xszheng2020/the_stack_dedup_python_hits_5 · Datasets at Hugging Face

080d591dcd18d5349f4e389d01e598d959bebf78

232

py

Python

tests/unit/executors/encoders/test_frameworks.py

NickCwh/jina

d3dec7b82d6301bce72a82ad6a41dddc944ead0b

[ "Apache-2.0" ]

null

tests/unit/executors/encoders/test_frameworks.py

NickCwh/jina

d3dec7b82d6301bce72a82ad6a41dddc944ead0b

[ "Apache-2.0" ]

null

tests/unit/executors/encoders/test_frameworks.py

NickCwh/jina

d3dec7b82d6301bce72a82ad6a41dddc944ead0b

[ "Apache-2.0" ]

null

import pytest from jina.executors.encoders.frameworks import BaseOnnxEncoder from jina.excepts import ModelCheckpointNotExist def test_raised_exception(): with pytest.raises(ModelCheckpointNotExist): BaseOnnxEncoder()

25.777778

62

0.818966

23

232

8.173913

0.695652

0.085106

0

0.125

232

8

63

29

0.926108

0

1

0.166667

true

0

0.5

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

f290df82121ee9c9600a7a71cc1add7aa646a9e9

188

py

Python

SelectAllElementsInView.py

gnomesoup/pyDynamo

dea046e96f7973fcb6c28a274a3092b246457551

[ "Unlicense", "MIT" ]

null

SelectAllElementsInView.py

gnomesoup/pyDynamo

dea046e96f7973fcb6c28a274a3092b246457551

[ "Unlicense", "MIT" ]

null

SelectAllElementsInView.py

gnomesoup/pyDynamo

dea046e96f7973fcb6c28a274a3092b246457551

[ "Unlicense", "MIT" ]

null

import clr import os clr.AddReference('RevitAPI') from Autodesk.Revit.DB import * clr.AddReference('RevitServices') import RevitServices from RevitServices.Persistence import DocumentMa

18.8

48

0.829787

22

188

7.090909

0.545455

0.115385

0

0.095745

188

9

49

20.888889

0.917647

0

0.111702

0

1

0

true

0

0.714286

0

0.714286

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

f2ae50797201246cdba1129302be063bb4e236e9

135

py

Python

torch_chemistry/nn/conv/__init__.py

0h-n0/pytorch_chemistry

14ca01ab2a30728016ce6c6793f119438a09ade5

[ "MIT" ]

7

2019-12-21T12:36:20.000Z

2022-01-15T11:05:25.000Z

torch_chemistry/nn/conv/__init__.py

0h-n0/pytorch-chemistry

14ca01ab2a30728016ce6c6793f119438a09ade5

[ "MIT" ]

null

torch_chemistry/nn/conv/__init__.py

0h-n0/pytorch-chemistry

14ca01ab2a30728016ce6c6793f119438a09ade5

[ "MIT" ]

1

2020-11-05T09:33:18.000Z

import torch import torch.nn as nn from .base_conv import GNNConv from .gcn_conv import GCNConv from .censnet_conv import CensNetConv

19.285714

37

0.82963

22

135

4.954545

0.545455

0.275229

0

0.140741

135

6

38

22.5

0.939655

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

4b3ed8c47591ea97e0c3d53ffcf61f6d32d5a56a

128

py

Python

server/externalrelations/webhookserver/admin.py

podyssea/Chatbot

c5e54da3493269e63bf486acc1da525fce4fa170

[ "MIT" ]

2

2019-03-31T15:28:39.000Z

2021-07-09T10:57:13.000Z

server/externalrelations/webhookserver/admin.py

modelorona/External-Relations-Chatbot

23dbfc99f4bb14b6cb6483cceb6b245cff963f1f

[ "MIT" ]

3

2021-03-09T13:58:14.000Z

2022-02-26T16:07:45.000Z

server/externalrelations/webhookserver/admin.py

podyssea/Chatbot

c5e54da3493269e63bf486acc1da525fce4fa170

[ "MIT" ]

null

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

21.333333

32

0.820313

17

128

6.176471

0.647059

0

0.117188

128

5

33

25.6

0.929204

0.203125

0

1

0

true

0

0.666667

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

4b46e9560b9adf35b83b33e82791b1a1edce0cd4

95

py

Python

tictactoe_project/setup.py

agryman/sean

11baf69c6eb9308266126bf9c8b1c67c6fd33afc

[ "MIT" ]

1

2020-03-28T18:17:52.000Z

tictactoe_project/setup.py

agryman/sean

11baf69c6eb9308266126bf9c8b1c67c6fd33afc

[ "MIT" ]

1

2022-01-21T21:33:00.000Z

tictactoe_project/setup.py

agryman/sean

11baf69c6eb9308266126bf9c8b1c67c6fd33afc

[ "MIT" ]

null

from setuptools import setup, find_packages setup(name='tictactoe', packages=find_packages())

23.75

49

0.810526

12

95

6.25

0.666667

0.32

0

0.084211

95

3

50

31.666667

0.862069

0

0.094737

0

1

0

true

0

0.5

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

29aebb7295b0760b9dbc72efea5fc618bae33f99

84

py

Python

nestedtensor/nn/__init__.py

seemethere/nestedtensor

b4190efc91f3cd4891ae370502b656cbb63e7def

[ "BSD-3-Clause" ]

1

2021-07-16T16:09:51.000Z

nestedtensor/nn/__init__.py

seemethere/nestedtensor

b4190efc91f3cd4891ae370502b656cbb63e7def

[ "BSD-3-Clause" ]

1

2021-04-17T11:15:19.000Z

nestedtensor/nn/__init__.py

seemethere/nestedtensor

b4190efc91f3cd4891ae370502b656cbb63e7def

[ "BSD-3-Clause" ]

null

from .mha import MultiheadAttention from .parameter import Parameter as NTParameter

28

47

0.857143

10

84

7.2

0.7

0

0.119048

84

2

48

42

0.972973

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

4b07a76c49e74fa826fad4da87066fc4d3d4f1e6

167

py

Python

setup.py

aaronbacher/RandomTransformationLayer

0174fe392aaefbc092cb26aa3b7c2f618562b1a9

[ "BSD-3-Clause" ]

null

setup.py

aaronbacher/RandomTransformationLayer

0174fe392aaefbc092cb26aa3b7c2f618562b1a9

[ "BSD-3-Clause" ]

null

setup.py

aaronbacher/RandomTransformationLayer

0174fe392aaefbc092cb26aa3b7c2f618562b1a9

[ "BSD-3-Clause" ]

null

from distutils.core import setup from Cython.Build import cythonize import numpy setup(ext_modules=cythonize('transform_cy.pyx'), include_dirs=[numpy.get_include()])

27.833333

84

0.820359

24

167

5.541667

0.708333

0

0.077844

167

5

85

33.4

0.863636

0

0.095808

0

1

0

true

0

0.75

0

0.75

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

d9eda1953d94cb78fc0096b7481d4ec40d23c209

5,137

py

Python

test/wecall_acceptance/genotyping/test_input_file.py

dylex/wecall

35d24cefa4fba549e737cd99329ae1b17dd0156b

[ "MIT" ]

8

2018-10-08T15:47:21.000Z

2021-11-09T07:13:05.000Z

test/wecall_acceptance/genotyping/test_input_file.py

dylex/wecall

35d24cefa4fba549e737cd99329ae1b17dd0156b

[ "MIT" ]

4

2018-11-05T09:16:27.000Z

2020-04-09T12:32:56.000Z

test/wecall_acceptance/genotyping/test_input_file.py

dylex/wecall

35d24cefa4fba549e737cd99329ae1b17dd0156b

[ "MIT" ]

4

2019-09-03T15:46:39.000Z

2021-06-04T07:28:33.000Z

# All content Copyright (C) 2018 Genomics plc from os.path import join from unittest import expectedFailure from wecall.genomics.variant import Variant from wecall.vcfutils.info_data import InfoData from wecall.vcfutils.schema import Schema from wecall.vcfutils.vcf_builder import VCFBuilder from wecall_test_drivers.base_test import BaseTest from wecall_test_drivers.svc_driver import SVCDriver class TestInputSpecification(BaseTest): def test_doesnt_give_a_flying_damn_about_spurious_filter_header(self): chrom = "22" variant = Variant(chrom, 11, "A", "C") schema = Schema() complex_filter_name = '.+-*\\/~@?!%^&><=\"\'(){}[]_|' schema.set_filter(complex_filter_name, 'unusual characters') gv_builder = VCFBuilder(join(self.work_dir, "genotype.vcf"), schema=schema) gv_builder.with_record_from_variant(variant, filters={complex_filter_name}) gv_builder.build().index() driver = SVCDriver(self) dodgy_sample = "bobs_your_uncle" driver.with_ref_sequence( "ACGCCCCCTGCAAAAAAAAAA", chrom=chrom, pos_from=0 ).with_read( "...........C.........", n_fwd=5, n_rev=5, chrom=chrom, sample_name=dodgy_sample ).with_genotype_alleles( gv_builder.compressed_filename ) expect = driver.call(expected_success=True) expect .with_output_vcf()\ .has_record_for_variant(variant)\ .with_sample(dodgy_sample)\ .has_genotype("1/1") def test_doesnt_give_a_flying_damn_about_spurious_filters(self): chrom = "22" variant = Variant(chrom, 11, "A", "C") gv_builder = VCFBuilder(join(self.work_dir, "genotype.vcf")) gv_builder.with_record_from_variant( variant, filters={"#$.:@$%$%^&**()7!"}) gv_builder.build().index() driver = SVCDriver(self) dodgy_sample = "bobs_your_uncle" driver.with_ref_sequence( "ACGCCCCCTGCAAAAAAAAAA", chrom=chrom, pos_from=0 ).with_read( "...........C.........", n_fwd=5, n_rev=5, chrom=chrom, sample_name=dodgy_sample ).with_genotype_alleles( gv_builder.compressed_filename ) expect = driver.call(expected_success=True) expect.with_output_vcf()\ .has_record_for_variant(variant)\ .with_sample(dodgy_sample)\ .has_genotype("1/1") def test_should_handle_complex_variant_input(self): chrom = "22" variant = Variant(chrom, 10, "CAA", "CA") gv_builder = VCFBuilder(join(self.work_dir, "genotype.vcf")) gv_builder.with_record_from_variant(variant) gv_builder.build().index() driver = SVCDriver(self) dodgy_sample = "bobs_your_uncle" driver.with_ref_sequence( "ACGCCCCCTGCAAAAAAAAAA", chrom=chrom, pos_from=0 ).with_read( "...........C.........", n_fwd=5, n_rev=5, chrom=chrom, sample_name=dodgy_sample ).with_genotype_alleles( gv_builder.compressed_filename ) expect = driver.call() expect.with_log()\ .input_variant_trimmed_warning(variant, Variant(chrom, 11, "A", "")) expect.with_output_vcf()\ .record_count(1) @expectedFailure # "Unskip test if parameter made public" def test_should_raise_if_output_ref_calls_is_switched_on(self): chrom = "22" variant = Variant(chrom, 10, "CAA", "CA") gv_builder = VCFBuilder(join(self.work_dir, "genotype.vcf")) gv_builder.with_record_from_variant(variant) gv_builder.build().index() driver = SVCDriver(self) dodgy_sample = "bobs_your_uncle" driver.with_ref_sequence( "ACGCCCCCTGCAAAAAAAAAA", chrom=chrom, pos_from=0 ).with_read( "...........C.........", n_fwd=5, n_rev=5, chrom=chrom, sample_name=dodgy_sample ).with_genotype_alleles( gv_builder.compressed_filename ).with_output_ref_calls(True) driver.call(False).genotyping_is_incompatible_with_outputting_reference_calls_error() def test_doesnt_give_a_flying_damn_about_spurious_info(self): chrom = "22" variant = Variant(chrom, 11, "A", "C") gv_builder = VCFBuilder(join(self.work_dir, "genotype.vcf")) gv_builder.with_record_from_variant(variant, info=InfoData(None, {"#f$@$e%$%^&k**()7!": ["#o$@$f%$%f^&**()7!"]})) gv_builder.build().index() driver = SVCDriver(self) dodgy_sample = "bobs_your_uncle" driver.with_ref_sequence( "ACGCCCCCTGCAAAAAAAAAA", chrom=chrom, pos_from=0 ).with_read( "...........C.........", n_fwd=5, n_rev=5, chrom=chrom, sample_name=dodgy_sample ).with_genotype_alleles( gv_builder.compressed_filename ) expect = driver.call(expected_success=True) expect.with_output_vcf() \ .has_record_for_variant(variant)\ .with_sample(dodgy_sample)\ .has_genotype("1/1")

36.692857

112

0.626825

601

5,137

5.009983

0.199667

0.059781

0.037861

0.02989

0.731319

0.724012

0.707074

0.642312

0

0.013104

0.242359

5,137

139

113

36.956835

0.760534

0.015963

0

0.693694

0

0.093428

0.041568

0

1

0.045045

false

0

0.072072

0

0.126126

0

null

0

1

0

null

0

5

8a11e4b7e2af3f74598584d5f6845be159074ae7

163

py

Python

PyMOTW/source/importlib/example/__init__.py

axetang/AxePython

3b517fa3123ce2e939680ad1ae14f7e602d446a6

[ "Apache-2.0" ]

1

2019-01-04T05:47:50.000Z

PyMOTW/source/importlib/example/__init__.py

axetang/AxePython

3b517fa3123ce2e939680ad1ae14f7e602d446a6

[ "Apache-2.0" ]

1

2020-07-18T03:52:03.000Z

2020-07-18T04:18:01.000Z

PyMOTW/source/importlib/example/__init__.py

axetang/AxePython

3b517fa3123ce2e939680ad1ae14f7e602d446a6

[ "Apache-2.0" ]

2

2021-03-06T04:28:32.000Z

2021-03-06T04:59:17.000Z

14.818182

55

0.705521

22

163

5.136364

1

0

0.035714

0.141104

163

10

56

16.3

0.771429

0.656442

0

0.581395

0

1

0

true

0

1

0

1

0

null

0

null

0

1

0

1

0

1

0

5

8a44e2f2c3888e8b4328df8380d22c69b3844951

147

py

Python

src/hebphonics/controllers/__init__.py

ohizkiya/hebphonics

60f46f2fbec6704c4598dfccaa4326b1e17b133a

[ "MIT" ]

null

src/hebphonics/controllers/__init__.py

ohizkiya/hebphonics

60f46f2fbec6704c4598dfccaa4326b1e17b133a

[ "MIT" ]

11

2020-11-20T20:23:00.000Z

2021-01-28T14:23:19.000Z

src/hebphonics/controllers/__init__.py

ohizkiya/hebphonics

60f46f2fbec6704c4598dfccaa4326b1e17b133a

[ "MIT" ]

1

2021-01-01T20:06:01.000Z

#!/usr/bin/env python # coding: utf-8 """Flask controllers.""" __all__ = ["jsend", "login", "hebphonics"] from . import jsend, login, hebphonics

18.375

42

0.666667

18

147

5.222222

0.833333

0.212766

0.425532

0

0.007874

0.136054

147

7

43

21

0.732283

0.360544

0

0.229885

0

1

0

false

0

0.5

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

5

8a48d4ef8babf6ad05d45a0da90e9dc110dc5246

75

py

Python

laspy/vlrs/__init__.py

CCInc/laspy

999306c92162fe6e4376960ac5b4df4368d5c3c7

[ "BSD-2-Clause" ]

240

2016-11-29T15:11:38.000Z

2022-03-30T20:22:42.000Z

laspy/vlrs/__init__.py

CCInc/laspy

999306c92162fe6e4376960ac5b4df4368d5c3c7

[ "BSD-2-Clause" ]

136

2016-11-28T16:38:05.000Z

2022-03-28T16:49:42.000Z

laspy/vlrs/__init__.py

CCInc/laspy

999306c92162fe6e4376960ac5b4df4368d5c3c7

[ "BSD-2-Clause" ]

76

2016-12-08T14:07:35.000Z

2022-03-16T00:41:17.000Z

from . import geotiff from .known import BaseKnownVLR from .vlr import VLR

18.75

31

0.8

11

75

5.454545

0.545455

0

0.16

75

3

32

25

0.952381

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

8a65d5e0780a9d154e594e3249f408e2d418272c

36

py

Python

run_train.py

thiagozampieri/gaia-loginface

ed1b9e6a4323718d070a659c59a9cf316a54fda2

[ "MIT" ]

null

run_train.py

thiagozampieri/gaia-loginface

ed1b9e6a4323718d070a659c59a9cf316a54fda2

[ "MIT" ]

null

run_train.py

thiagozampieri/gaia-loginface

ed1b9e6a4323718d070a659c59a9cf316a54fda2

[ "MIT" ]

null

import gaia.train as run run.train()

18

24

0.777778

7

36

4

0.714286

0

0.111111

36

2

25

18

0.875

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

5

8ad330d40365e2f54693c5adfdcabdb0c6463646

145

py

Python

spherov2/toy/sprk2.py

superfashi/spherov2.py

2f11b4e68ab4829972ba4e84bf23844c141f2266

[ "MIT" ]

1

2020-06-02T15:51:31.000Z

spherov2/toy/sprk2.py

superfashi/spherov2.py

2f11b4e68ab4829972ba4e84bf23844c141f2266

[ "MIT" ]

null

spherov2/toy/sprk2.py

superfashi/spherov2.py

2f11b4e68ab4829972ba4e84bf23844c141f2266

[ "MIT" ]

null

from spherov2.toy.bb8 import BB8 from spherov2.types import ToyType class Sprk2(BB8): toy_type = ToyType('Sphero SPRK+', 'SK-', 'SK', .06)

20.714286

56

0.696552

22

145

4.545455

0.636364

0.24

0

0.065574

0.158621

145

6

57

24.166667

0.754098

0

0.117241

0

1

0

false

0

0.5

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

76d5c5ff0da8a95cad203a56ad96389c8938d32e

5,118

py

Python

huaweicloud-sdk-dms/huaweicloudsdkdms/v2/__init__.py

githubmilesma/huaweicloud-sdk-python-v3

9d9449ed68a609ca65f0aa50b5b2a1c28445bf03

[ "Apache-2.0" ]

1

2021-04-16T07:59:28.000Z

huaweicloud-sdk-dms/huaweicloudsdkdms/v2/__init__.py

Lencof/huaweicloud-sdk-python-v3

d13dc4e2830a83e295be6e4de021999b3376e34e

[ "Apache-2.0" ]

null

huaweicloud-sdk-dms/huaweicloudsdkdms/v2/__init__.py

Lencof/huaweicloud-sdk-python-v3

d13dc4e2830a83e295be6e4de021999b3376e34e

[ "Apache-2.0" ]

1

2022-01-17T02:24:18.000Z

# coding: utf-8 from __future__ import absolute_import # import DmsClient from huaweicloudsdkdms.v2.dms_client import DmsClient from huaweicloudsdkdms.v2.dms_async_client import DmsAsyncClient # import models into sdk package from huaweicloudsdkdms.v2.model.batch_create_or_delete_queue_tag_request import BatchCreateOrDeleteQueueTagRequest from huaweicloudsdkdms.v2.model.batch_create_or_delete_queue_tag_response import BatchCreateOrDeleteQueueTagResponse from huaweicloudsdkdms.v2.model.batch_create_or_delete_tag_req import BatchCreateOrDeleteTagReq from huaweicloudsdkdms.v2.model.batch_create_or_delete_tag_req_tags import BatchCreateOrDeleteTagReqTags from huaweicloudsdkdms.v2.model.confirm_consumption_messages_req import ConfirmConsumptionMessagesReq from huaweicloudsdkdms.v2.model.confirm_consumption_messages_request import ConfirmConsumptionMessagesRequest from huaweicloudsdkdms.v2.model.confirm_consumption_messages_response import ConfirmConsumptionMessagesResponse from huaweicloudsdkdms.v2.model.confirm_dead_letters_messages_req import ConfirmDeadLettersMessagesReq from huaweicloudsdkdms.v2.model.confirm_dead_letters_messages_req_message import ConfirmDeadLettersMessagesReqMessage from huaweicloudsdkdms.v2.model.confirm_dead_letters_messages_request import ConfirmDeadLettersMessagesRequest from huaweicloudsdkdms.v2.model.confirm_dead_letters_messages_response import ConfirmDeadLettersMessagesResponse from huaweicloudsdkdms.v2.model.consume_deadletters_message import ConsumeDeadlettersMessage from huaweicloudsdkdms.v2.model.consume_deadletters_message_message import ConsumeDeadlettersMessageMessage from huaweicloudsdkdms.v2.model.consume_deadletters_message_request import ConsumeDeadlettersMessageRequest from huaweicloudsdkdms.v2.model.consume_deadletters_message_response import ConsumeDeadlettersMessageResponse from huaweicloudsdkdms.v2.model.consume_message import ConsumeMessage from huaweicloudsdkdms.v2.model.consume_message_message import ConsumeMessageMessage from huaweicloudsdkdms.v2.model.consume_messages_request import ConsumeMessagesRequest from huaweicloudsdkdms.v2.model.consume_messages_response import ConsumeMessagesResponse from huaweicloudsdkdms.v2.model.create_consumer_group_req import CreateConsumerGroupReq from huaweicloudsdkdms.v2.model.create_consumer_group_request import CreateConsumerGroupRequest from huaweicloudsdkdms.v2.model.create_consumer_group_resp_groups import CreateConsumerGroupRespGroups from huaweicloudsdkdms.v2.model.create_consumer_group_response import CreateConsumerGroupResponse from huaweicloudsdkdms.v2.model.create_queue_req import CreateQueueReq from huaweicloudsdkdms.v2.model.create_queue_request import CreateQueueRequest from huaweicloudsdkdms.v2.model.create_queue_response import CreateQueueResponse from huaweicloudsdkdms.v2.model.delete_queue_request import DeleteQueueRequest from huaweicloudsdkdms.v2.model.delete_queue_response import DeleteQueueResponse from huaweicloudsdkdms.v2.model.delete_specified_consumer_group_request import DeleteSpecifiedConsumerGroupRequest from huaweicloudsdkdms.v2.model.delete_specified_consumer_group_response import DeleteSpecifiedConsumerGroupResponse from huaweicloudsdkdms.v2.model.group_entity import GroupEntity from huaweicloudsdkdms.v2.model.list_consumer_groups_request import ListConsumerGroupsRequest from huaweicloudsdkdms.v2.model.list_consumer_groups_response import ListConsumerGroupsResponse from huaweicloudsdkdms.v2.model.list_queue_groups_resp_groups import ListQueueGroupsRespGroups from huaweicloudsdkdms.v2.model.list_queues_request import ListQueuesRequest from huaweicloudsdkdms.v2.model.list_queues_resp_queues import ListQueuesRespQueues from huaweicloudsdkdms.v2.model.list_queues_response import ListQueuesResponse from huaweicloudsdkdms.v2.model.send_message_entity import SendMessageEntity from huaweicloudsdkdms.v2.model.send_messages_req import SendMessagesReq from huaweicloudsdkdms.v2.model.send_messages_request import SendMessagesRequest from huaweicloudsdkdms.v2.model.send_messages_resp_messages import SendMessagesRespMessages from huaweicloudsdkdms.v2.model.send_messages_response import SendMessagesResponse from huaweicloudsdkdms.v2.model.show_project_tags_request import ShowProjectTagsRequest from huaweicloudsdkdms.v2.model.show_project_tags_resp_tags import ShowProjectTagsRespTags from huaweicloudsdkdms.v2.model.show_project_tags_response import ShowProjectTagsResponse from huaweicloudsdkdms.v2.model.show_queue_request import ShowQueueRequest from huaweicloudsdkdms.v2.model.show_queue_response import ShowQueueResponse from huaweicloudsdkdms.v2.model.show_queue_tags_request import ShowQueueTagsRequest from huaweicloudsdkdms.v2.model.show_queue_tags_response import ShowQueueTagsResponse from huaweicloudsdkdms.v2.model.show_quotas_request import ShowQuotasRequest from huaweicloudsdkdms.v2.model.show_quotas_resp_quotas import ShowQuotasRespQuotas from huaweicloudsdkdms.v2.model.show_quotas_resp_quotas_resources import ShowQuotasRespQuotasResources from huaweicloudsdkdms.v2.model.show_quotas_response import ShowQuotasResponse

81.238095

117

0.920477

559

5,118

8.13059

0.184258

0.254125

0.278328

0.326513

0.538174

0.524092

0.332453

0.142134

0.072607

0.047525

0

0.011478

0.046698

5,118

62

118

82.548387

0.920066

0.011919

0

1

0

true

0

1

0

1

0

null

1

0

null

0

1

0

1

0

5

76ea8b3aeacc180f09c1dd804ba1aee065c74697

227

py

Python

graphgallery/datasets/__init__.py

kisekizzz/GraphGallery

fd4a1f474c244f774397460ae95935638ef48f5b

[ "MIT" ]

null

graphgallery/datasets/__init__.py

kisekizzz/GraphGallery

fd4a1f474c244f774397460ae95935638ef48f5b

[ "MIT" ]

null

graphgallery/datasets/__init__.py

kisekizzz/GraphGallery

fd4a1f474c244f774397460ae95935638ef48f5b

[ "MIT" ]

null

from .dataset import Dataset from .in_memory_dataset import InMemoryDataset from .planetoid import Planetoid from .npz_dataset import NPZDataset from .ppi import PPI from .reddit import Reddit from .tu_dataset import TUDataset

28.375

46

0.845815

32

227

5.875

0.40625

0.276596

0

0.123348

227

7

47

32.428571

0.944724

0

1

0

true

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

5

0a08e6d49c00b5c3e0b57a9469f13ef038a2d0a6

49

py

Python

data/indicator/ME1/__init__.py

simonzabrocki/Anticipe

ad0e0aa39217a7e38ed10e1b3eb5be8e47d0e965

[ "MIT" ]

null

data/indicator/ME1/__init__.py

simonzabrocki/Anticipe

ad0e0aa39217a7e38ed10e1b3eb5be8e47d0e965

[ "MIT" ]

1

2022-01-27T07:44:44.000Z

data/indicator/ME1/__init__.py

simonzabrocki/Anticipe

ad0e0aa39217a7e38ed10e1b3eb5be8e47d0e965

[ "MIT" ]

null

from data.indicator.ME1.preprocess import config

24.5

48

0.857143

7

49

6

1

0

0.022222

0.081633

49

1

49

0.911111

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

0a0ed16fc973b3a217def6a264f87a4655c8119c

22

py

Python

tests/__init__.py

jbool24/CashWEB

da0f71956e95b70863bd8743372629609376f30b

[ "MIT" ]

null

tests/__init__.py

jbool24/CashWEB

da0f71956e95b70863bd8743372629609376f30b

[ "MIT" ]

null

tests/__init__.py

jbool24/CashWEB

da0f71956e95b70863bd8743372629609376f30b

[ "MIT" ]

null

# ./tests/__init__.py

11

21

0.681818

3

22

3.666667

1

0

0.090909

22

1

22

0.55

0.863636

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

5

0a16aefe0222857f03fe6a72857fd0b7db5ff5ae

375

py

Python

seq_graph_retro/layers/__init__.py

vsomnath/graphretro

2a88730da980be59541780e790cdb0fbdc581828

[ "MIT" ]

5

2022-02-21T08:26:15.000Z

2022-03-31T12:05:47.000Z

seq_graph_retro/layers/__init__.py

vsomnath/graphretro

2a88730da980be59541780e790cdb0fbdc581828

[ "MIT" ]

null

seq_graph_retro/layers/__init__.py

vsomnath/graphretro

2a88730da980be59541780e790cdb0fbdc581828

[ "MIT" ]

2

2022-03-11T15:33:33.000Z

2022-03-23T06:28:07.000Z

from seq_graph_retro.layers.reaction import AtomAttention, PairFeat from seq_graph_retro.layers.rnn import GRU, LSTM, MPNLayer from seq_graph_retro.layers.graph_transformer import (SublayerConnection, MultiHeadBlock, MultiHeadAttention, PositionwiseFeedForward) from seq_graph_retro.layers.encoder import GraphFeatEncoder, WLNEncoder, LogitEncoder, GTransEncoder

62.5

100

0.850667

42

375

7.380952

0.547619

0.090323

0.154839

0.219355

0.296774

0

0.098667

375

5

101

75

0.91716

0

1

0

true

0

0.8

0

0.8

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

5

0a17360497754b8b07462e61d7a4f08e8cf6fa1a

265

py

Python

Chapter 05/Chap05_Example5.6.py

bpbpublications/Programming-Techniques-using-Python

49b785f37e95a3aad1d36cef51e219ac56e5e9f0

[ "MIT" ]

null

Chapter 05/Chap05_Example5.6.py

bpbpublications/Programming-Techniques-using-Python

49b785f37e95a3aad1d36cef51e219ac56e5e9f0

[ "MIT" ]

null

Chapter 05/Chap05_Example5.6.py

bpbpublications/Programming-Techniques-using-Python

49b785f37e95a3aad1d36cef51e219ac56e5e9f0

[ "MIT" ]

null

import time from imp import reload import safetyprecautions print("I am inside testmodule") print("I am sleeping for 40 secs") print("--------------------------") time.sleep(40) reload(safetyprecautions) print("This is displayed after updation of module")

26.5

52

0.683019

34

265

5.323529

0.676471

0.243094

0.088398

0

0.017544

0.139623

265

9

53

29.444444

0.776316

0

0.449219

0.101563

0

1

0

true

0

0.333333

0

0.333333

0.444444

0

null

1

0

1

0

null

0

1

0

1

0

1

0

5

0a1bbd032b6538f787eda58f6052875670170207

39

py

Python

wacy/apps/__init__.py

thewaverguy/wacy

4a2ad247fad3b4af281392053d4ac50a67550a01

[ "Apache-2.0" ]

5

2021-03-11T17:41:10.000Z

2021-03-23T09:36:27.000Z

wacy/apps/__init__.py

thewaverguy/wacy

4a2ad247fad3b4af281392053d4ac50a67550a01

[ "Apache-2.0" ]

null

wacy/apps/__init__.py

thewaverguy/wacy

4a2ad247fad3b4af281392053d4ac50a67550a01

[ "Apache-2.0" ]

null

from wacy.apps.base.app import BaseApp

19.5

38

0.820513

7

39

4.571429

1

0

0.102564

39

1

39

0.914286

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

6a71722bbf8844642b0863038e7859984a07f1cb

175

py

Python

media_management_api/media_service/apps.py

Harvard-ATG/media_management_api

6ccc53c53def64f1976f6b21fd95abd68332a5b7

[ "BSD-3-Clause" ]

1

2017-09-25T19:55:49.000Z

media_management_api/media_service/apps.py

Harvard-ATG/media_management_api

6ccc53c53def64f1976f6b21fd95abd68332a5b7

[ "BSD-3-Clause" ]

32

2015-12-09T20:31:19.000Z

2022-03-11T23:33:50.000Z

media_management_api/media_service/apps.py

Harvard-ATG/media_management_api

6ccc53c53def64f1976f6b21fd95abd68332a5b7

[ "BSD-3-Clause" ]

1

2020-12-10T16:52:56.000Z

from django.apps import AppConfig class MediaServiceConfig(AppConfig): name = 'media_management_api.media_service' verbose_name = 'media_management_api.media_service'

35

55

0.817143

21

175

6.47619

0.619048

0.132353

0.279412

0.323529

0.5

0

0.114286

175

5

55

35

0.877419

0

0.386364

0

1

0

false

0

0.25

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

5

6ad89cbc01cd9317285f504f55adb9e0c2279392

357

py

Python

nnet/activation_func/_base.py

zhaoyan1117/NeuralNet

a0343dd469e981bf9b4f18db0209ca9bfaf58c4f

[ "BSD-2-Clause" ]

null

nnet/activation_func/_base.py

zhaoyan1117/NeuralNet

a0343dd469e981bf9b4f18db0209ca9bfaf58c4f

[ "BSD-2-Clause" ]

null

nnet/activation_func/_base.py

zhaoyan1117/NeuralNet

a0343dd469e981bf9b4f18db0209ca9bfaf58c4f

[ "BSD-2-Clause" ]

null

from __future__ import absolute_import from abc import ABCMeta, abstractmethod class ActivationFuncBase(object): __metaclass__ = ABCMeta @abstractmethod def apply(self, z): pass @abstractmethod def apply_scalar(self, s): pass @abstractmethod def mult_with_derivative(self, target, activated_z): pass

18.789474

56

0.697479

38

357

6.210526

0.605263

0.216102

0.186441

0

0.243697

357

18

57

19.833333

0.874074

0

0.461538

0

1

0.230769

false

0.230769

0.153846

0

0.538462

0

null

1

0

null

0

1

0

1

0

1

0

5

6ae2bfe9690305fe8239169ce4e1cac0913037a7

3,215

py

Python

testaskjunoace.py

jpvelsamy/hotdog

df45cdc0b9e6abfecd16a43f75f1671e51cbc47c

[ "Apache-2.0" ]

null

testaskjunoace.py

jpvelsamy/hotdog

df45cdc0b9e6abfecd16a43f75f1671e51cbc47c

[ "Apache-2.0" ]

null

testaskjunoace.py

jpvelsamy/hotdog

df45cdc0b9e6abfecd16a43f75f1671e51cbc47c

[ "Apache-2.0" ]

null

import logging import numpy as np import pandas as pd import sklearn from sklearn.model_selection import train_test_split from tensorflow import keras from tensorflow.keras import layers logger = logging.getLogger("ACE") class TestAskJunoACE: def __init__(self): self.k_fold_count = 4 self.num_epochs = 500 self.all_mae_histories = [] def fit_1(self, file_name): names = ["reach", "impressions", "results", "amount", "frequency", "clicks", "cpc", "ctr", "cpreach", "cpm", "engagement", "cpr"] data = pd.read_csv(file_name, engine='c', dtype='float64', names=names, header=0, skiprows=0) mean = data.mean(axis=0) data -= mean std = data.std(axis=0) data /= std x = data.iloc[:, 0:10] y = data.iloc[:, -1] x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25) model = keras.Sequential([ layers.Dense(64, activation="relu", input_shape=(x_train.shape[1],)), layers.Dense(64, activation="relu"), layers.Dense(1) ]) model.compile(optimizer="rmsprop", loss="mse", metrics=["mae"]) model.fit(x_train, y_train, epochs=130, batch_size=16, verbose=0) test_mse_score, test_mae_score = model.evaluate(x_test, y_test) logger.info(f'mse score #{test_mse_score}, mae score #{test_mae_score}') #https://stackoverflow.com/questions/40729162/merging-results-from-model-predict-with-original-pandas-dataframe y_hats = model.predict(x_test) y_test['preds'] = y_hats df_out = pd.merge(data, y_test[['preds']], how='left', left_index=True, right_index=True) df_out.to_csv('/home/jpvel/Desktop/outcome.csv', float_format='%.2f') def fit_2(self, file_name): names = ["reach", "impressions", "results", "amount", "frequency", "clicks", "cpc", "ctr", "cpreach", "cpm", "engagement", "cpr"] data = pd.read_csv(file_name, engine='c', dtype='float64', names=names, header=0, skiprows=0) mean = data.mean(axis=0) data -= mean std = data.std(axis=0) data /= std x = data.iloc[:, 0:10] y = data.iloc[:, -1] x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25) model = keras.Sequential([ layers.Dense(64, activation="relu", input_shape=(x_train.shape[1],)), layers.Dense(64, activation="relu"), layers.Dense(1) ]) model.compile(optimizer="rmsprop", loss="mse", metrics=["mae"]) model.fit(x_train, y_train, epochs=130, batch_size=16, verbose=0) test_mse_score, test_mae_score = model.evaluate(x_test, y_test) logger.info(f'mse score #{test_mse_score}, mae score #{test_mae_score}') #https://stackoverflow.com/questions/40729162/merging-results-from-model-predict-with-original-pandas-dataframe outcome = model.predict(x_test) y_test['preds'] = outcome df_out = pd.merge(data, y_test, how='left', left_index=True, right_index=True) logger.info(df_out.head(10)) df_out.to_csv('/home/jpvel/Desktop/outcome2.csv', float_format='%.2f')

44.652778

119

0.625505

446

3,215

4.318386

0.275785

0.025961

0.018692

0.047767

0.805815

0.728972

0.693666

0

0.030788

0.222084

3,215

72

120

44.652778

0.739304

0.068429

0

0.612903

0

0.137943

0.021042

0

1

0.048387

false

0

0.112903

0

0.177419

0

null

0

1

0

1

0

null

0

5

6af068ae69bc92b753b8d82591ccaed2c902f428

5,782

py

Python

tools/perf/page_sets/media_cns_cases.py

google-ar/chromium

2441c86a5fd975f09a6c30cddb57dfb7fc239699

[ "Apache-2.0", "BSD-3-Clause-No-Nuclear-License-2014", "BSD-3-Clause" ]

777

2017-08-29T15:15:32.000Z

2022-03-21T05:29:41.000Z

tools/perf/page_sets/media_cns_cases.py

harrymarkovskiy/WebARonARCore

2441c86a5fd975f09a6c30cddb57dfb7fc239699

[ "Apache-2.0", "BSD-3-Clause-No-Nuclear-License-2014", "BSD-3-Clause" ]

66

2017-08-30T18:31:18.000Z

2021-08-02T10:59:35.000Z

tools/perf/page_sets/media_cns_cases.py

harrymarkovskiy/WebARonARCore

2441c86a5fd975f09a6c30cddb57dfb7fc239699

[ "Apache-2.0", "BSD-3-Clause-No-Nuclear-License-2014", "BSD-3-Clause" ]

123

2017-08-30T01:19:34.000Z

2022-03-17T22:55:31.000Z

# Copyright 2014 The Chromium Authors. All rights reserved. # Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. from telemetry.page import page as page_module from telemetry import story class BasicPlayPage(page_module.Page): def __init__(self, url, page_set, name=''): super(BasicPlayPage, self).__init__(url=url, page_set=page_set, name=name) self.add_browser_metrics = True def PlayAction(self, action_runner): action_runner.PlayMedia(playing_event_timeout_in_seconds=60, ended_event_timeout_in_seconds=60) def RunPageInteractions(self, action_runner): self.PlayAction(action_runner) def SeekBeforeAndAfterPlayhead(self, action_runner): action_runner.PlayMedia(playing_event_timeout_in_seconds=60) # Wait for 1 second so that we know the play-head is at ~1s. action_runner.Wait(1) # Seek to before the play-head location. action_runner.SeekMedia(seconds=0.5, timeout_in_seconds=60, label='seek_warm') # Seek to after the play-head location. action_runner.SeekMedia(seconds=15, timeout_in_seconds=60, label='seek_cold') class SeekBeforeAndAfterPlayheadPage(BasicPlayPage): def __init__(self, url, page_set, name): super(SeekBeforeAndAfterPlayheadPage, self).__init__(url=url, page_set=page_set, name=name) self.add_browser_metrics = False def RunPageInteractions(self, action_runner): self.SeekBeforeAndAfterPlayhead(action_runner) class MediaCnsCasesPageSet(story.StorySet): """ Media benchmark on network constrained conditions. """ def __init__(self): super(MediaCnsCasesPageSet, self).__init__() urls_list = [ # pylint: disable=line-too-long 'file://tough_video_cases/video.html?id=no_constraints_webm&src=tulip2.webm&net=none', # pylint: disable=line-too-long 'file://tough_video_cases/video.html?id=cable_webm&src=tulip2.webm&net=cable', # pylint: disable=line-too-long 'file://tough_video_cases/video.html?id=wifi_webm&src=tulip2.webm&net=wifi', # pylint: disable=line-too-long 'file://tough_video_cases/video.html?id=no_constraints_ogv&src=tulip2.ogv&net=none', # pylint: disable=line-too-long 'file://tough_video_cases/video.html?id=cable_ogv&src=tulip2.ogv&net=cable', # pylint: disable=line-too-long 'file://tough_video_cases/video.html?id=wifi_ogv&src=tulip2.ogv&net=wifi', # pylint: disable=line-too-long 'file://tough_video_cases/video.html?id=no_constraints_mp4&src=tulip2.mp4&net=none', # pylint: disable=line-too-long 'file://tough_video_cases/video.html?id=cable_mp4&src=tulip2.mp4&net=cable', # pylint: disable=line-too-long 'file://tough_video_cases/video.html?id=wifi_mp4&src=tulip2.mp4&net=wifi', # pylint: disable=line-too-long 'file://tough_video_cases/video.html?id=no_constraints_wav&src=tulip2.wav&type=audio&net=none', # pylint: disable=line-too-long 'file://tough_video_cases/video.html?id=cable_wav&src=tulip2.wav&type=audio&net=cable', # pylint: disable=line-too-long 'file://tough_video_cases/video.html?id=wifi_wav&src=tulip2.wav&type=audio&net=wifi', # pylint: disable=line-too-long 'file://tough_video_cases/video.html?id=no_constraints_ogg&src=tulip2.ogg&type=audio&net=none', # pylint: disable=line-too-long 'file://tough_video_cases/video.html?id=cable_ogg&src=tulip2.ogg&type=audio&net=cable', # pylint: disable=line-too-long 'file://tough_video_cases/video.html?id=wifi_ogg&src=tulip2.ogg&type=audio&net=wifi', # pylint: disable=line-too-long 'file://tough_video_cases/video.html?id=no_constraints_mp3&src=tulip2.mp3&type=audio&net=none', # pylint: disable=line-too-long 'file://tough_video_cases/video.html?id=cable_mp3&src=tulip2.mp3&type=audio&net=cable', # pylint: disable=line-too-long 'file://tough_video_cases/video.html?id=wifi_mp3&src=tulip2.mp3&type=audio&net=wifi', # pylint: disable=line-too-long 'file://tough_video_cases/video.html?id=no_constraints_m4a&src=tulip2.m4a&type=audio&net=none', # pylint: disable=line-too-long 'file://tough_video_cases/video.html?id=cable_m4a&src=tulip2.m4a&type=audio&net=cable', # pylint: disable=line-too-long 'file://tough_video_cases/video.html?id=wifi_m4a&src=tulip2.m4a&type=audio&net=wifi' ] for url in urls_list: self.AddStory(BasicPlayPage(url, self)) urls_list2 = [ # pylint: disable=line-too-long 'file://tough_video_cases/video.html?id=wifi_mp3&src=tulip2.mp3&type=audio&net=wifi', # pylint: disable=line-too-long 'file://tough_video_cases/video.html?id=wifi_m4a&src=tulip2.m4a&type=audio&net=wifi', # pylint: disable=line-too-long 'file://tough_video_cases/video.html?id=wifi_ogg&src=tulip2.ogg&type=audio&net=wifi', # pylint: disable=line-too-long 'file://tough_video_cases/video.html?id=wifi_wav&src=tulip2.wav&type=audio&net=wifi', # pylint: disable=line-too-long 'file://tough_video_cases/video.html?id=wifi_mp4&src=tulip2.mp4&type=audio&net=wifi', # pylint: disable=line-too-long 'file://tough_video_cases/video.html?id=wifi_ogv&src=tulip2.ogv&type=audio&net=wifi', # pylint: disable=line-too-long 'file://tough_video_cases/video.html?id=wifi_webm&src=tulip2.webm&type=audio&net=wifi' ] for url in urls_list2: if url in urls_list: name = 'seek_' + url else: name = '' self.AddStory(SeekBeforeAndAfterPlayheadPage(url, self, name=name))

47.393443

101

0.699066

835

5,782

4.646707

0.148503

0.093814

0.12268

0.14433

0.794588

0.764433

0.728866

0.697938

0.647938

0

0.015589

0.167935

5,782

121

102

47.785124

0.790896

0.204773

0

0.115942

0

0.405797

0.50724

0.502194

0

1

0.101449

false

0

0.028986

0

0.173913

0

null

0

1

0

1

0

1

0

1

null

0

5

0a81eb32e4c38257c06b81ad370744bfa1131769

29

py

Python

k8s-deploy-external-dns/provider/__init__.py

Filippo125/k8s-deploy-external-dns

b83c74c7e00c9566088a232ce0c5da98b23f8198

[ "MIT" ]

null

k8s-deploy-external-dns/provider/__init__.py

Filippo125/k8s-deploy-external-dns

b83c74c7e00c9566088a232ce0c5da98b23f8198

[ "MIT" ]

null

k8s-deploy-external-dns/provider/__init__.py

Filippo125/k8s-deploy-external-dns

b83c74c7e00c9566088a232ce0c5da98b23f8198

[ "MIT" ]

null

from .povh import OVHProvider

29

0.862069

4

29

6.25

1

0

0.103448

29

1

29

0.961538

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

0ab464c6834affd58b59373c6b0f9bc1ca82876d

95

py

Python

test29.py

cherytony/test1

506ce4cab6f641beff817c81d7a616db29a7131d

[ "Apache-2.0" ]

null

test29.py

cherytony/test1

506ce4cab6f641beff817c81d7a616db29a7131d

[ "Apache-2.0" ]

null

test29.py

cherytony/test1

506ce4cab6f641beff817c81d7a616db29a7131d

[ "Apache-2.0" ]

null

print("Hello") import sys print(sys.version) import tensorflow as tf print(tf.__version__)

9.5

23

0.757895

14

95

4.857143

0.571429

0

0.136842

95

9

24

10.555556

0.829268

0

0.052632

0

1

0

true

0

0.4

0

0.4

0.6

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

0adc929657b93d6b766c909d3c2ba6f9e165ded8

39

py

Python

Src/Hosts/Silverlight/Tests/tests/manual/test_s_clock_rb/verification.py

jdhardy/dlr

dca078fbf9d103fad4dcabda76795a23d82106bc

[ "Apache-2.0" ]

null

Src/Hosts/Silverlight/Tests/tests/manual/test_s_clock_rb/verification.py

jdhardy/dlr

dca078fbf9d103fad4dcabda76795a23d82106bc

[ "Apache-2.0" ]

null

Src/Hosts/Silverlight/Tests/tests/manual/test_s_clock_rb/verification.py

jdhardy/dlr

dca078fbf9d103fad4dcabda76795a23d82106bc

[ "Apache-2.0" ]

null

from SL_util import * PositiveTest(0)

9.75

21

0.769231

6

39

4.833333

1

0

0.030303

0.153846

39

3

22

13

0.848485

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

5

0af4867553aa1f35b075b7edaaabaf94da3023a3

129

py

Python

gdeltdoc/__init__.py

Man-who-sold-the-world/gdelt-doc-api

9e2a5922aba7a56718fc6886e926e351e73597b4

[ "MIT" ]

null

gdeltdoc/__init__.py

Man-who-sold-the-world/gdelt-doc-api

9e2a5922aba7a56718fc6886e926e351e73597b4

[ "MIT" ]

null

gdeltdoc/__init__.py

Man-who-sold-the-world/gdelt-doc-api

9e2a5922aba7a56718fc6886e926e351e73597b4

[ "MIT" ]

null

from gdeltdoc.api_client import GdeltDoc from gdeltdoc.filters import Filters, near, repeat, multi_repeat __version__ = "1.3.0"

25.8

64

0.806202

19

129

5.157895

0.684211

0.244898

0

0.026316

0.116279

129

4

65

32.25

0.833333

0

0.03876

0

1

0

false

0

0.666667

0

0.666667

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

0afd7d04feca8eba88caab05f3b761663f018969

72

py

Python

server/jbei/rest/clients/ice/__init__.py

zhwycsz/edd

bdc1d2f8b5e375d3a1254829b9d2b460dd09ca12

[ "BSD-3-Clause-LBNL" ]

1

2020-04-07T03:14:52.000Z

server/jbei/rest/clients/ice/__init__.py

zhwycsz/edd

bdc1d2f8b5e375d3a1254829b9d2b460dd09ca12

[ "BSD-3-Clause-LBNL" ]

null

server/jbei/rest/clients/ice/__init__.py

zhwycsz/edd

bdc1d2f8b5e375d3a1254829b9d2b460dd09ca12

[ "BSD-3-Clause-LBNL" ]

null

# coding: utf-8 from .api import IceApi, IceApiException # noqa: F401

18

54

0.722222

10

72

5.2

1

0

0.067797

0.180556

72

3

55

24

0.813559

0.333333

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

7c8f76616e97f4b8db1394308640f991177e14b2

80

py

Python

PythonCurso01/aula121_docstrings/exemplo01/uma_linha.py

AlissonAnjos21/Aprendendo

9454d9e53ef9fb8bc61bf481b6592164f5bf8695

[ "MIT" ]

null

PythonCurso01/aula121_docstrings/exemplo01/uma_linha.py

AlissonAnjos21/Aprendendo

9454d9e53ef9fb8bc61bf481b6592164f5bf8695

[ "MIT" ]

null

PythonCurso01/aula121_docstrings/exemplo01/uma_linha.py

AlissonAnjos21/Aprendendo

9454d9e53ef9fb8bc61bf481b6592164f5bf8695

[ "MIT" ]

null

"""Uma linha de documentação""" variavel = 'valor' def funcao(): return 1

11.428571

31

0.6375

10

80

5.1

1

0

0.015873

0.2125

80

6

32

13.333333

0.793651

0.3125

0

0.102041

0

1

0.333333

false

0

0.333333

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

5

7c97aa6049b7ef8ab182c4473c5de87e84395ffa

126

py

Python

Own/Python/Tutorials/Animals/Cat.py

cychitivav/programming_exercises

e8e7ddb4ec4eea52ee0d3826a144c7dc97195e78

[ "MIT" ]

null

Own/Python/Tutorials/Animals/Cat.py

cychitivav/programming_exercises

e8e7ddb4ec4eea52ee0d3826a144c7dc97195e78

[ "MIT" ]

null

Own/Python/Tutorials/Animals/Cat.py

cychitivav/programming_exercises

e8e7ddb4ec4eea52ee0d3826a144c7dc97195e78

[ "MIT" ]

null

#Cristian Chitiva #cychitvav@unal.educo #16/Sept/2018 class Cat: def __init__(self, name): self.name = name

18

30

0.650794

17

126

4.588235

0.823529

0.205128

0

0.0625

0.238095

126

7

31

18

0.75

0.380952

0

1

0.333333

false

0

0.666667

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

7caa8f1c5bbe37f5b9b87c8b7b87e1dbfc9fc3f1

10,018

py

Python

opensfm/test/test_stats.py

ricklentz/OpenSfM

b44b5f2b533b6fce8055b3a5a98a59bc22ae2cf6

[ "BSD-2-Clause" ]

2,535

2015-01-04T17:59:20.000Z

2022-03-31T06:12:43.000Z

opensfm/test/test_stats.py

ricklentz/OpenSfM

b44b5f2b533b6fce8055b3a5a98a59bc22ae2cf6

[ "BSD-2-Clause" ]

752

2015-01-11T22:15:20.000Z

2022-03-31T15:23:47.000Z

opensfm/test/test_stats.py

ricklentz/OpenSfM

b44b5f2b533b6fce8055b3a5a98a59bc22ae2cf6

[ "BSD-2-Clause" ]

780

2015-01-15T15:06:00.000Z

2022-03-26T20:47:26.000Z

from opensfm import stats, types from opensfm.synthetic_data import synthetic_dataset, synthetic_scene def test_processing_statistics_normal( scene_synthetic: synthetic_scene.SyntheticInputData, ): reference = scene_synthetic.reconstruction dataset = synthetic_dataset.SyntheticDataSet( reference, scene_synthetic.exifs, scene_synthetic.features, scene_synthetic.tracks_manager, ) processing_statistics = stats.processing_statistics(dataset, [reference]) assert list(processing_statistics.keys()) == ["steps_times", "date", "area"] assert processing_statistics["steps_times"] == { "Feature Extraction": -1, "Features Matching": -1, "Tracks Merging": -1, "Reconstruction": -1, "Total Time": 0, } assert processing_statistics["date"] == "unknown" assert 3500 < processing_statistics["area"] < 3600 def test_processing_statistics_null( scene_synthetic: synthetic_scene.SyntheticInputData, null_scene: types.Reconstruction, ): dataset = synthetic_dataset.SyntheticDataSet( null_scene, scene_synthetic.exifs, scene_synthetic.features, scene_synthetic.tracks_manager, ) processing_statistics = stats.processing_statistics(dataset, [null_scene]) assert list(processing_statistics.keys()) == ["steps_times", "date", "area"] assert processing_statistics["steps_times"] == { "Feature Extraction": -1, "Features Matching": -1, "Tracks Merging": -1, "Reconstruction": -1, "Total Time": 0, } assert processing_statistics["date"] == "unknown" assert processing_statistics["area"] == -1 def test_features_statistics_normal( scene_synthetic: synthetic_scene.SyntheticInputData, ): reference = scene_synthetic.reconstruction dataset = synthetic_dataset.SyntheticDataSet( reference, scene_synthetic.exifs, scene_synthetic.features, scene_synthetic.tracks_manager, ) features_statistics = stats.features_statistics( dataset, scene_synthetic.tracks_manager, [reference] ) assert list(features_statistics.keys()) == [ "detected_features", "reconstructed_features", ] assert ( features_statistics["detected_features"] == features_statistics["reconstructed_features"] ) assert features_statistics["reconstructed_features"] == { "min": 303, "max": 1065, "mean": 841, "median": 884, } def test_features_statistics_null( scene_synthetic: synthetic_scene.SyntheticInputData, null_scene: types.Reconstruction, ): dataset = synthetic_dataset.SyntheticDataSet( null_scene, scene_synthetic.exifs, scene_synthetic.features, scene_synthetic.tracks_manager, ) features_statistics = stats.features_statistics( dataset, scene_synthetic.tracks_manager, [null_scene] ) assert list(features_statistics.keys()) == [ "detected_features", "reconstructed_features", ] assert ( features_statistics["detected_features"] == features_statistics["reconstructed_features"] ) assert features_statistics["reconstructed_features"] == { "min": -1, "max": -1, "mean": -1, "median": -1, } def test_reconstruction_statistics_normal( scene_synthetic: synthetic_scene.SyntheticInputData, ): reference = scene_synthetic.reconstruction dataset = synthetic_dataset.SyntheticDataSet( reference, scene_synthetic.exifs, scene_synthetic.features, scene_synthetic.tracks_manager, ) reconstruction_statistics = stats.reconstruction_statistics( dataset, scene_synthetic.tracks_manager, [reference] ) assert reconstruction_statistics["components"] == 1 assert not reconstruction_statistics["has_gps"] assert not reconstruction_statistics["has_gcp"] assert 4900 < reconstruction_statistics["initial_points_count"] < 5000 assert reconstruction_statistics["initial_shots_count"] == 20 assert 4900 < reconstruction_statistics["reconstructed_points_count"] < 5000 assert reconstruction_statistics["reconstructed_shots_count"] == 20 assert 16800 < reconstruction_statistics["observations_count"] < 16900 assert 3.3 < reconstruction_statistics["average_track_length"] < 3.4 assert 3.4 < reconstruction_statistics["average_track_length_over_two"] < 3.5 assert len(reconstruction_statistics["histogram_track_length"]) == 5 assert 0.15 < reconstruction_statistics["reprojection_error_normalized"] < 0.16 assert 1.25 < reconstruction_statistics["reprojection_error_pixels"] < 1.28 assert len(reconstruction_statistics["reprojection_histogram_normalized"][0]) == 30 assert len(reconstruction_statistics["reprojection_histogram_normalized"][1]) == 31 assert len(reconstruction_statistics["reprojection_histogram_pixels"][0]) == 30 assert len(reconstruction_statistics["reprojection_histogram_pixels"][1]) == 31 def test_reconstruction_statistics_null( scene_synthetic: synthetic_scene.SyntheticInputData, null_scene: types.Reconstruction, ): dataset = synthetic_dataset.SyntheticDataSet( null_scene, scene_synthetic.exifs, scene_synthetic.features, scene_synthetic.tracks_manager, ) reconstruction_statistics = stats.reconstruction_statistics( dataset, scene_synthetic.tracks_manager, [null_scene] ) assert reconstruction_statistics["components"] == 1 assert not reconstruction_statistics["has_gps"] assert not reconstruction_statistics["has_gcp"] assert 4900 < reconstruction_statistics["initial_points_count"] < 5000 assert reconstruction_statistics["initial_shots_count"] == 0 assert reconstruction_statistics["reconstructed_points_count"] == 0 assert reconstruction_statistics["reconstructed_shots_count"] == 0 assert reconstruction_statistics["observations_count"] == 0 assert reconstruction_statistics["average_track_length"] == -1 assert reconstruction_statistics["average_track_length_over_two"] == -1 assert len(reconstruction_statistics["histogram_track_length"]) == 0 assert reconstruction_statistics["reprojection_error_normalized"] == -1.0 assert reconstruction_statistics["reprojection_error_pixels"] == -1.0 assert len(reconstruction_statistics["reprojection_histogram_normalized"][0]) == 0 assert len(reconstruction_statistics["reprojection_histogram_normalized"][1]) == 0 assert len(reconstruction_statistics["reprojection_histogram_pixels"][0]) == 0 assert len(reconstruction_statistics["reprojection_histogram_pixels"][1]) == 0 def test_cameras_statistics_normal( scene_synthetic: synthetic_scene.SyntheticInputData, ): reference = scene_synthetic.reconstruction dataset = synthetic_dataset.SyntheticDataSet( reference, scene_synthetic.exifs, scene_synthetic.features, scene_synthetic.tracks_manager, ) cameras_statistics = stats.cameras_statistics(dataset, [reference]) assert cameras_statistics == { "1": { "initial_values": {"k1": -0.1, "k2": 0.01, "focal": 0.7}, "optimized_values": {"k1": -0.1, "k2": 0.01, "focal": 0.7}, "bias": { "rotation": [-0.0, -0.0, -0.0], "scale": 1.0, "translation": [0.0, 0.0, 0.0], }, } } def test_cameras_statistics_null( scene_synthetic: synthetic_scene.SyntheticInputData, null_scene: types.Reconstruction, ): dataset = synthetic_dataset.SyntheticDataSet( null_scene, scene_synthetic.exifs, scene_synthetic.features, scene_synthetic.tracks_manager, ) cameras_statistics = stats.cameras_statistics(dataset, [null_scene]) assert cameras_statistics == {} def test_rig_statistics_normal( scene_synthetic: synthetic_scene.SyntheticInputData, ): reference = scene_synthetic.reconstruction dataset = synthetic_dataset.SyntheticDataSet( reference, scene_synthetic.exifs, scene_synthetic.features, scene_synthetic.tracks_manager, ) rig_statistics = stats.rig_statistics(dataset, [reference]) assert rig_statistics == {} def test_rig_statistics_null( scene_synthetic: synthetic_scene.SyntheticInputData, null_scene: types.Reconstruction, ): dataset = synthetic_dataset.SyntheticDataSet( null_scene, scene_synthetic.exifs, scene_synthetic.features, scene_synthetic.tracks_manager, ) cameras_statistics = stats.rig_statistics(dataset, [null_scene]) assert cameras_statistics == {} def test_gps_errors_normal( scene_synthetic: synthetic_scene.SyntheticInputData, ): reference = scene_synthetic.reconstruction gps_errors = stats.gps_errors([reference]) assert gps_errors == {} def test_gps_errors_null( scene_synthetic: synthetic_scene.SyntheticInputData, null_scene: types.Reconstruction, ): gps_errors = stats.gps_errors([null_scene]) assert gps_errors == {} def test_gcp_errors_normal( scene_synthetic: synthetic_scene.SyntheticInputData, ): reference = scene_synthetic.reconstruction dataset = synthetic_dataset.SyntheticDataSet( reference, scene_synthetic.exifs, scene_synthetic.features, scene_synthetic.tracks_manager, ) gcp_errors = stats.gcp_errors(dataset, [reference]) assert gcp_errors == {} def test_gcp_errors_null( scene_synthetic: synthetic_scene.SyntheticInputData, null_scene: types.Reconstruction, ): dataset = synthetic_dataset.SyntheticDataSet( null_scene, scene_synthetic.exifs, scene_synthetic.features, scene_synthetic.tracks_manager, ) gcp_errors = stats.gcp_errors(dataset, [null_scene]) assert gcp_errors == {}

33.61745

87

0.712418

987

10,018

6.89463

0.101317

0.125496

0.047024

0.063483

0.911536

0.865393

0.797208

0.76723

0.689199

0.672594

0

0.019904

0.192553

10,018

297

88

33.73064

0.821362

0

0.611111

0

0.130465

0.069076

0

0.222222

1

0.055556

false

0

0.007937

0

0.063492

0

null

0

1

0

1

0

null

0

5

7cbaa967a7579192571a1e2bd188aed5f9e90329

1,754

py

Python

src/wheezy/http/tests/test_method.py

akornatskyy/wheezy.http

85f89fc5492e09f2049b8d7a25b0d2a09387060f

[ "MIT" ]

null

src/wheezy/http/tests/test_method.py

akornatskyy/wheezy.http

85f89fc5492e09f2049b8d7a25b0d2a09387060f

[ "MIT" ]

29

2020-07-18T04:32:17.000Z

2021-07-06T09:42:16.000Z

src/wheezy/http/tests/test_method.py

akornatskyy/wheezy.http

85f89fc5492e09f2049b8d7a25b0d2a09387060f

[ "MIT" ]

null

""" Unit tests for ``wheezy.http.method``. """ import unittest from unittest.mock import Mock from wheezy.http.method import accept_method from wheezy.http.response import HTTPResponse class AcceptMethodTestCase(unittest.TestCase): """Test the ``accept_method`` decorator.""" def test_exact_strategy(self): """A single HTTP method constraint check.""" mock_request = Mock() mock_handler = Mock(return_value=HTTPResponse()) for method in ["GET", "HEAD", "POST", "PUT"]: mock_request.reset_mock() mock_request.method = method handler = accept_method(method)(mock_handler) response = handler(mock_request) assert 200 == response.status_code for method in ["HEAD", "POST", "PUT"]: mock_request.reset_mock() mock_request.method = method handler = accept_method("GET")(mock_handler) response = handler(mock_request) assert 405 == response.status_code def test_one_of_strategy(self): """Multiple HTTP methods constraint check.""" mock_request = Mock() mock_handler = Mock(return_value=HTTPResponse()) for method in ["GET", "HEAD"]: mock_request.reset_mock() mock_request.method = method handler = accept_method(("GET", "HEAD"))(mock_handler) response = handler(mock_request) assert 200 == response.status_code for method in ["POST", "PUT"]: mock_request.reset_mock() mock_request.method = method handler = accept_method(("GET", "HEAD"))(mock_handler) response = handler(mock_request) assert 405 == response.status_code

34.392157

66

0.619156

192

1,754

5.442708

0.239583

0.147368

0.042105

0.076555

0.708134

0

0.00939

0.27138

1,754

50

67

35.08

0.808294

0.088369

0

0.628571

0

0.035533

0

0.114286

1

0.057143

false

0

0.114286

0

0.2

0

null

0

1

0

null

0

5

6b138e8ad017f0979c52c6c5e1b7da79cf726a9b

56

py

Python

api/lastfm.py

imsuwj/noambox

99404b773c16ee30fc3593b64cd91a8ff9aeaedf

[ "MIT" ]

3

2015-10-20T08:41:01.000Z

2017-08-08T17:45:59.000Z

api/lastfm.py

imsuwj/noambox

99404b773c16ee30fc3593b64cd91a8ff9aeaedf

[ "MIT" ]

null

api/lastfm.py

imsuwj/noambox

99404b773c16ee30fc3593b64cd91a8ff9aeaedf

[ "MIT" ]

null

import requests from handler.config import Config, data

18.666667

39

0.839286

8

56

5.875

0.75

0

0.125

56

2

40

28

0.959184

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

860b4025d89e0eb085eea3f0f81608b2dd2fd0cd

5,601

py

Python

python/paddle/sparse/functional/unary.py

Lieberk/Paddle

2eacef496854b6e8e3b06daaf1c83478c575fbb3

[ "Apache-2.0" ]

null

python/paddle/sparse/functional/unary.py

Lieberk/Paddle

2eacef496854b6e8e3b06daaf1c83478c575fbb3

[ "Apache-2.0" ]

null

python/paddle/sparse/functional/unary.py

Lieberk/Paddle

2eacef496854b6e8e3b06daaf1c83478c575fbb3

[ "Apache-2.0" ]

null

# Copyright (c) 2022 PaddlePaddle Authors. 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. __all__ = [] from paddle import _C_ops, in_dynamic_mode def relu(x, name=None): """ sparse relu activation, requiring x to be a sparse coo or sparse csr tensor. .. math:: out = max(x, 0) Parameters: x (Tensor): The input Sparse Tensor with data type float32, float64. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: A Sparse Tensor with the same data type and shape as ``x`` . Examples: .. code-block:: python import paddle from paddle.fluid.framework import _test_eager_guard with _test_eager_guard(): dense_x = paddle.to_tensor([-2, 0, 1], dtype='float32') sparse_x = dense_x.to_sparse_coo(1) out = paddle.sparse.functional.relu(sparse_x) """ assert in_dynamic_mode(), "Currently, Sparse API only support dynamic mode" if x.is_sparse_coo(): return _C_ops.final_state_sparse_coo_relu(x) elif x.is_sparse_csr(): return _C_ops.final_state_sparse_csr_relu(x) else: raise ValueError( "Currently, sparse.relu only support the input of SparseCooTensor or SparseCsrTensor" ) def tanh(x, name=None): """ sparse tanh activation, requiring x to be a sparse coo or sparse csr tensor. .. math:: out = tanh(x) Parameters: x (Tensor): The input Sparse Tensor with data type float32, float64. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: A Sparse Tensor with the same data type and shape as ``x`` . Examples: .. code-block:: python import paddle from paddle.fluid.framework import _test_eager_guard with _test_eager_guard(): dense_x = paddle.to_tensor([-2, 0, 1], dtype='float32') sparse_x = dense_x.to_sparse_coo(1) out = paddle.sparse.tanh(sparse_x) """ assert in_dynamic_mode(), "Currently, Sparse API only support dynamic mode" if x.is_sparse_coo(): return _C_ops.final_state_sparse_coo_tanh(x) elif x.is_sparse_csr(): return _C_ops.final_state_sparse_csr_tanh(x) else: raise ValueError( "Currently, sparse.tanh only support the input of SparseCooTensor or SparseCsrTensor" ) def sqrt(x, name=None): """ Calculate square root of x, requiring x to be a sparse coo or sparse csr tensor. .. math:: out = sqrt(x) Parameters: x (Tensor): The input Sparse Tensor with data type float32, float64. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: A Sparse Tensor with the same data type and shape as ``x`` . Examples: .. code-block:: python import paddle from paddle.fluid.framework import _test_eager_guard with _test_eager_guard(): dense_x = paddle.to_tensor([4, 0, 1], dtype='float32') sparse_x = dense_x.to_sparse_coo(1) out = paddle.sparse.sqrt(sparse_x) """ assert in_dynamic_mode(), "Currently, Sparse API only support dynamic mode" if x.is_sparse_coo(): return _C_ops.final_state_sparse_coo_sqrt(x) elif x.is_sparse_csr(): return _C_ops.final_state_sparse_csr_sqrt(x) else: raise ValueError( "Currently, sparse.sqrt only support the input of SparseCooTensor or SparseCsrTensor" ) def sin(x, name=None): """ Calculate sin of x, requiring x to be a sparse coo or sparse csr tensor. .. math:: out = sin(x) Parameters: x (Tensor): The input Sparse Tensor with data type float32, float64. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: A Sparse Tensor with the same data type and shape as ``x`` . Examples: .. code-block:: python import paddle from paddle.fluid.framework import _test_eager_guard with _test_eager_guard(): dense_x = paddle.to_tensor([-2, 0, 3], dtype='float32') sparse_x = dense_x.to_sparse_coo(1) out = paddle.sparse.sin(sparse_x) """ assert in_dynamic_mode(), "Currently, Sparse API only support dynamic mode" if x.is_sparse_coo(): return _C_ops.final_state_sparse_coo_sin(x) elif x.is_sparse_csr(): return _C_ops.final_state_sparse_csr_sin(x) else: raise ValueError( "Currently, sparse.sin only support the input of SparseCooTensor or SparseCsrTensor" )

31.466292

97

0.638993

764

5,601

4.501309

0.187173

0.041873

0.03722

0.034894

0.797906

0.757197

0.741785

0.692934

0

0.012102

0.277093

5,601

177

98

31.644068

0.837244

0.631494

0

0.47619

0

0.299135

0

0.095238

1

0.095238

false

0

0.02381

0

0.309524

0

null

0

1

0

null

0

5

86664b0dd9dac91d6fa00cf899f41b6b1858e394

3,930

py

Python

tools/perf/core/results_dashboard_unittest.py

sarang-apps/darshan_browser

173649bb8a7c656dc60784d19e7bb73e07c20daa

[ "BSD-3-Clause-No-Nuclear-License-2014", "BSD-3-Clause" ]

575

2015-06-18T23:58:20.000Z

2022-03-23T09:32:39.000Z

tools/perf/core/results_dashboard_unittest.py

sarang-apps/darshan_browser

173649bb8a7c656dc60784d19e7bb73e07c20daa

[ "BSD-3-Clause-No-Nuclear-License-2014", "BSD-3-Clause" ]

113

2015-05-04T09:58:14.000Z

2022-01-31T19:35:03.000Z

tools/perf/core/results_dashboard_unittest.py

sarang-apps/darshan_browser

173649bb8a7c656dc60784d19e7bb73e07c20daa

[ "BSD-3-Clause-No-Nuclear-License-2014", "BSD-3-Clause" ]

52

2015-07-14T10:40:50.000Z

2022-03-15T01:11:49.000Z

# Copyright 2018 The Chromium Authors. All rights reserved. # Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. import unittest import mock from mock import call from core import results_dashboard class ResultsDashboardTest(unittest.TestCase): def setUp(self): self.dummy_token_generator = lambda service_file, timeout: 'Arthur-Merlin' self.perf_data = {'foo': 1, 'bar': 2} self.dashboard_url = 'https://chromeperf.appspot.com' def testRetryForSendResultRetryException(self): def raise_retry_exception( url, histogramset_json, token_generator_callback): del url, histogramset_json # unused del token_generator_callback # unused raise results_dashboard.SendResultsRetryException('Should retry') with mock.patch('core.results_dashboard.time.sleep') as sleep_mock: with mock.patch('core.results_dashboard._SendHistogramJson', side_effect=raise_retry_exception) as m: upload_result = results_dashboard.SendResults( self.perf_data, 'dummy_benchmark', self.dashboard_url, send_as_histograms=True, token_generator_callback=self.dummy_token_generator, num_retries=5) self.assertFalse(upload_result) self.assertEqual(m.call_count, 5) self.assertEqual( sleep_mock.mock_calls, [call(15), call(30), call(60), call(120), call(240)]) def testNoRetryForSendResultFatalException(self): def raise_retry_exception( url, histogramset_json, token_generator_callback): del url, histogramset_json # unused del token_generator_callback # unused raise results_dashboard.SendResultsFatalException('Do not retry') with mock.patch('core.results_dashboard.time.sleep') as sleep_mock: with mock.patch('core.results_dashboard._SendHistogramJson', side_effect=raise_retry_exception) as m: upload_result = results_dashboard.SendResults( self.perf_data, 'dummy_benchmark', self.dashboard_url, send_as_histograms=True, token_generator_callback=self.dummy_token_generator, num_retries=5) self.assertFalse(upload_result) self.assertEqual(m.call_count, 1) self.assertFalse(sleep_mock.mock_calls) def testNoRetryForSuccessfulSendResult(self): with mock.patch('core.results_dashboard.time.sleep') as sleep_mock: with mock.patch('core.results_dashboard._SendHistogramJson') as m: upload_result = results_dashboard.SendResults( self.perf_data, 'dummy_benchmark', self.dashboard_url, send_as_histograms=True, token_generator_callback=self.dummy_token_generator, num_retries=5) self.assertTrue(upload_result) self.assertEqual(m.call_count, 1) self.assertFalse(sleep_mock.mock_calls) def testNoRetryAfterSucessfulSendResult(self): counter = [0] def raise_retry_exception_first_two_times( url, histogramset_json, token_generator_callback): del url, histogramset_json # unused del token_generator_callback # unused counter[0] += 1 if counter[0] <= 2: raise results_dashboard.SendResultsRetryException('Please retry') with mock.patch('core.results_dashboard.time.sleep') as sleep_mock: with mock.patch('core.results_dashboard._SendHistogramJson', side_effect=raise_retry_exception_first_two_times) as m: upload_result = results_dashboard.SendResults( self.perf_data, 'dummy_benchmark', self.dashboard_url, send_as_histograms=True, token_generator_callback=self.dummy_token_generator, num_retries=5) self.assertTrue(upload_result) self.assertEqual(m.call_count, 3) self.assertEqual( sleep_mock.mock_calls, [call(15), call(30)])

42.258065

79

0.708397

459

3,930

5.786492

0.246187

0.096386

0.082831

0.051205

0.739081

0.722139

0

0.011279

0.210433

3,930

92

80

42.717391

0.844666

0.050127

0

0.631579

0

0.118453

0.079506

0

0.157895

1

0.105263

false

0

0.052632

0

0.171053

0

null

0

1

0

null

0

5

86a11146e13501c9c6a54abae9650546fb715a24

3,217

py

Python

projects/models.py

robertruhiu/bac

2ab6d6b3fae806f96b1322f1d092ec8ff9860b1b

[ "MIT" ]

null

projects/models.py

robertruhiu/bac

2ab6d6b3fae806f96b1322f1d092ec8ff9860b1b

[ "MIT" ]

4

2020-06-05T19:32:33.000Z

2021-06-10T21:01:56.000Z

projects/models.py

robertruhiu/bac

2ab6d6b3fae806f96b1322f1d092ec8ff9860b1b

[ "MIT" ]

null

from django.contrib.auth.models import User from django.db import models # Create your models here. # TODO: add model for category to classify all projects using project category, can be multiple ie frontend, backend # TODO: categorise language into frontend, backend etc class Language(models.Model): name = models.CharField(max_length=140) def __str__(self): return self.name class Framework(models.Model): name = models.CharField(max_length=140) language = models.ForeignKey(Language, on_delete=models.DO_NOTHING) def __str__(self): return self.name class level(models.Model): name = models.CharField(max_length=140) language = models.ForeignKey(Language, on_delete=models.DO_NOTHING) def __str__(self): return self.name class Devtype(models.Model): name = models.CharField(max_length=140) def __str__(self): return self.name class Projecttype(models.Model): name = models.CharField(max_length=140) def __str__(self): return self.name class Project(models.Model): name = models.CharField(max_length=140) owner = models.ForeignKey(User, on_delete=models.CASCADE) description = models.CharField(max_length=500, blank=True, null=True, ) level = models.CharField(max_length=200, blank=True, null=True, ) concept = models.CharField(max_length=200, blank=True, null=True, ) projectimage1 = models.CharField(max_length=200, blank=True, null=True, ) projectimage2 = models.CharField(max_length=200, blank=True, null=True, ) projectimage3 = models.CharField(max_length=200, blank=True, null=True, ) projectimage4 = models.CharField(max_length=200, blank=True, null=True, ) projectimage5 = models.CharField(max_length=200, blank=True, null=True, ) projectimage6 = models.CharField(max_length=200, blank=True, null=True, ) projectimage7 = models.CharField(max_length=200, blank=True, null=True, ) projectimage8 = models.CharField(max_length=200, blank=True, null=True, ) projectimage9 = models.CharField(max_length=200, blank=True, null=True, ) projectimage10 = models.CharField(max_length=200, blank=True, null=True, ) requirement1 = models.CharField(max_length=200, blank=True, null=True, ) requirement2 = models.CharField(max_length=200, blank=True, null=True, ) requirement3 = models.CharField(max_length=200, blank=True, null=True, ) requirement4 = models.CharField(max_length=200, blank=True, null=True, ) requirement5 = models.CharField(max_length=200, blank=True, null=True, ) requirement6 = models.CharField(max_length=200, blank=True, null=True, ) requirement7 = models.CharField(max_length=200, blank=True, null=True, ) requirement8 = models.CharField(max_length=200, blank=True, null=True, ) requirement9 = models.CharField(max_length=200, blank=True, null=True, ) requirement10 = models.CharField(max_length=200, blank=True, null=True, ) framework = models.ForeignKey(Framework, on_delete=False, null=True) devtype = models.ForeignKey(Devtype, on_delete=False, null=True) projecttype = models.ForeignKey(Projecttype, on_delete=False, null=True) def __str__(self): return self.name

41.779221

116

0.733914

420

3,217

5.47619

0.192857

0.18913

0.226957

0.302609

0.67913

0.651739

0.641304

0.623043

0.202174

0

0.039971

0.152316

3,217

76

117

42.328947

0.803447

0.059683

0

0.363636

0

0.013158

0

1

0.109091

false

0

0.036364

0.109091

1

0

null

0

1

0

null

0

1

0

1

0

5

86cfe20fc2558f24232c36ffbb64f15bed7ebcf2

120

py

Python

loonflow/__init__.py

youjiajia/loonflow

0542e543ffea49b2eda864397b9875b6bf107dd5

[ "MIT" ]

null

loonflow/__init__.py

youjiajia/loonflow

0542e543ffea49b2eda864397b9875b6bf107dd5

[ "MIT" ]

null

loonflow/__init__.py

youjiajia/loonflow

0542e543ffea49b2eda864397b9875b6bf107dd5

[ "MIT" ]

null

from __future__ import absolute_import, unicode_literals from tasks import app as celery_app __all__ = ['celery_app']

20

56

0.816667

17

120

5.058824

0.647059

0.209302

0

0.133333

120

5

57

24

0.826923

0

0.083333

0

1

0

false

0

0.666667

0

0.666667

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

86ef99952a5376efe78273dc70b91ad2a7ddfd5c

46

py

Python

__init__.py

floort/buienbadge

178a090970e69891b3381a8d158b06d1b611c147

[ "Unlicense" ]

null

__init__.py

floort/buienbadge

178a090970e69891b3381a8d158b06d1b611c147

[ "Unlicense" ]

null

__init__.py

floort/buienbadge

178a090970e69891b3381a8d158b06d1b611c147

[ "Unlicense" ]

null

from buienbadge import service service.loop()

15.333333

30

0.826087

6

46

6.333333

0.833333

0

0.108696

46

2

31

23

0.926829

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

5

8118d32aa05d295c6dc173836bc206609862c9aa

271

py

Python

app/utils/open_api/__init__.py

maxzhenzhera/my_vocab_backend

2e9f968374e0bc2fcc0ae40830ca40f3cf5754d1

[ "MIT" ]

null

app/utils/open_api/__init__.py

maxzhenzhera/my_vocab_backend

2e9f968374e0bc2fcc0ae40830ca40f3cf5754d1

[ "MIT" ]

null

app/utils/open_api/__init__.py

maxzhenzhera/my_vocab_backend

2e9f968374e0bc2fcc0ae40830ca40f3cf5754d1

[ "MIT" ]

null

""" headers: Set-Cookie: description: Session cookie schema: type: string example: SESSIONID=abcde12345; Path=/ "\0Set-Cookie": description: CSRF token schema: type: string example: CSRFTOKEN=fghijk678910; Path=/; HttpOnly """

19.357143

55

0.634686

26

271

6.615385

0.692308

0.197674

0.186047

0.267442

0

0.059113

0.250923

271

13

56

20.846154

0.788177

0.9631

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

5

d493243ba40b13e8763016daae5929b87739c50c

126

py

Python

languages/python/design_stackinspection.py

Andilyn/learntosolveit

fd15345c74ef543e4e26f4691bf91cb6dac568a4

[ "BSD-3-Clause" ]

1

2021-04-09T04:15:24.000Z

languages/python/design_stackinspection.py

Andilyn/learntosolveit

fd15345c74ef543e4e26f4691bf91cb6dac568a4

[ "BSD-3-Clause" ]

null

languages/python/design_stackinspection.py

Andilyn/learntosolveit

fd15345c74ef543e4e26f4691bf91cb6dac568a4

[ "BSD-3-Clause" ]

1

2021-07-31T02:45:29.000Z

import sys def foo(): """blah""" print sys._getframe().f_back.f_locals[sys._getframe().f_code.co_name].__doc__ foo()

18

81

0.674603

20

126

3.75

0.7

0.293333

0.32

0

0.134921

126

6

82

21

0.688073

0

null

0

0.25

null

0.25

1

0

null

1

0

1

0

null

0

1

0

5

d4a504e8d7d216e49c2588b144a44a382e28a483

132

py

Python

python-arithmetic-operators.py

nabin-info/hackerrank.com

da66a470d2e97a093821bfe41eb233d51784b9cc

[ "MIT" ]

null

python-arithmetic-operators.py

nabin-info/hackerrank.com

da66a470d2e97a093821bfe41eb233d51784b9cc

[ "MIT" ]

null

python-arithmetic-operators.py

nabin-info/hackerrank.com

da66a470d2e97a093821bfe41eb233d51784b9cc

[ "MIT" ]

null

#!/usr/bin/python import sys a = int(raw_input().strip()) b = int(raw_input().strip()) print (a + b) print (a - b) print (a * b)

12

28

0.590909

24

132

3.166667

0.5

0.236842

0.276316

0.421053

0.276316

0

0.181818

132

10

29

13.2

0.703704

0.121212

0

1

0

false

0

0.166667

0

0.166667

0.5

1

0

null

1

0

null

0

1

0

5

d4ea50fda756725eb38446c5005404d8ecdcc6eb

362

py

Python

titration/utils/devices/serial_mock.py

kieransukachevin/AlkalinityTitrator

09b642ee1368278b7b4fc180bed50ff538a0938a

[ "MIT" ]

null

titration/utils/devices/serial_mock.py

kieransukachevin/AlkalinityTitrator

09b642ee1368278b7b4fc180bed50ff538a0938a

[ "MIT" ]

31

2021-06-29T17:53:56.000Z

2021-08-19T21:59:03.000Z

titration/utils/devices/serial_mock.py

kieransukachevin/AlkalinityTitrator

09b642ee1368278b7b4fc180bed50ff538a0938a

[ "MIT" ]

4

2021-02-12T23:21:17.000Z

2021-11-15T16:55:38.000Z

class Serial: def __init__(self, port=None, baudrate=None, timeout=None): pass def reset_output_buffer(self): pass def reset_input_buffer(self): pass def writable(self): return True def write(self, bytes): pass def flush(self): pass def readline(self): return b"DONE\r\n"

16.454545

63

0.58011

46

362

4.391304

0.543478

0.173267

0.163366

0.168317

0

0.328729

362

21

64

17.238095

0.831276

0

0.333333

0

0.022099

0

1

0.466667

false

0.333333

0

0.133333

0.666667

0

null

0

1

0

null

0

1

0

1

0

1

0

5

d4f43313db29aad0f1d413cf5527559585790e82

91

py

Python

Funcoes/exceptions.py

felipezago/ControleEstoque

229659c4f9888fd01df34375ec92af7a1f734d10

[ "MIT" ]

null

Funcoes/exceptions.py

felipezago/ControleEstoque

229659c4f9888fd01df34375ec92af7a1f734d10

[ "MIT" ]

null

Funcoes/exceptions.py

felipezago/ControleEstoque

229659c4f9888fd01df34375ec92af7a1f734d10

[ "MIT" ]

null

from psycopg2 import OperationalError class OperationalError(OperationalError): pass

15.166667

41

0.824176

8

91

9.375

0.75

0

0.012821

0.142857

91

5

42

18.2

0.948718

0

1

0

true

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

5

be2397b5fc6d613fe0be03080ccf42f5b3d01be6

47

py

Python

Python/Topics/Creating bytes/To_bytes()/main.py

drtierney/hyperskill-problems

b74da993f0ac7bcff1cbd5d89a3a1b06b05f33e0

[ "MIT" ]

5

2020-08-29T15:15:31.000Z

2022-03-01T18:22:34.000Z

Python/Topics/Creating bytes/To_bytes()/main.py

drtierney/hyperskill-problems

b74da993f0ac7bcff1cbd5d89a3a1b06b05f33e0

[ "MIT" ]

null

Python/Topics/Creating bytes/To_bytes()/main.py

drtierney/hyperskill-problems

b74da993f0ac7bcff1cbd5d89a3a1b06b05f33e0

[ "MIT" ]

1

2020-12-02T11:13:14.000Z

print(sum(int(input()).to_bytes(2, 'little')))

23.5

46

0.659574

8

47

3.75

1

0

0.022222

0.042553

47

1

47

0.644444

0

0.12766

0

1

0

true

0

1

0

null

0

1

0

null

0

1

0

1

0

5

076c58ad90deb0d4925ec6e95008e65878b03f89

242

py

Python

getting_started/any.py

AoEiuV020/LearningPython

aac0f3f99cfd3d03a96a3c0e41da8f82ea0b8c70

[ "MIT" ]

null

getting_started/any.py

AoEiuV020/LearningPython

aac0f3f99cfd3d03a96a3c0e41da8f82ea0b8c70

[ "MIT" ]

null

getting_started/any.py

AoEiuV020/LearningPython

aac0f3f99cfd3d03a96a3c0e41da8f82ea0b8c70

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- assert any(list(map(lambda x: x > 4, range(8)))) assert any(map(lambda x: x > 4, range(8))) assert not any(list(map(lambda x: x < 0, range(8)))) assert not any((map(lambda x: x < 0, range(8))))

30.25

52

0.61157

47

242

3.148936

0.361702

0.189189

0.27027

0.297297

0.783784

0.662162

0.567568

0.324324

0

0.04902

0.157025

242

7

53

34.571429

0.676471

0.177686

0

1

0

true

0

null

0

1

0

1

0

null

0

1

0

1

0

5

078a293fd5203a7a1c134fcd3b7753a539c60a7a

9,912

py

Python

test/test_blf_solver.py

Hasenpfote/image_packer

826f7899cd90372638c5d241314423df7991ceb3

[ "MIT" ]

3

2020-11-16T17:03:05.000Z

2021-08-14T12:16:29.000Z

test/test_blf_solver.py

Hasenpfote/image_packer

826f7899cd90372638c5d241314423df7991ceb3

[ "MIT" ]

null

test/test_blf_solver.py

Hasenpfote/image_packer

826f7899cd90372638c5d241314423df7991ceb3

[ "MIT" ]

1

2021-02-17T02:48:20.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- import logging import math import random import uuid from unittest import TestCase import sys sys.path.append('../') from image_packer import blf from image_packer import blf_solver class TestBlfSolver(TestCase): @classmethod def setUpClass(cls): logging.disable(logging.CRITICAL) @classmethod def tearDownClass(cls): logging.disable(logging.NOTSET) @staticmethod def next_power_of_2(x): return 2.0 ** math.ceil(math.log2(x)) @staticmethod def is_power_of_2(x): p = math.log2(x) return math.ceil(p) == math.floor(p) @staticmethod def make_random_pieces(width, height, num_pieces): if isinstance(width, tuple): min_width, max_width = width else: min_width, max_width = width, width if isinstance(height, tuple): min_height, max_height = height else: min_height, max_height = height, height pieces = list() for _ in range(num_pieces): w = random.randint(min_width, max_width) h = random.randint(min_height, max_height) pieces.append(blf.Piece(uid=uuid.uuid4(), size=blf.Size(w, h))) return pieces def test_calc_minimum_container_size(self): margin = blf.Thickness(top=1, right=1, bottom=1, left=1) regions = list() region1 = blf.Region( uid=uuid.uuid4(), top=margin.bottom + 10, right=margin.left + 10, bottom=margin.bottom, left=margin.left ) regions.append(region1) region2 = blf.Region( uid=uuid.uuid4(), top=margin.bottom + 5, right=region1.right + margin.right + margin.left + 5, bottom=margin.bottom, left=region1.right + margin.right + margin.left ) regions.append(region2) size = blf_solver.calc_minimum_container_size(regions, margin) self.assertEqual(size.width, region2.right + margin.right) self.assertEqual(size.height, region1.top + margin.top) def test_calc_container_size(self): margin = blf.Thickness(top=1, right=1, bottom=1, left=1) regions = list() region1 = blf.Region( uid=uuid.uuid4(), top=margin.bottom + 10, right=margin.left + 10, bottom=margin.bottom, left=margin.left ) regions.append(region1) region2 = blf.Region( uid=uuid.uuid4(), top=margin.bottom + 5, right=region1.right + margin.right + margin.left + 5, bottom=margin.bottom, left=region1.right + margin.right + margin.left ) regions.append(region2) container_width = 100 # size = blf_solver.calc_container_size(container_width, regions, margin, False, False) self.assertEqual(size.width, container_width) self.assertEqual(size.height, region1.top + margin.top) # size = blf_solver.calc_container_size(container_width, regions, margin, True, False) self.assertEqual(size.width, region2.right + margin.right) self.assertEqual(size.height, region1.top + margin.top) # size = blf_solver.calc_container_size(container_width, regions, margin, False, True) self.assertEqual(size.width, self.next_power_of_2(container_width)) self.assertEqual(size.height, self.next_power_of_2(region1.top + margin.top)) # size = blf_solver.calc_container_size(container_width, regions, margin, True, True) self.assertEqual(size.width, self.next_power_of_2(region2.right + margin.right)) self.assertEqual(size.height, self.next_power_of_2(region1.top + margin.top)) def test_calc_filling_rate(self): margin = blf.Thickness(top=1, right=1, bottom=1, left=1) regions = list() region1 = blf.Region( uid=uuid.uuid4(), top=margin.bottom + 10, right=margin.left + 10, bottom=margin.bottom, left=margin.left ) regions.append(region1) region2 = blf.Region( uid=uuid.uuid4(), top=margin.bottom + 5, right=region1.right + margin.right + margin.left + 5, bottom=margin.bottom, left=region1.right + margin.right + margin.left ) regions.append(region2) container_size = blf.Size(region2.right + margin.right, region1.top + margin.top) expected_area = sum(region.area for region in regions) / container_size.area area = blf_solver.calc_filling_rate(container_size, regions) self.assertAlmostEqual(area, expected_area) def test_default(self): pieces = self.make_random_pieces(width=(1, 64), height=(1, 64), num_pieces=10) result = blf_solver.solve(pieces=pieces, container_width=1) self.assertTrue(isinstance(result, tuple)) self.assertTrue(isinstance(result[0], int)) self.assertTrue(isinstance(result[1], int)) self.assertTrue(isinstance(result[2], list)) self.assertEqual(len(pieces), len(result[2])) def test_margin(self): pieces = self.make_random_pieces(width=(1, 64), height=(1, 64), num_pieces=10) options = { 'margin': blf.Thickness(top=1, right=1, bottom=1, left=1), } result = blf_solver.solve(pieces=pieces, container_width=1, options=options) self.assertTrue(isinstance(result, tuple)) self.assertTrue(isinstance(result[0], int)) self.assertTrue(isinstance(result[1], int)) self.assertTrue(isinstance(result[2], list)) self.assertEqual(len(pieces), len(result[2])) def test_collapse_margin(self): pieces = self.make_random_pieces(width=(1, 64), height=(1, 64), num_pieces=10) options = { 'margin': blf.Thickness(top=1, right=1, bottom=1, left=1), 'collapse_margin': True } result = blf_solver.solve(pieces=pieces, container_width=1, options=options) self.assertTrue(isinstance(result, tuple)) self.assertTrue(isinstance(result[0], int)) self.assertTrue(isinstance(result[1], int)) self.assertTrue(isinstance(result[2], list)) self.assertEqual(len(pieces), len(result[2])) def test_disable_auto_size(self): pieces = self.make_random_pieces(width=64, height=64, num_pieces=10) options = { 'margin': blf.Thickness(top=1, right=1, bottom=1, left=1), 'enable_auto_size': False } result = blf_solver.solve(pieces=pieces, container_width=66, options=options) self.assertTrue(isinstance(result, tuple)) self.assertTrue(isinstance(result[0], int)) self.assertTrue(isinstance(result[1], int)) self.assertTrue(isinstance(result[2], list)) self.assertEqual(len(pieces), len(result[2])) # with self.assertRaises(blf.LocationNotFoundError): blf_solver.solve(pieces=pieces, container_width=64, options=options) def test_force_pow2(self): pieces = self.make_random_pieces(width=(1, 64), height=(1, 64), num_pieces=10) options = { 'margin': blf.Thickness(top=1, right=1, bottom=1, left=1), 'force_pow2': True } result = blf_solver.solve(pieces=pieces, container_width=1, options=options) self.assertTrue(isinstance(result, tuple)) self.assertTrue(isinstance(result[0], int)) self.assertTrue(isinstance(result[1], int)) self.assertTrue(isinstance(result[2], list)) self.assertTrue(self.is_power_of_2(result[0])) self.assertTrue(self.is_power_of_2(result[1])) self.assertEqual(len(pieces), len(result[2])) def test_combination(self): keys = ('collapse_margin', 'enable_auto_size', 'force_pow2') patterns = ( (True, True, True), (True, False, False), (True, True, False), (True, False, True), (False, True, False), (False, True, True), (False, False, True), (False, False, False), ) pieces = self.make_random_pieces(width=(1, 64), height=(1, 64), num_pieces=10) margin = blf.Thickness(top=1, right=1, bottom=1, left=1) container_width = 66 for pattern in patterns: options = {k: v for k, v in zip(keys, pattern)} options['margin'] = margin result = blf_solver.solve(pieces=pieces, container_width=container_width, options=options) self.assertTrue(isinstance(result, tuple)) self.assertTrue(isinstance(result[0], int)) self.assertTrue(isinstance(result[1], int)) self.assertTrue(isinstance(result[2], list)) if options['force_pow2']: self.assertTrue(self.is_power_of_2(result[0])) self.assertTrue(self.is_power_of_2(result[1])) self.assertEqual(len(pieces), len(result[2])) def test_concurrent_processing(self): pieces = self.make_random_pieces(width=64, height=64, num_pieces=100) options = { 'margin': blf.Thickness(top=1, right=1, bottom=1, left=1), 'enable_auto_size': False } result = blf_solver.solve(pieces=pieces, container_width=66, options=options) self.assertTrue(isinstance(result, tuple)) self.assertTrue(isinstance(result[0], int)) self.assertTrue(isinstance(result[1], int)) self.assertTrue(isinstance(result[2], list)) self.assertEqual(len(pieces), len(result[2])) # with self.assertRaises(blf.LocationNotFoundError): blf_solver.solve(pieces=pieces, container_width=64, options=options)

38.27027

102

0.619552

1,198

9,912

4.996661

0.101002

0.074841

0.112262

0.140327

0.794688

0.763782

0.747077

0.743401

0.731874

0.724023

0

0.028723

0.258878

9,912

258

103

38.418605

0.786142

0.004338

0

0.592593

0

0.014907

0

0.240741

1

0.069444

false

0

0.037037

0.00463

0.125

0

null

0

1

0

null

0

5

079927797c3c3f572fc52109a1bd4bce7f2e61da

174

py

Python

feature/vectors/feature_vectors.py

hhk998402/NaiveBayesClassifier

ac7e7b8b67505e526376a1a8e96f25f5a1ac5705

[ "MIT" ]

27

2018-09-13T21:13:34.000Z

2022-02-05T21:48:54.000Z

feature/vectors/feature_vectors.py

hhk998402/NaiveBayesClassifier

ac7e7b8b67505e526376a1a8e96f25f5a1ac5705

[ "MIT" ]

null

feature/vectors/feature_vectors.py

hhk998402/NaiveBayesClassifier

ac7e7b8b67505e526376a1a8e96f25f5a1ac5705

[ "MIT" ]

28

2018-12-19T18:59:43.000Z

2022-03-05T20:00:11.000Z

from abc import ABC, abstractmethod class FeatureVectors(ABC): @abstractmethod def add(self, label, index, feature): """Add a feature to the container."""

19.333333

45

0.678161

21

174

5.619048

0.761905

0.288136

0

0.218391

174

8

46

21.75

0.867647

0.178161

0

1

0.25

false

0

0.25

0

0.75

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

07b385b14e6ebc1f4a04cfdc00bc8c54de87728b

85

py

Python

utils/__init__.py

KabirSingh114/DeepFake_Face_Detection

0cf1ce3e69a7e4a18adff889dab4f6db029a0f25

[ "MIT" ]

null

utils/__init__.py

KabirSingh114/DeepFake_Face_Detection

0cf1ce3e69a7e4a18adff889dab4f6db029a0f25

[ "MIT" ]

null

utils/__init__.py

KabirSingh114/DeepFake_Face_Detection

0cf1ce3e69a7e4a18adff889dab4f6db029a0f25

[ "MIT" ]

null

from .aug import cutout from .layers import ReLU6, HardSigmoid, HardSwish, Attention

28.333333

60

0.811765

11

85

6.272727

0.818182

0

0.013514

0.129412

85

2

61

42.5

0.918919

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

ed265dfb8956ab67677bc496a0399ae23d6829fd

298

py

Python

duckdb_engine/tests/conftest.py

marcua/duckdb_engine

31d91a1606164cef529332133a08b1cb8b246706

[ "MIT" ]

34

2020-10-02T10:49:04.000Z

2022-03-27T09:20:57.000Z

duckdb_engine/tests/conftest.py

marcua/duckdb_engine

31d91a1606164cef529332133a08b1cb8b246706

[ "MIT" ]

100

2020-10-24T06:26:02.000Z

2022-03-24T22:10:35.000Z

duckdb_engine/tests/conftest.py

marcua/duckdb_engine

31d91a1606164cef529332133a08b1cb8b246706

[ "MIT" ]

6

2021-04-30T13:36:11.000Z

2022-02-06T20:18:33.000Z

from pytest import fixture from sqlalchemy import create_engine from sqlalchemy.engine import Engine from sqlalchemy.engine.url import registry @fixture def engine() -> Engine: registry.register("duckdb", "duckdb_engine", "Dialect") return create_engine("duckdb:///:memory:")

24.833333

60

0.738255

35

298

6.2

0.428571

0.193548

0.184332

0.239631

0

0.161074

298

11

61

27.090909

0.868

0

0.15331

0

1

0.125

true

0

0.5

0

0.75

0

null

0

1

0

null

0

1

0

1

0

1

0

5

ed3a13bd5ee01563b1aef32009178ba1087a0788

115

py

Python

entropy/__init__.py

talhaanwarch/entropy

6686567c613474d6b46e080c453f4ae69f02bcbe

[ "BSD-3-Clause" ]

2

2020-04-28T12:50:26.000Z

2020-05-13T08:52:42.000Z

entropy/entropy/__init__.py

MahdadJafarzadeh/Zzzscoring

f5d22cb7a457412fcc575c5cc6d331286f117dbf

[ "MIT" ]

null

entropy/entropy/__init__.py

MahdadJafarzadeh/Zzzscoring

f5d22cb7a457412fcc575c5cc6d331286f117dbf

[ "MIT" ]

1

2020-07-14T13:48:56.000Z

# Import EntroPy objects from .utils import * from .entropy import * from .fractal import * __version__ = "0.1.1"

16.428571

24

0.721739

16

115

4.9375

0.5625

0.253165

0

0.031579

0.173913

115

6

25

19.166667

0.8

0.191304

0

0.054945

0

1

0

false

0

0.75

0

0.75

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

ed597d9c8847dd075928856b27a43ee5d04fb1d3

45

py

Python

shared_utils/__init__.py

Chotom/rl-db-indexing

16eaf0a3e3aef83b3fd077111e922dea6dd6a1f3

[ "MIT" ]

null

shared_utils/__init__.py

Chotom/rl-db-indexing

16eaf0a3e3aef83b3fd077111e922dea6dd6a1f3

[ "MIT" ]

null

shared_utils/__init__.py

Chotom/rl-db-indexing

16eaf0a3e3aef83b3fd077111e922dea6dd6a1f3

[ "MIT" ]

null

"""Utility functions for benchmark module """

22.5

41

0.755556

5

45

6.8

1

0

0.111111

45

2

42

22.5

0.85

0.844444

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

5

ed617d710b18f624aa219e59e193edf082866eb2

15,969

py

Python

sdk/search/azure-search-documents/tests/async_tests/test_index_live_async.py

malthe/azure-sdk-for-python

0394ca66256f18fd45975b75ceea0e2527208abf

[ "MIT" ]

null

sdk/search/azure-search-documents/tests/async_tests/test_index_live_async.py

malthe/azure-sdk-for-python

0394ca66256f18fd45975b75ceea0e2527208abf

[ "MIT" ]

null

sdk/search/azure-search-documents/tests/async_tests/test_index_live_async.py

malthe/azure-sdk-for-python

0394ca66256f18fd45975b75ceea0e2527208abf

[ "MIT" ]

null

# ------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. # -------------------------------------------------------------------------- import asyncio import functools import json from os.path import dirname, join, realpath import time import pytest from devtools_testutils import AzureMgmtTestCase, ResourceGroupPreparer from search_service_preparer import SearchServicePreparer from azure_devtools.scenario_tests.utilities import trim_kwargs_from_test_function CWD = dirname(realpath(__file__)) SCHEMA = open(join(CWD, "..", "hotel_schema.json")).read() BATCH = json.load(open(join(CWD, "..", "hotel_small.json"))) from azure.core.exceptions import HttpResponseError from azure.core.credentials import AzureKeyCredential from azure.search.documents import ( AutocompleteQuery, SearchQuery, SuggestQuery, ) from azure.search.documents.aio import SearchIndexClient def await_prepared_test(test_fn): """Synchronous wrapper for async test methods. Used to avoid making changes upstream to AbstractPreparer (which doesn't await the functions it wraps) """ @functools.wraps(test_fn) def run(test_class_instance, *args, **kwargs): trim_kwargs_from_test_function(test_fn, kwargs) loop = asyncio.get_event_loop() return loop.run_until_complete(test_fn(test_class_instance, **kwargs)) return run class SearchIndexClientTestAsync(AzureMgmtTestCase): @ResourceGroupPreparer(random_name_enabled=True) @SearchServicePreparer(schema=SCHEMA, index_batch=BATCH) @await_prepared_test async def test_async_get_document_count( self, api_key, endpoint, index_name, **kwargs ): client = SearchIndexClient( endpoint, index_name, AzureKeyCredential(api_key) ) async with client: assert await client.get_document_count() == 10 @ResourceGroupPreparer(random_name_enabled=True) @SearchServicePreparer(schema=SCHEMA, index_batch=BATCH) @await_prepared_test async def test_get_document(self, api_key, endpoint, index_name, **kwargs): client = SearchIndexClient( endpoint, index_name, AzureKeyCredential(api_key) ) async with client: for hotel_id in range(1, 11): result = await client.get_document(key=str(hotel_id)) expected = BATCH["value"][hotel_id - 1] assert result.get("hotelId") == expected.get("hotelId") assert result.get("hotelName") == expected.get("hotelName") assert result.get("description") == expected.get("description") @ResourceGroupPreparer(random_name_enabled=True) @SearchServicePreparer(schema=SCHEMA, index_batch=BATCH) @await_prepared_test async def test_get_document_missing(self, api_key, endpoint, index_name, **kwargs): client = SearchIndexClient( endpoint, index_name, AzureKeyCredential(api_key) ) async with client: with pytest.raises(HttpResponseError): await client.get_document(key="1000") @ResourceGroupPreparer(random_name_enabled=True) @SearchServicePreparer(schema=SCHEMA, index_batch=BATCH) @await_prepared_test async def test_get_search_simple(self, api_key, endpoint, index_name, **kwargs): client = SearchIndexClient( endpoint, index_name, AzureKeyCredential(api_key) ) async with client: results = [] async for x in await client.search(query="hotel"): results.append(x) assert len(results) == 7 results = [] async for x in await client.search(query="motel"): results.append(x) assert len(results) == 2 @ResourceGroupPreparer(random_name_enabled=True) @SearchServicePreparer(schema=SCHEMA, index_batch=BATCH) @await_prepared_test async def test_get_search_filter(self, api_key, endpoint, index_name, **kwargs): client = SearchIndexClient( endpoint, index_name, AzureKeyCredential(api_key) ) query = SearchQuery(search_text="WiFi") query.filter("category eq 'Budget'") query.select("hotelName", "category", "description") query.order_by("hotelName desc") async with client: results = [] async for x in await client.search(query=query): results.append(x) assert [x["hotelName"] for x in results] == sorted( [x["hotelName"] for x in results], reverse=True ) expected = { "category", "hotelName", "description", "@search.score", "@search.highlights", } assert all(set(x) == expected for x in results) assert all(x["category"] == "Budget" for x in results) @ResourceGroupPreparer(random_name_enabled=True) @SearchServicePreparer(schema=SCHEMA, index_batch=BATCH) @await_prepared_test async def test_get_search_counts(self, api_key, endpoint, index_name, **kwargs): client = SearchIndexClient( endpoint, index_name, AzureKeyCredential(api_key) ) query = SearchQuery(search_text="hotel") results = await client.search(query=query) assert await results.get_count() is None query = SearchQuery(search_text="hotel", include_total_result_count=True) results = await client.search(query=query) assert await results.get_count() == 7 @ResourceGroupPreparer(random_name_enabled=True) @SearchServicePreparer(schema=SCHEMA, index_batch=BATCH) @await_prepared_test async def test_get_search_coverage(self, api_key, endpoint, index_name, **kwargs): client = SearchIndexClient( endpoint, index_name, AzureKeyCredential(api_key) ) query = SearchQuery(search_text="hotel") results = await client.search(query=query) assert await results.get_coverage() is None query = SearchQuery(search_text="hotel", minimum_coverage=50.0) results = await client.search(query=query) cov = await results.get_coverage() assert isinstance(cov, float) assert cov >= 50.0 @ResourceGroupPreparer(random_name_enabled=True) @SearchServicePreparer(schema=SCHEMA, index_batch=BATCH) @await_prepared_test async def test_get_search_facets_none( self, api_key, endpoint, index_name, **kwargs ): client = SearchIndexClient( endpoint, index_name, AzureKeyCredential(api_key) ) query = SearchQuery(search_text="WiFi") query.select("hotelName", "category", "description") async with client: results = await client.search(query=query) assert await results.get_facets() is None @ResourceGroupPreparer(random_name_enabled=True) @SearchServicePreparer(schema=SCHEMA, index_batch=BATCH) @await_prepared_test async def test_get_search_facets_result( self, api_key, endpoint, index_name, **kwargs ): client = SearchIndexClient( endpoint, index_name, AzureKeyCredential(api_key) ) query = SearchQuery(search_text="WiFi", facets=["category"]) query.select("hotelName", "category", "description") async with client: results = await client.search(query=query) assert await results.get_facets() == { "category": [ {"value": "Budget", "count": 4}, {"value": "Luxury", "count": 1}, ] } @ResourceGroupPreparer(random_name_enabled=True) @SearchServicePreparer(schema=SCHEMA, index_batch=BATCH) @await_prepared_test async def test_autocomplete(self, api_key, endpoint, index_name, **kwargs): client = SearchIndexClient( endpoint, index_name, AzureKeyCredential(api_key) ) async with client: query = AutocompleteQuery(search_text="mot", suggester_name="sg") results = await client.autocomplete(query=query) assert results == [{"text": "motel", "query_plus_text": "motel"}] @ResourceGroupPreparer(random_name_enabled=True) @SearchServicePreparer(schema=SCHEMA, index_batch=BATCH) @await_prepared_test async def test_suggest(self, api_key, endpoint, index_name, **kwargs): client = SearchIndexClient( endpoint, index_name, AzureKeyCredential(api_key) ) async with client: query = SuggestQuery(search_text="mot", suggester_name="sg") results = await client.suggest(query=query) assert results == [ {"hotelId": "2", "text": "Cheapest hotel in town. Infact, a motel."}, {"hotelId": "9", "text": "Secret Point Motel"}, ] @ResourceGroupPreparer(random_name_enabled=True) @SearchServicePreparer(schema=SCHEMA, index_batch=BATCH) @await_prepared_test async def test_upload_documents_new(self, api_key, endpoint, index_name, **kwargs): client = SearchIndexClient( endpoint, index_name, AzureKeyCredential(api_key) ) DOCUMENTS = [ {"hotelId": "1000", "rating": 5, "rooms": [], "hotelName": "Azure Inn"}, {"hotelId": "1001", "rating": 4, "rooms": [], "hotelName": "Redmond Hotel"}, ] async with client: results = await client.upload_documents(DOCUMENTS) assert len(results) == 2 assert set(x.status_code for x in results) == {201} # There can be some lag before a document is searchable time.sleep(3) assert await client.get_document_count() == 12 for doc in DOCUMENTS: result = await client.get_document(key=doc["hotelId"]) assert result["hotelId"] == doc["hotelId"] assert result["hotelName"] == doc["hotelName"] assert result["rating"] == doc["rating"] assert result["rooms"] == doc["rooms"] @ResourceGroupPreparer(random_name_enabled=True) @SearchServicePreparer(schema=SCHEMA, index_batch=BATCH) @await_prepared_test async def test_upload_documents_existing( self, api_key, endpoint, index_name, **kwargs ): client = SearchIndexClient( endpoint, index_name, AzureKeyCredential(api_key) ) DOCUMENTS = [ {"hotelId": "1000", "rating": 5, "rooms": [], "hotelName": "Azure Inn"}, {"hotelId": "3", "rating": 4, "rooms": [], "hotelName": "Redmond Hotel"}, ] async with client: results = await client.upload_documents(DOCUMENTS) assert len(results) == 2 assert set(x.status_code for x in results) == {200, 201} @ResourceGroupPreparer(random_name_enabled=True) @SearchServicePreparer(schema=SCHEMA, index_batch=BATCH) @await_prepared_test async def test_delete_documents_existing( self, api_key, endpoint, index_name, **kwargs ): client = SearchIndexClient( endpoint, index_name, AzureKeyCredential(api_key) ) async with client: results = await client.delete_documents( [{"hotelId": "3"}, {"hotelId": "4"}] ) assert len(results) == 2 assert set(x.status_code for x in results) == {200} # There can be some lag before a document is searchable time.sleep(3) assert await client.get_document_count() == 8 with pytest.raises(HttpResponseError): await client.get_document(key="3") with pytest.raises(HttpResponseError): await client.get_document(key="4") @ResourceGroupPreparer(random_name_enabled=True) @SearchServicePreparer(schema=SCHEMA, index_batch=BATCH) @await_prepared_test async def test_delete_documents_missing( self, api_key, endpoint, index_name, **kwargs ): client = SearchIndexClient( endpoint, index_name, AzureKeyCredential(api_key) ) async with client: results = await client.delete_documents( [{"hotelId": "1000"}, {"hotelId": "4"}] ) assert len(results) == 2 assert set(x.status_code for x in results) == {200} # There can be some lag before a document is searchable time.sleep(3) assert await client.get_document_count() == 9 with pytest.raises(HttpResponseError): await client.get_document(key="1000") with pytest.raises(HttpResponseError): await client.get_document(key="4") @ResourceGroupPreparer(random_name_enabled=True) @SearchServicePreparer(schema=SCHEMA, index_batch=BATCH) @await_prepared_test async def test_merge_documents_existing( self, api_key, endpoint, index_name, **kwargs ): client = SearchIndexClient( endpoint, index_name, AzureKeyCredential(api_key) ) async with client: results = await client.merge_documents( [{"hotelId": "3", "rating": 1}, {"hotelId": "4", "rating": 2}] ) assert len(results) == 2 assert set(x.status_code for x in results) == {200} # There can be some lag before a document is searchable time.sleep(3) assert await client.get_document_count() == 10 result = await client.get_document(key="3") assert result["rating"] == 1 result = await client.get_document(key="4") assert result["rating"] == 2 @ResourceGroupPreparer(random_name_enabled=True) @SearchServicePreparer(schema=SCHEMA, index_batch=BATCH) @await_prepared_test async def test_merge_documents_missing( self, api_key, endpoint, index_name, **kwargs ): client = SearchIndexClient( endpoint, index_name, AzureKeyCredential(api_key) ) async with client: results = await client.merge_documents( [{"hotelId": "1000", "rating": 1}, {"hotelId": "4", "rating": 2}] ) assert len(results) == 2 assert set(x.status_code for x in results) == {200, 404} # There can be some lag before a document is searchable time.sleep(3) assert await client.get_document_count() == 10 with pytest.raises(HttpResponseError): await client.get_document(key="1000") result = await client.get_document(key="4") assert result["rating"] == 2 @ResourceGroupPreparer(random_name_enabled=True) @SearchServicePreparer(schema=SCHEMA, index_batch=BATCH) @await_prepared_test async def test_merge_or_upload_documents( self, api_key, endpoint, index_name, **kwargs ): client = SearchIndexClient( endpoint, index_name, AzureKeyCredential(api_key) ) async with client: results = await client.merge_or_upload_documents( [{"hotelId": "1000", "rating": 1}, {"hotelId": "4", "rating": 2}] ) assert len(results) == 2 assert set(x.status_code for x in results) == {200, 201} # There can be some lag before a document is searchable time.sleep(3) assert await client.get_document_count() == 11 result = await client.get_document(key="1000") assert result["rating"] == 1 result = await client.get_document(key="4") assert result["rating"] == 2

38.387019

88

0.626025

1,683

15,969

5.75104

0.124183

0.043186

0.06323

0.045459

0.785515

0.775597

0.754107

0.736026

0.716396

0

0.012096

0.264888

15,969

415

89

38.479518

0.81242

0.048281

0

0.581602

0

0.060634

0

0.139466

1

0.005935

false

0

0.038576

0

0.053412

0

null

0

1

0

null

0

5

ed6c9364ee5ca3b0c33fabcd7b2dfc63e73df6c3

34

py

Python

tasks/UDEMY/100_days/L025/main.py

AleksNeStu/projects

1a4c68dfbdcb77228f0f3617e58fd18fcb1f5dbb

[ "Apache-2.0" ]

2

2022-01-19T18:01:35.000Z

2022-02-06T06:54:38.000Z

tasks/UDEMY/100_days/L025/main.py

AleksNeStu/projects

1a4c68dfbdcb77228f0f3617e58fd18fcb1f5dbb

[ "Apache-2.0" ]

null

tasks/UDEMY/100_days/L025/main.py

AleksNeStu/projects

1a4c68dfbdcb77228f0f3617e58fd18fcb1f5dbb

[ "Apache-2.0" ]

null

# weather_data_from_csv = # # with

11.333333

25

0.735294

5

34

4.4

1

0

0.147059

34

3

26

11.333333

0.758621

0.823529

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

5

ed87e69c4e90baecffcf0005e7a0db42d3d1980e

45

py

Python

exercises/twelve-days/twelve_days.py

kishankj/python

82042de746128127502e109111e6c4e8ab002af6

[ "MIT" ]

1,177

2017-06-21T20:24:06.000Z

2022-03-29T02:30:55.000Z

exercises/twelve-days/twelve_days.py

kishankj/python

82042de746128127502e109111e6c4e8ab002af6

[ "MIT" ]

1,890

2017-06-18T20:06:10.000Z

2022-03-31T18:35:51.000Z

exercises/twelve-days/twelve_days.py

kishankj/python

82042de746128127502e109111e6c4e8ab002af6

[ "MIT" ]

1,095

2017-06-26T23:06:19.000Z

2022-03-29T03:25:38.000Z

def recite(start_verse, end_verse): pass

15

35

0.733333

7

45

4.428571

0.857143

0

0.177778

45

2

36

22.5

0.837838

0

1

0.5

false

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

5

71f486e66f0f2d778e3b2e6856334ab501aa84ee

136

py

Python

python/ray/_private/runtime_env/constants.py

mgelbart/ray

4cec2286572e368a4bd64aae467751a384eff62d

[ "Apache-2.0" ]

33

2020-05-27T14:25:24.000Z

2022-03-22T06:11:30.000Z

python/ray/_private/runtime_env/constants.py

mgelbart/ray

4cec2286572e368a4bd64aae467751a384eff62d

[ "Apache-2.0" ]

227

2021-10-01T08:00:01.000Z

2021-12-28T16:47:26.000Z

python/ray/_private/runtime_env/constants.py

mgelbart/ray

4cec2286572e368a4bd64aae467751a384eff62d

[ "Apache-2.0" ]

10

2018-04-27T10:50:59.000Z

2020-02-24T02:41:43.000Z

# Env var set by job manager to pass runtime env and metadata to subprocess RAY_JOB_CONFIG_JSON_ENV_VAR = "RAY_JOB_CONFIG_JSON_ENV_VAR"

45.333333

75

0.838235

26

136

4

0.576923

0.173077

0.230769

0.307692

0.423077

0

0.132353

136

2

76

68

0.881356

0.536765

0

0.442623

0

1

0

false

0

1

0

null

0

1

0

1

0

1

null

0

5

71f5abccabfda5ef012493efb78e2b2db9dee28e

96

py

Python

mysite/converters.py

wonjoonSeol/ScienceScape

8d8a3cb76193b6f85b7a2a6c7219e249237d64c8

[ "BSD-3-Clause" ]

5

2018-02-14T21:11:06.000Z

2020-02-23T14:53:11.000Z

mysite/converters.py

wonjoonSeol/ScienceScape

8d8a3cb76193b6f85b7a2a6c7219e249237d64c8

[ "BSD-3-Clause" ]

106

2018-02-09T00:31:05.000Z

2018-03-29T07:28:34.000Z

mysite/converters.py

wonjoonSeol/ScienceScape

8d8a3cb76193b6f85b7a2a6c7219e249237d64c8

[ "BSD-3-Clause" ]

6

2018-02-23T17:48:03.000Z

2020-05-14T13:39:36.000Z

class FilePath: regex = '[0-9]{4}' def to_python(self, value): return int(value)

16

30

0.583333

14

96

3.928571

0.928571

0

0.042254

0.260417

96

5

31

19.2

0.732394

0

0.083333

0

1

0.25

false

0

0.25

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

9c3d46a6d2ba07e68f5836e03d8d4a8a4530a02c

71

py

Python

8 kyu/L1: Set Alarm/L1: Set Alarm.py

anthonyjatoba/codewars

76b0d66dd1ba76a4d136b658920cdf85fd5c4b06

[ "MIT" ]

null

8 kyu/L1: Set Alarm/L1: Set Alarm.py

anthonyjatoba/codewars

76b0d66dd1ba76a4d136b658920cdf85fd5c4b06

[ "MIT" ]

2

2019-01-22T10:53:42.000Z

2019-01-31T08:02:48.000Z

8 kyu/L1: Set Alarm/L1: Set Alarm.py

anthonyjatoba/codewars

76b0d66dd1ba76a4d136b658920cdf85fd5c4b06

[ "MIT" ]

13

2019-01-22T10:37:42.000Z

2019-01-25T13:30:43.000Z

def set_alarm(employed, vacation): return employed and not vacation

35.5

36

0.788732

10

71

5.5

0.8

0

0.15493

71

2

36

35.5

0.916667

0

1

0.5

false

0

0.5

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

9c5267b4dc63a15ac4389a0f16d9d49b96ed03d9

110

py

Python

pyf/_map_int.py

snoopyjc/pythonizer

6b3683084f41f0aa06b1b4e652a0f00b19cceac1

[ "Artistic-2.0" ]

1

2022-03-13T22:08:25.000Z

pyf/_map_int.py

snoopyjc/pythonizer

6b3683084f41f0aa06b1b4e652a0f00b19cceac1

[ "Artistic-2.0" ]

21

2022-03-17T16:53:04.000Z

2022-03-31T23:55:24.000Z

pyf/_map_int.py

snoopyjc/pythonizer

6b3683084f41f0aa06b1b4e652a0f00b19cceac1

[ "Artistic-2.0" ]

null

def _map_int(*args): """Convert each element to an int""" return list(map(_int, _flatten(args)))

22

43

0.627273

16

110

4.0625

0.75

0.184615

0

0.218182

110

4

44

27.5

0.755814

0.272727

0

1

0.5

true

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

9c551f76d76a7c031ac7ea50a2d58a2e51b6c2d4

324

py

Python

toontown/cogdominium/DistCogdoLevelGameAI.py

TrueBlueDogemon/Toontown

ebed7fc3f2ef06a529cf02eda7ab46361aceef9d

[ "MIT" ]

1

2021-02-25T06:22:49.000Z

toontown/cogdominium/DistCogdoLevelGameAI.py

TrueBlueDogemon/Toontown

ebed7fc3f2ef06a529cf02eda7ab46361aceef9d

[ "MIT" ]

null

toontown/cogdominium/DistCogdoLevelGameAI.py

TrueBlueDogemon/Toontown

ebed7fc3f2ef06a529cf02eda7ab46361aceef9d

[ "MIT" ]

2

2020-11-08T03:38:35.000Z

2021-09-02T07:03:47.000Z

from direct.directnotify import DirectNotifyGlobal from toontown.cogdominium.DistCogdoGameAI import DistCogdoGameAI from otp.level.DistributedLevelAI import DistributedLevelAI class DistCogdoLevelGameAI(DistCogdoGameAI, DistributedLevelAI): notify = DirectNotifyGlobal.directNotify.newCategory("DistCogdoLevelGameAI")

40.5

80

0.876543

26

324

10.923077

0.576923

0

0.074074

324

7

81

46.285714

0.946667

0

0.06192

0

1

0

false

0

0.6

0

1

0

1

0

1

null

0

1

0

null

0

1

0

1

0

5

9c5d023bdce2ad6f340d7808605056f6530170c6

131

py

Python

app.py

leeroywking/potential-waddle

07b4055989c9851a23a87f78f74452da67942a94

[ "MIT" ]

null

app.py

leeroywking/potential-waddle

07b4055989c9851a23a87f78f74452da67942a94

[ "MIT" ]

null

app.py

leeroywking/potential-waddle

07b4055989c9851a23a87f78f74452da67942a94

[ "MIT" ]

null

from flask import Flask, request import requests as rq app = Flask(__name__) @app.route("/") def index(): return "hello world"

18.714286

32

0.709924

19

131

4.684211

0.789474

0

0.167939

131

7

33

18.714286

0.816514

0

0.090909

0

1

0.166667

false

0

0.333333

0.166667

0.666667

0

1

0

null

0

1

0

null

0

1

0

5

130b2aa2f75551d8af7e2a79b326f14112a7145a

11,071

py

Python

tests/test_grpc_functions.py

spring-operator/python2-function-invoker

60c6b7aeb344f971090774fc5e0c3e0c024d4a65

[ "Apache-2.0" ]

null

tests/test_grpc_functions.py

spring-operator/python2-function-invoker

60c6b7aeb344f971090774fc5e0c3e0c024d4a65

[ "Apache-2.0" ]

null

tests/test_grpc_functions.py

spring-operator/python2-function-invoker

60c6b7aeb344f971090774fc5e0c3e0c024d4a65

[ "Apache-2.0" ]

null

__copyright__ = ''' Copyright 2018 the original author or authors. 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. ''' __author__ = 'David Turanski' import sys if sys.version_info[0] != 2: raise RuntimeError("Requires Python 2") import grpc import unittest import subprocess import os import uuid import time from invoker import function_pb2_grpc as function from invoker import function_pb2 as message PYTHON = sys.executable class GrpcFunctionTest(unittest.TestCase): """ Assumes os.getcwd() is the project base directory """ @classmethod def setUpClass(cls): cls.workingdir = os.path.abspath("./invoker") cls.command = "%s function_invoker.py" % PYTHON def setUp(self): pass def tearDown(self): self.process.kill() def test_upper(self): port = find_free_port() env = { 'PYTHONPATH': '%s/tests/functions:$PYTHONPATH' % os.getcwd(), 'GRPC_PORT': str(port), 'FUNCTION_URI': 'file://%s/tests/functions/upper.py?handler=handle' % os.getcwd() } self.process = subprocess.Popen(self.command, cwd=self.workingdir, shell=True, env=env, preexec_fn=os.setsid, ) channel = grpc.insecure_channel('localhost:%s' % port) wait_until_channel_ready(channel) self.stub = function.MessageFunctionStub(channel) def generate_messages(): headers = { 'Content-Type': message.Message.HeaderValue(values=['text/plain']), 'correlationId': message.Message.HeaderValue(values=[str(uuid.uuid4())]) } messages = [ message.Message(payload="hello", headers=headers), message.Message(payload="world", headers=headers), message.Message(payload="foo", headers=headers), message.Message(payload="bar", headers=headers), ] for msg in messages: yield msg responses = self.stub.Call(generate_messages()) expected = ['HELLO', 'WORLD', 'FOO', 'BAR'] for response in responses: self.assertTrue(response.payload in expected) expected.remove(response.payload) self.assertEquals(0, len(expected)) def test_upper_no_correlation(self): port = find_free_port() env = { 'PYTHONPATH': '%s/tests/functions:$PYTHONPATH' % os.getcwd(), 'GRPC_PORT': str(port), 'FUNCTION_URI': 'file://%s/tests/functions/upper.py?handler=handle' % os.getcwd() } self.process = subprocess.Popen(self.command, cwd=self.workingdir, shell=True, env=env, preexec_fn=os.setsid, ) channel = grpc.insecure_channel('localhost:%s' % port) wait_until_channel_ready(channel) self.stub = function.MessageFunctionStub(channel) def generate_messages(): headers = {} messages = [ message.Message(payload="hello", headers=headers), message.Message(payload="world", headers=headers), message.Message(payload="foo", headers=headers), message.Message(payload="bar", headers=headers), ] for msg in messages: yield msg responses = self.stub.Call(generate_messages()) expected = ['HELLO', 'WORLD', 'FOO', 'BAR'] for response in responses: self.assertTrue(response.payload in expected) self.assertEquals([], response.headers['correlationId'].values) expected.remove(response.payload) self.assertEquals(0, len(expected)) def test_concat(self): port = find_free_port() env = { 'PYTHONPATH': '%s/tests/functions:$PYTHONPATH' % os.getcwd(), 'GRPC_PORT': str(port), 'FUNCTION_URI': 'file://%s/tests/functions/concat.py?handler=concat' % os.getcwd() } self.process = subprocess.Popen(self.command, cwd=self.workingdir, shell=True, env=env, preexec_fn=os.setsid, ) channel = grpc.insecure_channel('localhost:%s' % port) wait_until_channel_ready(channel) self.stub = function.MessageFunctionStub(channel) def generate_messages(): headers = { 'Content-Type': message.Message.HeaderValue(values=['application/json']), 'correlationId': message.Message.HeaderValue(values=[str(uuid.uuid4())]) } messages = [ message.Message(payload='{"foo":"bar","hello":"world"}', headers=headers), ] for msg in messages: yield msg responses = self.stub.Call(generate_messages()) for response in responses: self.assertEquals('{"result": "foobarhelloworld"}', response.payload) def test_accepts_application_json(self): port = find_free_port() env = { 'PYTHONPATH': '%s/tests/functions:$PYTHONPATH' % os.getcwd(), 'GRPC_PORT': str(port), 'FUNCTION_URI': 'file://%s/tests/functions/concat.py?handler=concat' % os.getcwd() } self.process = subprocess.Popen(self.command, cwd=self.workingdir, shell=True, env=env, preexec_fn=os.setsid, ) channel = grpc.insecure_channel('localhost:%d' % port) wait_until_channel_ready(channel) self.stub = function.MessageFunctionStub(channel) def generate_messages(): headers = { 'Content-Type': message.Message.HeaderValue(values=['application/json']), 'Accept': message.Message.HeaderValue(values=['application/json']), 'correlationId': message.Message.HeaderValue(values=[str(uuid.uuid4())]) } messages = [ message.Message(payload='{"foo":"bar","hello":"world"}', headers=headers), ] for msg in messages: yield msg responses = self.stub.Call(generate_messages()) for response in responses: self.assertEquals('{"result": "foobarhelloworld"}', response.payload) def test_accepts_text_plain(self): port = find_free_port() env = { 'PYTHONPATH': '%s/tests/functions:$PYTHONPATH' % os.getcwd(), 'GRPC_PORT': str(port), 'FUNCTION_URI': 'file://%s/tests/functions/concat.py?handler=concat' % os.getcwd() } self.process = subprocess.Popen(self.command, cwd=self.workingdir, shell=True, env=env, preexec_fn=os.setsid, ) channel = grpc.insecure_channel('localhost:%d' % port) wait_until_channel_ready(channel) self.stub = function.MessageFunctionStub(channel) def generate_messages(): headers = { 'Content-Type': message.Message.HeaderValue(values=['application/json']), 'Accept': message.Message.HeaderValue(values=['text/plain']), 'correlationId': message.Message.HeaderValue(values=[str(uuid.uuid4())]) } messages = [ message.Message(payload='{"foo":"bar","hello":"world"}', headers=headers), ] for msg in messages: yield msg responses = self.stub.Call(generate_messages()) for response in responses: self.assertEquals('{"result": "foobarhelloworld"}', response.payload) def test_accepts_not_supported(self): port = find_free_port() env = { 'PYTHONPATH': '%s/tests/functions:$PYTHONPATH' % os.getcwd(), 'GRPC_PORT': str(port), 'FUNCTION_URI': 'file://%s/tests/functions/concat.py?handler=concat' % os.getcwd() } self.process = subprocess.Popen(self.command, cwd=self.workingdir, shell=True, env=env, preexec_fn=os.setsid, ) channel = grpc.insecure_channel('localhost:%s' % port) wait_until_channel_ready(channel) self.stub = function.MessageFunctionStub(channel) def generate_messages(): headers = { 'Content-Type': message.Message.HeaderValue(values=['application/json']), 'Accept': message.Message.HeaderValue(values=['application/xml']), 'correlationId': message.Message.HeaderValue(values=[str(uuid.uuid4())]) } messages = [ message.Message(payload='{"foo":"bar","hello":"world"}', headers=headers), ] for msg in messages: yield msg try: responses = self.stub.Call(generate_messages()) self.assertEquals(grpc._channel._Rendezvous, type(responses)) # TODO: Investigate error handling # https://github.com/projectriff/python2-function-invoker/issues/5 except RuntimeError: pass import socket from contextlib import closing def find_free_port(): with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as s: s.bind(('', 0)) return s.getsockname()[1] def wait_until_channel_ready(channel): max_tries = 100 ready = grpc.channel_ready_future(channel) tries = 0 while not ready.done(): time.sleep(0.1) tries = tries + 1 if tries == max_tries: raise RuntimeError("cannot connect to gRPC server")

35.598071

94

0.541415

1,046

11,071

5.629063

0.200765

0.059443

0.055197

0.068444

0.756454

0.742188

0.735904

0

0.004294

0.347846

11,071

310

95

35.712903

0.811219

0.013368

0

0.638655

0

0.180451

0.054465

0

0.003226

0.037815

1

0.071429

false

0.008403

0.046218

0

0.12605

0

null

0

1

0

null

0

5

132128145d049bb2b5ef02ce4715c274426c1980

75

py

Python

wot/__init__.py

faro1219/wot

fbe5b63e64a54857d3aebd55d6313d06b39f74de

[ "BSD-3-Clause" ]

null

wot/__init__.py

faro1219/wot

fbe5b63e64a54857d3aebd55d6313d06b39f74de

[ "BSD-3-Clause" ]

null

wot/__init__.py

faro1219/wot

fbe5b63e64a54857d3aebd55d6313d06b39f74de

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- from .dataset import * from .dataset_util import *

18.75

27

0.653333

10

75

4.8

0.7

0.458333

0

0.016129

0.173333

75

3

28

25

0.758065

0.28

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

133bc8c41c3079f100ec28e9bfa319a30702a14c

7,554

py

Python

model-optimizer/extensions/middle/MulQuantizeFuse_test.py

giulio1979/dldt

e7061922066ccefc54c8dae6e3215308ce9559e1

[ "Apache-2.0" ]

1

2021-07-30T17:03:50.000Z

model-optimizer/extensions/middle/MulQuantizeFuse_test.py

Dipet/dldt

b2140c083a068a63591e8c2e9b5f6b240790519d

[ "Apache-2.0" ]

null

model-optimizer/extensions/middle/MulQuantizeFuse_test.py

Dipet/dldt

b2140c083a068a63591e8c2e9b5f6b240790519d

[ "Apache-2.0" ]

null

""" Copyright (C) 2018-2020 Intel Corporation Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ import unittest import numpy as np from extensions.middle.MulFakeQuantizeFuse import MulFakeQuantizeFuse from mo.middle.passes.eliminate_test import build_graph from mo.utils.ir_engine.compare_graphs import compare_graphs # The dictionary with nodes attributes used to build various graphs. A key is the name of the node and the value is the # dictionary with node attributes. nodes = { 'x': {'type': 'Parameter', 'kind': 'op', 'op': 'Parameter'}, 'x_data': {'value': None, 'shape': np.array([1, 64, 56, 56]), 'kind': 'data'}, 'mul_const': {'op': 'Const', 'type': 'Const', 'kind': 'op', 'value': None, 'shape': None}, 'mul_const_data': {'value': np.array([]), 'shape': np.array([]), 'kind': 'data'}, 'mul': {'op': 'Mul', 'kind': 'op'}, 'mul_data': {'value': np.array([]), 'shape': np.array([]), 'kind': 'data'}, 'mi_i': {'op': 'Const', 'type': 'Const', 'kind': 'op', 'value': None, 'shape': None}, 'mi_i_data': {'value': np.array([-10]), 'shape': np.array([]), 'kind': 'data'}, 'ma_i': {'op': 'Const', 'type': 'Const', 'kind': 'op', 'value': None, 'shape': None}, 'ma_i_data': {'value': np.array([10]), 'shape': np.array([]), 'kind': 'data'}, 'mi_o': {'op': 'Const', 'type': 'Const', 'kind': 'op', 'value': None, 'shape': None}, 'mi_o_data': {'value': np.array([]), 'shape': np.array([]), 'kind': 'data'}, 'ma_o': {'op': 'Const', 'type': 'Const', 'kind': 'op', 'value': None, 'shape': None}, 'ma_o_data': {'value': np.array([]), 'shape': np.array([]), 'kind': 'data'}, 'quantize': {'type': 'FakeQuantize', 'kind': 'op', 'op': 'FakeQuantize', 'levels': 2, 'keep_in_IR': True}, 'quantize_data': {'value': None, 'shape': np.array([1, 64, 56, 56]), 'kind': 'data'}, 'output': {'op': 'Result', 'kind': 'op'}, } edges = [ ('x', 'x_data'), ('mul_const', 'mul_const_data'), ('mul', 'mul_data'), ('mi_i', 'mi_i_data'), ('ma_i', 'ma_i_data'), ('mi_o', 'mi_o_data'), ('ma_o', 'ma_o_data'), ('quantize', 'quantize_data'), ('quantize_data', 'output'), ('x_data', 'mul', {'in': 0}), ('mul_const_data', 'mul', {'in': 1}), ('mul_data', 'quantize', {'in': 0}), ('mi_i_data', 'quantize', {'in': 1}), ('ma_i_data', 'quantize', {'in': 2}), ('mi_o_data', 'quantize', {'in': 3}), ('ma_o_data', 'quantize', {'in': 4}), ] edges_ref = [ ('x', 'x_data'), ('mul_const', 'mul_const_data'), ('mul', 'mul_data'), ('mi_i', 'mi_i_data'), ('ma_i', 'ma_i_data'), ('mi_o', 'mi_o_data'), ('ma_o', 'ma_o_data'), ('quantize', 'quantize_data'), ('quantize_data', 'output'), ('x_data', 'quantize', {'in': 0}), ('mi_i_data', 'quantize', {'in': 1}), ('ma_i_data', 'quantize', {'in': 2}), ('mi_o_data', 'quantize', {'in': 3}), ('ma_o_data', 'quantize', {'in': 4}), ('x_data', 'mul', {'in': 0}), ('mul_const_data', 'mul', {'in': 1}), ] class MulQuantizeFuseTest(unittest.TestCase): def test_1(self): graph = build_graph(nodes, edges, { 'mul_const_data': {'shape': np.array([3, 1, 1]), 'value': np.broadcast_to(np.array([1]), (3, 1, 1))}, 'quantize_data': {'shape': np.array([2, 3, 4, 4])}, 'mi_o_data': {'shape': np.array([1, 1, 1, 1]), 'value': np.broadcast_to(np.array([0]), (1, 1, 1, 1))}, 'ma_o_data': {'shape': np.array([1, 3, 1, 1]), 'value': np.broadcast_to(np.array([1]), (1, 3, 1, 1))}, }, nodes_with_edges_only=True) graph.stage = 'middle' graph_ref = build_graph(nodes, edges_ref, { 'quantize_data': {'shape': np.array([2, 3, 4, 4])}, 'mul_const_data': {'shape': np.array([3, 1, 1]), 'value': np.broadcast_to(np.array([1]), (3, 1, 1))}, 'mi_o_data': {'shape': np.array([1, 1, 1, 1]), 'value': np.broadcast_to(np.array([0]), (1, 1, 1, 1))}, 'ma_o_data': {'shape': np.array([1, 3, 1, 1]), 'value': np.broadcast_to(np.array([1]), (1, 3, 1, 1))}, 'mi_i_data': {'shape': np.array([3, 1, 1]), 'value': np.broadcast_to(np.array([-10]), (3, 1, 1))}, 'ma_i_data': {'shape': np.array([3, 1, 1]), 'value': np.broadcast_to(np.array([10]), (3, 1, 1))}, }, nodes_with_edges_only=True) MulFakeQuantizeFuse().find_and_replace_pattern(graph) (flag, resp) = compare_graphs(graph, graph_ref, 'output', check_op_attrs=True) self.assertTrue(flag, resp) def test_2(self): graph = build_graph(nodes, edges, { 'mul_const_data': {'shape': np.array([1]), 'value': np.array([-1])}, 'quantize_data': {'shape': np.array([2, 3, 4, 4])}, 'mi_o_data': {'shape': np.array([1]), 'value': np.array([0])}, 'ma_o_data': {'shape': np.array([1]), 'value': np.array([1])}, }, nodes_with_edges_only=True) graph.stage = 'middle' graph_ref = build_graph(nodes, edges_ref, { 'quantize_data': {'shape': np.array([2, 3, 4, 4])}, 'mul_const_data': {'shape': np.array([1]), 'value': np.array([-1])}, 'mi_o_data': {'shape': np.array([1]), 'value': np.array([1])}, 'ma_o_data': {'shape': np.array([1]), 'value': np.array([0])}, 'mi_i_data': {'shape': np.array([1]), 'value': np.array([10])}, 'ma_i_data': {'shape': np.array([1]), 'value': np.array([-10])}, }, nodes_with_edges_only=True) MulFakeQuantizeFuse().find_and_replace_pattern(graph) (flag, resp) = compare_graphs(graph, graph_ref, 'output', check_op_attrs=True) self.assertTrue(flag, resp) def test_3(self): graph = build_graph(nodes, edges, { 'mul_const_data': {'shape': np.array([3, 1, 1]), 'value': np.array([[[-1]], [[1]], [[-1]]])}, 'quantize_data': {'shape': np.array([2, 3, 4, 4])}, 'mi_o_data': {'shape': np.array([1, 1, 1, 1]), 'value': np.broadcast_to(np.array([0]), (1, 1, 1, 1))}, 'ma_o_data': {'shape': np.array([1, 1, 1, 1]), 'value': np.broadcast_to(np.array([1]), (1, 1, 1, 1))}, }, nodes_with_edges_only=True) graph.stage = 'middle' graph_ref = build_graph(nodes, edges_ref, { 'quantize_data': {'shape': np.array([2, 3, 4, 4])}, 'mul_const_data': {'shape': np.array([3, 1, 1]), 'value': np.array([[[-1]], [[1]], [[-1]]])}, 'mi_o_data': {'shape': np.array([1, 3, 1, 1]), 'value': np.array([[[1]], [[0]], [[1]]])}, 'ma_o_data': {'shape': np.array([1, 3, 1, 1]), 'value': np.array([[[0]], [[1]], [[0]]])}, 'mi_i_data': {'shape': np.array([1, 3, 1, 1]), 'value': np.array([[[10]], [[-10]], [[10]]])}, 'ma_i_data': {'shape': np.array([1, 3, 1, 1]), 'value': np.array([[[-10]], [[10]], [[-10]]])}, }, nodes_with_edges_only=True) MulFakeQuantizeFuse().find_and_replace_pattern(graph) (flag, resp) = compare_graphs(graph, graph_ref, 'output', check_op_attrs=True) self.assertTrue(flag, resp)

45.233533

119

0.548584

1,086

7,554

3.626151

0.127072

0.120874

0.115795

0.121889

0.748095

0.747842

0.74454

0.744286

0.742001

0.72321

0

0.03649

0.20188

7,554

166

120

45.506024

0.616686

0.095181

0

0.601695

0

0.245452

0

0.025424

1

0.025424

false

0.008475

0.042373

0

0.076271

0

null

0

1

0

null

0

5

13678837c4e767e0ef912669c109f4e31498dc5e

103

py

Python

firestore_collections/client.py

LasseRegin/firestore-collections

9a93f171f665c74078e36363e8fa3493e31e8f88

[ "MIT" ]

2

2020-12-02T18:11:43.000Z

2020-12-03T08:19:02.000Z

firestore_collections/client.py

LasseRegin/firestore-collections

9a93f171f665c74078e36363e8fa3493e31e8f88

[ "MIT" ]

null

firestore_collections/client.py

LasseRegin/firestore-collections

9a93f171f665c74078e36363e8fa3493e31e8f88

[ "MIT" ]

null

import os from google.cloud.firestore import Client client = Client(project=os.getenv('PROJECT_ID'))

17.166667

48

0.786408

15

103

5.333333

0.666667

0.3

0

0.106796

103

5

49

20.6

0.869565

0

0.097087

0

1

0

false

0

0.666667

0

0.666667

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

136f2fb3a15e5fcbaa9071150864ead93711f3e2

148

py

Python

my_education/urls.py

Williano/CV

b2954ac1753d7f31461c59cd7fe24ea13405cddf

[ "MIT" ]

null

my_education/urls.py

Williano/CV

b2954ac1753d7f31461c59cd7fe24ea13405cddf

[ "MIT" ]

3

2020-02-11T21:48:34.000Z

2021-06-10T18:38:09.000Z

my_education/urls.py

Williano/CV

b2954ac1753d7f31461c59cd7fe24ea13405cddf

[ "MIT" ]

1

2018-08-06T06:57:16.000Z

from django.urls import path from . import views app_name = 'my_education' urlpatterns = [ path('', views.my_education, name='my_education') ]

18.5

53

0.722973

20

148

5.15

0.55

0.320388

0.291262

0

0.155405

148

8

54

18.5

0.824

0

0.161074

0

1

0

false

0

0.333333

0

0.333333

0

1

0

null

1

0

1

0

null

0

1

0

5

1380a42abeaa93815e95399ed518e4ec43793750

181

py

Python

tests/test_paper_view.py

refraction-ray/myarxiv-app

c5850ea143915747cfd0b86ea08d77ac85cca943

[ "MIT" ]

2

2021-07-21T14:16:44.000Z

2021-08-07T14:11:26.000Z

tests/test_paper_view.py

refraction-ray/myarxiv-app

c5850ea143915747cfd0b86ea08d77ac85cca943

[ "MIT" ]

null

tests/test_paper_view.py

refraction-ray/myarxiv-app

c5850ea143915747cfd0b86ea08d77ac85cca943

[ "MIT" ]

null

def test_index(client): r = client.get("/") assert r.status_code == 200 def test_paper_page(client): r = client.get("/paper/1812.35598") assert r.status_code == 200

25.857143

39

0.657459

28

181

4.071429

0.5

0.122807

0.22807

0.280702

0.350877

0

0.102041

0.187845

181

7

40

25.857143

0.673469

0

0.333333

0

0.098901

0

0.333333

1

0.333333

false

0

0.333333

0

1

0

null

0

1

0

1

0

null

0

1

0

5

13a404deddefde695997acff7817a6ac88b9e8b3

51

py

Python

terrascript/cloudscale/d.py

amlodzianowski/python-terrascript

1111affe6cd30d9b8b7bc74ae4e27590f7d4dc49

[ "BSD-2-Clause" ]

null

terrascript/cloudscale/d.py

amlodzianowski/python-terrascript

1111affe6cd30d9b8b7bc74ae4e27590f7d4dc49

[ "BSD-2-Clause" ]

null

terrascript/cloudscale/d.py

amlodzianowski/python-terrascript

1111affe6cd30d9b8b7bc74ae4e27590f7d4dc49

[ "BSD-2-Clause" ]

null

# terrascript/cloudscale/d.py import terrascript

12.75

30

0.803922

6

51

6.833333

0.833333

0

0.117647

51

3

31

17

0.911111

0.529412

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

13b774d8773441547bf6571130cebcd2854b6dc9

44

py

Python

notebooks/_solutions/pandas_04_time_series_data9.py

jonasvdd/DS-python-data-analysis

835226f562ee0b0631d70e48a17c4526ff58a538

[ "BSD-3-Clause" ]

65

2017-03-21T09:15:40.000Z

2022-02-01T23:43:08.000Z

notebooks/_solutions/pandas_04_time_series_data9.py

jonasvdd/DS-python-data-analysis

835226f562ee0b0631d70e48a17c4526ff58a538

[ "BSD-3-Clause" ]

100

2016-12-15T03:44:06.000Z

2022-03-07T08:14:07.000Z

notebooks/_solutions/pandas_04_time_series_data9.py

jorisvandenbossche/ICES-python-data

63864947657f37cb26cb4e2dcd67ff106dffe9cd

[ "BSD-3-Clause" ]

52

2016-12-19T07:48:52.000Z

2022-02-19T17:53:48.000Z

data['2013':'2013'].mean().plot(kind='barh')

44

0.636364

7

44

4

0.857143

0

0.181818

0

44

1

44

0.454545

0

0.266667

0

1

0

true

0

1

0

null

0

1

0

1

0

null

0

1

0

5

13df6e33d1e722587140b9671351f9297ebbbafd

162

py

Python

flask_oauthlib/contrib/client/signals.py

PCMan/flask-oauthlib

3735210211ac0e50c4d32b887bbd61722dd175c7

[ "BSD-3-Clause" ]

1,292

2015-01-04T03:20:35.000Z

2022-03-23T11:08:15.000Z

flask_oauthlib/contrib/client/signals.py

PCMan/flask-oauthlib

3735210211ac0e50c4d32b887bbd61722dd175c7

[ "BSD-3-Clause" ]

217

2015-01-05T09:51:41.000Z

2020-09-05T04:41:52.000Z

flask_oauthlib/contrib/client/signals.py

PCMan/flask-oauthlib

3735210211ac0e50c4d32b887bbd61722dd175c7

[ "BSD-3-Clause" ]

496

2015-01-04T03:20:35.000Z

2022-03-19T08:31:42.000Z

from flask.signals import Namespace __all__ = ['request_token_fetched'] _signals = Namespace() request_token_fetched = _signals.signal('request-token-fetched')

23.142857

64

0.802469

19

162

6.315789

0.526316

0.3

0.475

0.433333

0

0.092593

162

6

65

27

0.816327

0

0.259259

0

1

0

false

0

0.25

0

0.25

0

1

0

null

1

0

1

0

null

0

5

13e63fa492e9906b2dcaebabfe9efc2f8fac1942

229

py

Python

example/sample/forms.py

ebspeter/django-editorjs-field

87016b6b14dc960b5e89bfce6fb3219e1e955e68

[ "Apache-2.0" ]

10

2019-06-08T21:20:05.000Z

2021-02-22T12:42:58.000Z

example/sample/forms.py

ebspeter/django-editorjs-field

87016b6b14dc960b5e89bfce6fb3219e1e955e68

[ "Apache-2.0" ]

8

2019-12-04T23:02:43.000Z

2022-02-10T08:19:24.000Z

example/sample/forms.py

ebspeter/django-editorjs-field

87016b6b14dc960b5e89bfce6fb3219e1e955e68

[ "Apache-2.0" ]

7

2020-02-08T17:52:35.000Z

2020-07-31T20:59:25.000Z

from django import forms from editorjs_field.widgets import EditorJsWidget class ArticleEditorForm(forms.Form): title = forms.CharField(label='Title') document = forms.CharField(label='Document', widget=EditorJsWidget)

28.625

71

0.790393

26

229

6.923077

0.615385

0.155556

0.211111

0

0.117904

229

7

72

32.714286

0.891089

0

0.056769

0

1

0

false

0

0.4

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

b9373210f973ac710bf99d9222271c161d3c16f4

99,268

py

Python

tests/compute/test_transform.py

ayasar70/dgl

2f45f4d2a94fad094c4dd388507b0e787c77062f

[ "Apache-2.0" ]

null

tests/compute/test_transform.py

ayasar70/dgl

2f45f4d2a94fad094c4dd388507b0e787c77062f

[ "Apache-2.0" ]

null

tests/compute/test_transform.py

ayasar70/dgl

2f45f4d2a94fad094c4dd388507b0e787c77062f

[ "Apache-2.0" ]

null

## # Copyright 2019-2021 Contributors # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from scipy import sparse as spsp import networkx as nx import numpy as np import os import dgl import dgl.function as fn import dgl.partition import backend as F import unittest import math from utils import parametrize_dtype from test_heterograph import create_test_heterograph3, create_test_heterograph4, create_test_heterograph5 D = 5 # line graph related def test_line_graph1(): N = 5 G = dgl.DGLGraph(nx.star_graph(N)).to(F.ctx()) G.edata['h'] = F.randn((2 * N, D)) L = G.line_graph(shared=True) assert L.number_of_nodes() == 2 * N assert F.allclose(L.ndata['h'], G.edata['h']) assert G.device == F.ctx() @parametrize_dtype def test_line_graph2(idtype): g = dgl.heterograph({ ('user', 'follows', 'user'): ([0, 1, 1, 2, 2],[2, 0, 2, 0, 1]) }, idtype=idtype) lg = dgl.line_graph(g) assert lg.number_of_nodes() == 5 assert lg.number_of_edges() == 8 row, col = lg.edges() assert np.array_equal(F.asnumpy(row), np.array([0, 0, 1, 2, 2, 3, 4, 4])) assert np.array_equal(F.asnumpy(col), np.array([3, 4, 0, 3, 4, 0, 1, 2])) lg = dgl.line_graph(g, backtracking=False) assert lg.number_of_nodes() == 5 assert lg.number_of_edges() == 4 row, col = lg.edges() assert np.array_equal(F.asnumpy(row), np.array([0, 1, 2, 4])) assert np.array_equal(F.asnumpy(col), np.array([4, 0, 3, 1])) g = dgl.heterograph({ ('user', 'follows', 'user'): ([0, 1, 1, 2, 2],[2, 0, 2, 0, 1]) }, idtype=idtype).formats('csr') lg = dgl.line_graph(g) assert lg.number_of_nodes() == 5 assert lg.number_of_edges() == 8 row, col = lg.edges() assert np.array_equal(F.asnumpy(row), np.array([0, 0, 1, 2, 2, 3, 4, 4])) assert np.array_equal(F.asnumpy(col), np.array([3, 4, 0, 3, 4, 0, 1, 2])) g = dgl.heterograph({ ('user', 'follows', 'user'): ([0, 1, 1, 2, 2],[2, 0, 2, 0, 1]) }, idtype=idtype).formats('csc') lg = dgl.line_graph(g) assert lg.number_of_nodes() == 5 assert lg.number_of_edges() == 8 row, col, eid = lg.edges('all') row = F.asnumpy(row) col = F.asnumpy(col) eid = F.asnumpy(eid).astype(int) order = np.argsort(eid) assert np.array_equal(row[order], np.array([0, 0, 1, 2, 2, 3, 4, 4])) assert np.array_equal(col[order], np.array([3, 4, 0, 3, 4, 0, 1, 2])) def test_no_backtracking(): N = 5 G = dgl.DGLGraph(nx.star_graph(N)) L = G.line_graph(backtracking=False) assert L.number_of_nodes() == 2 * N for i in range(1, N): e1 = G.edge_id(0, i) e2 = G.edge_id(i, 0) assert not L.has_edge_between(e1, e2) assert not L.has_edge_between(e2, e1) # reverse graph related @parametrize_dtype def test_reverse(idtype): g = dgl.DGLGraph() g = g.astype(idtype).to(F.ctx()) g.add_nodes(5) # The graph need not to be completely connected. g.add_edges([0, 1, 2], [1, 2, 1]) g.ndata['h'] = F.tensor([[0.], [1.], [2.], [3.], [4.]]) g.edata['h'] = F.tensor([[5.], [6.], [7.]]) rg = g.reverse() assert g.is_multigraph == rg.is_multigraph assert g.number_of_nodes() == rg.number_of_nodes() assert g.number_of_edges() == rg.number_of_edges() assert F.allclose(F.astype(rg.has_edges_between( [1, 2, 1], [0, 1, 2]), F.float32), F.ones((3,))) assert g.edge_id(0, 1) == rg.edge_id(1, 0) assert g.edge_id(1, 2) == rg.edge_id(2, 1) assert g.edge_id(2, 1) == rg.edge_id(1, 2) # test dgl.reverse # test homogeneous graph g = dgl.graph((F.tensor([0, 1, 2]), F.tensor([1, 2, 0]))) g.ndata['h'] = F.tensor([[0.], [1.], [2.]]) g.edata['h'] = F.tensor([[3.], [4.], [5.]]) g_r = dgl.reverse(g) assert g.number_of_nodes() == g_r.number_of_nodes() assert g.number_of_edges() == g_r.number_of_edges() u_g, v_g, eids_g = g.all_edges(form='all') u_rg, v_rg, eids_rg = g_r.all_edges(form='all') assert F.array_equal(u_g, v_rg) assert F.array_equal(v_g, u_rg) assert F.array_equal(eids_g, eids_rg) assert F.array_equal(g.ndata['h'], g_r.ndata['h']) assert len(g_r.edata) == 0 # without share ndata g_r = dgl.reverse(g, copy_ndata=False) assert g.number_of_nodes() == g_r.number_of_nodes() assert g.number_of_edges() == g_r.number_of_edges() assert len(g_r.ndata) == 0 assert len(g_r.edata) == 0 # with share ndata and edata g_r = dgl.reverse(g, copy_ndata=True, copy_edata=True) assert g.number_of_nodes() == g_r.number_of_nodes() assert g.number_of_edges() == g_r.number_of_edges() assert F.array_equal(g.ndata['h'], g_r.ndata['h']) assert F.array_equal(g.edata['h'], g_r.edata['h']) # add new node feature to g_r g_r.ndata['hh'] = F.tensor([0, 1, 2]) assert ('hh' in g.ndata) is False assert ('hh' in g_r.ndata) is True # add new edge feature to g_r g_r.edata['hh'] = F.tensor([0, 1, 2]) assert ('hh' in g.edata) is False assert ('hh' in g_r.edata) is True # test heterogeneous graph g = dgl.heterograph({ ('user', 'follows', 'user'): ([0, 1, 2, 4, 3 ,1, 3], [1, 2, 3, 2, 0, 0, 1]), ('user', 'plays', 'game'): ([0, 0, 2, 3, 3, 4, 1], [1, 0, 1, 0, 1, 0, 0]), ('developer', 'develops', 'game'): ([0, 1, 1, 2], [0, 0, 1, 1])}, idtype=idtype, device=F.ctx()) g.nodes['user'].data['h'] = F.tensor([0, 1, 2, 3, 4]) g.nodes['user'].data['hh'] = F.tensor([1, 1, 1, 1, 1]) g.nodes['game'].data['h'] = F.tensor([0, 1]) g.edges['follows'].data['h'] = F.tensor([0, 1, 2, 4, 3 ,1, 3]) g.edges['follows'].data['hh'] = F.tensor([1, 2, 3, 2, 0, 0, 1]) g_r = dgl.reverse(g) for etype_g, etype_gr in zip(g.canonical_etypes, g_r.canonical_etypes): assert etype_g[0] == etype_gr[2] assert etype_g[1] == etype_gr[1] assert etype_g[2] == etype_gr[0] assert g.number_of_edges(etype_g) == g_r.number_of_edges(etype_gr) for ntype in g.ntypes: assert g.number_of_nodes(ntype) == g_r.number_of_nodes(ntype) assert F.array_equal(g.nodes['user'].data['h'], g_r.nodes['user'].data['h']) assert F.array_equal(g.nodes['user'].data['hh'], g_r.nodes['user'].data['hh']) assert F.array_equal(g.nodes['game'].data['h'], g_r.nodes['game'].data['h']) assert len(g_r.edges['follows'].data) == 0 u_g, v_g, eids_g = g.all_edges(form='all', etype=('user', 'follows', 'user')) u_rg, v_rg, eids_rg = g_r.all_edges(form='all', etype=('user', 'follows', 'user')) assert F.array_equal(u_g, v_rg) assert F.array_equal(v_g, u_rg) assert F.array_equal(eids_g, eids_rg) u_g, v_g, eids_g = g.all_edges(form='all', etype=('user', 'plays', 'game')) u_rg, v_rg, eids_rg = g_r.all_edges(form='all', etype=('game', 'plays', 'user')) assert F.array_equal(u_g, v_rg) assert F.array_equal(v_g, u_rg) assert F.array_equal(eids_g, eids_rg) u_g, v_g, eids_g = g.all_edges(form='all', etype=('developer', 'develops', 'game')) u_rg, v_rg, eids_rg = g_r.all_edges(form='all', etype=('game', 'develops', 'developer')) assert F.array_equal(u_g, v_rg) assert F.array_equal(v_g, u_rg) assert F.array_equal(eids_g, eids_rg) # withour share ndata g_r = dgl.reverse(g, copy_ndata=False) for etype_g, etype_gr in zip(g.canonical_etypes, g_r.canonical_etypes): assert etype_g[0] == etype_gr[2] assert etype_g[1] == etype_gr[1] assert etype_g[2] == etype_gr[0] assert g.number_of_edges(etype_g) == g_r.number_of_edges(etype_gr) for ntype in g.ntypes: assert g.number_of_nodes(ntype) == g_r.number_of_nodes(ntype) assert len(g_r.nodes['user'].data) == 0 assert len(g_r.nodes['game'].data) == 0 g_r = dgl.reverse(g, copy_ndata=True, copy_edata=True) print(g_r) for etype_g, etype_gr in zip(g.canonical_etypes, g_r.canonical_etypes): assert etype_g[0] == etype_gr[2] assert etype_g[1] == etype_gr[1] assert etype_g[2] == etype_gr[0] assert g.number_of_edges(etype_g) == g_r.number_of_edges(etype_gr) assert F.array_equal(g.edges['follows'].data['h'], g_r.edges['follows'].data['h']) assert F.array_equal(g.edges['follows'].data['hh'], g_r.edges['follows'].data['hh']) # add new node feature to g_r g_r.nodes['user'].data['hhh'] = F.tensor([0, 1, 2, 3, 4]) assert ('hhh' in g.nodes['user'].data) is False assert ('hhh' in g_r.nodes['user'].data) is True # add new edge feature to g_r g_r.edges['follows'].data['hhh'] = F.tensor([1, 2, 3, 2, 0, 0, 1]) assert ('hhh' in g.edges['follows'].data) is False assert ('hhh' in g_r.edges['follows'].data) is True @parametrize_dtype def test_reverse_shared_frames(idtype): g = dgl.DGLGraph() g = g.astype(idtype).to(F.ctx()) g.add_nodes(3) g.add_edges([0, 1, 2], [1, 2, 1]) g.ndata['h'] = F.tensor([[0.], [1.], [2.]]) g.edata['h'] = F.tensor([[3.], [4.], [5.]]) rg = g.reverse(share_ndata=True, share_edata=True) assert F.allclose(g.ndata['h'], rg.ndata['h']) assert F.allclose(g.edata['h'], rg.edata['h']) assert F.allclose(g.edges[[0, 2], [1, 1]].data['h'], rg.edges[[1, 1], [0, 2]].data['h']) @unittest.skipIf(F._default_context_str == 'gpu', reason="GPU not implemented") def test_to_bidirected(): # homogeneous graph elist = [(0, 0), (0, 1), (1, 0), (1, 1), (2, 1), (2, 2)] num_edges = 7 g = dgl.graph(tuple(zip(*elist))) elist.append((1, 2)) elist = set(elist) big = dgl.to_bidirected(g) assert big.number_of_edges() == num_edges src, dst = big.edges() eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst)))) assert eset == set(elist) # heterogeneous graph elist1 = [(0, 0), (0, 1), (1, 0), (1, 1), (2, 1), (2, 2)] elist2 = [(0, 0), (0, 1)] g = dgl.heterograph({ ('user', 'wins', 'user'): tuple(zip(*elist1)), ('user', 'follows', 'user'): tuple(zip(*elist2)) }) g.nodes['user'].data['h'] = F.ones((3, 1)) elist1.append((1, 2)) elist1 = set(elist1) elist2.append((1, 0)) elist2 = set(elist2) big = dgl.to_bidirected(g) assert big.number_of_edges('wins') == 7 assert big.number_of_edges('follows') == 3 src, dst = big.edges(etype='wins') eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst)))) assert eset == set(elist1) src, dst = big.edges(etype='follows') eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst)))) assert eset == set(elist2) big = dgl.to_bidirected(g, copy_ndata=True) assert F.array_equal(g.nodes['user'].data['h'], big.nodes['user'].data['h']) def test_add_reverse_edges(): # homogeneous graph g = dgl.graph((F.tensor([0, 1, 3, 1]), F.tensor([1, 2, 0, 2]))) g.ndata['h'] = F.tensor([[0.], [1.], [2.], [1.]]) g.edata['h'] = F.tensor([[3.], [4.], [5.], [6.]]) bg = dgl.add_reverse_edges(g, copy_ndata=True, copy_edata=True) u, v = g.edges() ub, vb = bg.edges() assert F.array_equal(F.cat([u, v], dim=0), ub) assert F.array_equal(F.cat([v, u], dim=0), vb) assert F.array_equal(g.ndata['h'], bg.ndata['h']) assert F.array_equal(F.cat([g.edata['h'], g.edata['h']], dim=0), bg.edata['h']) bg.ndata['hh'] = F.tensor([[0.], [1.], [2.], [1.]]) assert ('hh' in g.ndata) is False bg.edata['hh'] = F.tensor([[0.], [1.], [2.], [1.], [0.], [1.], [2.], [1.]]) assert ('hh' in g.edata) is False # donot share ndata and edata bg = dgl.add_reverse_edges(g, copy_ndata=False, copy_edata=False) ub, vb = bg.edges() assert F.array_equal(F.cat([u, v], dim=0), ub) assert F.array_equal(F.cat([v, u], dim=0), vb) assert ('h' in bg.ndata) is False assert ('h' in bg.edata) is False # zero edge graph g = dgl.graph(([], [])) bg = dgl.add_reverse_edges(g, copy_ndata=True, copy_edata=True, exclude_self=False) # heterogeneous graph g = dgl.heterograph({ ('user', 'wins', 'user'): (F.tensor([0, 2, 0, 2, 2]), F.tensor([1, 1, 2, 1, 0])), ('user', 'plays', 'game'): (F.tensor([1, 2, 1]), F.tensor([2, 1, 1])), ('user', 'follows', 'user'): (F.tensor([1, 2, 1]), F.tensor([0, 0, 0])) }) g.nodes['game'].data['hv'] = F.ones((3, 1)) g.nodes['user'].data['hv'] = F.ones((3, 1)) g.edges['wins'].data['h'] = F.tensor([0, 1, 2, 3, 4]) bg = dgl.add_reverse_edges(g, copy_ndata=True, copy_edata=True, ignore_bipartite=True) assert F.array_equal(g.nodes['game'].data['hv'], bg.nodes['game'].data['hv']) assert F.array_equal(g.nodes['user'].data['hv'], bg.nodes['user'].data['hv']) u, v = g.all_edges(order='eid', etype=('user', 'wins', 'user')) ub, vb = bg.all_edges(order='eid', etype=('user', 'wins', 'user')) assert F.array_equal(F.cat([u, v], dim=0), ub) assert F.array_equal(F.cat([v, u], dim=0), vb) assert F.array_equal(F.cat([g.edges['wins'].data['h'], g.edges['wins'].data['h']], dim=0), bg.edges['wins'].data['h']) u, v = g.all_edges(order='eid', etype=('user', 'follows', 'user')) ub, vb = bg.all_edges(order='eid', etype=('user', 'follows', 'user')) assert F.array_equal(F.cat([u, v], dim=0), ub) assert F.array_equal(F.cat([v, u], dim=0), vb) u, v = g.all_edges(order='eid', etype=('user', 'plays', 'game')) ub, vb = bg.all_edges(order='eid', etype=('user', 'plays', 'game')) assert F.array_equal(u, ub) assert F.array_equal(v, vb) assert set(bg.edges['plays'].data.keys()) == {dgl.EID} assert set(bg.edges['follows'].data.keys()) == {dgl.EID} # donot share ndata and edata bg = dgl.add_reverse_edges(g, copy_ndata=False, copy_edata=False, ignore_bipartite=True) assert len(bg.edges['wins'].data) == 0 assert len(bg.edges['plays'].data) == 0 assert len(bg.edges['follows'].data) == 0 assert len(bg.nodes['game'].data) == 0 assert len(bg.nodes['user'].data) == 0 u, v = g.all_edges(order='eid', etype=('user', 'wins', 'user')) ub, vb = bg.all_edges(order='eid', etype=('user', 'wins', 'user')) assert F.array_equal(F.cat([u, v], dim=0), ub) assert F.array_equal(F.cat([v, u], dim=0), vb) u, v = g.all_edges(order='eid', etype=('user', 'follows', 'user')) ub, vb = bg.all_edges(order='eid', etype=('user', 'follows', 'user')) assert F.array_equal(F.cat([u, v], dim=0), ub) assert F.array_equal(F.cat([v, u], dim=0), vb) u, v = g.all_edges(order='eid', etype=('user', 'plays', 'game')) ub, vb = bg.all_edges(order='eid', etype=('user', 'plays', 'game')) assert F.array_equal(u, ub) assert F.array_equal(v, vb) # test the case when some nodes have zero degree # homogeneous graph g = dgl.graph((F.tensor([0, 1, 3, 1]), F.tensor([1, 2, 0, 2])), num_nodes=6) g.ndata['h'] = F.tensor([[0.], [1.], [2.], [1.], [1.], [1.]]) g.edata['h'] = F.tensor([[3.], [4.], [5.], [6.]]) bg = dgl.add_reverse_edges(g, copy_ndata=True, copy_edata=True) assert g.number_of_nodes() == bg.number_of_nodes() assert F.array_equal(g.ndata['h'], bg.ndata['h']) assert F.array_equal(F.cat([g.edata['h'], g.edata['h']], dim=0), bg.edata['h']) # heterogeneous graph g = dgl.heterograph({ ('user', 'wins', 'user'): (F.tensor([0, 2, 0, 2, 2]), F.tensor([1, 1, 2, 1, 0])), ('user', 'plays', 'game'): (F.tensor([1, 2, 1]), F.tensor([2, 1, 1])), ('user', 'follows', 'user'): (F.tensor([1, 2, 1]), F.tensor([0, 0, 0]))}, num_nodes_dict={ 'user': 5, 'game': 3 }) g.nodes['game'].data['hv'] = F.ones((3, 1)) g.nodes['user'].data['hv'] = F.ones((5, 1)) g.edges['wins'].data['h'] = F.tensor([0, 1, 2, 3, 4]) bg = dgl.add_reverse_edges(g, copy_ndata=True, copy_edata=True, ignore_bipartite=True) assert g.number_of_nodes('user') == bg.number_of_nodes('user') assert g.number_of_nodes('game') == bg.number_of_nodes('game') assert F.array_equal(g.nodes['game'].data['hv'], bg.nodes['game'].data['hv']) assert F.array_equal(g.nodes['user'].data['hv'], bg.nodes['user'].data['hv']) assert F.array_equal(F.cat([g.edges['wins'].data['h'], g.edges['wins'].data['h']], dim=0), bg.edges['wins'].data['h']) # test exclude_self g = dgl.heterograph({ ('A', 'r1', 'A'): (F.tensor([0, 0, 1, 1]), F.tensor([0, 1, 1, 2])), ('A', 'r2', 'A'): (F.tensor([0, 1]), F.tensor([1, 2])) }) g.edges['r1'].data['h'] = F.tensor([0, 1, 2, 3]) rg = dgl.add_reverse_edges(g, copy_edata=True, exclude_self=True) assert rg.num_edges('r1') == 6 assert rg.num_edges('r2') == 4 assert F.array_equal(rg.edges['r1'].data['h'], F.tensor([0, 1, 2, 3, 1, 3])) @unittest.skipIf(F._default_context_str == 'gpu', reason="GPU not implemented") def test_simple_graph(): elist = [(0, 1), (0, 2), (1, 2), (0, 1)] g = dgl.DGLGraph(elist, readonly=True) assert g.is_multigraph sg = dgl.to_simple_graph(g) assert not sg.is_multigraph assert sg.number_of_edges() == 3 src, dst = sg.edges() eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst)))) assert eset == set(elist) @unittest.skipIf(F._default_context_str == 'gpu', reason="GPU not implemented") def _test_bidirected_graph(): def _test(in_readonly, out_readonly): elist = [(0, 0), (0, 1), (1, 0), (1, 1), (2, 1), (2, 2)] num_edges = 7 g = dgl.DGLGraph(elist, readonly=in_readonly) elist.append((1, 2)) elist = set(elist) big = dgl.to_bidirected_stale(g, out_readonly) assert big.number_of_edges() == num_edges src, dst = big.edges() eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst)))) assert eset == set(elist) _test(True, True) _test(True, False) _test(False, True) _test(False, False) @unittest.skipIf(F._default_context_str == 'gpu', reason="GPU not implemented") def test_khop_graph(): N = 20 feat = F.randn((N, 5)) def _test(g): for k in range(4): g_k = dgl.khop_graph(g, k) # use original graph to do message passing for k times. g.ndata['h'] = feat for _ in range(k): g.update_all(fn.copy_u('h', 'm'), fn.sum('m', 'h')) h_0 = g.ndata.pop('h') # use k-hop graph to do message passing for one time. g_k.ndata['h'] = feat g_k.update_all(fn.copy_u('h', 'm'), fn.sum('m', 'h')) h_1 = g_k.ndata.pop('h') assert F.allclose(h_0, h_1, rtol=1e-3, atol=1e-3) # Test for random undirected graphs g = dgl.DGLGraph(nx.erdos_renyi_graph(N, 0.3)) _test(g) # Test for random directed graphs g = dgl.DGLGraph(nx.erdos_renyi_graph(N, 0.3, directed=True)) _test(g) @unittest.skipIf(F._default_context_str == 'gpu', reason="GPU not implemented") def test_khop_adj(): N = 20 feat = F.randn((N, 5)) g = dgl.DGLGraph(nx.erdos_renyi_graph(N, 0.3)) for k in range(3): adj = F.tensor(F.swapaxes(dgl.khop_adj(g, k), 0, 1)) # use original graph to do message passing for k times. g.ndata['h'] = feat for _ in range(k): g.update_all(fn.copy_u('h', 'm'), fn.sum('m', 'h')) h_0 = g.ndata.pop('h') # use k-hop adj to do message passing for one time. h_1 = F.matmul(adj, feat) assert F.allclose(h_0, h_1, rtol=1e-3, atol=1e-3) @unittest.skipIf(F._default_context_str == 'gpu', reason="GPU not implemented") def test_laplacian_lambda_max(): N = 20 eps = 1e-6 # test DGLGraph g = dgl.DGLGraph(nx.erdos_renyi_graph(N, 0.3)) l_max = dgl.laplacian_lambda_max(g) assert (l_max[0] < 2 + eps) # test batched DGLGraph ''' N_arr = [20, 30, 10, 12] bg = dgl.batch([ dgl.DGLGraph(nx.erdos_renyi_graph(N, 0.3)) for N in N_arr ]) l_max_arr = dgl.laplacian_lambda_max(bg) assert len(l_max_arr) == len(N_arr) for l_max in l_max_arr: assert l_max < 2 + eps ''' def create_large_graph(num_nodes, idtype=F.int64): row = np.random.choice(num_nodes, num_nodes * 10) col = np.random.choice(num_nodes, num_nodes * 10) spm = spsp.coo_matrix((np.ones(len(row)), (row, col))) spm.sum_duplicates() return dgl.from_scipy(spm, idtype=idtype) def get_nodeflow(g, node_ids, num_layers): batch_size = len(node_ids) expand_factor = g.number_of_nodes() sampler = dgl.contrib.sampling.NeighborSampler(g, batch_size, expand_factor=expand_factor, num_hops=num_layers, seed_nodes=node_ids) return next(iter(sampler)) # Disabled since everything will be on heterogeneous graphs @unittest.skipIf(F._default_context_str == 'gpu', reason="GPU not implemented") def test_partition_with_halo(): g = create_large_graph(1000) node_part = np.random.choice(4, g.number_of_nodes()) subgs, _, _ = dgl.transforms.partition_graph_with_halo(g, node_part, 2, reshuffle=True) for part_id, subg in subgs.items(): node_ids = np.nonzero(node_part == part_id)[0] lnode_ids = np.nonzero(F.asnumpy(subg.ndata['inner_node']))[0] orig_nids = F.asnumpy(subg.ndata['orig_id'])[lnode_ids] assert np.all(np.sort(orig_nids) == node_ids) assert np.all(F.asnumpy(subg.in_degrees(lnode_ids)) == F.asnumpy(g.in_degrees(orig_nids))) assert np.all(F.asnumpy(subg.out_degrees(lnode_ids)) == F.asnumpy(g.out_degrees(orig_nids))) @unittest.skipIf(os.name == 'nt', reason='Do not support windows yet') @unittest.skipIf(F._default_context_str == 'gpu', reason="METIS doesn't support GPU") @parametrize_dtype def test_metis_partition(idtype): # TODO(zhengda) Metis fails to partition a small graph. g = create_large_graph(1000, idtype=idtype) if idtype == F.int64: check_metis_partition(g, 0) check_metis_partition(g, 1) check_metis_partition(g, 2) check_metis_partition_with_constraint(g) else: assert_fail = False try: check_metis_partition(g, 1) except: assert_fail = True assert assert_fail def check_metis_partition_with_constraint(g): ntypes = np.zeros((g.number_of_nodes(),), dtype=np.int32) ntypes[0:int(g.number_of_nodes()/4)] = 1 ntypes[int(g.number_of_nodes()*3/4):] = 2 subgs = dgl.transforms.metis_partition(g, 4, extra_cached_hops=1, balance_ntypes=ntypes) if subgs is not None: for i in subgs: subg = subgs[i] parent_nids = F.asnumpy(subg.ndata[dgl.NID]) sub_ntypes = ntypes[parent_nids] print('type0:', np.sum(sub_ntypes == 0)) print('type1:', np.sum(sub_ntypes == 1)) print('type2:', np.sum(sub_ntypes == 2)) subgs = dgl.transforms.metis_partition(g, 4, extra_cached_hops=1, balance_ntypes=ntypes, balance_edges=True) if subgs is not None: for i in subgs: subg = subgs[i] parent_nids = F.asnumpy(subg.ndata[dgl.NID]) sub_ntypes = ntypes[parent_nids] print('type0:', np.sum(sub_ntypes == 0)) print('type1:', np.sum(sub_ntypes == 1)) print('type2:', np.sum(sub_ntypes == 2)) def check_metis_partition(g, extra_hops): subgs = dgl.transforms.metis_partition(g, 4, extra_cached_hops=extra_hops) num_inner_nodes = 0 num_inner_edges = 0 if subgs is not None: for part_id, subg in subgs.items(): lnode_ids = np.nonzero(F.asnumpy(subg.ndata['inner_node']))[0] ledge_ids = np.nonzero(F.asnumpy(subg.edata['inner_edge']))[0] num_inner_nodes += len(lnode_ids) num_inner_edges += len(ledge_ids) assert np.sum(F.asnumpy(subg.ndata['part_id']) == part_id) == len(lnode_ids) assert num_inner_nodes == g.number_of_nodes() print(g.number_of_edges() - num_inner_edges) if extra_hops == 0: return # partitions with node reshuffling subgs = dgl.transforms.metis_partition(g, 4, extra_cached_hops=extra_hops, reshuffle=True) num_inner_nodes = 0 num_inner_edges = 0 edge_cnts = np.zeros((g.number_of_edges(),)) if subgs is not None: for part_id, subg in subgs.items(): lnode_ids = np.nonzero(F.asnumpy(subg.ndata['inner_node']))[0] ledge_ids = np.nonzero(F.asnumpy(subg.edata['inner_edge']))[0] num_inner_nodes += len(lnode_ids) num_inner_edges += len(ledge_ids) assert np.sum(F.asnumpy(subg.ndata['part_id']) == part_id) == len(lnode_ids) nids = F.asnumpy(subg.ndata[dgl.NID]) # ensure the local node Ids are contiguous. parent_ids = F.asnumpy(subg.ndata[dgl.NID]) parent_ids = parent_ids[:len(lnode_ids)] assert np.all(parent_ids == np.arange(parent_ids[0], parent_ids[-1] + 1)) # count the local edges. parent_ids = F.asnumpy(subg.edata[dgl.EID])[ledge_ids] edge_cnts[parent_ids] += 1 orig_ids = subg.ndata['orig_id'] inner_node = F.asnumpy(subg.ndata['inner_node']) for nid in range(subg.number_of_nodes()): neighs = subg.predecessors(nid) old_neighs1 = F.gather_row(orig_ids, neighs) old_nid = F.asnumpy(orig_ids[nid]) old_neighs2 = g.predecessors(old_nid) # If this is an inner node, it should have the full neighborhood. if inner_node[nid]: assert np.all(np.sort(F.asnumpy(old_neighs1)) == np.sort(F.asnumpy(old_neighs2))) # Normally, local edges are only counted once. assert np.all(edge_cnts == 1) assert num_inner_nodes == g.number_of_nodes() print(g.number_of_edges() - num_inner_edges) @unittest.skipIf(F._default_context_str == 'gpu', reason="It doesn't support GPU") def test_reorder_nodes(): g = create_large_graph(1000) new_nids = np.random.permutation(g.number_of_nodes()) # TODO(zhengda) we need to test both CSR and COO. new_g = dgl.partition.reorder_nodes(g, new_nids) new_in_deg = new_g.in_degrees() new_out_deg = new_g.out_degrees() in_deg = g.in_degrees() out_deg = g.out_degrees() new_in_deg1 = F.scatter_row(in_deg, F.tensor(new_nids), in_deg) new_out_deg1 = F.scatter_row(out_deg, F.tensor(new_nids), out_deg) assert np.all(F.asnumpy(new_in_deg == new_in_deg1)) assert np.all(F.asnumpy(new_out_deg == new_out_deg1)) orig_ids = F.asnumpy(new_g.ndata['orig_id']) for nid in range(g.number_of_nodes()): neighs = F.asnumpy(g.successors(nid)) new_neighs1 = new_nids[neighs] new_nid = new_nids[nid] new_neighs2 = new_g.successors(new_nid) assert np.all(np.sort(new_neighs1) == np.sort(F.asnumpy(new_neighs2))) for nid in range(new_g.number_of_nodes()): neighs = F.asnumpy(new_g.successors(nid)) old_neighs1 = orig_ids[neighs] old_nid = orig_ids[nid] old_neighs2 = g.successors(old_nid) assert np.all(np.sort(old_neighs1) == np.sort(F.asnumpy(old_neighs2))) neighs = F.asnumpy(new_g.predecessors(nid)) old_neighs1 = orig_ids[neighs] old_nid = orig_ids[nid] old_neighs2 = g.predecessors(old_nid) assert np.all(np.sort(old_neighs1) == np.sort(F.asnumpy(old_neighs2))) @parametrize_dtype def test_compact(idtype): g1 = dgl.heterograph({ ('user', 'follow', 'user'): ([1, 3], [3, 5]), ('user', 'plays', 'game'): ([2, 3, 2], [4, 4, 5]), ('game', 'wished-by', 'user'): ([6, 5], [7, 7])}, {'user': 20, 'game': 10}, idtype=idtype, device=F.ctx()) g2 = dgl.heterograph({ ('game', 'clicked-by', 'user'): ([3], [1]), ('user', 'likes', 'user'): ([1, 8], [8, 9])}, {'user': 20, 'game': 10}, idtype=idtype, device=F.ctx()) g3 = dgl.heterograph({('user', '_E', 'user'): ((0, 1), (1, 2))}, {'user': 10}, idtype=idtype, device=F.ctx()) g4 = dgl.heterograph({('user', '_E', 'user'): ((1, 3), (3, 5))}, {'user': 10}, idtype=idtype, device=F.ctx()) def _check(g, new_g, induced_nodes): assert g.ntypes == new_g.ntypes assert g.canonical_etypes == new_g.canonical_etypes for ntype in g.ntypes: assert -1 not in induced_nodes[ntype] for etype in g.canonical_etypes: g_src, g_dst = g.all_edges(order='eid', etype=etype) g_src = F.asnumpy(g_src) g_dst = F.asnumpy(g_dst) new_g_src, new_g_dst = new_g.all_edges(order='eid', etype=etype) new_g_src_mapped = induced_nodes[etype[0]][F.asnumpy(new_g_src)] new_g_dst_mapped = induced_nodes[etype[2]][F.asnumpy(new_g_dst)] assert (g_src == new_g_src_mapped).all() assert (g_dst == new_g_dst_mapped).all() # Test default new_g1 = dgl.compact_graphs(g1) induced_nodes = {ntype: new_g1.nodes[ntype].data[dgl.NID] for ntype in new_g1.ntypes} induced_nodes = {k: F.asnumpy(v) for k, v in induced_nodes.items()} assert new_g1.idtype == idtype assert set(induced_nodes['user']) == set([1, 3, 5, 2, 7]) assert set(induced_nodes['game']) == set([4, 5, 6]) _check(g1, new_g1, induced_nodes) # Test with always_preserve given a dict new_g1 = dgl.compact_graphs( g1, always_preserve={'game': F.tensor([4, 7], idtype)}) assert new_g1.idtype == idtype induced_nodes = {ntype: new_g1.nodes[ntype].data[dgl.NID] for ntype in new_g1.ntypes} induced_nodes = {k: F.asnumpy(v) for k, v in induced_nodes.items()} assert set(induced_nodes['user']) == set([1, 3, 5, 2, 7]) assert set(induced_nodes['game']) == set([4, 5, 6, 7]) _check(g1, new_g1, induced_nodes) # Test with always_preserve given a tensor new_g3 = dgl.compact_graphs( g3, always_preserve=F.tensor([1, 7], idtype)) induced_nodes = {ntype: new_g3.nodes[ntype].data[dgl.NID] for ntype in new_g3.ntypes} induced_nodes = {k: F.asnumpy(v) for k, v in induced_nodes.items()} assert new_g3.idtype == idtype assert set(induced_nodes['user']) == set([0, 1, 2, 7]) _check(g3, new_g3, induced_nodes) # Test multiple graphs new_g1, new_g2 = dgl.compact_graphs([g1, g2]) induced_nodes = {ntype: new_g1.nodes[ntype].data[dgl.NID] for ntype in new_g1.ntypes} induced_nodes = {k: F.asnumpy(v) for k, v in induced_nodes.items()} assert new_g1.idtype == idtype assert new_g2.idtype == idtype assert set(induced_nodes['user']) == set([1, 3, 5, 2, 7, 8, 9]) assert set(induced_nodes['game']) == set([3, 4, 5, 6]) _check(g1, new_g1, induced_nodes) _check(g2, new_g2, induced_nodes) # Test multiple graphs with always_preserve given a dict new_g1, new_g2 = dgl.compact_graphs( [g1, g2], always_preserve={'game': F.tensor([4, 7], dtype=idtype)}) induced_nodes = {ntype: new_g1.nodes[ntype].data[dgl.NID] for ntype in new_g1.ntypes} induced_nodes = {k: F.asnumpy(v) for k, v in induced_nodes.items()} assert new_g1.idtype == idtype assert new_g2.idtype == idtype assert set(induced_nodes['user']) == set([1, 3, 5, 2, 7, 8, 9]) assert set(induced_nodes['game']) == set([3, 4, 5, 6, 7]) _check(g1, new_g1, induced_nodes) _check(g2, new_g2, induced_nodes) # Test multiple graphs with always_preserve given a tensor new_g3, new_g4 = dgl.compact_graphs( [g3, g4], always_preserve=F.tensor([1, 7], dtype=idtype)) induced_nodes = {ntype: new_g3.nodes[ntype].data[dgl.NID] for ntype in new_g3.ntypes} induced_nodes = {k: F.asnumpy(v) for k, v in induced_nodes.items()} assert new_g3.idtype == idtype assert new_g4.idtype == idtype assert set(induced_nodes['user']) == set([0, 1, 2, 3, 5, 7]) _check(g3, new_g3, induced_nodes) _check(g4, new_g4, induced_nodes) @unittest.skipIf(F._default_context_str == 'gpu', reason="GPU to simple not implemented") @parametrize_dtype def test_to_simple(idtype): # homogeneous graph g = dgl.graph((F.tensor([0, 1, 2, 1]), F.tensor([1, 2, 0, 2]))) g.ndata['h'] = F.tensor([[0.], [1.], [2.]]) g.edata['h'] = F.tensor([[3.], [4.], [5.], [6.]]) sg, wb = dgl.to_simple(g, writeback_mapping=True) u, v = g.all_edges(form='uv', order='eid') u = F.asnumpy(u).tolist() v = F.asnumpy(v).tolist() uv = list(zip(u, v)) eid_map = F.asnumpy(wb) su, sv = sg.all_edges(form='uv', order='eid') su = F.asnumpy(su).tolist() sv = F.asnumpy(sv).tolist() suv = list(zip(su, sv)) sc = F.asnumpy(sg.edata['count']) assert set(uv) == set(suv) for i, e in enumerate(suv): assert sc[i] == sum(e == _e for _e in uv) for i, e in enumerate(uv): assert eid_map[i] == suv.index(e) # shared ndata assert F.array_equal(sg.ndata['h'], g.ndata['h']) assert 'h' not in sg.edata # new ndata to sg sg.ndata['hh'] = F.tensor([[0.], [1.], [2.]]) assert 'hh' not in g.ndata sg = dgl.to_simple(g, writeback_mapping=False, copy_ndata=False) assert 'h' not in sg.ndata assert 'h' not in sg.edata # test coalesce edge feature sg = dgl.to_simple(g, copy_edata=True, aggregator='arbitrary') assert F.allclose(sg.edata['h'][1], F.tensor([4.])) sg = dgl.to_simple(g, copy_edata=True, aggregator='sum') assert F.allclose(sg.edata['h'][1], F.tensor([10.])) sg = dgl.to_simple(g, copy_edata=True, aggregator='mean') assert F.allclose(sg.edata['h'][1], F.tensor([5.])) # heterogeneous graph g = dgl.heterograph({ ('user', 'follow', 'user'): ([0, 1, 2, 1, 1, 1], [1, 3, 2, 3, 4, 4]), ('user', 'plays', 'game'): ([3, 2, 1, 1, 3, 2, 2], [5, 3, 4, 4, 5, 3, 3])}, idtype=idtype, device=F.ctx()) g.nodes['user'].data['h'] = F.tensor([0, 1, 2, 3, 4]) g.nodes['user'].data['hh'] = F.tensor([0, 1, 2, 3, 4]) g.edges['follow'].data['h'] = F.tensor([0, 1, 2, 3, 4, 5]) sg, wb = dgl.to_simple(g, return_counts='weights', writeback_mapping=True, copy_edata=True) g.nodes['game'].data['h'] = F.tensor([0, 1, 2, 3, 4, 5]) for etype in g.canonical_etypes: u, v = g.all_edges(form='uv', order='eid', etype=etype) u = F.asnumpy(u).tolist() v = F.asnumpy(v).tolist() uv = list(zip(u, v)) eid_map = F.asnumpy(wb[etype]) su, sv = sg.all_edges(form='uv', order='eid', etype=etype) su = F.asnumpy(su).tolist() sv = F.asnumpy(sv).tolist() suv = list(zip(su, sv)) sw = F.asnumpy(sg.edges[etype].data['weights']) assert set(uv) == set(suv) for i, e in enumerate(suv): assert sw[i] == sum(e == _e for _e in uv) for i, e in enumerate(uv): assert eid_map[i] == suv.index(e) # shared ndata assert F.array_equal(sg.nodes['user'].data['h'], g.nodes['user'].data['h']) assert F.array_equal(sg.nodes['user'].data['hh'], g.nodes['user'].data['hh']) assert 'h' not in sg.nodes['game'].data # new ndata to sg sg.nodes['user'].data['hhh'] = F.tensor([0, 1, 2, 3, 4]) assert 'hhh' not in g.nodes['user'].data # share edata feat_idx = F.asnumpy(wb[('user', 'follow', 'user')]) _, indices = np.unique(feat_idx, return_index=True) assert np.array_equal(F.asnumpy(sg.edges['follow'].data['h']), F.asnumpy(g.edges['follow'].data['h'])[indices]) sg = dgl.to_simple(g, writeback_mapping=False, copy_ndata=False) for ntype in g.ntypes: assert g.number_of_nodes(ntype) == sg.number_of_nodes(ntype) assert 'h' not in sg.nodes['user'].data assert 'hh' not in sg.nodes['user'].data # verify DGLGraph.edge_ids() after dgl.to_simple() # in case ids are not initialized in underlying coo2csr() u = F.tensor([0, 1, 2]) v = F.tensor([1, 2, 3]) eids = F.tensor([0, 1, 2]) g = dgl.graph((u, v)) assert F.array_equal(g.edge_ids(u, v), eids) sg = dgl.to_simple(g) assert F.array_equal(sg.edge_ids(u, v), eids) @parametrize_dtype def test_to_block(idtype): def check(g, bg, ntype, etype, dst_nodes, include_dst_in_src=True): if dst_nodes is not None: assert F.array_equal(bg.dstnodes[ntype].data[dgl.NID], dst_nodes) n_dst_nodes = bg.number_of_nodes('DST/' + ntype) if include_dst_in_src: assert F.array_equal( bg.srcnodes[ntype].data[dgl.NID][:n_dst_nodes], bg.dstnodes[ntype].data[dgl.NID]) g = g[etype] bg = bg[etype] induced_src = bg.srcdata[dgl.NID] induced_dst = bg.dstdata[dgl.NID] induced_eid = bg.edata[dgl.EID] bg_src, bg_dst = bg.all_edges(order='eid') src_ans, dst_ans = g.all_edges(order='eid') induced_src_bg = F.gather_row(induced_src, bg_src) induced_dst_bg = F.gather_row(induced_dst, bg_dst) induced_src_ans = F.gather_row(src_ans, induced_eid) induced_dst_ans = F.gather_row(dst_ans, induced_eid) assert F.array_equal(induced_src_bg, induced_src_ans) assert F.array_equal(induced_dst_bg, induced_dst_ans) def checkall(g, bg, dst_nodes, include_dst_in_src=True): for etype in g.etypes: ntype = g.to_canonical_etype(etype)[2] if dst_nodes is not None and ntype in dst_nodes: check(g, bg, ntype, etype, dst_nodes[ntype], include_dst_in_src) else: check(g, bg, ntype, etype, None, include_dst_in_src) g = dgl.heterograph({ ('A', 'AA', 'A'): ([0, 2, 1, 3], [1, 3, 2, 4]), ('A', 'AB', 'B'): ([0, 1, 3, 1], [1, 3, 5, 6]), ('B', 'BA', 'A'): ([2, 3], [3, 2])}, idtype=idtype, device=F.ctx()) g.nodes['A'].data['x'] = F.randn((5, 10)) g.nodes['B'].data['x'] = F.randn((7, 5)) g.edges['AA'].data['x'] = F.randn((4, 3)) g.edges['AB'].data['x'] = F.randn((4, 3)) g.edges['BA'].data['x'] = F.randn((2, 3)) g_a = g['AA'] def check_features(g, bg): for ntype in bg.srctypes: for key in g.nodes[ntype].data: assert F.array_equal( bg.srcnodes[ntype].data[key], F.gather_row(g.nodes[ntype].data[key], bg.srcnodes[ntype].data[dgl.NID])) for ntype in bg.dsttypes: for key in g.nodes[ntype].data: assert F.array_equal( bg.dstnodes[ntype].data[key], F.gather_row(g.nodes[ntype].data[key], bg.dstnodes[ntype].data[dgl.NID])) for etype in bg.canonical_etypes: for key in g.edges[etype].data: assert F.array_equal( bg.edges[etype].data[key], F.gather_row(g.edges[etype].data[key], bg.edges[etype].data[dgl.EID])) bg = dgl.to_block(g_a) check(g_a, bg, 'A', 'AA', None) check_features(g_a, bg) assert bg.number_of_src_nodes() == 5 assert bg.number_of_dst_nodes() == 4 bg = dgl.to_block(g_a, include_dst_in_src=False) check(g_a, bg, 'A', 'AA', None, False) check_features(g_a, bg) assert bg.number_of_src_nodes() == 4 assert bg.number_of_dst_nodes() == 4 dst_nodes = F.tensor([4, 3, 2, 1], dtype=idtype) bg = dgl.to_block(g_a, dst_nodes) check(g_a, bg, 'A', 'AA', dst_nodes) check_features(g_a, bg) g_ab = g['AB'] bg = dgl.to_block(g_ab) assert bg.idtype == idtype assert bg.number_of_nodes('SRC/B') == 4 assert F.array_equal(bg.srcnodes['B'].data[dgl.NID], bg.dstnodes['B'].data[dgl.NID]) assert bg.number_of_nodes('DST/A') == 0 checkall(g_ab, bg, None) check_features(g_ab, bg) dst_nodes = {'B': F.tensor([5, 6, 3, 1], dtype=idtype)} bg = dgl.to_block(g, dst_nodes) assert bg.number_of_nodes('SRC/B') == 4 assert F.array_equal(bg.srcnodes['B'].data[dgl.NID], bg.dstnodes['B'].data[dgl.NID]) assert bg.number_of_nodes('DST/A') == 0 checkall(g, bg, dst_nodes) check_features(g, bg) dst_nodes = {'A': F.tensor([4, 3, 2, 1], dtype=idtype), 'B': F.tensor([3, 5, 6, 1], dtype=idtype)} bg = dgl.to_block(g, dst_nodes=dst_nodes) checkall(g, bg, dst_nodes) check_features(g, bg) # test specifying lhs_nodes with include_dst_in_src src_nodes = {} for ntype in dst_nodes.keys(): # use the previous run to get the list of source nodes src_nodes[ntype] = bg.srcnodes[ntype].data[dgl.NID] bg = dgl.to_block(g, dst_nodes=dst_nodes, src_nodes=src_nodes) checkall(g, bg, dst_nodes) check_features(g, bg) # test without include_dst_in_src dst_nodes = {'A': F.tensor([4, 3, 2, 1], dtype=idtype), 'B': F.tensor([3, 5, 6, 1], dtype=idtype)} bg = dgl.to_block(g, dst_nodes=dst_nodes, include_dst_in_src=False) checkall(g, bg, dst_nodes, False) check_features(g, bg) # test specifying lhs_nodes without include_dst_in_src src_nodes = {} for ntype in dst_nodes.keys(): # use the previous run to get the list of source nodes src_nodes[ntype] = bg.srcnodes[ntype].data[dgl.NID] bg = dgl.to_block(g, dst_nodes=dst_nodes, include_dst_in_src=False, src_nodes=src_nodes) checkall(g, bg, dst_nodes, False) check_features(g, bg) @unittest.skipIf(F._default_context_str == 'gpu', reason="GPU not implemented") @parametrize_dtype def test_remove_edges(idtype): def check(g1, etype, g, edges_removed): src, dst, eid = g.edges(etype=etype, form='all') src1, dst1 = g1.edges(etype=etype, order='eid') if etype is not None: eid1 = g1.edges[etype].data[dgl.EID] else: eid1 = g1.edata[dgl.EID] src1 = F.asnumpy(src1) dst1 = F.asnumpy(dst1) eid1 = F.asnumpy(eid1) src = F.asnumpy(src) dst = F.asnumpy(dst) eid = F.asnumpy(eid) sde_set = set(zip(src, dst, eid)) for s, d, e in zip(src1, dst1, eid1): assert (s, d, e) in sde_set assert not np.isin(edges_removed, eid1).any() assert g1.idtype == g.idtype for fmt in ['coo', 'csr', 'csc']: for edges_to_remove in [[2], [2, 2], [3, 2], [1, 3, 1, 2]]: g = dgl.graph(([0, 2, 1, 3], [1, 3, 2, 4]), idtype=idtype).formats(fmt) g1 = dgl.remove_edges(g, F.tensor(edges_to_remove, idtype)) check(g1, None, g, edges_to_remove) g = dgl.from_scipy( spsp.csr_matrix(([1, 1, 1, 1], ([0, 2, 1, 3], [1, 3, 2, 4])), shape=(5, 5)), idtype=idtype).formats(fmt) g1 = dgl.remove_edges(g, F.tensor(edges_to_remove, idtype)) check(g1, None, g, edges_to_remove) g = dgl.heterograph({ ('A', 'AA', 'A'): ([0, 2, 1, 3], [1, 3, 2, 4]), ('A', 'AB', 'B'): ([0, 1, 3, 1], [1, 3, 5, 6]), ('B', 'BA', 'A'): ([2, 3], [3, 2])}, idtype=idtype) g2 = dgl.remove_edges(g, {'AA': F.tensor([2], idtype), 'AB': F.tensor([3], idtype), 'BA': F.tensor([1], idtype)}) check(g2, 'AA', g, [2]) check(g2, 'AB', g, [3]) check(g2, 'BA', g, [1]) g3 = dgl.remove_edges(g, {'AA': F.tensor([], idtype), 'AB': F.tensor([3], idtype), 'BA': F.tensor([1], idtype)}) check(g3, 'AA', g, []) check(g3, 'AB', g, [3]) check(g3, 'BA', g, [1]) g4 = dgl.remove_edges(g, {'AB': F.tensor([3, 1, 2, 0], idtype)}) check(g4, 'AA', g, []) check(g4, 'AB', g, [3, 1, 2, 0]) check(g4, 'BA', g, []) @parametrize_dtype def test_add_edges(idtype): # homogeneous graph g = dgl.graph(([0, 1], [1, 2]), idtype=idtype, device=F.ctx()) u = 0 v = 1 g = dgl.add_edges(g, u, v) assert g.device == F.ctx() assert g.number_of_nodes() == 3 assert g.number_of_edges() == 3 u = [0] v = [1] g = dgl.add_edges(g, u, v) assert g.device == F.ctx() assert g.number_of_nodes() == 3 assert g.number_of_edges() == 4 u = F.tensor(u, dtype=idtype) v = F.tensor(v, dtype=idtype) g = dgl.add_edges(g, u, v) assert g.device == F.ctx() assert g.number_of_nodes() == 3 assert g.number_of_edges() == 5 u, v = g.edges(form='uv', order='eid') assert F.array_equal(u, F.tensor([0, 1, 0, 0, 0], dtype=idtype)) assert F.array_equal(v, F.tensor([1, 2, 1, 1, 1], dtype=idtype)) g = dgl.add_edges(g, [], []) g = dgl.add_edges(g, 0, []) g = dgl.add_edges(g, [], 0) assert g.device == F.ctx() assert g.number_of_nodes() == 3 assert g.number_of_edges() == 5 u, v = g.edges(form='uv', order='eid') assert F.array_equal(u, F.tensor([0, 1, 0, 0, 0], dtype=idtype)) assert F.array_equal(v, F.tensor([1, 2, 1, 1, 1], dtype=idtype)) # node id larger than current max node id g = dgl.graph(([0, 1], [1, 2]), idtype=idtype, device=F.ctx()) u = F.tensor([0, 1], dtype=idtype) v = F.tensor([2, 3], dtype=idtype) g = dgl.add_edges(g, u, v) assert g.number_of_nodes() == 4 assert g.number_of_edges() == 4 u, v = g.edges(form='uv', order='eid') assert F.array_equal(u, F.tensor([0, 1, 0, 1], dtype=idtype)) assert F.array_equal(v, F.tensor([1, 2, 2, 3], dtype=idtype)) # has data g = dgl.graph(([0, 1], [1, 2]), idtype=idtype, device=F.ctx()) g.ndata['h'] = F.copy_to(F.tensor([1, 1, 1], dtype=idtype), ctx=F.ctx()) g.edata['h'] = F.copy_to(F.tensor([1, 1], dtype=idtype), ctx=F.ctx()) u = F.tensor([0, 1], dtype=idtype) v = F.tensor([2, 3], dtype=idtype) e_feat = {'h' : F.copy_to(F.tensor([2, 2], dtype=idtype), ctx=F.ctx()), 'hh' : F.copy_to(F.tensor([2, 2], dtype=idtype), ctx=F.ctx())} g = dgl.add_edges(g, u, v, e_feat) assert g.number_of_nodes() == 4 assert g.number_of_edges() == 4 u, v = g.edges(form='uv', order='eid') assert F.array_equal(u, F.tensor([0, 1, 0, 1], dtype=idtype)) assert F.array_equal(v, F.tensor([1, 2, 2, 3], dtype=idtype)) assert F.array_equal(g.ndata['h'], F.tensor([1, 1, 1, 0], dtype=idtype)) assert F.array_equal(g.edata['h'], F.tensor([1, 1, 2, 2], dtype=idtype)) assert F.array_equal(g.edata['hh'], F.tensor([0, 0, 2, 2], dtype=idtype)) # zero data graph g = dgl.graph(([], []), num_nodes=0, idtype=idtype, device=F.ctx()) u = F.tensor([0, 1], dtype=idtype) v = F.tensor([2, 2], dtype=idtype) e_feat = {'h' : F.copy_to(F.tensor([2, 2], dtype=idtype), ctx=F.ctx()), 'hh' : F.copy_to(F.tensor([2, 2], dtype=idtype), ctx=F.ctx())} g = dgl.add_edges(g, u, v, e_feat) assert g.number_of_nodes() == 3 assert g.number_of_edges() == 2 u, v = g.edges(form='uv', order='eid') assert F.array_equal(u, F.tensor([0, 1], dtype=idtype)) assert F.array_equal(v, F.tensor([2, 2], dtype=idtype)) assert F.array_equal(g.edata['h'], F.tensor([2, 2], dtype=idtype)) assert F.array_equal(g.edata['hh'], F.tensor([2, 2], dtype=idtype)) # bipartite graph g = dgl.heterograph( {('user', 'plays', 'game'): ([0, 1], [1, 2])}, idtype=idtype, device=F.ctx()) u = 0 v = 1 g = dgl.add_edges(g, u, v) assert g.device == F.ctx() assert g.number_of_nodes('user') == 2 assert g.number_of_nodes('game') == 3 assert g.number_of_edges() == 3 u = [0] v = [1] g = dgl.add_edges(g, u, v) assert g.device == F.ctx() assert g.number_of_nodes('user') == 2 assert g.number_of_nodes('game') == 3 assert g.number_of_edges() == 4 u = F.tensor(u, dtype=idtype) v = F.tensor(v, dtype=idtype) g = dgl.add_edges(g, u, v) assert g.device == F.ctx() assert g.number_of_nodes('user') == 2 assert g.number_of_nodes('game') == 3 assert g.number_of_edges() == 5 u, v = g.edges(form='uv') assert F.array_equal(u, F.tensor([0, 1, 0, 0, 0], dtype=idtype)) assert F.array_equal(v, F.tensor([1, 2, 1, 1, 1], dtype=idtype)) # node id larger than current max node id g = dgl.heterograph( {('user', 'plays', 'game'): ([0, 1], [1, 2])}, idtype=idtype, device=F.ctx()) u = F.tensor([0, 2], dtype=idtype) v = F.tensor([2, 3], dtype=idtype) g = dgl.add_edges(g, u, v) assert g.device == F.ctx() assert g.number_of_nodes('user') == 3 assert g.number_of_nodes('game') == 4 assert g.number_of_edges() == 4 u, v = g.edges(form='uv', order='eid') assert F.array_equal(u, F.tensor([0, 1, 0, 2], dtype=idtype)) assert F.array_equal(v, F.tensor([1, 2, 2, 3], dtype=idtype)) # has data g = dgl.heterograph( {('user', 'plays', 'game'): ([0, 1], [1, 2])}, idtype=idtype, device=F.ctx()) g.nodes['user'].data['h'] = F.copy_to(F.tensor([1, 1], dtype=idtype), ctx=F.ctx()) g.nodes['game'].data['h'] = F.copy_to(F.tensor([2, 2, 2], dtype=idtype), ctx=F.ctx()) g.edata['h'] = F.copy_to(F.tensor([1, 1], dtype=idtype), ctx=F.ctx()) u = F.tensor([0, 2], dtype=idtype) v = F.tensor([2, 3], dtype=idtype) e_feat = {'h' : F.copy_to(F.tensor([2, 2], dtype=idtype), ctx=F.ctx()), 'hh' : F.copy_to(F.tensor([2, 2], dtype=idtype), ctx=F.ctx())} g = dgl.add_edges(g, u, v, e_feat) assert g.number_of_nodes('user') == 3 assert g.number_of_nodes('game') == 4 assert g.number_of_edges() == 4 u, v = g.edges(form='uv', order='eid') assert F.array_equal(u, F.tensor([0, 1, 0, 2], dtype=idtype)) assert F.array_equal(v, F.tensor([1, 2, 2, 3], dtype=idtype)) assert F.array_equal(g.nodes['user'].data['h'], F.tensor([1, 1, 0], dtype=idtype)) assert F.array_equal(g.nodes['game'].data['h'], F.tensor([2, 2, 2, 0], dtype=idtype)) assert F.array_equal(g.edata['h'], F.tensor([1, 1, 2, 2], dtype=idtype)) assert F.array_equal(g.edata['hh'], F.tensor([0, 0, 2, 2], dtype=idtype)) # heterogeneous graph g = create_test_heterograph3(idtype) u = F.tensor([0, 2], dtype=idtype) v = F.tensor([2, 3], dtype=idtype) g = dgl.add_edges(g, u, v, etype='plays') assert g.number_of_nodes('user') == 3 assert g.number_of_nodes('game') == 4 assert g.number_of_nodes('developer') == 2 assert g.number_of_edges('plays') == 6 assert g.number_of_edges('develops') == 2 u, v = g.edges(form='uv', order='eid', etype='plays') assert F.array_equal(u, F.tensor([0, 1, 1, 2, 0, 2], dtype=idtype)) assert F.array_equal(v, F.tensor([0, 0, 1, 1, 2, 3], dtype=idtype)) assert F.array_equal(g.nodes['user'].data['h'], F.tensor([1, 1, 1], dtype=idtype)) assert F.array_equal(g.nodes['game'].data['h'], F.tensor([2, 2, 0, 0], dtype=idtype)) assert F.array_equal(g.edges['plays'].data['h'], F.tensor([1, 1, 1, 1, 0, 0], dtype=idtype)) # add with feature e_feat = {'h': F.copy_to(F.tensor([2, 2], dtype=idtype), ctx=F.ctx())} u = F.tensor([0, 2], dtype=idtype) v = F.tensor([2, 3], dtype=idtype) g.nodes['game'].data['h'] = F.copy_to(F.tensor([2, 2, 1, 1], dtype=idtype), ctx=F.ctx()) g = dgl.add_edges(g, u, v, data=e_feat, etype='develops') assert g.number_of_nodes('user') == 3 assert g.number_of_nodes('game') == 4 assert g.number_of_nodes('developer') == 3 assert g.number_of_edges('plays') == 6 assert g.number_of_edges('develops') == 4 u, v = g.edges(form='uv', order='eid', etype='develops') assert F.array_equal(u, F.tensor([0, 1, 0, 2], dtype=idtype)) assert F.array_equal(v, F.tensor([0, 1, 2, 3], dtype=idtype)) assert F.array_equal(g.nodes['developer'].data['h'], F.tensor([3, 3, 0], dtype=idtype)) assert F.array_equal(g.nodes['game'].data['h'], F.tensor([2, 2, 1, 1], dtype=idtype)) assert F.array_equal(g.edges['develops'].data['h'], F.tensor([0, 0, 2, 2], dtype=idtype)) @parametrize_dtype def test_add_nodes(idtype): # homogeneous Graphs g = dgl.graph(([0, 1], [1, 2]), idtype=idtype, device=F.ctx()) g.ndata['h'] = F.copy_to(F.tensor([1,1,1], dtype=idtype), ctx=F.ctx()) new_g = dgl.add_nodes(g, 1) assert g.number_of_nodes() == 3 assert new_g.number_of_nodes() == 4 assert F.array_equal(new_g.ndata['h'], F.tensor([1, 1, 1, 0], dtype=idtype)) # zero node graph g = dgl.graph(([], []), num_nodes=3, idtype=idtype, device=F.ctx()) g.ndata['h'] = F.copy_to(F.tensor([1,1,1], dtype=idtype), ctx=F.ctx()) g = dgl.add_nodes(g, 1, data={'h' : F.copy_to(F.tensor([2], dtype=idtype), ctx=F.ctx())}) assert g.number_of_nodes() == 4 assert F.array_equal(g.ndata['h'], F.tensor([1, 1, 1, 2], dtype=idtype)) # bipartite graph g = dgl.heterograph( {('user', 'plays', 'game'): ([0, 1], [1, 2])}, idtype=idtype, device=F.ctx()) g = dgl.add_nodes(g, 2, data={'h' : F.copy_to(F.tensor([2, 2], dtype=idtype), ctx=F.ctx())}, ntype='user') assert g.number_of_nodes('user') == 4 assert g.number_of_nodes('game') == 3 assert F.array_equal(g.nodes['user'].data['h'], F.tensor([0, 0, 2, 2], dtype=idtype)) g = dgl.add_nodes(g, 2, ntype='game') assert g.number_of_nodes('user') == 4 assert g.number_of_nodes('game') == 5 # heterogeneous graph g = create_test_heterograph3(idtype) g = dgl.add_nodes(g, 1, ntype='user') g = dgl.add_nodes(g, 2, data={'h' : F.copy_to(F.tensor([2, 2], dtype=idtype), ctx=F.ctx())}, ntype='game') assert g.number_of_nodes('user') == 4 assert g.number_of_nodes('game') == 4 assert g.number_of_nodes('developer') == 2 assert F.array_equal(g.nodes['user'].data['h'], F.tensor([1, 1, 1, 0], dtype=idtype)) assert F.array_equal(g.nodes['game'].data['h'], F.tensor([2, 2, 2, 2], dtype=idtype)) @parametrize_dtype def test_remove_edges(idtype): # homogeneous Graphs g = dgl.graph(([0, 1], [1, 2]), idtype=idtype, device=F.ctx()) e = 0 g = dgl.remove_edges(g, e) assert g.number_of_edges() == 1 u, v = g.edges(form='uv', order='eid') assert F.array_equal(u, F.tensor([1], dtype=idtype)) assert F.array_equal(v, F.tensor([2], dtype=idtype)) g = dgl.graph(([0, 1], [1, 2]), idtype=idtype, device=F.ctx()) e = [0] g = dgl.remove_edges(g, e) assert g.number_of_edges() == 1 u, v = g.edges(form='uv', order='eid') assert F.array_equal(u, F.tensor([1], dtype=idtype)) assert F.array_equal(v, F.tensor([2], dtype=idtype)) e = F.tensor([0], dtype=idtype) g = dgl.remove_edges(g, e) assert g.number_of_edges() == 0 # has node data g = dgl.graph(([0, 1], [1, 2]), idtype=idtype, device=F.ctx()) g.ndata['h'] = F.copy_to(F.tensor([1, 2, 3], dtype=idtype), ctx=F.ctx()) g = dgl.remove_edges(g, 1) assert g.number_of_edges() == 1 assert F.array_equal(g.ndata['h'], F.tensor([1, 2, 3], dtype=idtype)) # has edge data g = dgl.graph(([0, 1], [1, 2]), idtype=idtype, device=F.ctx()) g.edata['h'] = F.copy_to(F.tensor([1, 2], dtype=idtype), ctx=F.ctx()) g = dgl.remove_edges(g, 0) assert g.number_of_edges() == 1 assert F.array_equal(g.edata['h'], F.tensor([2], dtype=idtype)) # invalid eid assert_fail = False try: g = dgl.remove_edges(g, 1) except: assert_fail = True assert assert_fail # bipartite graph g = dgl.heterograph( {('user', 'plays', 'game'): ([0, 1], [1, 2])}, idtype=idtype, device=F.ctx()) e = 0 g = dgl.remove_edges(g, e) assert g.number_of_edges() == 1 u, v = g.edges(form='uv', order='eid') assert F.array_equal(u, F.tensor([1], dtype=idtype)) assert F.array_equal(v, F.tensor([2], dtype=idtype)) g = dgl.heterograph( {('user', 'plays', 'game'): ([0, 1], [1, 2])}, idtype=idtype, device=F.ctx()) e = [0] g = dgl.remove_edges(g, e) assert g.number_of_edges() == 1 u, v = g.edges(form='uv', order='eid') assert F.array_equal(u, F.tensor([1], dtype=idtype)) assert F.array_equal(v, F.tensor([2], dtype=idtype)) e = F.tensor([0], dtype=idtype) g = dgl.remove_edges(g, e) assert g.number_of_edges() == 0 # has data g = dgl.heterograph( {('user', 'plays', 'game'): ([0, 1], [1, 2])}, idtype=idtype, device=F.ctx()) g.nodes['user'].data['h'] = F.copy_to(F.tensor([1, 1], dtype=idtype), ctx=F.ctx()) g.nodes['game'].data['h'] = F.copy_to(F.tensor([2, 2, 2], dtype=idtype), ctx=F.ctx()) g.edata['h'] = F.copy_to(F.tensor([1, 2], dtype=idtype), ctx=F.ctx()) g = dgl.remove_edges(g, 1) assert g.number_of_edges() == 1 assert F.array_equal(g.nodes['user'].data['h'], F.tensor([1, 1], dtype=idtype)) assert F.array_equal(g.nodes['game'].data['h'], F.tensor([2, 2, 2], dtype=idtype)) assert F.array_equal(g.edata['h'], F.tensor([1], dtype=idtype)) # heterogeneous graph g = create_test_heterograph3(idtype) g.edges['plays'].data['h'] = F.copy_to(F.tensor([1, 2, 3, 4], dtype=idtype), ctx=F.ctx()) g = dgl.remove_edges(g, 1, etype='plays') assert g.number_of_edges('plays') == 3 u, v = g.edges(form='uv', order='eid', etype='plays') assert F.array_equal(u, F.tensor([0, 1, 2], dtype=idtype)) assert F.array_equal(v, F.tensor([0, 1, 1], dtype=idtype)) assert F.array_equal(g.edges['plays'].data['h'], F.tensor([1, 3, 4], dtype=idtype)) # remove all edges of 'develops' g = dgl.remove_edges(g, [0, 1], etype='develops') assert g.number_of_edges('develops') == 0 assert F.array_equal(g.nodes['user'].data['h'], F.tensor([1, 1, 1], dtype=idtype)) assert F.array_equal(g.nodes['game'].data['h'], F.tensor([2, 2], dtype=idtype)) assert F.array_equal(g.nodes['developer'].data['h'], F.tensor([3, 3], dtype=idtype)) # batched graph ctx = F.ctx() g1 = dgl.graph(([0, 1], [1, 2]), num_nodes=5, idtype=idtype, device=ctx) g2 = dgl.graph(([], []), idtype=idtype, device=ctx) g3 = dgl.graph(([2, 3, 4], [3, 2, 1]), idtype=idtype, device=ctx) bg = dgl.batch([g1, g2, g3]) bg_r = dgl.remove_edges(bg, 2) assert bg.batch_size == bg_r.batch_size assert F.array_equal(bg.batch_num_nodes(), bg_r.batch_num_nodes()) assert F.array_equal(bg_r.batch_num_edges(), F.tensor([2, 0, 2], dtype=F.int64)) bg_r = dgl.remove_edges(bg, [0, 2]) assert bg.batch_size == bg_r.batch_size assert F.array_equal(bg.batch_num_nodes(), bg_r.batch_num_nodes()) assert F.array_equal(bg_r.batch_num_edges(), F.tensor([1, 0, 2], dtype=F.int64)) bg_r = dgl.remove_edges(bg, F.tensor([0, 2], dtype=idtype)) assert bg.batch_size == bg_r.batch_size assert F.array_equal(bg.batch_num_nodes(), bg_r.batch_num_nodes()) assert F.array_equal(bg_r.batch_num_edges(), F.tensor([1, 0, 2], dtype=F.int64)) # batched heterogeneous graph g1 = dgl.heterograph({ ('user', 'follows', 'user'): ([0, 1], [1, 2]), ('user', 'plays', 'game'): ([1, 3], [0, 1]) }, num_nodes_dict={'user': 4, 'game': 3}, idtype=idtype, device=ctx) g2 = dgl.heterograph({ ('user', 'follows', 'user'): ([0, 2], [3, 4]), ('user', 'plays', 'game'): ([], []) }, num_nodes_dict={'user': 6, 'game': 2}, idtype=idtype, device=ctx) g3 = dgl.heterograph({ ('user', 'follows', 'user'): ([], []), ('user', 'plays', 'game'): ([1, 2], [1, 2]) }, idtype=idtype, device=ctx) bg = dgl.batch([g1, g2, g3]) bg_r = dgl.remove_edges(bg, 1, etype='follows') assert bg.batch_size == bg_r.batch_size ntypes = bg.ntypes for nty in ntypes: assert F.array_equal(bg.batch_num_nodes(nty), bg_r.batch_num_nodes(nty)) assert F.array_equal(bg_r.batch_num_edges('follows'), F.tensor([1, 2, 0], dtype=F.int64)) assert F.array_equal(bg_r.batch_num_edges('plays'), bg.batch_num_edges('plays')) bg_r = dgl.remove_edges(bg, 2, etype='plays') assert bg.batch_size == bg_r.batch_size for nty in ntypes: assert F.array_equal(bg.batch_num_nodes(nty), bg_r.batch_num_nodes(nty)) assert F.array_equal(bg.batch_num_edges('follows'), bg_r.batch_num_edges('follows')) assert F.array_equal(bg_r.batch_num_edges('plays'), F.tensor([2, 0, 1], dtype=F.int64)) bg_r = dgl.remove_edges(bg, [0, 1, 3], etype='follows') assert bg.batch_size == bg_r.batch_size for nty in ntypes: assert F.array_equal(bg.batch_num_nodes(nty), bg_r.batch_num_nodes(nty)) assert F.array_equal(bg_r.batch_num_edges('follows'), F.tensor([0, 1, 0], dtype=F.int64)) assert F.array_equal(bg.batch_num_edges('plays'), bg_r.batch_num_edges('plays')) bg_r = dgl.remove_edges(bg, [1, 2], etype='plays') assert bg.batch_size == bg_r.batch_size for nty in ntypes: assert F.array_equal(bg.batch_num_nodes(nty), bg_r.batch_num_nodes(nty)) assert F.array_equal(bg.batch_num_edges('follows'), bg_r.batch_num_edges('follows')) assert F.array_equal(bg_r.batch_num_edges('plays'), F.tensor([1, 0, 1], dtype=F.int64)) bg_r = dgl.remove_edges(bg, F.tensor([0, 1, 3], dtype=idtype), etype='follows') assert bg.batch_size == bg_r.batch_size for nty in ntypes: assert F.array_equal(bg.batch_num_nodes(nty), bg_r.batch_num_nodes(nty)) assert F.array_equal(bg_r.batch_num_edges('follows'), F.tensor([0, 1, 0], dtype=F.int64)) assert F.array_equal(bg.batch_num_edges('plays'), bg_r.batch_num_edges('plays')) bg_r = dgl.remove_edges(bg, F.tensor([1, 2], dtype=idtype), etype='plays') assert bg.batch_size == bg_r.batch_size for nty in ntypes: assert F.array_equal(bg.batch_num_nodes(nty), bg_r.batch_num_nodes(nty)) assert F.array_equal(bg.batch_num_edges('follows'), bg_r.batch_num_edges('follows')) assert F.array_equal(bg_r.batch_num_edges('plays'), F.tensor([1, 0, 1], dtype=F.int64)) @parametrize_dtype def test_remove_nodes(idtype): # homogeneous Graphs g = dgl.graph(([0, 1], [1, 2]), idtype=idtype, device=F.ctx()) n = 0 g = dgl.remove_nodes(g, n) assert g.number_of_nodes() == 2 assert g.number_of_edges() == 1 u, v = g.edges(form='uv', order='eid') assert F.array_equal(u, F.tensor([0], dtype=idtype)) assert F.array_equal(v, F.tensor([1], dtype=idtype)) g = dgl.graph(([0, 1], [1, 2]), idtype=idtype, device=F.ctx()) n = [1] g = dgl.remove_nodes(g, n) assert g.number_of_nodes() == 2 assert g.number_of_edges() == 0 g = dgl.graph(([0, 1], [1, 2]), idtype=idtype, device=F.ctx()) n = F.tensor([2], dtype=idtype) g = dgl.remove_nodes(g, n) assert g.number_of_nodes() == 2 assert g.number_of_edges() == 1 u, v = g.edges(form='uv', order='eid') assert F.array_equal(u, F.tensor([0], dtype=idtype)) assert F.array_equal(v, F.tensor([1], dtype=idtype)) # invalid nid assert_fail = False try: g.remove_nodes(3) except: assert_fail = True assert assert_fail # has node and edge data g = dgl.graph(([0, 0, 2], [0, 1, 2]), idtype=idtype, device=F.ctx()) g.ndata['hv'] = F.copy_to(F.tensor([1, 2, 3], dtype=idtype), ctx=F.ctx()) g.edata['he'] = F.copy_to(F.tensor([1, 2, 3], dtype=idtype), ctx=F.ctx()) g = dgl.remove_nodes(g, F.tensor([0], dtype=idtype)) assert g.number_of_nodes() == 2 assert g.number_of_edges() == 1 u, v = g.edges(form='uv', order='eid') assert F.array_equal(u, F.tensor([1], dtype=idtype)) assert F.array_equal(v, F.tensor([1], dtype=idtype)) assert F.array_equal(g.ndata['hv'], F.tensor([2, 3], dtype=idtype)) assert F.array_equal(g.edata['he'], F.tensor([3], dtype=idtype)) # node id larger than current max node id g = dgl.heterograph( {('user', 'plays', 'game'): ([0, 1], [1, 2])}, idtype=idtype, device=F.ctx()) n = 0 g = dgl.remove_nodes(g, n, ntype='user') assert g.number_of_nodes('user') == 1 assert g.number_of_nodes('game') == 3 assert g.number_of_edges() == 1 u, v = g.edges(form='uv', order='eid') assert F.array_equal(u, F.tensor([0], dtype=idtype)) assert F.array_equal(v, F.tensor([2], dtype=idtype)) g = dgl.heterograph( {('user', 'plays', 'game'): ([0, 1], [1, 2])}, idtype=idtype, device=F.ctx()) n = [1] g = dgl.remove_nodes(g, n, ntype='user') assert g.number_of_nodes('user') == 1 assert g.number_of_nodes('game') == 3 assert g.number_of_edges() == 1 u, v = g.edges(form='uv', order='eid') assert F.array_equal(u, F.tensor([0], dtype=idtype)) assert F.array_equal(v, F.tensor([1], dtype=idtype)) g = dgl.heterograph( {('user', 'plays', 'game'): ([0, 1], [1, 2])}, idtype=idtype, device=F.ctx()) n = F.tensor([0], dtype=idtype) g = dgl.remove_nodes(g, n, ntype='game') assert g.number_of_nodes('user') == 2 assert g.number_of_nodes('game') == 2 assert g.number_of_edges() == 2 u, v = g.edges(form='uv', order='eid') assert F.array_equal(u, F.tensor([0, 1], dtype=idtype)) assert F.array_equal(v, F.tensor([0 ,1], dtype=idtype)) # heterogeneous graph g = create_test_heterograph3(idtype) g.edges['plays'].data['h'] = F.copy_to(F.tensor([1, 2, 3, 4], dtype=idtype), ctx=F.ctx()) g = dgl.remove_nodes(g, 0, ntype='game') assert g.number_of_nodes('user') == 3 assert g.number_of_nodes('game') == 1 assert g.number_of_nodes('developer') == 2 assert g.number_of_edges('plays') == 2 assert g.number_of_edges('develops') == 1 assert F.array_equal(g.nodes['user'].data['h'], F.tensor([1, 1, 1], dtype=idtype)) assert F.array_equal(g.nodes['game'].data['h'], F.tensor([2], dtype=idtype)) assert F.array_equal(g.nodes['developer'].data['h'], F.tensor([3, 3], dtype=idtype)) u, v = g.edges(form='uv', order='eid', etype='plays') assert F.array_equal(u, F.tensor([1, 2], dtype=idtype)) assert F.array_equal(v, F.tensor([0, 0], dtype=idtype)) assert F.array_equal(g.edges['plays'].data['h'], F.tensor([3, 4], dtype=idtype)) u, v = g.edges(form='uv', order='eid', etype='develops') assert F.array_equal(u, F.tensor([1], dtype=idtype)) assert F.array_equal(v, F.tensor([0], dtype=idtype)) # batched graph ctx = F.ctx() g1 = dgl.graph(([0, 1], [1, 2]), num_nodes=5, idtype=idtype, device=ctx) g2 = dgl.graph(([], []), idtype=idtype, device=ctx) g3 = dgl.graph(([2, 3, 4], [3, 2, 1]), idtype=idtype, device=ctx) bg = dgl.batch([g1, g2, g3]) bg_r = dgl.remove_nodes(bg, 1) assert bg_r.batch_size == bg.batch_size assert F.array_equal(bg_r.batch_num_nodes(), F.tensor([4, 0, 5], dtype=F.int64)) assert F.array_equal(bg_r.batch_num_edges(), F.tensor([0, 0, 3], dtype=F.int64)) bg_r = dgl.remove_nodes(bg, [1, 7]) assert bg_r.batch_size == bg.batch_size assert F.array_equal(bg_r.batch_num_nodes(), F.tensor([4, 0, 4], dtype=F.int64)) assert F.array_equal(bg_r.batch_num_edges(), F.tensor([0, 0, 1], dtype=F.int64)) bg_r = dgl.remove_nodes(bg, F.tensor([1, 7], dtype=idtype)) assert bg_r.batch_size == bg.batch_size assert F.array_equal(bg_r.batch_num_nodes(), F.tensor([4, 0, 4], dtype=F.int64)) assert F.array_equal(bg_r.batch_num_edges(), F.tensor([0, 0, 1], dtype=F.int64)) # batched heterogeneous graph g1 = dgl.heterograph({ ('user', 'follows', 'user'): ([0, 1], [1, 2]), ('user', 'plays', 'game'): ([1, 3], [0, 1]) }, num_nodes_dict={'user': 4, 'game': 3}, idtype=idtype, device=ctx) g2 = dgl.heterograph({ ('user', 'follows', 'user'): ([0, 2], [3, 4]), ('user', 'plays', 'game'): ([], []) }, num_nodes_dict={'user': 6, 'game': 2}, idtype=idtype, device=ctx) g3 = dgl.heterograph({ ('user', 'follows', 'user'): ([], []), ('user', 'plays', 'game'): ([1, 2], [1, 2]) }, idtype=idtype, device=ctx) bg = dgl.batch([g1, g2, g3]) bg_r = dgl.remove_nodes(bg, 1, ntype='user') assert bg_r.batch_size == bg.batch_size assert F.array_equal(bg_r.batch_num_nodes('user'), F.tensor([3, 6, 3], dtype=F.int64)) assert F.array_equal(bg.batch_num_nodes('game'), bg_r.batch_num_nodes('game')) assert F.array_equal(bg_r.batch_num_edges('follows'), F.tensor([0, 2, 0], dtype=F.int64)) assert F.array_equal(bg_r.batch_num_edges('plays'), F.tensor([1, 0, 2], dtype=F.int64)) bg_r = dgl.remove_nodes(bg, 6, ntype='game') assert bg_r.batch_size == bg.batch_size assert F.array_equal(bg.batch_num_nodes('user'), bg_r.batch_num_nodes('user')) assert F.array_equal(bg_r.batch_num_nodes('game'), F.tensor([3, 2, 2], dtype=F.int64)) assert F.array_equal(bg.batch_num_edges('follows'), bg_r.batch_num_edges('follows')) assert F.array_equal(bg_r.batch_num_edges('plays'), F.tensor([2, 0, 1], dtype=F.int64)) bg_r = dgl.remove_nodes(bg, [1, 5, 6, 11], ntype='user') assert bg_r.batch_size == bg.batch_size assert F.array_equal(bg_r.batch_num_nodes('user'), F.tensor([3, 4, 2], dtype=F.int64)) assert F.array_equal(bg.batch_num_nodes('game'), bg_r.batch_num_nodes('game')) assert F.array_equal(bg_r.batch_num_edges('follows'), F.tensor([0, 1, 0], dtype=F.int64)) assert F.array_equal(bg_r.batch_num_edges('plays'), F.tensor([1, 0, 1], dtype=F.int64)) bg_r = dgl.remove_nodes(bg, [0, 3, 4, 7], ntype='game') assert bg_r.batch_size == bg.batch_size assert F.array_equal(bg.batch_num_nodes('user'), bg_r.batch_num_nodes('user')) assert F.array_equal(bg_r.batch_num_nodes('game'), F.tensor([2, 0, 2], dtype=F.int64)) assert F.array_equal(bg.batch_num_edges('follows'), bg_r.batch_num_edges('follows')) assert F.array_equal(bg_r.batch_num_edges('plays'), F.tensor([1, 0, 1], dtype=F.int64)) bg_r = dgl.remove_nodes(bg, F.tensor([1, 5, 6, 11], dtype=idtype), ntype='user') assert bg_r.batch_size == bg.batch_size assert F.array_equal(bg_r.batch_num_nodes('user'), F.tensor([3, 4, 2], dtype=F.int64)) assert F.array_equal(bg.batch_num_nodes('game'), bg_r.batch_num_nodes('game')) assert F.array_equal(bg_r.batch_num_edges('follows'), F.tensor([0, 1, 0], dtype=F.int64)) assert F.array_equal(bg_r.batch_num_edges('plays'), F.tensor([1, 0, 1], dtype=F.int64)) bg_r = dgl.remove_nodes(bg, F.tensor([0, 3, 4, 7], dtype=idtype), ntype='game') assert bg_r.batch_size == bg.batch_size assert F.array_equal(bg.batch_num_nodes('user'), bg_r.batch_num_nodes('user')) assert F.array_equal(bg_r.batch_num_nodes('game'), F.tensor([2, 0, 2], dtype=F.int64)) assert F.array_equal(bg.batch_num_edges('follows'), bg_r.batch_num_edges('follows')) assert F.array_equal(bg_r.batch_num_edges('plays'), F.tensor([1, 0, 1], dtype=F.int64)) @parametrize_dtype def test_add_selfloop(idtype): # homogeneous graph g = dgl.graph(([0, 0, 2], [2, 1, 0]), idtype=idtype, device=F.ctx()) g.edata['he'] = F.copy_to(F.tensor([1, 2, 3], dtype=idtype), ctx=F.ctx()) g.ndata['hn'] = F.copy_to(F.tensor([1, 2, 3], dtype=idtype), ctx=F.ctx()) g = dgl.add_self_loop(g) assert g.number_of_nodes() == 3 assert g.number_of_edges() == 6 u, v = g.edges(form='uv', order='eid') assert F.array_equal(u, F.tensor([0, 0, 2, 0, 1, 2], dtype=idtype)) assert F.array_equal(v, F.tensor([2, 1, 0, 0, 1, 2], dtype=idtype)) assert F.array_equal(g.edata['he'], F.tensor([1, 2, 3, 0, 0, 0], dtype=idtype)) # bipartite graph g = dgl.heterograph( {('user', 'plays', 'game'): ([0, 1, 2], [1, 2, 2])}, idtype=idtype, device=F.ctx()) # nothing will happend raise_error = False try: g = dgl.add_self_loop(g) except: raise_error = True assert raise_error g = create_test_heterograph5(idtype) g = dgl.add_self_loop(g, etype='follows') assert g.number_of_nodes('user') == 3 assert g.number_of_nodes('game') == 2 assert g.number_of_edges('follows') == 5 assert g.number_of_edges('plays') == 2 u, v = g.edges(form='uv', order='eid', etype='follows') assert F.array_equal(u, F.tensor([1, 2, 0, 1, 2], dtype=idtype)) assert F.array_equal(v, F.tensor([0, 1, 0, 1, 2], dtype=idtype)) assert F.array_equal(g.edges['follows'].data['h'], F.tensor([1, 2, 0, 0, 0], dtype=idtype)) assert F.array_equal(g.edges['plays'].data['h'], F.tensor([1, 2], dtype=idtype)) raise_error = False try: g = dgl.add_self_loop(g, etype='plays') except: raise_error = True assert raise_error @parametrize_dtype def test_remove_selfloop(idtype): # homogeneous graph g = dgl.graph(([0, 0, 0, 1], [1, 0, 0, 2]), idtype=idtype, device=F.ctx()) g.edata['he'] = F.copy_to(F.tensor([1, 2, 3, 4], dtype=idtype), ctx=F.ctx()) g = dgl.remove_self_loop(g) assert g.number_of_nodes() == 3 assert g.number_of_edges() == 2 assert F.array_equal(g.edata['he'], F.tensor([1, 4], dtype=idtype)) # bipartite graph g = dgl.heterograph( {('user', 'plays', 'game'): ([0, 1, 2], [1, 2, 2])}, idtype=idtype, device=F.ctx()) # nothing will happend raise_error = False try: g = dgl.remove_self_loop(g, etype='plays') except: raise_error = True assert raise_error g = create_test_heterograph4(idtype) g = dgl.remove_self_loop(g, etype='follows') assert g.number_of_nodes('user') == 3 assert g.number_of_nodes('game') == 2 assert g.number_of_edges('follows') == 2 assert g.number_of_edges('plays') == 2 u, v = g.edges(form='uv', order='eid', etype='follows') assert F.array_equal(u, F.tensor([1, 2], dtype=idtype)) assert F.array_equal(v, F.tensor([0, 1], dtype=idtype)) assert F.array_equal(g.edges['follows'].data['h'], F.tensor([2, 4], dtype=idtype)) assert F.array_equal(g.edges['plays'].data['h'], F.tensor([1, 2], dtype=idtype)) raise_error = False try: g = dgl.remove_self_loop(g, etype='plays') except: raise_error = True assert raise_error @parametrize_dtype def test_reorder_graph(idtype): g = dgl.graph(([0, 1, 2, 3, 4], [2, 2, 3, 2, 3]), idtype=idtype, device=F.ctx()) g.ndata['h'] = F.copy_to(F.randn((g.num_nodes(), 3)), ctx=F.ctx()) g.edata['w'] = F.copy_to(F.randn((g.num_edges(), 2)), ctx=F.ctx()) # call with default: node_permute_algo=None, edge_permute_algo='src' rg = dgl.reorder_graph(g) assert dgl.EID in rg.edata.keys() src = F.asnumpy(rg.edges()[0]) assert np.array_equal(src, np.sort(src)) # call with 'rcmk' node_permute_algo rg = dgl.reorder_graph(g, node_permute_algo='rcmk') assert dgl.NID in rg.ndata.keys() assert dgl.EID in rg.edata.keys() src = F.asnumpy(rg.edges()[0]) assert np.array_equal(src, np.sort(src)) # call with 'dst' edge_permute_algo rg = dgl.reorder_graph(g, edge_permute_algo='dst') dst = F.asnumpy(rg.edges()[1]) assert np.array_equal(dst, np.sort(dst)) # call with unknown edge_permute_algo raise_error = False try: dgl.reorder_graph(g, edge_permute_algo='none') except: raise_error = True assert raise_error # reorder back to original according to stored ids rg = dgl.reorder_graph(g, node_permute_algo='rcmk') rg2 = dgl.reorder_graph(rg, 'custom', permute_config={ 'nodes_perm': np.argsort(F.asnumpy(rg.ndata[dgl.NID]))}) assert F.array_equal(g.ndata['h'], rg2.ndata['h']) assert F.array_equal(g.edata['w'], rg2.edata['w']) # do not store ids rg = dgl.reorder_graph(g, store_ids=False) assert not dgl.NID in rg.ndata.keys() assert not dgl.EID in rg.edata.keys() # metis does not work on windows. if os.name == 'nt': pass else: # metis_partition may fail for small graph. mg = create_large_graph(1000).to(F.ctx()) # call with metis strategy, but k is not specified raise_error = False try: dgl.reorder_graph(mg, node_permute_algo='metis') except: raise_error = True assert raise_error # call with metis strategy, k is specified raise_error = False try: dgl.reorder_graph(mg, node_permute_algo='metis', permute_config={'k': 2}) except: raise_error = True assert not raise_error # call with qualified nodes_perm specified nodes_perm = np.random.permutation(g.num_nodes()) raise_error = False try: dgl.reorder_graph(g, node_permute_algo='custom', permute_config={ 'nodes_perm': nodes_perm}) except: raise_error = True assert not raise_error # call with unqualified nodes_perm specified raise_error = False try: dgl.reorder_graph(g, node_permute_algo='custom', permute_config={ 'nodes_perm': nodes_perm[:g.num_nodes() - 1]}) except: raise_error = True assert raise_error # call with unsupported strategy raise_error = False try: dgl.reorder_graph(g, node_permute_algo='cmk') except: raise_error = True assert raise_error # heterograph: not supported raise_error = False try: hg = dgl.heterogrpah({('user', 'follow', 'user'): ( [0, 1], [1, 2])}, idtype=idtype, device=F.ctx()) dgl.reorder_graph(hg) except: raise_error = True assert raise_error # TODO: shall we fix them? # add 'csc' format if needed #fg = g.formats('csr') #assert 'csc' not in sum(fg.formats().values(), []) #rfg = dgl.reorder_graph(fg) #assert 'csc' in sum(rfg.formats().values(), []) @unittest.skipIf(dgl.backend.backend_name == "tensorflow", reason="TF doesn't support a slicing operation") @parametrize_dtype def test_norm_by_dst(idtype): # Case1: A homogeneous graph g = dgl.graph(([0, 1, 1], [1, 1, 2]), idtype=idtype, device=F.ctx()) eweight = dgl.norm_by_dst(g) assert F.allclose(eweight, F.tensor([0.5, 0.5, 1.0])) # Case2: A heterogeneous graph g = dgl.heterograph({ ('user', 'follows', 'user'): ([0, 1], [1, 2]), ('user', 'plays', 'game'): ([0, 1, 1], [1, 1, 2]) }, idtype=idtype, device=F.ctx()) eweight = dgl.norm_by_dst(g, etype=('user', 'plays', 'game')) assert F.allclose(eweight, F.tensor([0.5, 0.5, 1.0])) @parametrize_dtype def test_module_add_self_loop(idtype): g = dgl.graph(([1, 1], [1, 2]), idtype=idtype, device=F.ctx()) g.ndata['h'] = F.randn((g.num_nodes(), 2)) g.edata['w'] = F.randn((g.num_edges(), 3)) # Case1: add self-loops with the default setting transform = dgl.AddSelfLoop() new_g = transform(g) assert new_g.device == g.device assert new_g.idtype == g.idtype assert new_g.num_nodes() == g.num_nodes() assert new_g.num_edges() == 4 src, dst = new_g.edges() eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst)))) assert eset == {(0, 0), (1, 1), (1, 2), (2, 2)} assert 'h' in new_g.ndata assert 'w' in new_g.edata # Case2: Remove self-loops first to avoid duplicate ones transform = dgl.AddSelfLoop(allow_duplicate=True) new_g = transform(g) assert new_g.device == g.device assert new_g.idtype == g.idtype assert new_g.num_nodes() == g.num_nodes() assert new_g.num_edges() == 5 src, dst = new_g.edges() eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst)))) assert eset == {(0, 0), (1, 1), (1, 2), (2, 2)} assert 'h' in new_g.ndata assert 'w' in new_g.edata # Create a heterogeneous graph g = dgl.heterograph({ ('user', 'plays', 'game'): ([0], [1]), ('user', 'follows', 'user'): ([1], [3]) }, idtype=idtype, device=F.ctx()) g.nodes['user'].data['h1'] = F.randn((4, 2)) g.edges['plays'].data['w1'] = F.randn((1, 3)) g.nodes['game'].data['h2'] = F.randn((2, 4)) g.edges['follows'].data['w2'] = F.randn((1, 5)) # Case3: add self-loops for a heterogeneous graph new_g = transform(g) assert new_g.device == g.device assert new_g.idtype == g.idtype assert new_g.ntypes == g.ntypes assert new_g.canonical_etypes == g.canonical_etypes for nty in new_g.ntypes: assert new_g.num_nodes(nty) == g.num_nodes(nty) assert new_g.num_edges('plays') == 1 assert new_g.num_edges('follows') == 5 assert 'h1' in new_g.nodes['user'].data assert 'h2' in new_g.nodes['game'].data assert 'w1' in new_g.edges['plays'].data assert 'w2' in new_g.edges['follows'].data # Case4: add self-etypes for a heterogeneous graph transform = dgl.AddSelfLoop(new_etypes=True) new_g = transform(g) assert new_g.device == g.device assert new_g.idtype == g.idtype assert new_g.ntypes == g.ntypes assert set(new_g.canonical_etypes) == { ('user', 'plays', 'game'), ('user', 'follows', 'user'), ('user', 'self', 'user'), ('game', 'self', 'game') } for nty in new_g.ntypes: assert new_g.num_nodes(nty) == g.num_nodes(nty) assert new_g.num_edges('plays') == 1 assert new_g.num_edges('follows') == 5 assert new_g.num_edges(('user', 'self', 'user')) == 4 assert new_g.num_edges(('game', 'self', 'game')) == 2 assert 'h1' in new_g.nodes['user'].data assert 'h2' in new_g.nodes['game'].data assert 'w1' in new_g.edges['plays'].data assert 'w2' in new_g.edges['follows'].data @parametrize_dtype def test_module_remove_self_loop(idtype): transform = dgl.RemoveSelfLoop() # Case1: homogeneous graph g = dgl.graph(([1, 1], [1, 2]), idtype=idtype, device=F.ctx()) g.ndata['h'] = F.randn((g.num_nodes(), 2)) g.edata['w'] = F.randn((g.num_edges(), 3)) new_g = transform(g) assert new_g.device == g.device assert new_g.idtype == g.idtype assert new_g.num_nodes() == g.num_nodes() assert new_g.num_edges() == 1 src, dst = new_g.edges() eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst)))) assert eset == {(1, 2)} assert 'h' in new_g.ndata assert 'w' in new_g.edata # Case2: heterogeneous graph g = dgl.heterograph({ ('user', 'plays', 'game'): ([0, 1], [1, 1]), ('user', 'follows', 'user'): ([1, 2], [2, 2]) }, idtype=idtype, device=F.ctx()) g.nodes['user'].data['h1'] = F.randn((3, 2)) g.edges['plays'].data['w1'] = F.randn((2, 3)) g.nodes['game'].data['h2'] = F.randn((2, 4)) g.edges['follows'].data['w2'] = F.randn((2, 5)) new_g = transform(g) assert new_g.device == g.device assert new_g.idtype == g.idtype assert new_g.ntypes == g.ntypes assert new_g.canonical_etypes == g.canonical_etypes for nty in new_g.ntypes: assert new_g.num_nodes(nty) == g.num_nodes(nty) assert new_g.num_edges('plays') == 2 assert new_g.num_edges('follows') == 1 assert 'h1' in new_g.nodes['user'].data assert 'h2' in new_g.nodes['game'].data assert 'w1' in new_g.edges['plays'].data assert 'w2' in new_g.edges['follows'].data @parametrize_dtype def test_module_add_reverse(idtype): transform = dgl.AddReverse() # Case1: Add reverse edges for a homogeneous graph g = dgl.graph(([0], [1]), idtype=idtype, device=F.ctx()) g.ndata['h'] = F.randn((g.num_nodes(), 3)) g.edata['w'] = F.randn((g.num_edges(), 2)) new_g = transform(g) assert new_g.device == g.device assert new_g.idtype == g.idtype assert g.num_nodes() == new_g.num_nodes() src, dst = new_g.edges() eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst)))) assert eset == {(0, 1), (1, 0)} assert F.allclose(g.ndata['h'], new_g.ndata['h']) assert F.allclose(g.edata['w'], F.narrow_row(new_g.edata['w'], 0, 1)) assert F.allclose(F.narrow_row(new_g.edata['w'], 1, 2), F.zeros((1, 2), F.float32, F.ctx())) # Case2: Add reverse edges for a homogeneous graph and copy edata transform = dgl.AddReverse(copy_edata=True) new_g = transform(g) assert new_g.device == g.device assert new_g.idtype == g.idtype assert g.num_nodes() == new_g.num_nodes() src, dst = new_g.edges() eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst)))) assert eset == {(0, 1), (1, 0)} assert F.allclose(g.ndata['h'], new_g.ndata['h']) assert F.allclose(g.edata['w'], F.narrow_row(new_g.edata['w'], 0, 1)) assert F.allclose(g.edata['w'], F.narrow_row(new_g.edata['w'], 1, 2)) # Case3: Add reverse edges for a heterogeneous graph g = dgl.heterograph({ ('user', 'plays', 'game'): ([0, 1], [1, 1]), ('user', 'follows', 'user'): ([1, 2], [2, 2]) }, device=F.ctx()) new_g = transform(g) assert new_g.device == g.device assert new_g.idtype == g.idtype assert g.ntypes == new_g.ntypes assert set(new_g.canonical_etypes) == { ('user', 'plays', 'game'), ('user', 'follows', 'user'), ('game', 'rev_plays', 'user')} for nty in g.ntypes: assert g.num_nodes(nty) == new_g.num_nodes(nty) src, dst = new_g.edges(etype='plays') eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst)))) assert eset == {(0, 1), (1, 1)} src, dst = new_g.edges(etype='follows') eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst)))) assert eset == {(1, 2), (2, 2), (2, 1)} src, dst = new_g.edges(etype='rev_plays') eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst)))) assert eset == {(1, 1), (1, 0)} # Case4: Enforce reverse edge types for symmetric canonical edge types transform = dgl.AddReverse(sym_new_etype=True) new_g = transform(g) assert new_g.device == g.device assert new_g.idtype == g.idtype assert g.ntypes == new_g.ntypes assert set(new_g.canonical_etypes) == { ('user', 'plays', 'game'), ('user', 'follows', 'user'), ('game', 'rev_plays', 'user'), ('user', 'rev_follows', 'user')} for nty in g.ntypes: assert g.num_nodes(nty) == new_g.num_nodes(nty) src, dst = new_g.edges(etype='plays') eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst)))) assert eset == {(0, 1), (1, 1)} src, dst = new_g.edges(etype='follows') eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst)))) assert eset == {(1, 2), (2, 2)} src, dst = new_g.edges(etype='rev_plays') eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst)))) assert eset == {(1, 1), (1, 0)} src, dst = new_g.edges(etype='rev_follows') eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst)))) assert eset == {(2, 1), (2, 2)} @unittest.skipIf(F._default_context_str == 'gpu', reason="GPU not supported for to_simple") @parametrize_dtype def test_module_to_simple(idtype): transform = dgl.ToSimple() g = dgl.graph(([0, 1, 1], [1, 2, 2]), idtype=idtype, device=F.ctx()) g.ndata['h'] = F.randn((g.num_nodes(), 2)) g.edata['w'] = F.tensor([[0.1], [0.2], [0.3]]) sg = transform(g) assert sg.device == g.device assert sg.idtype == g.idtype assert sg.num_nodes() == g.num_nodes() assert sg.num_edges() == 2 src, dst = sg.edges() eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst)))) assert eset == {(0, 1), (1, 2)} assert F.allclose(sg.edata['count'], F.tensor([1, 2])) assert F.allclose(sg.ndata['h'], g.ndata['h']) g = dgl.heterograph({ ('user', 'follows', 'user'): ([0, 1, 1], [1, 2, 2]), ('user', 'plays', 'game'): ([0, 1, 0], [1, 1, 1]) }) sg = transform(g) assert sg.device == g.device assert sg.idtype == g.idtype assert sg.ntypes == g.ntypes assert sg.canonical_etypes == g.canonical_etypes for nty in sg.ntypes: assert sg.num_nodes(nty) == g.num_nodes(nty) for ety in sg.canonical_etypes: assert sg.num_edges(ety) == 2 src, dst = sg.edges(etype='follows') eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst)))) assert eset == {(0, 1), (1, 2)} src, dst = sg.edges(etype='plays') eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst)))) assert eset == {(0, 1), (1, 1)} @parametrize_dtype def test_module_line_graph(idtype): transform = dgl.LineGraph() g = dgl.graph(([0, 1, 1], [1, 0, 2]), idtype=idtype, device=F.ctx()) g.ndata['h'] = F.tensor([[0.], [1.], [2.]]) g.edata['w'] = F.tensor([[0.], [0.1], [0.2]]) new_g = transform(g) assert new_g.device == g.device assert new_g.idtype == g.idtype assert new_g.num_nodes() == g.num_edges() src, dst = new_g.edges() eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst)))) assert eset == {(0, 1), (0, 2), (1, 0)} transform = dgl.LineGraph(backtracking=False) new_g = transform(g) assert new_g.device == g.device assert new_g.idtype == g.idtype assert new_g.num_nodes() == g.num_edges() src, dst = new_g.edges() eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst)))) assert eset == {(0, 2)} @parametrize_dtype def test_module_khop_graph(idtype): transform = dgl.KHopGraph(2) g = dgl.graph(([0, 1], [1, 2]), idtype=idtype, device=F.ctx()) g.ndata['h'] = F.randn((g.num_nodes(), 2)) new_g = transform(g) assert new_g.device == g.device assert new_g.idtype == g.idtype assert new_g.num_nodes() == g.num_nodes() assert F.allclose(g.ndata['h'], new_g.ndata['h']) src, dst = new_g.edges() eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst)))) assert eset == {(0, 2)} @parametrize_dtype def test_module_add_metapaths(idtype): g = dgl.heterograph({ ('person', 'author', 'paper'): ([0, 0, 1], [1, 2, 2]), ('paper', 'accepted', 'venue'): ([1], [0]), ('paper', 'rejected', 'venue'): ([2], [1]) }, idtype=idtype, device=F.ctx()) g.nodes['venue'].data['h'] = F.randn((g.num_nodes('venue'), 2)) g.edges['author'].data['h'] = F.randn((g.num_edges('author'), 3)) # Case1: keep_orig_edges is True metapaths = { 'accepted': [('person', 'author', 'paper'), ('paper', 'accepted', 'venue')], 'rejected': [('person', 'author', 'paper'), ('paper', 'rejected', 'venue')] } transform = dgl.AddMetaPaths(metapaths) new_g = transform(g) assert new_g.device == g.device assert new_g.idtype == g.idtype assert new_g.ntypes == g.ntypes assert set(new_g.canonical_etypes) == { ('person', 'author', 'paper'), ('paper', 'accepted', 'venue'), ('paper', 'rejected', 'venue'), ('person', 'accepted', 'venue'), ('person', 'rejected', 'venue') } for nty in new_g.ntypes: assert new_g.num_nodes(nty) == g.num_nodes(nty) for ety in g.canonical_etypes: assert new_g.num_edges(ety) == g.num_edges(ety) assert F.allclose(g.nodes['venue'].data['h'], new_g.nodes['venue'].data['h']) assert F.allclose(g.edges['author'].data['h'], new_g.edges['author'].data['h']) src, dst = new_g.edges(etype=('person', 'accepted', 'venue')) eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst)))) assert eset == {(0, 0)} src, dst = new_g.edges(etype=('person', 'rejected', 'venue')) eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst)))) assert eset == {(0, 1), (1, 1)} # Case2: keep_orig_edges is False transform = dgl.AddMetaPaths(metapaths, keep_orig_edges=False) new_g = transform(g) assert new_g.device == g.device assert new_g.idtype == g.idtype assert new_g.ntypes == g.ntypes assert len(new_g.canonical_etypes) == 2 for nty in new_g.ntypes: assert new_g.num_nodes(nty) == g.num_nodes(nty) assert F.allclose(g.nodes['venue'].data['h'], new_g.nodes['venue'].data['h']) src, dst = new_g.edges(etype=('person', 'accepted', 'venue')) eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst)))) assert eset == {(0, 0)} src, dst = new_g.edges(etype=('person', 'rejected', 'venue')) eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst)))) assert eset == {(0, 1), (1, 1)} @parametrize_dtype def test_module_compose(idtype): g = dgl.graph(([0, 1], [1, 2]), idtype=idtype, device=F.ctx()) transform = dgl.Compose([dgl.AddReverse(), dgl.AddSelfLoop()]) new_g = transform(g) assert new_g.device == g.device assert new_g.idtype == g.idtype assert new_g.num_edges() == 7 src, dst = new_g.edges() eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst)))) assert eset == {(0, 1), (1, 2), (1, 0), (2, 1), (0, 0), (1, 1), (2, 2)} @parametrize_dtype def test_module_gcnnorm(idtype): g = dgl.heterograph({ ('A', 'r1', 'A'): ([0, 1, 2], [0, 0, 1]), ('A', 'r2', 'B'): ([0, 0], [1, 1]), ('B', 'r3', 'B'): ([0, 1, 2], [0, 0, 1]) }, idtype=idtype, device=F.ctx()) g.edges['r3'].data['w'] = F.tensor([0.1, 0.2, 0.3]) transform = dgl.GCNNorm() new_g = transform(g) assert 'w' not in new_g.edges[('A', 'r2', 'B')].data assert F.allclose(new_g.edges[('A', 'r1', 'A')].data['w'], F.tensor([1./2, 1./math.sqrt(2), 0.])) assert F.allclose(new_g.edges[('B', 'r3', 'B')].data['w'], F.tensor([1./3, 2./3, 0.])) @unittest.skipIf(dgl.backend.backend_name != 'pytorch', reason='Only support PyTorch for now') @parametrize_dtype def test_module_ppr(idtype): g = dgl.graph(([0, 1, 2, 3, 4], [2, 3, 4, 5, 3]), idtype=idtype, device=F.ctx()) g.ndata['h'] = F.randn((6, 2)) transform = dgl.PPR(avg_degree=2) new_g = transform(g) assert new_g.idtype == g.idtype assert new_g.device == g.device assert new_g.num_nodes() == g.num_nodes() src, dst = new_g.edges() eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst)))) assert eset == {(0, 0), (0, 2), (0, 4), (1, 1), (1, 3), (1, 5), (2, 2), (2, 3), (2, 4), (3, 3), (3, 5), (4, 3), (4, 4), (4, 5), (5, 5)} assert F.allclose(g.ndata['h'], new_g.ndata['h']) assert 'w' in new_g.edata # Prior edge weights g.edata['w'] = F.tensor([0.1, 0.2, 0.3, 0.4, 0.5]) new_g = transform(g) src, dst = new_g.edges() eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst)))) assert eset == {(0, 0), (1, 1), (1, 3), (2, 2), (2, 3), (2, 4), (3, 3), (3, 5), (4, 3), (4, 4), (4, 5), (5, 5)} @unittest.skipIf(dgl.backend.backend_name != 'pytorch', reason='Only support PyTorch for now') @parametrize_dtype def test_module_heat_kernel(idtype): # Case1: directed graph g = dgl.graph(([0, 1, 2, 3, 4], [2, 3, 4, 5, 3]), idtype=idtype, device=F.ctx()) g.ndata['h'] = F.randn((6, 2)) transform = dgl.HeatKernel(avg_degree=1) new_g = transform(g) assert new_g.idtype == g.idtype assert new_g.device == g.device assert new_g.num_nodes() == g.num_nodes() assert F.allclose(g.ndata['h'], new_g.ndata['h']) assert 'w' in new_g.edata # Case2: weighted undirected graph g = dgl.graph(([0, 1, 2, 3], [1, 0, 3, 2]), idtype=idtype, device=F.ctx()) g.edata['w'] = F.tensor([0.1, 0.2, 0.3, 0.4]) new_g = transform(g) src, dst = new_g.edges() eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst)))) assert eset == {(0, 0), (1, 1), (2, 2), (3, 3)} @unittest.skipIf(dgl.backend.backend_name != 'pytorch', reason='Only support PyTorch for now') @parametrize_dtype def test_module_gdc(idtype): transform = dgl.GDC([0.1, 0.2, 0.1], avg_degree=1) g = dgl.graph(([0, 1, 2, 3, 4], [2, 3, 4, 5, 3]), idtype=idtype, device=F.ctx()) g.ndata['h'] = F.randn((6, 2)) new_g = transform(g) assert new_g.idtype == g.idtype assert new_g.device == g.device assert new_g.num_nodes() == g.num_nodes() src, dst = new_g.edges() eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst)))) assert eset == {(0, 0), (0, 2), (0, 4), (1, 1), (1, 3), (1, 5), (2, 2), (2, 3), (2, 4), (3, 3), (3, 5), (4, 3), (4, 4), (4, 5), (5, 5)} assert F.allclose(g.ndata['h'], new_g.ndata['h']) assert 'w' in new_g.edata # Prior edge weights g.edata['w'] = F.tensor([0.1, 0.2, 0.3, 0.4, 0.5]) new_g = transform(g) src, dst = new_g.edges() eset = set(zip(list(F.asnumpy(src)), list(F.asnumpy(dst)))) assert eset == {(0, 0), (1, 1), (2, 2), (3, 3), (4, 3), (4, 4), (5, 5)} @parametrize_dtype def test_module_node_shuffle(idtype): transform = dgl.NodeShuffle() g = dgl.heterograph({ ('A', 'r', 'B'): ([0, 1], [1, 2]), }, idtype=idtype, device=F.ctx()) new_g = transform(g) @unittest.skipIf(dgl.backend.backend_name != 'pytorch', reason='Only support PyTorch for now') @parametrize_dtype def test_module_drop_node(idtype): transform = dgl.DropNode() g = dgl.heterograph({ ('A', 'r', 'B'): ([0, 1], [1, 2]), }, idtype=idtype, device=F.ctx()) new_g = transform(g) assert new_g.idtype == g.idtype assert new_g.device == g.device assert new_g.ntypes == g.ntypes assert new_g.canonical_etypes == g.canonical_etypes @unittest.skipIf(dgl.backend.backend_name != 'pytorch', reason='Only support PyTorch for now') @parametrize_dtype def test_module_drop_edge(idtype): transform = dgl.DropEdge() g = dgl.heterograph({ ('A', 'r1', 'B'): ([0, 1], [1, 2]), ('C', 'r2', 'C'): ([3, 4, 5], [6, 7, 8]) }, idtype=idtype, device=F.ctx()) new_g = transform(g) assert new_g.idtype == g.idtype assert new_g.device == g.device assert new_g.ntypes == g.ntypes assert new_g.canonical_etypes == g.canonical_etypes @parametrize_dtype def test_module_add_edge(idtype): transform = dgl.AddEdge() g = dgl.heterograph({ ('A', 'r1', 'B'): ([0, 1, 2, 3, 4], [1, 2, 3, 4, 5]), ('C', 'r2', 'C'): ([0, 1, 2, 3, 4], [1, 2, 3, 4, 5]) }, idtype=idtype, device=F.ctx()) new_g = transform(g) assert new_g.num_edges(('A', 'r1', 'B')) == 6 assert new_g.num_edges(('C', 'r2', 'C')) == 6 assert new_g.idtype == g.idtype assert new_g.device == g.device assert new_g.ntypes == g.ntypes assert new_g.canonical_etypes == g.canonical_etypes @parametrize_dtype def test_module_random_walk_pe(idtype): transform = dgl.RandomWalkPE(2, 'rwpe') g = dgl.graph(([0, 1, 1], [1, 1, 0]), idtype=idtype, device=F.ctx()) new_g = transform(g) tgt = F.copy_to(F.tensor([[0., 0.5],[0.5, 0.75]]), g.device) assert F.allclose(new_g.ndata['rwpe'], tgt) @parametrize_dtype def test_module_laplacian_pe(idtype): transform = dgl.LaplacianPE(2, 'lappe') g = dgl.graph(([2, 1, 0, 3, 1, 1],[3, 0, 1, 3, 3, 1]), idtype=idtype, device=F.ctx()) new_g = transform(g) tgt = F.copy_to(F.tensor([[ 0.24971116, 0.], [ 0.11771496, 0.], [ 0.83237050, 1.], [ 0.48056933, 0.]]), g.device) # tensorflow has no abs() api if dgl.backend.backend_name == 'tensorflow': assert F.allclose(new_g.ndata['lappe'].__abs__(), tgt) # pytorch & mxnet else: assert F.allclose(new_g.ndata['lappe'].abs(), tgt) if __name__ == '__main__': test_partition_with_halo() test_module_heat_kernel(F.int32)

42.134126

117

0.598813

16,459

99,268

3.45829

0.035664

0.034557

0.044483

0.063018

0.824649

0.784751

0.743271

0.716058

0.690794

0.663352

0

0.034104

0.207499

99,268

2,355

118

42.152017

0.689424

0.04785

0

0.607873

0

0.051161

0

0.000425

0.360429

1

0.02863

false

0.000511

0.006135

0

0.036299

0.004601

0

null

0

1

0

1

0

null

0

5

b9570191d59a13f284d3694122d24aaa3eaa78dc

161

py

Python

mongoengine_todict/__init__.py

minazukie/mongoengine-todict

a2308845d0bf7a43969afefc12b9171fb754c160

[ "MIT" ]

2

2020-07-14T01:51:04.000Z

2020-07-14T02:29:38.000Z

mongoengine_todict/__init__.py

minazukie/mongoengine-todict

a2308845d0bf7a43969afefc12b9171fb754c160

[ "MIT" ]

null

mongoengine_todict/__init__.py

minazukie/mongoengine-todict

a2308845d0bf7a43969afefc12b9171fb754c160

[ "MIT" ]

null

# -*- coding: utf-8 -*- __version__ = "0.1.2" from mongoengine_todict.mixin import DocumentMixin, register_field __all__ = ("DocumentMixin", "register_field")

23

66

0.732919

19

161

5.631579

0.842105

0.392523

0.485981

0

0.028169

0.118012

161

6

67

26.833333

0.725352

0.130435

0

0.231884

0

1

0

false

0

0.333333

0

0.333333

0

1

0

null

1

0

1

0

null

0

1

0

5

b95ee9d243df941087a901e985e586e37e233cfc

84

py

Python

utils/worker/__init__.py

forcast-open/federated-api

cbcbeacf00a7cb53a22b26c8170bf146f476ac1a

[ "MIT" ]

null

utils/worker/__init__.py

forcast-open/federated-api

cbcbeacf00a7cb53a22b26c8170bf146f476ac1a

[ "MIT" ]

null

utils/worker/__init__.py

forcast-open/federated-api

cbcbeacf00a7cb53a22b26c8170bf146f476ac1a

[ "MIT" ]

null

#### Import sub-modules of the library #### from .worker import app, api, celery, db

42

43

0.690476

13

84

4.461538

0.923077

0

0.154762

84

2

44

42

0.816901

0.392857

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

b9715f15290cc81fe60c28a4b901fdb5f8035c4a

47

py

Python

qiubai/manage.py

shredstar/webcrawler

38284e4c568565a30e2bd06049d8fda84b055bed

[ "Apache-2.0" ]

null

qiubai/manage.py

shredstar/webcrawler

38284e4c568565a30e2bd06049d8fda84b055bed

[ "Apache-2.0" ]

null

qiubai/manage.py

shredstar/webcrawler

38284e4c568565a30e2bd06049d8fda84b055bed

[ "Apache-2.0" ]

null

from scrapy.cmdline import execute execute()

9.4

34

0.787234

6

47

6.166667

0.833333

0

0.148936

47

4

35

11.75

0.925

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

5

b988a1f1a5b293e2e8652b83c7f7b59bd480c5bd

19

py

Python

t2.py

soundaryathiagarajan/pythonprojects

174bd5aed96100e48e91d6a9552aa5b8ef1ce99a

[ "Apache-2.0" ]

null

t2.py

soundaryathiagarajan/pythonprojects

174bd5aed96100e48e91d6a9552aa5b8ef1ce99a

[ "Apache-2.0" ]

null

t2.py

soundaryathiagarajan/pythonprojects

174bd5aed96100e48e91d6a9552aa5b8ef1ce99a

[ "Apache-2.0" ]

null

print 'hi how r u'

9.5

18

0.631579

5

19

2.4

1

0

0.263158

19

1

19

0.857143

0

0.526316

0

null

0

null

1

0

null

0

1

0

null

0

1

0

1

0

5

b9a912b742f153c7edd214df0016201563ea3caa

153

py

Python

tests/web_platform/CSS2/positioning/test_positioning_float.py

jonboland/colosseum

cbf974be54fd7f6fddbe7285704cfaf7a866c5c5

[ "BSD-3-Clause" ]

71

2015-04-13T09:44:14.000Z

2019-03-24T01:03:02.000Z

tests/web_platform/CSS2/positioning/test_positioning_float.py

jonboland/colosseum

cbf974be54fd7f6fddbe7285704cfaf7a866c5c5

[ "BSD-3-Clause" ]

35

2019-05-06T15:26:09.000Z

2022-03-28T06:30:33.000Z

tests/web_platform/CSS2/positioning/test_positioning_float.py

jonboland/colosseum

cbf974be54fd7f6fddbe7285704cfaf7a866c5c5

[ "BSD-3-Clause" ]

139

2015-05-30T18:37:43.000Z

2019-03-27T17:14:05.000Z

from tests.utils import W3CTestCase class TestPositioningFloat(W3CTestCase): vars().update(W3CTestCase.find_tests(__file__, 'positioning-float-'))

25.5

73

0.797386

16

153

7.3125

0.8125

0

0.021583

0.091503

153

5

74

30.6

0.820144

0

0.117647

0

1

0

true

0

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

b9d4ac5ebde687c3a3f56410341cfe56155719b1

141

py

Python

AiXF/__init__.py

wzy916/wzy

5e491cc45c896fb1da79c63bae0e3fc3414a916e

[ "Apache-2.0" ]

null

AiXF/__init__.py

wzy916/wzy

5e491cc45c896fb1da79c63bae0e3fc3414a916e

[ "Apache-2.0" ]

null

AiXF/__init__.py

wzy916/wzy

5e491cc45c896fb1da79c63bae0e3fc3414a916e

[ "Apache-2.0" ]

null

from __future__ import absolute_import, unicode_literals from .celery import app as celery_app import pymysql pymysql.install_as_MySQLdb()

35.25

57

0.851064

20

141

5.55

0.6

0

0.113475

141

4

58

35.25

0.888

0

1

0

true

0

0.75

0

0.75

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

b9ec9ad6d7eb78ba0aa300929a6f9c4cab430aeb

88

py

Python

neptune/internal/cli/processes/__init__.py

jiji-online/neptune-cli

50cf680a80d141497f9331ab7cdaee49fcb90b0c

[ "Apache-2.0" ]

null

neptune/internal/cli/processes/__init__.py

jiji-online/neptune-cli

50cf680a80d141497f9331ab7cdaee49fcb90b0c

[ "Apache-2.0" ]

null

neptune/internal/cli/processes/__init__.py

jiji-online/neptune-cli

50cf680a80d141497f9331ab7cdaee49fcb90b0c

[ "Apache-2.0" ]

null

from .utils import build_process_command, recognize_execution_command, ExecutionCommand

44

87

0.897727

10

88

7.5

0.9

0

0.068182

88

1

88

0.914634

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

6a093b3b166bceee006b93349831064fbc3ae897

3,706

py

Python

pyaz/network/virtual_appliance/site/__init__.py

py-az-cli/py-az-cli

9a7dc44e360c096a5a2f15595353e9dad88a9792

[ "MIT" ]

null

pyaz/network/virtual_appliance/site/__init__.py

py-az-cli/py-az-cli

9a7dc44e360c096a5a2f15595353e9dad88a9792

[ "MIT" ]

null

pyaz/network/virtual_appliance/site/__init__.py

py-az-cli/py-az-cli

9a7dc44e360c096a5a2f15595353e9dad88a9792

[ "MIT" ]

1

2022-02-03T09:12:01.000Z

from .... pyaz_utils import _call_az def create(address_prefix, appliance_name, name, resource_group, allow=None, default=None, optimize=None): ''' Create an Azure network virtual appliance site. Required Parameters: - address_prefix -- Address Prefix of Network Virtual Appliance Site - appliance_name -- The name of Network Virtual Appliance - name -- The name of Network Virtual Appliance Site - resource_group -- Name of resource group. You can configure the default group using `az configure --defaults group=<name>` Optional Parameters: - allow -- Flag to control breakout of o365 allow category. - default -- Flag to control breakout of o365 default category. - optimize -- Flag to control breakout of o365 optimize category. ''' return _call_az("az network virtual-appliance site create", locals()) def update(address_prefix, appliance_name, name, resource_group, add=None, allow=None, default=None, force_string=None, optimize=None, remove=None, set=None): ''' Update an Azure network virtual appliance site. Required Parameters: - address_prefix -- Address Prefix of Network Virtual Appliance Site - appliance_name -- The name of Network Virtual Appliance - name -- The name of Network Virtual Appliance Site - resource_group -- Name of resource group. You can configure the default group using `az configure --defaults group=<name>` Optional Parameters: - add -- Add an object to a list of objects by specifying a path and key value pairs. Example: --add property.listProperty <key=value, string or JSON string> - allow -- Flag to control breakout of o365 allow category. - default -- Flag to control breakout of o365 default category. - force_string -- When using 'set' or 'add', preserve string literals instead of attempting to convert to JSON. - optimize -- Flag to control breakout of o365 optimize category. - remove -- Remove a property or an element from a list. Example: --remove property.list <indexToRemove> OR --remove propertyToRemove - set -- Update an object by specifying a property path and value to set. Example: --set property1.property2=<value> ''' return _call_az("az network virtual-appliance site update", locals()) def show(appliance_name, name, resource_group): ''' Show the detail of an Azure network virtual appliance site. Required Parameters: - appliance_name -- The name of Network Virtual Appliance - name -- The name of Network Virtual Appliance Site - resource_group -- Name of resource group. You can configure the default group using `az configure --defaults group=<name>` ''' return _call_az("az network virtual-appliance site show", locals()) def delete(appliance_name, name, resource_group, yes=None): ''' Delete an Azure network virtual appliance site. Required Parameters: - appliance_name -- The name of Network Virtual Appliance - name -- The name of Network Virtual Appliance Site - resource_group -- Name of resource group. You can configure the default group using `az configure --defaults group=<name>` Optional Parameters: - yes -- Do not prompt for confirmation. ''' return _call_az("az network virtual-appliance site delete", locals()) def list(appliance_name, resource_group): ''' List all Azure network virtual appliance site. Required Parameters: - appliance_name -- The name of Network Virtual Appliance - resource_group -- Name of resource group. You can configure the default group using `az configure --defaults group=<name>` ''' return _call_az("az network virtual-appliance site list", locals())

46.325

162

0.723961

491

3,706

5.384929

0.171079

0.111195

0.182678

0.163389

0.727307

0.704614

0.672088

0.625567

0.586989

0

0.006716

0.196438

3,706

79

163

46.911392

0.881128

0.711279

0

0.229777

0

1

0.454545

false

0

0.090909

0

1

0

null

0

1

0

1

0

null

0

1

0

5

6a0a1d9f756db55856ff45c2108ec718cf3ecc59

81

py

Python

ascii_letter_classifier/__init__.py

mlpipes/ascii-letter-classifier

ed46275b978911e0a03d95e6d1383110e227f7cd

[ "MIT" ]

2

2019-03-26T18:33:25.000Z

2019-03-26T18:33:28.000Z

ascii_letter_classifier/__init__.py

mlpipes/ascii-letter-classifier

ed46275b978911e0a03d95e6d1383110e227f7cd

[ "MIT" ]

null

ascii_letter_classifier/__init__.py

mlpipes/ascii-letter-classifier

ed46275b978911e0a03d95e6d1383110e227f7cd

[ "MIT" ]

null

from .config import AsciiLetterConfig from .model import ascii_letter_classifier

27

42

0.876543

10

81

6.9

0.8

0

0.098765

81

2

43

40.5

0.945205

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

6a0b729e1c39745c9310a4456f9616358fe9e221

47

py

Python

atest/testdata/standard_libraries/operating_system/files/result.py

gdw2/robot-framework

f25068edf1502e76ba8664d4b5ed1aebe0ee2434

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

4

2016-02-29T17:00:24.000Z

2019-06-27T08:49:13.000Z

atest/testdata/standard_libraries/operating_system/files/result.py

gdw2/robot-framework

f25068edf1502e76ba8664d4b5ed1aebe0ee2434

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

null

atest/testdata/standard_libraries/operating_system/files/result.py

gdw2/robot-framework

f25068edf1502e76ba8664d4b5ed1aebe0ee2434

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

2

2017-10-30T06:34:47.000Z

2019-03-12T07:23:08.000Z

result = u'Hyv\u00E4\u00E4 \u00FC\u00F6t\u00E4'

47

0.765957

8

47

4.5

0.75

0

0.318182

0.06383

47

1

47

0.5

0

0.729167

0

1

0

false

0

1

0

null

0

1

0

1

0

1

0

null

0

5

6a1f9d162710bb050df4dea2ca13d2475cc9ec34

168

py

Python

scripts/fips/docs.py

hyperknot/country-levels

3f200da4c806382273dce542ab79b65705be436b

[ "MIT" ]

28

2020-03-23T19:49:05.000Z

2022-03-19T14:31:56.000Z

scripts/fips/docs.py

hyperknot/country-level-id

3f200da4c806382273dce542ab79b65705be436b

[ "MIT" ]

16

2020-03-25T22:15:25.000Z

2020-06-11T19:00:14.000Z

scripts/fips/docs.py

hyperknot/country-level-id

3f200da4c806382273dce542ab79b65705be436b

[ "MIT" ]

5

2020-03-30T12:36:57.000Z

2021-10-08T22:42:03.000Z

#!/usr/bin/env python3 from country_levels_lib.fips.fips_docs import generate_fips_list def main(): generate_fips_list() if __name__ == "__main__": main()

14

64

0.72619

24

168

4.458333

0.708333

0.224299

0.299065

0

0.007092

0.160714

168

11

65

15.272727

0.751773

0.125

0

1

0

0.054795

0

1

0.2

true

0

0.2

0

0.4

0

1

0

null

1

0

1

0

null

0

1

0

5

dbefee0e3a84e5496ee66489191fddf406965e13

11,258

py

Python

plots.py

berkankadioglu/Bandits-Get-GANs

8cd5be8d12fc53ba96fe51b737b665ca9baaedef

[ "MIT" ]

null

plots.py

berkankadioglu/Bandits-Get-GANs

8cd5be8d12fc53ba96fe51b737b665ca9baaedef

[ "MIT" ]

null

plots.py

berkankadioglu/Bandits-Get-GANs

8cd5be8d12fc53ba96fe51b737b665ca9baaedef

[ "MIT" ]

null

import pickle import numpy as np import argparse import os import random import matplotlib.pyplot as plt import matplotlib.animation as animation import matplotlib as mpl from IPython.display import HTML import torch import torch.nn as nn import torch.nn.parallel import torch.backends.cudnn as cudnn import torch.optim as optim import torch.utils.data import torchvision.datasets as dset import torchvision.transforms as transforms import torchvision.utils as vutils # Load and standardize data # Original GAN with open('G_loss_orig', 'rb') as f: G_losses_orig = pickle.load(f) f.close() with open('D_loss_orig', 'rb') as f: D_losses_orig = pickle.load(f) f.close() with open('test_imgs_orig', 'rb') as f: img_list_orig = pickle.load(f) f.close() G_orig_mean = np.mean(G_losses_orig, axis=0) G_orig_std = np.std(G_losses_orig - G_orig_mean, axis=0) D_orig_mean = np.mean(D_losses_orig, axis=0) D_orig_std = np.std(D_losses_orig - D_orig_mean, axis=0) #################################################### # MAB GAN stat_reward = False conf_bound = False with open('G_loss_MAB'+'_stat_'+str(stat_reward)+'_ucb_'+str(conf_bound), 'rb') as f: G_losses_MAB_stat_false_conf_false = pickle.load(f) f.close() with open('D_loss_MAB'+'_stat_'+str(stat_reward)+'_ucb_'+str(conf_bound), 'rb') as f: D_losses_MAB_stat_false_conf_false = pickle.load(f) f.close() with open('test_imgs_MAB'+'_stat_'+str(stat_reward)+'_ucb_'+str(conf_bound), 'rb') as f: img_list_MAB_stat_false_conf_false = pickle.load(f) f.close() with open('k_list_MAB' + '_stat_' + str(stat_reward) + '_ucb_' + str(conf_bound), 'rb') as f: k_list_MAB_stat_false_conf_false = pickle.load(f) f.close() G_losses_MAB_stat_false_conf_false_mean = np.mean(G_losses_MAB_stat_false_conf_false, axis=0) G_losses_MAB_stat_false_conf_false_std = np.std(G_losses_MAB_stat_false_conf_false - G_losses_MAB_stat_false_conf_false_mean, axis=0) D_losses_MAB_stat_false_conf_false_mean = np.mean(D_losses_MAB_stat_false_conf_false, axis=0) D_losses_MAB_stat_false_conf_false_std = np.std(D_losses_MAB_stat_false_conf_false - D_losses_MAB_stat_false_conf_false_mean, axis=0) #################################################### stat_reward = True conf_bound = False with open('G_loss_MAB'+'_stat_'+str(stat_reward)+'_ucb_'+str(conf_bound), 'rb') as f: G_losses_MAB_stat_true_conf_false = pickle.load(f) with open('D_loss_MAB'+'_stat_'+str(stat_reward)+'_ucb_'+str(conf_bound), 'rb') as f: D_losses_MAB_stat_true_conf_false = pickle.load(f) with open('test_imgs_MAB'+'_stat_'+str(stat_reward)+'_ucb_'+str(conf_bound), 'rb') as f: img_list_MAB_stat_true_conf_false = pickle.load(f) # with open('k_list_MAB' + '_stat_' + str(stat_reward) + '_ucb_' + str(conf_bound), 'rb') as f: # k_list_MAB_stat_true_conf_false = pickle.load(f) G_losses_MAB_stat_true_conf_false_mean = np.mean(G_losses_MAB_stat_true_conf_false, axis=0) G_losses_MAB_stat_true_conf_false_std = np.std(G_losses_MAB_stat_true_conf_false - G_losses_MAB_stat_true_conf_false_mean, axis=0) D_losses_MAB_stat_true_conf_false_mean = np.mean(D_losses_MAB_stat_true_conf_false, axis=0) D_losses_MAB_stat_true_conf_false_std = np.std(D_losses_MAB_stat_true_conf_false - D_losses_MAB_stat_true_conf_false_mean, axis=0) #################################################### stat_reward = False conf_bound = True with open('G_loss_MAB'+'_stat_'+str(stat_reward)+'_ucb_'+str(conf_bound), 'rb') as f: G_losses_MAB_stat_false_conf_true = pickle.load(f) f.close() with open('D_loss_MAB'+'_stat_'+str(stat_reward)+'_ucb_'+str(conf_bound), 'rb') as f: D_losses_MAB_stat_false_conf_true = pickle.load(f) f.close() with open('test_imgs_MAB'+'_stat_'+str(stat_reward)+'_ucb_'+str(conf_bound), 'rb') as f: img_list_MAB_stat_false_conf_true = pickle.load(f) f.close() with open('k_list_MAB' + '_stat_' + str(stat_reward) + '_ucb_' + str(conf_bound), 'rb') as f: k_list_MAB_stat_false_conf_true = pickle.load(f) f.close() G_losses_MAB_stat_false_conf_true_mean = np.mean(G_losses_MAB_stat_false_conf_true, axis=0) G_losses_MAB_stat_false_conf_true_std = np.std(G_losses_MAB_stat_false_conf_true - G_losses_MAB_stat_false_conf_true_mean, axis=0) D_losses_MAB_stat_false_conf_true_mean = np.mean(D_losses_MAB_stat_false_conf_true, axis=0) D_losses_MAB_stat_false_conf_true_std = np.std(D_losses_MAB_stat_false_conf_true - D_losses_MAB_stat_false_conf_true_mean, axis=0) #################################################### stat_reward = True conf_bound = True with open('G_loss_MAB'+'_stat_'+str(stat_reward)+'_ucb_'+str(conf_bound), 'rb') as f: G_losses_MAB_stat_true_conf_true = pickle.load(f) f.close() with open('D_loss_MAB'+'_stat_'+str(stat_reward)+'_ucb_'+str(conf_bound), 'rb') as f: D_losses_MAB_stat_true_conf_true = pickle.load(f) f.close() with open('test_imgs_MAB'+'_stat_'+str(stat_reward)+'_ucb_'+str(conf_bound), 'rb') as f: img_list_MAB_stat_true_conf_true = pickle.load(f) f.close() with open('k_list_MAB' + '_stat_' + str(stat_reward) + '_ucb_' + str(conf_bound), 'rb') as f: k_list_MAB_stat_true_conf_true = pickle.load(f) f.close() G_losses_MAB_stat_true_conf_true_mean = np.mean(G_losses_MAB_stat_true_conf_true, axis=0) G_losses_MAB_stat_true_conf_true_std = np.std(G_losses_MAB_stat_true_conf_true - G_losses_MAB_stat_true_conf_true_mean, axis=0) D_losses_MAB_stat_true_conf_true_mean = np.mean(D_losses_MAB_stat_true_conf_true, axis=0) D_losses_MAB_stat_true_conf_true_std = np.std(D_losses_MAB_stat_true_conf_true - D_losses_MAB_stat_true_conf_true_mean, axis=0) #################################################### mpl.rcParams['lines.linewidth'] = .4 # Generator Plots plt.figure(figsize=(10,5)) plt.title("Generator Loss During Training") plt.plot(range(len(G_orig_mean)), G_orig_mean, label="Standard GAN") #plt.fill_between(range(len(G_orig_mean)), y1=G_orig_mean-G_orig_std, y2=G_orig_mean+G_orig_std) plt.plot(range(len(G_losses_MAB_stat_true_conf_true_mean)), G_losses_MAB_stat_true_conf_true_mean, label="MAB GAN stat. and UCB") #plt.fill_between(range(len(G_losses_MAB_stat_true_conf_true_mean)), # y1=G_losses_MAB_stat_true_conf_true_mean-G_losses_MAB_stat_true_conf_true_std, # y2=G_losses_MAB_stat_true_conf_true_mean+G_losses_MAB_stat_true_conf_true_std) plt.plot(range(len(G_losses_MAB_stat_false_conf_true_mean)), G_losses_MAB_stat_false_conf_true_mean, label="MAB GAN non-stat. and UCB") #plt.fill_between(range(len(G_losses_MAB_stat_false_conf_true_mean)), # y1=G_losses_MAB_stat_false_conf_true_mean-G_losses_MAB_stat_false_conf_true_std, # y2=G_losses_MAB_stat_false_conf_true_mean+G_losses_MAB_stat_false_conf_true_std) plt.plot(range(len(G_losses_MAB_stat_true_conf_false_mean)), G_losses_MAB_stat_true_conf_false_mean, label="MAB GAN stat. and no UCB") #plt.fill_between(range(len(G_losses_MAB_stat_true_conf_false_mean)), # y1=G_losses_MAB_stat_true_conf_false_mean-G_losses_MAB_stat_true_conf_false_std, # y2=G_losses_MAB_stat_true_conf_false_mean+G_losses_MAB_stat_true_conf_false_std) plt.plot(range(len(G_losses_MAB_stat_false_conf_false_mean)), G_losses_MAB_stat_false_conf_false_mean, label="MAB GAN non-stat. and no UCB") #plt.fill_between(range(len(G_losses_MAB_stat_false_conf_false_mean)), # y1=G_losses_MAB_stat_false_conf_false_mean-G_losses_MAB_stat_false_conf_false_std, # y2=G_losses_MAB_stat_false_conf_false_mean+G_losses_MAB_stat_false_conf_false_std) plt.xlabel("iterations") plt.ylabel("Generator Loss") plt.legend() plt.show() #################################################### # Discriminator Plots plt.figure(figsize=(10,5)) plt.title("Discriminator Loss During Training") plt.plot(range(len(D_orig_mean)), D_orig_mean, label="Standard GAN") #plt.fill_between(range(len(D_orig_mean)), y1=D_orig_mean-D_orig_std, y2=D_orig_mean+D_orig_std) plt.plot(range(len(D_losses_MAB_stat_true_conf_true_mean)), D_losses_MAB_stat_true_conf_true_mean, label="MAB GAN stat. and UCB") #plt.fill_between(range(len(D_losses_MAB_stat_true_conf_true_mean)), # y1=D_losses_MAB_stat_true_conf_true_mean-D_losses_MAB_stat_true_conf_true_std, # y2=D_losses_MAB_stat_true_conf_true_mean+D_losses_MAB_stat_true_conf_true_std) plt.plot(range(len(D_losses_MAB_stat_false_conf_true_mean)), D_losses_MAB_stat_false_conf_true_mean, label="MAB GAN non-stat. and UCB") #plt.fill_between(range(len(D_losses_MAB_stat_false_conf_true_mean)), # y1=D_losses_MAB_stat_false_conf_true_mean-D_losses_MAB_stat_false_conf_true_std, # y2=D_losses_MAB_stat_false_conf_true_mean+D_losses_MAB_stat_false_conf_true_std) plt.plot(range(len(D_losses_MAB_stat_true_conf_false_mean)), D_losses_MAB_stat_true_conf_false_mean, label="MAB GAN stat. and no UCB") #plt.fill_between(range(len(D_losses_MAB_stat_true_conf_false_mean)), # y1=D_losses_MAB_stat_true_conf_false_mean-D_losses_MAB_stat_true_conf_false_std, # y2=D_losses_MAB_stat_true_conf_false_mean+D_losses_MAB_stat_true_conf_false_std) plt.plot(range(len(D_losses_MAB_stat_false_conf_false_mean)), D_losses_MAB_stat_false_conf_false_mean, label="MAB GAN non-stat. and no UCB") #plt.fill_between(range(len(D_losses_MAB_stat_false_conf_false_mean)), # y1=D_losses_MAB_stat_false_conf_false_mean-D_losses_MAB_stat_false_conf_false_std, # y2=D_losses_MAB_stat_false_conf_false_mean+D_losses_MAB_stat_false_conf_false_std) plt.xlabel("iterations") plt.ylabel("Discriminator Loss") plt.legend() plt.show() #################################################### # Plot the fake images from the last epoch plt.subplot(1,2,1) plt.axis("off") plt.title("Fake Images for Standard GAN") plt.imshow(np.transpose(img_list_orig[-1],(1,2,0))) #plt.show() # Plot the fake images from the last epoch plt.subplot(1,2,2) plt.axis("off") plt.title("Fake Images for MAB GAN") plt.imshow(np.transpose(img_list_MAB_stat_true_conf_true[-1],(1,2,0))) plt.show() print() ####################################################### mpl.rcParams['lines.linewidth'] = 1 # Plot k plt.figure(figsize=(10,5)) plt.title("Number of Discriminator Updates per Iteration") plt.plot(np.ones((len(k_list_MAB_stat_true_conf_true[0]),)), label="Standard GAN") plt.plot(k_list_MAB_stat_true_conf_true[0], label="MAB GAN") plt.xlabel("iterations") plt.legend() plt.show() ####################################################### # Plot some training images # real_batch = next(iter(dataloader)) # plt.figure(figsize=(8,8)) # plt.axis("off") # plt.title("Training Images") # plt.imshow(np.transpose(vutils.make_grid(real_batch[0].to(device)[:64], padding=2, normalize=True).cpu(),(1,2,0))) # **Visualization of G’s progression** # # Remember how we saved the generator’s output on the fixed_noise batch # after every epoch of training. Now, we can visualize the training # progression of G with an animation. Press the play button to start the # animation. # #%%capture #fig = plt.figure(figsize=(8,8)) #plt.axis("off") #ims = [[plt.imshow(np.transpose(i,(1,2,0)), animated=True)] for i in img_list] #ani = animation.ArtistAnimation(fig, ims, interval=1000, repeat_delay=1000, blit=True) #HTML(ani.to_jshtml())

52.12037

140

0.75644

1,981

11,258

3.801615

0.081777

0.121763

0.179525

0.117514

0.832426

0.803612

0.790201

0.761253

0.719958

0.543354

0

0.008708

0.092112

11,258

215

141

52.362791

0.728109

0.298188

0

0.384058

0

0.121574

0

1

0

false

0

0.130435

0

0.130435

0.007246

0

null

0

1

0

null

0

5

dbf3f4d8f988efa6f80be843b6b91cbce4f8ee82

188

py

Python

store/admin.py

Kihara-tony/Bookstore

0023b86dc20297d22a3c01c340be9c50f6578109

[ "Unlicense" ]

null

store/admin.py

Kihara-tony/Bookstore

0023b86dc20297d22a3c01c340be9c50f6578109

[ "Unlicense" ]

5

2020-06-05T22:51:31.000Z

2021-09-08T01:16:57.000Z

store/admin.py

Kihara-tony/Bookstore

0023b86dc20297d22a3c01c340be9c50f6578109

[ "Unlicense" ]

null

from django.contrib import admin from .models import Teacher,Books,Profile # Register your models here. admin.site.register(Teacher) admin.site.register(Books) admin.site.register(Profile)

31.333333

41

0.824468

27

188

5.740741

0.481481

0.174194

0.329032

0

0.079787

188

6

42

31.333333

0.895954

0.138298

0

1

0

true

0

0.4

0

0.4

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

dbf93baa075190a525d65c3a6289cc630451fc3a

167

py

Python

family_api/admin.py

zchuhui/django-rest-framework-example

82ee470b581473a0e9f5772ede75a90f2dfe1c54

[ "Apache-2.0" ]

null

family_api/admin.py

zchuhui/django-rest-framework-example

82ee470b581473a0e9f5772ede75a90f2dfe1c54

[ "Apache-2.0" ]

null

family_api/admin.py

zchuhui/django-rest-framework-example

82ee470b581473a0e9f5772ede75a90f2dfe1c54

[ "Apache-2.0" ]

null

from django.contrib import admin # Register your models here. from .models import Relationship,Person admin.site.register(Relationship) admin.site.register(Person)

18.555556

39

0.814371

22

167

6.181818

0.545455

0.132353

0.25

0

0.107784

167

8

40

20.875

0.912752

0.155689

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

e01fcddc55beb5ab4224200b63c007cf3be993c6

4,481

py

Python

scripts/field/ds_tuto_3_0.py

G00dBye/YYMS

1de816fc842b6598d5b4b7896b6ab0ee8f7cdcfb

[ "MIT" ]

54

2019-04-16T23:24:48.000Z

2021-12-18T11:41:50.000Z

scripts/field/ds_tuto_3_0.py

G00dBye/YYMS

1de816fc842b6598d5b4b7896b6ab0ee8f7cdcfb

[ "MIT" ]

3

2019-05-19T15:19:41.000Z

2020-04-27T16:29:16.000Z

scripts/field/ds_tuto_3_0.py

G00dBye/YYMS

1de816fc842b6598d5b4b7896b6ab0ee8f7cdcfb

[ "MIT" ]

49

2020-11-25T23:29:16.000Z

2022-03-26T16:20:24.000Z

# Created by MechAviv # ID :: [931050000] # Hidden Street : Extraction Room 1 sm.curNodeEventEnd(True) sm.setTemporarySkillSet(0) sm.setInGameDirectionMode(True, True, False, False) sm.setStandAloneMode(True) def failMessage(crack): sm.chatScript("Tap the Control Key repeatedly to break the wall.") sm.showEffect("Effect/Direction6.img/effect/tuto/guide1/0", 3000, 0, -100, 20, 0, False, 0) sm.showEffect("Effect/Direction6.img/effect/tuto/breakEgg/" + str(crack), 6600, 0, 0, 1, 0, False, 0) if not "1" in sm.getQRValue(23206): sm.createQuestWithQRValue(23206, "1") sm.levelUntil(10) sm.sendDelay(3000) sm.showFieldEffect("demonSlayer/text12", 0) sm.sendDelay(5000) sm.forcedInput(1) sm.sendDelay(10) sm.forcedInput(0) sm.setSpeakerID(2159311) sm.removeEscapeButton() sm.setPlayerAsSpeaker() sm.setSpeakerType(3) sm.sendNext("........") sm.showEffect("Effect/Direction6.img/effect/tuto/balloonMsg0/14", 2000, 130, 50, 10, 0, False, 0) sm.sendDelay(2000) sm.setSpeakerID(2159311) sm.removeEscapeButton() sm.setPlayerAsSpeaker() sm.setSpeakerType(3) sm.sendNext("(I think I hear something...)") sm.showEffect("Effect/Direction6.img/effect/tuto/balloonMsg0/15", 2000, -130, 50, 10, 0, False, 0) sm.sendDelay(2000) sm.setSpeakerID(2159311) sm.removeEscapeButton() sm.setPlayerAsSpeaker() sm.setSpeakerType(3) sm.sendNext("(Where am I? Am I still alive...?)") sm.showEffect("Effect/Direction6.img/effect/tuto/balloonMsg0/16", 2000, 130, 50, 10, 0, False, 0) sm.sendDelay(2000) sm.setSpeakerID(2159311) sm.removeEscapeButton() sm.setPlayerAsSpeaker() sm.setSpeakerType(3) sm.sendNext("(Ugh... My energy... Something is stealing my energy!)") sm.showEffect("Effect/Direction6.img/effect/tuto/balloonMsg0/17", 2000, -130, 50, 10, 0, False, 0) sm.sendDelay(2000) sm.setSpeakerID(2159311) sm.removeEscapeButton() sm.setPlayerAsSpeaker() sm.setSpeakerType(3) sm.sendNext("(I must escape before they drain all my power!)") sm.setPatternInputCount(0) sm.chatScript("Tap the Control Key repeatedly to break the wall.") sm.showEffect("Effect/Direction6.img/effect/tuto/guide1/0", 3000, 0, -100, 20, 0, False, 0) while not sm.patternInputRequest("17#17#17#", 2, 2, 3000) and sm.getPatternInputCount() < 7: failMessage(0) sm.setPatternInputCount(0) sm.playSound("demonSlayer/punch", 100) sm.playSound("demonSlayer/crackEgg", 100) sm.chatScript("Tap the Control Key repeatedly to break the wall.") sm.showEffect("Effect/Direction6.img/effect/tuto/guide1/0", 3000, 0, -100, 20, 0, False, 0) sm.showEffect("Effect/Direction6.img/effect/tuto/breakEgg/0", 6600, 0, 0, 1, 0, False, 0) while not sm.patternInputRequest("17#17#17#", 2, 2, 3000) and sm.getPatternInputCount() < 7: failMessage(0) sm.setPatternInputCount(0) sm.playSound("demonSlayer/punch", 100) sm.playSound("demonSlayer/crackEgg", 100) sm.showEffect("Effect/Direction6.img/effect/tuto/balloonMsg0/7", 2000, 130, 100, 10, 0, False, 0) sm.chatScript("Tap the Control Key repeatedly to break the wall.") sm.showEffect("Effect/Direction6.img/effect/tuto/guide1/0", 3000, 0, -100, 20, 0, False, 0) sm.showEffect("Effect/Direction6.img/effect/tuto/breakEgg/0", 6600, 0, 0, 1, 0, False, 0) while not sm.patternInputRequest("17#17#17#", 2, 2, 3000) and sm.getPatternInputCount() < 7: failMessage(0) sm.setPatternInputCount(0) sm.playSound("demonSlayer/punch", 100) sm.playSound("demonSlayer/crackEgg", 100) sm.chatScript("Tap the Control Key repeatedly to break the wall.") sm.showEffect("Effect/Direction6.img/effect/tuto/guide1/0", 3000, 0, -100, 20, 0, False, 0) sm.showEffect("Effect/Direction6.img/effect/tuto/breakEgg/1", 6600, 0, 0, 1, 0, False, 0) while not sm.patternInputRequest("17#17#17#", 2, 2, 3000) and sm.getPatternInputCount() < 7: failMessage(1) sm.setPatternInputCount(0) sm.showEffect("Effect/Direction6.img/effect/tuto/breakEgg/0", 3600, 0, 0, 1, 0, False, 0) sm.sendDelay(3000) sm.showEffect("Effect/Direction6.img/effect/tuto/breakEgg/1", 3600, 0, 0, 1, 0, False, 0) sm.showEffect("Effect/Direction6.img/effect/tuto/balloonMsg1/1", 2000, -130, 50, 10, 0, False, 0) sm.playSound("demonSlayer/crackEgg", 100) sm.sendDelay(1000) sm.showEffect("Effect/Direction6.img/effect/tuto/breakEgg/2", 9000, 0, 0, 1, 0, False, 0) sm.showEffect("Effect/Direction6.img/effect/tuto/balloonMsg1/2", 2000, 130, 50, 10, 0, False, 0) sm.sendDelay(1000) sm.playSound("demonSlayer/breakEgg", 100) sm.showFieldEffect("demonSlayer/whiteOut", 0) sm.warpInstanceIn(931050020, 0)

35.283465

105

0.738451

660

4,481

5.013636

0.163636

0.023572

0.103354

0.160774

0.798429

0.793291

0.783318

0.779087

0.674222

0.650045

0

0.11663

0.096853

4,481

126

106

35.563492

0.701013

0.015845

0

0.634409

0

0.339083

0.192919

0

1

0.010753

false

0

0.010753

0

null

0

1

0

1

0

1

0

null

0

5

e026f95f826d4723e778086c5e7c53aac129329d

92

py

Python

shakenfist/exceptions.py

mcarden/shakenfist

cb90ffe81a3d0201949ddea8d4b36ce1b6c11246

[ "Apache-2.0" ]

null

shakenfist/exceptions.py

mcarden/shakenfist

cb90ffe81a3d0201949ddea8d4b36ce1b6c11246

[ "Apache-2.0" ]

null

shakenfist/exceptions.py

mcarden/shakenfist

cb90ffe81a3d0201949ddea8d4b36ce1b6c11246

[ "Apache-2.0" ]

null

class HTTPError(Exception): pass class VersionSpecificationError(Exception): pass

13.142857

43

0.76087

8

92

8.75

0.625

0.371429

0

0.173913

92

6

44

15.333333

0.921053

0

0.5

0

1

0

true

0.5

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

5

e04215b1a377992f554f9a6da13ebdda9f9ac242

1,088

py

Python

mywebsite/models.py

anton-donchev/mywebsite

1be02d08fc26477fc6dd7b0e7c94bf8de9c02bde

[ "MIT" ]

null

mywebsite/models.py

anton-donchev/mywebsite

1be02d08fc26477fc6dd7b0e7c94bf8de9c02bde

[ "MIT" ]

null

mywebsite/models.py

anton-donchev/mywebsite

1be02d08fc26477fc6dd7b0e7c94bf8de9c02bde

[ "MIT" ]

null

#!/usr/bin/env python # coding=utf-8 from werkzeug.security import generate_password_hash, check_password_hash from flask_login import UserMixin from datetime import datetime from mywebsite import db, login_manager @login_manager.user_loader def load_user(id): return Admin.query.get(int(id)) class Admin(UserMixin, db.Model): id = db.Column(db.Integer, primary_key=True) username = db.Column(db.String(64), index=True, unique=True) email = db.Column(db.String(120), index=True, unique=True) password_hash = db.Column(db.String(128)) status = db.Column(db.String(32), index=True) timestamp = db.Column(db.DateTime, index=True, default=datetime.utcnow) def __repr__(self): return f"<Admin {self.username}>" @property def password(self): raise AttributeError("'password' is not a readable attribute!") @password.setter def password(self, password): self.password_hash = generate_password_hash(password) def check_password(self, password): return check_password_hash(self.password_hash, password)

29.405405

75

0.725184

150

1,088

5.113333

0.426667

0.109518

0.078227

0.083442

0

0.012088

0.163603

1,088

36

76

30.222222

0.830769

0.030331

0

1

0

0.058879

0

1

0.208333

false

0.375

0.166667

0.125

0.791667

0

null

0

1

0

null

0

1

0

1

0

1

0

5

0edf3b1979c01112d2ac2ce8599c5385039d8532

3,282

py

Python

tests/test_minimap2_pipeline.py

ignasrum/hocort

5bf137ecaf8816cc8d951bb5168588eb87811097

[ "MIT" ]

null

tests/test_minimap2_pipeline.py

ignasrum/hocort

5bf137ecaf8816cc8d951bb5168588eb87811097

[ "MIT" ]

null

tests/test_minimap2_pipeline.py

ignasrum/hocort

5bf137ecaf8816cc8d951bb5168588eb87811097

[ "MIT" ]

null

from hocort.pipelines.minimap2 import Minimap2 import tempfile import os temp_dir = tempfile.TemporaryDirectory() path = os.path.dirname(__file__) idx = f'{path}/test_data/minimap2/genome.mmi' seq1 = f'{path}/test_data/sequences/sequences1.fastq' out1 = f'{temp_dir.name}/out1.fastq' seq2 = f'{path}/test_data/sequences/sequences2.fastq' out2 = f'{temp_dir.name}/out2.fastq' no_path = '' def test_pipeline_temp_dir(): path = '.' returncode = Minimap2(path).run(idx, seq1, out1, seq2=seq2, out2=out2) assert returncode == 0 def test_pipeline_mapq(): returncode = Minimap2().run(idx, seq1, out1, seq2=seq2, out2=out2, mapq=2) assert returncode == 0 def test_pipeline_idx_no_path(): returncode = Minimap2().run(no_path, seq1, out1) assert returncode == 1 def test_pipeline_seq1_no_path(): returncode = Minimap2().run(idx, no_path, out1) assert returncode == 1 def test_pipeline_out1_no_path(): returncode = Minimap2().run(idx, seq1, no_path) assert returncode == 0 def test_pipeline_seq1_seq2_no_path(): returncode = Minimap2().run(idx, no_path, out1, seq2=no_path) assert returncode == 1 def test_pipeline_seq2_no_path(): returncode = Minimap2().run(idx, seq1, out1, seq2=no_path) assert returncode == 0 def test_pipeline_hcfilter_true_1(): returncode = Minimap2().run(idx, seq1, out1, hcfilter='t') assert returncode == 0 def test_pipeline_hcfilter_false_1(): returncode = Minimap2().run(idx, seq1, out1, hcfilter='f') assert returncode == 0 def test_pipeline_hcfilter_true_2(): returncode = Minimap2().run(idx, seq1, out1, seq2=seq2, out2=out2, hcfilter='t') assert returncode == 0 def test_pipeline_hcfilter_false_2(): returncode = Minimap2().run(idx, seq1, out1, seq2=seq2, out2=out2, hcfilter='f') assert returncode == 0 def test_pipeline_1(): returncode = Minimap2().run(idx, seq1, out1) assert returncode == 0 def test_pipeline_2(): returncode = Minimap2().run(idx, seq1, out1, seq2=seq2, out2=out2) assert returncode == 0 def test_pipeline_custom_options_1(): options = [] returncode = Minimap2().run(idx, seq1, out1, options=options) assert returncode == 0 def test_pipeline_custom_options_2(): options = [] returncode = Minimap2().run(idx, seq1, out1, seq2=seq2, out2=out2, options=options) assert returncode == 0 def test_pipeline_sam_1(): intermediary = 'SAM' returncode = Minimap2().run(idx, seq1, out1, intermediary=intermediary) assert returncode == 0 def test_pipeline_sam_2(): intermediary = 'SAM' returncode = Minimap2().run(idx, seq1, out1, seq2=seq2, out2=out2, intermediary=intermediary) assert returncode == 0 def test_pipeline_sam_1(): intermediary = 'BAM' returncode = Minimap2().run(idx, seq1, out1, intermediary=intermediary) assert returncode == 0 def test_pipeline_sam_2(): intermediary = 'BAM' returncode = Minimap2().run(idx, seq1, out1, seq2=seq2, out2=out2, intermediary=intermediary) assert returncode == 0 def test_pipeline_seq2_no_out2(): returncode = Minimap2().run(idx, seq1, out1, seq2=seq2) assert returncode == 1 def test_pipeline_noseq2_out2(): returncode = Minimap2().run(idx, seq1, out1, out2=out2) assert returncode == 0

31.257143

97

0.708714

443

3,282

5.047404

0.108352

0.065742

0.140877

0.203936

0.850626

0.805009

0.743292

0.622093

0.453041

0.38864

0

0.055313

0.162706

3,282

104

98

31.557692

0.75837

0

0.432099

0

0.058196

0.053016

0

0.259259

1

0.259259

false

0

0.037037

0

0.296296

0

null

0

1

0

null

0

1

0

5

0eeb34c85c4c175585aaf02e5531af46cb4686c7

214

py

Python

mak/build_framework/configure/arch/mips.py

motor-dev/Motor

98cb099fe1c2d31e455ed868cc2a25eae51e79f0

[ "BSD-3-Clause" ]

4

2015-05-13T16:28:36.000Z

2017-05-24T15:34:14.000Z

mak/build_framework/configure/arch/mips.py

motor-dev/Motor

98cb099fe1c2d31e455ed868cc2a25eae51e79f0

[ "BSD-3-Clause" ]

null

mak/build_framework/configure/arch/mips.py

motor-dev/Motor

98cb099fe1c2d31e455ed868cc2a25eae51e79f0

[ "BSD-3-Clause" ]

1

2017-03-21T08:28:07.000Z

def configure(conf): conf.env.ARCHITECTURE = 'mips' conf.env.VALID_ARCHITECTURES = ['mips'] conf.env.ARCH_FAMILY = 'mips' conf.env.ARCH_LP64 = False conf.env.append_unique('DEFINES', ['_MIPS'])

30.571429

48

0.672897

28

214

4.964286

0.535714

0.251799

0.23741

0.215827

0

0.011236

0.168224

214

6

49

35.666667

0.769663

0

0.11215

0

1

0.166667

false

0

0.166667

0

1

0

null

1

0

1

0

null

0

5

0eec8e83919cdeb2106c1fcb7cebc1a419715cc5

114

py

Python

stataLogObject/Supports/__init__.py

sbaker-dev/stataLogObject

f72b80e4c69827f556d181dbfec66237c8464636

[ "MIT" ]

null

stataLogObject/Supports/__init__.py

sbaker-dev/stataLogObject

f72b80e4c69827f556d181dbfec66237c8464636

[ "MIT" ]

null

stataLogObject/Supports/__init__.py

sbaker-dev/stataLogObject

f72b80e4c69827f556d181dbfec66237c8464636

[ "MIT" ]

null

from .supports import clean_line, extract_values, clean_value, FOREST_DICT, methods_in_line from .Errors import *

38

91

0.833333

17

114

5.235294

0.764706

0

0.105263

114

2

92

57

0.872549

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

0ef47d2ba12968611e973f562249e06bb3e99777

258

py

Python

rotkehlchen/tests/unit/test_etherscan.py

rotkehlchenio/rotkehlchen

98f49cd3ed26c641fec03b78eff9fe1872385fbf

[ "BSD-3-Clause" ]

137

2018-03-05T11:53:29.000Z

2019-11-03T16:38:42.000Z

rotkehlchen/tests/unit/test_etherscan.py

rotkehlchenio/rotkehlchen

98f49cd3ed26c641fec03b78eff9fe1872385fbf

[ "BSD-3-Clause" ]

385

2018-03-08T12:43:41.000Z

2019-11-10T09:15:36.000Z

rotkehlchen/tests/unit/test_etherscan.py

rotkehlchenio/rotkehlchen

98f49cd3ed26c641fec03b78eff9fe1872385fbf

[ "BSD-3-Clause" ]

59

2018-03-08T10:08:27.000Z

2019-10-26T11:30:44.000Z

from rotkehlchen.externalapis.etherscan import _hashes_tuple_to_list def test_hashes_tuple_to_list(): hashes = {('0x1', 1), ('0x2', 2), ('0x3', 3), ('0x4', 4), ('0x5', 5)} assert _hashes_tuple_to_list(hashes) == ['0x1', '0x2', '0x3', '0x4', '0x5']

36.857143

79

0.639535

37

258

4.135135

0.594595

0.215686

0.254902

0.333333

0.339869

0

0.112613

0.139535

258

6

80

43

0.576577

0

0.116279

0

0.116279

0

0.25

1

0.25

false

0

0.25

0

0.5

0

null

1

0

1

0

null

0

1

0

5

0efcc647d5e2bea619fbe1de9c3ac6ac210090de

289

py

Python

playgrounds/test.py

Mariox222/htmlStruc

ebc4f9ae62b44b94a339ae34f0d8577738ff2801

[ "MIT" ]

null

playgrounds/test.py

Mariox222/htmlStruc

ebc4f9ae62b44b94a339ae34f0d8577738ff2801

[ "MIT" ]

null

playgrounds/test.py

Mariox222/htmlStruc

ebc4f9ae62b44b94a339ae34f0d8577738ff2801

[ "MIT" ]

null

from dataclasses import dataclass from dataclasses import field """ @dataclass class Node: #name: str = field(default="") attributes: list[str] = field(default_factory=list) #isCloseTag: bool = field(default=True) #depth: int = field(default=0) """ x: list[str] = list()

24.083333

55

0.681661

36

289

5.444444

0.555556

0.244898

0.214286

0

0.004202

0.176471

289

12

56

24.083333

0.819328

0

1

0

true

0

0.666667

0

0.666667

0

null

1

0

null

0

1

0

1

0

1

0

5

160b1ded5fb8662b107af9b3b8d464b9918e275b

359

py

Python

python/replicate/exceptions.py

kennyworkman/replicate

df9358847cdbb3d0e87018511e0a392d750d818a

[ "Apache-2.0" ]

810

2021-02-09T09:26:26.000Z

2022-03-25T14:06:13.000Z

python/replicate/exceptions.py

kennyworkman/replicate

df9358847cdbb3d0e87018511e0a392d750d818a

[ "Apache-2.0" ]

347

2021-02-08T07:24:29.000Z

2022-03-31T23:05:29.000Z

python/replicate/exceptions.py

kennyworkman/replicate

df9358847cdbb3d0e87018511e0a392d750d818a

[ "Apache-2.0" ]

43

2020-10-30T19:55:42.000Z

2021-01-18T22:41:49.000Z

from . import constants class DoesNotExist(Exception): pass class ReadError(Exception): pass class WriteError(Exception): pass class RepositoryConfigurationError(Exception): pass class IncompatibleRepositoryVersion(Exception): pass class CorruptedRepositorySpec(Exception): pass class ConfigNotFound(Exception): pass

11.966667

47

0.752089

31

359

8.709677

0.419355

0.337037

0.4

0

0.18663

359

29

48

12.37931

0.924658

0

0.466667

0

1

0

true

0.466667

0.066667

0

0.533333

0

null

1

0

null

0

1

0

1

0

5

16454461da9204839872985eeb76753164f40f68

312

py

Python

OST_helper/UI/fields/__init__.py

HomeletW/OST

5e359d00a547af194a2a1a2591a53c93d8f40b84

[ "MIT" ]

1

2020-07-31T16:43:13.000Z

OST_helper/UI/fields/__init__.py

HomeletW/OST

5e359d00a547af194a2a1a2591a53c93d8f40b84

[ "MIT" ]

null

OST_helper/UI/fields/__init__.py

HomeletW/OST

5e359d00a547af194a2a1a2591a53c93d8f40b84

[ "MIT" ]

null

from OST_helper.UI.fields.course_panel import CoursePair, CoursePanel from OST_helper.UI.fields.info_panel import OtherInfoPanel, PersonalInfoPanel from OST_helper.UI.fields.menu_bar import MenuBar from OST_helper.UI.fields.status_bar import StatusBar from OST_helper.UI.fields.utility_panel import UtilityPanel

52

77

0.875

47

312

5.595745

0.425532

0.13308

0.247148

0.285171

0.39924

0

0.070513

312

5

78

62.4

0.906897

0

1

0

true

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

165d63dc97b77ccebb9d275adb559016c6b9a146

151

py

Python

data/__init__.py

yezz123/Apollo

f27a655a6a5ee9186867b380840411feca21290f

[ "MIT" ]

18

2021-07-28T22:01:48.000Z

2022-02-07T10:57:21.000Z

data/__init__.py

yezz123/Apollo

f27a655a6a5ee9186867b380840411feca21290f

[ "MIT" ]

null

data/__init__.py

yezz123/Apollo

f27a655a6a5ee9186867b380840411feca21290f

[ "MIT" ]

5

2021-07-29T01:47:28.000Z

2022-01-05T02:04:54.000Z

#!/usr/bin/python3 from sqlalchemy import create_engine from sqlalchemy.ext.declarative import declarative_base from sqlalchemy.orm import sessionmaker

37.75

55

0.86755

20

151

6.45

0.65

0.325581

0

0.007194

0.07947

151

4

56

37.75

0.920863

0.112583

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

1695aedbbb339b924fdfe0e7abba346dbe8f3381

53

py

Python

weatherpy/__init__.py

cmcdowell/weatherpy

7da7bed9db946dfab9318d67aab5fbda1e4fed21

[ "MIT" ]

2

2015-11-28T09:01:47.000Z

2016-04-13T13:00:11.000Z

weatherpy/__init__.py

cmcdowell/weatherpy

7da7bed9db946dfab9318d67aab5fbda1e4fed21

[ "MIT" ]

null

weatherpy/__init__.py

cmcdowell/weatherpy

7da7bed9db946dfab9318d67aab5fbda1e4fed21

[ "MIT" ]

null

#!/usr/bin/env python from response import Response

13.25

29

0.773585

8

53

5.125

0.875

0

0.132075

53

3

30

17.666667

0.891304

0.377358

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

16d58b58f3b103201c63b5661f230d51a75528da

44

py

Python

config.py

marcusmguerrier/sql-challenge

0f0fb2236fa78509eec97cd2030c9f2e58f34d2c

[ "ADSL" ]

null

config.py

marcusmguerrier/sql-challenge

0f0fb2236fa78509eec97cd2030c9f2e58f34d2c

[ "ADSL" ]

null

config.py

marcusmguerrier/sql-challenge

0f0fb2236fa78509eec97cd2030c9f2e58f34d2c

[ "ADSL" ]

null

# username = postgres # password = postgres

14.666667

21

0.727273

4

44

8

0.75

0

0.181818

44

2

22

0.888889

0.886364

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

5

16ef541d2550e782b662ec4437a77a4ce60f5e55

5,836

py

Python

tests/artillery_transformer/test_dist.py

swagger-atlas/atlas

64a0a6e3107da9f7cf894880823badfa84e11f25

[ "Apache-2.0" ]

2

2019-04-25T10:22:53.000Z

2020-10-09T06:57:02.000Z

tests/artillery_transformer/test_dist.py

swagger-atlas/atlas

64a0a6e3107da9f7cf894880823badfa84e11f25

[ "Apache-2.0" ]

9

2019-04-11T17:27:57.000Z

2021-05-08T13:12:10.000Z

tests/artillery_transformer/test_dist.py

swagger-atlas/atlas

64a0a6e3107da9f7cf894880823badfa84e11f25

[ "Apache-2.0" ]

1

2019-04-18T22:18:37.000Z

import os from os import path from unittest import mock from atlas.modules.transformer.artillery.dist import ArtilleryDist, settings class TestDist: def test_start(self): instance = ArtilleryDist() instance.create_folder = mock.MagicMock() instance.copy_files = mock.MagicMock() instance.copy_folders = mock.MagicMock() instance.start() assert instance.create_folder.mock_calls == [ mock.call(settings.DIST_FOLDER), mock.call(settings.ARTILLERY_FOLDER, os.path.join(instance.path, settings.DIST_FOLDER)) ] instance.copy_files.assert_called_once() instance.copy_folders.assert_called_once() @mock.patch('atlas.modules.transformer.artillery.dist.shutil') @mock.patch('atlas.modules.transformer.artillery.dist.os.path.isfile') def test_copy_files_all_files(self, patch_os_is_file, patched_shell): patch_os_is_file.return_value = True instance = ArtilleryDist() instance.path = "" instance.copy_files() source_path = path.join(settings.BASE_DIR, "atlas", "modules", "data_provider", "artillery") source_files = [path.join(source_path, file) for file in os.listdir(source_path) if file not in {"constants.js", "settings.js", settings.ARTILLERY_RESOURCES}] d_path = path.join(settings.DIST_FOLDER, settings.ARTILLERY_FOLDER, settings.ARTILLERY_LIB_FOLDER) expected_sources = [((_file, d_path),) for _file in source_files] expected_sources.extend([ ( ( path.join(settings.INPUT_FOLDER, settings.ARTILLERY_FOLDER, settings.ARTILLERY_HOOK_FILE), path.join(settings.DIST_FOLDER, settings.ARTILLERY_FOLDER) ), ), ( ( path.join(settings.OUTPUT_FOLDER, settings.ARTILLERY_FOLDER, settings.ARTILLERY_FILE), path.join(settings.DIST_FOLDER, settings.ARTILLERY_FOLDER) ), ), ( ( path.join(settings.OUTPUT_FOLDER, settings.ARTILLERY_FOLDER, settings.ARTILLERY_YAML), path.join(settings.DIST_FOLDER, settings.ARTILLERY_FOLDER) ), ), ( ( path.join(settings.OUTPUT_FOLDER, settings.SWAGGER_FILE), path.join(settings.DIST_FOLDER) ), ) ]) assert patched_shell.copy.call_args_list == expected_sources @mock.patch('atlas.modules.transformer.artillery.dist.shutil') @mock.patch('atlas.modules.transformer.artillery.dist.os.path.isfile') def test_copy_files_no_files(self, patch_os_is_file, patched_shell): patch_os_is_file.return_value = False instance = ArtilleryDist() instance.path = "" instance.copy_files() expected_sources = [ ( ( path.join(settings.INPUT_FOLDER, settings.ARTILLERY_FOLDER, settings.ARTILLERY_HOOK_FILE), path.join(settings.DIST_FOLDER, settings.ARTILLERY_FOLDER) ), ), ( ( path.join(settings.OUTPUT_FOLDER, settings.ARTILLERY_FOLDER, settings.ARTILLERY_FILE), path.join(settings.DIST_FOLDER, settings.ARTILLERY_FOLDER) ), ), ( ( path.join(settings.OUTPUT_FOLDER, settings.ARTILLERY_FOLDER, settings.ARTILLERY_YAML), path.join(settings.DIST_FOLDER, settings.ARTILLERY_FOLDER) ), ), ( ( path.join(settings.OUTPUT_FOLDER, settings.SWAGGER_FILE), path.join(settings.DIST_FOLDER) ), ) ] assert patched_shell.copy.call_args_list == expected_sources @mock.patch('atlas.modules.transformer.artillery.dist.shutil') @mock.patch('atlas.modules.transformer.artillery.dist.os.path.exists') def test_copy_folders_destination_exists(self, patch_os_path_exists, patched_shell): patch_os_path_exists.return_value = True instance = ArtilleryDist() instance.path = "" instance.copy_folders() patched_shell.rmtree.assert_called() patched_shell.copytree.assert_called() @mock.patch('atlas.modules.transformer.artillery.dist.shutil') @mock.patch('atlas.modules.transformer.artillery.dist.os.path.exists') def test_copy_folders_destination_not_exists(self, patch_os_path_exists, patched_shell): patch_os_path_exists.return_value = False instance = ArtilleryDist() instance.path = "" instance.copy_folders() patched_shell.rmtree.assert_not_called() patched_shell.copytree.assert_called() @mock.patch('atlas.modules.transformer.artillery.dist.os.makedirs') @mock.patch('atlas.modules.transformer.artillery.dist.os.path.exists') def test_create_folder_path_exists(self, patch_os_path_exists, patched_makedir): patch_os_path_exists.return_value = True instance = ArtilleryDist() instance.path = "" instance.create_folder('folder') patched_makedir.assert_not_called() @mock.patch('atlas.modules.transformer.artillery.dist.os.makedirs') @mock.patch('atlas.modules.transformer.artillery.dist.os.path.exists') def test_create_folder_path_not_exists(self, patch_os_path_exists, patched_makedir): patch_os_path_exists.return_value = False instance = ArtilleryDist() instance.path = "" instance.create_folder('folder') patched_makedir.assert_called()

36.024691

110

0.63194

612

5,836

5.74183

0.124183

0.106431

0.130905

0.118384

0.791975

0.78173

0.776892

0.774047

0.758395

0.730222

0

0.271247

5,836

161

111

36.248447

0.82624

0

0.556452

0

0.118403

0.10658

0

0.08871

1

0.056452

false

0

0.032258

0

0.096774

0

null

0

1

0

null

0

5