xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

23e02b42f9834f452d669bf43e6ade3322053afe

45

py

Python

cflearn/data/__init__.py

carefree0910/carefree-learn

2043812afbe9c56f01ec1639961736313ee062ba

[ "MIT" ]

400

2020-07-05T18:55:49.000Z

2022-02-21T02:33:08.000Z

cflow/api/cv/data/__init__.py

carefree0910/carefree-flow

7035015a072cf8142074d01683889f90950d2939

[ "MIT" ]

82

2020-08-01T13:29:38.000Z

2021-10-09T07:13:44.000Z

cflearn/data/__init__.py

carefree0910/carefree-learn

2043812afbe9c56f01ec1639961736313ee062ba

[ "MIT" ]

34

2020-07-05T21:15:34.000Z

2021-12-20T08:45:17.000Z

from .core import * from .interface import *

15

24

0.733333

6

45

5.5

0.666667

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0.177778

45

2

25

22.5

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null

0

1

0

1

0

1

0

6

9b0d40641bbef7004d990876a4240fcad00c7ef2

45

py

Python

app/users/serializers/__init__.py

InNickF/django-template

a8a9e1e5cd8cf63543cc78ef4fbd6bce060a448b

[ "MIT" ]

3

2020-09-20T11:21:01.000Z

2021-01-31T18:55:54.000Z

app/users/serializers/__init__.py

InNickF/django-template

a8a9e1e5cd8cf63543cc78ef4fbd6bce060a448b

[ "MIT" ]

2

2020-09-21T09:53:32.000Z

2021-06-10T19:40:41.000Z

app/users/serializers/__init__.py

InNickF/django-template

a8a9e1e5cd8cf63543cc78ef4fbd6bce060a448b

[ "MIT" ]

2

2021-01-17T20:59:23.000Z

2021-01-31T18:55:58.000Z

"""Users serializers""" from .users import *

15

23

0.688889

5

45

6.2

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45

2

24

22.5

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true

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null

0

1

0

1

0

1

0

6

7b0ff51baec4951388f12a5b837fc3652e41ad1a

9,550

py

Python

tests/unit/workflows/nodejs_npm/test_actions.py

honey-insurance/aws-lambda-builders

908ad5f892b9a40ace7181fa53b949511c929055

[ "Apache-2.0" ]

180

2018-11-09T04:51:19.000Z

2020-08-06T21:43:20.000Z

tests/unit/workflows/nodejs_npm/test_actions.py

honey-insurance/aws-lambda-builders

908ad5f892b9a40ace7181fa53b949511c929055

[ "Apache-2.0" ]

108

2018-11-08T18:34:51.000Z

2020-08-12T17:59:41.000Z

tests/unit/workflows/nodejs_npm/test_actions.py

honey-insurance/aws-lambda-builders

908ad5f892b9a40ace7181fa53b949511c929055

[ "Apache-2.0" ]

91

2018-11-08T22:58:00.000Z

2020-08-17T21:15:31.000Z

from unittest import TestCase from mock import patch, call from parameterized import parameterized from aws_lambda_builders.actions import ActionFailedError from aws_lambda_builders.workflows.nodejs_npm.actions import ( NodejsNpmPackAction, NodejsNpmInstallAction, NodejsNpmrcAndLockfileCopyAction, NodejsNpmrcCleanUpAction, NodejsNpmLockFileCleanUpAction, NodejsNpmCIAction, ) from aws_lambda_builders.workflows.nodejs_npm.npm import NpmExecutionError class TestNodejsNpmPackAction(TestCase): @patch("aws_lambda_builders.workflows.nodejs_npm.utils.OSUtils") @patch("aws_lambda_builders.workflows.nodejs_npm.npm.SubprocessNpm") def test_tars_and_unpacks_npm_project(self, OSUtilMock, SubprocessNpmMock): osutils = OSUtilMock.return_value subprocess_npm = SubprocessNpmMock.return_value action = NodejsNpmPackAction( "artifacts", "scratch_dir", "manifest", osutils=osutils, subprocess_npm=subprocess_npm ) osutils.dirname.side_effect = lambda value: "/dir:{}".format(value) osutils.abspath.side_effect = lambda value: "/abs:{}".format(value) osutils.joinpath.side_effect = lambda a, b: "{}/{}".format(a, b) subprocess_npm.run.return_value = "package.tar" action.execute() subprocess_npm.run.assert_called_with(["pack", "-q", "file:/abs:/dir:manifest"], cwd="scratch_dir") osutils.extract_tarfile.assert_called_with("scratch_dir/package.tar", "artifacts") @patch("aws_lambda_builders.workflows.nodejs_npm.utils.OSUtils") @patch("aws_lambda_builders.workflows.nodejs_npm.npm.SubprocessNpm") def test_raises_action_failed_when_npm_fails(self, OSUtilMock, SubprocessNpmMock): osutils = OSUtilMock.return_value subprocess_npm = SubprocessNpmMock.return_value builder_instance = SubprocessNpmMock.return_value builder_instance.run.side_effect = NpmExecutionError(message="boom!") action = NodejsNpmPackAction( "artifacts", "scratch_dir", "manifest", osutils=osutils, subprocess_npm=subprocess_npm ) with self.assertRaises(ActionFailedError) as raised: action.execute() self.assertEqual(raised.exception.args[0], "NPM Failed: boom!") class TestNodejsNpmInstallAction(TestCase): @patch("aws_lambda_builders.workflows.nodejs_npm.npm.SubprocessNpm") def test_installs_npm_production_dependencies_for_npm_project(self, SubprocessNpmMock): subprocess_npm = SubprocessNpmMock.return_value action = NodejsNpmInstallAction("artifacts", subprocess_npm=subprocess_npm) action.execute() expected_args = ["install", "-q", "--no-audit", "--no-save", "--production", "--unsafe-perm"] subprocess_npm.run.assert_called_with(expected_args, cwd="artifacts") @patch("aws_lambda_builders.workflows.nodejs_npm.npm.SubprocessNpm") def test_can_set_mode(self, SubprocessNpmMock): subprocess_npm = SubprocessNpmMock.return_value action = NodejsNpmInstallAction("artifacts", subprocess_npm=subprocess_npm, is_production=False) action.execute() expected_args = ["install", "-q", "--no-audit", "--no-save", "--production=false", "--unsafe-perm"] subprocess_npm.run.assert_called_with(expected_args, cwd="artifacts") @patch("aws_lambda_builders.workflows.nodejs_npm.npm.SubprocessNpm") def test_raises_action_failed_when_npm_fails(self, SubprocessNpmMock): subprocess_npm = SubprocessNpmMock.return_value builder_instance = SubprocessNpmMock.return_value builder_instance.run.side_effect = NpmExecutionError(message="boom!") action = NodejsNpmInstallAction("artifacts", subprocess_npm=subprocess_npm) with self.assertRaises(ActionFailedError) as raised: action.execute() self.assertEqual(raised.exception.args[0], "NPM Failed: boom!") class TestNodejsNpmCIAction(TestCase): @patch("aws_lambda_builders.workflows.nodejs_npm.npm.SubprocessNpm") def test_tars_and_unpacks_npm_project(self, SubprocessNpmMock): subprocess_npm = SubprocessNpmMock.return_value action = NodejsNpmCIAction("sources", subprocess_npm=subprocess_npm) action.execute() subprocess_npm.run.assert_called_with(["ci"], cwd="sources") @patch("aws_lambda_builders.workflows.nodejs_npm.npm.SubprocessNpm") def test_raises_action_failed_when_npm_fails(self, SubprocessNpmMock): subprocess_npm = SubprocessNpmMock.return_value builder_instance = SubprocessNpmMock.return_value builder_instance.run.side_effect = NpmExecutionError(message="boom!") action = NodejsNpmCIAction("sources", subprocess_npm=subprocess_npm) with self.assertRaises(ActionFailedError) as raised: action.execute() self.assertEqual(raised.exception.args[0], "NPM Failed: boom!") class TestNodejsNpmrcAndLockfileCopyAction(TestCase): @parameterized.expand( [ [False, False], [True, False], [False, True], [True, True], ] ) @patch("aws_lambda_builders.workflows.nodejs_npm.utils.OSUtils") def test_copies_into_a_project_if_file_exists(self, npmrc_exists, package_lock_exists, OSUtilMock): osutils = OSUtilMock.return_value osutils.joinpath.side_effect = lambda a, b: "{}/{}".format(a, b) action = NodejsNpmrcAndLockfileCopyAction("artifacts", "source", osutils=osutils) osutils.file_exists.side_effect = [npmrc_exists, package_lock_exists] action.execute() filename_exists = { ".npmrc": npmrc_exists, "package-lock.json": package_lock_exists, } file_exists_calls = [call("source/{}".format(filename)) for filename in filename_exists] copy_file_calls = [ call("source/{}".format(filename), "artifacts") for filename, exists in filename_exists.items() if exists ] osutils.file_exists.assert_has_calls(file_exists_calls) osutils.copy_file.assert_has_calls(copy_file_calls) @patch("aws_lambda_builders.workflows.nodejs_npm.utils.OSUtils") def test_raises_action_failed_when_copying_fails(self, OSUtilMock): osutils = OSUtilMock.return_value osutils.joinpath.side_effect = lambda a, b: "{}/{}".format(a, b) osutils.copy_file.side_effect = OSError() action = NodejsNpmrcAndLockfileCopyAction("artifacts", "source", osutils=osutils) with self.assertRaises(ActionFailedError): action.execute() class TestNodejsNpmrcCleanUpAction(TestCase): @patch("aws_lambda_builders.workflows.nodejs_npm.utils.OSUtils") def test_removes_npmrc_if_npmrc_exists(self, OSUtilMock): osutils = OSUtilMock.return_value osutils.joinpath.side_effect = lambda a, b: "{}/{}".format(a, b) action = NodejsNpmrcCleanUpAction("artifacts", osutils=osutils) osutils.file_exists.side_effect = [True] action.execute() osutils.remove_file.assert_called_with("artifacts/.npmrc") @patch("aws_lambda_builders.workflows.nodejs_npm.utils.OSUtils") def test_skips_npmrc_removal_if_npmrc_doesnt_exist(self, OSUtilMock): osutils = OSUtilMock.return_value osutils.joinpath.side_effect = lambda a, b: "{}/{}".format(a, b) action = NodejsNpmrcCleanUpAction("artifacts", osutils=osutils) osutils.file_exists.side_effect = [False] action.execute() osutils.remove_file.assert_not_called() @patch("aws_lambda_builders.workflows.nodejs_npm.utils.OSUtils") def test_raises_action_failed_when_removing_fails(self, OSUtilMock): osutils = OSUtilMock.return_value osutils.joinpath.side_effect = lambda a, b: "{}/{}".format(a, b) osutils.remove_file.side_effect = OSError() action = NodejsNpmrcCleanUpAction("artifacts", osutils=osutils) with self.assertRaises(ActionFailedError): action.execute() class TestNodejsNpmLockFileCleanUpAction(TestCase): @patch("aws_lambda_builders.workflows.nodejs_npm.utils.OSUtils") def test_removes_dot_package_lock_if_exists(self, OSUtilMock): osutils = OSUtilMock.return_value osutils.joinpath.side_effect = lambda a, b, c: "{}/{}/{}".format(a, b, c) action = NodejsNpmLockFileCleanUpAction("artifacts", osutils=osutils) osutils.file_exists.side_effect = [True] action.execute() osutils.remove_file.assert_called_with("artifacts/node_modules/.package-lock.json") @patch("aws_lambda_builders.workflows.nodejs_npm.utils.OSUtils") def test_skips_lockfile_removal_if_it_doesnt_exist(self, OSUtilMock): osutils = OSUtilMock.return_value osutils.joinpath.side_effect = lambda a, b, c: "{}/{}/{}".format(a, b, c) action = NodejsNpmLockFileCleanUpAction("artifacts", osutils=osutils) osutils.file_exists.side_effect = [False] action.execute() osutils.remove_file.assert_not_called() @patch("aws_lambda_builders.workflows.nodejs_npm.utils.OSUtils") def test_raises_action_failed_when_removing_fails(self, OSUtilMock): osutils = OSUtilMock.return_value osutils.joinpath.side_effect = lambda a, b, c: "{}/{}/{}".format(a, b, c) osutils.remove_file.side_effect = OSError() action = NodejsNpmLockFileCleanUpAction("artifacts", osutils=osutils) with self.assertRaises(ActionFailedError): action.execute()

40.466102

117

0.718639

1,027

9,550

6.401168

0.133398

0.051415

0.051719

0.075145

0.808336

0.779586

0.764375

0.71874

0.703681

0.670216

0

0.000381

0.174974

9,550

235

118

40.638298

0.833989

0

0.620482

0

0.166492

0.108168

0

0.120482

1

0.090361

false

0

0.036145

0

0.162651

0

null

0

1

0

null

0

6

9e43cb94c88ac2bd2d63336288831b58d9b57cc0

28

py

Python

simple.py

saivenkat288/goCD-Test

54cbc274f37d7ef8ecd9de608789ef89a8fb3fa4

[ "Apache-2.0" ]

1

2021-08-09T10:17:13.000Z

simple.py

saivenkat288/goCD-Test

54cbc274f37d7ef8ecd9de608789ef89a8fb3fa4

[ "Apache-2.0" ]

null

simple.py

saivenkat288/goCD-Test

54cbc274f37d7ef8ecd9de608789ef89a8fb3fa4

[ "Apache-2.0" ]

null

print("Hey, Its working!!")

14

27

0.642857

4

28

4.5

1

0

0.107143

28

1

28

0.72

0

0.642857

0

1

0

true

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

9e44cd41605a4e45ff04c0276011bf4eae3ea27a

42

py

Python

lidardet/datasets/processor/__init__.py

Jiaolong/trajectory-prediction

3fd4e6253b44dfdc86e7c08e93c002baf66f2e46

[ "Apache-2.0" ]

6

2021-05-10T09:42:01.000Z

2022-01-04T08:03:42.000Z

lidardet/datasets/processor/__init__.py

Jiaolong/trajectory-prediction

3fd4e6253b44dfdc86e7c08e93c002baf66f2e46

[ "Apache-2.0" ]

3

2021-08-16T02:19:10.000Z

2022-01-10T02:05:48.000Z

lidardet/datasets/processor/__init__.py

Jiaolong/trajectory-prediction

3fd4e6253b44dfdc86e7c08e93c002baf66f2e46

[ "Apache-2.0" ]

1

2021-07-15T00:51:58.000Z

from .data_processor import DataProcessor

21

41

0.880952

5

42

7.2

1

0

0.095238

42

1

42

0.947368

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

9e7e147544db863076beb658f81087becee0b6b7

20

py

Python

examples/py30-0027-type-hints1.py

jwilk-forks/python-grammar-changes

5cbc14e520fadfef8539760a4ffdbe14b9d02f39

[ "MIT" ]

8

2020-11-21T22:39:41.000Z

2022-03-13T18:45:53.000Z

examples/py30-0027-type-hints1.py

jwilk-forks/python-grammar-changes

5cbc14e520fadfef8539760a4ffdbe14b9d02f39

[ "MIT" ]

1

2021-12-10T10:45:38.000Z

examples/py30-0027-type-hints1.py

jwilk-forks/python-grammar-changes

5cbc14e520fadfef8539760a4ffdbe14b9d02f39

[ "MIT" ]

1

2022-02-07T11:16:38.000Z

def f(x: str): pass

10

19

0.6

5

20

2.4

1

0

0.2

20

1

20

0.75

0

1

false

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

9e83d931b3bea5e28898303ee54c401b375ee99a

33

py

Python

cw_nets/unet_pytorch.py

dlindenbaum/cw-nets

141b8f9a01b8f75e6ce34be0e8c8a931d0559b7c

[ "Apache-2.0" ]

3

2018-07-14T07:45:29.000Z

2019-04-01T15:28:24.000Z

cw_nets/unet_pytorch.py

CosmiQ/cw-nets

7b78ac7e1f23b512def23ede52663970b2c87d6e

[ "Apache-2.0" ]

44

2018-07-12T17:13:20.000Z

2019-05-01T16:04:04.000Z

cw_nets/unet_pytorch.py

dlindenbaum/cw-nets

141b8f9a01b8f75e6ce34be0e8c8a931d0559b7c

[ "Apache-2.0" ]

1

2018-10-13T17:06:20.000Z

print("TODO-Not implemented yet")

33

0.787879

5

33

5.2

1

0

0.060606

33

1

33

0.83871

0

0.705882

0

1

0

true

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

7b459dfd9fadfe8508bab4f85b7f513fe02f990e

61

py

Python

tests/test_greet.py

terasakisatoshi/pydev_conda

dc26fed9d329a06151354e692c6d18ac342cf08c

[ "MIT" ]

null

tests/test_greet.py

terasakisatoshi/pydev_conda

dc26fed9d329a06151354e692c6d18ac342cf08c

[ "MIT" ]

null

tests/test_greet.py

terasakisatoshi/pydev_conda

dc26fed9d329a06151354e692c6d18ac342cf08c

[ "MIT" ]

null

from pydev_conda import greet def test_greet(): greet()

12.2

29

0.721311

9

61

4.666667

0.777778

0

0.196721

61

4

30

15.25

0.857143

0

1

0.333333

true

0

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

7b800911d241237bcc706e3f97f87cd8adf0474c

202

py

Python

src/utils/build_key.py

VitalyPetrov/asgi-ml

c297df2e3365cb8fd36fb8048db31e8f16d96fe7

[ "MIT" ]

1

2020-10-09T16:04:43.000Z

src/utils/build_key.py

VitalyPetrov/asgi-ml

c297df2e3365cb8fd36fb8048db31e8f16d96fe7

[ "MIT" ]

null

src/utils/build_key.py

VitalyPetrov/asgi-ml

c297df2e3365cb8fd36fb8048db31e8f16d96fe7

[ "MIT" ]

null

from hashlib import md5 from typing import Any, Callable def build_hashkey(func: Callable, *args: Any, **kwargs: Any) -> str: return md5(kwargs.get("features").json().encode("utf-8")).hexdigest()

28.857143

73

0.707921

29

202

4.896552

0.758621

0

0.017045

0.128713

202

6

74

33.666667

0.789773

0

0.064356

0

1

0.25

false

0

0.5

0.25

1

0

1

0

null

0

1

0

null

0

1

0

1

0

6

7bc4abcded457e23ca930818e1b41837e22ff7ee

990

py

Python

temboo/core/Library/SendGrid/WebAPI/Statistics/__init__.py

jordanemedlock/psychtruths

52e09033ade9608bd5143129f8a1bfac22d634dd

[ "Apache-2.0" ]

7

2016-03-07T02:07:21.000Z

2022-01-21T02:22:41.000Z

temboo/core/Library/SendGrid/WebAPI/Statistics/__init__.py

jordanemedlock/psychtruths

52e09033ade9608bd5143129f8a1bfac22d634dd

[ "Apache-2.0" ]

null

temboo/core/Library/SendGrid/WebAPI/Statistics/__init__.py

jordanemedlock/psychtruths

52e09033ade9608bd5143129f8a1bfac22d634dd

[ "Apache-2.0" ]

8

2016-06-14T06:01:11.000Z

2020-04-22T09:21:44.000Z

from temboo.Library.SendGrid.WebAPI.Statistics.GetAllTimeCategoryTotals import GetAllTimeCategoryTotals, GetAllTimeCategoryTotalsInputSet, GetAllTimeCategoryTotalsResultSet, GetAllTimeCategoryTotalsChoreographyExecution from temboo.Library.SendGrid.WebAPI.Statistics.GetCategoryStatistics import GetCategoryStatistics, GetCategoryStatisticsInputSet, GetCategoryStatisticsResultSet, GetCategoryStatisticsChoreographyExecution from temboo.Library.SendGrid.WebAPI.Statistics.ListAllCategories import ListAllCategories, ListAllCategoriesInputSet, ListAllCategoriesResultSet, ListAllCategoriesChoreographyExecution from temboo.Library.SendGrid.WebAPI.Statistics.RetrieveAggregates import RetrieveAggregates, RetrieveAggregatesInputSet, RetrieveAggregatesResultSet, RetrieveAggregatesChoreographyExecution from temboo.Library.SendGrid.WebAPI.Statistics.RetrieveStatistics import RetrieveStatistics, RetrieveStatisticsInputSet, RetrieveStatisticsResultSet, RetrieveStatisticsChoreographyExecution

165

219

0.924242

60

990

15.25

0.45

0.054645

0.092896

0.136612

0.224044

0

0.035354

990

5

220

198

0.958115

0

1

0

true

0

1

0

1

0

1

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

c86d74af8a4641dccf7d64383ceba5a0f5ec7b26

2,552

py

Python

day17/program.py

jredzepovic/AoC2020

aed66e27ea8a3e1f38457f8f8b21f9cdbf4d173d

[ "MIT" ]

null

day17/program.py

jredzepovic/AoC2020

aed66e27ea8a3e1f38457f8f8b21f9cdbf4d173d

[ "MIT" ]

null

day17/program.py

jredzepovic/AoC2020

aed66e27ea8a3e1f38457f8f8b21f9cdbf4d173d

[ "MIT" ]

null

from itertools import product import numpy as np def extend_grid_3d(current_grid): x, y, z = [], [], [] for loc in current_grid: x.append(loc[0]) y.append(loc[1]) z.append(loc[2]) grid = np.meshgrid( range(min(x) - 1, max(x) + 2), range(min(y) - 1, max(y) + 2), range(min(z) - 1, max(z) + 2)) return list(map(tuple, np.stack((grid[0].ravel(), grid[1].ravel(), grid[2].ravel()), axis=1))) def extend_grid_4d(current_grid): x, y, z, w = [], [], [], [] for loc in current_grid: x.append(loc[0]) y.append(loc[1]) z.append(loc[2]) w.append(loc[3]) grid = np.meshgrid( range(min(x) - 1, max(x) + 2), range(min(y) - 1, max(y) + 2), range(min(z) - 1, max(z) + 2), range(min(w) - 1, max(w) + 2)) return list(map(tuple, np.stack((grid[0].ravel(), grid[1].ravel(), grid[2].ravel(), grid[3].ravel()), axis=1))) def main(): # part 1 with open("./input.txt") as f: active = set([(i, j, 0) for i, l in enumerate(f.readlines()) for j, p in enumerate(l) if p == "#"]) transitions = list(product((-1, 0, 1), repeat=3)) transitions.remove((0, 0, 0)) for _ in range(6): next_grid = set() grid = extend_grid_3d(active) for x, y, z in grid: active_neighbors = sum((x + dx, y + dy, z + dz) in active for dx, dy, dz in transitions) if (x, y, z) in active and (active_neighbors == 2 or active_neighbors == 3): next_grid.add((x, y, z)) if (x, y, z) not in active and active_neighbors == 3: next_grid.add((x, y, z)) active = next_grid print(len(active)) # part 2 with open("./input.txt") as f: active = set([(i, j, 0, 0) for i, l in enumerate(f.readlines()) for j, p in enumerate(l) if p == "#"]) transitions = list(product((-1, 0, 1), repeat=4)) transitions.remove((0, 0, 0, 0)) for _ in range(6): next_grid = set() grid = extend_grid_4d(active) for x, y, z, w in grid: active_neighbors = sum((x + dx, y + dy, z + dz, w + dw) in active for dx, dy, dz, dw in transitions) if (x, y, z, w) in active and (active_neighbors == 2 or active_neighbors == 3): next_grid.add((x, y, z, w)) if (x, y, z, w) not in active and active_neighbors == 3: next_grid.add((x, y, z, w)) active = next_grid print(len(active)) if __name__ == "__main__": main()

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null

0

6

c8737a92b4e8d950d80241dbe25134323f70c768

5,985

py

Python

scripts/processed_data_generator.py

tugot17/Polish-Parties-Twitter-Activity

46c92b6a1d7a2d5a57f36b27c8dcb2ac3a5af6ef

[ "MIT" ]

null

scripts/processed_data_generator.py

tugot17/Polish-Parties-Twitter-Activity

46c92b6a1d7a2d5a57f36b27c8dcb2ac3a5af6ef

[ "MIT" ]

null

scripts/processed_data_generator.py

tugot17/Polish-Parties-Twitter-Activity

46c92b6a1d7a2d5a57f36b27c8dcb2ac3a5af6ef

[ "MIT" ]

null

from os.path import join, realpath, dirname, exists, basename from os import makedirs import pandas as pd from pandas import CategoricalDtype from tqdm.auto import tqdm from .coalitions import coalitions def generate_number_of_tweets_per_day(df, output_dir): # exclude retweets df = df[df.user_rt.isnull()] value_counts = df['date'].value_counts() df_value_counts = pd.DataFrame(value_counts) df_value_counts = df_value_counts.reset_index() df_value_counts.columns = ['date', 'number_of_tweets'] df_value_counts = df_value_counts.sort_values(by=['date']) if not exists(output_dir): makedirs(output_dir) path = join(output_dir, "number_of_tweets_per_day.csv") df_value_counts.to_csv(path, index=False) def generate_number_of_retweets_per_day(df, output_dir): df = df.dropna(subset=['user_rt']) value_counts = df['date'].value_counts() df_value_counts = pd.DataFrame(value_counts) df_value_counts = df_value_counts.reset_index() df_value_counts.columns = ['date', 'number_of_retweets'] df_value_counts = df_value_counts.sort_values(by=['date']) if not exists(output_dir): makedirs(output_dir) path = join(output_dir, "number_of_retweets_per_day.csv") df_value_counts.to_csv(path, index=False) def generate_number_of_retweets_for_users_tweets_per_day(df, output_dir): df_retweets_counts = df.groupby("date")['retweets_count'].sum().reset_index() df_retweets_counts = df_retweets_counts.sort_values(by=['date']) if not exists(output_dir): makedirs(output_dir) path = join(output_dir, "number_of_retweets_for_users_tweets_per_day.csv") df_retweets_counts.to_csv(path, index=False) def generate_number_of_likes_for_users_tweets_per_day(df, output_dir): df_likes_counts = df.groupby("date")['likes_count'].sum().reset_index() df_likes_counts = df_likes_counts.sort_values(by=['date']) if not exists(output_dir): makedirs(output_dir) path = join(output_dir, "number_of_likes_for_users_tweets_per_day.csv") df_likes_counts.to_csv(path, index=False) def generate_tweeting_activity_distribution_in_a_day(df, output_dir): # exclude retweets df = df[df.user_rt.isnull()] value_counts = pd.to_datetime(df['time']).dt.hour.value_counts(dropna=True) df_value_counts = pd.DataFrame(value_counts) df_value_counts = df_value_counts.reset_index() df_value_counts.columns = ['hour', 'number_of_tweets'] df_value_counts = df_value_counts.sort_values(by=['hour']) if not exists(output_dir): makedirs(output_dir) path = join(output_dir, "tweeting_activity_distribution_in_a_day.csv") df_value_counts.to_csv(path, index=False) def generate_tweeting_activity_distribution_in_a_week(df, output_dir): # exclude retweets df = df[df.user_rt.isnull()] value_counts = pd.to_datetime(df['date']).dt.day_name().value_counts(dropna=True) df_value_counts = pd.DataFrame(value_counts) df_value_counts = df_value_counts.reset_index() df_value_counts.columns = ['week_day', 'number_of_tweets'] cats = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'] cat_type = CategoricalDtype(categories=cats, ordered=True) df_value_counts['week_day'] = df_value_counts['week_day'].astype(cat_type) df_value_counts = df_value_counts.sort_values(by=['week_day']) if not exists(output_dir): makedirs(output_dir) path = join(output_dir, "tweeting_activity_distribution_in_a_week.csv") df_value_counts.to_csv(path, index=False) def generate_retweeting_activity_distribution_in_a_day(df, output_dir): df = df.dropna(subset=['user_rt']) value_counts = pd.to_datetime(df['time']).dt.hour.value_counts(dropna=True) df_value_counts = pd.DataFrame(value_counts) df_value_counts = df_value_counts.reset_index() df_value_counts.columns = ['hour', 'number_of_retweets'] df_value_counts = df_value_counts.sort_values(by=['hour']) if not exists(output_dir): makedirs(output_dir) path = join(output_dir, "retweeting_activity_distribution_in_a_day.csv") df_value_counts.to_csv(path, index=False) def generate_retweeting_activity_distribution_in_a_week(df, output_dir): df = df.dropna(subset=['user_rt']) value_counts = pd.to_datetime(df['date']).dt.day_name().value_counts(dropna=True) df_value_counts = pd.DataFrame(value_counts) df_value_counts = df_value_counts.reset_index() df_value_counts.columns = ['week_day', 'number_of_retweets'] cats = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'] cat_type = CategoricalDtype(categories=cats, ordered=True) df_value_counts['week_day'] = df_value_counts['week_day'].astype(cat_type) df_value_counts = df_value_counts.sort_values(by=['week_day']) if not exists(output_dir): makedirs(output_dir) path = join(output_dir, "retweeting_activity_distribution_in_a_week.csv") df_value_counts.to_csv(path, index=False) if __name__ == '__main__': data_dir_path = "data" for coalition_name in tqdm(coalitions.keys()): for party_name in coalitions[coalition_name]: save_dir = join("processed_data", coalition_name, party_name) df = pd.read_csv(join(data_dir_path, f"{party_name}.csv")) generate_number_of_tweets_per_day(df, join(save_dir)) generate_number_of_retweets_per_day(df, join(save_dir)) generate_number_of_retweets_for_users_tweets_per_day(df, join(save_dir)) generate_number_of_likes_for_users_tweets_per_day(df, join(save_dir)) generate_tweeting_activity_distribution_in_a_day(df, join(save_dir)) generate_tweeting_activity_distribution_in_a_week(df, join(save_dir)) generate_retweeting_activity_distribution_in_a_day(df, join(save_dir)) generate_retweeting_activity_distribution_in_a_week(df, join(save_dir))

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c8d59f767d1e53762d773da161d29b34be697973

90

py

Python

kipoiseq/extractors/__init__.py

KalinNonchev/kipoiseq

38d1134885e401198acd3883286dc55627cf12a6

[ "MIT" ]

2

2019-12-16T17:13:04.000Z

2021-07-29T12:05:47.000Z

kipoiseq/extractors/__init__.py

KalinNonchev/kipoiseq

38d1134885e401198acd3883286dc55627cf12a6

[ "MIT" ]

117

2020-04-22T12:46:45.000Z

2021-08-02T04:40:58.000Z

kipoiseq/extractors/__init__.py

KalinNonchev/kipoiseq

38d1134885e401198acd3883286dc55627cf12a6

[ "MIT" ]

null

from .base import * from .vcf import * from .vcf_seq import * from .vcf_matching import *

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363

py

Python

vesper/command/clip_exporter.py

HaroldMills/NFC

356b2234dc3c7d180282a597fa1e039ae79e03c6

[ "MIT" ]

null

vesper/command/clip_exporter.py

HaroldMills/NFC

356b2234dc3c7d180282a597fa1e039ae79e03c6

[ "MIT" ]

1

2015-01-12T12:41:29.000Z

vesper/command/clip_exporter.py

HaroldMills/NFC

356b2234dc3c7d180282a597fa1e039ae79e03c6

[ "MIT" ]

null

class ClipExporter: clip_query_set_select_related_args = None def begin_exports(self): pass def begin_subset_exports( self, station, mic_output, date, detector, clip_count): pass def export(self, clip): pass def end_subset_exports(self): pass def end_exports(self): pass

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6

a808bef29ee082d73f135a0f9fba51a82693d944

215

py

Python

src/vanchor/resources/__init__.py

AlexAsplund/Vanchor

cb5d1c95567ab9d9bd280e2ca3022e4a2da1fa67

[ "MIT" ]

12

2021-09-25T01:03:31.000Z

2022-02-04T09:13:00.000Z

src/vanchor/resources/__init__.py

AlexAsplund/Vanchor

cb5d1c95567ab9d9bd280e2ca3022e4a2da1fa67

[ "MIT" ]

13

2021-09-20T19:56:50.000Z

2022-01-10T13:08:32.000Z

src/vanchor/resources/__init__.py

AlexAsplund/Vanchor

cb5d1c95567ab9d9bd280e2ca3022e4a2da1fa67

[ "MIT" ]

1

2021-10-05T10:49:59.000Z

from .config import * from .events import * from .device_manager import * from .workers import * from .functions import * from .tools import * from .data import * from .main import * from .metrics import *

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a82c56e6c83c863d1bd739fff6aae57a9fa3eee4

1,156

py

Python

src/cosa_constants.py

charleshong3/cosa

20936a87ced9371736d7280d613821ae8d564fa3

[ "BSD-2-Clause" ]

35

2021-06-15T17:37:45.000Z

2022-03-10T10:50:31.000Z

src/cosa_constants.py

charleshong3/cosa

20936a87ced9371736d7280d613821ae8d564fa3

[ "BSD-2-Clause" ]

1

2021-08-07T17:52:04.000Z

2021-09-15T19:35:51.000Z

src/cosa_constants.py

charleshong3/cosa

20936a87ced9371736d7280d613821ae8d564fa3

[ "BSD-2-Clause" ]

7

2021-06-18T08:52:54.000Z

2022-03-08T15:39:40.000Z

#!/usr/bin/env python3 # j=7, v=3, prob - var _A = [ [1, 0, 0], # R [1, 0, 0], # S [0, 1, 1], # P [0, 1, 1], # Q [1, 1, 0], # C [1, 0, 1], # K [0, 1, 1], # N ] # assume 6 levels of ranks # v=3, i=6 var - rank _B = [ [1, 0, 1, 0, 0, 1], # Weights [0, 0, 0, 1, 1, 1], # Inputs [0, 1, 0, 0, 1, 1], # Outputs ] # for uneven mapping # v=3, i=6, i'=6 _Z = [ # Weights [ [1, 0, 0, 0, 0, 0], # mem 0 [0, 0, 0, 0, 0, 0], # mem 1 [1, 1, 1, 0, 0, 0], # mem 2 [0, 0, 0, 0, 0, 0], # mem 3 [0, 0, 0, 0, 0, 0], # mem 4 [1, 1, 1, 1, 1, 1], # mem 5 ], # Inputs [ [0, 0, 0, 0, 0, 0], # mem 0 [0, 0, 0, 0, 0, 0], # mem 1 [0, 0, 0, 0, 0, 0], # mem 2 [1, 1, 1, 1, 0, 0], # mem 3 [1, 1, 1, 1, 1, 0], # mem 4 [1, 1, 1, 1, 1, 1], # mem 5 ], # Outputs [ [0, 0, 0, 0, 0, 0], # mem 0 [1, 1, 0, 0, 0, 0], # mem 1 [0, 0, 0, 0, 0, 0], # mem 2 [0, 0, 0, 0, 0, 0], # mem 3 [1, 1, 1, 1, 1, 0], # mem 4 [1, 1, 1, 1, 1, 1], # mem 5 ], ]

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6

b57a3b8bb757613e8cd40079603a5ea0de8841ec

208

py

Python

asgi_correlation_id/__init__.py

lakshaythareja/asgi-correlation-id

c8febfdc04191087fb96b8d1843ad80e6f5cd080

[ "BSD-4-Clause" ]

null

asgi_correlation_id/__init__.py

lakshaythareja/asgi-correlation-id

c8febfdc04191087fb96b8d1843ad80e6f5cd080

[ "BSD-4-Clause" ]

null

asgi_correlation_id/__init__.py

lakshaythareja/asgi-correlation-id

c8febfdc04191087fb96b8d1843ad80e6f5cd080

[ "BSD-4-Clause" ]

null

from asgi_correlation_id.log_filters import correlation_id_filter from asgi_correlation_id.middleware import CorrelationIdMiddleware __all__ = ( 'CorrelationIdMiddleware', 'correlation_id_filter', )

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a92c3b2733eeda494ec4c11450a70b5759d0e42f

146

py

Python

python/tom/hmm/__init__.py

m7thon/tom

fde3e934083c8c91256350b00e4128e48b351a8c

[ "MIT" ]

7

2017-10-04T05:41:46.000Z

2021-07-18T01:31:36.000Z

python/tom/hmm/__init__.py

m7thon/tom

fde3e934083c8c91256350b00e4128e48b351a8c

[ "MIT" ]

1

2021-05-16T16:16:55.000Z

2021-05-20T09:21:30.000Z

python/tom/hmm/__init__.py

m7thon/tom

fde3e934083c8c91256350b00e4128e48b351a8c

[ "MIT" ]

1

2017-10-04T05:41:59.000Z

from .._tomlib import Hmm, Policy from ._hmm import random_HMM, convert_HMM_to_OOM, learn_EM #try: # from ._hmm import ghmm #except: # pass

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a93b1738e39802cd55ad28aad02d09302286e47e

486

py

Python

pdf_struct/core/__init__.py

koreyou/pdf-struct

2a1549f21e63c9291f4daf62d7832b87ab20f7fd

[ "Apache-2.0" ]

10

2021-11-08T14:40:23.000Z

2022-03-29T13:57:33.000Z

pdf_struct/core/__init__.py

koreyou/pdf-struct

2a1549f21e63c9291f4daf62d7832b87ab20f7fd

[ "Apache-2.0" ]

1

2022-03-04T11:48:16.000Z

2022-03-09T15:43:36.000Z

pdf_struct/core/__init__.py

koreyou/pdf-struct

2a1549f21e63c9291f4daf62d7832b87ab20f7fd

[ "Apache-2.0" ]

4

2021-12-25T22:12:06.000Z

2022-03-13T17:44:10.000Z

from pdf_struct.core import clustering from pdf_struct.core import data_statistics from pdf_struct.core import document from pdf_struct.core import download from pdf_struct.core import evaluation from pdf_struct.core import export from pdf_struct.core import feature_extractor from pdf_struct.core import predictor from pdf_struct.core import preprocessing from pdf_struct.core import structure_evaluation from pdf_struct.core import transition_labels from pdf_struct.core import utils

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8d242655e2ddceb02c06b2d36050828ad7b4852f

31

py

Python

livecoding/helper.py

akafliegdarmstadt/AkaPythonTutorial

ab05b5e0f00c02526d280d0c567d5192890dc399

[ "MIT" ]

null

livecoding/helper.py

akafliegdarmstadt/AkaPythonTutorial

ab05b5e0f00c02526d280d0c567d5192890dc399

[ "MIT" ]

1

2018-10-15T19:46:45.000Z

livecoding/helper.py

akafliegdarmstadt/AkaPythonTutorial

ab05b5e0f00c02526d280d0c567d5192890dc399

[ "MIT" ]

1

2018-10-10T18:39:56.000Z

def hallo(): print('hallo')

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null

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6

8d50947ee79bd00367f5849abd72ed9b154daf6b

16,911

py

Python

tests/api/online/settings.py

happz/settlers

961a6d2121ab6e89106f17017f026c60c77f16f9

[ "MIT" ]

1

2018-11-16T09:41:31.000Z

tests/api/online/settings.py

happz/settlers

961a6d2121ab6e89106f17017f026c60c77f16f9

[ "MIT" ]

15

2015-01-07T14:17:36.000Z

2019-04-29T13:26:43.000Z

tests/api/online/settings.py

happz/settlers

961a6d2121ab6e89106f17017f026c60c77f16f9

[ "MIT" ]

null

""" """ from tests.online import TestCase from tests import cmp_json_dicts import random class Email(TestCase): def test_empty_submit(self): reply = self.query('/settings/email') cmp_json_dicts(reply, { 'status': 400, 'form': { 'updated_fields': None, 'invalid_field': 'email', 'orig_fields': None }, 'error': { 'message': 'Please enter a value', 'params': {} } }) def test_invalid_param(self): i = random.randint(-20, 20) reply = self.query('/settings/email', data = {'__email': '%s' % i}) cmp_json_dicts(reply, { 'status': 400, 'form': { 'updated_fields': None, 'invalid_field': None, 'orig_fields': { '__email': '%s' % i } }, 'error': { 'message': 'The input field \'__email\' was not expected.', 'params': {} } }) def test_empty_action(self): reply = self.query('/settings/email', data = {'email': ''}) cmp_json_dicts(reply, { 'status': 400, 'form': { 'updated_fields': None, 'invalid_field': 'email', 'orig_fields': { 'email': '' } }, 'error': { 'message': 'Please enter a value', 'params': {} } }) def test_malformed_string(self): reply = self.query('/settings/email', data = {'email': 'foobar'}) cmp_json_dicts(reply, { 'status': 400, 'form': { 'updated_fields': None, 'invalid_field': 'email', 'orig_fields': { 'email': 'foobar' } }, 'error': { 'message': 'An email address must contain a single @', 'params': { } } }) def test_malformed_float(self): reply = self.query('/settings/email', data = {'email': 3.14}) cmp_json_dicts(reply, { 'status': 400, 'form': { 'updated_fields': None, 'invalid_field': 'email', 'orig_fields': { 'email': '3.14' } }, 'error': { 'message': 'An email address must contain a single @', 'params': { } } }) def test_proper(self): reply = self.query('/settings/email', data = {'email': self.config.get('online', 'email')}) cmp_json_dicts(reply, { 'status': 200, 'form': { 'updated_fields': { 'email': self.config.get('online', 'email') }, 'invalid_field': None, 'orig_fields': None } }) class AfterPassTurn(TestCase): def test_empty_submit(self): reply = self.query('/settings/after_pass_turn') cmp_json_dicts(reply, { 'status': 400, 'form': { 'updated_fields': None, 'invalid_field': 'action', 'orig_fields': None }, 'error': { 'message': 'Please enter a value', 'params': {} } }) def test_invalid_param(self): i = random.randint(-20, 20) reply = self.query('/settings/after_pass_turn', data = {'__action': i}) cmp_json_dicts(reply, { 'status': 400, 'form': { 'updated_fields': None, 'invalid_field': None, 'orig_fields': { '__action': '%i' % i } }, 'error': { 'message': 'The input field \'__action\' was not expected.', 'params': {} } }) def test_empty_action(self): reply = self.query('/settings/after_pass_turn', data = {'action': ''}) cmp_json_dicts(reply, { 'status': 400, 'form': { 'updated_fields': None, 'invalid_field': 'action', 'orig_fields': { 'action': '' } }, 'error': { 'message': 'Please enter a value', 'params': {} } }) def test_malformed_string(self): reply = self.query('/settings/after_pass_turn', data = {'action': 'foobar'}) cmp_json_dicts(reply, { 'status': 400, 'form': { 'updated_fields': None, 'invalid_field': 'action', 'orig_fields': { 'action': 'foobar' } }, 'error': { 'message': 'Please enter an integer value', 'params': { } } }) def test_malformed_float(self): reply = self.query('/settings/after_pass_turn', data = {'action': 3.14}) cmp_json_dicts(reply, { 'status': 400, 'form': { 'updated_fields': None, 'invalid_field': 'action', 'orig_fields': { 'action': '3.14' } }, 'error': { 'message': 'Please enter an integer value', 'params': { } } }) def test_malformed_oor(self): i = random.randint(-20, -5) reply = self.query('/settings/after_pass_turn', data = {'action': i}) cmp_json_dicts(reply, { 'status': 400, 'form': { 'updated_fields': None, 'invalid_field': 'action', 'orig_fields': { 'action': '%i' % i } }, 'error': { 'message': 'Value must be one of: 0; 1; 2 (not %i)' % i, 'params': { } } }) def test_random(self): i = random.randint(0, 2) reply = self.query('/settings/after_pass_turn', data = {'action': i}) cmp_json_dicts(reply, { 'status': 200, 'form': { 'updated_fields': { 'action': i }, 'invalid_field': None, 'orig_fields': None } }) class PerTablePage(TestCase): VALID_INPUTS = range(10, 61, 10) def get_rand_input(self): i = random.randint(0, len(self.VALID_INPUTS) - 1) return self.VALID_INPUTS[i] def test_empty_submit(self): reply = self.query('/settings/per_page') cmp_json_dicts(reply, { 'status': 400, 'form': { 'updated_fields': None, 'invalid_field': 'per_page', 'orig_fields': None }, 'error': { 'message': 'Please enter a value', 'params': {} } }) def test_invalid_param(self): i = random.randint(-20, 20) reply = self.query('/settings/per_page', data = {'__per_page': i}) cmp_json_dicts(reply, { 'status': 400, 'form': { 'updated_fields': None, 'invalid_field': None, 'orig_fields': { '__per_page': '%i' % i } }, 'error': { 'message': 'The input field \'__per_page\' was not expected.', 'params': { } } }) def test_empty(self): reply = self.query('/settings/per_page', data = {'per_page': ''}) cmp_json_dicts(reply, { 'status': 400, 'form': { 'updated_fields': None, 'invalid_field': 'per_page', 'orig_fields': { 'per_page': '' } }, 'error': { 'message': 'Please enter a value', 'params': {} } }) def test_malformed_int(self): while True: i = random.randint(-100, 100) if i in self.VALID_INPUTS: continue break reply = self.query('/settings/per_page', data = {'per_page': i}) cmp_json_dicts(reply, { 'status': 400, 'form': { 'updated_fields': None, 'invalid_field': 'per_page', 'orig_fields': { 'per_page': '%i' % i } }, 'error': { 'message': 'Value must be one of: 10; 20; 30; 40; 50; 60 (not %i)' % i, 'params': { } } }) def test_malformed_string(self): reply = self.query('/settings/per_page', data = {'per_page': 'foobar'}) cmp_json_dicts(reply, { 'status': 400, 'form': { 'updated_fields': None, 'invalid_field': 'per_page', 'orig_fields': { 'per_page': 'foobar' } }, 'error': { 'message': 'Please enter an integer value', 'params': { } } }) def test_random(self): i = self.get_rand_input() reply = self.query('/settings/per_page', data = {'per_page': i}) cmp_json_dicts(reply, { 'status': 200, 'form': { 'updated_fields': { 'per_page': i }, 'invalid_field': None, 'orig_fields': None } }) class Sound(TestCase): def test_empty_submit(self): reply = self.query('/settings/sound') cmp_json_dicts(reply, { 'status': 400, 'form': { 'updated_fields': None, 'invalid_field': 'sound', 'orig_fields': None }, 'error': { 'message': 'Please enter a value', 'params': {} } }) def test_invalid_param(self): i = random.randint(-20, 20) reply = self.query('/settings/sound', data = {'__sound': i}) cmp_json_dicts(reply, { 'status': 400, 'form': { 'updated_fields': None, 'invalid_field': None, 'orig_fields': { '__sound': '%i' % i } }, 'error': { 'message': 'The input field \'__sound\' was not expected.', 'params': { } } }) def test_empty_skin(self): reply = self.query('/settings/sound', data = {'sound': ''}) cmp_json_dicts(reply, { 'status': 400, 'form': { 'updated_fields': None, 'invalid_field': 'sound', 'orig_fields': { 'sound': '' } }, 'error': { 'message': 'Please enter a value', 'params': {} } }) def test_malformed_int(self): i = random.randint(-20, -10) reply = self.query('/settings/sound', data = {'sound': i}) cmp_json_dicts(reply, { 'status': 400, 'form': { 'updated_fields': None, 'invalid_field': 'sound', 'orig_fields': { 'sound': '%i' % i } }, 'error': { 'message': 'Value must be one of: 0; 1 (not %i)' % i, 'params': { } } }) def test_random(self): i = random.randint(0, 1) reply = self.query('/settings/sound', data = {'sound': i}) cmp_json_dicts(reply, { 'status': 200, 'form': { 'updated_fields': { 'sound': i }, 'invalid_field': None, 'orig_fields': None } }) class MyColor(TestCase): VALID_KINDS = ['settlers'] VALID_COLORS = ['pink', 'purple', 'dark_green', 'black', 'brown', 'light_blue', 'orange', 'green', 'dark_blue', 'red'] def get_rand_kind(self): return self.VALID_KINDS[0] def get_rand_color(self): i = random.randint(0, len(self.VALID_COLORS) - 1) return self.VALID_COLORS[i] def test_empty_submit(self): reply = self.query('/settings/color') cmp_json_dicts(reply, { 'status': 400, 'form': { 'updated_fields': None, 'invalid_field': 'color', 'orig_fields': None }, 'error': { 'message': 'Please enter a value', 'params': {} } }) def test_invalid_param_kind(self): i = random.randint(-20, 20) reply = self.query('/settings/color', data = {'__kind': i}) cmp_json_dicts(reply, { 'status': 400, 'form': { 'updated_fields': None, 'invalid_field': None, 'orig_fields': { '__kind': '%i' % i } }, 'error': { 'message': 'The input field \'__kind\' was not expected.', 'params': { } } }) def test_empty_kind(self): reply = self.query('/settings/color', data = {'kind': ''}) cmp_json_dicts(reply, { 'status': 400, 'form': { 'updated_fields': None, 'invalid_field': 'color', 'orig_fields': { 'kind': '' } }, 'error': { 'message': 'Please enter a value', 'params': {} } }) def test_empty(self): reply = self.query('/settings/color', data = {'kind': '', 'color': ''}) cmp_json_dicts(reply, { 'status': 400, 'form': { 'updated_fields': None, 'invalid_field': 'color', 'orig_fields': { 'color': '', 'kind': '' } }, 'error': { 'message': 'Please enter a value', 'params': {} } }) def test_malformed_kind_int(self): i = random.randint(-20, -10) color = self.get_rand_color() reply = self.query('/settings/color', data = {'kind': i, 'color': color}) cmp_json_dicts(reply, { 'status': 400, 'form': { 'updated_fields': None, 'invalid_field': 'kind', 'orig_fields': { 'kind': '%i' % i, 'color': color } }, 'error': { 'message': 'Value must be one of: settlers (not u\'%i\')' % i, 'params': { } } }) def test_malformed_kind_string(self): color = self.get_rand_color() reply = self.query('/settings/color', data = {'kind': 'foobar', 'color': color}) cmp_json_dicts(reply, { 'status': 400, 'form': { 'updated_fields': None, 'invalid_field': 'kind', 'orig_fields': { 'kind': 'foobar', 'color': color } }, 'error': { 'message': 'Value must be one of: settlers (not u\'foobar\')', 'params': { } } }) def test_malformed_color_int(self): return kind = self.get_rand_kind() color = random.randint(-20, -10) reply = self.query('/settings/color', data = {'kind': kind, 'color': color}) cmp_json_dicts(reply, { 'status': 400, 'form': { 'updated_fields': None, 'invalid_field': 'color', 'orig_fields': { 'kind': kind, 'color': '%i' % color } }, 'error': { 'message': 'Value must be one of: pink; purple; dark_green; black; brown; light_blue; orange; green; dark_blue; red (not u\'%i\')' % color, 'params': { } } }) def test_malformed_color_string(self): return kind = self.get_rand_kind() color = self.get_rand_color() reply = self.query('/settings/color', data = {'kind': kind, 'color': 'foobar'}) cmp_json_dicts(reply, { 'status': 400, 'form': { 'updated_fields': None, 'invalid_field': 'color', 'orig_fields': { 'kind': kind, 'color': 'foobar' } }, 'error': { 'message': 'Value must be one of: pink; purple; dark_green; black; brown; light_blue; orange; green; dark_blue; red (not u\'foobar\')', 'params': { } } }) class Board(TestCase): def test_empty_submit(self): reply = self.query('/settings/board_skin') cmp_json_dicts(reply, { 'status': 400, 'form': { 'updated_fields': None, 'invalid_field': 'skin', 'orig_fields': None }, 'error': { 'message': 'Please enter a value', 'params': {} } }) def test_invalid_param(self): i = random.randint(-20, 20) reply = self.query('/settings/board_skin', data = {'__skin': i}) cmp_json_dicts(reply, { 'status': 400, 'form': { 'updated_fields': None, 'invalid_field': None, 'orig_fields': { '__skin': '%i' % i } }, 'error': { 'message': 'The input field \'__skin\' was not expected.', 'params': { } } }) def test_empty_skin(self): reply = self.query('/settings/board_skin', data = {'skin': ''}) cmp_json_dicts(reply, { 'status': 400, 'form': { 'updated_fields': None, 'invalid_field': 'skin', 'orig_fields': { 'skin': '' } }, 'error': { 'message': 'Please enter a value', 'params': {} } }) def test_malformed_int(self): i = random.randint(-20, 20) reply = self.query('/settings/board_skin', data = {'skin': i}) cmp_json_dicts(reply, { 'status': 400, 'form': { 'updated_fields': None, 'invalid_field': 'skin', 'orig_fields': { 'skin': '%i' % i } }, 'error': { 'message': 'Value must be one of: real; simple (not u\'%i\')' % i, 'params': { } } }) def test_random(self): skins = ['simple', 'real'] i = random.randint(0, 1) skin = skins[i] reply = self.query('/settings/board_skin', data = {'skin': skin}) cmp_json_dicts(reply, { 'status': 200, 'form': { 'updated_fields': { 'skin': skin }, 'invalid_field': None, 'orig_fields': None } }) if __name__ == '__main__': unittest.main()

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0.090047

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null

0

1

0

null

0

6

570113d9e2b2057607af6cf330db571e53552eb1

224

py

Python

confo/Exceptions/EtcdExceptions.py

sambe-consulting/confo

3def0c151a45aa14849710da0daa678458d24d91

[ "Apache-2.0" ]

1

2021-03-21T20:55:12.000Z

confo/Exceptions/EtcdExceptions.py

sambe-consulting/confo

3def0c151a45aa14849710da0daa678458d24d91

[ "Apache-2.0" ]

6

2021-03-09T01:13:13.000Z

2021-03-20T05:57:59.000Z

confo/Exceptions/EtcdExceptions.py

sambe-consulting/confo

3def0c151a45aa14849710da0daa678458d24d91

[ "Apache-2.0" ]

1

2021-08-24T07:52:35.000Z

class Etcd3HostNotFoundException(Exception): pass class Etcd3PortNotFoundException(Exception): pass class ConfigurationNotSetException(Exception): pass class UnknownFormatInMainNameSpace(Exception): pass

17.230769

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224

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null

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6

5712886cfb5a86b05a786ff611bdf6a0db859daa

33

py

Python

django_user_agents/tests/__init__.py

claymcenter/django-user_agents

e2a0d92e371446c151b0830a911f44f8253c9376

[ "MIT" ]

null

django_user_agents/tests/__init__.py

claymcenter/django-user_agents

e2a0d92e371446c151b0830a911f44f8253c9376

[ "MIT" ]

null

django_user_agents/tests/__init__.py

claymcenter/django-user_agents

e2a0d92e371446c151b0830a911f44f8253c9376

[ "MIT" ]

1

2020-10-21T09:39:35.000Z

from .tests import MiddlewareTest

33

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7.25

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33

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null

0

1

0

1

0

1

0

6

5740d83f137d1d9e4a0a9cb102eae6bf26695008

170

py

Python

todo_app/admin.py

SpaceWalker0318/Todo-app-backend

b885a0c87e07584b689b2d43d923ce4233fc7738

[ "MIT" ]

null

todo_app/admin.py

SpaceWalker0318/Todo-app-backend

b885a0c87e07584b689b2d43d923ce4233fc7738

[ "MIT" ]

null

todo_app/admin.py

SpaceWalker0318/Todo-app-backend

b885a0c87e07584b689b2d43d923ce4233fc7738

[ "MIT" ]

null

from django.contrib import admin # Register your models here. from todo_app import models admin.site.register(models.UserProfile) admin.site.register(models.TodoItem)

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null

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1

0

1

0

6

5744319e78c4f5736d74739e3e78fcd00cdfb614

7,274

py

Python

tests/unit/test_upstream.py

Brickstertwo/git-commands

87fa9a6573dd426eecece098fbadc3f5550c8976

[ "MIT" ]

1

2018-10-17T11:09:32.000Z

tests/unit/test_upstream.py

Brickstertwo/git-commands

87fa9a6573dd426eecece098fbadc3f5550c8976

[ "MIT" ]

122

2015-01-06T19:10:23.000Z

2017-09-26T14:22:11.000Z

tests/unit/test_upstream.py

Brickster/git-commands

87fa9a6573dd426eecece098fbadc3f5550c8976

[ "MIT" ]

null

import unittest import mock from . import testutils from ..layers import GitUpstream from bin.commands import upstream @mock.patch('bin.commands.utils.git.is_empty_repository', return_value=False) class TestUpstream(unittest.TestCase): layer = GitUpstream @mock.patch('bin.commands.utils.git.current_branch', return_value='the-branch') @mock.patch('bin.commands.utils.execute.stdout') def test_upstream(self, mock_stdout, mock_currentbranch, mock_isemptyrepository): # setup expected_upstream = "the-upstream" upstream_info = "refs/heads/{}\n".format(expected_upstream) mock_stdout.return_value = upstream_info # when actual_upstream = upstream.upstream() # then self.assertEqual(actual_upstream, expected_upstream) mock_isemptyrepository.assert_called_once_with() mock_currentbranch.assert_called_once_with() mock_stdout.assert_called_once_with('git config --local branch.the-branch.merge') @mock.patch('bin.commands.utils.git.current_branch', return_value='the-branch') @mock.patch('bin.commands.utils.execute.stdout') def test_upstream_includeRemote_noUpstream(self, mock_stdout, mock_currentbranch, mock_isemptyrepository): # setup mock_stdout.return_value = '' # when actual_upstream = upstream.upstream() # then self.assertEqual(actual_upstream, '') mock_currentbranch.assert_called_once_with() mock_stdout.assert_called_once_with('git config --local branch.the-branch.merge') def test_upstream_repositoryIsEmpty(self, mock_isemptyrepository): # setup mock_isemptyrepository.return_value = True # when upstream_result = upstream.upstream() # then self.assertEqual(upstream_result, None) mock_isemptyrepository.assert_called_once_with() @mock.patch('bin.commands.utils.git.current_branch', return_value='the-branch') @mock.patch('bin.commands.utils.git.is_valid_reference', return_value=True) @mock.patch('bin.commands.utils.execute.stdout') def test_upstream_branchIncluded(self, mock_stdout, mock_isvalidreference, mock_currentbranch, mock_isemptyrepository): # setup branch_name = 'the-branch' expected_upstream = "the-upstream" upstream_info = "refs/heads/{}\n".format(expected_upstream) mock_stdout.return_value = upstream_info # when actual_upstream = upstream.upstream(branch=branch_name) # then self.assertEqual(actual_upstream, expected_upstream) mock_currentbranch.assert_not_called() mock_isvalidreference.assert_called_once_with(branch_name) mock_stdout.assert_called_once_with('git config --local branch.the-branch.merge') @mock.patch('bin.commands.utils.git.is_valid_reference', return_value=False) @mock.patch('bin.commands.utils.messages.error', side_effect=testutils.and_exit) def test_upstream_notAValidReference(self, mock_error, mock_isvalidreference, mock_isemptyrepository): # when try: upstream.upstream(branch='bad-branch') self.fail('expected to exit but did not') # pragma: no cover except SystemExit: pass mock_isvalidreference.assert_called_once_with('bad-branch') mock_error.assert_called_once_with("'bad-branch' is not a valid branch") @mock.patch('bin.commands.utils.git.current_branch', return_value='the-branch') @mock.patch('bin.commands.utils.execute.stdout') @mock.patch('bin.commands.utils.execute.check_output', return_value='the-remote') def test_upstream_includeRemote_always(self, mock_checkoutput, mock_stdout, mock_currentbranch, mock_isemptyrepository): # setup expected_upstream = "the-upstream" upstream_info = "refs/heads/{}\n".format(expected_upstream) mock_stdout.return_value = upstream_info # when actual_upstream = upstream.upstream(include_remote=upstream.IncludeRemote.ALWAYS) # then self.assertEqual(actual_upstream, 'the-remote/' + expected_upstream) mock_isemptyrepository.assert_called_once() mock_currentbranch.assert_called_once() mock_stdout.assert_called_once_with('git config --local branch.the-branch.merge') mock_checkoutput.assert_called_once_with('git config --local branch.the-branch.remote') @mock.patch('bin.commands.utils.git.current_branch', return_value='the-branch') @mock.patch('bin.commands.utils.execute.stdout') def test_upstream_includeRemote_never(self, mock_stdout, mock_currentbranch, mock_isemptyrepository): # setup expected_upstream = "the-upstream" upstream_info = "refs/heads/{}\n".format(expected_upstream) mock_stdout.return_value = upstream_info # when actual_upstream = upstream.upstream(include_remote=upstream.IncludeRemote.NEVER) # then self.assertEqual(actual_upstream, expected_upstream) mock_isemptyrepository.assert_called_once() mock_currentbranch.assert_called_once() mock_stdout.assert_called_once_with('git config --local branch.the-branch.merge') @mock.patch('bin.commands.utils.git.current_branch', return_value='the-branch') @mock.patch('bin.commands.utils.execute.stdout') @mock.patch('bin.commands.utils.execute.check_output', return_value='the-remote') def test_upstream_includeRemote_noneLocal_notLocal(self, mock_checkoutput, mock_stdout, mock_currentbranch, mock_isemptyrepository): # setup expected_upstream = "the-upstream" upstream_info = "refs/heads/{}\n".format(expected_upstream) mock_stdout.return_value = upstream_info # when actual_upstream = upstream.upstream(include_remote=upstream.IncludeRemote.NONE_LOCAL) # then self.assertEqual(actual_upstream, 'the-remote/' + expected_upstream) mock_isemptyrepository.assert_called_once() mock_currentbranch.assert_called_once() mock_stdout.assert_called_once_with('git config --local branch.the-branch.merge') mock_checkoutput.assert_called_once_with('git config --local branch.the-branch.remote') @mock.patch('bin.commands.utils.git.current_branch', return_value='the-branch') @mock.patch('bin.commands.utils.execute.stdout') @mock.patch('bin.commands.utils.execute.check_output', return_value='.') def test_upstream_includeRemote_noneLocal_isLocal(self, mock_checkoutput, mock_stdout, mock_currentbranch, mock_isemptyrepository): # setup expected_upstream = "the-upstream" upstream_info = "refs/heads/{}\n".format(expected_upstream) mock_stdout.return_value = upstream_info # when actual_upstream = upstream.upstream(include_remote=upstream.IncludeRemote.NONE_LOCAL) # then self.assertEqual(actual_upstream, expected_upstream) mock_isemptyrepository.assert_called_once() mock_currentbranch.assert_called_once() mock_stdout.assert_called_once_with('git config --local branch.the-branch.merge') mock_checkoutput.assert_called_once_with('git config --local branch.the-branch.remote')

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846

7,274

5.949173

0.109929

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0.079475

0.083449

0.849195

0.819591

0.796145

0.780052

0.758196

0

0.173082

7,274

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137

41.329545

0.836741

0.020484

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0.628571

0

0.218935

0.140462

0

0.32381

1

0.085714

false

0.009524

0.047619

0

0.152381

0

null

0

1

0

null

0

6

93f48fa5d89c272d91b0677c380d31cabfdfdeb2

40

py

Python

pdip/integrator/connection/types/file/connectors/csv/__init__.py

ahmetcagriakca/pdip

c4c16d5666a740154cabdc6762cd44d98b7bdde8

[ "MIT" ]

2

2021-12-09T21:07:46.000Z

2021-12-11T22:18:01.000Z

pdip/connection/file/connectors/csv/__init__.py

fmuyilmaz/pdip

f7e30b0c04d9e85ef46b0b7094fafd3ce18bccab

[ "MIT" ]

null

pdip/connection/file/connectors/csv/__init__.py

fmuyilmaz/pdip

f7e30b0c04d9e85ef46b0b7094fafd3ce18bccab

[ "MIT" ]

3

2021-11-15T00:47:00.000Z

2021-12-17T11:35:45.000Z

from .csv_connector import CsvConnector

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1

0

6

93fa9bb8e9f862e7049ff821692edf030f9269c2

159

py

Python

psi/app/api/__init__.py

lusi1990/betterlifepsi

8e7f8562967ab1816d8c25db3251c550a357f39c

[ "MIT" ]

33

2018-10-19T03:41:56.000Z

2022-01-23T16:26:02.000Z

psi/app/api/__init__.py

lusi1990/betterlifepsi

8e7f8562967ab1816d8c25db3251c550a357f39c

[ "MIT" ]

318

2018-09-23T15:16:54.000Z

2022-03-31T22:58:55.000Z

psi/app/api/__init__.py

lusi1990/betterlifepsi

8e7f8562967ab1816d8c25db3251c550a357f39c

[ "MIT" ]

19

2018-10-22T18:04:18.000Z

2021-12-06T19:49:05.000Z

# encoding=utf-8 from .sales_order import SalesOrderApi def init_all_apis(api): api.add_resource(SalesOrderApi, '/api/sales_order/<int:sales_order_id>')

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1

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6

f5227c2fa6368b43ec3f026e100c93b81f005a1c

250

py

Python

keys.py

lizKimita/Mshauri-Connect

24f6f67017eebf5ee1d2e08c9bf249108dee28a2

[ "MIT" ]

1

2019-06-20T08:23:22.000Z

keys.py

lizKimita/Mshauri-Connect

24f6f67017eebf5ee1d2e08c9bf249108dee28a2

[ "MIT" ]

16

2019-06-11T14:55:14.000Z

2021-09-08T01:02:58.000Z

keys.py

lizKimita/Mshauri-Connect

24f6f67017eebf5ee1d2e08c9bf249108dee28a2

[ "MIT" ]

null

business_shortcode = "174379" #lipa na mpesa code phone_number = "254740392957" mpesa_passkey = "bfb279f9aa9bdbcf158e97dd71a467cd2e0c893059b10f78e6b72ada1ed2c919" consumer_key = "nJKAXNYR4L0Jo3vbBu5C4oWVWuyASWZK" consumer_secret = "VirFCmLCWpQVOJL4"

41.666667

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1

null

0

1

0

6

f52b4ebc57c5911c5977f8b5074a7be35f7f512d

43

py

Python

addons14/base_technical_features/tests/__init__.py

odoochain/addons_oca

55d456d798aebe16e49b4a6070765f206a8885ca

[ "MIT" ]

1

2021-06-10T14:59:13.000Z

addons14/base_technical_features/tests/__init__.py

odoochain/addons_oca

55d456d798aebe16e49b4a6070765f206a8885ca

[ "MIT" ]

null

addons14/base_technical_features/tests/__init__.py

odoochain/addons_oca

55d456d798aebe16e49b4a6070765f206a8885ca

[ "MIT" ]

1

2021-04-09T09:44:44.000Z

from . import test_base_technical_features

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null

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1

0

1

0

1

0

6

f535cc78f7f056b66b85171c2e054fe6b54c9f11

285

py

Python

tests/test_10_cfunits.py

shoyer/cfgrib

fe11a1b638b1779e51da87eaa30f1f12b2d0911c

[ "Apache-2.0" ]

null

tests/test_10_cfunits.py

shoyer/cfgrib

fe11a1b638b1779e51da87eaa30f1f12b2d0911c

[ "Apache-2.0" ]

null

tests/test_10_cfunits.py

shoyer/cfgrib

fe11a1b638b1779e51da87eaa30f1f12b2d0911c

[ "Apache-2.0" ]

null

from __future__ import absolute_import, division, print_function, unicode_literals from cf2cdm import cfunits def test_are_convertible(): assert cfunits.are_convertible('m', 'm') assert cfunits.are_convertible('hPa', 'Pa') assert not cfunits.are_convertible('m', 'Pa')

25.909091

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0.308824

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0.004065

0.136842

285

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null

0

1

0

1

0

1

0

6

f5745f7c2245ec7e9341ca9b8a489b1bd7e8790d

2,054

py

Python

dev/Gems/CloudGemMetric/v1/AWS/common-code/AWSCommon/keyparts.py

kostenickj/lumberyard

e881f3023cc1840650eb7b133e605881d1d4330d

[ "AML" ]

null

dev/Gems/CloudGemMetric/v1/AWS/common-code/AWSCommon/keyparts.py

kostenickj/lumberyard

e881f3023cc1840650eb7b133e605881d1d4330d

[ "AML" ]

null

dev/Gems/CloudGemMetric/v1/AWS/common-code/AWSCommon/keyparts.py

kostenickj/lumberyard

e881f3023cc1840650eb7b133e605881d1d4330d

[ "AML" ]

null

class KeyParts(object): def __init__(self, key, sep): self.__key = key if self.__key.index("/") == 0: self.__key = self.__key[1:] self.__parts = self.__key.split(sep) @property def sensitivity_level(self): return self.raw_split(self.key_sensitivity) @property def source(self): return self.raw_split(self.key_source) @property def buildid(self): return self.raw_split(self.key_buildid) @property def datetime(self): return self.raw_split(self.key_datetime) @property def year(self): return int(self.raw_split(self.key_year)) @property def month(self): return int(self.raw_split(self.key_month)) @property def day(self): return int(self.raw_split(self.key_day)) @property def hour(self): return int(self.raw_split(self.key_hour)) @property def event(self): return self.raw_split(self.key_event) @property def filename(self): return self.__parts[11] @property def schema(self): return self.raw_split(self.key_schema) @property def key_source(self): return self.__parts[7] @property def key_buildid(self): return self.__parts[8] @property def key_year(self): return self.__parts[3] @property def key_month(self): return self.__parts[4] @property def key_day(self): return self.__parts[5] @property def key_hour(self): return self.__parts[6] @property def key_event(self): return self.__parts[1] @property def key_schema(self): return self.__parts[10] @property def key_datetime(self): return self.__parts[2] @property def key_sensitivity(self): return self.__parts[9] @property def path(self): return self.__key.replace(self.filename, "") def raw_split(self, value): return value.split("=")[1]

20.54

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2,054

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1

0

1

0

6

f578b750742d961ce0b0b9703f69ef9ef6507e90

7,760

py

Python

src/network/architecture.py

keyochali/handwritten-text-recognition

b2a26ac47dd6d6e0dfd128d841941db00aece748

[ "MIT" ]

2

2020-05-11T19:41:11.000Z

2021-11-08T15:53:45.000Z

src/network/architecture.py

keyochali/handwritten-text-recognition

b2a26ac47dd6d6e0dfd128d841941db00aece748

[ "MIT" ]

null

src/network/architecture.py

keyochali/handwritten-text-recognition

b2a26ac47dd6d6e0dfd128d841941db00aece748

[ "MIT" ]

1

2021-11-06T08:52:24.000Z

"""Networks to the Handwritten Text Recognition Model""" from tensorflow.keras.layers import Input, Conv2D, Bidirectional, LSTM, Dense from tensorflow.keras.layers import Dropout, BatchNormalization, MaxPooling2D from tensorflow.keras.layers import Reshape, Activation, LeakyReLU, PReLU from tensorflow.keras.constraints import MaxNorm from tensorflow.keras.optimizers import RMSprop from network.layers import FullGatedConv2D, GatedConv2D def bluche(input_size, output_size): """ Gated Convolucional Recurrent Neural Network by Bluche et al. Reference: Bluche, T., Messina, R.: Gated convolutional recurrent neural networks for multilingual handwriting recognition. In: Document Analysis and Recognition (ICDAR), 2017 14th IAPR International Conference on, vol. 1, pp. 646–651, 2017. URL: https://ieeexplore.ieee.org/document/8270042 Moysset, B. and Messina, R.: Are 2D-LSTM really dead for offline text recognition? In: International Journal on Document Analysis and Recognition (IJDAR) Springer Science and Business Media LLC URL: http://dx.doi.org/10.1007/s10032-019-00325-0 """ input_data = Input(name="input", shape=input_size) cnn = Reshape((input_size[0] // 2, input_size[1] // 2, input_size[2] * 4))(input_data) cnn = Conv2D(filters=8, kernel_size=(3,3), strides=(1,1), padding="same")(cnn) cnn = Activation(activation="tanh")(cnn) cnn = Dropout(rate=0.5)(cnn) cnn = Conv2D(filters=16, kernel_size=(2,4), strides=(2,4), padding="same")(cnn) cnn = Activation(activation="tanh")(cnn) cnn = Dropout(rate=0.5)(cnn) cnn = GatedConv2D(filters=16, kernel_size=(3,3), strides=(1,1), padding="same")(cnn) cnn = Conv2D(filters=32, kernel_size=(3,3), strides=(1,1), padding="same")(cnn) cnn = Activation(activation="tanh")(cnn) cnn = Dropout(rate=0.5)(cnn) cnn = GatedConv2D(filters=32, kernel_size=(3,3), strides=(1,1), padding="same")(cnn) cnn = Conv2D(filters=64, kernel_size=(2,4), strides=(2,4), padding="same")(cnn) cnn = Activation(activation="tanh")(cnn) cnn = Dropout(rate=0.5)(cnn) cnn = GatedConv2D(filters=64, kernel_size=(3,3), strides=(1,1), padding="same")(cnn) cnn = Conv2D(filters=128, kernel_size=(3,3), strides=(1,1), padding="same")(cnn) cnn = Activation(activation="tanh")(cnn) cnn = Dropout(rate=0.5)(cnn) cnn = MaxPooling2D(pool_size=(1,4), strides=(1,4), padding="valid")(cnn) shape = cnn.get_shape() blstm = Reshape((shape[1], shape[2] * shape[3]))(cnn) blstm = Bidirectional(LSTM(units=128, return_sequences=True, dropout=0.5))(blstm) blstm = Dense(units=128)(blstm) blstm = Activation(activation="tanh")(blstm) blstm = Bidirectional(LSTM(units=128, return_sequences=True, dropout=0.5))(blstm) blstm = Dense(units=output_size)(blstm) output_data = Activation(activation="softmax")(blstm) optimizer = RMSprop(learning_rate=4e-4) return (input_data, output_data, optimizer) def puigcerver(input_size, output_size): """ Convolucional Recurrent Neural Network by Puigcerver et al. Reference: Puigcerver, J.: Are multidimensional recurrent layers really necessary for handwritten text recognition? In: Document Analysis and Recognition (ICDAR), 2017 14th IAPR International Conference on, vol. 1, pp. 67–72. IEEE (2017) """ input_data = Input(name="input", shape=input_size) cnn = Conv2D(filters=16, kernel_size=(3,3), strides=(1,1), padding="same")(input_data) cnn = BatchNormalization()(cnn) cnn = LeakyReLU()(cnn) cnn = MaxPooling2D(pool_size=(2,2), strides=(2,2), padding="valid")(cnn) cnn = Conv2D(filters=32, kernel_size=(3,3), strides=(1,1), padding="same")(cnn) cnn = BatchNormalization()(cnn) cnn = LeakyReLU()(cnn) cnn = MaxPooling2D(pool_size=(2,2), strides=(2,2), padding="valid")(cnn) cnn = Dropout(rate=0.2)(cnn) cnn = Conv2D(filters=48, kernel_size=(3,3), strides=(1,1), padding="same")(cnn) cnn = BatchNormalization()(cnn) cnn = LeakyReLU()(cnn) cnn = MaxPooling2D(pool_size=(2,2), strides=(2,2), padding="valid")(cnn) cnn = Dropout(rate=0.2)(cnn) cnn = Conv2D(filters=64, kernel_size=(3,3), strides=(1,1), padding="same")(cnn) cnn = BatchNormalization()(cnn) cnn = LeakyReLU()(cnn) cnn = Dropout(rate=0.2)(cnn) cnn = Conv2D(filters=80, kernel_size=(3,3), strides=(1,1), padding="same")(cnn) cnn = BatchNormalization()(cnn) cnn = LeakyReLU()(cnn) shape = cnn.get_shape() blstm = Reshape((shape[1], shape[2] * shape[3]))(cnn) blstm = Bidirectional(LSTM(units=256, return_sequences=True, dropout=0.5))(blstm) blstm = Bidirectional(LSTM(units=256, return_sequences=True, dropout=0.5))(blstm) blstm = Bidirectional(LSTM(units=256, return_sequences=True, dropout=0.5))(blstm) blstm = Bidirectional(LSTM(units=256, return_sequences=True, dropout=0.5))(blstm) blstm = Bidirectional(LSTM(units=256, return_sequences=True, dropout=0.5))(blstm) blstm = Dropout(rate=0.5)(blstm) blstm = Dense(units=output_size)(blstm) output_data = Activation(activation="softmax")(blstm) optimizer = RMSprop(learning_rate=3e-4) return (input_data, output_data, optimizer) def flor(input_size, output_size): """Gated Convolucional Recurrent Neural Network by Flor.""" input_data = Input(name="input", shape=input_size) cnn = Conv2D(filters=16, kernel_size=(3,3), strides=(2,2), padding="same")(input_data) cnn = PReLU(shared_axes=[1,2])(cnn) cnn = BatchNormalization(renorm=True)(cnn) cnn = FullGatedConv2D(filters=16, kernel_size=(3,3), padding="same")(cnn) cnn = Conv2D(filters=32, kernel_size=(3,3), strides=(1,1), padding="same")(cnn) cnn = PReLU(shared_axes=[1,2])(cnn) cnn = BatchNormalization(renorm=True)(cnn) cnn = FullGatedConv2D(filters=32, kernel_size=(3,3), padding="same")(cnn) cnn = Conv2D(filters=40, kernel_size=(2,4), strides=(2,4), padding="same")(cnn) cnn = PReLU(shared_axes=[1,2])(cnn) cnn = BatchNormalization(renorm=True)(cnn) cnn = FullGatedConv2D(filters=40, kernel_size=(3,3), padding="same", kernel_constraint=MaxNorm(4, [0,1,2]))(cnn) cnn = Dropout(rate=0.2)(cnn) cnn = Conv2D(filters=48, kernel_size=(3,3), strides=(1,1), padding="same")(cnn) cnn = PReLU(shared_axes=[1,2])(cnn) cnn = BatchNormalization(renorm=True)(cnn) cnn = FullGatedConv2D(filters=48, kernel_size=(3,3), padding="same", kernel_constraint=MaxNorm(4, [0,1,2]))(cnn) cnn = Dropout(rate=0.2)(cnn) cnn = Conv2D(filters=56, kernel_size=(2,4), strides=(2,4), padding="same")(cnn) cnn = PReLU(shared_axes=[1,2])(cnn) cnn = BatchNormalization(renorm=True)(cnn) cnn = FullGatedConv2D(filters=56, kernel_size=(3,3), padding="same", kernel_constraint=MaxNorm(4, [0,1,2]))(cnn) cnn = Dropout(rate=0.2)(cnn) cnn = Conv2D(filters=64, kernel_size=(3,3), strides=(1,1), padding="same")(cnn) cnn = PReLU(shared_axes=[1,2])(cnn) cnn = BatchNormalization(renorm=True)(cnn) cnn = MaxPooling2D(pool_size=(1,2), strides=(1,2), padding="valid")(cnn) shape = cnn.get_shape() blstm = Reshape((shape[1], shape[2] * shape[3]))(cnn) blstm = Bidirectional(LSTM(units=128, return_sequences=True, dropout=0.5))(blstm) blstm = Dense(units=128)(blstm) blstm = Bidirectional(LSTM(units=128, return_sequences=True, dropout=0.5))(blstm) blstm = Dense(units=output_size)(blstm) output_data = Activation(activation="softmax")(blstm) optimizer = RMSprop(learning_rate=5e-4) return (input_data, output_data, optimizer)

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1

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null

0

6

1977f951e12f01b6bc1fb379ea50332d6748cac0

212

py

Python

stump/admin/__init__.py

The-Politico/politico-civic-stump

b66f4288841823d327a49563ffbc9ad1c826e247

[ "MIT" ]

null

stump/admin/__init__.py

The-Politico/politico-civic-stump

b66f4288841823d327a49563ffbc9ad1c826e247

[ "MIT" ]

null

stump/admin/__init__.py

The-Politico/politico-civic-stump

b66f4288841823d327a49563ffbc9ad1c826e247

[ "MIT" ]

null

from django.contrib import admin from stump.models import Appearance, AppearanceType from .appearance import AppearanceAdmin admin.site.register(AppearanceType) admin.site.register(Appearance, AppearanceAdmin)

26.5

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1

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6

19824a85fe31a60ed270f2fb4f9df3fd575bf951

2,964

py

Python

Classes/reservations.py

paulmouzas/Projects

cd2f489706bfb39a310d580e000c9f188cb9f305

[ "MIT" ]

1

2021-02-28T10:32:50.000Z

Classes/reservations.py

paulmouzas/Projects

cd2f489706bfb39a310d580e000c9f188cb9f305

[ "MIT" ]

null

Classes/reservations.py

paulmouzas/Projects

cd2f489706bfb39a310d580e000c9f188cb9f305

[ "MIT" ]

null

<<<<<<< HEAD """ **Airline / Hotel Reservation System** - Create a reservation system which books airline seats or hotel rooms. It charges various rates for particular sections of the plane or hotel. Example, first class is going to cost more than coach. Hotel rooms have penthouse suites which cost more. Keep track of when rooms will be available and can be scheduled. """ import datetime from calendar import monthrange current_year_month = (datetime.date.today().year, datetime.date.today().month) class Calendar(object): def __init__(self): self.reservations = [] def update(self, update): self.reservations.append(update) def printCalendar(self, month_year=current_year_month): for i in range(monthrange(month_year)[0], monthrange(month_year)[1]): print i class Reservation(object): def __init__(self, name, date, upgrade=False): self.name = name self.date = date self.upgrade = upgrade self.price = 99.99 if self.upgrade else 79.99 paul = Reservation('Paul', datetime.date.today()) calendar = Calendar() calendar.update(paul) print calendar.printCalendar() ======= """ **Airline / Hotel Reservation System** - Create a reservation system which books airline seats or hotel rooms. It charges various rates for particular sections of the plane or hotel. Example, first class is going to cost more than coach. Hotel rooms have penthouse suites which cost more. Keep track of when rooms will be available and can be scheduled. """ import datetime from calendar import monthrange month_names = {1:'January', 2:'February', 3:'March', 4:'April', 5:'May', 6:'June', 7:'July', 8:'August', 9:'September', 10:'October', 11:'November', 12:'December'} current_year, current_month = datetime.date.today().year, datetime.date.today().month class Calendar(object): def __init__(self): self.reservations = [] def update(self, update): self.reservations.append(update) def printCalendar(self, year=current_year, month=current_month): days_taken = [day.date.day for day in self.reservations if day.date.month==month] # days_taken is a list of days that are first_day = monthrange(year, month)[0] last_day = monthrange(year, month)[1]+1 print 'Month of %s' % month_names[month] for i in range(first_day, last_day): print "%d:\t %s" % (i, 'Not available' if i in days_taken else 'Available') class Reservation(object): def __init__(self, name, date, upgrade=False): self.name = name self.date = date self.upgrade = upgrade self.price = 99.99 if self.upgrade else 79.99 paul = Reservation('Paul', datetime.date.today()) calendar = Calendar() calendar.update(paul) >>>>>>> a5038f67d6e0c1379d346f5491464b9b1f2e80ad

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0

null

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null

0

1

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6

19935c3e1588ff3638c086ffae6bef0b24a3182f

26

py

Python

terrascript/dyn/__init__.py

vfoucault/python-terrascript

fe82b3d7e79ffa72b7871538f999828be0a115d0

[ "BSD-2-Clause" ]

null

terrascript/dyn/__init__.py

vfoucault/python-terrascript

fe82b3d7e79ffa72b7871538f999828be0a115d0

[ "BSD-2-Clause" ]

null

terrascript/dyn/__init__.py

vfoucault/python-terrascript

fe82b3d7e79ffa72b7871538f999828be0a115d0

[ "BSD-2-Clause" ]

null

"""2017-11-28 18:07:28"""

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5ff296ee852dc5cd378a67d4a8957e2446b327ee

193

py

Python

math/Sequences/ArithematicProgression/AP.py

CarbonDDR/al-go-rithms

8e65affbe812931b7dde0e2933eb06c0f44b4130

[ "CC0-1.0" ]

1,253

2017-06-06T07:19:25.000Z

2022-03-30T17:07:58.000Z

math/Sequences/ArithematicProgression/AP.py

rishabh99-rc/al-go-rithms

4df20d7ef7598fda4bc89101f9a99aac94cdd794

[ "CC0-1.0" ]

554

2017-09-29T18:56:01.000Z

2022-02-21T15:48:13.000Z

math/Sequences/ArithematicProgression/AP.py

rishabh99-rc/al-go-rithms

4df20d7ef7598fda4bc89101f9a99aac94cdd794

[ "CC0-1.0" ]

2,226

2017-09-29T19:59:59.000Z

2022-03-25T08:59:55.000Z

def ap(start,difference,terms): ans="AP IS : " + str(list(range(start,start+difference*terms,difference))) return (ans) def test(): return (ap(2,5,10)) test()

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2767b08e22c7c155f3af2ccf0e648bb394dea313

35

py

Python

authlib/specs/rfc7519/claims.py

tk193192/authlib

4c60a628f64c6d385a06ea55e416092726b94d07

[ "BSD-3-Clause" ]

2

2021-04-26T18:17:37.000Z

2021-04-28T21:39:45.000Z

authlib/specs/rfc7519/claims.py

tk193192/authlib

4c60a628f64c6d385a06ea55e416092726b94d07

[ "BSD-3-Clause" ]

4

2021-03-19T08:17:59.000Z

2021-06-10T19:34:36.000Z

authlib/specs/rfc7519/claims.py

tk193192/authlib

4c60a628f64c6d385a06ea55e416092726b94d07

[ "BSD-3-Clause" ]

2

2021-05-24T20:34:12.000Z

2022-03-26T07:46:17.000Z

from authlib.jose import JWTClaims

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27a32486a4ec187c578382c7af0a9691fb1d438d

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py

Python

auxpm/samplers.py

matt-graham/auxiliary-pm-mcmc

04e73508c1432ae5ac2fc867a9f794f95ce1d2f8

[ "MIT" ]

2

2016-01-26T19:59:42.000Z

2020-07-11T10:26:03.000Z

auxpm/samplers.py

matt-graham/auxiliary-pm-mcmc

04e73508c1432ae5ac2fc867a9f794f95ce1d2f8

[ "MIT" ]

null

auxpm/samplers.py

matt-graham/auxiliary-pm-mcmc

04e73508c1432ae5ac2fc867a9f794f95ce1d2f8

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Auxiliary Pseudo-Marginal Markov chain Monte Carlo samplers """ __authors__ = 'Matt Graham' __copyright__ = 'Copyright 2015, Matt Graham' __license__ = 'MIT' import numpy as np import mcmc_updates as mcmc class BaseAdaptiveMHSampler(object): """ Base class for adaptive Metropolis Hastings samplers. Implements a basic adaptive MH scheme which tunes scale parameters of the MH proposal distributions to achieve an acceptance rate in some target range. A derived class must implement a ``get_samples`` method with signature:: thetas, n_reject = get_samples(self, theta_init, n_sample) which returns the sampled states ``thetas`` and number of rejections ``n_reject`` made during a series of ``n_sample`` iterations of a MCMC update dynamic which includes (or solely consists of) a MH update step with proposal distribution parameterised by the ``proposal_scales`` attribute of this class as scale parameters. """ def __init__(self, prop_scales): """ Base class for adaptive Metropolis Hastings samplers. Parameters ---------- prop_scales : ndarray Array of values to initialise proposal distribution scale parameters to. """ self.prop_scales = prop_scales def get_samples(self, theta_init, n_sample): """ Perform a series of Markov chain updates. Parameters ---------- theta_init : ndarray State to initialise chain at, with shape ``(n_dim, )``. n_sample : integer Number of Markov chain updates to perform and so state samples to return. Returns ------- thetas : ndarray Two dimensional array of sampled chain states with shape ``(n_sample, n_dim)``. n_reject : integer or iterable For a Markov chain in which each state update contains only one Metropolis(-Hastings) accept step this is the number of rejected proposed updates during the ``n_sample`` updates. If each update contains multiple Metropolis(-Hastings) accept steps this is an iterable with each element corresponding to the rejection count for a particular accept step in the order they are performed in the overall update. """ raise NotImplementedError() def adaptive_run(self, theta_init, batch_size, n_batch, low_acc_thr, upp_acc_thr, adapt_factor_func, print_details=False, reject_count_index=-1,): """ Run MH Markov chain with proposal tuning to adapt acceptance rate. Performs batches of MH Markov chain updates, after each batch calculating an estimate of the current acceptance rate from the number of rejections in the last batch and is this falls outside some specified range, adjusting the MH proposal distribution scale parameters by some multiplicate or divisive adaption factor calculated as a function of the current batch number and overall number of batches. Parameters ---------- theta_init : ndarray State to start running chain from. batch_size : integer Number of samples (Markov chain updates) to compute for each batch. n_batch : integer Number of batches of updates (and so adaptions) to do in total. low_acc_thr : float Lower acceptance rate threshold, a batch estimated acceptance rate less than this will cause the proposal distribution scales to be divided by ``adapt_factor_func(b, n_batch)`` where ``b`` is the current batch number. upp_acc_thr : float Upper acceptance rate threshold, a batch estimated acceptance rate more than this will cause the proposal distribution scales to be multiplied by ``adapt_factor_func(b, n_batch)`` where ``b`` is the current batch number. adapt_factor_func : function or callable object Function which determines the factor by which the proposal distribution scale parameters are adjusted after each batch (if acceptance rate outside required interval). Function should have a signature of the form:: adapt_factor = adapt_factor_func(b, n_batch) where ``adapt_factor`` is a scalar floating-point value used to multiply / divide the proposal widths, ``b`` is the current batch number and ``n_batch`` is the total number of batches to be used. print_details : boolean Whether to print accept rate and adaption factor for each batch to standard out during a run. reject_count_index : integer Optional argument specifying index of which rejection count to use as adaption signal when ``get_samples`` method returns an iterable of rejection counts as its second argument, for example when each overall Markov chain update between successive sample is composed of several Metropolis(-Hastings) steps each with their own possibility of rejecting. The index specified should correspond to the rejection count of the MH step of which the proposal distribution is parameterised by the ``prop_scales`` parameters for the adaptive run to make any sense. Returns ------- thetas : ndarray Array of states sampled during all batches of adaptive run, of shape ``(n_batch * n_size, n_dim)``. prop_scales : ndarray Array of proposal scales after each batch of adaptive run, of shape ``(n_batch, n_dim)``. accept_rates : ndaray Array of acceptance rates for each batch of adaptive run, of shape ``(n_batch, )``. """ thetas = np.empty((n_batch * batch_size, theta_init.shape[0])) prop_scales = np.empty((n_batch, self.prop_scales.shape[0])) accept_rates = np.empty(n_batch) for b in range(n_batch): thetas[b*batch_size:(b+1)*batch_size], n_reject = ( self.get_samples(theta_init, batch_size)) # if multiple rejection counts present e.g. from multiple # Metropolis(-Hastings) accept steps during one overall update # for different parts of state, adapt only using last if hasattr(n_reject, '__len__') and reject_count_index: n_reject = n_reject[reject_count_index] accept_rates[b] = 1. - (n_reject * 1. / batch_size) theta_init = thetas[(b + 1) * batch_size - 1] adapt_factor = adapt_factor_func(b, n_batch) if accept_rates[b] < low_acc_thr: self.prop_scales /= adapt_factor elif accept_rates[b] > upp_acc_thr: self.prop_scales *= adapt_factor prop_scales[b] = self.prop_scales if print_details: print('Batch {0}: accept rate {1}, adapt factor {2}' .format(b + 1, accept_rates[b], adapt_factor)) return thetas, prop_scales, accept_rates class PMMHSampler(BaseAdaptiveMHSampler): """ Pseudo-marginal Metropolis Hastings sampler. Markov chain Monte Carlo sampler which uses pseudo-marginal Metropolis Hastings updates. In the pseudo-marginal framework only an unbiased noisy estimate of the (unnormalised) target density is available. """ def __init__(self, log_f_estimator, log_prop_density, prop_sampler, prop_scales, prng): """ Pseudo-Marginal Metropolis Hastings sampler. Parameters ---------- log_f_estimator : function or callable object Function which returns an unbiased estimate of the log density of the target distribution given current parameter state. Should have a call signature:: log_f_est = log_f_estimator(theta) where ``theta`` is state vector (as ndarray) to evaluate density at and log_f_est is the returned double log-density estimate. log_prop_density : function or callable object or None Function returning logarithm of parameter update proposal density at a given proposed parameter state given the current parameter state. Should have a call signature:: log_prop_dens = log_prop_density(theta_prop, theta_curr) where ``theta_prop`` is proposed parameter state to evaluate the log proposal density at, ``theta_curr`` is the parameter state to condition the proposal density on and ``log_prop_dens`` is the returned log proposal density value. Alternatively ``None`` may be passed which indicates a symmetric proposal density in which case a Metropolis update will be made. prop_sampler : function or callable object Function which returns a proposed new parameter state drawn from proposal distribution given a current parameter state. Should have a call signature:: theta_prop = prop_sampler(theta_curr, prop_scales) where ``theta_curr`` is the current parameter state vector (as a ndarray) which the proposal should be conditioned on, ``prop_scales`` is a ndarray of scale parameters for the proposal distribution (e.g. standard deviation for Gaussian proposals) and ``theta_prop`` is the returned random propsal distribution draw, again an ndarray. prop_scales : ndarray Array of values to initialise the scale parameters of the state proposal distribution to. If an initial adaptive run is performed by calling ``adaptive_run``, these parameters will be tuned to try to achieve an average accept rate in some prescribed interval. prng : RandomState Pseudo-random number generator object (either an instance of a ``numpy`` ``RandomState`` or an object with an equivalent interface) used to randomly sample accept decisions in MH accept step. """ super(PMMHSampler, self).__init__(prop_scales) self.log_f_estimator = log_f_estimator if log_prop_density is None: self.do_metropolis_update = True else: self.do_metropolis_update = False self.log_prop_density = log_prop_density self.prop_sampler = prop_sampler self.prng = prng def get_samples(self, theta_init, n_sample): """ Perform a series of Markov chain updates. Parameters ---------- theta_init : ndarray State to initialise chain at, with shape ``(n_dim, )``. n_sample : integer Number of Markov chain updates to perform and so state samples to return. Returns ------- thetas : ndarray Two dimensional array of sampled chain states with shape ``(n_sample, n_dim)``. n_reject : integer The number of rejected proposed updates during the ``n_sample`` updates. """ if hasattr(theta_init, 'shape'): thetas = np.empty((n_sample, theta_init.shape[0])) else: thetas = np.empty(n_sample) thetas[0] = theta_init log_f_est_curr = self.log_f_estimator(theta_init) n_reject = 0 for s in range(1, n_sample): if self.do_metropolis_update: thetas[s], log_f_est_curr, rejection = mcmc.metropolis_step( thetas[s-1], log_f_est_curr, self.log_f_estimator, self.prng, self.prop_sampler, self.prop_scales) else: thetas[s], log_f_est_curr, rejection = mcmc.met_hastings_step( thetas[s-1], log_f_est_curr, self.log_f_estimator, self.prng, self.prop_sampler, self.prop_scales, self.log_prop_density) if rejection: n_reject += 1 return thetas, n_reject class APMMetIndPlusMHSampler(BaseAdaptiveMHSampler): """ Auxiliary pseudo-marginal MI + MH sampler. Sampler in the auxiliary pseudo-marginal MCMC framework which uses Metropolis independence updates for the random draws and Metropolis-- Hastings updates for the parameter state. """ def __init__(self, log_f_estimator, log_prop_density, prop_sampler, prop_scales, u_sampler, prng): """ Auxiliary Pseudo-marginal MI + MH sampler. Parameters ---------- log_f_estimator : function or callable object Function which returns an unbiased estimate of the log density of the target distribution given current parameter state and random draws. Should have a call signature:: log_f_est, cached_res_out = log_f_estimator(u, theta, [, cached_res_in]) where ``u`` is the vector of auxiliary random draws used in the density estimator, ``theta`` is the state vector (as ndarray) to estimate the density at, ``cached_res_in`` is an optional input which can be provided if cached intermediate results deterministically calculated from the ``theta`` which it is wished to estimate the density at have been stored from a previous call, potentially speeding subsequent estimates, ``log_f_est`` is the calculated log-density estimate and ``cached_res_out`` are intermediate cached results determinstically calculated from the specified ``theta`` which can be used in subsequent calls to potentially speed further estimates of the log density for this ``theta`` value (if ``cached_res_in`` was specified then ``cached_res_out == cached_res_in``). log_prop_density : function or callable object or None Function returning logarithm of parameter update proposal density at a given proposed parameter state given the current parameter state. Should have a call signature:: log_prop_dens = log_prop_density(theta_prop, theta_curr) where ``theta_prop`` is proposed parameter state to evaluate the log proposal density at, ``theta_curr`` is the parameter state to condition the proposal density on and ``log_prop_dens`` is the returned log proposal density value. Alternatively ``None`` may be passed which indicates a symmetric proposal density in which case a Metropolis update will be made. prop_sampler : function or callable object Function which returns a proposed new parameter state drawn from proposal distribution given a current parameter state. Should have a call signature:: theta_prop = prop_sampler(theta_curr, prop_scales) where ``theta_curr`` is the current parameter state vector (as a ndarray) which the proposal should be conditioned on, ``prop_scales`` is a ndarray of scale parameters for the proposal distribution (e.g. standard deviation for Gaussian proposals) and ``theta_prop`` is the returned random propsal distribution draw, again an ndarray. prop_scales : ndarray Array of values to initialise the scale parameters of the state proposal distribution to. If an initial adaptive run is performed by calling ``adaptive_run``, these parameters will be tuned to try to achieve an average accept rate in some prescribed interval. u_sampler : function or callable object Function which returns an independent sample from the 'prior' distribution on the random draws :math:`q(u)`. prng : RandomState Pseudo-random number generator object (either an instance of a ``numpy`` ``RandomState`` or an object with an equivalent interface) used to randomly sample accept decisions in MH accept step. """ super(APMMetIndPlusMHSampler, self).__init__(prop_scales) self.log_f_estimator = log_f_estimator if log_prop_density is None: self.do_metropolis_update = True else: self.do_metropolis_update = False self.log_prop_density = log_prop_density self.prop_sampler = prop_sampler self.prop_scales = prop_scales self.u_sampler = u_sampler self.prng = prng def get_samples(self, theta_init, n_sample, u_init=None): """ Perform a series of Markov chain updates. Parameters ---------- theta_init : ndarray State to initialise parameters at, with shape ``(n_dim, )``. n_sample : integer Number of Markov chain updates to perform and so state samples to return. u_init : ndarray State to initialise random draws at. Optional, if not specified will be sampled from base density. Returns ------- thetas : ndarray Two dimensional array of sampled chain states with shape ``(n_sample, n_dim)``. (n_reject_1, n_reject_2) : tuple The number of rejected proposed updates during the ``n_sample`` updates, in the acceptance step for the random draw variable given the current state parameter (``n_reject_1``) and in the acceptance step for the state parameter given the current random draws (``n_reject_2``). """ if hasattr(theta_init, 'shape'): thetas = np.empty((n_sample, theta_init.shape[0])) else: thetas = np.empty(n_sample) thetas[0] = theta_init u = u_init if not u_init is None else self.u_sampler() log_f_est_curr, cached_res_curr = self.log_f_estimator(u, theta_init) n_reject_1 = 0 n_reject_2 = 0 for s in range(1, n_sample): ## Update u keeping theta fixed using MI # As for this update only u will be changed, cached results # for current theta calculated in previous log_f_estimator call # can be reused, hence pass these values to estimator (with no # theta value being needed in this case) and use only first # return value (as second will be equal to cached_res_curr) log_f_func_1 = lambda v: ( self.log_f_estimator(v, thetas[s-1], cached_res_curr)[0]) u, log_f_est_curr, rejection = mcmc.metropolis_indepedence_step( u, log_f_est_curr, log_f_func_1, self.prng, self.u_sampler) if rejection: n_reject_1 += 1 ## Update theta keeping u fixed using MH def log_f_func_2(theta): # save cached results from estimator evaluation for proposed # theta update so this can be saved to be used in # final call of log_f_func in slice sampling routine will # always be accepted update so cached results will be correct log_f_est, self._cached_res_prop = ( self.log_f_estimator(u, theta)) return log_f_est if self.do_metropolis_update: thetas[s], log_f_est_curr, rejection = mcmc.metropolis_step( thetas[s-1], log_f_est_curr, log_f_func_2, self.prng, self.prop_sampler, self.prop_scales) else: thetas[s], log_f_est_curr, rejection = mcmc.met_hastings_step( thetas[s-1], log_f_est_curr, log_f_func_2, self.prng, self.prop_sampler, self.prop_scales, self.log_prop_density) if rejection: n_reject_2 += 1 else: # if proposal accepted update current cached results cached_res_curr = self._cached_res_prop return thetas, (n_reject_1, n_reject_2) class APMEllSSPlusMHSampler(BaseAdaptiveMHSampler): """ Auxiliary pseudo-marginal ESS + MH sampler. Sampler in the auxiliary pseudo-marginal MCMC framework which uses elliptical slice sampling updates for the random draws and Metropolis-- Hastings updates for the parameter state. It is implicitly assumed the 'prior' :math:`q(u)` on the random draws is Gaussian in this case. """ def __init__(self, log_f_estimator, log_prop_density, prop_sampler, prop_scales, u_sampler, prng, max_slice_iters=1000): """ Auxiliary Pseudo-marginal ESS + MH sampler. Parameters ---------- log_f_estimator : function or callable object Function which returns an unbiased estimate of the log density of the target distribution given current parameter state and random draws. Should have a call signature:: log_f_est, cached_res_out = log_f_estimator(u, theta, [, cached_res_in]) where ``u`` is the vector of auxiliary random draws used in the density estimator, ``theta`` is the state vector (as ndarray) to estimate the density at, ``cached_res_in`` is an optional input which can be provided if cached intermediate results deterministically calculated from the ``theta`` which it is wished to estimate the density at have been stored from a previous call, potentially speeding subsequent estimates, ``log_f_est`` is the calculated log-density estimate and ``cached_res_out`` are intermediate cached results determinstically calculated from the specified ``theta`` which can be used in subsequent calls to potentially speed further estimates of the log density for this ``theta`` value (if ``cached_res_in`` was specified then ``cached_res_out == cached_res_in``). log_prop_density : function or callable object or None Function returning logarithm of parameter update proposal density at a given proposed parameter state given the current parameter state. Should have a call signature:: log_prop_dens = log_prop_density(theta_prop, theta_curr) where ``theta_prop`` is proposed parameter state to evaluate the log proposal density at, ``theta_curr`` is the parameter state to condition the proposal density on and ``log_prop_dens`` is the returned log proposal density value. Alternatively ``None`` may be passed which indicates a symmetric proposal density in which case a Metropolis update will be made. prop_sampler : function or callable object Function which returns a proposed new parameter state drawn from proposal distribution given a current parameter state. Should have a call signature:: theta_prop = prop_sampler(theta_curr, prop_scales) where ``theta_curr`` is the current parameter state vector (as a ndarray) which the proposal should be conditioned on, ``prop_scales`` is a ndarray of scale parameters for the proposal distribution (e.g. standard deviation for Gaussian proposals) and ``theta_prop`` is the returned random propsal distribution draw, again an ndarray. prop_scales : ndarray Array of values to initialise the scale parameters of the state proposal distribution to. If an initial adaptive run is performed by calling ``adaptive_run``, these parameters will be tuned to try to achieve an average accept rate in some prescribed interval. u_sampler : function or callable object Function which returns an independent sample from the 'prior' distribution on the random draws :math:`q(u)`. prng : RandomState Pseudo-random number generator object (either an instance of a ``numpy`` ``RandomState`` or an object with an equivalent interface) used to randomly sample accept decisions in MH accept step. max_slice_iters : integer Maximum number of elliptical slice shrinking iterations to perform. """ super(APMEllSSPlusMHSampler, self).__init__(prop_scales) self.log_f_estimator = log_f_estimator if log_prop_density is None: self.do_metropolis_update = True else: self.do_metropolis_update = False self.log_prop_density = log_prop_density self.prop_sampler = prop_sampler self.prop_scales = prop_scales self.prng = prng self.u_sampler = u_sampler self.max_slice_iters = max_slice_iters def elliptical_slice_sample_u_given_theta(self, u, log_f_est, log_f_func): """ Perform ESS on conditional density of random draws given state. Performs elliptical slice sampling conditional target density on auxiliary random draw variables given a parameter state. """ v = self.u_sampler() return mcmc.elliptical_slice_step(u, log_f_est, log_f_func, self.prng, v, self.max_slice_iters) def get_samples(self, theta_init, n_sample, u_init=None): """ Perform a series of Markov chain updates. Parameters ---------- theta_init : ndarray State to initialise parameters at, with shape ``(n_dim, )``. n_sample : integer Number of Markov chain updates to perform and so state samples to return. u_init : ndarray State to initialise random draws at. Optional, if not specified will be sampled from base density. Returns ------- thetas : ndarray Two dimensional array of sampled chain states with shape ``(n_sample, n_dim)``. n_reject : integer or iterable The number of rejected proposed updates during the ``n_sample`` updates. """ if hasattr(theta_init, 'shape'): thetas = np.empty((n_sample, theta_init.shape[0])) else: thetas = np.empty(n_sample) thetas[0] = theta_init u = u_init if u_init is not None else self.u_sampler() log_f_est_curr, self._cached_res_curr = ( self.log_f_estimator(u, theta_init)) n_reject = 0 for s in range(1, n_sample): ## Update u keeping theta fixed using ell-SS # As for this update only u will be changed, cached results # for current theta calculated in previous log_f_estimator call # can be reused, hence pass these values to estimator (with no # theta value being needed in this case) and use only first # return value (as second will be equal to cached_res_curr) log_f_func_1 = lambda v: ( self.log_f_estimator(v, thetas[s-1], self._cached_res_curr)[0]) u, log_f_est_curr = self.elliptical_slice_sample_u_given_theta( u, log_f_est_curr, log_f_func_1) ## Update theta keeping u fixed using MH def log_f_func_2(theta): # save cached results from estimator evaluation for proposed # theta update so this can be saved to be used in # final call of log_f_func in slice sampling routine will # always be accepted update so cached results will be correct log_f_est, self._cached_res_prop = ( self.log_f_estimator(u, theta)) return log_f_est if self.do_metropolis_update: thetas[s], log_f_est_curr, rejection = mcmc.metropolis_step( thetas[s-1], log_f_est_curr, log_f_func_2, self.prng, self.prop_sampler, self.prop_scales) else: thetas[s], log_f_est_curr, rejection = mcmc.met_hastings_step( thetas[s-1], log_f_est_curr, log_f_func_2, self.prng, self.prop_sampler, self.prop_scales, self.log_prop_density) if rejection: n_reject += 1 else: # if proposal accepted update current cached results self._cached_res_curr = self._cached_res_prop return thetas, n_reject class BaseAPMMetIndPlusSliceSampler(object): """ Abstract auxiliary pseudo-marginal MI + SS sampler base class. Sampler in the auxiliary pseudo-marginal MCMC framework which uses Metropolis independence updates for the random draws and some form of linear slice sampling in updates for parameter state. """ def __init__(self, log_f_estimator, u_sampler, prng, max_steps_out=0, max_slice_iters=1000): """ Abstract auxiliary Pseudo-marginal MI + SS sampler base class. Parameters ---------- log_f_estimator : function or callable object Function which returns an unbiased estimate of the log density of the target distribution given current parameter state and random draws. Should have a call signature:: log_f_est, cached_res_out = log_f_estimator(u, theta, [, cached_res_in]) where ``u`` is the vector of auxiliary random draws used in the density estimator, ``theta`` is the state vector (as ndarray) to estimate the density at, ``cached_res_in`` is an optional input which can be provided if cached intermediate results deterministically calculated from the ``theta`` which it is wished to estimate the density at have been stored from a previous call, potentially speeding subsequent estimates, ``log_f_est`` is the calculated log-density estimate and ``cached_res_out`` are intermediate cached results determinstically calculated from the specified ``theta`` which can be used in subsequent calls to potentially speed further estimates of the log density for this ``theta`` value (if ``cached_res_in`` was specified then ``cached_res_out == cached_res_in``). u_sampler : function or callable object Function which returns an independent sample from the 'prior' distribution on the random draws :math:`q(u)`. prng : RandomState Pseudo-random number generator object (either an instance of a ``numpy`` ``RandomState`` or an object with an equivalent interface) used to randomly sample accept decisions in MH accept step. max_steps_out : integer Maximum number of stepping out iterations to perform during slice sampling update (default 0). max_slice_iters : integer Maximum number of slice shrinking iterations to perform. """ self.log_f_estimator = log_f_estimator self.u_sampler = u_sampler self.prng = prng self.max_steps_out = max_steps_out self.max_slice_iters = max_slice_iters def slice_step(self, x_curr, log_f_curr, log_f_func, w): """ Perform a linear slice sampling step. Simply wraps external module function passing in fixed object level arguments for more convenient calling. """ return mcmc.linear_slice_step(x_curr, log_f_curr, log_f_func, w, self.prng, self.max_steps_out, self.max_slice_iters) def slice_sample_theta_given_u(self, theta, log_f_est, u): """ Perform SS on conditional density of state given random draws. Performs slice sampling along some line on conditional target density on parameter state given auxiliary random draw variables. Should be implemented by an derived class. """ raise NotImplementedError() def get_samples(self, theta_init, n_sample, u_init=None): """ Perform a series of Markov chain updates. Parameters ---------- theta_init : ndarray State to initialise parameters at, with shape ``(n_dim, )``. n_sample : integer Number of Markov chain updates to perform and so state samples to return. u_init : ndarray State to initialise random draws at. Optional, if not specified will be sampled from base density. Returns ------- thetas : ndarray Two dimensional array of sampled chain states with shape ``(n_sample, n_dim)``. n_reject : integer or iterable The number of rejected proposed updates during the ``n_sample`` updates. """ if hasattr(theta_init, 'shape'): thetas = np.empty((n_sample, theta_init.shape[0])) else: thetas = np.empty((n_sample, 1)) thetas[0] = theta_init u = u_init if u_init is not None else self.u_sampler() log_f_est_curr, self._cached_res_curr = ( self.log_f_estimator(u, theta_init)) n_reject = 0 for s in range(1, n_sample): ## Update u keeping theta fixed using MI # As for this update only u will be changed, cached results # for current theta calculated in previous log_f_estimator call # can be reused, hence pass these values to estimator (with no # theta value being needed in this case) and use only first # return value (as second will be equal to cached_res_curr) log_f_func_1 = lambda v: ( self.log_f_estimator(v, thetas[s-1], self._cached_res_curr)[0]) u, log_f_est_curr, rejection = mcmc.metropolis_indepedence_step( u, log_f_est_curr, log_f_func_1, self.prng, self.u_sampler) if rejection: n_reject += 1 ## Update theta given current u using SS # self.cached_res_curr also updated in this method thetas[s], log_f_est_curr = self.slice_sample_theta_gvn_u( thetas[s-1].copy(), log_f_est_curr, u) return thetas, n_reject class BaseAPMEllSSPlusSliceSampler(object): """ Abstract auxiliary pseudo-marginal ESS + SS sampler base class. Sampler in the auxiliary pseudo-marginal MCMC framework which uses elliptical slice sampling updates for the random draws and some form of linear slice sampling in updates for parameter state. It is implicitly assumed the 'prior' :math:`q(u)` on the random draws is Gaussian in this case. """ def __init__(self, log_f_estimator, u_sampler, prng, max_steps_out=0, max_slice_iters=1000): """ Abstract auxiliary Pseudo-marginal ESS + SS sampler base class. Parameters ---------- log_f_estimator : function or callable object Function which returns an unbiased estimate of the log density of the target distribution given current parameter state and random draws. Should have a call signature:: log_f_est, cached_res_out = log_f_estimator(u, theta, [, cached_res_in]) where ``u`` is the vector of auxiliary random draws used in the density estimator, ``theta`` is the state vector (as ndarray) to estimate the density at, ``cached_res_in`` is an optional input which can be provided if cached intermediate results deterministically calculated from the ``theta`` which it is wished to estimate the density at have been stored from a previous call, potentially speeding subsequent estimates, ``log_f_est`` is the calculated log-density estimate and ``cached_res_out`` are intermediate cached results determinstically calculated from the specified ``theta`` which can be used in subsequent calls to potentially speed further estimates of the log density for this ``theta`` value (if ``cached_res_in`` was specified then ``cached_res_out == cached_res_in``). u_sampler : function or callable object Function which returns an independent sample from the 'prior' distribution on the random draws :math:`q(u)`. prng : RandomState Pseudo-random number generator object (either an instance of a ``numpy`` ``RandomState`` or an object with an equivalent interface) used to randomly sample accept decisions in MH accept step. max_steps_out : integer Maximum number of stepping out iterations to perform during slice sampling update (default 0). max_slice_iters : integer Maximum number of slice shrinking iterations to perform (common to both elliptical and linear slice sampling updates). """ self.log_f_estimator = log_f_estimator self.u_sampler = u_sampler self.prng = prng self.max_steps_out = max_steps_out self.max_slice_iters = max_slice_iters def slice_step(self, x_curr, log_f_curr, log_f_func, w): """ Perform a linear slice sampling step. Simply wraps external module function passing in fixed object level arguments for more convenient calling. """ return mcmc.linear_slice_step(x_curr, log_f_curr, log_f_func, w, self.prng, self.max_steps_out, self.max_slice_iters) def elliptical_slice_sample_u_given_theta(self, u, log_f_est, log_f_func): """ Perform ESS on conditional density of random draws given state. Performs elliptical slice sampling conditional target density on auxiliary random draw variables given a parameter state. """ v = self.u_sampler() return mcmc.elliptical_slice_step(u, log_f_est, log_f_func, self.prng, v, self.max_slice_iters) def slice_sample_theta_given_u(self, theta, log_f_est, u): """ Perform SS on conditional density of state given random draws. Performs slice sampling along some line on conditional target density on parameter state given auxiliary random draw variables. Should be implemented by an derived class. """ raise NotImplementedError() def get_samples(self, theta_init, n_sample, u_init=None): """ Perform a series of Markov chain updates. Parameters ---------- theta_init : ndarray State to initialise parameters at, with shape ``(n_dim, )``. n_sample : integer Number of Markov chain updates to perform and so state samples to return. u_init : ndarray State to initialise random draws at. Optional, if not specified will be sampled from base density. Returns ------- thetas : ndarray Two dimensional array of sampled chain states with shape ``(n_sample, n_dim)``. """ if hasattr(theta_init, 'shape'): thetas = np.empty((n_sample, theta_init.shape[0])) else: thetas = np.empty((n_sample, 1)) thetas[0] = theta_init u = u_init if u_init is not None else self.u_sampler() log_f_est_curr, self._cached_res_curr = ( self.log_f_estimator(u, theta_init)) for s in range(1, n_sample): ## Update u keeping theta fixed using ell-SS log_f_func = lambda v: ( self.log_f_estimator(v, thetas[s-1], self._cached_res_curr)[0] # second output will be equal to cached_res_curr as # not changing theta ) u, log_f_est_curr = self.elliptical_slice_sample_u_given_theta( u, log_f_est_curr, log_f_func) ## Update theta given current u using SS # self.cached_res_curr also updated in this method thetas[s], log_f_est_curr = self.slice_sample_theta_gvn_u( thetas[s-1].copy(), log_f_est_curr, u) return thetas class APMMetIndPlusSeqSliceSampler(BaseAPMMetIndPlusSliceSampler): """ Auxiliary pseudo-marginal MI + sequential-SS sampler. Sampler in the auxiliary pseudo-marginal MCMC framework which uses Metropolis independence updates for the random draws and sequential (over axes) slice sampling in updates for parameter state. """ def __init__(self, log_f_estimator, u_sampler, prng, ws, max_steps_out=0, max_slice_iters=1000): """ Auxiliary pseudo-marginal MI + sequential-SS sampler. Parameters ---------- log_f_estimator : function or callable object Function which returns an unbiased estimate of the log density of the target distribution given current parameter state and random draws. Should have a call signature:: log_f_est, cached_res_out = log_f_estimator(u, theta, [, cached_res_in]) where ``u`` is the vector of auxiliary random draws used in the density estimator, ``theta`` is the state vector (as ndarray) to estimate the density at, ``cached_res_in`` is an optional input which can be provided if cached intermediate results deterministically calculated from the ``theta`` which it is wished to estimate the density at have been stored from a previous call, potentially speeding subsequent estimates, ``log_f_est`` is the calculated log-density estimate and ``cached_res_out`` are intermediate cached results determinstically calculated from the specified ``theta`` which can be used in subsequent calls to potentially speed further estimates of the log density for this ``theta`` value (if ``cached_res_in`` was specified then ``cached_res_out == cached_res_in``). u_sampler : function or callable object Function which returns an independent sample from the 'prior' distribution on the random draws :math:`q(u)`. prng : RandomState Pseudo-random number generator object (either an instance of a ``numpy`` ``RandomState`` or an object with an equivalent interface) used to randomly sample accept decisions in MH accept step. ws : ndarray Initial slice bracket widths to use when performing slice sampling sequentially on parameter state vector dimensions (i.e. `ws` should be same length as parameter state vector with a per dimension slice bracket width parameter being specified). max_steps_out : integer Maximum number of stepping out iterations to perform during slice sampling update (default 0). max_slice_iters : integer Maximum number of slice shrinking iterations to perform. """ super(APMMetIndPlusSeqSliceSampler, self).__init__( log_f_estimator, u_sampler, prng, max_steps_out, max_slice_iters) self.ws = ws def slice_sample_theta_gvn_u(self, theta, log_f_est, u): """ Perform seq-SS on conditional density of state given random draws. Performs slice sampling on conditional target density of each dimension of parameter state given rest of parameter state vector and auxiliary random draw variables, the dimension updates being peformed sequentially in a fixed ordinal ordering. """ for j in range(len(theta)): x_curr = theta[j] def log_f_func(x): # keep saving cached results from new estimator evaluations # final call of log_f_func in slice sampling routine will # always be accepted update so cached results will be correct log_f_est_, self._cached_res_curr = ( self.log_f_estimator(u, np.r_[theta[:j], x, theta[j+1:]]) ) return log_f_est_ x_new, log_f_est = self.slice_step( x_curr, log_f_est, log_f_func, self.ws[j]) theta[j] = x_new return theta, log_f_est class APMMetIndPlusRandDirSliceSampler(BaseAPMMetIndPlusSliceSampler): """ Auxiliary pseudo-marginal MI + random-direction-SS sampler. Sampler in the auxiliary pseudo-marginal MCMC framework which uses Metropolis independence updates for the random draws and slice sampling along a random direction in updates for parameter state. """ def __init__(self, log_f_estimator, u_sampler, prng, slc_dir_and_w_sampler, max_steps_out=0, max_slice_iters=1000): """ Auxiliary pseudo-marginal MI + random-direction-SS sampler. Parameters ---------- log_f_estimator : function or callable object Function which returns an unbiased estimate of the log density of the target distribution given current parameter state and random draws. Should have a call signature:: log_f_est, cached_res_out = log_f_estimator(u, theta, [, cached_res_in]) where ``u`` is the vector of auxiliary random draws used in the density estimator, ``theta`` is the state vector (as ndarray) to estimate the density at, ``cached_res_in`` is an optional input which can be provided if cached intermediate results deterministically calculated from the ``theta`` which it is wished to estimate the density at have been stored from a previous call, potentially speeding subsequent estimates, ``log_f_est`` is the calculated log-density estimate and ``cached_res_out`` are intermediate cached results determinstically calculated from the specified ``theta`` which can be used in subsequent calls to potentially speed further estimates of the log density for this ``theta`` value (if ``cached_res_in`` was specified then ``cached_res_out == cached_res_in``). u_sampler : function or callable object Function which returns an independent sample from the 'prior' distribution on the random draws :math:`q(u)`. prng : RandomState Pseudo-random number generator object (either an instance of a ``numpy`` ``RandomState`` or an object with an equivalent interface) used to randomly sample accept decisions in MH accept step. slc_dir_and_w_sampler : function or callable object Function which returns a vector specifying a random direction in the parameter state space along to slice sample along with a corresponding initial slice bracket width for this direction. Should have a call signature:: d, w = slc_dir_and_w_sampler() where ``d`` is a ndarray of same dimension as the parameter state and ``w`` is a (positive) floating point value specifying the corresponding initial slice bracket width parameter. max_steps_out : integer Maximum number of stepping out iterations to perform during slice sampling update (default 0). max_slice_iters : integer Maximum number of slice shrinking iterations to perform. """ super(APMMetIndPlusRandDirSliceSampler, self).__init__( log_f_estimator, u_sampler, prng, max_steps_out, max_slice_iters) self.slc_dir_and_w_sampler = slc_dir_and_w_sampler def slice_sample_theta_gvn_u(self, theta, log_f_est, u): """ Perform rd-SS on conditional density of state given random draws. Performs slice sampling along a random direction on conditional target density of parameter state given auxiliary random draw variables. """ d, w = self.slc_dir_and_w_sampler() def log_f_func(x): # keep saving cached results from new estimator evaluations # final call of log_f_func in slice sampling routine will # always be accepted update so cached results will be correct log_f_est_, self._cached_res_curr = ( self.log_f_estimator(u, theta + x * d) ) return log_f_est_ x_new, log_f_est = self.slice_step(0., log_f_est, log_f_func, w) return theta + x_new * d, log_f_est class APMEllSSPlusRandDirSliceSampler(BaseAPMEllSSPlusSliceSampler): """ Auxiliary pseudo-marginal ESS + random-direction-SS sampler. Sampler in the auxiliary pseudo-marginal MCMC framework which uses elliptical slice sampling updates for the random draws and slice sampling along a random direction in updates for parameter state. It is implicitly assumed the 'prior' :math:`q(u)` on the random draws is Gaussian in this case. """ def __init__(self, log_f_estimator, u_sampler, prng, slc_dir_and_w_sampler, max_steps_out=0, max_slice_iters=1000): """ Auxiliary pseudo-marginal ESS + random-direction-SS sampler. Parameters ---------- log_f_estimator : function or callable object Function which returns an unbiased estimate of the log density of the target distribution given current parameter state and random draws. Should have a call signature:: log_f_est, cached_res_out = log_f_estimator(u, theta, [, cached_res_in]) where ``u`` is the vector of auxiliary random draws used in the density estimator, ``theta`` is the state vector (as ndarray) to estimate the density at, ``cached_res_in`` is an optional input which can be provided if cached intermediate results deterministically calculated from the ``theta`` which it is wished to estimate the density at have been stored from a previous call, potentially speeding subsequent estimates, ``log_f_est`` is the calculated log-density estimate and ``cached_res_out`` are intermediate cached results determinstically calculated from the specified ``theta`` which can be used in subsequent calls to potentially speed further estimates of the log density for this ``theta`` value (if ``cached_res_in`` was specified then ``cached_res_out == cached_res_in``). u_sampler : function or callable object Function which returns an independent sample from the 'prior' distribution on the random draws :math:`q(u)`. prng : RandomState Pseudo-random number generator object (either an instance of a ``numpy`` ``RandomState`` or an object with an equivalent interface) used to randomly sample accept decisions in MH accept step. slc_dir_and_w_sampler : function or callable object Function which returns a vector specifying a random direction in the parameter state space along to slice sample along with a corresponding initial slice bracket width for this direction. Should have a call signature:: d, w = slc_dir_and_w_sampler() where ``d`` is a ndarray of same dimension as the parameter state and ``w`` is a (positive) floating point value specifying the corresponding initial slice bracket width parameter. max_steps_out : integer Maximum number of stepping out iterations to perform during slice sampling update (default 0). max_slice_iters : integer Maximum number of slice shrinking iterations to perform (common to both elliptical and linear slice sampling updates) """ super(APMEllSSPlusRandDirSliceSampler, self).__init__( log_f_estimator, u_sampler, prng, max_steps_out, max_slice_iters) self.slc_dir_and_w_sampler = slc_dir_and_w_sampler def slice_sample_theta_gvn_u(self, theta, log_f_est, u): """ Perform rd-SS on conditional density of state given random draws. Performs slice sampling along a random direction on conditional target density of parameter state given auxiliary random draw variables. """ d, w = self.slc_dir_and_w_sampler() def log_f_func(x): # keep saving cached results from new estimator evaluations # final call of log_f_func in slice sampling routine will # always be accepted update so cached results will be correct log_f_est_, self._cached_res_curr = ( self.log_f_estimator(u, theta + x * d) ) return log_f_est_ x_new, log_f_est = self.slice_step(0., log_f_est, log_f_func, w) return theta + x_new * d, log_f_est class APMEllSSPlusEllSSSampler(BaseAPMEllSSPlusSliceSampler): """ Auxiliary pseudo-marginal ESS + ESS sampler. Sampler in the auxiliary pseudo-marginal MCMC framework which uses elliptical slice sampling updates for both the random draws and parameter states. It is implicitly assumed the prior :math:`q(u)` on the random draws and the prior on the parameters :math:`p(\\theta)` are both Gaussian. """ def __init__(self, log_f_estimator, u_sampler, theta_sampler, prng, max_slice_iters=1000): """ Auxiliary pseudo-marginal ESS + ESS sampler. Parameters ---------- log_f_estimator : function or callable object Function which returns an unbiased estimate of the log density of the target likelihood (i.e. without Gaussian prior on parameter state) given current parameter state and random draws. Should have a call signature:: log_f_est, cached_res_out = log_f_estimator(u, theta, [, cached_res_in]) where ``u`` is the vector of auxiliary random draws used in the density estimator, ``theta`` is the state vector (as ndarray) to estimate the density at, ``cached_res_in`` is an optional input which can be provided if cached intermediate results deterministically calculated from the ``theta`` which it is wished to estimate the density at have been stored from a previous call, potentially speeding subsequent estimates, ``log_f_est`` is the calculated log-density estimate and ``cached_res_out`` are intermediate cached results determinstically calculated from the specified ``theta`` which can be used in subsequent calls to potentially speed further estimates of the log density for this ``theta`` value (if ``cached_res_in`` was specified then ``cached_res_out == cached_res_in``). u_sampler : function or callable object Function which returns an independent sample from the Gaussian prior distribution on the random draws :math:`q(u)`. theta_sampler : function or callable object Function which returns an independent sample from the Gaussian prior distribution on the parameters :math:`p(\\theta)`. prng : RandomState Pseudo-random number generator object (either an instance of a ``numpy`` ``RandomState`` or an object with an equivalent interface) used to randomly sample accept decisions in MH accept step. max_slice_iters : integer Maximum number of slice shrinking iterations to perform. """ super(APMEllSSPlusEllSSSampler, self).__init__( log_f_estimator, u_sampler, prng, None, max_slice_iters) self.theta_sampler = theta_sampler def slice_sample_theta_gvn_u(self, theta, log_f_est, u): """ Perform ESS on conditional density of state given random draws. Performs elliptical slice sampling on conditional target density of parameter state given auxiliary random draw variables. """ def log_f_func(theta): # keep saving cached results from new estimator evaluations # final call of log_f_func in slice sampling routine will # always be accepted update so cached results will be correct log_f_est_, self._cached_res_curr = ( self.log_f_estimator(u, theta) ) return log_f_est_ v = self.theta_sampler() return mcmc.elliptical_slice_step( theta, log_f_est, log_f_func, self.prng, v, self.max_slice_iters)

49.40566

79

0.641971

7,401

57,607

4.816376

0.060262

0.020199

0.015514

0.014784

0.864164

0.852129

0.840291

0.831313

0.819615

0.809376

0

0.003054

0.300936

57,607

1,165

80

49.448069

0.882099

0.633951

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0.697987

0

0.007457

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1

0.110738

false

0

0.006711

0

0.218121

0.010067

0

null

0

1

0

null

0

6

fd6a1be517a6b33b0fe1a38cba9957c136ed08ad

37

py

Python

python-logging/mymodule/sub/__init__.py

cgt212/example-code

739dadc5003b0a1f82cc05b3f40c168659ed31f3

[ "Apache-2.0" ]

null

python-logging/mymodule/sub/__init__.py

cgt212/example-code

739dadc5003b0a1f82cc05b3f40c168659ed31f3

[ "Apache-2.0" ]

null

python-logging/mymodule/sub/__init__.py

cgt212/example-code

739dadc5003b0a1f82cc05b3f40c168659ed31f3

[ "Apache-2.0" ]

null

from mymodule.sub.thing import Thing

18.5

36

0.837838

6

37

5.166667

0.833333

0

0.108108

37

1

37

0.939394

0

1

0

true

0

1

0

1

0

1

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null

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1

0

null

0

1

0

1

0

1

0

6

fda5205a76f7338320e3c6b08c4926f4b6fd1878

21

py

Python

ptfit/__init__.py

msyriac/ptfit

84a002f6cf0ff47a22e4c374d0b336d57e030b35

[ "BSD-2-Clause" ]

null

ptfit/__init__.py

msyriac/ptfit

84a002f6cf0ff47a22e4c374d0b336d57e030b35

[ "BSD-2-Clause" ]

null

ptfit/__init__.py

msyriac/ptfit

84a002f6cf0ff47a22e4c374d0b336d57e030b35

[ "BSD-2-Clause" ]

null

from .ptfit import *

10.5

20

0.714286

3

21

5

1

0

0.190476

21

1

21

0.882353

0

1

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true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

fdae93356ff62838465860e6c8bd4f30ee438e5d

193

py

Python

profiles_api/admin.py

csilouanos/profiles-reset-api

919648b82853d22c99dc54e54f4dcaa91964c527

[ "MIT" ]

null

profiles_api/admin.py

csilouanos/profiles-reset-api

919648b82853d22c99dc54e54f4dcaa91964c527

[ "MIT" ]

null

profiles_api/admin.py

csilouanos/profiles-reset-api

919648b82853d22c99dc54e54f4dcaa91964c527

[ "MIT" ]

null

from django.contrib import admin from profiles_api import models # Registers the UserProfile model as admin admin.site.register(models.UserProfile) admin.site.register(models.ProfileFeedItem)

27.571429

43

0.84456

26

193

6.230769

0.615385

0.111111

0.209877

0.283951

0

0.093264

193

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32.166667

0.925714

0.207254

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0.5

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0

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null

0

1

0

1

0

6

a9044a1a633ca05906619be6044590be0cec74b6

155

py

Python

tests/test_regex2.py

mannuan/dspider

bf1bbad375b3b61f800cb25d1c839659a66f3e12

[ "Apache-2.0" ]

15

2018-05-12T17:15:59.000Z

2020-09-06T04:32:47.000Z

tests/test_regex2.py

mannuan/dspider

bf1bbad375b3b61f800cb25d1c839659a66f3e12

[ "Apache-2.0" ]

null

tests/test_regex2.py

mannuan/dspider

bf1bbad375b3b61f800cb25d1c839659a66f3e12

[ "Apache-2.0" ]

2

2018-06-29T00:44:52.000Z

2020-07-07T01:58:03.000Z

# -*- coding:utf-8 -*- import re _str = '奥斯卡级hi空间大撒545谎单价(8)' print(re.sub(r'([^(]+)\([^)]+\)',r'\1',_str)) print(re.sub(r'[^(]+\(([^)]+)\)',r'\1',_str))

22.142857

45

0.470968

22

155

3.181818

0.5

0.2

0.285714

0.314286

0.457143

0

0.048951

0.077419

155

6

46

25.833333

0.440559

0.129032

0

0.413534

0

1

0

false

0

0.25

0

0.25

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1

0

null

0

1

0

1

0

1

0

null

0

1

0

6

e3000309b23a18d7ba6afdb32965ac922810c4dc

52,614

py

Python

dlkit/json_/assessment_authoring/managers.py

UOC/dlkit

a9d265db67e81b9e0f405457464e762e2c03f769

[ "MIT" ]

2

2018-02-23T12:16:11.000Z

2020-10-08T17:54:24.000Z

dlkit/json_/assessment_authoring/managers.py

UOC/dlkit

a9d265db67e81b9e0f405457464e762e2c03f769

[ "MIT" ]

87

2017-04-21T18:57:15.000Z

2021-12-13T19:43:57.000Z

dlkit/json_/assessment_authoring/managers.py

UOC/dlkit

a9d265db67e81b9e0f405457464e762e2c03f769

[ "MIT" ]

1

2018-03-01T16:44:25.000Z

"""JSON implementations of assessment.authoring managers.""" # pylint: disable=no-init # Numerous classes don't require __init__. # pylint: disable=too-many-public-methods,too-few-public-methods # Number of methods are defined in specification # pylint: disable=protected-access # Access to protected methods allowed in package json package scope # pylint: disable=too-many-ancestors # Inheritance defined in specification from . import profile from . import sessions from .. import utilities from ..osid import managers as osid_managers from ..primitives import Type from ..type.objects import TypeList from ..utilities import get_registry from dlkit.abstract_osid.osid import errors from dlkit.manager_impls.assessment_authoring import managers as assessment_authoring_managers class AssessmentAuthoringProfile(osid_managers.OsidProfile, assessment_authoring_managers.AssessmentAuthoringProfile): """The ``AssessmentAuthoringProfile`` describes the interoperability among assessment authoring services.""" def supports_assessment_part_lookup(self): """Tests if looking up assessment part is supported. return: (boolean) - ``true`` if assessment part lookup is supported, ``false`` otherwise *compliance: mandatory -- This method must be implemented.* """ # Implemented from template for # osid.resource.ResourceProfile.supports_resource_lookup return 'supports_assessment_part_lookup' in profile.SUPPORTS def supports_assessment_part_query(self): """Tests if querying assessment part is supported. return: (boolean) - ``true`` if assessment part query is supported, ``false`` otherwise *compliance: mandatory -- This method must be implemented.* """ # Implemented from template for # osid.resource.ResourceProfile.supports_resource_lookup return 'supports_assessment_part_query' in profile.SUPPORTS def supports_assessment_part_admin(self): """Tests if an assessment part administrative service is supported. return: (boolean) - ``true`` if assessment part administration is supported, ``false`` otherwise *compliance: mandatory -- This method must be implemented.* """ # Implemented from template for # osid.resource.ResourceProfile.supports_resource_lookup return 'supports_assessment_part_admin' in profile.SUPPORTS def supports_assessment_part_bank(self): """Tests if an assessment part bank lookup service is supported. return: (boolean) - ``true`` if an assessment part bank lookup service is supported, ``false`` otherwise *compliance: mandatory -- This method must be implemented.* """ # Implemented from template for # osid.resource.ResourceProfile.supports_resource_lookup return 'supports_assessment_part_bank' in profile.SUPPORTS def supports_assessment_part_bank_assignment(self): """Tests if an assessment part bank service is supported. return: (boolean) - ``true`` if assessment part bank assignment service is supported, ``false`` otherwise *compliance: mandatory -- This method must be implemented.* """ # Implemented from template for # osid.resource.ResourceProfile.supports_resource_lookup return 'supports_assessment_part_bank_assignment' in profile.SUPPORTS def supports_assessment_part_item(self): """Tests if an assessment part item service is supported for looking up assessment part and item mappings. return: (boolean) - ``true`` if assessment part item service is supported, ``false`` otherwise *compliance: mandatory -- This method must be implemented.* """ # Implemented from template for # osid.resource.ResourceProfile.supports_resource_lookup return 'supports_assessment_part_item' in profile.SUPPORTS def supports_assessment_part_item_design(self): """Tests if an assessment part item design session is supported. return: (boolean) - ``true`` if an assessment part item design service is supported, ``false`` otherwise *compliance: mandatory -- This method must be implemented.* """ # Implemented from template for # osid.resource.ResourceProfile.supports_resource_lookup return 'supports_assessment_part_item_design' in profile.SUPPORTS def supports_sequence_rule_lookup(self): """Tests if looking up sequence rule is supported. return: (boolean) - ``true`` if sequence rule lookup is supported, ``false`` otherwise *compliance: mandatory -- This method must be implemented.* """ # Implemented from template for # osid.resource.ResourceProfile.supports_resource_lookup return 'supports_sequence_rule_lookup' in profile.SUPPORTS def supports_sequence_rule_admin(self): """Tests if a sequence rule administrative service is supported. return: (boolean) - ``true`` if sequence rule administration is supported, ``false`` otherwise *compliance: mandatory -- This method must be implemented.* """ # Implemented from template for # osid.resource.ResourceProfile.supports_resource_lookup return 'supports_sequence_rule_admin' in profile.SUPPORTS def get_assessment_part_record_types(self): """Gets the supported ``AssessmentPart`` record types. return: (osid.type.TypeList) - a list containing the supported ``AssessmentPart`` record types *compliance: mandatory -- This method must be implemented.* """ # Implemented from template for # osid.resource.ResourceProfile.get_resource_record_types_template record_type_maps = get_registry('ASSESSMENT_PART_RECORD_TYPES', self._runtime) record_types = [] for record_type_map in record_type_maps: record_types.append(Type(**record_type_maps[record_type_map])) return TypeList(record_types) assessment_part_record_types = property(fget=get_assessment_part_record_types) def get_assessment_part_search_record_types(self): """Gets the supported ``AssessmentPart`` search record types. return: (osid.type.TypeList) - a list containing the supported ``AssessmentPart`` search record types *compliance: mandatory -- This method must be implemented.* """ # Implemented from template for # osid.resource.ResourceProfile.get_resource_record_types_template record_type_maps = get_registry('ASSESSMENT_PART_SEARCH_RECORD_TYPES', self._runtime) record_types = [] for record_type_map in record_type_maps: record_types.append(Type(**record_type_maps[record_type_map])) return TypeList(record_types) assessment_part_search_record_types = property(fget=get_assessment_part_search_record_types) def get_sequence_rule_record_types(self): """Gets the supported ``SequenceRule`` record types. return: (osid.type.TypeList) - a list containing the supported ``SequenceRule`` record types *compliance: mandatory -- This method must be implemented.* """ # Implemented from template for # osid.resource.ResourceProfile.get_resource_record_types_template record_type_maps = get_registry('SEQUENCE_RULE_RECORD_TYPES', self._runtime) record_types = [] for record_type_map in record_type_maps: record_types.append(Type(**record_type_maps[record_type_map])) return TypeList(record_types) sequence_rule_record_types = property(fget=get_sequence_rule_record_types) def get_sequence_rule_search_record_types(self): """Gets the supported ``SequenceRule`` search record types. return: (osid.type.TypeList) - a list containing the supported ``SequenceRule`` search record types *compliance: mandatory -- This method must be implemented.* """ # Implemented from template for # osid.resource.ResourceProfile.get_resource_record_types_template record_type_maps = get_registry('SEQUENCE_RULE_SEARCH_RECORD_TYPES', self._runtime) record_types = [] for record_type_map in record_type_maps: record_types.append(Type(**record_type_maps[record_type_map])) return TypeList(record_types) sequence_rule_search_record_types = property(fget=get_sequence_rule_search_record_types) def get_sequence_rule_enabler_record_types(self): """Gets the supported ``SequenceRuleEnabler`` record types. return: (osid.type.TypeList) - a list containing the supported ``SequenceRuleEnabler`` record types *compliance: mandatory -- This method must be implemented.* """ # Implemented from template for # osid.resource.ResourceProfile.get_resource_record_types_template record_type_maps = get_registry('SEQUENCE_RULE_ENABLER_RECORD_TYPES', self._runtime) record_types = [] for record_type_map in record_type_maps: record_types.append(Type(**record_type_maps[record_type_map])) return TypeList(record_types) sequence_rule_enabler_record_types = property(fget=get_sequence_rule_enabler_record_types) def get_sequence_rule_enabler_search_record_types(self): """Gets the supported ``SequenceRuleEnabler`` search record types. return: (osid.type.TypeList) - a list containing the supported ``SequenceRuleEnabler`` search record types *compliance: mandatory -- This method must be implemented.* """ # Implemented from template for # osid.resource.ResourceProfile.get_resource_record_types_template record_type_maps = get_registry('SEQUENCE_RULE_ENABLER_SEARCH_RECORD_TYPES', self._runtime) record_types = [] for record_type_map in record_type_maps: record_types.append(Type(**record_type_maps[record_type_map])) return TypeList(record_types) sequence_rule_enabler_search_record_types = property(fget=get_sequence_rule_enabler_search_record_types) class AssessmentAuthoringManager(osid_managers.OsidManager, AssessmentAuthoringProfile, assessment_authoring_managers.AssessmentAuthoringManager): """The assessment authoring manager provides access to assessment authoring sessions and provides interoperability tests for various aspects of this service. The sessions included in this manager are: * ``AssessmentPartLookupSession:`` a session to retrieve assessment part * ``AssessmentPartQuerySession:`` a session to query for assessment part * ``AssessmentPartSearchSession:`` a session to search for assessment part * ``AssessmentPartAdminSession:`` a session to create and delete assessment part * ``AssessmentPartNotificationSession:`` a session to receive notifications pertaining to assessment part changes * ``AssessmentPartBankSession:`` a session to look up assessment part bank mappings * ``AssessmentPartBankAssignmentSession:`` a session to manage assessment part to bank mappings * ``AssessmentPartSmartBankSession:`` a session to manage dynamic bank of assessment part * ``AssessmentPartItemSession:`` a session to look up assessment part to item mappings * ``AssessmentPartItemDesignSession:`` a session to map items to assessment parts * ``SequenceRuleLookupSession:`` a session to retrieve sequence rule * ``SequenceRuleQuerySession:`` a session to query for sequence rule * ``SequenceRuleSearchSession:`` a session to search for sequence rule * ``SequenceRuleAdminSession:`` a session to create and delete sequence rule * ``SequenceRuleNotificationSession:`` a session to receive notifications pertaining to sequence rule changes * ``SequenceRuleBankSession:`` a session to look up sequence rule bank mappings * ``SequenceRuleBankAssignmentSession:`` a session to manage sequence rule to bank mappings * ``SequenceRuleSmartBankSession:`` a session to manage dynamic bank of sequence rule * ``SequenceRuleEnablerLookupSession:`` a session to retrieve sequence rule enablers * ``SequenceRuleEnablerQuerySession:`` a session to query for sequence rule enablers * ``SequenceRuleEnablerSearchSession:`` a session to search for sequence rule enablers * ``SequenceRuleEnablerAdminSession:`` a session to create and delete sequence rule enablers * ``SequenceRuleEnablerNotificationSession:`` a session to receive notifications pertaining to sequence rule enabler changes * ``SequenceRuleEnablerBankSession:`` a session to look up sequence rule enabler bank mappings * ``SequenceRuleEnablerBankAssignmentSession:`` a session to manage sequence rule enabler to bank mappings * ``SequenceRuleEnablerSmartBankSession:`` a session to manage dynamic bank of sequence rule enablers * ``SequenceRuleEnableRuleLookupSession:`` a session to look up sequence rule enabler mappings * ``SequenceRuleEnablerRuleApplicationSession:`` a session to apply sequence rule enablers """ def __init__(self): osid_managers.OsidManager.__init__(self) @utilities.remove_null_proxy_kwarg def get_assessment_part_lookup_session(self): """Gets the ``OsidSession`` associated with the assessment part lookup service. return: (osid.assessment.authoring.AssessmentPartLookupSession) - an ``AssessmentPartLookupSession`` raise: OperationFailed - unable to complete request raise: Unimplemented - ``supports_assessment_part_lookup()`` is ``false`` *compliance: optional -- This method must be implemented if ``supports_assessment_part_lookup()`` is ``true``.* """ if not self.supports_assessment_part_lookup(): raise errors.Unimplemented() # pylint: disable=no-member return sessions.AssessmentPartLookupSession(runtime=self._runtime) assessment_part_lookup_session = property(fget=get_assessment_part_lookup_session) @utilities.remove_null_proxy_kwarg @utilities.arguments_not_none def get_assessment_part_lookup_session_for_bank(self, bank_id): """Gets the ``OsidSession`` associated with the assessment part lookup service for the given bank. arg: bank_id (osid.id.Id): the ``Id`` of the ``Bank`` return: (osid.assessment.authoring.AssessmentPartLookupSession) - an ``AssessmentPartLookupSession`` raise: NotFound - no ``Bank`` found by the given ``Id`` raise: NullArgument - ``bank_id`` is ``null`` raise: OperationFailed - unable to complete request raise: Unimplemented - ``supports_assessment_part_lookup()`` or ``supports_visible_federation()`` is ``false`` *compliance: optional -- This method must be implemented if ``supports_assessment_part_lookup()`` and ``supports_visible_federation()`` are ``true``.* """ if not self.supports_assessment_part_lookup(): raise errors.Unimplemented() ## # Also include check to see if the catalog Id is found otherwise raise errors.NotFound ## # pylint: disable=no-member return sessions.AssessmentPartLookupSession(bank_id, runtime=self._runtime) @utilities.remove_null_proxy_kwarg def get_assessment_part_query_session(self): """Gets the ``OsidSession`` associated with the assessment part query service. return: (osid.assessment.authoring.AssessmentPartQuerySession) - an ``AssessmentPartQuerySession`` raise: OperationFailed - unable to complete request raise: Unimplemented - ``supports_assessment_part_query()`` is ``false`` *compliance: optional -- This method must be implemented if ``supports_assessment_part_query()`` is ``true``.* """ if not self.supports_assessment_part_query(): raise errors.Unimplemented() # pylint: disable=no-member return sessions.AssessmentPartQuerySession(runtime=self._runtime) assessment_part_query_session = property(fget=get_assessment_part_query_session) @utilities.remove_null_proxy_kwarg @utilities.arguments_not_none def get_assessment_part_query_session_for_bank(self, bank_id): """Gets the ``OsidSession`` associated with the assessment part query service for the given bank. arg: bank_id (osid.id.Id): the ``Id`` of the ``Bank`` return: (osid.assessment.authoring.AssessmentPartQuerySession) - an ``AssessmentPartQuerySession`` raise: NotFound - no ``Bank`` found by the given ``Id`` raise: NullArgument - ``bank_id`` is ``null`` raise: OperationFailed - unable to complete request raise: Unimplemented - ``supports_assessment_part_query()`` or ``supports_visible_federation()`` is ``false`` *compliance: optional -- This method must be implemented if ``supports_assessment_part_query()`` and ``supports_visible_federation()`` are ``true``.* """ if not self.supports_assessment_part_query(): raise errors.Unimplemented() ## # Also include check to see if the catalog Id is found otherwise raise errors.NotFound ## # pylint: disable=no-member return sessions.AssessmentPartQuerySession(bank_id, runtime=self._runtime) @utilities.remove_null_proxy_kwarg def get_assessment_part_admin_session(self): """Gets the ``OsidSession`` associated with the assessment part administration service. return: (osid.assessment.authoring.AssessmentPartAdminSession) - an ``AssessmentPartAdminSession`` raise: OperationFailed - unable to complete request raise: Unimplemented - ``supports_assessment_part_admin()`` is ``false`` *compliance: optional -- This method must be implemented if ``supports_assessment_part_admin()`` is ``true``.* """ if not self.supports_assessment_part_admin(): raise errors.Unimplemented() # pylint: disable=no-member return sessions.AssessmentPartAdminSession(runtime=self._runtime) assessment_part_admin_session = property(fget=get_assessment_part_admin_session) @utilities.remove_null_proxy_kwarg @utilities.arguments_not_none def get_assessment_part_admin_session_for_bank(self, bank_id): """Gets the ``OsidSession`` associated with the assessment part administration service for the given bank. arg: bank_id (osid.id.Id): the ``Id`` of the ``Bank`` return: (osid.assessment.authoring.AssessmentPartAdminSession) - an ``AssessmentPartAdminSession`` raise: NotFound - no ``Bank`` found by the given ``Id`` raise: NullArgument - ``bank_id`` is ``null`` raise: OperationFailed - unable to complete request raise: Unimplemented - ``supports_assessment_part_admin()`` or ``supports_visible_federation()`` is ``false`` *compliance: optional -- This method must be implemented if ``supports_assessment_part_admin()`` and ``supports_visible_federation()`` are ``true``.* """ if not self.supports_assessment_part_admin(): raise errors.Unimplemented() ## # Also include check to see if the catalog Id is found otherwise raise errors.NotFound ## # pylint: disable=no-member return sessions.AssessmentPartAdminSession(bank_id, runtime=self._runtime) @utilities.remove_null_proxy_kwarg def get_assessment_part_bank_session(self): """Gets the ``OsidSession`` to lookup assessment part/bank mappings for assessment parts. return: (osid.assessment.authoring.AssessmentPartBankSession) - an ``AssessmentPartBankSession`` raise: OperationFailed - unable to complete request raise: Unimplemented - ``supports_assessment_part_bank()`` is ``false`` *compliance: optional -- This method must be implemented if ``supports_assessment_part_bank()`` is ``true``.* """ if not self.supports_assessment_part_bank(): raise errors.Unimplemented() # pylint: disable=no-member return sessions.AssessmentPartBankSession(runtime=self._runtime) assessment_part_bank_session = property(fget=get_assessment_part_bank_session) @utilities.remove_null_proxy_kwarg def get_assessment_part_bank_assignment_session(self): """Gets the ``OsidSession`` associated with assigning assessment part to bank. return: (osid.assessment.authoring.AssessmentPartBankAssignmentS ession) - an ``AssessmentPartBankAssignmentSession`` raise: OperationFailed - unable to complete request raise: Unimplemented - ``supports_assessment_part_bank_assignment()`` is ``false`` *compliance: optional -- This method must be implemented if ``supports_assessment_part_bank_assignment()`` is ``true``.* """ if not self.supports_assessment_part_bank_assignment(): raise errors.Unimplemented() # pylint: disable=no-member return sessions.AssessmentPartBankAssignmentSession(runtime=self._runtime) assessment_part_bank_assignment_session = property(fget=get_assessment_part_bank_assignment_session) @utilities.remove_null_proxy_kwarg def get_sequence_rule_lookup_session(self): """Gets the ``OsidSession`` associated with the sequence rule lookup service. return: (osid.assessment.authoring.SequenceRuleLookupSession) - a ``SequenceRuleLookupSession`` raise: OperationFailed - unable to complete request raise: Unimplemented - ``supports_sequence_rule_lookup()`` is ``false`` *compliance: optional -- This method must be implemented if ``supports_sequence_rule_lookup()`` is ``true``.* """ if not self.supports_sequence_rule_lookup(): raise errors.Unimplemented() # pylint: disable=no-member return sessions.SequenceRuleLookupSession(runtime=self._runtime) sequence_rule_lookup_session = property(fget=get_sequence_rule_lookup_session) @utilities.remove_null_proxy_kwarg @utilities.arguments_not_none def get_sequence_rule_lookup_session_for_bank(self, bank_id): """Gets the ``OsidSession`` associated with the sequence rule lookup service for the given bank. arg: bank_id (osid.id.Id): the ``Id`` of the ``Bank`` return: (osid.assessment.authoring.SequenceRuleLookupSession) - a ``SequenceRuleLookupSession`` raise: NotFound - no ``Bank`` found by the given ``Id`` raise: NullArgument - ``bank_id`` is ``null`` raise: OperationFailed - unable to complete request raise: Unimplemented - ``supports_sequence_rule_lookup()`` or ``supports_visible_federation()`` is ``false`` *compliance: optional -- This method must be implemented if ``supports_sequence_rule_lookup()`` and ``supports_visible_federation()`` are ``true``.* """ if not self.supports_sequence_rule_lookup(): raise errors.Unimplemented() ## # Also include check to see if the catalog Id is found otherwise raise errors.NotFound ## # pylint: disable=no-member return sessions.SequenceRuleLookupSession(bank_id, runtime=self._runtime) @utilities.remove_null_proxy_kwarg def get_sequence_rule_admin_session(self): """Gets the ``OsidSession`` associated with the sequence rule administration service. return: (osid.assessment.authoring.SequenceRuleAdminSession) - a ``SequenceRuleAdminSession`` raise: OperationFailed - unable to complete request raise: Unimplemented - ``supports_sequence_rule_admin()`` is ``false`` *compliance: optional -- This method must be implemented if ``supports_sequence_rule_admin()`` is ``true``.* """ if not self.supports_sequence_rule_admin(): raise errors.Unimplemented() # pylint: disable=no-member return sessions.SequenceRuleAdminSession(runtime=self._runtime) sequence_rule_admin_session = property(fget=get_sequence_rule_admin_session) @utilities.remove_null_proxy_kwarg @utilities.arguments_not_none def get_sequence_rule_admin_session_for_bank(self, bank_id): """Gets the ``OsidSession`` associated with the sequence rule administration service for the given bank. arg: bank_id (osid.id.Id): the ``Id`` of the ``Bank`` return: (osid.assessment.authoring.SequenceRuleAdminSession) - a ``SequenceRuleAdminSession`` raise: NotFound - no ``Bank`` found by the given ``Id`` raise: NullArgument - ``bank_id`` is ``null`` raise: OperationFailed - unable to complete request raise: Unimplemented - ``supports_sequence_rule_admin()`` or ``supports_visible_federation()`` is ``false`` *compliance: optional -- This method must be implemented if ``supports_sequence_rule_admin()`` and ``supports_visible_federation()`` are ``true``.* """ if not self.supports_sequence_rule_admin(): raise errors.Unimplemented() ## # Also include check to see if the catalog Id is found otherwise raise errors.NotFound ## # pylint: disable=no-member return sessions.SequenceRuleAdminSession(bank_id, runtime=self._runtime) @utilities.remove_null_proxy_kwarg @utilities.arguments_not_none def get_assessment_part_item_session(self, *args, **kwargs): """Gets the ``OsidSession`` associated with the assessment part item service. return: (osid.assessment.authoring.AssessmentPartItemSession) - an ``AssessmentPartItemSession`` raise: OperationFailed - unable to complete request raise: Unimplemented - ``supports_assessment_part_item()`` is ``false`` *compliance: optional -- This method must be implemented if ``supports_assessment_part_lookup()`` is ``true``.* """ if not self.supports_assessment_part_lookup(): # This is kludgy, but only until Tom fixes spec raise errors.Unimplemented() if self._proxy_in_args(*args, **kwargs): raise errors.InvalidArgument('A Proxy object was received but not expected.') # pylint: disable=no-member return sessions.AssessmentPartItemSession(runtime=self._runtime) assessment_part_item_session = property(fget=get_assessment_part_item_session) @utilities.remove_null_proxy_kwarg @utilities.arguments_not_none def get_assessment_part_item_session_for_bank(self, bank_id, *args, **kwargs): """Gets the ``OsidSession`` associated with the assessment part item service for the given bank. arg: bank_id (osid.id.Id): the ``Id`` of the ``Bank`` return: (osid.assessment.authoring.AssessmentPartItemSession) - an ``AssessmentPartItemSession`` raise: NotFound - no ``Bank`` found by the given ``Id`` raise: NullArgument - ``bank_id`` is ``null`` raise: OperationFailed - unable to complete request raise: Unimplemented - ``supports_assessment_part_item()`` or ``supports_visible_federation()`` is ``false`` *compliance: optional -- This method must be implemented if ``supports_assessment_part_item()`` and ``supports_visible_federation()`` are ``true``.* """ if not self.supports_assessment_part_lookup(): # This is kludgy, but only until Tom fixes spec raise errors.Unimplemented() if self._proxy_in_args(*args, **kwargs): raise errors.InvalidArgument('A Proxy object was received but not expected.') # Also include check to see if the catalog Id is found otherwise raise errors.NotFound # pylint: disable=no-member return sessions.AssessmentPartItemSession(bank_id, runtime=self._runtime) @utilities.remove_null_proxy_kwarg @utilities.arguments_not_none def get_assessment_part_item_design_session(self, *args, **kwargs): """Gets the ``OsidSession`` associated with the assessment part item design service. return: (osid.assessment.authoring.AssessmentPartItemDesignSession) - an ``AssessmentPartItemDesignSession`` raise: OperationFailed - unable to complete request raise: Unimplemented - ``supports_assessment_part_item_design()`` is ``false`` *compliance: optional -- This method must be implemented if ``supports_assessment_part_lookup()`` is ``true``.* """ if not self.supports_assessment_part_lookup(): # This is kludgy, but only until Tom fixes spec raise errors.Unimplemented() if self._proxy_in_args(*args, **kwargs): raise errors.InvalidArgument('A Proxy object was received but not expected.') # pylint: disable=no-member return sessions.AssessmentPartItemDesignSession(runtime=self._runtime) assessment_part_item_design_session = property(fget=get_assessment_part_item_design_session) @utilities.remove_null_proxy_kwarg @utilities.arguments_not_none def get_assessment_part_item_design_session_for_bank(self, bank_id, *args, **kwargs): """Gets the ``OsidSession`` associated with the assessment part item design service for the given bank. arg: bank_id (osid.id.Id): the ``Id`` of the ``Bank`` return: (osid.assessment.authoring.AssessmentPartItemDesignSession) - an ``AssessmentPartItemDesignSession`` raise: NotFound - no ``Bank`` found by the given ``Id`` raise: NullArgument - ``bank_id`` is ``null`` raise: OperationFailed - unable to complete request raise: Unimplemented - ``supports_assessment_part_item_design()`` or ``supports_visible_federation()`` is ``false`` *compliance: optional -- This method must be implemented if ``supports_assessment_part_item_design()`` and ``supports_visible_federation()`` are ``true``.* """ if not self.supports_assessment_part_lookup(): # This is kludgy, but only until Tom fixes spec raise errors.Unimplemented() if self._proxy_in_args(*args, **kwargs): raise errors.InvalidArgument('A Proxy object was received but not expected.') # Also include check to see if the catalog Id is found otherwise raise errors.NotFound # pylint: disable=no-member return sessions.AssessmentPartItemDesignSession(bank_id, runtime=self._runtime) class AssessmentAuthoringProxyManager(osid_managers.OsidProxyManager, AssessmentAuthoringProfile, assessment_authoring_managers.AssessmentAuthoringProxyManager): """The assessment authoring manager provides access to assessment authoring sessions and provides interoperability tests for various aspects of this service. Methods in this manager support the passing of a ``Proxy`` object. The sessions included in this manager are: * ``AssessmentPartLookupSession:`` a session to retrieve assessment part * ``AssessmentPartQuerySession:`` a session to query for assessment part * ``AssessmentPartSearchSession:`` a session to search for assessment part * ``AssessmentPartAdminSession:`` a session to create and delete assessment part * ``AssessmentPartNotificationSession:`` a session to receive notifications pertaining to assessment part changes * ``AssessmentPartBankSession:`` a session to look up assessment part bank mappings * ``AssessmentPartBankAssignmentSession:`` a session to manage assessment part to bank mappings * ``AssessmentPartSmartBankSession:`` a session to manage dynamic bank of assessment part * ``AssessmentPartItemSession:`` a session to look up assessment part to item mappings * ``AssessmentPartItemDesignSession:`` a session to map items to assessment parts * ``SequenceRuleLookupSession:`` a session to retrieve sequence rule * ``SequenceRuleQuerySession:`` a session to query for sequence rule * ``SequenceRuleSearchSession:`` a session to search for sequence rule * ``SequenceRuleAdminSession:`` a session to create and delete sequence rule * ``SequenceRuleNotificationSession:`` a session to receive notifications pertaining to sequence rule changes * ``SequenceRuleBankSession:`` a session to look up sequence rule bank mappings * ``SequenceRuleBankAssignmentSession:`` a session to manage sequence rule to bank mappings * ``SequenceRuleSmartBankSession:`` a session to manage dynamic bank of sequence rule * ``SequenceRuleEnablerLookupSession:`` a session to retrieve sequence rule enablers * ``SequenceRuleEnablerQuerySession:`` a session to query for sequence rule enablers * ``SequenceRuleEnablerSearchSession:`` a session to search for sequence rule enablers * ``SequenceRuleEnablerAdminSession:`` a session to create and delete sequence rule enablers * ``SequenceRuleEnablerNotificationSession:`` a session to receive notifications pertaining to sequence rule enabler changes * ``SequenceRuleEnablerBankSession:`` a session to look up sequence rule enabler bank mappings * ``SequenceRuleEnablerBankAssignmentSession:`` a session to manage sequence rule enabler to bank mappings * ``SequenceRuleEnablerSmartBankSession:`` a session to manage dynamic bank of sequence rule enablers * ``SequenceRuleEnableRuleLookupSession:`` a session to look up sequence rule enabler mappings * ``SequenceRuleEnablerRuleApplicationSession:`` a session to apply sequence rule enablers """ def __init__(self): osid_managers.OsidProxyManager.__init__(self) @utilities.arguments_not_none def get_assessment_part_lookup_session(self, proxy): """Gets the ``OsidSession`` associated with the assessment part lookup service. arg: proxy (osid.proxy.Proxy): a proxy return: (osid.assessment.authoring.AssessmentPartLookupSession) - an ``AssessmentPartLookupSession`` raise: NullArgument - ``proxy`` is ``null`` raise: OperationFailed - unable to complete request raise: Unimplemented - ``supports_assessment_part_lookup()`` is ``false`` *compliance: optional -- This method must be implemented if ``supports_assessment_part_lookup()`` is ``true``.* """ if not self.supports_assessment_part_lookup(): raise errors.Unimplemented() # pylint: disable=no-member return sessions.AssessmentPartLookupSession(proxy=proxy, runtime=self._runtime) @utilities.arguments_not_none def get_assessment_part_lookup_session_for_bank(self, bank_id, proxy): """Gets the ``OsidSession`` associated with the assessment part lookup service for the given bank. arg: bank_id (osid.id.Id): the ``Id`` of the ``Bank`` arg: proxy (osid.proxy.Proxy): a proxy return: (osid.assessment.authoring.AssessmentPartLookupSession) - an ``AssessmentPartLookupSession`` raise: NotFound - no ``Bank`` found by the given ``Id`` raise: NullArgument - ``bank_id or proxy is null`` raise: OperationFailed - unable to complete request raise: Unimplemented - ``supports_assessment_part_lookup()`` or ``supports_visible_federation()`` is ``false`` *compliance: optional -- This method must be implemented if ``supports_assessment_part_lookup()`` and ``supports_visible_federation()`` are ``true``.* """ if not self.supports_assessment_part_lookup(): raise errors.Unimplemented() ## # Also include check to see if the catalog Id is found otherwise raise errors.NotFound ## # pylint: disable=no-member return sessions.AssessmentPartLookupSession(bank_id, proxy, self._runtime) @utilities.arguments_not_none def get_assessment_part_query_session(self, proxy): """Gets the ``OsidSession`` associated with the assessment part query service. arg: proxy (osid.proxy.Proxy): a proxy return: (osid.assessment.authoring.AssessmentPartQuerySession) - an ``AssessmentPartQuerySession`` raise: NullArgument - ``proxy`` is ``null`` raise: OperationFailed - unable to complete request raise: Unimplemented - ``supports_assessment_part_query()`` is ``false`` *compliance: optional -- This method must be implemented if ``supports_assessment_part_query()`` is ``true``.* """ if not self.supports_assessment_part_query(): raise errors.Unimplemented() # pylint: disable=no-member return sessions.AssessmentPartQuerySession(proxy=proxy, runtime=self._runtime) @utilities.arguments_not_none def get_assessment_part_query_session_for_bank(self, bank_id, proxy): """Gets the ``OsidSession`` associated with the assessment part query service for the given bank. arg: bank_id (osid.id.Id): the ``Id`` of the ``Bank`` arg: proxy (osid.proxy.Proxy): a proxy return: (osid.assessment.authoring.AssessmentPartQuerySession) - an ``AssessmentPartQuerySession`` raise: NotFound - no ``Bank`` found by the given ``Id`` raise: NullArgument - ``bank_id or proxy is null`` raise: OperationFailed - unable to complete request raise: Unimplemented - ``supports_assessment_part_query()`` or ``supports_visible_federation()`` is ``false`` *compliance: optional -- This method must be implemented if ``supports_assessment_part_query()`` and ``supports_visible_federation()`` are ``true``.* """ if not self.supports_assessment_part_query(): raise errors.Unimplemented() ## # Also include check to see if the catalog Id is found otherwise raise errors.NotFound ## # pylint: disable=no-member return sessions.AssessmentPartQuerySession(bank_id, proxy, self._runtime) @utilities.arguments_not_none def get_assessment_part_admin_session(self, proxy): """Gets the ``OsidSession`` associated with the assessment part administration service. arg: proxy (osid.proxy.Proxy): a proxy return: (osid.assessment.authoring.AssessmentPartAdminSession) - an ``AssessmentPartAdminSession`` raise: NullArgument - ``proxy`` is ``null`` raise: OperationFailed - unable to complete request raise: Unimplemented - ``supports_assessment_part_admin()`` is ``false`` *compliance: optional -- This method must be implemented if ``supports_assessment_part_admin()`` is ``true``.* """ if not self.supports_assessment_part_admin(): raise errors.Unimplemented() # pylint: disable=no-member return sessions.AssessmentPartAdminSession(proxy=proxy, runtime=self._runtime) @utilities.arguments_not_none def get_assessment_part_admin_session_for_bank(self, bank_id, proxy): """Gets the ``OsidSession`` associated with the assessment part administration service for the given bank. arg: bank_id (osid.id.Id): the ``Id`` of the ``Bank`` arg: proxy (osid.proxy.Proxy): a proxy return: (osid.assessment.authoring.AssessmentPartAdminSession) - an ``AssessmentPartAdminSession`` raise: NotFound - no ``Bank`` found by the given ``Id`` raise: NullArgument - ``bank_id or proxy is null`` raise: OperationFailed - unable to complete request raise: Unimplemented - ``supports_assessment_part_admin()`` or ``supports_visible_federation()`` is ``false`` *compliance: optional -- This method must be implemented if ``supports_assessment_part_admin()`` and ``supports_visible_federation()`` are ``true``.* """ if not self.supports_assessment_part_admin(): raise errors.Unimplemented() ## # Also include check to see if the catalog Id is found otherwise raise errors.NotFound ## # pylint: disable=no-member return sessions.AssessmentPartAdminSession(bank_id, proxy, self._runtime) @utilities.arguments_not_none def get_assessment_part_bank_session(self, proxy): """Gets the ``OsidSession`` to lookup assessment part/bank mappings for assessment parts. arg: proxy (osid.proxy.Proxy): a proxy return: (osid.assessment.authoring.AssessmentPartBankSession) - an ``AssessmentPartBankSession`` raise: NullArgument - ``proxy`` is ``null`` raise: OperationFailed - unable to complete request raise: Unimplemented - ``supports_assessment_part_bank()`` is ``false`` *compliance: optional -- This method must be implemented if ``supports_assessment_part_bank()`` is ``true``.* """ if not self.supports_assessment_part_bank(): raise errors.Unimplemented() # pylint: disable=no-member return sessions.AssessmentPartBankSession(proxy=proxy, runtime=self._runtime) @utilities.arguments_not_none def get_assessment_part_bank_assignment_session(self, proxy): """Gets the ``OsidSession`` associated with assigning assessment part to bank. arg: proxy (osid.proxy.Proxy): a proxy return: (osid.assessment.authoring.AssessmentPartBankAssignmentS ession) - an ``AssessmentPartBankAssignmentSession`` raise: NullArgument - ``proxy`` is ``null`` raise: OperationFailed - unable to complete request raise: Unimplemented - ``supports_assessment_part_bank_assignment()`` is ``false`` *compliance: optional -- This method must be implemented if ``supports_assessment_part_bank_assignment()`` is ``true``.* """ if not self.supports_assessment_part_bank_assignment(): raise errors.Unimplemented() # pylint: disable=no-member return sessions.AssessmentPartBankAssignmentSession(proxy=proxy, runtime=self._runtime) @utilities.arguments_not_none def get_sequence_rule_lookup_session(self, proxy): """Gets the ``OsidSession`` associated with the sequence rule lookup service. arg: proxy (osid.proxy.Proxy): a proxy return: (osid.assessment.authoring.SequenceRuleLookupSession) - a ``SequenceRuleLookupSession`` raise: NullArgument - ``proxy`` is ``null`` raise: OperationFailed - unable to complete request raise: Unimplemented - ``supports_sequence_rule_lookup()`` is ``false`` *compliance: optional -- This method must be implemented if ``supports_sequence_rule_lookup()`` is ``true``.* """ if not self.supports_sequence_rule_lookup(): raise errors.Unimplemented() # pylint: disable=no-member return sessions.SequenceRuleLookupSession(proxy=proxy, runtime=self._runtime) @utilities.arguments_not_none def get_sequence_rule_lookup_session_for_bank(self, bank_id, proxy): """Gets the ``OsidSession`` associated with the sequence rule lookup service for the given bank. arg: bank_id (osid.id.Id): the ``Id`` of the ``Bank`` arg: proxy (osid.proxy.Proxy): a proxy return: (osid.assessment.authoring.SequenceRuleLookupSession) - a ``SequenceRuleLookupSession`` raise: NotFound - no ``Bank`` found by the given ``Id`` raise: NullArgument - ``bank_id or proxy is null`` raise: OperationFailed - unable to complete request raise: Unimplemented - ``supports_sequence_rule_lookup()`` or ``supports_visible_federation()`` is ``false`` *compliance: optional -- This method must be implemented if ``supports_sequence_rule_lookup()`` and ``supports_visible_federation()`` are ``true``.* """ if not self.supports_sequence_rule_lookup(): raise errors.Unimplemented() ## # Also include check to see if the catalog Id is found otherwise raise errors.NotFound ## # pylint: disable=no-member return sessions.SequenceRuleLookupSession(bank_id, proxy, self._runtime) @utilities.arguments_not_none def get_sequence_rule_admin_session(self, proxy): """Gets the ``OsidSession`` associated with the sequence rule administration service. arg: proxy (osid.proxy.Proxy): a proxy return: (osid.assessment.authoring.SequenceRuleAdminSession) - a ``SequenceRuleAdminSession`` raise: NullArgument - ``proxy`` is ``null`` raise: OperationFailed - unable to complete request raise: Unimplemented - ``supports_sequence_rule_admin()`` is ``false`` *compliance: optional -- This method must be implemented if ``supports_sequence_rule_admin()`` is ``true``.* """ if not self.supports_sequence_rule_admin(): raise errors.Unimplemented() # pylint: disable=no-member return sessions.SequenceRuleAdminSession(proxy=proxy, runtime=self._runtime) @utilities.arguments_not_none def get_sequence_rule_admin_session_for_bank(self, bank_id, proxy): """Gets the ``OsidSession`` associated with the sequence rule administration service for the given bank. arg: bank_id (osid.id.Id): the ``Id`` of the ``Bank`` arg: proxy (osid.proxy.Proxy): a proxy return: (osid.assessment.authoring.SequenceRuleAdminSession) - a ``SequenceRuleAdminSession`` raise: NotFound - no ``Bank`` found by the given ``Id`` raise: NullArgument - ``bank_id or proxy is null`` raise: OperationFailed - unable to complete request raise: Unimplemented - ``supports_sequence_rule_admin()`` or ``supports_visible_federation()`` is ``false`` *compliance: optional -- This method must be implemented if ``supports_sequence_rule_admin()`` and ``supports_visible_federation()`` are ``true``.* """ if not self.supports_sequence_rule_admin(): raise errors.Unimplemented() ## # Also include check to see if the catalog Id is found otherwise raise errors.NotFound ## # pylint: disable=no-member return sessions.SequenceRuleAdminSession(bank_id, proxy, self._runtime) @utilities.arguments_not_none def get_assessment_part_item_session(self, proxy): """Gets the ``OsidSession`` associated with the assessment part item service. return: (osid.assessment.authoring.AssessmentPartItemSession) - an ``AssessmentPartItemSession`` raise: OperationFailed - unable to complete request raise: Unimplemented - ``supports_assessment_part_item()`` is ``false`` *compliance: optional -- This method must be implemented if ``supports_assessment_part_lookup()`` is ``true``.* """ if not self.supports_assessment_part_lookup(): # This is kludgy, but only until Tom fixes spec raise errors.Unimplemented() # pylint: disable=no-member return sessions.AssessmentPartItemSession(proxy=proxy, runtime=self._runtime) assessment_part_item_session = property(fget=get_assessment_part_item_session) @utilities.arguments_not_none def get_assessment_part_item_session_for_bank(self, bank_id, proxy): """Gets the ``OsidSession`` associated with the assessment part item service for the given bank. arg: bank_id (osid.id.Id): the ``Id`` of the ``Bank`` return: (osid.assessment.authoring.AssessmentPartItemSession) - an ``AssessmentPartItemSession`` raise: NotFound - no ``Bank`` found by the given ``Id`` raise: NullArgument - ``bank_id`` is ``null`` raise: OperationFailed - unable to complete request raise: Unimplemented - ``supports_assessment_part_item()`` or ``supports_visible_federation()`` is ``false`` *compliance: optional -- This method must be implemented if ``supports_assessment_part_item()`` and ``supports_visible_federation()`` are ``true``.* """ if not self.supports_assessment_part_lookup(): # This is kludgy, but only until Tom fixes spec raise errors.Unimplemented() # Also include check to see if the catalog Id is found otherwise raise errors.NotFound # pylint: disable=no-member return sessions.AssessmentPartItemSession(bank_id, proxy=proxy, runtime=self._runtime) @utilities.arguments_not_none def get_assessment_part_item_design_session(self, proxy): """Gets the ``OsidSession`` associated with the assessment part item design service. return: (osid.assessment.authoring.AssessmentPartItemDesignSession) - an ``AssessmentPartItemDesignSession`` raise: OperationFailed - unable to complete request raise: Unimplemented - ``supports_assessment_part_item_design()`` is ``false`` *compliance: optional -- This method must be implemented if ``supports_assessment_part_lookup()`` is ``true``.* """ if not self.supports_assessment_part_lookup(): # This is kludgy, but only until Tom fixes spec raise errors.Unimplemented() # pylint: disable=no-member return sessions.AssessmentPartItemDesignSession(proxy=proxy, runtime=self._runtime) assessment_part_item_design_session = property(fget=get_assessment_part_item_design_session) @utilities.arguments_not_none def get_assessment_part_item_design_session_for_bank(self, bank_id, proxy): """Gets the ``OsidSession`` associated with the assessment part item design service for the given bank. arg: bank_id (osid.id.Id): the ``Id`` of the ``Bank`` return: (osid.assessment.authoring.AssessmentPartItemDesignSession) - an ``AssessmentPartItemDesignSession`` raise: NotFound - no ``Bank`` found by the given ``Id`` raise: NullArgument - ``bank_id`` is ``null`` raise: OperationFailed - unable to complete request raise: Unimplemented - ``supports_assessment_part_item_design()`` or ``supports_visible_federation()`` is ``false`` *compliance: optional -- This method must be implemented if ``supports_assessment_part_item_design()`` and ``supports_visible_federation()`` are ``true``.* """ if not self.supports_assessment_part_lookup(): # This is kludgy, but only until Tom fixes spec raise errors.Unimplemented() # Also include check to see if the catalog Id is found otherwise raise errors.NotFound # pylint: disable=no-member return sessions.AssessmentPartItemDesignSession(bank_id, proxy=proxy, runtime=self._runtime)

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6

e32de162c339beb09a447120cd19afe54eb08cc3

7,364

py

Python

resolution_dim_multiple.py

Theme-Maths/MultipleDegree-DifferentialEquationsSolver

8400c4d72ca9377ff46a64f16ac60c33b7bb3f44

[ "MIT" ]

null

resolution_dim_multiple.py

Theme-Maths/MultipleDegree-DifferentialEquationsSolver

8400c4d72ca9377ff46a64f16ac60c33b7bb3f44

[ "MIT" ]

null

resolution_dim_multiple.py

Theme-Maths/MultipleDegree-DifferentialEquationsSolver

8400c4d72ca9377ff46a64f16ac60c33b7bb3f44

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Ce fichier est chargé de résoudre les ED de dimension n, et d'interfacer les entrées utilisateur. Contient les fonctions de résolution à plusieurs dimensions : - solution_dim_2(g, y0, yp0, t0, T, h, methode='rk4') - solution_dim_n(g, Y0, t0, T, h, methode='rk4') - sol_exacte_dim_2(g, y0, yp0, t0, T, h) - sol_exacte_dim_n(g, Y0, t0, T, h) """ import numpy as np from scipy.integrate import odeint import schemas_1d, traces from math import cos, atan, pi, sin, exp def solution_dim_2(g, y0, yp0, t0, T, h, methode='rk4'): """ Résout une équation différentielle d'odre 2 avec la méthode choisie. Renvoie un tuple x, y de la solution calculé. Paramètres ---------- g : fonction du problème de Cauchy y0 : valeur de la solution initiale de y yp0 : valeur de la condition initiale de y' t0 : valeur de la borne inférieure de l'intervalle de résolution T : valeur de la borne supérieure de l'intervalle de résolution h : valeur du pas methode : méthodes de résolution possibles ('euler' ou 'rk4'). Vaut 'rk4' par défaut. Renvoi ------- t_list : liste des abscisses Y_list : liste des valeurs de calculées par la méthode """ # NOTATIONS : # y = y # yp = y' # ypp = y" = g(t0, y0, yp0) def F(t, Y): # on définit la fonction F du nouveau problème de Cauchy (y, yp) = Y ypp = g(t, y, yp) # on calcule la dernière coordonnée de ce que renverra F, à savoir ypp return [yp, ypp] Y0 = [y0, yp0] if methode == 'rk4': return schemas_1d.rk4_vect(F, Y0, t0, T, h) elif methode == 'euler': return schemas_1d.euler_vect(F, Y0, t0, T, h) else : raise ValueError("Méthode non reconnue. Les méthodes reconnues sont : 'rk4', 'euler'.") def solution_dim_n(g, Y0, t0, T, h, methode='rk4'): """ Résout une équation différentielle d'ordre n > 1 avec la méthode choisie. Renvoie un tuple (x, y) de la solution calculée. Paramètres ---------- g : fonction du problème de Cauchy (exemple dont la solution est exp(): g = lambda t, y, yp, ypp : ypp) Y0 : liste contenant les conditions initiales dans l'ordre suivant : Y0 = [y0, yp0, ... y_(n-1)0] t0 : valeur de la borne inférieure de l'intervalle de résolution T : valeur de la borne supérieure de l'intervalle de résolution h : valeur du pas methode : méthodes de résolution possibles : 'euler', 'rk4'. Vaut 'rk4' par défaut. Renvoi ------- t_list : liste des abscisses Y_list : liste des valeurs de calculées par la méthode """ def F(t, Y): # on définit la fonction F du nouveau problème de Cauchy y_np1 = g(t, *Y) # on calcule la dernière coordonnée de ce que renverra F, à savoir la valeur de la dérivées n+1ième de y en t return Y[1:]+[y_np1] if methode == 'rk4': return schemas_1d.rk4_vect(F, Y0, t0, T, h) elif methode == 'euler': return schemas_1d.euler_vect(F, Y0, t0, T, h) else : raise AttributeError("Méthode non reconnue. Les méthodes reconnues sont : 'rk4', 'euler'.") def sol_exacte_dim_2(g, y0, yp0, t0, T, h): """ Résout une équation différentielle d'ordre n avec scipy.integrate.odeint Renvoie un tuple x, y de la solution calculée. Paramètres ---------- g : fonction du problème de Cauchy y0 : valeur de la solution initiale de y yp0 : valeur de la condition initiale de y' t0 : valeur de la borne inférieure de l'intervalle de résolution T : valeur de la borne supérieure de l'intervalle de résolution h : valeur du pas Renvoi ------- t_list : liste des abscisses Y_list : liste des ordonnées de la 'solution exacte' trouvée """ def F(t, Y): # on définit la fonction F du nouveau problème de Cauchy (y, yp) = Y ypp = g(t, y, yp) return np.array([yp, ypp]) Y0 = np.array([y0, yp0]) x = np.linspace(t0, T, int((T-t0)/h)) # on construit la liste des abscisses Y = odeint(F, Y0, x, tfirst=True) return x, Y[:,0] def sol_exacte_dim_n(g, Y0, t0, T, h): """ Résout une équation différentielle d'ordre n avec odeint, méthode la plus précise implémentée dans Python. Renvoie un tuple x, y de la solution calculée. Paramètres ---------- g : fonction du problème de Cauchy Y0 : liste contenant les conditions initiales dans l'ordre suivant : Y = [y0, yp0, ... y_(n-1)0] t0 : valeur de la borne inférieure de l'intervalle de résolution T : valeur de la borne supérieure de l'intervalle de résolution h : valeur du pas Renvoi ------- t_list : liste des abscisses Y_list : liste des ordonnées de la 'solution exacte' trouvée """ Y0=np.array(Y0) t0 = np.array(t0) def F(t, Y): # on définit la fonction F du nouveau problème de Cauchy y_np1 = g(t, *Y) output = Y[1:] return np.append(output,y_np1) x = np.linspace(t0, T, int((T-t0)/h)) # on construit la liste des abscisses Y = odeint(F, Y0, x, tfirst=True) return x, Y[:,0] #%% COMMANDES DIRECTES # EXEMPLE 1 DE LA PRESENTATION ------------------------------------------------ # # Définition de la fonction g du problème de Cauchy # g = lambda t, y, yp : 3*yp - 20*y + 5 # # Définition des courbes que l'on veut tracer # x1, y1 = solution_dim_2(g, 0, 0, 0, 3, 0.01, methode='rk4') # x2, y2 = solution_dim_2(g, 0, 0, 0, 3, 0.01, methode='rk4') # x3, y3 = solution_dim_2(g, 0, 0, 0, 3, 0.1, methode='rk4') # xs, ys = sol_exacte_dim_2(g, 0, 0, 0, 3, 0.001) # # Tracé des courbes # traces.trace((x1, y1, 'Euler pas de 0.01'), (x2, y2, 'RK4 pas de 0.01'), (x3, y3, 'RK4 pas de 0.1'), sol=(xs, ys)) # EXEMPLE 2 DE LA PRESENTATION ------------------------------------------------ # # Définition de la fonction g à résoudre # g = lambda t, y, yp, ypp: 1/4 * (cos(t*ypp) - atan(yp)) # # Définition des courbes que l'on veut tracer # x1, y1 = solution_dim_n(g, [0, 0, 0], 0, 50, 0.2, methode='euler') # x2, y2 = solution_dim_n(g, [0, 0, 0], 0, 50, 0.1, methode='euler') # x3, y3 = solution_dim_n(g, [0, 0, 0], 0, 50, 0.05, methode='euler') # xs, ys = sol_exacte_dim_n(g, [0, 0, 0], 0, 50, 0.025) # # Traçage des courbes # traces.trace((x1, y1, 'Euler, pas de 0.2'),(x2, y2, 'Euler, pas de 0.1'), (x3, y3, 'Euler, pas de 0.05'), sol=(xs, ys)) # EXEMPLE DU PENDULE ---------------------------------------------------------- # # Définition de la fonction g à résoudre # g = lambda t, y, yp : -2*0.22*yp-4**2*sin(y) # # Définition des courbes que l'on veut tracer # x1, y1 = solution_dim_n(g, [1.3, 0], 0, 5, 0.001, methode='euler') # x2, y2 = solution_dim_n(g, [1.3,0], 0, 5, 0.001, methode='rk4') # xs, ys = sol_exacte_dim_n(g, [1.3,0], 0, 5, 0.001) # # Traçage des courbes # traces.trace((x1, y1, 'Euler, pas de 0.001'),(x2, y2, 'RK4, pas de 0.001'), sol=(xs, ys))

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0

0.488889

0

null

0

1

0

null

0

6

e3390e58dea4abbe0459fa110eba1be9496fe02a

61

py

Python

test.py

nsde/TPReplace

d3e8b6db420da49c34d17369b9753fe8ed1a92e4

[ "MIT" ]

null

test.py

nsde/TPReplace

d3e8b6db420da49c34d17369b9753fe8ed1a92e4

[ "MIT" ]

null

test.py

nsde/TPReplace

d3e8b6db420da49c34d17369b9753fe8ed1a92e4

[ "MIT" ]

null

import os print(os.listdir(r"C:\Users\xitzf\Desktop\blocks"))

30.5

51

0.770492

11

61

4.272727

0.909091

0

0.032787

61

2

51

30.5

0.79661

0

0.467742

0

1

0

true

0

0.5

0

0.5

1

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null

0

1

0

1

null

0

1

0

1

0

1

0

6

e33950bc8e0c5c69941aec80d91f16cd580e8ffa

24

py

Python

confrm/__init__.py

confrm/confrm

7d2b0b2f5efac243d9877509684d71acf4816dd6

[ "Apache-2.0" ]

1

2021-04-15T05:55:42.000Z

confrm/__init__.py

confrm/confrm

7d2b0b2f5efac243d9877509684d71acf4816dd6

[ "Apache-2.0" ]

33

2020-12-23T19:44:41.000Z

2021-01-26T20:53:01.000Z

confrm/__init__.py

confrm/confrm

7d2b0b2f5efac243d9877509684d71acf4816dd6

[ "Apache-2.0" ]

1

2021-01-07T11:06:35.000Z

from .confrm import APP

12

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1

0

6

e34c82bcbb0b62e20773fb195f17e0a49e8b4f1e

37

py

Python

ncc/callbacks/__init__.py

NCC-AI/ncc

c53379abcb21eb18268591239d02f69a148df6c5

[ "MIT" ]

null

ncc/callbacks/__init__.py

NCC-AI/ncc

c53379abcb21eb18268591239d02f69a148df6c5

[ "MIT" ]

null

ncc/callbacks/__init__.py

NCC-AI/ncc

c53379abcb21eb18268591239d02f69a148df6c5

[ "MIT" ]

null

from .callbacks import slack_logging

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1

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1

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6

e366f18dda2c3c4e031ce91ba556ac9380962e56

81

py

Python

fsm_eigenvalue/compute/__init__.py

petarmaric/fsm_eigenvalue

d4ca102cf2920ca41d31085f9e4bf1866d06a320

[ "BSD-3-Clause" ]

1

2021-03-09T13:16:17.000Z

fsm_eigenvalue/compute/__init__.py

petarmaric/fsm_eigenvalue

d4ca102cf2920ca41d31085f9e4bf1866d06a320

[ "BSD-3-Clause" ]

null

fsm_eigenvalue/compute/__init__.py

petarmaric/fsm_eigenvalue

d4ca102cf2920ca41d31085f9e4bf1866d06a320

[ "BSD-3-Clause" ]

null

from .core import perform_iteration from .parameter_sweep import parameter_sweep

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6

e3754bd635c94dad3d8bd69c188f3201731bfdae

119

py

Python

src/blog/views.py

hamdyadam97/blog-django-ar

2e2fec47cfe149c904f822503272a4b2fd90de0d

[ "bzip2-1.0.6" ]

null

src/blog/views.py

hamdyadam97/blog-django-ar

2e2fec47cfe149c904f822503272a4b2fd90de0d

[ "bzip2-1.0.6" ]

null

src/blog/views.py

hamdyadam97/blog-django-ar

2e2fec47cfe149c904f822503272a4b2fd90de0d

[ "bzip2-1.0.6" ]

null

from django.shortcuts import render def home(request): return render(request,'blog/index.html',{'title':'home'})

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null

0

1

0

1

0

6

8b696220b30c44fa269683be7b2dd86ec1a5e98c

114

py

Python

pyquadfilter/__init__.py

Kurene/pyquadfilter

89f678bd845fac556b46640e346b5503803e0e0d

[ "MIT" ]

1

2021-09-24T07:32:16.000Z

pyquadfilter/__init__.py

Kurene/pyquadfilter

89f678bd845fac556b46640e346b5503803e0e0d

[ "MIT" ]

null

pyquadfilter/__init__.py

Kurene/pyquadfilter

89f678bd845fac556b46640e346b5503803e0e0d

[ "MIT" ]

null

"""Top-level module for pyquadfilter""" from .core import PyQuadFilter from .plot import plot_frequency_response

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1

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6

8b711a23a662358ce47d2c710fe2ea644098d61e

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py

Python

treeHandler/__init__.py

Sreekiranar/tree-handler

3ceadfd0a50d2f02861531fcf693cd5c343c398c

[ "MIT" ]

1

2020-02-13T06:55:16.000Z

treeHandler/__init__.py

Sreekiranar/treeHandler

3ceadfd0a50d2f02861531fcf693cd5c343c398c

[ "MIT" ]

null

treeHandler/__init__.py

Sreekiranar/treeHandler

3ceadfd0a50d2f02861531fcf693cd5c343c398c

[ "MIT" ]

null

from .treeHandler import *

13.5

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6

8b8e521094722ff54569862900116d59450bb130

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py

Python

detection/keypoint/__init__.py

corenel/auto-traffic-camera-calib

a81d52b3a21b7cef37006cc93f764d93807293b0

[ "MIT" ]

3

2020-11-27T08:26:12.000Z

2021-08-24T02:53:45.000Z

detection/keypoint/__init__.py

corenel/auto-traffic-camera-calib

a81d52b3a21b7cef37006cc93f764d93807293b0

[ "MIT" ]

1

2021-07-17T20:15:59.000Z

detection/keypoint/__init__.py

corenel/auto-traffic-camera-calib

a81d52b3a21b7cef37006cc93f764d93807293b0

[ "MIT" ]

null

from .keypoint_detector import KeypointDetector

24

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1

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0

1

0

6

8bb7e35b22c001f952f06596fa9c83016c3cf865

7,544

py

Python

symmetry_functions.py

haakonvt/LearningTensorFlow

6988a15af2ac916ae1a5e23b2c5bde9630cc0519

[ "MIT" ]

5

2018-09-06T12:52:12.000Z

2020-05-09T01:40:12.000Z

symmetry_functions.py

haakonvt/LearningTensorFlow

6988a15af2ac916ae1a5e23b2c5bde9630cc0519

[ "MIT" ]

null

symmetry_functions.py

haakonvt/LearningTensorFlow

6988a15af2ac916ae1a5e23b2c5bde9630cc0519

[ "MIT" ]

4

2018-02-06T08:42:06.000Z

2019-04-16T11:23:06.000Z

from math import exp,cos,pi,tanh,sqrt # Faster than numpy for scalars import numpy as np """ ################# The cutoff functions ################# """ def cutoff_tanh(r,rc): """ Can take scalar and vector input of r and evaluate the cutoff function """ if type(r) == int: if r <= rc: return tanh(1-r/rc)**3 else: return 0. else: return np.tanh(1-r/rc)**3 * (r <= rc) def cutoff_cos(r,rc): """ Can take scalar, vector or matrix input of r and evaluate the cutoff function """ # r_SW_cut = 100#3.77118 if type(r) == int: if r <= rc and r < r_SW_cut: return 0.5*(cos(pi*r/rc)+1) else: return 0. else: return 0.5*(np.cos(pi*r/rc)+1) * (r <= rc)# * (r < r_SW_cut) """ ################# Single particle symmetry functions ################# """ def G1(r, rc, cutoff=cutoff_cos): r_cut = cutoff(r,rc) summation = np.sum( r_cut ) return summation def G2(r, rc, rs, eta, cutoff=cutoff_cos): r_cut = cutoff(r,rc) summation = np.sum( np.exp(-eta*(r-rs)**2)*r_cut ) return summation def G3(r, rc, kappa, cutoff=cutoff_cos): r_cut = cutoff(r,rc) summation = np.sum( np.cos(kappa*r)*r_cut ) return summation def G4(xyz, rc, eta, zeta, lambda_c, cutoff=cutoff_cos): """ xyz: [[x1 y1 z1] [x2 y2 z2] [x3 y3 z3] [x4 y4 z4]] """ r = np.linalg.norm(xyz,axis=1) N = len(r) r_cut = cutoff(r,rc) summation = 0 for j in range(N): # for k in range(N): # This double counts angles... as in the litterature # if j == k: # continue # Skip j=k for k in range(j+1,N): # Away with stupid double counting r_jk = np.linalg.norm(xyz[j] - xyz[k]) cos_theta = np.dot(xyz[j],xyz[k]) / (r[j]*r[k]) cutoff_ijk = r_cut[j] * r_cut[k] * cutoff(r_jk, rc) part_sum = (1+lambda_c * cos_theta)**zeta * exp(-eta*(r[j]**2+r[k]**2+r_jk**2)) summation += part_sum*cutoff_ijk summation *= 2**(1-zeta) # Normalization factor return summation def G5(xyz, rc, eta, zeta, lambda_c, cutoff=cutoff_cos): """ xyz: [[x1 y1 z1] [x2 y2 z2] [x3 y3 z3] [x4 y4 z4]] """ r = np.linalg.norm(xyz,axis=1) N = len(r) r_cut = cutoff(r,rc) summation = 0 for j in range(N): # for k in range(N): # This double counts angles... as in the litterature # if j == k: # continue # Skip j=k for k in range(j+1,N): # Away with stupid double counting cos_theta = np.dot(xyz[j],xyz[k]) / (r[j]*r[k]) cutoff_ijk = r_cut[j] * r_cut[k] part_sum = (1+lambda_c * cos_theta)**zeta * exp(-eta*(r[j]**2+r[k]**2)) summation += part_sum*cutoff_ijk summation *= 2**(1-zeta) return summation """ ################# N particle symmetry functions ################# """ def G1_N(r, type, rc, cutoff=cutoff_cos): """ r = [r1 , r2 , ..., rN] type = ["Hydrogen", "Oxygen", ..., "Carbon"] """ # TODO: May move this to symmetry_transform.py in stead... """ ################# Next functions are mainly for testing purposes i.e. plotting response-curves etc. ################# """ def G1_single_neighbor(r, rc, cutoff=cutoff_cos): return cutoff(r,rc) def G2_single_neighbor(r, rc, rs, eta, cutoff=cutoff_cos): r_cut = cutoff(r,rc) return np.exp(-eta*(r-rs)**2)*r_cut def G3_single_neighbor(r, rc, kappa, cutoff=cutoff_cos): r_cut = cutoff(r,rc) return np.cos(kappa*r)*r_cut def G4_single_neighbor_rjk(theta, rc, zeta, lambda_c, eta, cutoff=cutoff_cos, percent_of_rc=0.8): """ rij = rik = 0.8 Rc theta = 0,..,360 """ rij = percent_of_rc * rc cos_theta = np.cos(theta) rjk = sqrt(2) * rij * np.sqrt(1 - cos_theta) # Simplified law of cosines exp_factor = np.exp(-eta*(2*rij**2 + rjk**2)) angle_factor = 2**(1-zeta) * (1 + lambda_c * cos_theta)**zeta cutoff_factor = cutoff(rij, rc)**2 * cutoff(rjk, rc) return angle_factor * exp_factor * cutoff_factor def G4_single_neighbor_radial(r, zeta, lambda_c, eta): """ adfds """ theta = pi/3. # Constant at 60 degrees aka pi/3 exp_factor = np.exp(-eta*3*r**2) angle_factor = 2**(1-zeta) * (1 + lambda_c * np.cos(theta))**zeta return angle_factor * exp_factor def G4_single_neighbor_radial_cut(r, rc, zeta, lambda_c, eta, cutoff=cutoff_cos): """ With cutoff """ theta = pi/3. # Constant at 60 degrees aka pi/3 exp_factor = np.exp(-eta*3*r**2) angle_factor = 2**(1-zeta) * (1 + lambda_c * np.cos(theta))**zeta return angle_factor * exp_factor * cutoff(r, rc)**3 def G5_single_neighbor_radial_cut(r, rc, zeta, lambda_c, eta, cutoff=cutoff_cos): """ With cutoff """ theta = pi/3. # Constant at 60 degrees aka pi/3 exp_factor = np.exp(-eta * 2*r**2) angle_factor = 2**(1-zeta) * (1 + lambda_c * cos(theta))**zeta return angle_factor * exp_factor * cutoff(r, rc)**2 def G4_single_neighbor_2D(theta_grid, rc_grid, r_all, zeta, lambda_c, eta): cutoff = cutoff_cos rij = r_all # rij = rik cos_theta = np.cos(theta_grid) rjk = sqrt(2) * rij * np.sqrt(1 - cos_theta) # Simplified law of cosines exp_factor = np.exp(-eta*(2*rij**2 + rjk**2)) angle_factor = 2**(1-zeta) * (1 + lambda_c * cos_theta)**zeta cutoff_factor = cutoff(rij, rc_grid)**2 * cutoff(rjk, rc_grid) return angle_factor * exp_factor * cutoff_factor def G4_single_neighbor(theta, r_all, rc, zeta, lambda_c, eta): """ NB: Number 4, not 5 """ cutoff = cutoff_cos rij = r_all # rij = rik cos_theta = np.cos(theta) rjk = sqrt(2) * rij * np.sqrt(1 - cos_theta) # Simplified law of cosines exp_factor = np.exp(-eta*(2*rij**2 + rjk**2)) angle_factor = 2**(1-zeta) * (1 + lambda_c * cos_theta)**zeta cutoff_factor = cutoff(rij, rc)**2 * cutoff(rjk, rc) return angle_factor * exp_factor * cutoff_factor def G5_single_neighbor(theta, r_all, rc, zeta, lambda_c, eta): """ Assumes cutoffs to be normalized to 1 and is removed from eqs """ cutoff = cutoff_cos rij = r_all # Both equal exp_factor = np.exp(-eta*2*rij**2) angle_factor = 2**(1-zeta) * (1 + lambda_c * np.cos(theta))**zeta cutoff_factor = cutoff(rij, rc)**2 return angle_factor * exp_factor * cutoff_factor def G5_single_neighbor_radial(r, zeta, lambda_c, eta): """ Radial part of G5 when rij = rik """ theta = pi/3. # Constant at 60 degrees aka pi/3 exp_factor = np.exp(-eta*2*r**2) # rij = rik angle_factor = 2**(1-zeta) * (1 + lambda_c * np.cos(theta))**zeta return angle_factor * exp_factor def G5_single_neighbor_rjk(theta, rc, zeta, lambda_c, eta, cutoff=cutoff_cos, percent_of_rc=0.8): """ rij = rik = 0.8 Rc theta = 0,..,360 """ rij = percent_of_rc * rc cos_theta = np.cos(theta) exp_factor = np.exp(-eta*2*rij**2) angle_factor = 2**(1-zeta) * (1 + lambda_c * cos_theta)**zeta cutoff_factor = cutoff(rij, rc)**2 return angle_factor * exp_factor * cutoff_factor if __name__ == '__main__': """ Mainly for testing purpose """ print "This does absolutely nothing, I'm afraid dear!"

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6

8bbe6d5e2e40ba5708d40eec499289daf2f9bd49

5,462

py

Python

pymbs/processing/body.py

brutzl/pymbs

fb7c91435f56b5c4d460f82f081d5d1960fea886

[ "MIT" ]

null

pymbs/processing/body.py

brutzl/pymbs

fb7c91435f56b5c4d460f82f081d5d1960fea886

[ "MIT" ]

null

pymbs/processing/body.py

brutzl/pymbs

fb7c91435f56b5c4d460f82f081d5d1960fea886

[ "MIT" ]

null

from pymbs.common.abstractbody import AbstractBody from pymbs.symbolics import Matrix from .frame import Frame import pymbs.symbolics as symbolics class Body(AbstractBody): ''' Body holding mass and inertia properties ''' def __init__(self, name, mass=0, cg=symbolics.zeros((3,)), inertia=symbolics.zeros((3,3)), graph=None): ''' Constructor name: Name of the Body mass: Mass in kg (Scalar) cg: Centre Of Gravity (3x1 Vector) inertia: Inertia Tensor w.r.t. The Centre Of Gravity (symmetric 3x3 Matrix) ''' # Call MbsElement Constructor assert graph is not None AbstractBody.__init__(self, name, mass, cg, inertia, graph) # additional attributes self.index = None # body index, i.e. position in mass matrix self.children = [] # list of all children coordinate systems self.joint = None; # reference to parent joint # attributes used for calculation self.I_r = None # Position of Origin w.r.t. Inertial Frame self.I_v = None # Velocity of Origin w.r.t. Inertial Frame self.I_a = None # Acceleration of Origin w.r.t. Inertial Frame self.I_l = None # Centre of Gravity w.r.t. Inertial Frame self.I_R = None # Transformation Matrix, Inertial Frame <- Body Frame self.K_om = None # Angular Velocity w.r.t. (Body Frame if Explicit, Inertial Frame if Recursive!!!) self.K_al = None # Angular Acceleration w.r.t. (Body Frame if Explicit, Inertial Frame if Recursive!!!) self.CS_0 = self.addFrame('_int_CS_0') def addFrame(self, name, p=symbolics.zeros((3,)), R=symbolics.eye((3,3))): ''' Add A New Coordinate System To The List Of Children name: Name of the Coordinate System p: Position of the Coordinate System (3x1 Vector) R: Orientation of the Coordinate System (3x3 Matrix) ''' # Create a New Coordinate System cs = Frame(name=name, parentBody=self, p=p, R=R, graph=self.graph) # Append it to the List of Children self.children += [cs] # return new Coordinate System return cs class FlexibleBody(AbstractBody): ''' Body holding mass and inertia properties ''' def __init__(self, sid, name, mass=0, cg=symbolics.zeros((3,)), inertia=symbolics.zeros((3,3)), graph=None): ''' Constructor name: Name of the Body mass: Mass in kg (Scalar) cg: Centre Of Gravity (3x1 Vector) inertia: Inertia Tensor w.r.t. The Centre Of Gravity (symmetric 3x3 Matrix) ''' # Call MbsElement Constructor assert graph is not None AbstractBody.__init__(self, name, mass, cg, inertia, graph) # FlexibleBody object in Processing requires sid-object as well self.sid = sid # additional attributes self.index = None # body index, i.e. position in mass matrix self.children = [] # list of all children coordinate systems self.joint = None; # reference to parent joint # attributes used for calculation self.I_r = None # Position of Origin w.r.t. Inertial Frame self.I_v = None # Velocity of Origin w.r.t. Inertial Frame self.I_a = None # Acceleration of Origin w.r.t. Inertial Frame self.I_l = None # Centre of Gravity w.r.t. Inertial Frame self.I_R = None # Transformation Matrix, Inertial Frame <- Body Frame self.K_om = None # Angular Velocity w.r.t. (Body Frame if Explicit, Inertial Frame if Recursive!!!) self.K_al = None # Angular Acceleration w.r.t. (Body Frame if Explicit, Inertial Frame if Recursive!!!) #self.CS_0 = self.addFrame('_int_CS_0') # checking if the values of nelastq and nq (SID-File) are equal for nodes in self.sid.modal.frame.Knoten: if nodes.origin.originmatrix.nq != self.sid.modal.refmod.nelastq: raise NotImplementedError('the values of nelastq and nq (SID-File) must be equal') name_flexible_coordinates = 'flexible_coordinates' name_flexible_velocity = 'flexible_velocity' name_flexible_acceleration = 'flexible_acceleration' self.q = [graph.addVariable(name='q_%i_%s_%s' %(i+1,name_flexible_coordinates,self.name)) for i in range(self.sid.modal.refmod.nelastq)] self.q_vec = Matrix(self.q) self.qd = [graph.addVariable(name='qd_%i_%s_%s' %(i+1,name_flexible_velocity,self.name)) for i in range(self.sid.modal.refmod.nelastq)] self.qd_vec = Matrix(self.qd) self.qdd = [graph.addVariable(name='qdd_%i_%s_%s' %(i+1,name_flexible_acceleration,self.name)) for i in range(self.sid.modal.refmod.nelastq)] self.q0 = [0]*self.sid.modal.refmod.nelastq self.qd0 = [0]*self.sid.modal.refmod.nelastq def addFrame(self, name, p=symbolics.zeros((3,)), R=symbolics.eye((3,3))): ''' Add A New Coordinate System To The List Of Children name: Name of the Coordinate System p: Position of the Coordinate System (3x1 Vector) R: Orientation of the Coordinate System (3x3 Matrix) ''' # Create a New Coordinate System cs = Frame(name=name, parentBody=self, p=p, R=R, graph=self.graph) # Append it to the List of Children self.children += [cs] # return new Coordinate System return cs

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8bcc3a870bcbbeb41b4ff8351bbeb22f3d1ac191

80

py

Python

tests/test_sanity.py

ffreemt/freemt-utils

25bf192033235bb783005795f8c0bcdd8a79610f

[ "MIT" ]

null

tests/test_sanity.py

ffreemt/freemt-utils

25bf192033235bb783005795f8c0bcdd8a79610f

[ "MIT" ]

null

tests/test_sanity.py

ffreemt/freemt-utils

25bf192033235bb783005795f8c0bcdd8a79610f

[ "MIT" ]

null

''' sanity check ''' def test_sanity(): ''' sanity check ''' assert 1

10

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4.666667

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6

4770f7a6a8ed87a23f0ee21a13326e42b2c6c6b1

41,723

py

Python

NitroFE/time_based_features/weighted_window_features/weighted_window_features.py

NITRO-AI/NitroFE

08d5ccd2be7da4534bd1fb04b85d7c61ba1c017e

[ "Apache-2.0" ]

81

2021-10-31T12:20:10.000Z

2022-03-29T22:38:06.000Z

NitroFE/time_based_features/weighted_window_features/weighted_window_features.py

adbmd/NitroFE

327a54ffd5f9aaa19d05d7d87918757e3b0f5712

[ "Apache-2.0" ]

1

2021-11-02T14:21:48.000Z

NitroFE/time_based_features/weighted_window_features/weighted_window_features.py

adbmd/NitroFE

327a54ffd5f9aaa19d05d7d87918757e3b0f5712

[ "Apache-2.0" ]

7

2021-11-01T08:17:37.000Z

2022-01-01T19:06:06.000Z

from pandas.core.series import Series from NitroFE.time_based_features.weighted_window_features.weighted_windows import ( _barthann_window, _bartlett_window, _equal_window, _blackman_window, _blackmanharris_window, _bohman_window, _cosine_window, _exponential_window, _flattop_window, _gaussian_window, _hamming_window, _hann_window, _kaiser_window, _parzen_window, _triang_window, _weighted_moving_window, ) import numpy as np import pandas as pd from typing import Union, Callable class weighted_window_features: def __init__(self): self.params = {} pass def first_fit_params_save(self, function_name, **kwargs): if not function_name in self.params: self.params[function_name] = {} for _key in kwargs.keys(): self.params[function_name][_key] = kwargs[_key] def _template_feature_calculation( self, function_name, win_function, first_fit: bool = True, dataframe: Union[pd.DataFrame, pd.Series] = None, window: int = 3, min_periods: int = 1, symmetric: bool = False, operation: Callable = np.mean, operation_args: tuple = (), last_values_from_calculated: bool = False, **kwargs ): _function_name = function_name if not isinstance(operation_args, tuple): operation_args = (operation_args,) if first_fit: self.params[_function_name] = {} self.params[_function_name]["window"] = window self.params[_function_name]["min_periods"] = min_periods self.params[_function_name]["symmetric"] = symmetric self.params[_function_name]["operation"] = operation self.params[_function_name]["operation_args"] = operation_args self.params[_function_name][ "last_values_from_calculated" ] = last_values_from_calculated self.first_fit_params_save(_function_name, kwargs=kwargs) if not first_fit: if ( self.params[_function_name]["last_values_from_previous_run"] is None ) and (self.params[_function_name]["window"] != 1): raise ValueError( "First fit has not occured before. Kindly run first_fit=True for first fit instance," "and then proceed with first_fit=False for subsequent fits " ) dataframe = pd.concat( [ self.params[_function_name]["last_values_from_previous_run"], dataframe, ], axis=0, ) _return = dataframe.rolling( window=self.params[_function_name]["window"], min_periods=self.params[_function_name]["min_periods"], ).agg( lambda x: self.params[_function_name]["operation"]( win_function( data=x, window_size=self.params[_function_name]["window"], symmetric=self.params[_function_name]["symmetric"], **self.params[_function_name]["kwargs"] ), *self.params[_function_name]["operation_args"] ) ) if not first_fit: _return = _return.iloc[ self.params[_function_name]["len_last_values_from_previous_run"] : ] if not self.params[_function_name]["last_values_from_calculated"]: _last_values_from_previous_run = ( dataframe.iloc[1 - self.params[_function_name]["window"] :] if self.params[_function_name]["window"] != 1 else None ) else: _last_values_from_previous_run = ( _return.iloc[1 - self.params[_function_name]["window"] :] if self.params[_function_name]["window"] != 1 else None ) self.first_fit_params_save( _function_name, last_values_from_previous_run=_last_values_from_previous_run, len_last_values_from_previous_run=len(_last_values_from_previous_run), ) return _return def caluclate_weighted_moving_window_feature( self, dataframe: Union[pd.DataFrame, pd.Series], first_fit: bool = True, window: int = 3, min_periods: int = 1, operation: Callable = None, operation_args: tuple = (), ): """ Create weighted moving window feature A weighted average is an average that has multiplying factors to give different weights to data at different positions in the sample window. Mathematically, the weighted moving average is the convolution of the data with a fixed weighting function. In an n-day WMA the latest day has weight n, the second latest n-1, etc, down to one Parameters ---------- dataframe : Union[pd.DataFrame,pd.Series] dataframe/series over weighted rolling window feature is to be constructed first_fit : bool, optional Rolling features require past "window" number of values for calculation. Use True, when calculating for training data { in which case last "window" number of values will be saved } Use False, when calculating for testing/production data { in which case the, last "window" number of values, which are were saved during the last phase, will be utilized for calculation }, by default True window : int, optional Size of the rolling window, by default 3 min_periods : int, optional Minimum number of observations in window required to have a value, by default 1 operation : Callable, optional operation to perform over the weighted rolling window values, when None is passed, np.sum is used operation_args : tuple, optional additional agrument values to be sent for self defined operation function """ operation = np.sum if operation == None else operation _function_name = "caluclate_weighted_moving_window_feature" return self._template_feature_calculation( function_name=_function_name, win_function=_weighted_moving_window, first_fit=first_fit, dataframe=dataframe, window=window, min_periods=min_periods, symmetric=None, operation=operation, operation_args=operation_args, ) def caluclate_barthann_feature( self, dataframe: Union[pd.DataFrame, pd.Series], first_fit: bool = True, window: int = 3, min_periods: int = 1, symmetric: bool = False, operation: Callable = None, operation_args: tuple = (), ): """ Create Bartlett–Hann weighted rolling window feature Parameters ---------- dataframe : Union[pd.DataFrame,pd.Series] dataframe/series over which Bartlett–Hann weighted rolling window feature is to be constructed first_fit : bool, optional Rolling features require past "window" number of values for calculation. Use True, when calculating for training data { in which case last "window" number of values will be saved } Use False, when calculating for testing/production data { in which case the, last "window" number of values, which are were saved during the last phase, will be utilized for calculation }, by default True window : int, optional Size of the rolling window, by default 3 min_periods : int, optional Minimum number of observations in window required to have a value, by default 1 symmetric : bool, optional When True , generates a symmetric window, for use in filter design. When False, generates a periodic window, for use in spectral analysis, by default False operation : Callable, optional operation to perform over the weighted rolling window values, when None is passed, np.mean is used operation_args : tuple, optional additional agrument values to be sent for operation function """ operation = np.mean if operation == None else operation _function_name = "caluclate_barthann_feature" return self._template_feature_calculation( function_name=_function_name, win_function=_barthann_window, first_fit=first_fit, dataframe=dataframe, window=window, min_periods=min_periods, symmetric=symmetric, operation=operation, operation_args=operation_args, ) def caluclate_bartlett_feature( self, dataframe: Union[pd.DataFrame, pd.Series], first_fit: bool = True, window: int = 3, min_periods: int = 1, symmetric: bool = False, operation: Callable = None, operation_args: tuple = (), ): """ Create bartlett weighted rolling window feature Parameters ---------- dataframe : Union[pd.DataFrame,pd.Series] dataframe/series over which bartlett weighted rolling window feature is to be constructed first_fit : bool, optional Rolling features require past "window" number of values for calculation. Use True, when calculating for training data { in which case last "window" number of values will be saved } Use False, when calculating for testing/production data { in which case the, last "window" number of values, which are were saved during the last phase, will be utilized for calculation }, by default True window : int, optional Size of the rolling window, by default 3 min_periods : int, optional Minimum number of observations in window required to have a value, by default 1 symmetric : bool, optional When True , generates a symmetric window, for use in filter design. When False, generates a periodic window, for use in spectral analysis, by default False operation : Callable, optional operation to perform over the weighted rolling window values, when None is passed, np.mean is used operation_args : tuple, optional additional agrument values to be sent for operation function """ operation = np.mean if operation == None else operation _function_name = "caluclate_bartlett_feature" return self._template_feature_calculation( function_name=_function_name, win_function=_bartlett_window, first_fit=first_fit, dataframe=dataframe, window=window, min_periods=min_periods, symmetric=symmetric, operation=operation, operation_args=operation_args, ) def caluclate_equal_feature( self, dataframe: Union[pd.DataFrame, pd.Series], first_fit: bool = True, window: int = 3, min_periods: int = 1, operation: Callable = None, operation_args: tuple = (), ): """ Create equally weighted rolling window feature All elemets are weighted equally Parameters ---------- dataframe : Union[pd.DataFrame,pd.Series] dataframe/series over which equally weighted rolling window feature is to be constructed first_fit : bool, optional Rolling features require past "window" number of values for calculation. Use True, when calculating for training data { in which case last "window" number of values will be saved } Use False, when calculating for testing/production data { in which case the, last "window" number of values, which are were saved during the last phase, will be utilized for calculation }, by default True window : int, optional Size of the rolling window, by default 3 min_periods : int, optional Minimum number of observations in window required to have a value, by default 1 operation : Callable, optional operation to perform over the weighted rolling window values, when None is passed, np.mean is used operation_args : tuple, optional additional agrument values to be sent for operation function """ operation = np.mean if operation == None else operation _function_name = "caluclate_equal_feature" return self._template_feature_calculation( function_name=_function_name, win_function=_equal_window, first_fit=first_fit, dataframe=dataframe, window=window, min_periods=min_periods, symmetric=None, operation=operation, operation_args=operation_args, ) def caluclate_blackman_feature( self, dataframe: Union[pd.DataFrame, pd.Series], first_fit: bool = True, window: int = 3, min_periods: int = 1, symmetric: bool = False, operation: Callable = None, operation_args: tuple = (), ): """ Create blackman weighted rolling window feature Parameters ---------- dataframe : Union[pd.DataFrame,pd.Series] dataframe/series over which blackman weighted rolling window feature is to be constructed first_fit : bool, optional Rolling features require past "window" number of values for calculation. Use True, when calculating for training data { in which case last "window" number of values will be saved } Use False, when calculating for testing/production data { in which case the, last "window" number of values, which are were saved during the last phase, will be utilized for calculation }, by default True window : int, optional Size of the rolling window, by default 3 min_periods : int, optional Minimum number of observations in window required to have a value, by default 1 symmetric : bool, optional When True , generates a symmetric window, for use in filter design. When False, generates a periodic window, for use in spectral analysis, by default False operation : Callable, optional operation to perform over the weighted rolling window values, when None is passed, np.mean is used operation_args : tuple, optional additional agrument values to be sent for operation function """ operation = np.mean if operation == None else operation _function_name = "caluclate_blackman_feature" return self._template_feature_calculation( function_name=_function_name, win_function=_blackman_window, first_fit=first_fit, dataframe=dataframe, window=window, min_periods=min_periods, symmetric=symmetric, operation=operation, operation_args=operation_args, ) def caluclate_blackmanharris_feature( self, dataframe: Union[pd.DataFrame, pd.Series], first_fit: bool = True, window: int = 3, min_periods: int = 1, symmetric: bool = False, operation: Callable = None, operation_args: tuple = (), ): """ Create blackman-harris weighted rolling window feature Parameters ---------- dataframe : Union[pd.DataFrame,pd.Series] dataframe/series over which blackman-harris weighted rolling window feature is to be constructed first_fit : bool, optional Rolling features require past "window" number of values for calculation. Use True, when calculating for training data { in which case last "window" number of values will be saved } Use False, when calculating for testing/production data { in which case the, last "window" number of values, which are were saved during the last phase, will be utilized for calculation }, by default True window : int, optional Size of the rolling window, by default 3 min_periods : int, optional Minimum number of observations in window required to have a value, by default 1 symmetric : bool, optional When True , generates a symmetric window, for use in filter design. When False, generates a periodic window, for use in spectral analysis, by default False operation : Callable, optional operation to perform over the weighted rolling window values, when None is passed, np.mean is used operation_args : tuple, optional additional agrument values to be sent for operation function """ operation = np.mean if operation == None else operation _function_name = "caluclate_blackmanharris_feature" return self._template_feature_calculation( function_name=_function_name, win_function=_blackmanharris_window, first_fit=first_fit, dataframe=dataframe, window=window, min_periods=min_periods, symmetric=symmetric, operation=operation, operation_args=operation_args, ) def caluclate_bohman_feature( self, dataframe: Union[pd.DataFrame, pd.Series], first_fit: bool = True, window: int = 3, min_periods: int = 1, symmetric: bool = False, operation: Callable = None, operation_args: tuple = (), ): """ Create bohman weighted rolling window feature Parameters ---------- dataframe : Union[pd.DataFrame,pd.Series] dataframe/series over which bohman weighted rolling window feature is to be constructed first_fit : bool, optional Rolling features require past "window" number of values for calculation. Use True, when calculating for training data { in which case last "window" number of values will be saved } Use False, when calculating for testing/production data { in which case the, last "window" number of values, which are were saved during the last phase, will be utilized for calculation }, by default True window : int, optional Size of the rolling window, by default 3 min_periods : int, optional Minimum number of observations in window required to have a value, by default 1 symmetric : bool, optional When True , generates a symmetric window, for use in filter design. When False, generates a periodic window, for use in spectral analysis, by default False operation : Callable, optional operation to perform over the weighted rolling window values, when None is passed, np.mean is used operation_args : tuple, optional additional agrument values to be sent for operation function """ operation = np.mean if operation == None else operation _function_name = "caluclate_bohman_feature" return self._template_feature_calculation( function_name=_function_name, win_function=_bohman_window, first_fit=first_fit, dataframe=dataframe, window=window, min_periods=min_periods, symmetric=symmetric, operation=operation, operation_args=operation_args, ) def caluclate_cosine_feature( self, dataframe: Union[pd.DataFrame, pd.Series], first_fit: bool = True, window: int = 3, min_periods: int = 1, symmetric: bool = False, operation: Callable = None, operation_args: tuple = (), ): """ Create cosine weighted rolling window feature Parameters ---------- dataframe : Union[pd.DataFrame,pd.Series] dataframe/series over which cosine weighted rolling window feature is to be constructed first_fit : bool, optional Rolling features require past "window" number of values for calculation. Use True, when calculating for training data { in which case last "window" number of values will be saved } Use False, when calculating for testing/production data { in which case the, last "window" number of values, which are were saved during the last phase, will be utilized for calculation }, by default True window : int, optional Size of the rolling window, by default 3 min_periods : int, optional Minimum number of observations in window required to have a value, by default 1 symmetric : bool, optional When True , generates a symmetric window, for use in filter design. When False, generates a periodic window, for use in spectral analysis, by default False operation : Callable, optional operation to perform over the weighted rolling window values, when None is passed, np.mean is used operation_args : tuple, optional additional agrument values to be sent for operation function """ operation = np.mean if operation == None else operation _function_name = "caluclate_cosine_feature" return self._template_feature_calculation( function_name=_function_name, win_function=_cosine_window, first_fit=first_fit, dataframe=dataframe, window=window, min_periods=min_periods, symmetric=symmetric, operation=operation, operation_args=operation_args, ) def caluclate_exponential_feature( self, dataframe: Union[pd.DataFrame, pd.Series], first_fit: bool = True, window: int = 3, min_periods: int = 1, symmetric: bool = False, operation: Callable = None, operation_args: tuple = (), center: float = None, tau: float = 1, ): """ Create exponential weighted rolling window feature Parameters ---------- dataframe : Union[pd.DataFrame,pd.Series] dataframe/series over which exponential weighted rolling window feature is to be constructed first_fit : bool, optional Rolling features require past "window" number of values for calculation. Use True, when calculating for training data { in which case last "window" number of values will be saved } Use False, when calculating for testing/production data { in which case the, last "window" number of values, which are were saved during the last phase, will be utilized for calculation }, by default True window : int, optional Size of the rolling window, by default 3 min_periods : int, optional Minimum number of observations in window required to have a value, by default 1 symmetric : bool, optional When True , generates a symmetric window, for use in filter design. When False, generates a periodic window, for use in spectral analysis, by default False operation : Callable, optional operation to perform over the weighted rolling window values, when None is passed, np.mean is used operation_args : tuple, optional additional agrument values to be sent for operation function center : float , optional Parameter defining the center location of the window function. The default value if not given is center = (M-1) / 2. This parameter must take its default value for symmetric windows. tau : float , optional Parameter defining the decay. For center = 0 use tau = -(M-1) / ln(x) if x is the fraction of the window remaining at the end, by default 1 """ operation = np.mean if operation == None else operation _function_name = "caluclate_exponential_feature" return self._template_feature_calculation( function_name=_function_name, win_function=_exponential_window, first_fit=first_fit, dataframe=dataframe, window=window, min_periods=min_periods, symmetric=symmetric, operation=operation, operation_args=operation_args, center=center, tau=tau, ) def caluclate_flattop_feature( self, dataframe: Union[pd.DataFrame, pd.Series], first_fit: bool = True, window: int = 3, min_periods: int = 1, symmetric: bool = False, operation: Callable = None, operation_args: tuple = (), ): """ Create flattop weighted rolling window feature Parameters ---------- dataframe : Union[pd.DataFrame,pd.Series] dataframe/series over which flattop weighted rolling window feature is to be constructed first_fit : bool, optional Rolling features require past "window" number of values for calculation. Use True, when calculating for training data { in which case last "window" number of values will be saved } Use False, when calculating for testing/production data { in which case the, last "window" number of values, which are were saved during the last phase, will be utilized for calculation }, by default True window : int, optional Size of the rolling window, by default 3 min_periods : int, optional Minimum number of observations in window required to have a value, by default 1 symmetric : bool, optional When True , generates a symmetric window, for use in filter design. When False, generates a periodic window, for use in spectral analysis, by default False operation : Callable, optional operation to perform over the weighted rolling window values, when None is passed, np.mean is used operation_args : tuple, optional additional agrument values to be sent for operation function """ operation = np.mean if operation == None else operation _function_name = "caluclate_flattop_feature" return self._template_feature_calculation( function_name=_function_name, win_function=_flattop_window, first_fit=first_fit, dataframe=dataframe, window=window, min_periods=min_periods, symmetric=symmetric, operation=operation, operation_args=operation_args, ) def caluclate_gaussian_feature( self, dataframe: Union[pd.DataFrame, pd.Series], first_fit: bool = True, window: int = 3, min_periods: int = 1, symmetric: bool = False, operation: Callable = None, operation_args: tuple = (), std: float = 1, ): """ Create flattop gaussian rolling window feature Parameters ---------- dataframe : Union[pd.DataFrame,pd.Series] dataframe/series over which flattop gaussian rolling window feature is to be constructed first_fit : bool, optional Rolling features require past "window" number of values for calculation. Use True, when calculating for training data { in which case last "window" number of values will be saved } Use False, when calculating for testing/production data { in which case the, last "window" number of values, which are were saved during the last phase, will be utilized for calculation }, by default True window : int, optional Size of the rolling window, by default 3 min_periods : int, optional Minimum number of observations in window required to have a value, by default 1 symmetric : bool, optional When True , generates a symmetric window, for use in filter design. When False, generates a periodic window, for use in spectral analysis, by default False operation : Callable, optional operation to perform over the weighted rolling window values, when None is passed, np.mean is used operation_args : tuple, optional additional agrument values to be sent for operation function std : float, optional The standard deviation, sigma. """ operation = np.mean if operation == None else operation _function_name = "caluclate_gaussian_feature" return self._template_feature_calculation( function_name=_function_name, win_function=_gaussian_window, first_fit=first_fit, dataframe=dataframe, window=window, min_periods=min_periods, symmetric=symmetric, operation=operation, operation_args=operation_args, std=std, ) def caluclate_hamming_feature( self, dataframe: Union[pd.DataFrame, pd.Series], first_fit: bool = True, window: int = 3, min_periods: int = 1, symmetric: bool = False, operation: Callable = None, operation_args: tuple = (), ): """ Create flattop hamming rolling window feature Parameters ---------- dataframe : Union[pd.DataFrame,pd.Series] dataframe/series over which flattop hamming rolling window feature is to be constructed first_fit : bool, optional Rolling features require past "window" number of values for calculation. Use True, when calculating for training data { in which case last "window" number of values will be saved } Use False, when calculating for testing/production data { in which case the, last "window" number of values, which are were saved during the last phase, will be utilized for calculation }, by default True window : int, optional Size of the rolling window, by default 3 min_periods : int, optional Minimum number of observations in window required to have a value, by default 1 symmetric : bool, optional When True , generates a symmetric window, for use in filter design. When False, generates a periodic window, for use in spectral analysis, by default False operation : Callable, optional operation to perform over the weighted rolling window values, when None is passed, np.mean is used operation_args : tuple, optional additional agrument values to be sent for operation function """ operation = np.mean if operation == None else operation _function_name = "caluclate_hamming_feature" return self._template_feature_calculation( function_name=_function_name, win_function=_hamming_window, first_fit=first_fit, dataframe=dataframe, window=window, min_periods=min_periods, symmetric=symmetric, operation=operation, operation_args=operation_args, ) def caluclate_hann_feature( self, dataframe: Union[pd.DataFrame, pd.Series], first_fit: bool = True, window: int = 3, min_periods: int = 1, symmetric: bool = False, operation: Callable = None, operation_args: tuple = (), ): """ Create flattop hann rolling window feature Parameters ---------- dataframe : Union[pd.DataFrame,pd.Series] dataframe/series over which flattop hann rolling window feature is to be constructed first_fit : bool, optional Rolling features require past "window" number of values for calculation. Use True, when calculating for training data { in which case last "window" number of values will be saved } Use False, when calculating for testing/production data { in which case the, last "window" number of values, which are were saved during the last phase, will be utilized for calculation }, by default True window : int, optional Size of the rolling window, by default 3 min_periods : int, optional Minimum number of observations in window required to have a value, by default 1 symmetric : bool, optional When True , generates a symmetric window, for use in filter design. When False, generates a periodic window, for use in spectral analysis, by default False operation : Callable, optional operation to perform over the weighted rolling window values, when None is passed, np.mean is used operation_args : tuple, optional additional agrument values to be sent for operation function """ operation = np.mean if operation == None else operation _function_name = "caluclate_hann_feature" return self._template_feature_calculation( function_name=_function_name, win_function=_hann_window, first_fit=first_fit, dataframe=dataframe, window=window, min_periods=min_periods, symmetric=symmetric, operation=operation, operation_args=operation_args, ) def caluclate_kaiser_feature( self, dataframe: Union[pd.DataFrame, pd.Series], first_fit: bool = True, window: int = 3, min_periods: int = 1, symmetric: bool = False, beta: float = 7, operation: Callable = None, operation_args: tuple = (), ): """ Create flattop kaiser rolling window feature Parameters ---------- dataframe : Union[pd.DataFrame,pd.Series] dataframe/series over which flattop kaiser rolling window feature is to be constructed first_fit : bool, optional Rolling features require past "window" number of values for calculation. Use True, when calculating for training data { in which case last "window" number of values will be saved } Use False, when calculating for testing/production data { in which case the, last "window" number of values, which are were saved during the last phase, will be utilized for calculation }, by default True window : int, optional Size of the rolling window, by default 3 min_periods : int, optional Minimum number of observations in window required to have a value, by default 1 symmetric : bool, optional When True , generates a symmetric window, for use in filter design. When False, generates a periodic window, for use in spectral analysis, by default False beta : float, optional Shape parameter, determines trade-off between main-lobe width and side lobe level, by default 7 As beta gets large, the window narrows. operation : Callable, optional operation to perform over the weighted rolling window values, when None is passed, np.mean is used operation_args : tuple, optional additional agrument values to be sent for operation function """ operation = np.mean if operation == None else operation _function_name = "caluclate_kaiser_feature" return self._template_feature_calculation( function_name=_function_name, win_function=_kaiser_window, first_fit=first_fit, dataframe=dataframe, window=window, min_periods=min_periods, symmetric=symmetric, operation=operation, operation_args=operation_args, beta=beta, ) def caluclate_parzen_feature( self, dataframe: Union[pd.DataFrame, pd.Series], first_fit: bool = True, window: int = 3, min_periods: int = 1, symmetric: bool = False, operation: Callable = None, operation_args: tuple = (), ): """ Create flattop parzen rolling window feature Parameters ---------- dataframe : Union[pd.DataFrame,pd.Series] dataframe/series over which flattop parzen rolling window feature is to be constructed first_fit : bool, optional Rolling features require past "window" number of values for calculation. Use True, when calculating for training data { in which case last "window" number of values will be saved } Use False, when calculating for testing/production data { in which case the, last "window" number of values, which are were saved during the last phase, will be utilized for calculation }, by default True window : int, optional Size of the rolling window, by default 3 min_periods : int, optional Minimum number of observations in window required to have a value, by default 1 symmetric : bool, optional When True , generates a symmetric window, for use in filter design. When False, generates a periodic window, for use in spectral analysis, by default False operation : Callable, optional operation to perform over the weighted rolling window values, when None is passed, np.mean is used operation_args : tuple, optional additional agrument values to be sent for operation function """ operation = np.mean if operation == None else operation _function_name = "caluclate_parzen_feature" return self._template_feature_calculation( function_name=_function_name, win_function=_parzen_window, first_fit=first_fit, dataframe=dataframe, window=window, min_periods=min_periods, symmetric=symmetric, operation=operation, operation_args=operation_args, ) def caluclate_triang_feature( self, dataframe: Union[pd.DataFrame, pd.Series], first_fit: bool = True, window: int = 3, min_periods: int = 1, symmetric: bool = False, operation: Callable = None, operation_args: tuple = (), ): """ Create flattop triang rolling window feature Parameters ---------- dataframe : Union[pd.DataFrame,pd.Series] dataframe/series over which flattop triang rolling window feature is to be constructed first_fit : bool, optional Rolling features require past "window" number of values for calculation. Use True, when calculating for training data { in which case last "window" number of values will be saved } Use False, when calculating for testing/production data { in which case the, last "window" number of values, which are were saved during the last phase, will be utilized for calculation }, by default True window : int, optional Size of the rolling window, by default 3 min_periods : int, optional Minimum number of observations in window required to have a value, by default 1 symmetric : bool, optional When True , generates a symmetric window, for use in filter design. When False, generates a periodic window, for use in spectral analysis, by default False operation : Callable, optional operation to perform over the weighted rolling window values, when None is passed, np.mean is used operation_args : tuple, optional additional agrument values to be sent for operation function """ operation = np.mean if operation == None else operation _function_name = "caluclate_triang_feature" return self._template_feature_calculation( function_name=_function_name, win_function=_triang_window, first_fit=first_fit, dataframe=dataframe, window=window, min_periods=min_periods, symmetric=symmetric, operation=operation, operation_args=operation_args, )

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Python

sturn/stun/__init__.py

m32/sturn

ffc252db2a434daef33c5e819444b1d929a8599b

[ "MIT" ]

2

2021-07-11T21:24:37.000Z

2021-12-23T18:30:50.000Z

sturn/stun/__init__.py

m32/sturn

ffc252db2a434daef33c5e819444b1d929a8599b

[ "MIT" ]

null

sturn/stun/__init__.py

m32/sturn

ffc252db2a434daef33c5e819444b1d929a8599b

[ "MIT" ]

1

2021-12-24T01:07:21.000Z

from .stun import *

10

19

0.7

3

20

4.666667

1

0

0.2

20

1

20

0.875

0

1

0

true

0

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null

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1

0

null

0

1

0

1

0

1

0

6

4796ec376b4ed0889615feae4b523334d5744c98

1,638

py

Python

hackerrank/ginortS.py

FelixTheC/hackerrank_exercises

24eedbedebd122c53fd2cb6018cc3535d0d4c6a0

[ "MIT" ]

null

hackerrank/ginortS.py

FelixTheC/hackerrank_exercises

24eedbedebd122c53fd2cb6018cc3535d0d4c6a0

[ "MIT" ]

null

hackerrank/ginortS.py

FelixTheC/hackerrank_exercises

24eedbedebd122c53fd2cb6018cc3535d0d4c6a0

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ @created: 08.11.19 @author: felix """ import re def my_sort(string: str) -> str: lower_letters = re.findall(r'[a-z]', string) lower_letters.sort() upper_letters = re.findall(r'[A-Z]', string) upper_letters.sort() digits = [x for x in re.findall(r'[0-9]', string)] even = [x for x in digits if int(x) % 2 == 0] odd = [x for x in digits if int(x) % 2 != 0] even.sort() odd.sort() return ''.join(lower_letters + upper_letters + odd + even) if __name__ == '__main__': test_result = 'dddddddddddddddddeeeeeeeeeeeeeeefffffffffffggggggggggggggggghhhhhhhhhhhhhhhjjjjjjjjjjjjjjjjjjjjqqqqqqqqqqqqqqrrrrrrrrrrrrrrtttttttttttttttwwwwwwwwwwwwwwyyyyyyyyyyyyyyAAAAAAAAAAAAAAAAAAAABBBBBBBBBBCCCCCCCCCCCCCCCCCDDDDDDDDDDDEEEEEEEEEEEFFFFFFFFFFFFFFFGGGGGGGGGGGGGGHHHHHHHHHHHHHHHHHHHHHHHHHIIIIIIIIIIIIIIIIIIIIIIIIIIJJJJJJJJJJJJJJJJJJJJJJKKKKKKKKKKKKKKKKLLLLLLLLLLLLLLLLLMMMMMMMMMMMMMNNNNNNNNNNOOOOOOOOOOOPPPPPPPPPPPPPPPPPPQQQQQQQQQQQQQQRRRRRRRRRRRRRSSSSSSSSSSSSSSSSSTTTTTTTTTTTTTTTTTUUUUUUUUUUUUUUUVVVVVVVVVVVVVVVVVVVWWWWWWWWWWWWWWWWWWWWXXXXXXXXXXXXXYYYYYYYYYYZZZZZZZZZZZZZ111111111111111111111111111111111111111133333333333333333333333333333333333333333333333333355555555555555555555555555555555555555777777777777777777777777777777777777777777999999999999999999999999999999999999999999999900000000000002222222222222222222222222222222222222222222222222244444444444444444444444444444444444444444444446666666666666666666666666666666666666666666666666668888888888888888888888888888888888888888888888888' # nopep8 string = input() print(my_sort(string))

58.5

1,029

0.840659

101

1,638

13.465347

0.475248

0.026471

0.022059

0.015441

0.067647

0.030882

0

0.295378

0.088523

1,638

27

1,030

60.666667

0.615539

0.051282

0

0.662346

0.64744

0

1

0

1

0.0625

false

0

0.0625

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0.1875

0.0625

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null

0

1

0

1

0

1

null

1

0

6

47f48c04680b0fc028c0341372f898d4f326292b

20

py

Python

test.py

846395745/myProject

c4db486641d411485eab2bcc26def0f8a1318d66

[ "MIT" ]

1

2019-12-23T12:22:15.000Z

test.py

846395745/myProject

c4db486641d411485eab2bcc26def0f8a1318d66

[ "MIT" ]

null

test.py

846395745/myProject

c4db486641d411485eab2bcc26def0f8a1318d66

[ "MIT" ]

null

a = 123 print(a)

3.333333

8

0.5

4

20

2.5

0.75

0

0.230769

0.35

20

5

9

4

0.538462

0

1

0

false

0

0.5

1

0

null

0

1

0

1

0

1

0

null

0

1

0

6

9a23481c5c8c94dc6bf3a42693240d3dc146a28e

96

py

Python

vmachine/__init__.py

horus-4ever/python-chip8-emulator

a22b4003b0f2e6d333346927902661a6dd06d980

[ "MIT" ]

null

vmachine/__init__.py

horus-4ever/python-chip8-emulator

a22b4003b0f2e6d333346927902661a6dd06d980

[ "MIT" ]

null

vmachine/__init__.py

horus-4ever/python-chip8-emulator

a22b4003b0f2e6d333346927902661a6dd06d980

[ "MIT" ]

null

from .registers import * from .instructionset import * from .vcpu import * from .memory import *

24

29

0.760417

12

96

6.083333

0.5

0.410959

0

0.15625

96

4

30

24

0.901235

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

9a41cdd9cca7417079cd4d1c2752aab4d67bdae0

37

py

Python

zeroth/namesss/__init__.py

njvrzm/zeroth

26c000389403cd7e54dca7dfb9364b9fe50e161a

[ "MIT" ]

null

zeroth/namesss/__init__.py

njvrzm/zeroth

26c000389403cd7e54dca7dfb9364b9fe50e161a

[ "MIT" ]

null

zeroth/namesss/__init__.py

njvrzm/zeroth

26c000389403cd7e54dca7dfb9364b9fe50e161a

[ "MIT" ]

null

from .namesss import getNamesForYear

18.5

36

0.864865

4

37

8

1

0

0.108108

37

1

37

0.969697

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

d0005db1a2e08a9d801cd8b63bf0ba6675c55db9

49

py

Python

app/business/blog/__init__.py

Anioko/reusable

de6480bc23fb8cfff474985128be91f4dd391be6

[ "MIT" ]

null

app/business/blog/__init__.py

Anioko/reusable

de6480bc23fb8cfff474985128be91f4dd391be6

[ "MIT" ]

null

app/business/blog/__init__.py

Anioko/reusable

de6480bc23fb8cfff474985128be91f4dd391be6

[ "MIT" ]

null

from app.business.blog.views import blog # noqa

24.5

48

0.77551

8

49

4.75

0.875

0

0.142857

49

1

49

0.904762

0.081633

0

1

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true

0

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null

0

1

0

null

0

1

0

1

0

1

0

6

d09a47265de40fa28d63d23152163d2afe398393

32

py

Python

RandomBot/__init__.py

RandomBotDev/RandomBotCog

a00db232cf6eeb85293060a3ffaf6d44f4330450

[ "MIT" ]

null

RandomBot/__init__.py

RandomBotDev/RandomBotCog

a00db232cf6eeb85293060a3ffaf6d44f4330450

[ "MIT" ]

null

RandomBot/__init__.py

RandomBotDev/RandomBotCog

a00db232cf6eeb85293060a3ffaf6d44f4330450

[ "MIT" ]

1

2022-03-07T11:48:37.000Z

from RandomBot.MainCog import *

16

31

0.8125

4

32

6.5

1

0

0.125

32

1

32

0.928571

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

efecbbcd5adab5ef1bed01ae2353a20bf7d46be4

48

py

Python

tests/examples-bad/2.py

JohannesBuchner/pystrict3

f442a89ac6a23f4323daed8ef829d8e9e1197f90

[ "BSD-2-Clause" ]

1

2020-06-05T08:53:26.000Z

tests/examples-bad/2.py

JohannesBuchner/pystrict3

f442a89ac6a23f4323daed8ef829d8e9e1197f90

[ "BSD-2-Clause" ]

1

2020-06-04T13:47:19.000Z

2020-06-04T13:47:57.000Z

tests/examples-bad/2.py

JohannesBuchner/pystrict3

f442a89ac6a23f4323daed8ef829d8e9e1197f90

[ "BSD-2-Clause" ]

1

2020-11-07T17:02:46.000Z

def format(): pass ## bad, format is a keyword

16

33

0.666667

8

48

4

0.875

0

0.208333

48

2

34

24

0.842105

0.5

0

1

0.5

true

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

6

4bd4fec1d55acfabcdd266d3d2c4b9e7eead06a4

7,156

py

Python

models/unet.py

johnmartinsson/adversarial-representation-learning

86cd1489b0bdfa76bab37e313c6ab53304179f1e

[ "Apache-2.0" ]

null

models/unet.py

johnmartinsson/adversarial-representation-learning

86cd1489b0bdfa76bab37e313c6ab53304179f1e

[ "Apache-2.0" ]

null

models/unet.py

johnmartinsson/adversarial-representation-learning

86cd1489b0bdfa76bab37e313c6ab53304179f1e

[ "Apache-2.0" ]

null

import torch import torch.nn as nn import torch.nn.functional as F def double_conv(channels_in, channels_out): return nn.Sequential( nn.Conv2d(channels_in, channels_out, 3, padding=1), nn.BatchNorm2d(channels_out), nn.ReLU(), nn.Conv2d(channels_out, channels_out, 3, padding=1), nn.BatchNorm2d(channels_out), nn.ReLU() ) class UNetFilter(nn.Module): def __init__(self, channels_in, channels_out, chs=[32, 64, 128, 256, 512], image_width=64, image_height=64, noise_dim=10, activation='sigmoid', nb_classes=2, embedding_dim=16, use_cond=True): super().__init__() self.use_cond = use_cond self.width = image_width self.height = image_height self.activation = activation self.embed_condition = nn.Embedding(nb_classes, embedding_dim) # noise projection layer self.project_noise = nn.Linear(noise_dim, image_width//16 * image_height//16 * chs[4]) # condition projection layer self.project_cond = nn.Linear(embedding_dim, image_width//16 * image_height//16) self.dconv_down1 = double_conv(channels_in, chs[0]) self.pool_down1 = nn.MaxPool2d(2, stride=2) self.dconv_down2 = double_conv(chs[0], chs[1]) self.pool_down2 = nn.MaxPool2d(2, stride=2) self.dconv_down3 = double_conv(chs[1], chs[2]) self.pool_down3 = nn.MaxPool2d(2, stride=2) self.dconv_down4 = double_conv(chs[2], chs[3]) self.pool_down4 = nn.MaxPool2d(2, stride=2) self.dconv_down5 = double_conv(chs[3], chs[4]) if self.use_cond: self.dconv_up5 = double_conv(chs[4]+chs[4]+1+chs[3], chs[3]) else: self.dconv_up5 = double_conv(chs[4]+chs[4]+chs[3], chs[3]) self.dconv_up4 = double_conv(chs[3]+chs[2], chs[2]) self.dconv_up3 = double_conv(chs[2]+chs[1], chs[1]) self.dconv_up2 = double_conv(chs[1]+chs[0], chs[0]) self.dconv_up1 = nn.Conv2d(chs[0], channels_out, kernel_size=1) def forward(self, x, z, cond): noise = self.project_noise(z).reshape(x.shape[0], 512, x.shape[2]//16, x.shape[3]//16) cond_emb = self.embed_condition(cond) cond_emb = self.project_cond(cond_emb).reshape(x.shape[0], 1, x.shape[2]//16, x.shape[3]//16) conv1_down = self.dconv_down1(x) pool1 = self.pool_down1(conv1_down) conv2_down = self.dconv_down2(pool1) pool2 = self.pool_down2(conv2_down) conv3_down = self.dconv_down3(pool2) pool3 = self.pool_down3(conv3_down) conv4_down = self.dconv_down4(pool3) pool4 = self.pool_down4(conv4_down) conv5_down = self.dconv_down5(pool4) if self.use_cond: conv5_down = torch.cat((conv5_down, noise, cond_emb), dim=1) else: conv5_down = torch.cat((conv5_down, noise), dim=1) conv5_up = F.interpolate(conv5_down, scale_factor=2, mode='nearest') conv5_up = torch.cat((conv4_down, conv5_up), dim=1) conv5_up = self.dconv_up5(conv5_up) conv4_up = F.interpolate(conv5_up, scale_factor=2, mode='nearest') conv4_up = torch.cat((conv3_down, conv4_up), dim=1) conv4_up = self.dconv_up4(conv4_up) conv3_up = F.interpolate(conv4_up, scale_factor=2, mode='nearest') conv3_up = torch.cat((conv2_down, conv3_up), dim=1) conv3_up = self.dconv_up3(conv3_up) conv2_up = F.interpolate(conv3_up, scale_factor=2, mode='nearest') conv2_up = torch.cat((conv1_down, conv2_up), dim=1) conv2_up = self.dconv_up2(conv2_up) conv1_up = self.dconv_up1(conv2_up) if self.activation == 'sigmoid': x = torch.sigmoid(conv1_up) else: x = torch.tanh(conv1_up) return x class UNet(nn.Module): def __init__(self, channels_in, channels_out, chs=[8, 16, 32, 64, 128], image_width=64, image_height=64, noise_dim=10, activation='tanh', additive_noise=True): super().__init__() self.width = image_width self.height = image_height self.additive_noise = additive_noise self.activation = activation # noise projection layer if noise_dim is not None: if not additive_noise: self.project_noise = nn.Linear(noise_dim, image_width*image_height) self.dconv_down1 = double_conv(channels_in+1, chs[0]) else: self.project_noise = nn.Linear(noise_dim, channels_in*image_width*image_height) self.dconv_down1 = double_conv(channels_in, chs[0]) else: self.dconv_down1 = double_conv(channels_in, chs[0]) self.pool_down1 = nn.MaxPool2d(2, stride=2) self.dconv_down2 = double_conv(chs[0], chs[1]) self.pool_down2 = nn.MaxPool2d(2, stride=2) self.dconv_down3 = double_conv(chs[1], chs[2]) self.pool_down3 = nn.MaxPool2d(2, stride=2) self.dconv_down4 = double_conv(chs[2], chs[3]) self.pool_down4 = nn.MaxPool2d(2, stride=2) self.dconv_down5 = double_conv(chs[3], chs[4]) self.dconv_up5 = double_conv(chs[4]+chs[3], chs[4]) self.dconv_up4 = double_conv(chs[3]+chs[2], chs[3]) self.dconv_up3 = double_conv(chs[2]+chs[1], chs[2]) self.dconv_up2 = double_conv(chs[1]+chs[0], chs[1]) self.dconv_up1 = nn.Conv2d(chs[1], channels_out, kernel_size=1) def forward(self, x, z=None): if z is not None: if self.additive_noise: noise = self.project_noise(z).reshape(x.shape) x = x + noise else: noise = self.project_noise(z).reshape(x.shape[0], 1, x.shape[2], x.shape[3]) x = torch.cat((x, noise), dim=1) # concatenate along channel dimension conv1_down = self.dconv_down1(x) pool1 = self.pool_down1(conv1_down) conv2_down = self.dconv_down2(pool1) pool2 = self.pool_down2(conv2_down) conv3_down = self.dconv_down3(pool2) pool3 = self.pool_down3(conv3_down) conv4_down = self.dconv_down4(pool3) pool4 = self.pool_down4(conv4_down) conv5_down = self.dconv_down5(pool4) conv5_up = F.interpolate(conv5_down, scale_factor=2, mode='nearest') conv5_up = torch.cat((conv4_down, conv5_up), dim=1) conv5_up = self.dconv_up5(conv5_up) conv4_up = F.interpolate(conv4_down, scale_factor=2, mode='nearest') conv4_up = torch.cat((conv3_down, conv4_up), dim=1) conv4_up = self.dconv_up4(conv4_up) conv3_up = F.interpolate(conv3_down, scale_factor=2, mode='nearest') conv3_up = torch.cat((conv2_down, conv3_up), dim=1) conv3_up = self.dconv_up3(conv3_up) conv2_up = F.interpolate(conv2_down, scale_factor=2, mode='nearest') conv2_up = torch.cat((conv1_down, conv2_up), dim=1) conv2_up = self.dconv_up2(conv2_up) conv1_up = self.dconv_up1(conv2_up) if self.activation == 'sigmoid': x = torch.sigmoid(conv1_up) else: x = torch.tanh(conv1_up) return x

37.862434

195

0.629402

1,054

7,156

4.023719

0.102467

0.091252

0.05211

0.033954

0.80382

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0.785192

0.752888

0.723886

0.653619

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0.062569

0.245109

7,156

188

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false

0

0.021739

0.007246

0.094203

0

null

0

1

0

null

0

6

4bfa9c0cfb779d6b4fdbdd0f2917e034d14fb504

229

py

Python

src/mpls/mpls_controller.py

harpratap/nfv-mpls

bd7cb779a0ddf613f112fae860d149b7f8f0972f

[ "MIT" ]

null

src/mpls/mpls_controller.py

harpratap/nfv-mpls

bd7cb779a0ddf613f112fae860d149b7f8f0972f

[ "MIT" ]

null

src/mpls/mpls_controller.py

harpratap/nfv-mpls

bd7cb779a0ddf613f112fae860d149b7f8f0972f

[ "MIT" ]

null

class MPLSController: # host _host = None # port _port = None # has a label distribution protocol _label_distribution_protocol = None def getHost(self): return _host def getPort(self): return _port

13.470588

37

0.681223

27

229

5.518519

0.555556

0.228188

0.33557

0

0.262009

229

16

38

14.3125

0.881657

0.187773

0

1

0.25

false

0

0.25

1

0

1

0

null

1

0

null

0

1

0

1

0

6

ef6894bbfe2c86e66e2bbae6584d8f1c8bf63886

46

py

Python

vision/settings/__init__.py

JackGoldsworth/Vision

084330bec340596167944b623bc7b8d7d9c26b01

[ "MIT" ]

null

vision/settings/__init__.py

JackGoldsworth/Vision

084330bec340596167944b623bc7b8d7d9c26b01

[ "MIT" ]

1

2018-08-20T18:35:48.000Z

2019-01-10T02:56:12.000Z

vision/settings/__init__.py

JackGoldsworth/Vision

084330bec340596167944b623bc7b8d7d9c26b01

[ "MIT" ]

null

from .settings_handler import SettingsHandler

23

45

0.891304

5

46

8

1

0

0.086957

46

1

46

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

6

324770b7e7d379ad16c037efadb8859f7c70801f

25

py

Python

robotframework-ls/tests/robotframework_ls_tests/_resources/case_same_basename/directory/my_library.py

mardukbp/robotframework-lsp

57b4b2b14b712c9bf90577924a920fb9b9e831c7

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

92

2020-01-22T22:15:29.000Z

2022-03-31T05:19:16.000Z

robotframework-ls/tests/robotframework_ls_tests/_resources/case_same_basename/directory/my_library.py

mardukbp/robotframework-lsp

57b4b2b14b712c9bf90577924a920fb9b9e831c7

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

604

2020-01-25T17:13:27.000Z

2022-03-31T18:58:24.000Z

robotframework-ls/tests/robotframework_ls_tests/_resources/case_same_basename/directory/my_library.py

mardukbp/robotframework-lsp

57b4b2b14b712c9bf90577924a920fb9b9e831c7

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

39

2020-02-06T00:38:06.000Z

2022-03-15T06:14:19.000Z

def in_lib_2(): pass

8.333333

15

0.6

5

25

2.6

1

0

0.055556

0.28

25

2

16

12.5

0.666667

0

1

0.5

true

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

6

32479c50d88fc863dca5519fbbc0d501bea7ab11

91

py

Python

tools/dist_train.py

chetanmreddy/imvoxelnet

10dd35a96539af7b147be4bb03b0395cc164177e

[ "MIT" ]

1

2022-03-11T11:05:35.000Z

tools/dist_train.py

chetanmreddy/imvoxelnet

10dd35a96539af7b147be4bb03b0395cc164177e

[ "MIT" ]

null

tools/dist_train.py

chetanmreddy/imvoxelnet

10dd35a96539af7b147be4bb03b0395cc164177e

[ "MIT" ]

null

import os os.system('bash tools/dist_train.sh configs/imvoxelnet/imvoxelnet_scannet.py 2')

30.333333

80

0.824176

15

91

4.866667

0.866667

0

0.011765

0.065934

91

3

80

30.333333

0.847059

0

0.728261

0.434783

0

1

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true

0

0.5

0

0.5

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null

0

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0

1

null

0

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0

6

3270af60a9809cb2ae7836127962db973a4cdd5f

75

py

Python

bnf/test/fixtures/rules/__init__.py

Nikita-Boyarskikh/bnf

1293b0f2187593989e2484a7af9612477fa8bbe0

[ "MIT" ]

null

bnf/test/fixtures/rules/__init__.py

Nikita-Boyarskikh/bnf

1293b0f2187593989e2484a7af9612477fa8bbe0

[ "MIT" ]

null

bnf/test/fixtures/rules/__init__.py

Nikita-Boyarskikh/bnf

1293b0f2187593989e2484a7af9612477fa8bbe0

[ "MIT" ]

null

# flake8: noqa from .common import * from .llk import * from .lrk import *

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32b67ff4a1d3c45ab161c0d5ef092858421d702a

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py

Python

web3_erc20_predefined/predefined/bsc/__init__.py

kkristof200/py_web3_erc20_predefined

e95399bb14c61bb56e56f474937b0ace8565772b

[ "MIT" ]

null

web3_erc20_predefined/predefined/bsc/__init__.py

kkristof200/py_web3_erc20_predefined

e95399bb14c61bb56e56f474937b0ace8565772b

[ "MIT" ]

null

web3_erc20_predefined/predefined/bsc/__init__.py

kkristof200/py_web3_erc20_predefined

e95399bb14c61bb56e56f474937b0ace8565772b

[ "MIT" ]

null

from .busd import * from .wbnb import *

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6

32be6734f613b0b90f439ca6fdc9baa989e825d0

15,960

py

Python

tests/live_tests.py

gramedia-digital-nusantara/midtranspay

0367f8b261293e49ee8a6e395f6c44455212cf7b

[ "BSD-3-Clause" ]

6

2018-01-30T06:08:52.000Z

2021-02-15T12:41:40.000Z

tests/live_tests.py

derekjamescurtis/veritranspay

0367f8b261293e49ee8a6e395f6c44455212cf7b

[ "BSD-3-Clause" ]

8

2015-01-21T17:00:42.000Z

2017-07-06T05:26:30.000Z

tests/live_tests.py

derekjamescurtis/veritranspay

0367f8b261293e49ee8a6e395f6c44455212cf7b

[ "BSD-3-Clause" ]

6

2015-07-21T16:49:57.000Z

2017-07-05T07:55:35.000Z

import random import unittest import os import requests from requests import codes import veritranspay from veritranspay import request, veritrans, payment_types, response from veritranspay.response import status from . import fixtures from faker import Faker fake = Faker() SANDBOX_CLIENT_KEY = os.environ.get('SANDBOX_CLIENT_KEY', None) SANDBOX_SERVER_KEY = os.environ.get('SANDBOX_SERVER_KEY', None) RUN_ALL_ACCEPTANCE_TESTS = os.environ.get('RUN_ALL_ACCEPTANCE_TESTS', False) class LiveTests_Base(object): def setUp(self): if None in [SANDBOX_CLIENT_KEY, SANDBOX_SERVER_KEY]: self.skipTest("Live credentials not provided -- skipping tests") if not RUN_ALL_ACCEPTANCE_TESTS and \ self.VERSION != veritranspay.__version__: self.skipTest("Skipping this version of tests") expected = fixtures.CC_REQUEST self.expected = expected self.trans_details = request.TransactionDetails( order_id=expected['transaction_details']['order_id'], gross_amount=expected['transaction_details']['gross_amount']) self.cust_details = request.CustomerDetails( first_name=expected['customer_details']['first_name'], last_name=expected['customer_details']['last_name'], email=expected['customer_details']['email'], phone=expected['customer_details']['phone'], billing_address=request.Address( **expected['customer_details']['billing_address']), shipping_address=request.Address( **expected['customer_details']['shipping_address']) ) self.item_details = \ [request.ItemDetails(item_id=item['id'], price=item['price'], quantity=item['quantity'], name=item['name']) for item in expected['item_details']] def get_token(self, cc_num, client_key, secure=False): # try to get a token params = {'card_number': cc_num, 'card_exp_month': '12', 'card_exp_year': '2020', 'card_cvv': '123', 'secure': secure, 'gross_amount': 145000, 'client_key': client_key, } token_url = 'https://api.sandbox.midtrans.com/v2/token' resp = requests.get(token_url, params=params) if resp.status_code == codes.OK: return resp.json()['token_id'] else: self.fail("Failed retrieving token from server") class AcceptanceTests_v0_4(LiveTests_Base, unittest.TestCase): VERSION = 'v0.4' def test_success_cc_charge_request(self): # 1: get a token # on live, this step --MUST-- be performed by the web # application through the javascript library. token = self.get_token( random.choice(fixtures.CC_ACCEPTED), SANDBOX_CLIENT_KEY) # 2: Create a sandbox gateway gateway = veritrans.VTDirect( SANDBOX_SERVER_KEY, sandbox_mode=True) # 3: Create a charge request cc_payment = payment_types.CreditCard( bank=self.expected['credit_card']['bank'], token_id=token) charge_req = request.ChargeRequest( charge_type=cc_payment, transaction_details=self.trans_details, customer_details=self.cust_details, item_details=self.item_details) # 4: Submit our request resp = gateway.submit_charge_request(charge_req) self.assertIsInstance(resp, response.CreditCardChargeResponse) self.assertEqual(status.SUCCESS, resp.status_code) class AcceptanceTests_v0_5(LiveTests_Base, unittest.TestCase): VERSION = 'v0.5' def test_accept_challenged_charge_request(self): ''' Verify that we can accept challenged charge requests. ''' # 1: get a token # on live, this step --MUST-- be performed by the web # application through the javascript library. token = self.get_token( random.choice(fixtures.CC_CHALLENGED_FDS), SANDBOX_CLIENT_KEY) # 2: Create a sandbox gateway gateway = veritrans.VTDirect( SANDBOX_SERVER_KEY, sandbox_mode=True) # 3: Create a charge request cc_payment = payment_types.CreditCard( bank=self.expected['credit_card']['bank'], token_id=token) charge_req = request.ChargeRequest( charge_type=cc_payment, transaction_details=self.trans_details, customer_details=self.cust_details, item_details=self.item_details) # 4: Submit charge request # - verify we get a status_code of CHALLENGE back # - verify that we are returned a CreditCardChargeResponse resp = gateway.submit_charge_request(charge_req) self.assertIsInstance(resp, response.CreditCardChargeResponse) self.assertEqual(status.CHALLENGE, resp.status_code) # 5: Lookup the status of the transaction using the response # - verify can use CreditCareChargeResponse can as a StatusRequest # - verify we get a StatusResponse back # - verify the status_code is still CHALLENGE status_resp = gateway.submit_status_request(resp) self.assertIsInstance(status_resp, response.StatusResponse) self.assertEqual(status_resp.status_code, status.CHALLENGE) # 6: Approve the transaction! # - verify can build an ApprovalRequest # - verify we get an ApprovalResponse back # - verify the status_code is now SUCCESS approval_req = request.ApprovalRequest( status_resp.order_id) approval_resp = gateway.submit_approval_request( approval_req) self.assertIsInstance(approval_resp, response.ApproveResponse) self.assertEqual(approval_resp.status_code, status.SUCCESS) class AcceptanceTests_v0_6(LiveTests_Base, unittest.TestCase): VERSION = '0.9' def test_one_click(self): pass def test_two_click(self): pass def test_preauth_capture(self): pass class PermataVA_AcceptanceTests_v0_9(unittest.TestCase): VERSION = '0.9' def setUp(self): if None in [SANDBOX_CLIENT_KEY, SANDBOX_SERVER_KEY]: self.skipTest("Live credentials not provided -- skipping tests") if not RUN_ALL_ACCEPTANCE_TESTS and \ self.VERSION != veritranspay.__version__: self.skipTest("Skipping %s this version of tests" % (self.VERSION)) expected = fixtures.VIRTUALACCOUNTPERMATA_REQUEST self.expected = expected self.trans_details = request.TransactionDetails( order_id=expected['transaction_details']['order_id'], gross_amount=expected['transaction_details']['gross_amount']) self.cust_details = request.CustomerDetails( first_name=expected['customer_details']['first_name'], last_name=expected['customer_details']['last_name'], email=expected['customer_details']['email'], phone=expected['customer_details']['phone'], billing_address=request.Address( **expected['customer_details']['billing_address']), shipping_address=request.Address( **expected['customer_details']['shipping_address']) ) self.item_details = \ [request.ItemDetails(item_id=item['id'], price=item['price'], quantity=item['quantity'], name=item['name']) for item in expected['item_details']] def test_virtualaccountpermata(self): """ Verify Permata Virtual Account """ trans_details = self.trans_details trans_details.order_id = "".join([fake.random_letter() for _ in range(10)]) # 2: Create a sandbox gateway gateway = veritrans.VTDirect( SANDBOX_SERVER_KEY, sandbox_mode=True) # 3: Create a charge request payment = payment_types.VirtualAccountPermata() charge_req = request.ChargeRequest( charge_type=payment, transaction_details=trans_details, customer_details=self.cust_details, item_details=self.item_details) # 4: Submit our request resp = gateway.submit_charge_request(charge_req) self.assertIsInstance(resp, response.VirtualAccountPermataChargeResponse) self.assertEqual(status.PENDING, resp.status_code) self.assertEqual(self.trans_details.order_id, resp.order_id) class BriEpay_AcceptanceTests_v0_9(unittest.TestCase): VERSION = '0.9' def setUp(self): if None in [SANDBOX_CLIENT_KEY, SANDBOX_SERVER_KEY]: self.skipTest("Live credentials not provided -- skipping tests") if not RUN_ALL_ACCEPTANCE_TESTS and \ self.VERSION != veritranspay.__version__: self.skipTest("Skipping %s this version of tests" % self.VERSION) expected = fixtures.BRIEPAY_REQUEST self.expected = expected self.trans_details = request.TransactionDetails( order_id=expected['transaction_details']['order_id'], gross_amount=expected['transaction_details']['gross_amount']) self.cust_details = request.CustomerDetails( first_name=expected['customer_details']['first_name'], last_name=expected['customer_details']['last_name'], email=expected['customer_details']['email'], phone=expected['customer_details']['phone'], billing_address=request.Address( **expected['customer_details']['billing_address']), shipping_address=request.Address( **expected['customer_details']['shipping_address']) ) self.item_details = \ [request.ItemDetails(item_id=item['id'], price=item['price'], quantity=item['quantity'], name=item['name']) for item in expected['item_details']] def test_briepay(self): """ Verify Bri Epay payment method """ # 2: Create a sandbox gateway gateway = veritrans.VTDirect( SANDBOX_SERVER_KEY, sandbox_mode=True) # 3: Create a charge request payment = payment_types.BriEpay() charge_req = request.ChargeRequest( charge_type=payment, transaction_details=self.trans_details, customer_details=self.cust_details, item_details=self.item_details) # 4: Submit our request resp = gateway.submit_charge_request(charge_req) self.assertIsInstance(resp, response.EpayBriChargeResponse) self.assertEqual(status.PENDING, resp.status_code) self.assertEqual(self.trans_details.order_id, resp.order_id) class MandiriVA_AcceptanceTests_v0_9(unittest.TestCase): VERSION = '0.9' def setUp(self): if None in [SANDBOX_CLIENT_KEY, SANDBOX_SERVER_KEY]: self.skipTest("Live credentials not provided -- skipping tests") if not RUN_ALL_ACCEPTANCE_TESTS and \ self.VERSION != veritranspay.__version__: self.skipTest("Skipping %s this version of tests" % self.VERSION) expected = fixtures.VIRTUALACCOUNTMANDIRI_REQUEST self.expected = expected self.trans_details = request.TransactionDetails( order_id="".join([fake.random_letter() for _ in range(10)]), gross_amount=expected['transaction_details']['gross_amount']) self.cust_details = request.CustomerDetails( first_name=expected['customer_details']['first_name'], last_name=expected['customer_details']['last_name'], email=expected['customer_details']['email'], phone=expected['customer_details']['phone'], billing_address=request.Address( **expected['customer_details']['billing_address']), shipping_address=request.Address( **expected['customer_details']['shipping_address']) ) self.item_details = \ [request.ItemDetails(item_id=item['id'], price=item['price'], quantity=item['quantity'], name=item['name']) for item in expected['item_details']] def test_virtual_account_mandiri(self): """ Verify mandiri bill payment :return: """ # 2: Create a sandbox gateway gateway = veritrans.VTDirect( SANDBOX_SERVER_KEY, sandbox_mode=True) # 3: Create a charge request payment = payment_types.VirtualAccountMandiri(bill_info1=self.expected['echannel']['bill_info1'], bill_info2=self.expected['echannel']['bill_info2']) charge_req = request.ChargeRequest( charge_type=payment, transaction_details=self.trans_details, customer_details=self.cust_details, item_details=self.item_details) # 4: Submit our request resp = gateway.submit_charge_request(charge_req) self.assertIsInstance(resp, response.VirtualAccountMandiriChargeResponse) self.assertEqual(status.PENDING, resp.status_code) self.assertEqual(self.trans_details.order_id, resp.order_id) class GoPay_AcceptanceTests_v0_9(unittest.TestCase): VERSION = '0.9' def setUp(self): if None in [SANDBOX_CLIENT_KEY, SANDBOX_SERVER_KEY]: self.skipTest("Live credentials not provided -- skipping tests") if not RUN_ALL_ACCEPTANCE_TESTS and \ self.VERSION != veritranspay.__version__: self.skipTest("Skipping %s this version of tests" % self.VERSION) expected = fixtures.GOPAY_REQUEST self.expected = expected self.trans_details = request.TransactionDetails( order_id=expected['transaction_details']['order_id'], gross_amount=expected['transaction_details']['gross_amount']) self.cust_details = request.CustomerDetails( first_name=expected['customer_details']['first_name'], last_name=expected['customer_details']['last_name'], email=expected['customer_details']['email'], phone=expected['customer_details']['phone'], ) self.item_details = \ [request.ItemDetails(item_id=item['id'], price=item['price'], quantity=item['quantity'], name=item['name']) for item in expected['item_details']] def test_gopay(self): """ Verify GoPay payment method """ # 2: Create a sandbox gateway gateway = veritrans.VTDirect( SANDBOX_SERVER_KEY, sandbox_mode=True) # 3: Create a charge request payment = payment_types.GoPay() charge_req = request.ChargeRequest( charge_type=payment, transaction_details=self.trans_details, customer_details=self.cust_details, item_details=self.item_details) # 4: Submit our request resp = gateway.submit_charge_request(charge_req) self.assertIsInstance(resp, response.GoPayChargeResponse) self.assertEqual(status.PENDING, resp.status_code) self.assertEqual(self.trans_details.order_id, resp.order_id)

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null

0

6

08ab2cc86667f7b5599664a0dbe1be1b56370be8

123

py

Python

app/improving_agent/models/any_type.py

brettasmi/EvidARA

319bbe80ddb4d7d6aa4f1db005ad5461e015a8bc

[ "MIT" ]

null

app/improving_agent/models/any_type.py

brettasmi/EvidARA

319bbe80ddb4d7d6aa4f1db005ad5461e015a8bc

[ "MIT" ]

5

2020-06-25T21:47:50.000Z

2020-07-15T01:22:51.000Z

app/improving_agent/models/any_type.py

suihuanglab/evidARA

cf5b8bbdb9f90136c66b58c694acf2efc18ffc22

[ "MIT" ]

1

2020-03-23T10:39:59.000Z

class AnyType: def __init__(self): pass @staticmethod def get_value(self, value): return value

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123

5

0.714286

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0.317073

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false

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1

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null

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1

0

1

0

1

0

6

08d448164d3290e329010a8b9202706276607c36

94

py

Python

terrascript/ovh/__init__.py

amlodzianowski/python-terrascript

1111affe6cd30d9b8b7bc74ae4e27590f7d4dc49

[ "BSD-2-Clause" ]

null

terrascript/ovh/__init__.py

amlodzianowski/python-terrascript

1111affe6cd30d9b8b7bc74ae4e27590f7d4dc49

[ "BSD-2-Clause" ]

null

terrascript/ovh/__init__.py

amlodzianowski/python-terrascript

1111affe6cd30d9b8b7bc74ae4e27590f7d4dc49

[ "BSD-2-Clause" ]

null

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

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11

94

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7

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true

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0

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null

0

1

0

1

0

6

08efeff1de865af9ab4a8f08fe438929fb0a9876

42

py

Python

packaging_tutorial/example_pkg/hello_module.py

HFM3/pypi-template

d9407c0171f48dd35c06598600f6299850ea69a2

[ "Unlicense" ]

null

packaging_tutorial/example_pkg/hello_module.py

HFM3/pypi-template

d9407c0171f48dd35c06598600f6299850ea69a2

[ "Unlicense" ]

null

packaging_tutorial/example_pkg/hello_module.py

HFM3/pypi-template

d9407c0171f48dd35c06598600f6299850ea69a2

[ "Unlicense" ]

null

def hello_function(): print('HELLO!')

14

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null

0

1

0

1

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6

41262714c6da19e2b65742beb459a14e0a215487

86

py

Python

code/planner/__init__.py

jeguzzi/resilience

f6fb8be52a7f5a9dbf755ff7dbd5f8117c802a30

[ "MIT" ]

6

2019-05-23T22:52:56.000Z

2021-09-02T08:52:23.000Z

code/planner/__init__.py

hebinbing/resilience

f6fb8be52a7f5a9dbf755ff7dbd5f8117c802a30

[ "MIT" ]

null

code/planner/__init__.py

hebinbing/resilience

f6fb8be52a7f5a9dbf755ff7dbd5f8117c802a30

[ "MIT" ]

3

2019-10-17T07:51:16.000Z

2022-02-09T12:51:58.000Z

from .planner_abs import Planner from .planner_doors import DoorPlanner, CellPlanner

21.5

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86

6.454545

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1

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6

f5afd9072ed77cdc8ad81fa524a5eca4d50dc10b

121

py

Python

app/cogs/settings/__init__.py

fossabot/Starboard-2

798e2d04995ae7d920e76708b9ea8fae6f4af319

[ "MIT" ]

16

2021-01-19T19:12:00.000Z

2021-12-21T12:00:04.000Z

app/cogs/settings/__init__.py

Davi-the-Mudkip/Starboard-2

4de3c689ffef007e4f4a279251d107d890b69b15

[ "MIT" ]

15

2021-04-02T16:58:48.000Z

2022-03-28T06:09:49.000Z

app/cogs/settings/__init__.py

Davi-the-Mudkip/Starboard-2

4de3c689ffef007e4f4a279251d107d890b69b15

[ "MIT" ]

13

2021-01-21T14:26:00.000Z

2021-09-29T18:55:17.000Z

from app.classes.bot import Bot from . import settings_commands def setup(bot: Bot): settings_commands.setup(bot)

15.125

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1

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6

eb3c984a796a7c011b62d9b99c9febb0c58dd8b4

92

py

Python

pentagon/component/vpn/__init__.py

supaflysnooka/pentagon

7431cc29a80e090172b78abdf12d5da54d7f2455

[ "Apache-2.0" ]

null

pentagon/component/vpn/__init__.py

supaflysnooka/pentagon

7431cc29a80e090172b78abdf12d5da54d7f2455

[ "Apache-2.0" ]

null

pentagon/component/vpn/__init__.py

supaflysnooka/pentagon

7431cc29a80e090172b78abdf12d5da54d7f2455

[ "Apache-2.0" ]

null

from pentagon.component import ComponentBase import os class Vpn(ComponentBase): pass

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6.636364

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0.163043

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true

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0.5

0

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0

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0

null

0

1

0

null

0

1

0

1

0

6

de9920dae17bd40f291f9901725aac008020d82a

153

py

Python

01_basic-python/solution_condition.py

utecx/py-algorithms-4-automotive-engineering

45fa443b721efe6d887aaeeeae9b6867d71f2677

[ "MIT" ]

47

2020-04-20T14:12:20.000Z

2022-03-02T15:26:59.000Z

01_basic-python/solution_condition.py

utecx/py-algorithms-4-automotive-engineering

45fa443b721efe6d887aaeeeae9b6867d71f2677

[ "MIT" ]

6

2019-08-08T05:15:44.000Z

2020-03-27T09:39:06.000Z

01_basic-python/solution_condition.py

utecx/py-algorithms-4-automotive-engineering

45fa443b721efe6d887aaeeeae9b6867d71f2677

[ "MIT" ]

65

2019-07-01T06:09:48.000Z

2022-03-08T18:37:45.000Z

def check_value(x): if x == 0: print("Is zero", x) elif x > 0: print("Is positive", x) else: print("Is negative", x)

19.125

31

0.477124

23

153

3.130435

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0.194444

0.25

0

0.020619

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153

7

32

21.857143

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0

null

1

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null

0

1

0

6

dedf7a9d3dec8145688e43976274b55f740b23ce

72

py

Python

citizenscience/user/__init__.py

otwn/citizenscience

24ed9652ff896d46c7c7da9530c51fbd451d14ae

[ "BSD-3-Clause" ]

1

2021-07-06T19:44:15.000Z

fbone/user/__init__.py

pyeliteman/PDF-OCR-RTP

1833cda366ece33e6f6850dabec029d6a1502c74

[ "Apache-2.0" ]

null

fbone/user/__init__.py

pyeliteman/PDF-OCR-RTP

1833cda366ece33e6f6850dabec029d6a1502c74

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- from models import User from views import user

14.4

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1

0

1

0

6

dee61ee2bac8e2e1b692011d32e985f47e9a5049

21

py

Python

src/metricas/__init__.py

JLuisRojas/reconocimiento-de-voz

59282ffd6841f22e514a7055cb4d20ef97181b90

[ "MIT" ]

1

2021-12-03T00:01:09.000Z

src/metricas/__init__.py

JLuisRojas/reconocimiento-de-voz

59282ffd6841f22e514a7055cb4d20ef97181b90

[ "MIT" ]

2

2021-04-30T21:11:01.000Z

2021-08-25T16:00:42.000Z

src/metricas/__init__.py

JLuisRojas/reconocimiento-de-voz

59282ffd6841f22e514a7055cb4d20ef97181b90

[ "MIT" ]

null

from .wer import wer

10.5

20

0.761905

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4

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1

21

0.941176

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null

0

1

0

1

0

1

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6

720178181ed5bcab6b3dc4901a38c870b21d2e79

180

py

Python

yys/RegisterKeyModule.py

yangxu0110/yysScript

079101f57fb1a64b871924c988760d9e74063a71

[ "Apache-2.0" ]

62

2019-09-28T14:07:22.000Z

2022-02-25T05:54:47.000Z

yys/RegisterKeyModule.py

fishtank666/yysScript

079101f57fb1a64b871924c988760d9e74063a71

[ "Apache-2.0" ]

6

2019-11-12T11:08:36.000Z

2020-11-25T10:40:52.000Z

yys/RegisterKeyModule.py

fishtank666/yysScript

079101f57fb1a64b871924c988760d9e74063a71

[ "Apache-2.0" ]

24

2019-10-12T02:21:39.000Z

2021-11-13T07:32:25.000Z

# 生成激活码模块 class RegisterKeyUtil: def ValidateUserKey(userkey: str) -> bool: # todo pass def CreateUserKey(path: str) -> bool: # todo pass

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null

0

1

0

1

0

1

0

6

720b306ddd21576c4aaf2e51f6d4e052c293d0c6

28

py

Python

mcpi_functions.py

leha-code/mcpi-zero

fae9dbe1677fca64b9cc617e3a06858de2e220fd

[ "MIT" ]

2

2021-01-27T00:23:39.000Z

2021-01-27T15:46:38.000Z

mcpi_functions.py

mcpiscript/mcpi-zero

fae9dbe1677fca64b9cc617e3a06858de2e220fd

[ "MIT" ]

null

mcpi_functions.py

mcpiscript/mcpi-zero

fae9dbe1677fca64b9cc617e3a06858de2e220fd

[ "MIT" ]

null

def coming_soon(): pass

9.333333

18

0.642857

4

28

4.25

1

0

0.25

28

2

19

14

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0

1

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true

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

6

9d4542b4ee808d471aff9ee0b50fdfe05183bd98

48

py

Python

wrappers/__init__.py

kylestach/learn-you-a-soccer

e4163602ffb73b51d1b9ab35e75f2033f7179fff

[ "MIT" ]

24

2020-01-12T08:20:36.000Z

2022-03-17T13:07:30.000Z

wrappers/__init__.py

kylestach/learn-you-a-soccer

e4163602ffb73b51d1b9ab35e75f2033f7179fff

[ "MIT" ]

null

wrappers/__init__.py

kylestach/learn-you-a-soccer

e4163602ffb73b51d1b9ab35e75f2033f7179fff

[ "MIT" ]

10

2020-01-21T04:43:20.000Z

2021-11-02T02:50:56.000Z

# __init__.py from .normalized_actions import *

16

33

0.791667

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48

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0.125

48

2

34

24

0.785714

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1

0

true

0

1

0

1

0

1

0

null

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1

0

null

0

1

0

1

0

1

0

6

c2105be514f22bc8e73aa96bf2f2ec1e97ac1ed5

115

py

Python

annotate/admin.py

henryyang42/lifelog_annotation

586f44132508f59e97dda701bd5602d26b79a6f4

[ "MIT" ]

null

annotate/admin.py

henryyang42/lifelog_annotation

586f44132508f59e97dda701bd5602d26b79a6f4

[ "MIT" ]

null

annotate/admin.py

henryyang42/lifelog_annotation

586f44132508f59e97dda701bd5602d26b79a6f4

[ "MIT" ]

null

from django.contrib import admin from .models import * admin.site.register(Entry) admin.site.register(Annotation)

19.166667

32

0.808696

16

115

5.8125

0.625

0.236559

0.365591

0

0.095652

115

5

33

23

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0

1

0

true

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1

0

1

0

null

0

1

0

1

0

6

dfa90ccdbd251b8efecf8574615a12d9633f01e4

114

py

Python

KFR/build/examples/Debug/audio_low_quality.py

Asifadam93/FiltreMusical

dcd53bc41934f219fb9b3d5aef281099fb572a49

[ "BSD-3-Clause" ]

null

KFR/build/examples/Debug/audio_low_quality.py

Asifadam93/FiltreMusical

dcd53bc41934f219fb9b3d5aef281099fb572a49

[ "BSD-3-Clause" ]

null

KFR/build/examples/Debug/audio_low_quality.py

Asifadam93/FiltreMusical

dcd53bc41934f219fb9b3d5aef281099fb572a49

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python import dspplot dspplot.plot(r'audio_low_quality.wav', file='../svg/audio_low_quality.svg')

22.8

75

0.763158

19

114

4.368421

0.736842

0.192771

0.361446

0

0.061404

114

4

76

28.5

0.775701

0.175439

0

0.526882

0

1

0

true

0

0.5

0

0.5

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1

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null

0

1

0

1

0

1

null

0

1

0

1

0

6

a05ec202e173da996b727c6d7156b8f343c384d1

131

py

Python

ct/preprocess/__init__.py

ViktorStagge/CompressiveTransformer

644b363b1314f4f10c803ff4f014ff6d1a238fad

[ "MIT" ]

2

2020-10-26T10:08:37.000Z

2021-07-02T02:21:35.000Z

ct/preprocess/__init__.py

ViktorStagge/CompressiveTransformer

644b363b1314f4f10c803ff4f014ff6d1a238fad

[ "MIT" ]

null

ct/preprocess/__init__.py

ViktorStagge/CompressiveTransformer

644b363b1314f4f10c803ff4f014ff6d1a238fad

[ "MIT" ]

null

from ct.preprocess.tokenize import Tokenizer from ct.preprocess.dataset import preprocess from ct.preprocess.wma import wma as wma

32.75

44

0.847328

20

131

5.55

0.45

0.162162

0.432432

0

0.10687

131

3

45

43.666667

0.948718

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

a0a7c71bca46cc9409ebf4d801ee0d1ce28d9d6d

60

py

Python

products_app/Reset.py

sergiomastro/inventory-mgmt-app-py-master

56b89a11bec453537657a7ea10f51fa91307bf2d

[ "MIT" ]

1

2018-06-07T03:58:15.000Z

products_app/Reset.py

sergiomastro/inventory-mgmt-app-py-master

56b89a11bec453537657a7ea10f51fa91307bf2d

[ "MIT" ]

null

products_app/Reset.py

sergiomastro/inventory-mgmt-app-py-master

56b89a11bec453537657a7ea10f51fa91307bf2d

[ "MIT" ]

null

from app import reset_products_file reset_products_file()

20

36

0.85

9

60

5.222222

0.666667

0.553191

0.723404

0

0.116667

60

3

37

20

0.886792

0

1

0

true

0

0.5

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

a0b34aef92f144b3ba9e7de5897ec189e97871eb

4,511

py

Python

cybox/common/datetimewithprecision.py

siemens/python-cybox

b692a98c8a62bd696e2a0dda802ada7359853482

[ "BSD-3-Clause" ]

null

cybox/common/datetimewithprecision.py

siemens/python-cybox

b692a98c8a62bd696e2a0dda802ada7359853482

[ "BSD-3-Clause" ]

null

cybox/common/datetimewithprecision.py

siemens/python-cybox

b692a98c8a62bd696e2a0dda802ada7359853482

[ "BSD-3-Clause" ]

1

2019-04-16T18:37:32.000Z

# Copyright (c) 2014, The MITRE Corporation. All rights reserved. # See LICENSE.txt for complete terms. import cybox import cybox.bindings.cybox_common as common_binding import dateutil from datetime import datetime DATE_PRECISION_VALUES = ("year", "month", "day") TIME_PRECISION_VALUES = ("hour", "minute", "second") DATETIME_PRECISION_VALUES = DATE_PRECISION_VALUES + TIME_PRECISION_VALUES def parse_value(value): if not value: return None elif isinstance(value, datetime): return value return dateutil.parser.parse(value) def serialize_value(value): if not value: return None return value.isoformat() class DateTimeWithPrecision(cybox.Entity): _binding = common_binding _binding_class = common_binding.DateTimeWithPrecisionType _namespace = 'http://cybox.mitre.org/common-2' def __init__(self, value=None, precision='second'): super(DateTimeWithPrecision, self).__init__() self.value = value self.precision = precision @property def value(self): return self._value @value.setter def value(self, value): self._value = parse_value(value) @property def precision(self): return self._precision @precision.setter def precision(self, value): if value not in DATETIME_PRECISION_VALUES: raise ValueError("value must be one of [%s]" % ", ".join(x for x in DATETIME_PRECISION_VALUES)) self._precision = value def to_obj(self, return_obj=None, ns_info=None): self._collect_ns_info(ns_info) obj = self._binding_class() obj.valueOf_ = serialize_value(self.value) obj.precision = self._precision return obj @classmethod def from_obj(cls, obj): if not obj: return None return_obj = cls() return_obj.value = obj.valueOf_ return_obj.precision = obj.precision return return_obj def to_dict(self): value = serialize_value(self.value) if self.precision == 'second': return value dict_ = {} dict_['precision'] = self.precision dict_['value'] = value return dict_ @classmethod def from_dict(cls, dict_): if not dict_: return None return_obj = cls() if not isinstance(dict_, dict): return_obj.value = dict_ else: return_obj.precision = dict_.get('precision') return_obj.value = dict_.get('value') return return_obj class DateWithPrecision(cybox.Entity): _binding = common_binding _binding_class = common_binding.DateWithPrecisionType _namespace = 'http://cybox.mitre.org/common-2' def __init__(self, value=None, precision='day'): super(DateWithPrecision, self).__init__() self.value = value self.precision = precision @property def value(self): return self._value @value.setter def value(self, value): self._value = parse_value(value) if isinstance(self._value, datetime): self._value = self._value.date() @property def precision(self): return self._precision @precision.setter def precision(self, value): if value not in DATE_PRECISION_VALUES: raise ValueError("value must be one of [%s]" % ", ".join(x for x in DATE_PRECISION_VALUES)) self._precision = value def to_obj(self, return_obj=None, ns_info=None): self._collect_ns_info(ns_info) obj = self._binding_class() obj.valueOf_ = serialize_value(self.value) obj.precision = self._precision return obj @classmethod def from_obj(cls, obj): if not obj: return None return_obj = cls() return_obj.value = obj.valueOf_ return_obj.precision = obj.precision return return_obj def to_dict(self): value = serialize_value(self.value) if self.precision == 'day': return value dict_ = {} dict_['precision'] = self.precision dict_['value'] = value return dict_ @classmethod def from_dict(cls, dict_): if not dict_: return None return_obj = cls() if not isinstance(dict_, dict): return_obj.value = dict_ else: return_obj.precision = dict_.get('precision') return_obj.value = dict_.get('value') return return_obj

26.85119

107

0.630459

526

4,511

5.15019

0.155894

0.07309

0.046512

0.033961

0.761905

0.739756

0.698413

0

0.001843

0.278431

4,511

167

108

27.011976

0.830415

0.021946

0

0.782946

0

0.049444

0

1

0.155039

false

0

0.031008

0.426357

0

null

0

1

0

1

0

null

0

6

a0bfd6ab0ca423ed8a3ab37f8d4db02c20997664

51

py

Python

cfdm/core/meta/__init__.py

tsjackson-noaa/cfdm

a669677905badaced2eba87413288ac0bc2697fc

[ "MIT" ]

22

2018-11-07T18:16:22.000Z

2022-03-16T16:05:21.000Z

cfdm/core/meta/__init__.py

tsjackson-noaa/cfdm

a669677905badaced2eba87413288ac0bc2697fc

[ "MIT" ]

119

2019-04-08T08:00:24.000Z

2022-03-22T08:21:22.000Z

cfdm/core/meta/__init__.py

tsjackson-noaa/cfdm

a669677905badaced2eba87413288ac0bc2697fc

[ "MIT" ]

8

2019-04-09T10:12:26.000Z

2021-07-22T02:41:15.000Z

from .docstringrewrite import DocstringRewriteMeta

25.5

50

0.901961

4

51

11.5

1

0

0.078431

51

1

51

0.978723

0

1

0

true

0

1

0

1

0

1

0

1

null

0

1

0

null

0

1

0

1

0

1

0

6

cd07214a2b2bb586fbbe30af40b7635992db8cc6

72

py

Python

glue/dialogs/link_editor/qt/__init__.py

sergiopasra/glue

c25a217a122a11818382672c99cb21f57a30636f

[ "BSD-3-Clause" ]

null

glue/dialogs/link_editor/qt/__init__.py

sergiopasra/glue

c25a217a122a11818382672c99cb21f57a30636f

[ "BSD-3-Clause" ]

null

glue/dialogs/link_editor/qt/__init__.py

sergiopasra/glue

c25a217a122a11818382672c99cb21f57a30636f

[ "BSD-3-Clause" ]

null

from .link_editor import * # noqa from .link_equation import * # noqa

24

36

0.722222

10

72

5

0.6

0.32

0

0.194444

72

2

37

36

0.862069

0.125

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

cd855c4e472af9a7d55f8517ec75030ac037e339

6,819

py

Python

test/test_modbus_slave.py

eydam-prototyping/mp_modbus

8007c41dd16e6f71bd27b587628f57f38f27a7e0

[ "MIT" ]

2

2022-01-06T02:21:16.000Z

2022-03-08T07:55:43.000Z

test/test_modbus_slave.py

eydam-prototyping/mp_modbus

8007c41dd16e6f71bd27b587628f57f38f27a7e0

[ "MIT" ]

2

2021-12-10T15:56:52.000Z

2022-02-19T23:45:24.000Z

test/test_modbus_slave.py

eydam-prototyping/mp_modbus

8007c41dd16e6f71bd27b587628f57f38f27a7e0

[ "MIT" ]

3

2021-07-30T11:16:55.000Z

2022-01-05T18:19:55.000Z

import unittest from mp_modbus_slave import modbus_tcp_server from mp_modbus_frame import modbus_tcp_frame class Test(unittest.TestCase): def test_server_handle_message_1(self): srv = modbus_tcp_server("", 0, context={"co":{"startAddr": 1000, "registers": bytearray([0xFF, 0x00, 0x00, 0x00]*5)}}) msg = modbus_tcp_frame(transaction_id=1, unit_id=2, func_code=1, register=1000, length=2, fr_type="request") self.assertEqual(srv.handle_message(msg).data, bytearray([0x01])) msg = modbus_tcp_frame(transaction_id=1, unit_id=2, func_code=1, register=1001, length=2, fr_type="request") self.assertEqual(srv.handle_message(msg).data, bytearray([0x02])) msg = modbus_tcp_frame(transaction_id=1, unit_id=2, func_code=1, register=1000, length=3, fr_type="request") self.assertEqual(srv.handle_message(msg).data, bytearray([0x05])) def test_server_handle_message_2(self): srv = modbus_tcp_server("", 0, context={"di":{"startAddr": 1000, "registers": bytearray([0xFF, 0x00, 0x00, 0x00]*5)}}) msg = modbus_tcp_frame(transaction_id=1, unit_id=2, func_code=2, register=1000, length=2, fr_type="request") self.assertEqual(srv.handle_message(msg).data, bytearray([0x01])) msg = modbus_tcp_frame(transaction_id=1, unit_id=2, func_code=2, register=1001, length=2, fr_type="request") self.assertEqual(srv.handle_message(msg).data, bytearray([0x02])) msg = modbus_tcp_frame(transaction_id=1, unit_id=2, func_code=2, register=1000, length=3, fr_type="request") self.assertEqual(srv.handle_message(msg).data, bytearray([0x05])) def test_server_handle_message_3(self): srv = modbus_tcp_server("", 0, context={"hr":{"startAddr": 1000, "registers": bytearray([0x00, 0x01, 0x02, 0x03, 0x04, 0x05, 0x06, 0x07, 0x08, 0x09, 0x0A, 0x0B, 0x0C, 0x0D, 0x0E, 0x0F])}}) msg = modbus_tcp_frame(transaction_id=1, unit_id=2, func_code=3, register=1000, length=2, fr_type="request") self.assertEqual(srv.handle_message(msg).data, bytearray([0x00, 0x01, 0x02, 0x03])) msg = modbus_tcp_frame(transaction_id=1, unit_id=2, func_code=3, register=1001, length=2, fr_type="request") self.assertEqual(srv.handle_message(msg).data, bytearray([0x02, 0x03, 0x04, 0x05])) msg = modbus_tcp_frame(transaction_id=1, unit_id=2, func_code=3, register=1000, length=3, fr_type="request") self.assertEqual(srv.handle_message(msg).data, bytearray([0x00, 0x01, 0x02, 0x03, 0x04, 0x05])) def test_server_handle_message_4(self): srv = modbus_tcp_server("", 0, context={"ir":{"startAddr": 1000, "registers": bytearray([0x00, 0x01, 0x02, 0x03, 0x04, 0x05, 0x06, 0x07, 0x08, 0x09, 0x0A, 0x0B, 0x0C, 0x0D, 0x0E, 0x0F])}}) msg = modbus_tcp_frame(transaction_id=1, unit_id=2, func_code=4, register=1000, length=2, fr_type="request") self.assertEqual(srv.handle_message(msg).data, bytearray([0x00, 0x01, 0x02, 0x03])) msg = modbus_tcp_frame(transaction_id=1, unit_id=2, func_code=4, register=1001, length=2, fr_type="request") self.assertEqual(srv.handle_message(msg).data, bytearray([0x02, 0x03, 0x04, 0x05])) msg = modbus_tcp_frame(transaction_id=1, unit_id=2, func_code=4, register=1000, length=3, fr_type="request") self.assertEqual(srv.handle_message(msg).data, bytearray([0x00, 0x01, 0x02, 0x03, 0x04, 0x05])) def test_server_handle_message_5(self): srv = modbus_tcp_server("", 0, context={"co":{"startAddr": 1000, "registers": bytearray([0xFF, 0x00]*3)}}) msg = modbus_tcp_frame(transaction_id=1, unit_id=2, func_code=5, register=1000, fr_type="request", data=bytearray([0xFF, 0x00])) srv.handle_message(msg) self.assertEqual(srv.context["co"]["registers"], bytearray([0xFF, 0x00, 0xFF, 0x00, 0xFF, 0x00])) msg = modbus_tcp_frame(transaction_id=1, unit_id=2, func_code=5, register=1001, fr_type="request", data=bytearray([0x00, 0x00])) srv.handle_message(msg) self.assertEqual(srv.context["co"]["registers"], bytearray([0xFF, 0x00, 0x00, 0x00, 0xFF, 0x00])) def test_server_handle_message_6(self): srv = modbus_tcp_server("", 0, context={"hr":{"startAddr": 1000, "registers": bytearray([0x00, 0x00]*3)}}) msg = modbus_tcp_frame(transaction_id=1, unit_id=2, func_code=6, register=1000, fr_type="request", data=bytearray([0xFF, 0x00])) srv.handle_message(msg) self.assertEqual(srv.context["hr"]["registers"], bytearray([0xFF, 0x00, 0x00, 0x00, 0x00, 0x00])) msg = modbus_tcp_frame(transaction_id=1, unit_id=2, func_code=6, register=1001, fr_type="request", data=bytearray([0xAB, 0xCD])) srv.handle_message(msg) self.assertEqual(srv.context["hr"]["registers"], bytearray([0xFF, 0x00, 0xAB, 0xCD, 0x00, 0x00])) def test_server_handle_message_15(self): srv = modbus_tcp_server("", 0, context={"co":{"startAddr": 1000, "registers": bytearray([0xFF, 0x00]*4)}}) msg = modbus_tcp_frame(transaction_id=1, unit_id=2, func_code=15, register=1000, fr_type="request", data=bytearray([0x0f]), length=4) self.assertEqual(srv.handle_message(msg).get_frame(), bytearray([0x00, 0x01, 0x00, 0x00, 0x00, 0x06, 0x02, 0x0f, 0x03, 0xe8, 0x00, 0x04])) self.assertEqual(srv.context["co"]["registers"], bytearray([0xFF, 0x00, 0xFF, 0x00, 0xFF, 0x00, 0xFF, 0x00])) msg = modbus_tcp_frame(transaction_id=1, unit_id=2, func_code=15, register=1000, fr_type="request", data=bytearray([0x00]), length=2) self.assertEqual(srv.handle_message(msg).get_frame(), bytearray([0x00, 0x01, 0x00, 0x00, 0x00, 0x06, 0x02, 0x0f, 0x03, 0xe8, 0x00, 0x02])) self.assertEqual(srv.context["co"]["registers"], bytearray([0x00, 0x00, 0x00, 0x00, 0xFF, 0x00, 0xFF, 0x00])) def test_server_handle_message_16(self): srv = modbus_tcp_server("", 0, context={"hr":{"startAddr": 1000, "registers": bytearray([0x00, 0x00]*4)}}) msg = modbus_tcp_frame(transaction_id=1, unit_id=2, func_code=16, register=1000, fr_type="request", data=bytearray([0xAB, 0xCD, 0x12, 0x34]), length=2) self.assertEqual(srv.handle_message(msg).get_frame(), bytearray([0x00, 0x01, 0x00, 0x00, 0x00, 0x06, 0x02, 0x10, 0x03, 0xe8, 0x00, 0x02])) self.assertEqual(srv.context["hr"]["registers"], bytearray([0xAB, 0xCD, 0x12, 0x34, 0x00, 0x00, 0x00, 0x00])) msg = modbus_tcp_frame(transaction_id=1, unit_id=2, func_code=16, register=1001, fr_type="request", data=bytearray([0xAB, 0xCD]), length=1) self.assertEqual(srv.handle_message(msg).get_frame(), bytearray([0x00, 0x01, 0x00, 0x00, 0x00, 0x06, 0x02, 0x10, 0x03, 0xe9, 0x00, 0x01])) self.assertEqual(srv.context["hr"]["registers"], bytearray([0xAB, 0xCD, 0xAB, 0xCD, 0x00, 0x00, 0x00, 0x00]))

71.03125

196

0.691304

986

6,819

4.578093

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0.05494

0.095702

0.075321

0.95525

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0.90031

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197

71.03125

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0.176471

0

null

0

1

0

null

0

6

cd88ddbb62ea20fc330c2cbbcf5198044ea6c04a

51

py

Python

boa3/model/builtin/interop/storage/storagecontext/__init__.py

hal0x2328/neo3-boa

6825a3533384cb01660773050719402a9703065b

[ "Apache-2.0" ]

25

2020-07-22T19:37:43.000Z

2022-03-08T03:23:55.000Z

boa3/model/builtin/interop/storage/storagecontext/__init__.py

hal0x2328/neo3-boa

6825a3533384cb01660773050719402a9703065b

[ "Apache-2.0" ]

419

2020-04-23T17:48:14.000Z

2022-03-31T13:17:45.000Z

boa3/model/builtin/interop/storage/storagecontext/__init__.py

hal0x2328/neo3-boa

6825a3533384cb01660773050719402a9703065b

[ "Apache-2.0" ]

15

2020-05-21T21:54:24.000Z

2021-11-18T06:17:24.000Z

from .storagecontexttype import StorageContextType

25.5

50

0.901961

4

51

11.5

0.75

0

0.078431

51

1

51

0.978723

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0

null

0

1

0

1

0

1

0

6

26a7a7efea8830b102b86e7d98532d5aa6e8067d

26

py

Python

ttt_pkg/__init__.py

Alkatat/Tic-Tac-Toe

cdff8cd776a8463d45715ab41c1ebb2f386c68a2

[ "MIT" ]

null

ttt_pkg/__init__.py

Alkatat/Tic-Tac-Toe

cdff8cd776a8463d45715ab41c1ebb2f386c68a2

[ "MIT" ]

null

ttt_pkg/__init__.py

Alkatat/Tic-Tac-Toe

cdff8cd776a8463d45715ab41c1ebb2f386c68a2

[ "MIT" ]

null

from ttt_pkg.ttt import *

26

0.769231

5

26

3.8

0.8

0

0.153846

26

1

26

0.863636

0

1

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true

0

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1

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null

0

1

0

null

0

1

0

1

0

1

0

6

26fc939783feaae91ea55cb32501051a7bbee747

204

py

Python

fcn/keras_fcn/__init__.py

NickleDave/fcn-syl-seg

90f66cc716d5564dc297ba70720b31ada2ff062c

[ "BSD-3-Clause" ]

null

fcn/keras_fcn/__init__.py

NickleDave/fcn-syl-seg

90f66cc716d5564dc297ba70720b31ada2ff062c

[ "BSD-3-Clause" ]

null

fcn/keras_fcn/__init__.py

NickleDave/fcn-syl-seg

90f66cc716d5564dc297ba70720b31ada2ff062c

[ "BSD-3-Clause" ]

null

"""This subpackage is adapted from https://github.com/JihongJu/keras-fcn under MIT License, https://github.com/JihongJu/keras-fcn/blob/master/LICENSE""" from . import encoders, decoders, callbacks, blocks

68

79

0.784314

29

204

5.517241

0.724138

0.1375

0.175

0.275

0.375

0

0.078431

204

3

80

68

0.851064

0.715686

0

1

0

true

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1

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1

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1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

f808c28b47da5fa2f39ff6e16efaea4ba3fb3caa

1,116

py

Python

Lintcode/Ladder_29_F/64. Merge Sorted Array.py

ctc316/algorithm-python

ac4580d55e05e93e407c6156c9bb801808027d60

[ "MIT" ]

null

Lintcode/Ladder_29_F/64. Merge Sorted Array.py

ctc316/algorithm-python

ac4580d55e05e93e407c6156c9bb801808027d60

[ "MIT" ]

null

Lintcode/Ladder_29_F/64. Merge Sorted Array.py

ctc316/algorithm-python

ac4580d55e05e93e407c6156c9bb801808027d60

[ "MIT" ]

null

class Solution: """ @param: A: sorted integer array A which has m elements, but size of A is m+n @param: m: An integer @param: B: sorted integer array B which has n elements @param: n: An integer @return: nothing """ def mergeSortedArray(self, A, m, B, n): for i in range(n): A[m + i] = B[i] A.sort() class Solution: """ @param: A: sorted integer array A which has m elements, but size of A is m+n @param: m: An integer @param: B: sorted integer array B which has n elements @param: n: An integer @return: nothing """ def mergeSortedArray(self, A, m, B, n): i = m - 1 j = n - 1 index = m + n - 1 while i >= 0 and j >= 0: if A[i] > B[j]: A[index] = A[i] i -= 1 else: A[index] = B[j] j -= 1 index -= 1 while i >= 0: A[index] = A[i] i -= 1 index -= 1 while j >= 0: A[index] = B[j] j -= 1 index -= 1

22.77551

80

0.44086

162

1,116

3.037037

0.222222

0.105691

0.146341

0.077236

0.841463

0.800813

0.735772

0

0.022472

0.441756

1,116

49

81

22.77551

0.767255

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0

0.576923

0

1

0.076923

false

0

0.153846

0

null

0

1

0

null

0

6

f82f89eaa35c05fb7590a214dc8fadc69fc41451

27

py

Python

avalanche/benchmarks/scenarios/new_classes/__init__.py

PRISHIta123/avalanche

cf92e4e1b7135fedd04106a195eb1fb67b97c124

[ "MIT" ]

810

2018-10-08T15:49:05.000Z

2022-03-31T15:28:09.000Z

avalanche/benchmarks/scenarios/new_classes/__init__.py

PRISHIta123/avalanche

cf92e4e1b7135fedd04106a195eb1fb67b97c124

[ "MIT" ]

477

2021-03-01T17:50:51.000Z

2022-03-31T14:51:23.000Z

avalanche/benchmarks/scenarios/new_classes/__init__.py

PRISHIta123/avalanche

cf92e4e1b7135fedd04106a195eb1fb67b97c124

[ "MIT" ]

147

2018-10-08T15:49:18.000Z

2022-03-31T04:08:45.000Z

from .nc_scenario import *

13.5

26

0.777778

4

27

5

1

0

0.148148

27

1

27

0.869565

0

1

0

true

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1

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null

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null

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1

0

1

0

1

0

6

f85d516baeafdaf2dca8151502d7b875df9e198f

31,171

py

Python

tests/runtime/linux/finish_test.py

gaocegege/treadmill

04325d319c0ee912c066f07b88b674e84485f154

[ "Apache-2.0" ]

2

2017-03-20T07:13:33.000Z

2017-05-03T03:39:53.000Z

tests/runtime/linux/finish_test.py

gaocegege/treadmill

04325d319c0ee912c066f07b88b674e84485f154

[ "Apache-2.0" ]

12

2017-07-10T07:04:06.000Z

2017-07-26T09:32:54.000Z

tests/runtime/linux/finish_test.py

gaocegege/treadmill

04325d319c0ee912c066f07b88b674e84485f154

[ "Apache-2.0" ]

2

2017-05-04T11:25:32.000Z

2017-07-11T09:10:01.000Z

"""Unit test for treadmill.runtime.linux._finish. """ import datetime import os import shutil import tempfile import tarfile import time import unittest import kazoo import mock import yaml import treadmill import treadmill.rulefile from treadmill import firewall from treadmill import fs from treadmill import iptables from treadmill import utils from treadmill.apptrace import events from treadmill.runtime.linux import _finish as app_finish class LinuxRuntimeFinishTest(unittest.TestCase): """Tests for treadmill.runtime.linux._finish""" def setUp(self): # Access protected module _base_service # pylint: disable=W0212 self.root = tempfile.mkdtemp() self.tm_env = mock.Mock( root=self.root, # nfs_dir=os.path.join(self.root, 'mnt', 'nfs'), apps_dir=os.path.join(self.root, 'apps'), archives_dir=os.path.join(self.root, 'archives'), metrics_dir=os.path.join(self.root, 'metrics'), svc_cgroup=mock.Mock( spec_set=treadmill.services._base_service.ResourceService, ), svc_localdisk=mock.Mock( spec_set=treadmill.services._base_service.ResourceService, ), svc_network=mock.Mock( spec_set=treadmill.services._base_service.ResourceService, ), rules=mock.Mock( spec_set=treadmill.rulefile.RuleMgr, ), watchdogs=mock.Mock( spec_set=treadmill.watchdog.Watchdog, ), ) def tearDown(self): if self.root and os.path.isdir(self.root): shutil.rmtree(self.root) @mock.patch('kazoo.client.KazooClient', mock.Mock(set_spec=True)) @mock.patch('shutil.copy', mock.Mock()) @mock.patch('treadmill.appevents.post', mock.Mock()) @mock.patch('treadmill.utils.datetime_utcnow', mock.Mock( return_value=datetime.datetime(2015, 1, 22, 14, 14, 36, 537918))) @mock.patch('treadmill.appcfg.manifest.read', mock.Mock()) @mock.patch('treadmill.runtime.linux._finish._kill_apps_by_root', mock.Mock()) @mock.patch('treadmill.runtime.linux._finish._send_container_archive', mock.Mock()) @mock.patch('treadmill.sysinfo.hostname', mock.Mock(return_value='xxx.xx.com')) @mock.patch('treadmill.fs.archive_filesystem', mock.Mock(return_value=True)) @mock.patch('treadmill.apphook.cleanup', mock.Mock()) @mock.patch('treadmill.iptables.rm_ip_set', mock.Mock()) @mock.patch('treadmill.rrdutils.flush_noexc', mock.Mock()) @mock.patch('treadmill.subproc.call', mock.Mock(return_value=0)) @mock.patch('treadmill.subproc.check_call', mock.Mock()) @mock.patch('treadmill.subproc.invoke', mock.Mock()) @mock.patch('treadmill.zkutils.get', mock.Mock(return_value={ 'server': 'nonexist', 'auth': 'nonexist', })) def test_finish(self): """Tests container finish procedure and freeing of the resources. """ # Access protected module _kill_apps_by_root # pylint: disable=W0212 manifest = { 'app': 'proid.myapp', 'cell': 'test', 'cpu': '100%', 'disk': '100G', 'environment': 'dev', 'memory': '100M', 'name': 'proid.myapp#001', 'proid': 'foo', 'shared_network': False, 'task': '001', 'uniqueid': '0000000ID1234', 'archive': [ '/var/tmp/treadmill' ], 'endpoints': [ { 'port': 8000, 'name': 'http', 'real_port': 5000, 'proto': 'tcp', }, { 'port': 54321, 'type': 'infra', 'name': 'ssh', 'real_port': 54321, 'proto': 'tcp', } ], 'ephemeral_ports': { 'tcp': [45024], 'udp': [62422], }, 'services': [ { 'name': 'web_server', 'command': '/bin/false', 'restart': { 'limit': 3, 'interval': 60, }, } ], 'vring': { 'some': 'settings' } } treadmill.appcfg.manifest.read.return_value = manifest app_unique_name = 'proid.myapp-001-0000000ID1234' mock_cgroup_client = self.tm_env.svc_cgroup.make_client.return_value mock_ld_client = self.tm_env.svc_localdisk.make_client.return_value mock_nwrk_client = self.tm_env.svc_network.make_client.return_value localdisk = { 'block_dev': '/dev/foo', } mock_ld_client.get.return_value = localdisk network = { 'vip': '192.168.0.2', 'gateway': '192.168.254.254', 'veth': 'testveth.0', 'external_ip': '172.31.81.67', } mock_nwrk_client.get.return_value = network app_dir = os.path.join(self.tm_env.apps_dir, app_unique_name) # Create content in app root directory, verify that it is archived. fs.mkdir_safe(os.path.join(app_dir, 'root', 'xxx')) fs.mkdir_safe(os.path.join(app_dir, 'services')) # Simulate daemontools finish script, marking the app is done. with open(os.path.join(app_dir, 'exitinfo'), 'w') as f: f.write(yaml.dump({'service': 'web_server', 'rc': 0, 'sig': 0})) mock_zkclient = kazoo.client.KazooClient() mock_watchdog = mock.Mock() app_finish.finish(self.tm_env, mock_zkclient, app_dir, mock_watchdog) treadmill.subproc.check_call.assert_has_calls( [ mock.call( [ 's6_svc', '-d', app_dir, ] ), mock.call( [ 's6_svwait', '-d', app_dir, ] ), ] ) # All resource service clients are properly created self.tm_env.svc_cgroup.make_client.assert_called_with( os.path.join(app_dir, 'cgroups') ) self.tm_env.svc_localdisk.make_client.assert_called_with( os.path.join(app_dir, 'localdisk') ) self.tm_env.svc_network.make_client.assert_called_with( os.path.join(app_dir, 'network') ) treadmill.runtime.linux._finish._kill_apps_by_root.assert_called_with( os.path.join(app_dir, 'root') ) # Verify that we tested the archiving for the app root volume treadmill.fs.archive_filesystem.assert_called_with( '/dev/foo', os.path.join(app_dir, 'root'), os.path.join(app_dir, '001_xxx.xx.com_20150122_141436537918.tar'), mock.ANY ) # Verify that the file is uploaded by Uploader app = utils.to_obj(manifest) treadmill.runtime.linux._finish._send_container_archive\ .assert_called_with( mock_zkclient, app, os.path.join(app_dir, '001_xxx.xx.com_20150122_141436537918.tar.gz'), ) # Verify that the app folder was deleted self.assertFalse(os.path.exists(app_dir)) # Cleanup the block device mock_ld_client.delete.assert_called_with(app_unique_name) # Cleanup the cgroup resource mock_cgroup_client.delete.assert_called_with(app_unique_name) # Cleanup network resources mock_nwrk_client.get.assert_called_with(app_unique_name) self.tm_env.rules.unlink_rule.assert_has_calls( [ mock.call(chain=iptables.PREROUTING_DNAT, rule=firewall.DNATRule( proto='tcp', dst_ip='172.31.81.67', dst_port=5000, new_ip='192.168.0.2', new_port=8000 ), owner=app_unique_name), mock.call(chain=iptables.POSTROUTING_SNAT, rule=firewall.SNATRule( proto='tcp', src_ip='192.168.0.2', src_port=8000, new_ip='172.31.81.67', new_port=5000 ), owner=app_unique_name), mock.call(chain=iptables.PREROUTING_DNAT, rule=firewall.DNATRule( proto='tcp', dst_ip='172.31.81.67', dst_port=54321, new_ip='192.168.0.2', new_port=54321 ), owner=app_unique_name), mock.call(chain=iptables.POSTROUTING_SNAT, rule=firewall.SNATRule( proto='tcp', src_ip='192.168.0.2', src_port=54321, new_ip='172.31.81.67', new_port=54321 ), owner=app_unique_name), mock.call(chain=iptables.PREROUTING_DNAT, rule=firewall.DNATRule( proto='tcp', dst_ip='172.31.81.67', dst_port=45024, new_ip='192.168.0.2', new_port=45024 ), owner=app_unique_name), mock.call(chain=iptables.PREROUTING_DNAT, rule=firewall.DNATRule( proto='udp', dst_ip='172.31.81.67', dst_port=62422, new_ip='192.168.0.2', new_port=62422 ), owner=app_unique_name), ], any_order=True ) self.assertEqual(self.tm_env.rules.unlink_rule.call_count, 6) treadmill.iptables.rm_ip_set.assert_has_calls( [ mock.call(treadmill.iptables.SET_INFRA_SVC, '192.168.0.2,tcp:54321'), mock.call(treadmill.iptables.SET_INFRA_SVC, '192.168.0.2,tcp:45024'), mock.call(treadmill.iptables.SET_INFRA_SVC, '192.168.0.2,udp:62422'), mock.call(treadmill.iptables.SET_VRING_CONTAINERS, '192.168.0.2'), ], any_order=True ) self.assertEqual(treadmill.iptables.rm_ip_set.call_count, 4) mock_nwrk_client.delete.assert_called_with(app_unique_name) treadmill.appevents.post.assert_called_with( mock.ANY, events.FinishedTraceEvent( instanceid='proid.myapp#001', rc=0, signal=0, payload={ 'service': 'web_server', 'sig': 0, 'rc': 0 } ) ) treadmill.rrdutils.flush_noexc.assert_called_with( os.path.join(self.root, 'metrics', 'apps', app_unique_name + '.rrd') ) shutil.copy.assert_called_with( os.path.join(self.root, 'metrics', 'apps', app_unique_name + '.rrd'), os.path.join(app_dir, 'metrics.rrd') ) self.assertTrue(mock_watchdog.remove.called) @mock.patch('kazoo.client.KazooClient', mock.Mock(set_spec=True)) @mock.patch('shutil.copy', mock.Mock()) @mock.patch('treadmill.appevents.post', mock.Mock()) @mock.patch('treadmill.apphook.cleanup', mock.Mock()) @mock.patch('treadmill.runtime.linux._finish._kill_apps_by_root', mock.Mock()) @mock.patch('treadmill.appcfg.manifest.read', mock.Mock()) @mock.patch('treadmill.sysinfo.hostname', mock.Mock(return_value='myhostname')) @mock.patch('treadmill.cgroups.delete', mock.Mock()) @mock.patch('treadmill.cgutils.reset_memory_limit_in_bytes', mock.Mock(return_value=[])) @mock.patch('treadmill.fs.archive_filesystem', mock.Mock(return_value=True)) @mock.patch('treadmill.subproc.call', mock.Mock(return_value=0)) @mock.patch('treadmill.subproc.check_call', mock.Mock()) @mock.patch('treadmill.subproc.invoke', mock.Mock()) @mock.patch('treadmill.zkutils.get', mock.Mock(return_value=None)) @mock.patch('treadmill.rrdutils.flush_noexc', mock.Mock()) def test_finish_error(self): """Tests container finish procedure when app is improperly finished.""" manifest = { 'app': 'proid.myapp', 'cell': 'test', 'cpu': '100%', 'disk': '100G', 'environment': 'dev', 'memory': '100M', 'name': 'proid.myapp#001', 'proid': 'foo', 'shared_network': False, 'task': '001', 'uniqueid': '0000000001234', 'archive': [ '/var/tmp/treadmill' ], 'endpoints': [ { 'port': 8000, 'name': 'http', 'real_port': 5000, 'proto': 'tcp', } ], 'services': [ { 'name': 'web_server', 'command': '/bin/false', 'restart': { 'limit': 3, 'interval': 60, }, } ], 'ephemeral_ports': { 'tcp': [], 'udp': [], }, 'vring': { 'some': 'settings' } } treadmill.appcfg.manifest.read.return_value = manifest app_unique_name = 'proid.myapp-001-0000000001234' mock_ld_client = self.tm_env.svc_localdisk.make_client.return_value localdisk = { 'block_dev': '/dev/foo', } mock_ld_client.get.return_value = localdisk mock_nwrk_client = self.tm_env.svc_network.make_client.return_value network = { 'vip': '192.168.0.2', 'gateway': '192.168.254.254', 'veth': 'testveth.0', 'external_ip': '172.31.81.67', } mock_nwrk_client.get.return_value = network app_dir = os.path.join(self.tm_env.apps_dir, app_unique_name) # Create content in app root directory, verify that it is archived. fs.mkdir_safe(os.path.join(app_dir, 'root', 'xxx')) fs.mkdir_safe(os.path.join(app_dir, 'services')) # Simulate daemontools finish script, marking the app is done. with open(os.path.join(app_dir, 'exitinfo'), 'w') as f: f.write(yaml.dump({'service': 'web_server', 'rc': 1, 'sig': 3})) mock_zkclient = kazoo.client.KazooClient() mock_watchdog = mock.Mock() app_finish.finish( self.tm_env, mock_zkclient, app_dir, mock_watchdog ) treadmill.appevents.post.assert_called_with( mock.ANY, events.FinishedTraceEvent( instanceid='proid.myapp#001', rc=1, signal=3, payload={ 'service': 'web_server', 'sig': 3, 'rc': 1, } ) ) treadmill.rrdutils.flush_noexc.assert_called_with( os.path.join(self.root, 'metrics', 'apps', app_unique_name + '.rrd') ) shutil.copy.assert_called_with( os.path.join(self.tm_env.metrics_dir, 'apps', app_unique_name + '.rrd'), os.path.join(app_dir, 'metrics.rrd') ) self.assertTrue(mock_watchdog.remove.called) @mock.patch('kazoo.client.KazooClient', mock.Mock(set_spec=True)) @mock.patch('shutil.copy', mock.Mock()) @mock.patch('treadmill.appevents.post', mock.Mock()) @mock.patch('treadmill.appcfg.manifest.read', mock.Mock()) @mock.patch('treadmill.apphook.cleanup', mock.Mock()) @mock.patch('treadmill.runtime.linux._finish._kill_apps_by_root', mock.Mock()) @mock.patch('treadmill.sysinfo.hostname', mock.Mock(return_value='hostname')) @mock.patch('treadmill.fs.archive_filesystem', mock.Mock(return_value=True)) @mock.patch('treadmill.rulefile.RuleMgr.unlink_rule', mock.Mock()) @mock.patch('treadmill.subproc.call', mock.Mock(return_value=0)) @mock.patch('treadmill.subproc.check_call', mock.Mock()) @mock.patch('treadmill.subproc.invoke', mock.Mock()) @mock.patch('treadmill.zkutils.get', mock.Mock(return_value=None)) @mock.patch('treadmill.rrdutils.flush_noexc', mock.Mock()) def test_finish_aborted(self): """Tests container finish procedure when node is aborted. """ manifest = { 'app': 'proid.myapp', 'cell': 'test', 'cpu': '100%', 'disk': '100G', 'environment': 'dev', 'host_ip': '172.31.81.67', 'memory': '100M', 'name': 'proid.myapp#001', 'proid': 'foo', 'shared_network': False, 'task': '001', 'uniqueid': '0000000ID1234', 'archive': [ '/var/tmp/treadmill' ], 'endpoints': [ { 'port': 8000, 'name': 'http', 'real_port': 5000, 'proto': 'tcp', } ], 'services': [ { 'name': 'web_server', 'command': '/bin/false', 'restart': { 'limit': 3, 'interval': 60, }, } ], 'ephemeral_ports': { 'tcp': [], 'udp': [], }, 'vring': { 'some': 'settings' } } treadmill.appcfg.manifest.read.return_value = manifest app_unique_name = 'proid.myapp-001-0000000ID1234' mock_ld_client = self.tm_env.svc_localdisk.make_client.return_value localdisk = { 'block_dev': '/dev/foo', } mock_ld_client.get.return_value = localdisk mock_nwrk_client = self.tm_env.svc_network.make_client.return_value network = { 'vip': '192.168.0.2', 'gateway': '192.168.254.254', 'veth': 'testveth.0', 'external_ip': '172.31.81.67', } mock_nwrk_client.get.return_value = network app_dir = os.path.join(self.root, 'apps', app_unique_name) # Create content in app root directory, verify that it is archived. fs.mkdir_safe(os.path.join(app_dir, 'root', 'xxx')) fs.mkdir_safe(os.path.join(app_dir, 'services')) # Simulate daemontools finish script, marking the app is done. with open(os.path.join(app_dir, 'aborted'), 'w') as aborted: aborted.write('something went wrong') mock_zkclient = kazoo.client.KazooClient() mock_watchdog = mock.Mock() app_finish.finish( self.tm_env, mock_zkclient, app_dir, mock_watchdog ) treadmill.appevents.post( mock.ANY, events.AbortedTraceEvent( instanceid='proid.myapp#001', why=None, payload={ 'why': 'something went wrong', 'node': 'hostname', } ) ) treadmill.rrdutils.flush_noexc.assert_called_with( os.path.join(self.root, 'metrics', 'apps', app_unique_name + '.rrd') ) shutil.copy.assert_called_with( os.path.join(self.root, 'metrics', 'apps', app_unique_name + '.rrd'), os.path.join(app_dir, 'metrics.rrd') ) self.assertTrue(mock_watchdog.remove.called) @mock.patch('treadmill.subproc.check_call', mock.Mock(return_value=0)) def test_finish_no_manifest(self): """Test app finish on directory with no app.json. """ app_finish.finish(self.tm_env, None, self.root, mock.Mock()) @mock.patch('kazoo.client.KazooClient', mock.Mock(set_spec=True)) @mock.patch('shutil.copy', mock.Mock()) @mock.patch('treadmill.appevents.post', mock.Mock()) @mock.patch('treadmill.apphook.cleanup', mock.Mock()) @mock.patch('treadmill.utils.datetime_utcnow', mock.Mock( return_value=datetime.datetime(2015, 1, 22, 14, 14, 36, 537918))) @mock.patch('treadmill.appcfg.manifest.read', mock.Mock()) @mock.patch('treadmill.runtime.linux._finish._kill_apps_by_root', mock.Mock()) @mock.patch('treadmill.runtime.linux._finish._send_container_archive', mock.Mock()) @mock.patch('treadmill.sysinfo.hostname', mock.Mock(return_value='xxx.ms.com')) @mock.patch('treadmill.fs.archive_filesystem', mock.Mock(return_value=True)) @mock.patch('treadmill.iptables.rm_ip_set', mock.Mock()) @mock.patch('treadmill.rrdutils.flush_noexc', mock.Mock()) @mock.patch('treadmill.subproc.call', mock.Mock(return_value=0)) @mock.patch('treadmill.subproc.check_call', mock.Mock()) @mock.patch('treadmill.subproc.invoke', mock.Mock()) @mock.patch('treadmill.zkutils.get', mock.Mock(return_value={ 'server': 'nonexist', 'auth': 'nonexist', })) @mock.patch('treadmill.zkutils.put', mock.Mock()) def test_finish_no_resources(self): """Test app finish on directory when all resources are already freed. """ # Access protected module _kill_apps_by_root # pylint: disable=W0212 manifest = { 'app': 'proid.myapp', 'cell': 'test', 'cpu': '100%', 'disk': '100G', 'environment': 'dev', 'memory': '100M', 'name': 'proid.myapp#001', 'proid': 'foo', 'shared_network': False, 'task': '001', 'uniqueid': '0000000ID1234', 'archive': [ '/var/tmp/treadmill' ], 'endpoints': [ { 'port': 8000, 'name': 'http', 'real_port': 5000 }, { 'port': 54321, 'type': 'infra', 'name': 'ssh', 'real_port': 54321 } ], 'ephemeral_ports': { 'tcp': [45024], 'udp': [62422], }, 'services': [ { 'command': '/bin/false', 'restart_count': 3, 'name': 'web_server' } ], 'vring': { 'some': 'settings' } } treadmill.appcfg.manifest.read.return_value = manifest app_unique_name = 'proid.myapp-001-0000000ID1234' mock_cgroup_client = self.tm_env.svc_cgroup.make_client.return_value mock_ld_client = self.tm_env.svc_localdisk.make_client.return_value mock_nwrk_client = self.tm_env.svc_network.make_client.return_value # All resource managers return None mock_cgroup_client.get.return_value = None mock_ld_client.get.return_value = None mock_nwrk_client.get.return_value = None app_dir = os.path.join(self.tm_env.apps_dir, app_unique_name) # Create content in app root directory, verify that it is archived. fs.mkdir_safe(os.path.join(app_dir, 'root', 'xxx')) fs.mkdir_safe(os.path.join(app_dir, 'services')) # Simulate daemontools finish script, marking the app is done. with open(os.path.join(app_dir, 'exitinfo'), 'w') as f: f.write(yaml.dump({'service': 'web_server', 'rc': 0, 'sig': 0})) mock_zkclient = kazoo.client.KazooClient() mock_watchdog = mock.Mock() treadmill.runtime.linux._finish.finish( self.tm_env, mock_zkclient, app_dir, mock_watchdog ) treadmill.subproc.check_call.assert_has_calls( [ mock.call( [ 's6_svc', '-d', app_dir, ], ), mock.call( [ 's6_svwait', '-d', app_dir, ], ), ] ) self.tm_env.svc_cgroup.make_client.assert_called_with( os.path.join(app_dir, 'cgroups') ) self.tm_env.svc_localdisk.make_client.assert_called_with( os.path.join(app_dir, 'localdisk') ) self.tm_env.svc_network.make_client.assert_called_with( os.path.join(app_dir, 'network') ) treadmill.runtime.linux._finish._kill_apps_by_root.assert_called_with( os.path.join(app_dir, 'root') ) # Verify that the app folder was deleted self.assertFalse(os.path.exists(app_dir)) # Cleanup the network resources mock_nwrk_client.get.assert_called_with(app_unique_name) # Cleanup the block device mock_ld_client.delete.assert_called_with(app_unique_name) # Cleanup the cgroup resource mock_cgroup_client.delete.assert_called_with(app_unique_name) treadmill.appevents.post.assert_called_with( mock.ANY, events.FinishedTraceEvent( instanceid='proid.myapp#001', rc=0, signal=0, payload={ 'service': 'web_server', 'sig': 0, 'rc': 0 } ) ) treadmill.rrdutils.flush_noexc.assert_called_with( os.path.join(self.root, 'metrics', 'apps', app_unique_name + '.rrd') ) shutil.copy.assert_called_with( os.path.join(self.root, 'metrics', 'apps', app_unique_name + '.rrd'), os.path.join(app_dir, 'metrics.rrd') ) self.assertTrue(mock_watchdog.remove.called) def test__copy_metrics(self): """Test that metrics are copied safely. """ # Access protected module _copy_metrics # pylint: disable=W0212 with open(os.path.join(self.root, 'in.rrd'), 'w+'): pass app_finish._copy_metrics(os.path.join(self.root, 'in.rrd'), self.root) self.assertTrue(os.path.exists(os.path.join(self.root, 'metrics.rrd'))) os.unlink(os.path.join(self.root, 'metrics.rrd')) app_finish._copy_metrics(os.path.join(self.root, 'nosuchthing.rrd'), self.root) self.assertFalse( os.path.exists(os.path.join(self.root, 'metrics.rrd'))) def test__archive_logs(self): """Tests archiving local logs.""" # Access protected module _archive_logs # # pylint: disable=W0212 container_dir = os.path.join(self.root, 'xxx.yyy-1234-qwerty') fs.mkdir_safe(container_dir) archives_dir = os.path.join(self.root, 'archives') fs.mkdir_safe(archives_dir) sys_archive = os.path.join(archives_dir, 'xxx.yyy-1234-qwerty.sys.tar.gz') app_archive = os.path.join(archives_dir, 'xxx.yyy-1234-qwerty.app.tar.gz') app_finish._archive_logs(self.tm_env, container_dir) self.assertTrue(os.path.exists(sys_archive)) self.assertTrue(os.path.exists(app_archive)) os.unlink(sys_archive) os.unlink(app_archive) def _touch_file(path): """Touch file, appending path to container_dir.""" fpath = os.path.join(container_dir, path) fs.mkdir_safe(os.path.dirname(fpath)) open(fpath, 'w+').close() _touch_file('sys/foo/log/current') _touch_file('sys/bla/log/current') _touch_file('sys/bla/log/xxx') _touch_file('services/xxx/log/current') _touch_file('services/xxx/log/whatever') _touch_file('a.yml') _touch_file('a.rrd') _touch_file('log/current') _touch_file('whatever') app_finish._archive_logs(self.tm_env, container_dir) tar = tarfile.open(sys_archive) files = sorted([member.name for member in tar.getmembers()]) self.assertEqual( files, ['a.rrd', 'a.yml', 'log/current', 'sys/bla/log/current', 'sys/foo/log/current'] ) tar.close() tar = tarfile.open(app_archive) files = sorted([member.name for member in tar.getmembers()]) self.assertEqual( files, ['services/xxx/log/current'] ) tar.close() def test__archive_cleanup(self): """Tests cleanup of local logs.""" # Access protected module _ARCHIVE_LIMIT, _cleanup_archive_dir # # pylint: disable=W0212 fs.mkdir_safe(self.tm_env.archives_dir) # Cleanup does not care about file extensions, it will cleanup # oldest file if threshold is exceeded. app_finish._ARCHIVE_LIMIT = 20 file1 = os.path.join(self.tm_env.archives_dir, '1') with open(file1, 'w+') as f: f.write('x' * 10) app_finish._cleanup_archive_dir(self.tm_env) self.assertTrue(os.path.exists(file1)) os.utime(file1, (time.time() - 1, time.time() - 1)) file2 = os.path.join(self.tm_env.archives_dir, '2') with open(file2, 'w+') as f: f.write('x' * 10) app_finish._cleanup_archive_dir(self.tm_env) self.assertTrue(os.path.exists(file1)) with open(os.path.join(self.tm_env.archives_dir, '2'), 'w+') as f: f.write('x' * 15) app_finish._cleanup_archive_dir(self.tm_env) self.assertFalse(os.path.exists(file1)) self.assertTrue(os.path.exists(file2)) if __name__ == '__main__': unittest.main()

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Python

tnread/__init__.py

Uiuran/text-network-notebooks

d2744890df896def45047e7d266d21d1d5287533

[ "CC-BY-4.0" ]

null

tnread/__init__.py

Uiuran/text-network-notebooks

d2744890df896def45047e7d266d21d1d5287533

[ "CC-BY-4.0" ]

null

tnread/__init__.py

Uiuran/text-network-notebooks

d2744890df896def45047e7d266d21d1d5287533

[ "CC-BY-4.0" ]

null

from .main import * from .vis import *

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Python

sstcam_sandbox/d191128_pedestal_lab/extract_dc_tf.py

watsonjj/CHECLabPySB

91330d3a6f510a392f635bd7f4abd2f77871322c

[ "BSD-3-Clause" ]

null

sstcam_sandbox/d191128_pedestal_lab/extract_dc_tf.py

watsonjj/CHECLabPySB

91330d3a6f510a392f635bd7f4abd2f77871322c

[ "BSD-3-Clause" ]

null

sstcam_sandbox/d191128_pedestal_lab/extract_dc_tf.py

watsonjj/CHECLabPySB

91330d3a6f510a392f635bd7f4abd2f77871322c

[ "BSD-3-Clause" ]

1

2021-03-30T09:46:56.000Z

from sstcam_sandbox import get_checs, get_data from CHECLabPy.core.io import TIOReader from TargetCalibSB.tf import TFDC from TargetCalibSB.pedestal import PedestalTargetCalib from TargetCalibSB import get_cell_ids_for_waveform from tqdm import tqdm from glob import glob import re def process(tf_r0_paths, pedestal_path, tf_path): pedestal = PedestalTargetCalib.from_tcal(pedestal_path) # Parse amplitudes from filepath amplitudes = [] readers = [] for path in tf_r0_paths: regex_ped = re.search(r".+VPED_(\d+).tio", path) amplitudes.append(int(regex_ped.group(1))) readers.append(TIOReader(path)) # Instance TF class from first file tf = TFDC( readers[0].n_pixels, readers[0].n_samples - 32, readers[0].n_cells, amplitudes ) desc0 = "Generating TF" it = zip(amplitudes, readers) n_amp = len(amplitudes) for amplitude, reader in tqdm(it, total=n_amp, desc=desc0): amplitude_index = tf.get_input_amplitude_index(amplitude) for iwf, wfs in enumerate(reader): if wfs.missing_packets: continue # Skip to next file when enough hits are reached if iwf % 1000 == 0: if (tf.hits[..., amplitude_index] > 100).all(): break tf.add_to_tf( pedestal.subtract_pedestal(wfs, wfs.first_cell_id), wfs.first_cell_id, amplitude_index ) tf.save(tf_path) def main(): # tf_r0_paths = glob("/Users/Jason/Downloads/tempdata/d191128_pedestal_lab/dc_tf_tm_temp25/*.tio") # pedestal_path = "/Users/Jason/Downloads/tempdata/d191128_pedestal_lab/dc_tf_tm_temp25/VPED_1095_ped.tcal" # tf_path = get_data("d191128_pedestal_lab/dc_tf/before_25deg.h5") # process(tf_r0_paths, pedestal_path, tf_path) # tf_r0_paths = glob("/Users/Jason/Downloads/tempdata/d191128_pedestal_lab/dc_tf_tm_temp35/*.tio") # pedestal_path = "/Users/Jason/Downloads/tempdata/d191128_pedestal_lab/dc_tf_tm_temp25/VPED_1095_ped.tcal" # tf_path = get_data("d191128_pedestal_lab/dc_tf/before_35deg.h5") # process(tf_r0_paths, pedestal_path, tf_path) # tf_r0_paths = glob("/Users/Jason/Downloads/tempdata/d191128_pedestal_lab/dc_tf_tm_temp35_440pF_2/*.tio") # pedestal_path = "/Users/Jason/Downloads/tempdata/d191128_pedestal_lab/dc_tf_tm_temp25_440pF/VPED_1095_ped.tcal" # tf_path = get_data("d191128_pedestal_lab/dc_tf/after_35deg.h5") # process(tf_r0_paths, pedestal_path, tf_path) # # tf_r0_paths = glob("/Users/Jason/Downloads/tempdata/d191128_pedestal_lab/dc_tf_tm_temp25_440pF/*.tio") # pedestal_path = "/Users/Jason/Downloads/tempdata/d191128_pedestal_lab/dc_tf_tm_temp25_440pF/VPED_1095_ped.tcal" # tf_path = get_data("d191128_pedestal_lab/dc_tf/after_25deg.h5") # process(tf_r0_paths, pedestal_path, tf_path) # tf_r0_paths = glob("/Users/Jason/Downloads/tempdata/d191128_pedestal_lab/dc_tf_tm_temp25_440pF_3/*.tio") # pedestal_path = "/Users/Jason/Downloads/tempdata/d191128_pedestal_lab/dc_tf_tm_temp25_440pF_3/VPED_1095_ped.tcal" # tf_path = get_data("d191128_pedestal_lab/dc_tf/after_25deg_3.h5") # process(tf_r0_paths, pedestal_path, tf_path) # tf_r0_paths = glob("/Users/Jason/Downloads/tempdata/d191128_pedestal_lab/dc_tf_tm_temp35_440pF_3/*.tio") # pedestal_path = "/Users/Jason/Downloads/tempdata/d191128_pedestal_lab/dc_tf_tm_temp25_440pF_3/VPED_1095_ped.tcal" # tf_path = get_data("d191128_pedestal_lab/dc_tf/after_35deg_3.h5") # process(tf_r0_paths, pedestal_path, tf_path) # tf_r0_paths = glob("/Users/Jason/Downloads/tempdata/d191128_pedestal_lab/dc_tf_tm_temp25_100pF/*.tio") # pedestal_path = "/Users/Jason/Downloads/tempdata/d191128_pedestal_lab/dc_tf_tm_temp25_100pF/VPED_1095_ped.tcal" # tf_path = get_data("d191128_pedestal_lab/dc_tf/100pF_25deg.h5") # process(tf_r0_paths, pedestal_path, tf_path) # # tf_r0_paths = glob("/Users/Jason/Downloads/tempdata/d191128_pedestal_lab/dc_tf_tm_temp35_100pF/*.tio") # pedestal_path = "/Users/Jason/Downloads/tempdata/d191128_pedestal_lab/dc_tf_tm_temp25_100pF/VPED_1095_ped.tcal" # tf_path = get_data("d191128_pedestal_lab/dc_tf/100pF_35deg.h5") # process(tf_r0_paths, pedestal_path, tf_path) # tf_r0_paths = glob("/Users/Jason/Downloads/tempdata/d191128_pedestal_lab/dc_tf_tm_temp25_100_pF_1k/*.tio") # pedestal_path = "/Users/Jason/Downloads/tempdata/d191128_pedestal_lab/dc_tf_tm_temp25_100pF/VPED_1095_ped.tcal" # tf_path = get_data("d191128_pedestal_lab/dc_tf/100pF_1k_25deg.h5") # process(tf_r0_paths, pedestal_path, tf_path) tf_r0_paths = glob("/Users/Jason/Downloads/tempdata/d191128_pedestal_lab/dc_tf_tm_temp25_200_pF/*.tio") pedestal_path = "/Users/Jason/Downloads/tempdata/d191128_pedestal_lab/dc_tf_tm_temp25_200_pF/VPED_1095_ped.tcal" tf_path = get_data("d191128_pedestal_lab/dc_tf/200pF_25deg.h5") process(tf_r0_paths, pedestal_path, tf_path) tf_r0_paths = glob("/Users/Jason/Downloads/tempdata/d191128_pedestal_lab/dc_tf_tm_temp35_200_pF/*.tio") pedestal_path = "/Users/Jason/Downloads/tempdata/d191128_pedestal_lab/dc_tf_tm_temp25_200_pF/VPED_1095_ped.tcal" tf_path = get_data("d191128_pedestal_lab/dc_tf/200pF_35deg.h5") process(tf_r0_paths, pedestal_path, tf_path) if __name__ == '__main__': main()

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