xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

2d2ba7695d4a791fd93865ae5289987dfd5bbfad

124

py

Python

cupyx/distributed/__init__.py

Onkar627/cupy

8eef1ad5393c0a92c5065bc05137bf997f37044a

[ "MIT" ]

6,180

2016-11-01T14:22:30.000Z

2022-03-31T08:39:20.000Z

cupyx/distributed/__init__.py

Onkar627/cupy

8eef1ad5393c0a92c5065bc05137bf997f37044a

[ "MIT" ]

6,281

2016-12-22T07:42:31.000Z

2022-03-31T19:57:02.000Z

cupyx/distributed/__init__.py

Onkar627/cupy

8eef1ad5393c0a92c5065bc05137bf997f37044a

[ "MIT" ]

829

2017-02-23T05:46:12.000Z

2022-03-27T17:40:03.000Z

from cupyx.distributed._init import init_process_group # NOQA from cupyx.distributed._nccl_comm import NCCLBackend # NOQA

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py

Python

polyaxon/polyaxon/settings.py

elyase/polyaxon

1c19f059a010a6889e2b7ea340715b2bcfa382a0

[ "MIT" ]

null

polyaxon/polyaxon/settings.py

elyase/polyaxon

1c19f059a010a6889e2b7ea340715b2bcfa382a0

[ "MIT" ]

null

polyaxon/polyaxon/settings.py

elyase/polyaxon

1c19f059a010a6889e2b7ea340715b2bcfa382a0

[ "MIT" ]

null

from polyaxon.config_settings import * # noqa

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74446abef16ce9847fed1001afa98696f138c193

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py

Python

codewars/8kyu/mohamedashrafamin/Remove_exclamation_marks/main.py

mohamedashrafamin/Training_one

11748fdde85cdc9083e2b0bde7519b51a7acfa62

[ "MIT" ]

null

codewars/8kyu/mohamedashrafamin/Remove_exclamation_marks/main.py

mohamedashrafamin/Training_one

11748fdde85cdc9083e2b0bde7519b51a7acfa62

[ "MIT" ]

2

2019-01-22T10:53:42.000Z

2019-01-31T08:02:48.000Z

codewars/8kyu/mohamedashrafamin/Remove_exclamation_marks/main.py

mohamedashrafamin/Training_one

11748fdde85cdc9083e2b0bde7519b51a7acfa62

[ "MIT" ]

13

2019-01-22T10:37:42.000Z

2019-01-25T13:30:43.000Z

def remove_exclamation_marks(s): return s.replace('!', '') def remove_exclamation_marks1(s): return s.replace('!', '') # ---------------------------------------------------------------------------------------- benchmark: 2 tests --------------------------------------------------------------------------------------- # Name (time in ns) Min Max Mean StdDev Median IQR Outliers OPS (Mops/s) Rounds Iterations # --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- # remove_exclamation_marks1 95.3674 (1.0) 3,004.0741 (3.76) 201.5001 (1.0) 116.4286 (2.61) 190.7349 (1.0) 23.8419 (2.50) 491;5027 4.9628 (1.0) 49933 10 # remove_exclamation_marks 178.8139 (1.88) 798.7022 (1.0) 202.2819 (1.00) 44.6808 (1.0) 190.7349 (1.0) 9.5367 (1.0) 2755;3097 4.9436 (1.00) 49933 100 # ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

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744a0394a2976bb111391bd77fcff5619c0679d9

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py

Python

server/tests/test_noop.py

Neoteroi/BlackSheep-Azure-API

e4a7dae9fd3002fe6926c56a1b2ff65ba851e5cb

[ "MIT" ]

null

server/tests/test_noop.py

Neoteroi/BlackSheep-Azure-API

e4a7dae9fd3002fe6926c56a1b2ff65ba851e5cb

[ "MIT" ]

null

server/tests/test_noop.py

Neoteroi/BlackSheep-Azure-API

e4a7dae9fd3002fe6926c56a1b2ff65ba851e5cb

[ "MIT" ]

null

def test_noop(): pass def test_noop2(): pass

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74738713f129e5d45d002ba7b54ccd392628c763

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py

Python

meup/models/__init__.py

abheist/goldenSwan-backend

153e16bb829f113fb429131436324631f15ae064

[ "MIT" ]

null

meup/models/__init__.py

abheist/goldenSwan-backend

153e16bb829f113fb429131436324631f15ae064

[ "MIT" ]

9

2021-03-30T13:41:09.000Z

2022-03-12T00:32:50.000Z

meup/models/__init__.py

abheist/goldenSwan-backend

153e16bb829f113fb429131436324631f15ae064

[ "MIT" ]

null

from meup.models.Article import Article

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6

7482a5c85f43293612e381d3275e8842fd7c12f0

206

py

Python

RaspberryPi/SimpleAlertLog.py

fbleite/LandryMachineSensor

2ae3e903e9b20cea955189aa07ecd64a8bced960

[ "MIT" ]

null

RaspberryPi/SimpleAlertLog.py

fbleite/LandryMachineSensor

2ae3e903e9b20cea955189aa07ecd64a8bced960

[ "MIT" ]

7

2018-11-21T02:17:38.000Z

2019-04-19T02:34:11.000Z

RaspberryPi/SimpleAlertLog.py

fbleite/LandryMachineSensor

2ae3e903e9b20cea955189aa07ecd64a8bced960

[ "MIT" ]

null

import logging class SimpleAlertLog : def __init__(self, args): pass def alertCurrentStatus(self, laundryMachineStatus): logging.info(laundryMachineStatus.generateAlertMessage())

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6

77c1724c64c825b95ad6ba046b8af2a0bb7b2800

255

py

Python

utils/decorators/user_decorators.py

dawang-youy/Django-blog

529e7ef16d65170dc56cd628c34c5c9806138eed

[ "Apache-2.0" ]

null

utils/decorators/user_decorators.py

dawang-youy/Django-blog

529e7ef16d65170dc56cd628c34c5c9806138eed

[ "Apache-2.0" ]

null

utils/decorators/user_decorators.py

dawang-youy/Django-blog

529e7ef16d65170dc56cd628c34c5c9806138eed

[ "Apache-2.0" ]

null

def my_decorator(func): def wrapper(request,*args,**kwargs): print('这是定义的装饰器') print('判断用户是否登录，是否有相关权限') print(args,kwargs)#(<WSGIRequest: GET '/admin/index/'>,) {} return func(request,*args,**kwargs) return wrapper

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py

Python

pyroSAR/__init__.py

jiechencyz/pyroSAR

4c0e2294792b597f7663571ea8f0dbc98005868b

[ "MIT" ]

null

pyroSAR/__init__.py

jiechencyz/pyroSAR

4c0e2294792b597f7663571ea8f0dbc98005868b

[ "MIT" ]

null

pyroSAR/__init__.py

jiechencyz/pyroSAR

4c0e2294792b597f7663571ea8f0dbc98005868b

[ "MIT" ]

1

2019-10-17T03:02:43.000Z

from .drivers import *

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6

77cc67282eeacd2f4a27ad38d716432de4dc86f7

9,340

py

Python

src/stats2.py

KonstantinMack/fussball_vorhersagen

8213eaa59fe8c5e099716819c257af494a6db563

[ "MIT" ]

null

src/stats2.py

KonstantinMack/fussball_vorhersagen

8213eaa59fe8c5e099716819c257af494a6db563

[ "MIT" ]

null

src/stats2.py

KonstantinMack/fussball_vorhersagen

8213eaa59fe8c5e099716819c257af494a6db563

[ "MIT" ]

null

def get_avg_goaldiff_rolling(df, window=6, min_periods=4): """ Calculates rolling average goal difference per team at home and away """ home = df.groupby('HomeTeam').GoalDiff.apply(lambda x: x.rolling(window=window, min_periods=min_periods).mean().shift(1)) away = - df.groupby('AwayTeam').GoalDiff.apply(lambda x: x.rolling(window=window, min_periods=min_periods).mean().shift(1)) return home, away def get_avg_goaldiff_expanding(df): """ Calculates expanding average goal difference per team at home and away """ home = df.groupby('HomeTeam').GoalDiff.apply(lambda x: x.expanding().mean().shift(1)) away = - df.groupby('AwayTeam').GoalDiff.apply(lambda x: x.expanding().mean().shift(1)) return home, away def get_avg_goals_rolling(df, window=6, min_periods=4): """ Calculates rolling average goals scored/conceded for the league and per team at home and away """ Lg_HG = df["FTHG"].expanding().mean().shift() Lg_AG = df["FTAG"].expanding().mean().shift() H_avgG = df.groupby("HomeTeam")["FTHG"].apply(lambda x: x.rolling(window=window, min_periods=min_periods).mean().shift()) A_avgG = df.groupby("AwayTeam")["FTAG"].apply(lambda x: x.rolling(window=window, min_periods=min_periods).mean().shift()) H_avgG_c = df.groupby("HomeTeam")["FTAG"].apply(lambda x: x.rolling(window=window, min_periods=min_periods).mean().shift()) A_avgG_c = df.groupby("AwayTeam")["FTHG"].apply(lambda x: x.rolling(window=window, min_periods=min_periods).mean().shift()) return Lg_HG, Lg_AG, H_avgG, A_avgG, H_avgG_c, A_avgG_c def get_avg_goals_expanding(df): """ Calculates expanding average goals scored/conceded for the league and per team at home and away """ Lg_HG = df["FTHG"].expanding().mean().shift() Lg_AG = df["FTAG"].expanding().mean().shift() H_avgG = df.groupby("HomeTeam")["FTHG"].apply(lambda x: x.expanding().mean().shift()) A_avgG = df.groupby("AwayTeam")["FTAG"].apply(lambda x: x.expanding().mean().shift()) H_avgG_c = df.groupby("HomeTeam")["FTAG"].apply(lambda x: x.expanding().mean().shift()) A_avgG_c = df.groupby("AwayTeam")["FTHG"].apply(lambda x: x.expanding().mean().shift()) return Lg_HG, Lg_AG, H_avgG, A_avgG, H_avgG_c, A_avgG_c def get_avg_shots_rolling(df, window=6, min_periods=4): """ Calculates rolling average shots/shots on target fired/conceded for the league and per team at home and away """ Lg_HS = df["HS"].expanding().mean().shift() Lg_AS = df["AS"].expanding().mean().shift() Lg_HST = df["HST"].expanding().mean().shift() Lg_AST = df["AST"].expanding().mean().shift() H_avgS = df.groupby("HomeTeam")["HS"].apply(lambda x: x.rolling(window=window, min_periods=min_periods).mean().shift()) A_avgS = df.groupby("AwayTeam")["AS"].apply(lambda x: x.rolling(window=window, min_periods=min_periods).mean().shift()) H_avgS_c = df.groupby("HomeTeam")["AS"].apply(lambda x: x.rolling(window=window, min_periods=min_periods).mean().shift()) A_avgS_c = df.groupby("AwayTeam")["HS"].apply(lambda x: x.rolling(window=window, min_periods=min_periods).mean().shift()) H_avgST = df.groupby("HomeTeam")["HST"].apply(lambda x: x.rolling(window=window, min_periods=min_periods).mean().shift()) A_avgST = df.groupby("AwayTeam")["AST"].apply(lambda x: x.rolling(window=window, min_periods=min_periods).mean().shift()) H_avgST_c = df.groupby("HomeTeam")["AST"].apply(lambda x: x.rolling(window=window, min_periods=min_periods).mean().shift()) A_avgST_c = df.groupby("AwayTeam")["HST"].apply(lambda x: x.rolling(window=window, min_periods=min_periods).mean().shift()) return Lg_HS, Lg_AS, Lg_HST, Lg_AST, H_avgS, A_avgS, H_avgS_c, A_avgS_c, H_avgST, A_avgST, H_avgST_c, A_avgST_c def get_avg_shots_expanding(df): """ Calculates expanding average shots/shots on target fired/conceded for the league and per team at home and away """ Lg_HS = df["HS"].expanding().mean().shift() Lg_AS = df["AS"].expanding().mean().shift() Lg_HST = df["HST"].expanding().mean().shift() Lg_AST = df["AST"].expanding().mean().shift() H_avgS = df.groupby("HomeTeam")["HS"].apply(lambda x: x.expanding().mean().shift()) A_avgS = df.groupby("AwayTeam")["AS"].apply(lambda x: x.expanding().mean().shift()) H_avgS_c = df.groupby("HomeTeam")["AS"].apply(lambda x: x.expanding().mean().shift()) A_avgS_c = df.groupby("AwayTeam")["HS"].apply(lambda x: x.expanding().mean().shift()) H_avgST = df.groupby("HomeTeam")["HST"].apply(lambda x: x.expanding().mean().shift()) A_avgST = df.groupby("AwayTeam")["AST"].apply(lambda x: x.expanding().mean().shift()) H_avgST_c = df.groupby("HomeTeam")["AST"].apply(lambda x: x.expanding().mean().shift()) A_avgST_c = df.groupby("AwayTeam")["HST"].apply(lambda x: x.expanding().mean().shift()) return Lg_HS, Lg_AS, Lg_HST, Lg_AST, H_avgS, A_avgS, H_avgS_c, A_avgS_c, H_avgST, A_avgST, H_avgST_c, A_avgST_c def get_avg_corners_rolling(df, window=6, min_periods=4): """ Calculates rolling average corners for/against per team at home and away """ H_avgC = df.groupby("HomeTeam")["HC"].apply(lambda x: x.rolling(window=window, min_periods=min_periods).mean().shift()) A_avgC = df.groupby("AwayTeam")["AC"].apply(lambda x: x.rolling(window=window, min_periods=min_periods).mean().shift()) H_avgC_c = df.groupby("HomeTeam")["AC"].apply(lambda x: x.rolling(window=window, min_periods=min_periods).mean().shift()) A_avgC_c = df.groupby("AwayTeam")["HC"].apply(lambda x: x.rolling(window=window, min_periods=min_periods).mean().shift()) return H_avgC, A_avgC, H_avgC_c, A_avgC_c def get_avg_corners_expanding(df): """ Calculates expanding average corners for/against per team at home and away """ H_avgC = df.groupby("HomeTeam")["HC"].apply(lambda x: x.expanding().mean().shift()) A_avgC = df.groupby("AwayTeam")["AC"].apply(lambda x: x.expanding().mean().shift()) H_avgC_c = df.groupby("HomeTeam")["AC"].apply(lambda x: x.expanding().mean().shift()) A_avgC_c = df.groupby("AwayTeam")["HC"].apply(lambda x: x.expanding().mean().shift()) return H_avgC, A_avgC, H_avgC_c, A_avgC_c def get_stats(df2, mode="expanding", window=6, min_periods=4): df = df2.copy() if mode == "rolling": df["H_avgGD"], df["A_avgGD"] = get_avg_goaldiff_rolling(df, window, min_periods) df["Lg_HG"], df["Lg_AG"], df["H_avgG"], df["A_avgG"], df["H_avgG_c"], df["A_avgG_c"] = get_avg_goals_rolling(df, window, min_periods) df["Lg_HS"], df["Lg_AS"], df["Lg_HST"], df["Lg_AST"], df["H_avgS"], df["A_avgS"], df["H_avgS_c"], df["A_avgS_c"], df["H_avgST"], df["A_avgST"], df["H_avgST_c"], df["A_avgST_c"] = get_avg_shots_rolling(df, window, min_periods) else: df["H_avgGD"], df["A_avgGD"] = get_avg_goaldiff_expanding(df) df["Lg_HG"], df["Lg_AG"], df["H_avgG"], df["A_avgG"], df["H_avgG_c"], df["A_avgG_c"] = get_avg_goals_expanding(df) df["Lg_HS"], df["Lg_AS"], df["Lg_HST"], df["Lg_AST"], df["H_avgS"], df["A_avgS"], df["H_avgS_c"], df["A_avgS_c"], df["H_avgST"], df["A_avgST"], df["H_avgST_c"], df["A_avgST_c"] = get_avg_shots_expanding(df) return df def get_stats2(df2, window=6, min_periods=4): """ Calculates all stats as a mixture between the rolling and expanding versions of the respective stats """ df = df2.copy() H_avgGDr, A_avgGDr = get_avg_goaldiff_rolling(df, window, min_periods) H_avgGDe, A_avgGDe = get_avg_goaldiff_expanding(df) df["H_avgGD"] = (H_avgGDr + 2 * H_avgGDe) / 3 df["A_avgGD"] = (A_avgGDr + 2 * A_avgGDe) / 3 Lg_HG, Lg_AG, H_avgG, A_avgG, H_avgG_c, A_avgG_c = get_avg_goals_rolling(df, window, min_periods) Lg_HGe, Lg_AGe, H_avgGe, A_avgGe, H_avgG_ce, A_avgG_ce = get_avg_goals_expanding(df) df["Lg_HG"] = (Lg_HG + 2*Lg_HGe) / 3 df["Lg_AG"] = (Lg_AG + 2*Lg_AGe) / 3 df["H_avgG"] = (H_avgG + 2*H_avgGe) / 3 df["A_avgG"] = (A_avgG + 2*A_avgGe) / 3 df["H_avgG_c"] = (H_avgG_c + 2*H_avgG_ce) / 3 df["A_avgG_c"] = (A_avgG_c + 2*A_avgG_ce) / 3 Lg_HS, Lg_AS, Lg_HST, Lg_AST, H_avgS, A_avgS, H_avgS_c, A_avgS_c, H_avgST, A_avgST, H_avgST_c, A_avgST_c = get_avg_shots_rolling(df, window, min_periods) Lg_HSe, Lg_ASe, Lg_HSTe, Lg_ASTe, H_avgSe, A_avgSe, H_avgS_ce, A_avgS_ce, H_avgSTe, A_avgSTe, H_avgST_ce, A_avgST_ce = get_avg_shots_expanding(df) H_avgC, A_avgC, H_avgC_c, A_avgC_c = get_avg_corners_rolling(df, window, min_periods) H_avgCe, A_avgCe, H_avgC_ce, A_avgC_ce = get_avg_corners_expanding(df) df["Lg_HS"] = (Lg_HS + 2*Lg_HSe) / 3 df["Lg_AS"] = (Lg_AS + 2*Lg_ASe) / 3 df["Lg_HST"] = (Lg_HST + 2*Lg_HSTe) / 3 df["Lg_AST"] = (Lg_AST + 2*Lg_ASTe) / 3 df["H_avgS"] = (H_avgS + 2*H_avgSe) / 3 df["A_avgS"] = (A_avgS + 2*A_avgSe) / 3 df["H_avgS_c"] = (H_avgS_c + 2*H_avgS_ce) / 3 df["A_avgS_c"] = (A_avgS_c + 2*A_avgS_ce) / 3 df["H_avgST"] = (H_avgST + 2*H_avgSTe) / 3 df["A_avgST"] = (A_avgST + 2*A_avgSTe) / 3 df["H_avgST_c"] = (H_avgST_c + 2*H_avgST_ce) / 3 df["A_avgST_c"] = (A_avgST_c + 2*A_avgST_ce) / 3 df["H_avgC"] = (H_avgC + 2*H_avgCe) / 3 df["A_avgC"] = (A_avgC + 2*A_avgCe) / 3 df["H_avgC_c"] = (H_avgC_c + 2*H_avgC_ce) / 3 df["A_avgC_c"] = (A_avgC_c + 2*A_avgC_ce) / 3 return df

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7ae4cab312da5d5d9ec589b3b739a2dbec06136c

169

py

Python

relational/student_projects/2019_guth/models/MMI_Genetic/Genetic_MMI_Singlechoice.py

monthie/cogmods

62af4b8bf2effb77f26a8877d6a89949164d83f0

[ "MIT" ]

null

relational/student_projects/2019_guth/models/MMI_Genetic/Genetic_MMI_Singlechoice.py

monthie/cogmods

62af4b8bf2effb77f26a8877d6a89949164d83f0

[ "MIT" ]

11

2020-05-04T09:05:29.000Z

2021-04-08T13:22:34.000Z

relational/student_projects/2019_guth/models/MMI_Genetic/Genetic_MMI_Singlechoice.py

monthie/cogmods

62af4b8bf2effb77f26a8877d6a89949164d83f0

[ "MIT" ]

12

2020-05-02T09:36:14.000Z

2021-06-22T08:10:45.000Z

import Genetic_MMI class ModelApproachExp1(Genetic_MMI.ModelApproach): def __init__(self): Genetic_MMI.ModelApproach.__init__(self, 1, "MMI_singlechoice")

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Python

WebBrickLibs/EventHandlers/tests/TestEggNunciate.py

AndyThirtover/wb_gateway

69f9c870369085f4440033201e2fb263a463a523

[ "BSD-3-Clause" ]

null

WebBrickLibs/EventHandlers/tests/TestEggNunciate.py

AndyThirtover/wb_gateway

69f9c870369085f4440033201e2fb263a463a523

[ "BSD-3-Clause" ]

null

WebBrickLibs/EventHandlers/tests/TestEggNunciate.py

AndyThirtover/wb_gateway

69f9c870369085f4440033201e2fb263a463a523

[ "BSD-3-Clause" ]

null

# Copyright L.P.Klyne 2013 # Licenced under 3 clause BSD licence # $Id: TestEggNunciate.py 2612 2008-08-11 20:08:49Z graham.klyne $ # # Unit testing for WebBrick library functions (WbAccess.py) # See http://pyunit.sourceforge.net/pyunit.html # # NOTE: this is not strictly a unit test, in that it requires a WebBrick to be # available at the specified IP address import sys, logging, time import unittest from MiscLib.DomHelpers import * from EventLib.Event import Event, makeEvent from EventHandlers.BaseHandler import * from EventHandlers.EventRouterLoad import EventRouterLoader import EventHandlers.tests.TestEventLogger as TestEventLogger import Events from Utils import * # Configuration for the tests # a test with a single egg testConfigEggNunciate1 = """<?xml version="1.0" encoding="utf-8"?> <eventInterfaces> <eventInterface module='EventHandlers.tests.TestEventLogger' name='TestEventLogger'>  <eventtype type=""> <eventsource source="" > <event>  </event> </eventsource> </eventtype> </eventInterface> <eventInterface module='EventHandlers.TwistedReactor' name='TwistedReactor' debug="yes" /> <eventInterface module='EventHandlers.EggNunciate' name='EggNunciate'> <eventtype type="">  <eventsource source="time/runtime" > <event> <params> <testEq name="elapsed"> <value>5</value> </testEq> </params> <add name="defaultHigh" egg="lower" pri="0"/> <add name="defaultLow" egg="lower" pri="0"/> </event> </eventsource> </eventtype> <eventtype type="">  <eventsource source="time/second" > <event> <next/> </event> </eventsource> </eventtype> <eventtype type="">  <eventsource source="webbrick/100/DO/0" > <event> <params> <testEq name="state"> <value>1</value> </testEq> </params> <add name="garageOpenLow" egg="lower" pri="1"/> <add name="garageOpenHigh" egg="lower" pri="1"/> <add name="somethingActive" egg="lower" pri="1"/> </event> <event> <params> <testEq name="state"> <value>0</value> </testEq> </params> <delete name="garageOpenLow" egg="lower" pri="1"/> <delete name="garageOpenHigh" egg="lower" pri="1"/> <delete name="somethingActive" egg="lower" pri="1"/> </event> </eventsource>  <eventsource source="webbrick/100/DO/1" > <event> <params> <testEq name="state"> <value>1</value> </testEq> </params> <add name="AlarmOnLow" egg="lower" pri="1"/> <add name="AlarmOnHigh" egg="lower" pri="1"/> <add name="somethingActive" egg="lower" pri="1"/> </event> <event> <params> <testEq name="state"> <value>0</value> </testEq> </params> <delete name="AlarmOnLow" egg="lower" pri="1"/> <delete name="AlarmOnHigh" egg="lower" pri="1"/> <delete name="somethingActive" egg="lower" pri="1"/> </event> </eventsource> </eventtype>  <step id="defaultHigh" redMin="16" redMax="16" blueMin="16" blueMax="16" greenMin="16" greenMax="16"/> <step id="defaultLow" redMin="8" redMax="8" blueMin="8" blueMax="8" greenMin="8" greenMax="8"/> <step id="AlarmOnLow" redMin="33" redMax="33" blueMin="33" blueMax="33" greenMin="33" greenMax="33"/> <step id="AlarmOnHigh" redMin="47" redMax="47" blueMin="47" blueMax="47" greenMin="47" greenMax="47"/> <step id="garageOpenLow" redMin="32" redMax="32" blueMin="32" blueMax="32" greenMin="32" greenMax="32"/> <step id="garageOpenHigh" redMin="48" redMax="48" blueMin="48" blueMax="48" greenMin="48" greenMax="48"/> <step id="somethingActive" redMin="4" redMax="4" blueMin="4" blueMax="4" greenMin="4" greenMax="4"/>   <egg name="lower" adr="localhost:20999" redChn="0" greenChn="1" blueChn="2" cmdTemplate="/hid.spi?com=:DM%(redChn)s;%(red)s;%(greenChn)s;%(green)s;%(blueChn)s;%(blue)s:"/> </eventInterface> </eventInterfaces> """ # a test with two egg testConfigEggNunciate2 = """<?xml version="1.0" encoding="utf-8"?> <eventInterfaces> <eventInterface module='EventHandlers.tests.TestEventLogger' name='TestEventLogger'>  <eventtype type=""> <eventsource source="" > <event>  </event> </eventsource> </eventtype> </eventInterface> <eventInterface module='EventHandlers.TwistedReactor' name='TwistedReactor' debug="yes" /> <eventInterface module='EventHandlers.EggNunciate' name='EggNunciate'> <eventtype type="">  <eventsource source="time/runtime" > <event> <params> <testEq name="elapsed"> <value>5</value> </testEq> </params> <add name="defaultHigh" egg="lower" pri="0"/> <add name="defaultHigh" egg="upper" pri="0"/> <add name="defaultLow" egg="lower" pri="0"/> <add name="defaultLow" egg="upper" pri="0"/> </event> </eventsource> </eventtype> <eventtype type="">  <eventsource source="time/second" > <event> <next/> </event> </eventsource> </eventtype> <eventtype type="">  <eventsource source="webbrick/100/DO/0" > <event> <params> <testEq name="state"> <value>1</value> </testEq> </params> <add name="garageOpenLow" egg="lower" pri="1"/> <add name="garageOpenHigh" egg="lower" pri="1"/> <add name="somethingActive" egg="lower" pri="1"/> </event> <event> <params> <testEq name="state"> <value>0</value> </testEq> </params> <delete name="garageOpenLow" egg="lower" pri="1"/> <delete name="garageOpenHigh" egg="lower" pri="1"/> <delete name="somethingActive" egg="lower" pri="1"/> </event> </eventsource> </eventtype>  <step id="defaultHigh" redMin="16" redMax="16" blueMin="16" blueMax="16" greenMin="16" greenMax="16"/> <step id="defaultLow" redMin="8" redMax="8" blueMin="8" blueMax="8" greenMin="8" greenMax="8"/> <step id="garageOpenLow" redMin="32" redMax="32" blueMin="32" blueMax="32" greenMin="32" greenMax="32"/> <step id="garageOpenHigh" redMin="48" redMax="48" blueMin="48" blueMax="48" greenMin="48" greenMax="48"/> <step id="somethingActive" redMin="4" redMax="4" blueMin="4" blueMax="4" greenMin="4" greenMax="4"/>   <egg name="lower" adr="localhost:20999" redChn="0" greenChn="1" blueChn="2" cmdTemplate="/hid.spi?com=:DM%(redChn)s;%(red)s;%(greenChn)s;%(green)s;%(blueChn)s;%(blue)s:"/> <egg name="upper" adr="localhost:20999" redChn="3" greenChn="4" blueChn="5" cmdTemplate="/hid.spi?com=:DM%(redChn)s;%(red)s;%(greenChn)s;%(green)s;%(blueChn)s;%(blue)s:"/> </eventInterface> </eventInterfaces> """ class TestEggNunciate(unittest.TestCase): def setUp(self): self._log = logging.getLogger( "TestEggNunciate" ) self._log.debug( "\n\nsetUp" ) self.httpServer = None self.httpServer = TestHttpServer() self.httpServer.start() self.router = None self.loader = None def tearDown(self): self._log.debug( "\n\ntearDown" ) time.sleep(1) # allow twisted time if self.loader: self.loader.stop() # all tasks self.loader = None self.router = None if self.httpServer: self.httpServer.stop() time.sleep(5) def expectNhttp(self, cnt ): idx = 20 while (len(self.httpServer.requests()) < cnt) and (idx > 0): time.sleep(0.05) idx = idx - 1 if ( len(self.httpServer.requests()) != cnt): for req in self.httpServer.requests(): self._log.debug( "request %s", req ) self.assertEqual( len(self.httpServer.requests()), cnt) # Actual tests follow def testEggNunciateSingle(self): self._log.debug( "\ntestEggNunciateSingle" ) self.loader = EventRouterLoader() self.loader.loadHandlers( getDictFromXmlString(testConfigEggNunciate1) ) self.loader.start() # all tasks self.router = self.loader.getEventRouter() self.router.publish( EventAgent("TestEggNunciate"), Events.evtRuntime5 ) # set up base scene self.router.publish( EventAgent("TestEggNunciate"), Events.evtSecond5 ) # expect single HTTP request for default scene step 1. self._log.debug( "testHttpAction %s", self.httpServer.requests() ) self.expectNhttp( 1) self.assertEqual( self.httpServer.requests()[0], "/hid.spi?com=:DM0;16;1;16;2;16:" ) self.router.publish( EventAgent("TestEggNunciate"), Events.evtSecond5 ) # expect second HTTP request for default scene step 2. self._log.debug( "testHttpAction %s", self.httpServer.requests() ) self.expectNhttp( 2) self.assertEqual( self.httpServer.requests()[1], "/hid.spi?com=:DM0;8;1;8;2;8:" ) self.router.publish( EventAgent("TestEggNunciate"), Events.evtSecond5 ) # expect third HTTP request for default scene step 1. self._log.debug( "testHttpAction %s", self.httpServer.requests() ) self.expectNhttp( 3) self.assertEqual( self.httpServer.requests()[2], "/hid.spi?com=:DM0;16;1;16;2;16:" ) def testEggNunciateTwo(self): self._log.debug( "\ntestEggNunciateTwo" ) self.loader = EventRouterLoader() self.loader.loadHandlers( getDictFromXmlString(testConfigEggNunciate2) ) self.loader.start() # all tasks self.router = self.loader.getEventRouter() self.router.publish( EventAgent("TestEggNunciate"), Events.evtRuntime5 ) # set up base scene self.router.publish( EventAgent("TestEggNunciate"), Events.evtSecond5 ) # expect single HTTP request for default scene step 1. self._log.debug( "testHttpAction %s", self.httpServer.requests() ) self.expectNhttp( 2) self.assertEqual( self.httpServer.requests()[0], "/hid.spi?com=:DM3;16;4;16;5;16:" ) self.assertEqual( self.httpServer.requests()[1], "/hid.spi?com=:DM0;16;1;16;2;16:" ) self.router.publish( EventAgent("TestEggNunciate"), Events.evtSecond5 ) # expect second HTTP request for default scene step 2. self._log.debug( "testHttpAction %s", self.httpServer.requests() ) self.expectNhttp( 4) self.assertEqual( self.httpServer.requests()[3], "/hid.spi?com=:DM0;8;1;8;2;8:" ) self.assertEqual( self.httpServer.requests()[2], "/hid.spi?com=:DM3;8;4;8;5;8:" ) self.router.publish( EventAgent("TestEggNunciate"), Events.evtSecond5 ) # expect third HTTP request for default scene step 1. self._log.debug( "testHttpAction %s", self.httpServer.requests() ) self.expectNhttp( 6) self.assertEqual( self.httpServer.requests()[5], "/hid.spi?com=:DM0;16;1;16;2;16:" ) self.assertEqual( self.httpServer.requests()[4], "/hid.spi?com=:DM3;16;4;16;5;16:" ) def subCheckSeq(self, reqs): # each time tick causes another http request idx = len(self.httpServer.requests()) for i in range(len(reqs)): self.router.publish( EventAgent("TestEggNunciate"), Events.evtSecond5 ) self.expectNhttp( idx+1 ) hReq = self.httpServer.requests()[idx] self._log.debug( "testHttpAction (%u:%s) (%u:%s)", idx, hReq, i, reqs[i] ) self.assertEqual( hReq, reqs[i] ) idx = idx + 1 def subTestGarageOpen(self): self._log.debug( "\nsubTestGarageOpen" ) self.router.publish( EventAgent("TestEggNunciate"), Events.evtDO_0_on ) reqs = ["/hid.spi?com=:DM0;32;1;32;2;32:", "/hid.spi?com=:DM0;48;1;48;2;48:", "/hid.spi?com=:DM0;4;1;4;2;4:", "/hid.spi?com=:DM0;32;1;32;2;32:", "/hid.spi?com=:DM0;48;1;48;2;48:", "/hid.spi?com=:DM0;4;1;4;2;4:", ] self.subCheckSeq(reqs) def subTestGarageClose(self): self._log.debug( "\nsubTestGarageClose" ) self.router.publish( EventAgent("TestEggNunciate"), Events.evtDO_0_off ) reqs = ["/hid.spi?com=:DM0;16;1;16;2;16:", "/hid.spi?com=:DM0;8;1;8;2;8:", "/hid.spi?com=:DM0;16;1;16;2;16:", "/hid.spi?com=:DM0;8;1;8;2;8:", ] self.subCheckSeq(reqs) # Actual tests follow def testEggNunciateGarage(self): self._log.debug( "\ntestEggNunciateEvent" ) self.loader = EventRouterLoader() self.loader.loadHandlers( getDictFromXmlString(testConfigEggNunciate1) ) self.loader.start() # all tasks self.router = self.loader.getEventRouter() self.router.publish( EventAgent("TestEggNunciate"), Events.evtRuntime5 ) # set up base scene self.router.publish( EventAgent("TestEggNunciate"), Events.evtSecond5 ) # expect HTTP request for default scene 1 step 1. self._log.debug( "testHttpAction %s", self.httpServer.requests() ) self.expectNhttp( 1) hReq = self.httpServer.requests()[len(self.httpServer.requests())-1] self.assertEqual( hReq, "/hid.spi?com=:DM0;16;1;16;2;16:" ) self.subTestGarageOpen() self.expectNhttp( 7) self.subTestGarageClose() self.expectNhttp( 11) self.subTestGarageOpen() self.expectNhttp( 17) self.subTestGarageClose() self.expectNhttp( 21) def subTestAlarmOn(self): self._log.debug( "\nsubTestAlarmOn" ) self.router.publish( EventAgent("TestEggNunciate"), Events.evtDO_1_on ) reqs = ["/hid.spi?com=:DM0;33;1;33;2;33:", "/hid.spi?com=:DM0;47;1;47;2;47:", "/hid.spi?com=:DM0;4;1;4;2;4:", "/hid.spi?com=:DM0;33;1;33;2;33:", "/hid.spi?com=:DM0;47;1;47;2;47:", "/hid.spi?com=:DM0;4;1;4;2;4:", ] self.subCheckSeq(reqs) def subTestAlarmOff(self): self._log.debug( "\nsubTestAlarmOn" ) self.router.publish( EventAgent("TestEggNunciate"), Events.evtDO_1_off ) reqs = ["/hid.spi?com=:DM0;16;1;16;2;16:", "/hid.spi?com=:DM0;8;1;8;2;8:", "/hid.spi?com=:DM0;16;1;16;2;16:", "/hid.spi?com=:DM0;8;1;8;2;8:", ] self.subCheckSeq(reqs) # Actual tests follow def testEggNunciateAlarm(self): self._log.debug( "\ntestEggNunciateAlarm" ) self.loader = EventRouterLoader() self.loader.loadHandlers( getDictFromXmlString(testConfigEggNunciate1) ) self.loader.start() # all tasks self.router = self.loader.getEventRouter() self.router.publish( EventAgent("TestEggNunciate"), Events.evtRuntime5 ) # set up base scene self.router.publish( EventAgent("TestEggNunciate"), Events.evtSecond5 ) # expect HTTP request for default scene 1 step 1. self._log.debug( "testHttpAction %s", self.httpServer.requests() ) self.expectNhttp( 1) self.assertEqual( self.httpServer.requests()[0], "/hid.spi?com=:DM0;16;1;16;2;16:" ) self.subTestAlarmOn() self.subTestAlarmOff() self.subTestAlarmOn() self.subTestAlarmOff() # Actual tests follow def testEggNunciateGarageAlarm(self): self._log.debug( "\ntestEggNunciateGarageAlarm" ) self.loader = EventRouterLoader() self.loader.loadHandlers( getDictFromXmlString(testConfigEggNunciate1) ) self.loader.start() # all tasks self.router = self.loader.getEventRouter() self.router.publish( EventAgent("TestEggNunciate"), Events.evtRuntime5 ) # set up base scene self.router.publish( EventAgent("TestEggNunciate"), Events.evtSecond5 ) # expect HTTP request for default scene 1 step 1. self.expectNhttp( 1) hReq = self.httpServer.requests()[len(self.httpServer.requests())-1] self._log.debug( "testHttpAction %u:%s", (len(self.httpServer.requests())-1,hReq) ) self.assertEqual( hReq, "/hid.spi?com=:DM0;16;1;16;2;16:" ) self.subTestGarageOpen() self.expectNhttp( 7) self.router.publish( EventAgent("TestEggNunciate"), Events.evtDO_1_on ) reqs = ["/hid.spi?com=:DM0;33;1;33;2;33:", "/hid.spi?com=:DM0;47;1;47;2;47:", "/hid.spi?com=:DM0;4;1;4;2;4:", "/hid.spi?com=:DM0;32;1;32;2;32:", "/hid.spi?com=:DM0;48;1;48;2;48:", "/hid.spi?com=:DM0;4;1;4;2;4:", "/hid.spi?com=:DM0;33;1;33;2;33:", "/hid.spi?com=:DM0;47;1;47;2;47:", "/hid.spi?com=:DM0;4;1;4;2;4:", "/hid.spi?com=:DM0;32;1;32;2;32:", "/hid.spi?com=:DM0;48;1;48;2;48:", "/hid.spi?com=:DM0;4;1;4;2;4:", ] self.subCheckSeq(reqs) self.router.publish( EventAgent("TestEggNunciate"), Events.evtDO_0_off ) # garage closed reqs = ["/hid.spi?com=:DM0;33;1;33;2;33:", "/hid.spi?com=:DM0;47;1;47;2;47:", "/hid.spi?com=:DM0;4;1;4;2;4:", "/hid.spi?com=:DM0;33;1;33;2;33:", "/hid.spi?com=:DM0;47;1;47;2;47:", "/hid.spi?com=:DM0;4;1;4;2;4:", ] self.subCheckSeq(reqs) self.router.publish( EventAgent("TestEggNunciate"), Events.evtDO_1_off ) # Alarm reqs = ["/hid.spi?com=:DM0;16;1;16;2;16:", "/hid.spi?com=:DM0;8;1;8;2;8:", "/hid.spi?com=:DM0;16;1;16;2;16:", "/hid.spi?com=:DM0;8;1;8;2;8:", ] self.subCheckSeq(reqs) def testDummy(self): pass from MiscLib import TestUtils def getTestSuite(select="unit"): """ Get test suite select is one of the following: "unit" return suite of unit tests only "component" return suite of unit and component tests "all" return suite of unit, component and integration tests "pending" return suite of pending tests name a single named test to be run """ testdict = { "unit": [ "testEggNunciateSingle" , "testEggNunciateTwo" , "testEggNunciateGarage" , "testEggNunciateAlarm" , "testEggNunciateGarageAlarm" ], "component": [ "testDummy" ], "integration": [ "testDummy" ], "pending": [ "testDummy" ] } return TestUtils.getTestSuite(TestEggNunciate, testdict, select=select) # Run unit tests directly from command line if __name__ == "__main__": TestUtils.runTests("TestEggNunciate.log", getTestSuite, sys.argv)

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py

Python

chat/views.py

vlajna95/crazy_app

ffcda00e6247e01273be443a723ea4538d683420

[ "CC0-1.0" ]

null

chat/views.py

vlajna95/crazy_app

ffcda00e6247e01273be443a723ea4538d683420

[ "CC0-1.0" ]

null

chat/views.py

vlajna95/crazy_app

ffcda00e6247e01273be443a723ea4538d683420

[ "CC0-1.0" ]

null

from django.shortcuts import render def index(request): return render(request, 'chat/index.html') def room(request, room_name): return render(request, 'chat/room.html', {'user': request.user, 'room_name': room_name})

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24e79ee27cfc4ab49166ad541f0b70ebac828884

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py

Python

EqGNN/models/__init__.py

urielsinger/EqGNN

c757bb44b1750b08e37f8827363b947e5cc92396

[ "Apache-2.0" ]

1

2021-08-20T05:56:26.000Z

EqGNN/models/__init__.py

urielsinger/EqGNN

c757bb44b1750b08e37f8827363b947e5cc92396

[ "Apache-2.0" ]

null

EqGNN/models/__init__.py

urielsinger/EqGNN

c757bb44b1750b08e37f8827363b947e5cc92396

[ "Apache-2.0" ]

null

from EqGNN.models.GNN import GNN from EqGNN.models.EqGNN import GraphModule, AdversarialGraphModule, GraphModel

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6

24f8b43d463fbb9b8a25df3cd7be97d3e38eda32

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py

Python

fcs_api_py/__init__.py

JoaquinChartier/fcs_api_python

530da72d7fb26a569eb7106dfcb5ad6d320b171f

[ "MIT" ]

null

fcs_api_py/__init__.py

JoaquinChartier/fcs_api_python

530da72d7fb26a569eb7106dfcb5ad6d320b171f

[ "MIT" ]

null

fcs_api_py/__init__.py

JoaquinChartier/fcs_api_python

530da72d7fb26a569eb7106dfcb5ad6d320b171f

[ "MIT" ]

null

from .main import Forex

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1

0

6

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48

py

Python

qmpy/analysis/symmetry/__init__.py

WalterjhShen/qmpy

686e18cecbb82a6bb523249ac1779a99fb865350

[ "MIT" ]

2

2019-04-25T17:49:16.000Z

2019-06-01T01:36:04.000Z

qmpy/analysis/symmetry/__init__.py

WalterjhShen/qmpy

686e18cecbb82a6bb523249ac1779a99fb865350

[ "MIT" ]

10

2018-07-05T03:19:58.000Z

2019-03-24T13:05:14.000Z

qmpy/analysis/symmetry/__init__.py

WalterjhShen/qmpy

686e18cecbb82a6bb523249ac1779a99fb865350

[ "MIT" ]

1

2020-04-30T14:08:45.000Z

from routines import * from spacegroup import *

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70a808a9cb2063c1de09b134440db37af50fb8fc

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py

Python

prjcom/roc/migrations/0001_initial.py

ds-suyog/corporanalytics

797da93bdca33bef3d7eeb7b047c1b93fafea201

[ "MIT" ]

null

prjcom/roc/migrations/0001_initial.py

ds-suyog/corporanalytics

797da93bdca33bef3d7eeb7b047c1b93fafea201

[ "MIT" ]

3

2019-10-21T20:40:21.000Z

2019-12-08T15:42:32.000Z

prjcom/roc/migrations/0001_initial.py

ds-suyog/corporanalytics

797da93bdca33bef3d7eeb7b047c1b93fafea201

[ "MIT" ]

null

# Generated by Django 2.2.2 on 2019-06-15 06:18 from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='DefaultersCompanies', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('cin_number', models.CharField(max_length=100)), ('company_name', models.CharField(max_length=100)), ('dated', models.DateField()), ('insert_time', models.DateField()), ('created_at', models.DateTimeField(auto_now_add=True)), ('updated_at', models.DateTimeField(auto_now=True)), ], ), migrations.CreateModel( name='DefaultersDirectors', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('cin_number', models.CharField(max_length=100, unique=True)), ('company_name', models.CharField(max_length=100)), ('dated', models.DateField(null=True)), ('defaulting_year', models.CharField(max_length=100)), ('insert_time', models.DateField(null=True)), ('name', models.CharField(max_length=100)), ('signatory_id', models.CharField(max_length=100)), ('created_at', models.DateTimeField(auto_now_add=True)), ('updated_at', models.DateTimeField(auto_now=True)), ], ), migrations.CreateModel( name='DefaultersSecretaries', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('cin_number', models.CharField(max_length=100, unique=True)), ('company_name', models.CharField(max_length=100)), ('dated', models.DateField(null=True)), ('defaulting_year', models.CharField(max_length=100)), ('insert_time', models.DateField(null=True)), ('name', models.CharField(max_length=100)), ('signatory_id', models.CharField(max_length=100)), ('created_at', models.DateTimeField(auto_now_add=True)), ('updated_at', models.DateTimeField(auto_now=True)), ], ), migrations.CreateModel( name='DirectorDetails', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('address', models.CharField(max_length=100)), ('base_url', models.CharField(max_length=100)), ('companies', models.CharField(max_length=100)), ('din_number', models.CharField(max_length=100)), ('director_name', models.CharField(max_length=100)), ('dob', models.DateField(null=True)), ('father_lastname', models.CharField(max_length=100)), ('hhh', models.CharField(max_length=100)), ('insert_time', models.DateField(null=True)), ('status', models.FloatField()), ('storage_id', models.CharField(max_length=100)), ('update_time', models.DateField(null=True)), ('url', models.CharField(max_length=100)), ('vfv', models.CharField(max_length=100)), ('created_at', models.DateTimeField(auto_now_add=True)), ('updated_at', models.DateTimeField(auto_now=True)), ], ), migrations.CreateModel( name='DirectorDisqualifieds', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('cin_number', models.CharField(max_length=100)), ('company_name', models.CharField(max_length=100)), ('company_status', models.CharField(max_length=100)), ('din_number', models.CharField(max_length=100)), ('director_name', models.CharField(max_length=100)), ('helpdesk_ticket', models.CharField(max_length=100)), ('insert_time', models.DateField()), ('period_from', models.DateField()), ('period_till', models.DateField()), ('reason', models.CharField(max_length=100)), ('roc_code', models.CharField(max_length=100)), ('roc_name', models.CharField(max_length=100)), ('signatory_id', models.CharField(max_length=100)), ('status', models.CharField(max_length=100)), ('created_at', models.DateTimeField(auto_now_add=True)), ('updated_at', models.DateTimeField(auto_now=True)), ], ), migrations.CreateModel( name='RocData', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('agm_date', models.DateTimeField()), ('authorised_capital', models.IntegerField()), ('balancesheet_date', models.DateTimeField()), ('charges', models.CharField(max_length=100)), ('charges_storage_id', models.CharField(max_length=100)), ('cin_number', models.CharField(max_length=100, unique=True)), ('company_category', models.CharField(max_length=100)), ('company_class', models.CharField(max_length=100)), ('company_name', models.CharField(max_length=100)), ('company_status', models.CharField(max_length=100)), ('company_subcategory', models.CharField(max_length=100)), ('designated_partners', models.CharField(max_length=100)), ('email_id', models.CharField(max_length=100)), ('incorporation_country', models.CharField(max_length=100)), ('incorporation_date', models.DateTimeField()), ('insert_time', models.DateTimeField()), ('listed_type', models.CharField(max_length=100)), ('llp_status', models.CharField(max_length=100)), ('main_division', models.CharField(max_length=100)), ('members', models.IntegerField(blank=True, null=True)), ('obligation_contribution', models.IntegerField()), ('other_address', models.CharField(max_length=100)), ('paid_capital', models.IntegerField()), ('partners', models.CharField(max_length=100)), ('registered_address', models.CharField(max_length=100)), ('registration_number', models.CharField(max_length=100)), ('roc_code', models.CharField(max_length=100)), ('signatory', models.CharField(max_length=300)), ('signatory_storage_id', models.CharField(max_length=100)), ('solvency_filed_date', models.DateTimeField()), ('status', models.FloatField()), ('stock', models.CharField(max_length=100)), ('storage_id', models.CharField(max_length=100)), ('update_time', models.DateTimeField()), ('created_at', models.DateTimeField(auto_now_add=True)), ('updated_at', models.DateTimeField(auto_now=True)), ], ), ]

53.426573

114

0.569241

725

7,640

5.768276

0.154483

0.200861

0.241033

0.321377

0.78001

0.737207

0.63869

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0.609039

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142

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0

1

0

1

0

1

0

null

0

6

561ce6d9a07900986c6ca3d62308e171a6a20b09

392

py

Python

spira/core/all.py

qedalab/spira

32e4d2096e298b9fcc5952abd654312dc232a259

[ "MIT" ]

10

2018-07-13T09:46:21.000Z

2021-06-22T13:34:50.000Z

spira/core/all.py

qedalab/spira

32e4d2096e298b9fcc5952abd654312dc232a259

[ "MIT" ]

8

2018-09-09T11:32:40.000Z

2019-10-08T07:47:31.000Z

spira/core/all.py

qedalab/spira

32e4d2096e298b9fcc5952abd654312dc232a259

[ "MIT" ]

7

2019-01-17T18:50:17.000Z

2022-01-13T20:27:52.000Z

from spira.core.parameters.variables import * from spira.core.parameters.restrictions import * from spira.core.parameters.descriptor import * from spira.core.parameters.initializer import * from spira.core.transformable import * from spira.core.transformation import * from spira.core.transforms import * from spira.core.parameters.processors import * from spira.core.decorators import *

28

48

0.816327

50

392

6.4

0.28

0.253125

0.365625

0.475

0.3625

0

0.102041

392

13

49

30.153846

0.909091

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null

1

0

null

0

1

0

1

0

1

0

6

563ddee67c3972dab21cc345e9e15bc6348e4cca

112

py

Python

demo/master/master_app/new_conversations/__init__.py

darksigma/traceless

eed3a35e90b8bbbf272e1f324e1c28de7afe08da

[ "MIT" ]

1

2015-06-19T14:27:52.000Z

server/app/new_conversations/__init__.py

pratheeknagaraj/securechat

eed3a35e90b8bbbf272e1f324e1c28de7afe08da

[ "MIT" ]

null

server/app/new_conversations/__init__.py

pratheeknagaraj/securechat

eed3a35e90b8bbbf272e1f324e1c28de7afe08da

[ "MIT" ]

1

2016-04-09T19:25:11.000Z

from flask import Blueprint new_conversations = Blueprint('new_conversations', __name__) from . import routes

18.666667

60

0.8125

13

112

6.538462

0.615385

0.282353

0.588235

0

0.125

112

5

61

22.4

0.867347

0

0.151786

0

1

0

false

0

0.666667

0

0.666667

1

0

null

1

0

1

0

null

0

1

0

1

0

6

56696632cec5103022d3ea8dab2669aaab42436f

373

py

Python

probables/countminsketch/__init__.py

dekoza/pyprobables

9460471d7e391060874d342945b2352d0a35603f

[ "MIT" ]

null

probables/countminsketch/__init__.py

dekoza/pyprobables

9460471d7e391060874d342945b2352d0a35603f

[ "MIT" ]

null

probables/countminsketch/__init__.py

dekoza/pyprobables

9460471d7e391060874d342945b2352d0a35603f

[ "MIT" ]

null

''' count-min sketch submodule ''' from __future__ import (unicode_literals, absolute_import, print_function) from . countminsketch import (CountMinSketch, HeavyHitters, StreamThreshold, CountMeanSketch, CountMeanMinSketch) __all__ = ['CountMinSketch', 'HeavyHitters', 'StreamThreshold', 'CountMeanSketch', 'CountMeanMinSketch']

37.3

76

0.715818

27

373

9.481481

0.666667

0.203125

0.320313

0.4375

0.578125

0

0.190349

373

9

77

41.444444

0.847682

0.069705

0

0.218289

0

1

0

false

0

0.4

0

0.4

0.2

1

0

null

1

0

1

0

null

0

1

0

6

56816711d2404457a8b234d287d41eb902d5f3f2

341

py

Python

ufmtrends_sdk/__init__.py

UFM-Market-Trends/UFM-Market-Trends-SDK

12d94ca7a905caec3d8038f4df5631097cbcf1af

[ "CC0-1.0" ]

null

ufmtrends_sdk/__init__.py

UFM-Market-Trends/UFM-Market-Trends-SDK

12d94ca7a905caec3d8038f4df5631097cbcf1af

[ "CC0-1.0" ]

null

ufmtrends_sdk/__init__.py

UFM-Market-Trends/UFM-Market-Trends-SDK

12d94ca7a905caec3d8038f4df5631097cbcf1af

[ "CC0-1.0" ]

null

# This is so that we can write # `import ufmtrends_sdk` or `import test from ufmtrends_sdk` # instead of # `from ufmtrends_sdk.functions import test` from .functions import test, get_yearly_values, get_yearly_variation from .functions import get_yearonyear_variation from .functions import get_accumulated_values, get_accumulated_variation

42.625

72

0.832845

49

341

5.530612

0.44898

0.221402

0.210332

0.206642

0.228782

0

0.117302

341

8

72

42.625

0.900332

0.416422

0

1

0

true

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

3b1b8410f34818f7f5c68ed5dc8de7be56272b86

49

py

Python

alpinonaf/__init__.py

brdiep113/morphosyntactic_parser_nl

68519dbe3921f81a273dd8b3969878d6953bf41e

[ "Apache-2.0" ]

4

2018-01-17T05:34:37.000Z

2020-04-05T00:40:11.000Z

alpinonaf/__init__.py

brdiep113/morphosyntactic_parser_nl

68519dbe3921f81a273dd8b3969878d6953bf41e

[ "Apache-2.0" ]

26

2018-03-14T08:48:48.000Z

2018-05-15T15:45:35.000Z

morphosyntactic_parser_nl/alpinonaf/__init__.py

NLeSC/EviDENce

ff51f27e392076e51ad56236d039d38cb5fcadce

[ "Apache-2.0" ]

7

2016-02-05T08:06:58.000Z

2021-05-20T19:57:47.000Z

from .morph_syn_parser import parse, __version__

24.5

48

0.857143

7

49

5.142857

1

0

0.102041

49

1

49

0.818182

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

3b439ac9f28cdc083d7b00ba4e65324247d80034

5,770

py

Python

wikipedia_workload/de/uni-stuttgart/iaas/utility_calculation/cost_calculation.py

sgomezsaez/SCARF-Evaluation

a118039ddd62798ca93b78cb968d6ee8b15ec6f2

[ "Apache-2.0" ]

null

wikipedia_workload/de/uni-stuttgart/iaas/utility_calculation/cost_calculation.py

sgomezsaez/SCARF-Evaluation

a118039ddd62798ca93b78cb968d6ee8b15ec6f2

[ "Apache-2.0" ]

null

wikipedia_workload/de/uni-stuttgart/iaas/utility_calculation/cost_calculation.py

sgomezsaez/SCARF-Evaluation

a118039ddd62798ca93b78cb968d6ee8b15ec6f2

[ "Apache-2.0" ]

null

import constants as cs def calculate_price_aws_vm (vm_type, num_vms, usage_duration_hours, num_months, storage_size, data_transfer): # VM Price vm_price_per_hour = 0 if vm_type == cs.AWS_EC2_VM_T2_SMALL: vm_price_per_hour = cs.AWS_EC2_VM_T2_SMALL_PRICE * num_vms elif vm_type == cs.AWS_EC2_VM_M4_LARGE: vm_price_per_hour = cs.AWS_EC2_VM_M4_LARGE_PRICE * num_vms elif vm_type == cs.AWS_EC2_VM_M4_XLARGE: vm_price_per_hour = cs.AWS_EC2_VM_M4_XLARGE_PRICE * num_vms else: vm_price_per_hour = 0 vm_price = vm_price_per_hour * usage_duration_hours # Storage Price per GB Month storage_price = num_vms * cs.AWS_EC2_STORAGE_GB_MONTH_PRICE * storage_size * num_months # Data Transfer if data_transfer > 0: data_transfer_egress = float(data_transfer) - cs.AWS_EC2_DATA_TRANSFER_FREE_GB data_transfer_price = cs.AWS_EC2_DATA_TRANSFER_GB_PRICE * data_transfer_egress else: data_transfer_price = 0 return vm_price + storage_price + data_transfer_price def calculate_price_aws_vm_internal (vm_type, num_vms, usage_duration_hours, num_months, storage_size, data_transfer): # VM Price vm_price_per_hour = 0 if vm_type == cs.AWS_EC2_VM_T2_SMALL: vm_price_per_hour = cs.AWS_EC2_VM_T2_SMALL_PRICE * num_vms elif vm_type == cs.AWS_EC2_VM_M4_LARGE: vm_price_per_hour = cs.AWS_EC2_VM_M4_LARGE_PRICE * num_vms elif vm_type == cs.AWS_EC2_VM_M4_XLARGE: vm_price_per_hour = cs.AWS_EC2_VM_M4_XLARGE_PRICE * num_vms else: vm_price_per_hour = 0 vm_price = vm_price_per_hour * usage_duration_hours # Storage Price per GB Month storage_price = num_vms * cs.AWS_EC2_STORAGE_GB_MONTH_PRICE * storage_size * num_months return vm_price + storage_price def calculate_price_aws_rds (rds_type, num_rds, usage_duration_hours, num_months, storage_size, data_transfer_out): rds_price_per_hour = 0 if rds_type == cs.AWS_RDS_M4_LARGE: rds_price_per_hour = cs.AWS_RDS_DB_M4_LARGE_PRICE * num_rds rds_price = rds_price_per_hour * num_rds * usage_duration_hours storage_price = cs.AWS_RDS_STORAGE_GB_MONTH_PRICE * storage_size * num_months if data_transfer_out > 0: data_transfer_egress = float(data_transfer_out) - cs.AWS_RDS_DATA_TRANSFER_FREE_GB data_transfer_price = cs.AWS_RDS_DATA_TRANSFER_GB_PRICE * data_transfer_egress else: data_transfer_price = 0 return rds_price + storage_price + data_transfer_price def calculate_price_aws_beanstalk (vm_type, num_vms, usage_duration_hours, num_months, storage_size, data_transfer, num_elb): # VM Price vm_price_per_hour = 0 vm_price = calculate_price_aws_vm(vm_type, num_vms, usage_duration_hours, num_months, storage_size, data_transfer) # Load Balancing Price elb_price = num_elb * (cs.AWS_LB_PRICE * usage_duration_hours + data_transfer * cs.AWS_LB_DATA_TRANSFER_GB_PRICE) return vm_price + elb_price def calculate_price_aws_ecs (vm_type, num_vms, usage_duration_hours, num_months, storage_size, data_transfer, num_elb): # VM Price vm_price_per_hour = 0 vm_price = calculate_price_aws_vm(vm_type, num_vms, usage_duration_hours, num_months, storage_size, data_transfer) # Load Balancing Price elb_price = num_elb * (cs.AWS_LB_PRICE * usage_duration_hours + data_transfer * cs.AWS_LB_DATA_TRANSFER_GB_PRICE) return vm_price + elb_price def calculate_price_azure_vm (vm_type, num_vms, usage_duration_hours, num_months, storage_size, data_transfer): # VM Price vm_price_per_hour = 0 if vm_type == cs.AZURE_VM_DS1: vm_price_per_hour = cs.AZURE_VM_DS1_PRICE * num_vms elif vm_type == cs.AZURE_VM_DS2: vm_price_per_hour = cs.AZURE_VM_DS2_PRICE * num_vms elif vm_type == cs.AZURE_VM_DS3: vm_price_per_hour = cs.AZURE_VM_DS3_PRICE * num_vms else: vm_price_per_hour = 0 vm_price = vm_price_per_hour * usage_duration_hours # Data Transfer if data_transfer > 0: data_transfer_egress = float(data_transfer) - cs.AZURE_DATA_TRANSFER_FREE_GB data_transfer_price = cs.AZURE_EC2_DATA_TRANSFER_GB_PRICE * data_transfer_egress else: data_transfer_price = 0 return vm_price + data_transfer_price def calculate_price_azure_container (vm_type_master, vm_type_agents, num_vms_agents, usage_duration_hours, num_months, storage_size, data_transfer): # VM Price vm_price_master_per_hour = 0 if vm_type_master == cs.AZURE_VM_DS1: vm_price_master_per_hour = cs.AZURE_VM_DS1_PRICE * 1 elif vm_type_master == cs.AZURE_VM_DS2: vm_price_master_per_hour = cs.AZURE_VM_DS2_PRICE * 1 elif vm_type_master == cs.AZURE_VM_DS3: vm_price_master_per_hour = cs.AZURE_VM_DS3_PRICE * 1 else: vm_price_master_per_hour = 0 vm_price_master = vm_price_master_per_hour * usage_duration_hours vm_price_agent_per_hour = 0 if vm_type_agents == cs.AZURE_VM_DS1: vm_price_agent_per_hour = cs.AZURE_VM_DS1_PRICE * num_vms_agents elif vm_type_agents == cs.AZURE_VM_DS2: vm_price_agent_per_hour = cs.AZURE_VM_DS2_PRICE * num_vms_agents elif vm_type_agents == cs.AZURE_VM_DS3: vm_price_agent_per_hour = cs.AZURE_VM_DS3_PRICE * num_vms_agents else: vm_price_agent_per_hour = 0 vm_price_agents = vm_price_master_per_hour * usage_duration_hours # Data Transfer if data_transfer > 0: data_transfer_egress = float(data_transfer) - cs.AZURE_DATA_TRANSFER_FREE_GB data_transfer_price = cs.AZURE_EC2_DATA_TRANSFER_GB_PRICE * data_transfer_egress else: data_transfer_price = 0 return vm_price_master + vm_price_agents + data_transfer_price

36.289308

148

0.755459

944

5,770

4.049788

0.060381

0.163223

0.072195

0.073241

0.935653

0.886738

0.846194

0.818729

0.763013

0.6644

0

0.015812

0.188908

5,770

159

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36.289308

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0

null

0

1

0

null

0

6

3b6e00b63c9fb82f9fc27882a00ac566449920ba

5,845

py

Python

20_edgetpu-deeplab/01_float32/02_weight_quantization.py

khanfarhan10/PINTO_model_zoo

4cad2e506d8c0fb604aa7b5f84115a840ab59ba1

[ "MIT" ]

1,529

2019-12-11T13:36:23.000Z

2022-03-31T18:38:27.000Z

20_edgetpu-deeplab/01_float32/02_weight_quantization.py

khanfarhan10/PINTO_model_zoo

4cad2e506d8c0fb604aa7b5f84115a840ab59ba1

[ "MIT" ]

200

2020-01-06T09:24:42.000Z

2022-03-31T17:29:08.000Z

20_edgetpu-deeplab/01_float32/02_weight_quantization.py

khanfarhan10/PINTO_model_zoo

4cad2e506d8c0fb604aa7b5f84115a840ab59ba1

[ "MIT" ]

288

2020-02-21T14:56:02.000Z

2022-03-30T03:00:35.000Z

import tensorflow as tf ### Tensorflow v1.15.2 #tf.compat.v1.enable_eager_execution() graph_def_file="frozen_inference_graph_257_os16.pb" input_arrays=["ImageTensor"] output_arrays=['ArgMax'] input_tensor={"ImageTensor":[1,257,257,3]} # Weight Quantization - Input/Output=float32 #converter = tf.lite.TFLiteConverter.from_saved_model('./saved_model') converter = tf.lite.TFLiteConverter.from_frozen_graph(graph_def_file, input_arrays, output_arrays,input_tensor) #converter.experimental_new_converter = True converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE] tflite_quant_model = converter.convert() with open('./edgetpu_deeplab_257_os16_weight_quant.tflite', 'wb') as w: w.write(tflite_quant_model) print("Integer Quantization complete! - edgetpu_deeplab_257_os16_weight_quant.tflite") graph_def_file="frozen_inference_graph_257_os32.pb" input_arrays=["ImageTensor"] output_arrays=['ArgMax'] input_tensor={"ImageTensor":[1,257,257,3]} # Weight Quantization - Input/Output=float32 #converter = tf.lite.TFLiteConverter.from_saved_model('./saved_model') converter = tf.lite.TFLiteConverter.from_frozen_graph(graph_def_file, input_arrays, output_arrays,input_tensor) #converter.experimental_new_converter = True converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE] tflite_quant_model = converter.convert() with open('./edgetpu_deeplab_257_os32_weight_quant.tflite', 'wb') as w: w.write(tflite_quant_model) print("Integer Quantization complete! - edgetpu_deeplab_257_os32_weight_quant.tflite") graph_def_file="frozen_inference_graph_321_os16.pb" input_arrays=["ImageTensor"] output_arrays=['ArgMax'] input_tensor={"ImageTensor":[1,321,321,3]} # Weight Quantization - Input/Output=float32 #converter = tf.lite.TFLiteConverter.from_saved_model('./saved_model') converter = tf.lite.TFLiteConverter.from_frozen_graph(graph_def_file, input_arrays, output_arrays,input_tensor) #converter.experimental_new_converter = True converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE] tflite_quant_model = converter.convert() with open('./edgetpu_deeplab_321_os16_weight_quant.tflite', 'wb') as w: w.write(tflite_quant_model) print("Integer Quantization complete! - edgetpu_deeplab_321_os16_weight_quant.tflite") graph_def_file="frozen_inference_graph_321_os32.pb" input_arrays=["ImageTensor"] output_arrays=['ArgMax'] input_tensor={"ImageTensor":[1,321,321,3]} # Weight Quantization - Input/Output=float32 #converter = tf.lite.TFLiteConverter.from_saved_model('./saved_model') converter = tf.lite.TFLiteConverter.from_frozen_graph(graph_def_file, input_arrays, output_arrays,input_tensor) #converter.experimental_new_converter = True converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE] tflite_quant_model = converter.convert() with open('./edgetpu_deeplab_321_os32_weight_quant.tflite', 'wb') as w: w.write(tflite_quant_model) print("Integer Quantization complete! - edgetpu_deeplab_321_os32_weight_quant.tflite") graph_def_file="frozen_inference_graph_513_os16.pb" input_arrays=["ImageTensor"] output_arrays=['ArgMax'] input_tensor={"ImageTensor":[1,513,513,3]} # Weight Quantization - Input/Output=float32 #converter = tf.lite.TFLiteConverter.from_saved_model('./saved_model') converter = tf.lite.TFLiteConverter.from_frozen_graph(graph_def_file, input_arrays, output_arrays,input_tensor) #converter.experimental_new_converter = True converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE] tflite_quant_model = converter.convert() with open('./edgetpu_deeplab_513_os16_weight_quant.tflite', 'wb') as w: w.write(tflite_quant_model) print("Integer Quantization complete! - edgetpu_deeplab_513_os16_weight_quant.tflite") graph_def_file="frozen_inference_graph_513_os32.pb" input_arrays=["ImageTensor"] output_arrays=['ArgMax'] input_tensor={"ImageTensor":[1,513,513,3]} # Weight Quantization - Input/Output=float32 #converter = tf.lite.TFLiteConverter.from_saved_model('./saved_model') converter = tf.lite.TFLiteConverter.from_frozen_graph(graph_def_file, input_arrays, output_arrays,input_tensor) #converter.experimental_new_converter = True converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE] tflite_quant_model = converter.convert() with open('./edgetpu_deeplab_513_os32_weight_quant.tflite', 'wb') as w: w.write(tflite_quant_model) print("Integer Quantization complete! - edgetpu_deeplab_513_os32_weight_quant.tflite") graph_def_file="frozen_inference_graph_769_os16.pb" input_arrays=["ImageTensor"] output_arrays=['ArgMax'] input_tensor={"ImageTensor":[1,769,769,3]} # Weight Quantization - Input/Output=float32 #converter = tf.lite.TFLiteConverter.from_saved_model('./saved_model') converter = tf.lite.TFLiteConverter.from_frozen_graph(graph_def_file, input_arrays, output_arrays,input_tensor) #converter.experimental_new_converter = True converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE] tflite_quant_model = converter.convert() with open('./edgetpu_deeplab_769_os16_weight_quant.tflite', 'wb') as w: w.write(tflite_quant_model) print("Integer Quantization complete! - edgetpu_deeplab_769_os16_weight_quant.tflite") graph_def_file="frozen_inference_graph_769_os32.pb" input_arrays=["ImageTensor"] output_arrays=['ArgMax'] input_tensor={"ImageTensor":[1,769,769,3]} # Weight Quantization - Input/Output=float32 #converter = tf.lite.TFLiteConverter.from_saved_model('./saved_model') converter = tf.lite.TFLiteConverter.from_frozen_graph(graph_def_file, input_arrays, output_arrays,input_tensor) #converter.experimental_new_converter = True converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE] tflite_quant_model = converter.convert() with open('./edgetpu_deeplab_769_os32_weight_quant.tflite', 'wb') as w: w.write(tflite_quant_model) print("Integer Quantization complete! - edgetpu_deeplab_769_os32_weight_quant.tflite")

44.618321

111

0.821728

798

5,845

5.645363

0.075188

0.031964

0.042619

0.106548

0.985572

0.972475

0

0.037368

0.06142

5,845

131

112

44.618321

0.783813

0.221557

0

0.691358

0

0.330827

0.219372

0

1

0

false

0

0.012346

0

0.012346

0.098765

0

null

0

1

0

null

0

6

8eb58792f929a57521a8d7401ab81d64f831a901

95

py

Python

With_backend/Feasta/login/admin.py

HILAYTRIVEDI/Feasta_Backend

25c785e5ced66be05cabe44cb66cc42c8010831f

[ "MIT" ]

null

With_backend/Feasta/login/admin.py

HILAYTRIVEDI/Feasta_Backend

25c785e5ced66be05cabe44cb66cc42c8010831f

[ "MIT" ]

null

With_backend/Feasta/login/admin.py

HILAYTRIVEDI/Feasta_Backend

25c785e5ced66be05cabe44cb66cc42c8010831f

[ "MIT" ]

2

2020-10-27T18:46:54.000Z

2020-10-27T18:47:18.000Z

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

19

32

0.831579

15

95

5.133333

0.666667

0.207792

0

0.105263

95

4

33

23.75

0.905882

0

1

0

true

0

0.666667

0

0.666667

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

8eb963ac6381a8062b056aab855107f0f0898b80

143

py

Python

djangobb_forum/tests/__init__.py

tuffnatty/DjangoBB

423607675e9501f6ff5579d86c30540e377c4742

[ "BSD-3-Clause" ]

121

2016-02-16T09:05:15.000Z

2022-03-30T21:17:15.000Z

djangobb_forum/tests/__init__.py

tuffnatty/DjangoBB

423607675e9501f6ff5579d86c30540e377c4742

[ "BSD-3-Clause" ]

12

2015-05-09T09:27:05.000Z

2016-02-05T14:44:33.000Z

djangobb_forum/tests/__init__.py

tuffnatty/DjangoBB

423607675e9501f6ff5579d86c30540e377c4742

[ "BSD-3-Clause" ]

76

2016-02-11T16:35:52.000Z

2022-01-25T13:26:06.000Z

from .test_forum import * from .test_reputation import * from .test_profile import * from .test_utils import * from .test_templatetags import *

28.6

32

0.797203

20

143

5.45

0.4

0.366972

0.513761

0

0.132867

143

5

32

28.6

0.879032

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

8ed7b42aa31cb29427f7cea6456f289ec3390326

21,659

py

Python

blackjack.py

Collin-Campbell/games

c6c3bb9247e7d793e680ab6237f058ebae12728e

[ "MIT" ]

null

blackjack.py

Collin-Campbell/games

c6c3bb9247e7d793e680ab6237f058ebae12728e

[ "MIT" ]

null

blackjack.py

Collin-Campbell/games

c6c3bb9247e7d793e680ab6237f058ebae12728e

[ "MIT" ]

null

import random import time import sys instructions = """ General rules for how to play Blackjack can be found at: https://bicyclecards.com/how-to-play/blackjack/ Things to know: - An Ace counts as 1 or 11. Face cards count as 10. - A blackjack (A and K/Q/J/10) pays out x1.5 unless the dealer also has a blackjack, which results in a push. - A push or tie with the dealer results in no exchange of coins. - ‘Doubling down’ (possible if total of first two cards dealt equals 9, 10 or 11) doubles the wager. - Choosing to double down results in only being able to take one more card. - House/dealer wins if player goes over 21 first, regardless of dealer’s cards. - This version does not allow for splitting mainly due to the display. """ class Hand: def __init__(self,hand=[],display=[],value=0): self.hand = [] self.display = [] self.value = 0 def deal_card(self): possible_cards = ['A ','2 ','3 ','4 ','5 ','6 ','7 ','8 ','9 ','10','J ','Q ','K '] card = random.choice(possible_cards) card_display = [' -----','| |','| {} |'.format(card),'| |',' -----'] card_values = {'2':2,'3':3,'4':4,'5':5,'6':6,'7':7,'8':8,'9':9,'10':10,'J':10,'Q':10,'K':10,'A':1} self.hand.append(card) self.display.append(card_display) self.value += card_values[card.strip()] def print_value(self): if 'A ' in self.hand and (self.value + 10 < 22): print('{} or {}'.format(self.value, self.value+10)) else: print('{}'.format(self.value)) def print_hand(self): if len(self.display) == 1: for a in self.display[0]: print(a) elif len(self.display) == 2: for a,b in zip(*self.display): print(a + '\t' + b) elif len(self.display) == 3: for a,b,c in zip(*self.display): print(a + '\t' + b + '\t' + c) elif len(self.display) == 4: for a,b,c,d in zip(*self.display): print(a + '\t' + b + '\t' + c + '\t' + d) elif len(self.display) == 5: for a,b,c,d,e in zip(*self.display): print(a + '\t' + b + '\t' + c + '\t' + d + '\t' + e) elif len(self.display) == 6: for a,b,c,d,e,f in zip(*self.display): print(a + '\t' + b + '\t' + c + '\t' + d + '\t' + e + '\t' + f) elif len(self.display) == 7: for a,b,c,d,e,f,g in zip(*self.display): print(a + '\t' + b + '\t' + c + '\t' + d + '\t' + e + '\t' + f + '\t' + g) elif len(self.display) == 8: for a,b,c,d,e,f,g,h in zip(*self.display): print(a + '\t' + b + '\t' + c + '\t' + d + '\t' + e + '\t' + f + '\t' + g + '\t' + h) elif len(self.display) == 9: for a,b,c,d,e,f,g,h,i in zip(*self.display): print(a + '\t' + b + '\t' + c + '\t' + d + '\t' + e + '\t' + f + '\t' + g + '\t' + h + '\t' + i) elif len(self.display) == 10: for a,b,c,d,e,f,g,h,i,j in zip(*self.display): print(a + '\t' + b + '\t' + c + '\t' + d + '\t' + e + '\t' + f + '\t' + g + '\t' + h + '\t' + i + '\t' + j) def game(name,player_pot,wager): name = Hand() dealer = Hand() name.deal_card() name.deal_card() dealer.deal_card() print("\n\nDealer's up card:") dealer.print_hand() dealer.deal_card() # dealer's second card, not showing, but will use to check for blackjack time.sleep(2) print('\nYour hand:') name.print_hand() time.sleep(2) if 'A ' in name.hand and (name.value + 10) == 21: if 'A ' in dealer.hand and (dealer.value + 10) == 21: time.sleep(2) print("\nDealer's hand:") dealer.print_hand() time.sleep(2) print("\nWow, blackjacks all around..") return player_pot else: time.sleep(2) print('\nBlackjack! Congrats, you win the hand!') player_pot += (wager + (wager // 2)) return player_pot elif 'A ' in dealer.hand and (dealer.value + 10) == 21: time.sleep(2) print("\nDealer's hand:") dealer.print_hand() time.sleep(2) print("\nHouse wins with blackjack..") player_pot -= wager return player_pot print('\nYour total:') name.print_value() time.sleep(2) response = '' one_hit = '' if name.value in [9,10,11] and (player_pot >= wager*2): double_down = '' while double_down not in ['YES','NO','Y','N']: double_down = (input('\nWould you like to double down? (Y/N) ')).strip().upper() if double_down in ['YES','Y']: wager += wager response = 'HIT' one_hit = 'YES' # Since dealer's up card has been printed, can finish out dealer's hand on the backend: if 'A ' in dealer.hand and (dealer.value + 10) > 16 and (dealer.value + 10) < 22: dealer.value += 10 elif dealer.value < 17: dealer.deal_card() if 'A ' in dealer.hand and (dealer.value + 10) > 16 and (dealer.value + 10) < 22: dealer.value += 10 elif dealer.value < 17: dealer.deal_card() if 'A ' in dealer.hand and (dealer.value + 10) > 16 and (dealer.value + 10) < 22: dealer.value += 10 elif dealer.value < 17: dealer.deal_card() if 'A ' in dealer.hand and (dealer.value + 10) > 16 and (dealer.value + 10) < 22: dealer.value += 10 elif dealer.value < 17: dealer.deal_card() if 'A ' in dealer.hand and (dealer.value + 10) > 16 and (dealer.value + 10) < 22: dealer.value += 10 elif dealer.value < 17: dealer.deal_card() if 'A ' in dealer.hand and (dealer.value + 10) > 16 and (dealer.value + 10) < 22: dealer.value += 10 elif dealer.value < 17: dealer.deal_card() if 'A ' in dealer.hand and (dealer.value + 10) > 16 and (dealer.value + 10) < 22: dealer.value += 10 elif dealer.value < 17: dealer.deal_card() if 'A ' in dealer.hand and (dealer.value + 10) > 16 and (dealer.value + 10) < 22: dealer.value += 10 elif dealer.value < 17: dealer.deal_card() if 'A ' in dealer.hand and (dealer.value + 10) > 16 and (dealer.value + 10) < 22: dealer.value += 10 #dealer.value is dealer's final score #dealer.print_hand() is dealer's cards displayed time.sleep(1) while response not in ['HIT','STAY']: response = input('\nHit or Stay?: ').strip().upper() if response == 'STAY': if 'A ' in name.hand and (name.value + 10) < 22: name.value += 10 time.sleep(2) print("\nDealer's hand:") dealer.print_hand() time.sleep(2) print("\nDealer's total: {}".format(dealer.value)) time.sleep(2) if dealer.value > 21: print('\n\nDealer busted, you won the hand!') player_pot += wager return player_pot elif name.value > dealer.value: print('\n\nYou won the hand!') player_pot += wager return player_pot elif name.value == dealer.value: print('\n\nWhen push comes to shove..') return player_pot elif name.value < dealer.value: print('\n\nHouse wins.') player_pot -= wager return player_pot else: time.sleep(2) name.deal_card() print('\nYour hand:') name.print_hand() time.sleep(2) print('\nYour total:') name.print_value() time.sleep(1) if name.value > 21: print('\nBusted!') player_pot -= wager return player_pot response = '' if name.value == 21: response = 'STAY' if 'A ' in name.hand and (name.value + 10) == 21: name.value += 10 response = 'STAY' while response not in ['HIT','STAY'] and one_hit == '': response = input('\nHit or Stay?: ').strip().upper() if response == 'STAY' or one_hit == 'YES': if 'A ' in name.hand and (name.value + 10) < 22: name.value += 10 time.sleep(2) print("\nDealer's hand:") dealer.print_hand() time.sleep(2) print("\nDealer's total: {}".format(dealer.value)) time.sleep(2) if dealer.value > 21: print('\n\nDealer busted, you won the hand!') player_pot += wager return player_pot elif name.value > dealer.value: print('\n\nYou won the hand!') player_pot += wager return player_pot elif name.value == dealer.value: print('\n\nWhen push comes to shove..') return player_pot elif name.value < dealer.value: print('\n\nHouse wins.') player_pot -= wager return player_pot else: time.sleep(2) name.deal_card() print('\nYour hand:') name.print_hand() time.sleep(2) print('\nYour total:') name.print_value() time.sleep(1) if name.value > 21: print('\nBusted!') player_pot -= wager return player_pot response = '' if name.value == 21: response = 'STAY' if 'A ' in name.hand and (name.value + 10) == 21: name.value += 10 response = 'STAY' while response not in ['HIT','STAY']: response = input('\nHit or Stay?: ').strip().upper() if response == 'STAY': if 'A ' in name.hand and (name.value + 10) < 22: name.value += 10 time.sleep(2) print("\nDealer's hand:") dealer.print_hand() time.sleep(2) print("\nDealer's total: {}".format(dealer.value)) time.sleep(2) if dealer.value > 21: print('\n\nDealer busted, you won the hand!') player_pot += wager return player_pot elif name.value > dealer.value: print('\n\nYou won the hand!') player_pot += wager return player_pot elif name.value == dealer.value: print('\n\nWhen push comes to shove..') return player_pot elif name.value < dealer.value: print('\n\nHouse wins.') player_pot -= wager return player_pot else: time.sleep(2) name.deal_card() print('\nYour hand:') name.print_hand() time.sleep(2) print('\nYour total:') name.print_value() time.sleep(1) if name.value > 21: print('\nBusted!') player_pot -= wager return player_pot response = '' if name.value == 21: response = 'STAY' if 'A ' in name.hand and (name.value + 10) == 21: name.value += 10 response = 'STAY' while response not in ['HIT','STAY']: response = input('\nHit or Stay?: ').strip().upper() if response == 'STAY': if 'A ' in name.hand and (name.value + 10) < 22: name.value += 10 time.sleep(2) print("\nDealer's hand:") dealer.print_hand() time.sleep(2) print("\nDealer's total: {}".format(dealer.value)) time.sleep(2) if dealer.value > 21: print('\n\nDealer busted, you won the hand!') player_pot += wager return player_pot elif name.value > dealer.value: print('\n\nYou won the hand!') player_pot += wager return player_pot elif name.value == dealer.value: print('\n\nWhen push comes to shove..') return player_pot elif name.value < dealer.value: print('\n\nHouse wins.') player_pot -= wager return player_pot else: time.sleep(2) name.deal_card() print('\nYour hand:') name.print_hand() time.sleep(2) print('\nYour total:') name.print_value() time.sleep(1) if name.value > 21: print('\nBusted!') player_pot -= wager return player_pot response = '' if name.value == 21: response = 'STAY' if 'A ' in name.hand and (name.value + 10) == 21: name.value += 10 response = 'STAY' while response not in ['HIT','STAY']: response = input('\nHit or Stay?: ').strip().upper() if response == 'STAY': if 'A ' in name.hand and (name.value + 10) < 22: name.value += 10 time.sleep(2) print("\nDealer's hand:") dealer.print_hand() time.sleep(2) print("\nDealer's total: {}".format(dealer.value)) time.sleep(2) if dealer.value > 21: print('\n\nDealer busted, you won the hand!') player_pot += wager return player_pot elif name.value > dealer.value: print('\n\nYou won the hand!') player_pot += wager return player_pot elif name.value == dealer.value: print('\n\nWhen push comes to shove..') return player_pot elif name.value < dealer.value: print('\n\nHouse wins.') player_pot -= wager return player_pot else: time.sleep(2) name.deal_card() print('\nYour hand:') name.print_hand() time.sleep(2) print('\nYour total:') name.print_value() time.sleep(1) if name.value > 21: print('\nBusted!') player_pot -= wager return player_pot response = '' if name.value == 21: response = 'STAY' if 'A ' in name.hand and (name.value + 10) == 21: name.value += 10 response = 'STAY' while response not in ['HIT','STAY']: response = input('\nHit or Stay?: ').strip().upper() if response == 'STAY': if 'A ' in name.hand and (name.value + 10) < 22: name.value += 10 time.sleep(2) print("\nDealer's hand:") dealer.print_hand() time.sleep(2) print("\nDealer's total: {}".format(dealer.value)) time.sleep(2) if dealer.value > 21: print('\n\nDealer busted, you won the hand!') player_pot += wager return player_pot elif name.value > dealer.value: print('\n\nYou won the hand!') player_pot += wager return player_pot elif name.value == dealer.value: print('\n\nWhen push comes to shove..') return player_pot elif name.value < dealer.value: print('\n\nHouse wins.') player_pot -= wager return player_pot else: time.sleep(2) name.deal_card() print('\nYour hand:') name.print_hand() time.sleep(2) print('\nYour total:') name.print_value() time.sleep(1) if name.value > 21: print('\nBusted!') player_pot -= wager return player_pot response = '' if name.value == 21: response = 'STAY' if 'A ' in name.hand and (name.value + 10) == 21: name.value += 10 response = 'STAY' while response not in ['HIT','STAY']: response = input('\nHit or Stay?: ').strip().upper() if response == 'STAY': if 'A ' in name.hand and (name.value + 10) < 22: name.value += 10 time.sleep(2) print("\nDealer's hand:") dealer.print_hand() time.sleep(2) print("\nDealer's total: {}".format(dealer.value)) time.sleep(2) if dealer.value > 21: print('\n\nDealer busted, you won the hand!') player_pot += wager return player_pot elif name.value > dealer.value: print('\n\nYou won the hand!') player_pot += wager return player_pot elif name.value == dealer.value: print('\n\nWhen push comes to shove..') return player_pot elif name.value < dealer.value: print('\n\nHouse wins.') player_pot -= wager return player_pot else: time.sleep(2) name.deal_card() print('\nYour hand:') name.print_hand() time.sleep(2) print('\nYour total:') name.print_value() time.sleep(1) if name.value > 21: print('\nBusted!') player_pot -= wager return player_pot response = '' if name.value == 21: response = 'STAY' if 'A ' in name.hand and (name.value + 10) == 21: name.value += 10 response = 'STAY' while response not in ['HIT','STAY']: response = input('\nHit or Stay?: ').strip().upper() if response == 'STAY': if 'A ' in name.hand and (name.value + 10) < 22: name.value += 10 time.sleep(2) print("\nDealer's hand:") dealer.print_hand() time.sleep(2) print("\nDealer's total: {}".format(dealer.value)) time.sleep(2) if dealer.value > 21: print('\n\nDealer busted, you won the hand!') player_pot += wager return player_pot elif name.value > dealer.value: print('\n\nYou won the hand!') player_pot += wager return player_pot elif name.value == dealer.value: print('\n\nWhen push comes to shove..') return player_pot elif name.value < dealer.value: print('\n\nHouse wins.') player_pot -= wager return player_pot else: time.sleep(2) name.deal_card() print('\nYour hand:') name.print_hand() time.sleep(2) print('\nYour total:') name.print_value() time.sleep(1) if name.value > 21: print('\nBusted!') player_pot -= wager return player_pot response = '' if name.value == 21: response = 'STAY' if 'A ' in name.hand and (name.value + 10) == 21: name.value += 10 response = 'STAY' while response not in ['HIT','STAY']: response = input('\nHit or Stay?: ').strip().upper() if response == 'STAY': if 'A ' in name.hand and (name.value + 10) < 22: name.value += 10 time.sleep(2) print("\nDealer's hand:") dealer.print_hand() time.sleep(2) print("\nDealer's total: {}".format(dealer.value)) time.sleep(2) if dealer.value > 21: print('\n\nDealer busted, you won the hand!') player_pot += wager return player_pot elif name.value > dealer.value: print('\n\nYou won the hand!') player_pot += wager return player_pot elif name.value == dealer.value: print('\n\nWhen push comes to shove..') return player_pot elif name.value < dealer.value: print('\n\nHouse wins.') player_pot -= wager return player_pot if __name__ == '__main__': print(instructions) name = input('\nName, please: ') time.sleep(1) print('\n\nOkay {}, place your bet!'.format(name)) player_pot = 100 wager = 0 while wager <= 0 or wager > player_pot: try: wager = int((input('\nYou currently have {} coins, what will you wager? '.format(player_pot))).strip()) except ValueError: print('\n Please input an integer value.') continue time.sleep(3) player_pot = game(name,player_pot,wager) wager = 0 leave_table = '' while leave_table not in ['YES','Y']: time.sleep(3) if player_pot == 0: sys.exit('\n\nYou ran out of coins... better luck next time.\n\n') leave_table = (input("\n\nLeave table? ('Yes' or 'Y' to exit game, anything else to continue) ")).strip().upper() time.sleep(2) if leave_table in ['YES','Y']: sys.exit('\n\nFinal coin count: {} coins\n\n'.format(player_pot)) print('\n\nOkay {}, place your bet!'.format(name)) while wager <= 0 or wager > player_pot: try: wager = int((input('\nYou currently have {} coins, what will you wager? '.format(player_pot))).strip()) except ValueError: print('\n Please input an integer value.') continue time.sleep(3) player_pot = game(name,player_pot,wager) wager = 0

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d90b84e7c3c574de89fd179f23e8f6b161830000

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py

Python

halotools/empirical_models/sfr_models/zu_mandelbaum16.py

pllim/halotools

6499cff09e7e0f169e4f425ee265403f6be816e8

[ "BSD-3-Clause" ]

83

2015-01-15T14:54:16.000Z

2021-12-09T11:28:02.000Z

halotools/empirical_models/sfr_models/zu_mandelbaum16.py

pllim/halotools

6499cff09e7e0f169e4f425ee265403f6be816e8

[ "BSD-3-Clause" ]

579

2015-01-14T15:57:37.000Z

2022-01-13T18:58:44.000Z

halotools/empirical_models/sfr_models/zu_mandelbaum16.py

pllim/halotools

6499cff09e7e0f169e4f425ee265403f6be816e8

[ "BSD-3-Clause" ]

70

2015-01-14T15:15:58.000Z

2021-12-22T18:18:31.000Z

""" Module containing the `~halotools.empirical_models.ZuMandelbaum16QuenchingCens` and `~halotools.empirical_models.ZuMandelbaum16QuenchingSats` classes responsible for providing a mapping between halo mass and galaxy quenching designation. """ from __future__ import division, print_function, absolute_import import numpy as np from ..component_model_templates import BinaryGalpropModel __all__ = ('ZuMandelbaum16QuenchingCens', 'ZuMandelbaum16QuenchingSats') class ZuMandelbaum16QuenchingCens(BinaryGalpropModel): """ Model for the quiescent fraction of centrals as a function of halo mass defined by an exponential function of halo mass. See :ref:`zu_mandelbaum16_composite_model` for a tutorial on this model. """ def __init__(self, prim_haloprop_key='halo_m200m', **kwargs): """ Parameters ----------- prim_haloprop_key : string Name of the column of the halo table storing the mass-like variable the model is based on, e.g., 'halo_mvir' or 'halo_m200b'. Examples -------- >>> model = ZuMandelbaum16QuenchingCens() """ BinaryGalpropModel.__init__(self, galprop_name='quiescent', prim_haloprop_key=prim_haloprop_key) self.param_dict = self._retrieve_default_param_dict() self.gal_type = 'centrals' def mean_quiescent_fraction(self, **kwargs): r""" Quiescent fraction as a function of halo mass, modeled as an exponential: :math:`F_{\rm quiescent}(M_{\rm halo}) = 1 - {\rm exp}(-(M_{\rm halo}/M_{\rm char})^{\alpha})` Parameters ---------- prim_haloprop : array, optional Array of mass-like variable upon which occupation statistics are based. If ``prim_haloprop`` is not passed, then ``table`` keyword argument must be passed. table : object, optional Data table storing halo catalog. If ``table`` is not passed, then ``prim_haloprop`` keyword argument must be passed. Returns ------- quiescent_fraction : array_like Array containing mean fraction of quiescent galaxies. Examples -------- >>> model = ZuMandelbaum16QuenchingCens() >>> quiescent_fraction = model.mean_quiescent_fraction(prim_haloprop=1e12) """ if 'table' in list(kwargs.keys()): halo_mass = np.atleast_1d(kwargs['table'][self.prim_haloprop_key]) elif 'prim_haloprop' in list(kwargs.keys()): halo_mass = np.atleast_1d(kwargs['prim_haloprop']) else: raise KeyError("Must pass one of the following keyword arguments " "to mean_stellar_mass:\n``table`` or ``prim_haloprop``") mass_ratio = halo_mass/self.param_dict['quenching_mass_centrals'] exparg = mass_ratio**self.param_dict['quenching_exp_power_centrals'] result = 1 - np.exp(-exparg) result = np.where(result < 0, 0, result) result = np.where(result > 1, 1, result) return result def _retrieve_default_param_dict(self): d = {} d['quenching_mass_centrals'] = 10**12.2 d['quenching_exp_power_centrals'] = 0.38 return d class ZuMandelbaum16QuenchingSats(BinaryGalpropModel): """ Model for the quiescent fraction of satellites as a function of halo mass defined by an exponential function of halo mass. See :ref:`zu_mandelbaum16_composite_model` for a tutorial on this model. """ def __init__(self, prim_haloprop_key='halo_m200m', **kwargs): """ Parameters ----------- prim_haloprop_key : string Name of the column of the halo table storing the mass-like variable the model is based on, e.g., 'halo_mvir' or 'halo_m200b'. Examples -------- >>> model = ZuMandelbaum16QuenchingSats() """ BinaryGalpropModel.__init__(self, galprop_name='quiescent', prim_haloprop_key=prim_haloprop_key) self.param_dict = self._retrieve_default_param_dict() self.gal_type = 'satellites' def mean_quiescent_fraction(self, **kwargs): r""" Quiescent fraction as a function of halo mass, modeled as an exponential: :math:`F_{\rm quiescent}(M_{\rm halo}) = 1 - {\rm exp}(-(M_{\rm halo}/M_{\rm char})^{\alpha})` Parameters ---------- prim_haloprop : array, optional Array of mass-like variable upon which occupation statistics are based. If ``prim_haloprop`` is not passed, then ``table`` keyword argument must be passed. table : object, optional Data table storing halo catalog. If ``table`` is not passed, then ``prim_haloprop`` keyword argument must be passed. Returns ------- quiescent_fraction : array_like Array containing mean fraction of quiescent galaxies. Examples -------- >>> model = ZuMandelbaum16QuenchingSats() >>> quiescent_fraction = model.mean_quiescent_fraction(prim_haloprop=1e12) """ if 'table' in list(kwargs.keys()): halo_mass = np.atleast_1d(kwargs['table'][self.prim_haloprop_key]) elif 'prim_haloprop' in list(kwargs.keys()): halo_mass = np.atleast_1d(kwargs['prim_haloprop']) else: raise KeyError("Must pass one of the following keyword arguments " "to mean_stellar_mass:\n``table`` or ``prim_haloprop``") mass_ratio = halo_mass/self.param_dict['quenching_mass_satellites'] exparg = mass_ratio**self.param_dict['quenching_exp_power_satellites'] result = 1 - np.exp(-exparg) result = np.where(result < 0, 0, result) result = np.where(result > 1, 1, result) return result def _retrieve_default_param_dict(self): d = {} d['quenching_mass_satellites'] = 10**12.17 d['quenching_exp_power_satellites'] = 0.15 return d

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d93c4416d7cf133e45bc6c5f84e68480e03b88a5

28

py

Python

floris/tools/optimization/other/__init__.py

ElieKadoche/floris

d18f4d263ecabf502242592f9d60815a07c7b89c

[ "Apache-2.0" ]

91

2019-06-04T08:56:29.000Z

2022-03-13T17:39:22.000Z

floris/tools/optimization/other/__init__.py

ElieKadoche/floris

d18f4d263ecabf502242592f9d60815a07c7b89c

[ "Apache-2.0" ]

224

2019-04-08T22:03:45.000Z

2022-03-31T17:56:09.000Z

floris/tools/optimization/other/__init__.py

ElieKadoche/floris

d18f4d263ecabf502242592f9d60815a07c7b89c

[ "Apache-2.0" ]

97

2019-04-23T20:48:20.000Z

2022-03-29T08:17:02.000Z

from . import boundary_grid

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6

794254654f00f6fd4b817c6016e9d9c1874f374e

224

py

Python

espaloma/mm/__init__.py

jstaker7/espaloma

d80d280acd608dc04c93966afe15cc3cb74f65a8

[ "MIT" ]

null

espaloma/mm/__init__.py

jstaker7/espaloma

d80d280acd608dc04c93966afe15cc3cb74f65a8

[ "MIT" ]

null

espaloma/mm/__init__.py

jstaker7/espaloma

d80d280acd608dc04c93966afe15cc3cb74f65a8

[ "MIT" ]

null

import espaloma import espaloma.mm import espaloma.mm.angle import espaloma.mm.bond import espaloma.mm.energy import espaloma.mm.functional import espaloma.mm.geometry import espaloma.mm.nonbonded import espaloma.mm.torsion

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6

7955739b89d0d07687808e42cbe3b0c1835466e2

26

py

Python

Python/hellolies.py

saurabhcommand/Hello-world

647bad9da901a52d455f05ecc37c6823c22dc77e

[ "MIT" ]

1,428

2018-10-03T15:15:17.000Z

2019-03-31T18:38:36.000Z

Python/hellolies.py

saurabhcommand/Hello-world

647bad9da901a52d455f05ecc37c6823c22dc77e

[ "MIT" ]

1,162

2018-10-03T15:05:49.000Z

2018-10-18T14:17:52.000Z

Python/hellolies.py

saurabhcommand/Hello-world

647bad9da901a52d455f05ecc37c6823c22dc77e

[ "MIT" ]

3,909

2018-10-03T15:07:19.000Z

2019-03-31T18:39:08.000Z

print("Hello, pyLadies!")

13

25

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null

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null

0

1

0

1

0

6

8dc5ed5f7b2340bb23fcaf7a02ecec7450b01699

4,469

py

Python

Baixar arquivos Tesouro direto.py

ajzanella/Tesouro-Direto

b573ec3d777cc93d469514446701039a755c0e65

[ "MIT" ]

2

2020-04-25T23:31:59.000Z

2021-03-01T09:38:43.000Z

Baixar arquivos Tesouro direto.py

ajzanella/Tesouro-Direto

b573ec3d777cc93d469514446701039a755c0e65

[ "MIT" ]

1

2019-03-14T03:58:22.000Z

Baixar arquivos Tesouro direto.py

ajzanella/Tesouro-Direto

b573ec3d777cc93d469514446701039a755c0e65

[ "MIT" ]

1

2020-10-03T14:13:11.000Z

import datetime import requests import urllib.request from bs4 import BeautifulSoup import ssl import certifi base_url = "https://sisweb.tesouro.gov.br/apex/" query = "f?p=2031:2" url = base_url + query response = requests.get(url, verify=False) soup = BeautifulSoup(response.text, "html.parser") div = soup.find("div", {"class": "bl-body"}) #ass = div.find_all('a')[0] #for a in ass: # path = a['href'] # name = a.string # download_url = base_url + path # ssl._create_default_https_context = ssl._create_unverified_context # urllib.request.urlretrieve(download_url, "C:/Users/ajzan/Documents/GitHub/Tesouro-Direto/DataBase/Historic/{}.xls".format(name)) #Fazer um a um para adicionar na pasta correta #data de ontem x = (datetime.datetime.today()-datetime.timedelta(days = 1)).year #baixar ano anterior a = div.find_all('a')[0] path = a['href'] name = a.string download_url = base_url + path ssl._create_default_https_context = ssl._create_unverified_context urllib.request.urlretrieve(download_url, "C:/Users/ajzan/Documents/GitHub/Tesouro-Direto/DataBase/Historic/LFT/{}_%s.xls".format(name) %(x)) a = div.find_all('a')[1] path = a['href'] name = a.string download_url = base_url + path ssl._create_default_https_context = ssl._create_unverified_context urllib.request.urlretrieve(download_url, "C:/Users/ajzan/Documents/GitHub/Tesouro-Direto/DataBase/Historic/LTN/{}_%s.xls".format(name) %(x)) a = div.find_all('a')[2] path = a['href'] name = a.string download_url = base_url + path ssl._create_default_https_context = ssl._create_unverified_context urllib.request.urlretrieve(download_url, "C:/Users/ajzan/Documents/GitHub/Tesouro-Direto/DataBase/Historic/NTN-C/{}_%s.xls".format(name) %(x)) a = div.find_all('a')[3] path = a['href'] name = a.string download_url = base_url + path ssl._create_default_https_context = ssl._create_unverified_context urllib.request.urlretrieve(download_url, "C:/Users/ajzan/Documents/GitHub/Tesouro-Direto/DataBase/Historic/NTN-B/{}_%s.xls".format(name) %(x)) a = div.find_all('a')[4] path = a['href'] name = a.string download_url = base_url + path ssl._create_default_https_context = ssl._create_unverified_context urllib.request.urlretrieve(download_url, "C:/Users/ajzan/Documents/GitHub/Tesouro-Direto/DataBase/Historic/NTN-BPrincipal/{}_%s.xls".format(name) %(x)) a = div.find_all('a')[5] path = a['href'] name = a.string download_url = base_url + path ssl._create_default_https_context = ssl._create_unverified_context urllib.request.urlretrieve(download_url, "C:/Users/ajzan/Documents/GitHub/Tesouro-Direto/DataBase/Historic/NTN-F/{}_%s.xls".format(name) %(x)) ####baixar ano atual a = div.find_all('a')[6] path = a['href'] name = a.string download_url = base_url + path ssl._create_default_https_context = ssl._create_unverified_context urllib.request.urlretrieve(download_url, "C:/Users/ajzan/Documents/GitHub/Tesouro-Direto/DataBase/Historic/LFT/{}_%s.xls".format(name) %(x-1)) a = div.find_all('a')[7] path = a['href'] name = a.string download_url = base_url + path ssl._create_default_https_context = ssl._create_unverified_context urllib.request.urlretrieve(download_url, "C:/Users/ajzan/Documents/GitHub/Tesouro-Direto/DataBase/Historic/LTN/{}_%s.xls".format(name) %(x-1)) a = div.find_all('a')[8] path = a['href'] name = a.string download_url = base_url + path ssl._create_default_https_context = ssl._create_unverified_context urllib.request.urlretrieve(download_url, "C:/Users/ajzan/Documents/GitHub/Tesouro-Direto/DataBase/Historic/NTN-C/{}_%s.xls".format(name) %(x-1)) a = div.find_all('a')[9] path = a['href'] name = a.string download_url = base_url + path ssl._create_default_https_context = ssl._create_unverified_context urllib.request.urlretrieve(download_url, "C:/Users/ajzan/Documents/GitHub/Tesouro-Direto/DataBase/Historic/NTN-B/{}_%s.xls".format(name) %(x-1)) a = div.find_all('a')[10] path = a['href'] name = a.string download_url = base_url + path ssl._create_default_https_context = ssl._create_unverified_context urllib.request.urlretrieve(download_url, "C:/Users/ajzan/Documents/GitHub/Tesouro-Direto/DataBase/Historic/NTN-BPrincipal/{}_%s.xls".format(name) %(x-1)) a = div.find_all('a')[11] path = a['href'] name = a.string download_url = base_url + path ssl._create_default_https_context = ssl._create_unverified_context urllib.request.urlretrieve(download_url, "C:/Users/ajzan/Documents/GitHub/Tesouro-Direto/DataBase/Historic/NTN-F/{}_%s.xls".format(name) %(x-1))

36.040323

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0.044122

0.871027

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0

0.006816

0.080779

4,469

123

154

36.333333

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0.088834

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0.564706

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0.070588

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null

0

1

0

null

0

6

8dca97bf63914ac219d7a9fcdbbcca792d3ecd8c

25

py

Python

__init__.py

rtogo/SAPCostCenterHierarchy

5a5863562b37e2e5c12e0a7aeea3b94c9eeb2f50

[ "MIT" ]

null

__init__.py

rtogo/SAPCostCenterHierarchy

5a5863562b37e2e5c12e0a7aeea3b94c9eeb2f50

[ "MIT" ]

null

__init__.py

rtogo/SAPCostCenterHierarchy

5a5863562b37e2e5c12e0a7aeea3b94c9eeb2f50

[ "MIT" ]

null

from .process import run

12.5

24

0.8

4

25

5

1

0

0.16

25

1

25

0.952381

0

1

0

true

0

1

0

1

0

1

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null

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1

0

null

0

1

0

1

0

1

0

6

8dfcc63954738078588ba7602e05b079fe086d01

1,903

py

Python

Python/seedlist.py

tifuzeau/LifeGame

5c10990ccc04ceb412d5dd355f9e828748420456

[ "MIT" ]

null

Python/seedlist.py

tifuzeau/LifeGame

5c10990ccc04ceb412d5dd355f9e828748420456

[ "MIT" ]

null

Python/seedlist.py

tifuzeau/LifeGame

5c10990ccc04ceb412d5dd355f9e828748420456

[ "MIT" ]

null

# coding: utf8 # lang: python 3 from seed import Seed mini = [ [1], [1], [1], ] spaceships = [ [ 0, 1, 1 ], [ 1, 1, 0 ], [ 0, 0, 1 ], ] block_test = [ [ 1, 1 ], [ 1, 1 ], ] clown = [ [ 1, 0, 1], [ 1, 0, 1], [ 1, 1, 1], ] batellestart = [ [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, ], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, ], [ 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [ 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [ 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ], ] #{'name' : "", 'x' : , 'y' : , 'seed' : }, SeedList = [ {'name' : "mini", 'x' : 5, 'y' : 5, 'seed' : mini}, {'name' : "spaceships", 'x': 10, 'y': 10, 'seed' : spaceships}, {'name' : "block_test", 'x' : 5, 'y' : 5, 'seed' : block_test}, {'name' : "clown", 'x' : 50, 'y' : 30, 'seed' : clown}, {'name' : "batellestart", 'x' : 50, 'y' : 30, 'seed' : batellestart}, # {'name' : "", 'x' : , 'y' : ,'seed' : }, ]

35.90566

117

0.336837

451

1,903

1.414634

0.053215

0.92163

1.269592

1.548589

0.647335

0.575235

0.551724

0

0.324477

0.347346

1,903

52

118

36.596154

0.189211

0.058854

0

0.179487

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0.05098

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1

0

false

0

0.025641

0

0.025641

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1

null

1

0

1

0

1

0

null

0

6

5c3d25e5bd314d81f90fc97dfa5a53a3b9b19bee

120

py

Python

perm_security/TokenStrategy/TokenHandlerStrategy/__init__.py

TheJoeSmo/perm-security

2fd8ceb4fc72cce5889f55731056665a887399e1

[ "MIT" ]

null

perm_security/TokenStrategy/TokenHandlerStrategy/__init__.py

TheJoeSmo/perm-security

2fd8ceb4fc72cce5889f55731056665a887399e1

[ "MIT" ]

null

perm_security/TokenStrategy/TokenHandlerStrategy/__init__.py

TheJoeSmo/perm-security

2fd8ceb4fc72cce5889f55731056665a887399e1

[ "MIT" ]

null

from .TokenHandlerStrategy import TokenHandlerStrategy from .BasicTokenHandlerStrategy import BasicTokenHandlerStrategy

40

64

0.916667

8

120

13.75

0.5

0

0.066667

120

2

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60

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true

0

1

0

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1

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1

null

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1

0

null

0

1

0

1

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1

0

6

30e1e694ebef8321302ba82df471be4ab73d6384

37,723

py

Python

instances/passenger_demand/pas-20210421-2109-int12e/82.py

LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure

bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11

[ "BSD-3-Clause" ]

null

instances/passenger_demand/pas-20210421-2109-int12e/82.py

LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure

bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11

[ "BSD-3-Clause" ]

null

instances/passenger_demand/pas-20210421-2109-int12e/82.py

LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure

bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11

[ "BSD-3-Clause" ]

null

""" PASSENGERS """ numPassengers = 2755 passenger_arriving = ( (6, 6, 12, 5, 1, 0, 7, 6, 7, 3, 2, 0), # 0 (0, 9, 7, 3, 2, 0, 6, 8, 4, 2, 1, 0), # 1 (1, 7, 6, 1, 2, 0, 10, 7, 2, 5, 1, 0), # 2 (4, 3, 4, 1, 3, 0, 7, 8, 3, 7, 1, 0), # 3 (5, 11, 4, 1, 3, 0, 6, 6, 5, 3, 1, 0), # 4 (4, 8, 6, 4, 0, 0, 5, 11, 4, 4, 0, 0), # 5 (2, 8, 2, 4, 2, 0, 5, 5, 3, 2, 1, 0), # 6 (1, 7, 4, 3, 2, 0, 7, 3, 2, 6, 2, 0), # 7 (6, 11, 3, 1, 1, 0, 5, 6, 4, 0, 2, 0), # 8 (3, 7, 6, 3, 0, 0, 8, 10, 4, 4, 1, 0), # 9 (3, 5, 10, 2, 0, 0, 5, 4, 4, 8, 1, 0), # 10 (5, 7, 10, 2, 0, 0, 2, 14, 5, 5, 5, 0), # 11 (5, 9, 6, 6, 5, 0, 4, 5, 8, 2, 1, 0), # 12 (0, 8, 5, 4, 2, 0, 4, 6, 4, 3, 1, 0), # 13 (5, 4, 4, 4, 3, 0, 3, 9, 3, 7, 0, 0), # 14 (2, 4, 6, 1, 3, 0, 7, 9, 3, 4, 2, 0), # 15 (5, 7, 7, 3, 2, 0, 6, 7, 5, 2, 2, 0), # 16 (3, 8, 11, 3, 2, 0, 9, 7, 1, 2, 0, 0), # 17 (5, 5, 4, 0, 1, 0, 11, 11, 5, 6, 3, 0), # 18 (4, 6, 5, 4, 2, 0, 4, 7, 4, 2, 3, 0), # 19 (4, 10, 5, 3, 1, 0, 3, 3, 6, 6, 0, 0), # 20 (1, 9, 5, 6, 1, 0, 3, 5, 5, 5, 4, 0), # 21 (3, 9, 9, 1, 5, 0, 6, 14, 4, 7, 1, 0), # 22 (3, 10, 11, 5, 1, 0, 8, 14, 7, 5, 2, 0), # 23 (5, 5, 6, 4, 0, 0, 6, 7, 9, 6, 2, 0), # 24 (3, 3, 10, 3, 2, 0, 8, 8, 4, 4, 2, 0), # 25 (5, 5, 4, 3, 1, 0, 5, 5, 6, 5, 0, 0), # 26 (2, 6, 7, 4, 1, 0, 4, 13, 8, 7, 2, 0), # 27 (3, 7, 7, 6, 1, 0, 6, 12, 2, 3, 1, 0), # 28 (8, 9, 9, 6, 2, 0, 8, 7, 6, 2, 1, 0), # 29 (1, 5, 4, 2, 2, 0, 6, 7, 4, 8, 0, 0), # 30 (5, 8, 8, 2, 3, 0, 3, 8, 4, 7, 4, 0), # 31 (4, 11, 7, 2, 1, 0, 7, 4, 4, 3, 3, 0), # 32 (3, 3, 7, 3, 2, 0, 8, 9, 5, 2, 2, 0), # 33 (4, 14, 3, 1, 0, 0, 4, 4, 4, 1, 2, 0), # 34 (6, 4, 7, 5, 5, 0, 3, 6, 6, 10, 1, 0), # 35 (2, 7, 4, 3, 1, 0, 4, 6, 5, 4, 1, 0), # 36 (2, 9, 5, 1, 2, 0, 3, 6, 1, 5, 7, 0), # 37 (5, 7, 9, 4, 0, 0, 4, 4, 6, 8, 2, 0), # 38 (2, 10, 8, 2, 3, 0, 6, 13, 5, 5, 1, 0), # 39 (5, 8, 7, 2, 2, 0, 7, 9, 7, 5, 1, 0), # 40 (1, 11, 6, 3, 3, 0, 8, 8, 7, 7, 2, 0), # 41 (4, 4, 2, 1, 1, 0, 5, 7, 3, 2, 3, 0), # 42 (2, 8, 9, 4, 3, 0, 3, 9, 8, 3, 3, 0), # 43 (4, 7, 4, 3, 1, 0, 2, 5, 5, 2, 4, 0), # 44 (5, 5, 7, 4, 1, 0, 6, 6, 2, 7, 1, 0), # 45 (3, 4, 9, 4, 3, 0, 5, 7, 4, 4, 4, 0), # 46 (4, 12, 11, 4, 5, 0, 4, 11, 4, 8, 1, 0), # 47 (1, 5, 5, 4, 1, 0, 5, 7, 7, 6, 1, 0), # 48 (6, 8, 8, 1, 1, 0, 4, 9, 3, 5, 4, 0), # 49 (3, 10, 3, 2, 2, 0, 8, 10, 8, 4, 2, 0), # 50 (4, 9, 4, 4, 6, 0, 1, 8, 5, 3, 2, 0), # 51 (7, 8, 6, 3, 1, 0, 6, 3, 5, 6, 2, 0), # 52 (6, 10, 8, 4, 1, 0, 3, 7, 4, 4, 0, 0), # 53 (6, 9, 6, 4, 4, 0, 6, 5, 2, 5, 6, 0), # 54 (10, 5, 4, 5, 1, 0, 6, 8, 8, 1, 2, 0), # 55 (1, 6, 6, 3, 1, 0, 5, 5, 6, 6, 3, 0), # 56 (4, 5, 5, 6, 2, 0, 2, 13, 8, 3, 0, 0), # 57 (3, 12, 6, 5, 2, 0, 4, 9, 6, 4, 5, 0), # 58 (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), # 59 ) station_arriving_intensity = ( (3.1795818700614573, 8.15575284090909, 9.59308322622108, 7.603532608695652, 8.571634615384614, 5.708152173913044), # 0 (3.20942641205736, 8.246449918455387, 9.644898645029993, 7.6458772644927535, 8.635879807692307, 5.706206567028985), # 1 (3.238930172666081, 8.335801683501682, 9.695484147386459, 7.687289855072463, 8.69876923076923, 5.704201449275362), # 2 (3.268068107989464, 8.42371171875, 9.744802779562981, 7.727735054347824, 8.760245192307693, 5.702137092391305), # 3 (3.296815174129353, 8.510083606902358, 9.792817587832047, 7.767177536231884, 8.82025, 5.700013768115941), # 4 (3.3251463271875914, 8.594820930660775, 9.839491618466152, 7.805581974637681, 8.87872596153846, 5.697831748188405), # 5 (3.353036523266023, 8.677827272727273, 9.88478791773779, 7.842913043478261, 8.935615384615383, 5.695591304347826), # 6 (3.380460718466491, 8.75900621580387, 9.92866953191945, 7.879135416666666, 8.990860576923078, 5.693292708333334), # 7 (3.40739386889084, 8.83826134259259, 9.971099507283634, 7.914213768115941, 9.044403846153847, 5.6909362318840575), # 8 (3.4338109306409126, 8.915496235795453, 10.012040890102828, 7.9481127717391304, 9.0961875, 5.68852214673913), # 9 (3.459686859818554, 8.990614478114479, 10.051456726649528, 7.980797101449276, 9.146153846153846, 5.68605072463768), # 10 (3.4849966125256073, 9.063519652251683, 10.089310063196228, 8.012231431159421, 9.194245192307692, 5.683522237318841), # 11 (3.509715144863916, 9.134115340909089, 10.125563946015424, 8.042380434782608, 9.240403846153844, 5.680936956521738), # 12 (3.5338174129353224, 9.20230512678872, 10.160181421379605, 8.071208786231884, 9.284572115384616, 5.678295153985506), # 13 (3.5572783728416737, 9.267992592592593, 10.193125535561265, 8.098681159420288, 9.326692307692307, 5.6755971014492745), # 14 (3.5800729806848106, 9.331081321022726, 10.224359334832902, 8.124762228260868, 9.36670673076923, 5.672843070652174), # 15 (3.6021761925665783, 9.391474894781144, 10.25384586546701, 8.149416666666665, 9.404557692307693, 5.6700333333333335), # 16 (3.6235629645888205, 9.449076896569863, 10.281548173736075, 8.172609148550725, 9.4401875, 5.667168161231884), # 17 (3.64420825285338, 9.503790909090908, 10.307429305912597, 8.194304347826087, 9.473538461538464, 5.664247826086956), # 18 (3.664087013462101, 9.555520515046295, 10.331452308269066, 8.214466938405796, 9.504552884615384, 5.661272599637681), # 19 (3.683174202516827, 9.604169297138045, 10.353580227077975, 8.2330615942029, 9.533173076923077, 5.658242753623187), # 20 (3.7014447761194034, 9.649640838068178, 10.373776108611827, 8.250052989130435, 9.559341346153845, 5.655158559782609), # 21 (3.7188736903716704, 9.69183872053872, 10.3920029991431, 8.26540579710145, 9.582999999999998, 5.652020289855073), # 22 (3.7354359013754754, 9.730666527251683, 10.408223944944302, 8.279084692028986, 9.604091346153846, 5.6488282155797105), # 23 (3.75110636523266, 9.76602784090909, 10.422401992287917, 8.291054347826087, 9.62255769230769, 5.645582608695652), # 24 (3.7658600380450684, 9.797826244212962, 10.434500187446444, 8.301279438405798, 9.638341346153844, 5.642283740942029), # 25 (3.779671875914545, 9.825965319865318, 10.444481576692374, 8.309724637681159, 9.651384615384615, 5.63893188405797), # 26 (3.792516834942932, 9.85034865056818, 10.452309206298198, 8.316354619565217, 9.661629807692309, 5.635527309782609), # 27 (3.804369871232075, 9.870879819023568, 10.457946122536418, 8.321134057971014, 9.66901923076923, 5.632070289855072), # 28 (3.815205940883816, 9.887462407933501, 10.461355371679518, 8.324027626811594, 9.673495192307692, 5.628561096014493), # 29 (3.8249999999999997, 9.9, 10.4625, 8.325, 9.674999999999999, 5.625), # 30 (3.834164434143222, 9.910414559659088, 10.461641938405796, 8.324824387254901, 9.674452393617022, 5.620051511744128), # 31 (3.843131010230179, 9.920691477272728, 10.459092028985506, 8.324300980392156, 9.672821276595744, 5.612429710144928), # 32 (3.8519037563938614, 9.930829474431818, 10.45488668478261, 8.323434926470588, 9.670124202127658, 5.6022092203898035), # 33 (3.860486700767263, 9.940827272727272, 10.449062318840578, 8.32223137254902, 9.666378723404256, 5.589464667666167), # 34 (3.8688838714833755, 9.950683593749998, 10.441655344202898, 8.320695465686274, 9.661602393617022, 5.574270677161419), # 35 (3.8770992966751923, 9.96039715909091, 10.432702173913043, 8.318832352941177, 9.655812765957448, 5.556701874062968), # 36 (3.885137004475703, 9.96996669034091, 10.422239221014491, 8.316647181372549, 9.64902739361702, 5.536832883558221), # 37 (3.893001023017902, 9.979390909090908, 10.410302898550723, 8.314145098039214, 9.641263829787233, 5.514738330834581), # 38 (3.900695380434782, 9.988668536931817, 10.396929619565215, 8.31133125, 9.632539627659574, 5.490492841079459), # 39 (3.908224104859335, 9.997798295454546, 10.382155797101449, 8.308210784313726, 9.62287234042553, 5.464171039480259), # 40 (3.915591224424552, 10.006778906249998, 10.366017844202899, 8.304788848039216, 9.612279521276594, 5.435847551224389), # 41 (3.9228007672634266, 10.015609090909093, 10.348552173913044, 8.301070588235293, 9.600778723404256, 5.40559700149925), # 42 (3.929856761508952, 10.024287571022725, 10.329795199275361, 8.297061151960785, 9.5883875, 5.373494015492254), # 43 (3.936763235294117, 10.032813068181818, 10.309783333333334, 8.292765686274508, 9.575123404255319, 5.339613218390804), # 44 (3.9435242167519178, 10.041184303977271, 10.288552989130435, 8.288189338235293, 9.561003989361701, 5.304029235382309), # 45 (3.9501437340153456, 10.0494, 10.266140579710147, 8.28333725490196, 9.546046808510638, 5.266816691654173), # 46 (3.956625815217391, 10.05745887784091, 10.24258251811594, 8.278214583333332, 9.530269414893617, 5.228050212393803), # 47 (3.962974488491049, 10.065359659090909, 10.217915217391303, 8.272826470588234, 9.513689361702127, 5.187804422788607), # 48 (3.9691937819693086, 10.073101065340907, 10.19217509057971, 8.26717806372549, 9.49632420212766, 5.146153948025987), # 49 (3.9752877237851663, 10.080681818181816, 10.165398550724637, 8.261274509803922, 9.478191489361702, 5.103173413293353), # 50 (3.9812603420716113, 10.088100639204544, 10.137622010869565, 8.255120955882353, 9.459308776595744, 5.0589374437781105), # 51 (3.987115664961637, 10.09535625, 10.10888188405797, 8.248722549019607, 9.439693617021277, 5.013520664667666), # 52 (3.992857720588235, 10.10244737215909, 10.079214583333332, 8.24208443627451, 9.419363563829787, 4.966997701149425), # 53 (3.9984905370843995, 10.109372727272726, 10.04865652173913, 8.235211764705882, 9.398336170212765, 4.919443178410794), # 54 (4.00401814258312, 10.116131036931817, 10.017244112318838, 8.22810968137255, 9.376628989361702, 4.87093172163918), # 55 (4.0094445652173905, 10.122721022727271, 9.985013768115941, 8.220783333333333, 9.354259574468085, 4.821537956021989), # 56 (4.014773833120205, 10.129141406250001, 9.952001902173912, 8.213237867647058, 9.331245478723403, 4.771336506746626), # 57 (4.0200099744245525, 10.135390909090907, 9.91824492753623, 8.20547843137255, 9.307604255319148, 4.7204019990005), # 58 (0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 59 ) passenger_arriving_acc = ( (6, 6, 12, 5, 1, 0, 7, 6, 7, 3, 2, 0), # 0 (6, 15, 19, 8, 3, 0, 13, 14, 11, 5, 3, 0), # 1 (7, 22, 25, 9, 5, 0, 23, 21, 13, 10, 4, 0), # 2 (11, 25, 29, 10, 8, 0, 30, 29, 16, 17, 5, 0), # 3 (16, 36, 33, 11, 11, 0, 36, 35, 21, 20, 6, 0), # 4 (20, 44, 39, 15, 11, 0, 41, 46, 25, 24, 6, 0), # 5 (22, 52, 41, 19, 13, 0, 46, 51, 28, 26, 7, 0), # 6 (23, 59, 45, 22, 15, 0, 53, 54, 30, 32, 9, 0), # 7 (29, 70, 48, 23, 16, 0, 58, 60, 34, 32, 11, 0), # 8 (32, 77, 54, 26, 16, 0, 66, 70, 38, 36, 12, 0), # 9 (35, 82, 64, 28, 16, 0, 71, 74, 42, 44, 13, 0), # 10 (40, 89, 74, 30, 16, 0, 73, 88, 47, 49, 18, 0), # 11 (45, 98, 80, 36, 21, 0, 77, 93, 55, 51, 19, 0), # 12 (45, 106, 85, 40, 23, 0, 81, 99, 59, 54, 20, 0), # 13 (50, 110, 89, 44, 26, 0, 84, 108, 62, 61, 20, 0), # 14 (52, 114, 95, 45, 29, 0, 91, 117, 65, 65, 22, 0), # 15 (57, 121, 102, 48, 31, 0, 97, 124, 70, 67, 24, 0), # 16 (60, 129, 113, 51, 33, 0, 106, 131, 71, 69, 24, 0), # 17 (65, 134, 117, 51, 34, 0, 117, 142, 76, 75, 27, 0), # 18 (69, 140, 122, 55, 36, 0, 121, 149, 80, 77, 30, 0), # 19 (73, 150, 127, 58, 37, 0, 124, 152, 86, 83, 30, 0), # 20 (74, 159, 132, 64, 38, 0, 127, 157, 91, 88, 34, 0), # 21 (77, 168, 141, 65, 43, 0, 133, 171, 95, 95, 35, 0), # 22 (80, 178, 152, 70, 44, 0, 141, 185, 102, 100, 37, 0), # 23 (85, 183, 158, 74, 44, 0, 147, 192, 111, 106, 39, 0), # 24 (88, 186, 168, 77, 46, 0, 155, 200, 115, 110, 41, 0), # 25 (93, 191, 172, 80, 47, 0, 160, 205, 121, 115, 41, 0), # 26 (95, 197, 179, 84, 48, 0, 164, 218, 129, 122, 43, 0), # 27 (98, 204, 186, 90, 49, 0, 170, 230, 131, 125, 44, 0), # 28 (106, 213, 195, 96, 51, 0, 178, 237, 137, 127, 45, 0), # 29 (107, 218, 199, 98, 53, 0, 184, 244, 141, 135, 45, 0), # 30 (112, 226, 207, 100, 56, 0, 187, 252, 145, 142, 49, 0), # 31 (116, 237, 214, 102, 57, 0, 194, 256, 149, 145, 52, 0), # 32 (119, 240, 221, 105, 59, 0, 202, 265, 154, 147, 54, 0), # 33 (123, 254, 224, 106, 59, 0, 206, 269, 158, 148, 56, 0), # 34 (129, 258, 231, 111, 64, 0, 209, 275, 164, 158, 57, 0), # 35 (131, 265, 235, 114, 65, 0, 213, 281, 169, 162, 58, 0), # 36 (133, 274, 240, 115, 67, 0, 216, 287, 170, 167, 65, 0), # 37 (138, 281, 249, 119, 67, 0, 220, 291, 176, 175, 67, 0), # 38 (140, 291, 257, 121, 70, 0, 226, 304, 181, 180, 68, 0), # 39 (145, 299, 264, 123, 72, 0, 233, 313, 188, 185, 69, 0), # 40 (146, 310, 270, 126, 75, 0, 241, 321, 195, 192, 71, 0), # 41 (150, 314, 272, 127, 76, 0, 246, 328, 198, 194, 74, 0), # 42 (152, 322, 281, 131, 79, 0, 249, 337, 206, 197, 77, 0), # 43 (156, 329, 285, 134, 80, 0, 251, 342, 211, 199, 81, 0), # 44 (161, 334, 292, 138, 81, 0, 257, 348, 213, 206, 82, 0), # 45 (164, 338, 301, 142, 84, 0, 262, 355, 217, 210, 86, 0), # 46 (168, 350, 312, 146, 89, 0, 266, 366, 221, 218, 87, 0), # 47 (169, 355, 317, 150, 90, 0, 271, 373, 228, 224, 88, 0), # 48 (175, 363, 325, 151, 91, 0, 275, 382, 231, 229, 92, 0), # 49 (178, 373, 328, 153, 93, 0, 283, 392, 239, 233, 94, 0), # 50 (182, 382, 332, 157, 99, 0, 284, 400, 244, 236, 96, 0), # 51 (189, 390, 338, 160, 100, 0, 290, 403, 249, 242, 98, 0), # 52 (195, 400, 346, 164, 101, 0, 293, 410, 253, 246, 98, 0), # 53 (201, 409, 352, 168, 105, 0, 299, 415, 255, 251, 104, 0), # 54 (211, 414, 356, 173, 106, 0, 305, 423, 263, 252, 106, 0), # 55 (212, 420, 362, 176, 107, 0, 310, 428, 269, 258, 109, 0), # 56 (216, 425, 367, 182, 109, 0, 312, 441, 277, 261, 109, 0), # 57 (219, 437, 373, 187, 111, 0, 316, 450, 283, 265, 114, 0), # 58 (219, 437, 373, 187, 111, 0, 316, 450, 283, 265, 114, 0), # 59 ) passenger_arriving_rate = ( (3.1795818700614573, 6.524602272727271, 5.755849935732647, 3.0414130434782605, 1.7143269230769227, 0.0, 5.708152173913044, 6.857307692307691, 4.562119565217391, 3.8372332904884314, 1.6311505681818177, 0.0), # 0 (3.20942641205736, 6.597159934764309, 5.786939187017996, 3.0583509057971012, 1.7271759615384612, 0.0, 5.706206567028985, 6.908703846153845, 4.587526358695652, 3.857959458011997, 1.6492899836910773, 0.0), # 1 (3.238930172666081, 6.668641346801345, 5.817290488431875, 3.074915942028985, 1.7397538461538458, 0.0, 5.704201449275362, 6.959015384615383, 4.612373913043478, 3.8781936589545833, 1.6671603367003363, 0.0), # 2 (3.268068107989464, 6.738969375, 5.846881667737788, 3.091094021739129, 1.7520490384615384, 0.0, 5.702137092391305, 7.0081961538461535, 4.636641032608694, 3.897921111825192, 1.68474234375, 0.0), # 3 (3.296815174129353, 6.808066885521885, 5.875690552699228, 3.106871014492753, 1.76405, 0.0, 5.700013768115941, 7.0562, 4.66030652173913, 3.9171270351328187, 1.7020167213804713, 0.0), # 4 (3.3251463271875914, 6.87585674452862, 5.903694971079691, 3.122232789855072, 1.775745192307692, 0.0, 5.697831748188405, 7.102980769230768, 4.6833491847826085, 3.9357966473864603, 1.718964186132155, 0.0), # 5 (3.353036523266023, 6.942261818181818, 5.930872750642674, 3.137165217391304, 1.7871230769230766, 0.0, 5.695591304347826, 7.148492307692306, 4.705747826086957, 3.953915167095116, 1.7355654545454544, 0.0), # 6 (3.380460718466491, 7.007204972643096, 5.95720171915167, 3.1516541666666664, 1.7981721153846155, 0.0, 5.693292708333334, 7.192688461538462, 4.727481249999999, 3.97146781276778, 1.751801243160774, 0.0), # 7 (3.40739386889084, 7.0706090740740715, 5.982659704370181, 3.165685507246376, 1.8088807692307691, 0.0, 5.6909362318840575, 7.2355230769230765, 4.7485282608695645, 3.9884398029134536, 1.7676522685185179, 0.0), # 8 (3.4338109306409126, 7.132396988636362, 6.007224534061696, 3.179245108695652, 1.8192374999999996, 0.0, 5.68852214673913, 7.2769499999999985, 4.768867663043478, 4.004816356041131, 1.7830992471590905, 0.0), # 9 (3.459686859818554, 7.1924915824915825, 6.030874035989717, 3.19231884057971, 1.829230769230769, 0.0, 5.68605072463768, 7.316923076923076, 4.7884782608695655, 4.020582690659811, 1.7981228956228956, 0.0), # 10 (3.4849966125256073, 7.250815721801346, 6.053586037917737, 3.204892572463768, 1.8388490384615384, 0.0, 5.683522237318841, 7.355396153846153, 4.807338858695652, 4.0357240252784905, 1.8127039304503365, 0.0), # 11 (3.509715144863916, 7.30729227272727, 6.0753383676092545, 3.2169521739130427, 1.8480807692307688, 0.0, 5.680936956521738, 7.392323076923075, 4.825428260869565, 4.050225578406169, 1.8268230681818176, 0.0), # 12 (3.5338174129353224, 7.361844101430976, 6.096108852827762, 3.228483514492753, 1.8569144230769232, 0.0, 5.678295153985506, 7.427657692307693, 4.84272527173913, 4.0640725685518415, 1.840461025357744, 0.0), # 13 (3.5572783728416737, 7.414394074074074, 6.115875321336759, 3.2394724637681147, 1.8653384615384612, 0.0, 5.6755971014492745, 7.461353846153845, 4.859208695652172, 4.077250214224506, 1.8535985185185184, 0.0), # 14 (3.5800729806848106, 7.46486505681818, 6.134615600899742, 3.249904891304347, 1.873341346153846, 0.0, 5.672843070652174, 7.493365384615384, 4.874857336956521, 4.089743733933161, 1.866216264204545, 0.0), # 15 (3.6021761925665783, 7.513179915824915, 6.152307519280206, 3.259766666666666, 1.8809115384615382, 0.0, 5.6700333333333335, 7.523646153846153, 4.889649999999999, 4.101538346186803, 1.8782949789562287, 0.0), # 16 (3.6235629645888205, 7.55926151725589, 6.168928904241645, 3.26904365942029, 1.8880374999999998, 0.0, 5.667168161231884, 7.552149999999999, 4.903565489130435, 4.11261926949443, 1.8898153793139725, 0.0), # 17 (3.64420825285338, 7.603032727272725, 6.184457583547558, 3.2777217391304343, 1.8947076923076926, 0.0, 5.664247826086956, 7.578830769230771, 4.916582608695652, 4.122971722365039, 1.9007581818181813, 0.0), # 18 (3.664087013462101, 7.644416412037035, 6.198871384961439, 3.285786775362318, 1.9009105769230765, 0.0, 5.661272599637681, 7.603642307692306, 4.928680163043477, 4.132580923307626, 1.9111041030092588, 0.0), # 19 (3.683174202516827, 7.683335437710435, 6.2121481362467845, 3.2932246376811594, 1.9066346153846152, 0.0, 5.658242753623187, 7.626538461538461, 4.93983695652174, 4.14143209083119, 1.9208338594276086, 0.0), # 20 (3.7014447761194034, 7.719712670454542, 6.224265665167096, 3.3000211956521737, 1.911868269230769, 0.0, 5.655158559782609, 7.647473076923076, 4.950031793478261, 4.14951044344473, 1.9299281676136355, 0.0), # 21 (3.7188736903716704, 7.753470976430976, 6.23520179948586, 3.3061623188405793, 1.9165999999999994, 0.0, 5.652020289855073, 7.666399999999998, 4.959243478260869, 4.15680119965724, 1.938367744107744, 0.0), # 22 (3.7354359013754754, 7.784533221801346, 6.244934366966581, 3.311633876811594, 1.920818269230769, 0.0, 5.6488282155797105, 7.683273076923076, 4.967450815217392, 4.163289577977721, 1.9461333054503365, 0.0), # 23 (3.75110636523266, 7.812822272727271, 6.25344119537275, 3.3164217391304347, 1.9245115384615379, 0.0, 5.645582608695652, 7.6980461538461515, 4.974632608695652, 4.168960796915166, 1.9532055681818177, 0.0), # 24 (3.7658600380450684, 7.838260995370368, 6.260700112467866, 3.320511775362319, 1.9276682692307685, 0.0, 5.642283740942029, 7.710673076923074, 4.980767663043479, 4.173800074978577, 1.959565248842592, 0.0), # 25 (3.779671875914545, 7.860772255892254, 6.266688946015424, 3.3238898550724634, 1.9302769230769228, 0.0, 5.63893188405797, 7.721107692307691, 4.985834782608695, 4.177792630676949, 1.9651930639730635, 0.0), # 26 (3.792516834942932, 7.8802789204545425, 6.2713855237789184, 3.326541847826087, 1.9323259615384616, 0.0, 5.635527309782609, 7.729303846153846, 4.98981277173913, 4.180923682519278, 1.9700697301136356, 0.0), # 27 (3.804369871232075, 7.8967038552188535, 6.2747676735218505, 3.328453623188405, 1.9338038461538458, 0.0, 5.632070289855072, 7.735215384615383, 4.992680434782608, 4.183178449014567, 1.9741759638047134, 0.0), # 28 (3.815205940883816, 7.9099699263468, 6.276813223007711, 3.3296110507246373, 1.9346990384615383, 0.0, 5.628561096014493, 7.738796153846153, 4.994416576086956, 4.184542148671807, 1.9774924815867, 0.0), # 29 (3.8249999999999997, 7.92, 6.2775, 3.3299999999999996, 1.9349999999999996, 0.0, 5.625, 7.739999999999998, 4.994999999999999, 4.185, 1.98, 0.0), # 30 (3.834164434143222, 7.92833164772727, 6.276985163043477, 3.3299297549019604, 1.9348904787234043, 0.0, 5.620051511744128, 7.739561914893617, 4.994894632352941, 4.184656775362318, 1.9820829119318175, 0.0), # 31 (3.843131010230179, 7.936553181818182, 6.275455217391303, 3.329720392156862, 1.9345642553191487, 0.0, 5.612429710144928, 7.738257021276595, 4.994580588235293, 4.1836368115942015, 1.9841382954545455, 0.0), # 32 (3.8519037563938614, 7.944663579545454, 6.272932010869566, 3.329373970588235, 1.9340248404255314, 0.0, 5.6022092203898035, 7.736099361702125, 4.994060955882353, 4.181954673913044, 1.9861658948863634, 0.0), # 33 (3.860486700767263, 7.952661818181817, 6.269437391304347, 3.3288925490196077, 1.9332757446808508, 0.0, 5.589464667666167, 7.733102978723403, 4.993338823529411, 4.179624927536231, 1.9881654545454543, 0.0), # 34 (3.8688838714833755, 7.960546874999998, 6.264993206521739, 3.328278186274509, 1.9323204787234043, 0.0, 5.574270677161419, 7.729281914893617, 4.9924172794117645, 4.176662137681159, 1.9901367187499994, 0.0), # 35 (3.8770992966751923, 7.968317727272727, 6.259621304347825, 3.3275329411764707, 1.9311625531914893, 0.0, 5.556701874062968, 7.724650212765957, 4.9912994117647065, 4.173080869565217, 1.9920794318181818, 0.0), # 36 (3.885137004475703, 7.975973352272726, 6.253343532608695, 3.3266588725490194, 1.9298054787234038, 0.0, 5.536832883558221, 7.719221914893615, 4.989988308823529, 4.168895688405796, 1.9939933380681816, 0.0), # 37 (3.893001023017902, 7.983512727272726, 6.246181739130434, 3.325658039215685, 1.9282527659574464, 0.0, 5.514738330834581, 7.713011063829786, 4.988487058823528, 4.164121159420289, 1.9958781818181814, 0.0), # 38 (3.900695380434782, 7.990934829545453, 6.238157771739129, 3.3245324999999997, 1.9265079255319146, 0.0, 5.490492841079459, 7.7060317021276585, 4.98679875, 4.1587718478260856, 1.9977337073863632, 0.0), # 39 (3.908224104859335, 7.998238636363636, 6.229293478260869, 3.32328431372549, 1.924574468085106, 0.0, 5.464171039480259, 7.698297872340424, 4.984926470588236, 4.1528623188405795, 1.999559659090909, 0.0), # 40 (3.915591224424552, 8.005423124999998, 6.219610706521739, 3.321915539215686, 1.9224559042553186, 0.0, 5.435847551224389, 7.689823617021275, 4.982873308823529, 4.146407137681159, 2.0013557812499996, 0.0), # 41 (3.9228007672634266, 8.012487272727274, 6.209131304347826, 3.320428235294117, 1.920155744680851, 0.0, 5.40559700149925, 7.680622978723404, 4.980642352941175, 4.1394208695652175, 2.0031218181818184, 0.0), # 42 (3.929856761508952, 8.01943005681818, 6.1978771195652165, 3.3188244607843136, 1.9176774999999997, 0.0, 5.373494015492254, 7.670709999999999, 4.978236691176471, 4.131918079710144, 2.004857514204545, 0.0), # 43 (3.936763235294117, 8.026250454545455, 6.18587, 3.317106274509803, 1.9150246808510636, 0.0, 5.339613218390804, 7.660098723404254, 4.975659411764705, 4.123913333333333, 2.0065626136363637, 0.0), # 44 (3.9435242167519178, 8.032947443181817, 6.1731317934782615, 3.315275735294117, 1.91220079787234, 0.0, 5.304029235382309, 7.64880319148936, 4.972913602941175, 4.115421195652174, 2.008236860795454, 0.0), # 45 (3.9501437340153456, 8.03952, 6.159684347826087, 3.313334901960784, 1.9092093617021275, 0.0, 5.266816691654173, 7.63683744680851, 4.970002352941176, 4.106456231884058, 2.00988, 0.0), # 46 (3.956625815217391, 8.045967102272726, 6.1455495108695635, 3.3112858333333324, 1.9060538829787232, 0.0, 5.228050212393803, 7.624215531914893, 4.966928749999999, 4.097033007246376, 2.0114917755681816, 0.0), # 47 (3.962974488491049, 8.052287727272727, 6.130749130434782, 3.309130588235293, 1.9027378723404254, 0.0, 5.187804422788607, 7.610951489361701, 4.96369588235294, 4.087166086956521, 2.013071931818182, 0.0), # 48 (3.9691937819693086, 8.058480852272725, 6.115305054347826, 3.306871225490196, 1.899264840425532, 0.0, 5.146153948025987, 7.597059361702128, 4.960306838235294, 4.076870036231884, 2.014620213068181, 0.0), # 49 (3.9752877237851663, 8.064545454545453, 6.099239130434782, 3.3045098039215683, 1.8956382978723403, 0.0, 5.103173413293353, 7.582553191489361, 4.956764705882353, 4.066159420289854, 2.016136363636363, 0.0), # 50 (3.9812603420716113, 8.070480511363634, 6.082573206521739, 3.302048382352941, 1.8918617553191486, 0.0, 5.0589374437781105, 7.567447021276594, 4.953072573529411, 4.055048804347826, 2.0176201278409085, 0.0), # 51 (3.987115664961637, 8.076284999999999, 6.065329130434782, 3.299489019607843, 1.8879387234042553, 0.0, 5.013520664667666, 7.551754893617021, 4.949233529411765, 4.043552753623188, 2.0190712499999997, 0.0), # 52 (3.992857720588235, 8.081957897727271, 6.047528749999999, 3.2968337745098037, 1.8838727127659571, 0.0, 4.966997701149425, 7.5354908510638285, 4.945250661764706, 4.0316858333333325, 2.020489474431818, 0.0), # 53 (3.9984905370843995, 8.08749818181818, 6.0291939130434775, 3.294084705882353, 1.8796672340425529, 0.0, 4.919443178410794, 7.5186689361702115, 4.941127058823529, 4.019462608695651, 2.021874545454545, 0.0), # 54 (4.00401814258312, 8.092904829545454, 6.010346467391303, 3.2912438725490194, 1.8753257978723403, 0.0, 4.87093172163918, 7.501303191489361, 4.936865808823529, 4.006897644927535, 2.0232262073863634, 0.0), # 55 (4.0094445652173905, 8.098176818181816, 5.991008260869564, 3.288313333333333, 1.8708519148936167, 0.0, 4.821537956021989, 7.483407659574467, 4.9324699999999995, 3.994005507246376, 2.024544204545454, 0.0), # 56 (4.014773833120205, 8.103313125, 5.971201141304347, 3.285295147058823, 1.8662490957446805, 0.0, 4.771336506746626, 7.464996382978722, 4.927942720588234, 3.980800760869564, 2.02582828125, 0.0), # 57 (4.0200099744245525, 8.108312727272725, 5.950946956521738, 3.2821913725490197, 1.8615208510638295, 0.0, 4.7204019990005, 7.446083404255318, 4.923287058823529, 3.9672979710144918, 2.0270781818181813, 0.0), # 58 (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 59 ) passenger_allighting_rate = ( (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 0 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 1 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 2 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 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57 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 58 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 59 ) """ parameters for reproducibiliy. More information: https://numpy.org/doc/stable/reference/random/parallel.html """ #initial entropy entropy = 258194110137029475889902652135037600173 #index for seed sequence child child_seed_index = ( 1, # 0 81, # 1 )

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30f4b7e44a02f5ecd3c79e4a22a066b4b7a542da

16,978

py

Python

madminer/plotting/uncertainties.py

rbarrue/madminer

e1d17b2d6be48cc5686b5cbe5d01f62f6cd2450c

[ "MIT" ]

46

2019-06-29T14:56:00.000Z

2021-08-02T06:05:41.000Z

madminer/plotting/uncertainties.py

Sinclert/madminer

6556f164725baab6b79e5ffa2a64913a0ac50f7a

[ "MIT" ]

52

2019-06-18T18:42:58.000Z

2021-10-04T14:56:39.000Z

madminer/plotting/uncertainties.py

Sinclert/madminer

6556f164725baab6b79e5ffa2a64913a0ac50f7a

[ "MIT" ]

20

2019-06-17T15:29:49.000Z

2021-09-22T18:14:35.000Z

import logging import numpy as np from matplotlib import pyplot as plt, gridspec from ..sampling import SampleAugmenter from ..utils.morphing import NuisanceMorpher from ..utils.various import mdot, shuffle, sanitize_array logger = logging.getLogger(__name__) def plot_uncertainty( filename, theta, observable, obs_label, obs_range, n_bins=50, systematics=None, n_events=None, n_toys=100, linecolor="black", bandcolor1="#CC002E", bandcolor2="orange", ratio_range=(0.8, 1.2), ): """ Plots absolute and relative uncertainty bands in a histogram of one observable in a MadMiner file. Parameters ---------- filename : str Filename of a MadMiner HDF5 file. theta : ndarray, optional Which parameter points to use for histogramming the data. observable : str Which observable to plot, given by its name in the MadMiner file. obs_label : str x-axis label naming the observable. obs_range : tuple of two float Range to be plotted for the observable. n_bins : int Number of bins. Default value: 50. systematics : None or list of str, optional This can restrict which nuisance parameters are used to draw the uncertainty bands. Each entry of this list is the name of a systematic uncertainty (see `MadMiner.add_systematics()`). n_events : None or int, optional If not None, sets the number of events from the MadMiner file that will be analyzed and plotted. Default value: None. n_toys : int, optional Number of toy nuisance parameter vectors used to estimate the systematic uncertainties. Default value: 100. linecolor : str, optional Line color for central prediction. Default value: "black". bandcolor1 : str, optional Error band color for 1 sigma uncertainty. Default value: "#CC002E". bandcolor2 : str, optional Error band color for 2 sigma uncertainty. Default value: "orange". ratio_range : tuple of two floar y-axis range for the plots of the ratio to the central prediction. Default value: (0.8, 1.2). Returns ------- figure : Figure Plot as Matplotlib Figure instance. """ # Load data sa = SampleAugmenter(filename, include_nuisance_parameters=True) nuisance_morpher = NuisanceMorpher( sa.nuisance_parameters, list(sa.benchmarks.keys()), reference_benchmark=sa.reference_benchmark ) # Observable index obs_idx = list(sa.observables.keys()).index(observable) # Get event data (observations and weights) x, weights_benchmarks = sa.weighted_events() x = x[:, obs_idx] # Theta matrix theta_matrix = sa._get_theta_benchmark_matrix(theta) weights = mdot(theta_matrix, weights_benchmarks) # Remove negative weights x = x[weights >= 0.0] weights_benchmarks = weights_benchmarks[weights >= 0.0] weights = weights[weights >= 0.0] # Shuffle events x, weights, weights_benchmarks = shuffle(x, weights, weights_benchmarks) # Only analyze n_events if n_events is not None and n_events < x.shape[0]: x = x[:n_events] weights_benchmarks = weights_benchmarks[:n_events] weights = weights[:n_events] # Nuisance parameters n_nuisance_params = sa.n_nuisance_parameters nuisance_toys = np.random.normal(loc=0.0, scale=1.0, size=n_nuisance_params * n_toys) nuisance_toys = nuisance_toys.reshape(n_toys, n_nuisance_params) # Restrict nuisance parameters if systematics is not None: nuisance_parameters = [] for npar, (npar_syst, _, _) in sa.nuisance_parameters.items(): if npar_syst in systematics: nuisance_parameters.append(npar) for i in range(n_nuisance_params): if i not in nuisance_parameters: nuisance_toys[:, i] = 0.0 nuisance_toy_factors = np.array( [ nuisance_morpher.calculate_nuisance_factors(nuisance_toy, weights_benchmarks) for nuisance_toy in nuisance_toys ] ) # Shape (n_toys, n_events) nuisance_toy_factors = sanitize_array(nuisance_toy_factors, min_value=1.0e-2, max_value=100.0) # Shape (n_toys, n_events) # Calculate histogram for central prediction, not normalized histo, bin_edges = np.histogram(x, bins=n_bins, range=obs_range, weights=weights, density=False) # Calculate toy histograms, not normalized histos_toys_this_theta = [] for i_toy, nuisance_toy_factors_this_toy in enumerate(nuisance_toy_factors): toy_histo, _ = np.histogram( x, bins=n_bins, range=obs_range, weights=weights * nuisance_toy_factors_this_toy, density=False ) histos_toys_this_theta.append(toy_histo) histo_plus2sigma = np.percentile(histos_toys_this_theta, 97.5, axis=0) histo_plus1sigma = np.percentile(histos_toys_this_theta, 84.0, axis=0) histo_minus1sigma = np.percentile(histos_toys_this_theta, 16.0, axis=0) histo_minus2sigma = np.percentile(histos_toys_this_theta, 2.5, axis=0) # Calculate histogram for central prediction, normalized histo_norm, bin_edges_norm = np.histogram(x, bins=n_bins, range=obs_range, weights=weights, density=True) # Calculate toy histograms, normalized histos_toys_this_theta = [] for i_toy, nuisance_toy_factors_this_toy in enumerate(nuisance_toy_factors): toy_histo, _ = np.histogram( x, bins=n_bins, range=obs_range, weights=weights * nuisance_toy_factors_this_toy, density=True ) histos_toys_this_theta.append(toy_histo) histo_plus2sigma_norm = np.percentile(histos_toys_this_theta, 97.5, axis=0) histo_plus1sigma_norm = np.percentile(histos_toys_this_theta, 84.0, axis=0) histo_minus1sigma_norm = np.percentile(histos_toys_this_theta, 16.0, axis=0) histo_minus2sigma_norm = np.percentile(histos_toys_this_theta, 2.5, axis=0) # Prepare plotting def plot_mc(edges, histo_central, histo_m2, histo_m1, histo_p1, histo_p2, relative=False): bin_edges_ = np.repeat(edges, 2)[1:-1] histo_ = np.repeat(histo_central, 2) histo_m2_ = np.repeat(histo_m2, 2) histo_m1_ = np.repeat(histo_m1, 2) histo_p1_ = np.repeat(histo_p1, 2) histo_p2_ = np.repeat(histo_p2, 2) if relative: histo_m2_ /= histo_ histo_m1_ /= histo_ histo_p1_ /= histo_ histo_p2_ /= histo_ histo_ /= histo_ plt.fill_between(bin_edges_, histo_m2_, histo_p2_, facecolor=bandcolor2, edgecolor="none") plt.fill_between(bin_edges_, histo_m1_, histo_p1_, facecolor=bandcolor1, edgecolor="none") plt.plot(bin_edges_, histo_, color=linecolor, lw=1.5, ls="-") # Make plot fig = plt.figure(figsize=(10, 7)) gs = gridspec.GridSpec(2, 2, height_ratios=[2, 1]) # MC, absolute residuals ax = plt.subplot(gs[2]) plot_mc(bin_edges, histo, histo_minus2sigma, histo_minus1sigma, histo_plus1sigma, histo_plus2sigma, relative=True) plt.xlabel(obs_label) plt.ylabel(r"Relative to central pred.") plt.xlim(obs_range[0], obs_range[1]) plt.ylim(ratio_range[0], ratio_range[1]) # MC, absolute ax = plt.subplot(gs[0], sharex=ax) plot_mc(bin_edges, histo, histo_minus2sigma, histo_minus1sigma, histo_plus1sigma, histo_plus2sigma) plt.ylabel(r"Differential cross section [pb/bin]") plt.ylim(0.0, None) plt.setp(ax.get_xticklabels(), visible=False) # MC, relative residuals ax = plt.subplot(gs[3]) plot_mc( bin_edges_norm, histo_norm, histo_minus2sigma_norm, histo_minus1sigma_norm, histo_plus1sigma_norm, histo_plus2sigma_norm, relative=True, ) plt.xlabel(r"$p_{T,\gamma}$ [GeV]") plt.ylabel(r"Relative to central pred.") plt.xlim(obs_range[0], obs_range[1]) plt.ylim(ratio_range[0], ratio_range[1]) # MC, relative ax = plt.subplot(gs[1], sharex=ax) plot_mc( bin_edges_norm, histo_norm, histo_minus2sigma_norm, histo_minus1sigma_norm, histo_plus1sigma_norm, histo_plus2sigma_norm, ) plt.ylabel(r"Normalized distribution") plt.ylim(0.0, None) plt.setp(ax.get_xticklabels(), visible=False) # Return plt.tight_layout() return fig def plot_systematics( filename, theta, observable, obs_label, obs_range, n_bins=50, n_events=None, n_toys=100, linecolor="black", bandcolors=None, band_alpha=0.2, ratio_range=(0.8, 1.2), ): """ Plots absolute and relative uncertainty bands for all systematic uncertainties in a histogram of one observable in a MadMiner file. Parameters ---------- filename : str Filename of a MadMiner HDF5 file. theta : ndarray, optional Which parameter points to use for histogramming the data. observable : str Which observable to plot, given by its name in the MadMiner file. obs_label : str x-axis label naming the observable. obs_range : tuple of two float Range to be plotted for the observable. n_bins : int Number of bins. Default value: 50. n_events : None or int, optional If not None, sets the number of events from the MadMiner file that will be analyzed and plotted. Default value: None. n_toys : int, optional Number of toy nuisance parameter vectors used to estimate the systematic uncertainties. Default value: 100. linecolor : str, optional Line color for central prediction. Default value: "black". bandcolors : None or list of str, optional Error band colors. Default value: None. ratio_range : tuple of two float y-axis range for the plots of the ratio to the central prediction. Default value: (0.8, 1.2). Returns ------- figure : Figure Plot as Matplotlib Figure instance. """ # Colors if bandcolors is None: bandcolors = [f"C{i}" for i in range(10)] # Load data sa = SampleAugmenter(filename, include_nuisance_parameters=True) nuisance_morpher = NuisanceMorpher( sa.nuisance_parameters, list(sa.benchmarks.keys()), reference_benchmark=sa.reference_benchmark ) # Observable index obs_idx = list(sa.observables.keys()).index(observable) # Get event data (observations and weights) x, weights_benchmarks = sa.weighted_events() x = x[:, obs_idx] # Theta matrix theta_matrix = sa._get_theta_benchmark_matrix(theta) weights = mdot(theta_matrix, weights_benchmarks) # Remove negative weights x = x[weights >= 0.0] weights_benchmarks = weights_benchmarks[weights >= 0.0] weights = weights[weights >= 0.0] # Shuffle events x, weights, weights_benchmarks = shuffle(x, weights, weights_benchmarks) # Only analyze n_events if n_events is not None and n_events < x.shape[0]: x = x[:n_events] weights_benchmarks = weights_benchmarks[:n_events] weights = weights[:n_events] # Systematics n_systematics = len(sa.systematics) + 1 labels = list(sa.systematics.keys()) + ["combined"] # Nuisance parameters n_nuisance_params = sa.n_nuisance_parameters nuisance_toys = np.random.normal(loc=0.0, scale=1.0, size=n_systematics * n_nuisance_params * n_toys) nuisance_toys = nuisance_toys.reshape(n_systematics, n_toys, n_nuisance_params) # Restrict nuisance parameters all_nuisance_parameters = list(sa.nuisance_parameters.keys()) for i_syst, syst_name in enumerate(sa.systematics.keys()): n_used = n_nuisance_params used_nuisance_parameters = [] for npar, (npar_syst, _, _) in sa.nuisance_parameters.items(): if npar_syst == syst_name: used_nuisance_parameters.append(npar) for i in range(n_nuisance_params): if all_nuisance_parameters[i] not in used_nuisance_parameters: nuisance_toys[i_syst, :, i] = 0.0 n_used -= 1 logger.debug("Systematics %s based on %s nuisance parameters", syst_name, n_used) nuisance_toys = nuisance_toys.reshape(n_systematics * n_toys, n_nuisance_params) nuisance_toy_factors = np.array( [ nuisance_morpher.calculate_nuisance_factors(nuisance_toy, weights_benchmarks) for nuisance_toy in nuisance_toys ] ) # Shape (n_systematics*n_toys, n_events) nuisance_toy_factors = sanitize_array(nuisance_toy_factors, min_value=1.0e-2, max_value=100.0) # Shape (n_systematics*n_toys, n_events) # Calculate histogram for central prediction, not normalized histo, bin_edges = np.histogram(x, bins=n_bins, range=obs_range, weights=weights, density=False) # Calculate toy histograms, not normalized histos_toys_this_theta = [] for i_toy, nuisance_toy_factors_this_toy in enumerate(nuisance_toy_factors): toy_histo, _ = np.histogram( x, bins=n_bins, range=obs_range, weights=weights * nuisance_toy_factors_this_toy, density=False ) histos_toys_this_theta.append(toy_histo) histos_toys_this_theta = np.array(histos_toys_this_theta) histos_toys_this_theta = histos_toys_this_theta.reshape(n_systematics, n_toys, -1) # Shape (n_systematics, n_toys, v) histo_plus1sigma = np.percentile(histos_toys_this_theta, 84.0, axis=1) histo_minus1sigma = np.percentile(histos_toys_this_theta, 16.0, axis=1) # Calculate histogram for central prediction, normalized histo_norm, bin_edges_norm = np.histogram(x, bins=n_bins, range=obs_range, weights=weights, density=True) # Calculate toy histograms, normalized histos_toys_this_theta = [] for i_toy, nuisance_toy_factors_this_toy in enumerate(nuisance_toy_factors): toy_histo, _ = np.histogram( x, bins=n_bins, range=obs_range, weights=weights * nuisance_toy_factors_this_toy, density=True ) histos_toys_this_theta.append(toy_histo) histos_toys_this_theta = np.array(histos_toys_this_theta) histos_toys_this_theta = histos_toys_this_theta.reshape(n_systematics, n_toys, -1) # Shape (n_systematics, n_toys, n_bins) histo_plus1sigma_norm = np.percentile(histos_toys_this_theta, 84.0, axis=1) histo_minus1sigma_norm = np.percentile(histos_toys_this_theta, 16.0, axis=1) # Prepare plotting def plot_mc(edges, histo_central, histos_minus, histos_plus, relative=False): bin_edges_ = np.repeat(edges, 2)[1:-1] histo_central_ = np.repeat(histo_central, 2) histos_minus_ = [np.repeat(h, 2) for h in histos_minus] histos_plus_ = [np.repeat(h, 2) for h in histos_plus] if relative: histos_minus_ = [h / histo_central_ for h in histos_minus_] histos_plus_ = [h / histo_central_ for h in histos_plus_] histo_central_ /= histo_central_ for i, (histo_minus_, histo_plus_) in enumerate(zip(histos_minus_, histos_plus_)): plt.fill_between( bin_edges_, histo_minus_, histo_plus_, facecolor=bandcolors[i % len(bandcolors)], alpha=band_alpha, edgecolor="none", label=labels[i], ) plt.plot(bin_edges_, histo_minus_, color=bandcolors[i % len(bandcolors)], lw=1.0, ls="-") plt.plot(bin_edges_, histo_plus_, color=bandcolors[i % len(bandcolors)], lw=1.0, ls="-") plt.plot(bin_edges_, histo_central_, color=linecolor, lw=1.5, ls="-") # Make plot fig = plt.figure(figsize=(10, 7)) gs = gridspec.GridSpec(2, 2, height_ratios=[2, 1]) # MC, absolute residuals ax = plt.subplot(gs[2]) plot_mc(bin_edges, histo, histo_minus1sigma, histo_plus1sigma, relative=True) plt.xlabel(obs_label) plt.ylabel(r"Relative to central pred.") plt.xlim(obs_range[0], obs_range[1]) plt.ylim(ratio_range[0], ratio_range[1]) # MC, absolute ax = plt.subplot(gs[0], sharex=ax) plot_mc(bin_edges, histo, histo_minus1sigma, histo_plus1sigma) plt.legend() plt.ylabel(r"Differential cross section [pb/bin]") plt.ylim(0.0, None) plt.setp(ax.get_xticklabels(), visible=False) # MC, relative residuals ax = plt.subplot(gs[3]) plot_mc(bin_edges_norm, histo_norm, histo_minus1sigma_norm, histo_plus1sigma_norm, relative=True) plt.xlabel(obs_label) plt.ylabel(r"Relative to central pred.") plt.xlim(obs_range[0], obs_range[1]) plt.ylim(ratio_range[0], ratio_range[1]) # MC, relative ax = plt.subplot(gs[1], sharex=ax) plot_mc(bin_edges_norm, histo_norm, histo_minus1sigma_norm, histo_plus1sigma_norm) plt.legend() plt.ylabel(r"Normalized distribution") plt.ylim(0.0, None) plt.setp(ax.get_xticklabels(), visible=False) # Return plt.tight_layout() return fig

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30ffe8945bd775eb2a620fe9d38bc6c432a68239

2,921

py

Python

schunk_robots/schunk_lwa4d/scripts/move_lin.py

briefgw/brief_schunklwa4p_simulation

7b148225bfca2f2d3289a9b924d6afde7507b9e8

[ "BSD-3-Clause" ]

2

2019-03-27T11:01:08.000Z

2019-08-07T09:38:53.000Z

schunk_robots/schunk_lwa4d/scripts/move_lin.py

briefgw/brief_schunklwa4p_simulation

7b148225bfca2f2d3289a9b924d6afde7507b9e8

[ "BSD-3-Clause" ]

1

2019-04-01T11:20:08.000Z

2019-06-07T07:57:26.000Z

schunk_robots/schunk_lwa4d/scripts/move_lin.py

briefgw/brief_schunklwa4p_simulation

7b148225bfca2f2d3289a9b924d6afde7507b9e8

[ "BSD-3-Clause" ]

null

#! /usr/bin/env python import math import rospy from geometry_msgs.msg import Pose from cob_cartesian_controller.msg import Profile from simple_script_server.simple_script_server import simple_script_server import simple_cartesian_interface as sci def init_pos(): sss = simple_script_server() sss.move("arm", [[-0.00032004093963244884, -0.7064191894021441, -1.577532922958369e-06, 1.4183686971944311, 1.2084352562169443e-05, -0.6913530502577565, -0.0002663056533762642]]) if __name__ == '__main__': rospy.init_node('test_move_lin_interface') init_pos() pose = sci.gen_pose(pos=[0.0, 0.0, 0.9], rpy=[0.0, 0.0, 0.0]) profile = Profile() profile.vel = 0.1 profile.accl = 0.05 #profile.profile_type = Profile.SINOID profile.profile_type = Profile.RAMP success, message = sci.move_lin(pose, "world", profile) if success: rospy.loginfo(message) else: rospy.logerr(message) rospy.sleep(3.0) pose = sci.gen_pose(pos=[0.3, 0.0, 0.9], rpy=[0.0, 0.0, 0.0]) profile = Profile() profile.vel = 0.1 profile.accl = 0.05 #profile.profile_type = Profile.SINOID profile.profile_type = Profile.RAMP success, message = sci.move_lin(pose, "world", profile) if success: rospy.loginfo(message) else: rospy.logerr(message) rospy.sleep(3.0) pose = sci.gen_pose(pos=[0.3, 0.0, 0.8], rpy=[0.0, 0.0, 0.0]) profile = Profile() profile.vel = 0.1 profile.accl = 0.05 #profile.profile_type = Profile.SINOID profile.profile_type = Profile.RAMP success, message = sci.move_lin(pose, "world", profile) if success: rospy.loginfo(message) else: rospy.logerr(message) rospy.sleep(3.0) pose = sci.gen_pose(pos=[-0.3, 0.0, 0.8], rpy=[0.0, 0.0, 0.0]) profile = Profile() profile.vel = 0.1 profile.accl = 0.05 #profile.profile_type = Profile.SINOID profile.profile_type = Profile.RAMP success, message = sci.move_lin(pose, "world", profile) if success: rospy.loginfo(message) else: rospy.logerr(message) rospy.sleep(3.0) pose = sci.gen_pose(pos=[-0.3, 0.0, 0.9], rpy=[0.0, 0.0, 0.0]) profile = Profile() profile.vel = 0.1 profile.accl = 0.05 #profile.profile_type = Profile.SINOID profile.profile_type = Profile.RAMP success, message = sci.move_lin(pose, "world", profile) if success: rospy.loginfo(message) else: rospy.logerr(message) rospy.sleep(3.0) pose = sci.gen_pose(pos=[0.0, 0.0, 0.9], rpy=[0.0, 0.0, 0.0]) profile = Profile() profile.vel = 0.1 profile.accl = 0.05 #profile.profile_type = Profile.SINOID profile.profile_type = Profile.RAMP success, message = sci.move_lin(pose, "world", profile) if success: rospy.loginfo(message) else: rospy.logerr(message) rospy.sleep(3.0)

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null

0

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1

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null

0

6

a50ba269a585223f45e8d0fce2c263db7207fb1a

2,816

py

Python

draft_app/migrations/0001_initial.py

lopes05/battlerite_draft_graphql

69e3764e603b6162e3e5009647a0aafe5831d101

[ "MIT" ]

1

2020-03-02T21:33:09.000Z

draft_app/migrations/0001_initial.py

lopes05/battlerite_draft_graphql

69e3764e603b6162e3e5009647a0aafe5831d101

[ "MIT" ]

7

2021-03-19T00:42:11.000Z

2022-03-12T00:18:29.000Z

draft_app/migrations/0001_initial.py

lopes05/battlerite_draft_graphql

69e3764e603b6162e3e5009647a0aafe5831d101

[ "MIT" ]

null

# Generated by Django 3.0.3 on 2020-03-08 15:40 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Captain', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=100)), ], ), migrations.CreateModel( name='Champion', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=40, unique=True)), ('picture', models.ImageField(upload_to='')), ], ), migrations.CreateModel( name='Map', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=40, unique=True)), ('picture', models.ImageField(upload_to='')), ], ), migrations.CreateModel( name='Team', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('champion', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='draft_app.Champion')), ], ), migrations.CreateModel( name='DraftLobby', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('captain_a', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='captain_a', to='draft_app.Captain')), ('captain_b', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='captain_b', to='draft_app.Captain')), ('chosen_map', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='map', to='draft_app.Map')), ('team_a', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='team_a', to='draft_app.Team')), ('team_a_bans', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='bans_a', to='draft_app.Champion')), ('team_b', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='team_b', to='draft_app.Team')), ('team_b_bans', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='bans_b', to='draft_app.Champion')), ], ), ]

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0

1

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null

0

6

eb71b0a0ed00b83929cd60ffbedd20558daa58a5

39

py

Python

routes/admin/__init__.py

bilginfurkan/Anonimce

7d73c13ae8d5c873b6863878370ad83ec9ee5acc

[ "Apache-2.0" ]

2

2021-02-15T12:56:58.000Z

2021-02-21T12:38:47.000Z

routes/admin/__init__.py

bilginfurkan/Anonimce

7d73c13ae8d5c873b6863878370ad83ec9ee5acc

[ "Apache-2.0" ]

null

routes/admin/__init__.py

bilginfurkan/Anonimce

7d73c13ae8d5c873b6863878370ad83ec9ee5acc

[ "Apache-2.0" ]

null

from .admin import * from .api import *

19.5

20

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null

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1

0

1

0

1

0

6

eb8d9d20c0c168ad1cce80735928bb37e88f571f

5,239

py

Python

test/test_13_filtercapabilities.py

growell/svnhook

ceb337c472c5ec470286576a9bfca8f5b8ec424e

[ "Apache-2.0" ]

1

2015-11-23T21:05:19.000Z

test/test_13_filtercapabilities.py

growell/svnhook

ceb337c472c5ec470286576a9bfca8f5b8ec424e

[ "Apache-2.0" ]

null

test/test_13_filtercapabilities.py

growell/svnhook

ceb337c472c5ec470286576a9bfca8f5b8ec424e

[ "Apache-2.0" ]

null

#!/usr/bin/env python ###################################################################### # Test Client Capabilities Filter ###################################################################### import os, re, sys, unittest # Prefer local modules. mylib = os.path.normpath(os.path.join( os.path.dirname(__file__), '..')) if os.path.isdir(mylib): sys.path.insert(0, mylib) from test.base import HookTestCase # Test Hook and Configuration File testhook = 'start-commit' testconf = 'start-commit.xml' class TestFilterCapabilities(HookTestCase): def setUp(self): super(TestFilterCapabilities, self).setUp( re.sub(r'^test_?(.+)\.[^\.]+$', r'\1', os.path.basename(__file__))) def test_01_no_capabilities(self): """No client capabilities.""" # Define the hook configuration. self.writeConf(testconf, '''\ <?xml version="1.0"?> <Actions> <FilterCapabilities> <CapabilitiesRegex sense="false">mergeinfo</CapabilitiesRegex> <SendError>Capability missing: mergeinfo</SendError> </FilterCapabilities> </Actions> ''') # Call the script with a mismatch. p = self.callHook(testhook, self.repopath, self.username, '') (stdoutdata, stderrdata) = p.communicate() p.wait() # Verify the proper error is returned. self.assertRegexpMatches( stderrdata, r'Capability missing', 'Expected error message not found') # Verify a failure is indicated. self.assertTrue(p.returncode != 0, 'Expected error exit code is zero') def test_02_exact_match(self): """Exact capability match.""" # Define the hook configuration. self.writeConf(testconf, '''\ <?xml version="1.0"?> <Actions> <FilterCapabilities> <CapabilitiesRegex sense="false">mergeinfo</CapabilitiesRegex> <SendError>Capability missing: mergeinfo</SendError> </FilterCapabilities> </Actions> ''') # Call the script with a single capability that exactly # matches the desired capability. p = self.callHook(testhook, self.repopath, self.username, 'mergeinfo') (stdoutdata, stderrdata) = p.communicate() p.wait() # Verify that no error message is returned. self.assertRegexpMatches( stderrdata, r'(?s)^\s*$', 'Unexpected error message found') # Verify that no failure is indicated. self.assertTrue( p.returncode == 0, 'Expected success exit code is non-zero') def test_03_multiple_client_match(self): """Match capability in client list.""" # Define the hook configuration. self.writeConf(testconf, '''\ <?xml version="1.0"?> <Actions> <FilterCapabilities> <CapabilitiesRegex sense="false">mergeinfo</CapabilitiesRegex> <SendError>Capability missing: mergeinfo</SendError> </FilterCapabilities> </Actions> ''') # Call the script with multiple colon-delimited capabilities # - one of which matches the desired capability. p = self.callHook(testhook, self.repopath, self.username, 'mindread:mergeinfo:emote') (stdoutdata, stderrdata) = p.communicate() p.wait() # Verify that no error message is returned. self.assertRegexpMatches( stderrdata, r'(?s)^\s*$', 'Unexpected error message found') # Verify that no failure is indicated. self.assertTrue( p.returncode == 0, 'Expected success exit code is non-zero') def test_04_case_sensitive(self): """Case-sensitive client capabilities.""" # Define the hook configuration. self.writeConf(testconf, '''\ <?xml version="1.0"?> <Actions> <FilterCapabilities> <CapabilitiesRegex sense="false">mergeinfo</CapabilitiesRegex> <SendError>Capability missing: mergeinfo</SendError> </FilterCapabilities> </Actions> ''') # Call the script with a mismatch. p = self.callHook(testhook, self.repopath, self.username, 'MindRead:MergeInfo:Emote') (stdoutdata, stderrdata) = p.communicate() p.wait() # Verify the proper error is returned. self.assertRegexpMatches( stderrdata, r'Capability missing', 'Expected error message not found') # Verify a failure is indicated. self.assertTrue(p.returncode != 0, 'Expected error exit code is zero') # Allow manual execution of tests. if __name__=='__main__': suite = unittest.TestLoader()\ .loadTestsFromTestCase(TestFilterCapabilities) unittest.TextTestRunner(verbosity=2).run(suite) ########################### end of file ##############################

34.926667

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5,239

6.105263

0.277895

0.035172

0.017931

0.035862

0.734828

0

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0.300439

5,239

149

77

35.161074

0.784993

0.180378

0

0.75

0

0.399457

0.116457

0

0.083333

1

0.052083

false

0

0.020833

0

0.083333

0

null

0

1

0

1

0

null

0

6

ebd5c77583169affa3445c54f3e34877f364fe6f

3,706

py

Python

query_mpg.py

MicroServicesAIOps/ElasticSearchPython

75a9fb2a147fc735f962eb37bfa125409bab425a

[ "Apache-2.0" ]

null

query_mpg.py

MicroServicesAIOps/ElasticSearchPython

75a9fb2a147fc735f962eb37bfa125409bab425a

[ "Apache-2.0" ]

null

query_mpg.py

MicroServicesAIOps/ElasticSearchPython

75a9fb2a147fc735f962eb37bfa125409bab425a

[ "Apache-2.0" ]

null

import utils import pandas as pd # 创建mpg def generate_mpg_data(data_dir): df = pd.DataFrame(columns=['source', 'destination']) query_arr = [["source_workload", "destination_workload", "service", "service", "sum(istio_tcp_received_bytes_total) by (source_workload, destination_workload)"], ["source_workload", "destination_workload", "service", "service", "sum(istio_requests_total{destination_workload_namespace=\'sock-shop\'}) " "by (source_workload, destination_workload)"], ["pod", "instance", "container", "host", "sum(container_cpu_usage_seconds_total{namespace=\"sock-shop\", " "container!~\"POD|istio-proxy|\"}) by (instance, pod)"], ["instance", "pod", "host", "container", "sum(container_cpu_usage_seconds_total{namespace=\"sock-shop\", " "container!~\"POD|istio-proxy|\"}) by (instance, pod)"], ["kubernetes_pod_name", "source_workload", "container", "service", "sum(istio_requests_total{destination_workload_namespace='sock-shop', reporter='source'})" " by (kubernetes_pod_name, source_workload)"], ["source_workload", "kubernetes_pod_name", "service", "container", "sum(istio_requests_total{destination_workload_namespace='sock-shop', reporter='source'})" " by (kubernetes_pod_name, source_workload)"], ["kubernetes_pod_name", "destination_workload", "container", "service", "sum(istio_requests_total{destination_workload_namespace='sock-shop', reporter='destination'})" " by (kubernetes_pod_name, destination_workload)"], ["destination_workload", "kubernetes_pod_name", "service", "container", "sum(istio_requests_total{destination_workload_namespace='sock-shop', reporter='destination'})" " by (kubernetes_pod_name, destination_workload)"], ["kubernetes_pod_name", "source_workload", "container", "service", "sum(istio_tcp_received_bytes_total{destination_workload_namespace='sock-shop', reporter='source'})" " by (kubernetes_pod_name, source_workload)"], ["source_workload", "kubernetes_pod_name", "service", "container", "sum(istio_tcp_received_bytes_total{destination_workload_namespace='sock-shop', reporter='source'})" " by (kubernetes_pod_name, source_workload)"], ["kubernetes_pod_name", "destination_workload", "container", "service", "sum(istio_tcp_received_bytes_total{destination_workload_namespace='sock-shop', " "reporter='destination'}) by (kubernetes_pod_name, destination_workload)"], ["destination_workload", "kubernetes_pod_name", "service", "container", "sum(istio_tcp_received_bytes_total{destination_workload_namespace='sock-shop', " "reporter='destination'}) by (kubernetes_pod_name, destination_workload)"], ] for query_info in query_arr: results = utils.query_range_prom_data(query_info[-1], None, None, instant=True)['data']['result'] for result in results: metric = result['metric'] source = metric[query_info[0]] destination = metric[query_info[1]] df = df.append({'source': source + '_' + query_info[2], 'destination': destination + '_' + query_info[3]}, ignore_index=True) df = df.drop_duplicates() df.to_csv(data_dir + '_mpg.csv')

69.924528

118

0.618996

363

3,706

5.953168

0.176309

0.184637

0.125868

0.137436

0.795002

0.76261

0.752892

0.708931

0.687645

0

0.001788

0.245278

3,706

52

119

71.269231

0.770826

0.001349

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0.56

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0.318551

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1

0.02

false

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0.04

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0.06

0

null

0

1

0

1

0

null

0

6

ebd9833208bd8078770ffa548f791456f8a3d2fb

4,934

py

Python

tests/barriers/test_archive.py

felix781/market-access-python-frontend

3b0e49feb4fdf0224816326938a46002aa4a2b1c

[ "MIT" ]

1

2021-12-15T04:14:03.000Z

tests/barriers/test_archive.py

felix781/market-access-python-frontend

3b0e49feb4fdf0224816326938a46002aa4a2b1c

[ "MIT" ]

19

2019-12-11T11:32:47.000Z

2022-03-29T15:40:57.000Z

tests/barriers/test_archive.py

felix781/market-access-python-frontend

3b0e49feb4fdf0224816326938a46002aa4a2b1c

[ "MIT" ]

2

2021-02-09T09:38:45.000Z

2021-03-29T19:07:09.000Z

from http import HTTPStatus from django.urls import reverse from mock import patch from core.tests import MarketAccessTestCase class ArchiveTestCase(MarketAccessTestCase): @patch("utils.api.resources.APIResource.patch") def test_empty_reason(self, mock_patch): response = self.client.post( reverse("barriers:archive", kwargs={"barrier_id": self.barrier["id"]}) ) assert response.status_code == HTTPStatus.OK form = response.context["form"] assert form.is_valid() is False assert "reason" in form.errors assert mock_patch.called is False @patch("utils.api.resources.APIResource.patch") def test_duplicate_empty_explanation(self, mock_patch): response = self.client.post( reverse("barriers:archive", kwargs={"barrier_id": self.barrier["id"]}), data={"reason": "DUPLICATE"}, ) assert response.status_code == HTTPStatus.OK form = response.context["form"] assert form.is_valid() is False assert "duplicate_explanation" in form.errors assert mock_patch.called is False @patch("utils.api.resources.APIResource.patch") def test_non_a_barrier_empty_explanation(self, mock_patch): response = self.client.post( reverse("barriers:archive", kwargs={"barrier_id": self.barrier["id"]}), data={"reason": "NOT_A_BARRIER"}, ) assert response.status_code == HTTPStatus.OK form = response.context["form"] assert form.is_valid() is False assert "not_a_barrier_explanation" in form.errors assert mock_patch.called is False @patch("utils.api.resources.APIResource.patch") def test_other_empty_explanation(self, mock_patch): response = self.client.post( reverse("barriers:archive", kwargs={"barrier_id": self.barrier["id"]}), data={"reason": "OTHER"}, ) assert response.status_code == HTTPStatus.OK form = response.context["form"] assert form.is_valid() is False assert "other_explanation" in form.errors assert mock_patch.called is False @patch("utils.api.resources.APIResource.patch") def test_duplicate_success(self, mock_patch): response = self.client.post( reverse("barriers:archive", kwargs={"barrier_id": self.barrier["id"]}), data={"reason": "DUPLICATE", "duplicate_explanation": "Explanation"}, ) assert response.status_code == HTTPStatus.FOUND mock_patch.assert_called_with( id=self.barrier["id"], archived=True, archived_reason="DUPLICATE", archived_explanation="Explanation", ) @patch("utils.api.resources.APIResource.patch") def test_not_a_barrier_success(self, mock_patch): response = self.client.post( reverse("barriers:archive", kwargs={"barrier_id": self.barrier["id"]}), data={ "reason": "NOT_A_BARRIER", "not_a_barrier_explanation": "Explanation", }, ) assert response.status_code == HTTPStatus.FOUND mock_patch.assert_called_with( id=self.barrier["id"], archived=True, archived_reason="NOT_A_BARRIER", archived_explanation="Explanation", ) @patch("utils.api.resources.APIResource.patch") def test_other_success(self, mock_patch): response = self.client.post( reverse("barriers:archive", kwargs={"barrier_id": self.barrier["id"]}), data={"reason": "OTHER", "other_explanation": "Other Explanation"}, ) assert response.status_code == HTTPStatus.FOUND mock_patch.assert_called_with( id=self.barrier["id"], archived=True, archived_reason="OTHER", archived_explanation="Other Explanation", ) class UnarchiveTestCase(MarketAccessTestCase): @patch("utils.api.resources.APIResource.patch") def test_empty_reason(self, mock_patch): response = self.client.post( reverse("barriers:unarchive", kwargs={"barrier_id": self.barrier["id"]}) ) assert response.status_code == HTTPStatus.OK form = response.context["form"] assert form.is_valid() is False assert "reason" in form.errors assert mock_patch.called is False @patch("utils.api.resources.APIResource.patch") def test_success(self, mock_patch): response = self.client.post( reverse("barriers:unarchive", kwargs={"barrier_id": self.barrier["id"]}), data={"reason": "Reason for unarchiving"}, ) assert response.status_code == HTTPStatus.FOUND mock_patch.assert_called_with( id=self.barrier["id"], archived=False, unarchived_reason="Reason for unarchiving", )

38.850394

85

0.632955

537

4,934

5.636872

0.111732

0.065411

0.055831

0.06442

0.88107

0.877767

0.874463

0

0.247669

4,934

126

86

39.15873

0.815463

0

0.575221

0

0.202675

0.086137

0

0.247788

1

0.079646

false

0

0.035398

0

0.132743

0

null

0

1

0

null

0

6

696615a33763d3f5e34d089929055763e868b6c6

60

py

Python

testFunction.py

gtg3vv/cs3240-labdemo

8d49de60cdd5f9dd314da1225959bf95735e174a

[ "MIT" ]

null

testFunction.py

gtg3vv/cs3240-labdemo

8d49de60cdd5f9dd314da1225959bf95735e174a

[ "MIT" ]

null

testFunction.py

gtg3vv/cs3240-labdemo

8d49de60cdd5f9dd314da1225959bf95735e174a

[ "MIT" ]

null

def test_function(x): return x*x*x print(test_function(4))

15

23

0.75

12

60

3.583333

0.583333

0.55814

0

0.018519

0.1

60

4

23

15

0.777778

0

1

0.333333

false

0

0.333333

0.666667

0.333333

1

0

null

1

0

1

0

null

0

1

0

1

0

6

15d9da476eadece26d9c9f71048e3d8719e20882

27

py

Python

test3.py

Da-Tony-W/test1

95726b125a51c8712141f76a8d2511d68da99769

[ "MIT" ]

null

test3.py

Da-Tony-W/test1

95726b125a51c8712141f76a8d2511d68da99769

[ "MIT" ]

null

test3.py

Da-Tony-W/test1

95726b125a51c8712141f76a8d2511d68da99769

[ "MIT" ]

null

print("Hello, Mondasians")

13.5

26

0.740741

3

27

6.666667

1

0

0.074074

27

1

27

0.8

0

0.62963

0

1

0

true

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

c655b651da1c114da264d0e492d2d93f8da6fa94

100

py

Python

dvt/byod_dvt/fargate/validation/src/rules/__init__.py

joben/bring-your-own-data-labs

b580a2553220648e1273e585689267932812c732

[ "MIT-0" ]

null

dvt/byod_dvt/fargate/validation/src/rules/__init__.py

joben/bring-your-own-data-labs

b580a2553220648e1273e585689267932812c732

[ "MIT-0" ]

1

2020-12-07T05:03:04.000Z

dvt/byod_dvt/fargate/validation/src/rules/__init__.py

joben/bring-your-own-data-labs

b580a2553220648e1273e585689267932812c732

[ "MIT-0" ]

3

2020-10-13T08:52:38.000Z

2021-01-18T02:58:51.000Z

from .csv_header_rule import CsvHeaderRule from .filesize_encoding_rule import FileSizeEncodingRule

33.333333

56

0.9

12

100

7.166667

0.75

0.232558

0

0.08

100

2

57

50

0.934783

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

d6c20982e7ad8a076cc6b8d4d8312c43304e65d9

62

py

Python

brain_training/programming_challenges/src/T022/palindrome.py

kuzxnia/algoritms

eda3185f39d79a2657b7ef0da869fcc6b825889d

[ "MIT" ]

null

brain_training/programming_challenges/src/T022/palindrome.py

kuzxnia/algoritms

eda3185f39d79a2657b7ef0da869fcc6b825889d

[ "MIT" ]

null

brain_training/programming_challenges/src/T022/palindrome.py

kuzxnia/algoritms

eda3185f39d79a2657b7ef0da869fcc6b825889d

[ "MIT" ]

null

def is_palindrome(seq): return str(seq) == str(seq)[::-1]

20.666667

37

0.612903

10

62

3.7

0.7

0.324324

0

0.019231

0.16129

62

2

38

31

0.692308

0

1

0.5

false

0

0.5

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

d6ef13000ea2455e440a82c2bea154ca3013e854

434

py

Python

denoisers/base.py

parthe/VAMP

1a17af5aef4aaf9cc37bc3ae6b9c9b7d58de5b84

[ "BSD-3-Clause" ]

1

2021-12-08T03:15:04.000Z

denoisers/base.py

parthe/VAMP

1a17af5aef4aaf9cc37bc3ae6b9c9b7d58de5b84

[ "BSD-3-Clause" ]

null

denoisers/base.py

parthe/VAMP

1a17af5aef4aaf9cc37bc3ae6b9c9b7d58de5b84

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- """ Created on Fri Feb 5 18:38:10 2021 @author: Parthe """ from functools import partial class Denoiser(): def __init__(self, denoising_function, *args, **kwargs): self.denoising_function = partial(denoising_function, *args, **kwargs) self.__dict__.update(**kwargs) def __call__(self, *args, **kwargs): return self.denoising_function(*args, **kwargs)

22.842105

78

0.631336

50

434

5.16

0.62

0.263566

0.244186

0.313953

0.375969

0

0.035821

0.228111

434

19

79

22.842105

0.734328

0.172811

0

1

0.285714

false

0

0.142857

0.714286

0

null

1

0

null

0

1

0

1

0

6

ba399699970ab90393d6c4446632917433ad64fd

116

py

Python

zsplot/__init__.py

Zsailer/zsplot

ed0fe811e35d89ff929726a2e39688cecdf5fbb7

[ "MIT" ]

null

zsplot/__init__.py

Zsailer/zsplot

ed0fe811e35d89ff929726a2e39688cecdf5fbb7

[ "MIT" ]

null

zsplot/__init__.py

Zsailer/zsplot

ed0fe811e35d89ff929726a2e39688cecdf5fbb7

[ "MIT" ]

null

from .kde2d import kde2d from .correlations import (corr, resid, rhist, corr_resid, corr_resid_rhist) __all__ = []

23.2

76

0.767241

16

116

5.125

0.5

0.329268

0.341463

0

0.02

0.137931

116

4

77

29

0.8

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

ba586c482bdbf1eb4ab02e4fb4cfe7d019b85c79

130

py

Python

jobs/admin.py

Ascensiony/Software-Dev-Project

85513737ae4a4b76fa0cfdab579b037d33b72faf

[ "MIT" ]

2

2021-03-05T01:38:24.000Z

2021-03-19T21:11:14.000Z

jobs/admin.py

yabhi0807/Software-Dev-Project

85513737ae4a4b76fa0cfdab579b037d33b72faf

[ "MIT" ]

7

2021-04-08T21:12:42.000Z

2022-03-12T00:13:59.000Z

jobs/admin.py

venky012/ase-1-site

877e36344c82567d3ebc7b0f29a2757da2a7f071

[ "MIT" ]

4

2020-02-17T09:47:39.000Z

2020-02-22T12:11:18.000Z

from django.contrib import admin from .models import Jobs_details # Register your models here. admin.site.register(Jobs_details)

21.666667

33

0.823077

19

130

5.526316

0.631579

0.209524

0

0.115385

130

5

34

26

0.913043

0.2

0

1

0

true

0

0.666667

0

0.666667

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

ba81334f92a132a4d913d2577c9a58dfe3da3856

248

py

Python

python27/directoryCreationTest.py

xiemingzhi/pythonproject

9b860b274a71a84676a98f61cf6f4f283ff4bf78

[ "MIT" ]

null

python27/directoryCreationTest.py

xiemingzhi/pythonproject

9b860b274a71a84676a98f61cf6f4f283ff4bf78

[ "MIT" ]

null

python27/directoryCreationTest.py

xiemingzhi/pythonproject

9b860b274a71a84676a98f61cf6f4f283ff4bf78

[ "MIT" ]

1

2018-09-19T00:31:35.000Z

import os os.makedirs('bak_10_28_2014/TVepisodescopy/ad93e12b-2f85-4b20-9905-8f7687f4a97f/Images/English /') #os.makedirs('parent/thisisadirectorywith space ') #os.makedirs(r'thisisadirectorywith space ') #spacestr = 'thisisadirectorywith space '

35.428571

98

0.810484

29

248

6.827586

0.689655

0.151515

0

0.12931

0.064516

248

6

99

41.333333

0.724138

0.532258

0

0.734513

0.716814

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

1

null

0

1

0

1

0

6

bac0f0130f55f36ca620a503c08c4c73b15c053b

100

py

Python

pagina.py

Hugo1568/Real-time-facial-recognition-MTCNN

cba9b483e87b84af6414d01cb91a0024ed882e0d

[ "MIT" ]

3

2021-05-19T09:54:13.000Z

2021-11-05T09:05:05.000Z

pagina.py

Hugo1568/Real-time-facial-recognition-MTCNN

cba9b483e87b84af6414d01cb91a0024ed882e0d

[ "MIT" ]

1

2021-05-24T08:31:52.000Z

pagina.py

Hugo1568/Real-time-facial-recognition-MTCNN

cba9b483e87b84af6414d01cb91a0024ed882e0d

[ "MIT" ]

4

2021-05-24T08:22:14.000Z

2021-11-05T09:05:27.000Z

import streamlit as st from streamlit_webrtc import webrtc_streamer webrtc_streamer(key="example")

20

44

0.85

14

100

5.857143

0.642857

0.341463

0

0.1

100

4

45

25

0.911111

0

0.07

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bad623b8fd2195874383396db59b7f8b253a3c93

249

py

Python

bfgame/queries/__init__.py

ChrisLR/BasicDungeonRL

b293d40bd9a0d3b7aec41b5e1d58441165997ff1

[ "MIT" ]

3

2017-10-28T11:28:38.000Z

2018-09-12T09:47:00.000Z

bfgame/queries/__init__.py

ChrisLR/BasicDungeonRL

b293d40bd9a0d3b7aec41b5e1d58441165997ff1

[ "MIT" ]

null

bfgame/queries/__init__.py

ChrisLR/BasicDungeonRL

b293d40bd9a0d3b7aec41b5e1d58441165997ff1

[ "MIT" ]

null

from bfgame.queries.experience import Experience from bfgame.queries.restrictions import Restrictions from bfgame.queries.skills import Skills from bfgame.queries.specialability import SpecialAbility from bfgame.queries.visibility import Visibility

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6

243eae4d9d9ebdc274efde5057b3ba3e85026d11

74

py

Python

applied/tasks/aoex/__init__.py

ndoll1998/AppliedTransformers

76cbdef6fdd765b2178af71038a61e3e71e0cec9

[ "MIT" ]

3

2020-09-02T03:51:49.000Z

2020-09-18T14:09:48.000Z

applied/tasks/aoex/__init__.py

ndoll1998/AppliedTransformers

76cbdef6fdd765b2178af71038a61e3e71e0cec9

[ "MIT" ]

null

applied/tasks/aoex/__init__.py

ndoll1998/AppliedTransformers

76cbdef6fdd765b2178af71038a61e3e71e0cec9

[ "MIT" ]

2

2021-01-30T12:37:43.000Z

2021-05-19T06:29:31.000Z

from . import models from . import datasets from .trainer import Trainer

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6

2448bc1006a9478b4faa376d8f686d18ac4da327

2,365

py

Python

merge-files.py

joaofig/dublin-buses

b40896783643582aeaad3a7005c7aa4278d3b4e6

[ "MIT" ]

1

2020-01-22T19:13:36.000Z

merge-files.py

joaofig/dublin-buses

b40896783643582aeaad3a7005c7aa4278d3b4e6

[ "MIT" ]

1

2020-01-30T14:38:55.000Z

merge-files.py

joaofig/dublin-buses

b40896783643582aeaad3a7005c7aa4278d3b4e6

[ "MIT" ]

null

import dask.dataframe as dd import pandas as pd import numpy as np def dask_load_day(day): header = ['timestamp', 'line_id', 'direction', 'jrny_patt_id', 'time_frame', 'journey_id', 'operator', 'congestion', 'lon', 'lat', 'delay', 'block_id', 'vehicle_id', 'stop_id', 'at_stop'] types = {'timestamp': np.int64, 'journey_id': np.int32, 'congestion': np.int8, 'lon': np.float64, 'lat': np.float64, 'delay': np.int8, 'vehicle_id': np.int32, 'at_stop': np.int8} file_name = 'data/siri.201301{0:02d}.csv'.format(day) df = dd.read_csv(file_name, header=None, names=header, dtype=types, parse_dates=['time_frame'], infer_datetime_format=True) null_replacements = {'line_id': 0, 'stop_id': 0} df = df.fillna(value=null_replacements) df['line_id'] = df['line_id'].astype(np.int32) df['stop_id'] = df['stop_id'].astype(np.int32) df['timestamp'] = dd.to_datetime(df['timestamp'], unit='us') return df def pandas_load_day(day): header = ['timestamp', 'line_id', 'direction', 'jrny_patt_id', 'time_frame', 'journey_id', 'operator', 'congestion', 'lon', 'lat', 'delay', 'block_id', 'vehicle_id', 'stop_id', 'at_stop'] types = {'timestamp': np.int64, 'journey_id': np.int32, 'congestion': np.int8, 'lon': np.float64, 'lat': np.float64, 'delay': np.int8, 'vehicle_id': np.int32, 'at_stop': np.int8} file_name = 'data/siri.201301{0:02d}.csv'.format(day) df = pd.read_csv(file_name, header=None, names=header, dtype=types, parse_dates=['time_frame'], infer_datetime_format=True) null_replacements = {'line_id': 0, 'stop_id': 0} df = df.fillna(value=null_replacements) df['line_id'] = df['line_id'].astype(np.int32) df['stop_id'] = df['stop_id'].astype(np.int32) # df['timestamp'] = pd.to_datetime(df['timestamp'], unit='us') return df def run(): df = None for day in range(1, 32): print(day) day_df = pandas_load_day(day) if df is None: df = day_df else: df = df.append(day_df, ignore_index=True) df.to_csv('data/month.csv', index=False) if __name__ == '__main__': run()

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6

245ed034d789f6b8454a5b4fdb9cad2070e5893b

696

py

Python

pava/implementation/natives/sun/java2d/opengl/OGLSurfaceData.py

laffra/pava

54d10cf7f8def2f96e254c0356623d08f221536f

[ "MIT" ]

4

2017-03-30T16:51:16.000Z

2020-10-05T12:25:47.000Z

pava/implementation/natives/sun/java2d/opengl/OGLSurfaceData.py

laffra/pava

54d10cf7f8def2f96e254c0356623d08f221536f

[ "MIT" ]

null

pava/implementation/natives/sun/java2d/opengl/OGLSurfaceData.py

laffra/pava

54d10cf7f8def2f96e254c0356623d08f221536f

[ "MIT" ]

null

def add_native_methods(clazz): def initTexture__long__boolean__boolean__boolean__int__int__(a0, a1, a2, a3, a4, a5, a6): raise NotImplementedError() def initFBObject__long__boolean__boolean__boolean__int__int__(a0, a1, a2, a3, a4, a5, a6): raise NotImplementedError() def initFlipBackbuffer__long__(a0, a1): raise NotImplementedError() clazz.initTexture__long__boolean__boolean__boolean__int__int__ = initTexture__long__boolean__boolean__boolean__int__int__ clazz.initFBObject__long__boolean__boolean__boolean__int__int__ = initFBObject__long__boolean__boolean__boolean__int__int__ clazz.initFlipBackbuffer__long__ = initFlipBackbuffer__long__

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6

033ec08de916e40e952cee70655dddeca4bcde74

31,594

py

Python

code/src/features.py

bsm8734/BC_stage2_Tabular_data_Classification

e421360f3f6f9016c58bfff2dd20485206e4a365

[ "MIT" ]

null

code/src/features.py

bsm8734/BC_stage2_Tabular_data_Classification

e421360f3f6f9016c58bfff2dd20485206e4a365

[ "MIT" ]

null

code/src/features.py

bsm8734/BC_stage2_Tabular_data_Classification

e421360f3f6f9016c58bfff2dd20485206e4a365

[ "MIT" ]

null

import pandas as pd import numpy as np import os, sys, gc, random import datetime import dateutil.relativedelta # Machine learning from sklearn.preprocessing import LabelEncoder from sklearn.impute import SimpleImputer from sklearn.model_selection import StratifiedKFold from sklearn.metrics import roc_auc_score # Custom library from utils import seed_everything, print_score TOTAL_THRES = 300 # 구매액 임계값 SEED = 42 # 랜덤 시드 seed_everything(SEED) # 시드 고정 data_dir = '../input/train.csv' # os.environ['SM_CHANNEL_TRAIN'] model_dir = '../model' # os.environ['SM_MODEL_DIR'] ''' 입력인자로 받는 year_month에 대해 고객 ID별로 총 구매액이 구매액 임계값을 넘는지 여부의 binary label을 생성하는 함수 ''' def generate_label(df, year_month, total_thres=TOTAL_THRES, print_log=False): df = df.copy() # year_month에 해당하는 label 데이터 생성 df['year_month'] = df['order_date'].dt.strftime('%Y-%m') df.reset_index(drop=True, inplace=True) # year_month 이전 월의 고객 ID 추출 cust = df[df['year_month']<year_month]['customer_id'].unique() # year_month에 해당하는 데이터 선택 df = df[df['year_month']==year_month] # label 데이터프레임 생성 label = pd.DataFrame({'customer_id':cust}) label['year_month'] = year_month # year_month에 해당하는 고객 ID의 구매액의 합 계산 grped = df.groupby(['customer_id','year_month'], as_index=False)[['total']].sum() # label 데이터프레임과 merge하고 구매액 임계값을 넘었는지 여부로 label 생성 label = label.merge(grped, on=['customer_id','year_month'], how='left') label['total'].fillna(0.0, inplace=True) label['label'] = (label['total'] > total_thres).astype(int) # 고객 ID로 정렬 label = label.sort_values('customer_id').reset_index(drop=True) if print_log: print(f'{year_month} - final label shape: {label.shape}') return label def feature_preprocessing(train, test, features, do_imputing=True): x_tr = train.copy() x_te = test.copy() # 범주형 피처 이름을 저장할 변수 cate_cols = [] # 레이블 인코딩 for f in features: if x_tr[f].dtype.name == 'object': # 데이터 타입이 object(str)이면 레이블 인코딩 cate_cols.append(f) le = LabelEncoder() # train + test 데이터를 합쳐서 레이블 인코딩 함수에 fit le.fit(list(x_tr[f].values) + list(x_te[f].values)) # train 데이터 레이블 인코딩 변환 수행 x_tr[f] = le.transform(list(x_tr[f].values)) # test 데이터 레이블 인코딩 변환 수행 x_te[f] = le.transform(list(x_te[f].values)) print('categorical feature:', cate_cols) if do_imputing: # 중위값으로 결측치 채우기 imputer = SimpleImputer(strategy='median') x_tr[features] = imputer.fit_transform(x_tr[features]) x_te[features] = imputer.transform(x_te[features]) return x_tr, x_te def feature_engineering(df, year_month): df = df.copy() # year_month 이전 월 계산 d = datetime.datetime.strptime(year_month, "%Y-%m") prev_ym = d - dateutil.relativedelta.relativedelta(months=1) prev_ym = prev_ym.strftime('%Y-%m') # train, test 데이터 선택 train = df[df['order_date'] < prev_ym] test = df[df['order_date'] < year_month] # train, test 레이블 데이터 생성 train_label = generate_label(df, prev_ym)[['customer_id','year_month','label']] test_label = generate_label(df, year_month)[['customer_id','year_month','label']] # group by aggregation 함수 선언 agg_func = ['mean','max','min','sum','count','std','skew'] all_train_data = pd.DataFrame() for i, tr_ym in enumerate(train_label['year_month'].unique()): # group by aggretation 함수로 train 데이터 피처 생성 train_agg = train.loc[train['order_date'] < tr_ym].groupby(['customer_id']).agg(agg_func) # 멀티 레벨 컬럼을 사용하기 쉽게 1 레벨 컬럼명으로 변경 new_cols = [] for col in train_agg.columns.levels[0]: for stat in train_agg.columns.levels[1]: new_cols.append(f'{col}-{stat}') train_agg.columns = new_cols train_agg.reset_index(inplace = True) train_agg['year_month'] = tr_ym all_train_data = all_train_data.append(train_agg) all_train_data = train_label.merge(all_train_data, on=['customer_id', 'year_month'], how='left') features = all_train_data.drop(columns=['customer_id', 'label', 'year_month']).columns # group by aggretation 함수로 test 데이터 피처 생성 test_agg = test.groupby(['customer_id']).agg(agg_func) test_agg.columns = new_cols test_data = test_label.merge(test_agg, on=['customer_id'], how='left') # train, test 데이터 전처리 x_tr, x_te = feature_preprocessing(all_train_data, test_data, features) print('x_tr.shape', x_tr.shape, ', x_te.shape', x_te.shape) return x_tr, x_te, all_train_data['label'], features def feature_engineering1(df, year_month): df = df.copy() # customer_id 기준으로 pandas group by 후 total, quantity, price 누적합 계산 df['cumsum_total_by_cust_id'] = df.groupby(['customer_id'])['total'].cumsum() df['cumsum_quantity_by_cust_id'] = df.groupby(['customer_id'])['quantity'].cumsum() df['cumsum_price_by_cust_id'] = df.groupby(['customer_id'])['price'].cumsum() # product_id 기준으로 pandas group by 후 total, quantity, price 누적합 계산 df['cumsum_total_by_prod_id'] = df.groupby(['product_id'])['total'].cumsum() df['cumsum_quantity_by_prod_id'] = df.groupby(['product_id'])['quantity'].cumsum() df['cumsum_price_by_prod_id'] = df.groupby(['product_id'])['price'].cumsum() # order_id 기준으로 pandas group by 후 total, quantity, price 누적합 계산 df['cumsum_total_by_order_id'] = df.groupby(['order_id'])['total'].cumsum() df['cumsum_quantity_by_order_id'] = df.groupby(['order_id'])['quantity'].cumsum() df['cumsum_price_by_order_id'] = df.groupby(['order_id'])['price'].cumsum() # year_month 이전 월 계산 d = datetime.datetime.strptime(year_month, "%Y-%m") prev_ym = d - dateutil.relativedelta.relativedelta(months=1) prev_ym = prev_ym.strftime('%Y-%m') # train, test 데이터 선택 train = df[df['order_date'] < prev_ym] test = df[df['order_date'] < year_month] # train, test 레이블 데이터 생성 train_label = generate_label(df, prev_ym)[['customer_id', 'year_month', 'label']] test_label = generate_label(df, year_month)[['customer_id', 'year_month', 'label']] # group by aggregation 함수 선언 agg_func = ['mean', 'max', 'min', 'sum', 'count', 'std', 'skew'] agg_dict = { 'quantity': agg_func, 'price': agg_func, 'total': agg_func, 'cumsum_total_by_cust_id': agg_func, 'cumsum_quantity_by_cust_id': agg_func, 'cumsum_price_by_cust_id': agg_func, 'cumsum_total_by_prod_id': agg_func, 'cumsum_quantity_by_prod_id': agg_func, 'cumsum_price_by_prod_id': agg_func, 'cumsum_total_by_order_id': agg_func, 'cumsum_quantity_by_order_id': agg_func, 'cumsum_price_by_order_id': agg_func, 'order_id': ['nunique'], 'product_id': ['nunique'], } all_train_data = pd.DataFrame() for i, tr_ym in enumerate(train_label['year_month'].unique()): # group by aggretation 함수로 train 데이터 피처 생성 train_agg = train.loc[train['order_date'] < tr_ym].groupby(['customer_id']).agg(agg_dict) new_cols = [] for col in agg_dict.keys(): for stat in agg_dict[col]: if type(stat) is str: new_cols.append(f'{col}-{stat}') else: new_cols.append(f'{col}-mode') train_agg.columns = new_cols train_agg.reset_index(inplace=True) train_agg['year_month'] = tr_ym all_train_data = all_train_data.append(train_agg) all_train_data = train_label.merge(all_train_data, on=['customer_id', 'year_month'], how='left') features = all_train_data.drop(columns=['customer_id', 'label', 'year_month']).columns # group by aggretation 함수로 test 데이터 피처 생성 test_agg = test.groupby(['customer_id']).agg(agg_dict) test_agg.columns = new_cols test_data = test_label.merge(test_agg, on=['customer_id'], how='left') # train, test 데이터 전처리 x_tr, x_te = feature_preprocessing(all_train_data, test_data, features) print('x_tr.shape', x_tr.shape, ', x_te.shape', x_te.shape) return x_tr, x_te, all_train_data['label'], features # def get_year_month_list(df, year_month): # df = df.copy() # # df['year_month-mode'] = df['order_date'].dt.strftime('%Y-%m') # dd = df.groupby(['year_month-mode', 'customer_id'])['total'].sum() # cust_ids = df['customer_id'].unique() # # # year_month 이전 월 계산 # bef_12_d = datetime.datetime.strptime(year_month, "%Y-%m") # bef_12_prev_ym = bef_12_d - dateutil.relativedelta.relativedelta(months=12) # bef_12_prev_ym = bef_12_prev_ym.strftime('%Y-%m') # # # ddt = df[df['year_month-mode'] == bef_12_prev_ym] # # first_bef = [] # for id in cust_ids: # dd[:, bef_12_prev_ym] # # first_bef.append(dd.xs((id, bef_12_prev_ym))) # # # df['cycle_month'] = pd.Series(first_bef) # # print(df) def make_time_series_data(df, Input, year_month, stand): # 기준을 잡습니다. 기준은 여기서 %Y-%m 입니다. standard = ['customer_id'] + [stand] data = Input.copy() df = df.copy() data[stand] = pd.to_datetime(df['order_date']).dt.strftime(stand) data.order_date = pd.to_datetime(data['order_date']) # 월단위의 틀을 만들어주고, 기준으로 aggregation을 해준 다음에 merge를 해줄 것입니다 times = pd.date_range('2009-12-01', periods=(data.order_date.max() - data.order_date.min()).days + 1, freq='1d') customerid_frame = np.repeat(data.customer_id.unique(), len(times)) date_frame = np.tile(times, len(data.customer_id.unique())) frame = pd.DataFrame({'customer_id': customerid_frame, 'order_date': date_frame}) frame[stand] = pd.to_datetime(frame.order_date).dt.strftime(stand) # group by data_group = data.groupby(standard).sum().reset_index() frame_group = frame.groupby(standard).count().reset_index().drop(['order_date'], axis=1) # merge merge = pd.merge(frame_group, data_group, on=standard, how='left').fillna(0) merge = merge.rename(columns={stand: 'standard'}) merge_test = merge[merge['standard'] == year_month].drop(columns=['standard', 'quantity', 'price']) #.drop(merge.columns.tolist() - ['customer_id', 'total']) return merge_test def add_trend(df, year_month): df = df.copy() df['year_month'] = df['order_date'].dt.strftime('%Y-%m') # year_month 이전 월 계산 d = datetime.datetime.strptime(year_month, "%Y-%m") prev_ym = d - dateutil.relativedelta.relativedelta(months=1) # train과 test 데이터 생성 train = df[df['order_date'] < prev_ym] # 2009-12부터 2011-10 데이터 추출 test = df[df['order_date'] < year_month] # 2009-12부터 2011-11 데이터 추출 train_window_ym = [] test_window_ym = [] for month_back in [1, 2, 3, 5, 7, 12, 20, 23]: # 1개월, 2개월, ... 20개월, 23개월 전 year_month 파악 train_window_ym.append((prev_ym - dateutil.relativedelta.relativedelta(months=month_back)).strftime('%Y-%m')) test_window_ym.append((d - dateutil.relativedelta.relativedelta(months=month_back)).strftime('%Y-%m')) # aggregation 함수 선언 agg_func = ['max', 'min', 'sum', 'mean', 'count', 'std', 'skew'] # group by aggregation with Dictionary agg_dict = { 'quantity': agg_func, 'price': agg_func, 'total': agg_func, } # general statistics for train data with time series trend for i, tr_ym in enumerate(train_window_ym): # group by aggretation 함수로 train 데이터 피처 생성 train_agg = train.loc[train['year_month'] >= tr_ym].groupby(['customer_id']).agg( agg_dict) # 해당 year_month 이후부터 모든 데이터에 대한 aggregation을 실시 # 멀티 레벨 컬럼을 사용하기 쉽게 1 레벨 컬럼명으로 변경 new_cols = [] for level1, level2 in train_agg.columns: new_cols.append(f'{level1}-{level2}-{i}') train_agg.columns = new_cols train_agg.reset_index(inplace=True) if i == 0: train_data = train_agg else: train_data = train_data.merge(train_agg, on=['customer_id'], how='right') # general statistics for test data with time series trend for i, tr_ym in enumerate(test_window_ym): # group by aggretation 함수로 test 데이터 피처 생성 test_agg = test.loc[test['year_month'] >= tr_ym].groupby(['customer_id']).agg(agg_dict) # 멀티 레벨 컬럼을 사용하기 쉽게 1 레벨 컬럼명으로 변경 new_cols = [] for level1, level2 in test_agg.columns: new_cols.append(f'{level1}-{level2}-{i}') test_agg.columns = new_cols test_agg.reset_index(inplace=True) if i == 0: test_data = test_agg else: test_data = test_data.merge(test_agg, on=['customer_id'], how='right') return train_data, test_data def add_seasonality(df, year_month): df = df.copy() df['year_month'] = df['order_date'].dt.strftime('%Y-%m') # year_month 이전 월 계산 d = datetime.datetime.strptime(year_month, "%Y-%m") prev_ym = d - dateutil.relativedelta.relativedelta(months=1) # train과 test 데이터 생성 train = df[df['order_date'] < prev_ym] # 2009-12부터 2011-10 데이터 추출 test = df[df['order_date'] < year_month] # 2009-12부터 2011-11 데이터 추출 train_window_ym = [] test_window_ym = [] for month_back in [1, 6, 12, 18]: # 각 주기성을 파악하고 싶은 구간을 생성 train_window_ym.append( ( (prev_ym - dateutil.relativedelta.relativedelta(months=month_back)).strftime('%Y-%m'), (prev_ym - dateutil.relativedelta.relativedelta(months=month_back + 2)).strftime('%Y-%m') # 1~3, 6~8, 12~14, 18~20 Pair를 만들어준다 ) ) test_window_ym.append( ( (d - dateutil.relativedelta.relativedelta(months=month_back)).strftime('%Y-%m'), (d - dateutil.relativedelta.relativedelta(months=month_back + 2)).strftime('%Y-%m') ) ) # aggregation 함수 선언 agg_func = ['max', 'min', 'sum', 'mean', 'count', 'std', 'skew'] # group by aggregation with Dictionary agg_dict = { 'quantity': agg_func, 'price': agg_func, 'total': agg_func, } # seasonality for train data with time series for i, (tr_ym, tr_ym_3) in enumerate(train_window_ym): # group by aggretation 함수로 train 데이터 피처 생성 # 구간 사이에 존재하는 월들에 대해서 aggregation을 진행 train_agg = train.loc[(train['year_month'] >= tr_ym_3) & (train['year_month'] <= tr_ym)].groupby( ['customer_id']).agg(agg_dict) # 멀티 레벨 컬럼을 사용하기 쉽게 1 레벨 컬럼명으로 변경 new_cols = [] for level1, level2 in train_agg.columns: new_cols.append(f'{level1}-{level2}-season{i}') train_agg.columns = new_cols train_agg.reset_index(inplace=True) if i == 0: train_data = train_agg else: train_data = train_data.merge(train_agg, on=['customer_id'], how='right') # seasonality for test data with time series for i, (tr_ym, tr_ym_3) in enumerate(test_window_ym): # group by aggretation 함수로 train 데이터 피처 생성 test_agg = test.loc[(test['year_month'] >= tr_ym_3) & (test['year_month'] <= tr_ym)].groupby( ['customer_id']).agg(agg_dict) # 멀티 레벨 컬럼을 사용하기 쉽게 1 레벨 컬럼명으로 변경 new_cols = [] for level1, level2 in test_agg.columns: new_cols.append(f'{level1}-{level2}-season{i}') test_agg.columns = new_cols test_agg.reset_index(inplace=True) if i == 0: test_data = test_agg else: test_data = test_data.merge(test_agg, on=['customer_id'], how='right') return train_data, test_data def feature_engineering2(df, year_month): df = df.copy() # customer_id 기준으로 pandas group by 후 total, quantity, price 누적합 계산 df['cumsum_total_by_cust_id'] = df.groupby(['customer_id'])['total'].cumsum() df['cumsum_quantity_by_cust_id'] = df.groupby(['customer_id'])['quantity'].cumsum() df['cumsum_price_by_cust_id'] = df.groupby(['customer_id'])['price'].cumsum() # product_id 기준으로 pandas group by 후 total, quantity, price 누적합 계산 df['cumsum_total_by_prod_id'] = df.groupby(['product_id'])['total'].cumsum() df['cumsum_quantity_by_prod_id'] = df.groupby(['product_id'])['quantity'].cumsum() df['cumsum_price_by_prod_id'] = df.groupby(['product_id'])['price'].cumsum() # order_id 기준으로 pandas group by 후 total, quantity, price 누적합 계산 df['cumsum_total_by_order_id'] = df.groupby(['order_id'])['total'].cumsum() df['cumsum_quantity_by_order_id'] = df.groupby(['order_id'])['quantity'].cumsum() df['cumsum_price_by_order_id'] = df.groupby(['order_id'])['price'].cumsum() # oredr_ts df['order_ts'] = df['order_date'].astype(np.int64)//1e9 df['order_ts_diff'] = df.groupby(['customer_id'])['order_ts'].diff() df['quantity_diff'] = df.groupby(['customer_id'])['quantity'].diff() df['price_diff'] = df.groupby(['customer_id'])['price'].diff() df['total_diff'] = df.groupby(['customer_id'])['total'].diff() # mode df['month-mode'] = df['order_date'].dt.month df['year_month-mode'] = df['order_date'].dt.strftime('%Y-%m') # oredr_ts_plus === df['order_ts_plus'] = df[df['total'] > 0]['order_date'].astype(np.int64) // 1e9 df['order_ts_plus_diff'] = df[df['total'] > 0].groupby(['customer_id'])['order_ts'].diff() df['order_ts_plus'] = df['order_ts_plus'].fillna(0) df['order_ts_plus_diff'] = df['order_ts_plus_diff'].fillna(0) # df[~(df.order_id.str.contains('C'))].groupby(['customer_id'])['order_date'].last().astype(np.int64) // 1e9 # ================================================================================================ # year_month 이전 월 계산 d = datetime.datetime.strptime(year_month, "%Y-%m") prev_ym = d - dateutil.relativedelta.relativedelta(months=1) prev_ym = prev_ym.strftime('%Y-%m') # train, test 데이터 선택 train = df[df['order_date'] < prev_ym] test = df[df['order_date'] < year_month] # train, test 레이블 데이터 생성 train_label = generate_label(df, prev_ym)[['customer_id', 'year_month', 'label']] test_label = generate_label(df, year_month)[['customer_id', 'year_month', 'label']] # ================================================================================================ # 연월 피처 생성 target = datetime.datetime.strptime('2011-11', "%Y-%m") # 타겟 연월 prev = target - dateutil.relativedelta.relativedelta(years=1) # 전년 연월 prev = prev.strftime('%Y-%m') # 문자열로 변환 groupby = train.groupby(['customer_id', 'year_month-mode'])['total'].sum() # 고객별, 월별 total 합 groupby = groupby.unstack() # 월별을 컬럼으로 변환 prev_pprev_total = groupby.loc[:, [prev]] # 전년, 전전년 데이터만 추출 prev_pprev_total = prev_pprev_total.fillna(0) train_1224 = (prev_pprev_total['2010-11']) / 2 target = datetime.datetime.strptime('2011-12', "%Y-%m") # 타겟 연월 prev = target - dateutil.relativedelta.relativedelta(years=1) # 전년 연월 pprev = prev - dateutil.relativedelta.relativedelta(years=1) # 전전년 연월 prev, pprev = prev.strftime('%Y-%m'), pprev.strftime('%Y-%m') # 문자열로 변환 groupby = test.groupby(['customer_id', 'year_month-mode'])['total'].sum() # 고객별, 월별 total 합 groupby = groupby.unstack() # 월별을 컬럼으로 변환 prev_pprev_total = groupby.loc[:, [prev, pprev]] # 전년, 전전년 데이터만 추출 prev_pprev_total = prev_pprev_total.fillna(0) test_1224 = (prev_pprev_total['2010-12'] + prev_pprev_total['2009-12']) / 2 # ================================================================================================ # lambda 식 mode_f = lambda x: x.value_counts().index[0] # group by aggregation 함수 선언 agg_func = ['mean', 'max', 'min', 'sum', 'count', 'std', 'skew'] # agg_func = ['mean', 'max'] # , 'min', 'sum', 'count', 'std', 'skew'] agg_dict = { 'order_ts': ['first', 'last'], 'order_ts_diff': agg_func, 'order_ts_plus': ['first', 'last'], 'order_ts_plus_diff': agg_func, 'quantity_diff': agg_func, 'price_diff': agg_func, 'total_diff': agg_func, 'quantity': agg_func, 'price': agg_func, 'total': agg_func, 'cumsum_total_by_cust_id': agg_func, 'cumsum_quantity_by_cust_id': agg_func, 'cumsum_price_by_cust_id': agg_func, 'cumsum_total_by_prod_id': agg_func, 'cumsum_quantity_by_prod_id': agg_func, 'cumsum_price_by_prod_id': agg_func, 'cumsum_total_by_order_id': agg_func, 'cumsum_quantity_by_order_id': agg_func, 'cumsum_price_by_order_id': agg_func, 'order_id': ['nunique'], 'product_id': ['nunique'], 'month-mode': [mode_f], 'year_month-mode': [mode_f], } all_train_data = pd.DataFrame() for i, tr_ym in enumerate(train_label['year_month'].unique()): # group by aggretation 함수로 train 데이터 피처 생성 train_agg = train.loc[train['order_date'] < tr_ym].groupby(['customer_id']).agg(agg_dict) new_cols = [] for col in agg_dict.keys(): for stat in agg_dict[col]: if type(stat) is str: new_cols.append(f'{col}-{stat}') else: new_cols.append(f'{col}-mode') train_agg.columns = new_cols train_agg.reset_index(inplace=True) train_agg['year_month'] = tr_ym all_train_data = all_train_data.append(train_agg) all_train_data = train_label.merge(all_train_data, on=['customer_id', 'year_month'], how='left') all_train_data['cycle_1224'] = train_1224.to_numpy() # ================================================================================================ data = pd.read_csv("/opt/ml/code/input/train.csv", parse_dates=["order_date"]) # # baseline feature engineering # train, test, y, features = feature_engineering(data, '2011-12') # trend train_t, test_t = add_trend(data, year_month='2011-12') # seasonality train_s, test_s = add_seasonality(data, year_month='2011-12') # train 데이터 병합 all_train_data = all_train_data.merge(train_t, on=['customer_id'], how='left') all_train_data = all_train_data.merge(train_s, on=['customer_id'], how='left') all_train_data = all_train_data.fillna(0) # ================================================================================================ features = all_train_data.drop(columns=['customer_id', 'label', 'year_month']).columns print(features.shape) import csv with open("../output/feature.csv", 'w', newline='') as f: writer = csv.writer(f) for items in features.tolist(): print(items) writer.writerow([items]) test_agg = test.groupby(['customer_id']).agg(agg_dict) test_agg.columns = new_cols test_agg['cycle_1224'] = test_1224 test_data = test_label.merge(test_agg, on=['customer_id'], how='left') # test 데이터 병합 =================================================================================== test_data = test_data.merge(test_t, on=['customer_id'], how='left') test_data = test_data.merge(test_s, on=['customer_id'], how='left') test_data = test_data.fillna(0) # train, test 데이터 전처리 print(all_train_data.shape) print(test_data.shape) x_tr, x_te = feature_preprocessing(all_train_data, test_data, features) print('x_tr.shape', x_tr.shape, ', x_te.shape', x_te.shape) return x_tr, x_te, all_train_data['label'], features def feature_engineering3(df, year_month): my_pick = [ 'order_ts-last', 'order_ts-first', 'price_diff-skew', 'price-skew', 'order_ts_diff-max', 'quantity_diff-skew', 'cumsum_total_by_prod_id-skew', 'cumsum_price_by_prod_id-skew', 'cumsum_total_by_cust_id-skew', 'cumsum_quantity_by_prod_id-sum', 'quantity-skew', 'cumsum_total_by_order_id-skew', 'cumsum_price_by_cust_id-skew', 'cumsum_price_by_order_id-skew', 'year_month-mode', 'total_diff-skew', 'price-mean', 'cumsum_quantity_by_order_id-skew', 'cumsum_quantity_by_prod_id-skew', 'price_diff-mean', ] df = df.copy() # customer_id 기준으로 pandas group by 후 total, quantity, price 누적합 계산 df['cumsum_total_by_cust_id'] = df.groupby(['customer_id'])['total'].cumsum() df['cumsum_quantity_by_cust_id'] = df.groupby(['customer_id'])['quantity'].cumsum() df['cumsum_price_by_cust_id'] = df.groupby(['customer_id'])['price'].cumsum() # product_id 기준으로 pandas group by 후 total, quantity, price 누적합 계산 df['cumsum_total_by_prod_id'] = df.groupby(['product_id'])['total'].cumsum() df['cumsum_quantity_by_prod_id'] = df.groupby(['product_id'])['quantity'].cumsum() df['cumsum_price_by_prod_id'] = df.groupby(['product_id'])['price'].cumsum() # order_id 기준으로 pandas group by 후 total, quantity, price 누적합 계산 df['cumsum_total_by_order_id'] = df.groupby(['order_id'])['total'].cumsum() df['cumsum_quantity_by_order_id'] = df.groupby(['order_id'])['quantity'].cumsum() df['cumsum_price_by_order_id'] = df.groupby(['order_id'])['price'].cumsum() # oredr_ts df['order_ts'] = df['order_date'].astype(np.int64)//1e9 df['order_ts_diff'] = df.groupby(['customer_id'])['order_ts'].diff() df['quantity_diff'] = df.groupby(['customer_id'])['quantity'].diff() df['price_diff'] = df.groupby(['customer_id'])['price'].diff() df['total_diff'] = df.groupby(['customer_id'])['total'].diff() # mode df['month-mode'] = df['order_date'].dt.month df['year_month-mode'] = df['order_date'].dt.strftime('%Y-%m') # oredr_ts_plus === df['order_ts_plus'] = df[df['total'] > 0]['order_date'].astype(np.int64) // 1e9 df['order_ts_plus_diff'] = df[df['total'] > 0].groupby(['customer_id'])['order_ts'].diff() df['order_ts_plus'] = df['order_ts_plus'].fillna(0) df['order_ts_plus_diff'] = df['order_ts_plus_diff'].fillna(0) # df[~(df.order_id.str.contains('C'))].groupby(['customer_id'])['order_date'].last().astype(np.int64) // 1e9 # ================================================================================================ # year_month 이전 월 계산 d = datetime.datetime.strptime(year_month, "%Y-%m") prev_ym = d - dateutil.relativedelta.relativedelta(months=1) prev_ym = prev_ym.strftime('%Y-%m') # train, test 데이터 선택 train = df[df['order_date'] < prev_ym] test = df[df['order_date'] < year_month] # train, test 레이블 데이터 생성 train_label = generate_label(df, prev_ym)[['customer_id', 'year_month', 'label']] test_label = generate_label(df, year_month)[['customer_id', 'year_month', 'label']] #################################################################################### # year_month 이전 월 계산 bef_12_d1 = datetime.datetime.strptime(year_month, "%Y-%m") bef_12_prev_ym1 = bef_12_d1 - dateutil.relativedelta.relativedelta(months=12) bef_12_prev_ym1 = bef_12_prev_ym1.strftime('%Y-%m') merge_df_12_train = make_time_series_data(train, train, bef_12_prev_ym1, "%Y-%m") print(bef_12_prev_ym1) bef_24_d1 = datetime.datetime.strptime(year_month, "%Y-%m") bef_24_prev_ym1 = bef_24_d1 - dateutil.relativedelta.relativedelta(months=24) bef_24_prev_ym1 = bef_24_prev_ym1.strftime('%Y-%m') merge_df_24_train = make_time_series_data(train, train, bef_24_prev_ym1, "%Y-%m") print(bef_24_prev_ym1) merge_1224_train = merge_df_24_train.merge(merge_df_12_train, on=['customer_id'], how='left') series_1224_train = (merge_1224_train['total_x'] + merge_1224_train['total_y']) / 2 #################################################################################### # year_month 이전 월 계산 bef_12_d2 = datetime.datetime.strptime(prev_ym, "%Y-%m") bef_12_prev_ym2 = bef_12_d2 - dateutil.relativedelta.relativedelta(months=12) bef_12_prev_ym2 = bef_12_prev_ym2.strftime('%Y-%m') merge_df_12_test = make_time_series_data(test, test, bef_12_prev_ym2, "%Y-%m") print(bef_12_prev_ym2) bef_24_d2 = datetime.datetime.strptime(prev_ym, "%Y-%m") bef_24_prev_ym2 = bef_24_d2 - dateutil.relativedelta.relativedelta(months=24) bef_24_prev_ym2 = bef_24_prev_ym2.strftime('%Y-%m') merge_df_24_test = make_time_series_data(test, test, bef_24_prev_ym2, "%Y-%m") print(bef_24_prev_ym2) merge_1224_test = merge_df_24_test.merge(merge_df_12_test, on=['customer_id'], how='left') series_1224_test = (merge_1224_test['total_x'] + merge_1224_test['total_y']) / 2 #################################################################################### # lambda 식 mode_f = lambda x: x.value_counts().index[0] # group by aggregation 함수 선언 # agg_func = ['mean', 'max', 'min', 'sum', 'count', 'std', 'skew'] agg_func = ['mean', 'max'] # , 'min', 'sum', 'count', 'std', 'skew'] agg_dict = { 'order_ts': ['first', 'last'], 'order_ts_diff': agg_func, # 'order_ts_plus': ['first', 'last'], # 'order_ts_plus_diff': agg_func, # 'quantity_diff': agg_func, # 'price_diff': agg_func, # 'total_diff': agg_func, # 'quantity': agg_func, # 'price': agg_func, # 'total': agg_func, # 'cumsum_total_by_cust_id': agg_func, # 'cumsum_quantity_by_cust_id': agg_func, # 'cumsum_price_by_cust_id': agg_func, # 'cumsum_total_by_prod_id': agg_func, # 'cumsum_quantity_by_prod_id': agg_func, # 'cumsum_price_by_prod_id': agg_func, # 'cumsum_total_by_order_id': agg_func, # 'cumsum_quantity_by_order_id': agg_func, # 'cumsum_price_by_order_id': agg_func, # 'order_id': ['nunique'], # 'product_id': ['nunique'], # 'month-mode': [mode_f], # 'year_month-mode': [mode_f], } all_train_data = pd.DataFrame() for i, tr_ym in enumerate(train_label['year_month'].unique()): # group by aggretation 함수로 train 데이터 피처 생성 train_agg = train.loc[train['order_date'] < tr_ym].groupby(['customer_id']).agg(agg_dict) new_cols = [] for col in agg_dict.keys(): for stat in agg_dict[col]: if type(stat) is str: new_cols.append(f'{col}-{stat}') else: new_cols.append(f'{col}-mode') train_agg.columns = new_cols train_agg.reset_index(inplace=True) train_agg['year_month'] = tr_ym all_train_data = all_train_data.append(train_agg) all_train_data = train_label.merge(all_train_data, on=['customer_id', 'year_month'], how='left') all_train_data['cycle_1224'] = series_1224_train features = all_train_data.drop(columns=['customer_id', 'label', 'year_month']).columns import csv with open("../output/feature.csv", 'w', newline='') as f: writer = csv.writer(f) for items in features.tolist(): print(items) writer.writerow([items]) test_agg = test.groupby(['customer_id']).agg(agg_dict) test_agg.columns = new_cols test_agg['cycle_1224'] = series_1224_test test_data = test_label.merge(test_agg, on=['customer_id'], how='left') # train, test 데이터 전처리 x_tr, x_te = feature_preprocessing(all_train_data, test_data, features) # x_tr = x_tr[my_pick] # x_te = x_te[my_pick] # features = pd.Index(my_pick) print('x_tr.shape', x_tr.shape, ', x_te.shape', x_te.shape) return x_tr, x_te, all_train_data['label'], features if __name__ == '__main__': print('data_dir', data_dir)

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0363b5205df6dfc0e6790f0a27cda6b94a226324

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py

Python

kivymd/uix/menu/__init__.py

AnEx07/KivyMD

e4004a570ad3f1874b3540cc1b0c243b3037bba8

[ "MIT" ]

null

kivymd/uix/menu/__init__.py

AnEx07/KivyMD

e4004a570ad3f1874b3540cc1b0c243b3037bba8

[ "MIT" ]

null

kivymd/uix/menu/__init__.py

AnEx07/KivyMD

e4004a570ad3f1874b3540cc1b0c243b3037bba8

[ "MIT" ]

null

from .menu import MDDropdownMenu

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03657d0b752b4939d547063648aeac22904dfa9e

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py

Python

ci/util/urlencode.py

markleybros/yer-face

638566edfd80143ea75a7488ef6406faf88a1976

[ "MIT" ]

25

2018-02-27T03:39:43.000Z

2021-09-03T08:22:00.000Z

ci/util/urlencode.py

markleybros/yer-face

638566edfd80143ea75a7488ef6406faf88a1976

[ "MIT" ]

4

2019-03-21T02:48:44.000Z

2020-05-14T17:17:49.000Z

ci/util/urlencode.py

markleybros/yer-face

638566edfd80143ea75a7488ef6406faf88a1976

[ "MIT" ]

7

2018-05-14T06:51:55.000Z

2021-05-15T10:52:19.000Z

#!/usr/bin/env python3 import sys,urllib.parse print(urllib.parse.quote(sys.stdin.read().strip()))

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0.010638

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6

cee2da2296c99a00d048b44f20876551c7df2df9

152

py

Python

platform/radio/efr32_multiphy_configurator/pyradioconfig/parts/nerio/phys/Phys_Datasheet.py

PascalGuenther/gecko_sdk

2e82050dc8823c9fe0e8908c1b2666fb83056230

[ "Zlib" ]

82

2016-06-29T17:24:43.000Z

2021-04-16T06:49:17.000Z

platform/radio/efr32_multiphy_configurator/pyradioconfig/parts/nerio/phys/Phys_Datasheet.py

PascalGuenther/gecko_sdk

2e82050dc8823c9fe0e8908c1b2666fb83056230

[ "Zlib" ]

6

2022-01-12T18:22:08.000Z

2022-03-25T10:19:27.000Z

platform/radio/efr32_multiphy_configurator/pyradioconfig/parts/nerio/phys/Phys_Datasheet.py

PascalGuenther/gecko_sdk

2e82050dc8823c9fe0e8908c1b2666fb83056230

[ "Zlib" ]

56

2016-08-02T10:50:50.000Z

2021-07-19T08:57:34.000Z

from pyradioconfig.parts.jumbo.phys.Phys_Datasheet import PHYS_Datasheet class PHYS_Datasheet_Nerio(PHYS_Datasheet): # inherit from Jumbo pass

25.333333

72

0.815789

20

152

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0.131579

152

5

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null

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1

0

6

cef5d61c02e0d5e363ad73b1dabc998c0d26e6b1

44

py

Python

src/greetings/hello-hun.py

Gervercom/ef-01-git

cb2808f52ce6a1a8dc60bf913792a3d535b4083d

[ "MIT" ]

1

2019-09-05T18:34:21.000Z

src/greetings/hello-hun.py

Gervercom/ef-01-git

cb2808f52ce6a1a8dc60bf913792a3d535b4083d

[ "MIT" ]

20

2019-07-08T17:17:02.000Z

2019-07-09T20:20:28.000Z

src/greetings/hello-hun.py

Gervercom/ef-01-git

cb2808f52ce6a1a8dc60bf913792a3d535b4083d

[ "MIT" ]

23

2019-07-08T09:03:20.000Z

2019-08-01T01:27:54.000Z

print("Hello Hun, Say hello to Jung Un Kim")

44

0.727273

9

44

3.555556

0.888889

0

0.159091

44

1

44

0.864865

0

0.777778

0

1

0

true

0

1

0

null

0

1

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1

0

null

0

1

0

1

0

6

cefaa9bdc6b3c18e10a880c7f942bf0221c1d9a3

15,328

py

Python

python/smurff/test/test_centering.py

msteijaert/smurff

e6066d51e1640e9aad0118628ba72c9d662919fb

[ "MIT" ]

65

2017-06-23T14:01:58.000Z

2022-03-10T16:13:48.000Z

python/smurff/test/test_centering.py

msteijaert/smurff

e6066d51e1640e9aad0118628ba72c9d662919fb

[ "MIT" ]

143

2017-08-11T10:43:52.000Z

2021-09-23T17:07:51.000Z

python/smurff/test/test_centering.py

msteijaert/smurff

e6066d51e1640e9aad0118628ba72c9d662919fb

[ "MIT" ]

14

2018-05-17T18:33:28.000Z

2021-12-23T20:41:32.000Z

#!/usr/bin/env python import unittest import numpy as np import scipy as sp from smurff.center import mean, center, std, scale, center_and_scale class TestCentering(unittest.TestCase): def test_rows_centering(self): origin_matrix = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]) origin_matrix_mean = mean(origin_matrix, 'rows') centered_matrix = center(origin_matrix, 'rows', origin_matrix_mean) expected_matrix = np.array([[-1.5, -0.5, 0.5, 1.5], [-1.5, -0.5, 0.5, 1.5], [-1.5, -0.5, 0.5, 1.5]]) self.assertTrue(np.allclose(centered_matrix, expected_matrix)) def test_cols_centering(self): origin_matrix = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]) origin_matrix_mean = mean(origin_matrix, 'cols') centered_matrix = center(origin_matrix, 'cols', origin_matrix_mean) expected_matrix = np.array([[-4,-4, -4, -4], [ 0, 0, 0, 0], [ 4, 4, 4, 4]]) self.assertTrue(np.allclose(centered_matrix, expected_matrix)) def test_global_centering(self): origin_matrix = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]) origin_matrix_mean = mean(origin_matrix, 'global') centered_matrix = center(origin_matrix, 'global', origin_matrix_mean) expected_matrix = np.array([[-5.5, -4.5, -3.5, -2.5], [-1.5, -0.5, 0.5, 1.5], [ 2.5, 3.5, 4.5, 5.5]]) self.assertTrue(np.allclose(centered_matrix, expected_matrix)) def test_none_centering(self): origin_matrix = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]) origin_matrix_mean = mean(origin_matrix, 'none') centered_matrix = center(origin_matrix, 'none', origin_matrix_mean) expected_matrix = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]) self.assertTrue(np.allclose(centered_matrix, expected_matrix)) def test_dense_matrix_rows_scaling(self): origin_matrix = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]) origin_matrix_std = std(origin_matrix, 'rows') expected_matrix_std = [[1.11803399, 1.11803399, 1.11803399]] self.assertTrue(np.allclose(origin_matrix_std, expected_matrix_std)) scaled_matrix = scale(origin_matrix, 'rows', origin_matrix_std) expected_matrix = [[ 0.89442719, 1.78885438, 2.68328157, 3.57770876], [ 4.47213595, 5.36656315, 6.26099034, 7.15541753], [ 8.04984472, 8.94427191, 9.83869910, 10.73312629]] self.assertTrue(np.allclose(scaled_matrix, expected_matrix)) def test_sparse_matrix_rows_scaling(self): origin_matrix_rows = np.array([0, 0, 1, 1, 2, 2]) origin_matrix_cols = np.array([0, 1, 0, 1, 0, 1]) origin_matrix_vals = np.array([1, 2, 5, 6, 9, 10], dtype=sp.float64) origin_matrix = sp.sparse.coo_matrix((origin_matrix_vals, (origin_matrix_rows, origin_matrix_cols)), shape=(3, 4)) origin_matrix_std = std(origin_matrix, 'rows') expected_matrix_std = [[1.29099445, 4.50924975, 7.76745347]] self.assertTrue(np.allclose(origin_matrix_std, expected_matrix_std)) scaled_matrix = scale(origin_matrix, 'rows', origin_matrix_std) expected_matrix_rows = np.array([0, 1, 2, 0, 1, 2]) expected_matrix_cols = np.array([0, 0, 0, 1, 1, 1]) expected_matrix_vals = [ 0.7745966692414834 , 1.1088319064318592 , 1.1586809036417614 , 1.5491933384829668 , 1.3305982877182312 , 1.2874232262686236 ] expected_matrix = sp.sparse.coo_matrix((expected_matrix_vals, (expected_matrix_rows, expected_matrix_cols)), shape=(3, 4)) self.assertTrue(np.allclose(scaled_matrix.todense(), expected_matrix.todense())) def test_dense_matrix_cols_scaling(self): origin_matrix = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]) origin_matrix_std = std(origin_matrix, 'cols') expected_matrix_std = [[3.26598632, 3.26598632, 3.26598632, 3.26598632]] self.assertTrue(np.allclose(origin_matrix_std, expected_matrix_std)) scaled_matrix = scale(origin_matrix, 'cols', origin_matrix_std) expected_matrix = [[0.30618622, 0.61237244, 0.91855865, 1.22474487], [1.53093109, 1.83711731, 2.14330352, 2.44948974], [2.75567596, 3.06186218, 3.36804840, 3.67423461]] self.assertTrue(np.allclose(scaled_matrix, expected_matrix)) def test_sparse_matrix_cols_scaling(self): origin_matrix_rows = np.array([0, 0, 1, 1, 2, 2]) origin_matrix_cols = np.array([0, 1, 2, 3, 0, 1]) origin_matrix_vals = np.array([1, 2, 7, 8, 9, 10], dtype=sp.float64) origin_matrix = sp.sparse.coo_matrix((origin_matrix_vals, (origin_matrix_rows, origin_matrix_cols)), shape=(3, 4)) origin_matrix_std = std(origin_matrix, 'cols') expected_matrix_std = [[4.35889894, 4.76095229, 5.71547607, 6.53197265]] self.assertTrue(np.allclose(origin_matrix_std, expected_matrix_std)) scaled_matrix = scale(origin_matrix, 'cols', origin_matrix_std) expected_matrix_rows = np.array([0, 0, 1, 1, 2, 2]) expected_matrix_cols = np.array([0, 1, 2, 3, 0, 1]) expected_matrix_vals = [ 0.22941573387056174 , 0.42008402520840293 , 1.22474487139158920 , 1.22474487139158900 , 2.06474160483505550 , 2.10042012604201500 ] expected_matrix = sp.sparse.coo_matrix((expected_matrix_vals, (expected_matrix_rows, expected_matrix_cols)), shape=(3, 4)) self.assertTrue(np.allclose(scaled_matrix.todense(), expected_matrix.todense())) def test_dense_matrix_global_scaling(self): origin_matrix = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]) origin_matrix_std = std(origin_matrix, 'global') expected_matrix_std = 3.45205252953 self.assertTrue(np.allclose(origin_matrix_std, expected_matrix_std)) scaled_matrix = scale(origin_matrix, 'global', origin_matrix_std) expected_matrix = [[0.28968273, 0.57936546, 0.86904819, 1.15873092], [1.44841365, 1.73809638, 2.02777911, 2.31746184], [2.60714457, 2.89682730, 3.18651003, 3.47619276]] self.assertTrue(np.allclose(scaled_matrix, expected_matrix)) def test_sparse_matrix_global_scaling(self): origin_matrix_rows = np.array([0, 0, 1, 1, 2, 2]) origin_matrix_cols = np.array([0, 1, 2, 3, 0, 1]) origin_matrix_vals = np.array([1, 2, 7, 8, 9, 10], dtype=sp.float64) origin_matrix = sp.sparse.coo_matrix((origin_matrix_vals, (origin_matrix_rows, origin_matrix_cols)), shape=(3, 4)) origin_matrix_std = std(origin_matrix, 'global') expected_matrix_std = 3.43592135468 self.assertTrue(np.allclose(origin_matrix_std, expected_matrix_std)) scaled_matrix = scale(origin_matrix, 'global', origin_matrix_std) expected_matrix_rows = np.array([0, 0, 1, 1, 2, 2]) expected_matrix_cols = np.array([0, 1, 2, 3, 0, 1]) expected_matrix_vals = [ 0.2910427500435996 , 0.5820855000871992 , 2.0372992503051970 , 2.3283420003487967 , 2.6193847503923960 , 2.9104275004359956 ] expected_matrix = sp.sparse.coo_matrix((expected_matrix_vals, (expected_matrix_rows, expected_matrix_cols)), shape=(3, 4)) self.assertTrue(np.allclose(scaled_matrix.todense(), expected_matrix.todense())) def test_dense_matrix_none_scaling(self): origin_matrix = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]) origin_matrix_std = std(origin_matrix, 'none') scaled_matrix = scale(origin_matrix, 'none', origin_matrix_std) self.assertTrue(np.allclose(scaled_matrix, origin_matrix)) def test_sparse_matrix_none_scaling(self): origin_matrix_rows = np.array([0, 0, 1, 1, 2, 2]) origin_matrix_cols = np.array([0, 1, 2, 3, 0, 1]) origin_matrix_vals = np.array([1, 2, 7, 8, 9, 10], dtype=sp.float64) origin_matrix = sp.sparse.coo_matrix((origin_matrix_vals, (origin_matrix_rows, origin_matrix_cols)), shape=(3, 4)) origin_matrix_std = std(origin_matrix, 'none') scaled_matrix = scale(origin_matrix, 'none', origin_matrix_std) self.assertTrue(np.allclose(scaled_matrix.todense(), origin_matrix.todense())) def test_dense_matrix_rows_center_and_scale(self): origin_matrix = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]) centered_and_scaled_matrix, centered_and_scaled_matrix_mean, centered_and_scaled_matrix_std = center_and_scale(origin_matrix, 'rows') expected_matrix = [[-1.34164079, -0.4472136, 0.4472136, 1.34164079], [-1.34164079, -0.4472136, 0.4472136, 1.34164079], [-1.34164079, -0.4472136, 0.4472136, 1.34164079]] expected_matrix_mean = [2.5, 6.5, 10.5] expected_matrix_std = [1.11803399, 1.11803399, 1.11803399] self.assertTrue(np.allclose(centered_and_scaled_matrix, expected_matrix)) self.assertTrue(np.allclose(centered_and_scaled_matrix_mean, expected_matrix_mean)) self.assertTrue(np.allclose(centered_and_scaled_matrix_std, expected_matrix_std)) def test_dense_matrix_rows_center_and_scale_with_mean_only(self): origin_matrix = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]) centered_and_scaled_matrix, centered_and_scaled_matrix_mean, centered_and_scaled_matrix_std = center_and_scale(origin_matrix, 'rows', with_std=False) expected_matrix = np.array([[-1.5, -0.5, 0.5, 1.5], [-1.5, -0.5, 0.5, 1.5], [-1.5, -0.5, 0.5, 1.5]]) self.assertTrue(np.allclose(centered_and_scaled_matrix, expected_matrix)) self.assertEqual(centered_and_scaled_matrix_std, None) def test_dense_matrix_rows_center_and_scale_with_std_only(self): origin_matrix = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]) centered_and_scaled_matrix, centered_and_scaled_matrix_mean, centered_and_scaled_matrix_std = center_and_scale(origin_matrix, 'rows', with_mean=False) expected_matrix = [[ 0.89442719, 1.78885438, 2.68328157, 3.57770876], [ 4.47213595, 5.36656315, 6.26099034, 7.15541753], [ 8.04984472, 8.94427191, 9.83869910, 10.73312629]] self.assertTrue(np.allclose(centered_and_scaled_matrix, expected_matrix)) self.assertEqual(centered_and_scaled_matrix_mean, None) def test_dense_matrix_rows_center_and_scale_with_no_mean_and_std(self): origin_matrix = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]) centered_and_scaled_matrix, centered_and_scaled_matrix_mean, centered_and_scaled_matrix_std = center_and_scale(origin_matrix, 'rows', with_mean=False, with_std=False) self.assertTrue(np.allclose(centered_and_scaled_matrix, origin_matrix)) self.assertEqual(centered_and_scaled_matrix_mean, None) self.assertEqual(centered_and_scaled_matrix_std, None) def test_dense_matrix_cols_center_and_scale(self): origin_matrix = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]) centered_and_scaled_matrix, centered_and_scaled_matrix_mean, centered_and_scaled_matrix_std = center_and_scale(origin_matrix, 'cols') expected_matrix = [[-1.22474487, -1.22474487, -1.22474487, -1.22474487], [ 0., 0., 0., 0. ], [ 1.22474487, 1.22474487, 1.22474487, 1.22474487]] expected_matrix_mean = [5., 6., 7., 8.] expected_matrix_std = [3.26598632, 3.26598632, 3.26598632, 3.26598632] self.assertTrue(np.allclose(centered_and_scaled_matrix, expected_matrix)) self.assertTrue(np.allclose(centered_and_scaled_matrix_mean, expected_matrix_mean)) self.assertTrue(np.allclose(centered_and_scaled_matrix_std, expected_matrix_std)) def test_dense_matrix_global_center_and_scale(self): origin_matrix = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]) centered_and_scaled_matrix, centered_and_scaled_matrix_mean, centered_and_scaled_matrix_std = center_and_scale(origin_matrix, 'global') expected_matrix = [[-1.59325501, -1.30357228, -1.01388955, -0.72420682], [-0.43452409, -0.14484136, 0.14484136, 0.43452409], [ 0.72420682, 1.01388955, 1.30357228, 1.59325501]] expected_matrix_mean = 6.5 expected_matrix_std = 3.45205252953 self.assertTrue(np.allclose(centered_and_scaled_matrix, expected_matrix)) self.assertTrue(np.allclose(centered_and_scaled_matrix_mean, expected_matrix_mean)) self.assertTrue(np.allclose(centered_and_scaled_matrix_std, expected_matrix_std)) def test_dense_matrix_none_center_and_scale(self): origin_matrix = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]) centered_and_scaled_matrix, centered_and_scaled_matrix_mean, centered_and_scaled_matrix_std = center_and_scale(origin_matrix, 'none') self.assertTrue(np.allclose(centered_and_scaled_matrix, origin_matrix)) self.assertEqual(centered_and_scaled_matrix_mean, None) self.assertEqual(centered_and_scaled_matrix_std, None) if __name__ == '__main__': unittest.main()

57.841509

174

0.580441

1,893

15,328

4.396725

0.073957

0.155713

0.081701

0.110537

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0.831912

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0.802836

0.789139

0

0.161934

0.303823

15,328

264

175

58.060606

0.61803

0.001305

0

0.575107

0

0.009538

0

0.158798

1

0.081545

false

0

0.017167

0

0.103004

0

null

0

1

0

null

0

6

30035d9cb4198f6eeabe2b73ff0be200d7e89955

138

py

Python

deterministic_encryption_utils/encryption/extensions/__init__.py

seiferma/encviewfuse.commons

a747da3cd6daf39b0c26d4d497725e8863af1dd1

[ "MIT" ]

null

deterministic_encryption_utils/encryption/extensions/__init__.py

seiferma/encviewfuse.commons

a747da3cd6daf39b0c26d4d497725e8863af1dd1

[ "MIT" ]

null

deterministic_encryption_utils/encryption/extensions/__init__.py

seiferma/encviewfuse.commons

a747da3cd6daf39b0c26d4d497725e8863af1dd1

[ "MIT" ]

null

__all__ = ['ModificationTimeFilenameSaltProvider', 'ModificationTimeFileSaltProvider', 'NameFileSaltProvider', 'NameFilenameSaltProvider']

138

0.862319

5

138

23

1

0

0.036232

138

1

138

0.864662

0

0.805755

0.661871

0

1

0

false

0

1

0

1

null

0

1

0

1

0

1

null

0

6

30611a3feef6ea4a34a2f59acc6548e5fbc8874c

28

py

Python

jupyter_flashcards/__init__.py

patarapolw/jupyter-flashcards

ecab7550669b899d2d018556632f92387147e43d

[ "Apache-2.0" ]

3

2018-07-22T18:56:40.000Z

2020-02-04T21:03:30.000Z

jupyter_flashcards/__init__.py

patarapolw/jupyter-flashcards

ecab7550669b899d2d018556632f92387147e43d

[ "Apache-2.0" ]

null

jupyter_flashcards/__init__.py

patarapolw/jupyter-flashcards

ecab7550669b899d2d018556632f92387147e43d

[ "Apache-2.0" ]

3

2020-02-04T21:05:40.000Z

2021-09-10T19:09:37.000Z

from .app import Flashcards

14

27

0.821429

4

28

5.75

1

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0.142857

28

1

28

0.958333

0

1

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true

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1

0

1

0

1

0

6

306a6903b0e7d5236dc6e0bf077ce7197287a56b

20,916

py

Python

util/data/gen/kernel.appcore.dll.py

56kyle/bloons_auto

419d55b51d1cddc49099593970adf1c67985b389

[ "MIT" ]

null

util/data/gen/kernel.appcore.dll.py

56kyle/bloons_auto

419d55b51d1cddc49099593970adf1c67985b389

[ "MIT" ]

null

util/data/gen/kernel.appcore.dll.py

56kyle/bloons_auto

419d55b51d1cddc49099593970adf1c67985b389

[ "MIT" ]

null

symbols = [] exports = [{'type': 'function', 'name': 'AcquireStateLock', 'address': '0x7ffb398f3000'}, {'type': 'function', 'name': 'AddDependencyToProcessPackageGraph', 'address': '0x7ffb398f3010'}, {'type': 'function', 'name': 'AddExtensionProgId', 'address': '0x7ffb398f3020'}, {'type': 'function', 'name': 'AddPackageToFamilyXref', 'address': '0x7ffb398f3030'}, {'type': 'function', 'name': 'AppContainerDeriveSidFromMoniker', 'address': '0x7ffb398f3040'}, {'type': 'function', 'name': 'AppContainerFreeMemory', 'address': '0x7ffb398f3050'}, {'type': 'function', 'name': 'AppContainerLookupDisplayNameMrtReference', 'address': '0x7ffb398f3060'}, {'type': 'function', 'name': 'AppContainerLookupMoniker', 'address': '0x7ffb398f3070'}, {'type': 'function', 'name': 'AppContainerRegisterSid', 'address': '0x7ffb398f3080'}, {'type': 'function', 'name': 'AppContainerUnregisterSid', 'address': '0x7ffb398f3090'}, {'type': 'function', 'name': 'AppPolicyGetClrCompat', 'address': '0x7ffb398f30a0'}, {'type': 'function', 'name': 'AppPolicyGetCreateFileAccess', 'address': '0x7ffb398f30b0'}, {'type': 'function', 'name': 'AppPolicyGetLifecycleManagement', 'address': '0x7ffb398f30c0'}, {'type': 'function', 'name': 'AppPolicyGetMediaFoundationCodecLoading', 'address': '0x7ffb398f30d0'}, {'type': 'function', 'name': 'AppPolicyGetProcessTerminationMethod', 'address': '0x7ffb398f30e0'}, {'type': 'function', 'name': 'AppPolicyGetShowDeveloperDiagnostic', 'address': '0x7ffb398f30f0'}, {'type': 'function', 'name': 'AppPolicyGetThreadInitializationType', 'address': '0x7ffb398f3100'}, {'type': 'function', 'name': 'AppPolicyGetWindowingModel', 'address': '0x7ffb398f3110'}, {'type': 'function', 'name': 'AppXFreeMemory', 'address': '0x7ffb398f3120'}, {'type': 'function', 'name': 'AppXGetApplicationData', 'address': '0x7ffb398f3130'}, {'type': 'function', 'name': 'AppXGetDevelopmentMode', 'address': '0x7ffb398f3140'}, {'type': 'function', 'name': 'AppXGetOSMaxVersionTested', 'address': '0x7ffb398f3150'}, {'type': 'function', 'name': 'AppXGetOSMinVersion', 'address': '0x7ffb398f3160'}, {'type': 'function', 'name': 'AppXGetPackageCapabilities', 'address': '0x7ffb398f3170'}, {'type': 'function', 'name': 'AppXGetPackageSid', 'address': '0x7ffb398f3180'}, {'type': 'function', 'name': 'AppXLookupDisplayName', 'address': '0x7ffb398f3190'}, {'type': 'function', 'name': 'AppXLookupMoniker', 'address': '0x7ffb398f31a0'}, {'type': 'function', 'name': 'AppXUpdatePackageCapabilities', 'address': '0x7ffb398f31b0'}, {'type': 'function', 'name': 'ApplicationUserModelIdFromProductId', 'address': '0x7ffb398f31c0'}, {'type': 'function', 'name': 'BuildProcThreadAttributeListFromBlob', 'address': '0x7ffb398f2070'}, {'type': 'function', 'name': 'CheckIfStateChangeNotificationExists', 'address': '0x7ffb398f31d0'}, {'type': 'function', 'name': 'ClosePackageInfo', 'address': '0x7ffb398f31e0'}, {'type': 'function', 'name': 'CloseState', 'address': '0x7ffb398f31f0'}, {'type': 'function', 'name': 'CloseStateAtom', 'address': '0x7ffb398f3200'}, {'type': 'function', 'name': 'CloseStateChangeNotification', 'address': '0x7ffb398f3210'}, {'type': 'function', 'name': 'CloseStateContainer', 'address': '0x7ffb398f3220'}, {'type': 'function', 'name': 'CloseStateLock', 'address': '0x7ffb398f3230'}, {'type': 'function', 'name': 'CommitStateAtom', 'address': '0x7ffb398f3240'}, {'type': 'function', 'name': 'CouldMultiUserAppsBehaviorBePossibleForPackage', 'address': '0x7ffb398f3250'}, {'type': 'function', 'name': 'CreateStateAtom', 'address': '0x7ffb398f3260'}, {'type': 'function', 'name': 'CreateStateChangeNotification', 'address': '0x7ffb398f3270'}, {'type': 'function', 'name': 'CreateStateContainer', 'address': '0x7ffb398f3280'}, {'type': 'function', 'name': 'CreateStateLock', 'address': '0x7ffb398f3290'}, {'type': 'function', 'name': 'CreateStateSubcontainer', 'address': '0x7ffb398f32a0'}, {'type': 'function', 'name': 'DeleteStateAtomValue', 'address': '0x7ffb398f32b0'}, {'type': 'function', 'name': 'DeleteStateContainer', 'address': '0x7ffb398f32c0'}, {'type': 'function', 'name': 'DeleteStateContainerValue', 'address': '0x7ffb398f32d0'}, {'type': 'function', 'name': 'DuplicateStateContainerHandle', 'address': '0x7ffb398f32e0'}, {'type': 'function', 'name': 'EnumerateExtensionNames', 'address': '0x7ffb398f32f0'}, {'type': 'function', 'name': 'EnumerateStateAtomValues', 'address': '0x7ffb398f3300'}, {'type': 'function', 'name': 'EnumerateStateContainerItems', 'address': '0x7ffb398f3310'}, {'type': 'function', 'name': 'ExtensionProgIdExists', 'address': '0x7ffb398f3320'}, {'type': 'function', 'name': 'FindPackagesByPackageFamily', 'address': '0x7ffb398f3330'}, {'type': 'function', 'name': 'FormatApplicationUserModelId', 'address': '0x7ffb398f3340'}, {'type': 'function', 'name': 'FormatApplicationUserModelIdA', 'address': '0x7ffb398f3350'}, {'type': 'function', 'name': 'GenerateProcThreadAttributeBlob', 'address': '0x7ffb398f2270'}, {'type': 'function', 'name': 'GetAlternatePackageRoots', 'address': '0x7ffb398f3360'}, {'type': 'function', 'name': 'GetAppDataFolder', 'address': '0x7ffb398f3370'}, {'type': 'function', 'name': 'GetAppModelVersion', 'address': '0x7ffb398f3380'}, {'type': 'function', 'name': 'GetApplicationUserModelId', 'address': '0x7ffb398f3390'}, {'type': 'function', 'name': 'GetApplicationUserModelIdFromToken', 'address': '0x7ffb398f33a0'}, {'type': 'function', 'name': 'GetCurrentApplicationUserModelId', 'address': '0x7ffb398f33b0'}, {'type': 'function', 'name': 'GetCurrentPackageApplicationContext', 'address': '0x7ffb398f33c0'}, {'type': 'function', 'name': 'GetCurrentPackageApplicationResourcesContext', 'address': '0x7ffb398f33d0'}, {'type': 'function', 'name': 'GetCurrentPackageContext', 'address': '0x7ffb398f33e0'}, {'type': 'function', 'name': 'GetCurrentPackageFamilyName', 'address': '0x7ffb398f33f0'}, {'type': 'function', 'name': 'GetCurrentPackageFullName', 'address': '0x7ffb398f3400'}, {'type': 'function', 'name': 'GetCurrentPackageGlobalizationContext', 'address': '0x7ffb398f3410'}, {'type': 'function', 'name': 'GetCurrentPackageId', 'address': '0x7ffb398f3420'}, {'type': 'function', 'name': 'GetCurrentPackageInfo', 'address': '0x7ffb398f3430'}, {'type': 'function', 'name': 'GetCurrentPackageInfo2', 'address': '0x7ffb398f3440'}, {'type': 'function', 'name': 'GetCurrentPackageInfo3', 'address': '0x7ffb398f3450'}, {'type': 'function', 'name': 'GetCurrentPackagePath', 'address': '0x7ffb398f3460'}, {'type': 'function', 'name': 'GetCurrentPackagePath2', 'address': '0x7ffb398f3470'}, {'type': 'function', 'name': 'GetCurrentPackageResourcesContext', 'address': '0x7ffb398f3480'}, {'type': 'function', 'name': 'GetCurrentPackageSecurityContext', 'address': '0x7ffb398f3490'}, {'type': 'function', 'name': 'GetCurrentTargetPlatformContext', 'address': '0x7ffb398f34a0'}, {'type': 'function', 'name': 'GetEffectivePackageStatusForUser', 'address': '0x7ffb398f34b0'}, {'type': 'function', 'name': 'GetEffectivePackageStatusForUserSid', 'address': '0x7ffb398f34c0'}, {'type': 'function', 'name': 'GetExtensionApplicationUserModelId', 'address': '0x7ffb398f34d0'}, {'type': 'function', 'name': 'GetExtensionProgIds', 'address': '0x7ffb398f34e0'}, {'type': 'function', 'name': 'GetExtensionProperty', 'address': '0x7ffb398f34f0'}, {'type': 'function', 'name': 'GetExtensionProperty2', 'address': '0x7ffb398f3500'}, {'type': 'function', 'name': 'GetHivePath', 'address': '0x7ffb398f3510'}, {'type': 'function', 'name': 'GetPackageApplicationContext', 'address': '0x7ffb398f3520'}, {'type': 'function', 'name': 'GetPackageApplicationIds', 'address': '0x7ffb398f3530'}, {'type': 'function', 'name': 'GetPackageApplicationProperty', 'address': '0x7ffb398f3540'}, {'type': 'function', 'name': 'GetPackageApplicationPropertyString', 'address': '0x7ffb398f3550'}, {'type': 'function', 'name': 'GetPackageApplicationResourcesContext', 'address': '0x7ffb398f3560'}, {'type': 'function', 'name': 'GetPackageContext', 'address': '0x7ffb398f3570'}, {'type': 'function', 'name': 'GetPackageFamilyName', 'address': '0x7ffb398f3580'}, {'type': 'function', 'name': 'GetPackageFamilyNameFromProgId', 'address': '0x7ffb398f3590'}, {'type': 'function', 'name': 'GetPackageFamilyNameFromToken', 'address': '0x7ffb398f35a0'}, {'type': 'function', 'name': 'GetPackageFullName', 'address': '0x7ffb398f35b0'}, {'type': 'function', 'name': 'GetPackageFullNameFromToken', 'address': '0x7ffb398f35c0'}, {'type': 'function', 'name': 'GetPackageGlobalizationContext', 'address': '0x7ffb398f35d0'}, {'type': 'function', 'name': 'GetPackageGlobalizationProperty', 'address': '0x7ffb398f35e0'}, {'type': 'function', 'name': 'GetPackageId', 'address': '0x7ffb398f35f0'}, {'type': 'function', 'name': 'GetPackageInfo', 'address': '0x7ffb398f3600'}, {'type': 'function', 'name': 'GetPackageInfo2', 'address': '0x7ffb398f3610'}, {'type': 'function', 'name': 'GetPackageInfo3', 'address': '0x7ffb398f3620'}, {'type': 'function', 'name': 'GetPackageInstallTime', 'address': '0x7ffb398f3630'}, {'type': 'function', 'name': 'GetPackageOSMaxVersionTested', 'address': '0x7ffb398f3640'}, {'type': 'function', 'name': 'GetPackagePath', 'address': '0x7ffb398f3650'}, {'type': 'function', 'name': 'GetPackagePathByFullName', 'address': '0x7ffb398f3660'}, {'type': 'function', 'name': 'GetPackagePathByFullName2', 'address': '0x7ffb398f3670'}, {'type': 'function', 'name': 'GetPackagePathOnVolume', 'address': '0x7ffb398f3680'}, {'type': 'function', 'name': 'GetPackageProperty', 'address': '0x7ffb398f3690'}, {'type': 'function', 'name': 'GetPackagePropertyString', 'address': '0x7ffb398f36a0'}, {'type': 'function', 'name': 'GetPackageResourcesContext', 'address': '0x7ffb398f36b0'}, {'type': 'function', 'name': 'GetPackageResourcesProperty', 'address': '0x7ffb398f36c0'}, {'type': 'function', 'name': 'GetPackageSecurityContext', 'address': '0x7ffb398f36d0'}, {'type': 'function', 'name': 'GetPackageSecurityProperty', 'address': '0x7ffb398f36e0'}, {'type': 'function', 'name': 'GetPackageStatus', 'address': '0x7ffb398f36f0'}, {'type': 'function', 'name': 'GetPackageStatusForUser', 'address': '0x7ffb398f3700'}, {'type': 'function', 'name': 'GetPackageStatusForUserSid', 'address': '0x7ffb398f3710'}, {'type': 'function', 'name': 'GetPackageTargetPlatformProperty', 'address': '0x7ffb398f3720'}, {'type': 'function', 'name': 'GetPackageVolumeSisPath', 'address': '0x7ffb398f3730'}, {'type': 'function', 'name': 'GetPackagesByPackageFamily', 'address': '0x7ffb398f3740'}, {'type': 'function', 'name': 'GetProtocolAumid', 'address': '0x7ffb398f3750'}, {'type': 'function', 'name': 'GetProtocolProperty', 'address': '0x7ffb398f3760'}, {'type': 'function', 'name': 'GetPublisherCacheFolder', 'address': '0x7ffb398f3770'}, {'type': 'function', 'name': 'GetPublisherRootFolder', 'address': '0x7ffb398f3780'}, {'type': 'function', 'name': 'GetRoamingLastObservedChangeTime', 'address': '0x7ffb398f3790'}, {'type': 'function', 'name': 'GetSecureSystemAppDataFolder', 'address': '0x7ffb398f37a0'}, {'type': 'function', 'name': 'GetSerializedAtomBytes', 'address': '0x7ffb398f37b0'}, {'type': 'function', 'name': 'GetSharedLocalFolder', 'address': '0x7ffb398f37c0'}, {'type': 'function', 'name': 'GetStagedPackageOrigin', 'address': '0x7ffb398f37d0'}, {'type': 'function', 'name': 'GetStagedPackagePathByFullName', 'address': '0x7ffb398f37e0'}, {'type': 'function', 'name': 'GetStagedPackagePathByFullName2', 'address': '0x7ffb398f37f0'}, {'type': 'function', 'name': 'GetStateContainerDepth', 'address': '0x7ffb398f3800'}, {'type': 'function', 'name': 'GetStateFolder', 'address': '0x7ffb398f3810'}, {'type': 'function', 'name': 'GetStateRootFolder', 'address': '0x7ffb398f3820'}, {'type': 'function', 'name': 'GetStateRootFolderBase', 'address': '0x7ffb398f3830'}, {'type': 'function', 'name': 'GetStateSettingsFolder', 'address': '0x7ffb398f3840'}, {'type': 'function', 'name': 'GetStateVersion', 'address': '0x7ffb398f3850'}, {'type': 'function', 'name': 'GetSystemAppDataFolder', 'address': '0x7ffb398f3860'}, {'type': 'function', 'name': 'GetSystemAppDataKey', 'address': '0x7ffb398f3870'}, {'type': 'function', 'name': 'GetSystemMetadataPath', 'address': '0x7ffb398f3880'}, {'type': 'function', 'name': 'GetSystemMetadataPathForPackage', 'address': '0x7ffb398f3890'}, {'type': 'function', 'name': 'GetSystemMetadataPathForPackageFamily', 'address': '0x7ffb398f38a0'}, {'type': 'function', 'name': 'GetSystemStateRootFolder', 'address': '0x7ffb398f38b0'}, {'type': 'function', 'name': 'GetTargetPlatformContext', 'address': '0x7ffb398f38c0'}, {'type': 'function', 'name': 'IncrementPackageStatusVersion', 'address': '0x7ffb398f38d0'}, {'type': 'function', 'name': 'InvalidateAppModelVersionCache', 'address': '0x7ffb398f38e0'}, {'type': 'function', 'name': 'IsDeveloperModeEnabled', 'address': '0x7ffb398f38f0'}, {'type': 'function', 'name': 'IsDeveloperModePolicyApplied', 'address': '0x7ffb398f3900'}, {'type': 'function', 'name': 'IsMrtResourceRedirectionEnabled', 'address': '0x7ffb398f3910'}, {'type': 'function', 'name': 'IsOnDemandRegistrationSupportedForExtensionCategory', 'address': '0x7ffb398f3920'}, {'type': 'function', 'name': 'IsSideloadingEnabled', 'address': '0x7ffb398f3930'}, {'type': 'function', 'name': 'IsSideloadingPolicyApplied', 'address': '0x7ffb398f3940'}, {'type': 'function', 'name': 'OpenPackageInfoByFullName', 'address': '0x7ffb398f3950'}, {'type': 'function', 'name': 'OpenPackageInfoByFullNameForMachine', 'address': '0x7ffb398f3960'}, {'type': 'function', 'name': 'OpenPackageInfoByFullNameForUser', 'address': '0x7ffb398f3970'}, {'type': 'function', 'name': 'OpenState', 'address': '0x7ffb398f3980'}, {'type': 'function', 'name': 'OpenStateAtom', 'address': '0x7ffb398f3990'}, {'type': 'function', 'name': 'OpenStateExplicit', 'address': '0x7ffb398f39a0'}, {'type': 'function', 'name': 'OpenStateExplicitForUserSid', 'address': '0x7ffb398f39b0'}, {'type': 'function', 'name': 'OpenStateExplicitForUserSidString', 'address': '0x7ffb398f39c0'}, {'type': 'function', 'name': 'OverrideRoamingDataModificationTimesInRange', 'address': '0x7ffb398f39d0'}, {'type': 'function', 'name': 'PackageFamilyNameFromFullName', 'address': '0x7ffb398f39e0'}, {'type': 'function', 'name': 'PackageFamilyNameFromFullNameA', 'address': '0x7ffb398f39f0'}, {'type': 'function', 'name': 'PackageFamilyNameFromId', 'address': '0x7ffb398f3a00'}, {'type': 'function', 'name': 'PackageFamilyNameFromIdA', 'address': '0x7ffb398f3a10'}, {'type': 'function', 'name': 'PackageFamilyNameFromProductId', 'address': '0x7ffb398f3a20'}, {'type': 'function', 'name': 'PackageFullNameFromId', 'address': '0x7ffb398f3a30'}, {'type': 'function', 'name': 'PackageFullNameFromIdA', 'address': '0x7ffb398f3a40'}, {'type': 'function', 'name': 'PackageFullNameFromProductId', 'address': '0x7ffb398f3a50'}, {'type': 'function', 'name': 'PackageIdFromFullName', 'address': '0x7ffb398f3a60'}, {'type': 'function', 'name': 'PackageIdFromFullNameA', 'address': '0x7ffb398f3a70'}, {'type': 'function', 'name': 'PackageIdFromProductId', 'address': '0x7ffb398f3a80'}, {'type': 'function', 'name': 'PackageNameAndPublisherIdFromFamilyName', 'address': '0x7ffb398f3a90'}, {'type': 'function', 'name': 'PackageNameAndPublisherIdFromFamilyNameA', 'address': '0x7ffb398f3aa0'}, {'type': 'function', 'name': 'PackageRelativeApplicationIdFromProductId', 'address': '0x7ffb398f3ab0'}, {'type': 'function', 'name': 'PackageSidFromFamilyName', 'address': '0x7ffb398f3ac0'}, {'type': 'function', 'name': 'PackageSidFromProductId', 'address': '0x7ffb398f2ff0'}, {'type': 'function', 'name': 'ParseApplicationUserModelId', 'address': '0x7ffb398f3ad0'}, {'type': 'function', 'name': 'ParseApplicationUserModelIdA', 'address': '0x7ffb398f3ae0'}, {'type': 'function', 'name': 'ProductIdFromPackageFamilyName', 'address': '0x7ffb398f3af0'}, {'type': 'function', 'name': 'PsmActivateApplicationByToken', 'address': '0x7ffb398f4790'}, {'type': 'function', 'name': 'PsmAdjustActivationToken', 'address': '0x7ffb398f4910'}, {'type': 'function', 'name': 'PsmAdjustActivationTokenEx', 'address': '0x7ffb398f1130'}, {'type': 'function', 'name': 'PsmAdjustActivationTokenPkgClaim', 'address': '0x7ffb398f4960'}, {'type': 'function', 'name': 'PsmAdjustActivationTokenWithDynamicId', 'address': '0x7ffb398f4aa0'}, {'type': 'function', 'name': 'PsmCreateBrokerToken', 'address': '0x7ffb398f1140'}, {'type': 'function', 'name': 'PsmCreateMatchToken', 'address': '0x7ffb398f4af0'}, {'type': 'function', 'name': 'PsmQueryBackgroundActivationType', 'address': '0x7ffb398f1010'}, {'type': 'function', 'name': 'PsmRegisterApplicationProcess', 'address': '0x7ffb398f2570'}, {'type': 'function', 'name': 'PsmRegisterDesktopProcess', 'address': '0x7ffb398f4d80'}, {'type': 'function', 'name': 'PsmRegisterDesktopProcessWithAppContainerToken', 'address': '0x7ffb398f2580'}, {'type': 'function', 'name': 'PsmRegisterServiceProcess', 'address': '0x7ffb398f2470'}, {'type': 'function', 'name': 'PublishStateChangeNotification', 'address': '0x7ffb398f3b00'}, {'type': 'function', 'name': 'PublisherFromPackageFullName', 'address': '0x7ffb398f3b10'}, {'type': 'function', 'name': 'QueryStateAtomValueInfo', 'address': '0x7ffb398f3b20'}, {'type': 'function', 'name': 'QueryStateContainerCreatedNew', 'address': '0x7ffb398f3b30'}, {'type': 'function', 'name': 'QueryStateContainerItemInfo', 'address': '0x7ffb398f3b40'}, {'type': 'function', 'name': 'ReadStateAtomValue', 'address': '0x7ffb398f3b50'}, {'type': 'function', 'name': 'ReadStateContainerValue', 'address': '0x7ffb398f3b60'}, {'type': 'function', 'name': 'RefreshPackageInfo', 'address': '0x7ffb398f3b70'}, {'type': 'function', 'name': 'RegisterStateChangeNotification', 'address': '0x7ffb398f3b80'}, {'type': 'function', 'name': 'RegisterStateLock', 'address': '0x7ffb398f3b90'}, {'type': 'function', 'name': 'ReleaseStateLock', 'address': '0x7ffb398f3ba0'}, {'type': 'function', 'name': 'RemoveExtensionProgIds', 'address': '0x7ffb398f3bb0'}, {'type': 'function', 'name': 'RemovePackageFromFamilyXref', 'address': '0x7ffb398f3bc0'}, {'type': 'function', 'name': 'RemovePackageStatus', 'address': '0x7ffb398f3bd0'}, {'type': 'function', 'name': 'RemovePackageStatusForUser', 'address': '0x7ffb398f3be0'}, {'type': 'function', 'name': 'ResetState', 'address': '0x7ffb398f3bf0'}, {'type': 'function', 'name': 'SaveAlternatePackageRootPath', 'address': '0x7ffb398f3c00'}, {'type': 'function', 'name': 'SaveStateRootFolderPath', 'address': '0x7ffb398f3c10'}, {'type': 'function', 'name': 'SetExtensionProperty', 'address': '0x7ffb398f3c20'}, {'type': 'function', 'name': 'SetIsDeveloperModeEnabled', 'address': '0x7ffb398f3c30'}, {'type': 'function', 'name': 'SetIsSideloadingEnabled', 'address': '0x7ffb398f3c40'}, {'type': 'function', 'name': 'SetProtocolProperty', 'address': '0x7ffb398f3c50'}, {'type': 'function', 'name': 'SetRoamingLastObservedChangeTime', 'address': '0x7ffb398f3c60'}, {'type': 'function', 'name': 'SetStateVersion', 'address': '0x7ffb398f3c70'}, {'type': 'function', 'name': 'SharedLocalIsEnabled', 'address': '0x7ffb398f3c80'}, {'type': 'function', 'name': 'SubscribeStateChangeNotification', 'address': '0x7ffb398f3c90'}, {'type': 'function', 'name': 'UnregisterStateChangeNotification', 'address': '0x7ffb398f3ca0'}, {'type': 'function', 'name': 'UnregisterStateLock', 'address': '0x7ffb398f3cb0'}, {'type': 'function', 'name': 'UnsubscribeStateChangeNotification', 'address': '0x7ffb398f3cc0'}, {'type': 'function', 'name': 'UpdatePackageStatus', 'address': '0x7ffb398f3cd0'}, {'type': 'function', 'name': 'UpdatePackageStatusForUser', 'address': '0x7ffb398f3ce0'}, {'type': 'function', 'name': 'UpdatePackageStatusForUserSid', 'address': '0x7ffb398f3cf0'}, {'type': 'function', 'name': 'VerifyApplicationUserModelId', 'address': '0x7ffb398f3d00'}, {'type': 'function', 'name': 'VerifyApplicationUserModelIdA', 'address': '0x7ffb398f3d10'}, {'type': 'function', 'name': 'VerifyPackageFamilyName', 'address': '0x7ffb398f3d20'}, {'type': 'function', 'name': 'VerifyPackageFamilyNameA', 'address': '0x7ffb398f3d30'}, {'type': 'function', 'name': 'VerifyPackageFullName', 'address': '0x7ffb398f3d40'}, {'type': 'function', 'name': 'VerifyPackageFullNameA', 'address': '0x7ffb398f3d50'}, {'type': 'function', 'name': 'VerifyPackageId', 'address': '0x7ffb398f3d60'}, {'type': 'function', 'name': 'VerifyPackageIdA', 'address': '0x7ffb398f3d70'}, {'type': 'function', 'name': 'VerifyPackagePublisher', 'address': '0x7ffb398f3d80'}, {'type': 'function', 'name': 'VerifyPackagePublisherA', 'address': '0x7ffb398f3d90'}, {'type': 'function', 'name': 'VerifyPackageRelativeApplicationId', 'address': '0x7ffb398f3da0'}, {'type': 'function', 'name': 'VerifyPackageRelativeApplicationIdA', 'address': '0x7ffb398f3db0'}, {'type': 'function', 'name': 'WriteStateAtomValue', 'address': '0x7ffb398f3dc0'}, {'type': 'function', 'name': 'WriteStateContainerValue', 'address': 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062aa46b512766dda49213d2215cfa703a1c57eb

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relational/student_projects/2019_guth/models/MMI_Genetic/Genetic_MMI_Premiseorder.py

monthie/cogmods

62af4b8bf2effb77f26a8877d6a89949164d83f0

[ "MIT" ]

null

relational/student_projects/2019_guth/models/MMI_Genetic/Genetic_MMI_Premiseorder.py

monthie/cogmods

62af4b8bf2effb77f26a8877d6a89949164d83f0

[ "MIT" ]

11

2020-05-04T09:05:29.000Z

2021-04-08T13:22:34.000Z

relational/student_projects/2019_guth/models/MMI_Genetic/Genetic_MMI_Premiseorder.py

monthie/cogmods

62af4b8bf2effb77f26a8877d6a89949164d83f0

[ "MIT" ]

12

2020-05-02T09:36:14.000Z

2021-06-22T08:10:45.000Z

import Genetic_MMI class ModelApproachExp32(Genetic_MMI.ModelApproach): def __init__(self): Genetic_MMI.ModelApproach.__init__(self, 3, "MMI_premiseorder")

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247

py

Python

ctaplot/gammaboard/tests/test_gammaboard.py

vuillaut/CTAPLOT

3a7757f6e2487806ca87e41fcd123ccfbd4b964c

[ "MIT" ]

5

2018-07-03T16:13:29.000Z

2021-02-22T13:59:57.000Z

ctaplot/gammaboard/tests/test_gammaboard.py

vuillaut/CTAPLOT

3a7757f6e2487806ca87e41fcd123ccfbd4b964c

[ "MIT" ]

39

2020-03-10T16:52:45.000Z

2022-01-26T14:13:40.000Z

ctaplot/gammaboard/tests/test_gammaboard.py

vuillaut/CTAPLOT

3a7757f6e2487806ca87e41fcd123ccfbd4b964c

[ "MIT" ]

6

2019-11-22T10:01:23.000Z

2021-09-20T12:32:34.000Z

def test_find_dashboard(): from ctaplot.io import get get('dashboard.ipynb') def test_open_dashboard(): """ TODO: test and close notebook automatically """ # process = open_dashboard() # process.terminate() pass

17.642857

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0.651822

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247

5.571429

0.678571

0.089744

0

0.238866

247

13

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0.829787

0.368421

0

0.109489

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true

0.2

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null

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null

0

1

0

1

0

1

0

6

06395e454afbc6e0709c48a7a2a25b3d6d9485cd

74

py

Python

gravity/utils/__init__.py

ko-han/gravity

d3e1ff31e5cc3955d19acbbee8d10c2240191670

[ "Apache-2.0" ]

null

gravity/utils/__init__.py

ko-han/gravity

d3e1ff31e5cc3955d19acbbee8d10c2240191670

[ "Apache-2.0" ]

null

gravity/utils/__init__.py

ko-han/gravity

d3e1ff31e5cc3955d19acbbee8d10c2240191670

[ "Apache-2.0" ]

null

from .meta_clsss import * from .utils import * from .yaml_help import *

12.333333

25

0.72973

11

74

4.727273

0.636364

0.384615

0

0.189189

74

5

26

14.8

0.866667

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

06429cbe6acaf2f316bf6c20d7cf9a8dc9453274

40

py

Python

src/python/zquantum/core/wip/circuits/__init__.py

alexjuda2/z-quantum-core

c258100dbd091f0b22495b77b36399426ae9abac

[ "Apache-2.0" ]

24

2020-04-15T17:36:59.000Z

2022-01-25T05:02:14.000Z

src/python/zquantum/core/wip/circuits/__init__.py

alexjuda2/z-quantum-core

c258100dbd091f0b22495b77b36399426ae9abac

[ "Apache-2.0" ]

177

2020-04-23T15:19:59.000Z

2022-03-30T18:06:17.000Z

src/python/zquantum/core/wip/circuits/__init__.py

alexjuda2/z-quantum-core

c258100dbd091f0b22495b77b36399426ae9abac

[ "Apache-2.0" ]

19

2020-06-24T10:56:02.000Z

2021-09-30T13:02:21.000Z

from ...circuits import * # noqa: F403

20

39

0.65

5

40

5.2

1

0

0.09375

0.2

40

1

40

0.71875

0.25

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

06b285ce7f9ec3038ee5f3485fca543a0b0ae003

44

py

Python

make_it_sync/__init__.py

gordonwatts/make-it-sync

7e0885131a7e7a502ecc9a467a4e7121d2416730

[ "MIT" ]

2

2020-10-01T16:35:38.000Z

2021-09-17T17:57:12.000Z

make_it_sync/__init__.py

gordonwatts/make-it-sync

7e0885131a7e7a502ecc9a467a4e7121d2416730

[ "MIT" ]

2

2020-06-13T16:49:36.000Z

2020-06-13T23:43:50.000Z

make_it_sync/__init__.py

gordonwatts/make-it-sync

7e0885131a7e7a502ecc9a467a4e7121d2416730

[ "MIT" ]

1

2020-10-01T16:32:56.000Z

from .func_wrapper import make_sync # NOQA

22

43

0.795455

7

44

4.714286

1

0

0.159091

44

1

44

0.891892

0.090909

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

ebf9720cc3bb7c0090206f7dc8ea691a685c6b9c

3,698

py

Python

tests/unit/controllers/test_department_controller.py

Maxcutex/personal_ecommerce

be09fb20eae1b225523acde06f8e75effcc3676f

[ "MIT" ]

null

tests/unit/controllers/test_department_controller.py

Maxcutex/personal_ecommerce

be09fb20eae1b225523acde06f8e75effcc3676f

[ "MIT" ]

2

2019-05-21T08:44:29.000Z

2021-04-30T20:46:08.000Z

tests/unit/controllers/test_department_controller.py

Maxcutex/personal_ecommerce

be09fb20eae1b225523acde06f8e75effcc3676f

[ "MIT" ]

null

''' Unit tests for the Department Controller. ''' from unittest.mock import patch from app.controllers.department_controller import DepartmentController from tests.base_test_case import BaseTestCase from factories import DepartmentFactory class TestDepartmentController(BaseTestCase): ''' DepartmentController test class. ''' def setUp(self): self.BaseSetUp() @patch.object(DepartmentController, 'request_params_dict') def test_create_department_succeeds(self, mock_request_params): with self.app.app_context(): mock_request_params.return_value = { 'name': "eastern", 'description': "test description", } department_controller = DepartmentController(self.request_context) # Act result = department_controller.create_department() # Assert assert result.status_code == 201 assert result.get_json()['msg'] == 'OK' self.assertEqual( result.get_json()['payload']['department']['name'], "eastern" ) self.assertEqual( result.get_json()['payload']['department']['description'], "test description" ) def test_delete_department_succeeds(self): with self.app.app_context(): department = DepartmentFactory.create(name="test") department_controller = DepartmentController(self.request_context) # Act result = department_controller.delete_department(department.department_id) # Assert assert result.status_code == 200 assert result.get_json()['msg'] == 'Department successfully deleted' def test_delete_department_fails(self): with self.app.app_context(): department_controller = DepartmentController(self.request_context) # Act result = department_controller.delete_department(department_id=6) # Assert assert result.status_code == 404 assert result.get_json()['msg'] == 'Department not found' @patch.object(DepartmentController, 'request_params_dict') def test_update_department_succeeds(self, mock_request_params): with self.app.app_context(): department = DepartmentFactory.create(name="test") mock_request_params.return_value = { 'name': "eastern", 'description': "test description", } department_controller = DepartmentController(self.request_context) # Act result = department_controller.update_department(department.department_id) # Assert assert result.status_code == 200 assert result.get_json()['msg'] == 'OK' self.assertEqual( result.get_json()['payload']['department']['name'], "eastern" ) self.assertEqual( result.get_json()['payload']['department']['description'], "test description" ) @patch.object(DepartmentController, 'request_params_dict') def test_update_department_fails(self, mock_request_params): with self.app.app_context(): mock_request_params.return_value = { 'name': "eastern", 'description': "test description", } department_controller = DepartmentController(self.request_context) # Act result = department_controller.update_department(department_id=6) # Assert assert result.status_code == 404 assert result.get_json()['msg'] == 'Department not found'

35.219048

93

0.621147

338

3,698

6.553254

0.183432

0.108352

0.052822

0.031603

0.817607

0.804966

0.800451

0.787359

0.762528

0

0.006396

0.281233

3,698

104

94

35.557692

0.826938

0.035154

0

0.606061

0

0.116799

0

0.212121

1

0.090909

false

0

0.060606

0

0.166667

0

null

0

1

0

null

0

6

ebfaa1b90c608e4fe874233196cfde50a26ec8cd

26

py

Python

src/debugpy/_vendored/pydevd/tests_python/resources/my_code/my_code_on_entry.py

r3m0t/debugpy

090e3c3ef5758e5b316514c9d6f44f9b9b488cf1

[ "MIT" ]

695

2020-01-30T14:34:51.000Z

2022-03-31T09:31:57.000Z

src/debugpy/_vendored/pydevd/tests_python/resources/my_code/my_code_on_entry.py

r3m0t/debugpy

090e3c3ef5758e5b316514c9d6f44f9b9b488cf1

[ "MIT" ]

845

2020-01-29T23:53:36.000Z

2022-03-31T19:45:04.000Z

src/debugpy/_vendored/pydevd/tests_python/resources/my_code/my_code_on_entry.py

r3m0t/debugpy

090e3c3ef5758e5b316514c9d6f44f9b9b488cf1

[ "MIT" ]

66

2020-01-30T13:10:38.000Z

2022-03-29T07:11:17.000Z

print('my code on entry')

13

25

0.692308

5

26

3.6

1

0

0.153846

26

1

26

0.818182

0

0.615385

0

1

0

true

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

231c867a5071db465b7e8963fab57f60da620aa3

228

py

Python

celery_conf/tasks.py

rivendale/ethsigns

8ffecc264a54b00d5615c6fa36c9f661dce468b5

[ "MIT" ]

1

2021-01-24T04:27:57.000Z

celery_conf/tasks.py

rivendale/ethsigns

8ffecc264a54b00d5615c6fa36c9f661dce468b5

[ "MIT" ]

19

2021-02-07T18:24:31.000Z

2021-07-02T08:03:56.000Z

celery_conf/tasks.py

rivendale/ethsigns

8ffecc264a54b00d5615c6fa36c9f661dce468b5

[ "MIT" ]

null

"""Celery Tasks""" from ethsigns import celery_app @celery_app.task(name="sample_scheduler") def sample_scheduler(): """Sample to test `/celery` endpoint has scheduled tasks""" return dict(message="sample scheduler")

22.8

63

0.732456

29

228

5.62069

0.655172

0.276074

0

0.135965

228

9

64

25.333333

0.827411

0.289474

0

0.211921

0

1

0.25

true

0

0.25

0

0.75

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

003762c8c2241d61ff3268795856a14d4b4ea14d

1,718

py

Python

tests/sublime_plugin_lib/test_collections.py

my-personal-forks/dart-sublime-bundle

580127c13c3a97d7cb0c9ac09152f68cb665946c

[ "BSD-3-Clause" ]

145

2015-01-04T21:07:32.000Z

2021-10-16T21:35:20.000Z

tests/sublime_plugin_lib/test_collections.py

kkurian/dart-sublime-bundle

429324e43ea487f8c05a4fb7002c5fd2c8c56b42

[ "BSD-3-Clause" ]

93

2015-01-25T00:10:01.000Z

2021-02-01T12:11:31.000Z

tests/sublime_plugin_lib/test_collections.py

kkurian/dart-sublime-bundle

429324e43ea487f8c05a4fb7002c5fd2c8c56b42

[ "BSD-3-Clause" ]

45

2015-01-25T00:18:01.000Z

2021-02-25T18:09:08.000Z

# Copyright (c) 2014, Guillermo López-Anglada. Please see the AUTHORS file for details. # All rights reserved. Use of this source code is governed by a BSD-style # license that can be found in the LICENSE file.) import unittest import os from Dart.sublime_plugin_lib.collections import CircularArray class Test_CircularList(unittest.TestCase): def testCanInstantiate(self): ca = CircularArray(list(range(0, 5))) self.assertEqual(len(ca), 5) def testForward(self): ca = CircularArray(list(range(0, 5))) self.assertEqual(ca.forward(), 0) self.assertEqual(ca.forward(), 1) self.assertEqual(ca.forward(), 2) self.assertEqual(ca.forward(), 3) self.assertEqual(ca.forward(), 4) self.assertEqual(ca.forward(), 0) def testBackward(self): ca = CircularArray(list(range(0, 5))) self.assertEqual(ca.backward(), 4) self.assertEqual(ca.backward(), 3) self.assertEqual(ca.backward(), 2) self.assertEqual(ca.backward(), 1) self.assertEqual(ca.backward(), 0) self.assertEqual(ca.backward(), 4) def xtestBidirectionality(self): ca = CircularArray(list(range(0, 5))) self.assertEqual(ca.backward(), 4) self.assertEqual(ca.forward(), 0) self.assertEqual(ca.backward(), 4) self.assertEqual(ca.forward(), 0) self.assertEqual(ca.forward(), 1) self.assertEqual(ca.forward(), 2) self.assertEqual(ca.forward(), 3) self.assertEqual(ca.forward(), 4) self.assertEqual(ca.backward(), 3) self.assertEqual(ca.backward(), 2) self.assertEqual(ca.backward(), 1) self.assertEqual(ca.backward(), 0)

35.791667

87

0.64319

214

1,718

5.149533

0.308411

0.34029

0.370236

0.261343

0.677858

0.647005

0.606171

0

0.027571

0.218859

1,718

47

88

36.553191

0.793592

0.119325

0

0.756757

0

0.675676

1

0.108108

false

0

0.081081

0

0.216216

0

null

1

0

1

0

1

0

null

0

1

0

6

aea20186117137585f5254fde3254a736cea4847

239

py

Python

cogkge/adapter/graph_adapter.py

CogNLP/CogKGE

70d851d6489600c1e90eb25b0388a3ceba2f078c

[ "MIT" ]

1

2022-03-17T08:21:59.000Z

cogkge/adapter/graph_adapter.py

CogNLP/CogKGE

70d851d6489600c1e90eb25b0388a3ceba2f078c

[ "MIT" ]

null

cogkge/adapter/graph_adapter.py

CogNLP/CogKGE

70d851d6489600c1e90eb25b0388a3ceba2f078c

[ "MIT" ]

null

import torch def graph_adapter(func): def inner(*args, **kwargs): head_embedding, relation_embedding, tail_embedding = func(*args, **kwargs) return head_embedding, relation_embedding, tail_embedding return inner

23.9

82

0.719665

28

239

5.892857

0.5

0.121212

0.254545

0.363636

0.521212

0

0.192469

239

9

83

26.555556

0.854922

0

1

0.333333

false

0

0.166667

0

0.833333

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

aef7e1a8924e13c026a3d928bc76782001e5e4da

424

py

Python

Losses/RootRelativeSquaredError.py

recep-yildirim/Machine-Learning-Algorithms

dc4f4e6939631468246efc7537b1569007fee792

[ "MIT" ]

3

2021-05-12T13:13:52.000Z

2022-01-19T19:54:16.000Z

Losses/RootRelativeSquaredError.py

recep-yildirim/Machine-Learning-Algorithms

dc4f4e6939631468246efc7537b1569007fee792

[ "MIT" ]

null

Losses/RootRelativeSquaredError.py

recep-yildirim/Machine-Learning-Algorithms

dc4f4e6939631468246efc7537b1569007fee792

[ "MIT" ]

null

import numpy as np from Losses import Loss class RootRelativeSquaredError(Loss): def call(self, true_labels, predicted_labels): error = np.square(predicted_labels - true_labels) denominator = np.square(true_labels - np.mean(true_labels)) return np.sqrt(np.sum(error / denominator)) def __call__(self, true_labels, predicted_labels): return self.call(true_labels, predicted_labels)

32.615385

67

0.728774

55

424

5.363636

0.4

0.20339

0.19322

0.254237

0.244068

0

0.183962

424

13

68

32.615385

0.852601

0

1

0.222222

false

0

0.222222

0.111111

0.777778

0

null

1

0

null

0

1

0

1

0

6

aefb4403ac84e316b6668c15539d878e97963560

83

py

Python

code/11/fah_converter.py

TeamLab/introduction_to_pythoy_TEAMLAB_MOOC

ebf1ff02d6a341bfee8695eac478ff8297cb97e4

[ "MIT" ]

65

2017-11-01T01:57:21.000Z

2022-02-08T13:36:25.000Z

code/11/fah_converter.py

TeamLab/introduction_to_pythoy_TEAMLAB_MOOC

ebf1ff02d6a341bfee8695eac478ff8297cb97e4

[ "MIT" ]

9

2017-11-03T15:05:30.000Z

2018-05-17T03:18:36.000Z

code/11/fah_converter.py

TeamLab/introduction_to_pythoy_TEAMLAB_MOOC

ebf1ff02d6a341bfee8695eac478ff8297cb97e4

[ "MIT" ]

64

2017-11-01T01:57:23.000Z

2022-01-19T03:52:12.000Z

def covert_c_to_f(celsius_value): return celsius_value * 9.0 / 5 + 32 B = 155

16.6

39

0.686747

16

83

3.25

0.875

0.461538

0

0.123077

0.216867

83

4

40

20.75

0.676923

0

1

0.333333

false

0

0.333333

0.666667

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

4e36986381695f93a2ab378be0e749104622d4de

22

py

Python

cpcbccr/__init__.py

sakethramanujam/cpcbccr-python-client

658fa6d8c4c222e2fa19d96b52276403440952ff

[ "MIT" ]

2

2020-08-10T12:22:15.000Z

2021-12-28T14:17:48.000Z

cpcbccr/__init__.py

sakethramanujam/cpcbccr-python-client

658fa6d8c4c222e2fa19d96b52276403440952ff

[ "MIT" ]

null

cpcbccr/__init__.py

sakethramanujam/cpcbccr-python-client

658fa6d8c4c222e2fa19d96b52276403440952ff

[ "MIT" ]

1

2020-12-20T10:51:21.000Z

from .cpcbccr import *

22

0.772727

3

22

5.666667

1

0

0.136364

22

1

22

0.894737

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

9dcf58dd526d343689a225af97b04f0cd4e575ed

79

py

Python

vit.py

Junhojuno/vision-transfomer-tf2

4110ea9153214c08162a642272a909e72446e3d3

[ "MIT" ]

null

vit.py

Junhojuno/vision-transfomer-tf2

4110ea9153214c08162a642272a909e72446e3d3

[ "MIT" ]

null

vit.py

Junhojuno/vision-transfomer-tf2

4110ea9153214c08162a642272a909e72446e3d3

[ "MIT" ]

null

import tensorflow as tf class PatchEmbedding(tf.keras.layers.Layer): pass

15.8

44

0.772152

11

79

5.545455

0.909091

0

0.151899

79

4

45

19.75

0.910448

0

1

0

true

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

6

d1798109909dc4f88ca93ef6c5848236e78181fd

67

py

Python

danlp/datasets/__init__.py

nikaoz/danlp

1dd11a7301d2f8005f2b3a8a4fe3ccd15cfac74b

[ "BSD-3-Clause" ]

null

danlp/datasets/__init__.py

nikaoz/danlp

1dd11a7301d2f8005f2b3a8a4fe3ccd15cfac74b

[ "BSD-3-Clause" ]

null

danlp/datasets/__init__.py

nikaoz/danlp

1dd11a7301d2f8005f2b3a8a4fe3ccd15cfac74b

[ "BSD-3-Clause" ]

null

from .ddt import * from .wiki_ann import * from .word_sim import *

16.75

23

0.731343

11

67

4.272727

0.636364

0.425532

0

0.179104

67

3

24

22.333333

0.854545

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

ae03cadc03f377978fa0978717763c52c09d5f89

38

py

Python

Bioinformatics-basics-yt/rosalind-challenges/python-prelim/pythagoras.py

peranovicc/python

b669060eb539f5f23684ffa1de13e6c927cc0b06

[ "MIT" ]

null

Bioinformatics-basics-yt/rosalind-challenges/python-prelim/pythagoras.py

peranovicc/python

b669060eb539f5f23684ffa1de13e6c927cc0b06

[ "MIT" ]

null

Bioinformatics-basics-yt/rosalind-challenges/python-prelim/pythagoras.py

peranovicc/python

b669060eb539f5f23684ffa1de13e6c927cc0b06

[ "MIT" ]

null

x = 874 y = 923 print(x ** 2 + y ** 2)

12.666667

22

0.447368

9

38

1.888889

0.666667

0

0.307692

0.315789

38

3

22

12.666667

0.346154

0

1

0

false

0

0.333333

1

null

0

1

0

1

0

1

0

null

0

6

88a91fa56e7abad6cb12d5e3bdf7a8f583f3131e

33

py

Python

wikireader/browser/__init__.py

mke21/wikireader

767422a736498d5147650cae52a89da7d0c43b65

[ "MIT" ]

null

wikireader/browser/__init__.py

mke21/wikireader

767422a736498d5147650cae52a89da7d0c43b65

[ "MIT" ]

7

2020-11-12T20:16:14.000Z

2020-11-18T20:33:16.000Z

wikireader/browser/__init__.py

mke21/wikireader

767422a736498d5147650cae52a89da7d0c43b65

[ "MIT" ]

null

from .browser import run_browser

16.5

32

0.848485

5

33

5.4

0.8

0

0.121212

33

1

33

0.931034

0

1

0

true

0

1

0

1

0

1

0

null

0

1

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null

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1

0

1

0

1

0

6

88ad019cec010fd4b3c7997d821e8c8b0d793576

41,416

py

Python

tests/test_expr.py

JoaoFelipe/PyPosAST

94dc8cdc2568dcdc833c0c2a61ff52a4011d27ec

[ "MIT" ]

1

2015-10-17T18:43:43.000Z

tests/test_expr.py

JoaoFelipe/PyPosAST

94dc8cdc2568dcdc833c0c2a61ff52a4011d27ec

[ "MIT" ]

1

2016-02-01T02:22:08.000Z

2016-02-01T07:53:18.000Z

tests/test_expr.py

JoaoFelipe/PyPosAST

94dc8cdc2568dcdc833c0c2a61ff52a4011d27ec

[ "MIT" ]

1

2017-12-17T16:51:49.000Z

# coding: utf-8 # Copyright (c) 2016 Universidade Federal Fluminense (UFF) # This file is part of PyPosAST. # Please, consult the license terms in the LICENSE file. from __future__ import (absolute_import, division) import ast from .utils import get_nodes, NodeTestCase from .utils import only_python2, only_python3, only_python35, only_python36, only_python38 def nprint(nodes): """Print nodes""" for i, node in enumerate(nodes): print(i, node.lineno, node.col_offset) class TestExpr(NodeTestCase): # pylint: disable=missing-docstring, too-many-public-methods def test_name(self): code = ("#bla\n" "abc") nodes = get_nodes(code, ast.Name) self.assertPosition(nodes[0], (2, 0), (2, 3), (2, 3)) self.assertNoBeforeInnerAfter(nodes[0]) def test_name2(self): code = ("#bla\n" "(z)") nodes = get_nodes(code, ast.Name) self.assertPosition(nodes[0], (2, 0), (2, 3), (2, 3)) self.assertSimpleInnerPosition(nodes[0], (2, 1), (2, 2)) def test_name3(self): code = ("#bla\n" "( z )") nodes = get_nodes(code, ast.Name) self.assertPosition(nodes[0], (2, 0), (2, 5), (2, 5)) self.assertSimpleInnerPosition(nodes[0], (2, 2), (2, 3)) def test_name4(self): code = ("#bla\n" "((z))") nodes = get_nodes(code, ast.Name) self.assertPosition(nodes[0], (2, 0), (2, 5), (2, 5)) self.assertSimpleInnerPosition(nodes[0], (2, 2), (2, 3)) def test_name5(self): code = ("#bla\n" "((z\n" "))") nodes = get_nodes(code, ast.Name) self.assertPosition(nodes[0], (2, 0), (3, 2), (3, 2)) self.assertSimpleInnerPosition(nodes[0], (2, 2), (3, 0)) def test_num(self): code = ("#bla\n" "12") nodes = get_nodes(code, ast.Num) self.assertPosition(nodes[0], (2, 0), (2, 2), (2, 2)) self.assertNoBeforeInnerAfter(nodes[0]) @only_python2 def test_num2(self): """ Python 3 Num uses the minus as unaryop, USub """ code = ("#bla\n" "- 1245") nodes = get_nodes(code, ast.Num) self.assertPosition(nodes[0], (2, 0), (2, 7), (2, 7)) self.assertNoBeforeInnerAfter(nodes[0]) @only_python2 def test_num3(self): """ Python 3 Num uses the minus as unaryop, USub """ code = ("#bla\n" "- 0") nodes = get_nodes(code, ast.Num) self.assertPosition(nodes[0], (2, 0), (2, 4), (2, 4)) self.assertNoBeforeInnerAfter(nodes[0]) def test_num4(self): code = ("#bla\n" "0x1245") nodes = get_nodes(code, ast.Num) self.assertPosition(nodes[0], (2, 0), (2, 6), (2, 6)) self.assertNoBeforeInnerAfter(nodes[0]) def test_num5(self): code = ("#bla\n" "(2)") nodes = get_nodes(code, ast.Num) self.assertPosition(nodes[0], (2, 0), (2, 3), (2, 3)) self.assertSimpleInnerPosition(nodes[0], (2, 1), (2, 2)) def test_num6(self): code = ("#bla\n" "f(2)") nodes = get_nodes(code, ast.Num) self.assertPosition(nodes[0], (2, 2), (2, 3), (2, 3)) self.assertNoBeforeInnerAfter(nodes[0]) def test_str(self): code = ("#bla\n" "'ab\\\n" " cd\\\n" " ef'") nodes = get_nodes(code, ast.Str) self.assertPosition(nodes[0], (2, 0), (4, 4), (4, 4)) self.assertNoBeforeInnerAfter(nodes[0]) def test_str2(self): code = ("#bla\n" "'abcd'") nodes = get_nodes(code, ast.Str) self.assertPosition(nodes[0], (2, 0), (2, 6), (2, 6)) self.assertNoBeforeInnerAfter(nodes[0]) def test_str3(self): code = ("#bla\n" "('ab'\\\n" " 'cd'\n" " 'ef')") nodes = get_nodes(code, ast.Str) self.assertPosition(nodes[0], (2, 0), (4, 6), (4, 6)) self.assertSimpleInnerPosition(nodes[0], (2, 1), (4, 5)) def test_str4(self): code = ("#bla\n" "'ab' 'cd' 'ef'") nodes = get_nodes(code, ast.Str) self.assertPosition(nodes[0], (2, 0), (2, 14), (2, 14)) self.assertNoBeforeInnerAfter(nodes[0]) def test_attribute(self): code = ("#bla\n" "a.b") nodes = get_nodes(code, ast.Attribute) self.assertPosition(nodes[0], (2, 0), (2, 3), (2, 2)) self.assertOperation(nodes[0].op_pos[0], (2, 1), (2, 2), (2, 2), '.') self.assertNoBeforeInnerAfter(nodes[0]) def test_attribute2(self): code = ("#bla\n" "a.\\\n" "b") nodes = get_nodes(code, ast.Attribute) self.assertPosition(nodes[0], (2, 0), (3, 1), (2, 2)) self.assertOperation(nodes[0].op_pos[0], (2, 1), (2, 2), (2, 2), '.') self.assertNoBeforeInnerAfter(nodes[0]) def test_attribute3(self): code = ("#bla\n" "a.b.c") nodes = get_nodes(code, ast.Attribute) self.assertPosition(nodes[0], (2, 0), (2, 5), (2, 4)) self.assertOperation(nodes[0].op_pos[0], (2, 3), (2, 4), (2, 4), '.') self.assertPosition(nodes[1], (2, 0), (2, 3), (2, 2)) self.assertOperation(nodes[1].op_pos[0], (2, 1), (2, 2), (2, 2), '.') self.assertNoBeforeInnerAfter(nodes[0]) self.assertNoBeforeInnerAfter(nodes[1]) def test_attribute4(self): code = ("#bla\n" "a.\\\n" "b.c\\\n" ".d") nodes = get_nodes(code, ast.Attribute) self.assertPosition(nodes[0], (2, 0), (4, 2), (4, 1)) self.assertOperation(nodes[0].op_pos[0], (4, 0), (4, 1), (4, 1), '.') self.assertPosition(nodes[1], (2, 0), (3, 3), (3, 2)) self.assertOperation(nodes[1].op_pos[0], (3, 1), (3, 2), (3, 2), '.') self.assertPosition(nodes[2], (2, 0), (3, 1), (2, 2)) self.assertOperation(nodes[2].op_pos[0], (2, 1), (2, 2), (2, 2), '.') self.assertNoBeforeInnerAfter(nodes[0]) self.assertNoBeforeInnerAfter(nodes[1]) self.assertNoBeforeInnerAfter(nodes[2]) def test_attribute5(self): code = ("#bla\n" "(a.\\\n" " b)") nodes = get_nodes(code, ast.Attribute) self.assertPosition(nodes[0], (2, 0), (3, 5), (2, 3)) self.assertOperation(nodes[0].op_pos[0], (2, 2), (2, 3), (2, 3), '.') self.assertSimpleInnerPosition(nodes[0], (2, 1), (3, 4)) def test_ellipsis(self): code = ("#bla\n" "a[...]") nodes = get_nodes(code, ast.Ellipsis) self.assertPosition(nodes[0], (2, 2), (2, 5), (2, 5)) self.assertNoBeforeInnerAfter(nodes[0]) @only_python2 def test_ellipsis2(self): """ Invalid Python 3 syntax """ code = ("#bla\n" "a[.\\\n" "..]") nodes = get_nodes(code, ast.Ellipsis) self.assertPosition(nodes[0], (2, 2), (3, 2), (3, 2)) self.assertNoBeforeInnerAfter(nodes[0]) @only_python3 def test_ellipsis3(self): """ Invalid Python 2 syntax """ code = ("#bla\n" "a[(...)]") nodes = get_nodes(code, ast.Ellipsis) self.assertPosition(nodes[0], (2, 2), (2, 7), (2, 7)) self.assertSimpleInnerPosition(nodes[0], (2, 3), (2, 6)) def test_subscript(self): code = ("#bla\n" "a\\\n" "[1]") nodes = get_nodes(code, ast.Subscript) self.assertPosition(nodes[0], (2, 0), (3, 3), (3, 3)) self.assertOperation(nodes[0].op_pos[0], (3, 0), (3, 1), (3, 1), '[') self.assertOperation(nodes[0].op_pos[1], (3, 2), (3, 3), (3, 3), ']') self.assertNoBeforeInnerAfter(nodes[0]) def test_subscript2(self): code = ("#bla\n" "a[\n" "1]") nodes = get_nodes(code, ast.Subscript) self.assertPosition(nodes[0], (2, 0), (3, 2), (3, 2)) self.assertOperation(nodes[0].op_pos[0], (2, 1), (2, 2), (2, 2), '[') self.assertOperation(nodes[0].op_pos[1], (3, 1), (3, 2), (3, 2), ']') self.assertNoBeforeInnerAfter(nodes[0]) def test_subscript3(self): code = ("#bla\n" "a[1:\n" "2,\n" "3 ]") nodes = get_nodes(code, ast.Subscript) self.assertPosition(nodes[0], (2, 0), (4, 3), (4, 3)) self.assertOperation(nodes[0].op_pos[0], (2, 1), (2, 2), (2, 2), '[') self.assertOperation(nodes[0].op_pos[1], (4, 2), (4, 3), (4, 3), ']') self.assertNoBeforeInnerAfter(nodes[0]) def test_subscript4(self): code = ("#bla\n" "(a\n" "[1])") nodes = get_nodes(code, ast.Subscript) self.assertPosition(nodes[0], (2, 0), (3, 4), (3, 4)) self.assertOperation(nodes[0].op_pos[0], (3, 0), (3, 1), (3, 1), '[') self.assertOperation(nodes[0].op_pos[1], (3, 2), (3, 3), (3, 3), ']') self.assertSimpleInnerPosition(nodes[0], (2, 1), (3, 3)) def test_subscript5(self): code = (u"#bla\n" u"f('Ç', ns['u'])\n") nodes = get_nodes(code, ast.Subscript) self.assertPosition(nodes[0], (2, 7), (2, 14), (2, 14)) self.assertOperation(nodes[0].op_pos[0], (2, 9), (2, 10), (2, 10), '[') self.assertOperation(nodes[0].op_pos[1], (2, 13), (2, 14), (2, 14), ']') self.assertNoBeforeInnerAfter(nodes[0]) def test_tuple(self): code = ("#bla\n" "(\n" "1, 2,\n" "3\n" ")") nodes = get_nodes(code, ast.Tuple) self.assertPosition(nodes[0], (2, 0), (5, 1), (3, 1)) self.assertOperation(nodes[0].op_pos[0], (3, 1), (3, 2), (3, 2), ',') self.assertOperation(nodes[0].op_pos[1], (3, 4), (3, 5), (3, 5), ',') self.assertSimpleInnerPosition(nodes[0], (3, 0), (5, 0)) def test_tuple2(self): code = ("#bla\n" "(\n" ")") nodes = get_nodes(code, ast.Tuple) self.assertPosition(nodes[0], (2, 0), (3, 1), (3, 1)) self.assertNoBeforeInnerAfter(nodes[0]) self.assertEqual(nodes[0].op_pos, []) def test_tuple3(self): code = ("#bla\n" "(((0),\n" "1, 2,\n" "3\n" "))") nodes = get_nodes(code, ast.Tuple) self.assertPosition(nodes[0], (2, 0), (5, 2), (2, 5)) self.assertOperation(nodes[0].op_pos[0], (2, 5), (2, 6), (2, 6), ',') self.assertOperation(nodes[0].op_pos[1], (3, 1), (3, 2), (3, 2), ',') self.assertOperation(nodes[0].op_pos[2], (3, 4), (3, 5), (3, 5), ',') self.assertSimpleInnerPosition(nodes[0], (2, 2), (5, 0)) def test_tuple4(self): code = ("#bla\n" "1,") nodes = get_nodes(code, ast.Tuple) self.assertPosition(nodes[0], (2, 0), (2, 2), (2, 1)) self.assertOperation(nodes[0].op_pos[0], (2, 1), (2, 2), (2, 2), ',') self.assertNoBeforeInnerAfter(nodes[0]) def test_tuple5(self): code = ("#bla\n" "([1, 2], 3)") nodes = get_nodes(code, ast.Tuple) self.assertPosition(nodes[0], (2, 0), (2, 11), (2, 7)) self.assertOperation(nodes[0].op_pos[0], (2, 7), (2, 8), (2, 8), ',') self.assertSimpleInnerPosition(nodes[0], (2, 1), (2, 10)) def test_tuple6(self): code = ("#bla\n" "1, 2 ,") nodes = get_nodes(code, ast.Tuple) self.assertPosition(nodes[0], (2, 0), (2, 7), (2, 1)) self.assertOperation(nodes[0].op_pos[0], (2, 1), (2, 2), (2, 2), ',') self.assertOperation(nodes[0].op_pos[1], (2, 6), (2, 7), (2, 7), ',') self.assertNoBeforeInnerAfter(nodes[0]) def test_list(self): code = ("#bla\n" "[\n" "1, 2,\n" "3\n" "]") nodes = get_nodes(code, ast.List) self.assertPosition(nodes[0], (2, 0), (5, 1), (5, 1)) self.assertOperation(nodes[0].op_pos[0], (3, 1), (3, 2), (3, 2), ',') self.assertOperation(nodes[0].op_pos[1], (3, 4), (3, 5), (3, 5), ',') self.assertNoBeforeInnerAfter(nodes[0]) def test_list2(self): code = ("#bla\n" "[\n" "]") nodes = get_nodes(code, ast.List) self.assertPosition(nodes[0], (2, 0), (3, 1), (3, 1)) self.assertEqual(nodes[0].op_pos, []) self.assertNoBeforeInnerAfter(nodes[0]) def test_list3(self): code = ("#bla\n" "([(0),\n" "1, 2,\n" "3\n" "])") nodes = get_nodes(code, ast.List) self.assertPosition(nodes[0], (2, 0), (5, 2), (5, 2)) self.assertOperation(nodes[0].op_pos[0], (2, 5), (2, 6), (2, 6), ',') self.assertOperation(nodes[0].op_pos[1], (3, 1), (3, 2), (3, 2), ',') self.assertOperation(nodes[0].op_pos[2], (3, 4), (3, 5), (3, 5), ',') self.assertSimpleInnerPosition(nodes[0], (2, 1), (5, 1)) @only_python2 def test_repr(self): code = ("#bla\n" "`1`") nodes = get_nodes(code, ast.Repr) self.assertPosition(nodes[0], (2, 0), (2, 3), (2, 3)) self.assertOperation(nodes[0].op_pos[0], (2, 0), (2, 1), (2, 1), '`') self.assertOperation(nodes[0].op_pos[1], (2, 2), (2, 3), (2, 3), '`') self.assertNoBeforeInnerAfter(nodes[0]) @only_python2 def test_repr2(self): code = ("#bla\n" "``1``") nodes = get_nodes(code, ast.Repr) self.assertPosition(nodes[0], (2, 0), (2, 5), (2, 5)) self.assertOperation(nodes[0].op_pos[0], (2, 0), (2, 1), (2, 1), '`') self.assertOperation(nodes[0].op_pos[1], (2, 4), (2, 5), (2, 5), '`') self.assertNoBeforeInnerAfter(nodes[0]) @only_python2 def test_repr3(self): code = ("#bla\n" "``2\\\n" "``") nodes = get_nodes(code, ast.Repr) self.assertPosition(nodes[0], (2, 0), (3, 2), (3, 2)) self.assertOperation(nodes[0].op_pos[0], (2, 0), (2, 1), (2, 1), '`') self.assertOperation(nodes[0].op_pos[1], (3, 1), (3, 2), (3, 2), '`') self.assertNoBeforeInnerAfter(nodes[0]) @only_python2 def test_repr4(self): code = ("#bla\n" "(``2\n" "``)") nodes = get_nodes(code, ast.Repr) self.assertPosition(nodes[0], (2, 0), (3, 3), (3, 3)) self.assertOperation(nodes[0].op_pos[0], (2, 1), (2, 2), (2, 2), '`') self.assertOperation(nodes[0].op_pos[1], (3, 1), (3, 2), (3, 2), '`') self.assertSimpleInnerPosition(nodes[0], (2, 1), (3, 2)) def test_call(self): code = ("#bla\n" "fn(\n" "2)") nodes = get_nodes(code, ast.Call) self.assertPosition(nodes[0], (2, 0), (3, 2), (3, 2)) self.assertOperation(nodes[0].op_pos[0], (2, 2), (2, 3), (2, 3), '(') self.assertOperation(nodes[0].op_pos[1], (3, 1), (3, 2), (3, 2), ')') self.assertNoBeforeInnerAfter(nodes[0]) def test_call2(self): code = ("#bla\n" "fn(\n" "2,)") nodes = get_nodes(code, ast.Call) self.assertPosition(nodes[0], (2, 0), (3, 3), (3, 3)) self.assertOperation(nodes[0].op_pos[0], (2, 2), (2, 3), (2, 3), '(') self.assertOperation(nodes[0].op_pos[1], (3, 1), (3, 2), (3, 2), ',') self.assertOperation(nodes[0].op_pos[2], (3, 2), (3, 3), (3, 3), ')') self.assertNoBeforeInnerAfter(nodes[0]) def test_call3(self): code = ("#bla\n" "fn\\\n" "((\n" "2, 3))") nodes = get_nodes(code, ast.Call) self.assertPosition(nodes[0], (2, 0), (4, 6), (4, 6)) self.assertOperation(nodes[0].op_pos[0], (3, 0), (3, 1), (3, 1), '(') self.assertOperation(nodes[0].op_pos[1], (4, 5), (4, 6), (4, 6), ')') self.assertNoBeforeInnerAfter(nodes[0]) def test_call4(self): code = ("#bla\n" "fn()\\\n" "((\n" "2, 3))") nodes = get_nodes(code, ast.Call) self.assertPosition(nodes[0], (2, 0), (4, 6), (4, 6)) self.assertPosition(nodes[1], (2, 0), (2, 4), (2, 4)) self.assertOperation(nodes[0].op_pos[0], (3, 0), (3, 1), (3, 1), '(') self.assertOperation(nodes[0].op_pos[1], (4, 5), (4, 6), (4, 6), ')') self.assertNoBeforeInnerAfter(nodes[0]) self.assertOperation(nodes[1].op_pos[0], (2, 2), (2, 3), (2, 3), '(') self.assertOperation(nodes[1].op_pos[1], (2, 3), (2, 4), (2, 4), ')') self.assertNoBeforeInnerAfter(nodes[1]) def test_call5(self): code = ("#bla\n" "(fn(\n" "2))") nodes = get_nodes(code, ast.Call) self.assertPosition(nodes[0], (2, 0), (3, 3), (3, 3)) self.assertOperation(nodes[0].op_pos[0], (2, 3), (2, 4), (2, 4), '(') self.assertOperation(nodes[0].op_pos[1], (3, 1), (3, 2), (3, 2), ')') self.assertSimpleInnerPosition(nodes[0], (2, 1), (3, 2)) def test_compare(self): code = ("#bla\n" "2 < 3") nodes = get_nodes(code, ast.Compare) self.assertPosition(nodes[0], (2, 0), (2, 5), (2, 5)) self.assertOperation(nodes[0].op_pos[0], (2, 2), (2, 3), (2, 3), '<') self.assertNoBeforeInnerAfter(nodes[0]) def test_compare2(self): code = ("#bla\n" "2 < 3 <\\\n" " 5") nodes = get_nodes(code, ast.Compare) self.assertPosition(nodes[0], (2, 0), (3, 2), (3, 2)) self.assertOperation(nodes[0].op_pos[0], (2, 2), (2, 3), (2, 3), '<') self.assertOperation(nodes[0].op_pos[1], (2, 6), (2, 7), (2, 7), '<') self.assertNoBeforeInnerAfter(nodes[0]) def test_compare3(self): code = ("#bla\n" "(2 < 3 <\n" " 5)") nodes = get_nodes(code, ast.Compare) self.assertPosition(nodes[0], (2, 0), (3, 3), (3, 3)) self.assertOperation(nodes[0].op_pos[0], (2, 3), (2, 4), (2, 4), '<') self.assertOperation(nodes[0].op_pos[1], (2, 7), (2, 8), (2, 8), '<') self.assertSimpleInnerPosition(nodes[0], (2, 1), (3, 2)) @only_python35 def test_await(self): code = ("async def f():\n" " await 2") nodes = get_nodes(code, ast.Await) self.assertPosition(nodes[0], (2, 4), (2, 13), (2, 9)) self.assertOperation(nodes[0].op_pos[0], (2, 4), (2, 9), (2, 9), 'await') self.assertNoBeforeInnerAfter(nodes[0]) @only_python35 def test_await2(self): code = ("async def f():\n" " (await \n" "2)") nodes = get_nodes(code, ast.Await) self.assertPosition(nodes[0], (2, 4), (3, 2), (2, 10)) self.assertOperation(nodes[0].op_pos[0], (2, 5), (2, 10), (2, 10), 'await') self.assertSimpleInnerPosition(nodes[0], (2, 5), (3, 1)) def test_yield(self): code = ("#bla\n" "yield 2") nodes = get_nodes(code, ast.Yield) self.assertPosition(nodes[0], (2, 0), (2, 9), (2, 5)) self.assertOperation(nodes[0].op_pos[0], (2, 0), (2, 5), (2, 5), 'yield') self.assertNoBeforeInnerAfter(nodes[0]) def test_yield2(self): code = ("#bla\n" "(yield \n" "2)") nodes = get_nodes(code, ast.Yield) self.assertPosition(nodes[0], (2, 0), (3, 2), (2, 6)) self.assertOperation(nodes[0].op_pos[0], (2, 1), (2, 6), (2, 6), 'yield') self.assertSimpleInnerPosition(nodes[0], (2, 1), (3, 1)) def test_yield3(self): code = ("#bla\n" "yield") nodes = get_nodes(code, ast.Yield) self.assertPosition(nodes[0], (2, 0), (2, 5), (2, 5)) self.assertOperation(nodes[0].op_pos[0], (2, 0), (2, 5), (2, 5), 'yield') self.assertNoBeforeInnerAfter(nodes[0]) def test_yield4(self): code = ("#bla\n" "(yield \n" "2)\n" "(yield \n" "2)") nodes = get_nodes(code, ast.Yield) self.assertPosition(nodes[0], (2, 0), (3, 2), (2, 6)) self.assertOperation(nodes[0].op_pos[0], (2, 1), (2, 6), (2, 6), 'yield') self.assertSimpleInnerPosition(nodes[0], (2, 1), (3, 1)) self.assertPosition(nodes[1], (4, 0), (5, 2), (4, 6)) self.assertOperation(nodes[1].op_pos[0], (4, 1), (4, 6), (4, 6), 'yield') self.assertSimpleInnerPosition(nodes[1], (4, 1), (5, 1)) def test_generator_exp(self): code = ("#bla\n" "f(x\n" " for x in l\n" " if x)") nodes = get_nodes(code, ast.GeneratorExp) self.assertPosition(nodes[0], (2, 2), (4, 5), (2, 3)) self.assertNoBeforeInnerAfter(nodes[0]) def test_generator_exp2(self): code = ("#bla\n" "f(x\n" " for x in l\n" " if x)\n" "g(y\n" " for y in m\n" " if y)") nodes = get_nodes(code, ast.GeneratorExp) self.assertPosition(nodes[0], (2, 2), (4, 5), (2, 3)) self.assertNoBeforeInnerAfter(nodes[0]) self.assertPosition(nodes[1], (5, 2), (7, 5), (5, 3)) self.assertNoBeforeInnerAfter(nodes[1]) def test_dict_comp(self): code = ("#bla\n" "{x:2\n" " for x in l\n" " if x}") nodes = get_nodes(code, ast.DictComp) self.assertPosition(nodes[0], (2, 0), (4, 6), (4, 6)) self.assertNoBeforeInnerAfter(nodes[0]) def test_dict_comp2(self): code = ("#bla\n" "({x:2\n" " for x in l\n" " if x})") nodes = get_nodes(code, ast.DictComp) self.assertPosition(nodes[0], (2, 0), (4, 7), (4, 7)) self.assertSimpleInnerPosition(nodes[0], (2, 1), (4, 6)) def test_set_comp(self): code = ("#bla\n" "{x\n" " for x in l\n" " if x}") nodes = get_nodes(code, ast.SetComp) self.assertPosition(nodes[0], (2, 0), (4, 6), (4, 6)) self.assertNoBeforeInnerAfter(nodes[0]) def test_set_comp2(self): code = ("#bla\n" "({x\n" " for x in l\n" " if x})") nodes = get_nodes(code, ast.SetComp) self.assertPosition(nodes[0], (2, 0), (4, 7), (4, 7)) self.assertSimpleInnerPosition(nodes[0], (2, 1), (4, 6)) def test_list_comp(self): code = ("#bla\n" "[x\n" " for x in l\n" " if x]") nodes = get_nodes(code, ast.ListComp) self.assertPosition(nodes[0], (2, 0), (4, 6), (4, 6)) self.assertNoBeforeInnerAfter(nodes[0]) def test_list_comp2(self): code = ("#bla\n" "([x\n" " for x in l\n" " if x])") nodes = get_nodes(code, ast.ListComp) self.assertPosition(nodes[0], (2, 0), (4, 7), (4, 7)) self.assertSimpleInnerPosition(nodes[0], (2, 1), (4, 6)) @only_python36 def test_async_comp(self): code = ("#bla\n" "async def f():\n" " [x\n" " async for x in l\n" " if x]") nodes = get_nodes(code, ast.ListComp) self.assertPosition(nodes[0], (3, 4), (5, 10), (5, 10)) self.assertNoBeforeInnerAfter(nodes[0]) def test_set(self): code = ("#bla\n" "{x,\n" " 1,\n" " 3}") nodes = get_nodes(code, ast.Set) self.assertPosition(nodes[0], (2, 0), (4, 3), (4, 3)) self.assertOperation(nodes[0].op_pos[0], (2, 2), (2, 3), (2, 3), ',') self.assertOperation(nodes[0].op_pos[1], (3, 2), (3, 3), (3, 3), ',') self.assertNoBeforeInnerAfter(nodes[0]) def test_set2(self): code = ("#bla\n" "({x,\n" " 1,\n" " 3})") nodes = get_nodes(code, ast.Set) self.assertPosition(nodes[0], (2, 0), (4, 4), (4, 4)) self.assertOperation(nodes[0].op_pos[0], (2, 3), (2, 4), (2, 4), ',') self.assertOperation(nodes[0].op_pos[1], (3, 2), (3, 3), (3, 3), ',') self.assertSimpleInnerPosition(nodes[0], (2, 1), (4, 3)) def test_dict(self): code = ("#bla\n" "{}") nodes = get_nodes(code, ast.Dict) self.assertPosition(nodes[0], (2, 0), (2, 2), (2, 2)) self.assertEqual(nodes[0].op_pos, []) self.assertNoBeforeInnerAfter(nodes[0]) def test_dict2(self): code = ("#bla\n" "{1}, {1: x,\n" " 2: 1,\n" " 3: 3}") nodes = get_nodes(code, ast.Dict) self.assertPosition(nodes[0], (2, 5), (4, 7), (4, 7)) self.assertOperation(nodes[0].op_pos[0], (2, 7), (2, 8), (2, 8), ':') self.assertOperation(nodes[0].op_pos[1], (2, 10), (2, 11), (2, 11), ',') self.assertOperation(nodes[0].op_pos[2], (3, 3), (3, 4), (3, 4), ':') self.assertOperation(nodes[0].op_pos[3], (3, 6), (3, 7), (3, 7), ',') self.assertOperation(nodes[0].op_pos[4], (4, 3), (4, 4), (4, 4), ':') self.assertNoBeforeInnerAfter(nodes[0]) def test_dict3(self): code = ("#bla\n" "{1}, ({1: x,\n" " 2: 1,\n" " 3: 3})") nodes = get_nodes(code, ast.Dict) self.assertPosition(nodes[0], (2, 5), (4, 8), (4, 8)) self.assertOperation(nodes[0].op_pos[0], (2, 8), (2, 9), (2, 9), ':') self.assertOperation(nodes[0].op_pos[1], (2, 11), (2, 12), (2, 12), ',') self.assertOperation(nodes[0].op_pos[2], (3, 3), (3, 4), (3, 4), ':') self.assertOperation(nodes[0].op_pos[3], (3, 6), (3, 7), (3, 7), ',') self.assertOperation(nodes[0].op_pos[4], (4, 3), (4, 4), (4, 4), ':') self.assertSimpleInnerPosition(nodes[0], (2, 6), (4, 7)) def test_if_exp(self): code = ("#bla\n" "1 if 2\\\n" " else 3") nodes = get_nodes(code, ast.IfExp) self.assertPosition(nodes[0], (2, 0), (3, 8), (2, 4)) self.assertOperation(nodes[0].op_pos[0], (2, 2), (2, 4), (2, 4), 'if') self.assertOperation(nodes[0].op_pos[1], (3, 2), (3, 6), (3, 6), 'else') self.assertNoBeforeInnerAfter(nodes[0]) def test_if_exp2(self): code = ("#bla\n" "(1 if 2\n" " else 3)") nodes = get_nodes(code, ast.IfExp) self.assertPosition(nodes[0], (2, 0), (3, 9), (2, 5)) self.assertOperation(nodes[0].op_pos[0], (2, 3), (2, 5), (2, 5), 'if') self.assertOperation(nodes[0].op_pos[1], (3, 2), (3, 6), (3, 6), 'else') self.assertSimpleInnerPosition(nodes[0], (2, 1), (3, 8)) def test_if_exp3(self): code = ("#bla\n" "((1)if(2)else(3))\n" "((4)if(5)else(6))") nodes = get_nodes(code, ast.IfExp) self.assertPosition(nodes[0], (2, 0), (2, 17), (2, 6)) self.assertOperation(nodes[0].op_pos[0], (2, 4), (2, 6), (2, 6), 'if') self.assertOperation(nodes[0].op_pos[1], (2, 9), (2, 13), (2, 13), 'else') self.assertSimpleInnerPosition(nodes[0], (2, 1), (2, 16)) def test_lambda(self): code = ("#bla\n" "lambda x, y:\\\n" "x") nodes = get_nodes(code, ast.Lambda) self.assertPosition(nodes[0], (2, 0), (3, 1), (2, 12)) self.assertOperation(nodes[0].op_pos[0], (2, 0), (2, 6), (2, 6), 'lambda') self.assertOperation(nodes[0].op_pos[1], (2, 11), (2, 12), (2, 12), ':') self.assertNoBeforeInnerAfter(nodes[0]) def test_lambda2(self): code = ("#bla\n" "(lambda x, y:\n" "x)") nodes = get_nodes(code, ast.Lambda) self.assertPosition(nodes[0], (2, 0), (3, 2), (2, 13)) self.assertOperation(nodes[0].op_pos[0], (2, 1), (2, 7), (2, 7), 'lambda') self.assertOperation(nodes[0].op_pos[1], (2, 12), (2, 13), (2, 13), ':') self.assertSimpleInnerPosition(nodes[0], (2, 1), (3, 1)) def test_unary_op(self): code = ("#bla\n" "- a") nodes = get_nodes(code, ast.UnaryOp) self.assertPosition(nodes[0], (2, 0), (2, 3), (2, 1)) self.assertOperation(nodes[0].op_pos[0], (2, 0), (2, 1), (2, 1), '-') self.assertNoBeforeInnerAfter(nodes[0]) def test_unary_op2(self): code = ("#bla\n" "(-\n" "a)") nodes = get_nodes(code, ast.UnaryOp) self.assertPosition(nodes[0], (2, 0), (3, 2), (2, 2)) self.assertOperation(nodes[0].op_pos[0], (2, 1), (2, 2), (2, 2), '-') self.assertSimpleInnerPosition(nodes[0], (2, 1), (3, 1)) def test_binop(self): code = ("#bla\n" "ab+a") nodes = get_nodes(code, ast.BinOp) self.assertPosition(nodes[0], (2, 0), (2, 4), (2, 3)) self.assertOperation(nodes[0].op_pos[0], (2, 2), (2, 3), (2, 3), '+') self.assertNoBeforeInnerAfter(nodes[0]) def test_binop2(self): code = ("#bla\n" "a * b + c") nodes = get_nodes(code, ast.BinOp) self.assertPosition(nodes[0], (2, 0), (2, 9), (2, 7)) self.assertOperation(nodes[0].op_pos[0], (2, 6), (2, 7), (2, 7), '+') self.assertNoBeforeInnerAfter(nodes[0]) self.assertPosition(nodes[1], (2, 0), (2, 5), (2, 3)) self.assertOperation(nodes[1].op_pos[0], (2, 2), (2, 3), (2, 3), '*') self.assertNoBeforeInnerAfter(nodes[1]) def test_binop3(self): code = ("#bla\n" "(b + a)") nodes = get_nodes(code, ast.BinOp) self.assertPosition(nodes[0], (2, 0), (2, 7), (2, 4)) self.assertOperation(nodes[0].op_pos[0], (2, 3), (2, 4), (2, 4), '+') self.assertSimpleInnerPosition(nodes[0], (2, 1), (2, 6)) def test_bool_op(self): code = ("#bla\n" "a and b and c") nodes = get_nodes(code, ast.BoolOp) self.assertPosition(nodes[0], (2, 0), (2, 13), (2, 13)) self.assertOperation(nodes[0].op_pos[0], (2, 2), (2, 5), (2, 5), 'and') self.assertOperation(nodes[0].op_pos[1], (2, 8), (2, 11), (2, 11), 'and') self.assertNoBeforeInnerAfter(nodes[0]) def test_bool_op2(self): code = ("#bla\n" "a and b or c") nodes = get_nodes(code, ast.BoolOp) self.assertPosition(nodes[0], (2, 0), (2, 12), (2, 12)) self.assertOperation(nodes[0].op_pos[0], (2, 8), (2, 10), (2, 10), 'or') self.assertNoBeforeInnerAfter(nodes[0]) self.assertPosition(nodes[1], (2, 0), (2, 7), (2, 7)) self.assertOperation(nodes[1].op_pos[0], (2, 2), (2, 5), (2, 5), 'and') self.assertNoBeforeInnerAfter(nodes[1]) def test_bool_op3(self): code = ("#bla\n" "(a and b and c)") nodes = get_nodes(code, ast.BoolOp) self.assertPosition(nodes[0], (2, 0), (2, 15), (2, 15)) self.assertOperation(nodes[0].op_pos[0], (2, 3), (2, 6), (2, 6), 'and') self.assertOperation(nodes[0].op_pos[1], (2, 9), (2, 12), (2, 12), 'and') self.assertSimpleInnerPosition(nodes[0], (2, 1), (2, 14)) def test_bool_op4(self): code = ("#bla\n" "((a)and(b)and(c))\n" "((d)and(e)and(f))") nodes = get_nodes(code, ast.BoolOp) self.assertPosition(nodes[0], (2, 0), (2, 17), (2, 17)) self.assertOperation(nodes[0].op_pos[0], (2, 4), (2, 7), (2, 7), 'and') self.assertOperation(nodes[0].op_pos[1], (2, 10), (2, 13), (2, 13), 'and') self.assertSimpleInnerPosition(nodes[0], (2, 1), (2, 16)) self.assertPosition(nodes[1], (3, 0), (3, 17), (3, 17)) self.assertOperation(nodes[1].op_pos[0], (3, 4), (3, 7), (3, 7), 'and') self.assertOperation(nodes[1].op_pos[1], (3, 10), (3, 13), (3, 13), 'and') self.assertSimpleInnerPosition(nodes[1], (3, 1), (3, 16)) @only_python3 def test_starred(self): code = ("#bla\n" "a, * b = 1, 2, 3") nodes = get_nodes(code, ast.Starred) self.assertPosition(nodes[0], (2, 3), (2, 6), (2, 4)) self.assertOperation(nodes[0].op_pos[0], (2, 3), (2, 4), (2, 4), '*') self.assertNoBeforeInnerAfter(nodes[0]) @only_python3 def test_starred2(self): code = ("#bla\n" "a, (* b) = 1, 2, 3") nodes = get_nodes(code, ast.Starred) self.assertPosition(nodes[0], (2, 3), (2, 8), (2, 5)) self.assertOperation(nodes[0].op_pos[0], (2, 4), (2, 5), (2, 5), '*') self.assertSimpleInnerPosition(nodes[0], (2, 4), (2, 7)) @only_python3 def test_starred3(self): code = ("#bla\n" "a, *b = 1, 2, 3") nodes = get_nodes(code, ast.Starred) self.assertPosition(nodes[0], (2, 3), (2, 5), (2, 4)) self.assertOperation(nodes[0].op_pos[0], (2, 3), (2, 4), (2, 4), '*') self.assertNoBeforeInnerAfter(nodes[0]) @only_python35 def test_starred4(self): code = ("#bla\n" "f(*a, 5, *b)") nodes = get_nodes(code, ast.Starred) self.assertPosition(nodes[0], (2, 2), (2, 4), (2, 3)) self.assertOperation(nodes[0].op_pos[0], (2, 2), (2, 3), (2, 3), '*') self.assertNoBeforeInnerAfter(nodes[0]) self.assertPosition(nodes[1], (2, 9), (2, 11), (2, 10)) self.assertOperation(nodes[1].op_pos[0], (2, 9), (2, 10), (2, 10), '*') self.assertNoBeforeInnerAfter(nodes[1]) @only_python35 def test_starred5(self): code = ("#bla\n" "i, j, k, l, m = *a, 5, *b") nodes = get_nodes(code, ast.Starred) self.assertPosition(nodes[0], (2, 16), (2, 18), (2, 17)) self.assertOperation(nodes[0].op_pos[0], (2, 16), (2, 17), (2, 17), '*') self.assertNoBeforeInnerAfter(nodes[0]) self.assertPosition(nodes[1], (2, 23), (2, 25), (2, 24)) self.assertOperation(nodes[1].op_pos[0], (2, 23), (2, 24), (2, 24), '*') self.assertNoBeforeInnerAfter(nodes[1]) @only_python3 def test_name_constant(self): code = ("#bla\n" "None") nodes = get_nodes(code, ast.NameConstant) self.assertPosition(nodes[0], (2, 0), (2, 4), (2, 4)) self.assertNoBeforeInnerAfter(nodes[0]) @only_python3 def test_name_constant2(self): code = ("#bla\n" "True") nodes = get_nodes(code, ast.NameConstant) self.assertPosition(nodes[0], (2, 0), (2, 4), (2, 4)) self.assertNoBeforeInnerAfter(nodes[0]) @only_python3 def test_name_constant3(self): code = ("#bla\n" "False") nodes = get_nodes(code, ast.NameConstant) self.assertPosition(nodes[0], (2, 0), (2, 5), (2, 5)) self.assertNoBeforeInnerAfter(nodes[0]) @only_python3 def test_name_constant4(self): code = ("#bla\n" "(None)") nodes = get_nodes(code, ast.NameConstant) self.assertPosition(nodes[0], (2, 0), (2, 6), (2, 6)) self.assertSimpleInnerPosition(nodes[0], (2, 1), (2, 5)) @only_python3 def test_bytes(self): code = ("#bla\n" "b'ab\\\n" " cd\\\n" " ef'") nodes = get_nodes(code, ast.Bytes) self.assertPosition(nodes[0], (2, 0), (4, 4), (4, 4)) self.assertNoBeforeInnerAfter(nodes[0]) @only_python3 def test_bytes2(self): code = ("#bla\n" "b'abcd'") nodes = get_nodes(code, ast.Bytes) self.assertPosition(nodes[0], (2, 0), (2, 7), (2, 7)) self.assertNoBeforeInnerAfter(nodes[0]) @only_python3 def test_bytes3(self): code = ("#bla\n" "(b'ab'\\\n" " b'cd'\n" " b'ef')") nodes = get_nodes(code, ast.Bytes) self.assertPosition(nodes[0], (2, 0), (4, 7), (4, 7)) self.assertSimpleInnerPosition(nodes[0], (2, 1), (4, 6)) @only_python3 def test_bytes4(self): code = ("#bla\n" "b'ab' b'cd' b'ef'") nodes = get_nodes(code, ast.Bytes) self.assertPosition(nodes[0], (2, 0), (2, 17), (2, 17)) self.assertNoBeforeInnerAfter(nodes[0]) @only_python3 def test_yield_from(self): code = ("#bla\n" "yield from 2") nodes = get_nodes(code, ast.YieldFrom) self.assertPosition(nodes[0], (2, 0), (2, 13), (2, 10)) self.assertOperation(nodes[0].op_pos[0], (2, 0), (2, 10), (2, 10), 'yield from') self.assertNoBeforeInnerAfter(nodes[0]) @only_python3 def test_yield_from2(self): code = ("#bla\n" "yield \\\n" " from 2") nodes = get_nodes(code, ast.YieldFrom) self.assertPosition(nodes[0], (2, 0), (3, 8), (3, 5)) self.assertOperation(nodes[0].op_pos[0], (2, 0), (3, 5), (3, 5), 'yield from') self.assertNoBeforeInnerAfter(nodes[0]) @only_python3 def test_yield_from3(self): code = ("#bla\n" "(yield \n" "from 2)") nodes = get_nodes(code, ast.YieldFrom) self.assertPosition(nodes[0], (2, 0), (3, 8), (3, 4)) self.assertOperation(nodes[0].op_pos[0], (2, 1), (3, 4), (3, 4), 'yield from') self.assertSimpleInnerPosition(nodes[0], (2, 1), (3, 7)) @only_python3 def test_yield_from4(self): code = ("#bla\n" "(yield from(a))\n" "(yield from(b))") nodes = get_nodes(code, ast.YieldFrom) self.assertPosition(nodes[0], (2, 0), (2, 15), (2, 11)) self.assertOperation(nodes[0].op_pos[0], (2, 1), (2, 11), (2, 11), 'yield from') self.assertSimpleInnerPosition(nodes[0], (2, 1), (2, 14)) self.assertPosition(nodes[1], (3, 0), (3, 15), (3, 11)) self.assertOperation(nodes[1].op_pos[0], (3, 1), (3, 11), (3, 11), 'yield from') self.assertSimpleInnerPosition(nodes[1], (3, 1), (3, 14)) @only_python36 def test_joined_str(self): code = ("#bla\n" "a = 2\n" "f'{a}'\n") nodes = get_nodes(code, ast.JoinedStr) self.assertPosition(nodes[0], (3, 0), (3, 6), (3, 6)) self.assertNoBeforeInnerAfter(nodes[0]) @only_python36 def test_formatted_value(self): code = ("#bla\n" "a = 2\n" "f'{a}'\n") nodes = get_nodes(code, ast.FormattedValue) names = get_nodes(code, ast.Name) self.assertPosition(nodes[0], (3, 2), (3, 5), (3, 5)) self.assertNoBeforeInnerAfter(nodes[0]) self.assertPosition(names[0], (2, 0), (2, 1), (2, 1)) self.assertNoBeforeInnerAfter(names[0]) self.assertPosition(names[1], (3, 3), (3, 4), (3, 4)) self.assertNoBeforeInnerAfter(names[1]) @only_python36 def test_formatted_value2(self): code = ("#bla\n" "import decimal\n" "width, precision = 10, 4\n" "value = decimal.Decimal('12.34567')\n" "f'result: {value:{width}.{precision}}'\n") nodes = get_nodes(code, ast.FormattedValue) self.assertPosition(nodes[0], (5, 10), (5, 37), (5, 37)) self.assertNoBeforeInnerAfter(nodes[0]) @only_python36 def test_formatted_value3(self): code = ("#bla\n" "a = 1\n" "b = 2\n" "d = f'a: {a}; b: {b}'\n" "# other") nodes = get_nodes(code, ast.FormattedValue) self.assertPosition(nodes[0], (4, 9), (4, 12), (4, 12)) self.assertPosition(nodes[1], (4, 17), (4, 20), (4, 20)) self.assertNoBeforeInnerAfter(nodes[0]) self.assertNoBeforeInnerAfter(nodes[1]) @only_python36 def test_constant(self): code = ("#bla\n" "x = 2\n") # Constants are created by optimizers # Thus, we must simulate an optimizer tree = ast.parse(code) for node in ast.walk(tree): if isinstance(node, ast.Assign) and isinstance(node.value, ast.Num): node.value = ast.copy_location( ast.Constant(node.value.n), node.value ) nodes = get_nodes(code, ast.Constant, tree=tree) self.assertPosition(nodes[0], (2, 4), (2, 5), (2, 5)) self.assertNoBeforeInnerAfter(nodes[0]) @only_python38 def test_named_expr(self): code = ("#bla\n" "(a := 1)") nodes = get_nodes(code, ast.NamedExpr) self.assertPosition(nodes[0], (2, 0), (2, 8), (2, 5)) self.assertOperation(nodes[0].op_pos[0], (2, 3), (2, 5), (2, 5), ':=') self.assertSimpleInnerPosition(nodes[0], (2, 1), (2, 7))

38.779026

90

0.495726

5,558

41,416

3.619827

0.048219

0.095432

0.047666

0.059049

0.886973

0.853919

0.828471

0.787514

0.752274

0.708335

0

0.083853

0.298846

41,416

1,067

91

38.81537

0.608974

0.010672

0

0.550321

0

0.059773

0.001441

0

0.000147

0

0.388651

1

0.114561

false

0

0.005353

0

0.120985

0.002141

0

null

0

1

0

null

0

6

31ef3ee2384fa037ee5878d1f32578cf17d40414

77

py

Python

common/trainers/wikiqa_trainer.py

karkaroff/castor

881673f3dadb4f757fdfdf5d2ab9031e08512406

[ "Apache-2.0" ]

132

2017-04-02T12:31:55.000Z

2019-03-09T07:53:29.000Z

common/trainers/wikiqa_trainer.py

sudipta90/castor

fa2f59535c71a0fb4586afbe543b81ba812c8630

[ "Apache-2.0" ]

111

2017-04-01T23:00:24.000Z

2019-03-10T08:29:20.000Z

common/trainers/wikiqa_trainer.py

karkaroff/Castor

881673f3dadb4f757fdfdf5d2ab9031e08512406

[ "Apache-2.0" ]

53

2017-04-06T01:17:18.000Z

2019-02-27T03:10:35.000Z

from .qa_trainer import QATrainer class WikiQATrainer(QATrainer): pass

12.833333

33

0.779221

9

77

6.555556

0.888889

0

0.168831

77

5

34

15.4

0.921875

0

1

0

true

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

6

ee1fde0e35d709d947794857f3737bae80ea81cd

22

py

Python

catalogs/ngc2000-mid.py

spake/astrometry.net

12c76f4a44fe90a009eeb962f2ae28b0791829b8

[ "BSD-3-Clause" ]

4

2018-02-13T23:11:40.000Z

2021-09-30T16:02:22.000Z

catalogs/ngc2000-mid.py

spake/astrometry.net

12c76f4a44fe90a009eeb962f2ae28b0791829b8

[ "BSD-3-Clause" ]

null

catalogs/ngc2000-mid.py

spake/astrometry.net

12c76f4a44fe90a009eeb962f2ae28b0791829b8

[ "BSD-3-Clause" ]

1

2019-02-11T06:56:30.000Z

] ngc2000accurate = [

7.333333

19

0.681818

1

22

15

1

0

0.222222

0.181818

22

2

20

11

0.611111

0

null

0

null

0

1

null

0

1

0

1

0

null

0

1

0

6

ee738487bc2da929ac10faf9699fb85e76e3d49c

41

py

Python

service/__init__.py

janlingen/command_line_stocks

06860897d4114104d322486ec6bb86c36b53d637

[ "MIT" ]

6

2022-02-04T17:08:11.000Z

2022-02-09T14:04:16.000Z

service/__init__.py

janlingen/command_line_stocks

06860897d4114104d322486ec6bb86c36b53d637

[ "MIT" ]

null

service/__init__.py

janlingen/command_line_stocks

06860897d4114104d322486ec6bb86c36b53d637

[ "MIT" ]

null

from service.information_filter import *

20.5

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c9be6ae277ceb7221c015bb0ad230b27dcc35a38

5,482

py

Python

013.py

gconsidine/project-euler

4b3571b7544c1ebf52c4752e7dde7360aa61fc70

[ "MIT" ]

1

2017-04-22T11:40:01.000Z

013.py

gconsidine/project-euler

4b3571b7544c1ebf52c4752e7dde7360aa61fc70

[ "MIT" ]

null

013.py

gconsidine/project-euler

4b3571b7544c1ebf52c4752e7dde7360aa61fc70

[ "MIT" ]

null

#Greg Considine #Project Euler -- Problem 13 rawPaste = """37107287533902102798797998220837590246510135740250 46376937677490009712648124896970078050417018260538 74324986199524741059474233309513058123726617309629 91942213363574161572522430563301811072406154908250 23067588207539346171171980310421047513778063246676 89261670696623633820136378418383684178734361726757 28112879812849979408065481931592621691275889832738 44274228917432520321923589422876796487670272189318 47451445736001306439091167216856844588711603153276 70386486105843025439939619828917593665686757934951 62176457141856560629502157223196586755079324193331 64906352462741904929101432445813822663347944758178 92575867718337217661963751590579239728245598838407 58203565325359399008402633568948830189458628227828 80181199384826282014278194139940567587151170094390 35398664372827112653829987240784473053190104293586 86515506006295864861532075273371959191420517255829 71693888707715466499115593487603532921714970056938 54370070576826684624621495650076471787294438377604 53282654108756828443191190634694037855217779295145 36123272525000296071075082563815656710885258350721 45876576172410976447339110607218265236877223636045 17423706905851860660448207621209813287860733969412 81142660418086830619328460811191061556940512689692 51934325451728388641918047049293215058642563049483 62467221648435076201727918039944693004732956340691 15732444386908125794514089057706229429197107928209 55037687525678773091862540744969844508330393682126 18336384825330154686196124348767681297534375946515 80386287592878490201521685554828717201219257766954 78182833757993103614740356856449095527097864797581 16726320100436897842553539920931837441497806860984 48403098129077791799088218795327364475675590848030 87086987551392711854517078544161852424320693150332 59959406895756536782107074926966537676326235447210 69793950679652694742597709739166693763042633987085 41052684708299085211399427365734116182760315001271 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38298203783031473527721580348144513491373226651381 34829543829199918180278916522431027392251122869539 40957953066405232632538044100059654939159879593635 29746152185502371307642255121183693803580388584903 41698116222072977186158236678424689157993532961922 62467957194401269043877107275048102390895523597457 23189706772547915061505504953922979530901129967519 86188088225875314529584099251203829009407770775672 11306739708304724483816533873502340845647058077308 82959174767140363198008187129011875491310547126581 97623331044818386269515456334926366572897563400500 42846280183517070527831839425882145521227251250327 55121603546981200581762165212827652751691296897789 32238195734329339946437501907836945765883352399886 75506164965184775180738168837861091527357929701337 62177842752192623401942399639168044983993173312731 32924185707147349566916674687634660915035914677504 99518671430235219628894890102423325116913619626622 73267460800591547471830798392868535206946944540724 76841822524674417161514036427982273348055556214818 97142617910342598647204516893989422179826088076852 87783646182799346313767754307809363333018982642090 10848802521674670883215120185883543223812876952786 71329612474782464538636993009049310363619763878039 62184073572399794223406235393808339651327408011116 66627891981488087797941876876144230030984490851411 60661826293682836764744779239180335110989069790714 85786944089552990653640447425576083659976645795096 66024396409905389607120198219976047599490197230297 64913982680032973156037120041377903785566085089252 16730939319872750275468906903707539413042652315011 94809377245048795150954100921645863754710598436791 78639167021187492431995700641917969777599028300699 15368713711936614952811305876380278410754449733078 40789923115535562561142322423255033685442488917353 44889911501440648020369068063960672322193204149535 41503128880339536053299340368006977710650566631954 81234880673210146739058568557934581403627822703280 82616570773948327592232845941706525094512325230608 22918802058777319719839450180888072429661980811197 77158542502016545090413245809786882778948721859617 72107838435069186155435662884062257473692284509516 20849603980134001723930671666823555245252804609722 53503534226472524250874054075591789781264330331690""" sum = 0 for i in range(0, 100): sum += int(rawPaste[i*50+i:i*50+50+i]) print(sum) ''' I figured I might as well start off with a brute-force attempt since Python is capable of working with arbitrarily large values. Turns out no refinement was necessary -- the correct (non-truncated) sum was found in .024s. '''

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1

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c9e2ca7e4b70b39a36ea053ebab927f50e548bad

1,561

py

Python

src/scs_dfe/interface/interface.py

south-coast-science/scs_dfe_eng

05708b27ba65438d11fad0d947bcff3df37dc87d

[ "MIT" ]

null

src/scs_dfe/interface/interface.py

south-coast-science/scs_dfe_eng

05708b27ba65438d11fad0d947bcff3df37dc87d

[ "MIT" ]

null

src/scs_dfe/interface/interface.py

south-coast-science/scs_dfe_eng

05708b27ba65438d11fad0d947bcff3df37dc87d

[ "MIT" ]

2

2017-12-05T12:41:48.000Z

2019-09-29T14:41:30.000Z

""" Created on 20 Jun 2019 @author: Bruno Beloff (bruno.beloff@southcoastscience.com) An abstract system interface """ from abc import ABC, abstractmethod # -------------------------------------------------------------------------------------------------------------------- class Interface(ABC): """ classdocs """ # ---------------------------------------------------------------------------------------------------------------- @abstractmethod def status(self): pass @abstractmethod def null_datum(self): pass # ---------------------------------------------------------------------------------------------------------------- @abstractmethod def gas_sensors(self, host): pass @abstractmethod def pt1000(self, host): pass @abstractmethod def pt1000_adc(self, gain, rate): pass # ---------------------------------------------------------------------------------------------------------------- @abstractmethod def led(self): pass # ---------------------------------------------------------------------------------------------------------------- @abstractmethod def power_gases(self, on): # switches digital component only pass @abstractmethod def power_gps(self, on): pass @abstractmethod def power_modem(self, on): pass @abstractmethod def power_ndir(self, on): pass @abstractmethod def power_opc(self, on): pass

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null

0

1

0

1

0

6

a003ef79b149f0f62ae533496392b1cc94c5ef54

6

py

Python

test/parse/t00.py

timmartin/skulpt

2e3a3fbbaccc12baa29094a717ceec491a8a6750

[ "MIT" ]

2,671

2015-01-03T08:23:25.000Z

2022-03-31T06:15:48.000Z

test/parse/t00.py

csev/skulpt

9aa25b7dbf29f23ee8d3140d01a6f4353d12e66f

[ "MIT" ]

972

2015-01-05T08:11:00.000Z

2022-03-29T13:47:15.000Z

test/parse/t00.py

csev/skulpt

9aa25b7dbf29f23ee8d3140d01a6f4353d12e66f

[ "MIT" ]

845

2015-01-03T19:53:36.000Z

2022-03-29T18:34:22.000Z

y = 1

3

5

0.333333

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6

1

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0.333333

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1

0

1

0

1

0

null

0

6

4e43e4a9b228e0ea2ab64cc22a467b49e826c52d

112

py

Python

omnisonica/clients/test.py

smershon/omnisonica

0ef3cbabf30d27af227e4bf907ee907dc6445954

[ "MIT" ]

1

2016-05-12T23:20:40.000Z

omnisonica/clients/test.py

smershon/omnisonica

0ef3cbabf30d27af227e4bf907ee907dc6445954

[ "MIT" ]

null

omnisonica/clients/test.py

smershon/omnisonica

0ef3cbabf30d27af227e4bf907ee907dc6445954

[ "MIT" ]

null

import omni_redis, sys, track_worker print track_worker.get_original_release(omni_redis.get_track(sys.argv[1]))

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1

0

null

0

1

0

1

0

1

0

6

14cf442282e87bdb50ea23f3e334c908313c603e

22

py

Python

h2o-py/tests/pyunit_utils/__init__.py

huamichaelchen/h2o-3

2b52f2240652a1c73c1708762248c0773d0c073e

[ "Apache-2.0" ]

null

h2o-py/tests/pyunit_utils/__init__.py

huamichaelchen/h2o-3

2b52f2240652a1c73c1708762248c0773d0c073e

[ "Apache-2.0" ]

null

h2o-py/tests/pyunit_utils/__init__.py

huamichaelchen/h2o-3

2b52f2240652a1c73c1708762248c0773d0c073e

[ "Apache-2.0" ]

1

2020-01-22T19:10:37.000Z

from utilsPY import *

11

21

0.772727

3

22

5.666667

1

0

0.181818

22

1

22

0.944444

0

1

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true

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1

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1

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null

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0

1

0

1

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6

0963a40232962ba676d4cc79730cf1bb0023ce31

35

py

Python

build/lib/instabotpy/__init__.py

Krish-ds/Instagram_Bot

5404fd876102ad00ff9869f240094c74ae3e615a

[ "MIT" ]

1

2020-07-17T12:48:31.000Z

build/lib/instabotpy/__init__.py

Krish-ds/Instagram_Bot

5404fd876102ad00ff9869f240094c74ae3e615a

[ "MIT" ]

null

build/lib/instabotpy/__init__.py

Krish-ds/Instagram_Bot

5404fd876102ad00ff9869f240094c74ae3e615a

[ "MIT" ]

1

2021-04-10T17:13:25.000Z

from instabotpy.instabotpy import *

35

0.857143

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7.5

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0.085714

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1

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1

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null

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null

0

1

0

1

0

1

0

6

1174c1b6a61a4c75d4c0f2b8ae3a28bb82779f4a

389

py

Python

deepscratch/models/layers/layer.py

arnaldog12/deeplearning-from-scratch

afa1e3796f73b86d3b17bfbc88b7b57a5346fb9b

[ "MIT" ]

2

2019-03-14T16:08:18.000Z

2020-12-07T10:00:32.000Z

deepscratch/models/layers/layer.py

arnaldog12/deeplearning-from-scratch

afa1e3796f73b86d3b17bfbc88b7b57a5346fb9b

[ "MIT" ]

null

deepscratch/models/layers/layer.py

arnaldog12/deeplearning-from-scratch

afa1e3796f73b86d3b17bfbc88b7b57a5346fb9b

[ "MIT" ]

null

import numpy as np class Layer(object): def __init__(self): self.input_shape = None def forward(self, data): return NotImplementedError() def backward(self, grads): return NotImplementedError() def initialize(self, initializer, otimizer, input_shape, **kwargs): pass def output_shape(self): return NotImplementedError()

21.611111

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0.083333

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1

0

null

0

1

0

1

0

1

0

6

119c85d3811d76fb235c7edbe9c30bfb848b2790

26

py

Python

tests/test_bot/src/annofwd/__init__.py

Ketre3/vkquick

81b19111e2322d277bfbb89dae6a27fb70a9b8c7

[ "MIT" ]

null

tests/test_bot/src/annofwd/__init__.py

Ketre3/vkquick

81b19111e2322d277bfbb89dae6a27fb70a9b8c7

[ "MIT" ]

null

tests/test_bot/src/annofwd/__init__.py

Ketre3/vkquick

81b19111e2322d277bfbb89dae6a27fb70a9b8c7

[ "MIT" ]

null

from .main import annofwd

13

25

0.807692

4

26

5.25

1

0

0.153846

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1

26

0.954545

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

eebef1762eb3d8221a93916f6472be7ee79999d3

99

py

Python

scripts/generate_encoder_model.py

beckstev/MachineLearningSeminar

f6aa4affb0be5db40e64ba64236519fb53401b5f

[ "MIT" ]

null

scripts/generate_encoder_model.py

beckstev/MachineLearningSeminar

f6aa4affb0be5db40e64ba64236519fb53401b5f

[ "MIT" ]

23

2019-04-29T19:08:35.000Z

2020-09-25T23:46:14.000Z

scripts/generate_encoder_model.py

beckstev/MachineLearningSeminar

f6aa4affb0be5db40e64ba64236519fb53401b5f

[ "MIT" ]

1

2019-05-09T21:17:01.000Z

from dog_classifier.io.create_encoder_model import create_encoder_model create_encoder_model()

24.75

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99

5.642857

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1

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true

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0

null

0

1

0

1

0

6

eeedaa024a82ae2fc948dcfe450974bd9a6e91be

44

py

Python

extremecrawler/__init__.py

uehara1414/ExtremeCrawler

1ee22d82e88405bbc22d6bfd851f584fd4a0fd6c

[ "MIT" ]

null

extremecrawler/__init__.py

uehara1414/ExtremeCrawler

1ee22d82e88405bbc22d6bfd851f584fd4a0fd6c

[ "MIT" ]

null

extremecrawler/__init__.py

uehara1414/ExtremeCrawler

1ee22d82e88405bbc22d6bfd851f584fd4a0fd6c

[ "MIT" ]

null

from .extreme_crawler import ExtremeCrawler

22

43

0.886364

5

44

7.6

1

0

0.090909

44

1

44

0.95

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

e108eb54e31c7a70d2c6085b35f5ab086b6e3a74

61

py

Python

buzzni/ai/reco/mlserving/predictors/tensorflow/__init__.py

BuzzniAILab/mlserving

8b8add9dbe5cdd6392e0c87ee789492de0a1c70e

[ "MIT" ]

13

2020-08-23T17:35:53.000Z

2022-02-10T14:14:03.000Z

mlserving/predictors/tensorflow/__init__.py

orlevi111/ganesha

137cc388806fc98f7768298da01ebeddf03f9464

[ "MIT" ]

3

2020-08-20T21:09:01.000Z

2021-06-25T15:33:54.000Z

mlserving/predictors/tensorflow/__init__.py

orlevi111/ganesha

137cc388806fc98f7768298da01ebeddf03f9464

[ "MIT" ]

3

2021-04-12T01:56:22.000Z

2021-10-05T12:50:12.000Z

from .http import TFServingPrediction, TFServingRequestError

30.5

60

0.885246

5

61

10.8

1

0

0.081967

61

1

61

0.964286

0

1

0

true

0

1

0

1

0

1

0

1

null

0

1

0

null

0

1

0

1

0

1

0

6

010c39a8054dae42bdcde14d275c6a2ba4473be5

48

py

Python

tests.py

SykoTheKiD/Cords

aed15a492d116e22690b4fa34ce6c40d662a0aee

[ "MIT" ]

null

tests.py

SykoTheKiD/Cords

aed15a492d116e22690b4fa34ce6c40d662a0aee

[ "MIT" ]

null

tests.py

SykoTheKiD/Cords

aed15a492d116e22690b4fa34ce6c40d662a0aee

[ "MIT" ]

null

# !/usr/local/bin/python3 # TODO: Add unit tests

24

25

0.708333

8

48

4.25

1

0

0.02381

0.125

48

2

26

24

0.785714

0.916667

0

null

0

null

0

null

0

0.5

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

010ce9b2c833e3560c460a8e0397591daf485cd4

21

py

Python

cexapi/__init__.py

llazzaro/cex.io-api-python

654b66672180b7f34fb618b740080e47f9164a29

[ "MIT" ]

55

2015-01-02T05:24:43.000Z

2021-04-03T16:20:04.000Z

cexapi/__init__.py

llazzaro/cex.io-api-python

654b66672180b7f34fb618b740080e47f9164a29

[ "MIT" ]

6

2015-06-16T21:34:08.000Z

2020-06-27T18:00:14.000Z

cexapi/__init__.py

llazzaro/cex.io-api-python

654b66672180b7f34fb618b740080e47f9164a29

[ "MIT" ]

42

2016-09-16T05:27:17.000Z

2022-01-03T14:15:33.000Z

from cexapi import *

10.5

20

0.761905

3

21

5.333333

1

0

0.190476

21

1

21

0.941176

0

1

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true

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1

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1

0

1

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1

0

null

0

1

0

1

0

1

0

6

011c903e87fadb6fc5eb88b5542f7b8d5bc36081

180

py

Python

metadata-ingestion/src/datahub/metadata/com/linkedin/pegasus2avro/dataplatform/__init__.py

NathanFaught/datahub

f91062d82796cb56909623a5bc4d2d50edbb6a67

[ "Apache-2.0" ]

null

metadata-ingestion/src/datahub/metadata/com/linkedin/pegasus2avro/dataplatform/__init__.py

NathanFaught/datahub

f91062d82796cb56909623a5bc4d2d50edbb6a67

[ "Apache-2.0" ]

3

2022-02-14T13:39:45.000Z

2022-02-27T17:32:49.000Z

metadata-ingestion/src/datahub/metadata/com/linkedin/pegasus2avro/dataplatform/__init__.py

Jocker08/datahub

91eb3cc57e183fa1951349177e59df3f3897c8e0

[ "Apache-2.0" ]

null

from .....schema_classes import DataPlatformInfoClass from .....schema_classes import PlatformTypeClass DataPlatformInfo = DataPlatformInfoClass PlatformType = PlatformTypeClass

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py

Python

discordbot/bot_utils/__init__.py

rauenzi/discordbot.py

39bb98dae4e49487e6c6c597f85fc41c74b62bb8

[ "MIT" ]

31

2017-07-18T21:59:52.000Z

2021-11-30T10:52:13.000Z

discordbot/bot_utils/__init__.py

Sheeyre/discordbot.py

39bb98dae4e49487e6c6c597f85fc41c74b62bb8

[ "MIT" ]

3

2018-04-09T02:36:42.000Z

2019-04-18T18:46:49.000Z

discordbot/bot_utils/__init__.py

Sheeyre/discordbot.py

39bb98dae4e49487e6c6c597f85fc41c74b62bb8

[ "MIT" ]

22

2017-07-24T20:39:16.000Z

2021-05-17T21:26:17.000Z

from . import checks, config, formats, paginator

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