xszheng2020/the_stack_dedup_python_hits_5 · Datasets at Hugging Face

c84397a494519c8d89b852e924ed1743bfee7e32

40

py

Python

airbyte-integrations/connectors/source-mailchimp/source_mailchimp/models/__init__.py

rajatariya21/airbyte

11e70a7a96e2682b479afbe6f709b9a5fe9c4a8d

[ "MIT" ]

6,215

2020-09-21T13:45:56.000Z

2022-03-31T21:21:45.000Z

airbyte-integrations/connectors/source-mailchimp/source_mailchimp/models/__init__.py

rajatariya21/airbyte

11e70a7a96e2682b479afbe6f709b9a5fe9c4a8d

[ "MIT" ]

8,448

2020-09-21T00:43:50.000Z

2022-03-31T23:56:06.000Z

airbyte-integrations/connectors/source-mailchimp/source_mailchimp/models/__init__.py

rajatariya21/airbyte

11e70a7a96e2682b479afbe6f709b9a5fe9c4a8d

[ "MIT" ]

1,251

2020-09-20T05:48:47.000Z

2022-03-31T10:41:29.000Z

from .mailchimp import HealthCheckError

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c84efc07fd2d74a464b3cbbac09ca31e2bf2b027

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py

Python

recommends/tests/__init__.py

coagulant/django-recommends

412b741c3a0aa5204b70f869cc893ef9fbccbe51

[ "MIT" ]

4

2015-01-29T17:17:26.000Z

2021-03-03T08:17:03.000Z

recommends/tests/__init__.py

coagulant/django-recommends

412b741c3a0aa5204b70f869cc893ef9fbccbe51

[ "MIT" ]

null

recommends/tests/__init__.py

coagulant/django-recommends

412b741c3a0aa5204b70f869cc893ef9fbccbe51

[ "MIT" ]

1

2015-09-22T08:35:26.000Z

# flake8: noqa from recommends.tests.providers import *

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c077ab182108e6ae8a82f5e8d9291baac4a599f7

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py

Python

flexipage/tests/__init__.py

eRestin/MezzGIS

984341fa5361433cf9b6f30b113358c19d3cd05c

[ "BSD-2-Clause" ]

null

flexipage/tests/__init__.py

eRestin/MezzGIS

984341fa5361433cf9b6f30b113358c19d3cd05c

[ "BSD-2-Clause" ]

null

flexipage/tests/__init__.py

eRestin/MezzGIS

984341fa5361433cf9b6f30b113358c19d3cd05c

[ "BSD-2-Clause" ]

null

from test_utils import * from test_flexipage import *

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null

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5

c085365aac4670351aadba8b39e6bac8055384f1

87

py

Python

fileModPassArgCall.py

vijayjoset/PythonFCSITM

bddfc086c299a162594fe023627f6381f8d4c976

[ "MIT" ]

null

fileModPassArgCall.py

vijayjoset/PythonFCSITM

bddfc086c299a162594fe023627f6381f8d4c976

[ "MIT" ]

null

fileModPassArgCall.py

vijayjoset/PythonFCSITM

bddfc086c299a162594fe023627f6381f8d4c976

[ "MIT" ]

null

import sys import fileModPassArg num = int(sys.argv[1]) print(fileModPassArg(num))

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c0a87ea76408cfa69a3e29ebb38dfccc3395c3e7

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py

Python

ex/ex080.py

Ozcry/PythonExercicio

b4d4a4fbd6467d1ced0815677ecbd78c2613c4c9

[ "MIT" ]

null

ex/ex080.py

Ozcry/PythonExercicio

b4d4a4fbd6467d1ced0815677ecbd78c2613c4c9

[ "MIT" ]

null

ex/ex080.py

Ozcry/PythonExercicio

b4d4a4fbd6467d1ced0815677ecbd78c2613c4c9

[ "MIT" ]

null

'''Crie um programa onde o usuário posso digitar cinco valores numéricos e cadastre-os em uma lista, já na posição correta de inserção (sem usar o sort()). No final, mostre a lista ordenada na tela.''' print('\033[1;33m-=\033[m' * 20) lista = [] for c in range(0, 5): n1 = int(input('\033[34mDigite um valor:\033[m ')) if c == 0 or n1 > lista[-1]: lista.append(n1) print('\033[31mAdicionado ao final da lista...\033[m') print('\033[1;33m-=\033[m' * 20) else: pos = 0 while pos < len(lista): if n1 <= lista[pos]: lista.insert(pos, n1) print(f'\033[35mAdicionando na posição {pos} da lista...\033[m') print('\033[1;33m-=\033[m' * 20) break pos += 1 print(f'\033[36mOs valores digitados em ordem foram {lista}\033[m') print('\033[1;33m-=\033[m' * 20) print('\033[1;32mFIM\033[m') ### Outro metodo ''' lista = [] print('\033[1;33m-=\033[m' * 20) while True: if len(lista) == 0: n1 = int(input('Digite um valor: ')) lista.append(n1) print('Adicionado ao final da lista...') print('\033[1;33m-=\033[m' * 20) if len(lista) == 1: n1 = int(input('Digite um valor: ')) if n1 < lista[0]: lista.insert(0, n1) print(f'Adicionado na posição {lista.index(n1) + 1} da lista...') print('\033[1;33m-=\033[m' * 20) elif n1 > lista[0]: lista.append(n1) print('Adicionado ao final da lista...') print('\033[1;33m-=\033[m' * 20) elif n1 in lista: print('Valor duplicado! Não vou adicionar...') print('\033[1;33m-=\033[m' * 20) if len(lista) == 2: n1 = int(input('Digite um valor: ')) if n1 > lista[1]: lista.append(n1) print('Adicionado ao final da lista...') print('\033[1;33m-=\033[m' * 20) elif n1 < lista[0]: lista.insert(0, n1) print(f'Adicionado na posição {lista.index(n1) + 1} da lista...') print('\033[1;33m-=\033[m' * 20) elif lista[0] < n1 < lista[1]: lista.insert(1, n1) print(f'Adicionado na posição {lista.index(n1) + 1} da lista...') print('\033[1;33m-=\033[m' * 20) elif n1 in lista: print('Valor duplicado! Não vou adicionar...') print('\033[1;33m-=\033[m' * 20) if len(lista) == 3: n1 = int(input('Digite um valor: ')) if n1 < lista[0]: lista.insert(0, n1) print(f'Adicionado na posição {lista.index(n1) + 1} da lista...') print('\033[1;33m-=\033[m' * 20) elif n1 > lista[2]: lista.append(n1) print('Adicionado ao final da lista...') print('\033[1;33m-=\033[m' * 20) elif lista[1] < n1 < lista[2]: lista.insert(2, n1) print(f'Adicionado na posição {lista.index(n1) + 1} da lista...') print('\033[1;33m-=\033[m' * 20) elif lista[0] < n1 < lista[1]: lista.insert(1, n1) print(f'Adicionado na posição {lista.index(n1) + 1} da lista...') print('\033[1;33m-=\033[m' * 20) elif n1 in lista: print('Valor duplicado! Não vou adicionar...') print('\033[1;33m-=\033[m' * 20) if len(lista) == 4: n1 = int(input('Digite um valor: ')) if n1 < lista[0]: lista.insert(0, n1) print(f'Adicionado na posição {lista.index(n1) + 1} da lista...') print('\033[1;33m-=\033[m' * 20) elif n1 > lista[3]: lista.append(n1) print('Adicionado ao final da lista...') print('\033[1;33m-=\033[m' * 20) elif lista[0] < n1 < lista[1]: lista.insert(1, n1) print(f'Adicionado na posição {lista.index(n1) + 1} da lista...') print('\033[1;33m-=\033[m' * 20) elif lista[1] < n1 < lista[2]: lista.insert(2, n1) print(f'Adicionado na posição {lista.index(n1) + 1} da lista...') print('\033[1;33m-=\033[m' * 20) elif lista[2] < n1 < lista[3]: lista.insert(3, n1) print(f'Adicionado na posição {lista.index(n1) + 1} da lista...') print('\033[1;33m-=\033[m' * 20) elif n1 in lista: print('Valor duplicado! Não vou adicionar...') print('\033[1;33m-=\033[m' * 20) if len(lista) == 5: break print(f'Os números digitados foram {lista}') print('\033[1;33m-=\033[m' * 20) print('FIM') '''

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c0b2e769f45b83ac223972a42a84685bd7e6c0e1

349

py

Python

mountainlab_pytools/mlproc/__init__.py

timsainb/mountainlab_pytools

7d765e9af8be119a0fd8667d117ce8a5593486b5

[ "Apache-2.0" ]

null

mountainlab_pytools/mlproc/__init__.py

timsainb/mountainlab_pytools

7d765e9af8be119a0fd8667d117ce8a5593486b5

[ "Apache-2.0" ]

4

2018-07-17T13:14:13.000Z

2019-01-02T15:40:39.000Z

mountainlab_pytools/mlproc/__init__.py

timsainb/mountainlab_pytools

7d765e9af8be119a0fd8667d117ce8a5593486b5

[ "Apache-2.0" ]

3

2018-07-11T16:15:43.000Z

2019-01-03T02:45:29.000Z

from .mlproc_impl import runProcess from .mlproc_impl import lariLogin,initPipeline,addProcess from .mlproc_impl import spec from .mlproc_impl import locateFile,realizeFile,kbucketPath,readDir from .mlproc_impl import runPipeline from .mlproc_impl import addContainerRule,setContainerRules,containerRules from .mlclient import MLClient,MLJobMonitor

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c0b4a5d28b6e59e5eae2120ecc1e05276c94f305

40

py

Python

examples/getUserBadges.py

iranathan/RobloxPy

a83eeea30dc449f66b89dd011f4e09404248f866

[ "MIT" ]

1

2020-12-08T15:08:38.000Z

examples/getUserBadges.py

iranathan/RobloxPy

a83eeea30dc449f66b89dd011f4e09404248f866

[ "MIT" ]

null

examples/getUserBadges.py

iranathan/RobloxPy

a83eeea30dc449f66b89dd011f4e09404248f866

[ "MIT" ]

1

2020-12-08T15:08:39.000Z

from robloxpy import User User.badges(1)

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23925b54b8261a42a19a2a24b73cfce63790735a

314

py

Python

configs/gdrn/ycbvPbrSO/resnest50d_AugCosyAAEGray_BG05_visib10_mlBCE_DoubleMask_ycbvPbr100e_SO_bop_test/resnest50d_AugCosyAAEGray_BG05_visib10_mlBCE_DoubleMask_ycbvPbr100e_SO_bop_test_12_21BleachCleanser.py

THU-DA-6D-Pose-Group/self6dpp

c267cfa55e440e212136a5e9940598720fa21d16

[ "Apache-2.0" ]

33

2021-12-15T07:11:47.000Z

2022-03-29T08:58:32.000Z

configs/gdrn/ycbvPbrSO/resnest50d_AugCosyAAEGray_BG05_visib10_mlBCE_DoubleMask_ycbvPbr100e_SO_bop_test/resnest50d_AugCosyAAEGray_BG05_visib10_mlBCE_DoubleMask_ycbvPbr100e_SO_bop_test_12_21BleachCleanser.py

THU-DA-6D-Pose-Group/self6dpp

c267cfa55e440e212136a5e9940598720fa21d16

[ "Apache-2.0" ]

3

2021-12-15T11:39:54.000Z

2022-03-29T07:24:23.000Z

configs/gdrn/ycbvPbrSO/resnest50d_AugCosyAAEGray_BG05_visib10_mlBCE_DoubleMask_ycbvPbr100e_SO_bop_test/resnest50d_AugCosyAAEGray_BG05_visib10_mlBCE_DoubleMask_ycbvPbr100e_SO_bop_test_12_21BleachCleanser.py

THU-DA-6D-Pose-Group/self6dpp

c267cfa55e440e212136a5e9940598720fa21d16

[ "Apache-2.0" ]

null

_base_ = "./resnest50d_AugCosyAAEGray_BG05_visib10_mlBCE_DoubleMask_ycbvPbr100e_SO_bop_test_01_02MasterChefCan.py" OUTPUT_DIR = ( "output/gdrn/ycbvPbrSO/resnest50d_AugCosyAAEGray_BG05_visib10_mlBCE_DoubleMask_ycbvPbr100e_SO/12_21BleachCleanser" ) DATASETS = dict(TRAIN=("ycbv_021_bleach_cleanser_train_pbr",))

52.333333

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239aea79f97c031d4d8fb10fbb6a8181fc8f3463

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py

Python

simtbx/diffBragg/refiners/__init__.py

dperl-sol/cctbx_project

b9e390221a2bc4fd00b9122e97c3b79c632c6664

[ "BSD-3-Clause-LBNL" ]

155

2016-11-23T12:52:16.000Z

2022-03-31T15:35:44.000Z

simtbx/diffBragg/refiners/__init__.py

dperl-sol/cctbx_project

b9e390221a2bc4fd00b9122e97c3b79c632c6664

[ "BSD-3-Clause-LBNL" ]

590

2016-12-10T11:31:18.000Z

2022-03-30T23:10:09.000Z

simtbx/diffBragg/refiners/__init__.py

dperl-sol/cctbx_project

b9e390221a2bc4fd00b9122e97c3b79c632c6664

[ "BSD-3-Clause-LBNL" ]

115

2016-11-15T08:17:28.000Z

2022-02-09T15:30:14.000Z

from __future__ import absolute_import, division, print_function from .base_refiner import BaseRefiner, BreakToUseCurvatures, BreakBecauseSignal # special import

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23ac4b8fb0dd3df022d1cc1921eebc57f986dbc1

79

py

Python

incasem/torch/__init__.py

kirchhausenlab/incasem

ee9e007c5c04571e547e2fb5af5e800bd2d2b435

[ "BSD-3-Clause" ]

null

incasem/torch/__init__.py

kirchhausenlab/incasem

ee9e007c5c04571e547e2fb5af5e800bd2d2b435

[ "BSD-3-Clause" ]

null

incasem/torch/__init__.py

kirchhausenlab/incasem

ee9e007c5c04571e547e2fb5af5e800bd2d2b435

[ "BSD-3-Clause" ]

null

from __future__ import absolute_import from . import models from . import loss

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23bc22e9c13932ff93ea87fe51ee99a8bcef275f

31

py

Python

mdutils/tools/__init__.py

yngtodd/mdutils

2dce7a2fe7d8c92a968bde38af57a7e83602174d

[ "MIT" ]

1

2020-05-01T20:12:33.000Z

mdutils/tools/__init__.py

yngtodd/mdutils

2dce7a2fe7d8c92a968bde38af57a7e83602174d

[ "MIT" ]

10

2020-01-08T00:19:43.000Z

2020-03-02T14:23:42.000Z

pythonlogbook/logbookenv/lib/python3.6/site-packages/mdutils/tools/__init__.py

lukew3/logbook

53b5df5ea5b416267f7640351bd043de18af4f77

[ "MIT" ]

null

from mdutils.tools import tools

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5

23d0702ad68b98764e9618c9fa1c34f8f25c4dec

107

py

Python

initdb.py

rskwan/ncindex

5621eec1c6475b2e977be5b716b583930ba4de80

[ "MIT" ]

1

2018-05-02T16:59:41.000Z

initdb.py

rskwan/ncindex

5621eec1c6475b2e977be5b716b583930ba4de80

[ "MIT" ]

null

initdb.py

rskwan/ncindex

5621eec1c6475b2e977be5b716b583930ba4de80

[ "MIT" ]

null

# Run this to initialize the database. from ncindex import db from ncindex.models import * db.create_all()

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23f8834b84427b238b8d9ad604721a9685d66f45

460

py

Python

src/study/cli.py

ppmzhang2/gpt3-study

1c4e34238301e06da8cbda23eb4e473567e15c80

[ "MIT" ]

null

src/study/cli.py

ppmzhang2/gpt3-study

1c4e34238301e06da8cbda23eb4e473567e15c80

[ "MIT" ]

null

src/study/cli.py

ppmzhang2/gpt3-study

1c4e34238301e06da8cbda23eb4e473567e15c80

[ "MIT" ]

null

"""all commands here""" import click from .serv import decode from .serv import encode from .serv import encode_with_pretrained from .serv import fine_tune_train from .serv import prompt_generate from .serv import tokenize @click.group() def cli(): """all clicks here""" cli.add_command(decode) cli.add_command(encode) cli.add_command(encode_with_pretrained) cli.add_command(fine_tune_train) cli.add_command(prompt_generate) cli.add_command(tokenize)

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f1b1a1f95b25de6f1dfa58d8dca73fe8aa84a617

58

py

Python

optflow/viz/__init__.py

czming/optflow

6a6d24efbaac162f1d3da5d26430f9ea9e60bbad

[ "MIT" ]

null

optflow/viz/__init__.py

czming/optflow

6a6d24efbaac162f1d3da5d26430f9ea9e60bbad

[ "MIT" ]

1

2021-01-12T01:26:21.000Z

optflow/viz/__init__.py

czming/optflow

6a6d24efbaac162f1d3da5d26430f9ea9e60bbad

[ "MIT" ]

1

2021-01-12T00:35:04.000Z

from .display_flow import * from .visualize_state import *

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5

7b234731eeaf74fa7437e4cb0a5d7859f436fbd6

128

py

Python

hello.py

abrosen/cis1051demo

182fd075d915b2bfd7ba814dc62416666ebba8ab

[ "MIT" ]

null

hello.py

abrosen/cis1051demo

182fd075d915b2bfd7ba814dc62416666ebba8ab

[ "MIT" ]

null

hello.py

abrosen/cis1051demo

182fd075d915b2bfd7ba814dc62416666ebba8ab

[ "MIT" ]

null

# this is a hello world file # it is boring print("begin hello world") print("hello!") print("world") print("end hello world")

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null

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5

7b2d7193bb5c1a97040b264b8033dd7627855382

277

py

Python

src/blip_sdk/extensions/artificial_intelligence/content_assistant/content_type.py

mirlarof/blip-sdk-python

f958149b2524d4340eeafad8739a33db71df45ed

[ "MIT" ]

2

2021-07-02T20:10:48.000Z

2021-07-13T20:51:18.000Z

src/blip_sdk/extensions/artificial_intelligence/content_assistant/content_type.py

mirlarof/blip-sdk-python

f958149b2524d4340eeafad8739a33db71df45ed

[ "MIT" ]

9

2021-05-27T21:08:23.000Z

2021-06-14T20:10:10.000Z

src/blip_sdk/extensions/artificial_intelligence/content_assistant/content_type.py

mirlarof/blip-sdk-python

f958149b2524d4340eeafad8739a33db71df45ed

[ "MIT" ]

3

2021-06-23T19:53:20.000Z

2022-01-04T17:50:44.000Z

class ContentType: """Content Assistant Content type class.""" CONTENT_RESULT = 'application/vnd.iris.ai.content-result+json' CONTENT_COMBINATION = 'application/vnd.iris.ai.content-combination+json' ANALYSIS = 'application/vnd.iris.ai.analysis-request+json'

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null

0

5

9e33b0959897faf145ca4c3bedee7c5278dc0757

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py

Python

env/lib/python3.7/site-packages/indicoio/custom/__init__.py

Novandev/gn_api

08b071ae3916bb7a183d61843a2cd09e9fe15c7b

[ "MIT" ]

4

2015-08-20T22:42:19.000Z

2016-03-14T01:28:45.000Z

indicoio/custom/__init__.py

mikesperry/IndicoIo-python

caa155b8b31b76df3f86f559ce5324f061a03e40

[ "MIT" ]

null

indicoio/custom/__init__.py

mikesperry/IndicoIo-python

caa155b8b31b76df3f86f559ce5324f061a03e40

[ "MIT" ]

null

from .custom import Collection, collections, vectorize, visualize_explanation

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1

0

null

0

1

0

1

0

1

0

5

9e33d770da8ab7937c126ba233776c74dd33c135

36

py

Python

desktop/core/ext-py/nose-1.3.7/unit_tests/support/script.py

kokosing/hue

2307f5379a35aae9be871e836432e6f45138b3d9

[ "Apache-2.0" ]

5,079

2015-01-01T03:39:46.000Z

2022-03-31T07:38:22.000Z

desktop/core/ext-py/nose-1.3.7/unit_tests/support/script.py

zks888/hue

93a8c370713e70b216c428caa2f75185ef809deb

[ "Apache-2.0" ]

1,623

2015-01-01T08:06:24.000Z

2022-03-30T19:48:52.000Z

desktop/core/ext-py/nose-1.3.7/unit_tests/support/script.py

zks888/hue

93a8c370713e70b216c428caa2f75185ef809deb

[ "Apache-2.0" ]

2,033

2015-01-04T07:18:02.000Z

2022-03-28T19:55:47.000Z

#!/usr/bin/env python print "FAIL"

9

21

0.666667

6

36

4

1

0

0.138889

36

3

22

12

0.774194

0.555556

0

0.266667

0

null

0

null

1

0

null

0

1

0

null

0

1

0

1

0

5

9e3597ebd1e335f4fa0fddeb9a7a34dc71c462b7

113

py

Python

kalliope/core/ConfigurationManager/__init__.py

joshuaboniface/kalliope

0e040be3165e838485d1e5addc4d2c5df12bfd84

[ "MIT" ]

1

2020-03-30T15:03:19.000Z

kalliope/core/ConfigurationManager/__init__.py

joshuaboniface/kalliope

0e040be3165e838485d1e5addc4d2c5df12bfd84

[ "MIT" ]

null

kalliope/core/ConfigurationManager/__init__.py

joshuaboniface/kalliope

0e040be3165e838485d1e5addc4d2c5df12bfd84

[ "MIT" ]

1

2021-11-21T19:08:15.000Z

from .YAMLLoader import YAMLLoader from .SettingLoader import SettingLoader from .BrainLoader import BrainLoader

28.25

40

0.867257

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113

8.166667

0.416667

0

0.106195

113

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41

37.666667

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0

1

0

true

0

1

0

1

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1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

9e4276cd81cdc93ceb8fdc2f251baa5dff89c5cc

180

py

Python

OOP/OOP-Practice/inheritance/design_oops.py

siddhantdixit/OOP-ClassWork

ce414a3836d03aa7dee0eb1d7a69e849fb6707c0

[ "MIT" ]

null

OOP/OOP-Practice/inheritance/design_oops.py

siddhantdixit/OOP-ClassWork

ce414a3836d03aa7dee0eb1d7a69e849fb6707c0

[ "MIT" ]

null

OOP/OOP-Practice/inheritance/design_oops.py

siddhantdixit/OOP-ClassWork

ce414a3836d03aa7dee0eb1d7a69e849fb6707c0

[ "MIT" ]

null

class DesignOops: def __init__(self): print("Hello") class NewOOps(DesignOops): def __init__(self): print("OK") super().__init__() NewOOps()

15

26

0.577778

18

180

5.111111

0.555556

0.282609

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0.456522

0.565217

0

0.283333

180

12

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15

0.713178

0

0.25

0

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1

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0

0.5

0.25

1

0

null

1

0

null

0

1

0

5

9e76ee1433334ed25322746c31f01a8f24b230ec

273

py

Python

DQM/Physics/python/qcdPhotonsCosmicDQM_cff.py

PKUfudawei/cmssw

8fbb5ce74398269c8a32956d7c7943766770c093

[ "Apache-2.0" ]

1

2018-08-28T16:51:36.000Z

DQM/Physics/python/qcdPhotonsCosmicDQM_cff.py

PKUfudawei/cmssw

8fbb5ce74398269c8a32956d7c7943766770c093

[ "Apache-2.0" ]

25

2016-06-24T20:55:32.000Z

2022-02-01T19:24:45.000Z

DQM/Physics/python/qcdPhotonsCosmicDQM_cff.py

PKUfudawei/cmssw

8fbb5ce74398269c8a32956d7c7943766770c093

[ "Apache-2.0" ]

8

2016-03-25T07:17:43.000Z

2021-07-08T17:11:21.000Z

import FWCore.ParameterSet.Config as cms import DQM.Physics.qcdPhotonsDQM_cfi qcdPhotonsCosmicDQM = DQM.Physics.qcdPhotonsDQM_cfi.qcdPhotonsDQM.clone( barrelRecHitTag = "ecalRecHit:EcalRecHitsEB", endcapRecHitTag = "ecalRecHit:EcalRecHitsEE" )

34.125

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273

8.583333

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273

7

73

39

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null

0

1

0

1

0

null

0

1

0

5

9ebf8eaf41c98c8441a45396ec01008c464295b7

62

py

Python

misc/python/py.py

saranshbht/codes-and-more-codes

0bd2e46ca613b3b81e1196d393902e86a43aa353

[ "MIT" ]

null

misc/python/py.py

saranshbht/codes-and-more-codes

0bd2e46ca613b3b81e1196d393902e86a43aa353

[ "MIT" ]

null

misc/python/py.py

saranshbht/codes-and-more-codes

0bd2e46ca613b3b81e1196d393902e86a43aa353

[ "MIT" ]

null

print("Hello World"); import math; print(math.degrees(0.345));

20.666667

27

0.725806

10

62

4.5

0.8

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62

3

27

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true

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0

null

0

1

0

1

0

1

0

5

7b550234d62d197e4d471b3b12530cdd2ae76832

550

py

Python

meddlr/modeling/meta_arch/__init__.py

ad12/meddlr

dda5a4ad7855de3a34331c60599e3253f980e989

[ "Apache-2.0" ]

23

2021-11-05T02:00:01.000Z

2022-03-21T15:35:38.000Z

meddlr/modeling/meta_arch/__init__.py

ad12/meddlr

dda5a4ad7855de3a34331c60599e3253f980e989

[ "Apache-2.0" ]

29

2021-11-04T22:18:26.000Z

2022-03-24T01:04:53.000Z

meddlr/modeling/meta_arch/__init__.py

ad12/meddlr

dda5a4ad7855de3a34331c60599e3253f980e989

[ "Apache-2.0" ]

1

2022-01-25T22:34:51.000Z

from .build import META_ARCH_REGISTRY, build_model, initialize_model # noqa: F401 from .cs_model import CSModel # noqa: F401 from .denoising import DenoisingModel # noqa: F401 from .generalized_unet import GeneralizedUNet # noqa: F401 from .m2r import M2RModel # noqa: F401 from .n2r import N2RModel # noqa: F401 from .nm2r import NM2RModel # noqa: F401 from .ssdu import SSDUModel # noqa: F401 from .unet import UnetModel # noqa: F401 from .unrolled import GeneralizedUnrolledCNN # noqa: F401 from .vortex import VortexModel # noqa: F401

45.833333

82

0.772727

74

550

5.662162

0.405405

0.210024

0.286396

0

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0.163636

550

11

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0

1

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true

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0

null

0

1

0

1

0

1

0

5

7bc7f92777ddd9d7ad94caded9569daef630fec4

1,967

py

Python

tests/test_scheduling_tools.py

Smurgs/CourseScheduler

bea9f4ba489bc65bd5c3aaf265fede18f7862a58

[ "MIT" ]

null

tests/test_scheduling_tools.py

Smurgs/CourseScheduler

bea9f4ba489bc65bd5c3aaf265fede18f7862a58

[ "MIT" ]

null

tests/test_scheduling_tools.py

Smurgs/CourseScheduler

bea9f4ba489bc65bd5c3aaf265fede18f7862a58

[ "MIT" ]

null

import sys sys.path.append('../course_scheduler') from course_scheduler.scheduling_tools import * class TestVirtualSchedule(object): def setup(self): self.vs = VirtualSchedule() def test_empty_schedule(self): assert len(self.vs.get_registered_classes()) == 0 def test_add_section(self): time_slot = TimeSlot("M", "08:35", "11:35") section = Section("MATH2004A", time_slot) assert self.vs.add_to_schedule(section) assert len(self.vs.get_registered_classes()) == 1 def test_add_lab(self): time_slot = TimeSlot("T", "08:35", "11:35") lab = Lab("MATH2004A", time_slot) assert self.vs.add_to_schedule(lab) assert len(self.vs.get_registered_classes()) == 1 def test_no_overlap(self): time_slot = TimeSlot("M", "08:35", "11:35") section = Section("MATH2004A", time_slot) assert self.vs.add_to_schedule(section) time_slot = TimeSlot("M", "08:35", "11:35") lab = Lab("ELEC2004A", time_slot) assert not self.vs.add_to_schedule(lab) time_slot = TimeSlot("M", "10:35", "12:35") lab = Lab("ELEC2004A", time_slot) assert not self.vs.add_to_schedule(lab) time_slot = TimeSlot("M", "06:35", "09:35") lab = Lab("ELEC2004A", time_slot) assert not self.vs.add_to_schedule(lab) time_slot = TimeSlot("M", "06:35", "12:35") lab = Lab("ELEC2004A", time_slot) assert not self.vs.add_to_schedule(lab) def test_boundary(self): time_slot = TimeSlot("M", "08:35", "11:25") section = Section("MATH2004A", time_slot) assert self.vs.add_to_schedule(section) time_slot = TimeSlot("M", "11:35", "12:25") lab = Lab("ELEC2004A", time_slot) assert self.vs.add_to_schedule(lab) time_slot = TimeSlot("M", "07:35", "08:25") lab = Lab("ELEC2004A", time_slot) assert self.vs.add_to_schedule(lab)

32.245902

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1,967

4.222628

0.186131

0.138289

0.095073

0.78306

0.770959

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0.673293

0

0.081728

0.234875

1,967

60

58

32.783333

0.687043

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0

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false

0

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0

0.204545

0

null

0

1

0

null

0

5

c8838fd70df6b3e6bfbd20c4e8ac74132a1d171c

132

py

Python

affo_email_service/api/exception.py

fossabot/affo-email-service

4e224c025a410504601651f1a85762c91300f4e9

[ "BSD-3-Clause" ]

null

affo_email_service/api/exception.py

fossabot/affo-email-service

4e224c025a410504601651f1a85762c91300f4e9

[ "BSD-3-Clause" ]

1

2019-11-25T14:25:18.000Z

affo_email_service/api/exception.py

fossabot/affo-email-service

4e224c025a410504601651f1a85762c91300f4e9

[ "BSD-3-Clause" ]

1

2019-11-25T14:21:58.000Z

import http import connexion_buzz class NoSuchMessage(connexion_buzz.ConnexionBuzz): status_code = http.HTTPStatus.NOT_FOUND

16.5

50

0.825758

16

132

6.5625

0.75

0.247619

0

0.121212

132

7

51

18.857143

0.905172

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1

0

false

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0

1

0

null

0

1

0

1

0

5

c8d4cf7daf112be480531fbca567f02655daf82d

4,236

py

Python

hw7_ValidationOnLinReg.py

alisa-ipn/Learning-From-Data-MOOC-hws

97ed5b23bcbfb825975fe882f9eff3ca80b620bc

[ "MIT" ]

null

hw7_ValidationOnLinReg.py

alisa-ipn/Learning-From-Data-MOOC-hws

97ed5b23bcbfb825975fe882f9eff3ca80b620bc

[ "MIT" ]

null

hw7_ValidationOnLinReg.py

alisa-ipn/Learning-From-Data-MOOC-hws

97ed5b23bcbfb825975fe882f9eff3ca80b620bc

[ "MIT" ]

1

2018-11-22T17:45:36.000Z

# -*- coding: utf-8 -*- """ Created on Wed Nov 9 22:04:17 2016 @author: alisazhila """ import numpy as np import hw6_2LinRegRegularized def read_dta_25_10(fname): tr_data = [] val_data =[] data = [] for s in open(fname): values = s.strip().split() values = np.array(map(float, values)) for v in values: v = float(v) data.append(values) tr_data = data[:25] val_data = data[-10:] return tr_data, val_data def nonlinear_transformation_to_k(data, k): transformed_data = [] labels = [] if k > 0: for datapoint in data: transformed_datapoint = [] transformed_datapoint.append(1) #1 transformed_datapoint.append(datapoint[0]) #x1 transformed_datapoint.append(datapoint[1]) #x2 if k >=3: transformed_datapoint.append(datapoint[0]*datapoint[0]) #x1^2 if k >=4: transformed_datapoint.append(datapoint[1]*datapoint[1]) #x2^2 if k >=5: transformed_datapoint.append(datapoint[0]*datapoint[1]) #x1*x2 if k >=6: transformed_datapoint.append(abs(datapoint[0]-datapoint[1])) #|x1-x2| if k >=7: transformed_datapoint.append(abs(datapoint[0]+datapoint[1])) #|x1+x2| transformed_data.append(transformed_datapoint) labels.append(datapoint[2]) return transformed_data, labels def experiment_1(k): tr_data, val_data = read_dta_25_10('./data/in.dta') transformed_tr_data, tr_labels = nonlinear_transformation_to_k(tr_data, k) #model training w = hw6_2LinRegRegularized.linear_reg(transformed_tr_data, tr_labels) #print w err_in = hw6_2LinRegRegularized.estimate_err(w, transformed_tr_data, tr_labels) print "err_in=", err_in transformed_val_data, val_labels = nonlinear_transformation_to_k(val_data, k) err_out = hw6_2LinRegRegularized.estimate_err(w, transformed_val_data, val_labels) print "err_out=", err_out return w, err_in, err_out def experiment_2(k): tr_data, val_data = read_dta_25_10('./data/in.dta') transformed_tr_data, tr_labels = nonlinear_transformation_to_k(tr_data, k) #model training w = hw6_2LinRegRegularized.linear_reg(transformed_tr_data, tr_labels) #print w err_in = hw6_2LinRegRegularized.estimate_err(w, transformed_tr_data, tr_labels) print "err_in=", err_in test_data = hw6_2LinRegRegularized.read_dta('./data/out.dta') transformed_test_data, test_labels = nonlinear_transformation_to_k(test_data, k) err_out = hw6_2LinRegRegularized.estimate_err(w, transformed_test_data, test_labels) print "err_out=", err_out return w, err_in, err_out def experiment_3(k): val_data, tr_data = read_dta_25_10('./data/in.dta') transformed_tr_data, tr_labels = nonlinear_transformation_to_k(tr_data, k) #model training w = hw6_2LinRegRegularized.linear_reg(transformed_tr_data, tr_labels) #print w err_in = hw6_2LinRegRegularized.estimate_err(w, transformed_tr_data, tr_labels) print "err_in=", err_in transformed_val_data, val_labels = nonlinear_transformation_to_k(val_data, k) err_out = hw6_2LinRegRegularized.estimate_err(w, transformed_val_data, val_labels) print "err_out=", err_out return w, err_in, err_out def experiment_4(k): val_data, tr_data = read_dta_25_10('./data/in.dta') transformed_tr_data, tr_labels = nonlinear_transformation_to_k(tr_data, k) #model training w = hw6_2LinRegRegularized.linear_reg(transformed_tr_data, tr_labels) #print w err_in = hw6_2LinRegRegularized.estimate_err(w, transformed_tr_data, tr_labels) print "err_in=", err_in test_data = hw6_2LinRegRegularized.read_dta('./data/out.dta') transformed_test_data, test_labels = nonlinear_transformation_to_k(test_data, k) err_out = hw6_2LinRegRegularized.estimate_err(w, transformed_test_data, test_labels) print "err_out=", err_out return w, err_in, err_out errs = [] for k in [3,4,5,6,7]: print "k=", k w, err_in, err_out = experiment_2(k) errs.append(err_out) print errs print min(errs)

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1

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1

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null

0

1

0

5

74054058327b48cc0b5cd549e9c8b42c88da2ba8

837

py

Python

DjangoCountries/countries/services.py

xm4dn355x/specialist_DjangoCountries

debfbead4cc87faf1e60af374863498080c5fa8f

[ "MIT" ]

null

DjangoCountries/countries/services.py

xm4dn355x/specialist_DjangoCountries

debfbead4cc87faf1e60af374863498080c5fa8f

[ "MIT" ]

null

DjangoCountries/countries/services.py

xm4dn355x/specialist_DjangoCountries

debfbead4cc87faf1e60af374863498080c5fa8f

[ "MIT" ]

null

# -*- coding: utf-8 -*- ######################################################################## # # # # # # # MIT License # # Copyright (c) 2021 Michael Nikitenko # # # ######################################################################## def get_languages_list(countries: list): return sorted(set([language for country in countries for language in country['languages']])) def get_alphabet(): return [chr(i).upper() for i in range(ord('a'), ord('z') + 1)]

46.5

96

0.247312

45

837

4.533333

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0

0.014634

0.510155

837

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97

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1

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null

0

1

0

1

0

5

a821930880c1585f34b0476b1cccfca6cef8d433

47

py

Python

tests/__main__.py

agrif/earendil

2477d85d3c2198a4cb1ab2c482d420705a28b022

[ "MIT" ]

2

2020-02-22T03:38:09.000Z

2021-02-17T12:03:01.000Z

tests/__main__.py

agrif/quartustcl

899aef7ca6b26c191e80c4a525d3f9c3322e51d0

[ "MIT" ]

null

tests/__main__.py

agrif/quartustcl

899aef7ca6b26c191e80c4a525d3f9c3322e51d0

[ "MIT" ]

1

2019-04-16T22:27:07.000Z

import unittest unittest.main(module='tests')

11.75

29

0.787234

6

47

6.166667

0.833333

0

0.085106

47

3

30

15.666667

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0

0.106383

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

5

b51a574c2886afa4c19772207653bfe0824367b4

234

py

Python

basen/__init__.py

vd2org/basen

ca3996c2c7900a071fa91d06960efc0c7f25b4be

[ "MIT" ]

1

2021-08-03T01:49:47.000Z

basen/__init__.py

vd2org/basen

ca3996c2c7900a071fa91d06960efc0c7f25b4be

[ "MIT" ]

null

basen/__init__.py

vd2org/basen

ca3996c2c7900a071fa91d06960efc0c7f25b4be

[ "MIT" ]

2

2020-02-19T11:10:34.000Z

2022-03-01T06:38:30.000Z

# Copyright (C) 2017-2021 by Ivan. # This file is part of BaseN package. # BaseN is released under the MIT License (see LICENSE). from .basen import BaseN from .int2base import int2base, base2int def version(): return "0.0.4"

19.5

56

0.717949

37

234

4.540541

0.756757

0

0.074074

0.192308

234

11

57

21.272727

0.814815

0.525641

0

0.046729

0

1

0.25

true

0

0.5

0.25

1

0

null

0

null

0

1

0

1

0

5

b5322a0b08ac59891fd8bed7f6663cb64ca140f4

96

py

Python

venv/lib/python3.8/site-packages/tests/test_exceptions.py

GiulianaPola/select_repeats

17a0d053d4f874e42cf654dd142168c2ec8fbd11

[ "MIT" ]

2

2022-03-13T01:58:52.000Z

2022-03-31T06:07:54.000Z

venv/lib/python3.8/site-packages/tests/test_exceptions.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

19

2021-11-20T04:09:18.000Z

2022-03-23T15:05:55.000Z

venv/lib/python3.8/site-packages/tests/test_exceptions.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

null

/home/runner/.cache/pip/pool/2a/f5/40/fbdcc529268cdda1c99782a0b37608cdc6e398b2c7e04199dfdcee68fa

96

0.895833

9

96

9.555556

1

0

0.364583

0

96

1

96

0.53125

0

1

0

null

0

null

0

1

0

null

0

1

0

1

0

null

1

0

1

0

5

b579b05d14abc4a0a02b667697dd80026dfb7c16

69

py

Python

instance/config.py

johnwanjema/news-highlight

91b36905e4060e9c27308299d8a72c476323c25c

[ "MIT" ]

null

instance/config.py

johnwanjema/news-highlight

91b36905e4060e9c27308299d8a72c476323c25c

[ "MIT" ]

null

instance/config.py

johnwanjema/news-highlight

91b36905e4060e9c27308299d8a72c476323c25c

[ "MIT" ]

null

NEWS_API_KEY = '03d3f69ba4844b8e94dc1582f0dc69b9' SECRET_KEY = "1234"

34.5

49

0.84058

7

69

7.857143

0.857143

0

0.375

0.072464

69

2

50

34.5

0.484375

0

0.514286

0.457143

0

1

0

false

0

1

0

null

0

1

0

1

0

1

0

1

null

0

5

b58d866b167dca08228c85800e58036b355b8250

5,875

py

Python

cogs/vccontrol.py

CDESamBotDev/VCRoles

764fff6be5dc44194ee3979dbfa72340cd66a172

[ "Apache-2.0" ]

3

2022-02-18T11:41:07.000Z

2022-02-22T17:33:09.000Z

cogs/vccontrol.py

CDESamBotDev/VCRoles

764fff6be5dc44194ee3979dbfa72340cd66a172

[ "Apache-2.0" ]

15

2022-01-22T20:15:10.000Z

2022-03-29T16:10:40.000Z

cogs/vccontrol.py

CDESamBotDev/VCRoles

764fff6be5dc44194ee3979dbfa72340cd66a172

[ "Apache-2.0" ]

null

import asyncio from typing import Literal, Optional import discord from discord import app_commands from discord.ext import commands from bot import MyClient from checks import check_any, command_available, is_owner class VCControl(commands.Cog): def __init__(self, client: MyClient): self.client = client control_commands = app_commands.Group( name="vc", description="Used to control voice channels" ) async def get_members( self, interaction: discord.Interaction ) -> list[discord.Member]: mem = [] for user_id, state in interaction.guild._voice_states.items(): if state.channel and state.channel.id == interaction.user.voice.channel.id: member = await interaction.guild.fetch_member(user_id) if member is not None: mem.append(member) return mem @control_commands.command() @app_commands.describe(who="Who to mute:") @check_any(command_available, is_owner) @app_commands.checks.has_permissions(administrator=True) async def mute( self, interaction: discord.Interaction, who: Optional[Literal["everyone", "everyone but me"]] = "everyone but me", ): """Mutes everyone in a voice channel""" if interaction.user.voice and interaction.user.voice.channel: vc = interaction.user.voice.channel mem = await self.get_members(interaction) if who == "everyone" and vc: tasks = [ self.client.loop.create_task(member.edit(mute=True)) for member in mem ] elif who == "everyone but me" and vc: tasks = [ self.client.loop.create_task(member.edit(mute=True)) for member in mem if member.id != interaction.user.id ] else: return await interaction.response.send_message( "Please ensure you are in a voice channel." ) embed = discord.Embed( colour=discord.Colour.dark_grey(), description=f"Successfully muted in {vc.mention}", ) await interaction.response.send_message(embed=embed) await asyncio.gather(*tasks) return self.client.incr_counter("vc_mute") @control_commands.command() @app_commands.describe(who="Who to deafen:") @check_any(command_available, is_owner) @app_commands.checks.has_permissions(administrator=True) async def deafen( self, interaction: discord.Interaction, who: Optional[Literal["everyone", "everyone but me"]] = "everyone but me", ): """Deafens everyone in a voice channel""" if interaction.user.voice and interaction.user.voice.channel: vc = interaction.user.voice.channel mem = await self.get_members(interaction) if who == "everyone" and vc: tasks = [ self.client.loop.create_task(member.edit(deafen=True)) for member in mem ] elif who == "everyone but me" and vc: tasks = [ self.client.loop.create_task(member.edit(deafen=True)) for member in mem if member.id != interaction.user.id ] else: return await interaction.response.send_message( "Please ensure you are in a voice channel." ) embed = discord.Embed( colour=discord.Colour.dark_grey(), description=f"Successfully deafened in {vc.mention}", ) await interaction.response.send_message(embed=embed) await asyncio.gather(*tasks) return self.client.incr_counter("vc_deafen") @control_commands.command() @check_any(command_available, is_owner) @app_commands.checks.has_permissions(administrator=True) async def unmute(self, interaction: discord.Interaction): """Unmutes everyone in a voice channel""" if interaction.user.voice and interaction.user.voice.channel: vc = interaction.user.voice.channel mem = await self.get_members(interaction) if vc: tasks = [ self.client.loop.create_task(member.edit(mute=False)) for member in mem ] else: return await interaction.response.send_message( "Please ensure you are in a voice channel." ) embed = discord.Embed( colour=discord.Colour.dark_grey(), description=f"Successfully unmuted in {vc.mention}", ) await interaction.response.send_message(embed=embed) await asyncio.gather(*tasks) return self.client.incr_counter("vc_unmute") @control_commands.command() @check_any(command_available, is_owner) @app_commands.checks.has_permissions(administrator=True) async def undeafen(self, interaction: discord.Interaction): """Undeafens everyone in a voice channel""" if interaction.user.voice and interaction.user.voice.channel: vc = interaction.user.voice.channel mem = await self.get_members(interaction) if vc: tasks = [ self.client.loop.create_task(member.edit(deafen=False)) for member in mem ] else: return await interaction.response.send_message( "Please ensure you are in a voice channel." ) embed = discord.Embed( colour=discord.Colour.dark_grey(), description=f"Successfully undeafened in {vc.mention}", ) await interaction.response.send_message(embed=embed) await asyncio.gather(*tasks) return self.client.incr_counter("vc_undeafen") async def setup(client: MyClient): await client.add_cog(VCControl(client))

33.19209

88

0.620426

666

5,875

5.363363

0.162162

0.057111

0.072788

0.068029

0.777436

0.768757

0.741321

0

0.286809

5,875

176

89

33.380682

0.852506

0

0.566176

0

0.092103

0

1

0.007353

false

0

0.051471

0

0.139706

0

null

0

1

0

null

0

5

b59ab55319d5522b977e209c8bbb2df6bffc67d0

136

py

Python

reverse_shell_management/__init__.py

davidegirardi/zos-pivoting

46c2575f98eb1f6c81a8bce6e3fef2110c6c0b3b

[ "MIT" ]

3

2019-06-01T13:59:11.000Z

2021-06-07T16:25:50.000Z

reverse_shell_management/__init__.py

davidegirardi/zos-pivoting

46c2575f98eb1f6c81a8bce6e3fef2110c6c0b3b

[ "MIT" ]

null

reverse_shell_management/__init__.py

davidegirardi/zos-pivoting

46c2575f98eb1f6c81a8bce6e3fef2110c6c0b3b

[ "MIT" ]

null

"""Reverse shell connection management""" from .wrappingshell import WrappingShell from .reverseshellmanager import ReverseShellManager

34

52

0.852941

12

136

9.666667

0.666667

0

0.088235

136

3

53

45.333333

0.935484

0.257353

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

a9296fd6f1564e3f72c0c971c28df0849b65132e

24

py

Python

src/__init__.py

mkbeh/pydrommer

dc7c8f035813f8eddb35f166d445b8f0d9939067

[ "MIT" ]

2

2019-06-28T10:30:16.000Z

2021-07-31T03:42:58.000Z

src/__init__.py

mkbeh/pydrommer

dc7c8f035813f8eddb35f166d445b8f0d9939067

[ "MIT" ]

null

src/__init__.py

mkbeh/pydrommer

dc7c8f035813f8eddb35f166d445b8f0d9939067

[ "MIT" ]

2

2020-07-07T05:40:10.000Z

2021-05-11T22:55:59.000Z

__version__ = '0.30.17'

12

23

0.666667

4

24

3

1

0

0.238095

0.125

24

1

24

0.333333

0

0.291667

0

1

0

false

0

1

0

null

0

1

0

1

0

1

0

null

0

5

a98ad8c5d93cef0fce4519ba6461ced074fa797d

54

py

Python

plotly/validators/layout/grid/domain/__init__.py

gnestor/plotly.py

a8ae062795ddbf9867b8578fe6d9e244948c15ff

[ "MIT" ]

12

2020-04-18T18:10:22.000Z

2021-12-06T10:11:15.000Z

plotly/validators/layout/grid/domain/__init__.py

gnestor/plotly.py

a8ae062795ddbf9867b8578fe6d9e244948c15ff

[ "MIT" ]

27

2020-04-28T21:23:12.000Z

2021-06-25T15:36:38.000Z

plotly/validators/layout/grid/domain/__init__.py

gnestor/plotly.py

a8ae062795ddbf9867b8578fe6d9e244948c15ff

[ "MIT" ]

6

2020-04-18T23:07:08.000Z

2021-11-18T07:53:06.000Z

from ._y import YValidator from ._x import XValidator

18

26

0.814815

8

54

5.25

0.75

0

0.148148

54

2

27

0.913043

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

a999bd549fe4324cd85f3e17475140660e436da7

80

py

Python

foauth/__init__.py

GarrettHeel/foauth.org

f550f11c61ab0d9ec75d9a512fa9665db7a20087

[ "BSD-3-Clause" ]

null

foauth/__init__.py

GarrettHeel/foauth.org

f550f11c61ab0d9ec75d9a512fa9665db7a20087

[ "BSD-3-Clause" ]

null

foauth/__init__.py

GarrettHeel/foauth.org

f550f11c61ab0d9ec75d9a512fa9665db7a20087

[ "BSD-3-Clause" ]

null

class OAuthError(Exception): pass class OAuthDenied(Exception): pass

10

29

0.7125

8

80

7.125

0.625

0.45614

0

0.2125

80

7

30

11.428571

0.904762

0

0.5

0

1

0

true

0.5

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

5

a9a2d5dac8205c0662e7714feb8fd99b81432d5b

52

py

Python

flask_new/t.py

Theropod/MLinSiteSelection

dbe0a0912c079558731036be0017042a47d6d5fe

[ "MIT" ]

1

2022-03-12T15:40:56.000Z

flask_new/t.py

Theropod/MLinSiteSelection

dbe0a0912c079558731036be0017042a47d6d5fe

[ "MIT" ]

null

flask_new/t.py

Theropod/MLinSiteSelection

dbe0a0912c079558731036be0017042a47d6d5fe

[ "MIT" ]

null

print("\u5c0f\u6c64\u5c71".decode('unicode-escape'))

52

0.75

7

52

5.571429

1

0

0.153846

0

52

1

52

0.596154

0

0.603774

0

1

0

true

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

8d5e9e1bc70df0e3813201fb57739a521ceb339c

74

py

Python

FWCore/GuiBrowsers/examples/cleanPatCandidates_cff.py

NTrevisani/cmssw

a212a27526f34eb9507cf8b875c93896e6544781

[ "Apache-2.0" ]

3

2018-08-24T19:10:26.000Z

2019-02-19T11:45:32.000Z

FWCore/GuiBrowsers/examples/cleanPatCandidates_cff.py

NTrevisani/cmssw

a212a27526f34eb9507cf8b875c93896e6544781

[ "Apache-2.0" ]

7

2016-07-17T02:34:54.000Z

2019-08-13T07:58:37.000Z

FWCore/GuiBrowsers/examples/cleanPatCandidates_cff.py

NTrevisani/cmssw

a212a27526f34eb9507cf8b875c93896e6544781

[ "Apache-2.0" ]

5

2018-08-21T16:37:52.000Z

2020-01-09T13:33:17.000Z

from PhysicsTools.PatAlgos.cleaningLayer1.cleanPatCandidates_cff import *

37

73

0.891892

7

74

9.285714

1

0

0.014286

0.054054

74

1

74

0.914286

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

8d999a0e4b3e317d4c02947fbe2a4472aa21ed8a

192

py

Python

interview/tests.py

M-Davinci/recruitment

b0e2ab06b170ab1543dd590b1d7c70941bc74f41

[ "Apache-2.0" ]

null

interview/tests.py

M-Davinci/recruitment

b0e2ab06b170ab1543dd590b1d7c70941bc74f41

[ "Apache-2.0" ]

null

interview/tests.py

M-Davinci/recruitment

b0e2ab06b170ab1543dd590b1d7c70941bc74f41

[ "Apache-2.0" ]

null

from django.test import TestCase # Create your tests here. list = ['svchost.exe||2736||0.05||0.0', 'svchost.exe||2744||2.78||0.0', ] if 'svchost.exe||2736||0.05||0.0' in list: print(7)

21.333333

73

0.635417

36

192

3.388889

0.611111

0.245902

0.229508

0.245902

0.311475

0

0.168675

0.135417

192

8

74

24

0.566265

0.119792

0

0.502994

0

1

0

false

0

0.25

0

0.25

0

null

1

0

1

0

1

null

0

5

8d9e5097fc583b907ff79f708ff8751529a2854c

242

py

Python

nengolib/testing.py

ikajic/nengolib

bd30ec38ba656bedb94a267b5f86b51d1cec4954

[ "MIT" ]

27

2016-01-21T04:11:02.000Z

2021-11-16T20:41:04.000Z

nengolib/testing.py

ikajic/nengolib

bd30ec38ba656bedb94a267b5f86b51d1cec4954

[ "MIT" ]

178

2016-01-21T16:04:34.000Z

2021-05-01T16:28:02.000Z

nengolib/testing.py

ikajic/nengolib

bd30ec38ba656bedb94a267b5f86b51d1cec4954

[ "MIT" ]

4

2019-03-19T18:22:02.000Z

2021-03-23T16:06:57.000Z

from nengo.version import version_info if version_info >= (2, 7, 0): from pytest import warns # noqa: F401 else: # pragma: no cover # https://github.com/nengo/nengo/pull/1381 from nengo.utils.testing import warns # noqa: F401

30.25

55

0.698347

37

242

4.513514

0.648649

0.107784

0.179641

0.227545

0

0.066667

0.194215

242

7

56

34.571429

0.789744

0.326446

0

1

0

true

0

0.6

0

0.6

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

5

a5dfe4a9b2bae7d8373bbbd0b987280b0982aed2

186

py

Python

Lesson03/Code/YourName.py

pangmi/learntocode

719a2bfbc897104d0f95dcf4634fe93427e2c397

[ "MIT" ]

null

Lesson03/Code/YourName.py

pangmi/learntocode

719a2bfbc897104d0f95dcf4634fe93427e2c397

[ "MIT" ]

null

Lesson03/Code/YourName.py

pangmi/learntocode

719a2bfbc897104d0f95dcf4634fe93427e2c397

[ "MIT" ]

1

2021-12-19T18:01:06.000Z

# input() takes input from user with a prompting message, and returns the # user input as a string name = input('Please enter your name: ') print("Hello", name, name, name, name, name)

31

73

0.715054

30

186

4.433333

0.633333

0.240602

0.270677

0.240602

0

0.177419

186

5

74

37.2

0.869281

0.505376

0

0.325843

0

1

0

false

0

0.5

0

null

1

0

1

0

null

0

1

0

5

a5f2ba6667e305a4609d9f5390b09b72d052797c

1,180

py

Python

app/main/__init__.py

yorksdale/digital-marketplace

a37e79a8b8ac64e0e6f9e08803301eccdb18d7bc

[ "MIT" ]

null

app/main/__init__.py

yorksdale/digital-marketplace

a37e79a8b8ac64e0e6f9e08803301eccdb18d7bc

[ "MIT" ]

null

app/main/__init__.py

yorksdale/digital-marketplace

a37e79a8b8ac64e0e6f9e08803301eccdb18d7bc

[ "MIT" ]

null

from flask import Blueprint from dmcontent.content_loader import ContentLoader main = Blueprint('main', __name__) content_loader = ContentLoader('app/content') content_loader.load_manifest('digital-outcomes-and-specialists', 'briefs', 'edit_brief') content_loader.load_manifest('digital-outcomes-and-specialists', 'brief-responses', 'legacy_edit_brief_response') content_loader.load_manifest('digital-outcomes-and-specialists', 'brief-responses', 'edit_brief_response') content_loader.load_manifest('digital-outcomes-and-specialists', 'brief-responses', 'legacy_display_brief_response') content_loader.load_manifest('digital-outcomes-and-specialists', 'brief-responses', 'display_brief_response') content_loader.load_manifest('digital-outcomes-and-specialists-2', 'briefs', 'edit_brief') content_loader.load_manifest('digital-outcomes-and-specialists-2', 'brief-responses', 'edit_brief_response') content_loader.load_manifest('digital-outcomes-and-specialists-2', 'brief-responses', 'display_brief_response') @main.after_request def add_cache_control(response): response.cache_control.no_cache = True return response from .views import briefs from . import errors

43.703704

116

0.817797

147

1,180

6.272109

0.244898

0.140998

0.147505

0.21692

0.72885

0.707158

0.64859

0

0.002705

0.060169

1,180

26

117

45.384615

0.828674

0

0.454237

0.305932

0

1

0.055556

false

0

0.222222

0

0.333333

0.111111

0

null

0

1

0

1

0

1

0

null

0

5

f546bc2bc39dbde3cac4289ef478ad6455ba6672

73

py

Python

src/core.py

dmontemayor/msipipeline

f567fcc11458afe2afdd8932438e23801bb7d9cf

[ "MIT" ]

null

src/core.py

dmontemayor/msipipeline

f567fcc11458afe2afdd8932438e23801bb7d9cf

[ "MIT" ]

null

src/core.py

dmontemayor/msipipeline

f567fcc11458afe2afdd8932438e23801bb7d9cf

[ "MIT" ]

null

"""Core functions""" def noop(): """ noop function does nothing"""

12.166667

37

0.589041

8

73

5.375

0.875

0

0.205479

73

5

38

14.6

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1

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0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

f552142016fefd19dad03ac47af7c1b180307394

112

py

Python

lbworkflow/__init__.py

wearypossum4770/django-lb-workflow

8db36c7a8c5cf3aa2492048cad9fbf26d895c8c7

[ "MIT" ]

194

2017-04-24T15:28:16.000Z

2021-12-29T03:35:28.000Z

lbworkflow/__init__.py

wearypossum4770/django-lb-workflow

8db36c7a8c5cf3aa2492048cad9fbf26d895c8c7

[ "MIT" ]

17

2018-05-31T07:45:42.000Z

2021-12-16T08:55:44.000Z

lbworkflow/__init__.py

wearypossum4770/django-lb-workflow

8db36c7a8c5cf3aa2492048cad9fbf26d895c8c7

[ "MIT" ]

67

2017-05-18T02:28:28.000Z

2022-01-20T02:05:10.000Z

VERSION = (1, 0, 1, "alpha", 0) __version__ = "1.0.1" default_app_config = "lbworkflow.apps.LBWorkflowConfig"

18.666667

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0.696429

16

112

4.5

0.625

0.222222

0.25

0.277778

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0.072165

0.133929

112

5

56

22.4

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0

1

0

null

1

0

1

0

null

0

5

f576096fbbe0cd25ecb154be064adeba1b068cf4

175

py

Python

python/api-examples-source/metric.example3.py

blackary/docs

5e49a42219f0d09676c7f784cdd51cc8155cf8a2

[ "Apache-2.0" ]

12

2021-10-15T20:25:24.000Z

2022-03-05T10:56:55.000Z

docs/api-examples-source/metric.example3.py

linzhou-zhong/streamlit

fde1b548e4bf2d2e5a97b5c3fcf655d43134b342

[ "Apache-2.0" ]

148

2020-10-19T20:16:32.000Z

2022-03-31T03:34:25.000Z

docs/api-examples-source/metric.example3.py

linzhou-zhong/streamlit

fde1b548e4bf2d2e5a97b5c3fcf655d43134b342

[ "Apache-2.0" ]

33

2021-10-29T19:32:53.000Z

2022-03-31T19:43:47.000Z

import streamlit as st st.metric(label="Gas price", value=4, delta=-0.5, delta_color="inverse") st.metric(label="Active developers", value=123, delta=123, delta_color="off")

35

77

0.742857

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175

4.413793

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0.125

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0.05625

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1

0

1

0

5

f57b21e998b7a08f5d69fa2d3715d95eadd72251

97

py

Python

Term2/Session 17/2-request.py

theseana/apondaone

7cbf3572a86c73220329804fee1f3d03842ae902

[ "MIT" ]

null

Term2/Session 17/2-request.py

theseana/apondaone

7cbf3572a86c73220329804fee1f3d03842ae902

[ "MIT" ]

null

Term2/Session 17/2-request.py

theseana/apondaone

7cbf3572a86c73220329804fee1f3d03842ae902

[ "MIT" ]

null

import urllib.request import re contents = urllib.request.urlopen("https://www.nytimes.com/")

13.857143

61

0.752577

13

97

5.615385

0.769231

0.356164

0

0.103093

97

6

62

16.166667

0.83908

0

0.25

0

1

0

false

0

0.666667

0

0.666667

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

1956e07d2827bba4b3075ffba4942bb2182a7309

145

py

Python

collections/top_10_word.py

MailG/code_py

c21a27c871c5c42625aadf45d51a0ba325095739

[ "MIT" ]

null

collections/top_10_word.py

MailG/code_py

c21a27c871c5c42625aadf45d51a0ba325095739

[ "MIT" ]

null

collections/top_10_word.py

MailG/code_py

c21a27c871c5c42625aadf45d51a0ba325095739

[ "MIT" ]

null

import re from collections import Counter words = re.findall('\w+', open('top_10_word.py').read().lower()) print Counter(words).most_common(10)

24.166667

64

0.737931

23

145

4.521739

0.782609

0.230769

0

0.030303

0.089655

145

5

65

29

0.757576

0

0.117241

0

null

0

0.5

null

0.25

1

0

null

1

0

1

0

null

0

1

0

1

0

5

1967a1c2ca340413a16db12efd1a714ab962e4ff

2,652

py

Python

FibonacciSequence.py

Xzya/CodeAbbeySolutions

0a37eb246c24c1d74a6ff6c2ccf525444c5e787a

[ "MIT" ]

2

2021-07-25T13:41:48.000Z

2022-03-02T21:07:39.000Z

FibonacciSequence.py

Xzya/CodeAbbeySolutions

0a37eb246c24c1d74a6ff6c2ccf525444c5e787a

[ "MIT" ]

null

FibonacciSequence.py

Xzya/CodeAbbeySolutions

0a37eb246c24c1d74a6ff6c2ccf525444c5e787a

[ "MIT" ]

5

2015-10-29T16:11:43.000Z

2022-03-13T12:50:32.000Z

#input # 20 # 1777930954809416587147660791784794314784432111526800706093789579403138960940165075820050317562202766948028237512 # 5689768398165682472981133878451278523009637608647762675604795738876774718113916506327804992150205611581315356832469416472592015530113139666919895048261416544213718823613767148595520981851577168 # 1593326225701717188334037111425359127138512324945743711294024460075377172985524819472680355872170395569016093752628516876254232560412670420129021724057156769422272151448461996634558399312613 # 668996615388005031531000081241745415306766517246774551964595292186469 # 2830653773025598082345063352442424920351144475210443140761432888315880232178889808908956305371077582723774166955957876499339886412043139544385164571854387045842521 # 897889194859191704881857622613605161659692872156509128465291624947856903121114331700554055405737435019936805984303748490745 # 5789092068864820527338372482892113982249794889765 # 102334155 # 132980473367242282497284673037549604307310746277363901731233717012104672704818538889393037469151708867132489255106919564710136837304160825061948265664510600390158933 # 167889621328187018603839571160601156165718032465198173590271441192035550962902993642892477664171488276167058117358975773751425901845612997090658 # 11111460156937785151929026842503960837766832936 # 187341518601536966291015050946540312701895836604078191803255601777 # 43566776258854844738105 # 10108265416152526419683994794618270268165872518704428380856874159529924875319101159659894110659650591571332238987107046525767189192225493851401061174799065698263103347 # 40232462861844090389128434238541564732364078131780448061576898306103009199405081373822175980623530127985951663375242165249073319332045062588388761317543568249014325104512245165644739010 # 4953967011875066473162524925231604047727791871346061001150551747313593851366517214899257280600 # 3516470258181436632779942061407967017889567600656021732351687343954813226146279635109807764516348660489138275647928031738863616520008909996193084872671097173635385035177795631888400653504011257 # 941390895042587567453271223806288165311401367715034229502159202 # 1645645409178311156114050175340179094658577397657624573049761120640548215334513341070281 # 139423224561697880139724382870407283950070256587697307264108962948325571622863290691557658876222521294125 def find_fib(fib_numbers, num): while True: a, b = fib_numbers[-2:] c = a + b fib_numbers.append(c) if num == c: break n = int(input()) fib_numbers = [0, 1] for i in range(0, n): num = int(input()) if num not in fib_numbers: find_fib(fib_numbers, num) print(fib_numbers.index(num), "", end="")

64.682927

195

0.924208

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2,652

30.911392

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0.00819

0.013923

0.01638

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0.89232

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2,652

41

196

64.682927

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0

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0

0.071429

0

1

null

0

1

0

1

0

null

1

0

5

19752dfbcfbe693d07bff2073f771ad4d70fcbac

115

py

Python

tests/test_hello_world.py

polkafoundry/brownie-starter-kit

d1d672397ebb5152ecf0ab0485b387b873840d96

[ "MIT" ]

null

tests/test_hello_world.py

polkafoundry/brownie-starter-kit

d1d672397ebb5152ecf0ab0485b387b873840d96

[ "MIT" ]

null

tests/test_hello_world.py

polkafoundry/brownie-starter-kit

d1d672397ebb5152ecf0ab0485b387b873840d96

[ "MIT" ]

null

import pytest def test_call_get(hello_world): message = hello_world.get() assert message == "Hello world"

19.166667

35

0.721739

16

115

4.9375

0.625

0.379747

0.43038

0

0.182609

115

5

36

23

0.840426

0

0.095652

0

0.25

1

0.25

false

0

0.25

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

5

198f29276be3ef07dfeb16f340744978b4905b1e

258

py

Python

gencove/command/upload/exceptions.py

mislavcimpersak/gencove-cli

2ee9204609d4120c013392f892653ebe9f4a8f7e

[ "Apache-2.0" ]

1

2020-04-28T06:31:53.000Z

gencove/command/upload/exceptions.py

mislavcimpersak/gencove-cli

2ee9204609d4120c013392f892653ebe9f4a8f7e

[ "Apache-2.0" ]

null

gencove/command/upload/exceptions.py

mislavcimpersak/gencove-cli

2ee9204609d4120c013392f892653ebe9f4a8f7e

[ "Apache-2.0" ]

1

2021-07-29T08:24:51.000Z

"""Upload command exceptions""" class UploadError(Exception): """Upload related error.""" class UploadNotFound(Exception): """Upload related error.""" class SampleSheetError(Exception): """Error to generate the sample sheet for uploads."""

18.428571

57

0.70155

26

258

6.961538

0.653846

0.165746

0.243094

0.298343

0.353591

0

0.158915

258

13

58

19.846154

0.834101

0.453488

0

1

0

true

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

199082215a42261e3be2cf154a86dc10b7a63dc0

208

py

Python

Algorithm/complement.py

prathamsingh90/Implementation-of-RC5-Algorithm

db51583c8a4fa2de09e54b565dee63324c0cd6f7

[ "MIT" ]

null

Algorithm/complement.py

prathamsingh90/Implementation-of-RC5-Algorithm

db51583c8a4fa2de09e54b565dee63324c0cd6f7

[ "MIT" ]

null

Algorithm/complement.py

prathamsingh90/Implementation-of-RC5-Algorithm

db51583c8a4fa2de09e54b565dee63324c0cd6f7

[ "MIT" ]

3

2017-10-27T05:30:32.000Z

2020-02-16T13:36:26.000Z

w = '10110111111000010101000101100011' q = '10011110001101110111100110111001' a = bin(int(w,2))[2:].zfill(32) b = bin(int(q,2))[2:].zfill(32) print a,b l = bin(int(w,2) + int(q,2))[2:].zfill(32) print l

29.714286

43

0.649038

36

208

3.75

0.361111

0.133333

0.155556

0.2

0.266667

0

0.423077

0.125

208

7

44

29.714286

0.318681

0

0.315271

0

null

0

null

0.285714

0

null

0

1

0

1

0

1

0

1

0

null

0

1

0

5

19a5d2309db5643138dfaac660c737c6beadf39a

86

py

Python

util/website/errors.py

FellowHashbrown/omega-psi-py

4ea33cdbef15ffaa537f2c9e382de508c58093fc

[ "MIT" ]

4

2018-12-23T08:49:40.000Z

2021-03-25T16:51:43.000Z

util/website/errors.py

FellowHashbrown/omega-psi-py

4ea33cdbef15ffaa537f2c9e382de508c58093fc

[ "MIT" ]

23

2020-11-03T17:40:40.000Z

2022-02-01T17:12:59.000Z

util/website/errors.py

FellowHashbrown/omega-psi-py

4ea33cdbef15ffaa537f2c9e382de508c58093fc

[ "MIT" ]

1

2019-07-11T23:40:13.000Z

class InvalidJSONException(Exception): pass class UnmatchedFormatting(Exception): pass

43

0.872093

8

86

9.375

0.625

0.346667

0

0.05814

86

2

44

43

0.925926

0

1

0

true

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

5

271422c86030970ee427acd6638f0cd08c866894

192

py

Python

pdbuddy/formatters/__init__.py

emou/pdbuddy

5708c44803e46d06aca02a0402ebaec0c5ae4634

[ "MIT" ]

null

pdbuddy/formatters/__init__.py

emou/pdbuddy

5708c44803e46d06aca02a0402ebaec0c5ae4634

[ "MIT" ]

null

pdbuddy/formatters/__init__.py

emou/pdbuddy

5708c44803e46d06aca02a0402ebaec0c5ae4634

[ "MIT" ]

null

from __future__ import absolute_import from pdbuddy.formatters.base import BaseFormatter from pdbuddy.formatters.simple import SimpleFormatter __all__ = ['BaseFormatter', 'SimpleFormatter']

27.428571

53

0.84375

20

192

7.65

0.55

0.143791

0.27451

0

0.09375

192

6

54

32

0.87931

0

0.145833

0

1

0

false

0

0.75

0

0.75

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

272bfa76e780d34bece8627e1514bb43eb922bd7

205

py

Python

mysiteapp/posts/urls.py

kondoooooo/mysite

b5dd2b6699da55a086a138a76176c97c95a7940a

[ "MIT" ]

null

mysiteapp/posts/urls.py

kondoooooo/mysite

b5dd2b6699da55a086a138a76176c97c95a7940a

[ "MIT" ]

null

mysiteapp/posts/urls.py

kondoooooo/mysite

b5dd2b6699da55a086a138a76176c97c95a7940a

[ "MIT" ]

null

from django.urls import path from . import views # views.pyのindexを呼び出して、関数の中を実行する # urlpatterns = [url ( r'^$', views.index, name='index' )] urlpatterns = [ path ( '', views.index, name='index' ), ]

20.5

58

0.658537

24

205

5.625

0.541667

0.148148

0.207407

0.281481

0

0.170732

205

9

59

22.777778

0.794118

0.42439

0

0.043478

0

1

0

false

0

0.4

0

0.4

0

1

0

null

0

1

0

1

0

null

0

1

0

5

2740057060731c86901e355e802cfb5f484062ce

227

py

Python

FERNLV/__init__.py

otakbeku/FERNLV

43720d116985bdedbfb8e8b4591c0ca4f04f2054

[ "MIT" ]

1

2019-10-31T06:43:40.000Z

FERNLV/__init__.py

otakbeku/FERNLV

43720d116985bdedbfb8e8b4591c0ca4f04f2054

[ "MIT" ]

null

FERNLV/__init__.py

otakbeku/FERNLV

43720d116985bdedbfb8e8b4591c0ca4f04f2054

[ "MIT" ]

null

from __future__ import absolute_import import FERNLV.Camera as Camera import FERNLV.FaceRecognition as FaceRecognition import FERNLV.EigenUtils as EigenUtils import FERNLV.CountPerSec as CountPerSec import FERNLV.Utils as Utils

37.833333

48

0.876652

30

227

6.466667

0.366667

0.309278

0

0.101322

227

6

49

37.833333

0.95098

0

1

0

true

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

5

27436c101043734edb9ae38fe58ca6327f69dcc3

1,511

py

Python

app/api/limit_api.py

Ananto30/cap-em

c1241225c69d112bfb88ef6f6f8458da90e4b333

[ "MIT" ]

9

2020-06-05T11:10:24.000Z

2022-03-20T13:42:48.000Z

app/api/limit_api.py

Ananto30/cap-em

c1241225c69d112bfb88ef6f6f8458da90e4b333

[ "MIT" ]

27

2020-05-30T17:53:40.000Z

2021-07-21T08:40:24.000Z

app/api/limit_api.py

Ananto30/cap-em

c1241225c69d112bfb88ef6f6f8458da90e4b333

[ "MIT" ]

19

2020-05-31T06:09:01.000Z

2022-03-24T00:12:36.000Z

from flask_restful import reqparse, Resource from app.service.limit_service import LimitService class CheckLimit(Resource): def __init__(self, **kwargs): self.limit_service: LimitService = kwargs['limit_service'] @staticmethod def parse_args(): parser = reqparse.RequestParser() parser.add_argument('resource_name', type=str, required=True, location='json') parser.add_argument('access_id', type=str, required=True, location='json') return parser.parse_args() def post(self): args = self.parse_args() resource_name = args['resource_name'].strip() access_id = args['access_id'].strip() has_limit, access_in = self.limit_service.check_limit(resource_name, access_id) return { 'has_limit': has_limit, 'access_in': access_in } class AddUsage(Resource): def __init__(self, **kwargs): self.limit_service: LimitService = kwargs['limit_service'] @staticmethod def parse_args(): parser = reqparse.RequestParser() parser.add_argument('resource_name', type=str, required=True, location='json') parser.add_argument('access_id', type=str, required=True, location='json') return parser.parse_args() def post(self): args = self.parse_args() resource_name = args['resource_name'].strip() access_id = args['access_id'].strip() self.limit_service.add_usage(resource_name, access_id) return

29.627451

87

0.661813

177

1,511

5.367232

0.225989

0.101053

0.067368

0.08

0.781053

0.726316

0

0.221707

1,511

50

88

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false

0

0.057143

0

0.4

0

null

0

1

0

null

0

5

27c855390cf7620f00cdc5d25d9fef814764cbee

22,231

py

Python

mastic/tests/test_selection.py

ADicksonLab/mastic

58749c40fe364110e3e7be8aa79a89f32d956d09

[ "MIT" ]

5

2018-01-28T19:53:16.000Z

2021-02-21T01:53:08.000Z

mastic/tests/test_selection.py

salotz/mast

58749c40fe364110e3e7be8aa79a89f32d956d09

[ "MIT" ]

null

mastic/tests/test_selection.py

salotz/mast

58749c40fe364110e3e7be8aa79a89f32d956d09

[ "MIT" ]

1

2021-06-04T05:07:10.000Z

import doctest import unittest import numpy as np import numpy.testing as npt from mast import selection import mast.selection as mastsel class TestSelectionMember(unittest.TestCase): def setUp(self): self.member = 'a' self.selection_member = mastsel.SelectionMember(self.member) def tearDown(self): pass def test_constructor(self): pass def test_member(self): self.assertEqual(self.selection_member.member, self.member) def test_unselected_registry(self): self.assertEqual(self.selection_member.registry, []) def test_repr(self): pass def test_get_selections(self): sel0 = mastsel.Selection([self.selection_member], [0]) sel1 = mastsel.Selection([self.selection_member], [0], flags=['other_selection']) sel2 = mastsel.IndexedSelection([self.selection_member], [0]) meta_sel = mastsel.Selection([sel0, sel1, sel2], [0], flags=['meta-selection']) meta_meta_sel = mastsel.Selection([meta_sel], [0], flags=['meta-meta-selection']) sel_list = mastsel.SelectionsList([sel0, sel1, sel2], flags=['list-selection']) meta_sel_list = mastsel.Selection(sel_list, [0], flags=['meta-list-selection']) self.selection_member.get_selections(flags=['other_selection'], level=0) # level 0 self.assertIn(sel0, self.selection_member.get_selections(level=0)) self.assertIn(sel1, self.selection_member.get_selections(level=0)) self.assertIn(sel2, self.selection_member.get_selections(level=0)) self.assertNotIn(meta_sel, self.selection_member.get_selections(level=0)) self.assertNotIn(meta_meta_sel, self.selection_member.get_selections(level=0)) self.assertNotIn(sel_list, self.selection_member.get_selections(level=0)) self.assertNotIn(meta_sel_list, self.selection_member.get_selections(level=0)) # level 1 self.assertIn(sel0, self.selection_member.get_selections(level=1)) self.assertIn(sel1, self.selection_member.get_selections(level=1)) self.assertIn(sel2, self.selection_member.get_selections(level=1)) self.assertIn(meta_sel, self.selection_member.get_selections(level=1)) self.assertNotIn(meta_meta_sel, self.selection_member.get_selections(level=1)) self.assertIn(sel_list, self.selection_member.get_selections(level=1)) self.assertIn(meta_sel_list, self.selection_member.get_selections(level=1)) # level 2 self.assertIn(sel0, self.selection_member.get_selections(level=2)) self.assertIn(sel1, self.selection_member.get_selections(level=2)) self.assertIn(sel2, self.selection_member.get_selections(level=2)) self.assertIn(meta_sel, self.selection_member.get_selections(level=2)) self.assertIn(meta_meta_sel, self.selection_member.get_selections(level=2)) self.assertIn(sel_list, self.selection_member.get_selections(level=2)) self.assertIn(meta_sel_list, self.selection_member.get_selections(level=2)) # recursive # explicit syntax self.assertIn(sel0, self.selection_member.get_selections(level=None)) self.assertIn(sel1, self.selection_member.get_selections(level=None)) self.assertIn(sel2, self.selection_member.get_selections(level=None)) self.assertIn(meta_sel, self.selection_member.get_selections(level=None)) self.assertIn(meta_meta_sel, self.selection_member.get_selections(level=None)) self.assertIn(sel_list, self.selection_member.get_selections(level=None)) self.assertIn(meta_sel_list, self.selection_member.get_selections(level=None)) # implicit syntax self.assertIn(sel0, self.selection_member.get_selections()) self.assertIn(sel1, self.selection_member.get_selections()) self.assertIn(sel2, self.selection_member.get_selections()) self.assertIn(meta_sel, self.selection_member.get_selections()) self.assertIn(meta_meta_sel, self.selection_member.get_selections()) self.assertIn(sel_list, self.selection_member.get_selections()) self.assertIn(meta_sel_list, self.selection_member.get_selections()) # with selection criteria # level 0 only Selections self.assertIn(sel0, self.selection_member.get_selections( selection_types=[mastsel.Selection], level=0)) self.assertIn(sel1, self.selection_member.get_selections( selection_types=[mastsel.Selection], level=0)) self.assertNotIn(sel2, self.selection_member.get_selections( selection_types=[mastsel.Selection], level=0)) self.assertNotIn(meta_sel, self.selection_member.get_selections( selection_types=[mastsel.Selection], level=0)) self.assertNotIn(meta_meta_sel, self.selection_member.get_selections( selection_types=[mastsel.Selection], level=0)) self.assertNotIn(sel_list, self.selection_member.get_selections( selection_types=[mastsel.Selection], level=0)) self.assertNotIn(meta_sel_list, self.selection_member.get_selections( selection_types=[mastsel.Selection], level=0)) # level 0 only IndexedSelection self.assertNotIn(sel0, self.selection_member.get_selections( selection_types=[mastsel.IndexedSelection], level=0)) self.assertNotIn(sel1, self.selection_member.get_selections( selection_types=[mastsel.IndexedSelection], level=0)) self.assertIn(sel2, self.selection_member.get_selections( selection_types=[mastsel.IndexedSelection], level=0)) self.assertNotIn(meta_sel, self.selection_member.get_selections( selection_types=[mastsel.IndexedSelection], level=0)) self.assertNotIn(meta_meta_sel, self.selection_member.get_selections( selection_types=[mastsel.IndexedSelection], level=0)) self.assertNotIn(sel_list, self.selection_member.get_selections( selection_types=[mastsel.IndexedSelection], level=0)) self.assertNotIn(meta_sel_list, self.selection_member.get_selections( selection_types=[mastsel.IndexedSelection], level=0)) # level 0 only 'other_selection' flag # Don't know why this fails # self.assertNotIn(sel0, # self.selection_member.get_selections( # flags=['other_selection'], # level=0)) self.assertIn(sel1, self.selection_member.get_selections( flags=['other_selection'], level=0)) self.assertNotIn(sel2, self.selection_member.get_selections( flags=['other_selection'], level=0)) self.assertNotIn(meta_sel, self.selection_member.get_selections( flags=['other_selection'], level=0)) self.assertNotIn(meta_meta_sel, self.selection_member.get_selections( flags=['other_selection'], level=0)) self.assertNotIn(sel_list, self.selection_member.get_selections( flags=['other_selection'], level=0)) self.assertNotIn(meta_sel_list, self.selection_member.get_selections( flags=['other_selection'], level=0)) # recursive only Selections self.assertIn(sel0, self.selection_member.get_selections( selection_types=[mastsel.Selection], level=None)) self.assertIn(sel1, self.selection_member.get_selections( selection_types=[mastsel.Selection], level=None)) self.assertNotIn(sel2, self.selection_member.get_selections( selection_types=[mastsel.Selection], level=None)) self.assertIn(meta_sel, self.selection_member.get_selections( selection_types=[mastsel.Selection], level=None)) self.assertIn(meta_meta_sel, self.selection_member.get_selections( selection_types=[mastsel.Selection], level=None)) self.assertNotIn(sel_list, self.selection_member.get_selections( selection_types=[mastsel.Selection], level=None)) self.assertIn(meta_sel_list, self.selection_member.get_selections( selection_types=[mastsel.Selection], level=None)) # recursive only SelectionsList self.assertNotIn(sel0, self.selection_member.get_selections( selection_types=[mastsel.SelectionsList], level=None)) self.assertNotIn(sel1, self.selection_member.get_selections( selection_types=[mastsel.SelectionsList], level=None)) self.assertNotIn(sel2, self.selection_member.get_selections( selection_types=[mastsel.SelectionsList], level=None)) self.assertNotIn(meta_sel, self.selection_member.get_selections( selection_types=[mastsel.SelectionsList], level=None)) self.assertNotIn(meta_meta_sel, self.selection_member.get_selections( selection_types=[mastsel.SelectionsList], level=None)) self.assertIn(sel_list, self.selection_member.get_selections( selection_types=[mastsel.SelectionsList], level=None)) self.assertNotIn(meta_sel_list, self.selection_member.get_selections( selection_types=[mastsel.SelectionsList], level=None)) # recursive only 'list-selection' flag self.assertIn(sel0, self.selection_member.get_selections( flags=['list-selection'], level=None)) self.assertIn(sel1, self.selection_member.get_selections( flags=['list-selection'], level=None)) self.assertIn(sel2, self.selection_member.get_selections( flags=['list-selection'], level=None)) self.assertNotIn(meta_sel, self.selection_member.get_selections( flags=['list-selection'], level=None)) self.assertNotIn(meta_meta_sel, self.selection_member.get_selections( flags=['list-selection'], level=None)) self.assertIn(sel_list, self.selection_member.get_selections( flags=['list-selection'], level=None)) self.assertNotIn(meta_sel_list, self.selection_member.get_selections( flags=['list-selection'], level=None)) # recursive only 'meta-list-selection' flag self.assertIn(sel0, self.selection_member.get_selections( flags=['meta-list-selection'], level=None)) self.assertNotIn(sel1, self.selection_member.get_selections( flags=['meta-list-selection'], level=None)) self.assertNotIn(sel2, self.selection_member.get_selections( flags=['meta-list-selection'], level=None)) self.assertNotIn(meta_sel, self.selection_member.get_selections( flags=['meta-list-selection'], level=None)) self.assertNotIn(meta_meta_sel, self.selection_member.get_selections( flags=['meta-list-selection'], level=None)) self.asserttIn(sel_list, self.selection_member.get_selections( flags=['meta-list-selection'], level=None)) self.assertIn(meta_sel_list, self.selection_member.get_selections( flags=['meta-list-selection'], level=None)) def test_register_selection(self): pass class TestGenericSelection(unittest.TestCase): def setUp(self): self.member = 'a' self.selection_member = mastsel.SelectionMember(self.member) self.container = [self.selection_member] self.generic_selection = mastsel.GenericSelection(self.container) def tearDown(self): pass def test_constructor(self): with self.assertRaises(AssertionError): mastsel.GenericSelection(['a']) mastsel.GenericSelection([]) class TestSelection(unittest.TestCase): def setUp(self): self.members = ['a', 'b', 'c'] self.selection_members = [mastsel.SelectionMember(sel) for sel in self.members] self.sel_idxs = [0,2] self.selection = mastsel.Selection(self.selection_members, self.sel_idxs) def tearDown(self): pass def test_constructor(self): with self.assertRaises(AssertionError): mastsel.Selection(self.selection_members, 'a') mastsel.Selection(self.selection_members, ['a','b']) mastsel.Selection(self.selection_members, -1) mastsel.Selection(self.selection_members, [-1,-2]) mastsel.Selection(self.selection_members, [-1,2]) mastsel.Selection(self.selection_members, []) mastsel.Selection(self.members, [1]) def test_getitem(self): # the second element is the third from members self.assertEqual(self.selection[1], self.selection_members[self.sel_idxs[1]]) def test_selection_member_self_retrieval(self): for sel_memb in self.selection_members: for key, selection in sel_memb.registry: self.assertEqual(selection[key], sel_memb) class TestChainedSelection(unittest.TestCase): # set up Selection -0-> [Selection -0-> [SelectionMember]] def setUp(self): self.members = ['a', 'b', 'c'] self.selection_members = [mastsel.SelectionMember(sel) for sel in self.members] self.selection = mastsel.Selection(self.selection_members, [0]) self.selection_container = [self.selection] self.meta_selection = mastsel.Selection(self.selection_container, [0]) def test_chained_registry_assignment(self): # the first level is in the recursive get self.assertIn(self.selection, self.selection_members[0].get_selections()) # the first level is in the level=0 get self.assertIn(self.selection, self.selection_members[0].get_selections(level=0)) # specifying too many levels is ignored silently self.assertIn(self.selection, self.selection_members[0].get_selections(level=3)) # the second level selection is in the recursive get self.assertIn(self.meta_selection, self.selection_members[0].get_selections()) # the second level selection is not in the level=0 get self.assertNotIn(self.meta_selection, self.selection_members[0].get_selections(level=0)) # the second level selection is in the level=1 get self.assertIn(self.meta_selection, self.selection_members[0].get_selections(level=1)) for unselected_member in self.selection_members[1:]: # neither selection is in any other SelectionMember self.assertEqual(unselected_member.get_selections(), []) class TestIndexedSelection(unittest.TestCase): def setUp(self): self.members = ['a', 'b', 'c'] self.selection_members = [mastsel.SelectionMember(sel) for sel in self.members] self.selection_idxs = [0, 2] self.idx_selection = mastsel.IndexedSelection(self.selection_members, self.selection_idxs) def tearDown(self): pass def test_getitem(self): for idx in self.selection_idxs: self.assertEqual(self.idx_selection[idx], self.selection_members[idx]) def test_selection_member_self_retrieval(self): for sel_memb in self.selection_members: for key, selection in sel_memb.registry: self.assertEqual(selection[key], sel_memb) class TestCoordArray(unittest.TestCase): def setUp(self): self.array = np.array([[0,0,0], [1,1,1], [2,2,2]]) self.coords = mastsel.CoordArray(self.array) self.new_coord = np.array([3,3,3]) def test_add_coord(self): target_array = np.array([[0,0,0], [1,1,1], [2,2,2], [3,3,3]]) self.assertEqual(self.coords.add_coord(self.new_coord), 3) npt.assert_equal(self.coords.coords, target_array) with self.assertRaises(AssertionError): self.coords.add_coord(np.array([4,4,4,4])) self.coords.add_coord(np.array([2,2])) self.coords.add_coord(np.array([])) self.coords.add_coord([]) self.coords.add_coord({'a', 1}) def test_coord_setter(self): with self.assertRaises(AssertionError): self.coords.add_coord([]) self.coords.add_coord({'a', 1}) class TestCoordArraySelection(unittest.TestCase): def setUp(self): self.array = np.array([[0,0,0], [1,1,1], [2,2,2]]) self.coords = mastsel.CoordArray(self.array) self.sel_idxs = [0,2] self.coord_selection = mastsel.CoordArraySelection(self.coords, self.sel_idxs) def tearDown(self): pass def test_constructor(self): with self.assertRaises(AssertionError): mastsel.CoordArraySelection(self.coords, 'a') mastsel.CoordArraySelection(self.coords, ['a','b']) mastsel.CoordArraySelection(self.coords, -1) mastsel.CoordArraySelection(self.coords, [-1,-2]) mastsel.CoordArraySelection(self.coords, [-1,2]) mastsel.CoordArraySelection(self.coords, []) mastsel.CoordArraySelection({}, [1]) def test_getitem(self): for i, idx in enumerate(self.sel_idxs): npt.assert_equal(self.coord_selection.container[idx], self.array[idx]) npt.assert_equal(self.coord_selection.data[i], self.array[idx]) npt.assert_equal(self.coord_selection[i], self.array[idx]) def test_coords(self): target_coords = np.array([[0,0,0], [2,2,2]]) npt.assert_equal(target_coords, self.coord_selection.coords) class TestPoint(unittest.TestCase): def setUp(self): self.point1_coord = np.array([0,1,0]) self.point1 = mastsel.Point(self.point1_coord) self.array = np.array([[0,0,0], [1,1,1], [2,2,2]]) self.coord_array = mastsel.CoordArray(self.array) self.point2_coord = self.coord_array[0] self.point2 = mastsel.Point(self.point2_coord) self.point3_coord = np.array([0,1,0]) self.point3 = mastsel.Point(self.point3_coord) self.bad_point_2d = mastsel.CoordArray(np.array([0,1])) self.bad_point_4d = mastsel.CoordArray(np.array([0,1,2,3])) def tearDown(self): pass def test_constructor(self): with self.assertRaises(AssertionError): # wrong dimension points mastsel.Point(self.bad_point_2d) mastsel.Point(self.bad_point_4d) # from existing CoordArray mastsel.Point(coord_array=np.array([1,2,3])) mastsel.Point(coord_array=self.coord_array, array_idx=3) mastsel.Point(coord_array=self.coord_array, array_idx='b') def test_overlaps(self): self.assertFalse(self.point1.overlaps(self.point2)) self.assertTrue(self.point1.overlaps(self.point3)) with self.assertRaises(AssertionError): self.point1.overlaps(np.array([0,1,0])) self.point1.overlaps([0,1,0]) class TestSelectionType(unittest.TestCase): def setUp(self): pass def tearDown(self): pass class TestSelectionTypeLibrary(unittest.TestCase): def setUp(self): pass def tearDown(self): pass if __name__ == "__main__": from mast import selection # doctests print("\n\n\n Doc Tests\n-----------") nfail, ntests = doctest.testmod(selection, verbose=True) # unit tests print("\n\n\n Unit Tests\n-----------") unittest.main()

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null

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1

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1

0

null

0

5

27ea6de006bf921090c95a029566bffc31fafebf

46

py

Python

Bots/Consulado/check_consulado.py

victorathanasio/Personal-projects

94c870179cec32aa733a612a6faeb047df16d977

[ "MIT" ]

null

Bots/Consulado/check_consulado.py

victorathanasio/Personal-projects

94c870179cec32aa733a612a6faeb047df16d977

[ "MIT" ]

null

Bots/Consulado/check_consulado.py

victorathanasio/Personal-projects

94c870179cec32aa733a612a6faeb047df16d977

[ "MIT" ]

null

from program import * check_mudanca()

7.666667

22

0.652174

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46

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5

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null

0

1

0

1

0

5

fd934d2d052a596fdd403c25bf5821122aaf9d15

107

py

Python

befunge/__init__.py

malloc47/befunge.py

6bf3f5f667200c1d4f4a389c3c1e82780743db2c

[ "FSFAP" ]

1

2015-09-23T20:43:44.000Z

befunge/__init__.py

malloc47/befunge.py

6bf3f5f667200c1d4f4a389c3c1e82780743db2c

[ "FSFAP" ]

null

befunge/__init__.py

malloc47/befunge.py

6bf3f5f667200c1d4f4a389c3c1e82780743db2c

[ "FSFAP" ]

null

from befunge.interpreter import run from befunge.state import State from befunge.board import BefungeBoard

26.75

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0.859813

15

107

6.133333

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0.358696

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3

39

35.666667

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true

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null

0

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0

1

0

5

fd97c9a805ec0596db35cfc8b5a0a326745a6936

65

py

Python

_src/om2pyItem/scaffold/templates/__init__.py

s32n/OMOOC2py

33441de0b24cab1615b1a6cd7c83a7f769f781a3

[ "MIT" ]

95

2015-10-06T15:01:04.000Z

2017-04-12T09:37:35.000Z

_src/om2pyItem/scaffold/templates/__init__.py

s32n/OMOOC2py

33441de0b24cab1615b1a6cd7c83a7f769f781a3

[ "MIT" ]

117

2015-10-05T13:11:47.000Z

2017-01-21T13:04:18.000Z

_src/om2pyItem/scaffold/templates/__init__.py

s32n/OMOOC2py

33441de0b24cab1615b1a6cd7c83a7f769f781a3

[ "MIT" ]

180

2015-10-06T01:39:31.000Z

2017-04-28T03:52:21.000Z

# -*- coding: utf-8 -*- import sys #sys.path.append("..")

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5

8bf41a821d3d6672d1869a02aee3f0849fb8a2eb

150

py

Python

sm4.py

Preeti-Barua/Python

76d168617f2a92fa41d6af4ddf62450b6272ff84

[ "bzip2-1.0.6" ]

null

sm4.py

Preeti-Barua/Python

76d168617f2a92fa41d6af4ddf62450b6272ff84

[ "bzip2-1.0.6" ]

null

sm4.py

Preeti-Barua/Python

76d168617f2a92fa41d6af4ddf62450b6272ff84

[ "bzip2-1.0.6" ]

null

a="qwerty" print (a[ 0:3]) print(a[::2]) print(a[2:4]) print(a[-5:-1]) print(a[-1:-4]) print(a[::]) print(a[-6:-1]) print(a[-1:-4]) print(a[-6:-1])

11.538462

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0.093333

150

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null

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5

e31b086b5cbf63ffbf97c18392a223a4cda4b722

28

py

Python

test_ukz/test_midi/__init__.py

clauderichard/Ultrakazoid

619f1afd1fd55afb06e7d27b2bc30eee9929f660

[ "MIT" ]

null

test_ukz/test_midi/__init__.py

clauderichard/Ultrakazoid

619f1afd1fd55afb06e7d27b2bc30eee9929f660

[ "MIT" ]

null

test_ukz/test_midi/__init__.py

clauderichard/Ultrakazoid

619f1afd1fd55afb06e7d27b2bc30eee9929f660

[ "MIT" ]

null

from .test_byteutil import *

28

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4

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5.5

1

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1

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0.88

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null

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null

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1

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1

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5

e348debcffa1c942dd6bf4279358fb7bd92ed526

81

py

Python

plots/__init__.py

kuntzer/binfind

28f9cf9474e6b39a55a1a22d19ca8131a0408c84

[ "MIT" ]

null

plots/__init__.py

kuntzer/binfind

28f9cf9474e6b39a55a1a22d19ca8131a0408c84

[ "MIT" ]

null

plots/__init__.py

kuntzer/binfind

28f9cf9474e6b39a55a1a22d19ca8131a0408c84

[ "MIT" ]

null

from roc import roc from hist import hist from bar import errorbar import figures

20.25

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1

0

1

0

1

0

5

e369c8fc4ff6d108f079d26d2e3b496add4ad379

110

py

Python

novel.py

ILS-Z399/03-branching-plot-novel

a6d06cfc1d7281dcc0a95a8d81db40ad924c87e6

[ "MIT" ]

null

novel.py

ILS-Z399/03-branching-plot-novel

a6d06cfc1d7281dcc0a95a8d81db40ad924c87e6

[ "MIT" ]

null

novel.py

ILS-Z399/03-branching-plot-novel

a6d06cfc1d7281dcc0a95a8d81db40ad924c87e6

[ "MIT" ]

17

2017-09-13T13:48:02.000Z

2018-02-10T22:23:41.000Z

#!/usr/bin/python3 import sys assert sys.version_info >= (3,4), 'This script requires at least Python 3.4'

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8b8a9f73473e5b53cf76e9e1060787d907407343

386

py

Python

sgi/tests.py

jorgevilaca82/SGI

c3f13d9e3e8f04377d9e23636dc8e35ed5ace35a

[ "MIT" ]

null

sgi/tests.py

jorgevilaca82/SGI

c3f13d9e3e8f04377d9e23636dc8e35ed5ace35a

[ "MIT" ]

8

2019-12-07T13:13:34.000Z

2021-09-02T03:07:25.000Z

sgi/tests.py

jorgevilaca82/SGI

c3f13d9e3e8f04377d9e23636dc8e35ed5ace35a

[ "MIT" ]

null

from django.contrib.staticfiles import finders from django.contrib.staticfiles.storage import staticfiles_storage from django.test import TestCase class StaticFilesTest(TestCase): pass # ainda não funciona # def test_static_exists_at_desired_location(self): # absolute_path = finders.find('img/sapo.jpg') # assert staticfiles_storage.exists(absolute_path)

32.166667

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386

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null

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1

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1

0

5

8b8f33796a0e21b97dd343c4abaee9ae41e08818

88

py

Python

leveltwo/maze/hexagonal/__init__.py

LilianBoulard/LevelTwo

23013a53100875d77dfae99494d2ef415d12b0df

[ "MIT" ]

1

2021-05-03T08:21:36.000Z

leveltwo/maze/hexagonal/__init__.py

LilianBoulard/LevelTwo

23013a53100875d77dfae99494d2ef415d12b0df

[ "MIT" ]

2

2021-05-06T08:37:10.000Z

2021-05-06T14:08:46.000Z

leveltwo/maze/hexagonal/__init__.py

LilianBoulard/LevelTwo

23013a53100875d77dfae99494d2ef415d12b0df

[ "MIT" ]

null

from .editable import MazeEditableHexagonal from .playable import MazePlayableHexagonal

29.333333

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true

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null

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1

0

1

0

1

0

5

8bc0cd7d4b260a90c5719b1569bee010b0962496

1,073

py

Python

optimization/__init__.py

fberanizo/sin5006

96f7980b5ff61bd4af7852c9d733521edde540eb

[ "BSD-2-Clause" ]

null

optimization/__init__.py

fberanizo/sin5006

96f7980b5ff61bd4af7852c9d733521edde540eb

[ "BSD-2-Clause" ]

null

optimization/__init__.py

fberanizo/sin5006

96f7980b5ff61bd4af7852c9d733521edde540eb

[ "BSD-2-Clause" ]

null

from fitness_evaluator import RastriginFloatFitnessEvaluator from fitness_evaluator import RastriginBinaryFitnessEvaluator from fitness_evaluator import XSquareFloatFitnessEvaluator from fitness_evaluator import XSquareBinaryFitnessEvaluator from fitness_evaluator import XAbsoluteSquareFloatFitnessEvaluator from fitness_evaluator import XAbsoluteSquareBinaryFitnessEvaluator from fitness_evaluator import SineXSquareRootFloatFitnessEvaluator from fitness_evaluator import SineXSquareRootBinaryFitnessEvaluator from individual import Individual from individual_factory import RastriginFloatIndividualFactory from individual_factory import RastriginBinaryIndividualFactory from individual_factory import XSquareFloatIndividualFactory from individual_factory import XSquareBinaryIndividualFactory from individual_factory import XAbsoluteSquareFloatIndividualFactory from individual_factory import XAbsoluteSquareBinaryIndividualFactory from individual_factory import SineXSquareRootFloatIndividualFactory from individual_factory import SineXSquareRootBinaryIndividualFactory

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null

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1

0

5

4734d9cae5077679d95f3ef88b1e620f5c8e8cc7

87

py

Python

test/e2e/test_zip_download.py

ImageMarkup/isic-archive

7cd8097886d685ec629e2fcba079271fb77d028f

[ "Apache-2.0" ]

42

2015-12-12T14:05:46.000Z

2022-03-26T15:20:39.000Z

test/e2e/test_zip_download.py

ImageMarkup/isic-archive

7cd8097886d685ec629e2fcba079271fb77d028f

[ "Apache-2.0" ]

494

2015-07-09T16:14:12.000Z

2021-03-09T09:37:36.000Z

test/e2e/test_zip_download.py

ImageMarkup/uda

d221af3368baf3a06ecab67e69e9d0077426c8f9

[ "Apache-2.0" ]

12

2015-08-20T14:20:48.000Z

2020-10-20T01:14:44.000Z

import pytest @pytest.mark.skip('not implemented') def test_zip_download(): pass

12.428571

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12

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null

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null

0

1

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5

476f65b37cd34f246843f1436d04317a627eb417

171

py

Python

deeplearning_examples/loaders/__init__.py

dileep-kishore/deeplearning-examples

2b230ea17f366f602044d44cc8abcac419d4e521

[ "MIT" ]

null

deeplearning_examples/loaders/__init__.py

dileep-kishore/deeplearning-examples

2b230ea17f366f602044d44cc8abcac419d4e521

[ "MIT" ]

321

2017-11-23T20:37:03.000Z

2020-12-28T13:06:15.000Z

deeplearning_examples/loaders/__init__.py

dileep-kishore/deeplearning-examples

2b230ea17f366f602044d44cc8abcac419d4e521

[ "MIT" ]

null

import os BASEPATH = os.path.dirname(__file__).rsplit('/', 1)[0] DATAPATH = os.path.join(BASEPATH, 'data') def datapath(): return DATAPATH from .Churn import Churn

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1

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null

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1

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5

477c1e50a3dc10f1991cbbf405515e65a248a4f2

65

py

Python

dreamerv2/__init__.py

magamba/dreamerv2

ad7d54e256a03378bfd483e8a4c389ab2b444078

[ "MIT" ]

97

2021-07-08T07:05:22.000Z

2022-03-29T11:47:49.000Z

dreamerv2/__init__.py

magamba/dreamerv2

ad7d54e256a03378bfd483e8a4c389ab2b444078

[ "MIT" ]

2

2021-09-01T09:37:07.000Z

2022-01-28T15:59:54.000Z

dreamerv2/__init__.py

magamba/dreamerv2

ad7d54e256a03378bfd483e8a4c389ab2b444078

[ "MIT" ]

14

2021-07-08T07:51:47.000Z

2022-03-30T14:58:54.000Z

from . import models from . import training from . import utils

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5

9a1accad66cc8ca337b5c3a6e20937009406707a

87,803

py

Python

postproc/visualise/plotter.py

J-Massey/postproc

4552b0ad79072f5d217cf62632c08617ea3d2d82

[ "MIT" ]

null

postproc/visualise/plotter.py

J-Massey/postproc

4552b0ad79072f5d217cf62632c08617ea3d2d82

[ "MIT" ]

null

postproc/visualise/plotter.py

J-Massey/postproc

4552b0ad79072f5d217cf62632c08617ea3d2d82

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ @author: B. Font Garcia @description: Functions to plot 2D colormaps and CL-torch graphs. @contact: b.fontgarcia@soton.ac.uk """ # Imports import numpy as np import pandas as pd import matplotlib as mpl import matplotlib.pyplot as plt import matplotlib.colors as mpl_colors import matplotlib.patches as patches import matplotlib.colorbar as colorbar from matplotlib.lines import Line2D import matplotlib.animation as animation from mpl_toolkits.axes_grid1 import make_axes_locatable import seaborn as sns colors = sns.color_palette("husl", 14) plt.rc('text', usetex=True) plt.rc('font', family='sans-serif', size=16) # use 13(JFM) or 16(SNH) mpl.rc('xtick', labelsize=16) mpl.rc('ytick', labelsize=16) mpl.rcParams['text.latex.preamble'] = r'\usepackage{amsmath}' # for \text command plt.rcParams['animation.ffmpeg_path'] = r"/usr/bin/ffmpeg" mpl.rcParams['axes.linewidth'] = 0.5 plt.switch_backend('AGG') # png # plt.switch_backend('PS') # plt.switch_backend('PDF') # pdf # plt.switch_backend('TkAgg') # GUI # colors = ['black', 'orange', 'cyan', 'green', 'blue', 'red', 'magenta', 'yellow'] # colors = ['orange', 'cyan', 'green', 'blue', 'red', 'magenta', 'yellow'] markers = ['|', 's', '^', 'v', 'x', 'o', '*'] # markers = ['s', '^', 'v', 'x', 'o', '*'] # Functions # ------------------------------------------------------ def plot_history(f, t, label, file, title, **kwargs): """ Plot the history for the parameters printed to fort.9 :param f: Force [numpy 1D array] :param t: Time [numpy 1D array] :param label: Y label :param file: output fn name [string] :param title: graph tit :param kwargs: Select which additional information you want to include in the plot: 'St', 'CL_rms', 'CD_rms', 'n_periods', passing the corresponding values. E.g. 'St=0.2'. :return: - """ ax = plt.gca() fig = plt.gcf() # Show lines plt.plot(t, f, color='red', lw=1, label=r'$3\mathrm{length_scale}\,\, \mathrm{total}$') # Set limits ax.set_xlim(min(t), max(t)) ax.set_ylim(1.3 * min(f), 1.3 * max(f)) # Edit frame, labels and legend ax.axhline(linewidth=1) ax.axvline(linewidth=1) plt.xlabel(r'$torch/length_scale$') plt.ylabel(label) plt.title(title) # leg = plt.legend(loc='upper right') # leg.get_frame().set_edgecolor('black') # Annotations for key, value in kwargs.items(): if key == 'St': St_str = '{:.2f}'.format(value) my_str = r'$S_t=' + St_str + '$' plt.text(x=1.02 * max(t), y=1.4 * max(f), s=my_str, color='black') if key == 'CL_rms': CL_rms_str = '{:.2f}'.format(value) my_str = r'$\overline{C}_L=' + CL_rms_str + '$' plt.text(x=1.02 * max(t), y=1.2 * max(f), s=my_str, color='black') if key == 'CD_rms': CD_rms_str = '{:.2f}'.format(value) my_str = r'$\overline{C}_D=' + CD_rms_str + '$' plt.text(x=1.02 * max(t), y=1.0 * max(f), s=my_str, color='black') if key == 'n_periods': n_periods = str(value) my_str = r'$\textrm{periods}=' + n_periods + '$' plt.text(x=1.02 * max(t), y=0.8 * max(f), s=my_str, color='black') # Show plot and save figure plt.savefig(file, transparent=False, bbox_inches='tight') return def fully_defined_plot(x, y, file, x_label, y_label, title=None, colour='black', colours=None, l_label=None, marker=None, xlim=None, ylim=None): plt.style.use(['science', 'grid']) fig, ax = plt.subplots(figsize=(7, 5)) ax.set_title(title) ax.tick_params(bottom="on", top="on", right="on", which='both', direction='in', length=2) # Edit frame, labels and legend ax.set_xlabel(x_label) ax.set_ylabel(y_label) if xlim is not None: ax.set_xlim(xlim) if ylim is not None: ax.set_ylim(ylim) # Make legend manually if l_label and colours is not None: from matplotlib.lines import Line2D legend_elements = [] for idx, loop in enumerate(l_label): legend_elements.append(Line2D([0], [0], color=colours[idx], lw=4, label=loop)) ax.legend(handles=legend_elements, loc='lower right') ax.plot(x, y, color=colour, marker=marker) plt.savefig(file, bbox_inches='tight', transparent=False) return def domain_test_plot(means, var, file, y_label, title=None, doms=None): plt.style.use(['science', 'grid']) fig, ax = plt.subplots(figsize=(10, 3)) ax.set_title(title) n = np.arange(0, len(doms), 1) ax.tick_params(bottom="on", top="on", right="on", which='both', direction='in', length=2) # Edit frame, labels and legend ax.set_ylabel(y_label) ax.set_xlabel("Domain size") ax.set_xticks(n) ax.set_xticklabels(doms) ax.fill_between(n, 0.95 * means[-1], 1.05 * means[-1], alpha=0.5, color='C1') # Make legend manually # if l_label and colours is not None: from matplotlib.lines import Line2D legend_elements = [(Line2D([0], [0], color='C1', alpha=0.5, lw=4, label='$\pm 5 \% $ confidence interval'))] ax.legend(handles=legend_elements, loc='lower right') ax.scatter(n, means, color='k', marker='*') ax.errorbar(n, means, yerr=var, capsize=12) for idx, lab in enumerate(doms): ax.annotate(f'$\sigma^2 = {var[idx]:.3e}$', (n[idx], means[idx])) plt.savefig(file, bbox_inches='tight', transparent=False) return def plot_2D(u, file='test.pdf', **kwargs): """ Return nothing and saves the figure in the specified fn name. Args: cmap: matplotlib cmap. Eg: cmap = "seismic" lvls: number of levels of the contour. Eg: lvls = 100 lim: min and max values of the contour passed as array. Eg: lim = [-0.5, 0.5] file: Name of the fn to save the plot (recommended .pdf so it can be converted get .svg). Eg: fn = "dUdy.pdf" Kwargs: x=[xmin,xmax] is the x axis minimum and maximum specified Y=[ymin,ymax] is the Y axis minimum and maximum specified annotate: Boolean if annotations for min and max values of the field (and locations) are desired """ from matplotlib.ticker import FormatStrFormatter, MultipleLocator, FuncFormatter contour = kwargs.get('contour', False) contourf = kwargs.get('contourf', True) levels = kwargs.get('levels', 50) lim = kwargs.get('lim', [np.min(u), np.max(u)]) cmap = kwargs.get('cmap', 'Blues') scaling = kwargs.get('scaling', 1) shift = kwargs.get('shift', (0, 0)) xwindow = kwargs.get('xwindow', None) ywindow = kwargs.get('ywindow', None) title = kwargs.get('tit', '') n_ticks = kwargs.get('n_ticks', 10) n_decimals = kwargs.get('n_decimals', 2) case = kwargs.get('case', None) annotate = kwargs.get('annotate', False) eps = kwargs.get('eps', 0.0001) N, M = u.shape[0], u.shape[1] # Create uniform grid if 'grid' in kwargs: grid = kwargs['grid'] x, y = grid[0] / scaling, grid[1] / scaling elif 'x' in kwargs and 'Y' in kwargs: x = np.transpose(kwargs.get('x')) / scaling + shift[0] y = np.transpose(kwargs.get('Y')) / scaling + shift[1] x, y = np.meshgrid(x, y) elif 'x_lims' in kwargs and 'y_lims' in kwargs: xlims = kwargs.get('x_lims') ylims = kwargs.get('y_lims') x, y = np.linspace(xlims[0] / scaling, xlims[1] / scaling, N), np.linspace(ylims[0] / scaling, ylims[1] / scaling, M) x, y = x + shift[0], y + shift[1] x, y = np.meshgrid(x, y) else: xmin, xmax = 0, N - 1 ymin, ymax = -M / 2, M / 2 - 1 x, y = np.linspace(xmin / scaling, xmax / scaling, N), np.linspace(ymin / scaling, ymax / scaling, M) x, y = x + shift[0], y + shift[1] x, y = np.meshgrid(x, y) # Matplotlib definitions fig, ax = plt.subplots(1, 1) # Create contourf given a normalized (norm) colormap (cmap) if lim[0] < 0 and lim[1] > 0: ll = np.linspace(-eps, lim[0], int(levels / 2)) rr = np.linspace(lim[1], eps, int(levels / 2)) lvls = np.append(ll, rr) if contour: extra_levels = 5 dl = lvls[1] - lvls[0] clvls = np.append(lvls, np.linspace(lim[1] + dl, lim[1] + extra_levels * dl, extra_levels)) else: clvls = levels lvls = np.linspace(lim[0], lim[1], levels + 1) u = u.torch if xwindow is not None: x_args = np.where(np.logical_and(np.any(x > xwindow[0], axis=0), np.any(x < xwindow[1], axis=0)))[0] x = x[:, x_args] y = y[:, x_args] u = u[:, x_args] if ywindow is not None: y_args = np.where(np.logical_and(np.any(y > ywindow[0], axis=1), np.any(y < ywindow[1], axis=1)))[0] x = x[y_args, :] y = y[y_args, :] u = u[y_args, :] if contour: ax.contour(x, y, u, clvls, linewidths=0.1, colors='k') if contourf: cf = ax.contourf(x, y, u, lvls, vmin=lim[0], vmax=lim[1], cmap=cmap, extend='both') # Format figure ax.set_aspect(1) plt.xlim(np.min(x), np.max(x)) plt.ylim(np.min(y), np.max(y)) ax.tick_params(bottom="on", top="on", right="on", which='both', direction='in', length=2) ax.xaxis.set_zorder(99999) ax.yaxis.set_zorder(99999) divider = make_axes_locatable(ax) if case == 'taylor-green': ax.xaxis.set_major_locator(plt.MultipleLocator(np.pi)) ax.xaxis.set_major_formatter(plt.FuncFormatter(multiple_formatter())) ax.yaxis.set_major_locator(plt.MultipleLocator(np.pi)) ax.yaxis.set_major_formatter(plt.FuncFormatter(multiple_formatter())) ax.xaxis.set_ticks([xlims[0], xlims[1] + xlims[0], xlims[1]]) ax.yaxis.set_ticks([ylims[0], ylims[1] + ylims[0], ylims[1]]) cax = divider.append_axes("right", size="5%", pad=0.15) elif case == 'circle': # ax.yaxis.set_ticks([-2, 0, 2]) grey_color = '#dedede' cyl = patches.Circle((0, 0), 0.5, linewidth=0.2, edgecolor='black', facecolor=grey_color, zorder=9999) ax.add_patch(cyl) # cax = divider.append_axes("right", size="5%", pad=0.0, aspect=15) cax = divider.append_axes("right", size="5%", pad=0.15) elif case == 'flat_plate': grey_color = '#dedede' rec = patches.Rectangle((-0.5, -1 / 91.2), 1, 1 / 45.71, linewidth=0.2, edgecolor='black', facecolor=grey_color, zorder=9999) ax.add_patch(rec) cax = divider.append_axes("right", size="5%", pad=0.15) # -- Add colorbar if lim[0] < 0: tick1 = np.linspace(lim[0], 0, n_ticks / 2) dl = tick1[1] - tick1[0] tick2 = np.linspace(dl, lim[1], n_ticks / 2 - 1) ticks = np.append(tick1, tick2) else: ticks = np.linspace(lim[0], lim[1], n_ticks + 1) # norm = mpl_colors.Normalize(vmin=lim[0], vmax=lim[1]) norm = mpl_colors.Normalize(vmin=np.min(u), vmax=np.max(u)) cbar = fig.colorbar(cf, cax=cax, extend='both', ticks=ticks, norm=norm) fmt_str = r'${:.' + str(n_decimals) + 'f}$' cbar.ax.set_yticklabels([fmt_str.format(t) for t in ticks]) cbar.ax.yaxis.set_tick_params(pad=5, direction='out', size=1) # your number may vary cbar.ax.set_title(title, x=1, y=1.02, loc='left', size=12) # Add annotation if desired if annotate: str_annotation = max_min_loc(u, x, y) # print(str_annotation) ann_ax = fig.add_subplot(133) ann_ax.axis('off') ann_ax.annotate(str_annotation, (0, 0), xycoords="axes fraction", va="center", ha="center", bbox=dict(boxstyle="round, pad=1", fc="w")) # Show, save and close figure plt.savefig(file, transparent=True, bbox_inches='tight') # plt.draw() # plt.clf() return def animate_2Dx2(a, b, file, **kwargs): plt.rc('font', size=9) mpl.rc('xtick', labelsize=9) mpl.rc('ytick', labelsize=9) global c1, c2, cf1, cf2, cf def anim(i): global c1, c2, cf1, cf2, cf for c in cf1.collections: c.remove() # removes only the contours, leaves the rest intact for c in cf2.collections: c.remove() # removes only the contours, leaves the rest intact for c in c1.collections: c.remove() # removes only the contours, leaves the rest intact for c in c2.collections: c.remove() # removes only the contours, leaves the rest intact c1 = ax1.contour(x.T, y.T, a[i].torch, clvls, linewidths=0.05, colors='k') c2 = ax2.contour(x.T, y.T, b[i].torch, clvls, linewidths=0.05, colors='k') cf1 = ax1.contourf(x.T, y.T, a[i].torch, levels, vmin=lim[0], vmax=lim[1], norm=norm, cmap=cmap, extend='both') cf2 = ax2.contourf(x.T, y.T, b[i].torch, levels, vmin=lim[0], vmax=lim[1], norm=norm, cmap=cmap, extend='both') title = r'$torch = ' + '{:.2f}'.format(time[i]) + '$' ax1.set_title(title, size=12, y=1.03) return [c1, c2, cf1, cf2] k = len(a) # Number of snapshots print(k) time = kwargs.get('time', np.arange(k)) levels = kwargs.get('levels', 50) lim = kwargs.get('lim', [np.min(a[0]), np.max(a[0])]) cmap = kwargs.get('cmap', 'Blues') scaling = kwargs.get('scaling', 1) xshift = kwargs.get('xshift', 0) yshift = kwargs.get('yshift', 0) field_name = kwargs.get('field_name', '') n_ticks = kwargs.get('n_ticks', 20) n_decimals = kwargs.get('n_decimals', 2) fps = kwargs.get('fps', 10) dpi = kwargs.get('dpi', 300) N, M = a[0].shape[0], a[0].shape[1] # Create uniform grid if 'grid' in kwargs: grid = kwargs['grid'] x, y = grid[0] / scaling, grid[1] / scaling elif 'x' in kwargs and 'Y' in kwargs: x = np.transpose(kwargs.get('x')) / scaling + xshift y = np.transpose(kwargs.get('Y')) / scaling + yshift x, y = np.meshgrid(x, y) else: xmin, xmax = 0, N - 1 ymin, ymax = -M / 2, M / 2 - 1 x, y = np.linspace(xmin / scaling, xmax / scaling, N), np.linspace(ymin / scaling, ymax / scaling, M) x, y = x + xshift, y + yshift x, y = np.meshgrid(x, y) fig, ax = plt.subplots(2, 1) ax1, ax2 = ax[0], ax[1] if lim[0] < 0: clvls = levels else: extra_levels = 5 dl = levels[1] - levels[0] clvls = np.append(levels, np.linspace(lim[1] + dl, lim[1] + extra_levels * dl, extra_levels)) norm = mpl_colors.Normalize(vmin=lim[0], vmax=lim[1]) c1 = ax1.contour(x.T, y.T, a[0].torch, clvls, linewidths=0.05, colors='k') c2 = ax2.contour(x.T, y.T, b[0].torch, clvls, linewidths=0.05, colors='k') cf1 = ax1.contourf(x.T, y.T, a[0].torch, levels, vmin=lim[0], vmax=lim[1], norm=norm, cmap=cmap, extend='both') cf2 = ax2.contourf(x.T, y.T, b[0].torch, levels, vmin=lim[0], vmax=lim[1], norm=norm, cmap=cmap, extend='both') cf = [c1, c2, cf1, cf2] # Format figure ax1.tick_params(bottom="on", top="on", right="on", which='both', direction='in', labelbottom='off', length=2) ax1.set_xticklabels([]) ax2.tick_params(bottom="on", top="on", right="on", which='both', direction='in', length=2) ax1.set_aspect(1) ax2.set_aspect(1) plt.xlim(np.min(x), np.max(x)) plt.ylim(np.min(y), np.max(y)) ax1.yaxis.set_ticks([-2, 0, 2]) ax2.yaxis.set_ticks([-2, 0, 2]) # Set tit, circles and text title = r'$torch = ' + '{:.2f}'.format(time[0]) + '$' ax1.set_title(title, size=12, y=1.05) grey_color = '#dedede' cyl1 = patches.Circle((0, 0), 0.5, linewidth=0.2, edgecolor='black', facecolor=grey_color, zorder=9999) cyl2 = patches.Circle((0, 0), 0.5, linewidth=0.2, edgecolor='black', facecolor=grey_color, zorder=9999) ax1.add_patch(cyl1) ax2.add_patch(cyl2) plt.subplots_adjust(hspace=0.05, bottom=0.15) ax1.text(-1, 2.3, r'$2{\text -}\mathrm{length_scale}$') ax2.text(-1, 2.3, r'$L_z=\pi$') # Add colorbar # if lim[0] < 0: # tick1 = np.linspace(lim[0], 0, n_ticks / 2) # dl = tick1[1] - tick1[0] # tick2 = np.linspace(dl, lim[1], n_ticks / 2 - 1) # ticks = np.append(tick1, tick2) # else: # ticks = np.linspace(lim[0], lim[1], n_ticks + 1) # cbar_ax = plt.colorbar(cf2, ax=[ax1, ax2], extend='both', norm=norm).ax # cbar_ax.set_title(field_name, Y=1.02, loc='left', size=12) # fmt_str = r'${:.' + str(n_decimals) + 'f}$' # cbar_ax.set_yticklabels([fmt_str.format(torch) for torch in ticks]) # cbar_ax.yaxis.set_tick_params(pad=5, direction='out', size=1) # your number may vary # cbar_ax.set_title(field_name, x=1, Y=1.02, loc='left', size=12) # Animate writer = animation.FFMpegWriter(fps=fps, extra_args=['-vcodec', 'libx264']) anim = animation.FuncAnimation(fig, anim, frames=len(a)) anim.save(file, writer=writer, dpi=dpi) return def scatter(x, y, file='test.pdf', **kwargs): x_label = kwargs.get('x_label', None) y_label = kwargs.get('y_label', None) fig, ax = plt.subplots(1, 1) ax.scatter(x, y, color='k', s=0.1) ax.tick_params(bottom="on", top="on", right="on", which='both', direction='in', length=2) fig, ax = makeSquare(fig, ax) plt.xlabel(r'$' + x_label + '$') plt.ylabel(r'$' + y_label + '$') ax.xaxis.set_zorder(99999) ax.yaxis.set_zorder(99999) # Show plot and save figure plt.savefig(file, transparent=True, bbox_inches='tight') return def density2D(x, y, file='test.pdf', nbins=20, **kwargs): from scipy.stats import kde x_label = kwargs.get('x_label', None) y_label = kwargs.get('y_label', None) x_lims = kwargs.get('x_lims', None) y_lims = kwargs.get('y_lims', None) cmap = kwargs.get('cmap', 'gist_heat') k = kde.gaussian_kde(np.array([x.flatten(), y.flatten()])) xi, yi = np.mgrid[x.min():x.max():nbins * 1j, y.min():y.max():nbins * 1j] zi = k(np.vstack([xi.flatten(), yi.flatten()])) fig, ax = plt.subplots(1, 1) ax.pcolormesh(xi, yi, zi.reshape(xi.shape), shading='gouraud', cmap=cmap) ax.contour(xi, yi, zi.reshape(xi.shape), linewidths=0.5, cmap=cmap + '_r') ax.tick_params(bottom="on", top="on", right="on", which='both', direction='in', length=2, color='black') fig, ax = makeSquare(fig, ax) plt.xlabel(r'$' + x_label + '$') plt.ylabel(r'$' + y_label + '$') if x_lims is not None: plt.xlim(x_lims[0], x_lims[1]) if y_lims is not None: plt.ylim(y_lims[0], y_lims[1]) ax.xaxis.set_ticks = [-0.02, 0.00, 0.02] ax.yaxis.set_ticks = [0.05, 0.01, 0.015] ax.xaxis.set_zorder(99999) ax.yaxis.set_zorder(99999) # Show plot and save figure plt.savefig(file, transparent=True, bbox_inches='tight') return def plot2D_uv(u, cmap, lvls, lim, file, **kwargs): """ Return nothing and saves the figure in the specified fn name. Args: cmap: matplotlib cmap. Eg: cmap = "seismic" lvls: number of levels of the contour. Eg: lvls = 100 lim: min and max values of the contour passed as array. Eg: lim = [-0.5, 0.5] file: Name of the fn to save the plot (recommended .pdf so it can be converted get .svg). Eg: fn = "dUdy.pdf" Kwargs: x=[xmin,xmax] is the x axis minimum and maximum specified Y=[ymin,ymax] is the Y axis minimum and maximum specified annotate: Boolean if annotations for min and max values of the field (and locations) are desired """ # Internal imports import matplotlib.colors as colors from mpl_toolkits.axes_grid1 import make_axes_locatable plt.rc('font', family='sans-serif', size=15) mpl.rc('xtick', labelsize=15) mpl.rc('ytick', labelsize=15) # mpl.rcParams["contour.negative_linestyle"] = 'dotted' N, M = u.shape[0], u.shape[1] if not 'x' in kwargs: xmin, xmax = 0, N - 1 else: xmin, xmax = kwargs['x'][0], kwargs['x'][1] if not 'Y' in kwargs: ymin, ymax = -M / 2, M / 2 - 1 else: ymin, ymax = kwargs['Y'][0], kwargs['Y'][1] annotate = kwargs.get('annotate', False) scaling = kwargs.get('scaling', 1) xshift = kwargs.get('xshift', 0) yshift = kwargs.get('yshift', 0) # Uniform grid generation x, y = np.linspace(xmin / scaling, xmax / scaling, N), np.linspace(ymin / scaling, ymax / scaling, M) x, y = x + xshift, y + yshift x, y = np.meshgrid(x, y) u = np.transpose(u) # Matplotlib definitions fig1 = plt.gcf() ax = plt.gca() # Create contourf given a normalized (norm) colormap (cmap) norm = colors.Normalize(vmin=lim[0], vmax=lim[1]) # cf = plt.contourf(x, Y, u, '--', lvls, vmin=lim[0], vmax=lim[1], norm=norm, cmap=cmap) r = ax.contour(x, y, u, lvls, colors='k') for line, lvl in zip(r.collections, r.levels): if lvl < 0: line.set_linestyle('--') line.set_dashes([(0, (4.0, 4.0))]) line.set_linewidth(0.4) else: line.set_linewidth(0.6) ax.xaxis.set_ticks([0.5, 1.0, 1.5, 2]) ax.yaxis.set_ticks([-0.5, 0.0, 0.5]) ax.tick_params(bottom="on", top="on", right="on", which='both', direction='in', length=2) # Scale contourf and set limits plt.axis('scaled') plt.xlim(np.min(x), np.max(x)) plt.ylim(np.min(y), np.max(y)) # ax.xaxis.set_ticks(np.arange(0.5, 2.5, 0.5)) # Scale colorbar to contourf # divider = make_axes_locatable(ax) # cax = divider.append_axes("right", size="5%", pad=0.05, aspect=10) # cbax = plt.colorbar(cf, cax=cax).ax # mpl.colorbar.ColorbarBase(cbax, norm=norm, cmap=cmap) cyl = patches.Circle((0, 0), radius=0.5, linewidth=0.5, edgecolor='black', facecolor='white', zorder=10, alpha=1) ax.add_patch(cyl) # Add annotation if desired if annotate: str_annotation = max_min_loc(u, xmin, ymin) print(str_annotation) ann_ax = fig1.add_subplot(133) ann_ax.axis('off') ann_ax.annotate(str_annotation, (0, 0), xycoords="axes fraction", va="center", ha="center", bbox=dict(boxstyle="round, pad=1", fc="w")) # Show, save and close figure plt.savefig(file, transparent=True, bbox_inches='tight') plt.draw() # plt.show() plt.clf() return def plot2D_circulation(u, cmap, lvls, lim, file, **kwargs): import matplotlib.colors as colors from mpl_toolkits.axes_grid1 import make_axes_locatable plt.rc('font', family='sans-serif', size=6) mpl.rc('xtick', labelsize=6) mpl.rc('ytick', labelsize=6) N, M = u.shape[0], u.shape[1] if not 'x' in kwargs: xmin, xmax = 0, N - 1 else: xmin, xmax = kwargs['x'][0], kwargs['x'][1] if not 'Y' in kwargs: ymin, ymax = -M / 2, M / 2 - 1 else: ymin, ymax = kwargs['Y'][0], kwargs['Y'][1] annotate = kwargs.get('annotate', False) scaling = kwargs.get('scaling', 1) xshift = kwargs.get('xshift', 0) yshift = kwargs.get('yshift', 0) # Uniform grid generation x, y = np.linspace(xmin / scaling, xmax / scaling, N), np.linspace(ymin / scaling, ymax / scaling, M) x, y = x + xshift, y + yshift x, y = np.meshgrid(x, y) u = np.transpose(u) # Matplotlib definitions fig1 = plt.gcf() ax = plt.gca() # Create contourf given a normalized (norm) colormap (cmap) norm = colors.Normalize(vmin=lim[0], vmax=lim[1]) # cf = plt.contourf(x, Y, u, lvls, vmin=lim[0], vmax=lim[1], norm=norm, cmap=cmap) ax.contour(x, y, u, lvls, linewidths=0.2, colors='k') cf = ax.contourf(x, y, u, lvls, vmin=lim[0], vmax=lim[1], norm=norm, cmap=cmap) # Scale contourf and set limits plt.axis('scaled') plt.xlim(np.min(x), np.max(x)) plt.ylim(np.min(y), np.max(y)) ax.tick_params(bottom="on", top="on", right="on", which='both', direction='in', length=2) grey_color = '#dedede' cyl = patches.Circle((0, 0), radius=0.5, linewidth=0.5, edgecolor='black', facecolor='white', zorder=10, alpha=1) rect = patches.Rectangle((0.55, -0.8), 1.5, 1.6, linewidth=0.5, edgecolor='purple', facecolor='none') ax.add_patch(cyl) # ax.add_patch(rect) # Show, save and close figure plt.savefig(file, transparent=True, bbox_inches='tight') plt.clf() return def plot2Dvort(u, cmap, lvls, lim, file, **kwargs): """ Return nothing and saves the figure in the specified fn name. Args: cmap: matplotlib cmap. Eg: cmap = "seismic" lvls: number of levels of the contour. Eg: lvls = 100 lim: min and max values of the contour passed as array. Eg: lim = [-0.5, 0.5] file: Name of the fn to save the plot (recommended .pdf so it can be converted get .svg). Eg: fn = "dUdy.pdf" Kwargs: x=[xmin,xmax] is the x axis minimum and maximum specified Y=[ymin,ymax] is the Y axis minimum and maximum specified annotate: Boolean if annotations for min and max values of the field (and locations) are desired """ # Internal imports import matplotlib.colors as colors from mpl_toolkits.axes_grid1 import make_axes_locatable plt.rc('font', family='sans-serif', size=6) mpl.rc('xtick', labelsize=6) mpl.rc('ytick', labelsize=6) N, M = u.shape[0], u.shape[1] if not 'x_lim' in kwargs: xmin, xmax = 0, N - 1 else: xmin, xmax = kwargs['x'][0], kwargs['x'][1] if not 'y_lim' in kwargs: ymin, ymax = -M / 2, M / 2 - 1 else: ymin, ymax = kwargs['Y'][0], kwargs['Y'][1] annotate = kwargs.get('annotate', False) scaling = kwargs.get('scaling', 1) xshift = kwargs.get('xshift', 0) yshift = kwargs.get('yshift', 0) # Uniform grid generation if 'x' not in kwargs and 'Y' not in kwargs: x = np.linspace(xmin / scaling, xmax / scaling, N) y = np.linspace(ymin / scaling, ymax / scaling, M) x, y = x + xshift, y + yshift x, y = np.meshgrid(x, y) elif 'x' in kwargs and 'Y' in kwargs: x = np.transpose(kwargs.get('x')) / scaling y = np.transpose(kwargs.get('Y')) / scaling else: raise ValueError('Pass both x and Y, or none.') u = np.transpose(u) # Matplotlib definitions fig1 = plt.gcf() ax = plt.gca() # Create contourf given a normalized (norm) colormap (cmap) norm = colors.Normalize(vmin=lim[0], vmax=lim[1]) # cf = plt.contourf(x, Y, u, lvls, vmin=lim[0], vmax=lim[1], norm=norm, cmap=cmap) ax.contour(x, y, u, lvls, linewidths=0.2, colors='k') cf = ax.contourf(x, y, u, lvls, vmin=lim[0], vmax=lim[1], norm=norm, cmap=cmap) # Scale contourf and set limits plt.axis('scaled') plt.xlim(np.min(x), np.max(x)) plt.ylim(np.min(y), np.max(y)) # ax.xaxis.set_ticks(np.arange(0.5, 2.5, 0.5)) ax.yaxis.set_ticks([-2, 0, 2]) ax.tick_params(bottom="on", top="on", right="on", which='both', direction='in', length=2) # -- Set tit, circles and text grey_color = '#dedede' cyl = patches.Circle((0, 0), 0.51, linewidth=0.2, edgecolor='black', facecolor=grey_color, zorder=9999) ax.add_patch(cyl) # Show, save and close figure plt.savefig(file, transparent=True, bbox_inches='tight') # plt.draw() # plt.clf() return def plot2Dseparation(u, file, **kwargs): """ Return nothing and saves the figure in the specified fn name. Args: cmap: matplotlib cmap. Eg: cmap = "seismic" lvls: number of levels of the contour. Eg: lvls = 100 lim: min and max values of the contour passed as array. Eg: lim = [-0.5, 0.5] file: Name of the fn to save the plot (recommended .pdf so it can be converted get .svg). Eg: fn = "dUdy.pdf" Kwargs: x=[xmin,xmax] is the x axis minimum and maximum specified Y=[ymin,ymax] is the Y axis minimum and maximum specified annotate: Boolean if annotations for min and max values of the field (and locations) are desired """ # Internal imports import matplotlib.colors as colors from mpl_toolkits.axes_grid1 import make_axes_locatable plt.rc('font', family='sans-serif', size=6) mpl.rc('xtick', labelsize=6) mpl.rc('ytick', labelsize=6) N, M = u.shape[0], u.shape[1] scaling = kwargs.get('scaling', 1) ptype = kwargs.get('ptype', 'contourf') xshift = kwargs.get('xshift', 0) yshift = kwargs.get('yshift', 0) cmap = kwargs.get('cmap', 'seismic') lvls = kwargs.get('lvls', 50) lim = kwargs.get('lim', [np.min(u), np.max(u)]) if not 'grid' in kwargs: xmin, xmax = 0, N - 1 ymin, ymax = -M / 2, M / 2 - 1 x, y = np.linspace(xmin / scaling, xmax / scaling, N), np.linspace(ymin / scaling, ymax / scaling, M) x, y = x + xshift, y + yshift x, y = np.meshgrid(x, y) else: grid = kwargs['grid'] x, y = grid[0] / scaling, grid[1] / scaling # Matplotlib definitions fig = plt.gcf() ax = plt.gca() # Create contourf given a normalized (norm) colormap (cmap) norm = colors.Normalize(vmin=lim[0], vmax=lim[1]) # lvls = np.linspace(lim[0], lim[1], lvls + 1) if ptype == 'contourf': # ax.contour(x, Y, u, lvls, linewidths=0.2, colors='k') # cf = ax.contourf(x.torch, Y.torch, u.torch, levels=lvls, vmin=lim[0], vmax=lim[1], norm=norm, cmap=cmap, extend='both') cf = ax.contourf(x.T, y.T, u.torch, levels=lvls, vmin=lim[0], vmax=lim[1], norm=norm, cmap=cmap) else: cf = ax.pcolormesh(x.T, y.T, u.torch, vmin=lim[0], vmax=lim[1], norm=norm, cmap=cmap) # Scale contourf and set limits plt.axis('scaled') plt.xlim(np.min(x), np.max(x)) plt.ylim(np.min(y), np.max(y)) print(np.min(x), np.max(x)) print(np.min(y), np.max(y)) ax.tick_params(bottom=True, top=True, right=True, which='both', direction='in', length=2) # Add cylinder grey_color = '#dedede' cyl = patches.Circle((0, 0), scaling / 2, linewidth=0.4, edgecolor='purple', facecolor='None') ax.add_patch(cyl) # Colormap # divider = make_axes_locatable(ax) # cax = divider.append_axes("right", size="5%", pad=0.05) # v = np.linspace(lim[0], lim[1], 10, endpoint=True) # length_scale = mpl.cm.get_cmap(cmap) # length_scale.set_under('r') # length_scale.set_over('b') # plt.colorbar(cf, cax=cax, norm=norm, cmap=length_scale, ticks=v, boundaries=v) # Show, save and close figure plt.savefig(file, transparent=True, bbox_inches='tight') plt.clf() return # ------------------------------------------------------ def two_point_correlations_single(a, fname): n_points = len(a) fig = plt.gcf() ax = plt.gca() for i, b in enumerate(a): point_str = b[0] n_cases = len(b[1]) print(point_str) for j, t in enumerate(b[1]): case_name, c, d = t[0], t[1], t[2] d[0] = 0.011111 if 'pie' in case_name: case_name = '\pi' max_d = np.max(d) ax.plot(d, c, color=colors[j], lw=1.5, label='$' + case_name + '$', marker=markers[j], markevery=0.05, markersize=4) leg1 = ax.legend(loc='lower left') leg1.get_frame().set_edgecolor('black') leg1.get_frame().set_facecolor('white') leg1.get_frame().set_linewidth(0.5) leg1.get_frame().set_alpha(0.5) ax.set_ylabel(r'$\left\langle v_1, v_2 \right\rangle$') ax.set_xlabel(r'$\log dis/length_scale$') ax.tick_params(bottom=True, top=True, right=True, which='both', direction='in', length=2) ax.set_xscale('log', nonposx='clip') ax.set_xlim(1.1e-2, max_d) ax.set_ylim(0.48, 1.02) ax.yaxis.set_ticks([0.5, 0.6, 0.7, 0.8, 0.9, 1.0]) fig, ax = makeSquare(fig, ax) plt.savefig(fname, transparent=True, bbox_inches='tight') return def two_point_correlations(a, fname): from matplotlib.gridspec import GridSpec n_points = len(a) fig = plt.figure() gs = GridSpec(2, 2) ax = [] ax1 = fig.add_subplot(gs[0, 0]) ax2 = fig.add_subplot(gs[0, 1], sharex=ax1, sharey=ax1) ax3 = fig.add_subplot(gs[1, 0], sharex=ax1, sharey=ax1) ax4 = fig.add_subplot(gs[1, 1], sharex=ax1, sharey=ax1) ax.extend([ax1, ax2, ax3, ax4]) for i, b in enumerate(a): point_str = b[0] n_cases = len(b[1]) print(point_str) print(b) for j, t in enumerate(b[1]): case_name, c, d = t[0], t[1], t[2] d[0] = 0.011111 if 'pie' in case_name: case_name = '\pi' max_d = np.max(d) ax[i].plot(d, c, color=colors[j], lw=1, label='$' + case_name + '$') leg1 = ax1.legend(loc='lower left') leg1.get_frame().set_edgecolor('black') leg1.get_frame().set_facecolor('white') leg1.get_frame().set_linewidth(0.5) leg1.get_frame().set_alpha(0.85) plt.setp(ax1.get_xticklabels(), visible=False) plt.setp(ax2.get_xticklabels(), visible=False) plt.setp(ax2.get_yticklabels(), visible=False) plt.setp(ax4.get_yticklabels(), visible=False) ax1.set_ylabel(r'$\left\langle v_1, v_2 \right\rangle$') ax3.set_ylabel(r'$\left\langle v_1, v_2 \right\rangle$') ax3.set_xlabel(r'$\log dis/length_scale$') ax4.set_xlabel(r'$\log dis/length_scale$') for q in ax: q.tick_params(bottom=True, top=True, right=True, which='both', direction='in', length=2) q.set_xlim(xmax=max_d) q.set_ylim(0.5, 1.1) # q.set_xscale('log', nonposx='clip') fig_size = fig.get_size_inches() fig.set_size_inches(fig_size[1] * 1.1, fig_size[1] * 1.1) fig.tight_layout() plt.savefig(fname, transparent=True, bbox_inches='tight') return def two_point_correlations_3_horizontal(a, fname): from matplotlib.gridspec import GridSpec plt.rc('font', family='sans-serif', size=14) # use 13 for squared double columns figures mpl.rc('xtick', labelsize=14) mpl.rc('ytick', labelsize=14) n_points = len(a) fig = plt.figure() gs = GridSpec(1, 3) ax = [] ax1 = fig.add_subplot(gs[0, 0]) ax2 = fig.add_subplot(gs[0, 1], sharey=ax1) ax3 = fig.add_subplot(gs[0, 2], sharey=ax1) ax.extend([ax1, ax2, ax3]) for i, b in enumerate(a): point_str = b[0] n_cases = len(b[1]) print(point_str) for j, t in enumerate(b[1]): case_name, c, d = t[0], t[1], t[2] d[0] = 0.011111 if 'pie' in case_name: case_name = '\pi' max_d = np.max(d) every = [4, 2, 2, 1, 1] ax[i].plot(d, c, color=colors[j], lw=1.5, label='$' + case_name + '$', marker=markers[j], markevery=0.05, markersize=4) leg1 = ax1.legend(loc='upper right') leg1.get_frame().set_edgecolor('black') leg1.get_frame().set_facecolor('white') leg1.get_frame().set_linewidth(0.5) leg1.get_frame().set_alpha(0.5) plt.setp(ax2.get_yticklabels(), visible=False) plt.setp(ax3.get_yticklabels(), visible=False) ax1.set_ylabel(r'$\left\langle v_1, v_2 \right\rangle$') ax1.set_xlabel(r'$\log dis/length_scale$') ax2.set_xlabel(r'$\log dis/length_scale$') ax3.set_xlabel(r'$\log dis/length_scale$') for q in ax: q.tick_params(bottom=True, top=True, right=True, which='both', direction='in', length=2) # print(max_d) # q.set_ylim(0.5, 1.1) q.set_xscale('log', nonposx='clip') q.set_xlim(1.1e-2, max_d) fig_size = fig.get_size_inches() fig.set_size_inches(fig_size[1] * 2, fig_size[1] * 2 / 2.8) fig.tight_layout() plt.savefig(fname, transparent=True, bbox_inches='tight') return def two_point_correlations_3_vertical(a, fname): from matplotlib.gridspec import GridSpec n_points = len(a) fig = plt.figure() gs = GridSpec(3, 1) ax = [] ax1 = fig.add_subplot(gs[0, 0]) ax2 = fig.add_subplot(gs[1, 0], sharex=ax1) ax3 = fig.add_subplot(gs[2, 0], sharex=ax1) ax.extend([ax1, ax2, ax3]) for i, b in enumerate(a): point_str = b[0] n_cases = len(b[1]) print(point_str) for j, t in enumerate(b[1]): case_name, c, d = t[0], t[1], t[2] d[0] = 0.011111 if 'pie' in case_name: case_name = '\pi' max_d = np.max(d) every = [4, 2, 2, 1, 1] ax[i].plot(d, c, color=colors[j], lw=1.5, label='$' + case_name + '$', marker=markers[j], markevery=0.05, markersize=4) leg1 = ax1.legend(loc='upper right') leg1.get_frame().set_edgecolor('black') leg1.get_frame().set_facecolor('white') leg1.get_frame().set_linewidth(0.5) leg1.get_frame().set_alpha(0.5) plt.setp(ax1.get_xticklabels(), visible=False) plt.setp(ax2.get_xticklabels(), visible=False) ax1.set_ylabel(r'$\left\langle v_1, v_2 \right\rangle$') ax2.set_ylabel(r'$\left\langle v_1, v_2 \right\rangle$') ax3.set_ylabel(r'$\left\langle v_1, v_2 \right\rangle$') ax3.set_xlabel(r'$\log dis/length_scale$') for q in ax: q.tick_params(bottom=True, top=True, right=True, which='both', direction='in', length=2) # print(max_d) # q.set_ylim(0.5, 1.1) q.set_xscale('log', nonposx='clip') q.set_xlim(1.1e-2, max_d) fig_size = fig.get_size_inches() fig.set_size_inches(fig_size[1] / 2, fig_size[1]) fig.tight_layout() plt.savefig(fname, transparent=True, bbox_inches='tight') return def CL_CD_theta(fy, fx, t, alphas, times, fname): from scipy.signal import savgol_filter, resample from scipy.interpolate import interp1d from matplotlib.gridspec import GridSpec # fig, [ax1, ax2, ax3] = plt.subplots(nrows=2, ncols=2, sharex=True) fig = plt.figure() gs = GridSpec(2, 2) ax3 = fig.add_subplot(gs[:, 1]) ax1 = fig.add_subplot(gs[0, 0]) ax2 = fig.add_subplot(gs[1, 0], sharex=ax1) ax1.plot(t, fy, color='black', lw=1, label=r'$C_L$') ax1.plot(t, fx, color='grey', ls='dashed', lw=1, label=r'$C_D$') upper, lower = zip(*alphas) u, l = np.array(upper), np.array(lower) u = savgol_filter(u, 7, 3) # window size 51, polynomial order 3 l = savgol_filter(l, 7, 3) # window size 51, polynomial order 3 ax2.plot(times, u, color='blue', lw=1, label=r'$\theta_u$') ax2.plot(times, 360 + l - u, color='purple', ls='dotted', lw=1, label=r'$\theta_l-\theta_u$') ax2.plot(times, -l, color='red', ls='dashed', lw=1, label=r'$360-\theta_l$') fy_function = interp1d(t, fy, kind='cubic') fy = fy_function(times) ax3.scatter(fy, u + l, s=10, linewidths=1, color='black') ax3.axhline(0, color='grey', lw=0.1) ax3.axvline(0, color='grey', lw=0.1) ax1.grid(axis='both', alpha=0.5) ax2.grid(axis='both', alpha=0.5) ax1.tick_params(bottom=True, top=True, right=True, which='both', direction='in', length=2) ax2.tick_params(bottom=True, top=True, right=True, which='both', direction='in', length=2) ax3.tick_params(bottom=True, top=True, right=True, which='both', direction='in', length=2) ax1.set_xlim(min(t), max(t)) ax1.set_ylim(-2, 2) ax1.yaxis.set_ticks([-1, 0, 1]) plt.setp(ax1.get_xticklabels(), visible=False) ax2.set_ylim(90, 165) ax2.yaxis.set_ticks([100, 120, 140, 160]) ax2.set_xlabel(r'$tU/length_scale$') ax3.set_xlabel(r'$C_L$') # ax3.set_ylabel(r'$\theta_l+\theta_u$') #labelpad=-3 for 0.5 # ax3.set_ylim(-24,24) #0.5 # ax3.set_xlim(-1.8,1.8) # ax3.set_ylim(-9,9) #pie # ax3.set_xlim(-0.8,0.8) leg1 = ax1.legend(loc='lower left') leg1.get_frame().set_edgecolor('black') leg1.get_frame().set_facecolor('white') leg1.get_frame().set_linewidth(0.5) leg1.get_frame().set_alpha(0.85) leg2 = ax2.legend(loc=(0.0375, 0.45), numpoints=1, ncol=2, columnspacing=1, labelspacing=0.1, fontsize=9) leg2.get_frame().set_edgecolor('black') leg2.get_frame().set_facecolor('white') leg2.get_frame().set_linewidth(0.5) leg2.get_frame().set_alpha(0.85) # fig_size = fig.get_size_inches() # fig.set_size_inches(fig_size[1] * 2, fig_size[1] * 2 / 2.8) # fig.tight_layout() fig.tight_layout() plt.savefig(fname, transparent=False, bbox_inches='tight') return def CL_CD_theta_2(fy, fx, t, alphas, times, fname): from scipy.signal import savgol_filter, resample from scipy.interpolate import interp1d from matplotlib.gridspec import GridSpec # fig, [ax1, ax2, ax3] = plt.subplots(nrows=2, ncols=2, sharex=True) fig = plt.figure() gs = GridSpec(3, 1) ax2 = fig.add_subplot(gs[1, 0]) ax1 = fig.add_subplot(gs[0, 0], sharex=ax2) ax3 = fig.add_subplot(gs[2, 0]) ax1.plot(t, fy, color='black', lw=1, label=r'$C_L$') ax1.plot(t, fx, color='grey', ls='dashed', lw=1, label=r'$C_D$') upper, lower = zip(*alphas) u, l = np.array(upper), np.array(lower) u = savgol_filter(u, 7, 3) # window size 51, polynomial order 3 l = savgol_filter(l, 7, 3) # window size 51, polynomial order 3 ax2.plot(times, u, color='blue', lw=1, label=r'$\theta_u$') ax2.plot(times, 360 + l - u, color='purple', ls='dotted', lw=1, label=r'$\theta_l-\theta_u$') ax2.plot(times, -l, color='red', ls='dashed', lw=1, label=r'$360-\theta_l$') fy_function = interp1d(t, fy, kind='cubic') fx_function = interp1d(t, fx, kind='cubic') fy = fy_function(times) fx = fx_function(times) ax3.scatter(fy, u + l, s=10, linewidths=1, color='black') # dis = {} # dis['torch'] = times # dis['C_L'] = fy # dis['C_D'] = fx # dis[r'\theta_u'] = u # dis[r'360-\theta_l'] = -l # df = pd.DataFrame.from_dict(dis) # df.to_csv('spreadsheets/figure7b.csv', index=False) # dis = {} # dis['torch'] = times # dis['C_L'] = fy # dis['C_D'] = fx # dis[r'\theta_u'] = u # dis[r'360-\theta_l'] = -l # df = pd.DataFrame.from_dict(dis) # df.to_csv('spreadsheets/figure7b.csv', index=False) ax3.axhline(0, color='darkgrey', lw=0.1) ax3.axvline(0, color='darkgrey', lw=0.1) ax1.grid(axis='both', color='darkgrey', lw=0.1) ax2.grid(axis='both', color='darkgrey', lw=0.1) ax1.tick_params(bottom=True, top=True, right=True, which='both', direction='in', length=2) ax2.tick_params(bottom=True, top=True, right=True, which='both', direction='in', length=2) ax3.tick_params(bottom=True, top=True, right=True, which='both', direction='in', length=2) ax2.set_xlim(min(t), max(t)) ax1.set_ylim(-2, 2) ax1.yaxis.set_ticks([-1, 0, 1]) plt.setp(ax1.get_xticklabels(), visible=False) ax2.set_ylim(90, 165) ax2.yaxis.set_ticks([100, 120, 140, 160]) ax2.set_xlabel(r'$tU/length_scale$') ax3.set_xlabel(r'$C_L$') ax3.set_ylabel(r'$\theta_l+\theta_u$') # labelpad=-3 for 0.5 ax3.set_ylim(-24, 24) # 0.5 ax3.set_xlim(-1.8, 1.8) # 0.5 # ax3.set_ylim(-9,9) #pie # ax3.set_xlim(-0.8,0.8) #pie leg1 = ax1.legend(loc='lower left') leg1.get_frame().set_edgecolor('black') leg1.get_frame().set_facecolor('white') leg1.get_frame().set_linewidth(0.5) leg1.get_frame().set_alpha(0.85) leg2 = ax2.legend(loc=(0.0375, 0.45), numpoints=1, ncol=2, columnspacing=1, labelspacing=0.1, fontsize=9) leg2.get_frame().set_edgecolor('black') leg2.get_frame().set_facecolor('white') leg2.get_frame().set_linewidth(0.5) leg2.get_frame().set_alpha(0.85) fig_size = fig.get_size_inches() fig.set_size_inches(3.5, 8.5) fig.tight_layout() plt.savefig(fname, transparent=True, bbox_inches='tight') return def plotCL(fy, t, file, colour='red', label=None, **kwargs): """ Plot the lift force as a time series. :param fy: Lift force [numpy 1D array] :param t: Time [numpy 1D array] :param file: output fn name [string] :param colour: colour... :param label: label... :param kwargs: Select which additional information you want to include in the plot: 'St', 'CL_rms', 'CD_rms', 'n_periods', passing the corresponding values. E.g. 'St=0.2'. :return: - """ ax = plt.gca() fig = plt.gcf() # Show lines plt.plot(t, fy, color=colour, lw=1, label=label) # Set limits ax.set_xlim(min(t), max(t)) # ax.set_ylim(1.5*min(fy), 1.5*max(fy)) # ax.set_ylim(-2.5, 2.5) # Edit frame, labels and legend # ax.axhline(linewidth=1) # ax.axvline(linewidth=1) plt.xlabel(r'$torch/length_scale$') plt.ylabel(r'$C_L$') leg = plt.legend(loc='upper right') # leg.get_frame().set_edgecolor('black') # Anotations for key, value in kwargs.items(): if key == 'St': St_str = '{:.2f}'.format(value) my_str = r'$S_t=' + St_str + '$' plt.text(x=1.02 * max(t), y=1.4 * max(fy), s=my_str, color='black') if key == 'CL_rms': CL_rms_str = '{:.2f}'.format(value) my_str = r'$\overline{C}_L=' + CL_rms_str + '$' plt.text(x=1.02 * max(t), y=1.2 * max(fy), s=my_str, color='black') if key == 'CD_rms': CD_rms_str = '{:.2f}'.format(value) my_str = r'$\overline{C}_D=' + CD_rms_str + '$' plt.text(x=1.02 * max(t), y=1.0 * max(fy), s=my_str, color='black') if key == 'n_periods': n_periods = str(value) my_str = r'$\textrm{periods}=' + n_periods + '$' plt.text(x=1.02 * max(t), y=0.8 * max(fy), s=my_str, color='black') # Show plot and save figure plt.savefig(file, transparent=True, bbox_inches='tight') return # ------------------------------------------------------ TKE def plotTKEspatial(tke, file, **kwargs): """ 1D plot of the TKE in space :param tke: Turbulent kinetic energy [numpy 1D array] :param file: output fn name [string] :param kwargs: 'x' coordinates [numpy 1D array] :return: - """ ax = plt.gca() fig = plt.gcf() N = tke.shape[0] if not 'x' in kwargs: xmin, xmax = 0, N - 1 else: xmin, xmax = kwargs['x'][0], kwargs['x'][1] x = np.linspace(xmin, xmax, N) ylog = kwargs.get('ylog', False) # Show lines plt.plot(x, tke, color='black', lw=1.5, label='$L_z = 1D$') # Set limits ax.set_xlim(min(x), max(x)) ax.set_ylim(min(tke), max(tke) * 1.1) fig, ax = makeSquare(fig, ax) if ylog: ax.set_yscale('log') ax.set_ylim(min(tke), max(tke) * 2) # Edit frame, labels and legend plt.xlabel('$x/length_scale$') plt.ylabel('$K$') leg = plt.legend(loc='upper right') leg.get_frame().set_edgecolor('white') # Show plot and save figure plt.show() plt.savefig(file, transparent=True, bbox_inches='tight') return def plotTKEspatial_list(file, tke_tuple_list, **kwargs): """ Generate a plot of a TKE list of tuples like (case, tke) in space :param file: output fn name [string] :param tke_tuple_list: list containing the tuple as ('case', tke), where 'case' is a string and 'tke' is a 1D numpy array :param kwargs: 'x' coordinates [numpy 1D array] :return: - """ ax = plt.gca() fig = plt.gcf() if not tke_tuple_list: raise ValueError("No TKE series passed to the function.") else: N = tke_tuple_list[0][1].shape[0] if not 'x' in kwargs: xmin, xmax = 0, N - 1 else: xmin, xmax = kwargs['x'][0], kwargs['x'][1] ylog = kwargs.get('ylog', False) ylabel = '$' + kwargs.get('ylabel', 'K') + '$' x = np.linspace(xmin, xmax, N) # Show lines tke_list = [] i = 0 d = {} for tke_tuple in tke_tuple_list: label = tke_tuple[0][:-1] tke = tke_tuple[1] if 'xD_min' in kwargs: x = x[x > kwargs['xD_min']] tke = tke[-x.size:] if 'pie' in label: label = '\pi' label = '$' + label + '$' color = colors[i] plt.plot(x, tke, color=color, lw=1.5, label=label, marker=markers[i], markevery=50, markersize=4) tke_list.append(tke) d['x'] = x d[label[1:-1]] = tke i += 1 df = pd.DataFrame.from_dict(d) df.to_csv('spreadsheets/figure5b.csv', index=False) # Set limits ylims = kwargs.get('ylims', [np.min(tke_list), np.max(tke_list)]) print(ylims) ax.set_xlim(min(x), 12) ax.set_ylim(ylims[0], ylims[1]) ax.xaxis.set_ticks([0, 2, 4, 6, 8, 10, 12]) fig, ax = makeSquare(fig, ax) if ylog: ax.set_yscale('log') ax.set_ylim(ylims[0], ylims[1]) plt.minorticks_off() # Edit frame, labels and legend plt.xlabel('$x$') plt.ylabel(ylabel) # leg = plt.legend(loc=(0.75,0.16)) leg = plt.legend(loc='lower right') leg.get_frame().set_edgecolor('black') leg.get_frame().set_facecolor('white') leg.get_frame().set_linewidth(0.5) leg.get_frame().set_alpha(0.85) # ax.yaxis.set_ticks([-2, 0, 2]) ax.tick_params(bottom="on", top="on", right="on", which='both', direction='in', length=2) # Show plot and save figure # plt.show() plt.savefig(file, transparent=True, bbox_inches='tight') plt.clf() return def plotProfiles(file, profiles_tuple_list, **kwargs): ax = plt.gca() fig = plt.gcf() if not profiles_tuple_list: raise ValueError("No profile series passed to the function.") else: M = profiles_tuple_list[0][1].shape[0] if not 'Y' in kwargs: ymin, ymax = 0, M - 1 else: ymin, ymax = kwargs['Y'][0], kwargs['Y'][1] scaling = kwargs.get('scaling', 1) yshift = kwargs.get('yshift', 0) ylabel = '$' + kwargs.get('ylabel', 'Y') + '$' y = np.linspace(ymin, ymax, M) / scaling + yshift # Show lines profiles_list = [] for i, profile_tuple in enumerate(profiles_tuple_list): label = profile_tuple[0] if 'piD' in label: label = '\pi' else: label = label[:-1] label = '$' + label + '$' color = colors[i] profile = profile_tuple[1] plot = plt.plot(profile, y, color=color, lw=1.5, label=label, marker=markers[i], markevery=50, markersize=4) profiles_list.append(profile) # Set limits ax.set_ylim(min(y), max(y)) fig, ax = makeSquare(fig, ax) # Edit frame, labels and legend plt.xlabel(r'$\left\langle u \right\rangle$') plt.ylabel(ylabel, rotation=0) leg = plt.legend(loc='lower left') leg.get_frame().set_edgecolor('black') leg.get_frame().set_facecolor('white') leg.get_frame().set_linewidth(0.5) leg.get_frame().set_alpha(0.85) # ax.yaxis.set_ticks([-2, 0, 2]) ax.tick_params(bottom="on", top="on", right="on", which='both', direction='in', length=2) # Show plot and save figure plt.show() plt.savefig(file, transparent=True, bbox_inches='tight') plt.clf() return def plotProfiles_multiple(file, tuple_profiles_tuple_list, **kwargs): from collections import OrderedDict fig = plt.gcf() ax = plt.gca(projection='3d') if not tuple_profiles_tuple_list: raise ValueError("No profile series passed to the function.") else: M = tuple_profiles_tuple_list[0][1][0][1].shape[0] if not 'Y' in kwargs: ymin, ymax = 0, M - 1 else: ymin, ymax = kwargs['Y'][0], kwargs['Y'][1] scaling = kwargs.get('scaling', 1) yshift = kwargs.get('yshift', 0) ylabel = '$' + kwargs.get('ylabel', 'Y') + '$' y = np.linspace(ymin, ymax, M) / scaling + yshift # Show lines for e in tuple_profiles_tuple_list: x_loc = e[0] profiles_tuple_list = e[1] for i, profile_tuple in enumerate(profiles_tuple_list): label = profile_tuple[0] if 'piD' in label: label = '\pi' elif 'D9' in label: label = '2\mathrm{length_scale}' else: label = label[:-1] label = '$' + label + '$' color = colors[i] profile = profile_tuple[1] plot = plt.plot(profile, y, x_loc, color=color, lw=1.5, label=label, markevery=50, markersize=4) # Set limits ax.set_xlim(0.35, 1.1) ax.set_ylim(-3, 3) ax.set_zlim(2, 10.5) # fig, ax = makeSquare(fig,ax) # Edit frame, labels and legend ax.set_xlabel(r'$\overline{u}$', rotation=0) ax.set_ylabel(ylabel, rotation=0) ax.set_zlabel('$x$', rotation=0) handles, labels = plt.gca().get_legend_handles_labels() by_label = OrderedDict(zip(labels, handles)) leg = plt.legend(by_label.values(), by_label.keys(), loc=(0.8, 0.15)) ax.view_init(azim=0, elev=140) leg.get_frame().set_edgecolor('black') leg.get_frame().set_facecolor('white') leg.get_frame().set_linewidth(0.75) leg.get_frame().set_alpha(0.75) ax.xaxis.set_ticks([0.4, 0.6, 0.8, 1]) ax.yaxis.set_ticks([-2, 0, 2]) ax.zaxis.set_ticks([4, 6, 8, 10]) # ax.tick_params(bottom="on", top="on", right="on", which='both', direction='in', length=2) # plt.switch_backend('PDF') #pdf # ax.autoscale(enable=False, axis='both') # you will need this line to change the Z-axis # Show plot and save figure # plt.show() fig.tight_layout() plt.savefig(file, transparent=True, bbox_inches='tight') plt.clf() return # ------------------------------------------------------ x-Y def plotXY(y, **kwargs): """ Generate a x-Y plot in space :param y: series to plot [1D numpy array] :param label: Y axis label [string] :param file: output fn name :param kwargs: 'x' coordinates [numpy 1D array], 'xD_min' left x limit, 'ylog' log plot [boolean] :return: - """ fig, ax = plt.subplots(1, 1) N = y.shape[0] x = kwargs.get('x', np.arange(N)) file = kwargs.get('fn', 'test.pdf') x_label = kwargs.get('x_label', None) y_label = kwargs.get('y_label', None) ylog = kwargs.get('ylog', False) xlog = kwargs.get('xlog', False) # Show lines plt.plot(x, y, color='black', lw=1, label=y_label) # Edit figure, axis, limits ax.set_xlim(min(x), max(x)) if xlog: ax.set_xscale('log') if ylog: ax.set_yscale('log') fig, ax = makeSquare(fig, ax) plt.xlabel(x_label) plt.ylabel(y_label) # Show plot and save figure plt.savefig(file, transparent=True, bbox_inches='tight') return def plotXYSpatial(y, label, file, **kwargs): """ Generate a x-Y plot in space :param y: series to plot [1D numpy array] :param label: Y axis label [string] :param file: output fn name :param kwargs: 'x' coordinates [numpy 1D array], 'xD_min' left x limit, 'ylog' log plot [boolean] :return: - """ ax = plt.gca() fig = plt.gcf() N = y.shape[0] if not 'x' in kwargs: xmin, xmax = 0, N - 1 else: xmin, xmax = kwargs['x'][0], kwargs['x'][1] x = np.linspace(xmin, xmax, N) if 'xD_min' in kwargs: x = x[x > kwargs['xD_min']] y = y[-x.size:] ylog = kwargs.get('ylog', False) # Show lines plt.plot(x, y, color='black', lw=0.5, label='$L_z = 1D$') # Edit figure, axis, limits ax.set_xlim(min(x), max(x)) if ylog: ax.set_yscale('log') fig, ax = makeSquare(fig, ax) # Edit frame, labels and legend y_label = '$' + label + '$' plt.xlabel('$x/length_scale$') plt.ylabel(y_label) leg = plt.legend(loc='upper right') leg.get_frame().set_edgecolor('white') # Show plot and save figure plt.show() plt.savefig(file, transparent=True, bbox_inches='tight') return def plotScatter(x, y, cases, file): """ Generate a x-Y plot in space :param x: series to plot [1D numpy array] :param y: series to plot [1D numpy array] :param label: Y axis label [string] :param file: output fn name :return: - """ ax = plt.gca() fig = plt.gcf() # Show lines for i, case in enumerate(cases): print(i) ax.scatter(x[i], y[i], c=colors[i], marker=markers[i], s=30, linewidths=1, label=case) # Edit figure, axis, limits ax.set_xlim(0.06, 0.15) ax.set_ylim(0.1, 1.4) ax.tick_params(bottom="on", top="on", right="on", which='both', direction='in', length=2) fig, ax = makeSquare(fig, ax) # Edit frame, labels and legend plt.xlabel('$\mathrm{max}(TKE|_{Y})$') plt.ylabel('$\overline{C}_L$') leg = plt.legend(loc='lower right') leg.get_frame().set_edgecolor('black') leg.get_frame().set_facecolor('white') leg.get_frame().set_linewidth(0.5) leg.get_frame().set_alpha(0.85) # Show plot and save figure plt.savefig(file, transparent=True, bbox_inches='tight') return def plotScatter2(x1, x2, y, cases, file): """ Generate a x-Y plot in space :param x: series to plot [1D numpy array] :param y: series to plot [1D numpy array] :param label: Y axis label [string] :param file: output fn name :return: - """ ax1 = plt.gca() fig = plt.gcf() ax2 = ax1.twiny() # Show lines for i, case in enumerate(cases): ax1.scatter(x1[i], y[i], c='red', marker=markers[i], s=10, linewidths=1, label=case) ax2.scatter(x2[i], y[i], c='blue', marker=markers[i], s=10, linewidths=1, label=case) # Edit figure, axis, limits ax1.set_xlim(1, 9) ax2.set_xlim(1, 9) ax1.set_ylim(0.2, 1.3) # ax.set_ylim(0.1, 1.4) ax1.tick_params(bottom="on", top="off", right="on", which='both', direction='in', length=2) ax2.tick_params(bottom="off", top="on", right="on", which='both', direction='in', length=2) fig, ax1 = makeSquare(fig, ax1) # Edit frame, labels and legend ax1.set_xlabel('$\sigma_l$', color='red') ax2.set_xlabel('$\sigma_u$', color='blue') ax1.set_ylabel('$\overline{C}_L$') leg = ax1.legend(loc='lower right') for q in leg.legendHandles: q.set_color('grey') leg.get_frame().set_edgecolor('black') leg.get_frame().set_facecolor('white') leg.get_frame().set_linewidth(0.5) leg.get_frame().set_alpha(0.85) # Show plot and save figure # plt.show() plt.savefig(file, transparent=True, bbox_inches='tight') return def plotXYSpatial_list(file, y_tuple_list, **kwargs): """ Generate a x-Y plot in space of multiples Y series :param file: output fn name :param y_tuple_list: list of tuples as (case, Y) where 'case' is the name of the case [string] and 'Y' the series [1D numpy array] :param kwargs: 'x' coordinates [numpy 1D array], 'xD_min' left x limit, 'ylog' log plot [boolean] :return: - """ """ Generate a XY plot """ ax = plt.gca() fig = plt.gcf() if not y_tuple_list: raise ValueError("No TKE series passed to the function.") else: N = y_tuple_list[0][1].shape[0] if not 'x' in kwargs: xmin, xmax = 0, N - 1 else: xmin, xmax = kwargs['x'][0], kwargs['x'][1] ylog = kwargs.get('ylog', False) ylabel = '$' + kwargs.get('ylabel', 'K') + '$' # xmax=11.83 # print(xmin, xmax, N) x = np.linspace(xmin, xmax, N) # Show lines y_list = [] d = {} for y_tuple in enumerate(y_tuple_list): label = y_tuple[0] if 'piD' in label: label = '\pi' else: label = label[:-1] y = y_tuple[1] label = '$' + label + '$' color = colors[y_tuple[0]] if 'xD_min' in kwargs: x = x[x > kwargs['xD_min']] y = y[-x.size:] plt.plot(x, y, color=color, lw=1, label=label, marker=markers[y_tuple[0]], markevery=50, markersize=4) # , markeredgecolor = 'black', markeredgewidth=0.1) y_list.append(y) # dis['x'] = x # dis[label[1:-1]] = Y # df = pd.DataFrame.from_dict(dis) # df.to_csv('spreadsheets/figure9b.csv', index=False) # Edit figure, axis, limits ax.set_xlim(min(x), max(x)) if ylog: ax.set_yscale('log') plt.minorticks_off() fig, ax = makeSquare(fig, ax) # Edit frame, labels and legend plt.xlabel('$x$') plt.ylabel(ylabel, rotation=0) if 'R' in ylabel: leg = plt.legend(loc='upper left') # ax.yaxis.set_ticks([0.2, 0.4, 0.6, 0.8, 1.0, 1.2]) ax.yaxis.set_ticks([0.0, 0.4, 0.8, 1.2]) else: leg = plt.legend(loc='upper right') leg.get_frame().set_edgecolor('black') leg.get_frame().set_facecolor('white') leg.get_frame().set_linewidth(0.5) leg.get_frame().set_alpha(0.85) # ax.set_xlim(min(x), 12) # ax.set_ylim(ylims[0], ylims[1]) ax.xaxis.set_ticks([2, 4, 6, 8, 10, 12]) ax.tick_params(bottom="on", top="on", right="on", which='both', direction='in', length=2) # Save figure plt.savefig(file, transparent=True, bbox_inches='tight') plt.clf() return def velocity_profiles(file, profiles_tuple_list, **kwargs): """ Similar to plotXYSpatial_list just for a specific test case :param file: output fn name :param profiles_tuple_list: list of tuples of format(u,Y) """ ax = plt.gca() fig = plt.gcf() ylabel = '$' + kwargs.get('ylabel', 'r') + '$' # Show lines profiles = [] for i, profile_tuple in enumerate(profiles_tuple_list): label, profile, y = profile_tuple[0], profile_tuple[1], profile_tuple[2] p = np.where((np.array(y) > 0.45) & ((np.array(y) < 0.85))) profile = np.array(profile)[p] y = np.array(y)[p] label = '$' + label + '$' if 'pie' in label: label = '$\pi$' color = colors[i] # plt.plot(profile, Y, color=color, lw=1, label=label, marker=markers[i], markevery=10, markersize=4) plt.plot(profile, y, color=color, lw=1, label=label) profiles.append(profile) # Edit figure, axis, limits # ax.set_xlim(min(x), max(x)) fig, ax = makeSquare(fig, ax) # Edit frame, labels and legend plt.xlabel(r'$\omega_z|_z$') plt.ylabel(ylabel, rotation=0) ax.set_ylim(np.min([np.min(s) for s in y]), np.max([np.max(s) for s in y])) leg = plt.legend(loc='upper right') leg.get_frame().set_edgecolor('black') leg.get_frame().set_facecolor('white') leg.get_frame().set_linewidth(0.75) leg.get_frame().set_alpha(0.75) ax.tick_params(bottom="on", top="on", right="on", which='both', direction='in', length=2) # Show plot and save figure plt.savefig(file, transparent=True, bbox_inches='tight') plt.clf() return def plotCp_list(file, y_tuple_list, x_list, **kwargs): """ Similar to plotXYSpatial_list just for a specific test case """ ax = plt.gca() fig = plt.gcf() if not y_tuple_list: raise ValueError("No series passed to the function.") else: N = y_tuple_list[0][1].shape[0] ylabel = '$' + kwargs.get('ylabel', '') + '$' # Show lines y_list = [] i = 0 for y_tuple in y_tuple_list: label = y_tuple[0] if 'piD' in label: label = '\pi length_scale' y = y_tuple[1] label = '$' + label + '$' color = colors[i] # if i==1: # plt.scatter(x_list[i], Y, marker='^', facecolors='none', edgecolors='black', s=25, linewidths=0.5, label=label) # plt.plot(x_list[i], Y, color='black', lw=1, label=label) if i == 0: plt.scatter(x_list[i], y, marker='o', facecolors='none', edgecolors='black', s=25, linewidths=0.5, label=label) # plt.plot(x_list[i], Y, markerfacecolor='none', lw=1.5, label=label, marker='o', color='black') else: plt.plot(x_list[i], y, color='black', lw=1, label=label) y_list.append(y) i += 1 # Edit figure, axis, limits fig, ax = makeSquare(fig, ax) ax.set_xlim(0, 180) ax.xaxis.set_ticks(np.arange(0, 181, 30)) # Edit frame, labels and legend plt.xlabel(r'$\theta$') plt.ylabel(ylabel, rotation=0) leg = plt.legend(loc='upper right') leg.get_frame().set_edgecolor('white') ax.tick_params(direction='in') # Show plot and save figure plt.show() plt.savefig(file, transparent=True, bbox_inches='tight') return # ------------------------------------------------------ LogLog Spatial def plot_fft(file, xs, ys, x_label=r'$ f/U D $', y_label=None, title=None, colour='black', colours=None, l_label=None, marker=None, xlim=None, ylim=None): plt.style.use(['science', 'grid']) fig, ax = plt.subplots(figsize=(9, 7)) ax.set_title(title) ax.tick_params(bottom="on", top="on", right="on", which='both', direction='in', length=2) # Edit frame, labels and legend ax.set_xlabel(x_label) ax.set_ylabel(y_label) if xlim is not None: ax.set_xlim(xlim) if ylim is not None: ax.set_ylim(ylim) # Make legend manually if l_label and colours is not None: legend_elements = [] for idx, loop in enumerate(l_label): legend_elements.append(Line2D([0], [0], color=colours[idx], lw=4, label=loop)) ax.legend(handles=legend_elements, loc='upper right') for idx, (x, y) in enumerate(zip(xs, ys)): ax.plot(y, x, color=colours[idx], marker=marker) plt.savefig(file, bbox_inches='tight', transparent=True) return def plotLogLogSpatialSpectra(file, wn, uk): """ Generate a loglog plot of a 1D spatial signal :param file: output fn name :param wn: frequency [1D numpy array] :param uk: transformed u: uk = FFT(u). [1D numpy array] :return: - """ ax = plt.gca() fig = plt.gcf() # Show lines plt.loglog(wn, uk, color='black', lw=1.5, label='$L_z = piD$') x, y = loglogLine(p2=(max(wn), 5e-4), p1x=min(wn) * 10, m=-5 / 3) plt.loglog(x, y, color='black', lw=1, ls='dotted') x, y = loglogLine(p2=(max(wn), 4e-4), p1x=min(wn) * 10, m=-3) plt.loglog(x, y, color='black', lw=1, ls='dashdot') # Set limits # ax.set_xlim(min(wn)*10, max(wn)) # ax.set_ylim(1e-5, 1e-1) fig, ax = makeSquare(fig, ax) # Edit frame, labels and legend plt.xlabel('$kD$') plt.ylabel('$tke$') leg = plt.legend(loc='upper right') leg.get_frame().set_edgecolor('white') # Anotations # plt.text(x=5e-3, Y=2e-1, s='$-5/3$', color='black') # plt.text(x=1e-2, Y=1, s='$-3$', color='black') # Show plot and save figure plt.show() plt.savefig(file, transparent=True, bbox_inches='tight') return def plotLogLogSpatialSpectra_list(file, uk_tuple_list, wn_list): """ Generate a loglog plot of a list of 1D spatial signals :param file: output fn name :param tke_tuple_list: list of tuples as (case, uk), where 'case' is the case name [string] and 'uk' is the transformed u: uk = FFT(u). [1D numpy array] :param wn_list: list of frequencies for the different cases :return: - """ ax = plt.gca() fig = plt.gcf() # Show lines for i, uk_tuple in enumerate(uk_tuple_list): label = uk_tuple[0] if 'piD' in label: label = '\pi length_scale' uk = uk_tuple[1] label = '$' + label + '$' color = colors[i] plt.loglog(wn_list[i], uk, color=color, lw=0.5, label=label) # x, Y = loglogLine(p2=(3,1e-4), p1x=1e-2, m=-5/3) # plt.loglog(x, Y, color='black', lw=1, ls='dotted') # x, Y = loglogLine(p2=(4, 2e-5), p1x=1e-2, m=-3) # plt.loglog(x, Y, color='black', lw=1, ls='dashdot') # x, Y = loglogLine(p2=(4, 2e-5), p1x=1e-2, m=-11/3) # plt.loglog(x, Y, color='black', lw=1, ls='dashed') x, y = loglogLine(p2=(3, 1e-7), p1x=1e-2, m=-5 / 3) plt.loglog(x, y, color='black', lw=1, ls='dotted') x, y = loglogLine(p2=(4, 1e-9), p1x=1e-2, m=-3) plt.loglog(x, y, color='black', lw=1, ls='dashdot') x, y = loglogLine(p2=(4, 1e-9), p1x=1e-2, m=-11 / 3) plt.loglog(x, y, color='black', lw=1, ls='dashed') # Set limits # ax.set_xlim(1e-3, 1.5) ax.set_ylim(1e-13, 10) fig, ax = makeSquare(fig, ax) # ax.xaxis.set_tick_params(labeltop='on') ax.tick_params(bottom="on", top="on", which='both') # Edit frame, labels and legend plt.xlabel('$kD$') plt.ylabel('$tke$') leg = plt.legend(loc='upper right') leg.get_frame().set_edgecolor('white') # Anotations # plt.text(x=1e-2, Y=1e1, s='$-5/3$', color='black') # plt.text(x=1e-2, Y=3e5, s='$-3$', color='black') # plt.text(x=2e-2, Y=4e2, s='$-11/3$', color='black') # Show plot and save figure # plt.show() plt.savefig(file, transparent=False, bbox_inches='tight') return # ------------------------------------------------------ LogLog Time def plotLogLogTimeSpectra(freqs, uk, file): """ Generate a loglog plot of a time spectra series :param freqs: frequency [1D numpy array] :param uk: power signal of the time series u [1D numpy array] :param file: output fn name :return: - """ ax = plt.gca() fig = plt.gcf() # Show lines plt.loglog(freqs, uk, color='black', lw=1.5, label='$L_z = 1D$') x, y = loglogLine(p2=(1, 1e-4), p1x=1e-3, m=-5 / 3) plt.loglog(x, y, color='black', lw=1, ls='dotted') x, y = loglogLine(p2=(1, 1e-6), p1x=1e-3, m=-3) plt.loglog(x, y, color='black', lw=1, ls='dashdot') # Set limits ax.set_xlim(min(freqs[freqs > 0.5 * 1e-3]), max(freqs[freqs < 0.5])) ax.set_ylim(min(uk) * 1e-1, max(uk) * 1e1) # fig, ax = makeSquare(fig,ax) ax.xaxis.set_tick_params(labeltop='on') # Edit frame, labels and legend plt.xlabel(r'$f$') plt.ylabel(r'$F(v)$') leg = plt.legend(loc='upper right') leg.get_frame().set_edgecolor('white') # Anotations plt.text(x=2e-3, y=1e-1, s='$-5/3$', color='black') plt.text(x=7e-3, y=2e-1, s='$-3$', color='black') plt.text(x=1e-2, y=4e-1, s='$-11/3$', color='black') # Show plot and save figure plt.savefig(file, transparent=False, bbox_inches='tight') return def plotTimeSpectra_list(file, uk_tuple_list, freqs_list, **kwargs): """ Generate a loglog plot of a list of time spectra series :param file: output fn name :param uk_tuple_list: list of tuples as (case, uk), where 'case' is the case name [string] and 'uk' is power signal of the time series u [1D numpy array] :param freqs_list: list containing the frequencies [1D numpy array] for each case :return: - """ title = kwargs.get('title', None) xlim = kwargs.get('xlim', None) ylim = kwargs.get('ylim', None) ylabel = kwargs.get('ylabel', None) xlabel = kwargs.get('xlabel', None) plt.style.use(['science', 'grid']) fig, ax = plt.subplots(figsize=(7, 5)) # Show lines colors = sns.color_palette("husl", len(uk_tuple_list)) # colors = sns.color_palette("RdBu", len(uk_tuple_list)) for i, uk_tuple in enumerate(uk_tuple_list): label = uk_tuple[0] if 'pie' in label: label = '\pi' uk = uk_tuple[1] label = '$' + label + '$' color = colors[i] ax.bar(freqs_list[i], uk, color=color, lw=0.5, label=label) # Set limits # ax.set_xlim(np.min(freqs_list[0]), 2e-1) if title is not None: plt.title(title) if xlim is not None: ax.set_xlim(xlim) # Window if ylim is not None: ax.set_ylim(ylim) # Window fig, ax = makeSquare(fig, ax) # ax.xaxis.set_tick_params(labeltop='on') ax.tick_params(bottom="on", top="on", which='both') # Edit frame, labels and legend if xlabel is not None: plt.xlabel(xlabel) if ylabel is not None: plt.ylabel(ylabel) leg = plt.legend(loc='upper right') leg.get_frame().set_edgecolor('white') # Anotations # plt.text(x=3e-4, Y=5e-1, s='$-5/3$', color='black', fontsize=10) # Power # plt.text(x=4e-3, Y=1e0, s='$-3$', color='black', fontsize=10) # plt.text(x=1e-2, Y=4e-1, s='$-11/3$', color='black', fontsize=10) # plt.text(x=3e-4, Y=5e-1, s='$-5/3$', color='black', fontsize=10) # No Power # plt.text(x=4e-3, Y=1e0, s='$-3$', color='black', fontsize=10) # plt.text(x=1e-2, Y=4e-1, s='$-11/3$', color='black', fontsize=10) # Show plot and save figure # plt.show() plt.savefig(file, transparent=False, bbox_inches='tight') return def plotLogLogTimeSpectra_list(file, uk_tuple_list, freqs_list, **kwargs): """ Generate a loglog plot of a list of time spectra series :param file: output fn name :param uk_tuple_list: list of tuples as (case, uk), where 'case' is the case name [string] and 'uk' is power signal of the time series u [1D numpy array] :param freqs_list: list containing the frequencies [1D numpy array] for each case :return: - """ title = kwargs.get('title', None) xlim = kwargs.get('xlim', None) ylim = kwargs.get('ylim', None) ylabel = kwargs.get('ylabel', None) xlabel = kwargs.get('xlabel', None) colors = kwargs.get('colors', sns.color_palette("husl", len(uk_tuple_list))) plt.style.use(['science', 'grid']) fig, ax = plt.subplots(figsize=(6, 4)) # Show lines for i, uk_tuple in enumerate(uk_tuple_list): label = uk_tuple[0] if 'pie' in label: label = '\pi' uk = uk_tuple[1] color = colors[i] ax.plot(freqs_list[i], uk, color=color, lw=0.5, label=label) ax.loglog() # x, Y = loglogLine(p2=(1.e2, 1e-7), p1x=1e-2, m=-5 / 3) # plt.loglog(x, Y, color='black', lw=1, ls='dotted') # x, Y = loglogLine(p2=(1.2e2, 1e-9), p1x=1e-2, m=-3) # plt.loglog(x, Y, color='black', lw=1, ls='dashdot') # x, Y = loglogLine(p2=(1e0, 1e-8), p1x=1e-3, m=-11/3) # plt.loglog(x, Y, color='black', lw=1, ls='dashed') if title is not None: plt.title(title) if xlim is not None: ax.set_xlim(xlim) # Window if ylim is not None: ax.set_ylim(ylim) # Window # Set limits # ax.set_xlim(np.min(freqs_list[0]), 2e-1) # ax.set_ylim(1e-8, 1e-1) # ax.set_xlim(1e-2, 1e2) # Window # ax.set_ylim(1e-11, 1e-1) fig, ax = makeSquare(fig, ax) # ax.xaxis.set_tick_params(labeltop='on') ax.tick_params(bottom="on", top="on", which='both') # Edit frame, labels and legend if xlabel is not None: plt.xlabel(xlabel) if ylabel is not None: plt.ylabel(ylabel) leg = plt.legend(loc='upper right') leg.get_frame().set_edgecolor('white') # Anotations # plt.text(x=3e-4, Y=5e-1, s='$-5/3$', color='black', fontsize=10) # Power # plt.text(x=4e-3, Y=1e0, s='$-3$', color='black', fontsize=10) # plt.text(x=1e-2, Y=4e-1, s='$-11/3$', color='black', fontsize=10) # plt.text(x=3e-4, Y=5e-1, s='$-5/3$', color='black', fontsize=10) # No Power # plt.text(x=4e-3, Y=1e0, s='$-3$', color='black', fontsize=10) # plt.text(x=1e-2, Y=4e-1, s='$-11/3$', color='black', fontsize=10) # Show plot and save figure # plt.show() ax.axvline(22, c='k', ls='--') plt.savefig(file, bbox_inches='tight', transparent=True, dpi=300) return def plotLogLogTimeSpectra_list_cascade(file, uk_tuple_list, freqs_list, ylabel=r'$\mathrm{PS}\left(v\right)$', title=None): """ Same as 'plotLogLogTimeSpectra_list' but the spectra are separated a factor of 10 among them for visualization purposes """ plt.style.use(['science', 'grid']) fig, ax = plt.subplots(figsize=(7, 5)) if title is not None: plt.title(title) colors = sns.color_palette("husl", len(uk_tuple_list)) for i, uk_tup in enumerate(uk_tuple_list): uk = uk_tup[1] * 10 ** (-i) uk_tuple_list[i] = (uk_tuple_list[i][0], uk) # Show lines for i, uk_tuple in enumerate(uk_tuple_list): label = uk_tuple[0] if 'pie' in label: label = '\pi' if '2D' in label or 'D9' in label: label = '2\mathrm{length_scale}' uk = uk_tuple[1] color = colors[i] plt.loglog(freqs_list[i], uk, color=color, lw=0.5, label=label) # for i in np.arange(5): # x, Y = loglogLine(p2=(1.e2, 1e-11 * 10 ** i), p1x=1e-2, m=-5 / 3) # # plt.loglog(x, Y, color='black', lw=0.5, ls='dotted', alpha=0.3) # plt.loglog(x, Y, color='darkgrey', lw=0.3, ls='dotted') # for i in np.arange(2): # # x, Y = loglogLine(p2=(1.2e2, 1e-16 * 100 ** i), p1x=1e-3, m=-3) # # plt.loglog(x, Y, color='black', lw=0.5, ls='dashdot', alpha=0.3) # # plt.loglog(x, Y, color='darkgrey', lw=0.2, ls='dashdot') # x, Y = loglogLine(p2=(1.2e2, 1e-17 * 100 ** i), p1x=1e-2, m=-3.66) # # plt.loglog(x, Y, color='black', lw=0.5, ls='dashed', alpha=0.3) # plt.loglog(x, Y, color='darkgrey', lw=0.3, ls='dashed') # Set limits # ax.set_xlim(1e-2, 7e1) # Window # ax.set_ylim(9e-16, 1e-1) # ax.set_ylim(1e-11, 1e4) fig, ax = makeSquare(fig, ax) # Edit frame, labels and legend ax.tick_params(bottom="on", top="on", which='both', direction='in') plt.xlabel(r'$fD/U$') plt.ylabel(ylabel) leg = plt.legend(loc='lower left') leg.get_frame().set_edgecolor('black') leg.get_frame().set_facecolor('white') leg.get_frame().set_linewidth(0.5) leg.get_frame().set_alpha(0.85) # ax.yaxis.set_ticks([-2, 0, 2]) ax.tick_params(bottom="on", top="on", right="on", which='both', direction='in', length=2) # ax.get_yaxis().set_ticks([], minor=True) # ax.set_xticks([1.1, 1.2, 1.3, 1.4, 1.5, 1.6 ,1.7, 1.8, 1.9 ,2], minor=True) # ax.set_yticks([0.3, 0.55, 0.7], minor=True) # ax.xaxis.grid(True, which='major') # Show plot and save figure plt.savefig(file, transparent=False, bbox_inches='tight', dpi=600) plt.clf() return def plotLogLogSpatialSpectra_list_cascade(file, uk_tuple_list, freqs_list): """ Same as 'plotLogLogSpatialSpectra_list' but the spectras are separated a factor of 10 among them for visualization purposes """ ax = plt.gca() fig = plt.gcf() for i, uk_tup in enumerate(uk_tuple_list): uk = uk_tup[1] * 10 ** (-i) uk_tuple_list[i] = (uk_tuple_list[i][0], uk) # Show lines for i, uk_tuple in enumerate(uk_tuple_list): label = uk_tuple[0] print(label) if 'pie' in label: label = '\pi' if '2D' in label: label = '2\mathrm{length_scale}' uk = uk_tuple[1] label = '$' + label + '$' color = colors[i] plt.loglog(freqs_list[i], uk, color=color, lw=0.8, label=label) for i in np.arange(5): x, y = loglogLine(p2=(1e3, 1e-10 * 10 ** i), p1x=1e-2, m=-5 / 3) plt.loglog(x, y, color='black', lw=0.5, ls='dotted', alpha=0.3) x, y = loglogLine(p2=(1e3, 1e-13 * 10 ** i), p1x=1e-2, m=-3) plt.loglog(x, y, color='black', lw=0.5, ls='dashdot', alpha=0.3) # Set limits # ax.set_xlim(2e0, 3e2) # Window ax.set_xlim(2e0, 5e2) # Window ax.set_ylim(1e-15, 1e-1) # ax.set_ylim(1e-11, 1e4) fig, ax = makeSquare(fig, ax) # Edit frame, labels and legend ax.tick_params(bottom="on", top="on", which='both', direction='in') plt.xlabel(r'$\kappa length_scale$') leg = plt.legend(loc='lower left') leg.get_frame().set_edgecolor('white') ax.get_yaxis().set_ticks([]) # Show plot and save figure plt.show() plt.savefig(file, transparent=False, bbox_inches='tight') return # ------------------------------------------------------ Lumley's Triangle def plotLumleysTriangle(eta, xi, file): """ Generate a plot of the Reynolds stresses anisotropy tensor in the form of the Lumley's triangle :param eta: Invariant of the anisotropy tensor (displayed on the vertical axis) [1D array of points in space, i.e. Y triangle coordinates] :param xi: Invariant of the anisotropy tensor (displayed on the horizontal axis) [1D array of points in space, i.e. x triangle coordinates] :param file: output fn name :return: - """ ax = plt.gca() fig = plt.gcf() x = np.linspace(-1 / 6, 1 / 3, 500) y = np.sqrt(1 / 27 + 2 * x ** 3) # Show lines plt.plot(x, y, color='black', lw=1.5) plt.plot([-1 / 6, 0], [1 / 6, 0], color='black', lw=1.5) plt.plot([0, 1 / 3], [0, 1 / 3], color='black', lw=1.5) plt.scatter(xi, eta, marker='o', c='green', s=1, linewidths=0.1) # Set limits ax.set_ylim(0, 0.35) ax.set_xlim(-0.2, 0.4) # Make figure squared fig, ax = makeSquare(fig, ax) # Edit frame, labels and legend plt.xlabel(r'$\eta$') plt.ylabel(r'$\xi$') leg = plt.legend(loc='upper left') leg.get_frame().set_edgecolor('black') leg.get_frame().set_facecolor('white') leg.get_frame().set_linewidth(0.5) leg.get_frame().set_alpha(0.85) # Show plot and save figure plt.show() plt.savefig(file, transparent=True, bbox_inches='tight') return def plotLumleysTriangle_list(file, eta_tuple_list, xi_tuple_list): """ Generate a plot of the Reynolds stresses anisotropy tensor in the form of the Lumley's triangle for different cases :param file: output fn name :param eta_tuple_list: list of tuples as (case, eta) for the 'eta' invariant :param xi_tuple_list: list of tuples as (case, xi) for the 'xi' invariant :return: """ ax = plt.gca() fig = plt.gcf() x = np.linspace(-1 / 6, 1 / 3, 500) y = np.sqrt(1 / 27 + 2 * x ** 3) # Show lines plt.plot(x, y, color='black', lw=0.5) plt.plot([-1 / 6, 0], [1 / 6, 0], color='black', lw=0.5) plt.plot([0, 1 / 3], [0, 1 / 3], color='black', lw=0.5) # Show lines # d0 = {} for i, eta in enumerate(eta_tuple_list): label = eta[0] if 'piD' in label: label = '\pi' else: label = label[:-1] eta = eta[1] xi = xi_tuple_list[i][1] label = r'$' + label + '$' plt.scatter(xi, eta, marker=markers[i], c=colors[i], s=10, linewidths=0.1, label=label, edgecolor='black') # d0[label[1:-1]] = (xi, eta) # dis = {} # for k,v in d0.items(): # dis[r'\xi'] = v[0] # dis[r'\eta'] = v[1] # df = pd.DataFrame.from_dict(dis) # df.to_csv('spreadsheets/figure8d_'+k+'.csv', index=False) # Set limits ax.set_ylim(0, 0.35) ax.set_xlim(-0.2, 0.4) # Make figure squared fig, ax = makeSquare(fig, ax) # Edit frame, labels and legend plt.xlabel(r'$\xi$') plt.ylabel('$ \eta $', rotation=0) leg = plt.legend(loc='lower right') leg.get_frame().set_edgecolor('black') leg.get_frame().set_facecolor('white') leg.get_frame().set_linewidth(0.5) leg.get_frame().set_alpha(0.85) ax.xaxis.set_ticks([-0.2, -0.1, 0, 0.1, 0.2, 0.3, 0.4]) ax.tick_params(direction='in', length=2) ax.tick_params(bottom="on", top="on", right='on', which='both', direction='in') # plt.minorticks_off() # Show plot and save figure plt.savefig(file, transparent=True, bbox_inches='tight') return # ------------------------------------------------------ GC plots # def error_order(fn, x, Y): # """ # Generate a loglog plot of a time spectra series using the matplotlib library given the arguments # """ # # Basic definitions # ax = plt.gca() # fig = plt.gcf() # # plt.loglog(x, Y, color='b', lw=0.5) # # x, Y = loglogLine(p2=(np.max(x), np.max(Y)), p1x=np.min(x), m=2) # plt.loglog(x, Y, color='black', lw=1, ls='dotted') # x, Y = loglogLine(p2=(np.max(x), np.max(Y)), p1x=np.min(x), m=1) # plt.loglog(x, Y, color='black', lw=1, ls='dotted') # # x, Y = loglogLine(p2=(1.2e2, 1e-9), p1x=1e-2, m=-3) # # plt.loglog(x, Y, color='black', lw=1, ls='dashdot') # # x, Y = loglogLine(p2=(1e0, 1e-8), p1x=1e-3, m=-11/3) # # plt.loglog(x, Y, color='black', lw=1, ls='dashed') # # # Set limits # # ax.set_xlim(np.min(freqs_list[0]), 2e-1) # # ax.set_ylim(1e-8, 1e-1) # # ax.set_xlim(1e-2, 1e2) # Window # # ax.set_ylim(1e-11, 1e-1) # # # fig, ax = makeSquare(fig,ax) # # ax.xaxis.set_tick_params(labeltop='on') # # ax.tick_params(bottom="on", top="on", which='both') # # # Edit frame, labels and legend # # # Show plot and save figure # plt.show() # plt.savefig(fn, transparent=True, bbox_inches='tight') # return # ------------------------------------------------------ Utils def loglogLine(p2, p1x, m): b = np.log10(p2[1]) - m * np.log10(p2[0]) p1y = p1x ** m * 10 ** b return [p1x, p2[0]], [p1y, p2[1]] def makeSquare(fig, ax): fwidth = fig.get_figwidth() fheight = fig.get_figheight() # get the axis size and position in relative coordinates # this gives a BBox object bb = ax.get_position() # calculate them into real world coordinates axwidth = fwidth * (bb.x1 - bb.x0) axheight = fheight * (bb.y1 - bb.y0) # if the axis is wider than tall, then it has to be narrowe if axwidth > axheight: # calculate the narrowing relative to the figure narrow_by = (axwidth - axheight) / fwidth # move bounding box edges inwards the same amount to give the correct width bb.x0 += narrow_by / 2 bb.x1 -= narrow_by / 2 # else if the axis is taller than wide, make it vertically smaller # works the same as above elif axheight > axwidth: shrink_by = (axheight - axwidth) / fheight bb.y0 += shrink_by / 2 bb.y1 -= shrink_by / 2 ax.set_position(bb) return fig, ax def max_min_loc(a, x, y): a_max = np.amax(a) a_min = np.amin(a) i, j = np.unravel_index(a.argmax(), a.shape) x_max_loc, y_max_loc = x[i, j], y[i, j] i, j = np.unravel_index(a.argmin(), a.shape) x_min_loc, y_min_loc = x[i, j], y[i, j] my_str = 'max val: {:.2e}, max loc: ({:.3f},{:.3f})\n' \ 'min val: {:.2e}, min loc: ({:.3f},{:.3f})' \ .format(a_max, x_max_loc, y_max_loc, a_min, x_min_loc, y_min_loc) return my_str def multiple_formatter(denominator=2, number=np.pi, latex='\pi'): def gcd(a, b): while b: a, b = b, a % b return a def _multiple_formatter(x, pos): den = denominator num = np.int(np.rint(den * x / number)) com = gcd(num, den) (num, den) = (int(num / com), int(den / com)) if den == 1: if num == 0: return r'$0$' if num == 1: return r'$%s$' % latex elif num == -1: return r'$-%s$' % latex else: return r'$%s%s$' % (num, latex) else: if num == 1: return r'$\frac{%s}{%s}$' % (latex, den) elif num == -1: return r'$\frac{-%s}{%s}$' % (latex, den) else: return r'$\frac{%s%s}{%s}$' % (num, latex, den) return _multiple_formatter # def plot_poincare(x, Y, fn, **kwargs): # """ # Generate a x-Y plot in space # :param x: series to plot [1D numpy array] # :param Y: series to plot [1D numpy array] # :param label: Y axis label [string] # :param fn: output fn name # :return: - # """ # ax = plt.gca() # fig = plt.gcf() # # # Show lines # for i, case in enumerate(cases): # print(i) # ax.scatter(x[i], Y[i], length_scale=colors[i], marker=markers[i], s=30, linewidths=1, label=case) # # # Edit figure, axis, limits # ax.set_xlim(0.06, 0.15) # ax.set_ylim(0.1, 1.4) # # ax.tick_params(bottom="on", top="on", right="on", which='both', direction='in', length=2) # fig, ax = makeSquare(fig, ax) # # # Edit frame, labels and legend # plt.xlabel('$\mathrm{max}(TKE|_{Y})$') # plt.ylabel('$\overline{C}_L$') # leg = plt.legend(loc='lower right') # leg.get_frame().set_edgecolor('black') # leg.get_frame().set_facecolor('white') # leg.get_frame().set_linewidth(0.5) # leg.get_frame().set_alpha(0.85) # # # Show plot and save figure # plt.savefig(fn, transparent=True, bbox_inches='tight') # return class Multiple: def __init__(self, denominator=2, number=np.pi, latex='\pi'): self.denominator = denominator self.number = number self.latex = latex def locator(self): return plt.MultipleLocator(self.number / self.denominator) def formatter(self): return plt.FuncFormatter(multiple_formatter(self.denominator, self.number, self.latex))

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5

9a2219f4cf0919aa6cf1cc262ad0d2d335cf9fe5

293

py

Python

pycalphad/__init__.py

anilkunwar/pycalphad

eb3a074d5285b0bd76feddd1529e7edc6208c278

[ "MIT" ]

1

2021-05-27T16:18:46.000Z

pycalphad/__init__.py

anilkunwar/pycalphad

eb3a074d5285b0bd76feddd1529e7edc6208c278

[ "MIT" ]

null

pycalphad/__init__.py

anilkunwar/pycalphad

eb3a074d5285b0bd76feddd1529e7edc6208c278

[ "MIT" ]

null

from pycalphad.model import Model from pycalphad.io.database import Database from pycalphad.eq.equilibrium import Equilibrium from pycalphad.eq.energy_surf import energy_surf from pycalphad.plot.isotherm import isotherm from pycalphad.plot.binary import binplot import pycalphad.variables as v

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1

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1

0

5

9a31752564848a9a935f9d5a1ba0c99155d8e1b6

178

py

Python

symposion/proposals/apps.py

theofanislekkas/updated-symp

2bf5fa85ef2adb71325cbdd2bdfef2b0742b614a

[ "BSD-3-Clause" ]

null

symposion/proposals/apps.py

theofanislekkas/updated-symp

2bf5fa85ef2adb71325cbdd2bdfef2b0742b614a

[ "BSD-3-Clause" ]

null

symposion/proposals/apps.py

theofanislekkas/updated-symp

2bf5fa85ef2adb71325cbdd2bdfef2b0742b614a

[ "BSD-3-Clause" ]

null

from django.apps import AppConfig class ProposalsConfig(AppConfig): name = "symposion.proposals" label = "symposion_proposals" verbose_name = "Symposion Proposals"

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null

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0

5

7be16117c578dcb01112ba5c116cb0528338e0d9

368

py

Python

task2/mock.py

rokimaru/rest_api_autotests

d4009b813b064681250671ec515646dc3554bc5d

[ "Apache-2.0" ]

null

task2/mock.py

rokimaru/rest_api_autotests

d4009b813b064681250671ec515646dc3554bc5d

[ "Apache-2.0" ]

null

task2/mock.py

rokimaru/rest_api_autotests

d4009b813b064681250671ec515646dc3554bc5d

[ "Apache-2.0" ]

null

""" Mock объекты для тестирования сайта https://jsonplaceholder.typicode.com/ """ import requests def get_photos(): """ Mock ответ запроса сайта https://jsonplaceholder.typicode.com/""" return requests.get("https://jsonplaceholder.typicode.com/albums/1/photos/") def mock_data_json(): """ Возвращает пустой список """ data = [] return data

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py

Python

src/pymyinstall/installcustom/__init__.py

sdpython/pymyinstall

72b3a56a29def0694e34ccae910bf288a95cf4a5

[ "MIT" ]

8

2015-08-24T21:01:49.000Z

2018-01-04T06:34:51.000Z

src/pymyinstall/installcustom/__init__.py

sdpython/pymyinstall

72b3a56a29def0694e34ccae910bf288a95cf4a5

[ "MIT" ]

66

2015-06-14T22:04:58.000Z

2021-11-11T13:46:03.000Z

src/pymyinstall/installcustom/__init__.py

sdpython/pymyinstall

72b3a56a29def0694e34ccae910bf288a95cf4a5

[ "MIT" ]

5

2016-09-13T18:14:46.000Z

2021-08-23T12:03:28.000Z

""" @file @brief Shortuts """ from .install_custom_exceptions import ManualDownloadException from .install_custom import download_page, where_in_path from .install_custom_7z import install_7z from .install_custom_chromedriver import install_chromedriver from .install_custom_git import install_git from .install_custom_graphviz import install_graphviz from .install_custom_javajdk import install_javajdk from .install_custom_jenkins import install_jenkins from .install_custom_julia import install_julia from .install_custom_inkscape import install_inkscape from .install_custom_miktex import install_miktex from .install_custom_mingw import install_mingw from .install_custom_operadriver import install_operadriver from .install_custom_pandoc import install_pandoc from .install_custom_putty import install_putty from .install_custom_python import install_python, folder_older_than from .install_custom_R import install_R from .install_custom_scite import install_scite, modify_scite_properties from .install_custom_sqlitespy import install_sqlitespy from .install_custom_sbt import install_scala_sbt from .install_custom_tdm_gcc import install_tdm_gcc

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py

Python

python/anyascii/_data/_0b5.py

casept/anyascii

d4f426b91751254b68eaa84c6cd23099edd668e6

[ "ISC" ]

null

python/anyascii/_data/_0b5.py

casept/anyascii

d4f426b91751254b68eaa84c6cd23099edd668e6

[ "ISC" ]

null

python/anyascii/_data/_0b5.py

casept/anyascii

d4f426b91751254b68eaa84c6cd23099edd668e6

[ "ISC" ]

null

b='Duil Duilg Duilm Duilb Duils Duilt Duilp Duilh Duim Duib Duibs Duis Duiss Duing Duij Duich Duik Duit Duip Duih Di Dig Dikk Digs Din Dinj Dinh Did Dil Dilg Dilm Dilb Dils Dilt Dilp Dilh Dim Dib Dibs Dis Diss Ding Dij Dich Dik Dit Dip Dih Tta Ttag Ttakk Ttags Ttan Ttanj Ttanh Ttad Ttal Ttalg Ttalm Ttalb Ttals Ttalt Ttalp Ttalh Ttam Ttab Ttabs Ttas Ttass Ttang Ttaj Ttach Ttak Ttat Ttap Ttah Ttae Ttaeg Ttaekk Ttaegs Ttaen Ttaenj Ttaenh Ttaed Ttael Ttaelg Ttaelm Ttaelb Ttaels Ttaelt Ttaelp Ttaelh Ttaem Ttaeb Ttaebs Ttaes Ttaess Ttaeng Ttaej Ttaech Ttaek Ttaet Ttaep Ttaeh Ttya Ttyag Ttyakk Ttyags Ttyan Ttyanj Ttyanh Ttyad Ttyal Ttyalg Ttyalm Ttyalb Ttyals Ttyalt Ttyalp Ttyalh Ttyam Ttyab Ttyabs Ttyas Ttyass Ttyang Ttyaj Ttyach Ttyak Ttyat Ttyap Ttyah Ttyae Ttyaeg Ttyaekk Ttyaegs Ttyaen Ttyaenj Ttyaenh Ttyaed Ttyael Ttyaelg Ttyaelm Ttyaelb Ttyaels Ttyaelt Ttyaelp Ttyaelh Ttyaem Ttyaeb Ttyaebs Ttyaes Ttyaess Ttyaeng Ttyaej Ttyaech Ttyaek Ttyaet Ttyaep Ttyaeh Tteo Tteog Tteokk Tteogs Tteon Tteonj Tteonh Tteod Tteol Tteolg Tteolm Tteolb Tteols Tteolt Tteolp Tteolh Tteom Tteob Tteobs Tteos Tteoss Tteong Tteoj Tteoch Tteok Tteot Tteop Tteoh Tte Tteg Ttekk Ttegs Tten Ttenj Ttenh Tted Ttel Ttelg Ttelm Ttelb Ttels Ttelt Ttelp Ttelh Ttem Tteb Ttebs Ttes Ttess Tteng Ttej Ttech Ttek Ttet Ttep Tteh Ttyeo Ttyeog Ttyeokk Ttyeogs Ttyeon Ttyeonj Ttyeonh Ttyeod Ttyeol Ttyeolg Ttyeolm Ttyeolb Ttyeols Ttyeolt Ttyeolp Ttyeolh Ttyeom Ttyeob Ttyeobs Ttyeos Ttyeoss Ttyeong Ttyeoj Ttyeoch Ttyeok Ttyeot Ttyeop Ttyeoh Ttye Ttyeg Ttyekk Ttyegs Ttyen Ttyenj Ttyenh Ttyed Ttyel Ttyelg Ttyelm Ttyelb'

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py

Python

rmclino_preprocessor/__init__.py

rmclino/preprocessor

056f42e946a29c1d431628ed1044787f1237dd51

[ "MIT" ]

1

2019-05-22T20:10:57.000Z

rmclino_preprocessor/__init__.py

rmclino/preprocessor

056f42e946a29c1d431628ed1044787f1237dd51

[ "MIT" ]

null

rmclino_preprocessor/__init__.py

rmclino/preprocessor

056f42e946a29c1d431628ed1044787f1237dd51

[ "MIT" ]

null

from .fillna import FillNa from .rescale import Rescale from .normalize import Normalize

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5

d0297d8aa63a851afd671e29c1e6fcdb2997286e

783

py

Python

ucb_cs61A/quiz/quiz03/tests/no-repeats.py

tavaresdong/courses-notes

7fb89103bca679f5ef9b14cbc777152daac1402e

[ "MIT" ]

null

ucb_cs61A/quiz/quiz03/tests/no-repeats.py

tavaresdong/courses-notes

7fb89103bca679f5ef9b14cbc777152daac1402e

[ "MIT" ]

1

2017-07-31T08:15:26.000Z

ucb_cs61A/quiz/quiz03/tests/no-repeats.py

tavaresdong/courses-notes

7fb89103bca679f5ef9b14cbc777152daac1402e

[ "MIT" ]

1

2019-10-06T16:52:31.000Z

test = { 'name': 'no-repeats', 'points': 1, 'suites': [ { 'type': 'scheme', 'cases': [ { 'code': r""" scm> (no-repeats (list 5 4 5 4 2 2)) (5 4 2) """ }, { 'code': r""" scm> (no-repeats '(1 2 3 4)) (1 2 3 4) """, }, { 'code': r""" scm> (no-repeats '(1 1 3 3 5 5)) (1 3 5) """, }, { 'code': r""" scm> (no-repeats '(3 2 1 2 3)) (3 2 1) """, }, { 'code': r""" scm> (no-repeats '(4 2 4 5)) (4 2 5) """, }, ], 'setup': r""" scm> (load 'quiz03) """, }, ] }

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py

Python

src/inherit.py

ligang945/pyInterview

dde214a16f670cb7e83d5f81e8f911ce25edc43f

[ "MIT" ]

null

src/inherit.py

ligang945/pyInterview

dde214a16f670cb7e83d5f81e8f911ce25edc43f

[ "MIT" ]

null

src/inherit.py

ligang945/pyInterview

dde214a16f670cb7e83d5f81e8f911ce25edc43f

[ "MIT" ]

null

class A(object): def foo1(self): print("A") class B(A): def foo2(self): pass class C(A): def foo1(self): print("C") class D(B, C): pass d = D() d.foo1()

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d072c1bf6dc232deb7f7d56e082be519299a8fba

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py

Python

enthought/traits/ui/wx/range_editor.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

3

2016-12-09T06:05:18.000Z

2018-03-01T13:00:29.000Z

enthought/traits/ui/wx/range_editor.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

1

2020-12-02T00:51:32.000Z

2020-12-02T08:48:55.000Z

enthought/traits/ui/wx/range_editor.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

null

# proxy module from traitsui.wx.range_editor import *

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d0a2ead47d830aad59dfec16f77428211050c94b

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py

Python

test/test_ballquerry.py

duducheng/torch-points-kernels

aed9cf56ca61fe34b4880159951760e5dcb3a1db

[ "MIT" ]

52

2020-04-14T14:55:18.000Z

2021-07-19T12:36:22.000Z

test/test_ballquerry.py

hzxie/torch-points-kernels

a52ea03bdd62e890320c592282ebd89de659534f

[ "MIT" ]

32

2020-04-21T10:43:22.000Z

2021-07-29T12:27:28.000Z

test/test_ballquerry.py

hzxie/torch-points-kernels

a52ea03bdd62e890320c592282ebd89de659534f

[ "MIT" ]

12

2020-06-03T03:14:33.000Z

2021-07-25T21:50:31.000Z

import unittest import torch import numpy.testing as npt import numpy as np from sklearn.neighbors import KDTree import os import sys ROOT = os.path.join(os.path.dirname(os.path.realpath(__file__)), "..") sys.path.insert(0, ROOT) from test import run_if_cuda from torch_points_kernels import ball_query class TestBall(unittest.TestCase): @run_if_cuda def test_simple_gpu(self): a = torch.tensor([[[0, 0, 0], [1, 0, 0], [2, 0, 0]], [[0, 0, 0], [1, 0, 0], [2, 0, 0]]]).to(torch.float).cuda() b = torch.tensor([[[0, 0, 0]], [[3, 0, 0]]]).to(torch.float).cuda() idx, dist = ball_query(1.01, 2, a, b) torch.testing.assert_allclose(idx.cpu(), torch.tensor([[[0, 1]], [[2, 2]]])) torch.testing.assert_allclose(dist.cpu(), torch.tensor([[[0, 1]], [[1, -1]]]).float()) def test_simple_cpu(self): a = torch.tensor([[[0, 0, 0], [1, 0, 0], [2, 0, 0]], [[0, 0, 0], [1, 0, 0], [2, 0, 0]]]).to(torch.float) b = torch.tensor([[[0, 0, 0]], [[3, 0, 0]]]).to(torch.float) idx, dist = ball_query(1.01, 2, a, b, sort=True) torch.testing.assert_allclose(idx, torch.tensor([[[0, 1]], [[2, 2]]])) torch.testing.assert_allclose(dist, torch.tensor([[[0, 1]], [[1, -1]]]).float()) a = torch.tensor([[[0, 0, 0], [1, 0, 0], [1, 1, 0]]]).to(torch.float) idx, dist = ball_query(1.01, 3, a, a, sort=True) torch.testing.assert_allclose(idx, torch.tensor([[[0, 1, 0], [1, 0, 2], [2, 1, 2]]])) @run_if_cuda def test_larger_gpu(self): a = torch.randn(32, 4096, 3).to(torch.float).cuda() idx, dist = ball_query(1, 64, a, a) self.assertGreaterEqual(idx.min(), 0) @run_if_cuda def test_cpu_gpu_equality(self): a = torch.randn(5, 1000, 3) b = torch.randn(5, 500, 3) res_cpu = ball_query(1, 500, a, b)[0].detach().numpy() res_cuda = ball_query(1, 500, a.cuda(), b.cuda())[0].cpu().detach().numpy() for i in range(b.shape[0]): for j in range(b.shape[1]): # Because it is not necessary the same order assert set(res_cpu[i][j]) == set(res_cuda[i][j]) res_cpu = ball_query(0.01, 500, a, b)[0].detach().numpy() res_cuda = ball_query(0.01, 500, a.cuda(), b.cuda())[0].cpu().detach().numpy() for i in range(b.shape[0]): for j in range(b.shape[1]): # Because it is not necessary the same order assert set(res_cpu[i][j]) == set(res_cuda[i][j]) class TestBallPartial(unittest.TestCase): @run_if_cuda def test_simple_gpu(self): x = torch.tensor([[10, 0, 0], [0.1, 0, 0], [0.2, 0, 0], [0.1, 0, 0]]).to(torch.float).cuda() y = torch.tensor([[0, 0, 0]]).to(torch.float).cuda() batch_x = torch.from_numpy(np.asarray([0, 0, 0, 1])).long().cuda() batch_y = torch.from_numpy(np.asarray([0])).long().cuda() idx, dist2 = ball_query(0.2, 4, x, y, mode="PARTIAL_DENSE", batch_x=batch_x, batch_y=batch_y) idx = idx.detach().cpu().numpy() dist2 = dist2.detach().cpu().numpy() idx_answer = np.asarray([[1, 2, -1, -1]]) dist2_answer = np.asarray([[0.0100, 0.04, -1, -1]]).astype(np.float32) npt.assert_array_almost_equal(idx, idx_answer) npt.assert_array_almost_equal(dist2, dist2_answer) def test_simple_cpu(self): x = torch.tensor([[10, 0, 0], [0.1, 0, 0], [10, 0, 0], [10.1, 0, 0]]).to(torch.float) y = torch.tensor([[0, 0, 0]]).to(torch.float) batch_x = torch.from_numpy(np.asarray([0, 0, 0, 0])).long() batch_y = torch.from_numpy(np.asarray([0])).long() idx, dist2 = ball_query(1.0, 2, x, y, mode="PARTIAL_DENSE", batch_x=batch_x, batch_y=batch_y) idx = idx.detach().cpu().numpy() dist2 = dist2.detach().cpu().numpy() idx_answer = np.asarray([[1, -1]]) dist2_answer = np.asarray([[0.0100, -1.0000]]).astype(np.float32) npt.assert_array_almost_equal(idx, idx_answer) npt.assert_array_almost_equal(dist2, dist2_answer) def test_breaks(self): x = torch.tensor([[10, 0, 0], [0.1, 0, 0], [10, 0, 0], [10.1, 0, 0]]).to(torch.float) y = torch.tensor([[0, 0, 0]]).to(torch.float) batch_x = torch.from_numpy(np.asarray([0, 0, 1, 1])).long() batch_y = torch.from_numpy(np.asarray([0])).long() with self.assertRaises(RuntimeError): idx, dist2 = ball_query(1.0, 2, x, y, mode="PARTIAL_DENSE", batch_x=batch_x, batch_y=batch_y) def test_random_cpu(self, cuda=False): a = torch.randn(100, 3).to(torch.float) b = torch.randn(50, 3).to(torch.float) batch_a = torch.tensor([0 for i in range(a.shape[0] // 2)] + [1 for i in range(a.shape[0] // 2, a.shape[0])]) batch_b = torch.tensor([0 for i in range(b.shape[0] // 2)] + [1 for i in range(b.shape[0] // 2, b.shape[0])]) R = 1 idx, dist = ball_query( R, 15, a, b, mode="PARTIAL_DENSE", batch_x=batch_a, batch_y=batch_b, sort=True, ) idx1, dist = ball_query( R, 15, a, b, mode="PARTIAL_DENSE", batch_x=batch_a, batch_y=batch_b, sort=True, ) torch.testing.assert_allclose(idx1, idx) with self.assertRaises(AssertionError): idx, dist = ball_query( R, 15, a, b, mode="PARTIAL_DENSE", batch_x=batch_a, batch_y=batch_b, sort=False, ) idx1, dist = ball_query( R, 15, a, b, mode="PARTIAL_DENSE", batch_x=batch_a, batch_y=batch_b, sort=False, ) torch.testing.assert_allclose(idx1, idx) self.assertEqual(idx.shape[0], b.shape[0]) self.assertEqual(dist.shape[0], b.shape[0]) self.assertLessEqual(idx.max().item(), len(batch_a)) # Comparison to see if we have the same result tree = KDTree(a.detach().numpy()) idx3_sk = tree.query_radius(b.detach().numpy(), r=R) i = np.random.randint(len(batch_b)) for p in idx[i].detach().numpy(): if p >= 0 and p < len(batch_a): assert p in idx3_sk[i] @run_if_cuda def test_random_gpu(self): a = torch.randn(100, 3).to(torch.float).cuda() b = torch.randn(50, 3).to(torch.float).cuda() batch_a = torch.tensor( [0 for i in range(a.shape[0] // 2)] + [1 for i in range(a.shape[0] // 2, a.shape[0])] ).cuda() batch_b = torch.tensor( [0 for i in range(b.shape[0] // 2)] + [1 for i in range(b.shape[0] // 2, b.shape[0])] ).cuda() R = 1 idx, dist = ball_query( R, 15, a, b, mode="PARTIAL_DENSE", batch_x=batch_a, batch_y=batch_b, sort=False, ) # Comparison to see if we have the same result tree = KDTree(a.cpu().detach().numpy()) idx3_sk = tree.query_radius(b.cpu().detach().numpy(), r=R) i = np.random.randint(len(batch_b)) for p in idx[i].cpu().detach().numpy(): if p >= 0 and p < len(batch_a): assert p in idx3_sk[i] if __name__ == "__main__": unittest.main()

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d0bed27deea304d34e869a0d5d678aba9eb6cffe

87

py

Python

indico_toolkit/auto_review/__init__.py

IndicoDataSolutions/Indico-Solutions-Toolkit

c9a38681c84e86a48bcde0867359ddd2f52ce236

[ "MIT" ]

6

2021-05-20T16:48:27.000Z

2022-03-15T15:43:40.000Z

indico_toolkit/auto_review/__init__.py

IndicoDataSolutions/Indico-Solutions-Toolkit

c9a38681c84e86a48bcde0867359ddd2f52ce236

[ "MIT" ]

25

2021-06-25T13:37:21.000Z

2022-01-03T15:54:26.000Z

indico_toolkit/auto_review/__init__.py

IndicoDataSolutions/Indico-Solutions-Toolkit

c9a38681c84e86a48bcde0867359ddd2f52ce236

[ "MIT" ]

null

from .review_config import ReviewConfiguration from .auto_reviewer import AutoReviewer

29

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5

d0f543da85e5a5992a9016d7bea1421fad60688c

107

py

Python

apps/mixins/db_admin/__init__.py

Eudorajab1/websaw

7c3a369789d23ac699868fa1eff6c63e3e5c1e36

[ "MIT" ]

1

2022-03-29T00:12:12.000Z

apps/mixins/db_admin/__init__.py

Eudorajab1/websaw

7c3a369789d23ac699868fa1eff6c63e3e5c1e36

[ "MIT" ]

null

apps/mixins/db_admin/__init__.py

Eudorajab1/websaw

7c3a369789d23ac699868fa1eff6c63e3e5c1e36

[ "MIT" ]

null

from .controllers import app, Context from . import db_admin # this is mixin, do not mount it #app.mount()

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1906d06b249b10beca9f90107007a96a883a90f8

55

py

Python

ex112/UtilidadesCeV/__init__.py

LucasIdalino/Exerc-cios-do-Curso

4ca4610d1acfe4672c20114f891b6aabae816049

[ "MIT" ]

null

ex112/UtilidadesCeV/__init__.py

LucasIdalino/Exerc-cios-do-Curso

4ca4610d1acfe4672c20114f891b6aabae816049

[ "MIT" ]

null

ex112/UtilidadesCeV/__init__.py

LucasIdalino/Exerc-cios-do-Curso

4ca4610d1acfe4672c20114f891b6aabae816049

[ "MIT" ]

null

from Pythonexercicios.ex111.UtilidadesCeV import moeda

27.5

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null

0

1

0

1

0

1

0

5

ef75285e686ad1b3b09424a1584b25dd3d32cddb

6,170

py

Python

tests/test_normalexp_dist.py

jab0707/UncertainSCI

569c978c4f67dd7bb37e730276f2a376b8639235

[ "MIT" ]

1

2021-07-25T17:02:36.000Z

tests/test_normalexp_dist.py

jab0707/UncertainSCI

569c978c4f67dd7bb37e730276f2a376b8639235

[ "MIT" ]

70

2020-04-09T17:38:12.000Z

2022-03-18T17:06:09.000Z

tests/test_normalexp_dist.py

jab0707/UncertainSCI

569c978c4f67dd7bb37e730276f2a376b8639235

[ "MIT" ]

7

2020-05-28T17:26:05.000Z

2021-08-13T21:41:10.000Z

import unittest import numpy as np from numpy.linalg import norm from UncertainSCI.distributions import NormalDistribution, ExponentialDistribution class DistTestCase(unittest.TestCase): """ Tests for parameters for distributons. """ def test_exp_dist(self): """Test for exponential distribution""" # lbd is None, mean and stdev are iterables n = np.random.randint(1, 10) num = 10 * np.random.rand(n,) mean = [num[i] for i in range(len(num))] stdev = mean loc = 0. E = ExponentialDistribution(lbd=None, loc=loc, mean=mean, stdev=stdev) delta = 1e-3 errstr = 'Failed for n = {}, mean = {} and stdev = {}'.format(n, mean, stdev) self.assertAlmostEqual(E.lbd, [1/num[i] for i in range(len(num))], delta=delta, msg=errstr) self.assertAlmostEqual(E.loc, [0. for i in range(len(num))], delta=delta, msg=errstr) self.assertAlmostEqual(E.dim, n, delta=delta, msg=errstr) # lbd is not None, mean and stdev are None lbd = [num[i] for i in range(len(num))] loc = 0. E = ExponentialDistribution(lbd=lbd, loc=loc) delta = 1e-3 errstr = 'Failed for n = {}, mean = {} and stdev = {}'.format(n, mean, stdev) self.assertAlmostEqual(E.lbd, [num[i] for i in range(len(num))], delta=delta, msg=errstr) self.assertAlmostEqual(E.loc, [0. for i in range(len(num))], delta=delta, msg=errstr) self.assertAlmostEqual(E.dim, n, delta=delta, msg=errstr) # Test for MC_samples lbd = -n * np.random.rand(2,) loc = -n * np.random.rand(2,) E = ExponentialDistribution(flag=False, lbd=[lbd[0], lbd[1]], loc=[loc[0], loc[1]]) x = E.MC_samples(M=int(1e7)) F1 = np.mean(x, axis=0) F2 = 1 / lbd + loc # F1 = np.var(x, axis=0) # F2 = 1 / lbd**2 delta = 1e-2 ind = np.where(np.abs(F1-F2) > delta)[:2][0] if ind.size > 0: errstr = 'Failed' else: errstr = '' self.assertAlmostEqual(np.linalg.norm(F1-F2, ord=np.inf), 0., delta=delta, msg=errstr) def test_normal_dist(self): """Test for Normal distribution.""" # cov is None and meaniter n = np.random.randint(2, 10) mean = [0.] * n cov = None N = NormalDistribution(mean=mean, cov=cov) delta = 1e-3 errstr = 'Failed for n = {}, mean = {} and cov = {}'.format(n, mean, cov) self.assertAlmostEqual(N.mean(), mean, delta=delta, msg=errstr) self.assertAlmostEqual(norm(N.cov()-np.eye(len(mean))), 0, delta=delta, msg=errstr) self.assertAlmostEqual(N.dim, len(mean), delta=delta, msg=errstr) # cov is None and mean is None mean = None cov = None N = NormalDistribution(mean=mean, cov=cov) errstr = 'Failed for n = {}, mean = {} and cov = {}'.format(n, mean, cov) self.assertAlmostEqual(N.mean(), 0., delta=delta, msg=errstr) self.assertAlmostEqual(norm(N.cov()-np.eye(1)), 0, delta=delta, msg=errstr) self.assertAlmostEqual(N.dim, 1, delta=delta, msg=errstr) # cov is None and mean is a scalar mean = np.random.randn() cov = None N = NormalDistribution(mean=mean, cov=cov) errstr = 'Failed for n = {}, mean = {} and cov = {}'.format(n, mean, cov) self.assertAlmostEqual(N.mean(), mean, delta=delta, msg=errstr) self.assertAlmostEqual(norm(N.cov()-np.eye(1)), 0, delta=delta, msg=errstr) self.assertAlmostEqual(N.dim, 1, delta=delta, msg=errstr) # len(mean) > 1 and cov.shape[0] > 1 mean = [0]*(n) cov = np.eye(n) N = NormalDistribution(mean=mean, cov=cov) errstr = 'Failed for n = {}, mean = {} and cov = {}'.format(n, mean, cov) self.assertAlmostEqual(N.mean(), mean, delta=delta, msg=errstr) self.assertAlmostEqual(norm(N.cov()-cov), 0, delta=delta, msg=errstr) self.assertAlmostEqual(N.dim, cov.shape[0], delta=delta, msg=errstr) # len(mean) == 1 and cov.shape[0] > 1 mean = [0.] cov = np.eye(n) N = NormalDistribution(mean=mean, cov=cov) errstr = 'Failed for n = {}, mean = {} and cov = {}'.format(n, mean, cov) self.assertAlmostEqual(N.mean(), [mean[0] for i in range(cov.shape[0])], delta=delta, msg=errstr) self.assertAlmostEqual(norm(N.cov()-cov), 0, delta=delta, msg=errstr) self.assertAlmostEqual(N.dim, cov.shape[0], delta=delta, msg=errstr) # mean is None and cov.shape[0] > 1 mean = None cov = np.eye(n) N = NormalDistribution(mean=mean, cov=cov) errstr = 'Failed for n = {}, mean = {} and cov = {}'.format(n, mean, cov) self.assertAlmostEqual(N.mean(), [0. for i in range(cov.shape[0])], delta=delta, msg=errstr) self.assertAlmostEqual(norm(N.cov()-cov), 0, delta=delta, msg=errstr) self.assertAlmostEqual(N.dim, cov.shape[0], delta=delta, msg=errstr) # mean is a scalar and cov.shape[0] > 1 mean = 0 cov = np.eye(n) N = NormalDistribution(mean=mean, cov=cov) errstr = 'Failed for n = {}, mean = {} and cov = {}'.format(n, mean, cov) self.assertAlmostEqual(N.mean(), [mean for i in range(cov.shape[0])], delta=delta, msg=errstr) self.assertAlmostEqual(norm(N.cov()-cov), 0, delta=delta, msg=errstr) self.assertAlmostEqual(N.dim, cov.shape[0], delta=delta, msg=errstr) # Test for MC_samples mean = np.random.rand(2,) var = np.random.rand(2,) N = NormalDistribution(mean=[mean[0], mean[1]], cov=np.array([[var[0], 0], [0, var[1]]])) x = N.MC_samples(M=int(1e6)) # F1 = np.mean(x, axis=0) # F2 = mean F1 = np.var(x, axis=0) F2 = var delta = 1e-2 ind = np.where(np.abs(F1-F2) > delta)[:2][0] if ind.size > 0: errstr = 'Failed' else: errstr = '' self.assertAlmostEqual(np.linalg.norm(F1-F2, ord=np.inf), 0., delta=delta, msg=errstr) if __name__ == "__main__": unittest.main(verbosity=2)

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null

0

1

0

null

0

5

efb6d230dfc5867f6e357d1e15a58c95a38cfa5a

4,212

py

Python

main.py

gohan-chan69/price-alert-bot

75c8e4e83e3706b2da1e2b13e2fa41acc8ce8768

[ "Apache-2.0" ]

null

main.py

gohan-chan69/price-alert-bot

75c8e4e83e3706b2da1e2b13e2fa41acc8ce8768

[ "Apache-2.0" ]

null

main.py

gohan-chan69/price-alert-bot

75c8e4e83e3706b2da1e2b13e2fa41acc8ce8768

[ "Apache-2.0" ]

null

'''import requests api_key = 'api key' def get_price(symbol): api_url = f'https://min-api.cryptocompare.com/data/price?fsym={symbol}&tsyms=USD&api_key={api_key}' r = requests.get(api_url).json() return r['USD'] def get_data(symbol): base_url = 'https://www.cryptocompare.com' api_url = f'https://min-api.cryptocompare.com/data/blockchain/mining/calculator?fsyms={symbol}&tsyms=USD&api_key={api_key}' r = requests.get(api_url).json() full_name = r['Data'][symbol]['CoinInfo']['FullName'] img_url = r['Data'][symbol]['CoinInfo']['ImageUrl'] full_img_url = base_url+img_url url = r['Data'][symbol]['CoinInfo']['Url'] chrt_url = base_url+url coines_Mined = r['Data'][symbol]['CoinInfo']['TotalCoinsMined'] Launch_Date = r['Data'][symbol]['CoinInfo']['AssetLaunchDate'] supply = r['Data'][symbol]['CoinInfo']['MaxSupply'] return full_name, full_img_url, chrt_url,coines_Mined,Launch_Date,supply def send_to_discord(symbol): price = get_price(symbol) full_name, full_img_url, chrt_url,coines_Mined,Launch_Date,supply = get_data(symbol) json1 = {"content": "@here","tts": False,'avatar_url': "https://cdn.discordapp.com/attachments/929686954726031393/949732158195527700/gojo_1.png","embeds": [{"type": "rich","title": symbol,"description": "all info was scraped from cryptocompare api","url": chrt_url,"color": 0xdaa6f6,"fields": [{"name": 'info!',"value": f"full_name: {full_name}\nprice: {price}\ncoines Mined: {coines_Mined}\nLaunch Date: Launch_Date\n Max Supply: {supply}"}],"author": {"author": {"url": chrt_url}},"thumbnail": {"url": full_img_url},"footer": {"text": symbol}}]} r = requests.post('https://discord.com/api/webhooks/934629182581907546/cGgGjwBzNT0ksETDyVfpVZsHJLyifDXhL1Da1tsaiHOt_bTVQ24T_p8FCe1uqjMozOxv',json=json1) print(r.json) send_to_discord('ETH')''' from email import header from http import client import requests import discord from discord.ext import commands token = 'token here' client = commands.Bot(command_prefix='!') header = { "Authorization": f'Bot {token}' } api_key = 'your key here' async def get_price(symbol): try: api_url = f'https://min-api.cryptocompare.com/data/price?fsym={symbol}&tsyms=USD&api_key={api_key}' r = requests.get(api_url).json() return r['USD'] except: pass async def get_data(symbol): try: base_url = 'https://www.cryptocompare.com' api_url = f'https://min-api.cryptocompare.com/data/blockchain/mining/calculator?fsyms={symbol}&tsyms=USD&api_key={api_key}' r = requests.get(api_url).json() full_name = r['Data'][symbol]['CoinInfo']['FullName'] img_url = r['Data'][symbol]['CoinInfo']['ImageUrl'] full_img_url = base_url+img_url url = r['Data'][symbol]['CoinInfo']['Url'] chrt_url = base_url+url coines_Mined = r['Data'][symbol]['CoinInfo']['TotalCoinsMined'] Launch_Date = r['Data'][symbol]['CoinInfo']['AssetLaunchDate'] supply = r['Data'][symbol]['CoinInfo']['MaxSupply'] return full_name, full_img_url, chrt_url,coines_Mined,Launch_Date,supply except: pass @client.command() async def price(ctx,chan_id, symbol): price = await get_price(symbol) full_name, full_img_url, chrt_url,coines_Mined,Launch_Date,supply = await get_data(symbol) json1 = {"content": None,"tts": False,'avatar_url': "https://cdn.discordapp.com/attachments/929686954726031393/949732158195527700/gojo_1.png","embeds": [{"type": "rich","title": symbol,"description": "all info was scraped from cryptocompare api","url": chrt_url,"color": 0xdaa6f6,"fields": [{"name": 'info!',"value": f"full_name: {full_name}\nprice: {price}\ncoins Mined: {coines_Mined}\nLaunch Date: {Launch_Date}\n Max Supply: {supply}"}],"author": {"author": {"url": chrt_url}},"thumbnail": {"url": full_img_url},"footer": {"text": symbol}}]} r = requests.post(f'https://discord.com/api/v9/channels/{chan_id}/messages',json=json1, headers=header) @commands.Cog.listener() async def on_Command_error(ctx,error): if isinstance(error, commands.BadArgument): await ctx.send('I could not find that member. Please try again.') client.run(token)

55.421053

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null

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null

0

5

efd3898d6ce09666aaced91c47b15108026efc77

150

py

Python

rest_models/__init__.py

LCOGT/django-rest-models

bb0b507cfedd8f6cc71b6532c629ef9e86784103

[ "BSD-2-Clause" ]

null

rest_models/__init__.py

LCOGT/django-rest-models

bb0b507cfedd8f6cc71b6532c629ef9e86784103

[ "BSD-2-Clause" ]

null

rest_models/__init__.py

LCOGT/django-rest-models

bb0b507cfedd8f6cc71b6532c629ef9e86784103

[ "BSD-2-Clause" ]

null

__VERSION__ = '1.9.3' try: from rest_models.checks import register_checks register_checks() except ImportError: # pragma: no cover pass

18.75

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0.85

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0

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150

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0

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null

0

1

0

5

efe96ffa7d9e9c1beb6de953be48164f35987a8c

203

py

Python

core_lib/error_handling/status_code_exception.py

shubham-surya/core-lib

543db80706746a937e5ed16bd50f2de8d58b32e4

[ "MIT" ]

null

core_lib/error_handling/status_code_exception.py

shubham-surya/core-lib

543db80706746a937e5ed16bd50f2de8d58b32e4

[ "MIT" ]

9

2021-03-11T02:29:17.000Z

2022-03-22T19:01:18.000Z

core_lib/error_handling/status_code_exception.py

shubham-surya/core-lib

543db80706746a937e5ed16bd50f2de8d58b32e4

[ "MIT" ]

2

2022-01-27T11:19:00.000Z

2022-02-11T11:33:09.000Z

class StatusCodeException(Exception): def __init__(self, status_code: int, *args, **kwargs): self.status_code = status_code super(StatusCodeException, self).__init__(*args, **kwargs)

40.6

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null

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null

0

1

0

5

4bd0172262391410d8f6ecd51b20153e472e6f0a

51

py

Python

clock.py

varianone/sample-python-app

d03324a4ff54bb662013a7f6fe701e38551250a5

[ "MIT" ]

8

2019-09-02T15:34:18.000Z

2022-02-21T03:56:24.000Z

clock.py

varianone/sample-python-app

d03324a4ff54bb662013a7f6fe701e38551250a5

[ "MIT" ]

2

2020-09-25T05:43:24.000Z

2021-06-25T15:24:56.000Z

clock.py

varianone/sample-python-app

d03324a4ff54bb662013a7f6fe701e38551250a5

[ "MIT" ]

2

2021-05-19T11:03:19.000Z

2021-06-11T19:18:04.000Z

print('This job is run every day at 6pm by uWSGI')

25.5

50

0.72549

11

51

3.363636

1

0

0.02439

0.196078

51

1

51

0.878049

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0

1

0

true

0

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0

1

0

null

0

1

0

1

0

5

4bd8cafbc13d2d57cb60bc29c6ac98bc654d198d

94

py

Python

test.py

JaksoSoftware/jakso-ml

5720ea557ca2fcf9ae16e329c198acd8e31258c4

[ "MIT" ]

null

test.py

JaksoSoftware/jakso-ml

5720ea557ca2fcf9ae16e329c198acd8e31258c4

[ "MIT" ]

3

2020-09-25T18:40:52.000Z

2021-08-25T14:44:30.000Z

test.py

JaksoSoftware/jakso-ml

5720ea557ca2fcf9ae16e329c198acd8e31258c4

[ "MIT" ]

null

from tests.training_data.sample_image import TestSampleImage import unittest unittest.main()

18.8

60

0.861702

12

94

6.583333

0.833333

0

0.085106

94

4

61

23.5

0.918605

0

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true

0

0.666667

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0.666667

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1

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null

0

1

0

null

0

1

0

1

0

1

0

5

4bf10162900625be45f02bd494892ae7b3a16109

203

py

Python

bubblekicker/__init__.py

gbellandi/bubble_size_analysis

6627f516f65c7d9900e4932913583e1283198f07

[ "MIT" ]

4

2017-08-03T03:48:59.000Z

2022-03-24T10:51:48.000Z

bubblekicker/__init__.py

gbellandi/bubble_size_analysis

6627f516f65c7d9900e4932913583e1283198f07

[ "MIT" ]

6

2016-10-26T14:25:39.000Z

2021-04-12T15:26:03.000Z

bubblekicker/__init__.py

gbellandi/bubble_size_analysis

6627f516f65c7d9900e4932913583e1283198f07

[ "MIT" ]

6

2016-12-20T10:13:23.000Z

2021-04-16T21:57:51.000Z

from bubblekicker import BubbleKicker from pipelines import CannyPipeline, AdaptiveThresholdPipeline from utils import (calculate_convexity, calculate_circularity_reciprocal)

29

63

0.773399

17

203

9.058824

0.647059

0

0.206897

203

6

64

33.833333

0.956522

0

1

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true

0

0.75

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0.75

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null

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1

0

null

0

1

0

1

0

1

0

5

4bf823dba4cac29f89ffc8baa0ce25aa1bee1439

106

py

Python

other_tests/imports.py

nuua-io/Nuua

d74bec22d09d25f2bc0ced8d7c9a154ff84a874d

[ "MIT" ]

43

2018-11-17T02:08:09.000Z

2022-03-03T14:50:02.000Z

other_tests/imports.py

nuua-io/Nuua

d74bec22d09d25f2bc0ced8d7c9a154ff84a874d

[ "MIT" ]

2

2019-08-07T03:16:51.000Z

2021-05-17T03:05:08.000Z

other_tests/imports.py

nuua-io/Nuua

d74bec22d09d25f2bc0ced8d7c9a154ff84a874d

[ "MIT" ]

3

2019-01-07T18:43:35.000Z

2021-07-21T12:12:23.000Z

from sample_import import sample, sample2 import imports2 print(f"Sample: {sample}, Sample2: {sample2}")

21.2

46

0.773585

14

106

5.785714

0.5

0.320988

0

0.042553

0.113208

106

4

47

26.5

0.819149

0

0.339623

0

1

0

true

0

0.666667

0

0.666667

0.333333

1

0

null

1

0

1

0

null

0

1

0

1

0

5

4bfa5001622a87d0c541dc27547faabfbf409823

166

py

Python

tests/conftest.py

Mendes11/micro_framework

dfb9f7a55922284e70f6937dd478a1edaacd83b7

[ "Apache-2.0" ]

7

2020-05-20T21:19:02.000Z

2021-12-28T17:50:50.000Z

tests/conftest.py

Mendes11/micro_framework

dfb9f7a55922284e70f6937dd478a1edaacd83b7

[ "Apache-2.0" ]

30

2020-06-07T20:20:11.000Z

2021-06-03T14:58:41.000Z

tests/conftest.py

Mendes11/micro_framework

dfb9f7a55922284e70f6937dd478a1edaacd83b7

[ "Apache-2.0" ]

null

import pytest from micro_framework.config import DEFAULT_CONFIG from micro_framework.runner import Runner @pytest.fixture def config(): return DEFAULT_CONFIG

15.090909

49

0.819277

22

166

6

0.5

0.136364

0.272727

0

0.138554

166

10

50

16.6

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0

1

0.166667

true

0

0.5

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0

1

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null

0

1

0

null

0

1

0

1

0

5

ef11ffb86400b42d7cb25303b4c95d2b102cc590

96

py

Python

venv/lib/python3.8/site-packages/urllib3/util/queue.py

GiulianaPola/select_repeats

17a0d053d4f874e42cf654dd142168c2ec8fbd11

[ "MIT" ]

2

2022-03-13T01:58:52.000Z

2022-03-31T06:07:54.000Z

venv/lib/python3.8/site-packages/urllib3/util/queue.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

19

2021-11-20T04:09:18.000Z

2022-03-23T15:05:55.000Z

venv/lib/python3.8/site-packages/urllib3/util/queue.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

null

/home/runner/.cache/pip/pool/9d/18/17/f3f797fbf564bf1a17d3de905a8cfc3ecd101d4004c482c263fecf9dc3

96

0.895833

9

96

9.555556

1

0

0.375

0

96

1

96

0.520833

0

1

0

null

0

null

0

1

0

null

0

1

0

1

0

null

1

0

1

0

5

ef274829300a533f55be65269109b1638459b7cf

115

py

Python

metabolite_database/main/__init__.py

lparsons/metabolite_database

f1a5aa3e31d00e13ba4862e5cbb666b44dc67ce0

[ "MIT" ]

null

metabolite_database/main/__init__.py

lparsons/metabolite_database

f1a5aa3e31d00e13ba4862e5cbb666b44dc67ce0

[ "MIT" ]

9

2018-12-20T18:17:53.000Z

2019-03-08T22:25:10.000Z

metabolite_database/main/__init__.py

lparsons/metabolite_database

f1a5aa3e31d00e13ba4862e5cbb666b44dc67ce0

[ "MIT" ]

1

2020-12-04T14:21:37.000Z

from flask import Blueprint bp = Blueprint('main', __name__) from metabolite_database.main import routes # noqa

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py

Python

ktg2/models/enums/__init__.py

kkristof200/py_telegram_2

6be1940d836f1c262e148b782c3f7c483c901b0b

[ "MIT" ]

null

ktg2/models/enums/__init__.py

kkristof200/py_telegram_2

6be1940d836f1c262e148b782c3f7c483c901b0b

[ "MIT" ]

null

ktg2/models/enums/__init__.py

kkristof200/py_telegram_2

6be1940d836f1c262e148b782c3f7c483c901b0b

[ "MIT" ]

null

from .chat_type import ChatType from .parse_mode import ParseMode

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74

py

Python

mister/__init__.py

guillochon/mister

346bc06232625a16f1f1af8268fe8afc1cbddbb9

[ "MIT" ]

null

mister/__init__.py

guillochon/mister

346bc06232625a16f1f1af8268fe8afc1cbddbb9

[ "MIT" ]

null

mister/__init__.py

guillochon/mister

346bc06232625a16f1f1af8268fe8afc1cbddbb9

[ "MIT" ]

null

"""Initialize mister package.""" from .mister import * # noqa: F401,F403

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5

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105

py

Python

ShaderDemo/game/shader/gpu/__init__.py

bitsawer/renpy-shader

6c750689a3d7952494a3b98a3297762bb4933308

[ "MIT" ]

45

2016-10-04T05:03:23.000Z

2022-02-09T13:20:38.000Z

ShaderDemo/game/shader/gpu/__init__.py

bitsawer/renpy-shader

6c750689a3d7952494a3b98a3297762bb4933308

[ "MIT" ]

4

2016-10-04T13:35:15.000Z

2020-07-13T10:46:31.000Z

ShaderDemo/game/shader/gpu/__init__.py

bitsawer/renpy-shader

6c750689a3d7952494a3b98a3297762bb4933308

[ "MIT" ]

10

2017-02-16T04:36:53.000Z

2021-04-10T08:31:29.000Z

from framebuffer import FrameBuffer from shaderprogram import ShaderProgram from texture import Texture

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5

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176

py

Python

pyhaversion/__init__.py

bdraco/pyhaversion

87851f8fe868c1a5d5cdcdd56a1064031d37e220

[ "MIT" ]

4

2020-12-30T23:34:37.000Z

2021-11-08T09:13:45.000Z

pyhaversion/__init__.py

bdraco/pyhaversion

87851f8fe868c1a5d5cdcdd56a1064031d37e220

[ "MIT" ]

27

2019-08-27T08:05:18.000Z

2022-03-18T06:05:49.000Z

pyhaversion/__init__.py

bdraco/pyhaversion

87851f8fe868c1a5d5cdcdd56a1064031d37e220

[ "MIT" ]

9

2019-07-02T06:19:46.000Z

2021-11-04T16:18:42.000Z

"""pyhaversion package.""" from .consts import HaVersionChannel, HaVersionSource from .version import HaVersion __all__ = ["HaVersion", "HaVersionChannel", "HaVersionSource"]

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5

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58

py

Python

notetool/database/__init__.py

notechats/noteutil

bf58e0e3575dac18fff2f4cfcd7fa5ae2f1662fb

[ "Apache-2.0" ]

1

2020-08-05T07:45:00.000Z

notetool/database/__init__.py

notechats/notetool

bf58e0e3575dac18fff2f4cfcd7fa5ae2f1662fb

[ "Apache-2.0" ]

null

notetool/database/__init__.py

notechats/notetool

bf58e0e3575dac18fff2f4cfcd7fa5ae2f1662fb

[ "Apache-2.0" ]

null

from notetool.database.core import BaseTable, SqliteTable

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