xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

06f0db028f642ae4780997de45b1641ca0132cd6

23

py

Python

src/python/__init__.py

mesnardo/petibm-flapping

0a96126ec89bd22de9065ea2922eecd9d4cc110e

[ "BSD-3-Clause" ]

null

src/python/__init__.py

mesnardo/petibm-flapping

0a96126ec89bd22de9065ea2922eecd9d4cc110e

[ "BSD-3-Clause" ]

null

src/python/__init__.py

mesnardo/petibm-flapping

0a96126ec89bd22de9065ea2922eecd9d4cc110e

[ "BSD-3-Clause" ]

null

from .flapping import *

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b0a99b7770237b772213f4c77073bf8bca6266ca

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py

Python

plotly/graph_objs/layout/yaxis/__init__.py

mprostock/plotly.py

3471c3dfbf783927c203c676422260586514b341

[ "MIT" ]

12

2020-04-18T18:10:22.000Z

2021-12-06T10:11:15.000Z

plotly/graph_objs/layout/yaxis/__init__.py

Vesauza/plotly.py

e53e626d59495d440341751f60aeff73ff365c28

[ "MIT" ]

27

2020-04-28T21:23:12.000Z

2021-06-25T15:36:38.000Z

plotly/graph_objs/layout/yaxis/__init__.py

Vesauza/plotly.py

e53e626d59495d440341751f60aeff73ff365c28

[ "MIT" ]

6

2020-04-18T23:07:08.000Z

2021-11-18T07:53:06.000Z

from ._title import Title from plotly.graph_objs.layout.yaxis import title from ._tickformatstop import Tickformatstop from ._tickfont import Tickfont

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7c07e832bd0c0cd10d051e51f33029eccaf823de

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py

Python

src/core/toga/sources/__init__.py

luizoti/toga

3c49e685f325f1aba2ce048b253402d7e4519f97

[ "BSD-3-Clause" ]

1,261

2019-03-31T16:28:47.000Z

2022-03-31T09:01:23.000Z

src/core/toga/sources/__init__.py

luizoti/toga

3c49e685f325f1aba2ce048b253402d7e4519f97

[ "BSD-3-Clause" ]

597

2019-04-02T20:02:42.000Z

2022-03-30T10:28:47.000Z

src/core/toga/sources/__init__.py

luizoti/toga

3c49e685f325f1aba2ce048b253402d7e4519f97

[ "BSD-3-Clause" ]

318

2019-03-31T18:32:00.000Z

2022-03-30T18:07:13.000Z

from .accessors import to_accessor # noqa: F401 from .base import Source # noqa: F401 from .list_source import ListSource # noqa: F401 from .tree_source import TreeSource # noqa: F401 from .value_source import ValueSource # noqa: F401

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py

Python

mfrc522reader/__init__.py

bcurnow/mfrc522-reader

fc9293ef162f7a3223482c1bd3ea39ea0f1170fa

[ "Apache-2.0" ]

1

2021-01-06T16:47:22.000Z

mfrc522reader/__init__.py

bcurnow/mfrc522-reader

fc9293ef162f7a3223482c1bd3ea39ea0f1170fa

[ "Apache-2.0" ]

null

mfrc522reader/__init__.py

bcurnow/mfrc522-reader

fc9293ef162f7a3223482c1bd3ea39ea0f1170fa

[ "Apache-2.0" ]

null

# Bring the MFRC class up the top level to avoid having to import mfrc522reader.mfrc522.MFRC522 from mfrc522reader.mfrc522 import MFRC522 # noqa: F401

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9ff3599cff2fbc7200e5b32db7f775087195a51d

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py

Python

scripts/figures_for_paper/__init__.py

fang-ren/on_the_fly_assessment

102a7985d1765b11e6a7fdc1a11ac973cbc5fe3d

[ "BSD-3-Clause-LBNL" ]

1

2017-03-02T23:42:19.000Z

scripts/on_the_fly_assessment/__init__.py

fang-ren/on_the_fly_assessment

102a7985d1765b11e6a7fdc1a11ac973cbc5fe3d

[ "BSD-3-Clause-LBNL" ]

null

scripts/on_the_fly_assessment/__init__.py

fang-ren/on_the_fly_assessment

102a7985d1765b11e6a7fdc1a11ac973cbc5fe3d

[ "BSD-3-Clause-LBNL" ]

4

2017-08-07T15:12:17.000Z

2019-12-24T13:08:10.000Z

""" author: Fang Ren (SSRL) 4/27/2017 """

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6

c661855013101673976e18f73fd20d900249042e

1,677

py

Python

src/anaplanConnector/endpoints.py

matt-budd/anaplan-connector

885a9efc81973129dc76d86962e943aa3ec8b570

[ "MIT" ]

null

src/anaplanConnector/endpoints.py

matt-budd/anaplan-connector

885a9efc81973129dc76d86962e943aa3ec8b570

[ "MIT" ]

null

src/anaplanConnector/endpoints.py

matt-budd/anaplan-connector

885a9efc81973129dc76d86962e943aa3ec8b570

[ "MIT" ]

null

class Endpoints: def __init__(self,workspaceId=None,modelId=None): self.workspaceId = workspaceId self.modelId = modelId self.fileId = None self.auth = 'https://auth.anaplan.com' self.api = 'https://api.anaplan.com/2/0' self.token = f'{self.auth}/token/authenticate' self.workspaces = f'{self.api}/workspaces' def models(self): return f'{self.api}/workspaces/{self.workspaceId}/models' def files(self): return f'{self.api}/workspaces/{self.workspaceId}/models/{self.modelId}/files' def file(self): return f'{self.api}/workspaces/{self.workspaceId}/models/{self.modelId}/files/{self.fileId}' def processes(self): return f'{self.api}/workspaces/{self.workspaceId}/models/{self.modelId}/processes' def runProcess(self, processId): return f'{self.api}/workspaces/{self.workspaceId}/models/{self.modelId}/processes/{processId}/tasks' def chunk(self, fileId, chunkNum): return f'{self.api}/workspaces/{self.workspaceId}/models/{self.modelId}/files/{fileId}/chunks/{chunkNum}' def exports(self): return f'{self.api}/workspaces/{self.workspaceId}/models/{self.modelId}/exports' def startExport(self,exportId): return f'{self.api}/workspaces/{self.workspaceId}/models/{self.modelId}/exports/{exportId}/tasks' def taskStatus(self, exportId, taskId): return f'{self.api}/workspaces/{self.workspaceId}/models/{self.modelId}/exports/{exportId}/tasks/{taskId}' def getNumChunks(self, fileId): return f'{self.api}/workspaces/{self.workspaceId}/models/{self.modelId}/files/{fileId}/chunks'

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6

c675f662e9f0ac049c0c05214976d0ed4b9df2a1

3,574

py

Python

deepqmc/wavefunction/radial_functions.py

NLESC-JCER/DeepQMC

1e1ed24bf8e0de43be68bcd966425c119359c8b8

[ "Apache-2.0" ]

6

2019-12-10T22:49:51.000Z

2020-06-19T08:23:32.000Z

deepqmc/wavefunction/radial_functions.py

NLESC-JCER/QMC

1e1ed24bf8e0de43be68bcd966425c119359c8b8

[ "Apache-2.0" ]

10

2019-08-19T08:01:44.000Z

2020-01-07T12:09:51.000Z

deepqmc/wavefunction/radial_functions.py

NLESC-JCER/QMC

1e1ed24bf8e0de43be68bcd966425c119359c8b8

[ "Apache-2.0" ]

2

2019-09-30T22:48:15.000Z

2020-06-19T08:23:39.000Z

import torch def radial_slater( R, bas_n, bas_exp, xyz=None, derivative=0, jacobian=True): """Compute the radial part of STOs (or its derivative). Arguments: R {torch.tensor} -- distance between each electron and each atom bas_n {torch.tensor} -- principal quantum number bas_exp {torch.tensor} -- exponents of the exponential Keyword Arguments: xyz {torch.tensor} -- positions of the electrons (needed for derivative) (default: {None}) derivative {int} -- degree of the derivative (default: {0}) jacobian {bool} -- return the jacobian, i.e the sum of the gradients (default: {True}) Returns: torch.tensor -- values of each orbital radial part at each position """ if derivative == 0: return R**bas_n * torch.exp(-bas_exp * R) elif derivative > 0: rn = R**(bas_n) nabla_rn = (bas_n * R**(bas_n - 2)).unsqueeze(-1) * xyz er = torch.exp(-bas_exp * R) nabla_er = -(bas_exp * er).unsqueeze(-1) * \ xyz / R.unsqueeze(-1) if derivative == 1: if jacobian: nabla_rn = nabla_rn.sum(3) nabla_er = nabla_er.sum(3) return nabla_rn * er + rn * nabla_er else: return nabla_rn * \ er.unsqueeze(-1) + rn.unsqueeze(-1) * nabla_er elif derivative == 2: sum_xyz2 = (xyz**2).sum(3) lap_rn = bas_n * (3 * R**(bas_n - 2) + sum_xyz2 * (bas_n - 2) * R**(bas_n - 4)) lap_er = bas_exp**2 * er * sum_xyz2 / R**2 \ - 2 * bas_exp * er * sum_xyz2 / R**3 return lap_rn * er + 2 * \ (nabla_rn * nabla_er).sum(3) + rn * lap_er def radial_gaussian( R, bas_n, bas_exp, xyz=None, derivative=0, jacobian=True): """Compute the radial part of GTOs (or its derivative). Arguments: R {torch.tensor} -- distance between each electron and each atom bas_n {torch.tensor} -- principal quantum number bas_exp {torch.tensor} -- exponents of the exponential Keyword Arguments: xyz {torch.tensor} -- positions of the electrons (needed for derivative) (default: {None}) derivative {int} -- degree of the derivative (default: {0}) jacobian {bool} -- return the jacobian, i.e the sum of the gradients (default: {True}) Returns: torch.tensor -- values of each orbital radial part at each position """ if derivative == 0: return R**bas_n * torch.exp(-bas_exp * R**2) elif derivative > 0: rn = R**(bas_n) nabla_rn = (bas_n * R**(bas_n - 2)).unsqueeze(-1) * xyz er = torch.exp(-bas_exp * R**2) nabla_er = -2 * (bas_exp * er).unsqueeze(-1) * xyz if derivative == 1: if jacobian: nabla_rn = nabla_rn.sum(3) nabla_er = nabla_er.sum(3) return nabla_rn * er + rn * nabla_er else: return nabla_rn * \ er.unsqueeze(-1) + rn.unsqueeze(-1) * nabla_er elif derivative == 2: lap_rn = bas_n * (3 * R**(bas_n - 2) + (xyz**2).sum(3) * (bas_n - 2) * R**(bas_n - 4)) lap_er = 4 * bas_exp**2 * (xyz**2).sum(3) * er \ - 6 * bas_exp * er return lap_rn * er + 2 * \ (nabla_rn * nabla_er).sum(3) + rn * lap_er

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6

c686c15b9e3b0ce20f4fcceacb7366bca04f08d2

270

py

Python

blocks/kafka/__init__.py

severstal-digital/typed-blocks

276e65d22772057ba58198332406274d06b87788

[ "Apache-2.0" ]

null

blocks/kafka/__init__.py

severstal-digital/typed-blocks

276e65d22772057ba58198332406274d06b87788

[ "Apache-2.0" ]

null

blocks/kafka/__init__.py

severstal-digital/typed-blocks

276e65d22772057ba58198332406274d06b87788

[ "Apache-2.0" ]

null

from blocks.kafka.app import KafkaApp from blocks.kafka.events import Batch, CommitEvent, NoNewEvents from blocks.kafka.topics import InputTopic, OutputTopic from blocks.kafka.sources import KafkaSource from blocks.kafka.processors import KafkaProducer, OffsetCommitter

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c6984f6951db319db071cb9f8624bcd686f7ebc3

976

py

Python

scrabble_player/utils/board_configurations.py

CodyWoolaver/scrabble-player

1e81030e68b783b4842b345fd3b7db20a4f99891

[ "MIT" ]

null

scrabble_player/utils/board_configurations.py

CodyWoolaver/scrabble-player

1e81030e68b783b4842b345fd3b7db20a4f99891

[ "MIT" ]

1

2019-12-21T00:23:39.000Z

scrabble_player/utils/board_configurations.py

CodyWoolaver/ScrabblePlayer

1e81030e68b783b4842b345fd3b7db20a4f99891

[ "MIT" ]

null

# - DefaultTile # 1 TrippleWord # 2 TrippleLetter # 3 DoubleWord # 4 DoubleLetter # 5 Center DATA = { "Scrabble": ( 15, 15, "1--4---1---4--1" "-3---2---2---3-" "--3---4-4---3--" "4--3---4---3---" "----3-----3----" "-2---2---2---2-" "--4---4-4---4--" "1--4---5---4--1" "--4---4-4---4--" "-2---2---2---2-" "----3-----3----" "4--3---4---3---" "--3---4-4---3--" "-3---2---2---3-" "1--4---1---4--1" ), "Words With Friends": ( 15, 15, "---1--2-2--1---" "--4--3---3--4--" "-4--4-----4--4-" "1--2---3---2--1" "--4---4-4---4--" "-3---2---2---3-" "2---4-----4---2" "---3-------3---" "2---4-----4---2" "-3---2---2---3-" "--4---4-4---4--" "1--2---3---2--1" "-4--4-----4--4-" "--4--3---3--4--" "---1--2-2--1---" ) }

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c69d559012d0683664f4e35cc02818f3b1a40a87

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py

Python

rosys/communication/__init__.py

zauberzeug/rosys

10271c88ffd5dcc4fb8eec93d46fe4144a9e40d8

[ "MIT" ]

1

2022-02-20T08:21:07.000Z

rosys/communication/__init__.py

zauberzeug/rosys

10271c88ffd5dcc4fb8eec93d46fe4144a9e40d8

[ "MIT" ]

1

2022-03-08T12:46:09.000Z

rosys/communication/__init__.py

zauberzeug/rosys

10271c88ffd5dcc4fb8eec93d46fe4144a9e40d8

[ "MIT" ]

null

from .communication import Communication from .communication_factory import CommunicationFactory from .serial_communication import SerialCommunication from .web_communication import WebCommunication

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py

Python

src/datalabs/operations/aggregate/__init__.py

xcfcode/DataLab

d1a310de4986cb704b1fe3dea859452b8c14fc71

[ "Apache-2.0" ]

null

src/datalabs/operations/aggregate/__init__.py

xcfcode/DataLab

d1a310de4986cb704b1fe3dea859452b8c14fc71

[ "Apache-2.0" ]

null

src/datalabs/operations/aggregate/__init__.py

xcfcode/DataLab

d1a310de4986cb704b1fe3dea859452b8c14fc71

[ "Apache-2.0" ]

null

from .general import * from .aggregating import aggregating # from .text_classification import * # from .sequence_labeling import * # from .summarization import * # from .text_matching import * # from .kg_link_prediction import *

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05a14d872479130cb7320d6733aa4c5a9cf86919

44

py

Python

Helloworld.py

Ehsan746/Network-1

2587134ee4e6be585f37d588c276ca5acf29d6fd

[ "MIT" ]

null

Helloworld.py

Ehsan746/Network-1

2587134ee4e6be585f37d588c276ca5acf29d6fd

[ "MIT" ]

null

Helloworld.py

Ehsan746/Network-1

2587134ee4e6be585f37d588c276ca5acf29d6fd

[ "MIT" ]

null

import time print(time.strftime("%H : %M"))

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05b4636d7c1f721f3e42727ce1c917798670e05f

859

py

Python

nfv/nfv-common/nfv_common/debug/__init__.py

SidneyAn/nfv

5f0262a5b6ea4be59f977b9c587c483cbe0e373d

[ "Apache-2.0" ]

2

2020-02-07T19:01:36.000Z

2022-02-23T01:41:46.000Z

nfv/nfv-common/nfv_common/debug/__init__.py

SidneyAn/nfv

5f0262a5b6ea4be59f977b9c587c483cbe0e373d

[ "Apache-2.0" ]

1

2021-01-14T12:02:25.000Z

nfv/nfv-common/nfv_common/debug/__init__.py

SidneyAn/nfv

5f0262a5b6ea4be59f977b9c587c483cbe0e373d

[ "Apache-2.0" ]

2

2021-01-13T08:39:21.000Z

2022-02-09T00:21:55.000Z

# Copyright (c) 2015-2016 Wind River Systems, Inc. # # SPDX-License-Identifier: Apache-2.0 # from nfv_common.debug._debug_defs import DEBUG_LEVEL # noqa: F401 from nfv_common.debug._debug_log import debug_dump_loggers # noqa: F401 from nfv_common.debug._debug_log import debug_get_logger # noqa: F401 from nfv_common.debug._debug_log import debug_trace # noqa: F401 from nfv_common.debug._debug_module import debug_deregister_config_change_callback # noqa: F401 from nfv_common.debug._debug_module import debug_finalize # noqa: F401 from nfv_common.debug._debug_module import debug_get_config # noqa: F401 from nfv_common.debug._debug_module import debug_initialize # noqa: F401 from nfv_common.debug._debug_module import debug_register_config_change_callback # noqa: F401 from nfv_common.debug._debug_module import debug_reload_config # noqa: F401

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05dcad0a4251c714e16f9f9946953cb0b8e8c088

30

py

Python

autoedakit.py

ankitshaw/auto-eda

0836f64390e5c5268034c4808c6825a5aeffb227

[ "Apache-2.0" ]

null

autoedakit.py

ankitshaw/auto-eda

0836f64390e5c5268034c4808c6825a5aeffb227

[ "Apache-2.0" ]

null

autoedakit.py

ankitshaw/auto-eda

0836f64390e5c5268034c4808c6825a5aeffb227

[ "Apache-2.0" ]

null

print("Welcome to AutoEdaKit")

30

0.8

4

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null

0

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1

0

6

af15542db96df1f41a5761fe715fe0073513c3d9

2,492

py

Python

core/migrations/0037_remove_facet_models.py

kingsdigitallab/egomedia-django

7347fc96ca52ac195b388e43204d0a0faab0c88f

[ "MIT" ]

null

core/migrations/0037_remove_facet_models.py

kingsdigitallab/egomedia-django

7347fc96ca52ac195b388e43204d0a0faab0c88f

[ "MIT" ]

10

2021-04-06T18:17:44.000Z

2022-03-01T12:21:40.000Z

core/migrations/0037_remove_facet_models.py

kingsdigitallab/egomedia-django

7347fc96ca52ac195b388e43204d0a0faab0c88f

[ "MIT" ]

null

# Generated by Django 2.2.2 on 2019-08-07 10:46 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('core', '0036_add_facettype_to_basefacet'), ] operations = [ migrations.AlterUniqueTogether( name='focus', unique_together=None, ), migrations.RemoveField( model_name='focus', name='facet_type', ), migrations.AlterUniqueTogether( name='keyword', unique_together=None, ), migrations.RemoveField( model_name='keyword', name='facet_type', ), migrations.AlterUniqueTogether( name='method', unique_together=None, ), migrations.RemoveField( model_name='method', name='facet_type', ), migrations.RemoveField( model_name='projectpage', name='disciplines', ), migrations.RemoveField( model_name='projectpage', name='focus', ), migrations.RemoveField( model_name='projectpage', name='keywords', ), migrations.RemoveField( model_name='projectpage', name='methods', ), migrations.RemoveField( model_name='researcherpage', name='disciplines', ), migrations.RemoveField( model_name='researcherpage', name='focus', ), migrations.RemoveField( model_name='researcherpage', name='keywords', ), migrations.RemoveField( model_name='researcherpage', name='methods', ), migrations.RemoveField( model_name='themepage', name='disciplines', ), migrations.RemoveField( model_name='themepage', name='focus', ), migrations.RemoveField( model_name='themepage', name='keywords', ), migrations.RemoveField( model_name='themepage', name='methods', ), migrations.DeleteModel( name='Discipline', ), migrations.DeleteModel( name='Focus', ), migrations.DeleteModel( name='Keyword', ), migrations.DeleteModel( name='Method', ), ]

25.428571

52

0.504013

174

2,492

7.074713

0.252874

0.25589

0.316816

0.365556

0.703493

0.116978

0

0.012475

0.388844

2,492

97

53

25.690722

0.795798

0.018058

0

0.824176

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0.012679

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null

1

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1

0

null

0

6

af1bcd524f8d79099d180bc5b17ccac2ef92f8b7

39

py

Python

wifi_password/__main__.py

topdefaultuser/wifi-password

3ac73b8f48cb520158ca1eec93ed2261f4a6b61a

[ "MIT" ]

2,552

2021-01-25T21:43:11.000Z

2022-03-30T10:45:00.000Z

wifi_password/__main__.py

topdefaultuser/wifi-password

3ac73b8f48cb520158ca1eec93ed2261f4a6b61a

[ "MIT" ]

69

2021-01-25T22:15:58.000Z

2022-01-22T16:01:21.000Z

wifi_password/__main__.py

topdefaultuser/wifi-password

3ac73b8f48cb520158ca1eec93ed2261f4a6b61a

[ "MIT" ]

293

2021-01-26T12:44:54.000Z

2022-03-30T01:50:04.000Z

from wifi_password import main main()

9.75

30

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39

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null

0

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6

af5d1af13ddda9e452c400b539ca07bd6b2b3da4

33,130

py

Python

jogo_de_cartas_21.py

danilodelucio/Jogo_de_Cartas_21

28159c4967db03041830c23b555884db1830d8bf

[ "MIT" ]

null

jogo_de_cartas_21.py

danilodelucio/Jogo_de_Cartas_21

28159c4967db03041830c23b555884db1830d8bf

[ "MIT" ]

null

jogo_de_cartas_21.py

danilodelucio/Jogo_de_Cartas_21

28159c4967db03041830c23b555884db1830d8bf

[ "MIT" ]

null

from funcoes import * from random import randint from time import sleep # IDIOMA DO JOGO language = 0 while True: try: language = int(input('[1] ENGLISH\n[2] PORTUGUÊS\n-> ')) except: linha() print('PLEASE SELECT A LANGUAGE! / POR FAVOR, SELECIONE UM IDIOMA!') linha() continue if language == 1: linha() print('<<< English language selected >>>') linha() break elif language == 2: linha() print('<<< Idioma em Português selecionado >>>') linha() break else: linha() print('PLEASE SELECT A LANGUAGE! / POR FAVOR, SELECIONE UM IDIOMA!') linha() idiom(language, ' WELCOME TO 21 CARD GAME \n', ' BEM VINDO AO JOGO DE CARTAS 21 \n') # SINGLE PLAYER OR MULTIPLAYER MODE playerMode = 0 while True: try: if language == 1: playerMode = int(input('[1] SINGLE PLAYER\n[2] 1 VS 1\n[3] GAME RULES\n-> ')) elif language == 2: playerMode = int(input('[1] UM JOGADOR\n[2] 1 CONTRA 1\n[3] REGRAS DO JOGO\n-> ')) except: msgERROR(language) continue if playerMode == 1: linha() idiom(language, '<<< Single Player mode selected >>>', '<<< Modo de UM JOGADOR selecionado >>>') linha() break elif playerMode == 2: linha() idiom(language, '<<< 1 vs 1 mode selected >>>', '<<< Modo de 1 CONTRA 1 selecionado >>>') linha() break elif playerMode == 3: regras(language) continue else: msgERROR(language) continue # NAMES nome_Player1 = '' nome_Player2 = '' if playerMode == 1: while True: try: if language == 1: nome_Player1 = str(input('Type your name: ')).title().strip() elif language == 2: nome_Player1 = str(input('Digite seu nome: ')).title().strip() except: msgERROR(language) continue if nome_Player1.isnumeric(): msgERROR(language) continue elif nome_Player1 == '': msgERROR(language) continue else: break if playerMode == 2: # PLAYER 1 while True: try: if language == 1: nome_Player1 = str(input('Player1 name: ')).upper().strip() elif language == 2: nome_Player1 = str(input('Nome do Jogador1: ')).upper().strip() except: msgERROR(language) continue if nome_Player1 == '': msgERROR(language) continue elif nome_Player1.isnumeric(): msgERROR(language) continue else: break # PLAYER 2 while True: try: if language == 1: nome_Player2 = str(input('Player2 name: ')).upper().strip() elif language == 2: nome_Player2 = str(input('Nome do Jogador2: ')).upper().strip() except: msgERROR(language) continue if nome_Player2 == '': msgERROR(language) continue elif nome_Player2.isnumeric(): msgERROR(language) continue else: break print() # ACUMULADORES 01 vitoriasP1 = vitoriasP2 = derrotas = empates = partidas = 0 # PLAYER 1 MODE if playerMode == 1: while True: valor = ['A', 2, 3, 4, 5, 6, 7, 8, 9, 10, 'J', 'Q', 'K'] naipe = [] if language == 1: naipe = ['Spades', 'Hearts', 'Clubs', 'Dimonds'] elif language == 2: naipe = ['Espadas', 'Copas', 'Paus', 'Ouros'] # SORTEIO DE 2 CARTAS (VALORES E NAIPES) / CARTAS DE ENTRADA sorteio_valor1 = valor[randint(0, 12)] sorteio_valor2 = valor[randint(0, 12)] while True: if sorteio_valor2 == sorteio_valor1: sorteio_valor2 = valor[randint(0, 12)] elif sorteio_valor2 != sorteio_valor1: break sorteio_valor1_BOT = valor[randint(0, 12)] sorteio_valor2_BOT = valor[randint(0, 12)] while True: if sorteio_valor2_BOT == sorteio_valor1_BOT: sorteio_valor2_BOT = valor[randint(0, 12)] elif sorteio_valor2_BOT != sorteio_valor1_BOT: break sorteio_naipe1 = randint(0, 3) sorteio_naipe2 = randint(0, 3) while True: if sorteio_naipe2 == sorteio_naipe1: sorteio_naipe2 = randint(0, 3) elif sorteio_naipe2 != sorteio_naipe1: break sorteio_naipe1_BOT = randint(0, 3) sorteio_naipe2_BOT = randint(0, 3) while True: if sorteio_naipe2_BOT == sorteio_naipe1_BOT: sorteio_naipe2_BOT = randint(0, 3) elif sorteio_naipe2_BOT != sorteio_naipe1_BOT: break # MOSTRANDO O VALOR DA CARTA E O NAIPE idiom(language, 'SHUFFLING THE CARDS...', 'EMBARALHANDO AS CARTAS...') print() sleep(1) idiom(language, f'Cards from the player {nome_Player1}:', f'Cartas do jogador {nome_Player1}:') sleep(1) de = '' if language == 1: de = 'of' elif language == 2: de = 'de' carta1 = f'{sorteio_valor1} {de} {naipe[sorteio_naipe1]}' print(carta1) sleep(1) carta2 = f'{sorteio_valor2} {de} {naipe[sorteio_naipe2]}' print(carta2) sleep(1) print() cartas_Player1 = [carta1, carta2] idiom(language, f'Cards from BOT:', f'Cartas do BOT:') sleep(1) carta3 = f'{sorteio_valor1_BOT} {de} {naipe[sorteio_naipe1_BOT]}' print(carta3) sleep(1) carta4 = f'{sorteio_valor2_BOT} {de} {naipe[sorteio_naipe2_BOT]}' print(carta4) sleep(1) cartas_BOT = [carta3, carta4] # SOMA DAS CARTAS print() linha() soma1 = validacaoLetras(sorteio_valor1) + validacaoLetras(sorteio_valor2) soma_BOT = validacaoLetras(sorteio_valor1_BOT) + validacaoLetras(sorteio_valor2_BOT) if language == 1: print(f'The total sum of the two cards from {nome_Player1}: {soma1}') print() print(f'The total sum of the two cards from BOT: {soma_BOT}') elif language == 2: print(f'Soma total das duas cartas do {nome_Player1}: {soma1}') print() print(f'Soma total das duas cartas do BOT: {soma_BOT}') linha() # ACUMULADORES 02 somaFinal_Player1 = soma1 + 0 somaFinal_BOT = soma_BOT + 0 jogadaParada_BOT = 0 while True: comprar = '' while True: try: if language == 1: comprar = str(input(f'Do you wish to take another card {nome_Player1}? [Y/N] ')).upper().strip()[0] if comprar == 'Y': comprar = 'S' elif language == 2: comprar = str(input(f'Deseja comprar mais uma carta {nome_Player1}? [S/N] ')).upper().strip()[0] except: msgERROR(language) continue if comprar.isnumeric(): msgERROR(language) continue elif comprar == 'N' or comprar == 'S': break else: msgERROR(language) continue if comprar == 'S': # COMPRAR CARTA EXTRA sorteio_valorExtra = valor[randint(0, 12)] sorteio_naipeExtra = randint(0, 3) carta_Extra = f'{sorteio_valorExtra} {de} {naipe[sorteio_naipeExtra]}' sleep(1) print() idiom(language, f'Extra card: {carta_Extra}.', f'Carta Extra Sorteada: {carta_Extra}.') print() cartas_Player1.append(carta_Extra) somaFinal_Player1 += validacaoLetras(sorteio_valorExtra) sleep(1) idiom(language, f'Total sum: {somaFinal_Player1}.', f'Soma total: {somaFinal_Player1}.') linha() if somaFinal_Player1 == 21: vitoria(language) vitoriasP1 += 1 status(language, cartas_BOT, cartas_Player1, vitoriasP1) break if somaFinal_Player1 > 21: Player1_Estourou(language) derrotas += 1 status(language, cartas_BOT, cartas_Player1, vitoriasP1) break if somaFinal_Player1 == somaFinal_BOT and jogadaParada_BOT == 1: empate(language) empates += 1 status(language, cartas_BOT, cartas_Player1, vitoriasP1) break elif comprar == 'N': paradaPlayer1(language, nome_Player1, somaFinal_Player1) if jogadaParada_BOT == 1: idiom(language, f'The BOT stopped in {somaFinal_BOT}.', f'O BOT tinha parado no valor {somaFinal_BOT}.') linha() if somaFinal_BOT < somaFinal_Player1 < 21: break else: continue # JOGADA DO BOT sleep(1) idiom(language, "Now it's BOT's turn...", 'Agora é a vez do BOT...') sleep(1) if somaFinal_BOT == 21: BOT_venceu(language) derrotas += 1 status(language, cartas_BOT, cartas_Player1, vitoriasP1) break if somaFinal_BOT > 21: BOT_Estourou(language) vitoriasP1 += 1 status(language, cartas_BOT, cartas_Player1, vitoriasP1) break if comprar == 'N' and somaFinal_BOT > somaFinal_Player1 and jogadaParada_BOT == 1: break if comprar == 'S' and somaFinal_BOT == somaFinal_Player1 and somaFinal_BOT >= 18: paradaBOT(language, somaFinal_BOT) jogadaParada_BOT = 1 print() linha() continue if comprar == 'S' and somaFinal_BOT == somaFinal_Player1 and somaFinal_BOT < 18: # --------------------- / / ----------------------- # idiom(language, 'The BOT is taking a card... ', 'O BOT está comprando mais uma carta... ') sorteio_valor_extra_bot = valor[randint(0, 12)] sorteio_naipe_extra_bot = randint(0, 3) carta_extra_bot = f'{sorteio_valor_extra_bot} {de} {naipe[sorteio_naipe_extra_bot]}' sleep(1) print() idiom(language, f'Extra card: {carta_extra_bot}', f'Carta Extra Sorteada: {carta_extra_bot}') print() cartas_BOT.append(carta_extra_bot) somaFinal_BOT += validacaoLetras(sorteio_valor_extra_bot) sleep(1) idiom(language, f'Total sum of the cards from BOT: {somaFinal_BOT}.', f'Soma total das cartas do BOT: {somaFinal_BOT}.') linha() # --------------------- / / ----------------------- # if somaFinal_BOT == 21: BOT_venceu(language) derrotas += 1 status(language, cartas_BOT, cartas_Player1, vitoriasP1) break if somaFinal_BOT > 21: BOT_Estourou(language) vitoriasP1 += 1 status(language, cartas_BOT, cartas_Player1, vitoriasP1) break elif somaFinal_BOT > somaFinal_Player1: continue while comprar == 'N' and somaFinal_BOT == somaFinal_Player1 and somaFinal_BOT < 12: # --------------------- / / ----------------------- # idiom(language, 'The BOT is taking a card... ', 'O BOT está comprando mais uma carta... ') sorteio_valor_extra_bot = valor[randint(0, 12)] sorteio_naipe_extra_bot = randint(0, 3) carta_extra_bot = f'{sorteio_valor_extra_bot} {de} {naipe[sorteio_naipe_extra_bot]}' sleep(1) print() idiom(language, f'Extra card: {carta_extra_bot}', f'Carta Extra Sorteada: {carta_extra_bot}') print() cartas_BOT.append(carta_extra_bot) somaFinal_BOT += validacaoLetras(sorteio_valor_extra_bot) sleep(1) idiom(language, f'Total sum of the cards from BOT: {somaFinal_BOT}.', f'Soma total das cartas do BOT: {somaFinal_BOT}.') linha() # --------------------- / / ----------------------- # if somaFinal_BOT == 21: BOT_venceu(language) derrotas += 1 status(language, cartas_BOT, cartas_Player1, vitoriasP1) break if somaFinal_BOT > 21: BOT_Estourou(language) vitoriasP1 += 1 status(language, cartas_BOT, cartas_Player1, vitoriasP1) break elif somaFinal_BOT > somaFinal_Player1: break if comprar == 'N' and somaFinal_BOT == somaFinal_Player1: empate(language) empates += 1 status(language, cartas_BOT, cartas_Player1, vitoriasP1) break while comprar == 'N' and somaFinal_BOT < somaFinal_Player1: # --------------------- / / ----------------------- # idiom(language, 'The BOT is taking a card... ', 'O BOT está comprando mais uma carta... ') sorteio_valor_extra_bot = valor[randint(0, 12)] sorteio_naipe_extra_bot = randint(0, 3) carta_extra_bot = f'{sorteio_valor_extra_bot} {de} {naipe[sorteio_naipe_extra_bot]}' sleep(1) print() idiom(language, f'Extra card: {carta_extra_bot}', f'Carta Extra Sorteada: {carta_extra_bot}') print() cartas_BOT.append(carta_extra_bot) somaFinal_BOT += validacaoLetras(sorteio_valor_extra_bot) sleep(1) idiom(language, f'Total sum of the cards from BOT: {somaFinal_BOT}.', f'Soma total das cartas do BOT: {somaFinal_BOT}.') linha() # --------------------- / / ----------------------- # if somaFinal_BOT == 21: BOT_venceu(language) derrotas += 1 status(language, cartas_BOT, cartas_Player1, vitoriasP1) break if somaFinal_BOT > 21: BOT_Estourou(language) vitoriasP1 += 1 status(language, cartas_BOT, cartas_Player1, vitoriasP1) break elif somaFinal_BOT > somaFinal_Player1: break elif somaFinal_BOT == somaFinal_Player1: empate(language) empates += 1 status(language, cartas_BOT, cartas_Player1, vitoriasP1) break if comprar == 'N' and somaFinal_BOT == somaFinal_Player1: break if comprar == 'S' and somaFinal_BOT < somaFinal_Player1 and jogadaParada_BOT == 0: # --------------------- / / ----------------------- # idiom(language, 'The BOT is taking a card... ', 'O BOT está comprando mais uma carta... ') sorteio_valor_extra_bot = valor[randint(0, 12)] sorteio_naipe_extra_bot = randint(0, 3) carta_extra_bot = f'{sorteio_valor_extra_bot} {de} {naipe[sorteio_naipe_extra_bot]}' sleep(1) print() idiom(language, f'Extra card: {carta_extra_bot}', f'Carta Extra Sorteada: {carta_extra_bot}') print() cartas_BOT.append(carta_extra_bot) somaFinal_BOT += validacaoLetras(sorteio_valor_extra_bot) sleep(1) idiom(language, f'Total sum of the cards from BOT: {somaFinal_BOT}.', f'Soma total das cartas do BOT: {somaFinal_BOT}.') linha() # --------------------- / / ----------------------- # if somaFinal_BOT == 21: BOT_venceu(language) derrotas += 1 status(language, cartas_BOT, cartas_Player1, vitoriasP1) break if somaFinal_BOT > 21: BOT_Estourou(language) vitoriasP1 += 1 status(language, cartas_BOT, cartas_Player1, vitoriasP1) break if comprar == 'N' and somaFinal_BOT == somaFinal_Player1: empate(language) empates += 1 status(language, cartas_BOT, cartas_Player1, vitoriasP1) break if somaFinal_BOT < somaFinal_Player1: continue if comprar == 'N' and somaFinal_BOT > somaFinal_Player1: print() break if comprar == 'S' and somaFinal_BOT > somaFinal_Player1: paradaBOT(language, somaFinal_BOT) jogadaParada_BOT = 1 print() linha() continue if comprar == 'S' and somaFinal_BOT > 21: BOT_Estourou(language) vitoriasP1 += 1 statusFinal(vitoriasP1, derrotas, empates, partidas) continue if somaFinal_BOT == somaFinal_Player1 and somaFinal_BOT < 15: # --------------------- / / ----------------------- # idiom(language, 'The BOT is taking a card... ', 'O BOT está comprando mais uma carta... ') sorteio_valor_extra_bot = valor[randint(0, 12)] sorteio_naipe_extra_bot = randint(0, 3) carta_extra_bot = f'{sorteio_valor_extra_bot} {de} {naipe[sorteio_naipe_extra_bot]}' sleep(1) print() idiom(language, f'Extra card: {carta_extra_bot}', f'Carta Extra Sorteada: {carta_extra_bot}') print() cartas_BOT.append(carta_extra_bot) somaFinal_BOT += validacaoLetras(sorteio_valor_extra_bot) sleep(1) idiom(language, f'Total sum of the cards from BOT: {somaFinal_BOT}.', f'Soma total das cartas do BOT: {somaFinal_BOT}.') linha() # --------------------- / / ----------------------- # continue # DEFININDO VENCEDOR if somaFinal_BOT < somaFinal_Player1 < 21 and jogadaParada_BOT == 1: vitoria(language) vitoriasP1 += 1 print() linha() if somaFinal_Player1 < somaFinal_BOT < 21: BOT_venceu(language) derrotas += 1 status(language, cartas_BOT, cartas_Player1, vitoriasP1) # CONTINUAR sleep(1) continuar = '' while True: try: if language == 1: continuar = str(input('Do you wish to play again? [Y/N] ')).upper().strip()[0] if continuar == 'Y': continuar = 'S' elif language == 2: continuar = str(input('Deseja jogar de novo? [S/N] ')).upper().strip()[0] except: msgERROR(language) continue if continuar.isnumeric(): msgERROR(language) continue if continuar == 'S' or continuar == 'N': partidas += 1 break else: msgERROR(language) continue if continuar == 'N': break elif continuar == 'S': linha() print() continue # else: # print() # msgERROR(language) # continue # 1 VS 1 MODE elif playerMode == 2: while True: valor = ['A', 2, 3, 4, 5, 6, 7, 8, 9, 10, 'J', 'Q', 'K'] naipe = [] if language == 1: naipe = ['Spades', 'Hearts', 'Clubs', 'Dimonds'] elif language == 2: naipe = ['Espadas', 'Copas', 'Paus', 'Ouros'] # SORTEIO DE 2 CARTAS (VALORES E NAIPES) / CARTAS DE ENTRADA sorteio_valor1 = valor[randint(0, 12)] sorteio_valor2 = valor[randint(0, 12)] while True: if sorteio_valor2 == sorteio_valor1: sorteio_valor2 = valor[randint(0, 12)] elif sorteio_valor2 != sorteio_valor1: break sorteio_valor3 = valor[randint(0, 12)] sorteio_valor4 = valor[randint(0, 12)] while True: if sorteio_valor3 == sorteio_valor4: sorteio_valor3 = valor[randint(0, 12)] elif sorteio_valor3 != sorteio_valor4: break sorteio_naipe1 = randint(0, 3) sorteio_naipe2 = randint(0, 3) while True: if sorteio_naipe2 == sorteio_naipe1: sorteio_naipe2 = randint(0, 3) elif sorteio_naipe2 != sorteio_naipe1: break sorteio_naipe3 = randint(0, 3) sorteio_naipe4 = randint(0, 3) while True: if sorteio_naipe3 == sorteio_naipe4: sorteio_naipe3 = randint(0, 3) elif sorteio_naipe3 != sorteio_naipe4: break # MOSTRANDO O VALOR DA CARTA E O NAIPE idiom(language, 'SHUFFLING THE CARDS...', 'EMBARALHANDO AS CARTAS...') print() sleep(1) idiom(language, f'Cards from the player {nome_Player1}:', f'Cartas do jogador {nome_Player1}:') sleep(1) de = '' if language == 1: de = 'of' elif language == 2: de = 'de' carta1 = f'{sorteio_valor1} {de} {naipe[sorteio_naipe1]}' print(carta1) sleep(1) carta2 = f'{sorteio_valor2} {de} {naipe[sorteio_naipe2]}' print(carta2) sleep(1) print() cartas_Player1 = [carta1, carta2] idiom(language, f'Cards from the player {nome_Player2}:', f'Cartas do jogador {nome_Player2}:') sleep(1) carta3 = f'{sorteio_valor3} {de} {naipe[sorteio_naipe3]}' print(carta3) sleep(1) carta4 = f'{sorteio_valor4} {de} {naipe[sorteio_naipe4]}' print(carta4) sleep(1) cartas_Player2 = [carta3, carta4] # SOMA DAS CARTAS print() linha() soma1 = validacaoLetras(sorteio_valor1) + validacaoLetras(sorteio_valor2) soma2 = validacaoLetras(sorteio_valor3) + validacaoLetras(sorteio_valor4) if language == 1: print(f'The total sum of the two cards from {nome_Player1}: {soma1}') print() print(f'The total sum of the two cards from {nome_Player2}: {soma2}') elif language == 2: print(f'Soma total das duas cartas do {nome_Player1}: {soma1}') print() print(f'Soma total das duas cartas do {nome_Player2}: {soma2}') linha() # ACUMULADORES 02 somaFinal_Player1 = soma1 + 0 somaFinal_Player2 = soma2 + 0 Player1_Parou = Player2_Parou = 0 while True: comprar_Player1 = '' comprar_Player2 = '' # -------------------------- PLAYER 1 -------------------------- # while True and Player1_Parou == 0: try: if language == 1: comprar_Player1 = str(input(f'Do you wish to take another card {nome_Player1}? [Y/N] ')).upper().strip()[0] if comprar_Player1 == 'Y': comprar_Player1 = 'S' elif language == 2: comprar_Player1 = str(input(f'Deseja comprar mais uma carta {nome_Player1}? [S/N] ')).upper().strip()[0] except: msgERROR(language) if comprar_Player1.isnumeric(): continue elif comprar_Player1 == 'N' or comprar_Player1 == 'S': break if comprar_Player1 == 'N' and somaFinal_Player1 < somaFinal_Player2: break if comprar_Player1 == 'S': # COMPRAR CARTA EXTRA sorteio_valorExtra = valor[randint(0, 12)] sorteio_naipeExtra = randint(0, 3) carta_Extra = f'{sorteio_valorExtra} {de} {naipe[sorteio_naipeExtra]}' sleep(1) print() idiom(language, f'Extra card: {carta_Extra}.', f'Carta Extra Sorteada: {carta_Extra}.') print() cartas_Player1.append(carta_Extra) somaFinal_Player1 += validacaoLetras(sorteio_valorExtra) sleep(1) idiom(language, f'Total sum: {somaFinal_Player1}.', f'Soma total: {somaFinal_Player1}.') linha() if somaFinal_Player1 == 21: vitoriaPlayer1(language, nome_Player1) vitoriasP1 += 1 statusPlayers(language, nome_Player1, cartas_Player1, nome_Player2, cartas_Player2, vitoriasP1, vitoriasP2) break if somaFinal_Player1 > 21: Player1_Estourou(language) vitoriasP2 += 1 statusPlayers(language, nome_Player1, cartas_Player1, nome_Player2, cartas_Player2, vitoriasP1, vitoriasP2) break if Player2_Parou == 1 and somaFinal_Player1 > somaFinal_Player2: break # if somaFinal_Player1 == somaFinal_Player2: # empate(language) # empates += 1 # status(language, cartas_BOT, cartas_Player1, vitoriasP1) # break elif comprar_Player1 == 'N': paradaPlayer1(language, nome_Player1, somaFinal_Player1) Player1_Parou = 1 continue if Player1_Parou == 1 and Player2_Parou == 1: break # -------------------------- PLAYER 2 -------------------------- # while True and Player2_Parou == 0: try: if language == 1: comprar_Player2 = \ str(input(f'Do you wish to take another card {nome_Player2}? [Y/N] ')).upper().strip()[0] if comprar_Player2 == 'Y': comprar_Player2 = 'S' elif language == 2: comprar_Player2 = \ str(input(f'Deseja comprar mais uma carta {nome_Player2}? [S/N] ')).upper().strip()[0] except: msgERROR(language) if comprar_Player2.isnumeric(): continue elif comprar_Player2 == 'N' or comprar_Player2 == 'S': break if comprar_Player2 == 'N' and somaFinal_Player1 > somaFinal_Player2: break if comprar_Player2 == 'S': # COMPRAR CARTA EXTRA sorteio_valorExtra = valor[randint(0, 12)] sorteio_naipeExtra = randint(0, 3) carta_Extra = f'{sorteio_valorExtra} {de} {naipe[sorteio_naipeExtra]}' sleep(1) print() idiom(language, f'Extra card: {carta_Extra}.', f'Carta Extra Sorteada: {carta_Extra}.') print() cartas_Player2.append(carta_Extra) somaFinal_Player2 += validacaoLetras(sorteio_valorExtra) sleep(1) idiom(language, f'Total sum: {somaFinal_Player2}.', f'Soma total: {somaFinal_Player2}.') linha() if somaFinal_Player2 == 21: vitoriaPlayer2(language, nome_Player2) vitoriasP2 += 1 statusPlayers(language, nome_Player1, cartas_Player1, nome_Player2, cartas_Player2, vitoriasP1, vitoriasP2) break if somaFinal_Player2 > 21: Player2_Estourou(language) vitoriasP1 += 1 statusPlayers(language, nome_Player1, cartas_Player1, nome_Player2, cartas_Player2, vitoriasP1, vitoriasP2) break if Player1_Parou == 1 and somaFinal_Player2 > somaFinal_Player1: break if comprar_Player1 == 'N' and comprar_Player2 == 'N': break elif comprar_Player2 == 'N': paradaPlayer2(language, nome_Player2, somaFinal_Player2) Player2_Parou = 1 continue if Player1_Parou == 1 and Player2_Parou == 1: break # DECIDINDO VENCEDOR if somaFinal_Player2 < somaFinal_Player1 < 21: vitoriaPlayer1(language, nome_Player1) vitoriasP1 += 1 statusPlayers(language, nome_Player1, cartas_Player1, nome_Player2, cartas_Player2, vitoriasP1, vitoriasP2) elif somaFinal_Player1 < somaFinal_Player2 < 21: vitoriaPlayer2(language, nome_Player2) vitoriasP2 += 1 statusPlayers(language, nome_Player1, cartas_Player1, nome_Player2, cartas_Player2, vitoriasP1, vitoriasP2) elif somaFinal_Player1 == somaFinal_Player2: empate(language) empates += 1 statusPlayers(language, nome_Player1, cartas_Player1, nome_Player2, cartas_Player2, vitoriasP1, vitoriasP2) # CONTINUAR sleep(1) continuar = '' while True: try: if language == 1: continuar = str(input('Do you wish to play another match? [Y/N] ')).upper().strip()[0] if continuar == 'Y': continuar = 'S' elif language == 2: continuar = str(input('Deseja jogar outra partida? [S/N] ')).upper().strip()[0] except: msgERROR(language) continue if continuar == 'N' or continuar == 'S': partidas += 1 break else: msgERROR(language) continue if continuar == 'N': break elif continuar == 'S': linha() print() continue else: print() msgERROR(language) continue # ENCERRAMENTO DO PROGRAMA linha() sleep(1) idiom(language, 'FINALIZING SYSTEM...', 'ENCERRANDO O PROGRAMA...') sleep(1) print() if playerMode == 1: statusFinal(vitoriasP1, derrotas, empates, partidas) elif playerMode == 2: statusFinalPlayers(language, nome_Player1, vitoriasP1, nome_Player2, vitoriasP2, empates, partidas) sleep(1) print() assinatura(language) sleep(20)

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py

Python

run.py

Xitog/jyx

e84b886ddd3306475436b73d6c8542c9e7dbefe9

[ "MIT" ]

null

run.py

Xitog/jyx

e84b886ddd3306475436b73d6c8542c9e7dbefe9

[ "MIT" ]

3

2016-07-25T22:57:50.000Z

2016-07-27T21:12:08.000Z

run.py

Xitog/jyx

e84b886ddd3306475436b73d6c8542c9e7dbefe9

[ "MIT" ]

null

import jyx jyx.Jyx()

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py

Python

tests/api/test_mixin_with_tier_configuration_helper.py

JosePaniagua7/connect-processors-toolkit

0fa7de0e29ac52bd7633d2ef34a48a37b71c2714

[ "Apache-2.0" ]

null

tests/api/test_mixin_with_tier_configuration_helper.py

JosePaniagua7/connect-processors-toolkit

0fa7de0e29ac52bd7633d2ef34a48a37b71c2714

[ "Apache-2.0" ]

null

tests/api/test_mixin_with_tier_configuration_helper.py

JosePaniagua7/connect-processors-toolkit

0fa7de0e29ac52bd7633d2ef34a48a37b71c2714

[ "Apache-2.0" ]

null

import pytest import os from connect.client import ConnectClient, ClientError from connect.devops_testing import asserts from connect.processors_toolkit.requests.tier_configurations import TierConfigurationBuilder from connect.processors_toolkit.requests import RequestBuilder from connect.processors_toolkit.api.mixins import WithTierConfigurationHelper class Helper(WithTierConfigurationHelper): def __init__(self, client: ConnectClient): self.client = client BAD_REQUEST_400 = "400 Bad Request" ASSET_REQUEST_FILE = '/request_asset.json' TIER_CONFIG_REQUEST_FILE = '/request_tier_configuration.json' def test_helper_should_retrieve_a_tier_configuration_by_id(sync_client_factory, response_factory): tier_on_server = TierConfigurationBuilder() tier_on_server.with_tier_configuration_id('TC-9091-4850-9712') client = sync_client_factory([ response_factory(value=tier_on_server.raw(), status=200) ]) tc = Helper(client).find_tier_configuration('TC-9091-4850-9712') assert isinstance(tc, TierConfigurationBuilder) assert tc.tier_configuration_id() == 'TC-9091-4850-9712' def test_helper_should_match_all_tier_configurations(sync_client_factory, response_factory): content = [ TierConfigurationBuilder({'id': 'TC-000-000-001'}).raw(), TierConfigurationBuilder({'id': 'TC-000-000-002'}).raw() ] client = sync_client_factory([ response_factory(count=len(content), value=content) ]) templates = Helper(client).match_tier_configuration({}) assert len(templates) == 2 def test_helper_should_match_tier_configurations(sync_client_factory, response_factory): content = [ TierConfigurationBuilder({'id': 'TC-000-000-001'}).raw(), ] client = sync_client_factory([ response_factory(count=len(content), value=content) ]) templates = Helper(client).match_tier_configuration({'id': 'TC-000-000-001'}) assert len(templates) == 1 def test_helper_should_retrieve_a_tier_configuration_request_by_id(sync_client_factory, response_factory): tier_on_server = TierConfigurationBuilder() tier_on_server.with_tier_configuration_id('TC-9091-4850-9712') on_server = RequestBuilder() on_server.with_id('TCR-9091-4850-9712-001') on_server.with_type('setup') on_server.with_status('pending') on_server.with_tier_configuration(tier_on_server) client = sync_client_factory([ response_factory(value=on_server.raw(), status=200) ]) request = Helper(client).find_tier_configuration_request('TCR-9091-4850-9712-001') assert isinstance(request, RequestBuilder) assert request.id() == 'TCR-9091-4850-9712-001' def test_helper_should_match_all_tier_configuration_requests(sync_client_factory, response_factory): content = [ RequestBuilder({'id': 'TCR-000-000-001-001'}).raw(), RequestBuilder({'id': 'TCR-000-000-002-002'}).raw() ] client = sync_client_factory([ response_factory(count=len(content), value=content) ]) templates = Helper(client).match_tier_configuration_request({}) assert len(templates) == 2 def test_helper_should_match_tier_configuration_requests(sync_client_factory, response_factory): content = [ RequestBuilder({'id': 'TCR-000-000-001-001'}).raw(), ] client = sync_client_factory([ response_factory(count=len(content), value=content) ]) templates = Helper(client).match_tier_configuration_request({'id': 'TCR-000-000-001-001'}) assert len(templates) == 1 def test_helper_should_approve_a_tier_configuration_request(sync_client_factory, response_factory): tier_on_server = TierConfigurationBuilder() tier_on_server.with_tier_configuration_id('TC-8027-7606-7082') tier_on_server.with_tier_configuration_status('active') on_server = RequestBuilder() on_server.with_id('TCR-8027-7606-7082-001') on_server.with_type('setup') on_server.with_status('approved') on_server.with_tier_configuration(tier_on_server) client = sync_client_factory([ response_factory(value=on_server.raw(), status=200) ]) tier = on_server.tier_configuration() tier.with_tier_configuration_status('processing') request = RequestBuilder() request.with_id('PR-8027-7606-7082-001') request.with_type('setup') request.with_status('pending') request.with_tier_configuration(tier) request = Helper(client).approve_tier_configuration_request(request, 'TL-662-440-096') assert request.id() == 'TCR-8027-7606-7082-001' asserts.request_status(request.raw(), 'approved') def test_helper_should_approve_an_already_approved_tier_configuration_request(sync_client_factory, response_factory): exception = ClientError( message=BAD_REQUEST_400, status_code=400, error_code="TC_006", errors=["Tier configuration request status transition is not allowed."] ) client = sync_client_factory([ response_factory(exception=exception, status=exception.status_code) ]) tier = TierConfigurationBuilder() tier.with_tier_configuration_id('TC-8027-7606-7082') tier.with_tier_configuration_status('active') request = RequestBuilder() request.with_id('TCR-8027-7606-7082-001') request.with_type('setup') request.with_status('approved') request.with_tier_configuration(tier) request = Helper(client).approve_tier_configuration_request(request, 'TL-662-440-096') assert request.id() == 'TCR-8027-7606-7082-001' asserts.request_status(request.raw(), 'approved') def test_helper_should_fail_approving_a_tier_configuration_request(sync_client_factory, response_factory): exception = ClientError( message=BAD_REQUEST_400, status_code=400, error_code="TC_012", errors=[ "There is no tier configuration request template with such id." ] ) client = sync_client_factory([ response_factory(exception=exception, status=exception.status_code) ]) request = RequestBuilder() request.with_id('PR-8027-7606-7082-001') request.with_tier_configuration(TierConfigurationBuilder()) with pytest.raises(ClientError): Helper(client).approve_tier_configuration_request(request, 'TL-662-440-096') def test_helper_should_fail_a_tier_configuration_request(sync_client_factory, response_factory): reason = 'I don\'t like you :P' tier_on_server = TierConfigurationBuilder() tier_on_server.with_tier_configuration_id('TC-8027-7606-7082') tier_on_server.with_tier_configuration_status('processing') on_server = RequestBuilder() on_server.with_id('TCR-8027-7606-7082-001') on_server.with_type('setup') on_server.with_status('failed') on_server.with_tier_configuration(tier_on_server) on_server.with_reason(reason) client = sync_client_factory([ response_factory(value=on_server.raw(), status=200) ]) request = RequestBuilder() request.with_id('TCR-8027-7606-7082-001') request.with_status('pending') request.with_tier_configuration(tier_on_server) request = Helper(client).fail_tier_configuration_request(request, reason) assert request.id() == 'TCR-8027-7606-7082-001' asserts.request_status(request.raw(), 'failed') asserts.request_reason(request.raw(), reason) def test_helper_should_fail_an_already_failed_tier_configuration_request(sync_client_factory, response_factory): exception = ClientError( message=BAD_REQUEST_400, status_code=400, error_code="TC_006", errors=["Tier configuration request status transition is not allowed."] ) client = sync_client_factory([ response_factory(exception=exception, status=exception.status_code) ]) tier = TierConfigurationBuilder() tier.with_tier_configuration_id('TC-8027-7606-7082') tier.with_tier_configuration_status('processing') request = RequestBuilder() request.with_id('TCR-8027-7606-7082-001') request.with_type('setup') request.with_status('failed') request.with_tier_configuration(tier) request = Helper(client).fail_tier_configuration_request(request, 'It is my will') assert request.id() == 'TCR-8027-7606-7082-001' asserts.request_status(request.raw(), 'failed') def test_helper_should_fail_failing_a_tier_configuration_request(sync_client_factory, response_factory): exception = ClientError( message=BAD_REQUEST_400, status_code=400, error_code="VAL_001", errors=["reason: This field may not be blank."] ) client = sync_client_factory([ response_factory(exception=exception, status=exception.status_code) ]) request = RequestBuilder() request.with_id('TCR-8027-7606-7082-001') request.with_tier_configuration(TierConfigurationBuilder()) with pytest.raises(ClientError): Helper(client).fail_tier_configuration_request(request, "") def test_helper_should_inquire_a_tier_configuration_request(sync_client_factory, response_factory): tier = TierConfigurationBuilder() tier.with_tier_configuration_id('AS-8027-7606-7082') tier.with_tier_configuration_status('processing') on_server = RequestBuilder() on_server.with_id('TCR-8027-7606-7082-001') on_server.with_type('setup') on_server.with_status('inquiring') on_server.with_tier_configuration(tier) client = sync_client_factory([ response_factory(value=on_server.raw(), status=200) ]) request = RequestBuilder() request.with_id('TCR-8027-7606-7082-001') request.with_type('setup') request.with_status('pending') request.with_tier_configuration(tier) request = Helper(client).inquire_tier_configuration_request(request) assert request.id() == 'TCR-8027-7606-7082-001' asserts.request_status(request.raw(), 'inquiring') def test_helper_should_inquire_an_already_inquired_tier_configuration_request(sync_client_factory, response_factory): exception = ClientError( message=BAD_REQUEST_400, status_code=400, error_code="TC_006", errors=["Tier configuration request status transition is not allowed."] ) client = sync_client_factory([ response_factory(exception=exception, status=exception.status_code) ]) tier = TierConfigurationBuilder() tier.with_tier_configuration_id('TC-8027-7606-7082') tier.with_tier_configuration_status('processing') request = RequestBuilder() request.with_id('TCR-8027-7606-7082-001') request.with_type('setup') request.with_status('inquiring') request.with_tier_configuration(tier) request = Helper(client).inquire_tier_configuration_request(request) assert request.id() == 'TCR-8027-7606-7082-001' asserts.request_status(request.raw(), 'inquiring') def test_helper_should_fail_inquiring_a_tier_configuration_request(sync_client_factory, response_factory): exception = ClientError( message=BAD_REQUEST_400, status_code=400, error_code="TC_006", errors=["Some weird error..."] ) client = sync_client_factory([ response_factory(exception=exception, status=exception.status_code) ]) request = RequestBuilder() request.with_id('TCR-8027-7606-7082-001') request.with_tier_configuration(TierConfigurationBuilder()) with pytest.raises(ClientError): Helper(client).inquire_tier_configuration_request(request) def test_helper_should_update_a_request_tier_configuration_params(sync_client_factory, response_factory, load_json): on_server = RequestBuilder(load_json(os.path.dirname(__file__) + TIER_CONFIG_REQUEST_FILE)) after_update = RequestBuilder(load_json(os.path.dirname(__file__) + TIER_CONFIG_REQUEST_FILE)) after_update.with_param('TIER_SIGNATURE', 'the-tier-signature-updated') tier = after_update.tier_configuration() tier.with_tier_configuration_param('TIER_SIGNATURE', 'the-tier-signature-updated') after_update.with_tier_configuration(tier) client = sync_client_factory([ response_factory(value=on_server.raw(), status=200), response_factory(value=after_update.raw(), status=200) ]) request = RequestBuilder(load_json(os.path.dirname(__file__) + TIER_CONFIG_REQUEST_FILE)) request.with_param('TIER_SIGNATURE', 'the-tier-signature-updated') print(request.param('TIER_SIGNATURE')) request = Helper(client).update_tier_configuration_parameters(request) assert request.raw()['params'][0]['id'] == 'TIER_SIGNATURE' assert request.raw()['params'][0]['value'] == 'the-tier-signature-updated' asserts.tier_configuration_param_value_equal(request.raw(), 'TIER_SIGNATURE', 'the-tier-signature-updated') def test_helper_should_not_update_request_tier_configuration_params(sync_client_factory, response_factory, load_json): request = RequestBuilder(load_json(os.path.dirname(__file__) + TIER_CONFIG_REQUEST_FILE)) client = sync_client_factory([ response_factory(value=request.raw(), status=200), ]) request = Helper(client).update_tier_configuration_parameters(request) assert request.raw()['params'][0]['id'] == 'TIER_SIGNATURE' assert request.raw()['params'][0]['value'] == '' asserts.tier_configuration_param_value_equal(request.raw(), 'TIER_SIGNATURE', '')

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6

afc27d86b52a94a20737a42eb2ab75d60ff9041d

43

py

Python

image_gen/src/module/filters/__init__.py

Ovlic/ovlic.py

e776f5f84fbb15c12866a2d49997a21acde29fdb

[ "MIT" ]

null

image_gen/src/module/filters/__init__.py

Ovlic/ovlic.py

e776f5f84fbb15c12866a2d49997a21acde29fdb

[ "MIT" ]

null

image_gen/src/module/filters/__init__.py

Ovlic/ovlic.py

e776f5f84fbb15c12866a2d49997a21acde29fdb

[ "MIT" ]

null

def placeholder(): print("Placeholder")

21.5

24

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2

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true

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null

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1

0

1

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6

bbc4ab1669b61bc4a2fd42bb3f81e3c36812ed77

33

py

Python

memover/__main__.py

Alecktos/Directory-Tree-File-Mover

ac642ba0599534cdd248e56e8db842dbf1972496

[ "MIT" ]

1

2021-11-23T21:17:24.000Z

memover/__main__.py

Alecktos/Directory-Tree-File-Mover

ac642ba0599534cdd248e56e8db842dbf1972496

[ "MIT" ]

null

memover/__main__.py

Alecktos/Directory-Tree-File-Mover

ac642ba0599534cdd248e56e8db842dbf1972496

[ "MIT" ]

null

from . import main main.main()

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a52842e2e6d2b31fd06aa8008514a036c1e483ba

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py

Python

tests/instagram/tests_instagram_account.py

sgaynetdinov/instasave_bot

b54c1f53917b0a81dffd7a2e5e0980fa878b3d73

[ "MIT" ]

13

2017-08-16T18:48:43.000Z

2021-07-27T22:47:28.000Z

tests/instagram/tests_instagram_account.py

sgaynetdinov/instasave_bot

b54c1f53917b0a81dffd7a2e5e0980fa878b3d73

[ "MIT" ]

14

2019-01-17T07:49:23.000Z

2021-09-16T15:13:13.000Z

tests/instagram/tests_instagram_account.py

sgaynetdinov/instasave_bot

b54c1f53917b0a81dffd7a2e5e0980fa878b3d73

[ "MIT" ]

2

2019-06-08T23:34:53.000Z

2021-06-01T07:36:35.000Z

import unittest from unittest.mock import MagicMock, patch from urllib.error import HTTPError from bot.instagram import (Instagram, Instagram404Error, InstagramLinkError, urlopen) class InstagramAccountTestCase(unittest.TestCase): @patch('bot.instagram.urlopen') def test_get_photos_and_video_url__single_photo(self, mock): with open('tests/instagram/account.json') as fd: m = MagicMock() m.read.return_value = fd.read().encode() mock.return_value = m insta = Instagram.from_url('https://www.instagram.com/nasa/') self.assertEqual(len(insta.get_photos_and_video_url()), 1) self.assertEqual(insta.get_photos_and_video_url()[0], 'https://scontent-lax3-2.cdninstagram.com/v/account.jpg') @patch('bot.instagram.urlopen') def test_get_text(self, mock): with open('tests/instagram/account.json') as fd: m = MagicMock() m.read.return_value = fd.read().encode() mock.return_value = m insta = Instagram.from_url('https://www.instagram.com/nasa/') self.assertEqual(insta.get_text(), "NASA\n\nExplore the universe and discover our home planet with the official NASA Instagram account\nhttp://www.nasa.gov/") @patch('bot.instagram.urlopen') def test_get_text_if_not_url(self, mock): with open('tests/instagram/account.json') as fd: m = MagicMock() m.read.return_value = fd.read().encode() mock.return_value = m insta = Instagram.from_url('https://www.instagram.com/nasa/') del insta._content['external_url'] self.assertEqual(insta.get_text(), "NASA\n\nExplore the universe and discover our home planet with the official NASA Instagram account") @patch('bot.instagram.urlopen') def test_get_text_if_url_is_None(self, mock): with open('tests/instagram/account.json') as fd: m = MagicMock() m.read.return_value = fd.read().encode() mock.return_value = m insta = Instagram.from_url('https://www.instagram.com/nasa/') insta._content['external_url'] = None self.assertEqual(insta.get_text(), "NASA\n\nExplore the universe and discover our home planet with the official NASA Instagram account") @patch('bot.instagram.urlopen') def test_get_text_if_not_full_name(self, mock): with open('tests/instagram/account.json') as fd: m = MagicMock() m.read.return_value = fd.read().encode() mock.return_value = m insta = Instagram.from_url('https://www.instagram.com/nasa/') del insta._content['full_name'] self.assertEqual(insta.get_text(), "Explore the universe and discover our home planet with the official NASA Instagram account\nhttp://www.nasa.gov/") @patch('bot.instagram.urlopen') def test_full_name(self, mock): with open('tests/instagram/account.json') as fd: m = MagicMock() m.read.return_value = fd.read().encode() mock.return_value = m insta = Instagram.from_url('https://www.instagram.com/nasa/') self.assertEqual(insta._full_name, "NASA") @patch('bot.instagram.urlopen') def test_full_name_if_key_error(self, mock): with open('tests/instagram/account.json') as fd: m = MagicMock() m.read.return_value = fd.read().encode() mock.return_value = m insta = Instagram.from_url('https://www.instagram.com/nasa/') del insta._content['full_name'] self.assertEqual(insta._full_name, "") @patch('bot.instagram.urlopen') def test_text_empty(self, mock): with open('tests/instagram/account.json') as fd: m = MagicMock() m.read.return_value = fd.read().encode() mock.return_value = m insta = Instagram.from_url('https://www.instagram.com/nasa/') insta._content['biography'] = '' self.assertEqual(insta.get_text(), 'NASA')

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522

4,088

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0.053797

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4,088

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167

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0.171053

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null

0

1

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null

0

6

a56e2623f82bbcd348b15b560c6aea3a58f45033

181

py

Python

folder_compiler_static_website/__init__.py

d-krupke/folder_compiler_static_website

670d2895cdf3cd5e3b6cfd78344adf296fff951d

[ "MIT" ]

null

folder_compiler_static_website/__init__.py

d-krupke/folder_compiler_static_website

670d2895cdf3cd5e3b6cfd78344adf296fff951d

[ "MIT" ]

null

folder_compiler_static_website/__init__.py

d-krupke/folder_compiler_static_website

670d2895cdf3cd5e3b6cfd78344adf296fff951d

[ "MIT" ]

null

from .bibtex_processor import BibtexProcessor from .html_processor import HtmlProcessor from .jinja_processor import JinjaProcessor from .markdown_processor import MarkdownProcessor

45.25

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1

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6

a571e69b4d7df696efa313b435e93278ca472564

1,124

py

Python

financialmodelingprep/technical_indicators.py

Porter97/financialmodelingprep-python

fd1c14ac8ab36c34842dcc915c399ad4ed72ce2c

[ "MIT" ]

1

2021-03-15T17:18:25.000Z

financialmodelingprep/technical_indicators.py

Porter97/financialmodelingprep-python

fd1c14ac8ab36c34842dcc915c399ad4ed72ce2c

[ "MIT" ]

null

financialmodelingprep/technical_indicators.py

Porter97/financialmodelingprep-python

fd1c14ac8ab36c34842dcc915c399ad4ed72ce2c

[ "MIT" ]

1

2021-03-15T17:16:50.000Z

from financialmodelingprep.decorator import get_json_data class technical_indicators(): BASE_URL = 'https://financialmodelingprep.com' API_KEY = '' def __init__(self, API_KEY): self.API = API_KEY @get_json_data def stock_price(ticker: str, interval: str, period: str, indicator_type: str): ''' Earnings Calendar interval: | 1min | 5min | 15min | 30min | 1hour | 4hour | daily | type: | SMA | EMA | WMA | DEMA | TEMA | williams | RSI | ADX | standardDeviation ''' return f'{self.BASE_URL}/api/v3/technical_indicator/{interval}/{ticker}?period={period}&type={indicator_type}?apikey={self.API}' @get_json_data def stock_price(ticker: str, interval: str, period: str, indicator_type: str): ''' Earnings Calendar interval: | 1min | 5min | 15min | 30min | 1hour | 4hour | daily | type: | SMA | EMA | WMA | DEMA | TEMA | williams | RSI | ADX | standardDeviation ''' return f'{self.BASE_URL}/api/v3/technical_indicator/{interval}/{ticker}?period={period}&type={indicator_type}?apikey={self.API}'

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null

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6

36fd9674d88861f8bfe00e6a3d8d2175a079ecb9

413

py

Python

kissim/encoding/__init__.py

volkamerlab/kissim

35198a5efd4b651dd3952bf26ac5098fd1c4dfaa

[ "MIT" ]

15

2020-06-23T14:46:07.000Z

2022-02-03T04:23:56.000Z

kissim/encoding/__init__.py

volkamerlab/kissim

35198a5efd4b651dd3952bf26ac5098fd1c4dfaa

[ "MIT" ]

66

2020-11-05T11:45:21.000Z

2021-12-15T12:11:20.000Z

kissim/encoding/__init__.py

volkamerlab/kissim

35198a5efd4b651dd3952bf26ac5098fd1c4dfaa

[ "MIT" ]

3

2021-02-27T12:56:27.000Z

2022-02-03T04:23:57.000Z

""" Encode kinase pockets as subpocket-based structural fingerprint. """ from .fingerprint_base import FingerprintBase from .fingerprint import Fingerprint from .fingerprint_normalized import FingerprintNormalized from .fingerprint_generator_base import FingerprintGeneratorBase from .fingerprint_generator import FingerprintGenerator from .fingerprint_generator_normalized import FingerprintGeneratorNormalized

37.545455

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0.883777

39

413

9.179487

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0.251397

0.201117

0

0.082324

413

10

77

41.3

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0.154964

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1

0

true

0

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null

1

0

null

0

1

0

1

0

1

0

6

3c230d1d9882dafe642cfe266bafd27f94197090

53

py

Python

backend/initiatives/admin/__init__.py

danesjenovdan/izboljsajmo-maribor

cd2f388ceb89d7989952ab05154fd8e7341c2b2b

[ "CC0-1.0" ]

null

backend/initiatives/admin/__init__.py

danesjenovdan/izboljsajmo-maribor

cd2f388ceb89d7989952ab05154fd8e7341c2b2b

[ "CC0-1.0" ]

null

backend/initiatives/admin/__init__.py

danesjenovdan/izboljsajmo-maribor

cd2f388ceb89d7989952ab05154fd8e7341c2b2b

[ "CC0-1.0" ]

null

from .admin import * from .initiative_admin import *

17.666667

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53

5.714286

0.571429

0.55

0

0.150943

53

2

32

26.5

0.888889

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1

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true

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null

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1

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null

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1

0

1

0

1

0

6

3c4ecbeb702dd316495f951526939660fda7f495

447

py

Python

eggdriver/resources/math/__init__.py

PythonForChange/eggdriver

bcf1da6dcb2a8daf3144c7af8d1d04f8844be2fc

[ "MIT" ]

3

2021-09-25T01:22:31.000Z

2021-11-28T23:25:46.000Z

eggdriver/resources/math/__init__.py

PythonForChange/eggdriver

bcf1da6dcb2a8daf3144c7af8d1d04f8844be2fc

[ "MIT" ]

null

eggdriver/resources/math/__init__.py

PythonForChange/eggdriver

bcf1da6dcb2a8daf3144c7af8d1d04f8844be2fc

[ "MIT" ]

null

from eggdriver.resources.math.algorithms import solve, root from eggdriver.resources.math.theoric import * from eggdriver.resources.math.float import * from eggdriver.resources.math.polynomial import Polynomial from eggdriver.resources.math.constants import inf, e, pi from eggdriver.resources.math.functions import log, ln, cos, sin, tan from eggdriver.resources.math.linear import Vector, Matrix from eggdriver.resources.math.calculus import *

44.7

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0.474394

0.560647

0.172507

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0.091723

447

9

70

49.666667

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1

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true

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1

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null

0

1

0

1

0

6

3c5c6e9990c7d4fac835a2c442c43a70119e4610

56

py

Python

octopus_energy_api/__init__.py

euanacampbell/octopus_energy_api

61fe9bf659269636150d24957e5a11886ca142e7

[ "MIT" ]

2

2021-06-15T22:49:31.000Z

2021-07-31T14:39:37.000Z

octopus_energy_api/__init__.py

euanacampbell/octopus_energy_api

61fe9bf659269636150d24957e5a11886ca142e7

[ "MIT" ]

null

octopus_energy_api/__init__.py

euanacampbell/octopus_energy_api

61fe9bf659269636150d24957e5a11886ca142e7

[ "MIT" ]

1

2022-02-21T22:15:00.000Z

from octopus_energy_api.octopus_energy_api import oe_api

56

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true

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null

0

1

0

1

0

6

3c8cd5713c79193a7c627102b773a6f60f20bba3

40

py

Python

log/__init__.py

alexsmith2910/Strat_UN

57f79beb923cebed9ced940ccaea9df9172541fe

[ "MIT", "Unlicense" ]

null

log/__init__.py

alexsmith2910/Strat_UN

57f79beb923cebed9ced940ccaea9df9172541fe

[ "MIT", "Unlicense" ]

3

2020-10-10T11:10:55.000Z

2021-03-30T13:16:52.000Z

log/__init__.py

alexsmith2910/Strat_UN

57f79beb923cebed9ced940ccaea9df9172541fe

[ "MIT", "Unlicense" ]

null

from .log import log_write, error_write

20

39

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null

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1

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1

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6

b1bc60f136215ff744593f4d700e18afb7b549c6

96

py

Python

venv/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/backend.py

GiulianaPola/select_repeats

17a0d053d4f874e42cf654dd142168c2ec8fbd11

[ "MIT" ]

1

2021-11-07T22:40:27.000Z

venv/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/backend.py

GiulianaPola/select_repeats

17a0d053d4f874e42cf654dd142168c2ec8fbd11

[ "MIT" ]

19

2021-11-20T04:09:18.000Z

2022-03-23T15:05:55.000Z

venv/lib/python3.8/site-packages/cryptography/hazmat/backends/openssl/backend.py

GiulianaPola/select_repeats

17a0d053d4f874e42cf654dd142168c2ec8fbd11

[ "MIT" ]

null

/home/runner/.cache/pip/pool/7e/65/ee/200d991e43cd86a0907aab8ec16d798d074658390e3c0e8a43423cd2cf

96

0.895833

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null

1

0

1

0

6

b1c55e31c4de2cc4776b0ab7dbbbef1b7b6e1eaf

26

py

Python

PythonTestOne.py

HackTheAR/Transit

27598670b5d1f68d0c6a09ba14eaf8b6ebe88330

[ "Apache-2.0" ]

null

PythonTestOne.py

HackTheAR/Transit

27598670b5d1f68d0c6a09ba14eaf8b6ebe88330

[ "Apache-2.0" ]

null

PythonTestOne.py

HackTheAR/Transit

27598670b5d1f68d0c6a09ba14eaf8b6ebe88330

[ "Apache-2.0" ]

null

import os print("works")

6.5

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null

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1

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1

0

6

3ca2d0cbce80b402a14dc75dcab5623b9fd678ea

21,716

py

Python

Read_write_balorc_desp.py

luizschmall/tce_siconfi_inconsistencies

c55ed79332241a9d40dbb9528535a8fc8bf210d2

[ "MIT" ]

null

Read_write_balorc_desp.py

luizschmall/tce_siconfi_inconsistencies

c55ed79332241a9d40dbb9528535a8fc8bf210d2

[ "MIT" ]

null

Read_write_balorc_desp.py

luizschmall/tce_siconfi_inconsistencies

c55ed79332241a9d40dbb9528535a8fc8bf210d2

[ "MIT" ]

null

import pandas import string import math import csv import os from unicodedata import normalize def remover_acentos(txt): return normalize('NFKD', txt).encode('ASCII', 'ignore').decode('ASCII') def containsNumber(line): res = False numero = 0 if any(i.isdigit() for i in str(line)): res = True line = str(line).split(" ") for l in line: if any(k.isdigit() for k in l): l = l.replace(".", "") l = l.replace(",", "") l = l[:-2] + "." + l[-2:] try: numero = float(l) except: numero = 0 return res, numero def buscaKeyParts(diretorio, file, key): df = pandas.read_csv(diretorio + file, names=list(range(0, 10))) mask = df.applymap(lambda x: key.upper() in remover_acentos(str(x).upper())) # print(mask) df1 = df[mask.any(axis=1)] print(df1) i = 0 j = 0 resultado = [0, 0, 0, 0, 0, 0] if df1.empty == False: for (columnName, columnData) in df1.iteritems(): if key.upper() in remover_acentos(str(columnData.values[0]).upper()): j = 1 print('Colunm Name : ', columnName) print('Column Contents : ', columnData.values) if j == 1 and columnData.values[0] and ( isinstance(columnData.values[0], float) and math.isnan(columnData.values[0])) == False: containnumber1, containnumber2 = containsNumber(columnData.values[0]) print('contain number : ', containnumber1, containnumber2) if containnumber1 == True and i < 6: resultado[i] = containnumber2 i += 1 return resultado def main(): diretorio = "C:\\Users\\schmall\\Documents\\FGV\\Tese\\Balanços_PI\\BALORC\\ORIG\\RESULT2_despesas\\" files = os.listdir(diretorio) csv_files = [f for f in files if f.endswith('.csv')] files2 = [d for d in csv_files if 'tables' in d] new = "" despesas_correntes = [" ", " ", " ", " ", " ", " "] pessoal_encargos_sociais = [" ", " ", " ", " ", " ", " "] juros_encargos_divida = [" ", " ", " ", " ", " ", " "] outras_despesas_correntes = [" ", " ", " ", " ", " ", " "] despesas_capital = [" ", " ", " ", " ", " ", " "] investimentos = [" ", " ", " ", " ", " ", " "] inversoes_financeiras = [" ", " ", " ", " ", " ", " "] amortizacao_divida = [" ", " ", " ", " ", " ", " "] reserva_contingencia = [" ", " ", " ", " ", " ", " "] reserva_rpps = [" ", " ", " ", " ", " ", " "] subtotal_despesas = [" ", " ", " ", " ", " ", " "] amortizacao_divida_refinanciamento = [" ", " ", " ", " ", " ", " "] subtotal_refinanciamento_d = [" ", " ", " ", " ", " ", " "] for file in files2: print(file) file_parts = file.split(".") if file_parts[0] != new: with open(diretorio + new + "_tratado.csv", mode='a+') as balorc_file: balorc_writer = csv.writer(balorc_file, delimiter=';', quoting=csv.QUOTE_NONNUMERIC) if despesas_correntes[0] == 0 and despesas_correntes[1] == 0: balorc_writer.writerow(['DESPESAS CORRENTES', despesas_correntes[0], despesas_correntes[1], despesas_correntes[2], despesas_correntes[3], despesas_correntes[4], despesas_correntes[5]]) if pessoal_encargos_sociais[0] == 0 and pessoal_encargos_sociais[1] == 0: balorc_writer.writerow(['PESSOAL E ENCARGOS SOCIAIS', pessoal_encargos_sociais[0], pessoal_encargos_sociais[1], pessoal_encargos_sociais[2], pessoal_encargos_sociais[3], pessoal_encargos_sociais[4], pessoal_encargos_sociais[5]]) if juros_encargos_divida[0] == 0 and juros_encargos_divida[1] == 0: balorc_writer.writerow(['JUROS E ENCARGOS DA DIVIDA', juros_encargos_divida[0], juros_encargos_divida[1], juros_encargos_divida[2], juros_encargos_divida[3], juros_encargos_divida[4], juros_encargos_divida[5]]) if outras_despesas_correntes[0] == 0 and outras_despesas_correntes[1] == 0: balorc_writer.writerow(['OUTRAS DESPESAS CORRENTES', outras_despesas_correntes[0], outras_despesas_correntes[1], outras_despesas_correntes[2], outras_despesas_correntes[3], outras_despesas_correntes[4], outras_despesas_correntes[5]]) if despesas_capital[0] == 0 and despesas_capital[1] == 0: balorc_writer.writerow(['DESPESAS DE CAPITAL', despesas_capital[0], despesas_capital[1], despesas_capital[2], despesas_capital[3], despesas_capital[4], despesas_capital[5]]) if investimentos[0] == 0 and investimentos[1] == 0: balorc_writer.writerow(['INVESTIMENTOS', investimentos[0], investimentos[1], investimentos[2], investimentos[3], investimentos[4], investimentos[5]]) if inversoes_financeiras[0] == 0 and inversoes_financeiras[1] == 0: balorc_writer.writerow( ['INVERSOES FINANCEIRAS', inversoes_financeiras[0], inversoes_financeiras[1], inversoes_financeiras[2], inversoes_financeiras[3], inversoes_financeiras[4], inversoes_financeiras[5]]) if amortizacao_divida[0] == 0 and amortizacao_divida[1] == 0: balorc_writer.writerow(['AMORTIZACAO DA DIVIDA', amortizacao_divida[0], amortizacao_divida[1], amortizacao_divida[2], amortizacao_divida[3], amortizacao_divida[4], amortizacao_divida[5]]) if reserva_contingencia[0] == 0 and reserva_contingencia[1] == 0: balorc_writer.writerow(['RESERVA DE CONTINGENCIA', reserva_contingencia[0], reserva_contingencia[1], reserva_contingencia[2], reserva_contingencia[3], reserva_contingencia[4], reserva_contingencia[5]]) if reserva_rpps[0] == 0 and reserva_rpps[1] == 0: balorc_writer.writerow(['RESERVA DO RPPS', reserva_rpps[0], reserva_rpps[1], reserva_rpps[2], reserva_rpps[3], reserva_rpps[4], reserva_rpps[5]]) if subtotal_despesas[0] == 0 and subtotal_despesas[1] == 0: balorc_writer.writerow(['SUBTOTAL DAS DESPESAS', subtotal_despesas[0], subtotal_despesas[1], subtotal_despesas[2], subtotal_despesas[3], subtotal_despesas[4], subtotal_despesas[5]]) if amortizacao_divida_refinanciamento[0] == 0 and amortizacao_divida_refinanciamento[1] == 0: balorc_writer.writerow( ['AMORTIZACAO DA DIVIDA - REFINANCIAMENTO', amortizacao_divida_refinanciamento[0], amortizacao_divida_refinanciamento[1], amortizacao_divida_refinanciamento[2], amortizacao_divida_refinanciamento[3], amortizacao_divida_refinanciamento[4], amortizacao_divida_refinanciamento[5]]) if subtotal_refinanciamento_d[0] == 0 and subtotal_refinanciamento_d[1] == 0: balorc_writer.writerow(['SUBTOTAL COM REFINANCIAMENTO (XV)', subtotal_refinanciamento_d[0], subtotal_refinanciamento_d[1], subtotal_refinanciamento_d[2], subtotal_refinanciamento_d[3], subtotal_refinanciamento_d[4], subtotal_refinanciamento_d[5]]) new = file_parts[0] with open(diretorio + file_parts[0] + "_tratado.csv", mode='w+') as balorc_file: balorc_writer = csv.writer(balorc_file, delimiter=';', quoting=csv.QUOTE_NONNUMERIC) balorc_writer.writerow(["Key", "1", "2", "3", "4", "5", "6"]) despesas_correntes = buscaKeyParts(diretorio, file, 'DESPESAS CORRENTES') print("despesas_correntes", despesas_correntes) if despesas_correntes[0] != 0 or despesas_correntes[1] != 0: balorc_writer.writerow(['DESPESAS CORRENTES', despesas_correntes[0], despesas_correntes[1], despesas_correntes[2], despesas_correntes[3], despesas_correntes[4], despesas_correntes[5]]) pessoal_encargos_sociais = buscaKeyParts(diretorio, file, 'PESSOAL E ENCARGOS SOCIAIS') print("pessoal_encargos_sociais", pessoal_encargos_sociais) if pessoal_encargos_sociais[0] != 0 or pessoal_encargos_sociais[1] != 0: balorc_writer.writerow(['PESSOAL E ENCARGOS SOCIAIS', pessoal_encargos_sociais[0], pessoal_encargos_sociais[1], pessoal_encargos_sociais[2], pessoal_encargos_sociais[3], pessoal_encargos_sociais[4], pessoal_encargos_sociais[5]]) juros_encargos_divida = buscaKeyParts(diretorio, file, 'JUROS E ENCARGOS DA DIVIDA') print("juros_encargos_divida", juros_encargos_divida) if juros_encargos_divida[0] != 0 or juros_encargos_divida[1] != 0: balorc_writer.writerow(['JUROS E ENCARGOS DA DIVIDA', juros_encargos_divida[0], juros_encargos_divida[1], juros_encargos_divida[2], juros_encargos_divida[3], juros_encargos_divida[4], juros_encargos_divida[5]]) outras_despesas_correntes = buscaKeyParts(diretorio, file, 'OUTRAS DESPESAS CORRENTES') print("outras_despesas_correntes", outras_despesas_correntes) if outras_despesas_correntes[0] != 0 or outras_despesas_correntes[1] != 0: balorc_writer.writerow(['OUTRAS DESPESAS CORRENTES', outras_despesas_correntes[0], outras_despesas_correntes[1], outras_despesas_correntes[2], outras_despesas_correntes[3], outras_despesas_correntes[4], outras_despesas_correntes[5]]) despesas_capital = buscaKeyParts(diretorio, file, 'DESPESAS DE CAPITAL') print("despesas_capital", despesas_capital) if despesas_capital[0] != 0 or despesas_capital[1] != 0: balorc_writer.writerow(['DESPESAS DE CAPITAL', despesas_capital[0], despesas_capital[1], despesas_capital[2], despesas_capital[3], despesas_capital[4], despesas_capital[5]]) investimentos = buscaKeyParts(diretorio, file, 'INVESTIMENTOS') print("investimentos", investimentos) if investimentos[0] != 0 or investimentos[1] != 0: balorc_writer.writerow(['INVESTIMENTOS', investimentos[0], investimentos[1], investimentos[2], investimentos[3], investimentos[4], investimentos[5]]) inversoes_financeiras = buscaKeyParts(diretorio, file, 'INVERSOES FINANCEIRAS') print("inversoes_financeiras", inversoes_financeiras) if inversoes_financeiras[0] != 0 or inversoes_financeiras[1] != 0: balorc_writer.writerow( ['INVERSOES FINANCEIRAS', inversoes_financeiras[0], inversoes_financeiras[1], inversoes_financeiras[2], inversoes_financeiras[3], inversoes_financeiras[4], inversoes_financeiras[5]]) amortizacao_divida = buscaKeyParts(diretorio, file, 'AMORTIZACAO DA DIVIDA') print("amortizacao_divida", amortizacao_divida) if amortizacao_divida[0] != 0 or amortizacao_divida[1] != 0: balorc_writer.writerow(['AMORTIZACAO DA DIVIDA', amortizacao_divida[0], amortizacao_divida[1], amortizacao_divida[2], amortizacao_divida[3], amortizacao_divida[4], amortizacao_divida[5]]) reserva_contingencia = buscaKeyParts(diretorio, file, 'RESERVA DE CONTINGENCIA') print("reserva_contingencia", reserva_contingencia) if reserva_contingencia[0] != 0 or reserva_contingencia[1] != 0: balorc_writer.writerow(['RESERVA DE CONTINGENCIA', reserva_contingencia[0], reserva_contingencia[1], reserva_contingencia[2], reserva_contingencia[3], reserva_contingencia[4], reserva_contingencia[5]]) reserva_rpps = buscaKeyParts(diretorio, file, 'RESERVA DO RPPS') print("reserva_rpps", reserva_rpps) if reserva_rpps[0] != 0 or reserva_rpps[1] != 0: balorc_writer.writerow(['RESERVA DO RPPS', reserva_rpps[0], reserva_rpps[1], reserva_rpps[2], reserva_rpps[3], reserva_rpps[4], reserva_rpps[5]]) subtotal_despesas = buscaKeyParts(diretorio, file, 'SUBTOTAL DAS DESPESAS') print("subtotal_despesas", subtotal_despesas) if subtotal_despesas[0] != 0 or subtotal_despesas[1] != 0: balorc_writer.writerow(['SUBTOTAL DAS DESPESAS', subtotal_despesas[0], subtotal_despesas[1], subtotal_despesas[2], subtotal_despesas[3], subtotal_despesas[4], subtotal_despesas[5]]) amortizacao_divida_refinanciamento = buscaKeyParts(diretorio, file, 'AMORTIZACAO DA DIVIDA - REFINANCIAMENTO') print("amortizacao_divida_refinanciamento", amortizacao_divida_refinanciamento) if amortizacao_divida_refinanciamento[0] != 0 or amortizacao_divida_refinanciamento[1] != 0: balorc_writer.writerow( ['AMORTIZACAO DA DIVIDA - REFINANCIAMENTO', amortizacao_divida_refinanciamento[0], amortizacao_divida_refinanciamento[1], amortizacao_divida_refinanciamento[2], amortizacao_divida_refinanciamento[3], amortizacao_divida_refinanciamento[4], amortizacao_divida_refinanciamento[5]]) subtotal_refinanciamento_d = buscaKeyParts(diretorio, file, 'SUBTOTAL COM REFINANCIAMENTO (XV)') print("subtotal_refinanciamento_d", subtotal_refinanciamento_d) if subtotal_refinanciamento_d[0] != 0 or subtotal_refinanciamento_d[1] != 0: balorc_writer.writerow(['SUBTOTAL COM REFINANCIAMENTO (XV)', subtotal_refinanciamento_d[0], subtotal_refinanciamento_d[1], subtotal_refinanciamento_d[2], subtotal_refinanciamento_d[3], subtotal_refinanciamento_d[4], subtotal_refinanciamento_d[5]]) else: with open(diretorio + file_parts[0] + "_tratado.csv", mode='a+') as balorc_file: balorc_writer = csv.writer(balorc_file, delimiter=';', quoting=csv.QUOTE_NONNUMERIC) if despesas_correntes[0] == 0 and despesas_correntes[1] == 0: despesas_correntes = buscaKeyParts(diretorio, file, 'DESPESAS CORRENTES') print("despesas_correntes", despesas_correntes) if despesas_correntes[0] != 0 or despesas_correntes[1] != 0: balorc_writer.writerow(['DESPESAS CORRENTES', despesas_correntes[0], despesas_correntes[1], despesas_correntes[2], despesas_correntes[3], despesas_correntes[4], despesas_correntes[5]]) if pessoal_encargos_sociais[0] == 0 and pessoal_encargos_sociais[1] == 0: pessoal_encargos_sociais = buscaKeyParts(diretorio, file, 'PESSOAL E ENCARGOS SOCIAIS') print("pessoal_encargos_sociais", pessoal_encargos_sociais) if pessoal_encargos_sociais[0] != 0 or pessoal_encargos_sociais[1] != 0: balorc_writer.writerow(['PESSOAL E ENCARGOS SOCIAIS', pessoal_encargos_sociais[0], pessoal_encargos_sociais[1], pessoal_encargos_sociais[2], pessoal_encargos_sociais[3], pessoal_encargos_sociais[4], pessoal_encargos_sociais[5]]) if juros_encargos_divida[0] == 0 and juros_encargos_divida[1] == 0: juros_encargos_divida = buscaKeyParts(diretorio, file, 'JUROS E ENCARGOS DA DIVIDA') print("juros_encargos_divida", juros_encargos_divida) if juros_encargos_divida[0] != 0 or juros_encargos_divida[1] != 0: balorc_writer.writerow(['JUROS E ENCARGOS DA DIVIDA', juros_encargos_divida[0], juros_encargos_divida[1], juros_encargos_divida[2], juros_encargos_divida[3], juros_encargos_divida[4], juros_encargos_divida[5]]) if outras_despesas_correntes[0] == 0 and outras_despesas_correntes[1] == 0: outras_despesas_correntes = buscaKeyParts(diretorio, file, 'OUTRAS DESPESAS CORRENTES') print("outras_despesas_correntes", outras_despesas_correntes) if outras_despesas_correntes[0] != 0 or outras_despesas_correntes[1] != 0: balorc_writer.writerow(['OUTRAS DESPESAS CORRENTES', outras_despesas_correntes[0], outras_despesas_correntes[1], outras_despesas_correntes[2], outras_despesas_correntes[3], outras_despesas_correntes[4], outras_despesas_correntes[5]]) if despesas_capital[0] == 0 and despesas_capital[1] == 0: despesas_capital = buscaKeyParts(diretorio, file, 'DESPESAS DE CAPITAL') print("despesas_capital", despesas_capital) if despesas_capital[0] != 0 or despesas_capital[1] != 0: balorc_writer.writerow(['DESPESAS DE CAPITAL', despesas_capital[0], despesas_capital[1], despesas_capital[2], despesas_capital[3], despesas_capital[4], despesas_capital[5]]) if investimentos[0] == 0 and investimentos[1] == 0: investimentos = buscaKeyParts(diretorio, file, 'INVESTIMENTOS') print("investimentos", investimentos) if investimentos[0] != 0 or investimentos[1] != 0: balorc_writer.writerow(['INVESTIMENTOS', investimentos[0], investimentos[1], investimentos[2], investimentos[3], investimentos[4], investimentos[5]]) if inversoes_financeiras[0] == 0 and inversoes_financeiras[1] == 0: inversoes_financeiras = buscaKeyParts(diretorio, file, 'INVERSOES FINANCEIRAS') print("inversoes_financeiras", inversoes_financeiras) if inversoes_financeiras[0] != 0 or inversoes_financeiras[1] != 0: balorc_writer.writerow( ['INVERSOES FINANCEIRAS', inversoes_financeiras[0], inversoes_financeiras[1], inversoes_financeiras[2], inversoes_financeiras[3], inversoes_financeiras[4], inversoes_financeiras[5]]) if amortizacao_divida[0] == 0 and amortizacao_divida[1] == 0: amortizacao_divida = buscaKeyParts(diretorio, file, 'AMORTIZACAO DA DIVIDA') print("amortizacao_divida", amortizacao_divida) if amortizacao_divida[0] != 0 or amortizacao_divida[1] != 0: balorc_writer.writerow(['AMORTIZACAO DA DIVIDA', amortizacao_divida[0], amortizacao_divida[1], amortizacao_divida[2], amortizacao_divida[3], amortizacao_divida[4], amortizacao_divida[5]]) if reserva_contingencia[0] == 0 and reserva_contingencia[1] == 0: reserva_contingencia = buscaKeyParts(diretorio, file, 'RESERVA DE CONTINGENCIA') print("reserva_contingencia", reserva_contingencia) if reserva_contingencia[0] != 0 or reserva_contingencia[1] != 0: balorc_writer.writerow(['RESERVA DE CONTINGENCIA', reserva_contingencia[0], reserva_contingencia[1], reserva_contingencia[2], reserva_contingencia[3], reserva_contingencia[4], reserva_contingencia[5]]) if reserva_rpps[0] == 0 and reserva_rpps[1] == 0: reserva_rpps = buscaKeyParts(diretorio, file, 'RESERVA DO RPPS') print("reserva_rpps", reserva_rpps) if reserva_rpps[0] != 0 or reserva_rpps[1] != 0: balorc_writer.writerow(['RESERVA DO RPPS', reserva_rpps[0], reserva_rpps[1], reserva_rpps[2], reserva_rpps[3], reserva_rpps[4], reserva_rpps[5]]) if subtotal_despesas[0] == 0 and subtotal_despesas[1] == 0: subtotal_despesas = buscaKeyParts(diretorio, file, 'SUBTOTAL DAS DESPESAS') print("subtotal_despesas", subtotal_despesas) if subtotal_despesas[0] != 0 or subtotal_despesas[1] != 0: balorc_writer.writerow(['SUBTOTAL DAS DESPESAS', subtotal_despesas[0], subtotal_despesas[1], subtotal_despesas[2], subtotal_despesas[3], subtotal_despesas[4], subtotal_despesas[5]]) if amortizacao_divida_refinanciamento[0] == 0 and amortizacao_divida_refinanciamento[1] == 0: amortizacao_divida_refinanciamento = buscaKeyParts(diretorio, file, 'REFINANCIAMENTO DA DIVIDA - REFINANCIAMENTO') print("amortizacao_divida_refinanciamento", amortizacao_divida_refinanciamento) if amortizacao_divida_refinanciamento[0] != 0 or amortizacao_divida_refinanciamento[1] != 0: balorc_writer.writerow( ['REFINANCIAMENTO DA DIVIDA - REFINANCIAMENTO', amortizacao_divida_refinanciamento[0], amortizacao_divida_refinanciamento[1], amortizacao_divida_refinanciamento[2], amortizacao_divida_refinanciamento[3], amortizacao_divida_refinanciamento[4], amortizacao_divida_refinanciamento[5]]) if subtotal_refinanciamento_d[0] == 0 and subtotal_refinanciamento_d[1] == 0: subtotal_refinanciamento_d = buscaKeyParts(diretorio, file, 'SUBTOTAL COM REFINANCIAMENTO (XV)') print("subtotal_refinanciamento_d", subtotal_refinanciamento_d) if subtotal_refinanciamento_d[0] != 0 or subtotal_refinanciamento_d[1] != 0: balorc_writer.writerow(['SUBTOTAL COM REFINANCIAMENTO (XV)', subtotal_refinanciamento_d[0], subtotal_refinanciamento_d[1], subtotal_refinanciamento_d[2], subtotal_refinanciamento_d[3], subtotal_refinanciamento_d[4], subtotal_refinanciamento_d[5]]) if __name__ == "__main__": main()

75.141869

311

0.645607

2,311

21,716

5.791

0.061878

0.09654

0.059777

0.040798

0.891429

0.889487

0.875962

0.870881

0

0.029775

0.242172

21,716

288

312

75.402778

0.783436

0.000507

0

0.659389

0

0.109689

0.018165

0

1

0.017467

false

0

0.026201

0.004367

0.056769

0.135371

0

null

0

1

0

null

0

6

3cffc05e45986d3dc42e910e5174e501b7b56aa8

195

py

Python

src/dummynet/__init__.py

steinwurf/dummynet-python

4c72ed4c3a217ec6c69dfa00032b275b9bd6a40e

[ "BSD-3-Clause" ]

null

src/dummynet/__init__.py

steinwurf/dummynet-python

4c72ed4c3a217ec6c69dfa00032b275b9bd6a40e

[ "BSD-3-Clause" ]

null

src/dummynet/__init__.py

steinwurf/dummynet-python

4c72ed4c3a217ec6c69dfa00032b275b9bd6a40e

[ "BSD-3-Clause" ]

null

from .dummy_net import DummyNet from .dummy_net_factory import DummyNetFactory from .namespace_shell import NamespaceShell from .docker_shell import DockerShell from .host_shell import HostShell

32.5

46

0.871795

26

195

6.307692

0.538462

0.20122

0.146341

0

0.102564

195

5

47

39

0.937143

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

5956ec6e29720d187b0b60d95bd9bac544029e78

153

py

Python

905. Sort Array By Parity/905. Sort Array By Parity.py

JawadAsif/leetcode

13046653d8203ee3d8b524b7402f59c4e2bec7d0

[ "MIT" ]

null

905. Sort Array By Parity/905. Sort Array By Parity.py

JawadAsif/leetcode

13046653d8203ee3d8b524b7402f59c4e2bec7d0

[ "MIT" ]

null

905. Sort Array By Parity/905. Sort Array By Parity.py

JawadAsif/leetcode

13046653d8203ee3d8b524b7402f59c4e2bec7d0

[ "MIT" ]

null

class Solution(object): def sortArrayByParity(self, A): return ([x for x in A if x % 2 == 0] + [x for x in A if x % 2 == 1])

30.6

46

0.496732

26

153

2.923077

0.576923

0.105263

0.131579

0.184211

0.315789

0

0.041667

0.372549

153

4

47

38.25

0.75

0

1

0.25

false

0

0.25

0.75

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

abd2968bb5d2e8f41794423f3f052d1d4e7bd3af

301

py

Python

vivid/out_of_fold/boosting/__init__.py

upura/vivid

6139697d60656d4774aceae880f5a07d929124a8

[ "BSD-2-Clause-FreeBSD" ]

null

vivid/out_of_fold/boosting/__init__.py

upura/vivid

6139697d60656d4774aceae880f5a07d929124a8

[ "BSD-2-Clause-FreeBSD" ]

null

vivid/out_of_fold/boosting/__init__.py

upura/vivid

6139697d60656d4774aceae880f5a07d929124a8

[ "BSD-2-Clause-FreeBSD" ]

null

""" Boosting Module Gradient Boosted Decision Tree 系統のアルゴリズムを使った Out Of Fold を定義するモジュール """ from .lgbm import LGBMClassifierOutOfFold, LGBMRegressorOutOfFold from .xgboost import XGBoostRegressorOutOfFold, XGBoostClassifierOutOfFold, OptunaXGBClassifierOutOfFold, \ OptunaXGBRegressionOutOfFold

30.1

107

0.850498

23

301

11.130435

0.913043

0

0.106312

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9

108

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0.951673

0.27907

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null

0

1

0

1

0

1

0

6

abd47f3b6fbeb154d77e6a54c66e5aa9d493914a

81

py

Python

ubvi/optimization/__init__.py

trevorcampbell/ubvi

a71c14c9a1d588702f157f83a50619647856fd8b

[ "MIT" ]

5

2019-07-22T14:40:19.000Z

2020-10-15T13:23:08.000Z

ubvi/optimization/__init__.py

trevorcampbell/ubvi

a71c14c9a1d588702f157f83a50619647856fd8b

[ "MIT" ]

3

2019-10-02T20:22:56.000Z

2019-10-04T20:34:44.000Z

ubvi/optimization/__init__.py

trevorcampbell/ubvi

a71c14c9a1d588702f157f83a50619647856fd8b

[ "MIT" ]

2

2019-07-23T02:11:49.000Z

2019-10-24T06:57:23.000Z

from .adam import adam from .sgd import sgd from .simplex_sgd import simplex_sgd

20.25

36

0.814815

14

81

4.571429

0.357143

0.28125

0

0.148148

81

3

37

27

0.927536

0

1

0

true

0

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1

0

null

0

1

0

1

0

1

0

6

e631631cab6fd6ff7953124cce378bb10ea30475

34

wsgi

Python

annotator-store.wsgi

FUB-HCC/annotator-store

fb1f03d18770078f84c6a73a41cfba292235a59a

[ "MIT" ]

null

annotator-store.wsgi

FUB-HCC/annotator-store

fb1f03d18770078f84c6a73a41cfba292235a59a

[ "MIT" ]

null

annotator-store.wsgi

FUB-HCC/annotator-store

fb1f03d18770078f84c6a73a41cfba292235a59a

[ "MIT" ]

null

from run import app as application

34

0.852941

6

34

4.833333

1

0

0.147059

34

1

34

1

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

058a098556885dd6222851b0ac0695f739179ebc

231

py

Python

testsuite/utils/cached_property.py

itrofimow/yandex-taxi-testsuite

bea758af35ae19db929f4b2b99d2a2917ff4c147

[ "MIT" ]

null

testsuite/utils/cached_property.py

itrofimow/yandex-taxi-testsuite

bea758af35ae19db929f4b2b99d2a2917ff4c147

[ "MIT" ]

null

testsuite/utils/cached_property.py

itrofimow/yandex-taxi-testsuite

bea758af35ae19db929f4b2b99d2a2917ff4c147

[ "MIT" ]

null

# pylint: disable=import-only-modules # flake8: noqa import sys if sys.version_info > (3, 8): # pylint: disable=no-name-in-module from functools import cached_property else: from cached_property import cached_property

23.1

47

0.748918

33

231

5.121212

0.666667

0.248521

0.236686

0

0.015544

0.164502

231

9

48

25.666667

0.860104

0.354978

0

1

0

true

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0.6

0

0.6

0

null

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0

1

0

null

0

1

0

1

0

1

0

6

058cbecdb39437f68bfbce67210d01c0619bb488

49

py

Python

icarus_simulator/strategies/zone_bneck/__init__.py

RubenFr/ICARUS-framework

e57a1f50c3bb9522b2a279fee6b625628afd056f

[ "MIT" ]

5

2021-08-31T08:07:41.000Z

2022-01-04T02:09:25.000Z

icarus_simulator/strategies/zone_bneck/__init__.py

RubenFr/ICARUS-framework

e57a1f50c3bb9522b2a279fee6b625628afd056f

[ "MIT" ]

3

2021-09-23T09:06:35.000Z

2021-12-08T04:53:01.000Z

icarus_simulator/strategies/zone_bneck/__init__.py

RubenFr/ICARUS-framework

e57a1f50c3bb9522b2a279fee6b625628afd056f

[ "MIT" ]

2

2022-01-19T17:50:56.000Z

2022-03-06T18:59:41.000Z

from .detect_bneck_strat import DetectBneckStrat

24.5

48

0.897959

6

49

7

1

0

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49

1

49

0.933333

0

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0

true

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0

1

0

null

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0

null

0

1

0

1

0

1

0

6

55ab1e9def726a59e11e7f5e75a4148b40eb4682

1,391

py

Python

tests/test_swap_exceptions.py

tomgrin10/swap-exceptions

45747b7e61b974646d2a4eca2ceff9587f44d629

[ "MIT" ]

2

2020-08-28T14:48:12.000Z

2020-10-08T09:05:01.000Z

tests/test_swap_exceptions.py

tomgrin10/swap-exceptions

45747b7e61b974646d2a4eca2ceff9587f44d629

[ "MIT" ]

null

tests/test_swap_exceptions.py

tomgrin10/swap-exceptions

45747b7e61b974646d2a4eca2ceff9587f44d629

[ "MIT" ]

null

from swap_exceptions import swap_exceptions def test__swap_exceptions__context_manager(): # Arrange mapping = {ValueError: KeyError("AAAA")} exc_to_raise = ValueError() expected_raised_exc = mapping[type(exc_to_raise)] # Act raised_exc = None try: with swap_exceptions(mapping): raise exc_to_raise except Exception as e: raised_exc = e # Assert assert raised_exc is expected_raised_exc def test__swap_exceptions__decorator(): # Arrange mapping = {ValueError: KeyError("AAAA")} exc_to_raise = ValueError() expected_raised_exc = mapping[type(exc_to_raise)] @swap_exceptions(mapping) def foo(): raise exc_to_raise # Act raised_exc = None try: foo() except Exception as e: raised_exc = e # Assert assert raised_exc is expected_raised_exc def test__swap_exceptions__lambda_exception_target(): # Arrange mapping = {ValueError: lambda e: KeyError(e)} exc_to_raise = ValueError() expected_raised_exc = mapping[type(exc_to_raise)](exc_to_raise) # Act raised_exc = None try: with swap_exceptions(mapping): raise exc_to_raise except Exception as e: raised_exc = e # Assert assert type(raised_exc) is type(expected_raised_exc) assert raised_exc.args == expected_raised_exc.args

23.183333

67

0.677211

176

1,391

4.971591

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0.174857

0.114286

0.068571

0.715429

0.676571

0

0.25018

1,391

59

68

23.576271

0.838926

0.040259

0

0.702703

0

0.006038

0

0.108108

1

0.108108

false

0

0.027027

0

0.135135

0

null

0

1

0

1

0

null

0

6

55c01f89c4e6f9945d3408f49c4504a8632fcbf0

23

py

Python

contrib/diggext/drivers/devices/consoleservers/__init__.py

thekad/clusto

c141ea3ef4931c6a21fdf42845c6e9de5ee08caa

[ "BSD-3-Clause" ]

216

2015-01-10T17:03:25.000Z

2022-03-24T07:23:41.000Z

contrib/diggext/drivers/devices/consoleservers/__init__.py

thekad/clusto

c141ea3ef4931c6a21fdf42845c6e9de5ee08caa

[ "BSD-3-Clause" ]

23

2015-01-08T16:51:22.000Z

2021-03-13T12:56:04.000Z

contrib/diggext/drivers/devices/consoleservers/__init__.py

thekad/clusto

c141ea3ef4931c6a21fdf42845c6e9de5ee08caa

[ "BSD-3-Clause" ]

49

2015-01-08T00:13:17.000Z

2021-09-22T02:01:20.000Z

from opengear import *

11.5

22

0.782609

3

23

6

1

0

0.173913

23

1

23

0.947368

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

e96cc262caf292659ec3b967cff4f10c1e819e79

103

py

Python

argument_tuple_unpacking.py

shankar-shiv/CS1010E_Kattis_practice

9a8597b7ab61d5afa108a8b943ca2bb3603180c6

[ "MIT" ]

null

argument_tuple_unpacking.py

shankar-shiv/CS1010E_Kattis_practice

9a8597b7ab61d5afa108a8b943ca2bb3603180c6

[ "MIT" ]

null

argument_tuple_unpacking.py

shankar-shiv/CS1010E_Kattis_practice

9a8597b7ab61d5afa108a8b943ca2bb3603180c6

[ "MIT" ]

null

def f(a, b, c): print(f"a = {a}") print(f"b = {b}") print(f"c = {c}") t = (1, 2, 3) f(*t)

12.875

21

0.359223

23

103

1.608696

0.434783

0.486486

0

0.042254

0.31068

103

7

22

14.714286

0.478873

0

0.203884

0

1

0.166667

false

0

0.166667

0.5

1

0

1

null

1

0

1

0

1

0

null

0

1

0

6

e9a8cf6ace2b653fa05de7a56912eb72211ee231

65

py

Python

mappgene/subscripts/__init__.py

aavilaherrera/mappgene

899e54217221e18b3b7c32afb2dec0cae43f203c

[ "BSD-3-Clause" ]

7

2021-04-15T05:06:55.000Z

2022-02-23T22:18:49.000Z

mappgene/subscripts/__init__.py

aavilaherrera/mappgene

899e54217221e18b3b7c32afb2dec0cae43f203c

[ "BSD-3-Clause" ]

1

2021-07-16T23:50:15.000Z

mappgene/subscripts/__init__.py

aavilaherrera/mappgene

899e54217221e18b3b7c32afb2dec0cae43f203c

[ "BSD-3-Clause" ]

5

2021-04-16T05:03:56.000Z

2021-12-21T18:53:14.000Z

from .utilities import * from .vpipe import * from .ivar import *

21.666667

24

0.738462

9

65

5.333333

0.555556

0.416667

0

0.169231

65

3

25

21.666667

0.888889

0

1

0

true

0

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1

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1

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0

null

0

1

0

1

0

1

0

6

e9bddfe5b3d78a3c792aa4324772742f24c31003

32

py

Python

src/dbspy/core/spectrum/dbs/raw/__init__.py

ZhengKeli/PositronSpector

be0281fe50fe634183b6f239f03b7140c1dc0b7f

[ "MIT" ]

1

2019-06-18T09:23:42.000Z

src/dbspy/core/spectrum/dbs/raw/__init__.py

ZhengKeli/DBSpy

be0281fe50fe634183b6f239f03b7140c1dc0b7f

[ "MIT" ]

null

src/dbspy/core/spectrum/dbs/raw/__init__.py

ZhengKeli/DBSpy

be0281fe50fe634183b6f239f03b7140c1dc0b7f

[ "MIT" ]

null

from ._raw import Conf, Process

16

31

0.78125

5

32

4.8

1

0

0.15625

32

1

32

0.888889

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

e9c52ac09ab88cfc0a665bd36daa94992c03e4d4

30,722

py

Python

tests/conftest.py

BenSchZA/aquarius

2041605bc44ca03d95617fd30bc9ebf312f90beb

[ "Apache-2.0" ]

null

tests/conftest.py

BenSchZA/aquarius

2041605bc44ca03d95617fd30bc9ebf312f90beb

[ "Apache-2.0" ]

null

tests/conftest.py

BenSchZA/aquarius

2041605bc44ca03d95617fd30bc9ebf312f90beb

[ "Apache-2.0" ]

null

# Copyright 2018 Ocean Protocol Foundation # SPDX-License-Identifier: Apache-2.0 import copy import json import pytest from aquarius.constants import BaseURLs from aquarius.run import app app = app @pytest.fixture def base_ddo_url(): return BaseURLs.BASE_AQUARIUS_URL + '/assets/ddo' @pytest.fixture def client_with_no_data(): client = app.test_client() client.delete(BaseURLs.BASE_AQUARIUS_URL + '/assets/ddo') yield client @pytest.fixture def client(): client = app.test_client() client.delete(BaseURLs.BASE_AQUARIUS_URL + '/assets/ddo') post = client.post(BaseURLs.BASE_AQUARIUS_URL + '/assets/ddo', data=json.dumps(json_update), content_type='application/json') if post.status_code not in (200, 201): raise AssertionError(f'register asset failed: {post}') post2 = client.post(BaseURLs.BASE_AQUARIUS_URL + '/assets/ddo', data=json.dumps(json_dict), content_type='application/json') yield client client.delete( BaseURLs.BASE_AQUARIUS_URL + '/assets/ddo/%s' % json.loads(post.data.decode('utf-8'))['id']) client.delete( BaseURLs.BASE_AQUARIUS_URL + '/assets/ddo/%s' % json.loads(post2.data.decode('utf-8'))[ 'id']) json_dict = { "@context": "https://w3id.org/did/v1", "id": "did:op:0c184915b07b44c888d468be85a9b28253e80070e5294b1aaed81c2f0264e430", "created": "2019-05-22T08:44:27Z", "publicKey": [ { "id": "did:op:0c184915b07b44c888d468be85a9b28253e80070e5294b1aaed81c2f0264e430", "type": "EthereumECDSAKey", "owner": "0x00Bd138aBD70e2F00903268F3Db08f2D25677C9e" } ], "authentication": [ { "type": "RsaSignatureAuthentication2018", "publicKey": "did:op:0c184915b07b44c888d468be85a9b28253e80070e5294b1aaed81c2f0264e430" } ], "service": [ { "type": "authorization", "serviceEndpoint": "http://localhost:12001", "service": "SecretStore", "index": 0 }, { "type": "access", "serviceEndpoint": "http://localhost:8030/api/v1/brizo/services/consume", "purchaseEndpoint": "http://localhost:8030/api/v1/brizo/services/access/initialize", "index": 1, "templateId": "0x208aca4B0316C9996F085cbD57E01c11Bc0E7cb1", "name": "dataAssetAccessServiceAgreement", "creator": "", "serviceAgreementTemplate": { "contractName": "EscrowAccessSecretStoreTemplate", "events": [ { "name": "AgreementCreated", "actorType": "consumer", "handler": { "moduleName": "escrowAccessSecretStoreTemplate", "functionName": "fulfillLockRewardCondition", "version": "0.1" } } ], "fulfillmentOrder": [ "lockReward.fulfill", "accessSecretStore.fulfill", "escrowReward.fulfill" ], "conditionDependency": { "lockReward": [], "accessSecretStore": [], "escrowReward": [ "lockReward", "accessSecretStore" ] }, "conditions": [ { "name": "lockReward", "timelock": 0, "timeout": 0, "contractName": "LockRewardCondition", "functionName": "fulfill", "events": [ { "name": "Fulfilled", "actorType": "publisher", "handler": { "moduleName": "lockRewardCondition", "functionName": "fulfillAccessSecretStoreCondition", "version": "0.1" } } ], "parameters": [ { "name": "_rewardAddress", "type": "address", "value": "0x2AaC920AA4D10b80db9ed0E4EC04A3ff612F2bc6" }, { "name": "_amount", "type": "uint256", "value": "888000000000000000000000000000000" } ] }, { "name": "accessSecretStore", "timelock": 0, "timeout": 0, "contractName": "AccessSecretStoreCondition", "functionName": "fulfill", "events": [ { "name": "Fulfilled", "actorType": "publisher", "handler": { "moduleName": "accessSecretStore", "functionName": "fulfillEscrowRewardCondition", "version": "0.1" } }, { "name": "TimedOut", "actorType": "consumer", "handler": { "moduleName": "accessSecretStore", "functionName": "fulfillEscrowRewardCondition", "version": "0.1" } } ], "parameters": [ { "name": "_documentId", "type": "bytes32", "value": "0c184915b07b44c888d468be85a9b28253e80070e5294b1aaed81c2f0264e430" }, { "name": "_grantee", "type": "address", "value": "" } ] }, { "name": "escrowReward", "timelock": 0, "timeout": 0, "contractName": "EscrowReward", "functionName": "fulfill", "events": [ { "name": "Fulfilled", "actorType": "publisher", "handler": { "moduleName": "escrowRewardCondition", "functionName": "verifyRewardTokens", "version": "0.1" } } ], "parameters": [ { "name": "_amount", "type": "uint256", "value": "888000000000000000000000000000000" }, { "name": "_receiver", "type": "address", "value": "" }, { "name": "_sender", "type": "address", "value": "" }, { "name": "_lockCondition", "type": "bytes32", "value": "" }, { "name": "_releaseCondition", "type": "bytes32", "value": "" } ] } ] } }, { "type": "metadata", "serviceEndpoint": "http://localhost:5000/api/v1/aquarius/assets/ddo/did:op:0c184915b07b44c888d468be85a9b28253e80070e5294b1aaed81c2f0264e430", "attributes": { "main": { "name": "Ocean protocol white paper", "type": "dataset", "dateCreated": "2012-10-10T17:00:00Z", "datePublished": "2012-10-10T17:00:00Z", "author": "Ocean Protocol Foundation Ltd.", "license": "CC-BY", "price": "888000000000000000000000000000000", "files": [ { "checksum": "efb2c764274b745f5fc37f97c6b0e761", "contentType": "text/csv", "checksumType": "MD5", "contentLength": "4535431", "resourceId": "access-log2018-02-13-15-17-29-18386C502CAEA932", "index": 0 }, { "checksum": "efb2c764274b745f5fc37f97c6b0e761", "contentType": "text/csv", "contentLength": "4535431", "resourceId": "access-log2018-02-13-15-17-29-18386C502CAEA932", "index": 1 }, { "index": 2, "contentType": "text/csv", } ] }, "encryptedFiles": "<tests.resources.mocks.secret_store_mock.SecretStoreMock object at 0x7f8146a94710>.0c184915b07b44c888d468be85a9b28253e80070e5294b1aaed81c2f0264e430!![{\"url\": \"https://testocnfiles.blob.core.windows.net/testfiles/testzkp.pdf\", \"checksum\": \"efb2c764274b745f5fc37f97c6b0e761\", \"checksumType\": \"MD5\", \"contentLength\": \"4535431\", \"resourceId\": \"access-log2018-02-13-15-17-29-18386C502CAEA932\"}, {\"url\": \"s3://ocean-test-osmosis-data-plugin-dataseeding-1537375953/data.txt\", \"checksum\": \"efb2c764274b745f5fc37f97c6b0e761\", \"contentLength\": \"4535431\", \"resourceId\": \"access-log2018-02-13-15-17-29-18386C502CAEA932\"}, {\"url\": \"http://ipv4.download.thinkbroadband.com/5MB.zip\"}]!!0", "curation": { "rating": 0.93, "numVotes": 123, "schema": "Binary Voting" }, "additionalInformation": { "description": "Introduce the main concepts and vision behind ocean protocol", "copyrightHolder": "Ocean Protocol Foundation Ltd.", "workExample": "Text PDF", "inLanguage": "en", "categories": [ "white-papers" ], "tags": ["data exchange", "sharing", "curation", "bonding curve"], "links": [ { "url": "http://data.ceda.ac.uk/badc/ukcp09/data/gridded-land-obs/gridded-land-obs" "-daily/" }, { "url": "http://data.ceda.ac.uk/badc/ukcp09/data/gridded-land-obs/gridded-land-obs" "-averages-25km/" }, { "url": "http://data.ceda.ac.uk/badc/ukcp09/" } ], "updateFrequency": "yearly", "structuredMarkup": [ { "uri": "http://skos.um.es/unescothes/C01194/jsonld", "mediaType": "application/ld+json" }, { "uri": "http://skos.um.es/unescothes/C01194/turtle", "mediaType": "text/turtle" } ] } }, "index": 2 } ], "proof": { "type": "DDOIntegritySignature", "created": "2019-05-22T08:44:27Z", "creator": "0x00Bd138aBD70e2F00903268F3Db08f2D25677C9e", "signatureValue": "0xbd7b46b3ac664167bc70ac211b1a1da0baed9ead91613a5f02dfc25c1bb6e3ff40861b455017e8a587fd4e37b703436072598c3a81ec88be28bfe33b61554a471b" } } json_dict2 = { "@context": "https://w3id.org/did/v1", "id": "did:op:0c184915b07b44c888d468be85a9b28253e80070e5294b1aaed81c2f0264e430", "created": "2019-05-22T08:44:27Z", "publicKey": [ { "id": "did:op:0c184915b07b44c888d468be85a9b28253e80070e5294b1aaed81c2f0264e430", "type": "EthereumECDSAKey", "owner": "0x00Bd138aBD70e2F00903268F3Db08f2D25677C9e" } ], "authentication": [ { "type": "RsaSignatureAuthentication2018", "publicKey": "did:op:0c184915b07b44c888d468be85a9b28253e80070e5294b1aaed81c2f0264e430" } ], "service": [ { "type": "authorization", "serviceEndpoint": "http://localhost:12001", "service": "SecretStore", "index": 0 }, { "type": "access", "serviceEndpoint": "http://localhost:8030/api/v1/brizo/services/consume", "purchaseEndpoint": "http://localhost:8030/api/v1/brizo/services/access/initialize", "index": 1, "templateId": "0x208aca4B0316C9996F085cbD57E01c11Bc0E7cb1", "name": "dataAssetAccessServiceAgreement", "creator": "", "serviceAgreementTemplate": { "contractName": "EscrowAccessSecretStoreTemplate", "events": [ { "name": "AgreementCreated", "actorType": "consumer", "handler": { "moduleName": "escrowAccessSecretStoreTemplate", "functionName": "fulfillLockRewardCondition", "version": "0.1" } } ], "fulfillmentOrder": [ "lockReward.fulfill", "accessSecretStore.fulfill", "escrowReward.fulfill" ], "conditionDependency": { "lockReward": [], "accessSecretStore": [], "escrowReward": [ "lockReward", "accessSecretStore" ] }, "conditions": [ { "name": "lockReward", "timelock": 0, "timeout": 0, "contractName": "LockRewardCondition", "functionName": "fulfill", "events": [ { "name": "Fulfilled", "actorType": "publisher", "handler": { "moduleName": "lockRewardCondition", "functionName": "fulfillAccessSecretStoreCondition", "version": "0.1" } } ], "parameters": [ { "name": "_rewardAddress", "type": "address", "value": "0x2AaC920AA4D10b80db9ed0E4EC04A3ff612F2bc6" }, { "name": "_amount", "type": "uint256", "value": "888000000000000000000000000000000" } ] }, { "name": "accessSecretStore", "timelock": 0, "timeout": 0, "contractName": "AccessSecretStoreCondition", "functionName": "fulfill", "events": [ { "name": "Fulfilled", "actorType": "publisher", "handler": { "moduleName": "accessSecretStore", "functionName": "fulfillEscrowRewardCondition", "version": "0.1" } }, { "name": "TimedOut", "actorType": "consumer", "handler": { "moduleName": "accessSecretStore", "functionName": "fulfillEscrowRewardCondition", "version": "0.1" } } ], "parameters": [ { "name": "_documentId", "type": "bytes32", "value": "0c184915b07b44c888d468be85a9b28253e80070e5294b1aaed81c2f0264e430" }, { "name": "_grantee", "type": "address", "value": "" } ] }, { "name": "escrowReward", "timelock": 0, "timeout": 0, "contractName": "EscrowReward", "functionName": "fulfill", "events": [ { "name": "Fulfilled", "actorType": "publisher", "handler": { "moduleName": "escrowRewardCondition", "functionName": "verifyRewardTokens", "version": "0.1" } } ], "parameters": [ { "name": "_amount", "type": "uint256", "value": "888000000000000000000000000000000" }, { "name": "_receiver", "type": "address", "value": "" }, { "name": "_sender", "type": "address", "value": "" }, { "name": "_lockCondition", "type": "bytes32", "value": "" }, { "name": "_releaseCondition", "type": "bytes32", "value": "" } ] } ] } }, { "type": "metadata", "serviceEndpoint": "http://localhost:5000/api/v1/aquarius/assets/ddo/did:op:0c184915b07b44c888d468be85a9b28253e80070e5294b1aaed81c2f0264e430", "attributes": { "main": { "name": "Ocean protocol white paper", "type": "dataset", "dateCreated": "2012-10-10T17:00:00Z", "datePublished": "2012-10-10T17:00:00Z", "author": "Ocean Protocol Foundation Ltd.", "license": "CC-BY", "price": "888000000000000000000000000000000", "files": [ { "checksum": "efb2c764274b745f5fc37f97c6b0e761", "contentType": "text/csv", "checksumType": "MD5", "contentLength": "4535431", "resourceId": "access-log2018-02-13-15-17-29-18386C502CAEA932", "index": 0 }, { "checksum": "efb2c764274b745f5fc37f97c6b0e761", "contentType": "text/csv", "contentLength": "4535431", "resourceId": "access-log2018-02-13-15-17-29-18386C502CAEA932", "index": 1 }, { "index": 2, "contentType": "text/csv", } ], }, "encryptedFiles": "<tests.resources.mocks.secret_store_mock.SecretStoreMock object at 0x7f8146a94710>.0c184915b07b44c888d468be85a9b28253e80070e5294b1aaed81c2f0264e430!![{\"url\": \"https://testocnfiles.blob.core.windows.net/testfiles/testzkp.pdf\", \"checksum\": \"efb2c764274b745f5fc37f97c6b0e761\", \"checksumType\": \"MD5\", \"contentLength\": \"4535431\", \"resourceId\": \"access-log2018-02-13-15-17-29-18386C502CAEA932\"}, {\"url\": \"s3://ocean-test-osmosis-data-plugin-dataseeding-1537375953/data.txt\", \"checksum\": \"efb2c764274b745f5fc37f97c6b0e761\", \"contentLength\": \"4535431\", \"resourceId\": \"access-log2018-02-13-15-17-29-18386C502CAEA932\"}, {\"url\": \"http://ipv4.download.thinkbroadband.com/5MB.zip\"}]!!0", "curation": { "rating": 0.93, "numVotes": 123, "schema": "Binary Voting", "isListed": False }, "additionalInformation": { "description": "Introduce the main concepts and vision behind ocean protocol", "copyrightHolder": "Ocean Protocol Foundation Ltd.", "workExample": "Text PDF", "inLanguage": "en", "categories": [ "white-papers" ], "tags": ["data exchange", "sharing", "curation", "bonding curve"], "links": [ { "url": "http://data.ceda.ac.uk/badc/ukcp09/data/gridded-land-obs/gridded-land-obs" "-daily/" }, { "url": "http://data.ceda.ac.uk/badc/ukcp09/data/gridded-land-obs/gridded-land-obs" "-averages-25km/" }, { "url": "http://data.ceda.ac.uk/badc/ukcp09/" } ], "updateFrequency": "yearly", "structuredMarkup": [ { "uri": "http://skos.um.es/unescothes/C01194/jsonld", "mediaType": "application/ld+json" }, { "uri": "http://skos.um.es/unescothes/C01194/turtle", "mediaType": "text/turtle" } ] } }, "index": 2 } ], "proof": { "type": "DDOIntegritySignature", "created": "2019-05-22T08:44:27Z", "creator": "0x00Bd138aBD70e2F00903268F3Db08f2D25677C9e", "signatureValue": "0xbd7b46b3ac664167bc70ac211b1a1da0baed9ead91613a5f02dfc25c1bb6e3ff40861b455017e8a587fd4e37b703436072598c3a81ec88be28bfe33b61554a471b" } } json_dict_no_metadata = {"publisherId": "0x2"} json_dict_no_valid_metadata = {"publisherId": "0x4", "main": {}, "assetId": "002" } json_before = { "@context": "https://w3id.org/future-method/v1", "created": "2016-02-08T16:02:20Z", "id": "did:op:112233445566778899", "publicKey": [ { "id": "did:op:123456789abcdefghi#keys-1", "type": "RsaVerificationKey2018", "owner": "did:op:123456789abcdefghi", "publicKeyPem": "-----BEGIN PUBLIC KEY...END PUBLIC KEY-----\r\n" }, { "id": "did:op:123456789abcdefghi#keys-2", "type": "Ed25519VerificationKey2018", "owner": "did:op:123456789abcdefghi", "publicKeyBase58": "H3C2AVvLMv6gmMNam3uVAjZpfkcJCwDwnZn6z3wXmqPV" } ], "authentication": [ { "type": "RsaSignatureAuthentication2018", "publicKey": "did:op:123456789abcdefghi#keys-1" }, { "type": "ieee2410Authentication2018", "publicKey": "did:op:123456789abcdefghi#keys-2" } ], "proof": { "type": "UUIDSignature", "created": "2016-02-08T16:02:20Z", "creator": "did:example:8uQhQMGzWxR8vw5P3UWH1ja", "signatureValue": "QNB13Y7Q9...1tzjn4w==" }, "service": [ { "type": "Consume", "index": 0, "serviceEndpoint": "http://mybrizo.org/api/v1/brizo/services/consume?pubKey=${" "pubKey}&serviceId={serviceId}&url={url}" }, { "type": "Compute", "index": 1, "serviceEndpoint": "http://mybrizo.org/api/v1/brizo/services/compute?pubKey=${" "pubKey}&serviceId={serviceId}&algo={algo}&container={container}" }, { "type": "metadata", "index": 2, "serviceEndpoint": "http://myaquarius.org/api/v1/provider/assets/metadata/{did}", "attributes": { "main": { "name": "UK Weather information 2011", "type": "dataset", "dateCreated": "2012-10-10T17:00:00Z", "datePublished": "2012-10-10T17:00:00Z", "author": "Met Office", "license": "CC-BY", "files": [{ "index": 0, "contentLength": "4535431", "contentType": "text/csv", "encoding": "UTF-8", "compression": "zip", "resourceId": "access-log2018-02-13-15-17-29-18386C502CAEA932" } ], "price": "88888880000000000000", }, "encryptedFiles": "0xkasdhfkljhasdfkjasdhf", "curation": { "rating": 0.0, "numVotes": 0, "schema": "Binary Votting", "isListed": True }, "additionalInformation": { "description": "Weather information of UK including temperature and humidity", "copyrightHolder": "Met Office", "workExample": "stationId,latitude,longitude,datetime,temperature," "humidity /n 423432fsd,51.509865,-0.118092," "2011-01-01T10:55:11+00:00,7.2,68", "inLanguage": "en", "tags": ["weather", "uk", "2011", "temperature", "humidity"], "updateFrequency": "yearly", "structuredMarkup": [ {"uri": "http://skos.um.es/unescothes/C01194/jsonld", "mediaType": "application/ld+json"}, {"uri": "http://skos.um.es/unescothes/C01194/turtle", "mediaType": "text/turtle"} ], "links": [ { "name": "Sample of Asset Data", "type": "sample", "url": "https://foo.com/sample.csv" }, { "name": "Data Format Definition", "type": "format", "url": "https://foo.com/sample2.csv" } ] } } } ] } json_update = { "@context": "https://w3id.org/future-method/v1", "created": "2016-02-08T16:02:20Z", "id": "did:op:112233445566778899", "publicKey": [ { "id": "did:op:123456789abcdefghi#keys-1", "type": "RsaVerificationKey2018", "owner": "did:op:123456789abcdefghi", "publicKeyPem": "-----BEGIN PUBLIC KEY...END PUBLIC KEY-----\r\n" }, { "id": "did:op:123456789abcdefghi#keys-2", "type": "Ed25519VerificationKey2018", "owner": "did:op:123456789abcdefghi", "publicKeyBase58": "H3C2AVvLMv6gmMNam3uVAjZpfkcJCwDwnZn6z3wXmqPV" } ], "authentication": [ { "type": "RsaSignatureAuthentication2018", "publicKey": "did:op:123456789abcdefghi#keys-1" }, { "type": "ieee2410Authentication2018", "publicKey": "did:op:123456789abcdefghi#keys-2" } ], "proof": { "type": "UUIDSignature", "created": "2016-02-08T16:02:20Z", "creator": "did:example:8uQhQMGzWxR8vw5P3UWH1ja", "signatureValue": "QNB13Y7Q9...1tzjn4w==" }, "service": [ { "type": "Consume", "index": 0, "serviceEndpoint": "http://mybrizo.org/api/v1/brizo/services/consume?pubKey=${" "pubKey}&serviceId={serviceId}&url={url}" }, { "type": "Compute", "index": 1, "serviceEndpoint": "http://mybrizo.org/api/v1/brizo/services/compute?pubKey=${" "pubKey}&serviceId={serviceId}&algo={algo}&container={container}" }, { "type": "metadata", "index": 2, "serviceEndpoint": "http://myaquarius.org/api/v1/provider/assets/metadata/{did}", "attributes": { "main": { "name": "UK Weather information 2012", "type": "dataset", "dateCreated": "2012-02-01T10:55:11Z", "datePublished": "2012-02-01T10:55:11Z", "author": "Met Office", "license": "CC-BY", "files": [{ "index": 0, "contentLength": "4535431", "contentType": "text/csv", "encoding": "UTF-8", "compression": "zip", "resourceId": "access-log2018-02-13-15-17-29-18386C502CAEA932" }], "price": "15", }, "encryptedFiles": "0xkasdhfkljhasdfkjasdhf", "curation": { "rating": 8.0, "numVotes": 1, "schema": "Binary Votting", "isListed": True }, "additionalInformation": { "description": "Weather information of UK including temperature and humidity and white", "copyrightHolder": "Met Office", "workExample": "stationId,latitude,longitude,datetime,temperature," "humidity /n 423432fsd,51.509865,-0.118092," "2011-01-01T10:55:11+00:00,7.2,68", "inLanguage": "en", "tags": ["weather", "uk", "2011", "temperature", "humidity"], "updateFrecuency": "yearly", "structuredMarkup": [ {"uri": "http://skos.um.es/unescothes/C01194/jsonld", "mediaType": "application/ld+json"}, {"uri": "http://skos.um.es/unescothes/C01194/turtle", "mediaType": "text/turtle"} ], "links": [ { "name": "Sample of Asset Data", "type": "sample", "url": "https://foo.com/sample.csv" }, { "name": "Data Format Definition", "type": "format", "url": "https://foo.com/sample2.csv" } ] } } } ] } json_valid = { "main": { "name": "10 Monkey Species Small", "dateCreated": "2012-02-01T10:55:11Z", "author": "Mario", "license": "CC0: Public Domain", "price": "10", "files": [ { "index": 0, "contentType": "application/zip", "encoding": "UTF-8", "compression": "zip", "checksum": "2bf9d229d110d1976cdf85e9f3256c7f", "checksumType": "MD5", "contentLength": "12057507", "url": "https://s3.amazonaws.com/assets/training.zip" }, { "index": 1, "contentType": "text/txt", "encoding": "UTF-8", "compression": "none", "checksum": "354d19c0733c47ef3a6cce5b633116b0", "checksumType": "MD5", "contentLength": "928", "url": "https://s3.amazonaws.com/datacommons/monkey_labels.txt" }, { "index": 2, "contentType": "application/zip", "url": "https://s3.amazonaws.com/datacommons/validation.zip" } ], "type": "dataset", }, "additionalInformation":{ "description": "EXAMPLE ONLY ", "categories": [ "image" ], "tags": [ "image data", "classification", "animals" ], "workExample": "image path, id, label", "links": [ { "name": "example model", "url": "https://drive.google.com/open?id=1uuz50RGiAW8YxRcWeQVgQglZpyAebgSM" }, { "name": "example code", "type": "example code", "url": "https://github.com/slothkong/CNN_classification_10_monkey_species" }, { "url": "https://s3.amazonaws.com/datacommons/links/discovery/n5151.jpg", "name": "n5151.jpg", "type": "discovery" }, { "url": "https://s3.amazonaws.com/datacommons/links/sample/sample.zip", "name": "sample.zip", "type": "sample" } ], "copyrightHolder": "Unknown", "inLanguage": "en" } } test_assets = [] for i in range(10): a = copy.deepcopy(json_dict) a['id'] = a['id'][:-2] + str(i) +str(i) test_assets.append(a) json_request_consume = { 'requestId': "", 'consumerId': "", 'fixed_msg': "", 'sigEncJWT': "" }

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Retraces/UkraineBot

3d5d7f8aaa58fa0cb8b98733b8808e5dfbdb8b71

[ "MIT" ]

2

2022-03-13T01:58:52.000Z

2022-03-31T06:07:54.000Z

venv/lib/python3.8/site-packages/attr/__init__.py

DesmoSearch/Desmobot

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[ "MIT" ]

19

2021-11-20T04:09:18.000Z

2022-03-23T15:05:55.000Z

venv/lib/python3.8/site-packages/attr/__init__.py

DesmoSearch/Desmobot

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test/rai/test_counterfactual_component.py

Azure/RAI-vNext-Preview

be1eb5581a89de26e319184ed3cb95ab2e6d32d1

[ "MIT" ]

8

2022-01-11T19:41:04.000Z

2022-03-15T21:00:22.000Z

test/rai/test_counterfactual_component.py

Azure/RAI-vNext-Preview

be1eb5581a89de26e319184ed3cb95ab2e6d32d1

[ "MIT" ]

6

2022-01-10T21:42:42.000Z

2022-03-16T14:54:24.000Z

test/rai/test_counterfactual_component.py

Azure/RAI-vNext-Preview

be1eb5581a89de26e319184ed3cb95ab2e6d32d1

[ "MIT" ]

1

2022-02-11T17:45:33.000Z

# --------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # --------------------------------------------------------- import logging from azure.ai.ml import MLClient, dsl, Input from test.constants_for_test import Timeouts from test.utilities_for_test import submit_and_wait _logger = logging.getLogger(__file__) logging.basicConfig(level=logging.INFO) class TestCounterfactualComponent: def test_classification_all_args( self, ml_client: MLClient, component_config, registered_adult_model_id: str, rai_components, ): version_string = component_config["version"] @dsl.pipeline( compute="cpucluster", description="Test Counterfactual component with all arguments", experiment_name=f"TestCounterfactualComponent_test_classification_all_args_{version_string}", ) def test_counterfactual_classification( target_column_name, train_data, test_data, ): fetch_model_job = rai_components.fetch_model( model_id=registered_adult_model_id ) fetch_model_job.set_limits(timeout=Timeouts.DEFAULT_TIMEOUT) construct_job = rai_components.rai_constructor( title="Run built from DSL", task_type="classification", model_info_path=fetch_model_job.outputs.model_info_output_path, train_dataset=train_data, test_dataset=test_data, target_column_name=target_column_name, categorical_column_names='["Race", "Sex", "Workclass", "Marital Status", "Country", "Occupation"]', maximum_rows_for_test_dataset=5000, # Should be default classes="[]", # Should be default ) construct_job.set_limits(timeout=Timeouts.DEFAULT_TIMEOUT) counterfactual_job = rai_components.rai_counterfactual( rai_insights_dashboard=construct_job.outputs.rai_insights_dashboard, total_cfs=10, # Case sensitivity bug! method="random", desired_class="opposite", desired_range="[]", permitted_range='{"Capital Gain": [0, 20000], "Hours per week": [0, 20]}', features_to_vary='["Capital Gain", "Hours per week", "Age", "Country", "Sex"]', feature_importance=True, ) counterfactual_job.set_limits(timeout=Timeouts.COUNTERFACTUAL_TIMEOUT) gather_job = rai_components.rai_gather( constructor=construct_job.outputs.rai_insights_dashboard, insight_1=counterfactual_job.outputs.counterfactual, ) gather_job.set_limits(timeout=Timeouts.DEFAULT_TIMEOUT) gather_job.outputs.dashboard.mode = "upload" gather_job.outputs.ux_json.mode = "upload" return { "dashboard": gather_job.outputs.dashboard, "ux_json": gather_job.outputs.ux_json, } adult_train_pq = Input( type="uri_file", path=f"adult_train_pq:{version_string}", mode="download" ) adult_test_pq = Input( type="uri_file", path=f"adult_test_pq:{version_string}", mode="download" ) rai_pipeline = test_counterfactual_classification( target_column_name="income", train_data=adult_train_pq, test_data=adult_test_pq, ) rai_pipeline_job = submit_and_wait(ml_client, rai_pipeline) assert rai_pipeline_job is not None def test_regression_all_args( self, ml_client: MLClient, component_config, registered_boston_model_id: str, rai_components, ): version_string = component_config["version"] @dsl.pipeline( compute="cpucluster", description="Test Counterfactual component with all arguments", experiment_name=f"TestCounterfactualComponent_test_regression_all_args_{version_string}", ) def test_counterfactual_regression( target_column_name, train_data, test_data, ): fetch_model_job = rai_components.fetch_model( model_id=registered_boston_model_id ) fetch_model_job.set_limits(timeout=Timeouts.DEFAULT_TIMEOUT) construct_job = rai_components.rai_constructor( title="Run built from DSL", task_type="regression", model_info_path=fetch_model_job.outputs.model_info_output_path, train_dataset=train_data, test_dataset=test_data, target_column_name=target_column_name, categorical_column_names="[]", maximum_rows_for_test_dataset=5000, # Should be default classes="[]", # Should be default ) construct_job.set_limits(timeout=Timeouts.DEFAULT_TIMEOUT) counterfactual_job = rai_components.rai_counterfactual( rai_insights_dashboard=construct_job.outputs.rai_insights_dashboard, total_cfs=10, # Case sensitivity bug method="kdtree", desired_class="opposite", # Required argument bug... desired_range="[20, 100]", permitted_range='{"ZN": [0, 10], "AGE": [0, 50], "CRIM": [25, 50], "INDUS": [0, 10]}', features_to_vary='["ZN", "AGE", "CRIM", "INDUS"]', feature_importance=True, ) counterfactual_job.set_limits(timeout=Timeouts.COUNTERFACTUAL_TIMEOUT) gather_job = rai_components.rai_gather( constructor=construct_job.outputs.rai_insights_dashboard, insight_1=None, insight_4=counterfactual_job.outputs.counterfactual, ) gather_job.set_limits(timeout=120) gather_job.outputs.dashboard.mode = "upload" gather_job.outputs.ux_json.mode = "upload" return { "dashboard": gather_job.outputs.dashboard, "ux_json": gather_job.outputs.ux_json, } adult_train_pq = Input( type="uri_file", path=f"boston_train_pq:{version_string}", mode="download" ) adult_test_pq = Input( type="uri_file", path=f"boston_test_pq:{version_string}", mode="download" ) rai_pipeline = test_counterfactual_regression( target_column_name="y", train_data=adult_train_pq, test_data=adult_test_pq, ) rai_pipeline_job = submit_and_wait(ml_client, rai_pipeline) assert rai_pipeline_job is not None

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examples/requests-missing-timeout.py

bittner/bandit

87ecc4079ea50d77be13ed72bbf5ad2eb0673c64

[ "Apache-2.0" ]

null

examples/requests-missing-timeout.py

bittner/bandit

87ecc4079ea50d77be13ed72bbf5ad2eb0673c64

[ "Apache-2.0" ]

null

examples/requests-missing-timeout.py

bittner/bandit

87ecc4079ea50d77be13ed72bbf5ad2eb0673c64

[ "Apache-2.0" ]

null

import requests requests.get('https://gmail.com') requests.get('https://gmail.com', timeout=None) requests.get('https://gmail.com', timeout=5) requests.post('https://gmail.com') requests.post('https://gmail.com', timeout=None) requests.post('https://gmail.com', timeout=5) requests.put('https://gmail.com') requests.put('https://gmail.com', timeout=None) requests.put('https://gmail.com', timeout=5) requests.delete('https://gmail.com') requests.delete('https://gmail.com', timeout=None) requests.delete('https://gmail.com', timeout=5) requests.patch('https://gmail.com') requests.patch('https://gmail.com', timeout=None) requests.patch('https://gmail.com', timeout=5) requests.options('https://gmail.com') requests.options('https://gmail.com', timeout=None) requests.options('https://gmail.com', timeout=5) requests.head('https://gmail.com') requests.head('https://gmail.com', timeout=None) requests.head('https://gmail.com', timeout=5)

39.125

51

0.728435

135

939

5.066667

0.111111

0.307018

0.399123

0.409357

0.979532

0.78655

0

0.007769

0.040469

939

23

52

40.826087

0.751387

0

0.380192

0

1

0

true

0

0.045455

0

0.045455

0

null

1

0

null

0

1

0

6

75e90ccae8c4e15459f01ae8fdff9dbf976187ce

27

py

Python

build/lib/__init__.py

nirvanasupermind/qlang

c3264a343f19af0de1161b006c6ec2ee86e73882

[ "MIT" ]

null

build/lib/__init__.py

nirvanasupermind/qlang

c3264a343f19af0de1161b006c6ec2ee86e73882

[ "MIT" ]

null

build/lib/__init__.py

nirvanasupermind/qlang

c3264a343f19af0de1161b006c6ec2ee86e73882

[ "MIT" ]

null

from q import run, run_text

27

0.814815

6

27

3.5

0.833333

0

0.148148

27

1

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

6

f959d598d1f0ef52ccb401ef98f0d4b2e8038fed

351

py

Python

10/00/2.py

pylangstudy/201710

139cad34d40f23beac85800633ec2ed63d530bfd

[ "CC0-1.0" ]

null

10/00/2.py

pylangstudy/201710

139cad34d40f23beac85800633ec2ed63d530bfd

[ "CC0-1.0" ]

25

2017-10-03T00:12:53.000Z

2017-10-29T23:58:17.000Z

10/00/2.py

pylangstudy/201710

139cad34d40f23beac85800633ec2ed63d530bfd

[ "CC0-1.0" ]

null

from pathlib import * print(PurePosixPath('foo') == PurePosixPath('FOO')) print(PureWindowsPath('foo') == PureWindowsPath('FOO')) print(PureWindowsPath('FOO') in { PureWindowsPath('foo') }) print(PureWindowsPath('C:') < PureWindowsPath('d:')) print(PureWindowsPath('foo') == PurePosixPath('foo')) print(PureWindowsPath('foo') < PurePosixPath('foo'))

39

59

0.720798

34

351

7.441176

0.294118

0.426877

0.363636

0.3083

0.474308

0.332016

0

0.071225

351

8

60

43.875

0.776074

0

0.097143

0

1

0

true

0

0.142857

0

0.142857

0.857143

0

null

1

0

1

0

null

0

1

0

1

0

6

f9b48630c9ce3daa3c050f43bba5d93389259213

111

py

Python

templates/django_app_name/models.py

luiscberrocal/django_ansible_config

0eca2c7a7d7a515efbd143a7b33334f9a0c2f2c5

[ "MIT" ]

null

templates/django_app_name/models.py

luiscberrocal/django_ansible_config

0eca2c7a7d7a515efbd143a7b33334f9a0c2f2c5

[ "MIT" ]

8

2021-01-04T18:15:53.000Z

2021-03-14T13:53:31.000Z

templates/django_app_name/models.py

luiscberrocal/django_ansible_config

0eca2c7a7d7a515efbd143a7b33334f9a0c2f2c5

[ "MIT" ]

null

from django.db import models from django.utils.translation import gettext_lazy as _ # Create your models here.

27.75

54

0.81982

17

111

5.235294

0.764706

0.224719

0

0.135135

111

3

55

37

0.927083

0.216216

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

f9b926bb9fa22bcf78ab5a3f149f7d85d0e150bc

25

py

Python

liver_ct_segmentation_package/losses/__init__.py

qbic-pipelines/liver-ct-segmentation-package

9983741ae68b9906045a44f06837a69cf593a416

[ "MIT" ]

2

2020-04-03T23:02:00.000Z

2021-12-31T05:18:27.000Z

loss/__init__.py

ryanwongsa/open-images-2019-challenge

b49e0933451c4bf9b31a9a8faf1bd8ba3dee1cc5

[ "Apache-2.0" ]

67

2021-08-10T18:15:09.000Z

2022-03-31T18:15:15.000Z

loss/__init__.py

ryanwongsa/open-images-2019-challenge

b49e0933451c4bf9b31a9a8faf1bd8ba3dee1cc5

[ "Apache-2.0" ]

1

2021-08-10T12:47:02.000Z

from .focal_loss import *

25

0.8

4

25

4.75

1

0

0.12

25

1

25

0.863636

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

f9d1cfa06a274de156722594bf44e225c0b7cc74

578

py

Python

FirstStepsInPython/Basics/Exercise2 Conditional Statements/04.MetricConverter.py

Pittor052/SoftUni-Studies

1ee6341082f6ccfa45b3e82824c37722bcf2fb31

[ "MIT" ]

null

FirstStepsInPython/Basics/Exercise2 Conditional Statements/04.MetricConverter.py

Pittor052/SoftUni-Studies

1ee6341082f6ccfa45b3e82824c37722bcf2fb31

[ "MIT" ]

null

FirstStepsInPython/Basics/Exercise2 Conditional Statements/04.MetricConverter.py

Pittor052/SoftUni-Studies

1ee6341082f6ccfa45b3e82824c37722bcf2fb31

[ "MIT" ]

1

2021-10-07T18:30:42.000Z

number = float(input()) convert_from = input() convert_to = input() mm = str("mm") cm = str("cm") m = str("m") if convert_from == mm and convert_to == m: print(f"{number / 1000:.3f}") elif convert_from == m and convert_to == cm: print(f"{number * 100:.3f}") elif convert_from == cm and convert_to == m: print(f"{number / 100:.3f}") elif convert_from == cm and convert_to == mm: print(f"{number * 10:.3f}") elif convert_from == mm and convert_to == cm: print(f"{number / 10:.3f}") elif convert_from == m and convert_to == mm: print(f"{number * 1000:.3f}")

32.111111

45

0.624567

96

578

3.614583

0.197917

0.221902

0.207493

0.244957

0.806916

0.778098

0.73487

0.567723

0.26513

0

0.050955

0.185121

578

18

46

32.111111

0.685775

0

0.195164

0

1

0

false

0

0.333333

0

null

1

0

null

0

6

f9e9a6288b8071574cea9686463ea82a2c5bc0a7

36

py

Python

tests/test_travis.py

lbaumo/CLMM

678422fd173c27a2bad7017b0c095a7c833bbd32

[ "BSD-3-Clause" ]

null

tests/test_travis.py

lbaumo/CLMM

678422fd173c27a2bad7017b0c095a7c833bbd32

[ "BSD-3-Clause" ]

null

tests/test_travis.py

lbaumo/CLMM

678422fd173c27a2bad7017b0c095a7c833bbd32

[ "BSD-3-Clause" ]

null

def test_travis(): assert(True)

12

18

0.666667

5

36

4.6

1

0

0.194444

36

2

19

18

0.793103

0

0.5

1

0.5

true

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

6

ddff24d1d65e3ff0f9a95f35c80263360fbaef06

110

py

Python

day01/q1.py

anjaligoswami/dn-python

f723c159f20ef5e452fb76d6a0a0b9f55619a1a2

[ "MIT" ]

null

day01/q1.py

anjaligoswami/dn-python

f723c159f20ef5e452fb76d6a0a0b9f55619a1a2

[ "MIT" ]

null

day01/q1.py

anjaligoswami/dn-python

f723c159f20ef5e452fb76d6a0a0b9f55619a1a2

[ "MIT" ]

null

#https://www.hackerrank.com/challenges/py-hello-world/problem #qsn: print Hello, World! print("Hello, World!")

36.666667

61

0.754545

16

110

5.1875

0.6875

0.361446

0

0.054545

110

3

62

36.666667

0.798077

0.763636

0

0.52

0

1

0

true

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

fb0e66af76bb94a5b8faa57668624fe31d37a30d

119

py

Python

lsframe/__init__.py

thcasey3/lsframe

f1d667a305ecd860417b7d1cbbfa1bbfcc40107e

[ "MIT" ]

null

lsframe/__init__.py

thcasey3/lsframe

f1d667a305ecd860417b7d1cbbfa1bbfcc40107e

[ "MIT" ]

null

lsframe/__init__.py

thcasey3/lsframe

f1d667a305ecd860417b7d1cbbfa1bbfcc40107e

[ "MIT" ]

null

from .start import * from .intake import * from .engine import * from .tools import * from .version import __version__

19.833333

32

0.756303

16

119

5.375

0.4375

0.465116

0

0.168067

119

5

33

23.8

0.868687

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

fb2a2299801948629bfc8d649be2135067d963bd

105

py

Python

pyhive/__init__.py

bgms/PyHive

16a4896c17b5a592eec0ed929b1b1b93ff78331e

[ "Apache-2.0" ]

null

pyhive/__init__.py

bgms/PyHive

16a4896c17b5a592eec0ed929b1b1b93ff78331e

[ "Apache-2.0" ]

null

pyhive/__init__.py

bgms/PyHive

16a4896c17b5a592eec0ed929b1b1b93ff78331e

[ "Apache-2.0" ]

null

from __future__ import absolute_import from __future__ import unicode_literals __version__ = '0.6.5-rc1'

26.25

39

0.838095

15

105

4.933333

0.733333

0.27027

0.432432

0

0.042553

0.104762

105

3

40

35

0.744681

0

0.085714

0

1

0

false

0

0.666667

0

0.666667

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

fb305921bf534e7ae6475ac06e9ba721001ee125

4,527

py

Python

bluzelle/db.py

bluzelle/bluzelle-py

c0cd814cb0e021a51f76e75d2243935da81f99d3

[ "Apache-2.0" ]

1

2020-01-08T01:09:42.000Z

bluzelle/db.py

bluzelle/bluzelle-py

c0cd814cb0e021a51f76e75d2243935da81f99d3

[ "Apache-2.0" ]

null

bluzelle/db.py

bluzelle/bluzelle-py

c0cd814cb0e021a51f76e75d2243935da81f99d3

[ "Apache-2.0" ]

null

from pprint import pprint import asyncio import sys import json from bluzelle import bzapi from bluzelle.lib.udp.udp_support import * from bluzelle.lib.udp.test_udp import * class DB: def __init__(self, cpp_db): self.localhost_ip = "127.0.0.1" self.cpp_db = cpp_db def load_(self, *args, **kwargs): method_handle = getattr(self.cpp_db, kwargs['meth']) resp = method_handle(*args[1:]) return resp def create(self, *args, **kwargs): results = json.loads(self.load_(self, *args, **kwargs, meth = sys._getframe().f_code.co_name)) if 'result' in results: return results['result'] == 1 elif 'error' in results: raise Exception(results['error']) else: raise Exception("Unknown error") def update(self, *args, **kwargs): response = self.load_(self, *args, **kwargs, meth = sys._getframe().f_code.co_name) results = json.loads(response) if 'result' in results: return results['result'] == 1 elif 'error' in results: raise Exception(results['error']) else: raise Exception("Unknown error") def remove(self, *args, **kwargs): response = self.load_(self, *args, **kwargs, meth = sys._getframe().f_code.co_name) results = json.loads(response) if 'result' in results: return results['result'] == 1 elif 'error' in results: raise Exception(results['error']) else: raise Exception("Unknown error") def has(self, *args, **kwargs): response = self.load_(self, *args, **kwargs, meth = sys._getframe().f_code.co_name) results = json.loads(response) if 'result' in results: return results['result'] == 1 elif 'error' in results: raise Exception(results['error']) else: raise Exception("Unknown error") def read(self, *args, **kwargs): response = self.load_(self, *args, **kwargs, meth=sys._getframe().f_code.co_name) results = json.loads(response) if 'value' in results: return results['value'] elif 'error' in results: raise Exception(results['error']) else: raise Exception("Unknown error") def quick_read(self, *args, **kwargs): response = self.load_(self, *args, **kwargs, meth = sys._getframe().f_code.co_name) results = json.loads(response) if 'value' in results: return results['value'] elif 'error' in results: raise Exception(results['error']) else: raise Exception("Unknown error") def expire(self, *args, **kwargs): response = self.load_(self, *args, **kwargs, meth = sys._getframe().f_code.co_name) results = json.loads(response) if 'result' in results: return results['result'] == 1 elif 'error' in results: raise Exception(results['error']) else: raise Exception("Unknown error") def persist(self, *args, **kwargs): response = self.load_(self, *args, **kwargs, meth = sys._getframe().f_code.co_name) results = json.loads(response) if 'result' in results: return results['result'] == 1 elif 'error' in results: raise Exception(results['error']) else: raise Exception("Unknown error") def ttl(self, *args, **kwargs): response = self.load_(self, *args, **kwargs, meth = sys._getframe().f_code.co_name) results = json.loads(response) if 'error' in results: raise Exception(results['error']) elif 'ttl' in results: return results['ttl'] else: raise Exception("Unknown error") def keys(self): response = self.load_(self, meth = sys._getframe().f_code.co_name) results = json.loads(response) if 'keys' in results: return results['keys'] elif 'error' in results: raise Exception(results['error']) else: raise Exception("Unknown error") def size(self): response = self.load_(self, meth = sys._getframe().f_code.co_name) results = json.loads(response) if 'error' in results: raise Exception(results['error']) else: return results def swarm_status(self): response = self.cpp_db.swarm_status() return response

34.557252

102

0.5814

529

4,527

4.858223

0.119093

0.073541

0.103502

0.068482

0.802335

0.789494

0.787938

0

0.004056

0.292026

4,527

131

103

34.557252

0.797816

0

0.692982

0

0.079284

0

1

0.122807

false

0

0.061404

0

0.307018

0.008772

0

null

0

1

0

null

0

6

34e67b180c94ca398a5ae5240fb535e68aaa2ce0

32

py

Python

tests/syntax/scripts/lists.py

toddrme2178/pyccel

deec37503ab0c5d0bcca1a035f7909f7ce8ef653

[ "MIT" ]

null

tests/syntax/scripts/lists.py

toddrme2178/pyccel

deec37503ab0c5d0bcca1a035f7909f7ce8ef653

[ "MIT" ]

null

tests/syntax/scripts/lists.py

toddrme2178/pyccel

deec37503ab0c5d0bcca1a035f7909f7ce8ef653

[ "MIT" ]

null

[1,4,5] ['a','b','c'] [x,y,z,t]

8

13

0.3125

10

32

1

0

0.103448

0.09375

32

3

14

10.666667

0.241379

0

0.09375

0

1

0

true

0

1

null

0

1

0

1

0

null

0

1

0

6

34ec79304c4604478442217c40e849cb4fdb423c

2,457

py

Python

torchblocks/optims/utils.py

lonePatient/TorchBlocks

4a65d746cc8a396cb7df73ed4644d97ddf843e29

[ "MIT" ]

82

2020-06-23T05:51:08.000Z

2022-03-29T08:11:08.000Z

torchblocks/optims/utils.py

Raiselimit/TorchBlocks

a5baecb9a2470ff175087475630f2b7db3f7ef51

[ "MIT" ]

null

torchblocks/optims/utils.py

Raiselimit/TorchBlocks

a5baecb9a2470ff175087475630f2b7db3f7ef51

[ "MIT" ]

22

2020-06-23T05:51:10.000Z

2022-03-18T07:01:43.000Z

def get_optimizer_params(model, lr, lr_weight_decay_coef, num_layers): param_optimizer = list(model.named_parameters()) no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight'] if lr_weight_decay_coef < 1.0: optimizer_grouped_parameters = [ {'params': [ p for n, p in param_optimizer if 'bert.embeddings' not in n and 'bert.encoder' not in n and not any(nd in n for nd in no_decay)], 'weight_decay': 0.01}, {'params': [ p for n, p in param_optimizer if 'bert.embeddings' not in n and 'bert.encoder' not in n and any(nd in n for nd in no_decay)], 'weight_decay': 0.0}, {'params': [ p for n, p in param_optimizer if 'bert.embeddings' in n and not any(nd in n for nd in no_decay)], 'lr': lr * lr_weight_decay_coef ** (num_layers + 1), 'weight_decay': 0.01}, {'params': [ p for n, p in param_optimizer if 'bert.embeddings' in n and any(nd in n for nd in no_decay)], 'lr': lr * lr_weight_decay_coef ** (num_layers + 1), 'weight_decay': 0.0} ] for i in range(num_layers): optimizer_grouped_parameters.append( {'params': [ p for n, p in param_optimizer if 'bert.encoder.layer.{}.'.format(i) in n and any(nd in n for nd in no_decay)], 'lr': lr * lr_weight_decay_coef ** (num_layers - i), 'weight_decay': 0.0}) optimizer_grouped_parameters.append( {'params': [ p for n, p in param_optimizer if 'bert.encoder.layer.{}.'.format(i) in n and any(nd in n for nd in no_decay)], 'lr': lr * lr_weight_decay_coef ** (num_layers - i), 'weight_decay': 0.0}) else: optimizer_grouped_parameters = [ {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01}, {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0} ] return optimizer_grouped_parameters

49.14

95

0.499389

322

2,457

3.614907

0.127329

0.041237

0.068729

0.075601

0.844502

0.820447

0

0.015593

0.399674

2,457

49

96

50.142857

0.773559

0

0.693878

0

0.130399

0.018272

0

1

0.020408

false

0

0.040816

0

null

0

1

0

null

0

6

55236ca5633389a4594009c78cbffaf4a01fb7d8

81

py

Python

RecMet/__init__.py

rijulizer/RecMet

88863ef1ce1b9be8bbe1ae14580d6aa06b5cd738

[ "Apache-2.0" ]

null

RecMet/__init__.py

rijulizer/RecMet

88863ef1ce1b9be8bbe1ae14580d6aa06b5cd738

[ "Apache-2.0" ]

null

RecMet/__init__.py

rijulizer/RecMet

88863ef1ce1b9be8bbe1ae14580d6aa06b5cd738

[ "Apache-2.0" ]

null

from RecMet.PythonMetrics import Metrics from RecMet.PysparkMetrics import recmet

40.5

40

0.888889

10

81

7.2

0.6

0.277778

0

0.08642

81

2

41

40.5

0.972973

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

9b2afac5dc5bdc8eea15762ece1597360a7fb251

63,464

py

Python

src/qng/qng.py

misken/qng

8eeb43d60293b87536543b45e9ba5c62012d6c7e

[ "MIT" ]

null

src/qng/qng.py

misken/qng

8eeb43d60293b87536543b45e9ba5c62012d6c7e

[ "MIT" ]

null

src/qng/qng.py

misken/qng

8eeb43d60293b87536543b45e9ba5c62012d6c7e

[ "MIT" ]

null

__author__ = 'misken' import numpy as np import scipy.stats as stats import scipy.optimize import math def poissoninv(prob, mean): """ Return the cumulative inverse of the Poisson distribution. Useful for capacity planning approximations. Uses normal approximation to the Poisson distribution for mean > 50. Parameters ---------- mean : float mean of the Poisson distribution prob : percentile desired Returns ------- int minimum value, c, such that P(X>c) <= prob """ return stats.poisson.ppf(prob, mean) def erlangb_direct(load, c): """ Return the the probability of loss in M/G/c/c system. Parameters ---------- load : float average arrival rate * average service time (units are erlangs) c : int number of servers Returns ------- float probability arrival finds system full """ p = stats.poisson.pmf(c, load) / stats.poisson.cdf(c, load) return p def erlangb(load, c): """ Return the the probability of loss in M/G/c/c system using recursive approach. Much faster than direct computation via scipy.stats.poisson.pmf(c, load) / scipy.stats.poisson.cdf(c, load) Parameters ---------- load : float average arrival rate * average service time (units are erlangs) c : int number of servers Returns ------- float probability arrival finds system full """ invb = 1.0 for j in range(1, c + 1): invb = 1.0 + invb * j / load b = 1.0 / invb return b def erlangc(load, c): """ Return the the probability of delay in M/M/c/inf system using recursive Erlang B approach. Parameters ---------- load : float average arrival rate * average service time (units are erlangs) c : int number of servers Returns ------- float probability all servers busy """ rho = load / float(c) # if rho >= 1.0: # raise ValueError("rho must be less than 1.0") eb = erlangb(load, c) ec = 1.0 / (rho + (1 - rho) * (1.0 / eb)) return ec def erlangcinv(prob, load): """ Return the number of servers such that probability of delay in M/M/c/inf system is less than specified probability Parameters ---------- prob : float threshold delay probability load : float average arrival rate * average service time (units are erlangs) Returns ------- c : int number of servers """ c = np.ceil(load) ec = erlangc(load, c) if ec <= prob: return c else: while ec > prob: c += 1 ec = erlangc(load, c) return c def mmc_prob_n(n, arr_rate, svc_rate, c): """ Return the the probability of n customers in system in M/M/c/inf queue. Uses recursive approach from Tijms, H.C. (1994), "Stochastic Models: An Algorithmic Approach", John Wiley and Sons, Chichester (Section 4.5.1, p287) Parameters ---------- n : int number of customers for which probability is desired arr_rate : float average arrival rate to queueing system svc_rate : float average service rate (each server). 1/svc_rate is mean service time. c : int number of servers Returns ------- float probability n customers in system (in service plus in queue) """ rho = arr_rate / (svc_rate * float(c)) # Step 0: Initialization - p[0] is initialized to one via creation method pbar = np.ones(max(n + 1, c)) # Step 1: compute pbar for j in range(1, c): pbar[j] = arr_rate * pbar[j - 1] / (j * svc_rate) # Step 2: compute normalizing constant and normalize pbar gamma = np.sum(pbar) + rho * pbar[c - 1] / (1 - rho) p = pbar / gamma # Step 3: compute probs beyond c - 1 for j in range(c, n + 1): p[j] = p[c - 1] * (rho ** (j - c + 1)) return p[n] def mmc_mean_qsize(arr_rate, svc_rate, c): """ Return the the mean queue size in M/M/c/inf queue. Parameters ---------- arr_rate : float average arrival rate to queueing system svc_rate : float average service rate (each server). 1/svc_rate is mean service time. c : int number of servers Returns ------- float mean number of customers in queue """ rho = arr_rate / (svc_rate * float(c)) mean_qsize = (rho ** 2 / (1 - rho) ** 2) * mmc_prob_n(c - 1, arr_rate, svc_rate, c) return mean_qsize def mmc_mean_syssize(arr_rate, svc_rate, c): """ Return the the mean system size in M/M/c/inf queue. Parameters ---------- arr_rate : float average arrival rate to queueing system svc_rate : float average service rate (each server). 1/svc_rate is mean service time. c : int number of servers Returns ------- float mean number of customers in queue + service """ load = arr_rate / svc_rate rho = load / float(c) mean_qsize = (rho ** 2 / (1 - rho) ** 2) * mmc_prob_n(c - 1, arr_rate, svc_rate, c) mean_syssize = mean_qsize + load return mean_syssize def mmc_mean_qwait(arr_rate, svc_rate, c): """ Return the the mean wait in queue time in M/M/c/inf queue. Uses mmc_mean_qsize along with Little's Law. Parameters ---------- arr_rate : float average arrival rate to queueing system svc_rate : float average service rate (each server). 1/svc_rate is mean service time. c : int number of servers Returns ------- float mean wait time in queue """ return mmc_mean_qsize(arr_rate, svc_rate, c) / arr_rate def mmc_mean_systime(arr_rate, svc_rate, c): """ Return the mean time in system (wait in queue + service time) in M/M/c/inf queue. Uses mmc_mean_qsize along with Little's Law (via mmc_mean_qwait) and relationship between W and Wq.. Parameters ---------- arr_rate : float average arrival rate to queueing system svc_rate : float average service rate (each server). 1/svc_rate is mean service time. c : int number of servers Returns ------- float mean wait time in queue """ return mmc_mean_qwait(arr_rate, svc_rate, c) + 1 / svc_rate def mmc_prob_wait_normal(arr_rate, svc_rate, c): """ Return the approximate probability of waiting (i.e. erlang C) in M/M/c/inf queue using a normal approximation. Uses normal approximation approach by Kolesar and Green, "Insights on Service System Design from a Normal Approximation to Erlang's Delay Formula", POM, V7, No3, Fall 1998, pp282-293 Parameters ---------- arr_rate : float average arrival rate to queueing system svc_rate : float average service rate (each server). 1/svc_rate is mean service time. c : int number of servers Returns ------- float approximate probability of delay in queue """ load = arr_rate / svc_rate prob_wait = 1.0 - stats.norm.cdf(c - load - 0.5) / np.sqrt(load) return prob_wait def mgc_prob_wait_erlangc(arr_rate, svc_rate, c): """ Return the approximate probability of waiting in M/G/c/inf queue using Erlang-C as approximation. It's well known that the Erlang-C formula, P(W>0) in M/M/c is a good approximation for P(W>0) in M/G/c. See, for example, Tjims (1994) on p296 or Whitt (1993) "Approximations for the GI/G/m queue", Production and Operations Management, 2, 2. Parameters ---------- arr_rate : float average arrival rate to queueing system svc_rate : float average service rate (each server). 1/svc_rate is mean service time. c : int number of servers Returns ------- float approximate probability of delay in queue """ load = arr_rate / svc_rate prob_wait = erlangc(load, c) return prob_wait def mm1_qwait_cdf(t, arr_rate, svc_rate): """ Return P(Wq < t) in M/M/1/inf queue. Parameters ---------- t : float wait time of interest arr_rate : float average arrival rate to queueing system svc_rate : float average service rate (each server). 1/svc_rate is mean service time. Returns ------- float probability wait time in queue is < t """ rho = arr_rate / svc_rate term1 = rho term2 = -svc_rate * (1 - rho) * t prob_wq_lt_t = 1.0 - term1 * np.exp(term2) return prob_wq_lt_t def mmc_qwait_cdf(t, arr_rate, svc_rate, c): """ Return P(Wq < t) in M/M/c/inf queue. Parameters ---------- t : float wait time of interest arr_rate : float average arrival rate to queueing system svc_rate : float average service rate (each server). 1/svc_rate is mean service time. c : int number of servers Returns ------- float probability wait time in queue is < t """ rho = arr_rate / (svc_rate * float(c)) term1 = rho / (1 - rho) term2 = mmc_prob_n(c - 1, arr_rate, svc_rate, c) term3 = -c * svc_rate * (1 - rho) * t prob_wq_lt_t = 1.0 - term1 * term2 * np.exp(term3) return prob_wq_lt_t def mmc_qwait_cdf_inv(t, prob, arr_rate, svc_rate): """ Return the number of servers such that probability of delay < t in M/M/c/inf system is greater than specified prob Parameters ---------- t : float wait time threshold prob : float threshold delay probability arr_rate : float average arrival rate to queueing system svc_rate : float average service rate (each server). 1/svc_rate is mean service time. Returns ------- c : int number of servers """ c = math.ceil(arr_rate / svc_rate) pwait_lt_t = mmc_qwait_cdf(t, arr_rate, svc_rate, c) if pwait_lt_t >= prob: return c else: while pwait_lt_t < prob: c += 1 pwait_lt_t = mmc_qwait_cdf(t, arr_rate, svc_rate, c) return c def mm1_qwait_pctile(p, arr_rate, svc_rate): """ Return p'th percentile of P(Wq < t) in M/M/1/inf queue. Parameters ---------- p : float percentile of interest arr_rate : float average arrival rate to queueing system svc_rate : float average service rate (each server). 1/svc_rate is mean service time. Returns ------- float t such that P(wait time in queue is < t) = p """ # For initial guess, we'll use percentile from similar M/M/1 system init_guess = 1/svc_rate waitq_pctile = scipy.optimize.newton(_mm1_waitq_pctile_wrap,init_guess,args=(p, arr_rate, svc_rate)) return waitq_pctile def _mm1_waitq_pctile_wrap(t, p, arr_rate, svc_rate): return mm1_qwait_cdf(t, arr_rate, svc_rate) - p def mmc_qwait_pctile(p, arr_rate, svc_rate, c): """ Return p'th percentile of P(Wq < t) in M/M/c/inf queue. Parameters ---------- p : float percentile of interest arr_rate : float average arrival rate to queueing system svc_rate : float average service rate (each server). 1/svc_rate is mean service time. c : int number of servers Returns ------- float t such that P(wait time in queue is < t) = p """ # For initial guess, we'll use percentile from similar M/M/1 system init_guess = mm1_qwait_pctile(p, arr_rate, c * svc_rate) waitq_pctile = scipy.optimize.newton(_mmc_waitq_pctile_wrap,init_guess,args=(p, arr_rate, svc_rate, c)) return waitq_pctile def _mmc_waitq_pctile_wrap(t, p, arr_rate, svc_rate, c): return mmc_qwait_cdf(t, arr_rate, svc_rate, c) - p def mdc_mean_qwait_cosmetatos(arr_rate, svc_rate, c): """ Return the approximate mean queue wait in M/D/c/inf queue using Cosmetatos approximation. See Cosmetatos, George P. "Approximate explicit formulae for the average queueing time in the processes (M/D/r) and (D/M/r)." Infor 13.3 (1975): 328-331. Parameters ---------- arr_rate : float average arrival rate to queueing system svc_rate : float average service rate (each server). 1/svc_rate is mean service time. c : int number of servers Returns ------- float mean number of customers in queue """ rho = arr_rate / (svc_rate * float(c)) term1 = 0.5 term2 = (c - 1) * (np.sqrt(4 + 5 * c) - 2) / (16 * c) term3 = (1 - rho) / rho term4 = mmc_mean_qwait(arr_rate, svc_rate, c) mean_qwait = term1 * (1 + term2 * term3) * term4 return mean_qwait def mdc_mean_qsize_cosmetatos(arr_rate, svc_rate, c): """ Return the approximate mean queue size in M/D/c/inf queue using Cosmetatos approximation. See Cosmetatos, George P. "Approximate explicit formulae for the average queueing time in the processes (M/D/r) and (D/M/r)." Infor 13.3 (1975): 328-331. Parameters ---------- arr_rate : float average arrival rate to queueing system svc_rate : float average service rate (each server). 1/svc_rate is mean service time. c : int number of servers Returns ------- float mean number of customers in queue """ mean_qwait = mdc_mean_qwait_cosmetatos(arr_rate, svc_rate, c) mean_qsize = mean_qwait * arr_rate return mean_qsize def mgc_mean_qwait_kimura(arr_rate, svc_rate, c, cv2_svc_time): """ Return the approximate mean queue wait in M/G/c/inf queue using Kimura approximation. See Kimura, Toshikazu. "Approximations for multi-server queues: system interpolations." Queueing Systems 17.3-4 (1994): 347-382. It's based on interpolation between an M/D/c and a M/M/c queueing system. Parameters ---------- arr_rate : float average arrival rate to queueing system svc_rate : float average service rate (each server). 1/svc_rate is mean service time. c : int number of servers cv2_svc_time : float squared coefficient of variation for service time distribution Returns ------- float mean wait time in queue """ term1 = 1.0 + cv2_svc_time term2 = 2.0 * cv2_svc_time / mmc_mean_qwait(arr_rate, svc_rate, c) term3 = (1.0 - cv2_svc_time) / mdc_mean_qwait_cosmetatos(arr_rate, svc_rate, c) mean_qwait = term1 / (term2 + term3) return mean_qwait def mgc_mean_qsize_kimura(arr_rate, svc_rate, c, cv2_svc_time): """ Return the approximate mean queue size in M/G/c/inf queue using Kimura approximation. See Kimura, Toshikazu. "Approximations for multi-server queues: system interpolations." Queueing Systems 17.3-4 (1994): 347-382. It's based on interpolation between an M/D/c and a M/M/c queueing system. Parameters ---------- arr_rate : float average arrival rate to queueing system svc_rate : float average service rate (each server). 1/svc_rate is mean service time. c : int number of servers cv2_svc_time : float squared coefficient of variation for service time distribution Returns ------- float mean number of customers in queue """ mean_qwait = mgc_mean_qwait_kimura(arr_rate, svc_rate, c, cv2_svc_time) mean_qsize = mean_qwait * arr_rate return mean_qsize def mgc_qwait_cdf_whitt(t, arr_rate, svc_rate, c, cs2): """ Return the approximate P(Wq <= t) in M/G/c/inf queue using Whitt's G/C/c approximation. Comparison of Whitt's approximation with the van Hoorn and Tijms M/G/c specific approximation suggests that using Whitt's is sufficiently accurate and much easier in that we don't have to numerically integrate excess service time distributions. Whitt, Ward. "Approximations for the GI/G/m queue" Production and Operations Management 2, 2 (Spring 1993): 114-161. van Hoorn, Michiel Harpert, and Hendrik Cornelis Tijms. "Approximations for the waiting time distribution of the M/G/c queue." Performance Evaluation 2.1 (1982): 22-28. Parameters ---------- t : float wait time of interest arr_rate : float average arrival rate to queueing system svc_rate : float average service rate (each server). 1/svc_rate is mean service time. c : int number of servers cs2 : float squared coefficient of variation for service time distribution Returns ------- float ~ P(Wq <= t) """ pwait_lt_t = ggm_qwait_cdf_whitt(t, arr_rate, svc_rate, c, 1.0, cs2) return pwait_lt_t def mgc_mean_qwait_bjorklund(arr_rate, svc_rate, c, cv2_svc_time): """ Return the approximate mean queue wait in M/G/c/inf queue using Bjorklund and Elldin approximation. See Kimura, Toshikazu. "Approximations for multi-server queues: system interpolations." Queueing Systems 17.3-4 (1994): 347-382. It's based on interpolation between an M/D/c and a M/M/c queueing system. Parameters ---------- arr_rate : float average arrival rate to queueing system svc_rate : float average service rate (each server). 1/svc_rate is mean service time. c : int number of servers cv2_svc_time : float squared coefficient of variation for service time distribution Returns ------- float mean number of customers in queue """ term1 = cv2_svc_time * mmc_mean_qwait(arr_rate, svc_rate, c) term2 = (1.0 - cv2_svc_time) * mdc_mean_qwait_cosmetatos(arr_rate, svc_rate, c) mean_qwait = term1 + term2 return mean_qwait def mgc_mean_qsize_bjorklund(arr_rate, svc_rate, c, cv2_svc_time): """ Return the approximate mean queue size in M/G/c/inf queue using Bjorklund and Elldin approximation. See Kimura, Toshikazu. "Approximations for multi-server queues: system interpolations." Queueing Systems 17.3-4 (1994): 347-382. It's based on interpolation between an M/D/c and a M/M/c queueing system. Parameters ---------- arr_rate : float average arrival rate to queueing system svc_rate : float average service rate (each server). 1/svc_rate is mean service time. c : int number of servers cv2_svc_time : float squared coefficient of variation for service time distribution Returns ------- float mean number of customers in queue """ mean_qwait = mgc_mean_qwait_bjorklund(arr_rate, svc_rate, c, cv2_svc_time) mean_qsize = mean_qwait * arr_rate return mean_qsize def mgc_qcondwait_pctile_firstorder_2moment(prob, arr_rate, svc_rate, c, cv2_svc_time): """ Return an approximate conditional queue wait percentile in M/G/c/inf system. The approximation is based on a first order approximation using the M/M/c delay percentile. See Tijms, H.C. (1994), "Stochastic Models: An Algorithmic Approach", John Wiley and Sons, Chichester Chapter 4, p299-300 The percentile is conditional on Wq>0 (i.e. on event customer waits) This 1st order approximation is OK for 0<=CVSquared<=2 and prob>1-Prob(Delay) Note that for Prob(Delay) we use MMC as approximation for same quantity in MGC. Justification in Tijms (p296) Parameters ---------- arr_rate : float average arrival rate to queueing system svc_rate : float average service rate (each server). 1/svc_rate is mean service time. c : int number of servers cv2_svc_time : float squared coefficient of variation for service time distribution Returns ------- float t such that P(wait time in queue is < t | wait time in queue is > 0) = prob """ load = arr_rate / svc_rate # Compute corresponding prob for unconditional wait (see p274 of Tjims) equivalent_uncond_prob = 1.0 - (1.0 - prob) * erlangc(load, c) # Compute conditional wait time percentile for M/M/c system to use in approximation condwaitq_pctile_mmc = mmc_qwait_pctile(equivalent_uncond_prob, arr_rate, svc_rate, c) # First order approximation for conditional wait time in queue condwaitq_pctile = 0.5 * (1.0 + cv2_svc_time) * condwaitq_pctile_mmc return condwaitq_pctile def mgc_qcondwait_pctile_secondorder_2moment(prob, arr_rate, svc_rate, c, cv2_svc_time): """ Return an approximate conditional queue wait percentile in M/G/c/inf system. The approximation is based on a second order approximation using the M/M/c delay percentile. See Tijms, H.C. (1994), "Stochastic Models: An Algorithmic Approach", John Wiley and Sons, Chichester Chapter 4, p299-300 The percentile is conditional on Wq>0 (i.e. on event customer waits) This approximation is based on interpolation between corresponding M/M/c and M/D/c systems. Parameters ---------- arr_rate : float average arrival rate to queueing system svc_rate : float average service rate (each server). 1/svc_rate is mean service time. c : int number of servers cv2_svc_time : float squared coefficient of variation for service time distribution Returns ------- float t such that P(wait time in queue is < t | wait time in queue is > 0) = prob """ load = arr_rate / svc_rate # Compute corresponding prob for unconditional wait (see p274 of Tjims) equivalent_uncond_prob = 1.0 - (1.0 - prob) * erlangc(load, c) # Compute conditional wait time percentile for M/M/c system to use in approximation condwaitq_pctile_mmc = mmc_qwait_pctile(equivalent_uncond_prob, arr_rate, svc_rate, c) # Compute conditional wait time percentile for M/D/c system to use in approximation # TODO: implement mdc_qwait_pctile condqwait_pctile_mdc = mdc_waitq_pctile(equivalent_uncond_prob, arr_rate, svc_rate, c) # Second order approximation for conditional wait time in queue condwaitq_pctile = (1.0 - cv2_svc_time) * condqwait_pctile_mdc + cv2_svc_time * condwaitq_pctile_mmc return condwaitq_pctile def mg1_mean_qsize(arr_rate, svc_rate, cv2_svc_time): """ Return the mean queue size in M/G/1/inf queue using P-K formula. See any decent queueing book. Parameters ---------- arr_rate : float average arrival rate to queueing system svc_rate : float average service rate (each server). 1/svc_rate is mean service time. cv2_svc_time : float squared coefficient of variation for service time distribution Returns ------- float mean number of customers in queue """ rho = arr_rate / svc_rate mean_qsize = (arr_rate ** 2) * cv2_svc_time/(2 * (1.0 - rho)) return mean_qsize def mg1_mean_qwait(arr_rate, svc_rate, cs2): """ Return the mean queue wait in M/G/1/inf queue using P-K formula along with Little's Law. See any decent queueing book. Parameters ---------- arr_rate : float average arrival rate to queueing system svc_rate : float average service rate (each server). 1/svc_rate is mean service time. cs2 : float squared coefficient of variation for service time distribution Returns ------- float mean wait time in queue """ mean_qsize = mg1_mean_qsize(arr_rate, svc_rate, cs2) mean_qwait = mean_qsize / arr_rate return mean_qwait def gamma_0(m, rho): """ See p124 immediately after Eq 2.16. :param m: int number of servers :param rho: float lambda / (mu * m) :return: float """ term1 = 0.24 term2 = (1 - rho) * (m - 1) * (math.sqrt(4 + 5 * m) - 2 ) / (16 * m * rho) return min(term1, term2) def _ggm_mean_qwait_whitt_phi_1(m, rho): """ See p124 immediately after Eq 2.16. :param m: int number of servers :param rho: float lambda / (mu * m) :return: float """ return 1.0 + gamma_0(m, rho) def _ggm_mean_qwait_whitt_phi_2(m, rho): """ See p124 immediately after Eq 2.18. :param m: int number of servers :param rho: float lambda / (mu * m) :return: float """ return 1.0 - 4.0 * gamma_0(m, rho) def _ggm_mean_qwait_whitt_phi_3(m, rho): """ See p124 immediately after Eq 2.20. :param m: int number of servers :param rho: float lambda / (mu * m) :return: float """ term1 = _ggm_mean_qwait_whitt_phi_2(m, rho) term2 = math.exp(-2.0 * (1 - rho) / (3.0 * rho)) return term1 * term2 def _ggm_mean_qwait_whitt_phi_4(m, rho): """ See p125 , Eq 2.21. :param m: int number of servers :param rho: float lambda / (mu * m) :return: float """ term1 = 1.0 term2 = 0.5 * (_ggm_mean_qwait_whitt_phi_1(m, rho) + _ggm_mean_qwait_whitt_phi_3(m, rho)) return min(term1, term2) def _ggm_mean_qwait_whitt_psi_0(c2, m, rho): """ See p125 , Eq 2.22. :param c2: float common squared CV for both arrival and service process :param m: int number of servers :param rho: float lambda / (mu * m) :return: float """ if c2 >= 1: return 1.0 else: return _ggm_mean_qwait_whitt_phi_4(m, rho) ** (2 * (1 - c2)) def _ggm_mean_qwait_whitt_phi_0(rho, ca2, cs2, m): """ See p125 , Eq 2.25. :param rho: float lambda / (mu * m) :param ca2: float squared CV for arrival process :param cs2: float squared CV for service process :param m: int number of servers :return: float """ if ca2 >= cs2: term1 = _ggm_mean_qwait_whitt_phi_1(m, rho) * (4 * (ca2 - cs2) / (4 * ca2 - 3 * cs2)) term2 = (cs2 / (4 * ca2 - 3 * cs2)) * _ggm_mean_qwait_whitt_psi_0((ca2 + cs2) / 2.0, m, rho) return term1 + term2 else: term1 = _ggm_mean_qwait_whitt_phi_3(m, rho) * ((cs2 - ca2) / (2 * ca2 + 2 * cs2)) term2 = ( (cs2 + 3 * ca2) / (2 * ca2 + 2 * cs2) ) term3 = _ggm_mean_qwait_whitt_psi_0((ca2 + cs2) / 2.0, m, rho) check = term2 * term3 / term1 #print (check) return term1 + term2 * term3 def ggm_mean_qwait_whitt(arr_rate, svc_rate, m, ca2, cs2): """ Return the approximate mean queue wait in GI/G/c/inf queue using Whitt's 1993 approximation. See Whitt, Ward. "Approximations for the GI/G/m queue" Production and Operations Management 2, 2 (Spring 1993): 114-161. It's based on interpolations with corrections between an M/D/c, D/M/c and a M/M/c queueing systems. Parameters ---------- arr_rate : float average arrival rate to queueing system svc_rate : float average service rate (each server). 1/svc_rate is mean service time. c : int number of servers ca2 : float squared coefficient of variation for inter-arrival time distribution cs2 : float squared coefficient of variation for service time distribution Returns ------- float mean wait time in queue """ rho = arr_rate / (svc_rate * float(m)) if rho >= 1.0: raise ValueError("rho must be less than 1.0") # Now implement Eq 2.24 on p 125 # Hack - for some reason I can't get this approximation to match Table 2 in the above # reference for the case of D/M/m. However, if I use Eq 2.20 (specific for the D/M/m case), # I do match the expected results. So, for now, I'll trap for this case. if ca2 == 0 and cs2 == 1: qwait = dmm_mean_qwait_whitt(arr_rate, svc_rate, m) else: term1 = _ggm_mean_qwait_whitt_phi_0(rho, ca2, cs2, m) term2 = 0.5 * (ca2 + cs2) term3 = mmc_mean_qwait(arr_rate, svc_rate, m) qwait = term1 * term2 * term3 return qwait def ggm_prob_wait_whitt(arr_rate, svc_rate, m, ca2, cs2): """ Return the approximate P(Wq > 0) in GI/G/c/inf queue using Whitt's 1993 approximation. See Whitt, Ward. "Approximations for the GI/G/m queue" Production and Operations Management 2, 2 (Spring 1993): 114-161. It's based on interpolations with corrections between an M/D/c, D/M/c and a M/M/c queueing systems. Parameters ---------- arr_rate : float average arrival rate to queueing system svc_rate : float average service rate (each server). 1/svc_rate is mean service time. c : int number of servers ca2 : float squared coefficient of variation for inter-arrival time distribution cs2 : float squared coefficient of variation for service time distribution Returns ------- float mean wait time in queue """ rho = arr_rate / (svc_rate * float(m)) # For ca2 = 1 (e.g. Poisson arrivals), Whitt uses fact that Erlang-C works well for M/G/c if ca2 == 1: pwait = mgc_prob_wait_erlangc(arr_rate, svc_rate, m) else: pi = _ggm_prob_wait_whitt_pi(m, rho, ca2, cs2) pwait = min(pi, 1) return pwait def _ggm_prob_wait_whitt_z(ca2, cs2): """ Equation 3.8 on p139 of Whitt (1993). Used in approximation for P(Wq > 0) in GI/G/c/inf queue. See Whitt, Ward. "Approximations for the GI/G/m queue" Production and Operations Management 2, 2 (Spring 1993): 114-161. Parameters ---------- ca2 : float squared coefficient of variation for inter-arrival time distribution cs2 : float squared coefficient of variation for service time distribution Returns ------- float approximation for intermediate term z (see Eq 3.6) """ z = (ca2 + cs2) / (1.0 + cs2) return z def _ggm_prob_wait_whitt_gamma(m, rho, z): """ Equation 3.5 on p136 of Whitt (1993). Used in approximation for P(Wq > 0) in GI/G/c/inf queue. See Whitt, Ward. "Approximations for the GI/G/m queue" Production and Operations Management 2, 2 (Spring 1993): 114-161. Parameters ---------- m : int number of servers rho : float traffic intensity; arr_rate / (svc_rate * m) z : float intermediate term approximated in Eq 3.8 Returns ------- float intermediate term gamma (see Eq 3.5) """ term1 = m - m * rho - 0.5 term2 = np.sqrt(m * rho * z) gamma = term1 / term2 return gamma def _ggm_prob_wait_whitt_pi_6(m, rho, z): """ Part of Equation 3.11 on p139 of Whitt (1993). Used in approximation for P(Wq > 0) in GI/G/c/inf queue. See Whitt, Ward. "Approximations for the GI/G/m queue" Production and Operations Management 2, 2 (Spring 1993): 114-161. Parameters ---------- m : int number of servers rho : float traffic intensity; arr_rate / (svc_rate * m) z : float intermediate term approximated in Eq 3.8 Returns ------- float intermediate term pi_6 (see Eq 3.11) """ pi_6 = 1.0 - stats.norm.cdf((m - m * rho - 0.5) / np.sqrt(m * rho * z)) return pi_6 def _ggm_prob_wait_whitt_pi_5(m, rho, ca2, cs2): """ Part of Equation 3.11 on p139 of Whitt (1993). Used in approximation for P(Wq > 0) in GI/G/c/inf queue. See Whitt, Ward. "Approximations for the GI/G/m queue" Production and Operations Management 2, 2 (Spring 1993): 114-161. Parameters ---------- m : int number of servers rho : float traffic intensity; arr_rate / (svc_rate * m) ca2 : float squared coefficient of variation for inter-arrival time distribution cs2 : float squared coefficient of variation for service time distribution Returns ------- float intermediate term pi_5(see Eq 3.11) """ term1 = 2.0 * (1.0 - rho) * np.sqrt(m) / (1.0 + ca2) term2 = (1.0 - rho) * np.sqrt(m) term3 = erlangc(rho * m, m) * (1.0 - stats.norm.cdf(term1)) / (1.0 - stats.norm.cdf(term2)) pi_5 = min(1.0,term3) return pi_5 def _ggm_prob_wait_whitt_pi_4(m, rho, ca2, cs2): """ Part of Equation 3.11 on p139 of Whitt (1993). Used in approximation for P(Wq > 0) in GI/G/c/inf queue. See Whitt, Ward. "Approximations for the GI/G/m queue" Production and Operations Management 2, 2 (Spring 1993): 114-161. Parameters ---------- m : int number of servers rho : float traffic intensity; arr_rate / (svc_rate * m) ca2 : float squared coefficient of variation for inter-arrival time distribution cs2 : float squared coefficient of variation for service time distribution Returns ------- float intermediate term pi_5(see Eq 3.11) """ term1 = (1.0 + cs2) * (1.0 - rho) * np.sqrt(m) / (ca2 + cs2) term2 = (1.0 - rho) * np.sqrt(m) term3 = erlangc(rho * m, m) * (1.0 - stats.norm.cdf(term1)) / (1.0 - stats.norm.cdf(term2)) pi_4 = min(1.0,term3) return pi_4 def _ggm_prob_wait_whitt_pi_1(m, rho, ca2, cs2): """ Part of Equation 3.11 on p139 of Whitt (1993). Used in approximation for P(Wq > 0) in GI/G/c/inf queue. See Whitt, Ward. "Approximations for the GI/G/m queue" Production and Operations Management 2, 2 (Spring 1993): 114-161. Parameters ---------- m : int number of servers rho : float traffic intensity; arr_rate / (svc_rate * m) ca2 : float squared coefficient of variation for inter-arrival time distribution cs2 : float squared coefficient of variation for service time distribution Returns ------- float intermediate term pi_5(see Eq 3.11) """ pi_4 = _ggm_prob_wait_whitt_pi_4(m, rho, ca2, cs2) pi_5 = _ggm_prob_wait_whitt_pi_5(m, rho, ca2, cs2) pi_1 = (rho ** 2) * pi_4 + (1.0 - rho **2) * pi_5 return pi_1 def _ggm_prob_wait_whitt_pi_2(m, rho, ca2, cs2): """ Part of Equation 3.11 on p139 of Whitt (1993). Used in approximation for P(Wq > 0) in GI/G/c/inf queue. See Whitt, Ward. "Approximations for the GI/G/m queue" Production and Operations Management 2, 2 (Spring 1993): 114-161. Parameters ---------- m : int number of servers rho : float traffic intensity; arr_rate / (svc_rate * m) ca2 : float squared coefficient of variation for inter-arrival time distribution cs2 : float squared coefficient of variation for service time distribution Returns ------- float intermediate term pi_2(see Eq 3.11) """ pi_1 = _ggm_prob_wait_whitt_pi_1(m, rho, ca2, cs2) z = _ggm_prob_wait_whitt_z(ca2, cs2) pi_6 = _ggm_prob_wait_whitt_pi_6(m, rho, z) pi_2 = ca2 * pi_1 + (1.0 - ca2) * pi_6 return pi_2 def _ggm_prob_wait_whitt_pi_3(m, rho, ca2, cs2): """ Part of Equation 3.11 on p139 of Whitt (1993). Used in approximation for P(Wq > 0) in GI/G/c/inf queue. See Whitt, Ward. "Approximations for the GI/G/m queue" Production and Operations Management 2, 2 (Spring 1993): 114-161. Parameters ---------- m : int number of servers rho : float traffic intensity; arr_rate / (svc_rate * m) ca2 : float squared coefficient of variation for inter-arrival time distribution cs2 : float squared coefficient of variation for service time distribution Returns ------- float intermediate term pi_5(see Eq 3.11) """ z = _ggm_prob_wait_whitt_z(ca2, cs2) gamma = _ggm_prob_wait_whitt_gamma(m, rho, z) pi_2 = _ggm_prob_wait_whitt_pi_2(m, rho, ca2, cs2) pi_1 = _ggm_prob_wait_whitt_pi_1(m, rho, ca2, cs2) term1 = 2.0 * (1.0 - ca2) * (gamma - 0.5) term2 = 1.0 - term1 pi_3 = term1 * pi_2 + term2 * pi_1 return pi_3 def _ggm_prob_wait_whitt_pi(m, rho, ca2, cs2): """ Equation 3.10 on p139 of Whitt (1993). Used in approximation for P(Wq > 0) in GI/G/c/inf queue. See Whitt, Ward. "Approximations for the GI/G/m queue" Production and Operations Management 2, 2 (Spring 1993): 114-161. Parameters ---------- m : int number of servers rho : float traffic intensity; arr_rate / (svc_rate * m) ca2 : float squared coefficient of variation for inter-arrival time distribution cs2 : float squared coefficient of variation for service time distribution Returns ------- float intermediate term pi_5(see Eq 3.11) """ z = _ggm_prob_wait_whitt_z(ca2, cs2) gamma = _ggm_prob_wait_whitt_gamma(m, rho, z) if m <= 6 or gamma <= 0.5 or ca2 >= 1: pi = _ggm_prob_wait_whitt_pi_1(m, rho, ca2, cs2) elif m >= 7 and gamma >= 1.0 and ca2 < 1: pi = _ggm_prob_wait_whitt_pi_2(m, rho, ca2, cs2) else: pi = _ggm_prob_wait_whitt_pi_3(m, rho, ca2, cs2) return pi def _ggm_prob_wait_whitt_whichpi(m, rho, ca2, cs2): """ Equation 3.10 on p139 of Whitt (1993). Used in approximation for P(Wq > 0) in GI/G/c/inf queue. Primarily used for debugging and validation of the approximation implementation. See Whitt, Ward. "Approximations for the GI/G/m queue" Production and Operations Management 2, 2 (Spring 1993): 114-161. Parameters ---------- m : int number of servers rho : float traffic intensity; arr_rate / (svc_rate * m) ca2 : float squared coefficient of variation for inter-arrival time distribution cs2 : float squared coefficient of variation for service time distribution Returns ------- int the pi case used in the approximation (1, 2, or 3) """ z = _ggm_prob_wait_whitt_z(ca2, cs2) gamma = _ggm_prob_wait_whitt_gamma(m, rho, z) if m <= 6 or gamma <= 0.5 or ca2 >= 1: whichpi = 1 elif m >= 7 and gamma >= 1.0 and ca2 < 1: whichpi = 2 else: whichpi = 3 return whichpi def _ggm_qcondwait_whitt_ds3(cs2): """ Return the approximate E(V^3)/(EV)^2 where V is a service time; based on either a hyperexponential or Erlang distribution. Used in approximation of conditional wait time CDF (conditional on W>0). Whitt refers to conditional wait as D in his paper: See Whitt, Ward. "Approximations for the GI/G/m queue" Production and Operations Management 2, 2 (Spring 1993): 114-161. This is Equation 4.3 on p146. Note that there is a typo in the original paper in which the first term for Case 1 is shown as cubed, whereas it should be squared. This can be confirmed by seeing Eq 51 in Whitt's paper on the QNA (Bell Systems Technical Journal, Nov 1983). Parameters ---------- cs2 : float squared coefficient of variation for service time distribution Returns ------- float mean wait time in queue """ if cs2 >= 1: ds3 = 3.0 * cs2 * (1.0 + cs2) else: ds3 = (2 * cs2 + 1.0) * (cs2 + 1.0) return ds3 def ggm_qcondwait_whitt_cd2(rho, cs2): """ Return the approximate squared coefficient of conditional wait time (aka delay) in G/G/m queue See Whitt, Ward. "Approximations for the GI/G/m queue" Production and Operations Management 2, 2 (Spring 1993): 114-161. This is Equation 4.2 on p145. Parameters ---------- rho : float traffic intensity; arr_rate / (svc_rate * m) cs2 : float squared coefficient of variation for service time distribution Returns ------- float mean wait time in queue """ term1 = 2 * rho - 1.0 term2 = 4 * (1.0 - rho) * _ggm_qcondwait_whitt_ds3(cs2) term3 = 3.0 * (cs2 + 1.0) ** 2 cd2 = term1+ term2 / term3 return cd2 def ggm_qwait_whitt_cw2(arr_rate, svc_rate, m, ca2, cs2): """ Return the approximate squared coefficient of wait time in G/G/m queue See Whitt, Ward. "Approximations for the GI/G/m queue" Production and Operations Management 2, 2 (Spring 1993): 114-161. Parameters ---------- arr_rate : float average arrival rate to queueing system svc_rate : float average service rate (each server). 1/svc_rate is mean service time. c : int number of servers ca2 : float squared coefficient of variation for inter-arrival time distribution cs2 : float squared coefficient of variation for service time distribution Returns ------- float scv of wait time in queue """ rho = arr_rate / (svc_rate * float(m)) pwait = ggm_prob_wait_whitt(arr_rate, svc_rate, m, ca2, cs2) cd2 = ggm_qcondwait_whitt_cd2(rho, cs2) cw2 = (cd2 + 1 - pwait) / pwait return cw2 def ggm_qcondwait_whitt_ed(arr_rate, svc_rate, m, ca2, cs2): """ Return the approximate mean conditional wait time (aka delay) in G/G/m queue See Whitt, Ward. "Approximations for the GI/G/m queue" Production and Operations Management 2, 2 (Spring 1993): 114-161. Parameters ---------- arr_rate : float average arrival rate to queueing system svc_rate : float average service rate (each server). 1/svc_rate is mean service time. c : int number of servers ca2 : float squared coefficient of variation for inter-arrival time distribution cs2 : float squared coefficient of variation for service time distribution Returns ------- float variance of conditional wait time in queue """ pwait = ggm_prob_wait_whitt(arr_rate, svc_rate, m, ca2, cs2) meanwait = ggm_mean_qwait_whitt(arr_rate, svc_rate, m, ca2, cs2) / pwait return meanwait def ggm_qcondwait_whitt_vard(arr_rate, svc_rate, m, ca2, cs2): """ Return the approximate variance of conditional wait time (aka delay) in G/G/m queue See Whitt, Ward. "Approximations for the GI/G/m queue" Production and Operations Management 2, 2 (Spring 1993): 114-161. Parameters ---------- arr_rate : float average arrival rate to queueing system svc_rate : float average service rate (each server). 1/svc_rate is mean service time. c : int number of servers ca2 : float squared coefficient of variation for inter-arrival time distribution cs2 : float squared coefficient of variation for service time distribution Returns ------- float variance of conditional wait time in queue """ rho = arr_rate / (svc_rate * float(m)) pwait = ggm_prob_wait_whitt(arr_rate, svc_rate, m, ca2, cs2) cd2 = ggm_qcondwait_whitt_cd2(rho, cs2) meanwait = ggm_mean_qwait_whitt(arr_rate, svc_rate, m, ca2, cs2) vard = (meanwait ** 2) * cd2 / (pwait ** 2) return vard def ggm_qcondwait_whitt_ed2(arr_rate, svc_rate, m, ca2, cs2): """ Return the approximate 2nd moment of conditional wait time (aka delay) in G/G/m queue See Whitt, Ward. "Approximations for the GI/G/m queue" Production and Operations Management 2, 2 (Spring 1993): 114-161. Parameters ---------- arr_rate : float average arrival rate to queueing system svc_rate : float average service rate (each server). 1/svc_rate is mean service time. c : int number of servers ca2 : float squared coefficient of variation for inter-arrival time distribution cs2 : float squared coefficient of variation for service time distribution Returns ------- float variance of conditional wait time in queue """ pwait = ggm_prob_wait_whitt(arr_rate, svc_rate, m, ca2, cs2) vard = ggm_qcondwait_whitt_vard(arr_rate, svc_rate, m, ca2, cs2) meanwait = ggm_mean_qwait_whitt(arr_rate, svc_rate, m, ca2, cs2) # Compute conditional wait meandelay = meanwait / pwait ed2 = vard + meandelay ** 2 return ed2 def ggm_qwait_whitt_varw(arr_rate, svc_rate, m, ca2, cs2): """ Return the approximate variance of wait time (aka delay) in G/G/m queue See Whitt, Ward. "Approximations for the GI/G/m queue" Production and Operations Management 2, 2 (Spring 1993): 114-161. Parameters ---------- arr_rate : float average arrival rate to queueing system svc_rate : float average service rate (each server). 1/svc_rate is mean service time. c : int number of servers ca2 : float squared coefficient of variation for inter-arrival time distribution cs2 : float squared coefficient of variation for service time distribution Returns ------- float variance of conditional wait time in queue """ cw2 = ggm_qwait_whitt_cw2(arr_rate, svc_rate, m, ca2, cs2) meanwait = ggm_mean_qwait_whitt(arr_rate, svc_rate, m, ca2, cs2) varw = (meanwait ** 2) * cw2 return varw def ggm_qwait_whitt_ew2(arr_rate, svc_rate, m, ca2, cs2): """ Return the approximate 2nd moment of wait time in G/G/m queue See Whitt, Ward. "Approximations for the GI/G/m queue" Production and Operations Management 2, 2 (Spring 1993): 114-161. Parameters ---------- arr_rate : float average arrival rate to queueing system svc_rate : float average service rate (each server). 1/svc_rate is mean service time. c : int number of servers ca2 : float squared coefficient of variation for inter-arrival time distribution cs2 : float squared coefficient of variation for service time distribution Returns ------- float variance of conditional wait time in queue """ varw = ggm_qwait_whitt_varw(arr_rate, svc_rate, m, ca2, cs2) meanwait = ggm_mean_qwait_whitt(arr_rate, svc_rate, m, ca2, cs2) ew2 = varw + meanwait ** 2 return ew2 def ggm_mean_sojourn_whitt(arr_rate, svc_rate, m, ca2, cs2): """ Return the approximate soujourn time (wait + service) in G/G/m queue See Whitt, Ward. "Approximations for the GI/G/m queue" Production and Operations Management 2, 2 (Spring 1993): 114-161. Parameters ---------- arr_rate : float average arrival rate to queueing system svc_rate : float average service rate (each server). 1/svc_rate is mean service time. c : int number of servers ca2 : float squared coefficient of variation for inter-arrival time distribution cs2 : float squared coefficient of variation for service time distribution Returns ------- float variance of conditional wait time in queue """ meanwait = ggm_mean_qwait_whitt(arr_rate, svc_rate, m, ca2, cs2) sojourn = meanwait + 1.0 / svc_rate return sojourn def ggm_sojourn_whitt_var(arr_rate, svc_rate, m, ca2, cs2): """ Return the approximate variance of soujourn time (wait + service) in G/G/m queue See Whitt, Ward. "Approximations for the GI/G/m queue" Production and Operations Management 2, 2 (Spring 1993): 114-161. Parameters ---------- arr_rate : float average arrival rate to queueing system svc_rate : float average service rate (each server). 1/svc_rate is mean service time. c : int number of servers ca2 : float squared coefficient of variation for inter-arrival time distribution cs2 : float squared coefficient of variation for service time distribution Returns ------- float variance of conditional wait time in queue """ varwait = ggm_qwait_whitt_varw(arr_rate, svc_rate, m, ca2, cs2) sojourn = varwait + cs2 * (1.0 / svc_rate) ** 2 return sojourn def ggm_sojourn_whitt_et2(arr_rate, svc_rate, m, ca2, cs2): """ Return the approximate 2nd moment of soujourn time (wait + service) in G/G/m queue See Whitt, Ward. "Approximations for the GI/G/m queue" Production and Operations Management 2, 2 (Spring 1993): 114-161. Parameters ---------- arr_rate : float average arrival rate to queueing system svc_rate : float average service rate (each server). 1/svc_rate is mean service time. c : int number of servers ca2 : float squared coefficient of variation for inter-arrival time distribution cs2 : float squared coefficient of variation for service time distribution Returns ------- float variance of conditional wait time in queue """ varsojourn = ggm_sojourn_whitt_var(arr_rate, svc_rate, m, ca2, cs2) meansojourn = ggm_mean_sojourn_whitt(arr_rate, svc_rate, m, ca2, cs2) et2 = varsojourn + meansojourn ** 2 return et2 def ggm_sojourn_whitt_cv2(arr_rate, svc_rate, m, ca2, cs2): """ Return the approximate scv of soujourn time (wait + service) in G/G/m queue See Whitt, Ward. "Approximations for the GI/G/m queue" Production and Operations Management 2, 2 (Spring 1993): 114-161. Parameters ---------- arr_rate : float average arrival rate to queueing system svc_rate : float average service rate (each server). 1/svc_rate is mean service time. c : int number of servers ca2 : float squared coefficient of variation for inter-arrival time distribution cs2 : float squared coefficient of variation for service time distribution Returns ------- float variance of conditional wait time in queue """ varsojourn = ggm_sojourn_whitt_var(arr_rate, svc_rate, m, ca2, cs2) meansojourn = ggm_mean_sojourn_whitt(arr_rate, svc_rate, m, ca2, cs2) cv2 = varsojourn / meansojourn ** 2 return cv2 def ggm_mean_qsize_whitt(arr_rate, svc_rate, m, ca2, cs2): """ Return the approximate mean queue size in GI/G/c/inf queue using Whitt's 1993 approximation and Little's Law. See Whitt, Ward. "Approximations for the GI/G/m queue" Production and Operations Management 2, 2 (Spring 1993): 114-161. It's based on interpolations with corrections between an M/D/c, D/M/c and a M/M/c queueing systems. Parameters ---------- arr_rate : float average arrival rate to queueing system svc_rate : float average service rate (each server). 1/svc_rate is mean service time. c : int number of servers ca2 : float squared coefficient of variation for inter-arrival time distribution cs2 : float squared coefficient of variation for service time distribution Returns ------- float mean wait time in queue """ # Use Eq 2.24 on p 125 to compute mean wait time in queue qwait = ggm_mean_qwait_whitt(arr_rate, svc_rate, m, ca2, cs2) # Now use Little's Law return qwait * arr_rate def ggm_mean_syssize_whitt(arr_rate, svc_rate, m, ca2, cs2): """ Return the approximate mean system size in GI/G/c/inf queue using Whitt's 1993 approximation and Little's Law. See Whitt, Ward. "Approximations for the GI/G/m queue" Production and Operations Management 2, 2 (Spring 1993): 114-161. It's based on interpolations with corrections between an M/D/c, D/M/c and a M/M/c queueing systems. Parameters ---------- arr_rate : float average arrival rate to queueing system svc_rate : float average service rate (each server). 1/svc_rate is mean service time. c : int number of servers ca2 : float squared coefficient of variation for inter-arrival time distribution cs2 : float squared coefficient of variation for service time distribution Returns ------- float mean wait time in queue """ # Use Eq 2.24 on p 125 to compute mean wait time in queue mean_sojourn = ggm_mean_sojourn_whitt(arr_rate, svc_rate, m, ca2, cs2) # Now use Little's Law return mean_sojourn * arr_rate def dmm_mean_qwait_whitt(arr_rate, svc_rate, m, ca2=0.0, cs2=1.0): """ Return the approximate mean queue size in D/M/m/inf queue using Whitt's 1993 approximation. See Whitt, Ward. "Approximations for the GI/G/m queue" Production and Operations Management 2, 2 (Spring 1993): 114-161. Specifically, this approximation is Eq 2.20 on p124. This, along with mdm_mean_qwait_whitt are refinements of the Cosmetatos approximations. Parameters ---------- arr_rate : float average arrival rate to queueing system svc_rate : float average service rate (each server). 1/svc_rate is mean service time. c : int number of servers ca2 : float squared coefficient of variation for inter-arrival time distribution (0 for D) cs2 : float squared coefficient of variation for service time distribution (1 for M) Returns ------- float mean wait time in queue """ rho = arr_rate / (svc_rate * float(m)) # Now implement Eq 2.20 on p 124 term1 = _ggm_mean_qwait_whitt_phi_3(m, rho) term2 = 0.5 * (ca2 + cs2) term3 = mmc_mean_qwait(arr_rate, svc_rate, m) return term1 * term2 * term3 def mdm_mean_qwait_whitt(arr_rate, svc_rate, m, ca2=0.0, cs2=1.0): """ Return the approximate mean queue size in M/D/m/inf queue using Whitt's 1993 approximation. See Whitt, Ward. "Approximations for the GI/G/m queue" Production and Operations Management 2, 2 (Spring 1993): 114-161. Specifically, this approximation is Eq 2.16 on p124. This, along with dmm_mean_qwait_whitt are refinements of the Cosmetatos approximations. Parameters ---------- arr_rate : float average arrival rate to queueing system svc_rate : float average service rate (each server). 1/svc_rate is mean service time. c : int number of servers ca2 : float squared coefficient of variation for inter-arrival time distribution (0 for D) cs2 : float squared coefficient of variation for service time distribution (1 for M) Returns ------- float mean wait time in queue """ rho = arr_rate / (svc_rate * float(m)) # Now implement Eq 2.16 on p 124 term1 = _ggm_mean_qwait_whitt_phi_1(m, rho) term2 = 0.5 * (ca2 + cs2) term3 = mmc_mean_qwait(arr_rate, svc_rate, m) return term1 * term2 * term3 def fit_balanced_hyperexpon2(mean, cs2): """ Return the branching probability and rates for a balanced H2 distribution based on a specified mean and scv. Intended for scv's > 1. See Whitt, Ward. "Approximations for the GI/G/m queue" Production and Operations Management 2, 2 (Spring 1993): 114-161. Parameters ---------- cs2 : float squared coefficient of variation for desired distribution Returns ------- tuple (float p, float rate1, float rate2) branching probability and exponential rates """ p1 = 0.5 * (1 + np.sqrt((cs2-1) / (cs2+1))) p2 = 1 - p1 mu1 = 2 * p1 / mean mu2 = 2 * p2 / mean return (p1, mu1, mu2) def hyperexpon_cdf(x, probs, rates): """ Return the P(X < x) where X is hypergeometric with probabilities and exponential rates in lists probs and rates. Parameters ---------- probs : list of floats branching probabilities for hyperexponential probs : list of floats exponential rates Returns ------- float P(X<x) where X~hyperexponetial(probs, rates) """ sumproduct = sum([p * np.exp(-r * x) for (p, r) in zip(probs, rates)]) prob_lt_x = 1.0 - sumproduct return prob_lt_x def ggm_qcondwait_cdf_whitt(t, arr_rate, svc_rate, c, ca2, cs2): """ Return the approximate P(D <= t) where D = (W|W>0) in G/G/m queue using Whitt's two moment approximation. See Section 4 of Whitt, Ward. "Approximations for the GI/G/m queue" Production and Operations Management 2, 2 (Spring 1993): 114-161. It's based on an approach he originally used for G/G/1 queues in QNA. There are different cases based on the value of an approximation for the scv of D. Parameters ---------- t : float wait time of interest arr_rate : float average arrival rate to queueing system svc_rate : float average service rate (each server). 1/svc_rate is mean service time. c : int number of servers cv2_svc_time : float squared coefficient of variation for service time distribution Returns ------- float ~ P(D <= t | ) """ rho = arr_rate / (svc_rate * float(c)) ed = ggm_mean_qwait_whitt(arr_rate, svc_rate, c, ca2, cs2) / ggm_prob_wait_whitt(arr_rate, svc_rate, c, ca2, cs2) cd2 = ggm_qcondwait_whitt_cd2(rho,cs2) if cd2 > 1.01: # Hyperexponential approx p1, gamma1, gamma2 = fit_balanced_hyperexpon2(ed, cd2) p2 = 1.0 - p1 prob_wait_ltx = hyperexpon_cdf(t, [p1,p2], [gamma1, gamma2]) elif cd2 >= 0.99 and cd2 <= 1.01: # Exponential approx prob_wait_ltx = stats.expon.cdf(t,scale=ed) elif cd2 >= 0.501 and cd2 < 0.99: # Convolution of two exponentials approx vard = ggm_qcondwait_whitt_vard(arr_rate, svc_rate, c, ca2, cs2) gamma2 = 2.0 / (ed + np.sqrt(2 * vard - ed ** 2)) gamma1 = 1.0 / (ed - 1.0 / gamma2) prob_wait_gtx = (gamma1 * np.exp(-gamma2 * t) - gamma2 * np.exp(-gamma1 * t)) / (gamma1 - gamma2) prob_wait_ltx = 1.0 - prob_wait_gtx else: # Erlang approx gamma1 = 2.0 / ed prob_wait_gtx = np.exp(-gamma1 * t) * (1.0 + gamma1 * t) prob_wait_ltx = 1.0 - prob_wait_gtx return prob_wait_ltx def ggm_qwait_cdf_whitt(t, arr_rate, svc_rate, c, ca2, cs2): """ Return the approximate P(W <= t) in G/G/m queue using Whitt's two moment approximation for conditional wait and the P(W>0). See Section 4 of Whitt, Ward. "Approximations for the GI/G/m queue" Production and Operations Management 2, 2 (Spring 1993): 114-161. See ggm_qcondwait_cdf_whitt for more details. Parameters ---------- t : float wait time of interest arr_rate : float average arrival rate to queueing system svc_rate : float average service rate (each server). 1/svc_rate is mean service time. c : int number of servers cv2_svc_time : float squared coefficient of variation for service time distribution Returns ------- float ~ P(W <= t | ) """ qcondwait = ggm_qcondwait_cdf_whitt(t, arr_rate, svc_rate, c, ca2, cs2) pdelay = ggm_prob_wait_whitt(arr_rate, svc_rate, c, ca2, cs2) qwait = qcondwait * pdelay + (1.0 - pdelay) return qwait def ggm_qwait_pctile_whitt(p, arr_rate, svc_rate, c, ca2, cs2): """ Return approx p'th percentile of P(Wq < t) in G/G/c/inf queue using Whitt's two moment approximation for the wait time CDF Parameters ---------- p : float percentile of interest arr_rate : float average arrival rate to queueing system svc_rate : float average service rate (each server). 1/svc_rate is mean service time. c : int number of servers Returns ------- float t such that P(wait time in queue is < t) = p """ # For initial guess, we'll use percentile from similar M/M/1 system init_guess = mm1_qwait_pctile(p, arr_rate, c * svc_rate) waitq_pctile = scipy.optimize.newton(_ggm_waitq_pctile_whitt_wrap,init_guess,args=(p, arr_rate, svc_rate, c, ca2, cs2)) return waitq_pctile def _ggm_waitq_pctile_whitt_wrap(t, p, arr_rate, svc_rate, c, ca2, cs2): return ggm_qwait_cdf_whitt(t, arr_rate, svc_rate, c, ca2, cs2) - p def _ggm_qsize_prob_gt_0_whitt_5_2(arr_rate, svc_rate, c, ca2, cs2): """ Return the approximate P(Q>0) in G/G/m queue using Whitt's simple approximation involving rho and P(W>0). This approximation is exact for M/M/m and has strong theoretical support for GI/M/m. It's described by Whitt as "crude" but is "a useful quick approximation". See Section 5 of Whitt, Ward. "Approximations for the GI/G/m queue" Production and Operations Management 2, 2 (Spring 1993): 114-161. In particular, this is Equation 5.2. Parameters ---------- arr_rate : float average arrival rate to queueing system svc_rate : float average service rate (each server). 1/svc_rate is mean service time. c : int number of servers cv2_svc_time : float squared coefficient of variation for service time distribution Returns ------- float ~ P(Q > 0) """ rho = arr_rate / (svc_rate * float(c)) pdelay = ggm_prob_wait_whitt(arr_rate, svc_rate, c, ca2, cs2) prob_gt_0 = rho * pdelay return prob_gt_0 def _ggm_qsize_prob_gt_0_whitt_5_1(arr_rate, svc_rate, c, ca2, cs2): """ Return the approximate P(Q>0) in G/G/m queue using Whitt's approximation which is based on an exact expression for P(Q>0) given the CDF's of an interarrival time and a waiting time . This approximation is exact for M/M/m and has strong theoretical support for GI/M/m - see Equation 5.1. It is preferred to the cruder approximation given in Equation 5.2 (see ggm_qsize_prob_gt_0_whitt_5_2). See Section 5 of Whitt, Ward. "Approximations for the GI/G/m queue" Production and Operations Management 2, 2 (Spring 1993): 114-161. In particular, this is Equation 5.1. Parameters ---------- arr_rate : float average arrival rate to queueing system svc_rate : float average service rate (each server). 1/svc_rate is mean service time. c : int number of servers cv2_svc_time : float squared coefficient of variation for service time distribution Returns ------- float ~ P(Q > 0 """ rho = arr_rate / (svc_rate * float(c)) pdelay = ggm_prob_wait_whitt(arr_rate, svc_rate, c, ca2, cs2) # TODO - implement Equation 5.1 of Whitt (1995) return 0 def ggm_qsize_whitt_cq2(arr_rate, svc_rate, m, ca2, cs2): """ Return the approximate squared coefficient of queue size in G/G/m queue. See Whitt, Ward. "Approximations for the GI/G/m queue" Production and Operations Management 2, 2 (Spring 1993): 114-161. Equation 5.6. Parameters ---------- arr_rate : float average arrival rate to queueing system svc_rate : float average service rate (each server). 1/svc_rate is mean service time. c : int number of servers ca2 : float squared coefficient of variation for inter-arrival time distribution cs2 : float squared coefficient of variation for service time distribution Returns ------- float scv of number in queue """ eq = ggm_mean_qsize_whitt(arr_rate, svc_rate, m, ca2, cs2) cw2 = ggm_qwait_whitt_cw2(arr_rate, svc_rate, m, ca2, cs2) cq2 = (1/eq) + cw2 return cq2 def hyper_erlang_moment(rates, stages, probs, moment): terms = [probs[i - 1] * math.factorial(stages[i - 1] + moment - 1) * (1 / math.factorial(stages[i - 1] - 1)) * ( stages[i - 1] * rates[i - 1]) ** (-moment) for i in range(1, len(rates) + 1)] return sum(terms)

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9b4655608ca44fa36d02abb4baccf91772c878ef

35

py

Python

db/__init__.py

JonKoala/diariobot-scraper

8f1a44dc19d666139f5650ee64a8b14ab7c77b9a

[ "MIT" ]

1

2018-04-23T16:39:22.000Z

db/__init__.py

JonKoala/diariobot-datamining

c97095a7906fa984f4373cfbcdbf4576137d8e2f

[ "MIT" ]

null

db/__init__.py

JonKoala/diariobot-datamining

c97095a7906fa984f4373cfbcdbf4576137d8e2f

[ "MIT" ]

null

from .interface import Dbinterface

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17,086

py

Python

lib/data_allocation.py

stuckerc/ResDepth

c117409db4b5972583153b918521570bae3a9ec6

[ "MIT" ]

32

2021-07-30T17:35:40.000Z

2022-03-16T22:16:01.000Z

lib/data_allocation.py

stuckerc/ResDepth

c117409db4b5972583153b918521570bae3a9ec6

[ "MIT" ]

1

2022-02-07T21:28:19.000Z

2022-02-09T15:24:03.000Z

lib/data_allocation.py

stuckerc/ResDepth

c117409db4b5972583153b918521570bae3a9ec6

[ "MIT" ]

5

2021-07-30T17:35:48.000Z

2022-03-16T22:15:03.000Z

import numpy as np import sys from lib import fdutil, rasterutils STRATEGIES = ['5-crossval_vertical', '5-crossval_horizontal'] def _verify_inputs(fn_raster_in, allocation_strategy, test_stripe, crossval_training): """ Verifies the inputs of the function allocate_data(). :param fn_raster_in: str, path to the GeoTiff raster file :param allocation_strategy: str, allocation strategy (see parameter STRATEGIES) :param test_stripe: int, index of the test stripe (or validation stripe if cross-validation is enabled) :param crossval_training: bool, True if the raster is used for cross-validation (split into training and validation regions only), False otherwise """ # Check that the input GeoTiff raster exists if not fdutil.file_exists(fn_raster_in): print('Input raster does not exist: {}'.format(fn_raster_in)) sys.exit(1) if not isinstance(test_stripe, int): print("'test_stripe' must be an integer in the range [0,4].") sys.exit(1) if test_stripe > 4: print("'test_stripe' must be an integer in the range [0,4].") sys.exit(1) if allocation_strategy not in STRATEGIES: print("{} as 'allocation_strategy' is not a valid choice. Choose among: {}.".format(allocation_strategy, STRATEGIES)) sys.exit(1) if not isinstance(crossval_training, bool): print("'crossval_training' must be boolean.") sys.exit(1) def allocate_data(fn_raster_in, allocation_strategy, test_stripe=0, crossval_training=False): """ Splits a given raster into geographically separate stripes for training, validation, and testing. Assumption: the validation stripe is located to the right/bottom (east/south) of the test stripe (cyclic order). :param fn_raster_in: str, path to the GeoTiff raster file :param allocation_strategy: str, allocation strategy (see parameter STRATEGIES) :param test_stripe: int, index of the test stripe (or validation stripe if cross-validation is enabled) :param crossval_training: bool, True if the raster is used for cross-validation (split into training and validation regions only), False otherwise :return: returns three dictionaries train, val, and test, where each dictionary defines geographically rectangular regions. Each dictionary is composed of the following key-value pairs: x_extent: list of n tuples, where n denotes the number of rectangular regions (stripes). Each tuple defines the upper-left and lower-right x-coordinate of a rectangular region (stripe). y_extent: list of n tuples, where n denotes the number of rectangular regions (stripes). Each tuple defines the upper-left and lower-right y-coordinate of a rectangular region (stripe). Assumption: The i.th tuple of x_extent and i.th tuple of y_extent define a geographically rectangular region (stripe). """ # Check inputs _verify_inputs(fn_raster_in, allocation_strategy, test_stripe, crossval_training) if allocation_strategy == '5-crossval_vertical': train, val, test = _allocate_5crossval_vertical(fn_raster_in, test_stripe, crossval_training) elif allocation_strategy == '5-crossval_horizontal': train, val, test = _allocate_5crossval_horizontal(fn_raster_in, test_stripe, crossval_training) return train, val, test def _allocate_5crossval_vertical(fn_raster_in, test_stripe, crossval_training): """ Splits the geographic area of fn_raster_in into five equally large and mutually exclusive vertical stripes (north-south oriented) for training, validation, and testing (or training and validation only if cross-validation is enabled). Assumption: the validation stripe is located to the right (east) of the test stripe (cyclic order). :param fn_raster_in: str, path to the GeoTiff raster file :param test_stripe: int, index of the test stripe (or validation stripe if cross-validation is enabled) :param crossval_training: bool, True if the raster is used for cross-validation (split into training and validation regions only), False otherwise :return: returns three dictionaries train, val, and test, where each dictionary defines geographically rectangular regions (vertically oriented stripes). Each dictionary is composed of the following key-value pairs: x_extent: list of n tuples, where n denotes the number of rectangular regions (stripes). Each tuple defines the upper-left and lower-right x-coordinate of a rectangular region (stripe). y_extent: list of n tuples, where n denotes the number of rectangular regions (stripes). Each tuple defines the upper-left and lower-right y-coordinate of a rectangular region (stripe). Assumption: The i.th tuple of x_extent and i.th tuple of y_extent define a geographically rectangular region (stripe). """ # Get the extent of the input raster extent = rasterutils.get_raster_extent(fn_raster_in) cols = extent['cols'] rows = extent['rows'] # Compute the width of the stripes width = int(round(float(cols) * 0.2)) # Compute the extent in X-direction of the stripes x_start = 0 x_extent = [] for i in range(5): if i < 4: x_end = x_start + width - 1 else: x_end = cols - 1 x_extent.append((x_start, x_end)) x_start = x_end + 1 # Validation and test stripe: compute the extent in Y-direction y_val = [(0, rows - 1)] y_test = [(0, rows - 1)] if crossval_training is False: if test_stripe == 0: # Stripe order: | test | val | train | train | train | x_train = [(x_extent[2][0], x_extent[4][1])] x_val = [x_extent[1]] x_test = [x_extent[0]] y_train = [(0, rows - 1)] elif test_stripe == 1: # Stripe order: | train | test | val | train | train | x_train = [x_extent[0], (x_extent[3][0], x_extent[4][1])] x_val = [x_extent[2]] x_test = [x_extent[1]] y_train = [(0, rows - 1), (0, rows - 1)] elif test_stripe == 2: # Stripe order: | train | train | test | val | train | x_train = [(x_extent[0][0], x_extent[1][1]), x_extent[4]] x_val = [x_extent[3]] x_test = [x_extent[2]] y_train = [(0, rows - 1), (0, rows - 1)] elif test_stripe == 3: # Stripe order: | train | train | train | test | val | x_train = [(x_extent[0][0], x_extent[2][1])] x_val = [x_extent[4]] x_test = [x_extent[3]] y_train = [(0, rows - 1)] elif test_stripe == 4: # Stripe order: | val | train | train | train | test | x_train = [(x_extent[1][0], x_extent[3][1])] x_val = [x_extent[0]] x_test = [x_extent[4]] y_train = [(0, rows - 1)] test = {'x_extent': x_test, 'y_extent': y_test} else: if test_stripe == 0: # Stripe order: | val | train | train | train | train | x_train = [(x_extent[1][0], x_extent[4][1])] x_val = [x_extent[0]] y_train = [(0, rows - 1)] elif test_stripe == 1: # Stripe order: | train | val | train | train | train | x_train = [x_extent[0], (x_extent[2][0], x_extent[4][1])] x_val = [x_extent[1]] y_train = [(0, rows - 1), (0, rows - 1)] elif test_stripe == 2: # Stripe order: | train | train | val | train | train | x_train = [(x_extent[0][0], x_extent[1][1]), (x_extent[3][0], x_extent[4][1])] x_val = [x_extent[2]] y_train = [(0, rows - 1), (0, rows - 1)] elif test_stripe == 3: # Stripe order: | train | train | train | val | train | x_train = [(x_extent[0][0], x_extent[2][1]), x_extent[4]] x_val = [x_extent[3]] y_train = [(0, rows - 1), (0, rows - 1)] elif test_stripe == 4: # Stripe order: | train | train | train | train | val | x_train = [(x_extent[0][0], x_extent[3][1])] x_val = [x_extent[4]] y_train = [(0, rows - 1)] test = {} train = {'x_extent': x_train, 'y_extent': y_train} val = {'x_extent': x_val, 'y_extent': y_val} return train, val, test def _allocate_5crossval_horizontal(fn_raster_in, test_stripe, crossval_training): """ Splits the geographic area of fn_raster_in into five equally large and mutually exclusive horizontal stripes (west-east oriented) for training, validation, and testing (or training and validation only if cross-validation is enabled). Assumption: the validation stripe is located to the bottom (south) of the test stripe (cyclic order). :param fn_raster_in: str, path to the GeoTiff raster file :param test_stripe: int, index of the test stripe (or validation stripe if cross-validation is enabled) :param crossval_training: bool, True if the raster is used for cross-validation (split into training and validation regions only), False otherwise :return: returns three dictionaries train, val, and test, where each dictionary defines geographically rectangular regions (horizontally oriented stripes). Each dictionary is composed of the following key-value pairs: x_extent: list of n tuples, where n denotes the number of rectangular regions (stripes). Each tuple defines the upper-left and lower-right x-coordinate of a rectangular region (stripe). y_extent: list of n tuples, where n denotes the number of rectangular regions (stripes). Each tuple defines the upper-left and lower-right y-coordinate of a rectangular region (stripe). Assumption: The i.th tuple of x_extent and i.th tuple of y_extent define a geographically rectangular region (stripe). """ # Get the extent of the input raster extent = rasterutils.get_raster_extent(fn_raster_in) cols = extent['cols'] rows = extent['rows'] # Compute the height of the stripes height = int(round(float(rows) * 0.2)) # Compute the extent in Y-direction of the stripes y_start = 0 y_extent = [] for i in range(5): if i < 4: y_end = y_start + height - 1 else: y_end = rows - 1 y_extent.append((y_start, y_end)) y_start = y_end + 1 # Validation and test stripe: compute the extent in X-direction x_val = [(0, cols - 1)] x_test = [(0, cols - 1)] if crossval_training is False: if test_stripe == 0: # Stripe order: | test | val | train | train | train | y_train = [(y_extent[2][0], y_extent[4][1])] y_val = [y_extent[1]] y_test = [y_extent[0]] x_train = [(0, cols - 1)] elif test_stripe == 1: # Stripe order: | train | test | val | train | train | y_train = [y_extent[0], (y_extent[3][0], y_extent[4][1])] y_val = [y_extent[2]] y_test = [y_extent[1]] x_train = [(0, cols - 1), (0, cols - 1)] elif test_stripe == 2: # Stripe order: | train | train | test | val | train | y_train = [(y_extent[0][0], y_extent[1][1]), y_extent[4]] y_val = [y_extent[3]] y_test = [y_extent[2]] x_train = [(0, cols - 1), (0, cols - 1)] elif test_stripe == 3: # Stripe order: | train | train | train | test | val | y_train = [(y_extent[0][0], y_extent[2][1])] y_val = [y_extent[4]] y_test = [y_extent[3]] x_train = [(0, cols - 1)] elif test_stripe == 4: # Stripe order: | val | train | train | train | test | y_train = [(y_extent[1][0], y_extent[3][1])] y_val = [y_extent[0]] y_test = [y_extent[4]] x_train = [(0, cols - 1)] test = {'x_extent': x_test, 'y_extent': y_test} else: if test_stripe == 0: # Stripe order: | val | train | train | train | train | y_train = [(y_extent[1][0], y_extent[4][1])] y_val = [y_extent[0]] x_train = [(0, cols - 1)] elif test_stripe == 1: # Stripe order: | train | val | train | train | train | y_train = [y_extent[0], (y_extent[2][0], y_extent[4][1])] y_val = [y_extent[1]] x_train = [(0, cols - 1), (0, cols - 1)] elif test_stripe == 2: # Stripe order: | train | train | val | train | train | y_train = [(y_extent[0][0], y_extent[1][1]), (y_extent[3][0], y_extent[4][1])] y_val = [y_extent[2]] x_train = [(0, cols - 1), (0, cols - 1)] elif test_stripe == 3: # Stripe order: | train | train | train | val | train | y_train = [(y_extent[0][0], y_extent[2][1]), y_extent[4]] y_val = [y_extent[3]] x_train = [(0, cols - 1), (0, cols - 1)] elif test_stripe == 4: # Stripe order: | train | train | train | train | val | y_train = [(y_extent[0][0], y_extent[3][1])] y_val = [y_extent[4]] x_train = [(0, cols - 1)] test = {} train = {'x_extent': x_train, 'y_extent': y_train} val = {'x_extent': x_val, 'y_extent': y_val} return train, val, test def indices_from_area_defn(area_defn, tile_size): """ Returns the location (upper-left image coordinates) of valid patch positions. :param area_defn: dictionary, defines one or multiple rectangularly-shaped geographic regions from which DSM patches will be sampled. The dictionary is composed of the following key-value pairs: x_extent: list of n tuples, where n denotes the number of rectangular regions (stripes). Each tuple defines the upper-left and lower-right x-coordinate of a rectangular region (stripe). y_extent: list of n tuples, where n denotes the number of rectangular regions (stripes). Each tuple defines the upper-left and lower-right y-coordinate of a rectangular region (stripe). Assumption: The i.th tuple of x_extent and i.th tuple of y_extent define a geographically rectangular region (stripe). :param tile_size: int, tile size in pixels, :return: list of (y,x) tuples, upper-left image coordinates of valid patch positions. Note that the returned patch positions do not exceed the area specified in area_defn. """ # Initialize output list valid_positions = [] # Number of regions specified in area_defn num_regions = len(area_defn['x_extent']) for i in range(num_regions): # Extent of the i.th region x = area_defn['x_extent'][i] y = area_defn['y_extent'][i] # Compute valid x-coordinates of the i.th region x_start = x[0] x_end = x[1] - tile_size + 1 x_indices = np.linspace(x_start, x_end, x_end - x_start + 1, dtype=int) # Compute valid y-coordinates of the i.th region y_start = y[0] y_end = y[1] - tile_size + 1 y_indices = np.linspace(y_start, y_end, y_end - y_start + 1, dtype=int) for y in y_indices: for x in x_indices: valid_positions.append((y, x)) return valid_positions

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6

9b8b7a7c13f469661a4c01c42b1b6b2f8b43e089

74

py

Python

jacdac/water_level/__init__.py

microsoft/jacdac-python

712ad5559e29065f5eccb5dbfe029c039132df5a

[ "MIT" ]

1

2022-02-15T21:30:36.000Z

jacdac/water_level/__init__.py

microsoft/jacdac-python

712ad5559e29065f5eccb5dbfe029c039132df5a

[ "MIT" ]

null

jacdac/water_level/__init__.py

microsoft/jacdac-python

712ad5559e29065f5eccb5dbfe029c039132df5a

[ "MIT" ]

1

2022-02-08T19:32:45.000Z

# Autogenerated file. from .client import WaterLevelClient # type: ignore

24.666667

51

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6

9b8e4b00ef30709a480c5000399e967796cb57d2

47

py

Python

Phone/Pots.py

vidhurraj147/pythonexample

f595034cb6e4c317812f25d8d92501f62c2ccfee

[ "Apache-2.0" ]

null

Phone/Pots.py

vidhurraj147/pythonexample

f595034cb6e4c317812f25d8d92501f62c2ccfee

[ "Apache-2.0" ]

null

Phone/Pots.py

vidhurraj147/pythonexample

f595034cb6e4c317812f25d8d92501f62c2ccfee

[ "Apache-2.0" ]

null

def PotsImpl(): print ("I'm PotsImpl Phone")

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9b98a490f1080fa77f8a9ff10b6b3f0e8f32f99a

14,748

py

Python

python_modules/libraries/dagster-postgres/dagster_postgres_tests/test_run_storage.py

mcoleman-sain/dagster

97ec16d58e68a3a6f3c87010a4906fc34c35befb

[ "Apache-2.0" ]

null

python_modules/libraries/dagster-postgres/dagster_postgres_tests/test_run_storage.py

mcoleman-sain/dagster

97ec16d58e68a3a6f3c87010a4906fc34c35befb

[ "Apache-2.0" ]

null

python_modules/libraries/dagster-postgres/dagster_postgres_tests/test_run_storage.py

mcoleman-sain/dagster

97ec16d58e68a3a6f3c87010a4906fc34c35befb

[ "Apache-2.0" ]

null

import uuid import yaml from dagster.core.definitions.pipeline import ExecutionSelector, PipelineRunsFilter from dagster.core.events import DagsterEvent, DagsterEventType from dagster.core.instance import DagsterInstance from dagster.core.storage.pipeline_run import PipelineRun, PipelineRunStatus def build_run( run_id, pipeline_name, mode='default', tags=None, status=PipelineRunStatus.NOT_STARTED ): return PipelineRun( pipeline_name=pipeline_name, run_id=run_id, environment_dict=None, mode=mode, selector=ExecutionSelector(pipeline_name), step_keys_to_execute=None, tags=tags, status=status, ) def test_add_get_postgres_run_storage(clean_storage): run_storage = clean_storage run_id = str(uuid.uuid4()) run_to_add = build_run(pipeline_name='pipeline_name', run_id=run_id) added = run_storage.add_run(run_to_add) assert added fetched_run = run_storage.get_run_by_id(run_id) assert run_to_add == fetched_run assert run_storage.has_run(run_id) assert not run_storage.has_run(str(uuid.uuid4())) assert run_storage.get_runs() == [run_to_add] assert run_storage.get_runs(PipelineRunsFilter(pipeline_name='pipeline_name')) == [run_to_add] assert run_storage.get_runs(PipelineRunsFilter(pipeline_name='nope')) == [] run_storage.wipe() assert run_storage.get_runs() == [] def test_handle_run_event_pipeline_success_test(clean_storage): run_storage = clean_storage run_id = str(uuid.uuid4()) run_to_add = build_run(pipeline_name='pipeline_name', run_id=run_id) run_storage.add_run(run_to_add) dagster_pipeline_start_event = DagsterEvent( message='a message', event_type_value=DagsterEventType.PIPELINE_START.value, pipeline_name='pipeline_name', step_key=None, solid_handle=None, step_kind_value=None, logging_tags=None, ) run_storage.handle_run_event(run_id, dagster_pipeline_start_event) assert run_storage.get_run_by_id(run_id).status == PipelineRunStatus.STARTED run_storage.handle_run_event( str(uuid.uuid4()), # diff run DagsterEvent( message='a message', event_type_value=DagsterEventType.PIPELINE_SUCCESS.value, pipeline_name='pipeline_name', step_key=None, solid_handle=None, step_kind_value=None, logging_tags=None, ), ) assert run_storage.get_run_by_id(run_id).status == PipelineRunStatus.STARTED run_storage.handle_run_event( run_id, # correct run DagsterEvent( message='a message', event_type_value=DagsterEventType.PIPELINE_SUCCESS.value, pipeline_name='pipeline_name', step_key=None, solid_handle=None, step_kind_value=None, logging_tags=None, ), ) assert run_storage.get_run_by_id(run_id).status == PipelineRunStatus.SUCCESS def test_clear(clean_storage): storage = clean_storage run_id = str(uuid.uuid4()) storage.add_run(build_run(run_id=run_id, pipeline_name='some_pipeline')) assert len(storage.get_runs()) == 1 storage.wipe() assert list(storage.get_runs()) == [] def test_delete(clean_storage): storage = clean_storage run_id = str(uuid.uuid4()) storage.add_run(build_run(run_id=run_id, pipeline_name='some_pipeline')) assert len(storage.get_runs()) == 1 storage.delete_run(run_id) assert list(storage.get_runs()) == [] def test_fetch_by_filter(clean_storage): storage = clean_storage one = str(uuid.uuid4()) two = str(uuid.uuid4()) three = str(uuid.uuid4()) storage.add_run( build_run( run_id=one, pipeline_name='some_pipeline', tags={'tag': 'hello', 'tag2': 'world'}, status=PipelineRunStatus.SUCCESS, ) ) storage.add_run( build_run( run_id=two, pipeline_name='some_pipeline', tags={'tag': 'hello'}, status=PipelineRunStatus.FAILURE, ), ) storage.add_run( build_run(run_id=three, pipeline_name='other_pipeline', status=PipelineRunStatus.SUCCESS) ) assert len(storage.get_runs()) == 3 some_runs = storage.get_runs(PipelineRunsFilter(run_id=one)) count = storage.get_runs_count(PipelineRunsFilter(run_id=one)) assert len(some_runs) == 1 assert count == 1 assert some_runs[0].run_id == one some_runs = storage.get_runs(PipelineRunsFilter(pipeline_name='some_pipeline')) count = storage.get_runs_count(PipelineRunsFilter(pipeline_name='some_pipeline')) assert len(some_runs) == 2 assert count == 2 assert any(x.run_id == one for x in some_runs) assert any(x.run_id == two for x in some_runs) some_runs = storage.get_runs(PipelineRunsFilter(status=PipelineRunStatus.SUCCESS)) count = storage.get_runs_count(PipelineRunsFilter(status=PipelineRunStatus.SUCCESS)) assert len(some_runs) == 2 assert count == 2 assert any(x.run_id == one for x in some_runs) assert any(x.run_id == three for x in some_runs) some_runs = storage.get_runs(PipelineRunsFilter(tags={'tag': 'hello'})) count = storage.get_runs_count(PipelineRunsFilter(tags={'tag': 'hello'})) assert len(some_runs) == 2 assert count == 2 assert any(x.run_id == one for x in some_runs) assert any(x.run_id == two for x in some_runs) some_runs = storage.get_runs(PipelineRunsFilter(tags={'tag': 'hello', 'tag2': 'world'})) count = storage.get_runs_count(PipelineRunsFilter(tags={'tag': 'hello', 'tag2': 'world'})) assert len(some_runs) == 1 assert count == 1 assert some_runs[0].run_id == one some_runs = storage.get_runs( PipelineRunsFilter(pipeline_name="some_pipeline", tags={'tag': 'hello'}) ) count = storage.get_runs_count( PipelineRunsFilter(pipeline_name="some_pipeline", tags={'tag': 'hello'}) ) assert len(some_runs) == 2 assert count == 2 assert any(x.run_id == one for x in some_runs) assert any(x.run_id == two for x in some_runs) some_runs = storage.get_runs( PipelineRunsFilter( pipeline_name="some_pipeline", tags={'tag': 'hello'}, status=PipelineRunStatus.SUCCESS, ) ) count = storage.get_runs_count( PipelineRunsFilter( pipeline_name="some_pipeline", tags={'tag': 'hello'}, status=PipelineRunStatus.SUCCESS, ) ) assert len(some_runs) == 1 assert count == 1 assert some_runs[0].run_id == one # All filters some_runs = storage.get_runs( PipelineRunsFilter( run_id=one, pipeline_name="some_pipeline", tags={'tag': 'hello'}, status=PipelineRunStatus.SUCCESS, ) ) count = storage.get_runs_count( PipelineRunsFilter( run_id=one, pipeline_name="some_pipeline", tags={'tag': 'hello'}, status=PipelineRunStatus.SUCCESS, ) ) assert len(some_runs) == 1 assert count == 1 assert some_runs[0].run_id == one some_runs = storage.get_runs(PipelineRunsFilter()) count = storage.get_runs_count(PipelineRunsFilter()) assert len(some_runs) == 3 assert count == 3 def test_fetch_by_pipeline(clean_storage): storage = clean_storage one = str(uuid.uuid4()) two = str(uuid.uuid4()) storage.add_run(build_run(run_id=one, pipeline_name='some_pipeline')) storage.add_run(build_run(run_id=two, pipeline_name='some_other_pipeline')) assert len(storage.get_runs()) == 2 some_runs = storage.get_runs(PipelineRunsFilter(pipeline_name='some_pipeline')) assert len(some_runs) == 1 assert some_runs[0].run_id == one def test_fetch_count_by_tag(clean_storage): storage = clean_storage one = str(uuid.uuid4()) two = str(uuid.uuid4()) three = str(uuid.uuid4()) storage.add_run( build_run( run_id=one, pipeline_name='some_pipeline', tags={'mytag': 'hello', 'mytag2': 'world'} ) ) storage.add_run( build_run( run_id=two, pipeline_name='some_pipeline', tags={'mytag': 'goodbye', 'mytag2': 'world'} ) ) storage.add_run(build_run(run_id=three, pipeline_name='some_pipeline')) assert len(storage.get_runs()) == 3 run_count = storage.get_runs_count( PipelineRunsFilter(tags={'mytag': 'hello', 'mytag2': 'world'}) ) assert run_count == 1 run_count = storage.get_runs_count(PipelineRunsFilter(tags={'mytag2': 'world'})) assert run_count == 2 run_count = storage.get_runs_count(PipelineRunsFilter()) assert run_count == 3 def test_fetch_by_tags(clean_storage): storage = clean_storage one = str(uuid.uuid4()) two = str(uuid.uuid4()) three = str(uuid.uuid4()) storage.add_run( build_run( run_id=one, pipeline_name='some_pipeline', tags={'mytag': 'hello', 'mytag2': 'world'} ) ) storage.add_run( build_run( run_id=two, pipeline_name='some_pipeline', tags={'mytag': 'goodbye', 'mytag2': 'world'} ) ) storage.add_run(build_run(run_id=three, pipeline_name='some_pipeline')) assert len(storage.get_runs()) == 3 some_runs = storage.get_runs(PipelineRunsFilter(tags={'mytag': 'hello', 'mytag2': 'world'})) assert len(some_runs) == 1 assert some_runs[0].run_id == one some_runs = storage.get_runs(PipelineRunsFilter(tags={'mytag2': 'world'})) assert len(some_runs) == 2 assert any(x.run_id == one for x in some_runs) assert any(x.run_id == two for x in some_runs) some_runs = storage.get_runs(PipelineRunsFilter(tags={})) assert len(some_runs) == 3 def test_slice(clean_storage): storage = clean_storage one, two, three = sorted([str(uuid.uuid4()), str(uuid.uuid4()), str(uuid.uuid4())]) storage.add_run(build_run(run_id=one, pipeline_name='some_pipeline', tags={'mytag': 'hello'})) storage.add_run(build_run(run_id=two, pipeline_name='some_pipeline', tags={'mytag': 'hello'})) storage.add_run(build_run(run_id=three, pipeline_name='some_pipeline', tags={'mytag': 'hello'})) all_runs = storage.get_runs() assert len(all_runs) == 3 sliced_runs = storage.get_runs(cursor=three, limit=1) assert len(sliced_runs) == 1 assert sliced_runs[0].run_id == two all_runs = storage.get_runs(PipelineRunsFilter(pipeline_name='some_pipeline')) assert len(all_runs) == 3 sliced_runs = storage.get_runs( PipelineRunsFilter(pipeline_name='some_pipeline'), cursor=three, limit=1 ) assert len(sliced_runs) == 1 assert sliced_runs[0].run_id == two all_runs = storage.get_runs(PipelineRunsFilter(tags={'mytag': 'hello'})) assert len(all_runs) == 3 sliced_runs = storage.get_runs( PipelineRunsFilter(tags={'mytag': 'hello'}), cursor=three, limit=1 ) assert len(sliced_runs) == 1 assert sliced_runs[0].run_id == two def test_fetch_by_status(clean_storage): storage = clean_storage one = str(uuid.uuid4()) two = str(uuid.uuid4()) three = str(uuid.uuid4()) four = str(uuid.uuid4()) storage.add_run( build_run(run_id=one, pipeline_name='some_pipeline', status=PipelineRunStatus.NOT_STARTED) ) storage.add_run( build_run(run_id=two, pipeline_name='some_pipeline', status=PipelineRunStatus.STARTED) ) storage.add_run( build_run(run_id=three, pipeline_name='some_pipeline', status=PipelineRunStatus.STARTED) ) storage.add_run( build_run(run_id=four, pipeline_name='some_pipeline', status=PipelineRunStatus.FAILURE) ) assert { run.run_id for run in storage.get_runs(PipelineRunsFilter(status=PipelineRunStatus.NOT_STARTED)) } == {one} assert { run.run_id for run in storage.get_runs(PipelineRunsFilter(status=PipelineRunStatus.STARTED)) } == {two, three,} assert { run.run_id for run in storage.get_runs(PipelineRunsFilter(status=PipelineRunStatus.FAILURE)) } == {four} assert { run.run_id for run in storage.get_runs(PipelineRunsFilter(status=PipelineRunStatus.SUCCESS)) } == set() def test_fetch_by_status_cursored(clean_storage): storage = clean_storage one = str(uuid.uuid4()) two = str(uuid.uuid4()) three = str(uuid.uuid4()) four = str(uuid.uuid4()) storage.add_run( build_run(run_id=one, pipeline_name='some_pipeline', status=PipelineRunStatus.STARTED) ) storage.add_run( build_run(run_id=two, pipeline_name='some_pipeline', status=PipelineRunStatus.STARTED) ) storage.add_run( build_run(run_id=three, pipeline_name='some_pipeline', status=PipelineRunStatus.NOT_STARTED) ) storage.add_run( build_run(run_id=four, pipeline_name='some_pipeline', status=PipelineRunStatus.STARTED) ) cursor_four_runs = storage.get_runs( PipelineRunsFilter(status=PipelineRunStatus.STARTED), cursor=four ) assert len(cursor_four_runs) == 2 assert {run.run_id for run in cursor_four_runs} == {one, two} cursor_two_runs = storage.get_runs( PipelineRunsFilter(status=PipelineRunStatus.STARTED), cursor=two ) assert len(cursor_two_runs) == 1 assert {run.run_id for run in cursor_two_runs} == {one} cursor_one_runs = storage.get_runs( PipelineRunsFilter(status=PipelineRunStatus.STARTED), cursor=one ) assert not cursor_one_runs cursor_four_limit_one = storage.get_runs( filters=PipelineRunsFilter(status=PipelineRunStatus.STARTED), cursor=four, limit=1 ) assert len(cursor_four_limit_one) == 1 assert cursor_four_limit_one[0].run_id == two def test_load_from_config(hostname): url_cfg = ''' run_storage: module: dagster_postgres.run_storage class: PostgresRunStorage config: postgres_url: postgresql://test:test@{hostname}:5432/test '''.format( hostname=hostname ) explicit_cfg = ''' run_storage: module: dagster_postgres.run_storage class: PostgresRunStorage config: postgres_db: username: test password: test hostname: {hostname} db_name: test '''.format( hostname=hostname ) # pylint: disable=protected-access from_url = DagsterInstance.local_temp(overrides=yaml.safe_load(url_cfg))._run_storage from_explicit = DagsterInstance.local_temp(overrides=yaml.safe_load(explicit_cfg))._run_storage assert from_url.postgres_url == from_explicit.postgres_url

33.141573

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null

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null

0

6

9b9f516aad2811f21bd9b387290685f03e4c83d5

152

py

Python

src/z3c/objpath/__init__.py

zopefoundation/z3c.objpath

bc4947b105a6851d06d09a205f8fffcb8088507f

[ "ZPL-2.1" ]

1

2021-03-05T17:27:29.000Z

src/z3c/objpath/__init__.py

zopefoundation/z3c.objpath

bc4947b105a6851d06d09a205f8fffcb8088507f

[ "ZPL-2.1" ]

5

2018-03-12T17:28:42.000Z

2021-09-21T06:16:30.000Z

src/z3c/objpath/__init__.py

zopefoundation/z3c.objpath

bc4947b105a6851d06d09a205f8fffcb8088507f

[ "ZPL-2.1" ]

null

from z3c.objpath import path as _path # noqa: F401 from z3c.objpath.path import path # noqa: F401 from z3c.objpath.path import resolve # noqa: F401

30.4

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6

b5cec28b337bf7d0906cf99a63d07f0bf93efd59

49

py

Python

gadrionwrap/__init__.py

gadr1on/pilwrapper

79289ae5f1f74f8f3fc9fa47b9a64bb8eecee3e1

[ "MIT" ]

1

2020-08-05T01:25:16.000Z

gadrionwrap/__init__.py

gadr1on/pilwrapper

79289ae5f1f74f8f3fc9fa47b9a64bb8eecee3e1

[ "MIT" ]

null

gadrionwrap/__init__.py

gadr1on/pilwrapper

79289ae5f1f74f8f3fc9fa47b9a64bb8eecee3e1

[ "MIT" ]

null

from gadrionwrap.wrapper import findBestFontSize

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6

1fba5ce5b2b1121a7546dec53aecb152a759712a

11,676

py

Python

gem/tests/test_auth.py

praekelt/molo-gem

ab4f9a16719f6cfc1981ef448f1d42c457e1dad7

[ "BSD-2-Clause" ]

3

2017-08-03T22:37:13.000Z

2018-06-14T15:36:01.000Z

gem/tests/test_auth.py

praekelt/molo-gem

ab4f9a16719f6cfc1981ef448f1d42c457e1dad7

[ "BSD-2-Clause" ]

635

2016-01-12T07:23:46.000Z

2018-11-16T07:43:13.000Z

gem/tests/test_auth.py

praekelt/molo-gem

ab4f9a16719f6cfc1981ef448f1d42c457e1dad7

[ "BSD-2-Clause" ]

6

2017-05-11T09:50:34.000Z

2018-11-16T10:42:56.000Z

from django.core import mail from django.urls import reverse from django.conf import settings from django.conf.urls import url, include from django.contrib.auth import get_user_model from django.test.utils import override_settings from django.test import TestCase, Client, RequestFactory from django.contrib.auth.models import Group, Permission from allauth.socialaccount.models import SocialLogin, SocialAccount from wagtail.core import urls as wagtail_urls from wagtail.admin import urls as wagtailadmin_urls from gem.models import Invite from gem.tests.base import GemTestCaseMixin from gem.adapter import StaffUserSocialAdapter, StaffUserAdapter urlpatterns = [ url(r'^admin/', include(wagtailadmin_urls)), url(r'', include(wagtail_urls)), ] class TestAllAuth(GemTestCaseMixin, TestCase): def setUp(self): self.user = get_user_model().objects.create_superuser( username='superuser', email='superuser@email.com', password='pass') self.main = self.mk_main( title='main1', slug='main1', path='00010002', url_path='/main1/' ) self.site = self.main.get_site() self.user.profile.admin_sites.add(self.site) self.client = Client(SERVER_NAME=self.site.hostname) self.factory = RequestFactory() @override_settings(ENABLE_ALL_AUTH=True) def test_admin_login_view(self): res = self.client.get(reverse('wagtailadmin_login')) self.assertEqual(res.status_code, 200) # toDo: find a better way to handle conditional urls patterns, # because django only loads them once on server instantiation # self.assertTemplateUsed(res, 'wagtailadmin/social_login.html') @override_settings(ENABLE_ALL_AUTH=True) def test_admin_views_authed_user(self): self.client.force_login(self.user) res = self.client.get(reverse('wagtailadmin_login')) self.assertEqual(res.status_code, 302) self.assertEqual(settings.ENABLE_ALL_AUTH, True) self.assertEqual(res.url, '/admin/') res = self.client.get(res.url) self.assertEqual(res.status_code, 200) @override_settings(ENABLE_ALL_AUTH=True) def test_invite_create_view(self): req = self.factory.get("/") req.user = self.user req._wagtail_site = self.main.get_site() self.client.force_login(self.user) url = '/admin/gem/invite/create/' data = { 'email': 'testinvite@test.com' } res = self.client.post(url, data=data, request=req) subject = '{}: Admin site invitation'.format(self.site) self.assertEqual(res.status_code, 302) self.assertEqual(len(mail.outbox), 1) self.assertEqual(mail.outbox[0].subject, subject) self.assertEqual(mail.outbox[0].to, [data['email']]) self.assertEqual(mail.outbox[0].from_email, 'no-reply@gehosting.org') self.assertTrue( Invite.objects.filter(user=self.user).exists()) @override_settings(ENABLE_ALL_AUTH=True) def test_invite_edit_view(self): data = { 'email': 'testinvite@test.com' } req = self.factory.get("/") req.user = self.user req._wagtail_site = self.main.get_site() invite = Invite.objects.create( email=data['email'], user=self.user, site=self.site) self.client.force_login(self.user) url = '/admin/gem/invite/edit/{}/'.format(invite.pk) res = self.client.post(url, request=req) self.assertEqual(res.status_code, 200) self.assertContains(res, data['email']) res = self.client.post(url, data=data, request=req) self.assertEqual(res.status_code, 302) # Note: email sent on creation of invite object by signal # testing that a duplicate email was not sent on update self.assertEqual(len(mail.outbox), 1) def test_staff_social_adaptor(self): """ Test a front-end user getting an invite to admin site """ request = self.factory.get("/") request._wagtail_site = self.main.get_site() adaptor = StaffUserSocialAdapter(request=request) user = get_user_model().objects.create_user( username='testuser', email='testuser@email.com', password='pass' ) sociallogin = SocialLogin(user=user) group = Group.objects.filter().first() perm = Permission.objects.filter().first() self.assertFalse(adaptor.is_open_for_signup(request, sociallogin)) invite = Invite.objects.create( email=user.email, user=self.user, site=self.site) invite.groups.add(group) invite.permissions.add(perm) self.assertFalse(user.groups.all().exists()) self.assertFalse(user.user_permissions.all().exists()) adaptor.add_perms(user) invite.refresh_from_db() self.assertTrue(invite.is_accepted) self.assertTrue(user.groups.all().exists()) self.assertTrue(user.user_permissions.all().exists()) user.delete() invite.delete() def test_staff_social_adaptor_new_user(self): """ Test a new user getting an invite to admin site """ request = self.factory.get("/") request._wagtail_site = self.main.get_site() adaptor = StaffUserSocialAdapter(request=request) user = get_user_model()( username='testuser', email='testuser@email.com', password='pass' ) sociallogin = SocialLogin(user=user) group = Group.objects.filter().first() perm = Permission.objects.filter().first() self.assertFalse(user.pk) self.assertFalse(adaptor.is_open_for_signup(request, sociallogin)) invite = Invite.objects.create( email=user.email, user=self.user, site=self.site) invite.groups.add(group) invite.permissions.add(perm) adaptor.add_perms(user) invite.refresh_from_db() self.assertTrue(invite.is_accepted) self.assertTrue(user.groups.all().exists()) self.assertTrue(user.user_permissions.all().exists()) user.delete() invite.delete() def test_staff_social_adaptor_staff(self): """ Test a regular staff login """ request = self.factory.get("/") request._wagtail_site = self.main.get_site() adaptor = StaffUserSocialAdapter(request=request) user = get_user_model().objects.create_user( username='testuser', email='testuser@email.com', password='pass', is_staff=True, ) sociallogin = SocialLogin(user=user) group = Group.objects.filter().first() perm = Permission.objects.filter().first() user.groups.add(group) user.user_permissions.add(perm) self.assertFalse(adaptor.is_open_for_signup(request, sociallogin)) adaptor.add_perms(user) self.assertTrue(user.groups.all().exists()) self.assertTrue(user.user_permissions.all().exists()) user.delete() def test_staff_social_adaptor_superuser(self): """ Test a superuser login """ request = self.factory.get("/") request._wagtail_site = self.main.get_site() adaptor = StaffUserSocialAdapter(request=request) user = get_user_model().objects.create_user( username='testuser', email='testuser@email.com', is_superuser=True, password='pass' ) sociallogin = SocialLogin(user=user) self.assertFalse(adaptor.is_open_for_signup(request, sociallogin)) self.assertFalse(user.groups.all().exists()) self.assertFalse(user.user_permissions.all().exists()) adaptor.add_perms(user) self.assertFalse(user.groups.all().exists()) self.assertFalse(user.user_permissions.all().exists()) user.delete() def test_staff_user_adapter_new_user(self): adaptor = StaffUserAdapter() user = get_user_model()( username='testuser', email='testuser@email.com', password='pass' ) request = RequestFactory().post( data={ 'username': user.username, 'password': user.password }, path=reverse('wagtailadmin_login')) request._wagtail_site = self.main.get_site() self.assertFalse(adaptor.is_open_for_signup(request, None)) def test_staff_user_adapter_front_end_user(self): adaptor = StaffUserAdapter() user = get_user_model().objects.create( username='testuser', email='testuser@email.com', password='pass' ) request = RequestFactory().post( data={ 'username': user.username, 'password': user.password }, path=reverse('wagtailadmin_login')) request._wagtail_site = self.main.get_site() self.assertFalse(adaptor.is_open_for_signup(request, None)) def test_staff_user_adapter_staff_user(self): adaptor = StaffUserAdapter() user = get_user_model().objects.create( username='testuser', email='testuser@email.com', is_staff=True, password='pass' ) request = RequestFactory().post( data={ 'username': user.username, 'password': user.password }, path=reverse('wagtailadmin_login')) request._wagtail_site = self.main.get_site() self.assertFalse(adaptor.is_open_for_signup(request, None)) def test_staff_user_adapter_staff_user_perms(self): adaptor = StaffUserAdapter() group = Group.objects.filter().first() perm = Permission.objects.filter().first() user = get_user_model().objects.create( username='testuser', email='testuser@email.com', is_staff=True, password='pass' ) user.groups.add(group) user.user_permissions.add(perm) request = RequestFactory().post( data={ 'username': user.username, 'password': user.password }, path=reverse('wagtailadmin_login')) request._wagtail_site = self.main.get_site() self.assertFalse(adaptor.is_open_for_signup(request, None)) def test_user_delete(self): user = get_user_model().objects.create( username='testuser', email='testuser@email.com', is_staff=True, password='pass' ) SocialAccount.objects.create( user=user, provider='google', uid='1') user.delete() self.assertFalse( SocialAccount.objects.filter(user=user) ) class TestAllAuthDisabled(GemTestCaseMixin, TestCase): def setUp(self): self.user = get_user_model().objects.create_superuser( username='superuser', email='superuser@email.com', password='pass') self.main = self.mk_main( title='main1', slug='main1', path='00010002', url_path='/main1/' ) self.site = self.main.get_site() self.user.profile.admin_sites.add(self.site) self.factory = RequestFactory() @override_settings(ENABLE_ALL_AUTH=False) def test_login_all_auth_disabled(self): res = self.client.get(reverse('wagtailadmin_login')) self.assertEqual(res.status_code, 200) self.assertNotContains(res, '<span class="fa fa-google"></span>Google')

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0.63001

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11,676

5.452672

0.130534

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0.02016

0.0252

0.776004

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0.717206

0.702086

0.678286

0.615008

0

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0.25197

11,676

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0.812228

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0

0.003077

0.157692

1

0.061538

false

0.057692

0.053846

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null

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1

0

1

0

1

0

null

0

1

0

6

1ff3c80df93b2a7473307a99cc39ead522d1273f

43

py

Python

src/version_1/crystals/__init__.py

eragasa/pypospack

21cdecaf3b05c87acc532d992be2c04d85bfbc22

[ "MIT" ]

4

2018-01-18T19:59:56.000Z

2020-08-25T11:56:52.000Z

src/version_1/crystals/__init__.py

eragasa/pypospack

21cdecaf3b05c87acc532d992be2c04d85bfbc22

[ "MIT" ]

1

2018-04-22T23:02:13.000Z

src/version_1/crystals/__init__.py

eragasa/pypospack

21cdecaf3b05c87acc532d992be2c04d85bfbc22

[ "MIT" ]

1

2019-09-14T07:04:42.000Z

from pypospack2.crystals.atom import atom

14.333333

41

0.837209

6

43

6

0.833333

0

0.026316

0.116279

43

2

42

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true

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null

0

1

0

1

0

1

0

6

1ffa2421b413dc766aa8250a36c8fe6d21aa5eab

36

py

Python

keycloak_admin_aio/_resources/client_scopes/by_id/__init__.py

V-Mann-Nick/keycloak-admin-aio

83ac1af910e492a5864eb369aacfc0512e5c8c45

[ "Apache-2.0" ]

12

2021-11-08T18:03:09.000Z

2022-03-17T16:34:06.000Z

keycloak_admin_aio/_resources/client_scopes/by_id/__init__.py

V-Mann-Nick/keycloak-admin-aio

83ac1af910e492a5864eb369aacfc0512e5c8c45

[ "Apache-2.0" ]

null

keycloak_admin_aio/_resources/client_scopes/by_id/__init__.py

V-Mann-Nick/keycloak-admin-aio

83ac1af910e492a5864eb369aacfc0512e5c8c45

[ "Apache-2.0" ]

1

2021-11-14T13:55:30.000Z

from .by_id import ClientScopesById

18

35

0.861111

5

36

6

1

0

0.111111

36

1

36

0.9375

0

1

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true

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1

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null

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0

null

0

1

0

1

0

1

0

6

95306c3d8e61f43b252c8d0e2cb991eaaad6f24c

48

py

Python

scheduler/__init__.py

nielsrolf/scheduler

d7e03529cffd25c3db975f618a83861e25aff4a6

[ "Apache-2.0" ]

null

scheduler/__init__.py

nielsrolf/scheduler

d7e03529cffd25c3db975f618a83861e25aff4a6

[ "Apache-2.0" ]

1

2020-04-17T12:53:54.000Z

scheduler/__init__.py

nielsrolf/scheduler

d7e03529cffd25c3db975f618a83861e25aff4a6

[ "Apache-2.0" ]

null

from scheduler.schedule import Schedule # noqa

24

47

0.8125

6

48

6.5

0.833333

0

0.145833

48

1

48

0.95122

0.083333

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

1f226911a9a50dfb3e02f4bb0f2f980fbc4dd049

120

py

Python

makefile/practice/code_gen.py

jaebaek/Linker101

c6fbbbde0e280fd0b7d0c3247ad499f8c329cb29

[ "MIT" ]

5

2018-01-24T13:01:22.000Z

2020-11-19T18:29:10.000Z

makefile/practice/code_gen.py

jaebaek/Linker101

c6fbbbde0e280fd0b7d0c3247ad499f8c329cb29

[ "MIT" ]

null

makefile/practice/code_gen.py

jaebaek/Linker101

c6fbbbde0e280fd0b7d0c3247ad499f8c329cb29

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- import sys print "print \"I am code generated by " + sys.argv[0] + "\""

17.142857

60

0.583333

19

120

3.684211

0.894737

0

0.020619

0.191667

120

6

61

20

0.701031

0.35

0

1

0

0.315789

0

null

0

0.5

null

0.5

1

0

null

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1

0

1

0

null

0

1

0

1

0

1

0

6

1f4539986db245199b0716420813c0d9633bc2d0

182

py

Python

thai_parser.py

trangtops/typomorise

afce9d922d8f3b18e170c8d9ce1f887d7898a8cf

[ "MIT" ]

null

thai_parser.py

trangtops/typomorise

afce9d922d8f3b18e170c8d9ce1f887d7898a8cf

[ "MIT" ]

null

thai_parser.py

trangtops/typomorise

afce9d922d8f3b18e170c8d9ce1f887d7898a8cf

[ "MIT" ]

null

import json with open("eng_to_thai.json", "r") as f: eng_to_thai = json.load(f) def to_thai(eng_char): # global eng_to_thai return eng_to_thai.get(eng_char, eng_char)

18.2

46

0.703297

35

182

3.314286

0.457143

0.258621

0.310345

0.224138

0

0.181319

182

9

47

20.222222

0.778523

0.098901

0

0.104938

0

1

0.2

false

0

0.2

0.6

0

null

1

0

1

0

null

0

1

0

6

1f7e081d9fcb9c31452705f0361ff9d67c63f07c

73

py

Python

lang/py/cookbook/v2/source/cb2_4_6_exm_1.py

ch1huizong/learning

632267634a9fd84a5f5116de09ff1e2681a6cc85

[ "MIT" ]

null

lang/py/cookbook/v2/source/cb2_4_6_exm_1.py

ch1huizong/learning

632267634a9fd84a5f5116de09ff1e2681a6cc85

[ "MIT" ]

null

lang/py/cookbook/v2/source/cb2_4_6_exm_1.py

ch1huizong/learning

632267634a9fd84a5f5116de09ff1e2681a6cc85

[ "MIT" ]

null

for x in flatten([1, 2, [3, [], 4, [5, 6], 7, [8,], ], 9]): print x,

24.333333

59

0.383562

15

73

1.866667

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0

0.169811

0.273973

73

2

60

36.5

0.358491

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null

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null

0.5

1

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0

1

0

null

0

1

0

1

0

6

2f1393cb1520c83c7db9b9dbbc2f0f30470f06fd

35

py

Python

relativity/general/__init__.py

tdsymonds/relativity

89314f4a8b7003ae8ee3718ff5fc518c5bdb2973

[ "MIT" ]

null

relativity/general/__init__.py

tdsymonds/relativity

89314f4a8b7003ae8ee3718ff5fc518c5bdb2973

[ "MIT" ]

null

relativity/general/__init__.py

tdsymonds/relativity

89314f4a8b7003ae8ee3718ff5fc518c5bdb2973

[ "MIT" ]

null

from .general_relativity import *

17.5

34

0.8

4

35

6.75

1

0

0.142857

35

1

35

0.9

0

1

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true

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null

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null

0

1

0

1

0

1

0

6

2f7683295c5634d692d2a6cce955e1809ab9fc89

12,296

py

Python

tests/endtoend/test_ip_restrictions.py

satroutr/poppy

27417f86854d9e0a04726acc263ef0a2ce9f8f6e

[ "Apache-2.0" ]

3

2017-07-05T20:09:59.000Z

2018-11-27T22:02:57.000Z

tests/endtoend/test_ip_restrictions.py

satroutr/poppy

27417f86854d9e0a04726acc263ef0a2ce9f8f6e

[ "Apache-2.0" ]

24

2017-04-18T15:14:04.000Z

2019-03-20T19:09:07.000Z

tests/endtoend/test_ip_restrictions.py

satroutr/poppy

27417f86854d9e0a04726acc263ef0a2ce9f8f6e

[ "Apache-2.0" ]

8

2017-04-03T13:24:27.000Z

2021-11-08T20:28:10.000Z

# Copyright (c) 2015 Rackspace, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or # implied. # See the License for the specific language governing permissions and # limitations under the License. import subprocess import requests from tests.endtoend import base from tests.endtoend.utils import config class TestIpRestrictions(base.TestBase): @classmethod def setUpClass(cls): super(TestIpRestrictions, cls).setUpClass() cls.test_config = config.TestConfig() cls.check_preconditions() @classmethod def check_preconditions(cls): """Ensure our environment meets our needs to ensure a valid test.""" origin = cls.http_client.get("http://" + cls.default_origin) assert origin.status_code == 200 def setUp(self): super(TestIpRestrictions, self).setUp() self.service_name = base.random_string('E2E-IP-Restriction') self.cname_rec = [] self.service_location = '' def get_ipv4_address(self): return requests.get('https://api.ipify.org').text def get_ipv6_address(self): ifconfig_eth0 = subprocess.Popen( ['ifconfig', 'eth0'], stdout=subprocess.PIPE) ifconfig_eth0_global_scope = subprocess.Popen( ['grep', 'Scope:Global'], stdin=ifconfig_eth0.stdout, stdout=subprocess.PIPE) ifconfig_eth0_global_scope = ifconfig_eth0_global_scope.stdout.read() if ifconfig_eth0_global_scope == '': # assign an ipv6 address ipv6 = 'FE80:0000:0000:0000:0202:B3FF:FE1E:8329' else: ipv6_substring = ifconfig_eth0_global_scope.split( 'inet6 addr: ')[1] ipv6 = ipv6_substring.split('/64 Scope:Global\n')[0] return ipv6 def test_ip_blacklist(self): test_domain = "{0}.{1}".format( base.random_string('test-blacklist-ip'), self.dns_config.test_domain) domains = [{'domain': test_domain}] origins = [{ "origin": self.default_origin, "port": 80, "ssl": False, "rules": [{ "name": "default", "request_url": "/*", }], }] caching = [ {"name": "default", "ttl": 3600, "rules": [{"name": "default", "request_url": "/*"}]}] test_system_ipv4 = self.get_ipv4_address() test_system_ipv6 = self.get_ipv6_address() restrictions = [ {"name": "test_ip_blacklist", "access": "blacklist", "rules": [ {"name": "blacklist", "client_ip": test_system_ipv4, "request_url": "/*"}, {"name": "blacklist", "client_ip": test_system_ipv6, "request_url": "/*"}]}] resp = self.setup_service( service_name=self.service_name, domain_list=domains, origin_list=origins, caching_list=caching, restrictions_list=restrictions, flavor_id=self.poppy_config.flavor) self.service_location = resp.headers['location'] resp = self.poppy_client.get_service(location=self.service_location) links = resp.json()['links'] access_url = [link['href'] for link in links if link['rel'] == 'access_url'] rec = self.setup_cname(test_domain, access_url[0]) if rec: self.cname_rec.append(rec[0]) # Verify blacklisted IP cannot fetch cdn content cdn_url = 'http://' + test_domain resp = self.http_client.get(url=cdn_url) self.assertEqual(resp.status_code, 403) self.assertIn('Access Denied', resp.content) # Verify wpt can fetch cdn content wpt_result = self.run_webpagetest(url=cdn_url) test_region = wpt_result.keys()[0] wpt_response_text = \ wpt_result[ test_region]['data']['runs']['1']['firstView']['requests'][ 0]['headers']['response'][0] self.assertIn( 'HTTP/1.1 200', wpt_response_text) def test_ip_cidr_blacklist(self): test_domain = "{0}.{1}".format( base.random_string('test-blacklist-ip'), self.dns_config.test_domain) domains = [{'domain': test_domain}] origins = [{ "origin": self.default_origin, "port": 80, "ssl": False, "rules": [{ "name": "default", "request_url": "/*", }], }] caching = [ {"name": "default", "ttl": 3600, "rules": [{"name": "default", "request_url": "/*"}]}] test_system_ipv4_cidr = self.get_ipv4_address() + '/25' test_system_ipv6_cidr = self.get_ipv6_address() + '/100' restrictions = [ {"name": "test_ip_blacklist", "access": "blacklist", "rules": [ {"name": "blacklist", "client_ip": test_system_ipv4_cidr, "request_url": "/*"}, {"name": "blacklist", "client_ip": test_system_ipv6_cidr, "request_url": "/*"}]}] resp = self.setup_service( service_name=self.service_name, domain_list=domains, origin_list=origins, caching_list=caching, restrictions_list=restrictions, flavor_id=self.poppy_config.flavor) self.service_location = resp.headers['location'] resp = self.poppy_client.get_service(location=self.service_location) links = resp.json()['links'] access_url = [link['href'] for link in links if link['rel'] == 'access_url'] rec = self.setup_cname(test_domain, access_url[0]) if rec: self.cname_rec.append(rec[0]) # Verify blacklisted IP range cannot fetch cdn content cdn_url = 'http://' + test_domain resp = self.http_client.get(url=cdn_url) self.assertEqual(resp.status_code, 403) self.assertIn('Access Denied', resp.content) # Verify wpt can fetch cdn content # wpt accesses from a different country, which will not fall within # the blacklisted IP CIDR wpt_result = self.run_webpagetest(url=cdn_url) test_region = wpt_result.keys()[0] wpt_response_text = \ wpt_result[ test_region]['data']['runs']['1']['firstView']['requests'][ 0]['headers']['response'][0] self.assertIn( 'HTTP/1.1 200', wpt_response_text) def test_ip_whitelist(self): test_domain = "{0}.{1}".format( base.random_string('test-whitelist-ip'), self.dns_config.test_domain) domains = [{'domain': test_domain}] origins = [{ "origin": self.default_origin, "port": 80, "ssl": False, "rules": [{ "name": "default", "request_url": "/*", }], }] caching = [ {"name": "default", "ttl": 3600, "rules": [{"name": "default", "request_url": "/*"}]}] test_system_ipv4 = self.get_ipv4_address() test_system_ipv6 = self.get_ipv6_address() restrictions = [ {"name": "test_ip_whitelist", "access": "whitelist", "rules": [ {"name": "whitelist", "client_ip": test_system_ipv4, "request_url": "/*"}, {"name": "whitelist", "client_ip": test_system_ipv6, "request_url": "/*"}]}] resp = self.setup_service( service_name=self.service_name, domain_list=domains, origin_list=origins, caching_list=caching, restrictions_list=restrictions, flavor_id=self.poppy_config.flavor) self.service_location = resp.headers['location'] resp = self.poppy_client.get_service(location=self.service_location) links = resp.json()['links'] access_url = [link['href'] for link in links if link['rel'] == 'access_url'] rec = self.setup_cname(test_domain, access_url[0]) if rec: self.cname_rec.append(rec[0]) # Verify whitelisted IP can fetch cdn content cdn_url = 'http://' + test_domain resp = self.http_client.get(url=cdn_url) self.assertEqual(resp.status_code, 200) self.assertIn('Test Flask Site', resp.content) # Verify wpt cannot fetch cdn content wpt_result = self.run_webpagetest(url=cdn_url) test_region = wpt_result.keys()[0] wpt_response_text = \ wpt_result[ test_region]['data']['runs']['1']['firstView']['requests'][ 0]['headers']['response'][0] self.assertIn( 'HTTP/1.1 403 Forbidden', wpt_response_text) def test_ip_cidr_whitelist(self): test_domain = "{0}.{1}".format( base.random_string('test-whitelist-ip'), self.dns_config.test_domain) domains = [{'domain': test_domain}] origins = [{ "origin": self.default_origin, "port": 80, "ssl": False, "rules": [{ "name": "default", "request_url": "/*", }], }] caching = [ {"name": "default", "ttl": 3600, "rules": [{"name": "default", "request_url": "/*"}]}] test_system_ipv4_cidr = self.get_ipv4_address() + '/15' test_system_ipv6_cidr = self.get_ipv6_address() + '/42' restrictions = [ {"name": "test_ip_whitelist", "access": "whitelist", "rules": [ {"name": "whitelist", "client_ip": test_system_ipv4_cidr, "request_url": "/*"}, {"name": "whitelist", "client_ip": test_system_ipv6_cidr, "request_url": "/*"}]}] resp = self.setup_service( service_name=self.service_name, domain_list=domains, origin_list=origins, caching_list=caching, restrictions_list=restrictions, flavor_id=self.poppy_config.flavor) self.service_location = resp.headers['location'] resp = self.poppy_client.get_service(location=self.service_location) links = resp.json()['links'] access_url = [link['href'] for link in links if link['rel'] == 'access_url'] rec = self.setup_cname(test_domain, access_url[0]) if rec: self.cname_rec.append(rec[0]) # Verify whitelisted IP range can fetch cdn content cdn_url = 'http://' + test_domain resp = self.http_client.get(url=cdn_url) self.assertEqual(resp.status_code, 200) self.assertIn('Test Flask Site', resp.content) # Verify wpt cannot fetch cdn content. # wpt accesses from a different country, which will not fall within # the whitelisted IP CIDR. wpt_result = self.run_webpagetest(url=cdn_url) test_region = wpt_result.keys()[0] wpt_response_text = \ wpt_result[ test_region]['data']['runs']['1']['firstView']['requests'][ 0]['headers']['response'][0] self.assertIn( 'HTTP/1.1 403 Forbidden', wpt_response_text) def tearDown(self): self.poppy_client.delete_service(location=self.service_location) for record in self.cname_rec: self.dns_client.delete_record(record) super(TestIpRestrictions, self).tearDown()

36.164706

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0.555872

1,321

12,296

4.946253

0.165027

0.030609

0.029079

0.02816

0.768289

0.763085

0.747781

0.736762

0.721457

0

0.021196

0.317014

12,296

339

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36.271386

0.756847

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0

0.825279

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0

0.048327

1

0.037175

false

0

0.01487

0.003717

0.063197

0

null

0

1

0

1

0

null

0

6

c801ae076ec837db4add91d6432d00c543887cba

204

py

Python

tests/test_util.py

hsharrison/pyphase

adb3de4cb540553851c06b5d137a3a9c18cdf240

[ "MIT" ]

1

2020-03-22T10:58:47.000Z

tests/test_util.py

hsharrison/pyphase

adb3de4cb540553851c06b5d137a3a9c18cdf240

[ "MIT" ]

null

tests/test_util.py

hsharrison/pyphase

adb3de4cb540553851c06b5d137a3a9c18cdf240

[ "MIT" ]

null

import numpy as np from pyphase import util def test_wrap(): assert np.all(np.isclose(util.wrap([0, 2 * np.pi, -2 * np.pi, np.pi, -np.pi]), [0, 0, 0, -np.pi, -np.pi]))

22.666667

82

0.519608

36

204

2.916667

0.444444

0.228571

0.171429

0.228571

0

0.041958

0.29902

204

8

83

25.5

0.692308

0

0.2

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0.2

true

0

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0

null

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1

0

null

0

1

0

1

0

1

0

6

c8353babb1759e0e6f7452203f444985464550b7

2,638

py

Python

lightyear/functions/colors.py

divmain/lightyear

f328a2c5113edaab15565d0372e610aa8636ab90

[ "MIT" ]

1

2015-08-25T12:31:13.000Z

lightyear/functions/colors.py

divmain/lightyear

f328a2c5113edaab15565d0372e610aa8636ab90

[ "MIT" ]

null

lightyear/functions/colors.py

divmain/lightyear

f328a2c5113edaab15565d0372e610aa8636ab90

[ "MIT" ]

null

from decimal import Decimal from colorsys import hls_to_rgb, rgb_to_hls from . import bifunc from ..ly_types import Color, Distance @bifunc def darken(env, color, amount): if not isinstance(color, Color): raise ValueError('Cannot darken non-color:', str(color)) if isinstance(amount, Decimal): new_r = int(color._r - amount) new_g = int(color._g - amount) new_b = int(color._b - amount) color._r, color._g, color._b = (color if color >= 0 else 0 for color in (new_r, new_g, new_b)) return color elif isinstance(amount, Color): new_r = int(color._r - amount._r) new_g = int(color._g - amount._g) new_b = int(color._b - amount._b) color._r, color._g, color._b = (color if color >= 0 else 0 for color in (new_r, new_g, new_b)) return color elif isinstance(amount, Distance) and amount.unit == '%': r, g, b = float(color._r/255), float(color._g/255), float(color._b/255) h, l, s = rgb_to_hls(r, g, b) new_l = l * (100 - int(amount.value)) / 100 color._r, color._g, color._b = ( Decimal(255*c) if c >= 0 else Decimal(0) for c in hls_to_rgb(h, new_l, s)) return color raise ValueError('Cannot darken by value:', str(amount)) @bifunc def lighten(env, color, amount): if not isinstance(color, Color): raise ValueError('Cannot lighten non-color:', str(color)) if isinstance(amount, Decimal): new_r = int(color._r + amount) new_g = int(color._g + amount) new_b = int(color._b + amount) color._r, color._g, color._b = (color if color <= 255 else 255 for color in (new_r, new_g, new_b)) return color elif isinstance(amount, Color): new_r = int(color._r + amount._r) new_g = int(color._g + amount._g) new_b = int(color._b + amount._b) color._r, color._g, color._b = (color if color <= 255 else 255 for color in (new_r, new_g, new_b)) return color elif isinstance(amount, Distance) and amount.unit == '%': r, g, b = float(color._r/255), float(color._g/255), float(color._b/255) h, l, s = rgb_to_hls(r, g, b) new_l = l * (100 + int(amount.value)) / 100 color._r, color._g, color._b = ( Decimal(255*c) if c <= 255 else Decimal(255) for c in hls_to_rgb(h, new_l, s)) return color raise ValueError('Cannot lighten by value:', str(amount))

36.638889

79

0.566338

394

2,638

3.581218

0.119289

0.068037

0.046775

0.051028

0.860383

0.841956

0

0.033058

0.311979

2,638

71

80

37.15493

0.744353

0

0.551724

0

0.037149

0

1

0.034483

false

0

0.068966

0

0.206897

0

null

0

1

0

null

0

6

c098872c8ed4af54134960570fac093ba176d402

39

py

Python

ccimport/__init__.py

FindDefinition/ccimport

2be66fe4cdeb4daa915d2dfc75f2363c0c0bfb75

[ "MIT" ]

1

2021-11-23T08:36:48.000Z

ccimport/__init__.py

FindDefinition/ccimport

2be66fe4cdeb4daa915d2dfc75f2363c0c0bfb75

[ "MIT" ]

null

ccimport/__init__.py

FindDefinition/ccimport

2be66fe4cdeb4daa915d2dfc75f2363c0c0bfb75

[ "MIT" ]

1

2021-11-23T08:26:52.000Z

from .core import autoimport, ccimport

19.5

38

0.820513

5

39

6.4

1

0

0.128205

39

1

39

0.941176

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

c0998b67342a6eb6ad4cc9f221d2e0482870820b

1,259

py

Python

zoffimzoo/cards.py

normanjaeckel/ZoffImZoo

14f723f045090616d06c785b6199b0e1a9e25906

[ "MIT" ]

null

zoffimzoo/cards.py

normanjaeckel/ZoffImZoo

14f723f045090616d06c785b6199b0e1a9e25906

[ "MIT" ]

4

2021-03-19T01:58:02.000Z

2021-09-22T18:53:40.000Z

zoffimzoo/cards.py

normanjaeckel/ZoffImZoo

14f723f045090616d06c785b6199b0e1a9e25906

[ "MIT" ]

null

class Card: def __init__(self, name): self.name = name ALL_CARDS = [ Card("Elefant"), Card("Elefant"), Card("Elefant"), Card("Elefant"), Card("Elefant"), Card("Mücke"), Card("Mücke"), Card("Mücke"), Card("Mücke"), Card("Sardinen"), Card("Sardinen"), Card("Sardinen"), Card("Sardinen"), Card("Sardinen"), Card("Chamäleon"), Card("Barsch"), Card("Barsch"), Card("Barsch"), Card("Barsch"), Card("Barsch"), Card("Robbe"), Card("Robbe"), Card("Robbe"), Card("Robbe"), Card("Robbe"), Card("Eisbär"), Card("Eisbär"), Card("Eisbär"), Card("Eisbär"), Card("Eisbär"), Card("Krokodil"), Card("Krokodil"), Card("Krokodil"), Card("Krokodil"), Card("Krokodil"), Card("Orka"), Card("Orka"), Card("Orka"), Card("Orka"), Card("Orka"), Card("Löwe"), Card("Löwe"), Card("Löwe"), Card("Löwe"), Card("Löwe"), Card("Igel"), Card("Igel"), Card("Igel"), Card("Igel"), Card("Igel"), Card("Fuchs"), Card("Fuchs"), Card("Fuchs"), Card("Fuchs"), Card("Fuchs"), Card("Maus"), Card("Maus"), Card("Maus"), Card("Maus"), Card("Maus"), ]

18.514706

29

0.494837

131

1,259

4.717557

0.160305

0.088997

0.121359

0.142395

0.914239

0.86246

0.804207

0

0.258936

1,259

67

30

18.791045

0.662379

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0.907692

0

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0

1

0.015385

false

0

0.030769

0

null

0

1

0

1

0

null

0

6

23c0e87ea92f6aa2d7d9eb381b20f3101b8b4d66

212

py

Python

licenses/management/commands/check_for_translation_updates.py

snehal199/cc-licenses

d64c7293eb7be15ff3cd74cc5ff1536eb16794de

[ "MIT" ]

null

licenses/management/commands/check_for_translation_updates.py

snehal199/cc-licenses

d64c7293eb7be15ff3cd74cc5ff1536eb16794de

[ "MIT" ]

null

licenses/management/commands/check_for_translation_updates.py

snehal199/cc-licenses

d64c7293eb7be15ff3cd74cc5ff1536eb16794de

[ "MIT" ]

null

from django.core.management import BaseCommand from licenses.transifex import check_for_translation_updates class Command(BaseCommand): def handle(self, **options): check_for_translation_updates()

23.555556

60

0.79717

25

212

6.52

0.72

0.09816

0.233129

0.319018

0

0.136792

212

8

61

26.5

0.89071

0

1

0.2

false

0

0.4

0

0.8

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

23d2c33fb6e9b653ef444924b88ee528c63df1a0

121

py

Python

fancy/descriptor/__init__.py

susautw/fancy_descriptors

884331742a1c69671c71180db6a9a6532e0024cb

[ "MIT" ]

null

fancy/descriptor/__init__.py

susautw/fancy_descriptors

884331742a1c69671c71180db6a9a6532e0024cb

[ "MIT" ]

null

fancy/descriptor/__init__.py

susautw/fancy_descriptors

884331742a1c69671c71180db6a9a6532e0024cb

[ "MIT" ]

1

2021-04-09T13:34:47.000Z

from .method_descriptor_base import MethodDescriptor from .method_descriptor_factories import MethodDescriptorFactories

30.25

66

0.909091

12

121

8.833333

0.666667

0.188679

0.377358

0

0.07438

121

3

67

40.333333

0.946429

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

23ed24abf9e9da8310157d90f84322170778c116

346

py

Python

bitmovin_api_sdk/encoding/encodings/muxings/fmp4/drm/fairplay/__init__.py

jaythecaesarean/bitmovin-api-sdk-python

48166511fcb9082041c552ace55a9b66cc59b794

[ "MIT" ]

11

2019-07-03T10:41:16.000Z

2022-02-25T21:48:06.000Z

bitmovin_api_sdk/encoding/encodings/muxings/fmp4/drm/fairplay/__init__.py

jaythecaesarean/bitmovin-api-sdk-python

48166511fcb9082041c552ace55a9b66cc59b794

[ "MIT" ]

8

2019-11-23T00:01:25.000Z

2021-04-29T12:30:31.000Z

bitmovin_api_sdk/encoding/encodings/muxings/fmp4/drm/fairplay/__init__.py

jaythecaesarean/bitmovin-api-sdk-python

48166511fcb9082041c552ace55a9b66cc59b794

[ "MIT" ]

13

2020-01-02T14:58:18.000Z

2022-03-26T12:10:30.000Z

from bitmovin_api_sdk.encoding.encodings.muxings.fmp4.drm.fairplay.fairplay_api import FairplayApi from bitmovin_api_sdk.encoding.encodings.muxings.fmp4.drm.fairplay.customdata.customdata_api import CustomdataApi from bitmovin_api_sdk.encoding.encodings.muxings.fmp4.drm.fairplay.fair_play_drm_list_query_params import FairPlayDrmListQueryParams

86.5

132

0.901734

47

346

6.361702

0.425532

0.120401

0.150502

0.180602

0.571906

0

0.008982

0.034682

346

3

133

115.333333

0.886228

0

1

0

true

0

1

0

1

0

null

0

1

0

1

0

1

0

null

0

1

0

1

0

1

0

6

9b06f78aae8c60a4828e2a40657ef137f0e45e4c

347

py

Python

Prescient/models/__init__.py

RamonWill/Data-App

e4b28704940546156f9521c88eced73f1443ce7e

[ "MIT" ]

28

2020-10-07T04:40:42.000Z

2022-03-17T10:34:18.000Z

Prescient/models/__init__.py

RamonWill/Data-App

e4b28704940546156f9521c88eced73f1443ce7e

[ "MIT" ]

2

2021-01-16T18:48:56.000Z

2022-03-06T23:02:01.000Z

Prescient/models/__init__.py

RamonWill/Data-App

e4b28704940546156f9521c88eced73f1443ce7e

[ "MIT" ]

16

2020-09-28T17:30:39.000Z

2022-03-20T00:09:27.000Z

from Prescient.models.user import User, load_user from Prescient.models.watchlist import (WatchlistItems, Watchlist_Group, default_date) from Prescient.models.db_securities import (Available_Securities, Sector_Definitions)

49.571429

65

0.556196

28

347

6.678571

0.571429

0.208556

0.304813

0

0.403458

347

6

66

57.833333

0.903382

0

1

0

true

0

0.5

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

f19f358d44994efe578b6b7d9923073d0629e1b0

10,403

py

Python

markov_chain_neigh.py

Ultimawashi/CMC-CPS

883f0589f4c3f8e3c249c7eece3e368b44c9ba68

[ "MIT" ]

null

markov_chain_neigh.py

Ultimawashi/CMC-CPS

883f0589f4c3f8e3c249c7eece3e368b44c9ba68

[ "MIT" ]

null

markov_chain_neigh.py

Ultimawashi/CMC-CPS

883f0589f4c3f8e3c249c7eece3e368b44c9ba68

[ "MIT" ]

null

import numpy as np from tools import gauss, gauss2 from math import sqrt, pi from markov_chain import simu_mc, simu_mc_nonstat, calc_probaprio_mc def forward_neigh(A, p, gauss, g2, g3): """ Cette fonction calcule récursivement (mais ce n'est pas une fonction récursive!) les valeurs forward de la chaîne :param A: Matrice (2*2) de transition de la chaîne :param p: vecteur de taille 2 avec la probailité d'apparition a priori pour chaque classe :param gauss: numpy array (longeur de signal_noisy)*2 qui correspond aux valeurs des densité gaussiennes pour chaque élément du signal bruité :return: un vecteur de taille la longueur de la chaîne, contenant tous les forward (de 1 à n) """ proba2 = A @ g2[0] proba3 = A @ g3[0] forward = np.zeros((len(gauss), 2)) forward[0] = p * (gauss[0]*proba2*proba3) forward[0] = forward[0] / (forward[0].sum()) for l in range(1, len(gauss)): proba2 = A@g2[l] proba3 = A@g3[l] forward[l] = (gauss[l]*proba2*proba3) * (forward[l - 1] @ A) forward[l] = forward[l] / forward[l].sum() return forward def backward_neigh(A, gauss, g2, g3): """ Cette fonction calcule récursivement (mais ce n'est pas une fonction récursive!) les valeurs backward de la chaîne :param A: Matrice (2*2) de transition de la chaîne :param p: vecteur de taille 2 avec la probailité d'apparition a priori pour chaque classe :param gauss: numpy array (longeur de signal_noisy)*2 qui correspond aux valeurs des densité gaussiennes pour chaque élément du signal bruité :return: un vecteur de taille la longueur de la chaîne, contenant tous les backward (de 1 à n). Attention, si on calcule les backward en partant de la fin de la chaine, je conseille quand même d'ordonner le vecteur backward du début à la fin """ backward = np.zeros((len(gauss), 2)) backward[len(gauss) - 1] = np.ones(2) backward[len(gauss) - 1] = backward[len(gauss) - 1] / (backward[len(gauss) - 1].sum()) for k in reversed(range(0, len(gauss)-1)): proba2 = A @ g2[k+1] proba3 = A @ g3[k+1] backward[k] = A @ (backward[k + 1] * (gauss[k + 1]*proba2*proba3)) backward[k] = backward[k] / (backward[k].sum()) return backward def mpm_mc_neigh(signal_noisy, neighboursh, neighboursv, w, p, A, m1, sig1, m2, sig2): """ Cette fonction permet d'appliquer la méthode mpm pour retrouver notre signal d'origine à partir de sa version bruité et des paramètres du model. :param signal_noisy: Signal bruité (numpy array 1D de float) :param w: vecteur dont la première composante est la valeur de la classe w1 et la deuxième est la valeur de la classe w2 :param p: vecteur de taille 2 avec la probailité d'apparition a priori pour chaque classe :param A: Matrice (2*2) de transition de la chaîne :param m1: La moyenne de la première gaussienne :param sig1: La variance de la première gaussienne :param m2: La moyenne de la deuxième gaussienne :param sig2: La variance de la deuxième gaussienne :return: Un signal discret à 2 classe (numpy array 1D d'int) """ gausses = gauss(signal_noisy, m1, sig1, m2, sig2) g2 = gauss2(neighboursh, m1, sig1, m2, sig2) g3 = gauss2(neighboursv, m1, sig1, m2, sig2) alpha = forward_neigh(A, p, gausses, g2, g3) beta = backward_neigh(A, gausses, g2, g3) proba_apost = alpha * beta proba_apost = proba_apost / (proba_apost.sum(axis=1)[..., np.newaxis]) return w[np.argmax(proba_apost, axis=1)] def calc_param_EM_mc_neigh(signal_noisy, neighboursh, neighboursv, p, A, m1, sig1, m2, sig2): """ Cette fonction permet de calculer les nouveaux paramètres estimé pour une itération de EM :param signal_noisy: Signal bruité (numpy array 1D de float) :param p: vecteur de taille 2 avec la probailité d'apparition a priori pour chaque classe :param A: Matrice (2*2) de transition de la chaîne :param m1: La moyenne de la première gaussienne :param sig1: La variance de la première gaussienne :param m2: La moyenne de la deuxième gaussienne :param sig2: La variance de la deuxième gaussienne :return: tous les paramètres réestimés donc p, A, m1, sig1, m2, sig2 """ gausses = gauss(signal_noisy, m1, sig1, m2, sig2) g2 = gauss2(neighboursh, m1, sig1, m2, sig2) g3 = gauss2(neighboursv, m1, sig1, m2, sig2) proba2 = np.einsum('ij,kj->ki',A,g2) proba3 = np.einsum('ij,kj->ki',A,g3) alpha = forward_neigh(A, p, gausses, g2, g3) beta = backward_neigh(A, gausses, g2, g3) proba_apost = alpha * beta proba_apost = proba_apost / (proba_apost.sum(axis=1)[..., np.newaxis]) p = proba_apost.sum(axis=0)/proba_apost.shape[0] proba_c_apost = ( alpha[:-1, :, np.newaxis] * ((gausses[1:, np.newaxis, :]*proba2[1:, np.newaxis, :]*proba3[1:, np.newaxis, :]) * beta[1:, np.newaxis, :] * A[np.newaxis, :, :]) ) proba_c_apost = proba_c_apost / (proba_c_apost.sum(axis=(1, 2))[..., np.newaxis, np.newaxis]) A = np.transpose(np.transpose((proba_c_apost.sum(axis=0))) / (proba_apost[:-1:].sum(axis=0))) m1 = (proba_apost[:,0] * signal_noisy).sum()/proba_apost[:,0].sum() sig1 = np.sqrt((proba_apost[:,0]*((signal_noisy-m1)**2)).sum()/proba_apost[:,0].sum()) m2 = (proba_apost[:, 1] * signal_noisy).sum() / proba_apost[:, 1].sum() sig2 = np.sqrt((proba_apost[:, 1] * ((signal_noisy - m2) ** 2)).sum() / proba_apost[:, 1].sum()) return p, A, m1, sig1, m2, sig2 def estim_param_EM_mc_neigh(iter, signal_noisy, neighboursh, neighboursv, p, A, m1, sig1, m2, sig2): """ Cette fonction est l'implémentation de l'algorithme EM pour le modèle en question :param iter: Nombre d'itération choisie :param signal_noisy: Signal bruité (numpy array 1D de float) :param p: la valeur d'initialisation du vecteur de proba :param A: la valeur d'initialisation de la matrice de transition de la chaîne :param m1: la valeur d'initialisation de la moyenne de la première gaussienne :param sig1: la valeur d'initialisation de l'écart type de la première gaussienne :param m2: la valeur d'initialisation de la moyenne de la deuxième gaussienne :param sig2: la valeur d'initialisation de l'écart type de la deuxième gaussienne :return: Tous les paramètres réestimés à la fin de l'algorithme EM donc p, A, m1, sig1, m2, sig2 """ p_est = p A_est = A m1_est = m1 sig1_est = sig1 m2_est = m2 sig2_est = sig2 for i in range(iter): p_est, A_est, m1_est, sig1_est, m2_est, sig2_est = calc_param_EM_mc_neigh(signal_noisy, neighboursh, neighboursv, p_est, A_est, m1_est, sig1_est, m2_est, sig2_est) print({'iter':i,'p': p_est, 'A': A_est, 'm1': m1_est, 'sig1': sig1_est, 'm2': m2_est, 'sig2': sig2_est}) return p_est, A_est, m1_est, sig1_est, m2_est, sig2_est def calc_param_SEM_mc_neigh(signal_noisy, neighboursh, neighboursv, p, A, m1, sig1, m2, sig2): """ Cette fonction permet de calculer les nouveaux paramètres estimé pour une itération de EM :param signal_noisy: Signal bruité (numpy array 1D de float) :param p: vecteur de taille 2 avec la probailité d'apparition a priori pour chaque classe :param A: Matrice (2*2) de transition de la chaîne :param m1: La moyenne de la première gaussienne :param sig1: La variance de la première gaussienne :param m2: La moyenne de la deuxième gaussienne :param sig2: La variance de la deuxième gaussienne :return: tous les paramètres réestimés donc p, A, m1, sig1, m2, sig2 """ gausses = gauss(signal_noisy, m1, sig1, m2, sig2) g2 = gauss2(neighboursh, m1, sig1, m2, sig2) g3 = gauss2(neighboursv, m1, sig1, m2, sig2) proba2 = np.einsum('ij,kj->ki',A,g2) proba3 = np.einsum('ij,kj->ki',A,g3) alpha = forward_neigh(A, p, gausses, g2, g3) beta = backward_neigh(A, gausses, g2, g3) proba_init = alpha[0] * beta[0] proba_init = proba_init / proba_init.sum() tapost = ( ((gausses[1:, np.newaxis, :]*proba2[1:, np.newaxis, :]*proba3[1:, np.newaxis, :]) * beta[1:, np.newaxis, :] * A[np.newaxis, :, :]) ) tapost = tapost / tapost.sum(axis=2)[..., np.newaxis] signal = simu_mc_nonstat(signal_noisy.shape[0], proba_init, tapost) p,A = calc_probaprio_mc(signal, np.array([0,1])) m1 = ((signal==0) * signal_noisy).sum()/(signal==0).sum() sig1 = np.sqrt(((signal==0)*((signal_noisy-m1)**2)).sum()/(signal==0).sum()) m2 = ((signal==1) * signal_noisy).sum()/(signal==1).sum() sig2 = np.sqrt(((signal == 1) * ((signal_noisy - m2) ** 2)).sum() / (signal == 1).sum()) return p, A, m1, sig1, m2, sig2 def estim_param_SEM_mc_neigh(iter, signal_noisy, neighboursh, neighboursv, p, A, m1, sig1, m2, sig2): """ Cette fonction est l'implémentation de l'algorithme EM pour le modèle en question :param iter: Nombre d'itération choisie :param signal_noisy: Signal bruité (numpy array 1D de float) :param p: la valeur d'initialisation du vecteur de proba :param A: la valeur d'initialisation de la matrice de transition de la chaîne :param m1: la valeur d'initialisation de la moyenne de la première gaussienne :param sig1: la valeur d'initialisation de l'écart type de la première gaussienne :param m2: la valeur d'initialisation de la moyenne de la deuxième gaussienne :param sig2: la valeur d'initialisation de l'écart type de la deuxième gaussienne :return: Tous les paramètres réestimés à la fin de l'algorithme EM donc p, A, m1, sig1, m2, sig2 """ p_est = p A_est = A m1_est = m1 sig1_est = sig1 m2_est = m2 sig2_est = sig2 for i in range(iter): p_est, A_est, m1_est, sig1_est, m2_est, sig2_est = calc_param_SEM_mc_neigh(signal_noisy, neighboursh, neighboursv, p_est, A_est, m1_est, sig1_est, m2_est, sig2_est) print({'iter':i,'p': p_est, 'A': A_est, 'm1': m1_est, 'sig1': sig1_est, 'm2': m2_est, 'sig2': sig2_est}) return p_est, A_est, m1_est, sig1_est, m2_est, sig2_est

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f1b5044c5eb1cb284ff5a3ea9b87c2265ad5e875

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py

Python

jobs/__init__.py

eternalflow/push-money

d8ca1452b57f13b57c7a736c03f0287275f77950

[ "MIT" ]

3

2020-02-02T08:59:22.000Z

2020-05-05T09:18:52.000Z

jobs/__init__.py

eternalflow/push-money

d8ca1452b57f13b57c7a736c03f0287275f77950

[ "MIT" ]

5

2020-04-12T23:27:58.000Z

2020-05-05T12:27:54.000Z

jobs/__init__.py

eternalflow/push-money

d8ca1452b57f13b57c7a736c03f0287275f77950

[ "MIT" ]

4

2020-02-04T01:48:09.000Z

2020-04-26T10:37:07.000Z

from jobs.mailer import *

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py

Python

app/api_1_0/__init__.py

SherlockSheep/Snacks

62c622a641aa20421bd4f41ec268a14090763998

[ "MIT" ]

null

app/api_1_0/__init__.py

SherlockSheep/Snacks

62c622a641aa20421bd4f41ec268a14090763998

[ "MIT" ]

null

app/api_1_0/__init__.py

SherlockSheep/Snacks

62c622a641aa20421bd4f41ec268a14090763998

[ "MIT" ]

null

from flask import Blueprint api = Blueprint('api', __name__) from . import authentication, posts, users, comments, errors, register, tags, snacks, load_pic

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7bc3815bcec58aee7f58cee09f27406ec889c73a

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py

Python

src/process/process/domain/base/operation/__init__.py

ahmetcagriakca/pythondataintegrator

079b968d6c893008f02c88dbe34909a228ac1c7b

[ "MIT" ]

1

2020-12-18T21:37:28.000Z

src/process/process/domain/base/operation/__init__.py

ahmetcagriakca/pythondataintegrator

079b968d6c893008f02c88dbe34909a228ac1c7b

[ "MIT" ]

null

src/process/process/domain/base/operation/__init__.py

ahmetcagriakca/pythondataintegrator

079b968d6c893008f02c88dbe34909a228ac1c7b

[ "MIT" ]

1

2020-12-18T21:37:31.000Z

from process.domain.base.operation.DataOperationBase import DataOperationBase from process.domain.base.operation.DataOperationContactBase import DataOperationContactBase from process.domain.base.operation.DataOperationIntegrationBase import DataOperationIntegrationBase from process.domain.base.operation.DataOperationJobBase import DataOperationJobBase from process.domain.base.operation.DataOperationJobExecutionBase import DataOperationJobExecutionBase from process.domain.base.operation.DataOperationJobExecutionEventBase import DataOperationJobExecutionEventBase from process.domain.base.operation.DataOperationJobExecutionIntegrationBase import \ DataOperationJobExecutionIntegrationBase from process.domain.base.operation.DataOperationJobExecutionIntegrationEventBase import \ DataOperationJobExecutionIntegrationEventBase from process.domain.base.operation.DefinitionBase import DefinitionBase

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6

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py

Python

ppq/log/__init__.py

xiguadong/ppq

6c71adb3c2a8ca95967f101724b5e4b3e6f761ff

[ "Apache-2.0" ]

null

ppq/log/__init__.py

xiguadong/ppq

6c71adb3c2a8ca95967f101724b5e4b3e6f761ff

[ "Apache-2.0" ]

null

ppq/log/__init__.py

xiguadong/ppq

6c71adb3c2a8ca95967f101724b5e4b3e6f761ff

[ "Apache-2.0" ]

null

from .logger import NaiveLogger

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0

6

7bcf7b9b2a65d12388e1e2effb434332d78d92eb

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py

Python

fastapi_discord/models/__init__.py

abhishek0220/fastapi-discord

f06cc61a4e800eae5c09fd55329a74fbfc6e270e

[ "MIT" ]

2

2022-02-03T18:03:33.000Z

2022-03-21T10:54:41.000Z

fastapi_discord/models/__init__.py

abhishek0220/fastapi-discord

f06cc61a4e800eae5c09fd55329a74fbfc6e270e

[ "MIT" ]

null

fastapi_discord/models/__init__.py

abhishek0220/fastapi-discord

f06cc61a4e800eae5c09fd55329a74fbfc6e270e

[ "MIT" ]

null

from .guild import Guild from .user import User

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6

cda12a716a9774b9eaa5666cb7d95fee9566ea36

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py

Python

examples/planar_hand/analysis/plot_cost.py

lujieyang/irs_lqr

bc9cade6a3bb2fa2d76bdd5fe453030a7b28700f

[ "MIT" ]

6

2021-11-20T19:05:06.000Z

2022-01-31T00:10:41.000Z

examples/planar_hand/analysis/plot_cost.py

lujieyang/irs_lqr

bc9cade6a3bb2fa2d76bdd5fe453030a7b28700f

[ "MIT" ]

10

2021-07-24T19:50:36.000Z

2021-11-20T19:06:40.000Z

examples/planar_hand/analysis/plot_cost.py

lujieyang/irs_lqr

bc9cade6a3bb2fa2d76bdd5fe453030a7b28700f

[ "MIT" ]

1

2021-12-15T22:09:31.000Z

import numpy as np import matplotlib.pyplot as plt exact = np.loadtxt( "examples/planar_hand/analysis/planar_hand_exact.csv", delimiter=",") first_order = np.loadtxt( "examples/planar_hand/analysis/planar_hand_first_order.csv", delimiter=",") zero_order_B = np.loadtxt( "examples/planar_hand/analysis/planar_hand_zero_order_B.csv", delimiter=",") zero_order_AB = np.loadtxt( "examples/planar_hand/analysis/planar_hand_zero_order_AB.csv", delimiter=",") plt.figure() plt.plot(exact, marker='x', color='red', label='exact') plt.plot(first_order, marker='v', color='springgreen', label='first order') plt.plot(zero_order_AB, marker='^', color='blue', label='zero order') #plt.plot(zero_order_AB, marker='+', color='magenta', label='zero order_AB') plt.legend() plt.xlabel('iterations') plt.ylabel('Cost') plt.title("Planar Hand (Move Right)") plt.grid() plt.show() exact = np.loadtxt( "examples/planar_hand/analysis/planar_hand_spin_exact.csv", delimiter=",") first_order = np.loadtxt( "examples/planar_hand/analysis/planar_hand_spin_first_order.csv", delimiter=",") zero_order_B = np.loadtxt( "examples/planar_hand/analysis/planar_hand_spin_zero_order_B.csv", delimiter=",") zero_order_AB = np.loadtxt( "examples/planar_hand/analysis/planar_hand_spin_zero_order_AB.csv", delimiter=",") plt.figure() plt.plot(exact, marker='x', color='red', label='exact') plt.plot(first_order, marker='v', color='springgreen', label='first order') plt.plot(zero_order_AB, marker='^', color='blue', label='zero order') #plt.plot(zero_order_AB, marker='^', color='magenta', label='zero order_AB') plt.legend() plt.xlabel('iterations') plt.ylabel('Cost') plt.title("Planar Hand (Spin In-Place)") plt.grid() plt.show() exact = np.loadtxt( "examples/planar_hand/analysis/planar_hand_spin_second_exact.csv", delimiter=",") first_order = np.loadtxt( "examples/planar_hand/analysis/planar_hand_spin_second_first.csv", delimiter=",") zero_order = np.loadtxt( "examples/planar_hand/analysis/planar_hand_spin_second_zero.csv", delimiter=",") plt.figure() plt.plot(exact, marker='x', color='red', label='exact') plt.plot(first_order, marker='v', color='springgreen', label='first order') plt.plot(zero_order, marker='^', color='blue', label='zero order') plt.legend() plt.yscale('log') plt.xlabel('iterations') plt.ylabel('Cost (log scale)') plt.title("Planar Hand (Spin In-hand, Second-Order Sim)") plt.grid() plt.show()

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cde6252ebb1d2095703f323b7f4c13d249618dac

86

py

Python

annom/__init__.py

jcreinhold/annom

4ad65322bec7f038a2b3ce42f688672dc914c2e2

[ "Apache-2.0" ]

1

2021-03-06T17:42:32.000Z

annom/__init__.py

jcreinhold/annom

4ad65322bec7f038a2b3ce42f688672dc914c2e2

[ "Apache-2.0" ]

null

annom/__init__.py

jcreinhold/annom

4ad65322bec7f038a2b3ce42f688672dc914c2e2

[ "Apache-2.0" ]

null

from .errors import * from .layers import * from .loss import * from .models import *

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1

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6

a805f67e28db13fd611bdb9daf5bc380dc22985a

54

py

Python

src/pyannet/__init__.py

neo0311/pyannet

e485527b180a8bde962e0c3d5abd317c0b5dfad4

[ "MIT" ]

1

2022-03-01T22:48:05.000Z

src/pyannet/__init__.py

neo0311/pyannet

e485527b180a8bde962e0c3d5abd317c0b5dfad4

[ "MIT" ]

null

src/pyannet/__init__.py

neo0311/pyannet

e485527b180a8bde962e0c3d5abd317c0b5dfad4

[ "MIT" ]

null

import pyannet.data_prep import pyannet.neural_network

27

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versioning/__init__.py

nbag/python_utils

5c7039f7fe85dcb1dc71eac5b80b0d0225628f41

[ "MIT" ]

null

versioning/__init__.py

nbag/python_utils

5c7039f7fe85dcb1dc71eac5b80b0d0225628f41

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versioning/__init__.py

nbag/python_utils

5c7039f7fe85dcb1dc71eac5b80b0d0225628f41

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from .minimal_ext_cmd import minimal_ext_cmd from .pairing import n_pairing, reverse_n_pairing from .git_version import MAIN_BRANCHES, git_version, update_git_version

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__init__.py

mobilityhouse/cloudwatch_to_elastic

e638a4fa1607371e02f93f7f1c52d31204e7b1a2

[ "MIT" ]

4

2017-07-31T22:06:10.000Z

2021-06-05T16:16:18.000Z

__init__.py

mobilityhouse/cloudwatch_to_elastic

e638a4fa1607371e02f93f7f1c52d31204e7b1a2

[ "MIT" ]

null

__init__.py

mobilityhouse/cloudwatch_to_elastic

e638a4fa1607371e02f93f7f1c52d31204e7b1a2

[ "MIT" ]

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2021-06-05T16:16:22.000Z

from es_store import lambda_handler

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py

Python

tests/test_gui_navigation_frame.py

rbotter/pyDEA

2c8b4a70e8c071d580eff26a040efc22fc264045

[ "MIT" ]

29

2017-10-22T03:03:20.000Z

2022-03-21T09:15:22.000Z

tests/test_gui_navigation_frame.py

rbotter/pyDEA

2c8b4a70e8c071d580eff26a040efc22fc264045

[ "MIT" ]

6

2018-07-18T01:40:43.000Z

2021-04-11T00:38:30.000Z

tests/test_gui_navigation_frame.py

rbotter/pyDEA

2c8b4a70e8c071d580eff26a040efc22fc264045

[ "MIT" ]

20

2018-01-23T05:50:29.000Z

2022-02-22T05:04:56.000Z

import pytest from pyDEA.core.gui_modules.navigation_frame_gui import NavigationForTableFrame class TableFrameMock(object): def __init__(self): self.nb_rows = 100 self.display_data_called = False def display_data(self, row_index): self.display_data_called = True @pytest.fixture def nav_frame(request): nav_frame = NavigationForTableFrame(None, TableFrameMock()) request.addfinalizer(nav_frame.destroy) return nav_frame def test_reset_navigation(nav_frame): nav_frame.reset_navigation() assert nav_frame.current_page_str.get() == '1' assert nav_frame.text_var_nb_pages.get() == '1 pages' assert nav_frame.goto_spin.cget('to') == 1 def test_set_navigation(nav_frame): nav_frame.set_navigation(10) assert nav_frame.current_page_str.get() == '1' assert nav_frame.text_var_nb_pages.get() == '10 pages' assert nav_frame.goto_spin.cget('to') == 10 def test_on_page_change(nav_frame): nav_frame.set_navigation(5) nav_frame.current_page_str.set(3) nav_frame.on_page_change() assert nav_frame.table.display_data_called is True def test_on_page_change_more_than_max(nav_frame): nav_frame.set_navigation(5) nav_frame.current_page_str.set(7) nav_frame.on_page_change() assert nav_frame.table.display_data_called is True assert nav_frame.current_page_str.get() == '5' def test_on_page_change_negative(nav_frame): nav_frame.set_navigation(5) nav_frame.current_page_str.set(-7) nav_frame.on_page_change() assert nav_frame.table.display_data_called is True assert nav_frame.current_page_str.get() == '1' def test_on_page_change_invalid(nav_frame): nav_frame.set_navigation(5) nav_frame.current_page_str.set('text') nav_frame.on_page_change() assert nav_frame.table.display_data_called is True assert nav_frame.current_page_str.get() == '1' def test_show_prev_page_ok(nav_frame): nav_frame.set_navigation(5) nav_frame.current_page_str.set(3) nav_frame.show_prev_page() assert nav_frame.current_page_str.get() == '2' assert nav_frame.table.display_data_called is True def test_show_prev_page_invalid(nav_frame): nav_frame.set_navigation(5) nav_frame.current_page_str.set(1) nav_frame.show_prev_page() assert nav_frame.current_page_str.get() == '1' assert nav_frame.table.display_data_called is False def test_show_next_page_ok(nav_frame): nav_frame.set_navigation(5) nav_frame.current_page_str.set(3) nav_frame.show_next_page() assert nav_frame.current_page_str.get() == '4' assert nav_frame.table.display_data_called is True def test_show_next_page_invalid(nav_frame): nav_frame.set_navigation(5) nav_frame.current_page_str.set(5) nav_frame.show_next_page() assert nav_frame.current_page_str.get() == '5' assert nav_frame.table.display_data_called is False

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b5b3a24a809eff6fc31cb4a731d5f3a3ec485726

372

py

Python

src/bfs/python/simple/test.py

zakmandhro/Algorithms

de828b6dba9f3cbaf1cc0775c1ade03de57c8de1

[ "MIT" ]

13

2018-03-25T16:00:01.000Z

2022-03-07T23:10:32.000Z

src/bfs/python/simple/test.py

zakmandhro/Algorithms

de828b6dba9f3cbaf1cc0775c1ade03de57c8de1

[ "MIT" ]

1

2022-02-26T20:10:48.000Z

src/bfs/python/simple/test.py

zakmandhro/Algorithms

de828b6dba9f3cbaf1cc0775c1ade03de57c8de1

[ "MIT" ]

5

2021-06-02T05:43:13.000Z

2022-02-20T11:04:54.000Z

from bfs import bfs graph = [ [False, True, True, False, False, False], [False, False, False, True, True, False], [False, True, False, True, False, False], [False, False, False, False, True, True], [False, False, False, False, False, True], [False, False, False, False, False, False], ] # Should be [0, 1, 1, 2, 2, 3] print(bfs(graph, 0))

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a9112a74fa934aa95a65c0bb5389331901a45f8f

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py

Python

deepab/layers/__init__.py

antonkulaga/DeepAb

51a32d06d19815705bdbfb35a8a9518c17ec313a

[ "RSA-MD" ]

67

2021-07-02T08:31:10.000Z

2022-03-30T01:25:11.000Z

deepab/layers/__init__.py

antonkulaga/DeepAb

51a32d06d19815705bdbfb35a8a9518c17ec313a

[ "RSA-MD" ]

9

2021-08-18T10:32:27.000Z

2022-03-30T06:40:05.000Z

deepab/layers/__init__.py

antonkulaga/DeepAb

51a32d06d19815705bdbfb35a8a9518c17ec313a

[ "RSA-MD" ]

16

2021-07-17T08:33:30.000Z

2022-03-29T07:36:34.000Z

from .OuterConcatenation2D import *

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