95cd71382a51528

[ "Unlicense" ]

null

io_mesh_urho/utils.py

practicing01/Urho3D-Blender

820f03c34adda7594aa8ebc3f95cd71382a51528

[ "Unlicense" ]

null

io_mesh_urho/utils.py

practicing01/Urho3D-Blender

820f03c34adda7594aa8ebc3f95cd71382a51528

[ "Unlicense" ]

null

# # This script is licensed as public domain. # # http://docs.python.org/2/library/struct.html from xml.etree import ElementTree as ET from xml.dom import minidom import os import struct import array import logging log = logging.getLogger("ExportLogger") def enum(**enums): return type('Enum', (), enums) PathT...

27.607273

83

0.576264

890

7,592

4.898876

0.291011

0.022936

0.01445

0.024083

0.155963

0.124771

0.120642

0.059174

0

0.007742

0.285432

7,592

274

84

27.708029

0.795945

0.18638

0

0.147929

0

0.056462

0

1

0.16568

false

0.005917

0.035503

0.331361

0

null

0

null

0

1

0

809cbc92d834b903ea9a7f231c4069974f14b439

793

py

Python

751_ConcatenationCoincidence.py

joetache4/project-euler

82f9e25b414929d9f62d94905906ba2f57db7935

[ "MIT" ]

null

751_ConcatenationCoincidence.py

joetache4/project-euler

82f9e25b414929d9f62d94905906ba2f57db7935

[ "MIT" ]

null

751_ConcatenationCoincidence.py

joetache4/project-euler

82f9e25b414929d9f62d94905906ba2f57db7935

[ "MIT" ]

null

""" Joe Tacheron difficulty: TBD runtime: instant answer: 2.223561019313554106173177 *** 751 Concatenation Coincidence Find the only value of theta for which the concatenated sequence equals theta. Give your answer rounded to 24 places after the decimal point. """ from math import floor from decimal import getco...

19.341463

141

0.682219

126

793

4.285714

0.5

0.014815

0.038889

0.048148

0

0.108271

0.161412

793

41

142

19.341463

0.703759

0.351829

0

0.105263

0

0.064202

0

0.052632

1

0.052632

false

0

0.105263

0

0.210526

0.052632

0

null

0

null

0

1

0

809d6db57d7ccbeed3286156e788d7b40de4e64f

2,991

py

Python

apps/cuenta/views.py

mariomtzjr/podemos_test

5efaf02a19aa8c4849e3ad0108546e95af524126

[ "MIT" ]

null

apps/cuenta/views.py

mariomtzjr/podemos_test

5efaf02a19aa8c4849e3ad0108546e95af524126

[ "MIT" ]

null

apps/cuenta/views.py

mariomtzjr/podemos_test

5efaf02a19aa8c4849e3ad0108546e95af524126

[ "MIT" ]

null

import json from datetime import datetime, timedelta from collections import defaultdict from django.shortcuts import redirect from rest_framework import generics from rest_framework import status from rest_framework.response import Response from rest_framework.renderers import TemplateHTMLRenderer from api.serialize...

33.606742

82

0.63223

311

2,991

5.961415

0.321543

0.052859

0.037756

0.043689

0.157497

0.134844

0.097087

0

0.005991

0.27449

2,991

88

83

33.988636

0.848387

0.00769

0

0.089552

0

0.057991

0.016521

0

1

0.044776

false

0

0.19403

0

0.41791

0

null

0

null

0

1

0

809dce66f1ae4ba19a16cb38353f61a408805045

2,651

py

Python

BioClients/pubtator/Client.py

jeremyjyang/BioClients

b78ab2b948c79616fed080112e31d383346bec58

[ "CC0-1.0" ]

10

2020-05-26T07:29:14.000Z

2021-12-06T21:33:40.000Z

BioClients/pubtator/Client.py

jeremyjyang/BioClients

b78ab2b948c79616fed080112e31d383346bec58

[ "CC0-1.0" ]

1

2021-10-05T12:25:30.000Z

2021-10-05T17:05:56.000Z

BioClients/pubtator/Client.py

jeremyjyang/BioClients

b78ab2b948c79616fed080112e31d383346bec58

[ "CC0-1.0" ]

2

2021-03-16T03:20:24.000Z

2021-08-08T20:17:10.000Z

#!/usr/bin/env python3 """ Pubtator REST API client https://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/tmTools/RESTfulAPIs.html Formats: JSON, PubTator, BioC. Nomenclatures: Gene : NCBI Gene e.g. https://www.ncbi.nlm.nih.gov/sites/entrez?db=gene&term=145226 Disease : MEDIC (CTD, CTD_diseases.csv) e.g. http://ctdbase...

38.42029

138

0.692569

386

2,651

4.663212

0.450777

0.045

0.085

0.043333

0.106667

0.097778

0.072222

0.051111

0

0.016553

0.111279

2,651

68

139

38.985294

0.747453

0.296492

0

0.273495

0.036016

0

1

0

false

0

0.054054

0

0.054054

0

null

0

null

0

1

0

80a1edfb39244009248c251e763cdd1deed6666f

925

py

Python

2. Programming Fundamentals With Python (May 2021)/18. Mid Exam Preparation/More Exercises/02_array_modifier.py

kzborisov/SoftUni

ccb2b8850adc79bfb2652a45124c3ff11183412e

[ "MIT" ]

1

2021-02-07T07:51:12.000Z

2. Programming Fundamentals With Python (May 2021)/18. Mid Exam Preparation/More Exercises/02_array_modifier.py

kzborisov/softuni

9c5b45c74fa7d9748e9b3ea65a5ae4e15c142751

[ "MIT" ]

null

2. Programming Fundamentals With Python (May 2021)/18. Mid Exam Preparation/More Exercises/02_array_modifier.py

kzborisov/softuni

9c5b45c74fa7d9748e9b3ea65a5ae4e15c142751

[ "MIT" ]

null

class Modifier: def __init__(self, lst): self.lst = lst def swap(self, index_1, index_2): self.lst[index_1], self.lst[index_2] = self.lst[index_2], self.lst[index_1] def multiply(self, index_1, index_2): self.lst[index_1] = int(self.lst[index_1]) * int(self.lst[index_2]) def d...

26.428571

83

0.597838

137

925

3.854015

0.226277

0.145833

0.159091

0.098485

0.481061

0.422348

0.350379

0.291667

0.181818

0

0.035511

0.238919

925

34

84

27.205882

0.714489

0

0.230769

0

0.027027

0

1

0.153846

false

0

0.192308

0.038462

0

null

0

null

0

1

0

80a2a8d0820253e95e290a620678375fc27af9cc

6,023

py

Python

undeployed/subjects/chunking/chunk_bundle.py

NASA-DEVELOP/dnppy

8f7ef6f0653f5a4ea730ee557c72a2c89c06ce0b

[ "NASA-1.3" ]

65

2015-09-10T12:59:56.000Z

2022-02-27T22:09:03.000Z

undeployed/subjects/chunking/chunk_bundle.py

snowzm/dnppy

8f7ef6f0653f5a4ea730ee557c72a2c89c06ce0b

[ "NASA-1.3" ]

40

2015-04-08T19:23:30.000Z

2015-08-04T15:53:11.000Z

undeployed/subjects/chunking/chunk_bundle.py

snowzm/dnppy

8f7ef6f0653f5a4ea730ee557c72a2c89c06ce0b

[ "NASA-1.3" ]

45

2015-08-14T19:09:38.000Z

2022-02-15T18:53:16.000Z

__author__ = 'jwely' import numpy import os from chunk import chunk # from dnppy import raster # please see chunk_bundle.read() for dnppy.raster import class chunk_bundle(): """ Creates a chunk bundle object. it can be used to pass smaller pieces of raster data to complex functions and reduce me...

33.648045

123

0.62859

779

6,023

4.724005

0.322208

0.029348

0.031793

0.01712

0.1375

0.128804

0.099185

0.083424

0.063859

0

0.010024

0.304333

6,023

179

124

33.648045

0.868258

0.442305

0

0.235294

0

0.105246

0.05214

0

1

0.102941

false

0.029412

0.073529

0

0.294118

0.014706

0

null

0

null

0

1

0

80a2d988e41df3a0c79d453576a3823c0cb38741

58,965

py

Python

quart_app/beyondchaosmaster/wor.py

razzlestorm/BCRandomizer-API

2e5aec91c34b46e845bca695d3468eb8f3bae401

[ "MIT" ]

1

2021-06-15T03:54:53.000Z

quart_app/beyondchaosmaster/wor.py

razzlestorm/BCRandomizer-API

2e5aec91c34b46e845bca695d3468eb8f3bae401

[ "MIT" ]

1

2021-09-13T04:32:43.000Z

BeyondChaos/Wor.py

razzlestorm/BeyondChaosRandomizer

04a0acdcd9d4c3991a3e42cf1bba4299adda4435

[ "MIT" ]

null

import dataclasses from chestrandomizer import get_event_items from character import get_character, get_characters from dialoguemanager import get_dialogue, set_dialogue from locationrandomizer import get_location, get_locations, NPCBlock from monsterrandomizer import change_enemy_name from utils import (WOB_TREASURE_...

38.58966

540

0.602663

7,689

58,965

4.460918

0.151905

0.023557

0.022566

0.014927

0.406968

0.359592

0.326152

0.306997

0.284082

0.266414

0

0.121329

0.302501

58,965

1,527

541

38.614931

0.712653

0.218214

0

0.441132

0

0.000765

0.012208

0.001295

0

0.162612

0.000655

0.001529

1

0.013761

false

0.000765

0.005352

0

0.028287

0

null

0

null

0

1

0

80a53a59d7c9cc5fc9f43fec4ffd711ec190d7c2

3,742

py

Python

acs/acs/UtilitiesFWK/CommandLine.py

intel/test-framework-and-suites-for-android

3aae8452ae931437b3b5ac30f068dc22a8dc5b85

[ "Apache-2.0" ]

8

2018-09-14T01:34:01.000Z

2021-07-01T02:00:23.000Z

acs/acs/UtilitiesFWK/CommandLine.py

intel/test-framework-and-suites-for-android

3aae8452ae931437b3b5ac30f068dc22a8dc5b85

[ "Apache-2.0" ]

3

2019-09-10T11:39:50.000Z

2019-10-10T08:26:22.000Z

acs/acs/UtilitiesFWK/CommandLine.py

intel/test-framework-and-suites-for-android

3aae8452ae931437b3b5ac30f068dc22a8dc5b85

[ "Apache-2.0" ]

9

2018-10-11T15:14:03.000Z

2021-02-17T11:37:20.000Z

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

32.824561

103

0.613576

482

3,742

4.674274

0.348548

0.04261

0.024412

0.035952

0.110963

0.063027

0

0.009862

0.322555

3,742

113

104

33.115044

0.878895

0.504543

0

0.105263

0

0.013845

0

1

0.105263

false

0

0.052632

0

0.342105

0

null

0

null

0

1

0

80aa11171d981757abd23c45640f5c0e84c66506

9,132

py

Python

scripts/image_dataset.py

KentJames/crocodile

83c34c0530521774ba48063bb2357fc92a74d334

[ "Apache-2.0" ]

4

2015-02-10T17:26:50.000Z

2019-12-28T17:14:48.000Z

scripts/image_dataset.py

KentJames/crocodile

83c34c0530521774ba48063bb2357fc92a74d334

[ "Apache-2.0" ]

5

2015-03-19T12:15:08.000Z

2015-06-19T12:51:26.000Z

scripts/image_dataset.py

KentJames/crocodile

83c34c0530521774ba48063bb2357fc92a74d334

[ "Apache-2.0" ]

10

2015-03-05T18:21:19.000Z

2018-07-30T02:04:23.000Z

#!/bin/env python3 import sys import os project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) sys.path.append(project_root) import argparse import h5py import itertools import numpy import pylru from multiprocessing import Process, Array, Queue import ctypes import arl.test_support from crocodil...

38.209205

103

0.611695

1,281

9,132

4.278689

0.210773

0.021346

0.040321

0.015326

0.27568

0.207809

0.166575

0.135559

0.108009

0

0.015685

0.232041

9,132

238

104

38.369748

0.765863

0.102606

0

0.08046

0

0.005747

0.153337

0.002694

0

1

0.034483

false

0

0.074713

0.011494

0.137931

0.143678

0

null

0

null

0

1

0

80ac180f9a1f733a4e347f78769bceb79ce6c95d

719

py

Python

examples/api_csv_to_file.py

gjpower/python-sdk

e2f1bd7078afe0ed13364037992477a13ca8e4dc

[ "MIT" ]

18

2018-09-25T11:47:28.000Z

2021-12-14T20:28:39.000Z

examples/api_csv_to_file.py

gjpower/python-sdk

e2f1bd7078afe0ed13364037992477a13ca8e4dc

[ "MIT" ]

57

2018-11-08T12:40:30.000Z

2022-03-31T13:01:19.000Z

examples/api_csv_to_file.py

gjpower/python-sdk

e2f1bd7078afe0ed13364037992477a13ca8e4dc

[ "MIT" ]

34

2018-11-05T16:09:15.000Z

2022-03-08T10:51:34.000Z

import os from devo.api import Client, ClientConfig, TO_BYTES key = os.getenv('DEVO_API_KEY', None) secret = os.getenv('DEVO_API_SECRET', None) api = Client(auth={"key": key, "secret": secret}, address="https://apiv2-eu.devo.com/search/query", config=ClientConfig(response="csv", ...

29.958333

72

0.584145

93

719

4.44086

0.580645

0.050847

0.058111

0.072639

0

0.007547

0.262865

719

23

73

31.26087

0.771698

0

0.243394

0.03338

0

1

0

false

0

0.117647

0

0.117647

0.058824

0

null

0

null

0

1

0

80ac6e530ef3e3ca2f4277c6ca59011dab2aece5

2,246

py

Python

ovs/dal/lists/rolelist.py

mflu/openvstorage_centos

280a98d3e5d212d58297e0ffcecd325dfecef0f8

[ "Apache-2.0" ]

1

2015-08-29T16:36:40.000Z

ovs/dal/lists/rolelist.py

rootfs-analytics/openvstorage

6184822340faea1d2927643330a7aaa781d92d36

[ "Apache-2.0" ]

null

ovs/dal/lists/rolelist.py

rootfs-analytics/openvstorage

6184822340faea1d2927643330a7aaa781d92d36

[ "Apache-2.0" ]

null

# Copyright 2014 CloudFounders NV # # 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 writ...

35.650794

103

0.59528

262

2,246

5.061069

0.427481

0.045249

0.030166

0.052036

0.220211

0

0.006402

0.304541

2,246

62

104

36.225806

0.84251

0.355744

0

0.5

0

0.059657

0

1

0.107143

false

0

0.142857

0

0.428571

0

null

0

null

0

1

0

80ad6ed4380cff811437166d91bf4e659300cfcd

5,776

py

Python

urfiles/load.py

rikfaith/urfiles

95319aae9e6400075cd5ee4a35b1f3a5e32eb571

[ "MIT" ]

null

urfiles/load.py

rikfaith/urfiles

95319aae9e6400075cd5ee4a35b1f3a5e32eb571

[ "MIT" ]

null

urfiles/load.py

rikfaith/urfiles

95319aae9e6400075cd5ee4a35b1f3a5e32eb571

[ "MIT" ]

null

#!/usr/bin/env python3 # load.py -*-python-*- import csv import io import os import re import time import urfiles.db # pylint: disable=unused-import from urfiles.log import DEBUG, INFO, ERROR, FATAL class Load(): def __init__(self, directories, config, source=None, debug=False, md5file='md5sum.t...

32.818182

77

0.540166

701

5,776

4.35378

0.256776

0.026212

0.019659

0.018349

0.241809

0.23329

0.197248

0.179554

0

0.019078

0.346607

5,776

175

78

33.005714

0.789613

0.116343

0

0.341085

0

0.09654

0

1

0.054264

false

0

0.054264

0

0.162791

0

null

0

null

0

1

0

80b3ded7af8cd9841983dca1a2e3c26add8a24cb

2,109

py

Python

tomoproc/util/logger.py

KedoKudo/tomoproc

b20270e87af4ce7459004a6ed928037ae8573b1e

[ "MIT" ]

1

2020-07-19T21:12:33.000Z

tomoproc/util/logger.py

KedoKudo/xproc

b20270e87af4ce7459004a6ed928037ae8573b1e

[ "MIT" ]

null

tomoproc/util/logger.py

KedoKudo/xproc

b20270e87af4ce7459004a6ed928037ae8573b1e

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- """ Event and exception handeling with logger """ import functools import logging def create_logger(logfile=r"/tmp/tomoproc.log"): """Default logger for exception tracking""" logger = logging.getLogger("tomoproc_logger") logger.setLevel(logging.INFO) # ...

28.12

104

0.596017

258

2,109

4.670543

0.321705

0.049793

0.029876

0.041494

0.298755

0.253942

0

0.004496

0.261735

2,109

74

105

28.5

0.769428

0.158369

0

0.340426

0

0.021277

0.198622

0.096441

0

1

0.170213

false

0

0.042553

0

0.382979

0.085106

0

null

0

null

0

1

0

80b6a23d570751524a2f07ff3ef236f65d15c194

674

py

Python

trivial-forms/caller_1.py

mykespb/damba

1e16a6823fc2b307b023388f8dd61e5a83c6431b

[ "MIT" ]

null

trivial-forms/caller_1.py

mykespb/damba

1e16a6823fc2b307b023388f8dd61e5a83c6431b

[ "MIT" ]

null

trivial-forms/caller_1.py

mykespb/damba

1e16a6823fc2b307b023388f8dd61e5a83c6431b

[ "MIT" ]

null

#!python # caller_1.py # caller file for forms # Mikhail Kolodin, 2020 # ver. 2020-02-27 1.0 from forms_1 import * global_values = {'max': 5000, 'min': 100, 'name': 'Vasya'} def main(args): local_values = {'max': 3000, 'name': 'Kirill'} my_values = {**global_values, **local_values} temp = templates['ma...

21.741935

58

0.632047

90

674

4.5

0.477778

0.098765

0.093827

0.123457

0.355556

0

0.052336

0.206231

674

30

59

22.466667

0.704673

0.123145

0

0.352941

0

0.165529

0

1

0.058824

false

0

0.117647

0

0.235294

0.352941

0

null

0

null

0

1

0

80b8a6cce37192bf40ffca015c22b4b54d5f60e3

3,614

py

Python

sahara/config.py

hortonworksqe/sahara

b8edeaf2b6a475728bf9fd2ddc3a860dc6c23270

[ "Apache-2.0" ]

1

2016-04-13T17:07:05.000Z

sahara/config.py

hortonworksqe/sahara

b8edeaf2b6a475728bf9fd2ddc3a860dc6c23270

[ "Apache-2.0" ]

null

sahara/config.py

hortonworksqe/sahara

b8edeaf2b6a475728bf9fd2ddc3a860dc6c23270

[ "Apache-2.0" ]

null

# Copyright (c) 2013 Mirantis 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 agreed to in writ...

33.775701

79

0.633647

454

3,614

4.936123

0.4163

0.026774

0.021419

0.014279

0.024096

0

0.007634

0.275042

3,614

106

80

34.09434

0.84771

0.153016

0

0.052632

0

0.392646

0.018056

0

1

0.039474

false

0

0.052632

0

0.092105

0

null

0

null

0

1

0

80b8af7d0e63b2441f4f96ef7b409613e1e3bbf6

3,308

py

Python

scripts/python/set_districts_on_parking_places.py

grvl/grvl.github.io

1eff80b1dc01a612cc699f5f32e8ae342153e786

[ "MIT" ]

null

scripts/python/set_districts_on_parking_places.py

grvl/grvl.github.io

1eff80b1dc01a612cc699f5f32e8ae342153e786

[ "MIT" ]

null

scripts/python/set_districts_on_parking_places.py

grvl/grvl.github.io

1eff80b1dc01a612cc699f5f32e8ae342153e786

[ "MIT" ]

null

"""set_districts_on_parkgin_places.py set the district from SP, on the parking place. """ import json import math from sp_districts import get_districts, get_district_from_point _VAGAS_FILE = 'data/vagas/ZonaAzuVagas_DF_ID_latlong.json' _OUTPUT_FILE_RAW = 'data/vagas/vagas_latlong.csv' _OUTPUT_FILE_SCORED = 'data/v...

28.273504

78

0.58283

398

3,308

4.673367

0.301508

0.018817

0.027419

0.021505

0.146774

0.066667

0

0.005467

0.281137

3,308

116

79

28.517241

0.776703

0.137243

0

0.151899

0

0.168145

0.037247

0

1

0.050633

false

0

0.037975

0

0.101266

0.037975

0

null

0

null

0

1

0

80b9a3fc573c3c4f2095f1c187ca7ad44b7fa13f

10,744

py

Python

pybilt/bilayer_analyzer/leaflet.py

blakeaw/ORBILT

ed402dd496534dccd00f3e75b57007d944c58c1d

[ "MIT" ]

11

2019-07-29T16:21:53.000Z

2022-02-02T11:44:57.000Z

pybilt/bilayer_analyzer/leaflet.py

blakeaw/ORBILT

ed402dd496534dccd00f3e75b57007d944c58c1d

[ "MIT" ]

11

2019-05-15T09:30:05.000Z

2021-07-19T16:49:59.000Z

pybilt/bilayer_analyzer/leaflet.py

blakeaw/ORBILT

ed402dd496534dccd00f3e75b57007d944c58c1d

[ "MIT" ]

9

2019-08-12T11:14:45.000Z

2020-12-22T18:22:55.000Z

from __future__ import absolute_import from __future__ import division from __future__ import print_function from builtins import object # leaflet object class Leaflet(object): """ Create a bilayer Leaflet representation. This class object is used to group lipids together according to their bilayer leaflet. ...

39.5

135

0.625558

1,427

10,744

4.608269

0.131044

0.0441

0.023723

0.010645

0.519465

0.448145

0.411496

0.395377

0.368461

0.349148

0

0.001469

0.302867

10,744

271

136

39.645756

0.876502

0.493206

0

0.378378

0

0.080504

0

1

0.162162

false

0

0.036036

0.018018

0.414414

0.045045

0

null

0

null

0

1

0

80ba70e30ea60ba73100e502e98b6715cae9406e

11,797

py

Python

train_dqn.py

jamqd/EE239AS

d1e45d8878ac61e0b6af38d6ce24b9d3a87fa285

[ "MIT" ]

2

2020-08-24T08:09:39.000Z

2020-08-31T11:42:12.000Z

train_dqn.py

jamqd/EE239AS

d1e45d8878ac61e0b6af38d6ce24b9d3a87fa285

[ "MIT" ]

null

train_dqn.py

jamqd/EE239AS

d1e45d8878ac61e0b6af38d6ce24b9d3a87fa285

[ "MIT" ]

null

import torch from torch import optim import torch.nn.functional as F from dqn import DQN import run import gym import numpy as np from trajectory_dataset import TrajectoryDataset from torch.utils.tensorboard import SummaryWriter from run import collect_trajectories import os import datetime import qvalues import random...

36.981191

127

0.610325

1,474

11,797

4.675712

0.183175

0.029019

0.033517

0.018572

0.448491

0.386535

0.351857

0.298752

0.262188

0.25

0

0.008504

0.282275

11,797

318

128

37.097484

0.80548

0.080953

0

0.307359

0

0.063669

0.013125

0

1

0.021645

false

0

0.064935

0

0.108225

0.064935

0

null

0

null

0

1

0

80ba74d0606b498a8e643910a20eeff2e8a3db5b

4,105

py

Python

tests/ClientServer/interop_tools/client_sc_renew.py

workerVA/S2OPC

9a5b6008559501f46a4bc079beea2d6655b1bfe5

[ "ECL-2.0", "Apache-2.0" ]

8

2018-09-28T16:03:55.000Z

2021-09-23T09:07:10.000Z

tests/ClientServer/interop_tools/client_sc_renew.py

workerVA/S2OPC

9a5b6008559501f46a4bc079beea2d6655b1bfe5

[ "ECL-2.0", "Apache-2.0" ]

null

tests/ClientServer/interop_tools/client_sc_renew.py

workerVA/S2OPC

9a5b6008559501f46a4bc079beea2d6655b1bfe5

[ "ECL-2.0", "Apache-2.0" ]

1

2020-04-28T08:32:27.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- # Licensed to Systerel under one or more contributor license # agreements. See the NOTICE file distributed with this work # for additional information regarding copyright ownership. # Systerel licenses this file to you under the Apache # License, Version 2.0 (the "License...

37.318182

111

0.703045

571

4,105

4.935201

0.345009

0.064585

0.038325

0.046132

0.407026

0.351668

0.323989

0.310859

0

0.016273

0.206577

4,105

109

112

37.66055

0.848941

0.350548

0

0.416667

0

0.185383

0.007994

0

1

0.033333

false

0.016667

0.15

0

0.183333

0.133333

0

null

0

null

0

1

0

80bc2884ecba206ed21a7fb62256701a985367e5

832

py

Python

roblox/promotionchannels.py

speer-kinjo/ro.py

2d5b80aec8fd143b11101fbbfdf3b557f798a27f

[ "MIT" ]

28

2021-11-04T11:13:38.000Z

2022-03-11T05:00:16.000Z

roblox/promotionchannels.py

speer-kinjo/ro.py

2d5b80aec8fd143b11101fbbfdf3b557f798a27f

[ "MIT" ]

12

2021-11-24T06:25:24.000Z

2022-03-18T14:37:01.000Z

roblox/promotionchannels.py

speer-kinjo/ro.py

2d5b80aec8fd143b11101fbbfdf3b557f798a27f

[ "MIT" ]

21

2021-10-20T16:36:55.000Z

2022-03-27T21:43:53.000Z

""" This module contains classes intended to parse and deal with data from Roblox promotion channel endpoints. """ from typing import Optional class UserPromotionChannels: """ Represents a user's promotion channels. Attributes: facebook: A link to the user's Facebook profile. twitter: ...

27.733333

106

0.652644

103

832

5.116505

0.436893

0.104364

0.142315

0.056926

0.085389

0

0.240385

832

29

107

28.689655

0.833861

0.41226

0

0.13964

0.060811

0

1

0.2

false

0

0.1

0.5

0

null

0

null

0

1

0

80bcc9aff7166b320e85d3dea2785530a717ef20

1,256

py

Python

training/Toxic_CNN1_MCD.py

jsandersen/CMT

1be6e36b9a6042386395bc654c9dd4b579e6ce6d

[ "Apache-2.0" ]

null

training/Toxic_CNN1_MCD.py

jsandersen/CMT

1be6e36b9a6042386395bc654c9dd4b579e6ce6d

[ "Apache-2.0" ]

null

training/Toxic_CNN1_MCD.py

jsandersen/CMT

1be6e36b9a6042386395bc654c9dd4b579e6ce6d

[ "Apache-2.0" ]

null

import tensorflow as tf tf.compat.v1.disable_v2_behavior() from src.datasets.toxic import Toxic from src.models.cnn1 import getCNN1 from src.models.predict import predict_mcdropout import tensorflow as tf def build(): # config RANDOM_STATE = 1 VOCAB_SIZE = 20000 MAX_SEQUENCE_LENGTH = 500 ...

22.035088

83

0.636146

178

1,256

4.235955

0.410112

0.047745

0.067639

0.05305

0

0.03452

0.261943

1,256

57

84

22.035088

0.778857

0.030255

0

0.054054

0

0.091509

0.044518

0

1

0.027027

false

0

0.135135

0

0.162162

0.108108

0

null

0

null

0

1

0

80be7ef53052c878fdc38ea2f892c9d1a8f45ee3

1,691

py

Python

pal.py

pfreese/py_test

bf1cb713d63259c8b6db666924b69bd101b55674

[ "MIT" ]

null

pal.py

pfreese/py_test

bf1cb713d63259c8b6db666924b69bd101b55674

[ "MIT" ]

null

pal.py

pfreese/py_test

bf1cb713d63259c8b6db666924b69bd101b55674

[ "MIT" ]

null

def isPalindrome(r): rL = len(r) rHalf = rL // 2 for i in range(rHalf): if r[i] != r[rL - i - 1]: return False return True def longestPalindrome(s): sLen = len(s) if sLen < 2: return s if sLen == 2: if isPalindrome(s): return s else...

28.183333

90

0.591957

199

1,691

5.030151

0.246231

0.034965

0.017982

0.021978

0

0.020322

0.301597

1,691

59

91

28.661017

0.827265

0.009462

0

0.166667

0

0.002392

0

1

0.104167

false

0

0.354167

0.041667

0

null

0

null

0

1

0

80bece861972253f223e4ca2fe5fcdbcc32983b7

8,437

py

Python

ignite/contrib/handlers/time_profilers.py

Patil2099/ignite

5d01c306150345e081b41b9b623bd04a3f599448

[ "BSD-3-Clause" ]

null

ignite/contrib/handlers/time_profilers.py

Patil2099/ignite

5d01c306150345e081b41b9b623bd04a3f599448

[ "BSD-3-Clause" ]

null

ignite/contrib/handlers/time_profilers.py

Patil2099/ignite

5d01c306150345e081b41b9b623bd04a3f599448

[ "BSD-3-Clause" ]

null

from collections import OrderedDict import torch from ignite.engine import Engine, Events from ignite.handlers import Timer class BasicTimeProfiler(object): def __init__(self): self._dataflow_timer = Timer() self._processing_timer = Timer() self._event_handlers_timer = Timer() def ...

35.154167

112

0.644779

1,009

8,437

5.021804

0.132805

0.087231

0.080521

0.056444

0.523584

0.452339

0.402802

0.3657

0.319518

0.313203

0

0.001872

0.24037

8,437

239

113

35.301255

0.788735

0.012919

0

0.314917

0

0.142634

0.057078

0

1

0.110497

false

0

0.033149

0.005525

0.176796

0.016575

0

null

0

null

0

1

0

80bfb4b7a7a0c88d0a0cf52ced86fbe80cb85e15

4,678

py

Python

hopla/tests/test_converter.py

AGrigis/hopla

60147969267b8bf71aec774053d33fa797e2f668

[ "CECILL-B" ]

null

hopla/tests/test_converter.py

AGrigis/hopla

60147969267b8bf71aec774053d33fa797e2f668

[ "CECILL-B" ]

null

hopla/tests/test_converter.py

AGrigis/hopla

60147969267b8bf71aec774053d33fa797e2f668

[ "CECILL-B" ]

null

#! /usr/bin/env python ########################################################################## # Hopla - Copyright (C) AGrigis, 2015 # Distributed under the terms of the CeCILL-B license, as published by # the CEA-CNRS-INRIA. Refer to the LICENSE file or to # http://www.cecill.info/licences/Licence_CeCILL-B_V1-en.ht...

42.144144

78

0.551945

512

4,678

4.886719

0.263672

0.083133

0.043965

0.059952

0.572342

0.569145

0.534373

0.523181

0.464428

0.410072

0

0.021578

0.276828

4,678

110

79

42.527273

0.718002

0.161394

0

0.533333

0

0.088692

0.006699

0

0.08

1

0.053333

false

0

0.093333

0

0.16

0

null

0

null

0

1

0

80bff1f35b026d788a822b1166f2ed86dd9836a7

999

py

Python

quartz_metadata/handlers/on_mint.py

dipdup-net/quartz-metadata

78b90319359cbc641abdbbbfbf2fec59e601429b

[ "MIT" ]

null

quartz_metadata/handlers/on_mint.py

dipdup-net/quartz-metadata

78b90319359cbc641abdbbbfbf2fec59e601429b

[ "MIT" ]

null

quartz_metadata/handlers/on_mint.py

dipdup-net/quartz-metadata

78b90319359cbc641abdbbbfbf2fec59e601429b

[ "MIT" ]

null

from dipdup.context import HandlerContext from dipdup.models import Transaction from tortoise.exceptions import IntegrityError from quartz_metadata.manager import ResolveMetadataTaskManager from quartz_metadata.models import ResolveToken from quartz_metadata.types.ubisoft_quartz_minter.parameter.mint import MintParame...

31.21875

84

0.76977

106

999

7.037736

0.433962

0.053619

0.096515

0.061662

0.112601

0

0.176176

999

31

85

32.225806

0.90644

0

1

0

false

0.038462

0.269231

0

0.269231

0

null

0

null

0

1

0

80c133884608c388783cc004a5ae950066a8bd8a

1,161

py

Python

app/gws/lib/ows/formats/get_feature_info_response.py

gbd-consult/gbd-websuite

7212f41081c04614fdb4641e902d4de3424da8c5

[ "Apache-2.0" ]

3

2020-07-24T10:10:18.000Z

2022-03-16T10:22:04.000Z

app/gws/lib/ows/formats/get_feature_info_response.py

gbd-consult/gbd-websuite

7212f41081c04614fdb4641e902d4de3424da8c5

[ "Apache-2.0" ]

28

2020-03-03T17:35:58.000Z

2021-07-12T12:05:47.000Z

app/gws/lib/ows/formats/get_feature_info_response.py

gbd-consult/gbd-websuite

7212f41081c04614fdb4641e902d4de3424da8c5

[ "Apache-2.0" ]

1

2021-02-22T14:32:10.000Z

import gws.lib.feature import gws.lib.shape import gws.lib.xml2 # geoserver # # <GetFeatureInfoResponse> # <Layer name="...."> # <Feature id="..."> # <Attribute name="..." value="..."/> # <Attribute name="geometry" value="wkt"/> def parse(text, first_el, crs=None, invert_axis=None, **kwar...

27

73

0.511628

130

1,161

4.523077

0.361538

0.061224

0

0.002581

0.332472

1,161

42

74

27.642857

0.756129

0.153316

0

0.083077

0.022564

0

1

0.038462

false

0

0.115385

0

0.230769

0

null

0

null

0

1

0

80c2a4c56b25c8d0daa6417069d865c0369c616f

1,287

py

Python

imagefilter/imagefilter-rank.py

martinmcbride/python-imaging-book-examples

37e4ccf9b7b2fc3ff75b1fdb9f772de452a843b2

[ "MIT" ]

1

2021-08-22T17:09:44.000Z

imagefilter/imagefilter-rank.py

sthagen/python-imaging-book-examples

2a079c5271f9849bc90a33bed6f3288142035ea7

[ "MIT" ]

null

imagefilter/imagefilter-rank.py

sthagen/python-imaging-book-examples

2a079c5271f9849bc90a33bed6f3288142035ea7

[ "MIT" ]

1

2021-08-22T17:09:48.000Z

# Author: Martin McBride # Created: 2021-05-23 # Copyright (C) 2021, Martin McBride # License: MIT # Use the ranking filters. # Create a final image with all the filters. from PIL import Image, ImageFilter, ImageDraw, ImageFont image = Image.open('boat-small.jpg') min_image = image.filter(ImageFilter.MinFilter()) m...

29.25

56

0.706294

215

1,287

4.144186

0.339535

0.022447

0.089787

0.151515

0.304153

0.283951

0.257015

0

0.066313

0.121212

1,287

43

57

29.930233

0.721485

0.156177

0

0.068646

0

1

0

false

0

0.038462

0

0.038462

0

null

0

null

0

1

0

80c334bb39045d805035eb994c6817b18dd7c10e

4,974

py

Python

prepare_training_dataset.py

Koziev/masked_np_language_model

b2173682adb77b424ffa192f3030d8c8e78e88e2

[ "CC0-1.0" ]

null

prepare_training_dataset.py

Koziev/masked_np_language_model

b2173682adb77b424ffa192f3030d8c8e78e88e2

[ "CC0-1.0" ]

1

2022-03-04T14:48:02.000Z

2022-03-04T15:21:37.000Z

prepare_training_dataset.py

Koziev/masked_np_language_model

b2173682adb77b424ffa192f3030d8c8e78e88e2

[ "CC0-1.0" ]

null

""" Подготовка датасета для файнтюнинга ruT5 и ruGPT, чтобы модель могла подставлять NP в предложения. Используется неразмеченный текст и синтаксический парсер UDPipe для выделения именных групп. ATT: используются всякие локальные корпуса, которые я не выгружаю в общий доступ по разным соображениям. Тем не менее, не в...

40.112903

163

0.567752

588

4,974

4.683673

0.421769

0.017792

0.019608

0.025418

0.125272

0.082789

0.058097

0.021786

0

0.02

0.326498

4,974

123

164

40.439024

0.80209

0.237033

0

0.066667

0

0.146674

0.074539

0.013333

0

1

0.04

false

0.013333

0.093333

0.013333

0.16

0.053333

0

null

0

null

0

1

0

80c3575c2734a1ea3e2894cddf66ae5e01537fa7

601

py

Python

server/loaddata.py

deb17/nearby-places

0d05f888f3c90cd021c67d446bc16ccb59efc8bc

[ "MIT" ]

null

server/loaddata.py

deb17/nearby-places

0d05f888f3c90cd021c67d446bc16ccb59efc8bc

[ "MIT" ]

6

2021-03-09T13:19:32.000Z

2022-02-26T15:52:16.000Z

server/loaddata.py

deb17/nearby-places

0d05f888f3c90cd021c67d446bc16ccb59efc8bc

[ "MIT" ]

null

import csv from app import db from app.models import Feature def process_row(row): key, value = row[0], row[1] if '/' in value: values = [val.strip() for val in value.split('/')] for v in values: f = Feature(key=key, value=v) db.session.add(f) db.session.commit(...

25.041667

58

0.567388

84

601

3.940476

0.428571

0.108761

0.07855

0.084592

0.283988

0.169184

0

0.004762

0.301165

601

23

59

26.130435

0.783333

0

0.2

0

0.036606

0

1

0.05

false

0

0.15

0

0.2

0

null

0

null

0

1

0

80c8628f774c95ee9df7395733a5d80589d0278f

2,234

py

Python

detection_ctpn/utils/tf_utils.py

EuphoriaYan/Chinese-ancient-book-recognition-HSK

865736d16389037f555f0eea7ec6c4ab7e4319c9

[ "Apache-2.0" ]

null

detection_ctpn/utils/tf_utils.py

EuphoriaYan/Chinese-ancient-book-recognition-HSK

865736d16389037f555f0eea7ec6c4ab7e4319c9

[ "Apache-2.0" ]

null

detection_ctpn/utils/tf_utils.py

EuphoriaYan/Chinese-ancient-book-recognition-HSK

865736d16389037f555f0eea7ec6c4ab7e4319c9

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- """ File Name： tf_utils Description : tensorflow工具类 Author : mick.yi date： 2019/3/13 """ import tensorflow as tf def pad_to_fixed_size(input_tensor, fixed_size): """padding到固定长度, 在第二维度末位增加一个padding_flag, no_pad:1, pad:0. Parameter: input_ten...

25.976744

93

0.562668

346

2,234

3.531792

0.289017

0.063011

0.03437

0.03928

0.06874

0.021277

0

0.072196

0.249776

2,234

85

94

26.282353

0.656921

0.252014

0

0.01859

0

1

0.111111

false

0

0.027778

0

0.25

0

null

0

null

0

1

0

80c94708b0f74b66e6d49ed413132347b371694e

291

py

Python

InterviewBit/Scripting/TransformCSV.py

CRAZYGEEKS04/competitive-programming-1

f27b8a718761b7bfeb8ff9e294398ca1a294cb5d

[ "MIT" ]

2

2022-02-08T12:37:41.000Z

2022-03-09T03:48:56.000Z

InterviewBit/Scripting/TransformCSV.py

gauravsingh58/competitive-programming

fa5548f435cdf2aa059e1d6ab733885790c6a592

[ "MIT" ]

1

2020-10-10T16:14:54.000Z

InterviewBit/Scripting/TransformCSV.py

gauravsingh58/competitive-programming

fa5548f435cdf2aa059e1d6ab733885790c6a592

[ "MIT" ]

2

2021-01-23T14:35:48.000Z

2021-03-15T05:04:24.000Z

while True : try : text = input() arr = text.split(',') for i in range(len(arr)) : if i == 4 : continue if i == 6 : print("+", end = "") print(arr[4], end = "-") print(arr[6], end = "") else : print(arr[i], end = ",") print() except EOFError : break

17.117647

28

0.474227

40

291

3.45

0.55

0.173913

0.15942

0

0.02

0.312715

291

16

29

18.1875

0.67

0

0.013746

0

1

0

false

0

0.3125

0

null

0

null

0

1

0

80c9f68534b6e93dc236ef38e36dca90fb996522

15,621

py

Python

response_model/python/metric_learning/metric_eval.py

googlearchive/rgc-models

0dea94bbd54f591d82d95169e33d40bb55b6be94

[ "Apache-2.0" ]

1

2018-09-18T16:47:09.000Z

response_model/python/metric_learning/metric_eval.py

google/rgc-models

0dea94bbd54f591d82d95169e33d40bb55b6be94

[ "Apache-2.0" ]

null

response_model/python/metric_learning/metric_eval.py

google/rgc-models

0dea94bbd54f591d82d95169e33d40bb55b6be94

[ "Apache-2.0" ]

1

2022-01-12T12:44:17.000Z

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

41.991935

116

0.626272

1,934

15,621

4.795243

0.20424

0.013586

0.018223

0.009165

0.301919

0.233556

0.206168

0.191611

0.173927

0.16336

0

0.011724

0.279239

15,621

371

117

42.105121

0.811973

0.275334

0

0.069565

0

0.090115

0.004369

0

0.008086

0

1

0.008696

false

0

0.13913

0

0.147826

0.034783

0

null

0

null

0

1

0

80cd12bb7a5b93faddb12652ac494f409752a60f

8,192

py

Python

apps/graph.py

csgobeta/csgobetabot

4d37b0eb166d500869d9b271d417b61e95333824

[ "MIT" ]

9

2021-01-08T05:21:38.000Z

2021-12-10T12:35:59.000Z

apps/graph.py

csgobeta/csgobetabot

4d37b0eb166d500869d9b271d417b61e95333824

[ "MIT" ]

null

apps/graph.py

csgobeta/csgobetabot

4d37b0eb166d500869d9b271d417b61e95333824

[ "MIT" ]

2

2021-01-14T21:58:46.000Z

2022-01-23T23:21:15.000Z

import sys import os import inspect currentdir = os.path.dirname(os.path.abspath( inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(currentdir) sys.path.insert(0, parentdir) import matplotlib.pyplot as plt import matplotlib.dates as mdates import seaborn as sns import pandas as pd from datetim...

41.373737

111

0.521606

957

8,192

4.299896

0.241379

0.031106

0.027218

0.072904

0.667072

0.64034

0.615796

0.594897

0.564277

0.553584

0

0.055451

0.350586

8,192

197

112

41.583756

0.717857

0

0.43125

0

0.070068

0

1

0.00625

false

0.0125

0.08125

0

0.0875

0.0125

0

null

0

null

0

1

0

80cd62ec15badfd33866992ef09a6d8c71ac2b2f

5,443

py

Python

vicarui/src/vicarui/analysis/missions/cassini/set_info.py

joniumGit/moons

f5f8b7e23e707c8cf7e1081c4a1c0fcc22182d85

[ "MIT" ]

1

2021-07-16T06:30:37.000Z

vicarui/src/vicarui/analysis/missions/cassini/set_info.py

joniumGit/moons

f5f8b7e23e707c8cf7e1081c4a1c0fcc22182d85

[ "MIT" ]

null

vicarui/src/vicarui/analysis/missions/cassini/set_info.py

joniumGit/moons

f5f8b7e23e707c8cf7e1081c4a1c0fcc22182d85

[ "MIT" ]

1

2021-05-26T03:53:41.000Z

from .config import * from .funcs import norm, target_estimate from .helpers import ImageHelper from ...common import load_kernels_for_image, release_kernels from ....support import sci_2 def set_info( image: ImageWrapper, image_axis=None, analysis_axis=None, **config ): raw = imag...

40.924812

105

0.482271

587

5,443

4.250426

0.277683

0.028056

0.012024

0.028858

0.235271

0.177555

0.04489

0

0.019726

0.422561

5,443

132

106

41.234848

0.774101

0.00643

0

0.107143

0

0.017857

0.119127

0.019423

0.008929

0

1

0.017857

false

0

0.080357

0

0.116071

0

null

0

null

0

1

0

80cddf253a619a7f9e2bea2ab9271db73ba0ccb6

2,187

py

Python

python/tvm/contrib/msir/core/utils/info.py

Archermmt/tvm

8b900cec1a9c3cb453e159db4d497ebeb26ed289

[ "Apache-2.0" ]

null

python/tvm/contrib/msir/core/utils/info.py

Archermmt/tvm

8b900cec1a9c3cb453e159db4d497ebeb26ed289

[ "Apache-2.0" ]

null

python/tvm/contrib/msir/core/utils/info.py

Archermmt/tvm

8b900cec1a9c3cb453e159db4d497ebeb26ed289

[ "Apache-2.0" ]

null

import tvm import logging import numpy as np from collections.abc import Iterable from .namespace import MSIR_COLLECTION,MSIR_NAME,MSIR_TARGET def _get_logger(): if not MSIR_COLLECTION.get(MSIR_NAME.LOGGER): MSIR_COLLECTION.set(MSIR_NAME.LOGGER, logging.getLogger("MSIR")) return MSIR_COLLECTION.get(MS...

30.375

141

0.666667

303

2,187

4.640264

0.290429

0.021337

0.02845

0.044808

0.061878

0.044097

0

0.002809

0.1861

2,187

72

142

30.375

0.787079

0

0.038462

0

0.126143

0

0.096154

1

0.192308

false

0.019231

0.153846

0

0.461538

0.019231

0

null

0

null

0

1

0

80cf8406717293e85d168ad077e6680849900737

2,072

py

Python

src/python/dicomifier/bruker_to_dicom/modules/series.py

DimitriPapadopoulos/dicomifier

708e4e1c932f6411200aa010f857823dfcc495f1

[ "CECILL-B" ]

null

src/python/dicomifier/bruker_to_dicom/modules/series.py

DimitriPapadopoulos/dicomifier

708e4e1c932f6411200aa010f857823dfcc495f1

[ "CECILL-B" ]

null

src/python/dicomifier/bruker_to_dicom/modules/series.py

DimitriPapadopoulos/dicomifier

708e4e1c932f6411200aa010f857823dfcc495f1

[ "CECILL-B" ]

null

######################################################################### # Dicomifier - Copyright (C) Universite de Strasbourg # Distributed under the terms of the CeCILL-B license, as published by # the CEA-CNRS-INRIA. Refer to the LICENSE file or to # http://www.cecill.info/licences/Licence_CeCILL-B_V1-en.html # for...

42.285714

76

0.56805

214

2,072

5.350467

0.518692

0.052402

0.027948

0.031441

0.076856

0

0.017263

0.217181

2,072

48

77

43.166667

0.688656

0.188707

0

0.058824

0

0.352131

0.061639

0

1

0.029412

false

0

0.029412

0

0.088235

0

null

0

null

0

1

0

80cf9b726d141aa64359a81571dc5cc74e78eff7

12,365

py

Python

TranskribusDU/tasks/TablePrototypes/DU_ABPTableSkewed_txtTOMBS_sepSIO_line.py

Transkribus/TranskribusDU

61028ee5f5f39f435bf9c461f8073e75bca344ac

[ "BSD-3-Clause" ]

20

2017-01-24T20:08:25.000Z

2021-10-30T15:20:44.000Z

TranskribusDU/tasks/TablePrototypes/DU_ABPTableSkewed_txtTOMBS_sepSIO_line.py

Transkribus/TranskribusDU

61028ee5f5f39f435bf9c461f8073e75bca344ac

[ "BSD-3-Clause" ]

11

2017-06-27T11:41:42.000Z

2020-10-12T04:59:25.000Z

TranskribusDU/tasks/TablePrototypes/DU_ABPTableSkewed_txtTOMBS_sepSIO_line.py

Transkribus/TranskribusDU

61028ee5f5f39f435bf9c461f8073e75bca344ac

[ "BSD-3-Clause" ]

5

2017-01-12T15:55:34.000Z

2019-10-10T05:13:20.000Z

# -*- coding: utf-8 -*- """ *** Labelling is T O M B S It depends on the distance between the baseline and its above and below valid (S) cut Cuts are SIO Copyright Naver Labs Europe(C) 2018 JL Meunier Developed for the EU project READ. The READ project has received fun...

39.006309

135

0.510069

1,213

12,365

5.065128

0.309975

0.011393

0.020508

0.016276

0.125163

0.081055

0.072266

0.058919

0

0.011008

0.390214

12,365

316

136

39.129747

0.803846

0.228629

0

0.171123

0

0.037984

0.003374

0

0.010695

1

0.042781

false

0

0.085562

0

0.245989

0

null

0

null

0

1

0

80cfa666e310014576d03dad3c75588c0786534a

574

py

Python

LeetCode/2044. Count Number of Maximum Bitwise-OR Subsets/solution.py

InnoFang/oh-my-algorithms

f559dba371ce725a926725ad28d5e1c2facd0ab2

[ "Apache-2.0" ]

1

2017-03-31T15:24:01.000Z

LeetCode/2044. Count Number of Maximum Bitwise-OR Subsets/solution.py

InnoFang/Algorithm-Library

1896b9d8b1fa4cd73879aaecf97bc32d13ae0169

[ "Apache-2.0" ]

null

LeetCode/2044. Count Number of Maximum Bitwise-OR Subsets/solution.py

InnoFang/Algorithm-Library

1896b9d8b1fa4cd73879aaecf97bc32d13ae0169

[ "Apache-2.0" ]

null

""" 111 / 111 test cases passed. Runtime: 440 ms Memory Usage: 14.9 MB """ class Solution: def countMaxOrSubsets(self, nums: List[int]) -> int: count = largest = 0 def dfs(idx, res): nonlocal count, largest if idx == len(nums): if res > largest: ...

26.090909

56

0.444251

65

574

3.923077

0.492308

0.070588

0.054902

0.078431

0

0.064309

0.458188

574

21

57

27.333333

0.755627

0.114983

0

1

0.125

false

0

0.3125

0

null

0

null

0

1

0

80d4ab9196ae0d1428afc7cb93d38dd105b47bce

3,717

py

Python

ikalog/utils/icon_recoginizer/weapon.py

fetus-hina/IkaLog

bd476da541fcc296f792d4db76a6b9174c4777ad

[ "Apache-2.0" ]

285

2015-08-15T14:38:38.000Z

2022-02-18T15:00:06.000Z

ikalog/utils/icon_recoginizer/weapon.py

fetus-hina/IkaLog

bd476da541fcc296f792d4db76a6b9174c4777ad

[ "Apache-2.0" ]

323

2015-09-24T12:21:34.000Z

2018-05-06T16:34:54.000Z

ikalog/utils/icon_recoginizer/weapon.py

fetus-hina/IkaLog

bd476da541fcc296f792d4db76a6b9174c4777ad

[ "Apache-2.0" ]

72

2015-08-22T00:18:54.000Z

2022-02-18T14:44:20.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- # # IkaLog # ====== # Copyright (C) 2015 Takeshi HASEGAWA # # 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/l...

31.235294

75

0.641377

532

3,717

4.244361

0.327068

0.021258

0.019929

0.017715

0.153676

0.109832

0.099203

0.093003

0.079717

0

0.02546

0.239171

3,717

118

76

31.5

0.772984

0.181598

0

0.085714

0

0.013236

0.006949

0

1

0.157143

false

0

0.042857

0.014286

0.342857

0

null

0

null

0

1

0

80d598d25cb928d4e75ce7a8868f16d8dbc96650

11,867

py

Python

code/pytorch/utils/mujoco_solver.py

hzm2016/assistive-gym-robosuite

5c529f4444cc386383618bfa584341740a8468f9

[ "MIT" ]

1

2021-11-22T07:45:28.000Z

code/pytorch/utils/mujoco_solver.py

hzm2016/assistive-gym-robosuite

5c529f4444cc386383618bfa584341740a8468f9

[ "MIT" ]

null

code/pytorch/utils/mujoco_solver.py

hzm2016/assistive-gym-robosuite

5c529f4444cc386383618bfa584341740a8468f9

[ "MIT" ]

null

import math import os import random import numpy as np import torch from tensorboardX import SummaryWriter from tqdm import tqdm from ..methods import DDPG, TD3, SAC from envs.abb.models import utils class Solver(object): def __init__(self, args, env, project_path): self.args = args self.env = e...

42.08156

109

0.555911

1,365

11,867

4.594139

0.110623

0.059959

0.033487

0.035082

0.666082

0.603891

0.589858

0.57455

0.519375

0.502312

0

0.005186

0.333783

11,867

281

110

42.231317

0.788009

0.104913

0

0.446154

0

0.055126

0.007363

0

1

0.035897

false

0

0.046154

0

0.092308

0.097436

0

null

0

null

0

1

0

80d6549d6455eb3b023025b15786be0473b6bbe6

641

py

Python

code_example/w2_genMeanSd.py

koonyook/unsupervised-phase-supplementary

09ee8000c79465da8731b5323f2db9a25d7252ab

[ "MIT" ]

null

code_example/w2_genMeanSd.py

koonyook/unsupervised-phase-supplementary

09ee8000c79465da8731b5323f2db9a25d7252ab

[ "MIT" ]

null

code_example/w2_genMeanSd.py

koonyook/unsupervised-phase-supplementary

09ee8000c79465da8731b5323f2db9a25d7252ab

[ "MIT" ]

null

import numpy as np import pickle #this file will do #1. read from list of .pkl files that will become training data #2. save wMean.dat and wSd.dat for input in ["inputHeart/","inputBow/","inputAcrobat/"]: fileList=[ 'data_train.pkl', ] collect=[] for f in fileList: #data=np.load('input/'+f) #(2,-) dataLis...

22.103448

63

0.687988

101

641

4.356436

0.514851

0.036364

0.059091

0.077273

0

0.016393

0.143526

641

28

64

22.892857

0.785064

0.230889

0

0.141975

0

1

0

false

0

0.117647

0

0.117647

0.058824

0

null

0

null

0

1

0

80d6b3fdb21cbf34c945220abf23ee4dc73841af

10,553

py

Python

metadrive/component/road_network/node_road_network.py

liuzuxin/metadrive

850c207536531bc85179084acd7c30ab14a66111

[ "Apache-2.0" ]

125

2021-08-30T06:33:57.000Z

2022-03-31T09:02:44.000Z

metadrive/component/road_network/node_road_network.py

liuzuxin/metadrive

850c207536531bc85179084acd7c30ab14a66111

[ "Apache-2.0" ]

72

2021-08-30T16:23:41.000Z

2022-03-31T19:17:16.000Z

metadrive/component/road_network/node_road_network.py

liuzuxin/metadrive

850c207536531bc85179084acd7c30ab14a66111

[ "Apache-2.0" ]

20

2021-09-09T08:20:25.000Z

2022-03-24T13:24:07.000Z

import copy import logging from typing import List, Tuple, Dict import numpy as np from metadrive.component.lane.abs_lane import AbstractLane from metadrive.component.road_network.road import Road from metadrive.component.road_network.base_road_network import BaseRoadNetwork from metadrive.constants import Decoration ...

39.376866

120

0.607221

1,361

10,553

4.515797

0.166054

0.065897

0.023267

0.045558

0.318744

0.246827

0.150667

0.108363

0

0.004729

0.298683

10,553

267

121

39.524345

0.825699

0.16943

0

0.192308

0

0.02232

0

0.027473

1

0.120879

false

0

0.054945

0.005495

0.274725

0

null

0

null

0

1

0

80d8125a476591f35f2ad71b32650a81f028ecab

1,468

py

Python

generic_ui/RawTextWidget.py

STMicroelectronics/stm32ai-datalogger

0ba92ced44248e606a5cc68139fdfdc84489fa17

[ "BSD-3-Clause" ]

3

2021-06-28T13:41:12.000Z

2021-07-21T13:06:34.000Z

generic_ui/RawTextWidget.py

STMicroelectronics/stm32ai-datalogger

0ba92ced44248e606a5cc68139fdfdc84489fa17

[ "BSD-3-Clause" ]

null

generic_ui/RawTextWidget.py

STMicroelectronics/stm32ai-datalogger

0ba92ced44248e606a5cc68139fdfdc84489fa17

[ "BSD-3-Clause" ]

null

################################################################################### # Copyright (c) 2020-2021 STMicroelectronics. # All rights reserved. # This software is licensed under terms that can be found in the LICENSE file in # the root directory of this software component. # If no LICENSE file c...

38.631579

84

0.620572

160

1,468

5.5375

0.425

0.081264

0.031603

0.040632

0.469526

0

0.018739

0.200272

1,468

37

85

39.675676

0.735945

0.180518

0

0.356855

0

1

0.130435

false

0

0.086957

0

0.26087

0

null

0

null

0

1

0

80d88468c8e7365b40222afca17976c053b6b8f3

7,243

py

Python

test_QueryPharos.py

kevinxin90/RTX_BioThings_Explorer

16de49de9e0db75c7616a85c2592166ea055faa7

[ "Apache-2.0" ]

1

2018-05-24T13:16:57.000Z

test_QueryPharos.py

kevinxin90/RTX_BioThings_Explorer

16de49de9e0db75c7616a85c2592166ea055faa7

[ "Apache-2.0" ]

1

2018-06-01T02:04:23.000Z

2018-06-01T20:21:32.000Z

test_QueryPharos.py

kevinxin90/RTX_BioThings_Explorer

16de49de9e0db75c7616a85c2592166ea055faa7

[ "Apache-2.0" ]

null

import unittest from QueryPharos import QueryPharos class QueryPharosTestCase(unittest.TestCase): @classmethod def setUpClass(cls): cls.pharos = QueryPharos() def test_query_drug_name_to_targets(self): # bte_result = self.pharos.query_drug_name_to_targets('paclitaxel') # # TODO: B...

52.107914

114

0.54425

785

7,243

4.828025

0.322293

0.061741

0.044591

0.065172

0.407124

0.391821

0.366755

0.339842

0.262533

0.173615

0

0.058312

0.301533

7,243

138

115

52.485507

0.690848

0.316443

0

0.113924

0

0.273839

0

0.007246

0.101266

1

0.139241

false

0

0.025316

0

0.177215

0

null

0

null

0

1

0

80d969efc16e2fceb81dc2c5f9ae3ec495eea52f

2,016

py

Python

setup.py

MyGodIsHe/..-pytest-neo

5a7d3ef6754c03afeb01db189a80c55bba538de6

[ "MIT" ]

45

2019-03-07T12:12:11.000Z

2022-02-01T09:36:30.000Z

setup.py

MyGodIsHe/..-pytest-neo

5a7d3ef6754c03afeb01db189a80c55bba538de6

[ "MIT" ]

6

2019-03-14T09:37:51.000Z

2020-12-01T21:30:15.000Z

setup.py

MyGodIsHe/..-pytest-neo

5a7d3ef6754c03afeb01db189a80c55bba538de6

[ "MIT" ]

1

2019-03-30T22:45:58.000Z

from setuptools import setup import codecs # Copied from (and hacked): # https://github.com/pypa/virtualenv/blob/develop/setup.py#L42 def get_version(filename): import os import re here = os.path.dirname(os.path.abspath(__file__)) f = codecs.open(os.path.join(here, filename), encoding='utf-8') ve...

31.5

68

0.609623

227

2,016

5.286344

0.572687

0.110833

0.145833

0.13

0

0.01436

0.240079

2,016

63

69

32

0.76893

0.042163

0

0.435911

0.011417

0

1

0.018519

false

0

0.074074

0

0.111111

0

null

0

null

0

1

0

80da5c32e637d7a9cc6af9fdd36bc9ca02fad468

10,646

py

Python

ref_bot/cog/articlerefs.py

tser0f/ref_bot

8945992ec8802a88546494b503d7658cc53d80c5

[ "MIT" ]

null

ref_bot/cog/articlerefs.py

tser0f/ref_bot

8945992ec8802a88546494b503d7658cc53d80c5

[ "MIT" ]

1

2020-07-02T13:37:44.000Z

2020-07-07T03:09:50.000Z

ref_bot/cog/articlerefs.py

tser0f/ref_bot

8945992ec8802a88546494b503d7658cc53d80c5

[ "MIT" ]

null

import discord from discord.ext import commands from ref_bot.data_models import Article, Tag, ArticleOwner from ref_bot.article_scraper import scrape_article class ArticleRefs(commands.Cog): def __init__(self, bot, db_session): self.bot = bot self.db_session = db_session self._last_member =...

43.453061

191

0.603231

1,330

10,646

4.670677

0.132331

0.030425

0.038635

0.023181

0.469414

0.368802

0.302962

0.278815

0.250483

0.22537

0

0.004974

0.282454

10,646

244

192

43.631148

0.808221

0.059083

0

0.245902

0

0.010929

0.179257

0.006994

0

1

0.038251

false

0.005464

0.021858

0.120219

0

null

0

null

0

1

0

80ddf73885657b81588970d4b5f8599da4c9b6a7

2,060

py

Python

Discord Webhook Automation/discord_webhook.py

zYxDevs/Python_Scripts

74ed7df97c9287b966b4139f585ed3a1702f2d29

[ "MIT" ]

14

2021-10-02T14:17:06.000Z

2021-11-08T10:17:14.000Z

Discord Webhook Automation/discord_webhook.py

Naik-G/Python_Scripts

cd975036e126982aaa01da48c94cec1759af6d61

[ "MIT" ]

4

2021-10-03T05:35:11.000Z

2021-10-06T18:05:05.000Z

Discord Webhook Automation/discord_webhook.py

Naik-G/Python_Scripts

cd975036e126982aaa01da48c94cec1759af6d61

[ "MIT" ]

47

2021-10-02T12:07:07.000Z

2021-11-07T11:49:50.000Z

#!/usr/bin/env python3 # Path: Discord Webhook Automation/discord_webhook.py import requests discord_webhook_url = 'your webhook url' Message = { "content": "./Hello_World", "username": "Name for your discord webhook", "avatar_url": "Your Avatar Image URL", "tts": False, "embeds": [ { "title": "Tit...

29.428571

112

0.647087

280

2,060

4.721429

0.282143

0.083207

0.128593

0.079425

0.370651

0.360061

0

0.005736

0.23835

2,060

69

113

29.855072

0.836839

0.542233

0

0.105263

0

0.41402

0

1

0

false

0

0.026316

0

0.026316

0

null

0

null

0

1

0

80ddf8c8d244690e44871a0fc5d1f5d9d7730557

298

py

Python

Advertising/advertising.py

narenzhang/learnml

c6d5f4b84a7c9c23f93d03b06087f28772a52236

[ "Apache-2.0" ]

null

Advertising/advertising.py

narenzhang/learnml

c6d5f4b84a7c9c23f93d03b06087f28772a52236

[ "Apache-2.0" ]

null

Advertising/advertising.py

narenzhang/learnml

c6d5f4b84a7c9c23f93d03b06087f28772a52236

[ "Apache-2.0" ]

null

#!/usr/bin/python # _*_ coding : utf-8 _*_ import pandas as pd def run_main(): csv_path = 'Advertising.csv' # pandas 读取数据 data = pd.read_csv(csv_path) x = data[['TV', 'Radio', 'Newspaper']] y = data['Sales'] # 绘制1 plt.plot if __name__ == '__main__': run_main()

16.555556

42

0.583893

41

298

3.829268

0.731707

0.089172

0

0.008969

0.251678

298

18

43

16.555556

0.695067

0.184564

0

0.183333

0

1

0.111111

false

0

0.111111

0

0.222222

0

null

0

null

0

1

0

80df428c3bf27d5c6635b075ac58b6ddf1c4e21a

561

py

Python

python/Python - Settrade Open API Example - Equity.py

settrade/stt-open-api-sdk-example

b2644985ef41957df85a239a033a101435dff2c1

[ "MIT" ]

1

2022-03-03T20:15:34.000Z

python/Python - Settrade Open API Example - Equity.py

settrade/stt-open-api-sdk-example

b2644985ef41957df85a239a033a101435dff2c1

[ "MIT" ]

null

python/Python - Settrade Open API Example - Equity.py

settrade/stt-open-api-sdk-example

b2644985ef41957df85a239a033a101435dff2c1

[ "MIT" ]

null

import settrade.openapi from settrade.openapi import Investor ############################# login ############################# investor = Investor( app_id="8uuaMP1npccDixrg", app_secret="APX6wnqzk/yoVLIRyQ4ps4Fm13uzbC4tL5nyjAwwCKue", app_code="SANDBOX", broker_id="SANDBOX", is_auto_qu...

28.05

67

0.538324

49

561

6

0.714286

0.102041

0

0.042827

0.167558

561

20

68

28.05

0.586724

0.023173

0

0.23301

0.106796

0

1

0

false

0

0.133333

0

0.133333

0

null

0

null

0

1

0

80dfe6a1ff36490beaa2733bf4a9c540f4667373

2,135

py

Python

tests/examples/test_examples.py

897615138/tfsnippet-jill

2fc898a4def866c8d3c685168df1fa22083bb143

[ "MIT" ]

63

2018-06-06T11:56:40.000Z

2022-03-22T08:00:59.000Z

tests/examples/test_examples.py

897615138/tfsnippet-jill

2fc898a4def866c8d3c685168df1fa22083bb143

[ "MIT" ]

39

2018-07-04T12:40:53.000Z

2022-02-09T23:48:44.000Z

tests/examples/test_examples.py

897615138/tfsnippet-jill

2fc898a4def866c8d3c685168df1fa22083bb143

[ "MIT" ]

34

2018-06-25T09:59:22.000Z

2022-02-23T12:46:33.000Z

import codecs import copy import os import re import subprocess import sys import time import unittest from tfsnippet.utils import TemporaryDirectory, humanize_duration from tests.examples.helper import skipUnlessRunExamplesTests class ExamplesTestCase(unittest.TestCase): """ Test case to ensure all examples...

33.888889

78

0.526464

227

2,135

4.797357

0.444934

0.027548

0.025712

0

0.002978

0.37096

2,135

62

79

34.435484

0.807893

0.074005

0

0.09295

0.025065

0

1

0.043478

false

0

0.217391

0

0.304348

0.065217

0

null

0

null

0

1

0

80e29a5b17063378ea11a9ba5ec63b825ffd1e08

2,858

py

Python

Utils/fetcher.py

EchoAbstract/soma-fm-player

c1418033998c3fab74a649db98e230ced102e5fc

[ "MIT" ]

1

2019-03-04T10:35:42.000Z

Utils/fetcher.py

EchoAbstract/soma-fm-player

c1418033998c3fab74a649db98e230ced102e5fc

[ "MIT" ]

null

Utils/fetcher.py

EchoAbstract/soma-fm-player

c1418033998c3fab74a649db98e230ced102e5fc

[ "MIT" ]

null

import urllib2 from bs4 import BeautifulSoup from collections import defaultdict def fetch_html(): resp = urllib2.urlopen("http://somafm.com/listen/") html = resp.read() return html def make_soup(ingredients): return BeautifulSoup(ingredients, 'html.parser') def get_stations(soup): stations =...

27.747573

81

0.586424

334

2,858

4.817365

0.341317

0.021753

0.018645

0.024239

0

0.016058

0.280966

2,858

103

82

27.747573

0.76691

0.007698

0

0.108995

0.018342

0

1

0.09589

false

0

0.041096

0.013699

0.219178

0.027397

0

null

0

null

0

1

0

80e3d5f21275645dfb4b04506fb537a7e5daed1f

5,015

py

Python

Django elements/charts/data_preparation.py

LouisdeBruijn/Medium

afc66ee061c10b7107ba1661d2b9dfed0559dfc3

[ "MIT" ]

41

2020-05-03T19:32:37.000Z

2022-02-02T22:03:07.000Z

Django elements/charts/data_preparation.py

LouisdeBruijn/Medium

afc66ee061c10b7107ba1661d2b9dfed0559dfc3

[ "MIT" ]

2

2021-11-11T03:11:52.000Z

2021-12-16T01:51:13.000Z

Django elements/charts/data_preparation.py

LouisdeBruijn/Medium

afc66ee061c10b7107ba1661d2b9dfed0559dfc3

[ "MIT" ]

45

2020-03-29T02:43:24.000Z

2022-03-15T02:14:27.000Z

from .models import * from nltk.tokenize import TweetTokenizer from nltk.corpus import stopwords from sklearn.feature_extraction.text import TfidfVectorizer import seaborn as sns import numpy as np import time import re import json def hashtag_demographics(route, label): """Return most-used hashtags.""" data...

37.992424

137

0.637488

647

5,015

4.748068

0.239567

0.055013

0.046875

0.029297

0.361979

0.337891

0.318034

0.208008

0.17806

0.164388

0

0.008783

0.228116

5,015

131

138

38.282443

0.78481

0.031306

0

0.221053

0

0.07875

0.004375

0

1

0.042105

false

0

0.094737

0

0.178947

0

null

0

null

0

1

0

80e57ac43f1c3e92e78c3a47d232135e483fe654

2,838

py

Python

TaxiBJ/src/model/cnn.py

panzheyi/MF-STN

70d875d6b287a398b783e74031bb8237d44e5f8c

[ "MIT" ]

19

2019-10-28T09:41:51.000Z

2022-03-09T02:37:01.000Z

TaxiNYC/src/model/cnn.py

yoshall/MF-STN

70d875d6b287a398b783e74031bb8237d44e5f8c

[ "MIT" ]

null

TaxiNYC/src/model/cnn.py

yoshall/MF-STN

70d875d6b287a398b783e74031bb8237d44e5f8c

[ "MIT" ]

8

2020-11-20T09:02:30.000Z

2021-08-12T05:50:54.000Z

import numpy as np import mxnet as mx from mxnet import nd from mxnet.gluon import Block, HybridBlock, nn, rnn from config import ROWS, COLUMES, FLOW_OUTPUT_DIM, FLOW_OUTPUT_LEN from model.structure import MFDense, ResUnit N_LOC = ROWS * COLUMES class CNN(Block): """ Convolutional neural network """ ...

39.416667

125

0.559549

376

2,838

4.101064

0.279255

0.038911

0.025292

0.036965

0.182231

0.141375

0.076524

0

0.015539

0.29704

2,838

72

126

39.416667

0.757393

0.157858

0

0.045086

0

1

0.069767

false

0

0.139535

0.023256

0.27907

0

null

0

null

0

1

0

80eb165a96cff968c89b9f3c537a4b8ba8c0ae1a

7,315

py

Python

mujpy/muplot.py

RDeRenzi/mujpy

f7aa0eb97c3db668a1b099d00aba8e1bd41d4444

[ "MIT" ]

1

2017-09-10T15:55:23.000Z

mujpy/muplot.py

RDeRenzi/mujpy

f7aa0eb97c3db668a1b099d00aba8e1bd41d4444

[ "MIT" ]

1

2019-04-08T21:13:38.000Z

mujpy/muplot.py

RDeRenzi/mujpy

f7aa0eb97c3db668a1b099d00aba8e1bd41d4444

[ "MIT" ]

2

2019-03-26T11:47:29.000Z

2021-02-16T22:42:31.000Z

class multiplot(object): ''' plot class (let's see) ''' def __init__(self,time,asymm,title,nscan,histoLength): ''' input: if suite is the multiple run instance time, asymm - 1d and 2d numpy arrays e.g. from rebin(suite.time,suite.asymmetry_multirun(),(0,200...

43.541667

111

0.513192

907

7,315

4.033076

0.261301

0.036085

0.034445

0.019136

0.243849

0.220886

0.172499

0.13778

0.122471

0

0.029193

0.320984

7,315

167

112

43.802395

0.707268

0.277649

0

0.204545

0

0.029979

0

1

0.056818

false

0

0.056818

0

0.159091

0

null

0

null

0

1

0

80eb66e87e9597516ec5942a88129b383f122135

1,308

py

Python

tenning/layers/svdo.py

guilherme9820/Tenning

c0fe7695ef3dd791ea1083f39d6b312266fb0512

[ "MIT" ]

null

tenning/layers/svdo.py

guilherme9820/Tenning

c0fe7695ef3dd791ea1083f39d6b312266fb0512

[ "MIT" ]

null

tenning/layers/svdo.py

guilherme9820/Tenning

c0fe7695ef3dd791ea1083f39d6b312266fb0512

[ "MIT" ]

null

from tensorflow.keras.layers import Layer from tensorflow.keras.layers import Conv2D from tenning.generic_utils import get_object_config import tensorflow as tf class SVDO(Layer): """ Performs symmetric orthogonalization as detailed in the paper 'An Analysis of SVD for Deep Rotation Estimation' (h...

30.418605

99

0.632263

163

1,308

4.944785

0.521472

0.054591

0.047146

0.062035

0.076923

0

0.034483

0.246177

1,308

42

100

31.142857

0.782961

0.319572

0

0.015366

0

1

0.142857

false

0

0.190476

0

0.47619

0

null

0

null

0

1

0