xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

083cd985e8490fd2529b4838358dcdef0b1b20a5

92

py

Python

DisplayPane/Widgets/LabelEditor/PolygonEditor.py

CallumJHays/g26-egb320-2019

6dde6b5d2f72fac3928c5042a27dc50e978c3425

[ "MIT" ]

null

DisplayPane/Widgets/LabelEditor/PolygonEditor.py

CallumJHays/g26-egb320-2019

6dde6b5d2f72fac3928c5042a27dc50e978c3425

[ "MIT" ]

null

DisplayPane/Widgets/LabelEditor/PolygonEditor.py

CallumJHays/g26-egb320-2019

6dde6b5d2f72fac3928c5042a27dc50e978c3425

[ "MIT" ]

null

from .LabelEditorABC import LabelEditorABC class PolygonEditor(LabelEditorABC): pass

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6

f229f311f3f4ccf1acd2dfce868c83a1f8ef46e4

31

py

Python

pipeline/src/__init__.py

sawyerwatts/StopSpotDataPipeline

6537d0d1779d9ffa6a3096c02f4081d659c12a0e

[ "MIT" ]

3

2020-02-19T05:25:56.000Z

2020-02-22T21:31:34.000Z

pipeline/src/__init__.py

sawyerwatts/StopSpotDataPipeline

6537d0d1779d9ffa6a3096c02f4081d659c12a0e

[ "MIT" ]

69

2020-02-20T20:30:03.000Z

2020-05-29T01:20:05.000Z

pipeline/src/__init__.py

wolakdav/TeamBeeCapstoneProject

6957416273fda85a12e86408ae635d7491fb1035

[ "MIT" ]

4

2020-06-05T03:47:49.000Z

2020-12-21T01:17:02.000Z

from src.client import _Client

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0.83871

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f23978df56cad21b8a64bac428b7aa1b0c27293c

272

py

Python

tests/test_de.py

alexmisk/pyserde

bfa8629240950657f750464dbb80d8160a8f8070

[ "MIT" ]

null

tests/test_de.py

alexmisk/pyserde

bfa8629240950657f750464dbb80d8160a8f8070

[ "MIT" ]

10

2020-11-03T07:30:06.000Z

2021-09-01T06:47:13.000Z

tests/test_de.py

alexmisk/pyserde

bfa8629240950657f750464dbb80d8160a8f8070

[ "MIT" ]

null

from typing import Tuple, Union from serde.de import from_obj def test_from_obj(): assert not from_obj(int, None, False, True) assert "a" == from_obj(Union[int, str], "a", False, True) assert ("a", "b") == from_obj(Tuple[str, str], ("a", "b"), False, True)

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6

f27c1b296ba314e2b5a56250cea1770949c60d6f

85

py

Python

shop_simplevariations/tests/__init__.py

pjdelport/django-shop-simplevariations

72ecfe2ebe31ccbd51a745f954a15ce848c48be9

[ "BSD-3-Clause" ]

9

2015-03-14T20:55:06.000Z

2021-06-06T11:50:18.000Z

shop_simplevariations/tests/__init__.py

shyba/django-shop-simplevariations

e62e2cdddf4e4caed89860c191e94bc6fb6a3346

[ "BSD-3-Clause" ]

2

2016-08-10T18:54:19.000Z

2016-10-03T13:46:16.000Z

shop_simplevariations/tests/__init__.py

shyba/django-shop-simplevariations

e62e2cdddf4e4caed89860c191e94bc6fb6a3346

[ "BSD-3-Clause" ]

8

2015-01-08T18:00:07.000Z

2019-04-13T23:22:57.000Z

from .cart_modifier import * from .simplevariation_tags import * from .views import *

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f2910a5c61c89b31230f4cbac7d9b435ab4085ce

99

py

Python

taskflow/tests/test_task_run.py

jiangxianfu/smarttaskflow

c661d9776bc98823396d423e33b121933d4c3611

[ "MIT" ]

9

2020-02-25T01:23:10.000Z

2022-01-29T10:14:13.000Z

taskflow/tests/test_task_run.py

jiangxianfu/smarttaskflow

c661d9776bc98823396d423e33b121933d4c3611

[ "MIT" ]

null

taskflow/tests/test_task_run.py

jiangxianfu/smarttaskflow

c661d9776bc98823396d423e33b121933d4c3611

[ "MIT" ]

5

2020-02-23T14:32:56.000Z

2022-01-07T17:48:03.000Z

# -*- coding: utf-8 -*- def test_task_run(): print("test task run module") assert 1 == 1

14.142857

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6

4b2e090c6474fa2bd4eb423189555879fcc84e04

439

py

Python

src/pyconmech/init_template.py

yijiangh/conmech

9f24230f08587c5e62e3b482f8829f5ea449a169

[ "MIT" ]

10

2018-12-10T17:52:15.000Z

2021-05-12T05:49:34.000Z

src/pyconmech/init_template.py

yijiangh/conmech

9f24230f08587c5e62e3b482f8829f5ea449a169

[ "MIT" ]

32

2018-11-28T04:00:24.000Z

2020-03-14T21:20:38.000Z

src/pyconmech/init_template.py

yijiangh/conmech

9f24230f08587c5e62e3b482f8829f5ea449a169

[ "MIT" ]

1

2020-09-23T01:19:00.000Z

# https://github.com/jpanetta/MeshFEM/blob/master/python/init_template.py ################################################################################ # auto-generated from @PROJECT_SOURCE_DIR@/src/pyconmech/init_template.py ################################################################################ import sys as _sys _sys.path.insert(0, '@PROJECT_SOURCE_DIR@/src/pyconmech') from _pystiffness_checker import _StiffnessChecker

54.875

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0.12844

0.174312

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6

4b32ed49c9ab2b0ce0485bee3ada01060d5fb711

283

py

Python

xdnlp/bert/layers/__init__.py

mikuh/xdnlp

1da294659e276c59c620a9ebbab875f1d6fbb038

[ "MIT" ]

1

2022-02-08T03:27:32.000Z

xdnlp/bert/layers/__init__.py

mikuh/xdnlp

1da294659e276c59c620a9ebbab875f1d6fbb038

[ "MIT" ]

null

xdnlp/bert/layers/__init__.py

mikuh/xdnlp

1da294659e276c59c620a9ebbab875f1d6fbb038

[ "MIT" ]

null

from xdnlp.bert.layers.position_embedding import PositionEmbedding from xdnlp.bert.layers.self_attention_mask import SelfAttentionMask from xdnlp.bert.layers.on_device_embedding import OnDeviceEmbedding from xdnlp.bert.layers.transformer_encoder_block import TransformerEncoderBlock

56.6

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4b3a070740133baf9138a1d01a459a8ff6643dd3

4,061

py

Python

tests/grammpy_test/oldapi_tests/term-nonterm-grammar-handling_tests/NonterminalAddRemoveMixedTest.py

PatrikValkovic/grammpy

8308a1fd349bf9ea0d267360cc9a4ab20d1629e8

[ "MIT" ]

1

2021-02-04T12:41:08.000Z

tests/grammpy_test/oldapi_tests/term-nonterm-grammar-handling_tests/NonterminalAddRemoveMixedTest.py

PatrikValkovic/grammpy

8308a1fd349bf9ea0d267360cc9a4ab20d1629e8

[ "MIT" ]

3

2017-07-08T16:28:52.000Z

2020-04-23T18:06:24.000Z

tests/grammpy_test/oldapi_tests/term-nonterm-grammar-handling_tests/NonterminalAddRemoveMixedTest.py

PatrikValkovic/grammpy

8308a1fd349bf9ea0d267360cc9a4ab20d1629e8

[ "MIT" ]

1

2021-02-04T12:41:10.000Z

#!/usr/bin/env python """ :Author Patrik Valkovic :Created 03.08.2017 12:28 :Licence MIT Part of grammpy """ from unittest import TestCase, main from grammpy.old_api import Grammar from grammpy.old_api import Nonterminal class TempClass(Nonterminal): pass class Second(Nonterminal): pass class Third(Nonterminal): pass class NonterminalAddRemoveMixedTest(TestCase): def test_add_remove_add_one(self): gr = Grammar() self.assertEqual(gr.nonterms_count(), 0) self.assertFalse(gr.have_nonterm(TempClass)) self.assertIsNone(gr.get_nonterm(TempClass)) self.assertIsNone(gr.nonterm(TempClass)) self.assertEqual(gr.add_nonterm(TempClass), [TempClass]) self.assertEqual(gr.nonterms_count(), 1) self.assertIsNotNone(gr.get_nonterm(TempClass)) self.assertIsNotNone(gr.nonterm(TempClass)) self.assertEqual(gr.nonterm(TempClass), TempClass) self.assertEqual(gr.remove_nonterm(TempClass), [TempClass]) self.assertEqual(gr.nonterms_count(), 0) self.assertFalse(gr.have_nonterm(TempClass)) self.assertIsNone(gr.get_nonterm(TempClass)) self.assertIsNone(gr.nonterm(TempClass)) def test_addTwoRemoveOneAndAddThird(self): gr = Grammar() self.assertEqual(gr.add_nonterm(TempClass), [TempClass]) self.assertEqual(gr.add_nonterm(Second), [Second]) self.assertEqual(gr.nonterms_count(), 2) self.assertIsNotNone(gr.get_nonterm(TempClass)) self.assertIsNotNone(gr.nonterm(TempClass)) self.assertEqual(gr.get_nonterm(TempClass), TempClass) self.assertIsNotNone(gr.get_nonterm(Second)) self.assertIsNotNone(gr.nonterm(Second)) self.assertEqual(gr.get_nonterm(Second), Second) self.assertEqual(gr.remove_nonterm(Second), [Second]) self.assertEqual(gr.nonterms_count(), 1) self.assertIsNotNone(gr.get_nonterm(TempClass)) self.assertIsNotNone(gr.nonterm(TempClass)) self.assertEqual(gr.nonterm(TempClass), TempClass) self.assertIsNone(gr.get_nonterm(Second)) self.assertEqual(gr.add_nonterm(Third), [Third]) self.assertEqual(gr.nonterms_count(), 2) self.assertIsNotNone(gr.get_nonterm(TempClass)) self.assertIsNotNone(gr.nonterm(TempClass)) self.assertEqual(gr.get_nonterm(TempClass), TempClass) self.assertFalse(gr.have_nonterm(Second)) self.assertIsNone(gr.nonterm(Second)) self.assertIsNotNone(gr.get_nonterm(Third)) self.assertIsNotNone(gr.nonterm(Third)) self.assertEqual(gr.get_nonterm(Third), Third) def test_addThreeRemoveTwoInArray(self): gr = Grammar() self.assertEqual(gr.add_nonterm([TempClass, Second, Third]), [TempClass, Second, Third]) self.assertEqual(gr.nonterms_count(), 3) self.assertIsNotNone(gr.get_nonterm(TempClass)) self.assertIsNotNone(gr.nonterm(TempClass)) self.assertEqual(gr.nonterm(TempClass), TempClass) self.assertIsNotNone(gr.get_nonterm(Second)) self.assertIsNotNone(gr.nonterm(Second)) self.assertEqual(gr.nonterm(Second), Second) self.assertIsNotNone(gr.get_nonterm(Third)) self.assertIsNotNone(gr.nonterm(Third)) self.assertEqual(gr.nonterm(Third), Third) self.assertEqual(gr.remove_nonterm([Third, TempClass]), [Third, TempClass]) self.assertEqual(gr.nonterms_count(), 1) self.assertTrue(gr.have_nonterm(Second)) self.assertFalse(gr.have_nonterm(TempClass)) self.assertFalse(gr.have_nonterm(Third)) self.assertEqual(gr.add_nonterm(Third), [Third]) self.assertEqual(gr.nonterms_count(), 2) self.assertIsNotNone(gr.nonterm(Second)) self.assertEqual(gr.nonterm(Second), Second) self.assertIsNotNone(gr.get_nonterm(Second)) self.assertIsNotNone(gr.nonterm(Third)) self.assertEqual(gr.nonterm(Third), Third) self.assertIsNotNone(gr.get_nonterm(Third)) if __name__ == '__main__': main()

39.427184

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0.799569

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4,061

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6

4b63cc4511ffc6f06c746a09ef3301a5c735fe58

191

py

Python

server/common_models/__init__.py

Soopro/totoro

6be1af50496340ded9879a6450c8208ac9f97e72

[ "MIT" ]

null

server/common_models/__init__.py

Soopro/totoro

6be1af50496340ded9879a6450c8208ac9f97e72

[ "MIT" ]

null

server/common_models/__init__.py

Soopro/totoro

6be1af50496340ded9879a6450c8208ac9f97e72

[ "MIT" ]

1

2019-10-31T06:11:41.000Z

# coding=utf-8 from __future__ import absolute_import from .user import * from .media import * from .book import * from .category import * from .configuration import * from .notify import *

19.1

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6

299faf5bc659ee7d835f5448efd5eb8fa98854df

3,223

py

Python

data/load_mnist.py

gavinlive/perception

7b44d896a3f2ed3afb0376c394a5de3f8a4e4304

[ "MIT" ]

3

2019-03-18T16:16:20.000Z

2020-10-18T14:25:18.000Z

data/load_mnist.py

gavinlive/perception

7b44d896a3f2ed3afb0376c394a5de3f8a4e4304

[ "MIT" ]

13

2019-12-16T21:18:15.000Z

2021-07-27T18:55:01.000Z

data/load_mnist.py

gavinlive/perception

7b44d896a3f2ed3afb0376c394a5de3f8a4e4304

[ "MIT" ]

null

import pickle as cpk import numpy as np import random import os import tensorflow as tf def load_data(): dir_path = os.path.dirname(os.path.realpath(__file__)) filename= dir_path + '/rmnist_expanded_10.pkl' print("Loading data") with open(filename, 'rb') as fname: print("Opened data successfully") mnist = cpk.load(fname, encoding='latin1') # latin1 due to incompatibility between pickle in python2 and python3 mnist_data = np.array(mnist[0][0]) # (900, 784) mnist_labels = np.array(mnist[0][1]) # (900,) #mnist_labels_mat = np.zeros(list(np.shape(mnist_labels))+[10], dtype=np.int8) def insert_one(this_array, indx): this_array[indx] = 1 return this_array print("Creating labels") mnist_labels = np.array([insert_one(np.zeros([10]),x) for x in mnist_labels]) mnist_labels = mnist_labels.astype(np.float64) print("Finished creating labels") del mnist #mnist_test_data = mnist[1][0] # (10000, 784) #mnist_test_labels = mnist[1][1] # (10000,) data_idx = random.sample(range(900), 250) train_data_idx = data_idx[0:200] test_data_idx = data_idx[200:250] mnist_train_data = mnist_data[train_data_idx, :] mnist_train_labels = mnist_labels[train_data_idx] mnist_test_data = mnist_data[test_data_idx, :] mnist_test_labels = mnist_data[test_data_idx] mnist_train_data = np.reshape(mnist_train_data, [200, 28, 28]) mnist_test_data = np.reshape(mnist_test_data,[50, 28, 28]) print("Finished loading reduced-size MNIST dataset (200 training, 50 test)") return mnist_train_data, mnist_train_labels, mnist_test_data, mnist_test_labels def load_data_light(tr=1,te=2): dir_path = os.path.dirname(os.path.realpath(__file__)) filename= dir_path + '/rmnist_expanded_10.pkl' print("Loading data") with open(filename, 'rb') as fname: print("Opened data successfully") mnist = cpk.load(fname, encoding='latin1') # latin1 due to incompatibility between pickle in python2 and python3 mnist_data = np.array(mnist[0][0]) # (900, 784) mnist_labels = np.array(mnist[0][1]) # (900,) def insert_one(this_array, indx): this_array[indx] = 1 return this_array print("Creating labels") mnist_labels = np.array([insert_one(np.zeros([10]),x) for x in mnist_labels]) mnist_labels = mnist_labels.astype(np.float32) print("Finished creating labels") del mnist #mnist_test_data = mnist[1][0] # (10000, 784) #mnist_test_labels = mnist[1][1] # (10000,) data_idx = random.sample(range(900), tr+te) train_data_idx = data_idx[0:tr] test_data_idx = data_idx[tr:tr+te] mnist_train_data = mnist_data[train_data_idx, :] mnist_train_labels = mnist_labels[train_data_idx] mnist_test_data = mnist_data[test_data_idx, :] mnist_test_labels = mnist_data[test_data_idx] mnist_train_data = np.reshape(mnist_train_data, [tr, 28, 28]) mnist_test_data = np.reshape(mnist_test_data,[te, 28, 28]) print("Finished loading reduced-size MNIST dataset (%d training, %d test)" % (tr, te)) print(mnist_train_data.dtype) return mnist_train_data, mnist_train_labels, mnist_test_data, mnist_test_labels

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29a752dd2d61d85f75da7fa19325af7df767e39e

194

py

Python

sos4hjb/polynomials/__init__.py

TobiaMarcucci/sos4hjb

d8bd5c0179891ff09f11be48777bef148d952a2d

[ "MIT" ]

3

2020-07-05T17:36:06.000Z

2021-11-20T10:41:58.000Z

sos4hjb/polynomials/__init__.py

TobiaMarcucci/sos4hjb

d8bd5c0179891ff09f11be48777bef148d952a2d

[ "MIT" ]

null

sos4hjb/polynomials/__init__.py

TobiaMarcucci/sos4hjb

d8bd5c0179891ff09f11be48777bef148d952a2d

[ "MIT" ]

1

2022-01-25T06:39:56.000Z

from .variable import Variable from .basis_vector import BasisVector from .monomial_vector import MonomialVector from .chebyshev_vector import ChebyshevVector from .polynomial import Polynomial

32.333333

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0

6

29ea297041aaa45cc6f58d7b98f3feae2e31a411

158

py

Python

graphish/__init__.py

cmoore94/graphish

1908743374f48aed1dd17997765948a8c7befcd8

[ "MIT" ]

29

2019-06-19T17:13:16.000Z

2021-07-28T21:48:07.000Z

graphish/__init__.py

cmoore94/graphish

1908743374f48aed1dd17997765948a8c7befcd8

[ "MIT" ]

6

2019-08-02T19:56:06.000Z

2022-02-15T19:31:25.000Z

graphish/__init__.py

cmoore94/graphish

1908743374f48aed1dd17997765948a8c7befcd8

[ "MIT" ]

9

2019-07-29T10:59:08.000Z

2022-01-25T17:32:46.000Z

from graphish.connector import GraphConnector from graphish.search import Search from graphish.delete import Delete from graphish.mailfolder import MailFolder

39.5

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6

4b0a890880affa7ee7e611618c3651ae006102a8

26

py

Python

recOrder/__init__.py

mehta-lab/recOrder

67f2edb9ab13114dfe41d57e465ae24f961b0004

[ "Unlicense" ]

2

2022-01-19T21:13:32.000Z

2022-02-24T19:40:24.000Z

recOrder/__init__.py

mehta-lab/recOrder

67f2edb9ab13114dfe41d57e465ae24f961b0004

[ "Unlicense" ]

55

2021-06-24T18:53:18.000Z

2022-03-30T21:05:14.000Z

recOrder/__init__.py

mehta-lab/recOrder

67f2edb9ab13114dfe41d57e465ae24f961b0004

[ "Unlicense" ]

null

#todo: format overall init

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py

Python

dear_petition/petition/export/__init__.py

robert-w-gries/dear-petition

35244afc8e967b41ae5265ae31fd13b26e4e835a

[ "MIT" ]

4

2020-04-01T14:42:45.000Z

2021-12-12T21:11:11.000Z

dear_petition/petition/export/__init__.py

robert-w-gries/dear-petition

35244afc8e967b41ae5265ae31fd13b26e4e835a

[ "MIT" ]

142

2019-08-12T19:08:34.000Z

2022-03-29T23:05:35.000Z

dear_petition/petition/export/__init__.py

robert-w-gries/dear-petition

35244afc8e967b41ae5265ae31fd13b26e4e835a

[ "MIT" ]

8

2020-02-04T20:37:00.000Z

2021-03-28T13:28:32.000Z

from .main import generate_petition_pdf # noqa

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d99d33cefa4ad654506ccf81c4ad9d1ff622c3c9

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py

Python

scripts/device/temprature_controller/__init__.py

heptaliane/my_measurements_scripts

0c977a1677d7881a33863ab376cab48a387a7d52

[ "MIT" ]

null

scripts/device/temprature_controller/__init__.py

heptaliane/my_measurements_scripts

0c977a1677d7881a33863ab376cab48a387a7d52

[ "MIT" ]

null

scripts/device/temprature_controller/__init__.py

heptaliane/my_measurements_scripts

0c977a1677d7881a33863ab376cab48a387a7d52

[ "MIT" ]

null

from .interface import TempratureController from .cryocon_model62 import Cryocon_Model62

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d9a19a13f7b275d11cbe882398e7a8d22f45a2c6

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py

Python

Lib/importlib/machinery.py

oleksandr-pavlyk/cpython

eb002dbe0da9622245a355db5f0cd5aa2fc70b40

[ "0BSD" ]

52,316

2015-01-01T15:56:25.000Z

2022-03-31T23:19:01.000Z

Lib/importlib/machinery.py

dalakatt/cpython

2f49b97cc5426087b46515254b9a97a22ee8c807

[ "0BSD" ]

25,286

2015-03-03T23:18:02.000Z

2022-03-31T23:17:27.000Z

Lib/importlib/machinery.py

dalakatt/cpython

2f49b97cc5426087b46515254b9a97a22ee8c807

[ "0BSD" ]

31,623

2015-01-01T13:29:37.000Z

2022-03-31T19:55:06.000Z

"""The machinery of importlib: finders, loaders, hooks, etc.""" from ._bootstrap import ModuleSpec from ._bootstrap import BuiltinImporter from ._bootstrap import FrozenImporter from ._bootstrap_external import (SOURCE_SUFFIXES, DEBUG_BYTECODE_SUFFIXES, OPTIMIZED_BYTECODE_SUFFIXES, BYTECODE_SUFFIXES, EXTENSION_SUFFIXES) from ._bootstrap_external import WindowsRegistryFinder from ._bootstrap_external import PathFinder from ._bootstrap_external import FileFinder from ._bootstrap_external import SourceFileLoader from ._bootstrap_external import SourcelessFileLoader from ._bootstrap_external import ExtensionFileLoader from ._bootstrap_external import NamespaceLoader def all_suffixes(): """Returns a list of all recognized module suffixes for this process""" return SOURCE_SUFFIXES + BYTECODE_SUFFIXES + EXTENSION_SUFFIXES

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6

d9ec0299d5270d1f23f1810d918cb8a65b64ab4d

18,601

py

Python

shap/benchmark/measures.py

santanaangel/shap

1c1c4a45440f3475b8544251f9d9e5b43977cd0e

[ "MIT" ]

16,097

2016-12-01T20:01:26.000Z

2022-03-31T20:27:40.000Z

shap/benchmark/measures.py

santanaangel/shap

1c1c4a45440f3475b8544251f9d9e5b43977cd0e

[ "MIT" ]

2,217

2017-09-18T20:06:45.000Z

2022-03-31T21:00:25.000Z

shap/benchmark/measures.py

santanaangel/shap

1c1c4a45440f3475b8544251f9d9e5b43977cd0e

[ "MIT" ]

2,634

2017-06-29T21:30:46.000Z

2022-03-30T07:30:36.000Z

import numpy as np from tqdm.autonotebook import tqdm import gc import warnings import sklearn.utils _remove_cache = {} def remove_retrain(nmask, X_train, y_train, X_test, y_test, attr_test, model_generator, metric, trained_model, random_state): """ The model is retrained for each test sample with the important features set to a constant. If you want to know how important a set of features is you can ask how the model would be different if those features had never existed. To determine this we can mask those features across the entire training and test datasets, then retrain the model. If we apply compare the output of this retrained model to the original model we can see the effect produced by knowning the features we masked. Since for individualized explanation methods each test sample has a different set of most important features we need to retrain the model for every test sample to get the change in model performance when a specified fraction of the most important features are withheld. """ warnings.warn("The retrain based measures can incorrectly evaluate models in some cases!") # see if we match the last cached call global _remove_cache args = (X_train, y_train, X_test, y_test, model_generator, metric) cache_match = False if "args" in _remove_cache: if all(a is b for a,b in zip(_remove_cache["args"], args)) and np.all(_remove_cache["attr_test"] == attr_test): cache_match = True X_train, X_test = to_array(X_train, X_test) # how many features to mask assert X_train.shape[1] == X_test.shape[1] # this is the model we will retrain many times model_masked = model_generator() # mask nmask top features and re-train the model for each test explanation X_train_tmp = np.zeros(X_train.shape) X_test_tmp = np.zeros(X_test.shape) yp_masked_test = np.zeros(y_test.shape) tie_breaking_noise = const_rand(X_train.shape[1]) * 1e-6 last_nmask = _remove_cache.get("nmask", None) last_yp_masked_test = _remove_cache.get("yp_masked_test", None) for i in tqdm(range(len(y_test)), "Retraining for the 'remove' metric"): if cache_match and last_nmask[i] == nmask[i]: yp_masked_test[i] = last_yp_masked_test[i] elif nmask[i] == 0: yp_masked_test[i] = trained_model.predict(X_test[i:i+1])[0] else: # mask out the most important features for this test instance X_train_tmp[:] = X_train X_test_tmp[:] = X_test ordering = np.argsort(-attr_test[i,:] + tie_breaking_noise) X_train_tmp[:,ordering[:nmask[i]]] = X_train[:,ordering[:nmask[i]]].mean() X_test_tmp[i,ordering[:nmask[i]]] = X_train[:,ordering[:nmask[i]]].mean() # retrain the model and make a prediction model_masked.fit(X_train_tmp, y_train) yp_masked_test[i] = model_masked.predict(X_test_tmp[i:i+1])[0] # save our results so the next call to us can be faster when there is redundancy _remove_cache["nmask"] = nmask _remove_cache["yp_masked_test"] = yp_masked_test _remove_cache["attr_test"] = attr_test _remove_cache["args"] = args return metric(y_test, yp_masked_test) def remove_mask(nmask, X_train, y_train, X_test, y_test, attr_test, model_generator, metric, trained_model, random_state): """ Each test sample is masked by setting the important features to a constant. """ X_train, X_test = to_array(X_train, X_test) # how many features to mask assert X_train.shape[1] == X_test.shape[1] # mask nmask top features for each test explanation X_test_tmp = X_test.copy() tie_breaking_noise = const_rand(X_train.shape[1], random_state) * 1e-6 mean_vals = X_train.mean(0) for i in range(len(y_test)): if nmask[i] > 0: ordering = np.argsort(-attr_test[i,:] + tie_breaking_noise) X_test_tmp[i,ordering[:nmask[i]]] = mean_vals[ordering[:nmask[i]]] yp_masked_test = trained_model.predict(X_test_tmp) return metric(y_test, yp_masked_test) def remove_impute(nmask, X_train, y_train, X_test, y_test, attr_test, model_generator, metric, trained_model, random_state): """ The model is revaluated for each test sample with the important features set to an imputed value. Note that the imputation is done using a multivariate normality assumption on the dataset. This depends on being able to estimate the full data covariance matrix (and inverse) accuractly. So X_train.shape[0] should be significantly bigger than X_train.shape[1]. """ X_train, X_test = to_array(X_train, X_test) # how many features to mask assert X_train.shape[1] == X_test.shape[1] # keep nkeep top features for each test explanation C = np.cov(X_train.T) C += np.eye(C.shape[0]) * 1e-6 X_test_tmp = X_test.copy() yp_masked_test = np.zeros(y_test.shape) tie_breaking_noise = const_rand(X_train.shape[1], random_state) * 1e-6 mean_vals = X_train.mean(0) for i in range(len(y_test)): if nmask[i] > 0: ordering = np.argsort(-attr_test[i,:] + tie_breaking_noise) observe_inds = ordering[nmask[i]:] impute_inds = ordering[:nmask[i]] # impute missing data assuming it follows a multivariate normal distribution Coo_inv = np.linalg.inv(C[observe_inds,:][:,observe_inds]) Cio = C[impute_inds,:][:,observe_inds] impute = mean_vals[impute_inds] + Cio @ Coo_inv @ (X_test[i, observe_inds] - mean_vals[observe_inds]) X_test_tmp[i, impute_inds] = impute yp_masked_test = trained_model.predict(X_test_tmp) return metric(y_test, yp_masked_test) def remove_resample(nmask, X_train, y_train, X_test, y_test, attr_test, model_generator, metric, trained_model, random_state): """ The model is revaluated for each test sample with the important features set to resample background values. """ X_train, X_test = to_array(X_train, X_test) # how many features to mask assert X_train.shape[1] == X_test.shape[1] # how many samples to take nsamples = 100 # keep nkeep top features for each test explanation N,M = X_test.shape X_test_tmp = np.tile(X_test, [1, nsamples]).reshape(nsamples * N, M) tie_breaking_noise = const_rand(M) * 1e-6 inds = sklearn.utils.resample(np.arange(N), n_samples=nsamples, random_state=random_state) for i in range(N): if nmask[i] > 0: ordering = np.argsort(-attr_test[i,:] + tie_breaking_noise) X_test_tmp[i*nsamples:(i+1)*nsamples, ordering[:nmask[i]]] = X_train[inds, :][:, ordering[:nmask[i]]] yp_masked_test = trained_model.predict(X_test_tmp) yp_masked_test = np.reshape(yp_masked_test, (N, nsamples)).mean(1) # take the mean output over all samples return metric(y_test, yp_masked_test) def batch_remove_retrain(nmask_train, nmask_test, X_train, y_train, X_test, y_test, attr_train, attr_test, model_generator, metric): """ An approximation of holdout that only retraines the model once. This is alse called ROAR (RemOve And Retrain) in work by Google. It is much more computationally efficient that the holdout method because it masks the most important features in every sample and then retrains the model once, instead of retraining the model for every test sample like the holdout metric. """ warnings.warn("The retrain based measures can incorrectly evaluate models in some cases!") X_train, X_test = to_array(X_train, X_test) # how many features to mask assert X_train.shape[1] == X_test.shape[1] # mask nmask top features for each explanation X_train_tmp = X_train.copy() X_train_mean = X_train.mean(0) tie_breaking_noise = const_rand(X_train.shape[1]) * 1e-6 for i in range(len(y_train)): if nmask_train[i] > 0: ordering = np.argsort(-attr_train[i, :] + tie_breaking_noise) X_train_tmp[i, ordering[:nmask_train[i]]] = X_train_mean[ordering[:nmask_train[i]]] X_test_tmp = X_test.copy() for i in range(len(y_test)): if nmask_test[i] > 0: ordering = np.argsort(-attr_test[i, :] + tie_breaking_noise) X_test_tmp[i, ordering[:nmask_test[i]]] = X_train_mean[ordering[:nmask_test[i]]] # train the model with all the given features masked model_masked = model_generator() model_masked.fit(X_train_tmp, y_train) yp_test_masked = model_masked.predict(X_test_tmp) return metric(y_test, yp_test_masked) _keep_cache = {} def keep_retrain(nkeep, X_train, y_train, X_test, y_test, attr_test, model_generator, metric, trained_model, random_state): """ The model is retrained for each test sample with the non-important features set to a constant. If you want to know how important a set of features is you can ask how the model would be different if only those features had existed. To determine this we can mask the other features across the entire training and test datasets, then retrain the model. If we apply compare the output of this retrained model to the original model we can see the effect produced by only knowning the important features. Since for individualized explanation methods each test sample has a different set of most important features we need to retrain the model for every test sample to get the change in model performance when a specified fraction of the most important features are retained. """ warnings.warn("The retrain based measures can incorrectly evaluate models in some cases!") # see if we match the last cached call global _keep_cache args = (X_train, y_train, X_test, y_test, model_generator, metric) cache_match = False if "args" in _keep_cache: if all(a is b for a,b in zip(_keep_cache["args"], args)) and np.all(_keep_cache["attr_test"] == attr_test): cache_match = True X_train, X_test = to_array(X_train, X_test) # how many features to mask assert X_train.shape[1] == X_test.shape[1] # this is the model we will retrain many times model_masked = model_generator() # keep nkeep top features and re-train the model for each test explanation X_train_tmp = np.zeros(X_train.shape) X_test_tmp = np.zeros(X_test.shape) yp_masked_test = np.zeros(y_test.shape) tie_breaking_noise = const_rand(X_train.shape[1]) * 1e-6 last_nkeep = _keep_cache.get("nkeep", None) last_yp_masked_test = _keep_cache.get("yp_masked_test", None) for i in tqdm(range(len(y_test)), "Retraining for the 'keep' metric"): if cache_match and last_nkeep[i] == nkeep[i]: yp_masked_test[i] = last_yp_masked_test[i] elif nkeep[i] == attr_test.shape[1]: yp_masked_test[i] = trained_model.predict(X_test[i:i+1])[0] else: # mask out the most important features for this test instance X_train_tmp[:] = X_train X_test_tmp[:] = X_test ordering = np.argsort(-attr_test[i,:] + tie_breaking_noise) X_train_tmp[:,ordering[nkeep[i]:]] = X_train[:,ordering[nkeep[i]:]].mean() X_test_tmp[i,ordering[nkeep[i]:]] = X_train[:,ordering[nkeep[i]:]].mean() # retrain the model and make a prediction model_masked.fit(X_train_tmp, y_train) yp_masked_test[i] = model_masked.predict(X_test_tmp[i:i+1])[0] # save our results so the next call to us can be faster when there is redundancy _keep_cache["nkeep"] = nkeep _keep_cache["yp_masked_test"] = yp_masked_test _keep_cache["attr_test"] = attr_test _keep_cache["args"] = args return metric(y_test, yp_masked_test) def keep_mask(nkeep, X_train, y_train, X_test, y_test, attr_test, model_generator, metric, trained_model, random_state): """ The model is revaluated for each test sample with the non-important features set to their mean. """ X_train, X_test = to_array(X_train, X_test) # how many features to mask assert X_train.shape[1] == X_test.shape[1] # keep nkeep top features for each test explanation X_test_tmp = X_test.copy() yp_masked_test = np.zeros(y_test.shape) tie_breaking_noise = const_rand(X_train.shape[1], random_state) * 1e-6 mean_vals = X_train.mean(0) for i in range(len(y_test)): if nkeep[i] < X_test.shape[1]: ordering = np.argsort(-attr_test[i,:] + tie_breaking_noise) X_test_tmp[i,ordering[nkeep[i]:]] = mean_vals[ordering[nkeep[i]:]] yp_masked_test = trained_model.predict(X_test_tmp) return metric(y_test, yp_masked_test) def keep_impute(nkeep, X_train, y_train, X_test, y_test, attr_test, model_generator, metric, trained_model, random_state): """ The model is revaluated for each test sample with the non-important features set to an imputed value. Note that the imputation is done using a multivariate normality assumption on the dataset. This depends on being able to estimate the full data covariance matrix (and inverse) accuractly. So X_train.shape[0] should be significantly bigger than X_train.shape[1]. """ X_train, X_test = to_array(X_train, X_test) # how many features to mask assert X_train.shape[1] == X_test.shape[1] # keep nkeep top features for each test explanation C = np.cov(X_train.T) C += np.eye(C.shape[0]) * 1e-6 X_test_tmp = X_test.copy() yp_masked_test = np.zeros(y_test.shape) tie_breaking_noise = const_rand(X_train.shape[1], random_state) * 1e-6 mean_vals = X_train.mean(0) for i in range(len(y_test)): if nkeep[i] < X_test.shape[1]: ordering = np.argsort(-attr_test[i,:] + tie_breaking_noise) observe_inds = ordering[:nkeep[i]] impute_inds = ordering[nkeep[i]:] # impute missing data assuming it follows a multivariate normal distribution Coo_inv = np.linalg.inv(C[observe_inds,:][:,observe_inds]) Cio = C[impute_inds,:][:,observe_inds] impute = mean_vals[impute_inds] + Cio @ Coo_inv @ (X_test[i, observe_inds] - mean_vals[observe_inds]) X_test_tmp[i, impute_inds] = impute yp_masked_test = trained_model.predict(X_test_tmp) return metric(y_test, yp_masked_test) def keep_resample(nkeep, X_train, y_train, X_test, y_test, attr_test, model_generator, metric, trained_model, random_state): """ The model is revaluated for each test sample with the non-important features set to resample background values. """ # why broken? overwriting? X_train, X_test = to_array(X_train, X_test) # how many features to mask assert X_train.shape[1] == X_test.shape[1] # how many samples to take nsamples = 100 # keep nkeep top features for each test explanation N,M = X_test.shape X_test_tmp = np.tile(X_test, [1, nsamples]).reshape(nsamples * N, M) tie_breaking_noise = const_rand(M) * 1e-6 inds = sklearn.utils.resample(np.arange(N), n_samples=nsamples, random_state=random_state) for i in range(N): if nkeep[i] < M: ordering = np.argsort(-attr_test[i,:] + tie_breaking_noise) X_test_tmp[i*nsamples:(i+1)*nsamples, ordering[nkeep[i]:]] = X_train[inds, :][:, ordering[nkeep[i]:]] yp_masked_test = trained_model.predict(X_test_tmp) yp_masked_test = np.reshape(yp_masked_test, (N, nsamples)).mean(1) # take the mean output over all samples return metric(y_test, yp_masked_test) def batch_keep_retrain(nkeep_train, nkeep_test, X_train, y_train, X_test, y_test, attr_train, attr_test, model_generator, metric): """ An approximation of keep that only retraines the model once. This is alse called KAR (Keep And Retrain) in work by Google. It is much more computationally efficient that the keep method because it masks the unimportant features in every sample and then retrains the model once, instead of retraining the model for every test sample like the keep metric. """ warnings.warn("The retrain based measures can incorrectly evaluate models in some cases!") X_train, X_test = to_array(X_train, X_test) # how many features to mask assert X_train.shape[1] == X_test.shape[1] # mask nkeep top features for each explanation X_train_tmp = X_train.copy() X_train_mean = X_train.mean(0) tie_breaking_noise = const_rand(X_train.shape[1]) * 1e-6 for i in range(len(y_train)): if nkeep_train[i] < X_train.shape[1]: ordering = np.argsort(-attr_train[i, :] + tie_breaking_noise) X_train_tmp[i, ordering[nkeep_train[i]:]] = X_train_mean[ordering[nkeep_train[i]:]] X_test_tmp = X_test.copy() for i in range(len(y_test)): if nkeep_test[i] < X_test.shape[1]: ordering = np.argsort(-attr_test[i, :] + tie_breaking_noise) X_test_tmp[i, ordering[nkeep_test[i]:]] = X_train_mean[ordering[nkeep_test[i]:]] # train the model with all the features not given masked model_masked = model_generator() model_masked.fit(X_train_tmp, y_train) yp_test_masked = model_masked.predict(X_test_tmp) return metric(y_test, yp_test_masked) def local_accuracy(X_train, y_train, X_test, y_test, attr_test, model_generator, metric, trained_model): """ The how well do the features plus a constant base rate sum up to the model output. """ X_train, X_test = to_array(X_train, X_test) # how many features to mask assert X_train.shape[1] == X_test.shape[1] # keep nkeep top features and re-train the model for each test explanation yp_test = trained_model.predict(X_test) return metric(yp_test, strip_list(attr_test).sum(1)) def to_array(*args): return [a.values if str(type(a)).endswith("'pandas.core.frame.DataFrame'>") else a for a in args] def const_rand(size, seed=23980): """ Generate a random array with a fixed seed. """ old_seed = np.random.seed() np.random.seed(seed) out = np.random.rand(size) np.random.seed(old_seed) return out def const_shuffle(arr, seed=23980): """ Shuffle an array in-place with a fixed seed. """ old_seed = np.random.seed() np.random.seed(seed) np.random.shuffle(arr) np.random.seed(old_seed) def strip_list(attrs): """ This assumes that if you have a list of outputs you just want the second one (the second class is the '1' class). """ if isinstance(attrs, list): return attrs[1] else: return attrs

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false

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0

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null

0

6

d9f0e671999bca27f60afc3f2a6697179b56439d

26,542

py

Python

ironic-plugin-pike/ironic/tests/unit/drivers/modules/test_console_utils.py

saintifly/Server_Manage_Plugin

ae272e7e3ca065236cc7bc86c296ff9eb83f1bb9

[ "Apache-2.0" ]

null

ironic-plugin-pike/ironic/tests/unit/drivers/modules/test_console_utils.py

saintifly/Server_Manage_Plugin

ae272e7e3ca065236cc7bc86c296ff9eb83f1bb9

[ "Apache-2.0" ]

null

ironic-plugin-pike/ironic/tests/unit/drivers/modules/test_console_utils.py

saintifly/Server_Manage_Plugin

ae272e7e3ca065236cc7bc86c296ff9eb83f1bb9

[ "Apache-2.0" ]

1

2019-01-11T16:00:23.000Z

# coding=utf-8 # Copyright 2014 International Business Machines Corporation # All Rights Reserved. # # 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. """Test class for console_utils driver module.""" import errno import os import random import signal import string import subprocess import tempfile from ironic_lib import utils as ironic_utils import mock from oslo_config import cfg from oslo_service import loopingcall from oslo_utils import netutils import psutil from ironic.common import exception from ironic.drivers.modules import console_utils from ironic.drivers.modules import ipmitool as ipmi from ironic.tests.unit.db import base as db_base from ironic.tests.unit.db import utils as db_utils from ironic.tests.unit.objects import utils as obj_utils CONF = cfg.CONF INFO_DICT = db_utils.get_test_ipmi_info() class ConsoleUtilsTestCase(db_base.DbTestCase): def setUp(self): super(ConsoleUtilsTestCase, self).setUp() self.node = obj_utils.get_test_node( self.context, driver='fake_ipmitool', driver_info=INFO_DICT) self.info = ipmi._parse_driver_info(self.node) def test__get_console_pid_dir(self): pid_dir = '/tmp/pid_dir' self.config(terminal_pid_dir=pid_dir, group='console') dir = console_utils._get_console_pid_dir() self.assertEqual(pid_dir, dir) def test__get_console_pid_dir_tempdir(self): self.config(tempdir='/tmp/fake_dir') dir = console_utils._get_console_pid_dir() self.assertEqual(CONF.tempdir, dir) @mock.patch.object(os, 'makedirs', autospec=True) @mock.patch.object(os.path, 'exists', autospec=True) def test__ensure_console_pid_dir_exists(self, mock_path_exists, mock_makedirs): mock_path_exists.return_value = True mock_makedirs.side_effect = OSError pid_dir = console_utils._get_console_pid_dir() console_utils._ensure_console_pid_dir_exists() mock_path_exists.assert_called_once_with(pid_dir) self.assertFalse(mock_makedirs.called) @mock.patch.object(os, 'makedirs', autospec=True) @mock.patch.object(os.path, 'exists', autospec=True) def test__ensure_console_pid_dir_exists_fail(self, mock_path_exists, mock_makedirs): mock_path_exists.return_value = False mock_makedirs.side_effect = OSError pid_dir = console_utils._get_console_pid_dir() self.assertRaises(exception.ConsoleError, console_utils._ensure_console_pid_dir_exists) mock_path_exists.assert_called_once_with(pid_dir) mock_makedirs.assert_called_once_with(pid_dir) @mock.patch.object(console_utils, '_get_console_pid_dir', autospec=True) def test__get_console_pid_file(self, mock_dir): mock_dir.return_value = tempfile.gettempdir() expected_path = '%(tempdir)s/%(uuid)s.pid' % { 'tempdir': mock_dir.return_value, 'uuid': self.info['uuid']} path = console_utils._get_console_pid_file(self.info['uuid']) self.assertEqual(expected_path, path) mock_dir.assert_called_once_with() @mock.patch.object(console_utils, 'open', mock.mock_open(read_data='12345\n')) @mock.patch.object(console_utils, '_get_console_pid_file', autospec=True) def test__get_console_pid(self, mock_pid_file): tmp_file_handle = tempfile.NamedTemporaryFile() tmp_file = tmp_file_handle.name mock_pid_file.return_value = tmp_file pid = console_utils._get_console_pid(self.info['uuid']) mock_pid_file.assert_called_once_with(self.info['uuid']) self.assertEqual(pid, 12345) @mock.patch.object(console_utils, 'open', mock.mock_open(read_data='Hello World\n')) @mock.patch.object(console_utils, '_get_console_pid_file', autospec=True) def test__get_console_pid_not_a_num(self, mock_pid_file): tmp_file_handle = tempfile.NamedTemporaryFile() tmp_file = tmp_file_handle.name mock_pid_file.return_value = tmp_file self.assertRaises(exception.NoConsolePid, console_utils._get_console_pid, self.info['uuid']) mock_pid_file.assert_called_once_with(self.info['uuid']) def test__get_console_pid_file_not_found(self): self.assertRaises(exception.NoConsolePid, console_utils._get_console_pid, self.info['uuid']) @mock.patch.object(ironic_utils, 'unlink_without_raise', autospec=True) @mock.patch.object(os, 'kill', autospec=True) @mock.patch.object(console_utils, '_get_console_pid', autospec=True) def test__stop_console(self, mock_pid, mock_kill, mock_unlink): pid_file = console_utils._get_console_pid_file(self.info['uuid']) mock_pid.return_value = 12345 console_utils._stop_console(self.info['uuid']) mock_pid.assert_called_once_with(self.info['uuid']) mock_kill.assert_called_once_with(mock_pid.return_value, signal.SIGTERM) mock_unlink.assert_called_once_with(pid_file) @mock.patch.object(ironic_utils, 'unlink_without_raise', autospec=True) @mock.patch.object(os, 'kill', autospec=True) @mock.patch.object(console_utils, '_get_console_pid', autospec=True) def test__stop_console_nopid(self, mock_pid, mock_kill, mock_unlink): pid_file = console_utils._get_console_pid_file(self.info['uuid']) mock_pid.side_effect = exception.NoConsolePid(pid_path="/tmp/blah") self.assertRaises(exception.NoConsolePid, console_utils._stop_console, self.info['uuid']) mock_pid.assert_called_once_with(self.info['uuid']) self.assertFalse(mock_kill.called) mock_unlink.assert_called_once_with(pid_file) @mock.patch.object(ironic_utils, 'unlink_without_raise', autospec=True) @mock.patch.object(os, 'kill', autospec=True) @mock.patch.object(console_utils, '_get_console_pid', autospec=True) def test__stop_console_shellinabox_not_running(self, mock_pid, mock_kill, mock_unlink): pid_file = console_utils._get_console_pid_file(self.info['uuid']) mock_pid.return_value = 12345 mock_kill.side_effect = OSError(errno.ESRCH, 'message') console_utils._stop_console(self.info['uuid']) mock_pid.assert_called_once_with(self.info['uuid']) mock_kill.assert_called_once_with(mock_pid.return_value, signal.SIGTERM) mock_unlink.assert_called_once_with(pid_file) @mock.patch.object(ironic_utils, 'unlink_without_raise', autospec=True) @mock.patch.object(os, 'kill', autospec=True) @mock.patch.object(console_utils, '_get_console_pid', autospec=True) def test__stop_console_exception(self, mock_pid, mock_kill, mock_unlink): pid_file = console_utils._get_console_pid_file(self.info['uuid']) mock_pid.return_value = 12345 mock_kill.side_effect = OSError(2, 'message') self.assertRaises(exception.ConsoleError, console_utils._stop_console, self.info['uuid']) mock_pid.assert_called_once_with(self.info['uuid']) mock_kill.assert_called_once_with(mock_pid.return_value, signal.SIGTERM) mock_unlink.assert_called_once_with(pid_file) def _get_shellinabox_console(self, scheme): generated_url = ( console_utils.get_shellinabox_console_url(self.info['port'])) console_host = CONF.my_ip if netutils.is_valid_ipv6(console_host): console_host = '[%s]' % console_host http_url = "%s://%s:%s" % (scheme, console_host, self.info['port']) self.assertEqual(http_url, generated_url) def test_get_shellinabox_console_url(self): self._get_shellinabox_console('http') def test_get_shellinabox_console_https_url(self): # specify terminal_cert_dir in /etc/ironic/ironic.conf self.config(terminal_cert_dir='/tmp', group='console') # use https self._get_shellinabox_console('https') def test_make_persistent_password_file(self): filepath = '%(tempdir)s/%(node_uuid)s' % { 'tempdir': tempfile.gettempdir(), 'node_uuid': self.info['uuid']} password = ''.join([random.choice(string.ascii_letters) for n in range(16)]) console_utils.make_persistent_password_file(filepath, password) # make sure file exists self.assertTrue(os.path.exists(filepath)) # make sure the content is correct with open(filepath) as file: content = file.read() self.assertEqual(password, content) # delete the file os.unlink(filepath) @mock.patch.object(os, 'chmod', autospec=True) def test_make_persistent_password_file_fail(self, mock_chmod): mock_chmod.side_effect = IOError() filepath = '%(tempdir)s/%(node_uuid)s' % { 'tempdir': tempfile.gettempdir(), 'node_uuid': self.info['uuid']} self.assertRaises(exception.PasswordFileFailedToCreate, console_utils.make_persistent_password_file, filepath, 'password') @mock.patch.object(console_utils, 'open', mock.mock_open(read_data='12345\n')) @mock.patch.object(os.path, 'exists', autospec=True) @mock.patch.object(subprocess, 'Popen', autospec=True) @mock.patch.object(psutil, 'pid_exists', autospec=True) @mock.patch.object(console_utils, '_ensure_console_pid_dir_exists', autospec=True) @mock.patch.object(console_utils, '_stop_console', autospec=True) def test_start_shellinabox_console(self, mock_stop, mock_dir_exists, mock_pid_exists, mock_popen, mock_path_exists): mock_popen.return_value.poll.return_value = 0 mock_pid_exists.return_value = True mock_path_exists.return_value = True console_utils.start_shellinabox_console(self.info['uuid'], self.info['port'], 'ls&') mock_stop.assert_called_once_with(self.info['uuid']) mock_dir_exists.assert_called_once_with() mock_pid_exists.assert_called_once_with(12345) mock_popen.assert_called_once_with(mock.ANY, stdout=subprocess.PIPE, stderr=subprocess.PIPE) mock_popen.return_value.poll.assert_called_once_with() @mock.patch.object(console_utils, 'open', mock.mock_open(read_data='12345\n')) @mock.patch.object(os.path, 'exists', autospec=True) @mock.patch.object(subprocess, 'Popen', autospec=True) @mock.patch.object(psutil, 'pid_exists', autospec=True) @mock.patch.object(console_utils, '_ensure_console_pid_dir_exists', autospec=True) @mock.patch.object(console_utils, '_stop_console', autospec=True) def test_start_shellinabox_console_nopid(self, mock_stop, mock_dir_exists, mock_pid_exists, mock_popen, mock_path_exists): # no existing PID file before starting mock_stop.side_effect = exception.NoConsolePid('/tmp/blah') mock_popen.return_value.poll.return_value = 0 mock_pid_exists.return_value = True mock_path_exists.return_value = True console_utils.start_shellinabox_console(self.info['uuid'], self.info['port'], 'ls&') mock_stop.assert_called_once_with(self.info['uuid']) mock_dir_exists.assert_called_once_with() mock_pid_exists.assert_called_once_with(12345) mock_popen.assert_called_once_with(mock.ANY, stdout=subprocess.PIPE, stderr=subprocess.PIPE) mock_popen.return_value.poll.assert_called_once_with() @mock.patch.object(subprocess, 'Popen', autospec=True) @mock.patch.object(console_utils, '_ensure_console_pid_dir_exists', autospec=True) @mock.patch.object(console_utils, '_stop_console', autospec=True) def test_start_shellinabox_console_fail(self, mock_stop, mock_dir_exists, mock_popen): mock_popen.return_value.poll.return_value = 1 mock_popen.return_value.communicate.return_value = ('output', 'error') self.assertRaises(exception.ConsoleSubprocessFailed, console_utils.start_shellinabox_console, self.info['uuid'], self.info['port'], 'ls&') mock_stop.assert_called_once_with(self.info['uuid']) mock_dir_exists.assert_called_once_with() mock_popen.assert_called_once_with(mock.ANY, stdout=subprocess.PIPE, stderr=subprocess.PIPE) mock_popen.return_value.poll.assert_called_once_with() @mock.patch.object(console_utils, 'open', mock.mock_open(read_data='12345\n')) @mock.patch.object(os.path, 'exists', autospec=True) @mock.patch.object(subprocess, 'Popen', autospec=True) @mock.patch.object(psutil, 'pid_exists', autospec=True) @mock.patch.object(console_utils, '_ensure_console_pid_dir_exists', autospec=True) @mock.patch.object(console_utils, '_stop_console', autospec=True) def test_start_shellinabox_console_fail_no_pid(self, mock_stop, mock_dir_exists, mock_pid_exists, mock_popen, mock_path_exists): mock_popen.return_value.poll.return_value = 0 mock_pid_exists.return_value = False mock_popen.return_value.communicate.return_value = ('output', 'error') mock_path_exists.return_value = True self.assertRaises(exception.ConsoleSubprocessFailed, console_utils.start_shellinabox_console, self.info['uuid'], self.info['port'], 'ls&') mock_stop.assert_called_once_with(self.info['uuid']) mock_dir_exists.assert_called_once_with() mock_pid_exists.assert_called_once_with(12345) mock_popen.assert_called_once_with(mock.ANY, stdout=subprocess.PIPE, stderr=subprocess.PIPE) mock_popen.return_value.poll.assert_called_once_with() @mock.patch.object(subprocess, 'Popen', autospec=True) @mock.patch.object(console_utils, '_ensure_console_pid_dir_exists', autospec=True) @mock.patch.object(console_utils, '_stop_console', autospec=True) def test_start_shellinabox_console_fail_nopiddir(self, mock_stop, mock_dir_exists, mock_popen): mock_dir_exists.side_effect = exception.ConsoleError(message='fail') mock_popen.return_value.poll.return_value = 0 self.assertRaises(exception.ConsoleError, console_utils.start_shellinabox_console, self.info['uuid'], self.info['port'], 'ls&') mock_stop.assert_called_once_with(self.info['uuid']) mock_dir_exists.assert_called_once_with() self.assertFalse(mock_popen.called) @mock.patch.object(console_utils, '_stop_console', autospec=True) def test_stop_shellinabox_console(self, mock_stop): console_utils.stop_shellinabox_console(self.info['uuid']) mock_stop.assert_called_once_with(self.info['uuid']) @mock.patch.object(console_utils, '_stop_console', autospec=True) def test_stop_shellinabox_console_fail_nopid(self, mock_stop): mock_stop.side_effect = exception.NoConsolePid('/tmp/blah') console_utils.stop_shellinabox_console(self.info['uuid']) mock_stop.assert_called_once_with(self.info['uuid']) def test_get_socat_console_url_tcp(self): self.config(my_ip="10.0.0.1") url = console_utils.get_socat_console_url(self.info['port']) self.assertEqual("tcp://10.0.0.1:%s" % self.info['port'], url) def test_get_socat_console_url_tcp6(self): self.config(my_ip='::1') url = console_utils.get_socat_console_url(self.info['port']) self.assertEqual("tcp://[::1]:%s" % self.info['port'], url) def test_get_socat_console_url_tcp_with_address_conf(self): self.config(socat_address="10.0.0.1", group='console') url = console_utils.get_socat_console_url(self.info['port']) self.assertEqual("tcp://10.0.0.1:%s" % self.info['port'], url) @mock.patch.object(subprocess, 'Popen', autospec=True) @mock.patch.object(console_utils, '_get_console_pid_file', autospec=True) @mock.patch.object(console_utils, '_ensure_console_pid_dir_exists', autospec=True) @mock.patch.object(console_utils, '_stop_console', autospec=True) @mock.patch.object(loopingcall.FixedIntervalLoopingCall, 'start', autospec=True) def _test_start_socat_console_check_arg(self, mock_timer_start, mock_stop, mock_dir_exists, mock_get_pid, mock_popen): mock_timer_start.return_value = mock.Mock() mock_get_pid.return_value = '/tmp/%s.pid' % self.info['uuid'] console_utils.start_socat_console(self.info['uuid'], self.info['port'], 'ls&') mock_stop.assert_called_once_with(self.info['uuid']) mock_dir_exists.assert_called_once_with() mock_get_pid.assert_called_once_with(self.info['uuid']) mock_timer_start.assert_called_once_with(mock.ANY, interval=mock.ANY) mock_popen.assert_called_once_with(mock.ANY, stderr=subprocess.PIPE) return mock_popen.call_args[0][0] def test_start_socat_console_check_arg_default_timeout(self): args = self._test_start_socat_console_check_arg() self.assertIn('-T600', args) def test_start_socat_console_check_arg_timeout(self): self.config(terminal_timeout=1, group='console') args = self._test_start_socat_console_check_arg() self.assertIn('-T1', args) def test_start_socat_console_check_arg_timeout_disabled(self): self.config(terminal_timeout=0, group='console') args = self._test_start_socat_console_check_arg() self.assertNotIn('-T0', args) def test_start_socat_console_check_arg_bind_addr_default_ipv4(self): self.config(my_ip='10.0.0.1') args = self._test_start_socat_console_check_arg() self.assertIn('TCP4-LISTEN:%s,bind=10.0.0.1,reuseaddr' % self.info['port'], args) def test_start_socat_console_check_arg_bind_addr_ipv4(self): self.config(socat_address='10.0.0.1', group='console') args = self._test_start_socat_console_check_arg() self.assertIn('TCP4-LISTEN:%s,bind=10.0.0.1,reuseaddr' % self.info['port'], args) @mock.patch.object(os.path, 'exists', autospec=True) @mock.patch.object(subprocess, 'Popen', autospec=True) @mock.patch.object(psutil, 'pid_exists', autospec=True) @mock.patch.object(console_utils, '_get_console_pid', autospec=True) @mock.patch.object(console_utils, '_ensure_console_pid_dir_exists', autospec=True) @mock.patch.object(console_utils, '_stop_console', autospec=True) def test_start_socat_console(self, mock_stop, mock_dir_exists, mock_get_pid, mock_pid_exists, mock_popen, mock_path_exists): mock_popen.return_value.pid = 23456 mock_popen.return_value.poll.return_value = None mock_popen.return_value.communicate.return_value = (None, None) mock_get_pid.return_value = 23456 mock_path_exists.return_value = True console_utils.start_socat_console(self.info['uuid'], self.info['port'], 'ls&') mock_stop.assert_called_once_with(self.info['uuid']) mock_dir_exists.assert_called_once_with() mock_get_pid.assert_called_with(self.info['uuid']) mock_path_exists.assert_called_with(mock.ANY) mock_popen.assert_called_once_with(mock.ANY, stderr=subprocess.PIPE) @mock.patch.object(os.path, 'exists', autospec=True) @mock.patch.object(subprocess, 'Popen', autospec=True) @mock.patch.object(psutil, 'pid_exists', autospec=True) @mock.patch.object(console_utils, '_get_console_pid', autospec=True) @mock.patch.object(console_utils, '_ensure_console_pid_dir_exists', autospec=True) @mock.patch.object(console_utils, '_stop_console', autospec=True) def test_start_socat_console_nopid(self, mock_stop, mock_dir_exists, mock_get_pid, mock_pid_exists, mock_popen, mock_path_exists): # no existing PID file before starting mock_stop.side_effect = exception.NoConsolePid('/tmp/blah') mock_popen.return_value.pid = 23456 mock_popen.return_value.poll.return_value = None mock_popen.return_value.communicate.return_value = (None, None) mock_get_pid.return_value = 23456 mock_path_exists.return_value = True console_utils.start_socat_console(self.info['uuid'], self.info['port'], 'ls&') mock_stop.assert_called_once_with(self.info['uuid']) mock_dir_exists.assert_called_once_with() mock_get_pid.assert_called_with(self.info['uuid']) mock_path_exists.assert_called_with(mock.ANY) mock_popen.assert_called_once_with(mock.ANY, stderr=subprocess.PIPE) @mock.patch.object(subprocess, 'Popen', autospec=True) @mock.patch.object(console_utils, '_ensure_console_pid_dir_exists', autospec=True) @mock.patch.object(console_utils, '_stop_console', autospec=True) def test_start_socat_console_fail(self, mock_stop, mock_dir_exists, mock_popen): mock_popen.side_effect = OSError() mock_popen.return_value.pid = 23456 mock_popen.return_value.poll.return_value = 1 mock_popen.return_value.communicate.return_value = (None, 'error') self.assertRaises(exception.ConsoleSubprocessFailed, console_utils.start_socat_console, self.info['uuid'], self.info['port'], 'ls&') mock_stop.assert_called_once_with(self.info['uuid']) mock_dir_exists.assert_called_once_with() mock_popen.assert_called_once_with(mock.ANY, stderr=subprocess.PIPE) @mock.patch.object(subprocess, 'Popen', autospec=True) @mock.patch.object(console_utils, '_ensure_console_pid_dir_exists', autospec=True) @mock.patch.object(console_utils, '_stop_console', autospec=True) def test_start_socat_console_fail_nopiddir(self, mock_stop, mock_dir_exists, mock_popen): mock_dir_exists.side_effect = exception.ConsoleError(message='fail') self.assertRaises(exception.ConsoleError, console_utils.start_socat_console, self.info['uuid'], self.info['port'], 'ls&') mock_stop.assert_called_once_with(self.info['uuid']) mock_dir_exists.assert_called_once_with() mock_popen.assert_not_called() @mock.patch.object(console_utils, '_stop_console', autospec=True) def test_stop_socat_console(self, mock_stop): console_utils.stop_socat_console(self.info['uuid']) mock_stop.assert_called_once_with(self.info['uuid']) @mock.patch.object(console_utils.LOG, 'warning', autospec=True) @mock.patch.object(console_utils, '_stop_console', autospec=True) def test_stop_socat_console_fail_nopid(self, mock_stop, mock_log_warning): mock_stop.side_effect = exception.NoConsolePid('/tmp/blah') console_utils.stop_socat_console(self.info['uuid']) mock_stop.assert_called_once_with(self.info['uuid']) # LOG.warning() is called when _stop_console() raises NoConsolePid self.assertTrue(mock_log_warning.called)

46.16

78

0.634315

3,159

26,542

4.959797

0.081988

0.061271

0.070845

0.074036

0.828759

0.806676

0.784082

0.764488

0.748341

0.723002

0

0.007986

0.268744

26,542

574

79

46.240418

0.799268

0.036169

0

0.684902

0

0.0713

0.020075

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1

0.087527

false

0.017505

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0.133479

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null

0

1

0

null

0

6

8a31452d04c8ae9e47bed68e0e826fb663e642ef

11,408

py

Python

serial_scripts/mvpn/test_mvpn.py

vkolli/5.0_contrail-test

1793f169a94100400a1b2fafbad21daf5aa4d48a

[ "Apache-2.0" ]

null

serial_scripts/mvpn/test_mvpn.py

vkolli/5.0_contrail-test

1793f169a94100400a1b2fafbad21daf5aa4d48a

[ "Apache-2.0" ]

1

2021-06-01T22:18:29.000Z

serial_scripts/mvpn/test_mvpn.py

vkolli/5.0_contrail-test

1793f169a94100400a1b2fafbad21daf5aa4d48a

[ "Apache-2.0" ]

null

from tcutils.wrappers import preposttest_wrapper from compute_node_test import ComputeNodeFixture import test from common.mvpn.base import * from tcutils.traffic_utils.scapy_traffic_gen import ScapyTraffic from tcutils.traffic_utils.traffic_analyzer import TrafficAnalyzer class TestMVPNSingleVNSingleCompute(MVPNTestSingleVNSingleComputeBase): @classmethod def setUpClass(cls): super(TestMVPNSingleVNSingleCompute, cls).setUpClass() # end setUpClass @classmethod def tearDownClass(cls): super(TestMVPNSingleVNSingleCompute, cls).tearDownClass() # end tearDownClass @test.attr(type=['sanity']) @preposttest_wrapper def test_mvpn_single_vn_within_compute(self): ''' Test MVPN functionality when both multicast source and receivers are part of a single VN and also part of the same compute. ''' # Bringup MVPN setup ret_dict = self.bringup_mvpn_setup() vm_fixtures = ret_dict['vm_fixtures'] # Verify MVPN Type-1 routes route_type = 1 result = self.verify_mvpn_routes(route_type) # IGMP Join parameter details igmp = {'type': 0x22, # IGMPv3 Report 'numgrp': 1, # Number of group records 'record1': { 'rtype': 1, # Record type. INCLUDE 'maddr': '239.1.1.1', # Multicast group address 'srcaddrs': ['30.30.30.1'] # List of multicast source addresses }, } # Multicast Traffic details # IGMPv3 join is sent from vm2, not from vm3. So, that when multicast # source vm1 starts sending data traffic, vm2 only should receive the # traffic, not vm3. traffic = {'stream1': {'src':'vm1', # Multicast source 'rcvrs': ['vm2'], # Multicast receivers 'non_rcvrs': ['vm3'],# Non Multicast receivers 'maddr':'239.1.1.1', # Multicast group address 'count':10 # Num of packets } } # Send and verify IGMP reports and multicast data traffic result = self.send_verify_mcast(vm_fixtures, traffic, igmp) # IGMP Leave parameter details igmp = {'type': 0x22, # IGMPv3 Report 'numgrp': 1, # Number of group records 'record1': { 'rtype': 6, # Record type.BLOCK OLD SOURCES 'maddr': '239.1.1.1', # Multicast group address 'srcaddrs': ['30.30.30.1'] # List of multicast source addresses }, } # Send and verify IGMP reports and multicast traffic result = self.send_verify_mcast(vm_fixtures, traffic, igmp) # end test_mvpn_single_vn_within_compute class TestMVPNSingleVNMultiCompute(MVPNTestSingleVNMultiComputeBase): @classmethod def setUpClass(cls): super(TestMVPNSingleVNMultiCompute, cls).setUpClass() # end setUpClass @classmethod def tearDownClass(cls): super(TestMVPNSingleVNMultiCompute, cls).tearDownClass() # end tearDownClass @preposttest_wrapper def test_mvpn_single_vn_multi_compute(self): ''' Test MVPN functionality when both multicast source and receivers are part of a single VN. But, source and receivers are part of different computes ''' # Bringup MVPN setup ret_dict = self.bringup_mvpn_setup() vm_fixtures = ret_dict['vm_fixtures'] # Verify MVPN Type-1 routes route_type = 1 result = self.verify_mvpn_routes(route_type) # IGMP Join parameter details igmp = {'type': 0x22, # IGMPv3 Report 'numgrp': 1, # Number of group records 'record1': { 'rtype': 1, # Record type. INCLUDE 'maddr': '239.1.1.1', # Multicast group address 'srcaddrs': ['30.30.30.1'] # List of multicast source addresses }, } # Multicast Traffic details # IGMPv3 join is sent from vm3, not from vm2 and vm4. So, that when # multicast source vm1 starts sending data traffic, vm3 only should # receive the traffic, not vm2 and vm4. traffic = {'stream1':{'src':'vm1', # Multicast source 'rcvrs': ['vm3'], # Multicast receivers 'non_rcvrs': ['vm2','vm4'], # Non Multicast receivers 'maddr': '239.1.1.1', # Multicast group address 'count':10 # Num of packets } } # Send and verify IGMP reports and multicast traffic result = self.send_verify_mcast(vm_fixtures, traffic, igmp) # IGMP Leave parameter details igmp = {'type': 0x22, # IGMPv3 Report 'numgrp': 1, # Number of group records 'record1': { 'rtype': 6, # Record type.BLOCK OLD SOURCES 'maddr': '239.1.1.1', # Multicast group address 'srcaddrs': ['30.30.30.1'] # List of multicast source addresses }, } # Send and verify IGMP reports and multicast traffic result = self.send_verify_mcast(vm_fixtures, traffic, igmp) # end test_mvpn_single_vn_multi_compute class TestMVPNMultiVNSingleCompute(MVPNTestMultiVNSingleComputeBase): @classmethod def setUpClass(cls): super(TestMVPNMultiVNSingleCompute, cls).setUpClass() # end setUpClass @classmethod def tearDownClass(cls): super(TestMVPNMultiVNSingleCompute, cls).tearDownClass() # end tearDownClass @preposttest_wrapper def test_mvpn_multi_vn_single_compute(self): ''' Test MVPN functionality when both multicast source and receivers are part of a multiple VNs. But, source and receivers are part of different computes ''' # Bringup MVPN setup ret_dict = self.bringup_mvpn_setup() vm_fixtures = ret_dict['vm_fixtures'] # Verify MVPN Type-1 routes route_type = 1 result = self.verify_mvpn_routes(route_type) # IGMP Join parameters igmp = {'type': 0x22, # IGMPv3 Report 'numgrp': 1, # Number of group records 'record1': { 'rtype': 1, # Record type. INCLUDE 'maddr': '239.1.1.1', # Multicast group address 'srcaddrs': ['30.30.30.1'] # List of multicast source addresses }, } # Multicast Traffic details # IGMPv3 join is sent from vm2 and vm3, not from vm4. So, that when # multicast source vm1 starts sending data traffic, vm2 and vm3 only # should receive the traffic, not vm4. traffic = {'stream1': {'src':'vm1', # Multicast source 'rcvrs': ['vm2','vm3'], # Multicast receivers 'non_rcvrs': ['vm4'], # Non Multicast receivers 'maddr': '239.1.1.1', # Multicast group address 'count':10 # Num of packets } } # Send and verify IGMP reports and multicast traffic result = self.send_verify_mcast(vm_fixtures, traffic, igmp) # IGMP Leave parameters igmp = {'type': 0x22, # IGMPv3 Report 'numgrp': 1, # Number of group records 'record1': { 'rtype': 6, # Record type.BLOCK OLD SOURCES 'maddr': '239.1.1.1', # Multicast group address 'srcaddrs': ['30.30.30.1'] # List of multicast source addresses }, } # Send and verify IGMP reports and multicast traffic result = self.send_verify_mcast(vm_fixtures, traffic, igmp) # end test_mvpn_multi_vn_single_compute class TestMVPNMultiVNMultiCompute(MVPNTestMultiVNMultiComputeBase): @classmethod def setUpClass(cls): super(TestMVPNMultiVNMultiCompute, cls).setUpClass() # end setUpClass @classmethod def tearDownClass(cls): super(TestMVPNMultiVNMultiCompute, cls).tearDownClass() # end tearDownClass @preposttest_wrapper def test_mvpn_multi_vn_multi_compute(self): ''' Test MVPN functionality when both multicast source and receivers are part of a single VN. But, source and receivers are part of different computes ''' # Bringup MVPN setup ret_dict = self.bringup_mvpn_setup() vm_fixtures = ret_dict['vm_fixtures'] # Verify MVPN Type-1 routes route_type = 1 result = self.verify_mvpn_routes(route_type) # IGMP Join parameters igmp = {'type': 0x22, # IGMPv3 Report 'numgrp': 1, # Number of group records 'record1': { 'rtype': 1, # Record type. INCLUDE 'maddr': '239.1.1.1', # Multicast group address 'srcaddrs': ['30.30.30.1'] # List of multicast source addresses }, } # Multicast Traffic details # IGMPv3 join is sent from vm2 and vm3, not from vm4. So, that when # multicast source vm1 starts sending data traffic, vm2 and vm3 only # should receive the traffic, not vm4. traffic = {'stream1': {'src':'vm1', # Multicast source 'rcvrs': ['vm2', 'vm3'], # Multicast receivers 'non_rcvrs': ['vm4'], # Non Multicast receivers 'maddr': '239.1.1.1', # Multicast group address 'count':10 # Num of packets } } # Send and verify IGMP reports and multicast traffic result = self.send_verify_mcast(vm_fixtures, traffic, igmp) # IGMP Leave parameters igmp = {'type': 0x22, # IGMPv3 Report 'numgrp': 1, # Number of group records 'record1': { 'rtype': 6, # Record type.BLOCK OLD SOURCES 'maddr': '239.1.1.1', # Multicast group address 'srcaddrs': ['30.30.30.1'] # List of multicast source addresses }, } # Send and verify IGMP reports and multicast traffic result = self.send_verify_mcast(vm_fixtures, traffic, igmp) # end test_mvpn_multi_vn_multi_compute

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Python

oscar/lib/python2.7/site-packages/django_extensions/utils/deprecation.py

sainjusajan/django-oscar

466e8edc807be689b0a28c9e525c8323cc48b8e1

[ "BSD-3-Clause" ]

null

oscar/lib/python2.7/site-packages/django_extensions/utils/deprecation.py

sainjusajan/django-oscar

466e8edc807be689b0a28c9e525c8323cc48b8e1

[ "BSD-3-Clause" ]

null

oscar/lib/python2.7/site-packages/django_extensions/utils/deprecation.py

sainjusajan/django-oscar

466e8edc807be689b0a28c9e525c8323cc48b8e1

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- from __future__ import absolute_import from django.utils.deprecation import RemovedInNextVersionWarning class MarkedForDeprecationWarning(RemovedInNextVersionWarning): pass

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0a0d09a0b4eb4f56c25030174230ea51ea0306f6

48

py

Python

tests/test_views.py

codezeus/django-helpers

a28cc19e32cf41130e848c268d26c1858a7cf26a

[ "MIT" ]

null

tests/test_views.py

codezeus/django-helpers

a28cc19e32cf41130e848c268d26c1858a7cf26a

[ "MIT" ]

null

tests/test_views.py

codezeus/django-helpers

a28cc19e32cf41130e848c268d26c1858a7cf26a

[ "MIT" ]

null

import pytest from django_toolset import views

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6

0a27c814c08422d35bbb1097b53a775f175d023c

202

py

Python

tests/services/__init__.py

Bystroushaak/bottle-gui

5135a87e7f0be8e36c1fc8663f46d1dbe6e89a2a

[ "MIT" ]

3

2015-01-03T22:10:33.000Z

2015-01-04T16:48:45.000Z

tests/services/__init__.py

Bystroushaak/bottle-gui

5135a87e7f0be8e36c1fc8663f46d1dbe6e89a2a

[ "MIT" ]

4

2015-01-03T18:44:34.000Z

2020-09-26T08:02:18.000Z

tests/services/__init__.py

Bystroushaak/bottle-gui

5135a87e7f0be8e36c1fc8663f46d1dbe6e89a2a

[ "MIT" ]

null

#! /usr/bin/env python # -*- coding: utf-8 -*- # # Interpreter version: python 2.7 # # Imports ===================================================================== from hist import * from xex import *

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0a2a0398b1e8d96c44f4834cbda1121594c86452

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py

Python

code/ch5-viewmodels/services/user_service.py

mtgeekman/web-applications-with-fastapi-course

0ec278583542360fc6aaef7db5372a827e95deb8

[ "MIT" ]

225

2020-12-31T08:30:08.000Z

2022-03-30T14:14:47.000Z

code/ch5-viewmodels/services/user_service.py

mtgeekman/web-applications-with-fastapi-course

0ec278583542360fc6aaef7db5372a827e95deb8

[ "MIT" ]

10

2021-02-09T01:28:53.000Z

2022-02-25T19:03:49.000Z

code/ch5-viewmodels/services/user_service.py

mtgeekman/web-applications-with-fastapi-course

0ec278583542360fc6aaef7db5372a827e95deb8

[ "MIT" ]

145

2021-02-06T09:31:46.000Z

2022-03-26T19:18:20.000Z

def user_count() -> int: return 73_874

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0a4103067d07eac658207762e0d7bfaf098f7c9a

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py

Python

torch_geometric/graphgym/models/pooling.py

JinheonBaek/pytorch_geometric

dfd32d08a3d8191d6290e53458d4eda515d04fd6

[ "MIT" ]

4

2021-05-03T20:22:34.000Z

2021-12-11T03:19:07.000Z

torch_geometric/graphgym/models/pooling.py

JinheonBaek/pytorch_geometric

dfd32d08a3d8191d6290e53458d4eda515d04fd6

[ "MIT" ]

1

2021-09-10T06:36:13.000Z

2021-10-06T14:20:16.000Z

torch_geometric/graphgym/models/pooling.py

JinheonBaek/pytorch_geometric

dfd32d08a3d8191d6290e53458d4eda515d04fd6

[ "MIT" ]

2

2021-07-10T10:16:43.000Z

2021-11-04T07:36:55.000Z

from torch_scatter import scatter import torch_geometric.graphgym.register as register def global_add_pool(x, batch, size=None): """ Globally pool node embeddings into graph embeddings, via elementwise sum. Pooling function takes in node embedding [num_nodes x emb_dim] and batch (indices) and outputs graph embedding [num_graphs x emb_dim]. Args: x (torch.tensor): Input node embeddings batch (torch.tensor): Batch tensor that indicates which node belongs to which graph size (optional): Total number of graphs. Can be auto-inferred. Returns: Pooled graph embeddings """ size = batch.max().item() + 1 if size is None else size return scatter(x, batch, dim=0, dim_size=size, reduce='add') def global_mean_pool(x, batch, size=None): """ Globally pool node embeddings into graph embeddings, via elementwise mean. Pooling function takes in node embedding [num_nodes x emb_dim] and batch (indices) and outputs graph embedding [num_graphs x emb_dim]. Args: x (torch.tensor): Input node embeddings batch (torch.tensor): Batch tensor that indicates which node belongs to which graph size (optional): Total number of graphs. Can be auto-inferred. Returns: Pooled graph embeddings """ size = batch.max().item() + 1 if size is None else size return scatter(x, batch, dim=0, dim_size=size, reduce='mean') def global_max_pool(x, batch, size=None): """ Globally pool node embeddings into graph embeddings, via elementwise max. Pooling function takes in node embedding [num_nodes x emb_dim] and batch (indices) and outputs graph embedding [num_graphs x emb_dim]. Args: x (torch.tensor): Input node embeddings batch (torch.tensor): Batch tensor that indicates which node belongs to which graph size (optional): Total number of graphs. Can be auto-inferred. Returns: Pooled graph embeddings """ size = batch.max().item() + 1 if size is None else size return scatter(x, batch, dim=0, dim_size=size, reduce='max') pooling_dict = { 'add': global_add_pool, 'mean': global_mean_pool, 'max': global_max_pool } pooling_dict = {**register.pooling_dict, **pooling_dict}

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py

Python

clibs/tess2/__init__.py

filonik/clibs

d060d396515d1d4ba5a94cd5a10a6d728e42c295

[ "MIT" ]

null

clibs/tess2/__init__.py

filonik/clibs

d060d396515d1d4ba5a94cd5a10a6d728e42c295

[ "MIT" ]

null

clibs/tess2/__init__.py

filonik/clibs

d060d396515d1d4ba5a94cd5a10a6d728e42c295

[ "MIT" ]

null

from .tesselator import *

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6

0a6044b98521f2d3a0816b5d00e7cd24bf62aacb

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py

Python

flamio/user.py

Jkreid/flamio

c7d98b7e39f0a8e5792a236e9508632d294525b2

[ "MIT" ]

null

flamio/user.py

Jkreid/flamio

c7d98b7e39f0a8e5792a236e9508632d294525b2

[ "MIT" ]

11

2020-05-29T18:14:58.000Z

2021-07-21T02:41:14.000Z

flamio/user.py

Jkreid/flamio

c7d98b7e39f0a8e5792a236e9508632d294525b2

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Sat Jul 17 11:31:40 2021 @author: justi """ from abc import ABC, abstractmethod import flamio def flamio_method(method): def data_wrapped(user, *args, **kwargs): user.pre_method() value = method(user, *args, **kwargs) user.aft_method() return value return data_wrapped class User(ABC): #// User Methods ////////////////////////////////////////////////////////// def __init__(self, username, *args, info={}, **kwargs): self.username = username self.info = info super().__init__() @abstractmethod def save(self): # save self._info to saved data pass @abstractmethod def load(self): # set self._info by accessing saved data pass @abstractmethod def pre_method(self): pass @abstractmethod def aft_method(self): pass @property def info(self): return self._info @info.setter def info(self, info): self._info = info #// Flamio Methods //////////////////////////////////////////////////////// @flamio_method def create_track(self, *args, **kwargs): flamio.create_track(self.info, *args, **kwargs) @flamio_method def get_track(self, *args, **kwargs): return flamio.get_track(self.info, *args, **kwargs) @flamio_method def delete_track(self, *args, **kwargs): flamio.delete_track(self.info, *args, **kwargs) @flamio_method def create_tag(self, *args, **kwargs): flamio.create_tag(self.info, *args, **kwargs) @flamio_method def delete_tag(self, *args, **kwargs): flamio.delete_tag(self.info, *args, **kwargs) @flamio_method def get_tag(self, *args, **kwargs): return flamio.get_tag(self.info, *args, **kwargs) @flamio_method def rename_tag(self, *args, **kwargs): flamio.rename_tag(self.info, *args, **kwargs) @flamio_method def get_track_tags(self, *args, **kwargs): return flamio.get_track_tags(self.info, *args, **kwargs) @flamio_method def add_tag_to_track(self, *args, **kwargs): flamio.add_tag_to_track(self.info, *args, **kwargs) @flamio_method def remove_tag_from_track(self, *args, **kwargs): flamio.remove_tag_from_track(self.info, *args, **kwargs) @flamio_method def get_mix_tags(self, *args, **kwargs): return flamio.get_mix_tags(self.info, *args, **kwargs) @flamio_method def add_tag_to_mix(self, *args, **kwargs): flamio.add_tag_to_mix(self.info, *args, **kwargs) @flamio_method def remove_tag_from_mix(self, *args, **kwargs): flamio.remove_tag_from_mix(self.info, *args, **kwargs) @flamio_method def create_loop(self, *args, **kwargs): flamio.create_loop(self.info, *args, **kwargs) @flamio_method def get_loop(self, *args, **kwargs): return flamio.get_loop(self.info, *args, **kwargs) @flamio_method def delete_loop(self, *args, **kwargs): flamio.delete_loop(self.info, *args, **kwargs) @flamio_method def add_loop_time(self, *args, **kwargs): flamio.add_loop_time(self.info, *args, **kwargs) @flamio_method def get_loop_time(self, *args, **kwargs): return flamio.get_loop_time(self.info, *args, **kwargs) @flamio_method def edit_loop_time(self, *args, **kwargs): flamio.edit_loop_time(self.info, *args, **kwargs) @flamio_method def delete_loop_time(self, *args, **kwargs): flamio.delete_loop_time(self.info, *args, **kwargs) @flamio_method def multidelete_loop_times(self, *args, **kwargs): flamio.multidelete_loop_times(self.info, *args, **kwargs) @flamio_method def duplicate_loop_time(self, *args, **kwargs): flamio.duplicate_loop_time(self.info, *args, **kwargs) @flamio_method def move_loop_time(self, *args, **kwargs): flamio.move_loop_time(self.info, *args, **kwargs) @flamio_method def swap_loop_times(self, *args, **kwargs): flamio.swap_loop_times(self.info, *args, **kwargs) @flamio_method def create_skip(self, *args, **kwargs): flamio.create_skip(self.info, *args, **kwargs) @flamio_method def get_skip(self, *args, **kwargs): return flamio.get_skip(self.info, *args, **kwargs) @flamio_method def delete_skip(self, *args, **kwargs): flamio.delete_skip(self.info, *args, **kwargs) @flamio_method def add_skip_time(self, *args, **kwargs): flamio.add_skip_time(self.info, *args, **kwargs) @flamio_method def get_skip_time(self, *args, **kwargs): return flamio.get_skip_time(self.info, *args, **kwargs) @flamio_method def edit_skip_time(self, *args, **kwargs): flamio.edit_skip_time(self.info, *args, **kwargs) @flamio_method def delete_skip_time(self, *args, **kwargs): flamio.delete_skip_time(self.info, *args, **kwargs) @flamio_method def multidelete_skip_times(self, *args, **kwargs): flamio.multidelete_skip_times(self.info, *args, **kwargs) @flamio_method def duplicate_skip_time(self, *args, **kwargs): flamio.duplicate_skip_time(self.info, *args, **kwargs) @flamio_method def move_skip_time(self, *args, **kwargs): flamio.move_skip_time(self.info, *args, **kwargs) @flamio_method def swap_skip_times(self, *args, **kwargs): flamio.swap_skip_times(self.info, *args, **kwargs) @flamio_method def create_mix(self, *args, **kwargs): flamio.create_mix(self.info, *args, **kwargs) @flamio_method def get_mix(self, *args, **kwargs): return flamio.get_mix(self.info, *args, **kwargs) @flamio_method def delete_mix(self, *args, **kwargs): flamio.delete_mix(self.info, *args, **kwargs) @flamio_method def add_mix_item(self, *args, **kwargs): flamio.add_mix_item(self.info, *args, **kwargs) @flamio_method def get_mix_item(self, *args, **kwargs): return flamio.get_mix_item(self.info, *args, **kwargs) @flamio_method def edit_mix_item(self, *args, **kwargs): flamio.edit_mix_item(self.info, *args, **kwargs) @flamio_method def delete_mix_item(self, *args, **kwargs): flamio.delete_mix_item(self.info, *args, **kwargs) @flamio_method def multidelete_mix_items(self, *args, **kwargs): flamio.multidelete_mix_items(self.info, *args, **kwargs) @flamio_method def duplicate_mix_item(self, *args, **kwargs): flamio.duplicate_mix_item(self.info, *args, **kwargs) @flamio_method def move_mix_item(self, *args, **kwargs): flamio.move_mix_item(self.info, *args, **kwargs) @flamio_method def swap_mix_items(self, *args, **kwargs): flamio.swap_mix_items(self.info, *args, **kwargs) @flamio_method def add_mix_track(self, *args, **kwargs): flamio.add_mix_track(self.info, *args, **kwargs) @flamio_method def add_mix_pause(self, *args, **kwargs): flamio.add_mix_pause(self.info, *args, **kwargs) @flamio_method def add_mix_mix(self, *args, **kwargs): flamio.add_mix_mix(self.info, *args, **kwargs) @flamio_method def edit_mix_track(self, *args, **kwargs): flamio.edit_mix_track(self.info, *args, **kwargs) @flamio_method def edit_mix_pause(self, *args, **kwargs): flamio.edit_mix_pause(self.info, *args, **kwargs) @flamio_method def edit_mix_mix(self, *args, **kwargs): flamio.edit_mix_mix(self.info, *args, **kwargs) @flamio_method def get_track_play_info(self, *args, **kwargs): return flamio.get_track_play_info(self.info, *args, **kwargs) @flamio_method def get_item_play_info(self, *args, **kwargs): return flamio.get_item_play_info(self.info, *args, **kwargs) @flamio_method def get_mix_play_info(self, *args, **kwargs): return flamio.get_mix_play_info(self.info, *args, **kwargs)

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null

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1

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6a7da3df60c7069ffd260bfd4d586aa5bd98e007

253

py

Python

addons/hr/models/__init__.py

jjiege/odoo

fd5b8ad387c1881f349d125cbd56433f4d49398f

[ "MIT" ]

null

addons/hr/models/__init__.py

jjiege/odoo

fd5b8ad387c1881f349d125cbd56433f4d49398f

[ "MIT" ]

null

addons/hr/models/__init__.py

jjiege/odoo

fd5b8ad387c1881f349d125cbd56433f4d49398f

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Part of Odoo. See LICENSE file for full copyright and licensing details. from . import hr from . import res_config_settings from . import mail_alias from . import mail_channel from . import res_partner from . import res_users

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6a7db2cbbd35ce5421bf9dc46c63d0d880dfea45

1,460

py

Python

controllers/proc.py

himansu1997/eden

1e2cf2b00f55da46b1ce3e6b7ad44b5345d7a1dc

[ "MIT" ]

205

2015-01-20T08:26:09.000Z

2022-03-27T19:59:33.000Z

controllers/proc.py

himansu1997/eden

1e2cf2b00f55da46b1ce3e6b7ad44b5345d7a1dc

[ "MIT" ]

249

2015-02-10T09:56:35.000Z

2022-03-23T19:54:36.000Z

controllers/proc.py

himansu1997/eden

1e2cf2b00f55da46b1ce3e6b7ad44b5345d7a1dc

[ "MIT" ]

231

2015-02-10T09:33:17.000Z

2022-02-18T19:56:05.000Z

# -*- coding: utf-8 -*- """ Procurement A module to handle Procurement Currently handles Suppliers Planned Procurements Purchase Orders (POs) @ToDo: Extend to Purchase Requests (PRs) """ if not settings.has_module(c): raise HTTP(404, body="Module disabled: %s" % c) # ----------------------------------------------------------------------------- def index(): """ Module's Home Page """ return s3db.cms_index(c) # ----------------------------------------------------------------------------- def order(): """ RESTful CRUD controller """ return s3_rest_controller(rheader = s3db.proc_rheader, hide_filter = True, ) # ----------------------------------------------------------------------------- #def order_item(): # """ RESTful CRUD controller """ # return s3_rest_controller() # ----------------------------------------------------------------------------- def plan(): """ RESTful CRUD controller """ return s3_rest_controller(rheader = s3db.proc_rheader, hide_filter = True, ) # ----------------------------------------------------------------------------- def supplier(): """ RESTful CRUD controller """ return s3_rest_controller("org", "organisation") # END =========================================================================

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6a91a4a394e0bfc87d46febc7702653bfa739570

180

py

Python

class9/ex8/mytest/__init__.py

daveg999/Automation_class

d23652ecae56b790684971dda6e85a1d2367e22b

[ "Apache-2.0" ]

null

class9/ex8/mytest/__init__.py

daveg999/Automation_class

d23652ecae56b790684971dda6e85a1d2367e22b

[ "Apache-2.0" ]

null

class9/ex8/mytest/__init__.py

daveg999/Automation_class

d23652ecae56b790684971dda6e85a1d2367e22b

[ "Apache-2.0" ]

null

from mytest.simple import func1 from mytest.whatever import func2 from mytest.world import func3 from mytest.world import MyClass __all__ = ('func1', 'func2', 'func3', 'MyClass')

25.714286

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6aae89b56bc757f98c51d3d3665f0f64e58fb84a

35

py

Python

discord/types/widget.py

Harukomaze/disnake

541f5c9623a02be894cd1015dbb344070700cb87

[ "MIT" ]

null

discord/types/widget.py

Harukomaze/disnake

541f5c9623a02be894cd1015dbb344070700cb87

[ "MIT" ]

null

discord/types/widget.py

Harukomaze/disnake

541f5c9623a02be894cd1015dbb344070700cb87

[ "MIT" ]

null

from disnake.types.widget import *

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0ad15c947cfe5253b488718e212935b7005da018

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py

Python

tests/components/zeroconf/test_init.py

jonasjeeliasson/core

0301706fc631ad1f2cd2532667ba9dfe2f856198

[ "Apache-2.0" ]

1

2019-08-19T18:18:50.000Z

tests/components/zeroconf/test_init.py

jagadeeshvenkatesh/core

1bd982668449815fee2105478569f8e4b5670add

[ "Apache-2.0" ]

70

2020-08-05T07:20:00.000Z

2022-03-31T06:01:46.000Z

tests/components/zeroconf/test_init.py

jagadeeshvenkatesh/core

1bd982668449815fee2105478569f8e4b5670add

[ "Apache-2.0" ]

2

2020-06-03T20:24:39.000Z

2020-06-06T19:52:09.000Z

"""Test Zeroconf component setup process.""" from unittest.mock import patch from zeroconf import ( BadTypeInNameException, Error as ZeroconfError, InterfaceChoice, IPVersion, ServiceInfo, ServiceStateChange, ) from homeassistant.components import zeroconf from homeassistant.components.zeroconf import CONF_DEFAULT_INTERFACE, CONF_IPV6 from homeassistant.const import ( EVENT_HOMEASSISTANT_START, EVENT_HOMEASSISTANT_STARTED, EVENT_HOMEASSISTANT_STOP, ) from homeassistant.generated import zeroconf as zc_gen from homeassistant.setup import async_setup_component NON_UTF8_VALUE = b"ABCDEF\x8a" NON_ASCII_KEY = b"non-ascii-key\x8a" PROPERTIES = { b"macaddress": b"ABCDEF012345", b"non-utf8-value": NON_UTF8_VALUE, NON_ASCII_KEY: None, } HOMEKIT_STATUS_UNPAIRED = b"1" HOMEKIT_STATUS_PAIRED = b"0" _ROUTE_NO_LOOPBACK = ( { "attrs": [ ("RTA_TABLE", 254), ("RTA_DST", "224.0.0.251"), ("RTA_OIF", 4), ("RTA_PREFSRC", "192.168.1.5"), ], }, ) _ROUTE_LOOPBACK = ( { "attrs": [ ("RTA_TABLE", 254), ("RTA_DST", "224.0.0.251"), ("RTA_OIF", 4), ("RTA_PREFSRC", "127.0.0.1"), ], }, ) def service_update_mock(zeroconf, services, handlers, *, limit_service=None): """Call service update handler.""" for service in services: if limit_service is not None and service != limit_service: continue handlers[0](zeroconf, service, f"_name.{service}", ServiceStateChange.Added) def get_service_info_mock(service_type, name): """Return service info for get_service_info.""" return ServiceInfo( service_type, name, addresses=[b"\n\x00\x00\x14"], port=80, weight=0, priority=0, server="name.local.", properties=PROPERTIES, ) def get_service_info_mock_without_an_address(service_type, name): """Return service info for get_service_info without any addresses.""" return ServiceInfo( service_type, name, addresses=[], port=80, weight=0, priority=0, server="name.local.", properties=PROPERTIES, ) def get_homekit_info_mock(model, pairing_status): """Return homekit info for get_service_info for an homekit device.""" def mock_homekit_info(service_type, name): return ServiceInfo( service_type, name, addresses=[b"\n\x00\x00\x14"], port=80, weight=0, priority=0, server="name.local.", properties={b"md": model.encode(), b"sf": pairing_status}, ) return mock_homekit_info def get_zeroconf_info_mock(macaddress): """Return info for get_service_info for an zeroconf device.""" def mock_zc_info(service_type, name): return ServiceInfo( service_type, name, addresses=[b"\n\x00\x00\x14"], port=80, weight=0, priority=0, server="name.local.", properties={b"macaddress": macaddress.encode()}, ) return mock_zc_info def get_zeroconf_info_mock_manufacturer(manufacturer): """Return info for get_service_info for an zeroconf device.""" def mock_zc_info(service_type, name): return ServiceInfo( service_type, name, addresses=[b"\n\x00\x00\x14"], port=80, weight=0, priority=0, server="name.local.", properties={b"manufacturer": manufacturer.encode()}, ) return mock_zc_info async def test_setup(hass, mock_zeroconf): """Test configured options for a device are loaded via config entry.""" with patch.object( hass.config_entries.flow, "async_init" ) as mock_config_flow, patch.object( zeroconf, "HaServiceBrowser", side_effect=service_update_mock ) as mock_service_browser: mock_zeroconf.get_service_info.side_effect = get_service_info_mock assert await async_setup_component(hass, zeroconf.DOMAIN, {zeroconf.DOMAIN: {}}) hass.bus.async_fire(EVENT_HOMEASSISTANT_STARTED) await hass.async_block_till_done() assert len(mock_service_browser.mock_calls) == 1 expected_flow_calls = 0 for matching_components in zc_gen.ZEROCONF.values(): domains = set() for component in matching_components: if len(component) == 1: domains.add(component["domain"]) expected_flow_calls += len(domains) assert len(mock_config_flow.mock_calls) == expected_flow_calls # Test instance is set. assert "zeroconf" in hass.data assert await hass.components.zeroconf.async_get_instance() is mock_zeroconf async def test_setup_with_overly_long_url_and_name(hass, mock_zeroconf, caplog): """Test we still setup with long urls and names.""" with patch.object(hass.config_entries.flow, "async_init"), patch.object( zeroconf, "HaServiceBrowser", side_effect=service_update_mock ), patch( "homeassistant.components.zeroconf.get_url", return_value="https://this.url.is.way.too.long/very/deep/path/that/will/make/us/go/over/the/maximum/string/length/and/would/cause/zeroconf/to/fail/to/startup/because/the/key/and/value/can/only/be/255/bytes/and/this/string/is/a/bit/longer/than/the/maximum/length/that/we/allow/for/a/value", ), patch.object( hass.config, "location_name", "\u00dcBER \u00dcber German Umlaut long string long string long string long string long string long string long string long string long string long string long string long string long string long string long string long string long string long string long string long string long string long string long string long string long string long string long string long string long string long string long string long string long string long string long string long string long string long string long string long string long string long string long string long string long string long string long string long string long string long string", ): mock_zeroconf.get_service_info.side_effect = get_service_info_mock assert await async_setup_component(hass, zeroconf.DOMAIN, {zeroconf.DOMAIN: {}}) hass.bus.async_fire(EVENT_HOMEASSISTANT_START) await hass.async_block_till_done() assert "https://this.url.is.way.too.long" in caplog.text assert "German Umlaut" in caplog.text async def test_setup_with_default_interface(hass, mock_zeroconf): """Test default interface config.""" with patch.object(hass.config_entries.flow, "async_init"), patch.object( zeroconf, "HaServiceBrowser", side_effect=service_update_mock ): mock_zeroconf.get_service_info.side_effect = get_service_info_mock assert await async_setup_component( hass, zeroconf.DOMAIN, {zeroconf.DOMAIN: {CONF_DEFAULT_INTERFACE: True}} ) hass.bus.async_fire(EVENT_HOMEASSISTANT_STARTED) await hass.async_block_till_done() assert mock_zeroconf.called_with(interface_choice=InterfaceChoice.Default) async def test_setup_without_default_interface(hass, mock_zeroconf): """Test without default interface config.""" with patch.object(hass.config_entries.flow, "async_init"), patch.object( zeroconf, "HaServiceBrowser", side_effect=service_update_mock ): mock_zeroconf.get_service_info.side_effect = get_service_info_mock assert await async_setup_component( hass, zeroconf.DOMAIN, {zeroconf.DOMAIN: {CONF_DEFAULT_INTERFACE: False}} ) assert mock_zeroconf.called_with() async def test_setup_without_ipv6(hass, mock_zeroconf): """Test without ipv6.""" with patch.object(hass.config_entries.flow, "async_init"), patch.object( zeroconf, "HaServiceBrowser", side_effect=service_update_mock ): mock_zeroconf.get_service_info.side_effect = get_service_info_mock assert await async_setup_component( hass, zeroconf.DOMAIN, {zeroconf.DOMAIN: {CONF_IPV6: False}} ) hass.bus.async_fire(EVENT_HOMEASSISTANT_STARTED) await hass.async_block_till_done() assert mock_zeroconf.called_with(ip_version=IPVersion.V4Only) async def test_setup_with_ipv6(hass, mock_zeroconf): """Test without ipv6.""" with patch.object(hass.config_entries.flow, "async_init"), patch.object( zeroconf, "HaServiceBrowser", side_effect=service_update_mock ): mock_zeroconf.get_service_info.side_effect = get_service_info_mock assert await async_setup_component( hass, zeroconf.DOMAIN, {zeroconf.DOMAIN: {CONF_IPV6: True}} ) hass.bus.async_fire(EVENT_HOMEASSISTANT_STARTED) await hass.async_block_till_done() assert mock_zeroconf.called_with() async def test_setup_with_ipv6_default(hass, mock_zeroconf): """Test without ipv6 as default.""" with patch.object(hass.config_entries.flow, "async_init"), patch.object( zeroconf, "HaServiceBrowser", side_effect=service_update_mock ): mock_zeroconf.get_service_info.side_effect = get_service_info_mock assert await async_setup_component(hass, zeroconf.DOMAIN, {zeroconf.DOMAIN: {}}) hass.bus.async_fire(EVENT_HOMEASSISTANT_STARTED) await hass.async_block_till_done() assert mock_zeroconf.called_with() async def test_service_with_invalid_name(hass, mock_zeroconf, caplog): """Test we do not crash on service with an invalid name.""" with patch.object( zeroconf, "HaServiceBrowser", side_effect=service_update_mock ) as mock_service_browser: mock_zeroconf.get_service_info.side_effect = BadTypeInNameException assert await async_setup_component(hass, zeroconf.DOMAIN, {zeroconf.DOMAIN: {}}) hass.bus.async_fire(EVENT_HOMEASSISTANT_STARTED) await hass.async_block_till_done() assert len(mock_service_browser.mock_calls) == 1 assert "Failed to get info for device" in caplog.text async def test_zeroconf_match_macaddress(hass, mock_zeroconf): """Test configured options for a device are loaded via config entry.""" def http_only_service_update_mock(zeroconf, services, handlers): """Call service update handler.""" handlers[0]( zeroconf, "_http._tcp.local.", "Shelly108._http._tcp.local.", ServiceStateChange.Added, ) with patch.dict( zc_gen.ZEROCONF, { "_http._tcp.local.": [ {"domain": "shelly", "name": "shelly*", "macaddress": "FFAADD*"} ] }, clear=True, ), patch.object( hass.config_entries.flow, "async_init" ) as mock_config_flow, patch.object( zeroconf, "HaServiceBrowser", side_effect=http_only_service_update_mock ) as mock_service_browser: mock_zeroconf.get_service_info.side_effect = get_zeroconf_info_mock( "FFAADDCC11DD" ) assert await async_setup_component(hass, zeroconf.DOMAIN, {zeroconf.DOMAIN: {}}) hass.bus.async_fire(EVENT_HOMEASSISTANT_STARTED) await hass.async_block_till_done() assert len(mock_service_browser.mock_calls) == 1 assert len(mock_config_flow.mock_calls) == 1 assert mock_config_flow.mock_calls[0][1][0] == "shelly" async def test_zeroconf_match_manufacturer(hass, mock_zeroconf): """Test configured options for a device are loaded via config entry.""" def http_only_service_update_mock(zeroconf, services, handlers): """Call service update handler.""" handlers[0]( zeroconf, "_airplay._tcp.local.", "s1000._airplay._tcp.local.", ServiceStateChange.Added, ) with patch.dict( zc_gen.ZEROCONF, {"_airplay._tcp.local.": [{"domain": "samsungtv", "manufacturer": "samsung*"}]}, clear=True, ), patch.object( hass.config_entries.flow, "async_init" ) as mock_config_flow, patch.object( zeroconf, "HaServiceBrowser", side_effect=http_only_service_update_mock ) as mock_service_browser: mock_zeroconf.get_service_info.side_effect = ( get_zeroconf_info_mock_manufacturer("Samsung Electronics") ) assert await async_setup_component(hass, zeroconf.DOMAIN, {zeroconf.DOMAIN: {}}) hass.bus.async_fire(EVENT_HOMEASSISTANT_STARTED) await hass.async_block_till_done() assert len(mock_service_browser.mock_calls) == 1 assert len(mock_config_flow.mock_calls) == 1 assert mock_config_flow.mock_calls[0][1][0] == "samsungtv" async def test_zeroconf_no_match(hass, mock_zeroconf): """Test configured options for a device are loaded via config entry.""" def http_only_service_update_mock(zeroconf, services, handlers): """Call service update handler.""" handlers[0]( zeroconf, "_http._tcp.local.", "somethingelse._http._tcp.local.", ServiceStateChange.Added, ) with patch.dict( zc_gen.ZEROCONF, {"_http._tcp.local.": [{"domain": "shelly", "name": "shelly*"}]}, clear=True, ), patch.object( hass.config_entries.flow, "async_init" ) as mock_config_flow, patch.object( zeroconf, "HaServiceBrowser", side_effect=http_only_service_update_mock ) as mock_service_browser: mock_zeroconf.get_service_info.side_effect = get_zeroconf_info_mock( "FFAADDCC11DD" ) assert await async_setup_component(hass, zeroconf.DOMAIN, {zeroconf.DOMAIN: {}}) hass.bus.async_fire(EVENT_HOMEASSISTANT_STARTED) await hass.async_block_till_done() assert len(mock_service_browser.mock_calls) == 1 assert len(mock_config_flow.mock_calls) == 0 async def test_zeroconf_no_match_manufacturer(hass, mock_zeroconf): """Test configured options for a device are loaded via config entry.""" def http_only_service_update_mock(zeroconf, services, handlers): """Call service update handler.""" handlers[0]( zeroconf, "_airplay._tcp.local.", "s1000._airplay._tcp.local.", ServiceStateChange.Added, ) with patch.dict( zc_gen.ZEROCONF, {"_airplay._tcp.local.": [{"domain": "samsungtv", "manufacturer": "samsung*"}]}, clear=True, ), patch.object( hass.config_entries.flow, "async_init" ) as mock_config_flow, patch.object( zeroconf, "HaServiceBrowser", side_effect=http_only_service_update_mock ) as mock_service_browser: mock_zeroconf.get_service_info.side_effect = ( get_zeroconf_info_mock_manufacturer("Not Samsung Electronics") ) assert await async_setup_component(hass, zeroconf.DOMAIN, {zeroconf.DOMAIN: {}}) hass.bus.async_fire(EVENT_HOMEASSISTANT_STARTED) await hass.async_block_till_done() assert len(mock_service_browser.mock_calls) == 1 assert len(mock_config_flow.mock_calls) == 0 async def test_homekit_match_partial_space(hass, mock_zeroconf): """Test configured options for a device are loaded via config entry.""" with patch.dict( zc_gen.ZEROCONF, {"_hap._tcp.local.": [{"domain": "homekit_controller"}]}, clear=True, ), patch.object( hass.config_entries.flow, "async_init" ) as mock_config_flow, patch.object( zeroconf, "HaServiceBrowser", side_effect=lambda *args, **kwargs: service_update_mock( *args, **kwargs, limit_service="_hap._tcp.local." ), ) as mock_service_browser: mock_zeroconf.get_service_info.side_effect = get_homekit_info_mock( "LIFX bulb", HOMEKIT_STATUS_UNPAIRED ) assert await async_setup_component(hass, zeroconf.DOMAIN, {zeroconf.DOMAIN: {}}) hass.bus.async_fire(EVENT_HOMEASSISTANT_STARTED) await hass.async_block_till_done() assert len(mock_service_browser.mock_calls) == 1 assert len(mock_config_flow.mock_calls) == 1 assert mock_config_flow.mock_calls[0][1][0] == "lifx" async def test_homekit_match_partial_dash(hass, mock_zeroconf): """Test configured options for a device are loaded via config entry.""" with patch.dict( zc_gen.ZEROCONF, {"_hap._udp.local.": [{"domain": "homekit_controller"}]}, clear=True, ), patch.object( hass.config_entries.flow, "async_init" ) as mock_config_flow, patch.object( zeroconf, "HaServiceBrowser", side_effect=lambda *args, **kwargs: service_update_mock( *args, **kwargs, limit_service="_hap._udp.local." ), ) as mock_service_browser: mock_zeroconf.get_service_info.side_effect = get_homekit_info_mock( "Rachio-fa46ba", HOMEKIT_STATUS_UNPAIRED ) assert await async_setup_component(hass, zeroconf.DOMAIN, {zeroconf.DOMAIN: {}}) hass.bus.async_fire(EVENT_HOMEASSISTANT_STARTED) await hass.async_block_till_done() assert len(mock_service_browser.mock_calls) == 1 assert len(mock_config_flow.mock_calls) == 1 assert mock_config_flow.mock_calls[0][1][0] == "rachio" async def test_homekit_match_full(hass, mock_zeroconf): """Test configured options for a device are loaded via config entry.""" with patch.dict( zc_gen.ZEROCONF, {"_hap._udp.local.": [{"domain": "homekit_controller"}]}, clear=True, ), patch.object( hass.config_entries.flow, "async_init" ) as mock_config_flow, patch.object( zeroconf, "HaServiceBrowser", side_effect=lambda *args, **kwargs: service_update_mock( *args, **kwargs, limit_service="_hap._udp.local." ), ) as mock_service_browser: mock_zeroconf.get_service_info.side_effect = get_homekit_info_mock( "BSB002", HOMEKIT_STATUS_UNPAIRED ) assert await async_setup_component(hass, zeroconf.DOMAIN, {zeroconf.DOMAIN: {}}) hass.bus.async_fire(EVENT_HOMEASSISTANT_STARTED) await hass.async_block_till_done() assert len(mock_service_browser.mock_calls) == 1 assert len(mock_config_flow.mock_calls) == 1 assert mock_config_flow.mock_calls[0][1][0] == "hue" async def test_homekit_already_paired(hass, mock_zeroconf): """Test that an already paired device is sent to homekit_controller.""" with patch.dict( zc_gen.ZEROCONF, {"_hap._tcp.local.": [{"domain": "homekit_controller"}]}, clear=True, ), patch.object( hass.config_entries.flow, "async_init" ) as mock_config_flow, patch.object( zeroconf, "HaServiceBrowser", side_effect=lambda *args, **kwargs: service_update_mock( *args, **kwargs, limit_service="_hap._tcp.local." ), ) as mock_service_browser: mock_zeroconf.get_service_info.side_effect = get_homekit_info_mock( "tado", HOMEKIT_STATUS_PAIRED ) assert await async_setup_component(hass, zeroconf.DOMAIN, {zeroconf.DOMAIN: {}}) hass.bus.async_fire(EVENT_HOMEASSISTANT_STARTED) await hass.async_block_till_done() assert len(mock_service_browser.mock_calls) == 1 assert len(mock_config_flow.mock_calls) == 2 assert mock_config_flow.mock_calls[0][1][0] == "tado" assert mock_config_flow.mock_calls[1][1][0] == "homekit_controller" async def test_homekit_invalid_paring_status(hass, mock_zeroconf): """Test that missing paring data is not sent to homekit_controller.""" with patch.dict( zc_gen.ZEROCONF, {"_hap._tcp.local.": [{"domain": "homekit_controller"}]}, clear=True, ), patch.object( hass.config_entries.flow, "async_init" ) as mock_config_flow, patch.object( zeroconf, "HaServiceBrowser", side_effect=lambda *args, **kwargs: service_update_mock( *args, **kwargs, limit_service="_hap._tcp.local." ), ) as mock_service_browser: mock_zeroconf.get_service_info.side_effect = get_homekit_info_mock( "tado", b"invalid" ) assert await async_setup_component(hass, zeroconf.DOMAIN, {zeroconf.DOMAIN: {}}) hass.bus.async_fire(EVENT_HOMEASSISTANT_STARTED) await hass.async_block_till_done() assert len(mock_service_browser.mock_calls) == 1 assert len(mock_config_flow.mock_calls) == 1 assert mock_config_flow.mock_calls[0][1][0] == "tado" async def test_homekit_not_paired(hass, mock_zeroconf): """Test that an not paired device is sent to homekit_controller.""" with patch.dict( zc_gen.ZEROCONF, {"_hap._tcp.local.": [{"domain": "homekit_controller"}]}, clear=True, ), patch.object( hass.config_entries.flow, "async_init" ) as mock_config_flow, patch.object( zeroconf, "HaServiceBrowser", side_effect=service_update_mock ) as mock_service_browser: mock_zeroconf.get_service_info.side_effect = get_homekit_info_mock( "this_will_not_match_any_integration", HOMEKIT_STATUS_UNPAIRED ) assert await async_setup_component(hass, zeroconf.DOMAIN, {zeroconf.DOMAIN: {}}) hass.bus.async_fire(EVENT_HOMEASSISTANT_STARTED) await hass.async_block_till_done() assert len(mock_service_browser.mock_calls) == 1 assert len(mock_config_flow.mock_calls) == 1 assert mock_config_flow.mock_calls[0][1][0] == "homekit_controller" async def test_info_from_service_non_utf8(hass): """Test info_from_service handles non UTF-8 property keys and values correctly.""" service_type = "_test._tcp.local." info = zeroconf.info_from_service( get_service_info_mock(service_type, f"test.{service_type}") ) raw_info = info["properties"].pop("_raw", False) assert raw_info assert len(raw_info) == len(PROPERTIES) - 1 assert NON_ASCII_KEY not in raw_info assert len(info["properties"]) <= len(raw_info) assert "non-utf8-value" not in info["properties"] assert raw_info["non-utf8-value"] is NON_UTF8_VALUE async def test_info_from_service_with_addresses(hass): """Test info_from_service does not throw when there are no addresses.""" service_type = "_test._tcp.local." info = zeroconf.info_from_service( get_service_info_mock_without_an_address(service_type, f"test.{service_type}") ) assert info is None async def test_get_instance(hass, mock_zeroconf): """Test we get an instance.""" assert await async_setup_component(hass, zeroconf.DOMAIN, {zeroconf.DOMAIN: {}}) assert await hass.components.zeroconf.async_get_instance() is mock_zeroconf hass.bus.async_fire(EVENT_HOMEASSISTANT_STOP) await hass.async_block_till_done() assert len(mock_zeroconf.ha_close.mock_calls) == 1 async def test_removed_ignored(hass, mock_zeroconf): """Test we remove it when a zeroconf entry is removed.""" mock_zeroconf.get_service_info.side_effect = ZeroconfError def service_update_mock(zeroconf, services, handlers): """Call service update handler.""" handlers[0]( zeroconf, "_service.added", "name._service.added", ServiceStateChange.Added ) handlers[0]( zeroconf, "_service.updated", "name._service.updated", ServiceStateChange.Updated, ) handlers[0]( zeroconf, "_service.removed", "name._service.removed", ServiceStateChange.Removed, ) with patch.object(zeroconf, "HaServiceBrowser", side_effect=service_update_mock): assert await async_setup_component(hass, zeroconf.DOMAIN, {zeroconf.DOMAIN: {}}) hass.bus.async_fire(EVENT_HOMEASSISTANT_STARTED) await hass.async_block_till_done() assert len(mock_zeroconf.get_service_info.mock_calls) == 2 assert mock_zeroconf.get_service_info.mock_calls[0][1][0] == "_service.added" assert mock_zeroconf.get_service_info.mock_calls[1][1][0] == "_service.updated" async def test_async_detect_interfaces_setting_non_loopback_route(hass, mock_zeroconf): """Test without default interface config and the route returns a non-loopback address.""" with patch.object(hass.config_entries.flow, "async_init"), patch.object( zeroconf, "HaServiceBrowser", side_effect=service_update_mock ), patch( "homeassistant.components.zeroconf.IPRoute.route", return_value=_ROUTE_NO_LOOPBACK, ): mock_zeroconf.get_service_info.side_effect = get_service_info_mock assert await async_setup_component(hass, zeroconf.DOMAIN, {zeroconf.DOMAIN: {}}) hass.bus.async_fire(EVENT_HOMEASSISTANT_STARTED) await hass.async_block_till_done() assert mock_zeroconf.called_with(interface_choice=InterfaceChoice.Default) async def test_async_detect_interfaces_setting_loopback_route(hass, mock_zeroconf): """Test without default interface config and the route returns a loopback address.""" with patch.object(hass.config_entries.flow, "async_init"), patch.object( zeroconf, "HaServiceBrowser", side_effect=service_update_mock ), patch( "homeassistant.components.zeroconf.IPRoute.route", return_value=_ROUTE_LOOPBACK ): mock_zeroconf.get_service_info.side_effect = get_service_info_mock assert await async_setup_component(hass, zeroconf.DOMAIN, {zeroconf.DOMAIN: {}}) hass.bus.async_fire(EVENT_HOMEASSISTANT_STARTED) await hass.async_block_till_done() assert mock_zeroconf.called_with(interface_choice=InterfaceChoice.All) async def test_async_detect_interfaces_setting_empty_route(hass, mock_zeroconf): """Test without default interface config and the route returns nothing.""" with patch.object(hass.config_entries.flow, "async_init"), patch.object( zeroconf, "HaServiceBrowser", side_effect=service_update_mock ), patch("homeassistant.components.zeroconf.IPRoute.route", return_value=[]): mock_zeroconf.get_service_info.side_effect = get_service_info_mock assert await async_setup_component(hass, zeroconf.DOMAIN, {zeroconf.DOMAIN: {}}) hass.bus.async_fire(EVENT_HOMEASSISTANT_STARTED) await hass.async_block_till_done() assert mock_zeroconf.called_with(interface_choice=InterfaceChoice.All) async def test_async_detect_interfaces_setting_exception(hass, mock_zeroconf): """Test without default interface config and the route throws an exception.""" with patch.object(hass.config_entries.flow, "async_init"), patch.object( zeroconf, "HaServiceBrowser", side_effect=service_update_mock ), patch( "homeassistant.components.zeroconf.IPRoute.route", side_effect=AttributeError ): mock_zeroconf.get_service_info.side_effect = get_service_info_mock assert await async_setup_component(hass, zeroconf.DOMAIN, {zeroconf.DOMAIN: {}}) hass.bus.async_fire(EVENT_HOMEASSISTANT_STARTED) await hass.async_block_till_done() assert mock_zeroconf.called_with(interface_choice=InterfaceChoice.All)

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Python

tests/python/unittest/test_gluon_probability_v1.py

PawelGlomski-Intel/incubator-mxnet

13e9d572b3059ebe0d1d4f6d452db4f971375588

[ "Apache-2.0", "MIT" ]

2

2019-01-15T07:34:36.000Z

2019-06-13T04:46:31.000Z

tests/python/unittest/test_gluon_probability_v1.py

PawelGlomski-Intel/incubator-mxnet

13e9d572b3059ebe0d1d4f6d452db4f971375588

[ "Apache-2.0", "MIT" ]

27

2020-02-28T19:54:08.000Z

2020-09-20T02:39:46.000Z

tests/python/unittest/test_gluon_probability_v1.py

PawelGlomski-Intel/incubator-mxnet

13e9d572b3059ebe0d1d4f6d452db4f971375588

[ "Apache-2.0", "MIT" ]

5

2020-08-18T19:16:21.000Z

2020-09-10T20:30:44.000Z

# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you 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. """Test gluon.probability with HybridBlock.hybrid_forward api """ import mxnet as mx import numpy as _np from mxnet import np, npx, autograd from mxnet import gluon import mxnet.gluon.probability as mgp from mxnet.gluon.probability import StochasticBlock, StochasticSequential from mxnet.gluon import HybridBlock from mxnet.test_utils import use_np, assert_almost_equal from numpy.testing import assert_array_equal import pytest import scipy.stats as ss import scipy.special as scipy_special import itertools from numbers import Number def prob_to_logit(prob): return np.log(prob) - np.log1p(-prob) def _distribution_method_invoker(dist, func, *args): """Wrapper for invoking different types of class methods with one unified interface. Parameters ---------- dist : Distribution func : method """ if (len(args) == 0): out = getattr(dist, func) if callable(out): return out() else: return out return getattr(dist, func)(*args) def test_mgp_getF_v1(): # Test getF getF = mgp.utils.getF nd = mx.nd sym = mx.sym assert getF(nd.ones((2, 2)), nd.ones((2, 2))) == nd assert getF(sym.ones((2, 2)), sym.ones((2, 2))) == sym assert getF(1.0, 2.0) == nd # Test exception with pytest.raises(TypeError): getF(nd.ones((2, 2)), sym.ones((2, 2))) getF(sym.ones((2, 2)), nd.ones((2, 2))) @use_np def test_gluon_uniform_v1(): class TestUniform(HybridBlock): def __init__(self, func): super(TestUniform, self).__init__() self._func = func def hybrid_forward(self, F, low, high, *args): uniform = mgp.Uniform(low, high, validate_args=True) return _distribution_method_invoker(uniform, self._func, *args) shapes = [(), (1,), (2, 3), 6] # Test log_prob for shape, hybridize in itertools.product(shapes, [True, False]): low = np.random.uniform(-1, 1, shape) high = low + np.random.uniform(0.5, 1.5, shape) samples = np.random.uniform(low, high) net = TestUniform("log_prob") if hybridize: net.hybridize() for i in range(2): mx_out = net(low, high, samples).asnumpy() np_out = ss.uniform(low.asnumpy(), (high - low).asnumpy()).logpdf(samples.asnumpy()) assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) # Test cdf for shape, hybridize in itertools.product(shapes, [True, False]): low = np.random.uniform(-1, 1, shape) high = low + np.random.uniform(0.5, 1.5, shape) samples = np.random.uniform(low, high) net = TestUniform("cdf") if hybridize: net.hybridize() mx_out = net(low, high, samples).asnumpy() np_out = ss.uniform(low.asnumpy(), (high - low).asnumpy()).cdf(samples.asnumpy()) assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) # Test icdf for shape, hybridize in itertools.product(shapes, [True, False]): low = np.random.uniform(-1, 1, shape) high = low + np.random.uniform(0.5, 1.5, shape) samples = np.random.uniform(size=shape) net = TestUniform("icdf") if hybridize: net.hybridize() mx_out = net(low, high, samples).asnumpy() np_out = ss.uniform(low.asnumpy(), (high - low).asnumpy()).ppf(samples.asnumpy()) assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) # Test entropy for shape, hybridize in itertools.product(shapes, [True, False]): low = np.random.uniform(-1, 1, shape) high = low + np.random.uniform(0.5, 1.5, shape) net = TestUniform("entropy") if hybridize: net.hybridize() mx_out = net(low, high).asnumpy() np_out = ss.uniform(low.asnumpy(), (high - low).asnumpy()).entropy() assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) @use_np def test_gluon_normal_v1(): class TestNormal(HybridBlock): def __init__(self, func): super(TestNormal, self).__init__() self._func = func def hybrid_forward(self, F, loc, scale, *args): normal = mgp.Normal(loc, scale, validate_args=True) return _distribution_method_invoker(normal, self._func, *args) shapes = [(), (1,), (2, 3), 6] # Test log_prob for shape, hybridize in itertools.product(shapes, [True, False]): loc = np.random.uniform(-1, 1, shape) scale = np.random.uniform(0.5, 1.5, shape) samples = np.random.normal(size=shape) net = TestNormal("log_prob") if hybridize: net.hybridize() mx_out = net(loc, scale, samples).asnumpy() np_out = ss.norm(loc.asnumpy(), scale.asnumpy()).logpdf(samples.asnumpy()) assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) # Test cdf for shape, hybridize in itertools.product(shapes, [True, False]): loc = np.random.uniform(-1, 1, shape) scale = np.random.uniform(0.5, 1.5, shape) samples = np.random.normal(size=shape) net = TestNormal("cdf") if hybridize: net.hybridize() mx_out = net(loc, scale, samples).asnumpy() np_out = ss.norm(loc.asnumpy(), scale.asnumpy()).cdf(samples.asnumpy()) assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) # Test icdf for shape, hybridize in itertools.product(shapes, [True, False]): loc = np.random.uniform(-1, 1, shape) scale = np.random.uniform(0.5, 1.5, shape) samples = np.random.uniform(size=shape) net = TestNormal("icdf") if hybridize: net.hybridize() mx_out = net(loc, scale, samples).asnumpy() np_out = ss.norm(loc.asnumpy(), scale.asnumpy()).ppf(samples.asnumpy()) assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) # Test entropy for shape, hybridize in itertools.product(shapes, [True, False]): loc = np.random.uniform(-1, 1, shape) scale = np.random.uniform(0.5, 1.5, shape) net = TestNormal("entropy") if hybridize: net.hybridize() mx_out = net(loc, scale).asnumpy() np_out = ss.norm(loc.asnumpy(), scale.asnumpy()).entropy() assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) @use_np def test_gluon_laplace_v1(): class TestLaplace(HybridBlock): def __init__(self, func): super(TestLaplace, self).__init__() self._func = func def hybrid_forward(self, F, loc, scale, *args): laplace = mgp.Laplace(loc, scale, validate_args=True) return _distribution_method_invoker(laplace, self._func, *args) shapes = [(), (1,), (2, 3), 6] # Test log_prob for shape, hybridize in itertools.product(shapes, [True, False]): loc = np.random.uniform(-1, 1, shape) scale = np.random.uniform(0.5, 1.5, shape) samples = np.random.laplace(size=shape) net = TestLaplace("log_prob") if hybridize: net.hybridize() mx_out = net(loc, scale, samples).asnumpy() np_out = ss.laplace(loc.asnumpy(), scale.asnumpy()).logpdf(samples.asnumpy()) assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) # Test cdf for shape, hybridize in itertools.product(shapes, [True, False]): loc = np.random.uniform(-1, 1, shape) scale = np.random.uniform(0.5, 1.5, shape) samples = np.random.laplace(size=shape) net = TestLaplace("cdf") if hybridize: net.hybridize() mx_out = net(loc, scale, samples).asnumpy() np_out = ss.laplace(loc.asnumpy(), scale.asnumpy()).cdf(samples.asnumpy()) assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) # Test icdf for shape, hybridize in itertools.product(shapes, [True, False]): loc = np.random.uniform(-1, 1, shape) scale = np.random.uniform(0.5, 1.5, shape) samples = np.random.uniform(size=shape) net = TestLaplace("icdf") if hybridize: net.hybridize() mx_out = net(loc, scale, samples).asnumpy() np_out = ss.laplace(loc.asnumpy(), scale.asnumpy()).ppf(samples.asnumpy()) assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) # Test entropy for shape, hybridize in itertools.product(shapes, [True, False]): loc = np.random.uniform(-1, 1, shape) scale = np.random.uniform(0.5, 1.5, shape) net = TestLaplace("entropy") if hybridize: net.hybridize() mx_out = net(loc, scale).asnumpy() np_out = ss.laplace(loc.asnumpy(), scale.asnumpy()).entropy() assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) @use_np def test_gluon_cauchy_v1(): class TestCauchy(HybridBlock): def __init__(self, func): self._func = func super(TestCauchy, self).__init__() def hybrid_forward(self, F, loc, scale, *args): cauchy = mgp.Cauchy(loc, scale, F, validate_args=True) return _distribution_method_invoker(cauchy, self._func, *args) shapes = [(), (1,), (2, 3), 6] # Test sampling for shape, hybridize in itertools.product(shapes, [True, False]): loc = np.random.uniform(-1, 1, shape) scale = np.random.uniform(0.5, 1.5, shape) samples = np.random.normal(size=shape) net = TestCauchy("sample") if hybridize: net.hybridize() mx_out = net(loc, scale) desired_shape = (shape,) if isinstance(shape, Number) else shape assert mx_out.shape == desired_shape # Test log_prob for shape, hybridize in itertools.product(shapes, [True, False]): loc = np.random.uniform(-1, 1, shape) scale = np.random.uniform(0.5, 1.5, shape) samples = np.random.normal(size=shape) net = TestCauchy("log_prob") if hybridize: net.hybridize() mx_out = net(loc, scale, samples).asnumpy() np_out = ss.cauchy(loc.asnumpy(), scale.asnumpy()).logpdf(samples.asnumpy()) assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) # Test cdf for shape, hybridize in itertools.product(shapes, [True, False]): loc = np.random.uniform(-1, 1, shape) scale = np.random.uniform(0.5, 1.5, shape) samples = np.random.normal(size=shape) net = TestCauchy("cdf") if hybridize: net.hybridize() mx_out = net(loc, scale, samples).asnumpy() np_out = ss.cauchy(loc.asnumpy(), scale.asnumpy()).cdf(samples.asnumpy()) assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) # Test icdf for shape, hybridize in itertools.product(shapes, [True, False]): loc = np.random.uniform(-1, 1, shape) scale = np.random.uniform(0.5, 1.5, shape) samples = np.random.uniform(size=shape, low=1e-4, high=1.0-1e-4) net = TestCauchy("icdf") if hybridize: net.hybridize() mx_out = net(loc, scale, samples).asnumpy() np_out = ss.cauchy(loc.asnumpy(), scale.asnumpy()).ppf(samples.asnumpy()) assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) # Test entropy for shape, hybridize in itertools.product(shapes, [True, False]): loc = np.random.uniform(-1, 1, shape) scale = np.random.uniform(0.5, 1.5, shape) net = TestCauchy("entropy") if hybridize: net.hybridize() mx_out = net(loc, scale).asnumpy() np_out = ss.cauchy(loc.asnumpy(), scale.asnumpy()).entropy() assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) @use_np def test_gluon_half_cauchy_v1(): class TestHalfCauchy(HybridBlock): def __init__(self, func): super(TestHalfCauchy, self).__init__() self._func = func def hybrid_forward(self, F, scale, *args): half_normal = mgp.HalfCauchy(scale, F, validate_args=True) return getattr(half_normal, self._func)(*args) shapes = [(), (1,), (2, 3), 6] # Test sampling for shape, hybridize in itertools.product(shapes, [True, False]): scale = np.random.uniform(0.5, 1.5, shape) net = TestHalfCauchy("sample") if hybridize: net.hybridize() mx_out = net(scale).asnumpy() if isinstance(shape, Number): shape = (shape,) assert mx_out.shape == shape # Test log_prob for shape, hybridize in itertools.product(shapes, [True, False]): scale = np.random.uniform(0.5, 1.5, shape) samples = np.abs(np.random.normal(size=shape)) net = TestHalfCauchy("log_prob") if hybridize: net.hybridize() mx_out = net(scale, samples).asnumpy() np_out = ss.halfcauchy(0, scale.asnumpy()).logpdf(samples.asnumpy()) assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) # Test cdf for shape, hybridize in itertools.product(shapes, [True, False]): scale = np.random.uniform(0.5, 1.5, shape) samples = np.abs(np.random.normal(size=shape)) net = TestHalfCauchy("cdf") if hybridize: net.hybridize() mx_out = net(scale, samples).asnumpy() np_out = ss.halfcauchy(0, scale.asnumpy()).cdf(samples.asnumpy()) assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) # Test icdf for shape, hybridize in itertools.product(shapes, [True, False]): scale = np.random.uniform(0.5, 1.5, shape) samples = np.random.uniform(size=shape, high=1.0-1e-4) net = TestHalfCauchy("icdf") if hybridize: net.hybridize() mx_out = net(scale, samples).asnumpy() np_out = ss.halfcauchy(0, scale.asnumpy()).ppf(samples.asnumpy()) assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) @use_np def test_gluon_poisson_v1(): class TestPoisson(HybridBlock): def __init__(self, func): self._func = func super(TestPoisson, self).__init__() def hybrid_forward(self, F, rate, *args): poisson = mgp.Poisson(rate, F, validate_args=True) return _distribution_method_invoker(poisson, self._func, *args) shapes = [(1,), (2, 3), 6] # Test sampling for shape, hybridize in itertools.product(shapes, [False]): rate = np.random.uniform(0.5, 1.5, shape) net = TestPoisson("sample") if hybridize: net.hybridize() mx_out = net(rate).asnumpy() assert mx_out.shape == rate.shape # Test log_prob for shape, hybridize in itertools.product(shapes, [True, False]): rate = np.random.uniform(0.5, 1.5, shape) samples = np.random.randint(0, 5, shape).astype('float') net = TestPoisson("log_prob") if hybridize: net.hybridize() mx_out = net(rate, samples).asnumpy() np_out = ss.poisson(mu=rate.asnumpy()).logpmf(samples.asnumpy()) assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) @use_np def test_gluon_geometric_v1(): class TestGeometric(HybridBlock): def __init__(self, func, is_logit=False): super(TestGeometric, self).__init__() self._is_logit = is_logit self._func = func def hybrid_forward(self, F, params, *args): dist = mgp.Geometric(logit=params, validate_args=True) if self._is_logit else \ mgp.Geometric(prob=params, validate_args=True) return _distribution_method_invoker(dist, self._func, *args) shapes = [(), (1,), (2, 3), 6] # Test log_prob for shape, hybridize, use_logit in itertools.product(shapes, [True, False], [True, False]): prob = np.random.uniform(size=shape) sample = np.random.randint(0, 10, size=shape).astype('float32') param = prob if use_logit: param = prob_to_logit(param) net = TestGeometric("log_prob", use_logit) if hybridize: net.hybridize() mx_out = net(param, sample).asnumpy() np_out = ss.geom.logpmf(sample.asnumpy() + 1, prob.asnumpy()) assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) # Test variance for shape, hybridize, use_logit in itertools.product(shapes, [True, False], [True, False]): prob = np.random.uniform(size=shape) param = prob if use_logit: param = prob_to_logit(param) net = TestGeometric("variance", use_logit) if hybridize: net.hybridize() mx_out = net(param).asnumpy() np_out = ss.geom(prob.asnumpy()).var() assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) # Test entropy for shape, hybridize, use_logit in itertools.product(shapes, [True, False], [True, False]): # Add lower bound constraint, otherwise scipy would raise warning. prob = np.random.uniform(low=0.1, size=shape) param = prob if use_logit: param = prob_to_logit(param) net = TestGeometric("entropy", use_logit) if hybridize: net.hybridize() mx_out = net(param).asnumpy() np_out = ss.geom(prob.asnumpy()).entropy() assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) @use_np def test_gluon_negative_binomial_v1(): class TestNegativeBinomial(HybridBlock): def __init__(self, func, is_logit=False): super(TestNegativeBinomial, self).__init__() self._is_logit = is_logit self._func = func def hybrid_forward(self, F, n, params, *args): dist = mgp.NegativeBinomial(n=n, logit=params, validate_args=True) if self._is_logit else \ mgp.NegativeBinomial(n=n, prob=params, validate_args=True) return _distribution_method_invoker(dist, self._func, *args) shapes = [(), (1,), (2, 3), 6] # Test log_prob for shape, hybridize, use_logit in itertools.product(shapes, [True, False], [True, False]): n = np.random.randint(1, 10, size=shape).astype('float32') prob = np.random.uniform(low=0.2, high=0.6, size=shape).astype('float32') sample = np.random.randint(0, 10, size=shape).astype('float32') param = prob if use_logit: param = prob_to_logit(param) net = TestNegativeBinomial("log_prob", use_logit) if hybridize: net.hybridize() mx_out = net(n, param, sample).asnumpy() np_out = ss.nbinom(n=n.asnumpy(), p=prob.asnumpy() ).logpmf(sample.asnumpy()) assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) # Test mean and variance for shape, hybridize in itertools.product(shapes, [True, False]): for func in ['mean', 'variance']: for use_logit in [True, False]: n = np.random.randint(1, 10, size=shape).astype('float32') prob = np.random.uniform(low=0.2, high=0.6, size=shape).astype('float32') net = TestNegativeBinomial(func, use_logit) param = prob if use_logit: param = prob_to_logit(param) if hybridize: net.hybridize() mx_out = net(n, param).asnumpy() ss_nbinom = ss.nbinom(n=n.asnumpy(), p=1 - prob.asnumpy()) if func == 'mean': np_out = ss_nbinom.mean() else: np_out = ss_nbinom.var() assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) @use_np def test_gluon_exponential_v1(): class TestExponential(HybridBlock): def __init__(self, func): self._func = func super(TestExponential, self).__init__() def hybrid_forward(self, F, scale, *args): exponential = mgp.Exponential(scale, F, validate_args=True) return _distribution_method_invoker(exponential, self._func, *args) shapes = [(), (1,), (2, 3), 6] # Test log_prob for shape, hybridize in itertools.product(shapes, [True, False]): scale = np.random.uniform(0.5, 1.5, shape) samples = np.random.uniform(0.2, 1.2, size=shape) net = TestExponential("log_prob") if hybridize: net.hybridize() mx_out = net(scale, samples).asnumpy() np_out = ss.expon(scale=scale.asnumpy()).logpdf(samples.asnumpy()) assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) # Test cdf for shape, hybridize in itertools.product(shapes, [True, False]): scale = np.random.uniform(0.5, 1.5, shape) samples = np.random.uniform(0.2, 1.2, size=shape) net = TestExponential("cdf") if hybridize: net.hybridize() mx_out = net(scale, samples).asnumpy() np_out = ss.expon(scale=scale.asnumpy()).cdf(samples.asnumpy()) assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) # Test icdf for shape, hybridize in itertools.product(shapes, [True, False]): scale = np.random.uniform(0.5, 1.5, shape) samples = np.random.uniform(0.0, 1.0, size=shape) net = TestExponential("icdf") if hybridize: net.hybridize() mx_out = net(scale, samples).asnumpy() np_out = ss.expon(scale=scale.asnumpy()).ppf(samples.asnumpy()) assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) # Test entropy for shape, hybridize in itertools.product(shapes, [True, False]): scale = np.random.uniform(0.5, 1.5, shape) net = TestExponential("entropy") if hybridize: net.hybridize() mx_out = net(scale).asnumpy() np_out = ss.expon(scale=scale.asnumpy()).entropy() assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) @use_np def test_gluon_weibull_v1(): class TestWeibull(HybridBlock): def __init__(self, func): super(TestWeibull, self).__init__() self._func = func def hybrid_forward(self, F, concentration, scale, *args): weibull = mgp.Weibull(concentration, scale, F, validate_args=True) return _distribution_method_invoker(weibull, self._func, *args) shapes = [(), (1,), (2, 3), 6] # Test log_prob for shape, hybridize in itertools.product(shapes, [True, False]): concentration = np.random.uniform(size=shape) scale = np.random.uniform(size=shape) samples = np.random.uniform(size=shape) net = TestWeibull("log_prob") if hybridize: net.hybridize() mx_out = net(concentration, scale, samples).asnumpy() np_out = ss.weibull_min(c=concentration.asnumpy( ), scale=scale.asnumpy()).logpdf(samples.asnumpy()) assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) # Test cdf for shape, hybridize in itertools.product(shapes, [True, False]): concentration = np.random.uniform(size=shape) scale = np.random.uniform(size=shape) samples = np.random.uniform(size=shape) net = TestWeibull("cdf") if hybridize: net.hybridize() mx_out = net(concentration, scale, samples).asnumpy() np_out = ss.weibull_min(c=concentration.asnumpy( ), scale=scale.asnumpy()).cdf(samples.asnumpy()) assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) # Test icdf for shape, hybridize in itertools.product(shapes, [True, False]): concentration = np.random.uniform(size=shape) scale = np.random.uniform(size=shape) samples = np.random.uniform(size=shape) net = TestWeibull("icdf") if hybridize: net.hybridize() mx_out = net(concentration, scale, samples).asnumpy() np_out = ss.weibull_min(c=concentration.asnumpy( ), scale=scale.asnumpy()).ppf(samples.asnumpy()) assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) # Test entropy for shape, hybridize in itertools.product(shapes, [True, False]): concentration = np.random.uniform(size=shape) scale = np.random.uniform(size=shape) net = TestWeibull("entropy") if hybridize: net.hybridize() mx_out = net(concentration, scale).asnumpy() np_out = ss.weibull_min(c=concentration.asnumpy(), scale=scale.asnumpy()).entropy() assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) @use_np def test_gluon_pareto_v1(): class TestPareto(HybridBlock): def __init__(self, func): super(TestPareto, self).__init__() self._func = func def hybrid_forward(self, F, alpha, scale, *args): pareto = mgp.Pareto(alpha, scale, F, validate_args=True) return _distribution_method_invoker(pareto, self._func, *args) shapes = [(), (1,), (2, 3), 6] # Test log_prob for shape, hybridize in itertools.product(shapes, [True, False]): alpha = np.random.uniform(size=shape) scale = np.random.uniform(size=shape) samples = np.random.uniform(1, 2, size=shape) net = TestPareto("log_prob") if hybridize: net.hybridize() mx_out = net(alpha, scale, samples).asnumpy() np_out = ss.pareto(b=alpha.asnumpy(), scale=scale.asnumpy()).logpdf( samples.asnumpy()) assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) # Test cdf for shape, hybridize in itertools.product(shapes, [True, False]): alpha = np.random.uniform(size=shape) scale = np.random.uniform(size=shape) samples = np.random.uniform(1.0, 2.0, size=shape) net = TestPareto("cdf") if hybridize: net.hybridize() mx_out = net(alpha, scale, samples).asnumpy() np_out = ss.pareto(b=alpha.asnumpy(), scale=scale.asnumpy()).cdf( samples.asnumpy()) assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) # Test icdf for shape, hybridize in itertools.product(shapes, [True, False]): alpha = np.random.uniform(size=shape) scale = np.random.uniform(size=shape) samples = np.random.uniform(size=shape) net = TestPareto("icdf") if hybridize: net.hybridize() mx_out = net(alpha, scale, samples).asnumpy() np_out = ss.pareto(b=alpha.asnumpy(), scale=scale.asnumpy()).ppf( samples.asnumpy()) assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) # Test entropy for shape, hybridize in itertools.product(shapes, [True, False]): alpha = np.random.uniform(size=shape) scale = np.random.uniform(size=shape) net = TestPareto("entropy") if hybridize: net.hybridize() mx_out = net(alpha, scale).asnumpy() np_out = ss.pareto(b=alpha.asnumpy(), scale=scale.asnumpy()).entropy() assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) @use_np def test_gluon_gamma_v1(): class TestGamma(HybridBlock): def __init__(self, func): super(TestGamma, self).__init__() self._func = func def hybrid_forward(self, F, shape, scale, *args): gamma = mgp.Gamma(shape, scale, F, validate_args=True) return _distribution_method_invoker(gamma, self._func, *args) shapes = [(), (1,), (2, 3), 6] # Test log_prob for shape, hybridize in itertools.product(shapes, [True, False]): alpha = np.random.uniform(0.5, 1.5, shape) scale = np.random.uniform(0.5, 1.5, shape) samples = np.random.uniform(size=shape) net = TestGamma("log_prob") if hybridize: net.hybridize() mx_out = net(alpha, scale, samples).asnumpy() np_out = ss.gamma(a=alpha.asnumpy(), loc=0, scale=scale.asnumpy()).logpdf(samples.asnumpy()) assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) # Test `mean`, `var` and `entropy` for shape, hybridize in itertools.product(shapes, [True, False]): for func in ['mean', 'variance', 'entropy']: alpha = np.random.uniform(0.5, 1.5, shape) scale = np.random.uniform(0.5, 1.5, shape) net = TestGamma(func) if hybridize: net.hybridize() mx_out = net(alpha, scale).asnumpy() ss_gamma = ss.gamma(a=alpha.asnumpy(), loc=0, scale=scale.asnumpy()) if func == 'mean': np_out = ss_gamma.mean() elif func == 'variance': np_out = ss_gamma.var() else: np_out = ss_gamma.entropy() assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) @use_np def test_gluon_dirichlet_v1(): class TestDirichlet(HybridBlock): def __init__(self, func): super(TestDirichlet, self).__init__() self._func = func def hybrid_forward(self, F, alpha, *args): dirichlet = mgp.Dirichlet(alpha, F, validate_args=True) return _distribution_method_invoker(dirichlet, self._func, *args) event_shapes = [2, 4, 6] batch_shapes = [None, (2, 3)] # Test sampling for event_shape, batch_shape in itertools.product(event_shapes, batch_shapes): for hybridize in [True, False]: desired_shape = ( batch_shape if batch_shape is not None else ()) + (event_shape,) alpha = np.random.uniform(1.0, 5.0, size=desired_shape) net = TestDirichlet("sample") if hybridize: net.hybridize() mx_out = net(alpha).asnumpy() # Check shape assert mx_out.shape == desired_shape # Check simplex assert_almost_equal(mx_out.sum(-1), _np.ones_like(mx_out.sum(-1)), atol=1e-4, rtol=1e-3, use_broadcast=False) # Test log_prob # Scipy does not support batch `alpha`, thus we skip multi-dimensional batch_shape case. for event_shape, batch_shape in itertools.product(event_shapes, batch_shapes[:1]): for hybridize in [True, False]: desired_shape = ( batch_shape if batch_shape is not None else ()) + (event_shape,) alpha = np.random.uniform(1.0, 5.0, desired_shape) np_samples = _np.random.dirichlet( [10.0 / event_shape] * event_shape, size=batch_shape) net = TestDirichlet("log_prob") if hybridize: net.hybridize() mx_out = net(alpha, np.array(np_samples)).asnumpy() np_out = ss.dirichlet(alpha=alpha.asnumpy()).logpdf(np_samples) assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) # Test `mean`, `var` and `entropy` for event_shape, batch_shape in itertools.product(event_shapes, batch_shapes[:1]): for hybridize in [False]: for func in ['mean', 'variance', 'entropy']: desired_shape = ( batch_shape if batch_shape is not None else ()) + (event_shape,) alpha = np.random.uniform(1.0, 5.0, desired_shape) net = TestDirichlet(func) if hybridize: net.hybridize() mx_out = net(alpha).asnumpy() ss_dir = ss.dirichlet(alpha=alpha.asnumpy()) if func == 'mean': np_out = ss_dir.mean() elif func == 'variance': np_out = ss_dir.var() else: np_out = ss_dir.entropy() assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) @use_np def test_gluon_beta_v1(): class TestBeta(HybridBlock): def __init__(self, func): super(TestBeta, self).__init__() self._func = func def hybrid_forward(self, F, alpha, beta, *args): beta_dist = mgp.Beta(alpha, beta, F, validate_args=True) return _distribution_method_invoker(beta_dist, self._func, *args) shapes = [(), (1,), (2, 3), 6] # Test log_prob for shape, hybridize in itertools.product(shapes, [True, False]): alpha = np.random.uniform(0.5, 1.5, shape) beta = np.random.uniform(0.5, 1.5, shape) samples = np.random.uniform(size=shape) net = TestBeta("log_prob") if hybridize: net.hybridize() mx_out = net(alpha, beta, samples).asnumpy() np_out = ss.beta(alpha.asnumpy(), beta.asnumpy() ).logpdf(samples.asnumpy()) assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) # Test `mean`, `var` and `entropy` for shape, hybridize in itertools.product(shapes, [True, False]): for func in ['mean', 'variance', 'entropy']: alpha = np.random.uniform(0.5, 1.5, shape) beta = np.random.uniform(0.5, 1.5, shape) net = TestBeta(func) if hybridize: net.hybridize() mx_out = net(alpha, beta).asnumpy() ss_beta = ss.beta(alpha.asnumpy(), beta.asnumpy()) if func == 'mean': np_out = ss_beta.mean() elif func == 'variance': np_out = ss_beta.var() else: np_out = ss_beta.entropy() assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) @use_np def test_gluon_fisher_snedecor_v1(): class TestFisherSnedecor(HybridBlock): def __init__(self, func): super(TestFisherSnedecor, self).__init__() self._func = func def hybrid_forward(self, F, df1, df2, *args): beta_dist = mgp.FisherSnedecor(df1, df2, F, validate_args=True) return _distribution_method_invoker(beta_dist, self._func, *args) shapes = [(), (1,), (2, 3), 6] # Test log_prob for shape, hybridize in itertools.product(shapes, [True, False]): df1 = np.random.uniform(0.5, 1.5, shape) df2 = np.random.uniform(0.5, 1.5, shape) samples = np.random.uniform(size=shape) net = TestFisherSnedecor("log_prob") if hybridize: net.hybridize() mx_out = net(df1, df2, samples).asnumpy() np_out = ss.f(dfn=df1.asnumpy(), dfd=df2.asnumpy() ).logpdf(samples.asnumpy()) assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) # Test `mean` and `var` for shape, hybridize in itertools.product(shapes, [True, False]): for func in ['mean', 'variance']: df1 = np.random.uniform(0.5, 1.5, shape) df2 = np.random.uniform(4.0, 6.0, shape) net = TestFisherSnedecor(func) if hybridize: net.hybridize() mx_out = net(df1, df2).asnumpy() ss_f = ss.f(dfn=df1.asnumpy(), dfd=df2.asnumpy()) if func == 'mean': np_out = ss_f.mean() else: np_out = ss_f.var() assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) @use_np def test_gluon_student_t_v1(): class TestT(HybridBlock): def __init__(self, func): super(TestT, self).__init__() self._func = func def hybrid_forward(self, F, df, loc, scale, *args): t_dist = mgp.StudentT(df, loc, scale, F, validate_args=True) return _distribution_method_invoker(t_dist, self._func, *args) shapes = [(), (1,), (2, 3), 6] # Test log_prob for shape, hybridize in itertools.product(shapes, [True, False]): loc = np.zeros(shape) scale = np.random.uniform(0.5, 1.5, shape) df = np.random.uniform(2, 4, shape) samples = np.random.uniform(0, 4, size=shape) net = TestT("log_prob") if hybridize: net.hybridize() mx_out = net(df, loc, scale, samples).asnumpy() np_out = ss.t(loc=0, scale=scale.asnumpy(), df=df.asnumpy()).logpdf(samples.asnumpy()) assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) # Test `mean`, `var` and `entropy` for shape, hybridize in itertools.product(shapes, [False, True]): for func in ['mean', 'variance', 'entropy']: loc = np.zeros(shape) scale = np.random.uniform(0.5, 1.5, shape) df = np.random.uniform(3, 4, shape) net = TestT(func) if hybridize: net.hybridize() mx_out = net(df, loc, scale).asnumpy() ss_f = ss.t(loc=0, scale=scale.asnumpy(), df=df.asnumpy()) if func == 'mean': np_out = ss_f.mean() elif func == 'variance': np_out = ss_f.var() else: np_out = ss_f.entropy() assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) @use_np def test_gluon_gumbel_v1(): class TestGumbel(HybridBlock): def __init__(self, func): super(TestGumbel, self).__init__() self._func = func def hybrid_forward(self, F, loc, scale, *args): normal = mgp.Gumbel(loc, scale, F, validate_args=True) return getattr(normal, self._func)(*args) shapes = [(), (1,), (2, 3), 6] # Test log_prob for shape, hybridize in itertools.product(shapes, [True, False]): loc = np.random.uniform(-1, 1, shape) scale = np.random.uniform(0.5, 1.5, shape) samples = np.random.normal(size=shape) net = TestGumbel("log_prob") if hybridize: net.hybridize() mx_out = net(loc, scale, samples).asnumpy() np_out = ss.gumbel_r(loc=loc.asnumpy(), scale=scale.asnumpy()).logpdf(samples.asnumpy()) assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) # Test cdf for shape, hybridize in itertools.product(shapes, [True, False]): loc = np.random.uniform(-1, 1, shape) scale = np.random.uniform(0.5, 1.5, shape) samples = np.random.normal(size=shape) net = TestGumbel("cdf") if hybridize: net.hybridize() mx_out = net(loc, scale, samples).asnumpy() np_out = ss.gumbel_r(loc.asnumpy(), scale.asnumpy()).cdf(samples.asnumpy()) assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) # Test icdf for shape, hybridize in itertools.product(shapes, [True, False]): loc = np.random.uniform(-1, 1, shape) scale = np.random.uniform(0.5, 1.5, shape) samples = np.random.uniform(size=shape) net = TestGumbel("icdf") if hybridize: net.hybridize() mx_out = net(loc, scale, samples).asnumpy() np_out = ss.gumbel_r(loc.asnumpy(), scale.asnumpy()).ppf(samples.asnumpy()) assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) # Test entropy for shape, hybridize in itertools.product(shapes, [True, False]): loc = np.random.uniform(-1, 1, shape) scale = np.random.uniform(0.5, 1.5, shape) net = TestGumbel("entropy") if hybridize: net.hybridize() mx_out = net(loc, scale).asnumpy() np_out = ss.gumbel_r(loc.asnumpy(), scale.asnumpy()).entropy() assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) @use_np def test_gluon_multinomial_v1(): class TestMultinomial(HybridBlock): def __init__(self, func, num_events, total_count, is_logit, batch_shape=None, sample_shape=None): super(TestMultinomial, self).__init__() self._num_events = num_events self._total_count = total_count self._is_logit = is_logit self._func = func self._batch_shape = batch_shape self._sample_shape = sample_shape def hybrid_forward(self, F, params, *args): multinomial = ( mgp.Multinomial(self._num_events, logit=params, total_count=self._total_count, validate_args=True) if self._is_logit else mgp.Multinomial(self._num_events, prob=params, total_count=self._total_count, validate_args=True) ) if self._func == 'sample': return multinomial.sample(self._batch_shape) if self._func == 'sample_n': return multinomial.sample_n(self._sample_shape) return _distribution_method_invoker(multinomial, self._func, *args) def one_hot(a, num_classes): return np.identity(num_classes)[a] event_shapes = [2, 5, 10] batch_shapes = [None, (2, 3)] # , (4, 0, 5)] sample_shapes = [None, (2,), (3, 4)] # Test sampling for event_shape, batch_shape in itertools.product(event_shapes, batch_shapes): for use_logit, hybridize in itertools.product([True, False], [True, False]): prob = np.array(_np.random.dirichlet( [1 / event_shape] * event_shape, size=batch_shape)) param = prob if use_logit: param = np.log(param) net = TestMultinomial("sample", event_shape, _np.random.randint(1, 5), use_logit, batch_shape) if hybridize: net.hybridize() mx_out = net(param).asnumpy() desired_shape = batch_shape if batch_shape is not None else () assert mx_out.shape == desired_shape + (event_shape,) # Test sample_n for event_shape, batch_shape, sample_shape in itertools.product(event_shapes, batch_shapes, sample_shapes): for use_logit, hybridize in itertools.product([True, False], [True, False]): prob = np.array(_np.random.dirichlet( [1 / event_shape] * event_shape, size=batch_shape)) param = prob if use_logit: param = np.log(param) net = TestMultinomial("sample_n", event_shape, _np.random.randint(1, 5), use_logit, batch_shape, sample_shape) if hybridize: net.hybridize() mx_out = net(param).asnumpy() sample_shape = () if sample_shape is None else sample_shape desired_shape = sample_shape + \ (batch_shape if batch_shape is not None else ()) assert mx_out.shape == desired_shape + (event_shape,) # Test log_prob for event_shape, batch_shape, sample_shape in itertools.product(event_shapes, batch_shapes, sample_shapes): for use_logit, hybridize in itertools.product([True, False], [False]): prob = np.array(_np.random.dirichlet( [1 / event_shape] * event_shape, size=batch_shape)) eps = _np.finfo('float32').eps prob = np.clip(prob, eps, 1 - eps) param = prob sample_shape = () if sample_shape is None else sample_shape desired_shape = sample_shape + \ (batch_shape if batch_shape is not None else ()) samples = np.random.choice(event_shape, size=desired_shape) samples = one_hot(samples, event_shape) if use_logit: param = np.log(param) net = TestMultinomial("log_prob", event_shape, _np.random.randint(1, 5), use_logit) if hybridize: net.hybridize() mx_out = net(param, samples).asnumpy() # Check shape assert mx_out.shape == desired_shape @use_np def test_gluon_binomial_v1(): class TestBinomial(HybridBlock): def __init__(self, func, is_logit=False, n=1): super(TestBinomial, self).__init__() self._is_logit = is_logit self._func = func self._n = n def hybrid_forward(self, F, params, *args): dist = mgp.Binomial(n=self._n, logit=params, validate_args=True) \ if self._is_logit else \ mgp.Binomial(n=self._n, prob=params, validate_args=True) return _distribution_method_invoker(dist, self._func, *args) shapes = [(), (1,), (2, 3), 6] # Test sampling for shape, hybridize in itertools.product(shapes, [True, False]): for use_logit in [True, False]: n = _np.random.randint(5, 10) prob = np.random.uniform(low=0.1, size=shape) net = TestBinomial('sample', use_logit, n=float(n)) param = prob if use_logit: param = prob_to_logit(param) if hybridize: net.hybridize() mx_out = net(param).asnumpy() desired_shape = (shape,) if isinstance(shape, int) else shape assert mx_out.shape == desired_shape # Test sample_n prefix_shape = (2, 3) for shape in shapes: n = _np.random.randint(5, 10) prob = np.random.uniform(low=0.1, size=shape) dist = mgp.Binomial(n=n, prob=prob) samples = dist.sample_n(prefix_shape) assert samples.shape == (prefix_shape + prob.shape) # Test log_prob for shape, hybridize, use_logit in itertools.product(shapes, [True, False], [True, False]): n = _np.random.randint(5, 10) prob = np.random.uniform(low=0.1, size=shape) sample = np.random.randint(0, n, size=shape).astype('float32') param = prob if use_logit: param = prob_to_logit(param) net = TestBinomial("log_prob", use_logit, n=float(n)) if hybridize: net.hybridize() mx_out = net(param, sample).asnumpy() np_out = ss.binom(n=n, p=prob.asnumpy()).logpmf(sample.asnumpy()) assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) # Test mean and variance for shape, hybridize in itertools.product(shapes, [True, False]): for func in ['mean', 'variance']: for use_logit in [True, False]: n = _np.random.randint(5, 10) prob = np.random.uniform(low=0.1, size=shape) net = TestBinomial(func, use_logit, n=float(n)) param = prob if use_logit: param = prob_to_logit(param) if hybridize: net.hybridize() mx_out = net(param).asnumpy() ss_binom = ss.binom(n=n, p=prob.asnumpy()) if func == 'mean': np_out = ss_binom.mean() else: np_out = ss_binom.var() assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) @use_np @pytest.mark.flaky def test_gluon_bernoulli_v1(): class TestBernoulli(HybridBlock): def __init__(self, func, is_logit=False): super(TestBernoulli, self).__init__() self._is_logit = is_logit self._func = func def hybrid_forward(self, F, params, *args): bernoulli = mgp.Bernoulli(logit=params, validate_args=True) if self._is_logit else \ mgp.Bernoulli(prob=params, validate_args=True) return _distribution_method_invoker(bernoulli, self._func, *args) # Test log_prob shapes = [(), (1,), (2, 3), 6] for shape, hybridize, use_logit in itertools.product(shapes, [True, False], [True, False]): prob = np.random.uniform(size=shape) sample = npx.random.bernoulli(prob=0.5, size=shape) param = prob if use_logit: param = prob_to_logit(param) net = TestBernoulli("log_prob", use_logit) if hybridize: net.hybridize() mx_out = net(param, sample).asnumpy() np_out = _np.log(ss.bernoulli.pmf(sample.asnumpy(), prob.asnumpy())) assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) # Test variance for shape, hybridize, use_logit in itertools.product(shapes, [True, False], [True, False]): prob = np.random.uniform(size=shape) sample = npx.random.bernoulli(prob=0.5, size=shape) param = prob if use_logit: param = prob_to_logit(param) net = TestBernoulli("variance", use_logit) if hybridize: net.hybridize() mx_out = net(param).asnumpy() np_out = ss.bernoulli(prob.asnumpy()).var() assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) # Test entropy for shape, hybridize, use_logit in itertools.product(shapes, [True, False], [True, False]): prob = np.random.uniform(size=shape) sample = npx.random.bernoulli(prob=0.5, size=shape) param = prob if use_logit: param = prob_to_logit(param) net = TestBernoulli("entropy", use_logit) if hybridize: net.hybridize() mx_out = net(param).asnumpy() np_out = ss.bernoulli(prob.asnumpy()).entropy() assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) @use_np def test_relaxed_bernoulli_v1(): class TestRelaxedBernoulli(HybridBlock): def __init__(self, func, is_logit=False): super(TestRelaxedBernoulli, self).__init__() self._is_logit = is_logit self._func = func def hybrid_forward(self, F, params, *args): relaxed_bernoulli = mgp.RelaxedBernoulli(T=1.0, logit=params, validate_args=True)\ if self._is_logit else \ mgp.RelaxedBernoulli(T=1.0, prob=params, validate_args=True) if self._func == "sample": return relaxed_bernoulli.sample() return _distribution_method_invoker(relaxed_bernoulli, self._func, *args) def prob_to_logit(prob): return np.log(prob) - np.log1p(-prob) shapes = [(), (1,), (2, 3), 6] # Test sampling for shape, hybridize, use_logit in itertools.product(shapes, [True, False], [True, False]): prob = np.random.uniform(size=shape) param = prob if use_logit: param = prob_to_logit(param) param.attach_grad() net = TestRelaxedBernoulli("sample", use_logit) if hybridize: net.hybridize() with autograd.record(): mx_out = net(param) mx_out.backward() desired_shape = (shape,) if isinstance(shape, int) else shape assert param.grad.shape == desired_shape for shape, hybridize, use_logit in itertools.product(shapes, [True, False], [True, False]): prob = np.random.uniform(size=shape) sample = np.random.uniform(0.1, 0.9, size=shape) param = prob if use_logit: param = prob_to_logit(param) net = TestRelaxedBernoulli("log_prob", use_logit) if hybridize: net.hybridize() mx_out = net(param, sample).asnumpy() desired_shape = (shape,) if isinstance(shape, int) else shape assert mx_out.shape == desired_shape @use_np def test_gluon_categorical_v1(): class TestCategorical(HybridBlock): def __init__(self, func, is_logit=False, batch_shape=None, num_events=None, sample_shape=None): super(TestCategorical, self).__init__() self._is_logit = is_logit self._func = func self._batch_shape = batch_shape self._num_events = num_events self._sample_shape = sample_shape def hybrid_forward(self, F, params, *args): categorical = mgp.Categorical(self._num_events, logit=params, validate_args=True)\ if self._is_logit else \ mgp.Categorical(self._num_events, prob=params, validate_args=True) if self._func == "sample": return categorical.sample(self._batch_shape) if self._func == "sample_n": return categorical.sample_n(self._sample_shape) return _distribution_method_invoker(categorical, self._func, *args) event_shapes = [2, 5, 10] batch_shapes = [None, (2, 3)] # , (4, 0, 5)] sample_shapes = [(), (2,), (3, 4)] # Test sampling for event_shape, batch_shape in itertools.product(event_shapes, batch_shapes): for use_logit, hybridize in itertools.product([True, False], [True, False]): prob = np.array(_np.random.dirichlet( [1 / event_shape] * event_shape, size=batch_shape)) param = prob.astype('float32') if use_logit: param = np.log(param) net = TestCategorical("sample", use_logit, batch_shape, event_shape) if hybridize: net.hybridize() mx_out = net(param).asnumpy() desired_shape = batch_shape if batch_shape is not None else () assert mx_out.shape == desired_shape # Test sample_n for event_shape, batch_shape, sample_shape in itertools.product(event_shapes, batch_shapes, sample_shapes): for use_logit, hybridize in itertools.product([True, False], [True, False]): prob = np.array(_np.random.dirichlet( [1 / event_shape] * event_shape, size=batch_shape)) param = prob.astype('float32') if use_logit: param = np.log(param) net = TestCategorical("sample_n", is_logit=use_logit, batch_shape=batch_shape, num_events=event_shape, sample_shape=sample_shape ) if hybridize: net.hybridize() mx_out = net(param).asnumpy() desired_shape = sample_shape + \ (batch_shape if batch_shape is not None else ()) assert mx_out.shape == desired_shape # Test log_prob for event_shape, batch_shape, sample_shape in itertools.product(event_shapes, batch_shapes, sample_shapes): for use_logit, hybridize in itertools.product([True, False], [True, False]): prob = np.array(_np.random.dirichlet( [1 / event_shape] * event_shape, size=batch_shape)) eps = _np.finfo('float32').eps prob = np.clip(prob, eps, 1 - eps) param = prob.astype('float32') desired_shape = sample_shape + \ (batch_shape if batch_shape is not None else ()) samples = np.random.choice(event_shape, size=desired_shape) if use_logit: param = np.log(param) net = TestCategorical("log_prob", use_logit, batch_shape, event_shape) if hybridize: net.hybridize() mx_out = net(param, samples) # Check shape assert mx_out.shape == desired_shape # Check value log_pmf, indices = np.broadcast_arrays( np.log(prob), np.expand_dims(samples, -1)) if indices.ndim >= 1: indices = indices[..., :1] expect_log_prob = _np.take_along_axis( log_pmf, indices.astype('int'), axis=-1).asnumpy() assert_almost_equal(mx_out.asnumpy(), expect_log_prob.squeeze(), atol=1e-4, rtol=1e-3, use_broadcast=False) # Test enumerate_support for event_shape, batch_shape in itertools.product(event_shapes, batch_shapes): for use_logit, hybridize in itertools.product([True, False], [True, False]): prob = np.array(_np.random.dirichlet( [1 / event_shape] * event_shape, size=batch_shape)) param = prob.astype('float32') if use_logit: param = np.log(param) net = TestCategorical("enumerate_support", use_logit, batch_shape, event_shape) if hybridize: net.hybridize() mx_out = net(param).asnumpy() desired_shape = (event_shape,) + \ (batch_shape if batch_shape is not None else ()) assert mx_out.shape == desired_shape @use_np def test_gluon_one_hot_categorical_v1(): def one_hot(a, num_classes): return np.identity(num_classes)[a] class TestOneHotCategorical(HybridBlock): def __init__(self, func, is_logit=False, batch_shape=None, num_events=None): super(TestOneHotCategorical, self).__init__() self._is_logit = is_logit self._func = func self._batch_shape = batch_shape self._num_events = num_events def hybrid_forward(self, F, params, *args): categorical = mgp.OneHotCategorical(num_events=self._num_events, logit=params) \ if self._is_logit else \ mgp.OneHotCategorical(num_events=self._num_events, prob=params) if self._func == "sample": return categorical.sample(self._batch_shape) return _distribution_method_invoker(categorical, self._func, *args) event_shapes = [2, 5, 10] batch_shapes = [None, (2, 3)] # , (4, 0, 5)] sample_shapes = [(), (2,), (3, 4)] # Test sampling for event_shape, batch_shape in itertools.product(event_shapes, batch_shapes): for use_logit, hybridize in itertools.product([True, False], [True, False]): prob = np.array(_np.random.dirichlet( [1 / event_shape] * event_shape, size=batch_shape)) param = prob if use_logit: param = np.log(param) net = TestOneHotCategorical( "sample", use_logit, batch_shape, event_shape) if hybridize: net.hybridize() mx_out = net(param).asnumpy() desired_shape = batch_shape if batch_shape is not None else () assert mx_out.shape == desired_shape + (event_shape,) # Test log_prob for event_shape, batch_shape, sample_shape in itertools.product(event_shapes, batch_shapes, sample_shapes): for use_logit, hybridize in itertools.product([True, False], [True, False]): prob = np.array(_np.random.dirichlet( [1 / event_shape] * event_shape, size=batch_shape)) eps = _np.finfo('float32').eps prob = np.clip(prob, eps, 1 - eps) param = prob desired_shape = sample_shape + \ (batch_shape if batch_shape is not None else ()) samples = np.random.choice(event_shape, size=desired_shape) samples = one_hot(samples, event_shape) if use_logit: param = np.log(param) net = TestOneHotCategorical( "log_prob", use_logit, batch_shape, event_shape) if hybridize: net.hybridize() mx_out = net(param, samples) # Check shape assert mx_out.shape == desired_shape # Test enumerate support for event_shape, batch_shape in itertools.product(event_shapes, batch_shapes): for use_logit, hybridize in itertools.product([True, False], [True, False]): prob = np.array(_np.random.dirichlet( [1 / event_shape] * event_shape, size=batch_shape)) param = prob if use_logit: param = np.log(param) net = TestOneHotCategorical( "enumerate_support", use_logit, batch_shape, event_shape) if hybridize: net.hybridize() mx_out = net(param).asnumpy() desired_shape = batch_shape if batch_shape is not None else () assert mx_out.shape == (event_shape,) + \ desired_shape + (event_shape,) @use_np def test_relaxed_one_hot_categorical_v1(): class TestRelaxedOneHotCategorical(HybridBlock): def __init__(self, func, is_logit=False, batch_shape=None, num_events=None): super(TestRelaxedOneHotCategorical, self).__init__() self._is_logit = is_logit self._func = func self._batch_shape = batch_shape self._num_events = num_events def hybrid_forward(self, F, params, *args): categorical = mgp.RelaxedOneHotCategorical(T=1.0, num_events=self._num_events, logit=params) \ if self._is_logit else \ mgp.RelaxedOneHotCategorical( T=1.0, num_events=self._num_events, prob=params) if self._func == "sample": return categorical.sample(self._batch_shape) return _distribution_method_invoker(categorical, self._func, *args) event_shapes = [2, 5, 10] batch_shapes = [None, (2, 3)] # , (4, 0, 5)] sample_shapes = [(), (2,), (3, 4)] # Test sampling for event_shape, batch_shape in itertools.product(event_shapes, batch_shapes): for use_logit, hybridize in itertools.product([True, False], [True, False]): prob = np.array(_np.random.dirichlet( [1 / event_shape] * event_shape, size=batch_shape)) prob = prob.astype('float32') param = prob if use_logit: param = np.log(param) param.attach_grad() net = TestRelaxedOneHotCategorical( "sample", use_logit, batch_shape, event_shape) if hybridize: net.hybridize() with autograd.record(): mx_out = net(param) mx_out.backward() desired_shape = batch_shape if batch_shape is not None else () assert mx_out.shape == desired_shape + (event_shape,) assert param.grad.shape == param.shape # Test log_prob for event_shape, batch_shape, sample_shape in itertools.product(event_shapes, batch_shapes, sample_shapes): for use_logit, hybridize in itertools.product([True, False], [False]): prob = np.array(_np.random.dirichlet( [1 / event_shape] * event_shape, size=batch_shape)) eps = _np.finfo('float32').eps prob = np.clip(prob, eps, 1 - eps) param = prob desired_shape = sample_shape + \ (batch_shape if batch_shape is not None else ()) # Samples from a Relaxed One-hot Categorical lie on a simplex. samples = np.array(_np.random.dirichlet( [1 / event_shape] * event_shape, size=desired_shape)) if use_logit: param = np.log(param) net = TestRelaxedOneHotCategorical( "log_prob", use_logit, batch_shape, event_shape) if hybridize: net.hybridize() mx_out = net(param, samples) # Check shape assert mx_out.shape == desired_shape @use_np def test_gluon_mvn_v1(): class TestMVN(HybridBlock): def __init__(self, func, param_type): super(TestMVN, self).__init__() self._func = func # cov, precision or scale_tril self._param_type = param_type def hybrid_forward(self, F, loc, cov, *args): mvn = mgp.MultivariateNormal(loc=loc, **{self._param_type: cov}, validate_args=True) return _distribution_method_invoker(mvn, self._func, *args) def _stable_inv(cov): """ Force the precision matrix to be symmetric. """ precision = np.linalg.inv(cov) precision_t = np.swapaxes(precision, -1, -2) return (precision + precision_t) / 2 event_shapes = [3, 5] loc_shapes = [(), (2,), (4, 2)] cov_shapes = [(), (2,), (4, 2)] cov_func = { 'cov': lambda s: s, 'precision': lambda s: _stable_inv(s), 'scale_tril': lambda s: np.linalg.cholesky(s) } # Test sampling for loc_shape, cov_shape, event_shape in itertools.product(loc_shapes, cov_shapes, event_shapes): for cov_type in cov_func.keys(): for hybridize in [False]: loc = np.random.randn(*(loc_shape + (event_shape,))) _s = np.random.randn(*(cov_shape + (event_shape, event_shape))) loc.attach_grad() _s.attach_grad() # Full covariance matrix sigma = np.matmul(_s, np.swapaxes( _s, -1, -2)) + np.eye(event_shape) cov_param = cov_func[cov_type](sigma) net = TestMVN('sample', cov_type) if hybridize: net.hybridize() with autograd.record(): mx_out = net(loc, cov_param) desired_shape = (loc + sigma[..., 0]).shape assert mx_out.shape == desired_shape mx_out.backward() assert loc.grad.shape == loc.shape assert _s.grad.shape == _s.shape # Test log_prob for loc_shape, cov_shape, event_shape in itertools.product(loc_shapes, cov_shapes, event_shapes): for cov_type in cov_func.keys(): for hybridize in [True, False]: loc = np.random.randn(*(loc_shape + (event_shape,))) _s = np.random.randn(*(cov_shape + (event_shape, event_shape))) samples = np.random.normal( np.zeros_like(loc), np.ones_like(_s[..., 0])) loc.attach_grad() _s.attach_grad() # Full covariance matrix sigma = np.matmul(_s, np.swapaxes( _s, -1, -2)) + np.eye(event_shape) cov_param = cov_func[cov_type](sigma) net = TestMVN('log_prob', cov_type) if hybridize: net.hybridize() mx_out = net(loc, cov_param, samples) assert mx_out.shape == samples.shape[:-1] if mx_out.shape == (): mx_out_t = mx_out.asnumpy() else: mx_out_t = mx_out.flatten()[0].asnumpy() samples_t = samples.reshape(-1, event_shape).asnumpy()[0] # Select the first element in the batch, because scipy does not support batching. loc_t = loc.reshape(-1, event_shape)[0].asnumpy() sigma_t = sigma.reshape(-1, event_shape, event_shape)[0].asnumpy() scipy_mvn = ss.multivariate_normal(loc_t, sigma_t) ss_out = scipy_mvn.logpdf(samples_t) assert_almost_equal(mx_out_t, ss_out, atol=1e-4, rtol=1e-3, use_broadcast=False) # Test entropy for loc_shape, cov_shape, event_shape in itertools.product(loc_shapes, cov_shapes, event_shapes): for cov_type in cov_func.keys(): for hybridize in [True, False]: loc = np.random.randn(*(loc_shape + (event_shape,))) _s = np.random.randn(*(cov_shape + (event_shape, event_shape))) loc.attach_grad() _s.attach_grad() # Full covariance matrix sigma = np.matmul(_s, np.swapaxes( _s, -1, -2)) + np.eye(event_shape) cov_param = cov_func[cov_type](sigma) net = TestMVN('entropy', cov_type) if hybridize: net.hybridize() mx_out = net(loc, cov_param) assert mx_out.shape == sigma.shape[:-2] if mx_out.shape == (): mx_out_t = mx_out.asnumpy() else: mx_out_t = mx_out.flatten()[0].asnumpy() # Select the first element in the batch, because scipy does not support batching. loc_t = loc.reshape(-1, event_shape)[0].asnumpy() sigma_t = sigma.reshape(-1, event_shape, event_shape)[0].asnumpy() scipy_mvn = ss.multivariate_normal(loc_t, sigma_t) ss_out = scipy_mvn.entropy() assert_almost_equal(mx_out_t, ss_out, atol=1e-4, rtol=1e-3, use_broadcast=False) @use_np def test_gluon_half_normal_v1(): class TestHalfNormal(HybridBlock): def __init__(self, func): super(TestHalfNormal, self).__init__() self._func = func def hybrid_forward(self, F, scale, *args): half_normal = mgp.HalfNormal(scale, F, validate_args=True) return getattr(half_normal, self._func)(*args) shapes = [(), (1,), (2, 3), 6] # Test sampling for shape, hybridize in itertools.product(shapes, [True, False]): scale = np.random.uniform(0.5, 1.5, shape) net = TestHalfNormal("sample") if hybridize: net.hybridize() mx_out = net(scale).asnumpy() if isinstance(shape, Number): shape = (shape,) assert mx_out.shape == shape # Test log_prob for shape, hybridize in itertools.product(shapes, [True, False]): scale = np.random.uniform(0.5, 1.5, shape) samples = np.abs(np.random.normal(size=shape)) net = TestHalfNormal("log_prob") if hybridize: net.hybridize() mx_out = net(scale, samples).asnumpy() np_out = ss.halfnorm(0, scale.asnumpy()).logpdf(samples.asnumpy()) assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) # Test cdf for shape, hybridize in itertools.product(shapes, [True, False]): scale = np.random.uniform(0.5, 1.5, shape) samples = np.abs(np.random.normal(size=shape)) net = TestHalfNormal("cdf") if hybridize: net.hybridize() mx_out = net(scale, samples).asnumpy() np_out = ss.halfnorm(0, scale.asnumpy()).cdf(samples.asnumpy()) assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) # Test icdf for shape, hybridize in itertools.product(shapes, [True, False]): scale = np.random.uniform(0.5, 1.5, shape) samples = np.random.uniform(size=shape) net = TestHalfNormal("icdf") if hybridize: net.hybridize() mx_out = net(scale, samples).asnumpy() np_out = ss.halfnorm(0, scale.asnumpy()).ppf(samples.asnumpy()) assert_almost_equal(mx_out, np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) @use_np def test_affine_transform_v1(): r""" Test the correctness of affine transformation by performing it on a standard normal, since N(\mu, \sigma^2) = \mu + \sigma * N(0, 1) """ class TestAffineTransform(HybridBlock): def __init__(self, func): super(TestAffineTransform, self).__init__() self._func = func def hybrid_forward(self, F, loc, scale, *args): std_normal = mgp.Normal(F.np.zeros_like(loc), F.np.ones_like(scale), F) transforms = [mgp.AffineTransform(loc=0, scale=scale), mgp.AffineTransform(loc=loc, scale=1)] transformed_normal = mgp.TransformedDistribution( std_normal, transforms) if (len(args) == 0): return getattr(transformed_normal, self._func) return getattr(transformed_normal, self._func)(*args) shapes = [(1,), (2, 3), 6] # Test log_prob for shape, hybridize in itertools.product(shapes, [True, False]): loc = np.random.uniform(-1, 1, shape) loc.attach_grad() scale = np.random.uniform(0.5, 1.5, shape) scale.attach_grad() samples = np.random.normal(size=shape) net = TestAffineTransform('log_prob') if hybridize: net.hybridize() with autograd.record(): mx_out = net(loc, scale, samples) np_out = _np.log(ss.norm(loc.asnumpy(), scale.asnumpy()).pdf(samples.asnumpy())) assert_almost_equal(mx_out.asnumpy(), np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) mx_out.backward() loc_expected_grad = ((samples - loc) / scale ** 2).asnumpy() scale_expected_grad = (samples - loc) ** 2 * \ np.power(scale, -3) - (1 / scale) assert_almost_equal(loc.grad.asnumpy(), loc_expected_grad, atol=1e-4, rtol=1e-3, use_broadcast=False) assert_almost_equal(scale.grad.asnumpy(), scale_expected_grad, atol=1e-4, rtol=1e-3, use_broadcast=False) # Test sampling for shape, hybridize in itertools.product(shapes, [True, False]): loc = np.random.uniform(-1, 1, shape) loc.attach_grad() scale = np.random.uniform(0.5, 1.5, shape) scale.attach_grad() if not isinstance(shape, tuple): shape = (shape,) expected_shape = (4, 5) + shape net = TestAffineTransform('sample') mx_out = net(loc, scale, expected_shape).asnumpy() assert mx_out.shape == expected_shape @use_np def test_compose_transform_v1(): class TestComposeTransform(HybridBlock): def __init__(self, func): super(TestComposeTransform, self).__init__() self._func = func def hybrid_forward(self, F, loc, scale, *args): # Generate a log_normal distribution. std_normal = mgp.Normal(F.np.zeros_like(loc), F.np.ones_like(scale), F) transforms = mgp.ComposeTransform([ mgp.AffineTransform(loc=0, scale=scale), mgp.AffineTransform(loc=loc, scale=1), mgp.ExpTransform() ]) transformed_normal = mgp.TransformedDistribution( std_normal, transforms) if (len(args) == 0): return getattr(transformed_normal, self._func) return getattr(transformed_normal, self._func)(*args) shapes = [(1,), (2, 3), 6] # Test log_prob for shape, hybridize in itertools.product(shapes, [True, False]): loc = np.random.uniform(-1, 1, shape) loc.attach_grad() scale = np.random.uniform(0.5, 1.5, shape) scale.attach_grad() samples = np.random.uniform(1, 2, size=shape) net = TestComposeTransform('log_prob') if hybridize: net.hybridize() with autograd.record(): mx_out = net(loc, scale, samples) np_out = ss.lognorm(s=scale.asnumpy(), scale=np.exp( loc).asnumpy()).logpdf(samples.asnumpy()) assert_almost_equal(mx_out.asnumpy(), np_out, atol=1e-4, rtol=1e-3, use_broadcast=False) @use_np def test_cached_property_v1(): x = np.random.normal() x.attach_grad() scale = 0.1 class Dummy(object): def __init__(self, x): super(Dummy, self).__init__() self.x = x @mgp.cached_property def y(self): return scale * self.x + 1 with autograd.record(): obj = Dummy(x) obj.y.backward() assert_almost_equal(x.grad.asnumpy(), scale * np.ones((1,))) class DummyBlock(HybridBlock): def hybrid_forward(self, F, x): obj = Dummy(x) return obj.y x = np.random.normal() x.attach_grad() net = DummyBlock() with autograd.record(): y = net(x) y.backward() assert_almost_equal(x.grad.asnumpy(), scale * np.ones((1,))) x = np.random.normal() x.attach_grad() net.hybridize() with autograd.record(): y = net(x) y.backward() assert_almost_equal(x.grad.asnumpy(), scale * np.ones((1,))) @use_np def test_independent_v1(): class TestIndependent(HybridBlock): def __init__(self, event_dim, func): super(TestIndependent, self).__init__() self._event_dim = event_dim self._func = func def hybrid_forward(self, F, logit, *args): base_dist = mgp.Bernoulli(logit=logit) reshaped_dist = mgp.Independent(base_dist, self._event_dim) return getattr(reshaped_dist, self._func)(*args) event_shapes = [(1,), (4,), (2, 2)] batch_shapes = [(2, 3), (2,)] for (batch_shape, event_shape) in itertools.product(batch_shapes, event_shapes): for hybridize in [False, True]: for func in ['log_prob']: full_shape = batch_shape + event_shape logit = np.random.normal(0, 2, size=full_shape) samples = np.round(np.random.uniform(size=full_shape)) net = TestIndependent(len(event_shape), func) if hybridize: net.hybridize() mx_out = net(logit, samples) assert mx_out.shape == batch_shape @use_np def test_gluon_kl_v1(): def _test_zero_kl(p, shape): """Check if KL(p || p) = 0 Parameters ---------- p : Distribution """ mx_out = mgp.kl_divergence(p, p).asnumpy() np_out = _np.zeros(shape) assert_almost_equal(mx_out, np_out, atol=1e-3, rtol=1e-2, use_broadcast=False) def _test_monte_carlo(p, q, M=50000): r"""Check if KL(p || q) is approximately equal to 1/M * \Sum_{i=1}^{M} log(p(x_i) / q(x_i)), x_i ~ p(x) """ kl = mgp.kl_divergence(p, q) mc_approx = mgp.empirical_kl(p, q, M) assert_almost_equal(mc_approx.asnumpy(), kl.asnumpy(), atol=1e-1, rtol=1e-1, use_broadcast=False) def _dist_factory(dist, *param_funcs): """Generate a distribution object with parameters of random value. Parameters ---------- dist : Type A type of distribution. param_funcs : List A list of functions that generate valid parameters for `dist` """ params = [f() if callable(f) else f for f in param_funcs] return dist(*params) # could cause longer runtime and potential flaky tests monte_carlo_test = False repeated_times = 50000 shapes = [(), (1,), (2, 3), 6] # Test kl between same distributions # uniform for shape in shapes: dist = mgp.Uniform def low(): return np.random.uniform(0, 1, shape) def high(): return np.random.uniform(1, 2, shape) _test_zero_kl(_dist_factory(dist, low, high), shape) # normal, laplace, cauchy, gumbel for dist in [mgp.Normal, mgp.Laplace, mgp.Cauchy, mgp.Gumbel]: for shape in shapes: def loc(): return np.random.uniform(-1, 1, shape) def scale(): return np.random.uniform(0.5, 1.5, shape) _test_zero_kl(_dist_factory(dist, loc, scale), shape) if monte_carlo_test: _test_monte_carlo(_dist_factory(dist, loc, scale), _dist_factory(dist, loc, scale), repeated_times) # poisson for shape in shapes[1:]: dist = mgp.Poisson def rate(): return np.random.uniform(0.5, 1.5, shape) _test_zero_kl(_dist_factory(dist, rate), shape) if monte_carlo_test: _test_monte_carlo(_dist_factory(dist, rate), _dist_factory(dist, rate), repeated_times) # exponential, geometric for dist in [mgp.Exponential, mgp.Geometric]: for shape in shapes: def s(): return np.random.uniform(size=shape, low=1e-3) _test_zero_kl(_dist_factory(dist, s), shape) if monte_carlo_test: _test_monte_carlo(_dist_factory(dist, s), _dist_factory(dist, s), repeated_times) # pareto for shape in shapes: dist = mgp.Pareto def alpha(): return np.random.uniform(size=shape) def scale(): return np.random.uniform(size=shape) _test_zero_kl(_dist_factory(dist, alpha, scale), shape) for shape in shapes: dist = mgp.HalfNormal def scale(): return np.random.uniform(0.5, 1.5, shape) _test_zero_kl(_dist_factory(dist, scale), shape) if monte_carlo_test: _test_monte_carlo(_dist_factory(dist, scale), _dist_factory(dist, scale), repeated_times) # gamma, beta for dist in [mgp.Gamma, mgp.Beta]: for shape in shapes: def param1(): return np.random.uniform(0.5, 1.5, shape) def param2(): return np.random.uniform(0.5, 1.5, shape) _test_zero_kl(_dist_factory(dist, param1, param2), shape) if monte_carlo_test: _test_monte_carlo(_dist_factory(dist, param1, param2), _dist_factory(dist, param1, param2), 50000) # binomial for shape in shapes: n = _np.random.randint(5, 10) prob = np.random.uniform(low=0.1, size=shape) dist = mgp.Binomial(n=n, prob=prob) _test_zero_kl(dist, shape) # bernoulli for shape in shapes: prob = np.random.uniform(size=shape) dist = mgp.Bernoulli(prob=prob) _test_zero_kl(dist, shape) event_shapes = [3, 5, 10] loc_shapes = [(), (2,), (4, 2)] cov_shapes = [(), (2,), (4, 2)] for loc_shape, cov_shape, event_shape in itertools.product(loc_shapes, cov_shapes, event_shapes): loc = np.random.randn(*(loc_shape + (event_shape,))) _s = np.random.randn(*(cov_shape + (event_shape, event_shape))) sigma = np.matmul(_s, np.swapaxes(_s, -1, -2)) + np.eye(event_shape) dist = mgp.MultivariateNormal(loc, cov=sigma) desired_shape = (loc + sigma[..., 0]).shape[:-1] _test_zero_kl(dist, desired_shape) batch_shapes = loc_shapes # dirichlet for event_shape, batch_shape in itertools.product(event_shapes, batch_shapes): desired_shape = (batch_shape if batch_shape is not None else ()) dist = mgp.Dirichlet def alpha(): return np.random.uniform( 0.5, 1.5, size=(desired_shape + (event_shape,))) _test_zero_kl(_dist_factory(dist, alpha), desired_shape) if monte_carlo_test: _test_monte_carlo(_dist_factory(dist, alpha), _dist_factory(dist, alpha), 50000) # categorical, One-hot categorical for dist in [mgp.Categorical, mgp.OneHotCategorical]: for event_shape, batch_shape in itertools.product(event_shapes, batch_shapes): prob = (lambda: np.array(_np.random.dirichlet([1 / event_shape] * event_shape, size=batch_shape))) _test_zero_kl(_dist_factory(dist, event_shape, prob), batch_shape) if monte_carlo_test: _test_monte_carlo(_dist_factory(dist, event_shape, prob), _dist_factory(dist, event_shape, prob), repeated_times) # Test kl between different distributions # KL(Uniform || ...) for shape in shapes: rhs_dists = [ mgp.Normal(np.random.uniform(-1, 1, shape), np.random.uniform(0.5, 1.5, shape)), mgp.Gumbel(np.random.uniform(-1, 1, shape), np.random.uniform(0.5, 1.5, shape)), ] for rhs_dist in rhs_dists: low = np.random.uniform(-1, 1, shape) high = low + np.random.uniform(0.5, 1.5, shape) lhs_dist = mgp.Uniform(low, high) kl = mgp.kl_divergence(lhs_dist, rhs_dist) assert kl.shape == low.shape if monte_carlo_test: _test_monte_carlo(lhs_dist, rhs_dist, repeated_times) # KL(Exponential || ...) for shape in shapes: rhs_dists = [ mgp.Normal(np.random.uniform(-1, 1, shape), np.random.uniform(0.5, 1.5, shape)), mgp.Gumbel(np.random.uniform(-1, 1, shape), np.random.uniform(0.5, 1.5, shape)), mgp.Gamma(np.random.uniform(0.5, 1.5, shape), np.random.uniform(0.5, 1.5, shape)) ] for rhs_dist in rhs_dists: s = np.random.uniform(size=shape) lhs_dist = mgp.Exponential(s) kl = mgp.kl_divergence(lhs_dist, rhs_dist) assert kl.shape == s.shape if monte_carlo_test: _test_monte_carlo(lhs_dist, rhs_dist, repeated_times) @pytest.mark.garbage_expected @use_np def test_gluon_stochastic_block_v1(): class dummyBlock(StochasticBlock): """In this test case, we generate samples from a Gaussian parameterized by `loc` and `scale` and accumulate the KL-divergence between it and its prior and the l2 norm of `loc` into the block's loss storage.""" @StochasticBlock.collectLoss def hybrid_forward(self, F, loc, scale): qz = mgp.Normal(loc, scale) # prior pz = mgp.Normal(F.np.zeros_like(loc), F.np.ones_like(scale)) self.add_loss(mgp.kl_divergence(qz, pz)) self.add_loss((loc ** 2).sum(1)) return qz.sample() shape = (4, 4) for hybridize in [True, False]: net = dummyBlock() if hybridize: net.hybridize() loc = np.random.randn(*shape) scale = np.random.rand(*shape) mx_out = net(loc, scale).asnumpy() kl = net.losses[0].asnumpy() l2_norm = net.losses[1].asnumpy() assert mx_out.shape == loc.shape assert kl.shape == loc.shape assert l2_norm.shape == shape[:-1] @use_np def test_gluon_stochastic_block_exception_v1(): class problemBlock(StochasticBlock): def hybrid_forward(self, F, loc, scale): qz = mgp.Normal(loc, scale) # prior pz = mgp.Normal(F.np.zeros_like(loc), F.np.ones_like(scale)) self.add_loss(mgp.kl_divergence(qz, pz)) self.add_loss((loc ** 2).sum(1)) return qz.sample() shape = (4, 4) for hybridize in [True, False]: net = problemBlock() if hybridize: net.hybridize() loc = np.random.randn(*shape) scale = np.random.rand(*shape) with pytest.raises(ValueError): mx_out = net(loc, scale).asnumpy() @pytest.mark.garbage_expected @use_np def test_gluon_stochastic_sequential_v1(): class normalBlock(HybridBlock): def hybrid_forward(self, F, x): return (x + 1) class stochasticBlock(StochasticBlock): @StochasticBlock.collectLoss def hybrid_forward(self, F, x): self.add_loss(x ** 2) self.add_loss(x - 1) return (x + 1) class problemBlock(StochasticBlock): def hybrid_forward(self, F, x): self.add_loss(x ** 2) self.add_loss(x - 1) return (x + 1) shape = (4, 4) for hybridize in [True, False]: initial_value = np.ones(shape) net = StochasticSequential() net.add(stochasticBlock()) net.add(normalBlock()) net.add(stochasticBlock()) net.add(normalBlock()) if hybridize: net.hybridize() mx_out = net(initial_value).asnumpy() assert_almost_equal(mx_out, _np.ones(shape) * 5) accumulated_loss = net.losses assert len(accumulated_loss) == 2 assert_almost_equal(accumulated_loss[0][0].asnumpy(), _np.ones(shape)) assert_almost_equal( accumulated_loss[0][1].asnumpy(), _np.ones(shape) - 1) assert_almost_equal( accumulated_loss[1][0].asnumpy(), _np.ones(shape) * 9) assert_almost_equal( accumulated_loss[1][1].asnumpy(), _np.ones(shape) + 1) for hybridize in [True, False]: initial_value = np.ones(shape) net = StochasticSequential() net.add(stochasticBlock()) net.add(normalBlock()) net.add(problemBlock()) net.add(normalBlock()) if hybridize: net.hybridize() with pytest.raises(ValueError): mx_out = net(initial_value).asnumpy() @use_np def test_gluon_constraint_v1(): class TestConstraint(HybridBlock): def __init__(self, constraint_type): super(TestConstraint, self).__init__() self._constraint_type = getattr(mgp.constraint, constraint_type) def hybrid_forward(self, F, *params): value = params[0] constraint_param = params[1:] if len(constraint_param) == 0: constraint = self._constraint_type() else: constraint = self._constraint_type(*constraint_param) return constraint.check(value) _s = np.random.randn(5, 10, 10) psd_matrix = np.matmul(_s, np.swapaxes(_s, -1, -2)) + np.eye(_s.shape[-1]) constraints_zoo = [ # (constraint_type, constraint_param, test_samples) ('Real', (), [np.random.randn(2, 2)]), ('Boolean', (), [np.random.randint(0, 20, size=(2, 2)) % 2 == 0]), ('Interval', [np.zeros((2, 2)), np.ones( (2, 2))], [np.random.rand(2, 2)]), ('OpenInterval', [np.zeros((2, 2)), np.ones( (2, 2))], [np.random.rand(2, 2)]), ('HalfOpenInterval', [np.zeros((2, 2)), np.ones((2, 2))], [np.random.rand(2, 2)]), ('IntegerInterval', [np.zeros((2, 2)), np.ones((2, 2)) * 10], [np.random.randint(0, 10, size=(2, 2)).astype('float32')]), ('IntegerOpenInterval', [np.zeros((2, 2)), np.ones((2, 2)) * 10], [np.random.randint(1, 9, size=(2, 2)).astype('float32')]), ('IntegerHalfOpenInterval', [np.zeros((2, 2)), np.ones((2, 2)) * 10], [np.random.randint(1, 9, size=(2, 2)).astype('float32')]), ('GreaterThan', [np.zeros((2, 2))], [np.random.rand(2, 2)]), ('GreaterThanEq', [np.zeros((2, 2))], [np.random.rand(2, 2)]), ('LessThan', [np.ones((2, 2))], [np.random.rand(2, 2)]), ('LessThanEq', [np.ones((2, 2))], [np.random.rand(2, 2)]), ('IntegerGreaterThan', [np.zeros((2, 2))], [np.random.randint(1, 10, size=(2, 2)).astype('float32')]), ('IntegerGreaterThanEq', [np.zeros((2, 2))], [np.random.randint(0, 10, size=(2, 2)).astype('float32')]), ('IntegerLessThan', [np.ones((2, 2)) * 10], [np.random.randint(0, 9, size=(2, 2)).astype('float32')]), ('IntegerLessThanEq', [np.ones((2, 2)) * 10], [np.random.randint(0, 10, size=(2, 2)).astype('float32')]), ('Positive', (), [np.random.rand(2, 2)]), ('NonNegative', (), [np.random.rand(2, 2)]), ('PositiveInteger', (), [np.random.randint( 1, 5, size=(2, 2)).astype('float32')]), ('NonNegativeInteger', (), [np.random.randint( 0, 5, size=(2, 2)).astype('float32')]), ('Simplex', (), [npx.softmax(np.random.randn(4, 4), axis=-1)]), ('LowerTriangular', (), [np.tril(np.random.randn(5, 3, 3))]), ('LowerCholesky', (), [np.linalg.cholesky(psd_matrix)]), ('PositiveDefinite', (), [psd_matrix]), ] for (constraint_type, constraint_arg, test_samples) in constraints_zoo: for hybridize in [True, False]: net = TestConstraint(constraint_type) if hybridize: net.hybridize() for test_sample in test_samples: mx_out = net(test_sample, *constraint_arg).asnumpy() assert_almost_equal(mx_out, test_sample.asnumpy()) @use_np def test_gluon_domain_map_v1(): class TestDomainMap(HybridBlock): def __init__(self, constraint_type, bijective): super(TestDomainMap, self).__init__() self._constraint_type = getattr(mgp.constraint, constraint_type) def hybrid_forward(self, F, *params): value = params[0] constraint_param = params[1:] if len(constraint_param) == 0: constraint = self._constraint_type() else: constraint = self._constraint_type(*constraint_param) if bijective: bijector = mgp.biject_to(constraint) bijector.F = F value = bijector(value) else: transformation = mgp.transform_to(constraint) transformation.F = F value = transformation(value) return (value, constraint.check(value)) constraints_zoo = [ # (constraint_type, constraint_param) ('Positive', ()), ('GreaterThan', [np.random.randn(2, 2)]), ('GreaterThanEq', [np.random.randn(2, 2)]), ('LessThan', [np.random.randn(2, 2)]), ('Interval', [np.random.uniform(0, 1, (2, 2)), np.random.uniform(2, 3, (2, 2))]), ('HalfOpenInterval', [np.random.uniform( 0, 1, (2, 2)), np.random.uniform(2, 3, (2, 2))]) ] test_sample = np.random.randn(2, 2) for (constraint_type, constraint_arg) in constraints_zoo: for bijective in [True, False]: for hybridize in [True, False]: net = TestDomainMap(constraint_type, bijective) if hybridize: net.hybridize() constrained_out, constraint_status = net( test_sample, *constraint_arg) assert_almost_equal(constrained_out.asnumpy(), constraint_status.asnumpy())

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0aefd1e832608cb0768f999e50963866ceec5d5a

33

py

Python

odoo-13.0/venv/lib/python3.8/site-packages/PcxImagePlugin.py

VaibhavBhujade/Blockchain-ERP-interoperability

b5190a037fb6615386f7cbad024d51b0abd4ba03

[ "MIT" ]

2

2021-06-20T16:56:45.000Z

2021-06-20T17:30:18.000Z

odoo-13.0/venv/lib/python3.8/site-packages/PcxImagePlugin.py

VaibhavBhujade/Blockchain-ERP-interoperability

b5190a037fb6615386f7cbad024d51b0abd4ba03

[ "MIT" ]

null

odoo-13.0/venv/lib/python3.8/site-packages/PcxImagePlugin.py

VaibhavBhujade/Blockchain-ERP-interoperability

b5190a037fb6615386f7cbad024d51b0abd4ba03

[ "MIT" ]

null

from PIL.PcxImagePlugin import *

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6

7c20a5d0a36282a85e330e3fffbdb943e39489e5

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py

Python

multiworld/envs/mujoco/cameras.py

shikharbahl/multiworld

85b3200dc9a5821754c2d8ba2b8a7b6add874828

[ "MIT" ]

1

2019-01-30T20:55:15.000Z

multiworld/envs/mujoco/cameras.py

shikharbahl/multiworld

85b3200dc9a5821754c2d8ba2b8a7b6add874828

[ "MIT" ]

null

multiworld/envs/mujoco/cameras.py

shikharbahl/multiworld

85b3200dc9a5821754c2d8ba2b8a7b6add874828

[ "MIT" ]

null

import numpy as np def create_sawyer_camera_init( lookat=(0, 0.85, 0.3), distance=0.3, elevation=-35, azimuth=270, trackbodyid=-1, ): def init(camera): camera.lookat[0] = lookat[0] camera.lookat[1] = lookat[1] camera.lookat[2] = lookat[2] camera.distance = distance camera.elevation = elevation camera.azimuth = azimuth camera.trackbodyid = trackbodyid return init def init_sawyer_camera_v1(camera): """ Do not get so close that the arm crossed the camera plane """ camera.lookat[0] = 0 camera.lookat[1] = 1 camera.lookat[2] = 0.3 camera.distance = 0.35 camera.elevation = -35 camera.azimuth = 270 camera.trackbodyid = -1 def init_sawyer_camera_v2(camera): """ Top down basically. Sees through the arm. """ camera.lookat[0] = 0 camera.lookat[1] = 0.8 camera.lookat[2] = 0.3 camera.distance = 0.3 camera.elevation = -65 camera.azimuth = 270 camera.trackbodyid = -1 def init_sawyer_camera_v3(camera): """ Top down basically. Sees through the arm. """ camera.lookat[0] = 0 camera.lookat[1] = 0.85 camera.lookat[2] = 0.3 camera.distance = 0.3 camera.elevation = -35 camera.azimuth = 270 camera.trackbodyid = -1 def sawyer_pick_and_place_camera(camera): camera.lookat[0] = 0.0 camera.lookat[1] = .67 camera.lookat[2] = .1 camera.distance = .7 camera.elevation = 0 camera.azimuth = 180 camera.trackbodyid = 0 def init_sawyer_camera_v4(camera): """ This is the same camera used in old experiments (circa 6/7/2018) """ camera.lookat[0] = 0 camera.lookat[1] = 0.85 camera.lookat[2] = 0.3 camera.distance = 0.3 camera.elevation = -35 camera.azimuth = 270 camera.trackbodyid = -1 def sawyer_pick_and_place_camera_slanted_angle(camera): camera.lookat[0] = 0.0 camera.lookat[1] = .67 camera.lookat[2] = .1 camera.distance = .65 camera.elevation = -37.85 camera.azimuth = 180 camera.trackbodyid = 0 def init_sawyer_camera_v5(camera): """ Purposely zoomed out to be hard. """ camera.lookat[0] = 0 camera.lookat[1] = 0.85 camera.lookat[2] = 0.3 camera.distance = 1 camera.elevation = -35 camera.azimuth = 270 camera.trackbodyid = -1 def sawyer_xyz_reacher_camera(camera): # TODO: reformat or delete camera.trackbodyid = 0 camera.distance = 1.0 # 3rd person view cam_dist = 0.3 rotation_angle = 270 cam_pos = np.array([0, 1.0, 0.5, cam_dist, -30, rotation_angle]) for i in range(3): camera.lookat[i] = cam_pos[i] camera.distance = cam_pos[3] camera.elevation = cam_pos[4] camera.azimuth = cam_pos[5] camera.trackbodyid = -1 def sawyer_torque_reacher_camera(camera): # TODO: reformat or delete camera.trackbodyid = 0 camera.distance = 1.0 # 3rd person view cam_dist = 0.3 rotation_angle = 270 cam_pos = np.array([0, 1.0, 0.65, cam_dist, -30, rotation_angle]) for i in range(3): camera.lookat[i] = cam_pos[i] camera.distance = cam_pos[3] camera.elevation = cam_pos[4] camera.azimuth = cam_pos[5] camera.trackbodyid = -1 def sawyer_door_env_camera(camera): camera.trackbodyid = 0 camera.distance = 1.0 cam_dist = 0.1 rotation_angle = 0 cam_pos = np.array([0, 0.725, .9, cam_dist, -90, rotation_angle]) for i in range(3): camera.lookat[i] = cam_pos[i] camera.distance = cam_pos[3] camera.elevation = cam_pos[4] camera.azimuth = cam_pos[5] camera.trackbodyid = -1 def sawyer_door_env_camera_v2(camera): camera.trackbodyid = 0 camera.distance = 1.0 cam_dist = 0.1 rotation_angle = 0 cam_pos = np.array([.1, 0.55, .9, cam_dist, -90, rotation_angle]) for i in range(3): camera.lookat[i] = cam_pos[i] camera.distance = cam_pos[3] camera.elevation = cam_pos[4] camera.azimuth = cam_pos[5] camera.trackbodyid = -1 def sawyer_door_env_camera_v3(camera): camera.trackbodyid = 0 camera.distance = 1.0 # 3rd person view cam_dist = 0.25 rotation_angle = 360 cam_pos = np.array([-.2, .55, 0.6, cam_dist, -60, rotation_angle]) for i in range(3): camera.lookat[i] = cam_pos[i] camera.distance = cam_pos[3] camera.elevation = cam_pos[4] camera.azimuth = cam_pos[5] camera.trackbodyid = -1 def sawyer_pusher_camera_upright(camera): camera.trackbodyid = 0 camera.distance = .45 camera.lookat[0] = 0 camera.lookat[1] = 0.85 camera.lookat[2] = 0.45 camera.elevation = -50 camera.azimuth = 270 camera.trackbodyid = -1 def sawyer_pusher_camera_upright_v2(camera): camera.trackbodyid = 0 camera.distance = .45 camera.lookat[0] = 0 camera.lookat[1] = 0.85 camera.lookat[2] = 0.45 camera.elevation = -60 camera.azimuth = 270 camera.trackbodyid = -1 def sawyer_pusher_camera_top_down(camera): camera.trackbodyid = 0 cam_dist = 0.1 rotation_angle = 0 cam_pos = np.array([0, 0.6, .9, cam_dist, -90, rotation_angle]) for i in range(3): camera.lookat[i] = cam_pos[i] camera.distance = cam_pos[3] camera.elevation = cam_pos[4] camera.azimuth = cam_pos[5] camera.trackbodyid = -1

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null

0

1

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null

0

6

7c51fd04058be770247a4490232efadf8255b063

1,499

py

Python

NeoML/Python/test/svml.py

SAngeliuk/neoml_python

09e24dd726426ec880ff1793c287f03c3f1d362e

[ "ECL-2.0", "Apache-2.0" ]

1

2020-12-25T08:04:55.000Z

NeoML/Python/test/svml.py

SAngeliuk/neoml_python

09e24dd726426ec880ff1793c287f03c3f1d362e

[ "ECL-2.0", "Apache-2.0" ]

null

NeoML/Python/test/svml.py

SAngeliuk/neoml_python

09e24dd726426ec880ff1793c287f03c3f1d362e

[ "ECL-2.0", "Apache-2.0" ]

null

import numpy as np from scipy.sparse import csr_matrix def read( file_path, min_feature_count=0 ) : file = open(file_path, "r") data = [] column = [] row = [0] y = [] rowCount = 0; columnCount = 0 elementCount = 0 for line in file: cur = line.split(" ") y.append( int(cur[0]) ) for i in range(1, len(cur) ): item = cur[i].split(":") data.append( float(item[1]) ) column.append( int(item[0]) ) if int(item[0]) + 1 > columnCount: columnCount = int(item[0]) + 1 elementCount += 1 rowCount += 1 row.append( elementCount ) X = csr_matrix( ( np.array( data, np.float32 ), np.array( column, np.int32 ), row ), shape=( rowCount, max(columnCount, min_feature_count) ) ) return ( X, y ) def correct( file_path ) : file = open(file_path, "r") fileW = open("res.txt", "w") data = [] column = [] row = [0] y = [] rowCount = 0; columnCount = 0 elementCount = 0 for line in file: cur = line.split(" ") y.append( int(cur[0]) ) fileW.write( str( int( cur[0] ) - 1 ) ) for i in range(1, len(cur) ): item = cur[i].split(":") data.append( float(item[1]) ) column.append( int(item[0]) ) if int(item[0]) + 1 > columnCount: columnCount = int(item[0]) + 1 elementCount += 1 s = str(int(item[0]) - 1 ) + ":" + item[1] fileW.write( " " + s ) rowCount += 1 row.append( elementCount ) X = csr_matrix( ( np.array( data, np.float32 ), np.array( column, np.int32 ), row ), shape=( rowCount, columnCount ) ) return ( X, y )

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1

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1

0

null

0

6

7c8aa137d037945796fb4702c23c7eed165adb50

6,516

py

Python

tests/test_math/test_linalg.py

yeonsu-jung/PyElastica

fee87b9da22e310ff925c16fdc839bf8405c51a4

[ "MIT" ]

null

tests/test_math/test_linalg.py

yeonsu-jung/PyElastica

fee87b9da22e310ff925c16fdc839bf8405c51a4

[ "MIT" ]

1

2022-01-06T11:30:20.000Z

2022-02-07T07:11:22.000Z

tests/test_math/test_linalg.py

yeonsu-jung/PyElastica

fee87b9da22e310ff925c16fdc839bf8405c51a4

[ "MIT" ]

null

#!/usr/bin/env python3 __doc__ = ( """ Test scripts for linear algebra helpers in Elastica Numba implementation""" ) # System imports import numpy as np import pytest from numpy.testing import assert_allclose from elastica._linalg import ( _batch_matvec, _batch_matmul, _batch_cross, _batch_vec_oneD_vec_cross, _batch_dot, _batch_norm, _batch_product_i_k_to_ik, _batch_product_i_ik_to_k, _batch_product_k_ik_to_ik, _batch_vector_sum, _batch_matrix_transpose, ) @pytest.mark.parametrize("blocksize", [8, 32]) def test_batch_matvec(blocksize): input_matrix_collection = np.random.randn(3, 3, blocksize) input_vector_collection = np.random.randn(3, blocksize) test_vector_collection = _batch_matvec( input_matrix_collection, input_vector_collection ) correct_vector_collection = [ np.dot(input_matrix_collection[..., i], input_vector_collection[..., i]) for i in range(blocksize) ] correct_vector_collection = np.array(correct_vector_collection).T assert_allclose(test_vector_collection, correct_vector_collection) @pytest.mark.parametrize("blocksize", [8, 32]) def test_batch_matmul(blocksize): input_first_matrix_collection = np.random.randn(3, 3, blocksize) input_second_matrix_collection = np.random.randn(3, 3, blocksize) test_matrix_collection = _batch_matmul( input_first_matrix_collection, input_second_matrix_collection ) correct_matrix_collection = np.empty((3, 3, blocksize)) for i in range(blocksize): correct_matrix_collection[..., i] = np.dot( input_first_matrix_collection[..., i], input_second_matrix_collection[..., i], ) assert_allclose(test_matrix_collection, correct_matrix_collection) # TODO : Generalize to two dimensions @pytest.mark.parametrize("dim", [3]) @pytest.mark.parametrize("blocksize", [8, 32]) def test_batch_cross(dim, blocksize): input_first_vector_collection = np.random.randn(dim, blocksize) input_second_vector_collection = np.random.randn(dim, blocksize) test_vector_collection = _batch_cross( input_first_vector_collection, input_second_vector_collection ) correct_vector_collection = np.cross( input_first_vector_collection, input_second_vector_collection, axisa=0, axisb=0 ).T assert_allclose(test_vector_collection, correct_vector_collection) @pytest.mark.parametrize("blocksize", [8, 32]) def test_batch_vec_oneD_vec_cross(blocksize): input_first_vector_collection = np.random.randn(3, blocksize) input_second_vector = np.random.randn(3) test_vector_collection = _batch_vec_oneD_vec_cross( input_first_vector_collection, input_second_vector ) correct_vector_collection = np.cross( input_first_vector_collection, input_second_vector, axisa=0, axisb=0 ).T assert_allclose(test_vector_collection, correct_vector_collection) @pytest.mark.parametrize("blocksize", [8, 32]) def test_batch_dot(blocksize): input_first_vector_collection = np.random.randn(3, blocksize) input_second_vector_collection = np.random.randn(3, blocksize) test_vector_collection = _batch_dot( input_first_vector_collection, input_second_vector_collection ) correct_vector_collection = np.einsum( "ij,ij->j", input_first_vector_collection, input_second_vector_collection ) assert_allclose(test_vector_collection, correct_vector_collection) @pytest.mark.parametrize("blocksize", [8, 32]) def test_batch_norm(blocksize): input_first_vector_collection = np.random.randn(3, blocksize) test_vector_collection = _batch_norm(input_first_vector_collection) correct_vector_collection = np.sqrt( np.einsum( "ij,ij->j", input_first_vector_collection, input_first_vector_collection ) ) assert_allclose(test_vector_collection, correct_vector_collection) @pytest.mark.parametrize("blocksize", [8, 32]) def test_batch_product_i_k_to_ik(blocksize): input_first_vector_collection = np.random.randn(3) input_second_vector_collection = np.random.randn(blocksize) test_vector_collection = _batch_product_i_k_to_ik( input_first_vector_collection, input_second_vector_collection ) correct_vector_collection = np.einsum( "i,j->ij", input_first_vector_collection, input_second_vector_collection ) assert_allclose(test_vector_collection, correct_vector_collection) @pytest.mark.parametrize("blocksize", [8, 32]) def test_batch_product_i_ik_to_k(blocksize): input_first_vector_collection = np.random.randn(3) input_second_vector_collection = np.random.randn(3, blocksize) test_vector_collection = _batch_product_i_ik_to_k( input_first_vector_collection, input_second_vector_collection ) correct_vector_collection = np.einsum( "i,ij->j", input_first_vector_collection, input_second_vector_collection ) assert_allclose(test_vector_collection, correct_vector_collection) @pytest.mark.parametrize("blocksize", [8, 32]) def test_batch_product_k_ik_to_ik(blocksize): input_first_vector_collection = np.random.randn(blocksize) input_second_vector_collection = np.random.randn(3, blocksize) test_vector_collection = _batch_product_k_ik_to_ik( input_first_vector_collection, input_second_vector_collection ) correct_vector_collection = np.einsum( "j,ij->ij", input_first_vector_collection, input_second_vector_collection ) assert_allclose(test_vector_collection, correct_vector_collection) @pytest.mark.parametrize("blocksize", [8, 32]) def test_batch_vector_sum(blocksize): input_first_vector_collection = np.random.randn(3, blocksize) input_second_vector_collection = np.random.randn(3, blocksize) test_vector_collection = _batch_vector_sum( input_first_vector_collection, input_second_vector_collection ) correct_vector_collection = ( input_first_vector_collection + input_second_vector_collection ) assert_allclose(test_vector_collection, correct_vector_collection) @pytest.mark.parametrize("blocksize", [8, 32]) def test_batch_matrix_transpose(blocksize): input_matrix_collection = np.random.randn(3, 3, blocksize) test_matrix_collection = _batch_matrix_transpose(input_matrix_collection) correct_matrix_collection = np.einsum("ijk->jik", input_matrix_collection) assert_allclose(test_matrix_collection, correct_matrix_collection)

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py

Python

library/src/test/unit/__init__.py

unSAD-admin/unSAD

9f1d0e680a0086d140bc8d1c55fe21dd7de87df5

[ "Apache-2.0" ]

3

2019-11-01T04:51:51.000Z

2019-12-17T04:25:18.000Z

library/src/test/unit/__init__.py

unSAD-admin/unSAD

9f1d0e680a0086d140bc8d1c55fe21dd7de87df5

[ "Apache-2.0" ]

1

2019-11-11T18:29:36.000Z

library/src/test/unit/__init__.py

unSAD-admin/unSAD

9f1d0e680a0086d140bc8d1c55fe21dd7de87df5

[ "Apache-2.0" ]

2

2019-12-18T11:49:00.000Z

2020-03-27T20:06:15.000Z

# Created by Xinyu Zhu on 10/22/2019, 12:45 PM

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py

Python

idl2py/jd/ydn2md.py

RapidLzj/idl2py

193051cd8d01db0d125b8975713b885ad521a992

[ "MIT" ]

null

idl2py/jd/ydn2md.py

RapidLzj/idl2py

193051cd8d01db0d125b8975713b885ad521a992

[ "MIT" ]

null

idl2py/jd/ydn2md.py

RapidLzj/idl2py

193051cd8d01db0d125b8975713b885ad521a992

[ "MIT" ]

null

""" By Dr Jie Zheng -Q, NAOC v1 2019-04-27 """ import numpy as np from..util import * def ydn2md(): pass #;------------------------------------------------------------- #;+ #; NAME: #; YDN2MD #; PURPOSE: #; Convert from year and day number of year to month and day of month. #; CALLING SEQUENCE: #; YDN2MD,yr,dy,m,d #; INPUTS: #; yr = 4 digit year (like 1988), integer scalar #; dy = day number in year (like 310), integer scalar or vector #; #; OUTPUTS: #; m = month number (1-12, e.g. 11 = Nov) #; d = day of month (like 5). #; Note: On error returns m = d = -1. #; #; EXAMPLE: #; Find the month/day of days 155 and 255 in the year 2001 #; #; IDL> ydn2md, 2001, [155,255], m, d #; ==> m = [6,9] & d = [4,12] ; = June 4 and September 12 #; #; MODIFICATION HISTORY: #; Adapted from Johns Hopkins University/Applied Physics Laboratory #; Update to use VALUE_LOCATE, W. Landsman January 2001 #;- #;------------------------------------------------------------- # # PRO YDN2MD,YR,DY,M,D, help=hlp # # IF (N_PARAMS() LT 4) or keyword_set(hlp) THEN BEGIN # PRINT,' Convert from year and day number of year to month '+$ # 'and day of month.' # PRINT,' ydn2md,yr,dy,m,d' # PRINT,' yr = year (like 1988), scalar input' # PRINT,' dy = day number in year (like 310), scalar or vector input' # PRINT,' m = month number (like 11 = Nov). out' # PRINT,' d = day of month (like 5). out' # PRINT,' Note: On error returns m = d = -1.' # RETURN # ENDIF # # ; Days before start of each month. # YDAYS = [0,31,59,90,120,151,181,212,243,273,304,334,366] + 1 # # LEAP = (((YR MOD 4) EQ 0) AND ((YR MOD 100) NE 0)) OR $ # ((YR MOD 400) EQ 0) # # IF LEAP THEN YDAYS[2] = YDAYS[2:*] + 1 # M = VALUE_LOCATE(YDAYS, DY) + 1 # D = DY - YDAYS[M-1] + 1 # BAD = WHERE(M GT 12, NBAD) # # IF NBAD GT 0 THEN BEGIN # M[BAD] = -1 # D[BAD] = -1 # MESSAGE,'Error in Day Number',/CON # ENDIF # RETURN # # END

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py

Python

python/seldon/luigi/spark.py

smsahu/seldon-server

7f6dc5d405736e44205323f04ce431064dd854b3

[ "Apache-2.0" ]

1,645

2015-02-13T12:31:44.000Z

2022-03-17T07:50:05.000Z

python/seldon/luigi/spark.py

smsahu/seldon-server

7f6dc5d405736e44205323f04ce431064dd854b3

[ "Apache-2.0" ]

57

2015-03-26T16:00:23.000Z

2021-05-10T11:03:40.000Z

python/seldon/luigi/spark.py

smsahu/seldon-server

7f6dc5d405736e44205323f04ce431064dd854b3

[ "Apache-2.0" ]

371

2015-03-16T11:04:16.000Z

2022-02-27T01:16:02.000Z

import luigi from subprocess import call import logging from seldon.misc.item_similarity import * from seldon.misc.most_popular import * from luigi.contrib.spark import SparkSubmitTask # # Item Similarity # class ItemSimilaritySparkJob(luigi.Task): """ Spark job for running item similarity model """ inputPath = luigi.Parameter(default="/seldon-data/seldon-models/") outputPath = luigi.Parameter(default="/seldon-data/seldon-models/") client = luigi.Parameter(default="test") sparkDriverMemory = luigi.Parameter(default="1g") sparkExecutorMemory = luigi.Parameter(default="1g") startDay = luigi.IntParameter(default=1) days = luigi.IntParameter(default=1) itemType = luigi.IntParameter(-1) limit = luigi.IntParameter(default=100) minItemsPerUser = luigi.IntParameter(default=0) minUsersPerItem = luigi.IntParameter(default=0) maxUsersPerItem = luigi.IntParameter(default=2000000) dimsumThreshold =luigi.FloatParameter(default=0.1) sample = luigi.FloatParameter(default=1.0) def output(self): return luigi.LocalTarget("{}/{}/item-similarity/{}".format(self.outputPath,self.client,self.startDay)) def run(self): params = ["seldon-cli","model","--action","add","--client-name",self.client,"--model-name","similar-items","--inputPath",self.inputPath,"--outputPath",self.outputPath,"--startDay",str(self.startDay),"--days",str(self.days),"--sample",str(self.sample),"--itemType",str(self.itemType),"--limit",str(self.limit),"--minItemsPerUser",str(self.minItemsPerUser),"--minUsersPerItem",str(self.minUsersPerItem),"--maxUsersPerItem",str(self.maxUsersPerItem),"--dimsumThreshold",str(self.dimsumThreshold)] res = call(params) params = ["seldon-cli","model","--action","train","--client-name",self.client,"--model-name","similar-items","--spark-executor-memory",self.sparkExecutorMemory,"--spark-driver-memory",self.sparkDriverMemory] res = call(params) return res class SeldonItemSimilarity(luigi.Task): """ Item similarity model. Depends on spark job. Writes results to mysql db. """ startDay = luigi.IntParameter(default=1) client = luigi.Parameter(default="test") db_host = luigi.Parameter(default="mysql") db_user = luigi.Parameter(default="root") db_pass = luigi.Parameter(default="mypass") def requires(self): return ItemSimilaritySparkJob(client=self.client,startDay=self.startDay) def run(self): u = ItemSimilarityUploadMysql(self.client,self.db_host,self.db_user,self.db_pass) u.stream_and_upload(self.input().path) # # MF # class SeldonMatrixFactorization(luigi.Task): """ Matrix factorization using Spark """ inputPath = luigi.Parameter(default="/seldon-data/seldon-models/") outputPath = luigi.Parameter(default="/seldon-data/seldon-models/") client = luigi.Parameter(default="test") sparkDriverMemory = luigi.Parameter(default="1g") sparkExecutorMemory = luigi.Parameter(default="1g") startDay = luigi.IntParameter(default=1) days = luigi.IntParameter(default=1) rank = luigi.IntParameter(default=30) mf_lambda = luigi.FloatParameter(default=0.01) alpha = luigi.FloatParameter(default=1) iterations = luigi.IntParameter(default=5) def output(self): return luigi.LocalTarget("{}/{}/matrix-factorization/{}".format(self.outputPath,self.client,self.startDay)) def run(self): params = ["seldon-cli","model","--action","add","--client-name",self.client,"--model-name","matrix-factorization","--inputPath",self.inputPath,"--outputPath",self.outputPath,"--startDay",str(self.startDay),"--days",str(self.days),"--rank",str(self.rank),"--lambda",str(self.mf_lambda),"--alpha",str(self.alpha),"--iterations",str(self.iterations)] res = call(params) params = ["seldon-cli","model","--action","train","--client-name",self.client,"--model-name","matrix-factorization","--spark-executor-memory",self.sparkExecutorMemory,"--spark-driver-memory",self.sparkDriverMemory] res = call(params) return res class SeldonMatrixFactorizationClusters(luigi.Task): """ User Clustered Matrix factorization using Spark """ inputPath = luigi.Parameter(default="/seldon-data/seldon-models/") outputPath = luigi.Parameter(default="/seldon-data/seldon-models/") client = luigi.Parameter(default="test") sparkDriverMemory = luigi.Parameter(default="1g") sparkExecutorMemory = luigi.Parameter(default="1g") startDay = luigi.IntParameter(default=1) days = luigi.IntParameter(default=1) rank = luigi.IntParameter(default=30) mf_lambda = luigi.FloatParameter(default=0.01) alpha = luigi.FloatParameter(default=1) iterations = luigi.IntParameter(default=5) def output(self): return luigi.LocalTarget("{}/{}/matrix-factorization-clusters/{}".format(self.outputPath,self.client,self.startDay)) def run(self): params = ["seldon-cli","model","--action","add","--client-name",self.client,"--model-name","matrix-factorization-clusters","--inputPath",self.inputPath,"--outputPath",self.outputPath,"--startDay",str(self.startDay),"--days",str(self.days),"--rank",str(self.rank),"--lambda",str(self.mf_lambda),"--alpha",str(self.alpha),"--iterations",str(self.iterations)] res = call(params) params = ["seldon-cli","model","--action","train","--client-name",self.client,"--model-name","matrix-factorization-clusters","--spark-executor-memory",self.sparkExecutorMemory,"--spark-driver-memory",self.sparkDriverMemory] res = call(params) return res class SeldonMostPopularDim(luigi.Task): """ Most Popular by Dimension using Spark """ inputPath = luigi.Parameter(default="/seldon-data/seldon-models/") outputPath = luigi.Parameter(default="/seldon-data/seldon-models/") client = luigi.Parameter(default="test") sparkDriverMemory = luigi.Parameter(default="1g") sparkExecutorMemory = luigi.Parameter(default="1g") startDay = luigi.IntParameter(default=1) days = luigi.IntParameter(default=1) k = luigi.IntParameter(default=28) db_host = luigi.Parameter(default="mysql") db_port = luigi.IntParameter(default=3306) db_user = luigi.Parameter(default="root") db_pass = luigi.Parameter(default="mypass") def output(self): return luigi.LocalTarget("{}/{}/mostpopulardim/{}".format(self.outputPath,self.client,self.startDay)) def run(self): jdbc = "jdbc:mysql://"+self.db_host+":"+str(self.db_port)+"/"+self.client+"?characterEncoding=utf8&user="+self.db_user+"&password="+self.db_pass params = ["seldon-cli","model","--action","add","--client-name",self.client,"--model-name","mostpopulardim","--inputPath",self.inputPath,"--outputPath",self.outputPath,"--startDay",str(self.startDay),"--days",str(self.days),"--jdbc",jdbc,"--k",str(self.k)] res = call(params) params = ["seldon-cli","model","--action","train","--client-name",self.client,"--model-name","mostpopulardim","--spark-executor-memory",self.sparkExecutorMemory,"--spark-driver-memory",self.sparkDriverMemory] res = call(params) return res class MostPopularSparkJob(luigi.Task): """ Most Popular using Spark """ inputPath = luigi.Parameter(default="/seldon-data/seldon-models/") outputPath = luigi.Parameter(default="/seldon-data/seldon-models/") client = luigi.Parameter(default="test") sparkDriverMemory = luigi.Parameter(default="1g") sparkExecutorMemory = luigi.Parameter(default="1g") startDay = luigi.IntParameter(default=1) days = luigi.IntParameter(default=1) def output(self): return luigi.LocalTarget("{}/{}/mostpopular/{}".format(self.outputPath,self.client,self.startDay)) def run(self): params = ["seldon-cli","model","--action","add","--client-name",self.client,"--model-name","mostpopular","--inputPath",self.inputPath,"--outputPath",self.outputPath,"--startDay",str(self.startDay),"--days",str(self.days)] res = call(params) params = ["seldon-cli","model","--action","train","--client-name",self.client,"--model-name","mostpopular","--spark-executor-memory",self.sparkExecutorMemory,"--spark-driver-memory",self.sparkDriverMemory] res = call(params) return res class SeldonMostPopular(luigi.Task): """ Most Popular. Depends on spark job. Writes results to mysql db. """ startDay = luigi.IntParameter(default=1) client = luigi.Parameter(default="test") db_host = luigi.Parameter(default="mysql") db_user = luigi.Parameter(default="root") db_pass = luigi.Parameter(default="mypass") def requires(self): return MostPopularSparkJob(client=self.client,startDay=self.startDay) def run(self): u = MostPopularUploadMysql(self.client,self.db_host,self.db_user,self.db_pass) u.stream_and_upload(self.input().path) class SeldonSparkJob(SparkSubmitTask): """ Template for running a Spark Job """ app = "/home/seldon/libs/seldon-spark.jar" entry_class = "io.seldon.spark.mllib.SimilarItems" master = "spark://spark-master:7077" outputPath = luigi.Parameter(default="/seldon-data/seldon-models/") client = luigi.Parameter(default="test") startDay = luigi.IntParameter(default=17278) def app_options(self): return ["--client",self.client,"--zookeeper","zookeeper-1"] def output(self): return luigi.LocalTarget("{}/{}/item-similarity/{}".format(self.outputPath,self.client,self.startDay))

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6b04133a33c09306861fff8d4d2c162bef9235e7

26

py

Python

models/__init__.py

MarcAntoineAlex/DenseNAS-1

7957789aefcfaa569ae8705693b1eabce9161bcf

[ "Apache-2.0" ]

107

2020-06-15T09:55:11.000Z

2020-12-20T11:27:11.000Z

models/__init__.py

kayuksel/pytorch-GENet

e2dcda697ab04afaf88fb8f867405332bea3301b

[ "MIT" ]

7

2020-06-14T03:00:18.000Z

2020-12-07T07:10:10.000Z

models/__init__.py

kayuksel/pytorch-GENet

e2dcda697ab04afaf88fb8f867405332bea3301b

[ "MIT" ]

19

2020-06-14T08:35:33.000Z

2020-12-19T13:43:41.000Z

from .wideresnet import *

13

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79

py

Python

userSetup.py

Mikfr83/crab

d8f0c2a301017b686ace40bc3b3f74ffbc09e3ed

[ "MIT" ]

null

userSetup.py

Mikfr83/crab

d8f0c2a301017b686ace40bc3b3f74ffbc09e3ed

[ "MIT" ]

null

userSetup.py

Mikfr83/crab

d8f0c2a301017b686ace40bc3b3f74ffbc09e3ed

[ "MIT" ]

null

import maya.cmds maya.cmds.evalDeferred('import crab;crab.menu.initialize()')

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6b1af921694d4f3da65562cf4b2c89a44fc320a7

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py

Python

cmstack/cmlang/antlr_generator/lexer.py

he-actlab/cdstack

38f605cfa299bf97b5875a19f9fd811a2671d56f

[ "Apache-2.0" ]

null

cmstack/cmlang/antlr_generator/lexer.py

he-actlab/cdstack

38f605cfa299bf97b5875a19f9fd811a2671d56f

[ "Apache-2.0" ]

null

cmstack/cmlang/antlr_generator/lexer.py

he-actlab/cdstack

38f605cfa299bf97b5875a19f9fd811a2671d56f

[ "Apache-2.0" ]

null

# Generated from /home/kinzers/projects/cmstack.code/cmstack/cmlang/antlr_generator/CMLang.g4 by ANTLR 4.7.2 from antlr4 import * from io import StringIO from typing.io import TextIO import sys def serializedATN(): with StringIO() as buf: buf.write("\3\u608b\ua72a\u8133\ub9ed\u417c\u3be7\u7786\u5964\2C") buf.write("\u0217\b\1\4\2\t\2\4\3\t\3\4\4\t\4\4\5\t\5\4\6\t\6\4\7") buf.write("\t\7\4\b\t\b\4\t\t\t\4\n\t\n\4\13\t\13\4\f\t\f\4\r\t\r") buf.write("\4\16\t\16\4\17\t\17\4\20\t\20\4\21\t\21\4\22\t\22\4\23") buf.write("\t\23\4\24\t\24\4\25\t\25\4\26\t\26\4\27\t\27\4\30\t\30") buf.write("\4\31\t\31\4\32\t\32\4\33\t\33\4\34\t\34\4\35\t\35\4\36") buf.write("\t\36\4\37\t\37\4 \t \4!\t!\4\"\t\"\4#\t#\4$\t$\4%\t%") buf.write("\4&\t&\4\'\t\'\4(\t(\4)\t)\4*\t*\4+\t+\4,\t,\4-\t-\4.") buf.write("\t.\4/\t/\4\60\t\60\4\61\t\61\4\62\t\62\4\63\t\63\4\64") buf.write("\t\64\4\65\t\65\4\66\t\66\4\67\t\67\48\t8\49\t9\4:\t:") buf.write("\4;\t;\4<\t<\4=\t=\4>\t>\4?\t?\4@\t@\4A\tA\4B\tB\4C\t") buf.write("C\4D\tD\4E\tE\4F\tF\4G\tG\4H\tH\4I\tI\4J\tJ\4K\tK\4L\t") buf.write("L\4M\tM\4N\tN\3\2\3\2\3\3\3\3\3\4\3\4\3\5\3\5\3\6\3\6") buf.write("\3\7\3\7\3\b\3\b\3\t\3\t\3\n\3\n\3\13\3\13\3\f\3\f\3\r") buf.write("\3\r\3\16\3\16\3\17\3\17\3\20\3\20\3\21\3\21\3\22\3\22") buf.write("\3\22\3\22\3\23\3\23\3\23\3\23\3\23\3\23\3\24\3\24\3\24") buf.write("\3\24\3\25\3\25\3\25\3\25\3\25\3\26\3\26\3\26\3\26\3\26") buf.write("\3\26\3\26\3\26\3\27\3\27\3\27\3\27\3\27\3\27\3\30\3\30") buf.write("\3\30\3\30\3\30\3\30\3\30\3\31\3\31\3\31\3\31\3\31\3\31") buf.write("\3\32\3\32\3\32\3\32\3\32\3\32\3\33\3\33\3\33\3\33\3\33") buf.write("\3\33\3\33\3\34\3\34\3\34\3\34\3\34\3\34\3\34\3\34\3\34") buf.write("\3\34\3\35\3\35\3\35\3\35\3\35\3\35\3\35\3\35\3\35\3\35") buf.write("\3\36\3\36\3\36\3\36\3\36\3\36\3\37\3\37\3\37\3\37\3\37") buf.write("\3 \3 \3 \3!\3!\3!\3\"\3\"\3\"\3#\3#\3$\3$\3$\3$\3$\3") buf.write("$\3%\3%\3%\3%\3%\3%\3%\3&\3&\3&\3&\3&\3&\3&\3&\3&\3\'") buf.write("\3\'\3\'\3\'\3(\3(\3(\3(\3(\3(\3)\3)\3)\3)\3*\3*\3*\3") buf.write("*\3*\3*\3*\3+\3+\3+\3,\3,\3,\3,\3,\3,\3-\3-\3-\3-\3-\3") buf.write(".\3.\3.\3.\3.\3.\3.\3/\3/\3/\3\60\3\60\3\60\3\61\3\61") buf.write("\3\61\3\62\3\62\3\62\3\63\3\63\3\63\3\63\3\63\3\63\3\63") buf.write("\3\63\3\63\3\63\3\64\3\64\3\64\3\65\3\65\3\66\3\66\7\66") buf.write("\u0180\n\66\f\66\16\66\u0183\13\66\3\66\3\66\3\67\3\67") buf.write("\3\67\7\67\u018a\n\67\f\67\16\67\u018d\13\67\38\38\78") buf.write("\u0191\n8\f8\168\u0194\138\38\68\u0197\n8\r8\168\u0198") buf.write("\58\u019b\n8\39\39\39\69\u01a0\n9\r9\169\u01a1\3:\3:\3") buf.write(":\6:\u01a7\n:\r:\16:\u01a8\3;\3;\3;\6;\u01ae\n;\r;\16") buf.write(";\u01af\3<\3<\5<\u01b4\n<\3<\3<\3=\3=\5=\u01ba\n=\3>\3") buf.write(">\3?\6?\u01bf\n?\r?\16?\u01c0\3?\3?\3@\3@\5@\u01c7\n@") buf.write("\3@\5@\u01ca\n@\3@\3@\3A\3A\3A\3A\7A\u01d2\nA\fA\16A\u01d5") buf.write("\13A\3A\3A\3A\3A\3A\3B\3B\3B\3B\7B\u01e0\nB\fB\16B\u01e3") buf.write("\13B\3B\3B\3C\3C\3D\3D\3E\3E\3F\3F\3G\3G\3H\3H\3I\5I\u01f4") buf.write("\nI\3I\3I\3I\3I\5I\u01fa\nI\3J\3J\5J\u01fe\nJ\3J\3J\3") buf.write("K\6K\u0203\nK\rK\16K\u0204\3L\3L\6L\u0209\nL\rL\16L\u020a") buf.write("\3M\3M\5M\u020f\nM\3M\6M\u0212\nM\rM\16M\u0213\3N\3N\4") buf.write("\u0181\u01d3\2O\3\3\5\4\7\5\t\6\13\7\r\b\17\t\21\n\23") buf.write("\13\25\f\27\r\31\16\33\17\35\20\37\21!\22#\23%\24\'\25") buf.write(")\26+\27-\30/\31\61\32\63\33\65\34\67\359\36;\37= ?!A") buf.write("\"C#E$G%I&K\'M(O)Q*S+U,W-Y.[/]\60_\61a\62c\63e\64g\65") buf.write("i\66k\67m8o9q:s;u<w=y>{?}@\177A\u0081B\u0083C\u0085\2") buf.write("\u0087\2\u0089\2\u008b\2\u008d\2\u008f\2\u0091\2\u0093") buf.write("\2\u0095\2\u0097\2\u0099\2\u009b\2\3\2\17\4\2QQqq\4\2") buf.write("ZZzz\4\2DDdd\4\2\13\13\"\"\4\2\f\f\17\17\5\2C\\aac|\3") buf.write("\2\63;\3\2\62;\3\2\629\5\2\62;CHch\3\2\62\63\4\2GGgg\4") buf.write("\2--//\2\u0221\2\3\3\2\2\2\2\5\3\2\2\2\2\7\3\2\2\2\2\t") buf.write("\3\2\2\2\2\13\3\2\2\2\2\r\3\2\2\2\2\17\3\2\2\2\2\21\3") buf.write("\2\2\2\2\23\3\2\2\2\2\25\3\2\2\2\2\27\3\2\2\2\2\31\3\2") buf.write("\2\2\2\33\3\2\2\2\2\35\3\2\2\2\2\37\3\2\2\2\2!\3\2\2\2") buf.write("\2#\3\2\2\2\2%\3\2\2\2\2\'\3\2\2\2\2)\3\2\2\2\2+\3\2\2") buf.write("\2\2-\3\2\2\2\2/\3\2\2\2\2\61\3\2\2\2\2\63\3\2\2\2\2\65") buf.write("\3\2\2\2\2\67\3\2\2\2\29\3\2\2\2\2;\3\2\2\2\2=\3\2\2\2") buf.write("\2?\3\2\2\2\2A\3\2\2\2\2C\3\2\2\2\2E\3\2\2\2\2G\3\2\2") buf.write("\2\2I\3\2\2\2\2K\3\2\2\2\2M\3\2\2\2\2O\3\2\2\2\2Q\3\2") buf.write("\2\2\2S\3\2\2\2\2U\3\2\2\2\2W\3\2\2\2\2Y\3\2\2\2\2[\3") buf.write("\2\2\2\2]\3\2\2\2\2_\3\2\2\2\2a\3\2\2\2\2c\3\2\2\2\2e") buf.write("\3\2\2\2\2g\3\2\2\2\2i\3\2\2\2\2k\3\2\2\2\2m\3\2\2\2\2") buf.write("o\3\2\2\2\2q\3\2\2\2\2s\3\2\2\2\2u\3\2\2\2\2w\3\2\2\2") buf.write("\2y\3\2\2\2\2{\3\2\2\2\2}\3\2\2\2\2\177\3\2\2\2\2\u0081") buf.write("\3\2\2\2\2\u0083\3\2\2\2\3\u009d\3\2\2\2\5\u009f\3\2\2") buf.write("\2\7\u00a1\3\2\2\2\t\u00a3\3\2\2\2\13\u00a5\3\2\2\2\r") buf.write("\u00a7\3\2\2\2\17\u00a9\3\2\2\2\21\u00ab\3\2\2\2\23\u00ad") buf.write("\3\2\2\2\25\u00af\3\2\2\2\27\u00b1\3\2\2\2\31\u00b3\3") buf.write("\2\2\2\33\u00b5\3\2\2\2\35\u00b7\3\2\2\2\37\u00b9\3\2") buf.write("\2\2!\u00bb\3\2\2\2#\u00bd\3\2\2\2%\u00c1\3\2\2\2\'\u00c7") buf.write("\3\2\2\2)\u00cb\3\2\2\2+\u00d0\3\2\2\2-\u00d8\3\2\2\2") buf.write("/\u00de\3\2\2\2\61\u00e5\3\2\2\2\63\u00eb\3\2\2\2\65\u00f1") buf.write("\3\2\2\2\67\u00f8\3\2\2\29\u0102\3\2\2\2;\u010c\3\2\2") buf.write("\2=\u0112\3\2\2\2?\u0117\3\2\2\2A\u011a\3\2\2\2C\u011d") buf.write("\3\2\2\2E\u0120\3\2\2\2G\u0122\3\2\2\2I\u0128\3\2\2\2") buf.write("K\u012f\3\2\2\2M\u0138\3\2\2\2O\u013c\3\2\2\2Q\u0142\3") buf.write("\2\2\2S\u0146\3\2\2\2U\u014d\3\2\2\2W\u0150\3\2\2\2Y\u0156") buf.write("\3\2\2\2[\u015b\3\2\2\2]\u0162\3\2\2\2_\u0165\3\2\2\2") buf.write("a\u0168\3\2\2\2c\u016b\3\2\2\2e\u016e\3\2\2\2g\u0178\3") buf.write("\2\2\2i\u017b\3\2\2\2k\u017d\3\2\2\2m\u0186\3\2\2\2o\u019a") buf.write("\3\2\2\2q\u019c\3\2\2\2s\u01a3\3\2\2\2u\u01aa\3\2\2\2") buf.write("w\u01b3\3\2\2\2y\u01b9\3\2\2\2{\u01bb\3\2\2\2}\u01be\3") buf.write("\2\2\2\177\u01c9\3\2\2\2\u0081\u01cd\3\2\2\2\u0083\u01db") buf.write("\3\2\2\2\u0085\u01e6\3\2\2\2\u0087\u01e8\3\2\2\2\u0089") buf.write("\u01ea\3\2\2\2\u008b\u01ec\3\2\2\2\u008d\u01ee\3\2\2\2") buf.write("\u008f\u01f0\3\2\2\2\u0091\u01f9\3\2\2\2\u0093\u01fd\3") buf.write("\2\2\2\u0095\u0202\3\2\2\2\u0097\u0206\3\2\2\2\u0099\u020c") buf.write("\3\2\2\2\u009b\u0215\3\2\2\2\u009d\u009e\7*\2\2\u009e") buf.write("\4\3\2\2\2\u009f\u00a0\7+\2\2\u00a0\6\3\2\2\2\u00a1\u00a2") buf.write("\7}\2\2\u00a2\b\3\2\2\2\u00a3\u00a4\7\177\2\2\u00a4\n") buf.write("\3\2\2\2\u00a5\u00a6\7.\2\2\u00a6\f\3\2\2\2\u00a7\u00a8") buf.write("\7]\2\2\u00a8\16\3\2\2\2\u00a9\u00aa\7_\2\2\u00aa\20\3") buf.write("\2\2\2\u00ab\u00ac\7<\2\2\u00ac\22\3\2\2\2\u00ad\u00ae") 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buf.write("\2\2\2\u00d8\u00d9\7k\2\2\u00d9\u00da\7p\2\2\u00da\u00db") buf.write("\7r\2\2\u00db\u00dc\7w\2\2\u00dc\u00dd\7v\2\2\u00dd.\3") buf.write("\2\2\2\u00de\u00df\7q\2\2\u00df\u00e0\7w\2\2\u00e0\u00e1") buf.write("\7v\2\2\u00e1\u00e2\7r\2\2\u00e2\u00e3\7w\2\2\u00e3\u00e4") buf.write("\7v\2\2\u00e4\60\3\2\2\2\u00e5\u00e6\7u\2\2\u00e6\u00e7") buf.write("\7v\2\2\u00e7\u00e8\7c\2\2\u00e8\u00e9\7v\2\2\u00e9\u00ea") buf.write("\7g\2\2\u00ea\62\3\2\2\2\u00eb\u00ec\7r\2\2\u00ec\u00ed") buf.write("\7c\2\2\u00ed\u00ee\7t\2\2\u00ee\u00ef\7c\2\2\u00ef\u00f0") buf.write("\7o\2\2\u00f0\64\3\2\2\2\u00f1\u00f2\7u\2\2\u00f2\u00f3") buf.write("\7r\2\2\u00f3\u00f4\7t\2\2\u00f4\u00f5\7k\2\2\u00f5\u00f6") buf.write("\7p\2\2\u00f6\u00f7\7i\2\2\u00f7\66\3\2\2\2\u00f8\u00f9") buf.write("\7t\2\2\u00f9\u00fa\7g\2\2\u00fa\u00fb\7u\2\2\u00fb\u00fc") buf.write("\7g\2\2\u00fc\u00fd\7t\2\2\u00fd\u00fe\7x\2\2\u00fe\u00ff") buf.write("\7q\2\2\u00ff\u0100\7k\2\2\u0100\u0101\7t\2\2\u01018\3") 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buf.write("\2\2\2\u0204\u0205\3\2\2\2\u0205\u0096\3\2\2\2\u0206\u0208") buf.write("\7\60\2\2\u0207\u0209\5\u0089E\2\u0208\u0207\3\2\2\2\u0209") buf.write("\u020a\3\2\2\2\u020a\u0208\3\2\2\2\u020a\u020b\3\2\2\2") buf.write("\u020b\u0098\3\2\2\2\u020c\u020e\t\r\2\2\u020d\u020f\t") buf.write("\16\2\2\u020e\u020d\3\2\2\2\u020e\u020f\3\2\2\2\u020f") buf.write("\u0211\3\2\2\2\u0210\u0212\5\u0089E\2\u0211\u0210\3\2") buf.write("\2\2\u0212\u0213\3\2\2\2\u0213\u0211\3\2\2\2\u0213\u0214") buf.write("\3\2\2\2\u0214\u009a\3\2\2\2\u0215\u0216\t\16\2\2\u0216") buf.write("\u009c\3\2\2\2\32\2\u0181\u0189\u018b\u0192\u0198\u019a") buf.write("\u01a1\u01a8\u01af\u01b3\u01b9\u01c0\u01c6\u01c9\u01d3") buf.write("\u01e1\u01f3\u01f9\u01fd\u0204\u020a\u020e\u0213\3\b\2") buf.write("\2") return buf.getvalue() class CMLangLexer(Lexer): atn = ATNDeserializer().deserialize(serializedATN()) decisionsToDFA = [ DFA(ds, i) for i, ds in enumerate(atn.decisionToState) ] T__0 = 1 T__1 = 2 T__2 = 3 T__3 = 4 T__4 = 5 T__5 = 6 T__6 = 7 T__7 = 8 T__8 = 9 T__9 = 10 T__10 = 11 T__11 = 12 T__12 = 13 T__13 = 14 T__14 = 15 T__15 = 16 T__16 = 17 T__17 = 18 T__18 = 19 T__19 = 20 T__20 = 21 INPUT = 22 OUTPUT = 23 STATE = 24 PARAMETER = 25 SPRING = 26 RESERVOIR = 27 COMPONENT = 28 INDEX = 29 FLOW = 30 ARRAYMUL = 31 ARRAYDIV = 32 ARRAYRDIV = 33 POW = 34 BREAK = 35 RETURN = 36 FUNCTION = 37 FOR = 38 WHILE = 39 END = 40 GLOBAL = 41 IF = 42 CLEAR = 43 ELSE = 44 ELSEIF = 45 LE_OP = 46 GE_OP = 47 EQ_OP = 48 NE_OP = 49 TRANSPOSE = 50 NCTRANSPOSE = 51 SEMI = 52 STRING_LITERAL = 53 IDENTIFIER = 54 DECIMAL_INTEGER = 55 OCT_INTEGER = 56 HEX_INTEGER = 57 BIN_INTEGER = 58 IMAG_NUMBER = 59 FLOAT_NUMBER = 60 EQ = 61 WHITESPACE = 62 NEWLINE = 63 BLOCKCOMMENT = 64 LINECOMMENT = 65 channelNames = [ u"DEFAULT_TOKEN_CHANNEL", u"HIDDEN" ] modeNames = [ "DEFAULT_MODE" ] literalNames = [ "<INVALID>", "'('", "')'", "'{'", "'}'", "','", "'['", "']'", "':'", "'+'", "'-'", "'*'", "'/'", "'%'", "'<'", "'>'", "'?'", "'int'", "'float'", "'str'", "'bool'", "'complex'", "'input'", "'output'", "'state'", "'param'", "'spring'", "'reservoir'", "'component'", "'index'", "'flow'", "'.*'", "'.\\'", "'./'", "'^'", "'break'", "'return'", "'function'", "'for'", "'while'", "'end'", "'global'", "'if'", "'clear'", "'else'", "'elseif'", "'<='", "'>='", "'=='", "'!='", "'transpose'", "'.''", "';'", "'='" ] symbolicNames = [ "<INVALID>", "INPUT", "OUTPUT", "STATE", "PARAMETER", "SPRING", "RESERVOIR", "COMPONENT", "INDEX", "FLOW", "ARRAYMUL", "ARRAYDIV", "ARRAYRDIV", "POW", "BREAK", "RETURN", "FUNCTION", "FOR", "WHILE", "END", "GLOBAL", "IF", "CLEAR", "ELSE", "ELSEIF", "LE_OP", "GE_OP", "EQ_OP", "NE_OP", "TRANSPOSE", "NCTRANSPOSE", "SEMI", "STRING_LITERAL", "IDENTIFIER", "DECIMAL_INTEGER", "OCT_INTEGER", "HEX_INTEGER", "BIN_INTEGER", "IMAG_NUMBER", "FLOAT_NUMBER", "EQ", "WHITESPACE", "NEWLINE", "BLOCKCOMMENT", "LINECOMMENT" ] ruleNames = [ "T__0", "T__1", "T__2", "T__3", "T__4", "T__5", "T__6", "T__7", "T__8", "T__9", "T__10", "T__11", "T__12", "T__13", "T__14", "T__15", "T__16", "T__17", "T__18", "T__19", "T__20", "INPUT", "OUTPUT", "STATE", "PARAMETER", "SPRING", "RESERVOIR", "COMPONENT", "INDEX", "FLOW", "ARRAYMUL", "ARRAYDIV", "ARRAYRDIV", "POW", "BREAK", "RETURN", "FUNCTION", "FOR", "WHILE", "END", "GLOBAL", "IF", "CLEAR", "ELSE", "ELSEIF", "LE_OP", "GE_OP", "EQ_OP", "NE_OP", "TRANSPOSE", "NCTRANSPOSE", "SEMI", "STRING_LITERAL", "IDENTIFIER", "DECIMAL_INTEGER", "OCT_INTEGER", "HEX_INTEGER", "BIN_INTEGER", "IMAG_NUMBER", "FLOAT_NUMBER", "EQ", "WHITESPACE", "NEWLINE", "BLOCKCOMMENT", "LINECOMMENT", "NONDIGIT", "NON_ZERO_DIGIT", "DIGIT", "OCT_DIGIT", "HEX_DIGIT", "BIN_DIGIT", "POINT_FLOAT", "EXPONENT_FLOAT", "INT_PART", "FRACTION", "EXPONENT", "SIGN" ] grammarFileName = "CMLang.g4" def __init__(self, input=None, output:TextIO = sys.stdout): super().__init__(input, output) self.checkVersion("4.7.2") self._interp = LexerATNSimulator(self, self.atn, self.decisionsToDFA, PredictionContextCache()) self._actions = None self._predicates = None

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6

8628c79257b2a142662b9fcc9f2c67dcec552c6d

167

py

Python

api/models.py

zpx01/react-movie-rating

6869792d30fe8bf249bb7780f03ac1fbda4db3eb

[ "MIT" ]

null

api/models.py

zpx01/react-movie-rating

6869792d30fe8bf249bb7780f03ac1fbda4db3eb

[ "MIT" ]

null

api/models.py

zpx01/react-movie-rating

6869792d30fe8bf249bb7780f03ac1fbda4db3eb

[ "MIT" ]

null

from . import db class Movie(db.Model): id = db.Column(db.Integer, primary_key=True) title = db.Column(db.String(50)) rating = db.Column(db.Integer)

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py

Python

aiida/backends/sqlalchemy/migrations/versions/e15ef2630a1b_initial_schema.py

joepvd/aiida_core

6e9711046753332933f982971db1d7ac7e7ade58

[ "BSD-2-Clause" ]

1

2019-03-15T10:37:53.000Z

aiida/backends/sqlalchemy/migrations/versions/e15ef2630a1b_initial_schema.py

odarbelaeze/aiida_core

934b4ccdc73a993f2a6656caf516500470e3da08

[ "BSD-2-Clause" ]

null

aiida/backends/sqlalchemy/migrations/versions/e15ef2630a1b_initial_schema.py

odarbelaeze/aiida_core

934b4ccdc73a993f2a6656caf516500470e3da08

[ "BSD-2-Clause" ]

null

# -*- coding: utf-8 -*- ########################################################################### # Copyright (c), The AiiDA team. All rights reserved. # # This file is part of the AiiDA code. # # # # The code is hosted on GitHub at https://github.com/aiidateam/aiida_core # # For further information on the license, see the LICENSE.txt file # # For further information please visit http://www.aiida.net # ########################################################################### """Initial schema Revision ID: e15ef2630a1b Revises: Create Date: 2017-06-28 17:12:23.327195 """ from __future__ import division from __future__ import print_function from __future__ import absolute_import from alembic import op import sqlalchemy as sa from sqlalchemy.dialects import postgresql from sqlalchemy.orm.session import Session from aiida.backends.sqlalchemy.utils import install_tc # revision identifiers, used by Alembic. revision = 'e15ef2630a1b' down_revision = None branch_labels = None depends_on = None def upgrade(): op.create_table('db_dbuser', sa.Column('id', sa.INTEGER(), nullable=False), sa.Column('email', sa.VARCHAR(length=254), autoincrement=False, nullable=True), sa.Column('password', sa.VARCHAR(length=128), autoincrement=False, nullable=True), sa.Column('is_superuser', sa.BOOLEAN(), autoincrement=False, nullable=False), sa.Column('first_name', sa.VARCHAR(length=254), autoincrement=False, nullable=True), sa.Column('last_name', sa.VARCHAR(length=254), autoincrement=False, nullable=True), sa.Column('institution', sa.VARCHAR(length=254), autoincrement=False, nullable=True), sa.Column('is_staff', sa.BOOLEAN(), autoincrement=False, nullable=True), sa.Column('is_active', sa.BOOLEAN(), autoincrement=False, nullable=True), sa.Column('last_login', postgresql.TIMESTAMP(timezone=True), autoincrement=False, nullable=True), sa.Column('date_joined', postgresql.TIMESTAMP(timezone=True), autoincrement=False, nullable=True), sa.PrimaryKeyConstraint('id', name=u'db_dbuser_pkey'), postgresql_ignore_search_path=False ) op.create_index('ix_db_dbuser_email', 'db_dbuser', ['email'], unique=True) op.create_table('db_dbworkflow', sa.Column('id', sa.INTEGER(), nullable=False), sa.Column('uuid', postgresql.UUID(), autoincrement=False, nullable=True), sa.Column('ctime', postgresql.TIMESTAMP(timezone=True), autoincrement=False, nullable=True), sa.Column('mtime', postgresql.TIMESTAMP(timezone=True), autoincrement=False, nullable=True), sa.Column('user_id', sa.INTEGER(), autoincrement=False, nullable=True), sa.Column('label', sa.VARCHAR(length=255), autoincrement=False, nullable=True), sa.Column('description', sa.TEXT(), autoincrement=False, nullable=True), sa.Column('nodeversion', sa.INTEGER(), autoincrement=False, nullable=True), sa.Column('lastsyncedversion', sa.INTEGER(), autoincrement=False, nullable=True), sa.Column('state', sa.VARCHAR(length=255), autoincrement=False, nullable=True), sa.Column('report', sa.TEXT(), autoincrement=False, nullable=True), sa.Column('module', sa.TEXT(), autoincrement=False, nullable=True), sa.Column('module_class', sa.TEXT(), autoincrement=False, nullable=True), sa.Column('script_path', sa.TEXT(), autoincrement=False, nullable=True), sa.Column('script_md5', sa.VARCHAR(length=255), autoincrement=False, nullable=True), sa.ForeignKeyConstraint(['user_id'], [u'db_dbuser.id'], name=u'db_dbworkflow_user_id_fkey'), sa.PrimaryKeyConstraint('id', name=u'db_dbworkflow_pkey'), postgresql_ignore_search_path=False ) op.create_index('ix_db_dbworkflow_label', 'db_dbworkflow', ['label']) op.create_table('db_dbworkflowstep', sa.Column('id', sa.INTEGER(), nullable=False), sa.Column('parent_id', sa.INTEGER(), autoincrement=False, nullable=True), sa.Column('user_id', sa.INTEGER(), autoincrement=False, nullable=True), sa.Column('name', sa.VARCHAR(length=255), autoincrement=False, nullable=True), sa.Column('time', postgresql.TIMESTAMP(timezone=True), autoincrement=False, nullable=True), sa.Column('nextcall', sa.VARCHAR(length=255), autoincrement=False, nullable=True), sa.Column('state', sa.VARCHAR(length=255), autoincrement=False, nullable=True), sa.ForeignKeyConstraint(['parent_id'], [u'db_dbworkflow.id'], name=u'db_dbworkflowstep_parent_id_fkey'), sa.ForeignKeyConstraint(['user_id'], [u'db_dbuser.id'], name=u'db_dbworkflowstep_user_id_fkey'), sa.PrimaryKeyConstraint('id', name=u'db_dbworkflowstep_pkey'), sa.UniqueConstraint('parent_id', 'name', name=u'db_dbworkflowstep_parent_id_name_key'), postgresql_ignore_search_path=False ) op.create_table('db_dbcomputer', sa.Column('id', sa.INTEGER(), nullable=False), sa.Column('uuid', postgresql.UUID(), autoincrement=False, nullable=True), sa.Column('name', sa.VARCHAR(length=255), autoincrement=False, nullable=False), sa.Column('hostname', sa.VARCHAR(length=255), autoincrement=False, nullable=True), sa.Column('description', sa.TEXT(), autoincrement=False, nullable=True), sa.Column('enabled', sa.BOOLEAN(), autoincrement=False, nullable=True), sa.Column('transport_type', sa.VARCHAR(length=255), autoincrement=False, nullable=True), sa.Column('scheduler_type', sa.VARCHAR(length=255), autoincrement=False, nullable=True), sa.Column('transport_params', postgresql.JSONB(), autoincrement=False, nullable=True), sa.Column('metadata', postgresql.JSONB(), autoincrement=False, nullable=True), sa.PrimaryKeyConstraint('id', name=u'db_dbcomputer_pkey'), sa.UniqueConstraint('name', name=u'db_dbcomputer_name_key') ) op.create_table('db_dbauthinfo', sa.Column('id', sa.INTEGER(), nullable=False), sa.Column('aiidauser_id', sa.INTEGER(), autoincrement=False, nullable=True), sa.Column('dbcomputer_id', sa.INTEGER(), autoincrement=False, nullable=True), sa.Column('metadata', postgresql.JSONB(), autoincrement=False, nullable=True), sa.Column('auth_params', postgresql.JSONB(), autoincrement=False, nullable=True), sa.Column('enabled', sa.BOOLEAN(), autoincrement=False, nullable=True), sa.ForeignKeyConstraint(['aiidauser_id'], [u'db_dbuser.id'], name=u'db_dbauthinfo_aiidauser_id_fkey', ondelete=u'CASCADE', initially=u'DEFERRED', deferrable=True), sa.ForeignKeyConstraint(['dbcomputer_id'], [u'db_dbcomputer.id'], name=u'db_dbauthinfo_dbcomputer_id_fkey', ondelete=u'CASCADE', initially=u'DEFERRED', deferrable=True), sa.PrimaryKeyConstraint('id', name=u'db_dbauthinfo_pkey'), sa.UniqueConstraint('aiidauser_id', 'dbcomputer_id', name=u'db_dbauthinfo_aiidauser_id_dbcomputer_id_key') ) op.create_table('db_dbgroup', sa.Column('id', sa.INTEGER(), nullable=False), sa.Column('uuid', postgresql.UUID(), autoincrement=False, nullable=True), sa.Column('name', sa.VARCHAR(length=255), autoincrement=False, nullable=True), sa.Column('type', sa.VARCHAR(length=255), autoincrement=False, nullable=True), sa.Column('time', postgresql.TIMESTAMP(timezone=True), autoincrement=False, nullable=True), sa.Column('description', sa.TEXT(), autoincrement=False, nullable=True), sa.Column('user_id', sa.INTEGER(), autoincrement=False, nullable=True), sa.ForeignKeyConstraint(['user_id'], [u'db_dbuser.id'], name=u'db_dbgroup_user_id_fkey', ondelete=u'CASCADE', initially=u'DEFERRED', deferrable=True), sa.PrimaryKeyConstraint('id', name=u'db_dbgroup_pkey'), sa.UniqueConstraint('name', 'type', name=u'db_dbgroup_name_type_key') ) op.create_index('ix_db_dbgroup_name', 'db_dbgroup', ['name']) op.create_index('ix_db_dbgroup_type', 'db_dbgroup', ['type']) op.create_table('db_dbnode', sa.Column('id', sa.INTEGER(), nullable=False), sa.Column('uuid', postgresql.UUID(), autoincrement=False, nullable=True), sa.Column('type', sa.VARCHAR(length=255), autoincrement=False, nullable=True), sa.Column('label', sa.VARCHAR(length=255), autoincrement=False, nullable=True), sa.Column('description', sa.TEXT(), autoincrement=False, nullable=True), sa.Column('ctime', postgresql.TIMESTAMP(timezone=True), autoincrement=False, nullable=True), sa.Column('mtime', postgresql.TIMESTAMP(timezone=True), autoincrement=False, nullable=True), sa.Column('nodeversion', sa.INTEGER(), autoincrement=False, nullable=True), sa.Column('public', sa.BOOLEAN(), autoincrement=False, nullable=True), sa.Column('attributes', postgresql.JSONB(), autoincrement=False, nullable=True), sa.Column('extras', postgresql.JSONB(), autoincrement=False, nullable=True), sa.Column('dbcomputer_id', sa.INTEGER(), autoincrement=False, nullable=True), sa.Column('user_id', sa.INTEGER(), autoincrement=False, nullable=False), sa.ForeignKeyConstraint(['dbcomputer_id'], [u'db_dbcomputer.id'], name=u'db_dbnode_dbcomputer_id_fkey', ondelete=u'RESTRICT', initially=u'DEFERRED', deferrable=True), sa.ForeignKeyConstraint(['user_id'], [u'db_dbuser.id'], name=u'db_dbnode_user_id_fkey', ondelete=u'RESTRICT', initially=u'DEFERRED', deferrable=True), sa.PrimaryKeyConstraint('id', name=u'db_dbnode_pkey'),postgresql_ignore_search_path=False ) op.create_index('ix_db_dbnode_label', 'db_dbnode', ['label']) op.create_index('ix_db_dbnode_type', 'db_dbnode', ['type']) op.create_table('db_dbgroup_dbnodes', sa.Column('id', sa.INTEGER(), nullable=False), sa.Column('dbnode_id', sa.INTEGER(), autoincrement=False, nullable=True), sa.Column('dbgroup_id', sa.INTEGER(), autoincrement=False, nullable=True), sa.ForeignKeyConstraint(['dbgroup_id'], [u'db_dbgroup.id'], name=u'db_dbgroup_dbnodes_dbgroup_id_fkey', initially=u'DEFERRED', deferrable=True), sa.ForeignKeyConstraint(['dbnode_id'], [u'db_dbnode.id'], name=u'db_dbgroup_dbnodes_dbnode_id_fkey', initially=u'DEFERRED', deferrable=True), sa.PrimaryKeyConstraint('id', name=u'db_dbgroup_dbnodes_pkey') ) op.create_table('db_dblock', sa.Column('key', sa.VARCHAR(length=255), autoincrement=False, nullable=False), sa.Column('creation', postgresql.TIMESTAMP(timezone=True), autoincrement=False, nullable=True), sa.Column('timeout', sa.INTEGER(), autoincrement=False, nullable=True), sa.Column('owner', sa.VARCHAR(length=255), autoincrement=False, nullable=True), sa.PrimaryKeyConstraint('key', name=u'db_dblock_pkey') ) op.create_table('db_dbworkflowdata', sa.Column('id', sa.INTEGER(), nullable=False), sa.Column('parent_id', sa.INTEGER(), autoincrement=False, nullable=True), sa.Column('name', sa.VARCHAR(length=255), autoincrement=False, nullable=True), sa.Column('time', postgresql.TIMESTAMP(timezone=True), autoincrement=False, nullable=True), sa.Column('data_type', sa.VARCHAR(length=255), autoincrement=False, nullable=True), sa.Column('value_type', sa.VARCHAR(length=255), autoincrement=False, nullable=True), sa.Column('json_value', sa.TEXT(), autoincrement=False, nullable=True), sa.Column('aiida_obj_id', sa.INTEGER(), autoincrement=False, nullable=True), sa.ForeignKeyConstraint(['aiida_obj_id'], [u'db_dbnode.id'], name=u'db_dbworkflowdata_aiida_obj_id_fkey'), sa.ForeignKeyConstraint(['parent_id'], [u'db_dbworkflow.id'], name=u'db_dbworkflowdata_parent_id_fkey'), sa.PrimaryKeyConstraint('id', name=u'db_dbworkflowdata_pkey'), sa.UniqueConstraint('parent_id', 'name', 'data_type', name=u'db_dbworkflowdata_parent_id_name_data_type_key') ) op.create_table('db_dblink', sa.Column('id', sa.INTEGER(), nullable=False), sa.Column('input_id', sa.INTEGER(), autoincrement=False, nullable=True), sa.Column('output_id', sa.INTEGER(), autoincrement=False, nullable=True), sa.Column('label', sa.VARCHAR(length=255), autoincrement=False, nullable=False), sa.Column('type', sa.VARCHAR(length=255), autoincrement=False, nullable=True), sa.ForeignKeyConstraint(['input_id'], [u'db_dbnode.id'], name=u'db_dblink_input_id_fkey', initially=u'DEFERRED', deferrable=True), sa.ForeignKeyConstraint(['output_id'], [u'db_dbnode.id'], name=u'db_dblink_output_id_fkey', ondelete=u'CASCADE', initially=u'DEFERRED', deferrable=True), sa.PrimaryKeyConstraint('id', name=u'db_dblink_pkey'), ) op.create_index('ix_db_dblink_label', 'db_dblink', ['label']) op.create_table('db_dbworkflowstep_calculations', sa.Column('id', sa.INTEGER(), nullable=False), sa.Column('dbworkflowstep_id', sa.INTEGER(), autoincrement=False, nullable=True), sa.Column('dbnode_id', sa.INTEGER(), autoincrement=False, nullable=True), sa.ForeignKeyConstraint(['dbnode_id'], [u'db_dbnode.id'], name=u'db_dbworkflowstep_calculations_dbnode_id_fkey'), sa.ForeignKeyConstraint(['dbworkflowstep_id'], [u'db_dbworkflowstep.id'], name=u'db_dbworkflowstep_calculations_dbworkflowstep_id_fkey'), sa.PrimaryKeyConstraint('id', name=u'db_dbworkflowstep_calculations_pkey'), sa.UniqueConstraint('dbworkflowstep_id', 'dbnode_id', name=u'db_dbworkflowstep_calculations_dbworkflowstep_id_dbnode_id_key') ) op.create_table('db_dbpath', sa.Column('id', sa.INTEGER(), nullable=False), sa.Column('parent_id', sa.INTEGER(), autoincrement=False, nullable=True), sa.Column('child_id', sa.INTEGER(), autoincrement=False, nullable=True), sa.Column('depth', sa.INTEGER(), autoincrement=False, nullable=True), sa.Column('entry_edge_id', sa.INTEGER(), autoincrement=False, nullable=True), sa.Column('direct_edge_id', sa.INTEGER(), autoincrement=False, nullable=True), sa.Column('exit_edge_id', sa.INTEGER(), autoincrement=False, nullable=True), sa.ForeignKeyConstraint(['child_id'], [u'db_dbnode.id'], name=u'db_dbpath_child_id_fkey', initially=u'DEFERRED', deferrable=True), sa.ForeignKeyConstraint(['parent_id'], [u'db_dbnode.id'], name=u'db_dbpath_parent_id_fkey', initially=u'DEFERRED', deferrable=True), sa.PrimaryKeyConstraint('id', name=u'db_dbpath_pkey') ) op.create_table('db_dbcalcstate', sa.Column('id', sa.INTEGER(), nullable=False), sa.Column('dbnode_id', sa.INTEGER(), autoincrement=False, nullable=True), sa.Column('state', sa.VARCHAR(length=255), autoincrement=False, nullable=True), sa.Column('time', postgresql.TIMESTAMP(timezone=True), autoincrement=False, nullable=True), sa.ForeignKeyConstraint(['dbnode_id'], [u'db_dbnode.id'], name=u'db_dbcalcstate_dbnode_id_fkey', ondelete=u'CASCADE', initially=u'DEFERRED', deferrable=True), sa.PrimaryKeyConstraint('id', name=u'db_dbcalcstate_pkey'), sa.UniqueConstraint('dbnode_id', 'state', name=u'db_dbcalcstate_dbnode_id_state_key') ) op.create_index('ix_db_dbcalcstate_state', 'db_dbcalcstate', ['state']) op.create_table('db_dbsetting', sa.Column('id', sa.INTEGER(), nullable=False), sa.Column('key', sa.VARCHAR(length=255), autoincrement=False, nullable=False), sa.Column('val', postgresql.JSONB(), autoincrement=False, nullable=True), sa.Column('description', sa.VARCHAR(length=255), autoincrement=False, nullable=False), sa.Column('time', postgresql.TIMESTAMP(timezone=True), autoincrement=False, nullable=True), sa.PrimaryKeyConstraint('id', name=u'db_dbsetting_pkey'), sa.UniqueConstraint('key', name=u'db_dbsetting_key_key') ) op.create_index('ix_db_dbsetting_key', 'db_dbsetting', ['key']) op.create_table('db_dbcomment', sa.Column('id', sa.INTEGER(), nullable=False), sa.Column('uuid', postgresql.UUID(), autoincrement=False, nullable=True), sa.Column('dbnode_id', sa.INTEGER(), autoincrement=False, nullable=True), sa.Column('ctime', postgresql.TIMESTAMP(timezone=True), autoincrement=False, nullable=True), sa.Column('mtime', postgresql.TIMESTAMP(timezone=True), autoincrement=False, nullable=True), sa.Column('user_id', sa.INTEGER(), autoincrement=False, nullable=True), sa.Column('content', sa.TEXT(), autoincrement=False, nullable=True), sa.ForeignKeyConstraint(['dbnode_id'], [u'db_dbnode.id'], name=u'db_dbcomment_dbnode_id_fkey', ondelete=u'CASCADE', initially=u'DEFERRED', deferrable=True), sa.ForeignKeyConstraint(['user_id'], [u'db_dbuser.id'], name=u'db_dbcomment_user_id_fkey', ondelete=u'CASCADE', initially=u'DEFERRED', deferrable=True), sa.PrimaryKeyConstraint('id', name=u'db_dbcomment_pkey') ) op.create_table('db_dblog', sa.Column('id', sa.INTEGER(), nullable=False), sa.Column('time', postgresql.TIMESTAMP(timezone=True), autoincrement=False, nullable=True), sa.Column('loggername', sa.VARCHAR(length=255), autoincrement=False, nullable=True), sa.Column('levelname', sa.VARCHAR(length=255), autoincrement=False, nullable=True), sa.Column('objname', sa.VARCHAR(length=255), autoincrement=False, nullable=True), sa.Column('objpk', sa.INTEGER(), autoincrement=False, nullable=True), sa.Column('message', sa.TEXT(), autoincrement=False, nullable=True), sa.Column('metadata', postgresql.JSONB(), autoincrement=False, nullable=True), sa.PrimaryKeyConstraint('id', name=u'db_dblog_pkey') ) op.create_index('ix_db_dblog_levelname', 'db_dblog', ['levelname']) op.create_index('ix_db_dblog_loggername', 'db_dblog', ['loggername']) op.create_index('ix_db_dblog_objname', 'db_dblog', ['objname']) op.create_index('ix_db_dblog_objpk', 'db_dblog', ['objpk']) op.create_table('db_dbworkflowstep_sub_workflows', sa.Column('id', sa.INTEGER(), nullable=False), sa.Column('dbworkflowstep_id', sa.INTEGER(), autoincrement=False, nullable=True), sa.Column('dbworkflow_id', sa.INTEGER(), autoincrement=False, nullable=True), sa.ForeignKeyConstraint(['dbworkflow_id'], [u'db_dbworkflow.id'], name=u'db_dbworkflowstep_sub_workflows_dbworkflow_id_fkey'), sa.ForeignKeyConstraint(['dbworkflowstep_id'], [u'db_dbworkflowstep.id'], name=u'db_dbworkflowstep_sub_workflows_dbworkflowstep_id_fkey'), sa.PrimaryKeyConstraint('id', name=u'db_dbworkflowstep_sub_workflows_pkey'), sa.UniqueConstraint('dbworkflowstep_id', 'dbworkflow_id', name=u'db_dbworkflowstep_sub_workflo_dbworkflowstep_id_dbworkflow__key') ) # I get the session using the alembic connection # (Keep in mind that alembic uses the AiiDA SQLA # session) session = Session(bind=op.get_bind()) install_tc(session) def downgrade(): op.drop_table('db_dbworkflowstep_calculations') op.drop_table('db_dbworkflowstep_sub_workflows') op.drop_table('db_dbworkflowdata') op.drop_table('db_dbworkflowstep') op.drop_table('db_dbworkflow') op.drop_table('db_dbgroup_dbnodes') op.drop_table('db_dbgroup') op.drop_table('db_dblink') op.drop_table('db_dbpath') op.drop_table('db_dbcalcstate') op.drop_table('db_dbcomment') op.drop_table('db_dbnode') op.drop_table('db_dbauthinfo') op.drop_table('db_dbuser') op.drop_table('db_dbcomputer') op.drop_table('db_dblog') op.drop_table('db_dbsetting') op.drop_table('db_dblock')

66.309028

173

0.724302

2,455

19,097

5.442363

0.086762

0.075443

0.21211

0.229025

0.825387

0.777711

0.726293

0.705786

0.659681

0.567323

0

0.007748

0.107923

19,097

287

174

66.54007

0.776532

0.036341

0

0.290076

0

0.230567

0.077569

0

1

0.007634

false

0.003817

0.030534

0

0.038168

0.003817

0

null

0

1

0

null

0

6

86415dff82b7a45f70ba903723aa0dc9603b9375

374

py

Python

__init__.py

Leonardo-H/Energy-Efficient-RL

df5845bedcce16593c46724e88161f172c88e27b

[ "Apache-2.0" ]

null

__init__.py

Leonardo-H/Energy-Efficient-RL

df5845bedcce16593c46724e88161f172c88e27b

[ "Apache-2.0" ]

null

__init__.py

Leonardo-H/Energy-Efficient-RL

df5845bedcce16593c46724e88161f172c88e27b

[ "Apache-2.0" ]

null

from baselines.EERL.graph.advantage_learning_graph import adv_build_act, adv_build_train from baselines.EERL.graph.imitation_graph import imit_build_act, imit_build_train from baselines.EERL.graph.svgd_imitation_graph import svgd_imit_build_act, svgd_imit_build_train from baselines.EERL.graph.svgd_advantage_learning_graph import svgd_adv_build_act, svgd_adv_build_train

93.5

103

0.898396

60

374

5.133333

0.233333

0.168831

0.220779

0.285714

0.363636

0.25974

0

0.058824

374

4

103

93.5

0.875

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0

true

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1

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1

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1

0

null

0

1

0

1

0

1

0

6

864fb219f6ce94d4397b1987dc10b8871a6243e5

2,471

py

Python

tests/test_longest_palindromic.py

stachenov/PyLeetCode

cb13700d428854eff46a762542a63d691578d5b6

[ "Unlicense" ]

null

tests/test_longest_palindromic.py

stachenov/PyLeetCode

cb13700d428854eff46a762542a63d691578d5b6

[ "Unlicense" ]

null

tests/test_longest_palindromic.py

stachenov/PyLeetCode

cb13700d428854eff46a762542a63d691578d5b6

[ "Unlicense" ]

null

import pytest from problems.longest_palindromic import Solution @pytest.mark.parametrize("s,expected", [ ("", ""), ("a", "a"), ("aa", "aa"), ("aaa", "aaa"), ("aba", "aba"), ("abaa", "aba"), ("abaac", "aba"), ("aaba", "aba"), ("caaba", "aba"), ("abba", "abba"), ("abcba", "abcba"), ("abcac", "cac"), ("abacabxc", "bacab"), ("cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc", "cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc"), ]) def test(s, expected): assert Solution().longestPalindrome(s) == expected

107.434783

2,013

0.902469

48

2,471

46.4375

0.625

0.012113

0

0.042898

2,471

22

2,014

112.318182

0.942495

0

0.848644

0.809389

0

1

0

0.05

1

0.05

false

0

0.1

0

0.15

0

1

null

0

1

0

1

null

1

0

6

86856bb02d92ce188ce5e494d1ee917edcbc9a4e

9,964

py

Python

tests/test_alpaca_data_loader.py

andywitt1/LiuAlgoTrader

7e3ec8fb06cf6e616f2e91c99a7de83b5f048f70

[ "MIT" ]

null

tests/test_alpaca_data_loader.py

andywitt1/LiuAlgoTrader

7e3ec8fb06cf6e616f2e91c99a7de83b5f048f70

[ "MIT" ]

null

tests/test_alpaca_data_loader.py

andywitt1/LiuAlgoTrader

7e3ec8fb06cf6e616f2e91c99a7de83b5f048f70

[ "MIT" ]

null

from datetime import date, datetime import pandas as pd import pytest from pytz import timezone from liualgotrader.common import config from liualgotrader.common.data_loader import DataLoader # type: ignore from liualgotrader.common.types import DataConnectorType, TimeScale nyc = timezone("America/New_York") @pytest.mark.devtest def test_create_data_loader_default() -> bool: return bool(DataLoader(connector=DataConnectorType.alpaca)) @pytest.mark.devtest def test_apple_stock_latest_price() -> bool: print("test_apple_stock_latest_price") dl = DataLoader(TimeScale.minute, connector=DataConnectorType.alpaca) last_price = dl["AAPL"].close[-1] last_price_time = dl["AAPL"].close.index[-1] print(f"apple {last_price} @ {last_price_time}") return True @pytest.mark.devtest def test_apple_stock_current_price() -> bool: print("test_apple_stock_current_price") dl = DataLoader(TimeScale.minute, connector=DataConnectorType.alpaca) last_price = dl["AAPL"].close[-1] last_price_time = dl["AAPL"].close.index[-1] before_price = dl["AAPL"].close[-5] before_price_time = dl["AAPL"].close.index[-5] print( f"apple {last_price} @ {last_price_time}, before was {before_price}@{before_price_time}" ) return True @pytest.mark.devtest def test_apple_stock_current_price_range_int_minute() -> bool: print("test_apple_stock_current_price_range_int_minute") dl = DataLoader(TimeScale.minute, connector=DataConnectorType.alpaca) last_price_range = dl["AAPL"].close[-5:-1] # type:ignore print(last_price_range) return True @pytest.mark.devtest def test_apple_stock_current_price_range_int_day() -> bool: print("test_apple_stock_current_price_range_int_day") dl = DataLoader(TimeScale.day, connector=DataConnectorType.alpaca) last_price_range = dl["AAPL"].close[-6:-1] # type:ignore print(last_price_range) return True @pytest.mark.devtest def no_test_apple_stock_daily_price() -> bool: print("test_apple_stock_daily_price") dl = DataLoader(scale=TimeScale.day, connector=DataConnectorType.alpaca) last_price = dl["AAPL"].close[-1] last_price_time = dl["AAPL"].close.index[-1] print(last_price, last_price_time) before_price = dl["AAPL"].close[-5] print(f"before_price {before_price}, {dl['AAPL']}") print(f"apple {last_price} @ {last_price_time}, before was {before_price}") return True @pytest.mark.devtest def test_negative_current_price() -> bool: dl = DataLoader(TimeScale.minute, connector=DataConnectorType.alpaca) try: dl["DFGDFGDFG"].close[-1] except ValueError: return True return False @pytest.mark.devtest def test_apple_stock_close_price_range_str_day() -> bool: print("test_apple_stock_close_price_range_int_day") dl = DataLoader(TimeScale.day, connector=DataConnectorType.alpaca) last_price_range = dl["AAPL"].close[ "2021-01-01":"2021-01-05" # type:ignore ] # type:ignore print(last_price_range) return True @pytest.mark.devtest def test_apple_stock_close_price_range_str_minute() -> bool: print("test_apple_stock_close_price_range_str_minute") dl = DataLoader(TimeScale.minute, connector=DataConnectorType.alpaca) last_price_range = dl["AAPL"].close[ "2021-01-05 09:45:00":"2021-01-05 09:50:00" # type:ignore ] print(last_price_range) return True @pytest.mark.devtest def test_apple_stock_close_price_range_str_minute_int() -> bool: print("test_apple_stock_close_price_range_str_minute") dl = DataLoader(TimeScale.minute, connector=DataConnectorType.alpaca) last_price_range = dl["AAPL"].close[ "2021-12-15 09:45:00":-1 # type:ignore ] # type:ignore print(last_price_range) return True @pytest.mark.devtest def test_apple_stock_price_range_int_minute() -> bool: print("test_apple_stock_close_price_range_str_minute") dl = DataLoader(TimeScale.minute, connector=DataConnectorType.alpaca) last_price_range = dl["AAPL"][-5:-1] # type:ignore print(last_price_range) return True @pytest.mark.devtest def test_apple_stock_price_range_int_day() -> bool: print("test_apple_stock_price_range_int_day") dl = DataLoader(TimeScale.day, connector=DataConnectorType.alpaca) last_price_range = dl["AAPL"][-5:-1] # type:ignore print(last_price_range) return True @pytest.mark.devtest def test_apple_stock_price_range_date_day() -> bool: print("test_apple_stock_price_range_date_day") dl = DataLoader(TimeScale.day, connector=DataConnectorType.alpaca) last_price_range = dl["AAPL"]["2020-10-05":"2020-10-08"] # type:ignore print(last_price_range) return True @pytest.mark.devtest def test_apple_stock_price_range_date_int_day() -> bool: print("test_apple_stock_price_range_date_int_day") dl = DataLoader(TimeScale.day, connector=DataConnectorType.alpaca) last_price_range = dl["AAPL"]["2020-10-05":-1] # type:ignore print(last_price_range) return True @pytest.mark.devtest def test_apple_stock_price_range_date_int_min() -> bool: print("test_apple_stock_price_range_date_int_min") dl = DataLoader(TimeScale.minute, connector=DataConnectorType.alpaca) last_price_range = dl["AAPL"]["2020-10-05":-1] # type:ignore print(last_price_range) return True @pytest.mark.devtest def test_apple_stock_price_range_date_int_min_open() -> bool: print("test_apple_stock_price_range_date_int_min_open") dl = DataLoader(TimeScale.minute, connector=DataConnectorType.alpaca) last_price_range = dl["AAPL"]["2020-10-05":] # type:ignore print(last_price_range) return True @pytest.mark.devtest def test_apple_stock_price_open_range_date_int_min_open() -> bool: print("test_apple_stock_price_close_range_date_int_min_open") dl = DataLoader(TimeScale.minute, connector=DataConnectorType.alpaca) last_price_range = dl["AAPL"].open["2020-10-05":] # type:ignore print(last_price_range) return True @pytest.mark.devtest def test_apple_stock_price_range_date_min_open() -> bool: print("test_apple_stock_price_range_date_min_open") dl = DataLoader(TimeScale.minute, connector=DataConnectorType.alpaca) try: last_price_range = dl["AAPL"][:] # type:ignore print(last_price_range) except ValueError: return True return True @pytest.mark.devtest def test_apple_stock_price_open_range_date_min_open() -> bool: print("test_apple_stock_price_open_range_date_min_open") dl = DataLoader(TimeScale.minute, connector=DataConnectorType.alpaca) try: last_price_range = dl["AAPL"].open[:] # type:ignore print(last_price_range) except ValueError: return True return True @pytest.mark.devtest def test_apple_stock_price_range_date_min() -> bool: print("test_apple_stock_price_range_date_min") dl = DataLoader(TimeScale.minute, connector=DataConnectorType.alpaca) d1 = date(year=2021, month=2, day=1) d2 = date(year=2021, month=2, day=2) last_price_range = dl["AAPL"][d1:d2].between_time( # type:ignore "9:30", "16:00" ) # type:ignore print(last_price_range) return True @pytest.mark.devtest def test_apple_stock_price_range_date_min_mixed() -> bool: print("test_apple_stock_price_range_date_min_mixed") dl = DataLoader(TimeScale.minute, connector=DataConnectorType.alpaca) d1 = date(year=2021, month=2, day=1) last_price_range = dl["AAPL"][d1:"2021-02-02"].between_time( # type:ignore "9:30", "16:00" ) # type:ignore print(last_price_range) return True @pytest.mark.devtest def test_apple_stock_price_range_date_day_mixed() -> bool: print("test_apple_stock_price_range_date_day_mixed") dl = DataLoader(TimeScale.day, connector=DataConnectorType.alpaca) d1 = date(year=2021, month=2, day=1) last_price_range = dl["AAPL"][d1:"2021-02-02"] # type:ignore print(last_price_range) return True @pytest.mark.devtest def test_apple_stock_price_open_range_date_min_mixed() -> bool: print("test_apple_stock_price_range_date_min_mixed") dl = DataLoader(TimeScale.minute, connector=DataConnectorType.alpaca) d1 = datetime(year=2021, month=2, day=1, hour=3, minute=0) last_price_range = ( dl["AAPL"] .open[d1:"2021-02-01 21:00:00"] # type:ignore .between_time("9:30", "16:00") # type:ignore ) print(last_price_range) return True @pytest.mark.devtest def test_apple_stock_price_open_str() -> bool: print("test_apple_stock_price_open_str") dl = DataLoader(TimeScale.minute, connector=DataConnectorType.alpaca) d1 = date(year=2021, month=2, day=1) last_price_range = dl["AAPL"].open["2021-02-02 09:45:00"] print(last_price_range) return True @pytest.mark.devtest def test_apple_stock_price_open_date() -> bool: print("test_apple_stock_price_open_date") dl = DataLoader(TimeScale.minute, connector=DataConnectorType.alpaca) d1 = nyc.localize(datetime(year=2021, month=2, day=1, hour=9, minute=30)) last_price_range = dl["AAPL"].open[d1] print(last_price_range) return True @pytest.mark.devtest def test_get_symbols_alpaca() -> bool: print("test_get_symbols_alpaca") dl = DataLoader(TimeScale.minute, connector=DataConnectorType.alpaca) tickers = dl.data_api.get_symbols() print(len(tickers)) return True @pytest.mark.devtest def test_apple_update() -> bool: print("test_apple_stock_price_open_str") dl = DataLoader(TimeScale.minute, connector=DataConnectorType.alpaca) d1 = date(year=2021, month=2, day=1) last_price_range = dl["AAPL"][-1] print("after this") dl["AAPL"].loc["2021-02-02 09:46:00"] = [ 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, ] print(dl["AAPL"].loc["2021-02-02 09:46:00"]) return True

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86ba11df826b1a85b469e85b635d2a5d86cc38c0

142

py

Python

rsockets2/transport/__init__.py

freelancer1845/rsockets2-try

25b8b38e00925c3feb6c6e790624a35bc8689619

[ "Apache-2.0" ]

3

2020-05-08T09:45:44.000Z

2020-11-13T11:39:06.000Z

rsockets2/transport/__init__.py

freelancer1845/rsockets2-try

25b8b38e00925c3feb6c6e790624a35bc8689619

[ "Apache-2.0" ]

1

2022-01-27T08:07:22.000Z

rsockets2/transport/__init__.py

freelancer1845/rsockets2-try

25b8b38e00925c3feb6c6e790624a35bc8689619

[ "Apache-2.0" ]

1

2020-05-08T09:47:14.000Z

from .abstract_transport import AbstractTransport from .tcp_transport import TcpTransport from .websocket_transport import WebsocketTransport

35.5

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d4961ae1071073b986470319fdd2d6becb467c03

37,894

py

Python

instances/passenger_demand/pas-20210421-2109-int14000000000000001e/60.py

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

bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11

[ "BSD-3-Clause" ]

null

instances/passenger_demand/pas-20210421-2109-int14000000000000001e/60.py

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

bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11

[ "BSD-3-Clause" ]

null

instances/passenger_demand/pas-20210421-2109-int14000000000000001e/60.py

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

bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11

[ "BSD-3-Clause" ]

null

""" PASSENGERS """ numPassengers = 3241 passenger_arriving = ( (2, 4, 9, 5, 2, 0, 7, 7, 4, 5, 1, 0), # 0 (1, 9, 7, 4, 1, 0, 6, 6, 9, 6, 1, 0), # 1 (5, 6, 8, 4, 3, 0, 8, 11, 5, 2, 1, 0), # 2 (4, 5, 5, 4, 2, 0, 8, 15, 0, 4, 4, 0), # 3 (5, 11, 13, 4, 3, 0, 11, 6, 3, 1, 1, 0), # 4 (9, 6, 8, 3, 0, 0, 7, 6, 9, 7, 4, 0), # 5 (8, 12, 3, 4, 1, 0, 4, 12, 5, 4, 0, 0), # 6 (4, 13, 11, 2, 3, 0, 9, 9, 6, 5, 1, 0), # 7 (3, 8, 7, 7, 3, 0, 5, 9, 7, 5, 1, 0), # 8 (2, 5, 5, 1, 3, 0, 3, 3, 5, 6, 5, 0), # 9 (5, 8, 9, 3, 1, 0, 8, 12, 4, 3, 4, 0), # 10 (3, 12, 6, 3, 2, 0, 6, 6, 8, 6, 1, 0), # 11 (1, 10, 8, 7, 1, 0, 10, 7, 6, 5, 1, 0), # 12 (4, 9, 8, 3, 1, 0, 7, 11, 12, 3, 3, 0), # 13 (2, 10, 6, 3, 2, 0, 9, 7, 4, 7, 1, 0), # 14 (2, 6, 9, 2, 4, 0, 7, 12, 7, 8, 2, 0), # 15 (3, 14, 8, 5, 5, 0, 8, 10, 5, 5, 1, 0), # 16 (2, 8, 6, 5, 2, 0, 6, 5, 3, 7, 0, 0), # 17 (3, 11, 8, 2, 0, 0, 4, 3, 4, 7, 0, 0), # 18 (2, 7, 4, 2, 4, 0, 7, 4, 3, 6, 2, 0), # 19 (7, 16, 5, 3, 3, 0, 8, 6, 6, 4, 2, 0), # 20 (1, 6, 11, 5, 0, 0, 4, 11, 6, 9, 0, 0), # 21 (5, 8, 7, 5, 2, 0, 10, 11, 4, 3, 2, 0), # 22 (2, 8, 14, 3, 3, 0, 3, 8, 4, 4, 3, 0), # 23 (3, 7, 5, 5, 5, 0, 5, 8, 4, 5, 3, 0), # 24 (7, 5, 5, 4, 3, 0, 2, 5, 6, 6, 1, 0), # 25 (7, 12, 3, 6, 3, 0, 4, 10, 9, 3, 4, 0), # 26 (1, 10, 8, 2, 1, 0, 5, 13, 5, 5, 0, 0), # 27 (6, 4, 9, 6, 3, 0, 6, 8, 7, 7, 3, 0), # 28 (6, 9, 13, 7, 1, 0, 3, 10, 7, 3, 4, 0), # 29 (4, 5, 5, 2, 2, 0, 9, 14, 6, 7, 1, 0), # 30 (2, 7, 3, 3, 1, 0, 7, 7, 4, 4, 2, 0), # 31 (4, 5, 9, 0, 2, 0, 5, 15, 3, 2, 1, 0), # 32 (4, 10, 9, 2, 3, 0, 11, 8, 4, 4, 1, 0), # 33 (6, 11, 6, 3, 1, 0, 8, 4, 10, 6, 2, 0), # 34 (6, 4, 3, 4, 3, 0, 10, 9, 6, 6, 0, 0), # 35 (4, 6, 15, 4, 2, 0, 13, 8, 3, 6, 2, 0), # 36 (6, 9, 5, 5, 6, 0, 10, 14, 2, 7, 3, 0), # 37 (2, 7, 11, 7, 1, 0, 7, 7, 5, 3, 1, 0), # 38 (4, 11, 7, 4, 2, 0, 6, 13, 3, 2, 3, 0), # 39 (10, 12, 10, 3, 3, 0, 4, 10, 6, 7, 1, 0), # 40 (6, 6, 4, 1, 2, 0, 7, 9, 5, 4, 0, 0), # 41 (6, 7, 10, 5, 3, 0, 0, 15, 9, 7, 0, 0), # 42 (8, 11, 8, 2, 3, 0, 9, 12, 7, 1, 3, 0), # 43 (5, 12, 13, 3, 2, 0, 8, 12, 3, 4, 4, 0), # 44 (5, 8, 4, 6, 0, 0, 8, 9, 5, 3, 5, 0), # 45 (11, 18, 7, 2, 0, 0, 5, 13, 6, 3, 3, 0), # 46 (5, 9, 2, 5, 0, 0, 4, 8, 5, 3, 1, 0), # 47 (5, 9, 8, 7, 1, 0, 11, 10, 4, 4, 0, 0), # 48 (0, 10, 8, 4, 1, 0, 6, 7, 7, 6, 2, 0), # 49 (8, 8, 7, 4, 3, 0, 4, 11, 4, 8, 1, 0), # 50 (9, 12, 10, 3, 2, 0, 7, 9, 6, 7, 3, 0), # 51 (3, 8, 7, 4, 3, 0, 3, 11, 5, 4, 1, 0), # 52 (6, 8, 6, 3, 4, 0, 9, 10, 6, 5, 3, 0), # 53 (2, 17, 11, 4, 2, 0, 5, 14, 6, 12, 2, 0), # 54 (3, 8, 5, 3, 2, 0, 12, 9, 5, 4, 1, 0), # 55 (1, 6, 9, 4, 6, 0, 11, 10, 6, 8, 2, 0), # 56 (5, 6, 3, 5, 1, 0, 8, 9, 3, 6, 1, 0), # 57 (0, 11, 6, 5, 0, 0, 4, 13, 5, 4, 2, 0), # 58 (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), # 59 ) station_arriving_intensity = ( (3.7095121817383676, 9.515044981060607, 11.19193043059126, 8.87078804347826, 10.000240384615385, 6.659510869565219), # 0 (3.7443308140669203, 9.620858238197952, 11.252381752534994, 8.920190141908213, 10.075193108974359, 6.657240994867151), # 1 (3.7787518681104277, 9.725101964085297, 11.31139817195087, 8.968504830917876, 10.148564102564103, 6.654901690821256), # 2 (3.8127461259877085, 9.827663671875001, 11.368936576156813, 9.01569089673913, 10.22028605769231, 6.652493274456523), # 3 (3.8462843698175795, 9.928430874719417, 11.424953852470724, 9.061707125603865, 10.290291666666668, 6.6500160628019325), # 4 (3.879337381718857, 10.027291085770905, 11.479406888210512, 9.106512303743962, 10.358513621794872, 6.647470372886473), # 5 (3.9118759438103607, 10.12413181818182, 11.53225257069409, 9.150065217391306, 10.424884615384617, 6.644856521739131), # 6 (3.943870838210907, 10.218840585104518, 11.58344778723936, 9.19232465277778, 10.489337339743592, 6.64217482638889), # 7 (3.975292847039314, 10.311304899691358, 11.632949425164242, 9.233249396135266, 10.551804487179488, 6.639425603864735), # 8 (4.006112752414399, 10.401412275094698, 11.680714371786634, 9.272798233695653, 10.61221875, 6.636609171195653), # 9 (4.03630133645498, 10.489050224466892, 11.72669951442445, 9.310929951690824, 10.670512820512823, 6.633725845410628), # 10 (4.065829381279876, 10.5741062609603, 11.7708617403956, 9.347603336352659, 10.726619391025642, 6.630775943538648), # 11 (4.094667669007903, 10.656467897727273, 11.813157937017996, 9.382777173913043, 10.780471153846154, 6.627759782608695), # 12 (4.122786981757876, 10.736022647920176, 11.85354499160954, 9.416410250603866, 10.832000801282053, 6.624677679649759), # 13 (4.15015810164862, 10.81265802469136, 11.891979791488144, 9.448461352657004, 10.881141025641025, 6.621529951690821), # 14 (4.1767518107989465, 10.886261541193182, 11.928419223971721, 9.478889266304348, 10.92782451923077, 6.618316915760871), # 15 (4.202538891327675, 10.956720710578002, 11.96282017637818, 9.507652777777778, 10.971983974358976, 6.61503888888889), # 16 (4.227490125353625, 11.023923045998176, 11.995139536025421, 9.53471067330918, 11.013552083333336, 6.611696188103866), # 17 (4.25157629499561, 11.087756060606061, 12.025334190231364, 9.560021739130436, 11.052461538461543, 6.608289130434783), # 18 (4.274768182372451, 11.148107267554012, 12.053361026313912, 9.58354476147343, 11.088645032051284, 6.604818032910629), # 19 (4.297036569602966, 11.204864179994388, 12.079176931590974, 9.60523852657005, 11.122035256410259, 6.601283212560387), # 20 (4.318352238805971, 11.257914311079544, 12.102738793380466, 9.625061820652174, 11.152564903846153, 6.597684986413044), # 21 (4.338685972100283, 11.307145173961842, 12.124003499000287, 9.642973429951692, 11.180166666666667, 6.5940236714975855), # 22 (4.358008551604722, 11.352444281793632, 12.142927935768354, 9.658932140700484, 11.204773237179488, 6.590299584842997), # 23 (4.3762907594381035, 11.393699147727272, 12.159468991002571, 9.672896739130437, 11.226317307692307, 6.586513043478261), # 24 (4.393503377719247, 11.430797284915124, 12.173583552020853, 9.684826011473431, 11.244731570512819, 6.582664364432368), # 25 (4.409617188566969, 11.46362620650954, 12.185228506141103, 9.694678743961353, 11.259948717948719, 6.5787538647343), # 26 (4.424602974100088, 11.492073425662877, 12.194360740681233, 9.702413722826089, 11.271901442307694, 6.574781861413045), # 27 (4.438431516437421, 11.516026455527497, 12.200937142959157, 9.707989734299519, 11.280522435897437, 6.570748671497586), # 28 (4.4510735976977855, 11.535372809255753, 12.204914600292774, 9.711365564613528, 11.285744391025641, 6.566654612016909), # 29 (4.4625, 11.55, 12.20625, 9.7125, 11.287500000000001, 6.562500000000001), # 30 (4.47319183983376, 11.56215031960227, 12.205248928140096, 9.712295118464054, 11.286861125886526, 6.556726763701484), # 31 (4.4836528452685425, 11.574140056818184, 12.202274033816424, 9.711684477124184, 11.28495815602837, 6.547834661835751), # 32 (4.493887715792838, 11.585967720170455, 12.197367798913046, 9.710674080882354, 11.281811569148937, 6.535910757121439), # 33 (4.503901150895141, 11.597631818181819, 12.19057270531401, 9.709269934640524, 11.277441843971632, 6.521042112277196), # 34 (4.513697850063939, 11.609130859374998, 12.181931234903383, 9.707478043300654, 11.27186945921986, 6.503315790021656), # 35 (4.523282512787724, 11.62046335227273, 12.171485869565219, 9.705304411764708, 11.265114893617023, 6.482818853073463), # 36 (4.532659838554988, 11.631627805397729, 12.159279091183576, 9.70275504493464, 11.257198625886524, 6.4596383641512585), # 37 (4.5418345268542195, 11.642622727272729, 12.145353381642513, 9.699835947712419, 11.248141134751775, 6.433861385973679), # 38 (4.5508112771739135, 11.653446626420456, 12.129751222826087, 9.696553125000001, 11.23796289893617, 6.40557498125937), # 39 (4.559594789002558, 11.664098011363638, 12.11251509661836, 9.692912581699348, 11.22668439716312, 6.37486621272697), # 40 (4.568189761828645, 11.674575390625, 12.093687484903382, 9.68892032271242, 11.214326108156028, 6.34182214309512), # 41 (4.576600895140665, 11.684877272727276, 12.07331086956522, 9.684582352941177, 11.2009085106383, 6.3065298350824595), # 42 (4.584832888427111, 11.69500216619318, 12.051427732487923, 9.679904677287583, 11.186452083333334, 6.26907635140763), # 43 (4.592890441176471, 11.704948579545455, 12.028080555555556, 9.674893300653595, 11.17097730496454, 6.229548754789272), # 44 (4.600778252877237, 11.714715021306818, 12.003311820652177, 9.669554227941177, 11.15450465425532, 6.188034107946028), # 45 (4.6085010230179035, 11.724300000000003, 11.97716400966184, 9.663893464052288, 11.137054609929079, 6.144619473596536), # 46 (4.616063451086957, 11.733702024147728, 11.9496796044686, 9.65791701388889, 11.118647650709221, 6.099391914459438), # 47 (4.623470236572891, 11.742919602272728, 11.920901086956523, 9.651630882352942, 11.099304255319149, 6.052438493253375), # 48 (4.630726078964194, 11.751951242897727, 11.890870939009663, 9.645041074346407, 11.079044902482272, 6.003846272696985), # 49 (4.6378356777493615, 11.760795454545454, 11.85963164251208, 9.638153594771243, 11.057890070921987, 5.953702315508913), # 50 (4.6448037324168805, 11.769450745738636, 11.827225679347826, 9.630974448529413, 11.035860239361703, 5.902093684407797), # 51 (4.651634942455243, 11.777915625, 11.793695531400965, 9.623509640522876, 11.012975886524824, 5.849107442112278), # 52 (4.658334007352941, 11.786188600852274, 11.759083680555555, 9.615765175653596, 10.989257491134753, 5.794830651340996), # 53 (4.6649056265984665, 11.79426818181818, 11.723432608695653, 9.60774705882353, 10.964725531914894, 5.739350374812594), # 54 (4.671354499680307, 11.802152876420456, 11.686784797705313, 9.599461294934642, 10.939400487588653, 5.682753675245711), # 55 (4.677685326086957, 11.809841193181818, 11.649182729468599, 9.59091388888889, 10.913302836879433, 5.625127615358988), # 56 (4.683902805306906, 11.817331640625003, 11.610668885869565, 9.582110845588236, 10.886453058510638, 5.566559257871065), # 57 (4.690011636828645, 11.824622727272727, 11.57128574879227, 9.573058169934642, 10.858871631205675, 5.507135665500583), # 58 (0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 59 ) passenger_arriving_acc = ( (2, 4, 9, 5, 2, 0, 7, 7, 4, 5, 1, 0), # 0 (3, 13, 16, 9, 3, 0, 13, 13, 13, 11, 2, 0), # 1 (8, 19, 24, 13, 6, 0, 21, 24, 18, 13, 3, 0), # 2 (12, 24, 29, 17, 8, 0, 29, 39, 18, 17, 7, 0), # 3 (17, 35, 42, 21, 11, 0, 40, 45, 21, 18, 8, 0), # 4 (26, 41, 50, 24, 11, 0, 47, 51, 30, 25, 12, 0), # 5 (34, 53, 53, 28, 12, 0, 51, 63, 35, 29, 12, 0), # 6 (38, 66, 64, 30, 15, 0, 60, 72, 41, 34, 13, 0), # 7 (41, 74, 71, 37, 18, 0, 65, 81, 48, 39, 14, 0), # 8 (43, 79, 76, 38, 21, 0, 68, 84, 53, 45, 19, 0), # 9 (48, 87, 85, 41, 22, 0, 76, 96, 57, 48, 23, 0), # 10 (51, 99, 91, 44, 24, 0, 82, 102, 65, 54, 24, 0), # 11 (52, 109, 99, 51, 25, 0, 92, 109, 71, 59, 25, 0), # 12 (56, 118, 107, 54, 26, 0, 99, 120, 83, 62, 28, 0), # 13 (58, 128, 113, 57, 28, 0, 108, 127, 87, 69, 29, 0), # 14 (60, 134, 122, 59, 32, 0, 115, 139, 94, 77, 31, 0), # 15 (63, 148, 130, 64, 37, 0, 123, 149, 99, 82, 32, 0), # 16 (65, 156, 136, 69, 39, 0, 129, 154, 102, 89, 32, 0), # 17 (68, 167, 144, 71, 39, 0, 133, 157, 106, 96, 32, 0), # 18 (70, 174, 148, 73, 43, 0, 140, 161, 109, 102, 34, 0), # 19 (77, 190, 153, 76, 46, 0, 148, 167, 115, 106, 36, 0), # 20 (78, 196, 164, 81, 46, 0, 152, 178, 121, 115, 36, 0), # 21 (83, 204, 171, 86, 48, 0, 162, 189, 125, 118, 38, 0), # 22 (85, 212, 185, 89, 51, 0, 165, 197, 129, 122, 41, 0), # 23 (88, 219, 190, 94, 56, 0, 170, 205, 133, 127, 44, 0), # 24 (95, 224, 195, 98, 59, 0, 172, 210, 139, 133, 45, 0), # 25 (102, 236, 198, 104, 62, 0, 176, 220, 148, 136, 49, 0), # 26 (103, 246, 206, 106, 63, 0, 181, 233, 153, 141, 49, 0), # 27 (109, 250, 215, 112, 66, 0, 187, 241, 160, 148, 52, 0), # 28 (115, 259, 228, 119, 67, 0, 190, 251, 167, 151, 56, 0), # 29 (119, 264, 233, 121, 69, 0, 199, 265, 173, 158, 57, 0), # 30 (121, 271, 236, 124, 70, 0, 206, 272, 177, 162, 59, 0), # 31 (125, 276, 245, 124, 72, 0, 211, 287, 180, 164, 60, 0), # 32 (129, 286, 254, 126, 75, 0, 222, 295, 184, 168, 61, 0), # 33 (135, 297, 260, 129, 76, 0, 230, 299, 194, 174, 63, 0), # 34 (141, 301, 263, 133, 79, 0, 240, 308, 200, 180, 63, 0), # 35 (145, 307, 278, 137, 81, 0, 253, 316, 203, 186, 65, 0), # 36 (151, 316, 283, 142, 87, 0, 263, 330, 205, 193, 68, 0), # 37 (153, 323, 294, 149, 88, 0, 270, 337, 210, 196, 69, 0), # 38 (157, 334, 301, 153, 90, 0, 276, 350, 213, 198, 72, 0), # 39 (167, 346, 311, 156, 93, 0, 280, 360, 219, 205, 73, 0), # 40 (173, 352, 315, 157, 95, 0, 287, 369, 224, 209, 73, 0), # 41 (179, 359, 325, 162, 98, 0, 287, 384, 233, 216, 73, 0), # 42 (187, 370, 333, 164, 101, 0, 296, 396, 240, 217, 76, 0), # 43 (192, 382, 346, 167, 103, 0, 304, 408, 243, 221, 80, 0), # 44 (197, 390, 350, 173, 103, 0, 312, 417, 248, 224, 85, 0), # 45 (208, 408, 357, 175, 103, 0, 317, 430, 254, 227, 88, 0), # 46 (213, 417, 359, 180, 103, 0, 321, 438, 259, 230, 89, 0), # 47 (218, 426, 367, 187, 104, 0, 332, 448, 263, 234, 89, 0), # 48 (218, 436, 375, 191, 105, 0, 338, 455, 270, 240, 91, 0), # 49 (226, 444, 382, 195, 108, 0, 342, 466, 274, 248, 92, 0), # 50 (235, 456, 392, 198, 110, 0, 349, 475, 280, 255, 95, 0), # 51 (238, 464, 399, 202, 113, 0, 352, 486, 285, 259, 96, 0), # 52 (244, 472, 405, 205, 117, 0, 361, 496, 291, 264, 99, 0), # 53 (246, 489, 416, 209, 119, 0, 366, 510, 297, 276, 101, 0), # 54 (249, 497, 421, 212, 121, 0, 378, 519, 302, 280, 102, 0), # 55 (250, 503, 430, 216, 127, 0, 389, 529, 308, 288, 104, 0), # 56 (255, 509, 433, 221, 128, 0, 397, 538, 311, 294, 105, 0), # 57 (255, 520, 439, 226, 128, 0, 401, 551, 316, 298, 107, 0), # 58 (255, 520, 439, 226, 128, 0, 401, 551, 316, 298, 107, 0), # 59 ) passenger_arriving_rate = ( (3.7095121817383676, 7.612035984848484, 6.715158258354756, 3.5483152173913037, 2.000048076923077, 0.0, 6.659510869565219, 8.000192307692307, 5.322472826086956, 4.476772172236504, 1.903008996212121, 0.0), # 0 (3.7443308140669203, 7.696686590558361, 6.751429051520996, 3.5680760567632848, 2.0150386217948717, 0.0, 6.657240994867151, 8.060154487179487, 5.352114085144928, 4.500952701013997, 1.9241716476395903, 0.0), # 1 (3.7787518681104277, 7.780081571268237, 6.786838903170522, 3.58740193236715, 2.0297128205128203, 0.0, 6.654901690821256, 8.118851282051281, 5.381102898550726, 4.524559268780347, 1.9450203928170593, 0.0), # 2 (3.8127461259877085, 7.8621309375, 6.821361945694087, 3.6062763586956517, 2.044057211538462, 0.0, 6.652493274456523, 8.176228846153847, 5.409414538043478, 4.547574630462725, 1.965532734375, 0.0), # 3 (3.8462843698175795, 7.942744699775533, 6.854972311482434, 3.624682850241546, 2.0580583333333333, 0.0, 6.6500160628019325, 8.232233333333333, 5.437024275362319, 4.569981540988289, 1.9856861749438832, 0.0), # 4 (3.879337381718857, 8.021832868616723, 6.887644132926307, 3.6426049214975844, 2.0717027243589743, 0.0, 6.647470372886473, 8.286810897435897, 5.463907382246377, 4.591762755284204, 2.005458217154181, 0.0), # 5 (3.9118759438103607, 8.099305454545455, 6.919351542416455, 3.660026086956522, 2.084976923076923, 0.0, 6.644856521739131, 8.339907692307692, 5.490039130434783, 4.612901028277636, 2.0248263636363637, 0.0), # 6 (3.943870838210907, 8.175072468083613, 6.950068672343615, 3.6769298611111116, 2.0978674679487184, 0.0, 6.64217482638889, 8.391469871794873, 5.515394791666668, 4.633379114895743, 2.043768117020903, 0.0), # 7 (3.975292847039314, 8.249043919753085, 6.979769655098544, 3.693299758454106, 2.1103608974358976, 0.0, 6.639425603864735, 8.44144358974359, 5.5399496376811594, 4.653179770065696, 2.062260979938271, 0.0), # 8 (4.006112752414399, 8.321129820075758, 7.00842862307198, 3.709119293478261, 2.12244375, 0.0, 6.636609171195653, 8.489775, 5.563678940217391, 4.672285748714653, 2.0802824550189394, 0.0), # 9 (4.03630133645498, 8.391240179573513, 7.03601970865467, 3.724371980676329, 2.134102564102564, 0.0, 6.633725845410628, 8.536410256410257, 5.586557971014494, 4.690679805769779, 2.0978100448933783, 0.0), # 10 (4.065829381279876, 8.459285008768239, 7.06251704423736, 3.739041334541063, 2.145323878205128, 0.0, 6.630775943538648, 8.581295512820512, 5.608562001811595, 4.70834469615824, 2.1148212521920597, 0.0), # 11 (4.094667669007903, 8.525174318181818, 7.087894762210797, 3.7531108695652167, 2.156094230769231, 0.0, 6.627759782608695, 8.624376923076923, 5.6296663043478254, 4.725263174807198, 2.1312935795454546, 0.0), # 12 (4.122786981757876, 8.58881811833614, 7.112126994965724, 3.766564100241546, 2.1664001602564102, 0.0, 6.624677679649759, 8.665600641025641, 5.649846150362319, 4.741417996643816, 2.147204529584035, 0.0), # 13 (4.15015810164862, 8.650126419753088, 7.135187874892886, 3.779384541062801, 2.1762282051282047, 0.0, 6.621529951690821, 8.704912820512819, 5.669076811594202, 4.756791916595257, 2.162531604938272, 0.0), # 14 (4.1767518107989465, 8.709009232954545, 7.157051534383032, 3.7915557065217387, 2.1855649038461538, 0.0, 6.618316915760871, 8.742259615384615, 5.6873335597826085, 4.771367689588688, 2.177252308238636, 0.0), # 15 (4.202538891327675, 8.7653765684624, 7.177692105826908, 3.803061111111111, 2.194396794871795, 0.0, 6.61503888888889, 8.77758717948718, 5.7045916666666665, 4.785128070551272, 2.1913441421156, 0.0), # 16 (4.227490125353625, 8.81913843679854, 7.197083721615253, 3.8138842693236716, 2.202710416666667, 0.0, 6.611696188103866, 8.810841666666668, 5.720826403985508, 4.798055814410168, 2.204784609199635, 0.0), # 17 (4.25157629499561, 8.870204848484848, 7.215200514138818, 3.824008695652174, 2.2104923076923084, 0.0, 6.608289130434783, 8.841969230769234, 5.736013043478262, 4.810133676092545, 2.217551212121212, 0.0), # 18 (4.274768182372451, 8.918485814043208, 7.232016615788346, 3.8334179045893717, 2.2177290064102566, 0.0, 6.604818032910629, 8.870916025641026, 5.750126856884058, 4.8213444105255645, 2.229621453510802, 0.0), # 19 (4.297036569602966, 8.96389134399551, 7.247506158954584, 3.8420954106280196, 2.2244070512820517, 0.0, 6.601283212560387, 8.897628205128207, 5.76314311594203, 4.831670772636389, 2.2409728359988774, 0.0), # 20 (4.318352238805971, 9.006331448863634, 7.261643276028279, 3.8500247282608693, 2.2305129807692303, 0.0, 6.597684986413044, 8.922051923076921, 5.775037092391305, 4.841095517352186, 2.2515828622159084, 0.0), # 21 (4.338685972100283, 9.045716139169473, 7.274402099400172, 3.8571893719806765, 2.2360333333333333, 0.0, 6.5940236714975855, 8.944133333333333, 5.785784057971015, 4.849601399600115, 2.2614290347923682, 0.0), # 22 (4.358008551604722, 9.081955425434906, 7.285756761461012, 3.8635728562801934, 2.2409546474358972, 0.0, 6.590299584842997, 8.963818589743589, 5.79535928442029, 4.857171174307341, 2.2704888563587264, 0.0), # 23 (4.3762907594381035, 9.114959318181818, 7.295681394601543, 3.869158695652174, 2.2452634615384612, 0.0, 6.586513043478261, 8.981053846153845, 5.803738043478262, 4.863787596401028, 2.2787398295454544, 0.0), # 24 (4.393503377719247, 9.1446378279321, 7.304150131212511, 3.8739304045893723, 2.2489463141025636, 0.0, 6.582664364432368, 8.995785256410255, 5.810895606884059, 4.869433420808341, 2.286159456983025, 0.0), # 25 (4.409617188566969, 9.17090096520763, 7.311137103684661, 3.8778714975845405, 2.2519897435897436, 0.0, 6.5787538647343, 9.007958974358974, 5.816807246376811, 4.874091402456441, 2.2927252413019077, 0.0), # 26 (4.424602974100088, 9.193658740530301, 7.31661644440874, 3.880965489130435, 2.2543802884615385, 0.0, 6.574781861413045, 9.017521153846154, 5.821448233695653, 4.877744296272493, 2.2984146851325753, 0.0), # 27 (4.438431516437421, 9.212821164421996, 7.320562285775494, 3.8831958937198072, 2.256104487179487, 0.0, 6.570748671497586, 9.024417948717948, 5.824793840579711, 4.8803748571836625, 2.303205291105499, 0.0), # 28 (4.4510735976977855, 9.228298247404602, 7.322948760175664, 3.884546225845411, 2.257148878205128, 0.0, 6.566654612016909, 9.028595512820512, 5.826819338768117, 4.881965840117109, 2.3070745618511506, 0.0), # 29 (4.4625, 9.24, 7.32375, 3.885, 2.2575000000000003, 0.0, 6.562500000000001, 9.030000000000001, 5.8275, 4.8825, 2.31, 0.0), # 30 (4.47319183983376, 9.249720255681815, 7.323149356884057, 3.884918047385621, 2.257372225177305, 0.0, 6.556726763701484, 9.02948890070922, 5.827377071078432, 4.882099571256038, 2.312430063920454, 0.0), # 31 (4.4836528452685425, 9.259312045454546, 7.3213644202898545, 3.884673790849673, 2.2569916312056737, 0.0, 6.547834661835751, 9.027966524822695, 5.82701068627451, 4.880909613526569, 2.3148280113636366, 0.0), # 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42 (4.584832888427111, 9.356001732954544, 7.230856639492753, 3.8719618709150327, 2.2372904166666667, 0.0, 6.26907635140763, 8.949161666666667, 5.80794280637255, 4.820571092995169, 2.339000433238636, 0.0), # 43 (4.592890441176471, 9.363958863636363, 7.216848333333333, 3.8699573202614377, 2.2341954609929076, 0.0, 6.229548754789272, 8.93678184397163, 5.804935980392157, 4.811232222222222, 2.3409897159090907, 0.0), # 44 (4.600778252877237, 9.371772017045453, 7.201987092391306, 3.8678216911764705, 2.230900930851064, 0.0, 6.188034107946028, 8.923603723404256, 5.801732536764706, 4.80132472826087, 2.3429430042613633, 0.0), # 45 (4.6085010230179035, 9.379440000000002, 7.186298405797103, 3.8655573856209147, 2.2274109219858156, 0.0, 6.144619473596536, 8.909643687943262, 5.798336078431372, 4.790865603864735, 2.3448600000000006, 0.0), # 46 (4.616063451086957, 9.386961619318182, 7.16980776268116, 3.8631668055555552, 2.223729530141844, 0.0, 6.099391914459438, 8.894918120567375, 5.794750208333333, 4.77987184178744, 2.3467404048295455, 0.0), # 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52 (4.658334007352941, 9.428950880681818, 7.055450208333333, 3.8463060702614382, 2.1978514982269504, 0.0, 5.794830651340996, 8.791405992907801, 5.769459105392158, 4.703633472222222, 2.3572377201704544, 0.0), # 53 (4.6649056265984665, 9.435414545454544, 7.034059565217391, 3.843098823529412, 2.192945106382979, 0.0, 5.739350374812594, 8.771780425531915, 5.764648235294119, 4.689373043478261, 2.358853636363636, 0.0), # 54 (4.671354499680307, 9.441722301136364, 7.012070878623187, 3.8397845179738566, 2.1878800975177306, 0.0, 5.682753675245711, 8.751520390070922, 5.759676776960785, 4.674713919082125, 2.360430575284091, 0.0), # 55 (4.677685326086957, 9.447872954545453, 6.989509637681159, 3.8363655555555556, 2.1826605673758865, 0.0, 5.625127615358988, 8.730642269503546, 5.754548333333334, 4.65967309178744, 2.361968238636363, 0.0), # 56 (4.683902805306906, 9.453865312500001, 6.966401331521738, 3.832844338235294, 2.1772906117021273, 0.0, 5.566559257871065, 8.70916244680851, 5.749266507352941, 4.644267554347826, 2.3634663281250003, 0.0), # 57 (4.690011636828645, 9.459698181818181, 6.942771449275362, 3.8292232679738563, 2.1717743262411346, 0.0, 5.507135665500583, 8.687097304964539, 5.743834901960785, 4.628514299516908, 2.3649245454545453, 0.0), # 58 (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 59 ) passenger_allighting_rate = ( (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 0 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 1 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 2 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 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39 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 40 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 41 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 42 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 43 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 44 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 45 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 46 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 47 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 48 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 49 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 50 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 51 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 52 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 53 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 54 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 55 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 56 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 57 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 58 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 59 ) """ parameters for reproducibiliy. More information: https://numpy.org/doc/stable/reference/random/parallel.html """ #initial entropy entropy = 258194110137029475889902652135037600173 #index for seed sequence child child_seed_index = ( 1, # 0 59, # 1 )

113.116418

212

0.729139

5,147

37,894

5.366038

0.228483

0.312828

0.247656

0.469242

0.32847

0.327745

0

0.819053

0.119122

37,894

334

213

113.45509

0.008358

0.031958

0

0.202532

0

1

0

false

0.015823

0

null

1

0

1

0

1

0

null

0

6

d4f430c48f066f2bb72aee9f7b0be54914c560b6

26,491

py

Python

monitoring/tests/system/test_vpcsc.py

DaveCheez/google-cloud-python

fc03d4d41f13e9d13db7206438163b3a471fdabd

[ "Apache-2.0" ]

1

2019-06-14T10:11:59.000Z

monitoring/tests/system/test_vpcsc.py

DaveCheez/google-cloud-python

fc03d4d41f13e9d13db7206438163b3a471fdabd

[ "Apache-2.0" ]

null

monitoring/tests/system/test_vpcsc.py

DaveCheez/google-cloud-python

fc03d4d41f13e9d13db7206438163b3a471fdabd

[ "Apache-2.0" ]

1

2020-04-14T10:47:41.000Z

# -*- coding: utf-8 -*- # # Copyright 2019 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. # DO NOT MODIFY! AUTO-GENERATED! # This file is auto-generated on 2019-05-03. # flake8: noqa import os import pytest from google.api_core import exceptions from google.cloud import monitoring_v3 from google.cloud.monitoring_v3 import enums PROJECT_INSIDE = os.environ.get("PROJECT_ID", None) PROJECT_OUTSIDE = os.environ.get( "GOOGLE_CLOUD_TESTS_VPCSC_OUTSIDE_PERIMETER_PROJECT", None ) IS_INSIDE_VPCSC = os.environ.get("GOOGLE_CLOUD_TESTS_IN_VPCSC", "false") class TestVPCServiceControlV3(object): @staticmethod def _is_rejected(call): try: responses = call() # If we reach this line, then call() did not raise. The return # result must be either a google.api_core.page_iterator.Iterator # instance, or None. list(responses) except exceptions.PermissionDenied as e: return e.message == "Request is prohibited by organization's policy" except: pass return False @staticmethod def _do_test(delayed_inside, delayed_outside): if IS_INSIDE_VPCSC.lower() == "true": assert TestVPCServiceControlV3._is_rejected(delayed_outside) assert not (TestVPCServiceControlV3._is_rejected(delayed_inside)) else: assert not (TestVPCServiceControlV3._is_rejected(delayed_outside)) assert TestVPCServiceControlV3._is_rejected(delayed_inside) @pytest.mark.skipif( PROJECT_INSIDE is None, reason="Missing environment variable: PROJECT_ID" ) @pytest.mark.skipif( PROJECT_OUTSIDE is None, reason="Missing environment variable: GOOGLE_CLOUD_TESTS_VPCSC_OUTSIDE_PERIMETER_PROJECT", ) def test_create_alert_policy(self): client = monitoring_v3.AlertPolicyServiceClient() name_inside = client.project_path(PROJECT_INSIDE) delayed_inside = lambda: client.create_alert_policy(name_inside, {}) name_outside = client.project_path(PROJECT_OUTSIDE) delayed_outside = lambda: client.create_alert_policy(name_outside, {}) TestVPCServiceControlV3._do_test(delayed_inside, delayed_outside) @pytest.mark.skipif( PROJECT_INSIDE is None, reason="Missing environment variable: PROJECT_ID" ) @pytest.mark.skipif( PROJECT_OUTSIDE is None, reason="Missing environment variable: GOOGLE_CLOUD_TESTS_VPCSC_OUTSIDE_PERIMETER_PROJECT", ) def test_delete_alert_policy(self): client = monitoring_v3.AlertPolicyServiceClient() name_inside = client.alert_policy_path(PROJECT_INSIDE, "mock_alert_policy") delayed_inside = lambda: client.delete_alert_policy(name_inside) name_outside = client.alert_policy_path(PROJECT_OUTSIDE, "mock_alert_policy") delayed_outside = lambda: client.delete_alert_policy(name_outside) TestVPCServiceControlV3._do_test(delayed_inside, delayed_outside) @pytest.mark.skipif( PROJECT_INSIDE is None, reason="Missing environment variable: PROJECT_ID" ) @pytest.mark.skipif( PROJECT_OUTSIDE is None, reason="Missing environment variable: GOOGLE_CLOUD_TESTS_VPCSC_OUTSIDE_PERIMETER_PROJECT", ) def test_get_alert_policy(self): client = monitoring_v3.AlertPolicyServiceClient() name_inside = client.alert_policy_path(PROJECT_INSIDE, "mock_alert_policy") delayed_inside = lambda: client.get_alert_policy(name_inside) name_outside = client.alert_policy_path(PROJECT_OUTSIDE, "mock_alert_policy") delayed_outside = lambda: client.get_alert_policy(name_outside) TestVPCServiceControlV3._do_test(delayed_inside, delayed_outside) @pytest.mark.skipif( PROJECT_INSIDE is None, reason="Missing environment variable: PROJECT_ID" ) @pytest.mark.skipif( PROJECT_OUTSIDE is None, reason="Missing environment variable: GOOGLE_CLOUD_TESTS_VPCSC_OUTSIDE_PERIMETER_PROJECT", ) def test_list_alert_policies(self): client = monitoring_v3.AlertPolicyServiceClient() name_inside = client.project_path(PROJECT_INSIDE) delayed_inside = lambda: client.list_alert_policies(name_inside) name_outside = client.project_path(PROJECT_OUTSIDE) delayed_outside = lambda: client.list_alert_policies(name_outside) TestVPCServiceControlV3._do_test(delayed_inside, delayed_outside) @pytest.mark.skipif( PROJECT_INSIDE is None, reason="Missing environment variable: PROJECT_ID" ) @pytest.mark.skipif( PROJECT_OUTSIDE is None, reason="Missing environment variable: GOOGLE_CLOUD_TESTS_VPCSC_OUTSIDE_PERIMETER_PROJECT", ) def test_update_alert_policy(self): client = monitoring_v3.AlertPolicyServiceClient() name_inside = client.alert_policy_path(PROJECT_INSIDE, "mock_alert_policy") delayed_inside = lambda: client.update_alert_policy({"name": name_inside}) name_outside = client.alert_policy_path(PROJECT_OUTSIDE, "mock_alert_policy") delayed_outside = lambda: client.update_alert_policy({"name": name_outside}) TestVPCServiceControlV3._do_test(delayed_inside, delayed_outside) @pytest.mark.skipif( PROJECT_INSIDE is None, reason="Missing environment variable: PROJECT_ID" ) @pytest.mark.skipif( PROJECT_OUTSIDE is None, reason="Missing environment variable: GOOGLE_CLOUD_TESTS_VPCSC_OUTSIDE_PERIMETER_PROJECT", ) def test_create_group(self): client = monitoring_v3.GroupServiceClient() name_inside = client.project_path(PROJECT_INSIDE) delayed_inside = lambda: client.create_group(name_inside, {}) name_outside = client.project_path(PROJECT_OUTSIDE) delayed_outside = lambda: client.create_group(name_outside, {}) TestVPCServiceControlV3._do_test(delayed_inside, delayed_outside) @pytest.mark.skipif( PROJECT_INSIDE is None, reason="Missing environment variable: PROJECT_ID" ) @pytest.mark.skipif( PROJECT_OUTSIDE is None, reason="Missing environment variable: GOOGLE_CLOUD_TESTS_VPCSC_OUTSIDE_PERIMETER_PROJECT", ) def test_delete_group(self): client = monitoring_v3.GroupServiceClient() name_inside = client.group_path(PROJECT_INSIDE, "mock_group") delayed_inside = lambda: client.delete_group(name_inside) name_outside = client.group_path(PROJECT_OUTSIDE, "mock_group") delayed_outside = lambda: client.delete_group(name_outside) TestVPCServiceControlV3._do_test(delayed_inside, delayed_outside) @pytest.mark.skipif( PROJECT_INSIDE is None, reason="Missing environment variable: PROJECT_ID" ) @pytest.mark.skipif( PROJECT_OUTSIDE is None, reason="Missing environment variable: GOOGLE_CLOUD_TESTS_VPCSC_OUTSIDE_PERIMETER_PROJECT", ) def test_get_group(self): client = monitoring_v3.GroupServiceClient() name_inside = client.group_path(PROJECT_INSIDE, "mock_group") delayed_inside = lambda: client.get_group(name_inside) name_outside = client.group_path(PROJECT_OUTSIDE, "mock_group") delayed_outside = lambda: client.get_group(name_outside) TestVPCServiceControlV3._do_test(delayed_inside, delayed_outside) @pytest.mark.skipif( PROJECT_INSIDE is None, reason="Missing environment variable: PROJECT_ID" ) @pytest.mark.skipif( PROJECT_OUTSIDE is None, reason="Missing environment variable: GOOGLE_CLOUD_TESTS_VPCSC_OUTSIDE_PERIMETER_PROJECT", ) def test_list_group_members(self): client = monitoring_v3.GroupServiceClient() name_inside = client.project_path(PROJECT_INSIDE) delayed_inside = lambda: client.list_group_members(name_inside) name_outside = client.project_path(PROJECT_OUTSIDE) delayed_outside = lambda: client.list_group_members(name_outside) TestVPCServiceControlV3._do_test(delayed_inside, delayed_outside) @pytest.mark.skipif( PROJECT_INSIDE is None, reason="Missing environment variable: PROJECT_ID" ) @pytest.mark.skipif( PROJECT_OUTSIDE is None, reason="Missing environment variable: GOOGLE_CLOUD_TESTS_VPCSC_OUTSIDE_PERIMETER_PROJECT", ) def test_list_groups(self): client = monitoring_v3.GroupServiceClient() name_inside = client.project_path(PROJECT_INSIDE) delayed_inside = lambda: client.list_groups(name_inside) name_outside = client.project_path(PROJECT_OUTSIDE) delayed_outside = lambda: client.list_groups(name_outside) TestVPCServiceControlV3._do_test(delayed_inside, delayed_outside) @pytest.mark.skipif( PROJECT_INSIDE is None, reason="Missing environment variable: PROJECT_ID" ) @pytest.mark.skipif( PROJECT_OUTSIDE is None, reason="Missing environment variable: GOOGLE_CLOUD_TESTS_VPCSC_OUTSIDE_PERIMETER_PROJECT", ) def test_update_group(self): client = monitoring_v3.GroupServiceClient() name_inside = client.group_path(PROJECT_INSIDE, "mock_group") delayed_inside = lambda: client.update_group({"name": name_inside}) name_outside = client.group_path(PROJECT_OUTSIDE, "mock_group") delayed_outside = lambda: client.update_group({"name": name_outside}) TestVPCServiceControlV3._do_test(delayed_inside, delayed_outside) @pytest.mark.skipif( PROJECT_INSIDE is None, reason="Missing environment variable: PROJECT_ID" ) @pytest.mark.skipif( PROJECT_OUTSIDE is None, reason="Missing environment variable: GOOGLE_CLOUD_TESTS_VPCSC_OUTSIDE_PERIMETER_PROJECT", ) def test_create_metric_descriptor(self): client = monitoring_v3.MetricServiceClient() name_inside = client.project_path(PROJECT_INSIDE) delayed_inside = lambda: client.create_metric_descriptor(name_inside, {}) name_outside = client.project_path(PROJECT_OUTSIDE) delayed_outside = lambda: client.create_metric_descriptor(name_outside, {}) TestVPCServiceControlV3._do_test(delayed_inside, delayed_outside) @pytest.mark.skipif( PROJECT_INSIDE is None, reason="Missing environment variable: PROJECT_ID" ) @pytest.mark.skipif( PROJECT_OUTSIDE is None, reason="Missing environment variable: GOOGLE_CLOUD_TESTS_VPCSC_OUTSIDE_PERIMETER_PROJECT", ) def test_create_time_series(self): client = monitoring_v3.MetricServiceClient() name_inside = client.project_path(PROJECT_INSIDE) delayed_inside = lambda: client.create_time_series(name_inside, {}) name_outside = client.project_path(PROJECT_OUTSIDE) delayed_outside = lambda: client.create_time_series(name_outside, {}) TestVPCServiceControlV3._do_test(delayed_inside, delayed_outside) @pytest.mark.skipif( PROJECT_INSIDE is None, reason="Missing environment variable: PROJECT_ID" ) @pytest.mark.skipif( PROJECT_OUTSIDE is None, reason="Missing environment variable: GOOGLE_CLOUD_TESTS_VPCSC_OUTSIDE_PERIMETER_PROJECT", ) def test_delete_metric_descriptor(self): client = monitoring_v3.MetricServiceClient() name_inside = client.metric_descriptor_path( PROJECT_INSIDE, "mock_metric_descriptor" ) delayed_inside = lambda: client.delete_metric_descriptor(name_inside) name_outside = client.metric_descriptor_path( PROJECT_OUTSIDE, "mock_metric_descriptor" ) delayed_outside = lambda: client.delete_metric_descriptor(name_outside) TestVPCServiceControlV3._do_test(delayed_inside, delayed_outside) @pytest.mark.skipif( PROJECT_INSIDE is None, reason="Missing environment variable: PROJECT_ID" ) @pytest.mark.skipif( PROJECT_OUTSIDE is None, reason="Missing environment variable: GOOGLE_CLOUD_TESTS_VPCSC_OUTSIDE_PERIMETER_PROJECT", ) def test_get_metric_descriptor(self): client = monitoring_v3.MetricServiceClient() name_inside = client.metric_descriptor_path( PROJECT_INSIDE, "mock_metric_descriptor" ) delayed_inside = lambda: client.get_metric_descriptor(name_inside) name_outside = client.metric_descriptor_path( PROJECT_OUTSIDE, "mock_metric_descriptor" ) delayed_outside = lambda: client.get_metric_descriptor(name_outside) TestVPCServiceControlV3._do_test(delayed_inside, delayed_outside) @pytest.mark.skipif( PROJECT_INSIDE is None, reason="Missing environment variable: PROJECT_ID" ) @pytest.mark.skipif( PROJECT_OUTSIDE is None, reason="Missing environment variable: GOOGLE_CLOUD_TESTS_VPCSC_OUTSIDE_PERIMETER_PROJECT", ) def test_get_monitored_resource_descriptor(self): client = monitoring_v3.MetricServiceClient() name_inside = client.monitored_resource_descriptor_path( PROJECT_INSIDE, "mock_monitored_resource_descriptor" ) delayed_inside = lambda: client.get_monitored_resource_descriptor(name_inside) name_outside = client.monitored_resource_descriptor_path( PROJECT_OUTSIDE, "mock_monitored_resource_descriptor" ) delayed_outside = lambda: client.get_monitored_resource_descriptor(name_outside) TestVPCServiceControlV3._do_test(delayed_inside, delayed_outside) @pytest.mark.skipif( PROJECT_INSIDE is None, reason="Missing environment variable: PROJECT_ID" ) @pytest.mark.skipif( PROJECT_OUTSIDE is None, reason="Missing environment variable: GOOGLE_CLOUD_TESTS_VPCSC_OUTSIDE_PERIMETER_PROJECT", ) def test_list_metric_descriptors(self): client = monitoring_v3.MetricServiceClient() name_inside = client.project_path(PROJECT_INSIDE) delayed_inside = lambda: client.list_metric_descriptors(name_inside) name_outside = client.project_path(PROJECT_OUTSIDE) delayed_outside = lambda: client.list_metric_descriptors(name_outside) TestVPCServiceControlV3._do_test(delayed_inside, delayed_outside) @pytest.mark.skipif( PROJECT_INSIDE is None, reason="Missing environment variable: PROJECT_ID" ) @pytest.mark.skipif( PROJECT_OUTSIDE is None, reason="Missing environment variable: GOOGLE_CLOUD_TESTS_VPCSC_OUTSIDE_PERIMETER_PROJECT", ) def test_list_monitored_resource_descriptors(self): client = monitoring_v3.MetricServiceClient() name_inside = client.project_path(PROJECT_INSIDE) delayed_inside = lambda: client.list_monitored_resource_descriptors(name_inside) name_outside = client.project_path(PROJECT_OUTSIDE) delayed_outside = lambda: client.list_monitored_resource_descriptors( name_outside ) TestVPCServiceControlV3._do_test(delayed_inside, delayed_outside) @pytest.mark.skipif( PROJECT_INSIDE is None, reason="Missing environment variable: PROJECT_ID" ) @pytest.mark.skipif( PROJECT_OUTSIDE is None, reason="Missing environment variable: GOOGLE_CLOUD_TESTS_VPCSC_OUTSIDE_PERIMETER_PROJECT", ) def test_list_time_series(self): client = monitoring_v3.MetricServiceClient() name_inside = client.project_path(PROJECT_INSIDE) delayed_inside = lambda: client.list_time_series( name_inside, "", {}, enums.ListTimeSeriesRequest.TimeSeriesView.FULL ) name_outside = client.project_path(PROJECT_OUTSIDE) delayed_outside = lambda: client.list_time_series( name_outside, "", {}, enums.ListTimeSeriesRequest.TimeSeriesView.FULL ) TestVPCServiceControlV3._do_test(delayed_inside, delayed_outside) @pytest.mark.skipif( PROJECT_INSIDE is None, reason="Missing environment variable: PROJECT_ID" ) @pytest.mark.skipif( PROJECT_OUTSIDE is None, reason="Missing environment variable: GOOGLE_CLOUD_TESTS_VPCSC_OUTSIDE_PERIMETER_PROJECT", ) def test_create_notification_channel(self): client = monitoring_v3.NotificationChannelServiceClient() name_inside = client.project_path(PROJECT_INSIDE) delayed_inside = lambda: client.create_notification_channel(name_inside, {}) name_outside = client.project_path(PROJECT_OUTSIDE) delayed_outside = lambda: client.create_notification_channel(name_outside, {}) TestVPCServiceControlV3._do_test(delayed_inside, delayed_outside) @pytest.mark.skipif( PROJECT_INSIDE is None, reason="Missing environment variable: PROJECT_ID" ) @pytest.mark.skipif( PROJECT_OUTSIDE is None, reason="Missing environment variable: GOOGLE_CLOUD_TESTS_VPCSC_OUTSIDE_PERIMETER_PROJECT", ) def test_delete_notification_channel(self): client = monitoring_v3.NotificationChannelServiceClient() name_inside = client.notification_channel_path( PROJECT_INSIDE, "mock_notification_channel" ) delayed_inside = lambda: client.delete_notification_channel(name_inside) name_outside = client.notification_channel_path( PROJECT_OUTSIDE, "mock_notification_channel" ) delayed_outside = lambda: client.delete_notification_channel(name_outside) TestVPCServiceControlV3._do_test(delayed_inside, delayed_outside) @pytest.mark.skipif( PROJECT_INSIDE is None, reason="Missing environment variable: PROJECT_ID" ) @pytest.mark.skipif( PROJECT_OUTSIDE is None, reason="Missing environment variable: GOOGLE_CLOUD_TESTS_VPCSC_OUTSIDE_PERIMETER_PROJECT", ) def test_get_notification_channel(self): client = monitoring_v3.NotificationChannelServiceClient() name_inside = client.notification_channel_path( PROJECT_INSIDE, "mock_notification_channel" ) delayed_inside = lambda: client.get_notification_channel(name_inside) name_outside = client.notification_channel_path( PROJECT_OUTSIDE, "mock_notification_channel" ) delayed_outside = lambda: client.get_notification_channel(name_outside) TestVPCServiceControlV3._do_test(delayed_inside, delayed_outside) @pytest.mark.skipif( PROJECT_INSIDE is None, reason="Missing environment variable: PROJECT_ID" ) @pytest.mark.skipif( PROJECT_OUTSIDE is None, reason="Missing environment variable: GOOGLE_CLOUD_TESTS_VPCSC_OUTSIDE_PERIMETER_PROJECT", ) def test_get_notification_channel_descriptor(self): client = monitoring_v3.NotificationChannelServiceClient() name_inside = client.notification_channel_descriptor_path( PROJECT_INSIDE, "mock_notification_channel_descriptor" ) delayed_inside = lambda: client.get_notification_channel_descriptor(name_inside) name_outside = client.notification_channel_descriptor_path( PROJECT_OUTSIDE, "mock_notification_channel_descriptor" ) delayed_outside = lambda: client.get_notification_channel_descriptor( name_outside ) TestVPCServiceControlV3._do_test(delayed_inside, delayed_outside) @pytest.mark.skipif( PROJECT_INSIDE is None, reason="Missing environment variable: PROJECT_ID" ) @pytest.mark.skipif( PROJECT_OUTSIDE is None, reason="Missing environment variable: GOOGLE_CLOUD_TESTS_VPCSC_OUTSIDE_PERIMETER_PROJECT", ) def test_list_notification_channel_descriptors(self): client = monitoring_v3.NotificationChannelServiceClient() name_inside = client.project_path(PROJECT_INSIDE) delayed_inside = lambda: client.list_notification_channel_descriptors( name_inside ) name_outside = client.project_path(PROJECT_OUTSIDE) delayed_outside = lambda: client.list_notification_channel_descriptors( name_outside ) TestVPCServiceControlV3._do_test(delayed_inside, delayed_outside) @pytest.mark.skipif( PROJECT_INSIDE is None, reason="Missing environment variable: PROJECT_ID" ) @pytest.mark.skipif( PROJECT_OUTSIDE is None, reason="Missing environment variable: GOOGLE_CLOUD_TESTS_VPCSC_OUTSIDE_PERIMETER_PROJECT", ) def test_list_notification_channels(self): client = monitoring_v3.NotificationChannelServiceClient() name_inside = client.project_path(PROJECT_INSIDE) delayed_inside = lambda: client.list_notification_channels(name_inside) name_outside = client.project_path(PROJECT_OUTSIDE) delayed_outside = lambda: client.list_notification_channels(name_outside) TestVPCServiceControlV3._do_test(delayed_inside, delayed_outside) @pytest.mark.skipif( PROJECT_INSIDE is None, reason="Missing environment variable: PROJECT_ID" ) @pytest.mark.skipif( PROJECT_OUTSIDE is None, reason="Missing environment variable: GOOGLE_CLOUD_TESTS_VPCSC_OUTSIDE_PERIMETER_PROJECT", ) def test_update_notification_channel(self): client = monitoring_v3.NotificationChannelServiceClient() name_inside = client.notification_channel_path( PROJECT_INSIDE, "mock_notification_channel" ) delayed_inside = lambda: client.update_notification_channel( {"name": name_inside} ) name_outside = client.notification_channel_path( PROJECT_OUTSIDE, "mock_notification_channel" ) delayed_outside = lambda: client.update_notification_channel( {"name": name_outside} ) TestVPCServiceControlV3._do_test(delayed_inside, delayed_outside) @pytest.mark.skipif( PROJECT_INSIDE is None, reason="Missing environment variable: PROJECT_ID" ) @pytest.mark.skipif( PROJECT_OUTSIDE is None, reason="Missing environment variable: GOOGLE_CLOUD_TESTS_VPCSC_OUTSIDE_PERIMETER_PROJECT", ) def test_create_uptime_check_config(self): client = monitoring_v3.UptimeCheckServiceClient() name_inside = client.project_path(PROJECT_INSIDE) delayed_inside = lambda: client.create_uptime_check_config(name_inside, {}) name_outside = client.project_path(PROJECT_OUTSIDE) delayed_outside = lambda: client.create_uptime_check_config(name_outside, {}) TestVPCServiceControlV3._do_test(delayed_inside, delayed_outside) @pytest.mark.skipif( PROJECT_INSIDE is None, reason="Missing environment variable: PROJECT_ID" ) @pytest.mark.skipif( PROJECT_OUTSIDE is None, reason="Missing environment variable: GOOGLE_CLOUD_TESTS_VPCSC_OUTSIDE_PERIMETER_PROJECT", ) def test_delete_uptime_check_config(self): client = monitoring_v3.UptimeCheckServiceClient() name_inside = client.uptime_check_config_path( PROJECT_INSIDE, "mock_uptime_check_config" ) delayed_inside = lambda: client.delete_uptime_check_config(name_inside) name_outside = client.uptime_check_config_path( PROJECT_OUTSIDE, "mock_uptime_check_config" ) delayed_outside = lambda: client.delete_uptime_check_config(name_outside) TestVPCServiceControlV3._do_test(delayed_inside, delayed_outside) @pytest.mark.skipif( PROJECT_INSIDE is None, reason="Missing environment variable: PROJECT_ID" ) @pytest.mark.skipif( PROJECT_OUTSIDE is None, reason="Missing environment variable: GOOGLE_CLOUD_TESTS_VPCSC_OUTSIDE_PERIMETER_PROJECT", ) def test_get_uptime_check_config(self): client = monitoring_v3.UptimeCheckServiceClient() name_inside = client.uptime_check_config_path( PROJECT_INSIDE, "mock_uptime_check_config" ) delayed_inside = lambda: client.get_uptime_check_config(name_inside) name_outside = client.uptime_check_config_path( PROJECT_OUTSIDE, "mock_uptime_check_config" ) delayed_outside = lambda: client.get_uptime_check_config(name_outside) TestVPCServiceControlV3._do_test(delayed_inside, delayed_outside) @pytest.mark.skipif( PROJECT_INSIDE is None, reason="Missing environment variable: PROJECT_ID" ) @pytest.mark.skipif( PROJECT_OUTSIDE is None, reason="Missing environment variable: GOOGLE_CLOUD_TESTS_VPCSC_OUTSIDE_PERIMETER_PROJECT", ) def test_list_uptime_check_configs(self): client = monitoring_v3.UptimeCheckServiceClient() name_inside = client.project_path(PROJECT_INSIDE) delayed_inside = lambda: client.list_uptime_check_configs(name_inside) name_outside = client.project_path(PROJECT_OUTSIDE) delayed_outside = lambda: client.list_uptime_check_configs(name_outside) TestVPCServiceControlV3._do_test(delayed_inside, delayed_outside) @pytest.mark.skipif( PROJECT_INSIDE is None, reason="Missing environment variable: PROJECT_ID" ) @pytest.mark.skipif( PROJECT_OUTSIDE is None, reason="Missing environment variable: GOOGLE_CLOUD_TESTS_VPCSC_OUTSIDE_PERIMETER_PROJECT", ) def test_update_uptime_check_config(self): client = monitoring_v3.UptimeCheckServiceClient() name_inside = client.uptime_check_config_path( PROJECT_INSIDE, "mock_uptime_check_config" ) delayed_inside = lambda: client.update_uptime_check_config( {"name": name_inside} ) name_outside = client.uptime_check_config_path( PROJECT_OUTSIDE, "mock_uptime_check_config" ) delayed_outside = lambda: client.update_uptime_check_config( {"name": name_outside} ) TestVPCServiceControlV3._do_test(delayed_inside, delayed_outside)

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6

be0ca882a3db89c7a03c60e63dba3c68f62ed935

1,683

py

Python

molsysmt/tests/pbc/box_lengths_from_box_vectors/test_box_lengths_from_box_vectors.py

dprada/molsysmt

83f150bfe3cfa7603566a0ed4aed79d9b0c97f5d

[ "MIT" ]

null

molsysmt/tests/pbc/box_lengths_from_box_vectors/test_box_lengths_from_box_vectors.py

dprada/molsysmt

83f150bfe3cfa7603566a0ed4aed79d9b0c97f5d

[ "MIT" ]

null

molsysmt/tests/pbc/box_lengths_from_box_vectors/test_box_lengths_from_box_vectors.py

dprada/molsysmt

83f150bfe3cfa7603566a0ed4aed79d9b0c97f5d

[ "MIT" ]

null

""" Unit and regression test for the box_lengths_from_box_vectors module of the molsysmt package. """ # Import package, test suite, and other packages as needed import molsysmt as msm import numpy as np # Distance between atoms in space and time def test_box_lengths_from_box_vectors_1(): molsys = msm.convert(msm.demo['Met-enkephalin']['vacuum.msmpk'], to_form='molsysmt.MolSys') molsys = msm.build.solvate(molsys, box_geometry='cubic', clearance='14.0 angstroms', engine='PDBFixer') box = msm.get(molsys, target='system', box=True) lengths = msm.pbc.box_lengths_from_box_vectors(box) check = np.allclose(msm.puw.get_value(lengths, to_unit='nm'), [[3.1236, 3.1236, 3.1236]]) assert check def test_box_lengths_from_box_vectors_2(): molsys = msm.convert(msm.demo['Met-enkephalin']['vacuum.msmpk'], to_form='molsysmt.MolSys') molsys = msm.build.solvate(molsys, box_geometry='truncated octahedral', clearance='14.0 angstroms', engine='PDBFixer') box = msm.get(molsys, target='system', box=True) lengths = msm.pbc.box_lengths_from_box_vectors(box) check = np.allclose(msm.puw.get_value(lengths, to_unit='nm'), [[3.1236, 3.1236, 3.1236]]) assert check def test_box_lengths_from_box_vectors_3(): molsys = msm.convert(msm.demo['Met-enkephalin']['vacuum.msmpk'], to_form='molsysmt.MolSys') molsys = msm.build.solvate(molsys, box_geometry='rhombic dodecahedral', clearance='14.0 angstroms', engine='PDBFixer') box = msm.get(molsys, target='system', box=True) lengths = msm.pbc.box_lengths_from_box_vectors(box) check = np.allclose(msm.puw.get_value(lengths, to_unit='nm'), [[3.1236, 3.1236, 3.1236]]) assert check

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be20bf4c814f553043f910730684ae7fe8f2151e

46,629

py

Python

colour/plotting/diagrams.py

tjdcs/colour

09413da71b5da57408eb812797c5db1300d4791a

[ "BSD-3-Clause" ]

null

colour/plotting/diagrams.py

tjdcs/colour

09413da71b5da57408eb812797c5db1300d4791a

[ "BSD-3-Clause" ]

null

colour/plotting/diagrams.py

tjdcs/colour

09413da71b5da57408eb812797c5db1300d4791a

[ "BSD-3-Clause" ]

null

""" CIE Chromaticity Diagrams Plotting ================================== Defines the *CIE* chromaticity diagrams plotting objects: - :func:`colour.plotting.plot_chromaticity_diagram_CIE1931` - :func:`colour.plotting.plot_chromaticity_diagram_CIE1960UCS` - :func:`colour.plotting.plot_chromaticity_diagram_CIE1976UCS` - :func:`colour.plotting.plot_sds_in_chromaticity_diagram_CIE1931` - :func:`colour.plotting.plot_sds_in_chromaticity_diagram_CIE1960UCS` - :func:`colour.plotting.plot_sds_in_chromaticity_diagram_CIE1976UCS` """ from __future__ import annotations import bisect import matplotlib.pyplot as plt import numpy as np from matplotlib.collections import LineCollection from matplotlib.patches import Polygon from colour.algebra import normalise_maximum, normalise_vector from colour.colorimetry import ( MultiSpectralDistributions, SDS_ILLUMINANTS, SpectralDistribution, sd_to_XYZ, sds_and_msds_to_sds, ) from colour.hints import ( Any, ArrayLike, Boolean, Callable, Dict, Floating, Integer, List, Literal, NDArray, Optional, Sequence, Tuple, Union, cast, ) from colour.models import ( Luv_to_uv, Luv_uv_to_xy, UCS_to_uv, UCS_uv_to_xy, XYZ_to_Luv, XYZ_to_UCS, XYZ_to_xy, xy_to_XYZ, ) from colour.notation import HEX_to_RGB from colour.plotting import ( CONSTANTS_COLOUR_STYLE, CONSTANTS_ARROW_STYLE, XYZ_to_plotting_colourspace, artist, filter_cmfs, filter_illuminants, override_style, render, update_settings_collection, ) from colour.utilities import ( as_float_array, domain_range_scale, first_item, is_string, optional, tsplit, tstack, validate_method, ) __author__ = "Colour Developers" __copyright__ = "Copyright 2013 Colour Developers" __license__ = "New BSD License - https://opensource.org/licenses/BSD-3-Clause" __maintainer__ = "Colour Developers" __email__ = "colour-developers@colour-science.org" __status__ = "Production" __all__ = [ "plot_spectral_locus", "plot_chromaticity_diagram_colours", "plot_chromaticity_diagram", "plot_chromaticity_diagram_CIE1931", "plot_chromaticity_diagram_CIE1960UCS", "plot_chromaticity_diagram_CIE1976UCS", "plot_sds_in_chromaticity_diagram", "plot_sds_in_chromaticity_diagram_CIE1931", "plot_sds_in_chromaticity_diagram_CIE1960UCS", "plot_sds_in_chromaticity_diagram_CIE1976UCS", ] @override_style() def plot_spectral_locus( cmfs: Union[ MultiSpectralDistributions, str, Sequence[Union[MultiSpectralDistributions, str]], ] = "CIE 1931 2 Degree Standard Observer", spectral_locus_colours: Optional[Union[ArrayLike, str]] = None, spectral_locus_opacity: Floating = 1, spectral_locus_labels: Optional[Sequence] = None, method: Union[ Literal["CIE 1931", "CIE 1960 UCS", "CIE 1976 UCS"], str ] = "CIE 1931", **kwargs: Any, ) -> Tuple[plt.Figure, plt.Axes]: """ Plot the *Spectral Locus* according to given method. Parameters ---------- cmfs Standard observer colour matching functions used for computing the spectral locus boundaries. ``cmfs`` can be of any type or form supported by the :func:`colour.plotting.filter_cmfs` definition. spectral_locus_colours Colours of the *Spectral Locus*, if ``spectral_locus_colours`` is set to *RGB*, the colours will be computed according to the corresponding chromaticity coordinates. spectral_locus_opacity Opacity of the *Spectral Locus*. spectral_locus_labels Array of wavelength labels used to customise which labels will be drawn around the spectral locus. Passing an empty array will result in no wavelength labels being drawn. method *Chromaticity Diagram* method. Other Parameters ---------------- kwargs {:func:`colour.plotting.artist`, :func:`colour.plotting.render`}, See the documentation of the previously listed definitions. Returns ------- :class:`tuple` Current figure and axes. Examples -------- >>> plot_spectral_locus(spectral_locus_colours='RGB') # doctest: +ELLIPSIS (<Figure size ... with 1 Axes>, <...AxesSubplot...>) .. image:: ../_static/Plotting_Plot_Spectral_Locus.png :align: center :alt: plot_spectral_locus """ method = validate_method( method, ["CIE 1931", "CIE 1960 UCS", "CIE 1976 UCS"] ) spectral_locus_colours = optional( spectral_locus_colours, CONSTANTS_COLOUR_STYLE.colour.dark ) settings: Dict[str, Any] = {"uniform": True} settings.update(kwargs) _figure, axes = artist(**settings) cmfs = cast( MultiSpectralDistributions, first_item(filter_cmfs(cmfs).values()) ) illuminant = CONSTANTS_COLOUR_STYLE.colour.colourspace.whitepoint wavelengths = list(cmfs.wavelengths) equal_energy = np.array([1 / 3] * 2) if method == "cie 1931": ij = XYZ_to_xy(cmfs.values, illuminant) labels = cast( Tuple, optional( spectral_locus_labels, ( 390, 460, 470, 480, 490, 500, 510, 520, 540, 560, 580, 600, 620, 700, ), ), ) elif method == "cie 1960 ucs": ij = UCS_to_uv(XYZ_to_UCS(cmfs.values)) labels = cast( Tuple, optional( spectral_locus_labels, ( 420, 440, 450, 460, 470, 480, 490, 500, 510, 520, 530, 540, 550, 560, 570, 580, 590, 600, 610, 620, 630, 645, 680, ), ), ) elif method == "cie 1976 ucs": ij = Luv_to_uv(XYZ_to_Luv(cmfs.values, illuminant), illuminant) labels = cast( Tuple, optional( spectral_locus_labels, ( 420, 440, 450, 460, 470, 480, 490, 500, 510, 520, 530, 540, 550, 560, 570, 580, 590, 600, 610, 620, 630, 645, 680, ), ), ) pl_ij = np.reshape( tstack( [ np.linspace(ij[0][0], ij[-1][0], 20), np.linspace(ij[0][1], ij[-1][1], 20), ] ), (-1, 1, 2), ) sl_ij = np.copy(ij).reshape(-1, 1, 2) purple_line_colours: Optional[Union[ArrayLike, str]] if str(spectral_locus_colours).upper() == "RGB": spectral_locus_colours = normalise_maximum( XYZ_to_plotting_colourspace(cmfs.values), axis=-1 ) if method == "cie 1931": XYZ = xy_to_XYZ(pl_ij) elif method == "cie 1960 ucs": XYZ = xy_to_XYZ(UCS_uv_to_xy(pl_ij)) elif method == "cie 1976 ucs": XYZ = xy_to_XYZ(Luv_uv_to_xy(pl_ij)) purple_line_colours = normalise_maximum( XYZ_to_plotting_colourspace(np.reshape(XYZ, (-1, 3))), axis=-1 ) else: purple_line_colours = spectral_locus_colours for slp_ij, slp_colours in ( (pl_ij, purple_line_colours), (sl_ij, spectral_locus_colours), ): line_collection = LineCollection( np.concatenate([slp_ij[:-1], slp_ij[1:]], axis=1), colors=slp_colours, alpha=spectral_locus_opacity, zorder=CONSTANTS_COLOUR_STYLE.zorder.midground_scatter, ) axes.add_collection(line_collection) wl_ij = dict(zip(wavelengths, ij)) for label in labels: ij_l = wl_ij.get(label) if ij_l is None: continue ij_l = as_float_array([ij_l]) i, j = tsplit(ij_l) index = bisect.bisect(wavelengths, label) left = wavelengths[index - 1] if index >= 0 else wavelengths[index] right = ( wavelengths[index] if index < len(wavelengths) else wavelengths[-1] ) dx = wl_ij[right][0] - wl_ij[left][0] dy = wl_ij[right][1] - wl_ij[left][1] direction = np.array([-dy, dx]) normal = ( np.array([-dy, dx]) if np.dot( normalise_vector(ij_l - equal_energy), normalise_vector(direction), ) > 0 else np.array([dy, -dx]) ) normal = as_float_array(normalise_vector(normal) / 30) label_colour = ( spectral_locus_colours if is_string(spectral_locus_colours) else spectral_locus_colours[index] # type: ignore[index] ) axes.plot( (i, i + normal[0] * 0.75), (j, j + normal[1] * 0.75), color=label_colour, alpha=spectral_locus_opacity, zorder=CONSTANTS_COLOUR_STYLE.zorder.background_line, ) axes.plot( i, j, "o", color=label_colour, alpha=spectral_locus_opacity, zorder=CONSTANTS_COLOUR_STYLE.zorder.background_line, ) axes.text( i + normal[0], j + normal[1], label, clip_on=True, ha="left" if normal[0] >= 0 else "right", va="center", fontdict={"size": "small"}, zorder=CONSTANTS_COLOUR_STYLE.zorder.background_label, ) settings = {"axes": axes} settings.update(kwargs) return render(**kwargs) @override_style() def plot_chromaticity_diagram_colours( samples: Integer = 256, diagram_colours: Optional[Union[ArrayLike, str]] = None, diagram_opacity: Floating = 1, diagram_clipping_path: Optional[ArrayLike] = None, cmfs: Union[ MultiSpectralDistributions, str, Sequence[Union[MultiSpectralDistributions, str]], ] = "CIE 1931 2 Degree Standard Observer", method: Union[ Literal["CIE 1931", "CIE 1960 UCS", "CIE 1976 UCS"], str ] = "CIE 1931", **kwargs: Any, ) -> Tuple[plt.Figure, plt.Axes]: """ Plot the *Chromaticity Diagram* colours according to given method. Parameters ---------- samples Samples count on one axis when computing the *Chromaticity Diagram* colours. diagram_colours Colours of the *Chromaticity Diagram*, if ``diagram_colours`` is set to *RGB*, the colours will be computed according to the corresponding coordinates. diagram_opacity Opacity of the *Chromaticity Diagram*. diagram_clipping_path Path of points used to clip the *Chromaticity Diagram* colours. cmfs Standard observer colour matching functions used for computing the spectral locus boundaries. ``cmfs`` can be of any type or form supported by the :func:`colour.plotting.filter_cmfs` definition. method *Chromaticity Diagram* method. Other Parameters ---------------- kwargs {:func:`colour.plotting.artist`, :func:`colour.plotting.render`}, See the documentation of the previously listed definitions. Returns ------- :class:`tuple` Current figure and axes. Examples -------- >>> plot_chromaticity_diagram_colours(diagram_colours='RGB') ... # doctest: +ELLIPSIS (<Figure size ... with 1 Axes>, <...AxesSubplot...>) .. image:: ../_static/Plotting_Plot_Chromaticity_Diagram_Colours.png :align: center :alt: plot_chromaticity_diagram_colours """ method = validate_method( method, ["CIE 1931", "CIE 1960 UCS", "CIE 1976 UCS"] ) settings: Dict[str, Any] = {"uniform": True} settings.update(kwargs) _figure, axes = artist(**settings) diagram_colours = cast( ArrayLike, optional( diagram_colours, HEX_to_RGB(CONSTANTS_COLOUR_STYLE.colour.average) ), ) cmfs = cast( MultiSpectralDistributions, first_item(filter_cmfs(cmfs).values()) ) illuminant = CONSTANTS_COLOUR_STYLE.colour.colourspace.whitepoint if method == "cie 1931": spectral_locus = XYZ_to_xy(cmfs.values, illuminant) elif method == "cie 1960 ucs": spectral_locus = UCS_to_uv(XYZ_to_UCS(cmfs.values)) elif method == "cie 1976 ucs": spectral_locus = Luv_to_uv( XYZ_to_Luv(cmfs.values, illuminant), illuminant ) use_RGB_diagram_colours = str(diagram_colours).upper() == "RGB" if use_RGB_diagram_colours: ii, jj = np.meshgrid( np.linspace(0, 1, samples), np.linspace(1, 0, samples) ) ij = tstack([ii, jj]) if method == "cie 1931": XYZ = xy_to_XYZ(ij) elif method == "cie 1960 ucs": XYZ = xy_to_XYZ(UCS_uv_to_xy(ij)) elif method == "cie 1976 ucs": XYZ = xy_to_XYZ(Luv_uv_to_xy(ij)) diagram_colours = normalise_maximum( XYZ_to_plotting_colourspace(XYZ, illuminant), axis=-1 ) polygon = Polygon( spectral_locus if diagram_clipping_path is None else diagram_clipping_path, facecolor="none" if use_RGB_diagram_colours else np.hstack([diagram_colours, diagram_opacity]), edgecolor="none" if use_RGB_diagram_colours else np.hstack([diagram_colours, diagram_opacity]), zorder=CONSTANTS_COLOUR_STYLE.zorder.background_polygon, ) axes.add_patch(polygon) if use_RGB_diagram_colours: # Preventing bounding box related issues as per # https://github.com/matplotlib/matplotlib/issues/10529 image = axes.imshow( diagram_colours, interpolation="bilinear", extent=(0, 1, 0, 1), clip_path=None, alpha=diagram_opacity, zorder=CONSTANTS_COLOUR_STYLE.zorder.background_polygon, ) image.set_clip_path(polygon) settings = {"axes": axes} settings.update(kwargs) return render(**kwargs) @override_style() def plot_chromaticity_diagram( cmfs: Union[ MultiSpectralDistributions, str, Sequence[Union[MultiSpectralDistributions, str]], ] = "CIE 1931 2 Degree Standard Observer", show_diagram_colours: Boolean = True, show_spectral_locus: Boolean = True, method: Union[ Literal["CIE 1931", "CIE 1960 UCS", "CIE 1976 UCS"], str ] = "CIE 1931", **kwargs: Any, ) -> Tuple[plt.Figure, plt.Axes]: """ Plot the *Chromaticity Diagram* according to given method. Parameters ---------- cmfs Standard observer colour matching functions used for computing the spectral locus boundaries. ``cmfs`` can be of any type or form supported by the :func:`colour.plotting.filter_cmfs` definition. show_diagram_colours Whether to display the *Chromaticity Diagram* background colours. show_spectral_locus Whether to display the *Spectral Locus*. method *Chromaticity Diagram* method. Other Parameters ---------------- kwargs {:func:`colour.plotting.artist`, :func:`colour.plotting.diagrams.plot_spectral_locus`, :func:`colour.plotting.diagrams.plot_chromaticity_diagram_colours`, :func:`colour.plotting.render`}, See the documentation of the previously listed definitions. Returns ------- :class:`tuple` Current figure and axes. Examples -------- >>> plot_chromaticity_diagram() # doctest: +ELLIPSIS (<Figure size ... with 1 Axes>, <...AxesSubplot...>) .. image:: ../_static/Plotting_Plot_Chromaticity_Diagram.png :align: center :alt: plot_chromaticity_diagram """ method = validate_method( method, ["CIE 1931", "CIE 1960 UCS", "CIE 1976 UCS"] ) settings: Dict[str, Any] = {"uniform": True} settings.update(kwargs) _figure, axes = artist(**settings) cmfs = cast( MultiSpectralDistributions, first_item(filter_cmfs(cmfs).values()) ) if show_diagram_colours: settings = {"axes": axes, "method": method, "diagram_colours": "RGB"} settings.update(kwargs) settings["standalone"] = False settings["cmfs"] = cmfs plot_chromaticity_diagram_colours(**settings) if show_spectral_locus: settings = {"axes": axes, "method": method} settings.update(kwargs) settings["standalone"] = False settings["cmfs"] = cmfs plot_spectral_locus(**settings) if method == "cie 1931": x_label, y_label = "CIE x", "CIE y" elif method == "cie 1960 ucs": x_label, y_label = "CIE u", "CIE v" elif method == "cie 1976 ucs": x_label, y_label = ( "CIE u'", "CIE v'", ) title = f"{method.upper()} Chromaticity Diagram - {cmfs.strict_name}" settings.update( { "axes": axes, "standalone": True, "bounding_box": (0, 1, 0, 1), "title": title, "x_label": x_label, "y_label": y_label, } ) settings.update(kwargs) return render(**settings) @override_style() def plot_chromaticity_diagram_CIE1931( cmfs: Union[ MultiSpectralDistributions, str, Sequence[Union[MultiSpectralDistributions, str]], ] = "CIE 1931 2 Degree Standard Observer", show_diagram_colours: Boolean = True, show_spectral_locus: Boolean = True, **kwargs: Any, ) -> Tuple[plt.Figure, plt.Axes]: """ Plot the *CIE 1931 Chromaticity Diagram*. Parameters ---------- cmfs Standard observer colour matching functions used for computing the spectral locus boundaries. ``cmfs`` can be of any type or form supported by the :func:`colour.plotting.filter_cmfs` definition. show_diagram_colours Whether to display the *Chromaticity Diagram* background colours. show_spectral_locus Whether to display the *Spectral Locus*. Other Parameters ---------------- kwargs {:func:`colour.plotting.artist`, :func:`colour.plotting.diagrams.plot_chromaticity_diagram`, :func:`colour.plotting.render`}, See the documentation of the previously listed definitions. Returns ------- :class:`tuple` Current figure and axes. Examples -------- >>> plot_chromaticity_diagram_CIE1931() # doctest: +ELLIPSIS (<Figure size ... with 1 Axes>, <...AxesSubplot...>) .. image:: ../_static/Plotting_Plot_Chromaticity_Diagram_CIE1931.png :align: center :alt: plot_chromaticity_diagram_CIE1931 """ settings = dict(kwargs) settings.update({"method": "CIE 1931"}) return plot_chromaticity_diagram( cmfs, show_diagram_colours, show_spectral_locus, **settings ) @override_style() def plot_chromaticity_diagram_CIE1960UCS( cmfs: Union[ MultiSpectralDistributions, str, Sequence[Union[MultiSpectralDistributions, str]], ] = "CIE 1931 2 Degree Standard Observer", show_diagram_colours: Boolean = True, show_spectral_locus: Boolean = True, **kwargs: Any, ) -> Tuple[plt.Figure, plt.Axes]: """ Plot the *CIE 1960 UCS Chromaticity Diagram*. Parameters ---------- cmfs Standard observer colour matching functions used for computing the spectral locus boundaries. ``cmfs`` can be of any type or form supported by the :func:`colour.plotting.filter_cmfs` definition. show_diagram_colours Whether to display the *Chromaticity Diagram* background colours. show_spectral_locus Whether to display the *Spectral Locus*. Other Parameters ---------------- kwargs {:func:`colour.plotting.artist`, :func:`colour.plotting.diagrams.plot_chromaticity_diagram`, :func:`colour.plotting.render`}, See the documentation of the previously listed definitions. Returns ------- :class:`tuple` Current figure and axes. Examples -------- >>> plot_chromaticity_diagram_CIE1960UCS() # doctest: +ELLIPSIS (<Figure size ... with 1 Axes>, <...AxesSubplot...>) .. image:: ../_static/Plotting_Plot_Chromaticity_Diagram_CIE1960UCS.png :align: center :alt: plot_chromaticity_diagram_CIE1960UCS """ settings = dict(kwargs) settings.update({"method": "CIE 1960 UCS"}) return plot_chromaticity_diagram( cmfs, show_diagram_colours, show_spectral_locus, **settings ) @override_style() def plot_chromaticity_diagram_CIE1976UCS( cmfs: Union[ MultiSpectralDistributions, str, Sequence[Union[MultiSpectralDistributions, str]], ] = "CIE 1931 2 Degree Standard Observer", show_diagram_colours: Boolean = True, show_spectral_locus: Boolean = True, **kwargs: Any, ) -> Tuple[plt.Figure, plt.Axes]: """ Plot the *CIE 1976 UCS Chromaticity Diagram*. Parameters ---------- cmfs Standard observer colour matching functions used for computing the spectral locus boundaries. ``cmfs`` can be of any type or form supported by the :func:`colour.plotting.filter_cmfs` definition. show_diagram_colours Whether to display the *Chromaticity Diagram* background colours. show_spectral_locus Whether to display the *Spectral Locus*. Other Parameters ---------------- kwargs {:func:`colour.plotting.artist`, :func:`colour.plotting.diagrams.plot_chromaticity_diagram`, :func:`colour.plotting.render`}, See the documentation of the previously listed definitions. Returns ------- :class:`tuple` Current figure and axes. Examples -------- >>> plot_chromaticity_diagram_CIE1976UCS() # doctest: +ELLIPSIS (<Figure size ... with 1 Axes>, <...AxesSubplot...>) .. image:: ../_static/Plotting_Plot_Chromaticity_Diagram_CIE1976UCS.png :align: center :alt: plot_chromaticity_diagram_CIE1976UCS """ settings = dict(kwargs) settings.update({"method": "CIE 1976 UCS"}) return plot_chromaticity_diagram( cmfs, show_diagram_colours, show_spectral_locus, **settings ) @override_style() def plot_sds_in_chromaticity_diagram( sds: Union[ Sequence[Union[SpectralDistribution, MultiSpectralDistributions]], MultiSpectralDistributions, ], cmfs: Union[ MultiSpectralDistributions, str, Sequence[Union[MultiSpectralDistributions, str]], ] = "CIE 1931 2 Degree Standard Observer", chromaticity_diagram_callable: Callable = plot_chromaticity_diagram, method: Union[ Literal["CIE 1931", "CIE 1960 UCS", "CIE 1976 UCS"], str ] = "CIE 1931", annotate_kwargs: Optional[Union[Dict, List[Dict]]] = None, plot_kwargs: Optional[Union[Dict, List[Dict]]] = None, **kwargs: Any, ) -> Tuple[plt.Figure, plt.Axes]: """ Plot given spectral distribution chromaticity coordinates into the *Chromaticity Diagram* using given method. Parameters ---------- sds Spectral distributions or multi-spectral distributions to plot. `sds` can be a single :class:`colour.MultiSpectralDistributions` class instance, a list of :class:`colour.MultiSpectralDistributions` class instances or a list of :class:`colour.SpectralDistribution` class instances. cmfs Standard observer colour matching functions used for computing the spectral locus boundaries. ``cmfs`` can be of any type or form supported by the :func:`colour.plotting.filter_cmfs` definition. chromaticity_diagram_callable Callable responsible for drawing the *Chromaticity Diagram*. method *Chromaticity Diagram* method. annotate_kwargs Keyword arguments for the :func:`matplotlib.pyplot.annotate` definition, used to annotate the resulting chromaticity coordinates with their respective spectral distribution names. ``annotate_kwargs`` can be either a single dictionary applied to all the arrows with same settings or a sequence of dictionaries with different settings for each spectral distribution. The following special keyword arguments can also be used: - ``annotate`` : Whether to annotate the spectral distributions. plot_kwargs Keyword arguments for the :func:`matplotlib.pyplot.plot` definition, used to control the style of the plotted spectral distributions. `plot_kwargs`` can be either a single dictionary applied to all the plotted spectral distributions with the same settings or a sequence of dictionaries with different settings for each plotted spectral distributions. The following special keyword arguments can also be used: - ``illuminant`` : The illuminant used to compute the spectral distributions colours. The default is the illuminant associated with the whitepoint of the default plotting colourspace. ``illuminant`` can be of any type or form supported by the :func:`colour.plotting.filter_cmfs` definition. - ``cmfs`` : The standard observer colour matching functions used for computing the spectral distributions colours. ``cmfs`` can be of any type or form supported by the :func:`colour.plotting.filter_cmfs` definition. - ``normalise_sd_colours`` : Whether to normalise the computed spectral distributions colours. The default is *True*. - ``use_sd_colours`` : Whether to use the computed spectral distributions colours under the plotting colourspace illuminant. Alternatively, it is possible to use the :func:`matplotlib.pyplot.plot` definition ``color`` argument with pre-computed values. The default is *True*. Other Parameters ---------------- kwargs {:func:`colour.plotting.artist`, :func:`colour.plotting.diagrams.plot_chromaticity_diagram`, :func:`colour.plotting.render`}, See the documentation of the previously listed definitions. Returns ------- :class:`tuple` Current figure and axes. Examples -------- >>> A = SDS_ILLUMINANTS['A'] >>> D65 = SDS_ILLUMINANTS['D65'] >>> annotate_kwargs = [ ... {'xytext': (-25, 15), 'arrowprops':{'arrowstyle':'-'}}, ... {} ... ] >>> plot_kwargs = [ ... { ... 'illuminant': SDS_ILLUMINANTS['E'], ... 'markersize' : 15, ... 'normalise_sd_colours': True, ... 'use_sd_colours': True ... }, ... {'illuminant': SDS_ILLUMINANTS['E']}, ... ] >>> plot_sds_in_chromaticity_diagram( ... [A, D65], annotate_kwargs=annotate_kwargs, plot_kwargs=plot_kwargs) ... # doctest: +ELLIPSIS (<Figure size ... with 1 Axes>, <...AxesSubplot...>) .. image:: ../_static/Plotting_Plot_SDS_In_Chromaticity_Diagram.png :align: center :alt: plot_sds_in_chromaticity_diagram """ method = validate_method( method, ["CIE 1931", "CIE 1960 UCS", "CIE 1976 UCS"] ) sds_converted = sds_and_msds_to_sds(sds) settings: Dict[str, Any] = {"uniform": True} settings.update(kwargs) _figure, axes = artist(**settings) settings.update( { "axes": axes, "standalone": False, "method": method, "cmfs": cmfs, } ) chromaticity_diagram_callable(**settings) if method == "cie 1931": def XYZ_to_ij(XYZ: NDArray) -> NDArray: """ Convert given *CIE XYZ* tristimulus values to *ij* chromaticity coordinates. """ return XYZ_to_xy(XYZ) bounding_box = (-0.1, 0.9, -0.1, 0.9) elif method == "cie 1960 ucs": def XYZ_to_ij(XYZ: NDArray) -> NDArray: """ Convert given *CIE XYZ* tristimulus values to *ij* chromaticity coordinates. """ return UCS_to_uv(XYZ_to_UCS(XYZ)) bounding_box = (-0.1, 0.7, -0.2, 0.6) elif method == "cie 1976 ucs": def XYZ_to_ij(XYZ: NDArray) -> NDArray: """ Convert given *CIE XYZ* tristimulus values to *ij* chromaticity coordinates. """ return Luv_to_uv(XYZ_to_Luv(XYZ)) bounding_box = (-0.1, 0.7, -0.1, 0.7) annotate_settings_collection = [ { "annotate": True, "xytext": (-50, 30), "textcoords": "offset points", "arrowprops": CONSTANTS_ARROW_STYLE, "zorder": CONSTANTS_COLOUR_STYLE.zorder.midground_annotation, } for _ in range(len(sds_converted)) ] if annotate_kwargs is not None: update_settings_collection( annotate_settings_collection, annotate_kwargs, len(sds_converted) ) plot_settings_collection = [ { "color": CONSTANTS_COLOUR_STYLE.colour.brightest, "label": f"{sd.strict_name}", "marker": "o", "markeredgecolor": CONSTANTS_COLOUR_STYLE.colour.dark, "markeredgewidth": CONSTANTS_COLOUR_STYLE.geometry.short * 0.75, "markersize": ( CONSTANTS_COLOUR_STYLE.geometry.short * 6 + CONSTANTS_COLOUR_STYLE.geometry.short * 0.75 ), "zorder": CONSTANTS_COLOUR_STYLE.zorder.midground_line, "cmfs": cmfs, "illuminant": SDS_ILLUMINANTS[ CONSTANTS_COLOUR_STYLE.colour.colourspace.whitepoint_name ], "use_sd_colours": False, "normalise_sd_colours": False, } for sd in sds_converted ] if plot_kwargs is not None: update_settings_collection( plot_settings_collection, plot_kwargs, len(sds_converted) ) for i, sd in enumerate(sds_converted): plot_settings = plot_settings_collection[i] cmfs = cast( MultiSpectralDistributions, first_item(filter_cmfs(plot_settings.pop("cmfs")).values()), ) illuminant = cast( SpectralDistribution, first_item( filter_illuminants(plot_settings.pop("illuminant")).values() ), ) normalise_sd_colours = plot_settings.pop("normalise_sd_colours") use_sd_colours = plot_settings.pop("use_sd_colours") with domain_range_scale("1"): XYZ = sd_to_XYZ(sd, cmfs, illuminant) if use_sd_colours: if normalise_sd_colours: XYZ /= XYZ[..., 1] plot_settings["color"] = np.clip( XYZ_to_plotting_colourspace(XYZ), 0, 1 ) ij = XYZ_to_ij(XYZ) axes.plot(ij[0], ij[1], **plot_settings) if sd.name is not None and annotate_settings_collection[i]["annotate"]: annotate_settings = annotate_settings_collection[i] annotate_settings.pop("annotate") axes.annotate(sd.name, xy=ij, **annotate_settings) settings.update({"standalone": True, "bounding_box": bounding_box}) settings.update(kwargs) return render(**settings) @override_style() def plot_sds_in_chromaticity_diagram_CIE1931( sds: Union[ Sequence[Union[SpectralDistribution, MultiSpectralDistributions]], MultiSpectralDistributions, ], cmfs: Union[ MultiSpectralDistributions, str, Sequence[Union[MultiSpectralDistributions, str]], ] = "CIE 1931 2 Degree Standard Observer", chromaticity_diagram_callable_CIE1931: Callable = ( plot_chromaticity_diagram_CIE1931 ), annotate_kwargs: Optional[Union[Dict, List[Dict]]] = None, plot_kwargs: Optional[Union[Dict, List[Dict]]] = None, **kwargs: Any, ) -> Tuple[plt.Figure, plt.Axes]: """ Plot given spectral distribution chromaticity coordinates into the *CIE 1931 Chromaticity Diagram*. Parameters ---------- sds Spectral distributions or multi-spectral distributions to plot. `sds` can be a single :class:`colour.MultiSpectralDistributions` class instance, a list of :class:`colour.MultiSpectralDistributions` class instances or a list of :class:`colour.SpectralDistribution` class instances. cmfs Standard observer colour matching functions used for computing the spectral locus boundaries. ``cmfs`` can be of any type or form supported by the :func:`colour.plotting.filter_cmfs` definition. chromaticity_diagram_callable_CIE1931 Callable responsible for drawing the *CIE 1931 Chromaticity Diagram*. annotate_kwargs Keyword arguments for the :func:`matplotlib.pyplot.annotate` definition, used to annotate the resulting chromaticity coordinates with their respective spectral distribution names. ``annotate_kwargs`` can be either a single dictionary applied to all the arrows with same settings or a sequence of dictionaries with different settings for each spectral distribution. The following special keyword arguments can also be used: - ``annotate`` : Whether to annotate the spectral distributions. plot_kwargs Keyword arguments for the :func:`matplotlib.pyplot.plot` definition, used to control the style of the plotted spectral distributions. `plot_kwargs`` can be either a single dictionary applied to all the plotted spectral distributions with the same settings or a sequence of dictionaries with different settings for each plotted spectral distributions. The following special keyword arguments can also be used: - ``illuminant`` : The illuminant used to compute the spectral distributions colours. The default is the illuminant associated with the whitepoint of the default plotting colourspace. ``illuminant`` can be of any type or form supported by the :func:`colour.plotting.filter_cmfs` definition. - ``cmfs`` : The standard observer colour matching functions used for computing the spectral distributions colours. ``cmfs`` can be of any type or form supported by the :func:`colour.plotting.filter_cmfs` definition. - ``normalise_sd_colours`` : Whether to normalise the computed spectral distributions colours. The default is *True*. - ``use_sd_colours`` : Whether to use the computed spectral distributions colours under the plotting colourspace illuminant. Alternatively, it is possible to use the :func:`matplotlib.pyplot.plot` definition ``color`` argument with pre-computed values. The default is *True*. Other Parameters ---------------- kwargs {:func:`colour.plotting.artist`, :func:`colour.plotting.diagrams.plot_chromaticity_diagram`, :func:`colour.plotting.render`}, See the documentation of the previously listed definitions. Returns ------- :class:`tuple` Current figure and axes. Examples -------- >>> A = SDS_ILLUMINANTS['A'] >>> D65 = SDS_ILLUMINANTS['D65'] >>> plot_sds_in_chromaticity_diagram_CIE1931([A, D65]) ... # doctest: +ELLIPSIS (<Figure size ... with 1 Axes>, <...AxesSubplot...>) .. image:: ../_static/Plotting_\ Plot_SDS_In_Chromaticity_Diagram_CIE1931.png :align: center :alt: plot_sds_in_chromaticity_diagram_CIE1931 """ settings = dict(kwargs) settings.update({"method": "CIE 1931"}) return plot_sds_in_chromaticity_diagram( sds, cmfs, chromaticity_diagram_callable_CIE1931, annotate_kwargs=annotate_kwargs, plot_kwargs=plot_kwargs, **settings, ) @override_style() def plot_sds_in_chromaticity_diagram_CIE1960UCS( sds: Union[ Sequence[Union[SpectralDistribution, MultiSpectralDistributions]], MultiSpectralDistributions, ], cmfs: Union[ MultiSpectralDistributions, str, Sequence[Union[MultiSpectralDistributions, str]], ] = "CIE 1931 2 Degree Standard Observer", chromaticity_diagram_callable_CIE1960UCS: Callable = ( plot_chromaticity_diagram_CIE1960UCS ), annotate_kwargs: Optional[Union[Dict, List[Dict]]] = None, plot_kwargs: Optional[Union[Dict, List[Dict]]] = None, **kwargs: Any, ) -> Tuple[plt.Figure, plt.Axes]: """ Plot given spectral distribution chromaticity coordinates into the *CIE 1960 UCS Chromaticity Diagram*. Parameters ---------- sds Spectral distributions or multi-spectral distributions to plot. `sds` can be a single :class:`colour.MultiSpectralDistributions` class instance, a list of :class:`colour.MultiSpectralDistributions` class instances or a list of :class:`colour.SpectralDistribution` class instances. cmfs Standard observer colour matching functions used for computing the spectral locus boundaries. ``cmfs`` can be of any type or form supported by the :func:`colour.plotting.filter_cmfs` definition. chromaticity_diagram_callable_CIE1960UCS Callable responsible for drawing the *CIE 1960 UCS Chromaticity Diagram*. annotate_kwargs Keyword arguments for the :func:`matplotlib.pyplot.annotate` definition, used to annotate the resulting chromaticity coordinates with their respective spectral distribution names. ``annotate_kwargs`` can be either a single dictionary applied to all the arrows with same settings or a sequence of dictionaries with different settings for each spectral distribution. The following special keyword arguments can also be used: - ``annotate`` : Whether to annotate the spectral distributions. plot_kwargs Keyword arguments for the :func:`matplotlib.pyplot.plot` definition, used to control the style of the plotted spectral distributions. `plot_kwargs`` can be either a single dictionary applied to all the plotted spectral distributions with the same settings or a sequence of dictionaries with different settings for each plotted spectral distributions. The following special keyword arguments can also be used: - ``illuminant`` : The illuminant used to compute the spectral distributions colours. The default is the illuminant associated with the whitepoint of the default plotting colourspace. ``illuminant`` can be of any type or form supported by the :func:`colour.plotting.filter_cmfs` definition. - ``cmfs`` : The standard observer colour matching functions used for computing the spectral distributions colours. ``cmfs`` can be of any type or form supported by the :func:`colour.plotting.filter_cmfs` definition. - ``normalise_sd_colours`` : Whether to normalise the computed spectral distributions colours. The default is *True*. - ``use_sd_colours`` : Whether to use the computed spectral distributions colours under the plotting colourspace illuminant. Alternatively, it is possible to use the :func:`matplotlib.pyplot.plot` definition ``color`` argument with pre-computed values. The default is *True*. Other Parameters ---------------- kwargs {:func:`colour.plotting.artist`, :func:`colour.plotting.diagrams.plot_chromaticity_diagram`, :func:`colour.plotting.render`}, See the documentation of the previously listed definitions. Returns ------- :class:`tuple` Current figure and axes. Examples -------- >>> A = SDS_ILLUMINANTS['A'] >>> D65 = SDS_ILLUMINANTS['D65'] >>> plot_sds_in_chromaticity_diagram_CIE1960UCS([A, D65]) ... # doctest: +ELLIPSIS (<Figure size ... with 1 Axes>, <...AxesSubplot...>) .. image:: ../_static/Plotting_\ Plot_SDS_In_Chromaticity_Diagram_CIE1960UCS.png :align: center :alt: plot_sds_in_chromaticity_diagram_CIE1960UCS """ settings = dict(kwargs) settings.update({"method": "CIE 1960 UCS"}) return plot_sds_in_chromaticity_diagram( sds, cmfs, chromaticity_diagram_callable_CIE1960UCS, annotate_kwargs=annotate_kwargs, plot_kwargs=plot_kwargs, **settings, ) @override_style() def plot_sds_in_chromaticity_diagram_CIE1976UCS( sds: Union[ Sequence[Union[SpectralDistribution, MultiSpectralDistributions]], MultiSpectralDistributions, ], cmfs: Union[ MultiSpectralDistributions, str, Sequence[Union[MultiSpectralDistributions, str]], ] = "CIE 1931 2 Degree Standard Observer", chromaticity_diagram_callable_CIE1976UCS: Callable = ( plot_chromaticity_diagram_CIE1976UCS ), annotate_kwargs: Optional[Union[Dict, List[Dict]]] = None, plot_kwargs: Optional[Union[Dict, List[Dict]]] = None, **kwargs: Any, ) -> Tuple[plt.Figure, plt.Axes]: """ Plot given spectral distribution chromaticity coordinates into the *CIE 1976 UCS Chromaticity Diagram*. Parameters ---------- sds Spectral distributions or multi-spectral distributions to plot. `sds` can be a single :class:`colour.MultiSpectralDistributions` class instance, a list of :class:`colour.MultiSpectralDistributions` class instances or a list of :class:`colour.SpectralDistribution` class instances. cmfs Standard observer colour matching functions used for computing the spectral locus boundaries. ``cmfs`` can be of any type or form supported by the :func:`colour.plotting.filter_cmfs` definition. chromaticity_diagram_callable_CIE1976UCS Callable responsible for drawing the *CIE 1976 UCS Chromaticity Diagram*. annotate_kwargs Keyword arguments for the :func:`matplotlib.pyplot.annotate` definition, used to annotate the resulting chromaticity coordinates with their respective spectral distribution names. ``annotate_kwargs`` can be either a single dictionary applied to all the arrows with same settings or a sequence of dictionaries with different settings for each spectral distribution. The following special keyword arguments can also be used: - ``annotate`` : Whether to annotate the spectral distributions. plot_kwargs Keyword arguments for the :func:`matplotlib.pyplot.plot` definition, used to control the style of the plotted spectral distributions. `plot_kwargs`` can be either a single dictionary applied to all the plotted spectral distributions with the same settings or a sequence of dictionaries with different settings for each plotted spectral distributions. The following special keyword arguments can also be used: - ``illuminant`` : The illuminant used to compute the spectral distributions colours. The default is the illuminant associated with the whitepoint of the default plotting colourspace. ``illuminant`` can be of any type or form supported by the :func:`colour.plotting.filter_cmfs` definition. - ``cmfs`` : The standard observer colour matching functions used for computing the spectral distributions colours. ``cmfs`` can be of any type or form supported by the :func:`colour.plotting.filter_cmfs` definition. - ``normalise_sd_colours`` : Whether to normalise the computed spectral distributions colours. The default is *True*. - ``use_sd_colours`` : Whether to use the computed spectral distributions colours under the plotting colourspace illuminant. Alternatively, it is possible to use the :func:`matplotlib.pyplot.plot` definition ``color`` argument with pre-computed values. The default is *True*. Other Parameters ---------------- kwargs {:func:`colour.plotting.artist`, :func:`colour.plotting.diagrams.plot_chromaticity_diagram`, :func:`colour.plotting.render`}, See the documentation of the previously listed definitions. Returns ------- :class:`tuple` Current figure and axes. Examples -------- >>> A = SDS_ILLUMINANTS['A'] >>> D65 = SDS_ILLUMINANTS['D65'] >>> plot_sds_in_chromaticity_diagram_CIE1976UCS([A, D65]) ... # doctest: +ELLIPSIS (<Figure size ... with 1 Axes>, <...AxesSubplot...>) .. image:: ../_static/Plotting_\ Plot_SDS_In_Chromaticity_Diagram_CIE1976UCS.png :align: center :alt: plot_sds_in_chromaticity_diagram_CIE1976UCS """ settings = dict(kwargs) settings.update({"method": "CIE 1976 UCS"}) return plot_sds_in_chromaticity_diagram( sds, cmfs, chromaticity_diagram_callable_CIE1976UCS, annotate_kwargs=annotate_kwargs, plot_kwargs=plot_kwargs, **settings, )

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0.623732

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5.639784

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0.033851

0.019374

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0

1

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null

0

6

be21eb7ff5a888f1e1b13930f8acecabfbd7184c

1,345

py

Python

search_insert_position.py

ChiragSaini/June-LeetCoding-Challenge

8e3192c7c4cfbd5cf8718bdb1b041871585a0c69

[ "MIT" ]

null

search_insert_position.py

ChiragSaini/June-LeetCoding-Challenge

8e3192c7c4cfbd5cf8718bdb1b041871585a0c69

[ "MIT" ]

null

search_insert_position.py

ChiragSaini/June-LeetCoding-Challenge

8e3192c7c4cfbd5cf8718bdb1b041871585a0c69

[ "MIT" ]

null

######################## # * First Solution ######################## class Solution: def searchInsert(self, nums: List[int], target: int) -> int: low = 0 high = len(nums)-1 while low < high: mid = (low+high) // 2 if nums[mid] == target: return mid elif nums[mid] > target: high = mid-1 elif nums[mid] < target: low = mid+1 for i in range(len(nums)): if nums[i] >= target: return i return len(nums) ######################## # * Second Solution ######################## class Solution: def searchInsert(self, nums: List[int], target: int) -> int: # ? Border Cases if target > nums[-1]: return len(nums) if target <= nums[0]: return 0 ############ # * Binary Search here low = 0 high = len(nums)-1 while low < high: mid = (low+high) // 2 if nums[mid] == target: return mid elif nums[mid] > target: high = mid-1 elif nums[mid] < target: low = mid+1 # * Simple Traversal here for i in range(len(nums)): if nums[i] >= target: return i

29.23913

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0.402974

140

1,345

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0.235714

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0.125461

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0.01671

0.421561

1,345

46

65

29.23913

0.679949

0.069888

0

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false

0

0.314286

0

null

0

1

0

1

0

null

0

6

079c58dccf5598b4386d5a1d9775120393e99087

30

py

Python

experta/matchers/__init__.py

Kirito56/ExpertaMadman

e14ab93e6e86ef942be3ee5487425a6f483f0dad

[ "MIT" ]

null

experta/matchers/__init__.py

Kirito56/ExpertaMadman

e14ab93e6e86ef942be3ee5487425a6f483f0dad

[ "MIT" ]

null

experta/matchers/__init__.py

Kirito56/ExpertaMadman

e14ab93e6e86ef942be3ee5487425a6f483f0dad

[ "MIT" ]

null

from .rete import ReteMatcher

15

29

0.833333

4

30

6.25

1

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0.133333

30

1

30

0.961538

0

1

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true

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1

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1

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null

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1

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null

0

1

0

1

0

1

0

6

07cd8c1d2f18d7e0e61d519f5e313de0291e736a

25,729

py

Python

fixture/testhelpersm.py

IrinaSlobodchikova/marker

72f981134fb025a94348cd2bc829fa8430a01372

[ "Apache-2.0" ]

null

fixture/testhelpersm.py

IrinaSlobodchikova/marker

72f981134fb025a94348cd2bc829fa8430a01372

[ "Apache-2.0" ]

null

fixture/testhelpersm.py

IrinaSlobodchikova/marker

72f981134fb025a94348cd2bc829fa8430a01372

[ "Apache-2.0" ]

null

import re #import datetime from random import randrange import time class testHelperSM: def __init__(self, app): self.app = app # def find_region(self): # wd = self.app.wd # wd.find_element_by_xpath("//div[@id='mCSB_2_container']/ul/li[2]/label") # wd.find_element_by_xpath("//form[@id='frmSearch']//button[.='Поиск']") # def find_region2(self, reg_name): # wd = self.app.wd # self.app.wait_smBlock(600) # wd.find_element_by_xpath("//div[@id='aggregatesPlaceholder']/table/tbody/tr/td[2]/div/div/div[1]/span[2]").click() # wd.find_element_by_xpath("//div[@id='mCSB_6_container']/div/ul/li[20]/label").click() # wd.find_element_by_id("aggSearchText").click() # wd.find_element_by_id("aggSearchText").clear() # wd.find_element_by_id("aggSearchText").send_keys("%s" % reg_name) # wd.find_element_by_id("aggSearch").click() # wd.find_element_by_xpath("//div[@id='mCSB_7_container']/div/ul/li[6]/label").click() # wd.find_element_by_xpath("//div[@id='mCSB_7_container']/div/ul/li[6]/span[3]").click() # wd.find_element_by_xpath("//div[@id='mCSB_7_container']/div/ul/li[6]/label").click() # wd.find_element_by_xpath("//div[@id='mCSB_7_container']/div/ul/li[7]/label").click() # wd.find_element_by_xpath("//div[@id='mainAggDlgContent']//button[.='Применить фильтр']").click() # self.app.wait_smBlock(600) # self.press_search_button() # def find_region3(self): # wd = self.app.wd # self.app.wait_smBlock(600) # i = randrange(24) # wd.find_element_by_xpath("//div[@id='aggregatesPlaceholder']/table/tbody/tr[2]/td[1]/div/div/div[1]/span[2]").click() # self.app.wait_sm_artefact_Block(10) # if i > 0: #element = wd.find_element_by_xpath("//div[@id='mCSB_11_container']/div/ul/li[%s]/label" % i) #ActionChains(wd).move_to_element(element).perform() # wd.find_element_by_xpath("//div[@id='mCSB_11_container']/div/ul/li[%s]/label" % i).click() # else: # i = 2 # wd.find_element_by_xpath("//div[@id='mCSB_11_container']/div/ul/li[%s]/label" % i).click() # wd.find_element_by_xpath("//div[@id='mainAggDlgContent']//button[.='Применить фильтр']").click() # self.app.wait_smBlock(20) # self.press_search_button() # def find_in_container_number(self, range_container_numbers, container_number): # wd = self.app.wd # self.app.wait_smBlock(600) # spicok = [] # i = randrange(1, 4, 1) # if container_number == 0: # ct = randrange(1, range_container_numbers, 1) # else: # ct = container_number # if not self.is_sm_advSearch_is_displayed(): # if len(wd.find_elements_by_xpath("//div[@class='block-label']//a[.='Показать/скрыть']")) < 2: # wd.find_element_by_xpath("//div[@class='block-label']//a[.='Показать/скрыть']").click() # else: # wd.find_element_by_xpath("//div[@id='advSearch']/div[2]/a").click() # if i > 0 and ct > 0: # if ct == 1: # if i < 3: # wd.find_element_by_xpath("//div[@id='mCSB_1_container']/ul/li[%s]/label" % str(i)).click() # if i == 3: # i = 2 # wd.find_element_by_xpath("//div[@id='mCSB_1_container']/ul/li[%s]/label" % str(i)).click() # elif ct == 2: # try: # wd.find_element_by_xpath("//div[@id='mCSB_2_container']/ul/li[%s]/label" % str(i)).click() # except: # wd.find_element_by_xpath("//div[@id='mCSB_1_container']/ul/li[%s]/label" % str(i)).click() # elif ct == 3: # wd.find_element_by_xpath("//div[@id='mCSB_3_container']/ul/li[%s]/label" % str(i)).click() # elif ct == 4: # wd.find_element_by_xpath("//div[@id='mCSB_4_container']/ul/li[%s]/label" % str(i)).click() # elif ct == 5: # wd.find_element_by_xpath("//div[@id='mCSB_5_container']/ul/li[%s]/label" % str(i)).click() # elif ct == 6: # wd.find_element_by_xpath("//div[@id='mCSB_6_container']/ul/li[%s]/label" % str(i)).click() # elif ct == 7: # wd.find_element_by_xpath("//div[@id='mCSB_7_container']/ul/li[%s]/label" % str(i)).click() # elif ct == 8: # wd.find_element_by_xpath("//div[@id='mCSB_8_container']/ul/li[%s]/label" % str(i)).click() # elif ct == 9: # wd.find_element_by_xpath("//div[@id='mCSB_9_container']/ul/li[%s]/label" % str(i)).click() # elif ct == 10: # wd.find_element_by_xpath("//div[@id='mCSB_10_container']/ul/li[%s]/label" % str(i)).click() # else: # i = 2 # wd.find_element_by_xpath("//div[@id='mCSB_2_container']/ul/li[%s]/label" % str(i)).click() # self.press_search_button() # return i, ct def press_search_button(self): wd = self.app.wd wd.find_element_by_xpath("//form[@id='frmSearch']//button[.='Поиск']").click() # def is_sm_advSearch_is_displayed(self): # try: # text = self.app.wd.find_element_by_id("advSearchContent").value_of_css_property("display") # if text == 'block': # return True # except: # return False # def find_zakazchik_for_purchases_list(self): # wd = self.app.wd # self.app.wait_smBlock(600) # i = randrange(24) # wd.find_element_by_xpath( # "//div[@id='aggregatesPlaceholder']/table/tbody/tr[1]/td[3]/div[2]/div/div[1]/span[2]").click() # self.app.wait_sm_artefact_Block(10) # wd.find_element_by_id("aggSearchText").click() # wd.find_element_by_id("aggSearchText").clear() # wd.find_element_by_id("aggSearchText").send_keys("администрация") # wd.find_element_by_id("aggSearch").click() # self.app.wait_sm_artefact_Block(10) # if i > 0: # wd.find_element_by_xpath("//div[@id='mCSB_12_container']/div/ul/li[%s]/label" % i).click() # else: # i = 2 # wd.find_element_by_xpath("//div[@id='mCSB_12_container']/div/ul/li[%s]/label" % i).click() # wd.find_element_by_xpath("//div[@id='mainAggDlgContent']//button[.='Применить фильтр']").click() # self.app.wait_smBlock(600) # self.press_search_button() # ! not work # def search_in_opened_container(self): # wd = self.app.wd # self.app.wait_smBlock(600) # if not self.is_sm_advSearch_is_displayed(): # if len(wd.find_elements_by_xpath("//div[@class='block-label']//a[.='Показать/скрыть']")) < 2: # wd.find_element_by_xpath("//div[@class='block-label']//a[.='Показать/скрыть']").click() # else: # wd.find_element_by_xpath("//div[@id='advSearch']/div[2]/a").click() # i = randrange(1, 24, 1) # c = len(wd.find_elements_by_css_selector("span.agg-widget_btn")) # ct = randrange(c) # wd.find_elements_by_css_selector("span.agg-widget_btn")[ct].click() # self.app.wait_sm_artefact_Block(10) # #найти как кликнуть на элементе # wd.find_element_by_xpath("//div[@id='mainAggDlgContent']//button[.='Применить фильтр']").click() # self.app.wait_smBlock(600) # self.press_search_button() # def get_artef_parametrs(self, ct): # wd = self.app.wd # self.app.wait_smBlock(600) # for row in wd.find_elements_by_xpath("//div[@id='mCSB_%s_container']/ul/li[1]" % ct): # cells = row.find_elements_by_tag_name("span") # results = cells[0].find_element_by_tag_name("em").text # try: # parametr = cells[3].text # except: # parametr = cells[2].text # return parametr # def get_artef_param(self, ct): # wd = self.app.wd # param = self.get_artef_parametrs(ct) # return param # def is_smresult_not_0(self): # try: # text = self.get_total_results() # if text != '0': # return True # except: # return False # def check_results(self): # self.app.wait_smBlock(900) # if self.is_smresult_not_0(): # result = self.get_total_results() # return result # else: # return '0' # def get_total_results(self): # wd = self.app.wd # results = wd.find_element_by_xpath("//div[@class='panel_header']/h2").get_attribute("textContent") # #clear_result = wd.find_element_by_xpath("//div[@class='panel_header']/h2").get_attribute("textContent")[13:len(results)] # clear_result = results[13:len(results)] # return self.clear_result(clear_result) def create_contact_report_all_in_dif_row_tel_mail(self): wd = self.app.wd wd.maximize_window() self.app.wait_smBlock(600) wd.find_element_by_xpath("//div[@class='panel_header']//p[.='Контакты']").click() self.app.wait_sm_artefact_Block(10) wd.find_element_by_xpath("//label[@for='cb-3']").click() if not wd.find_element_by_id("cb-3").is_selected(): wd.find_element_by_id("cb-3").click() wd.find_element_by_xpath("//label[@for='rb-0']").click() if not wd.find_element_by_id("rb-0").is_selected(): wd.find_element_by_id("rb-0").click() wd.find_element_by_xpath("//div[@id='divReportContactsSettings']//button[.='Сформировать']").click() def create_contact_report_all_in_dif_row_tel_mail_zakazchiki(self): wd = self.app.wd wd.maximize_window() self.app.wait_smBlock(600) wd.find_element_by_xpath("//div[@class='panel_header']//p[.='Контакты']").click() self.app.wait_sm_artefact_Block(10) wd.find_element_by_xpath("//label[@for='cb-3']").click() if not wd.find_element_by_id("cb-3").is_selected(): wd.find_element_by_id("cb-3").click() wd.find_element_by_xpath("//label[@for='cb-8']").click() if not wd.find_element_by_id("cb-8").is_selected(): wd.find_element_by_id("cb-8").click() wd.find_element_by_xpath("//label[@for='cb-9']").click() if wd.find_element_by_id("cb-9").is_selected(): wd.find_element_by_id("cb-9").click() wd.find_element_by_xpath("//label[@for='rb-0']").click() if not wd.find_element_by_id("rb-0").is_selected(): wd.find_element_by_id("rb-0").click() wd.find_element_by_xpath("//div[@id='divReportContactsSettings']//button[.='Сформировать']").click() def create_contact_report_allinone_tel_mail(self): wd = self.app.wd self.app.wait_smBlock(600) wd.find_element_by_xpath("//div[@class='panel_header']//p[.='Контакты']").click() self.app.wait_sm_artefact_Block(10) wd.find_element_by_xpath("//label[@for='cb-3']").click() if not wd.find_element_by_id("cb-3").is_selected(): wd.find_element_by_id("cb-3").click() wd.find_element_by_xpath("//label[@for='rb-1']").click() if not wd.find_element_by_id("rb-1").is_selected(): wd.find_element_by_id("rb-1").click() wd.find_element_by_xpath("//div[@id='divReportContactsSettings']//button[.='Сформировать']").click() def create_contact_report_allinone_tel_mail_zakazchiki(self): wd = self.app.wd self.app.wait_smBlock(600) wd.find_element_by_xpath("//div[@class='panel_header']//p[.='Контакты']").click() self.app.wait_sm_artefact_Block(10) wd.find_element_by_xpath("//label[@for='cb-3']").click() if not wd.find_element_by_id("cb-3").is_selected(): wd.find_element_by_id("cb-3").click() wd.find_element_by_xpath("//label[@for='cb-8']").click() if not wd.find_element_by_id("cb-8").is_selected(): wd.find_element_by_id("cb-8").click() wd.find_element_by_xpath("//label[@for='cb-9']").click() if wd.find_element_by_id("cb-9").is_selected(): wd.find_element_by_id("cb-9").click() wd.find_element_by_xpath("//label[@for='rb-1']").click() if not wd.find_element_by_id("rb-1").is_selected(): wd.find_element_by_id("rb-1").click() wd.find_element_by_xpath("//div[@id='divReportContactsSettings']//button[.='Сформировать']").click() def create_contact_report_result(self): wd = self.app.wd self.app.wait_smBlock(600) wd.find_element_by_xpath("//div[@class='panel_header']//p[.='Результаты']").click() self.app.wait_sm_artefact_Block(10) wd.find_element_by_xpath("//div[@id='divReportSearchResultsSettings']//button[.='Сформировать']").click() def create_contact_report_statictic(self): wd = self.app.wd #добавить выбор чекбоксов self.app.wait_smBlock(600) wd.find_element_by_xpath("//div[@class='panel_header']//p[.='Статистика']").click() self.app.wait_sm_artefact_Block(10) wd.find_element_by_xpath("//div[@id='divReportStatisticsSettings']//button[.='Сформировать']").click() def create_contact_list_10000(self, cd2, text): wd = self.app.wd self.app.wait_smBlock(900) wd.find_element_by_xpath("//li[@id='UpdateList']//p[.='Добавить']").click() wd.find_element_by_xpath("//label[@for='sallResults']").click() if not wd.find_element_by_id("sallResults").is_selected(): wd.find_element_by_id("sallResults").click() wd.find_element_by_xpath("//input[@class='ui-autocomplete-input']").click() wd.find_element_by_xpath("//input[@class='ui-autocomplete-input']").clear() wd.find_element_by_xpath("//input[@class='ui-autocomplete-input']").send_keys(text % cd2) time.sleep(2) wd.find_element_by_xpath("//input[@class='ui-autocomplete-input']").click() wd.find_element_by_xpath("//div[@id='addOrUpdateEntitiesListSearchDlg']//button[.='Сохранить']").click() def create_purchases_company_list_50(self, cd2, text): wd = self.app.wd self.app.wait_smBlock(900) #выбор 50 self.select_all_50() #создание первых списка по первым 50 компаниям wd.find_element_by_xpath("//li[@id='UpdateList']//p[.='Добавить']").click() wd.find_element_by_xpath("//label[@for='scheckedResults']").click() if not wd.find_element_by_id("scheckedResults").is_selected(): wd.find_element_by_id("scheckedResults").click() wd.find_element_by_xpath("//input[@class='ui-autocomplete-input']").click() wd.find_element_by_xpath("//input[@class='ui-autocomplete-input']").clear() wd.find_element_by_xpath("//input[@class='ui-autocomplete-input']").click() wd.find_element_by_xpath("//input[@class='ui-autocomplete-input']").send_keys(text % cd2) time.sleep(2) wd.find_element_by_xpath("//input[@class='ui-autocomplete-input']").click() wd.find_element_by_xpath("//div[@id='addOrUpdateEntitiesListSearchDlg']//button[.='Сохранить']").click() def select_all_50(self): wd = self.app.wd wd.find_element_by_xpath("//label[@for='allItemsCb']").click() if not wd.find_element_by_id("allItemsCb").is_selected(): wd.find_element_by_id("allItemsCb").click() # def clear_result(self, s): # x = re.sub(" ", "", str(s)) # return x # def clear_spase_result(self, s): # x = re.sub(" ", "", str(s)) # return x def report_is_present_short(self, reestr_ex, report_type_ex, state_ex): wd = self.app.wd self.app.wait_smBlock(600) reestr = wd.find_element_by_xpath("//div[@id='reports']/div[3]/table/tbody/tr[1]/td[3]").text.rstrip() report_type = wd.find_element_by_xpath("//div[@id='reports']/div[3]/table/tbody/tr[1]/td[4]").text.rstrip() state = wd.find_element_by_xpath("//div[@id='reports']/div[3]/table/tbody/tr[1]/td[5]").text.rstrip() if state == "Создан" or state == state_ex: if report_type == report_type_ex: if reestr == reestr_ex: return True return False def report_is_present_date(self, cd2): wd = self.app.wd date = wd.find_element_by_xpath("//div[@id='reports']/div[3]/table/tbody/tr[1]/td[2]").text.rstrip() exp_date = "Сегодня " + cd2 cd2_hour = cd2[0:2] cd2_minute = cd2[3:5] exp_date2 = "Сегодня " + cd2_hour + ":" + str(int(cd2_minute) + 1) if date == exp_date or date == exp_date2: return True return False def monitoring_is_present(self, cd2, cd3, text, reestr_ex): wd = self.app.wd wd.refresh() self.app.wait_smBlock(600) date = wd.find_element_by_xpath("//div[@class='panel_layer']/div[2]/table/tbody/tr[1]/td[2]").text.rstrip() exp_date = "Сегодня " + cd3 cd2_hour = cd3[0:2] cd2_minute = cd3[3:5] exp_name = text[0:-3] + " " + cd2 exp_date2 = "Сегодня " + cd2_hour + ":" + str(int(cd2_minute) + 1) exp_date3 = "Сегодня " + cd2_hour + ":" + "0" + str(int(cd2_minute) + 1) reestr = wd.find_element_by_xpath("//div[@class='panel_layer']/div[2]/table/tbody/tr[1]/td[3]").text.rstrip() name = wd.find_element_by_xpath("//div[@class='panel_layer']//a[.='%s']" % exp_name).text.rstrip() #name = wd.find_element_by_xpath("//div[@class='panel_layer']/div[2]/table/tbody/tr[1]/td[4]").text.rstrip() if date == exp_date or date == exp_date2 or date == exp_date3: if reestr == reestr_ex: if name == exp_name: return True return False def click_on_monitoring_link(self, cd2, text): wd = self.app.wd self.app.wait_smBlock(600) exp_name = text[0:-3] + " " + cd2 wd.find_element_by_xpath("//div[@class='panel_layer']//a[.='%s']" % exp_name).click() def contact_or_purchases_list_is_present(self, cd2, text): wd = self.app.wd #проверить время self.app.wait_smBlock(600) cd_contact_list = wd.find_element_by_xpath("//div[@class='panel_layer']/div[2]/table/tbody/tr[1]/td[2]").text.rstrip() current_name = wd.find_element_by_xpath("//div[@class='panel_layer']/div[2]/table/tbody/tr[1]/td[3]").text.rstrip() created_name = text[0:-3] + " " + cd2 cd_contact_list_date = cd_contact_list[0:2] cd2_date = cd2[0:2] cd_contact_list_month = cd_contact_list[3:5] cd2_month = cd2[3:5] cd_contact_list_year = cd_contact_list[6:10] cd2_year = cd2[6:10] if len(cd_contact_list) == 18: cd_contact_list_hour = cd_contact_list[11:12] cd_contact_list_minute = cd_contact_list[13:15] else: cd_contact_list_hour = cd_contact_list[11:13] cd_contact_list_minute = cd_contact_list[14:16] cd2_hour = cd2[11:13] cd2_minute = cd2[14:16] if cd_contact_list_date == cd2_date: if cd_contact_list_month == cd2_month: if cd_contact_list_year == cd2_year: if cd_contact_list_hour == cd2_hour or cd_contact_list_hour == cd2_hour[1:2]: if cd_contact_list_minute == cd2_minute or cd_contact_list_minute == str(int(cd2_minute) + 1): if current_name.startswith(created_name): return True else: return False def ensure_link_work(self): wd = self.app.wd header = wd.find_element_by_css_selector("h1.clip").text return header.rstrip() def ensure_link_type2_work(self): wd = self.app.wd header = wd.find_element_by_css_selector("h2").text return header[0:8] def open_first_contact_list(self): wd = self.app.wd self.app.wait_smBlock(600) wd.find_element_by_xpath("//div[@class='panel_layer']/div[2]/table/tbody/tr[1]/td[3]/div/div[1]/a").click() def create_report_covladeltsy(self): wd = self.app.wd self.app.wait_smBlock(600) wd.find_element_by_xpath("//div[@class='panel_header']//p[.='Совладельцы']").click() wd.find_element_by_xpath("//div[@id='divReportCoownersSettings']//button[.='Сформировать']").click() wd.find_element_by_css_selector("div.toast-title").click() def create_report_affelir(self): wd = self.app.wd self.app.wait_smBlock(600) wd.find_element_by_xpath("//div[@class='panel_header']//p[.='Аффилированность']").click() wd.find_element_by_xpath("//div[@id='divReportAffilationSettings']//button[.='Сформировать']").click() def create_report_prices_zakazchik(self): wd = self.app.wd self.app.wait_smBlock(600) wd.find_element_by_xpath("//div[@class='panel_header']//p[.='Цены']").click() wd.find_element_by_xpath("//label[@for='rb-0']").click() if not wd.find_element_by_id("rb-0").is_selected(): wd.find_element_by_id("rb-0").click() #wd.find_element_by_xpath("//label[@for='cb-2']").click() #if not wd.find_element_by_id("cb-2").is_selected(): # wd.find_element_by_id("cb-2").click() #wd.find_element_by_xpath("//label[@for='cb-3']").click() #if not wd.find_element_by_id("cb-3").is_selected(): # wd.find_element_by_id("cb-3").click() #wd.find_element_by_xpath("//label[@for='cb-4']").click() #if not wd.find_element_by_id("cb-4").is_selected(): # wd.find_element_by_id("cb-4").click() wd.find_element_by_xpath("//div[@id='divReportPricesSettings']//button[.='Сформировать']").click() def create_report_prices_postavschik(self): wd = self.app.wd self.app.wait_smBlock(600) wd.find_element_by_xpath("//div[@class='panel_header']//p[.='Цены']").click() wd.find_element_by_xpath("//label[@for='rb-1']").click() if not wd.find_element_by_id("rb-1").is_selected(): wd.find_element_by_id("rb-1").click() #wd.find_element_by_xpath("//label[@for='cb-5']").click() #if not wd.find_element_by_xpath("//label[@for='cb-5']").is_selected(): # wd.find_element_by_xpath("//label[@for='cb-5']").click() #wd.find_element_by_xpath("//label[@for='cb-6']").click() #if not wd.find_element_by_xpath("//label[@for='cb-6']").is_selected(): # wd.find_element_by_xpath("//label[@for='cb-6']").click() wd.find_element_by_xpath("//div[@id='divReportPricesSettings']//button[.='Сформировать']").click() def create_report_rnpSuppliers(self): wd = self.app.wd self.app.wait_smBlock(600) wd.find_element_by_xpath("//div[@class='panel_header']//p[.='Поставщик в РНП']").click() wd.find_element_by_xpath("//div[@id='divRnpSuppliersSettings']//button[.='Сформировать']").click() def create_report_RnpParticipantsSettings(self): wd = self.app.wd self.app.wait_smBlock(600) wd.find_element_by_xpath("//div[@class='panel_header']//p[.='Участник в РНП']").click() wd.find_element_by_xpath("//div[@id='divRnpParticipantsSettings']//button[.='Сформировать']").click() def create_report_FasComplaintsSettings(self): wd = self.app.wd self.app.wait_smBlock(600) wd.find_element_by_xpath("//div[@class='panel_header']//p[.='ФАС']").click() wd.find_element_by_xpath("//div[@id='divFasComplaintsSettings']//button[.='Сформировать']").click() def save_requesr(self, cd2, text): wd = self.app.wd self.app.wait_smBlock(600) try: wd.find_element_by_link_text("Сохранить запрос").click() except: try: wd.find_element_by_link_text("Сохранить запрос/Мониторинг").click() except: try: wd.find_element_by_link_text("Сохранить запрос ").click() except: wd.find_element_by_link_text("Сохранить запрос/Мониторинг ").click() wd.find_element_by_id("requestName").click() wd.find_element_by_id("requestName").clear() wd.find_element_by_id("requestName").send_keys(text % cd2) time.sleep(2) wd.find_element_by_id("requestName").click() wd.find_element_by_xpath("//div[@id='divSaveRequest']//button[.='Сохранить']").click() def refresh_page(self): wd = self.app.wd wd.refresh() self.app.wait_smBlock(600) def contact_from_contact_rep_is_present(self): wd = self.app.wd pass def get_old_contact_list(self): pass def delete_report(self): pass def delete_first_contact_list(self): wd = self.app.wd self.app.wait_smBlock(600) #придумать как найти чекбокс, внизу чушь list = [] #for row in wd.find_element_by_xpath("//input[@class='row-cb']"): # cells = row.find_elements_by_tag_name("td") # id = cells[0].find_element_by_tag_name("input").get_attribute("data-id") wd.find_element_by_xpath("//div[@class='panel_layer']/div[2]/table/tbody/tr[1]/td[1]").click() if not wd.find_elements_by_xpath("//div[@class='panel_layer']/div[2]/table/tbody/tr[1]/td[1]").is_selected(): wd.find_element_by_xpath("//div[@class='panel_layer']/div[2]/table/tbody/tr[1]/td[1]").click() wd.find_element_by_id("btnDel").click() wd.find_element_by_xpath("//div[@id='dlgYesNo']//button[.='Да']").click()

47.122711

130

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3,615

25,729

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0.078008

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0.163343

0.186424

0.80183

0.783393

0.747883

0.709847

0.666007

0.65153

0

0.022762

0.212834

25,729

545

131

47.209174

0.700291

0.375257

0

0.585106

0

0.042553

0.229844

0.186067

0.003546

0

1

0.113475

false

0.010638

0

0.163121

0

null

0

1

0

1

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null

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6

07ec8ed3603f1cc7b31d1ed635abeed430f55bf6

25,009

py

Python

angr_platforms/tricore/rtl.py

shahinsba/angr-platforms

86f9ea90c396fb5561d0196a2d1a873e573b0294

[ "BSD-2-Clause" ]

null

angr_platforms/tricore/rtl.py

shahinsba/angr-platforms

86f9ea90c396fb5561d0196a2d1a873e573b0294

[ "BSD-2-Clause" ]

null

angr_platforms/tricore/rtl.py

shahinsba/angr-platforms

86f9ea90c396fb5561d0196a2d1a873e573b0294

[ "BSD-2-Clause" ]

null

#!/usr/bin/env python3 """ rtl.py A module for RTL functions. """ from pyvex.lifting.util import Type INT32_MAX_POS = 0x7fffffff #(1 << (32 - 1))-1 UINT32_MAX_POS = 0xffffffff INT32_MAX_NEG = -0x80000000 #-(1 << (32 - 1)) INT64_MAX_POS = 0x7fffffffffffffff INT64_MAX_NEG = -0x8000000000000000 SV_MASK = 0x10000000 # bit 29 of PSW ASV_MASK = 0x4000000 # bit 27 of PSW def carry(a, b, c): result_sum = a+b+c cond_32_lsb_z = (a+b+c) == 0 cond_smaller_a = (result_sum < a) cond_smaller_b = (result_sum < b) cond_smaller_c = (result_sum < c) cond_32_lsb_nz = cond_smaller_a | cond_smaller_b | cond_smaller_c return cond_32_lsb_z | cond_32_lsb_nz def overflow(val): """ Check Overflow for 32-bit values: - result > 0x7FFFFFFF or result < -0x80000000 """ return (val >> 32) != 0 def overflow_64(val): """ Check Overflow for 64-bit values: - result > 0xFFFFFFFFFFFFFFFF """ return (val >> 64) != 0 def advanced_overflow(val): """ Check advanced overflow for 32-bit values. """ return val[31] ^ val[30] def advanced_overflow_64(val): """ Check Advanced Overflow for 64-bit values. """ return val[63] ^ val[62] def set_usb(psw, C, V, SV, AV, SAV): """ Set User Status Bits. """ psw = (C << 31) | \ (V << 30) | \ (SV << 29) | \ (AV << 28) | \ (SAV << 27) return psw def extend_to_32_bits(val): val = (val << 31) | (val << 30) | (val << 29) | \ (val << 28) | (val << 27) | (val << 26) | \ (val << 25) | (val << 24) | (val << 23) | \ (val << 22) | (val << 21) | (val << 20) | \ (val << 19) | (val << 18) | (val << 17) | \ (val << 16) | (val << 15) | (val << 14) | \ (val << 13) | (val << 12) | (val << 11) | \ (val << 10) | (val << 9) | (val << 8) | \ (val << 7) | (val << 6) | (val << 5) | \ (val << 4) | (val << 3) | (val << 2) | \ (val << 1) | val return val def extend_to_16_bits(val): val = (val << 15) | (val << 14) | (val << 13) | \ (val << 12) | (val << 11) | (val << 10) | \ (val << 9) | (val << 8) | (val << 7) | \ (val << 6) | (val << 5) | (val << 4) | \ (val << 3) | (val << 2) | (val << 1) | val return val def extend_to_8_bits(val): val = (val << 7) | (val << 6) | (val << 5) | \ (val << 4) | (val << 3) | (val << 2) | \ (val << 1) | val return val def extend_to_6_bits(val): val = (val << 5) | (val << 4) | (val << 3) | \ (val << 2) | (val << 1) | val return val def extend_bits(val, bits): ret = 0 for i in range(bits+1): ret |= (val << bits-i) return ret def ssov(x, y): """ Saturation on signed overflow. """ max_pos = (1 << (y - 1)) - 1 max_neg = 1 << (y - 1) cond_x = extend_to_32_bits(x < max_pos) cond_max_neg = extend_to_32_bits(x > max_neg) ret = (x & cond_x & ~cond_max_neg) | \ (max_pos & ~cond_x & ~cond_max_neg) | \ (max_neg & ~cond_x & cond_max_neg) return ret def ssov16(x): """ Saturation on signed overflow. """ return x def ssov32(x, max_pos, max_neg): """ Saturation on signed overflow. :param x: Vex Constant (64-bit value). :param max_pos: Vex Constant (64-bit value). :param max_neg: Vex Constant (64-bit value). :return: x or max_pos or max_neg (32-bit value). """ cond_max_pos = extend_to_32_bits(x.signed > max_pos) cond_max_neg = extend_to_32_bits(x.signed < max_neg) ret = (max_pos & cond_max_pos & ~cond_max_neg) | \ (max_neg & ~cond_max_pos & cond_max_neg) | \ (x & ~cond_max_pos & ~cond_max_neg) return ret def ssov64(x): """ Saturation on signed overflow. """ return x def suov(x, y): """ Saturation on unsigned overflow. """ max_pos = (1 << y) - 1 cond_max_pos = extend_to_32_bits(x > max_pos) ret = (max_pos & cond_max_pos) | (x & ~cond_max_pos) return ret def suov16(x): """ Saturation on unsigned overflow. """ cond_x_neg = extend_to_16_bits((x >> 15) == 1) ret = x & (cond_x_neg^0xffff) return ret def suov32(x): """ Saturation on unsigned overflow. :param x: VexValue. """ max_pos = (1 << 32) - 1 cond_max_pos = extend_to_32_bits(x > max_pos) cond_neg = extend_to_32_bits(x < 0) ret = (max_pos & cond_max_pos & ~cond_neg) | \ (0 & ~cond_max_pos & cond_neg) | \ (x & ~cond_max_pos & ~cond_neg) return ret def suov32_sub(x): """ Saturation on unsigned overflow. :param x: VexValue. """ cond_pos = extend_to_32_bits(x.signed > 0) ret = x & cond_pos return ret def suov32_pos(x): """ Saturation on unsigned overflow. :param x: VexValue. """ cond_pos = extend_to_32_bits(x > UINT32_MAX_POS) ret = (UINT32_MAX_POS & cond_pos) | (x & ~cond_pos) return ret def suov64(x): """ Saturation on unsigned overflow. """ cond_x_neg = extend_bits((x[63] == 1), 64) ret = 0 | (x & ~cond_x_neg) return ret def extract_16s(reg, halfword): """ Return signed halfword value of register. :param reg: register to extract bits from it. :param halfword: 0 or 1 for corresponding halfwords. """ return (reg >> (halfword * 16)).cast_to(Type.int_16).cast_to(Type.int_32, signed=True) def sign_extend(val, bits=32): """ Sign extension. High-order bit of val is left extended. :param val: VexValue """ sign_bit = 1 << (bits - 1) return (val & (sign_bit - 1)) - (val & sign_bit) def sign_extend_2(val, width): """ Sign extension. High-order bit of val is left extended. :param val: VexValue :param width: int """ cond_sign_bit_1 = extend_to_32_bits((val & ((1 << width)-1)) == 1) mask_1 = ((0xffffffff >> width) << width) & cond_sign_bit_1 result = val | mask_1 return result def sign_extend_3(val, width, tmp): """ Sign extension. High-order bit of val is left extended. :param val: VexValue :param width: VexValue :param tmp: VexValue of 0xffffffff """ mask_sign_bit = (1 << (width-1)).cast_to(Type.int_32) cond_sign_bit_1 = extend_to_32_bits(val & mask_sign_bit == 1) mask_2 = ((tmp >> width) << width).cast_to(Type.int_32) & cond_sign_bit_1.cast_to(Type.int_32) result = val | mask_2 return result def twos_comp(val, bits): """compute 2's complement """ if val & (1 << (bits - 1)): val = val - (1 << bits) return val def twos_comp_2(val, bits): """compute 2's complement :param val: VexValue """ mask = 1 << (bits - 1) condition = extend_bits((val & mask) == mask, bits) val = (val - (1 << bits)) & condition return val def get_abs_val(val, bits): """ Compute absolute value :param val: VexValue """ mask = 1 << (bits - 1) ones = (mask << 1) - 1 condition = extend_to_32_bits(mask & (val & ones) == 0) result = (val & condition) | (((val ^ ones) + 1) & ~condition) return result def clo32(val): """ Count Leading Ones starting from bit 32. """ # pylint: disable=line-too-long first_bit = val[31] ^ 0x0 ctr = (1 & val[31]) + \ (1 & val[30]) + \ (1 & val[29] & val[30]) + \ (1 & val[28] & val[30] & val[29]) + \ (1 & val[27] & val[30] & val[29] & val[28]) + \ (1 & val[26] & val[30] & val[29] & val[28] & val[27]) + \ (1 & val[25] & val[30] & val[29] & val[28] & val[27] & val[26]) + \ (1 & val[24] & val[30] & val[29] & val[28] & val[27] & val[26] & val[25]) + \ (1 & val[23] & val[30] & val[29] & val[28] & val[27] & val[26] & val[25] & val[24]) + \ (1 & val[22] & val[30] & val[29] & val[28] & val[27] & val[26] & val[25] & val[24] & val[23]) + \ (1 & val[21] & val[30] & val[29] & val[28] & val[27] & val[26] & val[25] & val[24] & val[23] & val[22]) + \ (1 & val[20] & val[30] & val[29] & val[28] & val[27] & val[26] & val[25] & val[24] & val[23] & val[22] & val[21]) + \ (1 & val[19] & val[30] & val[29] & val[28] & val[27] & val[26] & val[25] & val[24] & val[23] & val[22] & val[21] & val[20]) + \ (1 & val[18] & val[30] & val[29] & val[28] & val[27] & val[26] & val[25] & val[24] & val[23] & val[22] & val[21] & val[20] & val[19]) + \ (1 & val[17] & val[30] & val[29] & val[28] & val[27] & val[26] & val[25] & val[24] & val[23] & val[22] & val[21] & val[20] & val[19] & val[18]) + \ (1 & val[16] & val[30] & val[29] & val[28] & val[27] & val[26] & val[25] & val[24] & val[23] & val[22] & val[21] & val[20] & val[19] & val[18] & val[17]) + \ (1 & val[15] & val[30] & val[29] & val[28] & val[27] & val[26] & val[25] & val[24] & val[23] & val[22] & val[21] & val[20] & val[19] & val[18] & val[17] & val[16]) + \ (1 & val[14] & val[30] & val[29] & val[28] & val[27] & val[26] & val[25] & val[24] & val[23] & val[22] & val[21] & val[20] & val[19] & val[18] & val[17] & val[16] & val[15]) + \ (1 & val[13] & val[30] & val[29] & val[28] & val[27] & val[26] & val[25] & val[24] & val[23] & val[22] & val[21] & val[20] & val[19] & val[18] & val[17] & val[16] & val[15] & val[14]) + \ (1 & val[12] & val[30] & val[29] & val[28] & val[27] & val[26] & val[25] & val[24] & val[23] & val[22] & val[21] & val[20] & val[19] & val[18] & val[17] & val[16] & val[15] & val[14] & val[13]) + \ (1 & val[11] & val[30] & val[29] & val[28] & val[27] & val[26] & val[25] & val[24] & val[23] & val[22] & val[21] & val[20] & val[19] & val[18] & val[17] & val[16] & val[15] & val[14] & val[13] & val[12]) + \ (1 & val[10] & val[30] & val[29] & val[28] & val[27] & val[26] & val[25] & val[24] & val[23] & val[22] & val[21] & val[20] & val[19] & val[18] & val[17] & val[16] & val[15] & val[14] & val[13] & val[12] & val[11]) + \ (1 & val[9] & val[30] & val[29] & val[28] & val[27] & val[26] & val[25] & val[24] & val[23] & val[22] & val[21] & val[20] & val[19] & val[18] & val[17] & val[16] & val[15] & val[14] & val[13] & val[12] & val[11] & val[10]) + \ (1 & val[8] & val[30] & val[29] & val[28] & val[27] & val[26] & val[25] & val[24] & val[23] & val[22] & val[21] & val[20] & val[19] & val[18] & val[17] & val[16] & val[15] & val[14] & val[13] & val[12] & val[11] & val[10] & val[9]) + \ (1 & val[7] & val[30] & val[29] & val[28] & val[27] & val[26] & val[25] & val[24] & val[23] & val[22] & val[21] & val[20] & val[19] & val[18] & val[17] & val[16] & val[15] & val[14] & val[13] & val[12] & val[11] & val[10] & val[9] & val[8]) + \ (1 & val[6] & val[30] & val[29] & val[28] & val[27] & val[26] & val[25] & val[24] & val[23] & val[22] & val[21] & val[20] & val[19] & val[18] & val[17] & val[16] & val[15] & val[14] & val[13] & val[12] & val[11] & val[10] & val[9] & val[8] & val[7]) + \ (1 & val[5] & val[30] & val[29] & val[28] & val[27] & val[26] & val[25] & val[24] & val[23] & val[22] & val[21] & val[20] & val[19] & val[18] & val[17] & val[16] & val[15] & val[14] & val[13] & val[12] & val[11] & val[10] & val[9] & val[8] & val[7] & val[6]) + \ (1 & val[4] & val[30] & val[29] & val[28] & val[27] & val[26] & val[25] & val[24] & val[23] & val[22] & val[21] & val[20] & val[19] & val[18] & val[17] & val[16] & val[15] & val[14] & val[13] & val[12] & val[11] & val[10] & val[9] & val[8] & val[7] & val[6] & val[5]) + \ (1 & val[3] & val[30] & val[29] & val[28] & val[27] & val[26] & val[25] & val[24] & val[23] & val[22] & val[21] & val[20] & val[19] & val[18] & val[17] & val[16] & val[15] & val[14] & val[13] & val[12] & val[11] & val[10] & val[9] & val[8] & val[7] & val[6] & val[5] & val[4]) + \ (1 & val[2] & val[30] & val[29] & val[28] & val[27] & val[26] & val[25] & val[24] & val[23] & val[22] & val[21] & val[20] & val[19] & val[18] & val[17] & val[16] & val[15] & val[14] & val[13] & val[12] & val[11] & val[10] & val[9] & val[8] & val[7] & val[6] & val[5] & val[4] & val[3]) + \ (1 & val[1] & val[30] & val[29] & val[28] & val[27] & val[26] & val[25] & val[24] & val[23] & val[22] & val[21] & val[20] & val[19] & val[18] & val[17] & val[16] & val[15] & val[14] & val[13] & val[12] & val[11] & val[10] & val[9] & val[8] & val[7] & val[6] & val[5] & val[4] & val[3] & val[2]) + \ (1 & val[0] & val[30] & val[29] & val[28] & val[27] & val[26] & val[25] & val[24] & val[23] & val[22] & val[21] & val[20] & val[19] & val[18] & val[17] & val[16] & val[15] & val[14] & val[13] & val[12] & val[11] & val[10] & val[9] & val[8] & val[7] & val[6] & val[5] & val[4] & val[3] & val[2] & val[1]) return ctr * first_bit def clo16(val): """ Count Leading Ones starting from bit 16. """ # pylint: disable=line-too-long first_bit = val[15] ^ 0x0 ctr = (1 & val[15]) + \ (1 & val[14]) + \ (1 & val[13] & val[14]) + \ (1 & val[12] & val[14] & val[13]) + \ (1 & val[11] & val[14] & val[13] & val[12]) + \ (1 & val[10] & val[14] & val[13] & val[12] & val[11]) + \ (1 & val[9] & val[14] & val[13] & val[12] & val[11] & val[10]) + \ (1 & val[8] & val[14] & val[13] & val[12] & val[11] & val[10] & val[9]) + \ (1 & val[7] & val[14] & val[13] & val[12] & val[11] & val[10] & val[9] & val[8]) + \ (1 & val[6] & val[14] & val[13] & val[12] & val[11] & val[10] & val[9] & val[8] & val[7]) + \ (1 & val[5] & val[14] & val[13] & val[12] & val[11] & val[10] & val[9] & val[8] & val[7] & val[6]) + \ (1 & val[4] & val[14] & val[13] & val[12] & val[11] & val[10] & val[9] & val[8] & val[7] & val[6] & val[5]) + \ (1 & val[3] & val[14] & val[13] & val[12] & val[11] & val[10] & val[9] & val[8] & val[7] & val[6] & val[5] & val[4]) + \ (1 & val[2] & val[14] & val[13] & val[12] & val[11] & val[10] & val[9] & val[8] & val[7] & val[6] & val[5] & val[4] & val[3]) + \ (1 & val[1] & val[14] & val[13] & val[12] & val[11] & val[10] & val[9] & val[8] & val[7] & val[6] & val[5] & val[4] & val[3] & val[2]) + \ (1 & val[0] & val[14] & val[13] & val[12] & val[11] & val[10] & val[9] & val[8] & val[7] & val[6] & val[5] & val[4] & val[3] & val[2] & val[1]) return ctr * first_bit def cls(val, disp): """ Count Leading Signs starting from bit disp. disp: 15 or 31 """ mask = 0x1 ctr = 0 sign_bit = disp # bit: 31 or 15 disp -= 1 # first bit is the sign bit while disp >= 0: cond = (val[sign_bit] ^ (((val & (mask << disp)) >> disp) & 0x1) == 0) ctr += (1 & cond) disp -= 1 return ctr def clz16(val): """ Count Leading Zeros starting from bit 16. """ # pylint: disable=line-too-long first_bit = val[15] ^ 0x1 ctr = (1 & (val[15]^1)) + \ (1 & (val[14]^1)) + \ (1 & (val[13]^1) & (val[14]^1)) + \ (1 & (val[12]^1) & (val[14]^1) & (val[13]^1)) + \ (1 & (val[11]^1) & (val[14]^1) & (val[13]^1) & (val[12]^1)) + \ (1 & (val[10]^1) & (val[14]^1) & (val[13]^1) & (val[12]^1) & (val[11]^1)) + \ (1 & (val[9] ^1) & (val[14]^1) & (val[13]^1) & (val[12]^1) & (val[11]^1) & (val[10]^1)) + \ (1 & (val[8] ^1) & (val[14]^1) & (val[13]^1) & (val[12]^1) & (val[11]^1) & (val[10]^1) & (val[9]^1)) + \ (1 & (val[7] ^1) & (val[14]^1) & (val[13]^1) & (val[12]^1) & (val[11]^1) & (val[10]^1) & (val[9]^1) & (val[8]^1)) + \ (1 & (val[6] ^1) & (val[14]^1) & (val[13]^1) & (val[12]^1) & (val[11]^1) & (val[10]^1) & (val[9]^1) & (val[8]^1) & (val[7]^1)) + \ (1 & (val[5] ^1) & (val[14]^1) & (val[13]^1) & (val[12]^1) & (val[11]^1) & (val[10]^1) & (val[9]^1) & (val[8]^1) & (val[7]^1) & (val[6]^1)) + \ (1 & (val[4] ^1) & (val[14]^1) & (val[13]^1) & (val[12]^1) & (val[11]^1) & (val[10]^1) & (val[9]^1) & (val[8]^1) & (val[7]^1) & (val[6]^1) & (val[5]^1)) + \ (1 & (val[3] ^1) & (val[14]^1) & (val[13]^1) & (val[12]^1) & (val[11]^1) & (val[10]^1) & (val[9]^1) & (val[8]^1) & (val[7]^1) & (val[6]^1) & (val[5]^1) & (val[4]^1)) + \ (1 & (val[2] ^1) & (val[14]^1) & (val[13]^1) & (val[12]^1) & (val[11]^1) & (val[10]^1) & (val[9]^1) & (val[8]^1) & (val[7]^1) & (val[6]^1) & (val[5]^1) & (val[4]^1) & (val[3]^1)) + \ (1 & (val[1] ^1) & (val[14]^1) & (val[13]^1) & (val[12]^1) & (val[11]^1) & (val[10]^1) & (val[9]^1) & (val[8]^1) & (val[7]^1) & (val[6]^1) & (val[5]^1) & (val[4]^1) & (val[3]^1) & (val[2]^1)) + \ (1 & (val[0] ^1) & (val[14]^1) & (val[13]^1) & (val[12]^1) & (val[11]^1) & (val[10]^1) & (val[9]^1) & (val[8]^1) & (val[7]^1) & (val[6]^1) & (val[5]^1) & (val[4]^1) & (val[3]^1) & (val[2]^1) & (val[1]^1)) return ctr * first_bit def clz32(val): """ Count Leading Zeros starting from bit 32. """ # pylint: disable=line-too-long first_bit = val[31] ^ 0x1 ctr = (1 & (val[31]^1)) + \ (1 & (val[30]^1)) + \ (1 & (val[29]^1) & (val[30]^1)) + \ (1 & (val[28]^1) & (val[30]^1) & (val[29]^1)) + \ (1 & (val[27]^1) & (val[30]^1) & (val[29]^1) & (val[28]^1)) + \ (1 & (val[26]^1) & (val[30]^1) & (val[29]^1) & (val[28]^1) & (val[27]^1)) + \ (1 & (val[25]^1) & (val[30]^1) & (val[29]^1) & (val[28]^1) & (val[27]^1) & (val[26]^1)) + \ (1 & (val[24]^1) & (val[30]^1) & (val[29]^1) & (val[28]^1) & (val[27]^1) & (val[26]^1) & (val[25]^1)) + \ (1 & (val[23]^1) & (val[30]^1) & (val[29]^1) & (val[28]^1) & (val[27]^1) & (val[26]^1) & (val[25]^1) & (val[24]^1)) + \ (1 & (val[22]^1) & (val[30]^1) & (val[29]^1) & (val[28]^1) & (val[27]^1) & (val[26]^1) & (val[25]^1) & (val[24]^1) & (val[23]^1)) + \ (1 & (val[21]^1) & (val[30]^1) & (val[29]^1) & (val[28]^1) & (val[27]^1) & (val[26]^1) & (val[25]^1) & (val[24]^1) & (val[23]^1) & (val[22]^1)) + \ (1 & (val[20]^1) & (val[30]^1) & (val[29]^1) & (val[28]^1) & (val[27]^1) & (val[26]^1) & (val[25]^1) & (val[24]^1) & (val[23]^1) & (val[22]^1) & (val[21]^1)) + \ (1 & (val[19]^1) & (val[30]^1) & (val[29]^1) & (val[28]^1) & (val[27]^1) & (val[26]^1) & (val[25]^1) & (val[24]^1) & (val[23]^1) & (val[22]^1) & (val[21]^1) & (val[20]^1)) + \ (1 & (val[18]^1) & (val[30]^1) & (val[29]^1) & (val[28]^1) & (val[27]^1) & (val[26]^1) & (val[25]^1) & (val[24]^1) & (val[23]^1) & (val[22]^1) & (val[21]^1) & (val[20]^1) & (val[19]^1)) + \ (1 & (val[17]^1) & (val[30]^1) & (val[29]^1) & (val[28]^1) & (val[27]^1) & (val[26]^1) & (val[25]^1) & (val[24]^1) & (val[23]^1) & (val[22]^1) & (val[21]^1) & (val[20]^1) & (val[19]^1) & (val[18]^1)) + \ (1 & (val[16]^1) & (val[30]^1) & (val[29]^1) & (val[28]^1) & (val[27]^1) & (val[26]^1) & (val[25]^1) & (val[24]^1) & (val[23]^1) & (val[22]^1) & (val[21]^1) & (val[20]^1) & (val[19]^1) & (val[18]^1) & (val[17]^1)) + \ (1 & (val[15]^1) & (val[30]^1) & (val[29]^1) & (val[28]^1) & (val[27]^1) & (val[26]^1) & (val[25]^1) & (val[24]^1) & (val[23]^1) & (val[22]^1) & (val[21]^1) & (val[20]^1) & (val[19]^1) & (val[18]^1) & (val[17]^1) & (val[16]^1)) + \ (1 & (val[14]^1) & (val[30]^1) & (val[29]^1) & (val[28]^1) & (val[27]^1) & (val[26]^1) & (val[25]^1) & (val[24]^1) & (val[23]^1) & (val[22]^1) & (val[21]^1) & (val[20]^1) & (val[19]^1) & (val[18]^1) & (val[17]^1) & (val[16]^1) & (val[15]^1)) + \ (1 & (val[13]^1) & (val[30]^1) & (val[29]^1) & (val[28]^1) & (val[27]^1) & (val[26]^1) & (val[25]^1) & (val[24]^1) & (val[23]^1) & (val[22]^1) & (val[21]^1) & (val[20]^1) & (val[19]^1) & (val[18]^1) & (val[17]^1) & (val[16]^1) & (val[15]^1) & (val[14]^1)) + \ (1 & (val[12]^1) & (val[30]^1) & (val[29]^1) & (val[28]^1) & (val[27]^1) & (val[26]^1) & (val[25]^1) & (val[24]^1) & (val[23]^1) & (val[22]^1) & (val[21]^1) & (val[20]^1) & (val[19]^1) & (val[18]^1) & (val[17]^1) & (val[16]^1) & (val[15]^1) & (val[14]^1) & (val[13]^1)) + \ (1 & (val[11]^1) & (val[30]^1) & (val[29]^1) & (val[28]^1) & (val[27]^1) & (val[26]^1) & (val[25]^1) & (val[24]^1) & (val[23]^1) & (val[22]^1) & (val[21]^1) & (val[20]^1) & (val[19]^1) & (val[18]^1) & (val[17]^1) & (val[16]^1) & (val[15]^1) & (val[14]^1) & (val[13]^1) & (val[12]^1)) + \ (1 & (val[10]^1) & (val[30]^1) & (val[29]^1) & (val[28]^1) & (val[27]^1) & (val[26]^1) & (val[25]^1) & (val[24]^1) & (val[23]^1) & (val[22]^1) & (val[21]^1) & (val[20]^1) & (val[19]^1) & (val[18]^1) & (val[17]^1) & (val[16]^1) & (val[15]^1) & (val[14]^1) & (val[13]^1) & (val[12]^1) & (val[11]^1)) + \ (1 & (val[9] ^1) & (val[30]^1) & (val[29]^1) & (val[28]^1) & (val[27]^1) & (val[26]^1) & (val[25]^1) & (val[24]^1) & (val[23]^1) & (val[22]^1) & (val[21]^1) & (val[20]^1) & (val[19]^1) & (val[18]^1) & (val[17]^1) & (val[16]^1) & (val[15]^1) & (val[14]^1) & (val[13]^1) & (val[12]^1) & (val[11]^1) & (val[10]^1)) + \ (1 & (val[8] ^1) & (val[30]^1) & (val[29]^1) & (val[28]^1) & (val[27]^1) & (val[26]^1) & (val[25]^1) & (val[24]^1) & (val[23]^1) & (val[22]^1) & (val[21]^1) & (val[20]^1) & (val[19]^1) & (val[18]^1) & (val[17]^1) & (val[16]^1) & (val[15]^1) & (val[14]^1) & (val[13]^1) & (val[12]^1) & (val[11]^1) & (val[10]^1) & (val[9]^1)) + \ (1 & (val[7] ^1) & (val[30]^1) & (val[29]^1) & (val[28]^1) & (val[27]^1) & (val[26]^1) & (val[25]^1) & (val[24]^1) & (val[23]^1) & (val[22]^1) & (val[21]^1) & (val[20]^1) & (val[19]^1) & (val[18]^1) & (val[17]^1) & (val[16]^1) & (val[15]^1) & (val[14]^1) & (val[13]^1) & (val[12]^1) & (val[11]^1) & (val[10]^1) & (val[9]^1) & (val[8]^1)) + \ (1 & (val[6] ^1) & (val[30]^1) & (val[29]^1) & (val[28]^1) & (val[27]^1) & (val[26]^1) & (val[25]^1) & (val[24]^1) & (val[23]^1) & (val[22]^1) & (val[21]^1) & (val[20]^1) & (val[19]^1) & (val[18]^1) & (val[17]^1) & (val[16]^1) & (val[15]^1) & (val[14]^1) & (val[13]^1) & (val[12]^1) & (val[11]^1) & (val[10]^1) & (val[9]^1) & (val[8]^1) & (val[7]^1)) + \ (1 & (val[5] ^1) & (val[30]^1) & (val[29]^1) & (val[28]^1) & (val[27]^1) & (val[26]^1) & (val[25]^1) & (val[24]^1) & (val[23]^1) & (val[22]^1) & (val[21]^1) & (val[20]^1) & (val[19]^1) & (val[18]^1) & (val[17]^1) & (val[16]^1) & (val[15]^1) & (val[14]^1) & (val[13]^1) & (val[12]^1) & (val[11]^1) & (val[10]^1) & (val[9]^1) & (val[8]^1) & (val[7]^1) & (val[6]^1)) + \ (1 & (val[4] ^1) & (val[30]^1) & (val[29]^1) & (val[28]^1) & (val[27]^1) & (val[26]^1) & (val[25]^1) & (val[24]^1) & (val[23]^1) & (val[22]^1) & (val[21]^1) & (val[20]^1) & (val[19]^1) & (val[18]^1) & (val[17]^1) & (val[16]^1) & (val[15]^1) & (val[14]^1) & (val[13]^1) & (val[12]^1) & (val[11]^1) & (val[10]^1) & (val[9]^1) & (val[8]^1) & (val[7]^1) & (val[6]^1) & (val[5]^1)) + \ (1 & (val[3] ^1) & (val[30]^1) & (val[29]^1) & (val[28]^1) & (val[27]^1) & (val[26]^1) & (val[25]^1) & (val[24]^1) & (val[23]^1) & (val[22]^1) & (val[21]^1) & (val[20]^1) & (val[19]^1) & (val[18]^1) & (val[17]^1) & (val[16]^1) & (val[15]^1) & (val[14]^1) & (val[13]^1) & (val[12]^1) & (val[11]^1) & (val[10]^1) & (val[9]^1) & (val[8]^1) & (val[7]^1) & (val[6]^1) & (val[5]^1) & (val[4]^1)) + \ (1 & (val[2] ^1) & (val[30]^1) & (val[29]^1) & (val[28]^1) & (val[27]^1) & (val[26]^1) & (val[25]^1) & (val[24]^1) & (val[23]^1) & (val[22]^1) & (val[21]^1) & (val[20]^1) & (val[19]^1) & (val[18]^1) & (val[17]^1) & (val[16]^1) & (val[15]^1) & (val[14]^1) & (val[13]^1) & (val[12]^1) & (val[11]^1) & (val[10]^1) & (val[9]^1) & (val[8]^1) & (val[7]^1) & (val[6]^1) & (val[5]^1) & (val[4]^1) & (val[3]^1)) + \ (1 & (val[1] ^1) & (val[30]^1) & (val[29]^1) & (val[28]^1) & (val[27]^1) & (val[26]^1) & (val[25]^1) & (val[24]^1) & (val[23]^1) & (val[22]^1) & (val[21]^1) & (val[20]^1) & (val[19]^1) & (val[18]^1) & (val[17]^1) & (val[16]^1) & (val[15]^1) & (val[14]^1) & (val[13]^1) & (val[12]^1) & (val[11]^1) & (val[10]^1) & (val[9]^1) & (val[8]^1) & (val[7]^1) & (val[6]^1) & (val[5]^1) & (val[4]^1) & (val[3]^1) & (val[2]^1)) + \ (1 & (val[0] ^1) & (val[30]^1) & (val[29]^1) & (val[28]^1) & (val[27]^1) & (val[26]^1) & (val[25]^1) & (val[24]^1) & (val[23]^1) & (val[22]^1) & (val[21]^1) & (val[20]^1) & (val[19]^1) & (val[18]^1) & (val[17]^1) & (val[16]^1) & (val[15]^1) & (val[14]^1) & (val[13]^1) & (val[12]^1) & (val[11]^1) & (val[10]^1) & (val[9]^1) & (val[8]^1) & (val[7]^1) & (val[6]^1) & (val[5]^1) & (val[4]^1) & (val[3]^1) & (val[2]^1) & (val[1]^1)) return ctr * first_bit def reverse16(n): result = n[0] << 15 | n[1] << 14 | \ n[2] << 13 | n[3] << 12 | \ n[4] << 11 | n[5] << 10 | \ n[6] << 9 | n[7] << 8 | \ n[8] << 7 | n[9] << 6 | \ n[10]<< 5 | n[11] << 4 | \ n[12]<< 3 | n[13] << 2 | \ n[14]<< 1 | n[15] return result

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58025967fa2de611203a39d31c54a0226281a0e6

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py

Python

mockfirestore/__init__.py

briggleman/python-mock-firestore

04720a7695f1826e9a1251dd2fd33324cecbbd43

[ "MIT" ]

null

mockfirestore/__init__.py

briggleman/python-mock-firestore

04720a7695f1826e9a1251dd2fd33324cecbbd43

[ "MIT" ]

null

mockfirestore/__init__.py

briggleman/python-mock-firestore

04720a7695f1826e9a1251dd2fd33324cecbbd43

[ "MIT" ]

1

2019-10-19T15:29:44.000Z

from .main import DocumentSnapshot, DocumentReference, Query, CollectionReference, MockFirestore

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py

Python

modelproject/modelproject/chxmodelproject.py

NumEconCopenhagen/projects-2019-chx

84cc06539b113d33464a8974fb4d9636acc3d1ba

[ "MIT" ]

null

modelproject/modelproject/chxmodelproject.py

NumEconCopenhagen/projects-2019-chx

84cc06539b113d33464a8974fb4d9636acc3d1ba

[ "MIT" ]

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2019-04-16T12:06:15.000Z

2019-05-15T23:53:45.000Z

modelproject/modelproject/chxmodelproject.py

NumEconCopenhagen/projects-2019-chx

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[ "MIT" ]

2

2020-04-02T10:51:19.000Z

2022-01-17T16:44:18.000Z

import numpy as np from scipy import optimize #%matplotlib inline import matplotlib.pyplot as plt def keynesian_cross(T, I, G, NX, a, b): """ Draws the Keynesian cross with the 45-degree line and the planned total spending as a function of total production. Args: T (float): Taxs a (float): Constant consumption, a>0 b (float): Marginal consumption rate, 0<b<1 I (float): Investment G (float): Public expenditure NX (float): Net export Return: Figure """ # The data vector to be plotted for production and aggregate expenditure: Y_arrey = np.linspace(0,300) AD_arrey = (a + b * (Y_arrey - T) + I + G + NX) degree = Y_arrey # The figure fig = plt.figure(figsize=(10,5)) ax = fig.add_subplot(1,1,1) ax.plot(Y_arrey, degree, label="45-degree line", color='lightblue',linewidth=3) ax.plot(Y_arrey, AD_arrey, label="AD=C+I+G+NX", color='darkorange',linewidth=3) ax.set_xlabel("Y") ax.set_ylabel("AD") ax.legend(loc="upper left") ax.grid() ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False) ax.spines['bottom'].set_visible(False) ax.spines['left'].set_visible(False) return def cross_equalibrium(T, I, G, NX, a, b): """ The equalibrium for the Keynesian cross where aggregate expenditure equals total production Args: T (float): Tax a (float): Constant consumption, a>0 b (float): Marginal consumption rate, 0<b<1 I (float): Investment G (float): Public expenditure NX (float): Net export Returns: Result: Production in equalibrium, Y (float) """ return 1/(1-b) * (I + G + NX + a - b*T) def keynesian_cross_NXshift(T, I, G, NX, a, b, delta_NX): """ Steady state for the Keynesian cross where aggregate expenditure equals total production Args: AD (float): Aggregate expenditure Y (float): Total production T (float): Tax a (float): Constant consumption, a>0 b (float): Marginal consumption rate, 0<b<1 I (float): Investment G (float): Public expenditure NX (float): Net export delta_NX (float): Net export shift in Returns: Result: Figure """ # The equation setup NX2 = NX + delta_NX Y_arrey = np.linspace(0,300) AD_arrey = (a + b * (Y_arrey - T) + I + G + NX) AD2_arrey = (a + b * (Y_arrey - T) + I + G + NX2) degree = Y_arrey # The figure fig = plt.figure(figsize=(10,6)) ax = fig.add_subplot(1,1,1) ax.plot(Y_arrey, degree, label="45-degree line", color='lightblue') ax.plot(Y_arrey, AD_arrey, label="AD=C+I+G+NX", color='orange') ax.plot(Y_arrey, AD2_arrey, label="AD'=C+I+G+NX'", color='red') ax.set_xlabel("Y") ax.set_ylabel("AD") ax.legend(loc="upper left") ax.grid() ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False) ax.spines['bottom'].set_visible(False) ax.spines['left'].set_visible(False) return def num_opt(Y_goal,T,I,G,a,b): """ Numerical optimazation to calculate value of NX to optain production goal Args: Y_goal (float): Production goal T (float): Tax a (float): Constant consumption, a>0 b (float): Marginal consumption rate, 0<b<1 I (float): Investment G (float): Public expenditure Returns: Result: NX (float) """ # Object function to be optimized: obj = lambda NX: (cross_equalibrium(T, I, G, NX, a, b) - Y_goal)**2 # Initial guess x0 = 10 return optimize.minimize(obj,x0) def keynesian_cross_NXshift_t(k, t, I, G, NX, a, b, delta_NX): """ Steady state for the Keynesian cross where aggregate expenditure equals total production Args: AD (float): Aggregate expenditure Y (float): Total production k (float): Base tax t (float): Marginal tax rate a (float): Constant consumption, a>0 b (float): Marginal consumption rate, 0<b<1 I (float): Investment G (float): Public expenditure NX (float): Net export delta_NX (float): Net export shift in Returns: Result: Figure """ # The equation setup and generating of data arreys: NX2 = NX + delta_NX Y_arrey = np.linspace(0,300) AD_arrey = (a + b * (Y_arrey - (k + b*t)) + I + G + NX) AD2_arrey = (a + b * (Y_arrey - (k + b*t)) + I + G + NX2) degree = Y_arrey # The figure: fig = plt.figure(figsize=(10,6)) ax = fig.add_subplot(1,1,1) ax.plot(Y_arrey, degree, label="45-degree line", color='lightblue') ax.plot(Y_arrey, AD_arrey, label="AD=C+I+G+NX", color='orange') ax.plot(Y_arrey, AD2_arrey, label="AD'=C+I+G+NX'", color='red') ax.set_xlabel("Y") ax.set_ylabel("AD") ax.legend(loc="upper left") ax.grid() ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False) ax.spines['bottom'].set_visible(False) ax.spines['left'].set_visible(False) return

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null

0

6

ed0a681f73db2b21175cc869d5444af5418367f8

104

py

Python

core/messenger/exceptions.py

anthill-arch/platform

ff45dc71b2f3141bbd95baaf4da7ff1d2ac24ca0

[ "MIT" ]

1

2018-11-30T21:56:14.000Z

core/messenger/exceptions.py

anthill-arch/platform

ff45dc71b2f3141bbd95baaf4da7ff1d2ac24ca0

[ "MIT" ]

null

core/messenger/exceptions.py

anthill-arch/platform

ff45dc71b2f3141bbd95baaf4da7ff1d2ac24ca0

[ "MIT" ]

null

class NotAuthenticatedError(Exception): pass class AuthenticationFailedError(Exception): pass

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104

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1

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null

0

1

0

6

ed2c8027b3616dd0462d0630ba3265e706057df8

89

py

Python

final_project/machinetranslation/__init__.py

yl-miao/xzceb-flask_eng_fr

916316b27fa447396a99314f41c643109ce22a7e

[ "Apache-2.0" ]

null

final_project/machinetranslation/__init__.py

yl-miao/xzceb-flask_eng_fr

916316b27fa447396a99314f41c643109ce22a7e

[ "Apache-2.0" ]

null

final_project/machinetranslation/__init__.py

yl-miao/xzceb-flask_eng_fr

916316b27fa447396a99314f41c643109ce22a7e

[ "Apache-2.0" ]

null

from . import translator #import sys #sys.path.append("./tests") from . import unit_tests

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ed2d8840bdda81ffe9f810a0e4a80dda3ea7871d

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py

Python

protobuf/__main__.py

Axonny/Protobuf

709e7d77f94e7482021c17fc18c441a1f2af5a1e

[ "MIT" ]

null

protobuf/__main__.py

Axonny/Protobuf

709e7d77f94e7482021c17fc18c441a1f2af5a1e

[ "MIT" ]

null

protobuf/__main__.py

Axonny/Protobuf

709e7d77f94e7482021c17fc18c441a1f2af5a1e

[ "MIT" ]

null

from protobuf.generate_class import main main()

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71f7088fe598802b25d2f6b8197de7cf0d4aa2e0

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py

Python

quarkchain/cluster/tests/test_shard_state.py

HAOYUatHZ/pyquarkchain

b2c7c02e4415aa26917c2cbb5e7571c9fef16c5b

[ "MIT" ]

1

2018-10-23T05:48:42.000Z

quarkchain/cluster/tests/test_shard_state.py

skji/pyquarkchain

090f9981b89b8873daaed36171a9bc9f27b10473

[ "MIT" ]

null

quarkchain/cluster/tests/test_shard_state.py

skji/pyquarkchain

090f9981b89b8873daaed36171a9bc9f27b10473

[ "MIT" ]

null

import random import unittest from fractions import Fraction from quarkchain.cluster.shard_state import ShardState from quarkchain.cluster.tests.test_utils import ( get_test_env, create_transfer_transaction, create_contract_creation_transaction, contract_creation_tx, ) from quarkchain.config import ConsensusType from quarkchain.core import CrossShardTransactionDeposit, CrossShardTransactionList from quarkchain.core import Identity, Address, TokenBalanceMap from quarkchain.diff import EthDifficultyCalculator from quarkchain.evm import opcodes from quarkchain.genesis import GenesisManager def create_default_shard_state( env, shard_id=0, diff_calc=None, posw_override=False, no_coinbase=False ): genesis_manager = GenesisManager(env.quark_chain_config) shard_size = next(iter(env.quark_chain_config.shards.values())).SHARD_SIZE full_shard_id = shard_size | shard_id if posw_override: posw_config = env.quark_chain_config.shards[full_shard_id].POSW_CONFIG posw_config.ENABLED = True if no_coinbase: env.quark_chain_config.shards[full_shard_id].COINBASE_AMOUNT = 0 shard_state = ShardState(env=env, full_shard_id=full_shard_id, diff_calc=diff_calc) shard_state.init_genesis_state(genesis_manager.create_root_block()) return shard_state class TestShardState(unittest.TestCase): def setUp(self): super().setUp() config = get_test_env().quark_chain_config self.root_coinbase = config.ROOT.COINBASE_AMOUNT self.shard_coinbase = next(iter(config.shards.values())).COINBASE_AMOUNT # to make test verification easier, assume following tax rate assert config.REWARD_TAX_RATE == 0.5 self.tax_rate = config.reward_tax_rate # type: Fraction self.genesis_token = config.genesis_token # type: int self.genesis_token_str = config.GENESIS_TOKEN # type: str def get_after_tax_reward(self, value: int) -> int: return value * self.tax_rate.numerator // self.tax_rate.denominator def test_shard_state_simple(self): env = get_test_env() state = create_default_shard_state(env) self.assertEqual(state.root_tip.height, 0) self.assertEqual(state.header_tip.height, 0) # make sure genesis minor block has the right coinbase after-tax self.assertEqual( state.header_tip.coinbase_amount_map.balance_map, {self.genesis_token: 2500000000000000000}, ) def test_init_genesis_state(self): env = get_test_env() state = create_default_shard_state(env) genesis_header = state.header_tip root_block = state.root_tip.create_block_to_append(nonce=1234) root_block.header.height = 0 root_block.finalize() new_genesis_block, _ = state.init_genesis_state(root_block) self.assertNotEqual( new_genesis_block.header.get_hash(), genesis_header.get_hash() ) # header tip is still the old genesis header self.assertEqual(state.header_tip, genesis_header) block = new_genesis_block.create_block_to_append() state.finalize_and_add_block(block) # extending new_genesis_block doesn't change header_tip due to root chain first consensus self.assertEqual(state.header_tip, genesis_header) self.assertEqual(genesis_header, state.db.get_minor_block_by_height(0).header) # extending the root block will change the header_tip root_block = root_block.create_block_to_append(nonce=1234).finalize() root_block.finalize() self.assertTrue(state.add_root_block(root_block)) # ideally header_tip should be block.header but we don't track tips on fork chains for the moment # and thus it reverted all the way back to genesis self.assertEqual(state.header_tip, new_genesis_block.header) self.assertEqual(new_genesis_block, state.db.get_minor_block_by_height(0)) def test_blocks_with_incorrect_version(self): env = get_test_env() state = create_default_shard_state(env=env) root_block = state.root_tip.create_block_to_append() root_block.header.version = 1 with self.assertRaisesRegexp(ValueError, "incorrect root block version"): state.add_root_block(root_block.finalize()) root_block.header.version = 0 state.add_root_block(root_block.finalize()) shard_block = state.create_block_to_mine() shard_block.header.version = 1 with self.assertRaisesRegexp(ValueError, "incorrect minor block version"): state.finalize_and_add_block(shard_block) shard_block.header.version = 0 state.finalize_and_add_block(shard_block) def test_gas_price(self): id_list = [Identity.create_random_identity() for _ in range(5)] acc_list = [Address.create_from_identity(i, full_shard_key=0) for i in id_list] env = get_test_env(genesis_account=acc_list[0], genesis_minor_quarkash=10000000) state = create_default_shard_state(env=env) # Add a root block to have all the shards initialized root_block = state.root_tip.create_block_to_append().finalize() state.add_root_block(root_block) # 5 tx per block, make 3 blocks for _ in range(3): for j in range(5): state.add_tx( create_transfer_transaction( shard_state=state, key=id_list[j].get_key(), from_address=acc_list[j], to_address=random.choice(acc_list), value=0, gas_price=42 if j == 0 else 0, ) ) b = state.create_block_to_mine(address=acc_list[1]) state.finalize_and_add_block(b) # for testing purposes, update percentile to take max gas price state.gas_price_suggestion_oracle.percentile = 100 gas_price = state.gas_price() self.assertEqual(gas_price, 42) # results should be cached (same header). updating oracle shouldn't take effect state.gas_price_suggestion_oracle.percentile = 50 gas_price = state.gas_price() self.assertEqual(gas_price, 42) def test_estimate_gas(self): id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_key=0) acc2 = Address.create_random_account(full_shard_key=0) env = get_test_env(genesis_account=acc1, genesis_minor_quarkash=10000000) state = create_default_shard_state(env=env) # Add a root block to have all the shards initialized root_block = state.root_tip.create_block_to_append().finalize() state.add_root_block(root_block) tx_gen = lambda data: create_transfer_transaction( shard_state=state, key=id1.get_key(), from_address=acc1, to_address=acc2, value=12345, data=data, ) tx = tx_gen(b"") estimate = state.estimate_gas(tx, acc1) self.assertEqual(estimate, 21000) tx = tx_gen(b"12123478123412348125936583475758") estimate = state.estimate_gas(tx, acc1) self.assertEqual(estimate, 23176) def test_execute_tx(self): id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_key=0) acc2 = Address.create_random_account(full_shard_key=0) env = get_test_env(genesis_account=acc1, genesis_minor_quarkash=10000000) state = create_default_shard_state(env=env) # Add a root block to have all the shards initialized root_block = state.root_tip.create_block_to_append().finalize() state.add_root_block(root_block) tx = create_transfer_transaction( shard_state=state, key=id1.get_key(), from_address=acc1, to_address=acc2, value=12345, ) # adding this line to make sure `execute_tx` would reset `gas_used` state.evm_state.gas_used = state.evm_state.gas_limit res = state.execute_tx(tx, acc1) self.assertEqual(res, b"") def test_add_tx_incorrect_from_shard_id(self): id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_key=1) acc2 = Address.create_random_account(full_shard_key=1) env = get_test_env(genesis_account=acc1, genesis_minor_quarkash=10000000) state = create_default_shard_state(env=env) # state is shard 0 but tx from shard 1 tx = create_transfer_transaction( shard_state=state, key=id1.get_key(), from_address=acc1, to_address=acc2, value=12345, ) self.assertFalse(state.add_tx(tx)) self.assertIsNone(state.execute_tx(tx, acc1)) def test_one_tx(self): id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_key=0) acc2 = Address.create_random_account(full_shard_key=0) acc3 = Address.create_random_account(full_shard_key=0) env = get_test_env(genesis_account=acc1, genesis_minor_quarkash=10000000) state = create_default_shard_state(env=env) # Add a root block to have all the shards initialized root_block = state.root_tip.create_block_to_append().finalize() state.add_root_block(root_block) tx = create_transfer_transaction( shard_state=state, key=id1.get_key(), from_address=acc1, to_address=acc2, value=12345, gas=50000, ) state.evm_state.gas_used = state.evm_state.gas_limit self.assertTrue(state.add_tx(tx)) block, i = state.get_transaction_by_hash(tx.get_hash()) self.assertEqual(block.tx_list[0], tx) self.assertEqual(block.header.create_time, 0) self.assertEqual(i, 0) # tx claims to use more gas than the limit and thus not included b1 = state.create_block_to_mine(address=acc3, gas_limit=49999) self.assertEqual(len(b1.tx_list), 0) b1 = state.create_block_to_mine(address=acc3) self.assertEqual(len(b1.tx_list), 1) # Should succeed state.finalize_and_add_block(b1) self.assertEqual(state.header_tip, b1.header) self.assertEqual( state.get_token_balance(id1.recipient, self.genesis_token), 10000000 - opcodes.GTXCOST - 12345, ) self.assertEqual( state.get_token_balance(acc2.recipient, self.genesis_token), 12345 ) # shard miner only receives a percentage of reward because of REWARD_TAX_RATE self.assertEqual( state.get_token_balance(acc3.recipient, self.genesis_token), self.get_after_tax_reward(opcodes.GTXCOST + self.shard_coinbase), ) # Check receipts self.assertEqual(len(state.evm_state.receipts), 1) self.assertEqual(state.evm_state.receipts[0].state_root, b"\x01") self.assertEqual(state.evm_state.receipts[0].gas_used, 21000) block, i = state.get_transaction_by_hash(tx.get_hash()) self.assertEqual(block, b1) self.assertEqual(i, 0) # Check receipts in storage resp = state.get_transaction_receipt(tx.get_hash()) self.assertIsNotNone(resp) block, i, r = resp self.assertEqual(block, b1) self.assertEqual(i, 0) self.assertEqual(r.success, b"\x01") self.assertEqual(r.gas_used, 21000) # Check Account has full_shard_key self.assertEqual( state.evm_state.get_full_shard_key(acc2.recipient), acc2.full_shard_key ) tx_list, _ = state.db.get_transactions_by_address(acc1) self.assertEqual(tx_list[0].value, 12345) tx_list, _ = state.db.get_transactions_by_address(acc2) self.assertEqual(tx_list[0].value, 12345) def test_duplicated_tx(self): id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_key=0) acc2 = Address.create_random_account(full_shard_key=0) acc3 = Address.create_random_account(full_shard_key=0) env = get_test_env(genesis_account=acc1, genesis_minor_quarkash=10000000) state = create_default_shard_state(env=env) # Add a root block to have all the shards initialized root_block = state.root_tip.create_block_to_append().finalize() state.add_root_block(root_block) tx = create_transfer_transaction( shard_state=state, key=id1.get_key(), from_address=acc1, to_address=acc2, value=12345, ) self.assertTrue(state.add_tx(tx)) self.assertFalse(state.add_tx(tx)) # already in tx_queue self.assertEqual(len(state.tx_queue), 1) self.assertEqual(len(state.tx_dict), 1) block, i = state.get_transaction_by_hash(tx.get_hash()) self.assertEqual(len(block.tx_list), 1) self.assertEqual(block.tx_list[0], tx) self.assertEqual(block.header.create_time, 0) self.assertEqual(i, 0) b1 = state.create_block_to_mine(address=acc3) self.assertEqual(len(b1.tx_list), 1) # Should succeed state.finalize_and_add_block(b1) self.assertEqual(state.header_tip, b1.header) self.assertEqual( state.get_token_balance(id1.recipient, self.genesis_token), 10000000 - opcodes.GTXCOST - 12345, ) self.assertEqual( state.get_token_balance(acc2.recipient, self.genesis_token), 12345 ) self.assertEqual( state.get_token_balance(acc3.recipient, self.genesis_token), self.get_after_tax_reward(opcodes.GTXCOST + self.shard_coinbase), ) # Check receipts self.assertEqual(len(state.evm_state.receipts), 1) self.assertEqual(state.evm_state.receipts[0].state_root, b"\x01") self.assertEqual(state.evm_state.receipts[0].gas_used, 21000) block, i = state.get_transaction_by_hash(tx.get_hash()) self.assertEqual(block, b1) self.assertEqual(i, 0) # tx already confirmed self.assertTrue(state.db.contain_transaction_hash(tx.get_hash())) self.assertFalse(state.add_tx(tx)) def test_add_invalid_tx_fail(self): id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_key=0) acc2 = Address.create_random_account(full_shard_key=0) env = get_test_env(genesis_account=acc1, genesis_minor_quarkash=10000000) state = create_default_shard_state(env=env) tx = create_transfer_transaction( shard_state=state, key=id1.get_key(), from_address=acc1, to_address=acc2, value=999999999999999999999, # insane ) self.assertFalse(state.add_tx(tx)) self.assertEqual(len(state.tx_queue), 0) def test_add_non_neighbor_tx_fail(self): id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_key=0) acc2 = Address.create_random_account(full_shard_key=3) # not acc1's neighbor acc3 = Address.create_random_account(full_shard_key=8) # acc1's neighbor env = get_test_env( genesis_account=acc1, genesis_minor_quarkash=10000000, shard_size=64 ) state = create_default_shard_state(env=env) # Add a root block to have all the shards initialized root_block = state.root_tip.create_block_to_append().finalize() state.add_root_block(root_block) tx = create_transfer_transaction( shard_state=state, key=id1.get_key(), from_address=acc1, to_address=acc2, value=0, gas=1000000, ) self.assertFalse(state.add_tx(tx)) self.assertEqual(len(state.tx_queue), 0) tx = create_transfer_transaction( shard_state=state, key=id1.get_key(), from_address=acc1, to_address=acc3, value=0, gas=1000000, ) self.assertTrue(state.add_tx(tx)) self.assertEqual(len(state.tx_queue), 1) def test_exceeding_xshard_limit(self): id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_key=0) acc2 = Address.create_random_account(full_shard_key=1) acc3 = Address.create_random_account(full_shard_key=0) env = get_test_env(genesis_account=acc1, genesis_minor_quarkash=10000000) # a huge number to make xshard tx limit become 0 so that no xshard tx can be # included in the block env.quark_chain_config.MAX_NEIGHBORS = 10 ** 18 state = create_default_shard_state(env=env) # Add a root block to have all the shards initialized root_block = state.root_tip.create_block_to_append().finalize() state.add_root_block(root_block) # add a xshard tx with large startgas tx = create_transfer_transaction( shard_state=state, key=id1.get_key(), from_address=acc1, to_address=acc2, value=12345, gas=state.get_xshard_gas_limit() + 1, ) self.assertFalse(state.add_tx(tx)) # xshard tx tx = create_transfer_transaction( shard_state=state, key=id1.get_key(), from_address=acc1, to_address=acc2, value=12345, gas=50000, ) self.assertTrue(state.add_tx(tx)) b1 = state.create_block_to_mine(address=acc3) self.assertEqual(len(b1.tx_list), 1) # inshard tx tx = create_transfer_transaction( shard_state=state, key=id1.get_key(), from_address=acc1, to_address=acc3, value=12345, gas=50000, ) self.assertTrue(state.add_tx(tx)) b1 = state.create_block_to_mine(address=acc3) self.assertEqual(len(b1.tx_list), 1) def test_two_tx_in_one_block(self): id1 = Identity.create_random_identity() id2 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_key=0) acc2 = Address.create_from_identity(id2, full_shard_key=0) acc3 = Address.create_random_account(full_shard_key=0) env = get_test_env( genesis_account=acc1, genesis_minor_quarkash=2000000 + opcodes.GTXCOST ) state = create_default_shard_state(env=env) # Add a root block to have all the shards initialized root_block = state.root_tip.create_block_to_append().finalize() state.add_root_block(root_block) state.add_tx( create_transfer_transaction( shard_state=state, key=id1.get_key(), from_address=acc1, to_address=acc2, value=1000000, ) ) b0 = state.create_block_to_mine(address=acc3) state.finalize_and_add_block(b0) self.assertEqual( state.get_token_balance(id1.recipient, self.genesis_token), 1000000 ) self.assertEqual( state.get_token_balance(acc2.recipient, self.genesis_token), 1000000 ) self.assertEqual( state.get_token_balance(acc3.recipient, self.genesis_token), self.get_after_tax_reward(opcodes.GTXCOST + self.shard_coinbase), ) # Check Account has full_shard_key self.assertEqual( state.evm_state.get_full_shard_key(acc2.recipient), acc2.full_shard_key ) state.add_tx( create_transfer_transaction( shard_state=state, key=id1.get_key(), from_address=acc1, to_address=Address( acc2.recipient, acc2.full_shard_key + 2 ), # set a different full shard id value=12345, gas=50000, ) ) state.add_tx( create_transfer_transaction( shard_state=state, key=id2.get_key(), from_address=acc2, to_address=acc1, value=54321, gas=40000, ) ) # Inshard gas limit is 40000 - 20000 b1 = state.create_block_to_mine( address=acc3, gas_limit=40000, xshard_gas_limit=20000 ) self.assertEqual(len(b1.tx_list), 0) b1 = state.create_block_to_mine( address=acc3, gas_limit=40000, xshard_gas_limit=0 ) self.assertEqual(len(b1.tx_list), 1) b1 = state.create_block_to_mine(address=acc3) self.assertEqual(len(b1.tx_list), 2) # Should succeed state.finalize_and_add_block(b1) self.assertEqual(state.header_tip, b1.header) self.assertEqual( state.get_token_balance(id1.recipient, self.genesis_token), 1000000 - opcodes.GTXCOST - 12345 + 54321, ) self.assertEqual( state.get_token_balance(acc2.recipient, self.genesis_token), 1000000 - opcodes.GTXCOST + 12345 - 54321, ) # 2 block rewards: 3 tx, 2 block rewards self.assertEqual( state.get_token_balance(acc3.recipient, self.genesis_token), self.get_after_tax_reward(opcodes.GTXCOST * 3 + self.shard_coinbase * 2), ) # Check receipts self.assertEqual(len(state.evm_state.receipts), 2) self.assertEqual(state.evm_state.receipts[0].state_root, b"\x01") self.assertEqual(state.evm_state.receipts[0].gas_used, 21000) self.assertEqual(state.evm_state.receipts[1].state_root, b"\x01") self.assertEqual(state.evm_state.receipts[1].gas_used, 42000) block, i = state.get_transaction_by_hash(b1.tx_list[0].get_hash()) self.assertEqual(block, b1) self.assertEqual(i, 0) block, i = state.get_transaction_by_hash(b1.tx_list[1].get_hash()) self.assertEqual(block, b1) self.assertEqual(i, 1) # Check acc2 full_shard_key doesn't change self.assertEqual( state.evm_state.get_full_shard_key(acc2.recipient), acc2.full_shard_key ) def test_fork_does_not_confirm_tx(self): """Tx should only be confirmed and removed from tx queue by the best chain""" id1 = Identity.create_random_identity() id2 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_key=0) acc2 = Address.create_from_identity(id2, full_shard_key=0) acc3 = Address.create_random_account(full_shard_key=0) env = get_test_env( genesis_account=acc1, genesis_minor_quarkash=2000000 + opcodes.GTXCOST ) state = create_default_shard_state(env=env) # Add a root block to have all the shards initialized root_block = state.root_tip.create_block_to_append().finalize() state.add_root_block(root_block) state.add_tx( create_transfer_transaction( shard_state=state, key=id1.get_key(), from_address=acc1, to_address=acc2, value=1000000, ) ) b0 = state.create_block_to_mine(address=acc3) b1 = state.create_block_to_mine(address=acc3) b0.tx_list = [] # make b0 empty state.finalize_and_add_block(b0) # tx is added back to queue in the end of create_block_to_mine self.assertEqual(len(state.tx_queue), 1) self.assertEqual(len(b1.tx_list), 1) state.finalize_and_add_block(b1) # b1 is a fork and does not remove the tx from queue self.assertEqual(len(state.tx_queue), 1) b2 = state.create_block_to_mine(address=acc3) state.finalize_and_add_block(b2) self.assertEqual(len(state.tx_queue), 0) def test_revert_fork_put_tx_back_to_queue(self): """Tx in the reverted chain should be put back to the queue""" id1 = Identity.create_random_identity() id2 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_key=0) acc2 = Address.create_from_identity(id2, full_shard_key=0) acc3 = Address.create_random_account(full_shard_key=0) env = get_test_env( genesis_account=acc1, genesis_minor_quarkash=2000000 + opcodes.GTXCOST ) state = create_default_shard_state(env=env) # Add a root block to have all the shards initialized root_block = state.root_tip.create_block_to_append().finalize() state.add_root_block(root_block) state.add_tx( create_transfer_transaction( shard_state=state, key=id1.get_key(), from_address=acc1, to_address=acc2, value=1000000, ) ) b0 = state.create_block_to_mine(address=acc3) b1 = state.create_block_to_mine(address=acc3) state.finalize_and_add_block(b0) self.assertEqual(len(state.tx_queue), 0) b1.tx_list = [] # make b1 empty state.finalize_and_add_block(b1) self.assertEqual(len(state.tx_queue), 0) b2 = b1.create_block_to_append() state.finalize_and_add_block(b2) # now b1-b2 becomes the best chain and we expect b0 to be reverted and put the tx back to queue self.assertEqual(len(state.tx_queue), 1) b3 = b0.create_block_to_append() state.finalize_and_add_block(b3) self.assertEqual(len(state.tx_queue), 1) b4 = b3.create_block_to_append() state.finalize_and_add_block(b4) # b0-b3-b4 becomes the best chain self.assertEqual(len(state.tx_queue), 0) def test_stale_block_count(self): id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_key=0) acc3 = Address.create_random_account(full_shard_key=0) env = get_test_env(genesis_account=acc1, genesis_minor_quarkash=10000000) state = create_default_shard_state(env=env) b1 = state.create_block_to_mine(address=acc3) b2 = state.create_block_to_mine(address=acc3) b2.header.create_time += 1 state.finalize_and_add_block(b1) self.assertEqual(state.db.get_block_count_by_height(1), 1) state.finalize_and_add_block(b2) self.assertEqual(state.db.get_block_count_by_height(1), 2) def test_xshard_tx_sent(self): id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_key=0) acc2 = Address.create_from_identity(id1, full_shard_key=1) acc3 = Address.create_random_account(full_shard_key=0) env = get_test_env(genesis_account=acc1, genesis_minor_quarkash=10000000) state = create_default_shard_state(env=env, shard_id=0) env1 = get_test_env(genesis_account=acc1, genesis_minor_quarkash=10000000) state1 = create_default_shard_state(env=env1, shard_id=1) # Add a root block to update block gas limit so that xshard tx can be included root_block = ( state.root_tip.create_block_to_append() .add_minor_block_header(state.header_tip) .add_minor_block_header(state1.header_tip) .finalize() ) state.add_root_block(root_block) tx = create_transfer_transaction( shard_state=state, key=id1.get_key(), from_address=acc1, to_address=acc2, value=888888, gas=opcodes.GTXXSHARDCOST + opcodes.GTXCOST, ) state.add_tx(tx) b1 = state.create_block_to_mine(address=acc3) self.assertEqual(len(b1.tx_list), 1) self.assertEqual(state.evm_state.gas_used, 0) # Should succeed state.finalize_and_add_block(b1) self.assertEqual(len(state.evm_state.xshard_list), 1) self.assertEqual( state.evm_state.xshard_list[0], CrossShardTransactionDeposit( tx_hash=tx.get_hash(), from_address=acc1, to_address=acc2, value=888888, gas_price=1, gas_token_id=self.genesis_token, transfer_token_id=self.genesis_token, ), ) self.assertEqual( state.get_token_balance(id1.recipient, self.genesis_token), 10000000 - 888888 - (opcodes.GTXCOST + opcodes.GTXXSHARDCOST), ) # Make sure the xshard gas is not used by local block self.assertEqual( state.evm_state.gas_used, opcodes.GTXCOST + opcodes.GTXXSHARDCOST ) # GTXXSHARDCOST is consumed by remote shard self.assertEqual( state.get_token_balance(acc3.recipient, self.genesis_token), self.get_after_tax_reward(opcodes.GTXCOST + self.shard_coinbase), ) def test_xshard_tx_insufficient_gas(self): id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_key=0) acc2 = Address.create_from_identity(id1, full_shard_key=1) acc3 = Address.create_random_account(full_shard_key=0) env = get_test_env(genesis_account=acc1, genesis_minor_quarkash=10000000) state = create_default_shard_state(env=env, shard_id=0) state.add_tx( create_transfer_transaction( shard_state=state, key=id1.get_key(), from_address=acc1, to_address=acc2, value=888888, gas=opcodes.GTXCOST, ) ) b1 = state.create_block_to_mine(address=acc3) self.assertEqual(len(b1.tx_list), 0) self.assertEqual(len(state.tx_queue), 0) def test_xshard_tx_received(self): id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_key=0) acc2 = Address.create_from_identity(id1, full_shard_key=16) acc3 = Address.create_random_account(full_shard_key=0) env0 = get_test_env( genesis_account=acc1, genesis_minor_quarkash=10000000, shard_size=64 ) env1 = get_test_env( genesis_account=acc1, genesis_minor_quarkash=10000000, shard_size=64 ) state0 = create_default_shard_state(env=env0, shard_id=0) state1 = create_default_shard_state(env=env1, shard_id=16) # Add a root block to allow later minor blocks referencing this root block to # be broadcasted root_block = ( state0.root_tip.create_block_to_append() .add_minor_block_header(state0.header_tip) .add_minor_block_header(state1.header_tip) .finalize() ) state0.add_root_block(root_block) state1.add_root_block(root_block) # Add one block in shard 0 b0 = state0.create_block_to_mine() state0.finalize_and_add_block(b0) b1 = state1.get_tip().create_block_to_append() b1.header.hash_prev_root_block = root_block.header.get_hash() tx = create_transfer_transaction( shard_state=state1, key=id1.get_key(), from_address=acc2, to_address=acc1, value=888888, gas=opcodes.GTXXSHARDCOST + opcodes.GTXCOST, gas_price=2, ) b1.add_tx(tx) # Add a x-shard tx from remote peer state0.add_cross_shard_tx_list_by_minor_block_hash( h=b1.header.get_hash(), tx_list=CrossShardTransactionList( tx_list=[ CrossShardTransactionDeposit( tx_hash=tx.get_hash(), from_address=acc2, to_address=acc1, value=888888, gas_price=2, gas_token_id=self.genesis_token, transfer_token_id=self.genesis_token, ) ] ), ) # Create a root block containing the block with the x-shard tx root_block = ( state0.root_tip.create_block_to_append() .add_minor_block_header(b0.header) .add_minor_block_header(b1.header) .finalize() ) state0.add_root_block(root_block) # Add b0 and make sure all x-shard tx's are added b2 = state0.create_block_to_mine(address=acc3) state0.finalize_and_add_block(b2) self.assertEqual( state0.get_token_balance(acc1.recipient, self.genesis_token), 10000000 + 888888, ) # Half collected by root self.assertEqual( state0.get_token_balance(acc3.recipient, self.genesis_token), self.get_after_tax_reward(opcodes.GTXXSHARDCOST * 2 + self.shard_coinbase), ) # X-shard gas used evmState0 = state0.evm_state self.assertEqual(evmState0.xshard_receive_gas_used, opcodes.GTXXSHARDCOST) def test_xshard_tx_received_exclude_non_neighbor(self): id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_key=0) acc2 = Address.create_from_identity(id1, full_shard_key=3) acc3 = Address.create_random_account(full_shard_key=0) env0 = get_test_env( genesis_account=acc1, genesis_minor_quarkash=10000000, shard_size=64 ) env1 = get_test_env( genesis_account=acc1, genesis_minor_quarkash=10000000, shard_size=64 ) state0 = create_default_shard_state(env=env0, shard_id=0) state1 = create_default_shard_state(env=env1, shard_id=3) b0 = state0.get_tip() b1 = state1.get_tip().create_block_to_append() tx = create_transfer_transaction( shard_state=state1, key=id1.get_key(), from_address=acc2, to_address=acc1, value=888888, gas=opcodes.GTXXSHARDCOST + opcodes.GTXCOST, gas_price=2, ) b1.add_tx(tx) # Create a root block containing the block with the x-shard tx root_block = ( state0.root_tip.create_block_to_append() .add_minor_block_header(b0.header) .add_minor_block_header(b1.header) .finalize() ) state0.add_root_block(root_block) b2 = state0.create_block_to_mine(address=acc3) state0.finalize_and_add_block(b2) self.assertEqual( state0.get_token_balance(acc1.recipient, self.genesis_token), 10000000 ) # Half collected by root self.assertEqual( state0.get_token_balance(acc3.recipient, self.genesis_token), self.get_after_tax_reward(self.shard_coinbase), ) # No xshard tx is processed on the receiving side due to non-neighbor evm_state0 = state0.evm_state self.assertEqual(evm_state0.xshard_receive_gas_used, 0) def test_xshard_from_root_block(self): id1 = Identity.create_random_identity() id2 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_key=0) acc2 = Address.create_from_identity(id2, full_shard_key=0) env = get_test_env(genesis_account=acc1, genesis_minor_quarkash=10000000) state = create_default_shard_state(env=env, shard_id=0) # Add a root block to update block gas limit so that xshard tx can be included root_block = ( state.root_tip.create_block_to_append() .add_minor_block_header(state.header_tip) .finalize( coinbase_tokens={env.quark_chain_config.genesis_token: 1000000}, coinbase_address=acc2, ) ) state.add_root_block(root_block) b0 = state.create_block_to_mine() state.finalize_and_add_block(b0) self.assertEqual( state.get_token_balance(acc2.recipient, self.genesis_token), 1000000 ) def test_xshard_for_two_root_blocks(self): id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_key=0) acc2 = Address.create_from_identity(id1, full_shard_key=1) acc3 = Address.create_random_account(full_shard_key=0) env0 = get_test_env(genesis_account=acc1, genesis_minor_quarkash=10000000) env1 = get_test_env(genesis_account=acc1, genesis_minor_quarkash=10000000) state0 = create_default_shard_state(env=env0, shard_id=0) state1 = create_default_shard_state(env=env1, shard_id=1) # Add a root block to allow later minor blocks referencing this root block to # be broadcasted root_block = ( state0.root_tip.create_block_to_append() .add_minor_block_header(state0.header_tip) .add_minor_block_header(state1.header_tip) .finalize() ) state0.add_root_block(root_block) state1.add_root_block(root_block) # Add one block in shard 0 b0 = state0.create_block_to_mine() state0.finalize_and_add_block(b0) b1 = state1.get_tip().create_block_to_append() b1.header.hash_prev_root_block = root_block.header.get_hash() tx = create_transfer_transaction( shard_state=state1, key=id1.get_key(), from_address=acc2, to_address=acc1, value=888888, gas=opcodes.GTXXSHARDCOST + opcodes.GTXCOST, ) b1.add_tx(tx) # Add a x-shard tx from state1 state0.add_cross_shard_tx_list_by_minor_block_hash( h=b1.header.get_hash(), tx_list=CrossShardTransactionList( tx_list=[ CrossShardTransactionDeposit( tx_hash=tx.get_hash(), from_address=acc2, to_address=acc1, value=888888, gas_price=2, gas_token_id=self.genesis_token, transfer_token_id=self.genesis_token, ) ] ), ) # Create a root block containing the block with the x-shard tx root_block0 = ( state0.root_tip.create_block_to_append() .add_minor_block_header(b0.header) .add_minor_block_header(b1.header) .finalize() ) state0.add_root_block(root_block0) b2 = state0.get_tip().create_block_to_append() state0.finalize_and_add_block(b2) b3 = b1.create_block_to_append() b3.header.hash_prev_root_block = root_block.header.get_hash() # Add a x-shard tx from state1 state0.add_cross_shard_tx_list_by_minor_block_hash( h=b3.header.get_hash(), tx_list=CrossShardTransactionList( tx_list=[ CrossShardTransactionDeposit( tx_hash=bytes(32), from_address=acc2, to_address=acc1, value=385723, gas_price=3, gas_token_id=self.genesis_token, transfer_token_id=self.genesis_token, ) ] ), ) root_block1 = ( state0.root_tip.create_block_to_append() .add_minor_block_header(b2.header) .add_minor_block_header(b3.header) .finalize() ) state0.add_root_block(root_block1) # Test x-shard gas limit when create_block_to_mine b6 = state0.create_block_to_mine(address=acc3, gas_limit=opcodes.GTXXSHARDCOST) self.assertEqual(b6.header.hash_prev_root_block, root_block1.header.get_hash()) # There are two x-shard txs: one is root block coinbase with zero gas, and another is from shard 1 b7 = state0.create_block_to_mine( address=acc3, gas_limit=2 * opcodes.GTXXSHARDCOST ) self.assertEqual(b7.header.hash_prev_root_block, root_block1.header.get_hash()) b8 = state0.create_block_to_mine( address=acc3, gas_limit=3 * opcodes.GTXXSHARDCOST ) self.assertEqual(b8.header.hash_prev_root_block, root_block1.header.get_hash()) # Add b0 and make sure all x-shard tx's are added b4 = state0.create_block_to_mine(address=acc3) self.assertEqual(b4.header.hash_prev_root_block, root_block1.header.get_hash()) state0.finalize_and_add_block(b4) self.assertEqual( state0.get_token_balance(acc1.recipient, self.genesis_token), 10000000 + 888888 + 385723, ) # Half collected by root self.assertEqual( state0.get_token_balance(acc3.recipient, self.genesis_token), self.get_after_tax_reward( opcodes.GTXXSHARDCOST * (2 + 3) + self.shard_coinbase ), ) # Check gas used for receiving x-shard tx self.assertEqual(state0.evm_state.gas_used, 18000) self.assertEqual(state0.evm_state.xshard_receive_gas_used, 18000) def test_xshard_gas_limit(self): id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_key=0) acc2 = Address.create_from_identity(id1, full_shard_key=16) acc3 = Address.create_random_account(full_shard_key=0) env0 = get_test_env( genesis_account=acc1, genesis_minor_quarkash=10000000, shard_size=64 ) env1 = get_test_env( genesis_account=acc1, genesis_minor_quarkash=10000000, shard_size=64 ) state0 = create_default_shard_state(env=env0, shard_id=0) state1 = create_default_shard_state(env=env1, shard_id=16) # Add a root block to allow later minor blocks referencing this root block to # be broadcasted root_block = ( state0.root_tip.create_block_to_append() .add_minor_block_header(state0.header_tip) .add_minor_block_header(state1.header_tip) .finalize() ) state0.add_root_block(root_block) state1.add_root_block(root_block) # Add one block in shard 1 with 2 x-shard txs b1 = state1.get_tip().create_block_to_append() b1.header.hash_prev_root_block = root_block.header.get_hash() tx0 = create_transfer_transaction( shard_state=state1, key=id1.get_key(), from_address=acc2, to_address=acc1, value=888888, gas=opcodes.GTXXSHARDCOST + opcodes.GTXCOST, gas_price=2, ) b1.add_tx(tx0) tx1 = create_transfer_transaction( shard_state=state1, key=id1.get_key(), from_address=acc2, to_address=acc1, value=111111, gas=opcodes.GTXXSHARDCOST + opcodes.GTXCOST, gas_price=2, ) b1.add_tx(tx1) # Add a x-shard tx from remote peer state0.add_cross_shard_tx_list_by_minor_block_hash( h=b1.header.get_hash(), tx_list=CrossShardTransactionList( tx_list=[ CrossShardTransactionDeposit( tx_hash=tx0.get_hash(), from_address=acc2, to_address=acc1, value=888888, gas_price=2, gas_token_id=self.genesis_token, transfer_token_id=self.genesis_token, ), CrossShardTransactionDeposit( tx_hash=tx1.get_hash(), from_address=acc2, to_address=acc1, value=111111, gas_price=2, gas_token_id=self.genesis_token, transfer_token_id=self.genesis_token, ), ] ), ) # Create a root block containing the block with the x-shard tx root_block = ( state0.root_tip.create_block_to_append() .add_minor_block_header(b1.header) .finalize( coinbase_tokens={env0.quark_chain_config.genesis_token: 1000000}, coinbase_address=acc1, ) ) state0.add_root_block(root_block) # Add b0 and make sure one x-shard tx's are added b2 = state0.create_block_to_mine( address=acc3, xshard_gas_limit=opcodes.GTXXSHARDCOST ) state0.finalize_and_add_block(b2, xshard_gas_limit=opcodes.GTXXSHARDCOST) # Root block coinbase does not consume xshard gas self.assertEqual( state0.get_token_balance(acc1.recipient, self.genesis_token), 10000000 + 1000000 + 888888, ) # Half collected by root self.assertEqual( state0.get_token_balance(acc3.recipient, self.genesis_token), self.get_after_tax_reward(opcodes.GTXXSHARDCOST * 2 + self.shard_coinbase), ) # X-shard gas used evmState0 = state0.evm_state self.assertEqual(evmState0.xshard_receive_gas_used, opcodes.GTXXSHARDCOST) # Add b2 and make sure all x-shard tx's are added b2 = state0.create_block_to_mine( address=acc3, xshard_gas_limit=opcodes.GTXXSHARDCOST ) state0.finalize_and_add_block(b2, xshard_gas_limit=opcodes.GTXXSHARDCOST) # Root block coinbase does not consume xshard gas self.assertEqual( state0.get_token_balance(acc1.recipient, self.genesis_token), 10000000 + 1000000 + 888888 + 111111, ) # Add b3 and make sure no x-shard tx's are added b3 = state0.create_block_to_mine( address=acc3, xshard_gas_limit=opcodes.GTXXSHARDCOST ) state0.finalize_and_add_block(b3, xshard_gas_limit=opcodes.GTXXSHARDCOST) # Root block coinbase does not consume xshard gas self.assertEqual( state0.get_token_balance(acc1.recipient, self.genesis_token), 10000000 + 1000000 + 888888 + 111111, ) b4 = state0.create_block_to_mine( address=acc3, xshard_gas_limit=opcodes.GTXXSHARDCOST ) state0.finalize_and_add_block(b4, xshard_gas_limit=opcodes.GTXXSHARDCOST) self.assertNotEqual( b2.meta.xshard_tx_cursor_info, b3.meta.xshard_tx_cursor_info ) self.assertEqual(b3.meta.xshard_tx_cursor_info, b4.meta.xshard_tx_cursor_info) b5 = state0.create_block_to_mine( address=acc3, gas_limit=opcodes.GTXXSHARDCOST, xshard_gas_limit=2 * opcodes.GTXXSHARDCOST, ) with self.assertRaises(ValueError): # xsahrd_gas_limit should be smaller than gas_limit state0.finalize_and_add_block( b5, gas_limit=opcodes.GTXXSHARDCOST, xshard_gas_limit=2 * opcodes.GTXXSHARDCOST, ) b6 = state0.create_block_to_mine( address=acc3, xshard_gas_limit=opcodes.GTXXSHARDCOST ) with self.assertRaises(ValueError): # xshard_gas_limit should be gas_limit // 2 state0.finalize_and_add_block(b6) def test_xshard_gas_limit_from_multiple_shards(self): id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_key=0) acc2 = Address.create_from_identity(id1, full_shard_key=16) acc3 = Address.create_from_identity(id1, full_shard_key=8) env0 = get_test_env( genesis_account=acc1, genesis_minor_quarkash=10000000, shard_size=64 ) env1 = get_test_env( genesis_account=acc1, genesis_minor_quarkash=10000000, shard_size=64 ) env2 = get_test_env( genesis_account=acc1, genesis_minor_quarkash=10000000, shard_size=64 ) state0 = create_default_shard_state(env=env0, shard_id=0) state1 = create_default_shard_state(env=env1, shard_id=16) state2 = create_default_shard_state(env=env1, shard_id=8) # Add a root block to allow later minor blocks referencing this root block to # be broadcasted root_block = ( state0.root_tip.create_block_to_append() .add_minor_block_header(state0.header_tip) .add_minor_block_header(state1.header_tip) .add_minor_block_header(state2.header_tip) .finalize() ) state0.add_root_block(root_block) state1.add_root_block(root_block) state2.add_root_block(root_block) # Add one block in shard 1 with 2 x-shard txs b1 = state1.get_tip().create_block_to_append() b1.header.hash_prev_root_block = root_block.header.get_hash() tx0 = create_transfer_transaction( shard_state=state1, key=id1.get_key(), from_address=acc2, to_address=acc1, value=888888, gas=opcodes.GTXXSHARDCOST + opcodes.GTXCOST, gas_price=2, ) b1.add_tx(tx0) tx1 = create_transfer_transaction( shard_state=state1, key=id1.get_key(), from_address=acc2, to_address=acc1, value=111111, gas=opcodes.GTXXSHARDCOST + opcodes.GTXCOST, gas_price=2, ) b1.add_tx(tx1) # Add a x-shard tx from remote peer state0.add_cross_shard_tx_list_by_minor_block_hash( h=b1.header.get_hash(), tx_list=CrossShardTransactionList( tx_list=[ CrossShardTransactionDeposit( tx_hash=tx0.get_hash(), from_address=acc2, to_address=acc1, value=888888, gas_price=2, gas_token_id=self.genesis_token, transfer_token_id=self.genesis_token, ), CrossShardTransactionDeposit( tx_hash=tx1.get_hash(), from_address=acc2, to_address=acc1, value=111111, gas_price=2, gas_token_id=self.genesis_token, transfer_token_id=self.genesis_token, ), ] ), ) # Add one block in shard 1 with 2 x-shard txs b2 = state2.get_tip().create_block_to_append() b2.header.hash_prev_root_block = root_block.header.get_hash() tx3 = create_transfer_transaction( shard_state=state1, key=id1.get_key(), from_address=acc2, to_address=acc1, value=12345, gas=opcodes.GTXXSHARDCOST + opcodes.GTXCOST, gas_price=2, ) b2.add_tx(tx3) # Add a x-shard tx from remote peer state0.add_cross_shard_tx_list_by_minor_block_hash( h=b2.header.get_hash(), tx_list=CrossShardTransactionList( tx_list=[ CrossShardTransactionDeposit( tx_hash=tx3.get_hash(), from_address=acc3, to_address=acc1, value=12345, gas_price=2, gas_token_id=self.genesis_token, transfer_token_id=self.genesis_token, ) ] ), ) # Create a root block containing the block with the x-shard tx root_block = ( state0.root_tip.create_block_to_append() .add_minor_block_header(b2.header) .add_minor_block_header(b1.header) .finalize( coinbase_tokens={env0.quark_chain_config.genesis_token: 1000000}, coinbase_address=acc1, ) ) state0.add_root_block(root_block) # Add b0 and make sure one x-shard tx's are added b2 = state0.create_block_to_mine(xshard_gas_limit=opcodes.GTXXSHARDCOST) state0.finalize_and_add_block(b2, xshard_gas_limit=opcodes.GTXXSHARDCOST) # Root block coinbase does not consume xshard gas self.assertEqual( state0.get_token_balance(acc1.recipient, self.genesis_token), 10000000 + 1000000 + 12345, ) # X-shard gas used evmState0 = state0.evm_state self.assertEqual(evmState0.xshard_receive_gas_used, opcodes.GTXXSHARDCOST) # Add b2 and make sure all x-shard tx's are added b2 = state0.create_block_to_mine(xshard_gas_limit=opcodes.GTXXSHARDCOST) state0.finalize_and_add_block(b2, xshard_gas_limit=opcodes.GTXXSHARDCOST) # Root block coinbase does not consume xshard gas self.assertEqual( state0.get_token_balance(acc1.recipient, self.genesis_token), 10000000 + 1000000 + 12345 + 888888, ) # Add b3 and make sure no x-shard tx's are added b3 = state0.create_block_to_mine(xshard_gas_limit=opcodes.GTXXSHARDCOST) state0.finalize_and_add_block(b3, xshard_gas_limit=opcodes.GTXXSHARDCOST) # Root block coinbase does not consume xshard gas self.assertEqual( state0.get_token_balance(acc1.recipient, self.genesis_token), 10000000 + 1000000 + 12345 + 888888 + 111111, ) def test_xshard_rootblock_coinbase(self): id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_key=0) acc2 = Address.create_from_identity(id1, full_shard_key=16) env0 = get_test_env( genesis_account=acc1, genesis_minor_quarkash=10000000, shard_size=64 ) env1 = get_test_env( genesis_account=acc1, genesis_minor_quarkash=10000000, shard_size=64 ) state0 = create_default_shard_state(env=env0, shard_id=0) state1 = create_default_shard_state(env=env1, shard_id=16) # Add a root block to allow later minor blocks referencing this root block to # be broadcasted root_block = ( state0.root_tip.create_block_to_append() .add_minor_block_header(state0.header_tip) .add_minor_block_header(state1.header_tip) .finalize() ) state0.add_root_block(root_block) state1.add_root_block(root_block) # Create a root block containing the block with the x-shard tx root_block = state0.root_tip.create_block_to_append().finalize( coinbase_tokens={env0.quark_chain_config.genesis_token: 1000000}, coinbase_address=acc1, ) state0.add_root_block(root_block) state1.add_root_block(root_block) # Add b0 and make sure one x-shard tx's are added b2 = state0.create_block_to_mine(xshard_gas_limit=opcodes.GTXXSHARDCOST) state0.finalize_and_add_block(b2, xshard_gas_limit=opcodes.GTXXSHARDCOST) # Root block coinbase does not consume xshard gas self.assertEqual( state0.get_token_balance(acc1.recipient, self.genesis_token), 10000000 + 1000000, ) # Add b0 and make sure one x-shard tx's are added b3 = state1.create_block_to_mine(xshard_gas_limit=opcodes.GTXXSHARDCOST) state1.finalize_and_add_block(b3, xshard_gas_limit=opcodes.GTXXSHARDCOST) # Root block coinbase does not consume xshard gas self.assertEqual( state1.get_token_balance(acc1.recipient, self.genesis_token), 10000000 ) def test_xshard_smart_contract(self): pass def test_xshard_sender_gas_limit(self): id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_key=0) acc2 = Address.create_from_identity(id1, full_shard_key=16) acc3 = Address.create_random_account(full_shard_key=0) env0 = get_test_env( genesis_account=acc1, genesis_minor_quarkash=10000000, shard_size=64 ) state0 = create_default_shard_state(env=env0, shard_id=0) # Add a root block to allow later minor blocks referencing this root block to # be broadcasted root_block = ( state0.root_tip.create_block_to_append() .add_minor_block_header(state0.header_tip) .finalize() ) state0.add_root_block(root_block) b0 = state0.get_tip().create_block_to_append() b0.header.hash_prev_root_block = root_block.header.get_hash() tx0 = create_transfer_transaction( shard_state=state0, key=id1.get_key(), from_address=acc1, to_address=acc2, value=888888, gas=b0.meta.evm_xshard_gas_limit + 1, gas_price=1, ) self.assertFalse(state0.add_tx(tx0)) b0.add_tx(tx0) with self.assertRaisesRegexp( RuntimeError, "xshard evm tx exceeds xshard gas limit" ): state0.finalize_and_add_block(b0) b2 = state0.create_block_to_mine( xshard_gas_limit=opcodes.GTXCOST * 9, include_tx=False ) b2.header.hash_prev_root_block = root_block.header.get_hash() tx2 = create_transfer_transaction( shard_state=state0, key=id1.get_key(), from_address=acc1, to_address=acc2, value=888888, gas=opcodes.GTXCOST * 10, gas_price=1, ) self.assertFalse(state0.add_tx(tx2, xshard_gas_limit=opcodes.GTXCOST * 9)) b2.add_tx(tx2) with self.assertRaisesRegexp( RuntimeError, "xshard evm tx exceeds xshard gas limit" ): state0.finalize_and_add_block(b2, xshard_gas_limit=opcodes.GTXCOST * 9) b1 = state0.get_tip().create_block_to_append() b1.header.hash_prev_root_block = root_block.header.get_hash() tx1 = create_transfer_transaction( shard_state=state0, key=id1.get_key(), from_address=acc1, to_address=acc2, value=888888, gas=b1.meta.evm_xshard_gas_limit, gas_price=1, ) b1.add_tx(tx1) state0.finalize_and_add_block(b1) def test_fork_resolve(self): id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_key=0) env = get_test_env(genesis_account=acc1, genesis_minor_quarkash=10000000) state = create_default_shard_state(env=env, shard_id=0) b0 = state.get_tip().create_block_to_append() b1 = state.get_tip().create_block_to_append() state.finalize_and_add_block(b0) self.assertEqual(state.header_tip, b0.header) # Fork happens, first come first serve state.finalize_and_add_block(b1) self.assertEqual(state.header_tip, b0.header) # Longer fork happens, override existing one b2 = b1.create_block_to_append() state.finalize_and_add_block(b2) self.assertEqual(state.header_tip, b2.header) def test_root_chain_first_consensus(self): id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_key=0) env0 = get_test_env(genesis_account=acc1, genesis_minor_quarkash=10000000) env1 = get_test_env(genesis_account=acc1, genesis_minor_quarkash=10000000) state0 = create_default_shard_state(env=env0, shard_id=0) state1 = create_default_shard_state(env=env1, shard_id=1) genesis = state0.header_tip # Add one block and prepare a fork b0 = state0.get_tip().create_block_to_append(address=acc1) b2 = state0.get_tip().create_block_to_append( address=Address.create_empty_account() ) state0.finalize_and_add_block(b0) state0.finalize_and_add_block(b2) b1 = state1.get_tip().create_block_to_append() evm_state = state1.run_block(b1) b1.finalize( evm_state=evm_state, coinbase_amount_map=TokenBalanceMap(evm_state.block_fee_tokens), ) root_block = ( state0.root_tip.create_block_to_append() .add_minor_block_header(genesis) .add_minor_block_header(b0.header) .add_minor_block_header(b1.header) .finalize() ) state0.add_root_block(root_block) b00 = b0.create_block_to_append() state0.finalize_and_add_block(b00) self.assertEqual(state0.header_tip, b00.header) # Create another fork that is much longer (however not confirmed by root_block) b3 = b2.create_block_to_append() state0.finalize_and_add_block(b3) b4 = b3.create_block_to_append() state0.finalize_and_add_block(b4) self.assertGreater(b4.header.height, b00.header.height) self.assertEqual(state0.header_tip, b00.header) def test_shard_state_add_root_block(self): id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_key=0) env0 = get_test_env(genesis_account=acc1, genesis_minor_quarkash=10000000) env1 = get_test_env(genesis_account=acc1, genesis_minor_quarkash=10000000) state0 = create_default_shard_state(env=env0, shard_id=0) state1 = create_default_shard_state(env=env1, shard_id=1) genesis = state0.header_tip # Add one block and prepare a fork b0 = state0.get_tip().create_block_to_append(address=acc1) b2 = state0.get_tip().create_block_to_append( address=Address.create_empty_account() ) state0.finalize_and_add_block(b0) state0.finalize_and_add_block(b2) b1 = state1.get_tip().create_block_to_append() evm_state = state1.run_block(b1) b1.finalize( evm_state=evm_state, coinbase_amount_map=TokenBalanceMap(evm_state.block_fee_tokens), ) # Add one empty root block empty_root = state0.root_tip.create_block_to_append().finalize() state0.add_root_block(empty_root) root_block = ( state0.root_tip.create_block_to_append() .add_minor_block_header(genesis) .add_minor_block_header(b0.header) .add_minor_block_header(b1.header) .finalize() ) root_block1 = ( state0.root_tip.create_block_to_append() .add_minor_block_header(genesis) .add_minor_block_header(b2.header) .add_minor_block_header(b1.header) .finalize() ) state0.add_root_block(root_block) b00 = b0.create_block_to_append() state0.finalize_and_add_block(b00) self.assertEqual(state0.header_tip, b00.header) # Create another fork that is much longer (however not confirmed by root_block) b3 = b2.create_block_to_append() state0.finalize_and_add_block(b3) b4 = b3.create_block_to_append() state0.finalize_and_add_block(b4) self.assertEqual(state0.header_tip, b00.header) self.assertEqual(state0.db.get_minor_block_by_height(2), b00) self.assertIsNone(state0.db.get_minor_block_by_height(3)) b5 = b1.create_block_to_append() self.assertFalse(state0.add_root_block(root_block1)) # Add one empty root block empty_root = root_block1.create_block_to_append().finalize() state0.add_root_block(empty_root) root_block2 = ( empty_root.create_block_to_append() .add_minor_block_header(b3.header) .add_minor_block_header(b4.header) .add_minor_block_header(b5.header) .finalize() ) self.assertTrue(state0.add_root_block(root_block2)) self.assertEqual(state0.header_tip, b4.header) self.assertEqual(state0.meta_tip, b4.meta) self.assertEqual(state0.root_tip, root_block2.header) self.assertEqual(state0.db.get_minor_block_by_height(2), b3) self.assertEqual(state0.db.get_minor_block_by_height(3), b4) def test_shard_reorg_by_adding_root_block(self): id1 = Identity.create_random_identity() id2 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_key=0) acc2 = Address.create_from_identity(id2, full_shard_key=0) env0 = get_test_env(genesis_account=acc1, genesis_minor_quarkash=10000000) state0 = create_default_shard_state(env=env0, shard_id=0) genesis = state0.header_tip # Add one block and include it in the root block b0 = state0.get_tip().create_block_to_append(address=acc1) b1 = state0.get_tip().create_block_to_append(address=acc2) root_block0 = ( state0.root_tip.create_block_to_append() .add_minor_block_header(genesis) .add_minor_block_header(b0.header) .finalize() ) root_block1 = ( state0.root_tip.create_block_to_append() .add_minor_block_header(genesis) .add_minor_block_header(b1.header) .finalize() ) state0.finalize_and_add_block(b0) state0.add_root_block(root_block0) self.assertEqual(state0.header_tip, b0.header) state0.finalize_and_add_block(b1) self.assertEqual(state0.header_tip, b0.header) # Add another root block with higher TD root_block1.header.total_difficulty += root_block1.header.difficulty root_block1.header.difficulty *= 2 self.assertTrue(state0.add_root_block(root_block1)) self.assertEqual(state0.header_tip, b1.header) self.assertEqual(state0.meta_tip, b1.meta) self.assertEqual(state0.root_tip, root_block1.header) self.assertEqual(state0.evm_state.trie.root_hash, b1.meta.hash_evm_state_root) def test_shard_state_add_root_block_too_many_minor_blocks(self): id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_key=0) env = get_test_env( genesis_account=acc1, genesis_minor_quarkash=10000000, shard_size=1 ) state = create_default_shard_state(env=env, shard_id=0) max_mblock_in_rblock = state.shard_config.max_blocks_per_shard_in_one_root_block headers = [state.header_tip] for i in range(max_mblock_in_rblock): b = state.get_tip().create_block_to_append(address=acc1) state.finalize_and_add_block(b) headers.append(b.header) root_block = ( state.root_tip.create_block_to_append() .extend_minor_block_header_list(headers) .finalize() ) # Too many blocks with self.assertRaisesRegexp( ValueError, "too many minor blocks in the root block" ): state.add_root_block(root_block) self.assertEqual( state.get_unconfirmed_header_list(), headers[:max_mblock_in_rblock] ) # 10 blocks is okay root_block.minor_block_header_list = headers[:max_mblock_in_rblock] root_block.finalize() state.add_root_block(root_block) def test_shard_state_fork_resolve_with_higher_root_chain(self): id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_key=0) env = get_test_env(genesis_account=acc1, genesis_minor_quarkash=10000000) state = create_default_shard_state(env=env, shard_id=0) b0 = state.get_tip() # genesis root_block = ( state.root_tip.create_block_to_append() .add_minor_block_header(b0.header) .finalize() ) self.assertEqual(state.header_tip, b0.header) self.assertTrue(state.add_root_block(root_block)) b1 = state.get_tip().create_block_to_append() b2 = state.get_tip().create_block_to_append(nonce=1) b2.header.hash_prev_root_block = root_block.header.get_hash() b3 = state.get_tip().create_block_to_append(nonce=2) b3.header.hash_prev_root_block = root_block.header.get_hash() state.finalize_and_add_block(b1) self.assertEqual(state.header_tip, b1.header) # Fork happens, although they have the same height, b2 survives since it confirms root block state.finalize_and_add_block(b2) self.assertEqual(state.header_tip, b2.header) # b3 confirms the same root block as b2, so it will not override b2 state.finalize_and_add_block(b3) self.assertEqual(state.header_tip, b2.header) def test_shard_state_difficulty(self): env = get_test_env() for shard_config in env.quark_chain_config.shards.values(): shard_config.GENESIS.DIFFICULTY = 10000 env.quark_chain_config.SKIP_MINOR_DIFFICULTY_CHECK = False diff_calc = EthDifficultyCalculator(cutoff=9, diff_factor=2048, minimum_diff=1) env.quark_chain_config.NETWORK_ID = ( 1 ) # other network ids will skip difficulty check state = create_default_shard_state(env=env, shard_id=0, diff_calc=diff_calc) # Check new difficulty b0 = state.create_block_to_mine(state.header_tip.create_time + 8) self.assertEqual( b0.header.difficulty, state.header_tip.difficulty // 2048 + state.header_tip.difficulty, ) b0 = state.create_block_to_mine(state.header_tip.create_time + 9) self.assertEqual(b0.header.difficulty, state.header_tip.difficulty) b0 = state.create_block_to_mine(state.header_tip.create_time + 17) self.assertEqual(b0.header.difficulty, state.header_tip.difficulty) b0 = state.create_block_to_mine(state.header_tip.create_time + 24) self.assertEqual( b0.header.difficulty, state.header_tip.difficulty - state.header_tip.difficulty // 2048, ) b0 = state.create_block_to_mine(state.header_tip.create_time + 35) self.assertEqual( b0.header.difficulty, state.header_tip.difficulty - state.header_tip.difficulty // 2048 * 2, ) def test_shard_state_recovery_from_root_block(self): id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_key=0) env = get_test_env(genesis_account=acc1, genesis_minor_quarkash=10000000) state = create_default_shard_state(env=env, shard_id=0) block_headers = [state.header_tip] block_meta = [state.meta_tip] for i in range(12): b = state.get_tip().create_block_to_append(address=acc1) state.finalize_and_add_block(b) block_headers.append(b.header) block_meta.append(b.meta) # add a fork b1 = state.db.get_minor_block_by_height(3) b1.header.create_time += 1 state.finalize_and_add_block(b1) self.assertEqual(state.db.get_minor_block_by_hash(b1.header.get_hash()), b1) root_block = state.root_tip.create_block_to_append() root_block.minor_block_header_list = block_headers[:5] root_block.finalize() state.add_root_block(root_block) recovered_state = ShardState(env=env, full_shard_id=2 | 0) recovered_state.init_from_root_block(root_block) self.assertEqual( recovered_state.db.get_minor_block_by_hash(b1.header.get_hash()), b1 ) self.assertEqual(recovered_state.root_tip, root_block.header) self.assertEqual(recovered_state.header_tip, block_headers[4]) self.assertEqual(recovered_state.confirmed_header_tip, block_headers[4]) self.assertEqual(recovered_state.meta_tip, block_meta[4]) self.assertEqual( recovered_state.evm_state.trie.root_hash, block_meta[4].hash_evm_state_root ) def test_shard_state_recovery_from_genesis(self): id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_key=0) env = get_test_env(genesis_account=acc1, genesis_minor_quarkash=10000000) state = create_default_shard_state(env=env, shard_id=0) block_headers = [state.header_tip] block_meta = [state.meta_tip] for i in range(12): b = state.get_tip().create_block_to_append(address=acc1) state.finalize_and_add_block(b) block_headers.append(b.header) block_meta.append(b.meta) # Add a few empty root blocks root_block = None for i in range(3): root_block = state.root_tip.create_block_to_append() root_block.finalize() state.add_root_block(root_block) recovered_state = ShardState(env=env, full_shard_id=2 | 0) # expect to recover from genesis recovered_state.init_from_root_block(root_block) genesis = state.db.get_minor_block_by_height(0) self.assertEqual(recovered_state.root_tip, root_block.header) self.assertEqual(recovered_state.header_tip, genesis.header) self.assertIsNone(recovered_state.confirmed_header_tip) self.assertEqual(recovered_state.meta_tip, genesis.meta) self.assertEqual( recovered_state.evm_state.trie.root_hash, genesis.meta.hash_evm_state_root ) def test_add_block_receipt_root_not_match(self): id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1) acc3 = Address.create_random_account(full_shard_key=0) env = get_test_env(genesis_account=acc1, genesis_minor_quarkash=10000000) state = create_default_shard_state(env=env) b1 = state.create_block_to_mine(address=acc3) # Should succeed state.finalize_and_add_block(b1) evm_state = state.run_block(b1) b1.finalize( evm_state=evm_state, coinbase_amount_map=b1.header.coinbase_amount_map ) b1.meta.hash_evm_receipt_root = bytes(32) def test_not_update_tip_on_root_fork(self): """ block's hash_prev_root_block must be on the same chain with root_tip to update tip. +--+ a. |r1| /+--+ / | +--+ / +--+ +--+ |r0|<----|m1|<---|m2| c. +--+ \ +--+ +--+ \ | | \+--+ | b. |r2|<----+ +--+ Initial state: r0 <- m1 Then adding r1, r2, m2 should not make m2 the tip because r1 is the root tip and r2 and r1 are not on the same root chain. """ id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_key=0) env = get_test_env(genesis_account=acc1, genesis_minor_quarkash=10000000) state = create_default_shard_state(env=env, shard_id=0) # m1 is the genesis block m1 = state.db.get_minor_block_by_height(0) r1 = state.root_tip.create_block_to_append() r2 = state.root_tip.create_block_to_append() r1.minor_block_header_list.append(m1.header) r1.finalize() state.add_root_block(r1) r2.minor_block_header_list.append(m1.header) r2.header.create_time = r1.header.create_time + 1 # make r2, r1 different r2.finalize() self.assertNotEqual(r1.header.get_hash(), r2.header.get_hash()) state.add_root_block(r2) self.assertEqual(state.root_tip, r1.header) m2 = m1.create_block_to_append(address=acc1) m2.header.hash_prev_root_block = r2.header.get_hash() state.finalize_and_add_block(m2) # m2 is added self.assertEqual(state.db.get_minor_block_by_hash(m2.header.get_hash()), m2) # but m1 should still be the tip self.assertEqual(state.header_tip, m1.header) def test_add_root_block_revert_header_tip(self): """ block's hash_prev_root_block must be on the same chain with root_tip to update tip. +--+ |r1|<-------------+ /+--+ | / | | +--+ / +--+ +--+ +--+ |r0|<----|m1|<---|m2| <---|m3| +--+ \ +--+ +--+ +--+ | \ | \ | \+--+. +--+ | |r2|<-----|r3| (r3 includes m2) | +--+ +--+ | | +--+ +-----+|r4| (r4 includes m1) +--+ Initial state: r0 <- m1 <- m2 Adding r1, r2, m3 makes r1 the root_tip, m3 the header_tip Adding r3 should change the root_tip to r3, header_tip to m2 Adding r4 (greater total diff) will reset root_tip to r4, header_tip to m2 """ id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_key=0) env = get_test_env(genesis_account=acc1, genesis_minor_quarkash=10000000) state = create_default_shard_state(env=env, shard_id=0) # m1 is the genesis block m1 = state.db.get_minor_block_by_height(0) m2 = state.get_tip().create_block_to_append(address=acc1) state.finalize_and_add_block(m2) r0 = state.root_tip r1 = r0.create_block_to_append() r2 = r0.create_block_to_append() r1.minor_block_header_list.append(m1.header) r1.finalize() state.add_root_block(r1) r2.minor_block_header_list.append(m1.header) r2.header.create_time = r1.header.create_time + 1 # make r2, r1 different r2.finalize() self.assertNotEqual(r1.header.get_hash(), r2.header.get_hash()) state.add_root_block(r2) self.assertEqual(state.root_tip, r1.header) m3 = state.create_block_to_mine(address=acc1) self.assertEqual(m3.header.hash_prev_root_block, r1.header.get_hash()) state.finalize_and_add_block(m3) r3 = r2.create_block_to_append(address=acc1) r3.add_minor_block_header(m2.header) r3.finalize() state.add_root_block(r3) self.assertEqual(state.root_tip, r3.header) self.assertEqual(state.header_tip, m2.header) # greater total diff r4 = r0.create_block_to_append(difficulty=r3.header.total_difficulty * 2) r4.minor_block_header_list.append(m1.header) r4.finalize() state.add_root_block(r4) self.assertEqual(state.root_tip, r4.header) self.assertEqual(state.header_tip, m2.header) def test_posw_fetch_previous_coinbase_address(self): acc = Address.create_from_identity( Identity.create_random_identity(), full_shard_key=0 ) env = get_test_env(genesis_account=acc, genesis_minor_quarkash=0) posw_window_len = 2 state = create_default_shard_state(env=env, shard_id=0) m = state.get_tip().create_block_to_append(address=acc) coinbase_blockcnt = state._get_posw_coinbase_blockcnt( m.header.hash_prev_minor_block, length=posw_window_len ) self.assertEqual(len(coinbase_blockcnt), 1) # Genesis state.finalize_and_add_block(m) # Note PoSW window size is 2 prev_addr = None for i in range(4): random_acc = Address.create_random_account(full_shard_key=0) m = state.get_tip().create_block_to_append(address=random_acc) coinbase_blockcnt = state._get_posw_coinbase_blockcnt( m.header.hash_prev_minor_block, length=posw_window_len ) self.assertEqual(len(coinbase_blockcnt), 2) # Count should all equal 1 self.assertEqual(len(set(coinbase_blockcnt.values())), 1) self.assertEqual(list(coinbase_blockcnt.values())[0], 1) if prev_addr: # Should always contain previous block's coinbase self.assertTrue(prev_addr in coinbase_blockcnt) state.finalize_and_add_block(m) prev_addr = random_acc.recipient # Cached should have certain items self.assertEqual(len(state.coinbase_addr_cache), 1) self.assertEqual(len(state.coinbase_addr_cache[2]), 5) def test_posw_coinbase_address_count_by_diff_length(self): acc = Address.create_from_identity( Identity.create_random_identity(), full_shard_key=0 ) env = get_test_env(genesis_account=acc, genesis_minor_quarkash=0) state = create_default_shard_state(env=env, shard_id=0) for i in range(4): random_acc = Address.create_random_account(full_shard_key=0) m = state.get_tip().create_block_to_append(address=random_acc) state.finalize_and_add_block(m) sum_cnt = lambda d: sum(d.values()) for length in range(1, 5): coinbase_blockcnt = state._get_posw_coinbase_blockcnt( m.header.get_hash(), length ) self.assertEqual(sum_cnt(coinbase_blockcnt), length) # Make sure internal cache state is correct self.assertEqual(len(state.coinbase_addr_cache), 4) def test_posw_coinbase_send_under_limit(self): id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_key=0) id2 = Identity.create_random_identity() acc2 = Address.create_from_identity(id2, full_shard_key=0) env = get_test_env(genesis_account=acc1, genesis_minor_quarkash=0) state = create_default_shard_state(env=env, shard_id=0, posw_override=True) state.shard_config.COINBASE_AMOUNT = 8 state.shard_config.POSW_CONFIG.TOTAL_STAKE_PER_BLOCK = 2 state.shard_config.POSW_CONFIG.WINDOW_SIZE = 4 # Add a root block to have all the shards initialized, also include the genesis from # another shard to allow x-shard tx TO that shard root_block = state.root_tip.create_block_to_append() root_block.add_minor_block_header( create_default_shard_state(env=env, shard_id=1).header_tip ) state.add_root_block(root_block.finalize()) m = state.get_tip().create_block_to_append(address=acc1) state.finalize_and_add_block(m) self.assertEqual(len(state.evm_state.sender_disallow_map), 2) self.assertEqual( state.get_token_balance(acc1.recipient, self.genesis_token), state.shard_config.COINBASE_AMOUNT // 2, # tax rate is 0.5 ) self.assertEqual( state.evm_state.sender_disallow_map, {bytes(20): 2, acc1.recipient: 2} ) # Try to send money from that account tx0 = create_transfer_transaction( shard_state=state, key=id1.get_key(), from_address=acc1, to_address=Address.create_empty_account(0), value=1, gas=21000, gas_price=0, ) res = state.execute_tx(tx0, acc1) self.assertIsNotNone(res, "tx should succeed") # Create a block including that tx, receipt should also report error self.assertTrue(state.add_tx(tx0)) m = state.create_block_to_mine(address=acc2) state.finalize_and_add_block(m) self.assertEqual( state.get_token_balance(acc1.recipient, self.genesis_token), state.shard_config.COINBASE_AMOUNT // 2 - 1, # tax rate is 0.5 ) self.assertEqual( state.evm_state.sender_disallow_map, {bytes(20): 2, acc1.recipient: 2, acc2.recipient: 2}, ) tx1 = create_transfer_transaction( shard_state=state, key=id1.get_key(), from_address=acc1, to_address=Address.create_empty_account(0), value=2, gas=21000, gas_price=0, ) res = state.execute_tx(tx1) self.assertIsNone(res, "tx should fail") # Create a block including that tx, receipt should also report error self.assertTrue(state.add_tx(tx1)) m = state.create_block_to_mine(address=acc2) state.finalize_and_add_block(m) self.assertEqual( state.get_token_balance(acc1.recipient, self.genesis_token), state.shard_config.COINBASE_AMOUNT // 2 - 1, # tax rate is 0.5 ) self.assertEqual( state.get_token_balance(acc2.recipient, self.genesis_token), state.shard_config.COINBASE_AMOUNT, # tax rate is 0.5 ) self.assertEqual( state.evm_state.sender_disallow_map, {acc1.recipient: 2, acc2.recipient: 4} ) tx2 = create_transfer_transaction( shard_state=state, key=id2.get_key(), from_address=acc2, to_address=Address.create_empty_account(0), value=5, gas=21000, gas_price=0, ) res = state.execute_tx(tx2) self.assertIsNone(res, "tx should fail") tx3 = create_transfer_transaction( shard_state=state, key=id2.get_key(), from_address=acc2, to_address=Address.create_empty_account(0), value=4, gas=21000, gas_price=0, ) res = state.execute_tx(tx3, acc2) self.assertIsNotNone(res, "tx should succeed") def test_posw_coinbase_send_equal_locked(self): id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_key=0) env = get_test_env(genesis_account=acc1, genesis_minor_quarkash=0) state = create_default_shard_state(env=env, shard_id=0, posw_override=True) state.shard_config.COINBASE_AMOUNT = 10 state.shard_config.POSW_CONFIG.TOTAL_STAKE_PER_BLOCK = 2 state.shard_config.POSW_CONFIG.WINDOW_SIZE = 4 # Add a root block to have all the shards initialized, also include the genesis from # another shard to allow x-shard tx TO that shard root_block = state.root_tip.create_block_to_append() root_block.add_minor_block_header( create_default_shard_state(env=env, shard_id=1).header_tip ) state.add_root_block(root_block.finalize()) m = state.create_block_to_mine(address=acc1) state.finalize_and_add_block(m) self.assertEqual(len(state.evm_state.sender_disallow_map), 2) self.assertEqual( state.get_token_balance(acc1.recipient, self.genesis_token), state.shard_config.COINBASE_AMOUNT // 2, # tax rate is 0.5 ) self.assertEqual( state.evm_state.sender_disallow_map, {bytes(20): 2, acc1.recipient: 2} ) # Try to send money from that account, the expected locked tokens are 4 tx0 = create_transfer_transaction( shard_state=state, key=id1.get_key(), from_address=acc1, to_address=Address.create_empty_account(0), value=1, gas=21000, gas_price=0, ) state.tx_queue.add_transaction(tx0.tx.to_evm_tx()) m = state.create_block_to_mine(address=acc1) state.finalize_and_add_block(m) r = state.get_transaction_receipt(tx0.get_hash()) self.assertEqual(r[2].success, b"\x01") # Success self.assertEqual( state.get_token_balance(acc1.recipient, self.genesis_token), state.shard_config.COINBASE_AMOUNT - 1, ) def test_posw_coinbase_send_above_locked(self): id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_key=0) env = get_test_env(genesis_account=acc1, genesis_minor_quarkash=0) state = create_default_shard_state(env=env, shard_id=0, posw_override=True) state.shard_config.COINBASE_AMOUNT = 10 state.shard_config.POSW_CONFIG.TOTAL_STAKE_PER_BLOCK = 2 state.shard_config.POSW_CONFIG.WINDOW_SIZE = 4 # Add a root block to have all the shards initialized, also include the genesis from # another shard to allow x-shard tx TO that shard root_block = state.root_tip.create_block_to_append() root_block.add_minor_block_header( create_default_shard_state(env=env, shard_id=1).header_tip ) state.add_root_block(root_block.finalize()) m = state.create_block_to_mine(address=acc1) state.finalize_and_add_block(m) self.assertEqual(len(state.evm_state.sender_disallow_map), 2) self.assertEqual( state.get_token_balance(acc1.recipient, self.genesis_token), state.shard_config.COINBASE_AMOUNT // 2, # tax rate is 0.5 ) self.assertEqual( state.evm_state.sender_disallow_map, {bytes(20): 2, acc1.recipient: 2} ) # Try to send money from that account, the expected locked tokens are 4 tx0 = create_transfer_transaction( shard_state=state, key=id1.get_key(), from_address=acc1, to_address=Address.create_empty_account(0), value=2, gas=21000, gas_price=0, ) state.tx_queue.add_transaction(tx0.tx.to_evm_tx()) m = state.create_block_to_mine(address=acc1) state.finalize_and_add_block(m) r = state.get_transaction_receipt(tx0.get_hash()) self.assertEqual(r[2].success, b"") # Failure self.assertEqual( state.get_token_balance(acc1.recipient, self.genesis_token), state.shard_config.COINBASE_AMOUNT, # tax rate is 0.5 ) def test_posw_validate_minor_block_seal(self): acc = Address(b"\x01" * 20, full_shard_key=0) env = get_test_env(genesis_account=acc, genesis_minor_quarkash=256) state = create_default_shard_state(env=env, shard_id=0, posw_override=True) # Force PoSW state.shard_config.CONSENSUS_TYPE = ConsensusType.POW_DOUBLESHA256 state.shard_config.POSW_CONFIG.TOTAL_STAKE_PER_BLOCK = 1 state.shard_config.POSW_CONFIG.WINDOW_SIZE = 256 state.shard_config.POSW_CONFIG.DIFF_DIVIDER = 1000 self.assertEqual( state.get_token_balance(acc.recipient, self.genesis_token), 256 ) genesis = Address(bytes(20), 0) self.assertEqual( state.get_token_balance(genesis.recipient, self.genesis_token), 0 ) # Genesis already has 1 block but zero stake, so no change to block diff m = state.get_tip().create_block_to_append(address=genesis, difficulty=1000) with self.assertRaises(ValueError): state.finalize_and_add_block(m) # Total stake * block PoSW is 256, so acc should pass the check no matter # how many blocks he mined before for _ in range(4): for nonce in range(4): # Try different nonce m = state.get_tip().create_block_to_append( address=acc, difficulty=1000, nonce=nonce ) state.validate_minor_block_seal(m) state.finalize_and_add_block(m) def test_posw_window_edge_cases(self): acc = Address(b"\x01" * 20, full_shard_key=0) env = get_test_env(genesis_account=acc, genesis_minor_quarkash=500) state = create_default_shard_state( env=env, shard_id=0, posw_override=True, no_coinbase=True ) # Force PoSW state.shard_config.CONSENSUS_TYPE = ConsensusType.POW_DOUBLESHA256 state.shard_config.POSW_CONFIG.TOTAL_STAKE_PER_BLOCK = 500 state.shard_config.POSW_CONFIG.WINDOW_SIZE = 2 state.shard_config.POSW_CONFIG.DIFF_DIVIDER = 1000 # Use 0 to denote blocks mined by others, 1 for blocks mined by acc, # stake * state per block = 1 for acc, 0 <- [curr], so current block # should enjoy the diff adjustment m = state.get_tip().create_block_to_append(address=acc, difficulty=1000) state.finalize_and_add_block(m) # Make sure stakes didn't change self.assertEqual( state.get_token_balance(acc.recipient, self.genesis_token), 500 ) # 0 <- 1 <- [curr], the window already has one block with PoSW benefit, # mining new blocks should fail m = state.get_tip().create_block_to_append(address=acc, difficulty=1000) with self.assertRaises(ValueError): state.finalize_and_add_block(m) def test_incorrect_coinbase_amount(self): env = get_test_env() state = create_default_shard_state(env=env) # Add a root block to have all the shards initialized root_block = state.root_tip.create_block_to_append().finalize() state.add_root_block(root_block) b = state.create_block_to_mine() evm_state = state.run_block(b) b.finalize( evm_state=evm_state, coinbase_amount_map=state.get_coinbase_amount_map(b.header.height), ) state.add_block(b) b = state.create_block_to_mine() wrong_coinbase = state.get_coinbase_amount_map(b.header.height) wrong_coinbase.add({self.genesis_token: +1}) b.finalize(evm_state=evm_state, coinbase_amount_map=wrong_coinbase) with self.assertRaises(ValueError): state.add_block(b) def test_shard_coinbase_decay(self): env = get_test_env() state = create_default_shard_state(env=env) coinbase = state.get_coinbase_amount_map(state.shard_config.EPOCH_INTERVAL) self.assertEqual( coinbase.balance_map, { env.quark_chain_config.genesis_token: state.shard_config.COINBASE_AMOUNT * env.quark_chain_config.BLOCK_REWARD_DECAY_FACTOR * env.quark_chain_config.REWARD_TAX_RATE }, ) coinbase = state.get_coinbase_amount_map(state.shard_config.EPOCH_INTERVAL + 1) self.assertEqual( coinbase.balance_map, { env.quark_chain_config.genesis_token: state.shard_config.COINBASE_AMOUNT * env.quark_chain_config.BLOCK_REWARD_DECAY_FACTOR * env.quark_chain_config.REWARD_TAX_RATE }, ) coinbase = state.get_coinbase_amount_map(state.shard_config.EPOCH_INTERVAL * 2) self.assertEqual( coinbase.balance_map, { env.quark_chain_config.genesis_token: state.shard_config.COINBASE_AMOUNT * env.quark_chain_config.BLOCK_REWARD_DECAY_FACTOR ** 2 * env.quark_chain_config.REWARD_TAX_RATE }, ) def test_enable_tx_timestamp(self): # whitelist acc1, make tx to acc2 # but do not whitelist acc2 and tx fails id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_key=0) id2 = Identity.create_random_identity() acc2 = Address.create_from_identity(id2, full_shard_key=0) acc3 = Address.create_random_account(full_shard_key=0) env = get_test_env(genesis_account=acc1, genesis_minor_quarkash=10000000) state = create_default_shard_state(env=env) # Add a root block to have all the shards initialized root_block = state.root_tip.create_block_to_append().finalize() state.add_root_block(root_block) tx = create_transfer_transaction( shard_state=state, key=id1.get_key(), from_address=acc1, to_address=acc2, value=5000000, gas=50000, ) self.assertTrue(state.add_tx(tx)) b1 = state.create_block_to_mine() self.assertEqual(len(b1.tx_list), 1) env.quark_chain_config.ENABLE_TX_TIMESTAMP = b1.header.create_time + 100 env.quark_chain_config.TX_WHITELIST_SENDERS = [acc1.recipient.hex()] b2 = state.create_block_to_mine() self.assertEqual(len(b2.tx_list), 1) state.finalize_and_add_block(b2) tx2 = create_transfer_transaction( shard_state=state, key=id2.get_key(), from_address=acc2, to_address=acc3, value=12345, gas=50000, ) env.quark_chain_config.ENABLE_TX_TIMESTAMP = None self.assertTrue(state.add_tx(tx2)) b3 = state.create_block_to_mine() self.assertEqual(len(b3.tx_list), 1) env.quark_chain_config.ENABLE_TX_TIMESTAMP = b1.header.create_time + 100 b4 = state.create_block_to_mine() self.assertEqual(len(b4.tx_list), 0) with self.assertRaisesRegexp( RuntimeError, "unwhitelisted senders not allowed before tx is enabled" ): state.finalize_and_add_block(b3) def test_enable_evm_timestamp_with_contract_create(self): id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_key=0) acc2 = Address.create_random_account(full_shard_key=0) env = get_test_env(genesis_account=acc1, genesis_minor_quarkash=10000000) state = create_default_shard_state(env=env) # Add a root block to have all the shards initialized root_block = state.root_tip.create_block_to_append().finalize() state.add_root_block(root_block) tx = create_contract_creation_transaction( shard_state=state, key=id1.get_key(), from_address=acc1, to_full_shard_key=0 ) self.assertTrue(state.add_tx(tx)) b1 = state.create_block_to_mine() self.assertEqual(len(b1.tx_list), 1) env.quark_chain_config.ENABLE_EVM_TIMESTAMP = b1.header.create_time + 100 b2 = state.create_block_to_mine() self.assertEqual(len(b2.tx_list), 0) with self.assertRaisesRegexp( RuntimeError, "smart contract tx is not allowed before evm is enabled" ): state.finalize_and_add_block(b1) def test_enable_evm_timestamp_with_contract_call(self): id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_key=0) acc2 = Address.create_random_account(full_shard_key=0) env = get_test_env(genesis_account=acc1, genesis_minor_quarkash=10000000) state = create_default_shard_state(env=env) # Add a root block to have all the shards initialized root_block = state.root_tip.create_block_to_append().finalize() state.add_root_block(root_block) tx = create_transfer_transaction( shard_state=state, key=id1.get_key(), from_address=acc1, to_address=acc2, value=12345, gas=50000, data=b"1234", ) self.assertTrue(state.add_tx(tx)) b1 = state.create_block_to_mine() self.assertEqual(len(b1.tx_list), 1) env.quark_chain_config.ENABLE_EVM_TIMESTAMP = b1.header.create_time + 100 b2 = state.create_block_to_mine() self.assertEqual(len(b2.tx_list), 0) with self.assertRaisesRegexp( RuntimeError, "smart contract tx is not allowed before evm is enabled" ): state.finalize_and_add_block(b1) def test_failed_transaction_gas(self): """in-shard revert contract transaction validating the failed transaction gas used """ id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_key=0) acc2 = Address.create_random_account(full_shard_key=0) env = get_test_env( genesis_account=acc1, genesis_minor_token_balances={self.genesis_token_str: 200 * 10 ** 18}, ) state = create_default_shard_state(env=env) # Create failed contract with revert operation contract_creation_with_revert_bytecode = ( "6080604052348015600f57600080fd5b50600080fdfe" ) """ pragma solidity ^0.5.1; contract RevertContract { constructor() public { revert(); } } """ # This transaction cost is calculated by remix, which is different than the opcodes.GTXCOST due to revert. FAILED_TRANSACTION_COST = 54416 tx = contract_creation_tx( shard_state=state, key=id1.get_key(), from_address=acc1, to_full_shard_key=acc1.full_shard_key, bytecode=contract_creation_with_revert_bytecode, gas_token_id=self.genesis_token, transfer_token_id=self.genesis_token, ) # Should succeed self.assertTrue(state.add_tx(tx)) b1 = state.create_block_to_mine(address=acc2) self.assertEqual(len(b1.tx_list), 1) state.finalize_and_add_block(b1) self.assertEqual(state.header_tip, b1.header) # Check receipts and make sure the transaction is failed self.assertEqual(len(state.evm_state.receipts), 1) self.assertEqual(state.evm_state.receipts[0].state_root, b"") self.assertEqual(state.evm_state.receipts[0].gas_used, FAILED_TRANSACTION_COST) # Make sure the FAILED_TRANSACTION_COST is consumed by the sender self.assertEqual( state.get_token_balance(id1.recipient, self.genesis_token), 200 * 10 ** 18 - FAILED_TRANSACTION_COST, ) # Make sure the accurate gas fee is obtained by the miner self.assertEqual( state.get_token_balance(acc2.recipient, self.genesis_token), self.get_after_tax_reward(FAILED_TRANSACTION_COST + self.shard_coinbase), ) self.assertEqual( b1.header.coinbase_amount_map.balance_map, { env.quark_chain_config.genesis_token: self.get_after_tax_reward( FAILED_TRANSACTION_COST + self.shard_coinbase ) }, )

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0.770201

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false

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0.033973

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null

0

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null

0

6

9c1075150e252d781f1bf9520197483078e4ba55

6,735

py

Python

DA-ESS-CbResponse/bin/tab_splunktalib/conf_manager/data_input_endpoints.py

hawkdavis/cb-response-splunk-app

120fe9d9a6a0d283cb7f91ff378ae33ba2f3cea6

[ "Apache-2.0" ]

null

DA-ESS-CbResponse/bin/tab_splunktalib/conf_manager/data_input_endpoints.py

hawkdavis/cb-response-splunk-app

120fe9d9a6a0d283cb7f91ff378ae33ba2f3cea6

[ "Apache-2.0" ]

null

DA-ESS-CbResponse/bin/tab_splunktalib/conf_manager/data_input_endpoints.py

hawkdavis/cb-response-splunk-app

120fe9d9a6a0d283cb7f91ff378ae33ba2f3cea6

[ "Apache-2.0" ]

null

import urllib import tab_splunktalib.common.xml_dom_parser as xdp from tab_splunktalib.conf_manager.request import content_request INPUT_ENDPOINT = "%s/servicesNS/%s/%s/data/inputs/%s" def _input_endpoint_ns(uri, owner, app, input_type): return INPUT_ENDPOINT % (uri, owner, app, input_type) def reload_data_input(splunkd_uri, session_key, owner, app_name, input_type, throw=False): """ :param splunkd_uri: splunkd uri, e.g. https://127.0.0.1:8089 :param session_key: splunkd session key :param owner: the owner (ACL user), e.g. "-", "nobody" :param app_name: the app"s name, e.g. "Splunk_TA_aws" :param input_type: name of the input type. if it is a script input, the input is "script", for modinput, say snow, the input is "snow" """ uri = _input_endpoint_ns(splunkd_uri, owner, app_name, input_type) uri += "/_reload" msg = "Failed to reload data input in app=%s: %s" % (app_name, input_type) try: content_request(uri, session_key, "GET", None, msg) except Exception: if throw: raise def create_data_input(splunkd_uri, session_key, owner, app_name, input_type, name, key_values): """ :param splunkd_uri: splunkd uri, e.g. https://127.0.0.1:8089 :param session_key: splunkd session key :param owner: the owner (ACL user), e.g. "-", "nobody" :param app_name: the app"s name, e.g. "Splunk_TA_aws" :param input_type: name of the input type. if it is a script input, the input is "script", for modinput, say snow, the input is "snow" :param name: The name of the input stanza to create. i.e. stanza [<input_type>://<name>] will be created. :param key_values: a K-V dict of details in the data input stanza. :return: None on success else raise exception """ key_values["name"] = name uri = _input_endpoint_ns(splunkd_uri, owner, app_name, input_type) msg = "Failed to create data input in app=%s: %s://%s" % ( app_name, input_type, name) content_request(uri, session_key, "POST", key_values, msg) def get_data_input(splunkd_uri, session_key, owner, app_name, input_type, name=None): """ :param splunkd_uri: splunkd uri, e.g. https://127.0.0.1:8089 :param session_key: splunkd session key :param owner: the owner (ACL user), e.g. "-", "nobody" :param app_name: the app"s name, e.g. "Splunk_TA_aws" :param input_type: name of the input type. if it is a script input, the input is "script", for modinput, say snow, the input is "snow" :param name: The name of the input stanza to create. i.e. stanza [<input_type>://<name>] will be deleted. :return: a list of stanzas in the input type, including metadata """ uri = _input_endpoint_ns(splunkd_uri, owner, app_name, input_type) if name: uri += urllib.quote("/" + name.replace("/", "%2F")) # get all the stanzas at one time uri += "?count=0&offset=0" msg = "Failed to get data input in app=%s: %s://%s" % ( app_name, input_type, name) content = content_request(uri, session_key, "GET", None, msg) return xdp.parse_conf_xml_dom(content) def update_data_input(splunkd_uri, session_key, owner, app_name, input_type, name, key_values): """ :param splunkd_uri: splunkd uri, e.g. https://127.0.0.1:8089 :param session_key: splunkd session key :param owner: the owner (ACL user), e.g. "-", "nobody" :param app_name: the app"s name, e.g. "Splunk_TA_aws" :param input_type: name of the input type. if it is a script input, the input is "script", for modinput, say snow, the input is "snow" :param name: The name of the input stanza to create. i.e. stanza [<input_type>://<name>] will be updated. :param key_values: a K-V dict of details in the data input stanza. :return: raise exception when failure """ if "name" in key_values: del key_values["name"] uri = _input_endpoint_ns(splunkd_uri, owner, app_name, input_type) uri += urllib.quote("/" + name.replace("/", "%2F")) msg = "Failed to update data input in app=%s: %s://%s" % ( app_name, input_type, name) content_request(uri, session_key, "POST", key_values, msg) def delete_data_input(splunkd_uri, session_key, owner, app_name, input_type, name): """ :param splunkd_uri: splunkd uri, e.g. https://127.0.0.1:8089 :param session_key: splunkd session key :param owner: the owner (ACL user), e.g. "-", "nobody" :param app_name: the app"s name, e.g. "Splunk_TA_aws" :param input_type: name of the input type. if it is a script input, the input is "script", for modinput, say snow, the input is "snow" :param name: The name of the input stanza to create. i.e. stanza [<input_type>://<name>] will be deleted. :return raise exception when failed """ uri = _input_endpoint_ns(splunkd_uri, owner, app_name, input_type) uri += urllib.quote("/" + name.replace("/", "%2F")) msg = "Failed to delete data input in app=%s: %s://%s" % ( app_name, input_type, name) content_request(uri, session_key, "DELETE", None, msg) def operate_data_input(splunkd_uri, session_key, owner, app_name, input_type, name, operation): """ :param splunkd_uri: splunkd uri, e.g. https://127.0.0.1:8089 :param session_key: splunkd session key :param owner: the owner (ACL user), e.g. "-", "nobody" :param app_name: the app"s name, e.g. "Splunk_TA_aws" :param input_type: name of the input type. if it is a script input, the input is "script", for modinput, say snow, the input is "snow" :param name: The name of the input stanza to create. i.e. stanza [<input_type>://<name>] will be operated. :param operation: must be "disable" or "enable" """ assert operation in ("disable", "enable") uri = _input_endpoint_ns(splunkd_uri, owner, app_name, input_type) uri += "/%s/%s" % (urllib.quote(name.replace("/", "%2F")), operation) msg = "Failed to %s data input in app=%s: %s://%s" % ( operation, app_name, input_type, name) content_request(uri, session_key, "POST", None, msg)

41.832298

78

0.605345

977

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4.006141

0.116684

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0.073582

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6

9c1583ee64efa2f240f83ba11c5e66f78c6d2ac0

16,940

py

Python

ui_automation_core/helpers/actions/mouse_action.py

Harshavardhanchowdary/python-ui-testing-automation

a624c6b945276c05722be2919d95aa9e5539d0d0

[ "MIT" ]

null

ui_automation_core/helpers/actions/mouse_action.py

Harshavardhanchowdary/python-ui-testing-automation

a624c6b945276c05722be2919d95aa9e5539d0d0

[ "MIT" ]

null

ui_automation_core/helpers/actions/mouse_action.py

Harshavardhanchowdary/python-ui-testing-automation

a624c6b945276c05722be2919d95aa9e5539d0d0

[ "MIT" ]

null

from enum import Enum, auto from selenium.webdriver.common.action_chains import ActionChains from selenium.webdriver.remote.webelement import WebElement from ui_automation_core.helpers import js_executor from ui_automation_core.helpers.web_element.locator import Locator from ui_automation_core.helpers.web_element.wait_states import ElementWaitState class ClickMethod(Enum): API_CLICK = auto() ACTION_CHAIN_CLICK = auto() JAVA_SCRIPT_CLICK = auto() class MouseAction: """ MouseAction class is a collection of Mouse Actions that you want to perform on an web element. """ def __init__(self, context): self.context = context def click_web_element(self, locator=None, click_method=ClickMethod.API_CLICK, wait_state=ElementWaitState.PRESENT, timeout=None): """ Simulates user clicking on an element with different click methods available. :param locator: Web element or a locator string on which the click action need to be performed :param click_method: Method to perform click and by default click_method=ClickMethod.API_CLICK Available methods are: API_CLICK JAVA_SCRIPT_CLICK ACTION_CHAIN_CLICK :param wait_state: he wait state for retrial. Choose state from ElementWaitState class. :param timeout: wait time before throwing any exception. If None, timeout defaults to 20 seconds. :return: self """ element_to_log = None try: if not isinstance(click_method, ClickMethod): raise TypeError(f'`{click_method}` must be an instance of ClickMethod.') if locator is None: raise ValueError('Please provide the string pattern or a web element to perform a click') if isinstance(locator, WebElement): element, element_to_log = locator, locator.get_attribute('outerHTML') else: element, element_to_log = Locator(self.context).get_element(locator, wait_state, True, timeout), locator if click_method is ClickMethod.API_CLICK: element.click() self.context.logger.info( f'Successfully clicked on the element {element_to_log}') if click_method is ClickMethod.JAVA_SCRIPT_CLICK: js_executor.execute_javascript('arguments[0].click();', element) self.context.logger.info( f'Successfully clicked on the element {element_to_log}') if click_method is ClickMethod.ACTION_CHAIN_CLICK: ActionChains(self.context.driver).click(element).perform() self.context.logger.info( f'Successfully clicked on the element {element_to_log}') return self except TypeError: self.context.logger.error(f'`{click_method}` must be an instance of ClickMethod') raise TypeError except ValueError: self.context.logger.error('String pattern is None. Please provide a valid pattern to locate the element.') raise ValueError except Exception as ex: self.context.logger.error( f'Unable to click on the element `{element_to_log}`.') self.context.logger.exception(ex) raise Exception( f'Unable to click on the element `{element_to_log}`. Error: {ex}') def double_click(self, locator=None, wait_state=ElementWaitState.PRESENT, timeout=None): """ Double-clicks an element. :param locator: Web element or a locator string on which the click action need to be performed :param wait_state: he wait state for retrial. Choose state from ElementWaitState class. :param timeout: wait time before throwing any exception. If None, timeout defaults to 20 seconds. :return: self """ element_to_log = None try: if locator is None: raise ValueError('Please provide the string pattern or a web element to perform a double click.') if isinstance(locator, WebElement): element, element_to_log = locator, locator.get_attribute('outerHTML') else: element, element_to_log = Locator(self.context).get_element(locator, wait_state, True, timeout) \ if locator is not None else None, locator ActionChains(self.context.driver).double_click(element).perform() self.context.logger.info( f'Successfully double clicked on element {element_to_log}') return self except ValueError: self.context.logger.error( 'String pattern is None. Please provide a valid pattern to locate the element and perform a ' 'click action.') raise ValueError except Exception as ex: self.context.logger.error(f'Unable to double click on element {element_to_log}.') self.context.logger.exception(ex) raise Exception( f'Unable to double click on element {element_to_log}. Error: {ex}') def context_click(self, locator=None, wait_state=ElementWaitState.PRESENT, timeout=None): """ Right-click on the given element. :param locator: Web element or a locator string on which the click action need to be performed :param wait_state: he wait state for retrial. Choose state from ElementWaitState class. :param timeout: wait time before throwing any exception. If None, timeout defaults to 20 seconds. :return: self """ element_to_log = None try: if locator is None: raise ValueError('Please provide the string pattern or a web element to perform a right click.') if isinstance(locator, WebElement): element, element_to_log = locator, locator.get_attribute('outerHTML') else: element, element_to_log = Locator(self.context).get_element(locator, wait_state, True, timeout) \ if locator is not None else None, locator ActionChains(self.context.driver).context_click(element).perform() self.context.logger.info( f'Successfully right clicked on element {element_to_log}') return self except ValueError: self.context.logger.error( 'String pattern is None. Please provide a valid pattern to locate the element and perform a ' 'right click action.') raise ValueError except Exception as ex: self.context.logger.error(f'Unable to right click on element {element_to_log}.') self.context.logger.exception(ex) raise Exception( f'Unable to right click on element {element_to_log}. Error: {ex}') def move_cursor_to_element(self, locator, wait_state=ElementWaitState.PRESENT, timeout=None): """ Simulate users hovering a mouse over the given element. :param locator: Web element or a locator string on which the click action need to be performed :param wait_state: he wait state for retrial. Choose state from ElementWaitState class. :param timeout: wait time before throwing any exception. If None, timeout defaults to 20 seconds. :return: self """ element_to_log = None try: if locator is None: raise ValueError('Please provide the string pattern or a web element to perform an action.') if isinstance(locator, WebElement): element, element_to_log = locator, locator.get_attribute('outerHTML') else: element, element_to_log = Locator(self.context).get_element(locator, wait_state, True, timeout) \ if locator is not None else None, locator ActionChains(self.context.driver).move_to_element(element).perform() self.context.logger.info( f'Successfully moved the cursor on to the element {element_to_log}') return self except ValueError: self.context.logger.error( 'String pattern is None. Please provide a valid pattern to locate the element and perform an ' 'action.') raise ValueError except Exception as ex: self.context.logger.error(f'Unable to move the cursor to the element {element_to_log}.') self.context.logger.exception(ex) raise Exception( f'Unable to move to the element {element_to_log}. Error: {ex}') def move_cursor_by_offset(self, x_offset, y_offset): """ Moving the mouse to an offset from current mouse position. :param x_offset: X offset to move to, as a positive or negative integer. :param y_offset: Y offset to move to, as a positive or negative integer. :return: self """ try: ActionChains(self.context.driver).move_by_offset( x_offset, y_offset).perform() self.context.logger.info( f'Successfully moved by offset {x_offset, y_offset}') return self except Exception as ex: self.context.logger.error(f'Unable to move by offset {x_offset, y_offset}.') self.context.logger.exception(ex) raise Exception( f'Unable to move by offset {x_offset, y_offset}. Error: {ex}') def move_cursor_to_element_by_offset(self, locator, x_offset, y_offset, wait_state=ElementWaitState.PRESENT, timeout=None): """ Simulate users hovering a mouse over the given element with the relative position (x, y) from the top-left corner of that element. :param locator: locator: Web element or a locator string on which the click action need to be performed :param x_offset: X offset to move to, as a positive or negative integer. :param y_offset: Y offset to move to, as a positive or negative integer. :param wait_state: he wait state for retrial. Choose state from ElementWaitState class. :param timeout: wait time before throwing any exception. If None, timeout defaults to 20 seconds. :return: self """ element_to_log = None try: element, element_to_log = (locator, locator.get_attribute('outerHTML')) \ if isinstance(locator, WebElement) \ else (Locator(self.context).get_element(locator, wait_state, True, timeout), locator) (ActionChains(self.context.driver).move_to_element_with_offset(element, x_offset, y_offset).perform()) self.context.logger.info(f'Successfully moved mouse pointer by an offset {x_offset, y_offset} ' f'on the element {element_to_log}') return self except Exception as ex: self.context.logger.error(f'Unable to move by an offset {x_offset, y_offset} ' f'on the element {element_to_log}') self.context.logger.exception(ex) raise Exception(f'Unable to move by an offset {x_offset, y_offset} to the ' f'element {element_to_log}. Error: {ex}') def drag_and_drop_to_object(self, source, target, wait_state=ElementWaitState.PRESENT, timeout=None): """ Drag an object and drop it onto another object. Holds down the left mouse button on the source element, then moves to the target element and releases the mouse button. :param source: The element to mouse down. (element to be moved). Can be a locator string or an web element. :param target: The element to mouse up. (destination location). Can be locator string or an web element :param wait_state: he wait state for retrial. Choose state from ElementWaitState class. :param timeout: wait time before throwing any exception. If None, timeout defaults to 20 seconds. :return: self """ trg_element = None trg_element_to_log = None src_element_to_log = None try: if source is None: raise ValueError( 'Please provide the `source` string pattern or a web element to perform drag and drop.') if target is None: raise ValueError( 'Please provide the `target` string pattern or a web element to perform a drag and drop.') if isinstance(source, WebElement): src_element, src_element_to_log = source, source.get_attribute('outerHTML') else: src_element, src_element_to_log = Locator(self.context).get_element(source, wait_state, True, timeout), source if isinstance(target, WebElement): src_element, trg_element_to_log = target, target.get_attribute('outerHTML') else: trg_element, trg_element_to_log = Locator(self.context).get_element(target, wait_state, True, timeout), target (ActionChains(self.context.driver).drag_and_drop(src_element, trg_element).perform()) self.context.logger.info(f'Successfully dragged from the source element ' f'{src_element_to_log} and dropped onto target element {trg_element_to_log}') return self except ValueError: self.context.logger.error( f'Locator pattern is None. Please provide a valid {"`source`" if source is None else "`target`"}' f' pattern to locate the element and perform a drag and drop operation.') raise ValueError except Exception as ex: self.context.logger.error(f'Unable to drag and drop on elements {src_element_to_log} ' f'and {trg_element_to_log}.') self.context.logger.exception(ex) raise Exception(f'Unable to drag and drop on elements {src_element_to_log} ' f'and {trg_element_to_log}. Error: {ex}') def drag_and_drop_by_offset(self, src_locator, x_offset, y_offset, wait_state=ElementWaitState.PRESENT, timeout=None): """ Drag an object and drop it to an offset location. Holds down the left mouse button on the source element, then moves to the target offset and releases the mouse button. :param src_locator: The element to mouse down. (element to be moved). Can be a locator string or an web element :param x_offset: X offset to move to :param y_offset: Y offset to move to. :param wait_state: he wait state for retrial. Choose state from ElementWaitState class. :param timeout: wait time before throwing any exception. If None, timeout defaults to 20 seconds. :return: self """ element_to_log = None try: if src_locator is None: raise ValueError( 'Please provide the `source` string pattern or a web element to perform drag and drop.') if isinstance(src_locator, WebElement): element, element_to_log = src_locator, src_locator.get_attribute('outerHTML') else: element, element_to_log = Locator(self.context).get_element(src_locator, wait_state, True, timeout), \ src_locator (ActionChains(self.context.driver) .drag_and_drop_by_offset(element, x_offset, y_offset).perform()) self.context.logger.info( f'Successfully moved the source element {element_to_log} by an offset {x_offset, y_offset}') return self except ValueError: self.context.logger.error( f'Locator pattern is None. Please provide a valid `source`' f' pattern to locate the element and perform a drag and drop operation.') raise ValueError except Exception as ex: self.context.logger.error( f'Unable to move the source element {element_to_log} by an offset {x_offset, y_offset}.') self.context.logger.exception(ex) raise Exception( f'Unable to move the source element {element_to_log} by an offset {x_offset, y_offset}. Error: {ex}')

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16,940

4.922484

0.081849

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0.057512

0.05761

0.838615

0.810935

0.790786

0.763204

0.735622

0.688185

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0.001287

0.311983

16,940

339

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0.875933

0.216942

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0.004651

0.252607

0.004978

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1

0.04186

false

0

0.027907

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0.130233

0

null

0

1

0

null

0

6

9c3e24b42abf1e4f5519bd685437d976c8ec34a9

263

py

Python

reminders/tests.py

BelikovYoav/Beyond-07-team-3

407efd58ed5d98fd0862601d792c0415464b45cc

[ "MIT" ]

null

reminders/tests.py

BelikovYoav/Beyond-07-team-3

407efd58ed5d98fd0862601d792c0415464b45cc

[ "MIT" ]

8

2022-02-28T17:05:35.000Z

2022-03-06T22:53:54.000Z

reminders/tests.py

BelikovYoav/Beyond-07-team-3

407efd58ed5d98fd0862601d792c0415464b45cc

[ "MIT" ]

5

2022-02-28T13:45:11.000Z

2022-03-06T15:26:54.000Z

from .class_tests.create_reminder_tests import * # noqa: F403 F401 from .class_tests.update_reminder_tests import * # noqa: F403 F401 from .class_tests.reminders_tests import * # noqa: F403 F401 from .class_tests.notification_tests import * # noqa: F403 F401

52.6

67

0.787072

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0.761421

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0.456853

0

0.105727

0.136882

263

4

68

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0.239544

0

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true

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null

0

1

0

1

0

1

0

6

92d618be3c0c6a273503ff2d722e169ff3a4ee45

30

py

Python

__init__.py

falgon/pelican_dynamic

611ed1666fc4014cc1ffee71ec2f18af399348f0

[ "MIT" ]

null

__init__.py

falgon/pelican_dynamic

611ed1666fc4014cc1ffee71ec2f18af399348f0

[ "MIT" ]

null

__init__.py

falgon/pelican_dynamic

611ed1666fc4014cc1ffee71ec2f18af399348f0

[ "MIT" ]

null

from .pelican_dynamic import *

30

0.833333

4

30

6

1

0

0.1

30

1

30

0.888889

0

1

0

true

0

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1

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1

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null

0

1

0

null

0

1

0

1

0

1

0

6

92d70b52ce2c89029477617e1ff993d157cc2e0e

1,465

py

Python

tests.py

plumdog/myhome

cc829fc5c76128adffb1049683194f16f18bb3a8

[ "MIT" ]

null

tests.py

plumdog/myhome

cc829fc5c76128adffb1049683194f16f18bb3a8

[ "MIT" ]

null

tests.py

plumdog/myhome

cc829fc5c76128adffb1049683194f16f18bb3a8

[ "MIT" ]

null

from datetime import datetime from dateutil.tz import tzutc from backend import get_post def test_backend_parsing(): content = '''Title: Example title Subtitle: Example subtitle Tags: tag1, tag2 Datetime: 2016-02-07 15:25:30+00:00 Live: True Content: Content here ''' post = get_post(content.splitlines()) assert post.title == 'Example title' assert post.subtitle == 'Example subtitle' assert post.tags == ['tag1', 'tag2'] assert post.datetime == datetime(2016, 2, 7, 15, 25, 30, tzinfo=tzutc()) def test_backend_parsing_no_tags(): content = '''Title: Example title Subtitle: Example subtitle Datetime: 2016-02-07 15:25:30+00:00 Content: Content here ''' post = get_post(content.splitlines()) assert post.title == 'Example title' assert post.subtitle == 'Example subtitle' assert not post.tags assert post.datetime == datetime(2016, 2, 7, 15, 25, 30, tzinfo=tzutc()) assert post.content.strip() == 'Content here' def test_backend_parsing_multiline_content(): content = '''Title: Example title Subtitle: Example subtitle Datetime: 2016-02-07 15:25:30+00:00 Content: Content here more here ''' post = get_post(content.splitlines()) assert post.title == 'Example title' assert post.subtitle == 'Example subtitle' assert not post.tags assert post.datetime == datetime(2016, 2, 7, 15, 25, 30, tzinfo=tzutc()) assert post.content.strip() == 'Content here\n\nmore here'

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1,465

4.945813

0.20197

0.119522

0.101594

0.062749

0.790837

0.744024

0.718127

0

0.078203

0.179522

1,465

66

77

22.19697

0.757072

0

0.727273

0

0.343345

0

0.318182

1

0.068182

false

0

0.068182

0

0.136364

0

null

0

1

0

1

0

null

0

6

138f970acbed1792052f8a985bddc53915515141

1,369

py

Python

flask__webservers/cookies/test.py

gil9red/SimplePyScripts

c191ce08fbdeb29377639184579e392057945154

[ "CC-BY-4.0" ]

117

2015-12-18T07:18:27.000Z

2022-03-28T00:25:54.000Z

flask__webservers/cookies/test.py

gil9red/SimplePyScripts

c191ce08fbdeb29377639184579e392057945154

[ "CC-BY-4.0" ]

8

2018-10-03T09:38:46.000Z

2021-12-13T19:51:09.000Z

flask__webservers/cookies/test.py

gil9red/SimplePyScripts

c191ce08fbdeb29377639184579e392057945154

[ "CC-BY-4.0" ]

28

2016-08-02T17:43:47.000Z

2022-03-21T08:31:12.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- __author__ = 'ipetrash' import requests session = requests.Session() rs = session.get('http://127.0.0.1:5001/get-cookies') print(rs, rs.url) print(rs.headers) print(rs.cookies) print(rs.json()) """ <Response [200]> http://127.0.0.1:5001/get-cookies {'Content-Type': 'application/json', 'Content-Length': '3', 'Server': 'Werkzeug/0.15.4 Python/3.7.3', 'Date': 'Wed, 24 Feb 2021 13:28:00 GMT'} <RequestsCookieJar[]> {} """ print() rs = session.post('http://127.0.0.1:5001/set-cookies', params=dict(a=123, b=3)) print(rs, rs.url) print(rs.headers) print(rs.cookies) print(rs.json()) """ <Response [200]> http://127.0.0.1:5001/set-cookies?a=123&b=3 {'Content-Type': 'application/json', 'Content-Length': '17', 'Set-Cookie': 'a=123; Path=/, b=3; Path=/', 'Server': 'Werkzeug/0.15.4 Python/3.7.3', 'Date': 'Wed, 24 Feb 2021 13:28:00 GMT'} <RequestsCookieJar[<Cookie a=123 for 127.0.0.1/>, <Cookie b=3 for 127.0.0.1/>]> {'ok': True} """ print() rs = session.get('http://127.0.0.1:5001/get-cookies') print(rs, rs.url) print(rs.headers) print(rs.cookies) print(rs.json()) """ <Response [200]> http://127.0.0.1:5001/get-cookies {'Content-Type': 'application/json', 'Content-Length': '30', 'Server': 'Werkzeug/0.15.4 Python/3.7.3', 'Date': 'Wed, 24 Feb 2021 13:28:00 GMT'} <RequestsCookieJar[]> {'a': '123', 'b': '3'} """

26.326923

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0.642075

236

1,369

3.707627

0.258475

0.112

0.045714

0.054857

0.795429

0.774857

0.730286

0.691429

0

0.133495

0.097151

1,369

51

188

26.843137

0.574434

0.03141

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0

0.213147

0

1

0

false

0

0.05

0

0.05

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0

null

0

1

0

1

0

1

0

null

0

1

0

6

13d50af8fe68859515ba6b641348e627b67c9cb0

111

py

Python

ld35/resources.py

seventhroot/ld35

0bdf3269b3b3a7a884d95c6bae0b1776509c2387

[ "MIT" ]

null

ld35/resources.py

seventhroot/ld35

0bdf3269b3b3a7a884d95c6bae0b1776509c2387

[ "MIT" ]

null

ld35/resources.py

seventhroot/ld35

0bdf3269b3b3a7a884d95c6bae0b1776509c2387

[ "MIT" ]

null

from pkg_resources import resource_filename def get(filename): return resource_filename('ld35', filename)

22.2

46

0.801802

14

111

6.142857

0.714286

0.372093

0

0.020619

0.126126

111

4

47

27.75

0.865979

0

0.036036

0

1

0.333333

false

0

0.333333

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

b92a55fb56c74ee48e73cf980c210d96ab7b524f

29

py

Python

cogs/memes/__init__.py

TCastus/ASTUSbot

348af14a2099e0eb2d69b0502d4c562bc88c72c4

[ "MIT" ]

4

2020-06-28T02:30:55.000Z

2021-03-22T10:44:26.000Z

cogs/memes/__init__.py

TCastus/ASTUSbot

348af14a2099e0eb2d69b0502d4c562bc88c72c4

[ "MIT" ]

23

2020-06-28T01:24:56.000Z

2021-09-22T14:13:30.000Z

cogs/memes/__init__.py

TCastus/ASTUSbot

348af14a2099e0eb2d69b0502d4c562bc88c72c4

[ "MIT" ]

3

2020-11-09T12:55:27.000Z

2020-12-03T12:00:39.000Z

from .cog_meme import CogMeme

29

0.862069

5

29

4.8

1

0

0.103448

29

1

29

0.923077

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

b9545c85158e61d3c849a79fc50debed37cfe984

119

py

Python

timetoken/__init__.py

mpavelka/python-timetoken

6248e438934ffc11e8be5c027bd9bef00a22dab6

[ "BSD-3-Clause" ]

null

timetoken/__init__.py

mpavelka/python-timetoken

6248e438934ffc11e8be5c027bd9bef00a22dab6

[ "BSD-3-Clause" ]

2

2018-08-14T19:04:28.000Z

2018-08-14T19:05:19.000Z

timetoken/__init__.py

mpavelka/python-timetoken

6248e438934ffc11e8be5c027bd9bef00a22dab6

[ "BSD-3-Clause" ]

1

2021-02-08T16:31:53.000Z

from .timetoken import TimeToken, TimeTokenException, TimeTokenExpired, InvalidTimeTokenSignature, TimeTokenParseError

59.5

118

0.890756

8

119

13.25

0.875

0

0.067227

119

1

119

0.954955

0

1

0

true

0

1

0

1

0

1

0

1

null

0

1

0

1

0

null

0

1

0

1

0

6

b961bfa0b9e646960be2e2b6bf315fc51315c507

64,034

py

Python

main/solutions/find_all_anagrams_in_a_string.py

techrabbit58/LeetCode30DaysMay2020Challenge

3798c5ce104e806372922a73b5ba66b29fc51dbd

[ "Unlicense" ]

1

2020-06-10T10:28:44.000Z

main/solutions/find_all_anagrams_in_a_string.py

techrabbit58/LeetCode30DaysMay2020Challenge

3798c5ce104e806372922a73b5ba66b29fc51dbd

[ "Unlicense" ]

null

main/solutions/find_all_anagrams_in_a_string.py

techrabbit58/LeetCode30DaysMay2020Challenge

3798c5ce104e806372922a73b5ba66b29fc51dbd

[ "Unlicense" ]

null

""" Week 3, Day 3: Find All Anagrams in a String Given a string s and a non-empty string p, find all the start indices of p's anagrams in s. Strings consists of lowercase English letters only and the length of both strings s and p will not be larger than 20, 100. The order of output does not matter. E x a m p l e s Input: s: "cbaebabacd" p: "abc" Output: [0, 6] Explanation: The substring with start index = 0 is "cba", which is an anagram of "abc". The substring with start index = 6 is "bac", which is an anagram of "abc". --- Input: s: "abab" p: "ab" Output: [0, 1, 2] Explanation: The substring with start index = 0 is "ab", which is an anagram of "ab". The substring with start index = 1 is "ba", which is an anagram of "ab". The substring with start index = 2 is "ab", which is an anagram of "ab". --- """ from collections import Counter from typing import List from itertools import islice class Solution: def findAnagrams(self, s: str, p: str) -> List[int]: """Slow.""" s = list(s) p = sorted(list(p)) return [j for j in range(len(s) - len(p) + 1) if sorted(s[j:j + len(p)]) == p] class SolutionV2: """Very slow.""" def findAnagrams(self, s: str, p: str) -> List[int]: starts = [k for k in range(len(s) - len(p) + 1) if s[k] in p] m = Counter(p) result = [] for j in starts: q = Counter(islice(s, j, j + len(p))) if not (q - m): result.append(j) return result class SolutionV3: """ I picked this solution from the discussion. It were contributed by Junaid Mansuri. It is clever and fast: O(n), and two orders of magnitude faster than the 1st approach. Great! """ def findAnagrams(self, s: str, p: str) -> List[int]: """ (1) Handle the corner case 'p is longer than s' by a guard clause. (2) Accumulate the hash values for s and p for the length of p at the same time. (3) Handle the common case that the first len(p) characters are already an anagram of p. (4) For the rest of s, walk through the sliding hash sum, index by index. (4.1) If there is a match in that 'sliding window' over s, append the start index i to the result. :param s: a string :param p: another string, of which anagrams shall be located in s :return: a list of indexes giving all start points of anagrams of p in s """ s_length, p_length, s_hash, p_hash, result = len(s), len(p), 0, 0, [] if p_length > s_length: return [] for k in range(p_length): s_hash, p_hash = s_hash + hash(s[k]), p_hash + hash(p[k]) if s_hash == p_hash: result.append(0) for k in range(p_length, s_length): s_hash += hash(s[k]) - hash(s[k - p_length]) if s_hash == p_hash: result.append(k - p_length + 1) return result if __name__ == '__main__': obj = SolutionV3() example = 'cbaebabacd' probe = 'abc' expected = [0, 6] print('Example: ', obj.findAnagrams(example, probe), '\nExpected:', expected, '\n') example = 'abab' probe = 'ab' expected = [0, 1, 2] print('Example: ', obj.findAnagrams(example, probe), '\nExpected:', expected, '\n') example = 'ababababab' probe = 'aab' expected = [0, 2, 4, 6] print('Example: ', obj.findAnagrams(example, probe), '\nExpected:', expected, '\n') example = \ "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaabaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa" probe = \ "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa" expected = [0, 10001] print('Example: ', obj.findAnagrams(example, probe), '\nExpected:', expected, '\n') example = \ 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fghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyz" probe = \ "abcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyz" expected = list(range(10063)) print('Example: ', obj.findAnagrams(example, probe), '\nExpected:', expected, '\n') # last line of code

500.265625

20,108

0.974654

593

64,034

105.198988

0.284992

0.000962

0.001058

0.001683

0.013994

0.012904

0.011814

0.010436

0.008159

0.005611

0

0.000843

0.01785

64,034

127

20,109

504.204724

0.99108

0.025986

0

0.275862

0

0.968434

0.966024

0

1

0

1

0.051724

false

0

0.051724

0

0.224138

0.086207

0

1

null

0

1

0

1

null

1

0

6

b9ef414657b07927b2a38ab2a073a4c6e7dab280

39,698

py

Python

nova/tests/api/openstack/test_server_actions.py

xushiwei/nova

f27956708b0aaeabb06125e6a72b4d61747934b7

[ "Apache-2.0" ]

1

2021-11-08T10:11:44.000Z

nova/tests/api/openstack/test_server_actions.py

xushiwei/nova

f27956708b0aaeabb06125e6a72b4d61747934b7

[ "Apache-2.0" ]

null

nova/tests/api/openstack/test_server_actions.py

xushiwei/nova

f27956708b0aaeabb06125e6a72b4d61747934b7

[ "Apache-2.0" ]

null

import base64 import datetime import json import stubout import webob from nova import context from nova import utils from nova import exception from nova import flags from nova.api.openstack import create_instance_helper from nova.compute import vm_states from nova.compute import instance_types import nova.db.api from nova import test from nova.tests.api.openstack import common from nova.tests.api.openstack import fakes FLAGS = flags.FLAGS def return_server_by_id(context, id): return stub_instance(id) def instance_update(context, instance_id, kwargs): return stub_instance(instance_id) def return_server_with_attributes(**kwargs): def _return_server(context, id): return stub_instance(id, **kwargs) return _return_server def return_server_with_state(vm_state, task_state=None): return return_server_with_attributes(vm_state=vm_state, task_state=task_state) def return_server_with_uuid_and_state(vm_state, task_state=None): def _return_server(context, id): return return_server_with_state(vm_state, task_state) return _return_server def stub_instance(id, metadata=None, image_ref="10", flavor_id="1", name=None, vm_state=None, task_state=None): if metadata is not None: metadata_items = [{'key':k, 'value':v} for k, v in metadata.items()] else: metadata_items = [{'key':'seq', 'value':id}] inst_type = instance_types.get_instance_type_by_flavor_id(int(flavor_id)) instance = { "id": int(id), "created_at": datetime.datetime(2010, 10, 10, 12, 0, 0), "updated_at": datetime.datetime(2010, 11, 11, 11, 0, 0), "admin_pass": "", "user_id": "fake", "project_id": "fake", "image_ref": image_ref, "kernel_id": "", "ramdisk_id": "", "launch_index": 0, "key_name": "", "key_data": "", "vm_state": vm_state or vm_states.ACTIVE, "task_state": task_state, "memory_mb": 0, "vcpus": 0, "local_gb": 0, "hostname": "", "host": "", "instance_type": dict(inst_type), "user_data": "", "reservation_id": "", "mac_address": "", "scheduled_at": utils.utcnow(), "launched_at": utils.utcnow(), "terminated_at": utils.utcnow(), "availability_zone": "", "display_name": name or "server%s" % id, "display_description": "", "locked": False, "metadata": metadata_items, "access_ip_v4": "", "access_ip_v6": "", "uuid": "aaaaaaaa-aaaa-aaaa-aaaa-aaaaaaaaaaaa", "virtual_interfaces": [], } instance["fixed_ips"] = { "address": '192.168.0.1', "floating_ips": [], } return instance class MockSetAdminPassword(object): def __init__(self): self.instance_id = None self.password = None def __call__(self, context, instance_id, password): self.instance_id = instance_id self.password = password class ServerActionsTest(test.TestCase): def setUp(self): self.maxDiff = None super(ServerActionsTest, self).setUp() self.flags(verbose=True) self.stubs = stubout.StubOutForTesting() fakes.stub_out_auth(self.stubs) self.stubs.Set(nova.db.api, 'instance_get', return_server_by_id) self.stubs.Set(nova.db.api, 'instance_update', instance_update) self.webreq = common.webob_factory('/v1.0/servers') def tearDown(self): self.stubs.UnsetAll() def test_server_change_password(self): body = {'changePassword': {'adminPass': '1234pass'}} req = webob.Request.blank('/v1.0/servers/1/action') req.method = 'POST' req.content_type = 'application/json' req.body = json.dumps(body) res = req.get_response(fakes.wsgi_app()) self.assertEqual(res.status_int, 501) def test_server_change_password_xml(self): req = webob.Request.blank('/v1.0/servers/1/action') req.method = 'POST' req.content_type = 'application/xml' req.body = '<changePassword adminPass="1234pass">' # res = req.get_response(fakes.wsgi_app()) # self.assertEqual(res.status_int, 501) def test_server_reboot(self): body = dict(server=dict( name='server_test', imageId=2, flavorId=2, metadata={}, personality={})) req = webob.Request.blank('/v1.0/servers/1/action') req.method = 'POST' req.content_type = 'application/json' req.body = json.dumps(body) res = req.get_response(fakes.wsgi_app()) def test_server_rebuild_accepted(self): body = { "rebuild": { "imageId": 2, }, } req = webob.Request.blank('/v1.0/servers/1/action') req.method = 'POST' req.content_type = 'application/json' req.body = json.dumps(body) res = req.get_response(fakes.wsgi_app()) self.assertEqual(res.status_int, 202) self.assertEqual(res.body, "") def test_server_rebuild_rejected_when_building(self): body = { "rebuild": { "imageId": 2, }, } state = vm_states.BUILDING new_return_server = return_server_with_state(state) self.stubs.Set(nova.db.api, 'instance_get', new_return_server) self.stubs.Set(nova.db, 'instance_get_by_uuid', return_server_with_uuid_and_state(state)) req = webob.Request.blank('/v1.0/servers/1/action') req.method = 'POST' req.content_type = 'application/json' req.body = json.dumps(body) res = req.get_response(fakes.wsgi_app()) self.assertEqual(res.status_int, 409) def test_server_rebuild_bad_entity(self): body = { "rebuild": { }, } req = webob.Request.blank('/v1.0/servers/1/action') req.method = 'POST' req.content_type = 'application/json' req.body = json.dumps(body) res = req.get_response(fakes.wsgi_app()) self.assertEqual(res.status_int, 400) def test_resize_server(self): req = self.webreq('/1/action', 'POST', dict(resize=dict(flavorId=3))) self.resize_called = False def resize_mock(*args): self.resize_called = True self.stubs.Set(nova.compute.api.API, 'resize', resize_mock) res = req.get_response(fakes.wsgi_app()) self.assertEqual(res.status_int, 202) self.assertEqual(self.resize_called, True) def test_resize_bad_flavor_fails(self): req = self.webreq('/1/action', 'POST', dict(resize=dict(derp=3))) self.resize_called = False def resize_mock(*args): self.resize_called = True self.stubs.Set(nova.compute.api.API, 'resize', resize_mock) res = req.get_response(fakes.wsgi_app()) self.assertEqual(res.status_int, 400) self.assertEqual(self.resize_called, False) def test_resize_raises_fails(self): req = self.webreq('/1/action', 'POST', dict(resize=dict(flavorId=3))) def resize_mock(*args): raise Exception('hurr durr') self.stubs.Set(nova.compute.api.API, 'resize', resize_mock) res = req.get_response(fakes.wsgi_app()) self.assertEqual(res.status_int, 500) def test_confirm_resize_server(self): req = self.webreq('/1/action', 'POST', dict(confirmResize=None)) self.resize_called = False def confirm_resize_mock(*args): self.resize_called = True self.stubs.Set(nova.compute.api.API, 'confirm_resize', confirm_resize_mock) res = req.get_response(fakes.wsgi_app()) self.assertEqual(res.status_int, 204) self.assertEqual(self.resize_called, True) def test_confirm_resize_server_fails(self): req = self.webreq('/1/action', 'POST', dict(confirmResize=None)) def confirm_resize_mock(*args): raise Exception('hurr durr') self.stubs.Set(nova.compute.api.API, 'confirm_resize', confirm_resize_mock) res = req.get_response(fakes.wsgi_app()) self.assertEqual(res.status_int, 400) def test_revert_resize_server(self): req = self.webreq('/1/action', 'POST', dict(revertResize=None)) self.resize_called = False def revert_resize_mock(*args): self.resize_called = True self.stubs.Set(nova.compute.api.API, 'revert_resize', revert_resize_mock) res = req.get_response(fakes.wsgi_app()) self.assertEqual(res.status_int, 202) self.assertEqual(self.resize_called, True) def test_revert_resize_server_fails(self): req = self.webreq('/1/action', 'POST', dict(revertResize=None)) def revert_resize_mock(*args): raise Exception('hurr durr') self.stubs.Set(nova.compute.api.API, 'revert_resize', revert_resize_mock) res = req.get_response(fakes.wsgi_app()) self.assertEqual(res.status_int, 400) def test_migrate_server(self): """This is basically the same as resize, only we provide the `migrate` attribute in the body's dict. """ req = self.webreq('/1/migrate', 'POST') self.resize_called = False def resize_mock(*args): self.resize_called = True self.stubs.Set(nova.compute.api.API, 'resize', resize_mock) res = req.get_response(fakes.wsgi_app()) self.assertEqual(res.status_int, 202) self.assertEqual(self.resize_called, True) def test_create_backup(self): """The happy path for creating backups""" self.flags(allow_admin_api=True) body = { 'createBackup': { 'name': 'Backup 1', 'backup_type': 'daily', 'rotation': 1, }, } req = webob.Request.blank('/v1.0/servers/1/action') req.method = 'POST' req.body = json.dumps(body) req.headers["content-type"] = "application/json" response = req.get_response(fakes.wsgi_app()) self.assertEqual(202, response.status_int) self.assertTrue(response.headers['Location']) def test_create_backup_admin_api_off(self): """The happy path for creating backups""" self.flags(allow_admin_api=False) body = { 'createBackup': { 'name': 'Backup 1', 'backup_type': 'daily', 'rotation': 1, }, } req = webob.Request.blank('/v1.0/servers/1/action') req.method = 'POST' req.body = json.dumps(body) req.headers["content-type"] = "application/json" response = req.get_response(fakes.wsgi_app()) self.assertEqual(400, response.status_int) def test_create_backup_with_metadata(self): self.flags(allow_admin_api=True) body = { 'createBackup': { 'name': 'Backup 1', 'backup_type': 'daily', 'rotation': 1, 'metadata': {'123': 'asdf'}, }, } req = webob.Request.blank('/v1.0/servers/1/action') req.method = 'POST' req.body = json.dumps(body) req.headers["content-type"] = "application/json" response = req.get_response(fakes.wsgi_app()) self.assertEqual(202, response.status_int) self.assertTrue(response.headers['Location']) def test_create_backup_with_too_much_metadata(self): self.flags(allow_admin_api=True) body = { 'createBackup': { 'name': 'Backup 1', 'backup_type': 'daily', 'rotation': 1, 'metadata': {'123': 'asdf'}, }, } for num in range(FLAGS.quota_metadata_items + 1): body['createBackup']['metadata']['foo%i' % num] = "bar" req = webob.Request.blank('/v1.0/servers/1/action') req.method = 'POST' req.body = json.dumps(body) req.headers["content-type"] = "application/json" response = req.get_response(fakes.wsgi_app()) self.assertEqual(413, response.status_int) def test_create_backup_no_name(self): """Name is required for backups""" self.flags(allow_admin_api=True) body = { 'createBackup': { 'backup_type': 'daily', 'rotation': 1, }, } req = webob.Request.blank('/v1.0/images') req.method = 'POST' req.body = json.dumps(body) req.headers["content-type"] = "application/json" response = req.get_response(fakes.wsgi_app()) self.assertEqual(400, response.status_int) def test_create_backup_no_rotation(self): """Rotation is required for backup requests""" self.flags(allow_admin_api=True) body = { 'createBackup': { 'name': 'Backup 1', 'backup_type': 'daily', }, } req = webob.Request.blank('/v1.0/images') req.method = 'POST' req.body = json.dumps(body) req.headers["content-type"] = "application/json" response = req.get_response(fakes.wsgi_app()) self.assertEqual(400, response.status_int) def test_create_backup_no_backup_type(self): """Backup Type (daily or weekly) is required for backup requests""" self.flags(allow_admin_api=True) body = { 'createBackup': { 'name': 'Backup 1', 'rotation': 1, }, } req = webob.Request.blank('/v1.0/images') req.method = 'POST' req.body = json.dumps(body) req.headers["content-type"] = "application/json" response = req.get_response(fakes.wsgi_app()) self.assertEqual(400, response.status_int) def test_create_backup_bad_entity(self): self.flags(allow_admin_api=True) body = {'createBackup': 'go'} req = webob.Request.blank('/v1.0/images') req.method = 'POST' req.body = json.dumps(body) req.headers["content-type"] = "application/json" response = req.get_response(fakes.wsgi_app()) self.assertEqual(400, response.status_int) class ServerActionsTestV11(test.TestCase): def setUp(self): self.maxDiff = None super(ServerActionsTestV11, self).setUp() self.stubs = stubout.StubOutForTesting() fakes.stub_out_auth(self.stubs) self.stubs.Set(nova.db.api, 'instance_get', return_server_by_id) self.stubs.Set(nova.db.api, 'instance_update', instance_update) fakes.stub_out_glance(self.stubs) fakes.stub_out_compute_api_snapshot(self.stubs) service_class = 'nova.image.glance.GlanceImageService' self.service = utils.import_object(service_class) self.context = context.RequestContext(1, None) self.service.delete_all() self.sent_to_glance = {} fakes.stub_out_glance_add_image(self.stubs, self.sent_to_glance) self.flags(allow_instance_snapshots=True) def tearDown(self): self.stubs.UnsetAll() def test_server_bad_body(self): body = {} req = webob.Request.blank('/v1.1/fake/servers/1/action') req.method = 'POST' req.content_type = 'application/json' req.body = json.dumps(body) res = req.get_response(fakes.wsgi_app()) self.assertEqual(res.status_int, 400) def test_server_unknown_action(self): body = {'sockTheFox': {'fakekey': '1234'}} req = webob.Request.blank('/v1.1/fake/servers/1/action') req.method = 'POST' req.content_type = 'application/json' req.body = json.dumps(body) res = req.get_response(fakes.wsgi_app()) self.assertEqual(res.status_int, 400) def test_server_change_password(self): mock_method = MockSetAdminPassword() self.stubs.Set(nova.compute.api.API, 'set_admin_password', mock_method) body = {'changePassword': {'adminPass': '1234pass'}} req = webob.Request.blank('/v1.1/fake/servers/1/action') req.method = 'POST' req.content_type = 'application/json' req.body = json.dumps(body) res = req.get_response(fakes.wsgi_app()) self.assertEqual(res.status_int, 202) self.assertEqual(mock_method.instance_id, '1') self.assertEqual(mock_method.password, '1234pass') def test_server_change_password_xml(self): mock_method = MockSetAdminPassword() self.stubs.Set(nova.compute.api.API, 'set_admin_password', mock_method) req = webob.Request.blank('/v1.1/fake/servers/1/action') req.method = 'POST' req.content_type = "application/xml" req.body = """<?xml version="1.0" encoding="UTF-8"?> <changePassword xmlns="http://docs.openstack.org/compute/api/v1.1" adminPass="1234pass"/>""" res = req.get_response(fakes.wsgi_app()) self.assertEqual(res.status_int, 202) self.assertEqual(mock_method.instance_id, '1') self.assertEqual(mock_method.password, '1234pass') def test_server_change_password_not_a_string(self): body = {'changePassword': {'adminPass': 1234}} req = webob.Request.blank('/v1.1/fake/servers/1/action') req.method = 'POST' req.content_type = 'application/json' req.body = json.dumps(body) res = req.get_response(fakes.wsgi_app()) self.assertEqual(res.status_int, 400) def test_server_change_password_bad_request(self): body = {'changePassword': {'pass': '12345'}} req = webob.Request.blank('/v1.1/fake/servers/1/action') req.method = 'POST' req.content_type = 'application/json' req.body = json.dumps(body) res = req.get_response(fakes.wsgi_app()) self.assertEqual(res.status_int, 400) def test_server_change_password_empty_string(self): body = {'changePassword': {'adminPass': ''}} req = webob.Request.blank('/v1.1/fake/servers/1/action') req.method = 'POST' req.content_type = 'application/json' req.body = json.dumps(body) res = req.get_response(fakes.wsgi_app()) self.assertEqual(res.status_int, 400) def test_server_change_password_none(self): body = {'changePassword': {'adminPass': None}} req = webob.Request.blank('/v1.1/fake/servers/1/action') req.method = 'POST' req.content_type = 'application/json' req.body = json.dumps(body) res = req.get_response(fakes.wsgi_app()) self.assertEqual(res.status_int, 400) def test_server_reboot_hard(self): body = dict(reboot=dict(type="HARD")) req = webob.Request.blank('/v1.1/fake/servers/1/action') req.method = 'POST' req.content_type = 'application/json' req.body = json.dumps(body) res = req.get_response(fakes.wsgi_app()) self.assertEqual(res.status_int, 202) def test_server_reboot_soft(self): body = dict(reboot=dict(type="SOFT")) req = webob.Request.blank('/v1.1/fake/servers/1/action') req.method = 'POST' req.content_type = 'application/json' req.body = json.dumps(body) res = req.get_response(fakes.wsgi_app()) self.assertEqual(res.status_int, 202) def test_server_reboot_incorrect_type(self): body = dict(reboot=dict(type="NOT_A_TYPE")) req = webob.Request.blank('/v1.1/fake/servers/1/action') req.method = 'POST' req.content_type = 'application/json' req.body = json.dumps(body) res = req.get_response(fakes.wsgi_app()) self.assertEqual(res.status_int, 400) def test_server_reboot_missing_type(self): body = dict(reboot=dict()) req = webob.Request.blank('/v1.1/fake/servers/1/action') req.method = 'POST' req.content_type = 'application/json' req.body = json.dumps(body) res = req.get_response(fakes.wsgi_app()) self.assertEqual(res.status_int, 400) def test_server_rebuild_accepted_minimum(self): new_return_server = return_server_with_attributes(image_ref='2') self.stubs.Set(nova.db.api, 'instance_get', new_return_server) body = { "rebuild": { "imageRef": "http://localhost/images/2", }, } req = webob.Request.blank('/v1.1/fake/servers/1/action') req.method = 'POST' req.content_type = 'application/json' req.body = json.dumps(body) res = req.get_response(fakes.wsgi_app()) self.assertEqual(res.status_int, 202) body = json.loads(res.body) self.assertEqual(body['server']['image']['id'], '2') self.assertEqual(len(body['server']['adminPass']), 16) def test_server_rebuild_rejected_when_building(self): body = { "rebuild": { "imageRef": "http://localhost/images/2", }, } state = vm_states.BUILDING new_return_server = return_server_with_state(state) self.stubs.Set(nova.db.api, 'instance_get', new_return_server) self.stubs.Set(nova.db, 'instance_get_by_uuid', return_server_with_uuid_and_state(state)) req = webob.Request.blank('/v1.1/fake/servers/1/action') req.method = 'POST' req.content_type = 'application/json' req.body = json.dumps(body) res = req.get_response(fakes.wsgi_app()) self.assertEqual(res.status_int, 409) def test_server_rebuild_accepted_with_metadata(self): metadata = {'new': 'metadata'} new_return_server = return_server_with_attributes(metadata=metadata) self.stubs.Set(nova.db.api, 'instance_get', new_return_server) body = { "rebuild": { "imageRef": "http://localhost/images/2", "metadata": metadata, }, } req = webob.Request.blank('/v1.1/fake/servers/1/action') req.method = 'POST' req.content_type = 'application/json' req.body = json.dumps(body) res = req.get_response(fakes.wsgi_app()) self.assertEqual(res.status_int, 202) body = json.loads(res.body) self.assertEqual(body['server']['metadata'], metadata) def test_server_rebuild_accepted_with_bad_metadata(self): body = { "rebuild": { "imageRef": "http://localhost/images/2", "metadata": "stack", }, } req = webob.Request.blank('/v1.1/fake/servers/1/action') req.method = 'POST' req.content_type = 'application/json' req.body = json.dumps(body) res = req.get_response(fakes.wsgi_app()) self.assertEqual(res.status_int, 400) def test_server_rebuild_bad_entity(self): body = { "rebuild": { "imageId": 2, }, } req = webob.Request.blank('/v1.1/fake/servers/1/action') req.method = 'POST' req.content_type = 'application/json' req.body = json.dumps(body) res = req.get_response(fakes.wsgi_app()) self.assertEqual(res.status_int, 400) def test_server_rebuild_bad_personality(self): body = { "rebuild": { "imageRef": "http://localhost/images/2", "personality": [{ "path": "/path/to/file", "contents": "INVALID b64", }] }, } req = webob.Request.blank('/v1.1/fake/servers/1/action') req.method = 'POST' req.content_type = 'application/json' req.body = json.dumps(body) res = req.get_response(fakes.wsgi_app()) self.assertEqual(res.status_int, 400) def test_server_rebuild_personality(self): body = { "rebuild": { "imageRef": "http://localhost/images/2", "personality": [{ "path": "/path/to/file", "contents": base64.b64encode("Test String"), }] }, } req = webob.Request.blank('/v1.1/fake/servers/1/action') req.method = 'POST' req.content_type = 'application/json' req.body = json.dumps(body) res = req.get_response(fakes.wsgi_app()) self.assertEqual(res.status_int, 202) body = json.loads(res.body) self.assertTrue('personality' not in body['server']) def test_server_rebuild_admin_pass(self): new_return_server = return_server_with_attributes(image_ref='2') self.stubs.Set(nova.db.api, 'instance_get', new_return_server) body = { "rebuild": { "imageRef": "http://localhost/images/2", "adminPass": "asdf", }, } req = webob.Request.blank('/v1.1/fake/servers/1/action') req.method = 'POST' req.content_type = 'application/json' req.body = json.dumps(body) res = req.get_response(fakes.wsgi_app()) self.assertEqual(res.status_int, 202) body = json.loads(res.body) self.assertEqual(body['server']['image']['id'], '2') self.assertEqual(body['server']['adminPass'], 'asdf') def test_server_rebuild_server_not_found(self): def server_not_found(self, instance_id): raise exception.InstanceNotFound(instance_id=instance_id) self.stubs.Set(nova.db.api, 'instance_get', server_not_found) body = { "rebuild": { "imageRef": "http://localhost/images/2", }, } req = webob.Request.blank('/v1.1/fake/servers/1/action') req.method = 'POST' req.content_type = 'application/json' req.body = json.dumps(body) res = req.get_response(fakes.wsgi_app()) self.assertEqual(res.status_int, 404) def test_resize_server(self): req = webob.Request.blank('/v1.1/fake/servers/1/action') req.content_type = 'application/json' req.method = 'POST' body_dict = dict(resize=dict(flavorRef="http://localhost/3")) req.body = json.dumps(body_dict) self.resize_called = False def resize_mock(*args): self.resize_called = True self.stubs.Set(nova.compute.api.API, 'resize', resize_mock) res = req.get_response(fakes.wsgi_app()) self.assertEqual(res.status_int, 202) self.assertEqual(self.resize_called, True) def test_resize_server_no_flavor(self): req = webob.Request.blank('/v1.1/fake/servers/1/action') req.content_type = 'application/json' req.method = 'POST' body_dict = dict(resize=dict()) req.body = json.dumps(body_dict) res = req.get_response(fakes.wsgi_app()) self.assertEqual(res.status_int, 400) def test_resize_server_no_flavor_ref(self): req = webob.Request.blank('/v1.1/fake/servers/1/action') req.content_type = 'application/json' req.method = 'POST' body_dict = dict(resize=dict(flavorRef=None)) req.body = json.dumps(body_dict) res = req.get_response(fakes.wsgi_app()) self.assertEqual(res.status_int, 400) def test_confirm_resize_server(self): req = webob.Request.blank('/v1.1/fake/servers/1/action') req.content_type = 'application/json' req.method = 'POST' body_dict = dict(confirmResize=None) req.body = json.dumps(body_dict) self.confirm_resize_called = False def cr_mock(*args): self.confirm_resize_called = True self.stubs.Set(nova.compute.api.API, 'confirm_resize', cr_mock) res = req.get_response(fakes.wsgi_app()) self.assertEqual(res.status_int, 204) self.assertEqual(self.confirm_resize_called, True) def test_revert_resize_server(self): req = webob.Request.blank('/v1.1/fake/servers/1/action') req.content_type = 'application/json' req.method = 'POST' body_dict = dict(revertResize=None) req.body = json.dumps(body_dict) self.revert_resize_called = False def revert_mock(*args): self.revert_resize_called = True self.stubs.Set(nova.compute.api.API, 'revert_resize', revert_mock) res = req.get_response(fakes.wsgi_app()) self.assertEqual(res.status_int, 202) self.assertEqual(self.revert_resize_called, True) def test_create_image(self): body = { 'createImage': { 'name': 'Snapshot 1', }, } req = webob.Request.blank('/v1.1/fake/servers/1/action') req.method = 'POST' req.body = json.dumps(body) req.headers["content-type"] = "application/json" response = req.get_response(fakes.wsgi_app()) self.assertEqual(202, response.status_int) location = response.headers['Location'] self.assertEqual('http://localhost/v1.1/images/123', location) def test_create_image_snapshots_disabled(self): """Don't permit a snapshot if the allow_instance_snapshots flag is False """ self.flags(allow_instance_snapshots=False) body = { 'createImage': { 'name': 'Snapshot 1', }, } req = webob.Request.blank('/v1.1/fake/servers/1/action') req.method = 'POST' req.body = json.dumps(body) req.headers["content-type"] = "application/json" response = req.get_response(fakes.wsgi_app()) self.assertEqual(400, response.status_int) def test_create_image_with_metadata(self): body = { 'createImage': { 'name': 'Snapshot 1', 'metadata': {'key': 'asdf'}, }, } req = webob.Request.blank('/v1.1/fake/servers/1/action') req.method = 'POST' req.body = json.dumps(body) req.headers["content-type"] = "application/json" response = req.get_response(fakes.wsgi_app()) self.assertEqual(202, response.status_int) location = response.headers['Location'] self.assertEqual('http://localhost/v1.1/images/123', location) def test_create_image_with_too_much_metadata(self): body = { 'createImage': { 'name': 'Snapshot 1', 'metadata': {}, }, } for num in range(FLAGS.quota_metadata_items + 1): body['createImage']['metadata']['foo%i' % num] = "bar" req = webob.Request.blank('/v1.1/fake/servers/1/action') req.method = 'POST' req.body = json.dumps(body) req.headers["content-type"] = "application/json" response = req.get_response(fakes.wsgi_app()) self.assertEqual(413, response.status_int) def test_create_image_no_name(self): body = { 'createImage': {}, } req = webob.Request.blank('/v1.1/fake/servers/1/action') req.method = 'POST' req.body = json.dumps(body) req.headers["content-type"] = "application/json" response = req.get_response(fakes.wsgi_app()) self.assertEqual(400, response.status_int) def test_create_image_bad_metadata(self): body = { 'createImage': { 'name': 'geoff', 'metadata': 'henry', }, } req = webob.Request.blank('/v1.1/fake/servers/1/action') req.method = 'POST' req.body = json.dumps(body) req.headers["content-type"] = "application/json" response = req.get_response(fakes.wsgi_app()) self.assertEqual(400, response.status_int) def test_create_backup(self): """The happy path for creating backups""" self.flags(allow_admin_api=True) body = { 'createBackup': { 'name': 'Backup 1', 'backup_type': 'daily', 'rotation': 1, }, } req = webob.Request.blank('/v1.1/fake/servers/1/action') req.method = 'POST' req.body = json.dumps(body) req.headers["content-type"] = "application/json" response = req.get_response(fakes.wsgi_app()) self.assertEqual(202, response.status_int) self.assertTrue(response.headers['Location']) class TestServerActionXMLDeserializerV11(test.TestCase): def setUp(self): self.deserializer = create_instance_helper.ServerXMLDeserializerV11() def tearDown(self): pass def test_create_image(self): serial_request = """ <createImage xmlns="http://docs.openstack.org/compute/api/v1.1" name="new-server-test"/>""" request = self.deserializer.deserialize(serial_request, 'action') expected = { "createImage": { "name": "new-server-test", }, } self.assertEquals(request['body'], expected) def test_create_image_with_metadata(self): serial_request = """ <createImage xmlns="http://docs.openstack.org/compute/api/v1.1" name="new-server-test"> <metadata> <meta key="key1">value1</meta> </metadata> </createImage>""" request = self.deserializer.deserialize(serial_request, 'action') expected = { "createImage": { "name": "new-server-test", "metadata": {"key1": "value1"}, }, } self.assertEquals(request['body'], expected) def test_change_pass(self): serial_request = """<?xml version="1.0" encoding="UTF-8"?> <changePassword xmlns="http://docs.openstack.org/compute/api/v1.1" adminPass="1234pass"/> """ request = self.deserializer.deserialize(serial_request, 'action') expected = { "changePassword": { "adminPass": "1234pass", }, } self.assertEquals(request['body'], expected) def test_change_pass_no_pass(self): serial_request = """<?xml version="1.0" encoding="UTF-8"?> <changePassword xmlns="http://docs.openstack.org/compute/api/v1.1"/> """ self.assertRaises(AttributeError, self.deserializer.deserialize, serial_request, 'action') def test_reboot(self): serial_request = """<?xml version="1.0" encoding="UTF-8"?> <reboot xmlns="http://docs.openstack.org/compute/api/v1.1" type="HARD"/>""" request = self.deserializer.deserialize(serial_request, 'action') expected = { "reboot": { "type": "HARD", }, } self.assertEquals(request['body'], expected) def test_reboot_no_type(self): serial_request = """<?xml version="1.0" encoding="UTF-8"?> <reboot xmlns="http://docs.openstack.org/compute/api/v1.1"/>""" self.assertRaises(AttributeError, self.deserializer.deserialize, serial_request, 'action') def test_resize(self): serial_request = """<?xml version="1.0" encoding="UTF-8"?> <resize xmlns="http://docs.openstack.org/compute/api/v1.1" flavorRef="http://localhost/flavors/3"/>""" request = self.deserializer.deserialize(serial_request, 'action') expected = { "resize": {"flavorRef": "http://localhost/flavors/3"}, } self.assertEquals(request['body'], expected) def test_resize_no_flavor_ref(self): serial_request = """<?xml version="1.0" encoding="UTF-8"?> <resize xmlns="http://docs.openstack.org/compute/api/v1.1"/>""" self.assertRaises(AttributeError, self.deserializer.deserialize, serial_request, 'action') def test_confirm_resize(self): serial_request = """<?xml version="1.0" encoding="UTF-8"?> <confirmResize xmlns="http://docs.openstack.org/compute/api/v1.1"/>""" request = self.deserializer.deserialize(serial_request, 'action') expected = { "confirmResize": None, } self.assertEquals(request['body'], expected) def test_revert_resize(self): serial_request = """<?xml version="1.0" encoding="UTF-8"?> <revertResize xmlns="http://docs.openstack.org/compute/api/v1.1"/>""" request = self.deserializer.deserialize(serial_request, 'action') expected = { "revertResize": None, } self.assertEquals(request['body'], expected) def test_rebuild(self): serial_request = """<?xml version="1.0" encoding="UTF-8"?> <rebuild xmlns="http://docs.openstack.org/compute/api/v1.1" name="new-server-test" imageRef="http://localhost/images/1"> <metadata> <meta key="My Server Name">Apache1</meta> </metadata> <personality> <file path="/etc/banner.txt">Mg==</file> </personality> </rebuild>""" request = self.deserializer.deserialize(serial_request, 'action') expected = { "rebuild": { "name": "new-server-test", "imageRef": "http://localhost/images/1", "metadata": { "My Server Name": "Apache1", }, "personality": [ {"path": "/etc/banner.txt", "contents": "Mg=="}, ], }, } self.assertDictMatch(request['body'], expected) def test_rebuild_minimum(self): serial_request = """<?xml version="1.0" encoding="UTF-8"?> <rebuild xmlns="http://docs.openstack.org/compute/api/v1.1" imageRef="http://localhost/images/1"/>""" request = self.deserializer.deserialize(serial_request, 'action') expected = { "rebuild": { "imageRef": "http://localhost/images/1", }, } self.assertDictMatch(request['body'], expected) def test_rebuild_no_imageRef(self): serial_request = """<?xml version="1.0" encoding="UTF-8"?> <rebuild xmlns="http://docs.openstack.org/compute/api/v1.1" name="new-server-test"> <metadata> <meta key="My Server Name">Apache1</meta> </metadata> <personality> <file path="/etc/banner.txt">Mg==</file> </personality> </rebuild>""" self.assertRaises(AttributeError, self.deserializer.deserialize, serial_request, 'action')

34.640489

79

0.582145

4,516

39,698

4.93822

0.069973

0.049774

0.034528

0.046859

0.844133

0.81799

0.794135

0.781669

0.745841

0.71508

0

0.019334

0.283415

39,698

1,145

80

34.670742

0.764615

0.01262

0

0.675241

0

0.20999

0.036604

0

0.096463

1

0.102894

false

0.042872

0.018221

0.005359

0.133976

0

null

0

1

0

null

0

6

6a436aee15f54c8a286f878586c1c3ace81544a5

156

py

Python

superlists/lists/views.py

Alfawuhn/test-driven-python

003f9a95ff8b3dd05f5b857a158781d1631f6d10

[ "Apache-2.0" ]

2

2015-02-12T04:25:29.000Z

2015-02-12T04:25:33.000Z

superlists/lists/views.py

Alfawuhn/test-driven-python

003f9a95ff8b3dd05f5b857a158781d1631f6d10

[ "Apache-2.0" ]

null

superlists/lists/views.py

Alfawuhn/test-driven-python

003f9a95ff8b3dd05f5b857a158781d1631f6d10

[ "Apache-2.0" ]

null

from django.http import HttpResponse # Create your views here. def home_page(request): return HttpResponse('<html><title>To-Do lists</title></html>')

22.285714

66

0.737179

22

156

5.181818

0.863636

0

0.128205

156

6

67

26

0.838235

0.147436

0

0.29771

0

1

0.333333

false

0

0.333333

1

0

1

0

null

0

1

0

null

0

1

0

1

0

6

e00b802934666522532aa2946ffcc2a8f3b2ae1c

16,929

py

Python

pybind/nos/v7_1_0/sflow/collector/__init__.py

shivharis/pybind

4e1c6d54b9fd722ccec25546ba2413d79ce337e6

[ "Apache-2.0" ]

null

pybind/nos/v7_1_0/sflow/collector/__init__.py

shivharis/pybind

4e1c6d54b9fd722ccec25546ba2413d79ce337e6

[ "Apache-2.0" ]

null

pybind/nos/v7_1_0/sflow/collector/__init__.py

shivharis/pybind

4e1c6d54b9fd722ccec25546ba2413d79ce337e6

[ "Apache-2.0" ]

1

2021-11-05T22:15:42.000Z

from operator import attrgetter import pyangbind.lib.xpathhelper as xpathhelper from pyangbind.lib.yangtypes import RestrictedPrecisionDecimalType, RestrictedClassType, TypedListType from pyangbind.lib.yangtypes import YANGBool, YANGListType, YANGDynClass, ReferenceType from pyangbind.lib.base import PybindBase from decimal import Decimal from bitarray import bitarray import __builtin__ class collector(PybindBase): """ This class was auto-generated by the PythonClass plugin for PYANG from YANG module brocade-sflow - based on the path /sflow/collector. Each member element of the container is represented as a class variable - with a specific YANG type. """ __slots__ = ('_pybind_generated_by', '_path_helper', '_yang_name', '_rest_name', '_extmethods', '__collector_ip_address','__collector_port_number','__use_vrf',) _yang_name = 'collector' _rest_name = 'collector' _pybind_generated_by = 'container' def __init__(self, *args, **kwargs): path_helper_ = kwargs.pop("path_helper", None) if path_helper_ is False: self._path_helper = False elif path_helper_ is not None and isinstance(path_helper_, xpathhelper.YANGPathHelper): self._path_helper = path_helper_ elif hasattr(self, "_parent"): path_helper_ = getattr(self._parent, "_path_helper", False) self._path_helper = path_helper_ else: self._path_helper = False extmethods = kwargs.pop("extmethods", None) if extmethods is False: self._extmethods = False elif extmethods is not None and isinstance(extmethods, dict): self._extmethods = extmethods elif hasattr(self, "_parent"): extmethods = getattr(self._parent, "_extmethods", None) self._extmethods = extmethods else: self._extmethods = False self.__collector_ip_address = YANGDynClass(base=[RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'(([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])\\.){3}([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])(%[\\p{N}\\p{L}]+)?'}),RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'((:|[0-9a-fA-F]{0,4}):)([0-9a-fA-F]{0,4}:){0,5}((([0-9a-fA-F]{0,4}:)?(:|[0-9a-fA-F]{0,4}))|(((25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])\\.){3}(25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])))(%[\\p{N}\\p{L}]+)?'}),], is_leaf=True, yang_name="collector-ip-address", rest_name="collector-ip-address", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'<ipv4/v6 address>; The IPv4/IPv6 address of the Sflow collector', u'cli-drop-node-name': None}}, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-sflow', defining_module='brocade-sflow', yang_type='inet:ip-address', is_config=True) self.__collector_port_number = YANGDynClass(base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), restriction_dict={'range': [u'1..65535']}), is_leaf=True, yang_name="collector-port-number", rest_name="collector-port-number", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'<1-65535> The port number used by the Sflow collector (default = 6343)', u'cli-drop-node-name': None, u'key-default': u'6343', u'cli-optional-in-sequence': None}}, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-sflow', defining_module='brocade-sflow', yang_type='uint32', is_config=True) self.__use_vrf = YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'((([a-zA-Z0-9_]([a-zA-Z0-9\\-_]){0,61})?[a-zA-Z0-9]\\.)*([a-zA-Z0-9_]([a-zA-Z0-9\\-_]){0,61})?[a-zA-Z0-9]\\.?)|\\.', 'length': [u'1..32']}), is_leaf=True, yang_name="use-vrf", rest_name="use-vrf", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Vrf to use for sending data to the collector (default = mgmt-vrf)', u'cli-optional-in-sequence': None, u'key-default': u'mgmt-vrf', u'cli-expose-key-name': None}}, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-sflow', defining_module='brocade-sflow', yang_type='common-def:vrf-name', is_config=True) load = kwargs.pop("load", None) if args: if len(args) > 1: raise TypeError("cannot create a YANG container with >1 argument") all_attr = True for e in self._pyangbind_elements: if not hasattr(args[0], e): all_attr = False break if not all_attr: raise ValueError("Supplied object did not have the correct attributes") for e in self._pyangbind_elements: nobj = getattr(args[0], e) if nobj._changed() is False: continue setmethod = getattr(self, "_set_%s" % e) if load is None: setmethod(getattr(args[0], e)) else: setmethod(getattr(args[0], e), load=load) def _path(self): if hasattr(self, "_parent"): return self._parent._path()+[self._yang_name] else: return [u'sflow', u'collector'] def _rest_path(self): if hasattr(self, "_parent"): if self._rest_name: return self._parent._rest_path()+[self._rest_name] else: return self._parent._rest_path() else: return [u'sflow', u'collector'] def _get_collector_ip_address(self): """ Getter method for collector_ip_address, mapped from YANG variable /sflow/collector/collector_ip_address (inet:ip-address) """ return self.__collector_ip_address def _set_collector_ip_address(self, v, load=False): """ Setter method for collector_ip_address, mapped from YANG variable /sflow/collector/collector_ip_address (inet:ip-address) If this variable is read-only (config: false) in the source YANG file, then _set_collector_ip_address is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_collector_ip_address() directly. """ parent = getattr(self, "_parent", None) if parent is not None and load is False: raise AttributeError("Cannot set keys directly when" + " within an instantiated list") if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=[RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'(([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])\\.){3}([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])(%[\\p{N}\\p{L}]+)?'}),RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'((:|[0-9a-fA-F]{0,4}):)([0-9a-fA-F]{0,4}:){0,5}((([0-9a-fA-F]{0,4}:)?(:|[0-9a-fA-F]{0,4}))|(((25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])\\.){3}(25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])))(%[\\p{N}\\p{L}]+)?'}),], is_leaf=True, yang_name="collector-ip-address", rest_name="collector-ip-address", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'<ipv4/v6 address>; The IPv4/IPv6 address of the Sflow collector', u'cli-drop-node-name': None}}, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-sflow', defining_module='brocade-sflow', yang_type='inet:ip-address', is_config=True) except (TypeError, ValueError): raise ValueError({ 'error-string': """collector_ip_address must be of a type compatible with inet:ip-address""", 'defined-type': "inet:ip-address", 'generated-type': """YANGDynClass(base=[RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'(([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])\\.){3}([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])(%[\\p{N}\\p{L}]+)?'}),RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'((:|[0-9a-fA-F]{0,4}):)([0-9a-fA-F]{0,4}:){0,5}((([0-9a-fA-F]{0,4}:)?(:|[0-9a-fA-F]{0,4}))|(((25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])\\.){3}(25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])))(%[\\p{N}\\p{L}]+)?'}),], is_leaf=True, yang_name="collector-ip-address", rest_name="collector-ip-address", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'<ipv4/v6 address>; The IPv4/IPv6 address of the Sflow collector', u'cli-drop-node-name': None}}, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-sflow', defining_module='brocade-sflow', yang_type='inet:ip-address', is_config=True)""", }) self.__collector_ip_address = t if hasattr(self, '_set'): self._set() def _unset_collector_ip_address(self): self.__collector_ip_address = YANGDynClass(base=[RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'(([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])\\.){3}([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])(%[\\p{N}\\p{L}]+)?'}),RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'((:|[0-9a-fA-F]{0,4}):)([0-9a-fA-F]{0,4}:){0,5}((([0-9a-fA-F]{0,4}:)?(:|[0-9a-fA-F]{0,4}))|(((25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])\\.){3}(25[0-5]|2[0-4][0-9]|[01]?[0-9]?[0-9])))(%[\\p{N}\\p{L}]+)?'}),], is_leaf=True, yang_name="collector-ip-address", rest_name="collector-ip-address", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'<ipv4/v6 address>; The IPv4/IPv6 address of the Sflow collector', u'cli-drop-node-name': None}}, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-sflow', defining_module='brocade-sflow', yang_type='inet:ip-address', is_config=True) def _get_collector_port_number(self): """ Getter method for collector_port_number, mapped from YANG variable /sflow/collector/collector_port_number (uint32) """ return self.__collector_port_number def _set_collector_port_number(self, v, load=False): """ Setter method for collector_port_number, mapped from YANG variable /sflow/collector/collector_port_number (uint32) If this variable is read-only (config: false) in the source YANG file, then _set_collector_port_number is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_collector_port_number() directly. """ parent = getattr(self, "_parent", None) if parent is not None and load is False: raise AttributeError("Cannot set keys directly when" + " within an instantiated list") if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), restriction_dict={'range': [u'1..65535']}), is_leaf=True, yang_name="collector-port-number", rest_name="collector-port-number", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'<1-65535> The port number used by the Sflow collector (default = 6343)', u'cli-drop-node-name': None, u'key-default': u'6343', u'cli-optional-in-sequence': None}}, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-sflow', defining_module='brocade-sflow', yang_type='uint32', is_config=True) except (TypeError, ValueError): raise ValueError({ 'error-string': """collector_port_number must be of a type compatible with uint32""", 'defined-type': "uint32", 'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), restriction_dict={'range': [u'1..65535']}), is_leaf=True, yang_name="collector-port-number", rest_name="collector-port-number", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'<1-65535> The port number used by the Sflow collector (default = 6343)', u'cli-drop-node-name': None, u'key-default': u'6343', u'cli-optional-in-sequence': None}}, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-sflow', defining_module='brocade-sflow', yang_type='uint32', is_config=True)""", }) self.__collector_port_number = t if hasattr(self, '_set'): self._set() def _unset_collector_port_number(self): self.__collector_port_number = YANGDynClass(base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), restriction_dict={'range': [u'1..65535']}), is_leaf=True, yang_name="collector-port-number", rest_name="collector-port-number", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'<1-65535> The port number used by the Sflow collector (default = 6343)', u'cli-drop-node-name': None, u'key-default': u'6343', u'cli-optional-in-sequence': None}}, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-sflow', defining_module='brocade-sflow', yang_type='uint32', is_config=True) def _get_use_vrf(self): """ Getter method for use_vrf, mapped from YANG variable /sflow/collector/use_vrf (common-def:vrf-name) """ return self.__use_vrf def _set_use_vrf(self, v, load=False): """ Setter method for use_vrf, mapped from YANG variable /sflow/collector/use_vrf (common-def:vrf-name) If this variable is read-only (config: false) in the source YANG file, then _set_use_vrf is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_use_vrf() directly. """ parent = getattr(self, "_parent", None) if parent is not None and load is False: raise AttributeError("Cannot set keys directly when" + " within an instantiated list") if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'((([a-zA-Z0-9_]([a-zA-Z0-9\\-_]){0,61})?[a-zA-Z0-9]\\.)*([a-zA-Z0-9_]([a-zA-Z0-9\\-_]){0,61})?[a-zA-Z0-9]\\.?)|\\.', 'length': [u'1..32']}), is_leaf=True, yang_name="use-vrf", rest_name="use-vrf", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Vrf to use for sending data to the collector (default = mgmt-vrf)', u'cli-optional-in-sequence': None, u'key-default': u'mgmt-vrf', u'cli-expose-key-name': None}}, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-sflow', defining_module='brocade-sflow', yang_type='common-def:vrf-name', is_config=True) except (TypeError, ValueError): raise ValueError({ 'error-string': """use_vrf must be of a type compatible with common-def:vrf-name""", 'defined-type': "common-def:vrf-name", 'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'((([a-zA-Z0-9_]([a-zA-Z0-9\\-_]){0,61})?[a-zA-Z0-9]\\.)*([a-zA-Z0-9_]([a-zA-Z0-9\\-_]){0,61})?[a-zA-Z0-9]\\.?)|\\.', 'length': [u'1..32']}), is_leaf=True, yang_name="use-vrf", rest_name="use-vrf", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Vrf to use for sending data to the collector (default = mgmt-vrf)', u'cli-optional-in-sequence': None, u'key-default': u'mgmt-vrf', u'cli-expose-key-name': None}}, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-sflow', defining_module='brocade-sflow', yang_type='common-def:vrf-name', is_config=True)""", }) self.__use_vrf = t if hasattr(self, '_set'): self._set() def _unset_use_vrf(self): self.__use_vrf = YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_dict={'pattern': u'((([a-zA-Z0-9_]([a-zA-Z0-9\\-_]){0,61})?[a-zA-Z0-9]\\.)*([a-zA-Z0-9_]([a-zA-Z0-9\\-_]){0,61})?[a-zA-Z0-9]\\.?)|\\.', 'length': [u'1..32']}), is_leaf=True, yang_name="use-vrf", rest_name="use-vrf", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Vrf to use for sending data to the collector (default = mgmt-vrf)', u'cli-optional-in-sequence': None, u'key-default': u'mgmt-vrf', u'cli-expose-key-name': None}}, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-sflow', defining_module='brocade-sflow', yang_type='common-def:vrf-name', is_config=True) collector_ip_address = __builtin__.property(_get_collector_ip_address, _set_collector_ip_address) collector_port_number = __builtin__.property(_get_collector_port_number, _set_collector_port_number) use_vrf = __builtin__.property(_get_use_vrf, _set_use_vrf) _pyangbind_elements = {'collector_ip_address': collector_ip_address, 'collector_port_number': collector_port_number, 'use_vrf': use_vrf, }

81.389423

984

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null

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1

0

null

0

6

e02bb86f60c9827be28be736f84f040de29c2e47

74

py

Python

notebooks/solutions/indexing_01.py

eseiver/Pandas-Tutorial-SciPyConf-2018

b6df2be699ea7ca12e2e6a7c7bde12bdc3565d62

[ "CC-BY-4.0" ]

43

2018-07-10T18:52:44.000Z

2021-05-04T21:26:49.000Z

notebooks/solutions/indexing_01.py

piyushpathak03/Pandas-Tutorial-SciPyConf-2018

fc68001e0a9346d2b9f30a31d0a66d10dde35114

[ "CC-BY-4.0" ]

8

2018-06-17T21:47:27.000Z

2018-07-11T22:31:17.000Z

notebooks/solutions/indexing_01.py

piyushpathak03/Pandas-Tutorial-SciPyConf-2018

fc68001e0a9346d2b9f30a31d0a66d10dde35114

[ "CC-BY-4.0" ]

47

2018-07-06T15:07:23.000Z

2020-11-07T07:44:20.000Z

flights[(flights.dep.dt.hour <= 6) | (flights.dep.dt.hour >= 18)]

24.666667

36

0.567568

11

74

3.818182

0.545455

0.47619

0.571429

0.761905

0

0.050847

0.202703

74

2

37

0.661017

0

1

0

true

0

1

0

null

1

0

1

0

null

0

1

0

6

0edfb98f8ae5da44620bae4ad4fb6b7054a9c651

25

py

Python

05. WINDOWS Python Setup/exe 1.py

AnmolTomer/Udemy---Colt-Steele-Modern-Python-Bootcamp-Codebook

5073fd92e38d95a1b7ecf3b9effb9c9683ce5ceb

[ "MIT" ]

3

2020-06-17T10:05:37.000Z

2021-12-14T17:24:21.000Z

05. WINDOWS Python Setup/exe 1.py

AnmolTomer/Udemy---Colt-Steele-Modern-Python-Bootcamp-Codebook

5073fd92e38d95a1b7ecf3b9effb9c9683ce5ceb

[ "MIT" ]

null

05. WINDOWS Python Setup/exe 1.py

AnmolTomer/Udemy---Colt-Steele-Modern-Python-Bootcamp-Codebook

5073fd92e38d95a1b7ecf3b9effb9c9683ce5ceb

[ "MIT" ]

4

2019-02-28T17:15:46.000Z

2020-04-26T05:56:57.000Z

print("Cosmic Commander")

25

0.8

3

25

6.666667

1

0

0.04

25

1

25

0.833333

0

0.615385

0

1

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true

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null

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1

0

1

0

null

0

1

0

1

0

6

1614955d94b7fae0ed23df30bbf8c52749e926a2

139

py

Python

amqpstorm/tests/__init__.py

mikemrm/amqpstorm

2a4ec4d72a81498e0774deda338f6aaf16570881

[ "MIT" ]

null

amqpstorm/tests/__init__.py

mikemrm/amqpstorm

2a4ec4d72a81498e0774deda338f6aaf16570881

[ "MIT" ]

null

amqpstorm/tests/__init__.py

mikemrm/amqpstorm

2a4ec4d72a81498e0774deda338f6aaf16570881

[ "MIT" ]

null

HOST = '127.0.0.1' USERNAME = 'guest' PASSWORD = 'guest' URI = 'amqp://guest:guest@127.0.0.1:5672/%2F' HTTP_URL = 'http://127.0.0.1:15672'

23.166667

45

0.633094

27

139

3.222222

0.518519

0.137931

0.172414

0.206897

0

0.225806

0.107914

139

5

46

27.8

0.475806

0

0.561151

0.266187

0

1

0

false

0.2

0

1

0

null

0

1

0

1

0

1

0

1

0

null

0

1

0

6

1619933d1891b8ee8970bf6c27f0b566306db31e

69

py

Python

cspark/EventTypeRouter.py

Matvey-Kuk/spark-python

69b8d8c708fd032077dcccb01a8466705b33c4a7

[ "MIT" ]

null

cspark/EventTypeRouter.py

Matvey-Kuk/spark-python

69b8d8c708fd032077dcccb01a8466705b33c4a7

[ "MIT" ]

102

2017-01-30T05:50:10.000Z

2022-03-07T18:56:23.000Z

cspark/EventTypeRouter.py

Matvey-Kuk/cspark-python

69b8d8c708fd032077dcccb01a8466705b33c4a7

[ "MIT" ]

null

from .Router import Router class EventTypeRouter(Router): pass

11.5

30

0.753623

8

69

6.5

0.75

0

0.188406

69

5

31

13.8

0.928571

0

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true

0.333333

0

0.666667

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null

0

1

0

null

0

1

0

1

0

6

165d499eb79bd13ee46ac0f3cbfaa8f76e0dd5d0

25

py

Python

game/blenderpanda/__init__.py

Kupoman/fafnir-demo

1e285296b49f00fa99672a242c8bfc4afd696ff4

[ "MIT" ]

1

2017-05-29T23:03:13.000Z

game/blenderpanda/__init__.py

Kupoman/fafnir-demo

1e285296b49f00fa99672a242c8bfc4afd696ff4

[ "MIT" ]

null

game/blenderpanda/__init__.py

Kupoman/fafnir-demo

1e285296b49f00fa99672a242c8bfc4afd696ff4

[ "MIT" ]

null

from .bpbase import init

12.5

24

0.8

4

25

5

1

0

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25

1

25

0.952381

0

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0

null

0

1

0

1

0

1

0

6

1682ffb1dea352c7f0a2a0679fdd5833491998b9

11,227

py

Python

tests/test_api/test_pep8.py

Joshua-Enrico/AirBnB_clone_v4

c603b7907826b60584597b258f42175d7c6bdd1a

[ "MIT" ]

null

tests/test_api/test_pep8.py

Joshua-Enrico/AirBnB_clone_v4

c603b7907826b60584597b258f42175d7c6bdd1a

[ "MIT" ]

null

tests/test_api/test_pep8.py

Joshua-Enrico/AirBnB_clone_v4

c603b7907826b60584597b258f42175d7c6bdd1a

[ "MIT" ]

1

2021-10-04T19:29:47.000Z

#!/usr/bin/python3 """ Contains the TestStateDocs classes """ from datetime import datetime import inspect import models import os from models import state from models.base_model import BaseModel import pep8 import unittest from api.v1 import app from api.v1.views import states as test_state from api.v1.views import amenities from api.v1.views import cities from api.v1.views import index from api.v1.views import places_reviews from api.v1.views import places_amenities from api.v1.views import places from api.v1.views import users State = state.State class TestStateDocs(unittest.TestCase): """Tests to check the documentation for all api files""" @classmethod def setUpClass(cls): """Set up for the doc tests""" cls.state_f = inspect.getmembers(State, inspect.isfunction) def test_pep8_conformance_app(self): """Test that models/state.py conforms to PEP8.""" pep8s = pep8.StyleGuide(quiet=True) result = pep8s.check_files(['api/v1/app.py']) self.assertEqual(result.total_errors, 0, "Found code style errors (and warnings).") def test_pep8_conformance_states(self): """Test that models/state.py conforms to PEP8.""" pep8s = pep8.StyleGuide(quiet=True) result = pep8s.check_files(['api/v1/views/states.py']) self.assertEqual(result.total_errors, 0, "Found code style errors (and warnings).") def test_pep8_conformance_amenities(self): """Test that models/state.py conforms to PEP8.""" pep8s = pep8.StyleGuide(quiet=True) result = pep8s.check_files(['api/v1/views/amenities.py']) self.assertEqual(result.total_errors, 0, "Found code style errors (and warnings).") def test_pep8_conformance_cities(self): """Test that models/state.py conforms to PEP8.""" pep8s = pep8.StyleGuide(quiet=True) result = pep8s.check_files(['api/v1/views/cities.py']) self.assertEqual(result.total_errors, 0, "Found code style errors (and warnings).") def test_pep8_conformance_places_rev(self): """Test that models/state.py conforms to PEP8.""" pep8s = pep8.StyleGuide(quiet=True) result = pep8s.check_files(['api/v1/views/places_reviews.py']) self.assertEqual(result.total_errors, 0, "Found code style errors (and warnings).") def test_pep8_conformance_places(self): """Test that models/state.py conforms to PEP8.""" pep8s = pep8.StyleGuide(quiet=True) result = pep8s.check_files(['api/v1/views/places.py']) self.assertEqual(result.total_errors, 0, "Found code style errors (and warnings).") def test_pep8_conformance_users(self): """Test that models/state.py conforms to PEP8.""" pep8s = pep8.StyleGuide(quiet=True) result = pep8s.check_files(['api/v1/views/users.py']) self.assertEqual(result.total_errors, 0, "Found code style errors (and warnings).") def test_pep8_conformance_index(self): """Test that models/state.py conforms to PEP8.""" pep8s = pep8.StyleGuide(quiet=True) result = pep8s.check_files(['api/v1/views/index.py']) self.assertEqual(result.total_errors, 0, "Found code style errors (and warnings).") def test_pep8_conformance_test_pep8(self): """Test that tests/test_models/test_state.py conforms to PEP8.""" pep8s = pep8.StyleGuide(quiet=True) result = pep8s.check_files(['tests/test_api/test_pep8.py']) self.assertEqual(result.total_errors, 0, "Found code style errors (and warnings).") def test_pep8_conformance_places_amenities(self): """Test that tests/test_models/test_state.py conforms to PEP8.""" pep8s = pep8.StyleGuide(quiet=True) result = pep8s.check_files(['api/v1/views/places_amenities.py']) self.assertEqual(result.total_errors, 0, "Found code style errors (and warnings).") # file docstring def test_state_module_docstring_app(self): """Test for the state.py module docstring""" self.assertIsNot(app.__doc__, None, "state.py needs a docstring") self.assertTrue(len(app.__doc__) >= 1, "state.py needs a docstring") def test_state_class_docstring_state(self): """Test for the State class docstring""" self.assertIsNot(test_state.__doc__, None, "State class needs a docstring") self.assertTrue(len(test_state.__doc__) >= 1, "State class needs a docstring") def test_state_class_docstring_amenities(self): """Test for the State class docstring""" self.assertIsNot(amenities.__doc__, None, "State class needs a docstring") self.assertTrue(len(amenities.__doc__) >= 1, "State class needs a docstring") def test_state_class_docstring_cities(self): """Test for the State class docstring""" self.assertIsNot(cities.__doc__, None, "State class needs a docstring") self.assertTrue(len(cities.__doc__) >= 1, "State class needs a docstring") def test_state_class_docstring_index(self): """Test for the State class docstring""" self.assertIsNot(index.__doc__, None, "State class needs a docstring") self.assertTrue(len(index.__doc__) >= 1, "State class needs a docstring") def test_state_class_docstring_rev(self): """Test for the State class docstring""" self.assertIsNot(places_reviews.__doc__, None, "State class needs a docstring") self.assertTrue(len(places_reviews.__doc__) >= 1, "State class needs a docstring") def test_state_class_docstring_places(self): """Test for the State class docstring""" self.assertIsNot(places.__doc__, None, "State class needs a docstring") self.assertTrue(len(places.__doc__) >= 1, "State class needs a docstring") def test_state_class_docstring_users(self): """Test for the State class docstring""" self.assertIsNot(users.__doc__, None, "State class needs a docstring") self.assertTrue(len(users.__doc__) >= 1, "State class needs a docstring") def test_state_class_docstring_amenities_rev(self): """Test for the State class docstring""" self.assertIsNot(places_amenities.__doc__, None, "State class needs a docstring") self.assertTrue(len(users.__doc__) >= 1, "State class needs a docstring") # dosctring tests def test_state_func_docstrings(self): """Test for the presence of docstrings in State methods""" for func in self.state_f: self.assertIsNot(func[1].__doc__, None, "{:s} method needs a docstring".format(func[0])) self.assertTrue(len(func[1].__doc__) >= 1, "{:s} method needs a docstring".format(func[0])) def test_state_func_docstrings_index(self): """Test for the presence of docstrings in State methods""" index_f = inspect.getmembers(index, inspect.isfunction) for func in index_f: self.assertIsNot(func[1].__doc__, None, "{:s} method needs a docstring".format(func[0])) self.assertTrue(len(func[1].__doc__) >= 1, "{:s} method needs a docstring".format(func[0])) def test_state_func_docstrings_user(self): """Test for the presence of docstrings in State methods""" index_f = inspect.getmembers(users, inspect.isfunction) for func in index_f: self.assertIsNot(func[1].__doc__, None, "{:s} method needs a docstring".format(func[0])) self.assertTrue(len(func[1].__doc__) >= 1, "{:s} method needs a docstring".format(func[0])) def test_state_func_docstrings_places(self): """Test for the presence of docstrings in State methods""" index_f = inspect.getmembers(places, inspect.isfunction) for func in index_f: self.assertIsNot(func[1].__doc__, None, "{:s} method needs a docstring".format(func[0])) self.assertTrue(len(func[1].__doc__) >= 1, "{:s} method needs a docstring".format(func[0])) def test_state_func_docstrings_states(self): """Test for the presence of docstrings in State methods""" index_f = inspect.getmembers(test_state, inspect.isfunction) for func in index_f: self.assertIsNot(func[1].__doc__, None, "{:s} method needs a docstring".format(func[0])) self.assertTrue(len(func[1].__doc__) >= 1, "{:s} method needs a docstring".format(func[0])) def test_state_func_docstrings_place_rev(self): """Test for the presence of docstrings in State methods""" index_f = inspect.getmembers(places_reviews, inspect.isfunction) for func in index_f: self.assertIsNot(func[1].__doc__, None, "{:s} method needs a docstring".format(func[0])) self.assertTrue(len(func[1].__doc__) >= 1, "{:s} method needs a docstring".format(func[0])) def test_state_func_docstrings_amenity_rev(self): """Test for the presence of docstrings in State methods""" index_f = inspect.getmembers(places_amenities, inspect.isfunction) for func in index_f: self.assertIsNot(func[1].__doc__, None, "{:s} method needs a docstring".format(func[0])) self.assertTrue(len(func[1].__doc__) >= 1, "{:s} method needs a docstring".format(func[0])) def test_state_func_docstrings_cities(self): """Test for the presence of docstrings in State methods""" index_f = inspect.getmembers(cities, inspect.isfunction) for func in index_f: self.assertIsNot(func[1].__doc__, None, "{:s} method needs a docstring".format(func[0])) self.assertTrue(len(func[1].__doc__) >= 1, "{:s} method needs a docstring".format(func[0])) def test_state_func_docstrings_amenities(self): """Test for the presence of docstrings in State methods""" index_f = inspect.getmembers(amenities, inspect.isfunction) for func in index_f: self.assertIsNot(func[1].__doc__, None, "{:s} method needs a docstring".format(func[0])) self.assertTrue(len(func[1].__doc__) >= 1, "{:s} method needs a docstring".format(func[0]))

45.088353

77

0.610849

1,362

11,227

4.80837

0.069016

0.032982

0.082455

0.038479

0.87647

0.848679

0.82608

0.81982

0.783173

0

0.016867

0.281821

11,227

248

78

45.270161

0.795362

0.127104

0

0.52514

0

0.17251

0.023029

0

0.256983

1

0.162011

false

0

0.094972

0

0.26257

0

null

0

1

0

null

0

6

16878f631cd695277870d207fede00a5db727143

27

py

Python

tests/sample_CASE.py

thomastan/pyexe

944a5e09ed2db4b9b7633bb1de77ad3eb777d958

[ "Apache-2.0" ]

47

2018-04-13T02:41:48.000Z

2022-02-07T15:55:33.000Z

tests/sample_CASE.py

thomastan/pyexe

944a5e09ed2db4b9b7633bb1de77ad3eb777d958

[ "Apache-2.0" ]

17

2018-04-09T03:12:43.000Z

2021-09-07T06:46:59.000Z

tests/sample_CASE.py

thomastan/pyexe

944a5e09ed2db4b9b7633bb1de77ad3eb777d958

[ "Apache-2.0" ]

11

2018-05-31T05:49:52.000Z

2021-12-17T06:20:12.000Z

print('mixed CASE module')

13.5

26

0.740741

4

27

5

1

0

0.111111

27

1

27

0.833333

0

0.62963

0

1

0

true

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

16921897190e63fed79fd3e90bd19da13692370c

145

py

Python

wagtail/wagtailcore/signals.py

balkantechnologies/BalkanCMS_core

68625199028fc96abb175e410a4a7a92c02cb261

[ "BSD-3-Clause" ]

1

2021-09-21T00:06:52.000Z

wagtail/wagtailcore/signals.py

balkantechnologies/BalkanCMS_core

68625199028fc96abb175e410a4a7a92c02cb261

[ "BSD-3-Clause" ]

1

2021-02-24T08:25:30.000Z

wagtail/wagtailcore/signals.py

balkantechnologies/BalkanCMS_core

68625199028fc96abb175e410a4a7a92c02cb261

[ "BSD-3-Clause" ]

1

2020-11-24T10:21:24.000Z

from django.dispatch import Signal page_published = Signal(providing_args=['instance']) page_unpublished = Signal(providing_args=['instance'])

24.166667

54

0.8

17

145

6.588235

0.647059

0.267857

0.339286

0.482143

0

0.082759

145

5

55

29

0.842105

0

0.110345

0

1

0

false

0

0.333333

0

0.333333

0

1

0

null

1

0

1

0

null

0

1

0

6

169be9adaaf883f774b866e1d842ce3657d29c32

29,902

py

Python

pypower/case118.py

Bengt/PYPOWER

78a0f8d4765d147f8237e9a905ef871508ecfee7

[ "BSD-3-Clause" ]

221

2015-01-03T23:18:11.000Z

2022-03-27T10:21:40.000Z

pypower/case118.py

Bengt/PYPOWER

78a0f8d4765d147f8237e9a905ef871508ecfee7

[ "BSD-3-Clause" ]

33

2015-05-12T08:48:02.000Z

2021-11-23T10:35:21.000Z

pypower/case118.py

Bengt/PYPOWER

78a0f8d4765d147f8237e9a905ef871508ecfee7

[ "BSD-3-Clause" ]

114

2015-02-02T15:07:38.000Z

2022-03-22T17:01:55.000Z

# Copyright (c) 1996-2015 PSERC. All rights reserved. # Use of this source code is governed by a BSD-style # license that can be found in the LICENSE file. """Power flow data for IEEE 118 bus test case. """ from numpy import array def case118(): """Power flow data for IEEE 118 bus test case. Please see L{caseformat} for details on the case file format. This data was converted from IEEE Common Data Format (ieee118cdf.txt) on 20-Sep-2004 by cdf2matp, rev. 1.11 See end of file for warnings generated during conversion. Converted from IEEE CDF file from: U{http://www.ee.washington.edu/research/pstca/} With baseKV data take from the PSAP format file from the same site, added manually on 10-Mar-2006. 08/25/93 UW ARCHIVE 100.0 1961 W IEEE 118 Bus Test Case @return: Power flow data for IEEE 118 bus test case. """ ppc = {"version": '2'} ##----- Power Flow Data -----## ## system MVA base ppc["baseMVA"] = 100.0 ## bus data # bus_i type Pd Qd Gs Bs area Vm Va baseKV zone Vmax Vmin ppc["bus"] = array([ [1, 2, 51, 27, 0, 0, 1, 0.955, 10.67, 138, 1, 1.06, 0.94], [2, 1, 20, 9, 0, 0, 1, 0.971, 11.22, 138, 1, 1.06, 0.94], [3, 1, 39, 10, 0, 0, 1, 0.968, 11.56, 138, 1, 1.06, 0.94], [4, 2, 39, 12, 0, 0, 1, 0.998, 15.28, 138, 1, 1.06, 0.94], [5, 1, 0, 0, 0, -40, 1, 1.002, 15.73, 138, 1, 1.06, 0.94], [6, 2, 52, 22, 0, 0, 1, 0.99, 13, 138, 1, 1.06, 0.94], [7, 1, 19, 2, 0, 0, 1, 0.989, 12.56, 138, 1, 1.06, 0.94], [8, 2, 28, 0, 0, 0, 1, 1.015, 20.77, 345, 1, 1.06, 0.94], [9, 1, 0, 0, 0, 0, 1, 1.043, 28.02, 345, 1, 1.06, 0.94], [10, 2, 0, 0, 0, 0, 1, 1.05, 35.61, 345, 1, 1.06, 0.94], [11, 1, 70, 23, 0, 0, 1, 0.985, 12.72, 138, 1, 1.06, 0.94], [12, 2, 47, 10, 0, 0, 1, 0.99, 12.2, 138, 1, 1.06, 0.94], [13, 1, 34, 16, 0, 0, 1, 0.968, 11.35, 138, 1, 1.06, 0.94], [14, 1, 14, 1, 0, 0, 1, 0.984, 11.5, 138, 1, 1.06, 0.94], [15, 2, 90, 30, 0, 0, 1, 0.97, 11.23, 138, 1, 1.06, 0.94], [16, 1, 25, 10, 0, 0, 1, 0.984, 11.91, 138, 1, 1.06, 0.94], [17, 1, 11, 3, 0, 0, 1, 0.995, 13.74, 138, 1, 1.06, 0.94], [18, 2, 60, 34, 0, 0, 1, 0.973, 11.53, 138, 1, 1.06, 0.94], [19, 2, 45, 25, 0, 0, 1, 0.963, 11.05, 138, 1, 1.06, 0.94], [20, 1, 18, 3, 0, 0, 1, 0.958, 11.93, 138, 1, 1.06, 0.94], [21, 1, 14, 8, 0, 0, 1, 0.959, 13.52, 138, 1, 1.06, 0.94], [22, 1, 10, 5, 0, 0, 1, 0.97, 16.08, 138, 1, 1.06, 0.94], [23, 1, 7, 3, 0, 0, 1, 1, 21, 138, 1, 1.06, 0.94], [24, 2, 13, 0, 0, 0, 1, 0.992, 20.89, 138, 1, 1.06, 0.94], [25, 2, 0, 0, 0, 0, 1, 1.05, 27.93, 138, 1, 1.06, 0.94], [26, 2, 0, 0, 0, 0, 1, 1.015, 29.71, 345, 1, 1.06, 0.94], [27, 2, 71, 13, 0, 0, 1, 0.968, 15.35, 138, 1, 1.06, 0.94], [28, 1, 17, 7, 0, 0, 1, 0.962, 13.62, 138, 1, 1.06, 0.94], [29, 1, 24, 4, 0, 0, 1, 0.963, 12.63, 138, 1, 1.06, 0.94], [30, 1, 0, 0, 0, 0, 1, 0.968, 18.79, 345, 1, 1.06, 0.94], [31, 2, 43, 27, 0, 0, 1, 0.967, 12.75, 138, 1, 1.06, 0.94], [32, 2, 59, 23, 0, 0, 1, 0.964, 14.8, 138, 1, 1.06, 0.94], [33, 1, 23, 9, 0, 0, 1, 0.972, 10.63, 138, 1, 1.06, 0.94], [34, 2, 59, 26, 0, 14, 1, 0.986, 11.3, 138, 1, 1.06, 0.94], [35, 1, 33, 9, 0, 0, 1, 0.981, 10.87, 138, 1, 1.06, 0.94], [36, 2, 31, 17, 0, 0, 1, 0.98, 10.87, 138, 1, 1.06, 0.94], [37, 1, 0, 0, 0, -25, 1, 0.992, 11.77, 138, 1, 1.06, 0.94], [38, 1, 0, 0, 0, 0, 1, 0.962, 16.91, 345, 1, 1.06, 0.94], [39, 1, 27, 11, 0, 0, 1, 0.97, 8.41, 138, 1, 1.06, 0.94], [40, 2, 66, 23, 0, 0, 1, 0.97, 7.35, 138, 1, 1.06, 0.94], [41, 1, 37, 10, 0, 0, 1, 0.967, 6.92, 138, 1, 1.06, 0.94], [42, 2, 96, 23, 0, 0, 1, 0.985, 8.53, 138, 1, 1.06, 0.94], [43, 1, 18, 7, 0, 0, 1, 0.978, 11.28, 138, 1, 1.06, 0.94], [44, 1, 16, 8, 0, 10, 1, 0.985, 13.82, 138, 1, 1.06, 0.94], [45, 1, 53, 22, 0, 10, 1, 0.987, 15.67, 138, 1, 1.06, 0.94], [46, 2, 28, 10, 0, 10, 1, 1.005, 18.49, 138, 1, 1.06, 0.94], [47, 1, 34, 0, 0, 0, 1, 1.017, 20.73, 138, 1, 1.06, 0.94], [48, 1, 20, 11, 0, 15, 1, 1.021, 19.93, 138, 1, 1.06, 0.94], [49, 2, 87, 30, 0, 0, 1, 1.025, 20.94, 138, 1, 1.06, 0.94], [50, 1, 17, 4, 0, 0, 1, 1.001, 18.9, 138, 1, 1.06, 0.94], [51, 1, 17, 8, 0, 0, 1, 0.967, 16.28, 138, 1, 1.06, 0.94], [52, 1, 18, 5, 0, 0, 1, 0.957, 15.32, 138, 1, 1.06, 0.94], [53, 1, 23, 11, 0, 0, 1, 0.946, 14.35, 138, 1, 1.06, 0.94], [54, 2, 113, 32, 0, 0, 1, 0.955, 15.26, 138, 1, 1.06, 0.94], [55, 2, 63, 22, 0, 0, 1, 0.952, 14.97, 138, 1, 1.06, 0.94], [56, 2, 84, 18, 0, 0, 1, 0.954, 15.16, 138, 1, 1.06, 0.94], [57, 1, 12, 3, 0, 0, 1, 0.971, 16.36, 138, 1, 1.06, 0.94], [58, 1, 12, 3, 0, 0, 1, 0.959, 15.51, 138, 1, 1.06, 0.94], [59, 2, 277, 113, 0, 0, 1, 0.985, 19.37, 138, 1, 1.06, 0.94], [60, 1, 78, 3, 0, 0, 1, 0.993, 23.15, 138, 1, 1.06, 0.94], [61, 2, 0, 0, 0, 0, 1, 0.995, 24.04, 138, 1, 1.06, 0.94], [62, 2, 77, 14, 0, 0, 1, 0.998, 23.43, 138, 1, 1.06, 0.94], [63, 1, 0, 0, 0, 0, 1, 0.969, 22.75, 345, 1, 1.06, 0.94], [64, 1, 0, 0, 0, 0, 1, 0.984, 24.52, 345, 1, 1.06, 0.94], [65, 2, 0, 0, 0, 0, 1, 1.005, 27.65, 345, 1, 1.06, 0.94], [66, 2, 39, 18, 0, 0, 1, 1.05, 27.48, 138, 1, 1.06, 0.94], [67, 1, 28, 7, 0, 0, 1, 1.02, 24.84, 138, 1, 1.06, 0.94], [68, 1, 0, 0, 0, 0, 1, 1.003, 27.55, 345, 1, 1.06, 0.94], [69, 3, 0, 0, 0, 0, 1, 1.035, 30, 138, 1, 1.06, 0.94], [70, 2, 66, 20, 0, 0, 1, 0.984, 22.58, 138, 1, 1.06, 0.94], [71, 1, 0, 0, 0, 0, 1, 0.987, 22.15, 138, 1, 1.06, 0.94], [72, 2, 12, 0, 0, 0, 1, 0.98, 20.98, 138, 1, 1.06, 0.94], [73, 2, 6, 0, 0, 0, 1, 0.991, 21.94, 138, 1, 1.06, 0.94], [74, 2, 68, 27, 0, 12, 1, 0.958, 21.64, 138, 1, 1.06, 0.94], [75, 1, 47, 11, 0, 0, 1, 0.967, 22.91, 138, 1, 1.06, 0.94], [76, 2, 68, 36, 0, 0, 1, 0.943, 21.77, 138, 1, 1.06, 0.94], [77, 2, 61, 28, 0, 0, 1, 1.006, 26.72, 138, 1, 1.06, 0.94], [78, 1, 71, 26, 0, 0, 1, 1.003, 26.42, 138, 1, 1.06, 0.94], [79, 1, 39, 32, 0, 20, 1, 1.009, 26.72, 138, 1, 1.06, 0.94], [80, 2, 130, 26, 0, 0, 1, 1.04, 28.96, 138, 1, 1.06, 0.94], [81, 1, 0, 0, 0, 0, 1, 0.997, 28.1, 345, 1, 1.06, 0.94], [82, 1, 54, 27, 0, 20, 1, 0.989, 27.24, 138, 1, 1.06, 0.94], [83, 1, 20, 10, 0, 10, 1, 0.985, 28.42, 138, 1, 1.06, 0.94], [84, 1, 11, 7, 0, 0, 1, 0.98, 30.95, 138, 1, 1.06, 0.94], [85, 2, 24, 15, 0, 0, 1, 0.985, 32.51, 138, 1, 1.06, 0.94], [86, 1, 21, 10, 0, 0, 1, 0.987, 31.14, 138, 1, 1.06, 0.94], [87, 2, 0, 0, 0, 0, 1, 1.015, 31.4, 161, 1, 1.06, 0.94], [88, 1, 48, 10, 0, 0, 1, 0.987, 35.64, 138, 1, 1.06, 0.94], [89, 2, 0, 0, 0, 0, 1, 1.005, 39.69, 138, 1, 1.06, 0.94], [90, 2, 163, 42, 0, 0, 1, 0.985, 33.29, 138, 1, 1.06, 0.94], [91, 2, 10, 0, 0, 0, 1, 0.98, 33.31, 138, 1, 1.06, 0.94], [92, 2, 65, 10, 0, 0, 1, 0.993, 33.8, 138, 1, 1.06, 0.94], [93, 1, 12, 7, 0, 0, 1, 0.987, 30.79, 138, 1, 1.06, 0.94], [94, 1, 30, 16, 0, 0, 1, 0.991, 28.64, 138, 1, 1.06, 0.94], [95, 1, 42, 31, 0, 0, 1, 0.981, 27.67, 138, 1, 1.06, 0.94], [96, 1, 38, 15, 0, 0, 1, 0.993, 27.51, 138, 1, 1.06, 0.94], [97, 1, 15, 9, 0, 0, 1, 1.011, 27.88, 138, 1, 1.06, 0.94], [98, 1, 34, 8, 0, 0, 1, 1.024, 27.4, 138, 1, 1.06, 0.94], [99, 2, 42, 0, 0, 0, 1, 1.01, 27.04, 138, 1, 1.06, 0.94], [100, 2, 37, 18, 0, 0, 1, 1.017, 28.03, 138, 1, 1.06, 0.94], [101, 1, 22, 15, 0, 0, 1, 0.993, 29.61, 138, 1, 1.06, 0.94], [102, 1, 5, 3, 0, 0, 1, 0.991, 32.3, 138, 1, 1.06, 0.94], [103, 2, 23, 16, 0, 0, 1, 1.001, 24.44, 138, 1, 1.06, 0.94], [104, 2, 38, 25, 0, 0, 1, 0.971, 21.69, 138, 1, 1.06, 0.94], [105, 2, 31, 26, 0, 20, 1, 0.965, 20.57, 138, 1, 1.06, 0.94], [106, 1, 43, 16, 0, 0, 1, 0.962, 20.32, 138, 1, 1.06, 0.94], [107, 2, 50, 12, 0, 6, 1, 0.952, 17.53, 138, 1, 1.06, 0.94], [108, 1, 2, 1, 0, 0, 1, 0.967, 19.38, 138, 1, 1.06, 0.94], [109, 1, 8, 3, 0, 0, 1, 0.967, 18.93, 138, 1, 1.06, 0.94], [110, 2, 39, 30, 0, 6, 1, 0.973, 18.09, 138, 1, 1.06, 0.94], [111, 2, 0, 0, 0, 0, 1, 0.98, 19.74, 138, 1, 1.06, 0.94], [112, 2, 68, 13, 0, 0, 1, 0.975, 14.99, 138, 1, 1.06, 0.94], [113, 2, 6, 0, 0, 0, 1, 0.993, 13.74, 138, 1, 1.06, 0.94], [114, 1, 8, 3, 0, 0, 1, 0.96, 14.46, 138, 1, 1.06, 0.94], [115, 1, 22, 7, 0, 0, 1, 0.96, 14.46, 138, 1, 1.06, 0.94], [116, 2, 184, 0, 0, 0, 1, 1.005, 27.12, 138, 1, 1.06, 0.94], [117, 1, 20, 8, 0, 0, 1, 0.974, 10.67, 138, 1, 1.06, 0.94], [118, 1, 33, 15, 0, 0, 1, 0.949, 21.92, 138, 1, 1.06, 0.94] ]) ## generator data # bus, Pg, Qg, Qmax, Qmin, Vg, mBase, status, Pmax, Pmin, Pc1, Pc2, # Qc1min, Qc1max, Qc2min, Qc2max, ramp_agc, ramp_10, ramp_30, ramp_q, apf ppc["gen"] = array([ [1, 0, 0, 15, -5, 0.955, 100, 1, 100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [4, 0, 0, 300, -300, 0.998, 100, 1, 100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [6, 0, 0, 50, -13, 0.99, 100, 1, 100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [8, 0, 0, 300, -300, 1.015, 100, 1, 100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [10, 450, 0, 200, -147, 1.05, 100, 1, 550, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [12, 85, 0, 120, -35, 0.99, 100, 1, 185, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [15, 0, 0, 30, -10, 0.97, 100, 1, 100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [18, 0, 0, 50, -16, 0.973, 100, 1, 100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [19, 0, 0, 24, -8, 0.962, 100, 1, 100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [24, 0, 0, 300, -300, 0.992, 100, 1, 100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [25, 220, 0, 140, -47, 1.05, 100, 1, 320, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [26, 314, 0, 1000, -1000, 1.015, 100, 1, 414, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [27, 0, 0, 300, -300, 0.968, 100, 1, 100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [31, 7, 0, 300, -300, 0.967, 100, 1, 107, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [32, 0, 0, 42, -14, 0.963, 100, 1, 100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [34, 0, 0, 24, -8, 0.984, 100, 1, 100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [36, 0, 0, 24, -8, 0.98, 100, 1, 100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [40, 0, 0, 300, -300, 0.97, 100, 1, 100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [42, 0, 0, 300, -300, 0.985, 100, 1, 100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [46, 19, 0, 100, -100, 1.005, 100, 1, 119, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [49, 204, 0, 210, -85, 1.025, 100, 1, 304, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [54, 48, 0, 300, -300, 0.955, 100, 1, 148, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [55, 0, 0, 23, -8, 0.952, 100, 1, 100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [56, 0, 0, 15, -8, 0.954, 100, 1, 100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [59, 155, 0, 180, -60, 0.985, 100, 1, 255, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [61, 160, 0, 300, -100, 0.995, 100, 1, 260, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [62, 0, 0, 20, -20, 0.998, 100, 1, 100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [65, 391, 0, 200, -67, 1.005, 100, 1, 491, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [66, 392, 0, 200, -67, 1.05, 100, 1, 492, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [69, 516.4, 0, 300, -300, 1.035, 100, 1, 805.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [70, 0, 0, 32, -10, 0.984, 100, 1, 100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [72, 0, 0, 100, -100, 0.98, 100, 1, 100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [73, 0, 0, 100, -100, 0.991, 100, 1, 100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [74, 0, 0, 9, -6, 0.958, 100, 1, 100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [76, 0, 0, 23, -8, 0.943, 100, 1, 100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [77, 0, 0, 70, -20, 1.006, 100, 1, 100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [80, 477, 0, 280, -165, 1.04, 100, 1, 577, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [85, 0, 0, 23, -8, 0.985, 100, 1, 100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [87, 4, 0, 1000, -100, 1.015, 100, 1, 104, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [89, 607, 0, 300, -210, 1.005, 100, 1, 707, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [90, 0, 0, 300, -300, 0.985, 100, 1, 100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [91, 0, 0, 100, -100, 0.98, 100, 1, 100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [92, 0, 0, 9, -3, 0.99, 100, 1, 100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [99, 0, 0, 100, -100, 1.01, 100, 1, 100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [100, 252, 0, 155, -50, 1.017, 100, 1, 352, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [103, 40, 0, 40, -15, 1.01, 100, 1, 140, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [104, 0, 0, 23, -8, 0.971, 100, 1, 100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [105, 0, 0, 23, -8, 0.965, 100, 1, 100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [107, 0, 0, 200, -200, 0.952, 100, 1, 100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [110, 0, 0, 23, -8, 0.973, 100, 1, 100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [111, 36, 0, 1000, -100, 0.98, 100, 1, 136, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [112, 0, 0, 1000, -100, 0.975, 100, 1, 100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [113, 0, 0, 200, -100, 0.993, 100, 1, 100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [116, 0, 0, 1000, -1000, 1.005, 100, 1, 100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] ]) ## branch data # fbus, tbus, r, x, b, rateA, rateB, rateC, ratio, angle, status, angmin, angmax ppc["branch"] = array([ [1, 2, 0.0303, 0.0999, 0.0254, 9900, 0, 0, 0, 0, 1, -360, 360], [1, 3, 0.0129, 0.0424, 0.01082, 9900, 0, 0, 0, 0, 1, -360, 360], [4, 5, 0.00176, 0.00798, 0.0021, 9900, 0, 0, 0, 0, 1, -360, 360], [3, 5, 0.0241, 0.108, 0.0284, 9900, 0, 0, 0, 0, 1, -360, 360], [5, 6, 0.0119, 0.054, 0.01426, 9900, 0, 0, 0, 0, 1, -360, 360], [6, 7, 0.00459, 0.0208, 0.0055, 9900, 0, 0, 0, 0, 1, -360, 360], [8, 9, 0.00244, 0.0305, 1.162, 9900, 0, 0, 0, 0, 1, -360, 360], [8, 5, 0, 0.0267, 0, 9900, 0, 0, 0.985, 0, 1, -360, 360], [9, 10, 0.00258, 0.0322, 1.23, 9900, 0, 0, 0, 0, 1, -360, 360], [4, 11, 0.0209, 0.0688, 0.01748, 9900, 0, 0, 0, 0, 1, -360, 360], [5, 11, 0.0203, 0.0682, 0.01738, 9900, 0, 0, 0, 0, 1, -360, 360], [11, 12, 0.00595, 0.0196, 0.00502, 9900, 0, 0, 0, 0, 1, -360, 360], [2, 12, 0.0187, 0.0616, 0.01572, 9900, 0, 0, 0, 0, 1, -360, 360], [3, 12, 0.0484, 0.16, 0.0406, 9900, 0, 0, 0, 0, 1, -360, 360], [7, 12, 0.00862, 0.034, 0.00874, 9900, 0, 0, 0, 0, 1, -360, 360], [11, 13, 0.02225, 0.0731, 0.01876, 9900, 0, 0, 0, 0, 1, -360, 360], [12, 14, 0.0215, 0.0707, 0.01816, 9900, 0, 0, 0, 0, 1, -360, 360], [13, 15, 0.0744, 0.2444, 0.06268, 9900, 0, 0, 0, 0, 1, -360, 360], [14, 15, 0.0595, 0.195, 0.0502, 9900, 0, 0, 0, 0, 1, -360, 360], [12, 16, 0.0212, 0.0834, 0.0214, 9900, 0, 0, 0, 0, 1, -360, 360], [15, 17, 0.0132, 0.0437, 0.0444, 9900, 0, 0, 0, 0, 1, -360, 360], [16, 17, 0.0454, 0.1801, 0.0466, 9900, 0, 0, 0, 0, 1, -360, 360], [17, 18, 0.0123, 0.0505, 0.01298, 9900, 0, 0, 0, 0, 1, -360, 360], [18, 19, 0.01119, 0.0493, 0.01142, 9900, 0, 0, 0, 0, 1, -360, 360], [19, 20, 0.0252, 0.117, 0.0298, 9900, 0, 0, 0, 0, 1, -360, 360], [15, 19, 0.012, 0.0394, 0.0101, 9900, 0, 0, 0, 0, 1, -360, 360], [20, 21, 0.0183, 0.0849, 0.0216, 9900, 0, 0, 0, 0, 1, -360, 360], [21, 22, 0.0209, 0.097, 0.0246, 9900, 0, 0, 0, 0, 1, -360, 360], [22, 23, 0.0342, 0.159, 0.0404, 9900, 0, 0, 0, 0, 1, -360, 360], [23, 24, 0.0135, 0.0492, 0.0498, 9900, 0, 0, 0, 0, 1, -360, 360], [23, 25, 0.0156, 0.08, 0.0864, 9900, 0, 0, 0, 0, 1, -360, 360], [26, 25, 0, 0.0382, 0, 9900, 0, 0, 0.96, 0, 1, -360, 360], [25, 27, 0.0318, 0.163, 0.1764, 9900, 0, 0, 0, 0, 1, -360, 360], [27, 28, 0.01913, 0.0855, 0.0216, 9900, 0, 0, 0, 0, 1, -360, 360], [28, 29, 0.0237, 0.0943, 0.0238, 9900, 0, 0, 0, 0, 1, -360, 360], [30, 17, 0, 0.0388, 0, 9900, 0, 0, 0.96, 0, 1, -360, 360], [8, 30, 0.00431, 0.0504, 0.514, 9900, 0, 0, 0, 0, 1, -360, 360], [26, 30, 0.00799, 0.086, 0.908, 9900, 0, 0, 0, 0, 1, -360, 360], [17, 31, 0.0474, 0.1563, 0.0399, 9900, 0, 0, 0, 0, 1, -360, 360], [29, 31, 0.0108, 0.0331, 0.0083, 9900, 0, 0, 0, 0, 1, -360, 360], [23, 32, 0.0317, 0.1153, 0.1173, 9900, 0, 0, 0, 0, 1, -360, 360], [31, 32, 0.0298, 0.0985, 0.0251, 9900, 0, 0, 0, 0, 1, -360, 360], [27, 32, 0.0229, 0.0755, 0.01926, 9900, 0, 0, 0, 0, 1, -360, 360], [15, 33, 0.038, 0.1244, 0.03194, 9900, 0, 0, 0, 0, 1, -360, 360], [19, 34, 0.0752, 0.247, 0.0632, 9900, 0, 0, 0, 0, 1, -360, 360], [35, 36, 0.00224, 0.0102, 0.00268, 9900, 0, 0, 0, 0, 1, -360, 360], [35, 37, 0.011, 0.0497, 0.01318, 9900, 0, 0, 0, 0, 1, -360, 360], [33, 37, 0.0415, 0.142, 0.0366, 9900, 0, 0, 0, 0, 1, -360, 360], [34, 36, 0.00871, 0.0268, 0.00568, 9900, 0, 0, 0, 0, 1, -360, 360], [34, 37, 0.00256, 0.0094, 0.00984, 9900, 0, 0, 0, 0, 1, -360, 360], [38, 37, 0, 0.0375, 0, 9900, 0, 0, 0.935, 0, 1, -360, 360], [37, 39, 0.0321, 0.106, 0.027, 9900, 0, 0, 0, 0, 1, -360, 360], [37, 40, 0.0593, 0.168, 0.042, 9900, 0, 0, 0, 0, 1, -360, 360], [30, 38, 0.00464, 0.054, 0.422, 9900, 0, 0, 0, 0, 1, -360, 360], [39, 40, 0.0184, 0.0605, 0.01552, 9900, 0, 0, 0, 0, 1, -360, 360], [40, 41, 0.0145, 0.0487, 0.01222, 9900, 0, 0, 0, 0, 1, -360, 360], [40, 42, 0.0555, 0.183, 0.0466, 9900, 0, 0, 0, 0, 1, -360, 360], [41, 42, 0.041, 0.135, 0.0344, 9900, 0, 0, 0, 0, 1, -360, 360], [43, 44, 0.0608, 0.2454, 0.06068, 9900, 0, 0, 0, 0, 1, -360, 360], [34, 43, 0.0413, 0.1681, 0.04226, 9900, 0, 0, 0, 0, 1, -360, 360], [44, 45, 0.0224, 0.0901, 0.0224, 9900, 0, 0, 0, 0, 1, -360, 360], [45, 46, 0.04, 0.1356, 0.0332, 9900, 0, 0, 0, 0, 1, -360, 360], [46, 47, 0.038, 0.127, 0.0316, 9900, 0, 0, 0, 0, 1, -360, 360], [46, 48, 0.0601, 0.189, 0.0472, 9900, 0, 0, 0, 0, 1, -360, 360], [47, 49, 0.0191, 0.0625, 0.01604, 9900, 0, 0, 0, 0, 1, -360, 360], [42, 49, 0.0715, 0.323, 0.086, 9900, 0, 0, 0, 0, 1, -360, 360], [42, 49, 0.0715, 0.323, 0.086, 9900, 0, 0, 0, 0, 1, -360, 360], [45, 49, 0.0684, 0.186, 0.0444, 9900, 0, 0, 0, 0, 1, -360, 360], [48, 49, 0.0179, 0.0505, 0.01258, 9900, 0, 0, 0, 0, 1, -360, 360], [49, 50, 0.0267, 0.0752, 0.01874, 9900, 0, 0, 0, 0, 1, -360, 360], [49, 51, 0.0486, 0.137, 0.0342, 9900, 0, 0, 0, 0, 1, -360, 360], [51, 52, 0.0203, 0.0588, 0.01396, 9900, 0, 0, 0, 0, 1, -360, 360], [52, 53, 0.0405, 0.1635, 0.04058, 9900, 0, 0, 0, 0, 1, -360, 360], [53, 54, 0.0263, 0.122, 0.031, 9900, 0, 0, 0, 0, 1, -360, 360], [49, 54, 0.073, 0.289, 0.0738, 9900, 0, 0, 0, 0, 1, -360, 360], [49, 54, 0.0869, 0.291, 0.073, 9900, 0, 0, 0, 0, 1, -360, 360], [54, 55, 0.0169, 0.0707, 0.0202, 9900, 0, 0, 0, 0, 1, -360, 360], [54, 56, 0.00275, 0.00955, 0.00732, 9900, 0, 0, 0, 0, 1, -360, 360], [55, 56, 0.00488, 0.0151, 0.00374, 9900, 0, 0, 0, 0, 1, -360, 360], [56, 57, 0.0343, 0.0966, 0.0242, 9900, 0, 0, 0, 0, 1, -360, 360], [50, 57, 0.0474, 0.134, 0.0332, 9900, 0, 0, 0, 0, 1, -360, 360], [56, 58, 0.0343, 0.0966, 0.0242, 9900, 0, 0, 0, 0, 1, -360, 360], [51, 58, 0.0255, 0.0719, 0.01788, 9900, 0, 0, 0, 0, 1, -360, 360], [54, 59, 0.0503, 0.2293, 0.0598, 9900, 0, 0, 0, 0, 1, -360, 360], [56, 59, 0.0825, 0.251, 0.0569, 9900, 0, 0, 0, 0, 1, -360, 360], [56, 59, 0.0803, 0.239, 0.0536, 9900, 0, 0, 0, 0, 1, -360, 360], [55, 59, 0.04739, 0.2158, 0.05646, 9900, 0, 0, 0, 0, 1, -360, 360], [59, 60, 0.0317, 0.145, 0.0376, 9900, 0, 0, 0, 0, 1, -360, 360], [59, 61, 0.0328, 0.15, 0.0388, 9900, 0, 0, 0, 0, 1, -360, 360], [60, 61, 0.00264, 0.0135, 0.01456, 9900, 0, 0, 0, 0, 1, -360, 360], [60, 62, 0.0123, 0.0561, 0.01468, 9900, 0, 0, 0, 0, 1, -360, 360], [61, 62, 0.00824, 0.0376, 0.0098, 9900, 0, 0, 0, 0, 1, -360, 360], [63, 59, 0, 0.0386, 0, 9900, 0, 0, 0.96, 0, 1, -360, 360], [63, 64, 0.00172, 0.02, 0.216, 9900, 0, 0, 0, 0, 1, -360, 360], [64, 61, 0, 0.0268, 0, 9900, 0, 0, 0.985, 0, 1, -360, 360], [38, 65, 0.00901, 0.0986, 1.046, 9900, 0, 0, 0, 0, 1, -360, 360], [64, 65, 0.00269, 0.0302, 0.38, 9900, 0, 0, 0, 0, 1, -360, 360], [49, 66, 0.018, 0.0919, 0.0248, 9900, 0, 0, 0, 0, 1, -360, 360], [49, 66, 0.018, 0.0919, 0.0248, 9900, 0, 0, 0, 0, 1, -360, 360], [62, 66, 0.0482, 0.218, 0.0578, 9900, 0, 0, 0, 0, 1, -360, 360], [62, 67, 0.0258, 0.117, 0.031, 9900, 0, 0, 0, 0, 1, -360, 360], [65, 66, 0, 0.037, 0, 9900, 0, 0, 0.935, 0, 1, -360, 360], [66, 67, 0.0224, 0.1015, 0.02682, 9900, 0, 0, 0, 0, 1, -360, 360], [65, 68, 0.00138, 0.016, 0.638, 9900, 0, 0, 0, 0, 1, -360, 360], [47, 69, 0.0844, 0.2778, 0.07092, 9900, 0, 0, 0, 0, 1, -360, 360], [49, 69, 0.0985, 0.324, 0.0828, 9900, 0, 0, 0, 0, 1, -360, 360], [68, 69, 0, 0.037, 0, 9900, 0, 0, 0.935, 0, 1, -360, 360], [69, 70, 0.03, 0.127, 0.122, 9900, 0, 0, 0, 0, 1, -360, 360], [24, 70, 0.00221, 0.4115, 0.10198, 9900, 0, 0, 0, 0, 1, -360, 360], [70, 71, 0.00882, 0.0355, 0.00878, 9900, 0, 0, 0, 0, 1, -360, 360], [24, 72, 0.0488, 0.196, 0.0488, 9900, 0, 0, 0, 0, 1, -360, 360], [71, 72, 0.0446, 0.18, 0.04444, 9900, 0, 0, 0, 0, 1, -360, 360], [71, 73, 0.00866, 0.0454, 0.01178, 9900, 0, 0, 0, 0, 1, -360, 360], [70, 74, 0.0401, 0.1323, 0.03368, 9900, 0, 0, 0, 0, 1, -360, 360], [70, 75, 0.0428, 0.141, 0.036, 9900, 0, 0, 0, 0, 1, -360, 360], [69, 75, 0.0405, 0.122, 0.124, 9900, 0, 0, 0, 0, 1, -360, 360], [74, 75, 0.0123, 0.0406, 0.01034, 9900, 0, 0, 0, 0, 1, -360, 360], [76, 77, 0.0444, 0.148, 0.0368, 9900, 0, 0, 0, 0, 1, -360, 360], [69, 77, 0.0309, 0.101, 0.1038, 9900, 0, 0, 0, 0, 1, -360, 360], [75, 77, 0.0601, 0.1999, 0.04978, 9900, 0, 0, 0, 0, 1, -360, 360], [77, 78, 0.00376, 0.0124, 0.01264, 9900, 0, 0, 0, 0, 1, -360, 360], [78, 79, 0.00546, 0.0244, 0.00648, 9900, 0, 0, 0, 0, 1, -360, 360], [77, 80, 0.017, 0.0485, 0.0472, 9900, 0, 0, 0, 0, 1, -360, 360], [77, 80, 0.0294, 0.105, 0.0228, 9900, 0, 0, 0, 0, 1, -360, 360], [79, 80, 0.0156, 0.0704, 0.0187, 9900, 0, 0, 0, 0, 1, -360, 360], [68, 81, 0.00175, 0.0202, 0.808, 9900, 0, 0, 0, 0, 1, -360, 360], [81, 80, 0, 0.037, 0, 9900, 0, 0, 0.935, 0, 1, -360, 360], [77, 82, 0.0298, 0.0853, 0.08174, 9900, 0, 0, 0, 0, 1, -360, 360], [82, 83, 0.0112, 0.03665, 0.03796, 9900, 0, 0, 0, 0, 1, -360, 360], [83, 84, 0.0625, 0.132, 0.0258, 9900, 0, 0, 0, 0, 1, -360, 360], [83, 85, 0.043, 0.148, 0.0348, 9900, 0, 0, 0, 0, 1, -360, 360], [84, 85, 0.0302, 0.0641, 0.01234, 9900, 0, 0, 0, 0, 1, -360, 360], [85, 86, 0.035, 0.123, 0.0276, 9900, 0, 0, 0, 0, 1, -360, 360], [86, 87, 0.02828, 0.2074, 0.0445, 9900, 0, 0, 0, 0, 1, -360, 360], [85, 88, 0.02, 0.102, 0.0276, 9900, 0, 0, 0, 0, 1, -360, 360], [85, 89, 0.0239, 0.173, 0.047, 9900, 0, 0, 0, 0, 1, -360, 360], [88, 89, 0.0139, 0.0712, 0.01934, 9900, 0, 0, 0, 0, 1, -360, 360], [89, 90, 0.0518, 0.188, 0.0528, 9900, 0, 0, 0, 0, 1, -360, 360], [89, 90, 0.0238, 0.0997, 0.106, 9900, 0, 0, 0, 0, 1, -360, 360], [90, 91, 0.0254, 0.0836, 0.0214, 9900, 0, 0, 0, 0, 1, -360, 360], [89, 92, 0.0099, 0.0505, 0.0548, 9900, 0, 0, 0, 0, 1, -360, 360], [89, 92, 0.0393, 0.1581, 0.0414, 9900, 0, 0, 0, 0, 1, -360, 360], [91, 92, 0.0387, 0.1272, 0.03268, 9900, 0, 0, 0, 0, 1, -360, 360], [92, 93, 0.0258, 0.0848, 0.0218, 9900, 0, 0, 0, 0, 1, -360, 360], [92, 94, 0.0481, 0.158, 0.0406, 9900, 0, 0, 0, 0, 1, -360, 360], [93, 94, 0.0223, 0.0732, 0.01876, 9900, 0, 0, 0, 0, 1, -360, 360], [94, 95, 0.0132, 0.0434, 0.0111, 9900, 0, 0, 0, 0, 1, -360, 360], [80, 96, 0.0356, 0.182, 0.0494, 9900, 0, 0, 0, 0, 1, -360, 360], [82, 96, 0.0162, 0.053, 0.0544, 9900, 0, 0, 0, 0, 1, -360, 360], [94, 96, 0.0269, 0.0869, 0.023, 9900, 0, 0, 0, 0, 1, -360, 360], [80, 97, 0.0183, 0.0934, 0.0254, 9900, 0, 0, 0, 0, 1, -360, 360], [80, 98, 0.0238, 0.108, 0.0286, 9900, 0, 0, 0, 0, 1, -360, 360], [80, 99, 0.0454, 0.206, 0.0546, 9900, 0, 0, 0, 0, 1, -360, 360], [92, 100, 0.0648, 0.295, 0.0472, 9900, 0, 0, 0, 0, 1, -360, 360], [94, 100, 0.0178, 0.058, 0.0604, 9900, 0, 0, 0, 0, 1, -360, 360], [95, 96, 0.0171, 0.0547, 0.01474, 9900, 0, 0, 0, 0, 1, -360, 360], [96, 97, 0.0173, 0.0885, 0.024, 9900, 0, 0, 0, 0, 1, -360, 360], [98, 100, 0.0397, 0.179, 0.0476, 9900, 0, 0, 0, 0, 1, -360, 360], [99, 100, 0.018, 0.0813, 0.0216, 9900, 0, 0, 0, 0, 1, -360, 360], [100, 101, 0.0277, 0.1262, 0.0328, 9900, 0, 0, 0, 0, 1, -360, 360], [92, 102, 0.0123, 0.0559, 0.01464, 9900, 0, 0, 0, 0, 1, -360, 360], [101, 102, 0.0246, 0.112, 0.0294, 9900, 0, 0, 0, 0, 1, -360, 360], [100, 103, 0.016, 0.0525, 0.0536, 9900, 0, 0, 0, 0, 1, -360, 360], [100, 104, 0.0451, 0.204, 0.0541, 9900, 0, 0, 0, 0, 1, -360, 360], [103, 104, 0.0466, 0.1584, 0.0407, 9900, 0, 0, 0, 0, 1, -360, 360], [103, 105, 0.0535, 0.1625, 0.0408, 9900, 0, 0, 0, 0, 1, -360, 360], [100, 106, 0.0605, 0.229, 0.062, 9900, 0, 0, 0, 0, 1, -360, 360], [104, 105, 0.00994, 0.0378, 0.00986, 9900, 0, 0, 0, 0, 1, -360, 360], [105, 106, 0.014, 0.0547, 0.01434, 9900, 0, 0, 0, 0, 1, -360, 360], [105, 107, 0.053, 0.183, 0.0472, 9900, 0, 0, 0, 0, 1, -360, 360], [105, 108, 0.0261, 0.0703, 0.01844, 9900, 0, 0, 0, 0, 1, -360, 360], [106, 107, 0.053, 0.183, 0.0472, 9900, 0, 0, 0, 0, 1, -360, 360], [108, 109, 0.0105, 0.0288, 0.0076, 9900, 0, 0, 0, 0, 1, -360, 360], [103, 110, 0.03906, 0.1813, 0.0461, 9900, 0, 0, 0, 0, 1, -360, 360], [109, 110, 0.0278, 0.0762, 0.0202, 9900, 0, 0, 0, 0, 1, -360, 360], [110, 111, 0.022, 0.0755, 0.02, 9900, 0, 0, 0, 0, 1, -360, 360], [110, 112, 0.0247, 0.064, 0.062, 9900, 0, 0, 0, 0, 1, -360, 360], [17, 113, 0.00913, 0.0301, 0.00768, 9900, 0, 0, 0, 0, 1, -360, 360], [32, 113, 0.0615, 0.203, 0.0518, 9900, 0, 0, 0, 0, 1, -360, 360], [32, 114, 0.0135, 0.0612, 0.01628, 9900, 0, 0, 0, 0, 1, -360, 360], [27, 115, 0.0164, 0.0741, 0.01972, 9900, 0, 0, 0, 0, 1, -360, 360], [114, 115, 0.0023, 0.0104, 0.00276, 9900, 0, 0, 0, 0, 1, -360, 360], [68, 116, 0.00034, 0.00405, 0.164, 9900, 0, 0, 0, 0, 1, -360, 360], [12, 117, 0.0329, 0.14, 0.0358, 9900, 0, 0, 0, 0, 1, -360, 360], [75, 118, 0.0145, 0.0481, 0.01198, 9900, 0, 0, 0, 0, 1, -360, 360], [76, 118, 0.0164, 0.0544, 0.01356, 9900, 0, 0, 0, 0, 1, -360, 360] ]) ##----- OPF Data -----## ## generator cost data # 1 startup shutdown n x1 y1 ... xn yn # 2 startup shutdown n c(n-1) ... c0 ppc["gencost"] = array([ [2, 0, 0, 3, 0.01, 40, 0], [2, 0, 0, 3, 0.01, 40, 0], [2, 0, 0, 3, 0.01, 40, 0], [2, 0, 0, 3, 0.01, 40, 0], [2, 0, 0, 3, 0.0222222, 20, 0], [2, 0, 0, 3, 0.117647, 20, 0], [2, 0, 0, 3, 0.01, 40, 0], [2, 0, 0, 3, 0.01, 40, 0], [2, 0, 0, 3, 0.01, 40, 0], [2, 0, 0, 3, 0.01, 40, 0], [2, 0, 0, 3, 0.0454545, 20, 0], [2, 0, 0, 3, 0.0318471, 20, 0], [2, 0, 0, 3, 0.01, 40, 0], [2, 0, 0, 3, 1.42857, 20, 0], [2, 0, 0, 3, 0.01, 40, 0], [2, 0, 0, 3, 0.01, 40, 0], [2, 0, 0, 3, 0.01, 40, 0], [2, 0, 0, 3, 0.01, 40, 0], [2, 0, 0, 3, 0.01, 40, 0], [2, 0, 0, 3, 0.526316, 20, 0], [2, 0, 0, 3, 0.0490196, 20, 0], [2, 0, 0, 3, 0.208333, 20, 0], [2, 0, 0, 3, 0.01, 40, 0], [2, 0, 0, 3, 0.01, 40, 0], [2, 0, 0, 3, 0.0645161, 20, 0], [2, 0, 0, 3, 0.0625, 20, 0], [2, 0, 0, 3, 0.01, 40, 0], [2, 0, 0, 3, 0.0255754, 20, 0], [2, 0, 0, 3, 0.0255102, 20, 0], [2, 0, 0, 3, 0.0193648, 20, 0], [2, 0, 0, 3, 0.01, 40, 0], [2, 0, 0, 3, 0.01, 40, 0], [2, 0, 0, 3, 0.01, 40, 0], [2, 0, 0, 3, 0.01, 40, 0], [2, 0, 0, 3, 0.01, 40, 0], [2, 0, 0, 3, 0.01, 40, 0], [2, 0, 0, 3, 0.0209644, 20, 0], [2, 0, 0, 3, 0.01, 40, 0], [2, 0, 0, 3, 2.5, 20, 0], [2, 0, 0, 3, 0.0164745, 20, 0], [2, 0, 0, 3, 0.01, 40, 0], [2, 0, 0, 3, 0.01, 40, 0], [2, 0, 0, 3, 0.01, 40, 0], [2, 0, 0, 3, 0.01, 40, 0], [2, 0, 0, 3, 0.0396825, 20, 0], [2, 0, 0, 3, 0.25, 20, 0], [2, 0, 0, 3, 0.01, 40, 0], [2, 0, 0, 3, 0.01, 40, 0], [2, 0, 0, 3, 0.01, 40, 0], [2, 0, 0, 3, 0.01, 40, 0], [2, 0, 0, 3, 0.277778, 20, 0], [2, 0, 0, 3, 0.01, 40, 0], [2, 0, 0, 3, 0.01, 40, 0], [2, 0, 0, 3, 0.01, 40, 0] ]) return ppc

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16aeb1434c61ec5f57f1e2b5b5461f487cd35aa7

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py

Python

src/flowket/deepar/ordering/raster.py

vigsterkr/FlowKet

0d8f301b5f51a1bab83021f10f65cfb5f2751079

[ "MIT" ]

21

2019-11-19T13:59:13.000Z

2021-12-03T10:26:30.000Z

src/flowket/deepar/ordering/raster.py

HUJI-Deep/PyKet

61238afd3fe1488d35c57d280675f544c559bd01

[ "MIT" ]

10

2019-11-15T12:07:28.000Z

2020-11-07T18:12:18.000Z

src/flowket/deepar/ordering/raster.py

HUJI-Deep/PyKet

61238afd3fe1488d35c57d280675f544c559bd01

[ "MIT" ]

11

2019-12-09T22:51:17.000Z

2021-11-29T22:05:41.000Z

import itertools def raster(input_size): return itertools.product(*[range(dim_size) for dim_size in input_size])

19.833333

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bc4a387faf3f2a1bff14de0ff7a001c24ef769b7

29

py

Python

kats/graphics/__init__.py

menefotto/Kats

3fc8a3f819502d45736734eabb3601f42a6b7759

[ "MIT" ]

1

2021-06-22T03:40:33.000Z

kats/graphics/__init__.py

menefotto/Kats

3fc8a3f819502d45736734eabb3601f42a6b7759

[ "MIT" ]

null

kats/graphics/__init__.py

menefotto/Kats

3fc8a3f819502d45736734eabb3601f42a6b7759

[ "MIT" ]

null

from . import plots # noqa

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6

bc8a6e32b83baae0018d70f7cf794bd1abd1040b

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py

Python

src/puLearning/__init__.py

hhelm10/pu-learning

eea5097192dbf384832b857d3e062ab2482fd1ae

[ "BSD-3-Clause" ]

null

src/puLearning/__init__.py

hhelm10/pu-learning

eea5097192dbf384832b857d3e062ab2482fd1ae

[ "BSD-3-Clause" ]

null

src/puLearning/__init__.py

hhelm10/pu-learning

eea5097192dbf384832b857d3e062ab2482fd1ae

[ "BSD-3-Clause" ]

null

from .puAdapter import *

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6

bc8abc09da8a75b66d974b4d2a9fe4e5ad26321f

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py

Python

katas/kyu_7/sum_up_the_random_string.py

the-zebulan/CodeWars

1eafd1247d60955a5dfb63e4882e8ce86019f43a

[ "MIT" ]

40

2016-03-09T12:26:20.000Z

2022-03-23T08:44:51.000Z

katas/kyu_7/sum_up_the_random_string.py

akalynych/CodeWars

1eafd1247d60955a5dfb63e4882e8ce86019f43a

[ "MIT" ]

null

katas/kyu_7/sum_up_the_random_string.py

akalynych/CodeWars

1eafd1247d60955a5dfb63e4882e8ce86019f43a

[ "MIT" ]

36

2016-11-07T19:59:58.000Z

2022-03-31T11:18:27.000Z

from re import findall def sum_from_string(string): return sum(int(a) for a in findall(r'\d+', string))

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py

Python

models/__init__.py

RyanWangZf/NRE-IF

738126d3ea06b396c67417e684400f510405f319

[ "MIT" ]

null

models/__init__.py

RyanWangZf/NRE-IF

738126d3ea06b396c67417e684400f510405f319

[ "MIT" ]

null

models/__init__.py

RyanWangZf/NRE-IF

738126d3ea06b396c67417e684400f510405f319

[ "MIT" ]

null

# -*- coding: utf-8 -*- from .PCNN_ONE import PCNN_ONE from .PCNN_ATT import PCNN_ATT from .PCNN_IF import PCNN_IF

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6

bcce8112a89725621d6bb584a8fded873d43b10f

29,448

py

Python

Outputs_win_app.py

stfbnc/mtsa_py

0dd14f0e51e3251f10b3da781867fbc7173608eb

[ "MIT" ]

null

Outputs_win_app.py

stfbnc/mtsa_py

0dd14f0e51e3251f10b3da781867fbc7173608eb

[ "MIT" ]

null

Outputs_win_app.py

stfbnc/mtsa_py

0dd14f0e51e3251f10b3da781867fbc7173608eb

[ "MIT" ]

null

import sys if sys.version_info[0] == 2: import Tkinter as tk from tkFileDialog import askdirectory else: import tkinter as tk from tkinter.filedialog import askdirectory import numpy as np import matplotlib matplotlib.use("TkAgg") import matplotlib.pyplot as plt from matplotlib.figure import Figure from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg import os ### set size for figures x_size = 7 y_size = 5 ############################################################################# ############################## SAVE_FREQ #################################### ############################################################################# def save_freq(arg0,arg2,arg3,arg4): path_file = askdirectory() path_file = os.path.join(path_file,'freq_perc.txt') f = open(path_file,'w') f.write('Frequencies for file %s\n\n' % (arg0)) f.write('Filter_round Frequency Period Percentage\n\n') for i in range(len(arg2)): f.write('%d %.8f %.2f %.2f\n' % (arg2[i],1.0 / arg3[i],arg3[i],arg4[i])) f.close() ############################################################################# ############################################################################# ############################## DETREND_PLOT ################################# ############################################################################# def detrend_plot(main_win,arg0,arg1,arg2): ticks = np.arange(0,len(arg0),len(arg0) / 7,dtype = int) t = np.arange(1,len(arg0) + 1,dtype = int) time = np.array(arg2,dtype = str) ticks_vec = t[ticks] time_label = time[ticks] def save_fig(): path_tot = askdirectory() plt.rc('text',usetex = True) plt.rc('font',family = 'serif') plt.plot(t,arg0 + arg1,'r',label = 'original') if not np.isscalar(arg1): plt.plot(t,arg1,'k',label = 'trend') plt.plot(t,arg0,'b',label = 'detrended') plt.legend(loc = 0) plt.xlim(float(entries[0].get()),float(entries[1].get())) plt.ylim(float(entries[2].get()),float(entries[3].get())) plt.xlabel(entries[4].get()) plt.ylabel(entries[5].get()) plt.title(entries[6].get()) plt.xticks(ticks,time_label) plt.margins(0.2) plt.subplots_adjust(bottom = 0.2) plt.savefig(os.path.join(path_tot,'ts.pdf')) plt.close() def screen_fig(): fig_ts = Figure(figsize = (x_size,y_size)) a = fig_ts.add_subplot(111) a.plot(t,arg0 + arg1,'r',label = 'original') if not np.isscalar(arg1): a.plot(t,arg1,'k',label = 'trend') a.plot(t,arg0,'b',label = 'detrended') a.legend(loc = 0) a.set_xlim(float(entries[0].get()),float(entries[1].get())) a.set_ylim(float(entries[2].get()),float(entries[3].get())) a.set_xlabel(entries[4].get(),fontsize = 15) a.set_ylabel(entries[5].get(),fontsize = 15) a.set_title(entries[6].get(),fontsize = 15) a.set_xticks(ticks_vec) a.set_xticklabels(time_label) fig_ts.tight_layout() canvas = FigureCanvasTkAgg(fig_ts,master = frame_1) canvas.get_tk_widget().grid(row = 0,column = 0) canvas.draw() def reset_fig(): for i in range(len(entries)): entries[i].delete(0,tk.END) entries[i].insert(0,values[i]) screen_fig() top = tk.Toplevel(main_win) top.geometry("%dx%d" % (int(main_win.winfo_screenwidth() * 0.93 * 0.85), int(main_win.winfo_screenheight() * 0.65))) top.wm_title("Time Series") top.resizable(width = False,height = False) frame_1 = tk.Frame(top) frame_1.grid(row = 0,column = 0) frame_2 = tk.Frame(top) frame_2.grid(row = 0,column = 1) names = ["X Limit (left)","X Limit (right)","Y Limit (bottom)","Y Limit (top)","X Label","Y Label","Title"] if not np.isscalar(arg1): values = [t[0],t[-1],np.min([np.min(arg0[~np.isnan(arg0)]),np.min(arg1[~np.isnan(arg1)]), np.min(arg0[~np.isnan(arg0)] + arg1[~np.isnan(arg1)])]) - 1.0, np.max([np.max(arg0[~np.isnan(arg0)]),np.max(arg1[~np.isnan(arg1)]), np.max(arg0[~np.isnan(arg0)] + arg1[~np.isnan(arg1)])]) + 1.0,'t','$X_t$', 'Time Series'] else: values = [t[0],t[-1],np.min(arg0[~np.isnan(arg0)]) - 1.0,np.max(arg0[~np.isnan(arg0)]) + 1.0, 't','$X_t$','Time Series'] entries = [] for i in range(len(names)): tk.Label(frame_2,text = names[i],font = "Verdana 13 bold").grid(row = 2 * i,column = 0, padx = int(main_win.winfo_screenwidth() * 0.01)) entries.append(tk.Entry(frame_2,width = 18)) entries[-1].insert(0,values[i]) entries[-1].grid(row = 2 * i,column = 1) for i in range(len(names)): tk.Label(frame_2,text = "").grid(row = 2 * i + 1,column = 0) screen_fig() tk.Button(frame_2,text = "Replot",font = "Verdana 13 bold",command = screen_fig).grid(row = 2 * len(names),column = 0) tk.Button(frame_2,text = "Save",font = "Verdana 13 bold",command = save_fig).grid(row = 2 * len(names),column = 1) tk.Label(frame_2,text = "").grid(row = 2 * len(names) + 1,column = 0) tk.Button(frame_2,text = "Reset",font = "Verdana 13 bold",command = reset_fig).grid(row = 2 * len(names) + 2,column = 0) ############################################################################# ############################################################################# ############################## SPECTRUM_PLOT ################################ ############################################################################# def spectrum_plot(main_win,arg0,arg1,arg2): def save_fig(): path_tot = askdirectory() plt.rc('text',usetex = True) plt.rc('font',family = 'serif') plt.plot(arg1,arg0,'b') if arg2 != 0: plt.plot((arg1[0],arg1[-1]),(arg2,arg2),'r') plt.xlabel(entries[0].get()) plt.ylabel(entries[1].get()) plt.xlim(float(entries[2].get()),float(entries[3].get())) plt.ylim(float(entries[4].get()),float(entries[5].get())) plt.title(entries[6].get()) plt.savefig(os.path.join(path_tot,'spectrum_in.pdf')) plt.close() def screen_fig(): fig_ts = Figure(figsize = (x_size,y_size)) a = fig_ts.add_subplot(111) a.plot(arg1,arg0,'b') if arg2 != 0: a.plot((arg1[0],arg1[-1]),(arg2,arg2),'r') a.set_xlabel(entries[0].get(),fontsize = 15) a.set_ylabel(entries[1].get(),fontsize = 15) a.set_xlim(float(entries[2].get()),float(entries[3].get())) a.set_ylim(float(entries[4].get()),float(entries[5].get())) a.set_title(entries[6].get(),fontsize = 15) fig_ts.tight_layout() canvas = FigureCanvasTkAgg(fig_ts,master = frame_1) canvas.get_tk_widget().grid(row = 0,column = 0) canvas.draw() def reset_fig(): for i in range(len(entries)): entries[i].delete(0,tk.END) entries[i].insert(0,values[i]) screen_fig() top = tk.Toplevel(main_win) top.geometry("%dx%d" % (int(main_win.winfo_screenwidth() * 0.93 * 0.85), int(main_win.winfo_screenheight() * 0.65))) if arg2 != 0: top.wm_title("Spectrum") else: top.wm_title("Spectrum of residuals") top.resizable(width = False,height = False) frame_1 = tk.Frame(top) frame_1.grid(row = 0,column = 0) frame_2 = tk.Frame(top) frame_2.grid(row = 0,column = 1) names = ["X Label","Y Label","X Limit (left)","X Limit (right)","Y Limit (bottom)","Y Limit (top)","Title"] if arg2 != 0: values = ['$\\nu$','$P(\\nu)$',0,arg1[-1],0,np.max(arg0) + 10.0,'LS spectrum (initial)'] else: values = ['$\\nu$','$P(\\nu)$',0,arg1[-1],0,np.max(arg0) + 10.0,'LS spectrum of residuals'] entries = [] for i in range(len(names)): tk.Label(frame_2,text = names[i],font = "Verdana 13 bold").grid(row = 2 * i,column = 0, padx = int(main_win.winfo_screenwidth() * 0.01)) entries.append(tk.Entry(frame_2,width = 18)) entries[-1].insert(0,values[i]) entries[-1].grid(row = 2 * i,column = 1) for i in range(len(names)): tk.Label(frame_2,text = "").grid(row = 2 * i + 1,column = 0) screen_fig() tk.Button(frame_2,text = "Replot",font = "Verdana 13 bold",command = screen_fig).grid(row = 2 * len(names),column = 0) tk.Button(frame_2,text = "Save",font = "Verdana 13 bold",command = save_fig).grid(row = 2 * len(names),column = 1) tk.Label(frame_2,text = "").grid(row = 2 * len(names) + 1,column = 0) tk.Button(frame_2,text = "Reset",font = "Verdana 13 bold",command = reset_fig).grid(row = 2 * len(names) + 2,column = 0) ############################################################################# ############################################################################# ############################## RES_PLOT ##################################### ############################################################################# def res_plot(main_win,arg0,arg1,arg2,arg3): ticks = np.arange(0,len(arg0),len(arg0) / 7,dtype = int) t = np.arange(1,len(arg0) + 1,dtype = int) time = np.array(arg3,dtype = str) ticks_vec = t[ticks] time_label = time[ticks] pn_norm_notnan = arg2[~np.isnan(arg2)] outlier_lim = 3.0 num_outliers_max = len(pn_norm_notnan[pn_norm_notnan > outlier_lim]) num_outliers_min = len(pn_norm_notnan[pn_norm_notnan < -outlier_lim]) num_outliers = num_outliers_max + num_outliers_min def save_fig(): path_tot = askdirectory() plt.figure(figsize = (12,9)) plt.rc('text',usetex = True) plt.rc('font',family = 'serif') plt.subplot(2,1,1) plt.plot(t,arg0) plt.xlim(int(entries[0].get()),int(entries[1].get())) plt.ylim(float(entries[5].get()),float(entries[6].get())) plt.xticks(ticks,'') plt.ylabel(entries[2].get()) plt.title(entries[4].get()) plt.margins(0.2) plt.subplots_adjust(hspace = 0.0) plt.subplot(2,1,2) sigma = '%.2f' % arg1 if int(matplotlib.__version__.split('.')[0]) == 2: plt.bar(t,arg2,width = 10,label = 'num outl = ' + str(num_outliers)) else: plt.bar(t,arg2,width = 0.1,label = 'num outl = ' + str(num_outliers)) plt.plot((t[0],t[-1]),(outlier_lim,outlier_lim),'r',label = '$\sigma$ = ' + sigma) plt.plot((t[0],t[-1]),(-outlier_lim,-outlier_lim),'r') plt.legend(loc = 0) plt.xlim(int(entries[0].get()),int(entries[1].get())) plt.ylim(float(entries[7].get()),float(entries[8].get())) plt.xticks(ticks,time_label) plt.ylabel(entries[3].get()) plt.margins(0.2) plt.subplots_adjust(hspace = 0.0) plt.savefig(os.path.join(path_tot,'res.pdf')) plt.close() def screen_fig(): fig_ts = Figure(figsize = (x_size,y_size)) a = fig_ts.add_subplot(211) a.plot(t,arg0) a.set_xlim(int(entries[0].get()),int(entries[1].get())) a.set_ylim(float(entries[5].get()),float(entries[6].get())) a.set_xticks(ticks_vec) a.set_xticklabels('') a.set_ylabel(entries[2].get(),fontsize = 15) a.set_title(entries[4].get(),fontsize = 15) b = fig_ts.add_subplot(212) sigma = '%.2f' % arg1 if int(matplotlib.__version__.split('.')[0]) == 2: b.bar(t,arg2,width = 10,label = 'num outl = ' + str(num_outliers)) else: b.bar(t,arg2,width = 0.1,label = 'num outl = ' + str(num_outliers)) b.plot((t[0],t[-1]),(outlier_lim,outlier_lim),'r',label = '$\sigma$ = ' + sigma) b.plot((t[0],t[-1]),(-outlier_lim,-outlier_lim),'r') b.legend(loc = 0) b.set_xlim(int(entries[0].get()),int(entries[1].get())) b.set_ylim(float(entries[7].get()),float(entries[8].get())) b.set_xticks(ticks) b.set_xticklabels(time_label) b.set_ylabel(entries[3].get(),fontsize = 15) fig_ts.tight_layout() canvas = FigureCanvasTkAgg(fig_ts,master = frame_1) canvas.get_tk_widget().grid(row = 0,column = 0) canvas.draw() def reset_fig(): for i in range(len(entries)): entries[i].delete(0,tk.END) entries[i].insert(0,values[i]) screen_fig() top = tk.Toplevel(main_win) top.geometry("%dx%d" % (int(main_win.winfo_screenwidth() * 0.93 * 0.85), int(main_win.winfo_screenheight() * 0.65))) top.wm_title("Residuals") top.resizable(width = False,height = False) frame_1 = tk.Frame(top) frame_1.grid(row = 0,column = 0) frame_2 = tk.Frame(top) frame_2.grid(row = 0,column = 1) names = ["X Limit (left)","X Limit (right)","Y Label (top)","Y Label (bottom)","Title", "Y1 Limit (bottom)","Y1 Limit (top)","Y2 Limit (bottom)","Y2 Limit (top)"] values = [t[0],t[-1],'$N_t$','$N_t^{norm}$','Residuals / Normalised residuals',np.min(arg0[~np.isnan(arg0)]) - 10.0, np.max(arg0[~np.isnan(arg0)]) + 10.0,np.min(arg2[~np.isnan(arg0)]) - 1.0,np.max(arg2[~np.isnan(arg0)]) + 1.0] entries = [] for i in range(len(names)): tk.Label(frame_2,text = names[i],font = "Verdana 13 bold").grid(row = 2 * i,column = 0, padx = int(main_win.winfo_screenwidth() * 0.01)) entries.append(tk.Entry(frame_2,width = 18)) entries[-1].insert(0,values[i]) entries[-1].grid(row = 2 * i,column = 1) for i in range(len(names)): tk.Label(frame_2,text = "").grid(row = 2 * i + 1,column = 0) screen_fig() tk.Button(frame_2,text = "Replot",font = "Verdana 13 bold",command = screen_fig).grid(row = 2 * len(names),column = 0) tk.Button(frame_2,text = "Save",font = "Verdana 13 bold",command = save_fig).grid(row = 2 * len(names),column = 1) tk.Label(frame_2,text = "").grid(row = 2 * len(names) + 1,column = 0) tk.Button(frame_2,text = "Reset",font = "Verdana 13 bold",command = reset_fig).grid(row = 2 * len(names) + 2,column = 0) ############################################################################# ############################################################################# ############################## DFA_PLOT ##################################### ############################################################################# def dfa_plot(main_win,arg0,arg1,arg2,arg3): def save_fig(): path_tot = askdirectory() plt.rc('text',usetex = True) plt.rc('font',family = 'serif') plt.plot(np.log(arg0),np.log(arg1),'o',label = '$H$ = ' + arg3) plt.plot(np.log(arg0),arg2,'r') plt.legend(loc = 0) plt.xlim(float(entries[0].get()),float(entries[1].get())) plt.ylim(float(entries[2].get()),float(entries[3].get())) plt.xlabel(entries[4].get()) plt.ylabel(entries[5].get()) plt.title(entries[6].get()) plt.savefig(os.path.join(path_tot,'dfa.pdf')) plt.close() def screen_fig(): fig_ts = Figure(figsize = (x_size,y_size)) a = fig_ts.add_subplot(111) a.plot(np.log(arg0),np.log(arg1),'o',label = '$H$ = ' + arg3) a.plot(np.log(arg0),arg2,'r') a.legend(loc = 0) a.set_xlim(float(entries[0].get()),float(entries[1].get())) a.set_ylim(float(entries[2].get()),float(entries[3].get())) a.set_xlabel(entries[4].get()) a.set_ylabel(entries[5].get()) a.set_title(entries[6].get()) fig_ts.tight_layout() canvas = FigureCanvasTkAgg(fig_ts,master = frame_1) canvas.get_tk_widget().grid(row = 0,column = 0) canvas.draw() def reset_fig(): for i in range(len(entries)): entries[i].delete(0,tk.END) entries[i].insert(0,values[i]) screen_fig() top = tk.Toplevel(main_win) top.geometry("%dx%d" % (int(main_win.winfo_screenwidth() * 0.93 * 0.85), int(main_win.winfo_screenheight() * 0.65))) top.wm_title("DFA") top.resizable(width = False,height = False) frame_1 = tk.Frame(top) frame_1.grid(row = 0,column = 0) frame_2 = tk.Frame(top) frame_2.grid(row = 0,column = 1) names = ["X Limit (left)","X Limit (right)","Y Limit (bottom)","Y Limit (top)","X Label","Y Label","Title"] values = [np.log(arg0[0]) - 0.3,np.log(arg0[-1]) + 0.3,np.min(np.log(arg1)) - 1.0,np.max(np.log(arg1)) + 1.0, 'log$(F(n))$','log$(n)$','DFA fit'] entries = [] for i in range(len(names)): tk.Label(frame_2,text = names[i],font = "Verdana 13 bold").grid(row = 2 * i,column = 0, padx = int(main_win.winfo_screenwidth() * 0.01)) entries.append(tk.Entry(frame_2,width = 18)) entries[-1].insert(0,values[i]) entries[-1].grid(row = 2 * i,column = 1) for i in range(len(names)): tk.Label(frame_2,text = "").grid(row = 2 * i + 1,column = 0) screen_fig() tk.Button(frame_2,text = "Replot",font = "Verdana 13 bold",command = screen_fig).grid(row = 2 * len(names),column = 0) tk.Button(frame_2,text = "Save",font = "Verdana 13 bold",command = save_fig).grid(row = 2 * len(names),column = 1) tk.Label(frame_2,text = "").grid(row = 2 * len(names) + 1,column = 0) tk.Button(frame_2,text = "Reset",font = "Verdana 13 bold",command = reset_fig).grid(row = 2 * len(names) + 2,column = 0) ############################################################################# ############################################################################# ############################## MDFA_PLOT #################################### ############################################################################# def mdfa_plot(main_win,arg0,arg1,arg2,arg3,arg4,arg5,arg6,arg7): def save_fig(): path_tot = askdirectory() plt.figure(figsize = (11,11)) plt.rc('text',usetex = True) plt.rc('font',family = 'serif') plt.subplot(2,2,1) plt.plot(np.log(arg0),np.log(arg1[0,:]),'b.') plt.plot(np.log(arg0),arg2[:,0],'b',label = 'q = -3') plt.plot(np.log(arg0),np.log(arg1[50,:]),'r.') plt.plot(np.log(arg0),arg2[:,50],'r',label = 'q = 0') plt.plot(np.log(arg0),np.log(arg1[-1,:]),'g.') plt.plot(np.log(arg0),arg2[:,-1],'g',label = 'q = 3') plt.legend(loc = 0) plt.xlim(float(entries[0].get()),float(entries[1].get())) plt.ylim(float(entries[2].get()),float(entries[3].get())) plt.xlabel(entries[4].get()) plt.ylabel(entries[5].get()) plt.title(entries[6].get()) plt.margins(0.2) plt.subplots_adjust(bottom = 0.2) plt.subplot(2,2,2) plt.plot(arg3,arg4,'b',label = 'h(q)') plt.plot((arg3[0],arg3[-1]),(arg5,arg5),'k',label = 'H') plt.legend(loc = 0) plt.xlim(float(entries[7].get()),float(entries[8].get())) plt.ylim(float(entries[9].get()),float(entries[10].get())) plt.xlabel(entries[11].get()) plt.ylabel(entries[12].get()) plt.title(entries[13].get()) plt.margins(0.2) plt.subplots_adjust(bottom = 0.2) plt.subplot(2,2,3) plt.plot(arg6,arg7,'b') plt.xlim(float(entries[14].get()),float(entries[15].get())) plt.ylim(float(entries[16].get()),float(entries[17].get())) plt.xlabel(entries[18].get()) plt.ylabel(entries[19].get()) plt.title(entries[20].get()) plt.margins(0.2) plt.subplots_adjust(bottom = 0.2) plt.savefig(os.path.join(path_tot,'mdfa.pdf')) plt.close() def screen_fig(): fig_ts = Figure(figsize = (x_size,y_size)) a = fig_ts.add_subplot(221) a.plot(np.log(arg0),np.log(arg1[0,:]),'b.') a.plot(np.log(arg0),arg2[:,0],'b',label = 'q = -3') a.plot(np.log(arg0),np.log(arg1[50,:]),'r.') a.plot(np.log(arg0),arg2[:,50],'r',label = 'q = 0') a.plot(np.log(arg0),np.log(arg1[-1,:]),'g.') a.plot(np.log(arg0),arg2[:,-1],'g',label = 'q = 3') a.legend(loc = 0) a.set_xlim(float(entries[0].get()),float(entries[1].get())) a.set_ylim(float(entries[2].get()),float(entries[3].get())) a.set_xlabel(entries[4].get()) a.set_ylabel(entries[5].get()) a.set_title(entries[6].get()) b = fig_ts.add_subplot(222) b.plot(arg3,arg4,'b',label = 'H(q)') b.plot((arg3[0],arg3[-1]),(arg5,arg5),'k',label = 'H') b.legend(loc = 0) b.set_xlim(float(entries[7].get()),float(entries[8].get())) b.set_ylim(float(entries[9].get()),float(entries[10].get())) b.set_xlabel(entries[11].get()) b.set_ylabel(entries[12].get()) b.set_title(entries[13].get()) c = fig_ts.add_subplot(223) c.plot(arg6,arg7,'b') c.set_xlim(float(entries[14].get()),float(entries[15].get())) c.set_ylim(float(entries[16].get()),float(entries[17].get())) c.set_xlabel(entries[18].get()) c.set_ylabel(entries[19].get()) c.set_title(entries[20].get()) fig_ts.tight_layout() canvas = FigureCanvasTkAgg(fig_ts,master = frame_1) canvas.get_tk_widget().grid(row = 0,column = 0) canvas.draw() def reset_fig(): for i in range(len(entries)): entries[i].delete(0,tk.END) entries[i].insert(0,values[i]) screen_fig() top = tk.Toplevel(main_win) top.geometry("%dx%d" % (int(main_win.winfo_screenwidth() * 0.93 * 0.85), int(main_win.winfo_screenheight() * 0.75))) top.wm_title("MFDFA") top.resizable(width = False,height = False) frame_1 = tk.Frame(top) frame_1.grid(row = 0,column = 0) frame_2 = tk.Frame(top) frame_2.grid(row = 0,column = 1) names = ["X1 Limit (left)","X1 Limit (right)","Y1 Limit (bottom)","Y1 Limit (top)","X1 Label","Y1 Label","Title1", "X2 Limit (left)","X2 Limit (right)","Y2 Limit (bottom)","Y2 Limit (top)","X2 Label","Y2 Label","Title2", "X3 Limit (left)","X3 Limit (right)","Y3 Limit (bottom)","Y3 Limit (top)","X3 Label","Y3 Label","Title3"] values = [np.log(arg0[0]),np.log(arg0[-1]),np.min([np.min(np.log(arg1[0,:])),np.min(arg2[:,0]), np.min(np.log(arg1[50,:])),np.min(arg2[:,50]),np.min(np.log(arg1[-1,:])),np.min(arg2[:,-1])]) - 1.0, np.max([np.max(np.log(arg1[0,:])),np.max(arg2[:,0]),np.max(np.log(arg1[50,:])),np.max(arg2[:,50]), np.max(np.log(arg1[-1,:])),np.max(arg2[:,-1])]) + 1.0,'log(n)','log(F(n))','MDFA fit', arg3[0],arg3[-1],np.min(arg4) - 0.1,np.max(arg4) + 0.1,'q','H(q)','Generalised Hurst exponent', np.min(arg6) - 0.2,np.max(arg6) + 0.2,np.min(arg7) - 0.2,1.2,'$\\alpha$','$f(\\alpha)$', 'Singularity spectrum'] entries = [] for i in range(len(names)): tk.Label(frame_2,text = names[i],font = "Verdana 13 bold").grid(row = i,column = 0, padx = int(main_win.winfo_screenwidth() * 0.01)) entries.append(tk.Entry(frame_2,width = 18)) entries[-1].insert(0,values[i]) entries[-1].grid(row = i,column = 1) screen_fig() tk.Label(frame_2,text = "").grid(row = len(names),column = 0) tk.Button(frame_2,text = "Replot",font = "Verdana 13 bold",command = screen_fig).grid(row = len(names) + 1,column = 0) tk.Button(frame_2,text = "Save",font = "Verdana 13 bold",command = save_fig).grid(row = len(names) + 1,column = 1) tk.Button(frame_2,text = "Reset",font = "Verdana 13 bold",command = reset_fig).grid(row = len(names) + 2,column = 0) ############################################################################# ############################################################################# ############################## MFDFA2_PLOT ################################## ############################################################################# def MFDFA2_plot(main_win,arg0,arg1,arg2,arg3,arg4,arg5): def save_fig(): path_tot = askdirectory() plt.figure(figsize = (12,9)) plt.rc('text',usetex = True) plt.rc('font',family = 'serif') plt.subplot(2,1,1) ax = plt.gca() if int(matplotlib.__version__.split('.')[0]) == 2: ax.set_facecolor('black') else: ax.set_axis_bgcolor('black') plt.plot(arg0,'y') plt.plot(0.5 * np.ones((len(arg0),)),'w') plt.plot(np.ones((len(arg0),)),'m') plt.plot(1.5 * np.ones((len(arg0),)),'r') plt.xlim(float(entries[0].get()),float(entries[1].get())) plt.ylim(float(entries[2].get()),float(entries[3].get())) plt.xlabel(entries[4].get()) plt.ylabel(entries[5].get()) plt.title(entries[6].get()) plt.margins(0.2) plt.subplots_adjust(hspace = 0.3) plt.subplot(2,1,2) plt.plot(arg1,arg2,'b',label = '$\mu$ = ' + arg4) plt.plot(arg1,arg3,'r',linewidth = 2.0,label = '$\sigma$ = ' + arg5) plt.legend(loc = 0) plt.xlim(float(entries[7].get()),float(entries[8].get())) plt.ylim(float(entries[9].get()),float(entries[10].get())) plt.ylabel(entries[11].get()) plt.xlabel(entries[12].get()) plt.title(entries[13].get()) plt.margins(0.2) plt.subplots_adjust(hspace = 0.3) plt.savefig(os.path.join(path_tot,'MFDFA2.pdf')) plt.close() def screen_fig(): fig_ts = Figure(figsize = (x_size,y_size)) a = fig_ts.add_subplot(211) ax = fig_ts.gca() if int(matplotlib.__version__.split('.')[0]) == 2: ax.set_facecolor('black') else: ax.set_axis_bgcolor('black') a.plot(arg0,'y') a.plot(0.5 * np.ones((len(arg0),)),'w') a.plot(np.ones((len(arg0),)),'m') a.plot(1.5 * np.ones((len(arg0),)),'r') a.set_xlim(float(entries[0].get()),float(entries[1].get())) a.set_ylim(float(entries[2].get()),float(entries[3].get())) a.set_xlabel(entries[4].get()) a.set_ylabel(entries[5].get()) a.set_title(entries[6].get()) b = fig_ts.add_subplot(212) b.plot(arg1,arg2,'b',label = '$\mu$ = ' + arg4) b.plot(arg1,arg3,'r',linewidth = 2.0,label = '$\sigma$ = ' + arg5) b.legend(loc = 0) b.set_xlim(float(entries[7].get()),float(entries[8].get())) b.set_ylim(float(entries[9].get()),float(entries[10].get())) b.set_ylabel(entries[11].get()) b.set_xlabel(entries[12].get()) b.set_title(entries[13].get()) fig_ts.tight_layout() canvas = FigureCanvasTkAgg(fig_ts,master = frame_1) canvas.get_tk_widget().grid(row = 0,column = 0) canvas.draw() def reset_fig(): for i in range(len(entries)): entries[i].delete(0,tk.END) entries[i].insert(0,values[i]) screen_fig() top = tk.Toplevel(main_win) top.geometry("%dx%d" % (int(main_win.winfo_screenwidth() * 0.93 * 0.85), int(main_win.winfo_screenheight() * 0.65))) top.wm_title("DFA") top.resizable(width = False,height = False) frame_1 = tk.Frame(top) frame_1.grid(row = 0,column = 0) frame_2 = tk.Frame(top) frame_2.grid(row = 0,column = 1) names = ["X1 Limit (left)","X1 Limit (right)","Y1 Limit (bottom)","Y1 Limit (top)","X1 Label (top)", "Y1 Label (top)","Title1 (top)","X2 Limit (left)","X2 Limit (right)","Y2 Limit (bottom)", "Y2 Limit (top)","X2 Label (top)","Y2 Label (bottom)","Title2 (bottom)"] values = [0,len(arg0),0,3,'time','$H_t$','local Hurst exponent',np.min(arg1) - 0.2,np.max(arg1) + 0.2, 0,np.max(arg2) * 11 / 10,'P($H_t$)','$H_t$','Prob distr of $H_t$'] entries = [] for i in range(len(names)): tk.Label(frame_2,text = names[i],font = "Verdana 13 bold").grid(row = i,column = 0, padx = int(main_win.winfo_screenwidth() * 0.01)) entries.append(tk.Entry(frame_2,width = 18)) entries[-1].insert(0,values[i]) entries[-1].grid(row = i,column = 1) screen_fig() tk.Label(frame_2,text = "").grid(row = len(names),column = 0) tk.Button(frame_2,text = "Replot",font = "Verdana 13 bold",command = screen_fig).grid(row = len(names) + 1,column = 0) tk.Button(frame_2,text = "Save",font = "Verdana 13 bold",command = save_fig).grid(row = len(names) + 1,column = 1) tk.Label(frame_2,text = "").grid(row = len(names) + 2,column = 0) tk.Button(frame_2,text = "Reset",font = "Verdana 13 bold",command = reset_fig).grid(row = len(names) + 3,column = 0) #############################################################################

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0.760645

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29,448

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null

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6

4c18328fc80b46e684fdcbe227c90aa8fd0e06bd

29,016

py

Python

openmdao/core/tests/test_des_vars_responses.py

Subraiz/OpenMDAO

ba247746e76fc3a46b768d0f09955ef58ee71ae4

[ "Apache-2.0" ]

null

openmdao/core/tests/test_des_vars_responses.py

Subraiz/OpenMDAO

ba247746e76fc3a46b768d0f09955ef58ee71ae4

[ "Apache-2.0" ]

null

openmdao/core/tests/test_des_vars_responses.py

Subraiz/OpenMDAO

ba247746e76fc3a46b768d0f09955ef58ee71ae4

[ "Apache-2.0" ]

null

""" Unit tests for the design_variable and response interface to system.""" import unittest import numpy as np from openmdao.api import Problem, NonlinearBlockGS, Group, IndepVarComp, ExecComp, ScipyKrylov, \ IndepVarComp, ScipyOptimizeDriver from openmdao.utils.assert_utils import assert_rel_error from openmdao.utils.mpi import MPI from openmdao.test_suite.components.sellar import SellarDerivatives, SellarDis1withDerivatives, \ SellarDis2withDerivatives try: from openmdao.vectors.petsc_vector import PETScVector except ImportError: PETScVector = None class TestDesVarsResponses(unittest.TestCase): def test_api_on_model(self): prob = Problem() prob.model = SellarDerivatives() prob.model.nonlinear_solver = NonlinearBlockGS() prob.model.add_design_var('x', lower=-100, upper=100) prob.model.add_design_var('z', lower=-100, upper=100) prob.model.add_objective('obj') prob.model.add_constraint('con1') prob.model.add_constraint('con2') prob.setup() des_vars = prob.model.get_design_vars() obj = prob.model.get_objectives() constraints = prob.model.get_constraints() self.assertEqual(set(des_vars.keys()), {'px.x', 'pz.z'}) self.assertEqual(set(obj.keys()), {'obj_cmp.obj'}) self.assertEqual(set(constraints.keys()), {'con_cmp1.con1', 'con_cmp2.con2'}) def test_api_response_on_model(self): prob = Problem() prob.model = SellarDerivatives() prob.model.nonlinear_solver = NonlinearBlockGS() prob.model.add_design_var('x', lower=-100, upper=100) prob.model.add_design_var('z', lower=-100, upper=100) prob.model.add_response('obj', type_="obj") prob.model.add_response('con1', type_="con") prob.model.add_response('con2', type_="con") prob.setup() des_vars = prob.model.get_design_vars() responses = prob.model.get_responses() obj = prob.model.get_objectives() constraints = prob.model.get_constraints() self.assertEqual(set(des_vars.keys()), {'px.x', 'pz.z'}) self.assertEqual(set(obj.keys()), {'obj_cmp.obj'}) self.assertEqual(set(constraints.keys()), {'con_cmp1.con1', 'con_cmp2.con2'}) self.assertEqual(set(responses.keys()), {'obj_cmp.obj', 'con_cmp1.con1', 'con_cmp2.con2'}) def test_api_list_on_model(self): prob = Problem() prob.model = SellarDerivatives() prob.model.nonlinear_solver = NonlinearBlockGS() prob.model.add_design_var('x', lower=-100, upper=100) prob.model.add_design_var('z', lower=[-100, -20], upper=[100, 20]) prob.model.add_objective('obj') prob.model.add_constraint('con1') prob.model.add_constraint('con2') prob.setup() des_vars = prob.model.get_design_vars() obj = prob.model.get_objectives() constraints = prob.model.get_constraints() self.assertEqual(set(des_vars.keys()), {'px.x', 'pz.z'}) self.assertEqual(set(obj.keys()), {'obj_cmp.obj',}) self.assertEqual(set(constraints.keys()), {'con_cmp1.con1', 'con_cmp2.con2'}) def test_api_array_on_model(self): prob = Problem() prob.model = SellarDerivatives() prob.model.nonlinear_solver = NonlinearBlockGS() prob.model.add_design_var('x', lower=-100, upper=100) prob.model.add_design_var('z', lower=np.array([-100, -20]), upper=np.array([100, 20])) prob.model.add_objective('obj') prob.model.add_constraint('con1') prob.model.add_constraint('con2') prob.setup() des_vars = prob.model.get_design_vars() obj = prob.model.get_objectives() constraints = prob.model.get_constraints() self.assertEqual(set(des_vars.keys()), {'px.x', 'pz.z'}) self.assertEqual(set(obj.keys()), {'obj_cmp.obj',}) self.assertEqual(set(constraints.keys()), {'con_cmp1.con1', 'con_cmp2.con2'}) def test_api_iter_on_model(self): prob = Problem() prob.model = SellarDerivatives() prob.model.nonlinear_solver = NonlinearBlockGS() prob.model.add_design_var('x', lower=-100, upper=100) prob.model.add_design_var('z', lower=range(-101, -99), upper=range(99, 101), indices=range(2)) prob.model.add_objective('obj') prob.model.add_constraint('con1') prob.model.add_constraint('con2') prob.setup() des_vars = prob.model.get_design_vars() obj = prob.model.get_objectives() constraints = prob.model.get_constraints() self.assertEqual(set(des_vars.keys()), {'px.x', 'pz.z'}) self.assertEqual(set(obj.keys()), {'obj_cmp.obj',}) self.assertEqual(set(constraints.keys()), {'con_cmp1.con1', 'con_cmp2.con2'}) def test_api_on_subsystems(self): prob = Problem() model = prob.model model.add_subsystem('px', IndepVarComp('x', 1.0)) model.add_subsystem('pz', IndepVarComp('z', np.array([5.0, 2.0]))) model.add_subsystem('d1', SellarDis1withDerivatives()) model.add_subsystem('d2', SellarDis2withDerivatives()) model.add_subsystem('obj_cmp', ExecComp('obj = x**2 + z[1] + y1 + exp(-y2)', z=np.array([0.0, 0.0]), x=0.0)) model.add_subsystem('con_cmp1', ExecComp('con1 = 3.16 - y1')) model.add_subsystem('con_cmp2', ExecComp('con2 = y2 - 24.0')) model.connect('px.x', ['d1.x', 'obj_cmp.x']) model.connect('pz.z', ['d1.z', 'd2.z', 'obj_cmp.z']) model.connect('d1.y1', ['d2.y1', 'obj_cmp.y1', 'con_cmp1.y1']) model.connect('d2.y2', ['d1.y2', 'obj_cmp.y2', 'con_cmp2.y2']) model.nonlinear_solver = NonlinearBlockGS() model.linear_solver = ScipyKrylov() px = prob.model.px px.add_design_var('x', lower=-100, upper=100) pz = prob.model.pz pz.add_design_var('z', lower=-100, upper=100) obj = prob.model.obj_cmp obj.add_objective('obj') con_comp1 = prob.model.con_cmp1 con_comp1.add_constraint('con1') con_comp2 = prob.model.con_cmp2 con_comp2.add_constraint('con2') prob.setup() des_vars = prob.model.get_design_vars() obj = prob.model.get_objectives() constraints = prob.model.get_constraints() self.assertEqual(set(des_vars.keys()), {'px.x', 'pz.z'}) self.assertEqual(set(obj.keys()), {'obj_cmp.obj',}) self.assertEqual(set(constraints.keys()), {'con_cmp1.con1', 'con_cmp2.con2'}) class TestDesvarOnModel(unittest.TestCase): def test_design_var_not_exist(self): prob = Problem() prob.model = SellarDerivatives() prob.model.nonlinear_solver = NonlinearBlockGS() prob.model.add_design_var('junk') with self.assertRaises(RuntimeError) as context: prob.setup() self.assertEqual(str(context.exception), "SellarDerivatives (<model>): Output not found for design variable 'junk'.") def test_desvar_affine_and_scaleradder(self): prob = Problem() prob.model = SellarDerivatives() prob.model.nonlinear_solver = NonlinearBlockGS() with self.assertRaises(ValueError) as context: prob.model.add_design_var('x', lower=-100, upper=100, ref=1.0, scaler=0.5) self.assertEqual(str(context.exception), 'Inputs ref/ref0 are mutually' ' exclusive with' ' scaler/adder') with self.assertRaises(ValueError) as context: prob.model.add_design_var('x', lower=-100, upper=100, ref=0.0, adder=0.5) self.assertEqual(str(context.exception), 'Inputs ref/ref0 are mutually' ' exclusive with' ' scaler/adder') with self.assertRaises(ValueError) as context: prob.model.add_design_var('x', lower=-100, upper=100, ref0=0.0, adder=0.5) self.assertEqual(str(context.exception), 'Inputs ref/ref0 are mutually' ' exclusive with' ' scaler/adder') with self.assertRaises(ValueError) as context: prob.model.add_design_var('x', lower=-100, upper=100, ref0=0.0, scaler=0.5) self.assertEqual(str(context.exception), 'Inputs ref/ref0 are mutually' ' exclusive with' ' scaler/adder') def test_desvar_affine_mapping(self): prob = Problem() prob.model = SellarDerivatives() prob.model.nonlinear_solver = NonlinearBlockGS() prob.model.add_design_var('x', lower=-100, upper=100, ref0=-100.0, ref=100) prob.model.add_design_var('z', lower=-100, upper=100) prob.model.add_objective('obj') prob.model.add_constraint('con1') prob.model.add_constraint('con2') prob.setup() des_vars = prob.model.get_design_vars() x_ref0 = des_vars['px.x']['ref0'] x_ref = des_vars['px.x']['ref'] x_scaler = des_vars['px.x']['scaler'] x_adder = des_vars['px.x']['adder'] self.assertAlmostEqual( x_scaler*(x_ref0 + x_adder), 0.0, places=12) self.assertAlmostEqual( x_scaler*(x_ref + x_adder), 1.0, places=12) def test_desvar_inf_bounds(self): # make sure no overflow when there is no specified upper/lower bound and significatn scaling prob = Problem() prob.model = SellarDerivatives() prob.model.nonlinear_solver = NonlinearBlockGS() prob.model.add_design_var('x', scaler=1e6) prob.model.add_objective('obj', scaler=1e6) prob.model.add_constraint('con1', scaler=1e6) prob.model.add_constraint('con2', scaler=1e6) prob.setup() des_vars = prob.model.get_design_vars() self.assertFalse(np.isinf(des_vars['px.x']['upper'])) self.assertFalse(np.isinf(-des_vars['px.x']['lower'])) responses = prob.model.get_responses() self.assertFalse(np.isinf(responses['con_cmp1.con1']['upper'])) self.assertFalse(np.isinf(responses['con_cmp2.con2']['upper'])) self.assertFalse(np.isinf(-responses['con_cmp1.con1']['lower'])) self.assertFalse(np.isinf(-responses['con_cmp2.con2']['lower'])) def test_desvar_invalid_name(self): prob = Problem() prob.model = SellarDerivatives() prob.model.nonlinear_solver = NonlinearBlockGS() with self.assertRaises(TypeError) as context: prob.model.add_design_var(42, lower=-100, upper=100, ref0=-100.0, ref=100) self.assertEqual(str(context.exception), 'SellarDerivatives: The name argument should ' 'be a string, got 42') def test_desvar_invalid_bounds(self): prob = Problem() prob.model = SellarDerivatives() prob.model.nonlinear_solver = NonlinearBlockGS() with self.assertRaises(TypeError) as context: prob.model.add_design_var('x', lower='foo', upper=[0, 100], ref0=-100.0, ref=100) self.assertEqual(str(context.exception), 'Expected values of lower to be an ' 'Iterable of numeric values, ' 'or a scalar numeric value. ' 'Got foo instead.') with self.assertRaises(ValueError) as context: prob.model.add_design_var('x', lower=0.0, upper=['a', 'b'], ref0=-100.0, ref=100) class TestConstraintOnModel(unittest.TestCase): def test_constraint_not_exist(self): prob = Problem() prob.model = SellarDerivatives() prob.model.nonlinear_solver = NonlinearBlockGS() prob.model.add_constraint('junk') with self.assertRaises(RuntimeError) as context: prob.setup() self.assertEqual(str(context.exception), "SellarDerivatives (<model>): Output not found for response 'junk'.") def test_constraint_affine_and_scaleradder(self): prob = Problem() prob.model = SellarDerivatives() prob.model.nonlinear_solver = NonlinearBlockGS() with self.assertRaises(ValueError) as context: prob.model.add_constraint('con1', lower=-100, upper=100, ref=1.0, scaler=0.5) self.assertEqual(str(context.exception), 'Inputs ref/ref0 are mutually' ' exclusive with' ' scaler/adder') with self.assertRaises(ValueError) as context: prob.model.add_constraint('con1', lower=-100, upper=100, ref=0.0, adder=0.5) self.assertEqual(str(context.exception), 'Inputs ref/ref0 are mutually' ' exclusive with' ' scaler/adder') with self.assertRaises(ValueError) as context: prob.model.add_constraint('x', lower=-100, upper=100, ref0=0.0, adder=0.5) self.assertEqual(str(context.exception), 'Inputs ref/ref0 are mutually' ' exclusive with' ' scaler/adder') with self.assertRaises(ValueError) as context: prob.model.add_constraint('con1', lower=-100, upper=100, ref0=0.0, scaler=0.5) self.assertEqual(str(context.exception), 'Inputs ref/ref0 are mutually' ' exclusive with' ' scaler/adder') def test_constraint_affine_mapping(self): prob = Problem() prob.model = SellarDerivatives() prob.model.nonlinear_solver = NonlinearBlockGS() prob.model.add_design_var('x', lower=-100, upper=100) prob.model.add_design_var('z', lower=-100, upper=100) prob.model.add_objective('obj') prob.model.add_constraint('con1', lower=-100, upper=100, ref0=-100.0, ref=100) prob.model.add_constraint('con2') prob.setup() constraints = prob.model.get_constraints() con1_ref0 = constraints['con_cmp1.con1']['ref0'] con1_ref = constraints['con_cmp1.con1']['ref'] con1_scaler = constraints['con_cmp1.con1']['scaler'] con1_adder = constraints['con_cmp1.con1']['adder'] self.assertAlmostEqual( con1_scaler*(con1_ref0 + con1_adder), 0.0, places=12) self.assertAlmostEqual( con1_scaler*(con1_ref + con1_adder), 1.0, places=12) def test_constraint_invalid_name(self): prob = Problem() prob.model = SellarDerivatives() prob.model.nonlinear_solver = NonlinearBlockGS() with self.assertRaises(TypeError) as context: prob.model.add_design_var(42, lower=-100, upper=100, ref0=-100.0, ref=100) self.assertEqual(str(context.exception), 'SellarDerivatives: The name argument should ' 'be a string, got 42') def test_constraint_invalid_bounds(self): prob = Problem() prob.model = SellarDerivatives() prob.model.nonlinear_solver = NonlinearBlockGS() with self.assertRaises(TypeError) as context: prob.model.add_design_var('x', lower='foo', upper=[0, 100], ref0=-100.0, ref=100) self.assertEqual(str(context.exception), 'Expected values of lower to' ' be an Iterable of numeric' ' values, or a scalar numeric' ' value. Got foo instead.') with self.assertRaises(ValueError) as context: prob.model.add_design_var('x', lower=0.0, upper=['a', 'b'], ref0=-100.0, ref=100) def test_constraint_invalid_name(self): prob = Problem() prob.model = SellarDerivatives() prob.model.nonlinear_solver = NonlinearBlockGS() with self.assertRaises(TypeError) as context: prob.model.add_constraint(42, lower=-100, upper=100, ref0=-100.0, ref=100) self.assertEqual(str(context.exception), 'SellarDerivatives: The name argument should ' 'be a string, got 42') def test_constraint_invalid_lower(self): prob = Problem() prob.driver = ScipyOptimizeDriver() prob.driver.options['optimizer'] = 'SLSQP' with self.assertRaises(TypeError) as context: prob.model.add_constraint('con1', lower='foo', upper=[0, 100], ref0=-100.0, ref=100) with self.assertRaises(TypeError) as context2: prob.model.add_constraint('con1', lower=['zero', 5], upper=[0, 100], ref0=-100.0, ref=100) msg = ("Argument 'lower' can not be a string ('foo' given). You can not " "specify a variable as lower bound. You can only provide constant " "float values") self.assertEqual(str(context.exception), msg) msg2 = ("Argument 'lower' can not be a string ('['zero', 5]' given). You can not " "specify a variable as lower bound. You can only provide constant " "float values") self.assertEqual(str(context2.exception), msg2) def test_constraint_invalid_upper(self): prob = Problem() prob.driver = ScipyOptimizeDriver() prob.driver.options['optimizer'] = 'SLSQP' with self.assertRaises(TypeError) as context: prob.model.add_constraint('con1', lower=0, upper='foo', ref0=-100.0, ref=100) with self.assertRaises(TypeError) as context2: prob.model.add_constraint('con1', lower=0, upper=[1, 'foo'], ref0=-100.0, ref=100) msg = ("Argument 'upper' can not be a string ('foo' given). You can not " "specify a variable as upper bound. You can only provide constant " "float values") self.assertEqual(str(context.exception), msg) msg2 = ("Argument 'upper' can not be a string ('[1, 'foo']' given). You can not " "specify a variable as upper bound. You can only provide constant " "float values") self.assertEqual(str(context2.exception), msg2) def test_constraint_invalid_equals(self): prob = Problem() prob.driver = ScipyOptimizeDriver() prob.driver.options['optimizer'] = 'SLSQP' with self.assertRaises(TypeError) as context: prob.model.add_constraint('con1', equals='foo') with self.assertRaises(TypeError) as context2: prob.model.add_constraint('con1', equals=[1, 'two']) msg = ("Argument 'equals' can not be a string ('foo' given). You can " "not specify a variable as equals bound. You can only provide " "constant float values") self.assertEqual(str(context.exception), msg) msg2 = ("Argument 'equals' can not be a string ('[1, 'two']' given). You can " "not specify a variable as equals bound. You can only provide " "constant float values") self.assertEqual(str(context2.exception), msg2) def test_constraint_invalid_indices(self): prob = Problem() prob.model = SellarDerivatives() prob.model.nonlinear_solver = NonlinearBlockGS() with self.assertRaises(ValueError) as context: prob.model.add_constraint('con1', lower=0.0, upper=5.0, indices='foo') self.assertEqual(str(context.exception), 'SellarDerivatives: If specified, response indices must ' 'be a sequence of integers.') with self.assertRaises(ValueError) as context: prob.model.add_constraint('con1', lower=0.0, upper=5.0, indices=1) self.assertEqual(str(context.exception), 'SellarDerivatives: If specified, response indices must ' 'be a sequence of integers.') with self.assertRaises(ValueError) as context: prob.model.add_constraint('con1', lower=0.0, upper=5.0, indices=[1, 'k']) self.assertEqual(str(context.exception), 'SellarDerivatives: If specified, response indices must ' 'be a sequence of integers.') # passing an iterator for indices should be valid prob.model.add_constraint('con1', lower=0.0, upper=5.0, indices=range(2)) def test_error_eq_ineq_con(self): prob = Problem() prob.model = SellarDerivatives() prob.model.nonlinear_solver = NonlinearBlockGS() with self.assertRaises(ValueError) as context: prob.model.add_constraint('con1', lower=0.0, upper=5.0, equals=3.0, indices='foo') msg = "SellarDerivatives: Constraint 'con1' cannot be both equality and inequality." self.assertEqual(str(context.exception), msg) @unittest.skipUnless(MPI and PETScVector, "MPI and PETSc are required.") class TestAddConstraintMPI(unittest.TestCase): N_PROCS = 2 def test_add_bad_con(self): # From a bug, this message didn't work in mpi. prob = Problem() model = prob.model sub = model.add_subsystem('sub', SellarDerivatives()) sub.nonlinear_solver = NonlinearBlockGS() sub.add_constraint('d1.junk', equals=0.0, cache_linear_solution=True) with self.assertRaises(RuntimeError) as context: prob.setup(mode='rev') msg = "SellarDerivatives (sub): Output not found for response 'd1.junk'." self.assertEqual(str(context.exception), msg) class TestObjectiveOnModel(unittest.TestCase): def test_obective_not_exist(self): prob = Problem() prob.model = SellarDerivatives() prob.model.nonlinear_solver = NonlinearBlockGS() prob.model.add_objective('junk') with self.assertRaises(RuntimeError) as context: prob.setup() self.assertEqual(str(context.exception), "SellarDerivatives (<model>): Output not found for response 'junk'.") def test_objective_affine_and_scaleradder(self): prob = Problem() prob.model = SellarDerivatives() prob.model.nonlinear_solver = NonlinearBlockGS() with self.assertRaises(TypeError) as context: prob.model.add_objective('con1', lower=-100, upper=100, ref=1.0, scaler=0.5) self.assertEqual(str(context.exception), "add_objective() got an unexpected keyword argument 'lower'") with self.assertRaises(ValueError) as context: prob.model.add_objective('con1', ref=0.0, scaler=0.5) self.assertEqual(str(context.exception), 'Inputs ref/ref0 are mutually' ' exclusive with' ' scaler/adder') with self.assertRaises(ValueError) as context: prob.model.add_objective('con1', ref=0.0, adder=0.5) self.assertEqual(str(context.exception), 'Inputs ref/ref0 are mutually' ' exclusive with' ' scaler/adder') with self.assertRaises(ValueError) as context: prob.model.add_objective('x', ref0=0.0, adder=0.5) self.assertEqual(str(context.exception), 'Inputs ref/ref0 are mutually' ' exclusive with' ' scaler/adder') with self.assertRaises(ValueError) as context: prob.model.add_objective('con1', ref0=0.0, scaler=0.5) self.assertEqual(str(context.exception), 'Inputs ref/ref0 are mutually' ' exclusive with' ' scaler/adder') def test_objective_affine_mapping(self): prob = Problem() prob.model = SellarDerivatives() prob.model.nonlinear_solver = NonlinearBlockGS() prob.model.add_design_var('x', lower=-100, upper=100) prob.model.add_design_var('z', lower=-100, upper=100) prob.model.add_objective('obj', ref0=1000, ref=1010) prob.model.add_objective('con2') prob.setup() objectives = prob.model.get_objectives() obj_ref0 = objectives['obj_cmp.obj']['ref0'] obj_ref = objectives['obj_cmp.obj']['ref'] obj_scaler = objectives['obj_cmp.obj']['scaler'] obj_adder = objectives['obj_cmp.obj']['adder'] self.assertAlmostEqual( obj_scaler*(obj_ref0 + obj_adder), 0.0, places=12) self.assertAlmostEqual( obj_scaler*(obj_ref + obj_adder), 1.0, places=12) def test_desvar_size_err(self): prob = Problem() prob.model = SellarDerivatives() prob.model.nonlinear_solver = NonlinearBlockGS() for name in ['lower', 'upper', 'adder', 'scaler', 'ref', 'ref0']: args = {name: -np.ones(2)*100} with self.assertRaises(Exception) as context: prob.model.add_design_var('z', indices=[1], **args) self.assertEqual(str(context.exception), "SellarDerivatives: When adding design var 'z', %s should have size 1 but instead has size 2." % name) def test_constraint_size_err(self): prob = Problem() prob.model = SellarDerivatives() prob.model.nonlinear_solver = NonlinearBlockGS() for name in ['lower', 'upper', 'equals', 'adder', 'scaler', 'ref', 'ref0']: args = {name: -np.ones(2)*100} with self.assertRaises(Exception) as context: prob.model.add_constraint('z', indices=[1], **args) self.assertEqual(str(context.exception), "SellarDerivatives: When adding constraint 'z', %s should have size 1 but instead has size 2." % name) def test_objective_size_err(self): prob = Problem() prob.model = SellarDerivatives() prob.model.nonlinear_solver = NonlinearBlockGS() for name in ['adder', 'scaler', 'ref', 'ref0']: args = {name: -np.ones(2)*100} with self.assertRaises(Exception) as context: prob.model.add_objective('z', index=1, **args) self.assertEqual(str(context.exception), "SellarDerivatives: When adding objective 'z', %s should have size 1 but instead has size 2." % name) def test_objective_invalid_name(self): prob = Problem() prob.model = SellarDerivatives() prob.model.nonlinear_solver = NonlinearBlockGS() with self.assertRaises(TypeError) as context: prob.model.add_objective(42, ref0=-100.0, ref=100) self.assertEqual(str(context.exception), 'SellarDerivatives: The name argument should ' 'be a string, got 42') def test_objective_invalid_index(self): prob = Problem() prob.model = SellarDerivatives() prob.model.nonlinear_solver = NonlinearBlockGS() with self.assertRaises(TypeError) as context: prob.model.add_objective('obj', index='foo') self.assertEqual(str(context.exception), 'SellarDerivatives: If specified, objective index must be an int.') prob.model.add_objective('obj', index=1) if __name__ == '__main__': unittest.main()

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0.061097

0.052141

0.819961

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29,016

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0

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4c454ec1e13a378a70f673408fb268f085c92162

3,682

py

Python

magnolia/python/utils/training.py

rashley-iqt/Magnolia

123d0fdbe81eb7bb6d76cbe9db6f793fa3105e8a

[ "Apache-2.0" ]

51

2016-12-16T04:00:07.000Z

2020-11-30T13:26:51.000Z

magnolia/python/utils/training.py

rashley-iqt/Magnolia

123d0fdbe81eb7bb6d76cbe9db6f793fa3105e8a

[ "Apache-2.0" ]

30

2016-12-22T22:26:16.000Z

2017-12-11T17:28:21.000Z

magnolia/python/utils/training.py

Lab41/Magnolia

123d0fdbe81eb7bb6d76cbe9db6f793fa3105e8a

[ "Apache-2.0" ]

35

2016-12-16T04:00:09.000Z

2021-03-27T03:04:44.000Z

import numpy as np def preprocess_l41_regression_batch(spec_batch, mask_batch=None, specs_batch=None): # should be dimensions of (batch size, time frame, frequency) spec_batch = spec_batch.transpose(0, 2, 1) scaled_spec_batch = scale_input_spectrogram_for_l41_model(spec_batch) if mask_batch is not None and specs_batch is None: # should be dimensions of (batch size, time frame, frequency, source) mask_batch = mask_batch.transpose(0, 3, 2, 1) mask_batch = convert_boolean_mask_for_l41_model(mask_batch) return scaled_spec_batch, mask_batch if specs_batch is not None and mask_batch is None: # should be dimensions of (batch size, time frame, frequency, source) specs_batch = specs_batch.transpose(0, 3, 2, 1) return scaled_spec_batch, np.abs(specs_batch) if specs_batch is not None and mask_batch is not None: # should be dimensions of (batch size, time frame, frequency, source) mask_batch = mask_batch.transpose(0, 3, 2, 1) mask_batch = convert_boolean_mask_for_l41_model(mask_batch) # should be dimensions of (batch size, time frame, frequency, source) specs_batch = specs_batch.transpose(0, 3, 2, 1) return scaled_spec_batch, mask_batch, np.abs(specs_batch) return scaled_spec_batch def preprocess_chimera_batch(spec_batch, mask_batch=None, specs_batch=None): # should be dimensions of (batch size, time frame, frequency) spec_batch = spec_batch.transpose(0, 2, 1) unscaled_spec_batch = np.abs(spec_batch) scaled_spec_batch = scale_input_spectrogram_for_l41_model(spec_batch) if mask_batch is not None and specs_batch is None: # should be dimensions of (batch size, time frame, frequency, source) mask_batch = mask_batch.transpose(0, 3, 2, 1) # mask_batch = convert_boolean_mask_for_chimera_model(mask_batch) return unscaled_spec_batch, scaled_spec_batch, mask_batch if specs_batch is not None and mask_batch is None: # should be dimensions of (batch size, time frame, frequency, source) specs_batch = specs_batch.transpose(0, 3, 2, 1) return unscaled_spec_batch, scaled_spec_batch, np.abs(specs_batch) if specs_batch is not None and mask_batch is not None: # should be dimensions of (batch size, time frame, frequency, source) mask_batch = mask_batch.transpose(0, 3, 2, 1) # mask_batch = convert_boolean_mask_for_chimera_model(mask_batch) # should be dimensions of (batch size, time frame, frequency, source) specs_batch = specs_batch.transpose(0, 3, 2, 1) return unscaled_spec_batch, scaled_spec_batch, mask_batch, np.abs(specs_batch) return unscaled_spec_batch, scaled_spec_batch def preprocess_l41_batch(spec_batch, mask_batch=None): # should be dimensions of (batch size, time frame, frequency) spec_batch = spec_batch.transpose(0, 2, 1) spec_batch = scale_input_spectrogram_for_l41_model(spec_batch) if mask_batch is not None: # should be dimensions of (batch size, time frame, frequency, source) mask_batch = mask_batch.transpose(0, 3, 2, 1) mask_batch = convert_boolean_mask_for_l41_model(mask_batch) return spec_batch, mask_batch return spec_batch def scale_input_spectrogram_for_l41_model(spec_batch): spec_batch = np.sqrt(np.abs(spec_batch)) return (spec_batch - spec_batch.min())/(spec_batch.max() - spec_batch.min()) def convert_boolean_mask_for_l41_model(mask_batch): return 2.0*mask_batch.astype(float) - 1.0 # def convert_boolean_mask_for_chimera_model(mask_batch): # return mask_batch.astype(float)

42.813953

86

0.730038

560

3,682

4.489286

0.085714

0.139618

0.072395

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0

null

0

6

4c6b0321fcebb0ede9c8cb42b6421abfd8b63fbe

7,188

py

Python

main_Experiement_SensitivityAnalysis.py

MaximilianJanetschek/Urban_Intermodal_Transportation

632caf668636448dc9290d54cf1c7b527c68a957

[ "MIT" ]

null

main_Experiement_SensitivityAnalysis.py

MaximilianJanetschek/Urban_Intermodal_Transportation

632caf668636448dc9290d54cf1c7b527c68a957

[ "MIT" ]

null

main_Experiement_SensitivityAnalysis.py

MaximilianJanetschek/Urban_Intermodal_Transportation

632caf668636448dc9290d54cf1c7b527c68a957

[ "MIT" ]

null

from Utilities.Data_Retrieval import * from Utilities.Requests import * from Utilities.Parameters import * from Utilities.Solution_Procedure import * run_CS = False run_TS = True run_PS = False # beta experiment on Physical sensitive beta setup: if run_CS: CSbetaset =[[1,2,3,12,2.5], [1,2,3,14,2.5], [1,2,3,17,2.5], [1,2,3,20,2.5], [1,2,3,22,2.5], [1,2,3,24,2.5], [1,2,3,27,2.5], [1,2,3,30,2.5], [1,2,3,34,2.5], [1,2,3,40,2.5], [1,2,3,44,2.5], [1,2,3,54,2.5], [1,2,3,60,2.5]] for i in range(0,len(CSbetaset)): if i >= 0: # prepare parameters CaseParameters = Parameters(varCost_Taxi=0.0023, fixCostTaxi=3.9, fixCost_PublicTransport=2.9, fixCost_Bike=1.5, maxNumber_of_Changes=4, beta=CSbetaset[i], waiting_time_bike=4, waiting_time_drive=4) # generate a Instance - see class Data Retrieval for detail BerlinInstance = InstanceNetwork(place='Berlin, Germany', networks=['drive', 'walk', 'bike']) # also possible drive and bike BerlinInstance.generateMultiModalGraph(parameters=CaseParameters.dictOfParameters) # get requests initializeRequests() requests = getNumberRequests(BerlinInstance, 300) # run model total_number = len(requests) counter = 1 start_time = time.time() for request_number in range(0, len(requests)): if request_number >= 271: request = requests[request_number] try: origin_point = (request.get('fromLat'), request.get('fromLon')) destination_point = (request.get('toLat'), request.get('toLon')) tourMulti = multi_mode_optimization_in_Arc_fromulation(origin_point, destination_point, BerlinInstance, CaseParameters.dictOfParameters) print(str(counter) +' out of ' + str(total_number) + ' requests are calculated') print("--- %s seconds ---" % round((time.time() - start_time), 2)) except nx.NetworkXNoPath: print("this does not work") print(str(counter) + ' out of ' + str(total_number) + ' requests are calculated') print("--- %s seconds ---" % round((time.time() - start_time), 2)) counter += 1 else: print('pass set ' + str(i) + ' as already generated') # beta experiment on Physical sensitive beta setup: if run_TS: TSbetaset = [[1,2,3,2,2.5], # [1,2,3,1.75,2.5], [1,2,3,1.7,2.5]] ''' [[1,2,3,9,2.5], [1,2,3,6,2.5], [1,2,3,3,2.5], [1,2,3,1,2.5], [2,4,6,1,5], [3,6,9,1,7.5], [4,8,12,1,10], [5,10,15,1,12.5], [6,12,18,1,15]] ''' for i in range(0,len(TSbetaset)): # set counter to last finished beta set, set to large number to skip physical test tun if i >= 0: # prepare parameters CaseParameters = Parameters(varCost_Taxi=0.0023, fixCostTaxi=3.9, fixCost_PublicTransport=2.9, fixCost_Bike=1.5, maxNumber_of_Changes=4, beta=TSbetaset[i], waiting_time_bike=4, waiting_time_drive=4) # generate a Instance - see class Data Retrieval for detail BerlinInstance = InstanceNetwork(place='Berlin, Germany', networks=['drive', 'walk', 'bike']) # also possible drive and bike BerlinInstance.generateMultiModalGraph(parameters=CaseParameters.dictOfParameters) # get requests initializeRequests() requests = getNumberRequests(BerlinInstance, 300) # run model total_number = len(requests) counter = 1 start_time = time.time() for request in requests: try: origin_point = (request.get('fromLat'), request.get('fromLon')) destination_point = (request.get('toLat'), request.get('toLon')) tourMulti = multi_mode_optimization_in_Arc_fromulation(origin_point, destination_point, BerlinInstance, CaseParameters.dictOfParameters) print(str(counter) +' out of ' + str(total_number) + ' requests are calculated') print("--- %s seconds ---" % round((time.time() - start_time), 2)) counter += 1 except nx.NetworkXNoPath: print("this does not work") # beta experiment on Physical sensitive beta setup: if run_PS: PSbetaset =[[1,2,3,12,2.5], [1,3,3,12,3.75], [1,5,3,12,6.25], [1,6,3,12,7.5], [1,7,3,12,8.75], [1,8,3,12,10], [1,9,3,12,11.25], [1,10,3,12,12.5], [1,11,3,12,13.75], [1,12,3,12,15], [1,14,3,12,17.5], [1,16,3,12,20], [1,18,3,12,22.5]] for i in range(0,len(PSbetaset)): # set counter to last finished beta set, set to large number to skip physical test tun if i >= 0: # prepare parameters CaseParameters = Parameters(varCost_Taxi=0.0023, fixCostTaxi=3.9, fixCost_PublicTransport=2.9, fixCost_Bike=1.5, maxNumber_of_Changes=4, beta=PSbetaset[i], waiting_time_bike=4, waiting_time_drive=4) # generate a Instance - see class Data Retrieval for detail BerlinInstance = InstanceNetwork(place='Berlin, Germany', networks=['drive', 'walk', 'bike']) # also possible drive and bike BerlinInstance.generateMultiModalGraph(parameters=CaseParameters.dictOfParameters) # get requests initializeRequests() requests = getNumberRequests(BerlinInstance, 300) # run model total_number = len(requests) counter = 1 start_time = time.time() for request in requests: try: origin_point = (request.get('fromLat'), request.get('fromLon')) destination_point = (request.get('toLat'), request.get('toLon')) tourMulti = multi_mode_optimization_in_Arc_fromulation(origin_point, destination_point, BerlinInstance, CaseParameters.dictOfParameters) print(str(counter) +' out of ' + str(total_number) + ' requests are calculated') print("--- %s seconds ---" % round((time.time() - start_time), 2)) counter += 1 except nx.NetworkXNoPath: print("this does not work")

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137

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0.01197

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0.019587

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null

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1

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null

0

6

4c730fa34525821f2f1eec318b7c26f55243b178

25

py

Python

calibration/__init__.py

najafian-lab/em-calibration

81693ddbf87e642cd66a0b375e25ca378c2752a8

[ "MIT" ]

1

2021-07-05T12:48:39.000Z

calibration/__init__.py

najafian-lab/em-calibration

81693ddbf87e642cd66a0b375e25ca378c2752a8

[ "MIT" ]

null

calibration/__init__.py

najafian-lab/em-calibration

81693ddbf87e642cd66a0b375e25ca378c2752a8

[ "MIT" ]

null

from calibration import *

25

0.84

3

25

7

1

0

0.12

25

1

25

0.954545

0

1

0

true

0

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null

0

1

0

null

0

1

0

1

0

1

0

6

910232a890e7bcb434bd5f904977f42ab169fbca

15,011

py

Python

sdk/python/pulumi_azure/network/network_security_group.py

adnang/pulumi-azure

32360d2f1e41e27d7fdd6522cb26d65e531f279f

[ "ECL-2.0", "Apache-2.0" ]

null

sdk/python/pulumi_azure/network/network_security_group.py

adnang/pulumi-azure

32360d2f1e41e27d7fdd6522cb26d65e531f279f

[ "ECL-2.0", "Apache-2.0" ]

null

sdk/python/pulumi_azure/network/network_security_group.py

adnang/pulumi-azure

32360d2f1e41e27d7fdd6522cb26d65e531f279f

[ "ECL-2.0", "Apache-2.0" ]

null

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import json import warnings import pulumi import pulumi.runtime from typing import Union from .. import utilities, tables class NetworkSecurityGroup(pulumi.CustomResource): location: pulumi.Output[str] """ Specifies the supported Azure location where the resource exists. Changing this forces a new resource to be created. """ name: pulumi.Output[str] """ The name of the security rule. """ resource_group_name: pulumi.Output[str] """ The name of the resource group in which to create the network security group. Changing this forces a new resource to be created. """ security_rules: pulumi.Output[list] """ A list of objects representing security rules, as defined below. * `access` (`str`) - Specifies whether network traffic is allowed or denied. Possible values are `Allow` and `Deny`. * `description` (`str`) - A description for this rule. Restricted to 140 characters. * `destination_address_prefix` (`str`) - CIDR or destination IP range or * to match any IP. Tags such as ‘VirtualNetwork’, ‘AzureLoadBalancer’ and ‘Internet’ can also be used. This is required if `destination_address_prefixes` is not specified. * `destination_address_prefixes` (`list`) - List of destination address prefixes. Tags may not be used. This is required if `destination_address_prefix` is not specified. * `destination_application_security_group_ids` (`list`) - A List of destination Application Security Group ID's * `destination_port_range` (`str`) - Destination Port or Range. Integer or range between `0` and `65535` or `*` to match any. This is required if `destination_port_ranges` is not specified. * `destination_port_ranges` (`list`) - List of destination ports or port ranges. This is required if `destination_port_range` is not specified. * `direction` (`str`) - The direction specifies if rule will be evaluated on incoming or outgoing traffic. Possible values are `Inbound` and `Outbound`. * `name` (`str`) - The name of the security rule. * `priority` (`float`) - Specifies the priority of the rule. The value can be between 100 and 4096. The priority number must be unique for each rule in the collection. The lower the priority number, the higher the priority of the rule. * `protocol` (`str`) - Network protocol this rule applies to. Can be `Tcp`, `Udp`, `Icmp`, or `*` to match all. * `source_address_prefix` (`str`) - CIDR or source IP range or * to match any IP. Tags such as ‘VirtualNetwork’, ‘AzureLoadBalancer’ and ‘Internet’ can also be used. This is required if `source_address_prefixes` is not specified. * `source_address_prefixes` (`list`) - List of source address prefixes. Tags may not be used. This is required if `source_address_prefix` is not specified. * `source_application_security_group_ids` (`list`) - A List of source Application Security Group ID's * `source_port_range` (`str`) - Source Port or Range. Integer or range between `0` and `65535` or `*` to match any. This is required if `source_port_ranges` is not specified. * `source_port_ranges` (`list`) - List of source ports or port ranges. This is required if `source_port_range` is not specified. """ tags: pulumi.Output[dict] """ A mapping of tags to assign to the resource. """ def __init__(__self__, resource_name, opts=None, location=None, name=None, resource_group_name=None, security_rules=None, tags=None, __props__=None, __name__=None, __opts__=None): """ Manages a network security group that contains a list of network security rules. Network security groups enable inbound or outbound traffic to be enabled or denied. > **NOTE on Network Security Groups and Network Security Rules:** This provider currently provides both a standalone Network Security Rule resource, and allows for Network Security Rules to be defined in-line within the Network Security Group resource. At this time you cannot use a Network Security Group with in-line Network Security Rules in conjunction with any Network Security Rule resources. Doing so will cause a conflict of rule settings and will overwrite rules. ## Example Usage ```python import pulumi import pulumi_azure as azure example_resource_group = azure.core.ResourceGroup("exampleResourceGroup", location="West US") example_network_security_group = azure.network.NetworkSecurityGroup("exampleNetworkSecurityGroup", location=example_resource_group.location, resource_group_name=example_resource_group.name, security_rule=[{ "name": "test123", "priority": 100, "direction": "Inbound", "access": "Allow", "protocol": "Tcp", "sourcePortRange": "*", "destinationPortRange": "*", "sourceAddressPrefix": "*", "destinationAddressPrefix": "*", }], tags={ "environment": "Production", }) ``` :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[str] location: Specifies the supported Azure location where the resource exists. Changing this forces a new resource to be created. :param pulumi.Input[str] name: The name of the security rule. :param pulumi.Input[str] resource_group_name: The name of the resource group in which to create the network security group. Changing this forces a new resource to be created. :param pulumi.Input[list] security_rules: A list of objects representing security rules, as defined below. :param pulumi.Input[dict] tags: A mapping of tags to assign to the resource. The **security_rules** object supports the following: * `access` (`pulumi.Input[str]`) - Specifies whether network traffic is allowed or denied. Possible values are `Allow` and `Deny`. * `description` (`pulumi.Input[str]`) - A description for this rule. Restricted to 140 characters. * `destination_address_prefix` (`pulumi.Input[str]`) - CIDR or destination IP range or * to match any IP. Tags such as ‘VirtualNetwork’, ‘AzureLoadBalancer’ and ‘Internet’ can also be used. This is required if `destination_address_prefixes` is not specified. * `destination_address_prefixes` (`pulumi.Input[list]`) - List of destination address prefixes. Tags may not be used. This is required if `destination_address_prefix` is not specified. * `destination_application_security_group_ids` (`pulumi.Input[list]`) - A List of destination Application Security Group ID's * `destination_port_range` (`pulumi.Input[str]`) - Destination Port or Range. Integer or range between `0` and `65535` or `*` to match any. This is required if `destination_port_ranges` is not specified. * `destination_port_ranges` (`pulumi.Input[list]`) - List of destination ports or port ranges. This is required if `destination_port_range` is not specified. * `direction` (`pulumi.Input[str]`) - The direction specifies if rule will be evaluated on incoming or outgoing traffic. Possible values are `Inbound` and `Outbound`. * `name` (`pulumi.Input[str]`) - The name of the security rule. * `priority` (`pulumi.Input[float]`) - Specifies the priority of the rule. The value can be between 100 and 4096. The priority number must be unique for each rule in the collection. The lower the priority number, the higher the priority of the rule. * `protocol` (`pulumi.Input[str]`) - Network protocol this rule applies to. Can be `Tcp`, `Udp`, `Icmp`, or `*` to match all. * `source_address_prefix` (`pulumi.Input[str]`) - CIDR or source IP range or * to match any IP. Tags such as ‘VirtualNetwork’, ‘AzureLoadBalancer’ and ‘Internet’ can also be used. This is required if `source_address_prefixes` is not specified. * `source_address_prefixes` (`pulumi.Input[list]`) - List of source address prefixes. Tags may not be used. This is required if `source_address_prefix` is not specified. * `source_application_security_group_ids` (`pulumi.Input[list]`) - A List of source Application Security Group ID's * `source_port_range` (`pulumi.Input[str]`) - Source Port or Range. Integer or range between `0` and `65535` or `*` to match any. This is required if `source_port_ranges` is not specified. * `source_port_ranges` (`pulumi.Input[list]`) - List of source ports or port ranges. This is required if `source_port_range` is not specified. """ if __name__ is not None: warnings.warn("explicit use of __name__ is deprecated", DeprecationWarning) resource_name = __name__ if __opts__ is not None: warnings.warn("explicit use of __opts__ is deprecated, use 'opts' instead", DeprecationWarning) opts = __opts__ if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.version is None: opts.version = utilities.get_version() if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = dict() __props__['location'] = location __props__['name'] = name if resource_group_name is None: raise TypeError("Missing required property 'resource_group_name'") __props__['resource_group_name'] = resource_group_name __props__['security_rules'] = security_rules __props__['tags'] = tags super(NetworkSecurityGroup, __self__).__init__( 'azure:network/networkSecurityGroup:NetworkSecurityGroup', resource_name, __props__, opts) @staticmethod def get(resource_name, id, opts=None, location=None, name=None, resource_group_name=None, security_rules=None, tags=None): """ Get an existing NetworkSecurityGroup resource's state with the given name, id, and optional extra properties used to qualify the lookup. :param str resource_name: The unique name of the resulting resource. :param str id: The unique provider ID of the resource to lookup. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[str] location: Specifies the supported Azure location where the resource exists. Changing this forces a new resource to be created. :param pulumi.Input[str] name: The name of the security rule. :param pulumi.Input[str] resource_group_name: The name of the resource group in which to create the network security group. Changing this forces a new resource to be created. :param pulumi.Input[list] security_rules: A list of objects representing security rules, as defined below. :param pulumi.Input[dict] tags: A mapping of tags to assign to the resource. The **security_rules** object supports the following: * `access` (`pulumi.Input[str]`) - Specifies whether network traffic is allowed or denied. Possible values are `Allow` and `Deny`. * `description` (`pulumi.Input[str]`) - A description for this rule. Restricted to 140 characters. * `destination_address_prefix` (`pulumi.Input[str]`) - CIDR or destination IP range or * to match any IP. Tags such as ‘VirtualNetwork’, ‘AzureLoadBalancer’ and ‘Internet’ can also be used. This is required if `destination_address_prefixes` is not specified. * `destination_address_prefixes` (`pulumi.Input[list]`) - List of destination address prefixes. Tags may not be used. This is required if `destination_address_prefix` is not specified. * `destination_application_security_group_ids` (`pulumi.Input[list]`) - A List of destination Application Security Group ID's * `destination_port_range` (`pulumi.Input[str]`) - Destination Port or Range. Integer or range between `0` and `65535` or `*` to match any. This is required if `destination_port_ranges` is not specified. * `destination_port_ranges` (`pulumi.Input[list]`) - List of destination ports or port ranges. This is required if `destination_port_range` is not specified. * `direction` (`pulumi.Input[str]`) - The direction specifies if rule will be evaluated on incoming or outgoing traffic. Possible values are `Inbound` and `Outbound`. * `name` (`pulumi.Input[str]`) - The name of the security rule. * `priority` (`pulumi.Input[float]`) - Specifies the priority of the rule. The value can be between 100 and 4096. The priority number must be unique for each rule in the collection. The lower the priority number, the higher the priority of the rule. * `protocol` (`pulumi.Input[str]`) - Network protocol this rule applies to. Can be `Tcp`, `Udp`, `Icmp`, or `*` to match all. * `source_address_prefix` (`pulumi.Input[str]`) - CIDR or source IP range or * to match any IP. Tags such as ‘VirtualNetwork’, ‘AzureLoadBalancer’ and ‘Internet’ can also be used. This is required if `source_address_prefixes` is not specified. * `source_address_prefixes` (`pulumi.Input[list]`) - List of source address prefixes. Tags may not be used. This is required if `source_address_prefix` is not specified. * `source_application_security_group_ids` (`pulumi.Input[list]`) - A List of source Application Security Group ID's * `source_port_range` (`pulumi.Input[str]`) - Source Port or Range. Integer or range between `0` and `65535` or `*` to match any. This is required if `source_port_ranges` is not specified. * `source_port_ranges` (`pulumi.Input[list]`) - List of source ports or port ranges. This is required if `source_port_range` is not specified. """ opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = dict() __props__["location"] = location __props__["name"] = name __props__["resource_group_name"] = resource_group_name __props__["security_rules"] = security_rules __props__["tags"] = tags return NetworkSecurityGroup(resource_name, opts=opts, __props__=__props__) def translate_output_property(self, prop): return tables._CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop def translate_input_property(self, prop): return tables._SNAKE_TO_CAMEL_CASE_TABLE.get(prop) or prop

77.376289

268

0.699021

1,986

15,011

5.128399

0.123364

0.045361

0.03299

0.037703

0.762592

0.753265

0.746588

0.74541

0.722337

0.7189

0

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0.21411

15,011

193

269

77.777202

0.857167

0.572314

0

0.036364

1

0

0.162017

0.027182

0

1

0.072727

false

0.018182

0.109091

0.036364

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0

null

0

1

0

1

0

null

0

6

e68e5d20601732f9ca0628ab99b2fd41f1f6668a

124

py

Python

dense_coattn/util/__init__.py

yuzhiw/Dense-CoAttention-Network

4bd82682b30a471edf19f6d88a87ef4399e7c4ba

[ "MIT" ]

1

2018-11-17T13:17:42.000Z

dense_coattn/util/__init__.py

yuzhiw/Dense-CoAttention-Network

4bd82682b30a471edf19f6d88a87ef4399e7c4ba

[ "MIT" ]

null

dense_coattn/util/__init__.py

yuzhiw/Dense-CoAttention-Network

4bd82682b30a471edf19f6d88a87ef4399e7c4ba

[ "MIT" ]

null

from .utils import Initializer, Drawer, Saver, Timer, Meter __all__ = ["Initializer", "Drawer", "Saver", "Timer", "Meter"]

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null

1

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1

0

null

0

1

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6

e6932792f25709082e1721d9dcdabeb173136243

31

py

Python

onyx/database/__init__.py

mudkipdev/onyx

333d23c1f83bb2f69a9f570ce874b9d05dc2edda

[ "MIT" ]

null

onyx/database/__init__.py

mudkipdev/onyx

333d23c1f83bb2f69a9f570ce874b9d05dc2edda

[ "MIT" ]

null

onyx/database/__init__.py

mudkipdev/onyx

333d23c1f83bb2f69a9f570ce874b9d05dc2edda

[ "MIT" ]

null

from .guild import CustomGuild

15.5

30

0.83871

4

31

6.5

1

0

0.129032

31

1

31

0.962963

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0

null

0

1

0

1

0

1

0

6

e6a72934634176da3823d49bfb079b1a3e7f34e1

93

py

Python

app/genetron/__init__.py

cangfengzhe/flask_genetron

792a22bc6550c8545e6345c43d7a2c9910ea84be

[ "MIT" ]

1

2016-12-12T10:51:15.000Z

app/genetron/__init__.py

cangfengzhe/flask_genetron

792a22bc6550c8545e6345c43d7a2c9910ea84be

[ "MIT" ]

null

app/genetron/__init__.py

cangfengzhe/flask_genetron

792a22bc6550c8545e6345c43d7a2c9910ea84be

[ "MIT" ]

null

from flask import Blueprint genetron = Blueprint('genetron', __name__) from . import views

15.5

42

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93

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1

0

null

0

1

0

1

0

6

e6aac7e4224b78b1796cad164e789d09b47835a3

5,028

py

Python

tests/gamestonk_terminal/stocks/options/test_tradier_view.py

minhhoang1023/GamestonkTerminal

195dc19b491052df080178c0cc6a9d535a91a704

[ "MIT" ]

null

tests/gamestonk_terminal/stocks/options/test_tradier_view.py

minhhoang1023/GamestonkTerminal

195dc19b491052df080178c0cc6a9d535a91a704

[ "MIT" ]

null

tests/gamestonk_terminal/stocks/options/test_tradier_view.py

minhhoang1023/GamestonkTerminal

195dc19b491052df080178c0cc6a9d535a91a704

[ "MIT" ]

null

# IMPORTATION THIRDPARTY import pytest # IMPORTATION INTERNAL from gamestonk_terminal.stocks.options import tradier_view @pytest.fixture(scope="module") def vcr_config(): return { "filter_headers": [("Authorization", "MOCK_TOKEN")], } @pytest.mark.vcr(record_mode="none") def test_red_highlight(recorder): result = tradier_view.red_highlight(val="MOCK TEXT") recorder.capture(result) @pytest.mark.vcr(record_mode="none") def test_green_highlight(recorder): result = tradier_view.green_highlight(val="MOCK TEXT") recorder.capture(result) @pytest.mark.vcr(record_mode="none") def test_check_valid_option_chains_headers(recorder): result = tradier_view.check_valid_option_chains_headers(headers="gamma,delta") recorder.capture(result) @pytest.mark.default_cassette("test_display_chains") @pytest.mark.vcr @pytest.mark.record_stdout @pytest.mark.parametrize( "calls_only, puts_only, min_sp, max_sp", [ (True, False, 80.0, 90.0), (False, True, 80.0, 90.0), (True, False, -1, -1), (False, True, -1, -1), (False, False, -1, -1), ], ) def test_display_chains(calls_only, max_sp, min_sp, mocker, puts_only): # MOCK EXPORT_DATA mocker.patch(target="gamestonk_terminal.stocks.options.tradier_view.export_data") # MOCK USE_COLOR mocker.patch.object(target=tradier_view.gtff, attribute="USE_COLOR", new=True) tradier_view.display_chains( ticker="AAPL", expiry="2022-02-25", to_display=["volume"], min_sp=min_sp, max_sp=max_sp, calls_only=calls_only, puts_only=puts_only, export="", ) @pytest.mark.default_cassette("test_plot_oi") @pytest.mark.vcr @pytest.mark.parametrize( "calls_only, puts_only, min_sp, max_sp", [ (True, False, 80.0, 90.0), (False, True, 80.0, 90.0), (True, False, -1, -1), (False, True, -1, -1), (True, True, -1, -1), (False, False, -1, -1), ], ) def test_plot_oi(calls_only, max_sp, min_sp, mocker, puts_only): # MOCK CHARTS mocker.patch( target="gamestonk_terminal.stocks.options.tradier_view.theme.visualize_output" ) # MOCK EXPORT_DATA mocker.patch(target="gamestonk_terminal.stocks.options.tradier_view.export_data") # MOCK USE_COLOR mocker.patch.object(target=tradier_view.gtff, attribute="USE_COLOR", new=True) tradier_view.plot_oi( ticker="AAPL", expiry="2022-02-25", min_sp=min_sp, max_sp=max_sp, calls_only=calls_only, puts_only=puts_only, export="", ) @pytest.mark.default_cassette("test_plot_oi") @pytest.mark.vcr @pytest.mark.parametrize( "calls_only, puts_only, min_sp, max_sp", [ (True, False, 80.0, 90.0), (False, True, 80.0, 90.0), (True, False, -1, -1), (False, True, -1, -1), (True, True, -1, -1), (False, False, -1, -1), ], ) def test_plot_vol(calls_only, max_sp, min_sp, mocker, puts_only): # MOCK CHARTS mocker.patch( target="gamestonk_terminal.stocks.options.tradier_view.theme.visualize_output" ) # MOCK EXPORT_DATA mocker.patch(target="gamestonk_terminal.stocks.options.tradier_view.export_data") # MOCK USE_COLOR mocker.patch.object(target=tradier_view.gtff, attribute="USE_COLOR", new=True) tradier_view.plot_vol( ticker="AAPL", expiry="2022-02-25", min_sp=min_sp, max_sp=max_sp, calls_only=calls_only, puts_only=puts_only, export="", ) @pytest.mark.default_cassette("test_plot_volume_open_interest") @pytest.mark.vcr @pytest.mark.parametrize( "min_sp, max_sp, min_vol", [ (80.0, 90.0, 0.0), (-1, -1, -1), ], ) def test_plot_volume_open_interest(max_sp, min_sp, min_vol, mocker): # MOCK CHARTS mocker.patch( target="gamestonk_terminal.stocks.options.tradier_view.theme.visualize_output" ) # MOCK EXPORT_DATA mocker.patch(target="gamestonk_terminal.stocks.options.tradier_view.export_data") # MOCK USE_COLOR mocker.patch.object(target=tradier_view.gtff, attribute="USE_COLOR", new=True) tradier_view.plot_volume_open_interest( ticker="AAPL", expiry="2022-02-25", min_sp=min_sp, max_sp=max_sp, min_vol=min_vol, export="", ) @pytest.mark.vcr @pytest.mark.record_stdout def test_display_historical(mocker): # MOCK CHARTS mocker.patch( target="gamestonk_terminal.stocks.options.tradier_view.theme.visualize_output" ) # MOCK EXPORT_DATA mocker.patch(target="gamestonk_terminal.stocks.options.tradier_view.export_data") # MOCK USE_COLOR mocker.patch.object(target=tradier_view.gtff, attribute="USE_COLOR", new=True) tradier_view.display_historical( ticker="AAPL", expiry="2022-02-25", strike=180.0, put=True, export="csv", raw=True, chain_id="", )

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0.026949

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0

null

0

1

0

null

0

6

e6d6f73ffc83062984873b9396f8302281a543a8

113,559

py

Python

onnxoptimizer/test/optimizer_test.py

462630221/optimizer

8f5a6e94ae841a0ac7431d339e3c290884ab02f5

[ "Apache-2.0" ]

1

2021-02-20T07:33:01.000Z

onnxoptimizer/test/optimizer_test.py

462630221/optimizer

8f5a6e94ae841a0ac7431d339e3c290884ab02f5

[ "Apache-2.0" ]

null

onnxoptimizer/test/optimizer_test.py

462630221/optimizer

8f5a6e94ae841a0ac7431d339e3c290884ab02f5

[ "Apache-2.0" ]

null

# SPDX-License-Identifier: Apache-2.0 from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals from collections import OrderedDict from typing import Sequence, Text, Any, Tuple, List, Callable, Optional, Dict, Union import io import unittest import os import numpy as np # type: ignore try: import torch import torchvision as tv has_tv = True except: has_tv = False import onnx from onnx import checker, helper, ModelProto, TensorProto, GraphProto, NodeProto, shape_inference from onnx import numpy_helper from onnx.numpy_helper import to_array try: import onnxruntime as rt has_ort = True except: has_ort = False import onnxoptimizer TensorShape = List[int] TensorShapes = Dict[Optional[str], TensorShape] LATEST_STABLE_OPSET_VERSION = 13 class TestOptimizer(unittest.TestCase): def _compare(self, model_opt: onnx.ModelProto, model_ori: onnx.ModelProto, n_times: int = 5, input_shapes: Optional[TensorShapes] = None, verbose=True) -> bool: """ :param input_shapes: Shapes of generated random inputs :param model_opt: The simplified ONNX model :param model_ori: The original ONNX model :param n_times: Generate n random inputs """ def get_shape_from_value_info_proto(v: onnx.ValueInfoProto) -> List[int]: return [dim.dim_value for dim in v.type.tensor_type.shape.dim] def get_value_info_all(m: onnx.ModelProto, name: str) -> Optional[onnx.ValueInfoProto]: for v in m.graph.value_info: if v.name == name: return v for v in m.graph.input: if v.name == name: return v for v in m.graph.output: if v.name == name: return v return None def get_shape(m: onnx.ModelProto, name: str) -> TensorShape: """ Note: This method relies on onnx shape inference, which is not reliable. So only use it on input or output tensors """ v = get_value_info_all(m, name) if v is not None: return get_shape_from_value_info_proto(v) raise RuntimeError('Cannot get shape of "{}"'.format(name)) def get_elem_type(m: onnx.ModelProto, name: str) -> Optional[int]: v = get_value_info_all(m, name) if v is not None: return v.type.tensor_type.elem_type return None def get_np_type_from_elem_type(elem_type: int) -> int: sizes = (None, np.float32, np.uint8, np.int8, np.uint16, np.int16, np.int32, np.int64, str, np.bool, np.float16, np.double, np.uint32, np.uint64, np.complex64, np.complex128, np.float16) assert len(sizes) == 17 size = sizes[elem_type] assert size is not None return size def get_input_names(model: onnx.ModelProto) -> List[str]: input_names = list(set([ipt.name for ipt in model.graph.input]) - set([x.name for x in model.graph.initializer])) return input_names def generate_rand_input(model, input_shapes: Optional[TensorShapes] = None): if input_shapes is None: input_shapes = {} input_names = get_input_names(model) full_input_shapes = {ipt: get_shape( model, ipt) for ipt in input_names} assert None not in input_shapes full_input_shapes.update(input_shapes) # type: ignore for key in full_input_shapes: if np.prod(full_input_shapes[key]) <= 0: raise RuntimeError( 'The shape of input "{}" has dynamic size, ' 'please set an input shape manually'.format(key)) inputs = {ipt: np.array(np.random.rand(*full_input_shapes[ipt]), dtype=get_np_type_from_elem_type(get_elem_type(model, ipt))) for ipt in input_names} return inputs def forward(model, inputs=None, input_shapes: Optional[TensorShapes] = None) -> Dict[str, np.ndarray]: if input_shapes is None: input_shapes = {} sess_options = rt.SessionOptions() sess_options.graph_optimization_level = rt.GraphOptimizationLevel(0) sess_options.log_severity_level = 3 sess = rt.InferenceSession(model.SerializeToString( ), sess_options=sess_options, providers=['CPUExecutionProvider']) if inputs is None: inputs = generate_rand_input(model, input_shapes=input_shapes) outputs = [x.name for x in sess.get_outputs()] run_options = rt.RunOptions() run_options.log_severity_level = 3 res = OrderedDict(zip(outputs, sess.run( outputs, inputs, run_options=run_options))) return res if input_shapes is None: input_shapes = {} onnx.checker.check_model(model_opt) for i in range(n_times): rand_input = generate_rand_input( model_opt, input_shapes=input_shapes) res_ori = forward(model_ori, inputs=rand_input) res_opt = forward(model_opt, inputs=rand_input) for name in res_opt.keys(): if not np.allclose(res_opt[name], res_ori[name], rtol=1e-4, atol=1e-5): if verbose: print("Tensor {} changes after optimization. The max diff is {}.".format( name, np.max(np.abs(res_opt[name] - res_ori[name])))) print("After optimization:") print(res_opt[name]) print("Before optimization:") print(res_ori[name]) print("----------------") return False return True # type: (Union[GraphProto, ModelProto], Sequence[Text], bool, **Any) -> ModelProto def _optimized(self, graph_or_model, opts, fixed_point=False, compare_result=True, **kwargs): if isinstance(graph_or_model, ModelProto): orig_model = graph_or_model else: opset_imports = kwargs.pop('opset_imports', None) if opset_imports is None: opset_imports = [helper.make_opsetid("", LATEST_STABLE_OPSET_VERSION)] orig_model = helper.make_model( graph_or_model, producer_name='onnx-test', opset_imports=opset_imports, **kwargs) checker.check_model(orig_model) optimized_model = onnxoptimizer.optimize(orig_model, opts, fixed_point) checker.check_model(optimized_model) if compare_result and len(optimized_model.graph.node) > 0: if has_ort: assert self._compare(optimized_model, orig_model) else: print("Skip onnxruntime test because it is not installed.") return optimized_model # input_types and output_types are lists of triples of (name, type, shape) # NOTE(daquexian): only values that change across loop iterations should be in `input_types` and `output_types`. The pseudocode showing how loop op works is: # loop_value_inputs = graph_value_inputs # while cond: # loop_value_outputs = body(loop_value_inputs) # loop_value_inputs = loop_value_outputs # graph_value_outputs = loop_value_outputs def _make_fake_loop_op(self, body_nodes, # type: Sequence[NodeProto] # type: Sequence[Tuple[TensorProto.DataType, Sequence[int], Text]] input_types, # type: Sequence[Tuple[TensorProto.DataType, Sequence[int], Text]] output_types, check_legality=True, ): # type: (...) -> List[NodeProto] if check_legality: assert len(input_types) == len(output_types) zero = helper.make_tensor( "trip_count_value", TensorProto.INT64, (), [1]) true = helper.make_tensor("condition", TensorProto.BOOL, (), [True]) # lcd is a dummy loop-carried dependency that only exists because # right now the schema checker is broken and assumes a variadic # input needs at least one value. graph_inputs = [helper.make_tensor_value_info("i", TensorProto.INT64, ()), helper.make_tensor_value_info("cond", TensorProto.BOOL, ())] for type, shape, name in input_types: graph_inputs.append( helper.make_tensor_value_info("_" + name, type, shape)) graph_outputs = [helper.make_tensor_value_info( "cond", TensorProto.BOOL, ())] for type, shape, name in output_types: graph_outputs.append( helper.make_tensor_value_info("_" + name, type, shape)) body_graph = helper.make_graph(body_nodes, "body_graph", graph_inputs, graph_outputs) loop_inputs = ["trip_count", "condition"] loop_inputs.extend([name for _, _, name in input_types]) # TODO: fix checker to accept 0-input variadic inputs if len(loop_inputs) == 2: loop_inputs.append("") loop_outputs = [name for _, _, name in output_types] retval_nodes = [ helper.make_node("Constant", [], ["trip_count"], value=zero), helper.make_node("Constant", [], ["condition"], value=true), helper.make_node("Loop", loop_inputs, loop_outputs, body=body_graph) ] return retval_nodes def _make_fake_if_op(self, true_nodes, # type: Sequence[NodeProto] false_nodes, # type: Sequence[NodeProto] # type: Sequence[Tuple[TensorProto.DataType, Sequence[int], Text]] output_types ): # type: (...) -> List[NodeProto] true = helper.make_tensor("condition", TensorProto.BOOL, (), [True]) true_graph = helper.make_graph(true_nodes, "true_graph", [], []) false_graph = helper.make_graph(false_nodes, "false_graph", [], []) if_inputs = ["condition"] if_outputs = [name for _, _, name in output_types] retval_nodes = [ helper.make_node("Constant", [], ["condition"], value=true), helper.make_node("If", if_inputs, if_outputs, then_branch=true_graph, else_branch=false_graph) ] return retval_nodes # fn is a function that takes a single node as argument # type: (GraphProto, Callable[[NodeProto], None]) -> None def _visit_all_nodes_recursive(self, graph, fn): for node in graph.node: fn(node) for attr in node.attribute: if attr.g is not None: self._visit_all_nodes_recursive(attr.g, fn) if len(attr.graphs): for gr in attr.graphs: self._visit_all_nodes_recursive(gr, fn) def test_get_available_passes(self): # type: () -> None # FIXME does not guarantees to be listing all graph = helper.make_graph([], "dummy_graph", [], []) list_of_passes = onnxoptimizer.get_available_passes() assert isinstance(list_of_passes, (list)) and len(list_of_passes) > 0 for pass_name in list_of_passes: # If pass_name is invalid it throws a RuntimeError self._optimized(graph, [pass_name]) def test_eliminate_identity_single_use(self): # type: () -> None nodes = [helper.make_node("Add", ["X", "Y"], ["A"]), helper.make_node("Identity", ["A"], ["B"])] nodes.extend(self._make_fake_loop_op( [helper.make_node("Identity", ["_B"], ["_B2"])], [(TensorProto.FLOAT, (5,), "B")], [(TensorProto.FLOAT, (5,), "B2")])) graph = helper.make_graph( nodes, "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (5,)), helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5,))], [helper.make_tensor_value_info("B", TensorProto.FLOAT, (5,)), helper.make_tensor_value_info("B2", TensorProto.FLOAT, (5,))]) optimized_model = self._optimized(graph, ["eliminate_identity"]) # All identity nodes should have been eliminated def check_identity(node): # type: (NodeProto) -> None assert node.op_type != "Identity" self._visit_all_nodes_recursive(optimized_model.graph, check_identity) # Use of the output from the Identity node in the main graph should # have been replaced with the input to the identity node assert len(optimized_model.graph.output) == 2 assert optimized_model.graph.output[0].name == "B" # Use of the output from the Identity node in the loop graph should # have been replaced with the input to that identity node assert len(optimized_model.graph.node[3].attribute[0].g.output) == 2 assert optimized_model.graph.node[3].attribute[0].g.output[1].name == "_B2" def test_eliminate_identity_graph_output(self): # type: () -> None add = helper.make_node("Add", ["X", "Y"], ["A"]) identity = helper.make_node("Identity", ["A"], ["B"]) graph = helper.make_graph( [add, identity], "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (5,)), helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5,))], [helper.make_tensor_value_info("B", TensorProto.FLOAT, (5,))]) optimized_model = self._optimized(graph, ["eliminate_identity"]) for node in optimized_model.graph.node: assert node.op_type != "Identity" assert len( optimized_model.graph.output) == 1 and optimized_model.graph.output[0].name == 'B' assert len(optimized_model.graph.node) == 1 def test_eliminate_identity_multiple_uses(self): # type: () -> None identity = helper.make_node("Identity", ["X"], ["Y"]) add = helper.make_node("Add", ["Z", "Y"], ["A"]) mul = helper.make_node("Mul", ["A", "Y"], ["B"]) graph = helper.make_graph( [identity, add, mul], "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (5,)), helper.make_tensor_value_info("Z", TensorProto.FLOAT, (5,))], [helper.make_tensor_value_info("B", TensorProto.FLOAT, (5,))]) optimized_model = self._optimized(graph, ["eliminate_identity"]) for node in optimized_model.graph.node: assert node.op_type != "Identity" assert len(optimized_model.graph.node) == 2 def test_not_fuse_non_nop_flatten(self): flatten = helper.make_node("Flatten", ["A"], ["B"], axis=2) graph = helper.make_graph( [flatten], "test", [helper.make_tensor_value_info( "A", TensorProto.FLOAT, (1, 10, 3, 1, 1))], [helper.make_tensor_value_info("B", TensorProto.FLOAT, (10, 3))]) optimized_model = self._optimized(graph, ["eliminate_nop_flatten"]) assert len(optimized_model.graph.node) == 1 assert optimized_model.graph.node[0].op_type == 'Flatten' def test_nop_flatten_axis0_graph_output(self): add = helper.make_node("Add", ["X", "Y"], ["A"]) flatten = helper.make_node("Flatten", ["A"], ["B"], axis=0) graph = helper.make_graph( [add, flatten], "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 10)), helper.make_tensor_value_info("Y", TensorProto.FLOAT, (1, 10)), ], [helper.make_tensor_value_info("B", TensorProto.FLOAT, (1, 10))], # the tensor_value_info of "A" is necessary to this optimizer value_info=[helper.make_tensor_value_info( "A", TensorProto.FLOAT, (1, 10))] ) # The existence of shape infos of graoh outputs is checked in _optimized optimized_model = self._optimized(graph, ["eliminate_nop_flatten"]) assert len(optimized_model.graph.node) == 1 assert optimized_model.graph.node[0].op_type == 'Add' def test_nop_flatten_axis0(self): flatten = helper.make_node("Flatten", ["A"], ["B"], axis=0) graph = helper.make_graph( [flatten], "test", [helper.make_tensor_value_info("A", TensorProto.FLOAT, (1, 10))], [helper.make_tensor_value_info("B", TensorProto.FLOAT, (1, 10))]) optimized_model = self._optimized(graph, ["eliminate_nop_flatten"]) assert len(optimized_model.graph.node) == 0 def test_nop_flatten_axis1(self): flatten = helper.make_node("Flatten", ["A"], ["B"], axis=1) graph = helper.make_graph( [flatten], "test", [helper.make_tensor_value_info("A", TensorProto.FLOAT, (2, 3))], [helper.make_tensor_value_info("B", TensorProto.FLOAT, (2, 3))]) optimized_model = self._optimized(graph, ["eliminate_nop_flatten"]) assert len(optimized_model.graph.node) == 0 def test_eliminate_duplicate_initializer(self): # type: () -> None add_1 = helper.make_node("Add", ["A", "I_0"], ["B"]) add_2 = helper.make_node("Add", ["B", "I_1"], ["C"]) i = np.random.rand(5).astype(np.float32) graph = helper.make_graph( [add_1, add_2], "test", [helper.make_tensor_value_info("A", TensorProto.FLOAT, (5,)), helper.make_tensor_value_info("I_0", TensorProto.FLOAT, (5,)), helper.make_tensor_value_info("I_1", TensorProto.FLOAT, (5,))], [helper.make_tensor_value_info("C", TensorProto.FLOAT, (5,))], [helper.make_tensor("I_0", TensorProto.FLOAT, dims=(5,), vals=i.tobytes(), raw=True), helper.make_tensor("I_1", TensorProto.FLOAT, dims=(5,), vals=i.tobytes(), raw=True)]) optimized_model = self._optimized( graph, ["eliminate_duplicate_initializer"]) assert len(optimized_model.graph.node) == 2 assert len(optimized_model.graph.initializer) == 1 assert len(optimized_model.graph.input) == 2 assert optimized_model.graph.node[0].input[1] == "I_0" def test_nop_cast(self): # type: () -> None cast = helper.make_node("Cast", ["A"], ["B"], to=TensorProto.FLOAT) graph = helper.make_graph( [cast], "test", [helper.make_tensor_value_info("A", TensorProto.FLOAT, (2, 3))], [helper.make_tensor_value_info("B", TensorProto.FLOAT, (2, 3))]) optimized_model = self._optimized(graph, ["eliminate_nop_cast"]) assert len(optimized_model.graph.node) == 0 def test_nop_transpose_graph_output(self): # type: () -> None add = helper.make_node("Add", ["X", "Y"], ["A"]) trans = helper.make_node("Transpose", ["A"], ["B"], perm=[0, 1]) graph = helper.make_graph( [add, trans], "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (2, 3)), helper.make_tensor_value_info("Y", TensorProto.FLOAT, (2, 3))], [helper.make_tensor_value_info("B", TensorProto.FLOAT, (2, 3))]) # The existence of shape infos of graoh outputs is checked in _optimized optimized_model = self._optimized(graph, ["eliminate_nop_transpose"]) def check_transpose(node): # type: (NodeProto) -> None assert node.op_type != "Transpose" self._visit_all_nodes_recursive(optimized_model.graph, check_transpose) assert len(optimized_model.graph.node) == 1 def test_nop_transpose(self): # type: () -> None nodes = [helper.make_node("Identity", ["A"], ["X"]), helper.make_node("Transpose", ["X"], ["Y"], perm=[0, 1])] nodes.extend(self._make_fake_loop_op( [helper.make_node("Transpose", ["_Y"], ["_Y2"], perm=[0, 1])], [(TensorProto.FLOAT, (2, 3), "Y")], [(TensorProto.FLOAT, (2, 3), "Y2")])) graph = helper.make_graph( nodes, "test", [helper.make_tensor_value_info("A", TensorProto.FLOAT, (2, 3))], [helper.make_tensor_value_info("Y", TensorProto.FLOAT, (2, 3)), helper.make_tensor_value_info("Y2", TensorProto.FLOAT, (2, 3))]) optimized_model = self._optimized(graph, ["eliminate_nop_transpose"]) def check_transpose(node): # type: (NodeProto) -> None assert node.op_type != "Transpose" self._visit_all_nodes_recursive(optimized_model.graph, check_transpose) # Use of the output from the Transpose node in the main graph should # have been replaced with the input to the identity node assert len(optimized_model.graph.output) == 2 assert optimized_model.graph.output[0].name == "Y" # Use of the output from the Transpose node in the loop graph should # have been replaced with the input to that identity node assert len(optimized_model.graph.node[3].attribute[0].g.output) == 2 assert optimized_model.graph.node[3].attribute[0].g.output[1].name == "_Y2" def test_nop_transpose_default(self): # type: () -> None trans = helper.make_node("Transpose", ["X"], ["Y"]) graph = helper.make_graph( [trans], "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (2, 3))], [helper.make_tensor_value_info("Y", TensorProto.FLOAT, (3, 2))]) optimized_model = self._optimized(graph, ["eliminate_nop_transpose"]) assert len(list(optimized_model.graph.node)) == 1 assert optimized_model.graph.node[0].op_type == "Transpose" def test_nop_pad_opset10(self): # type: () -> None nodes = [helper.make_node("Pad", ["X"], ["Y"], pads=[0, 0, 0, 0])] graph = helper.make_graph( nodes, "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (2, 3))], [helper.make_tensor_value_info("Y", TensorProto.FLOAT, (2, 3))]) assert len(graph.node) == 1 optimized_model = self._optimized( graph, ["eliminate_nop_pad"], False, opset_imports=[helper.make_opsetid("", 10)]) def check_pad(node): # type: (NodeProto) -> None assert node.op_type != "Pad" self._visit_all_nodes_recursive(optimized_model.graph, check_pad) assert len(optimized_model.graph.output) == 1 assert optimized_model.graph.output[0].name == "Y" assert len(optimized_model.graph.node) == 0 def test_nop_pad_graph_output(self): # type: () -> None add = helper.make_node("Add", ["X", "Y"], ["A"]) pad = helper.make_node("Pad", ["A", "Pads"], ["B"]) graph = helper.make_graph( [add, pad], "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (5,)), helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5,)), helper.make_tensor_value_info("Pads", TensorProto.INT64, (2,))], [helper.make_tensor_value_info("B", TensorProto.FLOAT, (5,))], [helper.make_tensor("Pads", TensorProto.INT64, dims=(2,), vals=np.array([0, 0]).astype( np.int64).tobytes(), raw=True)]) # The existence of shape infos of graoh outputs is checked in _optimized optimized_model = self._optimized(graph, ["eliminate_nop_pad"]) def check_pad(node): # type: (NodeProto) -> None assert node.op_type != "Pad" self._visit_all_nodes_recursive(optimized_model.graph, check_pad) assert len(optimized_model.graph.node) == 1 def test_nop_pad(self): # type: () -> None nodes = [helper.make_node("Pad", ["X", "Pads"], ["Y"])] graph = helper.make_graph( nodes, "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (2, 3)), helper.make_tensor_value_info("Pads", TensorProto.INT64, (4,))], [helper.make_tensor_value_info("Y", TensorProto.FLOAT, (2, 3))], [helper.make_tensor("Pads", TensorProto.INT64, dims=(4,), vals=np.array([0, 0, 0, 0]).astype( np.int64).tobytes(), raw=True)]) assert len(graph.node) == 1 optimized_model = self._optimized(graph, ["eliminate_nop_pad"]) def check_pad(node): # type: (NodeProto) -> None assert node.op_type != "Pad" self._visit_all_nodes_recursive(optimized_model.graph, check_pad) assert len(optimized_model.graph.output) == 1 assert optimized_model.graph.output[0].name == "Y" assert len(optimized_model.graph.node) == 0 def test_nop_pad_default_opset10(self): # type: () -> None trans = helper.make_node("Pad", ["X"], ["Y"], pads=[0, 0, 1, 1]) graph = helper.make_graph( [trans], "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (2, 3))], [helper.make_tensor_value_info("Y", TensorProto.FLOAT, (2, 4))]) optimized_model = self._optimized( graph, ["eliminate_nop_pad"], False, opset_imports=[helper.make_opsetid("", 10)]) assert len(list(optimized_model.graph.node)) == 1 assert optimized_model.graph.node[0].op_type == "Pad" def test_nop_pad_default(self): # type: () -> None trans = helper.make_node("Pad", ["X", "Pads"], ["Y"]) graph = helper.make_graph( [trans], "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (2, 3)), helper.make_tensor_value_info("Pads", TensorProto.INT64, (4,))], [helper.make_tensor_value_info("Y", TensorProto.FLOAT, (2, 4))], [helper.make_tensor("Pads", TensorProto.INT64, dims=(4,), vals=np.array([0, 1, 0, 0]).astype( np.int64).tobytes(), raw=True)]) optimized_model = self._optimized(graph, ["eliminate_nop_pad"]) assert len(list(optimized_model.graph.node)) == 1 assert optimized_model.graph.node[0].op_type == "Pad" def test_eliminate_unused_initializer(self): # type: () -> None add = helper.make_node("Add", ["X", "Y"], ["Z"]) graph = helper.make_graph( [add], "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 2)), helper.make_tensor_value_info("Y", TensorProto.FLOAT, (1, 2))], [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 2))], [helper.make_tensor("A", TensorProto.FLOAT, dims=(2, 3), vals=np.random.randn(2, 3).astype( np.float32).tobytes(), raw=True)]) optimized_model = self._optimized( graph, ["eliminate_unused_initializer"]) assert len(list(optimized_model.graph.initializer)) == 0 def test_eliminate_unused_initializer_input(self): # type: () -> None add = helper.make_node("Add", ["X", "Y"], ["Z"]) graph = helper.make_graph( [add], "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 2)), helper.make_tensor_value_info("Y", TensorProto.FLOAT, (1, 2)), helper.make_tensor_value_info("A", TensorProto.FLOAT, (2, 3))], [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 2))], [helper.make_tensor("A", TensorProto.FLOAT, dims=(2, 3), vals=np.random.randn(2, 3).astype( np.float32).tobytes(), raw=True)]) optimized_model = self._optimized( graph, ["eliminate_unused_initializer"]) assert len(list(optimized_model.graph.initializer)) == 0 assert len(optimized_model.graph.input) == 2 # type: () -> None def test_eliminate_unused_initializer_no_eliminate_used_default(self): add = helper.make_node("Add", ["X", "A"], ["Z"]) graph = helper.make_graph( [add], "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 2)), helper.make_tensor_value_info("A", TensorProto.FLOAT, (1, 2))], [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 2))], [helper.make_tensor("A", TensorProto.FLOAT, dims=(1, 2), vals=np.random.randn(1, 2).astype( np.float32).tobytes(), raw=True)]) optimized_model = self._optimized( graph, ["eliminate_unused_initializer"]) assert len(list(optimized_model.graph.initializer)) == 1 # type: () -> None def test_eliminate_unused_initializer_no_eliminate_used(self): nodes = [helper.make_node("Add", ["X", "A"], ["Z"])] nodes.extend(self._make_fake_loop_op( [helper.make_node("Add", ["_X", "A"], ["_Z2"])], [(TensorProto.FLOAT, (1, 2), "X")], [(TensorProto.FLOAT, (1, 2), "Z2")])) graph = helper.make_graph( nodes, "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 2)), helper.make_tensor_value_info("A", TensorProto.FLOAT, (1, 2))], [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 2))], [helper.make_tensor("A", TensorProto.FLOAT, dims=(1, 2), vals=np.random.randn(1, 2).astype( np.float32).tobytes(), raw=True)]) optimized_model = self._optimized( graph, ["eliminate_unused_initializer"]) # Add, Constant (trip count), Constant (cond), Loop assert len(list(optimized_model.graph.node)) == 4 assert optimized_model.graph.node[0].op_type == "Add" assert optimized_model.graph.output[0].name == "Z" # Add assert len(optimized_model.graph.node[3].attribute[0].g.node) == 1 assert optimized_model.graph.node[3].attribute[0].g.node[0].op_type == 'Add' assert optimized_model.graph.node[3].attribute[0].g.output[1].name == '_Z2' assert len(list(optimized_model.graph.initializer)) == 1 # type: () -> None def test_eliminate_unused_initializer_no_eliminate_output(self): add = helper.make_node("Add", ["X", "Y"], ["Z"]) graph = helper.make_graph( [add], "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 2)), helper.make_tensor_value_info("Y", TensorProto.FLOAT, (1, 2)), helper.make_tensor_value_info("A", TensorProto.FLOAT, (2, 3))], [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 2)), helper.make_tensor_value_info("A", TensorProto.FLOAT, (2, 3))], [helper.make_tensor("A", TensorProto.FLOAT, dims=(2, 3), vals=np.random.randn(2, 3).astype( np.float32).tobytes(), raw=True)]) optimized_model = self._optimized( graph, ["eliminate_unused_initializer"]) assert len(list(optimized_model.graph.initializer)) == 1 assert "Z" in [o.name for o in optimized_model.graph.output] def test_extract_constant_to_initializer(self): # type: () -> None conv = helper.make_node("Conv", ["X", "Y"], ["Z"]) constant = helper.make_node("Constant", [], ["A"], value=helper.make_tensor( name="bias", data_type=TensorProto.FLOAT, dims=(16, 1, 1), vals=np.random.randn(16).astype(np.float32).tolist())) add = helper.make_node("Add", ["Z", "A"], ["B"]) graph = helper.make_graph( [conv, constant, add], "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 3, 3)), helper.make_tensor_value_info("Y", TensorProto.FLOAT, (16, 5, 3, 3))], [helper.make_tensor_value_info( "B", TensorProto.FLOAT, (1, 16, 1, 1))], ) optimized_model = self._optimized( graph, ["extract_constant_to_initializer"]) self.assertEqual(len(optimized_model.graph.initializer), 1) init = optimized_model.graph.initializer[0] self.assertEqual(init.name, 'A') self.assertEqual(init.dims, [16, 1, 1]) self.assertEqual(init.data_type, TensorProto.FLOAT) self.assertEqual( [n.op_type for n in optimized_model.graph.node], ['Conv', 'Add']) def test_fuse_concats(self): # type: () -> None nodes = [helper.make_node("Concat", ["A", "B", "C"], ["X"], axis=0), helper.make_node("Concat", ["D", "E", "F"], ["Y"], axis=0), helper.make_node("Concat", ["X", "G", "Y"], ["Z"], axis=0)] graph = helper.make_graph( nodes, "test", [helper.make_tensor_value_info("A", TensorProto.FLOAT, (2, 3, 4)), helper.make_tensor_value_info("B", TensorProto.FLOAT, (4, 3, 4)), helper.make_tensor_value_info("C", TensorProto.FLOAT, (2, 3, 4)), helper.make_tensor_value_info("D", TensorProto.FLOAT, (4, 3, 4)), helper.make_tensor_value_info("E", TensorProto.FLOAT, (2, 3, 4)), helper.make_tensor_value_info("F", TensorProto.FLOAT, (4, 3, 4)), helper.make_tensor_value_info("G", TensorProto.FLOAT, (4, 3, 4))], [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (22, 3, 4))]) optimized_model = self._optimized( graph, ["fuse_consecutive_concats"], True) # two passes are needed to simplify the graph to its simplest state. assert len(optimized_model.graph.node) == 1 assert len(optimized_model.graph.node[0].input) == 7 assert optimized_model.graph.node[0].input == [ "A", "B", "C", "G", "D", "E", "F"] assert optimized_model.graph.node[0].op_type == "Concat" def test_fuse_concats_different_axis(self): # type: () -> None nodes = [helper.make_node("Concat", ["A", "B", "C"], ["X"], axis=0), helper.make_node("Concat", ["D", "E", "F"], ["Y"], axis=1), helper.make_node("Concat", ["X", "Y"], ["Z"], axis=2)] graph = helper.make_graph( nodes, "test", [helper.make_tensor_value_info("A", TensorProto.FLOAT, (2, 9, 4)), helper.make_tensor_value_info("B", TensorProto.FLOAT, (4, 9, 4)), helper.make_tensor_value_info("C", TensorProto.FLOAT, (2, 9, 4)), helper.make_tensor_value_info("D", TensorProto.FLOAT, (8, 3, 4)), helper.make_tensor_value_info("E", TensorProto.FLOAT, (8, 3, 4)), helper.make_tensor_value_info("F", TensorProto.FLOAT, (8, 3, 4))], [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (8, 9, 8))]) optimized_model = self._optimized( graph, ["fuse_consecutive_concats"]) assert optimized_model.graph == graph def test_fuse_transpose(self): # type: () -> None nodes = [helper.make_node("Transpose", ["X"], ["Y"], perm=[1, 0, 2]), helper.make_node("Transpose", ["Y"], ["Z"], perm=[2, 0, 1]), helper.make_node("Transpose", ["Z"], ["A"], perm=[2, 0, 1])] nodes.extend(self._make_fake_loop_op( [helper.make_node("Transpose", ["_X"], ["_Y2"], perm=[1, 0, 2]), helper.make_node("Transpose", ["_Y2"], ["_Y3"], perm=[2, 0, 1]), helper.make_node("Transpose", ["_Y3"], ["_Y4"], perm=[2, 0, 1])], [(TensorProto.FLOAT, (2, 3, 4), "X")], [(TensorProto.FLOAT, (2, 4, 3), "Y4")])) graph = helper.make_graph( nodes, "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (2, 3, 4))], [helper.make_tensor_value_info("A", TensorProto.FLOAT, (2, 4, 3)), helper.make_tensor_value_info("Y4", TensorProto.FLOAT, (4, 3, 2))]) original_model = helper.make_model(graph) shape_inference.infer_shapes(original_model) optimized_model = self._optimized( graph, ["fuse_consecutive_transposes"]) shape_inference.infer_shapes(optimized_model) # Transpose, Constant (trip count), Constant (cond), Loop assert len(list(optimized_model.graph.node)) == 4 # Transpose assert len(optimized_model.graph.node[3].attribute[0].g.node) == 1 def test_fuse_transpose_default_graph_output(self): # type: () -> None add = helper.make_node("Add", ["X", "Y"], ["A"]) trans1 = helper.make_node("Transpose", ["A"], ["B"]) trans2 = helper.make_node("Transpose", ["B"], ["C"]) graph = helper.make_graph( [add, trans1, trans2], "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (2, 3)), helper.make_tensor_value_info("Y", TensorProto.FLOAT, (2, 3))], [helper.make_tensor_value_info("C", TensorProto.FLOAT, (2, 3))]) # The existence of shape infos of graoh outputs is checked in _optimized optimized_model = self._optimized( graph, ["fuse_consecutive_transposes"]) def check_transpose(node): # type: (NodeProto) -> None assert node.op_type != "Transpose" self._visit_all_nodes_recursive(optimized_model.graph, check_transpose) assert len(optimized_model.graph.node) == 1 def test_fuse_transpose_default(self): # type: () -> None trans1 = helper.make_node("Transpose", ["X"], ["Y"]) trans2 = helper.make_node("Transpose", ["Y"], ["Z"]) graph = helper.make_graph( [trans1, trans2], "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (2, 3, 4))], [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (2, 3, 4))]) optimized_model = self._optimized( graph, ["fuse_consecutive_transposes"]) assert len(list(optimized_model.graph.node)) == 0 def test_fuse_transpose_default_no_fuse(self): # type: () -> None trans1 = helper.make_node("Transpose", ["X"], ["Y"]) trans2 = helper.make_node("Transpose", ["Y"], ["Z"], perm=[0, 1, 2]) graph = helper.make_graph( [trans1, trans2], "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (2, 3, 4))], [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (4, 3, 2))]) optimized_model = self._optimized( graph, ["fuse_consecutive_transposes"]) assert len(list(optimized_model.graph.node)) == 2 for node in optimized_model.graph.node: assert node.op_type == "Transpose" def test_fuse_transpose_into_gemm(self): # type: () -> None nodes = [helper.make_node("Transpose", ["X"], ["A"], perm=[1, 0]), helper.make_node("Transpose", ["Y"], ["B"], perm=[1, 0]), helper.make_node("Gemm", ["A", "B", "C"], ["Z"])] nodes.extend(self._make_fake_loop_op( [helper.make_node("Transpose", ["_X"], ["_A"], perm=[1, 0]), helper.make_node("Transpose", ["Y"], ["_B"], perm=[1, 0]), helper.make_node("Gemm", ["_A", "_B", "C"], ["_Z2"])], [(TensorProto.FLOAT, (2, 3), "X")], [(TensorProto.FLOAT, (3, 5), "Z2")])) graph = helper.make_graph( nodes, "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (2, 3)), helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5, 2)), helper.make_tensor_value_info("C", TensorProto.FLOAT, (3, 5))], [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (3, 5))]) optimized_model = self._optimized(graph, ["fuse_transpose_into_gemm"]) # Gemm, Constant (trip count), Constant (cond), Loop assert len(list(optimized_model.graph.node)) == 4 assert optimized_model.graph.node[0].op_type == "Gemm" # Gemm assert len(optimized_model.graph.node[3].attribute[0].g.node) == 1 assert optimized_model.graph.node[3].attribute[0].g.node[0].op_type == "Gemm" def test_fuse_add_bias_into_conv_with_scalar_bias(self): # type: () -> None nodes = [helper.make_node("Conv", ["X", "Y"], ["Z"]), helper.make_node("Add", ["Z", "A"], ["B"])] graph = helper.make_graph( nodes, "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 3, 3)), helper.make_tensor_value_info( "Y", TensorProto.FLOAT, (16, 5, 3, 3)), helper.make_tensor_value_info("A", TensorProto.FLOAT, ())], [helper.make_tensor_value_info( "B", TensorProto.FLOAT, (1, 16, 1, 1))], ) optimized_model = self._optimized(graph, ["fuse_add_bias_into_conv"]) # Unsqueeze, Conv assert len(optimized_model.graph.node) == 4 assert optimized_model.graph.node[0].op_type == 'Unsqueeze' assert optimized_model.graph.node[1].op_type == 'Constant' assert optimized_model.graph.node[2].op_type == 'Tile' assert optimized_model.graph.node[3].op_type == 'Conv' def test_fuse_add_bias_into_conv_use_weight_shape(self): # type: () -> None nodes = [helper.make_node("Conv", ["X", "Y"], ["Z"]), helper.make_node("Add", ["Z", "A"], ["B"])] # FIXME(daquexian): It looks like subgraph cannot get value info from parent subgraph # nodes.extend(self._make_fake_loop_op( # [helper.make_node("Conv", ["_X", "Y"], ["_Z"]), # helper.make_node("Add", ["_Z", "A"], ["_B2"])], # [(TensorProto.FLOAT, (1, 5, 3, 3), "X")], # [(TensorProto.FLOAT, (1, 16, 1, 1), "B2")])) graph = helper.make_graph( nodes, "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 3, 3)), helper.make_tensor_value_info( "Y", TensorProto.FLOAT, (16, 5, 3, 3)), helper.make_tensor_value_info("A", TensorProto.FLOAT, (16, 1, 1))], [helper.make_tensor_value_info( "B", TensorProto.FLOAT, (1, 16, 1, 1))], ) optimized_model = self._optimized(graph, ["fuse_add_bias_into_conv"]) # # Squeeze, Conv, Constant (trip count), Constant (condition), Loop # assert len(list(optimized_model.graph.node)) == 5 assert len(list(optimized_model.graph.node)) == 2 assert optimized_model.graph.node[0].op_type == 'Squeeze' assert optimized_model.graph.node[1].op_type == 'Conv' assert optimized_model.graph.output[0].name == 'B' # # Squeeze, Conv # assert len(optimized_model.graph.node[4].attribute[0].g.node) == 2 # assert optimized_model.graph.node[4].attribute[0].g.node[0].op_type == 'Squeeze' # assert optimized_model.graph.node[4].attribute[0].g.node[1].op_type == 'Conv' # # Output 1 since 0 is 'cond' # assert optimized_model.graph.node[4].attribute[0].g.output[1].name == 'B2' # type: () -> None def test_fuse_add_bias_into_conv_use_weight_shape_with_tile(self): conv = helper.make_node("Conv", ["X", "Y"], ["Z"]) add = helper.make_node("Add", ["Z", "A"], ["B"]) graph = helper.make_graph( [conv, add], "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 3, 3)), helper.make_tensor_value_info( "Y", TensorProto.FLOAT, (16, 5, 3, 3)), helper.make_tensor_value_info("A", TensorProto.FLOAT, (1,))], [helper.make_tensor_value_info( "B", TensorProto.FLOAT, (1, 16, 1, 1))], ) optimized_model = self._optimized(graph, ["fuse_add_bias_into_conv"]) assert len(list(optimized_model.graph.node)) == 3 assert len(optimized_model.graph.value_info) == 1 assert optimized_model.graph.value_info[0].type.tensor_type.elem_type == TensorProto.INT64 assert len( optimized_model.graph.value_info[0].type.tensor_type.shape.dim) == 1 assert optimized_model.graph.node[0].op_type == 'Constant' assert optimized_model.graph.node[1].op_type == 'Tile' assert optimized_model.graph.node[2].op_type == 'Conv' assert optimized_model.graph.output[0].name == 'B' def test_fuse_add_bias_into_conv_use_conv_shape(self): # type: () -> None sub = helper.make_node("Sub", ["M", "N"], ["Y"]) conv = helper.make_node("Conv", ["X", "Y"], ["Z"]) add = helper.make_node("Add", ["Z", "A"], ["B"]) graph = helper.make_graph( [sub, conv, add], "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 3, 3)), helper.make_tensor_value_info( "M", TensorProto.FLOAT, (16, 5, 3, 3)), helper.make_tensor_value_info( "N", TensorProto.FLOAT, (16, 5, 3, 3)), helper.make_tensor_value_info("A", TensorProto.FLOAT, (1, 16, 1, 1))], [helper.make_tensor_value_info( "B", TensorProto.FLOAT, (1, 16, 1, 1))], value_info=[ helper.make_tensor_value_info( "Z", TensorProto.FLOAT, (1, 16, 1, 1)) ], ) optimized_model = self._optimized(graph, ["fuse_add_bias_into_conv"]) assert len(optimized_model.graph.node) == 3 assert optimized_model.graph.node[0].op_type == 'Sub' assert optimized_model.graph.node[1].op_type == 'Squeeze' assert optimized_model.graph.node[2].op_type == 'Conv' assert optimized_model.graph.output[0].name == 'B' assert optimized_model.graph.output[0].type.tensor_type.elem_type == TensorProto.FLOAT assert len( optimized_model.graph.output[0].type.tensor_type.shape.dim) == 4 # type: () -> None def test_fuse_add_bias_into_conv_use_move_constant(self): conv = helper.make_node("Conv", ["X", "Y"], ["Z"]) constant = helper.make_node("Constant", [], ["A"], value=helper.make_tensor( name="bias", data_type=TensorProto.FLOAT, dims=(16, 1, 1), vals=np.random.randn(16).astype(np.float32).tolist())) add = helper.make_node("Add", ["Z", "A"], ["B"]) graph = helper.make_graph( [conv, constant, add], "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 3, 3)), helper.make_tensor_value_info("Y", TensorProto.FLOAT, (16, 5, 3, 3))], [helper.make_tensor_value_info( "B", TensorProto.FLOAT, (1, 16, 1, 1))], value_info=[ helper.make_tensor_value_info( "A", TensorProto.FLOAT, (16, 1, 1)), ] ) optimized_model = self._optimized(graph, ["fuse_add_bias_into_conv"]) assert len(optimized_model.graph.node) == 3 assert optimized_model.graph.node[0].op_type == 'Constant' assert optimized_model.graph.node[1].op_type == 'Squeeze' assert optimized_model.graph.node[2].op_type == 'Conv' assert optimized_model.graph.output[0].name == 'B' assert optimized_model.graph.output[0].type.tensor_type.elem_type == TensorProto.FLOAT assert len( optimized_model.graph.output[0].type.tensor_type.shape.dim) == 4 # type: () -> None def test_fuse_add_bias_into_conv_squeeze_1d_bias_no_fuse(self): conv = helper.make_node("Conv", ["X", "Y"], ["Z"]) add = helper.make_node("Add", ["Z", "A"], ["B"]) graph = helper.make_graph( [conv, add], "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 3, 3)), helper.make_tensor_value_info( "Y", TensorProto.FLOAT, (16, 5, 3, 3)), helper.make_tensor_value_info("A", TensorProto.FLOAT, (3,))], [helper.make_tensor_value_info( "B", TensorProto.FLOAT, (1, 16, 1, 3))], value_info=[ helper.make_tensor_value_info( "Z", TensorProto.FLOAT, (1, 16, 1, 1)), ] ) optimized_model = self._optimized(graph, ["fuse_add_bias_into_conv"]) assert len(list(optimized_model.graph.node)) == 2 assert optimized_model.graph.node[0].op_type == 'Conv' assert optimized_model.graph.node[1].op_type == 'Add' # type: () -> None def test_fuse_add_bias_into_conv_squeeze_3d_bias_no_fuse(self): conv = helper.make_node("Conv", ["X", "Y"], ["Z"]) add = helper.make_node("Add", ["Z", "A"], ["B"]) graph = helper.make_graph( [conv, add], "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 3, 3)), helper.make_tensor_value_info( "Y", TensorProto.FLOAT, (16, 5, 3, 3)), helper.make_tensor_value_info("A", TensorProto.FLOAT, (16, 3, 3))], [helper.make_tensor_value_info( "B", TensorProto.FLOAT, (1, 16, 3, 3))], value_info=[ helper.make_tensor_value_info( "Z", TensorProto.FLOAT, (1, 16, 1, 1)), ] ) optimized_model = self._optimized(graph, ["fuse_add_bias_into_conv"]) assert len(list(optimized_model.graph.node)) == 2 assert optimized_model.graph.node[0].op_type == 'Conv' assert optimized_model.graph.node[1].op_type == 'Add' # type: () -> None def test_fuse_add_bias_into_conv_squeeze_4d_bias_no_fuse(self): conv = helper.make_node("Conv", ["X", "Y"], ["Z"]) add = helper.make_node("Add", ["Z", "A"], ["B"]) graph = helper.make_graph( [conv, add], "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 3, 3)), helper.make_tensor_value_info( "Y", TensorProto.FLOAT, (16, 5, 3, 3)), helper.make_tensor_value_info("A", TensorProto.FLOAT, (1, 16, 3, 3))], [helper.make_tensor_value_info( "B", TensorProto.FLOAT, (1, 16, 3, 3))] ) optimized_model = self._optimized(graph, ["fuse_add_bias_into_conv"]) assert len(list(optimized_model.graph.node)) == 2 assert optimized_model.graph.node[0].op_type == 'Conv' assert optimized_model.graph.node[1].op_type == 'Add' def test_fuse_matmul_add_bias_into_gemm(self): # type: () -> None matmul = helper.make_node("MatMul", ["X", "Y"], ["Z"]) add = helper.make_node("Add", ["Z", "B"], ["A"]) graph = helper.make_graph( [matmul, add], "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (32, 10)), helper.make_tensor_value_info("Y", TensorProto.FLOAT, (10, 16)), helper.make_tensor_value_info("B", TensorProto.FLOAT, (16,))], [helper.make_tensor_value_info("A", TensorProto.FLOAT, (32, 16))] ) optimized_model = self._optimized( graph, ["fuse_matmul_add_bias_into_gemm"]) assert len(list(optimized_model.graph.node)) == 1 assert optimized_model.graph.node[0].op_type == "Gemm" def test_fuse_matmul_add_bias_into_gemm_2d_bias(self): # type: () -> None matmul = helper.make_node("MatMul", ["X", "Y"], ["Z"]) add = helper.make_node("Add", ["Z", "B"], ["A"]) graph = helper.make_graph( [matmul, add], "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (32, 10)), helper.make_tensor_value_info("Y", TensorProto.FLOAT, (10, 16)), helper.make_tensor_value_info("B", TensorProto.FLOAT, (1, 16))], [helper.make_tensor_value_info("A", TensorProto.FLOAT, (32, 16))] ) optimized_model = self._optimized( graph, ["fuse_matmul_add_bias_into_gemm"]) assert len(list(optimized_model.graph.node)) == 1 assert optimized_model.graph.node[0].op_type == "Gemm" # type: () -> None def test_fuse_matmul_add_bias_into_gemm_2d_bias_same_shape(self): matmul = helper.make_node("MatMul", ["X", "Y"], ["Z"]) add = helper.make_node("Add", ["Z", "B"], ["A"]) graph = helper.make_graph( [matmul, add], "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (32, 10)), helper.make_tensor_value_info("Y", TensorProto.FLOAT, (10, 16)), helper.make_tensor_value_info("B", TensorProto.FLOAT, (32, 16))], [helper.make_tensor_value_info("A", TensorProto.FLOAT, (32, 16))] ) optimized_model = self._optimized( graph, ["fuse_matmul_add_bias_into_gemm"]) assert len(list(optimized_model.graph.node)) == 1 assert optimized_model.graph.node[0].op_type == "Gemm" # type: () -> None def test_fuse_matmul_add_bias_into_gemm_2d_bias_bcast_no_fuse(self): matmul = helper.make_node("MatMul", ["X", "Y"], ["Z"]) add = helper.make_node("Add", ["Z", "B"], ["A"]) graph = helper.make_graph( [matmul, add], "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 10)), helper.make_tensor_value_info("Y", TensorProto.FLOAT, (10, 16)), helper.make_tensor_value_info("B", TensorProto.FLOAT, (16, 16))], [helper.make_tensor_value_info("A", TensorProto.FLOAT, (16, 16))] ) optimized_model = self._optimized( graph, ["fuse_matmul_add_bias_into_gemm"]) assert optimized_model.graph == graph # type: () -> None def test_fuse_matmul_add_bias_into_gemm_3d_matmul_no_fuse(self): matmul = helper.make_node("MatMul", ["X", "Y"], ["Z"]) add = helper.make_node("Add", ["Z", "B"], ["A"]) graph = helper.make_graph( [matmul, add], "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (2, 3, 4)), helper.make_tensor_value_info("Y", TensorProto.FLOAT, (2, 4, 3)), helper.make_tensor_value_info("B", TensorProto.FLOAT, (3, 3))], [helper.make_tensor_value_info("A", TensorProto.FLOAT, (2, 3, 3))] ) optimized_model = self._optimized( graph, ["fuse_matmul_add_bias_into_gemm"]) assert optimized_model.graph == graph # type: () -> None def test_fuse_matmul_add_bias_into_gemm_3d_bias_no_fuse(self): matmul = helper.make_node("MatMul", ["X", "Y"], ["Z"]) add = helper.make_node("Add", ["Z", "B"], ["A"]) graph = helper.make_graph( [matmul, add], "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (32, 10)), helper.make_tensor_value_info("Y", TensorProto.FLOAT, (10, 16)), helper.make_tensor_value_info("B", TensorProto.FLOAT, (4, 1, 16))], [helper.make_tensor_value_info("A", TensorProto.FLOAT, (32, 16))] ) # 3d bias for 2d matmul is not legal. So disable onnxruntime checking optimized_model = self._optimized( graph, ["fuse_matmul_add_bias_into_gemm"], compare_result=False) assert optimized_model.graph == graph # type: () -> None def test_fuse_matmul_add_bias_into_gemm_multiple_use_no_fuse(self): matmul = helper.make_node("MatMul", ["X", "Y"], ["Z"]) identity = helper.make_node("Identity", ["Z"], ["A1"]) add = helper.make_node("Add", ["Z", "B"], ["A2"]) graph = helper.make_graph( [matmul, add, identity], "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (32, 10)), helper.make_tensor_value_info("Y", TensorProto.FLOAT, (10, 16)), helper.make_tensor_value_info("B", TensorProto.FLOAT, (1, 16))], [helper.make_tensor_value_info("A1", TensorProto.FLOAT, (32, 16)), helper.make_tensor_value_info("A2", TensorProto.FLOAT, (32, 16))] ) optimized_model = self._optimized( graph, ["fuse_matmul_add_bias_into_gemm"]) assert optimized_model.graph == graph # type: () -> None def test_fuse_pad_into_conv_no_optional_value_opset10(self): pad = helper.make_node( "Pad", ["X"], ["P"], mode="constant", pads=[0, 0, 0, 0, 0, 0, 1, 1] ) conv = helper.make_node("Conv", ["P", "Y"], ["Z"]) graph = helper.make_graph( [pad, conv], "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 2, 2)), helper.make_tensor_value_info("Y", TensorProto.FLOAT, (16, 5, 3, 3))], [helper.make_tensor_value_info( "Z", TensorProto.FLOAT, (1, 16, 1, 1))] ) optimized_model = self._optimized( graph, ["fuse_pad_into_conv"], False, opset_imports=[helper.make_opsetid("", 10)]) assert len(list(optimized_model.graph.node)) == 1 assert optimized_model.graph.node[0].op_type == "Conv" assert optimized_model.graph.node[0].attribute[0].name == "pads" assert list(optimized_model.graph.node[0].attribute[0].ints) == [ 0, 0, 1, 1] def test_fuse_pad_into_conv_no_optional_value(self): # type: () -> None pad = helper.make_node( "Pad", ["X", "Pads"], ["P"], mode="constant" ) conv = helper.make_node("Conv", ["P", "Y"], ["Z"]) graph = helper.make_graph( [pad, conv], "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 2, 2)), helper.make_tensor_value_info("Pads", TensorProto.INT64, (8,)), helper.make_tensor_value_info("Y", TensorProto.FLOAT, (16, 5, 3, 3))], [helper.make_tensor_value_info( "Z", TensorProto.FLOAT, (1, 16, 1, 1))], [helper.make_tensor("Pads", TensorProto.INT64, dims=(8,), vals=np.array([0, 0, 0, 0, 0, 0, 1, 1]).astype( np.int64).tobytes(), raw=True)]) optimized_model = self._optimized(graph, ["fuse_pad_into_conv"]) assert len(list(optimized_model.graph.node)) == 1 assert optimized_model.graph.node[0].op_type == "Conv" assert optimized_model.graph.node[0].attribute[0].name == "pads" assert list(optimized_model.graph.node[0].attribute[0].ints) == [ 0, 0, 1, 1] def test_fuse_pad_into_conv_with_optional_value(self): # type: () -> None pad = helper.make_node( "Pad", ["X", "Pads", "Constant_value"], ["P"], mode="constant" ) conv = helper.make_node("Conv", ["P", "Y"], ["Z"]) graph = helper.make_graph( [pad, conv], "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 2, 2)), helper.make_tensor_value_info("Pads", TensorProto.INT64, (8,)), helper.make_tensor_value_info( "Constant_value", TensorProto.FLOAT, ()), helper.make_tensor_value_info("Y", TensorProto.FLOAT, (16, 5, 3, 3))], [helper.make_tensor_value_info( "Z", TensorProto.FLOAT, (1, 16, 1, 1))], [helper.make_tensor("Pads", TensorProto.INT64, dims=(8,), vals=np.array([0, 0, 0, 0, 0, 0, 1, 1]).astype( np.int64).tobytes(), raw=True), helper.make_tensor("Constant_value", TensorProto.FLOAT, dims=(), vals=np.array([0]).astype(np.float32).tobytes(), raw=True)]) optimized_model = self._optimized(graph, ["fuse_pad_into_conv"]) assert len(list(optimized_model.graph.node)) == 1 assert optimized_model.graph.node[0].op_type == "Conv" assert optimized_model.graph.node[0].attribute[0].name == "pads" assert list(optimized_model.graph.node[0].attribute[0].ints) == [ 0, 0, 1, 1] # type: () -> None def test_fuse_pad_into_conv_with_nonzero_optional_value(self): pad = helper.make_node( "Pad", ["X", "Pads", "Constant_value"], ["P"], mode="constant" ) conv = helper.make_node("Conv", ["P", "Y"], ["Z"]) graph = helper.make_graph( [pad, conv], "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 2, 2)), helper.make_tensor_value_info("Pads", TensorProto.INT64, (8,)), helper.make_tensor_value_info( "Constant_value", TensorProto.FLOAT, ()), helper.make_tensor_value_info("Y", TensorProto.FLOAT, (16, 5, 3, 3))], [helper.make_tensor_value_info( "Z", TensorProto.FLOAT, (1, 16, 1, 1))], [helper.make_tensor("Pads", TensorProto.INT64, dims=(8,), vals=np.array([0, 0, 0, 0, 0, 0, 1, 1]).astype( np.int64).tobytes(), raw=True), helper.make_tensor("Constant_value", TensorProto.FLOAT, dims=(), # non-zero Constant_value -> so no pad vals=np.array([25]).astype( np.float32).tobytes(), raw=True)]) optimized_model = self._optimized(graph, ["fuse_pad_into_conv"]) assert optimized_model.graph == graph def test_fuse_pad_into_conv_1d_opset10(self): # type: () -> None pad = helper.make_node( "Pad", ["X"], ["P"], mode="constant", pads=[0, 0, 1, 0, 0, 1] ) conv = helper.make_node("Conv", ["P", "Y"], ["Z"]) graph = helper.make_graph( [pad, conv], "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 30)), helper.make_tensor_value_info("Y", TensorProto.FLOAT, (16, 5, 32))], [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 16, 1))] ) optimized_model = self._optimized( graph, ["fuse_pad_into_conv"], False, opset_imports=[helper.make_opsetid("", 10)]) assert len(list(optimized_model.graph.node)) == 1 assert optimized_model.graph.node[0].op_type == "Conv" assert optimized_model.graph.node[0].attribute[0].name == "pads" assert list(optimized_model.graph.node[0].attribute[0].ints) == [1, 1] def test_fuse_pad_into_conv_1d(self): # type: () -> None pad = helper.make_node( "Pad", ["X", "Pads"], ["P"], mode="constant" ) conv = helper.make_node("Conv", ["P", "Y"], ["Z"]) graph = helper.make_graph( [pad, conv], "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 30)), helper.make_tensor_value_info("Pads", TensorProto.INT64, (6,)), helper.make_tensor_value_info("Y", TensorProto.FLOAT, (16, 5, 32))], [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (1, 16, 1))], [helper.make_tensor("Pads", TensorProto.INT64, dims=(6,), vals=np.array([0, 0, 1, 0, 0, 1]).astype( np.int64).tobytes(), raw=True)]) optimized_model = self._optimized(graph, ["fuse_pad_into_conv"]) assert len(list(optimized_model.graph.node)) == 1 assert optimized_model.graph.node[0].op_type == "Conv" assert optimized_model.graph.node[0].attribute[0].name == "pads" assert list(optimized_model.graph.node[0].attribute[0].ints) == [1, 1] # type: () -> None def test_fuse_pad_into_conv_existing_conv_pad_opset10(self): pad = helper.make_node( "Pad", ["X"], ["P"], mode="constant", pads=[0, 0, 0, 0, 0, 0, 1, 1] ) conv = helper.make_node( "Conv", ["P", "Y"], ["Z"], pads=[1, 1, 0, 0] ) graph = helper.make_graph( [pad, conv], "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 2, 2)), helper.make_tensor_value_info("Y", TensorProto.FLOAT, (16, 5, 4, 4))], [helper.make_tensor_value_info( "Z", TensorProto.FLOAT, (1, 16, 1, 1))] ) optimized_model = self._optimized( graph, ["fuse_pad_into_conv"], False, opset_imports=[helper.make_opsetid("", 10)]) assert len(list(optimized_model.graph.node)) == 1 assert optimized_model.graph.node[0].op_type == "Conv" assert optimized_model.graph.node[0].attribute[0].name == "pads" assert list(optimized_model.graph.node[0].attribute[0].ints) == [ 1, 1, 1, 1] def test_fuse_pad_into_conv_existing_conv_pad(self): # type: () -> None pad = helper.make_node( "Pad", ["X", "Pads"], ["P"], mode="constant" ) conv = helper.make_node( "Conv", ["P", "Y"], ["Z"], pads=[1, 1, 0, 0] ) graph = helper.make_graph( [pad, conv], "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 2, 2)), helper.make_tensor_value_info("Pads", TensorProto.INT64, (8,)), helper.make_tensor_value_info("Y", TensorProto.FLOAT, (16, 5, 4, 4))], [helper.make_tensor_value_info( "Z", TensorProto.FLOAT, (1, 16, 1, 1))], [helper.make_tensor("Pads", TensorProto.INT64, dims=(8,), vals=np.array([0, 0, 0, 0, 0, 0, 1, 1]).astype( np.int64).tobytes(), raw=True)]) optimized_model = self._optimized(graph, ["fuse_pad_into_conv"]) assert len(list(optimized_model.graph.node)) == 1 assert optimized_model.graph.node[0].op_type == "Conv" assert optimized_model.graph.node[0].attribute[0].name == "pads" assert list(optimized_model.graph.node[0].attribute[0].ints) == [ 1, 1, 1, 1] # type: () -> None def test_fuse_pad_into_conv_pad_feature_no_fuse_opset10(self): pad = helper.make_node( "Pad", ["X"], ["P"], mode="constant", pads=[0, 1, 0, 0, 0, 0, 0, 0] ) conv = helper.make_node("Conv", ["P", "Y"], ["Z"]) graph = helper.make_graph( [pad, conv], "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 4, 3, 3)), helper.make_tensor_value_info("Y", TensorProto.FLOAT, (16, 5, 3, 3))], [helper.make_tensor_value_info( "Z", TensorProto.FLOAT, (1, 16, 1, 1))] ) optimized_model = self._optimized( graph, ["fuse_pad_into_conv"], False, opset_imports=[helper.make_opsetid("", 10)]) assert optimized_model.graph == graph def test_fuse_pad_into_conv_pad_feature_no_fuse(self): # type: () -> None pad = helper.make_node( "Pad", ["X", "Pads"], ["P"], mode="constant" ) conv = helper.make_node("Conv", ["P", "Y"], ["Z"]) graph = helper.make_graph( [pad, conv], "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 4, 3, 3)), helper.make_tensor_value_info("Pads", TensorProto.INT64, (8,)), helper.make_tensor_value_info("Y", TensorProto.FLOAT, (16, 5, 3, 3))], [helper.make_tensor_value_info( "Z", TensorProto.FLOAT, (1, 16, 1, 1))], [helper.make_tensor("Pads", TensorProto.INT64, dims=(8,), vals=np.array([0, 1, 0, 0, 0, 0, 0, 0]).astype( np.int64).tobytes(), raw=True)]) optimized_model = self._optimized(graph, ["fuse_pad_into_conv"]) assert optimized_model.graph == graph # type: () -> None def test_fuse_pad_into_conv_negative_pad_no_fuse_opset10(self): pad = helper.make_node( "Pad", ["X"], ["P"], mode="constant", pads=[0, 0, 0, 0, 0, 0, -1, -1] ) conv = helper.make_node("Conv", ["P", "Y"], ["Z"]) graph = helper.make_graph( [pad, conv], "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 4, 4)), helper.make_tensor_value_info("Y", TensorProto.FLOAT, (16, 5, 3, 3))], [helper.make_tensor_value_info( "Z", TensorProto.FLOAT, (1, 16, 1, 1))] ) optimized_model = self._optimized( graph, ["fuse_pad_into_conv"], False, opset_imports=[helper.make_opsetid("", 10)]) assert optimized_model.graph == graph def test_fuse_pad_into_conv_negative_pad_no_fuse(self): # type: () -> None pad = helper.make_node( "Pad", ["X", "Pads"], ["P"], mode="constant" ) conv = helper.make_node("Conv", ["P", "Y"], ["Z"]) graph = helper.make_graph( [pad, conv], "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 4, 4)), helper.make_tensor_value_info("Pads", TensorProto.INT64, (8,)), helper.make_tensor_value_info("Y", TensorProto.FLOAT, (16, 5, 3, 3))], [helper.make_tensor_value_info( "Z", TensorProto.FLOAT, (1, 16, 1, 1))], [helper.make_tensor("Pads", TensorProto.INT64, dims=(8,), vals=np.array( [0, 0, 0, 0, 0, 0, -1, -1]).astype(np.int64).tobytes(), raw=True)]) optimized_model = self._optimized(graph, ["fuse_pad_into_conv"]) assert optimized_model.graph == graph # type: () -> None def test_fuse_pad_into_conv_reflection_pad_no_fuse_opset10(self): pad = helper.make_node( "Pad", ["X"], ["P"], mode="reflect", pads=[0, 0, 0, 0, 0, 0, 1, 1] ) conv = helper.make_node("Conv", ["P", "Y"], ["Z"]) graph = helper.make_graph( [pad, conv], "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 2, 2)), helper.make_tensor_value_info("Y", TensorProto.FLOAT, (16, 5, 3, 3))], [helper.make_tensor_value_info( "Z", TensorProto.FLOAT, (1, 16, 1, 1))] ) optimized_model = self._optimized( graph, ["fuse_pad_into_conv"], False, opset_imports=[helper.make_opsetid("", 10)]) assert optimized_model.graph == graph # type: () -> None def test_fuse_pad_into_conv_reflection_pad_no_fuse(self): pad = helper.make_node( "Pad", ["X", "Pads"], ["P"], mode="reflect" ) conv = helper.make_node("Conv", ["P", "Y"], ["Z"]) graph = helper.make_graph( [pad, conv], "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 5, 2, 2)), helper.make_tensor_value_info("Pads", TensorProto.INT64, (8,)), helper.make_tensor_value_info("Y", TensorProto.FLOAT, (16, 5, 3, 3))], [helper.make_tensor_value_info( "Z", TensorProto.FLOAT, (1, 16, 1, 1))], [helper.make_tensor("Pads", TensorProto.INT64, dims=(8,), vals=np.array([0, 0, 0, 0, 0, 0, 1, 1]).astype( np.int64).tobytes(), raw=True)]) optimized_model = self._optimized(graph, ["fuse_pad_into_conv"]) assert optimized_model.graph == graph def test_fuse_consecutive_squeezes(self): # type: () -> None nodes = [helper.make_node("Squeeze", ["X", "X_axes"], ["Y"]), helper.make_node("Squeeze", ["Y", "Y_axes"], ["Z"])] nodes.extend(self._make_fake_loop_op( [helper.make_node("Squeeze", ["_X", "X_axes"], ["_Y"]), helper.make_node("Squeeze", ["_Y", "Y_axes"], ["_Z2"])], [(TensorProto.FLOAT, (1, 1, 2, 3, 1, 1, 1, 1, 8, 9), "X")], [(TensorProto.FLOAT, (2, 3, 1, 8, 9), "Z2")])) initializers = [ helper.make_tensor(name, TensorProto.INT64, npa.shape, npa.tobytes(), raw=True) for name, npa in [('X_axes', np.array([0, 4, 5], dtype=np.int64)), ('Y_axes', np.array([0, 3], dtype=np.int64))] ] graph = helper.make_graph( nodes, "test", [helper.make_tensor_value_info( "X", TensorProto.FLOAT, (1, 1, 2, 3, 1, 1, 1, 1, 8, 9)), helper.make_tensor_value_info("X_axes", TensorProto.INT64, [3]), helper.make_tensor_value_info("Y_axes", TensorProto.INT64, [2])], [helper.make_tensor_value_info( "Z", TensorProto.FLOAT, (2, 3, 1, 8, 9))], initializer=initializers) optimized_model = self._optimized(graph, ["fuse_consecutive_squeezes"]) # Squeeze, Constant (trip count), Constant (cond), Loop assert optimized_model.graph.node[0].op_type == "Squeeze" for init in optimized_model.graph.initializer: if init.name == optimized_model.graph.node[0].input[1]: assert list(to_array(init)) == [0, 1, 4, 5, 6] assert len(list(optimized_model.graph.node)) == 4 def test_fuse_consecutive_squeezes_default(self): # type: () -> None squeeze1 = helper.make_node("Squeeze", ["X", "X_axes"], ["Y"]) squeeze2 = helper.make_node("Squeeze", ["Y", "Y_axes"], ["Z"]) squeeze3 = helper.make_node("Squeeze", ["Z", "Z_axes"], ["A"]) nodes = [squeeze1, squeeze2, squeeze3] initializers = [ helper.make_tensor(name, TensorProto.INT64, npa.shape, npa.tobytes(), raw=True) for name, npa in [('X_axes', np.array([0, 4, 5], dtype=np.int64)), ('Y_axes', np.array([0, 3], dtype=np.int64)), ('Z_axes', np.array([2], dtype=np.int64))] ] graph = helper.make_graph( nodes, "test", [helper.make_tensor_value_info( "X", TensorProto.FLOAT, (1, 1, 2, 3, 1, 1, 1, 1, 8, 9)), helper.make_tensor_value_info("X_axes", TensorProto.INT64, [3]), helper.make_tensor_value_info("Y_axes", TensorProto.INT64, [2]), helper.make_tensor_value_info("Z_axes", TensorProto.INT64, [1])], [helper.make_tensor_value_info( "A", TensorProto.FLOAT, (2, 3, 8, 9))], initializer=initializers) optimized_model = self._optimized(graph, ["fuse_consecutive_squeezes"]) assert optimized_model.graph.node[0].op_type == "Squeeze" for init in optimized_model.graph.initializer: if init.name == optimized_model.graph.node[0].input[1]: assert list(to_array(init)) == [0, 1, 4, 5, 6, 7] assert len(list(optimized_model.graph.node)) == 1 def test_fuse_consecutive_squeezes_random(self): # type: () -> None x_shape = [1, 1, 1, 3, 4, 1, 6, 1, 1, 9] s1_one_indices = [i for i, a in enumerate(x_shape) if a == 1] s1_axes = np.random.choice(s1_one_indices, size=np.random.randint( low=1, high=len(s1_one_indices) - 1), replace=False).astype(np.int64) s2_x_shape = [a for i, a in enumerate(x_shape) if i not in s1_axes] s2_one_indices = [i for i, a in enumerate(s2_x_shape) if a == 1] s2_axes = np.array(s2_one_indices).astype(np.int64) squeeze1 = helper.make_node("Squeeze", ["X", "X_axes"], ["Y"]) squeeze2 = helper.make_node("Squeeze", ["Y", "Y_axes"], ["Z"]) initializers = [ helper.make_tensor(name, TensorProto.INT64, npa.shape, npa.tobytes(), raw=True) for name, npa in [('X_axes', s1_axes), ('Y_axes', s2_axes)] ] nodes = [squeeze1, squeeze2] graph = helper.make_graph( nodes, "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, x_shape), helper.make_tensor_value_info( "X_axes", TensorProto.INT64, s1_axes.shape), helper.make_tensor_value_info("Y_axes", TensorProto.INT64, s2_axes.shape)], [helper.make_tensor_value_info( "Z", TensorProto.FLOAT, (3, 4, 6, 9))], initializer=initializers ) optimized_model = self._optimized(graph, ["fuse_consecutive_squeezes"]) assert optimized_model.graph.node[0].op_type == "Squeeze" for init in optimized_model.graph.initializer: if init.name == optimized_model.graph.node[0].input[1]: assert list(to_array(init)) == [0, 1, 2, 5, 7, 8] assert len(list(optimized_model.graph.node)) == 1 def test_fuse_consecutive_squeezes_multi_uses(self): # type: () -> None squeeze1 = helper.make_node("Squeeze", ["X", "X_axes"], ["Y"]) add = helper.make_node("Add", ["Y", "A"], ["Z2"]) squeeze2 = helper.make_node("Squeeze", ["Y", "Y_axes"], ["Z"]) initializers = [ helper.make_tensor(name, TensorProto.INT64, npa.shape, npa.tobytes(), raw=True) for name, npa in [('X_axes', np.array([0, 4, 5], dtype=np.int64)), ('Y_axes', np.array([0, 3], dtype=np.int64)), ] ] graph = helper.make_graph( [squeeze1, add, squeeze2], "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (1, 1, 2, 3, 1, 1, 1, 1, 8, 9)), helper.make_tensor_value_info("A", TensorProto.FLOAT, (1,)), helper.make_tensor_value_info("X_axes", TensorProto.INT64, [3]), helper.make_tensor_value_info("Y_axes", TensorProto.INT64, [2]), ], [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (2, 3, 1, 8, 9)), helper.make_tensor_value_info("Z2", TensorProto.FLOAT, (1, 2, 3, 1, 1, 8, 9))], initializer=initializers ) optimized_model = self._optimized(graph, ["fuse_consecutive_squeezes"]) assert optimized_model.graph.node[0].op_type == "Squeeze" assert optimized_model.graph.node[2].op_type == "Squeeze" assert optimized_model.graph.node[2].input[0] == "X" assert len(list(optimized_model.graph.node)) == 3 for init in optimized_model.graph.initializer: if init.name == optimized_model.graph.node[0].input[1]: assert list(to_array(init)) == [ 0, 4, 5] if init.name == optimized_model.graph.node[2].input[1]: assert list(to_array(init)) == [ 0, 1, 4, 5, 6] def test_fuse_consecutive_softmax_log_axis(self): # type: () -> None for axis in range(3): softmax = helper.make_node("Softmax", ["X"], ["Y"], axis=axis) log = helper.make_node("Log", ["Y"], ["Z"]) graph = helper.make_graph( [softmax, log], "test", [helper.make_tensor_value_info( "X", TensorProto.FLOAT, (5, 7, 11))], [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (5, 7, 11))]) optimized_model = self._optimized( graph, ["fuse_consecutive_log_softmax"]) assert optimized_model.graph.output[0].type.tensor_type.elem_type == TensorProto.FLOAT assert len(optimized_model.graph.output) == 1 assert len(optimized_model.graph.node) == 1 assert optimized_model.graph.node[0].op_type == "LogSoftmax" assert optimized_model.graph.node[0].attribute[0].name == "axis" assert optimized_model.graph.node[0].attribute[0].i == axis def test_fuse_consecutive_softmax_log_side_effect(self): # type: () -> None softmax = helper.make_node("Softmax", ["X"], ["Y"], axis=2) log = helper.make_node("Log", ["Y"], ["Z"]) graph = helper.make_graph( [softmax, log], "test", [helper.make_tensor_value_info( "X", TensorProto.FLOAT, (5, 7, 11))], [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (5, 7, 11)), helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5, 7, 11))]) optimized_model = self._optimized( graph, ["fuse_consecutive_log_softmax"]) assert graph == optimized_model.graph # type: () -> None def test_fuse_consecutive_softmax_log_multiple_out(self): softmax = helper.make_node("Softmax", ["X"], ["Y"], axis=2) log = helper.make_node("Log", ["Y"], ["Z"]) exp = helper.make_node("Exp", ["Z"], ["Z1"]) graph = helper.make_graph( [softmax, log, exp], "test", [helper.make_tensor_value_info( "X", TensorProto.FLOAT, (5, 7, 11))], [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (5, 7, 11)), helper.make_tensor_value_info("Z1", TensorProto.FLOAT, (5, 7, 11))]) optimized_model = self._optimized( graph, ["fuse_consecutive_log_softmax"]) assert len(optimized_model.graph.output) == 2 assert len(optimized_model.graph.node) == 2 assert optimized_model.graph.output[0].type.tensor_type.elem_type == TensorProto.FLOAT assert optimized_model.graph.output[1].type.tensor_type.elem_type == TensorProto.FLOAT assert optimized_model.graph.node[0].op_type == "LogSoftmax" assert optimized_model.graph.node[0].attribute[0].name == "axis" assert optimized_model.graph.node[0].attribute[0].i == 2 assert optimized_model.graph.node[1].op_type == "Exp" def test_preserve_value_info(self): # type: () -> None trans1 = helper.make_node("Transpose", ["X"], ["Y"], perm=[1, 0, 2]) trans2 = helper.make_node("Transpose", ["Y"], ["Z"], perm=[2, 0, 1]) trans3 = helper.make_node("Transpose", ["Z"], ["A"], perm=[2, 0, 1]) graph = helper.make_graph( [trans1, trans2, trans3], "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (2, 3, 4))], [helper.make_tensor_value_info("A", TensorProto.FLOAT, (2, 4, 3))]) vi = helper.make_tensor_value_info("Y", TensorProto.FLOAT, (3, 2, 4)) graph.value_info.extend([vi]) optimized_model = self._optimized(graph, ["nop"]) assert list(optimized_model.graph.value_info) == [vi] assert len(list(optimized_model.graph.node)) == 3 def test_split(self): # type: () -> None node = onnx.helper.make_node( 'Constant', inputs=[], outputs=['X'], value=onnx.helper.make_tensor( name='X', data_type=TensorProto.FLOAT, dims=[1], vals=[5], ), ) graph = helper.make_graph( [node], 'test-optimize-split', [], [helper.make_tensor_value_info('X', TensorProto.FLOAT, (1,))]) init_model = self._optimized(graph, ['split_init']) self.assertEqual(len(init_model.graph.node), 1) self.assertEqual(len(init_model.graph.output), 1) self.assertEqual(init_model.graph.node[0].op_type, 'Constant') predict_model = self._optimized(graph, ['split_predict']) self.assertEqual(len(predict_model.graph.node), 0) self.assertEqual(len(predict_model.graph.input), 1) self.assertEqual(predict_model.graph.input[0].name, 'X') def test_lift_lex_loop(self): # type: () -> None nodes = [helper.make_node("Identity", ["X"], ["Y"])] # 'lift_lexical_references' is legacy code and I don't know how it works. # More error occurs if I make this loop op legal. # So don't check legality here nodes.extend(self._make_fake_loop_op( [helper.make_node("Identity", ["X"], ["_Y2"]), helper.make_node("Identity", ["Y"], ["_Y3"])], [], [(TensorProto.FLOAT, (5,), "Y2"), (TensorProto.FLOAT, (5,), "Y3")], check_legality=False)) graph = helper.make_graph( nodes, "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (5,))], [helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5,)), helper.make_tensor_value_info("Y2", TensorProto.FLOAT, (5,))]) # "lift_lexical_references" pass produces a graph that does not conform to # the ONNX spec. Disable checking. optimized_model = self._optimized( graph, ["lift_lexical_references"], compare_result=False) assert len(optimized_model.graph.node) == 4 # body_graph, __control_inputs assert len(optimized_model.graph.node[3].attribute) == 2 assert optimized_model.graph.node[3].attribute[1].name == "__control_inputs" assert optimized_model.graph.node[3].attribute[1].strings[0] == b"X" assert optimized_model.graph.node[3].attribute[1].strings[1] == b"Y" def test_lift_lex_if(self): # type: () -> None nodes = [helper.make_node("Identity", ["X"], ["Y"])] nodes.extend(self._make_fake_if_op( [helper.make_node("Identity", ["X"], ["_Y2"]), helper.make_node("Identity", ["Y"], ["_Y3"])], [helper.make_node("Identity", ["X"], ["_Y2"]), helper.make_node("Identity", ["X"], ["_Y3"])], [(TensorProto.FLOAT, (5,), "Y2"), (TensorProto.FLOAT, (5,), "Y3")])) graph = helper.make_graph( nodes, "test", [helper.make_tensor_value_info("X", TensorProto.FLOAT, (5,))], [helper.make_tensor_value_info("Y", TensorProto.FLOAT, (5,)), helper.make_tensor_value_info("Y2", TensorProto.FLOAT, (5,))]) # "If" node now diverges from ONNX schema. Disable checking. optimized_model = self._optimized( graph, ["lift_lexical_references"], compare_result=False) # Identity, Constant (condition), If assert len(optimized_model.graph.node) == 3 # else_branch, then_branch, __control_inputs assert len(optimized_model.graph.node[2].attribute) == 3 assert optimized_model.graph.node[2].attribute[2].name == "__control_inputs" assert optimized_model.graph.node[2].attribute[2].strings[0] == b"X" assert optimized_model.graph.node[2].attribute[2].strings[1] == b"Y" def test_fuse_bn_into_conv_simple(self): # type: () -> None for (tensor_type, np_type) in [(TensorProto.FLOAT, np.float32)]: conv = helper.make_node("Conv", ["X", "W", "B"], ["Y"]) bn = helper.make_node("BatchNormalization", [ "Y", "scale", "b", "mean", "var"], ["Z"]) W = np.random.randn(3, 2, 5, 5).astype(np_type) + 2 B = np.random.randn(3,).astype(np_type) + 2 scale = np.random.randn(3,).astype(np_type) + 2 b = np.random.randn(3,).astype(np_type) + 2 mean = np.random.randn(3,).astype(np_type) + 2 var = np.abs(np.random.randn(3,).astype(np_type)) + 2 initializers = [ helper.make_tensor(name, tensor_type, npa.shape, npa.tobytes(), raw=True) for name, npa in [('W', W), ('B', B), ('scale', scale), ('b', b), ('mean', mean), ('var', var)] ] graph = helper.make_graph( [conv, bn], "test", [helper.make_tensor_value_info("X", tensor_type, (5, 2, 28, 28))], [helper.make_tensor_value_info( "Z", tensor_type, (5, 3, 24, 24))], initializer=initializers, value_info=[ helper.make_tensor_value_info( "Y", tensor_type, (5, 3, 24, 24)) ] ) optimized_model = self._optimized(graph, ["fuse_bn_into_conv"]) self.assertEqual(len(optimized_model.graph.node), 1) self.assertEqual(optimized_model.graph.node[0].op_type, 'Conv') self.assertEqual(len(optimized_model.graph.initializer), 2) new_W = numpy_helper.to_array(optimized_model.graph.initializer[0]) new_b = numpy_helper.to_array(optimized_model.graph.initializer[1]) f = scale / np.sqrt(var + 1e-5) np.testing.assert_almost_equal((B - mean) * f + b, new_b) np.testing.assert_almost_equal( W * f[:, np.newaxis, np.newaxis, np.newaxis], new_W) def _internal_test_deadend_elimination(self, fixed): # type: (bool) -> None softmax = helper.make_node("Softmax", ["X"], ["Y"], axis=2) log = helper.make_node("Log", ["Y"], ["Z"]) exp = helper.make_node("Exp", ["Z"], ["Z1"]) exp1 = helper.make_node("Log", ["Z"], ["Z2"]) exp2 = helper.make_node("Sqrt", ["Z1"], ["Z3"]) graph = helper.make_graph( [softmax, log, exp, exp1, exp2], "test", [helper.make_tensor_value_info( "X", TensorProto.FLOAT, (5, 7, 11))], [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (5, 7, 11))]) optimized_model = self._optimized( graph, ["eliminate_deadend"], fixed) assert len(optimized_model.graph.output) == 1 assert len(optimized_model.graph.node) == 2 assert optimized_model.graph.output[0].type.tensor_type.elem_type == TensorProto.FLOAT assert optimized_model.graph.node[0].op_type == "Softmax" assert optimized_model.graph.node[0].attribute[0].name == "axis" assert optimized_model.graph.node[0].attribute[0].i == 2 assert optimized_model.graph.node[1].op_type == "Log" def test_deadend_elimination_simple(self): # type: () -> None self._internal_test_deadend_elimination(False) def test_deadend_elimination_simple_fixed(self): # type: () -> None self._internal_test_deadend_elimination(True) def _get_argmax_output_shape(self, input_shape, axis, keepdims): assert keepdims output_shape = list(input_shape[:]) output_shape[axis] = 1 output_shape = tuple(output_shape) return output_shape # type: () -> None def test_eliminate_nop_monotone_argmax_basic_no_node_axis(self): input_shape = (5, 7, 11) for node_name in ["Exp"]: for axis in range(3): node = helper.make_node(node_name, ["X"], ["Y"]) argmax = helper.make_node("ArgMax", ["Y"], ["Z"], axis=axis) output_shape = self._get_argmax_output_shape( input_shape, axis, True) graph = helper.make_graph( [node, argmax], "test", [helper.make_tensor_value_info( "X", TensorProto.FLOAT, input_shape)], [helper.make_tensor_value_info("Z", TensorProto.INT64, output_shape)]) optimized_model = self._optimized( graph, ["eliminate_nop_monotone_argmax"]) assert len(optimized_model.graph.output) == 1 assert len(optimized_model.graph.node) == 1 assert optimized_model.graph.output[0].type.tensor_type.elem_type == TensorProto.INT64 assert optimized_model.graph.node[0].op_type == "ArgMax" assert optimized_model.graph.node[0].attribute[0].name == "axis" assert optimized_model.graph.node[0].attribute[0].i == axis # type: () -> None def test_eliminate_nop_monotone_argmax_basic_with_node_axis(self): input_shape = (5, 7, 11) for node_name in ["Softmax", "LogSoftmax"]: for axis_n in range(3): for axis_max in range(3): node = helper.make_node( node_name, ["X"], ["Y"], axis=axis_n) argmax = helper.make_node( "ArgMax", ["Y"], ["Z"], axis=axis_max) output_shape = self._get_argmax_output_shape( input_shape, axis_max, True) graph = helper.make_graph( [node, argmax], "test", [helper.make_tensor_value_info( "X", TensorProto.FLOAT, input_shape)], [helper.make_tensor_value_info("Z", TensorProto.INT64, output_shape)]) optimized_model = self._optimized( graph, ["eliminate_nop_monotone_argmax"]) if axis_max == axis_n: assert len(optimized_model.graph.output) == 1 assert len(optimized_model.graph.node) == 1 assert optimized_model.graph.output[0].type.tensor_type.elem_type == TensorProto.INT64 assert optimized_model.graph.node[0].op_type == "ArgMax" assert optimized_model.graph.node[0].attribute[0].name == "axis" assert optimized_model.graph.node[0].attribute[0].i == axis_max else: assert optimized_model.graph == graph # type: () -> None def test_eliminate_nop_monotone_argmax_multiple_out(self): input_shape = (5, 7, 11) for node_name in ["Exp"]: for axis in range(3): node = helper.make_node(node_name, ["X"], ["Y"]) node2 = helper.make_node(node_name, ["Y"], ["Z1"]) argmax = helper.make_node("ArgMax", ["Y"], ["Z"], axis=axis) argmax_output_shape = self._get_argmax_output_shape( input_shape, axis, True) graph = helper.make_graph( [node, node2, argmax], "test", [helper.make_tensor_value_info( "X", TensorProto.FLOAT, input_shape)], [helper.make_tensor_value_info("Z", TensorProto.INT64, argmax_output_shape), helper.make_tensor_value_info("Z1", TensorProto.FLOAT, input_shape)]) optimized_model = self._optimized( graph, ["eliminate_nop_monotone_argmax"]) assert optimized_model.graph == graph # type: () -> None def test_eliminate_nop_monotone_argmax_consecutive(self): # type: (GraphProto, ModelProto, bool, int) -> None input_shape = (5, 7, 11) def _assertion(graph, optimized_model, axis_aligned, true_axis): if axis_aligned: assert len(optimized_model.graph.output) == 1 assert len(optimized_model.graph.node) == 1 assert optimized_model.graph.output[0].type.tensor_type.elem_type == TensorProto.INT64 assert optimized_model.graph.node[0].op_type == "ArgMax" assert optimized_model.graph.node[0].attribute[0].name == "axis" assert optimized_model.graph.node[0].attribute[0].i == true_axis else: assert optimized_model.graph == graph # no axis X no axis test for node_name_0 in ["Exp"]: for node_name_1 in ["Exp"]: for axis in range(3): node = helper.make_node(node_name_0, ["X"], ["Y"]) node2 = helper.make_node(node_name_1, ["Y"], ["Y1"]) argmax = helper.make_node( "ArgMax", ["Y1"], ["Z"], axis=axis) output_shape = self._get_argmax_output_shape( input_shape, axis, True) graph = helper.make_graph( [node, node2, argmax], "test", [helper.make_tensor_value_info( "X", TensorProto.FLOAT, input_shape)], [helper.make_tensor_value_info("Z", TensorProto.INT64, output_shape)]) optimized_model = self._optimized( graph, ["eliminate_nop_monotone_argmax"], True) _assertion(graph, optimized_model, True, axis) # no axis X axis test for node_name_0 in ["Exp"]: for node_name_1 in ["Softmax", "LogSoftmax"]: for axis_0 in range(3): for axis_1 in range(3): node = helper.make_node(node_name_0, ["X"], ["Y"]) node2 = helper.make_node( node_name_1, ["Y"], ["Y1"], axis=axis_0) argmax = helper.make_node( "ArgMax", ["Y1"], ["Z"], axis=axis_1) output_shape = self._get_argmax_output_shape( input_shape, axis_1, True) graph = helper.make_graph( [node, node2, argmax], "test", [helper.make_tensor_value_info( "X", TensorProto.FLOAT, (5, 7, 11))], [helper.make_tensor_value_info("Z", TensorProto.INT64, output_shape)]) optimized_model = self._optimized( graph, ["eliminate_nop_monotone_argmax"], True) _assertion(graph, optimized_model, axis_0 == axis_1, axis_1) # axis X axis test for node_name_0 in ["Softmax", "LogSoftmax"]: for node_name_1 in ["Softmax", "LogSoftmax"]: for axis_0 in range(3): for axis_1 in range(3): for axis_2 in range(3): node = helper.make_node( node_name_0, ["X"], ["Y"], axis=axis_0) node2 = helper.make_node( node_name_1, ["Y"], ["Y1"], axis=axis_1) argmax = helper.make_node( "ArgMax", ["Y1"], ["Z"], axis=axis_2) output_shape = self._get_argmax_output_shape( input_shape, axis_2, True) graph = helper.make_graph( [node, node2, argmax], "test", [helper.make_tensor_value_info( "X", TensorProto.FLOAT, input_shape)], [helper.make_tensor_value_info("Z", TensorProto.INT64, output_shape)]) optimized_model = self._optimized( graph, ["eliminate_nop_monotone_argmax"], True) if axis_0 == axis_1: # we can reduce both of the monotonic ops _assertion(graph, optimized_model, axis_1 == axis_2, axis_2) elif axis_1 == axis_2: # we can reduce one of the monotonic ops assert len(optimized_model.graph.output) == 1 assert len(optimized_model.graph.node) == 2 assert optimized_model.graph.output[0].type.tensor_type.elem_type == TensorProto.INT64 assert optimized_model.graph.node[-1].op_type == "ArgMax" assert optimized_model.graph.node[-1].attribute[0].name == "axis" assert optimized_model.graph.node[-1].attribute[0].i == axis_2 else: # we can't reduce anything assert optimized_model.graph == graph def test_eliminate_nop_dropout(self): # type: () -> None node = helper.make_node("Dropout", ["X"], ["Y"]) node1 = helper.make_node("Log", ["Y"], ["Z"]) graph = helper.make_graph( [node, node1], "test", [helper.make_tensor_value_info( "X", TensorProto.FLOAT, (5, 7))], [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (5, 7))]) optimized_model = self._optimized( graph, ["eliminate_nop_dropout"], False) # we don't want to eliminate the dropoutin opset 12, # even when it';s an optional parameter (defaults to 0) assert optimized_model.graph == graph # type: () -> None def test_eliminate_nop_dropout_opset11_graph_output(self): node = helper.make_node("Log", ["X"], ["Y"]) node1 = helper.make_node("Dropout", ["Y"], ["Z"], ratio=0.0) graph = helper.make_graph( [node, node1], "test", [helper.make_tensor_value_info( "X", TensorProto.FLOAT, (5, 7))], [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (5, 7))]) optimized_model = self._optimized( graph, ["eliminate_nop_dropout"], False, opset_imports=[helper.make_opsetid("", 11)]) assert len(optimized_model.graph.output) == 1 assert len(optimized_model.graph.node) == 1 assert optimized_model.graph.node[0].op_type == "Log" assert optimized_model.graph.output[0].name == 'Z' def test_eliminate_nop_dropout_opset11(self): # type: () -> None for ratio in [0.0, 0.5]: node = helper.make_node("Dropout", ["X"], ["Y"], ratio=ratio) node1 = helper.make_node("Log", ["Y"], ["Z"]) graph = helper.make_graph( [node, node1], "test", [helper.make_tensor_value_info( "X", TensorProto.FLOAT, (5, 7))], [helper.make_tensor_value_info("Z", TensorProto.FLOAT, (5, 7))]) optimized_model = self._optimized( graph, ["eliminate_nop_dropout"], False, opset_imports=[helper.make_opsetid("", 11)]) if ratio > 0.0: assert optimized_model.graph == graph else: assert len(optimized_model.graph.output) == 1 assert len(optimized_model.graph.node) == 1 assert optimized_model.graph.node[0].op_type == "Log" def test_fuse_reduction_unsqueeze(self): # type: () -> None # type: (Tuple[int, ...], List[int], List[int], bool) -> Tuple[int, ...] def _calculate_post_transform_shape(input_shape, reduction_axes, unsqueeze_axes, keepdim): post_reduce_shape = None if keepdim: post_reduce_shape = tuple( [(x if i not in reduction_axes else 1) for i, x in enumerate(input_shape)]) else: post_reduce_shape = tuple( [x for i, x in enumerate(input_shape) if i not in reduction_axes]) post_unsqueeze_shape = list(post_reduce_shape) for ax in unsqueeze_axes: post_unsqueeze_shape.insert(ax, 1) return tuple(post_unsqueeze_shape) for reduction in ["ReduceL1", "ReduceL2", "ReduceLogSum", "ReduceLogSumExp", "ReduceMax", "ReduceMean", "ReduceMin", "ReduceProd", "ReduceSum", "ReduceSumSquare"]: for axes1 in [[1], [1, 2], [2]]: for axes2 in [[0], [0, 1], [1]]: for keepdim in [False, True]: input_shape = (5, 7, 9) output_shape = _calculate_post_transform_shape( input_shape, axes1, axes2, keepdim) # type: Tuple[int, ...] axes2_arr = np.array(axes2, dtype=np.int64) graph_input = [helper.make_tensor_value_info( "X", TensorProto.FLOAT, input_shape), helper.make_tensor_value_info("Y_axes", TensorProto.INT64, axes2_arr.shape)] graph_initializer = [ helper.make_tensor("Y_axes", TensorProto.INT64, axes2_arr.shape, axes2_arr.tobytes(), raw=True) ] if reduction in ("ReduceSum"): axes1_arr = np.array(axes1, dtype=np.int64) node = helper.make_node( reduction, ["X", "X_axes"], ["Y"], keepdims=keepdim) graph_input.append( helper.make_tensor_value_info("X_axes", TensorProto.INT64, axes1_arr.shape)) graph_initializer.append(helper.make_tensor("X_axes", TensorProto.INT64, axes1_arr.shape, axes1_arr.tobytes(), raw=True)) else: node = helper.make_node( reduction, ["X"], ["Y"], axes=axes1, keepdims=keepdim) node1 = helper.make_node( "Unsqueeze", ["Y", "Y_axes"], ["Z"]) graph = helper.make_graph( [node, node1], "test", graph_input, [helper.make_tensor_value_info( "Z", TensorProto.FLOAT, output_shape)], initializer=graph_initializer ) optimized_model = self._optimized( graph, ["fuse_consecutive_reduce_unsqueeze"], False) if keepdim or axes1 != axes2: assert optimized_model.graph == graph else: assert len(optimized_model.graph.output) == 1 assert len(optimized_model.graph.node) == 1 assert optimized_model.graph.output[0].type.tensor_type.elem_type == TensorProto.FLOAT assert optimized_model.graph.node[-1].op_type == reduction if reduction in ("ReduceSum"): for init in optimized_model.graph.initializer: if init.name == optimized_model.graph.node[-1].input[1]: assert list(to_array(init)) == axes1 else: assert optimized_model.graph.node[-1].attribute[0].name == "axes" assert optimized_model.graph.node[-1].attribute[0].ints == axes1 optimized_output_shape = tuple( x.dim_value for x in optimized_model.graph.output[0].type.tensor_type.shape.dim) assert optimized_output_shape == output_shape @unittest.skipUnless(has_tv, "This test needs torchvision") def test_torchvision_fasterrcnn_fpn(self): # type: () -> None model = tv.models.detection.fasterrcnn_resnet50_fpn(pretrained=False) x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)] with io.BytesIO() as f: torch.onnx.export(model, x, f, opset_version=11) model = onnx.load_model_from_string(f.getvalue()) self._optimized(model, onnxoptimizer.get_fuse_and_elimination_passes(), fixed_point=True) # maskrcnn is only supported in opset 11 and higher @unittest.skipUnless(has_tv, "This test needs torchvision") def test_torchvision_maskrcnn_fpn_opset11(self): # type: () -> None model = tv.models.detection.maskrcnn_resnet50_fpn(pretrained=False) x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)] with io.BytesIO() as f: torch.onnx.export(model, x, f, opset_version=11) model = onnx.load_model_from_string(f.getvalue()) self._optimized(model, onnxoptimizer.get_fuse_and_elimination_passes(), fixed_point=True) # keypointrcnn is only supported in opset 11 and higher @unittest.skipUnless(has_tv, "This test needs torchvision") def test_torchvision_keypointrcnn_fpn(self): # type: () -> None model = tv.models.detection.keypointrcnn_resnet50_fpn(pretrained=False) x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)] with io.BytesIO() as f: torch.onnx.export(model, x, f, opset_version=11) model = onnx.load_model_from_string(f.getvalue()) self._optimized(model, onnxoptimizer.get_fuse_and_elimination_passes(), fixed_point=True) @unittest.skipUnless(has_tv, "This test needs torchvision") def test_torchvision_shufflenet_v2(self): # type: () -> None model = tv.models.shufflenet_v2_x1_0(pretrained=False) x = torch.rand(1, 3, 224, 224) with io.BytesIO() as f: torch.onnx.export(model, x, f) model = onnx.load_model_from_string(f.getvalue()) self._optimized(model, onnxoptimizer.get_fuse_and_elimination_passes(), fixed_point=True) @unittest.skipUnless(has_tv, "This test needs torchvision") def test_torchvision_mnasnet(self): # type: () -> None model = tv.models.mnasnet1_0(pretrained=False) x = torch.rand(1, 3, 224, 224) with io.BytesIO() as f: torch.onnx.export(model, x, f) model = onnx.load_model_from_string(f.getvalue()) self._optimized(model, onnxoptimizer.get_fuse_and_elimination_passes(), fixed_point=True) @unittest.skipUnless(has_tv, "This test needs torchvision") def test_torchvision_deeplabv3(self): # type: () -> None model = tv.models.segmentation.deeplabv3_resnet50(pretrained=False) x = torch.rand(1, 3, 224, 224) with io.BytesIO() as f: torch.onnx.export(model, x, f) model = onnx.load_model_from_string(f.getvalue()) self._optimized(model, onnxoptimizer.get_fuse_and_elimination_passes(), fixed_point=True) if __name__ == '__main__': unittest.main()

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