xszheng2020/the_stack_dedup_python_hits_5 · Datasets at Hugging Face

fc73e0014ddf4e46bd3ef118ee80c755fe81d42d

165

py

Python

morepath_wiki/app.py

sgaist/morepath_wiki

4f03acd9484fef5f83cb15a47abb369adf614ee1

[ "BSD-3-Clause" ]

null

morepath_wiki/app.py

sgaist/morepath_wiki

4f03acd9484fef5f83cb15a47abb369adf614ee1

[ "BSD-3-Clause" ]

null

morepath_wiki/app.py

sgaist/morepath_wiki

4f03acd9484fef5f83cb15a47abb369adf614ee1

[ "BSD-3-Clause" ]

null

import morepath from . import storage class App(morepath.App): @morepath.reify def wiki(self): return storage.Storage(self.settings.storage.path)

16.5

58

0.709091

21

165

5.571429

0.619048

0.188034

0

0.193939

165

9

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18.333333

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0

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0.166667

false

0

0.333333

0.166667

0.833333

0

1

0

null

0

1

0

null

0

1

0

5

fc971cf998f29feff61bfdb6a587127041c5cfe2

129

py

Python

workSpace/mathTest.py

QuantumChamploo/NeilNeat

8ad51bfb59d313590ff9ef0909f59e5222dc1e9c

[ "BSD-3-Clause" ]

null

workSpace/mathTest.py

QuantumChamploo/NeilNeat

8ad51bfb59d313590ff9ef0909f59e5222dc1e9c

[ "BSD-3-Clause" ]

null

workSpace/mathTest.py

QuantumChamploo/NeilNeat

8ad51bfb59d313590ff9ef0909f59e5222dc1e9c

[ "BSD-3-Clause" ]

null

import tensorflow as tf import math print("showing different math methods") x = .906 print(math.tanh(x)) print(tf.math.tanh(x))

16.125

39

0.744186

22

129

4.363636

0.545455

0.166667

0.1875

0

0.026549

0.124031

129

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0

null

0

1

0

1

0

5

fca5f99a85210681227df5e05152ee4a668b96df

87

py

Python

bread/admin.py

brendanwelzien/django-custom-user

a2b28ca3cafccd9daacadb43533b6ec35b360c60

[ "MIT" ]

null

bread/admin.py

brendanwelzien/django-custom-user

a2b28ca3cafccd9daacadb43533b6ec35b360c60

[ "MIT" ]

5

2021-04-06T18:26:21.000Z

2021-09-22T19:41:17.000Z

bread/admin.py

brendanwelzien/django-custom-user

a2b28ca3cafccd9daacadb43533b6ec35b360c60

[ "MIT" ]

null

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

17.4

32

0.816092

13

87

5.461538

0.692308

0

0.114943

87

5

33

17.4

0.922078

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true

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0.666667

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null

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1

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null

0

1

0

1

0

1

0

5

5da24980a472514dd20933c5b2908994a5318823

67

py

Python

69. Sqrt(x).py

fossabot/leetcode-2

335f1aa3ee785320515c3d3f03c2cb2df3bc13ba

[ "MIT" ]

2

2018-02-26T09:12:19.000Z

2019-06-07T13:38:10.000Z

69. Sqrt(x).py

fossabot/leetcode-2

335f1aa3ee785320515c3d3f03c2cb2df3bc13ba

[ "MIT" ]

1

2018-12-24T07:03:34.000Z

69. Sqrt(x).py

fossabot/leetcode-2

335f1aa3ee785320515c3d3f03c2cb2df3bc13ba

[ "MIT" ]

2

2018-12-24T07:01:03.000Z

2019-06-07T13:38:07.000Z

class Solution: def mySqrt(self, x): return int(x**0.5)

22.333333

26

0.58209

11

67

3.545455

0.909091

0

0.040816

0.268657

67

3

26

22.333333

0.755102

0

1

0.333333

false

0

0.333333

1

0

1

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null

0

1

0

null

0

1

0

1

0

5

5dac2e93284c21cdc0a5e60e2c4fea17b5f04056

2,426

py

Python

gd/server/routes.py

nekitdev/gd.py

b9d5e29c09f953f54b9b648fb677e987d9a8e103

[ "MIT" ]

58

2020-09-30T16:51:22.000Z

2022-02-13T17:27:48.000Z

gd/server/routes.py

NeKitDS/gd.py

b9d5e29c09f953f54b9b648fb677e987d9a8e103

[ "MIT" ]

30

2019-07-29T12:03:41.000Z

2020-09-15T17:01:37.000Z

gd/server/routes.py

NeKitDS/gd.py

b9d5e29c09f953f54b9b648fb677e987d9a8e103

[ "MIT" ]

20

2019-12-06T03:16:57.000Z

2020-09-16T17:45:27.000Z

from gd.server.common import URL, web from gd.server.typing import Handler from gd.typing import Callable, Optional __all__ = ("get_route", "routes", "delete", "get", "head", "patch", "post", "put", "static") routes = web.RouteTableDef() def get_route( route: str, version: Optional[int] = None, prefix: str = "/api", version_format: str = "v{}" ) -> str: route = route.strip("/") if version is None: return (URL(prefix) / route).human_repr() return (URL(prefix) / version_format.format(version) / route).human_repr() def get( route: str, version: Optional[int] = None, prefix: str = "/api", version_format: str = "v{}", routes: web.RouteTableDef = routes, **kwargs, ) -> Callable[[Handler], Handler]: return routes.get(get_route(route, version, prefix, version_format), **kwargs) def post( route: str, version: Optional[int] = None, prefix: str = "/api", version_format: str = "v{}", routes: web.RouteTableDef = routes, **kwargs, ) -> Callable[[Handler], Handler]: return routes.post(get_route(route, version, prefix, version_format), **kwargs) def head( route: str, version: Optional[int] = None, prefix: str = "/api", version_format: str = "v{}", routes: web.RouteTableDef = routes, **kwargs, ) -> Callable[[Handler], Handler]: return routes.head(get_route(route, version, prefix, version_format), **kwargs) def put( route: str, version: Optional[int] = None, prefix: str = "/api", version_format: str = "v{}", routes: web.RouteTableDef = routes, **kwargs, ) -> Callable[[Handler], Handler]: return routes.put(get_route(route, version, prefix, version_format), **kwargs) def patch( route: str, version: Optional[int] = None, prefix: str = "/api", version_format: str = "v{}", routes: web.RouteTableDef = routes, **kwargs, ) -> Callable[[Handler], Handler]: return routes.patch(get_route(route, version, prefix, version_format), **kwargs) def delete( route: str, version: Optional[int] = None, prefix: str = "/api", version_format: str = "v{}", routes: web.RouteTableDef = routes, **kwargs, ) -> Callable[[Handler], Handler]: return routes.delete(get_route(route, version, prefix, version_format), **kwargs) def static(*, routes: web.RouteTableDef = routes, **kwargs) -> None: return routes.static(**kwargs)

27.258427

96

0.639324

289

2,426

5.269896

0.131488

0.119501

0.115561

0.105712

0.750492

0.728168

0.539068

0

0.19662

2,426

88

97

27.568182

0.781426

0

0.617647

0

0.039571

0

1

0.117647

false

0

0.044118

0.102941

0.294118

0

null

0

1

0

null

0

1

0

5

5dc0c2ea69c9994d66e7e1b249b868911e9b6cc3

66

py

Python

sbpy/obsutil/__init__.py

dirac-institute/sbpy

9eb0523610f497ba2d068a071aae05ebfd67ed9d

[ "BSD-3-Clause" ]

47

2018-07-26T04:21:51.000Z

2022-03-07T16:23:02.000Z

sbpy/obsutil/__init__.py

dirac-institute/sbpy

9eb0523610f497ba2d068a071aae05ebfd67ed9d

[ "BSD-3-Clause" ]

253

2018-07-24T12:12:57.000Z

2022-03-13T21:59:52.000Z

sbpy/obsutil/__init__.py

dirac-institute/sbpy

9eb0523610f497ba2d068a071aae05ebfd67ed9d

[ "BSD-3-Clause" ]

27

2018-07-20T05:25:44.000Z

2022-03-01T03:29:30.000Z

""" SBPy Module for observation planning """ from .core import *

11

36

0.69697

8

66

5.75

1

0

0.181818

66

5

37

13.2

0.851852

0.545455

0

1

0

true

0

1

0

1

0

1

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null

0

1

0

null

0

1

0

1

0

1

0

5

5dd6159bf7317416d90c34fb8457318a69622830

121

py

Python

Python3/Tornado/apps/pg/PG_Deposit/src/__init__.py

youngqqcn/QBlockChainNotes

85122049024dc5555705bf016312491a51966621

[ "MIT" ]

24

2018-11-01T03:36:43.000Z

2022-03-28T08:20:30.000Z

Python3/Tornado/apps/pg/PG_Deposit/src/__init__.py

songning4/QBlockChainNotes

d65ede073f5a20f728f41cc6850409693820cdb1

[ "MIT" ]

57

2019-12-04T08:26:47.000Z

2022-03-08T07:35:15.000Z

Python3/Tornado/apps/pg/PG_Deposit/src/__init__.py

youngqqcn/QBlockChainNotes

85122049024dc5555705bf016312491a51966621

[ "MIT" ]

11

2019-01-04T08:41:57.000Z

2022-03-16T03:51:36.000Z

import sys if sys.version_info < (3, 0): print("please use python3") raise Exception("please use python3 !!")

30.25

45

0.652893

17

121

4.588235

0.764706

0.230769

0.410256

0

0.042105

0.214876

121

4

45

30.25

0.778947

0

0.336134

0

1

0

true

0

0.25

0

0.25

1

0

null

1

0

1

0

null

0

1

0

5

5dde0011733b9679a3b681b37151283510e0287f

103

py

Python

custom_components/ge_kitchen/entities/fridge/__init__.py

joelmoses/ha_components

4a4c311337480f9482ece096b35b9f2b51427bcc

[ "MIT" ]

null

custom_components/ge_kitchen/entities/fridge/__init__.py

joelmoses/ha_components

4a4c311337480f9482ece096b35b9f2b51427bcc

[ "MIT" ]

null

custom_components/ge_kitchen/entities/fridge/__init__.py

joelmoses/ha_components

4a4c311337480f9482ece096b35b9f2b51427bcc

[ "MIT" ]

null

from .ge_fridge import GeFridge from .ge_freezer import GeFreezer from .ge_dispenser import GeDispenser

34.333333

37

0.864078

15

103

5.733333

0.6

0.209302

0

0.106796

103

3

37

34.333333

0.934783

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

f8d82f6a5c191a66d8083b76e50b22cb8b906eaa

106

py

Python

wallbox/__init__.py

Florian7843/wallbox

a13c44743c83a71af3711b8bfe7136c47e326f43

[ "Apache-2.0" ]

null

wallbox/__init__.py

Florian7843/wallbox

a13c44743c83a71af3711b8bfe7136c47e326f43

[ "Apache-2.0" ]

null

wallbox/__init__.py

Florian7843/wallbox

a13c44743c83a71af3711b8bfe7136c47e326f43

[ "Apache-2.0" ]

null

# Wallbox EV module __init__.py from wallbox.wallbox import Wallbox from wallbox.statuses import Statuses

26.5

37

0.839623

15

106

5.666667

0.533333

0.258824

0

0.122642

106

3

38

35.333333

0.913978

0.273585

0

1

0

true

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1

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1

0

null

1

0

1

0

null

0

1

0

1

0

5

5d0c1b4cab5c0b818abb18897441f6a251067168

1,342

py

Python

inline_query.py

codebam/telegram-bot

cb0942321c496557a217a534d0665f280600bfa1

[ "WTFPL" ]

null

inline_query.py

codebam/telegram-bot

cb0942321c496557a217a534d0665f280600bfa1

[ "WTFPL" ]

null

inline_query.py

codebam/telegram-bot

cb0942321c496557a217a534d0665f280600bfa1

[ "WTFPL" ]

null

def inline_query(bot, update): query = update.inline_query.inline_query.query results = list() results.append(InlineQueryResultArticle(id=uuid4(), title="Bold", input_message_content=InputTextMessageContent( "*%s*" % escape_markdown.escape_markdown(query), parse_mode=ParseMode.MARKDOWN))) results.append(InlineQueryResultArticle(id=uuid4(), title="Italic", input_message_content=InputTextMessageContent( "_%s_" % escape_markdown.escape_markdown(query), parse_mode=ParseMode.MARKDOWN))) results.append(InlineQueryResultArticle(id=uuid4(), title="Monospace", input_message_content=InputTextMessageContent( "`%s`" % escape_markdown.escape_markdown(query), parse_mode=ParseMode.MARKDOWN))) bot.answerInlineQuery(update.inline_query.inline_query.id, results=results)

58.347826

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0.469449

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6.988506

0.298851

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1,342

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false

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0

null

0

1

0

1

0

null

0

5

5d1177279f1db1312fc001b86be244cc032bc8c0

18,179

py

Python

diff_cover/tests/test_java_violations_reporter.py

kingchad1989/diff-cover

22b8b9c0e8ed38f1c1e72a38875e3c210a96da06

[ "Apache-2.0" ]

null

diff_cover/tests/test_java_violations_reporter.py

kingchad1989/diff-cover

22b8b9c0e8ed38f1c1e72a38875e3c210a96da06

[ "Apache-2.0" ]

null

diff_cover/tests/test_java_violations_reporter.py

kingchad1989/diff-cover

22b8b9c0e8ed38f1c1e72a38875e3c210a96da06

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- from __future__ import unicode_literals import os import xml.etree.cElementTree as etree from textwrap import dedent import unittest import mock from mock import patch from six import BytesIO from diff_cover.violationsreporters import base from diff_cover.command_runner import CommandError from diff_cover.violationsreporters.base import QualityReporter from diff_cover.violationsreporters.java_violations_reporter import ( Violation, checkstyle_driver, CheckstyleXmlDriver, FindbugsXmlDriver) def _patch_so_all_files_exist(): _mock_exists = patch.object(base.os.path, 'exists').start() _mock_exists.returnvalue = True def _setup_patch(return_value, status_code=0): mocked_process = mock.Mock() mocked_process.returncode = status_code mocked_process.communicate.return_value = return_value mocked_subprocess = mock.patch('diff_cover.command_runner.subprocess').start() mocked_subprocess.Popen.return_value = mocked_process return mocked_process class CheckstyleQualityReporterTest(unittest.TestCase): """Tests for checkstyle quality violations.""" def setUp(self): """Set up required files.""" _patch_so_all_files_exist() def tearDown(self): """Undo all patches.""" patch.stopall() def test_no_such_file(self): """Expect that we get no results.""" quality = QualityReporter(checkstyle_driver) result = quality.violations('') self.assertEqual(result, []) def test_no_java_file(self): """Expect that we get no results because no Python files.""" quality = QualityReporter(checkstyle_driver) file_paths = ['file1.coffee', 'subdir/file2.js'] for path in file_paths: result = quality.violations(path) self.assertEqual(result, []) def test_quality(self): """Integration test.""" # Patch the output of `checkstyle` _setup_patch(( dedent(""" [WARN] ../new_file.java:1:1: Line contains a tab character. [WARN] ../new_file.java:13: 'if' construct must use '{}'s. """).strip().encode('ascii'), '' )) expected_violations = [ Violation(1, 'Line contains a tab character.'), Violation(13, "'if' construct must use '{}'s."), ] # Parse the report quality = QualityReporter(checkstyle_driver) # Expect that the name is set self.assertEqual(quality.name(), 'checkstyle') # Measured_lines is undefined for a # quality reporter since all lines are measured self.assertEqual(quality.measured_lines('../new_file.java'), None) # Expect that we get violations for file1.java only # We're not guaranteed that the violations are returned # in any particular order. actual_violations = quality.violations('../new_file.java') self.assertEqual(len(actual_violations), len(expected_violations)) for expected in expected_violations: self.assertIn(expected, actual_violations) class CheckstyleXmlQualityReporterTest(unittest.TestCase): def setUp(self): _patch_so_all_files_exist() # Paths generated by git_path are always the given argument _git_path_mock = patch('diff_cover.violationsreporters.java_violations_reporter.GitPathTool').start() _git_path_mock.relative_path = lambda path: path _git_path_mock.absolute_path = lambda path: path def tearDown(self): """ Undo all patches. """ patch.stopall() def test_no_such_file(self): quality = QualityReporter(CheckstyleXmlDriver()) # Expect that we get no results result = quality.violations('') self.assertEqual(result, []) def test_no_java_file(self): quality = QualityReporter(CheckstyleXmlDriver()) file_paths = ['file1.coffee', 'subdir/file2.js'] # Expect that we get no results because no Java files for path in file_paths: result = quality.violations(path) self.assertEqual(result, []) def test_quality(self): # Patch the output of `checkstyle` _setup_patch(( dedent(""" <?xml version="1.0" encoding="UTF-8"?> <checkstyle version="8.0"> <file name="file1.java"> <error line="1" severity="error" message="Missing docstring"/> <error line="2" severity="error" message="Unused variable 'd'"/> <error line="2" severity="warning" message="TODO: Not the real way we'll store usages!"/> <error line="579" severity="error" message="Unable to import 'rooted_paths'"/> <error line="113" severity="error" message="Unused argument 'cls'"/> <error line="150" severity="error" message="error while code parsing ([Errno 2] No such file or directory)"/> <error line="149" severity="error" message="Comma not followed by a space"/> </file> <file name="path/to/file2.java"> <error line="100" severity="error" message="Access to a protected member"/> </file> </checkstyle> """).strip().encode('ascii'), '' )) expected_violations = [ Violation(1, 'error: Missing docstring'), Violation(2, "error: Unused variable 'd'"), Violation(2, "warning: TODO: Not the real way we'll store usages!"), Violation(579, "error: Unable to import 'rooted_paths'"), Violation(150, "error: error while code parsing ([Errno 2] No such file or directory)"), Violation(149, "error: Comma not followed by a space"), Violation(113, "error: Unused argument 'cls'") ] # Parse the report quality = QualityReporter(CheckstyleXmlDriver()) # Expect that the name is set self.assertEqual(quality.name(), 'checkstyle') # Measured_lines is undefined for a # quality reporter since all lines are measured self.assertEqual(quality.measured_lines('file1.java'), None) # Expect that we get violations for file1.java only # We're not guaranteed that the violations are returned # in any particular order. actual_violations = quality.violations('file1.java') self.assertEqual(len(actual_violations), len(expected_violations)) for expected in expected_violations: self.assertIn(expected, actual_violations) def test_quality_error(self): # Patch the output stderr/stdout and returncode of `checkstyle` _setup_patch(( dedent(""" <?xml version="1.0" encoding="UTF-8"?> <checkstyle version="8.0"> <file name="file1.java"> <error line="1" severity="error" message="Missing docstring"/> </file> </checkstyle> """), b'oops'), status_code=1) # Parse the report with patch('diff_cover.violationsreporters.java_violations_reporter.run_command_for_code') as code: code.return_value = 0 quality = QualityReporter(CheckstyleXmlDriver()) # Expect an error self.assertRaises(CommandError, quality.violations, 'file1.java') def test_quality_pregenerated_report(self): # When the user provides us with a pre-generated checkstyle report # then use that instead of calling checkstyle directly. checkstyle_reports = [ BytesIO(dedent(""" <?xml version="1.0" encoding="UTF-8"?> <checkstyle version="8.0"> <file name="path/to/file.java"> <error line="1" severity="error" message="Missing docstring"/> <error line="57" severity="warning" message="TODO the name of this method is a little bit confusing"/> </file> <file name="another/file.java"> <error line="41" severity="error" message="Specify string format arguments as logging function parameters"/> <error line="175" severity="error" message="Operator not preceded by a space"/> <error line="259" severity="error" message="Invalid name '' for type variable (should match [a-z_][a-z0-9_]{2,30}$)"/> </file> </checkstyle> """).strip().encode('utf-8')), BytesIO(dedent(""" <?xml version="1.0" encoding="UTF-8"?> <checkstyle version="8.0"> <file name="path/to/file.java"> <error line="183" severity="error" message="Invalid name '' for type argument (should match [a-z_][a-z0-9_]{2,30}$)"/> </file> <file name="another/file.java"> <error line="183" severity="error" message="Missing docstring"/> </file> </checkstyle> """).strip().encode('utf-8')) ] # Generate the violation report quality = QualityReporter(CheckstyleXmlDriver(), reports=checkstyle_reports) # Expect that we get the right violations expected_violations = [ Violation(1, 'error: Missing docstring'), Violation(57, 'warning: TODO the name of this method is a little bit confusing'), Violation(183, "error: Invalid name '' for type argument (should match [a-z_][a-z0-9_]{2,30}$)") ] # We're not guaranteed that the violations are returned # in any particular order. actual_violations = quality.violations('path/to/file.java') self.assertEqual(len(actual_violations), len(expected_violations)) for expected in expected_violations: self.assertIn(expected, actual_violations) class FindbugsQualityReporterTest(unittest.TestCase): def setUp(self): _patch_so_all_files_exist() # Paths generated by git_path are always the given argument _git_path_mock = patch('diff_cover.violationsreporters.java_violations_reporter.GitPathTool').start() _git_path_mock.relative_path = lambda path: path _git_path_mock.absolute_path = lambda path: path def tearDown(self): """ Undo all patches. """ patch.stopall() def test_no_such_file(self): quality = QualityReporter(FindbugsXmlDriver()) # Expect that we get no results result = quality.violations('') self.assertEqual(result, []) def test_no_java_file(self): quality = QualityReporter(FindbugsXmlDriver()) file_paths = ['file1.coffee', 'subdir/file2.js'] # Expect that we get no results because no Java files for path in file_paths: result = quality.violations(path) self.assertEqual(result, []) def test_quality_pregenerated_report(self): # When the user provides us with a pre-generated findbugs report # then use that instead of calling findbugs directly. findbugs_reports = [ BytesIO(dedent(""" <?xml version="1.0" encoding="UTF-8"?> <BugCollection sequence="0" release="" analysisTimestamp="1512755361404" version="3.0.1" timestamp="1512755226000"> <BugInstance instanceOccurrenceNum="0" instanceHash="1967bf8c4d25c6b964f30356014aa9fb" rank="20" abbrev="Dm" category="I18N" priority="3" type="DM_CONVERT_CASE" instanceOccurrenceMax="0"> <ShortMessage>Consider using Locale parameterized version of invoked method</ShortMessage> <LongMessage>Use of non-localized String.toUpperCase() or String.toLowerCase() in org.opensource.sample.file$1.isMultipart(HttpServletRequest)</LongMessage> <Class classname="org.opensource.sample.file$1" primary="true"> <SourceLine classname="org.opensource.sample.file$1" start="94" end="103" sourcepath="path/to/file.java" sourcefile="file.java"> <Message>At file.java:[lines 94-103]</Message> </SourceLine> <Message>In class org.opensource.sample.file$1</Message> </Class> <Method isStatic="false" classname="org.opensource.sample.file$1" signature="(Ljavax/servlet/http/HttpServletRequest;)Z" name="isMultipart" primary="true"> <SourceLine endBytecode="181" classname="org.opensource.sample.file$1" start="97" end="103" sourcepath="file1.java" sourcefile="file1.java" startBytecode="0" /> <Message>In method org.opensource.sample.file$1.isMultipart(HttpServletRequest)</Message> </Method> <SourceLine endBytecode="6" classname="org.opensource.sample.file$1" start="97" end="97" sourcepath="path/to/file.java" sourcefile="file.java" startBytecode="6" primary="true"> <Message>At file.java:[line 97]</Message> </SourceLine> <SourceLine role="SOURCE_LINE_ANOTHER_INSTANCE" endBytecode="55" classname="org.opensource.sample.file$1" start="103" end="104" sourcepath="another/file.java" sourcefile="file.java" startBytecode="55"> <Message>Another occurrence at file.java:[line 103, 104]</Message> </SourceLine> </BugInstance> </BugCollection> """).strip().encode('utf-8')), BytesIO(dedent(""" <?xml version="1.0" encoding="UTF-8"?> <BugCollection sequence="0" release="" analysisTimestamp="1512755361404" version="3.0.1" timestamp="1512755226000"> <BugInstance instanceOccurrenceNum="0" instanceHash="1967bf8c4d25c6b964f30356014aa9fb" rank="20" abbrev="Dm" category="I18N" priority="3" type="DM_CONVERT_CASE" instanceOccurrenceMax="0"> <ShortMessage>Consider using Locale parameterized version of invoked method</ShortMessage> <LongMessage>Use of non-localized String.toUpperCase() or String.toLowerCase() in org.opensource.sample.file$1.isMultipart(HttpServletRequest)</LongMessage> <Class classname="org.opensource.sample.file$1" primary="true"> <SourceLine classname="org.opensource.sample.file$1" start="94" end="103" sourcepath="path/to/file.java" sourcefile="file.java"> <Message>At file.java:[lines 94-103]</Message> </SourceLine> <Message>In class org.opensource.sample.file$1</Message> </Class> <Method isStatic="false" classname="org.opensource.sample.file$1" signature="(Ljavax/servlet/http/HttpServletRequest;)Z" name="isMultipart" primary="true"> <SourceLine endBytecode="181" classname="org.opensource.sample.file$1" start="97" end="103" sourcepath="file1.java" sourcefile="file1.java" startBytecode="0" /> <Message>In method org.opensource.sample.file$1.isMultipart(HttpServletRequest)</Message> </Method> <SourceLine endBytecode="6" classname="org.opensource.sample.file$1" start="183" end="183" sourcepath="path/to/file.java" sourcefile="file.java" startBytecode="6" primary="true"> <Message>At file.java:[line 97]</Message> </SourceLine> <SourceLine role="SOURCE_LINE_ANOTHER_INSTANCE" endBytecode="55" classname="org.opensource.sample.file$1" start="183" end="183" sourcepath="another/file.java" sourcefile="file.java" startBytecode="55"> <Message>Another occurrence at file.java:[line 183]</Message> </SourceLine> </BugInstance> </BugCollection> """).strip().encode('utf-8')), # this is a violation which is not bounded to a specific line. We'll skip those BytesIO(dedent(""" <?xml version="1.0" encoding="UTF-8"?> <BugCollection sequence="0" release="" analysisTimestamp="1512755361404" version="3.0.1" timestamp="1512755226000"> <BugInstance instanceOccurrenceNum="0" instanceHash="2820338ec68e2e75a81848c95d31167f" rank="19" abbrev="Se" category="BAD_PRACTICE" priority="3" type="SE_BAD_FIELD" instanceOccurrenceMax="0"> <ShortMessage>Non-transient non-serializable instance field in serializable class</ShortMessage> <LongMessage>Class org.opensource.sample.file defines non-transient non-serializable instance field</LongMessage> <SourceLine synthetic="true" classname="org.opensource.sample.file" sourcepath="path/to/file.java" sourcefile="file.java"> <Message>In file.java</Message> </SourceLine> </BugInstance> </BugCollection> """).strip().encode('utf-8')) ] # Generate the violation report quality = QualityReporter(FindbugsXmlDriver(), reports=findbugs_reports) # Expect that we get the right violations expected_violations = [ Violation(97, 'I18N: Consider using Locale parameterized version of invoked method'), Violation(183, 'I18N: Consider using Locale parameterized version of invoked method') ] # We're not guaranteed that the violations are returned # in any particular order. actual_violations = quality.violations('path/to/file.java') self.assertEqual(len(actual_violations), len(expected_violations)) for expected in expected_violations: self.assertIn(expected, actual_violations)

50.218232

225

0.61483

1,939

18,179

5.665291

0.162971

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0.041875

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0

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0.272072

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false

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null

0

1

0

1

0

1

0

null

0

5

5d495bd40958e0fd033c956c4ea2f478634d9da8

261

py

Python

mocasin/util/cleaner.py

tud-ccc/mocasin

6cf0a169e24d65d0fc859398f181dd500f928340

[ "0BSD" ]

1

2022-03-13T19:27:50.000Z

mocasin/util/cleaner.py

tud-ccc/mocasin

6cf0a169e24d65d0fc859398f181dd500f928340

[ "0BSD" ]

null

mocasin/util/cleaner.py

tud-ccc/mocasin

6cf0a169e24d65d0fc859398f181dd500f928340

[ "0BSD" ]

null

# Copyright (C) 2020 TU Dresden # Licensed under the ISC license (see LICENSE.txt) # # Authors: Andres Goens # import mocasin.tgff.tgffSimulation as tgff def _cleanup(): # if tgff._parsed_tgff_files != {}: # tgff._parsed_tgff_files = {} pass

20.076923

50

0.681992

34

261

5.029412

0.764706

0.116959

0.163743

0.222222

0

0.019417

0.210728

261

12

51

21.75

0.81068

0.804598

0

1

0.5

true

0.5

0

0.5

0

null

0

1

0

1

0

null

0

1

0

5

5d63d7f716a9f1fc60eadb8764d16a43c6bb8909

62

py

Python

envbash/__init__.py

scampersand/envbash

fc491fc90f4266ffe5df512822b4be555e6a758f

[ "MIT" ]

7

2018-12-28T03:00:09.000Z

2021-08-04T04:17:36.000Z

envbash/__init__.py

scampersand/envbash

fc491fc90f4266ffe5df512822b4be555e6a758f

[ "MIT" ]

2

2019-11-06T03:45:49.000Z

2019-11-07T17:41:15.000Z

envbash/__init__.py

scampersand/envbash

fc491fc90f4266ffe5df512822b4be555e6a758f

[ "MIT" ]

5

2019-11-05T21:35:47.000Z

2021-12-16T16:23:00.000Z

from .load import load_envbash from .read import read_envbash

20.666667

30

0.83871

10

62

5

0.5

0

0.129032

62

2

31

0.925926

0

1

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true

0

1

0

1

0

1

0

null

0

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0

null

0

1

0

1

0

1

0

5

5d66b4dd2b381a3614b2777e9e8dca6c616a647f

523

py

Python

paramiko_test2.py

marcabomb/pynet_week4

4b6dc7688b77c567280ee21b966582fd429b5556

[ "Apache-2.0" ]

null

paramiko_test2.py

marcabomb/pynet_week4

4b6dc7688b77c567280ee21b966582fd429b5556

[ "Apache-2.0" ]

null

paramiko_test2.py

marcabomb/pynet_week4

4b6dc7688b77c567280ee21b966582fd429b5556

[ "Apache-2.0" ]

null

import paramiko, time pynet_rtr2 = paramiko.SSHClient() pynet_rtr2.set_missing_host_key_policy(paramiko.AutoAddPolicy()) pynet_rtr2.connect('184.105.247.71', username='pyclass', password='88newclass') pynet_rtr2_shell = pynet_rtr2.invoke_shell() pynet_rtr2_shell.send('config t\n') time.sleep(.5) pynet_rtr2_shell.send('logging buffered 19999\n') time.sleep(.5) pynet_rtr2_shell.send('end\n') time.sleep(.5) pynet_rtr2_shell.send('sh run | i logging\n') time.sleep(.5) output = pynet_rtr2_shell.recv(6000) print output

26.15

79

0.783939

84

523

4.630952

0.47619

0.231362

0.215938

0.18509

0.22365

0

0.074074

0.070746

523

19

80

27.526316

0.726337

0

0.266667

0

0.172414

0

null

0.066667

null

0.066667

0

null

1

0

null

0

1

0

1

0

5

5d68ede46096b8ec5818dc120220e7ab53aeb3ca

1,683

py

Python

telegram_gcloner/utils/restricted.py

youzi2020520/TG-

0258de00b418643a048c8bb5810429e5ea9cab5f

[ "MIT" ]

1

2020-06-30T09:19:18.000Z

telegram_gcloner/utils/restricted.py

1035833776/telegram_gcloner

f365f4e09cc721b67413e5de2594f026f1d9da2e

[ "MIT" ]

null

telegram_gcloner/utils/restricted.py

1035833776/telegram_gcloner

f365f4e09cc721b67413e5de2594f026f1d9da2e

[ "MIT" ]

null

#!/usr/bin/python3 # -*- coding: utf-8 -*- import logging from functools import wraps from utils.config_loader import config logger = logging.getLogger(__name__) def restricted(func): @wraps(func) def wrapped(update, context, *args, **kwargs): if not update.effective_user: return user_id = update.effective_user.id ban_list = context.bot_data.get('ban', []) # access control. comment out one or the other as you wish. # if user_id in ban_list: if user_id in ban_list or user_id not in config.USER_IDS: logger.info("Unauthorized access denied for {} {}.".format(update.effective_user.full_name, user_id)) return return func(update, context, *args, **kwargs) return wrapped def restricted_user_ids(func): @wraps(func) def wrapped(update, context, *args, **kwargs): if not update.effective_user: return user_id = update.effective_user.id if user_id not in config.USER_IDS: logger.info("Unauthorized access denied for {} {}.".format(update.effective_user.full_name, user_id)) return return func(update, context, *args, **kwargs) return wrapped def restricted_admin(func): @wraps(func) def wrapped(update, context, *args, **kwargs): if not update.effective_user: return user_id = update.effective_user.id if user_id != config.USER_IDS[0]: logger.info("Unauthorized admin access denied for {} {}.".format(update.effective_user.full_name, user_id)) return return func(update, context, *args, **kwargs) return wrapped

33

119

0.645276

217

1,683

4.829493

0.262673

0.080153

0.163168

0.131679

0.752863

0.72042

0

0.002377

0.250149

1,683

50

120

33.66

0.828051

0.071895

0

0.684211

0

0.077022

0

1

0.157895

false

0

0.078947

0

0.473684

0

null

0

1

0

null

0

5

5371b88d78f2781505e3326b29cd78300e3cc8a8

14,251

py

Python

tests/miners/test_batch_easy_hard_miner.py

elias-ramzi/pytorch-metric-learning

de47c68ab19ba606979221c3629f74ea729eff29

[ "MIT" ]

1

2021-12-20T05:48:16.000Z

tests/miners/test_batch_easy_hard_miner.py

yl305237731/pytorch-metric-learning

dff4ae570db89dcb59a102f13f665502f9c1c7c6

[ "MIT" ]

null

tests/miners/test_batch_easy_hard_miner.py

yl305237731/pytorch-metric-learning

dff4ae570db89dcb59a102f13f665502f9c1c7c6

[ "MIT" ]

1

2021-05-07T08:09:39.000Z

import unittest import torch from pytorch_metric_learning.distances import LpDistance from pytorch_metric_learning.miners import BatchEasyHardMiner from pytorch_metric_learning.utils import loss_and_miner_utils as lmu from .. import TEST_DEVICE, TEST_DTYPES class TestBatchEasyHardMiner(unittest.TestCase): @classmethod def setUpClass(self): self.labels = torch.LongTensor([0, 0, 1, 1, 0, 2, 1, 1, 1]) self.a1_idx, self.p_idx, self.a2_idx, self.n_idx = lmu.get_all_pairs_indices( self.labels ) self.distance = LpDistance(normalize_embeddings=False) self.gt = { "batch_semihard_hard": { "miner": BatchEasyHardMiner( distance=self.distance, pos_strategy=BatchEasyHardMiner.SEMIHARD, neg_strategy=BatchEasyHardMiner.HARD, ), "easiest_triplet": -1, "hardest_triplet": -1, "easiest_pos_pair": 1, "hardest_pos_pair": 2, "easiest_neg_pair": 3, "hardest_neg_pair": 2, "expected": { "correct_a": torch.LongTensor([0, 7, 8]).to(TEST_DEVICE), "correct_p": [ torch.LongTensor([1, 6, 6]).to(TEST_DEVICE), torch.LongTensor([1, 8, 6]).to(TEST_DEVICE), ], "correct_n": [ torch.LongTensor([2, 5, 5]).to(TEST_DEVICE), torch.LongTensor([2, 5, 5]).to(TEST_DEVICE), ], }, }, "batch_hard_semihard": { "miner": BatchEasyHardMiner( distance=self.distance, pos_strategy=BatchEasyHardMiner.HARD, neg_strategy=BatchEasyHardMiner.SEMIHARD, ), "easiest_triplet": -1, "hardest_triplet": -1, "easiest_pos_pair": 3, "hardest_pos_pair": 6, "easiest_neg_pair": 7, "hardest_neg_pair": 4, "expected": { "correct_a": torch.LongTensor([0, 1, 6, 7, 8]).to(TEST_DEVICE), "correct_p": [torch.LongTensor([4, 4, 2, 2, 2]).to(TEST_DEVICE)], "correct_n": [ torch.LongTensor([5, 5, 1, 1, 1]).to(TEST_DEVICE), ], }, }, "batch_easy_semihard": { "miner": BatchEasyHardMiner( distance=self.distance, pos_strategy=BatchEasyHardMiner.EASY, neg_strategy=BatchEasyHardMiner.SEMIHARD, ), "easiest_triplet": -2, "hardest_triplet": -1, "easiest_pos_pair": 1, "hardest_pos_pair": 3, "easiest_neg_pair": 4, "hardest_neg_pair": 2, "expected": { "correct_a": torch.LongTensor([0, 1, 2, 3, 4, 6, 7, 8]).to( TEST_DEVICE ), "correct_p": [ torch.LongTensor([1, 0, 3, 2, 1, 7, 8, 7]).to(TEST_DEVICE), torch.LongTensor([1, 0, 3, 2, 1, 7, 6, 7]).to(TEST_DEVICE), ], "correct_n": [ torch.LongTensor([2, 3, 0, 1, 8, 4, 5, 5]).to(TEST_DEVICE), torch.LongTensor([2, 3, 4, 1, 8, 4, 5, 5]).to(TEST_DEVICE), torch.LongTensor([2, 3, 0, 5, 8, 4, 5, 5]).to(TEST_DEVICE), torch.LongTensor([2, 3, 4, 5, 8, 4, 5, 5]).to(TEST_DEVICE), ], }, }, "batch_hard_hard": { "miner": BatchEasyHardMiner( distance=self.distance, pos_strategy=BatchEasyHardMiner.HARD, neg_strategy=BatchEasyHardMiner.HARD, ), "easiest_triplet": 2, "hardest_triplet": 5, "easiest_pos_pair": 3, "hardest_pos_pair": 6, "easiest_neg_pair": 3, "hardest_neg_pair": 1, "expected": { "correct_a": torch.LongTensor([0, 1, 2, 3, 4, 6, 7, 8]).to( TEST_DEVICE ), "correct_p": [ torch.LongTensor([4, 4, 8, 8, 0, 2, 2, 2]).to(TEST_DEVICE) ], "correct_n": [ torch.LongTensor([2, 2, 1, 4, 3, 5, 5, 5]).to(TEST_DEVICE), torch.LongTensor([2, 2, 1, 4, 5, 5, 5, 5]).to(TEST_DEVICE), ], }, }, "batch_easy_hard": { "miner": BatchEasyHardMiner( distance=self.distance, pos_strategy=BatchEasyHardMiner.EASY, neg_strategy=BatchEasyHardMiner.HARD, ), "easiest_triplet": -2, "hardest_triplet": 2, "easiest_pos_pair": 1, "hardest_pos_pair": 3, "easiest_neg_pair": 3, "hardest_neg_pair": 1, "expected": { "correct_a": torch.LongTensor([0, 1, 2, 3, 4, 6, 7, 8]).to( TEST_DEVICE ), "correct_p": [ torch.LongTensor([1, 0, 3, 2, 1, 7, 8, 7]).to(TEST_DEVICE), torch.LongTensor([1, 0, 3, 2, 1, 7, 6, 7]).to(TEST_DEVICE), ], "correct_n": [ torch.LongTensor([2, 2, 1, 4, 3, 5, 5, 5]).to(TEST_DEVICE), torch.LongTensor([2, 2, 1, 4, 5, 5, 5, 5]).to(TEST_DEVICE), ], }, }, "batch_hard_easy": { "miner": BatchEasyHardMiner( distance=self.distance, pos_strategy=BatchEasyHardMiner.HARD, neg_strategy=BatchEasyHardMiner.EASY, ), "easiest_triplet": -4, "hardest_triplet": 3, "easiest_pos_pair": 3, "hardest_pos_pair": 6, "easiest_neg_pair": 8, "hardest_neg_pair": 3, "expected": { "correct_a": torch.LongTensor([0, 1, 2, 3, 4, 6, 7, 8]).to( TEST_DEVICE ), "correct_p": [ torch.LongTensor([4, 4, 8, 8, 0, 2, 2, 2]).to(TEST_DEVICE) ], "correct_n": [ torch.LongTensor([8, 8, 5, 0, 8, 0, 0, 0]).to(TEST_DEVICE) ], }, }, "batch_easy_easy": { "miner": BatchEasyHardMiner( distance=self.distance, pos_strategy=BatchEasyHardMiner.EASY, neg_strategy=BatchEasyHardMiner.EASY, ), "easiest_triplet": -7, "hardest_triplet": -1, "easiest_pos_pair": 1, "hardest_pos_pair": 3, "easiest_neg_pair": 8, "hardest_neg_pair": 3, "expected": { "correct_a": torch.LongTensor([0, 1, 2, 3, 4, 6, 7, 8]).to( TEST_DEVICE ), "correct_p": [ torch.LongTensor([1, 0, 3, 2, 1, 7, 8, 7]).to(TEST_DEVICE), torch.LongTensor([1, 0, 3, 2, 1, 7, 6, 7]).to(TEST_DEVICE), ], "correct_n": [ torch.LongTensor([8, 8, 5, 0, 8, 0, 0, 0]).to(TEST_DEVICE) ], }, }, "batch_easy_easy_with_min_val": { "miner": BatchEasyHardMiner( distance=self.distance, pos_strategy=BatchEasyHardMiner.EASY, neg_strategy=BatchEasyHardMiner.EASY, allowed_neg_range=[1, 7], allowed_pos_range=[1, 7], ), "easiest_triplet": -6, "hardest_triplet": -1, "easiest_pos_pair": 1, "hardest_pos_pair": 3, "easiest_neg_pair": 7, "hardest_neg_pair": 3, "expected": { "correct_a": torch.LongTensor([0, 1, 2, 3, 4, 6, 7, 8]).to( TEST_DEVICE ), "correct_p": [ torch.LongTensor([1, 0, 3, 2, 1, 7, 8, 7]).to(TEST_DEVICE), torch.LongTensor([1, 0, 3, 2, 1, 7, 6, 7]).to(TEST_DEVICE), ], "correct_n": [ torch.LongTensor([7, 8, 5, 0, 8, 0, 0, 1]).to(TEST_DEVICE) ], }, }, "batch_easy_all": { "miner": BatchEasyHardMiner( distance=self.distance, pos_strategy=BatchEasyHardMiner.EASY, neg_strategy=BatchEasyHardMiner.ALL, ), "easiest_triplet": 0, "hardest_triplet": 0, "easiest_pos_pair": 1, "hardest_pos_pair": 3, "easiest_neg_pair": 8, "hardest_neg_pair": 1, "expected": { "correct_a1": torch.LongTensor([0, 1, 2, 3, 4, 6, 7, 8]).to( TEST_DEVICE ), "correct_p": [ torch.LongTensor([1, 0, 3, 2, 1, 7, 8, 7]).to(TEST_DEVICE), torch.LongTensor([1, 0, 3, 2, 1, 7, 6, 7]).to(TEST_DEVICE), ], "correct_a2": self.a2_idx, "correct_n": [self.n_idx], }, }, "batch_all_easy": { "miner": BatchEasyHardMiner( distance=self.distance, pos_strategy=BatchEasyHardMiner.ALL, neg_strategy=BatchEasyHardMiner.EASY, ), "easiest_triplet": 0, "hardest_triplet": 0, "easiest_pos_pair": 1, "hardest_pos_pair": 6, "easiest_neg_pair": 8, "hardest_neg_pair": 3, "expected": { "correct_a1": self.a1_idx, "correct_p": [self.p_idx], "correct_a2": torch.LongTensor([0, 1, 2, 3, 4, 5, 6, 7, 8]).to( TEST_DEVICE ), "correct_n": [ torch.LongTensor([8, 8, 5, 0, 8, 0, 0, 0, 0]).to(TEST_DEVICE), ], }, }, "batch_all_all": { "miner": BatchEasyHardMiner( distance=self.distance, pos_strategy=BatchEasyHardMiner.ALL, neg_strategy=BatchEasyHardMiner.ALL, ), "easiest_triplet": 0, "hardest_triplet": 0, "easiest_pos_pair": 1, "hardest_pos_pair": 6, "easiest_neg_pair": 8, "hardest_neg_pair": 1, "expected": { "correct_a1": self.a1_idx, "correct_p": [self.p_idx], "correct_a2": self.a2_idx, "correct_n": [self.n_idx], }, }, } def test_dist_mining(self): for dtype in TEST_DTYPES: embeddings = torch.arange(9).type(dtype).unsqueeze(1).to(TEST_DEVICE) for miner in self.gt.keys(): cfg = self.gt[miner] miner = cfg["miner"] a1, p, a2, n = miner.mine( embeddings, self.labels, embeddings, self.labels ) self.helper(a1, p, a2, n, cfg["expected"]) self.assertTrue(miner.easiest_triplet == cfg["easiest_triplet"]) self.assertTrue(miner.hardest_triplet == cfg["hardest_triplet"]) self.assertTrue(miner.easiest_pos_pair == cfg["easiest_pos_pair"]) self.assertTrue(miner.hardest_pos_pair == cfg["hardest_pos_pair"]) self.assertTrue(miner.easiest_neg_pair == cfg["easiest_neg_pair"]) self.assertTrue(miner.hardest_neg_pair == cfg["hardest_neg_pair"]) def test_strategy_assertion(self): self.assertRaises(ValueError, lambda: BatchEasyHardMiner(pos_strategy="blah")) self.assertRaises( ValueError, lambda: BatchEasyHardMiner( pos_strategy="semihard", neg_strategy="semihard" ), ) self.assertRaises( ValueError, lambda: BatchEasyHardMiner(pos_strategy="all", neg_strategy="semihard"), ) self.assertRaises( ValueError, lambda: BatchEasyHardMiner(pos_strategy="semihard", neg_strategy="all"), ) def helper(self, a1, p, a2, n, gt): try: self.assertTrue(torch.equal(a1, gt["correct_a"])) self.assertTrue(torch.equal(a2, gt["correct_a"])) self.assertTrue(any(torch.equal(p, cn) for cn in gt["correct_p"])) self.assertTrue(any(torch.equal(n, cn) for cn in gt["correct_n"])) except: self.assertTrue(torch.equal(a1, gt["correct_a1"])) self.assertTrue(torch.equal(a2, gt["correct_a2"])) self.assertTrue(any(torch.equal(p, cn) for cn in gt["correct_p"])) self.assertTrue(any(torch.equal(n, cn) for cn in gt["correct_n"])) @classmethod def tearDown(self): torch.cuda.empty_cache() if __name__ == "__main__": unittest.main()

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coderedcms/models/__init__.py

mikiec84/coderedcms

d72de2118d777f23d9512dc348691d3d7b46d0e5

[ "BSD-3-Clause" ]

null

coderedcms/models/__init__.py

mikiec84/coderedcms

d72de2118d777f23d9512dc348691d3d7b46d0e5

[ "BSD-3-Clause" ]

null

coderedcms/models/__init__.py

mikiec84/coderedcms

d72de2118d777f23d9512dc348691d3d7b46d0e5

[ "BSD-3-Clause" ]

null

""" Models module entry point. Used to cleanly organize various models into files based on their purpose, but provide them all via a single `models` module. """ from .integration_models import * #noqa from .page_models import * #noqa from .snippet_models import * #noqa from .wagtailsettings_models import * #noqa

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__init__.py

Ashokkommi0001/my_packages

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[ "MIT" ]

null

__init__.py

Ashokkommi0001/my_packages

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[ "MIT" ]

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__init__.py

Ashokkommi0001/my_packages

28e99345c5abdbb9fabb80b1977e6631af713db7

[ "MIT" ]

null

from cal.function import add, sub, mul, mdiv, div, fdiv from Greet.greet import SayHello print(add(10,20)) print(SayHello('Ashok'))

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py

Python

guet/steps/check/__init__.py

AbhishekMashetty/pairprogrammingmasetty

0528d4999b472ec6d94058193275a505eaf2c762

[ "Apache-2.0" ]

13

2018-12-21T22:47:28.000Z

2021-12-17T14:27:35.000Z

guet/steps/check/__init__.py

chiptopher/guet

1099ee623311ba1d052237612efc9b06b7ff68bb

[ "Apache-2.0" ]

63

2018-08-30T11:19:12.000Z

2021-05-13T12:11:08.000Z

guet/steps/check/__init__.py

chiptopher/guet

1099ee623311ba1d052237612efc9b06b7ff68bb

[ "Apache-2.0" ]

7

2019-05-21T13:52:37.000Z

2022-01-30T22:57:21.000Z

from ._committers_exist import CommittersExistCheck from .check import Check from .git_required_check import GitRequiredCheck from .help_check import HelpCheck from .start_required_check import StartRequiredCheck from .version_check import VersionCheck

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53a0d1d515486909f717566d2466a18f44f895a4

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Python

src/__init__.py

burhanuddinbhopalwala/tiger-card-app

05693503b0ca4c11fc510e8a4d4d9ec1e025f6db

[ "MIT" ]

null

src/__init__.py

burhanuddinbhopalwala/tiger-card-app

05693503b0ca4c11fc510e8a4d4d9ec1e025f6db

[ "MIT" ]

null

src/__init__.py

burhanuddinbhopalwala/tiger-card-app

05693503b0ca4c11fc510e8a4d4d9ec1e025f6db

[ "MIT" ]

null

""" Tiger Card source implementation module Last Updated by: Burhanuddin Bhopalwala Created at: 11th Oct 2021 Last Modified: 11th Oct 2021 """

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py

Python

epytope/Data/pssms/arb/mat/A_2902_10.py

christopher-mohr/epytope

8ac9fe52c0b263bdb03235a5a6dffcb72012a4fd

[ "BSD-3-Clause" ]

7

2021-02-01T18:11:28.000Z

2022-01-31T19:14:07.000Z

epytope/Data/pssms/arb/mat/A_2902_10.py

christopher-mohr/epytope

8ac9fe52c0b263bdb03235a5a6dffcb72012a4fd

[ "BSD-3-Clause" ]

22

2021-01-02T15:25:23.000Z

2022-03-14T11:32:53.000Z

epytope/Data/pssms/arb/mat/A_2902_10.py

christopher-mohr/epytope

8ac9fe52c0b263bdb03235a5a6dffcb72012a4fd

[ "BSD-3-Clause" ]

4

2021-05-28T08:50:38.000Z

2022-03-14T11:45:32.000Z

A_2902_10 = {0: {'A': 0.21283531234228728, 'C': -0.1424941822777786, 'E': -0.012454045052777292, 'D': -0.0907347244603722, 'G': 0.2782523645596544, 'F': 0.4575326562208652, 'I': 0.24543406820627975, 'H': 0.06319328859502722, 'K': -0.22223087967952854, 'M': 0.2175977144649128, 'L': 0.10122086039687667, 'N': -0.20542238498391957, 'Q': -0.46001459265793515, 'P': -0.38707232029099503, 'S': -0.19539141495813572, 'R': -0.4147605801677689, 'T': -0.15715077916666323, 'W': 0.5856943171103324, 'V': -0.15705252930744518, 'Y': 0.8178155621532938}, 1: {'A': 0.3449301904547901, 'C': -0.15998196579388727, 'E': -0.9643054029477659, 'D': -0.09959362819134465, 'G': 0.3451194713296286, 'F': 0.32886814432453515, 'I': -0.2427745032304808, 'H': -0.9081754590241623, 'K': -0.9081754590241623, 'M': 0.8194172478417924, 'L': -0.042419192782268925, 'N': 0.3959331773300222, 'Q': -0.9279458548140068, 'P': -1.373744004833285, 'S': 0.37268517209550533, 'R': -1.2479901407674276, 'T': 0.32633760220030605, 'W': 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Python

python/basics/chapter_1_getting_started/hello_world.py

gabriel-miglioranza/python_crash_course

57db9d6b17b225a6aaa5451c3a3b567ffc426b37

[ "MIT" ]

null

python/basics/chapter_1_getting_started/hello_world.py

gabriel-miglioranza/python_crash_course

57db9d6b17b225a6aaa5451c3a3b567ffc426b37

[ "MIT" ]

null

python/basics/chapter_1_getting_started/hello_world.py

gabriel-miglioranza/python_crash_course

57db9d6b17b225a6aaa5451c3a3b567ffc426b37

[ "MIT" ]

null

# Hello, Python World! print("Hello world!")

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Python

kaltura_lib/KalturaClient/__init__.py

KameliaZhelyazkova/Media-Hopper-Initial-Project

c15ad7cbd23dcddc7463d510510916ffcc4954df

[ "CC0-1.0" ]

null

kaltura_lib/KalturaClient/__init__.py

KameliaZhelyazkova/Media-Hopper-Initial-Project

c15ad7cbd23dcddc7463d510510916ffcc4954df

[ "CC0-1.0" ]

null

kaltura_lib/KalturaClient/__init__.py

KameliaZhelyazkova/Media-Hopper-Initial-Project

c15ad7cbd23dcddc7463d510510916ffcc4954df

[ "CC0-1.0" ]

null

from Client import KalturaClient from Base import KalturaConfiguration

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Python

enthought/pyface/multi_toolbar_window.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

3

2016-12-09T06:05:18.000Z

2018-03-01T13:00:29.000Z

enthought/pyface/multi_toolbar_window.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

1

2020-12-02T00:51:32.000Z

2020-12-02T08:48:55.000Z

enthought/pyface/multi_toolbar_window.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

null

# proxy module from pyface.multi_toolbar_window import *

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0

1

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5

07215d4328b7362f355b9a3f505ea0ec52aaa777

60

py

Python

sandbox/customer/models.py

JSmith-BitFlipper/oscar-ecommerce-webauthn

faf085e0a046f3846a0ba88fff31e9a3b5bc9f10

[ "BSD-3-Clause" ]

14

2018-01-08T12:50:10.000Z

2021-12-26T18:38:14.000Z

sandbox/customer/models.py

JSmith-BitFlipper/oscar-ecommerce-webauthn

faf085e0a046f3846a0ba88fff31e9a3b5bc9f10

[ "BSD-3-Clause" ]

10

2018-03-01T14:17:05.000Z

2022-03-11T23:26:11.000Z

sandbox/customer/models.py

JSmith-BitFlipper/oscar-ecommerce-webauthn

faf085e0a046f3846a0ba88fff31e9a3b5bc9f10

[ "BSD-3-Clause" ]

4

2019-04-09T17:29:34.000Z

2020-06-07T14:46:23.000Z

from oscar.apps.customer.models import * # noqa isort:skip

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4ad1622c6cae7a4fcf3265cfbad138b1aaeeccac

41

py

Python

tests/components/samsungtv/__init__.py

domwillcode/home-assistant

f170c80bea70c939c098b5c88320a1c789858958

[ "Apache-2.0" ]

23

2017-11-15T21:03:53.000Z

2021-03-29T21:33:48.000Z

tests/components/samsungtv/__init__.py

jagadeeshvenkatesh/core

1bd982668449815fee2105478569f8e4b5670add

[ "Apache-2.0" ]

78

2020-07-23T07:13:08.000Z

2022-03-31T06:02:04.000Z

tests/components/samsungtv/__init__.py

jagadeeshvenkatesh/core

1bd982668449815fee2105478569f8e4b5670add

[ "Apache-2.0" ]

14

2018-08-19T16:28:26.000Z

2021-09-02T18:26:53.000Z

"""Tests for the samsungtv component."""

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null

0

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0

5

4adc1ce757728709d517d4cbccca09365e9e63a5

6,139

py

Python

odes.py

chapman-phys220-2018f/cw11-poor-social-skills-2-electric-boogaloo

c24227beefcd97483ff9150b6861e14a9eb24881

[ "MIT" ]

null

odes.py

chapman-phys220-2018f/cw11-poor-social-skills-2-electric-boogaloo

c24227beefcd97483ff9150b6861e14a9eb24881

[ "MIT" ]

null

odes.py

chapman-phys220-2018f/cw11-poor-social-skills-2-electric-boogaloo

c24227beefcd97483ff9150b6861e14a9eb24881

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- ### # Name: Trevor Kling # Student ID: 002270716 # Email: kling109@mail.chapman.edu # Course: PHYS220/MATH220/CPSC220 Fall 2018 # Assignment: CW 11 ### import numpy as np def eulerHelper(initPoint, delT): """ Helper Method for Euler's method for calculating differential equations. Parameters: ----------- initPoint: [float, float] The initial point u_k to be used for the approximation. Input as a vector in order to compute multiple functions simultaneously delT: float > 0 The change in time value associated with going from u_k to u_{k+1} Returns: -------- u_{k+1}: [float, float] The new point approximated by the method. """ J = np.matrix('0 1; -1 0') slopes = J @ initPoint return initPoint + (delT * slopes) def euler(N, u): """ Approximates the values of cos(t) and -sin(t) between 0 and 10 pi. Parameters: ----------- N: int > 0 The number of divisions to use for approximations. Defines delT and the number of values in the array returned. u: float The initial value of the function to be used. Returns: -------- eulerApprox: n by 2 array [[float,float]] An array of approximated function values for cos(t) and -sin(t) """ tRange = np.arange(0, 10*np.pi, 2*np.pi/N) eulerApprox = np.zeros((len(tRange)+1, 2)) delT = tRange[1] - tRange[0] eulerApprox[0] = u n = 0 for t in tRange: n += 1 eulerApprox[n] = eulerHelper(eulerApprox[n-1], delT) return eulerApprox def heunHelper(initPoint, delT): """ Helper Method for Heun's method for calculating differential equations. Parameters: ----------- initPoint: [float, float] The initial point u_k to be used for the approximation. Input as a vector in order to compute multiple functions simultaneously delT: float > 0 The change in time value associated with going from u_k to u_{k+1} Returns: -------- u_{k+1}: [float, float] The new point approximated by the method. """ nextApprox = eulerHelper(initPoint, delT) J = np.matrix('0 1; -1 0') return initPoint + (delT / 2)*((J @ (initPoint + nextApprox).reshape((2,1))).reshape(2)) def heun(N, u): """ Approximates the values of cos(t) and -sin(t) between 0 and 10 pi. Parameters: ----------- N: int > 0 The number of divisions to use for approximations. Defines delT and the number of values in the array returned. u: float The initial value of the function to be used. Returns: -------- heunApprox: n by 2 array [[float,float]] An array of approximated function values for cos(t) and -sin(t) """ tRange = np.arange(0, 10*np.pi, 2*np.pi/N) heunApprox = np.zeros((len(tRange)+1, 2)) delT = tRange[1] - tRange[0] heunApprox[0] = u n = 0 for t in tRange: n += 1 heunApprox[n] = heunHelper(heunApprox[n-1], delT) return heunApprox def rungeKuttaSecondHelper(initPoint, delT): """ Helper Method for the second-order Runge Kutta method for calculating differential equations. Parameters: ----------- initPoint: [float, float] The initial point u_k to be used for the approximation. Input as a vector in order to compute multiple functions simultaneously delT: float > 0 The change in time value associated with going from u_k to u_{k+1} Returns: -------- u_{k+1}: [float, float] The new point approximated by the method. """ J = np.matrix('0 1; -1 0') k1 = delT*(J @ initPoint) k2 = delT*(J @ (initPoint + (k1 / 2)).reshape((2,1))).reshape(2) return initPoint + k2 def rungeKuttaSecond(N, u): """ Approximates the values of cos(t) and -sin(t) between 0 and 10 pi. Parameters: ----------- N: int > 0 The number of divisions to use for approximations. Defines delT and the number of values in the array returned. u: float The initial value of the function to be used. Returns: -------- rksa: n by 2 array [[float,float]] An array of approximated function values for cos(t) and -sin(t) """ tRange = np.arange(0, 10*np.pi, 2*np.pi/N) rksa = np.zeros((len(tRange)+1, 2)) delT = tRange[1] - tRange[0] rksa[0] = u n = 0 for t in tRange: n+=1 rksa[n] = rungeKuttaSecondHelper(rksa[n-1], delT) return rksa def rungeKuttaFourthHelper(initPoint, delT): """ Helper Method for the fourth-order Runge Kutta method for calculating differential equations. Parameters: ----------- initPoint: [float, float] The initial point u_k to be used for the approximation. Input as a vector in order to compute multiple functions simultaneously delT: float > 0 The change in time value associated with going from u_k to u_{k+1} Returns: -------- u_{k+1}: [float, float] The new point approximated by the method. """ J = np.matrix('0 1; -1 0') k1 = delT*(J @ initPoint) k2 = delT*(J @ (initPoint + (k1 / 2)).reshape((2,1))).reshape(2) k3 = delT*(J @ (initPoint + (k2 / 2)).reshape((2,1))).reshape(2) k4 = delT*(J @ (initPoint+k3).reshape((2,1))).reshape(2) return initPoint + (k1 + 2*k2 + 2*k3 + k4)/6 def rungeKuttaFourth(N, u): """ Approximates the values of cos(t) and -sin(t) between 0 and 10 pi. Parameters: ----------- N: int > 0 The number of divisions to use for approximations. Defines delT and the number of values in the array returned. u: float The initial value of the function to be used. Returns: -------- rkfa: n by 2 array [[float,float]] An array of approximated function values for cos(t) and -sin(t) """ tRange = np.arange(0, 10*np.pi, 2*np.pi/N) rkfa = np.zeros((len(tRange)+1, 2)) delT = tRange[1] - tRange[0] rkfa[0] = u n = 0 for t in tRange: n+=1 rkfa[n] = rungeKuttaFourthHelper(rkfa[n-1], delT) return rkfa

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5

4ae45ae5c5ca8cb4f4214c2d46c0a52f3414e46e

5,601

py

Python

arrowStuff/arrow.py

andrewrkeyes/hw6

1d0607cbf7387b89c2ca3f761afa7b4750d7b31c

[ "MIT" ]

1

2019-11-23T18:33:33.000Z

arrowStuff/arrow.py

andrewrkeyes/hw6

1d0607cbf7387b89c2ca3f761afa7b4750d7b31c

[ "MIT" ]

null

arrowStuff/arrow.py

andrewrkeyes/hw6

1d0607cbf7387b89c2ca3f761afa7b4750d7b31c

[ "MIT" ]

1

2019-11-23T18:25:17.000Z

from graphics import * window = GraphWin("Arrow", 800, 550) def main(): value = 0; entry1 = Entry(Point(300, 50),20) entry1.draw(window) if value==1: label = Text(Point(window.getWidth()/2-5,20),"Honk") label.setSize(30) label.draw(window) if value==0: label = Text(Point(window.getWidth()/2-5,20),"Siren") label.setTextColor('red') label.setSize(30) label.draw(window) aLine = Line(Point(window.getWidth()/2,window.getHeight()/2+50), Point(window.getWidth()/2,window.getHeight()/2-50)) aLine.draw(window) bLine = Line(Point(window.getWidth()/2-50,window.getHeight()/2+50), Point(window.getWidth()/2+50,window.getHeight()/2-50)) bLine.draw(window) cLine = Line(Point(window.getWidth()/2-50,window.getHeight()/2-50), Point(window.getWidth()/2+50,window.getHeight()/2+50)) cLine.draw(window) setWidth(aLine,bLine,cLine) savedValue = entry1.getText() while True: k = window.checkKey() location = entry1.getText() if location != savedValue: savedValue = location print(location) undrawAll(aLine,bLine,cLine) if location == '1': aLine = Line(Point(window.getWidth()/2,100+10), Point(window.getWidth()/2,450)) aLine.draw(window) bLine = Line(Point(window.getWidth()/2+20,100), Point(window.getWidth()/2-80,window.getHeight()/2-80)) bLine.draw(window) cLine = Line(Point(window.getWidth()/2-20,100), Point(window.getWidth()/2+80,window.getHeight()/2-80)) cLine.draw(window) if location == '2': aLine = Line(Point(window.getWidth()/4,window.getHeight()*3/4), Point(window.getWidth()*3/4,window.getHeight()/4)) aLine.draw(window) bLine = Line(Point(window.getWidth()*3/4-150,window.getHeight()/4), Point(window.getWidth()*3/4+30,window.getHeight()/4)) bLine.draw(window) cLine = Line(Point(window.getWidth()*3/4,window.getHeight()/4+150), Point(window.getWidth()*3/4,window.getHeight()/4-30)) cLine.draw(window) if location == '3': aLine = Line(Point(100,window.getHeight()/2), Point(700-20,window.getHeight()/2)) aLine.draw(window) bLine = Line(Point(700,window.getHeight()/2+20), Point(window.getWidth()/2+180,window.getHeight()/2-90)) bLine.draw(window) cLine = Line(Point(700,window.getHeight()/2-20), Point(window.getWidth()/2+180,window.getHeight()/2+90)) cLine.draw(window) if location == '4': aLine = Line(Point(window.getWidth()/4,window.getHeight()/4), Point(window.getWidth()*3/4,window.getHeight()*3/4)) aLine.draw(window) bLine = Line(Point(window.getWidth()*3/4-150,window.getHeight()*3/4), Point(window.getWidth()*3/4+30,window.getHeight()*3/4)) bLine.draw(window) cLine = Line(Point(window.getWidth()*3/4,window.getHeight()*3/4-150), Point(window.getWidth()*3/4,window.getHeight()*3/4+30)) cLine.draw(window) if location == '5': aLine = Line(Point(window.getWidth()/2,100), Point(window.getWidth()/2,450-10)) aLine.draw(window) bLine = Line(Point(window.getWidth()/2+20,450), Point(window.getWidth()/2-80,window.getHeight()/2+80)) bLine.draw(window) cLine = Line(Point(window.getWidth()/2-20,450), Point(window.getWidth()/2+80,window.getHeight()/2+80)) cLine.draw(window) if location == '6': aLine = Line(Point(window.getWidth()/4,window.getHeight()*3/4), Point(window.getWidth()*3/4,window.getHeight()/4)) aLine.draw(window) bLine = Line(Point(window.getWidth()/4+150,window.getHeight()*3/4), Point(window.getWidth()/4-30,window.getHeight()*3/4)) bLine.draw(window) cLine = Line(Point(window.getWidth()/4,window.getHeight()*3/4-150), Point(window.getWidth()/4,window.getHeight()*3/4+30)) cLine.draw(window) if location == '7': aLine = Line(Point(100+20,window.getHeight()/2), Point(700,window.getHeight()/2)) aLine.draw(window) bLine = Line(Point(100,window.getHeight()/2+20), Point(window.getWidth()/2-180,window.getHeight()/2-90)) bLine.draw(window) cLine = Line(Point(100,window.getHeight()/2-20), Point(window.getWidth()/2-180,window.getHeight()/2+90)) cLine.draw(window) if location == '8': aLine = Line(Point(window.getWidth()/4,window.getHeight()/4), Point(window.getWidth()*3/4,window.getHeight()*3/4)) aLine.draw(window) bLine = Line(Point(window.getWidth()/4+150,window.getHeight()/4), Point(window.getWidth()/4-30,window.getHeight()/4)) bLine.draw(window) cLine = Line(Point(window.getWidth()/4,window.getHeight()/4+150), Point(window.getWidth()/4,window.getHeight()/4-30)) cLine.draw(window) setWidth(aLine,bLine,cLine) window.getMouse() # pause for click in window window.close() def undrawAll(aLine,bLine,cLine): aLine.undraw() bLine.undraw() cLine.undraw() def setWidth(aLine,bLine,cLine): aLine.setWidth(60) bLine.setWidth(60) cLine.setWidth(60) main()

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null

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5

4af9f958edd0d20628aa40d6a7c2bd4242639219

359

py

Python

app/helpers/__init__.py

netai/stockbag_backend

b5bbc09fea896bcb1c03091579f6de658bff4c13

[ "MIT" ]

null

app/helpers/__init__.py

netai/stockbag_backend

b5bbc09fea896bcb1c03091579f6de658bff4c13

[ "MIT" ]

null

app/helpers/__init__.py

netai/stockbag_backend

b5bbc09fea896bcb1c03091579f6de658bff4c13

[ "MIT" ]

null

from .auth_helper import AuthHelper from .api_error_helper import APIException, ResourceNotExistException, UnauthorizedException, \ ResourceExistException, InvalidAuthTokenException, InsufficientFundException from .user_helper import UserHelper from .holding_helper import HoldingHelper from .note_helper import NoteHelper from .fund_helper import FundHelper

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5

4afe87bcdc17b09e42328b21c064047d405076ea

547

py

Python

RecoBTag/Combined/python/combinedMVA_EventSetup_cff.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

852

2015-01-11T21:03:51.000Z

2022-03-25T21:14:00.000Z

RecoBTag/Combined/python/combinedMVA_EventSetup_cff.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

30,371

2015-01-02T00:14:40.000Z

2022-03-31T23:26:05.000Z

RecoBTag/Combined/python/combinedMVA_EventSetup_cff.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

3,240

2015-01-02T05:53:18.000Z

2022-03-31T17:24:21.000Z

import FWCore.ParameterSet.Config as cms # CombinedMVA V2 from RecoBTag.Combined.combinedMVAV2Computer_cfi import * from RecoBTag.Combined.candidateCombinedMVAV2Computer_cfi import * from RecoBTag.Combined.negativeCombinedMVAV2Computer_cfi import * from RecoBTag.Combined.positiveCombinedMVAV2Computer_cfi import * from RecoBTag.Combined.candidateNegativeCombinedMVAV2Computer_cfi import * from RecoBTag.Combined.candidatePositiveCombinedMVAV2Computer_cfi import * # Charge tagger from RecoBTag.Combined.candidateChargeBTagComputer_cfi import *

42.076923

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1

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1

0

1

0

5

ab2e1af6cdd361fabf6451661599dabc6bdd954c

165

py

Python

impersonate_permissions/apps.py

yunojuno/django-impersonate-permissions

a5381d2393abec1f40379c6d894736d42a3bcc4a

[ "MIT" ]

1

2020-08-27T23:09:13.000Z

impersonate_permissions/apps.py

yunojuno/django-impersonate-permissions

a5381d2393abec1f40379c6d894736d42a3bcc4a

[ "MIT" ]

null

impersonate_permissions/apps.py

yunojuno/django-impersonate-permissions

a5381d2393abec1f40379c6d894736d42a3bcc4a

[ "MIT" ]

null

from django.apps import AppConfig class ImpersonatePermissionsConfig(AppConfig): name = "impersonate_permissions" verbose_name = "Impersonate permissions"

23.571429

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1

0

null

0

1

0

5

ab32b1a4264ec3f5ba7da42b9515c95714e468a9

197

py

Python

multiplication_table.py

Kunalpod/codewars

8dc1af2f3c70e209471045118fd88b3ea1e627e5

[ "MIT" ]

null

multiplication_table.py

Kunalpod/codewars

8dc1af2f3c70e209471045118fd88b3ea1e627e5

[ "MIT" ]

null

multiplication_table.py

Kunalpod/codewars

8dc1af2f3c70e209471045118fd88b3ea1e627e5

[ "MIT" ]

null

#Kunal Gautam #Codewars : @Kunalpod #Problem name: Multiplication Table #Problem level: 6 kyu def multiplication_table(row,col): return [[x*y for y in range(1,col+1)] for x in range(1,row+1)]

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7

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null

0

1

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1

0

5

ab3a71a0db61ca5f4e4c0abd50c1afd6d85da705

71

py

Python

utils/__init__.py

AnotherTwinkle/visualpi

2bdda36c0db121253a8fa7642d4191fe7f970f8b

[ "MIT" ]

null

utils/__init__.py

AnotherTwinkle/visualpi

2bdda36c0db121253a8fa7642d4191fe7f970f8b

[ "MIT" ]

null

utils/__init__.py

AnotherTwinkle/visualpi

2bdda36c0db121253a8fa7642d4191fe7f970f8b

[ "MIT" ]

null

from .PIVALUE import PI from .EVALUE import E from .PHIVALUE import PHI

23.666667

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0.666667

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0

1

0

1

0

1

0

5

ab3ed91ed2e75fe69791bd6e22eb7bc882730520

97

py

Python

boomerang/exceptions.py

kdelwat/messengerplatform

1979c9d9a56958043bef919d8ef36bac7f78f74a

[ "MIT" ]

2

2018-08-12T03:42:22.000Z

2019-09-17T22:32:07.000Z

boomerang/exceptions.py

kdelwat/messengerplatform

1979c9d9a56958043bef919d8ef36bac7f78f74a

[ "MIT" ]

2

2021-03-25T21:43:02.000Z

2021-11-15T17:46:59.000Z

boomerang/exceptions.py

kdelwat/messengerplatform

1979c9d9a56958043bef919d8ef36bac7f78f74a

[ "MIT" ]

1

2019-09-17T22:49:54.000Z

class BoomerangException(Exception): pass class MessengerAPIException(Exception): pass

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5

db552e30f59493adafec5bc734f035c7cb5c7253

122

py

Python

HelloWorld/TestModel/admin.py

luoyefeiwu/learn_python

e888537c538309d2600a302c0c6e92456dd785c0

[ "Apache-2.0" ]

null

HelloWorld/TestModel/admin.py

luoyefeiwu/learn_python

e888537c538309d2600a302c0c6e92456dd785c0

[ "Apache-2.0" ]

null

HelloWorld/TestModel/admin.py

luoyefeiwu/learn_python

e888537c538309d2600a302c0c6e92456dd785c0

[ "Apache-2.0" ]

null

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

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null

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5

db67564e58ece9298d5cf76db2440be1970aca6c

181

py

Python

backend/migrations/6-update-product-keysv3.py

threefoldtech/threefold_connect

8c918ecaf673bb6c7b3cdf6e358cc577087fcdfa

[ "Apache-2.0" ]

1

2021-12-22T12:34:29.000Z

backend/migrations/6-update-product-keysv3.py

threefoldtech/threefold_connect

8c918ecaf673bb6c7b3cdf6e358cc577087fcdfa

[ "Apache-2.0" ]

209

2021-01-18T15:08:21.000Z

2022-03-25T12:33:18.000Z

backend/migrations/6-update-product-keysv3.py

threefoldtech/threefold_connect

8c918ecaf673bb6c7b3cdf6e358cc577087fcdfa

[ "Apache-2.0" ]

2

2021-02-17T04:34:25.000Z

2021-05-18T06:32:37.000Z

from database import update_table update_productkeys_sql = """ ALTER TABLE productkeys ADD COLUMN activated_directly boolean default false; """ update_table(update_productkeys_sql)

45.25

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null

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1

0

5

db6b0342fefe74caf66b6e55d04a81bece6bbc62

610

py

Python

src/meltano/core/cli_messages.py

hashdeps/meltano

19c52ea35c226a3a95e6ba523b93637a878328cc

[ "MIT" ]

null

src/meltano/core/cli_messages.py

hashdeps/meltano

19c52ea35c226a3a95e6ba523b93637a878328cc

[ "MIT" ]

null

src/meltano/core/cli_messages.py

hashdeps/meltano

19c52ea35c226a3a95e6ba523b93637a878328cc

[ "MIT" ]

null

"""Holds formatted CLI messages.""" GREETING = """ ████ █████ ░░███ ░░███ █████████████ ██████ ░███ ███████ ██████ ████████ ██████ ░░███░░███░░███ ███░░███ ░███ ░░░███░ ░░░░░███ ░░███░░███ ███░░███ ░███ ░███ ░███ ░███████ ░███ ░███ ███████ ░███ ░███ ░███ ░███ ░███ ░███ ░███ ░███░░░ ░███ ░███ ███ ███░░███ ░███ ░███ ░███ ░███ █████░███ █████░░██████ █████ ░░█████ ░░████████ ████ █████░░██████ ░░░░░ ░░░ ░░░░░ ░░░░░░ ░░░░░ ░░░░░ ░░░░░░░░ ░░░░ ░░░░░ ░░░░░░ ~ The DataOps OS ~ """

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0.5

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null

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5

db745aa56212af6a9c20e06ee9e4e5d6e27cf3c3

25,447

py

Python

tensorflow/contrib/quantize/python/quantize_parameterized_test.py

tianyapiaozi/tensorflow

fb3ce0467766a8e91f1da0ad7ada7c24fde7a73a

[ "Apache-2.0" ]

71

2017-05-25T16:02:15.000Z

2021-06-09T16:08:08.000Z

tensorflow/contrib/quantize/python/quantize_parameterized_test.py

tianyapiaozi/tensorflow

fb3ce0467766a8e91f1da0ad7ada7c24fde7a73a

[ "Apache-2.0" ]

133

2017-04-26T16:49:49.000Z

2019-10-15T11:39:26.000Z

tensorflow/contrib/quantize/python/quantize_parameterized_test.py

tianyapiaozi/tensorflow

fb3ce0467766a8e91f1da0ad7ada7c24fde7a73a

[ "Apache-2.0" ]

31

2018-09-11T02:17:17.000Z

2021-12-15T10:33:35.000Z

# Copyright 2017 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Parameterized unit tests for quantizing a Tensorflow graph.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow.contrib.layers.python.layers import layers from tensorflow.contrib.quantize.python import fold_batch_norms from tensorflow.contrib.quantize.python import quantize from tensorflow.python.framework import ops from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import init_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import nn_ops from tensorflow.python.ops import variable_scope from tensorflow.python.platform import googletest batch_norm = layers.batch_norm conv2d = layers.conv2d fully_connected = layers.fully_connected separable_conv2d = layers.separable_conv2d class QuantizeTest(test_util.TensorFlowTestCase): def _RunWithoutBatchNormTestOverParameters(self, test_fn): # TODO(suharshs): Use parameterized test once OSS TF supports it. parameters_list = [ # (activation, activation_op_name, with_bypass, delay) (nn_ops.relu6, 'Relu6', False, None), (nn_ops.relu, 'Relu', False, None), (array_ops.identity, 'Identity', False, None), (nn_ops.relu6, 'Relu6', False, 5000), (nn_ops.relu, 'Relu', False, 5000), (array_ops.identity, 'Identity', False, 5000), (nn_ops.relu6, 'Relu6', True, None), (nn_ops.relu, 'Relu', True, None), (array_ops.identity, 'Identity', True, None), (nn_ops.relu6, 'Relu6', True, 5000), (nn_ops.relu, 'Relu', True, 5000), (array_ops.identity, 'Identity', True, 5000), ] for params in parameters_list: # Test everything with resource variables and normal variables. test_fn(params[0], params[1], params[2], params[3], False) test_fn(params[0], params[1], params[2], params[3], True) def _AssertCorrectQuantizedGraphWithoutBatchNorm( self, graph, scope, layer, activation_op_name, with_bypass, delay, use_resource): quantization_node_name = 'FakeQuantWithMinMaxVars' weights_quant = graph.get_operation_by_name(scope + '/weights_quant/' + quantization_node_name) self.assertEqual(weights_quant.type, quantization_node_name) # Assemble the expected inputs. if use_resource: expected_inputs = [ scope + '/weights_quant/FakeQuantWithMinMaxVars/ReadVariableOp', scope + '/weights_quant/FakeQuantWithMinMaxVars/ReadVariableOp_1', ] if layer == 'DepthwiseConv2dNative': expected_inputs.append(scope + '/depthwise/ReadVariableOp') else: expected_inputs.append(scope + '/' + layer + '/ReadVariableOp') else: expected_inputs = [ scope + '/weights_quant/AssignMinLast', scope + '/weights_quant/AssignMaxLast', ] if layer == 'DepthwiseConv2dNative': expected_inputs.append(scope + '/depthwise_weights/read') else: expected_inputs.append(scope + '/weights/read') self._AssertInputOpsAre(weights_quant, expected_inputs) if delay and delay > 0: output_op_name = scope + '/weights_quant/delayed_quant/Switch_1' else: if layer == 'DepthwiseConv2dNative': output_op_name = scope + '/depthwise' else: output_op_name = scope + '/' + layer self._AssertOutputGoesToOps(weights_quant, graph, [output_op_name]) if with_bypass: conv_quant = graph.get_operation_by_name(scope + '/conv_quant/' + quantization_node_name) self.assertEqual(conv_quant.type, quantization_node_name) if use_resource: expected_inputs = [ scope + '/conv_quant/FakeQuantWithMinMaxVars/ReadVariableOp', scope + '/conv_quant/FakeQuantWithMinMaxVars/ReadVariableOp_1', scope + '/BiasAdd', ] else: expected_inputs = [ scope + '/conv_quant/AssignMinEma', scope + '/conv_quant/AssignMaxEma', scope + '/BiasAdd' ] self._AssertInputOpsAre(conv_quant, expected_inputs) output_op_name = (scope + '/conv_quant/delayed_quant/Switch_1' if delay else 'test/Add') self._AssertOutputGoesToOps(conv_quant, graph, [output_op_name]) act_quant = graph.get_operation_by_name('test/act_quant/' + quantization_node_name) self.assertEqual(act_quant.type, quantization_node_name) if use_resource: expected_inputs = [ 'test/act_quant/FakeQuantWithMinMaxVars/ReadVariableOp', 'test/act_quant/FakeQuantWithMinMaxVars/ReadVariableOp_1', 'test/' + activation_op_name, ] else: expected_inputs = [ 'test/act_quant/AssignMinEma', 'test/act_quant/AssignMaxEma', 'test/' + activation_op_name ] self._AssertInputOpsAre(act_quant, expected_inputs) output_op_name = ('test/act_quant/delayed_quant/Switch_1' if delay else 'control_dependency') self._AssertOutputGoesToOps(act_quant, graph, [output_op_name]) self._AssertIdempotent(graph) def testQuantize_Conv2dWithoutBatchNorm(self): self._RunWithoutBatchNormTestOverParameters( self._TestQuantize_Conv2dWithoutBatchNorm) def _TestQuantize_Conv2dWithoutBatchNorm(self, activation, activation_op_name, with_bypass, delay, use_resource): """Tests quantization: inputs -> Conv2d no batch norm -> Activation. Args: activation: Callable that returns an Operation, a factory method for the Activation. activation_op_name: String, name of the Activation operation. with_bypass: Bool, when true there is an extra connection added from inputs to just before Activation. delay: Int (optional), delay in number of steps until quantization starts. use_resource: Bool, when true uses resource variables. """ graph = ops.Graph() with graph.as_default(): variable_scope.get_variable_scope().set_use_resource(use_resource) batch_size, height, width, depth = 5, 128, 128, 3 inputs = array_ops.zeros((batch_size, height, width, depth)) stride = 1 if with_bypass else 2 out_depth = 3 if with_bypass else 32 activation_fn = None if with_bypass else activation scope = 'test/test2' if with_bypass else 'test' node = conv2d( inputs, out_depth, [5, 5], stride=stride, padding='SAME', weights_initializer=self._WeightInit(0.09), activation_fn=activation_fn, scope=scope) if with_bypass: node = math_ops.add(inputs, node, name='test/Add') node = activation(node, name='test/' + activation_op_name) update_barrier = control_flow_ops.no_op(name='update_barrier') with ops.control_dependencies([update_barrier]): array_ops.identity(node, name='control_dependency') quantize.Quantize(graph, True, quant_delay=delay) self._AssertCorrectQuantizedGraphWithoutBatchNorm( graph, scope, 'Conv2D', activation_op_name, with_bypass, delay, use_resource) def testQuantize_FCWithoutBatchNorm(self): self._RunWithoutBatchNormTestOverParameters( self._TestQuantize_FCWithoutBatchNorm) def _TestQuantize_FCWithoutBatchNorm(self, activation, activation_op_name, with_bypass, delay, use_resource): """Tests quantization: inputs -> FC no batch norm -> Activation. Args: activation: Callable that returns an Operation, a factory method for the Activation. activation_op_name: String, name of the Activation operation. with_bypass: Bool, when true there is an extra connection added from inputs to just before Activation. delay: Int (optional), delay in number of steps until quantization starts. use_resource: Bool, when true uses resource variables. """ graph = ops.Graph() with graph.as_default(): variable_scope.get_variable_scope().set_use_resource(use_resource) batch_size, depth = 5, 256 inputs = array_ops.zeros((batch_size, depth)) out_depth = 256 if with_bypass else 128 activation_fn = None if with_bypass else activation scope = 'test/test2' if with_bypass else 'test' node = fully_connected( inputs, out_depth, weights_initializer=self._WeightInit(0.03), activation_fn=activation_fn, scope=scope) if with_bypass: node = math_ops.add(inputs, node, name='test/Add') node = activation(node, name='test/' + activation_op_name) update_barrier = control_flow_ops.no_op(name='update_barrier') with ops.control_dependencies([update_barrier]): array_ops.identity(node, name='control_dependency') quantize.Quantize(graph, True, quant_delay=delay) self._AssertCorrectQuantizedGraphWithoutBatchNorm( graph, scope, 'MatMul', activation_op_name, with_bypass, delay, use_resource) def testQuantize_DepthwiseConv2dWithoutBatchNorm(self): self._RunWithoutBatchNormTestOverParameters( self._TestQuantize_DepthwiseConv2dWithoutBatchNorm) def _TestQuantize_DepthwiseConv2dWithoutBatchNorm( self, activation, activation_op_name, with_bypass, delay, use_resource): """Tests quantization: inputs -> DWConv2d no batch norm -> Activation. Args: activation: Callable that returns an Operation, a factory method for the Activation. activation_op_name: String, name of the Activation operation. with_bypass: Bool, when true there is an extra connection added from inputs to just before Activation. delay: Int (optional), delay in number of steps until quantization starts. use_resource: Bool, when true uses resource variables. """ graph = ops.Graph() with graph.as_default(): variable_scope.get_variable_scope().set_use_resource(use_resource) batch_size, height, width, depth = 5, 128, 128, 3 inputs = array_ops.zeros((batch_size, height, width, depth)) stride = 1 if with_bypass else 2 activation_fn = None if with_bypass else activation scope = 'test/test2' if with_bypass else 'test' node = separable_conv2d( inputs, None, [5, 5], stride=stride, depth_multiplier=1.0, padding='SAME', weights_initializer=self._WeightInit(0.09), activation_fn=activation_fn, scope=scope) if with_bypass: node = math_ops.add(inputs, node, name='test/Add') node = activation(node, name='test/' + activation_op_name) update_barrier = control_flow_ops.no_op(name='update_barrier') with ops.control_dependencies([update_barrier]): array_ops.identity(node, name='control_dependency') quantize.Quantize(graph, True, quant_delay=delay) self._AssertCorrectQuantizedGraphWithoutBatchNorm( graph, scope, 'DepthwiseConv2dNative', activation_op_name, with_bypass, delay, use_resource) def _RunBatchNormTestOverParameters(self, test_fn): # TODO(suharshs): Use parameterized test once OSS TF supports it. parameters_list = [ # (activation, activation_op_name, with_bypass, delay, fused_batch_norm) (nn_ops.relu6, 'Relu6', False, None, False), (nn_ops.relu, 'Relu', False, None, False), (array_ops.identity, 'Identity', False, None, False), (nn_ops.relu6, 'Relu6', False, 5000, False), (nn_ops.relu, 'Relu', False, 5000, False), (array_ops.identity, 'Identity', False, 5000, False), (nn_ops.relu6, 'Relu6', True, None, False), (nn_ops.relu, 'Relu', True, None, False), (array_ops.identity, 'Identity', True, None, False), (nn_ops.relu6, 'Relu6', True, 5000, False), (nn_ops.relu, 'Relu', True, 5000, False), (array_ops.identity, 'Identity', True, 5000, False), (nn_ops.relu6, 'Relu6', False, None, True), (nn_ops.relu, 'Relu', False, None, True), (array_ops.identity, 'Identity', False, None, True), (nn_ops.relu6, 'Relu6', False, 5000, True), (nn_ops.relu, 'Relu', False, 5000, True), (array_ops.identity, 'Identity', False, 5000, True), (nn_ops.relu6, 'Relu6', True, None, True), (nn_ops.relu, 'Relu', True, None, True), (array_ops.identity, 'Identity', True, None, True), (nn_ops.relu6, 'Relu6', True, 5000, True), (nn_ops.relu, 'Relu', True, 5000, True), (array_ops.identity, 'Identity', True, 5000, True) ] for params in parameters_list: # Test everything with resource variables and normal variables. test_fn(params[0], params[1], params[2], params[3], params[4], False) test_fn(params[0], params[1], params[2], params[3], params[4], True) def _AssertCorrectQuantizedGraphWithBatchNorm(self, graph, scope, layer, activation_op_name, with_bypass, delay, use_resource): quantization_node_name = 'FakeQuantWithMinMaxVars' weights_quant = graph.get_operation_by_name( scope + '/weights_quant/' + quantization_node_name) self.assertEqual(weights_quant.type, quantization_node_name) if use_resource: expected_inputs = [ scope + '/weights_quant/FakeQuantWithMinMaxVars/ReadVariableOp', scope + '/weights_quant/FakeQuantWithMinMaxVars/ReadVariableOp_1', ] else: expected_inputs = [ scope + '/weights_quant/' + 'AssignMinLast', scope + '/weights_quant/' + 'AssignMaxLast' ] expected_inputs.append(scope + '/mul_fold') self._AssertInputOpsAre(weights_quant, expected_inputs) if layer == 'DepthwiseConv2dNative': output_op_name = scope + ('/weights_quant/delayed_quant/Switch_1' if delay else '/depthwise_Fold') else: output_op_name = scope + ('/weights_quant/delayed_quant/Switch_1' if delay else '/' + layer + '_Fold') self._AssertOutputGoesToOps(weights_quant, graph, [output_op_name]) if with_bypass: conv_quant = graph.get_operation_by_name( scope + '/conv_quant/' + quantization_node_name) self.assertEqual(conv_quant.type, quantization_node_name) if use_resource: expected_inputs = [ scope + '/conv_quant/FakeQuantWithMinMaxVars/ReadVariableOp', scope + '/conv_quant/FakeQuantWithMinMaxVars/ReadVariableOp_1', ] else: expected_inputs = [ scope + '/conv_quant/AssignMinEma', scope + '/conv_quant/AssignMaxEma', ] expected_inputs.append(scope + '/add_fold') self._AssertInputOpsAre(conv_quant, expected_inputs) output_op_name = ( scope + '/conv_quant/delayed_quant/Switch_1' if delay else 'test/Add') self._AssertOutputGoesToOps(conv_quant, graph, [output_op_name]) act_quant = graph.get_operation_by_name( 'test/act_quant/' + quantization_node_name) self.assertEqual(act_quant.type, quantization_node_name) if use_resource: expected_inputs = [ 'test/act_quant/FakeQuantWithMinMaxVars/ReadVariableOp', 'test/act_quant/FakeQuantWithMinMaxVars/ReadVariableOp_1', ] else: expected_inputs = [ 'test/act_quant/AssignMinEma', 'test/act_quant/AssignMaxEma', ] expected_inputs.append('test/' + activation_op_name) self._AssertInputOpsAre(act_quant, expected_inputs) output_op_name = ('test/act_quant/delayed_quant/Switch_1' if delay else 'control_dependency') self._AssertOutputGoesToOps(act_quant, graph, [output_op_name]) self._AssertIdempotent(graph) def testQuantize_Conv2dWithBatchNorm(self): self._RunBatchNormTestOverParameters(self._TestQuantize_Conv2dWithBatchNorm) def _TestQuantize_Conv2dWithBatchNorm(self, activation, activation_op_name, with_bypass, delay, fused_batch_norm, use_resource): """Tests quantization: inputs -> Conv2d with batch norm -> Activation. Args: activation: Callable that returns an Operation, a factory method for the Activation. activation_op_name: String, name of the Activation operation. with_bypass: Bool, when true there is an extra connection added from inputs to just before Activation. delay: Int (optional), delay in number of steps until quantization starts. fused_batch_norm: Bool, when true use FusedBatchNorm. use_resource: Bool, when true uses resource variables. """ graph = ops.Graph() with graph.as_default(): variable_scope.get_variable_scope().set_use_resource(use_resource) batch_size, height, width, depth = 5, 128, 128, 3 inputs = array_ops.zeros((batch_size, height, width, depth)) stride = 1 if with_bypass else 2 out_depth = 3 if with_bypass else 32 scope = 'test/test2' if with_bypass else 'test' node = conv2d( inputs, out_depth, [5, 5], stride=stride, padding='SAME', weights_initializer=self._WeightInit(0.09), activation_fn=None, normalizer_fn=batch_norm, normalizer_params=self._BatchNormParams(fused_batch_norm), scope=scope) # Manually add a bypass (optional) and an activation. if with_bypass: node = math_ops.add(inputs, node, name='test/Add') node = activation(node, name='test/' + activation_op_name) update_barrier = control_flow_ops.no_op(name='update_barrier') with ops.control_dependencies([update_barrier]): array_ops.identity(node, name='control_dependency') fold_batch_norms.FoldBatchNorms(graph, is_training=True) quantize.Quantize(graph, True, quant_delay=delay) self._AssertCorrectQuantizedGraphWithBatchNorm( graph, scope, 'Conv2D', activation_op_name, with_bypass, delay, use_resource) def testQuantize_FCWithBatchNorm(self): self._RunBatchNormTestOverParameters(self._TestQuantize_FCWithBatchNorm) def _TestQuantize_FCWithBatchNorm(self, activation, activation_op_name, with_bypass, delay, fused_batch_norm, use_resource): """Tests quantization: inputs -> FC with batch norm -> Activation. Args: activation: Callable that returns an Operation, a factory method for the Activation. activation_op_name: String, name of the Activation operation. with_bypass: Bool, when true there is an extra connection added from inputs to just before Activation. delay: Int (optional), delay in number of steps until quantization starts. fused_batch_norm: Bool, when true use FusedBatchNorm. use_resource: Bool, when true uses resource variables. """ graph = ops.Graph() with graph.as_default(): variable_scope.get_variable_scope().set_use_resource(use_resource) batch_size, depth = 5, 256 inputs = array_ops.zeros((batch_size, depth)) out_depth = 256 if with_bypass else 128 scope = 'test/test2' if with_bypass else 'test' node = fully_connected( inputs, out_depth, weights_initializer=self._WeightInit(0.03), activation_fn=None, normalizer_fn=batch_norm, normalizer_params=self._BatchNormParams(fused_batch_norm), scope=scope) # Manually add a bypass (optional) and an activation. if with_bypass: node = math_ops.add(inputs, node, name='test/Add') node = activation(node, name='test/' + activation_op_name) update_barrier = control_flow_ops.no_op(name='update_barrier') with ops.control_dependencies([update_barrier]): array_ops.identity(node, name='control_dependency') fold_batch_norms.FoldBatchNorms(graph, is_training=True) quantize.Quantize(graph, True, quant_delay=delay) self._AssertCorrectQuantizedGraphWithBatchNorm( graph, scope, 'MatMul', activation_op_name, with_bypass, delay, use_resource) def testQuantize_DepthwiseConv2dWithBatchNorm(self): self._RunBatchNormTestOverParameters( self._TestQuantize_DepthwiseConv2dWithBatchNorm) def _TestQuantize_DepthwiseConv2dWithBatchNorm( self, activation, activation_op_name, with_bypass, delay, fused_batch_norm, use_resource): """Tests quantization: inputs -> DWConv2d with batch norm -> Activation. Args: activation: Callable that returns an Operation, a factory method for the Activation. activation_op_name: String, name of the Activation operation. with_bypass: Bool, when true there is an extra connection added from inputs to just before Activation. delay: Int (optional), delay in number of steps until quantization starts. fused_batch_norm: Bool, when true use FusedBatchNorm. use_resource: Bool, when true uses resource variables. """ graph = ops.Graph() with graph.as_default(): variable_scope.get_variable_scope().set_use_resource(use_resource) batch_size, height, width, depth = 5, 128, 128, 3 inputs = array_ops.zeros((batch_size, height, width, depth)) stride = 1 if with_bypass else 2 scope = 'test/test2' if with_bypass else 'test' node = separable_conv2d( inputs, None, [5, 5], stride=stride, depth_multiplier=1.0, padding='SAME', weights_initializer=self._WeightInit(0.09), activation_fn=None, normalizer_fn=batch_norm, normalizer_params=self._BatchNormParams(fused_batch_norm), scope=scope) # Manually add a bypass (optional) and an activation. if with_bypass: node = math_ops.add(inputs, node, name='test/Add') node = activation(node, name='test/' + activation_op_name) update_barrier = control_flow_ops.no_op(name='update_barrier') with ops.control_dependencies([update_barrier]): array_ops.identity(node, name='control_dependency') fold_batch_norms.FoldBatchNorms(graph, is_training=True) quantize.Quantize(graph, True, quant_delay=delay) self._AssertCorrectQuantizedGraphWithBatchNorm( graph, scope, 'DepthwiseConv2dNative', activation_op_name, with_bypass, delay, use_resource) def _AssertIdempotent(self, graph): # Ensure that calling the rewrite again doesn't change the graph. graph_def_before = str(graph.as_graph_def()) with graph.as_default(): # Ensuring that calling the rewrite again doesn't add more nodes. fold_batch_norms.FoldBatchNorms(graph, is_training=True) quantize.Quantize(graph, True) graph_def_after = str(graph.as_graph_def()) self.assertEqual(graph_def_before, graph_def_after) def _BatchNormParams(self, fused=False): return {'center': True, 'scale': True, 'decay': 1.0 - 0.003, 'fused': fused} def _WeightInit(self, stddev): """Returns truncated normal variable initializer. Function is defined purely to shorten the name so that it stops wrapping. Args: stddev: Standard deviation of normal variable. Returns: An initialized that initializes with a truncated normal variable. """ return init_ops.truncated_normal_initializer(stddev=stddev) def _AssertInputOpsAre(self, op, in_op_names): """Asserts that all inputs to op come from in_op_names (disregarding order). Args: op: Operation to check inputs for. in_op_names: List of strings, operations where all op's inputs should come from. """ expected_inputs = [in_op_name + ':0' for in_op_name in in_op_names] self.assertItemsEqual([t.name for t in op.inputs], expected_inputs) def _AssertOutputGoesToOps(self, op, graph, out_op_names): """Asserts that outputs from op go to out_op_names (and perhaps others). Args: op: Operation to check outputs for. graph: Graph where output operations are located. out_op_names: List of strings, operations where op's outputs should go. """ for out_op_name in out_op_names: out_op = graph.get_operation_by_name(out_op_name) self.assertIn(op.outputs[0].name, [str(t.name) for t in out_op.inputs]) if __name__ == '__main__': googletest.main()

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dbccbe75eb50798ed494eb3e0ff95b89b20c953d

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py

Python

applications/CoSimulationApplication/python_scripts/factories/data_transfer_operator_factory.py

lcirrott/Kratos

8406e73e0ad214c4f89df4e75e9b29d0eb4a47ea

[ "BSD-4-Clause" ]

2

2019-10-25T09:28:10.000Z

2019-11-21T12:51:46.000Z

applications/CoSimulationApplication/python_scripts/factories/data_transfer_operator_factory.py

lcirrott/Kratos

8406e73e0ad214c4f89df4e75e9b29d0eb4a47ea

[ "BSD-4-Clause" ]

13

2019-10-07T12:06:51.000Z

2020-02-18T08:48:33.000Z

applications/CoSimulationApplication/python_scripts/factories/data_transfer_operator_factory.py

lcirrott/Kratos

8406e73e0ad214c4f89df4e75e9b29d0eb4a47ea

[ "BSD-4-Clause" ]

null

from __future__ import print_function, absolute_import, division # makes these scripts backward compatible with python 2.6 and 2.7 from KratosMultiphysics.CoSimulationApplication.factories import base_factory def CreateDataTransferOperator(coupling_operation_settings): """This function creates and returns the Data Transfer Operator used for CoSimulation""" return base_factory.Create(coupling_operation_settings, [], "KratosMultiphysics.CoSimulationApplication.data_transfer_operators")

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91617b1083da208c320f58f95e127d23fd4bc57c

113

py

Python

mirumon/application/repo_protocol.py

mirumon/mirumon-backend

9b4d914b67dcc839ed8264f470e822dc22c98ad7

[ "MIT" ]

19

2020-01-25T22:52:09.000Z

2022-03-20T13:45:10.000Z

mirumon/application/repo_protocol.py

mirumon/mirumon-backend

9b4d914b67dcc839ed8264f470e822dc22c98ad7

[ "MIT" ]

15

2019-10-07T18:18:40.000Z

2020-10-17T15:47:39.000Z

mirumon/application/repo_protocol.py

mirumon/mirumon-backend

9b4d914b67dcc839ed8264f470e822dc22c98ad7

[ "MIT" ]

1

2020-01-20T14:16:29.000Z

from typing import Protocol class Repository(Protocol): """Base repository interface for typing and DI."""

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916753326eb3fec0ec7444f8ffa36d4c64dc3c4b

39

py

Python

tests/__init__.py

ltiao/lumberjax

5033b0c01ae86f15f2932395f4bb575ca853c8d2

[ "Apache-2.0" ]

null

tests/__init__.py

ltiao/lumberjax

5033b0c01ae86f15f2932395f4bb575ca853c8d2

[ "Apache-2.0" ]

null

tests/__init__.py

ltiao/lumberjax

5033b0c01ae86f15f2932395f4bb575ca853c8d2

[ "Apache-2.0" ]

null

"""Unit test package for lumberjax."""

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5

9185ee1cf5397ad396a9a073af6892d2f44f4b31

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py

Python

scripts/generate/headers.py

panchambharadwaj/remot3.it-connect

354293f5654f94c7254f53258ebab1c619215041

[ "MIT" ]

null

scripts/generate/headers.py

panchambharadwaj/remot3.it-connect

354293f5654f94c7254f53258ebab1c619215041

[ "MIT" ]

null

scripts/generate/headers.py

panchambharadwaj/remot3.it-connect

354293f5654f94c7254f53258ebab1c619215041

[ "MIT" ]

null

class GenerateHeaders(object): def __init__(self, developer_key): self.developer_key = developer_key def get_login_headers(self): return { 'developerkey': self.developer_key, 'content-type': "application/json", 'cache-control': "no-cache" } def get_session_headers(self, token): return { 'Content-Type': "application/json", 'developerkey': self.developer_key, 'token': token, }

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null

0

1

0

5

91918513b818ae031c0fe4ce023198b4dee7a6b0

6,139

py

Python

tests/test_rotated.py

tatuanb/monai_V1

41e492b61c78bb3c303f38b03fe9fdc74a3c2e96

[ "Apache-2.0" ]

1

2020-11-13T23:13:23.000Z

tests/test_rotated.py

catherine1996cn/MONAI

ff9bbfa82763de46cbac75553e340633e3d84ecb

[ "Apache-2.0" ]

2

2020-11-13T23:15:00.000Z

2020-11-16T14:54:08.000Z

tests/test_rotated.py

catherine1996cn/MONAI

ff9bbfa82763de46cbac75553e340633e3d84ecb

[ "Apache-2.0" ]

1

2021-11-18T22:37:40.000Z

# Copyright 2020 - 2021 MONAI Consortium # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # http://www.apache.org/licenses/LICENSE-2.0 # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest from typing import List, Tuple import numpy as np import scipy.ndimage import torch from parameterized import parameterized from monai.transforms import Rotated from tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, NumpyImageTestCase3D TEST_CASES_2D: List[Tuple] = [] for p in TEST_NDARRAYS: TEST_CASES_2D.append((p, -np.pi / 6, False, "bilinear", "border", False)) TEST_CASES_2D.append((p, -np.pi / 4, True, "bilinear", "border", False)) TEST_CASES_2D.append((p, np.pi / 4.5, True, "nearest", "reflection", False)) TEST_CASES_2D.append((p, -np.pi, False, "nearest", "zeros", False)) TEST_CASES_2D.append((p, np.pi / 2, False, "bilinear", "zeros", True)) TEST_CASES_3D: List[Tuple] = [] for p in TEST_NDARRAYS: TEST_CASES_3D.append((p, -np.pi / 6, False, "bilinear", "border", False)) TEST_CASES_3D.append((p, -np.pi / 4, True, "bilinear", "border", False)) TEST_CASES_3D.append((p, np.pi / 4.5, True, "nearest", "reflection", False)) TEST_CASES_3D.append((p, -np.pi, False, "nearest", "zeros", False)) TEST_CASES_3D.append((p, np.pi / 2, False, "bilinear", "zeros", True)) class TestRotated2D(NumpyImageTestCase2D): @parameterized.expand(TEST_CASES_2D) def test_correct_results(self, im_type, angle, keep_size, mode, padding_mode, align_corners): rotate_fn = Rotated(("img", "seg"), angle, keep_size, (mode, "nearest"), padding_mode, align_corners) rotated = rotate_fn({"img": im_type(self.imt[0]), "seg": im_type(self.segn[0])}) if keep_size: np.testing.assert_allclose(self.imt[0].shape, rotated["img"].shape) _order = 0 if mode == "nearest" else 1 if padding_mode == "border": _mode = "nearest" elif padding_mode == "reflection": _mode = "reflect" else: _mode = "constant" expected = scipy.ndimage.rotate( self.imt[0, 0], -np.rad2deg(angle), (0, 1), not keep_size, order=_order, mode=_mode, prefilter=False ) for k, v in rotated.items(): rotated[k] = v.cpu() if isinstance(v, torch.Tensor) else v good = np.sum(np.isclose(expected, rotated["img"][0], atol=1e-3)) self.assertLessEqual(np.abs(good - expected.size), 5, "diff at most 5 pixels") expected = scipy.ndimage.rotate( self.segn[0, 0], -np.rad2deg(angle), (0, 1), not keep_size, order=0, mode=_mode, prefilter=False ) expected = np.stack(expected).astype(int) self.assertLessEqual(np.count_nonzero(expected != rotated["seg"][0]), 30) class TestRotated3D(NumpyImageTestCase3D): @parameterized.expand(TEST_CASES_3D) def test_correct_results(self, im_type, angle, keep_size, mode, padding_mode, align_corners): rotate_fn = Rotated(("img", "seg"), [0, angle, 0], keep_size, (mode, "nearest"), padding_mode, align_corners) rotated = rotate_fn({"img": im_type(self.imt[0]), "seg": im_type(self.segn[0])}) if keep_size: np.testing.assert_allclose(self.imt[0].shape, rotated["img"].shape) _order = 0 if mode == "nearest" else 1 if padding_mode == "border": _mode = "nearest" elif padding_mode == "reflection": _mode = "reflect" else: _mode = "constant" expected = scipy.ndimage.rotate( self.imt[0, 0], np.rad2deg(angle), (0, 2), not keep_size, order=_order, mode=_mode, prefilter=False ) for k, v in rotated.items(): rotated[k] = v.cpu() if isinstance(v, torch.Tensor) else v good = np.sum(np.isclose(expected.astype(np.float32), rotated["img"][0], atol=1e-3)) self.assertLessEqual(np.abs(good - expected.size), 5, "diff at most 5 voxels.") expected = scipy.ndimage.rotate( self.segn[0, 0], np.rad2deg(angle), (0, 2), not keep_size, order=0, mode=_mode, prefilter=False ) expected = np.stack(expected).astype(int) self.assertLessEqual(np.count_nonzero(expected != rotated["seg"][0]), 160) class TestRotated3DXY(NumpyImageTestCase3D): @parameterized.expand(TEST_CASES_3D) def test_correct_results(self, im_type, angle, keep_size, mode, padding_mode, align_corners): rotate_fn = Rotated(("img", "seg"), [0, 0, angle], keep_size, (mode, "nearest"), padding_mode, align_corners) rotated = rotate_fn({"img": im_type(self.imt[0]), "seg": im_type(self.segn[0])}) if keep_size: np.testing.assert_allclose(self.imt[0].shape, rotated["img"].shape) _order = 0 if mode == "nearest" else 1 if padding_mode == "border": _mode = "nearest" elif padding_mode == "reflection": _mode = "reflect" else: _mode = "constant" expected = scipy.ndimage.rotate( self.imt[0, 0], -np.rad2deg(angle), (0, 1), not keep_size, order=_order, mode=_mode, prefilter=False ) for k, v in rotated.items(): rotated[k] = v.cpu() if isinstance(v, torch.Tensor) else v good = np.sum(np.isclose(expected, rotated["img"][0], atol=1e-3)) self.assertLessEqual(np.abs(good - expected.size), 5, "diff at most 5 voxels") expected = scipy.ndimage.rotate( self.segn[0, 0], -np.rad2deg(angle), (0, 1), not keep_size, order=0, mode=_mode, prefilter=False ) expected = np.stack(expected).astype(int) self.assertLessEqual(np.count_nonzero(expected != rotated["seg"][0]), 160) if __name__ == "__main__": unittest.main()

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919365b7f4b0e14645574583244a4b2fe659b8ec

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py

Python

src/backup/feature_engineering/feature_generator/categorical_var_encoder.py

wu-uw/OpenCompetition

9aa9d7a50ada1deb653d295dd8a7fe46321b9094

[ "Apache-2.0" ]

15

2019-12-22T14:26:47.000Z

2020-11-02T10:57:37.000Z

src/backup/feature_engineering/feature_generator/categorical_var_encoder.py

GT-JLU/OpenCompetition

5262fc5fa7efd7b483c1dc09cb7747dd75e37175

[ "Apache-2.0" ]

2

2020-02-03T07:10:11.000Z

2020-02-11T16:38:56.000Z

src/backup/feature_engineering/feature_generator/categorical_var_encoder.py

GT-JLU/OpenCompetition

5262fc5fa7efd7b483c1dc09cb7747dd75e37175

[ "Apache-2.0" ]

12

2020-01-06T14:16:52.000Z

2020-05-23T14:12:30.000Z

# coding = 'utf-8' def cat_encoder(df_list, method_list): """ Parameters ---------- df_list method_list: Can be one hot, ordinal or hashmap Returns ------- """

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919ade49e3634994a25a718d64d987c93c4f4f29

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py

Python

deimos/err.py

tfukushima/deimos

d4b84e24deac7ec3a26f651f2c69664c431e0c79

[ "Apache-2.0" ]

44

2015-01-02T00:24:05.000Z

2018-01-08T13:14:52.000Z

deimos/err.py

tfukushima/deimos

d4b84e24deac7ec3a26f651f2c69664c431e0c79

[ "Apache-2.0" ]

2

2017-01-30T12:49:16.000Z

2018-08-06T23:26:52.000Z

deimos/err.py

tfukushima/deimos

d4b84e24deac7ec3a26f651f2c69664c431e0c79

[ "Apache-2.0" ]

6

2015-01-29T02:20:09.000Z

2019-03-05T13:26:43.000Z

class Err(RuntimeError): pass

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91ad7e0431a676a52b067614059eea1837fe9dee

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py

Python

__init__.py

BARarch/qtimer

03d3f1be5dc08beeaab07b89ce48fb4f4c915e38

[ "MIT" ]

null

__init__.py

BARarch/qtimer

03d3f1be5dc08beeaab07b89ce48fb4f4c915e38

[ "MIT" ]

null

__init__.py

BARarch/qtimer

03d3f1be5dc08beeaab07b89ce48fb4f4c915e38

[ "MIT" ]

null

from .timers import timeit #from .argformater import formatArgs

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5

37dd5f0922c7cef41c25e202e86828854c7575ba

170

py

Python

ee250/lab04/part3/ultrasonicServer.py

lyashley/GrovePi-EE250

d337d6c5dea7f9c1548d75e6ac3f66e7883e315d

[ "MIT" ]

null

ee250/lab04/part3/ultrasonicServer.py

lyashley/GrovePi-EE250

d337d6c5dea7f9c1548d75e6ac3f66e7883e315d

[ "MIT" ]

null

ee250/lab04/part3/ultrasonicServer.py

lyashley/GrovePi-EE250

d337d6c5dea7f9c1548d75e6ac3f66e7883e315d

[ "MIT" ]

null

#Ultrasonic Sensor Server # # This code runs on your VM and receives a stream of packets holding ultrasonic # sensor data and prints it to stdout. Use a UDP socket here.

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5

530627a4d099e0f2ef44a53db21ed08643756d5f

206

py

Python

pcan/core/evaluation/__init__.py

SysCV/pcan

06416f1c96b7a86754828582d9a95b9ce0d327ba

[ "Apache-2.0" ]

271

2021-11-24T16:57:54.000Z

2022-03-31T02:00:38.000Z

pcan/core/evaluation/__init__.py

msg4rajesh/pcan

5328f42349e19ff1acaccd2c776804df972b9afe

[ "Apache-2.0" ]

10

2021-11-28T10:48:13.000Z

2022-03-11T09:59:30.000Z

pcan/core/evaluation/__init__.py

msg4rajesh/pcan

5328f42349e19ff1acaccd2c776804df972b9afe

[ "Apache-2.0" ]

36

2021-11-25T07:43:05.000Z

2022-03-08T04:08:48.000Z

from .eval_hooks import EvalHook, DistEvalHook from .mot import eval_mot from .mots import eval_mots from .mot import xyxy2xywh __all__ = ['eval_mot', 'eval_mots', 'EvalHook', 'DistEvalHook', 'xyxy2xywh']

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0.357143

0.266667

0.173333

0

0.01105

0.121359

206

6

77

34.333333

0.81768

0

0.223301

0

1

0

false

0

0.8

0

0.8

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

530cc11e191f653fa16a939369c7190a6049d495

11

py

Python

test.py

achiaver/introducaopython

10c192e680732fd1a244d30822f8e227a2b118dc

[ "MIT" ]

null

test.py

achiaver/introducaopython

10c192e680732fd1a244d30822f8e227a2b118dc

[ "MIT" ]

null

test.py

achiaver/introducaopython

10c192e680732fd1a244d30822f8e227a2b118dc

[ "MIT" ]

null

print(9+2)

5.5

10

0.636364

3

11

2.333333

1

0

0.2

0.090909

11

1

11

0.5

0

1

0

true

0

1

0

null

0

1

0

null

0

1

0

1

0

5

7272ac316df54be4b8a4bd2d058e2e5884ff9bdd

56

py

Python

pandas_polygon_api/__init__.py

jamesyrose/pandas_polygon_ap

df89f409d41f30880ed3c65efa982aed913a89ba

[ "MIT" ]

2

2020-11-22T21:02:21.000Z

2021-09-25T18:46:03.000Z

pandas_polygon_api/__init__.py

jamesyrose/pandas_polygon_api

df89f409d41f30880ed3c65efa982aed913a89ba

[ "MIT" ]

null

pandas_polygon_api/__init__.py

jamesyrose/pandas_polygon_api

df89f409d41f30880ed3c65efa982aed913a89ba

[ "MIT" ]

null

from pandas_polygon_api.polygon_api import PP_API as PPA

56

0.892857

11

56

4.181818

0.727273

0.434783

0

0.089286

56

1

56

0.901961

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

7274c8503ccd1af2c5613fc7313a8be3b80d8099

84

py

Python

use_live_db/__init__.py

bmampaey/django-testrunner-use_live_db

7c64233e0480eca388adc6223379d7d1b6d16426

[ "BSD-3-Clause" ]

1

2015-07-10T13:37:01.000Z

use_live_db/__init__.py

bmampaey/django-testrunner-use_live_db

7c64233e0480eca388adc6223379d7d1b6d16426

[ "BSD-3-Clause" ]

null

use_live_db/__init__.py

bmampaey/django-testrunner-use_live_db

7c64233e0480eca388adc6223379d7d1b6d16426

[ "BSD-3-Clause" ]

1

2020-03-15T13:36:32.000Z

from use_live_db.test_runner import ByPassableDBDjangoTestSuiteRunner as TestRunner

42

83

0.916667

10

84

7.4

1

0

0.071429

84

1

84

0.948718

0

1

0

true

1

0

1

0

1

0

null

0

1

0

null

0

1

0

5

728f73f4f0d327a22cc259a3ae8ae02f4c4dee5b

272

py

Python

pytorch_lightning/utilities/model_utils.py

KyleGoyette/pytorch-lightning

d6470bf1937e51e037a7f94a55ad76898e5ae103

[ "Apache-2.0" ]

3

2021-04-09T14:03:03.000Z

2021-04-10T02:58:23.000Z

pytorch_lightning/utilities/model_utils.py

KyleGoyette/pytorch-lightning

d6470bf1937e51e037a7f94a55ad76898e5ae103

[ "Apache-2.0" ]

1

2021-03-26T02:16:20.000Z

pytorch_lightning/utilities/model_utils.py

KyleGoyette/pytorch-lightning

d6470bf1937e51e037a7f94a55ad76898e5ae103

[ "Apache-2.0" ]

1

2021-09-16T15:14:11.000Z

from pytorch_lightning.utilities import rank_zero_deprecation rank_zero_deprecation( "`model_utils` package has been renamed to `model_helpers` since v1.2 and will be removed in v1.4" ) from pytorch_lightning.utilities.model_helpers import * # noqa: F403 E402 F401

34

102

0.805147

41

272

5.121951

0.707317

0.104762

0.190476

0.27619

0

0.055319

0.136029

272

7

103

38.857143

0.838298

0.073529

0

0.2

0.384

0

1

0

true

0

0.4

0

0.4

0

null

0

1

0

1

0

null

0

1

0

1

0

5

7297db07ddd88d9c5dcd068f46fe2258313d3a93

19,501

py

Python

python/vmaf/core/vmafexec_feature_extractor.py

xinglinsky/vmaf

55e60bd72eefef6d807bc8650f942349a19139f9

[ "BSD-2-Clause-Patent" ]

null

python/vmaf/core/vmafexec_feature_extractor.py

xinglinsky/vmaf

55e60bd72eefef6d807bc8650f942349a19139f9

[ "BSD-2-Clause-Patent" ]

null

python/vmaf/core/vmafexec_feature_extractor.py

xinglinsky/vmaf

55e60bd72eefef6d807bc8650f942349a19139f9

[ "BSD-2-Clause-Patent" ]

null

from vmaf import ExternalProgramCaller from vmaf.core.feature_extractor import VmafexecFeatureExtractorMixin, FeatureExtractor class FloatMotionFeatureExtractor(VmafexecFeatureExtractorMixin, FeatureExtractor): TYPE = "float_motion_feature" # VERSION = "1.0" VERSION = "1.1" # add debug features ATOM_FEATURES = ['motion2', 'motion', 'motion2_force_0', 'motion_force_0', ] ATOM_FEATURES_TO_VMAFEXEC_KEY_DICT = { 'motion2_force_0': 'motion2_force_0', 'motion_force_0': 'motion_force_0', 'motion2': 'motion2', 'motion': 'motion', } def _generate_result(self, asset): # routine to call the command-line executable and generate quality # scores in the log file. quality_width, quality_height = asset.quality_width_height log_file_path = self._get_log_file_path(asset) yuv_type=self._get_workfile_yuv_type(asset) ref_path=asset.ref_procfile_path dis_path=asset.dis_procfile_path w=quality_width h=quality_height logger = self.logger ExternalProgramCaller.call_vmafexec_single_feature('float_motion', yuv_type, ref_path, dis_path, w, h, log_file_path, logger, options=self.optional_dict) class IntegerMotionFeatureExtractor(VmafexecFeatureExtractorMixin, FeatureExtractor): TYPE = "integer_motion_feature" # VERSION = "1.0" # VERSION = "1.1" # vectorization VERSION = "1.2" # add debug features ATOM_FEATURES = ['motion2', 'motion', 'motion2_force_0', 'motion_force_0', ] ATOM_FEATURES_TO_VMAFEXEC_KEY_DICT = { 'motion2_force_0': 'integer_motion2_force_0', 'motion_force_0': 'integer_motion_force_0', 'motion2': 'integer_motion2', 'motion': 'integer_motion', } def _generate_result(self, asset): # routine to call the command-line executable and generate quality # scores in the log file. quality_width, quality_height = asset.quality_width_height log_file_path = self._get_log_file_path(asset) yuv_type=self._get_workfile_yuv_type(asset) ref_path=asset.ref_procfile_path dis_path=asset.dis_procfile_path w=quality_width h=quality_height logger = self.logger ExternalProgramCaller.call_vmafexec_single_feature('motion', yuv_type, ref_path, dis_path, w, h, log_file_path, logger, options=self.optional_dict) class FloatVifFeatureExtractor(VmafexecFeatureExtractorMixin, FeatureExtractor): TYPE = "float_VIF_feature" # VERSION = "1.0" VERSION = "1.1" # add debug features ATOM_FEATURES = [ 'vif_scale0', 'vif_scale1', 'vif_scale2', 'vif_scale3', 'vif', 'vif_num', 'vif_den', 'vif_num_scale0', 'vif_den_scale0', 'vif_num_scale1', 'vif_den_scale1', 'vif_num_scale2', 'vif_den_scale2', 'vif_num_scale3', 'vif_den_scale3', 'vif_scale0_egl_1', 'vif_scale1_egl_1', 'vif_scale2_egl_1', 'vif_scale3_egl_1', 'vif_egl_1', 'vif_num_egl_1', 'vif_den_egl_1', 'vif_num_scale0_egl_1', 'vif_den_scale0_egl_1', 'vif_num_scale1_egl_1', 'vif_den_scale1_egl_1', 'vif_num_scale2_egl_1', 'vif_den_scale2_egl_1', 'vif_num_scale3_egl_1', 'vif_den_scale3_egl_1', 'vif_scale0_egl_1.1', 'vif_scale1_egl_1.1', 'vif_scale2_egl_1.1', 'vif_scale3_egl_1.1', 'vif_egl_1.1', 'vif_num_egl_1.1', 'vif_den_egl_1.1', 'vif_num_scale0_egl_1.1', 'vif_den_scale0_egl_1.1', 'vif_num_scale1_egl_1.1', 'vif_den_scale1_egl_1.1', 'vif_num_scale2_egl_1.1', 'vif_den_scale2_egl_1.1', 'vif_num_scale3_egl_1.1', 'vif_den_scale3_egl_1.1', ] ATOM_FEATURES_TO_VMAFEXEC_KEY_DICT = { 'vif_scale0': 'vif_scale0', 'vif_scale1': 'vif_scale1', 'vif_scale2': 'vif_scale2', 'vif_scale3': 'vif_scale3', 'vif': 'vif', 'vif_num': 'vif_num', 'vif_den': 'vif_den', 'vif_num_scale0': 'vif_num_scale0', 'vif_den_scale0': 'vif_den_scale0', 'vif_num_scale1': 'vif_num_scale1', 'vif_den_scale1': 'vif_den_scale1', 'vif_num_scale2': 'vif_num_scale2', 'vif_den_scale2': 'vif_den_scale2', 'vif_num_scale3': 'vif_num_scale3', 'vif_den_scale3': 'vif_den_scale3', 'vif_scale0_egl_1': 'vif_scale0_egl_1', 'vif_scale1_egl_1': 'vif_scale1_egl_1', 'vif_scale2_egl_1': 'vif_scale2_egl_1', 'vif_scale3_egl_1': 'vif_scale3_egl_1', 'vif_egl_1': 'vif_egl_1', 'vif_num_egl_1': 'vif_num_egl_1', 'vif_den_egl_1': 'vif_den_egl_1', 'vif_num_scale0_egl_1': 'vif_num_scale0_egl_1', 'vif_den_scale0_egl_1': 'vif_den_scale0_egl_1', 'vif_num_scale1_egl_1': 'vif_num_scale1_egl_1', 'vif_den_scale1_egl_1': 'vif_den_scale1_egl_1', 'vif_num_scale2_egl_1': 'vif_num_scale2_egl_1', 'vif_den_scale2_egl_1': 'vif_den_scale2_egl_1', 'vif_num_scale3_egl_1': 'vif_num_scale3_egl_1', 'vif_den_scale3_egl_1': 'vif_den_scale3_egl_1', 'vif_scale0_egl_1.1': 'vif_scale0_egl_1.1', 'vif_scale1_egl_1.1': 'vif_scale1_egl_1.1', 'vif_scale2_egl_1.1': 'vif_scale2_egl_1.1', 'vif_scale3_egl_1.1': 'vif_scale3_egl_1.1', 'vif_egl_1.1': 'vif_egl_1.1', 'vif_num_egl_1.1': 'vif_num_egl_1.1', 'vif_den_egl_1.1': 'vif_den_egl_1.1', 'vif_num_scale0_egl_1.1': 'vif_num_scale0_egl_1.1', 'vif_den_scale0_egl_1.1': 'vif_den_scale0_egl_1.1', 'vif_num_scale1_egl_1.1': 'vif_num_scale1_egl_1.1', 'vif_den_scale1_egl_1.1': 'vif_den_scale1_egl_1.1', 'vif_num_scale2_egl_1.1': 'vif_num_scale2_egl_1.1', 'vif_den_scale2_egl_1.1': 'vif_den_scale2_egl_1.1', 'vif_num_scale3_egl_1.1': 'vif_num_scale3_egl_1.1', 'vif_den_scale3_egl_1.1': 'vif_den_scale3_egl_1.1', } def _generate_result(self, asset): # routine to call the command-line executable and generate quality # scores in the log file. quality_width, quality_height = asset.quality_width_height log_file_path = self._get_log_file_path(asset) yuv_type=self._get_workfile_yuv_type(asset) ref_path=asset.ref_procfile_path dis_path=asset.dis_procfile_path w=quality_width h=quality_height logger = self.logger ExternalProgramCaller.call_vmafexec_single_feature('float_vif', yuv_type, ref_path, dis_path, w, h, log_file_path, logger, options=self.optional_dict) class IntegerVifFeatureExtractor(VmafexecFeatureExtractorMixin, FeatureExtractor): TYPE = "integer_VIF_feature" # VERSION = "1.0" # VERSION = "1.1b" # vif_enhn_gain_limit with matching_matlab code # VERSION = "1.1c" # update boundary calculation # VERSION = "1.1d" # update to use log2f to replace log2f_approx # VERSION = "1.2" # fix vectorization corner cases VERSION = "1.3" # add debug features ATOM_FEATURES = [ 'vif_scale0', 'vif_scale1', 'vif_scale2', 'vif_scale3', 'vif', 'vif_num', 'vif_den', 'vif_num_scale0', 'vif_den_scale0', 'vif_num_scale1', 'vif_den_scale1', 'vif_num_scale2', 'vif_den_scale2', 'vif_num_scale3', 'vif_den_scale3', 'vif_scale0_egl_1', 'vif_scale1_egl_1', 'vif_scale2_egl_1', 'vif_scale3_egl_1', 'vif_egl_1', 'vif_num_egl_1', 'vif_den_egl_1', 'vif_num_scale0_egl_1', 'vif_den_scale0_egl_1', 'vif_num_scale1_egl_1', 'vif_den_scale1_egl_1', 'vif_num_scale2_egl_1', 'vif_den_scale2_egl_1', 'vif_num_scale3_egl_1', 'vif_den_scale3_egl_1', 'vif_scale0_egl_1.1', 'vif_scale1_egl_1.1', 'vif_scale2_egl_1.1', 'vif_scale3_egl_1.1', 'vif_egl_1.1', 'vif_num_egl_1.1', 'vif_den_egl_1.1', 'vif_num_scale0_egl_1.1', 'vif_den_scale0_egl_1.1', 'vif_num_scale1_egl_1.1', 'vif_den_scale1_egl_1.1', 'vif_num_scale2_egl_1.1', 'vif_den_scale2_egl_1.1', 'vif_num_scale3_egl_1.1', 'vif_den_scale3_egl_1.1', ] ATOM_FEATURES_TO_VMAFEXEC_KEY_DICT = { 'vif_scale0': 'integer_vif_scale0', 'vif_scale1': 'integer_vif_scale1', 'vif_scale2': 'integer_vif_scale2', 'vif_scale3': 'integer_vif_scale3', 'vif': 'integer_vif', 'vif_num': 'integer_vif_num', 'vif_den': 'integer_vif_den', 'vif_num_scale0': 'integer_vif_num_scale0', 'vif_den_scale0': 'integer_vif_den_scale0', 'vif_num_scale1': 'integer_vif_num_scale1', 'vif_den_scale1': 'integer_vif_den_scale1', 'vif_num_scale2': 'integer_vif_num_scale2', 'vif_den_scale2': 'integer_vif_den_scale2', 'vif_num_scale3': 'integer_vif_num_scale3', 'vif_den_scale3': 'integer_vif_den_scale3', 'vif_scale0_egl_1': 'integer_vif_scale0_egl_1', 'vif_scale1_egl_1': 'integer_vif_scale1_egl_1', 'vif_scale2_egl_1': 'integer_vif_scale2_egl_1', 'vif_scale3_egl_1': 'integer_vif_scale3_egl_1', 'vif_egl_1': 'integer_vif_egl_1', 'vif_num_egl_1': 'integer_vif_num_egl_1', 'vif_den_egl_1': 'integer_vif_den_egl_1', 'vif_num_scale0_egl_1': 'integer_vif_num_scale0_egl_1', 'vif_den_scale0_egl_1': 'integer_vif_den_scale0_egl_1', 'vif_num_scale1_egl_1': 'integer_vif_num_scale1_egl_1', 'vif_den_scale1_egl_1': 'integer_vif_den_scale1_egl_1', 'vif_num_scale2_egl_1': 'integer_vif_num_scale2_egl_1', 'vif_den_scale2_egl_1': 'integer_vif_den_scale2_egl_1', 'vif_num_scale3_egl_1': 'integer_vif_num_scale3_egl_1', 'vif_den_scale3_egl_1': 'integer_vif_den_scale3_egl_1', 'vif_scale0_egl_1.1': 'integer_vif_scale0_egl_1.1', 'vif_scale1_egl_1.1': 'integer_vif_scale1_egl_1.1', 'vif_scale2_egl_1.1': 'integer_vif_scale2_egl_1.1', 'vif_scale3_egl_1.1': 'integer_vif_scale3_egl_1.1', 'vif_egl_1.1': 'integer_vif_egl_1.1', 'vif_num_egl_1.1': 'integer_vif_num_egl_1.1', 'vif_den_egl_1.1': 'integer_vif_den_egl_1.1', 'vif_num_scale0_egl_1.1': 'integer_vif_num_scale0_egl_1.1', 'vif_den_scale0_egl_1.1': 'integer_vif_den_scale0_egl_1.1', 'vif_num_scale1_egl_1.1': 'integer_vif_num_scale1_egl_1.1', 'vif_den_scale1_egl_1.1': 'integer_vif_den_scale1_egl_1.1', 'vif_num_scale2_egl_1.1': 'integer_vif_num_scale2_egl_1.1', 'vif_den_scale2_egl_1.1': 'integer_vif_den_scale2_egl_1.1', 'vif_num_scale3_egl_1.1': 'integer_vif_num_scale3_egl_1.1', 'vif_den_scale3_egl_1.1': 'integer_vif_den_scale3_egl_1.1', } def _generate_result(self, asset): # routine to call the command-line executable and generate quality # scores in the log file. quality_width, quality_height = asset.quality_width_height log_file_path = self._get_log_file_path(asset) yuv_type=self._get_workfile_yuv_type(asset) ref_path=asset.ref_procfile_path dis_path=asset.dis_procfile_path w=quality_width h=quality_height logger = self.logger ExternalProgramCaller.call_vmafexec_single_feature('vif', yuv_type, ref_path, dis_path, w, h, log_file_path, logger, options=self.optional_dict) class FloatAdmFeatureExtractor(VmafexecFeatureExtractorMixin, FeatureExtractor): TYPE = "float_ADM_feature" # VERSION = "1.0" VERSION = "1.1" # add debug features ATOM_FEATURES = ['adm2', 'adm2_egl_1', 'adm2_egl_1.2', 'adm_scale0', 'adm_scale1', 'adm_scale2', 'adm_scale3', 'adm', 'adm_num', 'adm_den', 'adm_num_scale0', 'adm_den_scale0', 'adm_num_scale1', 'adm_den_scale1', 'adm_num_scale2', 'adm_den_scale2', 'adm_num_scale3', 'adm_den_scale3', ] ATOM_FEATURES_TO_VMAFEXEC_KEY_DICT = { 'adm2': 'adm2', 'adm2_egl_1': 'adm2_egl_1', 'adm2_egl_1.2': 'adm2_egl_1.2', 'adm_scale0': 'adm_scale0', 'adm_scale1': 'adm_scale1', 'adm_scale2': 'adm_scale2', 'adm_scale3': 'adm_scale3', 'adm': 'adm', 'adm_num': 'adm_num', 'adm_den': 'adm_den', 'adm_num_scale0': 'adm_num_scale0', 'adm_den_scale0': 'adm_den_scale0', 'adm_num_scale1': 'adm_num_scale1', 'adm_den_scale1': 'adm_den_scale1', 'adm_num_scale2': 'adm_num_scale2', 'adm_den_scale2': 'adm_den_scale2', 'adm_num_scale3': 'adm_num_scale3', 'adm_den_scale3': 'adm_den_scale3', } def _generate_result(self, asset): # routine to call the command-line executable and generate quality # scores in the log file. quality_width, quality_height = asset.quality_width_height log_file_path = self._get_log_file_path(asset) yuv_type=self._get_workfile_yuv_type(asset) ref_path=asset.ref_procfile_path dis_path=asset.dis_procfile_path w=quality_width h=quality_height logger = self.logger ExternalProgramCaller.call_vmafexec_single_feature('float_adm', yuv_type, ref_path, dis_path, w, h, log_file_path, logger, options=self.optional_dict) class IntegerPsnrFeatureExtractor(VmafexecFeatureExtractorMixin, FeatureExtractor): TYPE = 'integer_PSNR_feature' VERSION = "1.0" ATOM_FEATURES = ['psnr_y', 'psnr_cb', 'psnr_cr'] ATOM_FEATURES_TO_VMAFEXEC_KEY_DICT = { 'psnr_y': 'psnr_y', 'psnr_cb': 'psnr_cb', 'psnr_cr': 'psnr_cr', } def _generate_result(self, asset): # routine to call the command-line executable and generate quality # scores in the log file. quality_width, quality_height = asset.quality_width_height log_file_path = self._get_log_file_path(asset) yuv_type=self._get_workfile_yuv_type(asset) ref_path=asset.ref_procfile_path dis_path=asset.dis_procfile_path w=quality_width h=quality_height logger = self.logger ExternalProgramCaller.call_vmafexec_single_feature('psnr', yuv_type, ref_path, dis_path, w, h, log_file_path, logger, options=self.optional_dict) class IntegerAdmFeatureExtractor(VmafexecFeatureExtractorMixin, FeatureExtractor): TYPE = "integer_ADM_feature" # VERSION = "1.0" # VERSION = "1.1" # vectorization; small numerical diff introduced by adm_enhn_gain_limit VERSION = "1.2" # add debug features ATOM_FEATURES = ['adm2', 'adm2_egl_1', 'adm2_egl_1.1', 'adm2_egl_1.2', 'adm_scale0', 'adm_scale1', 'adm_scale2', 'adm_scale3', 'adm', 'adm_num', 'adm_den', 'adm_num_scale0', 'adm_den_scale0', 'adm_num_scale1', 'adm_den_scale1', 'adm_num_scale2', 'adm_den_scale2', 'adm_num_scale3', 'adm_den_scale3', ] ATOM_FEATURES_TO_VMAFEXEC_KEY_DICT = { 'adm2': 'integer_adm2', 'adm2_egl_1': 'integer_adm2_egl_1', 'adm2_egl_1.1': 'integer_adm2_egl_1.1', 'adm2_egl_1.2': 'integer_adm2_egl_1.2', 'adm_scale0': 'integer_adm_scale0', 'adm_scale1': 'integer_adm_scale1', 'adm_scale2': 'integer_adm_scale2', 'adm_scale3': 'integer_adm_scale3', 'adm': 'integer_adm', 'adm_num': 'integer_adm_num', 'adm_den': 'integer_adm_den', 'adm_num_scale0': 'integer_adm_num_scale0', 'adm_den_scale0': 'integer_adm_den_scale0', 'adm_num_scale1': 'integer_adm_num_scale1', 'adm_den_scale1': 'integer_adm_den_scale1', 'adm_num_scale2': 'integer_adm_num_scale2', 'adm_den_scale2': 'integer_adm_den_scale2', 'adm_num_scale3': 'integer_adm_num_scale3', 'adm_den_scale3': 'integer_adm_den_scale3', } def _generate_result(self, asset): # routine to call the command-line executable and generate quality # scores in the log file. quality_width, quality_height = asset.quality_width_height log_file_path = self._get_log_file_path(asset) yuv_type=self._get_workfile_yuv_type(asset) ref_path=asset.ref_procfile_path dis_path=asset.dis_procfile_path w=quality_width h=quality_height logger = self.logger ExternalProgramCaller.call_vmafexec_single_feature('adm', yuv_type, ref_path, dis_path, w, h, log_file_path, logger, options=self.optional_dict) class CIEDE2000FeatureExtractor(VmafexecFeatureExtractorMixin, FeatureExtractor): TYPE = 'CIEDE2000_feature' VERSION = "1.0" ATOM_FEATURES = ['ciede2000'] ATOM_FEATURES_TO_VMAFEXEC_KEY_DICT = { 'ciede2000': 'ciede2000', } def _generate_result(self, asset): # routine to call the command-line executable and generate quality # scores in the log file. quality_width, quality_height = asset.quality_width_height log_file_path = self._get_log_file_path(asset) yuv_type=self._get_workfile_yuv_type(asset) ref_path=asset.ref_procfile_path dis_path=asset.dis_procfile_path w=quality_width h=quality_height logger = self.logger ExternalProgramCaller.call_vmafexec_single_feature('ciede', yuv_type, ref_path, dis_path, w, h, log_file_path, logger, options=self.optional_dict)

39.555781

110

0.602533

2,493

19,501

4.165263

0.044525

0.075116

0.04478

0.0547

0.848902

0.825597

0.762134

0.725347

0.684322

0.672766

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0.300241

19,501

492

111

39.636179

0.710465

0.064868

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0

null

0

1

0

1

0

null

0

5

72cab85dce5416adaf9c1359aeaacd17fc472afe

199

py

Python

main/PluginDemos/MomentOfInertia/MomentOfInertia/Simulation/MomentOfInertia.py

JulianoGianlupi/nh-cc3d-4x-base-tool

c0f4aceebd4c5bf3ec39e831ef851e419b161259

[ "CC0-1.0" ]

null

main/PluginDemos/MomentOfInertia/MomentOfInertia/Simulation/MomentOfInertia.py

JulianoGianlupi/nh-cc3d-4x-base-tool

c0f4aceebd4c5bf3ec39e831ef851e419b161259

[ "CC0-1.0" ]

null

main/PluginDemos/MomentOfInertia/MomentOfInertia/Simulation/MomentOfInertia.py

JulianoGianlupi/nh-cc3d-4x-base-tool

c0f4aceebd4c5bf3ec39e831ef851e419b161259

[ "CC0-1.0" ]

1

2021-02-26T21:50:29.000Z

from cc3d import CompuCellSetup from .MomentOfInertiaSteppables import MomentOfInertiaPrinter CompuCellSetup.register_steppable(steppable=MomentOfInertiaPrinter(frequency=10)) CompuCellSetup.run()

28.428571

81

0.884422

17

199

10.294118

0.647059

0

0.016043

0.060302

199

6

82

33.166667

0.919786

0

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true

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0

null

0

1

0

1

0

5

72e2213ad064d11fc7ccf6e7e21a1a3da2c3c9c7

1,351

py

Python

setup.py

sunwj/sewar

4ea72fe3c501e597b27ffef4a83da3ba8c8c2c7b

[ "MIT" ]

null

setup.py

sunwj/sewar

4ea72fe3c501e597b27ffef4a83da3ba8c8c2c7b

[ "MIT" ]

null

setup.py

sunwj/sewar

4ea72fe3c501e597b27ffef4a83da3ba8c8c2c7b

[ "MIT" ]

null

from setuptools import setup def readme(): with open('README.md') as f: return f.read() setup(name='sewar', version='0.4', description='All image quality metrics you need in one package.', long_description=readme(), long_description_content_type="text/markdown", classifiers=[ 'Development Status :: 2 - Pre-Alpha', 'License :: OSI Approved :: MIT License', 'Operating System :: OS Independent', 'Programming Language :: Python', 'Programming Language :: Python :: 2', 'Programming Language :: Python :: 2.6', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.1', 'Programming Language :: Python :: 3.2', 'Programming Language :: Python :: 3.3', 'Programming Language :: Python :: 3.4', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', 'Topic :: Multimedia :: Graphics' ], keywords='image quality performance metric measure ergas q psnr pansharpening', url='https://github.com/andrewekhalel/sewar', author='Andrew Khalel', author_email='andrewekhalel@gmail.com', license='MIT', packages=['sewar'], test_suite='nose.collector', tests_require=['nose','Pillow'], install_requires=[ 'numpy', 'scipy' , 'Pillow' ], entry_points=""" [console_scripts] sewar = sewar.command_line:main """, zip_safe=False)

28.744681

80

0.689119

162

1,351

5.67284

0.604938

0.227421

0.299238

0.198041

0.05876

0

0.018198

0.145818

1,351

46

81

29.369565

0.778163

0

0.047619

0

0.641007

0.034049

0

1

0.02381

true

0

0.02381

0

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0

null

1

0

1

0

null

0

1

0

5

f43d66ec188ba931afffd0c4f702c02f9f787ced

74

py

Python

skrules/datasets/__init__.py

TomLaMantia/skope-rules

d9a777f84836905f726cb6221fe335cc1b935ae5

[ "MIT" ]

462

2018-02-19T07:56:48.000Z

2022-03-30T15:26:13.000Z

skrules/datasets/__init__.py

TomLaMantia/skope-rules

d9a777f84836905f726cb6221fe335cc1b935ae5

[ "MIT" ]

48

2018-02-22T16:33:14.000Z

2022-02-25T05:02:41.000Z

skrules/datasets/__init__.py

TomLaMantia/skope-rules

d9a777f84836905f726cb6221fe335cc1b935ae5

[ "MIT" ]

84

2018-02-28T08:36:36.000Z

2022-03-28T02:37:28.000Z

from .credit_data import load_credit_data __all__ = ['load_credit_data']

18.5

41

0.810811

11

74

4.636364

0.545455

0.588235

0.54902

0

0.108108

74

3

42

24.666667

0.772727

0

0.216216

0

1

0

false

0

0.5

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null

1

0

1

0

null

0

1

0

5

f46c1946073d2bff68fb2668b3c464c33da8a79f

3,594

py

Python

tests/processes/continuous/conftest.py

zaczw/stochastic

7de6ec2f9050120adfcffeebc94bfc17ec916150

[ "MIT" ]

268

2018-01-17T18:45:20.000Z

2022-03-28T06:05:30.000Z

tests/processes/continuous/conftest.py

zaczw/stochastic

7de6ec2f9050120adfcffeebc94bfc17ec916150

[ "MIT" ]

42

2018-07-11T02:17:43.000Z

2021-11-27T03:27:32.000Z

tests/processes/continuous/conftest.py

zaczw/stochastic

7de6ec2f9050120adfcffeebc94bfc17ec916150

[ "MIT" ]

56

2018-02-20T09:32:50.000Z

2022-02-15T15:39:37.000Z

"""Continuous-time process tests.""" import math import numpy as np import pytest # Floating point arithmetic comparison threshold @pytest.fixture(params=[10 ** -10]) def threshold(request): return request.param # Common @pytest.fixture(params=[1]) def t(request): return request.param @pytest.fixture(params=[16]) def n(request): return request.param # Generate some random times for the sample_at() method times_random = np.cumsum(np.abs(np.random.normal(size=16))) times_random_zero = np.cumsum([0] + list(np.abs(np.random.normal(size=16)))) @pytest.fixture(params=[times_random, times_random_zero]) def times(request): return request.param # Bessel @pytest.fixture(params=[0, 1, 1.1]) def dim_fixture(request): return request.param @pytest.fixture(params=[3]) def dim(request): return request.param # BrownianBridge @pytest.fixture(params=[3, 0, None]) def b(request): return request.param # BrownianMotion @pytest.fixture(params=[0, 1]) def drift(request): return request.param @pytest.fixture(params=[1]) def scale(request): return request.param # FractionalBrownianMotion @pytest.fixture(params=[0.2, 0.5, 0.7]) def hurst(request): return request.param # GammaProcess @pytest.fixture(params=[1, None]) def mean_fixture(request): return request.param @pytest.fixture(params=[1, None]) def scale_fixture(request): return request.param @pytest.fixture(params=[1, None]) def rate_fixture(request): return request.param @pytest.fixture(params=[1, None]) def variance_fixture(request): return request.param @pytest.fixture(params=[1]) def mean(request): return request.param @pytest.fixture(params=[1]) def variance(request): return request.param # GeometricBrownianMotion @pytest.fixture(params=[1]) def volatility(request): return request.param @pytest.fixture(params=[1]) def initial(request): return request.param # InverseGaussianProcess def mean_func_monotonic(t): return t def mean_func_not_monotonic(t): return 1 def mean_func_no_args(): return 1 @pytest.fixture(params=[mean_func_monotonic, None]) def mean_func(request): return request.param @pytest.fixture(params=[mean_func_not_monotonic, mean_func_no_args, 1]) def mean_func_invalid(request): return request.param # MultifractionalBrownianMotion def hurst_const(t): return 0.5 def hurst_sin(t): return math.sin(t) / 3 + 0.5 @pytest.fixture(params=[None, hurst_const, hurst_sin]) def hurst_func(request): return request.param def hurst_too_many_args(t, u): return 0.5 def hurst_out_of_range(t): return 1.1 @pytest.fixture(params=[0.5, hurst_too_many_args, hurst_out_of_range]) def hurst_invalid(request): return request.param # PoissonProcess @pytest.fixture(params=[16, None]) def n_fixture(request): return request.param @pytest.fixture(params=[1, None]) def length(request): return request.param @pytest.fixture(params=[1]) def rate(request): return request.param # MixedPoissonProcess @pytest.fixture(params=[np.random.uniform]) def rate_func(request): return request.param @pytest.fixture(params=[(1, 100), (1, 10)]) def rate_args(request): return request.param @pytest.fixture(params=[{"size": None}]) def rate_kwargs(request): return request.param @pytest.fixture(params=[0]) def rate_func_invalid(request): return request.param @pytest.fixture(params=[0]) def rate_args_invalid(request): return request.param @pytest.fixture(params=[0]) def rate_kwargs_invalid(request): return request.param

17.446602

76

0.730384

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3,594

5.101392

0.170974

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0.302027

0.488698

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0.365939

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0

0.020699

0.139677

3,594

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null

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1

0

null

0

1

0

1

0

5

f4761d9a6b8ba4daa29e8dc785b3c365b0b0d872

103

py

Python

xendit/network/__init__.py

glendaesutanto/xendit-python

f9b131882ff7d045f2e2c6518933d1594efba3e6

[ "MIT" ]

10

2020-10-31T23:34:34.000Z

2022-03-08T19:08:55.000Z

xendit/network/__init__.py

glendaesutanto/xendit-python

f9b131882ff7d045f2e2c6518933d1594efba3e6

[ "MIT" ]

22

2020-07-30T14:25:07.000Z

2022-03-31T03:55:46.000Z

xendit/network/__init__.py

glendaesutanto/xendit-python

f9b131882ff7d045f2e2c6518933d1594efba3e6

[ "MIT" ]

11

2020-07-28T08:09:40.000Z

2022-03-18T00:14:02.000Z

from .xendit_response import XenditResponse from .http_client_interface import HTTPClientInterface

25.75

55

0.864078

11

103

7.818182

0.818182

0

0.116505

103

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0

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0

null

0

1

0

1

0

1

0

5

be451531609a1a6cd5a01cf43666d2a05218e004

107

py

Python

make.py

venkat2319/Synth

1f6109d806abb8a0772809cfe42617bc4215a6ea

[ "MIT" ]

9

2015-10-23T02:20:46.000Z

2021-07-11T08:42:05.000Z

make.py

venkat2319/Synth

1f6109d806abb8a0772809cfe42617bc4215a6ea

[ "MIT" ]

null

make.py

venkat2319/Synth

1f6109d806abb8a0772809cfe42617bc4215a6ea

[ "MIT" ]

3

2015-10-08T01:52:14.000Z

2021-04-01T10:47:22.000Z

import os print "Synth Build Tool" os.system ("gcc -o bin/synth -include synth.h synth.c */*.c */*/*.c")

17.833333

69

0.635514

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107

3.578947

0.684211

0.058824

0

0.158879

107

5

70

21.4

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0

0.663551

0

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null

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1

0

null

0

1

0

1

0

5

be4c70dd952ed1f6ca260c8141ac3a2d3aeb9d22

18,591

py

Python

evaluation/plot_from_csv.py

Vivokas20/SKEL

d8766ceaa8aa766ea3580bbb61b747572ebfe77c

[ "Apache-2.0" ]

1

2022-01-20T14:57:30.000Z

evaluation/plot_from_csv.py

Vivokas20/SKEL

d8766ceaa8aa766ea3580bbb61b747572ebfe77c

[ "Apache-2.0" ]

null

evaluation/plot_from_csv.py

Vivokas20/SKEL

d8766ceaa8aa766ea3580bbb61b747572ebfe77c

[ "Apache-2.0" ]

null

import pandas as pd from matplotlib.backends.backend_pdf import PdfPages from matplotlib import pyplot as plt from matplotlib import rcParams import numpy as np flag_filter = False flag_summarise = False flag_both = False flag_union = False csv_list = [] name_list = [] filter = ['tests-examples/textbook/1', 'tests-examples/textbook/10', 'tests-examples/textbook/14', 'tests-examples/textbook/15', 'tests-examples/textbook/16', 'tests-examples/textbook/17', 'tests-examples/textbook/19', 'tests-examples/textbook/2', 'tests-examples/textbook/20', 'tests-examples/textbook/21', 'tests-examples/textbook/22', 'tests-examples/textbook/23', 'tests-examples/textbook/24', 'tests-examples/textbook/25', 'tests-examples/textbook/26', 'tests-examples/textbook/28', 'tests-examples/textbook/29', 'tests-examples/textbook/3', 'tests-examples/textbook/31', 'tests-examples/textbook/35', 'tests-examples/textbook/4', 'tests-examples/textbook/5', 'tests-examples/textbook/6', 'tests-examples/textbook/8', 'tests-examples/textbook/9', 'tests-examples/scythe/top_rated_posts/002', 'tests-examples/scythe/top_rated_posts/013', 'tests-examples/scythe/top_rated_posts/017', 'tests-examples/scythe/top_rated_posts/025', 'tests-examples/scythe/top_rated_posts/031', 'tests-examples/scythe/top_rated_posts/032', 'tests-examples/scythe/top_rated_posts/038', 'tests-examples/scythe/top_rated_posts/043', 'tests-examples/scythe/recent_posts/004', 'tests-examples/scythe/recent_posts/016', 'tests-examples/scythe/recent_posts/019', 'tests-examples/scythe/recent_posts/021', 'tests-examples/scythe/recent_posts/028', 'tests-examples/scythe/recent_posts/031', 'tests-examples/scythe/recent_posts/040', 'tests-examples/scythe/recent_posts/046', 'tests-examples/spider/architecture/0007', 'tests-examples/spider/architecture/0008', 'tests-examples/spider/architecture/0009', 'tests-examples/spider/architecture/0011', 'tests-examples/spider/architecture/0012', 'tests-examples/spider/architecture/0013', 'tests-examples/spider/architecture/0017'] summarise = ['tests-examples/textbook/10', 'tests-examples/textbook/14', 'tests-examples/textbook/15', 'tests-examples/textbook/17', 'tests-examples/textbook/18', 'tests-examples/textbook/22', 'tests-examples/textbook/25', 'tests-examples/textbook/4', 'tests-examples/textbook/5', 'tests-examples/textbook/6', 'tests-examples/textbook/7', 'tests-examples/textbook/8', 'tests-examples/textbook/9', 'tests-examples/scythe/top_rated_posts/001', 'tests-examples/scythe/top_rated_posts/002', 'tests-examples/scythe/top_rated_posts/004', 'tests-examples/scythe/top_rated_posts/006', 'tests-examples/scythe/top_rated_posts/007', 'tests-examples/scythe/top_rated_posts/008', 'tests-examples/scythe/top_rated_posts/009', 'tests-examples/scythe/top_rated_posts/012', 'tests-examples/scythe/top_rated_posts/013', 'tests-examples/scythe/top_rated_posts/014', 'tests-examples/scythe/top_rated_posts/016', 'tests-examples/scythe/top_rated_posts/019', 'tests-examples/scythe/top_rated_posts/021', 'tests-examples/scythe/top_rated_posts/027', 'tests-examples/scythe/top_rated_posts/028', 'tests-examples/scythe/top_rated_posts/029', 'tests-examples/scythe/top_rated_posts/034', 'tests-examples/scythe/top_rated_posts/036', 'tests-examples/scythe/top_rated_posts/037', 'tests-examples/scythe/top_rated_posts/038', 'tests-examples/scythe/top_rated_posts/043', 'tests-examples/scythe/top_rated_posts/047', 'tests-examples/scythe/top_rated_posts/048', 'tests-examples/scythe/top_rated_posts/049', 'tests-examples/scythe/top_rated_posts/051', 'tests-examples/scythe/top_rated_posts/055', 'tests-examples/scythe/top_rated_posts/057', 'tests-examples/scythe/recent_posts/009', 'tests-examples/scythe/recent_posts/011', 'tests-examples/scythe/recent_posts/016', 'tests-examples/scythe/recent_posts/040', 'tests-examples/scythe/recent_posts/045', 'tests-examples/scythe/recent_posts/051', 'tests-examples/spider/architecture/0003', 'tests-examples/spider/architecture/0009', 'tests-examples/spider/architecture/0011'] both = ['tests-examples/textbook/15', 'tests-examples/scythe/recent_posts/021', 'tests-examples/spider/architecture/0009', 'tests-examples/textbook/4', 'tests-examples/scythe/top_rated_posts/002', 'tests-examples/textbook/29', 'tests-examples/textbook/8', 'tests-examples/textbook/25', 'tests-examples/scythe/top_rated_posts/038', 'tests-examples/scythe/recent_posts/004', 'tests-examples/textbook/2', 'tests-examples/textbook/26', 'tests-examples/textbook/14', 'tests-examples/scythe/top_rated_posts/017', 'tests-examples/scythe/top_rated_posts/013', 'tests-examples/scythe/top_rated_posts/043', 'tests-examples/textbook/10', 'tests-examples/textbook/23', 'tests-examples/textbook/22', 'tests-examples/textbook/9', 'tests-examples/textbook/5', 'tests-examples/scythe/recent_posts/028', 'tests-examples/textbook/17', 'tests-examples/textbook/6', 'tests-examples/scythe/recent_posts/016', 'tests-examples/scythe/recent_posts/040', 'tests-examples/spider/architecture/0011', 'tests-examples/textbook/3'] union = ['tests-examples/scythe/top_rated_posts/029', 'tests-examples/textbook/29', 'tests-examples/textbook/31', 'tests-examples/textbook/4', 'tests-examples/textbook/21', 'tests-examples/textbook/28', 'tests-examples/textbook/15', 'tests-examples/textbook/20', 'tests-examples/scythe/recent_posts/045', 'tests-examples/scythe/top_rated_posts/057', 'tests-examples/spider/architecture/0009', 'tests-examples/scythe/recent_posts/016', 'tests-examples/scythe/top_rated_posts/031', 'tests-examples/scythe/top_rated_posts/013', 'tests-examples/scythe/top_rated_posts/027', 'tests-examples/spider/architecture/0007', 'tests-examples/scythe/recent_posts/051', 'tests-examples/scythe/recent_posts/021', 'tests-examples/scythe/top_rated_posts/036', 'tests-examples/scythe/top_rated_posts/007', 'tests-examples/scythe/recent_posts/028', 'tests-examples/scythe/top_rated_posts/038', 'tests-examples/scythe/recent_posts/004', 'tests-examples/scythe/top_rated_posts/021', 'tests-examples/scythe/top_rated_posts/037', 'tests-examples/scythe/top_rated_posts/051', 'tests-examples/textbook/8', 'tests-examples/spider/architecture/0003', 'tests-examples/textbook/16', 'tests-examples/scythe/top_rated_posts/016', 'tests-examples/scythe/top_rated_posts/048', 'tests-examples/scythe/top_rated_posts/028', 'tests-examples/scythe/top_rated_posts/004', 'tests-examples/textbook/3', 'tests-examples/scythe/top_rated_posts/006', 'tests-examples/scythe/recent_posts/009', 'tests-examples/scythe/top_rated_posts/009', 'tests-examples/textbook/9', 'tests-examples/textbook/2', 'tests-examples/scythe/top_rated_posts/017', 'tests-examples/spider/architecture/0011', 'tests-examples/textbook/19', 'tests-examples/scythe/recent_posts/046', 'tests-examples/textbook/14', 'tests-examples/scythe/recent_posts/040', 'tests-examples/scythe/recent_posts/019', 'tests-examples/textbook/24', 'tests-examples/spider/architecture/0012', 'tests-examples/textbook/25', 'tests-examples/textbook/5', 'tests-examples/scythe/top_rated_posts/001', 'tests-examples/spider/architecture/0013', 'tests-examples/textbook/1', 'tests-examples/scythe/top_rated_posts/049', 'tests-examples/textbook/23', 'tests-examples/textbook/17', 'tests-examples/scythe/recent_posts/011', 'tests-examples/scythe/top_rated_posts/012', 'tests-examples/scythe/top_rated_posts/032', 'tests-examples/textbook/10', 'tests-examples/scythe/recent_posts/031', 'tests-examples/scythe/top_rated_posts/047', 'tests-examples/textbook/7', 'tests-examples/scythe/top_rated_posts/019', 'tests-examples/scythe/top_rated_posts/008', 'tests-examples/textbook/26', 'tests-examples/scythe/top_rated_posts/025', 'tests-examples/textbook/6', 'tests-examples/scythe/top_rated_posts/043', 'tests-examples/scythe/top_rated_posts/014', 'tests-examples/textbook/22', 'tests-examples/scythe/top_rated_posts/002', 'tests-examples/textbook/18', 'tests-examples/scythe/top_rated_posts/034', 'tests-examples/scythe/top_rated_posts/055', 'tests-examples/spider/architecture/0008', 'tests-examples/textbook/35', 'tests-examples/spider/architecture/0017'] def greater_than(datas): # 1st data that must take the longest big = datas[0] small = datas[1] big = big[big.name.isin(small.name)].reset_index(drop=True) small = small[small.name.isin(big.name)].reset_index(drop=True) for n in big.index: if (float(big.real[n]) + 5) < float(small.real[n]): print(big.name[n]) def miscellaneous(datas): # 1st data that must take the longest no_opt = datas[0] opt = datas[1] a = no_opt[(no_opt.timeout == False) & (no_opt.ground_truth == True)] b = opt[(opt.timeout == False) & (opt.ground_truth == True)] c = a[a.name.isin(b.name)] d = a[~a.name.isin(b.name)] print(len(a)) print(len(b)) print(len(c)) print(d) ####################### Plot Functions ######################## def check(datas, name_list): for n in range(len(datas)): df = datas[n] df = df[df.timeout == False] programs = pd.isnull(df.programs) programs = programs[programs == True] if len(programs) > 0: return True, name_list[n] return False, None def time_plot(datas, names): fig, ax = plt.subplots() for n in range(len(datas)): df = datas[n] df = df[df.timeout == False] df = df.sort_values("real").reset_index(drop=True) df.index += 1 df = df.reset_index() fig = df.plot(label= names[n], xlabel="#Solved Instances", ylabel="Real Time (s)", x="index", y="real", style='.-', subplots=False, ax=ax) fig.set_ylim(-2,100) # baseline / filter fig.set_ylim(-1, 50) # summarise # df.plot(style='.-', markevery=5) fig = fig.get_figure() return fig def programs_plot(datas, names): fig, ax = plt.subplots() for n in range(len(datas)): df = datas[n] df = df[df.timeout == False] df = df.sort_values("programs").reset_index(drop=True) df.index += 1 df = df.reset_index() fig = df.plot(label= names[n], xlabel="#Solved Instances", ylabel="#Attempted programs", x="index", y="programs", style='.-', subplots=False, ax=ax) fig.set_ylim(-100, 2000) # baseline/summarise # fig.set_ylim(-100, 4000) # filter # fig.yaxis.set_major_formatter(mtick.PercentFormatter()) # fig = df.plot(label=names[n], xlabel="Instance", ylabel="Attempted programs", x="name", y="programs", subplots=False, ax=ax) fig = fig.get_figure() return fig def ground_truth(datas, names): index = [] solved = [] gtruth = [] for n in range(len(datas)): data = datas[n] df = data[data.timeout == False] df2 = data[data.ground_truth == True] name = names[n] if name.endswith("aggregate"): name = name[:-9] + "\n" + name[-9:] name = name[:14] + "\n" + name[14:] index.append(name) solved.append(len(df.index)) gtruth.append(len(df2.index)) df = pd.DataFrame({"solved": solved, "correct": gtruth}, index=index) rcParams.update({'figure.autolayout': True}) fig = df.plot(kind="bar", xlabel="Benchmark", ylabel="#Solved Instances", rot=0) # figsize = (6.4, 4.8) for p in fig.patches: fig.annotate(str(p.get_height()), (p.get_x() + p.get_width()/2, p.get_height() * 1.005), ha="center") # fig.legend(loc=(0.004,0.875)) fig.legend(loc="lower left") fig = fig.get_figure() return fig def solved_plot(datas, names): fig, ax = plt.subplots() index = [] values = [] fig.patch.set_visible(False) ax.axis('off') ax.axis('tight') for n in range(len(datas)): df = datas[n] index.append(names[n]) if n == 0: common = df[df.timeout == False] else: common_names = df[df.timeout == False].name common = common[common.name.isin(common_names)] tp = len(df[(df.timeout == False) & (df.ground_truth == True)]) fp = len(df[(df.timeout == False) & (df.ground_truth == False)]) fn = len(df[df.timeout == True]) values.append([round(tp/(tp+fp+fn), 4), round(tp/(tp+fp), 4), round(tp/(tp+fn), 4)]) for n in range(len(datas)): df = datas[n][datas[n].name.isin(common.name)] avg_time = df['real'].mean() avg_programs = df['programs'].mean() values[n].append(round(avg_time, 4)) values[n].append(round(avg_programs, 4)) values[n].append(len(datas[n][datas[n].timeout == False])) ax.table(cellText=values, rowLabels=index, colLabels=["Accuracy", "Precision", "Recall", "Real", "Programs", "Solved"], loc='center') fig.tight_layout() fig = fig.get_figure() return fig #################### FILES #################### dir = "evaluation/data/" # files = ["evaluation/data/textbook-no_sketch.csv", "evaluation/data/on/off_no_children.csv", "evaluation/data/on/on_no_children.csv", "evaluation/data/on/off_no_children_all_constraints.csv", "evaluation/data/on/off_no_children_constraints.csv"] # files = [dir+"st-no_sketch.csv", dir+"st-no_children.csv", dir+"st-no_root.csv"] # files = [dir+"st-no_sketch.csv", dir+"st-no_sketch_no_out_ctr.csv", dir+"st-no_sketch_no_out_ctr_new_opt.csv"] # files = [dir+"st-no_sketch_no_out_ctr_new_opt.csv", dir+"st-sketch_no_children_ctr_new_opt.csv", dir+"st-no_sketch_no_children_ctr_new_opt_flag.csv", dir+"new_no_children_off.csv", dir+"new_no_children_on.csv", dir+"new_no_sketch.csv", dir+"new_no_sketch_both.csv", dir+"new_no_children_on_both.csv", dir+"new_no_children_off_both.csv"] files = [dir+'no_sketch.csv', dir+'New/Off/no_children_off.csv', dir+'New/On/no_children_on.csv', dir+'New/Off/no_root_off.csv', dir+'New/On/no_root_on.csv'] out_file = "plots" ############ Baseline ################## files = [dir+'no_sketch.csv', dir+'New/Off/no_children_off.csv', dir+'New/Off/no_root_off.csv'] name_list = ["No sketch", "Sketch with no children", "Sketch with no root"] out_file = "Tese/baseline" ############ Optimization ################ files = [dir+'New/Off/no_children_off.csv', dir+'New/On/no_children_on.csv', dir+'New/Off/no_root_off.csv', dir+'New/On/no_root_on.csv'] name_list = ["Sketch with no children", "Sketch with no children optimized", "Sketch with no root", "Sketch with no root optimized"] out_file = "Tese/baseline_optimization" # files = [dir+'New/Off/no_children_off.csv', dir+'New/On/no_children_on.csv'] # name_list = ["Sketch with no children", "Sketch with no children optimized"] # out_file = "Tese/baseline_optimization_children" # files = [dir+'New/Off/no_root_off.csv', dir+'New/On/no_root_on.csv'] # name_list = ["Sketch with no root", "Sketch with no root optimized"] # out_file = "Tese/baseline_optimization_roots" # flag_filter = True # flag_summarise = True # flag_both = True # flag_union = True ################# PREPARATIONS ################# if flag_filter: # files = [dir + 'no_sketch.csv', dir + 'New/On/no_children_on.csv', dir + 'New/On/no_root_on.csv', dir + 'New/On/Filter/no_filter_on.csv', dir + 'New/On/Filter/only_filter_on.csv'] # files = [dir + 'no_sketch.csv', dir + 'New/Off/no_children_off.csv', dir + 'New/Off/no_root_off.csv', dir + 'New/Off/Filter/no_filter_off.csv', dir + 'New/Off/Filter/only_filter_off.csv'] # out_file = "filter_on" # out_file = "filter_off" files = [dir + 'New/On/no_children_on.csv', dir + 'New/On/Filter/no_root_no_filter_on.csv', dir + 'New/On/Filter/no_child_only_filter_on.csv', dir+'New/On/no_root_on.csv'] name_list = ["Sketch with no children", "Sketch with no root and no filter", "Sketch with no children except filter", "Sketch with no root"] out_file = "Tese/only_filter_optimized" elif flag_summarise: # files = [dir + 'no_sketch.csv', dir + 'New/On/no_children_on.csv', dir + 'New/On/no_root_on.csv', dir + 'New/On/Summarise/no_summarise_on.csv', dir+'New/On/Summarise/only_summarise_on.csv'] # files = [dir + 'no_sketch.csv', dir + 'New/Off/no_children_off.csv', dir + 'New/Off/no_root_off.csv', dir + 'New/Off/Summarise/no_summarise_off.csv', dir + 'New/Off/Summarise/only_summarise_off.csv'] # out_file = "summarise_on" # out_file = "summarise_off" files = [dir + 'New/On/no_children_on.csv', dir + 'New/On/Summarise/no_root_no_summarise_on.csv', dir + 'New/On/Summarise/no_child_only_summarise.csv', dir+'New/On/no_root_on.csv'] name_list = ["Sketch with no children", "Sketch with no root and no aggregate", "Sketch with no children except aggregate", "Sketch with no root"] out_file = "Tese/only_summarise_optimized" elif flag_both: files = [dir + 'New/On/no_children_on.csv', dir + 'New/On/Both/no_root_no_both_on.csv', dir + 'New/On/Both/sketch_no_child_only_both_on.csv', dir + 'New/On/no_root_on.csv'] # name_list = ["Sketch with no children", "Sketch with no root and no aggregate", "Sketch with no children except aggregate", "Sketch with no root"] out_file = "only_both_optimized" elif flag_union: files = [dir + 'New/On/no_children_on.csv', dir + 'New/On/Union/no_root_no_union_on.csv', dir + 'New/On/Union/sketch_no_child_only_both_on.csv', dir + 'New/On/no_root_on.csv'] # name_list = ["Sketch with no children", "Sketch with no root and no aggregate", "Sketch with no children except aggregate", "Sketch with no root"] out_file = "only_union_optimized" for file in files: csv_list.append(pd.read_csv(file)) if not name_list: for file in files: name_list.append(file.rsplit("/", 1)[1][:-4]) if flag_filter: new_list = [] for data in csv_list: new_list.append(data[data.name.isin(filter)]) csv_list = new_list elif flag_summarise: new_list = [] for data in csv_list: new_list.append(data[data.name.isin(summarise)]) csv_list = new_list elif flag_both: new_list = [] for data in csv_list: new_list.append(data[data.name.isin(both)]) csv_list = new_list elif flag_union: new_list = [] for data in csv_list: new_list.append(data[data.name.isin(union)]) csv_list = new_list ##################### RUN ##################### miscellaneous(csv_list) # sol = check(csv_list, name_list) # if sol[0]: # print("There are errors in csv: " + sol[1]) # else: # figs = [time_plot(csv_list, name_list), programs_plot(csv_list, name_list), ground_truth(csv_list, name_list), solved_plot(csv_list, name_list)] # # with PdfPages("evaluation/plots/"+out_file+".pdf") as pdf: # for fig in figs: # pdf.savefig(fig)

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Python

tests/__init__.py

luiscberrocal/requirement-auditor

47d88717b6c3754d10607034759c1d79dcf33d9e

[ "MIT" ]

1

2021-11-03T10:49:33.000Z

tests/__init__.py

luiscberrocal/requirement-auditor

47d88717b6c3754d10607034759c1d79dcf33d9e

[ "MIT" ]

null

tests/__init__.py

luiscberrocal/requirement-auditor

47d88717b6c3754d10607034759c1d79dcf33d9e

[ "MIT" ]

null

"""Unit test package for requirement_auditor."""

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py

Python

cltk/corpus/greek/tlg/author_geo.py

michiboo/cltk

f4ab93b836a995f88a007ed78246ea6db0bef377

[ "MIT" ]

2

2018-11-08T12:48:27.000Z

2018-11-08T17:01:55.000Z

cltk/corpus/greek/tlg/author_geo.py

michiboo/cltk

f4ab93b836a995f88a007ed78246ea6db0bef377

[ "MIT" ]

4

2021-02-02T23:07:04.000Z

2021-12-13T20:48:54.000Z

cltk/corpus/greek/tlg/author_geo.py

michiboo/cltk

f4ab93b836a995f88a007ed78246ea6db0bef377

[ "MIT" ]

1

2019-06-16T06:41:47.000Z

AUTHOR_GEO = {'Mesopotamia': ['2109'], 'Panormus [vel Panormum]': ['2393'], 'Hierapolis': ['0557', '1163', '1558'], 'Caloe+': ['3069'], 'Chonai': ['3094'], 'Theangela': ['1590'], 'Iconium': ['2112'], 'Mallos [vel Mallus]': ['2298'], 'Aspendos [vel Aspendus]': ['2681'], 'Elea': ['0595', '1434', '1461', '1562', '2607'], 'Tyros [vel Tyrus]': ['0563', '0666', '2034', '2340', '2383', '4346'], 'Colophon': ['0022', '0213', '0239', '0253', '0255', '0267', '1316', '1606', '1726', '2696'], 'Samos [vel Samus]': ['0137', '0198', '0242', '0260', '0471', '0537', '0679', '0686', '0707', '1181', '1263', '1339', '1446', '1494', '1506', '1597', '2192', '2372', '2560', '2566'], 'Lydia': ['0525', '1751', '4014'], 'Constantinopolis': ['0723', '0729', '2001', '2003', '2022', '2048', '2049', '2057', '2062', '2127', '2130', '2200', '2580', '2591', '2592', '2701', '2702', '2703', '2714', '2718', '2721', '2734', '2762', '2766', '2881', '2892', '2904', '2914', '2995', '3018', '3020', '3023', '3027', '3039', '3040', '3045', '3047', '3063', '3069', '3070', '3074', '3075', '3079', '3086', '3088', '3094', '3100', '3115', '3125', '3128', '3130', '3135', '3136', '3140', '3141', '3142', '3143', '3144', '3155', '3159', '3168', '3169', '3177', '3181', '3182', '3185', '3188', '4024', '4040', '4046', '4076', '4084', '4093', '4145', '4201', '4237', '4239', '4333', '9009', '9012', '9019', '9020', '9022', '9023'], 'Epidaurus [vel Epidaurum]': ['0201', '1828'], 'Ephesos [vel Ephesus]': ['0233', '0243', '0564', '0565', '0576', '0625', '0626', '0641', '0698', '1171', '1291', '1305', '1498', '1500', '1567', '1626', '1651', '2635', '2718', '4034', '4347'], 'Phrygia (Montanus)': ['1771'], 'Seleucia': ['1166', '2800'], 'Adramytteum': ['0174'], 'Lesbos [vel Lesbus]': ['0009', '0299', '0383', '0539', '0561', '1493', '3146'], 'Bulgaria': ['3014'], 'Myrlea': ['0655', '1199'], 'Lemnos [vel Lemnus]': ['0638', '0652', '1600'], 'Byzantium': ['0083', '0220', '0644', '0676', '1566', '1599', '1941', '2025', '2595', '4028'], 'Proconnesos [vel Proconnesus]': ['1182', '1871', '2326'], 'Thebae': ['0336', '0397', '0971', '2043', '2608'], 'Callatia [vel Callatis]': ['1917'], 'Antinoe': ['2055'], 'Heraclea': ['0127', '0703', '1251', '1409', '1427', '1496', '1544', '1752', '1846', '2300', '2633', '2636', '4003', '4126', '4145'], 'Palmyra': ['2178'], 'Palaestina': ['0526', '1398', '2021'], 'Phlius': ['1735', '1833', '2609'], 'Apamea': ['0024', '0661', '1052', '1542'], 'Croton (Democedes)': ['2218'], 'Argos': ['0369', '0392', '1292', '1314', '1324', '1376', '1625', '1678', '2212', '2612', '2630'], 'Ceos [vel Cea]': ['0199', '0261', '0690', '1192', '1634', '2306'], 'Cassandrea': ['1197'], 'Arelate [vel Arelas]': ['1377'], 'Leucadia': ['0380', '2386'], 'Smyrna': ['0036', '0255', '0693', '1421', '1622', '1724', '1987', '2046', '2314'], 'Cardia': ['1953'], 'Cydonia': ['1338'], 'Pella': ['0086', '1632', '1978', '1992'], 'Amasia': ['0099', '2060'], 'Sphettos': ['0673'], 'Priene': ['1223', '1523'], 'Megalopolis': ['0543', '1250', '1646'], 'Isauria': ['2800'], 'Side [vel Sida]': ['0281'], 'Tripolis': ['1719'], 'Macedonia': ['0048', '0616', '1288', '1577', '1709', '2037', '2697'], 'Imbros [vel Imbrus]': ['3147'], 'Curium': ['1273', '1420'], 'Patrae': ['1514', '2130'], 'Gaza': ['2048', '2449', '2578', '2598', '2806', '4001', '4094'], 'Abdera': ['0218', '0714', '1304', '1390', '1461', '1635', '2153'], 'Lugdunum': ['1447'], 'Amida': ['0718'], 'Lepreum [vel Lepreos]': ['1388'], 'Nyssa': ['1688', '1875', '2017'], 'Nicomedia': ['2200', '2638'], 'Neapolis (Samariae)': ['4075'], 'Amathus': ['2512'], 'Ascra': ['0020'], 'Perga': ['0550'], 'Sinope': ['0334', '0444', '0445', '0447', '1219'], 'Berytus': ['2881'], 'Paphos [vel Paphus]': ['1682'], 'Epiphania': ['4392'], 'Bithynia': ['0074', '1308', '2714'], 'Philadelphia': ['2580', '9008'], 'Lycia': ['2506', '4345'], 'Aphrodisias': ['0554', '0732', '1170'], 'Cappadocia': ['2058', '2158', '2499'], 'Mopsuestia': ['4135'], 'Heraclea (Ponti)': ['0097', '2185'], 'Lindos [vel Lindus]': ['0244', '1274'], 'Elaea': ['2652', '4344'], 'Myndos [vel Myndus]': ['2640'], 'Tralles': ['0585', '0744', '1004', '4018', '4088'], 'Scarphea': ['0205'], 'Borysthenis': ['1224', '1693'], 'Ilium [vel Troja]': ['0586'], 'Tegea': ['0306', '1680', '2215'], 'Syria': ['0015', '0630', '1441', '1766', '2461', '2798', '4138', '4393'], 'Delphi': ['1392', '2284'], 'Neocaesarea': ['2063'], 'Volsinii': ['0628'], 'Myra (Lyciae)': ['2904'], 'Aegyptus': ['0359', '0570', '0647', '1477', '1553', '1555', '3130', '4282'], 'Gerasa': ['0358'], 'Cos': ['0212', '0627', '1244', '1633', '2587'], 'Oenoanda': ['1321'], 'Mecyberna': ['1397'], 'Eleusis': ['2691'], 'Pygela': ['4390'], 'Agrigentum [vel Acragas]': ['1342', '1969'], 'Thessalonicensis': ['2592', '3015', '3145', '4021', '4083', '9023'], 'Antinoupolis': ['2596'], 'Plataeae [vel Plataea]': ['1908', '2482'], 'Oxyrhynchus': ['0608'], 'Carystos [vel Carystus]': ['0411', '0568', '1906'], 'Metapontum': ['1230', '1360', '1507', '2225', '2260', '2638'], 'Magnesia': ['2333', '2614'], 'Xanthos [vel Xanthus]': ['1503'], 'Tauromenium': ['1733'], 'Cythera': ['0379'], 'Thessalia': ['2417'], 'Chios [vel Chius]': ['0308', '0374', '0566', '1193', '1508', '1714', '2234', '2235', '2456'], 'Helenopolis': ['2111'], 'Oene': ['1343'], 'Aegina': ['0335', '0715'], 'Thurii': ['0016', '0246', '0324'], 'Pontos [vel Pontus]': ['0283', '1162', '4110'], 'Neapolis': ['1972'], 'Phalerum': ['0624'], 'Anazarba': ['0023', '0656'], 'Methymna': ['1723', '2331', '2384'], 'Tenedos [vel Tenedus]': ['1275', '2412'], 'Thyatira': ['1529'], 'Pitane': ['1172', '1210', '1486'], 'Panopolis': ['2045', '4038', '4319'], 'Croton': ['0766', '1341', '1362', '1509', '1549', '1596', '2229'], 'Selymbria': ['0331', '4201'], 'Arcadia': ['0249', '1556', '2606'], 'Apollonia': ['1319'], 'Eretria': ['0309', '1906'], 'Salamis': ['2511'], 'Lampsacum': ['0547', '1258', '1442', '1696', '1811'], 'Pharsalos [vel Pharsalus]': ['2605'], 'Caryanda': ['0065'], 'Amorgos [vel Amorgus]': ['0260'], 'Cnidos [vel Cnidus]': ['0067', '0845', '1358', '2193', '2568'], 'Sicilia': ['0060', '0695', '1273', '2634', '4235'], 'Soli': ['0382', '0653', '1225', '1264', '1270', '1287'], 'Corinthos [vel Corinthus]': ['0029', '0204', '0298', '0473', '0629', '1329', '2195', '2270', '2355', '2619', '2639'], 'Tyana': ['0619'], 'Byblos [vel Byblus]': ['1416'], 'Megara': ['0002', '0264', '1313', '1587', '1699', '2336'], 'Cyzicos [vel Cyzicus vel Cyzicum]': ['0207', '0688', '1232', '1525', '1636', '1704', '2319', '2326', '2328', '2610', '3157', '3158'], 'Gadara': ['0052', '1548', '1595', '2027'], 'Coptos [vel Coptus]': ['2119'], 'Erythrae': ['1435'], 'Sicyon': ['0372', '0378', '0473', '2162'], 'Sybaris': ['2228'], 'Carrhae': ['2157'], 'Aetolia': ['0216'], 'Olynthos [vel Olynthus]': ['0534', '1912', '4345'], 'Euboea': ['1174'], 'Camiros [vel Camirus]': ['0288'], 'Alexandria (Troadis)': ['0342', '1138', '1391', '1393'], 'Scythopolis': ['2877'], 'Capreae': ['1227'], 'Orchomenus': ['1260'], 'Sardis [vel Sardes]': ['0165', '0605', '1495', '2050', '4157'], 'Antiochia': ['1443', '1670', '1725', '1764', '2061', '2062', '2116', '2200', '2573', '2733', '2871', '4100', '4117', '4184', '4239', '4394'], 'Artemita': ['1164'], 'Cumae': ['0536', '1406', '2396'], 'Tarsos [vel Tarsus]': ['0146', '0592', '0700', '0706', '0720', '1146', '1173', '1206', '1748', '1954', '2294', '4134'], 'Caesarea (Palaestinae)': ['2018', '2042', '2064', '2577', '2591', '2816', '4029'], 'Paphlagonia': ['1577'], 'Oasis': ['1152'], 'Lacedaemon [vel Sparta]': ['0266', '0291', '1189', '1490', '1516', '1534', '1627', '1685'], 'Babylonia': ['0688', '1222', '1320', '2625'], 'Mytilene': ['0089', '0631', '0833', '1439', '1881', '1949', '1981', '2330', '4187'], 'Nilopolis': ['2052'], 'Nicaea': ['0385', '0655', '1083', '1431', '3142', '4000', '4031', '9012'], 'Patavium': ['4237'], 'Assos [vel Assus]': ['1269'], 'Calacte': ['1970'], 'Perinthos [vel Perinthus]': ['0606'], 'Samaria': ['0645', '1706'], 'Amisus': ['1266'], 'Sidon': ['1337', '2127', '2397'], 'Cnidos [vel Cnidus] (Calliphon)': ['2218'], 'Chalcis': ['0017', '0221', '0341', '0367', '1328', '2023', '2241'], 'Amphissa': ['1176'], 'Thasos [vel Thasus]': ['0463', '1923', '2231'], 'Numidia': ['0186'], 'Selinus': ['0241', '0377'], 'Oenoe': ['1343'], 'Ancyra': ['2041', '2084'], 'Chalcedon': ['0634', '1729', '2474'], 'Scepsis': ['1756', '1976'], 'Citium': ['0635', '0660', '1574'], 'Troezen': ['1301'], 'Phocis': ['3146'], 'Cyrrhus': ['4089'], 'Epiros [vel Epirus]': ['1638', '2025', '2160'], 'Carthago': ['2169'], 'Larissa': ['2627', '2697'], 'Myrina': ['4024'], 'Thera': ['2608'], 'Babylonia (fort. Aegypti)': ['1703'], 'Aegae': ['1504'], 'Constantia (Cypri)': ['2021'], 'Clazomenae': ['0713', '2307'], 'Rhegium': ['0293', '0900', '1437', '1438', '1470', '2275', '4391'], 'Leontini': ['0593'], 'Caesarea (Cappadociae)': ['2040', '2130'], 'Aphrodito (Aegypti)': ['2121'], 'Calabria': ['3159'], 'Teium': ['0237', '0259', '1141', '2334', '2536'], 'Mysia': ['0284'], 'Chersonesus': ['0210', '0570', '1346'], 'Ascalon': ['1143', '1643', '4072'], 'Phaselis': ['0329', '1294', '2565'], 'Gela': ['0413', '1175'], 'Epiphania (Syriae)': ['2733'], 'Syracusae': ['0005', '0035', '0095', '0247', '0330', '0338', '0487', '0521', '0552', '0578', '0639', '1145', '1175', '1341', '1591', '1654', '1715', '2240', '2244', '2387', '2968'], 'Eresos [vel Eresus]': ['0093', '1578'], 'Alexandria': ['0001', '0018', '0063', '0082', '0084', '0087', '0321', '0341', '0343', '0357', '0363', '0473', '0551', '0555', '0559', '0574', '0607', '0609', '0671', '0717', '0724', '0726', '0727', '0731', '0736', '1152', '1186', '1194', '1312', '1389', '1402', '1407', '1530', '1602', '1661', '1799', '1838', '1881', '1918', '2000', '2020', '2032', '2033', '2035', '2039', '2042', '2053', '2102', '2133', '2172', '2317', '2424', '2577', '2591', '2724', '2865', '2956', '2962', '2995', '3043', '4015', '4016', '4019', '4020', '4021', '4061', '4066', '4085', '4090', '4115', '4149', '4227', '4238', '4239', '4328', '9019', '9021'], 'Emesa': ['0743', '2881', '4124'], 'Daldis': ['0553'], 'Florentia': ['4237'], 'Stagira': ['0086'], 'Thebae (Aegypti)': ['2591'], 'Iasus': ['1262', '2246'], 'Nazianzus': ['2022'], 'Laranda': ['0522'], 'Cypros [vel Cyprus]': ['2532', '2860', '2969', '9006'], 'Leros [vel Lerus]': ['0245'], 'Pieria': ['1867'], 'Arabia': ['1608', '4340'], 'Naucratis': ['0008', '0542', '1469', '2182'], 'Delos': ['1425', '1663', '2594'], 'Lucania': ['1545'], 'Tragilos [vel Tragilus]': ['1200'], 'Pergamum': ['0057', '0079', '0722', '1245', '1254', '1698', '2392'], 'Chaeronea': ['0007'], 'Alabanda': ['2186'], 'Samosata': ['0062'], 'Thmuis': ['2966'], 'Rufinianae': ['2770'], 'Amastris (Paphlagoniae)': ['4397'], 'Cyrene [vel Cyrenae]': ['0222', '0533', '0584', '1450', '1814', '2006', '2237', '2613', '2729'], 'Massilia': ['1650'], 'Boeotia': ['0033', '1196'], 'Sigeum': ['1868'], 'Tanagra': ['0294'], 'Telmessos [vel Telmessus vel Telmissus]': ['2615'], 'Prusa': ['0612', '2051'], 'Hierosolyma': ['2110', '2766', '2797', '2956', '3173'], 'Corcyra [vel Cercyra]': ['1588'], 'Mauretania': ['1452'], 'Parium': ['1526'], 'Melos': ['0371', '0373'], 'Barce': ['1499'], 'Roma': ['0087', '0526', '0557', '0562', '0572', '0609', '0645', '0654', '1271', '1426', '1611', '1760', '2000', '2034', '2115', '2542', '2543', '2545', '2611', '4237'], 'Locri [vel Locrae vel Locra]': ['0601', '1734'], 'Antiochia (Pisidiae)': ['2701'], 'Messana [vel Messina]': ['0066', '1188'], 'Stymphalus [vel Stymphalum]': ['0058', '1683'], 'Paros': ['0232', '0251', '1800'], 'Olbiopolis': ['2187'], 'Petra': ['2189'], 'Monembasia': ['9018'], 'Hermione': ['0366', '0368'], 'Athenae': ['0003', '0006', '0010', '0011', '0014', '0017', '0019', '0026', '0027', '0028', '0029', '0030', '0032', '0034', '0059', '0085', '0086', '0198', '0203', '0236', '0246', '0250', '0252', '0254', '0262', '0263', '0301', '0302', '0303', '0314', '0319', '0320', '0325', '0365', '0370', '0375', '0427', '0433', '0465', '0483', '0496', '0497', '0508', '0516', '0517', '0535', '0537', '0540', '0541', '0549', '0583', '0591', '0610', '0713', '0724', '0750', '0876', '0897', '1087', '1125', '1147', '1150', '1184', '1205', '1276', '1289', '1303', '1307', '1399', '1400', '1426', '1433', '1491', '1553', '1583', '1584', '1609', '1692', '1780', '1782', '1843', '1848', '1907', '1911', '1912', '2027', '2031', '2051', '2141', '2151', '2171', '2178', '2219', '2232', '2255', '2291', '2303', '2305', '2313', '2600', '2607', '2645', '2699', '2766', '2903', '2904', '2937', '3139', '4013', '4017', '4036', '4066', '9019'], 'Phanagoria': ['2694'], 'Panium': ['2946'], 'Creta': ['0208', '0268', '1310', '1347', '2322', '9009'], 'Aenus': ['3170'], 'Persia': ['4361'], 'Himera': ['0292', '0981', '2304'], 'Mendes': ['1306', '2385', '2423', '2428'], 'Catana [vel Catina]': ['1259'], 'Gades': ['1890'], 'Tarentum [vel Taras]': ['0088', '0620', '0633', '1277', '1899', '2226', '2246'], 'Gabala': ['4139'], 'Damascos [vel Damascus]': ['0577', '1165', '2573', '2631', '2934', '4066'], 'Rhodos [vel Rhodus]': ['0001', '0089', '0211', '0215', '0265', '0344', '1052', '1124', '1207', '1240', '1244', '1281', '1354', '1357', '1383', '1430', '1679', '1687', '1732', '1915', '2354', '2357', '2364', '2367', '2628'], 'Samothraca [vel Samothrace vel Samothracia]': ['4046'], 'Miletos [vel Miletus]': ['0257', '0376', '0538', '0617', '0697', '0725', '0918', '1190', '1282', '1408', '1436', '1461', '1533', '1604', '1705', '1881', '2194', '2274', '2286', '2303', '2339', '2341', '2466', '2635'], 'Halicarnassus': ['0016', '0081', '1123', '1323', '1557'], 'Telos [vel Telus]': ['1355'], 'Laodicea': ['2074', '2586'], 'Lycopolis': ['2000', '2059', '4081'], 'Bena': ['0219']}

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fe6510bd1303a011bda9acf7e0ce3c592c45aaae

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py

Python

src/main.py

guritian/fl-noniid

0af633e4df54426ee499b6e27b42589cc23a1eee

[ "MIT" ]

3

2021-07-21T06:07:01.000Z

2021-12-27T06:54:54.000Z

src/main.py

guritian/fl-noniid

0af633e4df54426ee499b6e27b42589cc23a1eee

[ "MIT" ]

null

src/main.py

guritian/fl-noniid

0af633e4df54426ee499b6e27b42589cc23a1eee

[ "MIT" ]

null

import math import os import sys cur_path=os.path.abspath(os.path.dirname(__file__)) sys.path.insert(0, cur_path+"/..") import time from arg_parser import Parser from arg_parser1 import Parser1 from src.models.lenet1 import LeNet1 from src.models.lenetBN import LeNetBN from utils import Utils import torch import numpy as np import random from tqdm import tqdm from trainer import Trainer, Tester import copy from models import * from torch.utils.data import Dataset, DataLoader from sklearn.cluster import KMeans,DBSCAN from sklearn.manifold import TSNE from torch.utils.tensorboard import SummaryWriter writer = SummaryWriter('./log') # class FederatedLearning(): # def __init__(self, args): # self.args = args # # def run(self): # start = time.time() # # # Print arguments # if self.args.verbose: # print("Arguments:") # print(f"\t{self.args}") # # # Set training on CPU/GPU # device = "cpu" # if self.args.gpu is not None: # if torch.cuda.is_available(): # device = "cuda" # torch.cuda.set_device(self.args.gpu) # # # Set manual random seed # if not self.args.random_seed: # torch.manual_seed(42) # torch.cuda.manual_seed(42) # np.random.seed(42) # random.seed(42) # torch.backends.cudnn.deterministic = True # # utils = Utils() # # # Get dataset and data distribution over devices # train_dataset, test_dataset, device_idxs = utils.get_dataset_dist(self.args) # # # Get number of classes (MNIST: 10, CIFAR10: 10) # if self.args.dataset == "mnist": # num_classes = 10 # else: # num_classes = 10 # # # Set training model (VGG11/LeNet/ResNet18) # if self.args.model == "vgg11": # model = VGG("VGG11", num_classes) # elif self.args.model == "lenet": # model = LeNet(num_classes) # elif self.args.model == "lenetBN": # model = LeNetBN(num_classes) # else: # model = ResNet18(num_classes) # # # Optimization technique # if self.args.warmup_model: # weights = utils.warmup_model(model, train_dataset, test_dataset, device, self.args) # model.load_state_dict(weights) # # avg_weights_diff = [] # global_train_losses = [] # global_test_losses = [] # global_accuracies = [] # global_aucs = [] # global_kappas = [] # # # for round in tqdm(range(self.args.round)): # # Train step # print(f"\nRound {round+1} Training:") # # local_weights = [] # local_losses = [] # # # Select fraction of devices (minimum 1 device) # train_devices = random.sample( # range(self.args.num_devices), # max(1, int(self.args.num_devices*self.args.frac)) # ) # # print(f"\tDevices selected: {[x+1 for x in train_devices]}\n") # # # Train on each device and return weights and loss # for device_num in train_devices: # weights, loss = Trainer().train( # train_dataset, # device_idxs[device_num], # round, # device_num, # device, # copy.deepcopy(model), # Avoid continuously training same model on different devices # self.args # ) # # local_weights.append(weights) # local_losses.append(loss) # # if args.model == 'lenet': # avg_weights = utils.fed_avg(local_weights)# Federated averaging # elif args.model == 'lenetBN': # avg_weights = utils.communication(local_weights) # Federated averaging # # # model.load_state_dict(avg_weights) # Load new weights # # if self.args.cal_para_diff: # avg_weight_diff = utils.cal_avg_weight_diff(local_weights, avg_weights) # else: # avg_weight_diff = 0 # avg_loss = sum(local_losses)/len(local_losses) # # if self.args.cal_para_diff: # print(f"\n\tRound {round+1} | Average weight difference: {avg_weight_diff}") # # print(f"\tRound {round+1} | Average training loss: {avg_loss}\n") # # global_train_losses.append(avg_loss) # avg_weights_diff.append(avg_weight_diff) # # # # # Test step # print(f"Round {round+1} Testing:") # accuracy, loss, auc, kappa = Tester().test( # test_dataset, # round, # device, # copy.deepcopy(model), # self.args # ) # # print(f"\tRound {round+1} | Average accuracy: {accuracy}") # print(f"\tRound {round+1} | Average testing loss: {loss}\n") # print(f"\tRound {round+1} | Average AUC: {auc}") # print(f"\tRound {round+1} | Kappa: {kappa}\n") # # global_test_losses.append(loss) # global_accuracies.append(accuracy) # global_aucs.append(auc) # global_kappas.append(kappa) # # # # # # Quit early if satisfy certain situations # if accuracy >= self.args.train_until_acc/100: # print( # f"Accuracy reached {self.args.train_until_acc/100} in round {round+1}, stopping...") # break # if self.args.stop_if_improvment_lt: # if round > 0: # if global_accuracies[-2]+self.args.stop_if_improvment_lt/100 >= global_accuracies[-1]: # break # # end = time.time() # print(f"\nTime used: {time.strftime('%H:%M:%S', time.gmtime(end-start))}") # # # Print final results # if self.args.cal_para_diff: # print("\nAverage weight differences:") # print(f"\t{avg_weights_diff}\n") # # print("Losses on training data:") # print(f"\t{global_train_losses}\n") # print("Losses on testing data:") # print(f"\t{global_test_losses}\n") # print("Accuracies on testing data:") # print(f"\t{global_accuracies}\n") # print("Average AUCs on testing data:") # print(f"\t{global_aucs}\n") # print("Kappas on testing data:") # print(f"\t{global_kappas}\n") # # print(f"Final accuracy: {global_accuracies[-1]}") # print(f"Final loss: {global_test_losses[-1]}\n") # # # Write results to file # if self.args.save_results: # utils.save_results_to_file( # self.args, # avg_weights_diff, # global_train_losses, # global_test_losses, # global_accuracies, # global_aucs, # global_kappas # ) class FederatedLearning(): def __init__(self, args): self.args = args def run(self): start = time.time() # Print arguments if self.args.verbose: print("Arguments:") print(f"\t{self.args}") # Set training on CPU/GPU #device = "cpu" device = "cuda" if self.args.gpu is not None: if torch.cuda.is_available(): device = "cuda" #torch.cuda.set_device(self.args.gpu) # Set manual random seed if not self.args.random_seed: torch.manual_seed(42) torch.cuda.manual_seed(42) np.random.seed(42) random.seed(42) torch.backends.cudnn.deterministic = True utils = Utils() # Get dataset and data distribution over devices train_dataset, test_dataset, device_idxs = utils.get_dataset_dist(self.args) # Get number of classes (MNIST: 10, CIFAR10: 10) if self.args.dataset == "mnist": num_classes = 10 else: num_classes = 10 # Set training model (VGG11/LeNet/ResNet18) if self.args.model == "vgg11": model = VGG("VGG11", num_classes) elif self.args.model == "lenet": model = LeNet(num_classes) elif self.args.model == "lenetBN": model = LeNetBN(num_classes) else: model = ResNet18(num_classes) # Optimization technique if self.args.warmup_model: weights = utils.warmup_model(model, train_dataset, test_dataset, device, self.args) model.load_state_dict(weights) avg_weights_diff = [] global_train_losses = [] global_test_losses = [] global_accuracies = [] global_aucs = [] global_kappas = [] #每个client保存自己的BN层参数 device_weights = [] inital_weight = copy.deepcopy(model.state_dict()) for i in range(self.args.num_devices): device_weights.append(copy.deepcopy(model.state_dict())) for round in tqdm(range(self.args.round)): # Train step print(f"\nRound {round + 1} Training:") local_weights = [] local_losses = [] # Select fraction of devices (minimum 1 device) train_devices = random.sample( range(self.args.num_devices), max(1, int(self.args.num_devices * self.args.frac)) ) # #将local client BN层参数放进来 # for x in train_devices: # local_weights.append(device_weights[x]) print(f"\tDevices selected: {[x + 1 for x in train_devices]}\n") # Train on each device and return weights and loss for device_num in train_devices: model.load_state_dict(device_weights[device_num]) weights, loss = Trainer().train( train_dataset, device_idxs[device_num], round, device_num, device, copy.deepcopy(model), # Avoid continuously training same model on different devices self.args ) local_weights.append(weights) local_losses.append(loss) if args.model == 'lenet': avg_weights = utils.fed_avg(local_weights) # Federated averaging elif args.model == 'lenetBN': if args.frac == 1: avg_weights = utils.communication(args.frac,local_weights)# Federated BN else: avg_weights = utils.communication(args.frac,local_weights) # Federated BN model.load_state_dict(avg_weights) # Load new weights if self.args.cal_para_diff: avg_weight_diff = utils.cal_avg_weight_diff(local_weights, avg_weights) else: avg_weight_diff = 0 avg_loss = sum(local_losses) / len(local_losses) if self.args.cal_para_diff: print(f"\n\tRound {round + 1} | Average weight difference: {avg_weight_diff}") print(f"\tRound {round + 1} | Average training loss: {avg_loss}\n") global_train_losses.append(avg_loss) avg_weights_diff.append(avg_weight_diff) # Test step print(f"Round {round + 1} Testing:") i=0 accuracy, loss, auc, kappa = 0,0,0,0 #由于加入了BN层所以模型的测试更改为每个local都进行测试 for device_num in train_devices: model.load_state_dict(local_weights[i]) device_weights[device_num] = local_weights[i] temp_accuracy, temp_loss, temp_auc, temp_kappa = Tester().test( test_dataset, round, device, copy.deepcopy(model), self.args ) i+=1 print(f"\tDevice {device_num} Round {round + 1} | Average accuracy: {temp_accuracy}") print(f"\tDevice {device_num} Round {round + 1} | Average testing loss: {temp_loss}\n") print(f"\tDevice {device_num} Round {round + 1} | Average AUC: {temp_auc}") print(f"\tDevice {device_num} Round {round + 1} | Kappa: {temp_kappa}\n") accuracy += temp_accuracy loss += temp_loss auc += temp_auc kappa += temp_kappa accuracy /= len(local_weights) loss /= len(local_weights) auc /= len(local_weights) kappa /= len(local_weights) writer.add_scalar('accuracy', accuracy, round) global_test_losses.append(loss) global_accuracies.append(accuracy) global_aucs.append(auc) global_kappas.append(kappa) # Quit early if satisfy certain situations if accuracy >= self.args.train_until_acc / 100: print( f"Accuracy reached {self.args.train_until_acc / 100} in round {round + 1}, stopping...") break if self.args.stop_if_improvment_lt: if round > 0: if global_accuracies[-2] + self.args.stop_if_improvment_lt / 100 >= global_accuracies[-1]: break end = time.time() print(f"\nTime used: {time.strftime('%H:%M:%S', time.gmtime(end - start))}") # Print final results if self.args.cal_para_diff: print("\nAverage weight differences:") print(f"\t{avg_weights_diff}\n") print("Losses on training data:") print(f"\t{global_train_losses}\n") print("Losses on testing data:") print(f"\t{global_test_losses}\n") print("Accuracies on testing data:") print(f"\t{global_accuracies}\n") print("Average AUCs on testing data:") print(f"\t{global_aucs}\n") print("Kappas on testing data:") print(f"\t{global_kappas}\n") print(f"Final accuracy: {global_accuracies[-1]}") print(f"Final loss: {global_test_losses[-1]}\n") # Write results to file if self.args.save_results: utils.save_results_to_file( self.args, avg_weights_diff, global_train_losses, global_test_losses, global_accuracies, global_aucs, global_kappas ) class FederatedDecoupleLearning(): def __init__(self, args): self.args = args def run(self): start = time.time() # Print arguments if self.args.verbose: print("Arguments:") print(f"\t{self.args}") # Set training on CPU/GPU device = "cpu" if self.args.gpu is not None: if torch.cuda.is_available(): device = "cuda" #torch.cuda.set_device(self.args.gpu) # Set manual random seed if not self.args.random_seed: torch.manual_seed(42) torch.cuda.manual_seed(42) np.random.seed(42) random.seed(42) torch.backends.cudnn.deterministic = True utils = Utils() # Get dataset and data distribution over devices train_dataset, test_dataset, device_idxs = utils.get_dataset_dist(self.args) # Get number of classes (MNIST: 10, CIFAR10: 10) if self.args.dataset == "mnist": num_classes = 10 else: num_classes = 10 # Set training model (VGG11/LeNet/ResNet18) if self.args.model == "vgg11": model = VGG("VGG11", num_classes) elif self.args.model == "lenet": model = LeNet(num_classes) else: model = ResNet18(num_classes) # Optimization technique if self.args.warmup_model: weights = utils.warmup_model(model, train_dataset, test_dataset, device, self.args) model.load_state_dict(weights) avg_weights_diff = [] global_train_losses = [] global_test_losses = [] global_accuracies = [] global_aucs = [] global_kappas = [] #用来进行最后的各个client的fine tuning final_weight = [] for round in tqdm(range(self.args.round)): # Train step print(f"\nRound {round + 1} Training:") local_weights = [] local_losses = [] # Select fraction of devices (minimum 1 device) train_devices = random.sample( range(self.args.num_devices), max(1, int(self.args.num_devices * self.args.frac)) ) print(f"\tDevices selected: {[x + 1 for x in train_devices]}\n") # Train on each device and return weights and loss for device_num in train_devices: weights, loss = Trainer().train( train_dataset, device_idxs[device_num], round, device_num, device, copy.deepcopy(model), # Avoid continuously training same model on different devices self.args ) local_weights.append(weights) local_losses.append(loss) avg_weights = utils.fed_avg(local_weights) # Federated averaging model.load_state_dict(avg_weights) # Load new weights if self.args.cal_para_diff: avg_weight_diff = utils.cal_avg_weight_diff(local_weights, avg_weights) else: avg_weight_diff = 0 avg_loss = sum(local_losses) / len(local_losses) if self.args.cal_para_diff: print(f"\n\tRound {round + 1} | Average weight difference: {avg_weight_diff}") print(f"\tRound {round + 1} | Average training loss: {avg_loss}\n") global_train_losses.append(avg_loss) avg_weights_diff.append(avg_weight_diff) # Test step print(f"Round {round + 1} Testing:") accuracy, loss, auc, kappa = Tester().test( test_dataset, round, device, copy.deepcopy(model), self.args ) print(f"\tRound {round + 1} | Average accuracy: {accuracy}") print(f"\tRound {round + 1} | Average testing loss: {loss}\n") print(f"\tRound {round + 1} | Average AUC: {auc}") print(f"\tRound {round + 1} | Kappa: {kappa}\n") global_test_losses.append(loss) global_accuracies.append(accuracy) global_aucs.append(auc) global_kappas.append(kappa) #如果整个fedavg round已经结束了 if(round+1 == self.args.round ): final_weight = avg_weights # Quit early if satisfy certain situations if accuracy >= self.args.train_until_acc / 100: print( f"Accuracy reached {self.args.train_until_acc / 100} in round {round + 1}, stopping...") break if self.args.stop_if_improvment_lt: if round > 0: if global_accuracies[-2] + self.args.stop_if_improvment_lt / 100 >= global_accuracies[-1]: break #对所有的client进行一轮只训分类器 modelByDecouple = LeNet1(num_classes) modelByDecouple.load_state_dict(final_weight) local_decouple_accuracy = [] print(f"Final accuracy: {global_accuracies[-1]}") for device_num in range(10): # for device_num in range(self.args.num_devices): print(f"device {device_num} FedAvg Testing:") #每个client先测一下使用FedAvg的准确率 accuracy, loss, auc, kappa = Tester().test( test_dataset, round, device, copy.deepcopy(modelByDecouple), self.args ) print(f"\tdevice {device_num} | Average accuracy: {accuracy}") print(f"\tdevice {device_num} | Average testing loss: {loss}\n") print(f"\tdevice {device_num} | Average AUC: {auc}") print(f"\tdevice {device_num} | Kappa: {kappa}\n") #每个Local Client都使用 FedAvg算法得到的最终模型参数进行解耦分类器训练 weights, loss = Trainer().train( train_dataset, device_idxs[device_num], round, device_num, device, copy.deepcopy(modelByDecouple), # Avoid continuously training same model on different devices self.args ) #对经过fine tune的 Local Client 进行测试 model.load_state_dict(weights) # Test step print(f"device {device_num} FDL Testing:") accuracy, loss, auc, kappa = Tester().test( test_dataset, round, device, copy.deepcopy(model), self.args ) local_decouple_accuracy.append(accuracy) print(f"\tdevice {device_num} | Average accuracy: {accuracy}") print(f"\tdevice {device_num} | Average testing loss: {loss}\n") print(f"\tdevice {device_num} | Average AUC: {auc}") print(f"\tdevice {device_num} | Kappa: {kappa}\n") print("-----------------------------------------------------------------------") end = time.time() print(f"\nTime used: {time.strftime('%H:%M:%S', time.gmtime(end - start))}") print(f"Final accuracy: {global_accuracies[-1]}") print(f"经过fine tuning 后各个Client的测试准确率：") print(local_decouple_accuracy) print(f"Final loss: {global_test_losses[-1]}\n") # Write results to file if self.args.save_results: utils.save_results_to_file( self.args, avg_weights_diff, global_train_losses, global_test_losses, global_accuracies, global_aucs, global_kappas ) class FederatedLearning1(): def __init__(self, args): self.args = args def run(self): start = time.time() # Print arguments if self.args.verbose: print("Arguments:") print(f"\t{self.args}") # Set training on CPU/GPU device = "cpu" if self.args.gpu is not None: if torch.cuda.is_available(): device = "cuda" #torch.cuda.set_device(self.args.gpu) # Set manual random seed if not self.args.random_seed: torch.manual_seed(42) torch.cuda.manual_seed(42) np.random.seed(42) random.seed(42) torch.backends.cudnn.deterministic = True utils = Utils() # Get dataset and data distribution over devices # TODO client_labels 目前用来表示每个client上的数据分类 train_dataset, test_dataset, device_idxs,client_labels = utils.get_dataset_dist(self.args) # Get number of classes (MNIST: 10, CIFAR10: 10) if self.args.dataset == "mnist": num_classes = 10 else: num_classes = 10 # Set training model (VGG11/LeNet/ResNet18) if self.args.model == "vgg11": model = VGG("VGG11", num_classes) elif self.args.model == "lenet": model = LeNet(num_classes) else: model = ResNet18(num_classes) # Optimization technique if self.args.warmup_model: weights = utils.warmup_model(model, train_dataset, test_dataset, device, self.args) model.load_state_dict(weights) avg_weights_diff = [] global_train_losses = [] global_test_losses = [] global_accuracies = [] global_aucs = [] global_kappas = [] #用来进行最后的各个client的fine tuning final_weight = [] # 每个client保存自己的网络参数 device_weights = [] init_weights = copy.deepcopy(model.state_dict()) for i in range(self.args.num_devices): device_weights.append(copy.deepcopy(model.state_dict())) model_name = './model/client'+str(self.args.class_per_device)+"_"+ str(i) + ".pth" flag = os.path.exists() if flag: # 对每个client进行预训练，使 pretrain_epoch = self.args.pretrain_epoch for i in range(self.args.num_devices): weights, loss = Trainer().pre_train( epoch=pretrain_epoch, dataset=train_dataset, idxs=device_idxs[i], device_num=i, device=device, model=copy.deepcopy(model), # Avoid continuously training same model on different devices args=self.args ) device_weights[i] = weights model_name = "model/client" +str(self.args.class_per_device)+"_"+ str(i) + ".pth" torch.save(obj=weights, f=model_name) else: for i in range(self.args.num_devices): model_name = "model/client" +str(self.args.class_per_device)+"_" + str(i) + ".pth" device_weights[i] = torch.load(model_name) #对client进行聚类 kmeans_weights = [] for i in range(self.args.num_devices): first = True for param_tensor in device_weights[i]: numpy_para = device_weights[i][param_tensor].cpu().numpy() numpy_para = numpy_para.reshape(-1) if first: transform_feature = numpy_para first = False else: transform_feature = np.append(transform_feature,numpy_para) #print(transform_feature.shape) kmeans_weights.append(transform_feature) tsne = TSNE(n_components=2) tsne_weights = tsne.fit_transform(kmeans_weights) x_min, x_max = tsne_weights.min(0), tsne_weights.max(0) X_norm = (tsne_weights - x_min) / (x_max - x_min) # 归一化 ms = DBSCAN(eps=0.1, min_samples=5, metric='euclidean') ms.fit(X_norm) labels = ms.labels_ labels_unique = np.unique(labels) n_clusters_ = len(labels_unique) client_list_by_label = [[] for i in range(n_clusters_)] for i in range(self.args.num_devices): #在相应数据类别的列表中加入设备index client_list_by_label[labels[i]].append(i) #还是从原始模型开始训练 for i in range(self.args.num_devices): device_weights[i] = init_weights avg_weights = device_weights[0] for round in tqdm(range(self.args.round)): # Train step print(f"\nRound {round + 1} Training:") local_weights = [] local_losses = [] train_devices = [] #用于计数所有类别是否都加入到联邦学习中 #TODO 这只是一个比较粗略的方案真实情况下不可能事先知道所有类别 #用来判断包含数据分类总数是否达标 #class_set = set() # Select fraction of devices (minimum 1 device) #TODO 设置算法挑选包含所有分类数据的devices （目前假设每个client上数据分类已知，在labels） #while len(class_set)<10: # 此注释方法选取client 通过真实的数据类别没有考虑隐私性 # temp_train_devices = random.sample( # range(self.args.num_devices), # max(1, int(self.args.num_devices * self.args.frac)) # ) # for client_index in temp_train_devices: # #取出具体client的类别列表 # temp = set(client_labels[client_index]) # before_class_set_len = len(class_set) # class_set = class_set|temp # if(before_class_set_len != len(class_set)): # train_devices.append(client_index) #根据聚类的结果对client进行选择 #从每个簇中抽足够数量台参与模型聚合 for i in range(n_clusters_): #从每个簇中抽取client的数量 choose_num = math.ceil(self.args.num_devices * self.args.frac/n_clusters_) random_index = random.sample(range(len(client_list_by_label[i])),choose_num) #random_index = random.choice(range(len(client_list_by_label[i]))) for index in random_index: train_devices.append(client_list_by_label[i][index]) print(f"\tDevices selected: {[x + 1 for x in train_devices]}\n") # Train on each device and return weights and loss for device_num in train_devices: #第一轮时都采用预训练的local weight进行训练 if round == 0: model.load_state_dict(device_weights[device_num]) else: model.load_state_dict(avg_weights) weights, loss = Trainer().train( train_dataset, device_idxs[device_num], round, device_num, device, copy.deepcopy(model), # Avoid continuously training same model on different devices self.args ) local_weights.append(weights) local_losses.append(loss) avg_weights = utils.fed_avg(local_weights) # Federated averaging for device_num in train_devices: device_weights[device_num] = avg_weights model.load_state_dict(avg_weights) # Load new weights if self.args.cal_para_diff: avg_weight_diff = utils.cal_avg_weight_diff(local_weights, avg_weights) else: avg_weight_diff = 0 avg_loss = sum(local_losses) / len(local_losses) if self.args.cal_para_diff: print(f"\n\tRound {round + 1} | Average weight difference: {avg_weight_diff}") print(f"\tRound {round + 1} | Average training loss: {avg_loss}\n") global_train_losses.append(avg_loss) avg_weights_diff.append(avg_weight_diff) # Test step print(f"Round {round + 1} Testing:") accuracy, loss, auc, kappa = Tester().test( test_dataset, round, device, copy.deepcopy(model), self.args ) scalar = 'accuracy'+str(self.args.round)+"_"+str(self.args.class_per_device)+"_lr"+str(self.args.lr)+"_cluster"+str(n_clusters_) writer.add_scalar(scalar, accuracy, round) print(f"\tRound {round + 1} | Average accuracy: {accuracy}") print(f"\tRound {round + 1} | Average testing loss: {loss}\n") print(f"\tRound {round + 1} | Average AUC: {auc}") print(f"\tRound {round + 1} | Kappa: {kappa}\n") global_test_losses.append(loss) global_accuracies.append(accuracy) global_aucs.append(auc) global_kappas.append(kappa) #如果整个fedavg round已经结束了 if(round+1 == self.args.round ): final_weight = avg_weights # Quit early if satisfy certain situations if accuracy >= self.args.train_until_acc / 100: print( f"Accuracy reached {self.args.train_until_acc / 100} in round {round + 1}, stopping...") break if self.args.stop_if_improvment_lt: if round > 0: if global_accuracies[-2] + self.args.stop_if_improvment_lt / 100 >= global_accuracies[-1]: break # Write results to file if self.args.save_results: utils.save_results_to_file( self.args, avg_weights_diff, global_train_losses, global_test_losses, global_accuracies, global_aucs, global_kappas ) filename = str(self.args.round)+"_"+str(self.args.class_per_device)+"_lr"+str(self.args.lr) f = open("./results/"+filename+"_fed_cluster.txt","w") f.writelines(str(global_accuracies)) f.close() class CentralizedLearning(): def __init__(self, args): self.args = args def run(self): start = time.time() # Print arguments if self.args.verbose: print("Arguments:") print(f"\t{self.args}") # Set training on CPU/GPU device = "cpu" if self.args.gpu is not None: if torch.cuda.is_available(): device = "cuda" torch.cuda.set_device(self.args.gpu) # Set manual random seed if not self.args.random_seed: torch.manual_seed(42) torch.cuda.manual_seed(42) np.random.seed(42) random.seed(42) torch.backends.cudnn.deterministic = True utils = Utils() # Get dataset and data distribution over devices train_dataset, test_dataset, _ = utils.get_dataset_dist(self.args) # Get number of classes (MNIST: 10, CIFAR100: 10) if self.args.dataset == "mnist": num_classes = 10 else: num_classes = 10 # Set training model (VGG11/LeNet/ResNet18) if self.args.model == "vgg11": model = VGG("VGG11", num_classes) elif self.args.model == "lenet": model = LeNet(num_classes) else: model = ResNet18(num_classes) train_losses = [] test_losses = [] accuracies = [] aucs = [] kappas = [] if self.args.optim == "sgd": optimizer = torch.optim.SGD( model.parameters(), lr=self.args.lr, momentum=self.args.sgd_momentum ) elif self.args.optim == "adagrad": optimizer = torch.optim.Adagrad( model.parameters(), lr=self.args.lr ) else: optimizer = torch.optim.Adam( model.parameters(), lr=self.args.lr ) for epoch in tqdm(range(self.args.epoch)): # Train step print(f"Epoch {epoch+1} Training:") """ model.to(device) model.train() # Train mode dataloader = DataLoader( train_dataset, batch_size=self.args.bs, shuffle=True ) loss_function = nn.CrossEntropyLoss().to(device) batch_losses = [] for idx, (data, target) in enumerate(dataloader): data, target = data.to(device), target.to(device) model.zero_grad() output = model(data) loss = loss_function(output, target) loss.backward() optimizer.step() if not idx % 10: print(f"\tEpoch {epoch+1} | {idx*self.args.bs}/{len(train_dataset)} | Training loss: {loss.item()}") batch_losses.append(loss.item()) train_losses.append(sum(batch_losses)/len(batch_losses)) print(f"\nEpoch {epoch+1} | Average training loss: {loss}\n") """ weights, loss = Trainer().train( train_dataset, 0, epoch, 0, device, copy.deepcopy(model), self.args ) model.load_state_dict(weights) train_losses.append(loss) # Test step print(f"Epoch {epoch+1} Testing:") accuracy, loss, auc, kappa = Tester().test( test_dataset, epoch, device, model, self.args ) print(f"Epoch {epoch+1} | Accuracy: {accuracy}") print(f"Epoch {epoch+1} | Average testing loss: {loss}\n") print(f"Epoch {epoch+1} | Average AUC: {auc}") print(f"Epoch {epoch+1} | Kappa: {kappa}\n") test_losses.append(loss) accuracies.append(accuracy) aucs.append(auc) kappas.append(kappa) # Quit early if satisfy certain situations if accuracy >= self.args.train_until_acc/100: print( f"Accuracy reached {self.args.train_until_acc/100} in epoch {epoch+1}, stopping...") break if self.args.stop_if_improvment_lt: if epoch > 0: if accuracies[-2]+self.args.stop_if_improvment_lt/100 >= accuracies[-1]: break end = time.time() print(f"\nTime used: {time.strftime('%H:%M:%S', time.gmtime(end-start))}") # Print final results print("Losses on training data:") print(f"\t{train_losses}\n") print("Losses on testing data:") print(f"\t{test_losses}\n") print("Accuracies on testing data:") print(f"\t{accuracies}\n") print("Average AUCs on testing data:") print(f"\t{aucs}\n") print("Kappas on testing data:") print(f"\t{kappas}\n") print(f"Final accuracy: {accuracies[-1]}") print(f"Final loss: {test_losses[-1]}\n") # Write results to file if self.args.save_results: utils.save_results_to_file( self.args, [], train_losses, test_losses, accuracies, aucs, kappas ) if __name__ == "__main__": #non-iid(1) 50round args = Parser().parse() print(args) if args.learning == "f1" : FederatedLearning1(args).run() elif args.learning == "f" : FederatedLearning1(args).run() elif args.learning == "fd": FederatedDecoupleLearning(args).run() else: CentralizedLearning(args).run() # non-iid(2) 50round # args.class_per_device = 2 # FederatedLearning(args).run()

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fe6ded20c1ecf6e56b286e2383f16559b262d321

93

py

Python

predict/models.py

yen936/adaptic_public

905e287843c152d8a743a2a64ceac539aac96149

[ "MIT" ]

3

2019-05-18T14:26:18.000Z

2020-04-25T16:15:24.000Z

predict/models.py

yen936/adaptic_public

905e287843c152d8a743a2a64ceac539aac96149

[ "MIT" ]

2

2020-02-12T00:17:32.000Z

2020-06-05T20:53:28.000Z

predict/models.py

yen936/adaptic_public

905e287843c152d8a743a2a64ceac539aac96149

[ "MIT" ]

null

from django.db import models class Tickers(models.Model): tickers = models.TextField()

15.5

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0.741935

12

93

5.75

0.75

0.376812

0

0.16129

93

5

33

18.6

0.884615

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false

0

0.333333

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null

1

0

1

0

null

0

1

0

1

0

5

feb4a4564ca10f1be9cc52fe733b42a515245907

92

py

Python

five.py

kx-bipulroy/test

552c83a3ff88317997657841636437a8da063a0a

[ "MIT" ]

null

five.py

kx-bipulroy/test

552c83a3ff88317997657841636437a8da063a0a

[ "MIT" ]

1

2021-01-31T06:34:45.000Z

five.py

kx-bipulroy/test

552c83a3ff88317997657841636437a8da063a0a

[ "MIT" ]

null

def five(): print('Five') def six(): print('Six') def seven(): print('Seven')

10.222222

18

0.521739

12

92

4

0.416667

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0.25

92

8

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0

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null

0

1

0

1

0

5

feb6dfa4372e7c2adba843551b7549107497e8ef

9,193

py

Python

ApplicationCode/Problem1.py

darrahts/TeachableRobots

89d80aa4fda4e6b15ed2ab554ffdd81078867cef

[ "MIT" ]

3

2018-02-09T15:50:58.000Z

2021-09-21T00:11:23.000Z

ApplicationCode/Problem1.py

darrahts/TeachableRobots

89d80aa4fda4e6b15ed2ab554ffdd81078867cef

[ "MIT" ]

null

ApplicationCode/Problem1.py

darrahts/TeachableRobots

89d80aa4fda4e6b15ed2ab554ffdd81078867cef

[ "MIT" ]

null

# -*- coding: utf-8 -*- from RobotTracker import * import time from threading import Thread, Event def Problem1(): repeatCounter = 0 time.sleep(1) ############################################################################################ # origin r.SetGoal((0,0)) cv2.putText(r.textArea, "drive the robot to the origin", (0, 40), 2, .5, (100,200,100), 1) print("starting problem") t1 = time.time() time.sleep(1) while(abs(r.rLoc[0] - r.goal[0]) > .6 and abs(r.rLoc[1] - r.goal[1]) > .6): t2 = time.time() if(t2 - t1 > 60): if(repeatCounter == 0): cv2.putText(r.textArea, "the origin is where the 'X' and 'Y' axis intersect.", (0, 160), 2, .5, (100,200,100), 1) t1 = t2 repeatCounter += 1 elif(repeatCounter == 1): cv2.putText(r.textArea, "maybe you need some extra assistance", (0, 175), 2, .5, (100,200,100), 1) x = input() if(x == "continue"): break else: time.sleep(5) cv2.putText(r.textArea, "Nice Work!", (0, 300), 4, 1.2, (100,200,100), 1) repeatCounter = 0 time.sleep(3) r.textArea = np.zeros((r.frame.shape[0],550,3),dtype=np.uint8) ############################################################################################ # first quadrant r.SetGoal((0,0)) cv2.putText(r.textArea, "drive the robot into the first quadrant.", (0, 40), 2, .5, (100,200,100), 1) t1 = time.time() while(r.rLoc[0] < .5 and r.rLoc[1] < .5): t2 = time.time() if(t2 - t1 > 60): if(repeatCounter == 0): cv2.putText(r.textArea, "the first quadrant is top-right.", (0, 160), 2, .5, (100,200,100), 1) t1 = t2 repeatCounter += 1 elif(repeatCounter == 1): cv2.putText(r.textArea, "maybe you need some extra assistance", (0, 175), 2, .5, (100,200,100), 1) x = input() if(x == "continue"): break cv2.putText(r.textArea, "Awesome!", (0, 300), 4, 1.2, (100,200,100), 1) repeatCounter = 0 time.sleep(3) r.textArea = np.zeros((r.frame.shape[0],550,3),dtype=np.uint8) ############################################################################################ # x axis r.SetGoal((5,0)) cv2.putText(r.textArea, "drive the robot to any point on the 'X' axis", (0, 40), 2, .5, (100,200,100), 1) t1 = time.time() while(abs(r.rLoc[1] - r.goal[1]) > .6): t2 = time.time() if(t2 - t1 > 60): if(repeatCounter == 0): cv2.putText(r.textArea, "the 'X' axis is horizontal.", (0, 160), 2, .5, (100,200,100), 1) t1 = t2 repeatCounter += 1 elif(repeatCounter == 1): cv2.putText(r.textArea, "maybe you need some extra assistance", (0, 175), 2, .5, (100,200,100), 1) x = input() if(x == "continue"): break cv2.putText(r.textArea, "Great!", (0, 300), 4, 1.2, (100,200,100), 1) repeatCounter = 0 time.sleep(3) r.textArea = np.zeros((r.frame.shape[0],550,3),dtype=np.uint8) ############################################################################################ # y axis r.SetGoal((0,5)) cv2.putText(r.textArea, "drive the robot to any point on the 'y' axis", (0, 40), 2, .5, (100,200,100), 1) t1 = time.time() while(abs(r.rLoc[0] - r.goal[0]) > .6): t2 = time.time() if(t2 - t1 > 60): if(repeatCounter == 0): cv2.putText(r.textArea, "the 'Y' axis is vertical.", (0, 160), 2, .5, (100,200,100), 1) t1 = t2 repeatCounter += 1 elif(repeatCounter == 1): cv2.putText(r.textArea, "maybe you need some extra assistance", (0, 175), 2, .5, (100,200,100), 1) x = input() if(x == "continue"): break cv2.putText(r.textArea, "Good Job!", (0, 300), 4, 1.2, (100,200,100), 1) repeatCounter = 0 time.sleep(3) r.textArea = np.zeros((r.frame.shape[0],550,3),dtype=np.uint8) ############################################################################################ # pos or neg y values negY = True r.SetGoal((0,0)) if(r.rLoc[1] > 0): cv2.putText(r.textArea, "drive the robot to an area with negative 'y' values", (0, 40), 2, .5, (100,200,100), 1) else: cv2.putText(r.textArea, "drive the robot to an area with positive 'y' values", (0, 40), 2, .5, (100,200,100), 1) negY = False t1 = time.time() expression = "" if(negY): expression = "r.rLoc[1] > -.5" else: expression = "r.rLoc[1] < .5" while(eval(expression)): t2 = time.time() if(t2 - t1 > 60): if(repeatCounter == 0): if(negY): cv2.putText(r.textArea, "positive 'y' values are above the origin.", (0, 160), 2, .5, (100,200,100), 1) else: cv2.putText(r.textArea, "negative 'y' values are below the origin.", (0, 160), 2, .5, (100,200,100), 1) t1 = t2 repeatCounter += 1 elif(repeatCounter == 1): cv2.putText(r.textArea, "maybe you need some extra assistance", (0, 175), 2, .5, (100,200,100), 1) x = input() if(x == "continue"): break cv2.putText(r.textArea, "Excellent!", (0, 300), 4, 1.2, (100,200,100), 1) repeatCounter = 0 time.sleep(3) r.textArea = np.zeros((r.frame.shape[0],550,3),dtype=np.uint8) ############################################################################################ # pos x and pos/neg y quadrant = 1 expression = "" r.SetGoal((0,0)) if(r.rLoc[1] > 0): if(r.rLoc[0] > 0): cv2.putText(r.textArea, "drive the robot to an area with negative 'y' and 'x' values", (0, 40), 2, .5, (100,200,100), 1) quadrant = 3 expression = "r.rLoc[0] > -.5 or r.rLoc[1] > -.5" elif(r.rLoc[0] < 0): cv2.putText(r.textArea, "drive the robot to an area with negative 'y' and positive 'x' values", (0, 40), 2, .5, (100,200,100), 1) quadrant = 4 expression = "r.rLoc[0] < .5 or r.rLoc[1] > -.5" else: if(r.rLoc[0] > 0): cv2.putText(r.textArea, "drive the robot to an area with positive 'y' and negative 'x' values", (0, 40), 2, .5, (100,200,100), 1) quadrant = 2 expression = "r.rLoc[0] > -.5 or r.rLoc[1] < .5" elif(r.rLoc[0] < 0): cv2.putText(r.textArea, "drive the robot to an area with positive 'y' and 'x' values", (0, 40), 2, .5, (100,200,100), 1) quadrant = 1 expression = "r.rLoc[0] < .5 or r.rLoc[1] < .5" t1 = time.time() print(expression) while(eval(expression)): t2 = time.time() if(t2 - t1 > 60): if(repeatCounter == 0): if(quadrant == 1): cv2.putText(r.textArea, "All 'x' and 'y' values are positive in quadrant 1", (0, 160), 2, .5, (100,200,100), 1) elif(quadrant == 2): cv2.putText(r.textArea, "the quadrant with positive 'y' values and negative 'x' values", (0, 180), 2, .5, (100,200,100), 1) cv2.putText(r.textArea, "is in the top half of the coordinate plane", (0, 195), 2, .5, (100,200,100), 1) elif(quadrant == 3): cv2.putText(r.textArea, "the quadrant with all negative 'x' and 'y' values", (0, 180), 2, .5, (100,200,100), 1) cv2.putText(r.textArea, "is one that is to the left of the origin", (0, 195), 2, .5, (100,200,100), 1) elif(quadrant == 4): cv2.putText(r.textArea, "the quadrant with negative 'y' values and positive 'x' values", (0, 180), 2, .5, (100,200,100), 1) cv2.putText(r.textArea, "is in the bottom half of the coordinate plane", (0, 195), 2, .5, (100,200,100), 1) t1 = t2 repeatCounter += 1 elif(repeatCounter == 1): cv2.putText(r.textArea, "maybe you need some extra assistance", (0, 220), 2, .5, (100,200,100), 1) x = input() if(x == "continue"): break print(r.rLoc) cv2.putText(r.textArea, "Well Done!", (0, 300), 4, 1.2, (100,200,100), 1) repeatCounter = 0 time.sleep(3) r.textArea = np.zeros((r.frame.shape[0],550,3),dtype=np.uint8) r.finished = True ############################################################################################ if (__name__ == "__main__"): r = Robot() #r.SetGoal((2,2)) r.displayGoals = False problemThread = Thread(target=Problem1) problemThread.isDaemon = True e = Event() problemThread.start() r.Run() e.set() problemThread.join()

37.831276

143

0.464049

1,222

9,193

3.484452

0.109656

0.08666

0.090418

0.156177

0.788398

0.759981

0.736026

0.725928

0.710427

0

0.123174

0.307625

9,193

242

144

37.987603

0.545797

0.013597

0

0.608939

0

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0

1

0.005587

false

0

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0.01676

0

null

0

1

0

null

0

5

22b7c3db3028c791f45c4481c9591c79ebab75c8

173

py

Python

likelihoods/H0_F20/__init__.py

s-ilic/ECLAIR

d82e1cf96f4f3676120e94cd46a7ed7734002b0c

[ "MIT" ]

4

2020-04-23T03:30:27.000Z

2021-08-19T15:59:15.000Z

likelihoods/H0_F20/__init__.py

s-ilic/ECLAIR

d82e1cf96f4f3676120e94cd46a7ed7734002b0c

[ "MIT" ]

null

likelihoods/H0_F20/__init__.py

s-ilic/ECLAIR

d82e1cf96f4f3676120e94cd46a7ed7734002b0c

[ "MIT" ]

null

import numpy as np ### From Freedman et al., 2002.01550 def get_loglike(class_input, likes_input, class_run): return -0.5 * (class_run.h() * 100 - 69.6)**2. / 2.5**2.

28.833333

60

0.653179

32

173

3.375

0.78125

0.148148

0

0.146853

0.17341

173

5

61

34.6

0.608392

0.184971

0

1

0.333333

false

0

0.333333

1

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null

0

1

0

null

0

1

0

1

0

5

22cf297276725b1168ab2cffdd5e68e3c1f8f2c1

7,302

py

Python

cc_plugin_glider/required_var_attrs.py

ioos/cc-plugin-glider

582ec5b2f4a6713abc9eee8f8fbc54fcec9cee2a

[ "Apache-2.0" ]

2

2020-08-06T14:38:21.000Z

2021-07-11T23:23:40.000Z

cc_plugin_glider/required_var_attrs.py

ioos/cc-plugin-glider

582ec5b2f4a6713abc9eee8f8fbc54fcec9cee2a

[ "Apache-2.0" ]

24

2015-10-27T22:12:26.000Z

2020-05-20T17:33:42.000Z

cc_plugin_glider/required_var_attrs.py

ioos/cc-plugin-glider

582ec5b2f4a6713abc9eee8f8fbc54fcec9cee2a

[ "Apache-2.0" ]

11

2015-10-27T22:11:55.000Z

2020-09-30T19:43:57.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- ''' cc_plugin_glider/required_var_attrs.py Dictionary of required variables and their attributes Attributes with values set to None mean we only check that the attribute exists, not whether the value matches ''' required_var_attrs = { 'time': { 'dtype': 'f8', 'standard_name': 'time', 'units': 'seconds since 1970-01-01T00:00:00Z', 'calendar': 'gregorian', 'long_name': 'Time', 'observation_type': 'measured', }, 'lat': { 'standard_name': 'latitude', 'units': 'degrees_north', '_FillValue': None, 'ancillary_variables': None, 'comment': None, 'coordinate_reference_frame': None, 'long_name': None, 'observation_type': None, 'platform': None, 'reference': None, 'valid_max': None, 'valid_min': None }, 'lon': { 'standard_name': 'longitude', 'units': 'degrees_east', '_FillValue': None, 'ancillary_variables': None, 'comment': None, 'coordinate_reference_frame': None, 'long_name': None, 'observation_type': None, 'platform': None, 'reference': None, 'valid_max': None, 'valid_min': None }, 'trajectory': { 'cf_role': None, 'comment': None, 'long_name': None }, 'profile_id': { 'dtype': '<i4', '_FillValue': None, 'comment': None, 'long_name': None, 'valid_min': None, 'valid_max': None }, 'profile_time': { 'dtype': '<f8', 'standard_name': 'time', 'units': 'seconds since 1970-01-01T00:00:00Z', '_FillValue': None, 'comment': None, 'long_name': None, 'observation_type': None, 'platform': None, }, 'profile_lat': { 'dtype': '<f8', 'standard_name': 'latitude', 'units': 'degrees_north', '_FillValue': None, 'comment': None, 'long_name': None, 'observation_type': None, 'platform': None, 'valid_min': None, 'valid_max': None }, 'profile_lon': { 'dtype': '<f8', 'standard_name': 'longitude', 'units': 'degrees_east', '_FillValue': None, 'comment': None, 'long_name': None, 'observation_type': None, 'platform': None, 'valid_min': None, 'valid_max': None }, 'depth': { 'standard_name': 'depth', 'units': None, '_FillValue': None, 'accuracy': None, 'ancillary_variables': None, 'comment': None, 'instrument': None, 'long_name': None, 'observation_type': None, 'platform': None, 'positive': None, 'precision': None, 'reference_datum': None, 'resolution': None, 'standard_name': None, 'valid_max': None, 'valid_min': None }, 'pressure': { 'standard_name': 'sea_water_pressure', 'units': None, '_FillValue': None, 'accuracy': None, 'ancillary_variables': None, 'comment': None, 'instrument': None, 'long_name': None, 'observation_type': None, 'platform': None, 'positive': None, 'precision': None, 'reference_datum': None, 'resolution': None, 'standard_name': None, 'valid_max': None, 'valid_min': None }, 'temperature': { 'dtype': 'f8', 'standard_name': 'sea_water_temperature', 'units': 'degrees_C', '_FillValue': None, 'accuracy': None, 'ancillary_variables': None, 'instrument': None, 'long_name': None, 'observation_type': None, 'platform': None, 'precision': None, 'resolution': None, 'valid_max': None, 'valid_min': None }, 'conductivity': { 'dtype': 'f8', 'standard_name': 'sea_water_electrical_conductivity', 'units': None, '_FillValue': None, 'accuracy': None, 'ancillary_variables': None, 'instrument': None, 'long_name': None, 'observation_type': None, 'platform': None, 'precision': None, 'resolution': None, 'valid_max': None, 'valid_min': None }, 'salinity': { 'dtype': 'f8', 'standard_name': 'sea_water_practical_salinity', 'units': None, '_FillValue': None, 'accuracy': None, 'ancillary_variables': None, 'instrument': None, 'long_name': None, 'observation_type': None, 'platform': None, 'precision': None, 'resolution': None, 'valid_max': None, 'valid_min': None }, 'density': { 'dtype': 'f8', 'standard_name': 'sea_water_density', 'units': None, '_FillValue': None, 'accuracy': None, 'ancillary_variables': None, 'instrument': None, 'long_name': None, 'observation_type': None, 'platform': None, 'precision': None, 'resolution': None, 'valid_max': None, 'valid_min': None }, 'time_uv': { 'standard_name': 'time', 'units': 'seconds since 1970-01-01T00:00:00Z', '_FillValue': None, 'observation_type': None, }, 'lat_uv': { 'dtype': '<f8', 'standard_name': 'latitude', 'units': 'degrees_north', '_FillValue': None, 'comment': None, 'long_name': None, 'observation_type': None, 'platform': None, 'valid_min': None, 'valid_max': None }, 'lon_uv': { 'dtype': '<f8', 'standard_name': 'longitude', 'units': 'degrees_east', '_FillValue': None, 'comment': None, 'long_name': None, 'observation_type': None, 'platform': None, 'valid_min': None, 'valid_max': None }, 'u': { 'dtype': '<f8', 'standard_name': 'eastward_sea_water_velocity', 'units': 'm s-1', '_FillValue': None, 'comment': None, 'long_name': None, 'observation_type': None, 'platform': None, 'valid_min': None, 'valid_max': None }, 'v': { 'dtype': '<f8', 'standard_name': 'northward_sea_water_velocity', 'units': 'm s-1', '_FillValue': None, 'comment': None, 'long_name': None, 'observation_type': None, 'platform': None, 'valid_min': None, 'valid_max': None }, 'platform': { 'dtype': '<i4', '_FillValue': None, 'comment': None, 'id': None, 'instrument': None, 'long_name': None, 'type': None, 'wmo_id': None }, 'instrument_ctd': { 'dtype': '<i4', '_FillValue': None, 'calibration_date': None, 'calibration_report': None, 'comment': None, 'factory_calibrated': None, 'long_name': None, 'make_model': None, 'platform': None, 'serial_number': None, 'type': None } }

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7,302

5.292793

0.174174

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0.064681

0.086241

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0.718298

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0

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7,302

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26.747253

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null

0

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1

0

1

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null

0

5

22e1217641e2c0145c8d97db138dceea47196205

410

py

Python

retrieval/modules/__init__.py

phaedonmit/VL-BERT

1625c61b546788cede123b637d6c69dab43cad9c

[ "MIT" ]

1

2020-09-10T09:28:39.000Z

retrieval/modules/__init__.py

phaedonmit/VL-BERT

1625c61b546788cede123b637d6c69dab43cad9c

[ "MIT" ]

null

retrieval/modules/__init__.py

phaedonmit/VL-BERT

1625c61b546788cede123b637d6c69dab43cad9c

[ "MIT" ]

null

from .resnet_vlbert_for_pretraining_multitask import ResNetVLBERTForPretrainingMultitask from .resnet_vlbert_for_pretraining_translation_no_vision import ResNetVLBERTForPretrainingTranslationNoVision from .resnet_vlbert_for_distance_translation_no_vision import ResNetVLBERTDistanceTranslationNoVision from .resnet_vlbert_for_distance_translation_with_vision import ResNetVLBERTDistanceTranslationWithVision

58.571429

110

0.946341

38

410

9.631579

0.421053

0.10929

0.174863

0.20765

0.371585

0.20765

0

0.043902

410

6

111

68.333333

0.933673

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true

0

1

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1

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null

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1

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1

0

null

0

1

0

1

0

1

0

5

a3a60f6d4f64807418bedccb0eb4715cd0136bf0

52

py

Python

imagedataset_v1/__init.py__.py

IThinkthisisKazan/imagedataset

0f7c50502b00abec7392e715c5f160a535c1dd74

[ "MIT" ]

null

imagedataset_v1/__init.py__.py

IThinkthisisKazan/imagedataset

0f7c50502b00abec7392e715c5f160a535c1dd74

[ "MIT" ]

null

imagedataset_v1/__init.py__.py

IThinkthisisKazan/imagedataset

0f7c50502b00abec7392e715c5f160a535c1dd74

[ "MIT" ]

1

2021-06-03T03:05:42.000Z

from imagedataset_v1.core import find_and_separate

17.333333

50

0.884615

8

52

5.375

1

0

0.021277

0.096154

52

2

51

26

0.893617

0

1

0

true

0

1

0

1

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1

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null

0

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null

0

1

0

1

0

1

0

5

a3c13f9ebc133dad87c71aebd9762ec26eafc3d9

9,621

py

Python

tests/sflow_test.py

ljm625/sonic-utilities

28cca110b024a27943a50d2a7834ce8b6a8750d2

[ "Apache-2.0" ]

null

tests/sflow_test.py

ljm625/sonic-utilities

28cca110b024a27943a50d2a7834ce8b6a8750d2

[ "Apache-2.0" ]

6

2020-09-21T14:55:34.000Z

2021-02-24T07:21:08.000Z

tests/sflow_test.py

ljm625/sonic-utilities

28cca110b024a27943a50d2a7834ce8b6a8750d2

[ "Apache-2.0" ]

3

2020-09-24T12:21:48.000Z

2021-02-18T12:15:48.000Z

import os import sys import pytest import mock from click.testing import CliRunner from utilities_common.db import Db import show.main as show import config.main as config config.asic_type = mock.MagicMock(return_value = "broadcom") # Expected output for 'show sflow' show_sflow_output = ''+ \ """ sFlow Global Information: sFlow Admin State: up sFlow Polling Interval: 0 sFlow AgentID: default 2 Collectors configured: Name: prod IP addr: fe80::6e82:6aff:fe1e:cd8e UDP port: 6343 Name: ser5 IP addr: 172.21.35.15 UDP port: 6343 """ # Expected output for 'show sflow interface' show_sflow_intf_output = ''+ \ """ sFlow interface configurations +-------------+---------------+-----------------+ | Interface | Admin State | Sampling Rate | +=============+===============+=================+ | Ethernet0 | up | 2500 | +-------------+---------------+-----------------+ | Ethernet4 | up | 1000 | +-------------+---------------+-----------------+ | Ethernet112 | up | 1000 | +-------------+---------------+-----------------+ | Ethernet116 | up | 5000 | +-------------+---------------+-----------------+ """ class TestShowSflow(object): @classmethod def setup_class(cls): print("SETUP") os.environ["UTILITIES_UNIT_TESTING"] = "1" def test_show_sflow(self): runner = CliRunner() result = runner.invoke(show.cli.commands["sflow"], [], obj=Db()) print(result.exit_code, result.output) assert result.exit_code == 0 assert result.output == show_sflow_output def test_show_sflow_intf(self): runner = CliRunner() result = runner.invoke(show.cli.commands["sflow"].commands["interface"], \ [], obj=Db()) print(result.exit_code, result.output) assert result.exit_code == 0 assert result.output == show_sflow_intf_output def test_config_sflow_disable_enable(self): # config sflow <enable|disable> db = Db() runner = CliRunner() obj = {'db':db.cfgdb} #disable result = runner.invoke(config.config.commands["sflow"].\ commands["disable"], [], obj=obj) print(result.exit_code, result.output) assert result.exit_code == 0 # change the output global show_sflow_output show_sflow_output_local = show_sflow_output.replace(\ 'Admin State: up', \ 'Admin State: down') # run show and check result = runner.invoke(show.cli.commands["sflow"], [], obj=db) print(result.exit_code, result.output, show_sflow_output_local) assert result.exit_code == 0 assert result.output == show_sflow_output_local #enable result = runner.invoke(config.config.commands["sflow"].\ commands["enable"], [], obj=obj) print(result.exit_code, result.output) assert result.exit_code == 0 # run show and check result = runner.invoke(show.cli.commands["sflow"], [], obj=db) print(result.exit_code, result.output) assert result.exit_code == 0 assert result.output == show_sflow_output return def test_config_sflow_agent_id(self): db = Db() runner = CliRunner() obj = {'db':db.cfgdb} # mock netifaces.interface config.netifaces.interfaces = mock.MagicMock(return_value = "Ethernet0") # set agent-id result = runner.invoke(config.config.commands["sflow"].\ commands["agent-id"].commands["add"], ["Ethernet0"], obj=obj) print(result.exit_code, result.output) assert result.exit_code == 0 # change the output global show_sflow_output show_sflow_output_local = \ show_sflow_output.replace('default', 'Ethernet0') # run show and check result = runner.invoke(show.cli.commands["sflow"], [], obj=db) print(result.exit_code, result.output, show_sflow_output_local) assert result.exit_code == 0 assert result.output == show_sflow_output_local #del agent id result = runner.invoke(config.config.commands["sflow"].\ commands["agent-id"].commands["del"], [], obj=obj) print(result.exit_code, result.output) assert result.exit_code == 0 # run show and check result = runner.invoke(show.cli.commands["sflow"], [], obj=db) print(result.exit_code, result.output) assert result.exit_code == 0 assert result.output == show_sflow_output return def test_config_sflow_collector(self): db = Db() runner = CliRunner() obj = {'db':db.cfgdb} # del a collector result = runner.invoke(config.config.commands["sflow"].\ commands["collector"].commands["del"], ["prod"], obj=obj) print(result.exit_code, result.output) assert result.exit_code == 0 # change the output global show_sflow_output show_sflow_output_local = show_sflow_output.replace(\ "2 Collectors configured:\n\ Name: prod IP addr: fe80::6e82:6aff:fe1e:cd8e UDP port: 6343\n\ Name: ser5 IP addr: 172.21.35.15 UDP port: 6343", \ "1 Collectors configured:\n\ Name: ser5 IP addr: 172.21.35.15 UDP port: 6343") # run show and check result = runner.invoke(show.cli.commands["sflow"], [], obj=db) print(result.exit_code, result.output, show_sflow_output_local) assert result.exit_code == 0 assert result.output == show_sflow_output_local # add collector result = runner.invoke(config.config.commands["sflow"].\ commands["collector"].commands["add"], \ ["prod", "fe80::6e82:6aff:fe1e:cd8e"], obj=obj) assert result.exit_code == 0 # run show and check result = runner.invoke(show.cli.commands["sflow"], [], obj=db) print(result.exit_code, result.output) assert result.exit_code == 0 assert result.output == show_sflow_output return def test_config_sflow_polling_interval(self): db = Db() runner = CliRunner() obj = {'db':db.cfgdb} # set to 20 result = runner.invoke(config.config.commands["sflow"].\ commands["polling-interval"], ["20"], obj=obj) print(result.exit_code, result.output) assert result.exit_code == 0 # change the expected output global show_sflow_output show_sflow_output_local = show_sflow_output.replace(\ 'sFlow Polling Interval: 0', \ 'sFlow Polling Interval: 20') # run show and check result = runner.invoke(show.cli.commands["sflow"], [], obj=db) print(result.exit_code, result.output) assert result.exit_code == 0 assert result.output == show_sflow_output_local #reset to 0, no need to verify this one result = runner.invoke(config.config.commands["sflow"].\ commands["polling-interval"], ["0"], obj=obj) print(result.exit_code, result.output) assert result.exit_code == 0 return def test_config_sflow_intf_enable_disable(self): db = Db() runner = CliRunner() obj = {'db':db.cfgdb} # mock interface_name_is_valid config.interface_name_is_valid = mock.MagicMock(return_value = True) # intf enable result = runner.invoke(config.config.commands["sflow"].\ commands["interface"].commands["enable"], ["Ethernet1"], obj=obj) print(result.exit_code, result.output) assert result.exit_code == 0 # we can not use 'show sflow interface', becasue 'show sflow interface' # gets data from appDB, we need to fetch data from configDB for verification sflowSession = db.cfgdb.get_table('SFLOW_SESSION') assert sflowSession["Ethernet1"]["admin_state"] == "up" # intf disable result = runner.invoke(config.config.commands["sflow"].\ commands["interface"].commands["disable"], ["Ethernet1"], obj=obj) print(result.exit_code, result.output) assert result.exit_code == 0 # verify in configDb sflowSession = db.cfgdb.get_table('SFLOW_SESSION') assert sflowSession["Ethernet1"]["admin_state"] == "down" return def test_config_sflow_intf_sample_rate(self): db = Db() runner = CliRunner() obj = {'db':db.cfgdb} # mock interface_name_is_valid config.interface_name_is_valid = mock.MagicMock(return_value = True) # set sample-rate to 2500 result = runner.invoke(config.config.commands["sflow"].\ commands["interface"].commands["sample-rate"], \ ["Ethernet2", "2500"], obj=obj) print(result.exit_code, result.output) assert result.exit_code == 0 # we can not use 'show sflow interface', becasue 'show sflow interface' # gets data from appDB, we need to fetch data from configDB for verification sflowSession = db.cfgdb.get_table('SFLOW_SESSION') assert sflowSession["Ethernet2"]["sample_rate"] == "2500" return @classmethod def teardown_class(cls): print("TEARDOWN") os.environ["PATH"] = os.pathsep.join(os.environ["PATH"].split(os.pathsep)[:-1]) os.environ["UTILITIES_UNIT_TESTING"] = "0"

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87

0.592974

1,108

9,621

4.998195

0.131769

0.070423

0.098592

0.072228

0.789635

0.756771

0.746659

0.741062

0.705309

0

0.021902

0.264422

9,621

270

88

35.633333

0.760633

0.091051

0

0.640244

0

0.006098

0.084134

0.008836

0

0.195122

1

0.060976

false

0

0.04878

0

0.152439

0.128049

0

null

0

1

0

null

0

5

a3e82c8a09e3cdf034e4107949f6a1c704be8cef

261

py

Python

Programming/printVDWradii.py

MooersLab/jupyterlabpymolpysnipsplus

b886750d63372434df53d4d6d7cdad6cb02ae4e7

[ "MIT" ]

null

Programming/printVDWradii.py

MooersLab/jupyterlabpymolpysnipsplus

b886750d63372434df53d4d6d7cdad6cb02ae4e7

[ "MIT" ]

null

Programming/printVDWradii.py

MooersLab/jupyterlabpymolpysnipsplus

b886750d63372434df53d4d6d7cdad6cb02ae4e7

[ "MIT" ]

null

# Description: Print the van der Waals radii of the atoms in of a residue. # Source: https://www.pymolwiki.org/index.php/Sync """ cmd.do('iterate (resi ${1:101}), print(name + " %.2f" % vdw);') """ cmd.do('iterate (resi 101), print(name + " %.2f" % vdw);')

29

75

0.62069

41

261

3.95122

0.707317

0.061728

0.148148

0.197531

0.209877

0

0.041096

0.16092

261

8

76

32.625

0.69863

0.720307

0

0.75

0

1

0

true

0

1

0

null

0

1

0

1

0

1

0

null

0

1

0

1

0

5

a3f431b597cc2fea9d31060a613a1cac55bdab29

151

py

Python

what_apps/power/admin.py

SlashRoot/WHAT

69e78d01065142446234e77ea7c8c31e3482af29

[ "MIT" ]

null

what_apps/power/admin.py

SlashRoot/WHAT

69e78d01065142446234e77ea7c8c31e3482af29

[ "MIT" ]

null

what_apps/power/admin.py

SlashRoot/WHAT

69e78d01065142446234e77ea7c8c31e3482af29

[ "MIT" ]

null

from models import X10Module, X10ModuleCategory from django.contrib import admin admin.site.register(X10Module) admin.site.register(X10ModuleCategory)

30.2

47

0.860927

18

151

7.222222

0.555556

0.138462

0.261538

0

0.057143

0.072848

151

5

48

30.2

0.871429

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

a3fb5f87797542a3878db280699ec8d70f4927e0

22

py

Python

oi.py

QuintilianoNery/Estudos_Python

f8373abf11765fe3aa51f748a442f12b2b4b06ec

[ "MIT" ]

null

oi.py

QuintilianoNery/Estudos_Python

f8373abf11765fe3aa51f748a442f12b2b4b06ec

[ "MIT" ]

null

oi.py

QuintilianoNery/Estudos_Python

f8373abf11765fe3aa51f748a442f12b2b4b06ec

[ "MIT" ]

null

print ('Olá teste qa')

22

0.681818

4

22

3.75

1

0

0.136364

22

1

22

0.789474

0

0.521739

0

1

0

true

0

1

0

null

0

1

0

null

0

1

0

1

0

5

431294724b79d86b847a7c5876ac7213c69eceac

187

py

Python

pycompupipe/components/gui/__init__.py

xaedes/PyCompuPipe

c5243a875bb007fc67b02e1ed08b1d62b6ddc483

[ "MIT" ]

1

2015-12-22T16:59:08.000Z

pycompupipe/components/gui/__init__.py

xaedes/PyCompuPipe

c5243a875bb007fc67b02e1ed08b1d62b6ddc483

[ "MIT" ]

11

2016-01-06T13:06:43.000Z

2016-01-07T11:58:16.000Z

pycompupipe/components/gui/__init__.py

xaedes/PyCompuPipe

c5243a875bb007fc67b02e1ed08b1d62b6ddc483

[ "MIT" ]

null

#!/usr/bin/env python2 # -*- coding: utf-8 -*- from __future__ import absolute_import from .gui_element import GuiElement from .gui_manager import GuiManager from .interaction import *

20.777778

38

0.770053

25

187

5.48

0.68

0.10219

0

0.012346

0.13369

187

8

39

23.375

0.833333

0.229947

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

4332a0231f4547679daf906f230fe36fa7e04a90

56

py

Python

{{cookiecutter.project_name}}/src/parallel/__init__.py

luerhard/cookiecutter-computational-social-science

b1734c5ff853b30d1fe85098287db0e900a045e5

[ "MIT" ]

null

{{cookiecutter.project_name}}/src/parallel/__init__.py

luerhard/cookiecutter-computational-social-science

b1734c5ff853b30d1fe85098287db0e900a045e5

[ "MIT" ]

null

{{cookiecutter.project_name}}/src/parallel/__init__.py

luerhard/cookiecutter-computational-social-science

b1734c5ff853b30d1fe85098287db0e900a045e5

[ "MIT" ]

null

from .queued_multiprocessor import QueuedMultiProcessor

28

55

0.910714

5

56

10

1

0

0.071429

56

1

56

0.961538

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

4a2d3e84e6f37e9aa274b811ea55af98f6e66eb7

12,899

py

Python

vnpy/api/ib/test/test.py

OceanMT/vnpy_py3

0901e9381c54e615247eb753bac476a911c9ae5d

[ "MIT" ]

null

vnpy/api/ib/test/test.py

OceanMT/vnpy_py3

0901e9381c54e615247eb753bac476a911c9ae5d

[ "MIT" ]

null

vnpy/api/ib/test/test.py

OceanMT/vnpy_py3

0901e9381c54e615247eb753bac476a911c9ae5d

[ "MIT" ]

null

# encoding: UTF-8 import sys from time import sleep from vnib import IbApi ######################################################################## class TestApi(IbApi): print(sys._getframe().f_code.co_name) #---------------------------------------------------------------------- def __init__(self): """Constructor""" super(TestApi, self).__init__() #---------------------------------------------------------------------- def nextValidId(self, orderId): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def currentTime(self, time): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def connectAck(self): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def error(self, id_, errorCode, errorString): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def accountSummary(self, reqId, account, tag, value, curency): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def accountSummaryEnd(self, reqId): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def tickPrice(self, tickerId, field, price, canAutoExecute): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def tickSize(self, tickerId, field, size): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def tickOptionComputation(self, tickerId, tickType, impliedVol, delta, optPrice, pvDividend, gamma, vega, theta, undPrice): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def tickGeneric(self, tickerId, tickType, value): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def tickString(self, tickerId, tickType, value): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def tickEFP(self, tickerId, tickType, basisPoints, formattedBasisPoints, totalDividends, holdDays, futureLastTradeDate, dividendImpact, dividendsToLastTradeDate): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def orderStatus(self, orderId, status, filled, remaining, avgFillPrice, permId, parentId, lastFillPrice, clientId, whyHeld): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def openOrder(self, orderId, contract, order, orderState): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def openOrderEnd(self): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def winError(self, str_, lastError): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def connectionClosed(self): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def updateAccountValue(self, key, val, currency, accountName): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def updatePortfolio(self, contract, position, marketPrice, marketValue, averageCost, unrealizedPNL, realizedPNL, accountName): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def updateAccountTime(self, timeStamp): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def accountDownloadEnd(self, accountName): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def contractDetails(self, reqId, contractDetails): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def bondContractDetails(self, reqId, contractDetails): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def contractDetailsEnd(self, reqId): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def execDetails(self, reqId, contract, execution): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def execDetailsEnd(self, reqId): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def updateMktDepth(self, id_, position, operation, side, price, size): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def updateMktDepthL2(self, id_, position, marketMaker, operation, side, price, size): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def updateNewsBulletin(self, msgId, msgType, newsMessage, originExch): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def managedAccounts(self, accountsList): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def receiveFA(self, pFaDataType, cxml): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def historicalData(self, reqId, date, open_, high, low, close, volume, barCount, WAP, hasGaps): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def scannerParameters(self, xml): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def scannerData(self, reqId, rank, contractDetails, distance, benchmark, projection, legsStr): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def scannerDataEnd(self, reqId): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def realtimeBar(self, reqId, time, open_, high, low, close, volume, wap, count): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def fundamentalData(self, reqId, data): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def deltaNeutralValidation(self, reqId, underComp): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def tickSnapshotEnd(self, reqId): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def marketDataType(self, reqId, marketDataType): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def commissionReport(self, commissionReport): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def position(self, account, contract, position, avgCost): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def positionEnd(self): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def verifyMessageAPI(self, apiData): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def verifyCompleted(self, isSuccessful, errorText): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def displayGroupList(self, reqId, groups): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def displayGroupUpdated(self, reqId, contractInfo): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def verifyAndAuthMessageAPI(self, apiData, xyzChallange): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def verifyAndAuthCompleted(self, isSuccessful, errorText): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def positionMulti(self, reqId, account, modelCode, contract, pos, avgCost): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def positionMultiEnd(self, reqId): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def accountUpdateMulti(self, reqId, account, modelCode, key, value, currency): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def accountUpdateMultiEnd(self, reqId): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def securityDefinitionOptionalParameter(self, reqId, exchange, underlyingConId, tradingClass, multiplier, expirations, strikes): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def securityDefinitionOptionalParameterEnd(self, reqId): print(sys._getframe().f_code.co_name) print(locals()) #---------------------------------------------------------------------- def softDollarTiers(self, reqId, tiers): print(sys._getframe().f_code.co_name) print(locals()) if __name__ == '__main__': api = TestApi() n = api.eConnect('127.0.0.1', 7497, 123, False) print(n) #t = api.TwsConnectionTime() #print t # sleep(1) print('req time') api.reqCurrentTime() # sleep(1) api.reqAccountSummary(9001, "All", "AccountType") #print 'disconnect' #api.eDisconnect() input()

39.811728

166

0.40476

921

12,899

5.459283

0.218241

0.090692

0.181384

0.192721

0.535998

0.527247

0.521877

0.51432

0

0.002022

0.194666

12,899

324

167

39.811728

0.481998

0.316846

0

0.621622

0

0.004497

0

1

0.308108

false

0

0.016216

0

0.32973

0.621622

0

null

0

1

0

1

0

null

0

1

0

1

0

5

4a4df09740375f6642d9a7d667d5733ef697ba17

43

py

Python

pyperlin/__init__.py

duchesneaumathieu/pyperlin

4053dd343db7642d02ac8f3f5bdf1c713aa997ea

[ "MIT" ]

6

2021-11-19T09:03:13.000Z

2022-02-19T16:48:44.000Z

pyperlin/__init__.py

duchesneaumathieu/pyperlin

4053dd343db7642d02ac8f3f5bdf1c713aa997ea

[ "MIT" ]

1

2021-08-29T19:15:29.000Z

2021-09-01T21:41:44.000Z

pyperlin/__init__.py

duchesneaumathieu/pyperlin

4053dd343db7642d02ac8f3f5bdf1c713aa997ea

[ "MIT" ]

null

from .fractalperlin import FractalPerlin2D

21.5

42

0.883721

4

43

9.5

1

0

0.025641

0.093023

43

1

43

0.948718

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

4a5b24cb7b463ef725f936e2b1c0605b525eda54

90

py

Python

tests/PaxHeaders.47482/test-reconnect.py

xiaobinglu/openvswitch

b206a49997a51909d73fd5c11784c17aa885f76b

[ "Apache-2.0" ]

null

tests/PaxHeaders.47482/test-reconnect.py

xiaobinglu/openvswitch

b206a49997a51909d73fd5c11784c17aa885f76b

[ "Apache-2.0" ]

null

tests/PaxHeaders.47482/test-reconnect.py

xiaobinglu/openvswitch

b206a49997a51909d73fd5c11784c17aa885f76b

[ "Apache-2.0" ]

null

30 mtime=1365496689.514878595 30 atime=1440176559.469245606 30 ctime=1440177384.829299578

22.5

29

0.866667

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Python

tworaven_apps/utils/msg_helper.py

Mital188/TwoRavens

f84751b33fde26cd379d8120b3c6a6b5ed2c315d

[ "Apache-2.0" ]

20

2017-12-11T07:26:06.000Z

2021-11-22T16:16:20.000Z

tworaven_apps/utils/msg_helper.py

Mital188/TwoRavens

f84751b33fde26cd379d8120b3c6a6b5ed2c315d

[ "Apache-2.0" ]

849

2017-10-20T18:21:18.000Z

2022-02-18T02:45:44.000Z

tworaven_apps/utils/msg_helper.py

Mital188/TwoRavens

f84751b33fde26cd379d8120b3c6a6b5ed2c315d

[ "Apache-2.0" ]

1

2020-05-18T06:02:13.000Z

"""Convenience print methods""" def msg(message): """print a string to the screen""" print(message) def msgt(message): """Print a string, separated by dashes before and after""" print('-' * 40) msg(message) print('-' * 40)

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Python

python-sdk/nuscenes/eval/prediction/metrics.py

tanjiangyuan/Classification_nuScence

b94c4b0b6257fc1c048a676e3fd9e71183108d53

[ "Apache-2.0" ]

null

python-sdk/nuscenes/eval/prediction/metrics.py

tanjiangyuan/Classification_nuScence

b94c4b0b6257fc1c048a676e3fd9e71183108d53

[ "Apache-2.0" ]

null

python-sdk/nuscenes/eval/prediction/metrics.py

tanjiangyuan/Classification_nuScence

b94c4b0b6257fc1c048a676e3fd9e71183108d53

[ "Apache-2.0" ]

null

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py

Python

utils/optimizers.py

LqNoob/ML-From-Scratch

6312662acc6876d91b52570cf706a0988ffa783d

[ "MIT" ]

null

utils/optimizers.py

LqNoob/ML-From-Scratch

6312662acc6876d91b52570cf706a0988ffa783d

[ "MIT" ]

null

utils/optimizers.py

LqNoob/ML-From-Scratch

6312662acc6876d91b52570cf706a0988ffa783d

[ "MIT" ]

null

import numpy as np from data_manipulation import make_diagonal # Optimizers for models that use gradient methods for finding the # weights that minimizes the loss. # A good resource: # http://sebastianruder.com/optimizing-gradient-descent/index.html class GradientDescent(): def __init__(self, learning_rate, momentum=0): self.learning_rate = learning_rate self.momentum = momentum self.w_updt = np.array([]) def update(self, w, grad_wrt_w): if not self.w_updt.any(): self.w_updt = np.zeros(np.shape(w)) # Use momentum if set self.w_updt = self.momentum * self.w_updt + grad_wrt_w # Move against the gradient to minimize loss return w - self.learning_rate * self.w_updt class GradientDescent_(): def __init__(self, learning_rate, momentum=0): self.learning_rate = learning_rate self.momentum = momentum self.w_updt = np.array([]) def update(self, w, grad_func): # Initialize on first update if not self.w_updt.any(): self.w_updt = np.zeros(np.shape(w)) # Use momentum if set self.w_updt = self.momentum * self.w_updt + self.learning_rate * grad_func(w) # Move against the gradient to minimize loss return w - self.w_updt class NesterovAcceleratedGradient(): def __init__(self, learning_rate, momentum=0): self.learning_rate = learning_rate self.momentum = momentum self.w_updt = np.array([]) def update(self, w, grad_func): # Calculate the gradient of the loss a bit further down the slope from w grad_at_w = grad_func(w - self.momentum * self.w_updt) # Initialize on first update if not self.w_updt.any(): self.w_updt = np.zeros(np.shape(w)) # Use momentum if set self.w_updt = self.momentum * self.w_updt + self.learning_rate * grad_at_w # Move against the gradient to minimize loss return w - self.w_updt class Adagrad(): def __init__(self, learning_rate, momentum=0): self.learning_rate = .1 self.G = np.array([]) self.err = 1e-8 def update(self, w, grad_func): # Calculate the gradient of the loss at w grad_at_w = grad_func(w) # If not initialized if not self.G.any(): self.G = np.zeros(np.shape(w)) # Add the square of the gradient of the loss function at w self.G += np.power(grad_at_w, 2) # Adaptive gradient with higher learning rate for sparse data w_updt = self.learning_rate * np.linalg.pinv(np.sqrt(self.G + self.err)).T * grad_at_w return w - w_updt class Adadelta(): def __init__(self, learning_rate=0, momentum=0): self.Et = np.array([]) # Running average of theta self.Eg = np.array([]) # Running average of the gradient of theta self.w_updt = np.array([]) # Parameter update self.err = 1e-8 self.gamma = 0.1 def update(self, w, grad_func): # Calculate the gradient of the loss at w grad_at_w = grad_func(w) # If not initialized if not self.w_updt.any(): self.w_updt = np.zeros(np.shape(w)) self.Et = np.zeros(np.shape(w)) self.Eg = np.power(grad_at_w, 2) else: self.Et = self.gamma * self.Et + (1 - self.gamma) * np.power(self.w_updt, 2) self.Eg = self.gamma * self.Eg + (1 - self.gamma) * np.power(grad_at_w, 2) RMS_theta = np.sqrt(self.Et + self.err) RMS_grad = np.sqrt(self.Eg + self.err) # Adaptiv gradient with higher learning rate for sparse data self.w_updt = RMS_theta * np.linalg.pinv(RMS_grad).T * grad_at_w return w - self.w_updt class RMSprop(): def __init__(self, learning_rate=0.001, momentum=0): self.learning_rate = learning_rate self.Eg = np.array([]) # Running average of the gradient of theta self.err = 1e-8 self.gamma = 0.9 def update(self, w, grad_func): # Calculate the gradient of the loss at w grad_at_w = grad_func(w) # If not initialized if not self.Eg.any(): self.Eg = np.power(grad_at_w, 2) else: self.Eg = self.gamma * self.Eg + (1 - self.gamma) * np.power(grad_at_w, 2) # Adaptiv gradient with higher learning rate for sparse data self.w_updt = self.learning_rate * np.linalg.pinv(np.sqrt(self.Eg + self.err)).T * grad_at_w return w - self.w_updt

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py

Python

rx/operators/observable/toblocking.py

yutiansut/RxPY

c3bbba77f9ebd7706c949141725e220096deabd4

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

1

2018-11-16T09:07:13.000Z

rx/operators/observable/toblocking.py

yutiansut/RxPY

c3bbba77f9ebd7706c949141725e220096deabd4

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

null

rx/operators/observable/toblocking.py

yutiansut/RxPY

c3bbba77f9ebd7706c949141725e220096deabd4

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

1

2020-05-08T08:23:08.000Z

from rx.core import abc from rx.core.blockingobservable import BlockingObservable def to_blocking(source: abc.Observable): return BlockingObservable(source)

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py

Python

protoactor/schedulers/simple_scheduler.py

acolley/protoactor-python

94bb4220bbef7a7cee50f6829fcf4d4362e487c6

[ "Apache-2.0" ]

76

2017-02-03T16:09:14.000Z

2021-08-05T03:27:42.000Z

protoactor/schedulers/simple_scheduler.py

acolley/protoactor-python

94bb4220bbef7a7cee50f6829fcf4d4362e487c6

[ "Apache-2.0" ]

27

2017-02-14T13:38:47.000Z

2021-08-20T15:11:01.000Z

protoactor/schedulers/simple_scheduler.py

acolley/protoactor-python

94bb4220bbef7a7cee50f6829fcf4d4362e487c6

[ "Apache-2.0" ]

12

2017-02-07T02:10:26.000Z

2020-09-26T10:50:03.000Z

import asyncio from abc import ABCMeta, abstractmethod from datetime import timedelta from protoactor.actor import PID from protoactor.actor.actor_context import AbstractSenderContext, RootContext from protoactor.actor.cancel_token import CancelToken class AbstractSimpleScheduler(metaclass=ABCMeta): @abstractmethod async def schedule_tell_once(self, delay: timedelta, target: PID, message: any) -> None: raise NotImplementedError("Should Implement this method") @abstractmethod async def schedule_tell_repeatedly(self, delay: timedelta, interval: timedelta, target: PID, message: any, cancellation_token: CancelToken) -> None: raise NotImplementedError("Should Implement this method") @abstractmethod async def schedule_request_once(self, delay: timedelta, sender: PID, target: PID, message: any) -> None: raise NotImplementedError("Should Implement this method") @abstractmethod async def schedule_request_repeatedly(self, delay: timedelta, interval: timedelta, sender: PID, target: PID, message: any, cancellation_token: CancelToken) -> None: raise NotImplementedError("Should Implement this method") class SimpleScheduler(AbstractSimpleScheduler): def __init__(self, context: AbstractSenderContext = RootContext()): self._context = context async def schedule_tell_once(self, delay: timedelta, target: PID, message: any) -> None: async def schedule(): await asyncio.sleep(delay.total_seconds()) await self._context.send(target, message) asyncio.create_task(schedule()) async def schedule_tell_repeatedly(self, delay: timedelta, interval: timedelta, target: PID, message: any, cancellation_token: CancelToken) -> None: async def schedule(): await cancellation_token.wait(delay.total_seconds()) while True: if cancellation_token.triggered: return await self._context.send(target, message) await cancellation_token.wait(interval.total_seconds()) asyncio.create_task(schedule()) async def schedule_request_once(self, delay: timedelta, sender: PID, target: PID, message: any) -> None: async def schedule(): await asyncio.sleep(delay.total_seconds()) await self._context.request(target, message, sender) asyncio.create_task(schedule()) async def schedule_request_repeatedly(self, delay: timedelta, interval: timedelta, sender: PID, target: PID, message: any, cancellation_token: CancelToken) -> None: async def schedule(): await cancellation_token.cancellable_wait([], timeout=delay.total_seconds()) while True: if cancellation_token.triggered: return await self._context.request(target, message, sender) await cancellation_token.cancellable_wait([], timeout=interval.total_seconds()) asyncio.create_task(schedule())

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py

Python

src/test_cases/__init__.py

honeydev/Junior

743b3d700840b796c628bc69501e58e32406df1e

[ "MIT" ]

21

2019-09-17T07:20:34.000Z

2019-12-26T06:49:06.000Z

src/test_cases/__init__.py

honeydev/Junior

743b3d700840b796c628bc69501e58e32406df1e

[ "MIT" ]

24

2019-09-17T10:38:15.000Z

2021-03-09T18:28:12.000Z

src/test_cases/__init__.py

honeydev/Junior

743b3d700840b796c628bc69501e58e32406df1e

[ "MIT" ]

23

2019-10-08T06:58:54.000Z

2019-12-18T10:59:56.000Z

"""Пакет отвечающий за функционал прохождения тестов.""" from src.test_cases.models import *

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py

Python

relaax/server/common/saver/saver.py

deeplearninc/relaax

a0cf280486dc74dca3857c85ec0e4c34e88d6b2b

[ "MIT" ]

71

2017-01-25T00:26:20.000Z

2021-02-17T12:39:20.000Z

relaax/server/common/saver/saver.py

deeplearninc/relaax

a0cf280486dc74dca3857c85ec0e4c34e88d6b2b

[ "MIT" ]

69

2017-01-23T19:29:23.000Z

2018-08-21T13:26:39.000Z

relaax/server/common/saver/saver.py

deeplearninc/relaax

a0cf280486dc74dca3857c85ec0e4c34e88d6b2b

[ "MIT" ]

13

2017-01-23T21:18:09.000Z

2019-01-29T23:48:30.000Z

from __future__ import print_function from builtins import object class Saver(object): def checkpoint_ids(self): raise NotImplementedError def remove_checkpoint(self, checkpoint_id): raise NotImplementedError def restore_checkpoint(self, checkpoint_id): raise NotImplementedError def save_checkpoint(self, checkpoint_id): raise NotImplementedError

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py

Python

pipenv/cli/__init__.py

ehebert/pipenv

b771621274fcdb6980b4c9682bd2b2879e3354d1

[ "MIT" ]

3

2020-06-04T05:22:33.000Z

2020-09-23T19:44:02.000Z

pipenv/cli/__init__.py

ehebert/pipenv

b771621274fcdb6980b4c9682bd2b2879e3354d1

[ "MIT" ]

9

2019-12-05T00:49:12.000Z

2021-09-08T01:31:25.000Z

pipenv/cli/__init__.py

ehebert/pipenv

b771621274fcdb6980b4c9682bd2b2879e3354d1

[ "MIT" ]

1

2019-06-10T13:45:08.000Z

# -*- coding=utf-8 -*- from __future__ import absolute_import from .command import cli

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py

Python

Lib/site-packages/stripe/version.py

nemarugommula/ecommerce

60185e79655fbaf0fcad9e877a886fe9eb3c4451

[ "bzip2-1.0.6" ]

null

Lib/site-packages/stripe/version.py

nemarugommula/ecommerce

60185e79655fbaf0fcad9e877a886fe9eb3c4451

[ "bzip2-1.0.6" ]

13

2020-03-24T17:53:51.000Z

2022-02-10T20:01:14.000Z

Lib/site-packages/stripe/version.py

nemarugommula/ecommerce

60185e79655fbaf0fcad9e877a886fe9eb3c4451

[ "bzip2-1.0.6" ]

null

VERSION = "2.37.2"

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py

Python

ten-apps/04-journal/program.py

ryentzer/talkpython-courses

5f08b2be5a98f3d03571f416920585257775a918

[ "MIT" ]

null

ten-apps/04-journal/program.py

ryentzer/talkpython-courses

5f08b2be5a98f3d03571f416920585257775a918

[ "MIT" ]

null

ten-apps/04-journal/program.py

ryentzer/talkpython-courses

5f08b2be5a98f3d03571f416920585257775a918

[ "MIT" ]

null

# App 4 journal

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py

Python

abcview/view.py

OaklandPeters/abcview

d2b1fdd0dfc506570205f299a15e25c12525479f

[ "MIT" ]

null

abcview/view.py

OaklandPeters/abcview

d2b1fdd0dfc506570205f299a15e25c12525479f

[ "MIT" ]

null

abcview/view.py

OaklandPeters/abcview

d2b1fdd0dfc506570205f299a15e25c12525479f

[ "MIT" ]

null

import collections import abc import functools import meets import original_abcview class Cast(object): def __init__(self, obj, interface): self.obj = obj self.interface = interface def __enter__(self): view = original_abcview.ABCView(self.interface) wrapped = view(self.obj) return wrapped def __exit__(self, exc_type, exc_value, exc_traceback): pass class Surrogate(object): @abc.abstractproperty def AbstractParent(self): return NotImplemented def __init__(self, wrapped): """ wrapped: the original 'concrete' object AbstractParent: abstract class providing being used as a kind of 'restricted' viewpoint (a "type restriction") """ if not meets.meets(wrapped, self.AbstractParent): raise TypeError(str.format( "'wrapped' must be an instance of '{0}'.", self.AbstractParent )) if hasattr(wrapped, '_wrapped'): raise AttributeError("'wrapped' must not have a '_wrapped' method.") self._wrapped = wrapped def __getattr__(self, name): if name == '_wrapped': return getattr(self, '_wrapped') parent = self.AbstractParent parents_method = getattr(parent, name) # Not abstract on parent --> treat noramlly # ... should fallback to abstract method if not meets.is_abstract_method(parents_method): return parents_method # Abstract on parent --> use wrapped's implementation else: #@functools.wraps(parents_method) def redirection(*args, **kwargs): self_method = getattr(self._wrapped, name) return self_method(self, *args, **kwargs) return redirection def __repr__(self): if hasattr(self._wrapped, '__repr__'): return self._wrapped.__repr__() else: return object.__repr__(self) def __str__(self): if hasattr(self._wrapped, '__str__'): return self._wrapped.__str__() else: return object.__str__(self) class SequenceSurrogate(Surrogate): AbstractParent = collections.Sequence class MutableSequenceSurrogate(collections.MutableSequence, Surrogate): AbstractParent = collections.MutableSequence class ABCView(object): @abc.abstractproperty def AbstractParent(self): return NotImplemented def __init__(self, wrapped): """ wrapped: the original 'concrete' object AbstractParent: abstract class providing being used as a kind of 'restricted' viewpoint (a "type restriction") """ if not meets.meets(wrapped, self.AbstractParent): raise TypeError(str.format( "'wrapped' must be an instance of '{0}'.", self.AbstractParent )) if hasattr(wrapped, '_wrapped'): raise AttributeError("'wrapped' must not have a '_wrapped' method.") self._wrapped = wrapped def __repr__(self): if hasattr(self._wrapped, '__repr__'): return self._wrapped.__repr__() else: return object.__repr__(self) def __str__(self): if hasattr(self._wrapped, '__str__'): return self._wrapped.__str__() else: return object.__str__(self) class SequenceView(ABCView, collections.Sequence): AbstractParent = collections.Sequence # Re-implement abstract methods - referencing self._wrapped def __getitem__(self, key): return self._wrapped.__getitem__(key) def __len__(self): return self._wrapped.__len__() def __contains__(self, element): return self._wrapped.__contains__(element) def __iter__(self): return self._wrapped.__iter__() class MutableSequenceView(ABCView, collections.MutableSequence): AbstractParent = collections.MutableSequence # Re-implement abstract methods - referencing self._wrapped def __getitem__(self, key): return self._wrapped.__getitem__(key) def __len__(self): return self._wrapped.__len__() def __contains__(self, element): return self._wrapped.__contains__(element) def __iter__(self): return self._wrapped.__iter__() def __setitem__(self, key, value): return self._wrapped.__setitem__(key, value) def __delitem__(self, key): return self._wrapped.__delitem__(key) def insert(self, index, value): return self._wrapped.insert(index, value) class OriginalSequenceView(collections.Sequence): def __init__(self, wrapped): if not isinstance(wrapped, collections.Sequence): raise TypeError("'wrapped' must be a 'Sequence'.") if hasattr(wrapped, '_wrapped'): raise AttributeError("'wrapped' must not have a '_wrapped' method.") self._wrapped = wrapped # Re-implement abstract methods - referencing self._wrapped def __getitem__(self, key): return self._wrapped.__getitem__(key) def __len__(self): return self._wrapped.__len__() def __contains__(self, element): return self._wrapped.__contains__(element) def __iter__(self): return self._wrapped.__iter__() # def recast(surrogate): # """ # class SequenceView(collections.Sequence): # __getitem__ = recast('__geitem__') # @recast # def __setitem__(self, key, value): # pass # """ # if isinstance(surrogate, str): # name = surrogate # else: # name = surrogate.__name__ # # def wrapper(self, *args, **kwargs): # method = getattr(self._wrapped, name) # return method(*args, **kwargs) # return wrapper class OriginalMutableSequenceView(collections.MutableSequence): def __init__(self, wrapped): if not isinstance(wrapped, collections.MutableSequence): raise TypeError("'wrapped' must be a 'Sequence'.") if hasattr(wrapped, '_wrapped'): raise AttributeError("'wrapped' must not have a '_wrapped' method.") self._wrapped = wrapped # Re-implement abstract methods - referencing self._wrapped def __getitem__(self, key): return self._wrapped.__getitem__(key) def __len__(self): return self._wrapped.__len__() def __contains__(self, element): return self._wrapped.__contains__(element) def __iter__(self): return self._wrapped.__iter__() def __setitem__(self, key, value): return self._wrapped.__setitem__(key, value) def __delitem__(self, key): return self._wrapped.__delitem__(key) def insert(self, index, value): return self._wrapped.insert(index, value)

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71675f6d85bc9b873100525accf05561c18c2724

269,286

py

Python

openquake.hazardlib/openquake/hazardlib/gsim/edwards_fah_2013f_coeffs.py

rainzhop/ConvNetQuake

a3e6de3f7992eac72f1b9883fec36b8c7fdefd48

[ "MIT" ]

null

openquake.hazardlib/openquake/hazardlib/gsim/edwards_fah_2013f_coeffs.py

rainzhop/ConvNetQuake

a3e6de3f7992eac72f1b9883fec36b8c7fdefd48

[ "MIT" ]

null

openquake.hazardlib/openquake/hazardlib/gsim/edwards_fah_2013f_coeffs.py

rainzhop/ConvNetQuake

a3e6de3f7992eac72f1b9883fec36b8c7fdefd48

[ "MIT" ]

null

# -*- coding: utf-8 -*- # vim: tabstop=4 shiftwidth=4 softtabstop=4 # # Copyright (C) 2013-2016 GEM Foundation # # OpenQuake is free software: you can redistribute it and/or modify it # under the terms of the GNU Affero General Public License as published # by the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # OpenQuake is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Affero General Public License for more details. # # You should have received a copy of the GNU Affero General Public License # along with OpenQuake. If not, see <http://www.gnu.org/licenses/>. from openquake.hazardlib.gsim.base import CoeffsTable COEFFS_FORELAND_60Bars = CoeffsTable(sa_damping=5, table="""\ IMT a1 a2 a3 a4 a5 a6 a7 a8 a9 a10 a11 a12 a13 a14 a15 a16 a17 a18 a19 a20 a21 a22 a23 tau mean_phi_ss sigma_tot phi_11 phi_21 C2 Mc1 Mc2 Rc11 Rc21 pgv -6.68764117047698 5.17836168608062 -2.72357965923663 0.981772779887652 -0.18794348818416 0.0174845924015038 -0.000627568495085281 0.788457477603706 -0.220273537315267 0.00656134713848716 0.000254627802704765 -2.00824221704951 -0.733915029041026 0.223805612148217 -0.0123276023822747 0.920864248165468 0.576321803891029 -0.161286779113245 0.00936374316464263 -0.16656693325605 -0.113955629075892 0.0320828679553869 -0.00195427475302179 0.5010 0.4600 0.68015 0.00000 0.00000 0.00000 5 7 16 36 pga -4.87344448904996 5.72551392383395 -3.23511717883835 1.17530129227868 -0.225779044194617 0.0211047107082395 -0.000762337284651901 0.747546958399791 -0.285870266803578 0.0285222649722448 -0.00147459266470677 -1.83889798037392 -0.778285956887197 0.228987224612386 -0.0126059406885344 0.784942282308357 0.632772794901676 -0.173989445744905 0.0103259717600289 -0.152057234501119 -0.122671039034162 0.0345457666103071 -0.00216747273515938 0.3532 0.4600 0.57998 0.58000 0.47000 0.35000 5 7 16 36 0.01 -4.24464528701162 4.8375251725165 -2.7229788021309 1.02382946492236 -0.201504058458784 0.0191039108682566 -0.000695981815561547 0.676102162286005 -0.197438373890842 0.00683293074400265 3.14380736582837e-06 -1.67180936015094 -0.938907492152067 0.265562004945089 -0.0150244133016282 0.662201098362309 0.73425714009627 -0.196341719637515 0.0117916311952206 -0.125961932984455 -0.142795750920038 0.0389158522966839 -0.00245353561056639 0.3529 0.4600 0.57975 0.58000 0.47000 0.35000 5 7 16 36 0.02 -4.81531340249482 5.83807098342726 -3.3163640277755 1.20433893515254 -0.231062485564841 0.0215786965244617 -0.000779241623520298 1.33035870782831 -0.418504095360988 0.0308782068817182 -0.000860755253438791 -2.18609434033764 -0.807019480663948 0.259184150432205 -0.0153296962646344 0.724440912488775 0.738019892545604 -0.204538048512532 0.012648971445691 -0.112364107245305 -0.151466653629851 0.0416404194034116 -0.00268457610254328 0.3674 0.4570 0.58639 0.56796 0.46097 0.37408 5 7 16 36 0.03 -3.6050636356179 4.84893704647401 -2.81956042365627 1.06166675529961 -0.208650564924716 0.0197849744180356 -0.000721803529688156 -0.213105033590212 -0.118272005905898 0.0397195622106262 -0.00374139010241344 -0.303738306192 -1.06584969145193 0.227472727395779 -0.0108413452457289 -0.149657278890344 0.801411934907682 -0.178576889059417 0.00996897612210427 0.0181449535198095 -0.150360878054255 0.035400860421632 -0.00214099911738658 0.3749 0.4552 0.58975 0.56092 0.45569 0.38817 5 7 16 36 0.04 -3.51964328277704 5.18433961053195 -3.05123291874821 1.13119671193126 -0.219912395037152 0.0207387892660009 -0.00075453354046627 -1.04143248912517 -0.0412128788904239 0.0653048511379604 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-0.590741408046655 0.043272815634031 -0.00223865360099427 -0.563083067585731 0.122669342083253 -0.0126625877706851 0.000708154080833036 0.3244 0.4100 0.52282 0.53000 0.40000 0.40000 5 7 16 36 10 -8.20589620987515 3.26646849300338 -1.06278006635633 0.361098375861763 -0.0708012385106065 0.00688723487859206 -0.000262861786210615 1.5572334116826 -0.356898306386432 -0.00235683502439138 0.00208572021777938 -3.24271763091998 -0.255660621829228 0.166775624900545 -0.0104126803285788 1.76772257815016 0.0734893969252243 -0.0691078568544794 0.00415823683566276 -0.328631379970024 0.00636655422281645 0.00751331395567397 -0.000467751663634615 0.3293 0.4100 0.52588 0.53000 0.40000 0.40000 5 7 16 36 """) COEFFS_FORELAND_10Bars = CoeffsTable(sa_damping=5, table="""\ IMT a1 a2 a3 a4 a5 a6 a7 a8 a9 a10 a11 a12 a13 a14 a15 a16 a17 a18 a19 a20 a21 a22 a23 tau mean_phi_ss sigma_tot phi_11 phi_21 C2 Mc1 Mc2 Rc11 Rc21 pgv -5.26703737213063 3.13626055595851 -1.28721490767671 0.436079788498271 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2.38823737108443 -0.797186145143462 0.140929653423117 -0.0127867725606678 0.000459663944298618 1.44727887898932 0.0374573811057174 -0.111117633124041 0.00980381258510162 -4.31040069865685 0.0863626827552974 0.126909306333092 -0.00862457518071756 2.46139619644971 -0.225116044695943 -0.0145097864802452 0.000532802900031743 -0.457009639107258 0.0674806842676655 -0.00508071111283919 0.000430131135194113 0.3293 0.4100 0.52588 0.53000 0.40000 0.40000 5 7 16 36 8 -3.92072601500178 -2.65162634232736 2.5768640495621 -0.841209365490053 0.145397582002376 -0.0128884178980405 0.000453225640814314 1.13904880338633 0.122523751207596 -0.116448731986259 0.00974099211112658 -3.94041266194955 0.00989712703718198 0.127127141054346 -0.00818841672354807 2.29741042856605 -0.20748957690224 -0.0106722188786461 0.000109102800775492 -0.431553276544808 0.066547014196589 -0.00608771654680961 0.000518295539478288 0.3244 0.4100 0.52282 0.53000 0.40000 0.40000 5 7 16 36 10 -5.22076099376969 -1.23900957805002 1.7752748976784 -0.58631759135787 0.0995277529016549 -0.0086016006724535 0.000293483860485304 2.70286309864854 -0.952586915768218 0.0974738878185804 -0.00333130916422102 -5.94603095036781 1.43751971445192 -0.160491201877887 0.00947392815453608 3.32198656195457 -0.950282566950248 0.139425152495734 -0.00909394342526459 -0.610236415803407 0.196754717126982 -0.0323835824944728 0.00212760384684684 0.3293 0.4100 0.52588 0.53000 0.40000 0.40000 5 7 16 36 """) COEFFS_FORELAND_20Bars = CoeffsTable(sa_damping=5, table="""\ IMT a1 a2 a3 a4 a5 a6 a7 a8 a9 a10 a11 a12 a13 a14 a15 a16 a17 a18 a19 a20 a21 a22 a23 tau mean_phi_ss sigma_tot phi_11 phi_21 C2 Mc1 Mc2 Rc11 Rc21 pgv -5.98828131155829 4.22469803134838 -2.04120191203769 0.711900097679621 -0.134707909947453 0.0124643591916466 -0.000446133011460594 0.630842790775833 -0.0928130260632013 -0.0234695312288233 0.00237307994631626 -2.25270036839014 -0.586289153227759 0.202879758774249 -0.0116107156904395 1.12087240865561 0.443071405843821 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0.23075492450654 0.81627427629354 -0.256018593253181 0.0392576350800975 -0.00309110725256368 9.50507077009833e-05 1.36865888172568 -0.0321731675617039 -0.084335561452936 0.00767209803241737 -3.53821884330828 -0.298200396624397 0.192378418892592 -0.0123616529581177 2.01915586775386 0.0142324443090848 -0.0611596092179959 0.00355655494888518 -0.380505431429783 0.0244940206130155 0.00392959132396138 -0.000190373279017243 0.3244 0.4100 0.52282 0.53000 0.40000 0.40000 5 7 16 36 10.0 -6.43166037718965 0.781715417336081 0.419067880454194 -0.111703048232771 0.0111834875674998 -0.000347547879850276 -9.58880388830187e-06 1.39969757465226 -0.244919016206169 -0.0251821119076448 0.00351030447501622 -3.63841700023342 0.0319436321583016 0.106164012425708 -0.00652157603960815 2.09161717274124 -0.169762357847494 -0.0153647877577974 0.000563014058030854 -0.396033090352291 0.0577917572882087 -0.00409638232458104 0.000322671994810415 0.3293 0.4100 0.52588 0.53000 0.40000 0.40000 5 7 16 36 """) COEFFS_FORELAND_50Bars = CoeffsTable(sa_damping=5, table="""\ IMT a1 a2 a3 a4 a5 a6 a7 a8 a9 a10 a11 a12 a13 a14 a15 a16 a17 a18 a19 a20 a21 a22 a23 tau mean_phi_ss sigma_tot phi_11 phi_21 C2 Mc1 Mc2 Rc11 Rc21 pgv -7.07692104560858 5.53453557159042 -2.85638253778918 1.00455096336982 -0.189766212430622 0.0175339379429905 -0.000626946378448282 1.81360194718373 -0.789495658576955 0.10664113201381 -0.00537702655581446 -3.40075366709673 0.0695084444372301 0.0787711312860849 -0.00400441422151852 1.63540808826056 0.155336375482169 -0.0839712344789103 0.00486580061157626 -0.291743467951392 -0.0391738553207627 0.0182003147413057 -0.00113992749901153 0.5010 0.4600 0.68015 0.00000 0.00000 0.00000 5 7 16 36 pga -4.04537660224925 4.54792449211361 -2.52312602512793 0.947393883303524 -0.186633142737737 0.0177041231939612 -0.000645125482634016 0.567936855195552 -0.159096356116191 0.00189062729435474 0.000246448178818437 -1.67722490671008 -0.895375073520955 0.255212398467628 -0.0144070079256093 0.715702210915304 0.682852678952893 -0.185408762439109 0.0111290504165921 -0.142521524189518 -0.129561201230503 0.0361572032396897 -0.00228425426985864 0.3532 0.4600 0.57998 0.58000 0.47000 0.35000 5 7 16 36 0.01 -4.33129785720388 4.90730267582611 -2.71118914472022 1.00149016177602 -0.195318394943918 0.0184259980704779 -0.000669228385949504 1.15323979809213 -0.507528571770804 0.0673111371051944 -0.00367009353555145 -2.43765935600041 -0.429688018238893 0.164979880183164 -0.00886274315523457 1.08990004397154 0.445894984892876 -0.138638952003058 0.00822009691799743 -0.205937834474777 -0.0883910754473618 0.0279396332818193 -0.00176974999415353 0.3529 0.4600 0.57975 0.58000 0.47000 0.35000 5 7 16 36 0.02 -3.929015468659 4.5812092095625 -2.55942670675771 0.962816967078341 -0.189636103742108 0.0179804638488938 -0.000655141599622459 1.14586095542526 -0.286918953493489 0.00301111268208939 0.000951105727287412 -1.9950621947376 -0.946489805845478 0.290348333514757 -0.0174612593429473 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-0.000108743133007239 0.00961626900463011 -0.000583721048212146 0.3244 0.4100 0.52282 0.53000 0.40000 0.40000 5 7 16 36 10 -7.94617073706378 3.05358061930519 -0.96854271695036 0.328701833762793 -0.0641329222023237 0.00621123983538322 -0.000237030434091669 0.852119696321722 0.0960439733951024 -0.0905840253406567 0.00745876905697177 -2.46581768172236 -0.751883096438912 0.263733211281735 -0.0163560022914423 1.39627843374341 0.308820068352249 -0.114561775416081 0.00691668166539836 -0.265745759050759 -0.0332960394074053 0.0151080521904201 -0.000924908549758687 0.3293 0.4100 0.52588 0.53000 0.40000 0.40000 5 7 16 36 """) COEFFS_FORELAND_75Bars = CoeffsTable(sa_damping=5, table="""\ IMT a1 a2 a3 a4 a5 a6 a7 a8 a9 a10 a11 a12 a13 a14 a15 a16 a17 a18 a19 a20 a21 a22 a23 tau mean_phi_ss sigma_tot phi_11 phi_21 C2 Mc1 Mc2 Rc11 Rc21 pgv -6.57469117091242 4.99953275450203 -2.63630223447654 0.964945468210338 -0.186558236049026 0.0174520362677213 -0.000628362710479202 0.736128035784037 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0.269052720482077 -0.117584919033023 0.00733096314073971 -0.309694480512774 -0.03045665360681 0.0164743906214399 -0.00105052454446912 0.3244 0.4100 0.52282 0.53000 0.40000 0.40000 5 7 16 36 10 -9.17989619745715 4.20221145773307 -1.51629856201014 0.49830440089873 -0.0947967424221091 0.00904074598062506 -0.00033883529314331 3.40367123595467 -1.38456497146438 0.179501143627895 -0.00824080808115213 -5.27536565336961 0.858449863392392 -0.0286624327581027 0.000631169325975492 2.71178387228714 -0.437897146604191 0.0190964707092874 -0.000740423263877386 -0.48351810742625 0.0896778501059212 -0.0066743156842739 0.000309164016148211 0.3293 0.4100 0.52588 0.53000 0.40000 0.40000 5 7 16 36 """) COEFFS_FORELAND_120Bars = CoeffsTable(sa_damping=5, table="""\ IMT a1 a2 a3 a4 a5 a6 a7 a8 a9 a10 a11 a12 a13 a14 a15 a16 a17 a18 a19 a20 a21 a22 a23 tau mean_phi_ss sigma_tot phi_11 phi_21 C2 Mc1 Mc2 Rc11 Rc21 pgv -7.82108412777637 6.58780944333771 -3.55529537223548 1.25607833984171 -0.236374820444362 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Python

vps_api/api.wsgi

nioroso-x3/quikpod.link

363a612d5786bb2988dd28d8accd252b06c2cfc2

[ "MIT" ]

null

vps_api/api.wsgi

nioroso-x3/quikpod.link

363a612d5786bb2988dd28d8accd252b06c2cfc2

[ "MIT" ]

null

vps_api/api.wsgi

nioroso-x3/quikpod.link

363a612d5786bb2988dd28d8accd252b06c2cfc2

[ "MIT" ]

null

#!/usr/bin/python3 import sys sys.path.insert(0,"/opt/vps_api/") from api import app as application

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0.742574

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0.833333

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5

71db4db3530fc9da23f436642fa410c0ffa6ba26

185

py

Python

src/assignments/main_assignment2.py

acc-cosc-1336/cosc-1336-spring-2018-brianmiller7

78bb08379aba7a07838ed91643b8bf274f2227ae

[ "MIT" ]

null

src/assignments/main_assignment2.py

acc-cosc-1336/cosc-1336-spring-2018-brianmiller7

78bb08379aba7a07838ed91643b8bf274f2227ae

[ "MIT" ]

null

src/assignments/main_assignment2.py

acc-cosc-1336/cosc-1336-spring-2018-brianmiller7

78bb08379aba7a07838ed91643b8bf274f2227ae

[ "MIT" ]

null

from assignment2 import faculty_evaluation_result '''Write code to call the faculty_evaluation_result function with data of your choice''' faculty_evaluation_result(10,10,10,10,10,10)

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0.62069

0.135135

0.162162

0.081081

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0.097297

185

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89

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null

0

1

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1

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5

71db9841b0bc3cf1470afbb69fb44ea84944d424

22

py

Python

main.py

montych112/python

5504361da9e70f15e4feca90c9d560b931a35b69

[ "MIT" ]

null

main.py

montych112/python

5504361da9e70f15e4feca90c9d560b931a35b69

[ "MIT" ]

null

main.py

montych112/python

5504361da9e70f15e4feca90c9d560b931a35b69

[ "MIT" ]

null

print ("hello python")

22

0.727273

3

22

5.333333

1

0

0.090909

22

1

22

0.8

0

0.521739

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null

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null

0

1

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1

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5

71e36cd7a4eb80495aad996677eeb3d807e525c9

7,470

py

Python

bot/main.py

mirsaid-mirzohidov/ArchiveBot

35b8bce015ace2d345d1c966d5353189d84ffd27

[ "Apache-2.0" ]

2

2020-11-13T11:54:19.000Z

2021-07-26T17:20:27.000Z

bot/main.py

mirsaid-mirzohidov/ArchiveBot

35b8bce015ace2d345d1c966d5353189d84ffd27

[ "Apache-2.0" ]

null

bot/main.py

mirsaid-mirzohidov/ArchiveBot

35b8bce015ace2d345d1c966d5353189d84ffd27

[ "Apache-2.0" ]

null

from telebot.types import Message from config.config import admin from config.texts import * from button import music_button, book_button, main_btn, libr_menu_btn, playlist_menu_btn, exit_btn from button import playlist_menu_btn, exit_btn from __init__ import bot, db # Bussines logic @bot.message_handler(func=lambda message: message.chat.id in admin, commands=["start"]) def welc(message: Message): user_id = message.from_user.id # hello message msg=bot.send_message( user_id, hello_message_for_lord, reply_markup=main_btn, parse_mode='html') bot.register_next_step_handler(msg, main_handler) def main_handler(message: Message): user_id = message.from_user.id if message.text == "Library 📚": msg=bot.send_message( user_id, "Ok :)", reply_markup=libr_menu_btn) bot.register_next_step_handler(msg, libr_menu) elif message.text == "Playlist 🎵": msg=bot.send_message( user_id, "Ok :)", reply_markup=playlist_menu_btn) bot.register_next_step_handler(msg, playlist_menu) # elif message.text == "Pictures 📷": # pass ######################################## Library menu def libr_menu(message): user_id = message.from_user.id if message.text == "Books": msg = bot.send_message(user_id, "List of books:", reply_markup=book_button) bot.register_next_step_handler(msg, get_single_book) elif message.text == "Add book": msg = bot.send_message(user_id, "Send me a book", reply_markup=exit_btn) bot.register_next_step_handler(msg, handle_docs) def get_single_book(message: Message): user_id = message.from_user.id if message.text == "Orqaga": msg=bot.send_message( user_id, hello_message_for_lord, reply_markup=main_btn, parse_mode='html') bot.register_next_step_handler(msg, main_handler) else: book = db.get_book(message.text) try: msg = bot.send_document(user_id, book[0][2]) bot.register_next_step_handler(msg, get_single_book) except Exception as e: print(e) msg = bot.send_message(user_id, "List of books:", reply_markup=book_button) bot.register_next_step_handler(msg, get_single_book) def handle_docs(message): user_id = message.from_user.id if message.text == "Orqaga": msg=bot.send_message( user_id, hello_message_for_lord, reply_markup=main_btn, parse_mode='html') bot.register_next_step_handler(msg, main_handler) elif message.content_type == 'document': if(not db.book_exists(message.document.file_name)): db.add_book(file_name=message.document.file_name, file_id=message.document.file_id) try: bot.send_message(user_id, "Saved!") msg=bot.send_message( user_id, hello_message_for_lord, reply_markup=main_btn, parse_mode='html') bot.register_next_step_handler(msg, main_handler) except Exception as e: bot.send_message(user_id, str(e)) msg=bot.send_message( user_id, hello_message_for_lord, reply_markup=main_btn, parse_mode='html') bot.register_next_step_handler(msg, main_handler) else: bot.send_message(user_id, "Bu fayl bazada bor") msg=bot.send_message( user_id, hello_message_for_lord, reply_markup=main_btn, parse_mode='html') bot.register_next_step_handler(msg, main_handler) else: bot.send_message(user_id, "Error: <code>Message format not a document!</code>", parse_mode='html') msg = bot.send_message(user_id, "Send me a book!", reply_markup=exit_btn, parse_mode='html') bot.register_next_step_handler(msg, handle_docs) ######################################## Playlist menu def playlist_menu(message): user_id = message.from_user.id if message.text == "All musics": msg = bot.send_message(user_id, "List of musics:", reply_markup=music_button) bot.register_next_step_handler(msg, get_single_music) elif message.text == "Add music": msg = bot.send_message(user_id, "Send me a music", reply_markup=exit_btn) bot.register_next_step_handler(msg, add_music) def get_single_music(message: Message): user_id = message.from_user.id if message.text == "Orqaga": msg=bot.send_message( user_id, hello_message_for_lord, reply_markup=main_btn, parse_mode='html') bot.register_next_step_handler(msg, main_handler) else: music = db.get_music(message.text) try: msg = bot.send_audio(user_id, music[0][2]) bot.register_next_step_handler(msg, get_single_music) except Exception as e: print(e) def add_music(message): user_id = message.from_user.id if message.text == "Orqaga": msg=bot.send_message( user_id, hello_message_for_lord, reply_markup=main_btn, parse_mode='html') bot.register_next_step_handler(msg, main_handler) elif message.content_type == 'audio': if(not db.music_exists(message.json['audio']['file_name'])): db.add_music(file_name=message.json['audio']['file_name'], file_id=message.json['audio']['file_id']) try: bot.send_message(user_id, "Saved!") msg=bot.send_message( user_id, hello_message_for_lord, reply_markup=main_btn, parse_mode='html') bot.register_next_step_handler(msg, main_handler) except Exception as e: bot.send_message(user_id, str(e)) msg=bot.send_message( user_id, hello_message_for_lord, reply_markup=main_btn, parse_mode='html') bot.register_next_step_handler(msg, main_handler) else: bot.send_message(user_id, "Bu fayl bazada bor") msg=bot.send_message( user_id, hello_message_for_lord, reply_markup=main_btn, parse_mode='html') bot.register_next_step_handler(msg, main_handler) else: bot.send_message(user_id, "Error: <code>Message format not a audio!</code>", parse_mode='html') msg = bot.send_message(user_id, "Send me a music!", reply_markup=exit_btn, parse_mode='html') bot.register_next_step_handler(msg, add_music) # bot.enable_save_next_step_handlers(delay=2) # bot.load_next_step_handlers() try: if __name__ == '__main__': bot.infinity_polling() except Exception: db.close()

32.337662

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1

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1

0

1

0

null

0

5

e08209cb8d2112df9f33c0dcf802a57f1d4b4804

51

py

Python

qiskit_quantum_knn/encoding/__init__.py

thijsmie/qiskit-quantum-knn

7fbecab3644306cd601a7562b8f76a29d0190700

[ "Apache-2.0" ]

9

2020-12-29T02:12:36.000Z

2021-11-15T17:26:48.000Z

qiskit_quantum_knn/encoding/__init__.py

thijsmie/qiskit-quantum-knn

7fbecab3644306cd601a7562b8f76a29d0190700

[ "Apache-2.0" ]

5

2020-11-09T11:25:37.000Z

2021-11-02T11:13:40.000Z

qiskit_quantum_knn/encoding/__init__.py

thijsmie/qiskit-quantum-knn

7fbecab3644306cd601a7562b8f76a29d0190700

[ "Apache-2.0" ]

9

2020-11-11T20:19:00.000Z

2022-02-06T16:17:34.000Z

import qiskit_quantum_knn.encoding.analog as analog

51

0.901961

8

51

5.5

0.875

0

0.058824

51

1

51

0.916667

0

1

0

true

0

1

0

1

0

1

0

null

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1

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null

0

1

0

1

0

1

0

5

e0a7ba837cf6b7f5d11704b6f90a5522f8f95fa4

51

py

Python

Step-4-TrainingOverBLE/training/gym-arduino/gym_arduino/envs/__init__.py

kasey-/ArduinoDQNCar

cf1f2a74ea4f79808a3155fe9900c3207534d4e5

[ "MIT" ]

4

2020-03-29T07:01:34.000Z

2022-03-26T15:53:13.000Z

Step-4-TrainingOverBLE/training/gym-arduino/gym_arduino/envs/__init__.py

rkuo2000/ArduinoDQNCar

cf1f2a74ea4f79808a3155fe9900c3207534d4e5

[ "MIT" ]

null

Step-4-TrainingOverBLE/training/gym-arduino/gym_arduino/envs/__init__.py

rkuo2000/ArduinoDQNCar

cf1f2a74ea4f79808a3155fe9900c3207534d4e5

[ "MIT" ]

2

2021-06-29T09:25:23.000Z

2021-08-21T17:32:15.000Z

from gym_arduino.envs.arduino_env import ArduinoEnv

51

0.901961

8

51

5.5

0.875

0

0.058824

51

1

51

0.916667

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1

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true

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1

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1

0

1

0

null

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0

null

0

1

0

1

0

1

0

5

e0b4a792f73145c6f70a6ebc132776861e35f4d8

174

py

Python

ui/__init__.py

berendkleinhaneveld/Registrationshop

0d6f3ee5324865cdcb419369139f37c39dfe9a1c

[ "MIT" ]

25

2015-11-08T16:36:54.000Z

2022-01-20T16:03:28.000Z

ui/__init__.py

berendkleinhaneveld/Registrationshop

0d6f3ee5324865cdcb419369139f37c39dfe9a1c

[ "MIT" ]

2

2016-12-01T23:13:08.000Z

2017-07-25T02:40:49.000Z

ui/__init__.py

berendkleinhaneveld/Registrationshop

0d6f3ee5324865cdcb419369139f37c39dfe9a1c

[ "MIT" ]

10

2016-07-05T14:39:16.000Z

2022-01-01T02:05:55.000Z

from MainWindow import MainWindow from WindowDialog import WindowDialog from RenderController import RenderController from MultiRenderController import MultiRenderController

34.8

55

0.908046

16

174

9.875

0.375

0

0.091954

174

4

56

43.5

1

0

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

5

e0d5962a0309ce982ff8f4fa14c17ef8a55c64b9

175

py

Python

test/test_gradle_provider.py

alexcreasy/repour

ae808e1fe1b25eb7117530c6214b3a9ed0cc8887

[ "Apache-2.0" ]

5

2015-08-04T13:33:43.000Z

2021-11-17T16:56:28.000Z

test/test_gradle_provider.py

alexcreasy/repour

ae808e1fe1b25eb7117530c6214b3a9ed0cc8887

[ "Apache-2.0" ]

94

2016-05-17T19:18:42.000Z

2022-03-25T14:47:48.000Z

test/test_gradle_provider.py

alexcreasy/repour

ae808e1fe1b25eb7117530c6214b3a9ed0cc8887

[ "Apache-2.0" ]

18

2016-03-15T09:52:15.000Z

2021-05-05T18:19:36.000Z

# flake8: noqa import asyncio import tempfile import unittest import repour.adjust.gradle_provider as gradle_provider class TestGradleProvider(unittest.TestCase): pass

15.909091

55

0.822857

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175

6.761905

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0.197183

0

0.006579

0.131429

175

10

56

17.5

0.927632

0.068571

0

1

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true

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null

0

null

0

1

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1

0

5

1ccdcabb2a1d249140d7ea3374c1c83bcd0d2b34

26

py

Python

generate_templates.py

xueqing-chen/spyder_cninfo

3745de310c598e2dc79b9cb7ab25d540667592c6

[ "MIT" ]

null

generate_templates.py

xueqing-chen/spyder_cninfo

3745de310c598e2dc79b9cb7ab25d540667592c6

[ "MIT" ]

null

generate_templates.py

xueqing-chen/spyder_cninfo

3745de310c598e2dc79b9cb7ab25d540667592c6

[ "MIT" ]

null

def generate_template():

8.666667

24

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3

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6.333333

1

0

0.115385

26

2

25

13

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null

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5