xszheng2020/the_stack_dedup_python_hits_3 · Datasets at Hugging Face

2a7cc3a2ba45dcd79feceaaa86576ae6198b0caf

118

py

Python

Python/autori/autori.py

rvrheenen/OpenKattis

7fd59fcb54e86cdf10f56c580c218c62e584f391

[ "MIT" ]

12

2016-10-03T20:43:43.000Z

2021-06-12T17:18:42.000Z

Python/autori/autori.py

rvrheenen/OpenKattis

7fd59fcb54e86cdf10f56c580c218c62e584f391

[ "MIT" ]

null

Python/autori/autori.py

rvrheenen/OpenKattis

7fd59fcb54e86cdf10f56c580c218c62e584f391

[ "MIT" ]

10

2017-11-14T19:56:37.000Z

2021-02-02T07:39:57.000Z

line = input() abbr = line[0] for i in range(len(line)): if line[i] == "-": abbr += line[i+1] print(abbr)

16.857143

26

0.525424

20

118

3.1

0.6

0.258065

0

0.022727

0.254237

118

6

27

19.666667

0.681818

0

0.008475

0

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false

0

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0

null

1

0

1

0

null

0

3

2a7df1a1f3f69d0b66cc6a68a51f163e54917f1c

3,915

py

Python

covid_notebook/util/formatter.py

mtna/rds-python-examples

6cd52fe4ed1bc21acd591d575d0ea4a2dcd75d93

[ "Apache-2.0" ]

2

2020-05-15T18:11:04.000Z

2021-05-05T09:19:51.000Z

covid_notebook/util/formatter.py

mtna/rds-python-examples

6cd52fe4ed1bc21acd591d575d0ea4a2dcd75d93

[ "Apache-2.0" ]

null

covid_notebook/util/formatter.py

mtna/rds-python-examples

6cd52fe4ed1bc21acd591d575d0ea4a2dcd75d93

[ "Apache-2.0" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Wed May 6 11:24:11 2020 provides formatting for COVID-19 Notebook @author: seanlucas """ def html_catalog(catalog): metadata = catalog.get_metadata() dataproduct_rows = '' for dataproduct in metadata['dataProducts']: dataproduct_rows += f''' <tr style="border: 1px solid black"> <td style="text-align:left; border: 1px solid black">{dataproduct['name']}</td> <td style="text-align:left; border: 1px solid black">{dataproduct['id']}</td> </tr> ''' return f''' <html> <body> <div> <h1>{metadata['name']} ({metadata['id']})</h1> <p>{metadata['description']}</p> <table style="border: 1px solid black"> <tr style="border: 1px solid black"> <td colspan="2" style="text-align:center"><b>Dataproducts</b></td> </tr> <tr style="border: 1px solid black"> <td style="text-align:center; border: 1px solid black"><b>Name</b></td> <td style="text-align:center; border: 1px solid black"><b>ID</b></td> </tr> {dataproduct_rows} </table> </div> </body> </html> ''' def html_variables(dataproduct): metadata = dataproduct.get_variable() var_rows = '' for variable in metadata: class_id = '' try: class_id = variable['classificationId'] except KeyError: pass var_rows += f''' <tr style="border: 1px solid black"> <td style="text-align:left; border: 1px solid black">{variable['name']}</td> <td style="text-align:left; border: 1px solid black">{variable['id']}</td> <td style="text-align:left; border: 1px solid black">{variable['label']}</td> <td style="text-align:left; border: 1px solid black">{variable['dataType']}</td> <td style="text-align:left; border: 1px solid black">{class_id}</td> </tr> ''' return f''' <html> <body> <div> <table style="border: 1px solid black"> <tr style="border: 1px solid black"> <td colspan="5" style="text-align:center"><b>Variables</b></td> </tr> <tr style="border: 1px solid black"> <td style="text-align:center; border: 1px solid black"><b>Name</b></td> <td style="text-align:center; border: 1px solid black"><b>ID</b></td> <td style="text-align:center; border: 1px solid black"><b>Label</b></td> <td style="text-align:center; border: 1px solid black"><b>Data Type</b></td> <td style="text-align:center; border: 1px solid black"><b>Classification</b></td> </tr> {var_rows} </table> </div> </body> </html> ''' def html_classification(dataproduct, class_id, limit=20): metadata = dataproduct.get_classification(class_id) code_count = metadata['rootCodeCount'] codes = dataproduct.get_code(class_id, limit) code_rows = '' for code in codes: code_rows += f''' <tr style="border: 1px solid black"> <td style="text-align:left; border: 1px solid black">{code['codeValue']}</td> <td style="text-align:left; border: 1px solid black">{code['name']}</td> </tr> ''' return f''' <html> <body> <div> <h1>{metadata['id']}</h1> <p>Code Count: {code_count}</p> <table style="border: 1px solid black"> <tr style="border: 1px solid black"> <td colspan="2" style="text-align:center"><b>Codes</b></td> </tr> <tr style="border: 1px solid black"> <td style="text-align:center; border: 1px solid black"><b>Value</b></td> <td style="text-align:center; border: 1px solid black"><b>Label</b></td> </tr> {code_rows} </table> </div> </body> </html> '''

33.177966

93

0.558876

496

3,915

4.362903

0.159274

0.124769

0.194085

0.263401

0.677449

0.658503

0.633549

0.621534

0.591959

0

0.018698

0.262324

3,915

118

94

33.177966

0.730609

0.036271

0

0.564356

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0.19802

0.797875

0.30093

0

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0.029703

false

0.009901

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0.059406

0

null

0

1

0

1

0

null

0

3

aa6be4e924d5f2831927b299450e4354fc451b2e

320

py

Python

krun_ext_graal_ce_hotspot.py

vext01/new_benchmarking_experiment

3c2d834b6cb555a1997bf0f0236d6947c6fa4697

[ "Apache-2.0", "MIT-0", "MIT" ]

1

2019-09-15T21:21:03.000Z

krun_ext_graal_ce_hotspot.py

vext01/new_benchmarking_experiment

3c2d834b6cb555a1997bf0f0236d6947c6fa4697

[ "Apache-2.0", "MIT-0", "MIT" ]

9

2019-05-08T14:16:42.000Z

2019-12-09T10:56:45.000Z

krun_ext_graal_ce_hotspot.py

vext01/new_benchmarking_experiment

3c2d834b6cb555a1997bf0f0236d6947c6fa4697

[ "Apache-2.0", "MIT-0", "MIT" ]

2

2019-08-30T09:29:13.000Z

2019-11-20T20:59:09.000Z

#!/usr/bin/env python3 """ Graal CE (running the normal HotSpot compiler) script for use with Krun's ExternalSuiteVMDef. """ import sys from krun_ext_common import run, emit_process_exec_json _, benchmark, num_iters, param, instr = sys.argv emit_process_exec_json(run("graal-ce-hotspot", benchmark, int(num_iters)))

22.857143

74

0.775

49

320

4.836735

0.714286

0.059072

0.126582

0.160338

0

0.003546

0.11875

320

13

75

24.615385

0.836879

0.359375

0

0.081218

0

1

0

true

0

0.5

0

0.5

0

null

0

1

0

null

0

1

0

1

0

3

aa9d561fbe1b20f312f821da03f82c6947175010

86

py

Python

webu/auto/websocket.py

happyuc-project/webu.py

5a01124fc84d74df09a33d9dabe88b704cd5b6c6

[ "MIT" ]

null

webu/auto/websocket.py

happyuc-project/webu.py

5a01124fc84d74df09a33d9dabe88b704cd5b6c6

[ "MIT" ]

null

webu/auto/websocket.py

happyuc-project/webu.py

5a01124fc84d74df09a33d9dabe88b704cd5b6c6

[ "MIT" ]

null

from webu import ( Webu, WebsocketProvider, ) w3 = Webu(WebsocketProvider())

12.285714

30

0.674419

8

86

7.25

0.625

0.724138

0

0.014925

0.22093

86

6

31

14.333333

0.850746

0

1

0

false

0

0.2

0

0.2

0

1

0

null

1

0

1

0

null

0

3

aab648585ac526ae66daa1064ab201825f0e8da7

2,448

py

Python

chasscot/homepage/models.py

Chasscot-Doun/Chasscot

da46d29e38ef65b433a248397fd3f7ed6add3860

[ "MIT" ]

1

2021-05-10T18:37:54.000Z

chasscot/homepage/models.py

Chasscot-Doun/Chasscot

da46d29e38ef65b433a248397fd3f7ed6add3860

[ "MIT" ]

null

chasscot/homepage/models.py

Chasscot-Doun/Chasscot

da46d29e38ef65b433a248397fd3f7ed6add3860

[ "MIT" ]

null

from django.db import models import datetime # Create your models here. class Produit(models.Model): nom_bouteille = models.CharField(max_length=40, default='') description_bouteille = models.CharField(max_length=500, default='') millésime = models.IntegerField(default=datetime.date.today().year) saison = models.CharField(max_length=20, default='') quantité = models.FloatField(default=0.5) pourcentage_alcool = models.FloatField(default=16) photo = models.ImageField(upload_to='uploads/', default='') prix = models.FloatField(default=10) pourcentage_rabais = models.FloatField(default=0) prix_final = models.FloatField(default=10) class Membre(models.Model): nom = models.CharField(max_length=20) prénom = models.CharField(max_length=20) surnom = models.CharField(max_length=20) biographie = models.CharField(max_length=500) titre = models.CharField(max_length=20) photo_nain = models.ImageField(upload_to='uploads/', default='') photo_humain = models.ImageField(upload_to='uploads/', default='') class Utilisateur(models.Model): nom = models.CharField(max_length=100) prénom = models.CharField(max_length=100) nom_rue = models.CharField(max_length=250) numéro_de_rue = models.CharField(max_length=4) code_postal = models.CharField(max_length=4) #Ne pas oublier de forcer le int date_naissance = models.DateField() class Etiquette(models.Model): modele_etiquette = models.ImageField(upload_to='uploads/', default='') class Achat(models.Model): heure_achat_complet = models.DateTimeField(default=datetime.date.today()) jour_achat = models.DateTimeField(default=datetime.date.today().day) mois_achat = models.DateTimeField(default=datetime.date.today().month) année_achat = models.DateTimeField(default=datetime.date.today().year) class Client_B2B(models.Model): nom_contact = models.CharField(max_length=100) prénom_contact = models.CharField(max_length=100) nom_rue = models.CharField(max_length=250) numéro_de_rue = models.CharField(max_length=4) code_postal = models.CharField(max_length=4) # Ne pas oublier de forcer le int date_naissance = models.DateField() nom_établissement = models.CharField(max_length=100) logo_établissement = models.ImageField(upload_to='uploads/', default='') type_établissement = models.CharField(max_length=100) url_site = models.URLField(max_length=300)

45.333333

83

0.751634

314

2,448

5.681529

0.283439

0.105942

0.201794

0.269058

0.67657

0.54204

0.377803

0.206278

0

0.027713

0.13031

2,448

54

84

45.333333

0.81024

0.035539

0

0.173913

0

0.016964

0

1

0

false

0

0.043478

0

1

0

null

0

1

0

null

0

1

0

3

aabef2629d07d7b18486ad3418d52659d4f00f13

153

py

Python

4.py

BarisTeksin/project-euler

38a368d66fdd3bdc1d977059ba966fb7c1dcdc39

[ "MIT" ]

4

2020-04-18T21:05:13.000Z

2020-04-26T15:39:14.000Z

4.py

BarisTeksin/project-euler

38a368d66fdd3bdc1d977059ba966fb7c1dcdc39

[ "MIT" ]

null

4.py

BarisTeksin/project-euler

38a368d66fdd3bdc1d977059ba966fb7c1dcdc39

[ "MIT" ]

null

high = 0 for x in range(100,1000): for y in range(100,1000): if str(x*y) == str(x*y)[::-1] and x*y>high: high = x*y print(high)

19.125

51

0.509804

31

153

2.516129

0.451613

0.102564

0.25641

0.358974

0

0.149533

0.300654

153

7

52

21.857143

0.579439

0

1

0

false

0

0.166667

0

null

0

1

0

1

0

null

0

3

aad42316705611f0159a8c195377c4a50a574a93

369

py

Python

edu54book/propellerino.py

Clonexy700/edu54book

2a83f178947ddaf72ae6f94b502dfcf390ea9fe3

[ "Unlicense" ]

1

2019-12-24T08:44:32.000Z

edu54book/propellerino.py

Clonexy700/edu54book

2a83f178947ddaf72ae6f94b502dfcf390ea9fe3

[ "Unlicense" ]

null

edu54book/propellerino.py

Clonexy700/edu54book

2a83f178947ddaf72ae6f94b502dfcf390ea9fe3

[ "Unlicense" ]

null

from tkinter import * root = Tk() c = Canvas(root, width=500, height=500, bg='white') c.pack() c.create_oval(225, 235, 275, 285, width=2) c.create_oval(200, 210, 300, 310, width=2) c.create_oval(225, 80, 275, 210, width=2) c.create_oval(225, 310, 275, 450, width=2) c.create_oval(60, 240, 200, 285, width=2) c.create_oval(300, 240, 440, 285, width=2) root.mainloop()

26.357143

51

0.685637

72

369

3.430556

0.416667

0.17004

0.267206

0.263158

0.392713

0.323887

0

0.254658

0.127371

369

13

52

28.384615

0.512422

0

0.01355

0

1

0

false

0

0.090909

0

0.090909

0

null

0

1

0

1

0

null

0

3

2ad486eab3cc65ab2de10e2cdfe4c61e6463fcd8

1,147

py

Python

bspump/declarative/expression/value/eventexpr.py

chinese-soup/BitSwanPump

6ef71577cc1f166cff80876d28be37c791061bd2

[ "BSD-3-Clause" ]

1

2020-08-20T12:56:58.000Z

bspump/declarative/expression/value/eventexpr.py

chinese-soup/BitSwanPump

6ef71577cc1f166cff80876d28be37c791061bd2

[ "BSD-3-Clause" ]

null

bspump/declarative/expression/value/eventexpr.py

chinese-soup/BitSwanPump

6ef71577cc1f166cff80876d28be37c791061bd2

[ "BSD-3-Clause" ]

null

from ...abc import Expression class EVENT(Expression): """ The current event. Usage: ``` !EVENT `` """ def __init__(self, app, *, value): super().__init__(app) assert(value == "") def __call__(self, context, event, *args, **kwargs): return event class KWARGS(Expression): """ The current kwargs. Usage: ``` !KWARGS `` """ def __init__(self, app, *, value): super().__init__(app) assert(value == "") def __call__(self, context, event, *args, **kwargs): return kwargs class KWARG(Expression): """ The item from a kwargs. Usage: ``` !KWARG argname `` """ def __init__(self, app, *, value): super().__init__(app) self.ArgName = value def __call__(self, context, event, *args, **kwargs): return kwargs[self.ArgName] class ARGS(Expression): def __init__(self, app, *, value): super().__init__(app) assert(value == '') def __call__(self, context, event, *args, **kwargs): return args class ARG(Expression): def __init__(self, app, *, value): super().__init__(app) assert(value == '') self.ArgNumber = 0 def __call__(self, context, event, *args, **kwargs): return args[self.ArgNumber]

15.092105

53

0.646033

140

1,147

4.864286

0.2

0.051395

0.080764

0.10279

0.64464

0.599119

0.530103

0

0.001058

0.176112

1,147

75

54

15.293333

0.719577

0.135135

0

0.59375

0

0.125

1

0.3125

false

0

0.03125

0.15625

0.65625

0

null

0

null

0

1

0

1

0

3

2ae7f709e893ed3d1b2889a7294f6fa489eee7e1

804

py

Python

src/ci_workflow/ci_target.py

asifsmohammed/opensearch-build

f78859000d676d35c29b15e08bbf4310c4df05b9

[ "Apache-2.0" ]

1

2022-01-29T17:48:00.000Z

src/ci_workflow/ci_target.py

asifsmohammed/opensearch-build

f78859000d676d35c29b15e08bbf4310c4df05b9

[ "Apache-2.0" ]

1

2022-02-07T23:43:53.000Z

2022-02-10T19:56:41.000Z

src/ci_workflow/ci_target.py

tianleh/opensearch-build

460ad6b978c034d1dac672766c8dff67f51e4cd7

[ "Apache-2.0" ]

null

# SPDX-License-Identifier: Apache-2.0 # # The OpenSearch Contributors require contributions made to # this file be licensed under the Apache-2.0 license or a # compatible open source license. class CiTarget: version: str name: str snapshot: bool def __init__(self, version: str, name: str, snapshot: bool = True) -> None: self.version = version self.name = name self.snapshot = snapshot @property def opensearch_version(self) -> str: return self.version + "-SNAPSHOT" if self.snapshot else self.version @property def component_version(self) -> str: # BUG: the 4th digit is dictated by the component, it's not .0, this will break for 1.1.0.1 return self.version + ".0-SNAPSHOT" if self.snapshot else f"{self.version}.0"

30.923077

99

0.674129

113

804

4.743363

0.477876

0.123134

0.029851

0.063433

0.205224

0.108209

0

0.019417

0.231343

804

25

100

32.16

0.847896

0.337065

0

0.142857

0

0.068441

0

1

0.214286

false

0

0.142857

0.642857

0

null

0

null

0

1

0

1

0

3

2d4b25a72de45e992d60306dcfdbf1aa7077cbcd

1,474

py

Python

models.py

YazdanRa/gifoolak

c383ad095a2207f29a2f11d56da9aa7be289b67c

[ "MIT" ]

null

models.py

YazdanRa/gifoolak

c383ad095a2207f29a2f11d56da9aa7be289b67c

[ "MIT" ]

null

models.py

YazdanRa/gifoolak

c383ad095a2207f29a2f11d56da9aa7be289b67c

[ "MIT" ]

null

from datetime import datetime from peewee import * from playhouse.sqlite_ext import JSONField database = SqliteDatabase(None) class BaseModel(Model): id = PrimaryKeyField() created_at = TimestampField(constraints=[SQL('DEFAULT CURRENT_TIMESTAMP')]) updated_at = TimestampField(constraints=[SQL('DEFAULT CURRENT_TIMESTAMP')]) class Meta: database = database class User(BaseModel): chat_id = CharField(max_length=128, unique=True) first_name = CharField(max_length=128) last_name = CharField(max_length=128, null=True) username = CharField(max_length=128, null=True) language_code = CharField(max_length=20, null=True) is_bot = BooleanField(default=False) def __str__(self): return "{} {} (@{})".format(self.first_name, self.last_name, self.username) class Message(BaseModel): chat_id = CharField(max_length=128) username = CharField(max_length=128, null=True) text = CharField(max_length=128, null=True) date = DateTimeField(default=datetime.now) details = JSONField(default=dict, null=True) class Keyword(BaseModel): text = CharField(max_length=128) class Gif(BaseModel): user = ForeignKeyField(User, backref='gif') file_id = CharField(max_length=256) file_path = CharField(max_length=256) file_size = IntegerField() is_public = BooleanField(null=True) keywords = ManyToManyField(Keyword, backref='gif') KeywordGif = Gif.keywords.get_through_model()

28.346154

83

0.726594

180

1,474

5.761111

0.405556

0.12729

0.190935

0.162006

0.403086

0.298939

0.243009

0

0.025911

0.162144

1,474

51

84

28.901961

0.813765

0

0.057143

0

0.045455

0

1

0.028571

false

0

0.085714

0.028571

0.914286

0

null

0

1

0

null

0

1

0

3

2d5ce664099f0e8e3a8ec1030ab3679e4ebf48aa

273

py

Python

src/python/WMCore/BossAir/Oracle/DeleteJobs.py

khurtado/WMCore

f74e252412e49189a92962945a94f93bec81cd1e

[ "Apache-2.0" ]

21

2015-11-19T16:18:45.000Z

2021-12-02T18:20:39.000Z

src/python/WMCore/BossAir/Oracle/DeleteJobs.py

khurtado/WMCore

f74e252412e49189a92962945a94f93bec81cd1e

[ "Apache-2.0" ]

5,671

2015-01-06T14:38:52.000Z

2022-03-31T22:11:14.000Z

src/python/WMCore/BossAir/Oracle/DeleteJobs.py

khurtado/WMCore

f74e252412e49189a92962945a94f93bec81cd1e

[ "Apache-2.0" ]

67

2015-01-21T15:55:38.000Z

2022-02-03T19:53:13.000Z

#!/usr/bin/env python """ _DeleteJobs_ Oracle implementation for creating a deleting a job """ from WMCore.BossAir.MySQL.DeleteJobs import DeleteJobs as MySQLDeleteJobs class DeleteJobs(MySQLDeleteJobs): """ _DeleteJobs_ Delete jobs from bl_runjob """

16.058824

73

0.736264

31

273

6.322581

0.774194

0

0.179487

273

16

74

17.0625

0.875

0.465201

0

1

0

true

0

0.5

0

1

0

null

0

null

0

1

0

1

0

1

0

3

2d8070a9b85ef85dbeb373f5548431258c77a9d3

875

py

Python

solutions/python/2018/correctLineup.py

lucifer1198/Codesignal

07d6d6457b8b3a9f1c51118b0e8e44cce66ee039

[ "MIT" ]

2

2020-12-21T22:09:26.000Z

2021-01-01T15:40:01.000Z

solutions/python/2018/correctLineup.py

nsu1210/Codesignal

07d6d6457b8b3a9f1c51118b0e8e44cce66ee039

[ "MIT" ]

null

solutions/python/2018/correctLineup.py

nsu1210/Codesignal

07d6d6457b8b3a9f1c51118b0e8e44cce66ee039

[ "MIT" ]

1

2021-01-28T18:15:02.000Z

""" For the opening ceremony of the upcoming sports event an even number of athletes were picked. They formed a correct lineup, i.e. such a lineup in which no two boys or two girls stand together. The first person in the lineup was a girl. As a part of the performance, adjacent pairs of athletes (i.e. the first one together with the second one, the third one together with the fourth one, etc.) had to swap positions with each other. Given a list of athletes, return the list of athletes after the changes, i.e. after each adjacent pair of athletes is swapped. Example For athletes = [1, 2, 3, 4, 5, 6], the output should be correctLineup(athletes) = [2, 1, 4, 3, 6, 5]. """ def correctLineup(athletes): return [val for item in [a for a in zip([e for i, e in enumerate(athletes) if i % 2 != 0], [e for i, e in enumerate(athletes) if i % 2 == 0])] for val in item]

46.052632

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875

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0.022792

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875

18

164

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null

0

null

0

1

0

1

0

3

2db9d906c0209bf146eef0099d07fffe6b41d599

65

py

Python

usbclassifier/__init__.py

t-yui/usbclassifier

72a0be8991c34381ea72c5f969ae9d838c9ac158

[ "MIT" ]

4

2019-09-02T14:16:52.000Z

2020-12-09T16:13:05.000Z

usbclassifier/__init__.py

t-yui/usbclassifier

72a0be8991c34381ea72c5f969ae9d838c9ac158

[ "MIT" ]

null

usbclassifier/__init__.py

t-yui/usbclassifier

72a0be8991c34381ea72c5f969ae9d838c9ac158

[ "MIT" ]

1

2020-03-21T10:26:10.000Z

from .classifier import USBaggingClassifier __version__ = '0.1.1'

32.5

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0.092308

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null

0

1

0

null

0

1

0

3

2dbfdb14972de837af30ef8cd2a3801cb3e8ff95

753

py

Python

turbosms/routers.py

pmaigutyak/mp-turbosms

2b2089dbded95a49161e243e05a62fd0b568ab92

[ "0BSD" ]

7

2017-03-30T14:26:25.000Z

2021-09-25T14:31:43.000Z

turbosms/routers.py

pmaigutyak/mp-turbosms

2b2089dbded95a49161e243e05a62fd0b568ab92

[ "0BSD" ]

1

2019-04-20T15:38:13.000Z

2019-05-20T12:29:18.000Z

turbosms/routers.py

pmaigutyak/mp-turbosms

2b2089dbded95a49161e243e05a62fd0b568ab92

[ "0BSD" ]

null

class TurboSMSRouter(object): app_label = 'turbosms' db_name = 'turbosms' def db_for_read(self, model, **hints): if model._meta.app_label == self.app_label: return self.db_name return None def db_for_write(self, model, **hints): if model._meta.app_label == self.app_label: return self.db_name return None def allow_relation(self, obj1, obj2, **hints): if obj1._meta.app_label == self.app_label or \ obj2._meta.app_label == self.app_label: return False return None def allow_migrate(self, db, app_label, model_name=None, **hints): if app_label == self.app_label: return False return None

21.514286

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0.600266

99

753

4.30303

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0.225352

0.140845

0.176056

0.605634

0.549296

0.539906

0.347418

0

0.007648

0.305445

753

34

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22.147059

0.806883

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null

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1

0

null

0

1

0

3

2dc3fe0ac26ca288fee13059aacf28df1b63b630

163

py

Python

paw/constants/tests.py

keeperaft/personalaltwebsite

b8ad2679c2809c316e8f746ffe1b302460f336be

[ "MIT" ]

null

paw/constants/tests.py

keeperaft/personalaltwebsite

b8ad2679c2809c316e8f746ffe1b302460f336be

[ "MIT" ]

2

2020-06-05T20:35:57.000Z

2021-06-10T21:24:24.000Z

paw/constants/tests.py

keeperaft/personalaltwebsite

b8ad2679c2809c316e8f746ffe1b302460f336be

[ "MIT" ]

1

2019-04-10T02:03:35.000Z

BROWSER_IMPLICIT_WAIT_TIME = 5 MODAL_TRANSITION_WAIT_TIME = 2 BOOTSTRAP_SWITCH_TRANSITION_WAIT_TIME = 1 PAGE_LOADING_WAIT_TIME = 5 PAGE_LOADING_LONG_WAIT_TIME = 10

32.6

41

0.883436

27

163

4.703704

0.555556

0.314961

0.141732

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0.040268

0.08589

163

5

42

32.6

0.812081

0

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0

1

0

null

1

0

1

0

null

0

3

2dd1a49276fc87b7f280df5bf2a91398c277b7d1

532

py

Python

MyWork/OldFiles/Intermediate/ObjectOrientatedProgramming/ClassesObjects.py

minefarmer/100-Days-Python

b80b28d299342b490082ac301a0d8b176419f8f9

[ "Unlicense" ]

null

MyWork/OldFiles/Intermediate/ObjectOrientatedProgramming/ClassesObjects.py

minefarmer/100-Days-Python

b80b28d299342b490082ac301a0d8b176419f8f9

[ "Unlicense" ]

null

MyWork/OldFiles/Intermediate/ObjectOrientatedProgramming/ClassesObjects.py

minefarmer/100-Days-Python

b80b28d299342b490082ac301a0d8b176419f8f9

[ "Unlicense" ]

null

''' Class /is holding plate = True / attributes:/ / \ / tables_responsible = [4, 5, 6] waiter (object) \ \ / def take_order(table, order) / # takes order to chef methods:/ \ \ \def take_payment(amount): # add money to the restaurant '''

29.555556

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32

532

4.78125

0.84375

0.091503

0

0.015464

0.635338

532

18

59

29.555556

0.773196

0.969925

0

null

0

null

0

null

0

null

1

null

true

0

null

0

null

0

1

0

1

0

null

0

1

0

3

2de16c3fdb67e51a9ebf4990ca8d66b97638c3b8

72

py

Python

lightVeg/main.py

afiqhatta/hattaNum

b82267b048f7985b3bee0a33007118e4a1a80585

[ "MIT" ]

null

lightVeg/main.py

afiqhatta/hattaNum

b82267b048f7985b3bee0a33007118e4a1a80585

[ "MIT" ]

null

lightVeg/main.py

afiqhatta/hattaNum

b82267b048f7985b3bee0a33007118e4a1a80585

[ "MIT" ]

null

from lightVeg.datapoints.datapoint import * d = Data([1,0,0], [2,0,0])

18

43

0.666667

13

72

3.692308

0.769231

0.083333

0

0.095238

0.125

72

3

44

24

0.666667

0

1

0

false

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

3

2de24c10268c8b843abe3e0daae0d27ad3a1001b

344

py

Python

scfmsp/controlflowanalysis/instructions/InstructionBic.py

sepidehpouyan/SCF-MSP430

1d7565bf38d9f42e775031d4ea8515ff99bef778

[ "MIT" ]

1

2020-07-03T21:26:52.000Z

scfmsp/controlflowanalysis/instructions/InstructionBic.py

sepidehpouyan/SCF-MSP430

1d7565bf38d9f42e775031d4ea8515ff99bef778

[ "MIT" ]

null

scfmsp/controlflowanalysis/instructions/InstructionBic.py

sepidehpouyan/SCF-MSP430

1d7565bf38d9f42e775031d4ea8515ff99bef778

[ "MIT" ]

null

from scfmsp.controlflowanalysis.instructions.AbstractInstructionTwoRegisters import AbstractInstructionTwoRegisters class InstructionBic(AbstractInstructionTwoRegisters): name = 'bic' def get_execution_time(self): return self.clock def execute_judgment(self, ac): super(InstructionBic, self).execute_judgment(ac)

31.272727

115

0.790698

31

344

8.645161

0.677419

0.11194

0

0.142442

344

11

116

31.272727

0.908475

0

0.008696

0

1

0.285714

false

0

0.142857

0.857143

0

null

0

null

0

1

0

1

0

3

2de48398e5c4f46109ddf67f0c9c617ff7732359

713

py

Python

tests/mock_bbio/__init__.py

vt-sailbot/sailbot-20

b5d75cb82e4bc3e9c4e428a288c6ac98a4aa2c52

[ "MIT" ]

1

2019-09-26T12:05:57.000Z

tests/mock_bbio/__init__.py

vt-sailbot/sailbot-20

b5d75cb82e4bc3e9c4e428a288c6ac98a4aa2c52

[ "MIT" ]

5

2019-08-25T21:01:18.000Z

2020-09-04T02:56:40.000Z

tests/mock_bbio/__init__.py

vt-sailbot/sailbot-20

b5d75cb82e4bc3e9c4e428a288c6ac98a4aa2c52

[ "MIT" ]

null

import sys import types module_name = 'Adafruit_BBIO' # Create a type module_name Adafruit_BBIO = types.ModuleType(module_name) Adafruit_BBIO.GPIO = types.ModuleType(module_name + '.GPIO') Adafruit_BBIO.ADC = types.ModuleType(module_name + '.ADC') Adafruit_BBIO.PWM = types.ModuleType(module_name + '.PWM') Adafruit_BBIO.UART = types.ModuleType(module_name + '.UART') # Overwrite the default system module path to point to the type we just created sys.modules[module_name] = Adafruit_BBIO sys.modules[module_name + '.GPIO'] = Adafruit_BBIO.GPIO sys.modules[module_name + '.ADC'] = Adafruit_BBIO.ADC sys.modules[module_name + '.PWM'] = Adafruit_BBIO.PWM sys.modules[module_name + '.UART'] = Adafruit_BBIO.UART

35.65

79

0.772791

103

713

5.126214

0.252427

0.227273

0.198864

0.236742

0.287879

0

0.107994

713

19

80

37.526316

0.830189

0.14446

0

0.080725

0

1

0

false

0

0.153846

0

0.153846

0

null

1

0

null

0

3

93027f9c513a29a2ce3a6e99bcad683f2d76346f

127

py

Python

rio_stac/__init__.py

jonas-eberle/rio-stac

f46ecbaba3f9a324ab5546e1f3a5c2f922175969

[ "MIT" ]

null

rio_stac/__init__.py

jonas-eberle/rio-stac

f46ecbaba3f9a324ab5546e1f3a5c2f922175969

[ "MIT" ]

null

rio_stac/__init__.py

jonas-eberle/rio-stac

f46ecbaba3f9a324ab5546e1f3a5c2f922175969

[ "MIT" ]

null

"""rio-stac: Create STAC items from raster file.""" from rio_stac.stac import create_stac_item # noqa __version__ = "0.3.0"

21.166667

51

0.724409

21

127

4.047619

0.619048

0.164706

0

0.027778

0.149606

127

5

52

25.4

0.759259

0.401575

0

0.071429

0

1

0

false

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

3

93030f1a4d779ad15c572ebc533d877b010fec55

413

py

Python

tests/business/mock/mock_transport_stop_service.py

public-transport-quality-grades/oevgk18-generator

e467587d3d3c600a66139756e95bd84040d58c99

[ "MIT" ]

null

tests/business/mock/mock_transport_stop_service.py

public-transport-quality-grades/oevgk18-generator

e467587d3d3c600a66139756e95bd84040d58c99

[ "MIT" ]

3

2018-04-30T06:51:33.000Z

2018-05-30T18:22:58.000Z

tests/business/mock/mock_transport_stop_service.py

public-transport-quality-grades/oevgk18-generator

e467587d3d3c600a66139756e95bd84040d58c99

[ "MIT" ]

null

from typing import List from generator.business.model.transport_stop import TransportStop from . import mock_transport_stops def get_transport_stops(db) -> List[TransportStop]: return [ mock_transport_stops.stop_8503400, mock_transport_stops.stop_8503125, mock_transport_stops.stop_8591382, mock_transport_stops.stop_8593245, mock_transport_stops.stop_8504532 ]

27.533333

65

0.765133

50

413

5.92

0.42

0.331081

0.364865

0.371622

0

0.10355

0.181598

413

14

66

29.5

0.772189

0

1

0.090909

false

0

0.272727

0.090909

0.454545

0

null

1

0

null

0

3

934f94d34cd488990be0ab2c28fe4baafc2cbc17

90

py

Python

keyboards/inline/__init__.py

nomadroom/Media_Bot

59f70d1f1cbda254e6cc0f6a10c88dd05456f797

[ "MIT" ]

null

keyboards/inline/__init__.py

nomadroom/Media_Bot

59f70d1f1cbda254e6cc0f6a10c88dd05456f797

[ "MIT" ]

1

2021-09-12T17:38:29.000Z

keyboards/inline/__init__.py

nomadroom/Media_Bot

59f70d1f1cbda254e6cc0f6a10c88dd05456f797

[ "MIT" ]

1

2020-12-18T08:49:41.000Z

from aiogram.utils.callback_data import CallbackData some_callback = CallbackData("new")

22.5

52

0.833333

11

90

6.636364

0.818182

0

0.088889

90

3

53

30

0.890244

0

0.033333

0

1

0

false

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

3

935128fe3e82306431c5e1b59f7086bddbf5949f

3,831

py

Python

sgpublish/exporter/base.py

vfxetc/sgpublish

f6dcdb7d727ca78bc29ce76b91f13962628bfea1

[ "BSD-3-Clause" ]

3

2018-03-19T03:58:08.000Z

2020-09-30T17:47:16.000Z

sgpublish/exporter/base.py

vfxetc/sgpublish

f6dcdb7d727ca78bc29ce76b91f13962628bfea1

[ "BSD-3-Clause" ]

null

sgpublish/exporter/base.py

vfxetc/sgpublish

f6dcdb7d727ca78bc29ce76b91f13962628bfea1

[ "BSD-3-Clause" ]

2

2017-07-04T19:29:47.000Z

2019-07-19T01:15:43.000Z

import os from sgpublish.publisher import Publisher class Exporter(object): def __init__(self, workspace=None, filename_hint=None, publish_type=None): self._workspace = workspace self._filename_hint = filename_hint self._publish_type = publish_type @property def publish_type(self): """The type of publish to create for this exporter.""" return self._publish_type @property def filename_hint(self): """A filename for extracting info from, or using as a base to construct the final path if not supplied.""" return self._filename_hint @property def workspace(self): """The working directory, usually corresponds to an SGFS tag.""" return self._workspace or os.getcwd() def get_previous_publish_ids(self): """A set of previous publish IDs that current context was involved in. These publishes are used by the GUI to determine which publish stream to automatically select. Currently only supported in the Maya classes; please extend for your applications. """ return set() def record_publish_id(self, id_): """Save the new publish ID in the current scene/script/context. These publishes will later be returned by :meth:`get_previous_publish_ids`. Currently only supported in the Maya classes; please extend for your applications. """ pass def publish(self, link=None, name=None, export_kwargs=None, **publisher_kwargs): """Trigger a publish. This method only deals with setting up the publisher, and uses :meth:`export_publish` to do the work. :param export_kwargs: Passed to :meth:`export_publish`. :returns: The publisher used. """ type_ = self.publish_type if not type_: raise ValueError('cannot publish without type') publisher_kwargs.pop('type', None) with Publisher(link=link, type=type_, name=name, **publisher_kwargs) as publisher: # Record the ID before the export so that it is included. self.record_publish_id(publisher.id) # This is a hook that everyone should allow to go up the full chain. self.before_export_publish(publisher, **export_kwargs) # Completely overridable by children (without calling super). self.export_publish(publisher, **export_kwargs) return publisher def before_export_publish(self, publisher, **kwargs): pass def fields_for_review_version(self, **kwargs): return {} def export_publish(self, publisher, **kwargs): """Perform an export within the context of a publish. By default this simply calls :meth:`export` with the publish directory and no path. :param kwargs: Passed to :meth:`export_publish`. """ return self.export(publisher.directory, None, **kwargs) def export(self, directory, path, **kwargs): """Do the work of exporting. Must be implemented in subclasses. :param str directory: The directory to publish in. If ``path`` is present then this may be assumed equal to ``os.path.dirname(path)``. :param path: The path to export to. Will always be ``None`` when publishing, and future use of ``None`` is reserved for complex exports, such as geocaches. :type path: str or None :param kwargs: Extra keyword arguments passed from the exporting widgets. """ raise NotImplementedError()

33.605263

90

0.617593

457

3,831

5.059081

0.354486

0.03936

0.019464

0.019031

0.143599

0.086505

0.059689

0

0.309319

3,831

113

91

33.902655

0.873772

0.448969

0

0.131579

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0.01795

0

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0.289474

false

0.052632

0.026316

0.552632

0

null

0

null

0

1

0

1

0

1

0

3

9359bb5791113063725606fdee1bd52df26144cf

522

py

Python

src/app.py

RodrigoSantosRodrigues/simple-api-python-flask

508c104c6a2bf8ae50a3d72cfa640d4c7df723f7

[ "MIT" ]

null

src/app.py

RodrigoSantosRodrigues/simple-api-python-flask

508c104c6a2bf8ae50a3d72cfa640d4c7df723f7

[ "MIT" ]

null

src/app.py

RodrigoSantosRodrigues/simple-api-python-flask

508c104c6a2bf8ae50a3d72cfa640d4c7df723f7

[ "MIT" ]

null

from flask import Flask from .config import app_config from .controllers.HomeController import home_api def create_app(env_name): """ param: env_name Create app """ # app initiliazation app = Flask(__name__) app.config.from_object(app_config[env_name]) app.register_blueprint(home_api, url_prefix='/v1/api/home') @app.route('/') def index(): return 'My first Server Works!' @app.route('/greet') def hello(): return 'Hello from Server' return app

19.333333

62

0.655172

68

522

4.808824

0.441176

0.082569

0.079511

0

0.0025

0.233716

522

26

63

20.076923

0.815

0.091954

0

0.134884

0

1

0.214286

false

0

0.214286

0.142857

0.642857

0.071429

0

null

0

null

0

1

0

1

0

3

93633b3ca263da09d1e926963fa950ced7b3a83b

152

py

Python

p37_test.py

alpatine/project-euler-python

d731d2deebff4bfb812811921f56da7b984652c0

[ "MIT" ]

null

p37_test.py

alpatine/project-euler-python

d731d2deebff4bfb812811921f56da7b984652c0

[ "MIT" ]

null

p37_test.py

alpatine/project-euler-python

d731d2deebff4bfb812811921f56da7b984652c0

[ "MIT" ]

null

from unittest import TestCase from p37 import p37 class P37_Test(TestCase): def test_correct_answer(self): self.assertEqual(p37(), 748317)

21.714286

39

0.743421

21

152

5.238095

0.619048

0

0.112

0.177632

152

6

40

25.333333

0.768

0

0.2

1

0.2

false

0

0.4

0

0.8

0

1

0

null

0

1

0

null

0

1

0

3

937cc123645591552fca47c1bb0f57ee0a24ecef

872

py

Python

src/injecta/service/argument/DictArgument.py

DataSentics/injecta

090eeac6c76c43d40be71df678222a07b0a3c783

[ "MIT" ]

3

2021-09-27T12:55:00.000Z

2022-01-31T19:13:23.000Z

src/injecta/service/argument/DictArgument.py

DataSentics/injecta

090eeac6c76c43d40be71df678222a07b0a3c783

[ "MIT" ]

null

src/injecta/service/argument/DictArgument.py

DataSentics/injecta

090eeac6c76c43d40be71df678222a07b0a3c783

[ "MIT" ]

1

2021-03-04T09:12:05.000Z

from injecta.service.argument.ArgumentInterface import ArgumentInterface from injecta.service.class_.InspectedArgument import InspectedArgument class DictArgument(ArgumentInterface): def __init__(self, value: dict, name: str = None): self.__value = value self.__name = name @property def name(self): return self.__name def get_string_value(self): output = [] for key, sub_argument in self.__value.items(): output.append("{} = {}".format(key, sub_argument.get_string_value())) return ", ".join(output) def check_type_matches_definition(self, inspected_argument: InspectedArgument, services2_classes: dict, aliases2_services: dict): pass def __eq__(self, other: "DictArgument"): return self.name == other.name and self.get_string_value() == other.get_string_value()

32.296296

133

0.697248

99

872

5.808081

0.434343

0.062609

0.097391

0

0.002869

0.200688

872

26

134

33.538462

0.822095

0

0.024083

0

1

0.277778

false

0.055556

0.111111

0.611111

0

null

0

null

0

1

0

1

0

1

0

3

faa863ae21cdc714f4877ad1b7792f9b7c902577

411

py

Python

mayan/apps/sources/storages.py

darrenflexxu/Mayan-EDMS

6707365bfacd137e625ddc1b990168012246fa07

[ "Apache-2.0" ]

null

mayan/apps/sources/storages.py

darrenflexxu/Mayan-EDMS

6707365bfacd137e625ddc1b990168012246fa07

[ "Apache-2.0" ]

5

2021-03-19T22:56:45.000Z

2022-03-12T00:08:43.000Z

mayan/apps/sources/storages.py

Sumit-Kumar-Jha/mayan

5b7ddeccf080b9e41cc1074c70e27dfe447be19f

[ "Apache-2.0" ]

1

2020-07-29T21:03:27.000Z

from __future__ import unicode_literals from mayan.apps.storage.utils import get_storage_subclass from .settings import ( setting_staging_file_image_cache_storage, setting_staging_file_image_cache_storage_arguments, ) storage_staging_file_image_cache = get_storage_subclass( dotted_path=setting_staging_file_image_cache_storage.value )(**setting_staging_file_image_cache_storage_arguments.value)

31.615385

62

0.871046

55

411

5.854545

0.381818

0.170807

0.248447

0.326087

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0

0.085158

411

12

63

34.25

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1

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false

0

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0

null

0

1

0

null

0

1

0

3

faae335eeafa992b41a3b81b982e14bd079a747e

166

py

Python

lang/py/cookbook/v2/source/cb2_10_5_exm_1.py

ch1huizong/learning

632267634a9fd84a5f5116de09ff1e2681a6cc85

[ "MIT" ]

null

lang/py/cookbook/v2/source/cb2_10_5_exm_1.py

ch1huizong/learning

632267634a9fd84a5f5116de09ff1e2681a6cc85

[ "MIT" ]

null

lang/py/cookbook/v2/source/cb2_10_5_exm_1.py

ch1huizong/learning

632267634a9fd84a5f5116de09ff1e2681a6cc85

[ "MIT" ]

null

log_path = "/usr/local/nusphere/apache/logs/access_log" print "Percentage of requests that were client-cached: " + str( clientCachePercentage(log_path)) + '%'

41.5

63

0.728916

21

166

5.619048

0.857143

0.118644

0

0.138554

166

3

64

55.333333

0.825175

0

0.548193

0.253012

0

null

0

null

0.333333

1

0

null

0

1

0

null

0

1

0

3

fad1682f1dae35be9241db14077bdd418aa01ac9

211

py

Python

apps/enquiries/admin.py

FancyKat/django-portfolio

f261f8d3a37e5771f9f48a74a769b6e9b479d49d

[ "MIT" ]

null

apps/enquiries/admin.py

FancyKat/django-portfolio

f261f8d3a37e5771f9f48a74a769b6e9b479d49d

[ "MIT" ]

9

2022-03-22T04:30:50.000Z

2022-03-22T04:49:13.000Z

apps/enquiries/admin.py

FancyKat/django-portfolio

f261f8d3a37e5771f9f48a74a769b6e9b479d49d

[ "MIT" ]

null

from django.contrib import admin from .models import Enquiry class EnquiryAdmin(admin.ModelAdmin): list_display = ["name", "email", "phone_number", "message"] admin.site.register(Enquiry, EnquiryAdmin)

19.181818

63

0.753555

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211

6.28

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0.127962

211

10

64

21.1

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0

0.132701

0

1

0

false

0

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1

0

null

0

null

0

1

0

1

0

3

fad558c4f6575e6ff9099e80540fdc2055b56574

309

py

Python

state/db/db.py

andkononykhin/plenum

28dc1719f4b7e80d31dafbadb38cfec4da949886

[ "Apache-2.0" ]

148

2017-07-11T19:05:25.000Z

2022-03-16T21:31:20.000Z

state/db/db.py

andkononykhin/plenum

28dc1719f4b7e80d31dafbadb38cfec4da949886

[ "Apache-2.0" ]

561

2017-06-29T17:59:56.000Z

2022-03-09T15:47:14.000Z

state/db/db.py

andkononykhin/plenum

28dc1719f4b7e80d31dafbadb38cfec4da949886

[ "Apache-2.0" ]

378

2017-06-29T17:45:27.000Z

2022-03-26T07:27:59.000Z

from abc import abstractmethod class BaseDB: @abstractmethod def inc_refcount(self, key, value): raise NotImplementedError @abstractmethod def dec_refcount(self, key): raise NotImplementedError @abstractmethod def get(self, key): raise NotImplementedError

18.176471

39

0.692557

30

309

7.066667

0.533333

0.240566

0.141509

0.386792

0

0.252427

309

16

40

19.3125

0.917749

0

0.545455

0

1

0.272727

false

0

0.090909

0

0.454545

0

null

1

0

1

0

null

0

1

0

3

fadaf7e4d839c9ca608bd40d372cfaa3c2c5bf94

3,518

py

Python

mesos/cli/slave.py

mesosphere/mesos-cli

c65c8bcc3087a063d437698014e30dfd165a5257

[ "Apache-2.0" ]

77

2015-01-02T18:30:53.000Z

2019-06-11T06:10:06.000Z

mesos/cli/slave.py

mesosphere-backup/mesos-cli

c65c8bcc3087a063d437698014e30dfd165a5257

[ "Apache-2.0" ]

26

2015-01-31T23:23:34.000Z

2017-11-14T18:31:06.000Z

mesos/cli/slave.py

mesosphere-backup/mesos-cli

c65c8bcc3087a063d437698014e30dfd165a5257

[ "Apache-2.0" ]

24

2015-01-28T11:59:22.000Z

2019-06-11T01:55:29.000Z

# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import absolute_import, print_function import urlparse import requests import requests.exceptions from . import exceptions, log, mesos_file, util from .cfg import CURRENT as CFG class MesosSlave(object): def __init__(self, items): self.__items = items def __getitem__(self, name): return self.__items[name] def __str__(self): return self.key() def key(self): return self["pid"].split('@')[-1] @property def host(self): return "{0}://{1}:{2}".format( CFG["scheme"], self["hostname"], self["pid"].split(":")[-1]) @log.duration def fetch(self, url, **kwargs): try: return requests.get(urlparse.urljoin( self.host, url), timeout=CFG["response_timeout"], **kwargs) except requests.exceptions.ConnectionError: raise exceptions.SlaveDoesNotExist( "Unable to connect to the slave at {0}".format(self.host)) @util.CachedProperty(ttl=5) def state(self): return self.fetch("/slave(1)/state.json").json() @property def frameworks(self): return util.merge(self.state, "frameworks", "completed_frameworks") def task_executor(self, task_id): for fw in self.frameworks: for exc in util.merge(fw, "executors", "completed_executors"): if task_id in list(map( lambda x: x["id"], util.merge( exc, "completed_tasks", "tasks", "queued_tasks"))): return exc raise exceptions.MissingExecutor("No executor has a task by that id") def file_list(self, path): # The sandbox does not exist on the slave. if path == "": return [] resp = self.fetch("/files/browse.json", params={"path": path}) if resp.status_code == 404: return [] return resp.json() def file(self, task, path): return mesos_file.File(self, task, path) @util.CachedProperty(ttl=1) def stats(self): return self.fetch("/monitor/statistics.json").json() def executor_stats(self, _id): return list(filter(lambda x: x["executor_id"])) def task_stats(self, _id): eid = self.task_executor(_id)["id"] stats = list(filter( lambda x: x["executor_id"] == eid, self.stats )) # Tasks that are not yet in a RUNNING state have no stats. if len(stats) == 0: return {} else: return stats[0]["statistics"] @property @util.memoize def log(self): return mesos_file.File(self, path="/slave/log")

31.132743

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0.616543

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4.788288

0.380631

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0.026341

0.015052

0.047977

0.026341

0

0.007042

0.273451

3,518

112

80

31.410714

0.824726

0.241899

0

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0

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0.00906

0

1

0.208333

false

0

0.083333

0.138889

0.541667

0.013889

0

null

0

null

0

1

0

1

0

3

4f0003296fc304e903108cf4d0be48a1ad43b5e7

1,286

py

Python

tests/commands/test_node.py

o3seespy/o3seespy

4fdd942370df1ac8d454e361f651405717b8584c

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

16

2019-10-24T17:58:46.000Z

2022-03-01T19:48:06.000Z

tests/commands/test_node.py

o3seespy/o3seespy

4fdd942370df1ac8d454e361f651405717b8584c

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

5

2020-04-17T01:39:27.000Z

2020-12-18T05:07:58.000Z

tests/commands/test_node.py

o3seespy/o3seespy

4fdd942370df1ac8d454e361f651405717b8584c

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

6

2020-02-20T02:13:11.000Z

2021-11-01T19:08:41.000Z

import o3seespy as o3 import numpy as np def test_build_regular_node_mesh_2dxy(): xs = [1, 2, 3] ys = [3, 4] zs = 4.5 osi = o3.OpenSeesInstance(ndm=3, ndf=3) sn = o3.node.build_regular_node_mesh(osi, xs, ys, zs) sn = np.array(sn) print(sn.shape) assert len(sn.shape) == 2 # two axes assert sn.shape[0] == 3 # y-axis assert sn.shape[1] == 2 # x-axis assert sn[2][1].x == 3.0 assert sn[2][1].y == 4.0 assert sn[2][1].z == 4.5, sn[2][1].z # No z-axis zs = None osi = o3.OpenSeesInstance(ndm=2, ndf=3) sn = o3.node.build_regular_node_mesh(osi, xs, ys, zs) sn = np.array(sn) assert len(sn.shape) == 2 # two axes assert sn.shape[0] == 3 # x-axis assert sn.shape[1] == 2 # y-axis assert sn[2][1].x == 3.0 assert sn[2][1].y == 4.0 assert not hasattr(sn[2][1], 'z'), sn[1][2].z def test_build_regular_node_mesh_2dxz(): xs = [1, 2, 3] ys = [3] zs = [4.5, 6.0] osi = o3.OpenSeesInstance(ndm=3, ndf=3) sn = o3.node.build_regular_node_mesh(osi, xs, ys, zs) sn = np.array(sn) assert len(sn.shape) == 3 # three axes assert sn.shape[0] == 3 # y-axis assert sn.shape[1] == 1 # x-axis assert sn.shape[2] == 2 # x-axis assert sn[2][0][1].x == 3.0

26.791667

57

0.560653

245

1,286

2.865306

0.183673

0.148148

0.12963

0.14245

0.794872

0.762108

0.611111

0

0.082896

0.258942

1,286

47

58

27.361702

0.653725

0.067652

0

0.552632

0

0.000843

0

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1

0.052632

false

0

0.052632

0

0.105263

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0

null

0

1

0

1

0

null

0

1

0

3

877d074d84b0c3f4892f9d47b0b0226e66ec59d7

418

py

Python

coralillo/tests/utils_test.py

categulario/norm

232d3e25dcce2a1f698b429ecdedf5f8ee33c340

[ "MIT" ]

1

2020-10-11T06:40:33.000Z

coralillo/tests/utils_test.py

categulario/coralillo

232d3e25dcce2a1f698b429ecdedf5f8ee33c340

[ "MIT" ]

17

2017-08-22T16:52:03.000Z

2017-08-30T17:23:56.000Z

coralillo/tests/utils_test.py

categulario/norm

232d3e25dcce2a1f698b429ecdedf5f8ee33c340

[ "MIT" ]

4

2018-05-15T18:10:10.000Z

2020-09-01T08:58:55.000Z

from coralillo.utils import parse_embed def test_parse_embed(): array = ['object'] output = [['object', None]] assert parse_embed(array) == output array = ['object.field'] output = [['object', ['field']]] assert parse_embed(array) == output array = ['object.field', 'foo', 'object.var'] output = [['foo', None], ['object', ['field', 'var']]] assert parse_embed(array) == output

26.125

58

0.605263

48

418

5.145833

0.333333

0.202429

0.242915

0.255061

0.45749

0.348178

0

0.200957

418

15

59

27.866667

0.739521

0

0.272727

0

0.184211

0

0.272727

1

0.090909

false

0

0.090909

0

0.181818

0

null

1

0

null

0

3

8781575b880de0d2850ae80b1171e33686b73765

218

py

Python

vedastr_cstr/vedastr/models/bodies/feature_extractors/__init__.py

bsm8734/formula-image-latex-recognition

86d5070e8f907571a47967d64facaee246d92a35

[ "MIT" ]

13

2021-06-20T18:11:23.000Z

2021-12-07T18:06:42.000Z

vedastr_cstr/vedastr/models/bodies/feature_extractors/__init__.py

bsm8734/formula-image-latex-recognition

86d5070e8f907571a47967d64facaee246d92a35

[ "MIT" ]

9

2021-06-16T14:55:07.000Z

2021-06-23T14:45:36.000Z

vedastr_cstr/vedastr/models/bodies/feature_extractors/__init__.py

bsm8734/formula-image-latex-recognition

86d5070e8f907571a47967d64facaee246d92a35

[ "MIT" ]

6

2021-06-17T15:16:50.000Z

2021-07-05T20:41:26.000Z

from .builder import build_feature_extractor # noqa 401 from .decoders import build_brick, build_bricks, build_decoder # noqa 401 from .encoders import build_backbone, build_encoder, build_enhance_module # noqa 401

54.5

85

0.825688

31

218

5.516129

0.548387

0.192982

0.128655

0

0.047368

0.12844

218

3

86

72.666667

0.852632

0.119266

0

1

0

true

0

1

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1

0

null

0

1

0

null

0

1

0

1

0

3

87868a6eb63152a36a45d79106b350f4e4a5af31

152

py

Python

exemplos/teste.py

cirino/python

6c45b5305aebeeeebb7ffef335700e41cc0b6b3b

[ "MIT" ]

1

2018-05-06T01:25:28.000Z

exemplos/teste.py

cirino/python

6c45b5305aebeeeebb7ffef335700e41cc0b6b3b

[ "MIT" ]

1

2019-02-10T18:46:37.000Z

2019-02-12T21:17:50.000Z

exemplos/teste.py

cirino/python

6c45b5305aebeeeebb7ffef335700e41cc0b6b3b

[ "MIT" ]

null

print("Hello Word.") def nome(parameter_list): a = parameter_list.split(" ", 2) print(a) nome('dag cirino mano dev') nome('dagmar aparecido')

16.888889

36

0.664474

22

152

4.5

0.727273

0.262626

0

0.007937

0.171053

152

9

37

16.888889

0.777778

0

0.30719

0

1

0.166667

false

0

0.166667

0.333333

1

0

null

1

0

1

0

null

0

3

878a1229a4362eaf45a21edcb737d339fb83908c

224

py

Python

portfolio/blog/urls.py

selvianl/resume

4a056d57953c5d31ee1699eeacdf9f9bae85e896

[ "Apache-2.0" ]

null

portfolio/blog/urls.py

selvianl/resume

4a056d57953c5d31ee1699eeacdf9f9bae85e896

[ "Apache-2.0" ]

4

2021-03-18T21:49:30.000Z

2022-01-13T00:59:54.000Z

portfolio/blog/urls.py

selvianl/resume

4a056d57953c5d31ee1699eeacdf9f9bae85e896

[ "Apache-2.0" ]

null

from django.conf.urls import url from portfolio.blog.views import BlogFormView, BlogView urlpatterns = [ url(r'^add/$', BlogFormView.as_view(), name='blog_add'), url(r'^$', BlogView.as_view(), name='blog_index'), ]

28

60

0.700893

31

224

4.935484

0.580645

0.052288

0.130719

0.183007

0

0.125

224

8

61

28

0.780612

0

0.115556

0

1

0

false

0

0.333333

0

0.333333

0

null

0

1

0

1

0

null

0

1

0

3

87967b7fba18bbe3257b71e4b4b051dadf01ba7c

400

py

Python

app/models/Roles_Usuarios.py

pony012/PruebaServicioCucea

e28fc35beb8eefa3ed2de8b702f04a0f8ec8832f

[ "MIT" ]

null

app/models/Roles_Usuarios.py

pony012/PruebaServicioCucea

e28fc35beb8eefa3ed2de8b702f04a0f8ec8832f

[ "MIT" ]

null

app/models/Roles_Usuarios.py

pony012/PruebaServicioCucea

e28fc35beb8eefa3ed2de8b702f04a0f8ec8832f

[ "MIT" ]

null

from app.db import db_sql as db roles_usuarios = db.Table('roles_usuarios', db.Column('user_id', db.Integer(), db.ForeignKey('usuario.id')), db.Column('role_id', db.Integer(), db.ForeignKey('rol.id')))

36.363636

65

0.365

34

400

4.147059

0.5

0.085106

0.212766

0.184397

0.326241

0

0.5325

400

10

66

40

0.754011

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false

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0.125

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0

null

0

1

0

1

0

null

0

3

87bd2b7225cc173c5b88a75455771b83cb9b77d2

98

py

Python

FaceAPIWebRole/upload/forms.py

qwergram/CortanaVisionCSA

33e2e0266dec7d7eb65d4d3f3d5470f8dc601f48

[ "MIT" ]

null

FaceAPIWebRole/upload/forms.py

qwergram/CortanaVisionCSA

33e2e0266dec7d7eb65d4d3f3d5470f8dc601f48

[ "MIT" ]

null

FaceAPIWebRole/upload/forms.py

qwergram/CortanaVisionCSA

33e2e0266dec7d7eb65d4d3f3d5470f8dc601f48

[ "MIT" ]

null

from django import forms class UploadImageForm(forms.Form): imageupload = forms.ImageField()

19.6

36

0.77551

11

98

6.909091

0.818182

0

0.142857

98

4

37

24.5

0.904762

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false

0

0.333333

0

1

0

1

0

null

0

1

0

null

0

1

0

3

87c6a408e82b405b862a85871751638cf79ff84e

180

py

Python

output/models/ms_data/datatypes/facets/integer/integer_total_digits002_xsd/__init__.py

tefra/xsdata-w3c-tests

b6b6a4ac4e0ab610e4b50d868510a8b7105b1a5f

[ "MIT" ]

1

2021-08-14T17:59:21.000Z

output/models/ms_data/datatypes/facets/integer/integer_total_digits002_xsd/__init__.py

tefra/xsdata-w3c-tests

b6b6a4ac4e0ab610e4b50d868510a8b7105b1a5f

[ "MIT" ]

4

2020-02-12T21:30:44.000Z

2020-04-15T20:06:46.000Z

output/models/ms_data/datatypes/facets/integer/integer_total_digits002_xsd/__init__.py

tefra/xsdata-w3c-tests

b6b6a4ac4e0ab610e4b50d868510a8b7105b1a5f

[ "MIT" ]

null

from output.models.ms_data.datatypes.facets.integer.integer_total_digits002_xsd.integer_total_digits002 import ( FooType, Test, ) __all__ = [ "FooType", "Test", ]

18

112

0.722222

21

180

5.714286

0.714286

0.2

0.35

0

0.04

0.166667

180

9

113

20

0.76

0

0.061111

0

1

0

false

0

0.125

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0.125

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1

0

null

0

1

0

1

0

null

0

3

87ea7762b0a56f834137a9305f07a96cfcc0cfef

15,998

py

Python

tests/leguinncounter_tests.py

dunnesquared/sentencecow

5cd9b508043fe7b1064852ec2a6b0460944f23f7

[ "MIT" ]

1

2021-02-11T17:05:03.000Z

tests/leguinncounter_tests.py

dunnesquared/sentencecow

5cd9b508043fe7b1064852ec2a6b0460944f23f7

[ "MIT" ]

null

tests/leguinncounter_tests.py

dunnesquared/sentencecow

5cd9b508043fe7b1064852ec2a6b0460944f23f7

[ "MIT" ]

null

from random import randint from nose.tools import * # from legsewoc import * # from legsewoc.leguincounter import LeGuinCounter # from .context import legsewoc # from legsewoc.leguincounter import LeGuinCounter from .context import leguincounter from leguincounter import LeGuinCounter def test_init(): '''Test Cases: - Returns expected object - Check contents of attribute text - Check contentes of attribute sentences - Pass invalid argument - Pass empty string "" - Pass empty string " \n\t \r\n " ''' # Returns expected object text = "Blah! Blah, blah." lg = LeGuinCounter(text) expected = True assert_equal(isinstance(lg, LeGuinCounter), expected) # Check contents of attribute text expected = "Blah! Blah, blah." assert_equal(lg.text, expected) # Check contents of attribute sentences expected = ["Blah!", " Blah, blah."] assert_equal(lg.sentences, expected) # Pass invalid Argument text = None assert_raises(TypeError, LeGuinCounter, text) # Pass empty string "" text = "" expected = ("", []) lg = LeGuinCounter(text) assert_equal((lg.text, lg.sentences), expected) # Pass empty string " \n\t \r\n " text = " \n\t \r\n " expected = expected = (" \n\t \r\n ", []) lg = LeGuinCounter(text) assert_equal((lg.text, lg.sentences), expected) def test_parse(): '''Test Cases: # Pass valid argument # Pass invalid argument ''' # Pass valid argument text = "Blah! Blah, blah." lg = LeGuinCounter(text) lg.parse(text) expected = ["Blah!", " Blah, blah."] assert_equal(lg.sentences, expected) # Pass invalid argument text = None assert_raises(TypeError, lg.parse , text) def test_count_words(): '''Test Cases: - invalid input - empty string - empty string with spaces - non-empty string = 1 word - non-empty = random number of words ''' # invalid input text = "Blah! Blah, blah." lg = LeGuinCounter(text) assert_raises(TypeError, lg.count_words, 7) # empty string text = "" lg.parse(text) expected = 0 assert_equal(lg.count_words(text), expected) # empty string with spaces text = ''' ''' lg.parse(text) expected = 0 assert_equal(lg.count_words(text), expected) # non-empty string = 1 word text = "Eeyore" lg.parse(text) expected = 1 assert_equal(lg.count_words(text), expected) # random number of words val = randint(2, 1000) text = "Pizza! " * val expected = val assert_equal(lg.count_words(text), expected) def test_morethan(): '''Test Cases # valid sentence, invalid max = less than 1 # empty sentece, valid max # valid input; sentence equal to max # valid input; sentence less than max # valid input; greater than max # bad input types # default max ''' # valid sentence, invalid max = less than 1 text = "My name is Alex. What's yours?" lg = LeGuinCounter(text) max = 0 assert_raises(ValueError, lg.more_than, text, max) # empty sentece, valid max text = "" max = 3 assert_equal(lg.more_than(text, max), False) # valid input; sentence equal to max text = "My name is Alex. What's yours?" max = 6 assert_equal(lg.more_than(text, max), False) # valid input; sentence less than max text = "My name is Alex. What's yours?" max = 10 assert_equal(lg.more_than(text, max), False) # valid input; sentence less than max text = "My name is Alex. What's yours?" max = 3 assert_equal(lg.more_than(text, max), True) # bad input types assert_raises(TypeError, lg.more_than, 7, "whua?") # default max (20 words) assert_equal(lg.more_than(text), False) def test_sentence_morethan(): '''Test Cases # bad max value # no sentences over max # all sentences over max # some sentences over max ''' text = ''' This is a sample text. Do you like it? I hope so. We're having so much trouble getting this program finished. Bye for now!! ''' # bad max value lg = LeGuinCounter(text) max = 0 assert_raises(ValueError, lg.sentences_more_than, max) # no sentences over max max = 100 expected = [] assert_equal(lg.sentences_more_than(max), expected) # all sentences over max max = 1 actual = len(lg.sentences_more_than(max)) expected = 5 assert_equal(actual, expected) # some sentences over max = 3 actual = len(lg.sentences_more_than(max)) expected = 3 assert_equal(actual, expected) def test_mergenext(): '''Text Cases # Nothing to merge # Out of bounds to index: < 0 # Out of bounds to index: > len(sentences) # Out of bounds index: trying to merge the last element with something else # Merging when there is only one sentence in list # Merging when there are multiple sentences in list # Multiple merges until everything is just one sentence ''' # Nothing to merge text = "" lg = LeGuinCounter(text) before = len(lg.sentences) val = 0 assert_raises(ValueError, lg.merge_next, val) #lg.merge_next(0) #after = len(lg.sentences) #assert_equal(before, after) text = ''' This is a sample text. Do you like it? I hope so. We're having so much trouble getting this program finished. Bye for now!! ''' lg.parse(text) # Out of bounds to index: < 0 val = -100 assert_raises(IndexError, lg.merge_next, val) # Out of bounds to index: > len(sentences) val = 100 assert_raises(IndexError, lg.merge_next, val) # Out of bounds index: trying to merge the last element with something else val = len(lg.sentences) - 1 before = len(lg.sentences) lg.merge_next(val) after = len(lg.sentences) assert_equal(before, after) # Merging when there is only one sentence in list text = "This is a single sentence." lg.parse(text) before = len(lg.sentences) lg.merge_next(0) after = len(lg.sentences) assert_equal(before, after) # Merging when there are multiple sentences in list text = ''' This is a sample text. Do you like it? I hope so. We're having so much trouble getting this program finished. Bye for now!! ''' lg.parse(text) lg.merge_next(1) expected = 4 actual = len(lg.sentences) assert_equal(actual, expected) # Multiple merges until sentences can be merged no more! for n in range(10): lg.merge_next(0) expected = 1 actual = len(lg.sentences) assert_equal(actual, expected) # def test_split_sentence(): # # 1. Normal case, minimal white spacing # # Check first sentence # # Check second sentence # # Check list size # # # Setup # text = "This is a sentence with a footnote.[1] Crazy!" # split_pos = 38 # i = 0 # lg = LeGuinCounter(text) # lg.split_sentence(i, split_pos) # # # # Check first sentence # expected = 'This is a sentence with a footnote.[1]' # result = lg.sentences[i] # assert_equal(result, expected) # # # Check second sentence # expected = ' Crazy!' # result = lg.sentences[i+1] # assert_equal(result, expected) # # # Check size of list # expected = 2 # result = len(lg.sentences) # assert_equal(result, expected) # # # 2. Normal case, complicated whitespacing # text = '''This is a sentence with a footnote.[1] Crazy! It's followed by another.[2] And another.[3] This sentence is free. # Just insane. # Here's one last sentence with a footnote.[3] # This sentence is on a separate line, but still atttached to the previous sentence.''' # # # Check size BEFORE split # lg.parse(text) # expected = 4 # result = len(lg.sentences) # assert_equal(result, expected) # # # Split sentence 0 at split_pos = 38 # split_pos = 38 # i = 0 # lg.split_sentence(i, split_pos) # # # Test whether split worked: check sentences and list size # # Check first sentence # expected = 'This is a sentence with a footnote.[1]' # result = lg.sentences[i] # assert_equal(result, expected) # # # Check second sentence # expected = ' Crazy!' # result = lg.sentences[i+1] # assert_equal(result, expected) # # # Check size of list # expected = 5 # result = len(lg.sentences) # assert_equal(result, expected) # # # Test splitting the last sentence (4) at split pos 45 # i = 4 # split_pos = 45 # lg.split_sentence(i, split_pos) # # # Check first sentence # expected = "\nHere's one last sentence with a footnote.[3]" # result = lg.sentences[i] # assert_equal(result, expected) # # # Check second sentence # expected = ''' # This sentence is on a separate line, but still atttached to the previous sentence.''' # result = lg.sentences[i+1] # assert_equal(result, expected) # # # Check size of list # expected = 6 # result = len(lg.sentences) # assert_equal(result, expected) # # # #Test error conditions # # # Sentence index out of bounds # i = -1 # split_pos = 5 # assert_raises(IndexError, lg.split_sentence, i, split_pos) # # i = 1000 # assert_raises(IndexError, lg.split_sentence, i, split_pos) # # # # split pos out of bounds # i = 5 # split_pos = -5 # assert_raises(IndexError, lg.split_sentence, i, split_pos) # # split_pos = 1000 # assert_raises(IndexError, lg.split_sentence, i, split_pos) # # # # No sentences # lg.sentences = [] # i = 0 # split_pos = 3 # assert_raises(ValueError, lg.split_sentence, i, split_pos) # # # # Test: multiple splits # text = "0.1.2.3.4." # lg.parse(text) # # #Check size BEFORE split # expected = 1 # result = len(lg.sentences) # assert_equal(result, expected) # # # Do multiple splits # split_pos = 2 # for i in range(0, 4): # lg.split_sentence(i, split_pos) # # expected = 5 # result = len(lg.sentences) # assert_equal(result, expected) # # expected = ["0.", "1.", "2.", "3.", "4."] # result = lg.sentences # assert_equal(result, expected) # # # Split when there's nothing to split # text = "1." # lg.parse(text) # i, split_pos = 0, 1 # lg.split_sentence(i, split_pos) # lg.split_sentence(i, 0) # split again # # # Check sentence list length # expected = ['1', '.'] # result = lg.sentences # assert_equal(result, expected) # # # # Split with blank characters # text = "\n\t\r\n123!\n\t\r\n" # lg.parse(text) # i = 0 # split_pos = 4 # lg.split_sentence(i, split_pos) # # # Check sentence list length # expected = 1 # result = len(lg.sentences) # assert_equal(result, expected) # # # N.B. offset in textanalysis ignores leading white spaces of first sentence # # Trailing whitespaces discarded since sentence only take to terminating # # character # expected = "123!" # result = lg.sentences[i] # assert_equal(result, expected) # # # Split at end of sentence # text = "Pizza!" # lg.parse(text) # i, split_pos = 0, 6 # lg.split_sentence(i, split_pos) # # expected = 1 # result = len(lg.sentences) # assert_equal(result, expected) # # expected = "Pizza!" # result = lg.sentences[i] # assert_equal(result, expected) def test_split_sentence(): # 1. Normal case, minimal white spacing # Check first sentence # Check second sentence # Check list size # Setup text = "This is a sentence with a footnote.[1] Crazy!" i = 0 sub = "This is a sentence with a footnote.[1]" lg = LeGuinCounter(text) lg.split_sentence(i, sub) # Check first sentence expected = 'This is a sentence with a footnote.[1]' result = lg.sentences[i] assert_equal(result, expected) # Check second sentence expected = ' Crazy!' result = lg.sentences[i+1] assert_equal(result, expected) # Check size of list expected = 2 result = len(lg.sentences) assert_equal(result, expected) # 2. Normal case, complicated whitespacing text = '''This is a sentence with a footnote.[1] Crazy! It's followed by another.[2] And another.[3] This sentence is free. Just insane. Here's one last sentence with a footnote.[3] This sentence is on a separate line, but still atttached to the previous sentence.''' # Check size BEFORE split lg.parse(text) expected = 4 result = len(lg.sentences) assert_equal(result, expected) # Split sentence 0 at split_pos = 38 sub = 'This is a sentence with a footnote.[1]' i = 0 lg.split_sentence(i, sub) # Test whether split worked: check sentences and list size # Check first sentence expected = 'This is a sentence with a footnote.[1]' result = lg.sentences[i] assert_equal(result, expected) # Check second sentence expected = ' Crazy!' result = lg.sentences[i+1] assert_equal(result, expected) # Check size of list expected = 5 result = len(lg.sentences) assert_equal(result, expected) # Test splitting the last sentence (4) at split pos 45 i = 4 sub = "Here's one last sentence with a footnote.[3]" lg.split_sentence(i, sub) # Check first sentence expected = "\nHere's one last sentence with a footnote.[3]" result = lg.sentences[i] assert_equal(result, expected) # Check second sentence expected = ''' This sentence is on a separate line, but still atttached to the previous sentence.''' result = lg.sentences[i+1] assert_equal(result, expected) # Check size of list expected = 6 result = len(lg.sentences) assert_equal(result, expected) #Test error conditions # Sentence index out of bounds i = -1 sub= "Hello." assert_raises(IndexError, lg.split_sentence, i, sub) i = 1000 assert_raises(IndexError, lg.split_sentence, i, sub) # No sentences lg.sentences = [] i = 0 sub = "Hello" assert_raises(ValueError, lg.split_sentence, i, sub) #Substring empty text = "This is a sentence with a footnote.[1] Crazy!" lg.parse("This is a sentence with a footnote.[1] Crazy!") i = 0 sub = "" expected = ["This is a sentence with a footnote.[1] Crazy!"] result = lg.sentences assert_equal(result, expected) #Substring white characters only lg.sentences = [] i = 0 sub = " \n \r\t \n" assert_raises(ValueError, lg.split_sentence, i, sub) # Test: multiple splits text = "0.1.2.3.4." lg.parse(text) #Check size BEFORE split expected = 1 result = len(lg.sentences) assert_equal(result, expected) # Do multiple splits lg.split_sentence(0, "0.") lg.split_sentence(1, "1.") lg.split_sentence(2, "2.") lg.split_sentence(3, "3.") expected = 5 result = len(lg.sentences) assert_equal(result, expected) expected = ["0.", "1.", "2.", "3.", "4."] result = lg.sentences assert_equal(result, expected) # Split when there's nothing to split lg.split_sentence(2, "2.") expected = ["0.", "1.", "2.", "3.", "4."] result = lg.sentences assert_equal(result, expected) #Split again and again lg.split_sentence(2, "2") lg.split_sentence(2, "2") expected = ["0.", "1.", "2", ".", "3.", "4."] result = lg.sentences assert_equal(result, expected) # Split with blank characters text = "You!\n\t\r\n123!\n\t\r\n" lg.parse(text) i = 1 sub = "123!" lg.split_sentence(i, sub) # Check sentence list length expected = 2 result = len(lg.sentences) assert_equal(result, expected) expected = ["You!", "\n\t\r\n123!"] result = lg.sentences assert_equal(result, expected) #

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0.091324

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null

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null

0

3

87f9e852ff052564749065adb94a15868dd21a22

88

py

Python

aadhaar/secure_qr/__init__.py

vishaltanwar96/aadhaar-py

7d3fe865ef1ab9e087699ddf83def332ab701eea

[ "MIT" ]

6

2021-05-12T13:57:46.000Z

2021-12-20T10:59:57.000Z

aadhaar/secure_qr/__init__.py

vishaltanwar96/aadhaar-py

7d3fe865ef1ab9e087699ddf83def332ab701eea

[ "MIT" ]

12

2021-11-27T09:50:34.000Z

2022-03-12T01:04:45.000Z

aadhaar/secure_qr/__init__.py

vishaltanwar96/aadhaar-py

7d3fe865ef1ab9e087699ddf83def332ab701eea

[ "MIT" ]

null

from aadhaar.secure_qr.extractor import extract_data __all__ = [ "extract_data", ]

14.666667

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1

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354f9d3e53c6ec25711fdb4ba643a2b11227cfcd

5,305

py

Python

vcard/settings.py

hcpthanks/vCard

cc9a301f413961c398c355426013c0cc05fbb1b7

[ "MIT" ]

null

vcard/settings.py

hcpthanks/vCard

cc9a301f413961c398c355426013c0cc05fbb1b7

[ "MIT" ]

null

vcard/settings.py

hcpthanks/vCard

cc9a301f413961c398c355426013c0cc05fbb1b7

[ "MIT" ]

null

""" Django settings for vcard project. Generated by 'django-admin startproject' using Django 2.1.2. For more information on this file, see https://docs.djangoproject.com/en/2.1/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/2.1/ref/settings/ """ import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/2.1/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'oqc&3h1l*#u&fobkeyua92=-awh4wizv(cp_8srq-t)o=44r3g' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'guestbook', 'tinymce', 'taggit', 'blog', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'vcard.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [ os.path.join(BASE_DIR, 'templates') ], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'vcard.wsgi.application' # Database # https://docs.djangoproject.com/en/2.1/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } # Password validation # https://docs.djangoproject.com/en/2.1/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/2.1/topics/i18n/ LANGUAGE_CODE = 'zh-hans' TIME_ZONE = 'Asia/Shanghai' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/2.1/howto/static-files/ STATIC_URL = '/static/' #自定义静态资源目录 #指定使用项目根下的 static STATICFILES_DIRS = [ os.path.join(BASE_DIR, 'static'), ] # 自定义媒体根目录 MEDIA_URL = '/media/' MEDIA_ROOT = os.path.join(BASE_DIR, 'media') TINYMCE_DEFAULT_CONFIG = { # // General options 'mode': 'textareas', 'relative_urls': True, 'remove_script_host': False, 'urlconverter_callback': 'customURLConverter', 'theme': "advanced", 'plugins': "pagebreak,style,layer,table,save,advhr,advimage,advlink,emotions,iespell,inlinepopups,insertdatetime,preview,media,searchreplace,print,contextmenu,paste,directionality,fullscreen,noneditable,visualchars,nonbreaking,xhtmlxtras,template,wordcount,advlist,autosave", # // Theme options 'theme_advanced_buttons1': "save,newdocument,|,bold,italic,underline,strikethrough,|,justifyleft,justifycenter,justifyright,justifyfull,styleselect,formatselect,fontselect,fontsizeselect,fullscreen,code", 'theme_advanced_buttons2': "cut,copy,paste,pastetext,|,search,replace,|,bullist,numlist,|,outdent,indent,blockquote,|,undo,redo,|,link,unlink,anchor,image,cleanup,|,insertdate,inserttime,preview,|,forecolor,backcolor", 'theme_advanced_buttons3': "tablecontrols,|,hr,removeformat,visualaid,|,sub,sup,|,charmap,emotions,iespell,media,advhr,|,print,|,ltr,rtl", 'theme_advanced_toolbar_location': "top", 'theme_advanced_toolbar_align': "left", 'theme_advanced_statusbar_location': "bottom", 'theme_advanced_resizing': 'true', # // content_css: "/css/style.css", 'template_external_list_url': "lists/template_list.js", 'external_link_list_url': "lists/link_list.js", 'external_image_list_url': "lists/image_list.js", 'media_external_list_url': "lists/media_list.js", # // Style formats 'style_formats': [ {'title': 'Bold text', 'inline': 'strong'}, {'title': 'Red text', 'inline': 'span', 'styles': {'color': '#ff0000'}}, {'title': 'Help', 'inline': 'strong', 'classes': 'help'}, {'title': 'Table styles'}, {'title': 'Table row 1', 'selector': 'tr', 'classes': 'tablerow'} ], 'width': '700', 'height': '400' }

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null

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355d1cd2b9e3f646e7e1688cab7788f127e5575d

13,899

py

Python

src/typeclasses/migrations/0002_resave_attrs.py

reddcoin-project/ReddConnect

5c212683de6b80b81fd15ed05239c3a1b46c3afd

[ "BSD-3-Clause" ]

2

2019-02-24T00:20:47.000Z

2020-04-24T15:50:31.000Z

src/typeclasses/migrations/0002_resave_attrs.py

reddcoin-project/ReddConnect

5c212683de6b80b81fd15ed05239c3a1b46c3afd

[ "BSD-3-Clause" ]

null

src/typeclasses/migrations/0002_resave_attrs.py

reddcoin-project/ReddConnect

5c212683de6b80b81fd15ed05239c3a1b46c3afd

[ "BSD-3-Clause" ]

1

2019-01-05T15:51:37.000Z

# -*- coding: utf-8 -*- import datetime from south.db import db from south.v2 import DataMigration from django.db import models try: from django.contrib.auth import get_user_model except ImportError: # django < 1.5 from django.contrib.auth.models import User else: User = get_user_model() user_orm_label = '%s.%s' % (User._meta.app_label, User._meta.object_name) user_model_label = '%s.%s' % (User._meta.app_label, User._meta.module_name) user_ptr_name = '%s_ptr' % User._meta.object_name.lower() class Migration(DataMigration): depends_on = (('server', '0004_store_all_attrs'), ('objects', '0021_auto__del_objattribute'), ('players', '0020_auto__del_playerattribute'), ('scripts', '0013_auto__del_scriptattribute')) no_dry_run=True def forwards(self, orm): "Write your forwards methods here." # Note: Remember to use orm['appname.ModelName'] rather than "from appname.models..." for tmpattr in orm['server.TmpAttribute'].objects.all(): typ = tmpattr.db_obj_type dbid = tmpattr.db_obj_id if typ == 'objectdb': try: dbobj = orm['objects.ObjectDB'].objects.get(id=dbid) except: print "could not find objid %i" % dbid continue elif typ == 'playerdb': try: dbobj = orm['players.PlayerDB'].objects.get(id=dbid) except: print "could not find objid %i" % dbid continue elif typ == 'scriptdb': try: dbobj = orm['scripts.ScriptDB'].objects.get(id=dbid) except: print "could not find objid %i" % dbid continue else: print "Wrong object type to store on: %s" % typ continue dbattr = orm['typeclasses.Attribute'](db_key=tmpattr.db_key, db_value=tmpattr.db_value, db_lock_storage=tmpattr.db_lock_storage, db_date_created=tmpattr.db_date_created) dbattr.save() dbobj.db_attributes.add(dbattr) def backwards(self, orm): "Write your backwards methods here." raise RuntimeError("Cannot revert this migration.") models = { u'auth.group': { 'Meta': {'object_name': 'Group'}, u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '80'}), 'permissions': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['auth.Permission']", 'symmetrical': 'False', 'blank': 'True'}) }, u'auth.permission': { 'Meta': {'ordering': "(u'content_type__app_label', u'content_type__model', u'codename')", 'unique_together': "((u'content_type', u'codename'),)", 'object_name': 'Permission'}, 'codename': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'content_type': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['contenttypes.ContentType']"}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '50'}) }, user_model_label: { 'Meta': {'object_name': User.__name__, 'db_table': "'%s'" % User._meta.db_table}, 'date_joined': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}), 'email': ('django.db.models.fields.EmailField', [], {'max_length': '75', 'blank': 'True'}), 'first_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}), 'groups': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['auth.Group']", 'symmetrical': 'False', 'blank': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'is_active': ('django.db.models.fields.BooleanField', [], {'default': 'True'}), 'is_staff': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'is_superuser': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'last_login': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}), 'last_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}), 'password': ('django.db.models.fields.CharField', [], {'max_length': '128'}), 'user_permissions': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['auth.Permission']", 'symmetrical': 'False', 'blank': 'True'}), 'username': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '30'}) }, u'contenttypes.contenttype': { 'Meta': {'ordering': "('name',)", 'unique_together': "(('app_label', 'model'),)", 'object_name': 'ContentType', 'db_table': "'django_content_type'"}, 'app_label': ('django.db.models.fields.CharField', [], {'max_length': '100'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'model': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '100'}) }, u'server.serverconfig': { 'Meta': {'object_name': 'ServerConfig'}, 'db_key': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '64'}), 'db_value': ('django.db.models.fields.TextField', [], {'blank': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}) }, 'server.tmpattribute': { 'Meta': {'object_name': 'TmpAttribute'}, 'db_date_created': ('django.db.models.fields.DateTimeField', [], {}), 'db_key': ('django.db.models.fields.CharField', [], {'max_length': '255', 'db_index': 'True'}), 'db_lock_storage': ('django.db.models.fields.TextField', [], {'blank': 'True'}), 'db_obj_id': ('django.db.models.fields.IntegerField', [], {'null': 'True'}), 'db_obj_type': ('django.db.models.fields.CharField', [], {'max_length': '10', 'null': 'True'}), 'db_value': ('src.utils.picklefield.PickledObjectField', [], {'null': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}) }, u'typeclasses.attribute': { 'Meta': {'object_name': 'Attribute'}, 'db_date_created': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}), 'db_key': ('django.db.models.fields.CharField', [], {'max_length': '255', 'db_index': 'True'}), 'db_lock_storage': ('django.db.models.fields.TextField', [], {'blank': 'True'}), 'db_value': ('src.utils.picklefield.PickledObjectField', [], {'null': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}) }, u'objects.alias': { 'Meta': {'object_name': 'Alias'}, 'db_key': ('django.db.models.fields.CharField', [], {'max_length': '255', 'db_index': 'True'}), 'db_obj': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['objects.ObjectDB']"}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}) }, u'objects.objectdb': { 'Meta': {'object_name': 'ObjectDB'}, 'db_attributes': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['typeclasses.Attribute']", 'null': 'True', 'symmetrical': 'False'}), 'db_cmdset_storage': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}), 'db_date_created': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}), 'db_destination': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'destinations_set'", 'null': 'True', 'to': u"orm['objects.ObjectDB']"}), 'db_home': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'homes_set'", 'null': 'True', 'to': u"orm['objects.ObjectDB']"}), 'db_key': ('django.db.models.fields.CharField', [], {'max_length': '255', 'db_index': 'True'}), 'db_location': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'locations_set'", 'null': 'True', 'to': u"orm['objects.ObjectDB']"}), 'db_lock_storage': ('django.db.models.fields.TextField', [], {'blank': 'True'}), 'db_permissions': ('django.db.models.fields.CharField', [], {'max_length': '255', 'blank': 'True'}), 'db_player': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['players.PlayerDB']", 'null': 'True', 'blank': 'True'}), 'db_sessid': ('django.db.models.fields.IntegerField', [], {'null': 'True'}), 'db_typeclass_path': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}) }, u'objects.objectnick': { 'Meta': {'unique_together': "(('db_nick', 'db_type', 'db_obj'),)", 'object_name': 'ObjectNick'}, 'db_nick': ('django.db.models.fields.CharField', [], {'max_length': '255', 'db_index': 'True'}), 'db_obj': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['objects.ObjectDB']"}), 'db_real': ('django.db.models.fields.TextField', [], {}), 'db_type': ('django.db.models.fields.CharField', [], {'default': "'inputline'", 'max_length': '16', 'null': 'True', 'blank': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}) }, u'players.playerdb': { 'Meta': {'object_name': 'PlayerDB'}, 'db_attributes': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['typeclasses.Attribute']", 'null': 'True', 'symmetrical': 'False'}), 'db_cmdset_storage': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True'}), 'db_date_created': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}), 'db_is_connected': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'db_key': ('django.db.models.fields.CharField', [], {'max_length': '255', 'db_index': 'True'}), 'db_lock_storage': ('django.db.models.fields.TextField', [], {'blank': 'True'}), 'db_permissions': ('django.db.models.fields.CharField', [], {'max_length': '255', 'blank': 'True'}), 'db_typeclass_path': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'user': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['%s']" % user_orm_label, 'unique': 'True'}) }, u'players.playernick': { 'Meta': {'unique_together': "(('db_nick', 'db_type', 'db_obj'),)", 'object_name': 'PlayerNick'}, 'db_nick': ('django.db.models.fields.CharField', [], {'max_length': '255', 'db_index': 'True'}), 'db_obj': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['players.PlayerDB']"}), 'db_real': ('django.db.models.fields.TextField', [], {}), 'db_type': ('django.db.models.fields.CharField', [], {'default': "'inputline'", 'max_length': '16', 'null': 'True', 'blank': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}) }, u'scripts.scriptdb': { 'Meta': {'object_name': 'ScriptDB'}, 'db_attributes': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['typeclasses.Attribute']", 'null': 'True', 'symmetrical': 'False'}), 'db_date_created': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}), 'db_desc': ('django.db.models.fields.CharField', [], {'max_length': '255', 'blank': 'True'}), 'db_interval': ('django.db.models.fields.IntegerField', [], {'default': '-1'}), 'db_is_active': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'db_key': ('django.db.models.fields.CharField', [], {'max_length': '255', 'db_index': 'True'}), 'db_lock_storage': ('django.db.models.fields.TextField', [], {'blank': 'True'}), 'db_obj': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['objects.ObjectDB']", 'null': 'True', 'blank': 'True'}), 'db_permissions': ('django.db.models.fields.CharField', [], {'max_length': '255', 'blank': 'True'}), 'db_persistent': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'db_repeats': ('django.db.models.fields.IntegerField', [], {'default': '0'}), 'db_start_delay': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'db_typeclass_path': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}) } } complete_apps = ['server', 'typeclasses', 'objects', 'scripts', 'players'] symmetrical = True

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3569ad1085aab98b521635f2be1a6368983cfa79

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py

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setup.py

agronick/aiogear

9e4f2d18f05d91daea9e48de18b2d38f4811589f

[ "MIT" ]

null

setup.py

agronick/aiogear

9e4f2d18f05d91daea9e48de18b2d38f4811589f

[ "MIT" ]

null

setup.py

agronick/aiogear

9e4f2d18f05d91daea9e48de18b2d38f4811589f

[ "MIT" ]

null

from setuptools import setup setup( name='aiogear', version='0.2.4rc1', author='Sinan Nalkaya', author_email='sardok@gmail.com', url='https://github.com/sardok/aiogear', description='Asynchronous gearman protocol based on asyncio', packages=['aiogear'], classifiers=[ 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', ] )

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py

Python

Code_for_Signal_Processing/plot_corr_mx_85_concate_time_linux_v1.6.6.py

puyaraimondii/biometric-classification-of-frequency-following-responses

f5b5dca516592be451a3133acb8fa178519bc991

[ "MIT" ]

1

2021-04-20T14:47:40.000Z

Code_for_Signal_Processing/plot_corr_mx_85_concate_time_linux_v1.6.6.py

puyaraimondii/biometric-classification-of-frequency-following-responses

f5b5dca516592be451a3133acb8fa178519bc991

[ "MIT" ]

null

Code_for_Signal_Processing/plot_corr_mx_85_concate_time_linux_v1.6.6.py

puyaraimondii/biometric-classification-of-frequency-following-responses

f5b5dca516592be451a3133acb8fa178519bc991

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Thu Aug 9 17:02:59 2018 @author: bruce compared with version 1.6.4 the update is from correlation coefficient """ import pandas as pd import numpy as np from scipy import fftpack from scipy import signal import matplotlib.pyplot as plt from matplotlib.colors import LinearSegmentedColormap def correlation_matrix(corr_mx, cm_title): from matplotlib import pyplot as plt from matplotlib import cm as cm fig = plt.figure() ax1 = fig.add_subplot(111) #cmap = cm.get_cmap('jet', 30) cax = ax1.matshow(corr_mx, cmap='gray') #cax = ax1.imshow(df.corr(), interpolation="nearest", cmap=cmap) fig.colorbar(cax) ax1.grid(False) plt.title(cm_title) #plt.title('cross correlation of test and retest') ylabels=['T1','T2','T3','T4','T6','T7','T8','T9', 'T11', 'T12', 'T13', 'T14', 'T15', 'T16', 'T17', 'T18', 'T19', 'T20', 'T21', 'T22', 'T23', 'T25'] xlabels=['R1','R2','R3','R4','R6','R7','R8','R9', 'R11', 'R12', 'R13', 'R14', 'R15', 'R16', 'R17', 'R18', 'R19', 'R20', 'R21', 'R22', 'R23', 'R25'] ax1.set_xticks(np.arange(len(xlabels))) ax1.set_yticks(np.arange(len(ylabels))) ax1.set_xticklabels(xlabels,fontsize=6) ax1.set_yticklabels(ylabels,fontsize=6) # Add colorbar, make sure to specify tick locations to match desired ticklabels #fig.colorbar(cax, ticks=[.75,.8,.85,.90,.95,1]) # show digit in matrix corr_mx_array = np.asarray(corr_mx) for i in range(22): for j in range(22): c = corr_mx_array[j,i] ax1.text(i, j, round(c,2), va='center', ha='center') plt.show() def correlation_matrix_01(corr_mx, cm_title): # find the maximum in each row # input corr_mx is a dataframe # need to convert it into a array first #otherwise it is not working temp = np.asarray(corr_mx) output = (temp == temp.max(axis=1)[:,None]) # along rows fig = plt.figure() ax1 = fig.add_subplot(111) #cmap = cm.get_cmap('jet', 30) cs = ax1.matshow(output, cmap='gray') #cax = ax1.imshow(df.corr(), interpolation="nearest", cmap=cmap) fig.colorbar(cs) ax1.grid(False) plt.title(cm_title) ylabels=['T1','T2','T3','T4','T6','T7','T8','T9', 'T11', 'T12', 'T13', 'T14', 'T15', 'T16', 'T17', 'T18', 'T19', 'T20', 'T21', 'T22', 'T23', 'T25'] xlabels=['R1','R2','R3','R4','R6','R7','R8','R9', 'R11', 'R12', 'R13', 'R14', 'R15', 'R16', 'R17', 'R18', 'R19', 'R20', 'R21', 'R22', 'R23', 'R25'] ax1.set_xticks(np.arange(len(xlabels))) ax1.set_yticks(np.arange(len(ylabels))) ax1.set_xticklabels(xlabels,fontsize=6) ax1.set_yticklabels(ylabels,fontsize=6) plt.show() def correlation_matrix_rank(corr_mx, cm_title): temp = corr_mx #output = (temp == temp.max(axis=1)[:,None]) # along row output = temp.rank(axis=1, ascending=False) fig, ax1 = plt.subplots() im1 = ax1.matshow(output, cmap=plt.cm.Wistia) #cs = ax1.matshow(output) fig.colorbar(im1) ax1.grid(False) ylabels=['T1','T2','T3','T4','T6','T7','T8','T9', 'T11', 'T12', 'T13', 'T14', 'T15', 'T16', 'T17', 'T18', 'T19', 'T20', 'T21', 'T22', 'T23', 'T25'] xlabels=['R1','R2','R3','R4','R6','R7','R8','R9', 'R11', 'R12', 'R13', 'R14', 'R15', 'R16', 'R17', 'R18', 'R19', 'R20', 'R21', 'R22', 'R23', 'R25'] ax1.set_xticks(np.arange(len(xlabels))) ax1.set_yticks(np.arange(len(ylabels))) ax1.set_xticklabels(xlabels,fontsize=6) ax1.set_yticklabels(ylabels,fontsize=6) plt.title(cm_title) # show digit in matrix output = np.asarray(output) for i in range(22): for j in range(22): c = output[j,i] ax1.text(i, j, int(c), va='center', ha='center') plt.show() def correlation_matrix_comb(corr_mx, cm_title): fig, (ax2, ax3) = plt.subplots(1, 2) ylabels=['T1','T2','T3','T4','T6','T7','T8','T9', 'T11', 'T12', 'T13', 'T14', 'T15', 'T16', 'T17', 'T18', 'T19', 'T20', 'T21', 'T22', 'T23', 'T25'] xlabels=['R1','R2','R3','R4','R6','R7','R8','R9', 'R11', 'R12', 'R13', 'R14', 'R15', 'R16', 'R17', 'R18', 'R19', 'R20', 'R21', 'R22', 'R23', 'R25'] ''' # graph 1 grayscale im1 = ax1.matshow(corr_mx, cmap='gray') # colorbar need numpy version 1.13.1 #fig.colorbar(im1, ax=ax1) ax1.grid(False) ax1.set_title(cm_title) ax1.set_xticks(np.arange(len(xlabels))) ax1.set_yticks(np.arange(len(ylabels))) ax1.set_xticklabels(xlabels,fontsize=6) ax1.set_yticklabels(ylabels,fontsize=6) # show digit in matrix corr_mx_array = np.asarray(corr_mx) for i in range(22): for j in range(22): c = corr_mx_array[j,i] ax1.text(i, j, round(c,2), va='center', ha='center') ''' # graph 2 yellowscale corr_mx_rank = corr_mx.rank(axis=1, ascending=False) cmap_grey = LinearSegmentedColormap.from_list('mycmap', ['white', 'black']) im2 = ax2.matshow(corr_mx, cmap='viridis') # colorbar need numpy version 1.13.1 fig.colorbar(im2, ax=ax2) ax2.grid(False) ax2.set_title(cm_title) ax2.set_xticks(np.arange(len(xlabels))) ax2.set_yticks(np.arange(len(ylabels))) ax2.set_xticklabels(xlabels,fontsize=6) ax2.set_yticklabels(ylabels,fontsize=6) # Add colorbar, make sure to specify tick locations to match desired ticklabels # show digit in matrix corr_mx_rank = np.asarray(corr_mx_rank) for i in range(22): for j in range(22): c = corr_mx_rank[j,i] ax2.text(i, j, int(c), va='center', ha='center') # graph 3 # find the maximum in each row # input corr_mx is a dataframe # need to convert it into a array first #otherwise it is not working temp = np.asarray(corr_mx) output = (temp == temp.max(axis=1)[:,None]) # along rows im3 = ax3.matshow(output, cmap='gray') # colorbar need numpy version 1.13.1 #fig.colorbar(im3, ax=ax3) ax3.grid(False) ax3.set_title(cm_title) ax3.set_xticks(np.arange(len(xlabels))) ax3.set_yticks(np.arange(len(ylabels))) ax3.set_xticklabels(xlabels,fontsize=6) ax3.set_yticklabels(ylabels,fontsize=6) plt.show() def correlation_matrix_tt_01(corr_mx, cm_title): # find the maximum in each row # input corr_mx is a dataframe # need to convert it into a array first #otherwise it is not working temp = np.asarray(corr_mx) output = (temp == temp.max(axis=1)[:,None]) # along rows fig = plt.figure() ax1 = fig.add_subplot(111) #cmap = cm.get_cmap('jet', 30) cax = ax1.matshow(output, cmap='gray') #cax = ax1.imshow(df.corr(), interpolation="nearest", cmap=cmap) fig.colorbar(cax) ax1.grid(False) plt.title(cm_title) ylabels=['T1','T2','T3','T4','T6','T7','T8','T9', 'T11', 'T12', 'T13', 'T14', 'T15', 'T16', 'T17', 'T18', 'T19', 'T20', 'T21', 'T22', 'T23', 'T25'] xlabels=['T1','T2','T3','T4','T6','T7','T8','T9', 'T11', 'T12', 'T13', 'T14', 'T15', 'T16', 'T17', 'T18', 'T19', 'T20', 'T21', 'T22', 'T23', 'T25'] ax1.set_xticks(np.arange(len(xlabels))) ax1.set_yticks(np.arange(len(ylabels))) ax1.set_xticklabels(xlabels,fontsize=6) ax1.set_yticklabels(ylabels,fontsize=6) plt.show() def correlation_matrix_rr_01(corr_mx, cm_title): # find the maximum in each row # input corr_mx is a dataframe # need to convert it into a array first #otherwise it is not working temp = np.asarray(corr_mx) output = (temp == temp.max(axis=1)[:,None]) # along rows fig = plt.figure() ax1 = fig.add_subplot(111) #cmap = cm.get_cmap('jet', 30) cax = ax1.matshow(output, cmap='gray') #cax = ax1.imshow(df.corr(), interpolation="nearest", cmap=cmap) fig.colorbar(cax) ax1.grid(False) plt.title(cm_title) ylabels=['R1','R2','R3','R4','R6','R7','R8','R9', 'R11', 'R12', 'R13', 'R14', 'R15', 'R16', 'R17', 'R18', 'R19', 'R20', 'R21', 'R22', 'R23', 'R25'] xlabels=['R1','R2','R3','R4','R6','R7','R8','R9', 'R11', 'R12', 'R13', 'R14', 'R15', 'R16', 'R17', 'R18', 'R19', 'R20', 'R21', 'R22', 'R23', 'R25'] ax1.set_xticks(np.arange(len(xlabels))) ax1.set_yticks(np.arange(len(ylabels))) ax1.set_xticklabels(xlabels,fontsize=6) ax1.set_yticklabels(ylabels,fontsize=6) plt.show() # shrink value for correlation matrix # in order to use colormap -> 10 scale def shrink_value_03_1(corr_in1): corr_out1 = corr_in1.copy() # here dataframe.copy() must be used, otherwise input can also be changed when changing output for i in range (22): for j in range(22): if corr_in1.iloc[i, j] < 0.3: corr_out1.iloc[i, j] = 0.3 return corr_out1 def shrink_value_05_1(corr_in2): corr_out2 = corr_in2.copy() # here dataframe.copy() must be used, otherwise input can also be changed when changing output for i2 in range (22): for j2 in range(22): if corr_in2.iloc[i2, j2] < 0.5: corr_out2.iloc[i2, j2] = 0.5 return corr_out2 # not used!!!!!!!!!!!! # normalize the complex signal series def normalize_complex_arr(a): a_oo = a - a.real.min() - 1j*a.imag.min() # origin offsetted return a_oo/np.abs(a_oo).max() def improved_PCC(signal_in): output_corr = pd.DataFrame() for i in range(44): row_pcc_notremovemean = [] for j in range(44): sig_1 = signal_in.iloc[i, :] sig_2 = signal_in.iloc[j, :] pcc_notremovemean = np.abs(np.sum(sig_1 * sig_2) / np.sqrt(np.sum(sig_1*sig_1) * np.sum(sig_2 * sig_2))) row_pcc_notremovemean = np.append(row_pcc_notremovemean, pcc_notremovemean) output_corr = output_corr.append(pd.DataFrame(row_pcc_notremovemean.reshape(1,44)), ignore_index=True) output_corr = output_corr.iloc[22:44, 0:22] return output_corr ############################################################################### # import the pkl file #pkl_file=pd.read_pickle('/Users/bruce/Documents/uOttawa/Project/audio_brainstem_response/Data_BruceSunMaster_Studies/study2/study2DataFrame.pkl') df_EFR=pd.read_pickle('/home/bruce/Dropbox/4.Project/4.Code for Linux/df_EFR.pkl') # Mac # df_EFR=pd.read_pickle('/Users/bruce/Documents/uOttawa/Master‘s Thesis/4.Project/4.Code for Linux/df_EFR.pkl') # remove DC offset df_EFR_detrend = pd.DataFrame() for i in range(1408): # combine next two rows later df_EFR_detrend_data = pd.DataFrame(signal.detrend(df_EFR.iloc[i: i+1, 0:1024], type='constant').reshape(1,1024)) df_EFR_label = pd.DataFrame(df_EFR.iloc[i, 1024:1031].values.reshape(1,7)) df_EFR_detrend = df_EFR_detrend.append(pd.concat([df_EFR_detrend_data, df_EFR_label], axis=1, ignore_index=True)) # set the title of columns df_EFR_detrend.columns = np.append(np.arange(1024), ["Subject", "Sex", "Condition", "Vowel", "Sound Level", "Num", "EFR/FFR"]) df_EFR_detrend = df_EFR_detrend.reset_index(drop=True) df_EFR = df_EFR_detrend # Define window function win_kaiser = signal.kaiser(1024, beta=14) win_hamming = signal.hamming(1024) # average the df_EFR df_EFR_avg = pd.DataFrame() df_EFR_avg_win = pd.DataFrame() # average test1 and test2 for i in range(704): # combine next two rows later df_EFR_avg_t = pd.DataFrame(df_EFR.iloc[2*i: 2*i+2, 0:1024].mean(axis=0).values.reshape(1,1024)) # average those two rows # without window function df_EFR_avg_t = pd.DataFrame(df_EFR_avg_t.iloc[0,:].values.reshape(1,1024)) # without window function # implement the window function df_EFR_avg_t_window = pd.DataFrame((df_EFR_avg_t.iloc[0,:] * win_hamming).values.reshape(1,1024)) df_EFR_label = pd.DataFrame(df_EFR.iloc[2*i, 1024:1031].values.reshape(1,7)) df_EFR_avg = df_EFR_avg.append(pd.concat([df_EFR_avg_t, df_EFR_label], axis=1, ignore_index=True)) df_EFR_avg_win = df_EFR_avg_win.append(pd.concat([df_EFR_avg_t_window, df_EFR_label], axis=1, ignore_index=True)) # set the title of columns df_EFR_avg.columns = np.append(np.arange(1024), ["Subject", "Sex", "Condition", "Vowel", "Sound Level", "Num", "EFR/FFR"]) df_EFR_avg = df_EFR_avg.sort_values(by=["Condition", "Subject"]) df_EFR_avg = df_EFR_avg.reset_index(drop=True) df_EFR_avg_win.columns = np.append(np.arange(1024), ["Subject", "Sex", "Condition", "Vowel", "Sound Level", "Num", "EFR/FFR"]) df_EFR_avg_win = df_EFR_avg_win.sort_values(by=["Condition", "Subject"]) df_EFR_avg_win = df_EFR_avg_win.reset_index(drop=True) # average all the subjects , test and retest and keep one sound levels # filter by 'a vowel and 85Db' df_EFR_avg_sorted = df_EFR_avg.sort_values(by=["Sound Level", "Vowel","Condition", "Subject"]) df_EFR_avg_sorted = df_EFR_avg_sorted.reset_index(drop=True) df_EFR_avg_win_sorted = df_EFR_avg_win.sort_values(by=["Sound Level", "Vowel","Condition", "Subject"]) df_EFR_avg_win_sorted = df_EFR_avg_win_sorted.reset_index(drop=True) # filter55 65 75 sound levels and keep 85dB # keep vowel condition and subject df_EFR_avg_85 = pd.DataFrame(df_EFR_avg_sorted.iloc[528:, :]) df_EFR_avg_85 = df_EFR_avg_85.reset_index(drop=True) df_EFR_avg_win_85 = pd.DataFrame(df_EFR_avg_win_sorted.iloc[528:, :]) df_EFR_avg_win_85 = df_EFR_avg_win_85.reset_index(drop=True) # this part was replaced by upper part based on what I need to do ''' # average all the subjects , test and retest, different sound levels # filter by 'a vowel and 85Db' df_EFR_avg_sorted = df_EFR_avg.sort_values(by=["Vowel","Condition", "Subject", "Sound Level"]) df_EFR_avg_sorted = df_EFR_avg_sorted.reset_index(drop=True) # average sound levels and # keep vowel condition and subject df_EFR_avg_vcs = pd.DataFrame() for i in range(176): # combine next two rows later df_EFR_avg_vcs_t = pd.DataFrame(df_EFR_avg_sorted.iloc[4*i: 4*i+4, 0:1024].mean(axis=0).values.reshape(1,1024)) # average those two rows df_EFR_avg_vcs_label = pd.DataFrame(df_EFR_avg_sorted.iloc[4*i, 1024:1031].values.reshape(1,7)) df_EFR_avg_vcs = df_EFR_avg_vcs.append(pd.concat([df_EFR_avg_vcs_t, df_EFR_avg_vcs_label], axis=1, ignore_index=True), ignore_index=True) # set the title of columns df_EFR_avg_vcs.columns = np.append(np.arange(1024), ["Subject", "Sex", "Condition", "Vowel", "Sound Level", "Num", "EFR/FFR"]) #df_EFR_avg_vcs = df_EFR_avg_vcs.sort_values(by=["Condition", "Subject"]) ''' ''' # filter by 'a vowel and 85Db' df_EFR_a_85_test1 = df_EFR[(df_EFR['Vowel'] == 'a vowel') & (df_EFR['Sound Level'] == '85')] df_EFR_a_85_test1 = df_EFR_a_85_test1.reset_index(drop=True) df_EFR_a_85_avg = pd.DataFrame() # average test1 and test2 for i in range(44): df_EFR_a_85_avg_t = pd.DataFrame(df_EFR_a_85_test1.iloc[2*i: 2*i+2, 0:1024].mean(axis=0).values.reshape(1,1024)) df_EFR_a_85_label = pd.DataFrame(df_EFR_a_85_test1.iloc[2*i, 1024:1031].values.reshape(1,7)) df_EFR_a_85_avg = df_EFR_a_85_avg.append(pd.concat([df_EFR_a_85_avg_t, df_EFR_a_85_label], axis=1, ignore_index=True)) # set the title of columns df_EFR_a_85_avg.columns = np.append(np.arange(1024), ["Subject", "Sex", "Condition", "Vowel", "Sound Level", "Num", "EFR/FFR"]) df_EFR_a_85_avg = df_EFR_a_85_avg.sort_values(by=["Condition", "Subject"]) df_EFR_a_85_avg = df_EFR_a_85_avg.reset_index(drop=True) ''' ################################################## # Frequency Domain # parameters sampling_rate = 9606 # fs # sampling_rate = 9596.623 n = 1024 k = np.arange(n) T = n/sampling_rate # time of signal frq = k/T freq = frq[range(int(n/2))] n2 = 9606 k2 = np.arange(n2) T2 = n2/sampling_rate frq2 = k2/T2 freq2 = frq2[range(int(n2/2))] # zero padding # for df_EFR df_EFR_data = df_EFR.iloc[:, :1024] df_EFR_label = df_EFR.iloc[:, 1024:] df_EFR_mid = pd.DataFrame(np.zeros((1408, 95036))) df_EFR_withzero = pd.concat([df_EFR_data, df_EFR_mid, df_EFR_label], axis=1) # rename columns df_EFR_withzero.columns = np.append(np.arange(96060), ["Subject", "Sex", "Condition", "Vowel", "Sound Level", "Num", "EFR/FFR"]) # for df_EFR_avg_85 df_EFR_avg_85_data = df_EFR_avg_85.iloc[:, :1024] df_EFR_avg_85_label = df_EFR_avg_85.iloc[:, 1024:] df_EFR_avg_85_mid = pd.DataFrame(np.zeros((176, 8582))) df_EFR_avg_85_withzero = pd.concat([df_EFR_avg_85_data, df_EFR_avg_85_mid, df_EFR_avg_85_label], axis=1) # rename columns df_EFR_avg_85_withzero.columns = np.append(np.arange(9606), ["Subject", "Sex", "Condition", "Vowel", "Sound Level", "Num", "EFR/FFR"]) # df_EFR_avg_win_85 df_EFR_avg_win_85_data = df_EFR_avg_win_85.iloc[:, :1024] df_EFR_avg_win_85_label = df_EFR_avg_win_85.iloc[:, 1024:] df_EFR_avg_win_85_mid = pd.DataFrame(np.zeros((176, 8582))) df_EFR_avg_win_85_withzero = pd.concat([df_EFR_avg_win_85_data, df_EFR_avg_win_85_mid, df_EFR_avg_win_85_label], axis=1) df_EFR_avg_win_85_withzero.columns = np.append(np.arange(9606), ["Subject", "Sex", "Condition", "Vowel", "Sound Level", "Num", "EFR/FFR"]) # concatenate AENU temp1 = pd.concat([df_EFR_avg_85.iloc[0:44, 0:1024].reset_index(drop=True),df_EFR_avg_85.iloc[44:88, 0:1024].reset_index(drop=True)], axis=1) temp2 = pd.concat([df_EFR_avg_85.iloc[88:132, 0:1024].reset_index(drop=True), df_EFR_avg_85.iloc[132:176, 0:1024].reset_index(drop=True)], axis=1) df_EFR_avg_85_aenu = pd.concat([temp1, temp2], axis=1, ignore_index=True) df_EFR_avg_85_aenu_withzero = pd.concat([df_EFR_avg_85_aenu, pd.DataFrame(np.zeros((44, 36864)))] , axis=1) ''' # test############## # test(detrend) temp_test = np.asarray(df_EFR_avg_85_data.iloc[0, 0:1024]) temp_test_detrend = signal.detrend(temp_test) plt.figure() plt.subplot(2, 1, 1) plt.plot(temp_test) plt.subplot(2, 1, 2) plt.plot(temp_test_detrend) plt.show() # the raw data is already DC removed # test(zero padding) temp_EFR_1 = df_EFR_withzero.iloc[0, 0:1024] temp_EFR_2= df_EFR_withzero.iloc[0, 0:9606] temp_amplitude_spectrum_1 = np.abs((fftpack.fft(temp_EFR_1)/n)[range(int(n/2))]) temp_amplitude_spectrum_2 = np.abs((fftpack.fft(temp_EFR_2)/n2)[range(int(n2/2))]) plt.figure() plt.subplot(2, 1, 1) markers1 = [11, 21, 32, 43, 53, 64, 75] # which corresponds to 100 200....700Hz in frequency domain plt.plot(temp_amplitude_spectrum_1, '-D', markevery=markers1) plt.xlim(0, 100) plt.title('without zero padding') plt.subplot(2, 1, 2) #markers2 = [100, 200, 300, 400, 500, 600, 700] markers2 = [99, 199, 299, 399, 499, 599, 599] # which corresponds to 100 200....700Hz in frequency domain plt.plot(temp_amplitude_spectrum_2, '-D', markevery=markers2) plt.xlim(0, 1000) # plt.xscale('linear') plt.title('with zero padding') plt.show() # ################# ''' # Calculate the Amplitude Spectrum # create a new dataframe with zero-padding amplitude spectrum ''' # for df_EFR df_as_7= pd.DataFrame() for i in range(1408): temp_EFR = df_EFR_avg_85_withzero.iloc[i, 0:96060] temp_as = np.abs((fftpack.fft(temp_EFR)/n2)[range(int(n2/2))]) #df_as_7 = pd.concat([df_as_7, temp_as_7_t], axis=0) df_as_7 = df_as_7.append(pd.DataFrame(np.array([temp_as[1000], temp_as[2000], temp_as[3000], temp_as[4000], \ temp_as[5000], temp_as[6000], temp_as[7000]]).reshape(1,7)), ignore_index = True) df_as_7 = pd.concat([df_as_7, df_EFR_label], axis=1) # add labels on it # filter by 'a vowel and 85Db' df_as_7_test1 = df_as_7[(df_as_7['Vowel'] == 'a vowel') & (df_as_7['Sound Level'] == '85')] df_as_7_test1 = df_as_7_test1.reset_index(drop=True) ''' # for df_EFR_avg_vcs_withzero df_as_85_no0= pd.DataFrame() df_as_85= pd.DataFrame() df_as7_85= pd.DataFrame() df_as_win_85= pd.DataFrame() df_as7_win_85= pd.DataFrame() for i in range(176): #temp_aenu_EFR = df_EFR_avg_aenu_withzero.iloc[i, 0:9606] temp_as_no0 = np.abs((np.fft.fft(df_EFR_avg_85_data.iloc[i, :]))[range(int(n/2))]) df_as_85_no0 = df_as_85_no0.append(pd.DataFrame(temp_as_no0.reshape(1,512)), ignore_index = True) temp_as = np.abs((np.fft.fft(df_EFR_avg_85_withzero.iloc[i, 0:9606]))[range(int(n2/2))]) df_as_85 = df_as_85.append(pd.DataFrame(temp_as.reshape(1,4803)), ignore_index = True) df_as7_85 = df_as7_85.append(pd.DataFrame(np.array([temp_as[100], temp_as[200], temp_as[300], temp_as[400], \ temp_as[500], temp_as[600], temp_as[700]]).reshape(1,7)), ignore_index = True) temp_as_win = np.abs((np.fft.fft(df_EFR_avg_win_85_withzero.iloc[i, 0:9606]))[range(int(n2/2))]) df_as_win_85 = df_as_win_85.append(pd.DataFrame(temp_as_win.reshape(1,4803)), ignore_index = True) df_as7_win_85 = df_as7_win_85.append(pd.DataFrame(np.array([temp_as_win[100], temp_as_win[200], temp_as_win[300], temp_as_win[400], \ temp_as_win[500], temp_as_win[600], temp_as_win[700]]).reshape(1,7)), ignore_index = True) df_as_85_no0 = pd.concat([df_as_85_no0, df_EFR_avg_85_label], axis=1) # add labels on it df_as_85 = pd.concat([df_as_85, df_EFR_avg_85_label], axis=1) # add labels on it df_as7_85 = pd.concat([df_as7_85, df_EFR_avg_85_label], axis=1) # add labels on it df_as_win_85 = pd.concat([df_as_win_85, df_EFR_avg_win_85_label], axis=1) # add labels on it df_as7_win_85 = pd.concat([df_as7_win_85, df_EFR_avg_win_85_label], axis=1) # add labels on it # wothout zero padding df_as_85_aenu = pd.concat([df_as_85.iloc[0:44, :4803], df_as_85.iloc[44:88, :4803].reset_index(drop=True), df_as_85.iloc[88:132, :4803].reset_index(drop=True), df_as_85.iloc[132:176, :4803].reset_index(drop=True)], axis=1) df_as_85_1300_aenu = pd.concat([df_as_85.iloc[0:44, :1300], df_as_85.iloc[44:88, :1300].reset_index(drop=True), df_as_85.iloc[88:132, :1300].reset_index(drop=True), df_as_85.iloc[132:176, :1300].reset_index(drop=True)], axis=1) df_as_85_no0_1300 = df_as_85_no0.iloc[:, :139] df_as_85_no0_aenu = pd.concat([df_as_85_no0_1300.iloc[0:44, :], df_as_85_no0_1300.iloc[44:88, :].reset_index(drop=True), df_as_85_no0_1300.iloc[88:132, :].reset_index(drop=True), df_as_85_no0_1300.iloc[132:176, :].reset_index(drop=True)], axis=1) df_as7_85_aenu = pd.concat([df_as7_85.iloc[0:44, :7], df_as7_85.iloc[44:88, :7].reset_index(drop=True), df_as7_85.iloc[88:132, :7].reset_index(drop=True), df_as7_85.iloc[132:176, :7].reset_index(drop=True)], axis=1) # for efr_aenu df_aenu_as_85 = pd.DataFrame() df_aenu_as7_85 = pd.DataFrame() for i in range(44): #temp_aenu_EFR = df_EFR_avg_aenu_withzero.iloc[i, 0:9606] temp_as2 = np.abs((fftpack.fft(df_EFR_avg_85_aenu.iloc[i, 0:4096])/4096)[range(int(4096/2))]) df_aenu_as_85 = df_aenu_as_85.append(pd.DataFrame(temp_as2.reshape(1,2048)), ignore_index = True) df_aenu_as7_85 = df_aenu_as7_85.append(pd.DataFrame(np.array([temp_as2[43], temp_as2[85], temp_as2[128], temp_as2[170], \ temp_as2[213], temp_as2[256], temp_as2[298]]).reshape(1,7)), ignore_index = True) #df_aenu_as_85 = pd.concat([df_aenu_as_85, df_EFR_avg_85_label], axis=1) # add labels on it ''' # average test1 and test2 df_as_7_avg = pd.DataFrame() for i in range(44): df_as_7_avg1 = pd.DataFrame(df_as_7_test1.iloc[2*i: 2*i+1, 0:7].mean(axis=0).values.reshape(1,7)) df_as_7_label = pd.DataFrame(df_as_7_test1.iloc[2*i, 7:14].values.reshape(1,7)) df_as_7_avg_t = pd.concat([df_as_7_avg1, df_as_7_label], axis=1, ignore_index=True) df_as_7_avg = df_as_7_avg.append(df_as_7_avg_t) # set the title of columns df_as_7_avg.columns = np.append(np.arange(7), ["Subject", "Sex", "Condition", "Vowel", "Sound Level", "Num", "EFR/FFR"]) df_as_7_avg = df_as_7_avg.sort_values(by=["Condition", "Subject"]) df_as_7_avg = df_as_7_avg.reset_index(drop=True) ''' ''' # set a normalized AS df_as_7_avg_data= pd.DataFrame(df_as_7_avg.iloc[:, 0:7].astype(float)) df_as_7_avg_sum= pd.DataFrame(df_as_7_avg.iloc[:, 0:7]).sum(axis=1) df_as_7_avg_label= pd.DataFrame(df_as_7_avg.iloc[:, 7:14]) # normalize df_as_7_avg_norm = df_as_7_avg_data.div(df_as_7_avg_sum, axis=0) # add label df_as_7_avg_norm = pd.concat([df_as_7_avg_norm, df_as_7_avg_label], axis=1, ignore_index=True) ''' # normalization df_EFR_avg_85_aenu_norm = df_EFR_avg_85_aenu.div((df_EFR_avg_85_aenu.iloc[0:4096].abs()**2).sum()) df_aenu_as_85_1300_norm = df_aenu_as_85.iloc[:, :535].div((df_aenu_as_85.iloc[:, :535].abs()**2).sum()/1300) df_as_85_1300_aenu_norm = df_as_85_1300_aenu.div((df_as_85_1300_aenu.abs()**2).sum()/1300) # Calculate correlation # EFR corr_EFR_avg_85_a = df_EFR_avg_85.iloc[0:44, 0:1024].T.corr(method='pearson').iloc[22:44, 0:22] corr_EFR_avg_85_e = df_EFR_avg_85.iloc[44:88, 0:1024].T.corr(method='pearson').iloc[22:44, 0:22] corr_EFR_avg_85_n = df_EFR_avg_85.iloc[88:132, 0:1024].T.corr(method='pearson').iloc[22:44, 0:22] corr_EFR_avg_85_u = df_EFR_avg_85.iloc[132:176, 0:1024].T.corr(method='pearson').iloc[22:44, 0:22] corr_EFR_avg_85_aenu = df_EFR_avg_85_aenu.iloc[:, 0:4096].T.corr(method='pearson').iloc[22:44, 0:22] ''' corr_EFR_avg_85_a_t = df_EFR_avg_85.iloc[0:44, 0:1024].T.corr(method='pearson').iloc[0:22, 0:22] corr_EFR_avg_85_e_t = df_EFR_avg_85.iloc[44:88, 0:1024].T.corr(method='pearson').iloc[0:22, 0:22] corr_EFR_avg_85_n_t = df_EFR_avg_85.iloc[88:132, 0:1024].T.corr(method='pearson').iloc[0:22, 0:22] corr_EFR_avg_85_u_t = df_EFR_avg_85.iloc[132:176, 0:1024].T.corr(method='pearson').iloc[0:22, 0:22] corr_EFR_avg_85_a_re = df_EFR_avg_85.iloc[0:44, 0:1024].T.corr(method='pearson').iloc[22:44, 22:44] corr_EFR_avg_85_e_re = df_EFR_avg_85.iloc[44:88, 0:1024].T.corr(method='pearson').iloc[22:44, 22:44] corr_EFR_avg_85_n_re = df_EFR_avg_85.iloc[88:132, 0:1024].T.corr(method='pearson').iloc[22:44, 22:44] corr_EFR_avg_85_u_re = df_EFR_avg_85.iloc[132:176, 0:1024].T.corr(method='pearson').iloc[22:44, 22:44] ''' # AS corr_as_85_a = df_as_85.iloc[0:44, 0:1300].T.corr(method='pearson').iloc[22:44, 0:22] corr_as_85_e = df_as_85.iloc[44:88, 0:1300].T.corr(method='pearson').iloc[22:44, 0:22] corr_as_85_n = df_as_85.iloc[88:132, 0:1300].T.corr(method='pearson').iloc[22:44, 0:22] corr_as_85_u = df_as_85.iloc[132:176, 0:1300].T.corr(method='pearson').iloc[22:44, 0:22] corr_as_win_85_a = df_as_win_85.iloc[0:44, 0:1300].T.corr(method='pearson').iloc[22:44, 0:22] corr_as_win_85_e = df_as_win_85.iloc[44:88, 0:1300].T.corr(method='pearson').iloc[22:44, 0:22] corr_as_win_85_n = df_as_win_85.iloc[88:132, 0:1300].T.corr(method='pearson').iloc[22:44, 0:22] corr_as_win_85_u = df_as_win_85.iloc[132:176, 0:1300].T.corr(method='pearson').iloc[22:44, 0:22] corr_as_85_aenu = df_aenu_as_85.iloc[0:44, 0:2048].T.corr(method='pearson').iloc[22:44, 0:22] # here we use df_aenu_as_85.iloc[:, 0:535] to limit freq into 0 to 1300Hz corr_as_85_aenu_1300 = df_aenu_as_85.iloc[0:44, 0:535].T.corr(method='pearson').iloc[22:44, 0:22] corr_as_85_no0_aenu = df_as_85_no0_aenu.iloc[0:44, :].T.corr(method='pearson').iloc[22:44, 0:22] corr_as_85_no0_aenu = df_as_85_no0_aenu.iloc[0:44, :].T.corr(method='pearson').iloc[22:44, 0:22] corr_as7_85_aenu = df_as7_85_aenu.iloc[0:44, :].T.corr(method='pearson').iloc[22:44, 0:22] corr_aenu_as7_85 = df_aenu_as7_85.iloc[0:44, :].T.corr(method='pearson').iloc[22:44, 0:22] # calculate the improved PCC matrix corr_as_85_a_v2 = improved_PCC(df_as_85.iloc[0:44, 0:1300]) corr_as_85_e_v2 = improved_PCC(df_as_85.iloc[44:88, 0:1300]) corr_as_85_n_v2 = improved_PCC(df_as_85.iloc[88:132, 0:1300]) corr_as_85_u_v2 = improved_PCC(df_as_85.iloc[132:176, 0:1300]) corr_as_85_1300_aenu = improved_PCC(df_as_85_1300_aenu) # df_EFR + df_aenu_AS_1300 df_aenu_sum_85 = pd.concat([df_EFR_avg_85_aenu, df_aenu_as_85.iloc[:, :535]], axis=1) # df_aenu_sum_85 = pd.concat([df_EFR_avg_85_aenu_norm, df_aenu_as_85_1300_norm], axis=1) corr_sum_85_aenu = df_aenu_sum_85.iloc[0:44, 0:].T.corr(method='pearson').iloc[22:44, 0:22] # df_EFR + df_aenu_no0_as df_aenu_sum_85_v2 = pd.concat([df_EFR_avg_85_aenu, df_as_85_no0_aenu], axis=1) corr_sum_85_aenu_v2 = df_aenu_sum_85_v2.iloc[0:44, 0:].T.corr(method='pearson').iloc[22:44, 0:22] # concatenate df_EFR and df_as_85_1300_aenu df_aenu_sum_85_v3 = pd.concat([df_EFR_avg_85_aenu, df_as_85_1300_aenu], axis=1) # df_aenu_sum_85_v3 = pd.concat([df_EFR_avg_85_aenu_norm, df_as_85_1300_aenu_norm], axis=1) corr_sum_85_aenu_v3 = df_aenu_sum_85_v3.iloc[0:44, 0:].T.corr(method='pearson').iloc[22:44, 0:22] # improved PCC (not remove mean for as) # test for do not removing the mean of PCC corr_sum_85_aenu_v4 = pd.DataFrame() signal_in = df_aenu_sum_85_v3 for i in range(44): row_pcc_notremovemean = [] row_pcc = [] for j in range(44): sig_1 = signal_in.iloc[i, :].reset_index(drop=True) sig_2 = signal_in.iloc[j, :].reset_index(drop=True) sig_1_remove_mean = (sig_1 - sig_1.mean()).reset_index(drop=True) sig_2_remove_mean = (sig_2 - sig_2.mean()).reset_index(drop=True) # here EFR remove the mean but AS not # then normalize the energy of EFR and AS sig_1_p1 = sig_1_remove_mean.iloc[0:4096].div((sig_1_remove_mean.iloc[0:4096].abs()**2).sum()) sig_1_p2 = sig_1.iloc[4096:].div((sig_1.iloc[4096:].abs()**2).sum()/1300) sig_1_new = pd.concat([sig_1_p1, sig_1_p2]) sig_2_p1 = sig_2_remove_mean.iloc[0:4096].div((sig_2_remove_mean.iloc[0:4096].abs()**2).sum()) sig_2_p2 = sig_2.iloc[4096:].div((sig_2.iloc[4096:].abs()**2).sum()/1300) sig_2_new = pd.concat([sig_2_p1, sig_2_p2]) #sig_1_new = pd.concat([sig_1_remove_mean.iloc[0:4096], sig_1.iloc[4096:]]) #sig_2_new = pd.concat([sig_2_remove_mean.iloc[0:4096], sig_2.iloc[4096:]]) ''' pcc_notremovemean = np.abs(np.sum(sig_1 * sig_2) / np.sqrt(np.sum(sig_1*sig_1) * np.sum(sig_2 * sig_2))) pcc = np.abs(np.sum(sig_1_remove_mean * sig_2_remove_mean) / np.sqrt(np.sum(sig_1_remove_mean*sig_1_remove_mean) * np.sum(sig_2_remove_mean * sig_2_remove_mean))) ''' pcc_notremovemean = np.abs(np.sum(sig_1_new * sig_2_new) / np.sqrt(np.sum(sig_1_new*sig_1_new) * np.sum(sig_2_new * sig_2_new))) row_pcc_notremovemean = np.append(row_pcc_notremovemean, pcc_notremovemean) # row_pcc = np.append(row_pcc, pcc) # example if i==4 & j==5: plt.figure(1) ax1 = plt.subplot(211) ax1.plot(sig_1) ax1.plot(sig_2) ax2 = plt.subplot(212) ax2.plot(sig_1_remove_mean) ax2.plot(sig_2_remove_mean) ax1.set_title("original signal, norm corr = %.3f" % pcc_notremovemean) ax2.set_title("signal with mean removed(PCC), norm corr = %.3f" % pcc) plt.tight_layout() ax1.grid(True) ax2.grid(True) plt.show() corr_sum_85_aenu_v4 = corr_sum_85_aenu_v4.append(pd.DataFrame(row_pcc_notremovemean.reshape(1,44)), ignore_index=True) corr_sum_85_aenu_v4 = corr_sum_85_aenu_v4.iloc[22:44, 0:22] ''' corr_as_85_a_t = df_as_85.iloc[0:44, 0:48030].T.corr(method='pearson').iloc[0:22, 0:22] corr_as_85_e_t = df_as_85.iloc[44:88, 0:48030].T.corr(method='pearson').iloc[0:22, 0:22] corr_as_85_n_t = df_as_85.iloc[88:132, 0:48030].T.corr(method='pearson').iloc[0:22, 0:22] corr_as_85_u_t = df_as_85.iloc[132:176, 0:48030].T.corr(method='pearson').iloc[0:22, 0:22] corr_as_85_a_re = df_as_85.iloc[0:44, 0:48030].T.corr(method='pearson').iloc[22:44, 22:44] corr_as_85_e_re = df_as_85.iloc[44:88, 0:48030].T.corr(method='pearson').iloc[22:44, 22:44] corr_as_85_n_re = df_as_85.iloc[88:132, 0:48030].T.corr(method='pearson').iloc[22:44, 22:44] corr_as_85_u_re = df_as_85.iloc[132:176, 0:48030].T.corr(method='pearson').iloc[22:44, 22:44] ''' #AS7 corr_as7_85_a = df_as7_85.iloc[0:44, 0:7].T.corr(method='pearson').iloc[22:44, 0:22] corr_as7_85_e = df_as7_85.iloc[44:88, 0:7].T.corr(method='pearson').iloc[22:44, 0:22] corr_as7_85_n = df_as7_85.iloc[88:132, 0:7].T.corr(method='pearson').iloc[22:44, 0:22] corr_as7_85_u = df_as7_85.iloc[132:176, 0:7].T.corr(method='pearson').iloc[22:44, 0:22] ''' corr_as7_85_a_t = df_as7_85.iloc[0:44, 0:7].T.corr(method='pearson').iloc[0:22, 0:22] corr_as7_85_e_t = df_as7_85.iloc[44:88, 0:7].T.corr(method='pearson').iloc[0:22, 0:22] corr_as7_85_n_t = df_as7_85.iloc[88:132, 0:7].T.corr(method='pearson').iloc[0:22, 0:22] corr_as7_85_u_t = df_as7_85.iloc[132:176, 0:7].T.corr(method='pearson').iloc[0:22, 0:22] corr_as7_85_a_re = df_as7_85.iloc[0:44, 0:7].T.corr(method='pearson').iloc[22:44, 22:44] corr_as7_85_e_re = df_as7_85.iloc[44:88, 0:7].T.corr(method='pearson').iloc[22:44, 22:44] corr_as7_85_n_re = df_as7_85.iloc[88:132, 0:7].T.corr(method='pearson').iloc[22:44, 22:44] corr_as7_85_u_re = df_as7_85.iloc[132:176, 0:7].T.corr(method='pearson').iloc[22:44, 22:44] ''' # shrink # shrink the correlation range from 0.3 to 1 # EFR ''' corr_EFR_avg_85_a_shrink_03_1 = shrink_value_03_1(corr_EFR_avg_85_a) corr_EFR_avg_85_e_shrink_03_1 = shrink_value_03_1(corr_EFR_avg_85_e) corr_EFR_avg_85_n_shrink_03_1 = shrink_value_03_1(corr_EFR_avg_85_n) corr_EFR_avg_85_u_shrink_03_1 = shrink_value_03_1(corr_EFR_avg_85_u) ''' corr_EFR_avg_85_aenu_shrink_03_1 = shrink_value_03_1(corr_EFR_avg_85_aenu) # AS ''' corr_as_win_85_a_shrink_03_1 = shrink_value_03_1(corr_as_win_85_a) corr_as_win_85_e_shrink_03_1 = shrink_value_03_1(corr_as_win_85_e) corr_as_win_85_n_shrink_03_1 = shrink_value_03_1(corr_as_win_85_n) corr_as_win_85_u_shrink_03_1 = shrink_value_03_1(corr_as_win_85_u) ''' corr_as_85_aenu_shrink_03_1 = shrink_value_03_1(corr_as_85_aenu) # shrink the correlation range from 0.5 to 1 # EFR corr_EFR_avg_85_aenu_shrink_05_1 = shrink_value_05_1(corr_EFR_avg_85_aenu) # AS corr_as_85_aenu_shrink_05_1 = shrink_value_05_1(corr_as_85_aenu) # test # sum of time and frequency corelation matrix corr_sum_avg_85_aenu = (corr_EFR_avg_85_aenu + corr_as_85_aenu_1300).copy() corr_sum_avg_85_aenu_v2 = (corr_EFR_avg_85_aenu + corr_as_85_no0_aenu).copy() #corr_sum_avg_85_aenu = (corr_EFR_avg_85_aenu + corr_as_85_aenu).copy() # max of time and frequency corelation matrix # corr_max_avg_85_aenu = (corr_EFR_avg_85_aenu ? corr_as_85_aenu).copy() # plot the figure ''' # Correlation Matrix # EFR correlation_matrix(corr_EFR_avg_85_a, 'cross correlation of 85dB a_vowel in time domain') correlation_matrix(corr_EFR_avg_85_e, 'cross correlation of 85dB e_vowel in time domain') correlation_matrix(corr_EFR_avg_85_n, 'cross correlation of 85dB n_vowel in time domain') correlation_matrix(corr_EFR_avg_85_u, 'cross correlation of 85dB u_vowel in time domain') # AS correlation_matrix(corr_as_85_a, 'cross correlation of 85dB a_vowel in frequency domain') correlation_matrix(corr_as_85_e, 'cross correlation of 85dB e_vowel in frequency domain') correlation_matrix(corr_as_85_n, 'cross correlation of 85dB n_vowel in frequency domain') correlation_matrix(corr_as_85_u, 'cross correlation of 85dB u_vowel in frequency domain') # AS7 correlation_matrix(corr_as7_85_a, 'cross correlation of 85dB a_vowel in frequency domain 7') correlation_matrix(corr_as7_85_e, 'cross correlation of 85dB e_vowel in frequency domain 7') correlation_matrix(corr_as7_85_n, 'cross correlation of 85dB n_vowel in frequency domain 7') correlation_matrix(corr_as7_85_u, 'cross correlation of 85dB u_vowel in frequency domain 7') # Correlation Matrix witn 0 and 1 # EFR correlation_matrix_01(corr_EFR_avg_85_a, 'cross correlation of 85dB a_vowel in time domain') #correlation_matrix_tt_01(corr_EFR_avg_85_a_t, 'cross correlation of 85dB a_vowel in time domain') #correlation_matrix_rr_01(corr_EFR_avg_85_a_re, 'cross correlation of 85dB a_vowel in time domain') correlation_matrix_01(corr_EFR_avg_85_e, 'cross correlation of 85dB e_vowel in time domain') #correlation_matrix_tt_01(corr_EFR_avg_85_e_t, 'cross correlation of 85dB e_vowel in time domain') #correlation_matrix_rr_01(corr_EFR_avg_85_e_re, 'cross correlation of 85dB e_vowel in time domain') correlation_matrix_01(corr_EFR_avg_85_n, 'cross correlation of 85dB n_vowel in time domain') #correlation_matrix_tt_01(corr_EFR_avg_85_n_t, 'cross correlation of 85dB n_vowel in time domain') #correlation_matrix_rr_01(corr_EFR_avg_85_n_re, 'cross correlation of 85dB n_vowel in time domain') correlation_matrix_01(corr_EFR_avg_85_u, 'cross correlation of 85dB u_vowel in time domain') #correlation_matrix_tt_01(corr_EFR_avg_85_u_t, 'cross correlation of 85dB u_vowel in time domain') #correlation_matrix_rr_01(corr_EFR_avg_85_u_re, 'cross correlation of 85dB u_vowel in time domain') # Amplitude Spectrum correlation_matrix_01(corr_as_85_a, 'cross correlation of 85dB a_vowel in frequency domain') #correlation_matrix_tt_01(corr_as_85_a_t, 'cross correlation of 85dB a_vowel in frequency domain') #correlation_matrix_rr_01(corr_as_85_a_re, 'cross correlation of 85dB a_vowel in frequency domain') correlation_matrix_01(corr_as_85_e, 'cross correlation of 85dB e_vowel in frequency domain') #correlation_matrix_tt_01(corr_as_85_e_t, 'cross correlation of 85dB e_vowel in frequency domain') #correlation_matrix_rr_01(corr_as_85_e_re, 'cross correlation of 85dB e_vowel in frequency domain') correlation_matrix_01(corr_as_85_n, 'cross correlation of 85dB n_vowel in frequency domain') #correlation_matrix_tt_01(corr_as_85_n_t, 'cross correlation of 85dB n_vowel in frequency domain') #correlation_matrix_rr_01(corr_as_85_n_re, 'cross correlation of 85dB n_vowel in frequency domain') correlation_matrix_01(corr_as_85_u, 'cross correlation of 85dB u_vowel in frequency domain') #correlation_matrix_tt_01(corr_as_85_u_t, 'cross correlation of 85dB u_vowel in frequency domain') #correlation_matrix_rr_01(corr_as_85_u_re, 'cross correlation of 85dB u_vowel in frequency domain') # Amplitude Spectrum 7 points correlation_matrix_01(corr_as7_85_a, 'cross correlation of 85dB a_vowel in frequency domain 7') #correlation_matrix_tt_01(corr_as7_85_a_t, 'cross correlation of 85dB a_vowel in frequency domain 7') #correlation_matrix_rr_01(corr_as7_85_a_re, 'cross correlation of 85dB a_vowel in frequency domain 7') correlation_matrix_01(corr_as7_85_e, 'cross correlation of 85dB e_vowel in frequency domain 7') #correlation_matrix_tt_01(corr_as7_85_e_t, 'cross correlation of 85dB e_vowel in frequency domain 7') #correlation_matrix_rr_01(corr_as7_85_e_re, 'cross correlation of 85dB e_vowel in frequency domain 7') correlation_matrix_01(corr_as7_85_n, 'cross correlation of 85dB n_vowel in frequency domain 7') #correlation_matrix_tt_01(corr_as7_85_n_t, 'cross correlation of 85dB n_vowel in frequency domain 7') #correlation_matrix_rr_01(corr_as7_85_n_re, 'cross correlation of 85dB n_vowel in frequency domain 7') correlation_matrix_01(corr_as7_85_u, 'cross correlation of 85dB u_vowel in frequency domain 7') #correlation_matrix_tt_01(corr_as7_85_u_t, 'cross correlation of 85dB u_vowel in frequency domain 7') #correlation_matrix_rr_01(corr_as7_85_u_re, 'cross correlation of 85dB u_vowel in frequency domain 7') ''' # Correlation Matrix_both # EFR ''' correlation_matrix_comb(corr_EFR_avg_85_a, 'cross correlation of 85dB a_vowel in time domain') correlation_matrix_comb(corr_EFR_avg_85_e, 'cross correlation of 85dB e_vowel in time domain') correlation_matrix_comb(corr_EFR_avg_85_n, 'cross correlation of 85dB n_vowel in time domain') correlation_matrix_comb(corr_EFR_avg_85_u, 'cross correlation of 85dB u_vowel in time domain') ''' correlation_matrix_comb(corr_EFR_avg_85_aenu, 'cross correlation of 85dB aenu in time domain') correlation_matrix_comb(corr_EFR_avg_85_aenu_shrink_03_1, 'cross correlation of shrinked(0.3, 1) 85dB aenu in time domain') correlation_matrix_comb(corr_EFR_avg_85_aenu_shrink_05_1, 'cross correlation of shrinked(0.5, 1) 85dB aenu in time domain') # AS ''' correlation_matrix_comb(corr_as_85_a, 'cross correlation of 85dB a_vowel in frequency domain') correlation_matrix_comb(corr_as_85_e, 'cross correlation of 85dB e_vowel in frequency domain') correlation_matrix_comb(corr_as_85_n, 'cross correlation of 85dB n_vowel in frequency domain') correlation_matrix_comb(corr_as_85_u, 'cross correlation of 85dB u_vowel in frequency domain') ''' correlation_matrix_comb(corr_as_85_a_v2, 'cross correlation of 85dB a_vowel in frequency domain (improved PCC)') correlation_matrix_comb(corr_as_85_e_v2, 'cross correlation of 85dB e_vowel in frequency domain (improved PCC)') correlation_matrix_comb(corr_as_85_n_v2, 'cross correlation of 85dB n_vowel in frequency domain (improved PCC)') correlation_matrix_comb(corr_as_85_u_v2, 'cross correlation of 85dB u_vowel in frequency domain (improved PCC)') ''' correlation_matrix_comb(corr_as_win_85_a, 'cross correlation of 85dB a_vowel in frequency domain(hamming)') correlation_matrix_comb(corr_as_win_85_e, 'cross correlation of 85dB e_vowel in frequency domain(hamming)') correlation_matrix_comb(corr_as_win_85_n, 'cross correlation of 85dB n_vowel in frequency domain(hamming)') correlation_matrix_comb(corr_as_win_85_u, 'cross correlation of 85dB u_vowel in frequency domain(hamming)') ''' # no zero-padding correlation_matrix_comb(corr_as_85_no0_aenu, 'cross correlation of 85dB aenu in frequency domain(no zero padding)') # aenu -> as correlation_matrix_comb(corr_as_85_aenu, 'cross correlation of 85dB aenu in frequency domain') correlation_matrix_comb(corr_as_85_aenu_shrink_03_1, 'cross correlation of shrinked(0.3, 1) 85dB aenu in frequency domain') correlation_matrix_comb(corr_as_85_aenu_shrink_05_1, 'cross correlation of shrinked(0.5, 1) 85dB aenu in frequency domain') # zero padding -> as -> 0-1300Hz -> aenu # pcc do not remove mean correlation_matrix_comb(corr_as_85_1300_aenu, 'cross correlation of 85dB aenu in frequency domain(version2, improved PCC)') # AS7 ''' correlation_matrix_comb(corr_as7_85_a, 'cross correlation of 85dB a_vowel in frequency domain 7') correlation_matrix_comb(corr_as7_85_e, 'cross correlation of 85dB e_vowel in frequency domain 7') correlation_matrix_comb(corr_as7_85_n, 'cross correlation of 85dB n_vowel in frequency domain 7') correlation_matrix_comb(corr_as7_85_u, 'cross correlation of 85dB u_vowel in frequency domain 7') ''' correlation_matrix_comb(corr_as7_85_aenu, 'cross correlation of 85dB aenu in frequency domain 7(as7_aenu)') correlation_matrix_comb(corr_aenu_as7_85, 'cross correlation of 85dB aenu in frequency domain 7(aenu_as7)') # sum of EFR and AS # corr_EFR + corr_AS correlation_matrix_comb(corr_sum_avg_85_aenu, 'cross correlation of sum 85dB aenu in time and freq domain') correlation_matrix_comb(corr_sum_avg_85_aenu_v2, 'cross correlation of sum 85dB aenu in time and freq domain(version2)') # concat df_EFR + df_aenu_as 4096+535 correlation_matrix_comb(corr_sum_85_aenu, 'cross correlation of sum 85dB aenu in time and freq domain') # concat df_EFR + df_as_aenu 4096+5200 correlation_matrix_comb(corr_sum_85_aenu_v3, 'cross correlation of sum 85dB aenu in time and freq domain(version3)') # improved PCC correlation_matrix_comb(corr_sum_85_aenu_v4, 'cross correlation of sum 85dB aenu in time and freq domain (improved PCC)') # test corr_sum_85_aenu_v4.style.background_gradient(cmap='coolwarm')

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3579a60a21ebd74c9b69a1c76b463cbf6758ff39

224

py

Python

analyticlab/measure/__init__.py

xingrongtech/analyticlab

2827591db9b31ff38299712ed6c404ff30583f6f

[ "MIT" ]

13

2018-05-11T02:45:11.000Z

2021-07-17T22:19:04.000Z

analyticlab/measure/__init__.py

xingrongtech/analyticlab

2827591db9b31ff38299712ed6c404ff30583f6f

[ "MIT" ]

null

analyticlab/measure/__init__.py

xingrongtech/analyticlab

2827591db9b31ff38299712ed6c404ff30583f6f

[ "MIT" ]

2

2019-10-17T11:43:11.000Z

2019-11-27T10:54:28.000Z

# -*- coding: utf-8 -*- """ Created on Sun Feb 18 09:25:00 2018 @author: xingrongtech """ from . import ins, std, ACategory, BCategory from .basemeasure import BaseMeasure from .measure import Measure from .ins import Ins

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208

py

Python

topCoder/srms/200s/srm277/div2/sandwich_bar.py

ferhatelmas/algo

a7149c7a605708bc01a5cd30bf5455644cefd04d

[ "WTFPL" ]

25

2015-01-21T16:39:18.000Z

2021-05-24T07:01:24.000Z

topCoder/srms/200s/srm277/div2/sandwich_bar.py

gauravsingh58/algo

397859a53429e7a585e5f6964ad24146c6261326

[ "WTFPL" ]

2

2020-09-30T19:39:36.000Z

2020-10-01T17:15:16.000Z

topCoder/srms/200s/srm277/div2/sandwich_bar.py

ferhatelmas/algo

a7149c7a605708bc01a5cd30bf5455644cefd04d

[ "WTFPL" ]

15

2015-01-21T16:39:27.000Z

2020-10-01T17:00:22.000Z

class SandwichBar: def whichOrder(self, available, orders): for i, o in enumerate(orders): if all(map(lambda e: e in available, o.split())): return i return -1

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3580c0dd3f79543bbbc30b6fb81f79c1da8e9f30

187

py

Python

socialinsecurity.py

OmeletteDuFromageDat250/Protect-Magnus-Book

503dc78f46a33d7490383c646991edbfd690942e

[ "MIT" ]

null

socialinsecurity.py

OmeletteDuFromageDat250/Protect-Magnus-Book

503dc78f46a33d7490383c646991edbfd690942e

[ "MIT" ]

null

socialinsecurity.py

OmeletteDuFromageDat250/Protect-Magnus-Book

503dc78f46a33d7490383c646991edbfd690942e

[ "MIT" ]

1

2020-02-10T22:14:38.000Z

# configured as the entry point of the app, simply imports app to start application, just run 'flask run' to start from app import app app.config['MAX_CONTENT_LENGTH'] = 16 * 1024 * 1024

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358c6e2e2306e328879feed2952a8562bec7ccc4

25,391

py

Python

server.py

mitmedialab/MediaCloud-Discover

5c7dd2f2bfe2b9dbb79d52fc5500987cba63e038

[ "MIT" ]

null

server.py

mitmedialab/MediaCloud-Discover

5c7dd2f2bfe2b9dbb79d52fc5500987cba63e038

[ "MIT" ]

3

2018-04-20T18:19:15.000Z

2018-04-20T18:35:51.000Z

server.py

mitmedialab/MediaCloud-Discover

5c7dd2f2bfe2b9dbb79d52fc5500987cba63e038

[ "MIT" ]

null

from flask import render_template from flask import Flask from flask_cache import Cache from flask import jsonify from os import environ import os import logging.config import datetime import mediacloud import json import random # All country entity data data = {} mc_admin = None app = Flask(__name__) api_key = environ.get('MC_API_KEY') base_dir = os.path.dirname(os.path.abspath(__file__)) # setup logging with open(os.path.join(base_dir, 'server-logging.json'), 'r') as f: logging_config = json.load(f) logging_config['handlers']['file']['filename'] = os.path.join(base_dir, logging_config['handlers']['file']['filename']) logging.config.dictConfig(logging_config) logger = logging.getLogger(__name__) logger.info("---------------------------------------------------------------------------") # https://pythonhosted.org/Flask-Cache/ # Flask-Cache Filesystem Mode Parameters: # CACHE_DEFAULT_TIMEOUT # CACHE_DIR # CACHE_THRESHOLD # CACHE_ARGS # CACHE_OPTIONS cache = Cache(app, config={'CACHE_TYPE': 'filesystem', 'CACHE_DIR': './cache', 'CACHE_DEFAULT_TIMEOUT': '28800'}) # 8 hour cache COUNTRY_GEONAMES_ID_TO_APLHA3 = {3041565:"AND",290557:"ARE",1149361:"AFG",3576396:"ATG",3573511:"AIA",783754:"ALB",174982:"ARM",3351879:"AGO",6697173:"ATA",3865483:"ARG",5880801:"ASM",2782113:"AUT",2077456:"AUS",3577279:"ABW",661882:"ALA",587116:"AZE",3277605:"BIH",3374084:"BRB",1210997:"BGD",2802361:"BEL",2361809:"BFA",732800:"BGR",290291:"BHR",433561:"BDI",2395170:"BEN",3578476:"BLM",3573345:"BMU",1820814:"BRN",3923057:"BOL",7626844:"BES",3469034:"BRA",3572887:"BHS",1252634:"BTN",3371123:"BVT",933860:"BWA",630336:"BLR",3582678:"BLZ",6251999:"CAN",1547376:"CCK",203312:"COD",239880:"CAF",2260494:"COG",2658434:"CHE",2287781:"CIV",1899402:"COK",3895114:"CHL",2233387:"CMR",1814991:"CHN",3686110:"COL",3624060:"CRI",3562981:"CUB",3374766:"CPV",7626836:"CUW",2078138:"CXR",146669:"CYP",3077311:"CZE",2921044:"DEU",223816:"DJI",2623032:"DNK",3575830:"DMA",3508796:"DOM",2589581:"DZA",3658394:"ECU",453733:"EST",357994:"EGY",2461445:"ESH",338010:"ERI",2510769:"ESP",337996:"ETH",660013:"FIN",2205218:"FJI",3474414:"FLK",2081918:"FSM",2622320:"FRO",3017382:"FRA",2400553:"GAB",2635167:"GBR",3580239:"GRD",614540:"GEO",3381670:"GUF",3042362:"GGY",2300660:"GHA",2411586:"GIB",3425505:"GRL",2413451:"GMB",2420477:"GIN",3579143:"GLP",2309096:"GNQ",390903:"GRC",3474415:"SGS",3595528:"GTM",4043988:"GUM",2372248:"GNB",3378535:"GUY",1819730:"HKG",1547314:"HMD",3608932:"HND",3202326:"HRV",3723988:"HTI",719819:"HUN",1643084:"IDN",2963597:"IRL",294640:"ISR",3042225:"IMN",1269750:"IND",1282588:"IOT",99237:"IRQ",130758:"IRN",2629691:"ISL",3175395:"ITA",3042142:"JEY",3489940:"JAM",248816:"JOR",1861060:"JPN",192950:"KEN",1527747:"KGZ",1831722:"KHM",4030945:"KIR",921929:"COM",3575174:"KNA",1873107:"PRK",1835841:"KOR",831053:"XKX",285570:"KWT",3580718:"CYM",1522867:"KAZ",1655842:"LAO",272103:"LBN",3576468:"LCA",3042058:"LIE",1227603:"LKA",2275384:"LBR",932692:"LSO",597427:"LTU",2960313:"LUX",458258:"LVA",2215636:"LBY",2542007:"MAR",2993457:"MCO",617790:"MDA",3194884:"MNE",3578421:"MAF",1062947:"MDG",2080185:"MHL",718075:"MKD",2453866:"MLI",1327865:"MMR",2029969:"MNG",1821275:"MAC",4041468:"MNP",3570311:"MTQ",2378080:"MRT",3578097:"MSR",2562770:"MLT",934292:"MUS",1282028:"MDV",927384:"MWI",3996063:"MEX",1733045:"MYS",1036973:"MOZ",3355338:"NAM",2139685:"NCL",2440476:"NER",2155115:"NFK",2328926:"NGA",3617476:"NIC",2750405:"NLD",3144096:"NOR",1282988:"NPL",2110425:"NRU",4036232:"NIU",2186224:"NZL",286963:"OMN",3703430:"PAN",3932488:"PER",4030656:"PYF",2088628:"PNG",1694008:"PHL",1168579:"PAK",798544:"POL",3424932:"SPM",4030699:"PCN",4566966:"PRI",6254930:"PSE",2264397:"PRT",1559582:"PLW",3437598:"PRY",289688:"QAT",935317:"REU",798549:"ROU",6290252:"SRB",2017370:"RUS",49518:"RWA",102358:"SAU",2103350:"SLB",241170:"SYC",366755:"SDN",7909807:"SSD",2661886:"SWE",1880251:"SGP",3370751:"SHN",3190538:"SVN",607072:"SJM",3057568:"SVK",2403846:"SLE",3168068:"SMR",2245662:"SEN",51537:"SOM",3382998:"SUR",2410758:"STP",3585968:"SLV",7609695:"SXM",163843:"SYR",934841:"SWZ",3576916:"TCA",2434508:"TCD",1546748:"ATF",2363686:"TGO",1605651:"THA",1220409:"TJK",4031074:"TKL",1966436:"TLS",1218197:"TKM",2464461:"TUN",4032283:"TON",298795:"TUR",3573591:"TTO",2110297:"TUV",1668284:"TWN",149590:"TZA",690791:"UKR",226074:"UGA",5854968:"UMI",6252001:"USA",3439705:"URY",1512440:"UZB",3164670:"VAT",3577815:"VCT",3625428:"VEN",3577718:"VGB",4796775:"VIR",1562822:"VNM",2134431:"VUT",4034749:"WLF",4034894:"WSM",69543:"YEM",1024031:"MYT",953987:"ZAF",895949:"ZMB",878675:"ZWE"} COUNTRY_ALPHA_TO_LAT_LONG = {'XKX': {'lat': 42.60, 'long': 20.9 }, 'SSD': {'lat': 7.265, 'long': 30.054}, 'DZA': {'lat': 28.0, 'long': 3.0}, 'AGO': {'lat': -12.5, 'long': 18.5}, 'EGY': {'lat': 27.0, 'long': 30.0}, 'BGD': {'lat': 24.0, 'long': 90.0}, 'NER': {'lat': 16.0, 'long': 8.0}, 'LIE': {'lat': 47.1667, 'long': 9.5333}, 'NAM': {'lat': -22.0, 'long': 17.0}, 'BGR': {'lat': 43.0, 'long': 25.0}, 'BOL': {'lat': -17.0, 'long': -65.0}, 'GHA': {'lat': 8.0, 'long': -2.0}, 'CCK': {'lat': -12.5, 'long': 96.8333}, 'PAK': {'lat': 30.0, 'long': 70.0}, 'CPV': {'lat': 16.0, 'long': -24.0}, 'JOR': {'lat': 31.0, 'long': 36.0}, 'LBR': {'lat': 6.5, 'long': -9.5}, 'LBY': {'lat': 25.0, 'long': 17.0}, 'MYS': {'lat': 2.5, 'long': 112.5}, 'DOM': {'lat': 19.0, 'long': -70.6667}, 'PRI': {'lat': 18.25, 'long': -66.5}, 'MYT': {'lat': -12.8333, 'long': 45.1667}, 'PRK': {'lat': 40.0, 'long': 127.0}, 'PSE': {'lat': 32.0, 'long': 35.25}, 'TZA': {'lat': -6.0, 'long': 35.0}, 'BWA': {'lat': -22.0, 'long': 24.0}, 'KHM': {'lat': 13.0, 'long': 105.0}, 'UMI': {'lat': 19.2833, 'long': 166.6}, 'TTO': {'lat': 11.0, 'long': -61.0}, 'PRY': {'lat': -23.0, 'long': -58.0}, 'HKG': {'lat': 22.25, 'long': 114.1667}, 'SAU': {'lat': 25.0, 'long': 45.0}, 'LBN': {'lat': 33.8333, 'long': 35.8333}, 'SVN': {'lat': 46.0, 'long': 15.0}, 'BFA': {'lat': 13.0, 'long': -2.0}, 'SVK': {'lat': 48.6667, 'long': 19.5}, 'MRT': {'lat': 20.0, 'long': -12.0}, 'HRV': {'lat': 45.1667, 'long': 15.5}, 'CHL': {'lat': -30.0, 'long': -71.0}, 'CHN': {'lat': 35.0, 'long': 105.0}, 'KNA': {'lat': 17.3333, 'long': -62.75}, 'JAM': {'lat': 18.25, 'long': -77.5}, 'SMR': {'lat': 43.7667, 'long': 12.4167}, 'GIB': {'lat': 36.1833, 'long': -5.3667}, 'DJI': {'lat': 11.5, 'long': 43.0}, 'GIN': {'lat': 11.0, 'long': -10.0}, 'FIN': {'lat': 64.0, 'long': 26.0}, 'URY': {'lat': -33.0, 'long': -56.0}, 'VAT': {'lat': 41.9, 'long': 12.45}, 'STP': {'lat': 1.0, 'long': 7.0}, 'SYC': {'lat': -4.5833, 'long': 55.6667}, 'NPL': {'lat': 28.0, 'long': 84.0}, 'CXR': {'lat': -10.5, 'long': 105.6667}, 'LAO': {'lat': 18.0, 'long': 105.0}, 'YEM': {'lat': 15.0, 'long': 48.0}, 'BVT': {'lat': -54.4333, 'long': 3.4}, 'ZAF': {'lat': -29.0, 'long': 24.0}, 'KIR': {'lat': 1.4167, 'long': 173.0}, 'PHL': {'lat': 13.0, 'long': 122.0}, 'ROU': {'lat': 46.0, 'long': 25.0}, 'VIR': {'lat': 18.3333, 'long': -64.8333}, 'SYR': {'lat': 35.0, 'long': 38.0}, 'MAC': {'lat': 22.1667, 'long': 113.55}, 'NIC': {'lat': 13.0, 'long': -85.0}, 'MLT': {'lat': 35.8333, 'long': 14.5833}, 'KAZ': {'lat': 48.0, 'long': 68.0}, 'TCA': {'lat': 21.75, 'long': -71.5833}, 'PYF': {'lat': -15.0, 'long': -140.0}, 'NIU': {'lat': -19.0333, 'long': -169.8667}, 'DMA': {'lat': 15.4167, 'long': -61.3333}, 'GBR': {'lat': 54.0, 'long': -2.0}, 'BEN': {'lat': 9.5, 'long': 2.25}, 'GUF': {'lat': 4.0, 'long': -53.0}, 'BEL': {'lat': 50.8333, 'long': 4.0}, 'MSR': {'lat': 16.75, 'long': -62.2}, 'TGO': {'lat': 8.0, 'long': 1.1667}, 'DEU': {'lat': 51.0, 'long': 9.0}, 'GUM': {'lat': 13.4667, 'long': 144.7833}, 'LKA': {'lat': 7.0, 'long': 81.0}, 'FLK': {'lat': -51.75, 'long': -59.0}, 'PCN': {'lat': -24.7, 'long': -127.4}, 'GUY': {'lat': 5.0, 'long': -59.0}, 'CRI': {'lat': 10.0, 'long': -84.0}, 'COK': {'lat': -21.2333, 'long': -159.7667}, 'MAR': {'lat': 32.0, 'long': -5.0}, 'MNP': {'lat': 15.2, 'long': 145.75}, 'LSO': {'lat': -29.5, 'long': 28.5}, 'HUN': {'lat': 47.0, 'long': 20.0}, 'TKM': {'lat': 40.0, 'long': 60.0}, 'SUR': {'lat': 4.0, 'long': -56.0}, 'NLD': {'lat': 52.5, 'long': 5.75}, 'BMU': {'lat': 32.3333, 'long': -64.75}, 'HMD': {'lat': -53.1, 'long': 72.5167}, 'TCD': {'lat': 15.0, 'long': 19.0}, 'GEO': {'lat': 42.0, 'long': 43.5}, 'MNE': {'lat': 42.0, 'long': 19.0}, 'MNG': {'lat': 46.0, 'long': 105.0}, 'MHL': {'lat': 9.0, 'long': 168.0}, 'MTQ': {'lat': 14.6667, 'long': -61.0}, 'BLZ': {'lat': 17.25, 'long': -88.75}, 'NFK': {'lat': -29.0333, 'long': 167.95}, 'MMR': {'lat': 22.0, 'long': 98.0}, 'AFG': {'lat': 33.0, 'long': 65.0}, 'BDI': {'lat': -3.5, 'long': 30.0}, 'VGB': {'lat': 18.5, 'long': -64.5}, 'BLR': {'lat': 53.0, 'long': 28.0}, 'GRD': {'lat': 12.1167, 'long': -61.6667}, 'TKL': {'lat': -9.0, 'long': -172.0}, 'GRC': {'lat': 39.0, 'long': 22.0}, 'GRL': {'lat': 72.0, 'long': -40.0}, 'SHN': {'lat': -15.9333, 'long': -5.7}, 'AND': {'lat': 42.5, 'long': 1.6}, 'MOZ': {'lat': -18.25, 'long': 35.0}, 'TJK': {'lat': 39.0, 'long': 71.0}, 'THA': {'lat': 15.0, 'long': 100.0}, 'HTI': {'lat': 19.0, 'long': -72.4167}, 'MEX': {'lat': 23.0, 'long': -102.0}, 'ANT': {'lat': 12.25, 'long': -68.75}, 'ZWE': {'lat': -20.0, 'long': 30.0}, 'LCA': {'lat': 13.8833, 'long': -61.1333}, 'IND': {'lat': 20.0, 'long': 77.0}, 'LVA': {'lat': 57.0, 'long': 25.0}, 'BTN': {'lat': 27.5, 'long': 90.5}, 'VCT': {'lat': 13.25, 'long': -61.2}, 'VNM': {'lat': 16.0, 'long': 106.0}, 'NOR': {'lat': 62.0, 'long': 10.0}, 'CZE': {'lat': 49.75, 'long': 15.5}, 'ATF': {'lat': -43.0, 'long': 67.0}, 'ATG': {'lat': 17.05, 'long': -61.8}, 'FJI': {'lat': -18.0, 'long': 175.0}, 'IOT': {'lat': -6.0, 'long': 71.5}, 'HND': {'lat': 15.0, 'long': -86.5}, 'MUS': {'lat': -20.2833, 'long': 57.55}, 'ATA': {'lat': -90.0, 'long': 0.0}, 'LUX': {'lat': 49.75, 'long': 6.1667}, 'ISR': {'lat': 31.5, 'long': 34.75}, 'FSM': {'lat': 6.9167, 'long': 158.25}, 'PER': {'lat': -10.0, 'long': -76.0}, 'REU': {'lat': -21.1, 'long': 55.6}, 'IDN': {'lat': -5.0, 'long': 120.0}, 'VUT': {'lat': -16.0, 'long': 167.0}, 'MKD': {'lat': 41.8333, 'long': 22.0}, 'COD': {'lat': 0.0, 'long': 25.0}, 'COG': {'lat': -1.0, 'long': 15.0}, 'ISL': {'lat': 65.0, 'long': -18.0}, 'GLP': {'lat': 16.25, 'long': -61.5833}, 'ETH': {'lat': 8.0, 'long': 38.0}, 'COM': {'lat': -12.1667, 'long': 44.25}, 'COL': {'lat': 4.0, 'long': -72.0}, 'NGA': {'lat': 10.0, 'long': 8.0}, 'TWN': {'lat': 23.5, 'long': 121.0}, 'PRT': {'lat': 39.5, 'long': -8.0}, 'MDA': {'lat': 47.0, 'long': 29.0}, 'GGY': {'lat': 49.5, 'long': -2.56}, 'MDG': {'lat': -20.0, 'long': 47.0}, 'ECU': {'lat': -2.0, 'long': -77.5}, 'SEN': {'lat': 14.0, 'long': -14.0}, 'ESH': {'lat': 24.5, 'long': -13.0}, 'MDV': {'lat': 3.25, 'long': 73.0}, 'ASM': {'lat': -14.3333, 'long': -170.0}, 'SPM': {'lat': 46.8333, 'long': -56.3333}, 'SRB': {'lat': 44.0, 'long': 21.0}, 'FRA': {'lat': 46.0, 'long': 2.0}, 'LTU': {'lat': 56.0, 'long': 24.0}, 'RWA': {'lat': -2.0, 'long': 30.0}, 'ZMB': {'lat': -15.0, 'long': 30.0}, 'GMB': {'lat': 13.4667, 'long': -16.5667}, 'WLF': {'lat': -13.3, 'long': -176.2}, 'JEY': {'lat': 49.21, 'long': -2.13}, 'FRO': {'lat': 62.0, 'long': -7.0}, 'GTM': {'lat': 15.5, 'long': -90.25}, 'DNK': {'lat': 56.0, 'long': 10.0}, 'IMN': {'lat': 54.23, 'long': -4.55}, 'AUS': {'lat': -27.0, 'long': 133.0}, 'AUT': {'lat': 47.3333, 'long': 13.3333}, 'SJM': {'lat': 78.0, 'long': 20.0}, 'VEN': {'lat': 8.0, 'long': -66.0}, 'PLW': {'lat': 7.5, 'long': 134.5}, 'KEN': {'lat': 1.0, 'long': 38.0}, 'TUR': {'lat': 39.0, 'long': 35.0}, 'ALB': {'lat': 41.0, 'long': 20.0}, 'OMN': {'lat': 21.0, 'long': 57.0}, 'TUV': {'lat': -8.0, 'long': 178.0}, 'ITA': {'lat': 42.8333, 'long': 12.8333}, 'BRN': {'lat': 4.5, 'long': 114.6667}, 'TUN': {'lat': 34.0, 'long': 9.0}, 'RUS': {'lat': 60.0, 'long': 100.0}, 'BRB': {'lat': 13.1667, 'long': -59.5333}, 'BRA': {'lat': -10.0, 'long': -55.0}, 'CIV': {'lat': 8.0, 'long': -5.0}, 'TLS': {'lat': -8.55, 'long': 125.5167}, 'GNQ': {'lat': 2.0, 'long': 10.0}, 'USA': {'lat': 38.0, 'long': -97.0}, 'QAT': {'lat': 25.5, 'long': 51.25}, 'WSM': {'lat': -13.5833, 'long': -172.3333}, 'AZE': {'lat': 40.5, 'long': 47.5}, 'GNB': {'lat': 12.0, 'long': -15.0}, 'SWZ': {'lat': -26.5, 'long': 31.5}, 'TON': {'lat': -20.0, 'long': -175.0}, 'CAN': {'lat': 60.0, 'long': -95.0}, 'UKR': {'lat': 49.0, 'long': 32.0}, 'KOR': {'lat': 37.0, 'long': 127.5}, 'AIA': {'lat': 18.25, 'long': -63.1667}, 'CAF': {'lat': 7.0, 'long': 21.0}, 'CHE': {'lat': 47.0, 'long': 8.0}, 'CYP': {'lat': 35.0, 'long': 33.0}, 'BIH': {'lat': 44.0, 'long': 18.0}, 'SGP': {'lat': 1.3667, 'long': 103.8}, 'SGS': {'lat': -54.5, 'long': -37.0}, 'SOM': {'lat': 10.0, 'long': 49.0}, 'UZB': {'lat': 41.0, 'long': 64.0}, 'CMR': {'lat': 6.0, 'long': 12.0}, 'POL': {'lat': 52.0, 'long': 20.0}, 'KWT': {'lat': 29.3375, 'long': 47.6581}, 'ERI': {'lat': 15.0, 'long': 39.0}, 'GAB': {'lat': -1.0, 'long': 11.75}, 'CYM': {'lat': 19.5, 'long': -80.5}, 'ARE': {'lat': 24.0, 'long': 54.0}, 'EST': {'lat': 59.0, 'long': 26.0}, 'MWI': {'lat': -13.5, 'long': 34.0}, 'ESP': {'lat': 40.0, 'long': -4.0}, 'IRQ': {'lat': 33.0, 'long': 44.0}, 'SLV': {'lat': 13.8333, 'long': -88.9167}, 'MLI': {'lat': 17.0, 'long': -4.0}, 'IRL': {'lat': 53.0, 'long': -8.0}, 'IRN': {'lat': 32.0, 'long': 53.0}, 'ABW': {'lat': 12.5, 'long': -69.9667}, 'SLE': {'lat': 8.5, 'long': -11.5}, 'PAN': {'lat': 9.0, 'long': -80.0}, 'SDN': {'lat': 15.0, 'long': 30.0}, 'SLB': {'lat': -8.0, 'long': 159.0}, 'NZL': {'lat': -41.0, 'long': 174.0}, 'MCO': {'lat': 43.7333, 'long': 7.4}, 'JPN': {'lat': 36.0, 'long': 138.0}, 'KGZ': {'lat': 41.0, 'long': 75.0}, 'UGA': {'lat': 1.0, 'long': 32.0}, 'NCL': {'lat': -21.5, 'long': 165.5}, 'PNG': {'lat': -6.0, 'long': 147.0}, 'ARG': {'lat': -34.0, 'long': -64.0}, 'SWE': {'lat': 62.0, 'long': 15.0}, 'BHS': {'lat': 24.25, 'long': -76.0}, 'BHR': {'lat': 26.0, 'long': 50.55}, 'ARM': {'lat': 40.0, 'long': 45.0}, 'NRU': {'lat': -0.5333, 'long': 166.9167}, 'CUB': {'lat': 21.5, 'long': -80.0}} # ///////////////////////////////////////////////////////////////////////// def init(): global mc_admin global data mc_admin = mediacloud.api.AdminMediaCloud(api_key) logger.info('Media Cloud Interface created') logger.debug(api_key) logger.info('Loading entity cache...') load_country_cache() logger.info('Loading complete.') # Get list of cached countries, load them into country_cache # ///////////////////////////////////////////////////////////////////////// def load_country_cache(): with open(os.path.join(base_dir, 'whitelist.json')) as f: whitelist = json.load(f) for item in whitelist: filename = 'cache/{0}.json'.format( item['country_name'] ) try: with open(os.path.join(base_dir, filename), 'r') as country_json: logger.debug('Loading cache from {0}...'.format(filename)) country_data = json.load(country_json) data[country_data['id']] = country_data logger.debug('Cache file {0} loaded.'.format(filename)) # If JSON cannot be read, skip country except (ValueError, KeyError, IOError) as e: logger.error('Cannot read cache file {0}'.format(filename)) logger.error(e) pass # ///////////////////////////////////////////////////////////////////////// # // Application Root # // Default to United States context # ///////////////////////////////////////////////////////////////////////// @app.route('/') def root(): return render_template('index.html', data='9139487') # ///////////////////////////////////////////////////////////////////////// @app.route('/<int:country_id>/<string:entity_type>/<entity_id>') def entity_select(country_id, entity_type, entity_id): return render_template('index.html', data={'country_id': country_id, 'entity_id': entity_id, 'entity_type': entity_type}) # ///////////////////////////////////////////////////////////////////////// @app.route('/word_over_time/<int:collection_id>/<string:type>/<entity>') def words_over_time(collection_id, type, entity): ''' Helper to fetch sentences counts over the last year for an arbitrary query ''' last_n_days = 30 start_date = datetime.date.today()-datetime.timedelta(last_n_days) end_date = datetime.date.today()-datetime.timedelta(1) # yesterday fq = mc_admin.publish_date_query(start_date, end_date) start_datetime = datetime.datetime.strftime(start_date, '%Y-%m-%d') end_datetime = datetime.datetime.strftime(end_date, '%Y-%m-%d') if entity.isdigit(): if type == 'media': # Media Type sentences_over_time = mc_admin.sentenceCount('*', [ 'tags_id_media:{0}'.format(str(collection_id)), 'media_id:{0}'.format(entity), fq ], split=True, split_start_date=start_datetime, split_end_date=end_datetime)['split'] else: # Entity Type sentences_over_time = mc_admin.sentenceCount('*', [ 'tags_id_media:{0}'.format(str(collection_id)), 'tags_id_stories:{0}'.format(entity), fq ], split=True, split_start_date=start_datetime, split_end_date=end_datetime)['split'] else: # Word Type sentences_over_time = mc_admin.sentenceCount(entity, [ 'tags_id_media:({0})'.format(str(collection_id)), fq ], split=True, split_start_date=start_datetime, split_end_date=end_datetime)['split'] return jsonify(sentences_over_time) # ///////////////////////////////////////////////////////////////////////// def add_type(entity, type): entity['type'] = type return entity # ///////////////////////////////////////////////////////////////////////// def build_json_response(json_data): response = app.response_class( response=json.dumps(json_data), status=200, mimetype='application/json') return response # ///////////////////////////////////////////////////////////////////////// @app.route('/cache_data') def cache_data(): # Tag sets that hold tags on stories... NYT_LABELS_TAG_SET = 1963 # one tag per theme in a story (Jasmin's transfer-learning model) GEO_TAG_SET = 1011 # one tag per country/state stories are about (disambiguated) CLIFF_ORGS_TAG_SET = 2388 # one tag for each org mentioned in stories CLIFF_PEOPLE_TAG_SET = 2389 # one tag for each perosn mentioned in stories countries = { '34412193': 'China' } for country_id, country_name in countries.items(): data[country_id] = { 'name': country_name } logger.info('Getting Media for {0}...'.format(country_name)) data[country_id]['media'] = getBiggestMedia(country_id) logger.info('Getting Words for {0}...'.format(country_name)) data[country_id]['words'] = getTopWords(country_id) logger.info('Getting NYT Labels for {0}...'.format(country_name)) data[country_id]['labels'] = getEntities(country_id, NYT_LABELS_TAG_SET) logger.info('Getting Places for {0}...'.format(country_name)) data[country_id]['places'] = getEntities(country_id, GEO_TAG_SET) logger.info('Getting Organizations for {0}...'.format(country_name)) data[country_id]['orgs'] = getEntities(country_id, CLIFF_ORGS_TAG_SET) logger.info('Getting People for {0}...'.format(country_name)) data[country_id]['people'] = getEntities(country_id, CLIFF_PEOPLE_TAG_SET) response = build_json_response(data) clear_cache() cache_data() return response # ///////////////////////////////////////////////////////////////////////// @cache.cached(timeout=28800, key_prefix='cache_data') def cache_data(): return data # ///////////////////////////////////////////////////////////////////////// @app.route('/country_entities/<country_id>') def country_entities(country_id): FROM_EACH_TYPE = 8 # Pick random entities random.shuffle(data[country_id]['people']) random_people = data[country_id]['people'][: FROM_EACH_TYPE] random_people = [add_type(entity, 'person') for entity in random_people] random.shuffle(data[country_id]['labels']) random_labels = data[country_id]['labels'][: FROM_EACH_TYPE] random_labels = [add_type(entity, 'label') for entity in random_labels] random.shuffle(data[country_id]['orgs']) random_orgs = data[country_id]['orgs'][: FROM_EACH_TYPE] random_orgs = [add_type(entity, 'organization') for entity in random_orgs] random.shuffle(data[country_id]['places']) random_places = data[country_id]['places'][: FROM_EACH_TYPE] random_places = [add_type(entity, 'location') for entity in random_places] random.shuffle(data[country_id]['media']) random_media = data[country_id]['media'][: FROM_EACH_TYPE] random_media = [add_type(entity, 'media') for entity in random_media] random.shuffle(data[country_id]['words']) random_words = data[country_id]['words'][: FROM_EACH_TYPE] random_words = [add_type(entity, 'word') for entity in random_words] all_entities = random_labels + random_places + random_orgs + random_people + random_media + random_words response = build_json_response(all_entities) return response # ///////////////////////////////////////////////////////////////////////// def getEntities(collection_id, tag_set): entities = mc_admin.sentenceFieldCount('*',[ 'tags_id_media:{}'.format(collection_id), 'publish_date:NOW to NOW-3MONTH' ], tag_sets_id=tag_set, sample_size=5000) return entities # ///////////////////////////////////////////////////////////////////////// @app.route('/entity/<int:entity_id>') def entity(entity_id): entity = mc_admin.tag(entity_id) return jsonify(entity) # ///////////////////////////////////////////////////////////////////////// @app.route('/getTopWords/<int:collection_id>') def getTopWords(collection_id): word = mc_admin.wordCount('*', [ 'tags_id_media:{0}'.format(collection_id), 'publish_date:NOW to NOW-3MONTH' ], num_words=100, sample_size=5000) return word # ///////////////////////////////////////////////////////////////////////// @app.route('/media/<int:media_id>') def media(media_id): data = mc_admin.media( media_id ) return jsonify(data) # ///////////////////////////////////////////////////////////////////////// @app.route('/getBiggestMedia/<int:collection_id>') def getBiggestMedia(collection_id): media = mc_admin.mediaList(rows=10, tags_id=collection_id, sort='num_stories') return media # ///////////////////////////////////////////////////////////////////////// @app.route('/getGlobeData/<int:collection_id>') def getGlobeData(collection_id): lat_long_mag = [] geo_tags = mc_admin.sentenceFieldCount('tags_id_media:{0}'.format(collection_id), tag_sets_id=1011) country_tags = [t for t in geo_tags if int(t['tag'].split('_')[1]) in COUNTRY_GEONAMES_ID_TO_APLHA3.keys()] for t in country_tags: try: alpha3 = COUNTRY_GEONAMES_ID_TO_APLHA3[int(t['tag'].split('_')[1])] latlong = COUNTRY_ALPHA_TO_LAT_LONG[alpha3] lat_long_mag.append(latlong['lat']) lat_long_mag.append(latlong['long']) lat_long_mag.append(t['count']) logger.info(t) except Exception, e: logger.error('Failed on country lookup for {0}'.format(t)) country_tags.remove(t) data = [['seriesA', lat_long_mag]] return jsonify(data) # ///////////////////////////////////////////////////////////////////////// @app.route('/html/<path:name>') def projects(name): return render_template('/{0}'.format(name)) # ///////////////////////////////////////////////////////////////////////// @app.route('/sentences/<int:collection_id>/<string:type>/<entity>') def sentences(collection_id, type, entity): sample_size = 2000 if(entity.isdigit()): # Media Type if(type == 'media'): sentenceList = mc_admin.sentenceList('*', [ 'tags_id_media:{0}'.format(str(collection_id)), 'media_id:{0}'.format(entity), 'publish_date:NOW to NOW-3MONTH'], rows=sample_size, sort=mc_admin.SORT_RANDOM) # Entity Type else: sentenceList = mc_admin.sentenceList('*', [ 'tags_id_media:{0}'.format(str(collection_id)), 'tags_id_stories:{0}'.format(entity), 'publish_date:NOW to NOW-3MONTH'], rows=sample_size, sort=mc_admin.SORT_RANDOM) else: # Word Type sentenceList = mc_admin.sentenceList(entity, [ 'tags_id_media:{0}'.format(str(collection_id)), 'publish_date:NOW to NOW-3MONTH'], rows=sample_size, sort=mc_admin.SORT_RANDOM) return jsonify(sentenceList) # ///////////////////////////////////////////////////////////////////////// init() if __name__ == '__main__': app.run(debug=True, port=5000)

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1

0

1

0

null

0

1

0

3

359676a7bed26deb7a43c184082ef5a5ec9bc8ac

783

py

Python

ingestion/src/metadata/generated/schema/type/collectionDescriptor.py

juliarvalenti/OpenMetadata

ed4508ab2cbc53e16127b5d091bdef2156d3c412

[ "Apache-2.0" ]

null

ingestion/src/metadata/generated/schema/type/collectionDescriptor.py

juliarvalenti/OpenMetadata

ed4508ab2cbc53e16127b5d091bdef2156d3c412

[ "Apache-2.0" ]

null

ingestion/src/metadata/generated/schema/type/collectionDescriptor.py

juliarvalenti/OpenMetadata

ed4508ab2cbc53e16127b5d091bdef2156d3c412

[ "Apache-2.0" ]

null

# generated by datamodel-codegen: # filename: schema/type/collectionDescriptor.json # timestamp: 2021-09-27T15:46:37+00:00 from __future__ import annotations from typing import Optional from pydantic import AnyUrl, BaseModel, Field from . import profile class CollectionInfo(BaseModel): name: Optional[str] = Field( None, description='Unique name that identifies a collection.' ) documentation: Optional[str] = Field(None, description='Description of collection.') href: Optional[AnyUrl] = Field( None, description='URL of the API endpoint where given collections are available.', ) images: Optional[profile.ImageList] = None class SchemaForCollectionDescriptor(BaseModel): collection: Optional[CollectionInfo] = None

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null

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0

3

35acd8f295f6dfd95a79f459c59be91b8a56f970

299

py

Python

multi_tenant/managers.py

arineto/django-multi-tenant

713d555831b35a487f5a91494a20ee3d955a4b63

[ "MIT" ]

7

2016-07-13T12:49:26.000Z

2018-03-22T14:29:07.000Z

multi_tenant/managers.py

arineto/django-multi-tenant

713d555831b35a487f5a91494a20ee3d955a4b63

[ "MIT" ]

null

multi_tenant/managers.py

arineto/django-multi-tenant

713d555831b35a487f5a91494a20ee3d955a4b63

[ "MIT" ]

5

2016-07-20T13:09:41.000Z

2018-05-02T02:54:13.000Z

from django.db import models class TenantModelManager(models.Manager): """ This manager makes it easy to filter by tenant """ def by_tenant(self, tenant): return self.filter(tenant=tenant) def by_tenants(self, tenants): return self.filter(tenant__in=tenants)

21.357143

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false

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0

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null

0

1

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0

3

35e6f69b05fbe5ddf7f6a35922f57fd94ac2602e

232

py

Python

backend/web_app/tests/snapshots/snap_test_tk_graphql_requests.py

jsc-masshtab/vdi-server

3de49dec986ab26ffc6c073873fb9de5943809f9

[ "MIT" ]

2

2021-12-03T10:04:25.000Z

2022-01-12T06:26:39.000Z

backend/web_app/tests/snapshots/snap_test_tk_graphql_requests.py

jsc-masshtab/vdi-server

3de49dec986ab26ffc6c073873fb9de5943809f9

[ "MIT" ]

null

backend/web_app/tests/snapshots/snap_test_tk_graphql_requests.py

jsc-masshtab/vdi-server

3de49dec986ab26ffc6c073873fb9de5943809f9

[ "MIT" ]

null

# -*- coding: utf-8 -*- # snapshottest: v1 - https://goo.gl/zC4yUc from __future__ import unicode_literals from snapshottest import Snapshot snapshots = Snapshot() snapshots["test_request_thin_clients 1"] = {"thin_clients": []}

21.090909

63

0.732759

28

232

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0.21118

0

0.019802

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232

10

64

23.2

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0.267241

0

0.233533

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0

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false

0

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0

1

0

null

1

0

null

0

1

0

3

ea1432b0f85cbc82edf8fd9f37ad2dab2828e324

134

py

Python

hcec/edwards/testdata/encodepoint.py

duwu/hcd

590966016bc42f9d043c16ad8438148ca40eff89

[ "ISC" ]

131

2018-07-19T13:01:41.000Z

2021-12-26T12:27:33.000Z

hcec/edwards/testdata/encodepoint.py

duwu/hcd

590966016bc42f9d043c16ad8438148ca40eff89

[ "ISC" ]

32

2018-07-28T17:53:34.000Z

2022-01-06T05:32:46.000Z

hcec/edwards/testdata/encodepoint.py

duwu/hcd

590966016bc42f9d043c16ad8438148ca40eff89

[ "ISC" ]

101

2018-08-22T03:31:11.000Z

2022-03-17T09:01:24.000Z

import sys from ed25519 import * P = [] x = int(sys.argv[1]) P.append(x) y = int(sys.argv[2]) P.append(y) encodepointhex(P)

13.4

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134

3.416667

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1

0

null

0

1

0

1

0

null

0

3

ea3af54632833f0e21da2c2709dfe27358eb4392

156

py

Python

template.py

suddi/coding-challenges

f31b53790084dce1ad0be65ec1d61bf177cddb39

[ "MIT" ]

null

template.py

suddi/coding-challenges

f31b53790084dce1ad0be65ec1d61bf177cddb39

[ "MIT" ]

11

2020-01-09T06:53:45.000Z

2022-02-11T01:34:44.000Z

template.py

suddi/coding-challenges

f31b53790084dce1ad0be65ec1d61bf177cddb39

[ "MIT" ]

1

2017-03-18T17:19:43.000Z

# pylint: disable-msg=empty-docstring,unused-argument def solution(a): """ """ if __name__ == '__main__': import doctest doctest.testmod()

17.333333

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0.647436

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156

5.470588

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8

54

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0

1

0

null

0

1

0

3

ea4570a30f4803cd7a4cff196fba4f9b3401b86f

1,815

py

Python

discord_utils.py

CatTanker/cncnet-discord-bot

5c0bcbb6cda37052dfb29b6daa52770e4405f2d9

[ "MIT" ]

2

2022-01-12T20:40:37.000Z

2022-01-25T12:05:02.000Z

discord_utils.py

CatTanker/cncnet-discord-bot

5c0bcbb6cda37052dfb29b6daa52770e4405f2d9

[ "MIT" ]

11

2020-09-22T19:15:39.000Z

2020-10-12T09:16:03.000Z

discord_utils.py

CatTanker/cncnet-discord-bot

5c0bcbb6cda37052dfb29b6daa52770e4405f2d9

[ "MIT" ]

2

2021-04-12T17:15:01.000Z

2022-02-14T18:44:49.000Z

class DiscordParseException(Exception): """An exception that is thrown when parsing Discord's representation of a channel / role / user mention fails.""" def parse_discord_str(content_str: str, type_chars: str) -> int: """Parses Discord's representation of a channel / role / user mention into an ID.""" if content_str.startswith('<') and content_str.endswith('>') and content_str[1:-1].startswith(type_chars): return int(content_str[(1 + len(type_chars)):-1]) raise DiscordParseException(f"{content_str} is not a valid Discord-formatted ID representation for '{type_chars}'") def format_discord_str(discord_id: int, type_chars: str) -> str: """Formats an ID into a Discord's representation of a channel / role / user mention.""" return f"<{type_chars}{discord_id}>" # Shortcut functions def parse_channel(content_str: str) -> int: """Parses Discord's representation of a channel mention into an ID.""" return parse_discord_str(content_str, '#') def parse_role(content_str: str) -> int: """Parses Discord's representation of a role mention into an ID.""" return parse_discord_str(content_str, '@&') def parse_user(content_str: str) -> int: """Parses Discord's representation of a user mention into an ID.""" return parse_discord_str(content_str, '@!') def format_channel(discord_id: int) -> str: """Formats an ID into a Discord's representation of a channel mention.""" return format_discord_str(discord_id, '#') def format_role(discord_id: int) -> str: """Formats an ID into a Discord's representation of a role mention.""" return format_discord_str(discord_id, '@&') def format_user(discord_id: int) -> str: """Formats an ID into a Discord's representation of a user mention.""" return format_discord_str(discord_id, '@!')

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1,815

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1

0.421053

false

0

0.894737

0

null

0

1

0

null

0

1

0

1

0

3

ea564dc09567e06ddb042de2088bfdc60b3e4272

68

py

Python

Codewars/8kyu/removing-elements/Python/solution1.py

RevansChen/online-judge

ad1b07fee7bd3c49418becccda904e17505f3018

[ "MIT" ]

7

2017-09-20T16:40:39.000Z

2021-08-31T18:15:08.000Z

Codewars/8kyu/removing-elements/Python/solution1.py

RevansChen/online-judge

ad1b07fee7bd3c49418becccda904e17505f3018

[ "MIT" ]

null

Codewars/8kyu/removing-elements/Python/solution1.py

RevansChen/online-judge

ad1b07fee7bd3c49418becccda904e17505f3018

[ "MIT" ]

null

# Python - 3.6.0 remove_every_other = lambda my_list: my_list[::2]

17

49

0.705882

13

68

3.384615

0.846154

0.272727

0

0.068966

0.147059

68

3

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22.666667

0.689655

0.205882

0

1

0

false

0

1

0

null

1

0

1

0

null

0

3

ea6959f73343b9206b9c4c041bb1561a393d43b9

217

py

Python

jacquard/constants.py

peteowlett/jacquard

772fd633e521501688e0933482cba45f48c23ef9

[ "MIT" ]

null

jacquard/constants.py

peteowlett/jacquard

772fd633e521501688e0933482cba45f48c23ef9

[ "MIT" ]

null

jacquard/constants.py

peteowlett/jacquard

772fd633e521501688e0933482cba45f48c23ef9

[ "MIT" ]

null

"""Some general, project-level constants of little use outside Jacquard.""" import os import pathlib DEFAULT_CONFIG_FILE_PATH = pathlib.Path(os.environ.get( 'JACQUARD_CONFIG', '/etc/jacquard/config.cfg', ))

21.7

75

0.741935

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217

5.413793

0.724138

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0

0.133641

217

9

76

24.111111

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0.274648

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0

1

0

false

0

0.333333

0

0.333333

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1

0

null

0

1

0

null

0

1

0

3

ea6f5aa5bfecfbdff710ccbf3153d1cf7bb2a5d7

196

py

Python

app_portfolio_skills/urls.py

MichaelDoctor/Portfolio

41d9104ef6d34f8eb146230b19038b445351c713

[ "MIT" ]

null

app_portfolio_skills/urls.py

MichaelDoctor/Portfolio

41d9104ef6d34f8eb146230b19038b445351c713

[ "MIT" ]

4

2021-06-09T18:02:18.000Z

2022-01-13T03:06:24.000Z

app_portfolio_skills/urls.py

MichaelDoctor/Portfolio

41d9104ef6d34f8eb146230b19038b445351c713

[ "MIT" ]

null

from django.urls import path from .views import LanguagesView, FrameworksView urlpatterns = [ path('languages/', LanguagesView.as_view()), path('frameworks/', FrameworksView.as_view()) ]

24.5

49

0.739796

21

196

6.809524

0.619048

0.083916

0

0.132653

196

7

50

28

0.841176

0

0.107143

0

1

0

false

0

0.333333

0

0.333333

0

1

0

null

0

1

0

null

0

1

0

3

ea90870689cd4d924ec6998b6eb531534c4268e1

145

py

Python

fileconversions/conversions/png_to_pdf_conversion.py

wilbertom/fileconversions

c48fda9b2804524fc57d1f6963d09645825b0da6

[ "MIT" ]

null

fileconversions/conversions/png_to_pdf_conversion.py

wilbertom/fileconversions

c48fda9b2804524fc57d1f6963d09645825b0da6

[ "MIT" ]

null

fileconversions/conversions/png_to_pdf_conversion.py

wilbertom/fileconversions

c48fda9b2804524fc57d1f6963d09645825b0da6

[ "MIT" ]

null

from .command_conversion import CommandConversion class PngToPdf(CommandConversion): command_name = 'convert' output_extension = 'pdf'

20.714286

49

0.77931

14

145

7.857143

0.857143

0

0.151724

145

6

50

24.166667

0.894309

0

0.068966

0

1

0

false

0

0.25

0

1

0

1

0

null

0

1

0

null

0

1

0

3

ea92de22f54a91a1ac1404ae55750d761929c6ed

1,372

py

Python

app/http/controllers/TestController.py

Abeautifulsnow/masonite

f0ebb5ca05f5d88f21264e1cd0934435bd0a8791

[ "MIT" ]

null

app/http/controllers/TestController.py

Abeautifulsnow/masonite

f0ebb5ca05f5d88f21264e1cd0934435bd0a8791

[ "MIT" ]

1

2020-10-26T12:33:05.000Z

app/http/controllers/TestController.py

Abeautifulsnow/masonite

f0ebb5ca05f5d88f21264e1cd0934435bd0a8791

[ "MIT" ]

null

from app.jobs.TestJob import TestJob from src.masonite import Queue, Mail from src.masonite.request import Request from src.masonite.view import View class TestController: def __init__(self): self.test = True def show(self): return 'show' def v(self, view: View): return view.render('test') def change_header(self, request: Request): request.header('Content-Type', 'application/xml') return 'test' def change_status(self, request: Request): request.status(203) return 'test' def change_404(self, request: Request): request.status(404) return 'test' def testing(self): return 'test' def json_response(self): return {'id': 2} def post_test(self): return 'post_test' def json(self): return 'success' def bad(self): return 5 / 0 def keyerror(self): x = {'hello': 'world'} return x['test'] def session(self, request: Request): request.session.set('test', 'value') return 'session set' def queue(self, queue: Queue): # queue.driver('amqp').push(self.bad) queue.driver('amqp').push(TestJob, channel='default') return 'queued' def mail(self, mail: Mail): return mail.to('idmann509@gmail.com').template('test', {'test': 'mail'})

23.254237

80

0.603499

169

1,372

4.840237

0.349112

0.136919

0.08802

0.122249

0.075795

0

0.014911

0.266764

1,372

58

81

23.655172

0.798211

0.02551

0

0.097561

0

0.113109

0

1

0.365854

false

0

0.097561

0.195122

0.829268

0

null

0

null

0

1

0

1

0

3

575d7a08079287307546190b7fcbc56b378c6df1

7,462

py

Python

tests/test_grafana.py

bushelpowered/grafana-ldap-sync-script

1de90583ca4ab8cc828332b72f1f95756d099c1f

[ "Apache-2.0" ]

9

2020-07-17T06:36:23.000Z

2022-03-27T19:35:50.000Z

tests/test_grafana.py

bushelpowered/grafana-ldap-sync-script

1de90583ca4ab8cc828332b72f1f95756d099c1f

[ "Apache-2.0" ]

2

2021-08-19T13:25:32.000Z

2022-02-03T16:06:55.000Z

tests/test_grafana.py

bushelpowered/grafana-ldap-sync-script

1de90583ca4ab8cc828332b72f1f95756d099c1f

[ "Apache-2.0" ]

6

2021-01-05T18:46:36.000Z

2022-03-28T11:35:46.000Z

from unittest import TestCase from unittest.mock import patch, Mock from grafana_api.grafana_api import GrafanaClientError from script import grafana class delete_team_by_name(TestCase): @patch("script.grafana.grafana_api") @patch("script.grafana.configuration") def test_deletes_team(self, mock_config, mock_grafana_api): mock_config.DRY_RUN = False mock_grafana_api.teams = Mock() mock_grafana_api.teams.get_team_by_name.return_value = [{"id": "my_team_id"}] output = grafana.delete_team_by_name("my_team") self.assertEqual(output, True) self.assertEqual(mock_grafana_api.teams.delete_team.call_count, 1) mock_grafana_api.teams.delete_team.assert_called_with("my_team_id") @patch("script.grafana.grafana_api") def test_no_team_to_delete(self, mock_grafana_api): mock_grafana_api.teams = Mock() mock_grafana_api.teams.get_team_by_name.return_value = [] output = grafana.delete_team_by_name("my_team") self.assertEqual(output, False) self.assertFalse(mock_grafana_api.teams.delete_team.called) class delete_user_by_login(TestCase): @patch("script.grafana.grafana_api") @patch("script.grafana.configuration") def test_does_not_delete_admin(self, mock_config, mock_grafana_api): mock_config.DRY_RUN = False mock_grafana_api.admin = Mock() mock_grafana_api.admin.delete_user.return_value = True output = grafana.delete_user_by_login("admin") self.assertFalse(output) @patch("script.grafana.grafana_api") @patch("script.grafana.configuration") def test_deletes_user(self, mock_config, mock_grafana_api): mock_config.DRY_RUN = False mock_grafana_api.admin = Mock() mock_grafana_api.admin.delete_user.return_value = True mock_grafana_api.users.find_user = Mock() mock_grafana_api.users.find_user.return_value = {"id": "id_delete_me"} output = grafana.delete_user_by_login("delete_me") self.assertTrue(output) self.assertEqual(mock_grafana_api.admin.delete_user.call_count, 1) mock_grafana_api.admin.delete_user.called_with("id_delete_me") self.assertEquals(mock_grafana_api.users.find_user.call_count, 1) mock_grafana_api.users.find_user.called_with("delete_me") class create_folder(TestCase): @patch("script.grafana.grafana_api") @patch("script.grafana.configuration") def test_creates_folder(self, mock_config, mock_grafana_api): mock_config.DRY_RUN = False mock_grafana_api.folder.create_folder = Mock() mock_grafana_api.folder.create_folder.return_value = True output = grafana.create_folder("foo", "bar") self.assertTrue(output) self.assertEqual(mock_grafana_api.folder.create_folder.call_count, 1) mock_grafana_api.folder.create_folder.assert_called_with("foo", "bar") @patch("script.grafana.grafana_api") @patch("script.grafana.configuration") def test_catches_exception(self, mock_config, mock_grafana_api): mock_config.DRY_RUN = False mock_grafana_api.folder.create_folder = Mock() mock_grafana_api.folder.create_folder.side_effect = GrafanaClientError("something", "went", "wrong") output = grafana.create_folder("foo", "bar") self.assertFalse(output) self.assertEqual(mock_grafana_api.folder.create_folder.call_count, 1) mock_grafana_api.folder.create_folder.assert_called_with("foo", "bar") class get_members_of_team(TestCase): @patch("script.grafana.grafana_api") def test_returns_members_correctly(self, mock_grafana_api): mock_grafana_api.teams.get_team_members = Mock() mock_grafana_api.teams.get_team_members.return_value = [{"login": "user_login", "name": "name", "email": "mail"} ] output = grafana.get_members_of_team("my_team") self.assertEqual(output, [{"login": "user_login", "name": "name", "email": "mail"}]) class login_taken(TestCase): @patch("script.grafana.grafana_api") def test_login_is_taken(self, mock_grafana_api): mock_grafana_api.users.find_user = Mock() mock_grafana_api.users.find_user.return_value = "" output = grafana.login_taken("foo") self.assertTrue(output) @patch("script.grafana.grafana_api") def test_login_is_not_taken(self, mock_grafana_api): mock_grafana_api.users.find_user = Mock() mock_grafana_api.users.find_user.side_effect = GrafanaClientError("user", "not", "found") output = grafana.login_taken("foo") self.assertFalse(output) class exists_folder(TestCase): @patch("script.grafana.grafana_api") def test_login_is_taken(self, mock_grafana_api): mock_grafana_api.folder.get_folder = Mock() mock_grafana_api.folder.get_folder.return_value = "" output = grafana.exists_folder("foo") self.assertTrue(output) @patch("script.grafana.grafana_api") def test_login_is_not_taken(self, mock_grafana_api): mock_grafana_api.folder.get_folder = Mock() mock_grafana_api.folder.get_folder.side_effect = GrafanaClientError("user", "not", "found") output = grafana.exists_folder("foo") self.assertFalse(output) class get_id_of_team(TestCase): @patch("script.grafana.grafana_api") def test_team_exists(self, mock_grafana_api): mock_grafana_api.teams.get_team_by_name = Mock() mock_grafana_api.teams.get_team_by_name.return_value = [{"id": "my_team"}] output = grafana.get_id_of_team("my_team") self.assertEqual(output, "my_team") @patch("script.grafana.grafana_api") def test_team_not_existing(self, mock_grafana_api): mock_grafana_api.teams.get_team_by_name = Mock() mock_grafana_api.teams.get_team_by_name.return_value = [] output = grafana.get_id_of_team("my_team") self.assertFalse(output) class update_folder_permissions(TestCase): @patch("script.grafana.grafana_api") @patch("script.grafana.configuration") def test_update_input(self, mock_config, mock_grafana_api): mock_config.DRY_RUN = False mock_grafana_api.folder.update_folder_permissions = Mock() mock_grafana_api.folder.update_folder_permissions.return_value = [] grafana.update_folder_permissions("my_folder", [ { "id": "my_id", "permission": 1 } ]) self.assertEqual(mock_grafana_api.folder.update_folder_permissions.call_count, 1) mock_grafana_api.folder.update_folder_permissions.assert_called_with("my_folder", {"items": [ {"id": "my_id", "permission": 1 } ] })

39.068063

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0.642455

885

7,462

5.027119

0.090395

0.164082

0.179366

0.071926

0.837267

0.812093

0.739267

0.629804

0.578107

0.565745

0

0.001445

0.258108

7,462

190

109

39.273684

0.802204

0

0.536232

0

0.114313

0.071295

0

0.173913

1

0.101449

false

0

0.028986

0

0.188406

0

null

0

1

0

null

0

3

577674d6e6e440990896d02c1f571845c1da4ef4

272

py

Python

src/sima/simo/massunit.py

SINTEF/simapy

650b8c2f15503dad98e2bfc0d0788509593822c7

[ "MIT" ]

null

src/sima/simo/massunit.py

SINTEF/simapy

650b8c2f15503dad98e2bfc0d0788509593822c7

[ "MIT" ]

null

src/sima/simo/massunit.py

SINTEF/simapy

650b8c2f15503dad98e2bfc0d0788509593822c7

[ "MIT" ]

null

# Generated with MassUnit # from enum import Enum from enum import auto class MassUnit(Enum): """""" MG = auto() KG = auto() def label(self): if self == MassUnit.MG: return "Mg" if self == MassUnit.KG: return "kg"

18.133333

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0.536765

33

272

4.424242

0.454545

0.109589

0.191781

0

0.345588

272

15

32

18.133333

0.820225

0.084559

0

1

0

0.016667

0

1

0.1

false

0

0.2

0

0.8

0

null

0

1

0

1

0

null

0

1

0

3

5779baa8ebd68d27991e236edfabf315ce58575d

167

py

Python

New folder/Re7.py

piyushparastiwari/python-project

5dba0ef4e77f1d2528f510327de4224b60b1d4ba

[ "Apache-2.0" ]

null

New folder/Re7.py

piyushparastiwari/python-project

5dba0ef4e77f1d2528f510327de4224b60b1d4ba

[ "Apache-2.0" ]

null

New folder/Re7.py

piyushparastiwari/python-project

5dba0ef4e77f1d2528f510327de4224b60b1d4ba

[ "Apache-2.0" ]

null

import re st="amit and amita belongs to same family" for val in re.finditer("amit",st): print(val) lis=val.span() print(lis) print(type(lis))

18.555556

43

0.616766

27

167

3.814815

0.666667

0

0.251497

167

8

44

20.875

0.824

0

0.257862

0

1

0

false

0

0.142857

0

0.142857

0.428571

1

0

null

0

1

0

null

0

1

0

3

577c83bac9d62369bb796c7a1a418dc22207d901

318

py

Python

Python/Tests/TestData/TestAdapterTestB/InheritanceDerivedTest.py

nanshuiyu/pytools

9f9271fe8cf564b4f94e9456d400f4306ea77c23

[ "Apache-2.0" ]

null

Python/Tests/TestData/TestAdapterTestB/InheritanceDerivedTest.py

nanshuiyu/pytools

9f9271fe8cf564b4f94e9456d400f4306ea77c23

[ "Apache-2.0" ]

null

Python/Tests/TestData/TestAdapterTestB/InheritanceDerivedTest.py

nanshuiyu/pytools

9f9271fe8cf564b4f94e9456d400f4306ea77c23

[ "Apache-2.0" ]

null

import unittest import InheritanceBaseTest class DerivedClassTests(InheritanceBaseTest.BaseClassTests): def test_derived_pass(self): pass def test_derived_fail(self): self.assertTrue(False, "Force a failure in derived class test.") if __name__ == '__main__': unittest.main()

24.461538

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318

6.294118

0.617647

0.065421

0.130841

0

0.210692

318

12

74

26.5

0.85259

0

0.150327

0

0.111111

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0.222222

false

0.222222

0

0.555556

0

null

0

null

0

1

0

1

0

1

0

3

579297708304ce877306df0513a8afce6be83e37

12,978

py

Python

source/rttov_test/profile-datasets-py/div83/062.py

bucricket/projectMAScorrection

89489026c8e247ec7c364e537798e766331fe569

[ "BSD-3-Clause" ]

null

source/rttov_test/profile-datasets-py/div83/062.py

bucricket/projectMAScorrection

89489026c8e247ec7c364e537798e766331fe569

[ "BSD-3-Clause" ]

1

2022-03-12T12:19:59.000Z

source/rttov_test/profile-datasets-py/div83/062.py

bucricket/projectMAScorrection

89489026c8e247ec7c364e537798e766331fe569

[ "BSD-3-Clause" ]

null

""" Profile ../profile-datasets-py/div83/062.py file automaticaly created by prof_gen.py script """ self["ID"] = "../profile-datasets-py/div83/062.py" self["Q"] = numpy.array([ 2.135165, 2.544134, 3.283269, 4.205042, 4.999855, 5.317752, 5.205453, 5.320982, 5.907775, 6.095293, 6.191692, 6.191842, 6.146692, 6.080533, 6.027844, 6.021334, 6.013854, 5.993284, 5.947545, 5.859476, 5.707367, 5.45466 , 5.149923, 4.850026, 4.589789, 4.381871, 4.244622, 4.184272, 4.137703, 4.077603, 3.999644, 3.908545, 3.828035, 3.761756, 3.711596, 3.685716, 3.684286, 3.696326, 3.707896, 3.714566, 3.734056, 3.782776, 3.824185, 3.848895, 3.879375, 3.910525, 3.978284, 4.130243, 4.382681, 4.708588, 5.154323, 5.754207, 6.446238, 7.211108, 8.180523, 9.808144, 12.19045 , 14.78778 , 16.85582 , 18.19527 , 19.49502 , 20.34879 , 21.16945 , 22.4375 , 24.37611 , 26.75698 , 30.40398 , 36.18439 , 44.42083 , 55.27844 , 69.17471 , 87.64272 , 104.2721 , 117.5582 , 133.5732 , 155.2909 , 154.941 , 154.3332 , 165.0957 , 194.4842 , 252.885 , 340.3101 , 445.1148 , 547.7248 , 640.853 , 713.9849 , 773.9685 , 829.9586 , 885.8446 , 964.1026 , 1106.105 , 1219.651 , 944.1328 , 916.7358 , 890.4903 , 865.3355 , 841.2148 , 818.0752 , 795.8661 , 774.5406 , 754.055 ]) self["P"] = numpy.array([ 5.00000000e-03, 1.61000000e-02, 3.84000000e-02, 7.69000000e-02, 1.37000000e-01, 2.24400000e-01, 3.45400000e-01, 5.06400000e-01, 7.14000000e-01, 9.75300000e-01, 1.29720000e+00, 1.68720000e+00, 2.15260000e+00, 2.70090000e+00, 3.33980000e+00, 4.07700000e+00, 4.92040000e+00, 5.87760000e+00, 6.95670000e+00, 8.16550000e+00, 9.51190000e+00, 1.10038000e+01, 1.26492000e+01, 1.44559000e+01, 1.64318000e+01, 1.85847000e+01, 2.09224000e+01, 2.34526000e+01, 2.61829000e+01, 2.91210000e+01, 3.22744000e+01, 3.56505000e+01, 3.92566000e+01, 4.31001000e+01, 4.71882000e+01, 5.15278000e+01, 5.61260000e+01, 6.09895000e+01, 6.61253000e+01, 7.15398000e+01, 7.72396000e+01, 8.32310000e+01, 8.95204000e+01, 9.61138000e+01, 1.03017000e+02, 1.10237000e+02, 1.17778000e+02, 1.25646000e+02, 1.33846000e+02, 1.42385000e+02, 1.51266000e+02, 1.60496000e+02, 1.70078000e+02, 1.80018000e+02, 1.90320000e+02, 2.00989000e+02, 2.12028000e+02, 2.23442000e+02, 2.35234000e+02, 2.47408000e+02, 2.59969000e+02, 2.72919000e+02, 2.86262000e+02, 3.00000000e+02, 3.14137000e+02, 3.28675000e+02, 3.43618000e+02, 3.58966000e+02, 3.74724000e+02, 3.90893000e+02, 4.07474000e+02, 4.24470000e+02, 4.41882000e+02, 4.59712000e+02, 4.77961000e+02, 4.96630000e+02, 5.15720000e+02, 5.35232000e+02, 5.55167000e+02, 5.75525000e+02, 5.96306000e+02, 6.17511000e+02, 6.39140000e+02, 6.61192000e+02, 6.83667000e+02, 7.06565000e+02, 7.29886000e+02, 7.53628000e+02, 7.77790000e+02, 8.02371000e+02, 8.27371000e+02, 8.52788000e+02, 8.78620000e+02, 9.04866000e+02, 9.31524000e+02, 9.58591000e+02, 9.86067000e+02, 1.01395000e+03, 1.04223000e+03, 1.07092000e+03, 1.10000000e+03]) self["CO2"] = numpy.array([ 369.0472, 369.0461, 369.0418, 369.0364, 369.0272, 369.014 , 368.9971, 368.974 , 368.9548, 368.9298, 368.8917, 368.8367, 368.7727, 368.7118, 368.6468, 368.5718, 368.5218, 368.5058, 368.5028, 368.5008, 368.5119, 368.542 , 368.5811, 368.6212, 368.6653, 368.7144, 368.7534, 368.7935, 368.8235, 368.8565, 368.8935, 368.9336, 368.9856, 369.0416, 369.1946, 369.3996, 369.6246, 369.8796, 370.1486, 370.3456, 370.5446, 370.7746, 371.0386, 371.3156, 371.5916, 371.8805, 372.2945, 372.8095, 373.3224, 373.7442, 374.1831, 374.2768, 374.2726, 374.2973, 374.3609, 374.4303, 374.5314, 374.6365, 374.7087, 374.7782, 374.8217, 374.8544, 374.8731, 374.8786, 374.8759, 374.861 , 374.8396, 374.8074, 374.7734, 374.7403, 374.7041, 374.6662, 374.6269, 374.584 , 374.536 , 374.4828, 374.437 , 374.3912, 374.3472, 374.2982, 374.2463, 374.1886, 374.1284, 374.077 , 374.0341, 374.0028, 373.9803, 373.9624, 373.9454, 373.9202, 373.871 , 373.8335, 373.9406, 373.9539, 373.9667, 373.9771, 373.9871, 373.9958, 374.0041, 374.0121, 374.0198]) self["CO"] = numpy.array([ 1.044168 , 1.021437 , 0.9772578 , 0.9054302 , 0.803713 , 0.6758304 , 0.5907059 , 0.5901089 , 0.3854287 , 0.2633824 , 0.2040667 , 0.1702239 , 0.1136093 , 0.0496143 , 0.01022324, 0.00575758, 0.00531534, 0.00554938, 0.00580365, 0.00593863, 0.00606526, 0.0061711 , 0.00626554, 0.00633542, 0.00651423, 0.00678242, 0.00705851, 0.00736169, 0.00761043, 0.00788115, 0.00810314, 0.00834774, 0.00846046, 0.00857584, 0.00859602, 0.0085699 , 0.00856188, 0.00859618, 0.00863255, 0.00879777, 0.008989 , 0.00930631, 0.00978849, 0.01032786, 0.01123676, 0.01227285, 0.01416274, 0.01706003, 0.02045891, 0.02325589, 0.02657156, 0.02754204, 0.02778702, 0.02851259, 0.02991356, 0.03140779, 0.0328479 , 0.03440639, 0.0357939 , 0.03724762, 0.03847015, 0.03961269, 0.04054844, 0.04127447, 0.04184898, 0.04215827, 0.04238661, 0.04241467, 0.04242462, 0.04238266, 0.04235297, 0.04235829, 0.04237938, 0.04243321, 0.04246113, 0.04245561, 0.04239703, 0.04230487, 0.04214374, 0.04196034, 0.04173534, 0.04151467, 0.04130051, 0.04113266, 0.04099621, 0.04089188, 0.04082478, 0.0408119 , 0.04081871, 0.04083679, 0.04087654, 0.04090395, 0.04096279, 0.04099998, 0.04116401, 0.04140064, 0.04164224, 0.0418888 , 0.04214034, 0.04239694, 0.04265861]) self["T"] = numpy.array([ 205.66 , 214.138, 228.803, 244.185, 256.943, 262.499, 258.783, 249.46 , 239.723, 229.752, 223.18 , 221.108, 224.206, 226.735, 225.136, 218.183, 210.29 , 205.09 , 201.951, 200.133, 198.482, 196.528, 194.965, 194.055, 193.835, 194.059, 194.208, 194.173, 193.956, 193.718, 193.682, 193.933, 194.131, 194.705, 195.609, 196.61 , 197.675, 198.799, 199.776, 200.638, 201.537, 202.294, 202.856, 203.369, 203.973, 204.844, 205.793, 206.858, 207.907, 208.908, 209.816, 210.762, 211.53 , 211.957, 212.077, 211.765, 211.189, 210.949, 211.533, 212.365, 212.424, 212.319, 212.593, 213.367, 214.502, 215.702, 216.803, 217.925, 219.127, 220.43 , 221.803, 223.135, 224.442, 225.756, 227.146, 228.743, 230.533, 232.486, 234.56 , 236.706, 238.876, 240.984, 242.984, 244.886, 246.696, 248.41 , 249.974, 251.494, 253.024, 254.591, 256.096, 257.127, 254.785, 254.785, 254.785, 254.785, 254.785, 254.785, 254.785, 254.785, 254.785]) self["N2O"] = numpy.array([ 0.00045 , 0.00045 , 0.00045 , 0.00045 , 0.00087 , 0.00082 , 0.00045 , 0.00042 , 0.00133999, 0.00239998, 0.00516997, 0.00859995, 0.01236992, 0.01220993, 0.01250992, 0.01407992, 0.0165599 , 0.02100987, 0.02529985, 0.02979983, 0.03407981, 0.03503981, 0.03438982, 0.03376984, 0.02924987, 0.02439989, 0.01973992, 0.01793992, 0.01710993, 0.01630993, 0.01759993, 0.02691989, 0.03594986, 0.04469983, 0.0529298 , 0.05983978, 0.06655975, 0.07307973, 0.08438969, 0.1023196 , 0.1193496 , 0.1470594 , 0.1755893 , 0.2027892 , 0.2299491 , 0.254479 , 0.2758589 , 0.2846188 , 0.2926787 , 0.2999086 , 0.3061684 , 0.3113282 , 0.315218 , 0.3176777 , 0.3185374 , 0.3185369 , 0.3185361 , 0.3185353 , 0.3185346 , 0.3185342 , 0.3185338 , 0.3185335 , 0.3185333 , 0.3185329 , 0.3185322 , 0.3185315 , 0.3185303 , 0.3185285 , 0.3185259 , 0.3185224 , 0.318518 , 0.3185121 , 0.3185068 , 0.3185026 , 0.3184975 , 0.3184905 , 0.3184906 , 0.3184908 , 0.3184874 , 0.318478 , 0.3184594 , 0.3184316 , 0.3183982 , 0.3183655 , 0.3183359 , 0.3183126 , 0.3182935 , 0.3182756 , 0.3182578 , 0.3182329 , 0.3181877 , 0.3181515 , 0.3182393 , 0.318248 , 0.3182563 , 0.3182644 , 0.318272 , 0.3182794 , 0.3182865 , 0.3182933 , 0.3182998 ]) self["O3"] = numpy.array([ 0.7587884 , 0.6506303 , 0.4920414 , 0.4354072 , 0.4927165 , 0.6960063 , 1.086994 , 1.655471 , 2.345416 , 3.015182 , 3.040381 , 3.532388 , 3.956836 , 4.296324 , 4.369834 , 3.956136 , 3.575168 , 3.463419 , 3.454539 , 3.44159 , 3.335181 , 3.402581 , 3.532302 , 3.614202 , 3.602793 , 3.518435 , 3.352456 , 3.217207 , 3.064387 , 2.883768 , 2.693159 , 2.52414 , 2.357881 , 2.325821 , 2.490021 , 2.6827 , 2.71009 , 2.557481 , 2.390191 , 2.257812 , 2.018752 , 1.729413 , 1.550014 , 1.426195 , 1.229235 , 1.030666 , 0.8172317 , 0.6441773 , 0.5255887 , 0.4441519 , 0.3733641 , 0.3122102 , 0.2650703 , 0.2375863 , 0.2258832 , 0.2185519 , 0.2087325 , 0.1960951 , 0.1812439 , 0.164181 , 0.1450802 , 0.1258284 , 0.1086527 , 0.09469558, 0.08401265, 0.07566868, 0.06925349, 0.06385209, 0.05934416, 0.05564432, 0.05283704, 0.05088414, 0.04933126, 0.04790827, 0.04618753, 0.04385149, 0.04146417, 0.03934833, 0.03776906, 0.03677575, 0.03642339, 0.03630874, 0.03641958, 0.03672857, 0.03705864, 0.0372298 , 0.03721248, 0.03710618, 0.03694554, 0.03665513, 0.03590224, 0.03457798, 0.03937109, 0.03937217, 0.03937321, 0.0393742 , 0.03937515, 0.03937606, 0.03937694, 0.03937778, 0.03937858]) self["CH4"] = numpy.array([ 0.05500208, 0.05500206, 0.05500202, 0.09355291, 0.1077775 , 0.1368143 , 0.1482102 , 0.1591442 , 0.173895 , 0.2012118 , 0.2391385 , 0.2866422 , 0.3400379 , 0.3891816 , 0.4364604 , 0.4843851 , 0.5284008 , 0.5675666 , 0.6070874 , 0.6606711 , 0.7117169 , 0.7775898 , 0.8491726 , 0.9177535 , 1.036695 , 1.157895 , 1.274525 , 1.363954 , 1.442584 , 1.458114 , 1.474784 , 1.492634 , 1.511694 , 1.509474 , 1.507334 , 1.505314 , 1.503454 , 1.501804 , 1.506684 , 1.511824 , 1.517244 , 1.522934 , 1.528904 , 1.560684 , 1.585964 , 1.612404 , 1.640223 , 1.669413 , 1.697013 , 1.711792 , 1.727171 , 1.73038 , 1.730169 , 1.730588 , 1.731836 , 1.733073 , 1.734009 , 1.734974 , 1.735041 , 1.734998 , 1.734876 , 1.734715 , 1.734483 , 1.734181 , 1.733818 , 1.733374 , 1.732907 , 1.732417 , 1.731973 , 1.731654 , 1.73138 , 1.731228 , 1.731059 , 1.730866 , 1.730589 , 1.730201 , 1.729792 , 1.729333 , 1.728885 , 1.728424 , 1.727993 , 1.727552 , 1.727121 , 1.726794 , 1.726533 , 1.726347 , 1.726223 , 1.726126 , 1.72604 , 1.725904 , 1.725669 , 1.725533 , 1.726059 , 1.726146 , 1.726221 , 1.726285 , 1.726327 , 1.726367 , 1.726405 , 1.726442 , 1.726477 ]) self["CTP"] = 500.0 self["CFRACTION"] = 0.0 self["IDG"] = 0 self["ISH"] = 0 self["ELEVATION"] = 0.0 self["S2M"]["T"] = 254.785 self["S2M"]["Q"] = 754.054972021 self["S2M"]["O"] = 0.0393785839754 self["S2M"]["P"] = 875.53882 self["S2M"]["U"] = 0.0 self["S2M"]["V"] = 0.0 self["S2M"]["WFETC"] = 100000.0 self["SKIN"]["SURFTYPE"] = 0 self["SKIN"]["WATERTYPE"] = 1 self["SKIN"]["T"] = 254.785 self["SKIN"]["SALINITY"] = 35.0 self["SKIN"]["FOAM_FRACTION"] = 0.0 self["SKIN"]["FASTEM"] = numpy.array([ 3. , 5. , 15. , 0.1, 0.3]) self["ZENANGLE"] = 0.0 self["AZANGLE"] = 0.0 self["SUNZENANGLE"] = 0.0 self["SUNAZANGLE"] = 0.0 self["LATITUDE"] = -70.044 self["GAS_UNITS"] = 2 self["BE"] = 0.0 self["COSBK"] = 0.0 self["DATE"] = numpy.array([2007, 6, 1]) self["TIME"] = numpy.array([0, 0, 0])

58.197309

92

0.548467

1,907

12,978

3.730991

0.504982

0.011947

0.009276

0.013493

0.018693

0.011103

0.00759

0

0.704293

0.292803

12,978

222

93

58.459459

0.07093

0.00732

0

0.019352

0.00272

0

1

0

true

0

null

0

1

0

1

0

null

0

1

0

3

57d73df628277c009f64cbf8b7778091e214b9f2

346

py

Python

procrastinate/contrib/django/migrations/0001_baseline.py

t-eckert/procrastinate

f831565b00d67afa7a4291d734c8fe85074a360c

[ "MIT" ]

null

procrastinate/contrib/django/migrations/0001_baseline.py

t-eckert/procrastinate

f831565b00d67afa7a4291d734c8fe85074a360c

[ "MIT" ]

null

procrastinate/contrib/django/migrations/0001_baseline.py

t-eckert/procrastinate

f831565b00d67afa7a4291d734c8fe85074a360c

[ "MIT" ]

null

# Generated by Django 3.1 on 2020-08-22 16:57 from django.db import migrations import procrastinate.contrib.django class Migration(migrations.Migration): initial = True dependencies: list = [] operations = [ procrastinate.contrib.django.RunProcrastinateFile( filename="baseline-0.5.0.sql", ), ]

18.210526

58

0.66763

39

346

5.923077

0.769231

0.17316

0.225108

0

0.064151

0.234104

346

18

59

19.222222

0.807547

0.124277

0

1

0

0.059801

0

1

0

false

0

0.2

0

0.6

0

null

0

1

0

1

0

null

0

1

0

3

57e6f4680e3910d5aa9b8c572fe9a67674fa7993

155

py

Python

Exercício feitos pela primeira vez/ex025.py

Claayton/pythonExerciciosLinux

696cdb16983638418bd0d0d4fe44dc72662b9c97

[ "MIT" ]

1

2021-01-23T15:43:34.000Z

Exercício feitos pela primeira vez/ex025.py

Claayton/pythonExerciciosLinux

696cdb16983638418bd0d0d4fe44dc72662b9c97

[ "MIT" ]

null

Exercício feitos pela primeira vez/ex025.py

Claayton/pythonExerciciosLinux

696cdb16983638418bd0d0d4fe44dc72662b9c97

[ "MIT" ]

null

#Exercício025 name = str(input('Qual seu nome completo?: ')).strip().upper() print('Seu nome tem a palavra SILVA?: {}'.format('SILVA'in name)) print('xD')

31

65

0.677419

23

155

4.565217

0.782609

0.133333

0

0.021739

0.109677

155

4

66

38.75

0.73913

0.077419

0

0.457746

0

1

0

false

0

0.666667

1

0

null

0

1

0

null

0

1

0

3

57e84cf4f7c4b8be601c471d36c9ee834f3919ef

10,223

py

Python

tests/test_restapi_filtering.py

hafeezibbad/telegram-bot

0cbc35005ea5d076a8b3a243d794889532e69c4c

[ "Apache-2.0" ]

null

tests/test_restapi_filtering.py

hafeezibbad/telegram-bot

0cbc35005ea5d076a8b3a243d794889532e69c4c

[ "Apache-2.0" ]

2

2021-02-02T22:38:30.000Z

2021-06-02T01:27:14.000Z

tests/test_restapi_filtering.py

hafeezibbad/telegram-bot

0cbc35005ea5d076a8b3a243d794889532e69c4c

[ "Apache-2.0" ]

null

""" Module containing tests cases for testing Restapi calls for filtering and adding, removing dummy date. """ import json import string import random import unittest from datetime import datetime, timedelta from flask import url_for from botapp import create_app from botapp.models import MyBot, Message class ProceduresTest(unittest.TestCase): def setUp(self): self.app = create_app('testing') self.app_context = self.app.app_context() self.app_context.push() self.client = self.app.test_client() def tearDown(self): # Drop all collections MyBot.drop_collection() Message.drop_collection() self.app_context.pop() def get_api_headers(self): return { 'Accept': 'application/json', 'Content-Type': 'application/json' } def test_filter_messages_by_bot(self): for _ in range(3): Message(bot_id=1234).save() Message(bot_id=random.randint(1, 10)).save() # Get messages response = self.client.get( url_for('botapi.filter_messages_by_bot', bot_id=1234), headers=self.get_api_headers() ) self.assertEqual(response.status_code, 200) json_response = json.loads(response.data.decode('utf-8')) self.assertEqual(json_response['result'], 'success') self.assertEqual(len(json_response['messages']), 3) def test_filter_messages_by_username(self): for _ in range(3): Message(sender_username='TestUser1').save() Message(sender_username='TestUser' + str(random.randint(2, 10))).save() # Get messages response = self.client.get( url_for('botapi.filter_messages_by_username', username='TestUser1'), headers=self.get_api_headers() ) self.assertEqual(response.status_code, 200) json_response = json.loads(response.data.decode('utf-8')) self.assertEqual(json_response['result'], 'success') self.assertEqual(len(json_response['messages']), 3) def test_filter_messages_by_chatid(self): for _ in range(3): Message(chatid=123).save() Message(chatid=random.randint(200, 300)).save() # Get messages response = self.client.get( url_for('botapi.filter_messages_by_chatid', chatid=123), headers=self.get_api_headers() ) self.assertEqual(response.status_code, 200) json_response = json.loads(response.data.decode('utf-8')) self.assertEqual(json_response['result'], 'success') self.assertEqual(len(json_response['messages']), 3) def test_filter_messages_using_botid(self): # Add some dummy messages MyBot.generate_fake(1) Message.generate_fake(5) bot = MyBot(bot_id=11111, token='dummy-token', test_bot=True).save() self.assertIsNotNone(bot) for _ in range(3): Message(bot_id=bot.bot_id).save() self.assertEqual(Message.objects.count(), 5+3) # Get filtered messages response = self.client.get( url_for('botapi.filter_messages', botid=bot.bot_id, time_off=0, text='#', username='#', name='#'), headers=self.get_api_headers() ) self.assertEqual(response.status_code, 200) json_response = json.loads(response.data.decode('utf-8')) self.assertEqual(json_response['result'], 'success') self.assertEqual(len(json_response['messages']), 3) def test_filter_messages_using_time_off(self): # Add some dummy messages Message.generate_fake(5) for _ in range(5): Message(date=datetime.now()-timedelta(minutes=20)).save() self.assertEqual(Message.objects.count(), 5+5) # Get filtered messages response = self.client.get( url_for('botapi.filter_messages', botid=0, time_off=40, text='#', username='#', name='#'), headers=self.get_api_headers() ) self.assertEqual(response.status_code, 200) json_response = json.loads(response.data.decode('utf-8')) self.assertEqual(json_response['result'], 'success') self.assertEqual(len(json_response['messages']), 5) def test_filter_messages_using_text(self): # Add some dummy messages Message.generate_fake(5) for _ in range(5): Message(text_content='message:' + random.choice(string.ascii_letters)).save() self.assertEqual(Message.objects.count(), 5+5) # Get filtered messages response = self.client.get( url_for('botapi.filter_messages', botid=0, time_off=0, text='message', username='#', name='#'), headers=self.get_api_headers() ) self.assertEqual(response.status_code, 200) json_response = json.loads(response.data.decode('utf-8')) self.assertEqual(json_response['result'], 'success') self.assertEqual(len(json_response['messages']), 5) def test_filter_messages_using_username(self): # Add some dummy messages Message.generate_fake(5) for _ in range(5): Message(sender_username='testuser').save() self.assertEqual(Message.objects.count(), 5 + 5) # Get filtered messages response = self.client.get( url_for('botapi.filter_messages', botid=0, time_off=0, text='#', username='testuser', name='#'), headers=self.get_api_headers() ) self.assertEqual(response.status_code, 200) json_response = json.loads(response.data.decode('utf-8')) self.assertEqual(json_response['result'], 'success') self.assertEqual(len(json_response['messages']), 5) def test_filter_messages_using_user_firstname_lastname(self): # Add some dummy messages Message.generate_fake(5) Message(sender_firstname='testuser').save() Message(sender_lastname='usertest').save() Message(sender_firstname='test', sender_lastname='user').save() Message(sender_firstname='user', sender_lastname='test').save() self.assertEqual(Message.objects.count(), 5 + 4) # Get filtered messages response = self.client.get( url_for('botapi.filter_messages', botid=0, time_off=0, text='#', username='#', name='test'), headers=self.get_api_headers() ) self.assertEqual(response.status_code, 200) json_response = json.loads(response.data.decode('utf-8')) self.assertEqual(json_response['result'], 'success') self.assertEqual(len(json_response['messages']), 4) def test_filter_messages_using_no_criteria(self): # Add some dummy messages Message.generate_fake(5) Message(bot_id=1234).save() Message(date=datetime.now()-timedelta(hours=1.5)).save() Message(text_content='message1234').save() Message(sender_username='testuser').save() Message(sender_firstname='test', sender_lastname='user').save() self.assertEqual(Message.objects.count(), 5 + 5) # Get filtered messages response = self.client.get( url_for('botapi.filter_messages', botid=0, time_off=0, text='#', username='#', name='#'), headers=self.get_api_headers() ) self.assertEqual(response.status_code, 200) json_response = json.loads(response.data.decode('utf-8')) self.assertEqual(json_response['result'], 'success') self.assertEqual(len(json_response['messages']), 10) def test_filter_messages_using_all_criteria(self): # Add dummy messages Message.generate_fake(5) # Add partially matching messages. Message(date=datetime.now() - timedelta(minutes=30), # Un-match time. sender_username='tester1', sender_firstname='test', sender_lastname='bot', text_content='testmessage', bot_id=12345).save() Message(date=datetime.now() - timedelta(minutes=10), sender_username='tester2', # Non-matching sender-username. sender_firstname='test', sender_lastname='bot', text_content='testmessage', bot_id=12345).save() Message(date=datetime.now() - timedelta(minutes=10), sender_username='tester1', sender_firstname='abc', # Non-matching first-name, last-name sender_lastname='def', text_content='testmessage', bot_id=12345).save() Message(date=datetime.now() - timedelta(minutes=10), sender_username='tester1', sender_firstname='test', sender_lastname='bot', text_content='message', # Non-matching text content bot_id=12345).save() Message(date=datetime.now() - timedelta(minutes=10), sender_username='Tester1', sender_firstname='Test', sender_lastname='Bot', text_content='testmessage', bot_id=11111).save() # Non-matching botid # Add expected message. Message(date=datetime.now()-timedelta(minutes=10), sender_username='tester1', sender_firstname='test', sender_lastname='bot', text_content='testmessage', bot_id=12345).save() # Get filtered messages response = self.client.get( url_for('botapi.filter_messages', botid=12345, time_off=15, text='test', username='tester1', name='test'), headers=self.get_api_headers() ) self.assertEqual(response.status_code, 200) json_response = json.loads(response.data.decode('utf-8')) self.assertEqual(json_response['result'], 'success') self.assertEqual(len(json_response['messages']), 1)

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1

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null

0

3

17c3422fd5c753fc7b849ef8d958f4af1f54cc8e

91

py

Python

src/student1/apps.py

Ehsan-Home/drag_and_drop_in_tables

865079999e42a6f3dceb6814b69091580c1440b8

[ "MIT" ]

3

2020-03-08T09:14:38.000Z

2020-08-29T00:19:38.000Z

src/student1/apps.py

Ehsan-Home/drag_and_drop_in_tables

865079999e42a6f3dceb6814b69091580c1440b8

[ "MIT" ]

null

src/student1/apps.py

Ehsan-Home/drag_and_drop_in_tables

865079999e42a6f3dceb6814b69091580c1440b8

[ "MIT" ]

null

from django.apps import AppConfig class Student1Config(AppConfig): name = 'student1'

15.166667

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0.164835

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null

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1

0

null

0

1

0

3

17cca09d6607d4701575eebf9f9363e242db7714

618

py

Python

object_oriented_pandas/data_unit.py

AdamWclw/object_oriented_pandas

084d8cf12d71c96dc42d5cf7ac52030b1862f9c1

[ "MIT" ]

1

2021-03-12T12:32:04.000Z

object_oriented_pandas/data_unit.py

AdamWclw/object_oriented_pandas

084d8cf12d71c96dc42d5cf7ac52030b1862f9c1

[ "MIT" ]

null

object_oriented_pandas/data_unit.py

AdamWclw/object_oriented_pandas

084d8cf12d71c96dc42d5cf7ac52030b1862f9c1

[ "MIT" ]

null

# DATA UNIT CLASS class _Unit: def __init__(self, name, con_2_si, con_unit, si_unit): self._name = name self._con_2_si = con_2_si self._con_unit = con_unit self._si_unit = si_unit def get_name(self): return self._name def get_con_2_si(self): return self._con_2_si def get_con_unit(self): return self._con_unit def get_si_unit(self): return self._si_unit # DATA UNITS ENERGY = _Unit(name='Energy', con_2_si=1000 * 3600, con_unit='kWh', si_unit='Ws') DISTANCE = _Unit(name='Distance', con_2_si=1000, con_unit='km', si_unit='m')

22.888889

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0.650485

103

618

3.446602

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0.078873

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618

26

82

23.769231

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null

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null

0

1

0

1

0

3

17cd8ac3d98e5aa1290e36e5bb8980fb910b5466

259

py

Python

HARMAN International Software Engineer 2019/missingNumber.py

sivolko/codeforce

4b00c4c012780036e56d2f0e79adb2f5db7559df

[ "MIT" ]

null

HARMAN International Software Engineer 2019/missingNumber.py

sivolko/codeforce

4b00c4c012780036e56d2f0e79adb2f5db7559df

[ "MIT" ]

null

HARMAN International Software Engineer 2019/missingNumber.py

sivolko/codeforce

4b00c4c012780036e56d2f0e79adb2f5db7559df

[ "MIT" ]

null

def missing_number(nums): arr = [0 for _ in range(len(nums))] for i in range(len(nums)): if nums[i] < len(nums): arr[nums[i]] = -1 for i in range(len(nums)): if arr[i] != -1: return i return len(nums)

21.583333

39

0.498069

41

259

3.097561

0.341463

0.275591

0.23622

0.330709

0.314961

0

0.017751

0.34749

259

12

40

21.583333

0.733728

0

0.222222

0

1

0.111111

false

0

0.333333

0

null

1

0

null

0

3

17ebb1b0a232779c87fd66dd4cd9585253cf8344

1,196

py

Python

setup.py

whitespy/django-simple-metatags

5a085b64a3e1e8d4d1b1bd6b9a9eabcfa275912f

[ "MIT" ]

4

2017-04-09T20:29:46.000Z

2021-05-03T23:38:17.000Z

setup.py

whitespy/django-simple-metatags

5a085b64a3e1e8d4d1b1bd6b9a9eabcfa275912f

[ "MIT" ]

6

2017-04-09T20:38:40.000Z

2022-03-03T12:28:04.000Z

setup.py

whitespy/django-simple-metatags

5a085b64a3e1e8d4d1b1bd6b9a9eabcfa275912f

[ "MIT" ]

6

2016-12-05T16:00:17.000Z

2021-01-11T11:46:39.000Z

from setuptools import setup, find_packages setup( name='django-simple-metatags', version='2.0.3', description="The django application allows to add title, keywords and " "description meta tags to site's pages.", author='Andrey Butenko', author_email='whitespysoftware@gmail.com', url='https://github.com/whitespy/django-simple-metatags', long_description=open('README.rst', encoding='utf-8').read(), packages=find_packages(), include_package_data=True, platforms='any', classifiers=[ 'Environment :: Web Environment', 'Intended Audience :: Developers', 'Operating System :: OS Independent', 'Programming Language :: Python', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Programming Language :: Python :: 3.8', 'Programming Language :: Python :: 3.9', 'Programming Language :: Python :: 3.10', 'Framework :: Django', 'Framework :: Django :: 2.1', 'Framework :: Django :: 2.2', 'Framework :: Django :: 3.0', 'Framework :: Django :: 3.1', 'Framework :: Django :: 3.2', ], )

35.176471

75

0.601171

127

1,196

5.614173

0.566929

0.159888

0.210379

0.182328

0

0.027809

0.248328

1,196

33

76

36.242424

0.765295

0

0.576923

0.040134

0

1

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true

0

0.032258

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0.032258

0

null

0

1

0

null

0

1

0

3

aa124a0746565781aaf226d284979259a69cd9e9

671

py

Python

application/unused/acronymos/acronymos_source.py

Vahen/VahenWebsite

19a0f0f923874d66d8229e20e0df5b7916da5595

[ "MIT" ]

null

application/unused/acronymos/acronymos_source.py

Vahen/VahenWebsite

19a0f0f923874d66d8229e20e0df5b7916da5595

[ "MIT" ]

null

application/unused/acronymos/acronymos_source.py

Vahen/VahenWebsite

19a0f0f923874d66d8229e20e0df5b7916da5595

[ "MIT" ]

null

import random # Todo -> Terminer les corrections from typing import Set, List def create_acronyme(string: str) -> str: return ''.join([x[0] for x in string.split(" ")]) def pick_random_word_by_letter(letter: str, words: Set[str]) -> str: words_starting_by = find_list_words_starting_by(letter, words) return words_starting_by[random.randint(len(words_starting_by))] def find_list_words_starting_by(letter: str, words: Set[str]) -> List[str]: return [x for x in words if x.upper().startswith(letter.upper())] def unroll_acronyme(acronyme: str, words: Set[str]) -> str: return ' '.join([pick_random_word_by_letter(x, words) for x in acronyme])

30.5

77

0.724292

105

671

4.4

0.333333

0.140693

0.162338

0.090909

0.337662

0.125541

0

0.001742

0.14456

671

21

78

31.952381

0.803136

0.04769

0

0.00314

0

0.047619

0

1

0.363636

false

0

0.181818

0.272727

0.909091

0

null

0

null

0

1

0

1

0

1

0

3

aa457df01cd3ec06e6b098b140f070c64cd8b090

183

py

Python

v1/comments/urls.py

DucPhamTV/MaiTet

44a1465a3239808f6640592ba666d9c5449c0ef4

[ "MIT" ]

null

v1/comments/urls.py

DucPhamTV/MaiTet

44a1465a3239808f6640592ba666d9c5449c0ef4

[ "MIT" ]

15

2021-02-20T12:03:33.000Z

2021-07-26T10:15:03.000Z

v1/comments/urls.py

DucPhamTV/MaiTet

44a1465a3239808f6640592ba666d9c5449c0ef4

[ "MIT" ]

null

from rest_framework.routers import SimpleRouter from v1.comments.views import CommentViewSet router = SimpleRouter(trailing_slash=False) router.register('comments', CommentViewSet)

26.142857

47

0.846995

21

183

7.285714

0.714286

0

0.005952

0.081967

183

6

48

30.5

0.904762

0

0.043716

0

1

0

false

0

0.5

0

0.5

0

1

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null

0

1

0

null

0

1

0

3

a4acdfd6ab541d7f88b9a599f52e9aadab5c5f4b

200

py

Python

programming-language/cases/python/examples/py.test/example1.py

wdv4758h/notes

60fa483961245ec5bb264d3f28a885fb82a1c25e

[ "Unlicense" ]

136

2015-06-15T13:26:40.000Z

2022-03-03T07:47:31.000Z

programming-language/cases/python/examples/py.test/example1.py

wdv4758h/notes

60fa483961245ec5bb264d3f28a885fb82a1c25e

[ "Unlicense" ]

82

2017-01-06T06:32:55.000Z

2020-09-03T03:34:24.000Z

programming-language/cases/python/examples/py.test/example1.py

wdv4758h/notes

60fa483961245ec5bb264d3f28a885fb82a1c25e

[ "Unlicense" ]

18

2015-12-04T04:02:44.000Z

2022-02-24T03:48:57.000Z

#!/usr/bin/env python ''' you can use this command to run specific Python code: ..code-block:: python py.test example1.py ''' def f(x): return x + 1 def test_f(): assert f(0) == 1

10.526316

53

0.6

34

200

3.5

0.705882

0

0.026667

0.25

200

18

54

11.111111

0.766667

0.61

0

0.25

1

0.5

false

0

0.25

0.75

0

null

0

null

0

1

0

1

0

3

a4b14eb6b6c945a92b4dd50e0dc46ef1f9f9807c

683

py

Python

tests/rules/test_required_if.py

mateuszz0000/Validator

80dde6dd9bcbc4e0fb5815c1415c40e7357e98bd

[ "MIT" ]

null

tests/rules/test_required_if.py

mateuszz0000/Validator

80dde6dd9bcbc4e0fb5815c1415c40e7357e98bd

[ "MIT" ]

null

tests/rules/test_required_if.py

mateuszz0000/Validator

80dde6dd9bcbc4e0fb5815c1415c40e7357e98bd

[ "MIT" ]

null

from validator.rules import RequiredIf def test_required_if_01(): rule = RequiredIf("a") value_to_check = "abc" assert rule.check(value_to_check) rule = RequiredIf("a") value_to_check = ["a", "b", "c"] assert rule.check(value_to_check) def test_required_if_02(): rule = RequiredIf("a") value_to_check = "" assert not rule.check(value_to_check) rule = RequiredIf("a") value_to_check = [] assert not rule.check(value_to_check) rule = RequiredIf("a") value_to_check = "bcd" assert not rule.check(value_to_check) rule = RequiredIf("a") value_to_check = ["b", "c", "d"] assert not rule.check(value_to_check)

22.766667

41

0.658858

99

683

4.242424

0.232323

0.2

0.342857

0.285714

0.8

0.657143

0.585714

0

0.007421

0.210835

683

29

42

23.551724

0.7718

0

0.571429

0

0.026354

0

0.285714

1

0.095238

false

0

0.047619

0

0.142857

0

null

0

1

0

1

0

null

0

3

351b7102ef7d184476ebafb377acce37ce069fd2

119

py

Python

Tuples.py

rashidulhasanhridoy/LANGUAGE-PROFICIENCY-Python-HackerRank

46beecbf3a2468d6c598fe62a3e65c5f0c1395c8

[ "Apache-2.0" ]

1

2020-07-21T18:01:52.000Z

Tuples.py

rashidulhasanhridoy/LANGUAGE-PROFICIENCY-Python-HackerRank

46beecbf3a2468d6c598fe62a3e65c5f0c1395c8

[ "Apache-2.0" ]

null

Tuples.py

rashidulhasanhridoy/LANGUAGE-PROFICIENCY-Python-HackerRank

46beecbf3a2468d6c598fe62a3e65c5f0c1395c8

[ "Apache-2.0" ]

null

n = int(input()) integer_list = map(int, input().split()) integer_list = tuple(integer_list) print(hash(integer_list))

23.8

40

0.731092

18

119

4.611111

0.555556

0.53012

0

0.092437

119

4

41

29.75

0.768519

0

1

0

false

0

0.25

1

0

null

1

0

1

0

null

0

3

351e4169b883332b0f845dd71443900ace5793ff

627

py

Python

lego/settings/__init__.py

andrinelo/lego

9b53c8fe538d9107b980a70e2a21fb487cc3b290

[ "MIT" ]

null

lego/settings/__init__.py

andrinelo/lego

9b53c8fe538d9107b980a70e2a21fb487cc3b290

[ "MIT" ]

null

lego/settings/__init__.py

andrinelo/lego

9b53c8fe538d9107b980a70e2a21fb487cc3b290

[ "MIT" ]

null

import os import sys TESTING = 'test' in sys.argv[:2] DAPHNE_SERVER = 'daphne' in sys.argv from .base import * # noqa from .lego import * # noqa from .rest_framework import * # noqa from .search import * # noqa from .logging import * # noqa if TESTING: from .test import * # noqa else: if os.environ.get('ENV_CONFIG') in ['1', 'True', 'true']: from .production import * # noqa else: try: from .local import * # noqa except ImportError as e: raise ImportError('Couldn\'t load local settings lego.settings.local') DEFAULT_FROM_EMAIL = SERVER_EMAIL # noqa

24.115385

82

0.634769

84

627

4.666667

0.47619

0.204082

0.142857

0

0.004301

0.258373

627

25

83

25.08

0.83871

0.070175

0

0.1

0

0.062827

0

1

0

false

0

0.6

0

0.6

0

null

1

0

null

0

1

0

1

0

3

3525251f1d372db45b7ebad3ec881dff8481e8c7

444

py

Python

injectedConsole/plugin_help/__init__.py

ChenyangGao/SigilPlugin_injectedConsole

a35ee53671373db05e5ee66f0345d7ace06ddc0c

[ "BSD-3-Clause" ]

11

2021-03-04T02:19:48.000Z

2021-11-06T16:44:35.000Z

injectedConsole/plugin_help/__init__.py

fengdaokanhai/SigilPlugin_injectedConsole

a35ee53671373db05e5ee66f0345d7ace06ddc0c

[ "BSD-3-Clause" ]

1

2021-08-02T13:13:52.000Z

injectedConsole/plugin_help/__init__.py

fengdaokanhai/SigilPlugin_injectedConsole

a35ee53671373db05e5ee66f0345d7ace06ddc0c

[ "BSD-3-Clause" ]

4

2021-03-08T07:42:17.000Z

2021-11-06T16:44:52.000Z

#!/usr/bin/env python3 # coding: utf-8 from plugin_util.console import get_current_shell, list_shells from plugin_util.run import run_file, run_path, run, load from plugin_util.usepip import execute_pip, install, uninstall, ensure_import from plugin_util.urlimport import ( install_url_meta, remove_url_meta, install_path_hook as install_url_hook, remove_path_hook as remove_url_hook ) from .editor import * from .function import *

29.6

78

0.808559

70

444

4.8

0.5

0.119048

0.166667

0

0.005168

0.128378

444

14

79

31.714286

0.863049

0.078829

0

1

0

true

0

0.666667

0

0.666667

0

null

0

null

0

1

0

1

0

1

0

3

353178e9150c100aa45bb72cda027d20d79ce4d2

7,985

py

Python

lintcode/257.py

jianershi/algorithm

c3c38723b9c5f1cc745550d89e228f92fd4abfb2

[ "MIT" ]

1

2021-01-08T06:57:49.000Z

lintcode/257.py

jianershi/algorithm

c3c38723b9c5f1cc745550d89e228f92fd4abfb2

[ "MIT" ]

null

lintcode/257.py

jianershi/algorithm

c3c38723b9c5f1cc745550d89e228f92fd4abfb2

[ "MIT" ]

1

2021-01-08T06:57:52.000Z

""" 257. Longest String Chain https://www.lintcode.com/problem/longest-string-chain/description?_from=contest&&fromId=93 dp[i] longest string chian ending in i dp[i] = max(dp[i], dp[j] + 1) if distance between words[j] and word[i] is 1 for j in range [0, i) max(dp[i]) dp[i] = 1 longest increasing subsequence变形 """ class Solution: """ @param words: the list of word. @return: the length of the longest string chain. """ def longestStrChain(self, words): if not words: return 0 words = sorted(words, key=lambda x: len(x)) n = len(words) dp = [1] * n for i in range(n): for j in range(i - 1, -1 ,-1): if len(words[j]) == len(words[i]): continue if len(words[i]) - len(words[j]) > 1: break if self.is_valid(words[j], words[i]): dp[i] = max(dp[i], dp[j] + 1) return max(dp) def is_valid(self, word1, word2): i = 0 j = 0 diffed_by_one = False while i < len(word1) and j < len(word2): if word1[i] == word2[j]: i += 1 j += 1 else: if diffed_by_one: return False if j + 1 < len(word2) and word1[i] == word2[j + 1]: j += 1 else: return False diffed_by_one = True return True s = Solution() words=["uiykgmcc","jrgbss","mhkqodcpy","lkj","bwqktun","s","nrctyzifwytjblwy","wrp","scqlcwmxw","irqvnxdcxoejuu","gmlckvofwyifmrw","wbzbyrcppaljigvo","lk","kfeouqyyrer","efzzpvi","ubkcitcmwxk","txihn","mdwdmbtx","vuzvcoaif","jwmboqvhpqodsj","wscfvrfl","pzye","waxyoxftvrgqmkg","wwdidopozinxxn","dclpg","xjsvlxktxs","ajj","pvsdastm","tatjxhygidhn","feafycxdxagn","irqvnxxoeuu","kwjo","tztoovsyfwz","prllrw","sclmx","bbmjnwaxcwaml","gl","wiax","uzvcoaif","ztovyfwz","qxy","zuexoxyp","qxyyrl","pvsdasvtm","femafycxdxaagn","rspvccjcm","wvyiax","vst","efzi","fjmdcc","icsinrbpql","ctybiizlcr","ntyzfwytjblw","tatjxhygidhpn","e","kykizdandafusu","pnepuwcsxl","kfeuqyyrer","afplzhbqguu","hvajtj","prll","ildzdimea","zueoxp","ezi","lqr","jkaagljikwamaqvf","mlzwhkxsn","rspvccbcjjtcm","wscfvrl","m","msygukwlkrqboc","pifojogoveub","bkcmwx","jercgybhss","wrpi","aicsinkgrbpqli","aplzbuu","sclcmxw","atpepgsz","govrcuuglaer","bdxjpsvlxkytxs","uikgm","bm","wvyhiqax","znvaasgfvqi","hatpepgsz","hrzebpa","bnfz","lybtqrfzw","taxhygihn","bjnfzk","mhqp","ide","znvcaasgfvqi","ftv","afplzhbqsguuu","thn","pdbccbe","mxevopfoimgjww","fjmdrcce","rspvccjjcm","jv","motnfwohwule","xjsvlxtxs","bqeb","eug","jftavwgl","rzebpa","lybtqrfazw","zuexoxp","jercgybhsys","hajtj","bkcitcmwxk","mbpvxsdastvtm","mowlznwhkxsn","dvenn","rsacxe","tatjxhygihn","cotybiizlcr","bbmnaxaml","pkwrpsi","nqpdbccbkxens","mbpbovxsdastvtm","mj","pxpsvikwekuq","qeug","dmelddga","aicsinkgrbpxqli","bdxjpsvlxktytxs","pkrllrxw","jkgljikwmaqf","iddie","ctybiizcr","nyzfwytjblw","yvuhmiuehspi","keuqre","wzbypaigvo","sck","uzcoaf","dlpg","ubkcpitlscmwxk","molzwhkxsn","pepuwcsxl","laplm","dclpgc","mahkxqodcpy","sclcmx","hvrzebpaz","bgovrcuuglaer","clazpulmw","yvuyhmiuehspiq","wzbycpaljigvo","sceqalciwmxw","hjytflmvsgv","u","hjyvxytfflhmvsgv","jkgjikwmaqf","fefycxdxagn","ftvw","ofncgxrkqvcr","spvcjc","pvsdastvtm","kykzdandaus","wbzbycppaljigvo","haytpepgsz","jmowlznwhkxsn","aplzhbguu","zvyz","nfvqi","jfvtavwsgl","xejnllhfulns","zhhvbiqiw","jkgljikwmaqvf","tyizc","irqvnxcxoejuu","clvazzpulmw","oncgxrqvcr","qlupvpdkhrm","mtnfwohwule","wwdidopzozinxxn","auiykgmcc","wscfvrfyl","pfksmrullrxw","jwmoqvhpqods","ftavwg","iddiea","kcmw","ykkwjwo","pe","aplzbguu","eu","bbmnaxal","ntyswtnlab","zhhhvbhbiqiw","jwmoqvpqods","kykzdndaus","bbmjnaxcwaml","zunvcaasgfvqi","icsingrbpql","sceqalciwmsxyw","yvuhmiuehsp","bxjsvlxktxs","waxoxftvrgqmkg","cogxxpaknks","scllvazzpulmw","tatjxhygeidhpn","ftvwg","tyz","nafvqi","oby","pgzpkhqog","irqvnxxoejuu","oxwpkxlakcp","bnf","oxwnpkxlakcp","bwqktu","ufybbaozoqk","ntydswtnlab","zvyfz","znaafvqi","npdbccbke","mhkqocpy","kuq","bjnfz","taxhyihn","kwrpsi","qifepmcatbdjlf","lzwhks","kfeuqre","mxevopfoimgww","spvcjcm","oncgxrkqvcr","jftavwsgl","soifcbya","jpzyeg","jwmboqvhpqods","lapulm","jrgbhss","xejfnllhfulns","zhhhvbbiqiw","km","kuqre","scxlzlvazzpulmw","ztvyfwz","wbzbycpaljigvo","rzbpa","vsastm","uybaooqk","dn","ykwjwo","ufybmvbaozoqk","nknm","mbpvsdastvtm","dpgzpxykhqog","wzbypajigvo","bnjnfzk","eollbigtftpdrd","zhbiqiw","yvuhiuehp","zhhhvbhbiqiwg","pfksrullrxw","pzyeg","aplzhbqguu","z","hvrzecbpazw","clvazpulmw","tajxhygihn","pgzpxykhqog","fefyxdxagn","wimomuvhn","lqrzw","xejnlhfulns","jhrc","xsxxs","slmx","jrgss","uikgmc","ncgqvcr","womuhn","aryouvtnmme","uzco","zhhhvbiqiw","hjytflhmvsgv","znvaasfvqi","kuqr","ojrpp","ztoovyfwz","zvz","pxpsviweuq","ufybaooqk","xy","jfvvtavwksvgl","raiachv","bmnaxl","rspvccjjtcm","pgzpxkhqog","xhbtfnqebaj","sceqalciwmsxw","jssctk","uzvcoaf","fefydxagn","jhrvc","mbj","raiahv","nrtyzifwytjblwy","mhqcp","jkgjkwmaqf","wscfvrfylhi","lqrz","ahabucermswyrvl","wxoxftvrgqmkg","ku","uyaoq","mhqocp","ykwjo","vstm","ofncgxrkqvcwr","dqvh","taxyihn","idie","bwqtu","tztoovyfwz","rspvcccjjtcm","uojrpp","wmomuhn","cotycbiizlxcr","nrtyzfwytjblw","ocbya","sceqlciwmxw","ajtj","rspvccbcjjthcm","kfeuqyyre","dmelddg","txyihn","ubkcitlscmwxk","ntyswtnla","bdxjpstvlxktytxs","odqdvbh","pxpsvikeewekuq","mdwdmbdtux","vs","bma","wzbypigvo","qxyy","vsstm","hbtnqeba","hrzebpaz","xhbtfnjsqebbaj","ahaucermswyrv","ddmbtx","zhhbiqiw","pxpsvikewekuq","odqdvgbh","bxjpsvlxktxs","jsck","fjmdc","mdwdmbdtx","jqxyyrl","pxpsvikweuq","ctybizcr","dqvbh","lpl","lqrfzw","ufybaozoqk","znvaafvqi","yvuhmiuehp","hvrzebpazw","pfksrllrxw","alzuu","xjsvxtxs","afplzhbqguuu","icsingrbpqli","hjxytflhmvsgv","femafycxdxagn","uyaoqk","gmlckvofwyifrw","cinrbpql","jrcgbhss","oxwpkxlkcp","jkagljikwamaqvf","eollbigtftpdrdy","rspvcjcm","socbya","clapulm","qeb","kwrpi","efzpi","hbtfnqebaj","kykizdnandafusu","sclvazzpulmw","efzzpvvi","jfvvtavwsvgl","mhqocpy","v","mbpbvxsdastvtm","irqvnxouu","hvaajtj","ofnlcgxrkqvcwr","hbtqeba","hbtqeb","jwmqpds","ntrnlhujdslco","zv","npdbccbken","mhp","ddb","prllw","mddmbtx","clazpulm","cogxxpaknkse","bkitcmwxk","oxwpklkcp","tyiz","jwmqvpqods","waxyoxftvrgqmkgb","afplzhbbqsgujuu","bwtu","jercgbhss","rsacx","mahkqodcpy","cotycbiizlcr","ahabucermswyrv","lupvpkhr","dvnn","b","atpepsz","ncgxqvcr","qe","ubkcitlcmwxk","lyqrfzw","wimomuhn","bbmnaxl","motnfwohrwule","yvuyhmiuehspi","jfvvtavwsgl","rac","fefdxagn","bwqkctun","uotjrpp","ddbtx","afplzhbbqsguuu","xss","xsxs","wvyiqax","kykizdandaus","npdbccbkens","r","oxwnpkxjlakcp","tzmteoovsyfwz","kykizdnandafuspu","ahabulcermswyrvl","xjsxxs","qxyyr","ck","xhbtfnqebbaj","nqpdbccbkens","mpvsdastvtm","zuexqoxyp","gmlkvofwyifrw","kmw","txhn","kykizdandausu","molznwhkxsn","lupvpdkhr","jwmqvpds","bktcmwx","wyiax","hzvaajtj","ddbx","pifojogveub","naafvqi","motnfwjohrwule","odqvbh","aicsingrbpqli","jopzyeg","lybtqrfazrw","pijogveub","xzejfnllhfulns","scxllvazzpulmw","irqyvnxdcxfoejuu","cogxpaknks","pdkwrpsi","wzbycpajigvo","xjsxtxs","irqvnxdcxfoejuu","xhbtfnjqebbaj","uybaoqk","oncgxqvcr","aj","pepuwsxl","lytqrfzw","nkm","jrgs","pkrllrw","wscfvrfyli","bbmjnaxcaml","jftavwg","vuzvcozaif","pifjogveub","cmogxxpaknkse","cinrbql","scqlciwmxw","ztvyfz","mxyevopfoimgjpww","soicbya","lupvpdkhrm","ahaucermsyrv","ufybmvbaouzoqk","bdxjpsvlxktxs","hjxytfflhmvsgv","hjvxytfflhmvsgv","nqpdbccbzkxens","wr","kykzdndus","iddimea","fjmdrcc","efzzpi","vsdastm","btqeb","pfkrllrxw","ocby","irqvnxxouu","ildzpdimea","lzwhkxsn","ilddimea","ufybvbaozoqk","mxyevopfoimgjww","jhr","kcmwx","dvn","uzcof","glw","hbtnqebaj","riahv","w","qeugv","kfeuqyre","ilrdzpdimea","lplm","icinrbpql","scqlcmxw","bbmjnaxaml","e","rsac","bf","jwmqvpqds","tzteoovsyfwz","rc","lzwhkxs","jkgljikwamaqvf","tybizc","aplzuu","nrtyzifwytjblw","pze","bktcmwxk","uiykgmc","jsctk","npdbccbe","tybizcr"] print(s.longestStrChain(words))

133.083333

6,436

0.689167

772

7,985

7.11658

0.806995

0.003822

0.009829

0.003822

0.006735

0.005096

0

0.004341

0.076894

7,985

60

6,437

133.083333

0.741012

0.049092

0

0.166667

0

0.628073

0

1

0.055556

false

0

0.222222

0.027778

0

null

0

1

0

1

0

null

0

3

35485b9479958d2d47a02d4fdf1fd0230cf334e9

284

py

Python

6 kyu/Real Password Cracker.py

mwk0408/codewars_solutions

9b4f502b5f159e68024d494e19a96a226acad5e5

[ "MIT" ]

6

2020-09-03T09:32:25.000Z

2020-12-07T04:10:01.000Z

6 kyu/Real Password Cracker.py

mwk0408/codewars_solutions

9b4f502b5f159e68024d494e19a96a226acad5e5

[ "MIT" ]

1

2021-12-13T15:30:21.000Z

6 kyu/Real Password Cracker.py

mwk0408/codewars_solutions

9b4f502b5f159e68024d494e19a96a226acad5e5

[ "MIT" ]

null

from itertools import product from hashlib import sha1 def password_cracker(hash): for i in range(5): for j in product("abcdefghijklmnopqrstuvwxyz", repeat=i+1): if sha1("".join(j).encode('utf-8')).hexdigest()==hash: return "".join(j)

31.555556

67

0.612676

37

284

4.675676

0.702703

0.057803

0

0.023585

0.253521

284

9

68

31.555556

0.792453

0

0.108772

0.091228

0

1

0.142857

false

0.142857

0.285714

0

0.571429

0

null

0

1

0

null

0

1

0

1

0

3

101f3b4e80adc3534c5aa8016e5bdc4f35dfeaaa

138

py

Python

Projeto/pedebem/bar/apps.py

UFOP-CSI477/2021-02-atividades-UFOP-LucasAlmeida

07b66989c1868ef50b557c9acafcafb3f931a870

[ "MIT" ]

null

Projeto/pedebem/bar/apps.py

UFOP-CSI477/2021-02-atividades-UFOP-LucasAlmeida

07b66989c1868ef50b557c9acafcafb3f931a870

[ "MIT" ]

null

Projeto/pedebem/bar/apps.py

UFOP-CSI477/2021-02-atividades-UFOP-LucasAlmeida

07b66989c1868ef50b557c9acafcafb3f931a870

[ "MIT" ]

null

from django.apps import AppConfig class BarConfig(AppConfig): default_auto_field = 'django.db.models.BigAutoField' name = 'bar'

19.714286

56

0.746377

17

138

5.941176

0.882353

0

0.15942

138

6

57

23

0.87069

0

0.231884

0.210145

0

1

0

false

0

0.25

0

1

0

1

0

null

0

1

0

null

0

1

0

3

101feaa476e1a0515bfd0e32c44607c3249ef6f9

1,463

py

Python

splendor_sim/test/factories/test_json_validator.py

markbrockettrobson/SplendorBots

f78fab8cccf30c9cc4351308eedfca4203e55463

[ "MIT" ]

1

2019-06-19T02:00:32.000Z

splendor_sim/test/factories/test_json_validator.py

markbrockettrobson/SplendorBots

f78fab8cccf30c9cc4351308eedfca4203e55463

[ "MIT" ]

null

splendor_sim/test/factories/test_json_validator.py

markbrockettrobson/SplendorBots

f78fab8cccf30c9cc4351308eedfca4203e55463

[ "MIT" ]

null

import unittest import splendor_sim.src.factories.json_validator as json_validator class TestJsonValidator(unittest.TestCase): def setUp(self): self._schema = { "name": {"type": "string"}, "age": {"type": "integer", "min": 10, "required": True}, } self._json = {"name": "mark", "age": 10} def test_validate_json(self): # Arrange test_json_validator = json_validator.JsonValidator(self._schema) # Act # Assert self.assertTrue(test_json_validator.validate_json(self._json)) def test_validate_json_true_missing_non_required_field(self): # Arrange self._json = {"age": 10} test_json_validator = json_validator.JsonValidator(self._schema) # Act # Assert self.assertTrue(test_json_validator.validate_json(self._json)) def test_validate_json_false_incorrect_type(self): # Arrange self._json = {"name": "mark", "age": "10"} test_json_validator = json_validator.JsonValidator(self._schema) # Act # Assert self.assertFalse(test_json_validator.validate_json(self._json)) def test_validate_json_false_missing_required_field(self): # Arrange self._json = {"name": "mark"} test_json_validator = json_validator.JsonValidator(self._schema) # Act # Assert self.assertFalse(test_json_validator.validate_json(self._json))

32.511111

72

0.650034

163

1,463

5.466258

0.239264

0.204265

0.152637

0.085297

0.73064

0.59596

0

0.007201

0.240602

1,463

44

73

33.25

0.794779

0.051265

0

0.333333

0

0.053818

0

0.166667

1

0.208333

false

0

0.083333

0

0.333333

0

null

1

0

1

0

null

0

1

0

3

102756becedccecab8caa6b163783ea5218405f9

123

py

Python

TorrentPython/TorrentPython/Defines.py

reignofmiracle/RM_Torrent

a3f34d1868c0aeadc9c2c3e301f798dfae4fb3ff

[ "MIT" ]

1

2020-01-02T02:05:36.000Z

TorrentPython/TorrentPython/Defines.py

reignofmiracle/RM_Torrent

a3f34d1868c0aeadc9c2c3e301f798dfae4fb3ff

[ "MIT" ]

null

TorrentPython/TorrentPython/Defines.py

reignofmiracle/RM_Torrent

a3f34d1868c0aeadc9c2c3e301f798dfae4fb3ff

[ "MIT" ]

null

class Defines(object): PROTOCOL_ID = b'BitTorrent protocol' RM_CLIENT_ID = b'RM' RM_CLIENT_VERSION = b'0100'

17.571429

40

0.691057

18

123

4.444444

0.611111

0.075

0

0.041237

0.211382

123

6

41

20.5

0.783505

0

0.204918

0

1

0

false

0

1

0

1

0

null

0

1

0

null

0

1

0

3

102e541c574caf63f801d1b925046bf123411f44

61

py

Python

testes/teste_script.py

SuyKingsleigh/KitCast

12ece1fbccd252dfeb7c417bc51519d4cbe3546c

[ "MIT" ]

null

testes/teste_script.py

SuyKingsleigh/KitCast

12ece1fbccd252dfeb7c417bc51519d4cbe3546c

[ "MIT" ]

null

testes/teste_script.py

SuyKingsleigh/KitCast

12ece1fbccd252dfeb7c417bc51519d4cbe3546c

[ "MIT" ]

null

import sys for arg in sys.argv[1:]: print('arg: ' + arg)

15.25

24

0.590164

11

61

3.272727

0.727273

0

0.021277

0.229508

61

4

25

15.25

0.744681

0

0.080645

0

1

0

false

0

0.333333

0

0.333333

1

0

null

0

1

0

null

0

1

0

3

10465ab3d0634ad742495f49de255abb27420c04

593

py

Python

base/c1_monitor/monitor_frame.py

wwllong/py-design-pattern

7d4f870cf1be09c7b3b4d5329df78765b86ea451

[ "Apache-2.0" ]

1

2021-05-20T07:24:55.000Z

base/c1_monitor/monitor_frame.py

wwllong/py-design-pattern

7d4f870cf1be09c7b3b4d5329df78765b86ea451

[ "Apache-2.0" ]

null

base/c1_monitor/monitor_frame.py

wwllong/py-design-pattern

7d4f870cf1be09c7b3b4d5329df78765b86ea451

[ "Apache-2.0" ]

null

# 监听模式-框架模型 from abc import ABCMeta, abstractmethod # 引入ABCMeta 和 abstractmethod 来定义抽象类和抽象方法 class Observer(metaclass=ABCMeta): """"观察者的基类""" @abstractmethod def update(self, observable, object): pass class Observable: """"被观察者的基类""" def __init__(self): self.__observers = [] def addObserver(self, observer): self.__observers.append(observer) def removeObserver(self, observer): self.__observers.remove(observer) def notifyObserver(self, object=0): for o in self.__observers: o.update(self, object)

19.766667

41

0.657673

61

593

6.196721

0.52459

0.137566

0.084656

0.132275

0

0.002208

0.236088

593

29

42

20.448276

0.83223

0.111298

0

1

0.333333

false

0.066667

0

0.533333

0

null

0

null

0

1

0

1

0

1

0

3

105e3c9cfd68e3d3a94772a8e2750d96d2f760fa

93

py

Python

billsplit/apps.py

ksarthak4ever/Django-Bill_Splitting

59ee546f9e034dfd21effa30629b75e42b07da92

[ "MIT" ]

null

billsplit/apps.py

ksarthak4ever/Django-Bill_Splitting

59ee546f9e034dfd21effa30629b75e42b07da92

[ "MIT" ]

null

billsplit/apps.py

ksarthak4ever/Django-Bill_Splitting

59ee546f9e034dfd21effa30629b75e42b07da92

[ "MIT" ]

1

2021-10-07T16:17:02.000Z

from django.apps import AppConfig class BillsplitConfig(AppConfig): name = 'billsplit'

15.5

33

0.763441

10

93

7.1

0.9

0

0.16129

93

5

34

18.6

0.910256

0

0.096774

0

1

0

false

0

0.333333

0

1

0

1

0

null

0

1

0

null

0

1

0

3

1062057a42335c1cb315386ef8ebe99938be11a0

530

py

Python

compiler/codegen/codegen.py

raccoon-lang/raccoon

2a88039f271fbb0c370d1a6b57b2d67e3e9c6c9b

[ "Apache-2.0" ]

3

2020-12-28T18:10:49.000Z

2022-01-25T20:07:07.000Z

compiler/codegen/codegen.py

Valentine-Mario/raccoon

4dda2f6e5d227b43412d20729844ba394d6386f9

[ "Apache-2.0" ]

1

2021-08-23T21:09:30.000Z

compiler/codegen/codegen.py

Valentine-Mario/raccoon

4dda2f6e5d227b43412d20729844ba394d6386f9

[ "Apache-2.0" ]

1

2021-01-15T08:32:37.000Z

""" """ from copy import deepcopy from platform import machine class Codegen: """ """ def __init__(self, ast, semantic_info): self.word_size = 64 if '64' in machine() else 32 self.semantic_info = semantic_info self.ast = ast def __repr__(self): fields = deepcopy(vars(self)) string = ", ".join([f"{repr(key)}: {repr(val)}" for key, val in fields.items()]) return "{" + string + "}" def generate(self): return self def dumps(self): pass

20.384615

88

0.567925

65

530

4.446154

0.538462

0.124567

0.110727

0

0.016

0.292453

530

25

89

21.2

0.754667

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0.066667

0.133333

0.066667

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null

0

null

0

1

0

1

0

1

0

3

1068edd9c2cde925ca4eea48af2003a80af99ef9

678

py

Python

ridt/tests/systemtests/test_st25.py

riskaware-ltd/ridt

c0288a2f814b2749bdf73de7157f7477ca271aff

[ "MIT" ]

null

ridt/tests/systemtests/test_st25.py

riskaware-ltd/ridt

c0288a2f814b2749bdf73de7157f7477ca271aff

[ "MIT" ]

9

2020-09-18T08:22:39.000Z

2021-07-20T09:39:59.000Z

ridt/tests/systemtests/test_st25.py

riskaware-ltd/ridt

c0288a2f814b2749bdf73de7157f7477ca271aff

[ "MIT" ]

1

2021-06-22T21:53:20.000Z

import unittest from os import listdir from os.path import join from os import remove import shutil from ridt.config import ConfigFileParser from ridt.container import Domain from ridt.container.eddydiffusionrun import EddyDiffusionRun from ridt.data import BatchDataStore from ridt.data import DataStoreReader class ST25(unittest.TestCase): """System Test 25. Test the system is able to output the data store created during a batch run or single run mode to disk.""" def setUp(self) -> None: pass def tearDown(self) -> None: pass def test_verify(self): pass if __name__ == "__main__": unittest.main()

19.371429

60

0.710914

91

678

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678

34

61

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0.157895

0.526316

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0.736842

0

null

0

null

0

1

0

1

0

3

107bc069d3961ebf37c18165bf8960fa751bb0a6

21

py

Python

settings.py

nelldnine/symph-fb-slack

7cbac7218fc66a24e12878bd1ad9b4e1a2781ec9

[ "Apache-2.0" ]

null

settings.py

nelldnine/symph-fb-slack

7cbac7218fc66a24e12878bd1ad9b4e1a2781ec9

[ "Apache-2.0" ]

null

settings.py

nelldnine/symph-fb-slack

7cbac7218fc66a24e12878bd1ad9b4e1a2781ec9

[ "Apache-2.0" ]

null

FACEBOOK_KEY = 'XXXX'

21

0.761905

3

21

5

1

0

0.095238

21

1

21

0.789474

0

0.181818

0

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0

1

0

null

0

1

0

null

0

3

1083b7e694d8db2ab131f1b8c7874780b9245da3

666

py

Python

Geeks4Geeks/InterChangeQ.py

aparnamaleth/CodingPractice

937600b0dce20be023527cc036d1118312a140ea

[ "MIT" ]

null

Geeks4Geeks/InterChangeQ.py

aparnamaleth/CodingPractice

937600b0dce20be023527cc036d1118312a140ea

[ "MIT" ]

null

Geeks4Geeks/InterChangeQ.py

aparnamaleth/CodingPractice

937600b0dce20be023527cc036d1118312a140ea

[ "MIT" ]

null

class Queue: def __init__(self): self.input = [] self.output = [] self.size = 0 def push(self,data): self.input.append(data) self.size = self.size + 1 def pop(self): x = (self.size) for i in range(x/2): self.output.append(self.input.pop(0)) for i in range(x/2): self.input.append(self.output.pop()) for i in range(x/2): self.input.append(self.input.pop(0)) for i in range(x/2): self.output.append(self.input.pop(0)) while self.output: self.input.append(self.output.pop()) self.input.append(self.input.pop(0)) return self.input q = Queue() q.push(20) q.push(30) q.push(40) q.push(50) print q.pop()

20.8125

61

0.627628

118

666

3.508475

0.237288

0.23913

0.181159

0.10628

0.536232

0.44686

0.379227

0

0.033582

0.195195

666

31

62

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0.738806

0

0.357143

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null

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null

0.035714

0

null

1

0

null

0

1

0

3

52c19cf49bc3e20ccaba6c7c3afc80ddc76a4a5a

828

py

Python

linear-regression/src/my_kappa_calculator.py

yanshengjia/nlp

43398652b2cab9b85fd042f60e6f68c7b48697bc

[ "MIT" ]

1

2018-04-12T07:48:10.000Z

linear-regression/src/my_kappa_calculator.py

yanshengjia/nlp

43398652b2cab9b85fd042f60e6f68c7b48697bc

[ "MIT" ]

null

linear-regression/src/my_kappa_calculator.py

yanshengjia/nlp

43398652b2cab9b85fd042f60e6f68c7b48697bc

[ "MIT" ]

1

2018-05-02T06:53:29.000Z

# !/usr/bin/python # -*- coding:utf-8 -*- # Author: Shengjia Yan # Date: 2017-10-19 # Email: i@yanshengjia.com import numpy as np from quadratic_weighted_kappa import quadratic_weighted_kappa as qwk from quadratic_weighted_kappa import linear_weighted_kappa as lwk def assert_inputs(rater_a, rater_b): assert np.issubdtype(rater_a.dtype, np.integer), 'Integer array expected, got ' + str(rater_a.dtype) assert np.issubdtype(rater_b.dtype, np.integer), 'Integer array expected, got ' + str(rater_b.dtype) def quadratic_weighted_kappa(rater_a, rater_b, min_rating, max_rating): assert_inputs(rater_a, rater_b) return qwk(rater_a, rater_b, min_rating, max_rating) def linear_weighted_kappa(rater_a, rater_b, min_rating, max_rating): assert_inputs(rater_a, rater_b) return lwk(rater_a, rater_b, min_rating, max_rating)

36

101

0.78744

136

828

4.492647

0.330882

0.08838

0.126023

0.13748

0.628478

0.523732

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0.386252

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0

0.012228

0.111111

828

22

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37.636364

0.817935

0.123188

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0.416667

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0.25

false

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0.25

0

0.666667

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null

0

null

0

1

0

1

0

1

0

3

52d54f5fa4aaa53b074f49e11c11b9b420b21684

1,070

py

Python

registration/views.py

riggedCoinflip/mydjango

9f30effc0dccd95916f59a3b65d7e02bdd2827b5

[ "MIT" ]

null

registration/views.py

riggedCoinflip/mydjango

9f30effc0dccd95916f59a3b65d7e02bdd2827b5

[ "MIT" ]

1

2021-02-26T02:13:35.000Z

registration/views.py

riggedCoinflip/mydjango

9f30effc0dccd95916f59a3b65d7e02bdd2827b5

[ "MIT" ]

null

from django.contrib.auth import authenticate, login from django.http import HttpResponseRedirect from django.urls import reverse_lazy from django.views import generic from activate.verifiy import send_verification_email from users.forms import CustomUserCreationForm from users.models import User class SignupView(generic.CreateView): template_name = 'registration/signup.html' model = User form_class = CustomUserCreationForm success_url = reverse_lazy('welcome') def form_valid(self, form): self.object = form.save() user = self.object send_verification_email(user, self.request) # TODO messages.info(request, "Thanks for registering. You are now logged in.") user = authenticate(username=form.cleaned_data['username'], password=form.cleaned_data['password1'], ) login(self.request, user) return HttpResponseRedirect(self.get_success_url()) class WelcomeView(generic.TemplateView): template_name = 'registration/welcome.html'

34.516129

87

0.714953

120

1,070

6.25

0.525

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0.056

0

0.001178

0.206542

1,070

30

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35.666667

0.882214

0.071963

0

0.073663

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0.043478

false

0.043478

0.304348

0

0.695652

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null

0

null

0

1

0

1

0

1

0

3

52e449c0774455cb2b78f5505b5dee811827265a

7,698

py

Python

corona_data_collector/tests/test_consistent_length.py

tkarady/avid-covider-pipelines

43944410092ebab24e821b385de3735b757f0062

[ "MIT" ]

null

corona_data_collector/tests/test_consistent_length.py

tkarady/avid-covider-pipelines

43944410092ebab24e821b385de3735b757f0062

[ "MIT" ]

20

2020-04-16T16:26:07.000Z

2020-10-08T07:52:27.000Z

corona_data_collector/tests/test_consistent_length.py

tkarady/avid-covider-pipelines

43944410092ebab24e821b385de3735b757f0062

[ "MIT" ]

5

2020-04-16T16:36:35.000Z

2020-10-03T12:48:05.000Z

from corona_data_collector.DBToFileWriter import collect_row from corona_data_collector.config import values_to_convert from corona_data_collector import load_from_db, add_gps_coordinates, export_corona_bot_answers from dataflows import Flow, load, printer from avid_covider_pipelines.utils import get_parameters_from_pipeline_spec import random import logging logging.basicConfig(level=logging.INFO) def test_exposure_status_failure(): print("test_exposure_status_failure") record = { 'age': '72', 'sex': 0, 'locale': 'he', 'street': 'יונה סאלק', 'smoking': 1, 'version': '1.0.1', 'city_town': 'אשדוד', 'temperature': '36.3', 'met_above_18': '0', 'met_under_18': '0', 'general_feeling': 0, 'numPreviousReports': 0, 'chronic_hypertension': 1, 'id': 175130, 'created': '2020-03-25T16:32:38.997021', 'insulation_status': 0 } record_to_store = collect_row(record) print(record_to_store) assert len(record_to_store) > 0, 'failed to create a record that can be stored in file' print("OK") def test_expected_contact_with_patient(): print("test_expected_contact_with_patient") back_from_abroad_db = [169603, 169632, 169813] contact_with_patient_db = [10722, 10715, 10697] Flow( load_from_db.flow({ "where": "id in (%s)" % ", ".join(map(str, back_from_abroad_db + contact_with_patient_db)) }), add_gps_coordinates.flow({ "source_fields": get_parameters_from_pipeline_spec("pipeline-spec.yaml", "corona_data_collector", "corona_data_collector.add_gps_coordinates")["source_fields"], "get-coords-callback": lambda street, city: (random.uniform(29, 34), random.uniform(34, 36), int(street != city)) }), export_corona_bot_answers.flow({ "destination_output": "data/corona_data_collector/destination_output" }), ).process() contact_with_patient_key = values_to_convert['insulation_status']['contact-with-patient'] back_from_abroad_key = values_to_convert['insulation_status']['back-from-abroad'] contact_with_patient_array = [] back_from_abroad_array = [] counts = {"contact_with_patient": 0, "back_from_abroad": 0} def _test(row): if int(row["isolation"]) == contact_with_patient_key: counts["contact_with_patient"] += 1 contact_with_patient_array.append(int(row["id"])) if int(row["isolation"]) == back_from_abroad_key: assert int(row["id"]) in back_from_abroad_db counts["back_from_abroad"] += 1 back_from_abroad_array.append(int(row["id"])) Flow( load('data/corona_data_collector/destination_output/corona_bot_answers_25_3_2020_with_coords.csv'), load('data/corona_data_collector/destination_output/corona_bot_answers_22_3_2020_with_coords.csv'), _test, ).process() assert 3 == counts["contact_with_patient"], str(counts) assert 3 == counts["back_from_abroad"], str(counts) assert set(back_from_abroad_array) == set(back_from_abroad_db) assert set(contact_with_patient_array) == set(contact_with_patient_db) print("OK") def test_isolated_total_count(): print("test_isolated_total_count") db_isolated_id = [169603,169630,169632,169637,169690,169728,169753,169813,169829,169837,169882,169924,169930,170014,170042,170064,170067,170097,170099,170127,170184,170223,170234,170244,170263,170272,170289,170322,170326,170328,170350,170370,170390,170414,170428,170432,170436,170438,170442,170448,170453,170478,170479,170621,170629,170685,170735,170744,170777,170811,170878,170886,170903,170929,170936,170962,170970,170989,171009,171018,171078,171097,171123,171127,171132,171133,171142,171158,171162,171200,171201,171230,171256,171268,171283,171288,171290,171302,171323,171337,171342,171374,171399,171440,171472,171499,171506,171541,171571,171590,171599,171615,171686,171718,171720,171753,171823,171865,171900,171904,171907,171991,172048,172076,172153,172155,172163,172165,172218,172225,172231,172233,172236,172263,172276,172277,172316,172367,172373,172406,172419,172458,172483,172491,172492,172505,172511,172537,172542,172594,172596,172629,172637,172638,172644,172716,172727,172733,172749,172750,172789,172797,172808,172810,172894,172923,172925,172952,172956,172972,172995,173006,173077,173087,173112,173177,173178,173186,173199,173211,173222,173272,173275,173335,173336,173377,173436,173466,173507,173524,173579,173671,173768,173816,173965,173973,173979,173980,174018,174040,174049,174055,174063,174082,174084,174095,174099,174144,174146,174167,174202,174206,174232,174236,174239,174242,174258,174259,174263,174267,174271,174295,174313,174332,174350,174359,174369,174372,174374,174394,174405,174411,174443,174456,174470,174496,174506,174511,174541,174617,174652,174744,174768,174779,174813,174830,174840,174850,174859,174865,174890,174910,174997,175018,175025,175027,175056,175128,175154,175159,175167,175179,175235,175280,175290,175332,175339,175373,175424,175443,175455,175465,175470,175492,175503,175519,175537,175542,175628,175644,175684,175691,175730,175765,175773,175790,175831,175849,175857,175863,175880,175883,175887,175894,175908,175976,176035,176040,176046,176076,176124,176132,176198,176202,176211,176241,176288,176300,176340,176364,176386,176408,176435,176453,176466,176478,176490,176501,176534,176574,176613,176617,176674,176681,176804,176825,176827,176860,176889,176926,176930,177008,177045,177107,177113,177118,177122,177136,177207,177211,177238,177296,177363,177381,177409,177418,177426,177512,177559,177575,177608,177627,177721,177732,177780,177798,177810,177865,177870,177905,177945,177947,177953,178091,178118,178138,178186,178217,178252,178289,178304,178328,178420,178508,178511,178517,178525,178551,178603,178604,178681,178700,178713,178742,178750,178756,178781,178792,178836,178848,178867,178881,178910,178939,178955,179016,179033,179065,179066,179074,179160,179185,179212,179225,179250,179270,179281,179294,179338,179376,179418,179480,179492,179549,179594,179621,179661,179664,179669,179683,179702,179714,179758,179768,179769,179888,179982,180002,180010,180021,180027,180044,180074,180123,180125,180131,180136,180145,180169,180198,180271,180284,180383,180394,180438,180448,180478,180505,180511,180553,180575,180579,180587,180629,180725,180747,180795,180798,180840,180888,180941,180943,180944,180964,180991,181023,181037,181049,181120,181162,181164,181192,181218,181220,181230,181252,181304,181326,181339,181410,181445,181483,181520,181555,181562,181599,181630,181665] Flow( load_from_db.flow({ "where": "id in (%s)" % ", ".join(map(str, db_isolated_id)) }), add_gps_coordinates.flow({ "source_fields": get_parameters_from_pipeline_spec("pipeline-spec.yaml", "corona_data_collector", "corona_data_collector.add_gps_coordinates")["source_fields"], "get-coords-callback": lambda street, city: (random.uniform(29, 34), random.uniform(34, 36), int(street != city)) }), export_corona_bot_answers.flow({ "destination_output": "data/corona_data_collector/destination_output" }), ).process() counts = {"isolated": 0} def _test(row): if int(row["isolation"]) > 0: assert int(row["id"]) in db_isolated_id counts["isolated"] += 1 Flow( load('data/corona_data_collector/destination_output/corona_bot_answers_25_3_2020_with_coords.csv'), _test, ).process() assert 468 == counts["isolated"], str(counts) print("OK") if __name__ == "__main__": test_exposure_status_failure() test_expected_contact_with_patient() test_isolated_total_count() print("Great Success!")

71.943925

3,298

0.759548

1,039

7,698

5.40616

0.583253

0.029375

0.048068

0.020474

0.249065

0.209008

0.185508

0.166281

0

0.427014

0.106521

7,698

106

3,299

72.622642

0.389648

0

0.388889

0

0.168117

0.077563

0

0.088889

1

0.055556

false

0

0.077778

0

0.133333

0.1

0

null

0

1

0

1

0

1

0

null

0

3

5e0764b2930a571b47c8addd74be87438238db9d

3,371

py

Python

PyObjCTest/test_nsfilehandle.py

linuxfood/pyobjc-framework-Cocoa-test

3475890f165ab26a740f13d5afe4c62b4423a140

[ "MIT" ]

null

PyObjCTest/test_nsfilehandle.py

linuxfood/pyobjc-framework-Cocoa-test

3475890f165ab26a740f13d5afe4c62b4423a140

[ "MIT" ]

null

PyObjCTest/test_nsfilehandle.py

linuxfood/pyobjc-framework-Cocoa-test

3475890f165ab26a740f13d5afe4c62b4423a140

[ "MIT" ]

null

import Foundation from PyObjCTools.TestSupport import TestCase, min_os_level class TestNSFileHandle(TestCase): def testConstants(self): self.assertIsInstance(Foundation.NSFileHandleOperationException, str) self.assertIsInstance(Foundation.NSFileHandleReadCompletionNotification, str) self.assertIsInstance( Foundation.NSFileHandleReadToEndOfFileCompletionNotification, str ) self.assertIsInstance( Foundation.NSFileHandleConnectionAcceptedNotification, str ) self.assertIsInstance(Foundation.NSFileHandleDataAvailableNotification, str) self.assertIsInstance(Foundation.NSFileHandleNotificationDataItem, str) self.assertIsInstance(Foundation.NSFileHandleNotificationFileHandleItem, str) self.assertIsInstance(Foundation.NSFileHandleNotificationMonitorModes, str) def testMethods(self): f = Foundation.NSFileHandle.alloc().initWithFileDescriptor_closeOnDealloc_( 0, False ) self.assertArgIsBOOL(f.initWithFileDescriptor_closeOnDealloc_, 1) @min_os_level("10.6") def testMethods10_6(self): self.assertArgIsOut( Foundation.NSFileHandle.fileHandleForReadingFromURL_error_, 1 ) self.assertArgIsOut(Foundation.NSFileHandle.fileHandleForWritingToURL_error_, 1) self.assertArgIsOut(Foundation.NSFileHandle.fileHandleForUpdatingURL_error_, 1) @min_os_level("10.7") def testMethods10_7(self): self.assertArgIsBlock(Foundation.NSFileHandle.setReadabilityHandler_, 0, b"v@") self.assertArgIsBlock(Foundation.NSFileHandle.setWriteabilityHandler_, 0, b"v@") self.assertResultIsBlock(Foundation.NSFileHandle.readabilityHandler, b"v@") self.assertResultIsBlock(Foundation.NSFileHandle.writeabilityHandler, b"v@") @min_os_level("10.15") def testMethods10_15(self): self.assertArgIsOut( Foundation.NSFileHandle.readDataToEndOfFileAndReturnError_, 0 ) self.assertArgIsOut(Foundation.NSFileHandle.readDataUpToLength_error_, 1) self.assertResultIsBOOL(Foundation.NSFileHandle.writeData_error_) self.assertArgIsOut(Foundation.NSFileHandle.writeData_error_, 1) self.assertResultIsBOOL(Foundation.NSFileHandle.getOffset_error_) self.assertArgIsOut(Foundation.NSFileHandle.getOffset_error_, 0) self.assertArgIsOut(Foundation.NSFileHandle.getOffset_error_, 1) self.assertResultIsBOOL(Foundation.NSFileHandle.seekToEndReturningOffset_error_) self.assertArgIsOut(Foundation.NSFileHandle.seekToEndReturningOffset_error_, 0) self.assertArgIsOut(Foundation.NSFileHandle.seekToEndReturningOffset_error_, 1) self.assertResultIsBOOL(Foundation.NSFileHandle.seekToOffset_error_) self.assertArgIsOut(Foundation.NSFileHandle.seekToOffset_error_, 1) self.assertResultIsBOOL(Foundation.NSFileHandle.truncateAtOffset_error_) self.assertArgIsOut(Foundation.NSFileHandle.truncateAtOffset_error_, 1) self.assertResultIsBOOL(Foundation.NSFileHandle.synchronizeAndReturnError_) self.assertArgIsOut(Foundation.NSFileHandle.synchronizeAndReturnError_, 0) self.assertResultIsBOOL(Foundation.NSFileHandle.closeAndReturnError_) self.assertArgIsOut(Foundation.NSFileHandle.closeAndReturnError_, 0)

48.157143

88

0.770691

265

3,371

9.6

0.233962

0.224843

0.154088

0.220126

0.402516

0.284591

0

0.013347

0.155443

3,371

69

89

48.855072

0.880225

0

0.071429

0

0.00623

0

0.607143

1

0.089286

false

0

0.035714

0

0.142857

0

null

1

0

1

0

null

0

1

0

3

5e145878ae7e135adacc19d7438d7f356863d5bc

1,171

py

Python

infoblox_netmri/api/remote/models/subnet_network_explorer_summaries_section_grid_remote.py

IngmarVG-IB/infoblox-netmri

b0c725fd64aee1890d83917d911b89236207e564

[ "Apache-2.0" ]

null

infoblox_netmri/api/remote/models/subnet_network_explorer_summaries_section_grid_remote.py

IngmarVG-IB/infoblox-netmri

b0c725fd64aee1890d83917d911b89236207e564

[ "Apache-2.0" ]

null

infoblox_netmri/api/remote/models/subnet_network_explorer_summaries_section_grid_remote.py

IngmarVG-IB/infoblox-netmri

b0c725fd64aee1890d83917d911b89236207e564

[ "Apache-2.0" ]

null

19.847458

67

0.478224

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7.84507

0.366197

0.210054

0.274686

0.371634

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1,171

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19.847458

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null

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null

0

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5e327699c9961aa4b34d7ffaa20719c0711985bb

3,032

py

Python

simsalabim/dsp/snr.py

simklein/simsalabim

feade4cf0c95d89e9d845feda2b5c3693eceb5f0

[ "MIT" ]

null

simsalabim/dsp/snr.py

simklein/simsalabim

feade4cf0c95d89e9d845feda2b5c3693eceb5f0

[ "MIT" ]

null

simsalabim/dsp/snr.py

simklein/simsalabim

feade4cf0c95d89e9d845feda2b5c3693eceb5f0

[ "MIT" ]

null

import numpy as np def wada_snr(wav): # Direct blind estimation of the SNR of a speech signal. # # Paper on WADA SNR: # http://www.cs.cmu.edu/~robust/Papers/KimSternIS08.pdf # # This function was adapted from this matlab code: # https://labrosa.ee.columbia.edu/projects/snreval/#9 # init eps = 1e-10 # next 2 lines define a fancy curve derived from a gamma distribution -- see paper db_vals = np.arange(-20, 101) g_vals = np.array([0.40974774, 0.40986926, 0.40998566, 0.40969089, 0.40986186, 0.40999006, 0.41027138, 0.41052627, 0.41101024, 0.41143264, 0.41231718, 0.41337272, 0.41526426, 0.4178192 , 0.42077252, 0.42452799, 0.42918886, 0.43510373, 0.44234195, 0.45161485, 0.46221153, 0.47491647, 0.48883809, 0.50509236, 0.52353709, 0.54372088, 0.56532427, 0.58847532, 0.61346212, 0.63954496, 0.66750818, 0.69583724, 0.72454762, 0.75414799, 0.78323148, 0.81240985, 0.84219775, 0.87166406, 0.90030504, 0.92880418, 0.95655449, 0.9835349 , 1.01047155, 1.0362095 , 1.06136425, 1.08579312, 1.1094819 , 1.13277995, 1.15472826, 1.17627308, 1.19703503, 1.21671694, 1.23535898, 1.25364313, 1.27103891, 1.28718029, 1.30302865, 1.31839527, 1.33294817, 1.34700935, 1.3605727 , 1.37345513, 1.38577122, 1.39733504, 1.40856397, 1.41959619, 1.42983624, 1.43958467, 1.44902176, 1.45804831, 1.46669568, 1.47486938, 1.48269965, 1.49034339, 1.49748214, 1.50435106, 1.51076426, 1.51698915, 1.5229097 , 1.528578 , 1.53389835, 1.5391211 , 1.5439065 , 1.54858517, 1.55310776, 1.55744391, 1.56164927, 1.56566348, 1.56938671, 1.57307767, 1.57654764, 1.57980083, 1.58304129, 1.58602496, 1.58880681, 1.59162477, 1.5941969 , 1.59693155, 1.599446 , 1.60185011, 1.60408668, 1.60627134, 1.60826199, 1.61004547, 1.61192472, 1.61369656, 1.61534074, 1.61688905, 1.61838916, 1.61985374, 1.62135878, 1.62268119, 1.62390423, 1.62513143, 1.62632463, 1.6274027 , 1.62842767, 1.62945532, 1.6303307 , 1.63128026, 1.63204102]) # peak normalize, get magnitude, clip lower bound wav = np.array(wav) wav = wav / abs(wav).max() abs_wav = abs(wav) abs_wav[abs_wav < eps] = eps # calcuate statistics # E[|z|] v1 = max(eps, abs_wav.mean()) # E[log|z|] v2 = np.log(abs_wav).mean() # log(E[|z|]) - E[log(|z|)] v3 = np.log(v1) - v2 # table interpolation wav_snr_idx = None if any(g_vals < v3): wav_snr_idx = np.where(g_vals < v3)[0].max() # handle edge cases or interpolate if wav_snr_idx is None: wav_snr = db_vals[0] elif wav_snr_idx == len(db_vals) - 1: wav_snr = db_vals[-1] else: wav_snr = db_vals[wav_snr_idx] + \ (v3-g_vals[wav_snr_idx]) / (g_vals[wav_snr_idx+1] - \ g_vals[wav_snr_idx]) * (db_vals[wav_snr_idx+1] - db_vals[wav_snr_idx]) # Calculate SNR dEng = sum(wav**2) dFactor = 10**(wav_snr / 10) dNoiseEng = dEng / (1 + dFactor) # Noise energy dSigEng = dEng * dFactor / (1 + dFactor) # Signal energy snr = 10 * np.log10(dSigEng / dNoiseEng) return snr

57.207547

1,475

0.666887

491

3,032

4.03055

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0.012127

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3,032

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57.207547

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null

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1

0

1

0

null

0

3

eaaa69efcceb1b8352b1342b6299549cc66b1c6a

704

py

Python

pytablewriter/writer/text/__init__.py

shawalli/pytablewriter

2e3f84cb3c5676aa67711aa3e908b6e420c934b7

[ "MIT" ]

null

pytablewriter/writer/text/__init__.py

shawalli/pytablewriter

2e3f84cb3c5676aa67711aa3e908b6e420c934b7

[ "MIT" ]

null

pytablewriter/writer/text/__init__.py

shawalli/pytablewriter

2e3f84cb3c5676aa67711aa3e908b6e420c934b7

[ "MIT" ]

null

from ._borderless import BorderlessTableWriter from ._css import CssTableWriter from ._csv import CsvTableWriter from ._html import HtmlTableWriter from ._json import JsonTableWriter from ._jsonlines import JsonLinesTableWriter from ._latex import LatexMatrixWriter, LatexTableWriter from ._ltsv import LtsvTableWriter from ._markdown import MarkdownTableWriter from ._mediawiki import MediaWikiTableWriter from ._rst import RstCsvTableWriter, RstGridTableWriter, RstSimpleTableWriter from ._spacealigned import SpaceAlignedTableWriter from ._toml import TomlTableWriter from ._tsv import TsvTableWriter from ._unicode import BoldUnicodeTableWriter, UnicodeTableWriter from ._yaml import YamlTableWriter

41.411765

77

0.875

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704

8.823529

0.558824

0

0.096591

704

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true

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null

0

null

0

1

0

1

0

1

0

3

eac5df3ff69b83b9f50665bb87424eda3f56cbef

1,246

py

Python

boilerplate-polygon-area-calculator/shape_calculator.py

pablohema/FreeCodeCamp-Scientific-Computing--PythonCertification

d7fb13bed510bc191d84c20e414e545f5eb0c4f3

[ "MIT" ]

null

boilerplate-polygon-area-calculator/shape_calculator.py

pablohema/FreeCodeCamp-Scientific-Computing--PythonCertification

d7fb13bed510bc191d84c20e414e545f5eb0c4f3

[ "MIT" ]

null

boilerplate-polygon-area-calculator/shape_calculator.py

pablohema/FreeCodeCamp-Scientific-Computing--PythonCertification

d7fb13bed510bc191d84c20e414e545f5eb0c4f3

[ "MIT" ]

null

class Rectangle: def __init__(self, width, height): self.width = width self.height = height def __str__(self): return "Rectangle(width=" + str(self.width) + \ ", height=" + str(self.height) + ")" def set_width(self, width): self.width = width def set_height(self, height): self.height = height def get_area(self): return self.width * self.height def get_perimeter(self): return 2 * (self.width + self.height) def get_diagonal(self): return (self.width ** 2 + self.height ** 2) ** .5 def get_picture(self): if self.width > 50 or self.height > 50: return "Too big for picture." rectangle = ("*" * self.width + "\n") * self.height return rectangle def get_amount_inside(self, shape): max_width = self.width // shape.width max_height = self.height // shape.height return max_width * max_height class Square(Rectangle): def __init__(self, side): self.width = side self.height = side def set_side(self, side): self.width = side self.height = side def __str__(self): return "Square(side=" + str(self.width) + ")"

25.428571

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0.577849

154

1,246

4.487013

0.194805

0.182344

0.065123

0.057887

0.182344

0.109986

0

0.009185

0.300963

1,246

48

60

25.958333

0.784156

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0.342857

false

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0.142857

0.628571

0

null

0

null

0

1

0

1

0

3

eac82e41bcb43ffe9b9470edc7c59e27d7472a05

186

py

Python

setup.py

juliscrazy/Otto-Bot

6d5af7ddb7ffb318f0f78d3bf2cf27631a305b4f

[ "MIT" ]

null

setup.py

juliscrazy/Otto-Bot

6d5af7ddb7ffb318f0f78d3bf2cf27631a305b4f

[ "MIT" ]

null

setup.py

juliscrazy/Otto-Bot

6d5af7ddb7ffb318f0f78d3bf2cf27631a305b4f

[ "MIT" ]

null

from distutils.core import setup setup(name='Otto-Bot', version='1.0', description='Python Discord Bot', author='jul', author_email='julislazy@gmail.com', )

23.25

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0.634409

23

186

5.086957

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0.013793

0.22043

186

8

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0.272727

0

1

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true

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