xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

b9678f27461c722fc89e4edb93a357ffded7a5ae

132

py

Python

tests/missing_data/test_missing_data_air_passengers_DiscardRow_None.py

shaido987/pyaf

b9afd089557bed6b90b246d3712c481ae26a1957

[ "BSD-3-Clause" ]

377

2016-10-13T20:52:44.000Z

2022-03-29T18:04:14.000Z

tests/missing_data/test_missing_data_air_passengers_DiscardRow_None.py

ysdede/pyaf

b5541b8249d5a1cfdc01f27fdfd99b6580ed680b

[ "BSD-3-Clause" ]

160

2016-10-13T16:11:53.000Z

2022-03-28T04:21:34.000Z

tests/missing_data/test_missing_data_air_passengers_DiscardRow_None.py

ysdede/pyaf

b5541b8249d5a1cfdc01f27fdfd99b6580ed680b

[ "BSD-3-Clause" ]

63

2017-03-09T14:51:18.000Z

2022-03-27T20:52:57.000Z

import tests.missing_data.test_missing_data_air_passengers_generic as gen gen.test_air_passengers_missing_data('DiscardRow', None)

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132

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0

1

0

null

0

1

0

6

b96b22bb81b0a9151d3781c08783a7e680e8cd3d

1,226

gyp

Python

client/build/i18n_input_engine.gyp

zamorajavi/google-input-tools

fc9f11d80d957560f7accf85a5fc27dd23625f70

[ "Apache-2.0" ]

175

2015-01-01T12:40:33.000Z

2019-05-24T22:33:59.000Z

client/build/i18n_input_engine.gyp

zamorajavi/google-input-tools

fc9f11d80d957560f7accf85a5fc27dd23625f70

[ "Apache-2.0" ]

11

2015-01-19T16:30:56.000Z

2018-04-25T01:06:52.000Z

client/build/i18n_input_engine.gyp

zamorajavi/google-input-tools

fc9f11d80d957560f7accf85a5fc27dd23625f70

[ "Apache-2.0" ]

97

2015-01-19T15:35:29.000Z

2019-05-15T05:48:02.000Z

{ 'variables': { 'ENGINE_ROOT': '<(GOOGLE3)/i18n/input/engine', }, 'conditions': [ ['OS!="win"', { 'variables': { 'PROTOC': '<!(which protoc)', }, }], ], 'targets': [ { 'target_name': 'stubs', 'type': '<(library)', 'sources': [ '<(ENGINE_ROOT)/stubs/google3/base/commandlineflags.cc', '<(ENGINE_ROOT)/stubs/google3/base/commandlineflags_reporting.cc', '<(ENGINE_ROOT)/stubs/google3/base/init_google.cc', '<(ENGINE_ROOT)/stubs/google3/base/logging.cc', '<(ENGINE_ROOT)/stubs/google3/base/mutex.cc', '<(ENGINE_ROOT)/stubs/google3/base/scoped_ptr_internals.cc', '<(ENGINE_ROOT)/stubs/google3/base/sysinfo.cc', '<(ENGINE_ROOT)/stubs/google3/base/vlog_is_on.cc', ], 'conditions': [ ['OS=="win"', { 'sources': [ '<(ENGINE_ROOT)/stubs/google3/base/mutex-internal-win.cc', '<(ENGINE_ROOT)/stubs/posix/sys/mman.cc', '<(ENGINE_ROOT)/stubs/posix/sys/time.cc', '<(ENGINE_ROOT)/stubs/posix/unistd.cc', ], 'include_dirs': [ '<(ENGINE_ROOT)/stubs/posix/', ], }], ], }, ] }

29.190476

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0.527732

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true

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null

1

0

1

null

0

1

0

6

b9b7f5b3092a63cc5fca83642417fbe382b41966

1,376

py

Python

tests/comprehension_test.py

mxr/unkey

5245570cc3599a6750273f3f61d77e1fc99e3593

[ "MIT" ]

2

2021-08-04T04:50:39.000Z

2022-02-01T21:38:34.000Z

tests/comprehension_test.py

mxr/unkey

13188911df145b9ea6369fadd47a510948eb8b5b

[ "MIT" ]

null

tests/comprehension_test.py

mxr/unkey

13188911df145b9ea6369fadd47a510948eb8b5b

[ "MIT" ]

null

import pytest from unkey import _fix @pytest.mark.parametrize( ("s", "expected"), ( pytest.param("[x for x in d.keys()]", "[x for x in d]", id="attr list comp"), pytest.param( "[x for x in {}.keys()]", "[x for x in {}]", id="literal list comp" ), pytest.param( "[x for x in f().keys()]", "[x for x in f()]", id="func list comp" ), pytest.param("(x for x in d.keys())", "(x for x in d)", id="attr gen exp"), pytest.param("(x for x in {}.keys())", "(x for x in {})", id="literal gen exp"), pytest.param("(x for x in f().keys())", "(x for x in f())", id="func gen exp"), pytest.param("{x for x in d.keys()}", "{x for x in d}", id="attr set comp"), pytest.param( "{x for x in {}.keys()}", "{x for x in {}}", id="literal set comp" ), pytest.param("{x for x in f().keys()}", "{x for x in f()}", id="func set comp"), pytest.param( "{x:x for x in d.keys()}", "{x:x for x in d}", id="attr dict comp" ), pytest.param( "{x:x for x in {}.keys()}", "{x:x for x in {}}", id="literal dict comp" ), pytest.param( "{x:x for x in f().keys()}", "{x:x for x in f()}", id="func dict comp" ), ), ) def test_comprehensions(s, expected): assert _fix(s) == expected

37.189189

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1,376

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1,376

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false

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0

null

0

1

0

null

0

6

b9bdfa732965931492e679f92a50c4ba7a1297c5

26,194

py

Python

mayaSDK/PySide2/QtQml.py

FXTD-ODYSSEY/vscode-mayapy

7a21872f80b5b740fc653e79c3f9b5268e87b3c3

[ "MIT" ]

20

2019-09-20T00:30:22.000Z

2021-12-26T06:56:16.000Z

mayaSDK/PySide2/QtQml.py

minjiang999/vscode-mayapy

7a21872f80b5b740fc653e79c3f9b5268e87b3c3

[ "MIT" ]

5

2019-12-29T15:19:03.000Z

2022-03-29T16:54:19.000Z

mayaSDK/PySide2/QtQml.py

minjiang999/vscode-mayapy

7a21872f80b5b740fc653e79c3f9b5268e87b3c3

[ "MIT" ]

8

2019-09-23T05:46:44.000Z

2022-01-11T14:42:14.000Z

from PySide2.QtCore import QObject as _QObject class _Object(object): __dict__ = None class VolatileBool(object): """ VolatileBool objects contain a C++ volatile bool """ def __repr__(*args, **kwargs): """ x.__repr__() <==> repr(x) """ pass def __str__(*args, **kwargs): """ x.__str__() <==> str(x) """ pass def get(*args, **kwargs): """ B.get() -> Bool. Returns the value of the volatile boolean """ pass def set(*args, **kwargs): """ B.set(a) -> None. Sets the value of the volatile boolean """ pass __new__ = None class _Property(object): def __call__(*args, **kwargs): """ x.__call__(...) <==> x(...) """ pass def __init__(*args, **kwargs): """ x.__init__(...) initializes x; see help(type(x)) for signature """ pass def getter(*args, **kwargs): pass def read(*args, **kwargs): pass def setter(*args, **kwargs): pass def write(*args, **kwargs): pass __new__ = None class QQmlContext(_QObject): def __init__(*args, **kwargs): """ x.__init__(...) initializes x; see help(type(x)) for signature """ pass def baseUrl(*args, **kwargs): pass def contextObject(*args, **kwargs): pass def contextProperty(*args, **kwargs): pass def engine(*args, **kwargs): pass def isValid(*args, **kwargs): pass def nameForObject(*args, **kwargs): pass def parentContext(*args, **kwargs): pass def resolvedUrl(*args, **kwargs): pass def setBaseUrl(*args, **kwargs): pass def setContextObject(*args, **kwargs): pass def setContextProperty(*args, **kwargs): pass __new__ = None staticMetaObject = None class QQmlImageProviderBase(_Object): def flags(*args, **kwargs): pass def imageType(*args, **kwargs): pass Flag = None Flags = None ForceAsynchronousImageLoading = None Image = None ImageResponse = None ImageType = None Invalid = None Pixmap = None Texture = None __new__ = None class QQmlDebuggingEnabler(_Object): def __init__(*args, **kwargs): """ x.__init__(...) initializes x; see help(type(x)) for signature """ pass def connectToLocalDebugger(*args, **kwargs): pass def startTcpDebugServer(*args, **kwargs): pass DoNotWaitForClient = None StartMode = None WaitForClient = None __new__ = None class QQmlProperty(_Object): def __copy__(*args, **kwargs): pass def __eq__(*args, **kwargs): """ x.__eq__(y) <==> x==y """ pass def __ge__(*args, **kwargs): """ x.__ge__(y) <==> x>=y """ pass def __getattribute__(*args, **kwargs): """ x.__getattribute__('name') <==> x.name """ pass def __gt__(*args, **kwargs): """ x.__gt__(y) <==> x>y """ pass def __init__(*args, **kwargs): """ x.__init__(...) initializes x; see help(type(x)) for signature """ pass def __le__(*args, **kwargs): """ x.__le__(y) <==> x<=y """ pass def __lt__(*args, **kwargs): """ x.__lt__(y) <==> x<y """ pass def __ne__(*args, **kwargs): """ x.__ne__(y) <==> x!=y """ pass def connectNotifySignal(*args, **kwargs): pass def hasNotifySignal(*args, **kwargs): pass def index(*args, **kwargs): pass def isDesignable(*args, **kwargs): pass def isProperty(*args, **kwargs): pass def isResettable(*args, **kwargs): pass def isSignalProperty(*args, **kwargs): pass def isValid(*args, **kwargs): pass def isWritable(*args, **kwargs): pass def method(*args, **kwargs): pass def name(*args, **kwargs): pass def needsNotifySignal(*args, **kwargs): pass def object(*args, **kwargs): pass def property(*args, **kwargs): pass def propertyType(*args, **kwargs): pass def propertyTypeCategory(*args, **kwargs): pass def propertyTypeName(*args, **kwargs): pass def reset(*args, **kwargs): pass def type(*args, **kwargs): pass def read(*args, **kwargs): pass def write(*args, **kwargs): pass Invalid = None InvalidCategory = None List = None Normal = None Object = None Property = None PropertyTypeCategory = None SignalProperty = None Type = None __new__ = None class QQmlIncubationController(_Object): def __init__(*args, **kwargs): """ x.__init__(...) initializes x; see help(type(x)) for signature """ pass def engine(*args, **kwargs): pass def incubateFor(*args, **kwargs): pass def incubateWhile(*args, **kwargs): pass def incubatingObjectCount(*args, **kwargs): pass def incubatingObjectCountChanged(*args, **kwargs): pass __new__ = None class QQmlPropertyValueSource(_Object): def __init__(*args, **kwargs): """ x.__init__(...) initializes x; see help(type(x)) for signature """ pass def setTarget(*args, **kwargs): pass __new__ = None class QQmlNetworkAccessManagerFactory(_Object): def __init__(*args, **kwargs): """ x.__init__(...) initializes x; see help(type(x)) for signature """ pass def create(*args, **kwargs): pass __new__ = None class QQmlExtensionInterface(_Object): def __init__(*args, **kwargs): """ x.__init__(...) initializes x; see help(type(x)) for signature """ pass def initializeEngine(*args, **kwargs): pass __new__ = None class QJSValue(_Object): def __copy__(*args, **kwargs): pass def __init__(*args, **kwargs): """ x.__init__(...) initializes x; see help(type(x)) for signature """ pass def __nonzero__(*args, **kwargs): """ x.__nonzero__() <==> x != 0 """ pass def call(*args, **kwargs): pass def callAsConstructor(*args, **kwargs): pass def callWithInstance(*args, **kwargs): pass def deleteProperty(*args, **kwargs): pass def engine(*args, **kwargs): pass def equals(*args, **kwargs): pass def hasOwnProperty(*args, **kwargs): pass def hasProperty(*args, **kwargs): pass def isArray(*args, **kwargs): pass def isBool(*args, **kwargs): pass def isCallable(*args, **kwargs): pass def isDate(*args, **kwargs): pass def isError(*args, **kwargs): pass def isNull(*args, **kwargs): pass def isNumber(*args, **kwargs): pass def isObject(*args, **kwargs): pass def isQObject(*args, **kwargs): pass def isRegExp(*args, **kwargs): pass def isString(*args, **kwargs): pass def isUndefined(*args, **kwargs): pass def isVariant(*args, **kwargs): pass def property(*args, **kwargs): pass def prototype(*args, **kwargs): pass def setProperty(*args, **kwargs): pass def setPrototype(*args, **kwargs): pass def strictlyEquals(*args, **kwargs): pass def toBool(*args, **kwargs): pass def toDateTime(*args, **kwargs): pass def toInt(*args, **kwargs): pass def toNumber(*args, **kwargs): pass def toQObject(*args, **kwargs): pass def toString(*args, **kwargs): pass def toUInt(*args, **kwargs): pass def toVariant(*args, **kwargs): pass NullValue = None SpecialValue = None UndefinedValue = None __new__ = None class QJSEngine(_QObject): def __init__(*args, **kwargs): """ x.__init__(...) initializes x; see help(type(x)) for signature """ pass def collectGarbage(*args, **kwargs): pass def evaluate(*args, **kwargs): pass def globalObject(*args, **kwargs): pass def installExtensions(*args, **kwargs): pass def installTranslatorFunctions(*args, **kwargs): pass def newArray(*args, **kwargs): pass def newObject(*args, **kwargs): pass def newQObject(*args, **kwargs): pass def toScriptValue(*args, **kwargs): pass AllExtensions = None ConsoleExtension = None Extension = None Extensions = None GarbageCollectionExtension = None TranslationExtension = None __new__ = None staticMetaObject = None class QQmlError(_Object): def __copy__(*args, **kwargs): pass def __init__(*args, **kwargs): """ x.__init__(...) initializes x; see help(type(x)) for signature """ pass def __repr__(*args, **kwargs): """ x.__repr__() <==> repr(x) """ pass def column(*args, **kwargs): pass def description(*args, **kwargs): pass def isValid(*args, **kwargs): pass def line(*args, **kwargs): pass def object(*args, **kwargs): pass def setColumn(*args, **kwargs): pass def setDescription(*args, **kwargs): pass def setLine(*args, **kwargs): pass def setObject(*args, **kwargs): pass def setUrl(*args, **kwargs): pass def toString(*args, **kwargs): pass def url(*args, **kwargs): pass __new__ = None class QQmlParserStatus(_Object): def __init__(*args, **kwargs): """ x.__init__(...) initializes x; see help(type(x)) for signature """ pass def classBegin(*args, **kwargs): pass def componentComplete(*args, **kwargs): pass __new__ = None class QQmlFileSelector(_QObject): def __init__(*args, **kwargs): """ x.__init__(...) initializes x; see help(type(x)) for signature """ pass def setExtraSelectors(*args, **kwargs): pass def setSelector(*args, **kwargs): pass def get(*args, **kwargs): pass __new__ = None staticMetaObject = None class QQmlAbstractUrlInterceptor(_Object): def __init__(*args, **kwargs): """ x.__init__(...) initializes x; see help(type(x)) for signature """ pass def intercept(*args, **kwargs): pass DataType = None JavaScriptFile = None QmlFile = None QmldirFile = None UrlString = None __new__ = None class QQmlExpression(_QObject): def __init__(*args, **kwargs): """ x.__init__(...) initializes x; see help(type(x)) for signature """ pass def clearError(*args, **kwargs): pass def columnNumber(*args, **kwargs): pass def context(*args, **kwargs): pass def engine(*args, **kwargs): pass def error(*args, **kwargs): pass def evaluate(*args, **kwargs): pass def expression(*args, **kwargs): pass def hasError(*args, **kwargs): pass def lineNumber(*args, **kwargs): pass def notifyOnValueChanged(*args, **kwargs): pass def scopeObject(*args, **kwargs): pass def setExpression(*args, **kwargs): pass def setNotifyOnValueChanged(*args, **kwargs): pass def setSourceLocation(*args, **kwargs): pass def sourceFile(*args, **kwargs): pass __new__ = None staticMetaObject = None valueChanged = None class QQmlComponent(_QObject): def __init__(*args, **kwargs): """ x.__init__(...) initializes x; see help(type(x)) for signature """ pass def __nonzero__(*args, **kwargs): """ x.__nonzero__() <==> x != 0 """ pass def beginCreate(*args, **kwargs): pass def completeCreate(*args, **kwargs): pass def create(*args, **kwargs): pass def creationContext(*args, **kwargs): pass def errorString(*args, **kwargs): pass def errors(*args, **kwargs): pass def isError(*args, **kwargs): pass def isLoading(*args, **kwargs): pass def isNull(*args, **kwargs): pass def isReady(*args, **kwargs): pass def loadUrl(*args, **kwargs): pass def progress(*args, **kwargs): pass def setData(*args, **kwargs): pass def status(*args, **kwargs): pass def url(*args, **kwargs): pass Asynchronous = None CompilationMode = None Error = None Loading = None Null = None PreferSynchronous = None Ready = None Status = None __new__ = None progressChanged = None staticMetaObject = None statusChanged = None class QQmlIncubator(_Object): def __init__(*args, **kwargs): """ x.__init__(...) initializes x; see help(type(x)) for signature """ pass def __nonzero__(*args, **kwargs): """ x.__nonzero__() <==> x != 0 """ pass def clear(*args, **kwargs): pass def errors(*args, **kwargs): pass def forceCompletion(*args, **kwargs): pass def incubationMode(*args, **kwargs): pass def isError(*args, **kwargs): pass def isLoading(*args, **kwargs): pass def isNull(*args, **kwargs): pass def isReady(*args, **kwargs): pass def object(*args, **kwargs): pass def setInitialState(*args, **kwargs): pass def status(*args, **kwargs): pass def statusChanged(*args, **kwargs): pass Asynchronous = None AsynchronousIfNested = None Error = None IncubationMode = None Loading = None Null = None Ready = None Status = None Synchronous = None __new__ = None class QQmlTypesExtensionInterface(_Object): def __init__(*args, **kwargs): """ x.__init__(...) initializes x; see help(type(x)) for signature """ pass def registerTypes(*args, **kwargs): pass __new__ = None class QQmlPropertyMap(_QObject): def __init__(*args, **kwargs): """ x.__init__(...) initializes x; see help(type(x)) for signature """ pass def clear(*args, **kwargs): pass def contains(*args, **kwargs): pass def count(*args, **kwargs): pass def insert(*args, **kwargs): pass def isEmpty(*args, **kwargs): pass def keys(*args, **kwargs): pass def size(*args, **kwargs): pass def updateValue(*args, **kwargs): pass def value(*args, **kwargs): pass __new__ = None staticMetaObject = None valueChanged = None class QQmlScriptString(_Object): def __copy__(*args, **kwargs): pass def __eq__(*args, **kwargs): """ x.__eq__(y) <==> x==y """ pass def __ge__(*args, **kwargs): """ x.__ge__(y) <==> x>=y """ pass def __gt__(*args, **kwargs): """ x.__gt__(y) <==> x>y """ pass def __init__(*args, **kwargs): """ x.__init__(...) initializes x; see help(type(x)) for signature """ pass def __le__(*args, **kwargs): """ x.__le__(y) <==> x<=y """ pass def __lt__(*args, **kwargs): """ x.__lt__(y) <==> x<y """ pass def __ne__(*args, **kwargs): """ x.__ne__(y) <==> x!=y """ pass def booleanLiteral(*args, **kwargs): pass def isEmpty(*args, **kwargs): pass def isNullLiteral(*args, **kwargs): pass def isUndefinedLiteral(*args, **kwargs): pass def numberLiteral(*args, **kwargs): pass def stringLiteral(*args, **kwargs): pass __new__ = None class QJSValueIterator(_Object): def __init__(*args, **kwargs): """ x.__init__(...) initializes x; see help(type(x)) for signature """ pass def hasNext(*args, **kwargs): pass def name(*args, **kwargs): pass def next(*args, **kwargs): pass def value(*args, **kwargs): pass __new__ = None class QQmlListReference(_Object): def __copy__(*args, **kwargs): pass def __init__(*args, **kwargs): """ x.__init__(...) initializes x; see help(type(x)) for signature """ pass def append(*args, **kwargs): pass def at(*args, **kwargs): pass def canAppend(*args, **kwargs): pass def canAt(*args, **kwargs): pass def canClear(*args, **kwargs): pass def canCount(*args, **kwargs): pass def clear(*args, **kwargs): pass def count(*args, **kwargs): pass def isManipulable(*args, **kwargs): pass def isReadable(*args, **kwargs): pass def isValid(*args, **kwargs): pass def listElementType(*args, **kwargs): pass def object(*args, **kwargs): pass __new__ = None class QQmlFile(_Object): def __init__(*args, **kwargs): """ x.__init__(...) initializes x; see help(type(x)) for signature """ pass def __nonzero__(*args, **kwargs): """ x.__nonzero__() <==> x != 0 """ pass def clear(*args, **kwargs): pass def connectDownloadProgress(*args, **kwargs): pass def connectFinished(*args, **kwargs): pass def data(*args, **kwargs): pass def dataByteArray(*args, **kwargs): pass def error(*args, **kwargs): pass def isError(*args, **kwargs): pass def isLoading(*args, **kwargs): pass def isNull(*args, **kwargs): pass def isReady(*args, **kwargs): pass def load(*args, **kwargs): pass def size(*args, **kwargs): pass def status(*args, **kwargs): pass def url(*args, **kwargs): pass def isLocalFile(*args, **kwargs): pass def isSynchronous(*args, **kwargs): pass def urlToLocalFileOrQrc(*args, **kwargs): pass Error = None Loading = None Null = None Ready = None Status = None __new__ = None class ListProperty(_Property): def __init__(*args, **kwargs): """ x.__init__(...) initializes x; see help(type(x)) for signature """ pass class QQmlEngine(QJSEngine): def __init__(*args, **kwargs): """ x.__init__(...) initializes x; see help(type(x)) for signature """ pass def addImageProvider(*args, **kwargs): pass def addImportPath(*args, **kwargs): pass def addNamedBundle(*args, **kwargs): pass def addPluginPath(*args, **kwargs): pass def baseUrl(*args, **kwargs): pass def clearComponentCache(*args, **kwargs): pass def event(*args, **kwargs): pass def imageProvider(*args, **kwargs): pass def importPathList(*args, **kwargs): pass def importPlugin(*args, **kwargs): pass def incubationController(*args, **kwargs): pass def networkAccessManager(*args, **kwargs): pass def networkAccessManagerFactory(*args, **kwargs): pass def offlineStoragePath(*args, **kwargs): pass def outputWarningsToStandardError(*args, **kwargs): pass def pluginPathList(*args, **kwargs): pass def removeImageProvider(*args, **kwargs): pass def rootContext(*args, **kwargs): pass def setBaseUrl(*args, **kwargs): pass def setImportPathList(*args, **kwargs): pass def setIncubationController(*args, **kwargs): pass def setNetworkAccessManagerFactory(*args, **kwargs): pass def setOfflineStoragePath(*args, **kwargs): pass def setOutputWarningsToStandardError(*args, **kwargs): pass def setPluginPathList(*args, **kwargs): pass def setUrlInterceptor(*args, **kwargs): pass def trimComponentCache(*args, **kwargs): pass def urlInterceptor(*args, **kwargs): pass def contextForObject(*args, **kwargs): pass def objectOwnership(*args, **kwargs): pass def setContextForObject(*args, **kwargs): pass def setObjectOwnership(*args, **kwargs): pass CppOwnership = None JavaScriptOwnership = None ObjectOwnership = None __new__ = None quit = None staticMetaObject = None warnings = None class QQmlExtensionPlugin(_QObject, QQmlExtensionInterface): def __init__(*args, **kwargs): """ x.__init__(...) initializes x; see help(type(x)) for signature """ pass def baseUrl(*args, **kwargs): pass def initializeEngine(*args, **kwargs): pass def registerTypes(*args, **kwargs): pass __new__ = None staticMetaObject = None class QQmlApplicationEngine(QQmlEngine): def __init__(*args, **kwargs): """ x.__init__(...) initializes x; see help(type(x)) for signature """ pass def load(*args, **kwargs): pass def loadData(*args, **kwargs): pass def rootObjects(*args, **kwargs): pass __new__ = None objectCreated = None staticMetaObject = None def qmlRegisterType(*args, **kwargs): pass QML_HAS_ATTACHED_PROPERTIES = 1

15.06268

70

0.457433

2,182

26,194

5.238772

0.146196

0.25807

0.300061

0.322719

0.48316

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0.444755

0.426034

0.399703

0.28379

0

0.000399

0.425288

26,194

1,738

71

15.071346

0.758935

0.090861

0

0.657497

0

1

0.405777

false

0.405777

0.006878

0

0.598349

0

null

1

0

null

0

1

0

1

0

1

0

6

b9d1f0dd73f31b5e7afe7b66a7f352bc783bd42a

74

py

Python

app/engine/NoReturnFig/Loop/Break.py

normidar/py-visibility-coding-alpha

479d0c928f325178fc383dc1af0014cdf9771d1d

[ "MIT" ]

null

app/engine/NoReturnFig/Loop/Break.py

normidar/py-visibility-coding-alpha

479d0c928f325178fc383dc1af0014cdf9771d1d

[ "MIT" ]

null

app/engine/NoReturnFig/Loop/Break.py

normidar/py-visibility-coding-alpha

479d0c928f325178fc383dc1af0014cdf9771d1d

[ "MIT" ]

null

from ..NoReturnFig import NoReturnFig class Break(NoReturnFig): pass

14.8

37

0.77027

8

74

7.125

0.75

0

0.162162

74

4

38

18.5

0.919355

0

1

0

true

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

6

b9d7d6797a54ea35ab52e099269ba5314a8f950e

12,764

py

Python

cottonformation/res/servicecatalogappregistry.py

gitter-badger/cottonformation-project

354f1dce7ea106e209af2d5d818b6033a27c193c

[ "BSD-2-Clause" ]

5

2021-07-22T03:45:59.000Z

2021-12-17T21:07:14.000Z

cottonformation/res/servicecatalogappregistry.py

gitter-badger/cottonformation-project

354f1dce7ea106e209af2d5d818b6033a27c193c

[ "BSD-2-Clause" ]

1

2021-06-25T18:01:31.000Z

cottonformation/res/servicecatalogappregistry.py

gitter-badger/cottonformation-project

354f1dce7ea106e209af2d5d818b6033a27c193c

[ "BSD-2-Clause" ]

2

2021-06-27T03:08:21.000Z

2021-06-28T22:15:51.000Z

# -*- coding: utf-8 -*- """ This module """ import attr import typing from ..core.model import ( Property, Resource, Tag, GetAtt, TypeHint, TypeCheck, ) from ..core.constant import AttrMeta #--- Property declaration --- #--- Resource declaration --- @attr.s class Application(Resource): """ AWS Object Type = "AWS::ServiceCatalogAppRegistry::Application" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-servicecatalogappregistry-application.html Property Document: - ``rp_Name``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-servicecatalogappregistry-application.html#cfn-servicecatalogappregistry-application-name - ``p_Description``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-servicecatalogappregistry-application.html#cfn-servicecatalogappregistry-application-description - ``p_Tags``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-servicecatalogappregistry-application.html#cfn-servicecatalogappregistry-application-tags """ AWS_OBJECT_TYPE = "AWS::ServiceCatalogAppRegistry::Application" rp_Name: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "Name"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-servicecatalogappregistry-application.html#cfn-servicecatalogappregistry-application-name""" p_Description: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "Description"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-servicecatalogappregistry-application.html#cfn-servicecatalogappregistry-application-description""" p_Tags: typing.Dict[str, TypeHint.intrinsic_str] = attr.ib( default=None, validator=attr.validators.optional(attr.validators.deep_mapping(key_validator=attr.validators.instance_of(str), value_validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type))), metadata={AttrMeta.PROPERTY_NAME: "Tags"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-servicecatalogappregistry-application.html#cfn-servicecatalogappregistry-application-tags""" @property def rv_Id(self) -> GetAtt: """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-servicecatalogappregistry-application.html#aws-resource-servicecatalogappregistry-application-return-values""" return GetAtt(resource=self, attr_name="Id") @property def rv_Arn(self) -> GetAtt: """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-servicecatalogappregistry-application.html#aws-resource-servicecatalogappregistry-application-return-values""" return GetAtt(resource=self, attr_name="Arn") @attr.s class ResourceAssociation(Resource): """ AWS Object Type = "AWS::ServiceCatalogAppRegistry::ResourceAssociation" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-servicecatalogappregistry-resourceassociation.html Property Document: - ``rp_Application``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-servicecatalogappregistry-resourceassociation.html#cfn-servicecatalogappregistry-resourceassociation-application - ``rp_Resource``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-servicecatalogappregistry-resourceassociation.html#cfn-servicecatalogappregistry-resourceassociation-resource - ``rp_ResourceType``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-servicecatalogappregistry-resourceassociation.html#cfn-servicecatalogappregistry-resourceassociation-resourcetype """ AWS_OBJECT_TYPE = "AWS::ServiceCatalogAppRegistry::ResourceAssociation" rp_Application: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "Application"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-servicecatalogappregistry-resourceassociation.html#cfn-servicecatalogappregistry-resourceassociation-application""" rp_Resource: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "Resource"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-servicecatalogappregistry-resourceassociation.html#cfn-servicecatalogappregistry-resourceassociation-resource""" rp_ResourceType: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "ResourceType"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-servicecatalogappregistry-resourceassociation.html#cfn-servicecatalogappregistry-resourceassociation-resourcetype""" @property def rv_ApplicationArn(self) -> GetAtt: """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-servicecatalogappregistry-resourceassociation.html#aws-resource-servicecatalogappregistry-resourceassociation-return-values""" return GetAtt(resource=self, attr_name="ApplicationArn") @property def rv_ResourceArn(self) -> GetAtt: """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-servicecatalogappregistry-resourceassociation.html#aws-resource-servicecatalogappregistry-resourceassociation-return-values""" return GetAtt(resource=self, attr_name="ResourceArn") @property def rv_Id(self) -> GetAtt: """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-servicecatalogappregistry-resourceassociation.html#aws-resource-servicecatalogappregistry-resourceassociation-return-values""" return GetAtt(resource=self, attr_name="Id") @attr.s class AttributeGroup(Resource): """ AWS Object Type = "AWS::ServiceCatalogAppRegistry::AttributeGroup" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-servicecatalogappregistry-attributegroup.html Property Document: - ``rp_Attributes``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-servicecatalogappregistry-attributegroup.html#cfn-servicecatalogappregistry-attributegroup-attributes - ``rp_Name``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-servicecatalogappregistry-attributegroup.html#cfn-servicecatalogappregistry-attributegroup-name - ``p_Description``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-servicecatalogappregistry-attributegroup.html#cfn-servicecatalogappregistry-attributegroup-description - ``p_Tags``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-servicecatalogappregistry-attributegroup.html#cfn-servicecatalogappregistry-attributegroup-tags """ AWS_OBJECT_TYPE = "AWS::ServiceCatalogAppRegistry::AttributeGroup" rp_Attributes: dict = attr.ib( default=None, validator=attr.validators.instance_of(dict), metadata={AttrMeta.PROPERTY_NAME: "Attributes"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-servicecatalogappregistry-attributegroup.html#cfn-servicecatalogappregistry-attributegroup-attributes""" rp_Name: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "Name"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-servicecatalogappregistry-attributegroup.html#cfn-servicecatalogappregistry-attributegroup-name""" p_Description: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "Description"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-servicecatalogappregistry-attributegroup.html#cfn-servicecatalogappregistry-attributegroup-description""" p_Tags: typing.Dict[str, TypeHint.intrinsic_str] = attr.ib( default=None, validator=attr.validators.optional(attr.validators.deep_mapping(key_validator=attr.validators.instance_of(str), value_validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type))), metadata={AttrMeta.PROPERTY_NAME: "Tags"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-servicecatalogappregistry-attributegroup.html#cfn-servicecatalogappregistry-attributegroup-tags""" @property def rv_Id(self) -> GetAtt: """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-servicecatalogappregistry-attributegroup.html#aws-resource-servicecatalogappregistry-attributegroup-return-values""" return GetAtt(resource=self, attr_name="Id") @property def rv_Arn(self) -> GetAtt: """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-servicecatalogappregistry-attributegroup.html#aws-resource-servicecatalogappregistry-attributegroup-return-values""" return GetAtt(resource=self, attr_name="Arn") @attr.s class AttributeGroupAssociation(Resource): """ AWS Object Type = "AWS::ServiceCatalogAppRegistry::AttributeGroupAssociation" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-servicecatalogappregistry-attributegroupassociation.html Property Document: - ``rp_Application``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-servicecatalogappregistry-attributegroupassociation.html#cfn-servicecatalogappregistry-attributegroupassociation-application - ``rp_AttributeGroup``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-servicecatalogappregistry-attributegroupassociation.html#cfn-servicecatalogappregistry-attributegroupassociation-attributegroup """ AWS_OBJECT_TYPE = "AWS::ServiceCatalogAppRegistry::AttributeGroupAssociation" rp_Application: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "Application"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-servicecatalogappregistry-attributegroupassociation.html#cfn-servicecatalogappregistry-attributegroupassociation-application""" rp_AttributeGroup: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "AttributeGroup"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-servicecatalogappregistry-attributegroupassociation.html#cfn-servicecatalogappregistry-attributegroupassociation-attributegroup""" @property def rv_ApplicationArn(self) -> GetAtt: """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-servicecatalogappregistry-attributegroupassociation.html#aws-resource-servicecatalogappregistry-attributegroupassociation-return-values""" return GetAtt(resource=self, attr_name="ApplicationArn") @property def rv_AttributeGroupArn(self) -> GetAtt: """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-servicecatalogappregistry-attributegroupassociation.html#aws-resource-servicecatalogappregistry-attributegroupassociation-return-values""" return GetAtt(resource=self, attr_name="AttributeGroupArn") @property def rv_Id(self) -> GetAtt: """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-servicecatalogappregistry-attributegroupassociation.html#aws-resource-servicecatalogappregistry-attributegroupassociation-return-values""" return GetAtt(resource=self, attr_name="Id")

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6

b9e80da292d2c9f03d060b47d11ef742ae0f830d

229

py

Python

RNAPuzzles/rnapuzzles/views/user/__init__.py

whinyadventure/RNA-Puzzles

bbd147e1a0748a77b5e3424a93ad57bb430b5a0e

[ "Apache-2.0" ]

null

RNAPuzzles/rnapuzzles/views/user/__init__.py

whinyadventure/RNA-Puzzles

bbd147e1a0748a77b5e3424a93ad57bb430b5a0e

[ "Apache-2.0" ]

26

2019-10-08T11:11:25.000Z

2022-03-12T00:52:30.000Z

RNAPuzzles/rnapuzzles/views/user/__init__.py

whinyadventure/RNA-Puzzles

bbd147e1a0748a77b5e3424a93ad57bb430b5a0e

[ "Apache-2.0" ]

1

2020-05-11T18:51:04.000Z

from .detail import * from .signin import * from .signup import * from .update import * from .passwordUpdate import * from .passwordReset import * from .newPassword import * from .resetForm import * from .unconfirmedList import *

25.444444

30

0.768559

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229

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229

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1

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null

0

1

0

6

6a0aaa2cd589f7255a174e83c9cf3538a3359f9b

22

py

Python

cride/circles/views/__init__.py

AngelFA04/PlatziCride

ab37e6e7ff5b552ed799da22f562084592563680

[ "MIT" ]

null

cride/circles/views/__init__.py

AngelFA04/PlatziCride

ab37e6e7ff5b552ed799da22f562084592563680

[ "MIT" ]

null

cride/circles/views/__init__.py

AngelFA04/PlatziCride

ab37e6e7ff5b552ed799da22f562084592563680

[ "MIT" ]

null

from . import circles

11

21

0.772727

3

22

5.666667

1

0

0.181818

22

1

22

0.944444

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1

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true

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null

0

1

0

1

0

1

0

6

6a0d0818368942a4bfc0c3475d8a2e32d4b3175c

151

py

Python

google-cloud-sdk/platform/gsutil/third_party/rsa/tests/constants.py

bopopescu/searchparty

afdc2805cb1b77bd5ac9fdd1a76217f4841f0ea6

[ "Apache-2.0" ]

8

2016-02-08T11:59:31.000Z

2020-05-31T15:19:54.000Z

google-cloud-sdk/platform/gsutil/third_party/rsa/tests/constants.py

bopopescu/searchparty

afdc2805cb1b77bd5ac9fdd1a76217f4841f0ea6

[ "Apache-2.0" ]

1

2021-02-23T22:20:14.000Z

google-cloud-sdk/platform/gsutil/third_party/rsa/tests/constants.py

bopopescu/searchparty

afdc2805cb1b77bd5ac9fdd1a76217f4841f0ea6

[ "Apache-2.0" ]

7

2016-02-09T09:28:14.000Z

2020-07-25T19:03:36.000Z

# -*- coding: utf-8 -*- from rsa._compat import have_python3 if have_python3: from py3kconstants import * else: from py2kconstants import *

15.1

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19

151

5.421053

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0.041667

0.205298

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9

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true

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null

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1

0

1

0

1

0

6

dbea7b5668a6caab9724b591b737a24de13987bc

167

py

Python

obywatele/admin.py

testteammaciej/wikikracja

c530a90da5e9fbe7f506f98d7135de2eae3043c8

[ "MIT" ]

7

2016-02-21T17:25:54.000Z

2021-10-09T19:36:10.000Z

obywatele/admin.py

soma115/wikikracja

7715ca1daa4ca09888e1c7389ed5f8a2df29898b

[ "MIT" ]

19

2020-02-11T23:55:01.000Z

2022-03-31T18:11:56.000Z

obywatele/admin.py

testteammaciej/wikikracja

c530a90da5e9fbe7f506f98d7135de2eae3043c8

[ "MIT" ]

3

2016-01-20T22:34:58.000Z

2020-09-16T07:45:42.000Z

from django.contrib import admin # from django.contrib.auth.models import User # Register your models here. # admin.site.unregister(User) # admin.site.register(User)

23.857143

45

0.784431

24

167

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1

0

6

dbfc539a2f80a3db6bd2eca1dd01d62ffdd19006

48

py

Python

pyqtuidoc/fakemods/yselector.py

twardoch/pyqtuidoc

6c03d7f1311c36acd738fb05f2282dd1f7b5d85d

[ "MIT" ]

null

pyqtuidoc/fakemods/yselector.py

twardoch/pyqtuidoc

6c03d7f1311c36acd738fb05f2282dd1f7b5d85d

[ "MIT" ]

null

pyqtuidoc/fakemods/yselector.py

twardoch/pyqtuidoc

6c03d7f1311c36acd738fb05f2282dd1f7b5d85d

[ "MIT" ]

null

from PyQt5.QtWidgets import QLabel as YSelector

24

47

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7

48

5.857143

1

0

0.02381

0.125

48

1

48

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1

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null

0

1

0

1

0

1

0

6

e000f45c9400f1d0a092a11d2069952008125f5c

24

py

Python

__init__.py

smurfix/PushForKiCad

22b1110992ba5afd35dd92d14b380177d23e5914

[ "MIT" ]

24

2022-01-06T11:15:11.000Z

2022-02-06T09:51:39.000Z

__init__.py

smurfix/PushForKiCad

22b1110992ba5afd35dd92d14b380177d23e5914

[ "MIT" ]

13

2022-01-06T18:10:05.000Z

2022-03-06T12:52:40.000Z

__init__.py

smurfix/PushForKiCad

22b1110992ba5afd35dd92d14b380177d23e5914

[ "MIT" ]

2

2022-03-05T11:53:04.000Z

2022-03-07T00:55:27.000Z

from .src import plugin

12

23

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24

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1

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1

0

1

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6

e056c0d1175b95adb492297891efbad51804b931

89

py

Python

sdwan/sdwan/utils.py

sambyers/netauto_learning

22c1049bf86e188f774f1c977823abea2bb3abfe

[ "MIT" ]

null

sdwan/sdwan/utils.py

sambyers/netauto_learning

22c1049bf86e188f774f1c977823abea2bb3abfe

[ "MIT" ]

null

sdwan/sdwan/utils.py

sambyers/netauto_learning

22c1049bf86e188f774f1c977823abea2bb3abfe

[ "MIT" ]

null

from munch import munchify def dict_to_obj(dictionary): return munchify(dictionary)

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null

0

1

0

null

0

1

0

1

0

6

e0614371bd11dc505fbf930753981c97ce9b5e42

63,548

py

Python

pyspedas/mms/__init__.py

xnchu/pyspedas

62657581c0b6ed980fcd99ac34455a8b7a77dede

[ "MIT" ]

null

pyspedas/mms/__init__.py

xnchu/pyspedas

62657581c0b6ed980fcd99ac34455a8b7a77dede

[ "MIT" ]

null

pyspedas/mms/__init__.py

xnchu/pyspedas

62657581c0b6ed980fcd99ac34455a8b7a77dede

[ "MIT" ]

null

""" This module contains routines for loading MMS data """ from .mms_load_data import mms_load_data from .fgm.mms_curl import mms_curl from .fgm.mms_fgm_remove_flags import mms_fgm_remove_flags from .fgm.mms_fgm_set_metadata import mms_fgm_set_metadata from .scm.mms_scm_set_metadata import mms_scm_set_metadata from .edp.mms_edp_set_metadata import mms_edp_set_metadata from .dsp.mms_dsp_set_metadata import mms_dsp_set_metadata from .edi.mms_edi_set_metadata import mms_edi_set_metadata from .fpi.mms_fpi_set_metadata import mms_fpi_set_metadata from .mec.mms_mec_set_metadata import mms_mec_set_metadata from .hpca.mms_hpca_set_metadata import mms_hpca_set_metadata from .feeps.mms_feeps_correct_energies import mms_feeps_correct_energies from .feeps.mms_feeps_flat_field_corrections import mms_feeps_flat_field_corrections from .feeps.mms_feeps_active_eyes import mms_feeps_active_eyes from .feeps.mms_feeps_split_integral_ch import mms_feeps_split_integral_ch from .feeps.mms_feeps_remove_bad_data import mms_feeps_remove_bad_data from .feeps.mms_feeps_remove_sun import mms_feeps_remove_sun from .feeps.mms_feeps_omni import mms_feeps_omni from .feeps.mms_feeps_spin_avg import mms_feeps_spin_avg from .eis.mms_eis_omni import mms_eis_omni from .eis.mms_eis_spin_avg import mms_eis_spin_avg from .eis.mms_eis_set_metadata import mms_eis_set_metadata from pyspedas.mms.mec_ascii.mms_get_state_data import mms_get_state_data from .mms_config import CONFIG from pyspedas import tnames import re from pytplot import del_data from functools import wraps # the following decorator prints the loaded tplot variables after each load routine call def print_vars(func): def wrapper(*args, **kwargs): variables = func(*args, **kwargs) if variables is None: return None if kwargs.get('available') or CONFIG['download_only']: print('Available files:') else: print('Loaded variables:') for var in variables: print(var) return variables wrapper.__name__ = func.__name__ wrapper.__doc__ = func.__doc__ return wrapper @print_vars def mms_load_state(trange=['2015-10-16', '2015-10-17'], probe='1', level='def', datatypes=['pos', 'vel'], no_update=False, pred_or_def=True, suffix=''): """ This function loads the state (ephemeris and attitude) data from the ASCII files into tplot variables Parameters: trange : list of str time range of interest [starttime, endtime] with the format 'YYYY-MM-DD','YYYY-MM-DD'] or to specify more or less than a day ['YYYY-MM-DD/hh:mm:ss','YYYY-MM-DD/hh:mm:ss'] probe : str or list of str list of probes, valid values for MMS probes are ['1','2','3','4']. level : str indicates level of data (options: 'def' (definitive), 'pred' (predicted); default: def) datatypes : str or list of str no datatype for state data (options: 'pos', 'vel', 'spinras', 'spindec') suffix: str The tplot variable names will be given this suffix. By default, no suffix is added. no_update: bool Set this flag to preserve the original data. if not set and newer data is found the existing data will be overwritten Returns: List of tplot variables created. """ return mms_get_state_data(trange=trange, probe=probe, level=level, datatypes=datatypes, no_download=no_update, pred_or_def=pred_or_def, suffix=suffix) @print_vars def mms_load_fgm(trange=['2015-10-16', '2015-10-17'], probe='1', data_rate='srvy', level='l2', instrument='fgm', datatype='', varformat=None, varnames=[], suffix='', keep_flagged=False, get_support_data=True, time_clip=False, no_update=False, available=False, notplot=False, latest_version=False, major_version=False, min_version=None, cdf_version=None, spdf=False, always_prompt=False): """ This function loads FGM data into tplot variables Parameters: trange : list of str time range of interest [starttime, endtime] with the format 'YYYY-MM-DD','YYYY-MM-DD'] or to specify more or less than a day ['YYYY-MM-DD/hh:mm:ss','YYYY-MM-DD/hh:mm:ss'] probe : str or list of str list of probes, valid values for MMS probes are ['1','2','3','4']. data_rate : str or list of str instrument data rates for FGM include 'brst' 'fast' 'slow' 'srvy'. The default is 'srvy'. level : str indicates level of data processing. the default if no level is specified is 'l2' datatype : str or list of str no datatype for FGM instrument (all science data are loaded) get_support_data: bool Data with an attribute "VAR_TYPE" with a value of "support_data" will be loaded into tplot. By default, only loads in data with a "VAR_TYPE" attribute of "data". time_clip: bool Data will be clipped to the exact trange specified by the trange keyword. varformat: str The file variable formats to load into tplot. Wildcard character "*" is accepted. By default, all variables are loaded in. varnames: list of str List of variable names to load (if not specified, all data variables are loaded) suffix: str The tplot variable names will be given this suffix. By default, no suffix is added. notplot: bool If True, then data are returned in a hash table instead of being stored in tplot variables (useful for debugging, and access to multi-dimensional data products) available: bool If True, simply return the available data files (without downloading) for the requested paramters no_update: bool Set this flag to preserve the original data. if not set and newer data is found the existing data will be overwritten cdf_version: str Specify a specific CDF version # to load (e.g., cdf_version='4.3.0') min_version: str Specify a minimum CDF version # to load latest_version: bool Only grab the latest CDF version in the requested time interval major_version: bool Only open the latest major CDF version (e.g., X in vX.Y.Z) in the requested time interval keep_flagged: bool If True, don't remove flagged data (flagged data are set to NaNs by default, this keyword turns this off) always_prompt: bool Set this keyword to always prompt for the user's username and password; useful if you accidently save an incorrect password, or if your SDC password has changed spdf: bool If True, download the data from the SPDF instead of the SDC Returns: List of tplot variables created. """ if (varformat is not None) and (not keep_flagged) and (not available) and (not notplot): varformat_fetch = varformat+'|*_flag_*' else: varformat_fetch = varformat tvars = mms_load_data(trange=trange, notplot=notplot, probe=probe, data_rate=data_rate, level=level, instrument=instrument, datatype=datatype, varformat=varformat_fetch, varnames=varnames, suffix=suffix, get_support_data=get_support_data, time_clip=time_clip, no_update=no_update, available=available, latest_version=latest_version, major_version=major_version, min_version=min_version, cdf_version=cdf_version, spdf=spdf, always_prompt=always_prompt) if tvars == None or available or notplot or CONFIG['download_only']: return tvars # the probes will need to be strings beyond this point if isinstance(probe, list): probe = [str(p) for p in probe] # remove flagged data if not keep_flagged: mms_fgm_remove_flags(probe, data_rate, level, instrument, suffix=suffix) # Delete the flags variable if it was not originally requested if varformat is not None: regex = re.compile(varformat.replace("*", ".*")) tvars_to_delete = [tvar for tvar in tvars if not re.match(regex, tvar)] for tvar in tvars_to_delete: del_data(tvar) tvars.remove(tvar) mms_fgm_set_metadata(probe, data_rate, level, instrument, suffix=suffix) return tvars @print_vars def mms_load_hpca(trange=['2015-10-16', '2015-10-17'], probe='1', data_rate='srvy', level='l2', datatype='moments', get_support_data=None, time_clip=False, no_update=False, varformat=None, varnames=[], suffix='', center_measurement=False, available=False, notplot=False, latest_version=False, major_version=False, min_version=None, cdf_version=None, spdf=False, always_prompt=False): """ This function loads HPCA data into tplot variables Parameters: trange : list of str time range of interest [starttime, endtime] with the format 'YYYY-MM-DD','YYYY-MM-DD'] or to specify more or less than a day ['YYYY-MM-DD/hh:mm:ss','YYYY-MM-DD/hh:mm:ss'] probe : str or list of str list of probes, valid values for MMS probes are ['1','2','3','4']. data_rate : str or list of str instrument data rates for HPCA include 'brst', 'srvy'. The default is 'srvy'. level : str indicates level of data processing. the default if no level is specified is 'l2' datatype : str or list of str Valid datatypes for HPCA are 'moments' and 'ion'; the default is 'moments' get_support_data: bool Data with an attribute "VAR_TYPE" with a value of "support_data" will be loaded into tplot. By default, only loads in data with a "VAR_TYPE" attribute of "data". varformat: str The file variable formats to load into tplot. Wildcard character "*" is accepted. By default, all variables are loaded in. varnames: list of str List of variable names to load (if not specified, all data variables are loaded) time_clip: bool Data will be clipped to the exact trange specified by the trange keyword. suffix: str The tplot variable names will be given this suffix. By default, no suffix is added. center_measurement: bool If True, the CDF epoch variables are time-shifted to the middle of the accumulation interval by their DELTA_PLUS_VAR and DELTA_MINUS_VAR variable attributes notplot: bool If True, then data are returned in a hash table instead of being stored in tplot variables (useful for debugging, and access to multi-dimensional data products) available: bool If True, simply return the available data files (without downloading) for the requested paramters no_update: bool Set this flag to preserve the original data. if not set and newer data is found the existing data will be overwritten cdf_version: str Specify a specific CDF version # to load (e.g., cdf_version='4.3.0') min_version: str Specify a minimum CDF version # to load latest_version: bool Only grab the latest CDF version in the requested time interval major_version: bool Only open the latest major CDF version (e.g., X in vX.Y.Z) in the requested time interval always_prompt: bool Set this keyword to always prompt for the user's username and password; useful if you accidently save an incorrect password, or if your SDC password has changed spdf: bool If True, download the data from the SPDF instead of the SDC Returns: List of tplot variables created. """ if get_support_data is None: get_support_data = True tvars = mms_load_data(trange=trange, notplot=notplot, probe=probe, data_rate=data_rate, level=level, instrument='hpca', datatype=datatype, varformat=varformat, varnames=varnames, suffix=suffix, get_support_data=get_support_data, time_clip=time_clip, no_update=no_update, center_measurement=center_measurement, available=available, latest_version=latest_version, major_version=major_version, min_version=min_version, cdf_version=cdf_version, spdf=spdf, always_prompt=always_prompt) if tvars == None or available or notplot or CONFIG['download_only']: return tvars mms_hpca_set_metadata(probe=probe, suffix=suffix) return tvars @print_vars def mms_load_fpi(trange=['2015-10-16', '2015-10-17'], probe='1', data_rate='fast', level='l2', datatype=['des-moms', 'dis-moms'], varformat=None, varnames=[], suffix='', get_support_data=False, time_clip=False, no_update=False, center_measurement=False, available=False, notplot=False, latest_version=False, major_version=False, min_version=None, cdf_version=None, spdf=False, always_prompt=False): """ This function loads FPI data into tplot variables Parameters: trange : list of str time range of interest [starttime, endtime] with the format 'YYYY-MM-DD','YYYY-MM-DD'] or to specify more or less than a day ['YYYY-MM-DD/hh:mm:ss','YYYY-MM-DD/hh:mm:ss'] probe : str or list of str list of probes, valid values for MMS probes are ['1','2','3','4']. data_rate : str or list of str instrument data rates for FPI include 'brst', 'fast'. The default is 'srvy'. level : str indicates level of data processing. the default if no level is specified is 'l2' datatype : str or list of str Valid datatypes for FPI are: 'des-moms', 'dis-moms' (default) 'des-dist', 'dis-dist' get_support_data: bool Data with an attribute "VAR_TYPE" with a value of "support_data" will be loaded into tplot. By default, only loads in data with a "VAR_TYPE" attribute of "data". time_clip: bool Data will be clipped to the exact trange specified by the trange keyword. varformat: str The file variable formats to load into tplot. Wildcard character "*" is accepted. By default, all variables are loaded in. varnames: list of str List of variable names to load (if not specified, all data variables are loaded) suffix: str The tplot variable names will be given this suffix. By default, no suffix is added. center_measurement: bool If True, the CDF epoch variables are time-shifted to the middle of the accumulation interval by their DELTA_PLUS_VAR and DELTA_MINUS_VAR variable attributes notplot: bool If True, then data are returned in a hash table instead of being stored in tplot variables (useful for debugging, and access to multi-dimensional data products) available: bool If True, simply return the available data files (without downloading) for the requested paramters no_update: bool Set this flag to preserve the original data. if not set and newer data is found the existing data will be overwritten cdf_version: str Specify a specific CDF version # to load (e.g., cdf_version='4.3.0') min_version: str Specify a minimum CDF version # to load latest_version: bool Only grab the latest CDF version in the requested time interval major_version: bool Only open the latest major CDF version (e.g., X in vX.Y.Z) in the requested time interval always_prompt: bool Set this keyword to always prompt for the user's username and password; useful if you accidently save an incorrect password, or if your SDC password has changed spdf: bool If True, download the data from the SPDF instead of the SDC Returns: List of tplot variables created. """ tvars = mms_load_data(trange=trange, probe=probe, data_rate=data_rate, level=level, instrument='fpi', datatype=datatype, varformat=varformat, varnames=varnames, suffix=suffix, get_support_data=get_support_data, time_clip=time_clip, no_update=no_update, center_measurement=center_measurement, available=available, notplot=notplot, latest_version=latest_version, major_version=major_version, min_version=min_version, cdf_version=cdf_version, spdf=spdf, always_prompt=always_prompt) if tvars == None or available or notplot or CONFIG['download_only']: return tvars mms_fpi_set_metadata(probe, data_rate, datatype, level, suffix=suffix) return tvars @print_vars def mms_load_scm(trange=['2015-10-16', '2015-10-17'], probe='1', data_rate='srvy', level='l2', datatype='', varformat=None, varnames=[], suffix='', get_support_data=False, time_clip=True, no_update=False, available=False, notplot=False, latest_version=False, major_version=False, min_version=None, cdf_version=None, spdf=False, always_prompt=False): """ This function loads SCM data into tplot variables Parameters: trange : list of str time range of interest [starttime, endtime] with the format 'YYYY-MM-DD','YYYY-MM-DD'] or to specify more or less than a day ['YYYY-MM-DD/hh:mm:ss','YYYY-MM-DD/hh:mm:ss'] probe : str or list of str list of probes, valid values for MMS probes are ['1','2','3','4']. data_rate : str or list of str instrument data rates for SCM include ['brst' 'fast' 'slow' 'srvy']. The default is 'srvy'. level : str indicates level of data processing. the default if no level is specified is 'l2' datatype : str or list of str Valid datatypes for SCM are: ['scsrvy', 'cal', 'scb', 'scf', 'schb', 'scm', 'scs'] If no value is given the default is scsrvy for srvy data, and scb for brst data. get_support_data: bool Data with an attribute "VAR_TYPE" with a value of "support_data" will be loaded into tplot. By default, only loads in data with a "VAR_TYPE" attribute of "data". time_clip: bool Data will be clipped to the exact trange specified by the trange keyword. varformat: str The file variable formats to load into tplot. Wildcard character "*" is accepted. By default, all variables are loaded in. varnames: list of str List of variable names to load (if not specified, all data variables are loaded) suffix: str The tplot variable names will be given this suffix. By default, no suffix is added. notplot: bool If True, then data are returned in a hash table instead of being stored in tplot variables (useful for debugging, and access to multi-dimensional data products) available: bool If True, simply return the available data files (without downloading) for the requested paramters no_update: bool Set this flag to preserve the original data. if not set and newer data is found the existing data will be overwritten cdf_version: str Specify a specific CDF version # to load (e.g., cdf_version='4.3.0') min_version: str Specify a minimum CDF version # to load latest_version: bool Only grab the latest CDF version in the requested time interval major_version: bool Only open the latest major CDF version (e.g., X in vX.Y.Z) in the requested time interval always_prompt: bool Set this keyword to always prompt for the user's username and password; useful if you accidently save an incorrect password, or if your SDC password has changed spdf: bool If True, download the data from the SPDF instead of the SDC Returns: List of tplot variables created. """ if not isinstance(data_rate, list): data_rate = list([data_rate]) if isinstance(datatype, str) and datatype == '': # guess from data_rate datatype = list() for dr in data_rate: if dr == 'srvy': datatype.append('scsrvy') if dr == 'brst': datatype.extend(['scb', 'schb']) datatype = list(set(datatype)) # make it unique else: if not isinstance(datatype, list): datatype = list([datatype]) # ensure datatype does not contain empty string datatype = list(set([dt.strip() for dt in datatype])) if '' in datatype: datatype.remove('') tvars = mms_load_data(trange=trange, notplot=notplot, probe=probe, data_rate=data_rate, level=level, instrument='scm', datatype=datatype, varformat=varformat, varnames=varnames, suffix=suffix, get_support_data=get_support_data, time_clip=time_clip, no_update=no_update, available=available, latest_version=latest_version, major_version=major_version, min_version=min_version, cdf_version=cdf_version, spdf=spdf, always_prompt=always_prompt) if tvars == None or available or notplot or CONFIG['download_only']: return tvars coord = '' if level == 'l1a': coord = '123' elif level == 'l1b': coord = 'scm123' elif level == 'l2': coord = 'gse' if not isinstance(probe, list): probe = [probe] if not isinstance(datatype, list): datatype = [datatype] probe = [str(p) for p in probe] for p in probe: for dtype in datatype: mms_scm_set_metadata(tvars, p, dtype, coord, suffix=suffix) return tvars @print_vars def mms_load_mec(trange=['2015-10-16', '2015-10-17'], probe='1', data_rate='srvy', level='l2', datatype='ephts04d', varformat=None, varnames=[], suffix='', get_support_data=False, time_clip=False, no_update=False, available=False, notplot=False, latest_version=False, major_version=False, min_version=None, cdf_version=None, spdf=False, always_prompt=False): """ This function loads MEC data into tplot variables Parameters: trange : list of str time range of interest [starttime, endtime] with the format 'YYYY-MM-DD','YYYY-MM-DD'] or to specify more or less than a day ['YYYY-MM-DD/hh:mm:ss','YYYY-MM-DD/hh:mm:ss'] probe : str or list of str list of probes, valid values for MMS probes are ['1','2','3','4']. data_rate : str or list of str instrument data rates for MEC include ['brst', 'srvy']. The default is 'srvy'. level : str indicates level of data processing. the default if no level is specified is 'l2' datatype : str or list of str Valid datatypes for MEC are: ['ephts04d', 'epht89q', 'epht89d']; default is 'ephts04d' get_support_data: bool Data with an attribute "VAR_TYPE" with a value of "support_data" will be loaded into tplot. By default, only loads in data with a "VAR_TYPE" attribute of "data". time_clip: bool Data will be clipped to the exact trange specified by the trange keyword. varformat: str The file variable formats to load into tplot. Wildcard character "*" is accepted. By default, all variables are loaded in. varnames: list of str List of variable names to load (if not specified, all data variables are loaded) suffix: str The tplot variable names will be given this suffix. By default, no suffix is added. notplot: bool If True, then data are returned in a hash table instead of being stored in tplot variables (useful for debugging, and access to multi-dimensional data products) available: bool If True, simply return the available data files (without downloading) for the requested paramters no_update: bool Set this flag to preserve the original data. if not set and newer data is found the existing data will be overwritten cdf_version: str Specify a specific CDF version # to load (e.g., cdf_version='4.3.0') min_version: str Specify a minimum CDF version # to load latest_version: bool Only grab the latest CDF version in the requested time interval major_version: bool Only open the latest major CDF version (e.g., X in vX.Y.Z) in the requested time interval always_prompt: bool Set this keyword to always prompt for the user's username and password; useful if you accidently save an incorrect password, or if your SDC password has changed spdf: bool If True, download the data from the SPDF instead of the SDC Returns: List of tplot variables created. """ tvars = mms_load_data(trange=trange, probe=probe, data_rate=data_rate, level=level, instrument='mec', datatype=datatype, get_support_data=get_support_data, varformat=varformat, varnames=varnames, suffix=suffix, time_clip=time_clip, no_update=no_update, available=available, notplot=notplot, latest_version=latest_version, major_version=major_version, min_version=min_version, cdf_version=cdf_version, spdf=spdf, always_prompt=always_prompt) if tvars == None or available or notplot or CONFIG['download_only']: return tvars mms_mec_set_metadata(probe, data_rate, level, suffix=suffix) return tvars @print_vars def mms_load_feeps(trange=['2015-10-16', '2015-10-17'], probe='1', data_rate='srvy', level='l2', datatype='electron', varformat=None, varnames=[], get_support_data=True, suffix='', time_clip=False, no_update=False, available=False, notplot=False, no_flatfield_corrections=False, data_units=['count_rate', 'intensity'], latest_version=False, major_version=False, min_version=None, cdf_version=None, spdf=False, always_prompt=False): """ This function loads FEEPS data into tplot variables Parameters: trange : list of str time range of interest [starttime, endtime] with the format 'YYYY-MM-DD','YYYY-MM-DD'] or to specify more or less than a day ['YYYY-MM-DD/hh:mm:ss','YYYY-MM-DD/hh:mm:ss'] probe : str or list of str list of probes, valid values for MMS probes are ['1','2','3','4']. data_rate : str or list of str instrument data rates for FEEPS include ['brst', 'srvy']. The default is 'srvy'. level : str indicates level of data processing. the default if no level is specified is 'l2' datatype : str or list of str Valid datatypes for FEEPS are: L2, L1b: ['electron', 'ion'] L1a: ['electron-bottom', 'electron-top', 'ion-bottom', 'ion-top'] get_support_data: bool Data with an attribute "VAR_TYPE" with a value of "support_data" will be loaded into tplot. By default, only loads in data with a "VAR_TYPE" attribute of "data". time_clip: bool Data will be clipped to the exact trange specified by the trange keyword. varformat: str The file variable formats to load into tplot. Wildcard character "*" is accepted. By default, all variables are loaded in. varnames: list of str List of variable names to load (if not specified, all data variables are loaded) suffix: str The tplot variable names will be given this suffix. By default, no suffix is added. notplot: bool If True, then data are returned in a hash table instead of being stored in tplot variables (useful for debugging, and access to multi-dimensional data products) available: bool If True, simply return the available data files (without downloading) for the requested paramters no_update: bool Set this flag to preserve the original data. if not set and newer data is found the existing data will be overwritten cdf_version: str Specify a specific CDF version # to load (e.g., cdf_version='4.3.0') min_version: str Specify a minimum CDF version # to load latest_version: bool Only grab the latest CDF version in the requested time interval major_version: bool Only open the latest major CDF version (e.g., X in vX.Y.Z) in the requested time interval always_prompt: bool Set this keyword to always prompt for the user's username and password; useful if you accidently save an incorrect password, or if your SDC password has changed spdf: bool If True, download the data from the SPDF instead of the SDC Returns: List of tplot variables created. """ tvars = mms_load_data(trange=trange, notplot=notplot, probe=probe, data_rate=data_rate, level=level, instrument='feeps', datatype=datatype, varformat=varformat, varnames=varnames, get_support_data=get_support_data, suffix=suffix, time_clip=time_clip, no_update=no_update, available=available, latest_version=latest_version, major_version=major_version, min_version=min_version, cdf_version=cdf_version, spdf=spdf, always_prompt=always_prompt) if tvars == [] or available or notplot or CONFIG['download_only']: return tvars probes = probe if isinstance(probe, list) else [probe] data_rates = data_rate if isinstance(data_rate, list) else [data_rate] levels = level if isinstance(level, list) else [level] datatypes = datatype if isinstance(datatype, list) else [datatype] data_units = data_units if isinstance(data_units, list) else [data_units] probes = [str(p) for p in probes] mms_feeps_correct_energies(probes, data_rate, level=level, suffix=suffix) if not no_flatfield_corrections: mms_feeps_flat_field_corrections(probes=probes, data_rate=data_rate, suffix=suffix) for probe in probes: for datatype in datatypes: mms_feeps_remove_bad_data(probe=probe, data_rate=data_rate, datatype =datatype, level=level, suffix=suffix) for data_unit in data_units: eyes = mms_feeps_active_eyes(trange, probe, data_rate, datatype, level) split_vars = mms_feeps_split_integral_ch(data_unit, datatype, probe, suffix=suffix, data_rate=data_rate, level=level, sensor_eyes=eyes) sun_removed_vars = mms_feeps_remove_sun(eyes, trange, probe=probe, datatype=datatype, data_units=data_unit, data_rate=data_rate, level=level, suffix=suffix) omni_vars = mms_feeps_omni(eyes, probe=probe, datatype=datatype, data_units=data_unit, data_rate=data_rate, level=level, suffix=suffix) tvars = tvars + split_vars + sun_removed_vars + omni_vars tvars.append(mms_feeps_spin_avg(probe=probe, data_units=data_unit, datatype=datatype, data_rate=data_rate, level=level, suffix=suffix)) return tvars @print_vars def mms_load_eis(trange=['2015-10-16', '2015-10-17'], probe='1', data_rate='srvy', level='l2', datatype='extof', varformat=None, varnames=[], get_support_data=True, suffix='', time_clip=False, no_update=False, available=False, notplot=False, latest_version=False, major_version=False, min_version=None, cdf_version=None, spdf=False, always_prompt=False): """ This function loads EIS data into tplot variables Parameters: trange : list of str time range of interest [starttime, endtime] with the format 'YYYY-MM-DD','YYYY-MM-DD'] or to specify more or less than a day ['YYYY-MM-DD/hh:mm:ss','YYYY-MM-DD/hh:mm:ss'] probe : str or list of str list of probes, valid values for MMS probes are ['1','2','3','4']. data_rate : str or list of str instrument data rates for EIS include ['brst', 'srvy']. The default is 'srvy'. level : str indicates level of data processing. the default if no level is specified is 'l2' datatype : str or list of str Valid datatypes for EIS are: ['extof', 'phxtof', and 'electronenergy']; default is 'extof' get_support_data: bool Data with an attribute "VAR_TYPE" with a value of "support_data" will be loaded into tplot. By default, only loads in data with a "VAR_TYPE" attribute of "data". time_clip: bool Data will be clipped to the exact trange specified by the trange keyword. varformat: str The file variable formats to load into tplot. Wildcard character "*" is accepted. By default, all variables are loaded in. varnames: list of str List of variable names to load (if not specified, all data variables are loaded) suffix: str The tplot variable names will be given this suffix. By default, no suffix is added. notplot: bool If True, then data are returned in a hash table instead of being stored in tplot variables (useful for debugging, and access to multi-dimensional data products) available: bool If True, simply return the available data files (without downloading) for the requested paramters no_update: bool Set this flag to preserve the original data. if not set and newer data is found the existing data will be overwritten cdf_version: str Specify a specific CDF version # to load (e.g., cdf_version='4.3.0') min_version: str Specify a minimum CDF version # to load latest_version: bool Only grab the latest CDF version in the requested time interval major_version: bool Only open the latest major CDF version (e.g., X in vX.Y.Z) in the requested time interval always_prompt: bool Set this keyword to always prompt for the user's username and password; useful if you accidently save an incorrect password, or if your SDC password has changed spdf: bool If True, download the data from the SPDF instead of the SDC Returns: List of tplot variables created. """ tvars = mms_load_data(trange=trange, notplot=notplot, probe=probe, data_rate=data_rate, level=level, instrument='epd-eis', datatype=datatype, varformat=varformat, varnames=varnames, get_support_data=get_support_data, prefix='', suffix=suffix, time_clip=time_clip, no_update=no_update, available=available, latest_version=latest_version, major_version=major_version, min_version=min_version, cdf_version=cdf_version, spdf=spdf, always_prompt=always_prompt) if tvars == [] or available or notplot or CONFIG['download_only']: return tvars if not isinstance(probe, list): probe = [probe] if not isinstance(data_rate, list): data_rate = [data_rate] if not isinstance(datatype, list): datatype = [datatype] # the probes will need to be strings beyond this point if isinstance(probe, list): probe = [str(p) for p in probe] for probe_id in probe: for datatype_id in datatype: for data_rate_id in data_rate: if datatype_id == 'electronenergy': e_spin_avg_var = mms_eis_spin_avg(probe=probe_id, species='electron', datatype=datatype_id, data_rate=data_rate_id, suffix=suffix) # create non-spin averaged omni-directional spectra e_omni_spectra = mms_eis_omni(probe_id, species='electron', data_rate=data_rate_id, datatype=datatype_id) # create spin averaged omni-directional spectra e_omni_spectra_spin = mms_eis_omni(probe_id, species='electron', data_rate=data_rate_id, datatype=datatype_id, suffix=suffix+'_spin') # add the vars to the output if e_spin_avg_var is not None: for tvar in e_spin_avg_var: tvars.append(tvar) if e_omni_spectra is not None: tvars.append(e_omni_spectra) if e_omni_spectra_spin is not None: tvars.append(e_omni_spectra_spin) elif datatype_id == 'extof': p_spin_avg_var = mms_eis_spin_avg(probe=probe_id, species='proton', datatype=datatype_id, data_rate=data_rate_id, suffix=suffix) o_spin_avg_var = mms_eis_spin_avg(probe=probe_id, species='oxygen', datatype=datatype_id, data_rate=data_rate_id, suffix=suffix) a_spin_avg_var = mms_eis_spin_avg(probe=probe_id, species='alpha', datatype=datatype_id, data_rate=data_rate_id, suffix=suffix) # create non-spin averaged omni-directional spectra p_omni_spectra = mms_eis_omni(probe_id, species='proton', data_rate=data_rate_id, datatype=datatype_id, suffix=suffix) o_omni_spectra = mms_eis_omni(probe_id, species='oxygen', data_rate=data_rate_id, datatype=datatype_id, suffix=suffix) a_omni_spectra = mms_eis_omni(probe_id, species='alpha', data_rate=data_rate_id, datatype=datatype_id, suffix=suffix) # create spin averaged omni-directional spectra p_omni_spectra_spin = mms_eis_omni(probe_id, species='proton', data_rate=data_rate_id, datatype=datatype_id, suffix=suffix+'_spin') o_omni_spectra_spin = mms_eis_omni(probe_id, species='oxygen', data_rate=data_rate_id, datatype=datatype_id, suffix=suffix+'_spin') a_omni_spectra_spin = mms_eis_omni(probe_id, species='alpha', data_rate=data_rate_id, datatype=datatype_id, suffix=suffix+'_spin') # add the vars to the output if p_spin_avg_var is not None: for tvar in p_spin_avg_var: tvars.append(tvar) if o_spin_avg_var is not None: for tvar in o_spin_avg_var: tvars.append(tvar) if a_spin_avg_var is not None: for tvar in a_spin_avg_var: tvars.append(tvar) if p_omni_spectra is not None: tvars.append(p_omni_spectra) if o_omni_spectra is not None: tvars.append(o_omni_spectra) if a_omni_spectra is not None: tvars.append(a_omni_spectra) if p_omni_spectra_spin is not None: tvars.append(p_omni_spectra_spin) if o_omni_spectra_spin is not None: tvars.append(o_omni_spectra_spin) if a_omni_spectra_spin is not None: tvars.append(a_omni_spectra_spin) elif datatype_id == 'phxtof': p_spin_avg_var = mms_eis_spin_avg(probe=probe_id, species='proton', datatype=datatype_id, data_rate=data_rate_id, suffix=suffix) o_spin_avg_var = mms_eis_spin_avg(probe=probe_id, species='oxygen', datatype=datatype_id, data_rate=data_rate_id, suffix=suffix) # create non-spin averaged omni-directional spectra p_omni_spectra = mms_eis_omni(probe_id, species='proton', data_rate=data_rate_id, datatype=datatype_id, suffix=suffix) o_omni_spectra = mms_eis_omni(probe_id, species='oxygen', data_rate=data_rate_id, datatype=datatype_id, suffix=suffix) # create spin averaged omni-directional spectra p_omni_spectra_spin = mms_eis_omni(probe_id, species='proton', data_rate=data_rate_id, datatype=datatype_id, suffix=suffix+'_spin') o_omni_spectra_spin = mms_eis_omni(probe_id, species='oxygen', data_rate=data_rate_id, datatype=datatype_id, suffix=suffix+'_spin') # add the vars to the output if p_spin_avg_var is not None: for tvar in p_spin_avg_var: tvars.append(tvar) if o_spin_avg_var is not None: for tvar in o_spin_avg_var: tvars.append(tvar) if p_omni_spectra is not None: tvars.append(p_omni_spectra) if o_omni_spectra is not None: tvars.append(o_omni_spectra) if p_omni_spectra_spin is not None: tvars.append(p_omni_spectra_spin) if o_omni_spectra_spin is not None: tvars.append(o_omni_spectra_spin) mms_eis_set_metadata(tnames(tvars), data_rate=data_rate_id, datatype=datatype_id, suffix=suffix) return tnames(tvars) @print_vars def mms_load_edi(trange=['2016-10-16', '2016-10-17'], probe='1', data_rate='srvy', level='l2', datatype='efield', varformat=None, varnames=[], get_support_data=False, suffix='', time_clip=False, no_update=False, available=False, notplot=False, latest_version=False, major_version=False, min_version=None, cdf_version=None, spdf=False, always_prompt=False): """ This function loads EDI data into tplot variables Parameters: trange : list of str time range of interest [starttime, endtime] with the format 'YYYY-MM-DD','YYYY-MM-DD'] or to specify more or less than a day ['YYYY-MM-DD/hh:mm:ss','YYYY-MM-DD/hh:mm:ss'] probe : str or list of str list of probes, valid values for MMS probes are ['1','2','3','4']. data_rate : str or list of str instrument data rates for EDI include ['brst', 'fast', 'slow', 'srvy']. The default is 'srvy'. level : str indicates level of data processing. the default if no level is specified is 'l2' datatype : str or list of str Valid datatypes for EDI are: ['efield', 'amb']; default is 'efield' get_support_data: bool Data with an attribute "VAR_TYPE" with a value of "support_data" will be loaded into tplot. By default, only loads in data with a "VAR_TYPE" attribute of "data". time_clip: bool Data will be clipped to the exact trange specified by the trange keyword. varformat: str The file variable formats to load into tplot. Wildcard character "*" is accepted. By default, all variables are loaded in. varnames: list of str List of variable names to load (if not specified, all data variables are loaded) suffix: str The tplot variable names will be given this suffix. By default, no suffix is added. notplot: bool If True, then data are returned in a hash table instead of being stored in tplot variables (useful for debugging, and access to multi-dimensional data products) available: bool If True, simply return the available data files (without downloading) for the requested paramters no_update: bool Set this flag to preserve the original data. if not set and newer data is found the existing data will be overwritten cdf_version: str Specify a specific CDF version # to load (e.g., cdf_version='4.3.0') min_version: str Specify a minimum CDF version # to load latest_version: bool Only grab the latest CDF version in the requested time interval major_version: bool Only open the latest major CDF version (e.g., X in vX.Y.Z) in the requested time interval always_prompt: bool Set this keyword to always prompt for the user's username and password; useful if you accidently save an incorrect password, or if your SDC password has changed spdf: bool If True, download the data from the SPDF instead of the SDC Returns: List of tplot variables created. """ tvars = mms_load_data(trange=trange, notplot=notplot, probe=probe, data_rate=data_rate, level=level, instrument='edi', datatype=datatype, varformat=varformat, varnames=varnames, get_support_data=get_support_data, suffix=suffix, time_clip=time_clip, no_update=no_update, available=available, latest_version=latest_version, major_version=major_version, min_version=min_version, cdf_version=cdf_version, spdf=spdf, always_prompt=always_prompt) if tvars == None or available or notplot or CONFIG['download_only']: return tvars mms_edi_set_metadata(probe, data_rate, level, suffix=suffix) return tvars @print_vars def mms_load_edp(trange=['2015-10-16', '2015-10-17'], probe='1', data_rate='fast', level='l2', datatype='dce', varformat=None, varnames=[], get_support_data=False, suffix='', time_clip=True, no_update=False, available=False, notplot=False, latest_version=False, major_version=False, min_version=None, cdf_version=None, spdf=False, always_prompt=False): """ This function loads EDP data into tplot variables Parameters: trange : list of str time range of interest [starttime, endtime] with the format 'YYYY-MM-DD','YYYY-MM-DD'] or to specify more or less than a day ['YYYY-MM-DD/hh:mm:ss','YYYY-MM-DD/hh:mm:ss'] probe : str or list of str list of probes, valid values for MMS probes are ['1','2','3','4']. data_rate : str or list of str instrument data rates for EDP include ['brst', 'fast', 'slow', 'srvy']. The default is 'fast'. level : str indicates level of data processing. the default if no level is specified is 'l2' datatype : str or list of str Valid datatypes for EDP are: ['dce', 'dcv', 'ace', 'hmfe']; default is 'dce' get_support_data: bool Data with an attribute "VAR_TYPE" with a value of "support_data" will be loaded into tplot. By default, only loads in data with a "VAR_TYPE" attribute of "data". time_clip: bool Data will be clipped to the exact trange specified by the trange keyword. varformat: str The file variable formats to load into tplot. Wildcard character "*" is accepted. By default, all variables are loaded in. varnames: list of str List of variable names to load (if not specified, all data variables are loaded) suffix: str The tplot variable names will be given this suffix. By default, no suffix is added. notplot: bool If True, then data are returned in a hash table instead of being stored in tplot variables (useful for debugging, and access to multi-dimensional data products) available: bool If True, simply return the available data files (without downloading) for the requested paramters no_update: bool Set this flag to preserve the original data. if not set and newer data is found the existing data will be overwritten cdf_version: str Specify a specific CDF version # to load (e.g., cdf_version='4.3.0') min_version: str Specify a minimum CDF version # to load latest_version: bool Only grab the latest CDF version in the requested time interval major_version: bool Only open the latest major CDF version (e.g., X in vX.Y.Z) in the requested time interval always_prompt: bool Set this keyword to always prompt for the user's username and password; useful if you accidently save an incorrect password, or if your SDC password has changed spdf: bool If True, download the data from the SPDF instead of the SDC Returns: List of tplot variables created. """ tvars = mms_load_data(trange=trange, notplot=notplot, probe=probe, data_rate=data_rate, level=level, instrument='edp', datatype=datatype, varformat=varformat, varnames=varnames, get_support_data=get_support_data, suffix=suffix, time_clip=time_clip, no_update=no_update, available=available, latest_version=latest_version, major_version=major_version, min_version=min_version, cdf_version=cdf_version, spdf=spdf, always_prompt=always_prompt) if tvars == None or available or notplot or CONFIG['download_only']: return tvars mms_edp_set_metadata(probe, data_rate, level, suffix=suffix) return tvars @print_vars def mms_load_dsp(trange=['2015-10-16', '2015-10-17'], probe='1', data_rate='srvy', level='l2', datatype='bpsd', varformat=None, varnames=[], suffix='', get_support_data=False, time_clip=False, no_update=False, available=False, notplot=False, latest_version=False, major_version=False, min_version=None, cdf_version=None, spdf=False, always_prompt=False): """ This function loads DSP data into tplot variables Parameters: trange : list of str time range of interest [starttime, endtime] with the format 'YYYY-MM-DD','YYYY-MM-DD'] or to specify more or less than a day ['YYYY-MM-DD/hh:mm:ss','YYYY-MM-DD/hh:mm:ss'] probe : str or list of str list of probes, valid values for MMS probes are ['1','2','3','4']. data_rate : str or list of str instrument data rates for DSP include ['fast', 'slow', 'srvy']. The default is 'srvy'. level : str indicates level of data processing. the default if no level is specified is 'l2' datatype : str or list of str Valid datatypes for DSP are: ['epsd', 'bpsd', 'swd']; default is 'bpsd' get_support_data: bool Data with an attribute "VAR_TYPE" with a value of "support_data" will be loaded into tplot. By default, only loads in data with a "VAR_TYPE" attribute of "data". time_clip: bool Data will be clipped to the exact trange specified by the trange keyword. varformat: str The file variable formats to load into tplot. Wildcard character "*" is accepted. By default, all variables are loaded in. varnames: list of str List of variable names to load (if not specified, all data variables are loaded) suffix: str The tplot variable names will be given this suffix. By default, no suffix is added. notplot: bool If True, then data are returned in a hash table instead of being stored in tplot variables (useful for debugging, and access to multi-dimensional data products) available: bool If True, simply return the available data files (without downloading) for the requested paramters no_update: bool Set this flag to preserve the original data. if not set and newer data is found the existing data will be overwritten cdf_version: str Specify a specific CDF version # to load (e.g., cdf_version='4.3.0') min_version: str Specify a minimum CDF version # to load latest_version: bool Only grab the latest CDF version in the requested time interval major_version: bool Only open the latest major CDF version (e.g., X in vX.Y.Z) in the requested time interval always_prompt: bool Set this keyword to always prompt for the user's username and password; useful if you accidently save an incorrect password, or if your SDC password has changed spdf: bool If True, download the data from the SPDF instead of the SDC Returns: List of tplot variables created. """ tvars = mms_load_data(trange=trange, notplot=notplot, probe=probe, data_rate=data_rate, level=level, instrument='dsp', datatype=datatype, varformat=varformat, varnames=varnames, suffix=suffix, get_support_data=get_support_data, time_clip=time_clip, no_update=no_update, available=available, latest_version=latest_version, major_version=major_version, min_version=min_version, cdf_version=cdf_version, spdf=spdf, always_prompt=always_prompt) if tvars == None or available or notplot or CONFIG['download_only']: return tvars mms_dsp_set_metadata(probe, data_rate, level, suffix=suffix) return tvars @print_vars def mms_load_aspoc(trange=['2015-10-16', '2015-10-17'], probe='1', data_rate='srvy', level='l2', datatype='', varformat=None, varnames=[], get_support_data=False, suffix='', time_clip=False, no_update=False, available=False, notplot=False, latest_version=False, major_version=False, min_version=None, cdf_version=None, spdf=False, always_prompt=False): """ This function loads ASPOC data into tplot variables Parameters: trange : list of str time range of interest [starttime, endtime] with the format 'YYYY-MM-DD','YYYY-MM-DD'] or to specify more or less than a day ['YYYY-MM-DD/hh:mm:ss','YYYY-MM-DD/hh:mm:ss'] probe : str or list of str list of probes, valid values for MMS probes are ['1','2','3','4']. data_rate : str or list of str instrument data rates for ASPOC include 'srvy', 'sitl'. The default is 'srvy'. level : str indicates level of data processing. the default if no level is specified is 'l2' datatype : str or list of str Valid datatypes for ASPOC are: ['asp1', 'asp2', 'aspoc']; default is 'aspoc' get_support_data: bool Data with an attribute "VAR_TYPE" with a value of "support_data" will be loaded into tplot. By default, only loads in data with a "VAR_TYPE" attribute of "data". time_clip: bool Data will be clipped to the exact trange specified by the trange keyword. varformat: str The file variable formats to load into tplot. Wildcard character "*" is accepted. By default, all variables are loaded in. varnames: list of str List of variable names to load (if not specified, all data variables are loaded) suffix: str The tplot variable names will be given this suffix. By default, no suffix is added. notplot: bool If True, then data are returned in a hash table instead of being stored in tplot variables (useful for debugging, and access to multi-dimensional data products) available: bool If True, simply return the available data files (without downloading) for the requested paramters no_update: bool Set this flag to preserve the original data. if not set and newer data is found the existing data will be overwritten cdf_version: str Specify a specific CDF version # to load (e.g., cdf_version='4.3.0') min_version: str Specify a minimum CDF version # to load latest_version: bool Only grab the latest CDF version in the requested time interval major_version: bool Only open the latest major CDF version (e.g., X in vX.Y.Z) in the requested time interval always_prompt: bool Set this keyword to always prompt for the user's username and password; useful if you accidently save an incorrect password, or if your SDC password has changed spdf: bool If True, download the data from the SPDF instead of the SDC Returns: List of tplot variables created. """ if suffix == '': suffix = '_' + level else: suffix = '_' + level + suffix tvars = mms_load_data(trange=trange, notplot=notplot, probe=probe, data_rate=data_rate, level=level, instrument='aspoc', datatype=datatype, varformat=varformat, varnames=varnames, get_support_data=get_support_data, suffix=suffix, time_clip=time_clip, no_update=no_update, available=available, latest_version=latest_version, major_version=major_version, min_version=min_version, cdf_version=cdf_version, spdf=spdf, always_prompt=always_prompt) return tvars @print_vars def mms_load_fsm(trange=['2015-10-16', '2015-10-17'], probe='1', data_rate='brst', level='l3', datatype='8khz', get_support_data=False, time_clip=False, no_update=False, available=False, varformat=None, varnames=[], notplot=False, suffix='', latest_version=False, major_version=False, min_version=None, cdf_version=None, spdf=False, always_prompt=False): """ This function loads FSM data into tplot variables Parameters: trange : list of str time range of interest [starttime, endtime] with the format 'YYYY-MM-DD','YYYY-MM-DD'] or to specify more or less than a day ['YYYY-MM-DD/hh:mm:ss','YYYY-MM-DD/hh:mm:ss'] probe : str or list of str list of probes, valid values for MMS probes are ['1','2','3','4']. data_rate : str or list of str the current instrument data rate for FSM is 'brst' level : str indicates level of data processing. the default if no level is specified is 'l2' datatype : str or list of str Valid datatype for FSM is: 8khz get_support_data: bool Data with an attribute "VAR_TYPE" with a value of "support_data" will be loaded into tplot. By default, only loads in data with a "VAR_TYPE" attribute of "data". time_clip: bool Data will be clipped to the exact trange specified by the trange keyword. varformat: str The file variable formats to load into tplot. Wildcard character "*" is accepted. By default, all variables are loaded in. varnames: list of str List of variable names to load (if not specified, all data variables are loaded) suffix: str The tplot variable names will be given this suffix. By default, no suffix is added. notplot: bool If True, then data are returned in a hash table instead of being stored in tplot variables (useful for debugging, and access to multi-dimensional data products) available: bool If True, simply return the available data files (without downloading) for the requested paramters no_update: bool Set this flag to preserve the original data. if not set and newer data is found the existing data will be overwritten cdf_version: str Specify a specific CDF version # to load (e.g., cdf_version='4.3.0') min_version: str Specify a minimum CDF version # to load latest_version: bool Only grab the latest CDF version in the requested time interval major_version: bool Only open the latest major CDF version (e.g., X in vX.Y.Z) in the requested time interval always_prompt: bool Set this keyword to always prompt for the user's username and password; useful if you accidently save an incorrect password, or if your SDC password has changed spdf: bool If True, download the data from the SPDF instead of the SDC Returns: List of tplot variables created. """ tvars = mms_load_data(trange=trange, notplot=notplot, varformat=varformat, probe=probe, data_rate=data_rate, level=level, instrument='fsm', datatype=datatype, get_support_data=get_support_data, time_clip=time_clip, no_update=no_update, available=available, suffix=suffix, latest_version=latest_version, varnames=varnames, major_version=major_version, min_version=min_version, cdf_version=cdf_version, spdf=spdf, always_prompt=always_prompt) return tvars ''' the following wrappers allow users to import the load routines using the syntax: >>> from pyspedas.mms import fgm >>> fgm_data = fgm(...) and/or >>> import pyspedas >>> fgm_data = pyspedas.mms.fgm(...) ''' @wraps(mms_load_state) def state(*args, **kwargs): return mms_load_state(*args, **kwargs) @wraps(mms_load_fgm) def fgm(*args, **kwargs): return mms_load_fgm(*args, **kwargs) @wraps(mms_load_scm) def scm(*args, **kwargs): return mms_load_scm(*args, **kwargs) @wraps(mms_load_fsm) def fsm(*args, **kwargs): return mms_load_fsm(*args, **kwargs) @wraps(mms_load_edp) def edp(*args, **kwargs): return mms_load_edp(*args, **kwargs) @wraps(mms_load_edi) def edi(*args, **kwargs): return mms_load_edi(*args, **kwargs) @wraps(mms_load_fpi) def fpi(*args, **kwargs): return mms_load_fpi(*args, **kwargs) @wraps(mms_load_hpca) def hpca(*args, **kwargs): return mms_load_hpca(*args, **kwargs) @wraps(mms_load_eis) def eis(*args, **kwargs): return mms_load_eis(*args, **kwargs) @wraps(mms_load_feeps) def feeps(*args, **kwargs): return mms_load_feeps(*args, **kwargs) @wraps(mms_load_aspoc) def aspoc(*args, **kwargs): return mms_load_aspoc(*args, **kwargs) @wraps(mms_load_mec) def mec(*args, **kwargs): return mms_load_mec(*args, **kwargs) @wraps(mms_load_dsp) def dsp(*args, **kwargs): return mms_load_dsp(*args, **kwargs) @wraps(mms_curl) def curlometer(*args, **kwargs): return mms_curl(*args, **kwargs)

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1631cd2cb606a2379e7a111f471934645c5f7c19

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Python

tests/components/hunterdouglas_powerview/test_config_flow.py

gdt/core

72b0eb719e646cd8983f30ff797e7cd1cbe911e9

[ "Apache-2.0" ]

6

2017-11-15T09:56:41.000Z

2021-01-24T15:12:09.000Z

tests/components/hunterdouglas_powerview/test_config_flow.py

gdt/core

72b0eb719e646cd8983f30ff797e7cd1cbe911e9

[ "Apache-2.0" ]

87

2020-07-15T13:43:35.000Z

2022-03-23T07:43:10.000Z

tests/components/hunterdouglas_powerview/test_config_flow.py

gdt/core

72b0eb719e646cd8983f30ff797e7cd1cbe911e9

[ "Apache-2.0" ]

2

2021-11-19T23:20:40.000Z

2021-11-20T00:18:40.000Z

"""Test the Logitech Harmony Hub config flow.""" import asyncio import json from unittest.mock import AsyncMock, MagicMock, patch import pytest from homeassistant import config_entries from homeassistant.components import dhcp, zeroconf from homeassistant.components.hunterdouglas_powerview.const import DOMAIN from tests.common import MockConfigEntry, load_fixture HOMEKIT_DISCOVERY_INFO = zeroconf.ZeroconfServiceInfo( name="Hunter Douglas Powerview Hub._hap._tcp.local.", host="1.2.3.4", properties={"id": "AA::BB::CC::DD::EE::FF"}, ) ZEROCONF_DISCOVERY_INFO = zeroconf.ZeroconfServiceInfo( name="Hunter Douglas Powerview Hub._powerview._tcp.local.", host="1.2.3.4", ) DHCP_DISCOVERY_INFO = dhcp.DhcpServiceInfo( hostname="Hunter Douglas Powerview Hub", ip="1.2.3.4" ) DISCOVERY_DATA = [ ( config_entries.SOURCE_HOMEKIT, HOMEKIT_DISCOVERY_INFO, ), ( config_entries.SOURCE_DHCP, DHCP_DISCOVERY_INFO, ), (config_entries.SOURCE_ZEROCONF, ZEROCONF_DISCOVERY_INFO), ] def _get_mock_powerview_userdata(userdata=None, get_resources=None): mock_powerview_userdata = MagicMock() if not userdata: userdata = json.loads(load_fixture("hunterdouglas_powerview/userdata.json")) if get_resources: mock_powerview_userdata.get_resources = AsyncMock(side_effect=get_resources) else: mock_powerview_userdata.get_resources = AsyncMock(return_value=userdata) return mock_powerview_userdata def _get_mock_powerview_legacy_userdata(userdata=None, get_resources=None): mock_powerview_userdata_legacy = MagicMock() if not userdata: userdata = json.loads(load_fixture("hunterdouglas_powerview/userdata_v1.json")) if get_resources: mock_powerview_userdata_legacy.get_resources = AsyncMock( side_effect=get_resources ) else: mock_powerview_userdata_legacy.get_resources = AsyncMock(return_value=userdata) return mock_powerview_userdata_legacy def _get_mock_powerview_fwversion(fwversion=None, get_resources=None): mock_powerview_fwversion = MagicMock() if not fwversion: fwversion = json.loads(load_fixture("hunterdouglas_powerview/fwversion.json")) if get_resources: mock_powerview_fwversion.get_resources = AsyncMock(side_effect=get_resources) else: mock_powerview_fwversion.get_resources = AsyncMock(return_value=fwversion) return mock_powerview_fwversion async def test_user_form(hass): """Test we get the user form.""" result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": config_entries.SOURCE_USER} ) assert result["type"] == "form" assert result["errors"] == {} mock_powerview_userdata = _get_mock_powerview_userdata() with patch( "homeassistant.components.hunterdouglas_powerview.UserData", return_value=mock_powerview_userdata, ), patch( "homeassistant.components.hunterdouglas_powerview.async_setup_entry", return_value=True, ) as mock_setup_entry: result2 = await hass.config_entries.flow.async_configure( result["flow_id"], {"host": "1.2.3.4"}, ) await hass.async_block_till_done() assert result2["type"] == "create_entry" assert result2["title"] == "AlexanderHD" assert result2["data"] == { "host": "1.2.3.4", } assert len(mock_setup_entry.mock_calls) == 1 result3 = await hass.config_entries.flow.async_init( DOMAIN, context={"source": config_entries.SOURCE_USER} ) assert result3["type"] == "form" assert result3["errors"] == {} result4 = await hass.config_entries.flow.async_configure( result3["flow_id"], {"host": "1.2.3.4"}, ) assert result4["type"] == "abort" async def test_user_form_legacy(hass): """Test we get the user form with a legacy device.""" result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": config_entries.SOURCE_USER} ) assert result["type"] == "form" assert result["errors"] == {} mock_powerview_userdata = _get_mock_powerview_legacy_userdata() mock_powerview_fwversion = _get_mock_powerview_fwversion() with patch( "homeassistant.components.hunterdouglas_powerview.UserData", return_value=mock_powerview_userdata, ), patch( "homeassistant.components.hunterdouglas_powerview.ApiEntryPoint", return_value=mock_powerview_fwversion, ), patch( "homeassistant.components.hunterdouglas_powerview.async_setup_entry", return_value=True, ) as mock_setup_entry: result2 = await hass.config_entries.flow.async_configure( result["flow_id"], {"host": "1.2.3.4"}, ) await hass.async_block_till_done() assert result2["type"] == "create_entry" assert result2["title"] == "PowerView Hub Gen 1" assert result2["data"] == { "host": "1.2.3.4", } assert len(mock_setup_entry.mock_calls) == 1 result3 = await hass.config_entries.flow.async_init( DOMAIN, context={"source": config_entries.SOURCE_USER} ) assert result3["type"] == "form" assert result3["errors"] == {} result4 = await hass.config_entries.flow.async_configure( result3["flow_id"], {"host": "1.2.3.4"}, ) assert result4["type"] == "abort" @pytest.mark.parametrize("source, discovery_info", DISCOVERY_DATA) async def test_form_homekit_and_dhcp_cannot_connect(hass, source, discovery_info): """Test we get the form with homekit and dhcp source.""" ignored_config_entry = MockConfigEntry( domain=DOMAIN, data={}, source=config_entries.SOURCE_IGNORE ) ignored_config_entry.add_to_hass(hass) mock_powerview_userdata = _get_mock_powerview_userdata( get_resources=asyncio.TimeoutError ) with patch( "homeassistant.components.hunterdouglas_powerview.UserData", return_value=mock_powerview_userdata, ): result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": source}, data=discovery_info, ) assert result["type"] == "abort" assert result["reason"] == "cannot_connect" @pytest.mark.parametrize("source, discovery_info", DISCOVERY_DATA) async def test_form_homekit_and_dhcp(hass, source, discovery_info): """Test we get the form with homekit and dhcp source.""" ignored_config_entry = MockConfigEntry( domain=DOMAIN, data={}, source=config_entries.SOURCE_IGNORE ) ignored_config_entry.add_to_hass(hass) mock_powerview_userdata = _get_mock_powerview_userdata() with patch( "homeassistant.components.hunterdouglas_powerview.UserData", return_value=mock_powerview_userdata, ): result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": source}, data=discovery_info, ) assert result["type"] == "form" assert result["step_id"] == "link" assert result["errors"] is None assert result["description_placeholders"] == { "host": "1.2.3.4", "name": "Hunter Douglas Powerview Hub", } with patch( "homeassistant.components.hunterdouglas_powerview.UserData", return_value=mock_powerview_userdata, ), patch( "homeassistant.components.hunterdouglas_powerview.async_setup_entry", return_value=True, ) as mock_setup_entry: result2 = await hass.config_entries.flow.async_configure(result["flow_id"], {}) await hass.async_block_till_done() assert result2["type"] == "create_entry" assert result2["title"] == "Hunter Douglas Powerview Hub" assert result2["data"] == {"host": "1.2.3.4"} assert result2["result"].unique_id == "ABC123" assert len(mock_setup_entry.mock_calls) == 1 result3 = await hass.config_entries.flow.async_init( DOMAIN, context={"source": source}, data=discovery_info, ) assert result3["type"] == "abort" async def test_discovered_by_homekit_and_dhcp(hass): """Test we get the form with homekit and abort for dhcp source when we get both.""" mock_powerview_userdata = _get_mock_powerview_userdata() with patch( "homeassistant.components.hunterdouglas_powerview.UserData", return_value=mock_powerview_userdata, ): result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": config_entries.SOURCE_HOMEKIT}, data=HOMEKIT_DISCOVERY_INFO, ) assert result["type"] == "form" assert result["step_id"] == "link" with patch( "homeassistant.components.hunterdouglas_powerview.UserData", return_value=mock_powerview_userdata, ): result2 = await hass.config_entries.flow.async_init( DOMAIN, context={"source": config_entries.SOURCE_DHCP}, data=DHCP_DISCOVERY_INFO, ) assert result2["type"] == "abort" assert result2["reason"] == "already_in_progress" async def test_form_cannot_connect(hass): """Test we handle cannot connect error.""" result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": config_entries.SOURCE_USER} ) mock_powerview_userdata = _get_mock_powerview_userdata( get_resources=asyncio.TimeoutError ) with patch( "homeassistant.components.hunterdouglas_powerview.UserData", return_value=mock_powerview_userdata, ): result2 = await hass.config_entries.flow.async_configure( result["flow_id"], {"host": "1.2.3.4"}, ) assert result2["type"] == "form" assert result2["errors"] == {"base": "cannot_connect"} async def test_form_no_data(hass): """Test we handle no data being returned from the hub.""" result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": config_entries.SOURCE_USER} ) mock_powerview_userdata = _get_mock_powerview_userdata(userdata={"userData": {}}) with patch( "homeassistant.components.hunterdouglas_powerview.UserData", return_value=mock_powerview_userdata, ): result2 = await hass.config_entries.flow.async_configure( result["flow_id"], {"host": "1.2.3.4"}, ) assert result2["type"] == "form" assert result2["errors"] == {"base": "unknown"} async def test_form_unknown_exception(hass): """Test we handle unknown exception.""" result = await hass.config_entries.flow.async_init( DOMAIN, context={"source": config_entries.SOURCE_USER} ) mock_powerview_userdata = _get_mock_powerview_userdata(userdata={"userData": {}}) with patch( "homeassistant.components.hunterdouglas_powerview.UserData", return_value=mock_powerview_userdata, ): result2 = await hass.config_entries.flow.async_configure( result["flow_id"], {"host": "1.2.3.4"}, ) assert result2["type"] == "form" assert result2["errors"] == {"base": "unknown"}

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6

164bd05d32b19f9b79b2c0031c7c29bbae53e767

102

py

Python

terrascript/arukas/r.py

hugovk/python-terrascript

08fe185904a70246822f5cfbdc9e64e9769ec494

[ "BSD-2-Clause" ]

507

2017-07-26T02:58:38.000Z

2022-01-21T12:35:13.000Z

terrascript/arukas/r.py

hugovk/python-terrascript

08fe185904a70246822f5cfbdc9e64e9769ec494

[ "BSD-2-Clause" ]

135

2017-07-20T12:01:59.000Z

2021-10-04T22:25:40.000Z

terrascript/arukas/r.py

hugovk/python-terrascript

08fe185904a70246822f5cfbdc9e64e9769ec494

[ "BSD-2-Clause" ]

81

2018-02-20T17:55:28.000Z

2022-01-31T07:08:40.000Z

# terrascript/arukas/r.py import terrascript class arukas_container(terrascript.Resource): pass

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1669a984c539884229b910755834681fe5388538

194

py

Python

attendance_log/attendance_log/doctype/device_ip_details/device_ip_details.py

KaviyaPeriyasamy/attendance_log

fb9636067c54924f51ecd4df3cb66cbac829b6af

[ "MIT" ]

null

attendance_log/attendance_log/doctype/device_ip_details/device_ip_details.py

KaviyaPeriyasamy/attendance_log

fb9636067c54924f51ecd4df3cb66cbac829b6af

[ "MIT" ]

null

attendance_log/attendance_log/doctype/device_ip_details/device_ip_details.py

KaviyaPeriyasamy/attendance_log

fb9636067c54924f51ecd4df3cb66cbac829b6af

[ "MIT" ]

null

21.555556

49

0.793814

25

194

6.16

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194

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true

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null

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1

0

6

169981ceedf4a46996aee744b10545d746cd0b2f

68,076

py

Python

FasterRCNN_SE_Train/src/cntk/learners/__init__.py

springkim/FasterRCNN_SpringEdition

7771dcf310cd80a195c44839c83e65adcd5df37d

[ "MIT" ]

9

2017-10-03T14:02:29.000Z

2019-05-31T01:07:40.000Z

FasterRCNN_SE_Train/src/cntk/learners/__init__.py

springkim/FasterRCNN_SpringEdition

7771dcf310cd80a195c44839c83e65adcd5df37d

[ "MIT" ]

2

2018-06-21T11:09:36.000Z

2018-10-25T18:05:30.000Z

FasterRCNN_SE_Train/src/cntk/learners/__init__.py

springkim/FasterRCNN_SpringEdition

7771dcf310cd80a195c44839c83e65adcd5df37d

[ "MIT" ]

1

2019-03-22T02:16:17.000Z

# Copyright (c) Microsoft. All rights reserved. # Licensed under the MIT license. See LICENSE.md file in the project root # for full license information. # ============================================================================== ''' A learner tunes a set of parameters during the training process. One can use different learners for different sets of parameters. Currently, CNTK supports the following learning algorithms: - :func:`AdaDelta <adadelta>` - :func:`AdaGrad <adagrad>` - :func:`FSAdaGrad <fsadagrad>` - :func:`Adam <adam>` - :func:`MomentumSGD <momentum_sgd>` - :func:`Nesterov <nesterov>` - :func:`RMSProp <rmsprop>` - :func:`SGD <sgd>` - :func:`Learner with a customized update function <universal>` ''' from enum import Enum, unique import warnings import numpy as np import cntk.internal.utils as utils from .. import cntk_py, NDArrayView, asarray from cntk.internal import typemap from ..internal.swig_helper import map_if_possible @unique class UnitType(Enum): ''' Deprecated:: 2.2 Indicates whether the values in the schedule are specified on the per-sample or per-minibatch basis. ''' sample = 'sample' ''' Schedule contains per-sample values. ''' minibatch = 'minibatch' ''' Schedule contains per-minibatch values (and need to be re-scaled by the learner using the actual minibatch size in samples). ''' def default_unit_gain_value(): ''' Returns true if by default momentum is applied in the unit-gain fashion. ''' return cntk_py.default_unit_gain_value() def set_default_unit_gain_value(value): ''' Sets globally default unit-gain flag value. ''' cntk_py.set_default_unit_gain_value(value) def default_use_mean_gradient_value(): ''' Returns true if by default input gradient to learner is averaged. ''' return cntk_py.default_use_mean_gradient_value() def set_default_use_mean_gradient_value(value): ''' Sets globally default use_mean_gradient_value. ''' cntk_py.set_default_use_mean_gradient_value(value) # an internal method to verify that the learning rate schedule # has a proper (per-sample or per-MB schedule) type and raise # an exception otherwise def _verify_learning_rate_type(learning_rate): if not isinstance(learning_rate, cntk_py.training_double_parameter_schedule): raise ValueError('learning_rate type (%s) not supported. ' 'learning_rate must be a training schedule ' '(output of learning_rate_schedule() function)' % type(learning_rate)) # an internal method to verify that the mometum schedule # has a proper (per-MB or time-constant schedule) type and raise # an exception otherwise def _verify_momentum_type(momentum): if not isinstance(momentum, cntk_py.training_double_parameter_schedule): raise ValueError('momentum type (%s) not supported. ' 'momentum must be a training schedule ' '(output of momentum_schedule() or ' 'momentum_as_time_constant_schedule() function)' % type(momentum)) class Learner(cntk_py.Learner): ''' Abstraction for learning a subset of parameters of a learnable function using first order gradient values. For example momentum, AdaGrad, RMSProp, etc. are different types of learners with their own algorithms for learning parameter values using first order gradients. To instantiate a concrete learner, use the factory methods in this module. ''' def update(self, gradient_values, training_sample_count): ''' Update the parameters associated with this learner. Args: gradient_values (dict): maps :class:`~cntk.variables.Parameter` to a NumPy array containing the first order gradient values for the Parameter w.r.t. the training objective. training_sample_count (int): number of samples in the minibatch Returns: bool: `False` to indicate that learning has stopped for all of the parameters associated with this learner ''' var_nd_map = {var: NDArrayView.from_data(val) for var, val in gradient_values.items()} return super(Learner, self)._update(var_nd_map, training_sample_count) @property @typemap def parameters(self): ''' The set of parameters associated with this learner. ''' return super(Learner, self).parameters() def reset_learning_rate(self, learning_rate): ''' Resets the learning rate. The new schedule is adjusted to be relative to the current number of elapsed samples/sweeps: the 0 offset in the new schedule corresponds to the current value of elapsed samples/sweeps, and it takes effect from the current position in the training process onwards. Args: learning_rate (output of :func:`learning_parameter_schedule`) learning rate to reset to ''' _verify_learning_rate_type(learning_rate) if not learning_rate.is_minibatch_size_explicitly_specified: #If the schedule minibatch size is not explicitly specified, the learner's specification will take over if self.minibatch_size is not None and self.minibatch_size != self.ignored_minibatch_size: learning_rate.minibatch_size = self.minibatch_size return super(Learner, self).reset_learning_rate(learning_rate) def learning_rate(self): ''' Current learning rate schedule. ''' return super(Learner, self).learning_rate() IGNORE = Learner.ignored_minibatch_size ''' Indicate that the minibatch size is ignored in learning's hyper-parameter schedule. ''' class UserLearner(cntk_py.Learner): ''' Base class of all user-defined learners. To implement your own learning algorithm, derive from this class and override the :meth:`update`. Certain optimizers (such as AdaGrad) require additional storage. This can be allocated and initialized during construction. ''' def __init__(self, parameters, lr_schedule, as_numpy=True): super(UserLearner, self).__init__(parameters, lr_schedule) self.as_numpy = as_numpy self.__disown__() def _update(self, gradient_values, training_sample_count, sweep_end): ''' Update the parameters and related state associated with this learner. Args: gradient_values (dict): maps :class:`~cntk.variables.Parameter` to a NumPy array containing the gradient for the Parameter w.r.t. the training objective. training_sample_count (int): number of samples in the minibatch sweep_end (bool): if the data is fed by a conforming reader, this indicates whether a full pass over the dataset has just occurred. Returns: bool: `False` to indicate that learning has stopped for all of the parameters associated with this learner ''' map_if_possible(gradient_values) if self.as_numpy: var_nd_map = {var: asarray(gradient_values[var]) \ for var, val in gradient_values.items()} else: var_nd_map = gradient_values return self.update(gradient_values, training_sample_count, sweep_end) def update(self, gradient_values, training_sample_count, sweep_end): ''' Update the parameters associated with this learner. Args: gradient_values (dict): maps :class:`~cntk.variables.Parameter` to a NumPy array containing the first order gradient values for the Parameter w.r.t. the training objective. training_sample_count (int): number of samples in the minibatch sweep_end (bool): if the data is fed by a conforming reader, this indicates whether a full pass over the dataset has just occurred. Returns: bool: `False` to indicate that learning has stopped for all of the parameters associated with this learner ''' raise NotImplementedError('UserLearner.update must be overriden') def _prepare_training_parameter_list(schedule): if isinstance(schedule, list): return [(1, v) if isinstance(v, (float, int)) else v for v in schedule] else: return schedule @typemap def training_parameter_schedule(schedule, unit=UnitType.minibatch, epoch_size=None): ''' Deprecated:: 2.2 Create a training parameter schedule containing either per-sample (default) or per-minibatch values. Examples: >>> # Use a fixed value 0.01 for all samples >>> s = training_parameter_schedule(0.01) >>> s[0], s[1] (0.01, 0.01) >>> # Use 0.01 for the first 1000 samples, then 0.001 for the remaining ones >>> s = training_parameter_schedule([0.01, 0.001], epoch_size=1000) >>> s[0], s[1], s[1000], s[1001] (0.01, 0.01, 0.001, 0.001) >>> # Use 0.1 for the first 12 epochs, then 0.01 for the next 15, >>> # followed by 0.001 for the remaining ones, with a 100 samples in an epoch >>> s = training_parameter_schedule([(12, 0.1), (15, 0.01), (1, 0.001)], epoch_size=100) >>> s[0], s[1199], s[1200], s[2699], s[2700], s[5000] (0.1, 0.1, 0.01, 0.01, 0.001, 0.001) Args: schedule (float or list): if float, is the parameter schedule to be used for all samples. In case of list, the elements are used as the values for ``epoch_size`` samples. If list contains pair, the second element is used as a value for (``epoch_size`` x first element) samples unit (:class:`UnitType`): one of two * ``sample``: the returned schedule contains per-sample values * ``minibatch``: the returned schedule contains per-minibatch values. deprecated:: 2.2 Use minibatch_size parameter to specify the reference minbiatch size. epoch_size (optional, int): number of samples as a scheduling unit. Parameters in the schedule change their values every ``epoch_size`` samples. If no ``epoch_size`` is provided, this parameter is substituted by the size of the full data sweep, in which case the scheduling unit is the entire data sweep (as indicated by the MinibatchSource) and parameters change their values on the sweep-by-sweep basis specified by the ``schedule``. Returns: training parameter schedule See also: :func:`learning_rate_schedule` ''' if unit == UnitType.sample: ref_minibatch_size = 1 else: # unit == UnitType.minibatch ref_minibatch_size = cntk_py.training_double_parameter_schedule.ignored_minibatch_size if isinstance(schedule, cntk_py.training_double_parameter_schedule): schedule.is_minibatch_size_explicitly_specified = True #legacy learning parameter always have the specification return schedule if isinstance(schedule, (int, float)): if epoch_size is not None: warnings.warn('When providing the schedule as a number, epoch_size is ignored', RuntimeWarning) if UnitType(unit): schedule = cntk_py.training_double_parameter_schedule(*[schedule, ref_minibatch_size]) schedule.is_minibatch_size_explicitly_specified = True # legacy learning parameter always have the specification return schedule epoch_size = epoch_size if epoch_size is not None else cntk_py.training_double_parameter_schedule.full_data_sweep if isinstance(schedule, list) and UnitType(unit): schedule = _prepare_training_parameter_list(schedule) args = [schedule, epoch_size, ref_minibatch_size] schedule = cntk_py.training_double_parameter_schedule(*args) schedule.is_minibatch_size_explicitly_specified = True #legacy learning parameter always have the specification return schedule raise ValueError( 'schedule must be either a float or a list, not %s' % type(schedule)) @typemap def learning_parameter_schedule(schedule, minibatch_size=None, epoch_size=None): ''' Create a learning parameter schedule. Args: schedule (float or list): if float, is the parameter schedule to be used for all samples. In case of list [p_1, p_2, .., p_n], the i-th parameter p_i in the list is used as the value from the (``epoch_size`` * (i-1) + 1)-th sample to the (``epoch_size`` * i)-th sample. If list contains pair, i.e. [(num_epoch_1, p_1), (num_epoch_n, p_2), .., (num_epoch_n, p_n)], the i-th parameter is used as a value from the (``epoch_size`` * (num_epoch_0 + ... + num_epoch_2 + ... + num_epoch_(i-1) + 1)-th sample to the (``epoch_size`` * num_epoch_i)-th sample (taking num_epoch_0 = 0 as a special initialization). minibatch_size (int): an integer to specify the minibatch size that schedule are designed for. CNTK will scale the schedule internally so as to simulate the behavior of the schedule as much as possible to match the designed effect. If it is not specified, CNTK will set to the special value :attr:`IGNORE`. epoch_size (optional, int): number of samples as a scheduling unit. Parameters in the schedule change their values every ``epoch_size`` samples. If no ``epoch_size`` is provided, this parameter is substituted by the size of the full data sweep, in which case the scheduling unit is the entire data sweep (as indicated by the MinibatchSource) and parameters change their values on the sweep-by-sweep basis specified by the ``schedule``. Returns: learning parameter schedule ''' if isinstance(schedule, cntk_py.training_double_parameter_schedule): return schedule is_minibatch_size_explicitly_specified = True if minibatch_size == None: is_minibatch_size_explicitly_specified = False minibatch_size = 0 if isinstance(schedule, (int, float)): if epoch_size is not None: warnings.warn('When providing the schedule as a number, epoch_size is ignored', RuntimeWarning) schedule = cntk_py.training_double_parameter_schedule(*[schedule, minibatch_size]) schedule.is_minibatch_size_explicitly_specified = is_minibatch_size_explicitly_specified return schedule epoch_size = epoch_size if epoch_size is not None else cntk_py.training_double_parameter_schedule.full_data_sweep if isinstance(schedule, list): schedule = _prepare_training_parameter_list(schedule) args = [schedule, epoch_size, minibatch_size] schedule = cntk_py.training_double_parameter_schedule(*args) schedule.is_minibatch_size_explicitly_specified = is_minibatch_size_explicitly_specified return schedule raise ValueError( 'schedule must be either a float or a list, not %s' % type(schedule)) @typemap def learning_rate_schedule(lr, unit, epoch_size=None): ''' Deprecated:: 2.2 Create a learning rate schedule (using the same semantics as :func:`training_parameter_schedule`). Args: lr (float or list): see parameter ``schedule`` in :func:`training_parameter_schedule`. unit (:class:`UnitType`): see parameter ``unit`` in :func:`training_parameter_schedule`. deprecated:: 2.2 Use minibatch_size parameter to specify the reference minbiatch size instead. epoch_size (int): see parameter ``epoch_size`` in :func:`training_parameter_schedule`. Returns: learning rate schedule See also: :func:`training_parameter_schedule` ''' return training_parameter_schedule(lr, unit, epoch_size) @typemap def momentum_schedule(momentum, epoch_size=None, minibatch_size = None): ''' Create a per-minibatch momentum schedule (using the same semantics as :func:`training_parameter_schedule` with the `unit=UnitType.minibatch`). Args: momentum (float or list): see parameter ``schedule`` in :func:`training_parameter_schedule`. epoch_size (int): see parameter ``epoch_size`` in :func:`training_parameter_schedule`. minibatch_size (int): an integer to specify the reference minibatch size that schedule are designed for; CNTK will scale the schedule internally so as to simulate the behavior of the schedule as much as possible to match the designed effect. If you want to provide momentum values in a minibatch-size agnostic way, use :func:`momentum_as_time_constant_schedule`. Examples: >>> # Use a fixed momentum of 0.99 for all samples >>> m = momentum_schedule(0.99) >>> # Use the momentum value 0.99 for the first 1000 samples, >>> # then 0.9 for the remaining ones >>> m = momentum_schedule([0.99,0.9], 1000) >>> m[0], m[999], m[1000], m[1001] (0.99, 0.99, 0.9, 0.9) >>> # Use the momentum value 0.99 for the first 999 samples, >>> # then 0.88 for the next 888 samples, and 0.77 for the >>> # the remaining ones >>> m = momentum_schedule([(999,0.99),(888,0.88),(0, 0.77)]) >>> m[0], m[998], m[999], m[999+888-1], m[999+888] (0.99, 0.99, 0.88, 0.88, 0.77) Returns: momentum schedule ''' return learning_parameter_schedule(momentum, minibatch_size, epoch_size) @typemap def momentum_as_time_constant_schedule(momentum, epoch_size=None): ''' Create a momentum schedule in a minibatch-size agnostic way (using the same semantics as :func:`training_parameter_schedule` with `unit=UnitType.sample`). Deprecated:: 2.2 This is for legacy API. In this legacy API,:: #assume the desired minibatch size invariant constant momentum rate is: momentum_rate momentum_time_constant = -minibatch_size/np.log(momentum_rate) momentum = momentum_as_time_constant_schedule(momentum_time_constant) The equivalent code in the latest API, :: momentum = momentum_schedule(momentum_rate, minibatch_size = minibatch_size) Args: momentum (float or list): see parameter ``schedule`` in :func:`training_parameter_schedule`. epoch_size (int): see parameter ``epoch_size`` in :func:`training_parameter_schedule`. minibatch_size (int): an integer to specify the reference minibatch size that schedule are designed for; CNTK will scale the schedule internally so as to simulate the behavior of the schedule as much as possible to match the designed effect. CNTK specifies momentum in a minibatch-size agnostic way as the time constant (in samples) of a unit-gain 1st-order IIR filter. The value specifies the number of samples after which a gradient has an effect of 1/e=37%. If you want to specify the momentum per sample (or per minibatch), use :func:`momentum_schedule`. Examples: >>> # Use a fixed momentum of 1100 for all samples >>> m = momentum_as_time_constant_schedule(1100) >>> # Use the time constant 1100 for the first 1000 samples, >>> # then 1500 for the remaining ones >>> m = momentum_as_time_constant_schedule([1100, 1500], 1000) Returns: momentum as time constant schedule ''' if isinstance(momentum, (cntk_py.training_double_parameter_schedule)): #the legacy momentum as time constant schedule: the ref minibatch size is always 1, so it is specified by definition momentum.is_minibatch_size_explicitly_specified = True return momentum if isinstance(momentum, (int, float)): if epoch_size is not None: warnings.warn('When providing the schedule as a number, epoch_size is ignored', RuntimeWarning) momentum = cntk_py.momentum_as_time_constant_schedule(momentum) momentum.is_minibatch_size_explicitly_specified = True return momentum epoch_size = epoch_size if epoch_size is not None else cntk_py.training_double_parameter_schedule.full_data_sweep if isinstance(momentum, list): momentum = _prepare_training_parameter_list(momentum) args = [momentum, epoch_size, 1] #momentum constant schedule's reference minibatch size is always per sample momentum = cntk_py.training_double_parameter_schedule(*args) momentum = cntk_py.momentum_as_time_constant_schedule(momentum) momentum.is_minibatch_size_explicitly_specified = True return momentum raise ValueError( 'momentum must be either a float or a list, not %s' % type(momentum)) # TODO figure out how to pass infty to C++ in a portable way def _infer_ref_minibatch_size_from_legacy_use_mean_gradient(ref_minibatch_size, use_mean_gradient): if (ref_minibatch_size, use_mean_gradient) == (None, None): #if ref_minibatch_size and the legacy use_mean_gradient are neither specified return None if ref_minibatch_size is not None: if use_mean_gradient == True and ref_minibatch_size != cntk_py.Learner.ignored_minibatch_size: Warning( 'Learner reference minibatch size is specified while use_mean_gradient (depreated option) is specified to True. Learner reference minibatch size will override the mean gradient behavior') #if the ref_minibatch_size is specified, it overrides the legacay use_mean_gradient specification return ref_minibatch_size elif use_mean_gradient is not None: #if the ref_minibatch_size is NOT specified, the legacay use_mean_gradient specification take in the effect return cntk_py.Learner.ignored_minibatch_size if use_mean_gradient is True else None return None def _infer_learning_parameter_schedule(number_or_schedule, ref_minibatch_size, epoch_size, use_mean_gradient=None): #the input is a number, create a new training parameter if isinstance(number_or_schedule, (int, float)) or \ (isinstance(number_or_schedule, list) and all(isinstance(r, (int, float, tuple)) for r in number_or_schedule)): #default is per minibatch if the reference minibatch size is not specified. ref_minibatch_size = 0 if ref_minibatch_size is None else ref_minibatch_size schedule = learning_parameter_schedule(number_or_schedule, ref_minibatch_size, epoch_size) schedule.is_minibatch_size_explicitly_specified = ref_minibatch_size is not None return schedule elif isinstance(number_or_schedule, cntk_py.training_double_parameter_schedule): if not number_or_schedule.is_minibatch_size_explicitly_specified and ref_minibatch_size is not None: #If the schedule minibatch size is not explicitly specified, the learner's specification will take over number_or_schedule.minibatch_size = ref_minibatch_size #for backward compatibility: use_mean_gradient = True and lr.unit = UnitType.sample #this combination was there to avoid the double-scaling of gradients when the gradients are already mean gradients if use_mean_gradient and number_or_schedule.minibatch_size == 1: #override the learning rate's minibatch_size to IGNORE number_or_schedule.minibatch_size = IGNORE Warning('use_mean_gradient=True and learning_rate_schedule.unit=UnitType.sample is a deprecated combination. ' 'Please use the new learner APIs: see https://www.cntk.ai/pythondocs/cntk.learners.html for details.') return number_or_schedule else: raise ValueError('training parameter schedule type (%s) not supported. ' 'training parameter schedule must be a training schedule ' % type(number_or_schedule)) def _infer_learning_rate_schedule_and_ref_minibatch_size(use_mean_gradient, ref_minibatch_size, schedule, epoch_size): #if non-None reference_minibatch_size will take precedence otherwise according use_mean_gradient if it is True ref_minibatch_size = _infer_ref_minibatch_size_from_legacy_use_mean_gradient(ref_minibatch_size, use_mean_gradient) #if minibatch_size is not None, any schedules that are with unspecified reference minibatch size will be overrided. schedule = _infer_learning_parameter_schedule(schedule, ref_minibatch_size, epoch_size, use_mean_gradient) _verify_learning_rate_type(schedule) return schedule, ref_minibatch_size @typemap def sgd(parameters, lr, l1_regularization_weight=0.0, l2_regularization_weight=0.0, gaussian_noise_injection_std_dev=0.0, gradient_clipping_threshold_per_sample=np.inf, gradient_clipping_with_truncation=True, use_mean_gradient=None, minibatch_size=None, epoch_size=None): '''sgd(parameters, lr, l1_regularization_weight=0, l2_regularization_weight=0, gaussian_noise_injection_std_dev=0, gradient_clipping_threshold_per_sample=np.inf, gradient_clipping_with_truncation=True) Creates an SGD learner instance to learn the parameters. See [1] for more information on how to set the parameters. Args: parameters (list of parameters): list of network parameters to tune. These can be obtained by the '.parameters()' method of the root operator. lr (float, list, output of :func:`learning_parameter_schedule`): a learning rate in float, or a learning rate schedule. See also: :func:`learning_parameter_schedule` l1_regularization_weight (float, optional): the L1 regularization weight per sample, defaults to 0.0 l2_regularization_weight (float, optional): the L2 regularization weight per sample, defaults to 0.0 gaussian_noise_injection_std_dev (float, optional): the standard deviation of the Gaussian noise added to parameters post update, defaults to 0.0 gradient_clipping_threshold_per_sample (float, optional): clipping threshold per sample, defaults to infinity gradient_clipping_with_truncation (bool, default ``True``): use gradient clipping with truncation use_mean_gradient (bool, default ``False``): use averaged gradient as input to learner. Defaults to the value returned by :func:`default_use_mean_gradient_value()`. deprecated:: 2.2 Use minibatch_size parameter to specify the reference minbiatch size. minibatch_size (int, default ``None``): The minibatch size that the learner's parameters are designed or pre-tuned for. This size is usually set to the same as the minibatch data source's size. CNTK will perform automatic scaling of the parameters to enable efficient model parameter update implementation while approximate the behavior of pre-designed and pre-tuned parameters. In case that minibatch_size is not specified, CNTK will inherit the minibatch size from the learning rate schedule; if the learning rate schedule does not specify the minibatch_size, CNTK will set it to :attr:`IGNORE`. Setting minibatch_size to :attr:`IGNORE` will have the learner apply as it is preventing CNTK performing any hyper-parameter scaling. See also: :func:`learning_parameter_schedule` epoch_size (optional, int): number of samples as a scheduling unit for learning rate. See also: :func:`learning_parameter_schedule` Returns: :class:`~cntk.learners.Learner`: learner instance that can be passed to the :class:`~cntk.train.trainer.Trainer` See also: [1] L. Bottou. `Stochastic Gradient Descent Tricks <https://www.microsoft.com/en-us/research/publication/stochastic-gradient-tricks>`_. Neural Networks: Tricks of the Trade: Springer, 2012. ''' lr, minibatch_size = _infer_learning_rate_schedule_and_ref_minibatch_size(use_mean_gradient, minibatch_size, lr, epoch_size) gaussian_noise_injection_std_dev = \ training_parameter_schedule( gaussian_noise_injection_std_dev) additional_options = cntk_py.AdditionalLearningOptions() additional_options.l1_regularization_weight = l1_regularization_weight additional_options.l2_regularization_weight = l2_regularization_weight additional_options.gaussian_noise_injection_std_dev = gaussian_noise_injection_std_dev additional_options.gradient_clipping_threshold_per_sample = gradient_clipping_threshold_per_sample additional_options.gradient_clipping_with_truncation = gradient_clipping_with_truncation if minibatch_size is not None: additional_options.dict_options[cntk_py.Learner._MINIBATCH_SIZE] = cntk_py.SizeTWrapper(minibatch_size) #need this to make proper typed DictionaryValue opt = cntk_py.sgd_learner(parameters, lr, additional_options) opt.is_minibatch_size_explicitly_specified = minibatch_size is not None return opt @typemap def momentum_sgd(parameters, lr, momentum, unit_gain=default_unit_gain_value(), l1_regularization_weight=0.0, l2_regularization_weight=0.0, gaussian_noise_injection_std_dev=0.0, gradient_clipping_threshold_per_sample=np.inf, gradient_clipping_with_truncation=True, use_mean_gradient=None, minibatch_size=None, epoch_size=None): '''momentum_sgd(parameters, lr, momentum, unit_gain=default_unit_gain_value(), l1_regularization_weight=0.0, l2_regularization_weight=0, gaussian_noise_injection_std_dev=0, gradient_clipping_threshold_per_sample=np.inf, gradient_clipping_with_truncation=True) Creates a Momentum SGD learner instance to learn the parameters. Args: parameters (list of parameters): list of network parameters to tune. These can be obtained by the root operator's ``parameters``. lr (float, list, output of :func:`learning_parameter_schedule`): a learning rate in float, or a learning rate schedule. See also: :func:`learning_parameter_schedule` momentum (float, list, output of :func:`momentum_schedule`): momentum schedule. For additional information, please refer to the :cntkwiki:`this CNTK Wiki article <BrainScript-SGD-Block#converting-learning-rate-and-momentum-parameters-from-other-toolkits>`. unit_gain: when ``True``, momentum is interpreted as a unit-gain filter. Defaults to the value returned by :func:`default_unit_gain_value`. l1_regularization_weight (float, optional): the L1 regularization weight per sample, defaults to 0.0 l2_regularization_weight (float, optional): the L2 regularization weight per sample, defaults to 0.0 gaussian_noise_injection_std_dev (float, optional): the standard deviation of the Gaussian noise added to parameters post update, defaults to 0.0 gradient_clipping_threshold_per_sample (float, optional): clipping threshold per sample, defaults to infinity gradient_clipping_with_truncation (bool, default ``True``): use gradient clipping with truncation use_mean_gradient (bool, default ``False``): use averaged gradient as input to learner. Defaults to the value returned by :func:`default_use_mean_gradient_value()`. deprecated:: 2.2 Use minibatch_size parameter to specify the reference minbiatch size. minibatch_size (int, default ``None``): The minibatch size that the learner's parameters are designed or pre-tuned for. This size is usually set to the same as the minibatch data source's size. CNTK will perform automatic scaling of the parameters to enable efficient model parameter update implementation while approximate the behavior of pre-designed and pre-tuned parameters. In case that minibatch_size is not specified, CNTK will inherit the minibatch size from the learning rate schedule; if the learning rate schedule does not specify the minibatch_size, CNTK will set it to :attr:`IGNORE`. Setting minibatch_size to :attr:`IGNORE` will have the learner apply as it is preventing CNTK performing any hyper-parameter scaling. See also: :func:`learning_parameter_schedule` epoch_size (optional, int): number of samples as a scheduling unit for learning rate and momentum. See also: :func:`learning_parameter_schedule` Returns: :class:`~cntk.learners.Learner`: learner instance that can be passed to the :class:`~cntk.train.trainer.Trainer` ''' lr, minibatch_size = _infer_learning_rate_schedule_and_ref_minibatch_size(use_mean_gradient, minibatch_size, lr, epoch_size) momentum = _infer_learning_parameter_schedule(momentum, minibatch_size, epoch_size) _verify_momentum_type(momentum) gaussian_noise_injection_std_dev = \ training_parameter_schedule( gaussian_noise_injection_std_dev) additional_options = cntk_py.AdditionalLearningOptions() additional_options.l1_regularization_weight = l1_regularization_weight additional_options.l2_regularization_weight = l2_regularization_weight additional_options.gaussian_noise_injection_std_dev = gaussian_noise_injection_std_dev additional_options.gradient_clipping_threshold_per_sample = gradient_clipping_threshold_per_sample additional_options.gradient_clipping_with_truncation = gradient_clipping_with_truncation if minibatch_size is not None: additional_options.dict_options[cntk_py.Learner._MINIBATCH_SIZE] = cntk_py.SizeTWrapper(minibatch_size) #need this to make proper typed DictionaryValue opt = cntk_py.momentum_sgd_learner(parameters, lr, momentum, unit_gain, additional_options) opt.is_minibatch_size_explicitly_specified = minibatch_size is not None return opt @typemap def nesterov(parameters, lr, momentum, unit_gain=default_unit_gain_value(), l1_regularization_weight=0.0, l2_regularization_weight=0.0, gaussian_noise_injection_std_dev=0.0, gradient_clipping_threshold_per_sample=np.inf, gradient_clipping_with_truncation=True, use_mean_gradient=None, minibatch_size=None, epoch_size=None): '''nesterov(parameters, lr, momentum, unit_gain=default_unit_gain_value(), l1_regularization_weight=0, l2_regularization_weight=0, gaussian_noise_injection_std_dev=0, gradient_clipping_threshold_per_sample=np.inf, gradient_clipping_with_truncation=True) Creates a Nesterov SGD learner instance to learn the parameters. This was originally proposed by Nesterov [1] in 1983 and then shown to work well in a deep learning context by Sutskever, et al. [2]. Args: parameters (list of parameters): list of network parameters to tune. These can be obtained by the root operator's ``parameters``. lr (float, list, output of :func:`learning_parameter_schedule`): a learning rate in float, or a learning rate schedule. See also: :func:`learning_parameter_schedule` momentum (float, list, output of :func:`momentum_schedule`): momentum schedule. For additional information, please refer to the :cntkwiki:`this CNTK Wiki article <BrainScript-SGD-Block#converting-learning-rate-and-momentum-parameters-from-other-toolkits>`. unit_gain: when ``True``, momentum is interpreted as a unit-gain filter. Defaults to the value returned by :func:`default_unit_gain_value`. l1_regularization_weight (float, optional): the L1 regularization weight per sample, defaults to 0.0 l2_regularization_weight (float, optional): the L2 regularization weight per sample, defaults to 0.0 gaussian_noise_injection_std_dev (float, optional): the standard deviation of the Gaussian noise added to parameters post update, defaults to 0.0 gradient_clipping_threshold_per_sample (float, optional): clipping threshold per sample, defaults to infinity gradient_clipping_with_truncation (bool, default ``True``): use gradient clipping with truncation use_mean_gradient (bool, default ``False``): use averaged gradient as input to learner. Defaults to the value returned by :func:`default_use_mean_gradient_value()`. deprecated:: 2.2 Use minibatch_size parameter to specify the reference minbiatch size. minibatch_size (int, default ``None``): The minibatch size that the learner's parameters are designed or pre-tuned for. This size is usually set to the same as the minibatch data source's size. CNTK will perform automatic scaling of the parameters to enable efficient model parameter update implementation while approximate the behavior of pre-designed and pre-tuned parameters. In case that minibatch_size is not specified, CNTK will inherit the minibatch size from the learning rate schedule; if the learning rate schedule does not specify the minibatch_size, CNTK will set it to :attr:`IGNORE`. Setting minibatch_size to :attr:`IGNORE` will have the learner apply as it is preventing CNTK performing any hyper-parameter scaling. See also: :func:`learning_parameter_schedule` epoch_size (optional, int): number of samples as a scheduling unit for learning rate and momentum. See also: :func:`learning_parameter_schedule` Returns: :class:`~cntk.learners.Learner`: learner instance that can be passed to the :class:`~cntk.train.trainer.Trainer` See also: [1] Y. Nesterov. A Method of Solving a Convex Programming Problem with Convergence Rate O(1/ sqrt(k)). Soviet Mathematics Doklady, 1983. [2] I. Sutskever, J. Martens, G. Dahl, and G. Hinton. `On the Importance of Initialization and Momentum in Deep Learning <http://www.cs.toronto.edu/~fritz/absps/momentum.pdf>`_. Proceedings of the 30th International Conference on Machine Learning, 2013. ''' lr, minibatch_size = _infer_learning_rate_schedule_and_ref_minibatch_size(use_mean_gradient, minibatch_size, lr, epoch_size) momentum = _infer_learning_parameter_schedule(momentum, minibatch_size, epoch_size) _verify_momentum_type(momentum) gaussian_noise_injection_std_dev = \ training_parameter_schedule( gaussian_noise_injection_std_dev) additional_options = cntk_py.AdditionalLearningOptions() additional_options.l1_regularization_weight = l1_regularization_weight additional_options.l2_regularization_weight = l2_regularization_weight additional_options.gaussian_noise_injection_std_dev = gaussian_noise_injection_std_dev additional_options.gradient_clipping_threshold_per_sample = gradient_clipping_threshold_per_sample additional_options.gradient_clipping_with_truncation = gradient_clipping_with_truncation if minibatch_size is not None: additional_options.dict_options[cntk_py.Learner._MINIBATCH_SIZE] = cntk_py.SizeTWrapper(minibatch_size) #need this to make proper typed DictionaryValue opt=cntk_py.nesterov_learner(parameters, lr, momentum, unit_gain, additional_options) opt.is_minibatch_size_explicitly_specified = minibatch_size is not None return opt @typemap def adadelta(parameters, lr=learning_rate_schedule(1, UnitType.sample), rho=0.95, epsilon=1e-8, l1_regularization_weight=0.0, l2_regularization_weight=0.0, gaussian_noise_injection_std_dev=0.0, gradient_clipping_threshold_per_sample=np.inf, gradient_clipping_with_truncation=True, use_mean_gradient=None, minibatch_size=None, epoch_size=None): '''adadelta(parameters, lr, rho, epsilon, l1_regularization_weight=0, l2_regularization_weight=0, gaussian_noise_injection_std_dev=0, gradient_clipping_threshold_per_sample=np.inf, gradient_clipping_with_truncation=True) Creates an AdaDelta learner instance to learn the parameters. See [1] for more information. Args: parameters (list of parameters): list of network parameters to tune. These can be obtained by the root operator's ``parameters``. lr (float, list, output of :func:`learning_parameter_schedule`): a learning rate in float, or a learning rate schedule. See also: :func:`learning_parameter_schedule` rho (float): exponential smooth factor for each minibatch. epsilon (float): epsilon for sqrt. l1_regularization_weight (float, optional): the L1 regularization weight per sample, defaults to 0.0 l2_regularization_weight (float, optional): the L2 regularization weight per sample, defaults to 0.0 gaussian_noise_injection_std_dev (float, optional): the standard deviation of the Gaussian noise added to parameters post update, defaults to 0.0 gradient_clipping_threshold_per_sample (float, optional): clipping threshold per sample, defaults to infinity gradient_clipping_with_truncation (bool, default ``True``): use gradient clipping with truncation use_mean_gradient (bool, default ``False``): use averaged gradient as input to learner. Defaults to the value returned by :func:`default_use_mean_gradient_value()`. deprecated:: 2.2 Use minibatch_size parameter to specify the reference minbiatch size. minibatch_size (int, default ``None``): The minibatch size that the learner's parameters are designed or pre-tuned for. This size is usually set to the same as the minibatch data source's size. CNTK will perform automatic scaling of the parameters to enable efficient model parameter update implementation while approximate the behavior of pre-designed and pre-tuned parameters. In case that minibatch_size is not specified, CNTK will inherit the minibatch size from the learning rate schedule; if the learning rate schedule does not specify the minibatch_size, CNTK will set it to :attr:`IGNORE`. Setting minibatch_size to :attr:`IGNORE` will have the learner apply as it is preventing CNTK performing any hyper-parameter scaling. See also: :func:`learning_parameter_schedule` epoch_size (optional, int): number of samples as a scheduling unit for learning rate. See also: :func:`learning_parameter_schedule` Returns: :class:`~cntk.learners.Learner`: learner instance that can be passed to the :class:`~cntk.train.trainer.Trainer` See also [1] Matthew D. Zeiler, `ADADELTA: An Adaptive Learning Rate Method <https://arxiv.org/pdf/1212.5701.pdf>`_. ''' gaussian_noise_injection_std_dev = \ training_parameter_schedule( gaussian_noise_injection_std_dev) lr, minibatch_size = _infer_learning_rate_schedule_and_ref_minibatch_size(use_mean_gradient, minibatch_size, lr, epoch_size) additional_options = cntk_py.AdditionalLearningOptions() additional_options.l1_regularization_weight = l1_regularization_weight additional_options.l2_regularization_weight = l2_regularization_weight additional_options.gaussian_noise_injection_std_dev = gaussian_noise_injection_std_dev additional_options.gradient_clipping_threshold_per_sample = gradient_clipping_threshold_per_sample additional_options.gradient_clipping_with_truncation = gradient_clipping_with_truncation minibatch_size = _infer_ref_minibatch_size_from_legacy_use_mean_gradient(minibatch_size, use_mean_gradient) if minibatch_size is not None: additional_options.dict_options[cntk_py.Learner._MINIBATCH_SIZE] = cntk_py.SizeTWrapper(minibatch_size) #need this to make proper typed DictionaryValue opt = cntk_py.ada_delta_learner(parameters, lr, rho, epsilon, additional_options) opt.is_minibatch_size_explicitly_specified = minibatch_size is not None return opt @typemap def adagrad(parameters, lr, need_ave_multiplier=True, l1_regularization_weight=0.0, l2_regularization_weight=0.0, gaussian_noise_injection_std_dev=0.0, gradient_clipping_threshold_per_sample=np.inf, gradient_clipping_with_truncation=True, use_mean_gradient=None, minibatch_size=None, epoch_size=None): '''adagrad(parameters, lr, need_ave_multiplier=True, l1_regularization_weight=0, l2_regularization_weight=0, gaussian_noise_injection_std_dev=0, gradient_clipping_threshold_per_sample=np.inf, gradient_clipping_with_truncation=True) Creates an AdaGrad learner instance to learn the parameters. See [1] for more information. Args: parameters (list of parameters): list of network parameters to tune. These can be obtained by the root operator's ``parameters``. lr (float, list, output of :func:`learning_parameter_schedule`): a learning rate in float, or a learning rate schedule. See also: :func:`learning_parameter_schedule` need_ave_multiplier (bool, default): l1_regularization_weight (float, optional): the L1 regularization weight per sample, defaults to 0.0 l2_regularization_weight (float, optional): the L2 regularization weight per sample, defaults to 0.0 gaussian_noise_injection_std_dev (float, optional): the standard deviation of the Gaussian noise added to parameters post update, defaults to 0.0 gradient_clipping_threshold_per_sample (float, optional): clipping threshold per sample, defaults to infinity gradient_clipping_with_truncation (bool, default ``True``): use gradient clipping with truncation use_mean_gradient (bool, default ``False``): use averaged gradient as input to learner. Defaults to the value returned by :func:`default_use_mean_gradient_value()`. deprecated:: 2.2 Use minibatch_size parameter to specify the reference minbiatch size. minibatch_size (int, default ``None``): The minibatch size that the learner's parameters are designed or pre-tuned for. This size is usually set to the same as the minibatch data source's size. CNTK will perform automatic scaling of the parameters to enable efficient model parameter update implementation while approximate the behavior of pre-designed and pre-tuned parameters. In case that minibatch_size is not specified, CNTK will inherit the minibatch size from the learning rate schedule; if the learning rate schedule does not specify the minibatch_size, CNTK will set it to :attr:`IGNORE`. Setting minibatch_size to :attr:`IGNORE` will have the learner apply as it is preventing CNTK performing any hyper-parameter scaling. See also: :func:`learning_parameter_schedule` epoch_size (optional, int): number of samples as a scheduling unit for learning rate. See also: :func:`learning_parameter_schedule` Returns: :class:`~cntk.learners.Learner`: learner instance that can be passed to the :class:`~cntk.train.trainer.Trainer` See also: [1] J. Duchi, E. Hazan, and Y. Singer. `Adaptive Subgradient Methods for Online Learning and Stochastic Optimization <http://www.magicbroom.info/Papers/DuchiHaSi10.pdf>`_. The Journal of Machine Learning Research, 2011. ''' lr, minibatch_size = _infer_learning_rate_schedule_and_ref_minibatch_size(use_mean_gradient, minibatch_size, lr, epoch_size) gaussian_noise_injection_std_dev = \ training_parameter_schedule( gaussian_noise_injection_std_dev) additional_options = cntk_py.AdditionalLearningOptions() additional_options.l1_regularization_weight = l1_regularization_weight additional_options.l2_regularization_weight = l2_regularization_weight additional_options.gaussian_noise_injection_std_dev = gaussian_noise_injection_std_dev additional_options.gradient_clipping_threshold_per_sample = gradient_clipping_threshold_per_sample additional_options.gradient_clipping_with_truncation = gradient_clipping_with_truncation minibatch_size = _infer_ref_minibatch_size_from_legacy_use_mean_gradient(minibatch_size, use_mean_gradient) if minibatch_size is not None: additional_options.dict_options[cntk_py.Learner._MINIBATCH_SIZE] = cntk_py.SizeTWrapper(minibatch_size) #need this to make proper typed DictionaryValue opt = cntk_py.ada_grad_learner(parameters, lr, need_ave_multiplier, additional_options) opt.is_minibatch_size_explicitly_specified = minibatch_size is not None return opt @typemap def fsadagrad(parameters, lr, momentum, unit_gain=default_unit_gain_value(), variance_momentum=momentum_as_time_constant_schedule(720000), l1_regularization_weight=0.0, l2_regularization_weight=0.0, gaussian_noise_injection_std_dev=0.0, gradient_clipping_threshold_per_sample=np.inf, gradient_clipping_with_truncation=True, use_mean_gradient=None, minibatch_size=None, epoch_size=None): '''fsadagrad(parameters, lr, momentum, unit_gain=default_unit_gain_value(), variance_momentum=momentum_as_time_constant_schedule(720000), l1_regularization_weight=0, l2_regularization_weight=0, gaussian_noise_injection_std_dev=0, gradient_clipping_threshold_per_sample=np.inf, gradient_clipping_with_truncation=True) Creates an FSAdaGrad learner instance to learn the parameters. Args: parameters (list of parameters): list of network parameters to tune. These can be obtained by the root operator's ``parameters``. lr (float, list, output of :func:`learning_parameter_schedule`): a learning rate in float, or a learning rate schedule. See also: :func:`learning_parameter_schedule` momentum (float, list, output of :func:`momentum_schedule`): momentum schedule. For additional information, please refer to the :cntkwiki:`this CNTK Wiki article <BrainScript-SGD-Block#converting-learning-rate-and-momentum-parameters-from-other-toolkits>`. unit_gain: when ``True``, momentum is interpreted as a unit-gain filter. Defaults to the value returned by :func:`default_unit_gain_value`. variance_momentum (float, list, output of :func:`momentum_schedule` or :func:`momentum_as_time_constant_schedule`): variance momentum schedule. Defaults to ``momentum_as_time_constant_schedule(720000)``. l1_regularization_weight (float, optional): the L1 regularization weight per sample, defaults to 0.0 l2_regularization_weight (float, optional): the L2 regularization weight per sample, defaults to 0.0 gaussian_noise_injection_std_dev (float, optional): the standard deviation of the Gaussian noise added to parameters post update, defaults to 0.0 gradient_clipping_threshold_per_sample (float, optional): clipping threshold per sample, defaults to infinity gradient_clipping_with_truncation (bool, default ``True``): use gradient clipping with truncation use_mean_gradient (bool, default ``False``): use averaged gradient as input to learner. Defaults to the value returned by :func:`default_use_mean_gradient_value()`. deprecated:: 2.2 Use minibatch_size parameter to specify the reference minbiatch size. minibatch_size (int, default ``None``): The minibatch size that the learner's parameters are designed or pre-tuned for. This size is usually set to the same as the minibatch data source's size. CNTK will perform automatic scaling of the parameters to enable efficient model parameter update implementation while approximate the behavior of pre-designed and pre-tuned parameters. In case that minibatch_size is not specified, CNTK will inherit the minibatch size from the learning rate schedule; if the learning rate schedule does not specify the minibatch_size, CNTK will set it to :attr:`IGNORE`. Setting minibatch_size to :attr:`IGNORE` will have the learner apply as it is preventing CNTK performing any hyper-parameter scaling. See also: :func:`learning_parameter_schedule` epoch_size (optional, int): number of samples as a scheduling unit for learning rate, momentum and variance_momentum. See also: :func:`learning_parameter_schedule` Returns: :class:`~cntk.learners.Learner`: learner instance that can be passed to the :class:`~cntk.train.trainer.Trainer` ''' lr, minibatch_size = _infer_learning_rate_schedule_and_ref_minibatch_size(use_mean_gradient, minibatch_size, lr, epoch_size) momentum = _infer_learning_parameter_schedule(momentum, minibatch_size, epoch_size) _verify_momentum_type(momentum) variance_momentum = _infer_learning_parameter_schedule(variance_momentum, minibatch_size, epoch_size) _verify_momentum_type(variance_momentum) gaussian_noise_injection_std_dev = \ training_parameter_schedule( gaussian_noise_injection_std_dev) additional_options = cntk_py.AdditionalLearningOptions() additional_options.l1_regularization_weight = l1_regularization_weight additional_options.l2_regularization_weight = l2_regularization_weight additional_options.gaussian_noise_injection_std_dev = gaussian_noise_injection_std_dev additional_options.gradient_clipping_threshold_per_sample = gradient_clipping_threshold_per_sample additional_options.gradient_clipping_with_truncation = gradient_clipping_with_truncation minibatch_size = _infer_ref_minibatch_size_from_legacy_use_mean_gradient(minibatch_size, use_mean_gradient) if minibatch_size is not None: additional_options.dict_options[cntk_py.Learner._MINIBATCH_SIZE] = cntk_py.SizeTWrapper(minibatch_size) #need this to make proper typed DictionaryValue opt = cntk_py.fsada_grad_learner(parameters, lr, momentum, unit_gain, variance_momentum, additional_options) opt.is_minibatch_size_explicitly_specified = minibatch_size is not None return opt @typemap def adam(parameters, lr, momentum, unit_gain=default_unit_gain_value(), variance_momentum=momentum_as_time_constant_schedule(720000), l1_regularization_weight=0.0, l2_regularization_weight=0.0, gaussian_noise_injection_std_dev=0.0, gradient_clipping_threshold_per_sample=np.inf, gradient_clipping_with_truncation=True, use_mean_gradient=None, epsilon=1e-8, adamax=False, minibatch_size=None, epoch_size=None): '''adam(parameters, lr, momentum, unit_gain=default_unit_gain_value(), variance_momentum=momentum_as_time_constant_schedule(720000), l1_regularization_weight=0, l2_regularization_weight=0, gaussian_noise_injection_std_dev=0, gradient_clipping_threshold_per_sample=np.inf, gradient_clipping_with_truncation=True, epsilon=1e-8, adamax=False) Creates an Adam learner instance to learn the parameters. See [1] for more information. Args: parameters (list of parameters): list of network parameters to tune. These can be obtained by the root operator's ``parameters``. lr (float, list, output of :func:`learning_parameter_schedule`): a learning rate in float, or a learning rate schedule. See also: :func:`learning_parameter_schedule` momentum (float, list, output of :func:`momentum_schedule`): momentum schedule. Note that this is the beta1 parameter in the Adam paper [1]. For additional information, please refer to the :cntkwiki:`this CNTK Wiki article <BrainScript-SGD-Block#converting-learning-rate-and-momentum-parameters-from-other-toolkits>`. unit_gain: when ``True``, momentum is interpreted as a unit-gain filter. Defaults to the value returned by :func:`default_unit_gain_value`. variance_momentum (float, list, output of :func:`momentum_schedule` or :func:`momentum_as_time_constant_schedule`): variance momentum schedule. Note that this is the beta1 parameter in the Adam paper [1]. Defaults to ``momentum_as_time_constant_schedule(720000)``. l1_regularization_weight (float, optional): the L1 regularization weight per sample, defaults to 0.0 l2_regularization_weight (float, optional): the L2 regularization weight per sample, defaults to 0.0 gaussian_noise_injection_std_dev (float, optional): the standard deviation of the Gaussian noise added to parameters post update, defaults to 0.0 gradient_clipping_threshold_per_sample (float, optional): clipping threshold per sample, defaults to infinity gradient_clipping_with_truncation (bool, default ``True``): use gradient clipping with truncation use_mean_gradient (bool, default ``False``): use averaged gradient as input to learner. Defaults to the value returned by :func:`default_use_mean_gradient_value()`. deprecated:: 2.2 Use minibatch_size parameter to specify the reference minbiatch size. epsilon (float, optional): numerical stability constant, defaults to 1e-8 adamax: when ``True``, use infinity-norm variance momentum update instead of L2. Defaults to False minibatch_size (int, default ``None``): The minibatch size that the learner's parameters are designed or pre-tuned for. This size is usually set to the same as the minibatch data source's size. CNTK will perform automatic scaling of the parameters to enable efficient model parameter update implementation while approximate the behavior of pre-designed and pre-tuned parameters. In case that minibatch_size is not specified, CNTK will inherit the minibatch size from the learning rate schedule; if the learning rate schedule does not specify the minibatch_size, CNTK will set it to :attr:`IGNORE`. Setting minibatch_size to :attr:`IGNORE` will have the learner apply as it is preventing CNTK performing any hyper-parameter scaling. See also: :func:`learning_parameter_schedule` epoch_size (optional, int): number of samples as a scheduling unit for learning rate, momentum and variance_momentum. See also: :func:`learning_parameter_schedule` Returns: :class:`~cntk.learners.Learner`: learner instance that can be passed to the :class:`~cntk.train.trainer.Trainer` See also: [1] D. Kingma, J. Ba. `Adam: A Method for Stochastic Optimization <https://arxiv.org/abs/1412.6980>`_. International Conference for Learning Representations, 2015. ''' lr, minibatch_size = _infer_learning_rate_schedule_and_ref_minibatch_size(use_mean_gradient, minibatch_size, lr, epoch_size) momentum = _infer_learning_parameter_schedule(momentum, minibatch_size, epoch_size) _verify_momentum_type(momentum) variance_momentum = _infer_learning_parameter_schedule(variance_momentum, minibatch_size, epoch_size) _verify_momentum_type(variance_momentum) gaussian_noise_injection_std_dev = \ training_parameter_schedule( gaussian_noise_injection_std_dev) additional_options = cntk_py.AdditionalLearningOptions() additional_options.l1_regularization_weight = l1_regularization_weight additional_options.l2_regularization_weight = l2_regularization_weight additional_options.gaussian_noise_injection_std_dev = gaussian_noise_injection_std_dev additional_options.gradient_clipping_threshold_per_sample = gradient_clipping_threshold_per_sample additional_options.gradient_clipping_with_truncation = gradient_clipping_with_truncation if minibatch_size is not None: additional_options.dict_options[cntk_py.Learner._MINIBATCH_SIZE] = cntk_py.SizeTWrapper(minibatch_size) #need this to make proper typed DictionaryValue opt = cntk_py.adam_learner(parameters, lr, momentum, unit_gain, variance_momentum, epsilon, adamax, additional_options) opt.is_minibatch_size_explicitly_specified = minibatch_size is not None return opt @typemap def rmsprop(parameters, lr, gamma, inc, dec, max, min, need_ave_multiplier=True, l1_regularization_weight=0.0, l2_regularization_weight=0.0, gaussian_noise_injection_std_dev=0.0, gradient_clipping_threshold_per_sample=np.inf, gradient_clipping_with_truncation=True, use_mean_gradient=None, minibatch_size=None, epoch_size=None): '''rmsprop(parameters, lr, gamma, inc, dec, max, min, need_ave_multiplier=True, l1_regularization_weight=0, l2_regularization_weight=0, gaussian_noise_injection_std_dev=0, gradient_clipping_threshold_per_sample=np.inf, gradient_clipping_with_truncation=True) Creates an RMSProp learner instance to learn the parameters. Args: parameters (list of parameters): list of network parameters to tune. These can be obtained by the root operator's ``parameters``. lr (float, list, output of :func:`learning_parameter_schedule`): a learning rate in float, or a learning rate schedule. See also: :func:`learning_parameter_schedule` gamma (float): Trade-off factor for current and previous gradients. Common value is 0.95. Should be in range (0.0, 1.0) inc (float): Increasing factor when trying to adjust current learning_rate. Should be greater than 1 dec (float): Decreasing factor when trying to adjust current learning_rate. Should be in range (0.0, 1.0) max (float): Maximum scale allowed for the initial learning_rate. Should be greater than zero and min min (float): Minimum scale allowed for the initial learning_rate. Should be greater than zero need_ave_multiplier (bool, default ``True``): l1_regularization_weight (float, optional): the L1 regularization weight per sample, defaults to 0.0 l2_regularization_weight (float, optional): the L2 regularization weight per sample, defaults to 0.0 gaussian_noise_injection_std_dev (float, optional): the standard deviation of the Gaussian noise added to parameters post update, defaults to 0.0 gradient_clipping_threshold_per_sample (float, optional): clipping threshold per sample, defaults to infinity gradient_clipping_with_truncation (bool, default ``True``): use gradient clipping with truncation use_mean_gradient (bool, default ``False``): use averaged gradient as input to learner. Defaults to the value returned by :func:`default_use_mean_gradient_value()`. deprecated:: 2.2 Use minibatch_size parameter to specify the reference minbiatch size. minibatch_size (int, default ``None``): The minibatch size that the learner's parameters are designed or pre-tuned for. This size is usually set to the same as the minibatch data source's size. CNTK will perform automatic scaling of the parameters to enable efficient model parameter update implementation while approximate the behavior of pre-designed and pre-tuned parameters. In case that minibatch_size is not specified, CNTK will inherit the minibatch size from the learning rate schedule; if the learning rate schedule does not specify the minibatch_size, CNTK will set it to :attr:`IGNORE`. Setting minibatch_size to :attr:`IGNORE` will have the learner apply as it is preventing CNTK performing any hyper-parameter scaling. See also: :func:`learning_parameter_schedule` epoch_size (optional, int): number of samples as a scheduling unit for learning rate. See also: :func:`learning_parameter_schedule` Returns: :class:`~cntk.learners.Learner`: learner instance that can be passed to the :class:`~cntk.train.trainer.Trainer` ''' lr, minibatch_size = _infer_learning_rate_schedule_and_ref_minibatch_size(use_mean_gradient, minibatch_size, lr, epoch_size) gaussian_noise_injection_std_dev = \ training_parameter_schedule( gaussian_noise_injection_std_dev) additional_options = cntk_py.AdditionalLearningOptions() additional_options.l1_regularization_weight = l1_regularization_weight additional_options.l2_regularization_weight = l2_regularization_weight additional_options.gaussian_noise_injection_std_dev = gaussian_noise_injection_std_dev additional_options.gradient_clipping_threshold_per_sample = gradient_clipping_threshold_per_sample additional_options.gradient_clipping_with_truncation = gradient_clipping_with_truncation minibatch_size = _infer_ref_minibatch_size_from_legacy_use_mean_gradient(minibatch_size, use_mean_gradient) if minibatch_size is not None: additional_options.dict_options[cntk_py.Learner._MINIBATCH_SIZE] = cntk_py.SizeTWrapper(minibatch_size) #need this to make proper typed DictionaryValue opt = cntk_py.rmsprop_learner(parameters, lr, gamma, inc, dec, max, min, need_ave_multiplier, additional_options) opt.is_minibatch_size_explicitly_specified = minibatch_size is not None return opt @typemap def universal(update_func, parameters): ''' Creates a learner which uses a CNTK function to update the parameters. Args: update_func: function that takes parameters and gradients as arguments and returns a :class:`~cntk.ops.functions.Function` that performs the desired updates. The returned function updates the parameters by means of containing :func:`~cntk.ops.assign` operations. If ``update_func`` does not contain :func:`~cntk.ops.assign` operations the parameters will not be updated. parameters (list): list of network parameters to tune. These can be obtained by the root operator's `parameters`. Returns: :class:`~cntk.learners.Learner`: learner instance that can be passed to the :class:`~cntk.train.trainer.Trainer` Examples: >>> def my_adagrad(parameters, gradients): ... accumulators = [C.constant(0, shape=p.shape, dtype=p.dtype, name='accum') for p in parameters] ... update_funcs = [] ... for p, g, a in zip(parameters, gradients, accumulators): ... accum_new = C.assign(a, g * g) ... update_funcs.append(C.assign(p, p - 0.01 * g / C.sqrt(accum_new + 1e-6))) ... return C.combine(update_funcs) ... >>> x = C.input_variable((10,)) >>> y = C.input_variable((2,)) >>> z = C.layers.Sequential([C.layers.Dense(100, activation=C.relu), C.layers.Dense(2)])(x) >>> loss = C.cross_entropy_with_softmax(z, y) >>> learner = C.universal(my_adagrad, z.parameters) >>> trainer = C.Trainer(z, loss, learner) >>> # now trainer can be used as any other Trainer ''' from .. import constant args, _ = utils.get_python_function_arguments(update_func) if len(args) != 2: raise ValueError('update_func must be a function that accepts two arguments (parameters, gradients)') gradients = [] for p in parameters: if any(dim<0 for dim in p.shape): raise ValueError('parameter %s has inferred dimensions. Please create the learner after all parameter shapes have been determined'%str(p)) gradients.append(constant(0, shape=p.shape, dtype=p.dtype, name='grad')) #TODO: add additional options and learning context to the parameters of the updat_func so that the update function # can make use of the context and additional options result = update_func(parameters, gradients) return cntk_py.universal_learner(parameters, gradients, result)

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Python

SRC/Chapter_01-The-Basics/08_range_example.py

archeranimesh/HeadFirstPython

8e0c84871328a6bf3a8d723341be56298440f29b

[ "MIT" ]

1

2020-12-26T19:37:14.000Z

SRC/Chapter_01-The-Basics/08_range_example.py

archeranimesh/HeadFirstPython

8e0c84871328a6bf3a8d723341be56298440f29b

[ "MIT" ]

null

SRC/Chapter_01-The-Basics/08_range_example.py

archeranimesh/HeadFirstPython

8e0c84871328a6bf3a8d723341be56298440f29b

[ "MIT" ]

null

print(list(range(5))) print(list(range(5, 10))) print(list(range(0, 10, 2))) print(list(range(10, 0, -2))) print(list(range(0, 10, -2)))

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16b85881135eefe45aa0a9b794f4863f2eda78f1

98

py

Python

terrascript/triton/__init__.py

vutsalsinghal/python-terrascript

3b9fb5ad77453d330fb0cd03524154a342c5d5dc

[ "BSD-2-Clause" ]

null

terrascript/triton/__init__.py

vutsalsinghal/python-terrascript

3b9fb5ad77453d330fb0cd03524154a342c5d5dc

[ "BSD-2-Clause" ]

null

terrascript/triton/__init__.py

vutsalsinghal/python-terrascript

3b9fb5ad77453d330fb0cd03524154a342c5d5dc

[ "BSD-2-Clause" ]

null

# terrascript/triton/__init__.py import terrascript class triton(terrascript.Provider): pass

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bc6c3d2943dffd2db638fdc56eedb5f617d7fe41

47

py

Python

aiohttphelper/__init__.py

gregorybarille/AioCalls

15a8ffe56e656b641abcbc5a899b662e7fc2f0d8

[ "MIT" ]

1

2020-07-10T13:06:27.000Z

aiohttphelper/__init__.py

gregorybarille/AioCalls

15a8ffe56e656b641abcbc5a899b662e7fc2f0d8

[ "MIT" ]

null

aiohttphelper/__init__.py

gregorybarille/AioCalls

15a8ffe56e656b641abcbc5a899b662e7fc2f0d8

[ "MIT" ]

null

from .functions import get, put, post, delete

15.666667

45

0.744681

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47

5

1

0

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47

2

46

23.5

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6

bc7d118927d5343e885592d3c62eefb0a18867ce

51

py

Python

__init__.py

goodship1/CoinCommand

9b8f36d4b5241f9cb56cf68bf449c1af03c44a7a

[ "MIT" ]

2

2018-01-25T22:10:37.000Z

2020-02-13T16:49:55.000Z

__init__.py

goodship1/CoinCommand

9b8f36d4b5241f9cb56cf68bf449c1af03c44a7a

[ "MIT" ]

5

2018-03-03T23:35:21.000Z

2019-09-22T18:30:49.000Z

__init__.py

goodship1/CoinCommand

9b8f36d4b5241f9cb56cf68bf449c1af03c44a7a

[ "MIT" ]

null

from .CoincommandExceptions import CoinDoesntExist

25.5

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51

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1

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1

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6

bc825e0310b5e4859b0059b3ada81590c68d8418

569

py

Python

pyspedas/erg/__init__.py

pulupa/pyspedas

7228199cf16eca2a27d130f1e4985ef1e69462ea

[ "MIT" ]

3

2018-10-22T20:15:39.000Z

2019-03-06T18:03:35.000Z

pyspedas/erg/__init__.py

pulupa/pyspedas

7228199cf16eca2a27d130f1e4985ef1e69462ea

[ "MIT" ]

null

pyspedas/erg/__init__.py

pulupa/pyspedas

7228199cf16eca2a27d130f1e4985ef1e69462ea

[ "MIT" ]

2

2019-01-25T20:03:33.000Z

2019-07-05T19:53:30.000Z

from .satellite.erg.hep.hep import hep from .satellite.erg.lepe.lepe import lepe from .satellite.erg.lepi.lepi import lepi from .satellite.erg.mepe.mepe import mepe from .satellite.erg.mepi.mepi_nml import mepi_nml from .satellite.erg.mepi.mepi_tof import mepi_tof from .satellite.erg.mgf.mgf import mgf from .satellite.erg.orb.orb import orb from .satellite.erg.pwe.pwe_efd import pwe_efd from .satellite.erg.pwe.pwe_hfa import pwe_hfa from .satellite.erg.pwe.pwe_ofa import pwe_ofa from .satellite.erg.pwe.pwe_wfc import pwe_wfc from .satellite.erg.xep.xep import xep

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569

13

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1

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6

bcaccc0f170c85448aa391ca61f85defd86f0387

250

py

Python

examplepackage/tests/test_examplemodule.py

dionysisbacchus/python-project-template

1c25dc7022614b59c61bb99321c580afdeac17f2

[ "MIT" ]

null

examplepackage/tests/test_examplemodule.py

dionysisbacchus/python-project-template

1c25dc7022614b59c61bb99321c580afdeac17f2

[ "MIT" ]

null

examplepackage/tests/test_examplemodule.py

dionysisbacchus/python-project-template

1c25dc7022614b59c61bb99321c580afdeac17f2

[ "MIT" ]

3

2022-02-22T06:54:19.000Z

2022-02-23T20:06:13.000Z

import pytest from examplepackage.examplemodule import example_function @pytest.mark.parametrize("test_input,expected", [(2, 4), (3, 9), (5, 25)]) def test_example_function(test_input, expected): assert example_function(test_input) == expected

31.25

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250

7

75

35.714286

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1

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null

0

1

0

1

0

6

bcf9571cb6b1398073675c04d6c2d86ad989f69e

7,371

py

Python

cottonformation/res/inspector.py

gitter-badger/cottonformation-project

354f1dce7ea106e209af2d5d818b6033a27c193c

[ "BSD-2-Clause" ]

5

2021-07-22T03:45:59.000Z

2021-12-17T21:07:14.000Z

cottonformation/res/inspector.py

gitter-badger/cottonformation-project

354f1dce7ea106e209af2d5d818b6033a27c193c

[ "BSD-2-Clause" ]

1

2021-06-25T18:01:31.000Z

cottonformation/res/inspector.py

gitter-badger/cottonformation-project

354f1dce7ea106e209af2d5d818b6033a27c193c

[ "BSD-2-Clause" ]

2

2021-06-27T03:08:21.000Z

2021-06-28T22:15:51.000Z

# -*- coding: utf-8 -*- """ This module """ import attr import typing from ..core.model import ( Property, Resource, Tag, GetAtt, TypeHint, TypeCheck, ) from ..core.constant import AttrMeta #--- Property declaration --- #--- Resource declaration --- @attr.s class ResourceGroup(Resource): """ AWS Object Type = "AWS::Inspector::ResourceGroup" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-inspector-resourcegroup.html Property Document: - ``rp_ResourceGroupTags``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-inspector-resourcegroup.html#cfn-inspector-resourcegroup-resourcegrouptags """ AWS_OBJECT_TYPE = "AWS::Inspector::ResourceGroup" rp_ResourceGroupTags: typing.List[typing.Union[Tag, dict]] = attr.ib( default=None, converter=Tag.from_list, validator=attr.validators.deep_iterable(member_validator=attr.validators.instance_of(Tag), iterable_validator=attr.validators.instance_of(list)), metadata={AttrMeta.PROPERTY_NAME: "ResourceGroupTags"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-inspector-resourcegroup.html#cfn-inspector-resourcegroup-resourcegrouptags""" @property def rv_Arn(self) -> GetAtt: """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-inspector-resourcegroup.html#aws-resource-inspector-resourcegroup-return-values""" return GetAtt(resource=self, attr_name="Arn") @attr.s class AssessmentTemplate(Resource): """ AWS Object Type = "AWS::Inspector::AssessmentTemplate" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-inspector-assessmenttemplate.html Property Document: - ``rp_AssessmentTargetArn``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-inspector-assessmenttemplate.html#cfn-inspector-assessmenttemplate-assessmenttargetarn - ``rp_DurationInSeconds``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-inspector-assessmenttemplate.html#cfn-inspector-assessmenttemplate-durationinseconds - ``rp_RulesPackageArns``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-inspector-assessmenttemplate.html#cfn-inspector-assessmenttemplate-rulespackagearns - ``p_AssessmentTemplateName``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-inspector-assessmenttemplate.html#cfn-inspector-assessmenttemplate-assessmenttemplatename - ``p_UserAttributesForFindings``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-inspector-assessmenttemplate.html#cfn-inspector-assessmenttemplate-userattributesforfindings """ AWS_OBJECT_TYPE = "AWS::Inspector::AssessmentTemplate" rp_AssessmentTargetArn: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), metadata={AttrMeta.PROPERTY_NAME: "AssessmentTargetArn"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-inspector-assessmenttemplate.html#cfn-inspector-assessmenttemplate-assessmenttargetarn""" rp_DurationInSeconds: int = attr.ib( default=None, validator=attr.validators.instance_of(int), metadata={AttrMeta.PROPERTY_NAME: "DurationInSeconds"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-inspector-assessmenttemplate.html#cfn-inspector-assessmenttemplate-durationinseconds""" rp_RulesPackageArns: typing.List[TypeHint.intrinsic_str] = attr.ib( default=None, validator=attr.validators.deep_iterable(member_validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), iterable_validator=attr.validators.instance_of(list)), metadata={AttrMeta.PROPERTY_NAME: "RulesPackageArns"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-inspector-assessmenttemplate.html#cfn-inspector-assessmenttemplate-rulespackagearns""" p_AssessmentTemplateName: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "AssessmentTemplateName"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-inspector-assessmenttemplate.html#cfn-inspector-assessmenttemplate-assessmenttemplatename""" p_UserAttributesForFindings: typing.List[typing.Union[Tag, dict]] = attr.ib( default=None, converter=Tag.from_list, validator=attr.validators.optional(attr.validators.deep_iterable(member_validator=attr.validators.instance_of(Tag), iterable_validator=attr.validators.instance_of(list))), metadata={AttrMeta.PROPERTY_NAME: "UserAttributesForFindings"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-inspector-assessmenttemplate.html#cfn-inspector-assessmenttemplate-userattributesforfindings""" @property def rv_Arn(self) -> GetAtt: """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-inspector-assessmenttemplate.html#aws-resource-inspector-assessmenttemplate-return-values""" return GetAtt(resource=self, attr_name="Arn") @attr.s class AssessmentTarget(Resource): """ AWS Object Type = "AWS::Inspector::AssessmentTarget" Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-inspector-assessmenttarget.html Property Document: - ``p_AssessmentTargetName``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-inspector-assessmenttarget.html#cfn-inspector-assessmenttarget-assessmenttargetname - ``p_ResourceGroupArn``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-inspector-assessmenttarget.html#cfn-inspector-assessmenttarget-resourcegrouparn """ AWS_OBJECT_TYPE = "AWS::Inspector::AssessmentTarget" p_AssessmentTargetName: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "AssessmentTargetName"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-inspector-assessmenttarget.html#cfn-inspector-assessmenttarget-assessmenttargetname""" p_ResourceGroupArn: TypeHint.intrinsic_str = attr.ib( default=None, validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)), metadata={AttrMeta.PROPERTY_NAME: "ResourceGroupArn"}, ) """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-inspector-assessmenttarget.html#cfn-inspector-assessmenttarget-resourcegrouparn""" @property def rv_Arn(self) -> GetAtt: """Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-inspector-assessmenttarget.html#aws-resource-inspector-assessmenttarget-return-values""" return GetAtt(resource=self, attr_name="Arn")

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0

1

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null

0

6

4c2861eb6324559081276d32528bdabcbc7ccbf6

38

py

Python

tests/inline_test_resources/5/importB.py

thesynman/pybricksdev

6f34cfb7a5f26628fe3cedae1ce51ee6024f57b9

[ "MIT" ]

null

tests/inline_test_resources/5/importB.py

thesynman/pybricksdev

6f34cfb7a5f26628fe3cedae1ce51ee6024f57b9

[ "MIT" ]

null

tests/inline_test_resources/5/importB.py

thesynman/pybricksdev

6f34cfb7a5f26628fe3cedae1ce51ee6024f57b9

[ "MIT" ]

null

noway = 'should not import this file'

19

37

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null

0

1

0

1

0

6

4c3abb2a1ed1cbd85234cb3c5af8aefe1a81b75d

32,443

py

Python

dlp/tests/unit/gapic/v2/test_dlp_service_client_v2.py

udengcnf/gcloud

dd1714bd754e18739339e611c42a391ced27c614

[ "Apache-2.0" ]

null

dlp/tests/unit/gapic/v2/test_dlp_service_client_v2.py

udengcnf/gcloud

dd1714bd754e18739339e611c42a391ced27c614

[ "Apache-2.0" ]

null

dlp/tests/unit/gapic/v2/test_dlp_service_client_v2.py

udengcnf/gcloud

dd1714bd754e18739339e611c42a391ced27c614

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # # Copyright 2018 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Unit tests.""" import pytest from google.cloud import dlp_v2 from google.cloud.dlp_v2.proto import dlp_pb2 from google.protobuf import empty_pb2 class MultiCallableStub(object): """Stub for the grpc.UnaryUnaryMultiCallable interface.""" def __init__(self, method, channel_stub): self.method = method self.channel_stub = channel_stub def __call__(self, request, timeout=None, metadata=None, credentials=None): self.channel_stub.requests.append((self.method, request)) response = None if self.channel_stub.responses: response = self.channel_stub.responses.pop() if isinstance(response, Exception): raise response if response: return response class ChannelStub(object): """Stub for the grpc.Channel interface.""" def __init__(self, responses=[]): self.responses = responses self.requests = [] def unary_unary(self, method, request_serializer=None, response_deserializer=None): return MultiCallableStub(method, self) class CustomException(Exception): pass class TestDlpServiceClient(object): def test_inspect_content(self): # Setup Expected Response expected_response = {} expected_response = dlp_pb2.InspectContentResponse(**expected_response) # Mock the API response channel = ChannelStub(responses=[expected_response]) client = dlp_v2.DlpServiceClient(channel=channel) # Setup Request parent = client.project_path('[PROJECT]') response = client.inspect_content(parent) assert expected_response == response assert len(channel.requests) == 1 expected_request = dlp_pb2.InspectContentRequest(parent=parent) actual_request = channel.requests[0][1] assert expected_request == actual_request def test_inspect_content_exception(self): # Mock the API response channel = ChannelStub(responses=[CustomException()]) client = dlp_v2.DlpServiceClient(channel=channel) # Setup request parent = client.project_path('[PROJECT]') with pytest.raises(CustomException): client.inspect_content(parent) def test_redact_image(self): # Setup Expected Response redacted_image = b'28' extracted_text = 'extractedText998260012' expected_response = { 'redacted_image': redacted_image, 'extracted_text': extracted_text } expected_response = dlp_pb2.RedactImageResponse(**expected_response) # Mock the API response channel = ChannelStub(responses=[expected_response]) client = dlp_v2.DlpServiceClient(channel=channel) # Setup Request parent = client.project_path('[PROJECT]') response = client.redact_image(parent) assert expected_response == response assert len(channel.requests) == 1 expected_request = dlp_pb2.RedactImageRequest(parent=parent) actual_request = channel.requests[0][1] assert expected_request == actual_request def test_redact_image_exception(self): # Mock the API response channel = ChannelStub(responses=[CustomException()]) client = dlp_v2.DlpServiceClient(channel=channel) # Setup request parent = client.project_path('[PROJECT]') with pytest.raises(CustomException): client.redact_image(parent) def test_deidentify_content(self): # Setup Expected Response expected_response = {} expected_response = dlp_pb2.DeidentifyContentResponse( **expected_response) # Mock the API response channel = ChannelStub(responses=[expected_response]) client = dlp_v2.DlpServiceClient(channel=channel) # Setup Request parent = client.project_path('[PROJECT]') response = client.deidentify_content(parent) assert expected_response == response assert len(channel.requests) == 1 expected_request = dlp_pb2.DeidentifyContentRequest(parent=parent) actual_request = channel.requests[0][1] assert expected_request == actual_request def test_deidentify_content_exception(self): # Mock the API response channel = ChannelStub(responses=[CustomException()]) client = dlp_v2.DlpServiceClient(channel=channel) # Setup request parent = client.project_path('[PROJECT]') with pytest.raises(CustomException): client.deidentify_content(parent) def test_reidentify_content(self): # Setup Expected Response expected_response = {} expected_response = dlp_pb2.ReidentifyContentResponse( **expected_response) # Mock the API response channel = ChannelStub(responses=[expected_response]) client = dlp_v2.DlpServiceClient(channel=channel) # Setup Request parent = client.project_path('[PROJECT]') response = client.reidentify_content(parent) assert expected_response == response assert len(channel.requests) == 1 expected_request = dlp_pb2.ReidentifyContentRequest(parent=parent) actual_request = channel.requests[0][1] assert expected_request == actual_request def test_reidentify_content_exception(self): # Mock the API response channel = ChannelStub(responses=[CustomException()]) client = dlp_v2.DlpServiceClient(channel=channel) # Setup request parent = client.project_path('[PROJECT]') with pytest.raises(CustomException): client.reidentify_content(parent) def test_list_info_types(self): # Setup Expected Response expected_response = {} expected_response = dlp_pb2.ListInfoTypesResponse(**expected_response) # Mock the API response channel = ChannelStub(responses=[expected_response]) client = dlp_v2.DlpServiceClient(channel=channel) response = client.list_info_types() assert expected_response == response assert len(channel.requests) == 1 expected_request = dlp_pb2.ListInfoTypesRequest() actual_request = channel.requests[0][1] assert expected_request == actual_request def test_list_info_types_exception(self): # Mock the API response channel = ChannelStub(responses=[CustomException()]) client = dlp_v2.DlpServiceClient(channel=channel) with pytest.raises(CustomException): client.list_info_types() def test_create_inspect_template(self): # Setup Expected Response name = 'name3373707' display_name = 'displayName1615086568' description = 'description-1724546052' expected_response = { 'name': name, 'display_name': display_name, 'description': description } expected_response = dlp_pb2.InspectTemplate(**expected_response) # Mock the API response channel = ChannelStub(responses=[expected_response]) client = dlp_v2.DlpServiceClient(channel=channel) # Setup Request parent = client.organization_path('[ORGANIZATION]') response = client.create_inspect_template(parent) assert expected_response == response assert len(channel.requests) == 1 expected_request = dlp_pb2.CreateInspectTemplateRequest(parent=parent) actual_request = channel.requests[0][1] assert expected_request == actual_request def test_create_inspect_template_exception(self): # Mock the API response channel = ChannelStub(responses=[CustomException()]) client = dlp_v2.DlpServiceClient(channel=channel) # Setup request parent = client.organization_path('[ORGANIZATION]') with pytest.raises(CustomException): client.create_inspect_template(parent) def test_update_inspect_template(self): # Setup Expected Response name_2 = 'name2-1052831874' display_name = 'displayName1615086568' description = 'description-1724546052' expected_response = { 'name': name_2, 'display_name': display_name, 'description': description } expected_response = dlp_pb2.InspectTemplate(**expected_response) # Mock the API response channel = ChannelStub(responses=[expected_response]) client = dlp_v2.DlpServiceClient(channel=channel) # Setup Request name = client.organization_inspect_template_path( '[ORGANIZATION]', '[INSPECT_TEMPLATE]') response = client.update_inspect_template(name) assert expected_response == response assert len(channel.requests) == 1 expected_request = dlp_pb2.UpdateInspectTemplateRequest(name=name) actual_request = channel.requests[0][1] assert expected_request == actual_request def test_update_inspect_template_exception(self): # Mock the API response channel = ChannelStub(responses=[CustomException()]) client = dlp_v2.DlpServiceClient(channel=channel) # Setup request name = client.organization_inspect_template_path( '[ORGANIZATION]', '[INSPECT_TEMPLATE]') with pytest.raises(CustomException): client.update_inspect_template(name) def test_get_inspect_template(self): # Setup Expected Response name = 'name3373707' display_name = 'displayName1615086568' description = 'description-1724546052' expected_response = { 'name': name, 'display_name': display_name, 'description': description } expected_response = dlp_pb2.InspectTemplate(**expected_response) # Mock the API response channel = ChannelStub(responses=[expected_response]) client = dlp_v2.DlpServiceClient(channel=channel) response = client.get_inspect_template() assert expected_response == response assert len(channel.requests) == 1 expected_request = dlp_pb2.GetInspectTemplateRequest() actual_request = channel.requests[0][1] assert expected_request == actual_request def test_get_inspect_template_exception(self): # Mock the API response channel = ChannelStub(responses=[CustomException()]) client = dlp_v2.DlpServiceClient(channel=channel) with pytest.raises(CustomException): client.get_inspect_template() def test_list_inspect_templates(self): # Setup Expected Response next_page_token = '' inspect_templates_element = {} inspect_templates = [inspect_templates_element] expected_response = { 'next_page_token': next_page_token, 'inspect_templates': inspect_templates } expected_response = dlp_pb2.ListInspectTemplatesResponse( **expected_response) # Mock the API response channel = ChannelStub(responses=[expected_response]) client = dlp_v2.DlpServiceClient(channel=channel) # Setup Request parent = client.organization_path('[ORGANIZATION]') paged_list_response = client.list_inspect_templates(parent) resources = list(paged_list_response) assert len(resources) == 1 assert expected_response.inspect_templates[0] == resources[0] assert len(channel.requests) == 1 expected_request = dlp_pb2.ListInspectTemplatesRequest(parent=parent) actual_request = channel.requests[0][1] assert expected_request == actual_request def test_list_inspect_templates_exception(self): channel = ChannelStub(responses=[CustomException()]) client = dlp_v2.DlpServiceClient(channel=channel) # Setup request parent = client.organization_path('[ORGANIZATION]') paged_list_response = client.list_inspect_templates(parent) with pytest.raises(CustomException): list(paged_list_response) def test_delete_inspect_template(self): channel = ChannelStub() client = dlp_v2.DlpServiceClient(channel=channel) # Setup Request name = client.organization_inspect_template_path( '[ORGANIZATION]', '[INSPECT_TEMPLATE]') client.delete_inspect_template(name) assert len(channel.requests) == 1 expected_request = dlp_pb2.DeleteInspectTemplateRequest(name=name) actual_request = channel.requests[0][1] assert expected_request == actual_request def test_delete_inspect_template_exception(self): # Mock the API response channel = ChannelStub(responses=[CustomException()]) client = dlp_v2.DlpServiceClient(channel=channel) # Setup request name = client.organization_inspect_template_path( '[ORGANIZATION]', '[INSPECT_TEMPLATE]') with pytest.raises(CustomException): client.delete_inspect_template(name) def test_create_deidentify_template(self): # Setup Expected Response name = 'name3373707' display_name = 'displayName1615086568' description = 'description-1724546052' expected_response = { 'name': name, 'display_name': display_name, 'description': description } expected_response = dlp_pb2.DeidentifyTemplate(**expected_response) # Mock the API response channel = ChannelStub(responses=[expected_response]) client = dlp_v2.DlpServiceClient(channel=channel) # Setup Request parent = client.organization_path('[ORGANIZATION]') response = client.create_deidentify_template(parent) assert expected_response == response assert len(channel.requests) == 1 expected_request = dlp_pb2.CreateDeidentifyTemplateRequest( parent=parent) actual_request = channel.requests[0][1] assert expected_request == actual_request def test_create_deidentify_template_exception(self): # Mock the API response channel = ChannelStub(responses=[CustomException()]) client = dlp_v2.DlpServiceClient(channel=channel) # Setup request parent = client.organization_path('[ORGANIZATION]') with pytest.raises(CustomException): client.create_deidentify_template(parent) def test_update_deidentify_template(self): # Setup Expected Response name_2 = 'name2-1052831874' display_name = 'displayName1615086568' description = 'description-1724546052' expected_response = { 'name': name_2, 'display_name': display_name, 'description': description } expected_response = dlp_pb2.DeidentifyTemplate(**expected_response) # Mock the API response channel = ChannelStub(responses=[expected_response]) client = dlp_v2.DlpServiceClient(channel=channel) # Setup Request name = client.organization_deidentify_template_path( '[ORGANIZATION]', '[DEIDENTIFY_TEMPLATE]') response = client.update_deidentify_template(name) assert expected_response == response assert len(channel.requests) == 1 expected_request = dlp_pb2.UpdateDeidentifyTemplateRequest(name=name) actual_request = channel.requests[0][1] assert expected_request == actual_request def test_update_deidentify_template_exception(self): # Mock the API response channel = ChannelStub(responses=[CustomException()]) client = dlp_v2.DlpServiceClient(channel=channel) # Setup request name = client.organization_deidentify_template_path( '[ORGANIZATION]', '[DEIDENTIFY_TEMPLATE]') with pytest.raises(CustomException): client.update_deidentify_template(name) def test_get_deidentify_template(self): # Setup Expected Response name_2 = 'name2-1052831874' display_name = 'displayName1615086568' description = 'description-1724546052' expected_response = { 'name': name_2, 'display_name': display_name, 'description': description } expected_response = dlp_pb2.DeidentifyTemplate(**expected_response) # Mock the API response channel = ChannelStub(responses=[expected_response]) client = dlp_v2.DlpServiceClient(channel=channel) # Setup Request name = client.organization_deidentify_template_path( '[ORGANIZATION]', '[DEIDENTIFY_TEMPLATE]') response = client.get_deidentify_template(name) assert expected_response == response assert len(channel.requests) == 1 expected_request = dlp_pb2.GetDeidentifyTemplateRequest(name=name) actual_request = channel.requests[0][1] assert expected_request == actual_request def test_get_deidentify_template_exception(self): # Mock the API response channel = ChannelStub(responses=[CustomException()]) client = dlp_v2.DlpServiceClient(channel=channel) # Setup request name = client.organization_deidentify_template_path( '[ORGANIZATION]', '[DEIDENTIFY_TEMPLATE]') with pytest.raises(CustomException): client.get_deidentify_template(name) def test_list_deidentify_templates(self): # Setup Expected Response next_page_token = '' deidentify_templates_element = {} deidentify_templates = [deidentify_templates_element] expected_response = { 'next_page_token': next_page_token, 'deidentify_templates': deidentify_templates } expected_response = dlp_pb2.ListDeidentifyTemplatesResponse( **expected_response) # Mock the API response channel = ChannelStub(responses=[expected_response]) client = dlp_v2.DlpServiceClient(channel=channel) # Setup Request parent = client.organization_path('[ORGANIZATION]') paged_list_response = client.list_deidentify_templates(parent) resources = list(paged_list_response) assert len(resources) == 1 assert expected_response.deidentify_templates[0] == resources[0] assert len(channel.requests) == 1 expected_request = dlp_pb2.ListDeidentifyTemplatesRequest( parent=parent) actual_request = channel.requests[0][1] assert expected_request == actual_request def test_list_deidentify_templates_exception(self): channel = ChannelStub(responses=[CustomException()]) client = dlp_v2.DlpServiceClient(channel=channel) # Setup request parent = client.organization_path('[ORGANIZATION]') paged_list_response = client.list_deidentify_templates(parent) with pytest.raises(CustomException): list(paged_list_response) def test_delete_deidentify_template(self): channel = ChannelStub() client = dlp_v2.DlpServiceClient(channel=channel) # Setup Request name = client.organization_deidentify_template_path( '[ORGANIZATION]', '[DEIDENTIFY_TEMPLATE]') client.delete_deidentify_template(name) assert len(channel.requests) == 1 expected_request = dlp_pb2.DeleteDeidentifyTemplateRequest(name=name) actual_request = channel.requests[0][1] assert expected_request == actual_request def test_delete_deidentify_template_exception(self): # Mock the API response channel = ChannelStub(responses=[CustomException()]) client = dlp_v2.DlpServiceClient(channel=channel) # Setup request name = client.organization_deidentify_template_path( '[ORGANIZATION]', '[DEIDENTIFY_TEMPLATE]') with pytest.raises(CustomException): client.delete_deidentify_template(name) def test_create_dlp_job(self): # Setup Expected Response name = 'name3373707' job_trigger_name = 'jobTriggerName1819490804' expected_response = { 'name': name, 'job_trigger_name': job_trigger_name } expected_response = dlp_pb2.DlpJob(**expected_response) # Mock the API response channel = ChannelStub(responses=[expected_response]) client = dlp_v2.DlpServiceClient(channel=channel) # Setup Request parent = client.project_path('[PROJECT]') response = client.create_dlp_job(parent) assert expected_response == response assert len(channel.requests) == 1 expected_request = dlp_pb2.CreateDlpJobRequest(parent=parent) actual_request = channel.requests[0][1] assert expected_request == actual_request def test_create_dlp_job_exception(self): # Mock the API response channel = ChannelStub(responses=[CustomException()]) client = dlp_v2.DlpServiceClient(channel=channel) # Setup request parent = client.project_path('[PROJECT]') with pytest.raises(CustomException): client.create_dlp_job(parent) def test_list_dlp_jobs(self): # Setup Expected Response next_page_token = '' jobs_element = {} jobs = [jobs_element] expected_response = {'next_page_token': next_page_token, 'jobs': jobs} expected_response = dlp_pb2.ListDlpJobsResponse(**expected_response) # Mock the API response channel = ChannelStub(responses=[expected_response]) client = dlp_v2.DlpServiceClient(channel=channel) # Setup Request parent = client.project_path('[PROJECT]') paged_list_response = client.list_dlp_jobs(parent) resources = list(paged_list_response) assert len(resources) == 1 assert expected_response.jobs[0] == resources[0] assert len(channel.requests) == 1 expected_request = dlp_pb2.ListDlpJobsRequest(parent=parent) actual_request = channel.requests[0][1] assert expected_request == actual_request def test_list_dlp_jobs_exception(self): channel = ChannelStub(responses=[CustomException()]) client = dlp_v2.DlpServiceClient(channel=channel) # Setup request parent = client.project_path('[PROJECT]') paged_list_response = client.list_dlp_jobs(parent) with pytest.raises(CustomException): list(paged_list_response) def test_get_dlp_job(self): # Setup Expected Response name_2 = 'name2-1052831874' job_trigger_name = 'jobTriggerName1819490804' expected_response = { 'name': name_2, 'job_trigger_name': job_trigger_name } expected_response = dlp_pb2.DlpJob(**expected_response) # Mock the API response channel = ChannelStub(responses=[expected_response]) client = dlp_v2.DlpServiceClient(channel=channel) # Setup Request name = client.dlp_job_path('[PROJECT]', '[DLP_JOB]') response = client.get_dlp_job(name) assert expected_response == response assert len(channel.requests) == 1 expected_request = dlp_pb2.GetDlpJobRequest(name=name) actual_request = channel.requests[0][1] assert expected_request == actual_request def test_get_dlp_job_exception(self): # Mock the API response channel = ChannelStub(responses=[CustomException()]) client = dlp_v2.DlpServiceClient(channel=channel) # Setup request name = client.dlp_job_path('[PROJECT]', '[DLP_JOB]') with pytest.raises(CustomException): client.get_dlp_job(name) def test_delete_dlp_job(self): channel = ChannelStub() client = dlp_v2.DlpServiceClient(channel=channel) # Setup Request name = client.dlp_job_path('[PROJECT]', '[DLP_JOB]') client.delete_dlp_job(name) assert len(channel.requests) == 1 expected_request = dlp_pb2.DeleteDlpJobRequest(name=name) actual_request = channel.requests[0][1] assert expected_request == actual_request def test_delete_dlp_job_exception(self): # Mock the API response channel = ChannelStub(responses=[CustomException()]) client = dlp_v2.DlpServiceClient(channel=channel) # Setup request name = client.dlp_job_path('[PROJECT]', '[DLP_JOB]') with pytest.raises(CustomException): client.delete_dlp_job(name) def test_cancel_dlp_job(self): channel = ChannelStub() client = dlp_v2.DlpServiceClient(channel=channel) # Setup Request name = client.dlp_job_path('[PROJECT]', '[DLP_JOB]') client.cancel_dlp_job(name) assert len(channel.requests) == 1 expected_request = dlp_pb2.CancelDlpJobRequest(name=name) actual_request = channel.requests[0][1] assert expected_request == actual_request def test_cancel_dlp_job_exception(self): # Mock the API response channel = ChannelStub(responses=[CustomException()]) client = dlp_v2.DlpServiceClient(channel=channel) # Setup request name = client.dlp_job_path('[PROJECT]', '[DLP_JOB]') with pytest.raises(CustomException): client.cancel_dlp_job(name) def test_list_job_triggers(self): # Setup Expected Response next_page_token = '' job_triggers_element = {} job_triggers = [job_triggers_element] expected_response = { 'next_page_token': next_page_token, 'job_triggers': job_triggers } expected_response = dlp_pb2.ListJobTriggersResponse( **expected_response) # Mock the API response channel = ChannelStub(responses=[expected_response]) client = dlp_v2.DlpServiceClient(channel=channel) # Setup Request parent = client.project_path('[PROJECT]') paged_list_response = client.list_job_triggers(parent) resources = list(paged_list_response) assert len(resources) == 1 assert expected_response.job_triggers[0] == resources[0] assert len(channel.requests) == 1 expected_request = dlp_pb2.ListJobTriggersRequest(parent=parent) actual_request = channel.requests[0][1] assert expected_request == actual_request def test_list_job_triggers_exception(self): channel = ChannelStub(responses=[CustomException()]) client = dlp_v2.DlpServiceClient(channel=channel) # Setup request parent = client.project_path('[PROJECT]') paged_list_response = client.list_job_triggers(parent) with pytest.raises(CustomException): list(paged_list_response) def test_get_job_trigger(self): # Setup Expected Response name_2 = 'name2-1052831874' display_name = 'displayName1615086568' description = 'description-1724546052' expected_response = { 'name': name_2, 'display_name': display_name, 'description': description } expected_response = dlp_pb2.JobTrigger(**expected_response) # Mock the API response channel = ChannelStub(responses=[expected_response]) client = dlp_v2.DlpServiceClient(channel=channel) # Setup Request name = client.project_job_trigger_path('[PROJECT]', '[JOB_TRIGGER]') response = client.get_job_trigger(name) assert expected_response == response assert len(channel.requests) == 1 expected_request = dlp_pb2.GetJobTriggerRequest(name=name) actual_request = channel.requests[0][1] assert expected_request == actual_request def test_get_job_trigger_exception(self): # Mock the API response channel = ChannelStub(responses=[CustomException()]) client = dlp_v2.DlpServiceClient(channel=channel) # Setup request name = client.project_job_trigger_path('[PROJECT]', '[JOB_TRIGGER]') with pytest.raises(CustomException): client.get_job_trigger(name) def test_delete_job_trigger(self): channel = ChannelStub() client = dlp_v2.DlpServiceClient(channel=channel) # Setup Request name = 'name3373707' client.delete_job_trigger(name) assert len(channel.requests) == 1 expected_request = dlp_pb2.DeleteJobTriggerRequest(name=name) actual_request = channel.requests[0][1] assert expected_request == actual_request def test_delete_job_trigger_exception(self): # Mock the API response channel = ChannelStub(responses=[CustomException()]) client = dlp_v2.DlpServiceClient(channel=channel) # Setup request name = 'name3373707' with pytest.raises(CustomException): client.delete_job_trigger(name) def test_update_job_trigger(self): # Setup Expected Response name_2 = 'name2-1052831874' display_name = 'displayName1615086568' description = 'description-1724546052' expected_response = { 'name': name_2, 'display_name': display_name, 'description': description } expected_response = dlp_pb2.JobTrigger(**expected_response) # Mock the API response channel = ChannelStub(responses=[expected_response]) client = dlp_v2.DlpServiceClient(channel=channel) # Setup Request name = client.project_job_trigger_path('[PROJECT]', '[JOB_TRIGGER]') response = client.update_job_trigger(name) assert expected_response == response assert len(channel.requests) == 1 expected_request = dlp_pb2.UpdateJobTriggerRequest(name=name) actual_request = channel.requests[0][1] assert expected_request == actual_request def test_update_job_trigger_exception(self): # Mock the API response channel = ChannelStub(responses=[CustomException()]) client = dlp_v2.DlpServiceClient(channel=channel) # Setup request name = client.project_job_trigger_path('[PROJECT]', '[JOB_TRIGGER]') with pytest.raises(CustomException): client.update_job_trigger(name) def test_create_job_trigger(self): # Setup Expected Response name = 'name3373707' display_name = 'displayName1615086568' description = 'description-1724546052' expected_response = { 'name': name, 'display_name': display_name, 'description': description } expected_response = dlp_pb2.JobTrigger(**expected_response) # Mock the API response channel = ChannelStub(responses=[expected_response]) client = dlp_v2.DlpServiceClient(channel=channel) # Setup Request parent = client.project_path('[PROJECT]') response = client.create_job_trigger(parent) assert expected_response == response assert len(channel.requests) == 1 expected_request = dlp_pb2.CreateJobTriggerRequest(parent=parent) actual_request = channel.requests[0][1] assert expected_request == actual_request def test_create_job_trigger_exception(self): # Mock the API response channel = ChannelStub(responses=[CustomException()]) client = dlp_v2.DlpServiceClient(channel=channel) # Setup request parent = client.project_path('[PROJECT]') with pytest.raises(CustomException): client.create_job_trigger(parent)

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0.108844

0

null

0

1

0

null

0

6

910dd7c2ccdb8478d45a4d1037b5ebdbb57c27fd

49

py

Python

teste3.py

asalesg/python

885ff5fae1e685424863286082fbe49ca5f4efe7

[ "MIT" ]

null

teste3.py

asalesg/python

885ff5fae1e685424863286082fbe49ca5f4efe7

[ "MIT" ]

null

teste3.py

asalesg/python

885ff5fae1e685424863286082fbe49ca5f4efe7

[ "MIT" ]

null

lista = [0,5,10,15,5,10,20] print(lista.count(5))

24.5

27

0.653061

12

49

2.666667

0.666667

0.1875

0

0.26087

0.061224

49

2

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24.5

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1

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false

0

0.5

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null

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1

0

1

0

1

0

null

0

1

0

6

9129bbed156ecbcc8cc5c6c777c4e04f572f2563

125

py

Python

tests/test_match_parse.py

rickproza/twill

7a98e4912a8ff929a94e35d35e7a027472ee4f46

[ "MIT" ]

13

2020-04-18T15:17:58.000Z

2022-02-24T13:25:46.000Z

tests/test_match_parse.py

rickproza/twill

7a98e4912a8ff929a94e35d35e7a027472ee4f46

[ "MIT" ]

5

2020-04-04T21:16:00.000Z

2022-02-10T00:26:20.000Z

tests/test_match_parse.py

rickproza/twill

7a98e4912a8ff929a94e35d35e7a027472ee4f46

[ "MIT" ]

3

2020-06-06T17:26:19.000Z

2022-02-10T00:30:39.000Z

from .utils import execute_script def test_match_parse(url): execute_script('test_match_parse.twill', initial_url=url)

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null

0

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1

0

6

91454052c40a3924d575f59ff7f47c6043d9897f

45,806

py

Python

tests/api/v1_3_0/test_devices.py

wastorga/dnacentersdk

1a25aaef2eaa016fe54ebebbd7448919e0effa3f

[ "MIT" ]

null

tests/api/v1_3_0/test_devices.py

wastorga/dnacentersdk

1a25aaef2eaa016fe54ebebbd7448919e0effa3f

[ "MIT" ]

null

tests/api/v1_3_0/test_devices.py

wastorga/dnacentersdk

1a25aaef2eaa016fe54ebebbd7448919e0effa3f

[ "MIT" ]

null

# -*- coding: utf-8 -*- """DNACenterAPI devices API fixtures and tests. Copyright (c) 2019 Cisco and/or its affiliates. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ import pytest from tests.environment import DNA_CENTER_VERSION from tests.models.schema_validator import json_schema_validate pytestmark = pytest.mark.skipif(DNA_CENTER_VERSION != '1.3.0', reason='version does not match') def is_valid_get_module_info_by_id(obj): json_schema_validate('jsd_0db7da744c0b83d8_v1_3_0').validate(obj) return True def get_module_info_by_id(api): endpoint_result = api.devices.get_module_info_by_id( id='string' ) return endpoint_result @pytest.mark.devices def test_get_module_info_by_id(api): assert is_valid_get_module_info_by_id( get_module_info_by_id(api) ) def get_module_info_by_id_default(api): endpoint_result = api.devices.get_module_info_by_id( id='string' ) return endpoint_result @pytest.mark.devices def test_get_module_info_by_id_default(api): try: assert is_valid_get_module_info_by_id( get_module_info_by_id_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_delete_device_by_id(obj): json_schema_validate('jsd_1c894b5848eab214_v1_3_0').validate(obj) return True def delete_device_by_id(api): endpoint_result = api.devices.delete_device_by_id( id='string', is_force_delete=True ) return endpoint_result @pytest.mark.devices def test_delete_device_by_id(api): assert is_valid_delete_device_by_id( delete_device_by_id(api) ) def delete_device_by_id_default(api): endpoint_result = api.devices.delete_device_by_id( id='string', is_force_delete=None ) return endpoint_result @pytest.mark.devices def test_delete_device_by_id_default(api): try: assert is_valid_delete_device_by_id( delete_device_by_id_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_sync_devices_using_forcesync(obj): json_schema_validate('jsd_3b9ef9674429be4c_v1_3_0').validate(obj) return True def sync_devices_using_forcesync(api): endpoint_result = api.devices.sync_devices_using_forcesync( active_validation=True, force_sync=True, payload=[{}] ) return endpoint_result @pytest.mark.devices def test_sync_devices_using_forcesync(api): assert is_valid_sync_devices_using_forcesync( sync_devices_using_forcesync(api) ) def sync_devices_using_forcesync_default(api): endpoint_result = api.devices.sync_devices_using_forcesync( active_validation=True, force_sync=None, payload=None ) return endpoint_result @pytest.mark.devices def test_sync_devices_using_forcesync_default(api): try: assert is_valid_sync_devices_using_forcesync( sync_devices_using_forcesync_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_get_device_list(obj): json_schema_validate('jsd_20b19b52464b8972_v1_3_0').validate(obj) return True def get_device_list(api): endpoint_result = api.devices.get_device_list( associated_wlc_ip='value1,value2', collection_interval='value1,value2', collection_status='value1,value2', error_code='value1,value2', error_description='value1,value2', family='value1,value2', hostname='value1,value2', id='string', license_name='value1,value2', license_status='value1,value2', license_type='value1,value2', location='value1,value2', location_name='value1,value2', mac_address='value1,value2', management_ip_address='value1,value2', module_equpimenttype='value1,value2', module_name='value1,value2', module_operationstatecode='value1,value2', module_partnumber='value1,value2', module_servicestate='value1,value2', module_vendorequipmenttype='value1,value2', not_synced_for_minutes='value1,value2', platform_id='value1,value2', reachability_status='value1,value2', role='value1,value2', serial_number='value1,value2', series='value1,value2', software_type='value1,value2', software_version='value1,value2', type='value1,value2', up_time='value1,value2' ) return endpoint_result @pytest.mark.devices def test_get_device_list(api): assert is_valid_get_device_list( get_device_list(api) ) def get_device_list_default(api): endpoint_result = api.devices.get_device_list( associated_wlc_ip=None, collection_interval=None, collection_status=None, error_code=None, error_description=None, family=None, hostname=None, id=None, license_name=None, license_status=None, license_type=None, location=None, location_name=None, mac_address=None, management_ip_address=None, module_equpimenttype=None, module_name=None, module_operationstatecode=None, module_partnumber=None, module_servicestate=None, module_vendorequipmenttype=None, not_synced_for_minutes=None, platform_id=None, reachability_status=None, role=None, serial_number=None, series=None, software_type=None, software_version=None, type=None, up_time=None ) return endpoint_result @pytest.mark.devices def test_get_device_list_default(api): try: assert is_valid_get_device_list( get_device_list_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_get_device_interface_vlans(obj): json_schema_validate('jsd_288df9494f2a9746_v1_3_0').validate(obj) return True def get_device_interface_vlans(api): endpoint_result = api.devices.get_device_interface_vlans( id='string', interface_type='string' ) return endpoint_result @pytest.mark.devices def test_get_device_interface_vlans(api): assert is_valid_get_device_interface_vlans( get_device_interface_vlans(api) ) def get_device_interface_vlans_default(api): endpoint_result = api.devices.get_device_interface_vlans( id='string', interface_type=None ) return endpoint_result @pytest.mark.devices def test_get_device_interface_vlans_default(api): try: assert is_valid_get_device_interface_vlans( get_device_interface_vlans_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_get_polling_interval_for_all_devices(obj): json_schema_validate('jsd_38bd0b884b89a785_v1_3_0').validate(obj) return True def get_polling_interval_for_all_devices(api): endpoint_result = api.devices.get_polling_interval_for_all_devices( ) return endpoint_result @pytest.mark.devices def test_get_polling_interval_for_all_devices(api): assert is_valid_get_polling_interval_for_all_devices( get_polling_interval_for_all_devices(api) ) def get_polling_interval_for_all_devices_default(api): endpoint_result = api.devices.get_polling_interval_for_all_devices( ) return endpoint_result @pytest.mark.devices def test_get_polling_interval_for_all_devices_default(api): try: assert is_valid_get_polling_interval_for_all_devices( get_polling_interval_for_all_devices_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_get_device_interfaces_by_specified_range(obj): json_schema_validate('jsd_349c888443b89a58_v1_3_0').validate(obj) return True def get_device_interfaces_by_specified_range(api): endpoint_result = api.devices.get_device_interfaces_by_specified_range( device_id='string', records_to_return=0, start_index=0 ) return endpoint_result @pytest.mark.devices def test_get_device_interfaces_by_specified_range(api): assert is_valid_get_device_interfaces_by_specified_range( get_device_interfaces_by_specified_range(api) ) def get_device_interfaces_by_specified_range_default(api): endpoint_result = api.devices.get_device_interfaces_by_specified_range( device_id='string', records_to_return=0, start_index=0 ) return endpoint_result @pytest.mark.devices def test_get_device_interfaces_by_specified_range_default(api): try: assert is_valid_get_device_interfaces_by_specified_range( get_device_interfaces_by_specified_range_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_get_device_interface_count(obj): json_schema_validate('jsd_3d923b184dc9a4ca_v1_3_0').validate(obj) return True def get_device_interface_count(api): endpoint_result = api.devices.get_device_interface_count( ) return endpoint_result @pytest.mark.devices def test_get_device_interface_count(api): assert is_valid_get_device_interface_count( get_device_interface_count(api) ) def get_device_interface_count_default(api): endpoint_result = api.devices.get_device_interface_count( ) return endpoint_result @pytest.mark.devices def test_get_device_interface_count_default(api): try: assert is_valid_get_device_interface_count( get_device_interface_count_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_add_device(obj): json_schema_validate('jsd_4bb22af046fa8f08_v1_3_0').validate(obj) return True def add_device(api): endpoint_result = api.devices.add_device( active_validation=True, cliTransport='string', computeDevice=True, enablePassword='string', extendedDiscoveryInfo='string', httpPassword='string', httpPort='string', httpSecure=True, httpUserName='string', ipAddress=['string'], merakiOrgId=['string'], netconfPort='string', password='string', payload=None, serialNumber='string', snmpAuthPassphrase='string', snmpAuthProtocol='string', snmpMode='string', snmpPrivPassphrase='string', snmpPrivProtocol='string', snmpROCommunity='string', snmpRWCommunity='string', snmpRetry=0, snmpTimeout=0, snmpUserName='string', snmpVersion='string', type='COMPUTE_DEVICE', updateMgmtIPaddressList=[{'existMgmtIpAddress': 'string', 'newMgmtIpAddress': 'string'}], userName='string' ) return endpoint_result @pytest.mark.devices def test_add_device(api): assert is_valid_add_device( add_device(api) ) def add_device_default(api): endpoint_result = api.devices.add_device( active_validation=True, cliTransport=None, computeDevice=None, enablePassword=None, extendedDiscoveryInfo=None, httpPassword=None, httpPort=None, httpSecure=None, httpUserName=None, ipAddress=None, merakiOrgId=None, netconfPort=None, password=None, payload=None, serialNumber=None, snmpAuthPassphrase=None, snmpAuthProtocol=None, snmpMode=None, snmpPrivPassphrase=None, snmpPrivProtocol=None, snmpROCommunity=None, snmpRWCommunity=None, snmpRetry=None, snmpTimeout=None, snmpUserName=None, snmpVersion=None, type=None, updateMgmtIPaddressList=None, userName=None ) return endpoint_result @pytest.mark.devices def test_add_device_default(api): try: assert is_valid_add_device( add_device_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_get_interface_details(obj): json_schema_validate('jsd_4eb56a614cc9a2d2_v1_3_0').validate(obj) return True def get_interface_details(api): endpoint_result = api.devices.get_interface_details( device_id='string', name='string' ) return endpoint_result @pytest.mark.devices def test_get_interface_details(api): assert is_valid_get_interface_details( get_interface_details(api) ) def get_interface_details_default(api): endpoint_result = api.devices.get_interface_details( device_id='string', name=None ) return endpoint_result @pytest.mark.devices def test_get_interface_details_default(api): try: assert is_valid_get_interface_details( get_interface_details_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_get_device_interface_count_by_id(obj): json_schema_validate('jsd_5b8639224cd88ea7_v1_3_0').validate(obj) return True def get_device_interface_count_by_id(api): endpoint_result = api.devices.get_device_interface_count_by_id( device_id='string' ) return endpoint_result @pytest.mark.devices def test_get_device_interface_count_by_id(api): assert is_valid_get_device_interface_count_by_id( get_device_interface_count_by_id(api) ) def get_device_interface_count_by_id_default(api): endpoint_result = api.devices.get_device_interface_count_by_id( device_id='string' ) return endpoint_result @pytest.mark.devices def test_get_device_interface_count_by_id_default(api): try: assert is_valid_get_device_interface_count_by_id( get_device_interface_count_by_id_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_get_device_count(obj): json_schema_validate('jsd_5db21b8e43fab7d8_v1_3_0').validate(obj) return True def get_device_count(api): endpoint_result = api.devices.get_device_count( ) return endpoint_result @pytest.mark.devices def test_get_device_count(api): assert is_valid_get_device_count( get_device_count(api) ) def get_device_count_default(api): endpoint_result = api.devices.get_device_count( ) return endpoint_result @pytest.mark.devices def test_get_device_count_default(api): try: assert is_valid_get_device_count( get_device_count_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_get_ospf_interfaces(obj): json_schema_validate('jsd_70ad397649e9b4d3_v1_3_0').validate(obj) return True def get_ospf_interfaces(api): endpoint_result = api.devices.get_ospf_interfaces( ) return endpoint_result @pytest.mark.devices def test_get_ospf_interfaces(api): assert is_valid_get_ospf_interfaces( get_ospf_interfaces(api) ) def get_ospf_interfaces_default(api): endpoint_result = api.devices.get_ospf_interfaces( ) return endpoint_result @pytest.mark.devices def test_get_ospf_interfaces_default(api): try: assert is_valid_get_ospf_interfaces( get_ospf_interfaces_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_get_polling_interval_by_id(obj): json_schema_validate('jsd_82918a1b4d289c5c_v1_3_0').validate(obj) return True def get_polling_interval_by_id(api): endpoint_result = api.devices.get_polling_interval_by_id( id='string' ) return endpoint_result @pytest.mark.devices def test_get_polling_interval_by_id(api): assert is_valid_get_polling_interval_by_id( get_polling_interval_by_id(api) ) def get_polling_interval_by_id_default(api): endpoint_result = api.devices.get_polling_interval_by_id( id='string' ) return endpoint_result @pytest.mark.devices def test_get_polling_interval_by_id_default(api): try: assert is_valid_get_polling_interval_by_id( get_polling_interval_by_id_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_get_organization_list_for_meraki(obj): json_schema_validate('jsd_84b37ae54c59ab28_v1_3_0').validate(obj) return True def get_organization_list_for_meraki(api): endpoint_result = api.devices.get_organization_list_for_meraki( id='string' ) return endpoint_result @pytest.mark.devices def test_get_organization_list_for_meraki(api): assert is_valid_get_organization_list_for_meraki( get_organization_list_for_meraki(api) ) def get_organization_list_for_meraki_default(api): endpoint_result = api.devices.get_organization_list_for_meraki( id='string' ) return endpoint_result @pytest.mark.devices def test_get_organization_list_for_meraki_default(api): try: assert is_valid_get_organization_list_for_meraki( get_organization_list_for_meraki_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_get_functional_capability_by_id(obj): json_schema_validate('jsd_81bb4804405a8d2f_v1_3_0').validate(obj) return True def get_functional_capability_by_id(api): endpoint_result = api.devices.get_functional_capability_by_id( id='string' ) return endpoint_result @pytest.mark.devices def test_get_functional_capability_by_id(api): assert is_valid_get_functional_capability_by_id( get_functional_capability_by_id(api) ) def get_functional_capability_by_id_default(api): endpoint_result = api.devices.get_functional_capability_by_id( id='string' ) return endpoint_result @pytest.mark.devices def test_get_functional_capability_by_id_default(api): try: assert is_valid_get_functional_capability_by_id( get_functional_capability_by_id_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_get_isis_interfaces(obj): json_schema_validate('jsd_84ad8b0e42cab48a_v1_3_0').validate(obj) return True def get_isis_interfaces(api): endpoint_result = api.devices.get_isis_interfaces( ) return endpoint_result @pytest.mark.devices def test_get_isis_interfaces(api): assert is_valid_get_isis_interfaces( get_isis_interfaces(api) ) def get_isis_interfaces_default(api): endpoint_result = api.devices.get_isis_interfaces( ) return endpoint_result @pytest.mark.devices def test_get_isis_interfaces_default(api): try: assert is_valid_get_isis_interfaces( get_isis_interfaces_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_get_device_config_by_id(obj): json_schema_validate('jsd_84b33a9e480abcaf_v1_3_0').validate(obj) return True def get_device_config_by_id(api): endpoint_result = api.devices.get_device_config_by_id( network_device_id='string' ) return endpoint_result @pytest.mark.devices def test_get_device_config_by_id(api): assert is_valid_get_device_config_by_id( get_device_config_by_id(api) ) def get_device_config_by_id_default(api): endpoint_result = api.devices.get_device_config_by_id( network_device_id='string' ) return endpoint_result @pytest.mark.devices def test_get_device_config_by_id_default(api): try: assert is_valid_get_device_config_by_id( get_device_config_by_id_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_get_device_summary(obj): json_schema_validate('jsd_819f9aa54feab7bf_v1_3_0').validate(obj) return True def get_device_summary(api): endpoint_result = api.devices.get_device_summary( id='string' ) return endpoint_result @pytest.mark.devices def test_get_device_summary(api): assert is_valid_get_device_summary( get_device_summary(api) ) def get_device_summary_default(api): endpoint_result = api.devices.get_device_summary( id='string' ) return endpoint_result @pytest.mark.devices def test_get_device_summary_default(api): try: assert is_valid_get_device_summary( get_device_summary_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_get_device_by_id(obj): json_schema_validate('jsd_8fa8eb404a4a8d96_v1_3_0').validate(obj) return True def get_device_by_id(api): endpoint_result = api.devices.get_device_by_id( id='string' ) return endpoint_result @pytest.mark.devices def test_get_device_by_id(api): assert is_valid_get_device_by_id( get_device_by_id(api) ) def get_device_by_id_default(api): endpoint_result = api.devices.get_device_by_id( id='string' ) return endpoint_result @pytest.mark.devices def test_get_device_by_id_default(api): try: assert is_valid_get_device_by_id( get_device_by_id_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_get_interface_info_by_id(obj): json_schema_validate('jsd_ba9dc85b4b8a9a17_v1_3_0').validate(obj) return True def get_interface_info_by_id(api): endpoint_result = api.devices.get_interface_info_by_id( device_id='string' ) return endpoint_result @pytest.mark.devices def test_get_interface_info_by_id(api): assert is_valid_get_interface_info_by_id( get_interface_info_by_id(api) ) def get_interface_info_by_id_default(api): endpoint_result = api.devices.get_interface_info_by_id( device_id='string' ) return endpoint_result @pytest.mark.devices def test_get_interface_info_by_id_default(api): try: assert is_valid_get_interface_info_by_id( get_interface_info_by_id_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_register_device_for_wsa(obj): json_schema_validate('jsd_c9809b6744f8a502_v1_3_0').validate(obj) return True def register_device_for_wsa(api): endpoint_result = api.devices.register_device_for_wsa( macaddress='string', serial_number='string' ) return endpoint_result @pytest.mark.devices def test_register_device_for_wsa(api): assert is_valid_register_device_for_wsa( register_device_for_wsa(api) ) def register_device_for_wsa_default(api): endpoint_result = api.devices.register_device_for_wsa( macaddress=None, serial_number=None ) return endpoint_result @pytest.mark.devices def test_register_device_for_wsa_default(api): try: assert is_valid_register_device_for_wsa( register_device_for_wsa_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_update_device_role(obj): json_schema_validate('jsd_b9855ad54ae98156_v1_3_0').validate(obj) return True def update_device_role(api): endpoint_result = api.devices.update_device_role( active_validation=True, id='string', payload=None, role='string', roleSource='string' ) return endpoint_result @pytest.mark.devices def test_update_device_role(api): assert is_valid_update_device_role( update_device_role(api) ) def update_device_role_default(api): endpoint_result = api.devices.update_device_role( active_validation=True, id=None, payload=None, role=None, roleSource=None ) return endpoint_result @pytest.mark.devices def test_update_device_role_default(api): try: assert is_valid_update_device_role( update_device_role_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_get_device_config_for_all_devices(obj): json_schema_validate('jsd_b7bcaa084e2b90d0_v1_3_0').validate(obj) return True def get_device_config_for_all_devices(api): endpoint_result = api.devices.get_device_config_for_all_devices( ) return endpoint_result @pytest.mark.devices def test_get_device_config_for_all_devices(api): assert is_valid_get_device_config_for_all_devices( get_device_config_for_all_devices(api) ) def get_device_config_for_all_devices_default(api): endpoint_result = api.devices.get_device_config_for_all_devices( ) return endpoint_result @pytest.mark.devices def test_get_device_config_for_all_devices_default(api): try: assert is_valid_get_device_config_for_all_devices( get_device_config_for_all_devices_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_export_device_list(obj): json_schema_validate('jsd_cd98780f4888a66d_v1_3_0').validate(obj) return True def export_device_list(api): endpoint_result = api.devices.export_device_list( active_validation=True, deviceUuids=['string'], id='string', operationEnum='CREDENTIALDETAILS', parameters=['string'], password='string', payload=None ) return endpoint_result @pytest.mark.devices def test_export_device_list(api): assert is_valid_export_device_list( export_device_list(api) ) def export_device_list_default(api): endpoint_result = api.devices.export_device_list( active_validation=True, deviceUuids=None, id=None, operationEnum=None, parameters=None, password=None, payload=None ) return endpoint_result @pytest.mark.devices def test_export_device_list_default(api): try: assert is_valid_export_device_list( export_device_list_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_get_interface_by_ip(obj): json_schema_validate('jsd_cd8469e647caab0e_v1_3_0').validate(obj) return True def get_interface_by_ip(api): endpoint_result = api.devices.get_interface_by_ip( ip_address='string' ) return endpoint_result @pytest.mark.devices def test_get_interface_by_ip(api): assert is_valid_get_interface_by_ip( get_interface_by_ip(api) ) def get_interface_by_ip_default(api): endpoint_result = api.devices.get_interface_by_ip( ip_address='string' ) return endpoint_result @pytest.mark.devices def test_get_interface_by_ip_default(api): try: assert is_valid_get_interface_by_ip( get_interface_by_ip_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_get_network_device_by_ip(obj): json_schema_validate('jsd_d0a4b88145aabb51_v1_3_0').validate(obj) return True def get_network_device_by_ip(api): endpoint_result = api.devices.get_network_device_by_ip( ip_address='string' ) return endpoint_result @pytest.mark.devices def test_get_network_device_by_ip(api): assert is_valid_get_network_device_by_ip( get_network_device_by_ip(api) ) def get_network_device_by_ip_default(api): endpoint_result = api.devices.get_network_device_by_ip( ip_address='string' ) return endpoint_result @pytest.mark.devices def test_get_network_device_by_ip_default(api): try: assert is_valid_get_network_device_by_ip( get_network_device_by_ip_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_get_device_config_count(obj): json_schema_validate('jsd_888f585c49b88441_v1_3_0').validate(obj) return True def get_device_config_count(api): endpoint_result = api.devices.get_device_config_count( ) return endpoint_result @pytest.mark.devices def test_get_device_config_count(api): assert is_valid_get_device_config_count( get_device_config_count(api) ) def get_device_config_count_default(api): endpoint_result = api.devices.get_device_config_count( ) return endpoint_result @pytest.mark.devices def test_get_device_config_count_default(api): try: assert is_valid_get_device_config_count( get_device_config_count_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_get_device_by_serial_number(obj): json_schema_validate('jsd_d888ab6d4d59a8c1_v1_3_0').validate(obj) return True def get_device_by_serial_number(api): endpoint_result = api.devices.get_device_by_serial_number( serial_number='string' ) return endpoint_result @pytest.mark.devices def test_get_device_by_serial_number(api): assert is_valid_get_device_by_serial_number( get_device_by_serial_number(api) ) def get_device_by_serial_number_default(api): endpoint_result = api.devices.get_device_by_serial_number( serial_number='string' ) return endpoint_result @pytest.mark.devices def test_get_device_by_serial_number_default(api): try: assert is_valid_get_device_by_serial_number( get_device_by_serial_number_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_get_all_interfaces(obj): json_schema_validate('jsd_f5947a4c439a8bf0_v1_3_0').validate(obj) return True def get_all_interfaces(api): endpoint_result = api.devices.get_all_interfaces( ) return endpoint_result @pytest.mark.devices def test_get_all_interfaces(api): assert is_valid_get_all_interfaces( get_all_interfaces(api) ) def get_all_interfaces_default(api): endpoint_result = api.devices.get_all_interfaces( ) return endpoint_result @pytest.mark.devices def test_get_all_interfaces_default(api): try: assert is_valid_get_all_interfaces( get_all_interfaces_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_get_module_count(obj): json_schema_validate('jsd_8db939744649a782_v1_3_0').validate(obj) return True def get_module_count(api): endpoint_result = api.devices.get_module_count( device_id='string', name_list='value1,value2', operational_state_code_list='value1,value2', part_number_list='value1,value2', vendor_equipment_type_list='value1,value2' ) return endpoint_result @pytest.mark.devices def test_get_module_count(api): assert is_valid_get_module_count( get_module_count(api) ) def get_module_count_default(api): endpoint_result = api.devices.get_module_count( device_id=None, name_list=None, operational_state_code_list=None, part_number_list=None, vendor_equipment_type_list=None ) return endpoint_result @pytest.mark.devices def test_get_module_count_default(api): try: assert is_valid_get_module_count( get_module_count_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_get_modules(obj): json_schema_validate('jsd_eb8249e34f69b0f1_v1_3_0').validate(obj) return True def get_modules(api): endpoint_result = api.devices.get_modules( device_id='string', limit='string', name_list='value1,value2', offset='string', operational_state_code_list='value1,value2', part_number_list='value1,value2', vendor_equipment_type_list='value1,value2' ) return endpoint_result @pytest.mark.devices def test_get_modules(api): assert is_valid_get_modules( get_modules(api) ) def get_modules_default(api): endpoint_result = api.devices.get_modules( device_id=None, limit=None, name_list=None, offset=None, operational_state_code_list=None, part_number_list=None, vendor_equipment_type_list=None ) return endpoint_result @pytest.mark.devices def test_get_modules_default(api): try: assert is_valid_get_modules( get_modules_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_get_wireless_lan_controller_details_by_id(obj): json_schema_validate('jsd_f6826a8e41bba242_v1_3_0').validate(obj) return True def get_wireless_lan_controller_details_by_id(api): endpoint_result = api.devices.get_wireless_lan_controller_details_by_id( id='string' ) return endpoint_result @pytest.mark.devices def test_get_wireless_lan_controller_details_by_id(api): assert is_valid_get_wireless_lan_controller_details_by_id( get_wireless_lan_controller_details_by_id(api) ) def get_wireless_lan_controller_details_by_id_default(api): endpoint_result = api.devices.get_wireless_lan_controller_details_by_id( id='string' ) return endpoint_result @pytest.mark.devices def test_get_wireless_lan_controller_details_by_id_default(api): try: assert is_valid_get_wireless_lan_controller_details_by_id( get_wireless_lan_controller_details_by_id_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_sync_devices(obj): json_schema_validate('jsd_aeb9eb67460b92df_v1_3_0').validate(obj) return True def sync_devices(api): endpoint_result = api.devices.sync_devices( active_validation=True, cliTransport='string', computeDevice=True, enablePassword='string', extendedDiscoveryInfo='string', httpPassword='string', httpPort='string', httpSecure=True, httpUserName='string', ipAddress=['string'], merakiOrgId=['string'], netconfPort='string', password='string', payload=None, serialNumber='string', snmpAuthPassphrase='string', snmpAuthProtocol='string', snmpMode='string', snmpPrivPassphrase='string', snmpPrivProtocol='string', snmpROCommunity='string', snmpRWCommunity='string', snmpRetry=0, snmpTimeout=0, snmpUserName='string', snmpVersion='string', type='COMPUTE_DEVICE', updateMgmtIPaddressList=[{'existMgmtIpAddress': 'string', 'newMgmtIpAddress': 'string'}], userName='string' ) return endpoint_result @pytest.mark.devices def test_sync_devices(api): assert is_valid_sync_devices( sync_devices(api) ) def sync_devices_default(api): endpoint_result = api.devices.sync_devices( active_validation=True, cliTransport=None, computeDevice=None, enablePassword=None, extendedDiscoveryInfo=None, httpPassword=None, httpPort=None, httpSecure=None, httpUserName=None, ipAddress=None, merakiOrgId=None, netconfPort=None, password=None, payload=None, serialNumber=None, snmpAuthPassphrase=None, snmpAuthProtocol=None, snmpMode=None, snmpPrivPassphrase=None, snmpPrivProtocol=None, snmpROCommunity=None, snmpRWCommunity=None, snmpRetry=None, snmpTimeout=None, snmpUserName=None, snmpVersion=None, type=None, updateMgmtIPaddressList=None, userName=None ) return endpoint_result @pytest.mark.devices def test_sync_devices_default(api): try: assert is_valid_sync_devices( sync_devices_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_get_interface_by_id(obj): json_schema_validate('jsd_b888792d43baba46_v1_3_0').validate(obj) return True def get_interface_by_id(api): endpoint_result = api.devices.get_interface_by_id( id='string' ) return endpoint_result @pytest.mark.devices def test_get_interface_by_id(api): assert is_valid_get_interface_by_id( get_interface_by_id(api) ) def get_interface_by_id_default(api): endpoint_result = api.devices.get_interface_by_id( id='string' ) return endpoint_result @pytest.mark.devices def test_get_interface_by_id_default(api): try: assert is_valid_get_interface_by_id( get_interface_by_id_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_get_functional_capability_for_devices(obj): json_schema_validate('jsd_c3b3c9ef4e6b8a09_v1_3_0').validate(obj) return True def get_functional_capability_for_devices(api): endpoint_result = api.devices.get_functional_capability_for_devices( device_id='string', function_name='value1,value2' ) return endpoint_result @pytest.mark.devices def test_get_functional_capability_for_devices(api): assert is_valid_get_functional_capability_for_devices( get_functional_capability_for_devices(api) ) def get_functional_capability_for_devices_default(api): endpoint_result = api.devices.get_functional_capability_for_devices( device_id=None, function_name=None ) return endpoint_result @pytest.mark.devices def test_get_functional_capability_for_devices_default(api): try: assert is_valid_get_functional_capability_for_devices( get_functional_capability_for_devices_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_get_device_detail(obj): json_schema_validate('jsd_89b2fb144f5bb09b_v1_3_0').validate(obj) return True def get_device_detail(api): endpoint_result = api.devices.get_device_detail( identifier='string', search_by='string', timestamp=0 ) return endpoint_result @pytest.mark.devices def test_get_device_detail(api): assert is_valid_get_device_detail( get_device_detail(api) ) def get_device_detail_default(api): endpoint_result = api.devices.get_device_detail( identifier=None, search_by=None, timestamp=None ) return endpoint_result @pytest.mark.devices def test_get_device_detail_default(api): try: assert is_valid_get_device_detail( get_device_detail_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_get_network_device_by_pagination_range(obj): json_schema_validate('jsd_f49548c54be8a3e2_v1_3_0').validate(obj) return True def get_network_device_by_pagination_range(api): endpoint_result = api.devices.get_network_device_by_pagination_range( records_to_return=0, start_index=0 ) return endpoint_result @pytest.mark.devices def test_get_network_device_by_pagination_range(api): assert is_valid_get_network_device_by_pagination_range( get_network_device_by_pagination_range(api) ) def get_network_device_by_pagination_range_default(api): endpoint_result = api.devices.get_network_device_by_pagination_range( records_to_return=0, start_index=0 ) return endpoint_result @pytest.mark.devices def test_get_network_device_by_pagination_range_default(api): try: assert is_valid_get_network_device_by_pagination_range( get_network_device_by_pagination_range_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e def is_valid_retrieves_all_network_devices(obj): json_schema_validate('jsd_ffa748cc44e9a437_v1_3_0').validate(obj) return True def retrieves_all_network_devices(api): endpoint_result = api.devices.retrieves_all_network_devices( associated_wlc_ip='string', collection_interval='string', collection_status='string', error_code='string', family='string', hostname='string', limit='string', mac_address='string', management_ip_address='string', offset='string', platform_id='string', reachability_failure_reason='string', reachability_status='string', role='string', role_source='string', serial_number='string', series='string', software_type='string', software_version='string', type='string', up_time='string', vrf_name='string' ) return endpoint_result @pytest.mark.devices def test_retrieves_all_network_devices(api): assert is_valid_retrieves_all_network_devices( retrieves_all_network_devices(api) ) def retrieves_all_network_devices_default(api): endpoint_result = api.devices.retrieves_all_network_devices( associated_wlc_ip=None, collection_interval=None, collection_status=None, error_code=None, family=None, hostname=None, limit=None, mac_address=None, management_ip_address=None, offset=None, platform_id=None, reachability_failure_reason=None, reachability_status=None, role=None, role_source=None, serial_number=None, series=None, software_type=None, software_version=None, type=None, up_time=None, vrf_name=None ) return endpoint_result @pytest.mark.devices def test_retrieves_all_network_devices_default(api): try: assert is_valid_retrieves_all_network_devices( retrieves_all_network_devices_default(api) ) except Exception as original_e: with pytest.raises(TypeError, match="but instead we received None"): raise original_e

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9148cd2a1e8537ffec6d90651ed4e957a922ee7e

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Python

formwizard/tests/wizardtests/tests.py

djangsters/django-formwizard

7b35165f0340aae4e8302d5b05b0cb443f6c9904

[ "BSD-3-Clause" ]

4

2015-02-05T00:12:55.000Z

2017-08-14T10:37:20.000Z

formwizard/tests/wizardtests/tests.py

djangsters/django-formwizard

7b35165f0340aae4e8302d5b05b0cb443f6c9904

[ "BSD-3-Clause" ]

2

2017-05-18T20:21:03.000Z

2017-08-16T14:33:28.000Z

formwizard/tests/wizardtests/tests.py

djangsters/django-formwizard

7b35165f0340aae4e8302d5b05b0cb443f6c9904

[ "BSD-3-Clause" ]

4

2015-01-20T00:19:22.000Z

2017-11-24T15:17:02.000Z

from __future__ import with_statement import os from django.test import TestCase from django.conf import settings from django.contrib.auth.models import User import formwizard class WizardTests(object): urls = 'formwizard.tests.wizardtests.urls' def setUp(self): self.testuser, created = User.objects.get_or_create(username='testuser1') self.wizard_step_data[0]['form1-user'] = self.testuser.pk def test_initial_call(self): response = self.client.get(self.wizard_url) wizard = response.context['wizard'] self.assertEqual(response.status_code, 200) self.assertEqual(wizard['steps'].current, 'form1') self.assertEqual(wizard['steps'].step0, 0) self.assertEqual(wizard['steps'].step1, 1) self.assertEqual(wizard['steps'].last, 'form4') self.assertEqual(wizard['steps'].prev, None) self.assertEqual(wizard['steps'].next, 'form2') self.assertEqual(wizard['steps'].count, 4) def test_form_post_error(self): response = self.client.post(self.wizard_url, self.wizard_step_1_data) self.assertEqual(response.status_code, 200) self.assertEqual(response.context['wizard']['steps'].current, 'form1') self.assertEqual(response.context['wizard']['form'].errors, {'name': [u'This field is required.'], 'user': [u'This field is required.']}) def test_form_post_success(self): response = self.client.post(self.wizard_url, self.wizard_step_data[0]) wizard = response.context['wizard'] self.assertEqual(response.status_code, 200) self.assertEqual(wizard['steps'].current, 'form2') self.assertEqual(wizard['steps'].step0, 1) self.assertEqual(wizard['steps'].prev, 'form1') self.assertEqual(wizard['steps'].next, 'form3') def test_form_stepback(self): response = self.client.get(self.wizard_url) self.assertEqual(response.status_code, 200) self.assertEqual(response.context['wizard']['steps'].current, 'form1') response = self.client.post(self.wizard_url, self.wizard_step_data[0]) self.assertEqual(response.status_code, 200) self.assertEqual(response.context['wizard']['steps'].current, 'form2') response = self.client.post(self.wizard_url, { 'wizard_prev_step': response.context['wizard']['steps'].prev}) self.assertEqual(response.status_code, 200) self.assertEqual(response.context['wizard']['steps'].current, 'form1') def test_template_context(self): response = self.client.get(self.wizard_url) self.assertEqual(response.status_code, 200) self.assertEqual(response.context['wizard']['steps'].current, 'form1') self.assertEqual(response.context.get('another_var', None), None) response = self.client.post(self.wizard_url, self.wizard_step_data[0]) self.assertEqual(response.status_code, 200) self.assertEqual(response.context['wizard']['steps'].current, 'form2') self.assertEqual(response.context.get('another_var', None), True) def test_form_finish(self): response = self.client.get(self.wizard_url) self.assertEqual(response.status_code, 200) self.assertEqual(response.context['wizard']['steps'].current, 'form1') response = self.client.post(self.wizard_url, self.wizard_step_data[0]) self.assertEqual(response.status_code, 200) self.assertEqual(response.context['wizard']['steps'].current, 'form2') post_data = self.wizard_step_data[1] post_data['form2-file1'] = open(__file__) response = self.client.post(self.wizard_url, post_data) self.assertEqual(response.status_code, 200) self.assertEqual(response.context['wizard']['steps'].current, 'form3') response = self.client.post(self.wizard_url, self.wizard_step_data[2]) self.assertEqual(response.status_code, 200) self.assertEqual(response.context['wizard']['steps'].current, 'form4') response = self.client.post(self.wizard_url, self.wizard_step_data[3]) self.assertEqual(response.status_code, 200) all_data = response.context['form_list'] self.assertEqual(all_data[1]['file1'].read(), open(__file__).read()) del all_data[1]['file1'] self.assertEqual(all_data, [ {'name': u'Pony', 'thirsty': True, 'user': self.testuser}, {'address1': u'123 Main St', 'address2': u'Djangoland'}, {'random_crap': u'blah blah'}, [{'random_crap': u'blah blah'}, {'random_crap': u'blah blah'}]]) def test_cleaned_data(self): response = self.client.get(self.wizard_url) self.assertEqual(response.status_code, 200) response = self.client.post(self.wizard_url, self.wizard_step_data[0]) self.assertEqual(response.status_code, 200) post_data = self.wizard_step_data[1] post_data['form2-file1'] = open(__file__) response = self.client.post(self.wizard_url, post_data) self.assertEqual(response.status_code, 200) response = self.client.post(self.wizard_url, self.wizard_step_data[2]) self.assertEqual(response.status_code, 200) response = self.client.post(self.wizard_url, self.wizard_step_data[3]) self.assertEqual(response.status_code, 200) all_data = response.context['all_cleaned_data'] self.assertEqual(all_data['file1'].read(), open(__file__).read()) del all_data['file1'] self.assertEqual(all_data, { 'name': u'Pony', 'thirsty': True, 'user': self.testuser, 'address1': u'123 Main St', 'address2': u'Djangoland', 'random_crap': u'blah blah', 'formset-form4': [ {'random_crap': u'blah blah'}, {'random_crap': u'blah blah'}]}) def test_manipulated_data(self): response = self.client.get(self.wizard_url) self.assertEqual(response.status_code, 200) response = self.client.post(self.wizard_url, self.wizard_step_data[0]) self.assertEqual(response.status_code, 200) post_data = self.wizard_step_data[1] post_data['form2-file1'] = open(__file__) response = self.client.post(self.wizard_url, post_data) self.assertEqual(response.status_code, 200) response = self.client.post(self.wizard_url, self.wizard_step_data[2]) self.assertEqual(response.status_code, 200) self.client.cookies.pop('sessionid', None) self.client.cookies.pop('wizard_cookie_contact_wizard', None) response = self.client.post(self.wizard_url, self.wizard_step_data[3]) self.assertEqual(response.status_code, 200) self.assertEqual(response.context['wizard']['steps'].current, 'form1') def test_form_refresh(self): response = self.client.get(self.wizard_url) self.assertEqual(response.status_code, 200) self.assertEqual(response.context['wizard']['steps'].current, 'form1') response = self.client.post(self.wizard_url, self.wizard_step_data[0]) self.assertEqual(response.status_code, 200) self.assertEqual(response.context['wizard']['steps'].current, 'form2') response = self.client.post(self.wizard_url, self.wizard_step_data[0]) self.assertEqual(response.status_code, 200) self.assertEqual(response.context['wizard']['steps'].current, 'form2') post_data = self.wizard_step_data[1] post_data['form2-file1'] = open(__file__) response = self.client.post(self.wizard_url, post_data) self.assertEqual(response.status_code, 200) self.assertEqual(response.context['wizard']['steps'].current, 'form3') response = self.client.post(self.wizard_url, self.wizard_step_data[2]) self.assertEqual(response.status_code, 200) self.assertEqual(response.context['wizard']['steps'].current, 'form4') response = self.client.post(self.wizard_url, self.wizard_step_data[0]) self.assertEqual(response.status_code, 200) self.assertEqual(response.context['wizard']['steps'].current, 'form2') response = self.client.post(self.wizard_url, self.wizard_step_data[3]) self.assertEqual(response.status_code, 200) class SessionWizardTests(WizardTests, TestCase): wizard_url = '/wiz_session/' wizard_step_1_data = { 'session_contact_wizard-current_step': 'form1', } wizard_step_data = ( { 'form1-name': 'Pony', 'form1-thirsty': '2', 'session_contact_wizard-current_step': 'form1', }, { 'form2-address1': '123 Main St', 'form2-address2': 'Djangoland', 'session_contact_wizard-current_step': 'form2', }, { 'form3-random_crap': 'blah blah', 'session_contact_wizard-current_step': 'form3', }, { 'form4-INITIAL_FORMS': '0', 'form4-TOTAL_FORMS': '2', 'form4-MAX_NUM_FORMS': '0', 'form4-0-random_crap': 'blah blah', 'form4-1-random_crap': 'blah blah', 'session_contact_wizard-current_step': 'form4', } ) class CookieWizardTests(WizardTests, TestCase): wizard_url = '/wiz_cookie/' wizard_step_1_data = { 'cookie_contact_wizard-current_step': 'form1', } wizard_step_data = ( { 'form1-name': 'Pony', 'form1-thirsty': '2', 'cookie_contact_wizard-current_step': 'form1', }, { 'form2-address1': '123 Main St', 'form2-address2': 'Djangoland', 'cookie_contact_wizard-current_step': 'form2', }, { 'form3-random_crap': 'blah blah', 'cookie_contact_wizard-current_step': 'form3', }, { 'form4-INITIAL_FORMS': '0', 'form4-TOTAL_FORMS': '2', 'form4-MAX_NUM_FORMS': '0', 'form4-0-random_crap': 'blah blah', 'form4-1-random_crap': 'blah blah', 'cookie_contact_wizard-current_step': 'form4', } ) class WizardTestKwargs(TestCase): wizard_url = '/wiz_other_template/' wizard_step_1_data = { 'cookie_contact_wizard-current_step': 'form1', } wizard_step_data = ( { 'form1-name': 'Pony', 'form1-thirsty': '2', 'cookie_contact_wizard-current_step': 'form1', }, { 'form2-address1': '123 Main St', 'form2-address2': 'Djangoland', 'cookie_contact_wizard-current_step': 'form2', }, { 'form3-random_crap': 'blah blah', 'cookie_contact_wizard-current_step': 'form3', }, { 'form4-INITIAL_FORMS': '0', 'form4-TOTAL_FORMS': '2', 'form4-MAX_NUM_FORMS': '0', 'form4-0-random_crap': 'blah blah', 'form4-1-random_crap': 'blah blah', 'cookie_contact_wizard-current_step': 'form4', } ) urls = 'formwizard.tests.wizardtests.urls' def setUp(self): self.testuser, created = User.objects.get_or_create(username='testuser1') self.wizard_step_data[0]['form1-user'] = self.testuser.pk def test_template(self): templates = os.path.join(os.path.dirname(__file__), 'templates') with self.settings( TEMPLATE_DIRS=list(settings.TEMPLATE_DIRS) + [templates]): response = self.client.get(self.wizard_url) self.assertTemplateUsed(response, 'other_wizard_form.html')

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11,695

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null

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null

0

6

914c36194321c929b91469d5d4a236a46c7f5471

4,212

py

Python

tests/Python/Dynamic/PyATKDynamic_swellgain_test.py

D-J-Roberts/AudioTK

accf009d7238f32702eb1d5ee23c5148fc68e3bd

[ "BSD-3-Clause" ]

249

2015-01-05T13:36:26.000Z

2022-03-15T18:47:46.000Z

tests/Python/Dynamic/PyATKDynamic_swellgain_test.py

D-J-Roberts/AudioTK

accf009d7238f32702eb1d5ee23c5148fc68e3bd

[ "BSD-3-Clause" ]

22

2015-07-28T15:20:24.000Z

2020-07-11T14:18:19.000Z

tests/Python/Dynamic/PyATKDynamic_swellgain_test.py

D-J-Roberts/AudioTK

accf009d7238f32702eb1d5ee23c5148fc68e3bd

[ "BSD-3-Clause" ]

48

2015-08-15T12:08:13.000Z

2021-04-07T02:33:07.000Z

#!/usr/bin/env python from ATK.Core import DoubleInPointerFilter, DoubleOutPointerFilter from ATK.Dynamic import DoubleGainCompressorFilter, DoubleGainSwellFilter from ATK.Tools import DoubleApplyGainFilter sample_rate = 96000 def filter(input, ratio=4, threshold=1, softness=1): import numpy as np output = np.zeros(input.shape, dtype=np.float64) input2 = input**2 in2filter = DoubleInPointerFilter(input2, False) in2filter.input_sampling_rate = sample_rate infilter = DoubleInPointerFilter(input, False) infilter.input_sampling_rate = sample_rate gainfilter = DoubleGainCompressorFilter(1) gainfilter.input_sampling_rate = sample_rate gainfilter.set_input_port(0, in2filter, 0) gainfilter.threshold = threshold gainfilter.ratio = ratio gainfilter.softness = softness applygainfilter = DoubleApplyGainFilter(1) applygainfilter.input_sampling_rate = sample_rate applygainfilter.set_input_port(0, gainfilter, 0) applygainfilter.set_input_port(1, infilter, 0) outfilter = DoubleOutPointerFilter(output, False) outfilter.input_sampling_rate = sample_rate outfilter.set_input_port(0, applygainfilter, 0) outfilter.process(input.shape[1]) return output def colored_filter(input, ratio=4, threshold=1, softness=1): import numpy as np output = np.zeros(input.shape, dtype=np.float64) input2 = input**2 in2filter = DoubleInPointerFilter(input2, False) in2filter.input_sampling_rate = sample_rate infilter = DoubleInPointerFilter(input, False) infilter.input_sampling_rate = sample_rate gainfilter = DoubleGainSwellFilter(1) gainfilter.input_sampling_rate = sample_rate gainfilter.set_input_port(0, in2filter, 0) gainfilter.threshold = threshold gainfilter.ratio = ratio gainfilter.softness = softness applygainfilter = DoubleApplyGainFilter(1) applygainfilter.input_sampling_rate = sample_rate applygainfilter.set_input_port(0, gainfilter, 0) applygainfilter.set_input_port(1, infilter, 0) outfilter = DoubleOutPointerFilter(output, False) outfilter.input_sampling_rate = sample_rate outfilter.set_input_port(0, applygainfilter, 0) outfilter.process(input.shape[1]) return output def swell_test(): import numpy as np from numpy.testing import assert_almost_equal import os dirname = os.path.dirname(__file__) x = np.fromfile(dirname + os.sep + "input_swellgain.dat", dtype=np.float64).reshape(1, -1) ref = np.fromfile(dirname + os.sep + "output_swellgain.dat", dtype=np.float64).reshape(1, -1) out = filter(x, 1, 1, 1) assert_almost_equal(out, ref) def swell_1_1_test(): import numpy as np from numpy.testing import assert_almost_equal import os dirname = os.path.dirname(__file__) x = np.fromfile(dirname + os.sep + "input_swellgain.dat", dtype=np.float64).reshape(1, -1) ref = np.fromfile(dirname + os.sep + "output_swellgain_1_1.dat", dtype=np.float64).reshape(1, -1) out = colored_filter(x, 1, 1, 1) assert_almost_equal(out, ref) def swell_2_1_test(): import numpy as np from numpy.testing import assert_almost_equal import os dirname = os.path.dirname(__file__) x = np.fromfile(dirname + os.sep + "input_swellgain.dat", dtype=np.float64).reshape(1, -1) ref = np.fromfile(dirname + os.sep + "output_swellgain_2_1.dat", dtype=np.float64).reshape(1, -1) out = colored_filter(x, 2, 1, 1) assert_almost_equal(out, ref) if __name__ == "__main__": import numpy as np import matplotlib.pyplot as plt size = 1000 x = np.arange(10, size, dtype=np.float64).reshape(1, -1) / 100 x.tofile("input_swellgain.dat") out_1_1_1 = filter(x, 1, 1, 1) out_1_1_1.tofile("output_swellgain.dat") max_out_1_1 = colored_filter(x, 1, 1, 1) max_out_1_1.tofile("output_swellgain_1_1.dat") max_out_2_1 = colored_filter(x, 2, 1, 1) max_out_2_1.tofile("output_swellgain_2_1.dat") plt.figure() plt.loglog(x[0], out_1_1_1[0], label="compressor, ratio(1), threshold(1), softness(1)") plt.loglog(x[0], max_out_1_1[0], label="swell, ratio(1), threshold(1), softness(1)") plt.loglog(x[0], max_out_2_1[0], label="swell, ratio(2), threshold(1), softness(1)") plt.title("Swell gain") plt.legend(loc=4) plt.grid() plt.show()

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null

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6

e671a756af30787a22d288d1457e85890664447d

151

py

Python

Udemy/GeekUniversity/secao_4/ex8_conversor_kelvin_p_celsuis.py

SandboxGTASA/Python-1

bbb5f8bdf7d5110528e457b2a9ebdb2d67e40805

[ "MIT" ]

null

Udemy/GeekUniversity/secao_4/ex8_conversor_kelvin_p_celsuis.py

SandboxGTASA/Python-1

bbb5f8bdf7d5110528e457b2a9ebdb2d67e40805

[ "MIT" ]

null

Udemy/GeekUniversity/secao_4/ex8_conversor_kelvin_p_celsuis.py

SandboxGTASA/Python-1

bbb5f8bdf7d5110528e457b2a9ebdb2d67e40805

[ "MIT" ]

null

kelvin = float(input('Entre com a temperatura em Kelvin: ')) c = kelvin - 273.15 print(f'A temperatura em Kelvin convertida para Celsius é de: {c}')

25.166667

67

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25

151

4.28

0.72

0.224299

0.261682

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null

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6

e679bb586817f3b04e1a1806bc2b851af82b2dd0

124

py

Python

mapek_framework/src/test_mapek_framework/test_managed_system.py

imcatta/ros-mapek-framework

55e5aa299d322df4edea9b5898e969add122c445

[ "MIT" ]

1

2019-08-18T17:36:42.000Z

mapek_framework/src/test_mapek_framework/test_managed_system.py

imcatta/ros-mapek-framework

55e5aa299d322df4edea9b5898e969add122c445

[ "MIT" ]

4

2019-02-01T13:07:49.000Z

2019-02-04T14:10:46.000Z

mapek_framework/src/test_mapek_framework/test_managed_system.py

imcatta/ros-mapek-framework

55e5aa299d322df4edea9b5898e969add122c445

[ "MIT" ]

1

2019-09-06T13:03:51.000Z

import unittest from mapek_framework import ManagedSystem class ManagedSystemTestCase(unittest.TestCase): pass

20.666667

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124

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true

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null

0

1

0

1

0

6

e6956c35a56722f2ba4aac72cf5966b1d268d604

200

py

Python

webapp/manage.py

zhiwehu/IBookmark

b416f14f2b7ede4f38a00f386c2cdac01cbd740f

[ "Apache-2.0" ]

1

2020-04-01T11:11:37.000Z

webapp/manage.py

zhiwehu/IBookmark

b416f14f2b7ede4f38a00f386c2cdac01cbd740f

[ "Apache-2.0" ]

null

webapp/manage.py

zhiwehu/IBookmark

b416f14f2b7ede4f38a00f386c2cdac01cbd740f

[ "Apache-2.0" ]

2

2019-10-04T06:00:32.000Z

2021-02-03T08:08:27.000Z

#!/usr/bin/env python from django.core.management import execute_from_command_line import environment environment.setup_environ(__file__) if __name__ == "__main__": execute_from_command_line()

20

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0.81

26

200

5.5

0.730769

0.153846

0.251748

0.307692

0

0.105

200

9

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null

0

1

0

1

0

6

e69c9a56157705b84f1e8585b89a2f87f6564b96

3,211

py

Python

tests/test_backoff.py

alexferl/justbackoff

8d99124122e744b1e6a721742c2bcc96b7917993

[ "MIT" ]

null

tests/test_backoff.py

alexferl/justbackoff

8d99124122e744b1e6a721742c2bcc96b7917993

[ "MIT" ]

null

tests/test_backoff.py

alexferl/justbackoff

8d99124122e744b1e6a721742c2bcc96b7917993

[ "MIT" ]

null

import unittest from justbackoff import Backoff, to_ms, to_seconds class CustomAssertions: @staticmethod def assert_between(actual: float, low: float, high: float): if actual < low: raise AssertionError("Got {}, expecting >= {}".format(actual, low)) if actual > high: msg = "Got {}, expecting <= {}".format(actual, high) raise AssertionError(msg) class TestBackoff(unittest.TestCase, CustomAssertions): def setUp(self): self.b = Backoff(min_ms=100.0, max_ms=10000.0, factor=2.0) def test_defaults(self): self.assertEqual(self.b.duration(), to_seconds(100.0)) self.assertEqual(self.b.duration(), to_seconds(200.0)) self.assertEqual(self.b.duration(), to_seconds(400.0)) self.b.reset() self.assertEqual(self.b.duration(), to_seconds(100.0)) def test_factor(self): b = Backoff(min_ms=100, max_ms=10000, factor=1.5) self.assertEqual(b.duration(), to_seconds(100.0)) self.assertEqual(b.duration(), to_seconds(150.0)) self.assertEqual(b.duration(), to_seconds(225.0)) b.reset() self.assertEqual(b.duration(), to_seconds(100.0)) def test_for_attempt(self): self.assertEqual(self.b.for_attempt(0), to_seconds(100.0)) self.assertEqual(self.b.for_attempt(1), to_seconds(200.0)) self.assertEqual(self.b.for_attempt(2), to_seconds(400.0)) self.b.reset() self.assertEqual(self.b.for_attempt(0), to_seconds(100.0)) def test_get_attempt(self): self.assertEqual(self.b.attempt(), 0) self.assertEqual(self.b.duration(), to_seconds(100.0)) self.assertEqual(self.b.attempt(), 1) self.assertEqual(self.b.duration(), to_seconds(200.0)) self.assertEqual(self.b.attempt(), 2) self.assertEqual(self.b.duration(), to_seconds(400.0)) self.assertEqual(self.b.attempt(), 3) self.b.reset() self.assertEqual(self.b.attempt(), 0) self.assertEqual(self.b.duration(), to_seconds(100.0)) self.assertEqual(self.b.attempt(), 1) def test_jitter(self): b = Backoff(min_ms=100.0, max_ms=10000.0, factor=2.0, jitter=True) self.assertEqual(b.duration(), to_seconds(100.0)) self.assert_between(b.duration(), to_seconds(100.0), to_seconds(200.0)) self.assert_between(b.duration(), to_seconds(100.0), to_seconds(400.0)) b.reset() self.assertEqual(b.duration(), to_seconds(100.0)) def test_integers(self): b = Backoff(min_ms=100, max_ms=10000, factor=2) self.assertEqual(b.duration(), to_seconds(100.0)) self.assertEqual(b.duration(), to_seconds(200.0)) self.assertEqual(b.duration(), to_seconds(400.0)) b.reset() self.assertEqual(b.duration(), to_seconds(100.0)) def test_to_ms(self): self.assertEqual(10000, to_ms(10.0)) def test_min_bigger_than_max(self): b = Backoff(min_ms=10000.0, max_ms=1000.0, factor=2) self.assertEqual(b.duration(), 1.0) self.assertEqual(b.duration(), 1.0) self.assertEqual(b.duration(), 1.0) b.reset() self.assertEqual(b.duration(), 1.0)

37.337209

79

0.63843

458

3,211

4.344978

0.128821

0.248744

0.110553

0.180905

0.769849

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0.723116

0.671357

0.631658

0.603015

0

0.072492

0.205232

3,211

85

80

37.776471

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0

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false

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1

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0

1

0

null

0

1

0

6

e6b6b25cb2f1a9b15538ae2b6015c734c94717c7

10,335

py

Python

tests/pytests/unit/states/test_win_network.py

waynegemmell/salt

88056db3589cccab8956c2ae4f9b733acce89461

[ "Apache-2.0" ]

9,425

2015-01-01T05:59:24.000Z

2022-03-31T20:44:05.000Z

tests/pytests/unit/states/test_win_network.py

waynegemmell/salt

88056db3589cccab8956c2ae4f9b733acce89461

[ "Apache-2.0" ]

33,507

2015-01-01T00:19:56.000Z

2022-03-31T23:48:20.000Z

tests/pytests/unit/states/test_win_network.py

waynegemmell/salt

88056db3589cccab8956c2ae4f9b733acce89461

[ "Apache-2.0" ]

5,810

2015-01-01T19:11:45.000Z

2022-03-31T02:37:20.000Z

""" :codeauthor: Rahul Handay <rahulha@saltstack.com> """ import pytest import salt.states.win_network as win_network from tests.support.mock import MagicMock, patch @pytest.fixture def configure_loader_modules(): return {win_network: {}} def test_managed_missing_parameters(): """ Test to ensure that the named interface is configured properly. """ ret = { "name": "salt", "changes": {}, "result": False, "comment": ( "dns_proto must be one of the following: static, dhcp\n" "ip_proto must be one of the following: static, dhcp" ), } assert win_network.managed("salt") == ret def test_managed_static_enabled_false(): ret = { "name": "salt", "changes": {}, "result": True, "comment": "Interface 'salt' is up to date (already disabled)", } mock_false = MagicMock(return_value=False) with patch.dict(win_network.__salt__, {"ip.is_enabled": mock_false}): assert ( win_network.managed( "salt", dns_proto="static", ip_proto="static", enabled=False ) == ret ) def test_managed_test_true(): ret = { "name": "salt", "changes": {}, "result": False, "comment": "Failed to enable interface 'salt' to make changes", } mock_false = MagicMock(return_value=False) with patch.dict( win_network.__salt__, {"ip.is_enabled": mock_false, "ip.enable": mock_false} ), patch.dict(win_network.__opts__, {"test": False}): assert win_network.managed("salt", dns_proto="static", ip_proto="static") == ret def test_managed_validate_errors(): ret = { "name": "salt", "changes": {}, "result": False, "comment": ( "The following SLS configuration errors were " "detected:\n" "- First Error\n" "- Second Error" ), } mock_true = MagicMock(return_value=True) mock_validate = MagicMock(return_value=["First Error", "Second Error"]) with patch.dict(win_network.__salt__, {"ip.is_enabled": mock_true}), patch.object( win_network, "_validate", mock_validate ): assert win_network.managed("salt", dns_proto="static", ip_proto="static") == ret def test_managed_get_current_config_failed(): ret = { "name": "salt", "changes": {}, "result": False, "comment": "Unable to get current configuration for interface 'salt'", } mock_true = MagicMock(return_value=True) mock_false = MagicMock(return_value=False) mock_validate = MagicMock(return_value=[]) with patch.dict( win_network.__salt__, {"ip.is_enabled": mock_true, "ip.get_interface": mock_false}, ), patch.object(win_network, "_validate", mock_validate): assert win_network.managed("salt", dns_proto="dhcp", ip_proto="dhcp") == ret def test_managed_test_true_no_changes(): ret = { "name": "salt", "changes": {}, "result": True, "comment": "Interface 'salt' is up to date", } mock_true = MagicMock(return_value=True) mock_validate = MagicMock(return_value=[]) mock_get_int = MagicMock( return_value={ "DHCP enabled": "yes", "DNS servers configured through DHCP": "192.168.0.10", } ) with patch.dict( win_network.__salt__, {"ip.is_enabled": mock_true, "ip.get_interface": mock_get_int}, ), patch.dict(win_network.__opts__, {"test": True}), patch.object( win_network, "_validate", mock_validate ): assert win_network.managed("salt", dns_proto="dhcp", ip_proto="dhcp") == ret def test_managed_test_true_changes(): ret = { "name": "salt", "changes": {}, "result": None, "comment": ( "The following changes will be made to interface " "'salt':\n" "- DNS protocol will be changed to: dhcp" ), } mock_true = MagicMock(return_value=True) mock_validate = MagicMock(return_value=[]) mock_get_int = MagicMock( return_value={ "DHCP enabled": "no", "Statically Configured DNS Servers": "192.168.0.10", } ) with patch.dict( win_network.__salt__, {"ip.is_enabled": mock_true, "ip.get_interface": mock_get_int}, ), patch.dict(win_network.__opts__, {"test": True}), patch.object( win_network, "_validate", mock_validate ): assert win_network.managed("salt", dns_proto="dhcp", ip_proto="dhcp") == ret def test_managed_failed(): ret = { "name": "salt", "changes": {}, "result": False, "comment": "Failed to set desired configuration settings for interface 'salt'", } mock_true = MagicMock(return_value=True) mock_validate = MagicMock(return_value=[]) mock_get_int = MagicMock( return_value={ "DHCP enabled": "no", "Statically Configured DNS Servers": "192.168.0.10", } ) with patch.dict( win_network.__salt__, { "ip.is_enabled": mock_true, "ip.get_interface": mock_get_int, "ip.set_dhcp_dns": mock_true, "ip.set_dhcp_ip": mock_true, }, ), patch.dict(win_network.__opts__, {"test": False}), patch.object( win_network, "_validate", mock_validate ): assert win_network.managed("salt", dns_proto="dhcp", ip_proto="dhcp") == ret def test_managed(): ret = { "name": "salt", "changes": { "DHCP enabled": {"new": "yes", "old": "no"}, "DNS servers configured through DHCP": {"new": "192.168.0.10", "old": ""}, "Statically Configured DNS Servers": {"new": "", "old": "192.168.0.10"}, }, "result": True, "comment": "Successfully updated configuration for interface 'salt'", } mock_true = MagicMock(return_value=True) mock_validate = MagicMock(return_value=[]) mock_get_int = MagicMock( side_effect=[ { "DHCP enabled": "no", "Statically Configured DNS Servers": "192.168.0.10", }, { "DHCP enabled": "yes", "DNS servers configured through DHCP": "192.168.0.10", }, ] ) with patch.dict( win_network.__salt__, { "ip.is_enabled": mock_true, "ip.get_interface": mock_get_int, "ip.set_dhcp_dns": mock_true, "ip.set_dhcp_ip": mock_true, }, ), patch.dict(win_network.__opts__, {"test": False}), patch.object( win_network, "_validate", mock_validate ): assert win_network.managed("salt", dns_proto="dhcp", ip_proto="dhcp") == ret def test_managed_static_dns_clear(): expected = { "name": "salt", "changes": { "Statically Configured DNS Servers": {"new": "None", "old": "192.168.0.10"} }, "result": True, "comment": "Successfully updated configuration for interface 'salt'", } mock_true = MagicMock(return_value=True) mock_validate = MagicMock(return_value=[]) mock_get_int = MagicMock( side_effect=[ { "DHCP enabled": "no", "Statically Configured DNS Servers": "192.168.0.10", }, {"DHCP enabled": "no", "Statically Configured DNS Servers": "None"}, ] ) with patch.dict( win_network.__salt__, { "ip.is_enabled": mock_true, "ip.get_interface": mock_get_int, "ip.set_static_dns": mock_true, }, ), patch.dict(win_network.__opts__, {"test": False}), patch.object( win_network, "_validate", mock_validate ): ret = win_network.managed( "salt", dns_proto="static", dns_servers=[], ip_proto="dhcp" ) assert ret == expected def test_managed_static_dns(): expected = { "name": "salt", "changes": { "Statically Configured DNS Servers": {"new": "192.168.0.10", "old": "None"} }, "result": True, "comment": "Successfully updated configuration for interface 'salt'", } mock_true = MagicMock(return_value=True) mock_validate = MagicMock(return_value=[]) mock_get_int = MagicMock( side_effect=[ {"DHCP enabled": "no", "Statically Configured DNS Servers": "None"}, { "DHCP enabled": "no", "Statically Configured DNS Servers": "192.168.0.10", }, ] ) with patch.dict( win_network.__salt__, { "ip.is_enabled": mock_true, "ip.get_interface": mock_get_int, "ip.set_static_dns": mock_true, }, ), patch.dict(win_network.__opts__, {"test": False}), patch.object( win_network, "_validate", mock_validate ): ret = win_network.managed( "salt", dns_proto="static", dns_servers=["192.168.0.10"], ip_proto="dhcp", ) assert ret == expected def test_managed_static_dns_no_action(): expected = { "name": "salt", "changes": {}, "result": True, "comment": "Interface 'salt' is up to date", } mock_true = MagicMock(return_value=True) mock_validate = MagicMock(return_value=[]) mock_get_int = MagicMock( return_value={ "DHCP enabled": "no", "Statically Configured DNS Servers": "192.168.0.10", } ) with patch.dict( win_network.__salt__, { "ip.is_enabled": mock_true, "ip.get_interface": mock_get_int, "ip.set_static_dns": mock_true, }, ), patch.dict(win_network.__opts__, {"test": False}), patch.object( win_network, "_validate", mock_validate ): # Don't pass dns_servers ret = win_network.managed("salt", dns_proto="static", ip_proto="dhcp") assert ret == expected # Pass dns_servers=None ret = win_network.managed( "salt", dns_proto="static", dns_servers=None, ip_proto="dhcp" ) assert ret == expected

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null

0

1

0

null

0

6

e6c2807a6d162830d87e51e42c2b02735337f341

55

py

Python

test.py

RealityAbb/panSky

fadf7063094f809f679d0bcaafbd161054b6b63b

[ "Apache-2.0" ]

null

test.py

RealityAbb/panSky

fadf7063094f809f679d0bcaafbd161054b6b63b

[ "Apache-2.0" ]

null

test.py

RealityAbb/panSky

fadf7063094f809f679d0bcaafbd161054b6b63b

[ "Apache-2.0" ]

null

from CTFd.models import get_id print(get_id("1234567"))

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6

fc3895f4c5e42de64e0861e83b74e6a6914e1a8a

32

py

Python

nglview/theme/__init__.py

tovrstra/nglview

12ab36c78dffc04bc2dde1f4048bc61ca75d33a5

[ "MIT" ]

161

2020-07-28T14:05:57.000Z

2022-03-31T08:38:06.000Z

nglview/theme/__init__.py

tovrstra/nglview

12ab36c78dffc04bc2dde1f4048bc61ca75d33a5

[ "MIT" ]

123

2020-07-27T15:02:27.000Z

2022-03-30T18:31:51.000Z

nglview/theme/__init__.py

tovrstra/nglview

12ab36c78dffc04bc2dde1f4048bc61ca75d33a5

[ "MIT" ]

42

2020-07-28T09:50:06.000Z

2022-03-11T18:50:22.000Z

from .theme import ThemeManager

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1

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6

fc52e144812958fd47d18fcda49e62314066b23d

1,587

py

Python

python/easy/1470_Shuffle_the_Array.py

JackWang0107/leetcode

c02932190b639ef87a8d0fcd07d9cd6ec7344a67

[ "MIT" ]

1

2021-05-22T03:27:33.000Z

python/easy/1470_Shuffle_the_Array.py

JackWang0107/leetcode

c02932190b639ef87a8d0fcd07d9cd6ec7344a67

[ "MIT" ]

null

python/easy/1470_Shuffle_the_Array.py

JackWang0107/leetcode

c02932190b639ef87a8d0fcd07d9cd6ec7344a67

[ "MIT" ]

null

from typing import * class Solution: # 60 ms, faster than 61.39% of Python3 online submissions for Shuffle the Array. # 14.2 MB, less than 98.76% of Python3 online submissions for Shuffle the Array. def shuffle(self, nums: List[int], n: int) -> List[int]: ans = [] for i in range(n): ans.append(nums[i]) ans.append(nums[n + i]) return ans # 56 ms, faster than 84.56% of Python3 online submissions for Shuffle the Array. # 14.2 MB, less than 98.76% of Python3 online submissions for Shuffle the Array. def shuffle(self, nums: List[int], n: int) -> List[int]: ans = [] for x, y in zip(nums[:n], nums[n:]): ans.append(x) ans.append(y) return ans # 60 ms, faster than 61.39% of Python3 online submissions for Shuffle the Array. # 14.5 MB, less than 49.20% of Python3 online submissions for Shuffle the Array. def shuffle(self, nums: List[int], n: int) -> List[int]: left = nums[:n] right = nums[n:] ans = [ None ]* (n *2) ans[::2] = left ans[1::2] = right return ans # 60 ms, faster than 61.39% of Python3 online submissions for Shuffle the Array. # 14.5 MB, less than 49.20% of Python3 online submissions for Shuffle the Array. def shuffle(self, nums: List[int], n: int) -> List[int]: ans = [ None ] * ( n* 2) ans[::2] = nums[:n] ans[1::2] = nums[n:] return ans if __name__ == "__main__": so = Solution() print(so.shuffle(nums = [2,5,1,3,4,7], n = 3))

36.906977

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3.676113

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1,587

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null

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6

fca6b4abd9a3d599a91d273c5c04d4c4a0463e16

164

py

Python

Tensorflow/my_tensorflow/src/regularizers/__init__.py

hywel1994/mac-workspace

10c20555104ce6ebba77657c7605ce2b7fa2fc34

[ "MIT" ]

37

2018-10-18T06:19:08.000Z

2022-03-20T08:13:08.000Z

github/Algorithm_Interview_Notes-Chinese/_codes/my_tensorflow/src/regularizers/__init__.py

keeya/Interview

2b40108d39de9ca9e9ab069edb9d4fcf9fe5760c

[ "MIT" ]

1

2019-01-27T14:21:21.000Z

github/Algorithm_Interview_Notes-Chinese/_codes/my_tensorflow/src/regularizers/__init__.py

keeya/Interview

2b40108d39de9ca9e9ab069edb9d4fcf9fe5760c

[ "MIT" ]

13

2018-10-23T12:39:55.000Z

2022-02-25T10:54:01.000Z

""" 正则化函数 `Tensor -> Tensor or None` Examples: l2_regularizer = l2(0.01) tf.get_variable(..., regularizer=l2_regularizer, ...) """ from .L1L2 import *

16.4

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164

5

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164

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1

0

null

0

1

0

1

0

6

5dccee1d5537a67eb75dd0175f90ce68d50a316c

8,572

py

Python

tasks/libs/version_tests.py

Taiki-San/datadog-agent

40aa7e6d8a663dfda52ee5d35179fccc15e7ff51

[ "Apache-2.0" ]

1,611

2017-09-28T15:07:39.000Z

2022-03-31T18:23:05.000Z

tasks/libs/version_tests.py

Taiki-San/datadog-agent

40aa7e6d8a663dfda52ee5d35179fccc15e7ff51

[ "Apache-2.0" ]

3,946

2017-09-28T14:45:19.000Z

2022-03-31T20:19:36.000Z

tasks/libs/version_tests.py

Taiki-San/datadog-agent

40aa7e6d8a663dfda52ee5d35179fccc15e7ff51

[ "Apache-2.0" ]

916

2017-10-17T23:18:48.000Z

2022-03-30T09:26:14.000Z

import random import unittest from .version import Version class TestVersionComparison(unittest.TestCase): def _get_version(self, major, minor, patch, rc, devel): return Version(major, minor, patch=patch, rc=rc, devel=devel) def _get_random_version(self): return self._get_version( random.randint(0, 99), random.randint(0, 99), random.randint(0, 99), # For tests, rc must be non-0, as 0 signifies a release version, which would # break some tests like test_rc_higher and test_rc_lower random.randint(1, 99), random.choice([True, False]), ) def test_major_lower(self): version = self._get_random_version() increment = random.randint(1, 99) self.assertFalse( self._get_version(version.major, version.minor, version.patch, version.rc, version.devel) > self._get_version(version.major + increment, version.minor, version.patch, version.rc, version.devel) ) def test_major_higher(self): version = self._get_random_version() increment = random.randint(1, 99) self.assertTrue( self._get_version(version.major + increment, version.minor, version.patch, version.rc, version.devel) > self._get_version(version.major, version.minor, version.patch, version.rc, version.devel) ) def test_minor_lower(self): version = self._get_random_version() increment = random.randint(1, 99) self.assertFalse( self._get_version(version.major, version.minor, version.patch, version.rc, version.devel) > self._get_version(version.major, version.minor + increment, version.patch, version.rc, version.devel) ) def test_minor_higher(self): version = self._get_random_version() increment = random.randint(1, 99) self.assertTrue( self._get_version(version.major, version.minor + increment, version.patch, version.rc, version.devel) > self._get_version(version.major, version.minor, version.patch, version.rc, version.devel) ) def test_patch_lower(self): version = self._get_random_version() increment = random.randint(1, 99) self.assertFalse( self._get_version(version.major, version.minor, version.patch, version.rc, version.devel) > self._get_version(version.major, version.minor, version.patch + increment, version.rc, version.devel) ) def test_patch_higher(self): version = self._get_random_version() increment = random.randint(1, 99) self.assertTrue( self._get_version(version.major, version.minor, version.patch + increment, version.rc, version.devel) > self._get_version(version.major, version.minor, version.patch, version.rc, version.devel) ) def test_rc_lower_than_release(self): version = self._get_random_version() self.assertFalse( self._get_version(version.major, version.minor, version.patch, version.rc, version.devel) > self._get_version(version.major, version.minor, version.patch, None, version.devel) ) def test_release_higher_than_rc(self): version = self._get_random_version() self.assertTrue( self._get_version(version.major, version.minor, version.patch, None, version.devel) > self._get_version(version.major, version.minor, version.patch, version.rc, version.devel) ) def test_rc_lower(self): version = self._get_random_version() increment = random.randint(1, 99) self.assertFalse( self._get_version(version.major, version.minor, version.patch, version.rc, version.devel) > self._get_version(version.major, version.minor, version.patch, version.rc + increment, version.devel) ) def test_rc_higher(self): version = self._get_random_version() increment = random.randint(1, 99) self.assertTrue( self._get_version(version.major, version.minor, version.patch, version.rc + increment, version.devel) > self._get_version(version.major, version.minor, version.patch, version.rc, version.devel) ) def test_equal(self): version = self._get_random_version() self.assertFalse( self._get_version(version.major, version.minor, version.patch, version.rc, version.devel) > self._get_version(version.major, version.minor, version.patch, version.rc, version.devel) ) def test_absent_patch_equal_zero(self): version = self._get_random_version() self.assertFalse( self._get_version(version.major, version.minor, None, None, version.devel) > self._get_version(version.major, version.minor, 0, None, version.devel) ) def test_absent_patch_less_than_any(self): version = self._get_random_version() increment = random.randint(1, 99) self.assertTrue( self._get_version(version.major, version.minor, version.patch + increment, None, version.devel) > self._get_version(version.major, version.minor, None, None, version.devel) ) def test_devel_less_than_any(self): version = self._get_random_version() self.assertTrue( self._get_version(version.major, version.minor, version.patch, None, False) > self._get_version(version.major, version.minor, version.patch, None, True) ) def test_devel_less_than_rc(self): version = self._get_random_version() self.assertTrue( self._get_version(version.major, version.minor, version.patch, version.rc, False) > self._get_version(version.major, version.minor, version.patch, None, True) ) def test_devel_equal(self): version = self._get_random_version() self.assertTrue( self._get_version(version.major, version.minor, version.patch, None, True) == self._get_version(version.major, version.minor, version.patch, None, True) ) class TestNonDevelVersion(unittest.TestCase): version = Version(major=1, minor=0, devel=True) def test_non_devel_version(self): new_version = self.version.non_devel_version() expected_version = Version(major=1, minor=0) # 1.0.0 self.assertEqual(new_version, expected_version) class TestNextVersion(unittest.TestCase): version = Version(major=1, minor=0) def test_next_version_major(self): new_version = self.version.next_version(bump_major=True) expected_version = Version(major=2, minor=0) self.assertEqual(new_version, expected_version) def test_next_version_minor(self): new_version = self.version.next_version(bump_minor=True) expected_version = Version(major=1, minor=1) self.assertEqual(new_version, expected_version) def test_next_version_patch(self): new_version = self.version.next_version(bump_patch=True) expected_version = Version(major=1, minor=0, patch=1) self.assertEqual(new_version, expected_version) def test_next_version_major_rc(self): new_version = self.version.next_version(bump_major=True, rc=True) expected_version = Version(major=2, minor=0, rc=1) self.assertEqual(new_version, expected_version) def test_next_version_minor_rc(self): new_version = self.version.next_version(bump_minor=True, rc=True) expected_version = Version(major=1, minor=1, rc=1) self.assertEqual(new_version, expected_version) def test_next_version_patch_rc(self): new_version = self.version.next_version(bump_patch=True, rc=True) expected_version = Version(major=1, minor=0, patch=1, rc=1) self.assertEqual(new_version, expected_version) def test_next_version_rc(self): version = self.version.next_version(bump_patch=True, rc=True) # 1.0.1-rc.1 new_version = version.next_version(rc=True) expected_version = Version(major=1, minor=0, patch=1, rc=2) self.assertEqual(new_version, expected_version) def test_next_version_promote_rc(self): version = self.version.next_version(bump_patch=True, rc=True) # 1.0.1-rc.1 new_version = version.next_version(rc=False) expected_version = Version(major=1, minor=0, patch=1) self.assertEqual(new_version, expected_version) if __name__ == '__main__': unittest.main()

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0

null

0

1

0

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6

f8df4a44bfc676df0719491c5c618876d5b9eadf

35,038

py

Python

src/test/python/org/o3project/odenos/core/component/network/flow/ofpflow/test_ofp_flow_match.py

o3project/odenos

837d0d3d3c37482e843c40c5eeeac10646e68c65

[ "Apache-2.0" ]

26

2015-02-18T10:22:50.000Z

2020-06-18T05:07:54.000Z

src/test/python/org/o3project/odenos/core/component/network/flow/ofpflow/test_ofp_flow_match.py

o3project/odenos

837d0d3d3c37482e843c40c5eeeac10646e68c65

[ "Apache-2.0" ]

45

2015-02-20T00:40:45.000Z

2021-12-14T21:07:57.000Z

src/test/python/org/o3project/odenos/core/component/network/flow/ofpflow/test_ofp_flow_match.py

o3project/odenos

837d0d3d3c37482e843c40c5eeeac10646e68c65

[ "Apache-2.0" ]

30

2015-02-19T02:00:35.000Z

2017-02-18T15:28:09.000Z

# -*- coding:utf-8 -*- # Copyright 2015 NEC Corporation. # # # # Licensed under the Apache License, Version 2.0 (the "License"); # # you may not use this file except in compliance with the License. # # You may obtain a copy of the License at # # # # http://www.apache.org/licenses/LICENSE-2.0 # # # # Unless required by applicable law or agreed to in writing, software # # distributed under the License is distributed on an "AS IS" BASIS, # # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # # See the License for the specific language governing permissions and # # limitations under the License. # from org.o3project.odenos.core.component.network.flow.ofpflow.ofp_flow_match\ import OFPFlowMatch import unittest class OFPFlowMatchTest(unittest.TestCase): def setUp(self): self.target = OFPFlowMatch("OFPFlowMatch", "ofp_in_node", "ofp_in_port") self.target.in_phy_port = "in_phy_port" self.target.metadata = 11 self.target.metadata_mask = 12 self.target.eth_src = "eth_src" self.target.eth_src_mask = "eth_src_mask" self.target.eth_dst = "eth_dst" self.target.eth_dst_mask = "eth_dst_mask" self.target.vlan_vid = 13 self.target.vlan_vid_mask = 14 self.target.vlan_pcp = 15 self.target.eth_type = 16 self.target.ip_dscp = 21 self.target.ip_ecn = 22 self.target.ip_proto = 23 self.target.ipv4_src = "ipv4_src" self.target.ipv4_src_mask = "ipv4_src_mask" self.target.ipv4_dst = "ipv4_dst" self.target.ipv4_dst_mask = "ipv4_dst_mask" self.target.tcp_src = 31 self.target.tcp_dst = 32 self.target.udp_src = 33 self.target.udp_dst = 34 self.target.sctp_src = 35 self.target.sctp_dst = 36 self.target.icmpv4_type = 37 self.target.icmpv4_code = 38 self.target.arp_op = 40 self.target.arp_spa = "arp_spa" self.target.arp_spa_mask = "arp_spa_mask" self.target.arp_tpa = "arp_tpa" self.target.arp_tpa_mask = "arp_tpa_mask" self.target.arp_sha = "arp_sha" self.target.arp_sha_mask = "arp_sha_mask" self.target.arp_tha = "arp_tha" self.target.arp_tha_mask = "arp_tha_mask" self.target.ipv6_src = "ipv6_src" self.target.ipv6_src_mask = "ipv6_src_mask" self.target.ipv6_dst = "ipv6_dst" self.target.ipv6_dst_mask = "ipv6_dst_mask" self.target.ipv6_flabel = 50 self.target.ipv6_flabel_mask = 51 self.target.icmpv6_type = 52 self.target.icmpv6_code = 53 self.target.ipv6_nd_target = "ipv6_nd_target" self.target.ipv6_nd_sll = "ipv6_nd_sll" self.target.ipv6_nd_tll = "ipv6_nd_tll" self.target.mpls_label = 54 self.target.mpls_tc = 55 self.target.mpls_bos = 56 self.target.pbb_isid = 57 self.target.pbb_isid_mask = 58 self.target.tunnel_id = 59 self.target.tunnel_id_mask = 60 self.target.ipv6_exthdr = 61 self.target.ipv6_exthdr_mask = 62 def tearDown(self): self.target = None def test_constractor_Not_None(self): self.assertEqual(self.target._body[OFPFlowMatch.TYPE], "OFPFlowMatch") self.assertEqual(self.target._body[OFPFlowMatch.IN_NODE], "ofp_in_node") self.assertEqual(self.target._body[OFPFlowMatch.IN_PORT], "ofp_in_port") self.assertEqual(self.target._body[OFPFlowMatch.IN_PHY_PORT], "in_phy_port") self.assertEqual(self.target._body[OFPFlowMatch.METADATA], 11) self.assertEqual(self.target._body[OFPFlowMatch.METADATA_MASK], 12) self.assertEqual(self.target._body[OFPFlowMatch.ETH_SRC], "eth_src") self.assertEqual(self.target._body[OFPFlowMatch.ETH_SRC_MASK], "eth_src_mask") self.assertEqual(self.target._body[OFPFlowMatch.ETH_DST], "eth_dst") self.assertEqual(self.target._body[OFPFlowMatch.ETH_DST_MASK], "eth_dst_mask") self.assertEqual(self.target._body[OFPFlowMatch.VLAN_VID], 13) self.assertEqual(self.target._body[OFPFlowMatch.VLAN_VID_MASK], 14) self.assertEqual(self.target._body[OFPFlowMatch.VLAN_PCP], 15) self.assertEqual(self.target._body[OFPFlowMatch.ETH_TYPE], 16) self.assertEqual(self.target._body[OFPFlowMatch.IP_DSCP], 21) self.assertEqual(self.target._body[OFPFlowMatch.IP_ECN], 22) self.assertEqual(self.target._body[OFPFlowMatch.IP_PROTO], 23) self.assertEqual(self.target._body[OFPFlowMatch.IPV4_SRC], "ipv4_src") self.assertEqual(self.target._body[OFPFlowMatch.IPV4_SRC_MASK], "ipv4_src_mask") self.assertEqual(self.target._body[OFPFlowMatch.IPV4_DST], "ipv4_dst") self.assertEqual(self.target._body[OFPFlowMatch.IPV4_DST_MASK], "ipv4_dst_mask") self.assertEqual(self.target._body[OFPFlowMatch.TCP_SRC], 31) self.assertEqual(self.target._body[OFPFlowMatch.TCP_DST], 32) self.assertEqual(self.target._body[OFPFlowMatch.UDP_SRC], 33) self.assertEqual(self.target._body[OFPFlowMatch.UDP_DST], 34) self.assertEqual(self.target._body[OFPFlowMatch.SCTP_SRC], 35) self.assertEqual(self.target._body[OFPFlowMatch.SCTP_DST], 36) self.assertEqual(self.target._body[OFPFlowMatch.ICMPV4_TYPE], 37) self.assertEqual(self.target._body[OFPFlowMatch.ICMPV4_CODE], 38) self.assertEqual(self.target._body[OFPFlowMatch.ARP_OP], 40) self.assertEqual(self.target._body[OFPFlowMatch.ARP_SPA], "arp_spa") self.assertEqual(self.target._body[OFPFlowMatch.ARP_SPA_MASK], "arp_spa_mask") self.assertEqual(self.target._body[OFPFlowMatch.ARP_TPA], "arp_tpa") self.assertEqual(self.target._body[OFPFlowMatch.ARP_TPA_MASK], "arp_tpa_mask") self.assertEqual(self.target._body[OFPFlowMatch.ARP_SHA], "arp_sha") self.assertEqual(self.target._body[OFPFlowMatch.ARP_SHA_MASK], "arp_sha_mask") self.assertEqual(self.target._body[OFPFlowMatch.ARP_THA], "arp_tha") self.assertEqual(self.target._body[OFPFlowMatch.ARP_THA_MASK], "arp_tha_mask") self.assertEqual(self.target._body[OFPFlowMatch.IPV6_SRC], "ipv6_src") self.assertEqual(self.target._body[OFPFlowMatch.IPV6_SRC_MASK], "ipv6_src_mask") self.assertEqual(self.target._body[OFPFlowMatch.IPV6_DST], "ipv6_dst") self.assertEqual(self.target._body[OFPFlowMatch.IPV6_DST_MASK], "ipv6_dst_mask") self.assertEqual(self.target._body[OFPFlowMatch.IPV6_FLABEL], 50) self.assertEqual(self.target._body[OFPFlowMatch.IPV6_FLABEL_MASK], 51) self.assertEqual(self.target._body[OFPFlowMatch.ICMPV6_TYPE], 52) self.assertEqual(self.target._body[OFPFlowMatch.ICMPV6_CODE], 53) self.assertEqual(self.target._body[OFPFlowMatch.IPV6_ND_TARGET], "ipv6_nd_target") self.assertEqual(self.target._body[OFPFlowMatch.IPV6_ND_SLL], "ipv6_nd_sll") self.assertEqual(self.target._body[OFPFlowMatch.IPV6_ND_TLL], "ipv6_nd_tll") self.assertEqual(self.target._body[OFPFlowMatch.MPLS_LABEL], 54) self.assertEqual(self.target._body[OFPFlowMatch.MPLS_TC], 55) self.assertEqual(self.target._body[OFPFlowMatch.MPLS_BOS], 56) self.assertEqual(self.target._body[OFPFlowMatch.PBB_ISID], 57) self.assertEqual(self.target._body[OFPFlowMatch.PBB_ISID_MASK], 58) self.assertEqual(self.target._body[OFPFlowMatch.TUNNEL_ID], 59) self.assertEqual(self.target._body[OFPFlowMatch.TUNNEL_ID_MASK], 60) self.assertEqual(self.target._body[OFPFlowMatch.IPV6_EXTHDR], 61) self.assertEqual(self.target._body[OFPFlowMatch.IPV6_EXTHDR_MASK], 62) def test_constractor_None(self): self.target = OFPFlowMatch("OFPFlowMatch", "ofp_in_node", "ofp_in_port") self.assertEqual(self.target._body, {"in_node": "ofp_in_node", "type": "OFPFlowMatch", "in_port": "ofp_in_port"}) # IN_PHY_PORT def test_in_phy_port(self): self.assertEqual(self.target.in_phy_port, "in_phy_port") def test_in_phy_port_None(self): del self.target._body[OFPFlowMatch.IN_PHY_PORT] self.assertEqual(self.target.in_phy_port, None) # METADATA def test_metadata(self): self.assertEqual(self.target.metadata, 11) def test_metadata_None(self): del self.target._body[OFPFlowMatch.METADATA] self.assertEqual(self.target.metadata, None) # METADATA_MASK def test_metadata_mask(self): self.assertEqual(self.target.metadata_mask, 12) def test_metadata_mask_None(self): del self.target._body[OFPFlowMatch.METADATA_MASK] self.assertEqual(self.target.metadata_mask, None) # ETH_SRC def test_eth_src(self): self.assertEqual(self.target.eth_src, "eth_src") def test_eth_src_None(self): del self.target._body[OFPFlowMatch.ETH_SRC] self.assertEqual(self.target.eth_src, None) # ETH_SRC_MASK def test_eth_src_mask(self): self.assertEqual(self.target.eth_src_mask, "eth_src_mask") def test_eth_src_mask_None(self): del self.target._body[OFPFlowMatch.ETH_SRC_MASK] self.assertEqual(self.target.eth_src_mask, None) # ETH_DST def test_dl_dst(self): self.assertEqual(self.target.eth_dst, "eth_dst") def test_dl_dst_None(self): del self.target._body[OFPFlowMatch.ETH_DST] self.assertEqual(self.target.eth_dst, None) # ETH_DST_MASK def test_eth_dst_mask(self): self.assertEqual(self.target.eth_dst_mask, "eth_dst_mask") def test_eth_dst_mask_None(self): del self.target._body[OFPFlowMatch.ETH_DST_MASK] self.assertEqual(self.target.eth_dst_mask, None) # VLAN_VID def test_vlan_vid(self): self.assertEqual(self.target.vlan_vid, 13) def test_vlan_vid_None(self): del self.target._body[OFPFlowMatch.VLAN_VID] self.assertEqual(self.target.vlan_vid, None) # VLAN_VID_MASK def test_vlan_vid_mask(self): self.assertEqual(self.target.vlan_vid_mask, 14) def test_vlan_vid_mask_None(self): del self.target._body[OFPFlowMatch.VLAN_VID_MASK] self.assertEqual(self.target.vlan_vid_mask, None) # VLAN_PCP def test_vlan_pcp(self): self.assertEqual(self.target.vlan_pcp, 15) def test_vlan_pcp_None(self): del self.target._body[OFPFlowMatch.VLAN_PCP] self.assertEqual(self.target.vlan_pcp, None) # ETH_TYPE def test_eth_type(self): self.assertEqual(self.target.eth_type, 16) def test_eth_type_None(self): del self.target._body[OFPFlowMatch.ETH_TYPE] self.assertEqual(self.target.eth_type, None) # IP_DSCP def test_ip_dscp(self): self.assertEqual(self.target.ip_dscp, 21) def test_ip_dscp_None(self): del self.target._body[OFPFlowMatch.IP_DSCP] self.assertEqual(self.target.ip_dscp, None) # IP_ECN def test_ip_ecn(self): self.assertEqual(self.target.ip_ecn, 22) def test_ip_ecn_None(self): del self.target._body[OFPFlowMatch.IP_ECN] self.assertEqual(self.target.ip_ecn, None) # IP_PROTO def test_ip_proto(self): self.assertEqual(self.target.ip_proto, 23) def test_ip_proto_None(self): del self.target._body[OFPFlowMatch.IP_PROTO] self.assertEqual(self.target.ip_proto, None) # IPV4_SRC def test_ipv4_src(self): self.assertEqual(self.target.ipv4_src, "ipv4_src") def test_ipv4_src_None(self): del self.target._body[OFPFlowMatch.IPV4_SRC] self.assertEqual(self.target.ipv4_src, None) # IPV4_SRC_MASK def test_ipv4_src_mask(self): self.assertEqual(self.target.ipv4_src_mask, "ipv4_src_mask") def test_ipv4_src_mask_None(self): del self.target._body[OFPFlowMatch.IPV4_SRC_MASK] self.assertEqual(self.target.ipv4_src_mask, None) # IPV4_DST def test_ipv4_dst_mask(self): self.assertEqual(self.target.ipv4_dst, "ipv4_dst") def test_ipv4_dst_None(self): del self.target._body[OFPFlowMatch.IPV4_DST] self.assertEqual(self.target.ipv4_dst, None) # IPV4_DST_MASK def test_ipv4_dst_mask_mask(self): self.assertEqual(self.target.ipv4_dst_mask, "ipv4_dst_mask") def test_ipv4_dst_mask_None(self): del self.target._body[OFPFlowMatch.IPV4_DST_MASK] self.assertEqual(self.target.ipv4_dst_mask, None) # TCP_SRC def test_tcp_src(self): self.assertEqual(self.target.tcp_src, 31) def test_tcp_src_None(self): del self.target._body[OFPFlowMatch.TCP_SRC] self.assertEqual(self.target.tcp_src, None) # TCP_DST def test_tcp_dst(self): self.assertEqual(self.target.tcp_dst, 32) def test_tcp_dst_None(self): del self.target._body[OFPFlowMatch.TCP_DST] self.assertEqual(self.target.tcp_dst, None) # UDP_SRC def test_udp_src(self): self.assertEqual(self.target.udp_src, 33) def test_udp_src_None(self): del self.target._body[OFPFlowMatch.UDP_SRC] self.assertEqual(self.target.udp_src, None) # UDP_DST def test_udp_dst(self): self.assertEqual(self.target.udp_dst, 34) def test_udp_dst_None(self): del self.target._body[OFPFlowMatch.UDP_DST] self.assertEqual(self.target.udp_dst, None) # SCTP_SRC def test_sctp_src(self): self.assertEqual(self.target.sctp_src, 35) def test_sctp_src_None(self): del self.target._body[OFPFlowMatch.SCTP_SRC] self.assertEqual(self.target.sctp_src, None) # SCTP_DST def test_sctp_dst(self): self.assertEqual(self.target.sctp_dst, 36) def test_sctp_dst_None(self): del self.target._body[OFPFlowMatch.SCTP_DST] self.assertEqual(self.target.sctp_dst, None) # ICMPV4_TYPE def test_icmpv4_type(self): self.assertEqual(self.target.icmpv4_type, 37) def test_icmpv4_type_None(self): del self.target._body[OFPFlowMatch.ICMPV4_TYPE] self.assertEqual(self.target.icmpv4_type, None) # ICMPV4_CODE def test_icmpv4_code(self): self.assertEqual(self.target.icmpv4_code, 38) def test_icmpv4_code_None(self): del self.target._body[OFPFlowMatch.ICMPV4_CODE] self.assertEqual(self.target.icmpv4_code, None) # ARP_OP def test_arp_op(self): self.assertEqual(self.target.arp_op, 40) def test_arp_op_None(self): del self.target._body[OFPFlowMatch.ARP_OP] self.assertEqual(self.target.arp_op, None) # ARP_SPA def test_arp_spa(self): self.assertEqual(self.target.arp_spa, "arp_spa") def test_arp_spa_None(self): del self.target._body[OFPFlowMatch.ARP_SPA] self.assertEqual(self.target.arp_spa, None) # ARP_SPA_MASK def test_arp_spa_mask(self): self.assertEqual(self.target.arp_spa_mask, "arp_spa_mask") def test_arp_spa_mask_None(self): del self.target._body[OFPFlowMatch.ARP_SPA_MASK] self.assertEqual(self.target.arp_spa_mask, None) # ARP_TPA def test_arp_tpa(self): self.assertEqual(self.target.arp_tpa, "arp_tpa") def test_arp_tpa_None(self): del self.target._body[OFPFlowMatch.ARP_TPA] self.assertEqual(self.target.arp_tpa, None) # ARP_TPA_MASK def test_arp_tpa_mask(self): self.assertEqual(self.target.arp_tpa_mask, "arp_tpa_mask") def test_arp_tpa_mask_None(self): del self.target._body[OFPFlowMatch.ARP_TPA_MASK] self.assertEqual(self.target.arp_tpa_mask, None) # ARP_SHA def test_arp_sha(self): self.assertEqual(self.target.arp_sha, "arp_sha") def test_arp_sha_None(self): del self.target._body[OFPFlowMatch.ARP_SHA] self.assertEqual(self.target.arp_sha, None) # ARP_SHA_MASK def test_arp_sha_mask(self): self.assertEqual(self.target.arp_sha_mask, "arp_sha_mask") def test_arp_sha_mask_None(self): del self.target._body[OFPFlowMatch.ARP_SHA_MASK] self.assertEqual(self.target.arp_sha_mask, None) # ARP_THA def test_arp_tha(self): self.assertEqual(self.target.arp_tha, "arp_tha") def test_arp_tha_None(self): del self.target._body[OFPFlowMatch.ARP_THA] self.assertEqual(self.target.arp_tha, None) # ARP_THA_MASK def test_arp_tha_mask(self): self.assertEqual(self.target.arp_tha_mask, "arp_tha_mask") def test_arp_tha_mask_None(self): del self.target._body[OFPFlowMatch.ARP_THA_MASK] self.assertEqual(self.target.arp_tha_mask, None) # IPV6_SRC def test_ipv6_src(self): self.assertEqual(self.target.ipv6_src, "ipv6_src") def test_ipv6_src_None(self): del self.target._body[OFPFlowMatch.IPV6_SRC] self.assertEqual(self.target.ipv6_src, None) # IPV6_SRC_MASK def test_ipv6_src_mask(self): self.assertEqual(self.target.ipv6_src_mask, "ipv6_src_mask") def test_ipv6_src_mask_None(self): del self.target._body[OFPFlowMatch.IPV6_SRC_MASK] self.assertEqual(self.target.ipv6_src_mask, None) # IPV6_DST def test_ipv6_dst(self): self.assertEqual(self.target.ipv6_dst, "ipv6_dst") def test_ipv6_dst_None(self): del self.target._body[OFPFlowMatch.IPV6_DST] self.assertEqual(self.target.ipv6_dst, None) # IPV6_DST_MASK def test_ipv6_dst_mask(self): self.assertEqual(self.target.ipv6_dst_mask, "ipv6_dst_mask") def test_ipv6_dst_mask_None(self): del self.target._body[OFPFlowMatch.IPV6_DST_MASK] self.assertEqual(self.target.ipv6_dst_mask, None) # IPV6_FLABEL def test_ipv6_flabel(self): self.assertEqual(self.target.ipv6_flabel, 50) def test_ipv6_flabel_None(self): del self.target._body[OFPFlowMatch.IPV6_FLABEL] self.assertEqual(self.target.ipv6_flabel, None) # IPV6_FLABEL_MASK def test_ipv6_flabel_mask(self): self.assertEqual(self.target.ipv6_flabel_mask, 51) def test_ipv6_flabel_mask_None(self): del self.target._body[OFPFlowMatch.IPV6_FLABEL_MASK] self.assertEqual(self.target.ipv6_flabel_mask, None) # ICMPV6_TYPE def test_icmpv6_type(self): self.assertEqual(self.target.icmpv6_type, 52) def test_icmpv6_type_None(self): del self.target._body[OFPFlowMatch.ICMPV6_TYPE] self.assertEqual(self.target.icmpv6_type, None) # ICMPV6_CODE def test_icmpv6_code(self): self.assertEqual(self.target.icmpv6_code, 53) def test_icmpv6_code_None(self): del self.target._body[OFPFlowMatch.ICMPV6_CODE] self.assertEqual(self.target.icmpv6_code, None) # IPV6_ND_TARGET def test_ipv6_nd_target(self): self.assertEqual(self.target.ipv6_nd_target, "ipv6_nd_target") def test_ipv6_nd_target_None(self): del self.target._body[OFPFlowMatch.IPV6_ND_TARGET] self.assertEqual(self.target.ipv6_nd_target, None) # IPV6_ND_SLL def test_ipv6_nd_sll(self): self.assertEqual(self.target.ipv6_nd_sll, "ipv6_nd_sll") def test_ipv6_nd_sll_None(self): del self.target._body[OFPFlowMatch.IPV6_ND_SLL] self.assertEqual(self.target.ipv6_nd_sll, None) # IPV6_ND_TLL def test_ipv6_nd_tll(self): self.assertEqual(self.target.ipv6_nd_tll, "ipv6_nd_tll") def test_ipv6_nd_tll_None(self): del self.target._body[OFPFlowMatch.IPV6_ND_TLL] self.assertEqual(self.target.ipv6_nd_tll, None) # MPLS_LABEL def test_mpls_label(self): self.assertEqual(self.target.mpls_label, 54) def test_mpls_label_None(self): del self.target._body[OFPFlowMatch.MPLS_LABEL] self.assertEqual(self.target.mpls_label, None) # MPLS_TC def test_mpls_tc(self): self.assertEqual(self.target.mpls_tc, 55) def test_mpls_tc_None(self): del self.target._body[OFPFlowMatch.MPLS_TC] self.assertEqual(self.target.mpls_tc, None) # MPLS_BOS def test_mpls_bos(self): self.assertEqual(self.target.mpls_bos, 56) def test_mpls_bos_None(self): del self.target._body[OFPFlowMatch.MPLS_BOS] self.assertEqual(self.target.mpls_bos, None) # PBB_ISID def test_pbb_isid(self): self.assertEqual(self.target.pbb_isid, 57) def test_pbb_isid_None(self): del self.target._body[OFPFlowMatch.PBB_ISID] self.assertEqual(self.target.pbb_isid, None) # PBB_ISID_MASK def test_pbb_isid_mask(self): self.assertEqual(self.target.pbb_isid_mask, 58) def test_pbb_isid_mask_None(self): del self.target._body[OFPFlowMatch.PBB_ISID_MASK] self.assertEqual(self.target.pbb_isid_mask, None) # TUNNEL_ID def test_tunnel_id(self): self.assertEqual(self.target.tunnel_id, 59) def test_tunnel_id_None(self): del self.target._body[OFPFlowMatch.TUNNEL_ID] self.assertEqual(self.target.tunnel_id, None) # TUNNEL_ID_MASK def test_tunnel_id_mask(self): self.assertEqual(self.target.tunnel_id_mask, 60) def test_tunnel_id_mask_None(self): del self.target._body[OFPFlowMatch.TUNNEL_ID_MASK] self.assertEqual(self.target.tunnel_id_mask, None) # IPV6_EXTHDR def test_ipv6_exthdr(self): self.assertEqual(self.target.ipv6_exthdr, 61) def test_ipv6_exthdr_None(self): del self.target._body[OFPFlowMatch.IPV6_EXTHDR] self.assertEqual(self.target.ipv6_exthdr, None) # IPV6_EXTHDR_MASK def test_ipv6_exthdr_mask(self): self.assertEqual(self.target.ipv6_exthdr_mask, 62) def test_ipv6_exthdr_mask_None(self): del self.target._body[OFPFlowMatch.IPV6_EXTHDR_MASK] self.assertEqual(self.target.ipv6_exthdr_mask, None) def test_create_from_packed_Not_None(self): self.value = { OFPFlowMatch.TYPE: "OFPFlowMatch", OFPFlowMatch.IN_NODE: "ofp_in_node", OFPFlowMatch.IN_PORT: "ofp_in_port", OFPFlowMatch.IN_PHY_PORT: "in_phy_port", OFPFlowMatch.METADATA: 11, OFPFlowMatch.METADATA_MASK: 12, OFPFlowMatch.ETH_SRC: "eth_src", OFPFlowMatch.ETH_SRC_MASK: "eth_src_mask", OFPFlowMatch.ETH_DST: "eth_dst", OFPFlowMatch.ETH_DST_MASK: "eth_dst_mask", OFPFlowMatch.VLAN_VID: 13, OFPFlowMatch.VLAN_VID_MASK: 14, OFPFlowMatch.VLAN_PCP: 15, OFPFlowMatch.ETH_TYPE: 16, OFPFlowMatch.IP_DSCP: 21, OFPFlowMatch.IP_ECN: 22, OFPFlowMatch.IP_PROTO: 23, OFPFlowMatch.IPV4_SRC: "ipv4_src", OFPFlowMatch.IPV4_SRC_MASK: "ipv4_src_mask", OFPFlowMatch.IPV4_DST: "ipv4_dst", OFPFlowMatch.IPV4_DST_MASK: "ipv4_dst_mask", OFPFlowMatch.TCP_SRC: 31, OFPFlowMatch.TCP_DST: 32, OFPFlowMatch.UDP_SRC: 33, OFPFlowMatch.UDP_DST: 34, OFPFlowMatch.SCTP_SRC: 35, OFPFlowMatch.SCTP_DST: 36, OFPFlowMatch.ICMPV4_TYPE: 37, OFPFlowMatch.ICMPV4_CODE: 38, OFPFlowMatch.ARP_OP: 40, OFPFlowMatch.ARP_SPA: "arp_spa", OFPFlowMatch.ARP_SPA_MASK: "arp_spa_mask", OFPFlowMatch.ARP_TPA: "arp_tpa", OFPFlowMatch.ARP_TPA_MASK: "arp_tpa_mask", OFPFlowMatch.ARP_SHA: "arp_sha", OFPFlowMatch.ARP_SHA_MASK: "arp_sha_mask", OFPFlowMatch.ARP_THA: "arp_tha", OFPFlowMatch.ARP_THA_MASK: "arp_tha_mask", OFPFlowMatch.IPV6_SRC: "ipv6_src", OFPFlowMatch.IPV6_SRC_MASK: "ipv6_src_mask", OFPFlowMatch.IPV6_DST: "ipv6_dst", OFPFlowMatch.IPV6_DST_MASK: "ipv6_dst_mask", OFPFlowMatch.IPV6_FLABEL: 50, OFPFlowMatch.IPV6_FLABEL_MASK: 51, OFPFlowMatch.ICMPV6_TYPE: 52, OFPFlowMatch.ICMPV6_CODE: 53, OFPFlowMatch.IPV6_ND_TARGET: "ipv6_nd_target", OFPFlowMatch.IPV6_ND_SLL: "ipv6_nd_sll", OFPFlowMatch.IPV6_ND_TLL: "ipv6_nd_tll", OFPFlowMatch.MPLS_LABEL: 54, OFPFlowMatch.MPLS_TC: 55, OFPFlowMatch.MPLS_BOS: 56, OFPFlowMatch.PBB_ISID: 57, OFPFlowMatch.PBB_ISID_MASK: 58, OFPFlowMatch.TUNNEL_ID: 59, OFPFlowMatch.TUNNEL_ID_MASK: 60, OFPFlowMatch.IPV6_EXTHDR: 61, OFPFlowMatch.IPV6_EXTHDR_MASK: 62 } self.result = OFPFlowMatch.create_from_packed(self.value) self.assertEqual(self.result._body[OFPFlowMatch.TYPE], "OFPFlowMatch") self.assertEqual(self.result._body[OFPFlowMatch.IN_NODE], "ofp_in_node") self.assertEqual(self.result._body[OFPFlowMatch.IN_PORT], "ofp_in_port") self.assertEqual(self.result._body[OFPFlowMatch.IN_PHY_PORT], "in_phy_port") self.assertEqual(self.result._body[OFPFlowMatch.METADATA], 11) self.assertEqual(self.result._body[OFPFlowMatch.METADATA_MASK], 12) self.assertEqual(self.result._body[OFPFlowMatch.ETH_SRC], "eth_src") self.assertEqual(self.result._body[OFPFlowMatch.ETH_SRC_MASK], "eth_src_mask") self.assertEqual(self.result._body[OFPFlowMatch.ETH_DST], "eth_dst") self.assertEqual(self.result._body[OFPFlowMatch.ETH_DST_MASK], "eth_dst_mask") self.assertEqual(self.result._body[OFPFlowMatch.VLAN_VID], 13) self.assertEqual(self.result._body[OFPFlowMatch.VLAN_VID_MASK], 14) self.assertEqual(self.result._body[OFPFlowMatch.VLAN_PCP], 15) self.assertEqual(self.result._body[OFPFlowMatch.ETH_TYPE], 16) self.assertEqual(self.result._body[OFPFlowMatch.IP_DSCP], 21) self.assertEqual(self.result._body[OFPFlowMatch.IP_ECN], 22) self.assertEqual(self.result._body[OFPFlowMatch.IP_PROTO], 23) self.assertEqual(self.result._body[OFPFlowMatch.IPV4_SRC], "ipv4_src") self.assertEqual(self.result._body[OFPFlowMatch.IPV4_SRC_MASK], "ipv4_src_mask") self.assertEqual(self.result._body[OFPFlowMatch.IPV4_DST], "ipv4_dst") self.assertEqual(self.result._body[OFPFlowMatch.IPV4_DST_MASK], "ipv4_dst_mask") self.assertEqual(self.result._body[OFPFlowMatch.TCP_SRC], 31) self.assertEqual(self.result._body[OFPFlowMatch.TCP_DST], 32) self.assertEqual(self.result._body[OFPFlowMatch.UDP_SRC], 33) self.assertEqual(self.result._body[OFPFlowMatch.UDP_DST], 34) self.assertEqual(self.result._body[OFPFlowMatch.SCTP_SRC], 35) self.assertEqual(self.result._body[OFPFlowMatch.SCTP_DST], 36) self.assertEqual(self.result._body[OFPFlowMatch.ICMPV4_TYPE], 37) self.assertEqual(self.result._body[OFPFlowMatch.ICMPV4_CODE], 38) self.assertEqual(self.result._body[OFPFlowMatch.ARP_OP], 40) self.assertEqual(self.result._body[OFPFlowMatch.ARP_SPA], "arp_spa") self.assertEqual(self.result._body[OFPFlowMatch.ARP_SPA_MASK], "arp_spa_mask") self.assertEqual(self.result._body[OFPFlowMatch.ARP_TPA], "arp_tpa") self.assertEqual(self.result._body[OFPFlowMatch.ARP_TPA_MASK], "arp_tpa_mask") self.assertEqual(self.result._body[OFPFlowMatch.ARP_SHA], "arp_sha") self.assertEqual(self.result._body[OFPFlowMatch.ARP_SHA_MASK], "arp_sha_mask") self.assertEqual(self.result._body[OFPFlowMatch.ARP_THA], "arp_tha") self.assertEqual(self.result._body[OFPFlowMatch.ARP_THA_MASK], "arp_tha_mask") self.assertEqual(self.result._body[OFPFlowMatch.IPV6_SRC], "ipv6_src") self.assertEqual(self.result._body[OFPFlowMatch.IPV6_SRC_MASK], "ipv6_src_mask") self.assertEqual(self.result._body[OFPFlowMatch.IPV6_DST], "ipv6_dst") self.assertEqual(self.result._body[OFPFlowMatch.IPV6_DST_MASK], "ipv6_dst_mask") self.assertEqual(self.result._body[OFPFlowMatch.IPV6_FLABEL], 50) self.assertEqual(self.result._body[OFPFlowMatch.IPV6_FLABEL_MASK], 51) self.assertEqual(self.result._body[OFPFlowMatch.ICMPV6_TYPE], 52) self.assertEqual(self.result._body[OFPFlowMatch.ICMPV6_CODE], 53) self.assertEqual(self.result._body[OFPFlowMatch.IPV6_ND_TARGET], "ipv6_nd_target") self.assertEqual(self.result._body[OFPFlowMatch.IPV6_ND_SLL], "ipv6_nd_sll") self.assertEqual(self.result._body[OFPFlowMatch.IPV6_ND_TLL], "ipv6_nd_tll") self.assertEqual(self.result._body[OFPFlowMatch.MPLS_LABEL], 54) self.assertEqual(self.result._body[OFPFlowMatch.MPLS_TC], 55) self.assertEqual(self.result._body[OFPFlowMatch.MPLS_BOS], 56) self.assertEqual(self.result._body[OFPFlowMatch.PBB_ISID], 57) self.assertEqual(self.result._body[OFPFlowMatch.PBB_ISID_MASK], 58) self.assertEqual(self.result._body[OFPFlowMatch.TUNNEL_ID], 59) self.assertEqual(self.result._body[OFPFlowMatch.TUNNEL_ID_MASK], 60) self.assertEqual(self.result._body[OFPFlowMatch.IPV6_EXTHDR], 61) self.assertEqual(self.result._body[OFPFlowMatch.IPV6_EXTHDR_MASK], 62) def test_create_from_packed_None(self): self.value = {OFPFlowMatch.TYPE: "OFPFlowMatch", OFPFlowMatch.IN_NODE: "0456", OFPFlowMatch.IN_PORT: "0789"} self.result = OFPFlowMatch.create_from_packed(self.value) self.assertEqual(self.result._body, {OFPFlowMatch.TYPE: "OFPFlowMatch", OFPFlowMatch.IN_NODE: "0456", OFPFlowMatch.IN_PORT: "0789"}) def test_packed_object(self): self.result = self.target.packed_object() self.assertEqual(self.result[OFPFlowMatch.TYPE], "OFPFlowMatch") self.assertEqual(self.result[OFPFlowMatch.IN_NODE], "ofp_in_node") self.assertEqual(self.result[OFPFlowMatch.IN_PORT], "ofp_in_port") self.assertEqual(self.result[OFPFlowMatch.IN_PHY_PORT], "in_phy_port") self.assertEqual(self.result[OFPFlowMatch.METADATA], 11) self.assertEqual(self.result[OFPFlowMatch.METADATA_MASK], 12) self.assertEqual(self.result[OFPFlowMatch.ETH_SRC], "eth_src") self.assertEqual(self.result[OFPFlowMatch.ETH_SRC_MASK], "eth_src_mask") self.assertEqual(self.result[OFPFlowMatch.ETH_DST], "eth_dst") self.assertEqual(self.result[OFPFlowMatch.ETH_DST_MASK], "eth_dst_mask") self.assertEqual(self.result[OFPFlowMatch.VLAN_VID], 13) self.assertEqual(self.result[OFPFlowMatch.VLAN_VID_MASK], 14) self.assertEqual(self.result[OFPFlowMatch.VLAN_PCP], 15) self.assertEqual(self.result[OFPFlowMatch.ETH_TYPE], 16) self.assertEqual(self.result[OFPFlowMatch.IP_DSCP], 21) self.assertEqual(self.result[OFPFlowMatch.IP_ECN], 22) self.assertEqual(self.result[OFPFlowMatch.IP_PROTO], 23) self.assertEqual(self.result[OFPFlowMatch.IPV4_SRC], "ipv4_src") self.assertEqual(self.result[OFPFlowMatch.IPV4_SRC_MASK], "ipv4_src_mask") self.assertEqual(self.result[OFPFlowMatch.IPV4_DST], "ipv4_dst") self.assertEqual(self.result[OFPFlowMatch.IPV4_DST_MASK], "ipv4_dst_mask") self.assertEqual(self.result[OFPFlowMatch.TCP_SRC], 31) self.assertEqual(self.result[OFPFlowMatch.TCP_DST], 32) self.assertEqual(self.result[OFPFlowMatch.UDP_SRC], 33) self.assertEqual(self.result[OFPFlowMatch.UDP_DST], 34) self.assertEqual(self.result[OFPFlowMatch.SCTP_SRC], 35) self.assertEqual(self.result[OFPFlowMatch.SCTP_DST], 36) self.assertEqual(self.result[OFPFlowMatch.ICMPV4_TYPE], 37) self.assertEqual(self.result[OFPFlowMatch.ICMPV4_CODE], 38) self.assertEqual(self.result[OFPFlowMatch.ARP_OP], 40) self.assertEqual(self.result[OFPFlowMatch.ARP_SPA], "arp_spa") self.assertEqual(self.result[OFPFlowMatch.ARP_SPA_MASK], "arp_spa_mask") self.assertEqual(self.result[OFPFlowMatch.ARP_TPA], "arp_tpa") self.assertEqual(self.result[OFPFlowMatch.ARP_TPA_MASK], "arp_tpa_mask") self.assertEqual(self.result[OFPFlowMatch.ARP_SHA], "arp_sha") self.assertEqual(self.result[OFPFlowMatch.ARP_SHA_MASK], "arp_sha_mask") self.assertEqual(self.result[OFPFlowMatch.ARP_THA], "arp_tha") self.assertEqual(self.result[OFPFlowMatch.ARP_THA_MASK], "arp_tha_mask") self.assertEqual(self.result[OFPFlowMatch.IPV6_SRC], "ipv6_src") self.assertEqual(self.result[OFPFlowMatch.IPV6_SRC_MASK], "ipv6_src_mask") self.assertEqual(self.result[OFPFlowMatch.IPV6_DST], "ipv6_dst") self.assertEqual(self.result[OFPFlowMatch.IPV6_DST_MASK], "ipv6_dst_mask") self.assertEqual(self.result[OFPFlowMatch.IPV6_FLABEL], 50) self.assertEqual(self.result[OFPFlowMatch.IPV6_FLABEL_MASK], 51) self.assertEqual(self.result[OFPFlowMatch.ICMPV6_TYPE], 52) self.assertEqual(self.result[OFPFlowMatch.ICMPV6_CODE], 53) self.assertEqual(self.result[OFPFlowMatch.IPV6_ND_TARGET], "ipv6_nd_target") self.assertEqual(self.result[OFPFlowMatch.IPV6_ND_SLL], "ipv6_nd_sll") self.assertEqual(self.result[OFPFlowMatch.IPV6_ND_TLL], "ipv6_nd_tll") self.assertEqual(self.result[OFPFlowMatch.MPLS_LABEL], 54) self.assertEqual(self.result[OFPFlowMatch.MPLS_TC], 55) self.assertEqual(self.result[OFPFlowMatch.MPLS_BOS], 56) self.assertEqual(self.result[OFPFlowMatch.PBB_ISID], 57) self.assertEqual(self.result[OFPFlowMatch.PBB_ISID_MASK], 58) self.assertEqual(self.result[OFPFlowMatch.TUNNEL_ID], 59) self.assertEqual(self.result[OFPFlowMatch.TUNNEL_ID_MASK], 60) self.assertEqual(self.result[OFPFlowMatch.IPV6_EXTHDR], 61) self.assertEqual(self.result[OFPFlowMatch.IPV6_EXTHDR_MASK], 62) if __name__ == '__main__': unittest.main()

42.938725

90

0.680062

4,573

35,038

4.894817

0.038924

0.191655

0.242763

0.188751

0.888983

0.836356

0.711982

0.590198

0.312232

0.146533

0

0.024454

0.214539

35,038

815

91

42.991411

0.788888

0.045037

0

0.013468

0

0.05315

0

0.481481

1

0.19697

false

0

0.003367

0

0.20202

0

null

0

1

0

null

0

1

0

6

5d0f2bb186ffc7b9a92b6551237bf7d09fab5ce6

103

py

Python

blaze/compute/air/__init__.py

talumbau/blaze

66c9e61476f11d53f7b734664214537182397739

[ "BSD-3-Clause" ]

1

2018-01-24T08:54:04.000Z

blaze/compute/air/__init__.py

talumbau/blaze

66c9e61476f11d53f7b734664214537182397739

[ "BSD-3-Clause" ]

null

blaze/compute/air/__init__.py

talumbau/blaze

66c9e61476f11d53f7b734664214537182397739

[ "BSD-3-Clause" ]

null

from __future__ import absolute_import, division, print_function from .entrypoint import compile, run

25.75

64

0.84466

13

103

6.230769

0.769231

0

0.116505

103

3

65

34.333333

0.89011

0

1

0

true

0

1

0

1

0.5

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

5d1bd9fe989869ca0c0660a2f03f1cd70651cc37

248

py

Python

core/src/zeit/content/text/browser/tests/test_doctest.py

rickdg/vivi

16134ac954bf8425646d4ad47bdd1f372e089355

[ "BSD-3-Clause" ]

5

2019-05-16T09:51:29.000Z

2021-05-31T09:30:03.000Z

core/src/zeit/content/text/browser/tests/test_doctest.py

rickdg/vivi

16134ac954bf8425646d4ad47bdd1f372e089355

[ "BSD-3-Clause" ]

107

2019-05-24T12:19:02.000Z

2022-03-23T15:05:56.000Z

core/src/zeit/content/text/browser/tests/test_doctest.py

rickdg/vivi

16134ac954bf8425646d4ad47bdd1f372e089355

[ "BSD-3-Clause" ]

3

2020-08-14T11:01:17.000Z

2022-01-08T17:32:19.000Z

import zeit.cms.testing import zeit.content.text.testing def test_suite(): return zeit.cms.testing.FunctionalDocFileSuite( 'README.txt', package='zeit.content.text.browser', layer=zeit.content.text.testing.WSGI_LAYER)

24.8

51

0.717742

31

248

5.677419

0.548387

0.1875

0.255682

0.25

0

0.165323

248

9

52

27.555556

0.850242

0

0.141129

0.100806

0

1

0.142857

true

0

0.285714

0.142857

0.571429

0

null

0

1

0

1

0

null

0

1

0

1

0

6

5d543c23a7e260984b7ac5f5a9a21d89b2db2216

6,236

py

Python

simulation/behaviors.py

tmsquill/grove

afe2d6ff35a2db7d8cb1e3ff7e5a0e3b29f369f7

[ "MIT" ]

1

2016-11-23T16:59:01.000Z

simulation/behaviors.py

zivia/grove

afe2d6ff35a2db7d8cb1e3ff7e5a0e3b29f369f7

[ "MIT" ]

1

2020-04-26T19:28:42.000Z

simulation/behaviors.py

zivia/grove

afe2d6ff35a2db7d8cb1e3ff7e5a0e3b29f369f7

[ "MIT" ]

null

import entity from utils import Point, rand def move_north(agent=None, entities=None, environment=None): """ Behavior that causes an agent to move north one unit. :param agent: The agent to perform the behavior. :param entities: A list of entities in the simulation. :param environment: The environment containing the agent. :return: The updated agent. """ if environment.body.contains_point(Point(agent.body.top_left.position[0], agent.body.top_left.position[1] + 1)): for item in agent.inventory: item.body.top_left.position[1] += 1 item.body.bottom_right.position[1] += 1 agent.body.top_left.position[1] += 1 agent.body.bottom_right.position[1] += 1 agent.time += 1 return agent def move_east(agent=None, entities=None, environment=None): """ Behavior that causes an agent to move east one unit. :param agent: The agent to perform the behavior. :param entities: A list of entities in the simulation. :param environment: The environment containing the agent. :return: The updated agent. """ if environment.body.contains_point(Point(agent.body.bottom_right.position[0] + 1, agent.body.bottom_right.position[1])): for item in agent.inventory: item.body.top_left.position[0] += 1 item.body.bottom_right.position[0] += 1 agent.body.top_left.position[0] += 1 agent.body.bottom_right.position[0] += 1 agent.time += 1 return agent def move_south(agent=None, entities=None, environment=None): """ Behavior that causes an agent to move south one unit. :param agent: The agent to perform the behavior. :param entities: A list of entities in the simulation. :param environment: The environment containing the agent. :return: The updated agent. """ if environment.body.contains_point(Point(agent.body.bottom_right.position[0], agent.body.bottom_right.position[1] - 1)): for item in agent.inventory: item.body.top_left.position[1] -= 1 item.body.bottom_right.position[1] -= 1 agent.body.top_left.position[1] -= 1 agent.body.bottom_right.position[1] -= 1 agent.time += 1 return agent def move_west(agent=None, entities=None, environment=None): """ Behavior that causes an agent to move west one unit. :param agent: The agent to perform the behavior. :param entities: A list of entities in the simulation. :param environment: The environment containing the agent. :return: The updated agent. """ if environment.body.contains_point(Point(agent.body.top_left.position[0] - 1, agent.body.top_left.position[1])): for item in agent.inventory: item.body.top_left.position[0] -= 1 item.body.bottom_right.position[0] -= 1 agent.body.top_left.position[0] -= 1 agent.body.bottom_right.position[0] -= 1 agent.time += 1 return agent def pickup_food(agent=None, entities=None, environment=None): """ Behavior that causes an agent to pickup food. :param agent: The agent to perform the behavior. :param entities: A list of entities in the simulation. :param environment: The environment containing the agent. :return: The updated agent. """ foods = filter(lambda x: isinstance(x, entity.Food), entities) if not agent.holding_food: for food in foods: if agent.body.contains_rectangle(food.body) and food.interactable: agent.inventory.append(food) food.interactable = False agent.holding_food = True break agent.time += 1 return agent def drop_food(agent=None, entities=None, environment=None): """ Behavior that causes an agent to drop food. :param agent: The agent to perform the behavior. :param entities: A list of entities in the simulation. :param environment: The environment containing the agent. :return: The updated agent. """ nest = filter(lambda x: isinstance(x, entity.Nest), entities)[0] if agent.holding_food: for item in agent.inventory: agent.inventory.remove(item) if nest.body.contains_rectangle(agent.body): nest.food_count += 1 else: item.interactable = True agent.holding_food = False agent.time += 1 return agent def random_walk(agent=None, entities=None, environment=None): """ Behavior that causes an agent to walk in a random direction for one time step. :param agent: The agent to perform the behavior. :param entities: A list of entities in the simulation. :param environment: The environment containing the agent. :return: The updated agent. """ random_direction = rand.randint(0, 3) if random_direction == 0: return move_north(agent, entities, environment) elif random_direction == 1: return move_east(agent, entities, environment) elif random_direction == 2: return move_south(agent, entities, environment) elif random_direction == 3: return move_west(agent, entities, environment) return agent def return_home(agent=None, entities=None, environment=None): """ Behavior (naive) that causes an agent to return to the nest. :param agent: The agent to perform the behavior. :param entities: A list of entities in the simulation. :param environment: The environment containing the agent. :return: The updated agent. """ nest = filter(lambda x: isinstance(x, entity.Nest), entities)[0] agent.time += int(agent.body.top_left.distance_to(nest.body.top_left)) agent.body.top_left.position[1] = (nest.body.top_left.position[1] + nest.body.bottom_right.position[1]) / 2 agent.body.bottom_right.position[0] = (nest.body.top_left.position[0] + nest.body.bottom_right.position[0]) / 2 agent.body.bottom_right.position[1] = (nest.body.top_left.position[1] + nest.body.bottom_right.position[1]) / 2 agent.body.top_left.position[0] = (nest.body.top_left.position[0] + nest.body.bottom_right.position[0]) / 2 return agent

28.605505

124

0.669179

854

6,236

4.807963

0.094848

0.050414

0.05358

0.083293

0.830005

0.819776

0.760594

0.736727

0.728933

0.726498

0

0.015618

0.229955

6,236

217

125

28.737327

0.839442

0.314785

0

0.2625

0

1

0.1

false

0

0.025

0

0.275

0

null

0

1

0

null

0

6

538c39a701a8f10c699e5e979d44542d758f1f16

73

py

Python

src/cranet/metrics/__init__.py

shizuku/cranet

4c86ad16029ed76a74e22b5e5e4c21267d6b9996

[ "MIT" ]

4

2021-10-31T13:31:13.000Z

2021-12-11T08:45:36.000Z

src/cranet/metrics/__init__.py

Azathoth1729/cranet

4c86ad16029ed76a74e22b5e5e4c21267d6b9996

[ "MIT" ]

null

src/cranet/metrics/__init__.py

Azathoth1729/cranet

4c86ad16029ed76a74e22b5e5e4c21267d6b9996

[ "MIT" ]

2

2021-10-31T13:34:28.000Z

2021-11-21T09:11:46.000Z

from .classification import * from .common import * from .image import *

18.25

29

0.753425

9

73

6.111111

0.555556

0.363636

0

0.164384

73

3

30

24.333333

0.901639

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

53993a1bb828fc6a921c953e078dfc8940f4a60d

184

py

Python

umetnine/account/admin.py

jaanos/OPB-umetnine

f1fedd62e750317548510c412793d80c60b9e392

[ "MIT" ]

null

umetnine/account/admin.py

jaanos/OPB-umetnine

f1fedd62e750317548510c412793d80c60b9e392

[ "MIT" ]

null

umetnine/account/admin.py

jaanos/OPB-umetnine

f1fedd62e750317548510c412793d80c60b9e392

[ "MIT" ]

null

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

20.444444

32

0.793478

25

184

5.84

0.48

0.136986

0.219178

0

0.125

184

8

33

23

0.906832

0.418478

0

1

0

true

0

0.666667

0

0.666667

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

53aa7ff3e92639633021ae82763cdc6ae5481066

134

py

Python

coffin/conf/urls.py

spothero/coffin

9ea6a9173cbfed592c5b4776c489dba8d9280d52

[ "BSD-3-Clause" ]

null

coffin/conf/urls.py

spothero/coffin

9ea6a9173cbfed592c5b4776c489dba8d9280d52

[ "BSD-3-Clause" ]

null

coffin/conf/urls.py

spothero/coffin

9ea6a9173cbfed592c5b4776c489dba8d9280d52

[ "BSD-3-Clause" ]

null

from django.conf.urls import * handler404 = 'coffin.views.defaults.page_not_found' handler500 = 'coffin.views.defaults.server_error'

26.8

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0.80597

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false

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1

0

1

0

1

null

0

1

0

6

53d41097a07696ea306c9d943df32e690a1a8ed1

72

py

Python

pycocotools/loss.py

mintanwei/IPCLs-Net

04937df683216a090c0749cc90ab7e517dbab0fd

[ "MIT" ]

null

pycocotools/loss.py

mintanwei/IPCLs-Net

04937df683216a090c0749cc90ab7e517dbab0fd

[ "MIT" ]

null

pycocotools/loss.py

mintanwei/IPCLs-Net

04937df683216a090c0749cc90ab7e517dbab0fd

[ "MIT" ]

null

from torch import nn import torch from torch.nn import functional as F

14.4

36

0.805556

13

72

4.461538

0.538462

0.310345

0

0.180556

72

4

37

18

0.983051

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1

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true

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1

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1

0

null

0

1

0

1

0

1

0

6

53d915ed8952fb531f3395dc69a8724c25682022

47

py

Python

urls/__init__.py

AileenLumina/dwarf

5fc3b1b532290a474d17f84694dae1d0d53be7b4

[ "MIT" ]

null

urls/__init__.py

AileenLumina/dwarf

5fc3b1b532290a474d17f84694dae1d0d53be7b4

[ "MIT" ]

null

urls/__init__.py

AileenLumina/dwarf

5fc3b1b532290a474d17f84694dae1d0d53be7b4

[ "MIT" ]

null

import importlib # importlib.import_module()

9.4

27

0.787234

5

47

7.2

0.6

0

0.12766

47

4

28

11.75

0.878049

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true

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null

0

1

0

1

0

1

0

6

53e82b8a1f5a8b1b5a831baf0275f818ebf4aae4

1,776

py

Python

4 - camera & ysort/code/settings.py

aldrinbrillante/Zelda

83d74beca1e1d352a17fc4218cf1e2226d5788c3

[ "CC0-1.0" ]

null

4 - camera & ysort/code/settings.py

aldrinbrillante/Zelda

83d74beca1e1d352a17fc4218cf1e2226d5788c3

[ "CC0-1.0" ]

null

4 - camera & ysort/code/settings.py

aldrinbrillante/Zelda

83d74beca1e1d352a17fc4218cf1e2226d5788c3

[ "CC0-1.0" ]

null

# game setup WIDTH = 1280 HEIGTH = 720 FPS = 60 TILESIZE = 64 WORLD_MAP = [ ['x','x','x','x','x','x','x','x','x','x','x','x','x','x','x','x','x','x','x','x'], ['x',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ','x'], ['x',' ','p',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ','x'], ['x',' ',' ','x',' ',' ',' ',' ',' ','x','x','x','x','x',' ',' ',' ',' ',' ','x'], ['x',' ',' ','x',' ',' ',' ',' ',' ',' ',' ',' ',' ','x',' ',' ',' ',' ',' ','x'], ['x',' ',' ','x',' ',' ',' ',' ',' ',' ',' ',' ',' ','x',' ',' ',' ',' ',' ','x'], ['x',' ',' ','x',' ',' ',' ',' ',' ',' ',' ',' ',' ','x',' ',' ',' ',' ',' ','x'], ['x',' ',' ','x',' ',' ',' ',' ',' ',' ',' ',' ',' ','x',' ',' ',' ',' ',' ','x'], ['x',' ',' ','x',' ',' ',' ',' ',' ',' ',' ',' ',' ','x',' ',' ',' ',' ',' ','x'], ['x',' ',' ','x',' ',' ',' ',' ',' ',' ',' ',' ',' ','x',' ',' ',' ',' ',' ','x'], ['x',' ',' ','x',' ',' ',' ',' ',' ',' ',' ',' ',' ','x',' ',' ',' ',' ',' ','x'], ['x',' ',' ','x',' ',' ',' ',' ',' ',' ',' ',' ',' ','x','x','x',' ',' ',' ','x'], ['x',' ',' ',' ',' ',' ',' ','x',' ','x',' ',' ',' ',' ',' ',' ',' ',' ',' ','x'], ['x',' ',' ',' ',' ',' ','x','x','x','x','x',' ',' ',' ',' ',' ',' ',' ',' ','x'], ['x',' ',' ',' ',' ',' ',' ','x','x','x',' ',' ',' ',' ',' ',' ',' ',' ',' ','x'], ['x',' ',' ',' ',' ',' ',' ',' ','x',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ','x'], ['x',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ','x'], ['x',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ','x'], ['x',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ','x'], ['x','x','x','x','x','x','x','x','x','x','x','x','x','x','x','x','x','x','x','x'], ]

63.428571

83

0.091779

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1,776

1.306452

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0.685185

0

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1,776

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0

1

null

1

0

1

0

1

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null

0

6

53ef02c674653b5749ae7c259d02425420920e68

29,616

py

Python

code/python/FactSetOwnership/v1/fds/sdk/FactSetOwnership/api/fund_holdings_api.py

factset/enterprise-sdk

3fd4d1360756c515c9737a0c9a992c7451d7de7e

[ "Apache-2.0" ]

6

2022-02-07T16:34:18.000Z

2022-03-30T08:04:57.000Z

code/python/FactSetOwnership/v1/fds/sdk/FactSetOwnership/api/fund_holdings_api.py

factset/enterprise-sdk

3fd4d1360756c515c9737a0c9a992c7451d7de7e

[ "Apache-2.0" ]

2

2022-02-07T05:25:57.000Z

2022-03-07T14:18:04.000Z

code/python/FactSetOwnership/v1/fds/sdk/FactSetOwnership/api/fund_holdings_api.py

factset/enterprise-sdk

3fd4d1360756c515c9737a0c9a992c7451d7de7e

[ "Apache-2.0" ]

null

""" FactSet Ownership API FactSet’s Fund Ownership API gives access to both **Holdings** and **Holders** data. Factset's Holdings endpoints gives access to all the underlying securities and their position detils held within a given fund. Fund Types supported include Open-End Mutual Funds, Closed-end Mutual Funds, and Exchange Traded Funds. Security Holders information retrieves all \"holder types\" and their positions across institutions, funds, insiders, and stakeholders.The FactSet Ownership and Mutual Funds database collects global equity ownership data for approximately 50,000 institutions, 60,000 unique Mutual Fund portfolios, and 400,000 Insider/Stake holders from around 110 countries. For more details review our [Data Collection](https://my.apps.factset.com/oa/cms/oaAttachment/87e162be-f2d1-4f40-a85b-bfb1b020d270/20079) methodology. # noqa: E501 The version of the OpenAPI document: 1.1.0 Contact: api@factset.com Generated by: https://openapi-generator.tech """ import re # noqa: F401 import sys # noqa: F401 from multiprocessing.pool import ApplyResult import typing from fds.sdk.FactSetOwnership.api_client import ApiClient, Endpoint as _Endpoint from fds.sdk.FactSetOwnership.model_utils import ( # noqa: F401 check_allowed_values, check_validations, date, datetime, file_type, none_type, validate_and_convert_types ) from fds.sdk.FactSetOwnership.exceptions import ApiException from fds.sdk.FactSetOwnership.model.error_response import ErrorResponse from fds.sdk.FactSetOwnership.model.fund_holdings_request import FundHoldingsRequest from fds.sdk.FactSetOwnership.model.fund_holdings_response import FundHoldingsResponse class FundHoldingsApi(object): """NOTE: This class is auto generated by OpenAPI Generator Ref: https://openapi-generator.tech Do not edit the class manually. """ def __init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client = api_client self.get_ownership_holdings_endpoint = _Endpoint( settings={ 'response_type': ( { 200: (FundHoldingsResponse,), 400: (ErrorResponse,), 401: (ErrorResponse,), 403: (ErrorResponse,), 415: (ErrorResponse,), 500: (ErrorResponse,), }, None ), 'auth': [ 'FactSetApiKey', 'FactSetOAuth2' ], 'endpoint_path': '/factset-ownership/v1/fund-holdings', 'operation_id': 'get_ownership_holdings', 'http_method': 'GET', 'servers': None, }, params_map={ 'all': [ 'ids', 'date', 'topn', 'asset_type', 'currency', ], 'required': [ 'ids', ], 'nullable': [ ], 'enum': [ 'asset_type', ], 'validation': [ 'ids', ] }, root_map={ 'validations': { ('ids',): { 'max_items': 10, 'min_items': 1, }, }, 'allowed_values': { ('asset_type',): { "ALL": "ALL", "EQ": "EQ", "FI": "FI" }, }, 'openapi_types': { 'ids': ([str],), 'date': (str,), 'topn': (str,), 'asset_type': (str,), 'currency': (str,), }, 'attribute_map': { 'ids': 'ids', 'date': 'date', 'topn': 'topn', 'asset_type': 'assetType', 'currency': 'currency', }, 'location_map': { 'ids': 'query', 'date': 'query', 'topn': 'query', 'asset_type': 'query', 'currency': 'query', }, 'collection_format_map': { 'ids': 'csv', } }, headers_map={ 'accept': [ 'application/json' ], 'content_type': [], }, api_client=api_client ) self.post_ownership_holdings_endpoint = _Endpoint( settings={ 'response_type': ( { 200: (FundHoldingsResponse,), 400: (ErrorResponse,), 401: (ErrorResponse,), 403: (ErrorResponse,), 415: (ErrorResponse,), 500: (ErrorResponse,), }, None ), 'auth': [ 'FactSetApiKey', 'FactSetOAuth2' ], 'endpoint_path': '/factset-ownership/v1/fund-holdings', 'operation_id': 'post_ownership_holdings', 'http_method': 'POST', 'servers': None, }, params_map={ 'all': [ 'fund_holdings_request', ], 'required': [ 'fund_holdings_request', ], 'nullable': [ ], 'enum': [ ], 'validation': [ ] }, root_map={ 'validations': { }, 'allowed_values': { }, 'openapi_types': { 'fund_holdings_request': (FundHoldingsRequest,), }, 'attribute_map': { }, 'location_map': { 'fund_holdings_request': 'body', }, 'collection_format_map': { } }, headers_map={ 'accept': [ 'application/json' ], 'content_type': [ 'application/json' ] }, api_client=api_client ) @staticmethod def apply_kwargs_defaults(kwargs, return_http_data_only, async_req): kwargs["async_req"] = async_req kwargs["_return_http_data_only"] = return_http_data_only kwargs["_preload_content"] = kwargs.get("_preload_content", True) kwargs["_request_timeout"] = kwargs.get("_request_timeout", None) kwargs["_check_input_type"] = kwargs.get("_check_input_type", True) kwargs["_check_return_type"] = kwargs.get("_check_return_type", True) kwargs["_spec_property_naming"] = kwargs.get("_spec_property_naming", False) kwargs["_content_type"] = kwargs.get("_content_type") kwargs["_host_index"] = kwargs.get("_host_index") def get_ownership_holdings( self, ids, **kwargs ) -> FundHoldingsResponse: """Get underlying holdings information for a requested fund identifer. # noqa: E501 Gets holdings information for list of fund identifiers. The service allows you to filter by the TopN holdings and Asset Type. # noqa: E501 This method makes a synchronous HTTP request. Returns the http data only Args: ids ([str]): List of requested fund identifiers. ***ids limit** = 10 per request* Keyword Args: date (str): Date of holdings expressed in YYYY-MM-DD format. The fund-holdings endpoint will default to latest month-end close.. [optional] topn (str): Limits number of holdings or holders displayed by the top *n* securities based on positions Market Value. Default is ALL, otherwise use number to limit number.. [optional] if omitted the server will use the default value of "ALL" asset_type (str): Filter holdings by the following major asset classes - * **EQ** = Equity * **FI** = Fixed Income * **ALL** = ALL . [optional] if omitted the server will use the default value of "EQ" currency (str): Currency code for adjusting prices. Default is Local. For a list of currency ISO codes, visit [Online Assistant Page 1470](https://oa.apps.factset.com/pages/1470).. [optional] _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _content_type (str/None): force body content-type. Default is None and content-type will be predicted by allowed content-types and body. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. Returns: FundHoldingsResponse Response Object """ self.apply_kwargs_defaults(kwargs=kwargs, return_http_data_only=True, async_req=False) kwargs['ids'] = \ ids return self.get_ownership_holdings_endpoint.call_with_http_info(**kwargs) def get_ownership_holdings_with_http_info( self, ids, **kwargs ) -> typing.Tuple[FundHoldingsResponse, int, typing.MutableMapping]: """Get underlying holdings information for a requested fund identifer. # noqa: E501 Gets holdings information for list of fund identifiers. The service allows you to filter by the TopN holdings and Asset Type. # noqa: E501 This method makes a synchronous HTTP request. Returns http data, http status and headers Args: ids ([str]): List of requested fund identifiers. ***ids limit** = 10 per request* Keyword Args: date (str): Date of holdings expressed in YYYY-MM-DD format. The fund-holdings endpoint will default to latest month-end close.. [optional] topn (str): Limits number of holdings or holders displayed by the top *n* securities based on positions Market Value. Default is ALL, otherwise use number to limit number.. [optional] if omitted the server will use the default value of "ALL" asset_type (str): Filter holdings by the following major asset classes - * **EQ** = Equity * **FI** = Fixed Income * **ALL** = ALL . [optional] if omitted the server will use the default value of "EQ" currency (str): Currency code for adjusting prices. Default is Local. For a list of currency ISO codes, visit [Online Assistant Page 1470](https://oa.apps.factset.com/pages/1470).. [optional] _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _content_type (str/None): force body content-type. Default is None and content-type will be predicted by allowed content-types and body. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. Returns: FundHoldingsResponse Response Object int Http Status Code dict Dictionary of the response headers """ self.apply_kwargs_defaults(kwargs=kwargs, return_http_data_only=False, async_req=False) kwargs['ids'] = \ ids return self.get_ownership_holdings_endpoint.call_with_http_info(**kwargs) def get_ownership_holdings_async( self, ids, **kwargs ) -> "ApplyResult[FundHoldingsResponse]": """Get underlying holdings information for a requested fund identifer. # noqa: E501 Gets holdings information for list of fund identifiers. The service allows you to filter by the TopN holdings and Asset Type. # noqa: E501 This method makes a asynchronous HTTP request. Returns the http data, wrapped in ApplyResult Args: ids ([str]): List of requested fund identifiers. ***ids limit** = 10 per request* Keyword Args: date (str): Date of holdings expressed in YYYY-MM-DD format. The fund-holdings endpoint will default to latest month-end close.. [optional] topn (str): Limits number of holdings or holders displayed by the top *n* securities based on positions Market Value. Default is ALL, otherwise use number to limit number.. [optional] if omitted the server will use the default value of "ALL" asset_type (str): Filter holdings by the following major asset classes - * **EQ** = Equity * **FI** = Fixed Income * **ALL** = ALL . [optional] if omitted the server will use the default value of "EQ" currency (str): Currency code for adjusting prices. Default is Local. For a list of currency ISO codes, visit [Online Assistant Page 1470](https://oa.apps.factset.com/pages/1470).. [optional] _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _content_type (str/None): force body content-type. Default is None and content-type will be predicted by allowed content-types and body. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. Returns: ApplyResult[FundHoldingsResponse] """ self.apply_kwargs_defaults(kwargs=kwargs, return_http_data_only=True, async_req=True) kwargs['ids'] = \ ids return self.get_ownership_holdings_endpoint.call_with_http_info(**kwargs) def get_ownership_holdings_with_http_info_async( self, ids, **kwargs ) -> "ApplyResult[typing.Tuple[FundHoldingsResponse, int, typing.MutableMapping]]": """Get underlying holdings information for a requested fund identifer. # noqa: E501 Gets holdings information for list of fund identifiers. The service allows you to filter by the TopN holdings and Asset Type. # noqa: E501 This method makes a asynchronous HTTP request. Returns http data, http status and headers, wrapped in ApplyResult Args: ids ([str]): List of requested fund identifiers. ***ids limit** = 10 per request* Keyword Args: date (str): Date of holdings expressed in YYYY-MM-DD format. The fund-holdings endpoint will default to latest month-end close.. [optional] topn (str): Limits number of holdings or holders displayed by the top *n* securities based on positions Market Value. Default is ALL, otherwise use number to limit number.. [optional] if omitted the server will use the default value of "ALL" asset_type (str): Filter holdings by the following major asset classes - * **EQ** = Equity * **FI** = Fixed Income * **ALL** = ALL . [optional] if omitted the server will use the default value of "EQ" currency (str): Currency code for adjusting prices. Default is Local. For a list of currency ISO codes, visit [Online Assistant Page 1470](https://oa.apps.factset.com/pages/1470).. [optional] _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _content_type (str/None): force body content-type. Default is None and content-type will be predicted by allowed content-types and body. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. Returns: ApplyResult[(FundHoldingsResponse, int, typing.Dict)] """ self.apply_kwargs_defaults(kwargs=kwargs, return_http_data_only=False, async_req=True) kwargs['ids'] = \ ids return self.get_ownership_holdings_endpoint.call_with_http_info(**kwargs) def post_ownership_holdings( self, fund_holdings_request, **kwargs ) -> FundHoldingsResponse: """Get holdings for a list of funds. # noqa: E501 Gets Holding information for a long list of Fund objects. # noqa: E501 This method makes a synchronous HTTP request. Returns the http data only Args: fund_holdings_request (FundHoldingsRequest): Requesting Underlying Holdings for a list of Fund Identifiers. Keyword Args: _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _content_type (str/None): force body content-type. Default is None and content-type will be predicted by allowed content-types and body. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. Returns: FundHoldingsResponse Response Object """ self.apply_kwargs_defaults(kwargs=kwargs, return_http_data_only=True, async_req=False) kwargs['fund_holdings_request'] = \ fund_holdings_request return self.post_ownership_holdings_endpoint.call_with_http_info(**kwargs) def post_ownership_holdings_with_http_info( self, fund_holdings_request, **kwargs ) -> typing.Tuple[FundHoldingsResponse, int, typing.MutableMapping]: """Get holdings for a list of funds. # noqa: E501 Gets Holding information for a long list of Fund objects. # noqa: E501 This method makes a synchronous HTTP request. Returns http data, http status and headers Args: fund_holdings_request (FundHoldingsRequest): Requesting Underlying Holdings for a list of Fund Identifiers. Keyword Args: _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _content_type (str/None): force body content-type. Default is None and content-type will be predicted by allowed content-types and body. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. Returns: FundHoldingsResponse Response Object int Http Status Code dict Dictionary of the response headers """ self.apply_kwargs_defaults(kwargs=kwargs, return_http_data_only=False, async_req=False) kwargs['fund_holdings_request'] = \ fund_holdings_request return self.post_ownership_holdings_endpoint.call_with_http_info(**kwargs) def post_ownership_holdings_async( self, fund_holdings_request, **kwargs ) -> "ApplyResult[FundHoldingsResponse]": """Get holdings for a list of funds. # noqa: E501 Gets Holding information for a long list of Fund objects. # noqa: E501 This method makes a asynchronous HTTP request. Returns the http data, wrapped in ApplyResult Args: fund_holdings_request (FundHoldingsRequest): Requesting Underlying Holdings for a list of Fund Identifiers. Keyword Args: _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _content_type (str/None): force body content-type. Default is None and content-type will be predicted by allowed content-types and body. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. Returns: ApplyResult[FundHoldingsResponse] """ self.apply_kwargs_defaults(kwargs=kwargs, return_http_data_only=True, async_req=True) kwargs['fund_holdings_request'] = \ fund_holdings_request return self.post_ownership_holdings_endpoint.call_with_http_info(**kwargs) def post_ownership_holdings_with_http_info_async( self, fund_holdings_request, **kwargs ) -> "ApplyResult[typing.Tuple[FundHoldingsResponse, int, typing.MutableMapping]]": """Get holdings for a list of funds. # noqa: E501 Gets Holding information for a long list of Fund objects. # noqa: E501 This method makes a asynchronous HTTP request. Returns http data, http status and headers, wrapped in ApplyResult Args: fund_holdings_request (FundHoldingsRequest): Requesting Underlying Holdings for a list of Fund Identifiers. Keyword Args: _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _content_type (str/None): force body content-type. Default is None and content-type will be predicted by allowed content-types and body. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. Returns: ApplyResult[(FundHoldingsResponse, int, typing.Dict)] """ self.apply_kwargs_defaults(kwargs=kwargs, return_http_data_only=False, async_req=True) kwargs['fund_holdings_request'] = \ fund_holdings_request return self.post_ownership_holdings_endpoint.call_with_http_info(**kwargs)

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rcnn/lib/python3.6/site-packages/tensorflow/graph_util/__init__.py

dreamingweaver/making_passportImage

68f23411780ff82abe934dfae5fc04acb80f2c49

[ "MIT" ]

1

2019-01-12T13:17:32.000Z

rcnn/lib/python3.6/site-packages/tensorflow/graph_util/__init__.py

dreamingweaver/making_passportImage

68f23411780ff82abe934dfae5fc04acb80f2c49

[ "MIT" ]

null

rcnn/lib/python3.6/site-packages/tensorflow/graph_util/__init__.py

dreamingweaver/making_passportImage

68f23411780ff82abe934dfae5fc04acb80f2c49

[ "MIT" ]

null

# This file is MACHINE GENERATED! Do not edit. # Generated by: tensorflow/python/tools/api/generator/create_python_api.py script. """Helpers to manipulate a tensor graph in python. """ from __future__ import print_function from tensorflow.python.framework.graph_util import convert_variables_to_constants from tensorflow.python.framework.graph_util import extract_sub_graph from tensorflow.python.framework.graph_util import must_run_on_cpu from tensorflow.python.framework.graph_util import remove_training_nodes from tensorflow.python.framework.graph_util import tensor_shape_from_node_def_name del print_function

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54fea51f6d1cb4c372a2272b257c00584e640961

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py

Python

adventures/bucketlist/admin.py

lakivisi-zz/adventures

f094ac3fa1d5c85d65650c9cdc2ff2f60f9189a5

[ "MIT" ]

null

adventures/bucketlist/admin.py

lakivisi-zz/adventures

f094ac3fa1d5c85d65650c9cdc2ff2f60f9189a5

[ "MIT" ]

null

adventures/bucketlist/admin.py

lakivisi-zz/adventures

f094ac3fa1d5c85d65650c9cdc2ff2f60f9189a5

[ "MIT" ]

1

2021-01-14T21:27:32.000Z

from django.contrib import admin from bucketlist.models import Bucketlist, Item admin.site.register((Bucketlist, Item))

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073308290954ca159881f4e1d0c513bbe9ea4754

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py

Python

unittest/functions.test.py

mokpolar/devops-eng-training

2cf327a37e4575991f2846f42cad03f3cbab770d

[ "MIT" ]

null

unittest/functions.test.py

mokpolar/devops-eng-training

2cf327a37e4575991f2846f42cad03f3cbab770d

[ "MIT" ]

1

2021-05-17T07:43:26.000Z

unittest/functions.test.py

mokpolar/devops-eng-training

2cf327a37e4575991f2846f42cad03f3cbab770d

[ "MIT" ]

9

2021-05-06T06:00:18.000Z

2021-05-15T08:30:47.000Z

# TODO(everyone): 더하기, 빼기, 곱하기, 나누기 함수 테스트 케이스 작성 import pytest

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py

Python

soccer/exceptions.py

elimt/soccer-cli

400665812bbe2d29b8ead7627713f38fa688bd75

[ "MIT" ]

1,185

2015-08-19T15:52:35.000Z

2022-03-27T19:28:36.000Z

soccer/exceptions.py

elimt/soccer-cli

400665812bbe2d29b8ead7627713f38fa688bd75

[ "MIT" ]

129

2015-09-01T18:32:21.000Z

2022-02-13T06:35:38.000Z

soccer/exceptions.py

elimt/soccer-cli

400665812bbe2d29b8ead7627713f38fa688bd75

[ "MIT" ]

312

2015-09-01T17:58:15.000Z

2022-03-27T19:29:55.000Z

class IncorrectParametersException(Exception): pass class APIErrorException(Exception): pass

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py

Python

papi_sdk/tests/mocked_data/overview.py

stanislav-losev/papi-sdk-python

4a296745d626ef13c6d1170e9d3569cb1c37eb3c

[ "MIT" ]

1

2022-02-01T08:53:24.000Z

papi_sdk/tests/mocked_data/overview.py

stanislav-losev/papi-sdk-python

4a296745d626ef13c6d1170e9d3569cb1c37eb3c

[ "MIT" ]

2

2021-01-18T07:57:29.000Z

2021-06-23T11:04:14.000Z

papi_sdk/tests/mocked_data/overview.py

stanislav-losev/papi-sdk-python

4a296745d626ef13c6d1170e9d3569cb1c37eb3c

[ "MIT" ]

3

2020-12-30T13:09:45.000Z

2020-12-30T13:42:33.000Z

overview_response = { "debug": None, "error": None, "status": "ok", "data": [ { "endpoint": "api/b2b/v3/general/contract/data/info/", "is_active": True, "is_debug_mode": False, "is_limited": True, "requests_number": 100, "seconds_number": 86400, }, { "endpoint": "api/b2b/v3/general/document/closing_documents/download/", "is_active": True, "is_debug_mode": False, "is_limited": True, "requests_number": 100, "seconds_number": 86400, }, { "endpoint": "api/b2b/v3/general/document/closing_documents/info/", "is_active": True, "is_debug_mode": False, "is_limited": True, "requests_number": 100, "seconds_number": 86400, }, { "endpoint": "api/b2b/v3/general/financial/info/", "is_active": True, "is_debug_mode": False, "is_limited": True, "requests_number": 100, "seconds_number": 86400, }, { "endpoint": "api/b2b/v3/hotel/info/", "is_active": True, "is_debug_mode": False, "is_limited": True, "requests_number": 30, "seconds_number": 60, }, { "endpoint": "api/b2b/v3/hotel/info/dump/", "is_active": True, "is_debug_mode": False, "is_limited": True, "requests_number": 100, "seconds_number": 86400, }, { "endpoint": "api/b2b/v3/hotel/info/incremental_dump/", "is_active": True, "is_debug_mode": False, "is_limited": True, "requests_number": 100, "seconds_number": 86400, }, { "endpoint": "api/b2b/v3/hotel/matching/dump/", "is_active": True, "is_debug_mode": False, "is_limited": True, "requests_number": 100, "seconds_number": 86400, }, { "endpoint": "api/b2b/v3/hotel/order/booking/finish/", "is_active": True, "is_debug_mode": False, "is_limited": True, "requests_number": 30, "seconds_number": 60, }, { "endpoint": "api/b2b/v3/hotel/order/booking/finish/status/", "is_active": True, "is_debug_mode": False, "is_limited": True, "requests_number": 30, "seconds_number": 60, }, { "endpoint": "api/b2b/v3/hotel/order/booking/form/", "is_active": True, "is_debug_mode": False, "is_limited": True, "requests_number": 30, "seconds_number": 60, }, { "endpoint": "api/b2b/v3/hotel/order/cancel/", "is_active": True, "is_debug_mode": False, "is_limited": True, "requests_number": 30, "seconds_number": 60, }, { "endpoint": "api/b2b/v3/hotel/order/document/info_invoice/download/", "is_active": True, "is_debug_mode": False, "is_limited": True, "requests_number": 30, "seconds_number": 60, }, { "endpoint": "api/b2b/v3/hotel/order/document/single_act/download/", "is_active": True, "is_debug_mode": False, "is_limited": True, "requests_number": 30, "seconds_number": 60, }, { "endpoint": "api/b2b/v3/hotel/order/document/voucher/download/", "is_active": True, "is_debug_mode": False, "is_limited": True, "requests_number": 30, "seconds_number": 60, }, { "endpoint": "api/b2b/v3/hotel/order/info/", "is_active": True, "is_debug_mode": False, "is_limited": True, "requests_number": 30, "seconds_number": 60, }, { "endpoint": "api/b2b/v3/hotel/reviews/dump/", "is_active": True, "is_debug_mode": False, "is_limited": True, "requests_number": 100, "seconds_number": 86400, }, { "endpoint": "api/b2b/v3/hotel/static/", "is_active": True, "is_debug_mode": False, "is_limited": True, "requests_number": 100, "seconds_number": 86400, }, { "endpoint": "api/b2b/v3/search/serp/hotels/", "is_active": True, "is_debug_mode": False, "is_limited": True, "requests_number": 30, "seconds_number": 60, }, { "endpoint": "api/b2b/v3/search/hp/", "is_active": True, "is_debug_mode": False, "is_limited": True, "requests_number": 30, "seconds_number": 60, }, { "endpoint": "api/b2b/v3/search/multicomplete/", "is_active": True, "is_debug_mode": False, "is_limited": True, "requests_number": 30, "seconds_number": 60, }, { "endpoint": "api/b2b/v3/ordergroup/order/add/", "is_active": True, "is_debug_mode": False, "is_limited": True, "requests_number": 30, "seconds_number": 60, }, { "endpoint": "api/b2b/v3/ordergroup/create/", "is_active": True, "is_debug_mode": False, "is_limited": True, "requests_number": 30, "seconds_number": 60, }, { "endpoint": "api/b2b/v3/ordergroup/disband/", "is_active": True, "is_debug_mode": False, "is_limited": True, "requests_number": 30, "seconds_number": 60, }, { "endpoint": "api/b2b/v3/ordergroup/document/invoice/download/", "is_active": True, "is_debug_mode": False, "is_limited": True, "requests_number": 30, "seconds_number": 60, }, { "endpoint": "api/b2b/v3/ordergroup/info/", "is_active": True, "is_debug_mode": False, "is_limited": True, "requests_number": 30, "seconds_number": 60, }, { "endpoint": "api/b2b/v3/ordergroup/pay/overpay/", "is_active": True, "is_debug_mode": False, "is_limited": True, "requests_number": 30, "seconds_number": 60, }, { "endpoint": "api/b2b/v3/ordergroup/order/remove/", "is_active": True, "is_debug_mode": False, "is_limited": True, "requests_number": 30, "seconds_number": 60, }, { "endpoint": "api/b2b/v3/overview/", "is_active": True, "is_debug_mode": False, "is_limited": True, "requests_number": 100, "seconds_number": 86400, }, { "endpoint": "api/b2b/v3/profiles/create/", "is_active": True, "is_debug_mode": False, "is_limited": True, "requests_number": 30, "seconds_number": 60, }, { "endpoint": "api/b2b/v3/profiles/delete/", "is_active": True, "is_debug_mode": False, "is_limited": True, "requests_number": 30, "seconds_number": 60, }, { "endpoint": "api/b2b/v3/profiles/disable/", "is_active": True, "is_debug_mode": False, "is_limited": True, "requests_number": 30, "seconds_number": 60, }, { "endpoint": "api/b2b/v3/profiles/edit/", "is_active": True, "is_debug_mode": False, "is_limited": True, "requests_number": 30, "seconds_number": 60, }, { "endpoint": "api/b2b/v3/profiles/list/", "is_active": True, "is_debug_mode": False, "is_limited": True, "requests_number": 30, "seconds_number": 60, }, { "endpoint": "api/b2b/v3/profiles/restore/", "is_active": True, "is_debug_mode": False, "is_limited": True, "requests_number": 30, "seconds_number": 60, }, { "endpoint": "api/b2b/v3/search/serp/region/", "is_active": True, "is_debug_mode": False, "is_limited": True, "requests_number": 30, "seconds_number": 60, }, { "endpoint": "api/affiliate/v3/overview/", "is_active": True, "is_debug_mode": False, "is_limited": True, "requests_number": 30, "seconds_number": 60, }, ], }

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py

Python

tests/test_my_package.py

lucianojardim/pyskeleton

19b67f1e57c19bd882271be06b0605b6ba355db0

[ "MIT" ]

null

tests/test_my_package.py

lucianojardim/pyskeleton

19b67f1e57c19bd882271be06b0605b6ba355db0

[ "MIT" ]

null

tests/test_my_package.py

lucianojardim/pyskeleton

19b67f1e57c19bd882271be06b0605b6ba355db0

[ "MIT" ]

null

""" tests for my_package """ from .context import my_package def test_increment(): """ test increment """ assert my_package.increment(3) == 4 def test_decrement(): """ test decrement """ assert my_package.decrement(3) == 2 def test_double(): """ test double """ assert my_package.double(3) == 6

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1

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6

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131

py

Python

ranking/admin.py

ShitamatsugeFactory/KokoWalk_Server

9af36f750a512aa56635b04a190589d76822bc86

[ "MIT" ]

3

2017-01-01T07:34:54.000Z

2017-01-04T02:18:37.000Z

ranking/admin.py

ShitamatsugeFactory/KokoWalk_Server

9af36f750a512aa56635b04a190589d76822bc86

[ "MIT" ]

null

ranking/admin.py

ShitamatsugeFactory/KokoWalk_Server

9af36f750a512aa56635b04a190589d76822bc86

[ "MIT" ]

null

from django.contrib import admin from .models import Ranking @admin.register(Ranking) class Ranking(admin.ModelAdmin): pass

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6

ab26df89f6ef8af4754cf6ce61455085d2b634e1

32

py

Python

dlex_impl/deep_sets/src/datasets/__init__.py

dvtrung/dl-torch

b49e57d10d32bb223e2d7643f2579ccc32c63a9a

[ "MIT" ]

null

dlex_impl/deep_sets/src/datasets/__init__.py

dvtrung/dl-torch

b49e57d10d32bb223e2d7643f2579ccc32c63a9a

[ "MIT" ]

null

dlex_impl/deep_sets/src/datasets/__init__.py

dvtrung/dl-torch

b49e57d10d32bb223e2d7643f2579ccc32c63a9a

[ "MIT" ]

null

from .modelnet import ModelNet40

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db7354bd91d165215e863d1dc8f6118a73eba28f

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py

Python

tests/unit/test_modulegraph/testdata/nspkg/src/parent/namedpkg/parent.py

hawkhai/pyinstaller

016a24479b34de161792c72dde455a81ad4c78ae

[ "Apache-2.0" ]

9,267

2015-01-01T04:08:45.000Z

2022-03-31T11:42:38.000Z

tests/unit/test_modulegraph/testdata/nspkg/src/parent/namedpkg/parent.py

jeremysanders/pyinstaller

321b24f9a9a5978337735816b36ca6b4a90a2fb4

[ "Apache-2.0" ]

5,150

2015-01-01T12:09:56.000Z

2022-03-31T18:06:12.000Z

tests/unit/test_modulegraph/testdata/nspkg/src/parent/namedpkg/parent.py

jeremysanders/pyinstaller

321b24f9a9a5978337735816b36ca6b4a90a2fb4

[ "Apache-2.0" ]

2,101

2015-01-03T10:25:27.000Z

2022-03-30T11:04:42.000Z

""" parent packages """ import sys

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0

6

dbc23709c7cec8f99f3984d24ca2dcaf6002878b

191

py

Python

statsmodels/sandbox/tools/__init__.py

yarikoptic/statsmodels

f990cb1a1ef0c9883c9394444e6f9d027efabec6

[ "BSD-3-Clause" ]

34

2018-07-13T11:30:46.000Z

2022-01-05T13:48:10.000Z

venv/lib/python3.6/site-packages/statsmodels/sandbox/tools/__init__.py

HeyWeiPan/vnpy_crypto

844381797a475a01c05a4e162592a5a6e3a48032

[ "MIT" ]

7

2015-11-20T08:33:04.000Z

2020-07-24T19:34:39.000Z

venv/lib/python3.6/site-packages/statsmodels/sandbox/tools/__init__.py

HeyWeiPan/vnpy_crypto

844381797a475a01c05a4e162592a5a6e3a48032

[ "MIT" ]

28

2015-04-01T20:02:25.000Z

2021-07-03T00:09:28.000Z

'''some helper function for principal component and time series analysis Status ------ pca : tested against matlab pcasvd : tested against matlab ''' from .tools_pca import * #pca, pcasvd

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91a69bf2e3b09b5c2d2ab9c4930e98ed67a67ef3

181

py

Python

graphPlot/__init__.py

francisp336/graphPlot

2e12535aebf39b65c93a16fe8de9b657555392e8

[ "MIT" ]

null

graphPlot/__init__.py

francisp336/graphPlot

2e12535aebf39b65c93a16fe8de9b657555392e8

[ "MIT" ]

1

2020-09-29T22:01:56.000Z

graphPlot/__init__.py

francisp336/graphPlot

2e12535aebf39b65c93a16fe8de9b657555392e8

[ "MIT" ]

null

import numpy as np import matplotlib.pyplot as plt import matplotlib.patches as ptc import random as r from .graphPlot import * # def _unit_test(): # TODO define unit test

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6

91e6edfa77d27dad68c6edfea1331fb3d2c2dab7

32,178

py

Python

Tests/scripts/hook_validations/tests/integration_test.py

TBE-Comp/content

3a27b1c779dec1e4ee918ca2da77538238dd10f0

[ "MIT" ]

7

2020-09-24T22:38:01.000Z

2021-07-14T15:58:35.000Z

Tests/scripts/hook_validations/tests/integration_test.py

TBE-Comp/content

3a27b1c779dec1e4ee918ca2da77538238dd10f0

[ "MIT" ]

7

2021-03-25T23:09:39.000Z

2021-09-23T23:27:14.000Z

Tests/scripts/hook_validations/tests/integration_test.py

TBE-Comp/content

3a27b1c779dec1e4ee918ca2da77538238dd10f0

[ "MIT" ]

2

2020-12-08T17:03:33.000Z

2021-07-13T18:32:06.000Z

from Tests.scripts.hook_validations.integration import IntegrationValidator def test_removed_docker_image_on_existing_integration(): validator = IntegrationValidator("temp_file", check_git=False) validator.old_integration = { "script": { "dockerimage": "test" } } validator.current_integration = { "script": { "no": "dockerimage" } } assert validator.is_docker_image_changed(), "The script validator couldn't find the docker image as changed" def test_updated_docker_image_on_existing_integration(): validator = IntegrationValidator("temp_file", check_git=False) validator.old_integration = { "script": { "dockerimage": "test" } } validator.current_integration = { "script": { "dockerimage": "test1" } } assert validator.is_docker_image_changed(), "The script validator couldn't find the docker image as changed" def test_not_changed_docker_image_on_existing_integration(): validator = IntegrationValidator("temp_file", check_git=False) validator.old_integration = {} validator.current_integration = {} assert validator.is_docker_image_changed() is False, "The script validator couldn't find the docker "\ "image as changed" def test_added_docker_image_on_existing_integration(): validator = IntegrationValidator("temp_file", check_git=False) validator.old_integration = {} validator.current_integration = { "script": { "dockerimage": "test1" } } assert validator.is_docker_image_changed(), "The script validator couldn't find the docker image as changed" def test_added_required_field_in_integration(): validator = IntegrationValidator("temp_file", check_git=False) validator.old_integration = { "configuration": [ { "name": "test", "required": False } ] } validator.current_integration = { "configuration": [ { "name": "test", "required": True } ] } assert validator.is_added_required_fields(), "The script validator couldn't find the new required fields" def test_changed_required_field_to_not_required_in_integration(): validator = IntegrationValidator("temp_file", check_git=False) validator.old_integration = { "configuration": [ { "name": "test", "required": True } ] } validator.current_integration = { "configuration": [ { "name": "test", "required": False } ] } assert validator.is_added_required_fields() is False, "The script validator found the change to not reuquired " \ "as a one who breaks backward compatability" def test_not_changed_required_field_in_integration(): validator = IntegrationValidator("temp_file", check_git=False) validator.old_integration = { "configuration": [ { "name": "test", "required": True } ] } validator.current_integration = { "configuration": [ { "name": "test", "required": True } ] } assert validator.is_added_required_fields() is False, "The script validator found a backward compatability " \ "change although no such change was done" def test_not_changed_required_field_scenario2_in_integration(): validator = IntegrationValidator("temp_file", check_git=False) validator.old_integration = { "configuration": [ { "name": "test", "required": False } ] } validator.current_integration = { "configuration": [ { "name": "test", "required": False } ] } assert validator.is_added_required_fields() is False, "The script validator found a backward compatability " \ "change although no such change was done" def test_configuration_extraction(): validator = IntegrationValidator("temp_file", check_git=False) integration_json = { "configuration": [ { "name": "test", "required": False }, { "name": "test1", "required": True } ] } expected = { "test": False, "test1": True } assert validator._get_field_to_required_dict(integration_json) == expected, "Failed to extract configuration" def test_not_changed_context_in_integration(): validator = IntegrationValidator("temp_file", check_git=False) validator.old_integration = { "commands": [ { "name": "test", "outputs": [ { "contextPath": "test" } ] } ] } validator.current_integration = { "commands": [ { "name": "test", "outputs": [ { "contextPath": "test" } ] } ] } assert validator.is_changed_context_path() is False, "The script validator found a backward compatability " \ "change although no such change was done" def test_changed_context_in_integration(): validator = IntegrationValidator("temp_file", check_git=False) validator.old_integration = { "script": { "commands": [ { "name": "test", "outputs": [ { "contextPath": "test" } ] } ] } } validator.current_integration = { "script": { "commands": [ { "name": "test", "outputs": [ { "contextPath": "changed that" } ] } ] } } assert validator.is_changed_context_path(), "The script validator didn't find a backward compatability " \ "issue although the context path has changed" def test_added_context_in_integration(): validator = IntegrationValidator("temp_file", check_git=False) validator.old_integration = { "commands": [ { "name": "test", "outputs": [ { "contextPath": "test" } ] } ] } validator.current_integration = { "commands": [ { "name": "test", "outputs": [ { "contextPath": "test" }, { "contextPath": "changed that" } ] } ] } assert validator.is_changed_context_path() is False, "The script validator didn't find a backward compatability " \ "issue although the context path has changed" def test_added_new_command_context_path_in_integration(): validator = IntegrationValidator("temp_file", check_git=False) validator.old_integration = { "commands": [ { "name": "test", "outputs": [ { "contextPath": "test" } ] } ] } validator.current_integration = { "commands": [ { "name": "test", "outputs": [ { "contextPath": "test" } ] }, { "name": "test2", "outputs": [ { "contextPath": "new command" } ] } ] } assert validator.is_changed_context_path() is False, "The script validator found a backward compatibility " \ "issue although the context path has not changed" def test_changed_required_arg_for_command_in_integration(): validator = IntegrationValidator("temp_file", check_git=False) validator.old_integration = { "script": { "commands": [ { "name": "test", "arguments": [ { "name": "test" } ] } ] } } validator.current_integration = { "script": { "commands": [ { "name": "test", "arguments": [ { "name": "test", "required": True } ] } ] } } assert validator.is_changed_command_name_or_arg(), "The script validator did not found a backward compatibility " \ "issue although the command was added with required arg" def test_added_required_arg_for_command_in_integration(): validator = IntegrationValidator("temp_file", check_git=False) validator.old_integration = { "script": { "commands": [ { "name": "test", "arguments": [ { "name": "test" } ] } ] } } validator.current_integration = { "script": { "commands": [ { "name": "test", "arguments": [ { "name": "test", }, { "name": "test1", "required": True } ] } ] } } assert validator.is_changed_command_name_or_arg(), "The script validator did not found a backward compatibility " \ "issue although the command was added with required arg" def test_renamed_arg_in_command_in_integration(): validator = IntegrationValidator("temp_file", check_git=False) validator.old_integration = { "script": { "commands": [ { "name": "test", "arguments": [ { "name": "test" } ] } ] } } validator.current_integration = { "script": { "commands": [ { "name": "test", "arguments": [ { "name": "test1", } ] } ] } } assert validator.is_changed_command_name_or_arg(), "The script validator did not found a backward compatibility " \ "issue although the command args were renamed" def test_not_requires_arg_in_command_in_integration(): validator = IntegrationValidator("temp_file", check_git=False) validator.old_integration = { "commands": [ { "name": "test", "arguments": [ { "name": "test" } ] } ] } validator.current_integration = { "commands": [ { "name": "test", "arguments": [ { "name": "test" }, { "name": "test1", } ] } ] } assert validator.is_changed_command_name_or_arg() is False, "The script validator found a backward compatibility " \ "issue although a new not required command was added" def test_not_changed_command_in_integration(): validator = IntegrationValidator("temp_file", check_git=False) validator.old_integration = { "script": { "commands": [ { "name": "test", "arguments": [ { "name": "test" } ] } ] } } validator.current_integration = { "script": { "commands": [ { "name": "test", "arguments": [ { "name": "test" } ] } ] } } assert validator.is_changed_command_name_or_arg() is False, "The script validator found a backward compatibility " \ "issue although the commands haven't changed" def test_no_duplicate_params(): validator = IntegrationValidator("temp_file", check_git=False) validator.current_integration = { "configuration": [ { "name": "test" }, { "name": "tes1", } ] } assert validator.is_there_duplicate_params() is False, \ "The integration validator found duplicated params although there are none" def test_duplicated_params(): validator = IntegrationValidator("temp_file", check_git=False) validator.current_integration = { "configuration": [ { "name": "test" }, { "name": "test", } ] } assert validator.is_there_duplicate_params(), \ "The integration validator did not find duplicated params although there are duplicates" def test_no_duplicate_args(): validator = IntegrationValidator("temp_file", check_git=False) validator.current_integration = { "script": { "commands": [ { "name": "testing", "arguments": [ { "name": "test1" }, { "name": "test2" } ] } ] } } assert validator.is_there_duplicate_args() is False, \ "The integration validator found duplicated args although there are none" def test_duplicated_argss(): validator = IntegrationValidator("temp_file", check_git=False) validator.current_integration = { "script": { "commands": [ { "name": "testing", "arguments": [ { "name": "test" }, { "name": "test" } ] } ] } } assert validator.is_there_duplicate_args(), \ "The integration validator did not find duplicated args although there are duplicates" def test_is_changed_subtype_non_changed(): validator = IntegrationValidator("temp_file", check_git=False) validator.current_integration = { "script": { "type": "python", "subtype": "python3" } } validator.old_integration = { "script": { "type": "python", "subtype": "python3" } } assert validator.is_changed_subtype(), \ "The integration validator found changed subtype while it is valid" def test_is_changed_subtype_changed(): validator = IntegrationValidator("temp_file", check_git=False) validator.current_integration = { "script": { "type": "python", "subtype": "python3" } } validator.old_integration = { "script": { "type": "python", "subtype": "python2" } } assert validator.is_changed_subtype() is False, \ "The integration validator did not find changed subtype while it is changed" def test_valid_subtype_lies(): validator = IntegrationValidator("temp_file", check_git=False) validator.current_integration = { "script": { "type": "python", "subtype": "lies" } } validator.old_integration = None assert validator.is_valid_subtype() is False, \ "The integration validator found valid subtype while it is invalid" def test_is_default_arguments_non_default(): validator = IntegrationValidator("temp_file", check_git=False) validator.current_integration = { "script": { "commands": [ { "name": "file", "arguments": [ { "name": "file", "required": True, "default": False }, { "name": "verbose" } ] } ] } } validator.old_integration = None assert validator.is_default_arguments() is False, \ "The integration validator did not find invalid arg (needed to be default and not required)" def test_is_default_arguments_ok(): validator = IntegrationValidator("temp_file", check_git=False) validator.current_integration = { "script": { "commands": [ { "name": "email", "arguments": [ { "name": "email", "required": False, "default": True }, { "name": "verbose" } ] } ] } } validator.old_integration = None assert validator.is_default_arguments() is True, \ "The integration validator found an invalid command arg while it is valid" def test_is_outputs_for_reputations_commands_valid(): validator = IntegrationValidator("temp_file", check_git=False) validator.current_integration = { "script": { "commands": [ { "name": "panorama-commit-status", "outputs": [ { "contextPath": "Panorama.Commit.JobID", "description": "Job ID of the configuration to be committed", "type": "number" }, { "contextPath": "DBotScore.does.not.matter" } ] } ] } } validator.old_integration = None assert validator.is_outputs_for_reputations_commands_valid() is True, \ "The integration validator found invalid command outputs while it is valid" validator_email = IntegrationValidator("temp_file", check_git=False) validator_email.current_integration = { "script": { "commands": [ { "name": "email", "outputs": [ { "contextPath": "DBotScore.Indicator", "description": "The indicator that was tested.", "type": "string" }, { "contextPath": "DBotScore.Type", "description": "The indicator type.", "type": "string" }, { "contextPath": "DBotScore.Vendor", "description": "Vendor used to calculate the score.", "type": "string" }, { "contextPath": "DBotScore.Sc0re", "description": "The actual score.", "type": "int" }, { "contextPath": "Email.To", "description": "email to", "type": "string" }, ] } ] } } validator_email.old_integration = None assert validator_email.is_outputs_for_reputations_commands_valid() is False, \ "The integration validator did not find the invalid command output - DBotScore.Sc0re" validator_file = IntegrationValidator("temp_file", check_git=False) validator_file.current_integration = { "script": { "commands": [ { "name": "file", "outputs": [ { "contextPath": "DBotScore.Indicator", "description": "The indicator that was tested.", "type": "string" }, { "contextPath": "DBotScore.Type", "description": "The indicator type.", "type": "string" }, { "contextPath": "DBotScore.Vendor", "description": "Vendor used to calculate the score.", "type": "string" }, { "contextPath": "DBotScore.Score", "description": "The actual score.", "type": "int" }, { "contextPath": "File.Md5", "description": "The MD5 hash of the file.", "type": "string" }, ] } ] } } validator_file.old_integration = None assert validator_file.is_outputs_for_reputations_commands_valid() is False, \ "The integration validator did not find the invalid command output - File.Md5" validator_ip = IntegrationValidator("temp_file", check_git=False) validator_ip.current_integration = { "script": { "commands": [ { "name": "ip", "outputs": [ { "contextPath": "DBotScore.Indicator", "description": "The indicator that was tested.", "type": "string" }, { "contextPath": "DBotScore.Type", "description": "The indicator type.", "type": "string" }, { "contextPath": "DBotScore.Vendor", "description": "Vendor used to calculate the score.", "type": "string" }, { "contextPath": "DBotScore.Score", "description": "The actual score.", "type": "int" }, { "contextPath": "IP.Address", "description": "IP address", "type": "string" }, ] } ] } } validator_ip.old_integration = None assert validator_ip.is_outputs_for_reputations_commands_valid() is True, \ "The integration validator found invalid command outputs while it is valid" def test_valid_new_beta_integration(): validator = IntegrationValidator("temp_file", check_git=False) validator.old_integration = {} validator.current_integration = { "commonfields": { "id": "newIntegration" }, "name": "newIntegration", "display": "newIntegration (Beta)", "beta": True, } assert validator.is_valid_beta_integration(is_new=True) is True, \ "The Beta validator did not validate a new valid integration" def test_new_beta_integration_missing_beta_in_display(): validator = IntegrationValidator("temp_file", check_git=False) validator.old_integration = {} validator.current_integration = { "commonfields": { "id": "newIntegration" }, "name": "newIntegration", "display": "newIntegration", "beta": True, } assert validator.is_valid_beta_integration(is_new=True) is False, \ "The Beta validator approved the integration" \ "but it should have fail it for missing beta substring in 'display' field" def test_new_beta_integration_with_beta_substring_in_id(): validator = IntegrationValidator("temp_file", check_git=False) validator.old_integration = {} validator.current_integration = { "commonfields": { "id": "newIntegration beta" }, "name": "newIntegration", "display": "newIntegration (Beta)", "beta": True, } assert validator.is_valid_beta_integration(is_new=True) is False, \ "The beta validator approved the new beta integration," \ " but it should fail it because the 'id' field has a 'beta' substring in it. " \ "the validator should not allow it for new integration" def test_new_beta_integration_with_beta_substring_in_name(): validator = IntegrationValidator("temp_file", check_git=False) validator.old_integration = {} validator.current_integration = { "commonfields": { "id": "newIntegration" }, "name": "newIntegration beta", "display": "newIntegration (Beta)", "beta": True, } assert validator.is_valid_beta_integration(is_new=True) is False, \ "The beta validator approved the new beta integration," \ " but it should fail it because the 'name' field has a 'beta' substring in it. " \ "the validator should not allow it for new integration" def test_cahnged_beta_integration_with_beta_substring_in_is_and_name(): validator = IntegrationValidator("temp_file", check_git=False) validator.old_integration = { "commonfields": { "id": "newIntegration beta" }, "name": "newIntegration beta", "display": "newIntegration (Beta)", "beta": True, } validator.current_integration = { "commonfields": { "id": "newIntegration beta" }, "name": "newIntegration beta", "display": "newIntegration changed (Beta)", "beta": True, } assert validator.is_valid_beta_integration() is True, \ "The Beta validator failed the integration" \ "but it should have approved" def test_changed_beta_integration_without_beta_field(): validator = IntegrationValidator("temp_file", check_git=False) validator.old_integration = { "commonfields": { "id": "newIntegration beta" }, "name": "newIntegration beta", "display": "newIntegration (Beta)", } validator.current_integration = { "commonfields": { "id": "newIntegration beta" }, "name": "newIntegration beta", "display": "newIntegration changed (Beta)", } assert validator.is_valid_beta_integration() is False, \ "The Beta validator approved the integration" \ "but it should have fail it because it is missing 'beta' field with the value true" def test_proxy_sanity_check(): validator = IntegrationValidator("temp_file", check_git=False) validator.current_integration = { "configuration": [ { "name": "proxy", "type": 8, "display": "Use system proxy settings", "required": False } ] } assert validator.is_proxy_configured_correctly() def test_proxy_wrong_type(): validator = IntegrationValidator("temp_file", check_git=False) validator.current_integration = { "configuration": [ { "name": "proxy", "type": 9, "display": "Use system proxy settings", "required": False } ] } assert validator.is_proxy_configured_correctly() is False def test_proxy_wrong_display(): validator = IntegrationValidator("temp_file", check_git=False) validator.current_integration = { "configuration": [ { "name": "proxy", "type": 8, "display": "bla", "required": False } ] } assert validator.is_proxy_configured_correctly() is False def test_proxy_wrong_required(): validator = IntegrationValidator("temp_file", check_git=False) validator.current_integration = { "configuration": [ { "name": "proxy", "type": 8, "display": "Use system proxy settings", "required": True } ] } assert validator.is_proxy_configured_correctly() is False def test_insecure_wrong_display(): validator = IntegrationValidator("temp_file", check_git=False) validator.current_integration = { "configuration": [ { "name": "insecure", "type": 8, "display": "Use system proxy settings", "required": False } ] } assert validator.is_insecure_configured_correctly() is False def test_unsecure_wrong_display(): validator = IntegrationValidator("temp_file", check_git=False) validator.current_integration = { "configuration": [ { "name": "unsecure", "type": 8, "display": "Use system proxy settings", "required": False } ] } assert validator.is_insecure_configured_correctly() is False def test_unsecure_correct_display(): validator = IntegrationValidator("temp_file", check_git=False) validator.current_integration = { "configuration": [ { "name": "unsecure", "type": 8, "display": "Trust any certificate (not secure)", "required": False } ] } assert validator.is_insecure_configured_correctly() def test_is_valid_category(): validator_siem = IntegrationValidator("temp_file", check_git=False) validator_siem.current_integration = {"category": "Analytics & SIEMM"} assert validator_siem.is_valid_category() is False validator_endpoint = IntegrationValidator("temp_file", check_git=False) validator_endpoint.current_integration = {"category": "Endpoint"} assert validator_endpoint.is_valid_category()

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null

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null

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6

72755bc746d04b3e5d1fb511a7512ba186454e41

25,748

py

Python

sunshine_conversations_client/__init__.py

zendesk/sunshine-conversations-python

2d0240681b809ffd8ff0e9ed58b33aae844d29f6

[ "Apache-2.0" ]

4

2020-09-27T14:28:25.000Z

2022-02-02T13:51:29.000Z

sunshine_conversations_client/__init__.py

zendesk/sunshine-conversations-python

2d0240681b809ffd8ff0e9ed58b33aae844d29f6

[ "Apache-2.0" ]

3

2021-09-30T18:18:58.000Z

2021-12-04T07:55:23.000Z

sunshine_conversations_client/__init__.py

zendesk/sunshine-conversations-python

2d0240681b809ffd8ff0e9ed58b33aae844d29f6

[ "Apache-2.0" ]

5

2020-11-07T02:08:18.000Z

2021-12-07T17:10:23.000Z

# coding: utf-8 # flake8: noqa """ Sunshine Conversations API The version of the OpenAPI document: 9.4.5 Generated by: https://openapi-generator.tech """ from __future__ import absolute_import __version__ = "9.4.6" # import apis into sdk package from sunshine_conversations_client.api.activities_api import ActivitiesApi from sunshine_conversations_client.api.app_keys_api import AppKeysApi from sunshine_conversations_client.api.apps_api import AppsApi from sunshine_conversations_client.api.attachments_api import AttachmentsApi from sunshine_conversations_client.api.clients_api import ClientsApi from sunshine_conversations_client.api.conversations_api import ConversationsApi from sunshine_conversations_client.api.custom_integration_api_keys_api import CustomIntegrationApiKeysApi from sunshine_conversations_client.api.integrations_api import IntegrationsApi from sunshine_conversations_client.api.messages_api import MessagesApi from sunshine_conversations_client.api.o_auth_endpoints_api import OAuthEndpointsApi from sunshine_conversations_client.api.participants_api import ParticipantsApi from sunshine_conversations_client.api.switchboard_actions_api import SwitchboardActionsApi from sunshine_conversations_client.api.switchboard_integrations_api import SwitchboardIntegrationsApi from sunshine_conversations_client.api.switchboards_api import SwitchboardsApi from sunshine_conversations_client.api.users_api import UsersApi from sunshine_conversations_client.api.webhooks_api import WebhooksApi # import ApiClient from sunshine_conversations_client.api_client import ApiClient from sunshine_conversations_client.configuration import Configuration from sunshine_conversations_client.exceptions import OpenApiException from sunshine_conversations_client.exceptions import ApiTypeError from sunshine_conversations_client.exceptions import ApiValueError from sunshine_conversations_client.exceptions import ApiKeyError from sunshine_conversations_client.exceptions import ApiException # import models into sdk package from sunshine_conversations_client.model.accept_control_body import AcceptControlBody from sunshine_conversations_client.model.action import Action from sunshine_conversations_client.model.action_subset import ActionSubset from sunshine_conversations_client.model.activity import Activity from sunshine_conversations_client.model.activity_all_of import ActivityAllOf from sunshine_conversations_client.model.activity_post import ActivityPost from sunshine_conversations_client.model.activity_post_all_of import ActivityPostAllOf from sunshine_conversations_client.model.activity_types import ActivityTypes from sunshine_conversations_client.model.android import Android from sunshine_conversations_client.model.android_all_of import AndroidAllOf from sunshine_conversations_client.model.android_update import AndroidUpdate from sunshine_conversations_client.model.android_update_all_of import AndroidUpdateAllOf from sunshine_conversations_client.model.api_key import ApiKey from sunshine_conversations_client.model.app import App from sunshine_conversations_client.model.app_create_body import AppCreateBody from sunshine_conversations_client.model.app_key import AppKey from sunshine_conversations_client.model.app_key_create_body import AppKeyCreateBody from sunshine_conversations_client.model.app_key_list_response import AppKeyListResponse from sunshine_conversations_client.model.app_key_response import AppKeyResponse from sunshine_conversations_client.model.app_list_filter import AppListFilter from sunshine_conversations_client.model.app_list_response import AppListResponse from sunshine_conversations_client.model.app_response import AppResponse from sunshine_conversations_client.model.app_settings import AppSettings from sunshine_conversations_client.model.app_sub_schema import AppSubSchema from sunshine_conversations_client.model.app_update_body import AppUpdateBody from sunshine_conversations_client.model.apple import Apple from sunshine_conversations_client.model.apple_all_of import AppleAllOf from sunshine_conversations_client.model.apple_update import AppleUpdate from sunshine_conversations_client.model.attachment_delete_body import AttachmentDeleteBody from sunshine_conversations_client.model.attachment_media_token_body import AttachmentMediaTokenBody from sunshine_conversations_client.model.attachment_media_token_response import AttachmentMediaTokenResponse from sunshine_conversations_client.model.attachment_response import AttachmentResponse from sunshine_conversations_client.model.attachment_schema import AttachmentSchema from sunshine_conversations_client.model.attachment_upload_body import AttachmentUploadBody from sunshine_conversations_client.model.author import Author from sunshine_conversations_client.model.author_webhook import AuthorWebhook from sunshine_conversations_client.model.buy import Buy from sunshine_conversations_client.model.carousel_message import CarouselMessage from sunshine_conversations_client.model.carousel_message_display_settings import CarouselMessageDisplaySettings from sunshine_conversations_client.model.client import Client from sunshine_conversations_client.model.client_add_event import ClientAddEvent from sunshine_conversations_client.model.client_add_event_all_of import ClientAddEventAllOf from sunshine_conversations_client.model.client_add_event_all_of_payload import ClientAddEventAllOfPayload from sunshine_conversations_client.model.client_association import ClientAssociation from sunshine_conversations_client.model.client_create import ClientCreate from sunshine_conversations_client.model.client_list_response import ClientListResponse from sunshine_conversations_client.model.client_remove_event import ClientRemoveEvent from sunshine_conversations_client.model.client_remove_event_all_of import ClientRemoveEventAllOf from sunshine_conversations_client.model.client_remove_event_all_of_payload import ClientRemoveEventAllOfPayload from sunshine_conversations_client.model.client_response import ClientResponse from sunshine_conversations_client.model.client_type import ClientType from sunshine_conversations_client.model.client_update_event import ClientUpdateEvent from sunshine_conversations_client.model.client_update_event_all_of import ClientUpdateEventAllOf from sunshine_conversations_client.model.client_update_event_all_of_payload import ClientUpdateEventAllOfPayload from sunshine_conversations_client.model.confirmation import Confirmation from sunshine_conversations_client.model.content import Content from sunshine_conversations_client.model.conversation import Conversation from sunshine_conversations_client.model.conversation_all_of import ConversationAllOf from sunshine_conversations_client.model.conversation_create_body import ConversationCreateBody from sunshine_conversations_client.model.conversation_create_event import ConversationCreateEvent from sunshine_conversations_client.model.conversation_create_event_all_of import ConversationCreateEventAllOf from sunshine_conversations_client.model.conversation_create_event_all_of_payload import ConversationCreateEventAllOfPayload from sunshine_conversations_client.model.conversation_join_event import ConversationJoinEvent from sunshine_conversations_client.model.conversation_join_event_all_of import ConversationJoinEventAllOf from sunshine_conversations_client.model.conversation_join_event_all_of_payload import ConversationJoinEventAllOfPayload from sunshine_conversations_client.model.conversation_leave_event import ConversationLeaveEvent from sunshine_conversations_client.model.conversation_leave_event_all_of import ConversationLeaveEventAllOf from sunshine_conversations_client.model.conversation_leave_event_all_of_payload import ConversationLeaveEventAllOfPayload from sunshine_conversations_client.model.conversation_list_filter import ConversationListFilter from sunshine_conversations_client.model.conversation_list_response import ConversationListResponse from sunshine_conversations_client.model.conversation_message_delivery_channel_event import ConversationMessageDeliveryChannelEvent from sunshine_conversations_client.model.conversation_message_delivery_channel_event_all_of import ConversationMessageDeliveryChannelEventAllOf from sunshine_conversations_client.model.conversation_message_delivery_failure_event import ConversationMessageDeliveryFailureEvent from sunshine_conversations_client.model.conversation_message_delivery_failure_event_all_of import ConversationMessageDeliveryFailureEventAllOf from sunshine_conversations_client.model.conversation_message_delivery_payload import ConversationMessageDeliveryPayload from sunshine_conversations_client.model.conversation_message_delivery_payload_destination import ConversationMessageDeliveryPayloadDestination from sunshine_conversations_client.model.conversation_message_delivery_payload_external_messages import ConversationMessageDeliveryPayloadExternalMessages from sunshine_conversations_client.model.conversation_message_delivery_payload_message import ConversationMessageDeliveryPayloadMessage from sunshine_conversations_client.model.conversation_message_delivery_user_event import ConversationMessageDeliveryUserEvent from sunshine_conversations_client.model.conversation_message_event import ConversationMessageEvent from sunshine_conversations_client.model.conversation_message_event_all_of import ConversationMessageEventAllOf from sunshine_conversations_client.model.conversation_message_event_all_of_payload import ConversationMessageEventAllOfPayload from sunshine_conversations_client.model.conversation_postback_event import ConversationPostbackEvent from sunshine_conversations_client.model.conversation_postback_event_all_of import ConversationPostbackEventAllOf from sunshine_conversations_client.model.conversation_postback_event_all_of_payload import ConversationPostbackEventAllOfPayload from sunshine_conversations_client.model.conversation_read_event import ConversationReadEvent from sunshine_conversations_client.model.conversation_read_event_all_of import ConversationReadEventAllOf from sunshine_conversations_client.model.conversation_read_event_all_of_payload import ConversationReadEventAllOfPayload from sunshine_conversations_client.model.conversation_remove_event import ConversationRemoveEvent from sunshine_conversations_client.model.conversation_remove_event_all_of import ConversationRemoveEventAllOf from sunshine_conversations_client.model.conversation_remove_event_all_of_payload import ConversationRemoveEventAllOfPayload from sunshine_conversations_client.model.conversation_response import ConversationResponse from sunshine_conversations_client.model.conversation_truncated import ConversationTruncated from sunshine_conversations_client.model.conversation_type import ConversationType from sunshine_conversations_client.model.conversation_typing_event import ConversationTypingEvent from sunshine_conversations_client.model.conversation_typing_event_all_of import ConversationTypingEventAllOf from sunshine_conversations_client.model.conversation_typing_event_all_of_payload import ConversationTypingEventAllOfPayload from sunshine_conversations_client.model.conversation_update_body import ConversationUpdateBody from sunshine_conversations_client.model.custom import Custom from sunshine_conversations_client.model.custom_all_of import CustomAllOf from sunshine_conversations_client.model.custom_update import CustomUpdate from sunshine_conversations_client.model.destination import Destination from sunshine_conversations_client.model.device import Device from sunshine_conversations_client.model.event_sub_schema import EventSubSchema from sunshine_conversations_client.model.extra_channel_options import ExtraChannelOptions from sunshine_conversations_client.model.extra_channel_options_messenger import ExtraChannelOptionsMessenger from sunshine_conversations_client.model.field import Field from sunshine_conversations_client.model.file_message import FileMessage from sunshine_conversations_client.model.form_message import FormMessage from sunshine_conversations_client.model.form_response_message import FormResponseMessage from sunshine_conversations_client.model.image_message import ImageMessage from sunshine_conversations_client.model.inline_object import InlineObject from sunshine_conversations_client.model.instagram import Instagram from sunshine_conversations_client.model.instagram_all_of import InstagramAllOf from sunshine_conversations_client.model.instagram_update import InstagramUpdate from sunshine_conversations_client.model.instagram_update_all_of import InstagramUpdateAllOf from sunshine_conversations_client.model.integration import Integration from sunshine_conversations_client.model.integration_api_key import IntegrationApiKey from sunshine_conversations_client.model.integration_api_key_list_response import IntegrationApiKeyListResponse from sunshine_conversations_client.model.integration_api_key_response import IntegrationApiKeyResponse from sunshine_conversations_client.model.integration_id import IntegrationId from sunshine_conversations_client.model.integration_list_filter import IntegrationListFilter from sunshine_conversations_client.model.integration_list_response import IntegrationListResponse from sunshine_conversations_client.model.integration_response import IntegrationResponse from sunshine_conversations_client.model.integration_type import IntegrationType from sunshine_conversations_client.model.integration_update import IntegrationUpdate from sunshine_conversations_client.model.integration_update_base import IntegrationUpdateBase from sunshine_conversations_client.model.ios import Ios from sunshine_conversations_client.model.ios_all_of import IosAllOf from sunshine_conversations_client.model.ios_update import IosUpdate from sunshine_conversations_client.model.ios_update_all_of import IosUpdateAllOf from sunshine_conversations_client.model.item import Item from sunshine_conversations_client.model.line import Line from sunshine_conversations_client.model.line_all_of import LineAllOf from sunshine_conversations_client.model.line_update import LineUpdate from sunshine_conversations_client.model.link import Link from sunshine_conversations_client.model.links import Links from sunshine_conversations_client.model.list_message import ListMessage from sunshine_conversations_client.model.location_message import LocationMessage from sunshine_conversations_client.model.location_message_coordinates import LocationMessageCoordinates from sunshine_conversations_client.model.location_message_location import LocationMessageLocation from sunshine_conversations_client.model.location_request import LocationRequest from sunshine_conversations_client.model.mailgun import Mailgun from sunshine_conversations_client.model.mailgun_all_of import MailgunAllOf from sunshine_conversations_client.model.mailgun_update import MailgunUpdate from sunshine_conversations_client.model.mailgun_update_all_of import MailgunUpdateAllOf from sunshine_conversations_client.model.match_criteria import MatchCriteria from sunshine_conversations_client.model.match_criteria_base import MatchCriteriaBase from sunshine_conversations_client.model.match_criteria_mailgun import MatchCriteriaMailgun from sunshine_conversations_client.model.match_criteria_mailgun_all_of import MatchCriteriaMailgunAllOf from sunshine_conversations_client.model.match_criteria_messagebird import MatchCriteriaMessagebird from sunshine_conversations_client.model.match_criteria_messagebird_all_of import MatchCriteriaMessagebirdAllOf from sunshine_conversations_client.model.match_criteria_twilio import MatchCriteriaTwilio from sunshine_conversations_client.model.match_criteria_twilio_all_of import MatchCriteriaTwilioAllOf from sunshine_conversations_client.model.match_criteria_whatsapp import MatchCriteriaWhatsapp from sunshine_conversations_client.model.match_criteria_whatsapp_all_of import MatchCriteriaWhatsappAllOf from sunshine_conversations_client.model.message import Message from sunshine_conversations_client.model.message_bird_update import MessageBirdUpdate from sunshine_conversations_client.model.message_list_response import MessageListResponse from sunshine_conversations_client.model.message_override import MessageOverride from sunshine_conversations_client.model.message_override_apple import MessageOverrideApple from sunshine_conversations_client.model.message_override_line import MessageOverrideLine from sunshine_conversations_client.model.message_override_messenger import MessageOverrideMessenger from sunshine_conversations_client.model.message_override_payload import MessageOverridePayload from sunshine_conversations_client.model.message_override_whatsapp import MessageOverrideWhatsapp from sunshine_conversations_client.model.message_post import MessagePost from sunshine_conversations_client.model.message_post_response import MessagePostResponse from sunshine_conversations_client.model.message_webhook import MessageWebhook from sunshine_conversations_client.model.messagebird import Messagebird from sunshine_conversations_client.model.messagebird_all_of import MessagebirdAllOf from sunshine_conversations_client.model.messenger import Messenger from sunshine_conversations_client.model.messenger_all_of import MessengerAllOf from sunshine_conversations_client.model.messenger_update import MessengerUpdate from sunshine_conversations_client.model.meta import Meta from sunshine_conversations_client.model.offer_control_body import OfferControlBody from sunshine_conversations_client.model.page import Page from sunshine_conversations_client.model.participant import Participant from sunshine_conversations_client.model.participant_join_body import ParticipantJoinBody from sunshine_conversations_client.model.participant_leave_body import ParticipantLeaveBody from sunshine_conversations_client.model.participant_leave_body_participant_id import ParticipantLeaveBodyParticipantId from sunshine_conversations_client.model.participant_leave_body_user_external_id import ParticipantLeaveBodyUserExternalId from sunshine_conversations_client.model.participant_leave_body_user_id import ParticipantLeaveBodyUserId from sunshine_conversations_client.model.participant_list_response import ParticipantListResponse from sunshine_conversations_client.model.participant_response import ParticipantResponse from sunshine_conversations_client.model.participant_sub_schema import ParticipantSubSchema from sunshine_conversations_client.model.participant_with_user_external_id import ParticipantWithUserExternalId from sunshine_conversations_client.model.participant_with_user_id import ParticipantWithUserId from sunshine_conversations_client.model.pass_control_body import PassControlBody from sunshine_conversations_client.model.postback import Postback from sunshine_conversations_client.model.postback_webhook import PostbackWebhook from sunshine_conversations_client.model.prechat_capture import PrechatCapture from sunshine_conversations_client.model.profile import Profile from sunshine_conversations_client.model.quoted_message import QuotedMessage from sunshine_conversations_client.model.quoted_message_external_message_id import QuotedMessageExternalMessageId from sunshine_conversations_client.model.quoted_message_message import QuotedMessageMessage from sunshine_conversations_client.model.referral import Referral from sunshine_conversations_client.model.referral_details import ReferralDetails from sunshine_conversations_client.model.reply import Reply from sunshine_conversations_client.model.source import Source from sunshine_conversations_client.model.source_webhook import SourceWebhook from sunshine_conversations_client.model.status import Status from sunshine_conversations_client.model.switchboard import Switchboard from sunshine_conversations_client.model.switchboard_accept_control import SwitchboardAcceptControl from sunshine_conversations_client.model.switchboard_accept_control_all_of import SwitchboardAcceptControlAllOf from sunshine_conversations_client.model.switchboard_accept_control_all_of_payload import SwitchboardAcceptControlAllOfPayload from sunshine_conversations_client.model.switchboard_accept_control_failure import SwitchboardAcceptControlFailure from sunshine_conversations_client.model.switchboard_accept_control_failure_all_of import SwitchboardAcceptControlFailureAllOf from sunshine_conversations_client.model.switchboard_accept_control_failure_all_of_payload import SwitchboardAcceptControlFailureAllOfPayload from sunshine_conversations_client.model.switchboard_integration import SwitchboardIntegration from sunshine_conversations_client.model.switchboard_integration_create_body import SwitchboardIntegrationCreateBody from sunshine_conversations_client.model.switchboard_integration_list_response import SwitchboardIntegrationListResponse from sunshine_conversations_client.model.switchboard_integration_response import SwitchboardIntegrationResponse from sunshine_conversations_client.model.switchboard_integration_update_body import SwitchboardIntegrationUpdateBody from sunshine_conversations_client.model.switchboard_integration_webhook import SwitchboardIntegrationWebhook from sunshine_conversations_client.model.switchboard_list_response import SwitchboardListResponse from sunshine_conversations_client.model.switchboard_offer_control import SwitchboardOfferControl from sunshine_conversations_client.model.switchboard_offer_control_all_of import SwitchboardOfferControlAllOf from sunshine_conversations_client.model.switchboard_offer_control_all_of_payload import SwitchboardOfferControlAllOfPayload from sunshine_conversations_client.model.switchboard_offer_control_failure import SwitchboardOfferControlFailure from sunshine_conversations_client.model.switchboard_pass_control import SwitchboardPassControl from sunshine_conversations_client.model.switchboard_pass_control_all_of import SwitchboardPassControlAllOf from sunshine_conversations_client.model.switchboard_pass_control_all_of_payload import SwitchboardPassControlAllOfPayload from sunshine_conversations_client.model.switchboard_pass_control_failure import SwitchboardPassControlFailure from sunshine_conversations_client.model.switchboard_response import SwitchboardResponse from sunshine_conversations_client.model.switchboard_update_body import SwitchboardUpdateBody from sunshine_conversations_client.model.target import Target from sunshine_conversations_client.model.telegram import Telegram from sunshine_conversations_client.model.telegram_all_of import TelegramAllOf from sunshine_conversations_client.model.telegram_update import TelegramUpdate from sunshine_conversations_client.model.template_message import TemplateMessage from sunshine_conversations_client.model.text_message import TextMessage from sunshine_conversations_client.model.twilio import Twilio from sunshine_conversations_client.model.twilio_all_of import TwilioAllOf from sunshine_conversations_client.model.twilio_update import TwilioUpdate from sunshine_conversations_client.model.twitter import Twitter from sunshine_conversations_client.model.twitter_all_of import TwitterAllOf from sunshine_conversations_client.model.twitter_update import TwitterUpdate from sunshine_conversations_client.model.user import User from sunshine_conversations_client.model.user_all_of import UserAllOf from sunshine_conversations_client.model.user_create_body import UserCreateBody from sunshine_conversations_client.model.user_merge_event import UserMergeEvent from sunshine_conversations_client.model.user_merge_event_all_of import UserMergeEventAllOf from sunshine_conversations_client.model.user_merge_event_all_of_payload import UserMergeEventAllOfPayload from sunshine_conversations_client.model.user_merge_event_all_of_payload_merged_clients import UserMergeEventAllOfPayloadMergedClients from sunshine_conversations_client.model.user_merge_event_all_of_payload_merged_conversations import UserMergeEventAllOfPayloadMergedConversations from sunshine_conversations_client.model.user_merge_event_all_of_payload_merged_users import UserMergeEventAllOfPayloadMergedUsers from sunshine_conversations_client.model.user_response import UserResponse from sunshine_conversations_client.model.user_truncated import UserTruncated from sunshine_conversations_client.model.user_update_body import UserUpdateBody from sunshine_conversations_client.model.viber import Viber from sunshine_conversations_client.model.viber_all_of import ViberAllOf from sunshine_conversations_client.model.viber_update import ViberUpdate from sunshine_conversations_client.model.web import Web from sunshine_conversations_client.model.web_all_of import WebAllOf from sunshine_conversations_client.model.web_update import WebUpdate from sunshine_conversations_client.model.web_update_all_of import WebUpdateAllOf from sunshine_conversations_client.model.webhook import Webhook from sunshine_conversations_client.model.webhook_body import WebhookBody from sunshine_conversations_client.model.webhook_create_body import WebhookCreateBody from sunshine_conversations_client.model.webhook_list_response import WebhookListResponse from sunshine_conversations_client.model.webhook_response import WebhookResponse from sunshine_conversations_client.model.webhook_sub_schema import WebhookSubSchema from sunshine_conversations_client.model.webview import Webview from sunshine_conversations_client.model.whats_app_update import WhatsAppUpdate from sunshine_conversations_client.model.whats_app_update_all_of import WhatsAppUpdateAllOf from sunshine_conversations_client.model.whatsapp import Whatsapp from sunshine_conversations_client.model.whatsapp_all_of import WhatsappAllOf

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false

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1

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null

0

1

0

1

0

6

72761545ae336a19e68ea01bb8ef61ca83d8229d

1,282

py

Python

demo_pshape.py

sam1902/pshape

b94b474ecd528284307907d85455e6252946fb95

[ "BSD-3-Clause" ]

null

demo_pshape.py

sam1902/pshape

b94b474ecd528284307907d85455e6252946fb95

[ "BSD-3-Clause" ]

null

demo_pshape.py

sam1902/pshape

b94b474ecd528284307907d85455e6252946fb95

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python3 """Module doc""" from pshape import pshape import torch import numpy as np def main(): """Main function""" print(">>> pshape(np.arange(10).reshape(5,2,1), heading=True)") print() pshape(np.arange(10).reshape(5,2,1), heading=True) print() print(">>> pshape(np.eye(4), np.arange(10).reshape(5,2,1), heading=True)") print() pshape(np.eye(4), np.arange(10).reshape(5,2,1), heading=True) print() print(">>> cool_arr1 = np.random.rand(123,4,2,1)") print(">>> cool_arr2 = np.random.rand(123,4,2,2)") print(">>> cool_arr3 = np.random.rand(123,4,2,3)") cool_arr1 = np.random.rand(123,4,2,1) cool_arr2 = np.random.rand(123,4,2,2) cool_arr3 = np.random.rand(123,4,2,3) print(">>> pshape(cool_arr1, cool_arr2, cool_arr3)") print() pshape(cool_arr1, cool_arr2, cool_arr3) print() print(">>> pshape(cool_arr1, np.arange(12).reshape(3,4), cool_arr3)") print() pshape(cool_arr1, np.arange(12).reshape(3,4), cool_arr3) print() print(">>> pshape(torch.arange(12).view(3,4), np.arange(12).reshape(3,4), cool_arr3)") print() pshape(torch.arange(12).view(3,4), np.arange(12).reshape(3,4), cool_arr3, heading=True) print() if __name__ == "__main__": main()

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91

0.620125

210

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0.185714

0.143043

0.093628

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0.828349

0.527958

0

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0.165367

1,282

43

92

29.813953

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0.035881

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0.3125

0

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0

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0

0.09375

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0

null

0

1

0

null

0

1

0

6

72976487139c1c744f4018393d1580ee68ac1e88

16,745

py

Python

mayan/apps/user_management/tests/test_api.py

Fourdee/mayan-edms

39a94f8b4fed519a3b20ab419e920ea53c11eb84

[ "Apache-2.0" ]

4

2019-02-17T08:35:42.000Z

2019-03-28T06:02:11.000Z

mayan/apps/user_management/tests/test_api.py

zhoubear/mayan-edms

e9bc10a056c3379b57115c6e83022f48c6298e1d

[ "Apache-2.0" ]

1

2018-10-11T13:01:34.000Z

mayan/apps/user_management/tests/test_api.py

prezi/mayan-edms

e9bc10a056c3379b57115c6e83022f48c6298e1d

[ "Apache-2.0" ]

3

2019-01-29T13:21:57.000Z

2019-10-27T03:20:15.000Z

from __future__ import unicode_literals from django.contrib.auth import get_user_model from django.contrib.auth.models import Group from rest_framework import status from rest_api.tests import BaseAPITestCase from ..permissions import ( permission_group_create, permission_group_delete, permission_group_edit, permission_group_view, permission_user_create, permission_user_delete, permission_user_edit, permission_user_view ) from .literals import ( TEST_GROUP_2_NAME, TEST_GROUP_2_NAME_EDITED, TEST_USER_2_EMAIL, TEST_USER_2_PASSWORD, TEST_USER_2_USERNAME, TEST_USER_2_USERNAME_EDITED, TEST_USER_2_PASSWORD_EDITED ) class UserManagementUserAPITestCase(BaseAPITestCase): def setUp(self): super(UserManagementUserAPITestCase, self).setUp() self.login_user() # User create def _create_user(self): return get_user_model().objects.create_user( username=TEST_USER_2_USERNAME, email=TEST_USER_2_EMAIL, password=TEST_USER_2_PASSWORD ) def _request_user_create(self): return self.post( viewname='rest_api:user-list', data={ 'email': TEST_USER_2_EMAIL, 'password': TEST_USER_2_PASSWORD, 'username': TEST_USER_2_USERNAME, } ) def test_user_create_no_permission(self): response = self._request_user_create() self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN) # Default two users, the test admin and the test user self.assertEqual(get_user_model().objects.count(), 2) def test_user_create_with_permission(self): self.grant_permission(permission=permission_user_create) response = self._request_user_create() self.assertEqual(response.status_code, status.HTTP_201_CREATED) user = get_user_model().objects.get(pk=response.data['id']) self.assertEqual(user.username, TEST_USER_2_USERNAME) self.assertEqual(get_user_model().objects.count(), 3) def _request_create_user_with_extra_data(self): return self.post( viewname='rest_api:user-list', data={ 'email': TEST_USER_2_EMAIL, 'password': TEST_USER_2_PASSWORD, 'username': TEST_USER_2_USERNAME, 'groups_pk_list': self.groups_pk_list } ) def test_user_create_with_group_no_permission(self): group_1 = Group.objects.create(name=TEST_GROUP_2_NAME) self.groups_pk_list = '{}'.format(group_1.pk) response = self._request_create_user_with_extra_data() self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN) def test_user_create_with_group_with_permission(self): group_1 = Group.objects.create(name=TEST_GROUP_2_NAME) self.groups_pk_list = '{}'.format(group_1.pk) self.grant_permission(permission=permission_user_create) response = self._request_create_user_with_extra_data() self.assertEqual(response.status_code, status.HTTP_201_CREATED) user = get_user_model().objects.get(pk=response.data['id']) self.assertEqual(user.username, TEST_USER_2_USERNAME) self.assertQuerysetEqual(user.groups.all(), (repr(group_1),)) def test_user_create_with_groups_no_permission(self): group_1 = Group.objects.create(name='test group 1') group_2 = Group.objects.create(name='test group 2') self.groups_pk_list = '{},{}'.format(group_1.pk, group_2.pk) response = self._request_create_user_with_extra_data() self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN) def test_user_create_with_groups_with_permission(self): group_1 = Group.objects.create(name='test group 1') group_2 = Group.objects.create(name='test group 2') self.groups_pk_list = '{},{}'.format(group_1.pk, group_2.pk) self.grant_permission(permission=permission_user_create) response = self._request_create_user_with_extra_data() self.assertEqual(response.status_code, status.HTTP_201_CREATED) user = get_user_model().objects.get(pk=response.data['id']) self.assertEqual(user.username, TEST_USER_2_USERNAME) self.assertQuerysetEqual( user.groups.all().order_by('name'), (repr(group_1), repr(group_2)) ) # User login def test_user_create_login(self): self._create_user() self.assertTrue( self.login( username=TEST_USER_2_USERNAME, password=TEST_USER_2_PASSWORD ) ) # User password change def test_user_create_login_password_change_no_access(self): user = self._create_user() self.patch( viewname='rest_api:user-detail', args=(user.pk,), data={ 'password': TEST_USER_2_PASSWORD_EDITED, } ) self.assertFalse( self.client.login( username=TEST_USER_2_USERNAME, password=TEST_USER_2_PASSWORD_EDITED ) ) def test_user_create_login_password_change_with_access(self): user = self._create_user() self.grant_access(permission=permission_user_edit, obj=user) self.patch( viewname='rest_api:user-detail', args=(user.pk,), data={ 'password': TEST_USER_2_PASSWORD_EDITED, } ) self.assertTrue( self.client.login( username=TEST_USER_2_USERNAME, password=TEST_USER_2_PASSWORD_EDITED ) ) # User edit def _request_user_edit_via_put(self, user): return self.put( viewname='rest_api:user-detail', args=(user.pk,), data={'username': TEST_USER_2_USERNAME_EDITED} ) def test_user_edit_via_put_no_access(self): user = self._create_user() response = self._request_user_edit_via_put(user=user) self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN) user.refresh_from_db() self.assertEqual(user.username, TEST_USER_2_USERNAME) def test_user_edit_via_put_with_access(self): user = self._create_user() self.grant_access(permission=permission_user_edit, obj=user) response = self._request_user_edit_via_put(user=user) self.assertEqual(response.status_code, status.HTTP_200_OK) user.refresh_from_db() self.assertEqual(user.username, TEST_USER_2_USERNAME_EDITED) def _request_user_edit_via_patch(self, user): return self.patch( viewname='rest_api:user-detail', args=(user.pk,), data={'username': TEST_USER_2_USERNAME_EDITED} ) def test_user_edit_via_patch_no_access(self): user = self._create_user() response = self._request_user_edit_via_patch(user=user) self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN) user.refresh_from_db() self.assertEqual(user.username, TEST_USER_2_USERNAME) def test_user_edit_via_patch_with_access(self): user = self._create_user() self.grant_access(permission=permission_user_edit, obj=user) response = self._request_user_edit_via_patch(user=user) self.assertEqual(response.status_code, status.HTTP_200_OK) user.refresh_from_db() self.assertEqual(user.username, TEST_USER_2_USERNAME_EDITED) def _request_user_edit_via_patch_with_extra_data(self, user, group): return self.patch( viewname='rest_api:user-detail', args=(user.pk,), data={'groups_pk_list': '{}'.format(group.pk)} ) def test_user_edit_add_groups_via_patch_no_access(self): group = Group.objects.create(name=TEST_GROUP_2_NAME) user = self._create_user() response = self._request_user_edit_via_patch_with_extra_data( user=user, group=group ) self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN) user.refresh_from_db() self.assertEqual(user.username, TEST_USER_2_USERNAME) self.assertQuerysetEqual( user.groups.all(), () ) def test_user_edit_add_groups_via_patch_with_access(self): group = Group.objects.create(name=TEST_GROUP_2_NAME) user = self._create_user() self.grant_access(permission=permission_user_edit, obj=user) response = self._request_user_edit_via_patch_with_extra_data( user=user, group=group ) self.assertEqual(response.status_code, status.HTTP_200_OK) user.refresh_from_db() self.assertEqual(user.username, TEST_USER_2_USERNAME) self.assertQuerysetEqual( user.groups.all(), (repr(group),) ) # User delete def _request_user_delete(self, user): return self.delete( viewname='rest_api:user-detail', args=(user.pk,) ) def test_user_delete_no_access(self): user = self._create_user() response = self._request_user_delete(user=user) self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN) self.assertTrue(get_user_model().objects.filter(pk=user.pk).exists()) def test_user_delete_with_access(self): user = self._create_user() self.grant_access(permission=permission_user_delete, obj=user) response = self._request_user_delete(user=user) self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT) self.assertFalse(get_user_model().objects.filter(pk=user.pk).exists()) # User view def _request_user_group_view(self, user): return self.get( viewname='rest_api:users-group-list', args=(user.pk,) ) def test_user_group_list_no_access(self): group = Group.objects.create(name=TEST_GROUP_2_NAME) user = self._create_user() user.groups.add(group) response = self._request_user_group_view(user=user) self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN) def test_user_group_list_with_user_access(self): group = Group.objects.create(name=TEST_GROUP_2_NAME) user = self._create_user() user.groups.add(group) self.grant_access(permission=permission_user_view, obj=user) response = self._request_user_group_view(user=user) self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertEqual(response.data['count'], 0) def test_user_group_list_with_group_access(self): group = Group.objects.create(name=TEST_GROUP_2_NAME) user = self._create_user() user.groups.add(group) self.grant_access(permission=permission_group_view, obj=group) response = self._request_user_group_view(user=user) self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN) def test_user_group_list_with_access(self): group = Group.objects.create(name=TEST_GROUP_2_NAME) user = self._create_user() user.groups.add(group) self.grant_access(permission=permission_user_view, obj=user) self.grant_access(permission=permission_group_view, obj=group) response = self._request_user_group_view(user=user) self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertEqual(response.data['count'], 1) def _request_user_group_add(self, user, group): return self.post( viewname='rest_api:users-group-list', args=(user.pk,), data={ 'group_pk_list': '{}'.format(group.pk) } ) def test_user_group_add_no_access(self): group = Group.objects.create(name=TEST_GROUP_2_NAME) user = self._create_user() response = self._request_user_group_add(user=user, group=group) self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN) user.refresh_from_db() self.assertEqual(group.user_set.first(), None) def test_user_group_add_with_user_access(self): group = Group.objects.create(name=TEST_GROUP_2_NAME) user = self._create_user() self.grant_access(permission=permission_user_edit, obj=user) response = self._request_user_group_add(user=user, group=group) self.assertEqual(response.status_code, status.HTTP_201_CREATED) # FIXME: Should this endpoint return a 201 or a 200 since # the user is being edited and there is not resource creation # happening. user.refresh_from_db() self.assertEqual(group.user_set.first(), None) def test_user_group_add_with_group_access(self): group = Group.objects.create(name=TEST_GROUP_2_NAME) user = self._create_user() self.grant_access(permission=permission_group_view, obj=group) response = self._request_user_group_add(user=user, group=group) self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN) # FIXME: Should this endpoint return a 201 or a 200 since # the user is being edited and there is not resource creation # happening. user.refresh_from_db() self.assertEqual(group.user_set.first(), None) def test_user_group_add_with_access(self): group = Group.objects.create(name=TEST_GROUP_2_NAME) user = self._create_user() self.grant_access(permission=permission_user_edit, obj=user) self.grant_access(permission=permission_group_view, obj=group) response = self._request_user_group_add(user=user, group=group) self.assertEqual(response.status_code, status.HTTP_201_CREATED) # FIXME: Should this endpoint return a 201 or a 200 since # the user is being edited and there is not resource creation # happening. user.refresh_from_db() self.assertEqual(group.user_set.first(), user) class UserManagementGroupAPITestCase(BaseAPITestCase): def setUp(self): super(UserManagementGroupAPITestCase, self).setUp() self.login_user() def _request_group_create(self): return self.post( viewname='rest_api:group-list', data={ 'name': TEST_GROUP_2_NAME } ) def test_group_create_no_permission(self): response = self._request_group_create() self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN) self.assertFalse( TEST_GROUP_2_NAME in list(Group.objects.values_list('name', flat=True)) ) def test_group_create_with_permission(self): self.grant_permission(permission=permission_group_create) response = self._request_group_create() self.assertEqual(response.status_code, status.HTTP_201_CREATED) self.assertTrue( TEST_GROUP_2_NAME in list(Group.objects.values_list('name', flat=True)) ) def _request_group_edit_via_patch(self): return self.patch( viewname='rest_api:group-detail', args=(self.group.pk,), data={ 'name': TEST_GROUP_2_NAME_EDITED } ) def test_group_edit_via_patch_no_access(self): self.group = Group.objects.create(name=TEST_GROUP_2_NAME) response = self._request_group_edit_via_patch() self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN) self.group.refresh_from_db() self.assertEqual(self.group.name, TEST_GROUP_2_NAME) def test_group_edit_via_patch_with_access(self): self.group = Group.objects.create(name=TEST_GROUP_2_NAME) self.grant_access(permission=permission_group_edit, obj=self.group) response = self._request_group_edit_via_patch() self.assertEqual(response.status_code, status.HTTP_200_OK) self.group.refresh_from_db() self.assertEqual(self.group.name, TEST_GROUP_2_NAME_EDITED) def _request_group_delete(self): return self.delete( viewname='rest_api:group-detail', args=(self.group.pk,) ) def test_group_delete_no_access(self): self.group = Group.objects.create(name=TEST_GROUP_2_NAME) response = self._request_group_delete() self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN) self.assertTrue( TEST_GROUP_2_NAME in list(Group.objects.values_list('name', flat=True)) ) def test_group_delete_with_access(self): self.group = Group.objects.create(name=TEST_GROUP_2_NAME) self.grant_access(permission=permission_group_delete, obj=self.group) response = self._request_group_delete() self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT) self.assertFalse( TEST_GROUP_2_NAME in list(Group.objects.values_list('name', flat=True)) )

38.318078

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null

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729aec100312ae793527a7e94b62e9476eb868b9

113

py

Python

bodyhands/__init__.py

cvlab-stonybrook/BodyHands

dcfe470f6fd31a048d4d17d4ae9a2a524538b380

[ "MIT" ]

1

2022-03-06T08:18:33.000Z

bodyhands/__init__.py

cvlab-stonybrook/BodyHands

dcfe470f6fd31a048d4d17d4ae9a2a524538b380

[ "MIT" ]

null

bodyhands/__init__.py

cvlab-stonybrook/BodyHands

dcfe470f6fd31a048d4d17d4ae9a2a524538b380

[ "MIT" ]

null

from .config import * from .data import * from .evaluation import * from .modeling import * from .utils import *

18.833333

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5.533333

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f42cb37aa2d3c697c189931afe7aa988e5cfa79d

293

py

Python

pkg_radish_selenium/radish_selenium/__init__.py

bbielicki/radish-bdd-extensions

7f1317461af23a70f2a551b66299b54e296af32f

[ "BSD-3-Clause" ]

4

2019-09-19T21:25:26.000Z

2019-11-10T06:09:06.000Z

pkg_radish_selenium/radish_selenium/__init__.py

bbielicki/radish-bdd-extensions

7f1317461af23a70f2a551b66299b54e296af32f

[ "BSD-3-Clause" ]

null

pkg_radish_selenium/radish_selenium/__init__.py

bbielicki/radish-bdd-extensions

7f1317461af23a70f2a551b66299b54e296af32f

[ "BSD-3-Clause" ]

2

2019-09-17T11:26:59.000Z

2020-01-23T20:20:43.000Z

# © 2019 Nokia # Licensed under the BSD 3 Clause license # SPDX-License-Identifier: BSD-3-Clause import os def get_radish_selenium_dir(): return os.path.abspath(os.path.dirname(__file__)) def get_radish_selenium_etc_dir(): return os.path.join(get_radish_selenium_dir(), 'etc')

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0.131068

0.247573

0.194175

0

0.023904

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293

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18.3125

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true

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null

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1

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6

f439440399888de027a2324c8c89546e0f24d846

7,422

py

Python

povary/apps/events/migrations/0006_auto__add_field_event_visits_num__add_field_eventcategory_visits_num.py

TorinAsakura/cooking

cf0c78f613fa9ce0fcd4ec7a397ab880d9dd631a

[ "BSD-3-Clause" ]

null

povary/apps/events/migrations/0006_auto__add_field_event_visits_num__add_field_eventcategory_visits_num.py

TorinAsakura/cooking

cf0c78f613fa9ce0fcd4ec7a397ab880d9dd631a

[ "BSD-3-Clause" ]

null

povary/apps/events/migrations/0006_auto__add_field_event_visits_num__add_field_eventcategory_visits_num.py

TorinAsakura/cooking

cf0c78f613fa9ce0fcd4ec7a397ab880d9dd631a

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- import datetime from south.db import db from south.v2 import SchemaMigration from django.db import models class Migration(SchemaMigration): def forwards(self, orm): # Adding field 'Event.visits_num' db.add_column('events_event', 'visits_num', self.gf('django.db.models.fields.PositiveIntegerField')(default=0), keep_default=False) # Adding field 'EventCategory.visits_num' db.add_column('events_eventcategory', 'visits_num', self.gf('django.db.models.fields.PositiveIntegerField')(default=0), keep_default=False) def backwards(self, orm): # Deleting field 'Event.visits_num' db.delete_column('events_event', 'visits_num') # Deleting field 'EventCategory.visits_num' db.delete_column('events_eventcategory', 'visits_num') models = { 'cities_light.city': { 'Meta': {'unique_together': "(('region', 'name'),)", 'object_name': 'City'}, 'alternate_names': ('django.db.models.fields.TextField', [], {'default': "''", 'null': 'True', 'blank': 'True'}), 'country': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['cities_light.Country']"}), 'display_name': ('django.db.models.fields.CharField', [], {'max_length': '200'}), 'geoname_id': ('django.db.models.fields.IntegerField', [], {'unique': 'True', 'null': 'True', 'blank': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'latitude': ('django.db.models.fields.DecimalField', [], {'null': 'True', 'max_digits': '8', 'decimal_places': '5', 'blank': 'True'}), 'longitude': ('django.db.models.fields.DecimalField', [], {'null': 'True', 'max_digits': '8', 'decimal_places': '5', 'blank': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '200', 'db_index': 'True'}), 'name_ascii': ('django.db.models.fields.CharField', [], {'db_index': 'True', 'max_length': '200', 'blank': 'True'}), 'region': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['cities_light.Region']", 'null': 'True', 'blank': 'True'}), 'search_names': ('cities_light.models.ToSearchTextField', [], {'default': "''", 'max_length': '4000', 'db_index': 'True', 'blank': 'True'}), 'slug': ('autoslug.fields.AutoSlugField', [], {'unique_with': '()', 'max_length': '50', 'populate_from': 'None'}) }, 'cities_light.country': { 'Meta': {'object_name': 'Country'}, 'alternate_names': ('django.db.models.fields.TextField', [], {'default': "''", 'null': 'True', 'blank': 'True'}), 'code2': ('django.db.models.fields.CharField', [], {'max_length': '2', 'unique': 'True', 'null': 'True', 'blank': 'True'}), 'code3': ('django.db.models.fields.CharField', [], {'max_length': '3', 'unique': 'True', 'null': 'True', 'blank': 'True'}), 'continent': ('django.db.models.fields.CharField', [], {'max_length': '2', 'db_index': 'True'}), 'geoname_id': ('django.db.models.fields.IntegerField', [], {'unique': 'True', 'null': 'True', 'blank': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '200'}), 'name_ascii': ('django.db.models.fields.CharField', [], {'db_index': 'True', 'max_length': '200', 'blank': 'True'}), 'slug': ('autoslug.fields.AutoSlugField', [], {'unique_with': '()', 'max_length': '50', 'populate_from': 'None'}), 'tld': ('django.db.models.fields.CharField', [], {'db_index': 'True', 'max_length': '5', 'blank': 'True'}) }, 'cities_light.region': { 'Meta': {'unique_together': "(('country', 'name'),)", 'object_name': 'Region'}, 'alternate_names': ('django.db.models.fields.TextField', [], {'default': "''", 'null': 'True', 'blank': 'True'}), 'country': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['cities_light.Country']"}), 'display_name': ('django.db.models.fields.CharField', [], {'max_length': '200'}), 'geoname_code': ('django.db.models.fields.CharField', [], {'db_index': 'True', 'max_length': '50', 'null': 'True', 'blank': 'True'}), 'geoname_id': ('django.db.models.fields.IntegerField', [], {'unique': 'True', 'null': 'True', 'blank': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '200', 'db_index': 'True'}), 'name_ascii': ('django.db.models.fields.CharField', [], {'db_index': 'True', 'max_length': '200', 'blank': 'True'}), 'slug': ('autoslug.fields.AutoSlugField', [], {'unique_with': '()', 'max_length': '50', 'populate_from': 'None'}) }, 'events.event': { 'Meta': {'object_name': 'Event'}, 'category': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'events'", 'to': "orm['events.EventCategory']"}), 'city': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['cities_light.City']", 'null': 'True', 'blank': 'True'}), 'country': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['cities_light.Country']", 'null': 'True', 'blank': 'True'}), 'created': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}), 'description': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}), 'end_date': ('django.db.models.fields.DateTimeField', [], {}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'published': ('django.db.models.fields.BooleanField', [], {'default': 'True'}), 'slug': ('django.db.models.fields.SlugField', [], {'unique': 'True', 'max_length': '50'}), 'start_date': ('django.db.models.fields.DateTimeField', [], {}), 'title': ('django.db.models.fields.CharField', [], {'max_length': '255'}), 'updated': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}), 'visits_num': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}) }, 'events.eventcategory': { 'Meta': {'object_name': 'EventCategory'}, 'created': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}), 'description': ('django.db.models.fields.TextField', [], {}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'published': ('django.db.models.fields.BooleanField', [], {'default': 'True'}), 'slug': ('django.db.models.fields.SlugField', [], {'unique': 'True', 'max_length': '50'}), 'title': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}), 'updated': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}), 'visits_num': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}) } } complete_apps = ['events']

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764

7,422

5.311518

0.143979

0.100542

0.172499

0.246427

0.810251

0.783144

0.715377

0.706013

0.686792

0.66412

0

0.01034

0.192132

7,422

98

153

75.734694

0.666444

0.02277

0

0.423529

0

0.555326

0.286286

0

1

0.023529

false

0

0.047059

0

0.105882

0

null

0

1

0

1

0

null

0

6

f4399ebf5dd63b964e0c1dae5cd95ec230b4ffd9

420

py

Python

src/modules/priors/prior.py

Constantin771/Improved_srVAE

c1dc8662a077fa8b43ed14d77d5491f50faed79f

[ "MIT" ]

60

2020-06-11T11:06:15.000Z

2022-03-31T14:35:19.000Z

src/modules/priors/prior.py

Constantin771/Improved_srVAE

c1dc8662a077fa8b43ed14d77d5491f50faed79f

[ "MIT" ]

9

2020-06-28T09:45:28.000Z

2020-12-30T15:20:19.000Z

src/modules/priors/prior.py

Constantin771/Improved_srVAE

c1dc8662a077fa8b43ed14d77d5491f50faed79f

[ "MIT" ]

9

2020-07-28T12:03:32.000Z

2022-03-31T14:34:08.000Z

import torch import torch.nn as nn class Prior(nn.Module): def __init__(self): super().__init__() def sample(self, **kwargs): raise NotImplementedError def log_p(self, input, **kwargs): return self.forward(z) def forward(self, input, **kwargs): raise NotImplementedError def __str__(self): raise NotImplementedError if __name__ == "__main__": pass

17.5

39

0.635714

48

420

5.125

0.541667

0.292683

0.243902

0.268293

0

0.259524

420

23

40

18.26087

0.790997

0

0.2

0

0.019048

0

1

0.333333

false

0.066667

0.133333

0.066667

0.6

0

null

1

0

null

0

1

0

1

0

1

0

6

f48002125b3db6047ffea0bb08eec9a4ef22c59d

36

py

Python

pose_tracking_3d/__init__.py

WildflowerSchools/wf-3d-pose-tracking

0ba099e3d573ecbc39773617c540360668fcee9a

[ "MIT" ]

1

2019-12-06T21:15:36.000Z

pose_tracking_3d/__init__.py

WildflowerSchools/wf-3d-pose-tracking

0ba099e3d573ecbc39773617c540360668fcee9a

[ "MIT" ]

1

2019-12-15T23:49:06.000Z

2019-12-16T20:20:52.000Z

pose_tracking_3d/__init__.py

WildflowerSchools/wf-3d-pose-tracking

0ba099e3d573ecbc39773617c540360668fcee9a

[ "MIT" ]

2

2019-12-06T19:46:07.000Z

2019-12-11T22:38:15.000Z

from pose_tracking_3d.core import *

18

35

0.833333

6

36

4.666667

1

0

0.03125

0.111111

36

1

36

0.84375

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

be797d25bdb54a612514ab0799e16c4b21917fe2

167

py

Python

cpab/gpu/__init__.py

freifeld/cpabDiffeo

22df6cdbd7111b9ae3e7f1c0e31ff85e92d281a6

[ "MIT" ]

17

2016-03-16T21:35:36.000Z

2021-11-11T04:16:21.000Z

cpab/gpu/__init__.py

freifeld/cpabDiffeo

22df6cdbd7111b9ae3e7f1c0e31ff85e92d281a6

[ "MIT" ]

null

cpab/gpu/__init__.py

freifeld/cpabDiffeo

22df6cdbd7111b9ae3e7f1c0e31ff85e92d281a6

[ "MIT" ]

4

2016-08-12T23:02:09.000Z

2019-03-14T18:20:36.000Z

import os dirname_of_cuda_files = os.path.abspath(os.path.dirname(str(__file__))) if __name__ == "__main__": print 'dirname_of_cuda_files:',dirname_of_cuda_files

27.833333

71

0.784431

26

167

4.230769

0.538462

0.245455

0.354545

0.490909

0

0.095808

167

6

72

27.833333

0.728477

0

0.178571

0.130952

0

null

0

0.25

null

0.25

1

0

null

1

0

1

0

null

0

1

0

6

be9c67abdf4450516929b46cb9d0a2bd1cd7a8ff

166

py

Python

misc/pyschemes_fun.py

diegopacheco/python-playground

8e6ba427df6922fb578c2328babbf3466687ccbf

[ "Unlicense" ]

null

misc/pyschemes_fun.py

diegopacheco/python-playground

8e6ba427df6922fb578c2328babbf3466687ccbf

[ "Unlicense" ]

null

misc/pyschemes_fun.py

diegopacheco/python-playground

8e6ba427df6922fb578c2328babbf3466687ccbf

[ "Unlicense" ]

null

from pyschemes import Scheme, validators from collections import Iterable print( str( Scheme(int).validate(10) )) print( str( Scheme(Iterable).validate([1, 2]) ))

27.666667

49

0.740964

22

166

5.590909

0.636364

0.130081

0.227642

0

0.027586

0.126506

166

5

50

33.2

0.82069

0

1

0

true

0

0.5

0

0.5

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

fe57f8c8c300823790f15c1c2940a65808751edc

1,380

py

Python

tests/test_provider_invidian_libvirt.py

mjuenema/python-terrascript

6d8bb0273a14bfeb8ff8e950fe36f97f7c6e7b1d

[ "BSD-2-Clause" ]

507

2017-07-26T02:58:38.000Z

2022-01-21T12:35:13.000Z

tests/test_provider_invidian_libvirt.py

mjuenema/python-terrascript

6d8bb0273a14bfeb8ff8e950fe36f97f7c6e7b1d

[ "BSD-2-Clause" ]

135

2017-07-20T12:01:59.000Z

2021-10-04T22:25:40.000Z

tests/test_provider_invidian_libvirt.py

mjuenema/python-terrascript

6d8bb0273a14bfeb8ff8e950fe36f97f7c6e7b1d

[ "BSD-2-Clause" ]

81

2018-02-20T17:55:28.000Z

2022-01-31T07:08:40.000Z

# tests/test_provider_invidian_libvirt.py # Automatically generated by tools/makecode.py (24-Sep-2021 15:21:04 UTC) def test_provider_import(): import terrascript.provider.invidian.libvirt def test_resource_import(): from terrascript.resource.invidian.libvirt import libvirt_cloudinit_disk from terrascript.resource.invidian.libvirt import libvirt_domain from terrascript.resource.invidian.libvirt import libvirt_ignition from terrascript.resource.invidian.libvirt import libvirt_network from terrascript.resource.invidian.libvirt import libvirt_pool from terrascript.resource.invidian.libvirt import libvirt_volume def test_datasource_import(): from terrascript.data.invidian.libvirt import libvirt_network_dns_host_template from terrascript.data.invidian.libvirt import libvirt_network_dns_srv_template from terrascript.data.invidian.libvirt import ( libvirt_network_dnsmasq_options_template, ) # TODO: Shortcut imports without namespace for official and supported providers. # TODO: This has to be moved into a required_providers block. # def test_version_source(): # # import terrascript.provider.invidian.libvirt # # t = terrascript.provider.invidian.libvirt.libvirt() # s = str(t) # # assert 'https://github.com/invidian/terraform-provider-libvirt' in s # assert '0.6.10-rc1' in s

30.666667

83

0.784058

174

1,380

6.04023

0.413793

0.185538

0.179829

0.239772

0.549001

0.466223

0.175071

0

0.014382

0.143478

1,380

44

84

31.363636

0.874788

0.365217

0

1

0

0.022727

0

1

0.2

true

0

0.866667

0

1.066667

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

1

0

6

fe7dd5a7728664c1b1e9772fa5df5fda1c08e621

69

py

Python

CAIL2020/cocr/torchocr/networks/heads/__init__.py

ShenDezhou/CAIL

c4cfa98ab4ecedbce34a7a5a186830486047540c

[ "Apache-2.0" ]

71

2020-07-16T01:49:27.000Z

2022-03-27T16:55:00.000Z

CAIL2020/cocr/torchocr/networks/heads/__init__.py

ShenDezhou/CAIL

c4cfa98ab4ecedbce34a7a5a186830486047540c

[ "Apache-2.0" ]

11

2020-09-18T14:26:25.000Z

2022-02-09T23:49:33.000Z

CAIL2020/cocr/torchocr/networks/necks/__init__.py

ShenDezhou/CAIL

c4cfa98ab4ecedbce34a7a5a186830486047540c

[ "Apache-2.0" ]

16

2020-07-15T07:24:30.000Z

2022-03-19T05:41:11.000Z

# -*- coding: utf-8 -*- # @Time : 2020/5/15 17:42 # @Author : THU

23

28

0.492754

11

69

3.090909

1

0

0.230769

0.246377

69

3

29

23

0.423077

0.913043

0

null

0

null

0

null

0

null

1

null

true

0

null

1

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null

0

1

0

1

0

1

0

null

0

1

0

6

fe91f139723675c70a0d84c29b7f358dc9272a66

1,432

py

Python

tests/datastructures/trees/test_serialize_tree.py

sikakente/educative-io-python

be6e6c3534bf76e6f77addce16d1ab0c40e3e48d

[ "MIT" ]

1

2021-12-28T21:19:53.000Z

tests/datastructures/trees/test_serialize_tree.py

sikakente/educative-io-python

be6e6c3534bf76e6f77addce16d1ab0c40e3e48d

[ "MIT" ]

72

2022-02-01T18:18:47.000Z

2022-03-13T12:31:26.000Z

tests/datastructures/trees/test_serialize_tree.py

sikakente/educative-io-python

be6e6c3534bf76e6f77addce16d1ab0c40e3e48d

[ "MIT" ]

null

import unittest import pytest from datastructures.trees.binary_tree_utils import BinaryTree, NULL_MARKER from datastructures.trees.serialize_tree import Codec, Codec2 @pytest.mark.parametrize("values", [ ([2, 1, 3]), ([]), ([1, 2]), ([1, NULL_MARKER, 2]), ([5, 3, 6, 2, 4, NULL_MARKER, NULL_MARKER, 1]) ]) def test_serialize_deserialize_tree(values): tree = BinaryTree().build_tree(values) serializer = Codec() deserializer = Codec() serialized_tree = serializer.serialize(tree.root) deserialized_tree = deserializer.deserialize(serialized_tree) BinaryTree().preorder(tree.root, []) BinaryTree().preorder(deserialized_tree, []) assert BinaryTree().preorder(tree.root, []) == BinaryTree().preorder(deserialized_tree, []) @pytest.mark.parametrize("values", [ ([2, 1, 3]), ([]), ([1, 2]), ([1, NULL_MARKER, 2]), ([5, 3, 6, 2, 4, NULL_MARKER, NULL_MARKER, 1]) ]) def test_serialize_deserialize_tree(values): tree = BinaryTree().build_tree(values) serializer = Codec2() deserializer = Codec2() serialized_tree = serializer.serialize(tree.root) deserialized_tree = deserializer.deserialize(serialized_tree) BinaryTree().preorder(tree.root, []) BinaryTree().preorder(deserialized_tree, []) assert BinaryTree().preorder(tree.root, []) == BinaryTree().preorder(deserialized_tree, []) if __name__ == '__main__': unittest.main()

31.822222

95

0.683659

161

1,432

5.857143

0.236025

0.152704

0.093319

0.110286

0.776246

0

0.024066

0.15852

1,432

44

96

32.545455

0.758506

0

0.736842

0

0.013966

0

0.052632

1

0.052632

false

0

0.105263

0

0.157895

0

null

0

1

0

1

0

null

0

6

22ab98ecd9c94f3ed445f7ea1e896080f788d740

974

py

Python

ShowMhd.py

ZvikaDia/LearnGitHub

c9f15e01fc795b08440fb30fdf2d0afcfd728fbc

[ "BSD-2-Clause" ]

null

ShowMhd.py

ZvikaDia/LearnGitHub

c9f15e01fc795b08440fb30fdf2d0afcfd728fbc

[ "BSD-2-Clause" ]

1

2021-08-19T10:20:53.000Z

2021-08-19T10:27:35.000Z

ShowMhd.py

ZvikaDia/LearnGitHub

c9f15e01fc795b08440fb30fdf2d0afcfd728fbc

[ "BSD-2-Clause" ]

null

import SimpleITK as sitk import matplotlib.pylab as plt ct_scans = sitk.GetArrayFromImage(sitk.ReadImage(r"D:\DIACARDIO\DATA\Camus\training\patient0002\patient0002_4CH_ES_gt.mhd", sitk.sitkFloat32)) plt.figure() plt.gray() plt.imshow(ct_scans[0]) # plt.subplots_adjust(0,0,1,1,0.01,0.01) # for i in range(ct_scans.shape[0]): # plt.subplot(5,6,i+1), plt.imshow(ct_scans[i]), plt.axis('off') # # use plt.savefig(...) here if you want to save the images as .jpg, e.g., plt.savefig (r"D:\DeleteMe\20\test_gt.png") ct_scans = sitk.GetArrayFromImage(sitk.ReadImage(r"D:\DIACARDIO\DATA\Camus\training\patient0002\patient0002_4CH_ES.mhd", sitk.sitkFloat32)) plt.figure() plt.gray() plt.imshow(ct_scans[0]) # plt.subplots_adjust(0,0,1,1,0.01,0.01) # for i in range(ct_scans.shape[0]): # plt.subplot(5,6,i+1), plt.imshow(ct_scans[i]), plt.axis('off') # # use plt.savefig(...) here if you want to save the images as .jpg, e.g., plt.savefig (r"D:\DeleteMe\20\test.png")

46.380952

142

0.717659

179

974

3.815642

0.324022

0.081991

0.064422

0.093704

0.916545

0

0.063636

0.096509

974

21

143

46.380952

0.7125

0.446612

0

0.5

0

0.351607

0

1

0

false

0

0.166667

0

0.166667

0

null

0

1

0

null

0

6

22d682bd051b7e91419cbfd52b1e75f59c3df840

776

py

Python

steam/client/builtins/__init__.py

oczkers/steam-httpx

77c4c63a5b9937839f62acac01f8e07ca765f20c

[ "MIT" ]

1

2019-10-01T11:33:44.000Z

steam/client/builtins/__init__.py

wynick27/steam

deb1390f319d91d0fec14c32514f1a5f95d4647b

[ "MIT" ]

null

steam/client/builtins/__init__.py

wynick27/steam

deb1390f319d91d0fec14c32514f1a5f95d4647b

[ "MIT" ]

null

""" All high level features of :class:`steam.client.SteamClient` are implemented here in separate submodules. """ from steam.client.builtins.account import Account from steam.client.builtins.user import User from steam.client.builtins.web import Web from steam.client.builtins.unified_messages import UnifiedMessages from steam.client.builtins.leaderboards import Leaderboards from steam.client.builtins.gameservers import GameServers from steam.client.builtins.friends import Friends from steam.client.builtins.apps import Apps class BuiltinBase(GameServers, UnifiedMessages, Account, User, Web, Leaderboards, Friends, Apps): """ This object is used as base to implement all high level functionality. The features are separated into submodules. """ pass

40.842105

105

0.804124

101

776

6.168317

0.415842

0.158909

0.192616

0.295345

0

0.125

776

18

106

43.111111

0.917526

0.283505

0

1

0

true

0.1

0.8

0

0.9

0

null

0

1

0

null

0

1

0

1

0

6

a3db5a9ea9a2244850491175f9b783b611e2df44

156

py

Python

socket_programming/using_telnet.py

disooqi/learn-with-corey-schafer

d76b4e30190ed4d514455ddbb07cae96e6ed4d2e

[ "MIT" ]

2

2018-03-06T22:27:11.000Z

2020-10-04T06:14:27.000Z

socket_programming/using_telnet.py

disooqi/learn-with-corey-schafer

d76b4e30190ed4d514455ddbb07cae96e6ed4d2e

[ "MIT" ]

null

socket_programming/using_telnet.py

disooqi/learn-with-corey-schafer

d76b4e30190ed4d514455ddbb07cae96e6ed4d2e

[ "MIT" ]

1

2018-03-06T22:27:14.000Z

import getpass import telnetlib from telnetlib import Telnet with Telnet('localhost', 9600) as tn: tn.write('disooqi'.encode('ascii')) # tn.interact()

19.5

39

0.737179

21

156

5.47619

0.714286

0

0.02963

0.134615

156

7

40

22.285714

0.822222

0.083333

0

0.148936

0

1

0

true

0.2

0.6

0

0.6

0

1

0

null

0

1

0

null

0

1

0

1

0

6

a3e6c41c13851cd73965d5c7209ec44eabb4392e

44

py

Python

mne_qt_browser/figure.py

marsipu/mne-qt-browser

6b67dd5af1ef0e39590ffacfbd4dffd8bd7d273d

[ "BSD-3-Clause" ]

1

2021-11-01T08:59:15.000Z

mne_qt_browser/figure.py

mscheltienne/mne-qt-browser

6c2431632c577fb41f2c1fa25dfbe4e19205da69

[ "BSD-3-Clause" ]

null

mne_qt_browser/figure.py

mscheltienne/mne-qt-browser

6c2431632c577fb41f2c1fa25dfbe4e19205da69

[ "BSD-3-Clause" ]

null

from ._pg_figure import MNEQtBrowser # noqa

44

0.818182

6

44

5.666667

1

0

0.136364

44

1

44

0.894737

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

431a3b3236d9250faac854d90248e62b7cf16b52

101

py

Python

accounts/filters.py

yamansener199/CS308-Project

11b915c891494278db73dede565554704cbc8ae2

[ "Apache-2.0" ]

1

2021-11-13T11:35:40.000Z

accounts/filters.py

yamansener199/CS308-Project

11b915c891494278db73dede565554704cbc8ae2

[ "Apache-2.0" ]

null

accounts/filters.py

yamansener199/CS308-Project

11b915c891494278db73dede565554704cbc8ae2

[ "Apache-2.0" ]

2

2021-11-11T14:22:38.000Z

2021-11-13T11:35:42.000Z

import django_filters from django_filters import DateFilter, CharFilter from .models import *

16.833333

50

0.792079

12

101

6.5

0.583333

0.333333

0

0.178218

101

5

51

20.2

0.939759

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

4a375f3eef82646da3adb5a7927be5b831428df3

261

py

Python

coveo-functools/coveo_functools/flex/__init__.py

coveooss/coveo-python-oss

08f5048e12449392f3acaeff85e075711543daab

[ "Apache-2.0" ]

7

2021-03-02T19:30:30.000Z

2022-03-08T12:24:50.000Z

coveo-functools/coveo_functools/flex/__init__.py

coveooss/coveo-python-oss

08f5048e12449392f3acaeff85e075711543daab

[ "Apache-2.0" ]

29

2021-01-21T16:45:33.000Z

2021-12-10T12:09:26.000Z

coveo-functools/coveo_functools/flex/__init__.py

coveooss/coveo-python-oss

08f5048e12449392f3acaeff85e075711543daab

[ "Apache-2.0" ]

null

# backward compatibility stuff and import shortcuts from coveo_functools.flex.decorator import flex, RAW_KEY # noqa: F401 from coveo_functools.flex.deserializer import deserialize # noqa: F401 from coveo_functools.flex.types import * # noqa: F401,F403

43.5

72

0.793103

35

261

5.8

0.542857

0.133005

0.26601

0.325123

0.295567

0

0.053812

0.145594

261

5

73

52.2

0.856502

0.333333

0

1

0

true

0

1

0

1

0

null

0

1

0

1

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null

0

1

0

1

0

1

0

6

4a907bcfa0dc0ff154f451b28b77c0b96f47c1a2

2,768

py

Python

ivy_tests/test_ivy/test_functional/test_core/test_logic.py

MudasserAfzal/ivy

d43b5da54651ebf1183913acee1279b881e84245

[ "Apache-2.0" ]

1

2022-02-28T03:20:54.000Z

ivy_tests/test_ivy/test_functional/test_core/test_logic.py

MudasserAfzal/ivy

d43b5da54651ebf1183913acee1279b881e84245

[ "Apache-2.0" ]

null

ivy_tests/test_ivy/test_functional/test_core/test_logic.py

MudasserAfzal/ivy

d43b5da54651ebf1183913acee1279b881e84245

[ "Apache-2.0" ]

null

""" Collection of tests for unified logic functions """ # global import pytest import numpy as np # local import ivy import ivy.functional.backends.numpy import ivy_tests.test_ivy.helpers as helpers # logical_and @pytest.mark.parametrize( "x1_n_x2", [([True, True], [False, True]), ([[0.]], [[1.]])]) @pytest.mark.parametrize( "dtype", ['float32']) @pytest.mark.parametrize( "tensor_fn", [ivy.array, helpers.var_fn]) def test_logical_and(x1_n_x2, dtype, tensor_fn, dev, call): # smoke test x1, x2 = x1_n_x2 x1 = tensor_fn(x1, dtype, dev) x2 = tensor_fn(x2, dtype, dev) ret = ivy.logical_and(x1, x2) # type test assert ivy.is_array(ret) # cardinality test assert ret.shape == x1.shape # value test assert np.allclose(call(ivy.logical_and, x1, x2), ivy.functional.backends.numpy.logical_and(ivy.to_numpy(x1), ivy.to_numpy(x2))) # compilation test if call in [helpers.torch_call]: # pytorch scripting does not support .type() method return if not ivy.array_mode(): helpers.assert_compilable(ivy.logical_and) # logical_or @pytest.mark.parametrize( "x1_n_x2", [([True, True], [False, True]), ([[0.]], [[1.]])]) @pytest.mark.parametrize( "dtype", ['float32']) @pytest.mark.parametrize( "tensor_fn", [ivy.array, helpers.var_fn]) def test_logical_or(x1_n_x2, dtype, tensor_fn, dev, call): # smoke test x1, x2 = x1_n_x2 x1 = tensor_fn(x1, dtype, dev) x2 = tensor_fn(x2, dtype, dev) ret = ivy.logical_or(x1, x2) # type test assert ivy.is_array(ret) # cardinality test assert ret.shape == x1.shape # value test assert np.allclose(call(ivy.logical_or, x1, x2), ivy.functional.backends.numpy.logical_or(ivy.to_numpy(x1), ivy.to_numpy(x2))) # compilation test if call in [helpers.torch_call]: # pytorch scripting does not support .type() method return if not ivy.array_mode(): helpers.assert_compilable(ivy.logical_or) # logical_not @pytest.mark.parametrize( "x", [[True, True], [[0.]]]) @pytest.mark.parametrize( "dtype", ['float32']) @pytest.mark.parametrize( "tensor_fn", [ivy.array, helpers.var_fn]) def test_logical_not(x, dtype, tensor_fn, dev, call): # smoke test x = tensor_fn(x, dtype, dev) ret = ivy.logical_not(x) # type test assert ivy.is_array(ret) # cardinality test assert ret.shape == x.shape # value test assert np.allclose(call(ivy.logical_not, x), ivy.functional.backends.numpy.logical_not(ivy.to_numpy(x))) # compilation test if call in [helpers.torch_call]: # pytorch scripting does not support .type() method return if not ivy.array_mode(): helpers.assert_compilable(ivy.logical_not)

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null

0

6

43cfdf154d0ec1ce3831edaaeb95a6d991105d96

3,410

py

Python

tests/datasets/test_multi_datamodule.py

facebookresearch/pythia

079740bee4b357a7b1b866d35e2f1fad6edba8a4

[ "BSD-3-Clause" ]

3,252

2018-07-27T02:32:24.000Z

2020-05-07T17:54:46.000Z

tests/datasets/test_multi_datamodule.py

facebookresearch/pythia

079740bee4b357a7b1b866d35e2f1fad6edba8a4

[ "BSD-3-Clause" ]

209

2018-07-30T06:39:59.000Z

2020-05-04T22:03:48.000Z

tests/datasets/test_multi_datamodule.py

facebookresearch/pythia

079740bee4b357a7b1b866d35e2f1fad6edba8a4

[ "BSD-3-Clause" ]

431

2018-07-27T04:17:37.000Z

2020-05-05T13:58:02.000Z

# Copyright (c) Facebook, Inc. and its affiliates. import functools import torch from mmf.datasets.lightning_multi_datamodule import LightningMultiDataModule from mmf.datasets.mmf_dataset_builder import MMFDatasetBuilder from mmf.datasets.multi_datamodule import MultiDataModule from omegaconf import OmegaConf from tests.datasets.test_mmf_dataset_builder import SimpleMMFDataset class MultiDataModuleTestObject(MultiDataModule): def __init__(self, batch_size): self.batch_size = batch_size config = OmegaConf.create( { "use_features": True, "annotations": { "train": "not_a_real_annotations_dataset", "val": "not_a_real_annotations_dataset", }, "features": { "train": "not_a_real_features_dataset", "val": "not_a_real_features_dataset", }, "dataset_config": {"simple": 0}, } ) self.config = config self.dataset_list = [] dataset_builder = MMFDatasetBuilder( "simple", functools.partial(SimpleMMFDataset, num_examples=100) ) dataset_builder.train_dataloader = self._get_dataloader dataset_builder.val_dataloader = self._get_dataloader dataset_builder.test_dataloader = self._get_dataloader self.datamodules = {"simple": dataset_builder} def _get_dataloader(self): dataset = SimpleMMFDataset( num_examples=100, dataset_name="simple", dataset_type="val", config=self.config, ) dataloader = torch.utils.data.DataLoader( dataset=dataset, batch_size=self.batch_size, shuffle=False, num_workers=1, drop_last=False, ) return dataloader class LightningDataModuleTestObject(LightningMultiDataModule): def __init__(self, batch_size): self.batch_size = batch_size config = OmegaConf.create( { "use_features": True, "annotations": { "train": "not_a_real_annotations_dataset", "val": "not_a_real_annotations_dataset", }, "features": { "train": "not_a_real_features_dataset", "val": "not_a_real_features_dataset", }, "dataset_config": {"simple": 0}, } ) self.config = config self.dataset_list = [] dataset_builder = MMFDatasetBuilder( "simple", functools.partial(SimpleMMFDataset, num_examples=100) ) dataset_builder.train_dataloader = self._get_dataloader dataset_builder.val_dataloader = self._get_dataloader dataset_builder.test_dataloader = self._get_dataloader self.datamodules = {"simple": dataset_builder} def _get_dataloader(self): dataset = SimpleMMFDataset( num_examples=100, dataset_name="simple", dataset_type="val", config=self.config, ) dataloader = torch.utils.data.DataLoader( dataset=dataset, batch_size=self.batch_size, shuffle=False, num_workers=1, drop_last=False, ) return dataloader

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null

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1

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1

0

null

0

6

43d39546ea2d1046b2d090b13c9a78e4f68b1b01

246

py

Python

Python/B2-Wuerfel/Wuerfel.py

frankyhub/Calliope

335f0ef5ca9bcf57e14166319501ec9086bc09bf

[ "MIT" ]

null

Python/B2-Wuerfel/Wuerfel.py

frankyhub/Calliope

335f0ef5ca9bcf57e14166319501ec9086bc09bf

[ "MIT" ]

null

Python/B2-Wuerfel/Wuerfel.py

frankyhub/Calliope

335f0ef5ca9bcf57e14166319501ec9086bc09bf

[ "MIT" ]

null

def on_button_pressed_a(): basic.show_number(randint(1, 6)) input.on_button_pressed(Button.A, on_button_pressed_a) def on_button_pressed_b(): basic.show_number(randint(1, 4)) input.on_button_pressed(Button.B, on_button_pressed_b)

30.75

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0.772358

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0.569767

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246

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null

1

0

1

0

null

0

1

0

6

78f38e5a158b625177f169e9748e7a88cbc9ecd2

4,594

py

Python

graphql_compiler/tests/test_graphql_pretty_print.py

manesioz/graphql-compiler

b23be9c4a8e26f8c82e741625e04f7c9ac2e623b

[ "Apache-2.0" ]

521

2017-07-18T23:56:25.000Z

2022-03-25T16:39:06.000Z

graphql_compiler/tests/test_graphql_pretty_print.py

manesioz/graphql-compiler

b23be9c4a8e26f8c82e741625e04f7c9ac2e623b

[ "Apache-2.0" ]

740

2017-07-19T01:52:42.000Z

2021-09-30T11:15:00.000Z

graphql_compiler/tests/test_graphql_pretty_print.py

manesioz/graphql-compiler

b23be9c4a8e26f8c82e741625e04f7c9ac2e623b

[ "Apache-2.0" ]

56

2017-07-18T23:56:14.000Z

2021-10-30T08:08:56.000Z

# Copyright 2017-present Kensho Technologies, LLC. from textwrap import dedent import unittest from ..query_formatting.graphql_formatting import pretty_print_graphql class GraphQLPrettyPrintTests(unittest.TestCase): def test_graphql_pretty_print_indentation(self) -> None: bad_query = """{ Animal { name @output(out_name: "name") } }""" four_space_output = dedent( """\ { Animal { name @output(out_name: "name") } } """ ) two_space_output = dedent( """\ { Animal { name @output(out_name: "name") } } """ ) self.assertEqual(four_space_output, pretty_print_graphql(bad_query)) self.assertEqual(two_space_output, pretty_print_graphql(bad_query, use_four_spaces=False)) def test_filter_directive_order(self) -> None: bad_query = """{ Animal @filter(value: ["$name"], op_name: "name_or_alias") { uuid @filter(value: ["$max_uuid"], op_name: "<=") out_Entity_Related { ...on Species{ name @output(out_name: "related_species") } } } }""" expected_output = dedent( """\ { Animal @filter(op_name: "name_or_alias", value: ["$name"]) { uuid @filter(op_name: "<=", value: ["$max_uuid"]) out_Entity_Related { ... on Species { name @output(out_name: "related_species") } } } } """ ) self.assertEqual(expected_output, pretty_print_graphql(bad_query)) def test_args_not_in_schema(self) -> None: bad_query = """{ Animal @filter(value: ["$name"], unknown_arg: "value", op_name: "name_or_alias") { uuid @filter(value: ["$max_uuid"], op_name: "<=") out_Entity_Related { ...on Species{ name @output(out_name: "related_species") } } } }""" expected_output = dedent( """\ { Animal @filter(op_name: "name_or_alias", value: ["$name"], unknown_arg: "value") { uuid @filter(op_name: "<=", value: ["$max_uuid"]) out_Entity_Related { ... on Species { name @output(out_name: "related_species") } } } } """ ) self.assertEqual(expected_output, pretty_print_graphql(bad_query)) def test_missing_args(self) -> None: bad_query = """{ Animal @filter(value: ["$name"]) { uuid @filter(value: ["$max_uuid"], op_name: "<=") out_Entity_Related { ...on Species{ name @output(out_name: "related_species") } } } }""" expected_output = dedent( """\ { Animal @filter(value: ["$name"]) { uuid @filter(op_name: "<=", value: ["$max_uuid"]) out_Entity_Related { ... on Species { name @output(out_name: "related_species") } } } } """ ) self.assertEqual(expected_output, pretty_print_graphql(bad_query)) def test_other_directive(self) -> None: bad_query = """{ Animal @filter(value: ["$name"]) { uuid @filter(value: ["$max_uuid"], op_name: "<=") out_Entity_Related @other(arg1: "val1", arg2: "val2") { ...on Species{ name @output(out_name: "related_species") } } } }""" expected_output = dedent( """\ { Animal @filter(value: ["$name"]) { uuid @filter(op_name: "<=", value: ["$max_uuid"]) out_Entity_Related @other(arg1: "val1", arg2: "val2") { ... on Species { name @output(out_name: "related_species") } } } } """ ) self.assertEqual(expected_output, pretty_print_graphql(bad_query))

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98

0.44471

380

4,594

5.047368

0.163158

0.037539

0.074557

0.097497

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0.741919

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4,594

154

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0.111111

0

null

0

1

0

null

0

6

78ff0ff94b158b8a0b4a0bdd82c57578f958bc59

121

py

Python

hydra/plugins/plugin.py

andrewjong/hydra

c2faea0f137164721e73d4d0143f9e03554daae4

[ "MIT" ]

2

2021-02-06T00:23:56.000Z

2021-03-08T17:31:49.000Z

hydra/plugins/plugin.py

andrewjong/hydra

c2faea0f137164721e73d4d0143f9e03554daae4

[ "MIT" ]

4

2021-10-06T22:51:46.000Z

2022-02-27T12:53:27.000Z

hydra/plugins/plugin.py

andrewjong/hydra

c2faea0f137164721e73d4d0143f9e03554daae4

[ "MIT" ]

null

17.285714

70

0.727273

18

121

4.888889

0.888889

0

0.198347

121

6

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20.166667

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0

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true

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null

0

1

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null

0

1

0

1

0

6

78ffa1be2666509203b8ad6638c6f9b74cdd04a0

45

py

Python

RobotMovingPanel/features/__init__.py

Vlad12344/Pulseapi_Integration

2acf93a17dd2911328141886b8724134fff84f00

[ "MIT" ]

null

RobotMovingPanel/features/__init__.py

Vlad12344/Pulseapi_Integration

2acf93a17dd2911328141886b8724134fff84f00

[ "MIT" ]

null

RobotMovingPanel/features/__init__.py

Vlad12344/Pulseapi_Integration

2acf93a17dd2911328141886b8724134fff84f00

[ "MIT" ]

null

from RobotMovingPanel.features.TCP import tcp

45

0.888889

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null

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1

0

1

0

6

602893babf2340c835d0745a7c7a285b961f55ee

21,295

py

Python

demo_scripts/variable_type_config.py

groverpr/aws-fraud-detector-samples

a1f178ee4389416b93750abb1db622f74a6b3cb4

[ "MIT-0" ]

null

demo_scripts/variable_type_config.py

groverpr/aws-fraud-detector-samples

a1f178ee4389416b93750abb1db622f74a6b3cb4

[ "MIT-0" ]

null

demo_scripts/variable_type_config.py

groverpr/aws-fraud-detector-samples

a1f178ee4389416b93750abb1db622f74a6b3cb4

[ "MIT-0" ]

1

2022-01-25T20:48:22.000Z

RECIPE = \ { "Registration_FakeAccountCreationByBots": { "data_path": "data/Registration_FakeAccountCreationByBots_100k.csv", "variable_mappings": [ { "variable_name": "first_name", "variable_type": "SHIPPING_NAME", "data_type": "STRING" }, { "variable_name": "last_name", "variable_type": "BILLING_NAME", "data_type": "STRING" }, { "variable_name": "ip_address", "variable_type": "IP_ADDRESS", "data_type": "STRING" }, { "variable_name": "honeypot_hits_with_given_user_agent_last_hour", "variable_type": "NUMERIC", "data_type": "FLOAT" }, { "variable_name": "email_address", "variable_type": "EMAIL_ADDRESS", "data_type": "STRING" }, { "variable_name": "user_agent", "variable_type": "USERAGENT", "data_type": "STRING" }, { "variable_name": "honeypot_hits_with_given_ip_address_last_hour", "variable_type": "NUMERIC", "data_type": "FLOAT" } ], "label_mappings": { "FRAUD": ["fraud"], "LEGIT": ["legit"] } }, "Registration_FakeAccountCreationByHumans": { "data_path": "data/Registration_FakeAccountCreationByHumans_100k.csv", "variable_mappings": [ { "variable_name": "first_name", "variable_type": "SHIPPING_NAME", "data_type": "STRING" }, { "variable_name": "last_name", "variable_type": "BILLING_NAME", "data_type": "STRING" }, { "variable_name": "ip_address", "variable_type": "IP_ADDRESS", "data_type": "STRING" }, { "variable_name": "user_agent", "variable_type": "USERAGENT", "data_type": "STRING" }, { "variable_name": "email_address", "variable_type": "EMAIL_ADDRESS", "data_type": "STRING" }, { "variable_name": "date_of_birth", "variable_type": "FREE_FORM_TEXT", "data_type": "STRING" }, { "variable_name": "email_domain", "variable_type": "CATEGORICAL", "data_type": "STRING" } ], "label_mappings": { "FRAUD": ["fraud"], "LEGIT": ["legit"] } }, "Transactions_CardNotPresentOnlineTransactions": { "data_path": "data/Transactions_CardNotPresentOnlineTransactions_100k.csv", "variable_mappings": [ { "variable_name": "ip_address", "variable_type": "IP_ADDRESS", "data_type": "STRING" }, { "variable_name": "user_agent", "variable_type": "USERAGENT", "data_type": "STRING" }, { "variable_name": "email_address", "variable_type": "EMAIL_ADDRESS", "data_type": "STRING" }, { "variable_name": "fingerprint", "variable_type": "FINGERPRINT", "data_type": "STRING" }, { "variable_name": "phone_number", "variable_type": "PHONE_NUMBER", "data_type": "STRING" }, { "variable_name": "billing_address", "variable_type": "BILLING_ADDRESS_L1", "data_type": "STRING" }, { "variable_name": "billing_city", "variable_type": "BILLING_CITY", "data_type": "STRING" }, { "variable_name": "billing_postal", "variable_type": "BILLING_ZIP", "data_type": "STRING" }, { "variable_name": "billing_state", "variable_type": "BILLING_STATE", "data_type": "STRING" }, { "variable_name": "billing_country", "variable_type": "BILLING_COUNTRY", "data_type": "STRING" }, { "variable_name": "merchant_id", "variable_type": "CATEGORICAL", "data_type": "STRING" }, { "variable_name": "card_bin", "variable_type": "CARD_BIN", "data_type": "INTEGER" }, { "variable_name": "product_id", "variable_type": "CATEGORICAL", "data_type": "STRING" }, { "variable_name": "product_category", "variable_type": "CATEGORICAL", "data_type": "STRING" }, { "variable_name": "transaction_amount", "variable_type": "NUMERIC", "data_type": "FLOAT" }, { "variable_name": "shipping_address", "variable_type": "SHIPPING_ADDRESS_L1", "data_type": "STRING" }, { "variable_name": "shipping_city", "variable_type": "SHIPPING_CITY", "data_type": "STRING" }, { "variable_name": "shipping_postal", "variable_type": "SHIPPING_ZIP", "data_type": "STRING" }, { "variable_name": "shipping_state", "variable_type": "SHIPPING_STATE", "data_type": "STRING" }, { "variable_name": "shipping_country", "variable_type": "SHIPPING_COUNTRY", "data_type": "STRING" } ], "label_mappings": { "FRAUD": ["fraud"], "LEGIT": ["legit"] } }, "Transactions_LoyaltyPayments": { "data_path": "data/Transactions_LoyaltyPayments_100k.csv", "variable_mappings": [ { "variable_name": "ip_address", "variable_type": "IP_ADDRESS", "data_type": "STRING" }, { "variable_name": "user_agent", "variable_type": "USERAGENT", "data_type": "STRING" }, { "variable_name": "email_address", "variable_type": "EMAIL_ADDRESS", "data_type": "STRING" }, { "variable_name": "is_code_transferred", "variable_type": "CATEGORICAL", "data_type": "STRING" }, { "variable_name": "is_postal_in_txn_same_as_postal_in_acnt", "variable_type": "CATEGORICAL", "data_type": "STRING" }, { "variable_name": "shipping_city", "variable_type": "SHIPPING_CITY", "data_type": "STRING" }, { "variable_name": "shipping_postal", "variable_type": "SHIPPING_ZIP", "data_type": "STRING" }, { "variable_name": "shipping_state", "variable_type": "SHIPPING_STATE", "data_type": "STRING" }, { "variable_name": "shipping_country", "variable_type": "SHIPPING_COUNTRY", "data_type": "STRING" }, { "variable_name": "loyalty_card_type", "variable_type": "CATEGORICAL", "data_type": "STRING" }, { "variable_name": "transaction_amount", "variable_type": "NUMERIC", "data_type": "FLOAT" }, { "variable_name": "count_previous_redemptions_device", "variable_type": "NUMERIC", "data_type": "FLOAT" }, { "variable_name": "count_previous_redemptions_ip", "variable_type": "NUMERIC", "data_type": "FLOAT" }, { "variable_name": "count_of_txns_loyalty_card_last_day", "variable_type": "NUMERIC", "data_type": "FLOAT" }, { "variable_name": "amount_of_txns_loyalty_card_last_day", "variable_type": "NUMERIC", "data_type": "FLOAT" }, { "variable_name": "total_reward_points", "variable_type": "NUMERIC", "data_type": "FLOAT" }, { "variable_name": "coupon_code", "variable_type": "CATEGORICAL", "data_type": "STRING" }, { "variable_name": "device_id", "variable_type": "CATEGORICAL", "data_type": "STRING" } ], "label_mappings": { "FRAUD": ["fraud"], "LEGIT": ["legit"] } }, "Abuse_FreeTrialReferralAbuse": { "data_path": "data/Abuse_FreeTrialReferralAbuse_100k.csv", "variable_mappings": [ { "variable_name": "referral_code", "variable_type": "CATEGORICAL", "data_type": "STRING" }, { "variable_name": "first_name", "variable_type": "SHIPPING_NAME", "data_type": "STRING" }, { "variable_name": "card_bin", "variable_type": "CARD_BIN", "data_type": "INTEGER" }, { "variable_name": "last_name", "variable_type": "BILLING_NAME", "data_type": "STRING" }, { "variable_name": "email_address", "variable_type": "EMAIL_ADDRESS", "data_type": "STRING" }, { "variable_name": "ip_address", "variable_type": "IP_ADDRESS", "data_type": "STRING" }, { "variable_name": "phone_number", "variable_type": "PHONE_NUMBER", "data_type": "STRING" }, { "variable_name": "postal_code", "variable_type": "BILLING_ZIP", "data_type": "STRING" }, { "variable_name": "referral_medium", "variable_type": "CATEGORICAL", "data_type": "STRING" } ], "label_mappings": { "FRAUD": ["fraud"], "LEGIT": ["legit"] } }, "ContentModeration_FakeReviews": { "data_path": "data/ContentModeration_FakeReviews_100k.csv", "variable_mappings": [ { "variable_name": "hour_of_review", "variable_type": "NUMERIC", "data_type": "FLOAT" }, { "variable_name": "asin", "variable_type": "CATEGORICAL", "data_type": "STRING" }, { "variable_name": "review_text", "variable_type": "FREE_FORM_TEXT", "data_type": "STRING" }, { "variable_name": "rating", "variable_type": "NUMERIC", "data_type": "FLOAT" } ], "label_mappings": { "FRAUD": ["fraud"], "LEGIT": ["legit"] } }, "Insurance_FraudulentAutoInsuranceClaims": { "data_path": "data/Insurance_FraudulentAutoInsuranceClaims_100k.csv", "variable_mappings": [ { "variable_name": "first_name", "variable_type": "SHIPPING_NAME", "data_type": "STRING" }, { "variable_name": "last_name", "variable_type": "BILLING_NAME", "data_type": "STRING" }, { "variable_name": "policy_id", "variable_type": "ORDER_ID", "data_type": "STRING" }, { "variable_name": "policy_deductable", "variable_type": "NUMERIC", "data_type": "FLOAT" }, { "variable_name": "customer_age", "variable_type": "NUMERIC", "data_type": "FLOAT" }, { "variable_name": "policy_annual_premium", "variable_type": "NUMERIC", "data_type": "FLOAT" }, { "variable_name": "incident_severity", "variable_type": "NUMERIC", "data_type": "FLOAT" }, { "variable_name": "vehicle_claim", "variable_type": "NUMERIC", "data_type": "FLOAT" }, { "variable_name": "incident_hour", "variable_type": "NUMERIC", "data_type": "FLOAT" }, { "variable_name": "num_injuries", "variable_type": "NUMERIC", "data_type": "FLOAT" }, { "variable_name": "num_claims_past_year", "variable_type": "NUMERIC", "data_type": "FLOAT" }, { "variable_name": "injury_claim", "variable_type": "NUMERIC", "data_type": "FLOAT" }, { "variable_name": "num_vehicles_involved", "variable_type": "NUMERIC", "data_type": "FLOAT" }, { "variable_name": "num_witnesses", "variable_type": "NUMERIC", "data_type": "FLOAT" }, { "variable_name": "incident_type", "variable_type": "CATEGORICAL", "data_type": "STRING" }, { "variable_name": "police_report_available", "variable_type": "CATEGORICAL", "data_type": "STRING" } ], "label_mappings": { "FRAUD": ["fraud"], "LEGIT": ["legit"] } }, "Advertisement_AdClickFraud": { "data_path": "data/Advertisement_AdClickFraud_20k.csv", "variable_mappings": [ { "variable_name": "ip_address", "variable_type": "IP_ADDRESS", "data_type": "STRING" }, { "variable_name": "user_agent", "variable_type": "USERAGENT", "data_type": "STRING" }, { "variable_name": "campaign_id", "variable_type": "CATEGORICAL", "data_type": "STRING" }, { "variable_name": "publisher_id", "variable_type": "CATEGORICAL", "data_type": "STRING" }, { "variable_name": "time_between_clicks_minutes", "variable_type": "NUMERIC", "data_type": "FLOAT" }, { "variable_name": "click_id", "variable_type": "CATEGORICAL", "data_type": "STRING" }, { "variable_name": "app_category_id", "variable_type": "CATEGORICAL", "data_type": "STRING" } ], "label_mappings": { "FRAUD": ["fraud"], "LEGIT": ["legit"] } } }

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6065cdf1306b7e7a7bcc249359b334450efed9ac

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py

Python

net_ner/task/__init__.py

renjunxiang/ccks2019_el

67b7b35312c06248ea1deccbfb37cf5d8e5c6376

[ "MIT" ]

99

2019-08-01T01:04:54.000Z

2022-03-17T09:00:14.000Z

net_ner/task/__init__.py

ZhouXiaoLeilei/ccks2019_el-1

67b7b35312c06248ea1deccbfb37cf5d8e5c6376

[ "MIT" ]

5

2019-08-06T02:16:20.000Z

2021-12-12T15:37:27.000Z

net_ner/task/__init__.py

ZhouXiaoLeilei/ccks2019_el-1

67b7b35312c06248ea1deccbfb37cf5d8e5c6376

[ "MIT" ]

18

2019-08-10T11:18:29.000Z

2022-03-15T04:44:52.000Z

from .Locate_Entity import Locate_Entity, slice_entity

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1

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1

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6

60ed1a174d34aacfa7b01dbf21c63f1db5b18160

6,838

py

Python

scripts/networkDesign.py

johodges/datadriven-wildfire-spread

f391c6322b70c28c0016eecc3d324c6078b03dc9

[ "Apache-2.0" ]

null

scripts/networkDesign.py

johodges/datadriven-wildfire-spread

f391c6322b70c28c0016eecc3d324c6078b03dc9

[ "Apache-2.0" ]

null

scripts/networkDesign.py

johodges/datadriven-wildfire-spread

f391c6322b70c28c0016eecc3d324c6078b03dc9

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- """ Created on Fri Apr 20 14:05:20 2018 @author: JHodges """ import tensorflow as tf def cnnModel3(features, labels, mode): """Model function for CNN.""" dconv = True sz = 50 n_dimensions = 13 #n_dimensions = int(features["x"].get_shape().as_list()[1]/(sz**2)) print("MODE=%s\nInput Dimensions=%s"%(mode,n_dimensions)) ks1 = [10,10] ks2 = [10,10] ks3 = [10,10] fs1 = 32 fs2 = 64 fs3 = 2 # Input Layer input_layer = tf.reshape(features["x"], [-1, sz, sz, n_dimensions]) dropOut_layer = tf.layers.dropout(input_layer,rate=0.5) #print(input_layer.shape) # Convolutional Layer #1 conv1 = tf.layers.conv2d( inputs=dropOut_layer, filters=fs1, kernel_size=ks1, padding="same", activation=tf.nn.leaky_relu, name="conv1") pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2) # Convolutional Layer #2 and Pooling Layer #2 conv2 = tf.layers.conv2d( inputs=pool1, filters=fs2, kernel_size=ks2, padding="same", activation=tf.nn.leaky_relu, name="conv2") pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2) pool2flat = tf.reshape(pool2,[-1,pool2.shape[1]*pool2.shape[2]*pool2.shape[3]]) if dconv: dense1 = tf.layers.dense(inputs=pool2flat, units=int(sz*sz*2), activation=tf.nn.leaky_relu) dense1_rs = tf.reshape(dense1,[-1,sz,sz,2]) dconv1 = tf.layers.conv2d_transpose( inputs=dense1_rs,filters=fs3, kernel_size=ks3, padding="same", activation=tf.nn.leaky_relu, name="dconv1") dconv1flat = tf.reshape(dconv1,[-1,dconv1.shape[1]*dconv1.shape[2]*dconv1.shape[3]]) denseOut = tf.layers.dense(inputs=dconv1flat, units=int(sz*sz*2), activation=tf.nn.tanh) print("Input Layer Dimensions:\t",input_layer.shape) print("Dropout Layer Dimensions:\t",dropOut_layer.shape) print("First Conv Layer Dim:\t",conv1.shape) print("First Pool Layer Dim:\t",pool1.shape) print("Second Conv Layer Dim:\t", conv2.shape) print("Second Pool Layer Dim:\t", pool2.shape) print("Classify Layer Dim:\t", dense1.shape) print("Deconv Layer Dim:\t", dconv1.shape) print("Output Layer Dim:\t",denseOut.shape) else: denseOut = tf.layers.dense(inputs=pool2flat, units=int(sz*sz*2), activation=tf.nn.tanh) logits = tf.reshape(denseOut,[-1,int(sz*sz*2)]) predicted_classes = tf.argmax(input=tf.reshape(dense1,[-1,int(sz*sz),2]), axis=2) if mode == tf.estimator.ModeKeys.PREDICT: predictions = { 'class_ids': predicted_classes,#[:, tf.newaxis], 'probabilities': tf.nn.softmax(logits), 'logits': logits, } return tf.estimator.EstimatorSpec(mode, predictions=predictions) loss = tf.reduce_sum(abs(tf.cast(labels,tf.float32)-tf.cast(logits,tf.float32))**2)**0.5 label_rs = tf.reshape(labels,[-1,int(sz*sz),2]) label_classes = tf.argmax(input=label_rs,axis=2) accuracy = tf.metrics.accuracy(labels=label_classes,predictions=predicted_classes,name='acc_op') metrics = {'accuracy': accuracy} tf.summary.scalar('accuracy', accuracy[1]) if mode == tf.estimator.ModeKeys.EVAL: return tf.estimator.EstimatorSpec(mode,loss=loss,eval_metric_ops=metrics) if mode == tf.estimator.ModeKeys.TRAIN: optimizer = tf.train.GradientDescentOptimizer(learning_rate=10**-4) train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step()) return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op) def cnnModel4(features, labels, mode): """Model function for CNN.""" dropoutRate = 0.25 if mode == tf.estimator.ModeKeys.TRAIN else 0.0 (sz,n_dimensions) = (50,13) (ks1,fs1,ks2,fs2,ks3,fs3) = ([10,10],32,[10,10],64,[10,10],2) lrelu = tf.nn.leaky_relu #n_dimensions = int(features["x"].get_shape().as_list()[1]/(sz**2)) print("MODE=%s\nInput Dimensions=%s"%(mode,n_dimensions)) # Input Layer input_layer = tf.reshape(features["x"], [-1, sz, sz, n_dimensions]) # Convolutional Layer #1 conv1 = tf.layers.conv2d(inputs=input_layer,filters=fs1,kernel_size=ks1,padding="same",activation=lrelu,name="conv1") pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2) # Convolutional Layer #2 and Pooling Layer #2 conv2 = tf.layers.conv2d(inputs=pool1,filters=fs2,kernel_size=ks2,padding="same",activation=lrelu,name="conv2") pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2) pool2flat = tf.reshape(pool2,[-1,pool2.shape[1]*pool2.shape[2]*pool2.shape[3]]) # Dense classification layer dense1 = tf.layers.dense(inputs=pool2flat, units=int(sz*sz*2), activation=lrelu) # Dropout layer dropOut_layer = tf.layers.dropout(dense1,rate=dropoutRate) dense1_rs = tf.reshape(dropOut_layer,[-1,sz,sz,2]) dconv1 = tf.layers.conv2d_transpose(inputs=dense1_rs,filters=fs3,kernel_size=ks3,padding="same",activation=lrelu,name="dconv1") dconv1flat = tf.reshape(dconv1,[-1,dconv1.shape[1]*dconv1.shape[2]*dconv1.shape[3]]) # Output layer denseOut = tf.layers.dense(inputs=dconv1flat, units=int(sz*sz*2), activation=tf.nn.tanh) logits = tf.reshape(denseOut,[-1,int(sz*sz*2)]) predicted_classes = tf.argmax(input=tf.reshape(dense1,[-1,int(sz*sz),2]), axis=2) # Print sizes for debugging print("Input Layer Dimensions:\t",input_layer.shape) print("First Conv Layer Dim:\t",conv1.shape) print("First Pool Layer Dim:\t",pool1.shape) print("Second Conv Layer Dim:\t", conv2.shape) print("Second Pool Layer Dim:\t", pool2.shape) print("Classify Layer Dim:\t", dense1.shape) print("Dropout Layer Dimensions:\t",dropOut_layer.shape) print("Deconv Layer Dim:\t", dconv1.shape) print("Output Layer Dim:\t",denseOut.shape) if mode == tf.estimator.ModeKeys.PREDICT: predictions = {'class_ids': predicted_classes,'probabilities': tf.nn.softmax(logits),'logits': logits} return tf.estimator.EstimatorSpec(mode, predictions=predictions) #loss = tf.reduce_sum(abs(tf.cast(labels,tf.float32)-tf.cast(logits,tf.float32))**2)**0.5 loss = tf.losses.sigmoid_cross_entropy(labels,logits) if mode == tf.estimator.ModeKeys.TRAIN: optimizer = tf.train.GradientDescentOptimizer(learning_rate=10**-4) train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step()) return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)

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null

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1

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null

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6

71c62ab4879ea1f548ed7cf43cbd971c494b3dbd

25

py

Python

labtools/__main__.py

Jejulia/labtools

fbd5e6d9857e2403feda47fd683e7a2d0532b600

[ "MIT" ]

null

labtools/__main__.py

Jejulia/labtools

fbd5e6d9857e2403feda47fd683e7a2d0532b600

[ "MIT" ]

null

labtools/__main__.py

Jejulia/labtools

fbd5e6d9857e2403feda47fd683e7a2d0532b600

[ "MIT" ]

null

import labtools as lb

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0

1

0

1

0

6

71cb60f27edd018e3882af6dccfb9181ed5d4dcb

44

py

Python

gym_fabrikatioRL/__init__.py

malerinc/fabricatio-rl

414ec7cacd0e4316882bb93109930ad8c257cf7f

[ "MIT" ]

3

2021-08-09T15:40:36.000Z

2022-03-18T07:31:16.000Z

gym_fabrikatioRL/__init__.py

malerinc/fabricatio-rl

414ec7cacd0e4316882bb93109930ad8c257cf7f

[ "MIT" ]

null

gym_fabrikatioRL/__init__.py

malerinc/fabricatio-rl

414ec7cacd0e4316882bb93109930ad8c257cf7f

[ "MIT" ]

null

from gym.envs.registration import register

14.666667

42

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43

22

0.948718

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null

0

1

0

1

0

1

0

6

e0b3b5d46f632529071d69810a97f8aa2d59e9dd

21

py

Python

invoke_tools/cloud/__init__.py

VJftw/invoke-tools

9584a1f8a402118310b6f2a495062f388fc8dc3a

[ "MIT" ]

2

2017-07-02T02:46:58.000Z

2018-07-24T03:36:30.000Z

invoke_tools/cloud/__init__.py

VJftw/invoke-tools

9584a1f8a402118310b6f2a495062f388fc8dc3a

[ "MIT" ]

null

invoke_tools/cloud/__init__.py

VJftw/invoke-tools

9584a1f8a402118310b6f2a495062f388fc8dc3a

[ "MIT" ]

1

2019-11-27T14:43:03.000Z

from .aws import AWS

10.5

20

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1

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null

0

1

0

1

0

1

0

6

e0be315d5602fa72ecd0629df16722e7a4480341

134

py

Python

__init__.py

fuzzyVineStone/PygamePlus

e17e536c82aec5275d21261b233f5b1dd2083747

[ "MIT" ]

null

__init__.py

fuzzyVineStone/PygamePlus

e17e536c82aec5275d21261b233f5b1dd2083747

[ "MIT" ]

null

__init__.py

fuzzyVineStone/PygamePlus

e17e536c82aec5275d21261b233f5b1dd2083747

[ "MIT" ]

null

from .audio import * from .event import * from .graphics import * from .keyboard import * from .physics import * from .time import *

16.75

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1

0

1

0

1

0

6

e0e160fd2d76274dc3ea7103fefc3d37e92db99b

4,513

py

Python

data/level/level10216.py

levelupai/match3-level-similarity

cc9b28b8741b41bea1273c8bc9b4d265d79a1dca

[ "Apache-2.0" ]

null

data/level/level10216.py

levelupai/match3-level-similarity

cc9b28b8741b41bea1273c8bc9b4d265d79a1dca

[ "Apache-2.0" ]

6

2020-07-04T02:53:08.000Z

2022-03-11T23:53:14.000Z

data/level/level10216.py

levelupai/match3-level-similarity

cc9b28b8741b41bea1273c8bc9b4d265d79a1dca

[ "Apache-2.0" ]

3

2019-12-31T11:42:59.000Z

2021-03-28T20:06:13.000Z

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e0fa55e4200d0e84f069807b27139c601e515801

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Python

tests/nlu_core_tests/training_tests/classifiers/multi_classifier_dl_tests.py

askmetoo/nlu

e9ce74895ac44f6b61610f0926e4ab08759f04f7

[ "Apache-2.0" ]

1

2021-12-26T07:56:38.000Z

tests/nlu_core_tests/training_tests/classifiers/multi_classifier_dl_tests.py

avanishiitk/nlu

ca244fe422923084196f6f6f4c5c41723e46d537

[ "Apache-2.0" ]

null

tests/nlu_core_tests/training_tests/classifiers/multi_classifier_dl_tests.py

avanishiitk/nlu

ca244fe422923084196f6f6f4c5c41723e46d537

[ "Apache-2.0" ]

null

from sklearn.metrics import classification_report import unittest from nlu import * import tests.test_utils as t import pandas as pd class MultiClassifierDlTests(unittest.TestCase): def test_multi_classifier_dl_training(self): # The y column must be a string seperated with ```,``` . Custom seperators can be configured by passing test_df = self.load_multi_classifier_dl_dataset() # test_df.columns = ['y_str','text'] # test_df['y'] = test_df.y_str.str.split(',') # test_df.y = test_df.y.astype('stringArray')#pd.arrays. # test_df.y = test_df.y.astype(list[str])#pd.arrays. print(test_df.y) print(test_df) print(test_df.dtypes) # test_df.drop('y_str',inplace=True,axis=1) train_df = test_df pipe = nlu.load('train.multi_classifier',verbose=True,) #: java.lang.IllegalArgumentException: requirement failed: The label column MultiClassifierDLApproach_cbfe97978b3c__labelColumn type is StringType and it's not compatible. Compatible types are ArrayType(StringType). # pipe['multi_classifier_dl'].setMaxEpochs(2) # pipe.print_info() pipe = pipe.fit(train_df) df = pipe.predict(train_df) print(df.columns) for c in df.columns : print (df[c]) # # print(df[['multi_classifier_classes','y']]) # print(df[['multi_classifier_confidences','y']]) df = pipe.predict(test_df) print(df.columns) for c in df.columns : print (df[c]) # print(df[['multi_classifier_classes','y']]) # print(df[['multi_classifier_confidence','y']]) df.dropna(inplace=True) # print (classification_report(df['y'], df['multi_classifier_classes'])) # Too heavy running on github actions # # def test_multi_classifier_dl_custom_embeds_doc_level(self): # test_df = self.load_multi_classifier_dl_dataset() # # test_df.columns = ['y_str','text'] # test_df.columns = ['y','text'] # # # # print(test_df.y) # print(test_df) # print(test_df.dtypes) # # # test_df.drop('y_str',inplace=True,axis=1) # train_df = test_df # # pipe = nlu.load('embed_sentence.bert train.multi_classifier',verbose=True,) # #: java.lang.IllegalArgumentException: requirement failed: The label column MultiClassifierDLApproach_cbfe97978b3c__labelColumn type is StringType and it's not compatible. Compatible types are ArrayType(StringType). # # # pipe['multi_classifier_dl'].setMaxEpochs(2) # pipe.print_info() # pipe = pipe.fit(train_df) # df = pipe.predict(train_df) # print(df.columns) # print(df[['multi_classifier','y']]) # print(df[['multi_classifier_confidence','y']]) # df = pipe.predict(test_df) # print(df.columns) # print(df[['multi_classifier','y']]) # print(df[['multi_classifier_confidence','y']]) # df.dropna(inplace=True) # print (classification_report(df['y'], df['multi_classifier'])) # # def test_multi_classifier_dl_custom_embeds_sentence_level(self): # test_path = self.load_multi_classifier_dl_dataset() # test_df = pd.read_csv(test_path) # train_df = test_df # train_df.columns = ['y','text'] # test_df.columns = ['y','text'] # pipe = nlu.load('embed_sentence.bert train.multi_classifier',verbose=True,) # pipe['multi_classifier_dl'].setMaxEpochs(2) # pipe = pipe.fit(train_df) # df = pipe.predict(train_df, output_level='sentence') # print(df.columns) # print(df[['category','y']]) # df = pipe.predict(test_df, output_level='sentence') # print(df.columns) # print(df[['category','y']]) # # Eval results # from sklearn.metrics import classification_report # print (classification_report(df['y'], df['category'])) # # # def test_multi_classifier_dl_custom_embeds_auto_level(self): # test_path = self.load_multi_classifier_dl_dataset() # test_df = pd.read_csv(test_path) # train_df = test_df # train_df.columns = ['y','text'] # test_df.columns = ['y','text'] # pipe = nlu.load('embed_sentence.bert train.multi_classifier',verbose=True,) # pipe['multi_classifier_dl'].setMaxEpochs(2) # pipe = pipe.fit(train_df) # df = pipe.predict(train_df) # print(df.columns) # print(df[['category','y']]) # df = pipe.predict(test_df) # print(df.columns) # print(df[['category','y']]) # # Eval results # from sklearn.metrics import classification_report # print (classification_report(df['y'], df['category'])) # def load_multi_classifier_dl_dataset(self): # #relative from tests/nlu_core_tests/training_tests/classifiers # p = '/home/loan/Documents/freelancework/jsl/nlu/4realnlugit/tests/datasets/multi_classifier_dl/e2e-dataset/testset_w_refs.csv' # return pd.read_csv(p) def load_multi_classifier_dl_dataset(self): output_file_name = 'e2e_test.csv' output_folder = 'multi_classifier_dl/' # data_url = "http://ckl-it.de/wp-content/uploads/2020/12/testset_w_refs.csv" data_url = "http://ckl-it.de/wp-content/uploads/2020/12/e2e.csv" return pd.read_csv(t.download_dataset(data_url,output_file_name,output_folder)).iloc[0:100] # output_file_name = 'news_category_test.csv' # output_folder = 'multi_classifier_dl/' # data_dir = '../../../datasets/' # data_url = "https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/resources/en/classifier-dl/news_Category/news_category_test.csv" # return t.download_dataset(data_url,output_file_name,output_folder,data_dir) # # def load_classifier_dl_dataset(self): # #relative from tests/nlu_core_tests/training_tests/classifiers # output_file_name = 'news_category_test.csv' # output_folder = 'classifier_dl/' # data_dir = t.create_dataset_dir_if_not_exist_and_get_path() # t.create_path_if_not_exist(data_dir + output_file_name) # data_url = "https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/resources/en/classifier-dl/news_Category/news_category_test.csv" # return t.download_dataset(data_url,output_file_name,output_folder,data_dir) if __name__ == '__main__': unittest.main()

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Python

Week10/Day5/sortingtry.py

malharlakdawala/DevelopersInstitute

3d6a9fb0002670878105d983edca432f635bce6d

[ "MIT" ]

null

Week10/Day5/sortingtry.py

malharlakdawala/DevelopersInstitute

3d6a9fb0002670878105d983edca432f635bce6d

[ "MIT" ]

null

Week10/Day5/sortingtry.py

malharlakdawala/DevelopersInstitute

3d6a9fb0002670878105d983edca432f635bce6d

[ "MIT" ]

1

2021-10-09T19:01:08.000Z

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