xszheng2020/the_stack_dedup_python_hits_4 · Datasets at Hugging Face

bdf14975d6521c1daf5a7ef9a7ee88fad5142051

538

py

Python

application/mod_default/forms.py

calitaz/logisticdemandsflask

a15d30ddd91a629fd23d94a42b9ec47c3b5b9755

[ "MIT" ]

null

application/mod_default/forms.py

calitaz/logisticdemandsflask

a15d30ddd91a629fd23d94a42b9ec47c3b5b9755

[ "MIT" ]

null

application/mod_default/forms.py

calitaz/logisticdemandsflask

a15d30ddd91a629fd23d94a42b9ec47c3b5b9755

[ "MIT" ]

null

# Import forms dependences from flask_wtf import FlaskForm from wtforms import IntegerField, DecimalField from wtforms.validators import DataRequired # Define SMA / MMS form class SMAForm(FlaskForm): base = IntegerField('base', validators=[DataRequired("Type the base of evaluation/Digite a base de avaliação")]) period = IntegerField('period', validators=[DataRequired("Type the period /Digite o periodo")]) demand = DecimalField('demand', validators=[DataRequired("Type the demand /Digite a demanda para este periodo")])

41.384615

117

0.771375

65

538

6.369231

0.523077

0.15942

0.188406

0.210145

0

0.135688

538

12

118

44.833333

0.890323

0.085502

0

0.315574

0

1

0

false

0

0.428571

0

1

0

null

0

1

0

null

0

1

0

1

0

4

bdf3f6999a59426ef47dbde77b810ac60c9c1d94

225

py

Python

cbpos/uihandler.py

juliustip/cb

e57470694314f37e1ed090fd94ae438f4f7c8093

[ "MIT" ]

1

2017-10-09T17:31:29.000Z

cbpos/uihandler.py

juliustip/cb

e57470694314f37e1ed090fd94ae438f4f7c8093

[ "MIT" ]

null

cbpos/uihandler.py

juliustip/cb

e57470694314f37e1ed090fd94ae438f4f7c8093

[ "MIT" ]

3

2015-08-20T11:44:55.000Z

2021-03-17T15:37:57.000Z

class BaseUIHandler(object): def handle_first_run(self): pass def init(self): raise NotImplementedError('No UI handler specified!') return False def start(self): return 1

20.454545

61

0.604444

25

225

5.36

0.8

0

0.006536

0.32

225

10

62

22.5

0.869281

0

0.106667

0

1

0.375

false

0.125

0

0.125

0.75

0

1

0

null

0

null

0

1

0

1

0

1

0

4

da1a66b4d77b7ba51689e636096b43e52bc0b90a

139

py

Python

curso/models.py

latreta/dvestagio

d18f7c7184748c7b88e335ae9ffd2bdcc197d14f

[ "MIT" ]

null

curso/models.py

latreta/dvestagio

d18f7c7184748c7b88e335ae9ffd2bdcc197d14f

[ "MIT" ]

null

curso/models.py

latreta/dvestagio

d18f7c7184748c7b88e335ae9ffd2bdcc197d14f

[ "MIT" ]

null

from django.db import models # Create your models here. class Curso(models.Model): nome = models.CharField(max_length=100,unique=True)

27.8

55

0.769784

21

139

5.047619

0.857143

0

0.024793

0.129496

139

5

55

27.8

0.85124

0.172662

0

1

0

false

0

0.333333

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

da27510153443bf188e25bd3413d9e832423d877

42

py

Python

start/Intro to Lists/fix01/fixup2.py

codermoji-contrib/python

764bffaf0e92270be196aa5728f255aaaf5b8150

[ "MIT" ]

null

start/Intro to Lists/fix01/fixup2.py

codermoji-contrib/python

764bffaf0e92270be196aa5728f255aaaf5b8150

[ "MIT" ]

null

start/Intro to Lists/fix01/fixup2.py

codermoji-contrib/python

764bffaf0e92270be196aa5728f255aaaf5b8150

[ "MIT" ]

null

items = ['water', 1, 'food'] print(items)

14

28

0.595238

6

42

4.166667

0.833333

0

0.027778

0.142857

42

2

29

21

0.666667

0

0.214286

0

1

0

false

0

0.5

1

0

null

0

1

0

null

0

1

0

4

da45d8b62434ee193cad9d23200a7c52c558d0a8

115

py

Python

blog/urls.py

HyunJW/Article-writing-advice-system-based-on-GPT2-language-model

9abfa7ed02f2d4120883b09011bf7dafa3c3ade0

[ "Apache-2.0" ]

1

2020-05-23T08:42:39.000Z

blog/urls.py

ahnyujin/Django-proj

002c18c8a5c61bf6a4ae8ae7c56bc276afebf2d2

[ "Apache-2.0" ]

null

blog/urls.py

ahnyujin/Django-proj

002c18c8a5c61bf6a4ae8ae7c56bc276afebf2d2

[ "Apache-2.0" ]

null

from django.urls import path from . import views urlpatterns = [ path('', views.post_list, name='post_list'), ]

23

48

0.704348

16

115

4.9375

0.625

0.202532

0

0.156522

115

5

49

23

0.814433

0

0.077586

0

1

0

false

0

0.4

0

0.4

0

1

0

null

1

0

1

0

null

0

1

0

4

da7ec7e2ca1870196c6d605a1c4bcae9be3dea00

622

py

Python

lusee/__init__.py

lusee-night/luseepy

064a72ac1fb9635d428da1889f2e5ed429707c24

[ "MIT" ]

null

lusee/__init__.py

lusee-night/luseepy

064a72ac1fb9635d428da1889f2e5ed429707c24

[ "MIT" ]

5

2022-02-11T09:08:55.000Z

2022-02-17T10:17:00.000Z

lusee/__init__.py

lusee-night/luseepy

064a72ac1fb9635d428da1889f2e5ed429707c24

[ "MIT" ]

null

# -- FIXME -- # -mxp- commented out imports since the hardcoded cache breaks # execution on Singularity and otherwise has undesirable side # effects. There is a separate cache in the LunarCalendar class # now, which can be propagated to other modules. It behaves # gracefully e.g. optionally cleaned up, has db name capability etc. #from . import lunar_calendar as calendar from .observation import LObservation from .lunar_satellite import LSatellite, ObservedSatellite from .LBeam import LBeam, grid2healpix from .simulation import Simulator from . import sky_models as sky from . import mono_sky_models as monosky

36.588235

68

0.797428

87

622

5.643678

0.735632

0.0611

0.044807

0

0.001905

0.155949

622

16

69

38.875

0.933333

0.57717

0

0.0625

0

1

0

true

0

1

0

1

0

null

0

null

0

1

0

1

0

1

0

1

0

4

da892f2f8a6bd538464c45d23066d4f398ce5b34

41

py

Python

upto7-12-2020/nameing.py

nikhilsamninan/python-files

15198459081097058a939b40b5e8ef754e578fe0

[ "Apache-2.0" ]

null

upto7-12-2020/nameing.py

nikhilsamninan/python-files

15198459081097058a939b40b5e8ef754e578fe0

[ "Apache-2.0" ]

null

upto7-12-2020/nameing.py

nikhilsamninan/python-files

15198459081097058a939b40b5e8ef754e578fe0

[ "Apache-2.0" ]

null

p=(input("enter The word")) print(p[-1])

13.666667

27

0.609756

8

41

3.125

0.875

0

0.027027

0.097561

41

2

28

20.5

0.648649

0

0.341463

0

1

0

false

0

0.5

1

0

null

0

1

0

null

0

1

0

4

da8f36147e9332ad7023bef5a72bc53b89bc7206

587

py

Python

server/app/settings.py

edwinzhng/contract-scan-dash

d923b0b618eba558dba70ffd0838909a657ecbe4

[ "MIT" ]

null

server/app/settings.py

edwinzhng/contract-scan-dash

d923b0b618eba558dba70ffd0838909a657ecbe4

[ "MIT" ]

null

server/app/settings.py

edwinzhng/contract-scan-dash

d923b0b618eba558dba70ffd0838909a657ecbe4

[ "MIT" ]

null

import os from pydantic import BaseSettings class Settings(BaseSettings): telegram_webhook_host = os.environ.get("TELEGRAM_WEBHOOK_HOST") telegram_bot_token: str = os.environ.get("TELEGRAM_BOT_TOKEN") ftmscan_api_key: str = os.environ.get("FTMSCAN_API_KEY") scrape_sleep_sec: int = os.environ.get("SCRAPE_SLEEP_SEC") postgres_db: str = os.environ.get("POSTGRES_DB") postgres_user: str = os.environ.get("POSTGRES_USER") postgres_pw: str = os.environ.get("POSTGRES_PASSWORD") postgres_port: int = os.environ.get("POSTGRES_PORT") settings = Settings()

30.894737

67

0.746167

82

587

5.04878

0.341463

0.173913

0.231884

0.181159

0.166667

0

0.136286

587

18

68

32.611111

0.816568

0

0.211244

0.035775

0

1

0

false

0.083333

0.166667

0

0.916667

0

null

0

1

0

null

0

1

0

1

0

4

da91b1f57e265258ebca982e5d37a92a3dbb8566

30

py

Python

bin/resource.py

hazzashirt/obsidian-service

fdc617900eb9390f36c18a5c3de3f277c84feff5

[ "BSD-2-Clause" ]

null

bin/resource.py

hazzashirt/obsidian-service

fdc617900eb9390f36c18a5c3de3f277c84feff5

[ "BSD-2-Clause" ]

null

bin/resource.py

hazzashirt/obsidian-service

fdc617900eb9390f36c18a5c3de3f277c84feff5

[ "BSD-2-Clause" ]

null

# people # locations # tools

10

12

0.666667

3

30

6.666667

1

0

0.233333

30

3

13

10

0.869565

0.733333

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

null

0

1

0

4

16f5fb90523db1a3818adddd2872a243522378ae

2,068

py

Python

chat_wars_database/app/business_exchange/migrations/0002_statsbyday.py

ricardochaves/chat-wars-database

597f192fb6ddf290c6c7477cf8c7d0ca654925f6

[ "MIT" ]

1

2019-12-30T19:16:52.000Z

chat_wars_database/app/business_exchange/migrations/0002_statsbyday.py

ricardochaves/chat-wars-database

597f192fb6ddf290c6c7477cf8c7d0ca654925f6

[ "MIT" ]

null

chat_wars_database/app/business_exchange/migrations/0002_statsbyday.py

ricardochaves/chat-wars-database

597f192fb6ddf290c6c7477cf8c7d0ca654925f6

[ "MIT" ]

null

# Generated by Django 3.0.1 on 2019-12-30 21:15 import django.db.models.deletion from django.db import migrations from django.db import models class Migration(migrations.Migration): dependencies = [("business_core", "0003_auto_20191230_2115"), ("business_exchange", "0001_initial")] operations = [ migrations.CreateModel( name="StatsByDay", fields=[ ("id", models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name="ID")), ("date", models.DateField()), ("units", models.IntegerField()), ("average_value", models.DecimalField(decimal_places=2, max_digits=8)), ("mean_value", models.IntegerField()), ("min_value", models.IntegerField()), ("max_value", models.IntegerField()), ("deerhorn_castle_seller", models.IntegerField(default=0)), ("dragonscale_castle_seller", models.IntegerField(default=0)), ("highnest_castle_seller", models.IntegerField(default=0)), ("moonlight_castle_seller", models.IntegerField(default=0)), ("potato_castle_seller", models.IntegerField(default=0)), ("sharkteeth_castle_seller", models.IntegerField(default=0)), ("wolfpack_castle_seller", models.IntegerField(default=0)), ("deerhorn_castle_buyer", models.IntegerField(default=0)), ("dragonscale_castle_buyer", models.IntegerField(default=0)), ("highnest_castle_buyer", models.IntegerField(default=0)), ("moonlight_castle_buyer", models.IntegerField(default=0)), ("potato_castle_buyer", models.IntegerField(default=0)), ("sharkteeth_castle_buyer", models.IntegerField(default=0)), ("wolfpack_castle_buyer", models.IntegerField(default=0)), ("item", models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to="business_core.Item")), ], ) ]

50.439024

114

0.619923

202

2,068

6.123762

0.371287

0.261924

0.282943

0.29426

0.508488

0

0.03263

0.244197

2,068

40

115

51.7

0.758797

0.02176

0

1

0

0.22761

0.144978

0

1

0

false

0

0.088235

0

0.176471

0

null

1

0

1

0

null

0

4

16fc99d7435f927a8a4760669cdf8ed7af76c6da

167

py

Python

les-1/perkavic/2-12.py

AtlasDev/TICT-V1PROG-15

9595300fce48f18b643d60629db3821487c1a7b6

[ "MIT" ]

6

2016-09-12T19:21:29.000Z

2018-09-02T18:36:37.000Z

les-1/perkavic/2-12.py

AtlasDev/TICT-V1PROG-15

9595300fce48f18b643d60629db3821487c1a7b6

[ "MIT" ]

null

les-1/perkavic/2-12.py

AtlasDev/TICT-V1PROG-15

9595300fce48f18b643d60629db3821487c1a7b6

[ "MIT" ]

null

import math; # A print(1 + 2 + 3 + 4 + 5 + 6 + 7); # B print(round((65 + 57 + 45) / 3)); # C print(math.pow(2, 20)); # D print(4356 // 61); # E print(4365 % 61);

9.823529

33

0.48503

32

167

2.53125

0.75

0

0.237705

0.269461

167

16

34

10.4375

0.42623

0.053892

0

1

0

true

0

0.166667

0

0.166667

0.833333

0

null

0

1

0

1

0

null

0

1

0

1

0

4

16fddbf3cc34a12774b5a92b7060c110f851bbb2

1,264

py

Python

wdim/orm/query.py

chrisseto/Still

3e4df26b824227472e5f487905779deafc76b4dd

[ "MIT" ]

null

wdim/orm/query.py

chrisseto/Still

3e4df26b824227472e5f487905779deafc76b4dd

[ "MIT" ]

null

wdim/orm/query.py

chrisseto/Still

3e4df26b824227472e5f487905779deafc76b4dd

[ "MIT" ]

null

import abc class BaseQuery(abc.ABC): def __and__(self, other): return And(self, other) def __or__(self, other): return Or(self, other) def __add__(self, other): return self & other class JointQuery(BaseQuery, metaclass=abc.ABCMeta): @property def queries(self): return self._queries def __init__(self, *queries): self._queries = queries def __iter__(self): return self.queries def __repr__(self): return '<{}({})>'.format( self.__class__.__name__, ', '.join([q.__repr__() for q in self.queries]) ) def __str__(self): return self.__repr__() class Query(BaseQuery, metaclass=abc.ABCMeta): @property def name(self): return self._name @property def value(self): return self._value def __init__(self, field, value): self._field = field self._name = field._name self._value = field.parse(value) def __repr__(self): return '<{}({}, {!r})>'.format(self.__class__.__name__, self.name, self.value) def __str__(self): return self.__repr__() class Equals(Query): pass class And(JointQuery): pass class Or(JointQuery): pass

18.057143

86

0.596519

145

1,264

4.675862

0.227586

0.117994

0.123894

0.082596

0.271386

0.20059

0.085546

0

0.281646

1,264

69

87

18.318841

0.746696

0

0.272727

0

0.018987

0

1

0.295455

false

0.068182

0.022727

0.25

0.704545

0

null

0

null

0

1

0

1

0

1

0

4

e50f8a60deb191f5c2260c4222fec78c7e51bc60

713

py

Python

test/functions/lambda3.py

kylebarron/MagicPython

da6fa0793e2c85d3bf7709ff1d4f65ccf468db11

[ "MIT" ]

1,482

2015-10-16T21:59:32.000Z

2022-03-30T11:44:40.000Z

test/functions/lambda3.py

kylebarron/MagicPython

da6fa0793e2c85d3bf7709ff1d4f65ccf468db11

[ "MIT" ]

226

2015-10-15T15:53:44.000Z

2022-03-25T03:08:27.000Z

test/functions/lambda3.py

kylebarron/MagicPython

da6fa0793e2c85d3bf7709ff1d4f65ccf468db11

[ "MIT" ]

129

2015-10-20T02:41:49.000Z

2022-03-22T01:44:36.000Z

anon = lambda lambda: 42 anon : source.python : source.python = : keyword.operator.assignment.python, source.python : source.python lambda : meta.lambda-function.python, source.python, storage.type.function.lambda.python : meta.function.lambda.parameters.python, meta.lambda-function.python, source.python lambda : meta.function.lambda.parameters.python, meta.lambda-function.python, source.python, storage.type.function.lambda.python : : meta.lambda-function.python, punctuation.section.function.lambda.begin.python, source.python : source.python 42 : constant.numeric.dec.python, source.python

47.533333

135

0.673212

78

713

6.153846

0.24359

0.25

0.3375

0.2

0.695833

0.504167

0

0.00722

0.223001

713

14

136

50.928571

0.859206

0

0.272727

0

null

0

null

0

null

1

0

null

0

1

0

4

e531d824abbcc5d33c9e24616b212ac2e1f9e390

208

py

Python

tests/model/test_direction.py

jonashellmann/informaticup21-team-chillow

f2e519af0a5d9a9368d62556703cfb1066ebb58f

[ "MIT" ]

3

2021-01-17T23:32:07.000Z

2022-01-30T14:49:16.000Z

tests/model/test_direction.py

jonashellmann/informaticup21-team-chillow

f2e519af0a5d9a9368d62556703cfb1066ebb58f

[ "MIT" ]

2

2021-01-17T13:37:56.000Z

2021-04-14T12:28:49.000Z

tests/model/test_direction.py

jonashellmann/informaticup21-team-chillow

f2e519af0a5d9a9368d62556703cfb1066ebb58f

[ "MIT" ]

2

2021-04-02T14:53:38.000Z

2021-04-20T11:10:17.000Z

import unittest from chillow.model.direction import Direction class DirectionTest(unittest.TestCase): def test_should_have_four_different_directions(self): self.assertEqual(len(Direction), 4)

20.8

57

0.793269

25

208

6.4

0.8

0

0.005556

0.134615

208

9

58

23.111111

0.883333

0

0.2

1

0.2

false

0

0.4

0

0.8

0

1

0

null

0

1

0

null

0

1

0

1

0

4

e55521627e9499934a37781d50c0379eeda2365a

172

py

Python

src/stagemodel/__init__.py

ramittal/stagemodel

9d8df177860ed5077a27c072ba1936c6864fad47

[ "BSD-2-Clause" ]

null

src/stagemodel/__init__.py

ramittal/stagemodel

9d8df177860ed5077a27c072ba1936c6864fad47

[ "BSD-2-Clause" ]

2

2021-01-04T18:50:00.000Z

2021-10-24T00:06:23.000Z

src/stagemodel/__init__.py

ramittal/stagemodel

9d8df177860ed5077a27c072ba1936c6864fad47

[ "BSD-2-Clause" ]

1

2020-12-14T23:26:52.000Z

from .node_model import OverallModel, StudyModel from .composite_model import StagewiseModel, TwoStageModel, ReverseTwoStageModel from .utils import solve_ls, result_to_df

43

80

0.866279

21

172

6.857143

0.761905

0.152778

0

0.093023

172

3

81

57.333333

0.923077

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

e5857c87731d4d16645528c08e9a1b7001c234ff

98

py

Python

src/api/admin.py

lerdem/chart

b152426e9a218b3d1c2acc5e0cea5cd8c52d1f8d

[ "Apache-2.0" ]

null

src/api/admin.py

lerdem/chart

b152426e9a218b3d1c2acc5e0cea5cd8c52d1f8d

[ "Apache-2.0" ]

null

src/api/admin.py

lerdem/chart

b152426e9a218b3d1c2acc5e0cea5cd8c52d1f8d

[ "Apache-2.0" ]

null

from django.contrib import admin from .models import RandomData admin.site.register(RandomData)

16.333333

32

0.826531

13

98

6.230769

0.692308

0

0.112245

98

6

33

16.333333

0.931034

0

1

0

true

0

0.666667

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

4

e5b58971eeeef02bfcba4b3766c0055bc239ffb8

145

py

Python

logger.py

Kevin-Luk-Hub/twitter-test

63d2726d568cac866364ef4a66f293764e338bd7

[ "MIT" ]

null

logger.py

Kevin-Luk-Hub/twitter-test

63d2726d568cac866364ef4a66f293764e338bd7

[ "MIT" ]

null

logger.py

Kevin-Luk-Hub/twitter-test

63d2726d568cac866364ef4a66f293764e338bd7

[ "MIT" ]

null

import logging logging.basicConfig(filename='test.log', level=logging.INFO, format='%(asctime)s:%(levelname)s:%(message)s')

29

67

0.648276

17

145

5.529412

0.764706

0

0.17931

145

4

68

36.25

0.789916

0

0.310345

0.255172

0

1

0

true

0

0.333333

0

0.333333

0

1

0

null

0

1

0

null

0

1

0

1

0

4

f913e6fd2d6dca483273e497f9bb358d685880a7

55

py

Python

Tests/Runnable1/r_pythonapi_t.py

jwilk/Pyrex

83dfbae1261788933472e3f9c501ad74c61a37c5

[ "Apache-2.0" ]

5

2019-05-26T20:48:36.000Z

2021-07-09T01:38:38.000Z

Tests/Runnable1/r_pythonapi_t.py

jwilk/Pyrex

83dfbae1261788933472e3f9c501ad74c61a37c5

[ "Apache-2.0" ]

null

Tests/Runnable1/r_pythonapi_t.py

jwilk/Pyrex

83dfbae1261788933472e3f9c501ad74c61a37c5

[ "Apache-2.0" ]

1

2022-02-10T07:14:58.000Z

from r_pythonapi import spam x = spam() print repr(x)

11

28

0.727273

10

55

3.9

0.8

0

0.181818

55

4

29

13.75

0.866667

0

null

0

0.333333

null

0.333333

1

0

null

0

1

0

null

0

1

0

1

0

4

f9154e805e7e4bb2f179eabec87b6d8ee12219fc

133

py

Python

flow54/__init__.py

corygoates/Flow54

d24fe113afb932df6a910b560c6d491693b87592

[ "MIT" ]

null

flow54/__init__.py

corygoates/Flow54

d24fe113afb932df6a910b560c6d491693b87592

[ "MIT" ]

null

flow54/__init__.py

corygoates/Flow54

d24fe113afb932df6a910b560c6d491693b87592

[ "MIT" ]

null

from flow54.diamond_airfoil import DiamondAirfoil import flow54.compressible_tools as compressible_tools from flow54.cone import Cone

44.333333

54

0.894737

18

133

6.444444

0.555556

0.172414

0

0.04918

0.082707

133

3

55

44.333333

0.901639

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

f925923e62565f3d9391d917521f4f8cc49a6ba8

62,255

py

Python

fgfa_rfcn/core/tester_online.py

sdroh1027/track_to_learn

26dc83328bd29612a80e7d2429cdfd32926d0af6

[ "MIT" ]

null

fgfa_rfcn/core/tester_online.py

sdroh1027/track_to_learn

26dc83328bd29612a80e7d2429cdfd32926d0af6

[ "MIT" ]

null

fgfa_rfcn/core/tester_online.py

sdroh1027/track_to_learn

26dc83328bd29612a80e7d2429cdfd32926d0af6

[ "MIT" ]

null

from multiprocessing.pool import ThreadPool as Pool import cPickle import os import time import mxnet as mx import numpy as np import math import dill from module import MutableModule from utils import image from bbox.bbox_transform import bbox_pred, clip_boxes from nms.nms import py_nms_wrapper, cpu_nms_wrapper, gpu_nms_wrapper from nms.seq_nms import seq_nms from utils.PrefetchingIter import PrefetchingIter from collections import deque from instance import Instance from tester import get_resnet_output, draw_all_detection, prepare_data import logging logger = logging.getLogger("lgr") def roi_crop_embed(img_height, img_width, pred_boxes, cur_embed, cfg): # using cur_embed & bbox_pred, make new instance wise embed layer # bbox_pred[4:8] has (x1,y1) (x2,y2) coordinates scale_h = cur_embed.shape[2] / img_height scale_w = cur_embed.shape[3] / img_width zeros = np.zeros([300, 1]) pred_boxes_on_feat = np.zeros([300, 4]) pred_boxes_on_feat[:, 0] = pred_boxes[:, 0] * scale_w # x1 pred_boxes_on_feat[:, 1] = pred_boxes[:, 1] * scale_h # y1 pred_boxes_on_feat[:, 2] = pred_boxes[:, 2] * scale_w # x2 pred_boxes_on_feat[:, 3] = pred_boxes[:, 3] * scale_h # y2 pred_boxes_on_feat = clip_boxes(pred_boxes_on_feat, cur_embed.shape[-2:]) pred_boxes_on_feat = np.hstack((zeros, pred_boxes_on_feat[:, 0:])) pred_boxes_on_feat = mx.nd.array(pred_boxes_on_feat, mx.gpu()) sym_bbox = mx.sym.Variable('bbox_with_delta') sym_cur_embed = mx.sym.Variable('cur_embed') #new_embed = mx.sym.ROIPooling(data=sym_cur_embed, rois=sym_bbox, pooled_size=(cfg.TEST.EMBED_SIZE, cfg.TEST.EMBED_SIZE), # spatial_scale=1.0, name='new_embed') new_embed = mx.sym.contrib.ROIAlign(data=sym_cur_embed, rois=sym_bbox, pooled_size=(cfg.TEST.EMBED_SIZE, cfg.TEST.EMBED_SIZE), spatial_scale=1.0, name='new_embed') new_embed_normalized = mx.sym.L2Normalization(data=new_embed, mode='channel', name='new_embed_normalized') ex = new_embed_normalized.bind(mx.gpu(), {'cur_embed': cur_embed, 'bbox_with_delta': pred_boxes_on_feat}) cropped_embed = ex.forward() return cropped_embed[0] def roi_crop_embed_nd(img_height, img_width, pred_boxes, cur_embed, cfg): # using cur_embed & bbox_pred, make new instance wise embed layer # bbox_pred[4:8] has (x1,y1) (x2,y2) coordinates scale_h = cur_embed.shape[2] / img_height scale_w = cur_embed.shape[3] / img_width zeros = np.zeros([300, 1]) pred_boxes_on_feat = np.zeros([300, 4]) pred_boxes_on_feat[:, 0] = pred_boxes[:, 0] * scale_w # x1 pred_boxes_on_feat[:, 1] = pred_boxes[:, 1] * scale_h # y1 pred_boxes_on_feat[:, 2] = pred_boxes[:, 2] * scale_w # x2 pred_boxes_on_feat[:, 3] = pred_boxes[:, 3] * scale_h # y2 pred_boxes_on_feat = clip_boxes(pred_boxes_on_feat, cur_embed.shape[-2:]) pred_boxes_on_feat = np.hstack((zeros, pred_boxes_on_feat[:, 0:])) pred_boxes_on_feat = mx.nd.array(pred_boxes_on_feat, mx.gpu()) new_embed = mx.nd.contrib.ROIAlign(data=cur_embed, rois=pred_boxes_on_feat, pooled_size=(cfg.TEST.EMBED_SIZE, cfg.TEST.EMBED_SIZE), spatial_scale=1.0, name='new_embed') new_embed_normalized = mx.nd.L2Normalization(data=new_embed, mode='channel', name='new_embed_normalized') return new_embed_normalized def im_detect_all(predictor, data_batch, data_names, scales, cfg): output_all = predictor.predict(data_batch) data_dict_all = [dict(zip(data_names, data_batch.data[i])) for i in xrange(len(data_batch.data))] scores_all = [] pred_boxes_all = [] iscores_all = [] # instance prediction ipred_boxes_all = [] # instance prediction rois_all = [] cropped_embeds_all = [] psroipooled_cls_rois_all = [] # for debugging # cur_embeds_all = [] # # new_embeds_all = [] # # sliced_all = [] # for output, data_dict, scale in zip(output_all, data_dict_all, scales): if cfg.TEST.HAS_RPN: rois = output['rois_output'].asnumpy()[:, 1:] else: rois = data_dict['rois'].asnumpy().reshape((-1, 5))[:, 1:] im_shape = data_dict['data'].shape # save output scores = output['cls_prob_reshape_output'].asnumpy()[0] bbox_deltas = output['bbox_pred_reshape_output'].asnumpy()[0] iscores = output['inst_prob_reshape_output'].asnumpy()[0] ibbox_deltas = output['ibbox_pred_reshape_output'].asnumpy()[0] cur_embed = output['cur_embed_output'] psroipooled_cls_rois_nd = output['psroipooled_cls_rois_output'] #unnormalize_weight = output['unnormalize_weight_output'] #new_embed = output['new_embed_output'].asnumpy()[0] #sliced = output['sliced_bbox_output'].asnumpy()[0] # post processing pred_boxes = bbox_pred(rois, bbox_deltas) pred_boxes = clip_boxes(pred_boxes, im_shape[-2:]) # Clip boxes to image boundaries. ipred_boxes = bbox_pred(rois, ibbox_deltas) ipred_boxes = clip_boxes(ipred_boxes, im_shape[-2:]) # for cropping, scale pred boxes to size of embed feature img_height =data_batch.data[0][1][0][0].asnumpy() img_width =data_batch.data[0][1][0][1].asnumpy() cropped_embed = roi_crop_embed_nd(img_height, img_width, rois, cur_embed, cfg) #nd_array is faster cropped_embed = cropped_embed.asnumpy() # we used scaled image & roi to train, so it is necessary to transform them back pred_boxes = pred_boxes / scale ipred_boxes = ipred_boxes / scale scores_all.append(scores) pred_boxes_all.append(pred_boxes) iscores_all.append(iscores) ipred_boxes_all.append(ipred_boxes) rois_all.append(rois) #cur_embeds_all.append(cur_embed) #new_embeds_all.append(new_embed) #sliced_all.append(sliced) cropped_embeds_all.append(cropped_embed) psroipooled_cls_rois_all.append(psroipooled_cls_rois_nd) debug = 0 if debug is True: cur_embed = cur_embed.asnumpy()[0] feat_cache = output['feat_cache'].asnumpy()[0] #plot_tensor(cur_embed, 16) #plot_tensor(feat_cache, 16) return zip(scores_all, pred_boxes_all, rois_all, data_dict_all, iscores_all, ipred_boxes_all, cropped_embeds_all, psroipooled_cls_rois_all) def pred_eval_ot(gpu_id, feat_predictors, aggr_predictors, test_data, imdb, cfg, vis=False, thresh=1e-3, logger=None, ignore_cache=True): """ wrapper for calculating offline validation for faster data analysis in this example, all threshold are set by hand :param predictor: Predictor :param test_data: data iterator, must be non-shuffle :param imdb: image database :param vis: controls visualization :param thresh: valid detection threshold :return: """ det_file = os.path.join(imdb.result_path, imdb.name + '_'+ str(gpu_id)) if cfg.TEST.SEQ_NMS == True: det_file += '_raw' print 'det_file=',det_file if os.path.exists(det_file) and not ignore_cache: with open(det_file, 'rb') as fid: all_boxes, frame_ids = cPickle.load(fid) return all_boxes, frame_ids assert vis or not test_data.shuffle data_names = [k[0] for k in test_data.provide_data[0]] num_images = test_data.size roidb_frame_ids = [x['frame_id'] for x in test_data.roidb] if not isinstance(test_data, PrefetchingIter): test_data = PrefetchingIter(test_data) nms = py_nms_wrapper(cfg.TEST.NMS) # limit detections to max_per_image over all classes max_per_image = cfg.TEST.max_per_image # all detections are collected into: # all_boxes[cls][image] = N x 5 array of detections in # (x1, y1, x2, y2, score) all_boxes = [[[] for _ in range(num_images)] for _ in range(imdb.num_classes)] all_boxes_inst = [[[] for _ in range(num_images)] for _ in range(imdb.num_classes)] frame_ids = np.zeros(num_images, dtype=np.int) roidb_idx = -1 roidb_offset = -1 idx = 0 all_frame_interval = cfg.TEST.KEY_FRAME_INTERVAL * 2 + 1 data_time, net_time, post_time,seq_time = 0.0, 0.0, 0.0,0.0 t = time.time() # loop through all the test data for im_info, key_frame_flag, data_batch in test_data: t1 = time.time() - t t = time.time() ################################################# # new video # ################################################# # empty lists and append padding images # do not do prediction yet if key_frame_flag == 0: roidb_idx += 1 roidb_offset = -1 # init data_lsit and feat_list for a new video data_list = deque(maxlen=all_frame_interval) feat_list = deque(maxlen=all_frame_interval) image, feat = get_resnet_output(feat_predictors, data_batch, data_names) # append cfg.TEST.KEY_FRAME_INTERVAL+1 padding images in the front (first frame) while len(data_list) < cfg.TEST.KEY_FRAME_INTERVAL+1: data_list.append(image) feat_list.append(feat) ginst_ID = 0 ginst_mem = [] # list for instance class sim_array_global = [] # similarity array list logger.info('prepared for a new video') ################################################# # main part of the loop # ################################################# elif key_frame_flag == 2: # keep appending data to the lists without doing prediction until the lists contain 2 * cfg.TEST.KEY_FRAME_INTERVAL objects if len(data_list) < all_frame_interval - 1: image, feat = get_resnet_output(feat_predictors, data_batch, data_names) data_list.append(image) feat_list.append(feat) else: scales = [iim_info[0, 2] for iim_info in im_info] image, feat = get_resnet_output(feat_predictors, data_batch, data_names) data_list.append(image) feat_list.append(feat) prepare_data(data_list, feat_list, data_batch) pred_result = im_detect_all(aggr_predictors, data_batch, data_names, scales, cfg) roidb_offset += 1 # frame number in this snippet frame_ids[idx] = roidb_frame_ids[roidb_idx] + roidb_offset t2 = time.time() - t t = time.time() ginst_ID_prev = ginst_ID ginst_ID, out_im, out_im2, out_im_linst = process_link_pred_result(imdb.classes, pred_result, imdb.num_classes, thresh, cfg, nms, all_boxes, all_boxes_inst, idx, max_per_image, vis, data_list[cfg.TEST.KEY_FRAME_INTERVAL].asnumpy(), scales, ginst_mem, sim_array_global, ginst_ID) ginst_ID_now = ginst_ID idx += test_data.batch_size t3 = time.time() - t t = time.time() data_time += t1 net_time += t2 post_time += t3 print 'testing {}/{} data {:.4f}s net {:.4f}s post {:.4f}s GID:{} #GInsts:{}'.format(idx, num_images, data_time / idx * test_data.batch_size, net_time / idx * test_data.batch_size, post_time / idx * test_data.batch_size, ginst_ID, len(ginst_mem)) if logger: logger.info('testing {}/{} data {:.4f}s net {:.4f}s post {:.4f}s GID:{} #GInsts:{}'.format(idx, num_images, data_time / idx * test_data.batch_size, net_time / idx * test_data.batch_size, post_time / idx * test_data.batch_size, ginst_ID, len(ginst_mem))) ################################################# # end part of a video # ################################################# elif key_frame_flag == 1: # last frame of a video end_counter = 0 image, feat = get_resnet_output(feat_predictors, data_batch, data_names) while end_counter < cfg.TEST.KEY_FRAME_INTERVAL + 1: data_list.append(image) feat_list.append(feat) prepare_data(data_list, feat_list, data_batch) pred_result = im_detect_all(aggr_predictors, data_batch, data_names, scales, cfg) roidb_offset += 1 frame_ids[idx] = roidb_frame_ids[roidb_idx] + roidb_offset t2 = time.time() - t t = time.time() ginst_ID_prev = ginst_ID ginst_ID, out_im, out_im2, out_im_linst = process_link_pred_result(imdb.classes, pred_result, imdb.num_classes, thresh, cfg, nms, all_boxes, all_boxes_inst, idx, max_per_image, vis, data_list[cfg.TEST.KEY_FRAME_INTERVAL].asnumpy(), scales, ginst_mem, sim_array_global, ginst_ID) ginst_ID_now = ginst_ID idx += test_data.batch_size t3 = time.time() - t t = time.time() data_time += t1 net_time += t2 post_time += t3 print 'testing {}/{} data {:.4f}s net {:.4f}s post {:.4f}s GID:{} #GInsts:{}'.format(idx, num_images, data_time / idx * test_data.batch_size, net_time / idx * test_data.batch_size, post_time / idx * test_data.batch_size, ginst_ID, len(ginst_mem)) if logger: logger.info('testing {}/{} data {:.4f}s net {:.4f}s post {:.4f}s GID:{} #GInsts:{}'.format(idx, num_images, data_time / idx * test_data.batch_size, net_time / idx * test_data.batch_size, post_time / idx * test_data.batch_size, ginst_ID, len(ginst_mem))) end_counter += 1 with open(det_file, 'wb') as f: cPickle.dump((all_boxes_inst, frame_ids), f, protocol=cPickle.HIGHEST_PROTOCOL) return all_boxes_inst, frame_ids def pred_eval_multiprocess_ot(gpu_num, key_predictors, cur_predictors, test_datas, imdb, cfg, vis=False, thresh=1e-3, logger=None, ignore_cache=True): assert cfg.TEST.SEQ_NMS==False if gpu_num == 1: res = [pred_eval_ot(0, key_predictors[0], cur_predictors[0], test_datas[0], imdb, cfg, vis, thresh, logger, ignore_cache), ] else: #multigpu not supported yet assert gpu_num == 1 from multiprocessing.pool import ThreadPool as Pool pool = Pool(processes=gpu_num) multiple_results = [pool.apply_async(pred_eval_ot, args=( i, key_predictors[i], cur_predictors[i], test_datas[i], imdb, cfg, vis, thresh, logger, ignore_cache)) for i in range(gpu_num)] pool.close() pool.join() res = [res.get() for res in multiple_results] info_str = imdb.evaluate_detections_multiprocess_select_idx(res, -5) info_str2 = imdb.evaluate_detections_multiprocess_select_idx(res, -4) info_str3 = imdb.evaluate_detections_multiprocess_select_idx(res, -3) info_str4 = imdb.evaluate_detections_multiprocess_select_idx(res, -2) info_str5 = imdb.evaluate_detections_multiprocess_select_idx(res, -1) if logger: logger.info('evaluate detections: \n{}'.format(info_str)) logger.info('evaluate detections: \n{}'.format(info_str2)) logger.info('evaluate detections: \n{}'.format(info_str3)) logger.info('evaluate detections: \n{}'.format(info_str4)) logger.info('evaluate detections: \n{}'.format(info_str5)) def draw_all_ginst(im_array, inst_mem, class_names, scale, cfg, frame_now, threshold=0.1): # sidong """ visualize all detections in one image :param im_array: [b=1 c h w] in rgb :param inst_mem_now: local instance array :param inst_mem: global instance array :param class_names: list of names in imdb :param scale: visualize the scaled image :param IDs: linked global IDs of local instances. tuple. :return: """ import cv2 import random color_white = (255, 255, 255) im = image.transform_inverse(im_array, cfg.network.PIXEL_MEANS) # change to bgr im = cv2.cvtColor(im, cv2.COLOR_RGB2BGR) for j, inst in enumerate(inst_mem): if inst.cls == 0: # '__background__': continue if inst.detected_idx[-1] == frame_now: if not inst.color: inst.color = (random.randint(0, 256), random.randint(0, 256), random.randint(0, 256)) # generate a random color bbox = inst.bbox[:4] * scale cls = inst.cls score = inst.cls_score #if score < threshold: # continue bbox = map(int, bbox) cv2.rectangle(im, (bbox[0], bbox[1]), (bbox[2], bbox[3]), color=inst.color, thickness=2) cv2.putText(im, 'LID:%d %.3s %.2f GID:%s %.3s s:%.2f' % (inst.LID, class_names[cls], score, inst.GID, class_names[inst.cls_high], inst.sim), (bbox[0], bbox[1] + 10), color=color_white, fontFace=cv2.FONT_HERSHEY_COMPLEX, fontScale=0.5) return im def draw_all_linst(im_array, inst_mem_now, inst_mem, class_names, scale, cfg, threshold=0.01): # sidong """ visualize all detections in one image :param im_array: [b=1 c h w] in rgb :param inst_mem_now: local instance array :param inst_mem: global instance array :param class_names: list of names in imdb :param scale: visualize the scaled image :param IDs: linked global IDs of local instances. tuple. :return: """ import cv2 import random color_white = (255, 255, 255) im = image.transform_inverse(im_array, cfg.network.PIXEL_MEANS) # change to bgr im = cv2.cvtColor(im, cv2.COLOR_RGB2BGR) for j, inst in enumerate(inst_mem_now): if inst.cls == 0: # '__background__': continue if inst.cls_score < threshold: continue if inst.linked_to_gidx >= 0: inst = inst_mem[inst.linked_to_gidx] if not inst.color: inst.color = (random.randint(0, 256), random.randint(0, 256), random.randint(0, 256)) # generate a random color bbox = inst.bbox[:4] * scale cls = inst.cls score = inst.cls_score bbox = map(int, bbox) cv2.rectangle(im, (bbox[0], bbox[1]), (bbox[2], bbox[3]), color=inst.color, thickness=2) cv2.putText(im, 'ID:%d %.3s %.2f GID:%s %.3s s:%.2f' % (inst.LID, class_names[cls], score, inst.GID, class_names[inst.cls_high], inst.sim), (bbox[0], bbox[1] + 10), color=color_white, fontFace=cv2.FONT_HERSHEY_COMPLEX, fontScale=0.5) else: color = (random.randint(0, 256), random.randint(0, 256), random.randint(0, 256)) # generate a random color bbox = inst.bbox[:4] * scale cls = inst.cls score = inst.cls_score #if score < threshold: # continue bbox = map(int, bbox) cv2.rectangle(im, (bbox[0], bbox[1]), (bbox[2], bbox[3]), color=color, thickness=2) cv2.putText(im, 'LID:%d %.3s %.2f' % (inst.LID, class_names[cls], score), (bbox[0], bbox[1] + 10), color=color_white, fontFace=cv2.FONT_HERSHEY_COMPLEX, fontScale=0.5) return im def divide_L2_norm(vector): #assume that the vector is 2-demensional list (2048L, cfg.TEST.EMBED_SIZE, cfg.TEST.EMBED_SIZE) return vector / np.linalg.norm(vector, axis=0) # vector <type 'tuple'>: (2048, 8,8) def compare_embed(A, B): C = A * B # C vector: <type 'tuple'>: (2048, 8,8) sum = np.sum(C, axis=0) # return vector: <type 'tuple'>: (8,8) return sum.sum()/sum.size def normalize(ndarray): return ndarray/(ndarray.square().sum().sqrt()) def compare_embed_filtered(A, B, normed_score_map_A, normed_score_map_B): # score_map : mxnet nd array (7*7) # output: cosine similarity C = A * B # C vector: <type 'tuple'>: (2048, z,z) (z*z vectors, length: 2048) cos_sim = np.sum(C, axis=0) # return vector: <type 'tuple'>: (z,z) ggrid = normed_score_map_A * normed_score_map_B # (7, 7) grid matrix grid1 = mx.nd.softmax(data=ggrid.reshape(-3), axis=-1, temperature=0.05).reshape((7,7)).asnumpy() filtered_sim1 = grid1 * cos_sim sum1 = filtered_sim1.sum() #grid11 = mx.nd.softmax(data=ggrid.reshape(-3), axis=-1, temperature=0.1).reshape((7, 7)).asnumpy() #soft_A = mx.nd.softmax(normed_score_map_A.reshape(-3), axis=-1, temperature=0.1) #aa = soft_A.asnumpy().reshape((7,7)) #soft_B = mx.nd.softmax(normed_score_map_B.reshape(-3), axis=-1, temperature=0.1) #bb = soft_B.asnumpy().reshape((7,7)) #grid2 = mx.nd.softmax((soft_A * soft_B), axis=-1, temperature=0.01).reshape(7,7).asnumpy() #filtered_sim2 = grid2 * cos_sim #sum2 = filtered_sim2.sum() #AAA = score_map_A.asnumpy() #BBB = score_map_B.asnumpy() #DDD = ggrid.asnumpy() return sum1 def Area(box): return (box[2] - box[0] + 1) * (box[3] - box[1] + 1) def compute_IOU(box_A,box_B, area_A, area_B): xx1 = max(box_A[0],box_B[0]) yy1 = max(box_A[1],box_B[1]) xx2 = min(box_A[2],box_B[2]) yy2 = min(box_A[3],box_B[3]) w = max(0.0, xx2 - xx1 + 1) h = max(0.0, yy2 - yy1 + 1) inter = w * h ovr = inter / (area_A + area_B - inter) return ovr def compute_IOM(box_me,box_B): area_A = Area(box_me) xx1 = max(box_me[0], box_B[0]) yy1 = max(box_me[1], box_B[1]) xx2 = min(box_me[2], box_B[2]) yy2 = min(box_me[3], box_B[3]) w = max(0.0, xx2 - xx1 + 1) h = max(0.0, yy2 - yy1 + 1) inter = w * h ovr = inter / (area_A) return ovr def compute_relative_dist(inst_A, inst_B): rad_A = math.sqrt(Area(inst_B.bbox)) rad_B = math.sqrt(Area(inst_B.bbox)) dx = inst_A.center[0] - inst_B.center[0] dy = inst_A.center[1] - inst_B.center[1] dis = math.sqrt(dx ** 2 + dy ** 2) return ((rad_A + rad_B) - dis)/(rad_A + rad_B) def compute_locality(inst_A, inst_B): rd = compute_relative_dist(inst_A, inst_B) return (0 if rd <= 0 else rd) def ginst_to_box_and_score(inst): dynamic_average = inst.cls_score_acc / len(inst.detected_idx) box_and_score = np.hstack( (inst.bbox, inst.cls_score, inst.cls_score_high, inst.cls_score_reliable, dynamic_average, max(dynamic_average, inst.cls_score) )) return box_and_score def ginst_to_box_and_score_cls_scores(inst): dynamic_average = inst.cls_score_acc / len(inst.detected_idx) box_and_score = np.hstack( (inst.bbox, inst.cls_score, inst.cls_score_high, inst.cls_score_reliable, dynamic_average, max(dynamic_average, inst.cls_score) )) return box_and_score def linst_to_box_and_score(inst): box_and_score = np.hstack( (inst.bbox, inst.cls_score, inst.cls_score_high, inst.cls_score, inst.cls_score, inst.cls_score)) return box_and_score def draw_all_rois(im_array, rois, scale, cfg, threshold=0.1): # sidong """ visualize all detections in one image :param im_array: [b=1 c h w] in rgb :param detections: [ numpy.ndarray([[x1 y1 x2 y2 score]]) for j in classes ] :param class_names: list of names in imdb :param scale: visualize the scaled image :return: """ import cv2 import random color_white = (255, 255, 255) im = image.transform_inverse(im_array, cfg.network.PIXEL_MEANS) # change to bgr im = cv2.cvtColor(im, cv2.COLOR_RGB2BGR) color = (random.randint(0, 256), random.randint(0, 256), random.randint(0, 256)) # generate a random color #print rois for det in rois: bbox = det[:4] #* scale # scaling is not needed for rois #score = det[-1] #if score < threshold: # continue bbox = map(int, bbox) cv2.rectangle(im, (bbox[0], bbox[1]), (bbox[2], bbox[3]), color=color, thickness=2) #cv2.putText(im, '%s %.3f' % (class_names[j], score), (bbox[0], bbox[1] + 10), # color=color_white, fontFace=cv2.FONT_HERSHEY_COMPLEX, fontScale=0.5) cv2.putText(im, '%s' % ('rois'), (bbox[0], bbox[1] + 10), color=color_white, fontFace=cv2.FONT_HERSHEY_COMPLEX, fontScale=0.5) return im def process_link_pred_result(classes, pred_result, num_classes, thresh, cfg, nms, all_boxes, all_boxes_inst, idx, max_per_image, vis, center_image, scales, inst_mem, sim_array_global, ginst_ID): global logger for delta, (scores, boxes, rois, data_dict, iscores, ipred_boxes, embed_feat, psroipooled_cls_rois) in enumerate(pred_result): #16th frame -> cat(10) -> local box(3, 4th in rois) local_inst_ID = 0 inst_mem_now = [] # intra-frame inst memory per_cls_box_idx_over_th = [[]] per_cls_box_idx_nms = [[]] all_cls_box_idx_nms = [] for j in range(1, num_classes): #logger.debug('[%dth frame]test boxes of class: (%d, %s)' % (idx+delta, j, classes[j])) indexes = np.where(scores[:, j] > thresh)[0] per_cls_box_idx_over_th.append(indexes) cls_scores = scores[indexes, j, np.newaxis] cls_boxes = boxes[indexes, 4:8] if cfg.CLASS_AGNOSTIC else boxes[indexes, j * 4:(j + 1) * 4] cls_dets = np.hstack((cls_boxes, cls_scores)) if cfg.TEST.SEQ_NMS: all_boxes[j][idx + delta]=cls_dets # all_boxes[31L(class)][frame_idx + batch_size][300L(box_idx)][5L(x,y,w,h,score)] else: #cls_dets=np.float32(cls_dets) keep = nms(cls_dets) all_boxes[j][idx + delta] = cls_dets[keep, :] per_cls_box_idx_nms.append(indexes[keep]) #all_cls_box_idx_nms.extend(indexes[keep]) #inst_mem_cls = [[] for _ in range(num_classes)] ## intra-class inst memory #for j in range(1, num_classes): box_indexes = per_cls_box_idx_nms[j] for i, box_idx in enumerate(box_indexes): # for dets of class j, box = all_boxes[j][idx + delta][i] new_inst = Instance(LID=local_inst_ID, frame_idx=idx + delta, cls=j, cls_score=box[-1], bbox=box[0:4], embed_feat=embed_feat[box_idx], atten=normalize(psroipooled_cls_rois[box_idx][j])) #make instances for outputs new_inst.cls_scores = box_idx local_inst_ID += 1 #inst_mem_cls[j].append(new_inst) inst_mem_now.append(new_inst) #inst_mem_now.extend(inst_mem_cls[j]) iou_array_final = np.zeros((1,len(inst_mem_now))) sim_array_final = np.zeros((1,len(inst_mem_now))) sim_array_final_th = np.zeros((1,len(inst_mem_now)), int) linked_array = np.zeros((len(inst_mem), len(inst_mem_now))) gidx_linked = [] #logger.debug('[%dth frame] @@@ inter frame phase start @@@', idx + delta) len_local = len(inst_mem_now) len_global = len(inst_mem) if len_local > 0 and len_global > 0 : iou_array = np.zeros((len_global, len_local)) # matrix of iou coef_array = np.zeros((len_global, len_local)) # matrix of coeff sim_array = np.zeros((len_global, len_local)) # matrix of similarity g_rel_array = np.zeros((len_global, len_local)) # matrix of reliability same_cls_score_array = np.zeros((len_global, len_local)) # matrix of reliability l_rel_array = np.zeros((len_global, len_local)) # matrix of reliability g to l #loc_array = np.zeros((len_global, len_local)) # matrix of relative distance area1 = np.empty(len_global) for i, ginst in enumerate(inst_mem): area1[i] = Area(ginst.bbox2) area2 = np.empty(len_local) for j, linst in enumerate(inst_mem_now): area2[j] = Area(linst.bbox2) # Similarity matrix generation (global <-> local inst) for i, ginst in enumerate(inst_mem): # loop for instances in this frame for j, linst in enumerate(inst_mem_now): coeff = 0 #compare_embed(ginst.embed_feat, linst.embed_feat) # input embed_feat <type 'tuple'>: (2048, 8,8) coeff_filtered = compare_embed_filtered(ginst.embed_feat, linst.embed_feat, ginst.atten, linst.atten) iou = compute_IOU(ginst.bbox2, linst.bbox2, area1[i], area2[j]) #use virtual bbox similar = coeff_filtered * iou #g_reliability = similar * ginst.cls_score_reliable same_cls_score = linst.cls_score * (ginst.cls_high == linst.cls) l_reliability = similar * same_cls_score #loc = compute_locality(ginst, linst) coeff_th = (coeff > cfg.TEST.COEFF_THRESH_INTER) iou_th = (iou > cfg.TEST.IOU_THRESH_INTRA) similar_th = (similar > cfg.TEST.IOU_THRESH_INTRA * cfg.TEST.COEFF_THRESH_INTRA) sim_array[i, j] = similar iou_array[i, j] = iou coef_array[i, j] = coeff_filtered # #g_rel_array[i, j] = g_reliability same_cls_score_array[i, j] = same_cls_score l_rel_array[i, j] = l_reliability #loc_array[i, j] = loc logger.debug('[%dth frame] ginst[%d](GID:%d, %.5s(%.2f) lastf:%d)) VS linst[%d](LID:%d, %.5s(%.2f)): filt_coef:%.2f IOU:%.2f(%.1s) coef:%.2f(%.1s) sim:%.2f(%.1s) lrel:%.2f' % ( idx + delta, i, ginst.GID, classes[ginst.cls], ginst.cls_score, ginst.detected_idx[-1], j, linst.LID, classes[linst.cls], linst.cls_score, coeff_filtered, iou, iou_th, coeff, coeff_th, similar, similar_th, l_reliability)) sim_array_th = (sim_array[:, :] > cfg.TEST.COEFF_THRESH_INTRA * cfg.TEST.IOU_THRESH_INTRA) g_rel_array_th = (g_rel_array[:, :] > cfg.TEST.REL_THRESH_INTRA) l_rel_array_th = (l_rel_array[:, :] > cfg.TEST.REL_THRESH_INTRA) #sim_array_global.append(sim_array) #for debugging link_array = np.zeros((len_global, len_local)) sim_array_tmp = sim_array.copy() l_rel_array_tmp = l_rel_array.copy() sim_array_hor = sim_array.copy() l_rel_array_hor = l_rel_array.copy() sim_array_ver = sim_array.copy() l_rel_array_ver = l_rel_array.copy() same_cls_score_array_tmp = same_cls_score_array.copy() trial = 0 linked_num = 0 # ginst linking while l_rel_array_tmp.max() > cfg.TEST.REL_THRESH_INTRA and linked_num < min(15, len_global): #i = l_rel_array_tmp.argmax() i = same_cls_score_array_tmp.argmax() gi = i / len_local li = i % len_local trial += 1 if l_rel_array_tmp[gi, li] > cfg.TEST.REL_THRESH_INTRA: link_array[gi, li] = 1 sim_array_tmp[gi, :] = -1 sim_array_tmp[:, li] = -1 l_rel_array_tmp[gi, :] = -1 l_rel_array_tmp[:, li] = -1 sim_array_hor[gi, :] = -1 sim_array_ver[:, li] = -1 l_rel_array_hor[gi, :] = -1 l_rel_array_ver[:, li] = -1 same_cls_score_array_tmp[gi, :] = -1 same_cls_score_array_tmp[:, li] = -1 gidx_linked.append(gi) inst_mem[gi].update_inter_frame(inst_mem_now[li], sim_array[gi,li]) linked_num += 1 else: sim_array_tmp[gi, li] = -1 l_rel_array_tmp[gi, li] = -1 sim_array_hor[gi, li] = -1 sim_array_ver[gi, li] = -1 l_rel_array_hor[gi, li] = -1 l_rel_array_ver[gi, li] = -1 same_cls_score_array_tmp[gi, li] = -1 while 0:#sim_array_tmp.max() > cfg.TEST.IOU_THRESH_INTER * cfg.TEST.COEFF_THRESH_INTER: i = sim_array_tmp.argmax() gi = i / len(inst_mem_now) li = i % len(inst_mem_now) link_array[gi, li] = 2 sim_array_tmp[gi, :] = -2 sim_array_tmp[:, li] = -2 l_rel_array_tmp[gi, :] = -2 l_rel_array_tmp[:, li] = -2 sim_array_hor[gi, :] = -2 sim_array_ver[:, li] = -2 l_rel_array_hor[gi, :] = -2 l_rel_array_ver[:, li] = -2 gidx_linked.append(gi) inst_mem[gi].update_inter_frame(inst_mem_now[li], sim_array[gi, li]) l_rel_array_ver_th = (l_rel_array_ver[:, :] > cfg.TEST.REL_THRESH_INTRA) l_rel_array_hor_th = (l_rel_array_hor[:, :] > cfg.TEST.REL_THRESH_INTRA) # ginst guided linst supression (for not linked) if cfg.TEST.LSUP: for i, ginst in enumerate(inst_mem): if link_array[i, :].max() > 0: lindex_to_suppress = np.where(l_rel_array_ver_th[i, :] == True)[0] sim_array_tmp[:, lindex_to_suppress] = -3 for k in lindex_to_suppress: inst_mem_now[k].l_suppressed.append(idx + delta) # linst guided ginst suppresion (for not linked) if cfg.TEST.GSUP: for j, linst in enumerate(inst_mem_now): if link_array[:, j].max() > 0: gindex_to_suppress = np.where(l_rel_array_hor_th[:, j] == True)[0] sim_array_tmp[gindex_to_suppress, :] = -4 for k in gindex_to_suppress: inst_mem[k].g_suppressed.append(idx + delta) iou_array_final = iou_array.copy() sim_array_final = sim_array.copy() sim_array_final_th = sim_array_th.copy() rel_array_final = l_rel_array.copy() rel_array_final_th = l_rel_array_th.copy() linked_array = link_array.copy() # delete some ginsts gidx_to_delete = [] for i, ginst in enumerate(inst_mem): delete = 0 last_frame = ginst.detected_idx[-1] if last_frame < (idx + delta - cfg.TEST.GINST_LIFE_FRAME): logger.debug('[%dth frame] ginst[%d](GID:%d, %s, %.3f, frame:%d) is deleted (old frame)' % ( idx + delta, i, inst_mem[i].GID, classes[inst_mem[i].cls], inst_mem[i].cls_score, inst_mem[i].detected_idx[-1])) delete = 1 elif ginst.g_suppressed: logger.debug('[%dth frame] ginst[%d](GID:%d, %s, %.3f, frame:%d) is deleted (suppressed)' % ( idx + delta, i, inst_mem[i].GID, classes[inst_mem[i].cls], inst_mem[i].cls_score, inst_mem[i].detected_idx[-1])) delete = 1 elif 0:#ginst.cls_score_reliable < 0.05: logger.debug('[%dth frame] ginst[%d](GID:%d, %s, %.3f, frame:%d) is deleted (low reliability)' % ( idx + delta, i, inst_mem[i].GID, classes[inst_mem[i].cls], inst_mem[i].cls_score, inst_mem[i].detected_idx[-1])) delete = 1 if delete == 1: gidx_to_delete.append(i) if last_frame == idx + delta: gidx_linked.remove(i) gidx_to_delete.reverse() for i in gidx_to_delete: del inst_mem[i] iou_array_final, sim_array_final, sim_array_final_th, rel_array_final, rel_array_final_th, linked_array = \ np.delete( (iou_array_final, sim_array_final, sim_array_final_th, rel_array_final, rel_array_final_th, linked_array), (gidx_to_delete), axis=1) # link global insts & local insts for i, ginst in enumerate(inst_mem): indexes_linked = np.argwhere(linked_array[i, :] >= 1) if len(indexes_linked) == 0: logger.debug('[%dth frame] ginst[%d](GID:%d, %s, %.3f, frame:%d) is not linked to anyone' % ( idx + delta, i, inst_mem[i].GID, classes[inst_mem[i].cls], inst_mem[i].cls_score, inst_mem[i].detected_idx[-1])) elif len(indexes_linked) == 1: index_linked = int(indexes_linked[0]) linst = inst_mem_now[index_linked] sim = sim_array_final[i][index_linked] logger.debug( '[%dth frame] ginst[%d](GID:%d, %s, %.3f, frame:%d) is updated by linst[%d](LID:%d, %s, %.3f)' % ( idx + delta, i, inst_mem[i].GID, classes[inst_mem[i].cls], inst_mem[i].cls_score, inst_mem[i].detected_idx[-1], index_linked, linst.LID, classes[linst.cls], linst.cls_score)) #ginst.update_inter_frame(linst, sim) else: logger.debug('# of linked indexes %d' % (len(indexes_linked))) raise NotImplementedError # this should not happen #if idx == 88: # 0.6 # logger.debug('debug_point') # make new ginst # translate inst_mem_now into box array (for evaluation) gidx_linked_from_lidx = [None for _ in range(len(inst_mem_now))] list_new_ginsts = [] if len(inst_mem_now) > 0: for j, linst in enumerate(inst_mem_now): if linst.l_suppressed: # if suppressed logger.debug( '[%dth frame] linst[%d](LID:%d, %s, %.3f) is discarded because of suppression' % ( idx + delta, j, linst.LID, classes[linst.cls], linst.cls_score)) continue gidx_linked_from_lidx[j] = np.argwhere(linked_array[:, j] >= 1) if len(gidx_linked_from_lidx[j]): # if linked to ginst assert len(gidx_linked_from_lidx[j]) == 1 gidx = gidx_linked_from_lidx[j][0][0] ginst = inst_mem[gidx] box_and_score = ginst_to_box_and_score(ginst) linst.cls_high = ginst.cls_high linst.linked_to_gidx = gidx else: # if not linked if linst.cls_score >= cfg.TEST.SCORE_THRESH: if len(inst_mem) > 0: iou_sum = iou_array[gidx_linked, j].sum() #compute_IOM(box_me=, box_B=) # exception handling if iou_sum > 0.5: # if linst overlaps with linked ginsts of this frame iou_max_index = np.argmax(iou_array_final[:, j]) if linst.cls == inst_mem[iou_max_index].cls and linst.cls_score < inst_mem[iou_max_index].cls_score: logger.debug( '[%dth frame] linst[%d](LID:%d, %s, %.3f) will be remained linst because of overlap(%.2f) with ginsts{g_linked}'.format( g_linked=gidx_linked) % (idx + delta, j, linst.LID, classes[linst.cls], linst.cls_score, iou_sum)) box_and_score = linst_to_box_and_score(linst) continue logger.debug('[%dth frame] linst[%d](LID:%d, %s, %.3f) became new ginst_memory[%d]' % ( idx + delta, j, linst.LID, classes[linst.cls], linst.cls_score, ginst_ID)) new_ginst = linst.make_global_inst(ginst_ID) list_new_ginsts.append(new_ginst) linst.linked_to = [ginst_ID] linst.linked_to_gidx = len(inst_mem) + len(list_new_ginsts) - 1 ginst_ID = ginst_ID + 1 box_and_score = ginst_to_box_and_score(new_ginst) else: logger.debug( '[%dth frame] linst[%d](LID:%d, %s, %.3f) will be remained linst because of low cls_score' % ( idx + delta, j, linst.LID, classes[linst.cls], linst.cls_score)) box_and_score = linst_to_box_and_score(linst) all_boxes_inst[linst.cls_high][idx + delta].append(box_and_score) for i in range(num_classes): all_boxes_inst[i][idx + delta] = np.array(all_boxes_inst[i][idx + delta]) inst_mem.extend(list_new_ginsts) out_im_ginst = 0 out_im_linst = 0 if vis: out_im_ginst = draw_all_ginst(center_image, inst_mem, classes, scales[delta], cfg, idx+delta) out_im_linst = draw_all_linst(center_image, inst_mem_now, inst_mem, classes, scales[delta], cfg) #out_im2 = draw_all_rois(center_image, rois, scales[delta], cfg) # print rois from RPN out_im = 0 if cfg.TEST.SEQ_NMS == False and max_per_image > 0 and cfg.TEST.DISPLAY[0]: image_scores = np.hstack([all_boxes[j][idx + delta][:, -1] for j in range(1, num_classes)]) if len(image_scores) > max_per_image: image_thresh = np.sort(image_scores)[-max_per_image] for j in range(1, num_classes): keep = np.where(all_boxes[j][idx + delta][:, -1] >= image_thresh)[0] all_boxes[j][idx + delta] = all_boxes[j][idx + delta][keep, :] if vis: boxes_this_image = [[]] + [all_boxes[j][idx + delta] for j in range(1, num_classes)] out_im = draw_all_detection(center_image, boxes_this_image, classes, scales[delta], cfg) return ginst_ID, out_im, out_im_ginst, out_im_linst def process_link_pred_result2(classes, pred_result, num_classes, thresh, cfg, nms, all_boxes, all_boxes_inst, idx, max_per_image, vis, center_image, scales, inst_mem, sim_array_global, ginst_ID): global logger for delta, (scores, boxes, rois, data_dict, iscores, ipred_boxes, embed_feat, psroipooled_cls_rois) in enumerate(pred_result): #16th frame -> cat(10) -> local box(3, 4th in rois) local_inst_ID = 0 inst_mem_now = [] # intra-frame inst memory per_cls_box_idx_over_th = [[]] per_cls_box_idx_nms = [[]] all_cls_box_idx_nms = [] for j in range(1, num_classes): # logger.debug('[%dth frame]test boxes of class: (%d, %s)' % (idx+delta, j, classes[j])) indexes = np.where(scores[:, j] > thresh)[0] per_cls_box_idx_over_th.append(indexes) cls_scores = scores[indexes, j, np.newaxis] cls_boxes = boxes[indexes, 4:8] if cfg.CLASS_AGNOSTIC else boxes[indexes, j * 4:(j + 1) * 4] cls_dets = np.hstack((cls_boxes, cls_scores)) if cfg.TEST.SEQ_NMS: all_boxes[j][ idx + delta] = cls_dets # all_boxes[31L(class)][frame_idx + batch_size][300L(box_idx)][5L(x,y,w,h,score)] else: # cls_dets=np.float32(cls_dets) keep = nms(cls_dets) all_boxes[j][idx + delta] = cls_dets[keep, :] per_cls_box_idx_nms.append(indexes[keep]) # all_cls_box_idx_nms.extend(indexes[keep]) #merging scores_max = scores[:,1:].max(axis=1) index_tf = (scores_max > thresh) cls_scores = scores[index_tf] cls_highest = scores[index_tf, 1:].argmax(axis=1) + 1 cls_boxes = boxes[index_tf, 4:8] if cfg.CLASS_AGNOSTIC else boxes[index_tf, j * 4:(j + 1) * 4] embed_feats = embed_feat[index_tf] psroipooled_cls_roiss = psroipooled_cls_rois[index_tf] for i in range(len(cls_scores)): new_inst = Instance(LID=local_inst_ID, frame_idx=idx + delta, cls=cls_highest[i], cls_score=cls_scores[i][cls_highest[i]], bbox=cls_boxes[i], embed_feat=embed_feats[i], atten=psroipooled_cls_roiss[i]) # make instances for outputs new_inst.cls_scores = cls_scores[i] new_inst.cls_scores_acc = cls_scores[i] local_inst_ID += 1 inst_mem_now.append(new_inst) logger.debug('[%dth frame] %d linsts created' % (idx + delta, len(inst_mem_now))) iou_array_final = np.zeros((1,len(inst_mem_now))) sim_array_final = np.zeros((1,len(inst_mem_now))) sim_array_final_th = np.zeros((1,len(inst_mem_now)), int) linked_array = np.zeros((len(inst_mem), len(inst_mem_now))) gidx_linked = [] #logger.debug('[%dth frame] @@@ inter frame phase start @@@', idx + delta) if len(inst_mem) > 0 and len(inst_mem_now) > 0 : cls_sim_array = np.zeros((len(inst_mem), len(inst_mem_now))) iou_array = np.zeros((len(inst_mem), len(inst_mem_now))) # matrix of iou coef_array = np.zeros((len(inst_mem), len(inst_mem_now))) # matrix of coeff sim_array = np.zeros((len(inst_mem), len(inst_mem_now))) # matrix of similarity g_rel_array = np.zeros((len(inst_mem), len(inst_mem_now))) # matrix of reliability l_rel_array = np.zeros((len(inst_mem), len(inst_mem_now))) # matrix of reliability g to l loc_array = np.zeros((len(inst_mem), len(inst_mem_now))) # matrix of relative distance area1 = np.empty(len(inst_mem)) for i, ginst in enumerate(inst_mem): area1[i] = Area(ginst.bbox2) area2 = np.empty(len(inst_mem_now)) for j, linst in enumerate(inst_mem_now): area2[j] = Area(linst.bbox2) # Similarity matrix generation (global <-> local inst) for i, ginst in enumerate(inst_mem): # loop for instances in this frame for j, linst in enumerate(inst_mem_now): cls_sim = (linst.cls_scores * ginst.cls_scores).sum() coeff = 0 #compare_embed(ginst.embed_feat, linst.embed_feat) # input embed_feat <type 'tuple'>: (2048, 8,8) iou = compute_IOU(ginst.bbox2, linst.bbox2, area1[i], area2[j]) #use virtual bbox similar = iou * cls_sim #original ver g_reliability = 0 # similar * ginst.cls_score_reliable l_reliability = 0 # similar * linst.cls_scores[ginst.cls] loc = 0 # compute_locality(ginst, linst) coeff_th = 0 # (coeff > cfg.TEST.COEFF_THRESH_INTER) iou_th = (iou > cfg.TEST.IOU_THRESH_INTRA) similar_th = (similar > cfg.TEST.IOU_THRESH_INTRA * cfg.TEST.COEFF_THRESH_INTRA) cls_sim_array[i, j] = cls_sim iou_array[i, j] = iou sim_array[i, j] = similar #coef_array[i, j] = coeff #g_rel_array[i, j] = g_reliability #l_rel_array[i, j] = l_reliability #loc_array[i, j] = loc logger.debug('[%dth frame] ginst[%d](GID:%d, %.5s(%.2f) lastf:%d)) VS linst[%d](LID:%d, %.5s(%.2f)): cls_sim:%.2f IOU:%.2f(%.1s) coef:%.2f(%.1s) sim:%.2f(%.1s) lrel:%.2f' % ( idx + delta, i, ginst.GID, classes[ginst.cls], ginst.cls_score, ginst.detected_idx[-1], j, linst.LID, classes[linst.cls], linst.cls_score, cls_sim, iou, iou_th, coeff, coeff_th, similar, similar_th, l_reliability)) sim_array_th = (sim_array[:, :] > cfg.TEST.COEFF_THRESH_INTRA * cfg.TEST.IOU_THRESH_INTRA) g_rel_array_th = (g_rel_array[:, :] > cfg.TEST.REL_THRESH_INTRA) l_rel_array_th = (l_rel_array[:, :] > cfg.TEST.REL_THRESH_INTRA) #sim_array_global.append(sim_array) #for debugging link_array = np.zeros((len(inst_mem), len(inst_mem_now))) sim_array_tmp = sim_array.copy() l_rel_array_tmp = l_rel_array.copy() sim_array_hor = sim_array.copy() l_rel_array_hor = l_rel_array.copy() sim_array_ver = sim_array.copy() l_rel_array_ver = l_rel_array.copy() # ginst linking while 0: # l_rel_array_tmp.max() > cfg.TEST.REL_THRESH_INTRA: i = l_rel_array_tmp.argmax() gi = i / len(inst_mem_now) li = i % len(inst_mem_now) link_array[gi, li] = 1 sim_array_tmp[gi, :] = -1 sim_array_tmp[:, li] = -1 l_rel_array_tmp[gi, :] = -1 l_rel_array_tmp[:, li] = -1 sim_array_hor[gi, :] = -1 sim_array_ver[:, li] = -1 l_rel_array_hor[gi, :] = -1 l_rel_array_ver[:, li] = -1 gidx_linked.append(gi) inst_mem[gi].update_inter_frame(inst_mem_now[li], sim_array[gi,li]) while sim_array_tmp.max() > 0: # cfg.TEST.IOU_THRESH_INTER * cfg.TEST.COEFF_THRESH_INTER: i = sim_array_tmp.argmax() gi = i / len(inst_mem_now) li = i % len(inst_mem_now) link_array[gi, li] = 2 sim_array_tmp[gi, :] = -2 sim_array_tmp[:, li] = -2 l_rel_array_tmp[gi, :] = -2 l_rel_array_tmp[:, li] = -2 sim_array_hor[gi, :] = -2 sim_array_ver[:, li] = -2 l_rel_array_hor[gi, :] = -2 l_rel_array_ver[:, li] = -2 gidx_linked.append(gi) inst_mem[gi].update_inter_frame(inst_mem_now[li], sim_array[gi, li]) inst_mem[gi].cls_scores_acc += inst_mem_now[li].cls_scores l_rel_array_ver_th = (l_rel_array_ver[:, :] > cfg.TEST.REL_THRESH_INTRA) l_rel_array_hor_th = (l_rel_array_hor[:, :] > cfg.TEST.REL_THRESH_INTRA) # ginst guided linst supression (for not linked) if cfg.TEST.LSUP: for i, ginst in enumerate(inst_mem): if link_array[i, :].max() > 0: lindex_to_suppress = np.where(l_rel_array_ver_th[i, :] == True)[0] sim_array_tmp[:, lindex_to_suppress] = -3 for k in lindex_to_suppress: inst_mem_now[k].l_suppressed.append(idx + delta) # linst guided ginst suppresion (for not linked) if cfg.TEST.GSUP: for j, linst in enumerate(inst_mem_now): if link_array[:, j].max() > 0: gindex_to_suppress = np.where(l_rel_array_hor_th[:, j] == True)[0] sim_array_tmp[gindex_to_suppress, :] = -4 for k in gindex_to_suppress: inst_mem[k].g_suppressed.append(idx + delta) iou_array_final = iou_array.copy() sim_array_final = sim_array.copy() sim_array_final_th = sim_array_th.copy() rel_array_final = l_rel_array.copy() rel_array_final_th = l_rel_array_th.copy() linked_array = link_array.copy() # delete some ginsts gidx_to_delete = [] for i, ginst in enumerate(inst_mem): delete = 0 last_frame = ginst.detected_idx[-1] if last_frame < (idx + delta - cfg.TEST.GINST_LIFE_FRAME): logger.debug('[%dth frame] ginst[%d](GID:%d, %s, %.3f, frame:%d) is deleted (old frame)' % ( idx + delta, i, inst_mem[i].GID, classes[inst_mem[i].cls], inst_mem[i].cls_score, inst_mem[i].detected_idx[-1])) delete = 1 elif ginst.g_suppressed: logger.debug('[%dth frame] ginst[%d](GID:%d, %s, %.3f, frame:%d) is deleted (suppressed)' % ( idx + delta, i, inst_mem[i].GID, classes[inst_mem[i].cls], inst_mem[i].cls_score, inst_mem[i].detected_idx[-1])) delete = 1 elif 0:#ginst.cls_score_reliable < 0.05: logger.debug('[%dth frame] ginst[%d](GID:%d, %s, %.3f, frame:%d) is deleted (low reliability)' % ( idx + delta, i, inst_mem[i].GID, classes[inst_mem[i].cls], inst_mem[i].cls_score, inst_mem[i].detected_idx[-1])) delete = 1 if delete == 1: gidx_to_delete.append(i) if last_frame == idx + delta: gidx_linked.remove(i) gidx_to_delete.reverse() for i in gidx_to_delete: del inst_mem[i] iou_array_final, sim_array_final, sim_array_final_th, rel_array_final, rel_array_final_th, linked_array = \ np.delete( (iou_array_final, sim_array_final, sim_array_final_th, rel_array_final, rel_array_final_th, linked_array), (gidx_to_delete), axis=1) # link global insts & local insts for i, ginst in enumerate(inst_mem): indexes_linked = np.argwhere(linked_array[i, :] >= 1) if len(indexes_linked) == 0: logger.debug('[%dth frame] ginst[%d](GID:%d, %s, %.3f, frame:%d) is not linked to anyone' % ( idx + delta, i, inst_mem[i].GID, classes[inst_mem[i].cls], inst_mem[i].cls_score, inst_mem[i].detected_idx[-1])) elif len(indexes_linked) == 1: index_linked = int(indexes_linked[0]) linst = inst_mem_now[index_linked] sim = sim_array_final[i][index_linked] logger.debug( '[%dth frame] ginst[%d](GID:%d, %s, %.3f, frame:%d) is updated by linst[%d](LID:%d, %s, %.3f)' % ( idx + delta, i, inst_mem[i].GID, classes[inst_mem[i].cls], inst_mem[i].cls_score, inst_mem[i].detected_idx[-1], index_linked, linst.LID, classes[linst.cls], linst.cls_score)) #ginst.update_inter_frame(linst, sim) else: logger.debug('# of linked indexes %d' % (len(indexes_linked))) raise NotImplementedError # this should not happen #if idx == 88: # 0.6 # logger.debug('debug_point') # make new ginst # translate inst_mem_now into box array (for evaluation) gidx_linked_from_lidx = [None for _ in range(len(inst_mem_now))] list_new_ginsts = [] if len(inst_mem_now) > 0: for j, linst in enumerate(inst_mem_now): if linst.l_suppressed: # if suppressed logger.debug( '[%dth frame] linst[%d](LID:%d, %s, %.3f) is discarded because of suppression' % ( idx + delta, j, linst.LID, classes[linst.cls], linst.cls_score)) continue gidx_linked_from_lidx[j] = np.argwhere(linked_array[:, j] >= 1) if len(gidx_linked_from_lidx[j]): # if linked to ginst assert len(gidx_linked_from_lidx[j]) == 1 gidx = gidx_linked_from_lidx[j][0][0] ginst = inst_mem[gidx] box_and_score = ginst_to_box_and_score_cls_scores(ginst) linst.cls_high = ginst.cls_scores_acc[1:].argmax() + 1 # this cls result is only accurate for dynamic averaging result linst.linked_to_gidx = gidx else: # if not linked if linst.cls_score >= cfg.TEST.SCORE_THRESH: if len(inst_mem) > 0: iou_sum = iou_array[gidx_linked, j].sum() #compute_IOM(box_me=, box_B=) # exception handling if iou_sum > 0.5: # if linst overlaps with linked ginsts of this frame iou_max_index = np.argmax(iou_array_final[:, j]) if linst.cls == inst_mem[iou_max_index].cls and linst.cls_score < inst_mem[iou_max_index].cls_score: logger.debug( '[%dth frame] linst[%d](LID:%d, %s, %.3f) will be remained linst because of overlap(%.2f) with ginsts{g_linked}'.format( g_linked=gidx_linked) % (idx + delta, j, linst.LID, classes[linst.cls], linst.cls_score, iou_sum)) box_and_score = linst_to_box_and_score(linst) continue logger.debug('[%dth frame] linst[%d](LID:%d, %s, %.3f) became new ginst_memory[%d]' % ( idx + delta, j, linst.LID, classes[linst.cls], linst.cls_score, ginst_ID)) new_ginst = linst.make_global_inst(ginst_ID) new_ginst.cls_scores = linst.cls_scores new_ginst.cls_scores_acc = linst.cls_scores_acc list_new_ginsts.append(new_ginst) linst.linked_to = [ginst_ID] linst.linked_to_gidx = len(inst_mem) + len(list_new_ginsts) - 1 ginst_ID = ginst_ID + 1 box_and_score = ginst_to_box_and_score_cls_scores(new_ginst) else: logger.debug( '[%dth frame] linst[%d](LID:%d, %s, %.3f) will be remained linst because of low cls_score' % ( idx + delta, j, linst.LID, classes[linst.cls], linst.cls_score)) box_and_score = linst_to_box_and_score(linst) all_boxes_inst[linst.cls_high][idx + delta].append(box_and_score) for i in range(num_classes): all_boxes_inst[i][idx + delta] = np.array(all_boxes_inst[i][idx + delta]) keep = nms(all_boxes_inst[i][idx + delta]) all_boxes[i][idx + delta] = all_boxes_inst[i][idx + delta][keep, :] inst_mem.extend(list_new_ginsts) out_im_ginst = 0 out_im_linst = 0 if vis: out_im_ginst = draw_all_ginst(center_image, inst_mem, classes, scales[delta], cfg, idx+delta) out_im_linst = draw_all_linst(center_image, inst_mem_now, inst_mem, classes, scales[delta], cfg) #out_im2 = draw_all_rois(center_image, rois, scales[delta], cfg) # print rois from RPN out_im = 0 return ginst_ID, out_im, out_im_ginst, out_im_linst

52.359125

197

0.549819

8,200

62,255

3.883902

0.068415

0.034947

0.018839

0.012246

0.778448

0.74686

0.720077

0.686919

0.667891

0.660952

0

0.019669

0.336873

62,255

1,188

198

52.403199

0.751786

0.112473

0

0.640678

0

0.027119

0.051447

0.002355

0

0.00565

0

null

0

0.028249

null

0.00339

0

null

0

1

0

1

0

null

0

1

0

4

f9274a552f7bd23d0303ea41ab7da1df9847093c

902

py

Python

cookbook/variable-number-of-products/serializer/tasks.py

jramirez857/projects

de47cf98cc6b4cc14117924b623ded989ab870d7

[ "Apache-2.0" ]

44

2020-07-07T13:35:51.000Z

2022-03-25T21:45:43.000Z

cookbook/variable-number-of-products/serializer/tasks.py

jramirez857/projects

de47cf98cc6b4cc14117924b623ded989ab870d7

[ "Apache-2.0" ]

20

2020-10-15T01:33:28.000Z

2022-03-18T14:43:16.000Z

cookbook/variable-number-of-products/serializer/tasks.py

jramirez857/projects

de47cf98cc6b4cc14117924b623ded989ab870d7

[ "Apache-2.0" ]

4

2021-10-17T09:21:05.000Z

2022-02-06T22:38:50.000Z

import json from random import randint def variable(): """ A task that generates a variable number of products (keys are filenames, values are products) """ return {f'{x}.txt': str(x) for x in range(randint(1, 5))} def many_products_one_variable(): """ A task that generates a fixed-size product ('one') and a variable-size product ('variable') """ return { 'one': 1, 'variable': {f'{x}.txt': str(x) for x in range(randint(1, 5))} } def variable_downstream(upstream): """ A task that dumps to JSON the output of "variable" """ return json.dumps(upstream['variable']) def many_products_one_variable_downstream(upstream): """ A task that dumps to JSON the output "variable" of "many_products_one_variable" """ return json.dumps(upstream['many_products_one_variable']['variable'])

24.378378

76

0.631929

121

902

4.595041

0.330579

0.035971

0.064748

0.165468

0.580935

0.410072

0.31295

0

0.007331

0.243902

902

37

77

24.378378

0.807918

0.350333

0

1

0

0.13035

0.050584

0

1

0.285714

false

0

0.142857

0

0.714286

0

null

0

1

0

1

0

null

0

1

0

1

0

4

006053e36cabd5815ea8362f0b2abbdee31ba0e6

172

py

Python

freetrade/__init__.py

DainisGorbunovs/freetrade

1f589b9bbf607dc2f1017b29b664d97dd49379b7

[ "MIT" ]

32

2019-05-21T17:18:18.000Z

2022-02-01T22:14:19.000Z

freetrade/__init__.py

DainisGorbunovs/freetrade

1f589b9bbf607dc2f1017b29b664d97dd49379b7

[ "MIT" ]

3

2020-05-09T14:05:31.000Z

2021-02-08T19:54:37.000Z

freetrade/__init__.py

DainisGorbunovs/freetrade

1f589b9bbf607dc2f1017b29b664d97dd49379b7

[ "MIT" ]

1

2021-02-06T13:20:54.000Z

from .credentials import Credentials from .auth import Auth from .api import API from .index import Index from .datastore import DataStore from .freetrade import FreeTrade

24.571429

36

0.825581

24

172

5.916667

0.333333

0

0.139535

172

6

37

28.666667

0.959459

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

007e0576100f8772376edd8a3a0f458719ded107

262

py

Python

Deprecated/helper.py

JTermens/MacroFlexEngine

568ad52c5682569ee7c7331859f5ffd38a368478

[ "MIT" ]

null

Deprecated/helper.py

JTermens/MacroFlexEngine

568ad52c5682569ee7c7331859f5ffd38a368478

[ "MIT" ]

null

Deprecated/helper.py

JTermens/MacroFlexEngine

568ad52c5682569ee7c7331859f5ffd38a368478

[ "MIT" ]

1

2020-07-04T12:49:16.000Z

class BytesIntEncoder: @staticmethod def encode(s: str) -> int: return int.from_bytes(s.encode(), byteorder='big') @staticmethod def decode(i: int) -> str: return i.to_bytes(((i.bit_length() + 7) // 8), byteorder='big').decode()

29.111111

80

0.610687

34

262

4.617647

0.588235

0.191083

0

0.009756

0.217557

262

8

81

32.75

0.756098

0

0.285714

0

0.022901

0

1

0.285714

false

0

0.285714

0.714286

0

null

0

1

0

null

0

1

0

1

0

4

0093401ef41fd70e9204836c286d771218ecc070

270

py

Python

conversationinsights/policies/__init__.py

osswangxining/conversationinsights-dialogue

07490b6307667b0d0ddc2c4fb8aa4f8d7b853df9

[ "Apache-2.0" ]

8

2017-10-10T02:18:09.000Z

2019-12-16T15:14:13.000Z

conversationinsights/policies/__init__.py

osswangxining/conversationinsights-dialogue

07490b6307667b0d0ddc2c4fb8aa4f8d7b853df9

[ "Apache-2.0" ]

null

conversationinsights/policies/__init__.py

osswangxining/conversationinsights-dialogue

07490b6307667b0d0ddc2c4fb8aa4f8d7b853df9

[ "Apache-2.0" ]

2

2018-06-26T02:03:41.000Z

2018-08-06T10:54:46.000Z

from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals from conversationinsights.policies.policy import Policy from conversationinsights.policies.trainer import PolicyTrainer

33.75

63

0.892593

31

270

7.16129

0.451613

0.18018

0.288288

0

0.092593

270

7

64

38.571429

0.906122

0

1

0

true

0

1

0

1

0.166667

0

null

0

1

0

1

0

null

0

1

0

1

0

4

00bf141f94a868228065f0f60baee56784b30f41

1,077

py

Python

catkin_ws/build/mobrob_util/cmake/mobrob_util-genmsg-context.py

KaibiaoRuan/mobile-robot-controller

ac931b925d42c152a214385f1c0af2fa4acfea73

[ "MIT" ]

null

catkin_ws/build/mobrob_util/cmake/mobrob_util-genmsg-context.py

KaibiaoRuan/mobile-robot-controller

ac931b925d42c152a214385f1c0af2fa4acfea73

[ "MIT" ]

null

catkin_ws/build/mobrob_util/cmake/mobrob_util-genmsg-context.py

KaibiaoRuan/mobile-robot-controller

ac931b925d42c152a214385f1c0af2fa4acfea73

[ "MIT" ]

null

# generated from genmsg/cmake/pkg-genmsg.context.in messages_str = "/home/pi/catkin_ws/src/mobrob_util/msg/ME439SensorsRaw.msg;/home/pi/catkin_ws/src/mobrob_util/msg/ME439SensorsProcessed.msg;/home/pi/catkin_ws/src/mobrob_util/msg/ME439WheelSpeeds.msg;/home/pi/catkin_ws/src/mobrob_util/msg/ME439MotorCommands.msg;/home/pi/catkin_ws/src/mobrob_util/msg/ME439WheelAngles.msg;/home/pi/catkin_ws/src/mobrob_util/msg/ME439WheelDisplacements.msg;/home/pi/catkin_ws/src/mobrob_util/msg/ME439PathSpecs.msg;/home/pi/catkin_ws/src/mobrob_util/msg/ME439WaypointXY.msg" services_str = "" pkg_name = "mobrob_util" dependencies_str = "geometry_msgs;std_msgs" langs = "gencpp;geneus;genlisp;gennodejs;genpy" dep_include_paths_str = "mobrob_util;/home/pi/catkin_ws/src/mobrob_util/msg;geometry_msgs;/opt/ros/melodic/share/geometry_msgs/cmake/../msg;std_msgs;/opt/ros/melodic/share/std_msgs/cmake/../msg" PYTHON_EXECUTABLE = "/usr/bin/python2" package_has_static_sources = '' == 'TRUE' genmsg_check_deps_script = "/opt/ros/melodic/share/genmsg/cmake/../../../lib/genmsg/genmsg_check_deps.py"

89.75

506

0.814299

168

1,077

4.970238

0.35119

0.131737

0.129341

0.150898

0.401198

0.348503

0.276647

0

0.023969

0.031569

1,077

11

507

97.909091

0.776606

0.045497

0

1

0.222222

0.802144

0.77193

0

1

0

false

0

null

0

1

null

0

4

00c82d9fb28f4e943c2d9fe797c707bc819f7ebe

446

py

Python

pyPractise/jcp007.py

enyaooshigaolo/MyPython

67dc3f6ff596545ab70e11a573a6031232128711

[ "Apache-2.0" ]

null

pyPractise/jcp007.py

enyaooshigaolo/MyPython

67dc3f6ff596545ab70e11a573a6031232128711

[ "Apache-2.0" ]

null

pyPractise/jcp007.py

enyaooshigaolo/MyPython

67dc3f6ff596545ab70e11a573a6031232128711

[ "Apache-2.0" ]

null

''' Created on 2017年1月6日 @author: Think 【程序7】题目：输出特殊图案，请在c环境中运行，看一看，Very Beautiful! 1.程序分析：字符共有256个。不同字符，图形不一样。　　　　　　 2.程序源代码： ''' import os import sys def jcp007(): a = 176 b = 219 print(chr(b),chr(a),chr(a),chr(a),chr(b)) print(chr(a),chr(b),chr(a),chr(a),chr(a)) print(chr(a),chr(a),chr(a),chr(b),chr(a)) print(chr(a),chr(b),chr(a),chr(a),chr(a)) print(chr(b),chr(a),chr(a),chr(a),chr(b)) jcp007()

18.583333

45

0.576233

85

446

3.023529

0.352941

0.280156

0.40856

0.311284

0.486381

0.470817

0.420233

0

0.065574

0.179372

446

24

46

18.583333

0.636612

0.278027

0

0.363636

0

1

0.090909

false

0

0.181818

0

0.272727

0.454545

0

null

1

0

null

0

1

0

4

00cd714d767de17e1832353533e04b8196f78b65

1,194

py

Python

src/apiUtils.py

prakharbahuguna/PyPlyServer

d5b17feb288352c63283ccd1c1c107339870c89d

[ "MIT" ]

null

src/apiUtils.py

prakharbahuguna/PyPlyServer

d5b17feb288352c63283ccd1c1c107339870c89d

[ "MIT" ]

null

src/apiUtils.py

prakharbahuguna/PyPlyServer

d5b17feb288352c63283ccd1c1c107339870c89d

[ "MIT" ]

null

__author__ = 'matt' import json import spotipy import spotipy.util as util import sys import os datapath = os.path.dirname(os.path.realpath(__file__)) + '/data' sys.path.insert(0, datapath) DATA_PATH = os.path.join(datapath, "API_info") class APIUtils(): def __init__(self): self.apiInfo = json.load(open(DATA_PATH)) def getAPI_JSON(self): return self.apiInfo def getSpotifyClientID(self): return self.apiInfo["spotifyClientID"] def getSpotifyClientSecret(self): return self.apiInfo["spotifyClientSecret"] def getSpotifyRedirectURI(self): return self.apiInfo["spotifyRedirectURI"] def getSpotifyTokenURL(self): return self.apiInfo["spotifyTokenURL"] def getFacebookID(self): return self.apiInfo["facebookID"] def getFacebookSecret(self): return self.apiInfo["facebookSecret"] def getSpotifyToken(self, user): return util.prompt_for_user_token(username=user, scope="playlist-modify-public", client_id=self.apiInfo["spotifyClientID"], client_secret=self.apiInfo["spotifyClientSecret"], redirect_uri=self.apiInfo["spotifyRedirectURI"])

29.121951

141

0.697655

129

1,194

6.286822

0.44186

0.149199

0.120838

0.181258

0

0.001038

0.193467

1,194

41

141

29.121951

0.841122

0

0.152301

0.01841

0

1

0.310345

false

0

0.172414

0.275862

0.793103

0

null

0

1

0

null

0

1

0

1

0

4

00d0ae858849631bb5ae475f200a17fc53bd96d6

40

py

Python

scripts/npc/stage43npc.py

kleric/initiald

f05579c8919d6bbecce8e67da1a807cb522cde99

[ "MIT" ]

null

scripts/npc/stage43npc.py

kleric/initiald

f05579c8919d6bbecce8e67da1a807cb522cde99

[ "MIT" ]

null

scripts/npc/stage43npc.py

kleric/initiald

f05579c8919d6bbecce8e67da1a807cb522cde99

[ "MIT" ]

null

sm.teleportToPortal(6) chr.lapCount = 0

20

23

0.775

6

40

5.166667

1

0

0.055556

0.1

40

2

24

20

0.805556

0

1

0

true

0

1

0

null

0

1

0

null

0

1

0

4

dac3ce4d32d0bedcb6ef8737693d615e60e8a015

90

py

Python

mci/__init__.py

brighthive/master-client-index

b5aeb600d7e9e7c07a0b57deb2b4d89b3f90fd2f

[ "MIT" ]

2

2019-05-29T14:10:56.000Z

2019-06-27T02:53:01.000Z

mci/__init__.py

brighthive/master-client-index

b5aeb600d7e9e7c07a0b57deb2b4d89b3f90fd2f

[ "MIT" ]

22

2019-05-01T21:19:53.000Z

2020-07-01T23:15:21.000Z

mci/__init__.py

brighthive/master-client-index

b5aeb600d7e9e7c07a0b57deb2b4d89b3f90fd2f

[ "MIT" ]

1

2020-04-29T18:18:31.000Z

from mci.app import create_app from mci.config.config import ConfigurationFactory, Config

30

58

0.855556

13

90

5.846154

0.538462

0.184211

0

0.1

90

2

59

45

0.938272

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

dacb85325eb3841f43baa12361e8d497e4bb9546

304

py

Python

starfish/pipeline/features/spots/decoder/iss.py

Xiaojieqiu/starfish

426480fcfeda4b8b1eb9371a818ff20275ac898d

[ "MIT" ]

1

2018-10-07T03:53:43.000Z

starfish/pipeline/features/spots/decoder/iss.py

Xiaojieqiu/starfish

426480fcfeda4b8b1eb9371a818ff20275ac898d

[ "MIT" ]

null

starfish/pipeline/features/spots/decoder/iss.py

Xiaojieqiu/starfish

426480fcfeda4b8b1eb9371a818ff20275ac898d

[ "MIT" ]

null

from ._base import DecoderAlgorithmBase class IssDecoder(DecoderAlgorithmBase): def __init__(self, **kwargs): pass @classmethod def add_arguments(cls, group_parser): pass def decode(self, intensities, codebook): return codebook.decode_per_hyb_max(intensities)

21.714286

55

0.710526

32

304

6.4375

0.75

0

0.213816

304

13

56

23.384615

0.861925

0

0.222222

0

1

0.333333

false

0.222222

0.111111

0.666667

0

null

0

null

0

1

0

1

0

1

0

4

dad228857b28e23f9be130a53cee8b6b30d5d9bf

246

py

Python

tools/bin/pythonSrc/pychecker-0.8.18/test_input/test64.py

YangHao666666/hawq

10cff8350f1ba806c6fec64eb67e0e6f6f24786c

[ "Artistic-1.0-Perl", "ISC", "bzip2-1.0.5", "TCL", "Apache-2.0", "BSD-3-Clause-No-Nuclear-License-2014", "MIT", "PostgreSQL", "BSD-3-Clause" ]

450

2015-09-05T09:12:51.000Z

2018-08-30T01:45:36.000Z

tools/bin/pythonSrc/pychecker-0.8.18/test_input/test64.py

YangHao666666/hawq

10cff8350f1ba806c6fec64eb67e0e6f6f24786c

[ "Artistic-1.0-Perl", "ISC", "bzip2-1.0.5", "TCL", "Apache-2.0", "BSD-3-Clause-No-Nuclear-License-2014", "MIT", "PostgreSQL", "BSD-3-Clause" ]

1,274

2015-09-22T20:06:16.000Z

2018-08-31T22:14:00.000Z

tools/bin/pythonSrc/pychecker-0.8.18/test_input/test64.py

YangHao666666/hawq

10cff8350f1ba806c6fec64eb67e0e6f6f24786c

[ "Artistic-1.0-Perl", "ISC", "bzip2-1.0.5", "TCL", "Apache-2.0", "BSD-3-Clause-No-Nuclear-License-2014", "MIT", "PostgreSQL", "BSD-3-Clause" ]

278

2015-09-21T19:15:06.000Z

2018-08-31T00:36:51.000Z

'''spurious warnings reported by Andrew Dalke ''' import sys, string, re import array class _Anything: pass _anything = _Anything() def outer(): def x(): return _anything def y(): return dir(string.translate) x()

13.666667

45

0.638211

30

246

5.1

0.7

0.143791

0

0.256098

246

17

46

14.470588

0.836066

0.170732

0

1

0.3

false

0.1

0.2

0.8

0

null

0

null

0

1

0

1

0

1

0

4

dae6bdf1d5cc0a69559c1ae3e57e8574435f6fdb

174

py

Python

src/loading_multi_subject_data/read_mat.py

AlexLamson/JINS-blink

7a8afabca7e38176ddd15fa29e5192c9d6b7e8fe

[ "MIT" ]

null

src/loading_multi_subject_data/read_mat.py

AlexLamson/JINS-blink

7a8afabca7e38176ddd15fa29e5192c9d6b7e8fe

[ "MIT" ]

1

2018-06-22T18:27:38.000Z

src/loading_multi_subject_data/read_mat.py

AlexLamson/JINS-blink

7a8afabca7e38176ddd15fa29e5192c9d6b7e8fe

[ "MIT" ]

null

import scipy.io # mat = scipy.io.loadmat('105_label0.mat') # print(mat['time_stamps'].flatten()) mat = scipy.io.loadmat('categories.mat') print(mat['categories'].flatten())

24.857143

42

0.712644

25

174

4.88

0.48

0.172131

0.163934

0.278689

0

0.024845

0.074713

174

6

43

29

0.732919

0.436782

0

0.252632

0

1

0

false

0

0.333333

0

0.333333

1

0

null

0

1

0

1

0

null

0

1

0

4

daedc4de58f74353afef9cde9dc5d78d00f9f1df

695

py

Python

src/errors/channel.py

xrenata/krema

9923988266d1c7c27fe793d642ea34747744d449

[ "MIT" ]

2

2021-08-10T08:24:18.000Z

2022-02-02T14:21:25.000Z

src/errors/channel.py

xrenata/krema

9923988266d1c7c27fe793d642ea34747744d449

[ "MIT" ]

null

src/errors/channel.py

xrenata/krema

9923988266d1c7c27fe793d642ea34747744d449

[ "MIT" ]

1

2022-01-01T19:53:39.000Z

""" Errors about channels. """ class FetchChannelFailed(Exception): """Raises when fetching a channel is failed.""" pass class FetchChannelMessagesFailed(Exception): """Raises when fetching messages from channel is failed.""" pass class FetchChannelMessageFailed(Exception): """Raises when fetching A message from channel is failed.""" pass class BulkDeleteMessagesFailed(Exception): """Raises when purge messages from channel is failed.""" pass class EditChannelFailed(Exception): """Raises when editing the channel is failed.""" pass class DeleteChannelFailed(Exception): """Raises when deleting the channel is failed.""" pass

17.375

64

0.709353

73

695

6.753425

0.356164

0.182556

0.231237

0.464503

0.20284

0.146045

0

0.194245

695

39

65

17.820513

0.880357

0.447482

0

0.5

0

1

0

true

0.5

0

0.5

0

null

0

1

0

null

0

1

0

4

daf1f7dfb844b08e7a439ec4621db16ea9122b96

1,642

py

Python

absympla/core/types/participant.py

4U360/ABSympla

041b7b338c66228c76feac9628b2c698ae00137c

[ "MIT" ]

null

absympla/core/types/participant.py

4U360/ABSympla

041b7b338c66228c76feac9628b2c698ae00137c

[ "MIT" ]

null

absympla/core/types/participant.py

4U360/ABSympla

041b7b338c66228c76feac9628b2c698ae00137c

[ "MIT" ]

null

from decimal import Decimal from collections import namedtuple from typing import Iterator CustomForm = namedtuple('CustomForm', ["id", "name", "value"]) class Participant(object): __data = {} def __init__(self, **kwargs): self.__data = {**kwargs} @property def data(self) -> dict: return self.__data @property def id(self) -> int: return self.data.get("id") @property def order_id(self) -> str: return self.data.get("order_id") @property def first_name(self) -> str: return self.data.get("first_name", "") @property def last_name(self) -> str: return self.data.get("last_name", "") @property def email(self) -> str: return self.data.get("email", "") @property def ticket_number(self) -> str: return self.data.get("ticket_number", "") @property def ticket_num_qr_code(self) -> str: return self.data.get("ticket_num_qr_code", "") @property def ticket_name(self) -> str: return self.data.get("ticket_name", "") @property def pdv_user(self) -> str: return self.data.get("pdv_user", "") @property def ticket_sale_price(self) -> float: return self.data.get("ticket_sale_price", 0) @property def ticket_sale_price_decimal(self) -> Decimal: return Decimal(self.ticket_sale_price) @property def checkin(self) -> dict: return self.data.get("checkin", {}) @property def custom_form(self) -> Iterator[CustomForm]: for form in self.data.get("custom_form", []): yield CustomForm(**form)

24.147059

62

0.611449

202

1,642

4.777228

0.227723

0.116062

0.174093

0.193782

0.353368

0.230052

0.15544

0

0.000811

0.249086

1,642

68

63

24.147059

0.781833

0

0.27451

0

0.08521

0

1

0.294118

false

0

0.058824

0.254902

0.647059

0

null

0

1

0

null

0

1

0

1

0

4

9716ab34f2ae55fec3184c30dee65cce990ee577

189

py

Python

Cura/Uranium/UM/Qt/Bindings/__init__.py

TIAO-JI-FU/3d-printing-with-moveo-1

100ecfd1208fe1890f8bada946145d716b2298eb

[ "MIT" ]

null

Cura/Uranium/UM/Qt/Bindings/__init__.py

TIAO-JI-FU/3d-printing-with-moveo-1

100ecfd1208fe1890f8bada946145d716b2298eb

[ "MIT" ]

null

Cura/Uranium/UM/Qt/Bindings/__init__.py

TIAO-JI-FU/3d-printing-with-moveo-1

100ecfd1208fe1890f8bada946145d716b2298eb

[ "MIT" ]

null

31.5

67

0.756614

29

189

4.931034

0.931034

0

0.032051

0.174603

189

5

68

37.8

0.884615

0.941799

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

4

974f778bd0ee1c12515ef2bdc490b22e9fee9ce7

103

py

Python

version_metadata/__main__.py

bonitalam/ihec-ecosystems

55c1c7248422705d07405e7dd72e13f18e092935

[ "Apache-2.0" ]

7

2015-11-17T06:52:52.000Z

2021-09-28T18:45:15.000Z

version_metadata/__main__.py

bonitalam/ihec-ecosystems

55c1c7248422705d07405e7dd72e13f18e092935

[ "Apache-2.0" ]

87

2015-08-31T02:21:39.000Z

2022-02-25T18:59:27.000Z

version_metadata/__main__.py

bonitalam/ihec-ecosystems

55c1c7248422705d07405e7dd72e13f18e092935

[ "Apache-2.0" ]

3

2019-10-04T16:05:21.000Z

2021-08-04T21:19:16.000Z

from .config import Config from .main import main if __name__ == '__main__': main(Config.sys())

10.3

27

0.68932

14

103

4.5

0.5

0

0.194175

103

9

28

11.444444

0.759036

0

0.079208

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

4

9766a854b1b36ba3838d22326da0499d2f45738c

100

py

Python

jawfish/static/script/Lib/browser/websocket.py

NeolithEra/jawfish

22fe222e607f0ad275860c75d81ab41114a18eb3

[ "MIT" ]

52

2016-08-08T15:08:19.000Z

2022-03-23T09:48:53.000Z

jawfish/static/script/Lib/browser/websocket.py

NeolithEra/jawfish

22fe222e607f0ad275860c75d81ab41114a18eb3

[ "MIT" ]

6

2016-10-09T19:50:49.000Z

2019-08-17T15:34:21.000Z

jawfish/static/script/Lib/browser/websocket.py

NeolithEra/jawfish

22fe222e607f0ad275860c75d81ab41114a18eb3

[ "MIT" ]

15

2017-02-03T03:08:57.000Z

2021-08-04T06:11:15.000Z

from browser import window import javascript WebSocket = javascript.JSConstructor(window.WebSocket)

25

54

0.86

11

100

7.818182

0.636364

0

0.09

100

4

54

25

0.945055

0

1

0

false

0

0.666667

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

4

978447600bd72802137eebd65fe4cd6a59e7eeac

39

py

Python

.history/ClassFiles/Control Flow/BreakStatement_20210101215307.py

minefarmer/Comprehensive-Python

f97b9b83ec328fc4e4815607e6a65de90bb8de66

[ "Unlicense" ]

null

.history/ClassFiles/Control Flow/BreakStatement_20210101215307.py

minefarmer/Comprehensive-Python

f97b9b83ec328fc4e4815607e6a65de90bb8de66

[ "Unlicense" ]

null

.history/ClassFiles/Control Flow/BreakStatement_20210101215307.py

minefarmer/Comprehensive-Python

f97b9b83ec328fc4e4815607e6a65de90bb8de66

[ "Unlicense" ]

null

''' Break statement '''

3.9

27

0.358974

2

39

7

1

0

0.487179

39

10

28

3.9

0.7

0.384615

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

null

0

1

0

4

97971067c78d2fef1d7750883fb0a158e5963307

287

py

Python

planner/urls.py

miichaek/Planner-react_imp0

64d20d4070ca759cc53e97ce4e7c828caef5c5a9

[ "MIT" ]

null

planner/urls.py

miichaek/Planner-react_imp0

64d20d4070ca759cc53e97ce4e7c828caef5c5a9

[ "MIT" ]

null

planner/urls.py

miichaek/Planner-react_imp0

64d20d4070ca759cc53e97ce4e7c828caef5c5a9

[ "MIT" ]

null

from django.urls import path from .views import SignUpView from planner import views # from django.urls import path from django.contrib import admin from django.urls import path from planner import views urlpatterns = [ # path('signup/', SignUpView.as_view(), name='signup'), ]

17.9375

59

0.756098

40

287

5.4

0.375

0.185185

0.194444

0.277778

0.388889

0

0.160279

287

15

60

19.133333

0.896266

0.285714

0

0.5

0

1

0

false

0

0.75

0

0.75

0

null

0

1

0

null

0

1

0

1

0

4

97b0112f78fbbf9229c2fd7f1c367f3e0842e96d

214

py

Python

__init__.py

MichaelSchwabe/conv-ebnas-abgabe

f463d7bbd9b514597e19d25007913f7994cbbf7c

[ "MIT" ]

6

2021-11-03T07:20:48.000Z

2021-11-10T08:20:44.000Z

__init__.py

MichaelSchwabe/conv-ebnas-abgabe

f463d7bbd9b514597e19d25007913f7994cbbf7c

[ "MIT" ]

1

2021-11-02T21:10:51.000Z

2021-11-02T21:11:05.000Z

__init__.py

MichaelSchwabe/conv-ebnas-abgabe

f463d7bbd9b514597e19d25007913f7994cbbf7c

[ "MIT" ]

null

from .evolution import Evolution from .genome_handler import GenomeHandler import tensorflow as tf print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU'))) __all__ = ['Evolution', 'GenomeHandler']

42.8

74

0.794393

27

214

6.037037

0.740741

0

0.093458

214

5

75

42.8

0.840206

0

0.209302

0

1

0

false

0

0.6

0

0.6

0.2

1

0

null

0

1

0

null

0

1

0

1

0

4

97cdfcbc3ffaf27fc94cf343eb45cfae8fd97295

84

py

Python

swift/lib/utils.py

sun3shines/swift-1.7.4

980d2cd98b6cad82d3e182a26f608292ba51c37a

[ "Apache-2.0" ]

null

swift/lib/utils.py

sun3shines/swift-1.7.4

980d2cd98b6cad82d3e182a26f608292ba51c37a

[ "Apache-2.0" ]

null

swift/lib/utils.py

sun3shines/swift-1.7.4

980d2cd98b6cad82d3e182a26f608292ba51c37a

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- def file_decrypt(data,mode,storetype): return data

9.333333

38

0.619048

11

84

4.636364

0.909091

0

0.015152

0.214286

84

8

39

10.5

0.757576

0.25

0

1

0.5

false

0

0.5

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

c105d41c0af7706ed6d8b29c3a9faace55e58306

582

py

Python

test_dir/python/good_morning.py

ekzemplaro/data_base_language

e77030367ffc595f1fac8583f03f9a3ce5eb1611

[ "MIT", "Unlicense" ]

3

2015-05-12T16:44:27.000Z

2021-02-09T00:39:24.000Z

test_dir/python/good_morning.py

ekzemplaro/data_base_language

e77030367ffc595f1fac8583f03f9a3ce5eb1611

[ "MIT", "Unlicense" ]

null

test_dir/python/good_morning.py

ekzemplaro/data_base_language

e77030367ffc595f1fac8583f03f9a3ce5eb1611

[ "MIT", "Unlicense" ]

null

#! /usr/bin/python # -*- coding: utf-8 -*- # # Jan/06/2019 # --------------------------------------------------------------- import socket import os import cgi # --------------------------------------------------------------- print("Content-Type: text/html") print("") print("Good Morning") print("Good Morning") print(socket.gethostname() + "") ip = socket.gethostbyname(socket.gethostname()) print(ip + "") print(cgi.escape(os.environ["REMOTE_ADDR"]) + "") print("Good Morning") # ---------------------------------------------------------------

27.714286

65

0.419244

53

582

4.584906

0.528302

0.098765

0.197531

0.209877

0.234568

0

0.013258

0.092784

582

20

66

29.1

0.44697

0.424399

0

0.25

0

0.304878

0

1

0

false

0

0.25

0

0.25

0.666667

0

null

0

1

0

1

0

null

0

1

0

4

c11876222bd3467e7ed5f513f12e4fd227163ed3

220

py

Python

helpers/common/visualization.py

BDALab/datascience_helpers

333752a49ad02e0ea3ba2bfefb32c6bfddf28172

[ "MIT" ]

1

2019-07-22T14:16:23.000Z

helpers/common/visualization.py

BDALab/datascience_helpers

333752a49ad02e0ea3ba2bfefb32c6bfddf28172

[ "MIT" ]

null

helpers/common/visualization.py

BDALab/datascience_helpers

333752a49ad02e0ea3ba2bfefb32c6bfddf28172

[ "MIT" ]

1

2019-12-03T17:34:50.000Z

def starify_pval(pval): if pval > 0.05: return "" else: if pval <= 0.001: return "***" if pval <= 0.01: return "**" if pval <= 0.05: return "*"

20

25

0.381818

25

220

3.32

0.4

0.289157

0.337349

0.216867

0.361446

0

0.111111

0.468182

220

10

26

22

0.598291

0

0.027273

0

1

0.1

false

0

0.5

0

1

0

null

1

0

null

0

4

c12048d56a39d56fe5a070cdf60f8281ebd061d9

325

py

Python

breadp/__init__.py

tgweber/breadp

12b97b9d2d997b1345a8e026690d57b3286a04d0

[ "Apache-2.0" ]

null

breadp/__init__.py

tgweber/breadp

12b97b9d2d997b1345a8e026690d57b3286a04d0

[ "Apache-2.0" ]

null

breadp/__init__.py

tgweber/breadp

12b97b9d2d997b1345a8e026690d57b3286a04d0

[ "Apache-2.0" ]

null

################################################################################ # Copyright: Tobias Weber 2020 # # Apache 2.0 License # # This file contains code related to all breadp module # ################################################################################ class ChecksNotRunException(Exception): pass

27.083333

80

0.375385

21

325

5.809524

1

0

0.020478

0.098462

325

11

81

29.545455

0.395904

0.307692

0

1

0

true

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

4

c1a964667833aea3f60cf46bd71b09eaa0be964b

1,116

py

Python

miriam/_utility.py

troydai/Miriam

2e7d51edc1efa4c5ea0bf8ea6eaf913b23246edd

[ "MIT" ]

1

2017-06-27T22:07:20.000Z

miriam/_utility.py

troydai/Miriam

2e7d51edc1efa4c5ea0bf8ea6eaf913b23246edd

[ "MIT" ]

null

miriam/_utility.py

troydai/Miriam

2e7d51edc1efa4c5ea0bf8ea6eaf913b23246edd

[ "MIT" ]

null

from argparse import Namespace from azure.storage.blob import BlockBlobService from azure.batch import BatchServiceClient def config_logging(args: Namespace): import logging log_level = [logging.WARNING, logging.INFO, logging.DEBUG][args.verbose] if args.verbose < 3 else logging.DEBUG logging.basicConfig(format='%(levelname)-8s %(name)-10s %(message)s', level=log_level) def get_logger(scope: str = None): import logging return logging.getLogger('miriam').getChild(scope) if scope else logging.getLogger('miriam') def get_command_string(*args): return "/bin/bash -c 'set -e; set -o pipefail; {}; wait'".format(';'.join(args)) def create_batch_client(settings: dict) -> BatchServiceClient: from azure.batch.batch_auth import SharedKeyCredentials cred = SharedKeyCredentials(settings['azurebatch']['account'], settings['azurebatch']['key']) return BatchServiceClient(cred, settings['azurebatch']['endpoint']) def create_storage_client(settings: dict) -> BlockBlobService: return BlockBlobService(settings['azurestorage']['account'], settings['azurestorage']['key'])

38.482759

115

0.749104

132

1,116

6.25

0.477273

0.032727

0.033939

0

0.004061

0.117384

1,116

28

116

39.857143

0.833503

0

0.111111

0

0.163082

0

1

0.277778

false

0

0.333333

0.111111

0.833333

0

null

0

null

0

1

0

1

0

4

c1fb1e6b70a7422d316a8abd3562d9aa194cfc94

154

py

Python

code-samples/code-samples/indentation.py

ryan-blunden/intro-python

1bc5d2ac5c0d46c6bc1ff081817b5cae3b5625ee

[ "Apache-2.0" ]

null

code-samples/code-samples/indentation.py

ryan-blunden/intro-python

1bc5d2ac5c0d46c6bc1ff081817b5cae3b5625ee

[ "Apache-2.0" ]

null

code-samples/code-samples/indentation.py

ryan-blunden/intro-python

1bc5d2ac5c0d46c6bc1ff081817b5cae3b5625ee

[ "Apache-2.0" ]

null

import os import sys def my_function(): a = 10 b = 20 def my_function_2(): a = 10 b = 20 def my_function_3(): a = 10 b = 20

8.555556

20

0.525974

27

154

2.814815

0.444444

0.197368

0.513158

0.236842

0.5

0

0.145833

0.376623

154

17

21

9.058824

0.645833

0

0.545455

0

1

0.272727

false

0

0.181818

0

0.454545

0

1

0

null

0

1

0

null

0

1

0

4

a9e29d4ba6dad042a4821392dee1c2886cc100fd

89

py

Python

funtime/apps.py

Olliej91/funtime

12c173a58f2d69fb8960585aaa7f78f39a0e125e

[ "MIT" ]

null

funtime/apps.py

Olliej91/funtime

12c173a58f2d69fb8960585aaa7f78f39a0e125e

[ "MIT" ]

1

2020-10-27T19:23:40.000Z

funtime/apps.py

Olliej91/funtime

12c173a58f2d69fb8960585aaa7f78f39a0e125e

[ "MIT" ]

null

from django.apps import AppConfig class FuntimeConfig(AppConfig): name = 'funtime'

14.833333

33

0.752809

10

89

6.7

0.9

0

0.168539

89

5

34

17.8

0.905405

0

0.078652

0

1

0

false

0

0.333333

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

e710d928dcebf33e00756d120724f518e7fd31f5

713

py

Python

configuration_py/tests/unit/test_load.py

Ferroman/configuration.py

c6fb46685a66d09bb17d76d3ce686ecef21769ac

[ "MIT" ]

5

2017-03-29T23:16:27.000Z

2021-09-02T10:08:57.000Z

configuration_py/tests/unit/test_load.py

Ferroman/configuration.py

c6fb46685a66d09bb17d76d3ce686ecef21769ac

[ "MIT" ]

null

configuration_py/tests/unit/test_load.py

Ferroman/configuration.py

c6fb46685a66d09bb17d76d3ce686ecef21769ac

[ "MIT" ]

1

2017-04-20T08:55:21.000Z

# from unittest import TestCase # # from mock import patch # # from configuration_py.configuration_load import load # class TestGetAvailableConfigEnvList(TestCase): # @patch('configuration_py.configuration_py._normalize_environment_label', return_value={'test': ''}) # @patch('configuration_py.configuration_py._get_available_config_environments_list') # @patch('configuration_py.configuration_py._load_yaml_config_by_name') # def test_should_call_load_yaml_config_by_name_with_correct_parameters(self, load_mock, available_mock, normalize_mock): # config_name = 'test' # config_folder = './config' # load(config_name, config_name) # load_mock.assert_called_once_with(config_name, config_folder)

44.5625

121

0.812062

89

713

6

0.404494

0.196629

0.209738

0.185393

0.271536

0

0.091164

713

15

122

47.533333

0.824074

0.957924

0

null

0

null

0

null

0

null

1

null

true

0

null

0

null

0

1

0

1

0

null

0

1

0

4

e72e1d5e2ea7c269c2f318be968b1df7419b7eea

69

py

Python

modular/Demo01/Crypt01.py

walkingtyphoon/Python-workspace

e872bce82b2bac3dd5d809f8576345ccc1c6afb7

[ "Apache-2.0" ]

null

modular/Demo01/Crypt01.py

walkingtyphoon/Python-workspace

e872bce82b2bac3dd5d809f8576345ccc1c6afb7

[ "Apache-2.0" ]

null

modular/Demo01/Crypt01.py

walkingtyphoon/Python-workspace

e872bce82b2bac3dd5d809f8576345ccc1c6afb7

[ "Apache-2.0" ]

null

import crypt password = crypt.crypt("610628", "hjm") print(password)

17.25

39

0.73913

9

69

5.666667

0.666667

0

0.096774

0.101449

69

4

40

17.25

0.725806

0

0.128571

0

1

0

false

0.666667

0.333333

0

0.333333

1

0

null

0

1

0

null

0

1

0

4

e7307c7fd4d033bd1a474da1bfd16f19839d9cce

59

py

Python

tests/__init__.py

CoronaWhy/vt

a190be746a2d57974c1f0ed1558ae392c41113f2

[ "MIT" ]

null

tests/__init__.py

CoronaWhy/vt

a190be746a2d57974c1f0ed1558ae392c41113f2

[ "MIT" ]

11

2020-04-06T15:41:50.000Z

2020-07-03T21:08:26.000Z

tests/__init__.py

CoronaWhy/vt

a190be746a2d57974c1f0ed1558ae392c41113f2

[ "MIT" ]

19

2020-03-29T12:29:37.000Z

2020-06-29T16:24:28.000Z

# -*- coding: utf-8 -*- """Tests for ``coronawhy_vt``."""

14.75

33

0.508475

7

59

4.142857

1

0

0.02

0.152542

59

3

34

19.666667

0.56

0.847458

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

4

e732fda45dc41ed4de174139b441e65a3cacfc42

327

py

Python

python/ErrorMessage.py

chepiga1995/houserielt

49c5e5c3aa7ef483fc2c05e15bd64c16786b61f0

[ "MIT" ]

null

python/ErrorMessage.py

chepiga1995/houserielt

49c5e5c3aa7ef483fc2c05e15bd64c16786b61f0

[ "MIT" ]

null

python/ErrorMessage.py

chepiga1995/houserielt

49c5e5c3aa7ef483fc2c05e15bd64c16786b61f0

[ "MIT" ]

null

fields = 'Incorrect some fields' login = 'Incorrect password or login' location = 'Incorrect location' wrong = 'Something goes wrong. Maybe some fields are incorrect. Please connect with administrations' load = 'not loaded' change = 'Somefing change on site. Please connect with administrations' not_found = 'Article not found'

46.714286

100

0.782875

42

327

6.071429

0.595238

0.078431

0.133333

0.25098

0

0.143731

327

7

101

46.714286

0.910714

0

0.740854

0

1

0

false

0.142857

0

null

0

1

0

1

0

1

0

null

0

1

0

4

e77c0308c15d95d39c8d4c2e4d7df197085075f0

84

py

Python

blackjax/__init__.py

junpenglao/blackjax

4abebc51df841ee6fdfd71cffc21668aa69eb9d0

[ "Apache-2.0" ]

null

blackjax/__init__.py

junpenglao/blackjax

4abebc51df841ee6fdfd71cffc21668aa69eb9d0

[ "Apache-2.0" ]

null

blackjax/__init__.py

junpenglao/blackjax

4abebc51df841ee6fdfd71cffc21668aa69eb9d0

[ "Apache-2.0" ]

null

from . import hmc, inference __version__ = "0.0.1" __all__ = ["hmc", "inference"]

14

30

0.654762

11

84

4.272727

0.727273

0.510638

0

0.042857

0.166667

84

5

31

16.8

0.628571

0

0.202381

0

1

0

false

0

0.333333

0

0.333333

0

1

0

null

1

0

1

0

null

0

1

0

4

e78572e11d6c3388a545b46a345b56a5b248baa1

1,852

py

Python

setup.py

uv-Ic/python-cloud-utils

af19992b311e39d5207a0baea7ae78683005f9b1

[ "Apache-2.0" ]

null

setup.py

uv-Ic/python-cloud-utils

af19992b311e39d5207a0baea7ae78683005f9b1

[ "Apache-2.0" ]

null

setup.py

uv-Ic/python-cloud-utils

af19992b311e39d5207a0baea7ae78683005f9b1

[ "Apache-2.0" ]

null

# Copyright 2017 Google Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://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 setuptools import setup LICENSE = '''Copyright 2017 Google Inc. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at https://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.''' VERSION = '1.0.42' setup(name='cloud-utils', version=VERSION, description='Python Cloud Utilities', author='Arie Abramovici', author_email='beast@google.com', url='https://github.com/google/python-cloud-utils', license=LICENSE, homepage='https://github.com/google/python-cloud-utils', packages=['cloud_utils'], entry_points={'console_scripts': ['list_instances = cloud_utils.list_instances:main']}, install_requires=['boto', 'boto3', 'botocore', 'google-api-python-client==1.6.2', 'urllib3>=1.24.2', 'google-auth', 'texttable', 'python-dateutil', 'pytz', 'kubernetes'], zip_safe=False)

42.090909

106

0.726242

265

1,852

5.041509

0.415094

0.08982

0.038922

0.047904

0.711078

0.657186

0

0.01888

0.170626

1,852

43

107

43.069767

0.850911

0.296436

0

0.695112

0.048099

0

1

0

false

0

0.04

0

0.04

0

null

0

1

0

1

0

1

0

null

0

4

e7d1442a41fdd48d14644af5d51cababab86cbf7

44

py

Python

qtools2/__init__.py

jkpr/QTools2

629caa0bd322cb2eeeb94d5a0a8180a2e3fddf74

[ "MIT" ]

1

2018-03-08T06:32:58.000Z

qtools2/__init__.py

jkpr/QTools2

629caa0bd322cb2eeeb94d5a0a8180a2e3fddf74

[ "MIT" ]

16

2017-02-07T22:49:14.000Z

2019-11-25T16:50:58.000Z

qtools2/__init__.py

jkpr/QTools2

629caa0bd322cb2eeeb94d5a0a8180a2e3fddf74

[ "MIT" ]

2

2016-09-22T16:04:25.000Z

2018-03-01T07:42:27.000Z

__all__ = ['convert'] __version__ = '0.2.7'

14.666667

21

0.636364

6

44

3.333333

1

0

0.078947

0.136364

44

2

22

0.447368

0

0.272727

0

1

0

false

0

1

0

null

0

1

0

1

0

null

0

4

99ca3d0ac8e03b84f414aa48fccb3e0251831f56

219

py

Python

matrix_old/python/rgbmatrix/__init__.py

hammal/macapar

05fb84b8f5e967ed6d3edb0891ac58674e6b60bc

[ "MIT" ]

3

2020-03-05T18:07:44.000Z

2020-03-06T00:59:02.000Z

matrix_old/python/rgbmatrix/__init__.py

hammal/macapar

05fb84b8f5e967ed6d3edb0891ac58674e6b60bc

[ "MIT" ]

11

2020-10-12T01:35:43.000Z

2020-12-02T10:44:35.000Z

matrix_old/python/rgbmatrix/__init__.py

hammal/macapar

05fb84b8f5e967ed6d3edb0891ac58674e6b60bc

[ "MIT" ]

null

# -*- coding: utf-8 -*- from __future__ import absolute_import __version__ = "0.0.1" __author__ = "Christoph Friedrich <christoph.friedrich@vonaffenfels.de>" from .core import RGBMatrix, FrameCanvas, RGBMatrixOptions

27.375

72

0.771689

25

219

6.24

0.76

0.230769

0

0.020619

0.114155

219

7

73

31.285714

0.783505

0.09589

0

0.316327

0.188776

0

1

0

false

0

0.5

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

4

99e97a13709fb5200ff670d0edfcecd8b66de8a9

103

py

Python

tests/transformations/test_ozone_transf_difference.py

shaido987/pyaf

b9afd089557bed6b90b246d3712c481ae26a1957

[ "BSD-3-Clause" ]

377

2016-10-13T20:52:44.000Z

2022-03-29T18:04:14.000Z

tests/transformations/test_ozone_transf_difference.py

ysdede/pyaf

b5541b8249d5a1cfdc01f27fdfd99b6580ed680b

[ "BSD-3-Clause" ]

160

2016-10-13T16:11:53.000Z

2022-03-28T04:21:34.000Z

tests/transformations/test_ozone_transf_difference.py

ysdede/pyaf

b5541b8249d5a1cfdc01f27fdfd99b6580ed680b

[ "BSD-3-Clause" ]

63

2017-03-09T14:51:18.000Z

2022-03-27T20:52:57.000Z

import tests.transformations.test_ozone_transf_generic as gen gen.test_transformation('Difference');

20.6

61

0.854369

13

103

6.461538

0.846154

0

0.067961

103

4

62

25.75

0.875

0

0.098039

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

4

99f1b557075dcb4a7598225b8b89eb4b97bb20de

1,053

py

Python

flask/test/model_tests/test_beneficiary.py

boxwise/boxtribute

b87d3bf52e29cb485d98e669b4b43d1934abf310

[ "Apache-2.0" ]

3

2020-10-17T06:37:10.000Z

2021-06-08T16:58:38.000Z

flask/test/model_tests/test_beneficiary.py

boxwise/boxtribute

b87d3bf52e29cb485d98e669b4b43d1934abf310

[ "Apache-2.0" ]

166

2020-10-25T20:45:32.000Z

2022-03-28T08:18:26.000Z

flask/test/model_tests/test_beneficiary.py

boxwise/boxtribute

b87d3bf52e29cb485d98e669b4b43d1934abf310

[ "Apache-2.0" ]

4

2021-01-01T18:03:57.000Z

2022-03-10T08:43:23.000Z

import pytest from boxwise_flask.models.beneficiary import Beneficiary from playhouse.shortcuts import model_to_dict @pytest.mark.usefixtures("default_base") @pytest.mark.usefixtures("default_beneficiary") def test_beneficiary_model(default_base, default_beneficiary): queried_beneficiary = Beneficiary.get_by_id(default_beneficiary["id"]) queried_beneficiary_dict = model_to_dict(queried_beneficiary) assert queried_beneficiary_dict["id"] == default_beneficiary["id"] assert queried_beneficiary_dict["comments"] == default_beneficiary["comments"] assert queried_beneficiary_dict["created_on"] == default_beneficiary["created_on"] assert queried_beneficiary_dict["created_by"] == default_beneficiary["created_by"] assert queried_beneficiary_dict["deleted"] == default_beneficiary["deleted"] assert queried_beneficiary_dict["family_id"] == default_beneficiary["family_id"] assert queried_beneficiary_dict["seq"] == default_beneficiary["seq"] assert queried_beneficiary_dict["base"]["id"] == default_base["id"]

50.142857

86

0.797721

122

1,053

6.491803

0.237705

0.25

0.282828

0.164141

0

0.092118

1,053

20

87

52.65

0.828452

0

0.132004

0

0.5

1

0.0625

false

0

0.1875

0

0.25

0

null

1

0

null

0

1

0

4

820f5f8329f74aa18011b8f7720f1857d292be60

930

py

Python

p1_basic/day08_15filefunction/day12/04_闭包.py

dong-pro/fullStackPython

5ad8662f7b57f14c8529e7eaf64290eeda773557

[ "Apache-2.0" ]

1

2020-04-03T01:32:05.000Z

p1_basic/day08_15filefunction/day12/04_闭包.py

dong-pro/fullStackPython

5ad8662f7b57f14c8529e7eaf64290eeda773557

[ "Apache-2.0" ]

null

p1_basic/day08_15filefunction/day12/04_闭包.py

dong-pro/fullStackPython

5ad8662f7b57f14c8529e7eaf64290eeda773557

[ "Apache-2.0" ]

null

# 闭包. 在内层函数中访问外层函数的变量 # 闭包的作用: # 1. 可以保护你的变量不收侵害 # 2. 可以让一个变量常驻内存 # 查看函数是否是闭包 # a = 10 # 不安全的 # # def outer(): # global a # a = 20 # # def outer_2(): # global a # a = 30 # # outer_2() # outer() # # print(a) # def outer(): # a = 10 # 常驻内存, 为了inner执行的时候有值. # def inner(): # print(a) # return inner # # fn = outer() # print("fdsafasd") # print("fdsafasd") # print("fdsafasd") # # fn() # 调用的时机是不定的. # 超简易爬虫 from urllib.request import urlopen # def outer(): # # 常驻内存 # s = urlopen("http://tu.baidu.com/").read() # def getContent(): # 闭包 # return s # return getContent # # print("爬取内容.....") # pa = outer() # # ret = pa() # print(ret) # # ret = pa() # print(ret) # # ret = pa() # print(ret) # # ret = pa() # print(ret) # def func(): # a = 10 # def inner(): # print(a) # print(inner.__closure__) # 如果打印的是None. 不是闭包. 如果不是None, 就是闭包 # # func()

14.307692

65

0.52043

110

930

4.345455

0.418182

0.066946

0.083682

0.108787

0

0.020958

0.28172

930

65

66

14.307692

0.694611

0.822581

0

1

0

true

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

4

822c9bee21bd9dc068caa22b972747e97ad9ae13

133

py

Python

gym-projectile/gym_projectile/__init__.py

DerwenAI/gym_projectile

bb9ace323a96858660ce4345ad1a2335620a98a8

[ "MIT" ]

null

gym-projectile/gym_projectile/__init__.py

DerwenAI/gym_projectile

bb9ace323a96858660ce4345ad1a2335620a98a8

[ "MIT" ]

1

2020-04-27T15:49:10.000Z

2020-04-30T21:36:08.000Z

gym-projectile/gym_projectile/__init__.py

DerwenAI/gym_projectile

bb9ace323a96858660ce4345ad1a2335620a98a8

[ "MIT" ]

null

from gym.envs.registration import register register( id="projectile-v0", entry_point="gym_projectile.envs:Projectile_v0", )

19

52

0.759398

17

133

5.764706

0.647059

0.244898

0

0.017241

0.12782

133

6

53

22.166667

0.827586

0

0.345865

0.24812

0

1

0

true

0

0.2

0

0.2

0

1

0

null

1

0

1

0

null

0

1

0

4

82313795832550f8e51462fb83673a1e55705d1f

186

py

Python

core/models/service.py

themightychris/prevention-point

a92f98b25d32dd30bb33e7cb1ac7f10439f5203f

[ "MIT" ]

null

core/models/service.py

themightychris/prevention-point

a92f98b25d32dd30bb33e7cb1ac7f10439f5203f

[ "MIT" ]

null

core/models/service.py

themightychris/prevention-point

a92f98b25d32dd30bb33e7cb1ac7f10439f5203f

[ "MIT" ]

null

from django.db import models from core.models import Program class Service(models.Model): name = models.CharField(max_length=100) available = models.BooleanField(default=False)

26.571429

50

0.77957

25

186

5.76

0.76

0

0.018634

0.134409

186

6

51

31

0.875776

0

1

0

false

0

0.4

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

8243bfab93b15f1f84946bd5e25f1f371b426016

51

py

Python

user_orders/filters/__init__.py

Vitamal/shop

facf04da00b8b674f2d8024aca4dae272a0c3de8

[ "MIT" ]

null

user_orders/filters/__init__.py

Vitamal/shop

facf04da00b8b674f2d8024aca4dae272a0c3de8

[ "MIT" ]

null

user_orders/filters/__init__.py

Vitamal/shop

facf04da00b8b674f2d8024aca4dae272a0c3de8

[ "MIT" ]

null

from .filter_backends import RegistrationDateFilter

51

0.921569

5

51

9.2

1

0

0.058824

51

1

51

0.958333

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

8247e2a14d31eabc717f22d7702dd0d3efb55084

3,130

py

Python

users/migrations/0002_auto_20200916_2135.py

Gagan-Shenoy/sushiksha-website

a41991df1a1d46336cbf019e83add5df56dde363

[ "Apache-2.0" ]

1

2021-06-18T08:04:08.000Z

users/migrations/0002_auto_20200916_2135.py

Gagan-Shenoy/sushiksha-website

a41991df1a1d46336cbf019e83add5df56dde363

[ "Apache-2.0" ]

1

2021-02-10T16:12:58.000Z

users/migrations/0002_auto_20200916_2135.py

Gagan-Shenoy/sushiksha-website

a41991df1a1d46336cbf019e83add5df56dde363

[ "Apache-2.0" ]

null

# Generated by Django 3.1.1 on 2020-09-16 16:05 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('users', '0001_initial'), ] operations = [ migrations.AddField( model_name='profile', name='address', field=models.TextField(blank=True, default=None, null=True), ), migrations.AddField( model_name='profile', name='batch', field=models.CharField(choices=[('2011', '2011'), ('2012', '2012'), ('2013', '2013'), ('2014', '2014'), ('2015', '2015'), ('2016', '2016'), ('2017', '2017'), ('2018', '2018'), ('2019', '2019'), ('2020', '2020'), ('2021', '2021')], default='2019', max_length=10), ), migrations.AddField( model_name='profile', name='college', field=models.CharField(blank=True, default=None, max_length=300, null=True), ), migrations.AddField( model_name='profile', name='facebook', field=models.URLField(blank=True, default=None, null=True), ), migrations.AddField( model_name='profile', name='github', field=models.URLField(blank=True, default=None, null=True), ), migrations.AddField( model_name='profile', name='guidance', field=models.TextField(blank=True, default=None, null=True), ), migrations.AddField( model_name='profile', name='instagram', field=models.URLField(blank=True, default=None, null=True), ), migrations.AddField( model_name='profile', name='linkedin', field=models.URLField(blank=True, default=None, null=True), ), migrations.AddField( model_name='profile', name='name', field=models.CharField(blank=True, default=None, max_length=100, null=True), ), migrations.AddField( model_name='profile', name='okr', field=models.URLField(blank=True, default=None, null=True), ), migrations.AddField( model_name='profile', name='phone', field=models.PositiveBigIntegerField(blank=True, default=None, null=True), ), migrations.AddField( model_name='profile', name='points', field=models.PositiveIntegerField(default=0), ), migrations.AddField( model_name='profile', name='profession', field=models.CharField(blank=True, default=None, max_length=100, null=True), ), migrations.AddField( model_name='profile', name='role', field=models.CharField(choices=[('Mentee', 'Mentee'), ('Mentor', 'Mentor')], default='Mentee', max_length=10), ), migrations.AddField( model_name='profile', name='twitter', field=models.URLField(blank=True, default=None, null=True), ), ]

35.168539

274

0.542492

298

3,130

5.627517

0.234899

0.161002

0.205725

0.241503

0.700656

0.655337

0.627907

0.512224

0

0.05771

0.307987

3,130

88

275

35.568182

0.716528

0.014377

0

0.670732

1

0

0.110607

0

1

0

false

0

0.012195

0

0.04878

0

null

0

1

0

1

0

1

0

null

0

4

413524096aedb286a8643aca113d3b6baa8b40e8

271

py

Python

twint/__init__.py

wl-lab/twint

81416f59a3b1aa50797e765fb09a993b0a6a3e4e

[ "MIT" ]

null

twint/__init__.py

wl-lab/twint

81416f59a3b1aa50797e765fb09a993b0a6a3e4e

[ "MIT" ]

null

twint/__init__.py

wl-lab/twint

81416f59a3b1aa50797e765fb09a993b0a6a3e4e

[ "MIT" ]

1

2020-11-05T11:36:19.000Z

from .config import Config from .errors import * from .get import get_user_id, search_url, profile_feed_url from .parser import parse_tweets from .search import TwintSearch from .token import TokenGetter from .user_agents import default_user_agent, get_random_user_agent

33.875

66

0.845018

42

271

5.166667

0.5

0.082949

0

0.114391

271

7

67

38.714286

0.904167

0

1

0

true

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

4

415d3efebe108902df82eab9d771d7cfee088f65

118

py

Python

polyaxon/sidecar/apps.py

wbuchwalter/polyaxon

a01396ea86a74082c457bfbc2c91d283b6ff6fba

[ "MIT" ]

null

polyaxon/sidecar/apps.py

wbuchwalter/polyaxon

a01396ea86a74082c457bfbc2c91d283b6ff6fba

[ "MIT" ]

null

polyaxon/sidecar/apps.py

wbuchwalter/polyaxon

a01396ea86a74082c457bfbc2c91d283b6ff6fba

[ "MIT" ]

null

from django.apps import AppConfig class SideCarConfig(AppConfig): name = 'sidecar' verbose_name = 'SideCar'

16.857143

33

0.728814

13

118

6.538462

0.769231

0.258824

0

0.186441

118

6

34

19.666667

0.885417

0

0.118644

0

1

0

false

0

0.25

0

1

0

1

0

null

1

0

1

0

null

0

1

0

4

41710b10f1b6dc7c37feb446f0611a7ee9a5e736

469

py

Python

NiLBS/weighting/weighting_function_mlp.py

joemarch010/NILBS

c6568818ec8acdb0fe4bd8d197278f0abb361d0b

[ "MIT" ]

2

2021-04-01T07:55:11.000Z

2021-12-10T02:57:59.000Z

NiLBS/weighting/weighting_function_mlp.py

joemarch010/NILBS

c6568818ec8acdb0fe4bd8d197278f0abb361d0b

[ "MIT" ]

null

NiLBS/weighting/weighting_function_mlp.py

joemarch010/NILBS

c6568818ec8acdb0fe4bd8d197278f0abb361d0b

[ "MIT" ]

null

from NiLBS.weighting.weighting_function import WeightingFunction class WeightingFunctionMLP(WeightingFunction): """ Weighting function backed by an MLP """ def __init__(self, mlp): self.mlp = mlp def generate_query(self, x, pose): return None def generate_query_set(self, X, pose): return None def evaluate(self, x, pose): return None def evaluate_set(self, X, pose): return None

14.212121

64

0.637527

55

469

5.272727

0.418182

0.068966

0.124138

0.206897

0.368966

0.206897

0

0.283582

469

33

65

14.212121

0.863095

0.074627

0

0.333333

0

1

0.416667

false

0

0.083333

0.333333

0.916667

0

null

0

1

0

null

0

1

0

1

0

4

41a9b2622939181ac5441a98ff8da6deab1624f7

1,948

py

Python

src/parse_XML_with_BeautifulSoup.py

marianoju/selectDiff

7210787df39cf9bf80a7e3578e8cf0b13390009d

[ "MIT" ]

null

src/parse_XML_with_BeautifulSoup.py

marianoju/selectDiff

7210787df39cf9bf80a7e3578e8cf0b13390009d

[ "MIT" ]

null

src/parse_XML_with_BeautifulSoup.py

marianoju/selectDiff

7210787df39cf9bf80a7e3578e8cf0b13390009d

[ "MIT" ]

null

from bs4 import BeautifulSoup import urllib.request import re import os # reads XML file from online source url = 'https://stackoverflow.com/feeds' # parses XML document and returns a `BeautifulSoup` object (aka soup) with urllib.request.urlopen(url) as response: soup = BeautifulSoup(response.read(), "xml") # functions that handle content # replaces HTML tags with nothing def strip_tags(description): return re.sub('<.*?>', '', description) def substitute_whitespaces(text): return re.sub('\s+', ' ', text).strip() # alternatively: return ' '.join(text.split()) def cleanse_text(ore): return strip_tags(substitute_whitespaces(ore)) # functions that handle URL strings # removes the protocol from the URL string def remove_protocol(url): return re.sub('^http://|^https://', '', url) # substitutes all characters from the URL string # with exception of letters, digits and underscores def substitute_but_alnum(string): return re.sub(r'\W+', '_', string) # '\W == [^a-zA-Z0-9_] def sanitize_string(var): return substitute_but_alnum(remove_protocol(var)) # preprares directory structure for writing files def mkdir(directory): if not os.path.exists(directory): os.makedirs(directory) # finds all entries in the soup object # and writes summary to TXT files # in source-dedicated directories for entry in soup.find_all('entry'): filename = f'{sanitize_string(entry.id.string)}.txt' url = sanitize_string(url) directory = '/'.join(['../data', url]) path = '/'.join([directory, filename]) # TODO: add entry.published.string to filename or path mkdir(directory) with open(path, 'w') as file: pure_text = '' # .stripped_strings is a Beautiful Soup generator # intended to remove extra whitespace for string in entry.summary.stripped_strings: pure_text += cleanse_text(string) file.write(pure_text)

30.4375

69

0.695072

258

1,948

5.158915

0.457364

0.024042

0.033058

0.024042

0

0.001896

0.187885

1,948

63

70

30.920635

0.839444

0.358316

0

0.095779

0.030844

0

0.015873

0

1

0.212121

false

0

0.121212

0.181818

0.515152

0

null

0

null

0

1

0

1

0

1

0

4

41ad3ff9869de80e0e72d926b5611192782f82d3

189

py

Python

blog/admin.py

ashutoshacharya24/miniblog

166283e1a82bbe8bef6e97cec4e8c3750438244d

[ "MIT" ]

null

blog/admin.py

ashutoshacharya24/miniblog

166283e1a82bbe8bef6e97cec4e8c3750438244d

[ "MIT" ]

null

blog/admin.py

ashutoshacharya24/miniblog

166283e1a82bbe8bef6e97cec4e8c3750438244d

[ "MIT" ]

null

from django.contrib import admin from .models import Post # Register your models here. @admin.register(Post) class PostModelAdmin(admin.ModelAdmin): list_display = ['id', 'title', 'desc']

27

39

0.761905

25

189

5.72

0.72

0

0.116402

189

6

40

31.5

0.856287

0.137566

0

0.068323

0

1

0

false

0

0.4

0

0.8

0

1

0

null

0

1

0

null

0

1

0

1

0

4

41c8c02adec8d5ece65873a7bb3c0eeb74524ebb

2,322

py

Python

tests/test_digits.py

yekmolsoheil/persiantools

8c5ea181640408bfd31d69d8b240114e3f44d101

[ "MIT" ]

66

2020-09-14T18:29:49.000Z

2022-03-22T08:36:18.000Z

tests/test_digits.py

yekmolsoheil/persiantools

8c5ea181640408bfd31d69d8b240114e3f44d101

[ "MIT" ]

9

2020-09-03T22:39:14.000Z

2022-03-14T19:54:31.000Z

tests/test_digits.py

yekmolsoheil/persiantools

8c5ea181640408bfd31d69d8b240114e3f44d101

[ "MIT" ]

8

2021-02-23T05:01:38.000Z

2022-01-27T22:55:10.000Z

from unittest import TestCase import pytest from persiantools import digits class TestDigits(TestCase): def test_en_to_fa(self): self.assertEqual(digits.en_to_fa("0987654321"), "۰۹۸۷۶۵۴۳۲۱") self.assertEqual(digits.en_to_fa("0987654321"), "۰۹۸۷۶۵۴۳۲۱") self.assertEqual(digits.en_to_fa("۰۹۸۷۶۵۴۳۲۱"), "۰۹۸۷۶۵۴۳۲۱") self.assertEqual(digits.en_to_fa("+0987654321 abcd"), "+۰۹۸۷۶۵۴۳۲۱ abcd") with pytest.raises(TypeError): digits.en_to_fa(12345) def test_ar_to_fa(self): self.assertEqual(digits.ar_to_fa("٠٩٨٧٦٥٤٣٢١"), "۰۹۸۷۶۵۴۳۲۱") self.assertEqual(digits.ar_to_fa("٠٩٨٧٦٥٤٣٢١"), "۰۹۸۷۶۵۴۳۲۱") orig = "0987٦٥٤٣۲۱" converted = digits.en_to_fa(orig) converted = digits.ar_to_fa(converted) self.assertEqual(converted, "۰۹۸۷۶۵۴۳۲۱") def test_fa_to_en(self): self.assertEqual(digits.fa_to_en("۰۹۸۷۶۵۴۳۲۱"), "0987654321") def test_fa_to_ar(self): self.assertEqual(digits.fa_to_ar("۰۹۸۷۶۵۴۳۲۱"), "٠٩٨٧٦٥٤٣٢١") self.assertEqual(digits.fa_to_ar(" ۰۹۸۷۶۵۴۳۲۱"), " ٠٩٨٧٦٥٤٣٢١") def test_to_letter(self): self.assertEqual(digits.to_word(1), "یک") self.assertEqual(digits.to_word(12), "دوازده") self.assertEqual(digits.to_word(49), "چهل و نه") self.assertEqual(digits.to_word(77), "هفتاد و هفت") self.assertEqual(digits.to_word(250), "دویست و پنجاه") self.assertEqual(digits.to_word(809), "هشتصد و نه") self.assertEqual(digits.to_word(1001), "یک هزار و یک") self.assertEqual(digits.to_word(3512), "سه هزار و پانصد و دوازده") self.assertEqual(digits.to_word(10000), "ده هزار") self.assertEqual(digits.to_word(20010), "بیست هزار و ده") self.assertEqual(digits.to_word(10001), "ده هزار و یک") self.assertEqual(digits.to_word(500253), "پانصد هزار و دویست و پنجاه و سه") self.assertEqual(digits.to_word(6000123), "شش میلیون و یکصد و بیست و سه") self.assertEqual(digits.to_word(1000000985), "یک میلیارد و نهصد و هشتاد و پنج") self.assertEqual(digits.to_word(100000000000004), "یکصد تریلیون و چهار") self.assertEqual(digits.to_word(-305), "منفی سیصد و پنج") with pytest.raises(digits.OutOfRangeException): digits.to_word(1000000000000001)

41.464286

87

0.679587

314

2,322

4.850318

0.235669

0.256074

0.344714

0.241628

0.575837

0.45174

0.35719

0.278398

0.079448

0

0.159018

0.192937

2,322

55

88

42.218182

0.653682

0

0.142857

0

0.196813

0

0.619048

1

0.119048

false

0

0.071429

0

0.214286

0

null

1

0

null

0

1

0

4

68be81b47f2848218f9d9fd521faecdb17f0d06e

7,639

py

Python

src/conferences/forms.py

skiry/Conference-Management-System

0c8d6206e910787eb5c0432a5c78579752624ae5

[ "MIT" ]

1

2020-03-24T13:14:38.000Z

src/conferences/forms.py

skiry/Conference-Management-System

0c8d6206e910787eb5c0432a5c78579752624ae5

[ "MIT" ]

8

2019-07-03T21:32:20.000Z

2022-03-11T23:50:59.000Z

src/conferences/forms.py

skiry/conference-management-system

0c8d6206e910787eb5c0432a5c78579752624ae5

[ "MIT" ]

null

from __future__ import unicode_literals from django import forms from django.urls import reverse from crispy_forms.helper import FormHelper from crispy_forms.layout import Layout, Submit, HTML, Field from . import models class AddConference(forms.Form): name = forms.CharField() website = forms.CharField() info = forms.CharField() start_date = forms.DateField() abstract_date = forms.DateField() submission_date = forms.DateField() bidding_date = forms.DateField() presentation_date = forms.DateField() end_date = forms.DateField() def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.helper = FormHelper() self.helper.layout = Layout( Field("name", placeholder="Name"), Field("website", placeholder="Website"), Field("info", placeholder="Information"), Field("start_date", placeholder="Starting Date"), Field("abstract_date", placeholder="Abstract Deadline Date"), Field("submission_date", placeholder="Submission Deadline Date"), Field("bidding_date", placeholder="Bidding Deadline Date"), Field("presentation_date", placeholder="Presentation Deadline Date"), Field("end_date", placeholder="Ending Date"), Submit("create_conference", "Create new conference", css_class="btn btn-lg btn-primary btn-block"), ) class PostponeDeadlines(forms.Form): abstract_date = forms.DateField() submission_date = forms.DateField() presentation_date = forms.DateField() end_date = forms.DateField() def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.helper = FormHelper() self.helper.layout = Layout( Field("abstract_date", placeholder="Abstract Deadline Date"), Field("submission_date", placeholder="Submission Deadline Date"), Field("presentation_date", placeholder="Presentation Deadline Date"), Field("end_date", placeholder="Ending Date"), Submit("update_conference", "Update conference", css_class="btn btn-lg btn-primary btn-block"), ) class SubmitProposal(forms.Form): title = forms.CharField(max_length=128) abstract = forms.FileField() full_paper = forms.FileField(required=False) meta_info = forms.CharField(max_length=10000, required=False) def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.helper = FormHelper() self.helper.layout = Layout( Field("title", placeholder="Paper Title"), Field("abstract", placeholder="Paper Abstract"), Field("full_paper", placeholder="Full Paper"), Field("meta_info", placeholder="General Information Behind the Paper"), Submit("submit_proposal", "Submit your proposal", css_class="btn btn-lg btn-primary btn-block") ) class CreateSection(forms.Form): section_name = forms.CharField(max_length=128) def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.helper = FormHelper() self.helper.layout = Layout( Field("section_name", placeholder="Category Name"), Submit("create_section", "Create the section", css_class="btn btn-lg btn-primary btn-block") ) class AddSectionToConference(forms.Form): section_name = forms.CharField(max_length=128) def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.helper = FormHelper() self.helper.layout = Layout( Field("section_name", placeholder="Add a tag for this conference"), Submit("add_section", "Add the section to this conference", css_class="btn btn-lg btn-primary btn-block") ) class UpdateSubmission(forms.Form): title = forms.CharField(max_length=128) abstract = forms.FileField() full_paper = forms.FileField(required=False) meta_info = forms.CharField(max_length=10000, required=False) def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.helper = FormHelper() self.helper.layout = Layout( Field("title", placeholder="Paper Title"), Field("abstract", placeholder="Paper Abstract"), Field("full_paper", placeholder="Full Paper"), Field("meta_info", placeholder="General Information Behind the Paper"), Submit("update_submission", "Update this Submission", css_class="btn btn-lg btn-primary btn-block") ) class EnrollPcMember(forms.Form): description = forms.CharField(max_length=1024) def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.helper = FormHelper() self.helper.layout = Layout( Field("description", placeholder="Why would you be a good PC Member?"), Submit("submit_enrollment", "Submit your proposal", css_class="btn btn-lg btn-primary btn-block") ) class BidSubmission(forms.Form): bidding = forms.ChoiceField(choices=models.BiddingValues.CHOICES, required=True) def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.helper = FormHelper() self.helper.layout = Layout( Field("bidding", placeholder="What's your bidding on this submission?"), Submit("submit_bidding", "Submit your bid", css_class="btn btn-lg btn-primary btn-block") ) class CommentSubmission(forms.Form): remark = forms.CharField(max_length=1024) def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.helper = FormHelper() self.helper.layout = Layout( Field("remark", placeholder="What do you have to add?"), Submit("submit_remark", "Submit your remark", css_class="btn btn-lg btn-primary btn-block") ) class SectionAssignment(forms.Form): section_name = forms.CharField(max_length=128) def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.helper = FormHelper() self.helper.layout = Layout( Field("section_name", placeholder="Add a section for this submission"), Submit("add_section", "Add the section to this submission", css_class="btn btn-lg btn-primary btn-block") ) class JoinPaper(forms.Form): agreement = forms.CharField(max_length=128, disabled=True, required=False) payment = forms.CharField(max_length=128, disabled=True, required=False) def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.helper = FormHelper() self.helper.layout = Layout( Field("agreement", placeholder="Do you agree with Terms and Conditions?"), Field("payment", placeholder="You will pay the fee at the entrance!"), Submit("add_section", "Yes! Confirm Registration!", css_class="btn btn-lg btn-primary btn-block") ) class SessionChairAssignment(forms.Form): section_name = forms.CharField(max_length=128) pc_member_name = forms.CharField(max_length=128) def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.helper = FormHelper() self.helper.layout = Layout( Field("section_name", placeholder="Section's name"), Field("pc_member_name", placeholder="User's name"), Submit("add_section", "Assign PC member as session chair", css_class="btn btn-lg btn-primary btn-block") )

37.446078

117

0.648383

855

7,639

5.582456

0.150877

0.050283

0.046302

0.062644

0.707102

0.674419

0.639011

0

0.007582

0.223066

7,639

203

118

37.630542

0.79663

0

0.545455

0

0.232491

0

1

0.077922

false

0

0.038961

0

0.396104

0

null

0

1

0

1

0

null

0

4

ec0f43ae20986b83c4ec355473812225f5445794

77

py

Python

histonia.py

intgr/histonia

7e79bfc3aca057bc9530015cdf79001ed6873548

[ "MIT" ]

3

2021-06-16T22:08:41.000Z

2021-12-24T10:20:39.000Z

histonia.py

intgr/histonia

7e79bfc3aca057bc9530015cdf79001ed6873548

[ "MIT" ]

2

2021-12-24T10:16:29.000Z

2022-02-28T19:04:32.000Z

histonia.py

intgr/histonia

7e79bfc3aca057bc9530015cdf79001ed6873548

[ "MIT" ]

null

""" the file was left blank with much intent and purpose Poetry needs it """

12.833333

28

0.727273

13

77

4.307692

1

0

0.194805

77

5

29

15.4

0.903226

0.883117

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

4

ec2e3cb47c10ac12451d994331ec654a7440d6e2

215

py

Python

launcher/api/python/notifier/drivers/base_driver.py

davidvoler/ate_meteor

d7ac20638a30e941e0ca8740499743bc26dd57be

[ "MIT" ]

null

launcher/api/python/notifier/drivers/base_driver.py

davidvoler/ate_meteor

d7ac20638a30e941e0ca8740499743bc26dd57be

[ "MIT" ]

2

2015-08-06T14:08:39.000Z

2015-09-29T09:47:26.000Z

launcher/api/python/notifier/drivers/base_driver.py

davidvoler/ate_meteor

d7ac20638a30e941e0ca8740499743bc26dd57be

[ "MIT" ]

null

__author__ = 'davidl' class BaseMonitorDriver(object): def notify(self,fixture_id, info): print (info) def notify_blocking_request(self,fixture_id, info): print (info) return True

19.545455

55

0.665116

25

215

5.4

0.64

0.133333

0.192593

0.251852

0.385185

0

0.24186

215

10

56

21.5

0.828221

0

0.285714

0

0.027907

0

1

0.285714

false

0

0.571429

0.285714

1

0

null

0

1

0

null

0

1

0

4

6b5b92ccfa8bc32fbb6e2ba1cfa2cf310ae136eb

352

py

Python

enthought/plugins/__init__.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

3

2016-12-09T06:05:18.000Z

2018-03-01T13:00:29.000Z

enthought/plugins/__init__.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

1

2020-12-02T00:51:32.000Z

2020-12-02T08:48:55.000Z

enthought/plugins/__init__.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

null

#------------------------------------------------------------------------------ # Copyright (c) 2007 by Enthought, Inc. # All rights reserved. #------------------------------------------------------------------------------ """ Plug-ins for the Envisage application framework. Part of the EnvisagePlugins project of the Enthought Tool Suite. """

32

79

0.397727

26

352

5.384615

0.846154

0.071429

0

0.012739

0.107955

352

10

80

35.2

0.433121

0.934659

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

null

0

1

0

4

6b5f147115e3fba44655c0c54e122bb84acff70c

257

py

Python

crm/forms.py

SorryLegacy/Django-CRM

42db8b6a9354fa989c3dad925c1d7fb927b8489f

[ "MIT" ]

null

crm/forms.py

SorryLegacy/Django-CRM

42db8b6a9354fa989c3dad925c1d7fb927b8489f

[ "MIT" ]

null

crm/forms.py

SorryLegacy/Django-CRM

42db8b6a9354fa989c3dad925c1d7fb927b8489f

[ "MIT" ]

null

from django import forms class OrderForm(forms.Form): name = forms.CharField(max_length=200, widget=forms.TextInput(attrs={'class' : 'form-control'})) phone = forms.CharField(max_length=15, widget=forms.TextInput(attrs={'class' : 'form-control'}))

42.833333

100

0.731518

34

257

5.470588

0.529412

0.150538

0.182796

0.247312

0.44086

0

0.021739

0.105058

257

6

101

42.833333

0.786957

0

0.131783

0

1

0

false

0

0.25

0

1

0

null

0

1

0

1

0

null

0

1

0

4

6ba83ac0842966978178b0d42d88e3386e537a1f

147

py

Python

app/core/apps.py

ctiagotb/recipe-app-api

e906de3a2de8aeecae0315bcd5ea7027c7a52abf

[ "MIT" ]

null

app/core/apps.py

ctiagotb/recipe-app-api

e906de3a2de8aeecae0315bcd5ea7027c7a52abf

[ "MIT" ]

null

app/core/apps.py

ctiagotb/recipe-app-api

e906de3a2de8aeecae0315bcd5ea7027c7a52abf

[ "MIT" ]

null

from django.apps import AppConfig # docker-compose run app sh -c "python manage.py startapp core" class CoreConfig(AppConfig): name = 'core'

21

63

0.741497

21

147

5.190476

0.904762

0

0.170068

147

6

64

24.5

0.893443

0.414966

0

0.047619

0

1

0

false

0

0.333333

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

6bad82049270acdefce3fed170dd16137501d4bd

94

py

Python

src/bee/ext/texts/builder.py

awenhaowenchao/bee

cac7522f8994aa3067c6e0a1bb3613de5c577129

[ "MIT" ]

4

2019-11-12T05:01:42.000Z

2022-02-23T01:52:11.000Z

src/bee/ext/texts/builder.py

awenhaowenchao/bee

cac7522f8994aa3067c6e0a1bb3613de5c577129

[ "MIT" ]

6

2021-03-19T08:13:39.000Z

2022-03-02T15:00:19.000Z

src/bee/ext/texts/builder.py

awenhaowenchao/bee

cac7522f8994aa3067c6e0a1bb3613de5c577129

[ "MIT" ]

null

from bytebuffer import ByteBuffer class Builder(ByteBuffer): name = "ext.texts.Builder"

15.666667

33

0.755319

11

94

6.454545

0.727273

0

0.159574

94

5

34

18.8

0.898734

0

0.180851

0

1

0

false

0

0.333333

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

6bd2e05381d0b38c12589df747f4418426743cfe

888

py

Python

tests/parsers/test_nwchem_nwcpymatgen_1/__init__.py

nkeilbart/aiida-nwchem

d5199b8a94fc2ec8ed30d3370ceac3826312b757

[ "MIT" ]

1

2019-12-12T15:54:58.000Z

tests/parsers/test_nwchem_nwcpymatgen_1/__init__.py

nkeilbart/aiida-nwchem

d5199b8a94fc2ec8ed30d3370ceac3826312b757

[ "MIT" ]

10

2017-11-16T15:53:39.000Z

2021-12-07T16:34:18.000Z

tests/parsers/test_nwchem_nwcpymatgen_1/__init__.py

nkeilbart/aiida-nwchem

d5199b8a94fc2ec8ed30d3370ceac3826312b757

[ "MIT" ]

6

2018-08-14T13:26:30.000Z

2021-12-31T14:37:31.000Z

# -*- coding: utf-8 -*- ########################################################################### # Copyright (c), The AiiDA team. All rights reserved. # # This file is part of the AiiDA code. # # # # The code is hosted on GitHub at https://github.com/aiidateam/aiida_core # # For further information on the license, see the LICENSE.txt file # # For further information please visit http://www.aiida.net # ########################################################################### from distutils.version import StrictVersion from aiida.orm.data.structure import has_pymatgen, get_pymatgen_version def skip_condition(): return not (has_pymatgen() and StrictVersion(get_pymatgen_version()) == StrictVersion('4.5.3'))

52.235294

80

0.480856

84

888

4.988095

0.666667

0.038186

0.100239

0

0.00624

0.278153

888

17

80

52.235294

0.647426

0.5

0

0.018797

0

1

0.2

true

0

0.4

0.2

0.8

0

null

0

null

0

1

0

1

0

4

6be98df8aecb83fac87b58652fa22db13a231a77

1,020

py

Python

get_passphrase/resolvers/standard_resolvers.py

sammck/get-passphrase

ee043811cfda64481d04005245b8b7aaaf85a1ff

[ "MIT" ]

2

2022-02-22T20:13:10.000Z

2022-02-22T23:30:11.000Z

get_passphrase/resolvers/standard_resolvers.py

sammck/get-passphrase

ee043811cfda64481d04005245b8b7aaaf85a1ff

[ "MIT" ]

null

get_passphrase/resolvers/standard_resolvers.py

sammck/get-passphrase

ee043811cfda64481d04005245b8b7aaaf85a1ff

[ "MIT" ]

null

from typing import List, Type from ..resolver import PassphraseResolver from .fixed_resolver import FixedPassphraseResolver from .empty_resolver import EmptyPassphraseResolver from .none_resolver import NonePassphraseResolver from .inline_resolver import InlinePassphraseResolver from .env_resolver import EnvPassphraseResolver from .fd_resolver import FdPassphraseResolver from .stdin_resolver import StdinPassphraseResolver from .file_resolver import FilePassphraseResolver from .getpass_resolver import GetpassPassphraseResolver from .keyring_resolver import KeyringPassphraseResolver from .any_resolver import AnyPassphraseResolver standard_resolver_classes: List[Type[PassphraseResolver]] = [ FixedPassphraseResolver, EmptyPassphraseResolver, NonePassphraseResolver, InlinePassphraseResolver, EnvPassphraseResolver, FdPassphraseResolver, StdinPassphraseResolver, FilePassphraseResolver, GetpassPassphraseResolver, KeyringPassphraseResolver, AnyPassphraseResolver, ]

32.903226

61

0.851961

81

1,020

10.567901

0.382716

0.196262

0

0.115686

1,020

30

62

34

0.949002

0

1

0

true

0.923077

0.5

0

0.5

0

1

null

0

null

0

1

0

4

d40b159a07cc5269e0652c70284fa576a84fe03c

278

py

Python

src/get_nth_even_number.py

nosrac77/code-katas

741c9e60ba5fa53b55964e324ae6d1a34c310ccd

[ "MIT" ]

null

src/get_nth_even_number.py

nosrac77/code-katas

741c9e60ba5fa53b55964e324ae6d1a34c310ccd

[ "MIT" ]

null

src/get_nth_even_number.py

nosrac77/code-katas

741c9e60ba5fa53b55964e324ae6d1a34c310ccd

[ "MIT" ]

null

"""Kata: Get nth even number - Return the nth even number. #1 Best Practices Solution by tedmiston, tachyonlabs, OQth, and others. def nth_even(n): return n * 2 - 2 """ def nth_even(n): """Function I wrote that returns the nth even number.""" return (n * 2) - 2

21.384615

71

0.654676

45

278

4

0.555556

0.194444

0.216667

0.211111

0

0.023256

0.226619

278

12

72

23.166667

0.813953

0.784173

0

1

0.5

false

0

1

0

null

0

1

0

null

0

1

0

1

0

4

d40c0b7847163c34bf287ae17838d3531d449964

86

py

Python

autoupdate/__init__.py

HelloKuki/autoupdateServer

4fa17a25dd3cf9916543d194e425b4d6c6a6ad07

[ "MIT" ]

null

autoupdate/__init__.py

HelloKuki/autoupdateServer

4fa17a25dd3cf9916543d194e425b4d6c6a6ad07

[ "MIT" ]

null

autoupdate/__init__.py

HelloKuki/autoupdateServer

4fa17a25dd3cf9916543d194e425b4d6c6a6ad07

[ "MIT" ]

null

# coding=utf-8 import sqlite3 import sys reload(sys) sys.setdefaultencoding('utf-8')

12.285714

31

0.767442

13

86

5.076923

0.615385

0.121212

0

0.038961

0.104651

86

6

32

14.333333

0.818182

0.139535

0

0.069444

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

4

d416ead5ffc3fe68d1d2fd6b322235f979541e05

1,750

py

Python

codewars/8kyu/dinamuh/FindTheSmallestIntegerInTheArray/test_bench.py

dinamuh/Training_one

d18e8fb12608ce1753162c20252ca928c4df97ab

[ "MIT" ]

null

codewars/8kyu/dinamuh/FindTheSmallestIntegerInTheArray/test_bench.py

dinamuh/Training_one

d18e8fb12608ce1753162c20252ca928c4df97ab

[ "MIT" ]

2

2019-01-22T10:53:42.000Z

2019-01-31T08:02:48.000Z

codewars/8kyu/dinamuh/FindTheSmallestIntegerInTheArray/test_bench.py

dinamuh/Training_one

d18e8fb12608ce1753162c20252ca928c4df97ab

[ "MIT" ]

13

2019-01-22T10:37:42.000Z

2019-01-25T13:30:43.000Z

from main import findSmallestInt from main import find_smallest_int def test(benchmark): assert benchmark(find_smallest_int, [78, 56, 232, 12, 11, 43]) == 11 def test2(benchmark): assert benchmark(findSmallestInt, [78, 56, 232, 12, 11, 43]) == 11 ''''''''' ---------------------------------------------------------------------------------------- benchmark: 2 tests ---------------------------------------------------------------------------------------- Name (time in ns) Min Max Mean StdDev Median IQR Outliers OPS (Mops/s) Rounds Iterations ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- test2 289.5294 (1.0) 4,301.0000 (1.05) 310.3681 (1.0) 64.4600 (1.03) 297.8235 (1.0) 9.8235 (1.0) 5478;17077 3.2220 (1.0) 193462 17 test 308.5500 (1.07) 4,098.0500 (1.0) 329.0544 (1.06) 62.6260 (1.0) 316.7000 (1.06) 10.9000 (1.11) 3728;13181 3.0390 (0.94) 154298 20 ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Legend: Outliers: 1 Standard Deviation from Mean; 1.5 IQR (InterQuartile Range) from 1st Quartile and 3rd Quartile. OPS: Operations Per Second, computed as 1 / Mean ============================================================================ 2 passed in 4.74 seconds ======== '''''''''''''''

67.307692

196

0.334857

157

1,750

3.707006

0.611465

0.024055

0.04811

0.030928

0.051546

0

0.148283

0.217714

1,750

25

197

70

0.276844

0

0.111111

0

0.222222

0.83945

0.369266

0

0.111111

1

0.111111

false

0.055556

0.111111

0

0.222222

0

null

0

1

0

1

0

null

0

1

0

4

d43a4bb1306684ae6b8150699d8683448e677593

48

py

Python

Idat_Python2022/Semana_9/Ejercicio05.py

Kennethguerra3/Python_Ejercicio_2022

cf1297cf1e1585eba699e32c02993818c3d9ecbf

[ "MIT" ]

null

Idat_Python2022/Semana_9/Ejercicio05.py

Kennethguerra3/Python_Ejercicio_2022

cf1297cf1e1585eba699e32c02993818c3d9ecbf

[ "MIT" ]

null

Idat_Python2022/Semana_9/Ejercicio05.py

Kennethguerra3/Python_Ejercicio_2022

cf1297cf1e1585eba699e32c02993818c3d9ecbf

[ "MIT" ]

null

for i in range(5): print("Hola por ",i," vez")

16

28

0.583333

10

48

2.8

0.9

0

0.025641

0.1875

48

2

29

24

0.692308

0

0.270833

0

1

0

false

0

0.5

1

0

null

0

1

0

null

0

1

0

4

d44ba054d13bad6eec9e78065efe9ff3ae90ee88

814

py

Python

app/api/wallet/main.py

JimmyMow/21-wallet

bcd45d8f0d0c7147359051c15ab51d81f61148cb

[ "MIT" ]

null

app/api/wallet/main.py

JimmyMow/21-wallet

bcd45d8f0d0c7147359051c15ab51d81f61148cb

[ "MIT" ]

null

app/api/wallet/main.py

JimmyMow/21-wallet

bcd45d8f0d0c7147359051c15ab51d81f61148cb

[ "MIT" ]

null

#!/usr/bin/python3 import sys import json from os.path import expanduser from two1.wallet import Two1Wallet class Wallet(): def __init__(self): with open('{}/.two1/wallet/default_wallet.json'.format(expanduser('~'))) as data_file: wallet_data = json.load(data_file) self.two1Wallet = Two1Wallet.import_from_mnemonic(mnemonic=wallet_data['master_seed']) def address(self): return self.two1Wallet.get_payout_address() def confirmed(self): return self.two1Wallet.confirmed_balance() def unconfirmed(self): return self.two1Wallet.unconfirmed_balance() def history(self): return self.two1Wallet.transaction_history() wallet = None wallet = wallet or Wallet() del sys.argv[0] for arg in sys.argv: method = getattr(wallet, arg) print(json.dumps({ arg: method() }))

23.257143

92

0.730958

108

814

5.351852

0.453704

0.121107

0.096886

0.16609

0

0.015873

0.148649

814

34

93

23.941176

0.818182

0.020885

0

0.059194

0.044081

0

1

0.217391

false

0

0.217391

0.173913

0.652174

0.043478

0

null

0

1

0

null

0

1

0

1

0

4

d44f4cd9f597c78d6a9248b4d5047525fdf2586b

14,068

py

Python

tests/test_criteria.py

lohithn4/NowTrade

ac04499731130297135b3526325191bd2cb36343

[ "MIT" ]

87

2015-11-09T07:11:32.000Z

2021-12-16T03:13:09.000Z

tests/test_criteria.py

lohithn4/NowTrade

ac04499731130297135b3526325191bd2cb36343

[ "MIT" ]

14

2015-09-28T18:24:18.000Z

2020-04-22T15:17:26.000Z

tests/test_criteria.py

lohithn4/NowTrade

ac04499731130297135b3526325191bd2cb36343

[ "MIT" ]

34

2015-10-12T13:26:09.000Z

2022-01-15T20:16:23.000Z

import unittest import numpy as np import pandas as pd from nowtrade import criteria from testing_data import msft_data, msft_close_name from nowtrade.symbol_list import Symbol from nowtrade.action import Long, Short, LongExit, ShortExit from nowtrade.strategy import LONG, SHORT, LONG_EXIT, SHORT_EXIT, NO_ACTION class TestCriteria(unittest.TestCase): def setUp(self): self.data = pd.DataFrame([[0, 5, 10.0, 0, NO_ACTION, 0, 10, -0.10, 0.01], [5, 4, 12.0, 5, LONG, 1, 20, -0.20, 0.02], [10, 3, 8.0, 10, NO_ACTION, 1, 30, -0.30, 0.01], [15, 2, 6.0, 15, LONG_EXIT, 0, 0, -0.20, 0.00], [20, 1, 9.0, 20, NO_ACTION, 0, -10, -0.10, -0.01], [25, 0, 10.0, 25, SHORT, -1, -20, 0.0, -0.02], [30, -1, 11.0, 30, SHORT_EXIT, 0, 0, 0.10, -0.03]], columns=['ONE', 'TWO', 'THREE', 'ONE_CLONE', 'ACTIONS_ONE', 'STATUS_ONE', 'PL_ONE', 'CHANGE_VALUE_ONE', 'CHANGE_PERCENT_ONE'], index=pd.date_range('20100601', periods=7)) self.one = Symbol('ONE') self.two = Symbol('TWO') self.three = Symbol('THREE') self.one_clone = Symbol('ONE_CLONE') class TestBarsSinceAction(TestCriteria): def test_bars_since_action(self): crit = criteria.BarsSinceAction(self.one, Long(), 2) self.assertEquals(str(crit), 'BarsSinceAction(symbol=ONE, action=1, periods=2, condition=NONE)') crit = criteria.BarsSinceLongExit(self.one, 3) self.assertTrue(crit.apply(self.data)) crit = criteria.BarsSinceShortExit(self.one, 2) self.assertFalse(crit.apply(self.data)) crit = criteria.BarsSinceShortExit(self.one, 1) self.assertFalse(crit.apply(self.data)) crit = criteria.BarsSinceShortExit(self.one, 0) self.assertTrue(crit.apply(self.data)) crit = criteria.BarsSinceLong(self.one, 4) self.assertFalse(crit.apply(self.data)) crit = criteria.BarsSinceLong(self.one, 5) self.assertTrue(crit.apply(self.data)) crit = criteria.BarsSinceLong(self.one, 6) self.assertFalse(crit.apply(self.data)) crit = criteria.BarsSinceShortExit(self.one, 0, 'under') self.assertFalse(crit.apply(self.data)) crit = criteria.BarsSinceShortExit(self.one, 1, 'under') self.assertTrue(crit.apply(self.data)) crit = criteria.BarsSinceShortExit(self.one, 2, 'under') self.assertTrue(crit.apply(self.data)) crit = criteria.BarsSinceLong(self.one, 5, 'under') self.assertFalse(crit.apply(self.data)) crit = criteria.BarsSinceLong(self.one, 6, 'under') self.assertTrue(crit.apply(self.data)) crit = criteria.BarsSinceLong(self.one, 7, 'under') self.assertTrue(crit.apply(self.data)) crit = criteria.BarsSinceShortExit(self.one, 1, 'over') self.assertFalse(crit.apply(self.data)) crit = criteria.BarsSinceShortExit(self.one, 0, 'over') self.assertFalse(crit.apply(self.data)) crit = criteria.BarsSinceShort(self.one, 0, 'over') self.assertTrue(crit.apply(self.data)) crit = criteria.BarsSinceLong(self.one, 4, 'over') self.assertTrue(crit.apply(self.data)) crit = criteria.BarsSinceLong(self.one, 5, 'over') self.assertFalse(crit.apply(self.data)) crit = criteria.BarsSinceLong(self.one, 6, 'over') self.assertFalse(crit.apply(self.data)) class TestInMarket(TestCriteria): def test_in_market(self): crit = criteria.InMarket(self.one) self.assertEqual(crit.__repr__(), crit.label) self.assertFalse(crit.apply(self.data)) self.assertTrue(crit.apply(self.data[:-1])) self.assertTrue(crit.apply(self.data[:2])) self.assertTrue(crit.apply(self.data[:3])) class TestIsLong(TestCriteria): def test_is_long(self): crit = criteria.IsLong(self.one) self.assertFalse(crit.apply(self.data)) self.assertFalse(crit.apply(self.data[:-1])) self.assertTrue(crit.apply(self.data[:3])) self.assertFalse(crit.apply(pd.DataFrame())) class TestIsShort(TestCriteria): def test_is_short(self): crit = criteria.IsShort(self.one) self.assertFalse(crit.apply(self.data)) self.assertTrue(crit.apply(self.data[:-1])) self.assertFalse(crit.apply(self.data[:2])) self.assertFalse(crit.apply(pd.DataFrame())) class TestIsYear(TestCriteria): def test_is_year(self): crit = criteria.IsYear(2012) self.assertEqual(crit.__repr__(), 'IsYear_2012') self.assertFalse(crit.apply(self.data).any()) crit = criteria.IsYear(2010) self.assertTrue(crit.apply(self.data).all()) class TestIsMonth(TestCriteria): def test_is_month(self): crit = criteria.IsMonth(6) self.assertEqual(crit.__repr__(), 'IsMonth_6') self.assertTrue(crit.apply(self.data).all()) crit = criteria.IsMonth(1) self.assertFalse(crit.apply(self.data).any()) class TestIsDay(TestCriteria): def test_is_day(self): crit = criteria.IsDay(7) self.assertEqual(crit.__repr__(), 'IsDay_7') self.assertTrue(crit.apply(self.data)[-1]) crit = criteria.IsDay(8) self.assertFalse(crit.apply(self.data)[-1]) class TestIsWeekDay(TestCriteria): def test_is_week_day(self): crit = criteria.IsWeekDay(0) self.assertEqual(crit.__repr__(), 'IsWeekDay_0') self.assertTrue(crit.apply(self.data)[-1]) crit = criteria.IsWeekDay(4) self.assertTrue(crit.apply(self.data)[-4]) crit = criteria.IsWeekDay(3) self.assertFalse(crit.apply(self.data)[-1]) class TestPositions(TestCriteria): def test_position(self): crit = criteria.Above('ONE', 5) value = crit.apply(pd.DataFrame()) self.assertEqual(value, False) value = crit.apply(self.data.head(2)) self.assertEqual(value, False) value = crit.apply(self.data.head(3)) self.assertEqual(value, True) crit = criteria.Above('TWO', 4, 3) value = crit.apply(self.data.head(3)) self.assertEqual(value, True) crit = criteria.Below('TWO', 6, 2) value = crit.apply(pd.DataFrame()) self.assertFalse(value) value = crit.apply(self.data) self.assertEqual(value, True) crit = criteria.Below('ONE', 5) value = crit.apply(self.data.head(1)) self.assertEqual(value, True) value = crit.apply(self.data.head(2)) self.assertEqual(value, False) value = crit.apply(self.data.head(3)) self.assertEqual(value, False) crit = criteria.Above('ONE', 'TWO') value = crit.apply(self.data.head(1)) self.assertEqual(value, False) value = crit.apply(self.data.head(2)) self.assertEqual(value, True) crit = criteria.Below('ONE', 'TWO') value = crit.apply(self.data.head(1)) self.assertEqual(value, True) value = crit.apply(self.data.head(2)) self.assertEqual(value, False) crit = criteria.Equals('ONE', 10) value = crit.apply(pd.DataFrame()) self.assertEqual(value, False) value = crit.apply(self.data.head(2)) self.assertEqual(value, False) crit = criteria.Equal('ONE', 10) value = crit.apply(self.data.head(2)) self.assertEqual(value, False) value = crit.apply(self.data.head(3)) self.assertEqual(value, True) value = crit.apply(self.data.head(4)) self.assertEqual(value, False) crit = criteria.Above('ONE', 10, 1) value = crit.apply(self.data.head(4)) self.assertEqual(value, True) crit = criteria.Equals('ONE', 12, 3) self.assertEqual(value, True) crit = criteria.Equals('ONE', 'ONE_CLONE', 2) value = crit.apply(self.data) self.assertEqual(value, True) class TestInRange(TestCriteria): def test_in_range(self): crit = criteria.InRange(str(self.one), 10, 20) ret = crit.apply(self.data.head(2)) self.assertFalse(ret) ret = crit.apply(self.data.head(3)) self.assertTrue(ret) ret = crit.apply(self.data.head(4)) self.assertTrue(ret) ret = crit.apply(self.data.head(5)) self.assertTrue(ret) ret = crit.apply(self.data.head(6)) self.assertFalse(ret) crit = criteria.InRange(str(self.one), str(self.two), str(self.three)) ret = crit.apply(self.data.head(1)) self.assertFalse(ret) ret = crit.apply(self.data.head(2)) self.assertTrue(ret) ret = crit.apply(self.data.head(3)) self.assertFalse(ret) crit = criteria.InRange(str(self.one), -1, 1) ret = crit.apply(self.data.head(1)) self.assertTrue(ret) crit = criteria.InRange(str(self.two), 4, str(self.three)) ret = crit.apply(self.data.head(1)) self.assertTrue(ret) crit = criteria.InRange(str(self.two), str(self.one), 6) ret = crit.apply(self.data.head(1)) self.assertTrue(ret) class TestCrossing(TestCriteria): def test_crossing(self): crit = criteria.CrossingAbove(str(self.one), str(self.two)) self.assertEquals(crit.__repr__(), 'CrossingAbove(param1=ONE, param2=TWO)') self.assertTrue(crit.apply(self.data.head(2))) crit = criteria.CrossingBelow(str(self.one), str(self.two)) self.assertEquals(crit.__repr__(), 'CrossingBelow(param1=ONE, param2=TWO)') self.assertFalse(crit.apply(self.data.head(2))) crit = criteria.CrossingBelow(str(self.three), str(self.one)) self.assertFalse(crit.apply(self.data.head(2))) self.assertTrue(crit.apply(self.data.head(3))) crit = criteria.CrossingAbove(str(self.one), 7) self.assertFalse(crit.apply(self.data.head(2))) crit = criteria.CrossingBelow(str(self.two), 2) self.assertFalse(crit.apply(self.data.head(4))) self.assertTrue(crit.apply(self.data.head(5))) class TestNot(TestCriteria): def test_not(self): inRangeCriteria = criteria.InRange(str(self.one), 10, 20) crit = criteria.Not(inRangeCriteria) ret = crit.apply(self.data.head(2)) self.assertTrue(ret) ret = crit.apply(self.data.head(3)) self.assertFalse(ret) ret = crit.apply(self.data.head(4)) self.assertFalse(ret) ret = crit.apply(self.data.head(5)) self.assertFalse(ret) ret = crit.apply(self.data.head(6)) self.assertTrue(ret) class TestStopLoss(TestCriteria): def test_stop_loss(self): crit = criteria.StopLoss(self.one, -0.2) self.assertFalse(crit.apply(self.data)) self.assertFalse(crit.apply(self.data[:-2])) self.assertTrue(crit.apply(self.data[:-3])) crit = criteria.StopLoss(self.one, -0.02, percent=True) self.assertTrue(crit.apply(self.data)) self.assertTrue(crit.apply(self.data[:-1])) self.assertFalse(crit.apply(self.data[:-2])) crit = criteria.StopLoss(self.one, -0.1, short=True) self.assertTrue(crit.apply(self.data)) self.assertFalse(crit.apply(self.data[:-1])) crit = criteria.StopLoss(self.one, -0.02, short=True, percent=True) self.assertFalse(crit.apply(self.data)) self.assertFalse(crit.apply(self.data[:-1])) self.assertTrue(crit.apply(self.data[:2])) class TestTakeProfit(TestCriteria): def test_take_profit(self): crit = criteria.TakeProfit(self.one, 20) self.assertEqual(crit.__repr__(), crit.label) self.assertTrue(crit.apply(self.data[:2])) self.assertFalse(crit.apply(self.data[:-1])) crit_short = criteria.TakeProfit(self.one, 20, short=True) self.assertFalse(crit_short.apply(self.data[:2])) self.assertTrue(crit_short.apply(self.data[:-1])) self.data['PL_ONE'] = np.nan self.assertFalse(crit.apply(self.data)) class TestTrailingStop(TestCriteria): def test_trailing_stop(self): crit = criteria.TrailingStop(self.one, 0.009, short=False, percent=True) self.assertFalse(crit.apply(self.data[:1])) self.assertFalse(crit.apply(self.data[:2])) self.assertTrue(crit.apply(self.data[:3])) crit = criteria.TrailingStop(self.one, 0.019, short=False, percent=True) self.assertFalse(crit.apply(self.data[:1])) self.assertFalse(crit.apply(self.data[:2])) self.assertFalse(crit.apply(self.data[:3])) self.assertTrue(crit.apply(self.data[:4])) crit = criteria.TrailingStop(self.one, 0.09, short=False, percent=False) self.assertTrue(crit.apply(self.data[:1])) crit = criteria.TrailingStop(self.one, 0.19, short=False, percent=False) self.assertFalse(crit.apply(self.data[:1])) self.assertTrue(crit.apply(self.data[:2])) crit = criteria.TrailingStop(self.one, 0.009, short=True, percent=True) self.assertTrue(crit.apply(self.data[:1])) crit = criteria.TrailingStop(self.one, 0.019, short=True, percent=True) self.assertFalse(crit.apply(self.data[:1])) self.assertTrue(crit.apply(self.data[:2])) crit = criteria.TrailingStop(self.one, 0.09, short=True, percent=False) self.assertFalse(crit.apply(self.data[:1])) self.assertFalse(crit.apply(self.data[:2])) self.assertFalse(crit.apply(self.data[:3])) self.assertTrue(crit.apply(self.data[:4])) crit = criteria.TrailingStop(self.one, 0.19, short=True, percent=False) self.assertFalse(crit.apply(self.data[:1])) self.assertFalse(crit.apply(self.data[:2])) self.assertFalse(crit.apply(self.data[:3])) self.assertFalse(crit.apply(self.data[:4])) self.assertTrue(crit.apply(self.data[:5])) if __name__ == "__main__": unittest.main()

45.380645

159

0.629158

1,822

14,068

4.801866

0.085071

0.122414

0.172363

0.221511

0.762487

0.742142

0.725111

0.65859

0.595611

0.512973

0

0.029278

0.218226

14,068

309

160

45.527508

0.76623

0

0.510274

0

0.028504

0.005473

0

0.44863

1

0.054795

false

0

0.027397

0

0.136986

0

null

0

1

0

1

0

null

0

1

0

4

d455d98950f2555d11cbe9c7b480bcab60f76269

328

py

Python

my_func.py

shamurti/Python_Class

e4da52ad0b5061e0fa127a03157be43a08ebe28b

[ "Apache-2.0" ]

null

my_func.py

shamurti/Python_Class

e4da52ad0b5061e0fa127a03157be43a08ebe28b

[ "Apache-2.0" ]

null

my_func.py

shamurti/Python_Class

e4da52ad0b5061e0fa127a03157be43a08ebe28b

[ "Apache-2.0" ]

null

#!/usr/bin/env python print "This is before the function" a = 100 def my_func(): b = a + 1 print "hello there! This is from inside the function" print (r"This is 'b = a + 1' from inside the function: {}").format(b) print "This is text after the function" print "This is 'a' from outside the function: {}".format(a)

19.294118

71

0.661585

57

328

3.789474

0.45614

0.138889

0.152778

0.194444

0

0.019305

0.210366

328

16

72

20.5

0.814672

0.060976

0

0.638158

0

null

0

null

0.625

0

null

0

1

0

1

0

null

0

1

0

1

0

4

d46de1b52cd5d98da18d5aa0e8bbe43447b0fce5

104

py

Python

DIFFSUM.py

ankitpipalia/codechef-solutions

d10e7f15b74a11655b0e53953a8e2bc7efbf7377

[ "MIT" ]

1

2022-01-23T08:13:17.000Z

DIFFSUM.py

ankitpipalia/codechef-solutions

d10e7f15b74a11655b0e53953a8e2bc7efbf7377

[ "MIT" ]

null

DIFFSUM.py

ankitpipalia/codechef-solutions

d10e7f15b74a11655b0e53953a8e2bc7efbf7377

[ "MIT" ]

null

num1 = int(input()) num2 = int(input()) if num1 > num2: print(num1-num2) else: print(num1+num2)

14.857143

20

0.615385

16

104

4

0.4375

0.375

0.40625

0

0.096386

0.201923

104

7

21

14.857143

0.674699

0

1

0

false

0

0.333333

1

0

null

1

0

1

0

null

0

4

d46de846c70479b72a53bd512bf1e75d075fbb69

103

py

Python

app/models/role.py

chenke91/ihaveablog

64000723589d3f5a074bd09f045cb5d6c3daf6dd

[ "MIT" ]

null

app/models/role.py

chenke91/ihaveablog

64000723589d3f5a074bd09f045cb5d6c3daf6dd

[ "MIT" ]

null

app/models/role.py

chenke91/ihaveablog

64000723589d3f5a074bd09f045cb5d6c3daf6dd

[ "MIT" ]

null

from app import db from ._base import SessionMixin class Role: ADMIN = 'admin' NOMAL = 'nomal'

17.166667

31

0.68932

14

103

5

0.714286

0

0.23301

103

6

32

17.166667

0.886076

0

0.096154

0

1

0

false

0

0.4

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

d4726d9190f425dccd950f9967d25436455cd6ec

161

py

Python

mdptools/utils/__init__.py

mholdg16/py-mdptools

ae986edc2097e97cb73331d66f0051ca9f5bd15c

[ "MIT" ]

1

2021-12-15T13:22:48.000Z

mdptools/utils/__init__.py

mholdg16/py-mdptools

ae986edc2097e97cb73331d66f0051ca9f5bd15c

[ "MIT" ]

2

2021-11-09T23:43:48.000Z

2021-11-13T20:41:12.000Z

mdptools/utils/__init__.py

mholdg16/py-mdptools

ae986edc2097e97cb73331d66f0051ca9f5bd15c

[ "MIT" ]

null

"""Utilities """ from .utils import * from .highlight import highlight from .format_str import format_str, format_tup, to_identifier from .prism import to_prism

23

61

0.795031

23

161

5.347826

0.478261

0.146341

0

0.124224

161

6

62

26.833333

0.87234

0.055901

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

2e61568880d771fdf8caee17590b6be8b4b26831

57

py

Python

__main__.py

TomCreeper/goldmine

8407b787aeb042630b4d35083f4267cceca35078

[ "MIT" ]

2

2017-07-10T13:05:18.000Z

2017-07-10T13:05:20.000Z

__main__.py

Tominous/goldmine

8407b787aeb042630b4d35083f4267cceca35078

[ "MIT" ]

null

__main__.py

Tominous/goldmine

8407b787aeb042630b4d35083f4267cceca35078

[ "MIT" ]

null

"""Module level wrapper for Goldmine.""" import goldmine

19

40

0.754386

7

57

6.142857

0.857143

0

0.122807

57

2

41

28.5

0.86

0.596491

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

2e86f7e5657915d195f709a234cd50f11ac93d65

22,206

py

Python

cryspy/A_functions_base/flip_ratio.py

ikibalin/rhochi

1ca03f18dc72006322a101ed877cdbba33ed61e7

[ "MIT" ]

null

cryspy/A_functions_base/flip_ratio.py

ikibalin/rhochi

1ca03f18dc72006322a101ed877cdbba33ed61e7

[ "MIT" ]

null

cryspy/A_functions_base/flip_ratio.py

ikibalin/rhochi

1ca03f18dc72006322a101ed877cdbba33ed61e7

[ "MIT" ]

null

from typing import Callable import numpy def calc_iint( beam_polarization: float, flipper_efficiency: float, f_nucl, f_m_perp, matrix_u, func_extinction: Callable = None, flag_beam_polarization: bool = False, flag_flipper_efficiency: bool = False, flag_f_nucl: bool = False, flag_f_m_perp: bool = False, dict_in_out: dict = None, flag_use_precalculated_data: bool = False): """Calculate integrated intensities. It is supposed that crystal is not rotate during the calculations (orientation matrix is the same for all reflections) """ if dict_in_out is None: flag_dict = False dict_in_out_keys = [] else: flag_dict = True dict_in_out_keys = dict_in_out.keys() p_u = beam_polarization p_d = beam_polarization*(2*flipper_efficiency-1) flag_f_plus_sq = flag_f_nucl or flag_f_m_perp flag_f_minus_sq = flag_f_nucl or flag_f_m_perp flag_f_m_perp_xy_sq = flag_f_m_perp f_n = numpy.atleast_1d(f_nucl) f_m_perp = numpy.atleast_1d(f_m_perp) f_n_sq = numpy.square(numpy.abs(f_n)) f_m_perp_x = f_m_perp[0]*matrix_u[0] + f_m_perp[1]*matrix_u[1] + f_m_perp[2]*matrix_u[2] f_m_perp_y = f_m_perp[0]*matrix_u[3] + f_m_perp[1]*matrix_u[4] + f_m_perp[2]*matrix_u[5] f_m_perp_z = f_m_perp[0]*matrix_u[6] + f_m_perp[1]*matrix_u[7] + f_m_perp[2]*matrix_u[8] f_m_perp_x_sq = numpy.square(numpy.abs(f_m_perp_x)) f_m_perp_y_sq = numpy.square(numpy.abs(f_m_perp_y)) f_m_perp_z_sq = numpy.square(numpy.abs(f_m_perp_z)) f_n_f_m_perp_z = 2.*(f_n.real * f_m_perp_z.real + f_n.imag * f_m_perp_z.imag) f_plus_sq = f_n_sq + f_m_perp_z_sq + f_n_f_m_perp_z f_minus_sq = f_n_sq + f_m_perp_z_sq - f_n_f_m_perp_z f_m_perp_xy_sq = f_m_perp_x_sq + f_m_perp_y_sq if func_extinction is None: dder_y_plus, dder_y_minus, dder_y_m_perp_xy = {}, {}, {} y_plus = numpy.ones_like(f_plus_sq) y_minus = numpy.ones_like(f_minus_sq) y_m_perp_xy = numpy.ones_like(f_m_perp_xy_sq) else: y_plus, dder_y_plus = func_extinction(f_plus_sq, flag_f_sq=flag_f_plus_sq) y_minus, dder_y_minus = func_extinction(f_minus_sq, flag_f_sq=flag_f_minus_sq) y_m_perp_xy, dder_y_m_perp_xy = func_extinction(f_m_perp_xy_sq, flag_f_sq=flag_f_m_perp_xy_sq) chiral_term = 2.*(f_m_perp_x.imag * f_m_perp_y.real - f_m_perp_x.real * f_m_perp_y.imag) if flag_dict: dict_in_out["chiral_term"] = chiral_term iint_plus = 0.5*((1.+p_u)*y_plus*f_plus_sq + (1.-p_u)*y_minus*f_minus_sq) + \ y_m_perp_xy * f_m_perp_xy_sq + \ p_u * chiral_term iint_minus = 0.5*((1.-p_d)*y_plus*f_plus_sq + (1.+p_d)*y_minus*f_minus_sq) + \ y_m_perp_xy * f_m_perp_xy_sq - \ p_d * chiral_term if flag_dict: dict_in_out["iint_plus"] = iint_plus dict_in_out["iint_minus"] = iint_minus dict_in_out["y_plus"] = y_plus dict_in_out["f_plus_sq"] = f_plus_sq dict_in_out["y_minus"] = y_minus dict_in_out["f_minus_sq"] = f_minus_sq dict_in_out["y_m_perp_xy"] = y_m_perp_xy dict_in_out["f_m_perp_xy_sq"] = f_m_perp_xy_sq dder_plus = {} dder_minus = {} if flag_beam_polarization: dder_plus["beam_polarization"] = 0.5*(y_plus*f_plus_sq - y_minus*f_minus_sq + 2.*chiral_term) * \ numpy.ones_like(beam_polarization) dder_minus["beam_polarization"] = 0.5*(-y_plus*f_plus_sq + y_minus*f_minus_sq - 2.*chiral_term) * \ numpy.ones_like(beam_polarization)*(2.*flipper_efficiency-1.) if flag_flipper_efficiency: dder_minus["flipper_efficiency"] = beam_polarization*(-y_plus*f_plus_sq + y_minus*f_minus_sq - 2.*chiral_term) * \ numpy.ones_like(flipper_efficiency) if flag_f_nucl: f_plus_sq_f_n_real = 2. * (f_n.real + f_m_perp_z.real) * numpy.ones_like(f_n.real) f_plus_sq_f_n_imag = 2. * (f_n.imag + f_m_perp_z.imag) * numpy.ones_like(f_n.imag) f_minus_sq_f_n_real = 2. * (f_n.real - f_m_perp_z.real)* numpy.ones_like(f_n.real) f_minus_sq_f_n_imag = 2. * (f_n.imag - f_m_perp_z.imag) * numpy.ones_like(f_n.imag) y_plus_f_n_real, y_minus_f_n_real = 0, 0 y_plus_f_n_imag, y_minus_f_n_imag = 0, 0 if "f_sq" in dder_y_plus.keys(): y_plus_f_n_real = dder_y_plus["f_sq"]*f_plus_sq_f_n_real y_plus_f_n_imag = dder_y_plus["f_sq"]*f_plus_sq_f_n_imag if "f_sq" in dder_y_minus.keys(): y_minus_f_n_real = dder_y_minus["f_sq"]*f_minus_sq_f_n_real y_minus_f_n_imag = dder_y_minus["f_sq"]*f_minus_sq_f_n_imag dder_plus["f_nucl_real"] = 0.5*( (1.+p_u)*(y_plus*f_plus_sq_f_n_real+y_plus_f_n_real*f_plus_sq) + (1.-p_u)*(y_minus*f_minus_sq_f_n_real+y_minus_f_n_real*f_minus_sq)) dder_plus["f_nucl_imag"] = 0.5*( (1.+p_u)*(y_plus*f_plus_sq_f_n_imag+y_plus_f_n_imag*f_plus_sq) + (1.-p_u)*(y_minus*f_minus_sq_f_n_imag+y_minus_f_n_imag*f_minus_sq)) dder_minus["f_nucl_real"] = 0.5*( (1.-p_d)*(y_plus*f_plus_sq_f_n_real+y_plus_f_n_real*f_plus_sq) + (1.+p_d)*(y_minus*f_minus_sq_f_n_real+y_minus_f_n_real*f_minus_sq)) dder_minus["f_nucl_imag"] = 0.5*( (1.-p_d)*(y_plus*f_plus_sq_f_n_imag+y_plus_f_n_imag*f_plus_sq) + (1.+p_d)*(y_minus*f_minus_sq_f_n_imag+y_minus_f_n_imag*f_minus_sq)) if flag_f_m_perp: f_plus_sq_f_m_perp_z_real = 2. * (f_n.real + f_m_perp_z.real) * numpy.ones_like(f_m_perp_z.real) f_plus_sq_f_m_perp_z_imag = 2. * (f_n.imag + f_m_perp_z.imag) * numpy.ones_like(f_m_perp_z.imag) f_minus_sq_f_m_perp_z_real = -2. * (f_n.real - f_m_perp_z.real) * numpy.ones_like(f_m_perp_z.real) f_minus_sq_f_m_perp_z_imag = -2. * (f_n.imag - f_m_perp_z.imag) * numpy.ones_like(f_m_perp_z.imag) f_m_perp_xy_sq_f_m_perp_x_real = 2 * f_m_perp_x.real * numpy.ones_like(f_m_perp_x.real) f_m_perp_xy_sq_f_m_perp_x_imag = 2 * f_m_perp_x.imag * numpy.ones_like(f_m_perp_x.imag) f_m_perp_xy_sq_f_m_perp_y_real = 2 * f_m_perp_y.real * numpy.ones_like(f_m_perp_y.real) f_m_perp_xy_sq_f_m_perp_y_imag = 2 * f_m_perp_y.imag * numpy.ones_like(f_m_perp_y.imag) chiral_term_f_m_perp_x_real = -2 * f_m_perp_y.imag * numpy.ones_like(f_m_perp_x.real) chiral_term_f_m_perp_x_imag = 2 * f_m_perp_y.real * numpy.ones_like(f_m_perp_x.imag) chiral_term_f_m_perp_y_real = 2 * f_m_perp_x.imag * numpy.ones_like(f_m_perp_y.real) chiral_term_f_m_perp_y_imag = -2 * f_m_perp_x.real * numpy.ones_like(f_m_perp_y.imag) y_plus_f_m_perp_z_real, y_plus_f_m_perp_z_imag = 0, 0 y_minus_f_m_perp_z_real, y_minus_f_m_perp_z_imag = 0, 0 y_m_perp_xy_f_m_perp_x_real, y_m_perp_xy_f_m_perp_x_imag = 0, 0 y_m_perp_xy_f_m_perp_y_real, y_m_perp_xy_f_m_perp_y_imag = 0, 0 if "f_sq" in dder_y_plus.keys(): y_plus_f_m_perp_z_real = dder_y_plus["f_sq"]*f_plus_sq_f_m_perp_z_real y_plus_f_m_perp_z_imag = dder_y_plus["f_sq"]*f_plus_sq_f_m_perp_z_imag if "f_sq" in dder_y_minus.keys(): y_minus_f_m_perp_z_real = dder_y_minus["f_sq"]*f_minus_sq_f_m_perp_z_real y_minus_f_m_perp_z_imag = dder_y_minus["f_sq"]*f_minus_sq_f_m_perp_z_imag if "f_sq" in dder_y_m_perp_xy.keys(): y_m_perp_xy_f_m_perp_x_real = dder_y_m_perp_xy["f_sq"]*f_m_perp_xy_sq_f_m_perp_x_real y_m_perp_xy_f_m_perp_x_imag = dder_y_m_perp_xy["f_sq"]*f_m_perp_xy_sq_f_m_perp_x_imag y_m_perp_xy_f_m_perp_y_real = dder_y_m_perp_xy["f_sq"]*f_m_perp_xy_sq_f_m_perp_y_real y_m_perp_xy_f_m_perp_y_imag = dder_y_m_perp_xy["f_sq"]*f_m_perp_xy_sq_f_m_perp_y_imag dder_plus_f_m_perp_x_real = \ y_m_perp_xy_f_m_perp_x_real * f_m_perp_xy_sq + \ y_m_perp_xy * f_m_perp_xy_sq_f_m_perp_x_real +\ p_u * chiral_term_f_m_perp_x_real dder_plus_f_m_perp_x_imag = \ y_m_perp_xy_f_m_perp_x_imag * f_m_perp_xy_sq + \ y_m_perp_xy * f_m_perp_xy_sq_f_m_perp_x_imag +\ p_u * chiral_term_f_m_perp_x_imag dder_plus_f_m_perp_y_real = \ y_m_perp_xy_f_m_perp_y_real * f_m_perp_xy_sq + \ y_m_perp_xy * f_m_perp_xy_sq_f_m_perp_y_real +\ p_u * chiral_term_f_m_perp_y_real dder_plus_f_m_perp_y_imag = \ y_m_perp_xy_f_m_perp_y_imag * f_m_perp_xy_sq + \ y_m_perp_xy * f_m_perp_xy_sq_f_m_perp_y_imag +\ p_u * chiral_term_f_m_perp_y_imag dder_plus_f_m_perp_z_real = 0.5*( (1.+p_u)*(y_plus*f_plus_sq_f_m_perp_z_real+y_plus_f_m_perp_z_real*f_plus_sq) + (1.-p_u)*(y_minus*f_minus_sq_f_m_perp_z_real+y_minus_f_m_perp_z_real*f_minus_sq)) dder_plus_f_m_perp_z_imag = 0.5*( (1.+p_u)*(y_plus*f_plus_sq_f_m_perp_z_imag+y_plus_f_m_perp_z_imag*f_plus_sq) + (1.-p_u)*(y_minus*f_minus_sq_f_m_perp_z_imag+y_minus_f_m_perp_z_imag*f_minus_sq)) dder_plus_f_m_perp_1_real = \ dder_plus_f_m_perp_x_real*matrix_u[0] + \ dder_plus_f_m_perp_y_real*matrix_u[3] + \ dder_plus_f_m_perp_z_real*matrix_u[6] dder_plus_f_m_perp_1_imag = \ dder_plus_f_m_perp_x_imag*matrix_u[0] + \ dder_plus_f_m_perp_y_imag*matrix_u[3] + \ dder_plus_f_m_perp_z_imag*matrix_u[6] dder_plus_f_m_perp_2_real = \ dder_plus_f_m_perp_x_real*matrix_u[1] + \ dder_plus_f_m_perp_y_real*matrix_u[4] + \ dder_plus_f_m_perp_z_real*matrix_u[7] dder_plus_f_m_perp_2_imag = \ dder_plus_f_m_perp_x_imag*matrix_u[1] + \ dder_plus_f_m_perp_y_imag*matrix_u[4] + \ dder_plus_f_m_perp_z_imag*matrix_u[7] dder_plus_f_m_perp_3_real = \ dder_plus_f_m_perp_x_real*matrix_u[2] + \ dder_plus_f_m_perp_y_real*matrix_u[5] + \ dder_plus_f_m_perp_z_real*matrix_u[8] dder_plus_f_m_perp_3_imag = \ dder_plus_f_m_perp_x_imag*matrix_u[2] + \ dder_plus_f_m_perp_y_imag*matrix_u[5] + \ dder_plus_f_m_perp_z_imag*matrix_u[8] dder_plus["f_m_perp_real"] = numpy.stack([ dder_plus_f_m_perp_1_real, dder_plus_f_m_perp_2_real, dder_plus_f_m_perp_3_real], axis=0) dder_plus["f_m_perp_imag"] = numpy.stack([ dder_plus_f_m_perp_1_imag, dder_plus_f_m_perp_2_imag, dder_plus_f_m_perp_3_imag], axis=0) dder_minus_f_m_perp_x_real = \ y_m_perp_xy_f_m_perp_x_real * f_m_perp_xy_sq + \ y_m_perp_xy * f_m_perp_xy_sq_f_m_perp_x_real -\ p_d * chiral_term_f_m_perp_x_real dder_minus_f_m_perp_x_imag = \ y_m_perp_xy_f_m_perp_x_imag * f_m_perp_xy_sq + \ y_m_perp_xy * f_m_perp_xy_sq_f_m_perp_x_imag -\ p_d * chiral_term_f_m_perp_x_imag dder_minus_f_m_perp_y_real = \ y_m_perp_xy_f_m_perp_y_real * f_m_perp_xy_sq + \ y_m_perp_xy * f_m_perp_xy_sq_f_m_perp_y_real -\ p_d * chiral_term_f_m_perp_y_real dder_minus_f_m_perp_y_imag = \ y_m_perp_xy_f_m_perp_y_imag * f_m_perp_xy_sq + \ y_m_perp_xy * f_m_perp_xy_sq_f_m_perp_y_imag -\ p_d * chiral_term_f_m_perp_y_imag dder_minus_f_m_perp_z_real = 0.5*( (1.-p_d)*(y_plus*f_plus_sq_f_m_perp_z_real+y_plus_f_m_perp_z_real*f_plus_sq) + (1.+p_d)*(y_minus*f_minus_sq_f_m_perp_z_real+y_minus_f_m_perp_z_real*f_minus_sq)) dder_minus_f_m_perp_z_imag = 0.5*( (1.-p_d)*(y_plus*f_plus_sq_f_m_perp_z_imag+y_plus_f_m_perp_z_imag*f_plus_sq) + (1.+p_d)*(y_minus*f_minus_sq_f_m_perp_z_imag+y_minus_f_m_perp_z_imag*f_minus_sq)) dder_minus_f_m_perp_1_real = \ dder_minus_f_m_perp_x_real*matrix_u[0] + \ dder_minus_f_m_perp_y_real*matrix_u[3] + \ dder_minus_f_m_perp_z_real*matrix_u[6] dder_minus_f_m_perp_1_imag = \ dder_minus_f_m_perp_x_imag*matrix_u[0] + \ dder_minus_f_m_perp_y_imag*matrix_u[3] + \ dder_minus_f_m_perp_z_imag*matrix_u[6] dder_minus_f_m_perp_2_real = \ dder_minus_f_m_perp_x_real*matrix_u[1] + \ dder_minus_f_m_perp_y_real*matrix_u[4] + \ dder_minus_f_m_perp_z_real*matrix_u[7] dder_minus_f_m_perp_2_imag = \ dder_minus_f_m_perp_x_imag*matrix_u[1] + \ dder_minus_f_m_perp_y_imag*matrix_u[4] + \ dder_minus_f_m_perp_z_imag*matrix_u[7] dder_minus_f_m_perp_3_real = \ dder_minus_f_m_perp_x_real*matrix_u[2] + \ dder_minus_f_m_perp_y_real*matrix_u[5] + \ dder_minus_f_m_perp_z_real*matrix_u[8] dder_minus_f_m_perp_3_imag = \ dder_minus_f_m_perp_x_imag*matrix_u[2] + \ dder_minus_f_m_perp_y_imag*matrix_u[5] + \ dder_minus_f_m_perp_z_imag*matrix_u[8] dder_minus["f_m_perp_real"] = numpy.stack([ dder_minus_f_m_perp_1_real, dder_minus_f_m_perp_2_real, dder_minus_f_m_perp_3_real], axis=0) dder_minus["f_m_perp_imag"] = numpy.stack([ dder_minus_f_m_perp_1_imag, dder_minus_f_m_perp_2_imag, dder_minus_f_m_perp_3_imag], axis=0) extinction_keys = dder_y_plus.keys() if len(extinction_keys) != 0: for key in extinction_keys: if key == "f_sq": pass else: dder_plus[key] = \ 0.5*((1.+p_u)*f_plus_sq*dder_y_plus[key] + (1.-p_u)*f_minus_sq*dder_y_minus[key]) + \ f_m_perp_xy_sq * dder_y_m_perp_xy[key] dder_minus[key] = \ 0.5*((1.-p_d)*f_plus_sq*dder_y_plus[key] + (1.+p_d)*f_minus_sq*dder_y_minus[key]) + \ f_m_perp_xy_sq * dder_y_m_perp_xy[key] return iint_plus, iint_minus, dder_plus, dder_minus def calc_flip_ratio_by_iint( iint_plus, iint_minus, c_lambda2: float = None, iint_2hkl = None, flag_iint_plus: bool = False, flag_iint_minus: bool = False, flag_c_lambda2: bool = False, flag_iint_2hkl: bool = False): """Calculate flip ratio by given integrated intensities. """ dder = {} cond = (c_lambda2 is None) or (iint_2hkl is None) if cond: signal_plus = iint_plus signal_minus = iint_minus else: signal_plus = iint_plus + c_lambda2*iint_2hkl signal_minus = iint_minus + c_lambda2*iint_2hkl flip_ratio = signal_plus/signal_minus if flag_iint_plus: dder["iint_plus"] = numpy.ones_like(iint_plus) / signal_minus if flag_iint_minus: dder["iint_minus"] = -1. * flip_ratio * numpy.ones_like(iint_minus)/ signal_minus if flag_c_lambda2: dder["c_lambda2"] = numpy.ones_like(c_lambda2)*iint_2hkl / signal_minus - \ flip_ratio * numpy.ones_like(c_lambda2)*iint_2hkl/ signal_minus if flag_iint_2hkl: dder["iint_2hkl"] = numpy.ones_like(iint_2hkl)*c_lambda2 / signal_minus - \ flip_ratio * numpy.ones_like(iint_2hkl)*c_lambda2/ signal_minus return flip_ratio, dder def calc_asymmetry_by_iint( iint_plus, iint_minus, c_lambda2: float = None, iint_2hkl = None, flag_iint_plus: bool = False, flag_iint_minus: bool = False, flag_c_lambda2: bool = False, flag_iint_2hkl: bool = False): """Calculate asymmetry (I^+ - I^-)/(I^+ + I^-). """ dder = {} cond = (c_lambda2 is None) or (iint_2hkl is None) if cond: contamination = 0. else: contamination = 2.*c_lambda2*iint_2hkl denom = (iint_plus + iint_minus + contamination) asymmetry = (iint_plus - iint_minus) / denom if flag_iint_plus: dder["iint_plus"] = (1. / denom - (iint_plus - iint_minus) / numpy.square(denom)) * \ numpy.ones_like(iint_plus) if flag_iint_minus: dder["iint_minus"] = (-1. / denom - (iint_plus - iint_minus) / numpy.square(denom)) * \ numpy.ones_like(iint_minus) if flag_c_lambda2: dder["c_lambda2"] = 2.*iint_2hkl*( - (iint_plus - iint_minus) / numpy.square(denom)) * \ numpy.ones_like(c_lambda2) if flag_iint_2hkl: dder["iint_2hkl"] = 2.*c_lambda2*( - (iint_plus - iint_minus) / numpy.square(denom)) * \ numpy.ones_like(iint_2hkl) return asymmetry, dder def calc_intensities_by_structure_factors( beam_polarization: float, flipper_efficiency: float, f_nucl, f_m_perp, matrix_u, func_extinction: Callable = None, c_lambda2: float=None, f_nucl_2hkl=None, f_m_perp_2hkl=None, flag_beam_polarization: bool = False, flag_flipper_efficiency: bool = False, flag_f_nucl: bool = False, flag_f_m_perp: bool = False, flag_c_lambda2: bool = False, flag_f_nucl_2hkl: bool = False, flag_f_m_perp_2hkl: bool = False, dict_in_out: dict = None, flag_use_precalculated_data: bool = False): """Calculate flip ratio by given structure factors """ if dict_in_out is None: flag_dict = False dict_in_out_keys = [] else: flag_dict = True dict_in_out_keys = dict_in_out.keys() iint_plus, iint_minus, dder_plus_hkl, dder_minus_hkl = calc_iint( beam_polarization, flipper_efficiency, f_nucl, f_m_perp, matrix_u, func_extinction=func_extinction, flag_beam_polarization=flag_beam_polarization, flag_flipper_efficiency=flag_flipper_efficiency, flag_f_nucl=flag_f_nucl, flag_f_m_perp=flag_f_m_perp, dict_in_out=dict_in_out, flag_use_precalculated_data=flag_use_precalculated_data) cond = (c_lambda2 is None) or (f_nucl_2hkl is None) or (f_m_perp_2hkl is None) if cond: c_lambda2 = None iint_2hkl = None flag_iint_2hkl = False dder_2hkl = {} else: iint_plus_2hkl, iint_minus_2hkl, dder_plus_2hkl, dder_minus_2hkl = calc_iint( 0, 0, f_nucl_2hkl, f_m_perp_2hkl, matrix_u, func_extinction=func_extinction, flag_beam_polarization=False, flag_flipper_efficiency=False, flag_f_nucl=flag_f_nucl_2hkl, flag_f_m_perp=flag_f_m_perp_2hkl, dict_in_out=None, flag_use_precalculated_data=False) iint_2hkl = iint_plus_2hkl dder_2hkl = dder_plus_2hkl flag_iint_2hkl = len(dder_2hkl.keys()) > 0 iint_plus += c_lambda2*iint_2hkl iint_minus += c_lambda2*iint_2hkl if flag_f_nucl_2hkl: dder_2hkl["f_nucl_2hkl_real"] = dder_2hkl.pop("f_nucl_real") dder_2hkl["f_nucl_2hkl_imag"] = dder_2hkl.pop("f_nucl_imag") if flag_f_m_perp_2hkl: dder_2hkl["f_m_perp_2hkl_real"] = dder_2hkl.pop("f_m_perp_real") dder_2hkl["f_m_perp_2hkl_imag"] = dder_2hkl.pop("f_m_perp_imag") dder_plus = {} dder_minus = {} keys_plus = dder_plus_hkl.keys() keys_minus = dder_minus_hkl.keys() keys_2h = dder_2hkl.keys() set_key = set(keys_plus) | set(keys_minus) | set(keys_2h) for key in set_key: flag_1 = key in keys_plus flag_2 = key in keys_minus flag_3 = key in keys_2h if flag_1: term_1 = dder_plus_hkl[key] if flag_2: term_2 = dder_minus_hkl[key] if flag_3: term_3 = dder_2hkl[key] if flag_1 and flag_3: dder_plus[key] = term_1 + c_lambda2 * term_3 elif flag_1: dder_plus[key] = term_1 elif flag_3: dder_plus[key] = c_lambda2 * term_3 if flag_2 and flag_3: dder_minus[key] = term_2 + c_lambda2 * term_3 elif flag_2: dder_minus[key] = term_2 elif flag_3: dder_minus[key] = c_lambda2 * term_3 if flag_c_lambda2: dder_plus["c_lambda2"] = iint_2hkl dder_minus["c_lambda2"] = iint_2hkl return iint_plus, iint_minus, dder_plus, dder_minus def calc_flip_ratio_by_structure_factors( beam_polarization: float, flipper_efficiency: float, f_nucl, f_m_perp, matrix_u, func_extinction: Callable = None, c_lambda2: float=None, f_nucl_2hkl=None, f_m_perp_2hkl=None, flag_beam_polarization: bool = False, flag_flipper_efficiency: bool = False, flag_f_nucl: bool = False, flag_f_m_perp: bool = False, flag_c_lambda2: bool = False, flag_f_nucl_2hkl: bool = False, flag_f_m_perp_2hkl: bool = False, dict_in_out: dict = None, flag_use_precalculated_data: bool = False): """Calculate flip ratio by given structure factors """ if dict_in_out is None: flag_dict = False dict_in_out_keys = [] else: flag_dict = True dict_in_out_keys = dict_in_out.keys() iint_plus, iint_minus, dder_plus, dder_minus = calc_intensities_by_structure_factors( beam_polarization, flipper_efficiency, f_nucl, f_m_perp, matrix_u, func_extinction=func_extinction, c_lambda2=c_lambda2, f_nucl_2hkl=f_nucl_2hkl, f_m_perp_2hkl=f_m_perp_2hkl, flag_beam_polarization=flag_beam_polarization, flag_flipper_efficiency=flag_flipper_efficiency, flag_f_nucl=flag_f_nucl, flag_f_m_perp=flag_f_m_perp, flag_c_lambda2=flag_c_lambda2, flag_f_nucl_2hkl=flag_f_nucl_2hkl, flag_f_m_perp_2hkl=flag_f_m_perp_2hkl, dict_in_out=dict_in_out, flag_use_precalculated_data=flag_use_precalculated_data) flip_ration = iint_plus/iint_minus dder = {} keys_plus = dder_plus.keys() keys_minus = dder_minus.keys() set_key = set(keys_plus) | set(keys_minus) for key in set_key: flag_1 = key in keys_plus flag_2 = key in keys_minus if flag_1 and flag_2: dder[key] = dder_plus[key]/iint_minus - flip_ration*dder_minus[key]/iint_minus elif flag_1: dder[key] = dder_plus[key]/iint_minus elif flag_2: dder[key] = - flip_ration*dder_minus[key]/iint_minus return flip_ration, dder

47.04661

122

0.672476

4,112

22,206

3.027237

0.027237

0.128535

0.135443

0.039364

0.867288

0.816115

0.757873

0.70196

0.638817

0.547638

0

0.019492

0.237593

22,206

471

123

47.146497

0.715771

0.016392

0

0.273632

0

0.023628

0

1

0.012438

false

0.002488

0.004975

0

0.029851

0

null

0

1

0

null

0

4

5cf39c9d7caa61ad13736a237ac01ce3af23ed24

27

py

Python

dpath/version.py

calebcase/dpath-python

52b531695c2b7a9ef49aa42ba50ed605bf3fed92

[ "MIT" ]

null

dpath/version.py

calebcase/dpath-python

52b531695c2b7a9ef49aa42ba50ed605bf3fed92

[ "MIT" ]

null

dpath/version.py

calebcase/dpath-python

52b531695c2b7a9ef49aa42ba50ed605bf3fed92

[ "MIT" ]

null

import os VERSION="1.4.0"

6.75

15

0.666667

6

27

3

1

0

0.130435

0.148148

27

3

16

9

0.652174

0

0.185185

0

1

0

false

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

4

cf07362d66fd192309e46983530a86b65c76e706

712

py

Python

easyw/watermark/service/user.py

GooZy/Image-Watermark

faaba8da5b33372f2fa1d187752da4288aefeb43

[ "MIT" ]

5

2018-12-17T08:15:08.000Z

2021-10-30T16:52:56.000Z

easyw/watermark/service/user.py

GooZy/Image-Watermark

faaba8da5b33372f2fa1d187752da4288aefeb43

[ "MIT" ]

1

2018-12-17T08:52:48.000Z

2018-12-20T07:11:53.000Z

easyw/watermark/service/user.py

GooZy/Image-Watermark

faaba8da5b33372f2fa1d187752da4288aefeb43

[ "MIT" ]

1

2019-06-18T07:24:02.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- # @Time : 2018/3/25 20:18 # @Author : GUO Ziyao from easyw.common.db import query_db from easyw.common.utils import hash_password class UserService(object): @classmethod def get_user_by_name(cls, username): return query_db('SELECT * FROM users WHERE username=?', [username]) @classmethod def get_user_by_name_and_password(cls, username, password): return query_db('SELECT * FROM users WHERE username=? AND password=?', [username, hash_password(password)]) @classmethod def add_user(cls, username, password): query_db("INSERT INTO users(username, password) VALUES (?, ?)", [username, hash_password(password)])

32.363636

115

0.696629

94

712

5.106383

0.478723

0.058333

0.0625

0.0875

0.283333

0.170833

0

0.020478

0.176966

712

21

116

33.904762

0.798635

0.126404

0

0.25

0

0.223301

0

1

0.25

false

0.416667

0.166667

0.666667

0

null

0

null

0

1

0

1

0

1

0

4

cf183ac95c30b71cdf89ad3c9ba9ac247e47d17f

70

py

Python

secret_santa/controller_service/__init__.py

jacobboesch/secret_santa_program

f5b75614e716302930e5980beb1c79171e9b5451

[ "MIT" ]

null

secret_santa/controller_service/__init__.py

jacobboesch/secret_santa_program

f5b75614e716302930e5980beb1c79171e9b5451

[ "MIT" ]

null

secret_santa/controller_service/__init__.py

jacobboesch/secret_santa_program

f5b75614e716302930e5980beb1c79171e9b5451

[ "MIT" ]

null

from secret_santa.controller_service.email_service import EmailService

70

0.928571

9

70

6.888889

0.888889

0

0.042857

70

1

70

0.925373

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

cf1cc6f8d9f2322da10a18351e5414e29dbabba4

49

py

Python

config.py

PatrickLopesF/kucoin-trading-bot

b69077e5eea278d28d1fc686762dff9578b80bae

[ "MIT" ]

null

config.py

PatrickLopesF/kucoin-trading-bot

b69077e5eea278d28d1fc686762dff9578b80bae

[ "MIT" ]

null

config.py

PatrickLopesF/kucoin-trading-bot

b69077e5eea278d28d1fc686762dff9578b80bae

[ "MIT" ]

null

api_key = '' api_secret = '' api_passphrase = ''

12.25

19

0.632653

6

49

4.666667

0.666667

0

0.183673

49

3

20

16.333333

0.7

0

1

0

false

0.333333

0

1

0

null

0

1

0

null

0

1

0

4

cf27a0112b4fe4ef571cb653637df903abaf5afe

192

py

Python

binreconfiguration/strategy/maxfreespace.py

vialette/binreconfiguration

57cc024fdcf9b083a830270176ade185b65a85d0

[ "MIT" ]

null

binreconfiguration/strategy/maxfreespace.py

vialette/binreconfiguration

57cc024fdcf9b083a830270176ade185b65a85d0

[ "MIT" ]

null

binreconfiguration/strategy/maxfreespace.py

vialette/binreconfiguration

57cc024fdcf9b083a830270176ade185b65a85d0

[ "MIT" ]

null

from .descendinggaugedstrategy import DescendingGaugedStrategy from .gauge import FreeSpace class MaxFreeSpace(DescendingGaugedStrategy): def _gauge(self, item): return FreeSpace(item)

19.2

62

0.828125

18

192

8.777778

0.611111

0

0.114583

192

9

63

21.333333

0.929412

0

1

0.2

false

0

0.4

0.2

1

0

1

0

null

0

1

0

null

0

1

0

4

cf5884124f24b69198865312c0c41c6f1f8e5784

87

py

Python

py_tdlib/constructors/get_file.py

Mr-TelegramBot/python-tdlib

2e2d21a742ebcd439971a32357f2d0abd0ce61eb

[ "MIT" ]

24

2018-10-05T13:04:30.000Z

2020-05-12T08:45:34.000Z

py_tdlib/constructors/get_file.py

MrMahdi313/python-tdlib

2e2d21a742ebcd439971a32357f2d0abd0ce61eb

[ "MIT" ]

3

2019-06-26T07:20:20.000Z

2021-05-24T13:06:56.000Z

py_tdlib/constructors/get_file.py

MrMahdi313/python-tdlib

2e2d21a742ebcd439971a32357f2d0abd0ce61eb

[ "MIT" ]

5

2018-10-05T14:29:28.000Z

2020-08-11T15:04:10.000Z

from ..factory import Method class getFile(Method): file_id = None # type: "int32"

14.5

32

0.701149

12

87

5

0.916667

0

0.028169

0.183908

87

5

33

17.4

0.816901

0.149425

0

1

0

false

0

0.333333

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

4

cf8899f74cabe6150ba74551918867e396ebf30d

203

py

Python

app/utils/__init__.py

Landers1037/pan

483b9fa8ad5bf30375c155d6bdf5db9d2bd063e2

[ "MIT" ]

null

app/utils/__init__.py

Landers1037/pan

483b9fa8ad5bf30375c155d6bdf5db9d2bd063e2

[ "MIT" ]

null

app/utils/__init__.py

Landers1037/pan

483b9fa8ad5bf30375c155d6bdf5db9d2bd063e2

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Time: 2020-05-26 20:45 # Author: Landers1037 # Mail: liaorenj@gmail.com # File: __init__.py from .secure_name import secure_name from .auto_category import auto_category

25.375

40

0.714286

30

203

4.566667

0.8

0.145985

0

0.1

0.162562

203

8

40

25.375

0.705882

0.527094

0

1

0

true

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

4

d8569e28d961d9277732532778d42103783c243d

2,540

py

Python

test_day03.py

Yolgie/AdventOfCode2017

bf823b049659c6c528b354f0895cf311ad69ad5d

[ "MIT" ]

null

test_day03.py

Yolgie/AdventOfCode2017

bf823b049659c6c528b354f0895cf311ad69ad5d

[ "MIT" ]

null

test_day03.py

Yolgie/AdventOfCode2017

bf823b049659c6c528b354f0895cf311ad69ad5d

[ "MIT" ]

null

import unittest from day03 import SpiralMemory, Grid, calculateSpiralPosition class part01Tests(unittest.TestCase): spiralMemory = SpiralMemory() def setUp(self): super().setUp() self.spiralMemory.test = 1 def test_sample(self): self.assertEqual(self.spiralMemory.process(["1"]), 0) self.assertEqual(self.spiralMemory.process(["12"]), 3) self.assertEqual(self.spiralMemory.process(["23"]), 2) self.assertEqual(self.spiralMemory.process(["1024"]), 31) class part02(unittest.TestCase): spiralMemory = SpiralMemory() def setUp(self): super().setUp() self.spiralMemory.test = 2 def test_gird(self): grid = Grid() self.assertEqual(grid.calculateNext(), 1) self.assertEqual(grid.calculateNext(), 2) self.assertEqual(grid.calculateNext(), 4) self.assertEqual(grid.calculateNext(), 5) self.assertEqual(grid.calculateNext(), 10) self.assertEqual(grid.calculateNext(), 11) self.assertEqual(grid.calculateNext(), 23) self.assertEqual(grid.calculateNext(), 25) self.assertEqual(grid.calculateNext(), 26) self.assertEqual(grid.calculateNext(), 54) self.assertEqual(grid.calculateNext(), 57) def test_calculate_spiral_position(self): self.assertEqual(calculateSpiralPosition(1), (0, 0)) self.assertEqual(calculateSpiralPosition(2), (1, 0)) self.assertEqual(calculateSpiralPosition(3), (1, 1)) self.assertEqual(calculateSpiralPosition(4), (0, 1)) self.assertEqual(calculateSpiralPosition(5), (-1, 1)) self.assertEqual(calculateSpiralPosition(6), (-1, 0)) def test_first_greater(self): self.assertEqual(self.spiralMemory.process(["0"]), 1) self.assertEqual(self.spiralMemory.process(["1"]), 2) self.assertEqual(self.spiralMemory.process(["2"]), 4) self.assertEqual(self.spiralMemory.process(["3"]), 4) self.assertEqual(self.spiralMemory.process(["4"]), 5) self.assertEqual(self.spiralMemory.process(["5"]), 10) self.assertEqual(self.spiralMemory.process(["6"]), 10) self.assertEqual(self.spiralMemory.process(["7"]), 10) self.assertEqual(self.spiralMemory.process(["8"]), 10) self.assertEqual(self.spiralMemory.process(["10"]), 11) self.assertEqual(self.spiralMemory.process(["400"]), 747) self.assertEqual(self.spiralMemory.process(["800"]), 806)

40.967742

66

0.646063

262

2,540

6.236641

0.187023

0.302938

0.186047

0.30355

0.534884

0.369645

0.100367

0

0.047548

0.205118

2,540

61

67

41.639344

0.761763

0

0.12

0

0.010488

0

0.66

1

0.12

false

0

0.04

0

0.24

0

null

1

0

null

0

1

0

4

d85a1f47291c7f09ebf88c1f0fd3daab680740e3

303

py

Python

exercicios_utfpr/ex015.py

MatheusTG/python

5ec8701ffcdc5ac5a3e6e75dcd789bdec84612ad

[ "MIT" ]

null

exercicios_utfpr/ex015.py

MatheusTG/python

5ec8701ffcdc5ac5a3e6e75dcd789bdec84612ad

[ "MIT" ]

null

exercicios_utfpr/ex015.py

MatheusTG/python

5ec8701ffcdc5ac5a3e6e75dcd789bdec84612ad

[ "MIT" ]

null

hora_trabalho = int(input("Digite o número de horas trabalhadas: ")) salario_min = int(input("Digite o valor do salario minímo: ")) horaextra = int(input("Digite o número de horas extras trabalhadas: ")) print(f"A salario é igual a R$: {salario_min / 8 * hora_trabalho + salario_min / 4 * horaextra}")

50.5

97

0.726073

47

303

4.574468

0.531915

0.111628

0.195349

0.209302

0.260465

0

0.007782

0.151815

303

5

98

60.6

0.828794

0

0.673267

0

1

0

false

0

0.25

0

null

0

1

0

1

0

1

0

null

0

4

d85bc5e8ea40ce97ce5fd1c2867322c48e514b11

13

py

Python

epa_aqs_api/output.py

ab7289/python-epa-aqs

ca60febdb9430b0b6a26be878320b29dca7d6fed

[ "MIT" ]

null

epa_aqs_api/output.py

ab7289/python-epa-aqs

ca60febdb9430b0b6a26be878320b29dca7d6fed

[ "MIT" ]

null

epa_aqs_api/output.py

ab7289/python-epa-aqs

ca60febdb9430b0b6a26be878320b29dca7d6fed

[ "MIT" ]

null

# import csv

6.5

12

0.692308

2

13

4.5

1

0

0.230769

13

1

13

0.9

0.769231

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

null

0

1

0

4

d88be36131951c5ac2daef7eb577c5ef15dd00c0

8,098

py

Python

test/crypten/test_context.py

ribhu97/PySyft

ee36eb95a01f4e6736e87b8856a3e7788054ac30

[ "Apache-2.0" ]

1

2021-04-09T10:23:06.000Z

test/crypten/test_context.py

ribhu97/PySyft

ee36eb95a01f4e6736e87b8856a3e7788054ac30

[ "Apache-2.0" ]

null

test/crypten/test_context.py

ribhu97/PySyft

ee36eb95a01f4e6736e87b8856a3e7788054ac30

[ "Apache-2.0" ]

null

import pytest import crypten import torch as th import torch.nn as nn import torch.nn.functional as F import syft as sy from syft.frameworks.crypten.context import run_multiworkers, run_party from syft.frameworks.crypten.model import OnnxModel from syft.frameworks.crypten import utils th.set_num_threads(1) # Define an example network class ExampleNet(nn.Module): def __init__(self): super(ExampleNet, self).__init__() self.conv1 = nn.Conv2d(1, 16, kernel_size=5, padding=0) self.fc1 = nn.Linear(16 * 12 * 12, 100) self.fc2 = nn.Linear(100, 2) def forward(self, x): out = self.conv1(x) out = F.relu(out) out = F.max_pool2d(out, 2) out = out.view(-1, 16 * 12 * 12) out = self.fc1(out) out = F.relu(out) out = self.fc2(out) return out def test_context_plan(workers): # alice and bob n_workers = 2 alice = workers["alice"] bob = workers["bob"] alice_tensor_ptr = th.tensor([42, 53, 3, 2]).tag("crypten_data").send(alice) bob_tensor_ptr = th.tensor([101, 32, 29, 2]).tag("crypten_data").send(bob) @run_multiworkers([alice, bob], master_addr="127.0.0.1") @sy.func2plan() def plan_func(model=None, crypten=crypten): # pragma: no cover alice_tensor = crypten.load("crypten_data", 0) bob_tensor = crypten.load("crypten_data", 1) crypt = alice_tensor + bob_tensor result = crypt.get_plain_text() return result return_values = plan_func() expected_value = th.tensor([143, 85, 32, 4]) # A toy function is ran at each party, and they should all decrypt # a tensor with value [143, 85] for rank in range(n_workers): assert th.all( return_values[rank] == expected_value ), "Crypten party with rank {} don't match expected value {} != {}".format( rank, return_values[rank], expected_value ) def test_context_jail(workers): # alice and bob n_workers = 2 alice = workers["alice"] bob = workers["bob"] alice_tensor_ptr = th.tensor([42, 53, 3, 2]).tag("crypten_data").send(alice) bob_tensor_ptr = th.tensor([101, 32, 29, 2]).tag("crypten_data").send(bob) @run_multiworkers([alice, bob], master_addr="127.0.0.1") def jail_func(crypten=crypten): # pragma: no cover alice_tensor = crypten.load("crypten_data", 0) bob_tensor = crypten.load("crypten_data", 1) crypt = alice_tensor + bob_tensor result = crypt.get_plain_text() return result return_values = jail_func() expected_value = th.tensor([143, 85, 32, 4]) # A toy function is ran at each party, and they should all decrypt # a tensor with value [143, 85] for rank in range(n_workers): assert th.all( return_values[rank] == expected_value ), "Crypten party with rank {} don't match expected value {} != {}".format( rank, return_values[rank], expected_value ) def test_context_jail_with_model(workers): dummy_input = th.empty(1, 1, 28, 28) pytorch_model = ExampleNet() alice = workers["alice"] bob = workers["bob"] alice_tensor_ptr = th.tensor(dummy_input).tag("crypten_data").send(alice) @run_multiworkers( [alice, bob], master_addr="127.0.0.1", model=pytorch_model, dummy_input=dummy_input ) def run_encrypted_eval(): # pragma: no cover rank = crypten.communicator.get().get_rank() t = crypten.load("crypten_data", 0) model.encrypt() # noqa: F821 out = model(t) # noqa: F821 model.decrypt() # noqa: F821 out = out.get_plain_text() return model, out # noqa: F821 result = run_encrypted_eval() # compare out assert th.all(result[0][1] == result[1][1]) def test_context_jail_with_model_failures(workers): dummy_input = th.empty(1, 1, 28, 28) pytorch_model = ExampleNet() alice = workers["alice"] bob = workers["bob"] alice_tensor_ptr = th.tensor(dummy_input).tag("crypten_data").send(alice) @run_multiworkers([alice, bob], master_addr="127.0.0.1", model=pytorch_model) def run_encrypted_eval(): # pragma: no cover rank = crypten.communicator.get().get_rank() t = crypten.load("crypten_data", 0) model.encrypt() # noqa: F821 out = model(t) # noqa: F821 model.decrypt() # noqa: F821 out = out.get_plain_text() return model, out # noqa: F821 with pytest.raises(ValueError): result = run_encrypted_eval() @run_multiworkers([alice, bob], master_addr="127.0.0.1", model=5) def run_encrypted_eval(): # pragma: no cover rank = crypten.communicator.get().get_rank() t = crypten.load("crypten_data", 0) model.encrypt() # noqa: F821 out = model(t) # noqa: F821 model.decrypt() # noqa: F821 out = out.get_plain_text() return model, out # noqa: F821 with pytest.raises(TypeError): result = run_encrypted_eval() @run_multiworkers([alice, bob], master_addr="127.0.0.1", model=pytorch_model, dummy_input=73) def run_encrypted_eval(): # pragma: no cover rank = crypten.communicator.get().get_rank() t = crypten.load("crypten_data", 0) model.encrypt() # noqa: F821 out = model(t) # noqa: F821 model.decrypt() # noqa: F821 out = out.get_plain_text() return model, out # noqa: F821 with pytest.raises(TypeError): result = run_encrypted_eval() def test_run_party(): expected = th.tensor(5) def party(): # pragma: no cover t = crypten.cryptensor(expected) return t.get_plain_text() t = run_party(None, party, 0, 1, "127.0.0.1", 15463, (), {}) result = utils.unpack_values(t) assert result == expected def test_duplicate_ids(workers): # alice and bob n_workers = 2 alice = workers["alice"] alice2 = workers["alice"] @run_multiworkers([alice, alice2], master_addr="127.0.0.1") def jail_func(crypten=crypten): # pragma: no cover pass with pytest.raises(RuntimeError): return_values = jail_func() def test_context_plan_with_model(workers): dummy_input = th.empty(1, 1, 28, 28) pytorch_model = ExampleNet() alice = workers["alice"] bob = workers["bob"] alice_tensor_ptr = th.tensor(dummy_input).tag("crypten_data").send(alice) @run_multiworkers( [alice, bob], master_addr="127.0.0.1", model=pytorch_model, dummy_input=dummy_input ) @sy.func2plan() def plan_func_model(model=None, crypten=crypten): # noqa: F821 t = crypten.load("crypten_data", 0) model.encrypt() out = model(t) model.decrypt() out = out.get_plain_text() return model, out result = plan_func_model() assert th.all(result[0][1] == result[1][1]) def test_context_plan_with_model_private(workers): """ Test if we can run remote inference (using data that is not on our local paty) using a private model (model that is not known locally) """ dummy_input = th.empty(1, 1, 28, 28) pytorch_model = ExampleNet() alice = workers["alice"] bob = workers["bob"] data_alice = th.tensor(dummy_input).tag("crypten_data").send(alice) model = OnnxModel.fromModel(pytorch_model, dummy_input).tag("crypten_model") # Model is known only by Bob and Alice and the data is at the local party alice_model_ptr = model.send(alice) bob_model_ptr = model.send(bob) @run_multiworkers([alice, bob], master_addr="127.0.0.1") @sy.func2plan() def plan_func_model(crypten=crypten): # noqa: F821 data = crypten.load("crypten_data", 0) # This should load the crypten model that is found at all parties model = crypten.load_model("crypten_model") model.encrypt() out = model(data) model.decrypt() out = out.get_plain_text() return out result = plan_func_model() assert th.all(result[0] == result[1])

29.992593

97

0.633366

1,126

8,098

4.376554

0.146536

0.040179

0.010146

0.012175

0.726664

0.716315

0.701907

0.678977

0.670657

0

0.043273

0.240924

8,098

269

98

30.104089

0.758419

0.108051

0

0.666667

0

0.071798

0

0.032787

1

0.10929

false

0.005464

0.04918

0

0.218579

0

null

0

1

0

1

0

null

0

4