xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

041f8a2e2d465dac715827f2f1568c5808c50c25

109

py

Python

signalfx_azure_function_python/version.py

claranet/signalfx-azure-function-python

e343d1f25615c7343a588a04f5edc8331ff544ca

[ "Apache-2.0" ]

null

signalfx_azure_function_python/version.py

claranet/signalfx-azure-function-python

e343d1f25615c7343a588a04f5edc8331ff544ca

[ "Apache-2.0" ]

1

2021-02-17T14:08:46.000Z

signalfx_azure_function_python/version.py

claranet/signalfx-azure-function-python

e343d1f25615c7343a588a04f5edc8331ff544ca

[ "Apache-2.0" ]

null

name = "signalfx-azure-function-python" version = "1.0.1" user_agent = f"signalfx_azure_function/{version}"

21.8

49

0.752294

16

109

4.9375

0.6875

0.329114

0.531646

0

0.030303

0.091743

109

4

50

27.25

0.767677

0

0.623853

0.577982

0

1

0

false

0

1

0

null

1

0

1

0

1

null

0

6

043e794cf6d16e7d01417fbdd4a093412ea9a042

32

py

Python

rasloader/__init__.py

ttruttmann/rasloader

87995d4dc64ae72e094e2171591335d0d3733408

[ "MIT" ]

1

2022-02-16T03:49:51.000Z

rasloader/__init__.py

ttruttmann/rasloader

87995d4dc64ae72e094e2171591335d0d3733408

[ "MIT" ]

null

rasloader/__init__.py

ttruttmann/rasloader

87995d4dc64ae72e094e2171591335d0d3733408

[ "MIT" ]

null

from .rasloader import RasLoader

32

0.875

4

32

7

0.75

0

0.09375

32

1

32

0.965517

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

045cb4795d7f44dc08bf536b5b969bd4ca469aca

25

py

Python

Algorithms/Util/__init__.py

grayy921013/RecSys

ce0683b86755935c943722cbba5541931978498e

[ "Apache-2.0" ]

null

Algorithms/Util/__init__.py

grayy921013/RecSys

ce0683b86755935c943722cbba5541931978498e

[ "Apache-2.0" ]

null

Algorithms/Util/__init__.py

grayy921013/RecSys

ce0683b86755935c943722cbba5541931978498e

[ "Apache-2.0" ]

null

from .enums import Field

12.5

24

0.8

4

25

5

1

0

0.16

25

1

25

0.952381

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

f0c0b65819250aeeb4d1d52449b01afc7e5788f9

1,996

py

Python

cg_security/hasher.py

ConnectSW/cg_security

6eb9807183854cb96f0a7c53501b968569578c7a

[ "MIT" ]

null

cg_security/hasher.py

ConnectSW/cg_security

6eb9807183854cb96f0a7c53501b968569578c7a

[ "MIT" ]

null

cg_security/hasher.py

ConnectSW/cg_security

6eb9807183854cb96f0a7c53501b968569578c7a

[ "MIT" ]

null

import hashlib from .security_exception import SecurityException def blake2b(plaintext) -> str: """ Cria o hash Blake2b da string :param plaintext: String a ser feito o hash :type plaintext: str :return: Hash :rtype: str """ try: return hashlib.blake2b(plaintext.encode()).hexdigest() except Exception as e: raise SecurityException(str(e)) def blake2s(plaintext): """ Cria o hash Blake2s da string :param plaintext: String a ser feito o hash :type plaintext: str :return: Hash :rtype: str """ try: return hashlib.blake2s(plaintext.encode()).hexdigest() except Exception as e: raise SecurityException(str(e)) def md5_hash(plaintext): """ Cria o hash MD5 da string :param plaintext: String a ser feito o hash :type plaintext: str :return: Hash :rtype: str """ try: return hashlib.md5(plaintext.encode()).hexdigest() except Exception as e: raise SecurityException(str(e)) def sha1_hash(plaintext): """ Cria o hash SHA1 da string :param plaintext: String a ser feito o hash :type plaintext: str :return: Hash :rtype: str """ try: return hashlib.sha1(plaintext.encode()).hexdigest() except Exception as e: raise SecurityException(str(e)) def sha256_hash(plaintext): """ Cria o hash SHA256 da string :param plaintext: String a ser feito o hash :type plaintext: str :return: Hash :rtype: str """ try: return hashlib.sha256(plaintext.encode()).hexdigest() except Exception as e: raise SecurityException(str(e)) def sha512_hash(plaintext): """ Cria o hash SHA512 da string :param plaintext: String a ser feito o hash :type plaintext: str :return: Hash :rtype: str """ try: return hashlib.sha512(plaintext.encode()).hexdigest() except Exception as e: raise SecurityException(str(e))

23.209302

62

0.634269

246

1,996

5.126016

0.138211

0.047581

0.042823

0.104679

0.842982

0.773196

0

0.020534

0.268036

1,996

86

63

23.209302

0.842574

0.358717

0

0.5625

0

1

0.1875

false

0

0.0625

0

0.4375

0

null

0

1

0

null

0

6

f0d84faefbbb55023852873eddb5e7c9fe579f36

28

py

Python

main_2.py

jessicafinnie/testGitProjectForClass

eff728cc5eb3e8370729a378dbca6226f310477c

[ "MIT" ]

null

main_2.py

jessicafinnie/testGitProjectForClass

eff728cc5eb3e8370729a378dbca6226f310477c

[ "MIT" ]

null

main_2.py

jessicafinnie/testGitProjectForClass

eff728cc5eb3e8370729a378dbca6226f310477c

[ "MIT" ]

null

print("Hello again, world!")

28

0.714286

4

28

5

1

0

0.071429

28

1

28

0.769231

0

0.655172

0

1

0

true

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

0b0161e4d690d3bc961d632ecca9a94ac592e7a5

152

py

Python

stats-essentials/data/weather.py

chyld/demoX

27f26a553aeb6682173f6b1b8dc8969101993324

[ "MIT" ]

16

2018-09-21T23:14:35.000Z

2022-01-21T10:38:52.000Z

stats-essentials/data/weather.py

chyld/demoX

27f26a553aeb6682173f6b1b8dc8969101993324

[ "MIT" ]

null

stats-essentials/data/weather.py

chyld/demoX

27f26a553aeb6682173f6b1b8dc8969101993324

[ "MIT" ]

27

2018-01-08T22:59:38.000Z

2022-02-09T06:44:38.000Z

from pprint import pprint import requests r = requests.get('http://api.openweathermap.org/data/2.5/weather?q=London&APPID={APIKEY}') pprint(r.json())

21.714286

90

0.75

24

152

4.75

0.791667

0.210526

0

0.014286

0.078947

152

6

91

25.333333

0.8

0

0.25

0.466667

0

1

0

false

0

0.5

0

0.5

1

0

null

1

0

1

0

1

0

null

0

1

0

1

0

6

0b0b23a900cfd52f65edf59de035656db09ad4da

168

py

Python

lume/src/infrastructure/services/logger/print_logger.py

alice-biometrics/lume

a5605f75f05c9007e164e0644dd34f83dfbdfc7d

[ "MIT" ]

15

2020-03-20T19:33:07.000Z

2022-01-07T15:11:08.000Z

lume/src/infrastructure/services/logger/print_logger.py

alice-biometrics/lume

a5605f75f05c9007e164e0644dd34f83dfbdfc7d

[ "MIT" ]

18

2020-04-07T10:53:30.000Z

2022-01-24T07:13:39.000Z

lume/src/infrastructure/services/logger/print_logger.py

alice-biometrics/lume

a5605f75f05c9007e164e0644dd34f83dfbdfc7d

[ "MIT" ]

null

from lume.src.domain.services.logger import Logger class PrintLogger(Logger): def log(self, logging_level, message): print(f"{logging_level}: {message}")

24

50

0.720238

22

168

5.409091

0.772727

0.201681

0.319328

0

0.154762

168

6

51

28

0.838028

0

0.154762

0

1

0.25

false

0

0.25

0

0.75

0.25

1

0

null

1

0

1

0

null

0

1

0

1

0

6

9bf8fb256889f7bd1d8f1be1e5b57ae06f8da239

69

py

Python

usecase_2_calling_v2.py

docktermj/python-future-proofing-apis

ae8ac783a14c6f9d4050ad2545c82f96fb990a5c

[ "Apache-2.0" ]

null

usecase_2_calling_v2.py

docktermj/python-future-proofing-apis

ae8ac783a14c6f9d4050ad2545c82f96fb990a5c

[ "Apache-2.0" ]

null

usecase_2_calling_v2.py

docktermj/python-future-proofing-apis

ae8ac783a14c6f9d4050ad2545c82f96fb990a5c

[ "Apache-2.0" ]

null

from api_version_2 import stable_api stable_api(1, 2) stable_api(1)

13.8

36

0.811594

14

69

3.642857

0.5

0.529412

0.392157

0

0.065574

0.115942

69

4

37

17.25

0.770492

0

1

0

true

0

0.333333

0

0.333333

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

acc3263bbbd98cd69fd0b1982db742b42a06b382

237

py

Python

swagger_server/test/test_ols.py

bigbio/sdrfcheck-api

c634113cfbcc3ea81b9127a67d76975adbe6aec5

[ "Apache-2.0" ]

null

swagger_server/test/test_ols.py

bigbio/sdrfcheck-api

c634113cfbcc3ea81b9127a67d76975adbe6aec5

[ "Apache-2.0" ]

1

2020-04-30T08:52:21.000Z

2020-04-30T08:58:30.000Z

swagger_server/test/test_ols.py

bigbio/sdrfcheck-api

c634113cfbcc3ea81b9127a67d76975adbe6aec5

[ "Apache-2.0" ]

null

def test_besthit(): assert False def test_get_term(): assert False def test_get_ancestors(): assert False def test_search(): assert False def test_suggest(): assert False def test_select(): assert False

10.304348

25

0.679325

32

237

4.78125

0.34375

0.27451

0.457516

0.588235

0.27451

0

0.244726

237

22

26

10.772727

0.854749

0

0.5

0

0.5

1

0.5

true

0

0.5

0

null

1

0

null

0

1

0

1

0

6

c582ad7fb26907b8c1e9561889e4123ef92da1ef

3,886

py

Python

results/models.py

lilbex/bitcom

c0d09155b655de3ebe84851f24e5c07ef60da611

[ "MIT" ]

null

results/models.py

lilbex/bitcom

c0d09155b655de3ebe84851f24e5c07ef60da611

[ "MIT" ]

null

results/models.py

lilbex/bitcom

c0d09155b655de3ebe84851f24e5c07ef60da611

[ "MIT" ]

null

from django.db import models class agentname(models.Model): name_id = models.IntegerField(primary_key=True, editable=False) firstname = models.CharField(max_length=200) lastname = models.CharField(max_length=200) email = models.CharField(max_length=200) phone = models.CharField(max_length=200) pollingunit_uniqueid = models.IntegerField() class announced_lga_results(models.Model): result_id = models.IntegerField(primary_key=True, editable=False) lga_name = models.CharField(max_length=200) party_abbreviation = models.CharField(max_length=50) party_score = models.IntegerField() entered_by_user = models.CharField(max_length=200) date_entered = models.DateTimeField() user_ip_address = models.CharField(max_length=200) class announced_pu_results(models.Model): result_id = models.AutoField(primary_key=True, editable=False) polling_unit_uniqueid = models.CharField(max_length=200) party_abbreviation = models.CharField(max_length=50) party_score = models.IntegerField() entered_by_user = models.CharField(max_length=7) date_entered = models.DateTimeField() user_ip_address = models.CharField(max_length=100) class announced_state_results(models.Model): result_id = models.IntegerField(primary_key=True, editable=False) state_name = models.CharField(max_length=200) party_abbreviation = models.CharField(max_length=50) party_score = models.IntegerField() entered_by_user = models.CharField(max_length=200) date_entered = models.DateTimeField() user_ip_address = models.CharField(max_length=100) class announced_ward_results(models.Model): result_id = models.IntegerField(primary_key=True, editable=False) ward_name = models.CharField(max_length=200) party_abbreviation = models.CharField(max_length=50) party_score = models.IntegerField() entered_by_user = models.CharField(max_length=200) date_entered = models.DateTimeField() user_ip_address = models.CharField(max_length=100) class lga(models.Model): uniqueid = models.IntegerField(primary_key=True, editable=False) lga_id = models.IntegerField() lga_name = models.CharField(max_length=200) state_id = models.IntegerField() lga_description = models.TextField() entered_by_user = models.CharField(max_length=200) date_entered = models.DateTimeField(max_length=200) user_ip_address = models.CharField(max_length=200) class party(models.Model): id = models.IntegerField(primary_key=True, editable=False) partyid = models.CharField(max_length=200) partyname = models.CharField(max_length=50) class polling_unit(models.Model): uniqueid = models.IntegerField(primary_key=True, editable=False) polling_unit_id = models.IntegerField() ward_id = models.IntegerField() lga_id = models.IntegerField() uniquewardid = models.IntegerField() polling_unit_number = models.CharField(max_length=200) polling_unit_name = models.CharField(max_length=200) polling_unit_description = models.TextField() lat = models.CharField(max_length=50) long = models.CharField(max_length=200) entered_by_user = models.CharField(max_length=200) date_entered = models.DateTimeField() user_ip_address = models.CharField(max_length=200) class states(models.Model): state_id = models.IntegerField(unique=True, primary_key=True, editable=False) state_name = models.CharField(max_length=200) class ward(models.Model): uniqueid = models.IntegerField(unique=True, primary_key=True, editable=False) ward_id = models.IntegerField() ward_name = models.CharField(max_length=50) lga_id = models.IntegerField() ward_description = models.TextField() entered_by_user = models.CharField(max_length=200) date_entered = models.DateTimeField() user_ip_address = models.CharField(max_length=50)

39.252525

81

0.764539

494

3,886

5.753036

0.119433

0.114004

0.221675

0.295567

0.822308

0.738916

0.710415

0.654469

0.619282

0.574243

0

0.029219

0.136902

3,886

98

82

39.653061

0.818128

0

0.506329

0

1

0

false

0

0.012658

0

1

0

null

0

1

0

null

0

1

0

6

c58b30ff4fa86fcf902e661e654dad96f910108c

135

py

Python

stuff_for_python_socket/trick2/__init__.py

elafontaine/socket_presentation

06113fd3c3eec257b45951cc26471ff0bbf1af0f

[ "MIT" ]

null

stuff_for_python_socket/trick2/__init__.py

elafontaine/socket_presentation

06113fd3c3eec257b45951cc26471ff0bbf1af0f

[ "MIT" ]

null

stuff_for_python_socket/trick2/__init__.py

elafontaine/socket_presentation

06113fd3c3eec257b45951cc26471ff0bbf1af0f

[ "MIT" ]

null

if __name__ == '__main__': print("{file} is main".format(file=__file__)) else: print("{file} is loaded".format(file=__file__))

27

51

0.666667

18

135

4.111111

0.5

0.243243

0.297297

0

0.140741

135

5

51

27

0.637931

0

0.281481

0

1

0

true

0

0.5

1

0

null

1

0

1

0

null

0

1

0

1

0

6

c5aa1ecc6d9e753df84b01e1f6c18d7caa9a5034

10,793

py

Python

src/tests/test_pagure_lib_add_user_to_project.py

yifengyou/learn-pagure

e54ba955368918c92ad2be6347b53bb2c24a228c

[ "Unlicense" ]

null

src/tests/test_pagure_lib_add_user_to_project.py

yifengyou/learn-pagure

e54ba955368918c92ad2be6347b53bb2c24a228c

[ "Unlicense" ]

null

src/tests/test_pagure_lib_add_user_to_project.py

yifengyou/learn-pagure

e54ba955368918c92ad2be6347b53bb2c24a228c

[ "Unlicense" ]

null

# -*- coding: utf-8 -*- """ (c) 2017 - Copyright Red Hat Inc Authors: Pierre-Yves Chibon <pingou@pingoured.fr> """ from __future__ import unicode_literals, absolute_import import unittest import sys import os from mock import patch, MagicMock sys.path.insert( 0, os.path.join(os.path.dirname(os.path.abspath(__file__)), "..") ) import pagure.lib.query import tests class PagureLibAddUserToProjecttests(tests.Modeltests): """ Tests for pagure.lib.query.add_user_to_project """ def setUp(self): """ Set up the environnment, ran before every tests. """ super(PagureLibAddUserToProjecttests, self).setUp() tests.create_projects(self.session) item = pagure.lib.model.User( user="bar", fullname="bar baz", password="foo", default_email="bar@bar.com", ) self.session.add(item) item = pagure.lib.model.UserEmail(user_id=3, email="bar@bar.com") self.session.add(item) self.session.commit() # Before repo = pagure.lib.query._get_project(self.session, "test") self.assertEqual(len(repo.users), 0) msg = pagure.lib.query.add_user_to_project( session=self.session, project=repo, new_user="foo", user="pingou" ) self.session.commit() self.assertEqual(msg, "User added") # After repo = pagure.lib.query._get_project(self.session, "test") self.assertEqual(len(repo.users), 1) self.assertEqual(repo.users[0].user, "foo") self.assertEqual(repo.admins[0].user, "foo") @patch("pagure.lib.notify.send_email", MagicMock(return_value=True)) def test_re_add_user_to_project_default(self): """ Update an existing user but to the same access level. """ repo = pagure.lib.query._get_project(self.session, "test") # Try adding the same user with the same access self.assertRaises( pagure.exceptions.PagureException, pagure.lib.query.add_user_to_project, session=self.session, project=repo, new_user="foo", user="pingou", access="admin", ) @patch("pagure.lib.notify.send_email", MagicMock(return_value=True)) def test_update_user_to_project_default(self): """Update an existing user without any required group membership.""" repo = pagure.lib.query._get_project(self.session, "test") # Update the access of the user msg = pagure.lib.query.add_user_to_project( session=self.session, project=repo, new_user="foo", user="pingou", access="commit", ) self.session.commit() self.assertEqual(msg, "User access updated") self.assertEqual(len(repo.users), 1) self.assertEqual(repo.users[0].user, "foo") self.assertEqual(repo.committers[0].user, "foo") @patch("pagure.lib.notify.send_email", MagicMock(return_value=True)) def test_update_user_to_project_require_packager_on_all(self): """ Update an existing user but required group membership on all projects. """ repo = pagure.lib.query._get_project(self.session, "test") config = {"*": ["packager"]} # Update the access of the user self.assertRaises( pagure.exceptions.PagureException, pagure.lib.query.add_user_to_project, session=self.session, project=repo, new_user="foo", user="pingou", access="admin", required_groups=config, ) self.session.commit() self.assertEqual(len(repo.users), 1) self.assertEqual(repo.users[0].user, "foo") self.assertEqual(repo.committers[0].user, "foo") @patch("pagure.lib.notify.send_email", MagicMock(return_value=True)) def test_update_user_to_project_require_packager_on_st(self): """ Update an existing user but required group membership on all projects match *st. """ repo = pagure.lib.query._get_project(self.session, "test") config = {"*st": ["packager"]} # Update the access of the user self.assertRaises( pagure.exceptions.PagureException, pagure.lib.query.add_user_to_project, session=self.session, project=repo, new_user="foo", user="pingou", access="admin", required_groups=config, ) self.session.commit() self.assertEqual(len(repo.users), 1) self.assertEqual(repo.users[0].user, "foo") self.assertEqual(repo.committers[0].user, "foo") @patch("pagure.lib.notify.send_email", MagicMock(return_value=True)) def test_update_user_to_project_require_packager_on_te(self): """ Update an existing user but required group membership on all projects match te*. """ repo = pagure.lib.query._get_project(self.session, "test") config = {"te*": ["packager"]} # Update the access of the user self.assertRaises( pagure.exceptions.PagureException, pagure.lib.query.add_user_to_project, session=self.session, project=repo, new_user="foo", user="pingou", access="admin", required_groups=config, ) self.session.commit() self.assertEqual(len(repo.users), 1) self.assertEqual(repo.users[0].user, "foo") self.assertEqual(repo.committers[0].user, "foo") @patch("pagure.lib.notify.send_email", MagicMock(return_value=True)) def test_update_user_to_project_require_packager_on_test(self): """ Update an existing user but required group membership on a specific project: test. """ repo = pagure.lib.query._get_project(self.session, "test") config = {"test": ["packager"]} # Update the access of the user self.assertRaises( pagure.exceptions.PagureException, pagure.lib.query.add_user_to_project, session=self.session, project=repo, new_user="foo", user="pingou", access="admin", required_groups=config, ) self.session.commit() self.assertEqual(len(repo.users), 1) self.assertEqual(repo.users[0].user, "foo") self.assertEqual(repo.committers[0].user, "foo") @patch("pagure.lib.notify.send_email", MagicMock(return_value=True)) def test_add_user_to_test2_require_packager_on_test(self): """ Add user to project test2 while the configuration requires group membership on the project test. """ repo = pagure.lib.query._get_project(self.session, "test2") self.assertEqual(len(repo.users), 0) config = {"test": ["packager"]} # Add the user pagure.lib.query.add_user_to_project( session=self.session, project=repo, new_user="foo", user="pingou", access="admin", required_groups=config, ) self.session.commit() self.assertEqual(len(repo.users), 1) self.assertEqual(repo.users[0].user, "foo") self.assertEqual(repo.committers[0].user, "foo") class PagureLibAddUserToProjectWithGrouptests(PagureLibAddUserToProjecttests): """ Tests for pagure.lib.query.add_user_to_project """ def setUp(self): """ Set up the environnment, ran before every tests. """ super(PagureLibAddUserToProjectWithGrouptests, self).setUp() # Create group msg = pagure.lib.query.add_group( self.session, group_name="packager", display_name="packager", description="The Fedora packager groups", group_type="user", user="pingou", is_admin=False, blacklist=[], ) self.session.commit() self.assertEqual(msg, "User `pingou` added to the group `packager`.") # Add user to group group = pagure.lib.query.search_groups( self.session, group_name="packager" ) msg = pagure.lib.query.add_user_to_group( self.session, username="bar", group=group, user="pingou", is_admin=True, ) self.session.commit() self.assertEqual(msg, "User `bar` added to the group `packager`.") @patch("pagure.lib.notify.send_email", MagicMock(return_value=True)) def test_add_user_to_test_require_packager_on_test(self): """ Add user to project test while the configuration requires group membership on the project test. """ repo = pagure.lib.query._get_project(self.session, "test") self.assertEqual(len(repo.users), 1) config = {"test": ["packager"]} # Add the user to the project pagure.lib.query.add_user_to_project( session=self.session, project=repo, new_user="bar", user="pingou", access="commit", required_groups=config, ) self.session.commit() repo = pagure.lib.query._get_project(self.session, "test") self.assertEqual(len(repo.users), 2) self.assertEqual(repo.users[0].user, "foo") self.assertEqual(repo.committers[0].user, "foo") self.assertEqual(repo.users[1].user, "bar") self.assertEqual(repo.committers[1].user, "bar") @patch("pagure.lib.notify.send_email", MagicMock(return_value=True)) def test_add_user_to_test_require_packager(self): """ Add user to project test while the configuration requires group membership on all the projects. """ repo = pagure.lib.query._get_project(self.session, "test") self.assertEqual(len(repo.users), 1) config = {"*": ["packager"]} # Add the user to the project pagure.lib.query.add_user_to_project( session=self.session, project=repo, new_user="bar", user="pingou", access="commit", required_groups=config, ) self.session.commit() repo = pagure.lib.query._get_project(self.session, "test") self.assertEqual(len(repo.users), 2) self.assertEqual(repo.users[0].user, "foo") self.assertEqual(repo.committers[0].user, "foo") self.assertEqual(repo.users[1].user, "bar") self.assertEqual(repo.committers[1].user, "bar") if __name__ == "__main__": unittest.main(verbosity=2)

33.623053

78

0.604281

1,245

10,793

5.082731

0.116466

0.071271

0.064159

0.040455

0.832965

0.813053

0.799147

0.770386

0.760272

0.727402

0

0.005882

0.275364

10,793

320

79

33.728125

0.803222

0.123784

0

0.690909

0

0.088562

0.027451

0

0.2

1

0.05

false

0.004545

0.031818

0

0.090909

0

null

0

1

0

null

0

6

c5c45df4c969a48aa83cf4183515174e9440ad72

117

py

Python

ccc_client_dev.py

ohsu-comp-bio/ccc_client

433a7fad3d8e6817362678b783110f38ca81e0a7

[ "MIT" ]

null

ccc_client_dev.py

ohsu-comp-bio/ccc_client

433a7fad3d8e6817362678b783110f38ca81e0a7

[ "MIT" ]

null

ccc_client_dev.py

ohsu-comp-bio/ccc_client

433a7fad3d8e6817362678b783110f38ca81e0a7

[ "MIT" ]

null

""" ccc_client entrypoint script """ import ccc_client.cli if __name__ == "__main__": ccc_client.cli.cli_main()

14.625

29

0.717949

16

117

4.5

0.5625

0.375

0.333333

0

0.145299

117

7

30

16.714286

0.72

0.239316

0

0.098765

0

1

0

true

0

0.333333

0

0.333333

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

c5ff71800837220027bc1b54d138c4b03b406cbc

126

py

Python

TextCNN/data/MR/__init__.py

wangtao666666/NLP

6c1507b532800ef2f40fcf8450c3eb414816302f

[ "MIT" ]

2

2021-05-09T13:17:37.000Z

2021-06-06T08:58:53.000Z

TextCNN/data/MR/__init__.py

wangtao666666/NLP

6c1507b532800ef2f40fcf8450c3eb414816302f

[ "MIT" ]

null

TextCNN/data/MR/__init__.py

wangtao666666/NLP

6c1507b532800ef2f40fcf8450c3eb414816302f

[ "MIT" ]

1

2020-11-04T06:33:21.000Z

# -*- coding: utf-8 -*- # @Time : 2020/10/15 上午10:59 # @Author : TaoWang # @Description : from .MR_Dataset import MR_Dataset

18

34

0.650794

18

126

4.444444

0.888889

0.225

0

0.125

0.174603

126

7

34

18

0.644231

0.642857

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

a85096ce0206606764119c3d0362bf1068fcb4a5

1,387

py

Python

2021/dayFour/dayFour.py

joshuagornall/Advent-of-Code

8f1e3affb8504309c418dd64b45d1e1912462da8

[ "MIT" ]

null

2021/dayFour/dayFour.py

joshuagornall/Advent-of-Code

8f1e3affb8504309c418dd64b45d1e1912462da8

[ "MIT" ]

null

2021/dayFour/dayFour.py

joshuagornall/Advent-of-Code

8f1e3affb8504309c418dd64b45d1e1912462da8

[ "MIT" ]

null

# Part 1 data = data.split("\r\n\r\n") nums = list(map(int, data[0].split(","))) boards = [] for k in data[1:]: boards.append([]) for j in k.splitlines(): boards[-1].append(list(map(int, j.split()))) for num in nums: for board in boards: for row in board: for i in range(len(row)): if row[i] == num: row[i] = None if any(all(x == None for x in row) for row in board) or any(all(row[i] == None for row in board) for i in range(len(board[0]))): print(sum(x or 0 for row in board for x in row) * num) exit(0) # Part 2 data = data.split("\r\n\r\n") nums = list(map(int, data[0].split(","))) boards = [] for k in data[1:]: boards.append([]) for j in k.splitlines(): boards[-1].append(list(map(int, j.split()))) lb = None for num in nums: bi = 0 while bi < len(boards): board = boards[bi] for row in board: for i in range(len(row)): if row[i] == num: row[i] = None if any(all(x == None for x in row) for row in board) or any(all(row[i] == None for row in board) for i in range(len(board[0]))): lb = board del boards[bi] else: bi += 1 if len(boards) == 0: break print(sum(x or 0 for row in lb for x in row) * num)

27.196078

136

0.504686

234

1,387

2.991453

0.17094

0.068571

0.091429

0.13

0.804286

0.762857

0.705714

0

0.017486

0.340303

1,387

50

137

27.74

0.747541

0.009373

0

0.65

0

0.013129

0

1

0

false

0

0.05

0

null

0

1

0

null

0

6

a87c3780e4800a9d4c8355c60e2d800797ba6bcc

177

py

Python

tests/base_classifier.py

bsteubing/multifunctional

92c6e427374b151a8ea57c9be9e9245c717b9d46

[ "BSD-3-Clause" ]

1

2021-02-03T22:01:08.000Z

tests/base_classifier.py

bsteubing/multifunctional

92c6e427374b151a8ea57c9be9e9245c717b9d46

[ "BSD-3-Clause" ]

null

tests/base_classifier.py

bsteubing/multifunctional

92c6e427374b151a8ea57c9be9e9245c717b9d46

[ "BSD-3-Clause" ]

null

import pytest import multifunctional as mf from multifunctional.classifiers.base import BaseMFClassifier from bw2data.tests import bw2test @bw2test def test_dummy(): pass

17.7

61

0.824859

22

177

6.590909

0.727273

0

0.019608

0.135593

177

9

62

19.666667

0.928105

0

1

0.142857

true

0.142857

0.571429

0

0.714286

0

1

0

null

0

1

0

null

0

1

0

1

0

6

a89cc305d82a83f765b3eed51db6206405092143

2,539

py

Python

epytope/Data/pssms/smm/mat/B_18_01_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/smm/mat/B_18_01_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/smm/mat/B_18_01_10.py

christopher-mohr/epytope

8ac9fe52c0b263bdb03235a5a6dffcb72012a4fd

[ "BSD-3-Clause" ]

4

2021-05-28T08:50:38.000Z

2022-03-14T11:45:32.000Z

B_18_01_10 = {0: {'A': 0.397, 'C': 0.192, 'E': -0.169, 'D': -0.682, 'G': 0.447, 'F': -0.363, 'I': -0.22, 'H': 0.05, 'K': 0.573, 'M': -0.668, 'L': -0.095, 'N': -0.126, 'Q': 0.287, 'P': 0.229, 'S': 0.084, 'R': 0.264, 'T': -0.198, 'W': 0.125, 'V': 0.143, 'Y': -0.27}, 1: {'A': 0.077, 'C': 0.0, 'E': -0.871, 'D': 0.533, 'G': -0.777, 'F': 0.263, 'I': 0.148, 'H': -0.0, 'K': 0.938, 'M': 0.013, 'L': -0.726, 'N': -0.273, 'Q': -0.538, 'P': -0.114, 'S': 0.928, 'R': 0.056, 'T': 0.081, 'W': 0.155, 'V': 0.028, 'Y': 0.078}, 2: {'A': -0.027, 'C': -0.009, 'E': -0.14, 'D': 0.271, 'G': 0.606, 'F': -0.518, 'I': 0.009, 'H': -0.051, 'K': 0.371, 'M': -0.464, 'L': 0.06, 'N': -0.149, 'Q': -0.269, 'P': 0.555, 'S': 0.062, 'R': 0.065, 'T': -0.002, 'W': -0.312, 'V': -0.048, 'Y': -0.011}, 3: {'A': -0.047, 'C': 0.015, 'E': -0.014, 'D': -0.041, 'G': 0.021, 'F': -0.039, 'I': 0.024, 'H': 0.014, 'K': 0.028, 'M': -0.073, 'L': -0.057, 'N': 0.146, 'Q': -0.005, 'P': 0.121, 'S': -0.011, 'R': -0.0, 'T': 0.066, 'W': -0.049, 'V': -0.016, 'Y': -0.082}, 4: {'A': -0.173, 'C': 0.038, 'E': 0.058, 'D': -0.017, 'G': 0.007, 'F': -0.108, 'I': 0.027, 'H': 0.1, 'K': 0.227, 'M': 0.001, 'L': -0.193, 'N': -0.097, 'Q': 0.019, 'P': 0.111, 'S': 0.007, 'R': 0.05, 'T': -0.089, 'W': 0.001, 'V': -0.043, 'Y': 0.074}, 5: {'A': -0.009, 'C': -0.096, 'E': 0.045, 'D': 0.109, 'G': 0.051, 'F': -0.126, 'I': -0.016, 'H': -0.06, 'K': 0.015, 'M': -0.0, 'L': -0.066, 'N': -0.075, 'Q': 0.156, 'P': 0.079, 'S': -0.006, 'R': -0.016, 'T': 0.146, 'W': -0.083, 'V': -0.017, 'Y': -0.03}, 6: {'A': -0.101, 'C': -0.108, 'E': 0.11, 'D': -0.014, 'G': -0.015, 'F': -0.131, 'I': 0.029, 'H': 0.096, 'K': 0.103, 'M': -0.028, 'L': -0.075, 'N': 0.029, 'Q': 0.181, 'P': 0.157, 'S': 0.052, 'R': -0.022, 'T': -0.025, 'W': -0.04, 'V': -0.061, 'Y': -0.136}, 7: {'A': -0.409, 'C': 0.344, 'E': 0.089, 'D': 0.197, 'G': 0.032, 'F': -0.434, 'I': 0.211, 'H': -0.255, 'K': 0.828, 'M': -0.471, 'L': -0.126, 'N': 0.006, 'Q': 0.153, 'P': 0.233, 'S': 0.073, 'R': 0.396, 'T': 0.02, 'W': -0.229, 'V': -0.242, 'Y': -0.417}, 8: {'A': -0.01, 'C': 0.005, 'E': -0.004, 'D': 0.01, 'G': 0.006, 'F': 0.01, 'I': 0.003, 'H': -0.0, 'K': -0.001, 'M': 0.006, 'L': 0.003, 'N': 0.001, 'Q': -0.001, 'P': 0.004, 'S': -0.01, 'R': 0.003, 'T': -0.013, 'W': -0.001, 'V': -0.011, 'Y': -0.002}, 9: {'A': 0.16, 'C': 0.0, 'E': 0.0, 'D': 0.0, 'G': 0.264, 'F': -0.548, 'I': 0.191, 'H': 0.0, 'K': 0.373, 'M': -0.625, 'L': 0.004, 'N': 0.0, 'Q': -0.049, 'P': 0.0, 'S': 0.188, 'R': 0.129, 'T': 0.542, 'W': 0.136, 'V': 0.189, 'Y': -0.953}, -1: {'con': 4.64684}}

2,539

0.389917

618

2,539

1.597087

0.279935

0.02229

0.009119

0.012158

0.039514

0

0.368471

0.163056

2,539

1

2,539

0.096

0

0.079921

0

1

0

false

0

1

null

0

1

0

1

0

null

0

6

a8ac1180e759cda7753f63c34af1669ac95511dc

4,885

py

Python

tethys_services/base.py

quyendong/tethys

99bcb524d5b2021b88d5fa15b7ed6b8acb460997

[ "BSD-2-Clause" ]

1

2020-10-08T20:38:33.000Z

tethys_services/base.py

quyendong/tethys

99bcb524d5b2021b88d5fa15b7ed6b8acb460997

[ "BSD-2-Clause" ]

1

2018-04-14T19:40:54.000Z

tethys_services/base.py

quyendong/tethys

99bcb524d5b2021b88d5fa15b7ed6b8acb460997

[ "BSD-2-Clause" ]

1

2021-09-07T14:47:11.000Z

""" ******************************************************************************** * Name: base.py * Author: Nathan Swain * Created On: 2014 * Copyright: (c) Brigham Young University 2014 * License: BSD 2-Clause ******************************************************************************** """ from tethys_dataset_services.valid_engines import VALID_ENGINES, VALID_SPATIAL_ENGINES from tethys_apps.cli.cli_colors import pretty_output, FG_WHITE class DatasetService: """ Used to define dataset services for apps. """ def __init__(self, name, type, endpoint, apikey=None, username=None, password=None): """ Constructor """ self.name = name # Validate the types if type in VALID_ENGINES: self.type = type self.engine = VALID_ENGINES[type] else: engine_key_list = list(VALID_ENGINES) if len(VALID_ENGINES) > 2: comma_separated_types = ', '.join('"{0}"'.format(t) for t in engine_key_list[:-1]) last_type = '"{0}"'.format(engine_key_list[-1]) valid_types_string = '{0}, and {1}'.format(comma_separated_types, last_type) elif len(VALID_ENGINES) == 2: valid_types_string = '"{0}" and "{1}"'.format(engine_key_list[0], engine_key_list[1]) else: valid_types_string = '"{0}"'.format(engine_key_list[0]) raise ValueError('The value "{0}" is not a valid for argument "type" of DatasetService. Valid values for ' '"type" argument include {1}.'.format(type, valid_types_string)) self.endpoint = endpoint self.apikey = apikey self.username = username self.password = password with pretty_output(FG_WHITE) as p: p.write('DEPRECATION WARNING: Storing connection credentials for Dataset Services in the app.py is a ' 'security leak. App configuration for Dataset Services will be deprecated in version 1.2.') def __repr__(self): """ String representation """ return '<DatasetService: type={0}, api_endpoint={1}>'.format(self.type, self.endpoint) class SpatialDatasetService: """ Used to define spatial dataset services for apps. """ def __init__(self, name, type, endpoint, apikey=None, username=None, password=None): """ Constructor """ self.name = name # Validate the types if type in VALID_SPATIAL_ENGINES: self.type = type self.engine = VALID_SPATIAL_ENGINES[type] else: spatial_engine_key_list = list(VALID_SPATIAL_ENGINES) if len(VALID_SPATIAL_ENGINES) > 2: comma_separated_types = ', '.join('"{0}"'.format(t) for t in spatial_engine_key_list[:-1]) last_type = '"{0}"'.format(spatial_engine_key_list[-1]) valid_types_string = '{0}, and {1}'.format(comma_separated_types, last_type) elif len(VALID_SPATIAL_ENGINES) == 2: valid_types_string = '"{0}" and "{1}"'.format(spatial_engine_key_list[0], spatial_engine_key_list[1]) else: valid_types_string = '"{0}"'.format(spatial_engine_key_list[0]) raise ValueError('The value "{0}" is not a valid for argument "type" of SpatialDatasetService.' ' Valid values for "type" argument include {1}.'.format(type, valid_types_string)) self.endpoint = endpoint self.apikey = apikey self.username = username self.password = password with pretty_output(FG_WHITE) as p: p.write('DEPRECATION WARNING: Storing connection credentials for Spatial Dataset Services ' 'in the app.py is a security leak. App configuration for Spatial Dataset Services ' 'will be deprecated in version 1.2.') def __repr__(self): """ String representation """ return '<SpatialDatasetService: type={0}, api_endpoint={1}>'.format(self.type, self.endpoint) class WpsService: """ Used to define dataset services for apps. """ def __init__(self, name, endpoint, username=None, password=None): """ Constructor """ self.name = name self.endpoint = endpoint self.username = username self.password = password with pretty_output(FG_WHITE) as p: p.write('DEPRECATION WARNING: Storing connection credentials for WPS Services in the app.py is a security ' 'leak. App configuration for WPS Services will be deprecated in version 1.2.') def __repr__(self): """ String representation """ return '<WpsService: name={0}, endpoint={1}>'.format(self.name, self.endpoint)

38.164063

119

0.587922

558

4,885

4.94086

0.18638

0.039173

0.056583

0.030468

0.804498

0.770403

0.757708

0.733043

0.694958

0.669568

0

0.015023

0.277789

4,885

127

120

38.464567

0.76644

0.11566

0

0.441176

0

0.243217

0.010901

0

1

0.088235

false

0.088235

0.029412

0

0.205882

0

null

0

1

0

1

0

null

0

1

0

6

a8c72ab4f0940b3dcc1ebf7dffe0aea35e2a1e49

29

py

Python

ccdl/__init__.py

xabgesagtx/ccdl

c6931d38c6068b6c48b033e2619a75157cbd6ac2

[ "MIT" ]

1

2015-12-10T16:41:52.000Z

ccdl/__init__.py

xabgesagtx/ccdl

c6931d38c6068b6c48b033e2619a75157cbd6ac2

[ "MIT" ]

null

ccdl/__init__.py

xabgesagtx/ccdl

c6931d38c6068b6c48b033e2619a75157cbd6ac2

[ "MIT" ]

1

2022-03-21T07:24:56.000Z

from .ccdl import CcDownload

14.5

28

0.827586

4

29

6

1

0

0.137931

29

1

29

0.96

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

7690aa8625abf3609eda5c764fde4bc287acdb9e

12,274

py

Python

SimModel_Python_API/simmodel_swig/Release/SimMaterial_GlazingMaterial_Glazing.py

EnEff-BIM/EnEffBIM-Framework

6328d39b498dc4065a60b5cc9370b8c2a9a1cddf

[ "MIT" ]

3

2016-05-30T15:12:16.000Z

2022-03-22T08:11:13.000Z

SimModel_Python_API/simmodel_swig/Release/SimMaterial_GlazingMaterial_Glazing.py

EnEff-BIM/EnEffBIM-Framework

6328d39b498dc4065a60b5cc9370b8c2a9a1cddf

[ "MIT" ]

21

2016-06-13T11:33:45.000Z

2017-05-23T09:46:52.000Z

SimModel_Python_API/simmodel_swig/Release/SimMaterial_GlazingMaterial_Glazing.py

EnEff-BIM/EnEffBIM-Framework

6328d39b498dc4065a60b5cc9370b8c2a9a1cddf

[ "MIT" ]

null

# This file was automatically generated by SWIG (http://www.swig.org). # Version 3.0.7 # # Do not make changes to this file unless you know what you are doing--modify # the SWIG interface file instead. from sys import version_info if version_info >= (2, 6, 0): def swig_import_helper(): from os.path import dirname import imp fp = None try: fp, pathname, description = imp.find_module('_SimMaterial_GlazingMaterial_Glazing', [dirname(__file__)]) except ImportError: import _SimMaterial_GlazingMaterial_Glazing return _SimMaterial_GlazingMaterial_Glazing if fp is not None: try: _mod = imp.load_module('_SimMaterial_GlazingMaterial_Glazing', fp, pathname, description) finally: fp.close() return _mod _SimMaterial_GlazingMaterial_Glazing = swig_import_helper() del swig_import_helper else: import _SimMaterial_GlazingMaterial_Glazing del version_info try: _swig_property = property except NameError: pass # Python < 2.2 doesn't have 'property'. def _swig_setattr_nondynamic(self, class_type, name, value, static=1): if (name == "thisown"): return self.this.own(value) if (name == "this"): if type(value).__name__ == 'SwigPyObject': self.__dict__[name] = value return method = class_type.__swig_setmethods__.get(name, None) if method: return method(self, value) if (not static): if _newclass: object.__setattr__(self, name, value) else: self.__dict__[name] = value else: raise AttributeError("You cannot add attributes to %s" % self) def _swig_setattr(self, class_type, name, value): return _swig_setattr_nondynamic(self, class_type, name, value, 0) def _swig_getattr_nondynamic(self, class_type, name, static=1): if (name == "thisown"): return self.this.own() method = class_type.__swig_getmethods__.get(name, None) if method: return method(self) if (not static): return object.__getattr__(self, name) else: raise AttributeError(name) def _swig_getattr(self, class_type, name): return _swig_getattr_nondynamic(self, class_type, name, 0) def _swig_repr(self): try: strthis = "proxy of " + self.this.__repr__() except: strthis = "" return "<%s.%s; %s >" % (self.__class__.__module__, self.__class__.__name__, strthis,) try: _object = object _newclass = 1 except AttributeError: class _object: pass _newclass = 0 try: import weakref weakref_proxy = weakref.proxy except: weakref_proxy = lambda x: x import base import SimMaterial_GlazingMaterial_Gas import SimMaterial_Default_Default class SimMaterial_GlazingMaterial_Glazing(SimMaterial_GlazingMaterial_Gas.SimMaterial_GlazingMaterial): __swig_setmethods__ = {} for _s in [SimMaterial_GlazingMaterial_Gas.SimMaterial_GlazingMaterial]: __swig_setmethods__.update(getattr(_s, '__swig_setmethods__', {})) __setattr__ = lambda self, name, value: _swig_setattr(self, SimMaterial_GlazingMaterial_Glazing, name, value) __swig_getmethods__ = {} for _s in [SimMaterial_GlazingMaterial_Gas.SimMaterial_GlazingMaterial]: __swig_getmethods__.update(getattr(_s, '__swig_getmethods__', {})) __getattr__ = lambda self, name: _swig_getattr(self, SimMaterial_GlazingMaterial_Glazing, name) __repr__ = _swig_repr def SimMaterial_Name(self, *args): return _SimMaterial_GlazingMaterial_Glazing.SimMaterial_GlazingMaterial_Glazing_SimMaterial_Name(self, *args) def SimMaterial_Thick(self, *args): return _SimMaterial_GlazingMaterial_Glazing.SimMaterial_GlazingMaterial_Glazing_SimMaterial_Thick(self, *args) def SimMaterial_Cond(self, *args): return _SimMaterial_GlazingMaterial_Glazing.SimMaterial_GlazingMaterial_Glazing_SimMaterial_Cond(self, *args) def SimMaterial_OpticalDataType(self, *args): return _SimMaterial_GlazingMaterial_Glazing.SimMaterial_GlazingMaterial_Glazing_SimMaterial_OpticalDataType(self, *args) def SimMaterial_WndwGlassSpectralDataSetName(self, *args): return _SimMaterial_GlazingMaterial_Glazing.SimMaterial_GlazingMaterial_Glazing_SimMaterial_WndwGlassSpectralDataSetName(self, *args) def SimMaterial_SolarTransAtNrmlIncent(self, *args): return _SimMaterial_GlazingMaterial_Glazing.SimMaterial_GlazingMaterial_Glazing_SimMaterial_SolarTransAtNrmlIncent(self, *args) def SimMaterial_FrontSideSolarReflectAtNrmlIncent(self, *args): return _SimMaterial_GlazingMaterial_Glazing.SimMaterial_GlazingMaterial_Glazing_SimMaterial_FrontSideSolarReflectAtNrmlIncent(self, *args) def SimMaterial_BackSideSolarReflectAtNrmlIncent(self, *args): return _SimMaterial_GlazingMaterial_Glazing.SimMaterial_GlazingMaterial_Glazing_SimMaterial_BackSideSolarReflectAtNrmlIncent(self, *args) def SimMaterial_VisTransAtNrmlIncent(self, *args): return _SimMaterial_GlazingMaterial_Glazing.SimMaterial_GlazingMaterial_Glazing_SimMaterial_VisTransAtNrmlIncent(self, *args) def SimMaterial_FrontSideVisReflectAtNrmlIncent(self, *args): return _SimMaterial_GlazingMaterial_Glazing.SimMaterial_GlazingMaterial_Glazing_SimMaterial_FrontSideVisReflectAtNrmlIncent(self, *args) def SimMaterial_BackSideVisReflectAtNrmlIncent(self, *args): return _SimMaterial_GlazingMaterial_Glazing.SimMaterial_GlazingMaterial_Glazing_SimMaterial_BackSideVisReflectAtNrmlIncent(self, *args) def SimMaterial_InfraredTransAtNrmlIncent(self, *args): return _SimMaterial_GlazingMaterial_Glazing.SimMaterial_GlazingMaterial_Glazing_SimMaterial_InfraredTransAtNrmlIncent(self, *args) def SimMaterial_FrontSideInfraredHemisphEmis(self, *args): return _SimMaterial_GlazingMaterial_Glazing.SimMaterial_GlazingMaterial_Glazing_SimMaterial_FrontSideInfraredHemisphEmis(self, *args) def SimMaterial_BackSideInfraredHemisphEmis(self, *args): return _SimMaterial_GlazingMaterial_Glazing.SimMaterial_GlazingMaterial_Glazing_SimMaterial_BackSideInfraredHemisphEmis(self, *args) def SimMaterial_DirtCorrectFactorForSolar_VisTrans(self, *args): return _SimMaterial_GlazingMaterial_Glazing.SimMaterial_GlazingMaterial_Glazing_SimMaterial_DirtCorrectFactorForSolar_VisTrans(self, *args) def SimMaterial_SolarDiffusing(self, *args): return _SimMaterial_GlazingMaterial_Glazing.SimMaterial_GlazingMaterial_Glazing_SimMaterial_SolarDiffusing(self, *args) def SimMaterial_YoungsModulus(self, *args): return _SimMaterial_GlazingMaterial_Glazing.SimMaterial_GlazingMaterial_Glazing_SimMaterial_YoungsModulus(self, *args) def SimMaterial_PoissonsRatio(self, *args): return _SimMaterial_GlazingMaterial_Glazing.SimMaterial_GlazingMaterial_Glazing_SimMaterial_PoissonsRatio(self, *args) def MatProp_GlazingSpectralData_Name(self, *args): return _SimMaterial_GlazingMaterial_Glazing.SimMaterial_GlazingMaterial_Glazing_MatProp_GlazingSpectralData_Name(self, *args) def MatProp_GlazingSpectralData_Wavelength_1_800(self, *args): return _SimMaterial_GlazingMaterial_Glazing.SimMaterial_GlazingMaterial_Glazing_MatProp_GlazingSpectralData_Wavelength_1_800(self, *args) def MatProp_GlazingSpectralData_Trans_1_800(self, *args): return _SimMaterial_GlazingMaterial_Glazing.SimMaterial_GlazingMaterial_Glazing_MatProp_GlazingSpectralData_Trans_1_800(self, *args) def MatProp_GlazingSpectralData_FrontReflect_1_800(self, *args): return _SimMaterial_GlazingMaterial_Glazing.SimMaterial_GlazingMaterial_Glazing_MatProp_GlazingSpectralData_FrontReflect_1_800(self, *args) def MatProp_GlazingSpectralData_BackReflect_1_800(self, *args): return _SimMaterial_GlazingMaterial_Glazing.SimMaterial_GlazingMaterial_Glazing_MatProp_GlazingSpectralData_BackReflect_1_800(self, *args) def __init__(self, *args): this = _SimMaterial_GlazingMaterial_Glazing.new_SimMaterial_GlazingMaterial_Glazing(*args) try: self.this.append(this) except: self.this = this def _clone(self, f=0, c=None): return _SimMaterial_GlazingMaterial_Glazing.SimMaterial_GlazingMaterial_Glazing__clone(self, f, c) __swig_destroy__ = _SimMaterial_GlazingMaterial_Glazing.delete_SimMaterial_GlazingMaterial_Glazing __del__ = lambda self: None SimMaterial_GlazingMaterial_Glazing_swigregister = _SimMaterial_GlazingMaterial_Glazing.SimMaterial_GlazingMaterial_Glazing_swigregister SimMaterial_GlazingMaterial_Glazing_swigregister(SimMaterial_GlazingMaterial_Glazing) class SimMaterial_GlazingMaterial_Glazing_sequence(base.sequence_common): __swig_setmethods__ = {} for _s in [base.sequence_common]: __swig_setmethods__.update(getattr(_s, '__swig_setmethods__', {})) __setattr__ = lambda self, name, value: _swig_setattr(self, SimMaterial_GlazingMaterial_Glazing_sequence, name, value) __swig_getmethods__ = {} for _s in [base.sequence_common]: __swig_getmethods__.update(getattr(_s, '__swig_getmethods__', {})) __getattr__ = lambda self, name: _swig_getattr(self, SimMaterial_GlazingMaterial_Glazing_sequence, name) __repr__ = _swig_repr def __init__(self, *args): this = _SimMaterial_GlazingMaterial_Glazing.new_SimMaterial_GlazingMaterial_Glazing_sequence(*args) try: self.this.append(this) except: self.this = this def assign(self, n, x): return _SimMaterial_GlazingMaterial_Glazing.SimMaterial_GlazingMaterial_Glazing_sequence_assign(self, n, x) def begin(self, *args): return _SimMaterial_GlazingMaterial_Glazing.SimMaterial_GlazingMaterial_Glazing_sequence_begin(self, *args) def end(self, *args): return _SimMaterial_GlazingMaterial_Glazing.SimMaterial_GlazingMaterial_Glazing_sequence_end(self, *args) def rbegin(self, *args): return _SimMaterial_GlazingMaterial_Glazing.SimMaterial_GlazingMaterial_Glazing_sequence_rbegin(self, *args) def rend(self, *args): return _SimMaterial_GlazingMaterial_Glazing.SimMaterial_GlazingMaterial_Glazing_sequence_rend(self, *args) def at(self, *args): return _SimMaterial_GlazingMaterial_Glazing.SimMaterial_GlazingMaterial_Glazing_sequence_at(self, *args) def front(self, *args): return _SimMaterial_GlazingMaterial_Glazing.SimMaterial_GlazingMaterial_Glazing_sequence_front(self, *args) def back(self, *args): return _SimMaterial_GlazingMaterial_Glazing.SimMaterial_GlazingMaterial_Glazing_sequence_back(self, *args) def push_back(self, *args): return _SimMaterial_GlazingMaterial_Glazing.SimMaterial_GlazingMaterial_Glazing_sequence_push_back(self, *args) def pop_back(self): return _SimMaterial_GlazingMaterial_Glazing.SimMaterial_GlazingMaterial_Glazing_sequence_pop_back(self) def detach_back(self, pop=True): return _SimMaterial_GlazingMaterial_Glazing.SimMaterial_GlazingMaterial_Glazing_sequence_detach_back(self, pop) def insert(self, *args): return _SimMaterial_GlazingMaterial_Glazing.SimMaterial_GlazingMaterial_Glazing_sequence_insert(self, *args) def erase(self, *args): return _SimMaterial_GlazingMaterial_Glazing.SimMaterial_GlazingMaterial_Glazing_sequence_erase(self, *args) def detach(self, position, r, erase=True): return _SimMaterial_GlazingMaterial_Glazing.SimMaterial_GlazingMaterial_Glazing_sequence_detach(self, position, r, erase) def swap(self, x): return _SimMaterial_GlazingMaterial_Glazing.SimMaterial_GlazingMaterial_Glazing_sequence_swap(self, x) __swig_destroy__ = _SimMaterial_GlazingMaterial_Glazing.delete_SimMaterial_GlazingMaterial_Glazing_sequence __del__ = lambda self: None SimMaterial_GlazingMaterial_Glazing_sequence_swigregister = _SimMaterial_GlazingMaterial_Glazing.SimMaterial_GlazingMaterial_Glazing_sequence_swigregister SimMaterial_GlazingMaterial_Glazing_sequence_swigregister(SimMaterial_GlazingMaterial_Glazing_sequence) # This file is compatible with both classic and new-style classes.

45.970037

154

0.786704

1,251

12,274

7.183054

0.135891

0.33274

0.396617

0.29379

0.676052

0.643

0.640775

0.553639

0.517805

0.443134

0

0.004492

0.147629

12,274

266

155

46.142857

0.854426

0.023953

0

0.255102

1

0

0.019215

0.006015

0

1

0.239796

false

0.010204

0.066327

0.209184

0.647959

0

null

1

0

1

0

null

0

1

0

1

0

6

7691b0973118054b83bedab1ece63281bd28fefe

49

py

Python

usage_demo/my_other_program.py

aroberge/importhooks

57483ce24d265d391587f6321954f2ed60f04afd

[ "MIT" ]

null

usage_demo/my_other_program.py

aroberge/importhooks

57483ce24d265d391587f6321954f2ed60f04afd

[ "MIT" ]

null

usage_demo/my_other_program.py

aroberge/importhooks

57483ce24d265d391587f6321954f2ed60f04afd

[ "MIT" ]

null

# my_other_program.py import my_program # noqa

12.25

25

0.77551

8

49

4.375

0.75

0

0.163265

49

3

26

16.333333

0.853659

0.489796

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

76c50d7ff0229d1e0e8ecd450f289bf586b425fa

372

py

Python

fabrik_full_body/output_writer.py

Atiehmerikh/FABRIK_Full_Body

ae31fd059e65db18cbcf5fcdb43cfad3e015a359

[ "MIT" ]

7

2020-09-29T23:49:05.000Z

2022-02-07T12:51:11.000Z

fabrik_full_body/output_writer.py

Atiehmerikh/FABRIK_Full_Body

ae31fd059e65db18cbcf5fcdb43cfad3e015a359

[ "MIT" ]

2

2020-07-09T18:46:31.000Z

2022-03-17T06:42:41.000Z

fabrik_full_body/output_writer.py

Atiehmerikh/FABRIK_python

ae31fd059e65db18cbcf5fcdb43cfad3e015a359

[ "MIT" ]

5

2021-03-15T12:50:16.000Z

2022-02-11T11:10:36.000Z

from fabrik_full_body.singleton import Singleton class OutputWriter(metaclass=Singleton): def __init__(self, base_address="./outputs/", angles_file_address = "angles.txt"): self.base_address = base_address self.angles_file_address = base_address + angles_file_address def angle_writer(self): return open(self.angles_file_address, "w")

41.333333

86

0.741935

47

372

5.468085

0.489362

0.171206

0.264591

0.163424

0

0.166667

372

8

87

46.5

0.829032

0

0.056452

0

1

0.285714

false

0

0.142857

0.714286

0

null

0

1

0

1

0

null

0

1

0

1

0

6

76c5b0599de622291a540c654c4951f237f6e8b0

42

py

Python

chrome_handler/__init__.py

kaito1002/chrome_handler

d9ec0229a57e370e05cd2eef51c2dc18abf5c463

[ "MIT" ]

null

chrome_handler/__init__.py

kaito1002/chrome_handler

d9ec0229a57e370e05cd2eef51c2dc18abf5c463

[ "MIT" ]

null

chrome_handler/__init__.py

kaito1002/chrome_handler

d9ec0229a57e370e05cd2eef51c2dc18abf5c463

[ "MIT" ]

null

from .chrome_handler import ChromeHandler

21

41

0.880952

5

42

7.2

1

0

0.095238

42

1

42

0.947368

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

76c9b903c76be1afe3aa574aaab0c0f54fe035ec

59,578

py

Python

tests/test_wsman.py

Samathy/pypsrp

196b4279581319930f2133860bce2f901a8bb127

[ "MIT" ]

null

tests/test_wsman.py

Samathy/pypsrp

196b4279581319930f2133860bce2f901a8bb127

[ "MIT" ]

null

tests/test_wsman.py

Samathy/pypsrp

196b4279581319930f2133860bce2f901a8bb127

[ "MIT" ]

null

import os import requests import sys import uuid import pytest import pypsrp.wsman as pypsrp_wsman from pypsrp.encryption import WinRMEncryption from pypsrp.exceptions import AuthenticationError, WinRMError, \ WinRMTransportError, WSManFaultError from pypsrp.negotiate import HTTPNegotiateAuth from pypsrp.wsman import OptionSet, SelectorSet, WSMan, WSManAction, \ NAMESPACES, _TransportHTTP try: from unittest.mock import MagicMock except ImportError: from mock import MagicMock try: import requests_credssp except ImportError: requests_credssp = None if sys.version_info[0] == 2 and sys.version_info[1] < 7: # pragma: no cover # ElementTree in Python 2.6 does not support namespaces so we need to use # lxml instead for this version from lxml import etree as ET else: # pragma: no cover import xml.etree.ElementTree as ET class _TransportTest(object): def __init__(self, expected_action=None): self.endpoint = "testendpoint" self.expected_action = expected_action def send(self, xml): # ensure wsman is always sending a byte string assert isinstance(xml, bytes) if self.expected_action is None: # see what happens if the text is XML but not a WSManFault message raise WinRMTransportError("http", 401, "not an XML response") req = ET.fromstring(xml) action = req.find("s:Header/wsa:Action", NAMESPACES).text if action == self.expected_action: return '''<s:Envelope xml:lang="en-US" xmlns:s="http://www.w3.org/2003/05/soap-envelope" xmlns:wsa="http://schemas.xmlsoap.org/ws/2004/08/addressing"> <s:Header> <wsa:RelatesTo>uuid:00000000-0000-0000-0000-000000000000</wsa:RelatesTo> </s:Header> <s:Body>body</s:Body> </s:Envelope>''' else: # we want to set a non XML message as the response text to verify # the parsing failure is checked and the original exception is # raised error_msg = '''<s:Envelope xml:lang="en-US" xmlns:s="http://www.w3.org/2003/05/soap-envelope" xmlns:a="http://schemas.xmlsoap.org/ws/2004/08/addressing" xmlns:x="http://schemas.xmlsoap.org/ws/2004/09/transfer" xmlns:e="http://schemas.xmlsoap.org/ws/2004/08/eventing" xmlns:n="http://schemas.xmlsoap.org/ws/2004/09/enumeration" xmlns:w="http://schemas.dmtf.org/wbem/wsman/1/wsman.xsd" xmlns:rsp="http://schemas.microsoft.com/wbem/wsman/1/windows/shell" xmlns:p="http://schemas.microsoft.com/wbem/wsman/1/wsman.xsd"><s:Header><a:Action>http://schemas.xmlsoap.org/ws/2004/08/addressing/fault</a:Action><a:MessageID>uuid:4DB571F9-F8DE-48FD-872C-2AF08D996249</a:MessageID><a:To>http://schemas.xmlsoap.org/ws/2004/08/addressing/role/anonymous</a:To><a:RelatesTo>uuid:eaa98952-3188-458f-b265-b03ace115f20</a:RelatesTo><s:NotUnderstood qname="wsman:ResourceUri" xmlns:wsman="http://schemas.dmtf.org/wbem/wsman/1/wsman.xsd" /> </s:Header> <s:Body> <s:Fault> <s:Code> <s:Value>IllegalAction</s:Value> </s:Code> <s:Reason> <s:Text xml:lang="">Illegal action '%s', expecting '%s'</s:Text> </s:Reason> </s:Fault> </s:Body> </s:Envelope>''' % (action, self.expected_action) raise WinRMTransportError("http", 500, error_msg) class TestWSMan(object): def test_wsman_defaults(self): actual = WSMan("") assert actual.max_envelope_size == 153600 assert actual.max_payload_size < actual.max_envelope_size assert actual.operation_timeout == 20 assert isinstance(actual.session_id, str) # verify we get a unique session id each time this is initialised new_wsman = WSMan("") assert actual.session_id != new_wsman.session_id def test_override_default(self): actual = WSMan("", 8192, 30) assert actual.max_envelope_size == 8192 assert actual.max_payload_size < actual.max_envelope_size assert actual.operation_timeout == 30 def test_invoke_command(self, monkeypatch): def mockuuid(): return uuid.UUID("00000000-0000-0000-0000-000000000000") monkeypatch.setattr(uuid, 'uuid4', mockuuid) wsman = WSMan("") wsman.transport = _TransportTest(WSManAction.COMMAND) actual = wsman.command("", None) assert actual.tag == "{http://www.w3.org/2003/05/soap-envelope}Body" assert actual.text == "body" def test_invoke_connect(self, monkeypatch): def mockuuid(): return uuid.UUID("00000000-0000-0000-0000-000000000000") monkeypatch.setattr(uuid, 'uuid4', mockuuid) wsman = WSMan("") wsman.transport = _TransportTest(WSManAction.CONNECT) actual = wsman.connect("", None) assert actual.tag == "{http://www.w3.org/2003/05/soap-envelope}Body" assert actual.text == "body" def test_invoke_create(self, monkeypatch): def mockuuid(): return uuid.UUID("00000000-0000-0000-0000-000000000000") monkeypatch.setattr(uuid, 'uuid4', mockuuid) wsman = WSMan("") wsman.transport = _TransportTest(WSManAction.CREATE) actual = wsman.create("", None) assert actual.tag == "{http://www.w3.org/2003/05/soap-envelope}Body" assert actual.text == "body" def test_invoke_disconnect(self, monkeypatch): def mockuuid(): return uuid.UUID("00000000-0000-0000-0000-000000000000") monkeypatch.setattr(uuid, 'uuid4', mockuuid) wsman = WSMan("") wsman.transport = _TransportTest(WSManAction.DISCONNECT) actual = wsman.disconnect("", None) assert actual.tag == "{http://www.w3.org/2003/05/soap-envelope}Body" assert actual.text == "body" def test_invoke_enumerate(self, monkeypatch): def mockuuid(): return uuid.UUID("00000000-0000-0000-0000-000000000000") monkeypatch.setattr(uuid, 'uuid4', mockuuid) wsman = WSMan("") wsman.transport = _TransportTest(WSManAction.ENUMERATE) actual = wsman.enumerate("", None) assert actual.tag == "{http://www.w3.org/2003/05/soap-envelope}Body" assert actual.text == "body" def test_invoke_delete(self, monkeypatch): def mockuuid(): return uuid.UUID("00000000-0000-0000-0000-000000000000") monkeypatch.setattr(uuid, 'uuid4', mockuuid) wsman = WSMan("") wsman.transport = _TransportTest(WSManAction.DELETE) actual = wsman.delete("", None) assert actual.tag == "{http://www.w3.org/2003/05/soap-envelope}Body" assert actual.text == "body" def test_invoke_get(self, monkeypatch): def mockuuid(): return uuid.UUID("00000000-0000-0000-0000-000000000000") monkeypatch.setattr(uuid, 'uuid4', mockuuid) wsman = WSMan("") wsman.transport = _TransportTest(WSManAction.GET) actual = wsman.get("", None) assert actual.tag == "{http://www.w3.org/2003/05/soap-envelope}Body" assert actual.text == "body" def test_invoke_pull(self, monkeypatch): def mockuuid(): return uuid.UUID("00000000-0000-0000-0000-000000000000") monkeypatch.setattr(uuid, 'uuid4', mockuuid) wsman = WSMan("") wsman.transport = _TransportTest(WSManAction.PULL) actual = wsman.pull("", None) assert actual.tag == "{http://www.w3.org/2003/05/soap-envelope}Body" assert actual.text == "body" def test_invoke_put(self, monkeypatch): def mockuuid(): return uuid.UUID("00000000-0000-0000-0000-000000000000") monkeypatch.setattr(uuid, 'uuid4', mockuuid) wsman = WSMan("") wsman.transport = _TransportTest(WSManAction.PUT) actual = wsman.put("", None) assert actual.tag == "{http://www.w3.org/2003/05/soap-envelope}Body" assert actual.text == "body" def test_invoke_receive(self, monkeypatch): def mockuuid(): return uuid.UUID("00000000-0000-0000-0000-000000000000") monkeypatch.setattr(uuid, 'uuid4', mockuuid) wsman = WSMan("") wsman.transport = _TransportTest(WSManAction.RECEIVE) actual = wsman.receive("", None) assert actual.tag == "{http://www.w3.org/2003/05/soap-envelope}Body" assert actual.text == "body" def test_invoke_reconnect(self, monkeypatch): def mockuuid(): return uuid.UUID("00000000-0000-0000-0000-000000000000") monkeypatch.setattr(uuid, 'uuid4', mockuuid) wsman = WSMan("") wsman.transport = _TransportTest(WSManAction.RECONNECT) actual = wsman.reconnect("", None) assert actual.tag == "{http://www.w3.org/2003/05/soap-envelope}Body" assert actual.text == "body" def test_invoke_send(self, monkeypatch): def mockuuid(): return uuid.UUID("00000000-0000-0000-0000-000000000000") monkeypatch.setattr(uuid, 'uuid4', mockuuid) wsman = WSMan("") wsman.transport = _TransportTest(WSManAction.SEND) actual = wsman.send("", None) assert actual.tag == "{http://www.w3.org/2003/05/soap-envelope}Body" assert actual.text == "body" def test_invoke_signal(self, monkeypatch): def mockuuid(): return uuid.UUID("00000000-0000-0000-0000-000000000000") monkeypatch.setattr(uuid, 'uuid4', mockuuid) wsman = WSMan("") wsman.transport = _TransportTest(WSManAction.SIGNAL) actual = wsman.signal("", None) assert actual.tag == "{http://www.w3.org/2003/05/soap-envelope}Body" assert actual.text == "body" def test_get_header_no_locale(self): wsman = WSMan("") actual = wsman._create_header("action", "resource", None, None, None) actual_data_locale = actual.find("wsmv:DataLocale", NAMESPACES) actual_locale = actual.find("wsman:Locale", NAMESPACES) xml = NAMESPACES['xml'] assert actual_data_locale.attrib["{%s}lang" % xml] == "en-US" assert actual_locale.attrib["{%s}lang" % xml] == "en-US" def test_get_header_explicit_locale(self): wsman = WSMan("", locale="en-GB") actual = wsman._create_header("action", "resource", None, None, None) actual_data_locale = actual.find("wsmv:DataLocale", NAMESPACES) actual_locale = actual.find("wsman:Locale", NAMESPACES) xml = NAMESPACES['xml'] assert actual_data_locale.attrib["{%s}lang" % xml] == "en-GB" assert actual_locale.attrib["{%s}lang" % xml] == "en-GB" def test_get_header_explicit_data_locale(self): wsman = WSMan("", data_locale="en-GB") actual = wsman._create_header("action", "resource", None, None, None) actual_data_locale = actual.find("wsmv:DataLocale", NAMESPACES) actual_locale = actual.find("wsman:Locale", NAMESPACES) xml = NAMESPACES['xml'] assert actual_data_locale.attrib["{%s}lang" % xml] == "en-GB" assert actual_locale.attrib["{%s}lang" % xml] == "en-US" def test_get_header_explicit_both_locale(self): wsman = WSMan("", locale="en-AU", data_locale="en-GB") actual = wsman._create_header("action", "resource", None, None, None) actual_data_locale = actual.find("wsmv:DataLocale", NAMESPACES) actual_locale = actual.find("wsman:Locale", NAMESPACES) xml = NAMESPACES['xml'] assert actual_data_locale.attrib["{%s}lang" % xml] == "en-GB" assert actual_locale.attrib["{%s}lang" % xml] == "en-AU" def test_invoke_mismatch_id(self, monkeypatch): def mockuuid(): return uuid.UUID("00000000-0000-0000-0000-000000000001") monkeypatch.setattr(uuid, 'uuid4', mockuuid) wsman = WSMan("") wsman.transport = _TransportTest(WSManAction.SEND) with pytest.raises(WinRMError) as exc: wsman.send("", None) assert str(exc.value) == \ "Received related id does not match related expected message " \ "id: Sent: uuid:00000000-0000-0000-0000-000000000001, Received: " \ "uuid:00000000-0000-0000-0000-000000000000" def test_invoke_transport_error(self): wsman = WSMan("") wsman.transport = _TransportTest() with pytest.raises(WinRMTransportError) as exc: wsman.send("", None) error_msg = "Bad HTTP response returned from the server. Code: 401, " \ "Content: 'not an XML response'" assert str(exc.value) == error_msg assert exc.value.code == 401 assert exc.value.protocol == "http" assert exc.value.message == error_msg assert exc.value.response_text == "not an XML response" def test_invoke_wsman_fault(self): # we set Create and send Send to cause the test transport to fire the # error we want wsman = WSMan("") wsman.transport = _TransportTest(WSManAction.CREATE) with pytest.raises(WSManFaultError) as exc: wsman.send("", None) error_msg = \ "Received a WSManFault message. (Code: IllegalAction, Reason: " \ "Illegal action '%s', expecting '%s')" \ % (WSManAction.SEND, WSManAction.CREATE) assert str(exc.value) == error_msg assert exc.value.code == "IllegalAction" assert exc.value.machine is None assert exc.value.message == error_msg assert exc.value.provider is None assert exc.value.provider_fault is None assert exc.value.reason == "Illegal action '%s', expecting '%s'" \ % (WSManAction.SEND, WSManAction.CREATE) def test_raise_native_wsman_fault(self): xml_text = ''' <s:Envelope xml:lang="en-US" xmlns:s="http://www.w3.org/2003/05/soap-envelope" xmlns:a="http://schemas.xmlsoap.org/ws/2004/08/addressing" xmlns:x="http://schemas.xmlsoap.org/ws/2004/09/transfer" xmlns:e="http://schemas.xmlsoap.org/ws/2004/08/eventing" xmlns:n="http://schemas.xmlsoap.org/ws/2004/09/enumeration" xmlns:w="http://schemas.dmtf.org/wbem/wsman/1/wsman.xsd" xmlns:rsp="http://schemas.microsoft.com/wbem/wsman/1/windows/shell" xmlns:p="http://schemas.microsoft.com/wbem/wsman/1/wsman.xsd"><s:Header><a:Action>http://schemas.xmlsoap.org/ws/2004/08/addressing/fault</a:Action><a:MessageID>uuid:4DB571F9-F8DE-48FD-872C-2AF08D996249</a:MessageID><a:To>http://schemas.xmlsoap.org/ws/2004/08/addressing/role/anonymous</a:To><a:RelatesTo>uuid:eaa98952-3188-458f-b265-b03ace115f20</a:RelatesTo><s:NotUnderstood qname="wsman:ResourceUri" xmlns:wsman="http://schemas.dmtf.org/wbem/wsman/1/wsman.xsd" /> </s:Header> <s:Body> <s:Fault> <s:Code> <s:Value>s:MustUnderstand</s:Value> </s:Code> <s:Reason> <s:Text xml:lang="">The WS-Management service cannot process a SOAP header in the request that is marked as mustUnderstand by the client. This could be caused by the use of a version of the protocol which is not supported, or may be an incompatibility between the client and server implementations. </s:Text> </s:Reason> </s:Fault> </s:Body> </s:Envelope>''' with pytest.raises(WSManFaultError) as exc: raise WSMan._parse_wsman_fault(xml_text) assert exc.value.code == "s:MustUnderstand" assert exc.value.machine is None assert exc.value.message == \ "Received a WSManFault message. (Code: s:MustUnderstand, " \ "Reason: The WS-Management service cannot process a SOAP header " \ "in the request that is marked as mustUnderstand by the client. " \ " This could be caused by the use of a version of the protocol " \ "which is not supported, or may be an incompatibility between " \ "the client and server implementations.)" assert exc.value.provider is None assert exc.value.provider_fault is None assert exc.value.provider_path is None assert exc.value.reason == \ "The WS-Management service cannot process a SOAP header in the " \ "request that is marked as mustUnderstand by the client. This " \ "could be caused by the use of a version of the protocol which " \ "is not supported, or may be an incompatibility between the " \ "client and server implementations." def test_raise_native_wsman_fault_no_reason(self): xml_text = ''' <s:Envelope xml:lang="en-US" xmlns:s="http://www.w3.org/2003/05/soap-envelope" xmlns:a="http://schemas.xmlsoap.org/ws/2004/08/addressing" xmlns:x="http://schemas.xmlsoap.org/ws/2004/09/transfer" xmlns:e="http://schemas.xmlsoap.org/ws/2004/08/eventing" xmlns:n="http://schemas.xmlsoap.org/ws/2004/09/enumeration" xmlns:w="http://schemas.dmtf.org/wbem/wsman/1/wsman.xsd" xmlns:rsp="http://schemas.microsoft.com/wbem/wsman/1/windows/shell" xmlns:p="http://schemas.microsoft.com/wbem/wsman/1/wsman.xsd"><s:Header><a:Action>http://schemas.xmlsoap.org/ws/2004/08/addressing/fault</a:Action><a:MessageID>uuid:4DB571F9-F8DE-48FD-872C-2AF08D996249</a:MessageID><a:To>http://schemas.xmlsoap.org/ws/2004/08/addressing/role/anonymous</a:To><a:RelatesTo>uuid:eaa98952-3188-458f-b265-b03ace115f20</a:RelatesTo><s:NotUnderstood qname="wsman:ResourceUri" xmlns:wsman="http://schemas.dmtf.org/wbem/wsman/1/wsman.xsd" /> </s:Header> <s:Body> <s:Fault> <s:Code> <s:Value>s:Unknown</s:Value> </s:Code> </s:Fault> </s:Body> </s:Envelope>''' with pytest.raises(WSManFaultError) as exc: raise WSMan._parse_wsman_fault(xml_text) assert exc.value.code == "s:Unknown" assert exc.value.machine is None assert exc.value.message == "Received a WSManFault message. " \ "(Code: s:Unknown)" assert exc.value.provider is None assert exc.value.provider_fault is None assert exc.value.provider_path is None assert exc.value.reason is None def test_raise_wsman_fault_with_wsman_fault(self): xml_text = r''' <s:Envelope xml:lang="en-US" xmlns:s="http://www.w3.org/2003/05/soap-envelope" xmlns:a="http://schemas.xmlsoap.org/ws/2004/08/addressing" xmlns:x="http://schemas.xmlsoap.org/ws/2004/09/transfer" xmlns:e="http://schemas.xmlsoap.org/ws/2004/08/eventing" xmlns:n="http://schemas.xmlsoap.org/ws/2004/09/enumeration" xmlns:w="http://schemas.dmtf.org/wbem/wsman/1/wsman.xsd" xmlns:p="http://schemas.microsoft.com/wbem/wsman/1/wsman.xsd"> <s:Header> <a:Action>http://schemas.dmtf.org/wbem/wsman/1/wsman/fault</a:Action> <a:MessageID>uuid:348D9DCE-B99B-4EBD-A90B-624854B032BB</a:MessageID> <a:To>http://schemas.xmlsoap.org/ws/2004/08/addressing/role/anonymous</a:To> <a:RelatesTo>uuid:a82b5f24-7a6c-4170-8cd1-d2031b1203fd</a:RelatesTo> </s:Header> <s:Body> <s:Fault> <s:Code> <s:Value>s:Sender</s:Value> <s:Subcode> <s:Value>w:InvalidParameter</s:Value> </s:Subcode> </s:Code> <s:Reason> <s:Text xml:lang="">The parameter is incorrect. </s:Text> </s:Reason> <s:Detail> <w:FaultDetail>http://schemas.dmtf.org/wbem/wsman/1/wsman/faultDetail/InvalidValue</w:FaultDetail> <f:WSManFault xmlns:f="http://schemas.microsoft.com/wbem/wsman/1/wsmanfault" Code="87" Machine="SERVER2016.domain.local"> <f:Message><f:ProviderFault provider="Shell cmd plugin" path="%systemroot%\system32\winrscmd.dll">The parameter is incorrect. </f:ProviderFault></f:Message> </f:WSManFault> </s:Detail> </s:Fault> </s:Body> </s:Envelope>''' with pytest.raises(WSManFaultError) as exc: raise WSMan._parse_wsman_fault(xml_text) assert exc.value.code == 87 assert exc.value.machine == "SERVER2016.domain.local" assert exc.value.message == \ "Received a WSManFault message. (Code: 87, Machine: " \ "SERVER2016.domain.local, Reason: The parameter is incorrect., " \ "Provider: Shell cmd plugin, Provider Path: %systemroot%\\" \ "system32\\winrscmd.dll, Provider Fault: The parameter is " \ "incorrect.)" assert exc.value.provider == "Shell cmd plugin" assert exc.value.provider_fault == "The parameter is incorrect." assert exc.value.provider_path == \ "%systemroot%\\system32\\winrscmd.dll" assert exc.value.reason == "The parameter is incorrect." def test_raise_wsman_fault_without_provider(self): xml_text = r''' <s:Envelope xml:lang="en-US" xmlns:s="http://www.w3.org/2003/05/soap-envelope" xmlns:a="http://schemas.xmlsoap.org/ws/2004/08/addressing" xmlns:x="http://schemas.xmlsoap.org/ws/2004/09/transfer" xmlns:e="http://schemas.xmlsoap.org/ws/2004/08/eventing" xmlns:n="http://schemas.xmlsoap.org/ws/2004/09/enumeration" xmlns:w="http://schemas.dmtf.org/wbem/wsman/1/wsman.xsd" xmlns:p="http://schemas.microsoft.com/wbem/wsman/1/wsman.xsd"> <s:Header> <a:Action>http://schemas.dmtf.org/wbem/wsman/1/wsman/fault</a:Action> <a:MessageID>uuid:EE71C444-1658-4B3F-916D-54CE43B68BC9</a:MessageID> <a:To>http://schemas.xmlsoap.org/ws/2004/08/addressing/role/anonymous</a:To> <a:RelatesTo>uuid.761ca906-0bf0-41bb-a9d9-4cbbca986aeb</a:RelatesTo> </s:Header> <s:Body> <s:Fault> <s:Code> <s:Value>s:Sender</s:Value> <s:Subcode> <s:Value>w:SchemaValidationError</s:Value> </s:Subcode> </s:Code> <s:Reason> <s:Text xml:lang="">The SOAP XML in the message does not match the corresponding XML schema definition. Change the XML and retry. </s:Text> </s:Reason> <s:Detail> <f:WSManFault xmlns:f="http://schemas.microsoft.com/wbem/wsman/1/wsmanfault" Code="2150858817" Machine="SERVER2008.domain.local"> <f:Message>The Windows Remote Shell cannot process the request. The SOAP packet contains an element Argument that is invalid. Retry the request with the correct XML element. </f:Message> </f:WSManFault> </s:Detail> </s:Fault> </s:Body> </s:Envelope>''' with pytest.raises(WSManFaultError) as exc: raise WSMan._parse_wsman_fault(xml_text) assert exc.value.code == 2150858817 assert exc.value.machine == "SERVER2008.domain.local" assert exc.value.message == \ "Received a WSManFault message. (Code: 2150858817, Machine: " \ "SERVER2008.domain.local, Reason: The Windows Remote Shell " \ "cannot process the request. The SOAP packet contains an " \ "element Argument that is invalid. Retry the request with the " \ "correct XML element.)" assert exc.value.provider is None assert exc.value.provider_fault is None assert exc.value.provider_path is None assert exc.value.reason == \ "The Windows Remote Shell cannot process the request. The SOAP " \ "packet contains an element Argument that is invalid. Retry the " \ "request with the correct XML element." def test_wsman_update_envelope_size_explicit(self): wsman = WSMan("") wsman.update_max_payload_size(4096) assert wsman.max_envelope_size == 4096 # this next value is dependent on a lot of things such as python # version and rounding differences, we will just assert against a range assert 1450 <= wsman.max_payload_size <= 1835 @pytest.mark.parametrize('wsman_conn', # we just want to validate against different env # set on a server [[False, 'test_wsman_update_envelope_size_150']], indirect=True) def test_wsman_update_envelope_size_150(self, wsman_conn): wsman_conn.update_max_payload_size() assert wsman_conn.max_envelope_size == 153600 # this next value is dependent on a lot of things such as python # version and rounding differences, we will just assert against a range assert 113574 <= wsman_conn.max_payload_size <= 113952 @pytest.mark.parametrize('wsman_conn', # we just want to validate against different env # set on a server [[False, 'test_wsman_update_envelope_size_500']], indirect=True) def test_wsman_update_envelope_size_500(self, wsman_conn): wsman_conn.update_max_payload_size() assert wsman_conn.max_envelope_size == 512000 # this next value is dependent on a lot of things such as python # version and rounding differences, we will just assert against a range assert 382374 <= wsman_conn.max_payload_size <= 382752 @pytest.mark.parametrize('wsman_conn', # we just want to validate against different env # set on a server [[False, 'test_wsman_update_envelope_size_4096']], indirect=True) def test_wsman_update_envelope_size_4096(self, wsman_conn): wsman_conn.update_max_payload_size() assert wsman_conn.max_envelope_size == 4194304 # this next value is dependent on a lot of things such as python # version and rounding differences, we will just assert against a range assert 3144102 <= wsman_conn.max_payload_size <= 3144480 class TestOptionSet(object): def test_set_no_options(self): option_set = OptionSet() actual = option_set.pack() assert len(actual.attrib.keys()) == 1 assert actual.attrib['{http://www.w3.org/2003/05/soap-envelope}' 'mustUnderstand'] == 'true' assert actual.tag == \ "{http://schemas.dmtf.org/wbem/wsman/1/wsman.xsd}OptionSet" assert actual.text is None assert list(actual) == [] assert str(option_set) == "{}" def test_set_one_option(self): option_set = OptionSet() option_set.add_option("key", "value") actual = option_set.pack() assert len(actual.attrib.keys()) == 1 assert actual.attrib['{http://www.w3.org/2003/05/soap-envelope}' 'mustUnderstand'] == 'true' assert actual.tag == \ "{http://schemas.dmtf.org/wbem/wsman/1/wsman.xsd}OptionSet" assert actual.text is None children = list(actual) assert len(children) == 1 assert len(children[0].attrib.keys()) == 1 assert children[0].attrib['Name'] == "key" assert children[0].tag == \ "{http://schemas.dmtf.org/wbem/wsman/1/wsman.xsd}Option" assert children[0].text == "value" assert str(option_set) == "{'key': 'value'}" def test_set_one_option_with_attributes(self): option_set = OptionSet() option_set.add_option("key", "value", {"attrib1": "value1", "attrib2": "value2"}) actual = option_set.pack() assert len(actual.attrib.keys()) == 1 assert actual.attrib['{http://www.w3.org/2003/05/soap-envelope}' 'mustUnderstand'] == 'true' assert actual.tag == \ "{http://schemas.dmtf.org/wbem/wsman/1/wsman.xsd}OptionSet" assert actual.text is None children = list(actual) assert len(children) == 1 assert len(children[0].attrib.keys()) == 3 assert children[0].attrib['Name'] == "key" assert children[0].attrib['attrib1'] == "value1" assert children[0].attrib['attrib2'] == "value2" assert children[0].tag == \ "{http://schemas.dmtf.org/wbem/wsman/1/wsman.xsd}Option" assert children[0].text == "value" assert str(option_set) == "{'key': 'value'}" def test_set_multiple_options(self): option_set = OptionSet() option_set.add_option("key1", "value1") option_set.add_option("key2", "value2") actual = option_set.pack() assert len(actual.attrib.keys()) == 1 assert actual.attrib['{http://www.w3.org/2003/05/soap-envelope}' 'mustUnderstand'] == 'true' assert actual.tag == \ "{http://schemas.dmtf.org/wbem/wsman/1/wsman.xsd}OptionSet" assert actual.text is None children = list(actual) assert len(children) == 2 assert len(children[0].attrib.keys()) == 1 assert children[0].attrib['Name'] == "key1" assert children[0].tag == \ "{http://schemas.dmtf.org/wbem/wsman/1/wsman.xsd}Option" assert children[0].text == "value1" assert len(children[1].attrib.keys()) == 1 assert children[1].attrib['Name'] == "key2" assert children[1].tag == \ "{http://schemas.dmtf.org/wbem/wsman/1/wsman.xsd}Option" assert children[1].text == "value2" assert str(option_set) == "{'key1': 'value1', 'key2': 'value2'}" class TestSelectorSet(object): def test_set_no_options(self): selector_set = SelectorSet() actual = selector_set.pack() assert len(actual.attrib.keys()) == 0 assert actual.tag == \ "{http://schemas.dmtf.org/wbem/wsman/1/wsman.xsd}SelectorSet" assert actual.text is None assert list(actual) == [] assert str(selector_set) == "{}" def test_set_one_option(self): selector_set = SelectorSet() selector_set.add_option("key", "value") actual = selector_set.pack() assert len(actual.attrib.keys()) == 0 assert actual.tag == \ "{http://schemas.dmtf.org/wbem/wsman/1/wsman.xsd}SelectorSet" assert actual.text is None children = list(actual) assert len(children) == 1 assert len(children[0].attrib.keys()) == 1 assert children[0].attrib['Name'] == "key" assert children[0].tag == \ "{http://schemas.dmtf.org/wbem/wsman/1/wsman.xsd}Selector" assert children[0].text == "value" assert str(selector_set) == "{'key': 'value'}" def test_set_one_option_with_attributes(self): selector_set = SelectorSet() selector_set.add_option("key", "value", {"attrib1": "value1", "attrib2": "value2"}) actual = selector_set.pack() assert len(actual.attrib.keys()) == 0 assert actual.tag == \ "{http://schemas.dmtf.org/wbem/wsman/1/wsman.xsd}SelectorSet" assert actual.text is None children = list(actual) assert len(children) == 1 assert len(children[0].attrib.keys()) == 3 assert children[0].attrib['Name'] == "key" assert children[0].attrib['attrib1'] == "value1" assert children[0].attrib['attrib2'] == "value2" assert children[0].tag == \ "{http://schemas.dmtf.org/wbem/wsman/1/wsman.xsd}Selector" assert children[0].text == "value" assert str(selector_set) == "{'key': 'value'}" def test_set_multiple_options(self): selector_set = SelectorSet() selector_set.add_option("key1", "value1") selector_set.add_option("key2", "value2") actual = selector_set.pack() assert len(actual.attrib.keys()) == 0 assert actual.tag == \ "{http://schemas.dmtf.org/wbem/wsman/1/wsman.xsd}SelectorSet" assert actual.text is None children = list(actual) assert len(children) == 2 assert len(children[0].attrib.keys()) == 1 assert children[0].attrib['Name'] == "key1" assert children[0].tag == \ "{http://schemas.dmtf.org/wbem/wsman/1/wsman.xsd}Selector" assert children[0].text == "value1" assert len(children[1].attrib.keys()) == 1 assert children[1].attrib['Name'] == "key2" assert children[1].tag == \ "{http://schemas.dmtf.org/wbem/wsman/1/wsman.xsd}Selector" assert children[1].text == "value2" assert str(selector_set) == "{'key1': 'value1', 'key2': 'value2'}" class TestTransportHTTP(object): def test_not_supported_auth(self): with pytest.raises(ValueError) as err: _TransportHTTP("", "", auth="fake") assert str(err.value) == \ "The specified auth 'fake' is not supported, please select one " \ "of 'basic, certificate, credssp, kerberos, negotiate, ntlm'" def test_invalid_encryption_value(self): with pytest.raises(ValueError) as err: _TransportHTTP("", "", encryption="fake") assert str(err.value) == \ "The encryption value 'fake' must be auto, always, or never" def test_encryption_always_not_valid_auth_ssl(self): with pytest.raises(ValueError) as err: _TransportHTTP("", "", auth="basic", encryption="always", ssl=True) assert str(err.value) == \ "Cannot use message encryption with auth 'basic', either set " \ "encryption='auto' or use one of the following auth providers: " \ "credssp, kerberos, negotiate, ntlm" def test_encryption_auto_not_valid_auth_no_ssl(self): with pytest.raises(ValueError) as err: _TransportHTTP("", "", auth="basic", encryption="auto", ssl=False) assert str(err.value) == \ "Cannot use message encryption with auth 'basic', either set " \ "encryption='never', use ssl=True or use one of the following " \ "auth providers: credssp, kerberos, negotiate, ntlm" def test_build_basic_no_username(self): transport = _TransportHTTP("") with pytest.raises(ValueError) as err: transport._build_auth_basic(None) assert str(err.value) == \ "For basic auth, the username must be specified" def test_build_basic_no_password(self): transport = _TransportHTTP("", username="user") with pytest.raises(ValueError) as err: transport._build_auth_basic(None) assert str(err.value) == \ "For basic auth, the password must be specified" def test_build_basic(self): transport = _TransportHTTP("", username="user", password="pass", auth="basic") session = transport._build_session() assert transport.encryption is None assert isinstance(session.auth, requests.auth.HTTPBasicAuth) assert session.auth.username == "user" assert session.auth.password == "pass" def test_build_certificate_no_key_pem(self): transport = _TransportHTTP("") with pytest.raises(ValueError) as err: transport._build_auth_certificate(None) assert str(err.value) == \ "For certificate auth, the path to the certificate key pem file " \ "must be specified with certificate_key_pem" def test_build_certificate_no_pem(self): transport = _TransportHTTP("", certificate_key_pem="path") with pytest.raises(ValueError) as err: transport._build_auth_certificate(None) assert str(err.value) == \ "For certificate auth, the path to the certificate pem file " \ "must be specified with certificate_pem" def test_build_certificate_not_ssl(self): transport = _TransportHTTP("", certificate_key_pem="path", certificate_pem="path", ssl=False) with pytest.raises(ValueError) as err: transport._build_auth_certificate(None) assert str(err.value) == "For certificate auth, SSL must be used" def test_build_certificate(self): transport = _TransportHTTP("", auth="certificate", certificate_key_pem="key_pem", certificate_pem="pem") session = transport._build_session() assert transport.encryption is None assert session.auth is None assert session.cert == ("pem", "key_pem") assert session.headers['Authorization'] == \ "http://schemas.dmtf.org/wbem/wsman/1/wsman/secprofile/" \ "https/mutual" @pytest.mark.skipif( requests_credssp, reason="only raises if requests-credssp is not installed", ) def test_build_credssp_not_imported(self): transport = _TransportHTTP("", username="user", password="password") with pytest.raises( ImportError, match=( r"Cannot use CredSSP auth as requests-credssp is not " r"installed: No module named '?requests_credssp'?" ), ): transport._build_auth_credssp(None) def test_build_credssp_no_username(self): transport = _TransportHTTP("") with pytest.raises(ValueError) as err: transport._build_auth_credssp(None) assert str(err.value) == \ "For credssp auth, the username must be specified" def test_build_credssp_no_password(self): transport = _TransportHTTP("", username="user") with pytest.raises(ValueError) as err: transport._build_auth_credssp(None) assert str(err.value) == \ "For credssp auth, the password must be specified" def test_build_credssp_no_kwargs(self): credssp = pytest.importorskip("requests_credssp") transport = _TransportHTTP("", username="user", password="pass", auth="credssp") session = transport._build_session() assert isinstance(session.auth, credssp.HttpCredSSPAuth) assert session.auth.disable_tlsv1_2 is False assert session.auth.minimum_version == 2 assert session.auth.password == 'pass' assert session.auth.username == 'user' def test_build_credssp_with_kwargs(self): credssp = pytest.importorskip("requests_credssp") transport = _TransportHTTP("", username="user", password="pass", auth="credssp", credssp_auth_mechanism="kerberos", credssp_disable_tlsv1_2=True, credssp_minimum_version=5) session = transport._build_session() assert isinstance(session.auth, credssp.HttpCredSSPAuth) assert session.auth.disable_tlsv1_2 is True assert session.auth.minimum_version == 5 assert session.auth.password == 'pass' assert session.auth.username == 'user' def test_build_kerberos(self): transport = _TransportHTTP("", auth="kerberos") session = transport._build_session() assert isinstance(session.auth, HTTPNegotiateAuth) assert session.auth.auth_provider == "kerberos" assert session.auth.delegate is False assert session.auth.hostname_override is None assert session.auth.password is None assert session.auth.send_cbt is True assert session.auth.service == 'WSMAN' assert session.auth.username is None assert session.auth.wrap_required is False def test_build_kerberos_with_kwargs(self): transport = _TransportHTTP("", auth="kerberos", username="user", ssl=False, password="pass", negotiate_delegate=True, negotiate_hostname_override="host", negotiate_send_cbt=False, negotiate_service="HTTP") session = transport._build_session() assert isinstance(session.auth, HTTPNegotiateAuth) assert session.auth.auth_provider == "kerberos" assert session.auth.delegate is True assert session.auth.hostname_override == "host" assert session.auth.password == "pass" assert session.auth.send_cbt is False assert session.auth.service == 'HTTP' assert session.auth.username == "user" assert session.auth.wrap_required is True def test_build_negotiate(self): transport = _TransportHTTP("") session = transport._build_session() assert isinstance(session.auth, HTTPNegotiateAuth) assert session.auth.auth_provider == "negotiate" assert session.auth.delegate is False assert session.auth.hostname_override is None assert session.auth.password is None assert session.auth.send_cbt is True assert session.auth.service == 'WSMAN' assert session.auth.username is None assert session.auth.wrap_required is False def test_build_negotiate_with_kwargs(self): transport = _TransportHTTP("", auth="negotiate", username="user", ssl=False, password="pass", negotiate_delegate=True, negotiate_hostname_override="host", negotiate_send_cbt=False, negotiate_service="HTTP") session = transport._build_session() assert isinstance(session.auth, HTTPNegotiateAuth) assert session.auth.auth_provider == "negotiate" assert session.auth.delegate is True assert session.auth.hostname_override == "host" assert session.auth.password == "pass" assert session.auth.send_cbt is False assert session.auth.service == 'HTTP' assert session.auth.username == "user" assert session.auth.wrap_required is True def test_build_ntlm(self): transport = _TransportHTTP("", auth="ntlm") session = transport._build_session() assert isinstance(session.auth, HTTPNegotiateAuth) assert session.auth.auth_provider == "ntlm" assert session.auth.delegate is False assert session.auth.hostname_override is None assert session.auth.password is None assert session.auth.send_cbt is True assert session.auth.service == 'WSMAN' assert session.auth.username is None assert session.auth.wrap_required is False def test_build_ntlm_with_kwargs(self): transport = _TransportHTTP("", auth="ntlm", username="user", ssl=False, password="pass", negotiate_delegate=True, negotiate_hostname_override="host", negotiate_send_cbt=False, negotiate_service="HTTP", cert_validation=False) session = transport._build_session() assert isinstance(session.auth, HTTPNegotiateAuth) assert session.auth.auth_provider == "ntlm" assert session.auth.delegate is True assert session.auth.hostname_override == "host" assert session.auth.password == "pass" assert session.auth.send_cbt is False assert session.auth.service == 'HTTP' assert session.auth.username == "user" assert session.auth.wrap_required is True def test_build_session_default(self): transport = _TransportHTTP("") session = transport._build_session() assert session.headers['User-Agent'] == "Python PSRP Client" assert session.trust_env is True assert isinstance(session.auth, HTTPNegotiateAuth) assert 'http' not in session.proxies assert 'https' not in session.proxies assert session.verify is True def test_build_session_cert_validate(self): transport = _TransportHTTP("", cert_validation=True) session = transport._build_session() assert session.verify is True def test_build_session_cert_validate_env(self): transport = _TransportHTTP("", cert_validation=True) os.environ['REQUESTS_CA_BUNDLE'] = 'path_to_REQUESTS_CA_CERT' try: session = transport._build_session() finally: del os.environ['REQUESTS_CA_BUNDLE'] assert session.verify == 'path_to_REQUESTS_CA_CERT' def test_build_session_cert_validate_path_override_env(self): transport = _TransportHTTP("", cert_validation="kwarg_path") os.environ['REQUESTS_CA_BUNDLE'] = 'path_to_REQUESTS_CA_CERT' try: session = transport._build_session() finally: del os.environ['REQUESTS_CA_BUNDLE'] assert session.verify == 'kwarg_path' def test_build_session_cert_no_validate(self): transport = _TransportHTTP("", cert_validation=False) session = transport._build_session() assert session.verify is False def test_build_session_cert_no_validate_override_env(self): transport = _TransportHTTP("", cert_validation=False) os.environ['REQUESTS_CA_BUNDLE'] = 'path_to_REQUESTS_CA_CERT' try: session = transport._build_session() finally: del os.environ['REQUESTS_CA_BUNDLE'] assert session.verify is False def test_build_session_proxies_default(self): transport = _TransportHTTP("server") session = transport._build_session() assert 'http' not in session.proxies assert 'https' not in session.proxies def test_build_session_proxies_env(self): transport = _TransportHTTP("server") os.environ['https_proxy'] = "https://envproxy" try: session = transport._build_session() finally: del os.environ['https_proxy'] assert 'http' not in session.proxies assert session.proxies["https"] == "https://envproxy" def test_build_session_proxies_kwarg(self): transport = _TransportHTTP("server", proxy="https://kwargproxy") session = transport._build_session() assert 'http' not in session.proxies assert session.proxies["https"] == "https://kwargproxy" def test_build_session_proxies_kwarg_non_ssl(self): transport = _TransportHTTP("server", proxy="http://kwargproxy", ssl=False) session = transport._build_session() assert session.proxies["http"] == "http://kwargproxy" assert 'https' not in session.proxies def test_build_session_proxies_env_kwarg_override(self): transport = _TransportHTTP("server", proxy="https://kwargproxy") os.environ['https_proxy'] = "https://envproxy" try: session = transport._build_session() finally: del os.environ['https_proxy'] assert 'http' not in session.proxies assert session.proxies['https'] == "https://kwargproxy" def test_build_session_proxies_env_no_proxy_override(self): transport = _TransportHTTP("server", no_proxy=True) os.environ['https_proxy'] = "https://envproxy" try: session = transport._build_session() finally: del os.environ['https_proxy'] assert 'http' not in session.proxies assert 'https' not in session.proxies def test_build_session_proxies_kwarg_ignore_no_proxy(self): transport = _TransportHTTP("server", proxy="https://kwargproxy", no_proxy=True) session = transport._build_session() assert 'http' not in session.proxies assert session.proxies['https'] == "https://kwargproxy" def test_send_without_encryption(self, monkeypatch): send_mock = MagicMock() monkeypatch.setattr(_TransportHTTP, "_send_request", send_mock) transport = _TransportHTTP("server") transport.send(b"message") assert send_mock.call_count == 1 actual_request = send_mock.call_args[0][0] assert actual_request.body == b"message" assert actual_request.url == "https://server:5986/wsman" assert actual_request.headers['content-type'] == "application/soap+xml;charset=UTF-8" def test_send_with_encryption(self, monkeypatch): send_mock = MagicMock() def send_request(self, *args, **kwargs): self.session.auth.contexts['server'] = MagicMock() return send_mock(*args, **kwargs) wrap_mock = MagicMock() wrap_mock.return_value = "multipart/encrypted", b"wrapped" monkeypatch.setattr(_TransportHTTP, "_send_request", send_request) monkeypatch.setattr(WinRMEncryption, "wrap_message", wrap_mock) transport = _TransportHTTP("server", ssl=False) transport.send(b"message") transport.send(b"message 2") assert send_mock.call_count == 3 actual_request1 = send_mock.call_args_list[0][0][0] actual_request2 = send_mock.call_args_list[1][0][0] actual_request3 = send_mock.call_args_list[2][0][0] assert actual_request1.body is None assert actual_request1.url == "http://server:5985/wsman" assert actual_request2.body == b"wrapped" assert actual_request2.headers['content-type'] == \ 'multipart/encrypted;protocol="application/' \ 'HTTP-SPNEGO-session-encrypted";boundary="Encrypted Boundary"' assert actual_request2.url == "http://server:5985/wsman" assert actual_request3.body == b"wrapped" assert actual_request3.headers['content-type'] == \ 'multipart/encrypted;protocol="application/' \ 'HTTP-SPNEGO-session-encrypted";boundary="Encrypted Boundary"' assert actual_request3.url == "http://server:5985/wsman" assert wrap_mock.call_count == 2 assert wrap_mock.call_args_list[0][0][0] == b"message" assert wrap_mock.call_args_list[1][0][0] == b"message 2" def test_send_default(self, monkeypatch): response = requests.Response() response.status_code = 200 response._content = b"content" response.headers['content-type'] = "application/soap+xml;charset=UTF-8" send_mock = MagicMock() send_mock.return_value = response monkeypatch.setattr(requests.Session, "send", send_mock) transport = _TransportHTTP("server", ssl=True) session = transport._build_session() transport.session = session request = requests.Request('POST', transport.endpoint, data=b"data") prep_request = session.prepare_request(request) actual = transport._send_request(prep_request) assert actual == b"content" assert send_mock.call_count == 1 assert send_mock.call_args[0] == (prep_request,) assert send_mock.call_args[1]['timeout'] == (30, 30) def test_send_timeout_kwargs(self, monkeypatch): response = requests.Response() response.status_code = 200 response._content = b"content" response.headers['content-type'] = "application/soap+xml;charset=UTF-8" send_mock = MagicMock() send_mock.return_value = response monkeypatch.setattr(requests.Session, "send", send_mock) transport = _TransportHTTP("server", ssl=True, connection_timeout=20, read_timeout=25) session = transport._build_session() transport.session = session request = requests.Request('POST', transport.endpoint, data=b"data") prep_request = session.prepare_request(request) actual = transport._send_request(prep_request) assert actual == b"content" assert send_mock.call_count == 1 assert send_mock.call_args[0] == (prep_request,) assert send_mock.call_args[1]['timeout'] == (20, 25) def test_send_auth_error(self, monkeypatch): response = requests.Response() response.status_code = 401 send_mock = MagicMock() send_mock.return_value = response monkeypatch.setattr(requests.Session, "send", send_mock) transport = _TransportHTTP("server", ssl=True) session = transport._build_session() transport.session = session request = requests.Request('POST', transport.endpoint, data=b"data") prep_request = session.prepare_request(request) with pytest.raises(AuthenticationError) as err: transport._send_request(prep_request) assert str(err.value) == "Failed to authenticate the user None with " \ "negotiate" def test_send_winrm_error_blank(self, monkeypatch): response = requests.Response() response.status_code = 500 response._content = b"" send_mock = MagicMock() send_mock.return_value = response monkeypatch.setattr(requests.Session, "send", send_mock) transport = _TransportHTTP("server", ssl=True) session = transport._build_session() transport.session = session request = requests.Request('POST', transport.endpoint, data=b"data") prep_request = session.prepare_request(request) with pytest.raises(WinRMTransportError) as err: transport._send_request(prep_request) assert str(err.value) == "Bad HTTP response returned from the " \ "server. Code: 500, Content: ''" assert err.value.code == 500 assert err.value.protocol == 'http' assert err.value.response_text == '' def test_send_winrm_error_content(self, monkeypatch): response = requests.Response() response.status_code = 500 response._content = b"error msg" send_mock = MagicMock() send_mock.return_value = response monkeypatch.setattr(requests.Session, "send", send_mock) transport = _TransportHTTP("server", ssl=True) session = transport._build_session() transport.session = session request = requests.Request('POST', transport.endpoint, data=b"data") prep_request = session.prepare_request(request) with pytest.raises(WinRMTransportError) as err: transport._send_request(prep_request) assert str(err.value) == "Bad HTTP response returned from the " \ "server. Code: 500, Content: 'error msg'" assert err.value.code == 500 assert err.value.protocol == 'http' assert err.value.response_text == 'error msg' def test_send_winrm_encrypted_single(self, monkeypatch): response = requests.Response() response.status_code = 200 response._content = b"content" response.headers['content-type'] = \ 'multipart/encrypted;protocol="application/HTTP-SPNEGO-session-' \ 'encrypted";boundary="Encrypted Boundary"' send_mock = MagicMock() send_mock.return_value = response unwrap_mock = MagicMock() unwrap_mock.return_value = b"unwrapped content" monkeypatch.setattr(requests.Session, "send", send_mock) monkeypatch.setattr(WinRMEncryption, "unwrap_message", unwrap_mock) transport = _TransportHTTP("server", ssl=False) transport.encryption = WinRMEncryption(None, None) session = transport._build_session() transport.session = session request = requests.Request('POST', transport.endpoint, data=b"data") prep_request = session.prepare_request(request) actual = transport._send_request(prep_request) assert actual == b"unwrapped content" assert send_mock.call_count == 1 assert send_mock.call_args[0] == (prep_request,) assert send_mock.call_args[1]['timeout'] == (30, 30) assert unwrap_mock.call_count == 1 assert unwrap_mock.call_args[0] == (b"content", "Encrypted Boundary") assert unwrap_mock.call_args[1] == {} def test_send_winrm_encrypted_multiple(self, monkeypatch): response = requests.Response() response.status_code = 200 response._content = b"content" response.headers['content-type'] = \ 'multipart/x-multi-encrypted;protocol="application/HTTP-CredSSP-' \ 'session-encrypted";boundary="Encrypted Boundary"' send_mock = MagicMock() send_mock.return_value = response unwrap_mock = MagicMock() unwrap_mock.return_value = b"unwrapped content" monkeypatch.setattr(requests.Session, "send", send_mock) monkeypatch.setattr(WinRMEncryption, "unwrap_message", unwrap_mock) transport = _TransportHTTP("server", ssl=False) transport.encryption = WinRMEncryption(None, None) session = transport._build_session() transport.session = session request = requests.Request('POST', transport.endpoint, data=b"data") prep_request = session.prepare_request(request) actual = transport._send_request(prep_request) assert actual == b"unwrapped content" assert send_mock.call_count == 1 assert send_mock.call_args[0] == (prep_request,) assert send_mock.call_args[1]['timeout'] == (30, 30) assert unwrap_mock.call_count == 1 assert unwrap_mock.call_args[0] == (b"content", "Encrypted Boundary") assert unwrap_mock.call_args[1] == {} @pytest.mark.parametrize('ssl, server, port, path, expected', [ [True, 'server', 5986, 'wsman', 'https://server:5986/wsman'], [False, 'server', 5985, 'wsman', 'http://server:5985/wsman'], [False, 'server', 5985, 'iis-wsman', 'http://server:5985/iis-wsman'], [True, '127.0.0.1', 443, 'wsman', 'https://127.0.0.1:443/wsman'], [False, '2001:0db8:0a0b:12f0:0000:0000:0000:0001', 80, 'path', 'http://[2001:db8:a0b:12f0::1]:80/path'], [False, '2001:db8:a0b:12f0::1', 80, 'path', 'http://[2001:db8:a0b:12f0::1]:80/path'], [False, '2001:0db8:0a0b:12f0:0001:0001:0001:0001', 5985, 'wsman', 'http://[2001:db8:a0b:12f0:1:1:1:1]:5985/wsman'], [False, 'FE80::0202:B3FF:FE1E:8329', 5985, 'wsman', 'http://[fe80::202:b3ff:fe1e:8329]:5985/wsman'], [True, '[2001:0db8:0a0b:12f0:0000:0000:0000:0001]', 5986, 'wsman', 'https://[2001:0db8:0a0b:12f0:0000:0000:0000:0001]:5986/wsman'], ]) def test_endpoint_forms(self, ssl, server, port, path, expected): actual = _TransportHTTP._create_endpoint(ssl, server, port, path) assert actual == expected

46.472699

929

0.623838

6,969

59,578

5.194863

0.073755

0.016048

0.027705

0.013673

0.845869

0.809077

0.768693

0.739939

0.71983

0.708809

0

0.040773

0.255715

59,578

1,281

930

46.508977

0.775658

0.02088

0

0.642923

0

0.023127

0.302348

0.041213

0

0.303423

1

0.092507

false

0.018501

0.019426

0.012951

0.13136

0

null

0

1

0

null

0

6

4f29288604459253d7cb9e3d68a91d869abee8de

201

py

Python

src/c3tools/lib/__init__.py

conao3/python-c3tools

2ad0259d2036ef37a67ae6774efb47e3ab0fface

[ "Apache-2.0" ]

null

src/c3tools/lib/__init__.py

conao3/python-c3tools

2ad0259d2036ef37a67ae6774efb47e3ab0fface

[ "Apache-2.0" ]

null

src/c3tools/lib/__init__.py

conao3/python-c3tools

2ad0259d2036ef37a67ae6774efb47e3ab0fface

[ "Apache-2.0" ]

null

from . import openapi # noqa from . import pydantic # noqa from . import random # noqa from . import stdin # noqa from . import string # noqa from . import subr # noqa from . import yaml # noqa

25.125

30

0.686567

28

201

4.928571

0.357143

0.507246

0.608696

0

0.243781

201

7

31

28.714286

0.907895

0.169154

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

4f58a870209c7214a98dfab5efb00fcaf42d5382

332

py

Python

clpy/statistics/__init__.py

fixstars/clpy

693485f85397cc110fa45803c36c30c24c297df0

[ "BSD-3-Clause" ]

142

2018-06-07T07:43:10.000Z

2021-10-30T21:06:32.000Z

clpy/statistics/__init__.py

fixstars/clpy

693485f85397cc110fa45803c36c30c24c297df0

[ "BSD-3-Clause" ]

282

2018-06-07T08:35:03.000Z

2021-03-31T03:14:32.000Z

clpy/statistics/__init__.py

fixstars/clpy

693485f85397cc110fa45803c36c30c24c297df0

[ "BSD-3-Clause" ]

19

2018-06-19T11:07:53.000Z

2021-05-13T20:57:04.000Z

# Functions from the following NumPy document # http://docs.scipy.org/doc/numpy/reference/routines.statistics.html # "NOQA" to suppress flake8 warning from clpy.statistics import correlation # NOQA from clpy.statistics import histogram # NOQA from clpy.statistics import meanvar # NOQA from clpy.statistics import order # NOQA

36.888889

68

0.795181

45

332

5.866667

0.577778

0.121212

0.272727

0.363636

0.318182

0

0.003472

0.13253

332

8

69

41.5

0.913194

0.493976

0

1

0

true

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

4f69378eb6da1f4b15ae8c5cef08139c57633129

33

py

Python

deepstack/intelligencelayer/shared/scene/__init__.py

mayop/DeepStack

8b05c0a69dce65513638def0a8a21c87fd8409f1

[ "Apache-2.0" ]

1

2021-01-03T05:47:42.000Z

deepstack/intelligencelayer/shared/scene/__init__.py

robmarkcole/DeepStack

8b05c0a69dce65513638def0a8a21c87fd8409f1

[ "Apache-2.0" ]

null

deepstack/intelligencelayer/shared/scene/__init__.py

robmarkcole/DeepStack

8b05c0a69dce65513638def0a8a21c87fd8409f1

[ "Apache-2.0" ]

null

from .process import SceneModel

16.5

32

0.818182

4

33

6.75

1

0

0.151515

33

1

33

0.964286

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

4fac9249a4507ce0b7812ed76dd2b4ea6b84810e

127

py

Python

model/__init__.py

Eren-Corn0712/CV_DL-Contrastive-Learning

c59ba5e2ae31c14ef4e175c79e3575e2cc7c439c

[ "MIT" ]

null

model/__init__.py

Eren-Corn0712/CV_DL-Contrastive-Learning

c59ba5e2ae31c14ef4e175c79e3575e2cc7c439c

[ "MIT" ]

null

model/__init__.py

Eren-Corn0712/CV_DL-Contrastive-Learning

c59ba5e2ae31c14ef4e175c79e3575e2cc7c439c

[ "MIT" ]

null

from .clrnet import CLRBackbone, CLRLinearClassifier, CLRClassifier # TODO: If you want to use more model, can add on this

31.75

68

0.771654

18

127

5.444444

1

0

0.181102

127

3

69

42.333333

0.942308

0.409449

0

0.333333

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

1

0

6

96d69a530e823000fd1676f01d9052bc2fbcbbf2

13

py

Python

models/__init__.py

gezekun/End-to-end-Lane-Detection-with-Convolution-and-Transformer

db243ad33a80069f4223598945d8c45c0af3c335

[ "MIT" ]

null

models/__init__.py

gezekun/End-to-end-Lane-Detection-with-Convolution-and-Transformer

db243ad33a80069f4223598945d8c45c0af3c335

[ "MIT" ]

null

models/__init__.py

gezekun/End-to-end-Lane-Detection-with-Convolution-and-Transformer

db243ad33a80069f4223598945d8c45c0af3c335

[ "MIT" ]

null

# 2022 03 01

6.5

12

0.615385

3

13

2.666667

1

0

0.888889

0.307692

13

1

13

0

0.769231

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

1

0

null

0

1

0

6

96e9b2c4ce5fb14474fd6f0b1a0934bcfb5b92cf

919

py

Python

tests/threatbutt_test.py

ivanlei/threatbutt

faff507a4bebfa585d3044427111418c257c34ec

[ "Apache-2.0" ]

55

2015-04-25T07:22:18.000Z

2021-05-23T15:04:52.000Z

tests/threatbutt_test.py

ivanlei/threatbutt

faff507a4bebfa585d3044427111418c257c34ec

[ "Apache-2.0" ]

null

tests/threatbutt_test.py

ivanlei/threatbutt

faff507a4bebfa585d3044427111418c257c34ec

[ "Apache-2.0" ]

8

2015-04-27T03:51:49.000Z

2021-04-28T22:17:18.000Z

# -*- coding: utf-8 -*- from io import StringIO from mock import patch from threatbutt import ThreatButt def test_ioc(): with patch('sys.stdout', new=StringIO()) as fake_out: tb = ThreatButt() tb.clown_strike_ioc('127.0.0.1') assert len(fake_out.getvalue()) def test_md5(): with patch('sys.stdout', new=StringIO()) as fake_out: tb = ThreatButt() tb.bespoke_md5('d41d8cd98f00b204e9800998ecf8427e') assert len(fake_out.getvalue()) def test_ioc_maltego(): with patch('sys.stdout', new=StringIO()) as fake_out: tb = ThreatButt(maltegofy=True) tb.clown_strike_ioc('127.0.0.1') assert len(fake_out.getvalue()) def test_md5_maltego(): with patch('sys.stdout', new=StringIO()) as fake_out: tb = ThreatButt(maltegofy=True) tb.bespoke_md5('d41d8cd98f00b204e9800998ecf8427e') assert len(fake_out.getvalue())

26.257143

58

0.660501

121

919

4.85124

0.289256

0.0954

0.081772

0.122658

0.843271

0.831346

0.606474

0

0.081044

0.207835

919

34

59

27.029412

0.725275

0.022851

0

0.695652

0

0.136161

0.071429

0

0.173913

1

0.173913

false

0

0.130435

0

0.304348

0

null

0

1

0

null

0

6

96f42af5481a62e2bde02c9e2613ca7b569a6324

34

py

Python

tests/unittests/durable_functions/activity_trigger_no_anno/main.py

vrdmr/azure-functions-python-worker

7c9bcc4cc647f4b80a606a1e039d7cf9f3db9624

[ "MIT" ]

null

tests/unittests/durable_functions/activity_trigger_no_anno/main.py

vrdmr/azure-functions-python-worker

7c9bcc4cc647f4b80a606a1e039d7cf9f3db9624

[ "MIT" ]

null

tests/unittests/durable_functions/activity_trigger_no_anno/main.py

vrdmr/azure-functions-python-worker

7c9bcc4cc647f4b80a606a1e039d7cf9f3db9624

[ "MIT" ]

null

def main(input): return input

11.333333

16

0.676471

5

34

4.6

0.8

0

0.235294

34

2

17

0.884615

0

1

0.5

false

0

0.5

1

0

1

0

null

0

1

0

null

0

1

0

1

0

6

96f4e56b165e717caa6240e5ba13778f3a0000b3

66

py

Python

sample/sample.py

eaybek/utilio

5299558adce907ac40ef46da3e2ce8d4b5e16324

[ "MIT" ]

null

sample/sample.py

eaybek/utilio

5299558adce907ac40ef46da3e2ce8d4b5e16324

[ "MIT" ]

null

sample/sample.py

eaybek/utilio

5299558adce907ac40ef46da3e2ce8d4b5e16324

[ "MIT" ]

null

from utilio.utilio import Utilio class Utilio(object): pass

11

32

0.742424

9

66

5.444444

0.666667

0

0.19697

66

5

33

13.2

0.924528

0

1

0

true

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

6

8c6246e872f4ef25f0b1c211a1f937d90135e2ea

25

py

Python

fun.py

omribrand/MyNewRepo

4315095715451944ec5d8ea02a6b5136a93fc5e7

[ "MIT" ]

null

fun.py

omribrand/MyNewRepo

4315095715451944ec5d8ea02a6b5136a93fc5e7

[ "MIT" ]

null

fun.py

omribrand/MyNewRepo

4315095715451944ec5d8ea02a6b5136a93fc5e7

[ "MIT" ]

null

def bka(a): return 2*a

8.333333

12

0.6

6

25

2.5

0.833333

0

0.052632

0.24

25

2

13

12.5

0.736842

0

1

0.5

false

0

0.5

1

0

1

0

null

0

1

0

null

0

1

0

1

0

6

4fb2bfc1cd80130fe83c4c692e0d3f8c0c33c061

28

py

Python

tcopy/__init__.py

Bogdanp/tcopy

bfb072d47317a22e133e7ca8e400cafcf304f85e

[ "MIT" ]

4

2015-04-06T14:48:25.000Z

2020-08-15T00:09:23.000Z

tcopy/__init__.py

Bogdanp/tcopy

bfb072d47317a22e133e7ca8e400cafcf304f85e

[ "MIT" ]

null

tcopy/__init__.py

Bogdanp/tcopy

bfb072d47317a22e133e7ca8e400cafcf304f85e

[ "MIT" ]

null

from tco import tco # noqa

14

27

0.714286

5

28

4

0.8

0

0.25

28

1

28

0.952381

0.142857

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

8b0189a40e9f73ddfc01fa6402b45f76ffdefef4

4,056

py

Python

Source/WebCore/Modules/webgpu/WHLSL/WHLSLBuildStandardLibraryFunctionMap.py

jacadcaps/webkitty

9aebd2081349f9a7b5d168673c6f676a1450a66d

[ "BSD-2-Clause" ]

6

2021-07-05T16:09:39.000Z

2022-03-06T22:44:42.000Z

Source/WebCore/Modules/webgpu/WHLSL/WHLSLBuildStandardLibraryFunctionMap.py

jacadcaps/webkitty

9aebd2081349f9a7b5d168673c6f676a1450a66d

[ "BSD-2-Clause" ]

7

2022-03-15T13:25:39.000Z

2022-03-15T13:25:44.000Z

Source/WebCore/Modules/webgpu/WHLSL/WHLSLBuildStandardLibraryFunctionMap.py

jacadcaps/webkitty

9aebd2081349f9a7b5d168673c6f676a1450a66d

[ "BSD-2-Clause" ]

null

#!/usr/bin/env python # # Copyright (C) 2014 Apple Inc. All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions # are met: # 1. Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # 2. Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # # THIS SOFTWARE IS PROVIDED BY APPLE INC. AND ITS CONTRIBUTORS ``AS IS'' # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, # THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR # PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL APPLE INC. OR ITS CONTRIBUTORS # BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF # THE POSSIBILITY OF SUCH DAMAGE. import sys import re regularExpression = re.compile("/\* Functions named (.*) \*/") infile = open(sys.argv[1], "r") contents = infile.read() infile.close() outfile = open(sys.argv[2], "w") outfile.write(""" /* * Copyright (C) 2019 Apple Inc. All rights reserved. * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions * are met: * 1. Redistributions of source code must retain the above copyright * notice, this list of conditions and the following disclaimer. * 2. Redistributions in binary form must reproduce the above copyright * notice, this list of conditions and the following disclaimer in the * documentation and/or other materials provided with the distribution. * * THIS SOFTWARE IS PROVIDED BY APPLE INC. AND ITS CONTRIBUTORS ``AS IS'' * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, * THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL APPLE INC. OR ITS CONTRIBUTORS * BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR * CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF * SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS * INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN * CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF * THE POSSIBILITY OF SUCH DAMAGE. */ #include "config.h" #include "WHLSLStandardLibraryFunctionMap.h" #if ENABLE(WEBGPU) namespace WebCore { namespace WHLSL { HashMap<String, SubstringLocation> computeStandardLibraryFunctionMap() { HashMap<String, SubstringLocation> result; """) # FIXME: Compute StandardLibraryFunctionMap at build-time https://bugs.webkit.org/show_bug.cgi?id=199448 previous = 0 previousName = "" boundary = 0 for match in regularExpression.finditer(contents): if previous == 0: previous = match.start() boundary = previous previousName = match.group(1) continue outfile.write(" result.add(\"" + previousName + "\"_str, SubstringLocation { " + str(previous) + ", " + str(match.start()) + " });\n") previous = match.start() previousName = match.group(1) outfile.write(" result.add(\"" + previousName + "\"_str, SubstringLocation { " + str(previous) + ", " + str(len(contents)) + " });\n") outfile.write(""" return result; } unsigned firstFunctionOffsetInStandardLibrary() { """) outfile.write(" return " + str(boundary) + ";\n") outfile.write(""" } } } #endif """) outfile.close()

36.540541

141

0.735207

530

4,056

5.620755

0.332075

0.016113

0.022826

0.030883

0.731118

0

0.007454

0.173077

4,056

110

142

36.872727

0.880739

0.3464

0

0.147059

0

0.713252

0.041127

0

0.009091

0

1

0

false

0

0.029412

0

0.044118

0

null

0

1

0

1

0

null

0

6

8ca57d60888fa3136e119e2392629b7982bdb971

33

py

Python

highcharts/highcharts/__init__.py

Jbrunn/python-highcharts

a4c488ae5c2e125616efad5a722f3dfd8a9bc450

[ "MIT" ]

370

2015-10-07T20:13:10.000Z

2022-03-31T03:43:17.000Z

highcharts/highcharts/__init__.py

Jbrunn/python-highcharts

a4c488ae5c2e125616efad5a722f3dfd8a9bc450

[ "MIT" ]

67

2016-03-14T12:18:44.000Z

2022-02-24T09:24:31.000Z

highcharts/highcharts/__init__.py

Jbrunn/python-highcharts

a4c488ae5c2e125616efad5a722f3dfd8a9bc450

[ "MIT" ]

159

2016-02-25T15:07:52.000Z

2022-03-12T13:04:14.000Z

from .highcharts import Highchart

33

0.878788

4

33

7.25

1

0

0.090909

33

1

33

0.966667

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

8cbb25d411bc5b5ca7f91784d5c0f70ff00ac433

6,324

py

Python

nets/unet.py

mymsimple/table-detect

99a659346449734b905f7be165d30ff667b6cf93

[ "MIT" ]

null

nets/unet.py

mymsimple/table-detect

99a659346449734b905f7be165d30ff667b6cf93

[ "MIT" ]

null

nets/unet.py

mymsimple/table-detect

99a659346449734b905f7be165d30ff667b6cf93

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Thu Sep 9 23:11:51 2020 table line detect @author: chineseocr """ from tensorflow.keras.models import Model from tensorflow.keras.layers import Input, concatenate, Conv2D, MaxPooling2D, BatchNormalization, UpSampling2D from tensorflow.keras.layers import LeakyReLU def unet(input_shape=(512, 512, 3),num_classes=1): inputs = Input(shape=input_shape) # 512 use_bias=False down0a = Conv2D(16, (3, 3), padding='same',use_bias=use_bias)(inputs) down0a = BatchNormalization()(down0a) down0a = LeakyReLU(alpha=0.1)(down0a) down0a = Conv2D(16, (3, 3), padding='same',use_bias=use_bias)(down0a) down0a = BatchNormalization()(down0a) down0a = LeakyReLU(alpha=0.1)(down0a) down0a_pool = MaxPooling2D((2, 2), strides=(2, 2))(down0a) # 256 down0 = Conv2D(32, (3, 3), padding='same',use_bias=use_bias)(down0a_pool) down0 = BatchNormalization()(down0) down0 = LeakyReLU(alpha=0.1)(down0) down0 = Conv2D(32, (3, 3), padding='same',use_bias=use_bias)(down0) down0 = BatchNormalization()(down0) down0 =LeakyReLU(alpha=0.1)(down0) down0_pool = MaxPooling2D((2, 2), strides=(2, 2))(down0) # 128 down1 = Conv2D(64, (3, 3), padding='same',use_bias=use_bias)(down0_pool) down1 = BatchNormalization()(down1) down1 = LeakyReLU(alpha=0.1)(down1) down1 = Conv2D(64, (3, 3), padding='same',use_bias=use_bias)(down1) down1 = BatchNormalization()(down1) down1 = LeakyReLU(alpha=0.1)(down1) down1_pool = MaxPooling2D((2, 2), strides=(2, 2))(down1) # 64 down2 = Conv2D(128, (3, 3), padding='same',use_bias=use_bias)(down1_pool) down2 = BatchNormalization()(down2) down2 = LeakyReLU(alpha=0.1)(down2) down2 = Conv2D(128, (3, 3), padding='same',use_bias=use_bias)(down2) down2 = BatchNormalization()(down2) down2 = LeakyReLU(alpha=0.1)(down2) down2_pool = MaxPooling2D((2, 2), strides=(2, 2))(down2) # 32 down3 = Conv2D(256, (3, 3), padding='same',use_bias=use_bias)(down2_pool) down3 = BatchNormalization()(down3) down3 = LeakyReLU(alpha=0.1)(down3) down3 = Conv2D(256, (3, 3), padding='same',use_bias=use_bias)(down3) down3 = BatchNormalization()(down3) down3 = LeakyReLU(alpha=0.1)(down3) down3_pool = MaxPooling2D((2, 2), strides=(2, 2))(down3) # 16 down4 = Conv2D(512, (3, 3), padding='same',use_bias=use_bias)(down3_pool) down4 = BatchNormalization()(down4) down4 = LeakyReLU(alpha=0.1)(down4) down4 = Conv2D(512, (3, 3), padding='same',use_bias=use_bias)(down4) down4 = BatchNormalization()(down4) down4 = LeakyReLU(alpha=0.1)(down4) down4_pool = MaxPooling2D((2, 2), strides=(2, 2))(down4) # 8 center = Conv2D(1024, (3, 3), padding='same',use_bias=use_bias)(down4_pool) center = BatchNormalization()(center) center = LeakyReLU(alpha=0.1)(center) center = Conv2D(1024, (3, 3), padding='same',use_bias=use_bias)(center) center = BatchNormalization()(center) center = LeakyReLU(alpha=0.1)(center) # center up4 = UpSampling2D((2, 2))(center) up4 = concatenate([down4, up4], axis=3) up4 = Conv2D(512, (3, 3), padding='same',use_bias=use_bias)(up4) up4 = BatchNormalization()(up4) up4 = LeakyReLU(alpha=0.1)(up4) up4 = Conv2D(512, (3, 3), padding='same',use_bias=use_bias)(up4) up4 = BatchNormalization()(up4) up4 = LeakyReLU(alpha=0.1)(up4) up4 = Conv2D(512, (3, 3), padding='same',use_bias=use_bias)(up4) up4 = BatchNormalization()(up4) up4 = LeakyReLU(alpha=0.1)(up4) # 16 up3 = UpSampling2D((2, 2))(up4) up3 = concatenate([down3, up3], axis=3) up3 = Conv2D(256, (3, 3), padding='same',use_bias=use_bias)(up3) up3 = BatchNormalization()(up3) up3 = LeakyReLU(alpha=0.1)(up3) up3 = Conv2D(256, (3, 3), padding='same',use_bias=use_bias)(up3) up3 = BatchNormalization()(up3) up3 = LeakyReLU(alpha=0.1)(up3) up3 = Conv2D(256, (3, 3), padding='same',use_bias=use_bias)(up3) up3 = BatchNormalization()(up3) up3 = LeakyReLU(alpha=0.1)(up3) # 32 up2 = UpSampling2D((2, 2))(up3) up2 = concatenate([down2, up2], axis=3) up2 = Conv2D(128, (3, 3), padding='same',use_bias=use_bias)(up2) up2 = BatchNormalization()(up2) up2 = LeakyReLU(alpha=0.1)(up2) up2 = Conv2D(128, (3, 3), padding='same',use_bias=use_bias)(up2) up2 = BatchNormalization()(up2) up2 = LeakyReLU(alpha=0.1)(up2) up2 = Conv2D(128, (3, 3), padding='same',use_bias=use_bias)(up2) up2 = BatchNormalization()(up2) up2 = LeakyReLU(alpha=0.1)(up2) # 64 up1 = UpSampling2D((2, 2))(up2) up1 = concatenate([down1, up1], axis=3) up1 = Conv2D(64, (3, 3), padding='same',use_bias=use_bias)(up1) up1 = BatchNormalization()(up1) up1 = LeakyReLU(alpha=0.1)(up1) up1 = Conv2D(64, (3, 3), padding='same',use_bias=use_bias)(up1) up1 = BatchNormalization()(up1) up1 = LeakyReLU(alpha=0.1)(up1) up1 = Conv2D(64, (3, 3), padding='same',use_bias=use_bias)(up1) up1 = BatchNormalization()(up1) up1 = LeakyReLU(alpha=0.1)(up1) # 128 up0 = UpSampling2D((2, 2))(up1) up0 = concatenate([down0, up0], axis=3) up0 = Conv2D(32, (3, 3), padding='same',use_bias=use_bias)(up0) up0 = BatchNormalization()(up0) up0 = LeakyReLU(alpha=0.1)(up0) up0 = Conv2D(32, (3, 3), padding='same',use_bias=use_bias)(up0) up0 = BatchNormalization()(up0) up0 = LeakyReLU(alpha=0.1)(up0) up0 = Conv2D(32, (3, 3), padding='same',use_bias=use_bias)(up0) up0 = BatchNormalization()(up0) up0 = LeakyReLU(alpha=0.1)(up0) # 256 up0a = UpSampling2D((2, 2))(up0) up0a = concatenate([down0a, up0a], axis=3) up0a = Conv2D(16, (3, 3), padding='same',use_bias=use_bias)(up0a) up0a = BatchNormalization()(up0a) up0a = LeakyReLU(alpha=0.1)(up0a) up0a = Conv2D(16, (3, 3), padding='same',use_bias=use_bias)(up0a) up0a = BatchNormalization()(up0a) up0a = LeakyReLU(alpha=0.1)(up0a) up0a = Conv2D(16, (3, 3), padding='same',use_bias=use_bias)(up0a) up0a = BatchNormalization()(up0a) up0a = LeakyReLU(alpha=0.1)(up0a) # 512 classify = Conv2D(num_classes, (1, 1), activation='sigmoid')(up0a) model = Model(inputs=inputs, outputs=classify) return model

38.797546

110

0.646743

891

6,324

4.499439

0.088664

0.113495

0.071838

0.103767

0.813669

0.798204

0.757795

0.74233

0

0.110727

0.175996

6,324

162

111

39.037037

0.658607

0.026407

0

0.666667

0

0.022008

0

1

0.00813

false

0

0.02439

0

0.04065

0

null

0

1

0

null

0

6

5069c9abbef88f46faee541c6e023d234368e699

125

py

Python

tests/setup-transaction_generator/test_setup.py

metronotes-testing/transaction-generator

98f00e7041ce01f80d0df2f3fc58c0157552ad97

[ "MIT" ]

1

2020-01-30T05:25:36.000Z

tests/setup-transaction_generator/test_setup.py

harmony-one/transaction-generator

98f00e7041ce01f80d0df2f3fc58c0157552ad97

[ "MIT" ]

null

tests/setup-transaction_generator/test_setup.py

harmony-one/transaction-generator

98f00e7041ce01f80d0df2f3fc58c0157552ad97

[ "MIT" ]

null

import pytest import transaction_generator as tx_gen @pytest.fixture(scope="session", autouse=True) def setup(): pass

13.888889

46

0.76

17

125

5.470588

0.882353

0

0.144

125

8

47

15.625

0.869159

0

0.056

0

1

0.2

true

0.2

0.4

0

0.6

0

1

0

null

0

1

0

null

0

1

0

1

0

6

50c9782d4a6b4a7570201b5f44808e9f18869182

132

py

Python

pose_format/tensorflow/masked/__init__.py

yairc2223/pose-format

6556433193582f8a7ed80d58d19ec11749e8606b

[ "MIT" ]

11

2020-09-02T02:58:23.000Z

2022-01-20T09:17:26.000Z

pose_format/tensorflow/masked/__init__.py

yairc2223/pose-format

6556433193582f8a7ed80d58d19ec11749e8606b

[ "MIT" ]

5

2021-12-10T15:48:59.000Z

2022-02-21T15:53:20.000Z

pose_format/tensorflow/masked/__init__.py

yairc2223/pose-format

6556433193582f8a7ed80d58d19ec11749e8606b

[ "MIT" ]

6

2020-09-21T02:21:26.000Z

2022-02-05T17:18:44.000Z

from pose_format.tensorflow.masked.tensor import MaskedTensor from pose_format.tensorflow.masked.tensorflow import MaskedTensorflow

44

69

0.893939

16

132

7.25

0.5625

0.137931

0.241379

0.413793

0.517241

0

0.060606

132

2

70

66

0.935484

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

50d050397171e66f93442e98d2a2871b8d0c4f08

2,376

py

Python

xndtools/kernel_generator/tests/test_test_mixed.py

xnd-project/xndtools

9478f31954091d861ce538ba278f7f888e23d19b

[ "BSD-3-Clause" ]

3

2019-11-12T16:01:26.000Z

2020-06-27T19:27:27.000Z

xndtools/kernel_generator/tests/test_test_mixed.py

xnd-project/xndtools

9478f31954091d861ce538ba278f7f888e23d19b

[ "BSD-3-Clause" ]

4

2018-04-25T17:12:43.000Z

2018-08-23T18:17:24.000Z

xndtools/kernel_generator/tests/test_test_mixed.py

xnd-project/xndtools

9478f31954091d861ce538ba278f7f888e23d19b

[ "BSD-3-Clause" ]

6

2018-05-04T08:10:40.000Z

2019-03-19T10:00:21.000Z

import pytest from xndtools.kernel_generator.utils import NormalizedTypeMap from xnd import xnd m = pytest.importorskip("test_mixed") long_t = NormalizedTypeMap()('long') def assert_equal(x, y): assert x == y and x.dtype == y.dtype def test_mixed_matrices_CF_inout(): a = xnd([[10, 20], [30, 40]], type=f'2 * 2 * {long_t}') b = xnd([[5, 6], [7, 8]], type=f'!2 * 2 * {long_t}') r = m.test_mixed_matrices_inout_CF(a, b) assert_equal(r, xnd(26, type=long_t)) a = xnd([[10, 20], [30, 40]], type=f'!2 * 2 * {long_t}') b = xnd([[5, 6], [7, 8]], type=f'2 * 2 * {long_t}') with pytest.raises(ValueError, match=r'.* must be C-contiguous .*'): r = m.test_mixed_matrices_inout_CF(a, b) a = xnd([[10, 20], [30, 40]], type=f'2 * 2 * {long_t}') b = xnd([[5, 6], [7, 8]], type=f'2 * 2 * {long_t}') with pytest.raises(ValueError, match=r'.* must be F-contiguous .*'): r = m.test_mixed_matrices_inout_CF(a, b) def test_mixed_matrices_FC_inout(): a = xnd([[10, 20], [30, 40]], type=f'!2 * 2 * {long_t}') b = xnd([[5, 6], [7, 8]], type=f'2 * 2 * {long_t}') r = m.test_mixed_matrices_inout_FC(a, b) assert_equal(r, xnd(37, type=long_t)) def test_mixed_matrices_CC_inout(): a = xnd([[10, 20], [30, 40]], type=f'2 * 2 * {long_t}') b = xnd([[5, 6], [7, 8]], type=f'2 * 2 * {long_t}') r = m.test_mixed_matrices_inout_CC(a, b) assert_equal(r, xnd(27, type=long_t)) a = xnd([[10, 20], [30, 40]], type=f'!2 * 2 * {long_t}') b = xnd([[5, 6], [7, 8]], type=f'!2 * 2 * {long_t}') with pytest.raises(ValueError, match=r'.* must be C-contiguous .*'): r = m.test_mixed_matrices_inout_CC(a, b) def test_mixed_matrices_FF_inout(): a = xnd([[10, 20], [30, 40]], type=f'!2 * 2 * {long_t}') b = xnd([[5, 6], [7, 8]], type=f'!2 * 2 * {long_t}') r = m.test_mixed_matrices_inout_FF(a, b) assert_equal(r, xnd(36, type=long_t)) a = xnd([[10, 20], [30, 40]], type=f'2 * 2 * {long_t}') b = xnd([[5, 6], [7, 8]], type=f'2 * 2 * {long_t}') with pytest.raises(ValueError, match=r'.* must be F-contiguous .*'): r = m.test_mixed_matrices_inout_FF(a, b)

30.857143

72

0.520202

395

2,376

2.936709

0.136709

0.090517

0.082759

0.096552

0.790517

0.702586

0.685345

0

0.07907

0.276094

2,376

76

73

31.263158

0.595349

0

0.728814

0

0.160354

0

0.101695

1

0.084746

false

0

0.067797

0

0.152542

0

null

0

1

0

1

0

null

0

6

0ff32fc3b840e09f50cb50e169fb701aecb71f60

23,947

py

Python

core/app.py

Volentix/EZEOS

611048ace245e0b5776fe23b0eb32352a01300ae

[ "MIT" ]

12

2018-07-06T01:49:45.000Z

2019-05-15T20:54:07.000Z

core/app.py

Volentix/EZEOS

611048ace245e0b5776fe23b0eb32352a01300ae

[ "MIT" ]

1

2018-11-02T14:31:48.000Z

core/app.py

Volentix/EZEOS

611048ace245e0b5776fe23b0eb32352a01300ae

[ "MIT" ]

4

2018-07-17T21:07:37.000Z

2019-05-15T06:32:42.000Z

#!/usr/bin/env python # coding: utf-8 from tkinter import Tk from gui.ui import UI from core import DOCKER_CONTAINER_NAME from core import TIMEOUT from core import btc, bch, dash, eth, ltc, neo, xmr import subprocess import os import signal def run(): global app root = Tk() app = UI(root) # Application getCleosCommand() # print(app.tabPanel.producer.get()) root.lift() root.attributes('-topmost', True) root.attributes('-topmost', False) root.mainloop() def handler(signum, frame): raise RuntimeError("End of time") def getCleosCommand(): # TODO The docker has to be removed since was deprecated. global DOCKER_COMMAND DOCKER_COMMAND = ['docker', 'exec', DOCKER_CONTAINER_NAME] CLEOS_COMMAND = ['/opt/eosio/bin/cleos', '-h'] global cleos # try: # subprocess.check_output(DOCKER_COMMAND+CLEOS_COMMAND) # except OSError as e: # cleos = ['cleos'] # except Exception as e: # cleos = ['cleos'] # else: # cleos = ['docker', 'exec', DOCKER_CONTAINER_NAME, '/opt/eosio/bin/cleos'] try: subprocess.check_output(['cleos', '-h']) except OSError as e: app.outputPanel.logger('Can not find the cleos command.\n' + str(e)) except Exception as e: app.outputPanel.logger('Something went wrong \n' + str(e)) else: cleos = ['cleos'] # Logic functions def getProducerInfo(): try: out = subprocess.run(cleos + ['--url', app.tabPanel.producer.get(), 'get', 'info'], timeout=TIMEOUT, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) out = out.stdout.decode('utf-8') except subprocess.TimeoutExpired as e: print(e) out = 'Timeout. Producer is not available\n' + str(e) except Exception as e: print(e) out = 'Could not get info.\n' + str(e) finally: app.outputPanel.logger(out) def getBlockInfo(): try: out = subprocess.run(cleos + ['--url', app.tabPanel.producer.get(), 'get', 'block', app.tabPanel.blockNumber.get()], timeout=TIMEOUT, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) out = out.stdout.decode('utf-8') except subprocess.TimeoutExpired as e: print(e) out = 'Timeout. Can not get block info\n' + str(e) except Exception as e: print(e) out = 'Could not get block info.\n' + str(e) finally: app.outputPanel.logger(out) def getBlockProducers(): try: out = subprocess.run(cleos + ['--url', app.tabPanel.producer.get(), 'system', 'listproducers'], timeout=TIMEOUT, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) out = out.stdout.decode('utf-8') except subprocess.TimeoutExpired as e: print(e) out = 'Timeout. Can not get producer list\n' + str(e) except Exception as e: print(e) out = "Could not get producer list.\n" + str(e) finally: app.outputPanel.logger(out) def getWalletList(): try: out = subprocess.run(cleos + ['wallet', 'list'], timeout=TIMEOUT, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) out = out.stdout.decode('utf-8') except subprocess.TimeoutExpired as e: print(e) out = 'Timeout. Can not get wallet list\n' + str(e) except Exception as e: print(e) out = "Could not get wallet list. \n" + str(e) finally: app.outputPanel.logger(out) def getWalletListFilesystem(): if 'docker' in cleos: # docker exec eos ls /root/eosio-wallet | egrep '\.wallet$' out = b"Found wallets in filesystem inside docker container:\n> /root/eosio-wallet\n\n" com = " ".join(DOCKER_COMMAND + ['ls', '/root/eosio-wallet', '|', 'egrep', '\.wallet$']) out += subprocess.check_output(com, shell=True) else: # ls ~/eosio-wallet | egrep '\.wallet$' out = b"Found wallets in filesystem:\n> ~/eosio-wallet\n\n" com = " ".join(['ls', '~/eosio-wallet', '|', 'egrep', '\.wallet$']) out += subprocess.check_output(com, shell=True) app.outputPanel.logger(out) def createWallet(): toConsole = app.tabPanel.toConsole.get() if 'docker' in cleos: # docker - cleos wallet create -n twal --file /root/twal saved indide docker /root/ try: if toConsole == '--to-console': out = subprocess.run(cleos + ['wallet', 'create', '-n', app.tabPanel.walletName.get(), '--to-console'], timeout=TIMEOUT, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) out = out.stdout.decode('utf-8') elif toConsole == '--file': out = subprocess.run(cleos + ['wallet', 'create', '-n', app.tabPanel.walletName.get(), '--file', "/root/" + app.tabPanel.walletName.get()], timeout=TIMEOUT, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) out = out.stdout.decode('utf-8') except subprocess.TimeoutExpired as e: print(e) out = 'Timeout. Can not create wallet\n' + str(e) except Exception as e: print(e) out = "Could not create wallet.\n" + str(e) finally: app.tabPanel.openWalletName.insert(0, app.tabPanel.walletName.get()) app.outputPanel.logger(out) else: walletDir = os.environ['HOME'] + '/eosio-wallet' if not os.path.exists(walletDir): os.makedirs(walletDir) try: if toConsole == '--to-console': out = subprocess.run(cleos + ['wallet', 'create', '-n', app.tabPanel.walletName.get(), '--to-console'], timeout=TIMEOUT, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) out = out.stdout.decode('utf-8') elif toConsole == '--file': out = subprocess.run(cleos + ['wallet', 'create', '-n', app.tabPanel.walletName.get(), '--file', walletDir + "/" + app.tabPanel.walletName.get()], timeout=TIMEOUT, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) out = out.stdout.decode('utf-8') except subprocess.TimeoutExpired as e: print(e) out = 'Timeout. Can not create wallet\n' + str(e) except Exception as e: print(e) out = "Could not create wallet.\n" + str(e) finally: app.tabPanel.openWalletName.insert(0, app.tabPanel.walletName.get()) app.outputPanel.logger(out) def openWallet(): try: out = subprocess.run(cleos + ['wallet', 'open', '-n', app.tabPanel.openWalletName.get()], timeout=TIMEOUT, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) out = out.stdout.decode('utf-8') except subprocess.TimeoutExpired as e: print(e) out = 'Timeout. Can not open the wallet\n' + str(e) except Exception as e: print(e) out = 'Could not open the wallet.\n' + str(e) finally: if 'Opened' in out: out += "\nRemember this wallet as default for this core session!" app.outputPanel.logger(out) def unlockWallet(password): try: out = subprocess.run(cleos + ['wallet', 'unlock', '-n', app.tabPanel.openWalletName.get(), '--password', password], timeout=TIMEOUT, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) out = out.stdout.decode('utf-8') except subprocess.TimeoutExpired as e: print(e) out = 'Timeout. Unlock the wallet\n' + str(e) except Exception as e: print(e) out = 'Could not unlock the wallet.\n' + str(e) finally: app.outputPanel.logger(out) def showKeys(): try: out = subprocess.run(cleos + ['wallet', 'keys'], timeout=TIMEOUT, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) out = out.stdout.decode('utf-8') except subprocess.TimeoutExpired as e: print(e) out = 'Timeout. Can not show keys\n' + str(e) except Exception as e: print(e) out = 'Could not show keys.\n' + str(e) finally: app.outputPanel.logger(out) def showPrivateKeys(password): try: out = subprocess.run(cleos + ['wallet', 'private_keys', '-n', app.tabPanel.openWalletName.get(), '--password', password], timeout=TIMEOUT, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) out = out.stdout.decode('utf-8') except subprocess.TimeoutExpired as e: print(e) out = 'Timeout. Can not show private keys\n' + str(e) except Exception as e: print(e) out = 'Could not show private keys.\n' + str(e) finally: app.outputPanel.logger(out) def importKey(key): try: out = subprocess.run(cleos + ['wallet', 'import', '-n', app.tabPanel.openWalletName.get(), '--private-key', key], timeout=TIMEOUT, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) out = out.stdout.decode('utf-8') except subprocess.TimeoutExpired as e: print(e) out = 'Timeout. Can not import the key\n' + str(e) except Exception as e: print(e) out = 'Could not import the key.\n' + str(e) finally: app.outputPanel.logger(out) def createKeys(): # TODO add --tofile feature try: out = subprocess.run(cleos + ['create', 'key', '--to-console'], timeout=TIMEOUT, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) out = out.stdout.decode('utf-8') except subprocess.TimeoutExpired as e: print(e) out = 'Timeout. Can not create keys\n' + str(e) except Exception as e: print(e) out = 'Could not create keys.\n' + str(e) finally: app.outputPanel.logger(out) def compileContract(): cpp = app.tabPanel.contractFileCPP.get() wasm = app.tabPanel.contractFileWASM.get() wast = app.tabPanel.contractFileWAST.get() abi = app.tabPanel.contractFileABI.get() try: out = subprocess.run(['eosio-cpp', '-o', wasm, cpp, '--abigen'], timeout=TIMEOUT+60, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) out = out.stdout.decode('utf-8') except subprocess.TimeoutExpired as e: print(e) out = 'Timeout. Can not compile contract\n' + str(e) except Exception as e: print(e) out = 'Could not compile contract.\n' + str(e) finally: if 'error' in out: app.outputPanel.logger(out) else: app.outputPanel.logger("Compile successful\n\n" + out) try: out = subprocess.run(['eosio-cpp', '-o', wast, cpp, '--abigen'], timeout=TIMEOUT+60, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) out = out.stdout.decode('utf-8') except subprocess.TimeoutExpired as e: print(e) out = 'Timeout. Can not compile contract\n' + str(e) except Exception as e: print(e) out = 'Could not compile contract.\n' + str(e) finally: if 'error' in out: app.outputPanel.logger(out) else: app.outputPanel.logger("Compile successful\n\n" + out) def setContract(): cpp = app.tabPanel.contractFileCPP.get() wasm = app.tabPanel.contractFileWASM.get() wast = app.tabPanel.contractFileWAST.get() abi = app.tabPanel.contractFileABI.get() try: out_code = subprocess.run(cleos + ['--url', app.tabPanel.producer.get(), 'set', 'code', app.tabPanel.accountName.get(), wasm], timeout=TIMEOUT, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) out_abi = subprocess.run(cleos + ['--url', app.tabPanel.producer.get(), 'set', 'abi', app.tabPanel.accountName.get(), abi], timeout=TIMEOUT, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) out_code = out_code.stdout.decode('utf-8') out_abi = out_abi.stdout.decode('utf-8') out = str(out_code) + str(out_abi) except subprocess.TimeoutExpired as e: print(e) out = 'Timeout. Can not set contract\n' + str(e) except Exception as e: print(e) out = 'Could not set contract.\n' + str(e) finally: app.outputPanel.logger("Contract successfully pished to the net.\n\n" + out) def getAccountBalance(): try: out = subprocess.run(cleos + ['--url', app.tabPanel.producer.get() ,'get', 'currency', 'balance', 'eosio.token', app.tabPanel.accountName.get()], timeout=TIMEOUT, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) out = out.stdout.decode('utf-8') except subprocess.TimeoutExpired as e: print(e) out = 'Timeout. Can not get account balance\n' + str(e) except Exception as e: print(e) out = "Could not get account balance. \n" + str(e) finally: app.outputPanel.logger(out) def getAccountDetails(): try: out = subprocess.run(cleos + ['--url', app.tabPanel.producer.get() ,'get', 'account', app.tabPanel.accountName.get()], timeout=TIMEOUT, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) out = out.stdout.decode('utf-8') except subprocess.TimeoutExpired as e: print(e) out = 'Timeout. Can not get account details\n' + str(e) except Exception as e: print(e) out = "Could not get account details. \n" + str(e) finally: app.outputPanel.logger(out) def getAccountActions(): try: out = subprocess.run(cleos + ['--url', app.tabPanel.producer.get(), 'get', 'actions', app.tabPanel.accountName.get()], timeout=TIMEOUT, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) out = out.stdout.decode('utf-8') except subprocess.TimeoutExpired as e: print(e) out = 'Timeout. Can not get account actions\n' + str(e) except Exception as e: print(e) out = "Could not get account actions. \n" + str(e) finally: app.outputPanel.logger(out) def getAccountCode(): try: out = subprocess.run(cleos + ['--url', app.tabPanel.producer.get(), 'get', 'code', app.tabPanel.accountName.get()], timeout=TIMEOUT, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) out = out.stdout.decode('utf-8') except subprocess.TimeoutExpired as e: print(e) out = 'Timeout. Can not get account code\n' + str(e) except Exception as e: print(e) out = "Could not get account code. \n" + str(e) finally: app.outputPanel.logger(out) def getAccountAbi(): try: out = subprocess.run(cleos + ['--url', app.tabPanel.producer.get(), 'get', 'abi', app.tabPanel.accountName.get()], timeout=TIMEOUT, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) out = out.stdout.decode('utf-8') except subprocess.TimeoutExpired as e: print(e) out = 'Timeout. Can not get account abi\n' + str(e) except Exception as e: print(e) out = "Could not get account abi. \n" + str(e) finally: app.outputPanel.logger(out) def getAccountTable(): try: out = subprocess.run(cleos + ['--url', app.tabPanel.producer.get(), 'get', 'table', app.tabPanel.accountName.get(), app.tabPanel.accountScope.get(), app.tabPanel.accountTable.get(), '-L', app.tabPanel.accountLower.get(), '-l', app.tabPanel.accountLimit.get()], timeout=TIMEOUT, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) out = out.stdout.decode('utf-8') except subprocess.TimeoutExpired as e: print(e) out = 'Timeout. Can not get account table\n' + str(e) except Exception as e: print(e) out = "Could not get account table. \n" + str(e) finally: app.outputPanel.logger(out) def buyRam(): creator = app.tabPanel.accountCreator.get() owner = app.tabPanel.accountOwner.get() ram = app.tabPanel.ram.get() # #buy ram for yourself # cleos system buyram someaccount1 someaccount1 "10 EOS" # # #buy ram for someone else # cleos system buyram someaccount1 someaccount2 "1 EOS" try: out = subprocess.run(cleos + ['--url', app.tabPanel.producer.get(), 'system', 'buyram', creator, owner, ram], timeout=TIMEOUT, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) out = out.stdout.decode('utf-8') except subprocess.TimeoutExpired as e: print(e) out = 'Timeout. Can not buy RAM\n' + str(e) except Exception as e: print(e) out = "Could not get but RAM. \n" + str(e) finally: app.outputPanel.logger(out) def stakeNet(): creator = app.tabPanel.accountCreator.get() owner = app.tabPanel.accountOwner.get() net = app.tabPanel.net.get() cpu = app.tabPanel.cpu.get() # cleos system delegatebw accountname1 accountname2 "1 SYS" "1 SYS" try: out = subprocess.run(cleos + ['--url', app.tabPanel.producer.get(), 'system', 'delegatebw', creator, owner, net, cpu], timeout=TIMEOUT, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) out = out.stdout.decode('utf-8') except subprocess.TimeoutExpired as e: print(e) out = 'Timeout. Can not stake NET\n' + str(e) except Exception as e: print(e) out = "Could not get stake NET. \n" + str(e) finally: app.outputPanel.logger(out) def createAccount(): creator = app.tabPanel.accountCreator.get() owner = app.tabPanel.accountOwner.get() activeKey = app.tabPanel.accountActiveKey.get() ownerKey = app.tabPanel.accountOwnerKey.get() cpu = app.tabPanel.cpu.get() net = app.tabPanel.net.get() ram = app.tabPanel.ram.get() permission = creator + '@active' # cleos -u http://IP-HERE:8888 system newaccount --stake-net "0.1000 EOS" --stake-cpu "0.1000 EOS" --buy-ram-kbytes 8 eosio myDesiredAccountName Public key Public key try: out = subprocess.run(cleos + ['--url', app.tabPanel.producer.get(), 'system', 'newaccount', creator, owner, ownerKey, activeKey, '--stake-net', net, '--stake-cpu', cpu, '--buy-ram', ram, '--transfer', '-p', permission], timeout=TIMEOUT, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) out = out.stdout.decode('utf-8') except subprocess.TimeoutExpired as e: print(e) out = 'Timeout. Can not stake NET\n' + str(e) except Exception as e: print(e) out = "Could not get stake NET. \n" + str(e) finally: app.outputPanel.logger(out) def setWalletDir(): stop = stopKeosd(False) run = runKeosd(False) app.outputPanel.logger(stop + '\n' + run) def stopKeosd(flag): if flag: try: out = subprocess.run(cleos + ['wallet', 'stop'], timeout=TIMEOUT, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) out = out.stdout.decode('utf-8') except subprocess.TimeoutExpired as e: print(e) out = 'Timeout. Can not stop keosd\n' + str(e) except Exception as e: print(e) out = "Could not stop keosd. \n" + str(e) finally: app.outputPanel.logger(out) else: try: out = subprocess.run(cleos + ['wallet', 'stop'], timeout=TIMEOUT, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) out = out.stdout.decode('utf-8') except subprocess.TimeoutExpired as e: print(e) out = 'Timeout. Can not stop keosd\n' + str(e) except Exception as e: print(e) out = "Could not stop keosd. \n" + str(e) finally: return out def runKeosd(flag): # TODO rewrite function if flag: try: out = os.spawnl(os.P_NOWAIT, 'keosd', '--wallet-dir', '~/eosio-wallet') except Exception as e: print('Could not run keosd by default path: ' + str(e)) out = "Could not run keosd by default path: " + str(e) finally: app.outputPanel.logger(str(out)) else: try: out = os.spawnl(os.P_NOWAIT, 'keosd', '--wallet-dir', app.tabPanel.walletDir.get()) except Exception as e: print('Could not run keosd ' + str(e)) out = "Could not run keosd " + str(e) finally: return str(out) # Currency operations def getBtcBalance(address): signal.signal(signal.SIGALRM, handler) signal.alarm(TIMEOUT) try: out = btc.getBalance(address) except RuntimeError as e: print(e) out = 'Can not get BTC balance. Timeout error.\n' + str(e) except Exception as e: print(e) out = 'Can not get BTC balance.\n' + str(e) finally: signal.alarm(0) app.outputPanel.logger(out) def getEthBalance(address): signal.signal(signal.SIGALRM, handler) signal.alarm(TIMEOUT) try: out = eth.getBalance(address) except RuntimeError as e: print(e) out = 'Can not get ETH balance. Timeout error.\n' + str(e) except Exception as e: print(e) out = 'Can not get ETH balance.\n' + str(e) finally: signal.alarm(0) app.outputPanel.logger(out) def getXmrBalance(address): signal.signal(signal.SIGALRM, handler) signal.alarm(TIMEOUT) try: out = xmr.getBalance(address) except RuntimeError as e: print(e) out = 'Can not get XMR balance. Timeout error.\n' + str(e) except Exception as e: print(e) out = 'Can not get XMR balance.\n' + str(e) finally: signal.alarm(0) app.outputPanel.logger(out) def getNeoBalance(address): signal.signal(signal.SIGALRM, handler) signal.alarm(TIMEOUT) try: out = neo.getBalance(address) except RuntimeError as e: print(e) out = 'Can not get NEO balance. Timeout error.\n' + str(e) except Exception as e: print(e) out = 'Can not get NEO balance.\n' + str(e) finally: signal.alarm(0) app.outputPanel.logger(out) def getLtcBalance(address): signal.signal(signal.SIGALRM, handler) signal.alarm(TIMEOUT) try: out = ltc.getBalance(address) except RuntimeError as e: print(e) out = 'Can not get LTC balance. Timeout error.\n' + str(e) except Exception as e: print(e) out = 'Can not get LTC balance.\n' + str(e) finally: signal.alarm(0) app.outputPanel.logger(out) def getBchBalance(address): signal.signal(signal.SIGALRM, handler) signal.alarm(TIMEOUT) try: out = bch.getBalance(address) except RuntimeError as e: print(e) out = 'Can not get BCH balance. Timeout error.\n' + str(e) except Exception as e: print(e) out = 'Can not get BCH balance.\n' + str(e) finally: signal.alarm(0) app.outputPanel.logger(out) def getDashBalance(address): signal.signal(signal.SIGALRM, handler) signal.alarm(TIMEOUT) try: out = dash.getBalance(address) except RuntimeError as e: print(e) out = 'Can not get DASH balance. Timeout error.\n' + str(e) except Exception as e: print(e) out = 'Can not get DASH balance.\n' + str(e) finally: signal.alarm(0) app.outputPanel.logger(out)

35.902549

268

0.587798

2,886

23,947

4.867983

0.094248

0.015375

0.024201

0.042281

0.813652

0.781835

0.755783

0.728237

0.7266

0.678198

0

0.004007

0.280954

23,947

666

269

35.956456

0.811894

0.042594

0

0.689408

0

0.15176

0.000961

0

0.001502

0

1

0.064632

false

0.007181

0.021544

0

0.089767

0.122083

0

null

0

1

0

null

0

6

0ff925beaf5ef2567704c3c1920f77502dc58969

4,747

py

Python

pyflux/gas/tests/gas_llev_tests_skewt.py

ThomasHoppe/pyflux

297f2afc2095acd97c12e827dd500e8ea5da0c0f

[ "BSD-3-Clause" ]

2,091

2016-04-01T02:52:10.000Z

2022-03-29T11:38:15.000Z

pyflux/gas/tests/gas_llev_tests_skewt.py

EricSchles/pyflux

297f2afc2095acd97c12e827dd500e8ea5da0c0f

[ "BSD-3-Clause" ]

160

2016-04-26T14:52:18.000Z

2022-03-15T02:09:07.000Z

pyflux/gas/tests/gas_llev_tests_skewt.py

EricSchles/pyflux

297f2afc2095acd97c12e827dd500e8ea5da0c0f

[ "BSD-3-Clause" ]

264

2016-05-02T14:03:31.000Z

2022-03-29T07:48:20.000Z

import numpy as np import pyflux as pf noise = np.random.normal(0,1,200) data = np.zeros(200) for i in range(1,len(data)): data[i] = 1.0*data[i-1] + noise[i] countdata = np.random.poisson(3,200) def test_skewt_couple_terms(): """ Tests latent variable list length is correct, and that the estimated latent variables are not nan """ model = pf.GASLLEV(data=data, family=pf.Skewt()) x = model.fit() assert(len(model.latent_variables.z_list) == 4) lvs = np.array([i.value for i in model.latent_variables.z_list]) assert(len(lvs[np.isnan(lvs)]) == 0) def test_skewt_couple_terms_integ(): """ Tests latent variable list length is correct, and that the estimated latent variables are not nan """ model = pf.GASLLEV(data=data, integ=1, family=pf.Skewt()) x = model.fit() assert(len(model.latent_variables.z_list) == 4) lvs = np.array([i.value for i in model.latent_variables.z_list]) assert(len(lvs[np.isnan(lvs)]) == 0) def test_skewt_bbvi(): """ Tests an GAS model estimated with BBVI and that the length of the latent variable list is correct, and that the estimated latent variables are not nan """ model = pf.GASLLEV(data=data, family=pf.Skewt()) x = model.fit('BBVI',iterations=100) assert(len(model.latent_variables.z_list) == 4) lvs = np.array([i.value for i in model.latent_variables.z_list]) assert(len(lvs[np.isnan(lvs)]) == 0) def test_skewt_bbvi_mini_batch(): """ Tests an ARIMA model estimated with BBVI and that the length of the latent variable list is correct, and that the estimated latent variables are not nan """ model = pf.GASLLEV(data=data, family=pf.Skewt()) x = model.fit('BBVI',iterations=100, mini_batch=32) assert(len(model.latent_variables.z_list) == 4) lvs = np.array([i.value for i in model.latent_variables.z_list]) assert(len(lvs[np.isnan(lvs)]) == 0) def test_skewt_bbvi_elbo(): """ Tests that the ELBO increases """ model = pf.GASLLEV(data=data, family=pf.Skewt()) x = model.fit('BBVI',iterations=100, record_elbo=True) assert(x.elbo_records[-1]>x.elbo_records[0]) def test_skewt_bbvi_mini_batch_elbo(): """ Tests that the ELBO increases """ model = pf.GASLLEV(data=data, family=pf.Skewt()) x = model.fit('BBVI',iterations=100, mini_batch=32, record_elbo=True) assert(x.elbo_records[-1]>x.elbo_records[0]) def test_skewt_mh(): """ Tests an GAS model estimated with Metropolis-Hastings and that the length of the latent variable list is correct, and that the estimated latent variables are not nan """ model = pf.GASLLEV(data=data, family=pf.Skewt()) x = model.fit('M-H',nsims=300) assert(len(model.latent_variables.z_list) == 4) lvs = np.array([i.value for i in model.latent_variables.z_list]) assert(len(lvs[np.isnan(lvs)]) == 0) """ Uncomment in future if Skewt becomes more robust def test_skewt_laplace(): Tests an GAS model estimated with Laplace approximation and that the length of the latent variable list is correct, and that the estimated latent variables are not nan model = pf.GASLLEV(data=data, family=pf.Skewt()) x = model.fit('Laplace') assert(len(model.latent_variables.z_list) == 4) lvs = np.array([i.value for i in model.latent_variables.z_list]) assert(len(lvs[np.isnan(lvs)]) == 0) """ def test_skewt_pml(): """ Tests a PML model estimated with Laplace approximation and that the length of the latent variable list is correct, and that the estimated latent variables are not nan """ model = pf.GASLLEV(data=data, family=pf.Skewt()) x = model.fit('PML') assert(len(model.latent_variables.z_list) == 4) lvs = np.array([i.value for i in model.latent_variables.z_list]) assert(len(lvs[np.isnan(lvs)]) == 0) def test_skewt_predict_length(): """ Tests that the prediction dataframe length is equal to the number of steps h """ model = pf.GASLLEV(data=data, family=pf.Skewt()) x = model.fit() x.summary() assert(model.predict(h=5).shape[0] == 5) def test_skewt_predict_is_length(): """ Tests that the prediction IS dataframe length is equal to the number of steps h """ model = pf.GASLLEV(data=data, family=pf.Skewt()) x = model.fit() assert(model.predict_is(h=5).shape[0] == 5) def test_skewt_predict_nans(): """ Tests that the predictions are not nans model = pf.GASLLEV(data=data, family=pf.Skewt()) """ model = pf.GASLLEV(data=data, family=pf.Skewt()) x = model.fit() x.summary() assert(len(model.predict(h=5).values[np.isnan(model.predict(h=5).values)]) == 0) """ def test_skewt_predict_is_nans(): Tests that the in-sample predictions are not nans model = pf.GASLLEV(data=data, family=pf.Skewt()) x = model.fit() x.summary() assert(len(model.predict_is(h=5).values[np.isnan(model.predict_is(h=5).values)]) == 0) """

33.195804

87

0.709711

793

4,747

4.147541

0.128625

0.095774

0.059593

0.076619

0.901186

0.857708

0.839465

0.816358

0.799331

0

0.016316

0.147883

4,747

142

88

33.429577

0.796786

0.240152

0

0.569231

0

0.00805

0

0.261538

1

0.169231

false

0

0.030769

0

0.2

0

null

0

1

0

null

0

6

ba06bd7bbbbf073ea68c4060dc53dffd638beb95

62

py

Python

libs/query_set.py

jessamynsmith/quotations

b2a9b70190756fa261840faea181860b166e253f

[ "MIT" ]

2

2015-05-01T19:44:41.000Z

2015-07-17T13:52:46.000Z

libs/query_set.py

jessamynsmith/quotations

b2a9b70190756fa261840faea181860b166e253f

[ "MIT" ]

13

2019-10-18T17:06:52.000Z

2022-02-10T07:37:30.000Z

libs/query_set.py

jessamynsmith/quotations

b2a9b70190756fa261840faea181860b166e253f

[ "MIT" ]

3

2015-05-06T15:38:30.000Z

2015-07-26T21:12:32.000Z

def get_random(query_set): return query_set.order_by('?')

20.666667

34

0.725806

10

62

4.1

0.8

0.390244

0

0.129032

62

2

35

31

0.759259

0

0.016129

0

1

0.5

false

0

0.5

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

e83bd0da7cea890b9cffd07d8619934a628dd1e1

34,860

py

Python

tests/unit/test_property_wizard.py

rnag/dataclass-wizard

5bccf63daea217aff6e77fd00ed8f7bed87f0377

[ "Apache-2.0" ]

19

2021-11-05T20:29:56.000Z

2022-03-31T02:51:25.000Z

tests/unit/test_property_wizard.py

rnag/dataclass-wizard

5bccf63daea217aff6e77fd00ed8f7bed87f0377

[ "Apache-2.0" ]

6

2021-10-20T23:24:04.000Z

2022-03-01T18:49:14.000Z

tests/unit/test_property_wizard.py

rnag/dataclass-wizard

5bccf63daea217aff6e77fd00ed8f7bed87f0377

[ "Apache-2.0" ]

null

import logging from collections import defaultdict from dataclasses import dataclass, field from datetime import datetime from typing import Union, List, ClassVar, DefaultDict, Set import pytest from dataclass_wizard import property_wizard from ..conftest import Literal, Annotated, PY39_OR_ABOVE, PY310_OR_ABOVE log = logging.getLogger(__name__) def test_property_wizard_does_not_affect_normal_properties(): """ The `property_wizard` should not otherwise affect normal properties (i.e. ones that don't have their property names (or underscored names) annotated as a dataclass field. """ @dataclass class Vehicle(metaclass=property_wizard): def __post_init__(self): self.wheels = 4 self._my_prop = 0 @property def wheels(self) -> int: return self._wheels @wheels.setter def wheels(self, wheels: Union[int, str]): self._wheels = int(wheels) @property def _my_prop(self) -> int: return self.my_prop @_my_prop.setter def _my_prop(self, my_prop: Union[int, str]): self.my_prop = int(my_prop) + 5 v = Vehicle() log.debug(v) assert v.wheels == 4 assert v._my_prop == 5 # These should all result in a `TypeError`, as neither `wheels` nor # `_my_prop` are valid arguments to the constructor, as they are just # normal properties. with pytest.raises(TypeError): _ = Vehicle(wheels=3) with pytest.raises(TypeError): _ = Vehicle('6') with pytest.raises(TypeError): _ = Vehicle(_my_prop=2) v.wheels = '123' assert v.wheels == 123, 'Expected assignment to use the setter method' v._my_prop = '5' assert v._my_prop == 10, 'Expected assignment to use the setter method' def test_property_wizard_does_not_affect_read_only_properties(): """ The `property_wizard` should not otherwise affect properties which are read-only (i.e. ones which don't define a `setter` method) """ @dataclass class Vehicle(metaclass=property_wizard): list_of_wheels: list = field(default_factory=list) @property def wheels(self) -> int: return len(self.list_of_wheels) v = Vehicle() log.debug(v) assert v.wheels == 0 # AttributeError: can't set attribute with pytest.raises(AttributeError): v.wheels = 3 v = Vehicle(list_of_wheels=[1, 2, 1]) assert v.wheels == 3 v.list_of_wheels = [0] assert v.wheels == 1 def test_property_wizard_does_not_error_when_forward_refs_are_declared(): """ Using `property_wizard` when the dataclass has a forward reference defined in a type annotation. """ @dataclass class Vehicle(metaclass=property_wizard): fire_truck: 'Truck' cars: List['Car'] = field(default_factory=list) _wheels: Union[int, str] = 4 @property def wheels(self) -> int: return self._wheels @wheels.setter def wheels(self, wheels: Union[int, str]): self._wheels = int(wheels) @dataclass class Car: tires: int @dataclass class Truck: color: str truck = Truck('red') v = Vehicle(fire_truck=truck) log.debug(v) assert v.wheels == 4 v = Vehicle(fire_truck=truck, wheels=3) log.debug(v) assert v.wheels == 3 v = Vehicle(truck, [Car(4)], '6') log.debug(v) assert v.wheels == 6, 'The constructor should use our setter method' v.wheels = '123' assert v.wheels == 123, 'Expected assignment to use the setter method' def test_property_wizard_with_public_property_and_underscored_field(): """ Using `property_wizard` when the dataclass has an public property and an underscored field name. """ @dataclass class Vehicle(metaclass=property_wizard): _wheels: Union[int, str] = 4 @property def wheels(self) -> int: return self._wheels @wheels.setter def wheels(self, wheels: Union[int, str]): self._wheels = int(wheels) v = Vehicle() log.debug(v) assert v.wheels == 4 # Note that my IDE complains here, and suggests `_wheels` as a possible # keyword argument to the constructor method; however, that's wrong and # will error if you try it way. v = Vehicle(wheels=3) log.debug(v) assert v.wheels == 3 v = Vehicle('6') log.debug(v) assert v.wheels == 6, 'The constructor should use our setter method' v.wheels = '123' assert v.wheels == 123, 'Expected assignment to use the setter method' def test_property_wizard_with_public_property_and_field(): """ Using `property_wizard` when the dataclass has both a property and field name *without* a leading underscore. """ @dataclass class Vehicle(metaclass=property_wizard): # The value of `wheels` here will be ignored, since `wheels` is simply # re-assigned on the following property definition. wheels: Union[int, str] = 4 @property def wheels(self) -> int: return self._wheels @wheels.setter def wheels(self, wheels: Union[int, str]): self._wheels = int(wheels) v = Vehicle() log.debug(v) assert v.wheels == 0 v = Vehicle(wheels=3) log.debug(v) assert v.wheels == 3 v = Vehicle('6') log.debug(v) assert v.wheels == 6, 'The constructor should use our setter method' v.wheels = '123' assert v.wheels == 123, 'Expected assignment to use the setter method' @pytest.mark.skipif(not PY310_OR_ABOVE, reason='requires Python 3.10 or higher') def test_property_wizard_with_public_property_and_field_with_or(): """ Using `property_wizard` when the dataclass has both a property and field name *without* a leading underscore, and using the OR ("|") operator in Python 3.10+, instead of the `typing.Union` usage. """ @dataclass class Vehicle(metaclass=property_wizard): # The value of `wheels` here will be ignored, since `wheels` is simply # re-assigned on the following property definition. wheels: int | str = 4 @property def wheels(self) -> int: return self._wheels @wheels.setter def wheels(self, wheels: Union[int, str]): self._wheels = int(wheels) v = Vehicle() log.debug(v) assert v.wheels == 0 v = Vehicle(wheels=3) log.debug(v) assert v.wheels == 3 v = Vehicle('6') log.debug(v) assert v.wheels == 6, 'The constructor should use our setter method' v.wheels = '123' assert v.wheels == 123, 'Expected assignment to use the setter method' def test_property_wizard_with_underscored_property_and_public_field(): """ Using `property_wizard` when the dataclass has an underscored property and a public field name. """ @dataclass class Vehicle(metaclass=property_wizard): wheels: Union[int, str] = 4 @property def _wheels(self) -> int: return self._wheels @_wheels.setter def _wheels(self, wheels: Union[int, str]): self._wheels = int(wheels) v = Vehicle() log.debug(v) assert v.wheels == 4 v = Vehicle(wheels=3) log.debug(v) assert v.wheels == 3 v = Vehicle('6') log.debug(v) assert v.wheels == 6, 'The constructor should use our setter method' v.wheels = '123' assert v.wheels == 123, 'Expected assignment to use the setter method' def test_property_wizard_with_underscored_property_and_field(): """ Using `property_wizard` when the dataclass has both a property and field name with a leading underscore. Note: this approach is generally *not* recommended, because the IDE won't know that the property or field name will be transformed to a public field name without the leading underscore, so it won't offer the desired type hints and auto-completion here. """ @dataclass class Vehicle(metaclass=property_wizard): # The value of `_wheels` here will be ignored, since `_wheels` is # simply re-assigned on the following property definition. _wheels: Union[int, str] = 4 @property def _wheels(self) -> int: return self._wheels @_wheels.setter def _wheels(self, wheels: Union[int, str]): self._wheels = int(wheels) v = Vehicle() log.debug(v) assert v.wheels == 0 # Note that my IDE complains here, and suggests `_wheels` as a possible # keyword argument to the constructor method; however, that's wrong and # will error if you try it way. v = Vehicle(wheels=3) log.debug(v) assert v.wheels == 3 v = Vehicle('6') log.debug(v) assert v.wheels == 6, 'The constructor should use our setter method' v.wheels = '123' assert v.wheels == 123, 'Expected assignment to use the setter method' def test_property_wizard_with_public_property_and_annotated_field(): """ Using `property_wizard` when the dataclass has both a property and field name *without* a leading underscore, and the field is a :class:`typing.Annotated` type. """ @dataclass class Vehicle(metaclass=property_wizard): # The value of `wheels` here will be ignored, since `wheels` is simply # re-assigned on the following property definition. wheels: Annotated[Union[int, str], field(default=4)] = None @property def wheels(self) -> int: return self._wheels @wheels.setter def wheels(self, wheels: Union[int, str]): self._wheels = int(wheels) v = Vehicle() log.debug(v) assert v.wheels == 4 v = Vehicle(wheels=3) log.debug(v) assert v.wheels == 3 v = Vehicle('6') log.debug(v) assert v.wheels == 6, 'The constructor should use our setter method' v.wheels = '123' assert v.wheels == 123, 'Expected assignment to use the setter method' def test_property_wizard_with_private_property_and_annotated_field_with_no_useful_extras(): """ Using `property_wizard` when the dataclass has both a property and field name with a leading underscore, and the field is a :class:`typing.Annotated` type without any extras that are a :class:`dataclasses.Field` type. """ @dataclass class Vehicle(metaclass=property_wizard): # The value of `wheels` here will be ignored, since `wheels` is simply # re-assigned on the following property definition. _wheels: Annotated[Union[int, str], 'Hello world!', 123] = None @property def _wheels(self) -> int: return self._wheels @_wheels.setter def _wheels(self, wheels: Union[int, str]): self._wheels = int(wheels) v = Vehicle() log.debug(v) assert v.wheels == 0 v = Vehicle(wheels=3) log.debug(v) assert v.wheels == 3 v = Vehicle('6') log.debug(v) assert v.wheels == 6, 'The constructor should use our setter method' v.wheels = '123' assert v.wheels == 123, 'Expected assignment to use the setter method' def test_property_wizard_with_multiple_inheritance(): """ When using multiple inheritance or when extending from more than one class, and if any of the super classes define properties that should also be `dataclass` fields, then the recommended approach is to define the `property_wizard` metaclass on each class that has such properties. Note that the last class in the below example (Car) doesn't need to use this metaclass, as it doesn't have any properties that meet this condition. """ @dataclass class VehicleWithWheels(metaclass=property_wizard): _wheels: Union[int, str] = field(default=4) @property def wheels(self) -> int: return self._wheels @wheels.setter def wheels(self, wheels: Union[int, str]): self._wheels = int(wheels) @dataclass class Vehicle(VehicleWithWheels, metaclass=property_wizard): _windows: Union[int, str] = field(default=6) @property def windows(self) -> int: return self._windows @windows.setter def windows(self, windows: Union[int, str]): self._windows = int(windows) @dataclass class Car(Vehicle): my_list: List[str] = field(default_factory=list) v = Car() log.debug(v) assert v.wheels == 4 assert v.windows == 6 assert v.my_list == [] # Note that my IDE complains here, and suggests `_wheels` as a possible # keyword argument to the constructor method; however, that's wrong and # will error if you try it way. v = Car(wheels=3, windows=5, my_list=['hello', 'world']) log.debug(v) assert v.wheels == 3 assert v.windows == 5 assert v.my_list == ['hello', 'world'] v = Car('6', '7', ['testing']) log.debug(v) assert v.wheels == 6, 'The constructor should use our setter method' assert v.windows == 7, 'The constructor should use our setter method' assert v.my_list == ['testing'] v.wheels = '123' assert v.wheels == 123, 'Expected assignment to use the setter method' v.windows = '321' assert v.windows == 321, 'Expected assignment to use the setter method' # NOTE: the below test cases are added for coverage purposes def test_property_wizard_with_public_property_and_underscored_field_without_default_value(): """ Using `property_wizard` when the dataclass has a public property, and an underscored field *without* a default value explicitly set. """ @dataclass class Vehicle(metaclass=property_wizard): _wheels: Union[int, str] @property def wheels(self) -> int: return self._wheels @wheels.setter def wheels(self, wheels: Union[int, str]): self._wheels = int(wheels) v = Vehicle() log.debug(v) assert v.wheels == 0 v = Vehicle(wheels=3) log.debug(v) assert v.wheels == 3 v = Vehicle('6') log.debug(v) assert v.wheels == 6, 'The constructor should use our setter method' v.wheels = '123' assert v.wheels == 123, 'Expected assignment to use the setter method' def test_property_wizard_with_public_property_and_underscored_field_with_default_factory(): """ Using `property_wizard` when the dataclass has a public property, and an underscored field has only `default_factory` set. """ @dataclass class Vehicle(metaclass=property_wizard): _wheels: Union[int, str] = field(default_factory=str) @property def wheels(self) -> int: return self._wheels @wheels.setter def wheels(self, wheels: Union[int, str]): self._wheels = int(wheels) with pytest.raises(ValueError): # Setter raises ValueError, as `wheels` will be a string by default _ = Vehicle() v = Vehicle(wheels=3) log.debug(v) assert v.wheels == 3 v = Vehicle('6') log.debug(v) assert v.wheels == 6, 'The constructor should use our setter method' v.wheels = '123' assert v.wheels == 123, 'Expected assignment to use the setter method' def test_property_wizard_with_public_property_and_underscored_field_without_default_or_default_factory(): """ Using `property_wizard` when the dataclass has a public property, and an underscored field has neither `default` or `default_factory` set. """ @dataclass class Vehicle(metaclass=property_wizard): _wheels: Union[int, str] = field() @property def wheels(self) -> int: return self._wheels @wheels.setter def wheels(self, wheels: Union[int, str]): self._wheels = int(wheels) v = Vehicle() log.debug(v) assert v.wheels == 0 v = Vehicle(wheels=3) log.debug(v) assert v.wheels == 3 v = Vehicle('6') log.debug(v) assert v.wheels == 6, 'The constructor should use our setter method' v.wheels = '123' assert v.wheels == 123, 'Expected assignment to use the setter method' def test_property_wizard_with_underscored_property_and_public_field_without_default_value(): """ Using `property_wizard` when the dataclass has an underscored property, and a public field *without* a default value explicitly set. """ @dataclass class Vehicle(metaclass=property_wizard): wheels: Union[int, str] @property def _wheels(self) -> int: return self._wheels @_wheels.setter def _wheels(self, wheels: Union[int, str]): self._wheels = int(wheels) v = Vehicle() log.debug(v) assert v.wheels == 0 v = Vehicle(wheels=3) log.debug(v) assert v.wheels == 3 v = Vehicle('6') log.debug(v) assert v.wheels == 6, 'The constructor should use our setter method' v.wheels = '123' assert v.wheels == 123, 'Expected assignment to use the setter method' def test_property_wizard_with_public_property_and_public_field_is_property(): """ Using `property_wizard` when the dataclass has an underscored property, and a public field is also defined as a property. """ @dataclass class Vehicle(metaclass=property_wizard): # The value of `wheels` here will be ignored, since `wheels` is simply # re-assigned on the following property definition. wheels = property # Defines the default value for `wheels`, since it won't work if we # define it above. The `init=False` is needed since otherwise IDEs # seem to suggest `_wheels` as a parameter to the constructor method, # which shouldn't be the case. # # Note: if are *ok* with the default value for the type (0 in this # case), then you can remove the below line and annotate the above # line instead as `wheels: Union[int, str] = property` _wheels: Union[int, str] = field(default=4, init=False) @wheels def wheels(self) -> int: return self._wheels @wheels.setter def wheels(self, wheels: Union[int, str]): self._wheels = int(wheels) v = Vehicle() log.debug(v) assert v.wheels == 4 v = Vehicle(wheels=3) log.debug(v) assert v.wheels == 3 v = Vehicle('6') log.debug(v) assert v.wheels == 6, 'The constructor should use our setter method' v.wheels = '123' assert v.wheels == 123, 'Expected assignment to use the setter method' def test_property_wizard_with_underscored_property_and_public_field_with_default(): """ Using `property_wizard` when the dataclass has an underscored property, and the public field has `default` set. """ @dataclass class Vehicle(metaclass=property_wizard): wheels: Union[int, str] = field(default=2) @property def _wheels(self) -> int: return self._wheels @_wheels.setter def _wheels(self, wheels: Union[int, str]): self._wheels = int(wheels) v = Vehicle() log.debug(v) assert v.wheels == 2 v = Vehicle(wheels=3) log.debug(v) assert v.wheels == 3 v = Vehicle('6') log.debug(v) assert v.wheels == 6, 'The constructor should use our setter method' v.wheels = '123' assert v.wheels == 123, 'Expected assignment to use the setter method' def test_property_wizard_with_underscored_property_and_public_field_with_default_factory(): """ Using `property_wizard` when the dataclass has an underscored property, and the public field has only `default_factory` set. """ @dataclass class Vehicle(metaclass=property_wizard): wheels: Union[int, str] = field(default_factory=str) @property def _wheels(self) -> int: return self._wheels @_wheels.setter def _wheels(self, wheels: Union[int, str]): self._wheels = int(wheels) with pytest.raises(ValueError): # Setter raises ValueError, as `wheels` will be a string by default _ = Vehicle() v = Vehicle(wheels=3) log.debug(v) assert v.wheels == 3 v = Vehicle('6') log.debug(v) assert v.wheels == 6, 'The constructor should use our setter method' v.wheels = '123' assert v.wheels == 123, 'Expected assignment to use the setter method' def test_property_wizard_with_underscored_property_and_public_field_without_default_or_default_factory(): """ Using `property_wizard` when the dataclass has an underscored property, and the public field has neither `default` or `default_factory` set. """ @dataclass class Vehicle(metaclass=property_wizard): wheels: Union[int, str] = field() @property def _wheels(self) -> int: return self._wheels @_wheels.setter def _wheels(self, wheels: Union[int, str]): self._wheels = int(wheels) v = Vehicle() log.debug(v) assert v.wheels == 0 v = Vehicle(wheels=3) log.debug(v) assert v.wheels == 3 v = Vehicle('6') log.debug(v) assert v.wheels == 6, 'The constructor should use our setter method' v.wheels = '123' assert v.wheels == 123, 'Expected assignment to use the setter method' def test_property_wizard_where_annotated_type_contains_none(): """ Using `property_wizard` when the annotated type for the dataclass field associated with a property is here a :class:`Union` type that contains `None`. As such, the field is technically an `Optional` so the default value will be `None` if no value is specified via the constructor. """ @dataclass class Vehicle(metaclass=property_wizard): wheels: Union[int, str, None] @property def _wheels(self) -> int: return self._wheels @_wheels.setter def _wheels(self, wheels: Union[int, str]): self._wheels = int(wheels) # TypeError: int() argument is `None` with pytest.raises(TypeError): _ = Vehicle() v = Vehicle(wheels=3) log.debug(v) assert v.wheels == 3 v = Vehicle('6') log.debug(v) assert v.wheels == 6, 'The constructor should use our setter method' v.wheels = '123' assert v.wheels == 123, 'Expected assignment to use the setter method' def test_property_wizard_with_literal_type(): """ Using `property_wizard` when the dataclass field associated with a property is annotated with a :class:`Literal` type. """ @dataclass class Vehicle(metaclass=property_wizard): # Annotate `wheels` as a literal that should only be set to 1 or 0 # (similar to how the binary numeral system works, for example) # # Note: we can assign a default value for `wheels` explicitly, so that # the IDE doesn't complain when we omit the argument to the # constructor method, but it's technically not required. wheels: Literal[1, '1', 0, '0'] @property def _wheels(self) -> int: return self._wheels @_wheels.setter def _wheels(self, wheels: Union[int, str]): self._wheels = int(wheels) v = Vehicle() log.debug(v) assert v.wheels == 1 # The IDE should display a warning (`wheels` only accepts [0, 1]), however # it won't prevent the assignment here. v = Vehicle(wheels=3) log.debug(v) assert v.wheels == 3 # The IDE should display no warning here, as this is an acceptable value v = Vehicle('1') log.debug(v) assert v.wheels == 1, 'The constructor should use our setter method' v.wheels = '123' assert v.wheels == 123, 'Expected assignment to use the setter method' def test_property_wizard_with_concrete_type(): """ Using `property_wizard` when the dataclass field associated with a property is annotated with a non-generic type, such as a `str` or `int`. """ @dataclass class Vehicle(metaclass=property_wizard): wheels: int @property def _wheels(self) -> int: return self._wheels @_wheels.setter def _wheels(self, wheels: Union[int, str]): self._wheels = int(wheels) v = Vehicle() log.debug(v) assert v.wheels == 0 v = Vehicle(wheels=3) log.debug(v) assert v.wheels == 3 v = Vehicle('1') log.debug(v) assert v.wheels == 1, 'The constructor should use our setter method' v.wheels = '123' assert v.wheels == 123, 'Expected assignment to use the setter method' def test_property_wizard_with_concrete_type_and_default_factory_raises_type_error(): """ Using `property_wizard` when the dataclass field associated with a property is annotated with a non-generic type, such as a `datetime`, which doesn't have a no-args constructor. Since `property_wizard` is not able to instantiate a new `datetime`, the default value should be ``None``. """ @dataclass class Vehicle(metaclass=property_wizard): # Date when the vehicle was sold sold_dt: datetime @property def _sold_dt(self) -> int: return self._sold_dt @_sold_dt.setter def _sold_dt(self, sold_dt: datetime): """Save the datetime with the year set to `2010`""" self._sold_dt = sold_dt.replace(year=2010) # AttributeError: 'NoneType' object has no attribute 'replace' with pytest.raises(AttributeError): _ = Vehicle() dt = datetime(2020, 1, 1, 12, 0, 0) # Jan. 1 2020 12:00 PM expected_dt = datetime(2010, 1, 1, 12, 0, 0) # Jan. 1 2010 12:00 PM v = Vehicle(sold_dt=dt) log.debug(v) assert v.sold_dt != dt assert v.sold_dt == expected_dt, 'The constructor should use our setter ' \ 'method' dt = datetime.min expected_dt = datetime.min.replace(year=2010) v.sold_dt = dt assert v.sold_dt == expected_dt, 'Expected assignment to use the setter ' \ 'method' def test_property_wizard_with_generic_type_which_is_not_supported(): """ Using `property_wizard` when the dataclass field associated with a property is annotated with a generic type other than one of the supported types (e.g. Literal and Union). """ @dataclass class Vehicle(metaclass=property_wizard): # Date when the vehicle was sold sold_dt: ClassVar[datetime] @property def _sold_dt(self) -> int: return self._sold_dt @_sold_dt.setter def _sold_dt(self, sold_dt: datetime): """Save the datetime with the year set to `2010`""" self._sold_dt = sold_dt.replace(year=2010) v = Vehicle() log.debug(v) dt = datetime(2020, 1, 1, 12, 0, 0) # Jan. 1 2020 12:00 PM expected_dt = datetime(2010, 1, 1, 12, 0, 0) # Jan. 1 2010 12:00 PM # TypeError: __init__() got an unexpected keyword argument 'sold_dt' # Note: This is expected because the field for the property is a # `ClassVar`, and even `dataclasses` excludes this annotated type # from the constructor. with pytest.raises(TypeError): _ = Vehicle(sold_dt=dt) # Our property should still work as expected, however v.sold_dt = dt assert v.sold_dt == expected_dt, 'Expected assignment to use the setter ' \ 'method' def test_property_wizard_with_mutable_types_v1(): """ The `property_wizard` handles mutable collections (e.g. subclasses of list, dict, and set) as expected. The defaults for these mutable types should use a `default_factory` so we can observe the expected behavior. """ @dataclass class Vehicle(metaclass=property_wizard): wheels: List[Union[int, str]] # _wheels: List[Union[int, str]] = field(init=False) inverse_bool_set: Set[bool] # Not needed, but we can also define this as below if we want to # inverse_bool_set: Annotated[Set[bool], field(default_factory=set)] # We'll need the `field(default_factory=...)` syntax here, because # otherwise the default_factory will be `defaultdict()`, which is not what # we want. wheels_dict: Annotated[ DefaultDict[str, List[str]], field(default_factory=lambda: defaultdict(list)) ] @property def wheels(self) -> List[int]: return self._wheels @wheels.setter def wheels(self, wheels: List[Union[int, str]]): self._wheels = [int(w) for w in wheels] @property def inverse_bool_set(self) -> Set[bool]: return self._inverse_bool_set @inverse_bool_set.setter def inverse_bool_set(self, bool_set: Set[bool]): # Confirm that we're passed in the right type when no value is set via # the constructor (i.e. from the `property_wizard` metaclass) assert isinstance(bool_set, set) self._inverse_bool_set = {not b for b in bool_set} @property def wheels_dict(self) -> int: return self._wheels_dict @wheels_dict.setter def wheels_dict(self, wheels: Union[int, str]): self._wheels_dict = wheels v1 = Vehicle(wheels=['1', '2', '3'], inverse_bool_set={True, False}, wheels_dict=defaultdict(list, key=['value'])) v1.wheels_dict['key2'].append('another value') log.debug(v1) v2 = Vehicle() v2.wheels.append(4) v2.wheels_dict['a'].append('5') v2.inverse_bool_set.add(True) log.debug(v2) v3 = Vehicle() v3.wheels.append(1) v3.wheels_dict['b'].append('2') v3.inverse_bool_set.add(False) log.debug(v3) assert v1.wheels == [1, 2, 3] assert v1.inverse_bool_set == {False, True} assert v1.wheels_dict == {'key': ['value'], 'key2': ['another value']} assert v2.wheels == [4] assert v2.inverse_bool_set == {True} assert v2.wheels_dict == {'a': ['5']} assert v3.wheels == [1] assert v3.inverse_bool_set == {False} assert v3.wheels_dict == {'b': ['2']} def test_property_wizard_with_mutable_types_v2(): """ The `property_wizard` handles mutable collections (e.g. subclasses of list, dict, and set) as expected. The defaults for these mutable types should use a `default_factory` so we can observe the expected behavior. In this version, we explicitly pass in the `field(default_factory=...)` syntax for all field properties, though it's technically not needed. """ @dataclass class Vehicle(metaclass=property_wizard): wheels: Annotated[List[int], field(default_factory=list)] _wheels_list: list = field(default_factory=list) @property def wheels_list(self) -> list: return self._wheels_list @wheels_list.setter def wheels_list(self, wheels): self._wheels_list = wheels @property def wheels(self) -> list: return self._wheels @wheels.setter def wheels(self, wheels): self._wheels = wheels v1 = Vehicle(wheels=[1, 2], wheels_list=[2, 1]) v1.wheels.append(3) v1.wheels_list.insert(0, 3) log.debug(v1) v2 = Vehicle() log.debug(v2) v2.wheels.append(2) v2.wheels.append(1) v2.wheels_list.append(1) v2.wheels_list.append(2) v3 = Vehicle() log.debug(v3) v3.wheels.append(1) v3.wheels.append(1) v3.wheels_list.append(5) v3.wheels_list.append(5) assert v1.wheels == [1, 2, 3] assert v1.wheels_list == [3, 2, 1] assert v2.wheels == [2, 1] assert v2.wheels_list == [1, 2] assert v3.wheels == [1, 1] assert v3.wheels_list == [5, 5] @pytest.mark.skipif(not PY39_OR_ABOVE, reason='requires Python 3.9 or higher') def test_property_wizard_with_mutable_types_with_parameterized_standard_collections(): """ Test case for mutable types with a Python 3.9 specific feature: parameterized standard collections. As such, this test case is only expected to pass for Python 3.9+. """ @dataclass class Vehicle(metaclass=property_wizard): wheels: list[Union[int, str]] # _wheels: List[Union[int, str]] = field(init=False) inverse_bool_set: set[bool] # Not needed, but we can also define this as below if we want to # inverse_bool_set: Annotated[Set[bool], field(default_factory=set)] # We'll need the `field(default_factory=...)` syntax here, because # otherwise the default_factory will be `defaultdict()`, which is not what # we want. wheels_dict: Annotated[ defaultdict[str, List[str]], field(default_factory=lambda: defaultdict(list)) ] @property def wheels(self) -> List[int]: return self._wheels @wheels.setter def wheels(self, wheels: List[Union[int, str]]): self._wheels = [int(w) for w in wheels] @property def inverse_bool_set(self) -> Set[bool]: return self._inverse_bool_set @inverse_bool_set.setter def inverse_bool_set(self, bool_set: Set[bool]): # Confirm that we're passed in the right type when no value is set via # the constructor (i.e. from the `property_wizard` metaclass) assert isinstance(bool_set, set) self._inverse_bool_set = {not b for b in bool_set} @property def wheels_dict(self) -> int: return self._wheels_dict @wheels_dict.setter def wheels_dict(self, wheels: Union[int, str]): self._wheels_dict = wheels v1 = Vehicle(wheels=['1', '2', '3'], inverse_bool_set={True, False}, wheels_dict=defaultdict(list, key=['value'])) v1.wheels_dict['key2'].append('another value') log.debug(v1) v2 = Vehicle() v2.wheels.append(4) v2.wheels_dict['a'].append('5') v2.inverse_bool_set.add(True) log.debug(v2) v3 = Vehicle() v3.wheels.append(1) v3.wheels_dict['b'].append('2') v3.inverse_bool_set.add(False) log.debug(v3) assert v1.wheels == [1, 2, 3] assert v1.inverse_bool_set == {False, True} assert v1.wheels_dict == {'key': ['value'], 'key2': ['another value']} assert v2.wheels == [4] assert v2.inverse_bool_set == {True} assert v2.wheels_dict == {'a': ['5']} assert v3.wheels == [1] assert v3.inverse_bool_set == {False} assert v3.wheels_dict == {'b': ['2']}

29.392917

105

0.635657

4,686

34,860

4.59347

0.070849

0.033821

0.049524

0.041812

0.797538

0.770035

0.75252

0.732729

0.718792

0.703136

0

0.020404

0.26331

34,860

1,185

106

29.417722

0.817764

0.254016

0

0.791908

0

0.094476

0

0.174855

1

0.137283

false

0

0.011561

0.049133

0.297688

0

null

0

1

0

1

0

null

0

6

e86175919abeb092ddf35854c597c02131ebd4bd

23

py

Python

stat_tools/__init__.py

tilmantroester/simple_bootstrap

4cecd2a789adad7700842de02cb051c46af3647b

[ "MIT" ]

null

stat_tools/__init__.py

tilmantroester/simple_bootstrap

4cecd2a789adad7700842de02cb051c46af3647b

[ "MIT" ]

null

stat_tools/__init__.py

tilmantroester/simple_bootstrap

4cecd2a789adad7700842de02cb051c46af3647b

[ "MIT" ]

null

from . import bootstrap

23

0.826087

3

23

6.333333

1

0

0.130435

23

1

23

0.95

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

e86ab204920d37c7269fd123ac7a7323a5856aec

37

py

Python

download.py

easygittool/EasyGitTool

55ce8aaa6756715e864afdfb3b420d62eef84437

[ "Apache-2.0" ]

1

2019-02-09T11:18:29.000Z

download.py

easygittool/EasyGitTool

55ce8aaa6756715e864afdfb3b420d62eef84437

[ "Apache-2.0" ]

null

download.py

easygittool/EasyGitTool

55ce8aaa6756715e864afdfb3b420d62eef84437

[ "Apache-2.0" ]

null

import os import os.rename(src, dst)

12.333333

19

0.756757

7

37

4

0.714286

0.571429

0

0.135135

37

3

19

12.333333

0.875

0

null

0

0.666667

null

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

2cd9d28ece47e42057e4d6218cb1fc0b40c58037

46,346

py

Python

surveyequivalence/scoring_functions.py

DavidXu999/surveyequivalence

83fc7f3095f7d58a32d5624afa8ec83895073277

[ "MIT" ]

null

surveyequivalence/scoring_functions.py

DavidXu999/surveyequivalence

83fc7f3095f7d58a32d5624afa8ec83895073277

[ "MIT" ]

3

2021-11-30T22:57:59.000Z

2022-02-03T04:44:45.000Z

surveyequivalence/scoring_functions.py

DavidXu999/surveyequivalence

83fc7f3095f7d58a32d5624afa8ec83895073277

[ "MIT" ]

1

2021-07-28T14:27:18.000Z

import random from abc import ABC, abstractmethod from math import log2 from typing import Sequence import numpy as np import pandas as pd from sklearn.metrics import precision_score, recall_score, f1_score, roc_auc_score from .combiners import DiscreteDistributionPrediction, NumericPrediction, DiscretePrediction frac_cache = dict() def frac(n:int): """ Calculate the frac: n! """ if n <= 0: return 1 if n in frac_cache: return frac_cache[n] frac_cache[n] = frac(n-1) * n return frac_cache[n] def comb(m:int,n:int): """ Calculate the combination number: pick m items from n items """ if m>n: return 0 elif m==n: return 1 return (frac(n)/frac(n-m))/frac(m) def mode(data): """ Calculate the mode in the data. """ freq = dict() for i in data: if not i in freq: freq[i]=1 else: freq[i]+=1 max_freq = max(freq.values()) modes = [k for k in freq if freq[k] == max_freq] return np.random.choice(modes,1,replace=False)[0] class Scorer(ABC): """ Scorer Class. An abstract class. Parameters ---------- num_virtual_raters: the number of virtual raters drawn when calculating the score. Higher num_virtural_rater makes the varience of score lower. num_ref_raters_per_virtual_rater: A virtual rater is the combined rating of a randomly selected set of num_ref_raters_per_virtual_rater non-null ratings for each column ref_rater_combiner: The way to combine the ref_raters. Default: Combine with majority vote for discrete labels; mean for continuous labels verbosity: verbosity value from 1 to 4 indicating increased verbosity. """ @abstractmethod def __init__(self, num_virtual_raters=100, num_ref_raters_per_virtual_rater=1, ref_rater_combiner="majority_vote", verbosity=0): self.num_virtual_raters = num_virtual_raters self.num_ref_raters_per_virtual_rater = num_ref_raters_per_virtual_rater self.ref_rater_combiner = ref_rater_combiner self.verbosity = verbosity pass @staticmethod @abstractmethod def score(classifier_predictions: Sequence, rater_labels: Sequence) -> float: pass def expected_score_anonymous_raters(self, classifier_predictions, W, num_virtual_raters=None, num_ref_raters_per_virtual_rater=None, ref_rater_combiner=None, verbosity=None): """ This implementation generates sample virtual raters, scores each, and takes the mean Some scoring functions override this with a closed-form solution for the expectation Parameters ---------- classifier_predictions: Scoring predictions W: The item and rating dataset num_virtual_raters: (the same with instance property if None) the number of virtual raters drawn when calculating the score. Higher num_virtural_rater makes the varience of score lower. num_ref_raters_per_virtual_rater: (the same with instance property if None) A virtual rater is the combined rating of a randomly selected set of num_ref_raters_per_virtual_rater non-null ratings for each column ref_rater_combiner: (the same with instance property if None) The way to combine the ref_raters. Default: Combine with majority vote for discrete labels; mean for continuous labels verbosity: (the same with instance property if None) verbosity value from 1 to 4 indicating increased verbosity. Returns ------- A scalar expected score """ if not num_virtual_raters: num_virtual_raters = self.num_virtual_raters if not num_ref_raters_per_virtual_rater: num_ref_raters_per_virtual_rater = self.num_ref_raters_per_virtual_rater if not ref_rater_combiner: ref_rater_combiner = self.ref_rater_combiner if not verbosity: verbosity = self.verbosity # create a bunch of virtual raters (samples) # for each virtual rater, pick a random combination randomly selected set of num_ref_raters_per_virtual_rater non-null ratings for each column virtual_raters_collection = [] if ref_rater_combiner=="majority_vote": for _, virtual_rater_i in W.iterrows(): vals = virtual_rater_i.dropna().values if len(vals) > 0: ratings_for_i = [] num = min(len(vals),num_ref_raters_per_virtual_rater) for _ in range(num_virtual_raters): #select num_ref_raters_per_virtual_rater reference raters, and combine them to produce virtual rater label ratings_for_i.append(mode(np.random.choice(vals, num, replace=True))) virtual_raters_collection.append(ratings_for_i) else: raise NotImplementedError() # one row for each item; num_virtual_raters columns virtual_raters_matrix = np.array(virtual_raters_collection) # iterate through the columns (virtual raters) of samples_matrix, scoring each scores = [self.score(classifier_predictions, virtual_rater) for virtual_rater in virtual_raters_matrix.T] non_null_scores = [score for score in scores if not pd.isna(score)] if len(non_null_scores) == 0: if verbosity > 2: print("\t\t\tNo non-null scores") return None # take average score across virtual rateres retval = sum(non_null_scores) / len(non_null_scores) if verbosity > 2: print(f"\t\tnon_null_scores = {non_null_scores}; returning mean: {retval}") return retval def expected_score_non_anonymous_raters(self, classifier_predictions, W, verbosity=None): """ A virtual rater is a column of W Parameters ---------- classifier_predictions: Scoring predictions W: The item and rating dataset verbosity: (the same with instance property if None) verbosity value from 1 to 4 indicating increased verbosity. Returns ------- A scalar expected score """ if not verbosity: verbosity = self.verbosity # one sample for each column scores = [self.score(classifier_predictions, W[col]) for col in W.columns] non_null_scores = [score for score in scores if not pd.isna(score)] if len(non_null_scores) == 0: if verbosity > 2: print("\t\t\tNo non-null scores") return None retval = sum(non_null_scores) / len(non_null_scores) if verbosity > 2: print(f"\t\tnon_null_scores = {non_null_scores}; returning mean: {retval}") return retval def expected_score(self, classifier_predictions: Sequence, raters: Sequence, W, anonymous=False, verbosity=None): """ Computes the expected score of the classifier against a random rater. With anonymous flag, compute expected score against a randomly selected label for each item With non-anonymous, compute the expected score against a randomly selected column. Parameters ---------- classifier_predictions: Predictions to be scored raters: Which columns of W to use as reference raters to score the predictions against W: The item and rating dataset. anonymous: if False, then a random rater is a column from W; if True, then labels in a column are not necessarily from the same rater. verbosity: (the same with instance property if None) verbosity value from 1 to 4 indicating increased printed feedback during execution. Returns ------- Expected score of the classifier against a random rater. """ if not verbosity: verbosity = self.verbosity if verbosity > 2: print(f"\t\tScoring predictions = {classifier_predictions} vs. ref raters {raters}") if verbosity > 4: print(f"ref_ratings = \n{W.loc[:, list(raters)]}") if not anonymous: return self.expected_score_non_anonymous_raters(classifier_predictions, W[raters], verbosity=verbosity) else: return self.expected_score_anonymous_raters(classifier_predictions, W[raters], verbosity=verbosity) class Scorer_for_Hard_Classifier(Scorer): """ Scorer class for hard classifier (whose output is a DiscretePrediction) Computes numeric socre for a sequence of classifier DiscretePredictions: - .score() yields actual score against a sequence of reference labels - .expected_score() yields expected score against a matrix of reference labels Note that the current implementation of survey equivalence centering on c_0 and plotting both assume that higher scores are better. """ def __init__(self, num_virtual_raters=100, num_ref_raters_per_virtual_rater=1, ref_rater_combiner="majority_vote", verbosity=0): super().__init__(num_virtual_raters=num_virtual_raters, num_ref_raters_per_virtual_rater=num_ref_raters_per_virtual_rater, ref_rater_combiner=ref_rater_combiner, verbosity=verbosity) pass @staticmethod @abstractmethod def score(classifier_predictions: Sequence[DiscretePrediction], rater_labels: Sequence) -> float: pass def expected_score_anonymous_raters(self, classifier_predictions: Sequence[DiscretePrediction], W, num_virtual_raters=None, num_ref_raters_per_virtual_rater=None, ref_rater_combiner=None, verbosity=None): return super().expected_score_anonymous_raters(classifier_predictions, W, num_virtual_raters=num_virtual_raters, num_ref_raters_per_virtual_rater=num_ref_raters_per_virtual_rater, ref_rater_combiner=ref_rater_combiner, verbosity=verbosity) def expected_score_non_anonymous_raters(self, classifier_predictions: Sequence[DiscretePrediction], W, verbosity=None): return super().expected_score_non_anonymous_raters(classifier_predictions, W, verbosity=verbosity) def expected_score(self, classifier_predictions: Sequence[DiscretePrediction], raters: Sequence, W, anonymous=False, verbosity=None): return super().expected_score(classifier_predictions, raters, W, anonymous=anonymous, verbosity=verbosity) class Scorer_for_Soft_Classifier(Scorer): """ Scorer class for soft classifier (whose output is a DiscreteDistributionPrediction) Computes numeric socre for a sequence of classifier DiscreteDistributionPredictions: - .score() yields actual score against a sequence of reference labels - .expected_score() yields expected score against a matrix of reference labels Note that the current implementation of survey equivalence centering on c_0 and plotting both assume that higher scores are better. Currently, this only affects the CrossEntropy scorer, which we have negated from the traditional definition. """ def __init__(self, num_virtual_raters=100, num_ref_raters_per_virtual_rater=1, ref_rater_combiner="majority_vote", verbosity=0): super().__init__(num_virtual_raters=num_virtual_raters, num_ref_raters_per_virtual_rater=num_ref_raters_per_virtual_rater, ref_rater_combiner=ref_rater_combiner, verbosity=verbosity) pass @staticmethod @abstractmethod def score(classifier_predictions: Sequence[DiscreteDistributionPrediction], rater_labels: Sequence) -> float: pass def expected_score_anonymous_raters(self, classifier_predictions: Sequence[DiscreteDistributionPrediction], W, num_virtual_raters=None, num_ref_raters_per_virtual_rater=None, ref_rater_combiner=None, verbosity=None): return super().expected_score_anonymous_raters(classifier_predictions, W, num_virtual_raters=num_virtual_raters, num_ref_raters_per_virtual_rater=num_ref_raters_per_virtual_rater, ref_rater_combiner=ref_rater_combiner, verbosity=verbosity) def expected_score_non_anonymous_raters(self, classifier_predictions: Sequence[DiscreteDistributionPrediction], W, verbosity=None): return super().expected_score_non_anonymous_raters(classifier_predictions, W, verbosity=verbosity) def expected_score(self, classifier_predictions: Sequence[DiscreteDistributionPrediction], raters: Sequence, W, anonymous=False, verbosity=None): return super().expected_score(classifier_predictions, raters, W, anonymous=anonymous, verbosity=verbosity) class Scorer_for_Numeric_Classifier(Scorer): """ Scorer class for numeric classifier (whose output is a NumericPrediction) Computes numeric socre for a sequence of classifier NumericPredictions: - .score() yields actual score against a sequence of reference labels - .expected_score() yields expected score against a matrix of reference labels Note that the current implementation of survey equivalence centering on c_0 and plotting both assume that higher scores are better. Currently, this only affects the CrossEntropy scorer, which we have negated from the traditional definition. """ def __init__(self, num_virtual_raters=100, num_ref_raters_per_virtual_rater=1, ref_rater_combiner="mean", verbosity=0): super().__init__(num_virtual_raters=num_virtual_raters, num_ref_raters_per_virtual_rater=num_ref_raters_per_virtual_rater, ref_rater_combiner=ref_rater_combiner, verbosity=verbosity) pass @staticmethod @abstractmethod def score(classifier_predictions: Sequence[NumericPrediction], rater_labels: Sequence) -> float: pass def expected_score_anonymous_raters(self, classifier_predictions: Sequence[NumericPrediction], W, num_virtual_raters=None, num_ref_raters_per_virtual_rater=None, ref_rater_combiner=None, verbosity=None): return super().expected_score_anonymous_raters(classifier_predictions, W, num_virtual_raters=num_virtual_raters, num_ref_raters_per_virtual_rater=num_ref_raters_per_virtual_rater, ref_rater_combiner=ref_rater_combiner, verbosity=verbosity) def expected_score_non_anonymous_raters(self, classifier_predictions: Sequence[NumericPrediction], W, verbosity=None): return super().expected_score_non_anonymous_raters(classifier_predictions, W, verbosity=verbosity) def expected_score(self, classifier_predictions: Sequence[NumericPrediction], raters: Sequence, W, anonymous=False, verbosity=None): return super().expected_score(classifier_predictions, raters, W, anonymous=anonymous, verbosity=verbosity) class Correlation(Scorer_for_Numeric_Classifier): """ Computes the Pearson correlation coefficient. """ def __init__(self): super().__init__() @staticmethod def score(classifier_predictions: Sequence[NumericPrediction], rater_labels: Sequence[str], verbosity=0 ): """ Parameters ---------- classifier_predictions: numeric values rater_labels: sequence of labels, which should be numeric values verbosity: Returns ------- Pearson correlation coefficient """ if verbosity > 3: print(f'\t\t\tcorrelation: preds={classifier_predictions}, labels={list(rater_labels)}') if len(classifier_predictions) != len(rater_labels): print("ALERT: classifier_prediction and rater_labels not of same length; skipping") print("") return None # have to remove items where either pred or label is missing good_items = [(pred.value, label) \ for (pred, label) in zip(classifier_predictions, rater_labels) \ if pred and (not pd.isna(pred.value)) and (not pd.isna(label))] if len(good_items) == 0: if verbosity > 0: print("ALERT: no items with both prediction and label; skipping\n") return None else: # note that zip(*tups) unzips a list of tuples non_null_preds, non_null_labels = zip(*good_items) if verbosity > 3: print(f'\t\t\tcorrelation: non null preds={non_null_preds}, non null labels={list(non_null_labels)}') # [convert_to_number(l) for l in rater_labels] retval = np.corrcoef(non_null_preds, non_null_labels)[1, 0] if verbosity > 2: print(f"\t\t\tcorrelation: returning score = {retval}") return retval class AgreementScore(Scorer_for_Hard_Classifier): """ Agreement Scorer. Discrete labels and predictions """ def __init__(self, num_virtual_raters=100, num_ref_raters_per_virtual_rater=1, ref_rater_combiner="majority_vote", verbosity=0): super().__init__(num_virtual_raters=num_virtual_raters, num_ref_raters_per_virtual_rater=num_ref_raters_per_virtual_rater, ref_rater_combiner=ref_rater_combiner, verbosity=verbosity) def expected_score_anonymous_raters(self, classifier_predictions, W, num_virtual_raters=None, num_ref_raters_per_virtual_rater=None, ref_rater_combiner=None, verbosity=None): """ A virtual rater is a majority vote from a group of num_ref_raters_per_virtual_rater randomly selected non-null ratings. Closed-form solution for the expectation, so we ignore the num_virtual_raters parameter Parameters ---------- classifier_predictions: Scoring predictions W: The item and rating dataset verbosity: verbosity value from 1 to 4 indicating increased verbosity. Returns ------- A scalar expected score """ if not num_virtual_raters: num_virtual_raters = self.num_virtual_raters if not num_ref_raters_per_virtual_rater: num_ref_raters_per_virtual_rater = self.num_ref_raters_per_virtual_rater if not ref_rater_combiner: ref_rater_combiner = self.ref_rater_combiner if not verbosity: verbosity = self.verbosity # iterate through the rows # for each row: # get the frequency of matches among the ratings tot = 0 ct = 0 for (row, pred) in zip([row for _, row in W.iterrows()], classifier_predictions): # count frequency of each value counts = row.dropna().value_counts() tot_counts=np.sum(counts) if len(counts) == 0: # no non-null labels for this item; skip it continue # NOTE: the fast combination calculation for majority vote rule is only for binary case if len(counts) > 2 and num_ref_raters_per_virtual_rater > 1: return super().expected_score_anonymous_raters(classifier_predictions,W,num_virtual_raters=num_virtual_raters,num_ref_raters_per_virtual_rater=num_ref_raters_per_virtual_rater,ref_rater_combiner=ref_rater_combiner,verbosity=verbosity) # majority vote of the reference panel for particular label: freqs[] freqs = counts/np.sum(counts) for label, count in counts.items(): # calculate the probability of majority vote's outcomes sum = 0 for ii in range(int((num_ref_raters_per_virtual_rater)/2)+1): i = int((num_ref_raters_per_virtual_rater+1)/2) + ii # i is the number of votes # if there is a tie, choose one randomly # pick i from the current label, and the rest from other labels if i*2 == num_ref_raters_per_virtual_rater: sum += comb(i,count)*comb(num_ref_raters_per_virtual_rater-i,tot_counts-count)/2 # else elif i*2 > num_ref_raters_per_virtual_rater: sum += comb(i,count)*comb(num_ref_raters_per_virtual_rater-i,tot_counts-count) freqs[label]=sum/comb(num_ref_raters_per_virtual_rater,tot_counts) ct += 1 if pred.value in freqs: tot += freqs[pred.value] else: # predicted label never occurred in row, so no agreements to add to tot, but still increment ct pass if ct > 0: return tot / ct else: return None @staticmethod def score(classifier_predictions: Sequence[DiscretePrediction], rater_labels: Sequence[str], verbosity=0): """ Agreement score measures the normalized number of times that the predictor matched the label. Akin to a typical accuracy score. Parameters ---------- classifier_predictions: numeric values rater_labels: sequence of labels, which should be numeric values verbosity: Returns ------- Agreement score """ assert len(classifier_predictions) == len(rater_labels) tot_score = sum([pred.value == label for (pred, label) in \ zip(classifier_predictions, rater_labels)]) / \ len(classifier_predictions) return tot_score class CrossEntropyScore(Scorer_for_Soft_Classifier): """ Cross Entropy Scorer """ def __init__(self, num_virtual_raters=100, num_ref_raters_per_virtual_rater=1, ref_rater_combiner="majority_vote", verbosity=0): super().__init__(num_virtual_raters=num_virtual_raters, num_ref_raters_per_virtual_rater=num_ref_raters_per_virtual_rater, ref_rater_combiner=ref_rater_combiner, verbosity=verbosity) def expected_score_anonymous_raters(self, classifier_predictions, W, num_virtual_raters=None, num_ref_raters_per_virtual_rater=None, ref_rater_combiner=None, verbosity=None): """ A virtual rater is a majority vote from a group of num_ref_raters_per_virtual_rater randomly selected non-null ratings. Closed-form solution for the expectation, so we ignore the num_virtual_raters parameter Parameters ---------- classifier_predictions: Scoring predictions W: The item and rating dataset verbosity: verbosity value from 1 to 4 indicating increased verbosity. Returns ------- A scalar expected score """ if not num_virtual_raters: num_virtual_raters = self.num_virtual_raters if not num_ref_raters_per_virtual_rater: num_ref_raters_per_virtual_rater = self.num_ref_raters_per_virtual_rater if not ref_rater_combiner: ref_rater_combiner = self.ref_rater_combiner if not verbosity: verbosity = self.verbosity # iterate through the rows # for each row: # -- get the probability of each label # -- use those as weights, with score for when that label happens tot = 0 ct = 0 for (row, pred) in zip([row for _, row in W.iterrows()], classifier_predictions): # count frequency of each value counts = row.dropna().value_counts() tot_counts=np.sum(counts) if len(counts) == 0: # no non-null labels for this item; skip it continue # NOTE: the fast combination calculation for majority vote rule is only for binary case if len(counts) > 2 and num_ref_raters_per_virtual_rater > 1: return super().expected_score_anonymous_raters(classifier_predictions,W,num_virtual_raters=num_virtual_raters,num_ref_raters_per_virtual_rater=num_ref_raters_per_virtual_rater,ref_rater_combiner=ref_rater_combiner,verbosity=verbosity) # majority vote of the reference panel for particular label: freqs[] freqs = counts/np.sum(counts) for label, count in counts.items(): # calculate the probability of majority vote's outcomes sum = 0 for ii in range(int((num_ref_raters_per_virtual_rater)/2)+1): i = int((num_ref_raters_per_virtual_rater+1)/2) + ii # i is the number of votes # if there is a tie, choose one randomly # pick i from the current label, and the rest from other labels if i*2 == num_ref_raters_per_virtual_rater: sum += comb(i,count)*comb(num_ref_raters_per_virtual_rater-i,tot_counts-count)/2 # else elif i*2 > num_ref_raters_per_virtual_rater: sum += comb(i,count)*comb(num_ref_raters_per_virtual_rater-i,tot_counts-count) freqs[label]=sum/comb(num_ref_raters_per_virtual_rater,tot_counts) item_tot = 0 for label, freq in freqs.items(): # We use the negated cross-entropy, so that higher scores will be better, # which is true of all the other scoring functions. # If we used the standard cross-entropy, scores would be positive, and higher scores would be worse # Several things would have to be generalized in equivalence.py to allow for higher scores # being worse, including plotting and centering. score = freq * log2(pred.label_probability(label)) item_tot += score tot += item_tot ct += 1 if ct > 0: return tot / ct else: return None @staticmethod def score(classifier_predictions: Sequence[DiscreteDistributionPrediction], rater_labels: Sequence[str], verbosity=0): """ Calculates the Cross Entropy of the two labels. >>> CrossEntropyScore.score([DiscreteDistributionPrediction(['a', 'b'], prs) for prs in [[.3, .7], [.4, .6], [.6, .4]]], ['b', 'b', 'b']) 0.594597099859 >>> CrossEntropyScore.score([DiscreteDistributionPrediction(['a', 'b'], prs) for prs in [[.3, .7], [.4, .6], [.6, .4]]], ['a', 'b', 'b']) 0.87702971998 Parameters ---------- classifier_predictions: numeric values rater_labels: sequence of labels, which should be numeric values verbosity: Returns ------- Cross Entropy score """ assert len(classifier_predictions) == len(rater_labels); if verbosity > 2: print(f'\n-------\n\t\tpredictions: {classifier_predictions[:10]}') print(f'\n--------\n\t\tlabels: {rater_labels[:10]}') def item_score(pred, label): if pred is None: return None if label is None: return None return log2(pred.label_probability(label)) # if pred.value == label: # return log2(pred.label_probability(label)) # else: # return (log2(1-pred.label_probability(label))) # compute mean score over all items seq = list() for (pred, label) in zip(classifier_predictions, rater_labels): score = item_score(pred, label) if score is not None: seq.append(score) if len(seq) == 0: return None return np.mean(seq) # Scorer.rob_median_of_means(pd.Series(seq), 1) class PrecisionScore(Scorer): """ Only implemented for binary labels where one of the labels is "pos" and binary predictions. Calculate the expected probability of (pos rating | pos prediction). (True positives divided by all positives). """ def __init__(self): super().__init__() def expected_score_anonymous_raters(self, classifier_predictions, W, num_virtual_raters=None, verbosity=0): """ A virtual rater is a randomly selected non-null rating for each column. Closed-form solution for the expectation, so we ignore the num_virtual_raters parameter Parameters ---------- classifier_predictions: Scoring predictions W: The item and rating dataset verbosity: verbosity value from 1 to 4 indicating increased verbosity. Returns ------- A scalar expected score """ # iterate through the rows # for each row: # -- count it only if the prediction is "positive" # -- get the frequency of positive among the ratings tot = 0 ct = 0 for (row, pred) in zip([row for _, row in W.iterrows()], classifier_predictions): # count frequency of each value counts = row.dropna().value_counts() freqs = counts/np.sum(counts) if len(counts) == 0: # no non-null labels for this item continue elif pred.value != "pos": # no impact on precision if classifier didn't predict positive continue tot += freqs['pos'] ct += 1 if ct > 0: return tot / ct else: return None @staticmethod def score(classifier_predictions: Sequence[DiscreteDistributionPrediction], rater_labels: Sequence[str], verbosity=0, average: str = 'micro') -> float: """ Precision score. This function uses sklearn's precision function. >>> PrecisionScore.score([DiscreteDistributionPrediction(['a', 'b'], prs) for prs in [[.3, .7], [.4, .6], [.6, .4]]], ['b', 'b', 'b'], 'micro') 0.6666666666666666 >>> PrecisionScore.score([DiscreteDistributionPrediction(['a', 'b'], prs) for prs in [[.3, .7], [.4, .6], [.6, .4]]], ['a', 'b', 'b'], 'micro') 0.3333333333333333 Parameters ---------- classifier_predictions: numeric values rater_labels: sequence of labels, which should be numeric values verbosity: average: macro or micro averaging Returns ------- Precision Score """ assert len(classifier_predictions) == len(rater_labels); if verbosity > 2: print(f'\n-------\n\t\tpredictions: {classifier_predictions[:10]}') print(f'\n--------\n\t\tlabels: {rater_labels[:10]}') new_pred = list() new_label = list() for (pred, label) in zip(classifier_predictions, rater_labels): if pred is not None and label is not None: new_pred.append(pred) new_label.append(label) return precision_score(new_label, [p.value for p in new_pred], average=average) class RecallScore(Scorer): def __init__(self): super().__init__() @staticmethod def score(classifier_predictions: Sequence[DiscreteDistributionPrediction], rater_labels: Sequence[str], verbosity=0, average: str = 'micro') -> float: """ Recall score. This function uses sklearn's recall function. >>> RecallScore.score([DiscreteDistributionPrediction(['a', 'b'], prs) for prs in [[.3, .7], [.4, .6], [.6, .4]]], ['b', 'b', 'b'], 'micro') 0.6666666666666666 >>> RecallScore.score([DiscreteDistributionPrediction(['a', 'b'], prs) for prs in [[.3, .7], [.4, .6], [.6, .4]]], ['b', 'b', 'b'], 'macro') 0.5 >>> RecallScore.score([DiscreteDistributionPrediction(['a', 'b'], prs) for prs in [[.3, .7], [.4, .6], [.6, .4]]], ['a', 'b', 'b'], 'micro') 0.3333333333333333 >>> RecallScore.score([DiscreteDistributionPrediction(['a', 'b'], prs) for prs in [[.3, .7], [.4, .6], [.6, .4]]], ['a', 'b', 'b'], 'macro') 0.25 Parameters ---------- classifier_predictions: numeric values rater_labels: sequence of labels, which should be numeric values verbosity: average: macro or micro averaging Returns ------- Recall Score """ assert len(classifier_predictions) == len(rater_labels); if verbosity > 2: print(f'\n-------\n\t\tpredictions: {classifier_predictions[:10]}') print(f'\n--------\n\t\tlabels: {rater_labels[:10]}') new_pred = list() new_label = list() for (pred, label) in zip(classifier_predictions, rater_labels): if pred is not None and label is not None: new_pred.append(pred) new_label.append(label) return recall_score(new_label, [p.value for p in new_pred], average=average) class F1Score(Scorer): def __init__(self): super().__init__() @staticmethod def score(classifier_predictions: Sequence[DiscreteDistributionPrediction], rater_labels: Sequence[str], verbosity=0, average: str = 'micro') -> float: """ F1 score. This function uses sklearn's F1 function. >>> F1Score.score([DiscreteDistributionPrediction(['a', 'b'], prs) for prs in [[.3, .7], [.4, .6], [.6, .4]]], ['b', 'b', 'b'], 'micro') 0.6666666666666666 >>> F1Score.score([DiscreteDistributionPrediction(['a', 'b'], prs) for prs in [[.3, .7], [.4, .6], [.6, .4]]], ['b', 'b', 'b'], 'macro') 0.39759036144 >>> F1Score.score([DiscreteDistributionPrediction(['a', 'b'], prs) for prs in [[.3, .7], [.4, .6], [.6, .4]]], ['a', 'b', 'b'], 'micro') 0.3333333333333333 >>> F1Score.score([DiscreteDistributionPrediction(['a', 'b'], prs) for prs in [[.3, .7], [.4, .6], [.6, .4]]], ['a', 'b', 'b'], 'macro') 0.25 Parameters ---------- classifier_predictions: numeric values rater_labels: sequence of labels, which should be numeric values verbosity: average: macro or micro averaging Returns ------- F1 Score """ assert len(classifier_predictions) == len(rater_labels); if verbosity > 2: print(f'\n-------\n\t\tpredictions: {classifier_predictions[:10]}') print(f'\n--------\n\t\tlabels: {rater_labels[:10]}') new_pred = list() new_label = list() for (pred, label) in zip(classifier_predictions, rater_labels): if pred is not None and label is not None: new_pred.append(pred) new_label.append(label) return f1_score(new_label, [p.value for p in new_pred], average=average) class AUCScore(Scorer): def __init__(self): super().__init__() @staticmethod def score(classifier_predictions: Sequence[DiscreteDistributionPrediction], rater_labels: Sequence[str], verbosity=0) -> float: """ AUC score. This function uses sklearn's AUC function, but does not work in many cases with multiple labels. Parameters ---------- classifier_predictions: numeric values rater_labels: sequence of labels, which should be numeric values verbosity: Returns ------- AUC Score """ assert len(classifier_predictions) == len(rater_labels); if verbosity > 2: print(f'\n-------\n\t\tpredictions: {classifier_predictions[:10]}') print(f'\n--------\n\t\tlabels: {rater_labels[:10]}') new_pred = list() new_label = list() for (pred, label) in zip(classifier_predictions, rater_labels): if pred is not None and label is not None: new_pred.append(pred) new_label.append(label) if len(set(new_label)) == 1: return np.nan if len(set(new_label)) == 2: return roc_auc_score(new_label, [p.value_prob for p in new_pred]) if len(set(new_label)) > 2: print("multiclass AUC not implemented") return np.nan class DMIScore_for_Hard_Classifier(Scorer_for_Hard_Classifier): def __init__(self, num_virtual_raters=100, num_ref_raters_per_virtual_rater=1, ref_rater_combiner="majority_vote", verbosity=0): super().__init__(num_virtual_raters=num_virtual_raters, num_ref_raters_per_virtual_rater=num_ref_raters_per_virtual_rater, ref_rater_combiner=ref_rater_combiner, verbosity=verbosity) def expected_score_anonymous_raters(self, classifier_predictions: Sequence[DiscretePrediction], W, num_virtual_raters=None, num_ref_raters_per_virtual_rater=None, ref_rater_combiner=None, verbosity=None): if not num_virtual_raters: num_virtual_raters = self.num_virtual_raters if not num_ref_raters_per_virtual_rater: num_ref_raters_per_virtual_rater = self.num_ref_raters_per_virtual_rater if not ref_rater_combiner: ref_rater_combiner = self.ref_rater_combiner if not verbosity: verbosity = self.verbosity W_np = W.to_numpy() # Use index to represent the labels label_to_idx = dict() idx = 0 for pred in classifier_predictions: if pred.value not in label_to_idx: label_to_idx[pred.value] = idx idx += 1 for item_labels in W_np: for label in item_labels: if label not in label_to_idx: label_to_idx[label] = idx idx += 1 if num_ref_raters_per_virtual_rater>1 and idx>2: raise NotImplementedError() # calculate the freq matrix freqs_matrix = np.zeros((idx,idx)) for (row, pred) in zip([row for _, row in W.iterrows()], classifier_predictions): counts = row.dropna().value_counts() tot_counts=np.sum(counts) label_prob = np.zeros(idx) for label, count in counts.items(): # calculate the probability of majority vote's outcomes sum = 0 for ii in range(int((num_ref_raters_per_virtual_rater)/2)+1): i = int((num_ref_raters_per_virtual_rater+1)/2) + ii # i is the number of votes # if there is a tie, choose one randomly # pick i from the current label, and the rest from other labels if i*2 == num_ref_raters_per_virtual_rater: sum += comb(i,count)*comb(num_ref_raters_per_virtual_rater-i,tot_counts-count)/2 # else elif i*2 > num_ref_raters_per_virtual_rater: sum += comb(i,count)*comb(num_ref_raters_per_virtual_rater-i,tot_counts-count) label_prob[label_to_idx[label]]=sum/comb(num_ref_raters_per_virtual_rater,tot_counts) freqs_matrix[label_to_idx[pred.value]] += label_prob # normalization freqs_matrix = freqs_matrix / np.sum(freqs_matrix) DMI=np.abs(np.linalg.det(freqs_matrix)) return DMI @staticmethod def score(classifier_predictions: Sequence[DiscretePrediction], rater_labels: Sequence[str], verbosity=0) -> float: """ DMI score. Parameters ---------- classifier_predictions: the (hard) classifier's predictions for all items rater_labels: sequence of labels from the reference rater verbosity: Returns ------- DMI Score """ assert len(classifier_predictions) == len(rater_labels) # Use index to represent the labels label_to_idx = dict() idx = 0 for pred in classifier_predictions: if pred.value not in label_to_idx: label_to_idx[pred.value] = idx idx += 1 for label in rater_labels: if label not in label_to_idx: label_to_idx[label] = idx idx += 1 # calculate the freq matrix freqs_matrix = np.zeros((idx,idx)) for (pred, label) in zip(classifier_predictions, rater_labels): freqs_matrix[label_to_idx[pred.value]][label_to_idx[label]] += 1 # normalization freqs_matrix = freqs_matrix / np.sum(freqs_matrix) DMI=np.abs(np.linalg.det(freqs_matrix)) return DMI class DMIScore_for_Soft_Classifier(Scorer_for_Soft_Classifier): def __init__(self, num_virtual_raters=100, num_ref_raters_per_virtual_rater=1, ref_rater_combiner="majority_vote", verbosity=0): super().__init__(num_virtual_raters=num_virtual_raters, num_ref_raters_per_virtual_rater=num_ref_raters_per_virtual_rater, ref_rater_combiner=ref_rater_combiner, verbosity=verbosity) def expected_score_anonymous_raters(self, classifier_predictions: Sequence[DiscreteDistributionPrediction], W, num_virtual_raters=None, num_ref_raters_per_virtual_rater=None, ref_rater_combiner=None, verbosity=None): if not num_virtual_raters: num_virtual_raters = self.num_virtual_raters if not num_ref_raters_per_virtual_rater: num_ref_raters_per_virtual_rater = self.num_ref_raters_per_virtual_rater if not ref_rater_combiner: ref_rater_combiner = self.ref_rater_combiner if not verbosity: verbosity = self.verbosity W_np = W.to_numpy() # Create a dictionary to map label names to enumerated index values (0, 1 for binary labels) num_distinct_labels = len(classifier_predictions[0].label_names) label_idx_map = dict(zip(classifier_predictions[0].label_names,range(num_distinct_labels))) # if W has any labels that are not in the classifier's output, we have an all 0 column in the matrix # and DMI is 0 for item_labels in W_np: for label in item_labels: if label not in label_idx_map: return 0 if num_ref_raters_per_virtual_rater>1 and num_distinct_labels>2: raise NotImplementedError() # calculate the freq matrix; joint distribution of classifier output and reference rater labels freqs_matrix = np.zeros((num_distinct_labels, num_distinct_labels)) for (row, pred) in zip([row for _, row in W.iterrows()], classifier_predictions): # each row is one item counts = row.dropna().value_counts() # a dict that maps from label names to frequency of that label among reference raters tot_counts=np.sum(counts) # if target panel size is 1, we could work directly with counts/tot_counts as probabilities. # more generally, we need the probabilities of different majority vote outcomes # rather than probabilities of different labels from individual raters # majority_prob will be a mapping from labels to the probability of a majority of raters giving that label majority_prob = np.zeros(num_distinct_labels) for label, count in counts.items(): sum = 0 for ii in range(int((num_ref_raters_per_virtual_rater)/2)+1): i = int((num_ref_raters_per_virtual_rater+1)/2) + ii # i is the number of votes # if there is a tie, choose one randomly # pick i from the current label, and the rest from other labels if i*2 == num_ref_raters_per_virtual_rater: sum += comb(i,count)*comb(num_ref_raters_per_virtual_rater-i,tot_counts-count)/2 # else elif i*2 > num_ref_raters_per_virtual_rater: sum += comb(i,count)*comb(num_ref_raters_per_virtual_rater-i,tot_counts-count) majority_prob[label_idx_map[label]]=sum/comb(num_ref_raters_per_virtual_rater,tot_counts) # get joint probability distribution of classifier output and target panel output for this item # add that to the accumulating overall matrix; we will normalize later to make it a joint probability distribution freqs_matrix += np.array(pred.probabilities).reshape(-1,1) * majority_prob # normalization freqs_matrix = freqs_matrix / np.sum(freqs_matrix) # DMI is determinant of the normalized matrix DMI=np.abs(np.linalg.det(freqs_matrix)) return DMI @staticmethod def score(classifier_predictions: Sequence[DiscreteDistributionPrediction], rater_labels: Sequence[str], verbosity=0) -> float: """ DMI score. Parameters ---------- classifier_predictions: the (soft) classifier's predictions for all items rater_labels: sequence of labels from the reference rater verbosity: Returns ------- DMI Score """ assert len(classifier_predictions) == len(rater_labels) # Use index to represent the labels idx = len(classifier_predictions[0].label_names) label_to_idx = dict(zip(classifier_predictions[0].label_names,range(idx))) for label in rater_labels: if label not in label_to_idx: return 0 # calculate the freq matrix freqs_matrix = np.zeros((idx,idx)) for (pred, label) in zip(classifier_predictions, rater_labels): freqs_matrix[label_to_idx[label]] += pred.probabilities # normalization freqs_matrix = freqs_matrix / np.sum(freqs_matrix) DMI=np.abs(np.linalg.det(freqs_matrix)) return DMI

42.363803

250

0.636064

5,660

46,346

4.965901

0.075265

0.046963

0.04184

0.0523

0.796919

0.773259

0.748817

0.735689

0.710748

0.699932

0

0.012973

0.27983

46,346

1,094

251

42.363803

0.829134

0.302378

0

0.73271

0

0.001869

0.042715

0.018146

0

0.014953

1

0.08785

false

0.016822

0.014953

0.016822

0.216822

0.042991

0

null

0

1

0

1

0

null

0

6

2ce2eee8d89f06ffa737492055a7d3050e36fa30

47

py

Python

sandbox/utils.py

IvanaEscobar/sandbox

71d62af2c112686c5ce26def35593247cf6a0ccc

[ "MIT" ]

null

sandbox/utils.py

IvanaEscobar/sandbox

71d62af2c112686c5ce26def35593247cf6a0ccc

[ "MIT" ]

3

2022-02-15T23:32:52.000Z

2022-03-28T21:35:12.000Z

sandbox/utils.py

IvanaEscobar/sandbox

71d62af2c112686c5ce26def35593247cf6a0ccc

[ "MIT" ]

null

def testFunction ( var ): return var + 10

11.75

25

0.617021

6

47

4.833333

0.833333

0

0.060606

0.297872

47

3

26

15.666667

0.818182

0

1

0.5

false

0

0.5

1

0

1

0

null

0

1

0

null

0

1

0

1

0

6

2ce89d44b2c024f94cf4c603ae079d5d050a4038

2,096

py

Python

tests/test_rsa_crt_fault_attack.py

timgates42/featherduster

76954f234d9056bbaafa00b066725c7d8de9c9c4

[ "BSD-3-Clause" ]

1,026

2016-05-05T17:27:39.000Z

2022-03-31T20:58:42.000Z

tests/test_rsa_crt_fault_attack.py

timgates42/featherduster

76954f234d9056bbaafa00b066725c7d8de9c9c4

[ "BSD-3-Clause" ]

82

2016-05-06T13:02:14.000Z

2022-03-27T19:51:25.000Z

tests/test_rsa_crt_fault_attack.py

timgates42/featherduster

76954f234d9056bbaafa00b066725c7d8de9c9c4

[ "BSD-3-Clause" ]

165

2016-05-05T17:33:38.000Z

2022-03-10T01:39:14.000Z

import cryptanalib as ca d = 8276730537354063873046700086606934013390657907137764360442957114148983703392213920905379521933961200662349418726972470589061811064510563973168097131091762900935884000158700608857487894152877574467022635471921054922438416372847738044720341171693728509510304733152582025735997457366640962042446371702796650429270558556364971206673220641102736535785495085270088144553219777365472362543536538109527913412532336001148762959211359919986044869355021796538441331705044929239323402165504467704827987337504638532978042282749691349759655331384162387839085847376059025035741851624666333548281288846373129193232087364721195558785 N = 10582801803234222023721351326204905463469321586937873866285899804705438289550794516434678042160644904163578778193959630189545495390487529871810472562828953980775617092992054819000569379567214328036625223601593768074993103126020801627845266883277896935534900134552457628778290646235319742652095517837642111737039755900571013123362947713434696280050067990761280634722593133366154815295209776044603345322953117132250103892984014819690279192133597065661729463436631976729813726128619125291814410455706189604700693994757946995645898522397403679829590407298131095142520403589090633192841184074504222946814847796446760184239L e = 0x10001 badsig = 8864006670929406852925128423419283000101606264931546544565004118285111530579411512945707908758302944358049245159881240365195456784997978690049780123607312704598614883100338341770324555702622215028874055295233188087028972047396168538381606028425952850084870840815139465808335221537979841948906784351532564396334588129879467099436972117704102514869368975631607984728653576512470131900790248497082055162110680555834874277167971776484142044468048689414584647613377242581544618782866426893967031773962301853433165727478082390333355401924553292504003746966703113454750999893154821514523123639279610673613179496062870087972L message = 2 print 'Testing RSA-CRT fault attack...' try: assert(ca.rsa_crt_fault_attack(badsig, message, N, verbose=True) == d) except: exit('RSA-CRT fault attack is broken!')

116.444444

626

0.962309

41

2,096

49.121951

0.682927

0.008937

0.016385

0.025323

0

0.906693

0.023378

2,096

17

627

123.294118

0.077186

0

0.02958

0

1

0.00334

0

0.090909

0

null

0

0.090909

null

0.090909

0

1

null

0

1

0

1

0

null

1

0

1

0

6

fa5f164def109b09dfbbd12bdeedb6d6af991903

984

py

Python

dashboard/core/templatetags/core_extras.py

hebergui/webtrade

338fbf334b6ba173296635b380b53b088a87bb95

[ "Apache-2.0" ]

null

dashboard/core/templatetags/core_extras.py

hebergui/webtrade

338fbf334b6ba173296635b380b53b088a87bb95

[ "Apache-2.0" ]

null

dashboard/core/templatetags/core_extras.py

hebergui/webtrade

338fbf334b6ba173296635b380b53b088a87bb95

[ "Apache-2.0" ]

null

import json import statistics from django import template from django.template.defaultfilters import stringfilter from django.utils.safestring import mark_safe register = template.Library() @register.filter @stringfilter def split(value, arg): return value.split(arg) @register.filter(is_safe=True) def js(obj): return mark_safe(json.dumps(obj)) @register.filter def get_attr(obj, attr): return getattr(obj, attr) @register.filter def get_sum(li): return sum(li) @register.filter def get_min(li): return min(li) @register.filter def get_median(li): return statistics.median(li) @register.filter def get_mean(li): return statistics.mean(li) @register.filter def get_max(li): return max(li) @register.filter def get_abs(a): return abs(a) @register.filter def index(indexable, i): if indexable is not None: return indexable[i] @register.filter def get_pl(pru, price): return round(100 * (price - pru) / pru, 2)

14.909091

55

0.720528

144

984

4.847222

0.333333

0.22063

0.219198

0.229226

0.157593

0

0.004902

0.170732

984

65

56

15.138462

0.85049

0

0.243902

0

1

0.268293

false

0

0.121951

0.243902

0.658537

0

null

1

0

null

0

1

0

1

0

6

d71bb519364d3c81f3c5a098c4b6698613f64631

18

py

Python

test.py

danielim/chenpy

502e00e610ce47469d75777f5c1224a316dda4f0

[ "MIT" ]

null

test.py

danielim/chenpy

502e00e610ce47469d75777f5c1224a316dda4f0

[ "MIT" ]

null

test.py

danielim/chenpy

502e00e610ce47469d75777f5c1224a316dda4f0

[ "MIT" ]

null

import tldrwiki

4.5

15

0.777778

2

18

7

1

0

0.222222

18

3

16

6

1

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

d74711cccb093dafff7fd329dab02fe7da3c629b

48

py

Python

pytest-ex/ch1/test_two.py

leonhmi75/learning-materials

7342bf14e41ee2d1bf1b0b9b52f626318597a75e

[ "MIT" ]

1

2019-05-01T05:25:22.000Z

pytest-ex/ch1/test_two.py

leon-lei/learning-materials

7342bf14e41ee2d1bf1b0b9b52f626318597a75e

[ "MIT" ]

null

pytest-ex/ch1/test_two.py

leon-lei/learning-materials

7342bf14e41ee2d1bf1b0b9b52f626318597a75e

[ "MIT" ]

null

def test_failing(): assert(1,2,3) == (3,2,1)

24

28

0.5625

10

48

2.6

0.7

0

0.15

0.166667

48

2

28

24

0.5

0

0.5

1

0.5

true

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

6

d79485dd00e3715f1bb3fe9967ca9dadecc1d7ff

41

py

Python

test.py

osirisguitar/gpt2-generator

a78af4f341f24f0452a44336309c6c9cbe09011f

[ "MIT" ]

null

test.py

osirisguitar/gpt2-generator

a78af4f341f24f0452a44336309c6c9cbe09011f

[ "MIT" ]

null

test.py

osirisguitar/gpt2-generator

a78af4f341f24f0452a44336309c6c9cbe09011f

[ "MIT" ]

null

print("Message one") print("Message two")

20.5

20

0.731707

6

41

5

0.666667

0.8

0

0.073171

41

2

21

20.5

0.789474

0

0.52381

0

1

0

true

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

d7973249303aefe10d10f81cc67f24b5061d4732

284

py

Python

app/static.py

renato-farias/openstack-instances-monitoring

8b3aaa5276404f79036b2ec8fbaa32d37b3d7cf5

[ "Apache-2.0" ]

18

2016-05-13T13:16:03.000Z

2021-06-01T23:37:13.000Z

app/static.py

renato-farias/openstack-instances-monitoring

8b3aaa5276404f79036b2ec8fbaa32d37b3d7cf5

[ "Apache-2.0" ]

2

2016-06-20T09:24:30.000Z

2017-04-06T14:41:44.000Z

app/static.py

renato-farias/openstack-instances-monitoring

8b3aaa5276404f79036b2ec8fbaa32d37b3d7cf5

[ "Apache-2.0" ]

9

2016-05-15T20:12:23.000Z

2018-01-18T12:18:53.000Z

# -*- coding: utf-8 -*- from flask import current_app def index(resource=None): return current_app.send_static_file('index.html') def instances(): return current_app.send_static_file('instances.html') def report(): return current_app.send_static_file('report.html')

18.933333

57

0.732394

40

284

4.95

0.475

0.20202

0.242424

0.30303

0.454545

0

0.004082

0.137324

284

14

58

20.285714

0.804082

0.073944

0

0.1341

0

1

0.428571

false

0

0.142857

0.428571

1

0

null

1

0

null

0

1

0

1

0

6

ad4357264dc72a492e2e7707307ba1d582488d9a

9,013

py

Python

src/performant/tools.py

babe269/performant

416c07b06a1288ad16d50e275fcd78c3ffee3cfb

[ "MIT" ]

null

src/performant/tools.py

babe269/performant

416c07b06a1288ad16d50e275fcd78c3ffee3cfb

[ "MIT" ]

null

src/performant/tools.py

babe269/performant

416c07b06a1288ad16d50e275fcd78c3ffee3cfb

[ "MIT" ]

null

def plotFormants(fileNames, vowels, language, centroid): #Requirements if running on Colab: install !pip install praat-parselmouth !pip install tgt #cd into a directory to store wav, txt and textgrid files before running. import os import requests import pandas as pd import urllib.request import parselmouth import tgt import plotly.express as px import numpy as np vl = [] f1l = [] f2l = [] #List of vowels for NZE vowels = vowels print(os.getcwd()) for fileName in fileNames: #Call WebMaus Basic Api to generate TextGrids. print(" "+"└── "+ fileName) headers = { 'content-type': 'multipart/form-data', } files = { 'SIGNAL': (fileName + '.wav', open(fileName + '.wav', 'rb')), 'LANGUAGE': (None, language), 'OUTFORMAT': (None, 'TextGrid'), 'TEXT': (fileName + '.txt', open(fileName + '.txt', 'rb')), } result = requests.post('https://clarin.phonetik.uni-muenchen.de/BASWebServices/services/runMAUSBasic', files=files) decodeResponse = result.content.decode("utf-8") xmlSplit = decodeResponse.split("<downloadLink>") downURL = xmlSplit[1].split("</downloadLink>") downURL = downURL[0] urllib.request.urlretrieve(downURL, fileName+".TextGrid") #Use parselmouth + Textgrid tools to obtain formant information ps = parselmouth.Sound(fileName + ".wav") formants = ps.to_formant_burg() tg = tgt.io.read_textgrid(fileName + '.TextGrid') mau = tg.get_tier_by_name("MAU") mauObjs = mau.intervals for i in vowels: for j in mauObjs: if i in j.text: start = j.start_time end = j.end_time mid = (start + end) / 2 f1 = formants.get_value_at_time(1, mid, "HERTZ") f2 = formants.get_value_at_time(2, mid, "HERTZ") vl.append(j.text) f1l.append(f1) f2l.append(f2) #Store formant values in dataframe. d = {'vowel': vl, 'f1': f1l, 'f2': f2l} df=pd.DataFrame(d) display(df) if centroid == True: f1Centroid = df.groupby('vowel')['f1'].apply(lambda x: np.mean(x.tolist(), axis=0)) f2Centroid = df.groupby('vowel')['f2'].apply(lambda x: np.mean(x.tolist(), axis=0)) d = {'f1': f1Centroid, 'f2': f2Centroid} finaldf=pd.DataFrame(d) fig = px.scatter(finaldf, x="f2", y="f1",color= finaldf.index, text = finaldf.index, width=1000, height=900) fig.update_layout( font_family="Helvetica", font_color="black", font = {"size": 20} ) fig.update_xaxes( tickangle = 90, title_text = "F2 (Hz)", title_font = {"size": 20}, title_standoff = 20 ) fig.update_yaxes( tickangle = 90, title_text = "F1 (Hz)", title_font = {"size": 20}, title_standoff = 20 ) fig.update_layout({ 'plot_bgcolor': '#ffffff', 'paper_bgcolor': '#ffffff', 'yaxis_gridcolor':'#e5e5ea', 'xaxis_gridcolor':'#e5e5ea' }) fig.update_traces(textposition='top center') fig.update_yaxes(autorange="reversed") fig.update_xaxes(autorange="reversed") fig.update_xaxes(tickangle=0) fig.update_yaxes(tickangle=0) fig.show() else: finaldf = df fig = px.scatter(finaldf, x="f2", y="f1",color= "vowel", text = "vowel", width=1000, height=900) fig.update_layout( font_family="Helvetica", font_color="black", font = {"size": 20} ) fig.update_xaxes( tickangle = 90, title_text = "F2 (Hz)", title_font = {"size": 20}, title_standoff = 20 ) fig.update_yaxes( tickangle = 90, title_text = "F1 (Hz)", title_font = {"size": 20}, title_standoff = 20 ) fig.update_layout({ 'plot_bgcolor': '#ffffff', 'paper_bgcolor': '#ffffff', 'yaxis_gridcolor':'#e5e5ea', 'xaxis_gridcolor':'#e5e5ea' }) fig.update_traces(textposition='top center') fig.update_yaxes(autorange="reversed") fig.update_xaxes(autorange="reversed") fig.update_xaxes(tickangle=0) fig.update_yaxes(tickangle=0) fig.show() return finaldf def plotPath(folderNames, vowels, language): import os import requests import pandas as pd import urllib.request import parselmouth import tgt import plotly.express as px import plotly.graph_objects as go import numpy as np originpath = os.getcwd() data = [] for index,folder in enumerate(folderNames): vl = [] f1l = [] f2l = [] path = os.getcwd() +"\\"+ folder os.chdir(path) print(os.getcwd()) namelist = os.listdir(path) fileNames =[] for name in namelist: unique = name.split(".")[0] if unique not in fileNames: fileNames.append(unique) for fileName in fileNames: #Call WebMaus Basic Api to generate TextGrids. print(" "+"└── "+ fileName) headers = { 'content-type': 'multipart/form-data', } files = { 'SIGNAL': (fileName + '.wav', open(fileName + '.wav', 'rb')), 'LANGUAGE': (None, language), 'OUTFORMAT': (None, 'TextGrid'), 'TEXT': (fileName + '.txt', open(fileName + '.txt', 'rb')), } result = requests.post('https://clarin.phonetik.uni-muenchen.de/BASWebServices/services/runMAUSBasic', files=files) decodeResponse = result.content.decode("utf-8") xmlSplit = decodeResponse.split("<downloadLink>") downURL = xmlSplit[1].split("</downloadLink>") downURL = downURL[0] urllib.request.urlretrieve(downURL, fileName+".TextGrid") #Use parselmouth + Textgrid tools to obtain formant information ps = parselmouth.Sound(fileName + ".wav") formants = ps.to_formant_burg() tg = tgt.io.read_textgrid(fileName + '.TextGrid') mau = tg.get_tier_by_name("MAU") mauObjs = mau.intervals for i in vowels: for j in mauObjs: if i in j.text: start = j.start_time end = j.end_time mid = (start + end) / 2 f1 = formants.get_value_at_time(1, mid, "HERTZ") f2 = formants.get_value_at_time(2, mid, "HERTZ") vl.append(j.text) f1l.append(f1) f2l.append(f2) if folder == "truth": d = {'vowel': vl, 'f1': f1l, 'f2': f2l} df=pd.DataFrame(d) f1Centroid = df.groupby('vowel')['f1'].apply(lambda x: np.mean(x.tolist(), axis=0)) f2Centroid = df.groupby('vowel')['f2'].apply(lambda x: np.mean(x.tolist(), axis=0)) d = { 'f1': f1Centroid, 'f2': f2Centroid} rDF=pd.DataFrame(d) rDF = rDF.reset_index() else: #Store formant values in dataframe. d = {'vowel': vl, 'f1': f1l, 'f2': f2l} df=pd.DataFrame(d) f1Centroid = df.groupby('vowel')['f1'].apply(lambda x: np.mean(x.tolist(), axis=0)) f2Centroid = df.groupby('vowel')['f2'].apply(lambda x: np.mean(x.tolist(), axis=0)) d = {'f1': f1Centroid, 'f2': f2Centroid, 'steps': folder, 'point': index} df2=pd.DataFrame(d) data.append(df2) os.chdir(originpath) full = pd.concat(data) df = full fig = px.line(full, x="f2", y="f1",color=df.index, width=1000, height=900, line_shape= 'spline', text = 'steps', line_group=df.index, ) if 'truth' in folderNames: for i, d in enumerate(fig.data): for index, row in rDF.iterrows(): if d.legendgroup == row.vowel: fig.add_trace(go.Scatter(x=[row.f2], y = [row.f1], mode = "markers+text", showlegend=False, marker_color=d.line.color, text=row.vowel, textfont=dict( size=30, color=d.line.color ))) fig.update_layout( font_family="Helvetica", font_color="black", font = {"size": 20} ) fig.update_xaxes( tickangle = 90, title_text = "F2 (Hz)", title_font = {"size": 20}, title_standoff = 20 ) fig.update_yaxes( tickangle = 90, title_text = "F1 (Hz)", title_font = {"size": 20}, title_standoff = 20 ) fig.update_layout({ 'plot_bgcolor': '#ffffff', 'paper_bgcolor': '#ffffff', 'yaxis_gridcolor':'#e5e5ea', 'xaxis_gridcolor':'#e5e5ea' }) fig.update_traces(textposition='top center') fig.update_yaxes(autorange="reversed") fig.update_xaxes(autorange="reversed") fig.update_xaxes(tickangle=0) fig.update_yaxes(tickangle=0) fig.show() os.chdir(originpath) return df

35.070039

163

0.562854

1,048

9,013

4.753817

0.211832

0.048776

0.018065

0.016861

0.786632

0.778804

0.76676

0

0.02678

0.295684

9,013

257

164

35.070039

0.757089

0.051925

0

0.774468

0

0.125351

0

1

0.008511

false

0

0.07234

0

0.089362

0.017021

0

null

0

1

0

1

0

null

0

6

a8ec05d1330ae68b60864f963ee382af8fb81175

28

py

Python

datacatalog/formats/tulane/__init__.py

SD2E/python-datacatalog

51ab366639505fb6e8a14cd6b446de37080cd20d

[ "CNRI-Python" ]

null

datacatalog/formats/tulane/__init__.py

SD2E/python-datacatalog

51ab366639505fb6e8a14cd6b446de37080cd20d

[ "CNRI-Python" ]

2

2019-07-25T15:39:04.000Z

2019-10-21T15:31:46.000Z

datacatalog/formats/tulane/__init__.py

SD2E/python-datacatalog

51ab366639505fb6e8a14cd6b446de37080cd20d

[ "CNRI-Python" ]

1

2019-10-15T14:33:44.000Z

from .convert import Tulane

14

27

0.821429

4

28

5.75

1

0

0.142857

28

1

28

0.958333

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

a8ef3b58fe05e8f36fb47a9deaf7d16e84e3090d

6,515

py

Python

seqgra/learner/dna.py

gifford-lab/seqgra

3c7547878ecda4c00572746b8a07e0d614c9dbef

[ "MIT" ]

null

seqgra/learner/dna.py

gifford-lab/seqgra

3c7547878ecda4c00572746b8a07e0d614c9dbef

[ "MIT" ]

null

seqgra/learner/dna.py

gifford-lab/seqgra

3c7547878ecda4c00572746b8a07e0d614c9dbef

[ "MIT" ]

2

2021-06-14T20:27:40.000Z

2021-06-14T20:29:29.000Z

"""MIT - CSAIL - Gifford Lab - seqgra Abstract base class for learners @author: Konstantin Krismer """ from __future__ import annotations from typing import List import itertools import warnings import numpy as np import pandas as pd from sklearn.preprocessing import MultiLabelBinarizer from seqgra import ExampleSet from seqgra.learner import MultiClassClassificationLearner from seqgra.learner import MultiLabelClassificationLearner from seqgra.learner import DNAHelper from seqgra.model import ModelDefinition class DNAMultiClassClassificationLearner(MultiClassClassificationLearner): def __init__(self, model_definition: ModelDefinition, data_dir: str, output_dir: str, validate_data: bool = True, gpu_id: int = 0, silent: bool = False) -> None: super().__init__(model_definition, data_dir, output_dir, validate_data, gpu_id, silent=silent) self.alphabet_size: int = 4 def encode_x(self, x: List[str]): return np.stack([DNAHelper.convert_dense_to_one_hot_encoding(seq) for seq in x]) def decode_x(self, x): return np.stack([DNAHelper.convert_one_hot_to_dense_encoding(seq) for seq in x]) def encode_y(self, y: List[str]): if self.definition.labels is None: raise Exception("unknown labels, call parse_examples_data or " "load_model first") labels = np.array(self.definition.labels) return np.vstack([ex == labels for ex in y]) def decode_y(self, y): if self.definition.labels is None: raise Exception("unknown labels, call parse_examples_data or " "load_model first") labels = np.array(self.definition.labels) if isinstance(y, list): y = np.asarray(y) elif not isinstance(y, np.ndarray): raise Exception("y is neither list nor np.ndarry") if y.dtype == np.float32 or y.dtype == np.float64 or \ y.dtype == np.float_: # binarize y true_idx = np.argmax(y, axis=1) y = np.zeros(y.shape) y[np.arange(len(y)), true_idx] = 1 y = y.astype(bool) elif y.dtype == np.int8 or y.dtype == np.int16 or \ y.dtype == np.int32 or y.dtype == np.int64 or \ y.dtype == np.uint8 or y.dtype == np.uint16 or \ y.dtype == np.uint32 or y.dtype == np.uint64 or \ y.dtype == np.intp or y.dtype == np.uintp or \ y.dtype == np.int_: y = y.astype(bool) elif y.dtype != np.bool_: raise Exception("y has invalid data type; valid data types " "include bool, int, float") decoded_y = np.vstack([labels[ex] for ex in y]) decoded_y = list(itertools.chain(*decoded_y)) return decoded_y def parse_examples_data(self, file_name: str) -> ExampleSet: df = pd.read_csv(file_name, sep="\t", dtype={"x": "string", "y": "string"}) df = df.fillna("") x: List[str] = df["x"].tolist() y: List[str] = df["y"].tolist() if self.validate_data: self.check_sequence(x) self.check_labels(y) return ExampleSet(x, y) def check_sequence(self, x: List[str]) -> bool: return DNAHelper.check_sequence(x) class DNAMultiLabelClassificationLearner(MultiLabelClassificationLearner): def __init__(self, model_definition: ModelDefinition, data_dir: str, output_dir: str, validate_data: bool = True, gpu_id: int = 0, silent: bool = False) -> None: super().__init__(model_definition, data_dir, output_dir, validate_data, gpu_id, silent=silent) self.alphabet_size: int = 4 def encode_x(self, x: List[str]): return np.stack([DNAHelper.convert_dense_to_one_hot_encoding(seq) for seq in x]) def decode_x(self, x): return np.stack([DNAHelper.convert_one_hot_to_dense_encoding(seq) for seq in x]) def encode_y(self, y: List[str]): if self.definition.labels is None: raise Exception("unknown labels, call parse_examples_data or " "load_model first") y = [ex.split("|") for ex in y] mlb = MultiLabelBinarizer(classes=self.definition.labels) with warnings.catch_warnings(): warnings.simplefilter("ignore") y = mlb.fit_transform(y).astype(bool) return y def decode_y(self, y): if self.definition.labels is None: raise Exception("unknown labels, call parse_examples_data or " "load_model first") labels = np.array(self.definition.labels) if isinstance(y, list): y = np.asarray(y) elif not isinstance(y, np.ndarray): raise Exception("y is neither list nor np.ndarry") if y.dtype == np.float32 or y.dtype == np.float64 or \ y.dtype == np.float_: # binarize y y = np.greater(y, 0.5).astype(bool) elif y.dtype == np.int8 or y.dtype == np.int16 or \ y.dtype == np.int32 or y.dtype == np.int64 or \ y.dtype == np.uint8 or y.dtype == np.uint16 or \ y.dtype == np.uint32 or y.dtype == np.uint64 or \ y.dtype == np.intp or y.dtype == np.uintp or \ y.dtype == np.int_: y = y.astype(bool) elif y.dtype != np.bool_: raise Exception("y has invalid data type; valid data types " "include bool, int, float") decoded_y = [labels[ex] for ex in y] decoded_y = ["|".join(ex) for ex in decoded_y] return decoded_y def parse_examples_data(self, file_name: str) -> ExampleSet: df = pd.read_csv(file_name, sep="\t", dtype={"x": "string", "y": "string"}) df = df.fillna("") x: List[str] = df["x"].tolist() y: List[str] = df["y"].replace(np.nan, "", regex=True).tolist() if self.validate_data: self.check_sequence(x) self.check_labels(y) return ExampleSet(x, y) def check_sequence(self, x: List[str]) -> bool: return DNAHelper.check_sequence(x)

38.550296

74

0.57452

835

6,515

4.338922

0.186826

0.049683

0.066243

0.757107

0.743307

0

0.009905

0.318189

6,515

168

75

38.779762

0.805718

0.018419

0

0.763359

0

0.07484

0

1

0.10687

false

0

0.091603

0.045802

0.305344

0

null

0

1

0

1

0

null

0

6

a8ff1bc176272e500a07aebd74e964e8b2d647c7

179

py

Python

django_compat_patcher/fixers/__init__.py

jayvdb/django-compat-patcher

09a60f1fa7e860a32d506c92d684997492385dda

[ "MIT" ]

null

django_compat_patcher/fixers/__init__.py

jayvdb/django-compat-patcher

09a60f1fa7e860a32d506c92d684997492385dda

[ "MIT" ]

null

django_compat_patcher/fixers/__init__.py

jayvdb/django-compat-patcher

09a60f1fa7e860a32d506c92d684997492385dda

[ "MIT" ]

null

from __future__ import absolute_import, print_function, unicode_literals from . import django1_6, django1_7, django1_8, django1_9, django1_10, django1_11, django2_0, django2_1

29.833333

102

0.832402

27

179

4.962963

0.666667

0

0.113208

0.111732

179

5

103

35.8

0.72956

0

1

0

true

0

1

0

1

0.5

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

d15130120eee6120e788b34ff44aa50929cf2fbc

65

py

Python

dart/experiments/__init__.py

fpirovan/NoiseInjection

d1a8c90aaf45d435d40c476a2d2e74258920ff22

[ "RSA-MD" ]

null

dart/experiments/__init__.py

fpirovan/NoiseInjection

d1a8c90aaf45d435d40c476a2d2e74258920ff22

[ "RSA-MD" ]

null

dart/experiments/__init__.py

fpirovan/NoiseInjection

d1a8c90aaf45d435d40c476a2d2e74258920ff22

[ "RSA-MD" ]

2

2020-11-06T06:57:35.000Z

2021-04-26T13:23:35.000Z

from .tools import * from .net import * from .framework import *

16.25

24

0.723077

9

65

5.222222

0.555556

0.425532

0

0.184615

65

3

25

21.666667

0.886792

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

0f00006967ad3f1b9a9299831e27a0fa80919718

69

py

Python

6.py

BarisTeksin/project-euler

38a368d66fdd3bdc1d977059ba966fb7c1dcdc39

[ "MIT" ]

4

2020-04-18T21:05:13.000Z

2020-04-26T15:39:14.000Z

6.py

BarisTeksin/project-euler

38a368d66fdd3bdc1d977059ba966fb7c1dcdc39

[ "MIT" ]

null

6.py

BarisTeksin/project-euler

38a368d66fdd3bdc1d977059ba966fb7c1dcdc39

[ "MIT" ]

null

print(sum(x for x in range(101))**2 - sum(x**2 for x in range(101)))

34.5

68

0.623188

17

69

2.529412

0.470588

0.186047

0.27907

0.511628

0.651163

0

0.137931

0.15942

69

1

69

0.603448

0

1

0

true

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

0f3b6de130bfdb209b8347e2c0a0e27691a2f8cf

9,075

py

Python

torchensemble/tests/test_all_models_multi_input.py

e-eight/Ensemble-Pytorch

83250759d3075795805d8c90f425191ab262b1c4

[ "BSD-3-Clause" ]

null

torchensemble/tests/test_all_models_multi_input.py

e-eight/Ensemble-Pytorch

83250759d3075795805d8c90f425191ab262b1c4

[ "BSD-3-Clause" ]

null

torchensemble/tests/test_all_models_multi_input.py

e-eight/Ensemble-Pytorch

83250759d3075795805d8c90f425191ab262b1c4

[ "BSD-3-Clause" ]

1

2021-07-02T07:44:20.000Z

import torch import pytest import numpy as np import torch.nn as nn from torch.utils.data import TensorDataset, DataLoader import torchensemble from torchensemble.utils import io from torchensemble.utils.logging import set_logger # All classifiers all_clf = [ torchensemble.FusionClassifier, torchensemble.VotingClassifier, torchensemble.BaggingClassifier, torchensemble.GradientBoostingClassifier, torchensemble.SnapshotEnsembleClassifier, torchensemble.AdversarialTrainingClassifier, torchensemble.FastGeometricClassifier, ] # All regressors all_reg = [ torchensemble.FusionRegressor, torchensemble.VotingRegressor, torchensemble.BaggingRegressor, torchensemble.GradientBoostingRegressor, torchensemble.SnapshotEnsembleRegressor, torchensemble.AdversarialTrainingRegressor, torchensemble.FastGeometricRegressor, ] np.random.seed(0) torch.manual_seed(0) device = torch.device("cpu") logger = set_logger("pytest_all_models_multiple_input") # Base estimator class MLP_clf(nn.Module): def __init__(self): super(MLP_clf, self).__init__() self.linear1 = nn.Linear(2, 2) self.linear2 = nn.Linear(2, 2) def forward(self, X_1, X_2): X_1 = X_1.view(X_1.size()[0], -1) X_2 = X_2.view(X_2.size()[0], -1) output_1 = self.linear1(X_1) output_1 = self.linear2(output_1) output_2 = self.linear1(X_2) output_2 = self.linear2(output_2) return 0.5 * output_1 + 0.5 * output_2 class MLP_reg(nn.Module): def __init__(self): super(MLP_reg, self).__init__() self.linear1 = nn.Linear(2, 2) self.linear2 = nn.Linear(2, 1) def forward(self, X_1, X_2): X_1 = X_1.view(X_1.size()[0], -1) X_2 = X_2.view(X_2.size()[0], -1) output_1 = self.linear1(X_1) output_1 = self.linear2(output_1) output_2 = self.linear1(X_2) output_2 = self.linear2(output_2) return 0.5 * output_1 + 0.5 * output_2 # Training data X_train = torch.Tensor( np.array(([0.1, 0.1], [0.2, 0.2], [0.3, 0.3], [0.4, 0.4])) ) y_train_clf = torch.LongTensor(np.array(([0, 0, 1, 1]))) y_train_reg = torch.FloatTensor(np.array(([0.1, 0.2, 0.3, 0.4]))) y_train_reg = y_train_reg.view(-1, 1) # Testing data numpy_X_test = np.array(([0.5, 0.5], [0.6, 0.6])) X_test = torch.Tensor(numpy_X_test) y_test_clf = torch.LongTensor(np.array(([1, 0]))) y_test_reg = torch.FloatTensor(np.array(([0.5, 0.6]))) y_test_reg = y_test_reg.view(-1, 1) @pytest.mark.parametrize("clf", all_clf) def test_clf_class(clf): """ This unit test checks the training and evaluating stage of all classifiers. """ epochs = 1 n_estimators = 2 model = clf(estimator=MLP_clf, n_estimators=n_estimators, cuda=False) # Optimizer model.set_optimizer("Adam", lr=1e-3, weight_decay=5e-4) # Scheduler (Snapshot Ensemble Excluded) if not isinstance(model, torchensemble.SnapshotEnsembleClassifier): model.set_scheduler("MultiStepLR", milestones=[2, 4]) # Prepare data with multiple inputs train = TensorDataset(X_train, X_train, y_train_clf) train_loader = DataLoader(train, batch_size=2, shuffle=False) test = TensorDataset(X_test, X_test, y_test_clf) test_loader = DataLoader(test, batch_size=2, shuffle=False) # Snapshot Ensemble needs more epochs if isinstance(model, torchensemble.SnapshotEnsembleClassifier): epochs = 6 # Train model.fit(train_loader, epochs=epochs, test_loader=test_loader) # Evaluate model.evaluate(test_loader) # Predict for _, elem in enumerate(test_loader): data, target = io.split_data_target(elem, device) model.predict(*data) break # Reload new_model = clf(estimator=MLP_clf, n_estimators=n_estimators, cuda=False) io.load(new_model) new_model.evaluate(test_loader) for _, elem in enumerate(test_loader): data, target = io.split_data_target(elem, device) new_model.predict(*data) break @pytest.mark.parametrize("clf", all_clf) def test_clf_object(clf): """ This unit test checks the training and evaluating stage of all classifiers. """ epochs = 1 n_estimators = 2 model = clf(estimator=MLP_clf(), n_estimators=n_estimators, cuda=False) # Optimizer model.set_optimizer("Adam", lr=1e-3, weight_decay=5e-4) # Scheduler (Snapshot Ensemble Excluded) if not isinstance(model, torchensemble.SnapshotEnsembleClassifier): model.set_scheduler("MultiStepLR", milestones=[2, 4]) # Prepare data with multiple inputs train = TensorDataset(X_train, X_train, y_train_clf) train_loader = DataLoader(train, batch_size=2, shuffle=False) test = TensorDataset(X_test, X_test, y_test_clf) test_loader = DataLoader(test, batch_size=2, shuffle=False) # Snapshot Ensemble needs more epochs if isinstance(model, torchensemble.SnapshotEnsembleClassifier): epochs = 6 # Train model.fit(train_loader, epochs=epochs, test_loader=test_loader) # Evaluate model.evaluate(test_loader) # Predict for _, elem in enumerate(test_loader): data, target = io.split_data_target(elem, device) model.predict(*data) break # Reload new_model = clf(estimator=MLP_clf(), n_estimators=n_estimators, cuda=False) io.load(new_model) new_model.evaluate(test_loader) for _, elem in enumerate(test_loader): data, target = io.split_data_target(elem, device) new_model.predict(*data) break @pytest.mark.parametrize("reg", all_reg) def test_reg_class(reg): """ This unit test checks the training and evaluating stage of all regressors. """ epochs = 1 n_estimators = 2 model = reg(estimator=MLP_reg, n_estimators=n_estimators, cuda=False) # Optimizer model.set_optimizer("Adam", lr=1e-3, weight_decay=5e-4) # Scheduler (Snapshot Ensemble Excluded) if not isinstance(model, torchensemble.SnapshotEnsembleRegressor): model.set_scheduler("MultiStepLR", milestones=[2, 4]) # Prepare data with multiple inputs train = TensorDataset(X_train, X_train, y_train_reg) train_loader = DataLoader(train, batch_size=2, shuffle=False) test = TensorDataset(X_test, X_test, y_test_reg) test_loader = DataLoader(test, batch_size=2, shuffle=False) # Snapshot Ensemble needs more epochs if isinstance(model, torchensemble.SnapshotEnsembleRegressor): epochs = 6 # Train model.fit(train_loader, epochs=epochs, test_loader=test_loader) # Evaluate model.evaluate(test_loader) # Predict for _, elem in enumerate(test_loader): data, target = io.split_data_target(elem, device) model.predict(*data) break # Reload new_model = reg(estimator=MLP_reg, n_estimators=n_estimators, cuda=False) io.load(new_model) new_model.evaluate(test_loader) for _, elem in enumerate(test_loader): data, target = io.split_data_target(elem, device) new_model.predict(*data) break @pytest.mark.parametrize("reg", all_reg) def test_reg_object(reg): """ This unit test checks the training and evaluating stage of all regressors. """ epochs = 1 n_estimators = 2 model = reg(estimator=MLP_reg(), n_estimators=n_estimators, cuda=False) # Optimizer model.set_optimizer("Adam", lr=1e-3, weight_decay=5e-4) # Scheduler (Snapshot Ensemble Excluded) if not isinstance(model, torchensemble.SnapshotEnsembleRegressor): model.set_scheduler("MultiStepLR", milestones=[2, 4]) # Prepare data with multiple inputs train = TensorDataset(X_train, X_train, y_train_reg) train_loader = DataLoader(train, batch_size=2, shuffle=False) test = TensorDataset(X_test, X_test, y_test_reg) test_loader = DataLoader(test, batch_size=2, shuffle=False) # Snapshot Ensemble needs more epochs if isinstance(model, torchensemble.SnapshotEnsembleRegressor): epochs = 6 # Train model.fit(train_loader, epochs=epochs, test_loader=test_loader) # Evaluate model.evaluate(test_loader) # Predict for _, elem in enumerate(test_loader): data, target = io.split_data_target(elem, device) model.predict(*data) break # Reload new_model = reg(estimator=MLP_reg(), n_estimators=n_estimators, cuda=False) io.load(new_model) new_model.evaluate(test_loader) for _, elem in enumerate(test_loader): data, target = io.split_data_target(elem, device) new_model.predict(*data) break def test_split_data_target_invalid_data_type(): with pytest.raises(ValueError) as excinfo: io.split_data_target(0.0, device, logger) assert "Invalid dataloader" in str(excinfo.value) def test_split_data_target_invalid_list_length(): with pytest.raises(ValueError) as excinfo: io.split_data_target([0.0], device, logger) assert "should at least contain two tensors" in str(excinfo.value)

29.464286

79

0.695537

1,222

9,075

4.943535

0.121113

0.04635

0.029796

0.028141

0.814104

0.798212

0.779672

0.770733

0.760967

0

0.023093

0.198347

9,075

307

80

29.560261

0.807285

0.106997

0

0.644809

0

0.019983

0.003997

0

0.010929

1

0.054645

false

0

0.043716

0

0.120219

0

null

0

1

0

null

0

6

0f3c0f60bd1240b2462f9d145d8774d9b29c0816

34

py

Python

path_finding/rl_algo.py

Maarten1999/Minor_ML3_Snake_AI

8a579634c94feb8f73b9bf00db78d6852993d3f6

[ "MIT" ]

null

path_finding/rl_algo.py

Maarten1999/Minor_ML3_Snake_AI

8a579634c94feb8f73b9bf00db78d6852993d3f6

[ "MIT" ]

null

path_finding/rl_algo.py

Maarten1999/Minor_ML3_Snake_AI

8a579634c94feb8f73b9bf00db78d6852993d3f6

[ "MIT" ]

null

from stable_baselines import DQN

11.333333

32

0.852941

5

34

5.6

1

0

0.147059

34

2

33

17

0.965517

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

0f4abb67d40c804ea603d252baf08947e2775d3e

251

py

Python

src/layers/__init__.py

nkoppe/tf_Transformer_SerieseData

502bb4c917b47139503a9d59300108313f507bd6

[ "MIT" ]

null

src/layers/__init__.py

nkoppe/tf_Transformer_SerieseData

502bb4c917b47139503a9d59300108313f507bd6

[ "MIT" ]

null

src/layers/__init__.py

nkoppe/tf_Transformer_SerieseData

502bb4c917b47139503a9d59300108313f507bd6

[ "MIT" ]

null

from .Attention import Attention from .PositionalEncoding import PositionalEncoding from .ScaledDotProductAttention import ScaledDotProductAttention from .LayerNormalization import LayerNormalization from .MultiHeadAttention import MultiHeadAttention

41.833333

64

0.900398

20

251

11.3

0.35

0

0.079681

251

5

65

50.2

0.978355

0

1

0

true

0

1

0

1

0

1

0

1

null

0

1

0

null

0

1

0

1

0

1

0

6

0f5d02eb86018865398a9002e028991094f09a4f

123

py

Python

utility/print_range.py

polde-live/learnpython

ff8ec96db1951d99797205d0bd491e542152a36f

[ "Unlicense" ]

null

utility/print_range.py

polde-live/learnpython

ff8ec96db1951d99797205d0bd491e542152a36f

[ "Unlicense" ]

null

utility/print_range.py

polde-live/learnpython

ff8ec96db1951d99797205d0bd491e542152a36f

[ "Unlicense" ]

null

def print_range(x): print "Printing for %d" %x, range(1, x-1) xs = range(1,25); for x in xs: print_range(x)

13.666667

45

0.577236

24

123

2.875

0.458333

0.289855

0.318841

0

0.054945

0.260163

123

8

46

15.375

0.703297

0

0.121951

0

null

0

null

0.6

1

0

null

1

0

1

0

null

0

1

0

1

0

6

0f7bef1be2ac5de8d51156c9fa037d2d838bde1f

19

py

Python

chat/tests.py

Safintim/aiohttp-chat

eef9e14d67070176678a99af10c729a057c4cd00

[ "MIT" ]

1

2019-02-05T20:14:41.000Z

chat/tests.py

Safintim/aiohttp-chat

eef9e14d67070176678a99af10c729a057c4cd00

[ "MIT" ]

157

2019-02-12T18:07:28.000Z

2022-02-10T07:14:24.000Z

chat/tests.py

Safintim/aiohttp-chat

eef9e14d67070176678a99af10c729a057c4cd00

[ "MIT" ]

null

# TODO write tests

9.5

18

0.736842

3

19

4.666667

1

0

0.210526

19

1

19

0.933333

0.842105

0

null

0

null

0

null

0

1

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

7e3bfa4d428dce49d313005b9fc42561cf2bf5b7

103

py

Python

spylib/utils/misc.py

SatelCreative/spylib

d7f5129c4f0bba3cbc7a2925fdee389dbbde75f1

[ "MIT" ]

1

2022-03-11T18:19:32.000Z

spylib/utils/misc.py

SatelCreative/spylib

d7f5129c4f0bba3cbc7a2925fdee389dbbde75f1

[ "MIT" ]

32

2020-08-14T19:49:09.000Z

2022-03-31T22:18:09.000Z

spylib/utils/misc.py

SatelCreative/spylib

d7f5129c4f0bba3cbc7a2925fdee389dbbde75f1

[ "MIT" ]

null

from shortuuid import ShortUUID def get_unique_id() -> str: return ShortUUID().random(length=10)

17.166667

40

0.737864

14

103

5.285714

0.857143

0

0.022989

0.15534

103

5

41

20.6

0.827586

0

1

0.333333

true

0

0.333333

1

0

1

0

null

0

1

0

null

0

1

0

1

0

6

7e408ad0fc6ba490f8107341c72b857a699ca195

37

py

Python

sciapp/__init__.py

pengguanjun/imagepy

d96ef98c2c3e93d368131fd2753bce164e1247cd

[ "BSD-4-Clause" ]

1

2020-08-17T04:18:35.000Z

sciapp/__init__.py

cycleuser/imagepy

5dc1a9a8137280c5215287392ba1b23d368bd7e9

[ "BSD-4-Clause" ]

null

sciapp/__init__.py

cycleuser/imagepy

5dc1a9a8137280c5215287392ba1b23d368bd7e9

[ "BSD-4-Clause" ]

null

from .app import App, Manager, Source

37

0.783784

6

37

4.833333

0.833333

0

0.135135

37

1

37

0.90625

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

7e628a7702baf8fc310c2e0fde616ef5abfdb26f

37,729

py

Python

instances/passenger_demand/pas-20210421-2109-int12e/5.py

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

bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11

[ "BSD-3-Clause" ]

null

instances/passenger_demand/pas-20210421-2109-int12e/5.py

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

bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11

[ "BSD-3-Clause" ]

null

instances/passenger_demand/pas-20210421-2109-int12e/5.py

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

bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11

[ "BSD-3-Clause" ]

null

""" PASSENGERS """ numPassengers = 2713 passenger_arriving = ( (0, 6, 4, 4, 1, 0, 4, 5, 3, 6, 1, 0), # 0 (4, 10, 2, 0, 1, 0, 5, 8, 5, 5, 3, 0), # 1 (1, 6, 3, 2, 5, 0, 4, 4, 5, 5, 1, 0), # 2 (3, 10, 6, 3, 3, 0, 5, 5, 4, 2, 0, 0), # 3 (2, 6, 4, 1, 0, 0, 7, 8, 6, 6, 1, 0), # 4 (4, 9, 8, 3, 3, 0, 4, 7, 6, 7, 0, 0), # 5 (2, 6, 8, 4, 1, 0, 5, 6, 5, 0, 5, 0), # 6 (2, 8, 4, 5, 2, 0, 5, 6, 4, 5, 2, 0), # 7 (4, 5, 7, 3, 3, 0, 4, 12, 5, 2, 3, 0), # 8 (3, 7, 4, 3, 3, 0, 6, 8, 9, 2, 4, 0), # 9 (7, 2, 4, 4, 2, 0, 1, 10, 4, 5, 2, 0), # 10 (1, 9, 6, 4, 3, 0, 4, 5, 7, 4, 1, 0), # 11 (2, 8, 5, 2, 1, 0, 1, 8, 5, 2, 1, 0), # 12 (6, 7, 8, 4, 0, 0, 2, 5, 4, 3, 1, 0), # 13 (4, 5, 3, 3, 3, 0, 6, 6, 12, 3, 1, 0), # 14 (2, 12, 6, 2, 2, 0, 3, 5, 7, 5, 4, 0), # 15 (4, 8, 7, 4, 3, 0, 7, 4, 4, 4, 3, 0), # 16 (1, 5, 5, 3, 1, 0, 4, 10, 0, 8, 2, 0), # 17 (2, 12, 8, 2, 3, 0, 7, 8, 4, 7, 4, 0), # 18 (2, 2, 6, 2, 2, 0, 6, 9, 4, 2, 1, 0), # 19 (5, 6, 10, 2, 0, 0, 4, 5, 0, 2, 2, 0), # 20 (2, 6, 11, 1, 1, 0, 5, 9, 4, 3, 1, 0), # 21 (4, 14, 5, 3, 3, 0, 4, 10, 2, 8, 1, 0), # 22 (4, 8, 10, 7, 2, 0, 7, 12, 2, 5, 4, 0), # 23 (3, 3, 7, 3, 4, 0, 6, 8, 4, 5, 2, 0), # 24 (4, 9, 3, 1, 3, 0, 9, 10, 7, 5, 4, 0), # 25 (4, 6, 6, 5, 0, 0, 4, 5, 2, 5, 3, 0), # 26 (2, 10, 4, 4, 3, 0, 6, 9, 4, 3, 2, 0), # 27 (5, 8, 6, 5, 2, 0, 8, 6, 8, 4, 3, 0), # 28 (7, 5, 6, 2, 4, 0, 8, 7, 8, 3, 4, 0), # 29 (5, 6, 6, 1, 4, 0, 5, 5, 3, 1, 0, 0), # 30 (3, 8, 8, 2, 1, 0, 9, 6, 12, 5, 4, 0), # 31 (8, 9, 3, 6, 1, 0, 6, 6, 4, 6, 2, 0), # 32 (1, 6, 6, 1, 0, 0, 2, 6, 2, 5, 4, 0), # 33 (7, 10, 12, 3, 2, 0, 6, 11, 8, 6, 3, 0), # 34 (6, 8, 9, 4, 2, 0, 12, 11, 6, 1, 3, 0), # 35 (3, 6, 4, 4, 2, 0, 8, 10, 3, 4, 1, 0), # 36 (3, 10, 6, 3, 5, 0, 3, 10, 4, 4, 3, 0), # 37 (4, 10, 4, 6, 2, 0, 2, 4, 6, 4, 3, 0), # 38 (4, 11, 3, 3, 1, 0, 3, 8, 4, 6, 1, 0), # 39 (4, 8, 5, 2, 0, 0, 2, 9, 4, 6, 1, 0), # 40 (4, 7, 7, 1, 1, 0, 7, 10, 7, 1, 4, 0), # 41 (2, 2, 6, 6, 2, 0, 5, 10, 4, 2, 4, 0), # 42 (5, 4, 7, 5, 0, 0, 9, 10, 6, 2, 2, 0), # 43 (5, 3, 2, 3, 1, 0, 8, 4, 2, 7, 0, 0), # 44 (4, 4, 7, 3, 2, 0, 7, 11, 2, 2, 4, 0), # 45 (2, 9, 8, 2, 3, 0, 8, 7, 7, 5, 3, 0), # 46 (5, 8, 6, 1, 2, 0, 2, 9, 11, 5, 1, 0), # 47 (3, 4, 8, 2, 3, 0, 8, 9, 2, 3, 0, 0), # 48 (6, 12, 9, 2, 2, 0, 4, 7, 5, 6, 4, 0), # 49 (7, 5, 7, 4, 0, 0, 5, 6, 7, 2, 1, 0), # 50 (5, 8, 4, 4, 0, 0, 5, 10, 7, 7, 1, 0), # 51 (4, 8, 6, 8, 4, 0, 7, 11, 2, 4, 1, 0), # 52 (3, 13, 6, 3, 0, 0, 4, 6, 1, 4, 1, 0), # 53 (2, 8, 5, 3, 3, 0, 7, 7, 3, 5, 1, 0), # 54 (5, 7, 5, 3, 1, 0, 7, 10, 3, 4, 0, 0), # 55 (3, 8, 2, 2, 1, 0, 2, 5, 5, 5, 0, 0), # 56 (3, 8, 5, 1, 2, 0, 7, 7, 4, 4, 1, 0), # 57 (7, 7, 7, 4, 0, 0, 4, 8, 4, 3, 2, 0), # 58 (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), # 59 ) station_arriving_intensity = ( (3.1795818700614573, 8.15575284090909, 9.59308322622108, 7.603532608695652, 8.571634615384614, 5.708152173913044), # 0 (3.20942641205736, 8.246449918455387, 9.644898645029993, 7.6458772644927535, 8.635879807692307, 5.706206567028985), # 1 (3.238930172666081, 8.335801683501682, 9.695484147386459, 7.687289855072463, 8.69876923076923, 5.704201449275362), # 2 (3.268068107989464, 8.42371171875, 9.744802779562981, 7.727735054347824, 8.760245192307693, 5.702137092391305), # 3 (3.296815174129353, 8.510083606902358, 9.792817587832047, 7.767177536231884, 8.82025, 5.700013768115941), # 4 (3.3251463271875914, 8.594820930660775, 9.839491618466152, 7.805581974637681, 8.87872596153846, 5.697831748188405), # 5 (3.353036523266023, 8.677827272727273, 9.88478791773779, 7.842913043478261, 8.935615384615383, 5.695591304347826), # 6 (3.380460718466491, 8.75900621580387, 9.92866953191945, 7.879135416666666, 8.990860576923078, 5.693292708333334), # 7 (3.40739386889084, 8.83826134259259, 9.971099507283634, 7.914213768115941, 9.044403846153847, 5.6909362318840575), # 8 (3.4338109306409126, 8.915496235795453, 10.012040890102828, 7.9481127717391304, 9.0961875, 5.68852214673913), # 9 (3.459686859818554, 8.990614478114479, 10.051456726649528, 7.980797101449276, 9.146153846153846, 5.68605072463768), # 10 (3.4849966125256073, 9.063519652251683, 10.089310063196228, 8.012231431159421, 9.194245192307692, 5.683522237318841), # 11 (3.509715144863916, 9.134115340909089, 10.125563946015424, 8.042380434782608, 9.240403846153844, 5.680936956521738), # 12 (3.5338174129353224, 9.20230512678872, 10.160181421379605, 8.071208786231884, 9.284572115384616, 5.678295153985506), # 13 (3.5572783728416737, 9.267992592592593, 10.193125535561265, 8.098681159420288, 9.326692307692307, 5.6755971014492745), # 14 (3.5800729806848106, 9.331081321022726, 10.224359334832902, 8.124762228260868, 9.36670673076923, 5.672843070652174), # 15 (3.6021761925665783, 9.391474894781144, 10.25384586546701, 8.149416666666665, 9.404557692307693, 5.6700333333333335), # 16 (3.6235629645888205, 9.449076896569863, 10.281548173736075, 8.172609148550725, 9.4401875, 5.667168161231884), # 17 (3.64420825285338, 9.503790909090908, 10.307429305912597, 8.194304347826087, 9.473538461538464, 5.664247826086956), # 18 (3.664087013462101, 9.555520515046295, 10.331452308269066, 8.214466938405796, 9.504552884615384, 5.661272599637681), # 19 (3.683174202516827, 9.604169297138045, 10.353580227077975, 8.2330615942029, 9.533173076923077, 5.658242753623187), # 20 (3.7014447761194034, 9.649640838068178, 10.373776108611827, 8.250052989130435, 9.559341346153845, 5.655158559782609), # 21 (3.7188736903716704, 9.69183872053872, 10.3920029991431, 8.26540579710145, 9.582999999999998, 5.652020289855073), # 22 (3.7354359013754754, 9.730666527251683, 10.408223944944302, 8.279084692028986, 9.604091346153846, 5.6488282155797105), # 23 (3.75110636523266, 9.76602784090909, 10.422401992287917, 8.291054347826087, 9.62255769230769, 5.645582608695652), # 24 (3.7658600380450684, 9.797826244212962, 10.434500187446444, 8.301279438405798, 9.638341346153844, 5.642283740942029), # 25 (3.779671875914545, 9.825965319865318, 10.444481576692374, 8.309724637681159, 9.651384615384615, 5.63893188405797), # 26 (3.792516834942932, 9.85034865056818, 10.452309206298198, 8.316354619565217, 9.661629807692309, 5.635527309782609), # 27 (3.804369871232075, 9.870879819023568, 10.457946122536418, 8.321134057971014, 9.66901923076923, 5.632070289855072), # 28 (3.815205940883816, 9.887462407933501, 10.461355371679518, 8.324027626811594, 9.673495192307692, 5.628561096014493), # 29 (3.8249999999999997, 9.9, 10.4625, 8.325, 9.674999999999999, 5.625), # 30 (3.834164434143222, 9.910414559659088, 10.461641938405796, 8.324824387254901, 9.674452393617022, 5.620051511744128), # 31 (3.843131010230179, 9.920691477272728, 10.459092028985506, 8.324300980392156, 9.672821276595744, 5.612429710144928), # 32 (3.8519037563938614, 9.930829474431818, 10.45488668478261, 8.323434926470588, 9.670124202127658, 5.6022092203898035), # 33 (3.860486700767263, 9.940827272727272, 10.449062318840578, 8.32223137254902, 9.666378723404256, 5.589464667666167), # 34 (3.8688838714833755, 9.950683593749998, 10.441655344202898, 8.320695465686274, 9.661602393617022, 5.574270677161419), # 35 (3.8770992966751923, 9.96039715909091, 10.432702173913043, 8.318832352941177, 9.655812765957448, 5.556701874062968), # 36 (3.885137004475703, 9.96996669034091, 10.422239221014491, 8.316647181372549, 9.64902739361702, 5.536832883558221), # 37 (3.893001023017902, 9.979390909090908, 10.410302898550723, 8.314145098039214, 9.641263829787233, 5.514738330834581), # 38 (3.900695380434782, 9.988668536931817, 10.396929619565215, 8.31133125, 9.632539627659574, 5.490492841079459), # 39 (3.908224104859335, 9.997798295454546, 10.382155797101449, 8.308210784313726, 9.62287234042553, 5.464171039480259), # 40 (3.915591224424552, 10.006778906249998, 10.366017844202899, 8.304788848039216, 9.612279521276594, 5.435847551224389), # 41 (3.9228007672634266, 10.015609090909093, 10.348552173913044, 8.301070588235293, 9.600778723404256, 5.40559700149925), # 42 (3.929856761508952, 10.024287571022725, 10.329795199275361, 8.297061151960785, 9.5883875, 5.373494015492254), # 43 (3.936763235294117, 10.032813068181818, 10.309783333333334, 8.292765686274508, 9.575123404255319, 5.339613218390804), # 44 (3.9435242167519178, 10.041184303977271, 10.288552989130435, 8.288189338235293, 9.561003989361701, 5.304029235382309), # 45 (3.9501437340153456, 10.0494, 10.266140579710147, 8.28333725490196, 9.546046808510638, 5.266816691654173), # 46 (3.956625815217391, 10.05745887784091, 10.24258251811594, 8.278214583333332, 9.530269414893617, 5.228050212393803), # 47 (3.962974488491049, 10.065359659090909, 10.217915217391303, 8.272826470588234, 9.513689361702127, 5.187804422788607), # 48 (3.9691937819693086, 10.073101065340907, 10.19217509057971, 8.26717806372549, 9.49632420212766, 5.146153948025987), # 49 (3.9752877237851663, 10.080681818181816, 10.165398550724637, 8.261274509803922, 9.478191489361702, 5.103173413293353), # 50 (3.9812603420716113, 10.088100639204544, 10.137622010869565, 8.255120955882353, 9.459308776595744, 5.0589374437781105), # 51 (3.987115664961637, 10.09535625, 10.10888188405797, 8.248722549019607, 9.439693617021277, 5.013520664667666), # 52 (3.992857720588235, 10.10244737215909, 10.079214583333332, 8.24208443627451, 9.419363563829787, 4.966997701149425), # 53 (3.9984905370843995, 10.109372727272726, 10.04865652173913, 8.235211764705882, 9.398336170212765, 4.919443178410794), # 54 (4.00401814258312, 10.116131036931817, 10.017244112318838, 8.22810968137255, 9.376628989361702, 4.87093172163918), # 55 (4.0094445652173905, 10.122721022727271, 9.985013768115941, 8.220783333333333, 9.354259574468085, 4.821537956021989), # 56 (4.014773833120205, 10.129141406250001, 9.952001902173912, 8.213237867647058, 9.331245478723403, 4.771336506746626), # 57 (4.0200099744245525, 10.135390909090907, 9.91824492753623, 8.20547843137255, 9.307604255319148, 4.7204019990005), # 58 (0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 59 ) passenger_arriving_acc = ( (0, 6, 4, 4, 1, 0, 4, 5, 3, 6, 1, 0), # 0 (4, 16, 6, 4, 2, 0, 9, 13, 8, 11, 4, 0), # 1 (5, 22, 9, 6, 7, 0, 13, 17, 13, 16, 5, 0), # 2 (8, 32, 15, 9, 10, 0, 18, 22, 17, 18, 5, 0), # 3 (10, 38, 19, 10, 10, 0, 25, 30, 23, 24, 6, 0), # 4 (14, 47, 27, 13, 13, 0, 29, 37, 29, 31, 6, 0), # 5 (16, 53, 35, 17, 14, 0, 34, 43, 34, 31, 11, 0), # 6 (18, 61, 39, 22, 16, 0, 39, 49, 38, 36, 13, 0), # 7 (22, 66, 46, 25, 19, 0, 43, 61, 43, 38, 16, 0), # 8 (25, 73, 50, 28, 22, 0, 49, 69, 52, 40, 20, 0), # 9 (32, 75, 54, 32, 24, 0, 50, 79, 56, 45, 22, 0), # 10 (33, 84, 60, 36, 27, 0, 54, 84, 63, 49, 23, 0), # 11 (35, 92, 65, 38, 28, 0, 55, 92, 68, 51, 24, 0), # 12 (41, 99, 73, 42, 28, 0, 57, 97, 72, 54, 25, 0), # 13 (45, 104, 76, 45, 31, 0, 63, 103, 84, 57, 26, 0), # 14 (47, 116, 82, 47, 33, 0, 66, 108, 91, 62, 30, 0), # 15 (51, 124, 89, 51, 36, 0, 73, 112, 95, 66, 33, 0), # 16 (52, 129, 94, 54, 37, 0, 77, 122, 95, 74, 35, 0), # 17 (54, 141, 102, 56, 40, 0, 84, 130, 99, 81, 39, 0), # 18 (56, 143, 108, 58, 42, 0, 90, 139, 103, 83, 40, 0), # 19 (61, 149, 118, 60, 42, 0, 94, 144, 103, 85, 42, 0), # 20 (63, 155, 129, 61, 43, 0, 99, 153, 107, 88, 43, 0), # 21 (67, 169, 134, 64, 46, 0, 103, 163, 109, 96, 44, 0), # 22 (71, 177, 144, 71, 48, 0, 110, 175, 111, 101, 48, 0), # 23 (74, 180, 151, 74, 52, 0, 116, 183, 115, 106, 50, 0), # 24 (78, 189, 154, 75, 55, 0, 125, 193, 122, 111, 54, 0), # 25 (82, 195, 160, 80, 55, 0, 129, 198, 124, 116, 57, 0), # 26 (84, 205, 164, 84, 58, 0, 135, 207, 128, 119, 59, 0), # 27 (89, 213, 170, 89, 60, 0, 143, 213, 136, 123, 62, 0), # 28 (96, 218, 176, 91, 64, 0, 151, 220, 144, 126, 66, 0), # 29 (101, 224, 182, 92, 68, 0, 156, 225, 147, 127, 66, 0), # 30 (104, 232, 190, 94, 69, 0, 165, 231, 159, 132, 70, 0), # 31 (112, 241, 193, 100, 70, 0, 171, 237, 163, 138, 72, 0), # 32 (113, 247, 199, 101, 70, 0, 173, 243, 165, 143, 76, 0), # 33 (120, 257, 211, 104, 72, 0, 179, 254, 173, 149, 79, 0), # 34 (126, 265, 220, 108, 74, 0, 191, 265, 179, 150, 82, 0), # 35 (129, 271, 224, 112, 76, 0, 199, 275, 182, 154, 83, 0), # 36 (132, 281, 230, 115, 81, 0, 202, 285, 186, 158, 86, 0), # 37 (136, 291, 234, 121, 83, 0, 204, 289, 192, 162, 89, 0), # 38 (140, 302, 237, 124, 84, 0, 207, 297, 196, 168, 90, 0), # 39 (144, 310, 242, 126, 84, 0, 209, 306, 200, 174, 91, 0), # 40 (148, 317, 249, 127, 85, 0, 216, 316, 207, 175, 95, 0), # 41 (150, 319, 255, 133, 87, 0, 221, 326, 211, 177, 99, 0), # 42 (155, 323, 262, 138, 87, 0, 230, 336, 217, 179, 101, 0), # 43 (160, 326, 264, 141, 88, 0, 238, 340, 219, 186, 101, 0), # 44 (164, 330, 271, 144, 90, 0, 245, 351, 221, 188, 105, 0), # 45 (166, 339, 279, 146, 93, 0, 253, 358, 228, 193, 108, 0), # 46 (171, 347, 285, 147, 95, 0, 255, 367, 239, 198, 109, 0), # 47 (174, 351, 293, 149, 98, 0, 263, 376, 241, 201, 109, 0), # 48 (180, 363, 302, 151, 100, 0, 267, 383, 246, 207, 113, 0), # 49 (187, 368, 309, 155, 100, 0, 272, 389, 253, 209, 114, 0), # 50 (192, 376, 313, 159, 100, 0, 277, 399, 260, 216, 115, 0), # 51 (196, 384, 319, 167, 104, 0, 284, 410, 262, 220, 116, 0), # 52 (199, 397, 325, 170, 104, 0, 288, 416, 263, 224, 117, 0), # 53 (201, 405, 330, 173, 107, 0, 295, 423, 266, 229, 118, 0), # 54 (206, 412, 335, 176, 108, 0, 302, 433, 269, 233, 118, 0), # 55 (209, 420, 337, 178, 109, 0, 304, 438, 274, 238, 118, 0), # 56 (212, 428, 342, 179, 111, 0, 311, 445, 278, 242, 119, 0), # 57 (219, 435, 349, 183, 111, 0, 315, 453, 282, 245, 121, 0), # 58 (219, 435, 349, 183, 111, 0, 315, 453, 282, 245, 121, 0), # 59 ) passenger_arriving_rate = ( (3.1795818700614573, 6.524602272727271, 5.755849935732647, 3.0414130434782605, 1.7143269230769227, 0.0, 5.708152173913044, 6.857307692307691, 4.562119565217391, 3.8372332904884314, 1.6311505681818177, 0.0), # 0 (3.20942641205736, 6.597159934764309, 5.786939187017996, 3.0583509057971012, 1.7271759615384612, 0.0, 5.706206567028985, 6.908703846153845, 4.587526358695652, 3.857959458011997, 1.6492899836910773, 0.0), # 1 (3.238930172666081, 6.668641346801345, 5.817290488431875, 3.074915942028985, 1.7397538461538458, 0.0, 5.704201449275362, 6.959015384615383, 4.612373913043478, 3.8781936589545833, 1.6671603367003363, 0.0), # 2 (3.268068107989464, 6.738969375, 5.846881667737788, 3.091094021739129, 1.7520490384615384, 0.0, 5.702137092391305, 7.0081961538461535, 4.636641032608694, 3.897921111825192, 1.68474234375, 0.0), # 3 (3.296815174129353, 6.808066885521885, 5.875690552699228, 3.106871014492753, 1.76405, 0.0, 5.700013768115941, 7.0562, 4.66030652173913, 3.9171270351328187, 1.7020167213804713, 0.0), # 4 (3.3251463271875914, 6.87585674452862, 5.903694971079691, 3.122232789855072, 1.775745192307692, 0.0, 5.697831748188405, 7.102980769230768, 4.6833491847826085, 3.9357966473864603, 1.718964186132155, 0.0), # 5 (3.353036523266023, 6.942261818181818, 5.930872750642674, 3.137165217391304, 1.7871230769230766, 0.0, 5.695591304347826, 7.148492307692306, 4.705747826086957, 3.953915167095116, 1.7355654545454544, 0.0), # 6 (3.380460718466491, 7.007204972643096, 5.95720171915167, 3.1516541666666664, 1.7981721153846155, 0.0, 5.693292708333334, 7.192688461538462, 4.727481249999999, 3.97146781276778, 1.751801243160774, 0.0), # 7 (3.40739386889084, 7.0706090740740715, 5.982659704370181, 3.165685507246376, 1.8088807692307691, 0.0, 5.6909362318840575, 7.2355230769230765, 4.7485282608695645, 3.9884398029134536, 1.7676522685185179, 0.0), # 8 (3.4338109306409126, 7.132396988636362, 6.007224534061696, 3.179245108695652, 1.8192374999999996, 0.0, 5.68852214673913, 7.2769499999999985, 4.768867663043478, 4.004816356041131, 1.7830992471590905, 0.0), # 9 (3.459686859818554, 7.1924915824915825, 6.030874035989717, 3.19231884057971, 1.829230769230769, 0.0, 5.68605072463768, 7.316923076923076, 4.7884782608695655, 4.020582690659811, 1.7981228956228956, 0.0), # 10 (3.4849966125256073, 7.250815721801346, 6.053586037917737, 3.204892572463768, 1.8388490384615384, 0.0, 5.683522237318841, 7.355396153846153, 4.807338858695652, 4.0357240252784905, 1.8127039304503365, 0.0), # 11 (3.509715144863916, 7.30729227272727, 6.0753383676092545, 3.2169521739130427, 1.8480807692307688, 0.0, 5.680936956521738, 7.392323076923075, 4.825428260869565, 4.050225578406169, 1.8268230681818176, 0.0), # 12 (3.5338174129353224, 7.361844101430976, 6.096108852827762, 3.228483514492753, 1.8569144230769232, 0.0, 5.678295153985506, 7.427657692307693, 4.84272527173913, 4.0640725685518415, 1.840461025357744, 0.0), # 13 (3.5572783728416737, 7.414394074074074, 6.115875321336759, 3.2394724637681147, 1.8653384615384612, 0.0, 5.6755971014492745, 7.461353846153845, 4.859208695652172, 4.077250214224506, 1.8535985185185184, 0.0), # 14 (3.5800729806848106, 7.46486505681818, 6.134615600899742, 3.249904891304347, 1.873341346153846, 0.0, 5.672843070652174, 7.493365384615384, 4.874857336956521, 4.089743733933161, 1.866216264204545, 0.0), # 15 (3.6021761925665783, 7.513179915824915, 6.152307519280206, 3.259766666666666, 1.8809115384615382, 0.0, 5.6700333333333335, 7.523646153846153, 4.889649999999999, 4.101538346186803, 1.8782949789562287, 0.0), # 16 (3.6235629645888205, 7.55926151725589, 6.168928904241645, 3.26904365942029, 1.8880374999999998, 0.0, 5.667168161231884, 7.552149999999999, 4.903565489130435, 4.11261926949443, 1.8898153793139725, 0.0), # 17 (3.64420825285338, 7.603032727272725, 6.184457583547558, 3.2777217391304343, 1.8947076923076926, 0.0, 5.664247826086956, 7.578830769230771, 4.916582608695652, 4.122971722365039, 1.9007581818181813, 0.0), # 18 (3.664087013462101, 7.644416412037035, 6.198871384961439, 3.285786775362318, 1.9009105769230765, 0.0, 5.661272599637681, 7.603642307692306, 4.928680163043477, 4.132580923307626, 1.9111041030092588, 0.0), # 19 (3.683174202516827, 7.683335437710435, 6.2121481362467845, 3.2932246376811594, 1.9066346153846152, 0.0, 5.658242753623187, 7.626538461538461, 4.93983695652174, 4.14143209083119, 1.9208338594276086, 0.0), # 20 (3.7014447761194034, 7.719712670454542, 6.224265665167096, 3.3000211956521737, 1.911868269230769, 0.0, 5.655158559782609, 7.647473076923076, 4.950031793478261, 4.14951044344473, 1.9299281676136355, 0.0), # 21 (3.7188736903716704, 7.753470976430976, 6.23520179948586, 3.3061623188405793, 1.9165999999999994, 0.0, 5.652020289855073, 7.666399999999998, 4.959243478260869, 4.15680119965724, 1.938367744107744, 0.0), # 22 (3.7354359013754754, 7.784533221801346, 6.244934366966581, 3.311633876811594, 1.920818269230769, 0.0, 5.6488282155797105, 7.683273076923076, 4.967450815217392, 4.163289577977721, 1.9461333054503365, 0.0), # 23 (3.75110636523266, 7.812822272727271, 6.25344119537275, 3.3164217391304347, 1.9245115384615379, 0.0, 5.645582608695652, 7.6980461538461515, 4.974632608695652, 4.168960796915166, 1.9532055681818177, 0.0), # 24 (3.7658600380450684, 7.838260995370368, 6.260700112467866, 3.320511775362319, 1.9276682692307685, 0.0, 5.642283740942029, 7.710673076923074, 4.980767663043479, 4.173800074978577, 1.959565248842592, 0.0), # 25 (3.779671875914545, 7.860772255892254, 6.266688946015424, 3.3238898550724634, 1.9302769230769228, 0.0, 5.63893188405797, 7.721107692307691, 4.985834782608695, 4.177792630676949, 1.9651930639730635, 0.0), # 26 (3.792516834942932, 7.8802789204545425, 6.2713855237789184, 3.326541847826087, 1.9323259615384616, 0.0, 5.635527309782609, 7.729303846153846, 4.98981277173913, 4.180923682519278, 1.9700697301136356, 0.0), # 27 (3.804369871232075, 7.8967038552188535, 6.2747676735218505, 3.328453623188405, 1.9338038461538458, 0.0, 5.632070289855072, 7.735215384615383, 4.992680434782608, 4.183178449014567, 1.9741759638047134, 0.0), # 28 (3.815205940883816, 7.9099699263468, 6.276813223007711, 3.3296110507246373, 1.9346990384615383, 0.0, 5.628561096014493, 7.738796153846153, 4.994416576086956, 4.184542148671807, 1.9774924815867, 0.0), # 29 (3.8249999999999997, 7.92, 6.2775, 3.3299999999999996, 1.9349999999999996, 0.0, 5.625, 7.739999999999998, 4.994999999999999, 4.185, 1.98, 0.0), # 30 (3.834164434143222, 7.92833164772727, 6.276985163043477, 3.3299297549019604, 1.9348904787234043, 0.0, 5.620051511744128, 7.739561914893617, 4.994894632352941, 4.184656775362318, 1.9820829119318175, 0.0), # 31 (3.843131010230179, 7.936553181818182, 6.275455217391303, 3.329720392156862, 1.9345642553191487, 0.0, 5.612429710144928, 7.738257021276595, 4.994580588235293, 4.1836368115942015, 1.9841382954545455, 0.0), # 32 (3.8519037563938614, 7.944663579545454, 6.272932010869566, 3.329373970588235, 1.9340248404255314, 0.0, 5.6022092203898035, 7.736099361702125, 4.994060955882353, 4.181954673913044, 1.9861658948863634, 0.0), # 33 (3.860486700767263, 7.952661818181817, 6.269437391304347, 3.3288925490196077, 1.9332757446808508, 0.0, 5.589464667666167, 7.733102978723403, 4.993338823529411, 4.179624927536231, 1.9881654545454543, 0.0), # 34 (3.8688838714833755, 7.960546874999998, 6.264993206521739, 3.328278186274509, 1.9323204787234043, 0.0, 5.574270677161419, 7.729281914893617, 4.9924172794117645, 4.176662137681159, 1.9901367187499994, 0.0), # 35 (3.8770992966751923, 7.968317727272727, 6.259621304347825, 3.3275329411764707, 1.9311625531914893, 0.0, 5.556701874062968, 7.724650212765957, 4.9912994117647065, 4.173080869565217, 1.9920794318181818, 0.0), # 36 (3.885137004475703, 7.975973352272726, 6.253343532608695, 3.3266588725490194, 1.9298054787234038, 0.0, 5.536832883558221, 7.719221914893615, 4.989988308823529, 4.168895688405796, 1.9939933380681816, 0.0), # 37 (3.893001023017902, 7.983512727272726, 6.246181739130434, 3.325658039215685, 1.9282527659574464, 0.0, 5.514738330834581, 7.713011063829786, 4.988487058823528, 4.164121159420289, 1.9958781818181814, 0.0), # 38 (3.900695380434782, 7.990934829545453, 6.238157771739129, 3.3245324999999997, 1.9265079255319146, 0.0, 5.490492841079459, 7.7060317021276585, 4.98679875, 4.1587718478260856, 1.9977337073863632, 0.0), # 39 (3.908224104859335, 7.998238636363636, 6.229293478260869, 3.32328431372549, 1.924574468085106, 0.0, 5.464171039480259, 7.698297872340424, 4.984926470588236, 4.1528623188405795, 1.999559659090909, 0.0), # 40 (3.915591224424552, 8.005423124999998, 6.219610706521739, 3.321915539215686, 1.9224559042553186, 0.0, 5.435847551224389, 7.689823617021275, 4.982873308823529, 4.146407137681159, 2.0013557812499996, 0.0), # 41 (3.9228007672634266, 8.012487272727274, 6.209131304347826, 3.320428235294117, 1.920155744680851, 0.0, 5.40559700149925, 7.680622978723404, 4.980642352941175, 4.1394208695652175, 2.0031218181818184, 0.0), # 42 (3.929856761508952, 8.01943005681818, 6.1978771195652165, 3.3188244607843136, 1.9176774999999997, 0.0, 5.373494015492254, 7.670709999999999, 4.978236691176471, 4.131918079710144, 2.004857514204545, 0.0), # 43 (3.936763235294117, 8.026250454545455, 6.18587, 3.317106274509803, 1.9150246808510636, 0.0, 5.339613218390804, 7.660098723404254, 4.975659411764705, 4.123913333333333, 2.0065626136363637, 0.0), # 44 (3.9435242167519178, 8.032947443181817, 6.1731317934782615, 3.315275735294117, 1.91220079787234, 0.0, 5.304029235382309, 7.64880319148936, 4.972913602941175, 4.115421195652174, 2.008236860795454, 0.0), # 45 (3.9501437340153456, 8.03952, 6.159684347826087, 3.313334901960784, 1.9092093617021275, 0.0, 5.266816691654173, 7.63683744680851, 4.970002352941176, 4.106456231884058, 2.00988, 0.0), # 46 (3.956625815217391, 8.045967102272726, 6.1455495108695635, 3.3112858333333324, 1.9060538829787232, 0.0, 5.228050212393803, 7.624215531914893, 4.966928749999999, 4.097033007246376, 2.0114917755681816, 0.0), # 47 (3.962974488491049, 8.052287727272727, 6.130749130434782, 3.309130588235293, 1.9027378723404254, 0.0, 5.187804422788607, 7.610951489361701, 4.96369588235294, 4.087166086956521, 2.013071931818182, 0.0), # 48 (3.9691937819693086, 8.058480852272725, 6.115305054347826, 3.306871225490196, 1.899264840425532, 0.0, 5.146153948025987, 7.597059361702128, 4.960306838235294, 4.076870036231884, 2.014620213068181, 0.0), # 49 (3.9752877237851663, 8.064545454545453, 6.099239130434782, 3.3045098039215683, 1.8956382978723403, 0.0, 5.103173413293353, 7.582553191489361, 4.956764705882353, 4.066159420289854, 2.016136363636363, 0.0), # 50 (3.9812603420716113, 8.070480511363634, 6.082573206521739, 3.302048382352941, 1.8918617553191486, 0.0, 5.0589374437781105, 7.567447021276594, 4.953072573529411, 4.055048804347826, 2.0176201278409085, 0.0), # 51 (3.987115664961637, 8.076284999999999, 6.065329130434782, 3.299489019607843, 1.8879387234042553, 0.0, 5.013520664667666, 7.551754893617021, 4.949233529411765, 4.043552753623188, 2.0190712499999997, 0.0), # 52 (3.992857720588235, 8.081957897727271, 6.047528749999999, 3.2968337745098037, 1.8838727127659571, 0.0, 4.966997701149425, 7.5354908510638285, 4.945250661764706, 4.0316858333333325, 2.020489474431818, 0.0), # 53 (3.9984905370843995, 8.08749818181818, 6.0291939130434775, 3.294084705882353, 1.8796672340425529, 0.0, 4.919443178410794, 7.5186689361702115, 4.941127058823529, 4.019462608695651, 2.021874545454545, 0.0), # 54 (4.00401814258312, 8.092904829545454, 6.010346467391303, 3.2912438725490194, 1.8753257978723403, 0.0, 4.87093172163918, 7.501303191489361, 4.936865808823529, 4.006897644927535, 2.0232262073863634, 0.0), # 55 (4.0094445652173905, 8.098176818181816, 5.991008260869564, 3.288313333333333, 1.8708519148936167, 0.0, 4.821537956021989, 7.483407659574467, 4.9324699999999995, 3.994005507246376, 2.024544204545454, 0.0), # 56 (4.014773833120205, 8.103313125, 5.971201141304347, 3.285295147058823, 1.8662490957446805, 0.0, 4.771336506746626, 7.464996382978722, 4.927942720588234, 3.980800760869564, 2.02582828125, 0.0), # 57 (4.0200099744245525, 8.108312727272725, 5.950946956521738, 3.2821913725490197, 1.8615208510638295, 0.0, 4.7204019990005, 7.446083404255318, 4.923287058823529, 3.9672979710144918, 2.0270781818181813, 0.0), # 58 (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 59 ) passenger_allighting_rate = ( (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 0 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 1 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 2 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 3 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 4 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 5 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 6 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 7 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 8 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 9 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 10 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 11 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 12 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 13 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 14 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 15 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 16 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 17 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 18 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 19 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 20 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 21 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 22 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 23 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 24 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 25 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 26 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 27 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 28 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 29 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 30 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 31 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 32 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 33 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 34 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 35 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 36 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 37 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 38 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 39 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 40 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 41 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 42 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 43 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 44 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 45 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 46 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 47 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 48 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 49 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 50 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 51 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 52 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 53 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 54 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 55 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 56 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 57 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 58 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 59 ) """ parameters for reproducibiliy. More information: https://numpy.org/doc/stable/reference/random/parallel.html """ #initial entropy entropy = 258194110137029475889902652135037600173 #index for seed sequence child child_seed_index = ( 1, # 0 4, # 1 )

112.623881

213

0.727955

5,147

37,729

5.333981

0.22382

0.314708

0.249144

0.472062

0.331682

0.329788

0

0.818154

0.119643

37,729

334

214

112.961078

0.0084

0.032097

0

0.202532

0

1

0

false

0.015823

0

null

1

0

1

0

1

0

null

0

6

7e6e555640e9d566e30c1077470fabffd0e0fe28

77

py

Python

speckle_pattern/__init__.py

jankoslavic/speckle_pattern

7c2233a36b8870b3b5d712e21d18f0fb4f5da6e0

[ "MIT" ]

16

2019-05-31T20:06:03.000Z

2022-01-04T06:42:28.000Z

speckle_pattern/__init__.py

EntPyle/speckle_pattern

7c2233a36b8870b3b5d712e21d18f0fb4f5da6e0

[ "MIT" ]

null

speckle_pattern/__init__.py

EntPyle/speckle_pattern

7c2233a36b8870b3b5d712e21d18f0fb4f5da6e0

[ "MIT" ]

12

2018-12-18T18:08:37.000Z

2021-11-13T00:17:07.000Z

from .speckle import generate_and_save, generate_lines, generate_checkerboard

77

0.896104

10

77

6.5

0.8

0

0.064935

77

1

77

0.902778

0

1

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

0e185a315c65c0f483135ff29705243a6196dc0b

135

py

Python

query_builder/query_builder_empty.py

mbkr1992/weather-analytical-portal-rest-api

f4af9b308aa0ca02103bb81e7446ab641b5d7e2a

[ "MIT" ]

null

query_builder/query_builder_empty.py

mbkr1992/weather-analytical-portal-rest-api

f4af9b308aa0ca02103bb81e7446ab641b5d7e2a

[ "MIT" ]

null

query_builder/query_builder_empty.py

mbkr1992/weather-analytical-portal-rest-api

f4af9b308aa0ca02103bb81e7446ab641b5d7e2a

[ "MIT" ]

null

from query_builder.query_builder import QueryBuilder class QueryBuilderEmpty(QueryBuilder): def build(self, params): pass

22.5

52

0.77037

15

135

6.8

0.8

0.235294

0

0.17037

135

6

53

22.5

0.910714

0

1

0.25

false

0.25

0

0.75

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

0e1932656b40274d93c0706e3ff1b0b460b18dcf

40

py

Python

rl_trader/engine/rl_environment/types/test/env_market_types.test.py

AlexandreMahdhaoui/rl_trader

5bda02622c7e17c4e6f28a90c510cfe8f914f7a8

[ "Apache-2.0" ]

null

rl_trader/engine/rl_environment/types/test/env_market_types.test.py

AlexandreMahdhaoui/rl_trader

5bda02622c7e17c4e6f28a90c510cfe8f914f7a8

[ "Apache-2.0" ]

null

rl_trader/engine/rl_environment/types/test/env_market_types.test.py

AlexandreMahdhaoui/rl_trader

5bda02622c7e17c4e6f28a90c510cfe8f914f7a8

[ "Apache-2.0" ]

null

# TODO: PairMarketValue # TODO: Market

10

23

0.725

4

40

7.25

0.75

0

0.175

40

3

24

13.333333

0.878788

0.85

0

null

0

null

0

null

0

0.333333

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

0e3826056d8eeaf711fea6bb9e7987cfedb97770

2,799

py

Python

radioxenon_ml/solve/matrix_values.py

sczyz/radioxenon_ml

73398f0060e88616c7652a72bdedf7f93ea17a20

[ "MIT" ]

null

radioxenon_ml/solve/matrix_values.py

sczyz/radioxenon_ml

73398f0060e88616c7652a72bdedf7f93ea17a20

[ "MIT" ]

null

radioxenon_ml/solve/matrix_values.py

sczyz/radioxenon_ml

73398f0060e88616c7652a72bdedf7f93ea17a20

[ "MIT" ]

1

2018-04-23T20:52:43.000Z

# -*- coding: utf-8 -*- """ Created on Tue May 8 17:19:53 2018 @author: Steven """ import numpy as np def j_matrix_val(S,D,f): """ Determines the variance of the number of counts in each channel i of the vectorized version of the 2D coincidence spectrum for the qth iteration: -S(np.array) is the original experimental spectrum -D(np.array) is the determined variance value from variance() -f(np.array) is the reference spectrum for each isotope as well as background Equations are taken from the quite excellent paper: Lowrey, Justin D., and Steven R.F. Biegalski. “Comparison of Least- Squares vs. Maximum Likelihood Estimation for Standard Spectrum Technique of Β−γ Coincidence Spectrum Analysis.” Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms 270 (January 2012): 116–19. https://doi.org/10.1016/j.nimb.2011.09.005. """ J_temp = np.zeros((np.shape(f)[1],np.shape(f)[0])) J = np.zeros((np.shape(f)[1],1)) for j in range(np.shape(f)[1]): #loop over # of isotopes for i in range(np.shape(f)[0]): #loop over # of array elements J_temp[j,i] = (S[i]*f[i,j])/D[i] #Eqn. 7 J[j] = np.sum(J_temp[j]) #sum all columns to make a column vector return J def k_matrix_val(D,f): """ Determines the variance of the number of counts in each channel i of the vectorized version of the 2D coincidence spectrum for the qth iteration: -D(np.array) is the determined variance value from variance() -f(np.array) is the reference spectrum for each isotope as well as background Equations are taken from the quite excellent paper: Lowrey, Justin D., and Steven R.F. Biegalski. “Comparison of Least- Squares vs. Maximum Likelihood Estimation for Standard Spectrum Technique of Β−γ Coincidence Spectrum Analysis.” Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms 270 (January 2012): 116–19. https://doi.org/10.1016/j.nimb.2011.09.005. """ K_element_temp = np.zeros((np.shape(f)[1],np.shape(f)[0])) K = np.zeros((np.shape(f)[1],np.shape(f)[1])) for m in range(np.shape(f)[1]): #loop over # of isotopes for j in range(np.shape(f)[1]): #loop over # of isotopes again for i in range(np.shape(f)[0]): #loop over # of array elements K_element_temp[j,i] = (f[i,m]*f[i,j])/D[i] #Eqn. 7 K[m,j] = np.sum(K_element_temp[j]) #sum all elements to make an entry in the array return K

39.985714

95

0.622008

442

2,799

3.918552

0.294118

0.048499

0.055427

0.04157

0.829099

0.819861

0.809469

0.795612

0

0.040766

0.272597

2,799

69

96

40.565217

0.807957

0.656306

0

0.222222

0

1

0.111111

false

0

0.055556

0

0.277778

0

null

0

1

0

null

0

6

0e93d555d556779c60518cc909097718895e4273

49

py

Python

my-webapp/my_domain.py

BernardNotarianni/spike-concourse

eb84f52c96688e94bdd6ee61d348257c8eee0040

[ "MIT" ]

null

my-webapp/my_domain.py

BernardNotarianni/spike-concourse

eb84f52c96688e94bdd6ee61d348257c8eee0040

[ "MIT" ]

1

2019-06-09T13:12:33.000Z

my-webapp/my_domain.py

BernardNotarianni/spike-concourse

eb84f52c96688e94bdd6ee61d348257c8eee0040

[ "MIT" ]

null

def my_message(): return "Hello Concourse!"

12.25

29

0.673469

6

49

5.333333

1

0

0.204082

49

3

30

16.333333

0.820513

0

0.333333

0

1

0.5

true

0

0.5

1

0

1

0

null

0

1

0

null

0

1

0

1

0

6

0ea6dd3e534279d8de4104b149933cf528ec0bd1

974

py

Python

findost/serverApp/database/DatabaseError.py

BongOST/FindOST

bef3c8991b7494ad08c06ed1c9fb0bc41996ec8b

[ "Apache-2.0" ]

1

2017-11-12T03:10:25.000Z

findost/serverApp/database/DatabaseError.py

BongOST/FindOST

bef3c8991b7494ad08c06ed1c9fb0bc41996ec8b

[ "Apache-2.0" ]

null

findost/serverApp/database/DatabaseError.py

BongOST/FindOST

bef3c8991b7494ad08c06ed1c9fb0bc41996ec8b

[ "Apache-2.0" ]

null

# -*- coding:utf-8 -*- __author__ = 'lee' """ 数据库自定义异常 """ class ExistError(Exception): def __init__(self, exist, message): self.exist = exist self.message = message def __str__(self): return repr(self.message) class NotExistError(Exception): def __init__(self, notexist, message): self.notexist = notexist self.message = message def __str__(self): return repr(self.message) class OutRangeError(Exception): def __init__(self, table, message): self.table = table self.message = message def __str__(self): return repr(self.message) class InsertError(Exception): def __init__(self, table, message): self.table = table self.message = message def __str__(self): return repr(self.message) class CommonError(Exception): def __init__(self, message): self.message = message def __str__(self): return repr(self.message)

20.723404

42

0.635524

107

974

5.373832

0.214953

0.210435

0.13913

0.173913

0.62087

0

0.001381

0.256674

974

46

43

21.173913

0.792818

0.020534

0

0.633333

0

0.003205

0

1

0.333333

false

0

0.166667

0.666667

0

null

1

0

1

0

1

0

null

0

1

0

1

0

6

7d1d6343b048ef31eb6f2a0fed69b1e88ee35132

128

py

Python

app/api/__init__.py

yc19890920/flask-blog

d2aa57bd876e41a18a791c0b110bb31b86133ead

[ "MIT" ]

null

app/api/__init__.py

yc19890920/flask-blog

d2aa57bd876e41a18a791c0b110bb31b86133ead

[ "MIT" ]

null

app/api/__init__.py

yc19890920/flask-blog

d2aa57bd876e41a18a791c0b110bb31b86133ead

[ "MIT" ]

null

from flask import Blueprint api = Blueprint('api', __name__) from . import authentication, posts, comments, errors # users

25.6

61

0.742188

15

128

6.066667

0.733333

0.263736

0

0.171875

128

5

61

25.6

0.858491

0.039063

0

0.025424

0

1

0

false

0

0.666667

0

0.666667

1

0

null

1

0

1

0

null

0

1

0

1

0

6

ade50d3dc4937799eaa3095e5d8a35a75955fdd7

162

py

Python

tests/unit/test_values.py

prog-serhii/MyPorfolio

ab8d06925650a4b5809a33669a5d315dc87c56ec

[ "MIT" ]

null

tests/unit/test_values.py

prog-serhii/MyPorfolio

ab8d06925650a4b5809a33669a5d315dc87c56ec

[ "MIT" ]

null

tests/unit/test_values.py

prog-serhii/MyPorfolio

ab8d06925650a4b5809a33669a5d315dc87c56ec

[ "MIT" ]

null

import random from decimal import Decimal from domain.values import Money def test_equality(): assert Money('uah', Decimal()) == Money('uah', Decimal(50))

18

63

0.722222

22

162

5.272727

0.590909

0.137931

0.258621

0

0.014599

0.154321

162

8

64

20.25

0.832117

0

0.037037

0

0.2

1

0.2

true

0

0.6

0

0.8

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

ade83ed2a02d34b8387677d6ded173699b1ca4d8

1,404

py

Python

src/colleges/migrations/0004_auto_20160120_1231.py

Busaka/excellence

1cd19770285584d61aeddd77d6c1dd83e2fd04ba

[ "MIT" ]

null

src/colleges/migrations/0004_auto_20160120_1231.py

Busaka/excellence

1cd19770285584d61aeddd77d6c1dd83e2fd04ba

[ "MIT" ]

null

src/colleges/migrations/0004_auto_20160120_1231.py

Busaka/excellence

1cd19770285584d61aeddd77d6c1dd83e2fd04ba

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Generated by Django 1.9 on 2016-01-20 12:31 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('colleges', '0003_auto_20160120_1034'), ] operations = [ migrations.AlterField( model_name='college', name='college_logo', field=models.ImageField(upload_to='colleges/college_photos'), ), migrations.AlterField( model_name='college', name='college_photo1', field=models.ImageField(upload_to='colleges/college_photos'), ), migrations.AlterField( model_name='college', name='college_photo2', field=models.ImageField(upload_to='colleges/college_photos'), ), migrations.AlterField( model_name='college', name='college_photo3', field=models.ImageField(upload_to='colleges/college_photos'), ), migrations.AlterField( model_name='college', name='college_photo4', field=models.ImageField(upload_to='colleges/college_photos'), ), migrations.AlterField( model_name='college', name='college_photo5', field=models.ImageField(upload_to='colleges/college_photos'), ), ]

30.521739

73

0.597578

132

1,404

6.113636

0.348485

0.163569

0.185874

0.215613

0.72119

0.662949

0.600991

0

0.036036

0.288462

1,404

45

74

31.2

0.771772

0.046296

0

0.631579

1

0

0.219311

0.120509

0

1

0

false

0

0.052632

0

0.131579

0

null

0

1

0

1

0

1

0

1

0

null

0

6

adf3d8f8208184767f05cf55b646aae17c4a25b1

8,295

py

Python

tests/test_templates.py

clokep/mwcomposerfromhell

02ba160ad55ee7fc1b69834cd5cb256e98a52648

[ "0BSD" ]

3

2019-10-03T06:46:19.000Z

2021-09-25T13:39:32.000Z

tests/test_templates.py

clokep/mwcomposerfromhell

02ba160ad55ee7fc1b69834cd5cb256e98a52648

[ "0BSD" ]

1

2020-03-18T07:24:28.000Z

2020-05-07T11:58:59.000Z

tests/test_templates.py

clokep/mwcomposerfromhell

02ba160ad55ee7fc1b69834cd5cb256e98a52648

[ "0BSD" ]

1

2021-07-05T11:30:13.000Z

import mwparserfromhell from mwcomposerfromhell import ( ArticleResolver, compose, Namespace, WikicodeToHtmlComposer, ) def _get_composer(templates): resolver = ArticleResolver() resolver.add_namespace("Template", Namespace(templates)) return WikicodeToHtmlComposer(resolver=resolver) def test_simple(): """Render a simple template.""" # A simple template that's just a string. template = "This is a test" templates = {"temp": mwparserfromhell.parse(template)} # Parse the main content. wikicode = mwparserfromhell.parse("{{temp}}") # Render the result. composer = _get_composer(templates) assert composer.compose(wikicode) == "" + template + "" def test_with_args(): """Render a content with a template that has arguments.""" # Template that uses both a position and keyword argument. templates = {"temp": mwparserfromhell.parse('This is a "{{{1}}}" "{{{key}}}"')} # Parse the main content. wikicode = mwparserfromhell.parse("{{temp|foobar|key=value}}") # Render the result. composer = _get_composer(templates) assert composer.compose(wikicode) == 'This is a "foobar" "value"' def test_with_default_args(): """Render a template where arguments fall back to default values.""" # Template that uses a position argument and a keyword argument, both with # defaults. templates = { "temp": mwparserfromhell.parse('This is a "{{{1|first}}}" "{{{key|second}}}"') } # Parse the main content. wikicode = mwparserfromhell.parse("{{temp}}") # Render the result. composer = _get_composer(templates) assert composer.compose(wikicode) == 'This is a "first" "second"' def test_with_blank_default_args(): """Render a template where arguments fall back to blank values.""" # Template that uses a position argument and a keyword argument, both with # blank defaults. templates = {"temp": mwparserfromhell.parse('This is a "{{{1|}}}" "{{{key|}}}"')} # Parse the main content. wikicode = mwparserfromhell.parse("{{temp}}") # Render the result. composer = _get_composer(templates) assert composer.compose(wikicode) == 'This is a "" ""' def test_with_replaced_default_arg(): """A default argument that is another replacement.""" # Template that uses a position argument and a keyword argument, both with # defaults. templates = { "temp": mwparserfromhell.parse( 'This is a "{{{1|foo {{{default}}}}}}" "{{{key|foo {{{default}}}}}}"' ) } # Parse the main content. wikicode = mwparserfromhell.parse("{{temp|default=bar}}") # Render the result. composer = _get_composer(templates) assert composer.compose(wikicode) == 'This is a "foo bar" "foo bar"' def test_without_default_args(): """Render a template where arguments fall back to their keys.""" # Template that uses a position argument and a keyword argument, without # defaults. template = 'This is a "{{{1}}}" "{{{key}}}"' templates = {"temp": mwparserfromhell.parse(template)} # Parse the main content. wikicode = mwparserfromhell.parse("{{temp}}") # Render the result. composer = _get_composer(templates) assert composer.compose(wikicode) == "" + template + "" def test_complex_name(): """A template name that gets rendered via a different template.""" templates = { "text": mwparserfromhell.parse("{{{1}}}"), "temp": mwparserfromhell.parse("This is a test"), } # Parse the main content. The name of the template is given by another template wikicode = mwparserfromhell.parse("{{t{{text|em}}p}}") # Render the result. composer = _get_composer(templates) assert composer.compose(wikicode) == "This is a test" def test_complex_parameter_name(): """A template name that gets rendered via a different template.""" templates = { "text": mwparserfromhell.parse("{{{1}}}"), "temp": mwparserfromhell.parse('This is a "{{{1}}}" "{{{key}}}"'), } # Parse the main content. The name of the template is given by another template wikicode = mwparserfromhell.parse("{{temp|first|k{{text|ey}}=second}}") # Render the result. composer = _get_composer(templates) assert composer.compose(wikicode) == 'This is a "first" "second"' def test_complex_parameter_value(): """A template name that gets rendered via a different template.""" templates = { "text": mwparserfromhell.parse("{{{1}}}"), "temp": mwparserfromhell.parse('This is a "{{{1}}}" "{{{key}}}"'), } # Parse the main content. The name of the template is given by another template wikicode = mwparserfromhell.parse("{{temp|fi{{text|rst}}|key={{text|sec}}ond}}") # Render the result. composer = _get_composer(templates) assert composer.compose(wikicode) == 'This is a "first" "second"' def test_complex_arg(): """An argument name gets generated in a complex fashion.""" templates = { "text": mwparserfromhell.parse("{{{1}}}"), "temp": mwparserfromhell.parse( 'This is a "{{{ {{text|1}} }}}" "{{{ {{text|key}} }}}"' ), } # Parse the main content. The name of the template is given by another template wikicode = mwparserfromhell.parse("{{temp|first|key=second}}") # Render the result. composer = _get_composer(templates) assert composer.compose(wikicode) == 'This is a "first" "second"' def test_spaces(): """Spaces around a template name should be ignored.""" # A simple template that's just a string. template = "This is a test" templates = {"temp": mwparserfromhell.parse(template)} # Parse the main content. wikicode = mwparserfromhell.parse("{{ temp }}") # Render the result. composer = _get_composer(templates) assert composer.compose(wikicode) == "" + template + "" def test_spaces_with_parameter(): """Spaces around keyword parameters should be removed.""" # Template that uses both a position and keyword argument. templates = {"temp": mwparserfromhell.parse('This is a "{{{1}}}" "{{{key}}}"')} # Parse the main content. wikicode = mwparserfromhell.parse("{{temp| foobar | key = value}}") # Render the result. composer = _get_composer(templates) assert composer.compose(wikicode) == 'This is a " foobar " "value"' def test_capitalization(): """MediaWiki treats the first character as case-insensitive.""" # A simple template that's just a string. template = "This is a test" templates = {"temp": mwparserfromhell.parse(template)} # Parse the main content. wikicode = mwparserfromhell.parse("{{Temp}}") # Render the result. composer = _get_composer(templates) assert composer.compose(wikicode) == "" + template + "" def test_wikilink(): """Parameters in Wikilinks should be replaced.""" templates = {"temp": mwparserfromhell.parse("[[{{{1}}}|See more at {{{1}}}]]")} # Parse the main content. wikicode = mwparserfromhell.parse("{{temp|foobar}}") # Render the result. composer = _get_composer(templates) assert ( composer.compose(wikicode) == '<a href="/wiki/Foobar" title="Foobar">See more at foobar</a>' ) def test_externallink(): """Parameters in external links should be replaced.""" templates = {"temp": mwparserfromhell.parse("[https://{{{1}}}.com {{{1}}}]")} # Parse the main content. wikicode = mwparserfromhell.parse("{{temp|foobar}}") # Render the result. composer = _get_composer(templates) assert ( composer.compose(wikicode) == '<a href="https://foobar.com">foobar</a>' ) def test_unknown(): """An unknown template gets rendered as is.""" content = "{{temp}}" wikicode = mwparserfromhell.parse(content) # Render the result. assert compose(wikicode) == "" + content + "" def test_unknown_duplicate(): """An unknown template gets rendered as is.""" content = "{{temp}}{{temp}}" wikicode = mwparserfromhell.parse(content) # Render the result. assert compose(wikicode) == "" + content + ""

32.529412

86

0.6434

971

8,295

5.424305

0.123584

0.143535

0.029239

0.054111

0.814505

0.800456

0.78147

0.771597

0.754129

0

0.002571

0.203014

8,295

254

87

32.65748

0.794131

0.283303

0

0.512195

0

0.00813

0.225623

0.02184

0

0.138211

1

0.146341

false

0

0.01626

0

0.170732

0

null

0

1

0

null

0

6

adfcf06ac1569b8f6c285d4e3ed2141c330c5572

28,042

py

Python

scicopia/app/parser/ScicopiaLexer.py

pikatech/Scicopia

dcdb3b4f55b9111fa3b4fe78afdb07bb2ceb9985

[ "MIT" ]

null

scicopia/app/parser/ScicopiaLexer.py

pikatech/Scicopia

dcdb3b4f55b9111fa3b4fe78afdb07bb2ceb9985

[ "MIT" ]

9

2021-07-24T16:12:03.000Z

2021-07-24T16:58:19.000Z

scicopia/app/parser/ScicopiaLexer.py

pikatech/Scicopia

dcdb3b4f55b9111fa3b4fe78afdb07bb2ceb9985

[ "MIT" ]

1

2021-06-18T16:00:06.000Z

# Generated from Scicopia.g4 by ANTLR 4.9.2 from antlr4 import * from io import StringIO import sys if sys.version_info[1] > 5: from typing import TextIO else: from typing.io import TextIO def serializedATN(): with StringIO() as buf: buf.write("\3\u608b\ua72a\u8133\ub9ed\u417c\u3be7\u7786\u5964\2\23") buf.write("\u00c5\b\1\4\2\t\2\4\3\t\3\4\4\t\4\4\5\t\5\4\6\t\6\4\7") buf.write("\t\7\4\b\t\b\4\t\t\t\4\n\t\n\4\13\t\13\4\f\t\f\4\r\t\r") buf.write("\4\16\t\16\4\17\t\17\4\20\t\20\4\21\t\21\4\22\t\22\4\23") buf.write("\t\23\4\24\t\24\4\25\t\25\4\26\t\26\3\2\3\2\3\3\3\3\3") buf.write("\4\3\4\3\5\3\5\3\5\6\5\67\n\5\r\5\16\58\3\5\3\5\3\5\6") buf.write("\5>\n\5\r\5\16\5?\3\5\3\5\3\5\7\5E\n\5\f\5\16\5H\13\5") buf.write("\5\5J\n\5\3\6\3\6\3\6\3\6\6\6P\n\6\r\6\16\6Q\3\7\3\7\3") buf.write("\7\3\7\3\7\3\7\5\7Z\n\7\6\7\\\n\7\r\7\16\7]\3\7\3\7\5") buf.write("\7b\n\7\3\7\3\7\3\7\3\7\3\7\7\7i\n\7\f\7\16\7l\13\7\5") buf.write("\7n\n\7\3\7\3\7\3\7\3\7\3\7\7\7u\n\7\f\7\16\7x\13\7\6") buf.write("\7z\n\7\r\7\16\7{\5\7~\n\7\3\b\3\b\3\b\6\b\u0083\n\b\r") buf.write("\b\16\b\u0084\3\t\3\t\3\n\6\n\u008a\n\n\r\n\16\n\u008b") buf.write("\3\13\6\13\u008f\n\13\r\13\16\13\u0090\3\f\6\f\u0094\n") buf.write("\f\r\f\16\f\u0095\3\f\3\f\3\r\6\r\u009b\n\r\r\r\16\r\u009c") buf.write("\3\r\3\r\3\16\3\16\6\16\u00a3\n\16\r\16\16\16\u00a4\3") buf.write("\17\3\17\3\17\6\17\u00aa\n\17\r\17\16\17\u00ab\3\20\3") buf.write("\20\3\21\3\21\3\22\3\22\7\22\u00b4\n\22\f\22\16\22\u00b7") buf.write("\13\22\3\23\3\23\3\24\3\24\3\25\3\25\3\26\6\26\u00c0\n") buf.write("\26\r\26\16\26\u00c1\3\26\3\26\2\2\27\3\3\5\4\7\5\t\6") buf.write("\13\7\r\b\17\t\21\n\23\13\25\f\27\r\31\16\33\17\35\20") buf.write("\37\21!\22#\2%\2\'\2)\2+\23\3\2\6\6\2--//GGUU\4\2--//") buf.write("\t\2##%(,.\60\61=B`a\u0080\u0080\5\2\13\f\16\17\"\"\5") buf.write("\u024b\2C\2\\\2c\2|\2\u00ac\2\u00ac\2\u00b7\2\u00b7\2") buf.write("\u00bc\2\u00bc\2\u00c2\2\u00d8\2\u00da\2\u00f8\2\u00fa") buf.write("\2\u02c3\2\u02c8\2\u02d3\2\u02e2\2\u02e6\2\u02ee\2\u02ee") buf.write("\2\u02f0\2\u02f0\2\u0372\2\u0376\2\u0378\2\u0379\2\u037c") buf.write("\2\u037f\2\u0381\2\u0381\2\u0388\2\u0388\2\u038a\2\u038c") buf.write("\2\u038e\2\u038e\2\u0390\2\u03a3\2\u03a5\2\u03f7\2\u03f9") buf.write("\2\u0483\2\u048c\2\u0531\2\u0533\2\u0558\2\u055b\2\u055b") buf.write("\2\u0563\2\u0589\2\u05d2\2\u05ec\2\u05f2\2\u05f4\2\u0622") buf.write("\2\u064c\2\u0670\2\u0671\2\u0673\2\u06d5\2\u06d7\2\u06d7") buf.write("\2\u06e7\2\u06e8\2\u06f0\2\u06f1\2\u06fc\2\u06fe\2\u0701") buf.write("\2\u0701\2\u0712\2\u0712\2\u0714\2\u0731\2\u074f\2\u07a7") buf.write("\2\u07b3\2\u07b3\2\u07cc\2\u07ec\2\u07f6\2\u07f7\2\u07fc") buf.write("\2\u07fc\2\u0802\2\u0817\2\u081c\2\u081c\2\u0826\2\u0826") buf.write("\2\u082a\2\u082a\2\u0842\2\u085a\2\u0862\2\u086c\2\u08a2") buf.write("\2\u08b6\2\u08b8\2\u08bf\2\u0906\2\u093b\2\u093f\2\u093f") buf.write("\2\u0952\2\u0952\2\u095a\2\u0963\2\u0973\2\u0982\2\u0987") buf.write("\2\u098e\2\u0991\2\u0992\2\u0995\2\u09aa\2\u09ac\2\u09b2") buf.write("\2\u09b4\2\u09b4\2\u09b8\2\u09bb\2\u09bf\2\u09bf\2\u09d0") buf.write("\2\u09d0\2\u09de\2\u09df\2\u09e1\2\u09e3\2\u09f2\2\u09f3") buf.write("\2\u09fe\2\u09fe\2\u0a07\2\u0a0c\2\u0a11\2\u0a12\2\u0a15") buf.write("\2\u0a2a\2\u0a2c\2\u0a32\2\u0a34\2\u0a35\2\u0a37\2\u0a38") buf.write("\2\u0a3a\2\u0a3b\2\u0a5b\2\u0a5e\2\u0a60\2\u0a60\2\u0a74") buf.write("\2\u0a76\2\u0a87\2\u0a8f\2\u0a91\2\u0a93\2\u0a95\2\u0aaa") buf.write("\2\u0aac\2\u0ab2\2\u0ab4\2\u0ab5\2\u0ab7\2\u0abb\2\u0abf") buf.write("\2\u0abf\2\u0ad2\2\u0ad2\2\u0ae2\2\u0ae3\2\u0afb\2\u0afb") buf.write("\2\u0b07\2\u0b0e\2\u0b11\2\u0b12\2\u0b15\2\u0b2a\2\u0b2c") buf.write("\2\u0b32\2\u0b34\2\u0b35\2\u0b37\2\u0b3b\2\u0b3f\2\u0b3f") buf.write("\2\u0b5e\2\u0b5f\2\u0b61\2\u0b63\2\u0b73\2\u0b73\2\u0b85") buf.write("\2\u0b85\2\u0b87\2\u0b8c\2\u0b90\2\u0b92\2\u0b94\2\u0b97") buf.write("\2\u0b9b\2\u0b9c\2\u0b9e\2\u0b9e\2\u0ba0\2\u0ba1\2\u0ba5") buf.write("\2\u0ba6\2\u0baa\2\u0bac\2\u0bb0\2\u0bbb\2\u0bd2\2\u0bd2") buf.write("\2\u0c07\2\u0c0e\2\u0c10\2\u0c12\2\u0c14\2\u0c2a\2\u0c2c") buf.write("\2\u0c3b\2\u0c3f\2\u0c3f\2\u0c5a\2\u0c5c\2\u0c62\2\u0c63") buf.write("\2\u0c82\2\u0c82\2\u0c87\2\u0c8e\2\u0c90\2\u0c92\2\u0c94") buf.write("\2\u0caa\2\u0cac\2\u0cb5\2\u0cb7\2\u0cbb\2\u0cbf\2\u0cbf") buf.write("\2\u0ce0\2\u0ce0\2\u0ce2\2\u0ce3\2\u0cf3\2\u0cf4\2\u0d07") buf.write("\2\u0d0e\2\u0d10\2\u0d12\2\u0d14\2\u0d3c\2\u0d3f\2\u0d3f") buf.write("\2\u0d50\2\u0d50\2\u0d56\2\u0d58\2\u0d61\2\u0d63\2\u0d7c") buf.write("\2\u0d81\2\u0d87\2\u0d98\2\u0d9c\2\u0db3\2\u0db5\2\u0dbd") buf.write("\2\u0dbf\2\u0dbf\2\u0dc2\2\u0dc8\2\u0e03\2\u0e32\2\u0e34") buf.write("\2\u0e35\2\u0e42\2\u0e48\2\u0e83\2\u0e84\2\u0e86\2\u0e86") buf.write("\2\u0e89\2\u0e8a\2\u0e8c\2\u0e8c\2\u0e8f\2\u0e8f\2\u0e96") buf.write("\2\u0e99\2\u0e9b\2\u0ea1\2\u0ea3\2\u0ea5\2\u0ea7\2\u0ea7") buf.write("\2\u0ea9\2\u0ea9\2\u0eac\2\u0ead\2\u0eaf\2\u0eb2\2\u0eb4") buf.write("\2\u0eb5\2\u0ebf\2\u0ebf\2\u0ec2\2\u0ec6\2\u0ec8\2\u0ec8") buf.write("\2\u0ede\2\u0ee1\2\u0f02\2\u0f02\2\u0f42\2\u0f49\2\u0f4b") buf.write("\2\u0f6e\2\u0f8a\2\u0f8e\2\u1002\2\u102c\2\u1041\2\u1041") buf.write("\2\u1052\2\u1057\2\u105c\2\u105f\2\u1063\2\u1063\2\u1067") buf.write("\2\u1068\2\u1070\2\u1072\2\u1077\2\u1083\2\u1090\2\u1090") buf.write("\2\u10a2\2\u10c7\2\u10c9\2\u10c9\2\u10cf\2\u10cf\2\u10d2") buf.write("\2\u10fc\2\u10fe\2\u124a\2\u124c\2\u124f\2\u1252\2\u1258") buf.write("\2\u125a\2\u125a\2\u125c\2\u125f\2\u1262\2\u128a\2\u128c") buf.write("\2\u128f\2\u1292\2\u12b2\2\u12b4\2\u12b7\2\u12ba\2\u12c0") buf.write("\2\u12c2\2\u12c2\2\u12c4\2\u12c7\2\u12ca\2\u12d8\2\u12da") buf.write("\2\u1312\2\u1314\2\u1317\2\u131a\2\u135c\2\u1382\2\u1391") buf.write("\2\u13a2\2\u13f7\2\u13fa\2\u13ff\2\u1403\2\u166e\2\u1671") buf.write("\2\u1681\2\u1683\2\u169c\2\u16a2\2\u16ec\2\u16f3\2\u16fa") buf.write("\2\u1702\2\u170e\2\u1710\2\u1713\2\u1722\2\u1733\2\u1742") buf.write("\2\u1753\2\u1762\2\u176e\2\u1770\2\u1772\2\u1782\2\u17b5") buf.write("\2\u17d9\2\u17d9\2\u17de\2\u17de\2\u1822\2\u1879\2\u1882") buf.write("\2\u1886\2\u1889\2\u18aa\2\u18ac\2\u18ac\2\u18b2\2\u18f7") buf.write("\2\u1902\2\u1920\2\u1952\2\u196f\2\u1972\2\u1976\2\u1982") buf.write("\2\u19ad\2\u19b2\2\u19cb\2\u1a02\2\u1a18\2\u1a22\2\u1a56") buf.write("\2\u1aa9\2\u1aa9\2\u1b07\2\u1b35\2\u1b47\2\u1b4d\2\u1b85") buf.write("\2\u1ba2\2\u1bb0\2\u1bb1\2\u1bbc\2\u1be7\2\u1c02\2\u1c25") buf.write("\2\u1c4f\2\u1c51\2\u1c5c\2\u1c7f\2\u1c82\2\u1c8a\2\u1ceb") buf.write("\2\u1cee\2\u1cf0\2\u1cf3\2\u1cf7\2\u1cf8\2\u1d02\2\u1dc1") buf.write("\2\u1e02\2\u1f17\2\u1f1a\2\u1f1f\2\u1f22\2\u1f47\2\u1f4a") buf.write("\2\u1f4f\2\u1f52\2\u1f59\2\u1f5b\2\u1f5b\2\u1f5d\2\u1f5d") buf.write("\2\u1f5f\2\u1f5f\2\u1f61\2\u1f7f\2\u1f82\2\u1fb6\2\u1fb8") buf.write("\2\u1fbe\2\u1fc0\2\u1fc0\2\u1fc4\2\u1fc6\2\u1fc8\2\u1fce") buf.write("\2\u1fd2\2\u1fd5\2\u1fd8\2\u1fdd\2\u1fe2\2\u1fee\2\u1ff4") buf.write("\2\u1ff6\2\u1ff8\2\u1ffe\2\u2073\2\u2073\2\u2081\2\u2081") buf.write("\2\u2092\2\u209e\2\u2104\2\u2104\2\u2109\2\u2109\2\u210c") buf.write("\2\u2115\2\u2117\2\u2117\2\u211b\2\u211f\2\u2126\2\u2126") buf.write("\2\u2128\2\u2128\2\u212a\2\u212a\2\u212c\2\u212f\2\u2131") buf.write("\2\u213b\2\u213e\2\u2141\2\u2147\2\u214b\2\u2150\2\u2150") buf.write("\2\u2185\2\u2186\2\u2c02\2\u2c30\2\u2c32\2\u2c60\2\u2c62") buf.write("\2\u2ce6\2\u2ced\2\u2cf0\2\u2cf4\2\u2cf5\2\u2d02\2\u2d27") buf.write("\2\u2d29\2\u2d29\2\u2d2f\2\u2d2f\2\u2d32\2\u2d69\2\u2d71") buf.write("\2\u2d71\2\u2d82\2\u2d98\2\u2da2\2\u2da8\2\u2daa\2\u2db0") buf.write("\2\u2db2\2\u2db8\2\u2dba\2\u2dc0\2\u2dc2\2\u2dc8\2\u2dca") buf.write("\2\u2dd0\2\u2dd2\2\u2dd8\2\u2dda\2\u2de0\2\u2e31\2\u2e31") buf.write("\2\u3007\2\u3008\2\u3033\2\u3037\2\u303d\2\u303e\2\u3043") buf.write("\2\u3098\2\u309f\2\u30a1\2\u30a3\2\u30fc\2\u30fe\2\u3101") buf.write("\2\u3107\2\u3130\2\u3133\2\u3190\2\u31a2\2\u31bc\2\u31f2") buf.write("\2\u3201\2\u3402\2\u4db7\2\u4e02\2\u9fec\2\ua002\2\ua48e") buf.write("\2\ua4d2\2\ua4ff\2\ua502\2\ua60e\2\ua612\2\ua621\2\ua62c") buf.write("\2\ua62d\2\ua642\2\ua670\2\ua681\2\ua69f\2\ua6a2\2\ua6e7") buf.write("\2\ua719\2\ua721\2\ua724\2\ua78a\2\ua78d\2\ua7b0\2\ua7b2") buf.write("\2\ua7b9\2\ua7f9\2\ua803\2\ua805\2\ua807\2\ua809\2\ua80c") buf.write("\2\ua80e\2\ua824\2\ua842\2\ua875\2\ua884\2\ua8b5\2\ua8f4") buf.write("\2\ua8f9\2\ua8fd\2\ua8fd\2\ua8ff\2\ua8ff\2\ua90c\2\ua927") buf.write("\2\ua932\2\ua948\2\ua962\2\ua97e\2\ua986\2\ua9b4\2\ua9d1") buf.write("\2\ua9d1\2\ua9e2\2\ua9e6\2\ua9e8\2\ua9f1\2\ua9fc\2\uaa00") buf.write("\2\uaa02\2\uaa2a\2\uaa42\2\uaa44\2\uaa46\2\uaa4d\2\uaa62") buf.write("\2\uaa78\2\uaa7c\2\uaa7c\2\uaa80\2\uaab1\2\uaab3\2\uaab3") buf.write("\2\uaab7\2\uaab8\2\uaabb\2\uaabf\2\uaac2\2\uaac2\2\uaac4") buf.write("\2\uaac4\2\uaadd\2\uaadf\2\uaae2\2\uaaec\2\uaaf4\2\uaaf6") buf.write("\2\uab03\2\uab08\2\uab0b\2\uab10\2\uab13\2\uab18\2\uab22") buf.write("\2\uab28\2\uab2a\2\uab30\2\uab32\2\uab5c\2\uab5e\2\uab67") buf.write("\2\uab72\2\uabe4\2\uac02\2\ud7a5\2\ud7b2\2\ud7c8\2\ud7cd") buf.write("\2\ud7fd\2\uf902\2\ufa6f\2\ufa72\2\ufadb\2\ufb02\2\ufb08") buf.write("\2\ufb15\2\ufb19\2\ufb1f\2\ufb1f\2\ufb21\2\ufb2a\2\ufb2c") buf.write("\2\ufb38\2\ufb3a\2\ufb3e\2\ufb40\2\ufb40\2\ufb42\2\ufb43") buf.write("\2\ufb45\2\ufb46\2\ufb48\2\ufbb3\2\ufbd5\2\ufd3f\2\ufd52") buf.write("\2\ufd91\2\ufd94\2\ufdc9\2\ufdf2\2\ufdfd\2\ufe72\2\ufe76") buf.write("\2\ufe78\2\ufefe\2\uff23\2\uff3c\2\uff43\2\uff5c\2\uff68") buf.write("\2\uffc0\2\uffc4\2\uffc9\2\uffcc\2\uffd1\2\uffd4\2\uffd9") buf.write("\2\uffdc\2\uffde\2\2\3\r\3\17\3(\3*\3<\3>\3?\3A\3O\3R") buf.write("\3_\3\u0082\3\u00fc\3\u0282\3\u029e\3\u02a2\3\u02d2\3") buf.write("\u0302\3\u0321\3\u032f\3\u0342\3\u0344\3\u034b\3\u0352") buf.write("\3\u0377\3\u0382\3\u039f\3\u03a2\3\u03c5\3\u03ca\3\u03d1") buf.write("\3\u0402\3\u049f\3\u04b2\3\u04d5\3\u04da\3\u04fd\3\u0502") buf.write("\3\u0529\3\u0532\3\u0565\3\u0602\3\u0738\3\u0742\3\u0757") buf.write("\3\u0762\3\u0769\3\u0802\3\u0807\3\u080a\3\u080a\3\u080c") buf.write("\3\u0837\3\u0839\3\u083a\3\u083e\3\u083e\3\u0841\3\u0857") buf.write("\3\u0862\3\u0878\3\u0882\3\u08a0\3\u08e2\3\u08f4\3\u08f6") buf.write("\3\u08f7\3\u0902\3\u0917\3\u0922\3\u093b\3\u0982\3\u09b9") buf.write("\3\u09c0\3\u09c1\3\u0a02\3\u0a02\3\u0a12\3\u0a15\3\u0a17") buf.write("\3\u0a19\3\u0a1b\3\u0a35\3\u0a62\3\u0a7e\3\u0a82\3\u0a9e") buf.write("\3\u0ac2\3\u0ac9\3\u0acb\3\u0ae6\3\u0b02\3\u0b37\3\u0b42") buf.write("\3\u0b57\3\u0b62\3\u0b74\3\u0b82\3\u0b93\3\u0c02\3\u0c4a") buf.write("\3\u0c82\3\u0cb4\3\u0cc2\3\u0cf4\3\u1005\3\u1039\3\u1085") buf.write("\3\u10b1\3\u10d2\3\u10ea\3\u1105\3\u1128\3\u1152\3\u1174") buf.write("\3\u1178\3\u1178\3\u1185\3\u11b4\3\u11c3\3\u11c6\3\u11dc") buf.write("\3\u11dc\3\u11de\3\u11de\3\u1202\3\u1213\3\u1215\3\u122d") buf.write("\3\u1282\3\u1288\3\u128a\3\u128a\3\u128c\3\u128f\3\u1291") buf.write("\3\u129f\3\u12a1\3\u12aa\3\u12b2\3\u12e0\3\u1307\3\u130e") buf.write("\3\u1311\3\u1312\3\u1315\3\u132a\3\u132c\3\u1332\3\u1334") buf.write("\3\u1335\3\u1337\3\u133b\3\u133f\3\u133f\3\u1352\3\u1352") buf.write("\3\u135f\3\u1363\3\u1402\3\u1436\3\u1449\3\u144c\3\u1482") buf.write("\3\u14b1\3\u14c6\3\u14c7\3\u14c9\3\u14c9\3\u1582\3\u15b0") buf.write("\3\u15da\3\u15dd\3\u1602\3\u1631\3\u1646\3\u1646\3\u1682") buf.write("\3\u16ac\3\u1702\3\u171b\3\u18a2\3\u18e1\3\u1901\3\u1901") buf.write("\3\u1a02\3\u1a02\3\u1a0d\3\u1a34\3\u1a3c\3\u1a3c\3\u1a52") buf.write("\3\u1a52\3\u1a5e\3\u1a85\3\u1a88\3\u1a8b\3\u1ac2\3\u1afa") buf.write("\3\u1c02\3\u1c0a\3\u1c0c\3\u1c30\3\u1c42\3\u1c42\3\u1c74") buf.write("\3\u1c91\3\u1d02\3\u1d08\3\u1d0a\3\u1d0b\3\u1d0d\3\u1d32") buf.write("\3\u1d48\3\u1d48\3\u2002\3\u239b\3\u2482\3\u2545\3\u3002") buf.write("\3\u3430\3\u4402\3\u4648\3\u6802\3\u6a3a\3\u6a42\3\u6a60") buf.write("\3\u6ad2\3\u6aef\3\u6b02\3\u6b31\3\u6b42\3\u6b45\3\u6b65") buf.write("\3\u6b79\3\u6b7f\3\u6b91\3\u6f02\3\u6f46\3\u6f52\3\u6f52") buf.write("\3\u6f95\3\u6fa1\3\u6fe2\3\u6fe3\3\u7002\3\u87ee\3\u8802") buf.write("\3\u8af4\3\ub002\3\ub120\3\ub172\3\ub2fd\3\ubc02\3\ubc6c") buf.write("\3\ubc72\3\ubc7e\3\ubc82\3\ubc8a\3\ubc92\3\ubc9b\3\ud402") buf.write("\3\ud456\3\ud458\3\ud49e\3\ud4a0\3\ud4a1\3\ud4a4\3\ud4a4") buf.write("\3\ud4a7\3\ud4a8\3\ud4ab\3\ud4ae\3\ud4b0\3\ud4bb\3\ud4bd") buf.write("\3\ud4bd\3\ud4bf\3\ud4c5\3\ud4c7\3\ud507\3\ud509\3\ud50c") buf.write("\3\ud50f\3\ud516\3\ud518\3\ud51e\3\ud520\3\ud53b\3\ud53d") buf.write("\3\ud540\3\ud542\3\ud546\3\ud548\3\ud548\3\ud54c\3\ud552") buf.write("\3\ud554\3\ud6a7\3\ud6aa\3\ud6c2\3\ud6c4\3\ud6dc\3\ud6de") buf.write("\3\ud6fc\3\ud6fe\3\ud716\3\ud718\3\ud736\3\ud738\3\ud750") buf.write("\3\ud752\3\ud770\3\ud772\3\ud78a\3\ud78c\3\ud7aa\3\ud7ac") buf.write("\3\ud7c4\3\ud7c6\3\ud7cd\3\ue802\3\ue8c6\3\ue902\3\ue945") buf.write("\3\uee02\3\uee05\3\uee07\3\uee21\3\uee23\3\uee24\3\uee26") buf.write("\3\uee26\3\uee29\3\uee29\3\uee2b\3\uee34\3\uee36\3\uee39") buf.write("\3\uee3b\3\uee3b\3\uee3d\3\uee3d\3\uee44\3\uee44\3\uee49") buf.write("\3\uee49\3\uee4b\3\uee4b\3\uee4d\3\uee4d\3\uee4f\3\uee51") buf.write("\3\uee53\3\uee54\3\uee56\3\uee56\3\uee59\3\uee59\3\uee5b") buf.write("\3\uee5b\3\uee5d\3\uee5d\3\uee5f\3\uee5f\3\uee61\3\uee61") buf.write("\3\uee63\3\uee64\3\uee66\3\uee66\3\uee69\3\uee6c\3\uee6e") buf.write("\3\uee74\3\uee76\3\uee79\3\uee7b\3\uee7e\3\uee80\3\uee80") buf.write("\3\uee82\3\uee8b\3\uee8d\3\uee9d\3\ueea3\3\ueea5\3\ueea7") buf.write("\3\ueeab\3\ueead\3\ueebd\3\2\4\ua6d8\4\ua702\4\ub736\4") buf.write("\ub742\4\ub81f\4\ub822\4\ucea3\4\uceb2\4\uebe2\4\uf802") buf.write("\4\ufa1f\4\u0109\2\u0302\2\u0371\2\u0485\2\u048b\2\u0593") buf.write("\2\u05bf\2\u05c1\2\u05c1\2\u05c3\2\u05c4\2\u05c6\2\u05c7") buf.write("\2\u05c9\2\u05c9\2\u0612\2\u061c\2\u064d\2\u0661\2\u0672") buf.write("\2\u0672\2\u06d8\2\u06de\2\u06e1\2\u06e6\2\u06e9\2\u06ea") buf.write("\2\u06ec\2\u06ef\2\u0713\2\u0713\2\u0732\2\u074c\2\u07a8") buf.write("\2\u07b2\2\u07ed\2\u07f5\2\u0818\2\u081b\2\u081d\2\u0825") buf.write("\2\u0827\2\u0829\2\u082b\2\u082f\2\u085b\2\u085d\2\u08d6") buf.write("\2\u08e3\2\u08e5\2\u0905\2\u093c\2\u093e\2\u0940\2\u0951") buf.write("\2\u0953\2\u0959\2\u0964\2\u0965\2\u0983\2\u0985\2\u09be") buf.write("\2\u09be\2\u09c0\2\u09c6\2\u09c9\2\u09ca\2\u09cd\2\u09cf") buf.write("\2\u09d9\2\u09d9\2\u09e4\2\u09e5\2\u0a03\2\u0a05\2\u0a3e") buf.write("\2\u0a3e\2\u0a40\2\u0a44\2\u0a49\2\u0a4a\2\u0a4d\2\u0a4f") buf.write("\2\u0a53\2\u0a53\2\u0a72\2\u0a73\2\u0a77\2\u0a77\2\u0a83") buf.write("\2\u0a85\2\u0abe\2\u0abe\2\u0ac0\2\u0ac7\2\u0ac9\2\u0acb") buf.write("\2\u0acd\2\u0acf\2\u0ae4\2\u0ae5\2\u0afc\2\u0b01\2\u0b03") buf.write("\2\u0b05\2\u0b3e\2\u0b3e\2\u0b40\2\u0b46\2\u0b49\2\u0b4a") buf.write("\2\u0b4d\2\u0b4f\2\u0b58\2\u0b59\2\u0b64\2\u0b65\2\u0b84") buf.write("\2\u0b84\2\u0bc0\2\u0bc4\2\u0bc8\2\u0bca\2\u0bcc\2\u0bcf") buf.write("\2\u0bd9\2\u0bd9\2\u0c02\2\u0c05\2\u0c40\2\u0c46\2\u0c48") buf.write("\2\u0c4a\2\u0c4c\2\u0c4f\2\u0c57\2\u0c58\2\u0c64\2\u0c65") buf.write("\2\u0c83\2\u0c85\2\u0cbe\2\u0cbe\2\u0cc0\2\u0cc6\2\u0cc8") buf.write("\2\u0cca\2\u0ccc\2\u0ccf\2\u0cd7\2\u0cd8\2\u0ce4\2\u0ce5") buf.write("\2\u0d02\2\u0d05\2\u0d3d\2\u0d3e\2\u0d40\2\u0d46\2\u0d48") buf.write("\2\u0d4a\2\u0d4c\2\u0d4f\2\u0d59\2\u0d59\2\u0d64\2\u0d65") buf.write("\2\u0d84\2\u0d85\2\u0dcc\2\u0dcc\2\u0dd1\2\u0dd6\2\u0dd8") buf.write("\2\u0dd8\2\u0dda\2\u0de1\2\u0df4\2\u0df5\2\u0e33\2\u0e33") buf.write("\2\u0e36\2\u0e3c\2\u0e49\2\u0e50\2\u0eb3\2\u0eb3\2\u0eb6") buf.write("\2\u0ebb\2\u0ebd\2\u0ebe\2\u0eca\2\u0ecf\2\u0f1a\2\u0f1b") buf.write("\2\u0f37\2\u0f37\2\u0f39\2\u0f39\2\u0f3b\2\u0f3b\2\u0f40") buf.write("\2\u0f41\2\u0f73\2\u0f86\2\u0f88\2\u0f89\2\u0f8f\2\u0f99") buf.write("\2\u0f9b\2\u0fbe\2\u0fc8\2\u0fc8\2\u102d\2\u1040\2\u1058") buf.write("\2\u105b\2\u1060\2\u1062\2\u1064\2\u1066\2\u1069\2\u106f") buf.write("\2\u1073\2\u1076\2\u1084\2\u108f\2\u1091\2\u1091\2\u109c") buf.write("\2\u109f\2\u135f\2\u1361\2\u1714\2\u1716\2\u1734\2\u1736") buf.write("\2\u1754\2\u1755\2\u1774\2\u1775\2\u17b6\2\u17d5\2\u17df") buf.write("\2\u17df\2\u180d\2\u180f\2\u1887\2\u1888\2\u18ab\2\u18ab") buf.write("\2\u1922\2\u192d\2\u1932\2\u193d\2\u1a19\2\u1a1d\2\u1a57") buf.write("\2\u1a60\2\u1a62\2\u1a7e\2\u1a81\2\u1a81\2\u1ab2\2\u1ac0") buf.write("\2\u1b02\2\u1b06\2\u1b36\2\u1b46\2\u1b6d\2\u1b75\2\u1b82") buf.write("\2\u1b84\2\u1ba3\2\u1baf\2\u1be8\2\u1bf5\2\u1c26\2\u1c39") buf.write("\2\u1cd2\2\u1cd4\2\u1cd6\2\u1cea\2\u1cef\2\u1cef\2\u1cf4") buf.write("\2\u1cf6\2\u1cf9\2\u1cfb\2\u1dc2\2\u1dfb\2\u1dfd\2\u1e01") buf.write("\2\u20d2\2\u20f2\2\u2cf1\2\u2cf3\2\u2d81\2\u2d81\2\u2de2") buf.write("\2\u2e01\2\u302c\2\u3031\2\u309b\2\u309c\2\ua671\2\ua674") buf.write("\2\ua676\2\ua67f\2\ua6a0\2\ua6a1\2\ua6f2\2\ua6f3\2\ua804") buf.write("\2\ua804\2\ua808\2\ua808\2\ua80d\2\ua80d\2\ua825\2\ua829") buf.write("\2\ua882\2\ua883\2\ua8b6\2\ua8c7\2\ua8e2\2\ua8f3\2\ua928") buf.write("\2\ua92f\2\ua949\2\ua955\2\ua982\2\ua985\2\ua9b5\2\ua9c2") buf.write("\2\ua9e7\2\ua9e7\2\uaa2b\2\uaa38\2\uaa45\2\uaa45\2\uaa4e") buf.write("\2\uaa4f\2\uaa7d\2\uaa7f\2\uaab2\2\uaab2\2\uaab4\2\uaab6") buf.write("\2\uaab9\2\uaaba\2\uaac0\2\uaac1\2\uaac3\2\uaac3\2\uaaed") buf.write("\2\uaaf1\2\uaaf7\2\uaaf8\2\uabe5\2\uabec\2\uabee\2\uabef") buf.write("\2\ufb20\2\ufb20\2\ufe02\2\ufe11\2\ufe22\2\ufe31\2\u01ff") buf.write("\3\u01ff\3\u02e2\3\u02e2\3\u0378\3\u037c\3\u0a03\3\u0a05") buf.write("\3\u0a07\3\u0a08\3\u0a0e\3\u0a11\3\u0a3a\3\u0a3c\3\u0a41") buf.write("\3\u0a41\3\u0ae7\3\u0ae8\3\u1002\3\u1004\3\u103a\3\u1048") buf.write("\3\u1081\3\u1084\3\u10b2\3\u10bc\3\u1102\3\u1104\3\u1129") buf.write("\3\u1136\3\u1175\3\u1175\3\u1182\3\u1184\3\u11b5\3\u11c2") buf.write("\3\u11cc\3\u11ce\3\u122e\3\u1239\3\u1240\3\u1240\3\u12e1") buf.write("\3\u12ec\3\u1302\3\u1305\3\u133e\3\u133e\3\u1340\3\u1346") buf.write("\3\u1349\3\u134a\3\u134d\3\u134f\3\u1359\3\u1359\3\u1364") buf.write("\3\u1365\3\u1368\3\u136e\3\u1372\3\u1376\3\u1437\3\u1448") buf.write("\3\u14b2\3\u14c5\3\u15b1\3\u15b7\3\u15ba\3\u15c2\3\u15de") buf.write("\3\u15df\3\u1632\3\u1642\3\u16ad\3\u16b9\3\u171f\3\u172d") buf.write("\3\u1a03\3\u1a0c\3\u1a35\3\u1a3b\3\u1a3d\3\u1a40\3\u1a49") buf.write("\3\u1a49\3\u1a53\3\u1a5d\3\u1a8c\3\u1a9b\3\u1c31\3\u1c38") buf.write("\3\u1c3a\3\u1c41\3\u1c94\3\u1ca9\3\u1cab\3\u1cb8\3\u1d33") buf.write("\3\u1d38\3\u1d3c\3\u1d3c\3\u1d3e\3\u1d3f\3\u1d41\3\u1d47") buf.write("\3\u1d49\3\u1d49\3\u6af2\3\u6af6\3\u6b32\3\u6b38\3\u6f53") buf.write("\3\u6f80\3\u6f91\3\u6f94\3\ubc9f\3\ubca0\3\ud167\3\ud16b") buf.write("\3\ud16f\3\ud174\3\ud17d\3\ud184\3\ud187\3\ud18d\3\ud1ac") buf.write("\3\ud1af\3\ud244\3\ud246\3\uda02\3\uda38\3\uda3d\3\uda6e") buf.write("\3\uda77\3\uda77\3\uda86\3\uda86\3\uda9d\3\udaa1\3\udaa3") buf.write("\3\udab1\3\ue002\3\ue008\3\ue00a\3\ue01a\3\ue01d\3\ue023") buf.write("\3\ue025\3\ue026\3\ue028\3\ue02c\3\ue8d2\3\ue8d8\3\ue946") buf.write("\3\ue94c\3\u0102\20\u01f1\209\2\62\2;\2\u0662\2\u066b") buf.write("\2\u06f2\2\u06fb\2\u07c2\2\u07cb\2\u0968\2\u0971\2\u09e8") buf.write("\2\u09f1\2\u0a68\2\u0a71\2\u0ae8\2\u0af1\2\u0b68\2\u0b71") buf.write("\2\u0be8\2\u0bf1\2\u0c68\2\u0c71\2\u0ce8\2\u0cf1\2\u0d68") buf.write("\2\u0d71\2\u0de8\2\u0df1\2\u0e52\2\u0e5b\2\u0ed2\2\u0edb") buf.write("\2\u0f22\2\u0f2b\2\u1042\2\u104b\2\u1092\2\u109b\2\u17e2") buf.write("\2\u17eb\2\u1812\2\u181b\2\u1948\2\u1951\2\u19d2\2\u19db") buf.write("\2\u1a82\2\u1a8b\2\u1a92\2\u1a9b\2\u1b52\2\u1b5b\2\u1bb2") buf.write("\2\u1bbb\2\u1c42\2\u1c4b\2\u1c52\2\u1c5b\2\ua622\2\ua62b") buf.write("\2\ua8d2\2\ua8db\2\ua902\2\ua90b\2\ua9d2\2\ua9db\2\ua9f2") buf.write("\2\ua9fb\2\uaa52\2\uaa5b\2\uabf2\2\uabfb\2\uff12\2\uff1b") buf.write("\2\u04a2\3\u04ab\3\u1068\3\u1071\3\u10f2\3\u10fb\3\u1138") buf.write("\3\u1141\3\u11d2\3\u11db\3\u12f2\3\u12fb\3\u1452\3\u145b") buf.write("\3\u14d2\3\u14db\3\u1652\3\u165b\3\u16c2\3\u16cb\3\u1732") buf.write("\3\u173b\3\u18e2\3\u18eb\3\u1c52\3\u1c5b\3\u1d52\3\u1d5b") buf.write("\3\u6a62\3\u6a6b\3\u6b52\3\u6b5b\3\ud7d0\3\ud801\3\ue952") buf.write("\3\ue95b\3\u00da\2\3\3\2\2\2\2\5\3\2\2\2\2\7\3\2\2\2\2") buf.write("\t\3\2\2\2\2\13\3\2\2\2\2\r\3\2\2\2\2\17\3\2\2\2\2\21") buf.write("\3\2\2\2\2\23\3\2\2\2\2\25\3\2\2\2\2\27\3\2\2\2\2\31\3") buf.write("\2\2\2\2\33\3\2\2\2\2\35\3\2\2\2\2\37\3\2\2\2\2!\3\2\2") buf.write("\2\2+\3\2\2\2\3-\3\2\2\2\5/\3\2\2\2\7\61\3\2\2\2\tI\3") buf.write("\2\2\2\13K\3\2\2\2\r}\3\2\2\2\17\177\3\2\2\2\21\u0086") buf.write("\3\2\2\2\23\u0089\3\2\2\2\25\u008e\3\2\2\2\27\u0093\3") buf.write("\2\2\2\31\u009a\3\2\2\2\33\u00a2\3\2\2\2\35\u00a9\3\2") buf.write("\2\2\37\u00ad\3\2\2\2!\u00af\3\2\2\2#\u00b1\3\2\2\2%\u00b8") buf.write("\3\2\2\2\'\u00ba\3\2\2\2)\u00bc\3\2\2\2+\u00bf\3\2\2\2") buf.write("-.\7$\2\2.\4\3\2\2\2/\60\7)\2\2\60\6\3\2\2\2\61\62\7<") buf.write("\2\2\62\b\3\2\2\2\63\66\5\23\n\2\64\65\7/\2\2\65\67\5") buf.write("\33\16\2\66\64\3\2\2\2\678\3\2\2\28\66\3\2\2\289\3\2\2") buf.write("\29J\3\2\2\2:;\5\33\16\2;<\7/\2\2<>\3\2\2\2=:\3\2\2\2") buf.write(">?\3\2\2\2?=\3\2\2\2?@\3\2\2\2@A\3\2\2\2AF\5\23\n\2BC") buf.write("\7/\2\2CE\5\33\16\2DB\3\2\2\2EH\3\2\2\2FD\3\2\2\2FG\3") buf.write("\2\2\2GJ\3\2\2\2HF\3\2\2\2I\63\3\2\2\2I=\3\2\2\2J\n\3") buf.write("\2\2\2KO\5\33\16\2LM\5\'\24\2MN\5\33\16\2NP\3\2\2\2OL") buf.write("\3\2\2\2PQ\3\2\2\2QO\3\2\2\2QR\3\2\2\2R\f\3\2\2\2ST\5") buf.write("\37\20\2TU\t\2\2\2U[\5!\21\2VY\7/\2\2WZ\5\23\n\2XZ\5\25") buf.write("\13\2YW\3\2\2\2YX\3\2\2\2Z\\\3\2\2\2[V\3\2\2\2\\]\3\2") buf.write("\2\2][\3\2\2\2]^\3\2\2\2^~\3\2\2\2_b\5\23\n\2`b\5\25\13") buf.write("\2a_\3\2\2\2a`\3\2\2\2by\3\2\2\2cm\7/\2\2dn\5\23\n\2e") buf.write("j\5\25\13\2fg\7.\2\2gi\5\25\13\2hf\3\2\2\2il\3\2\2\2j") buf.write("h\3\2\2\2jk\3\2\2\2kn\3\2\2\2lj\3\2\2\2md\3\2\2\2me\3") buf.write("\2\2\2nz\3\2\2\2op\5\37\20\2pq\5\25\13\2qv\5!\21\2rs\7") buf.write(".\2\2su\5\25\13\2tr\3\2\2\2ux\3\2\2\2vt\3\2\2\2vw\3\2") buf.write("\2\2wz\3\2\2\2xv\3\2\2\2yc\3\2\2\2yo\3\2\2\2z{\3\2\2\2") buf.write("{y\3\2\2\2{|\3\2\2\2|~\3\2\2\2}S\3\2\2\2}a\3\2\2\2~\16") buf.write("\3\2\2\2\177\u0082\5\23\n\2\u0080\u0081\7)\2\2\u0081\u0083") buf.write("\5\23\n\2\u0082\u0080\3\2\2\2\u0083\u0084\3\2\2\2\u0084") buf.write("\u0082\3\2\2\2\u0084\u0085\3\2\2\2\u0085\20\3\2\2\2\u0086") buf.write("\u0087\7/\2\2\u0087\22\3\2\2\2\u0088\u008a\5#\22\2\u0089") buf.write("\u0088\3\2\2\2\u008a\u008b\3\2\2\2\u008b\u0089\3\2\2\2") buf.write("\u008b\u008c\3\2\2\2\u008c\24\3\2\2\2\u008d\u008f\5%\23") buf.write("\2\u008e\u008d\3\2\2\2\u008f\u0090\3\2\2\2\u0090\u008e") buf.write("\3\2\2\2\u0090\u0091\3\2\2\2\u0091\26\3\2\2\2\u0092\u0094") buf.write("\5#\22\2\u0093\u0092\3\2\2\2\u0094\u0095\3\2\2\2\u0095") buf.write("\u0093\3\2\2\2\u0095\u0096\3\2\2\2\u0096\u0097\3\2\2\2") buf.write("\u0097\u0098\7\60\2\2\u0098\30\3\2\2\2\u0099\u009b\5\33") buf.write("\16\2\u009a\u0099\3\2\2\2\u009b\u009c\3\2\2\2\u009c\u009a") buf.write("\3\2\2\2\u009c\u009d\3\2\2\2\u009d\u009e\3\2\2\2\u009e") buf.write("\u009f\t\3\2\2\u009f\32\3\2\2\2\u00a0\u00a3\5#\22\2\u00a1") buf.write("\u00a3\5%\23\2\u00a2\u00a0\3\2\2\2\u00a2\u00a1\3\2\2\2") buf.write("\u00a3\u00a4\3\2\2\2\u00a4\u00a2\3\2\2\2\u00a4\u00a5\3") buf.write("\2\2\2\u00a5\34\3\2\2\2\u00a6\u00aa\5#\22\2\u00a7\u00aa") buf.write("\5%\23\2\u00a8\u00aa\5)\25\2\u00a9\u00a6\3\2\2\2\u00a9") buf.write("\u00a7\3\2\2\2\u00a9\u00a8\3\2\2\2\u00aa\u00ab\3\2\2\2") buf.write("\u00ab\u00a9\3\2\2\2\u00ab\u00ac\3\2\2\2\u00ac\36\3\2") buf.write("\2\2\u00ad\u00ae\7*\2\2\u00ae \3\2\2\2\u00af\u00b0\7+") buf.write("\2\2\u00b0\"\3\2\2\2\u00b1\u00b5\t\6\2\2\u00b2\u00b4\t") buf.write("\7\2\2\u00b3\u00b2\3\2\2\2\u00b4\u00b7\3\2\2\2\u00b5\u00b3") buf.write("\3\2\2\2\u00b5\u00b6\3\2\2\2\u00b6$\3\2\2\2\u00b7\u00b5") buf.write("\3\2\2\2\u00b8\u00b9\t\b\2\2\u00b9&\3\2\2\2\u00ba\u00bb") buf.write("\4.\61\2\u00bb(\3\2\2\2\u00bc\u00bd\t\4\2\2\u00bd*\3\2") buf.write("\2\2\u00be\u00c0\t\5\2\2\u00bf\u00be\3\2\2\2\u00c0\u00c1") buf.write("\3\2\2\2\u00c1\u00bf\3\2\2\2\u00c1\u00c2\3\2\2\2\u00c2") buf.write("\u00c3\3\2\2\2\u00c3\u00c4\b\26\2\2\u00c4,\3\2\2\2\34") buf.write("\28?FIQY]ajmvy{}\u0084\u008b\u0090\u0095\u009c\u00a2\u00a4") buf.write("\u00a9\u00ab\u00b5\u00c1\3\b\2\2") return buf.getvalue() class ScicopiaLexer(Lexer): atn = ATNDeserializer().deserialize(serializedATN()) decisionsToDFA = [ DFA(ds, i) for i, ds in enumerate(atn.decisionToState) ] T__0 = 1 T__1 = 2 T__2 = 3 DASH = 4 NUM = 5 COMPOUND = 6 APOSTROPHE = 7 NOT = 8 ALPHA = 9 DIGITS = 10 ABBREV = 11 CHARGED = 12 ALPHANUM = 13 STRING = 14 LPAR = 15 RPAR = 16 WHITESPACE = 17 channelNames = [ u"DEFAULT_TOKEN_CHANNEL", u"HIDDEN" ] modeNames = [ "DEFAULT_MODE" ] literalNames = [ "<INVALID>", "'\"'", "'''", "':'", "'-'", "'('", "')'" ] symbolicNames = [ "<INVALID>", "DASH", "NUM", "COMPOUND", "APOSTROPHE", "NOT", "ALPHA", "DIGITS", "ABBREV", "CHARGED", "ALPHANUM", "STRING", "LPAR", "RPAR", "WHITESPACE" ] ruleNames = [ "T__0", "T__1", "T__2", "DASH", "NUM", "COMPOUND", "APOSTROPHE", "NOT", "ALPHA", "DIGITS", "ABBREV", "CHARGED", "ALPHANUM", "STRING", "LPAR", "RPAR", "LETTER", "DIGIT", "PCT", "ASCII", "WHITESPACE" ] grammarFileName = "Scicopia.g4" def __init__(self, input=None, output:TextIO = sys.stdout): super().__init__(input, output) self.checkVersion("4.9.2") self._interp = LexerATNSimulator(self, self.atn, self.decisionsToDFA, PredictionContextCache()) self._actions = None self._predicates = None

68.899263

103

0.61415

6,062

28,042

2.836028

0.307489

0.158213

0.098418

0.025593

0.065437

0.03839

0.020126

0.017973

0.014367

0

0.349965

0.125918

28,042

406

104

69.068966

0.351434

0.001462

0

1

0.853093

0.681894

0.671786

0

1

0.005155

false

0

0.012887

0

0.087629

0

null

0

1

0

1

0

1

null

0

6

bc031334e22f739f970afae8284b1de7a3fbf622

3,772

py

Python

src/models/lla7.py

Neuralwood-Net/face-recognizer-9000

b7804355927540bf07ce70cfe44dac6988a9b8cc

[ "MIT" ]

null

src/models/lla7.py

Neuralwood-Net/face-recognizer-9000

b7804355927540bf07ce70cfe44dac6988a9b8cc

[ "MIT" ]

null

src/models/lla7.py

Neuralwood-Net/face-recognizer-9000

b7804355927540bf07ce70cfe44dac6988a9b8cc

[ "MIT" ]

null

import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim class FleetwoodNet7V1(nn.Module): def __init__( self, num_classes, init_weights=True, ): super().__init__() # Convolutional Layers self.features = nn.Sequential( nn.Conv2d(1, 64, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2, stride=2), nn.Conv2d(64, 128, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2, stride=2), nn.Conv2d(128, 256, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(256, 256, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2, stride=2), ) self.avgpool = nn.AdaptiveAvgPool2d((8, 8)) # Fully-connected classification layers self.classifier = nn.Sequential( nn.Linear(256 * 8 * 8, 4096), nn.ReLU(inplace=True), nn.Dropout(), nn.Linear(4096, 4096), nn.ReLU(inplace=True), nn.Dropout(), nn.Linear(4096, num_classes), ) if init_weights: self._initialize_weights() def forward(self, x): x = self.features(x) x = self.avgpool(x) x = torch.flatten(x, 1) x = self.classifier(x) return x def _initialize_weights(self): for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') if m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.BatchNorm2d): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) elif isinstance(m, nn.Linear): nn.init.normal_(m.weight, 0, 0.01) nn.init.constant_(m.bias, 0) class FleetwoodNet9V2(nn.Module): def __init__( self, num_classes, ): super().__init__() # Convolutional Layers self.features = nn.Sequential( nn.Conv2d(1, 128, kernel_size=3, padding=1), nn.BatchNorm2d(128), nn.ReLU(inplace=True), nn.Conv2d(128, 128, kernel_size=3, padding=1), nn.BatchNorm2d(128), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2, stride=2), nn.Conv2d(128, 256, kernel_size=3, padding=1), nn.BatchNorm2d(256), nn.ReLU(inplace=True), nn.Conv2d(256, 256, kernel_size=3, padding=1), nn.BatchNorm2d(256), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2, stride=2), nn.Conv2d(256, 512, kernel_size=3, padding=1), nn.BatchNorm2d(512), nn.ReLU(inplace=True), nn.Conv2d(512, 512, kernel_size=3, padding=1), nn.BatchNorm2d(512), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2, stride=2), ) self.avgpool = nn.AdaptiveAvgPool2d((8, 8)) # Fully-connected classification layers self.classifier = nn.Sequential( nn.Linear(512 * 8 * 8, 4096), nn.ReLU(inplace=True), nn.Dropout(), nn.Linear(4096, 4096), nn.ReLU(inplace=True), nn.Dropout(), nn.Linear(4096, num_classes), ) def forward(self, x): x = self.features(x) x = self.avgpool(x) x = torch.flatten(x, 1) x = self.classifier(x) return x

31.173554

86

0.526511

452

3,772

4.283186

0.165929

0.082645

0.094008

0.122934

0.805269

0.78874

0.757231

0

0.077456

0.349682

3,772

120

87

31.433333

0.711781

0.031018

0

0.69697

0

0.003014

0

1

0.050505

false

0

0.040404

0

0.131313

0

null

0

1

0

null

0

6

70b4f2f4ae759f100993ad441e0e904b1a1b8cda

202

py

Python

iterm2_images/__init__.py

crowsonkb/iterm2-images

74839760cc8df7c3688c0611f2838251f1f7ce4c

[ "MIT" ]

null

iterm2_images/__init__.py

crowsonkb/iterm2-images

74839760cc8df7c3688c0611f2838251f1f7ce4c

[ "MIT" ]

null

iterm2_images/__init__.py

crowsonkb/iterm2-images

74839760cc8df7c3688c0611f2838251f1f7ce4c

[ "MIT" ]

null

"""Inline images and file transfers for iTerm2.""" from .payloads import FileEsc, ImageLenUnit, ImageDim, ImageEsc __all__ = ['FileEsc', 'ImageLenUnit', 'ImageDim', 'ImageEsc'] __version__ = '0.1.0'

25.25

63

0.722772

23

202

6

0.782609

0.275362

0.391304

0.507246

0

0.022727

0.128713

202

7

64

28.857143

0.761364

0.217822

0

0.263158

0

1

0

false

0

0.333333

0

0.333333

0

1

0

null

1

0

1

0

null

0

1

0

6

cb445ec5444cd991521ff653a8f4d9d4947022a5

23

py

Python

__init__.py

p3lim/pycaddy

06f3f81ac4963ea9f33e4cebfbc26a192797c060

[ "Unlicense" ]

null

__init__.py

p3lim/pycaddy

06f3f81ac4963ea9f33e4cebfbc26a192797c060

[ "Unlicense" ]

null

__init__.py

p3lim/pycaddy

06f3f81ac4963ea9f33e4cebfbc26a192797c060

[ "Unlicense" ]

null

from .pycaddy import *

11.5

22

0.73913

3

23

5.666667

1

0

0.173913

23

1

23

0.894737

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

cbdd353e24127433a693c7b17b59da30a2b8a103

47

py

Python

giant/__init__.py

lixar/giant

fba966e4389b80b38bee1067ad9173adf4eaa5b5

[ "MIT" ]

null

giant/__init__.py

lixar/giant

fba966e4389b80b38bee1067ad9173adf4eaa5b5

[ "MIT" ]

2

2016-05-26T14:40:07.000Z

2017-04-13T21:07:16.000Z

giant/__init__.py

lixar/giant

fba966e4389b80b38bee1067ad9173adf4eaa5b5

[ "MIT" ]

null

#!/usr/bin/env python from .giant import giant

15.666667

24

0.744681

8

47

4.375

0.875

0

0.12766

47

3

24

15.666667

0.853659

0.425532

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

1dc83bf38dea68e5fa59aec50cd42cf81c0bc8e7

115

py

Python

Lab1/lab1_boiko.py

Nickas47/python_savka

31101bba6a7e75bc398136d01e5e0cb9d68df097

[ "Apache-2.0" ]

null

Lab1/lab1_boiko.py

Nickas47/python_savka

31101bba6a7e75bc398136d01e5e0cb9d68df097

[ "Apache-2.0" ]

null

Lab1/lab1_boiko.py

Nickas47/python_savka

31101bba6a7e75bc398136d01e5e0cb9d68df097

[ "Apache-2.0" ]

null

print("'Python': \n Lab1 Mykola Boiko, pm-12343") print('Savka, '*44+'Savka') print(123.8+((11-21.1/2)/(87-32.2)))

28.75

49

0.617391

22

115

3.227273

0.818182

0

0.216981

0.078261

115

3

50

38.333333

0.45283

0

0.452174

0

1

0

true

0

1

0

null

0

1

0

1

0

1

0

null

0

1

0

1

0

6

1dce902408db5ee1414702b0dd430e71f3feee8c

35

py

Python

cytominer_eval/transform/__init__.py

michaelbornholdt/cytominer-eval

97b471dd4141d29bfcb06921cb1e294596c39ecf

[ "BSD-3-Clause" ]

4

2020-06-11T20:31:17.000Z

2021-02-12T04:12:43.000Z

cytominer_eval/transform/__init__.py

michaelbornholdt/cytominer-eval

97b471dd4141d29bfcb06921cb1e294596c39ecf

[ "BSD-3-Clause" ]

46

2020-06-16T11:31:49.000Z

2021-12-07T10:52:00.000Z

cytominer_eval/transform/__init__.py

michaelbornholdt/cytominer-eval

97b471dd4141d29bfcb06921cb1e294596c39ecf

[ "BSD-3-Clause" ]

6

2020-06-11T18:36:31.000Z

2021-04-15T19:38:52.000Z

from .transform import metric_melt

17.5

34

0.857143

5

35

5.8

1

0

0.114286

35

1

35

0.935484

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

1de087e7cf06560132b5a3fa47fcfc082d7d2542

552

py

Python

wrappers/python/tests/non_secrets/test_add_wallet_record.py

sklump/indy-sdk

ee05a89ddf60b42f7483bebf2d89a936e12730df

[ "Apache-2.0" ]

636

2017-05-25T07:45:43.000Z

2022-03-23T22:30:34.000Z

wrappers/python/tests/non_secrets/test_add_wallet_record.py

Nick-1979/indy-sdk

e5f812e14962f0d51cf96f843033754ff841ce30

[ "Apache-2.0" ]

731

2017-05-29T07:15:08.000Z

2022-03-31T07:55:58.000Z

wrappers/python/tests/non_secrets/test_add_wallet_record.py

Nick-1979/indy-sdk

e5f812e14962f0d51cf96f843033754ff841ce30

[ "Apache-2.0" ]

904

2017-05-25T07:45:49.000Z

2022-03-31T07:43:31.000Z

import pytest from indy import error from tests.non_secrets.common import * @pytest.mark.asyncio async def test_add_wallet_record_works(wallet_handle): await non_secrets.add_wallet_record(wallet_handle, type_, id1, value1, tags1) @pytest.mark.asyncio async def test_add_wallet_record_works_for_duplicate(wallet_handle): await non_secrets.add_wallet_record(wallet_handle, type_, id1, value1, tags1) with pytest.raises(error.WalletItemAlreadyExists): await non_secrets.add_wallet_record(wallet_handle, type_, id1, value1, tags1)

32.470588

85

0.811594

79

552

5.316456

0.367089

0.107143

0.178571

0.128571

0.719048

0

0.018405

0.11413

552

16

86

34.5

0.840491

0

0.454545

0

1

0

true

0

0.272727

0

0.272727

0

null

0

1

0

null

0

1

0

6

1df03d6f373e17c7e521ba9e893650c959234f49

124

py

Python

src/mercs/algo/__init__.py

MattiasDC/mercs

466962e254c4f56f4a16a31b1a3d7bd893c8e23e

[ "MIT" ]

11

2020-01-28T16:15:53.000Z

2021-05-20T08:05:42.000Z

src/mercs/algo/__init__.py

MattiasDC/mercs

466962e254c4f56f4a16a31b1a3d7bd893c8e23e

[ "MIT" ]

null

src/mercs/algo/__init__.py

MattiasDC/mercs

466962e254c4f56f4a16a31b1a3d7bd893c8e23e

[ "MIT" ]

4

2020-02-06T09:02:28.000Z

2022-02-14T09:42:04.000Z

from .new_prediction import (mi, mrai, it, rw) from .selection import base_selection_algorithm, random_selection_algorithm

31

75

0.830645

17

124

5.764706

0.705882

0.367347

0

0.104839

124

3

76

41.333333

0.882883

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

1df1820777129248ae5138dd09511cef7f75f054

6,106

py

Python

pymccrgb/tests/test_core.py

stgl/pymccrgb

dc8ad2e46cbe6ff8081c32fa11bce68f869baafa

[ "MIT" ]

3

2020-11-30T12:49:14.000Z

2021-11-12T00:32:32.000Z

pymccrgb/tests/test_core.py

rmsare/pymccrgb

dc8ad2e46cbe6ff8081c32fa11bce68f869baafa

[ "MIT" ]

20

2019-06-18T19:10:00.000Z

2019-11-14T22:55:10.000Z

pymccrgb/tests/test_core.py

rmsare/pymccrgb

dc8ad2e46cbe6ff8081c32fa11bce68f869baafa

[ "MIT" ]

3

2019-06-14T00:39:25.000Z

2019-10-30T14:07:33.000Z

""" Test Python MCC bindings and MCC-RGB algorithm """ import os import pytest import unittest import numpy as np from context import pymccrgb TEST_DATA_DIR = os.path.join(os.path.dirname(__file__), "data") TEST_OUTPUT_DIR = os.path.join(os.path.dirname(__file__), "output") TEST_SCALES = [0.5, 1.0, 1.5] TEST_TOLS = [0.01, 0.05, 0.3, 0.5, 1.0] SEED_VALUE = 42 class MCCTestCase(unittest.TestCase): def setUp(self): self.data = pymccrgb.ioutils.read_las( os.path.join(TEST_DATA_DIR, "points_rgb.laz") ) def _test_classify_ground_mcc(self, scale, tol): test = pymccrgb.core.classify_ground_mcc(self.data, scale, tol) true = np.load( os.path.join(TEST_OUTPUT_DIR, f"classification_mcc_{scale}_{tol}.npy"), allow_pickle=True, ) self.assertSequenceEqual( test.tolist(), true.tolist(), f"MCC ground classification is incorrect for scale {scale} and height tolerance {tol}", ) def test_mcc_classification(self): for scale in TEST_SCALES: for tol in TEST_TOLS: self._test_classify_ground_mcc(scale, tol) def test_mcc_default(self): test_points, test_labels = pymccrgb.core.mcc(self.data, verbose=True) true_points, true_labels = np.load( os.path.join(TEST_OUTPUT_DIR, f"ground_labels_mcc_default.npy"), allow_pickle=True, ) self.assertTrue( np.allclose(test_points, true_points), "Ground points are incorrect for default MCC configuration", ) self.assertSequenceEqual( test_labels.tolist(), true_labels.tolist(), "Classification is incorrect for default MCC configuration", ) def test_mcc_default_las_codes(self): test_points, test_labels = pymccrgb.core.mcc(self.data, verbose=True, use_las_codes=True) true_points, true_labels = np.load( os.path.join(TEST_OUTPUT_DIR, f"ground_labels_mcc_default.npy"), allow_pickle=True, ) self.assertTrue( np.allclose(test_points, true_points), "Ground points are incorrect for default MCC configuration using LAS codes", ) true_labels[true_labels == 0] = 4 true_labels[true_labels == 1] = 2 self.assertSequenceEqual( test_labels.tolist(), true_labels.tolist(), "Classification is incorrect for default MCC configuration using LAS codes", ) class MCCRGBTestCase(unittest.TestCase): def setUp(self): self.data = pymccrgb.ioutils.read_las( os.path.join(TEST_DATA_DIR, "points_rgb.laz") ) def test_mcc_rgb_default(self): test_points, test_labels = pymccrgb.core.mcc_rgb( self.data, seed=SEED_VALUE, verbose=True ) true_points, true_labels = np.load( os.path.join(TEST_OUTPUT_DIR, f"ground_labels_mccrgb_default.npy"), allow_pickle=True, ) self.assertTrue( np.allclose(test_points, true_points), "Ground points are incorrect for default MCC-RGB configuration", ) self.assertSequenceEqual( test_labels.tolist(), true_labels.tolist(), "Classification is incorrect for default MCC-RGB configuration", ) def test_mcc_default_las_codes(self): test_points, test_labels = pymccrgb.core.mcc_rgb(self.data, seed=SEED_VALUE, verbose=True, use_las_codes=True) true_points, true_labels = np.load( os.path.join(TEST_OUTPUT_DIR, f"ground_labels_mccrgb_default.npy"), allow_pickle=True, ) self.assertTrue( np.allclose(test_points, true_points), "Ground points are incorrect for default MCC configuration using LAS codes", ) true_labels[true_labels == 0] = 4 true_labels[true_labels == 1] = 2 self.assertSequenceEqual( test_labels.tolist(), true_labels.tolist(), "Classification is incorrect for default MCC configuration using LAS codes", ) def test_mcc_rgb_default_parallel(self): test_points, test_labels = pymccrgb.core.mcc_rgb( self.data, seed=SEED_VALUE, n_jobs=2, verbose=True ) true_points, true_labels = np.load( os.path.join(TEST_OUTPUT_DIR, f"ground_labels_mccrgb_default.npy"), allow_pickle=True, ) self.assertTrue( np.allclose(test_points, true_points), "Ground points are incorrect for default MCC-RGB configuration with parallelization", ) self.assertSequenceEqual( test_labels.tolist(), true_labels.tolist(), "Classification is incorrect for default MCC-RGB configuration with parallelization", ) def test_mcc_rgb_two_training_tols(self): test_points, test_labels = pymccrgb.core.mcc_rgb( self.data, tols=[1.0, 0.3, 0.3], scales=[0.5, 1.0, 1.5], training_tols=[1.0, 0.3], training_scales=[0.5, 0.5], seed=SEED_VALUE, verbose=True, ) true_points, true_labels = np.load( os.path.join(TEST_OUTPUT_DIR, f"ground_labels_mccrgb_twotols_1.0_0.3.npy"), allow_pickle=True, ) self.assertTrue( np.allclose(test_points, true_points), "Ground points are incorrect for MCC-RGB using training tols 1.0 and 0.3", ) self.assertSequenceEqual( test_labels.tolist(), true_labels.tolist(), "Classification is incorrect for MCC-RGB using training tols 1.0 and 0.3", )

36.130178

99

0.592368

721

6,106

4.775312

0.122053

0.058089

0.031949

0.063898

0.821667

0.803079

0.796108

0.767645

0.754865

0

0.014639

0.317557

6,106

168

100

36.345238

0.811615

0.007534

0

0.513699

0

0.195803

0.038004

0

0.089041

1

0.068493

false

0

0.034247

0

0.116438

0

null

0

1

0

null

0

6

38467ebf6ab127d5ff882dae11a281a4fa48316b

96

py

Python

venv/lib/python3.8/site-packages/aiohttp/web_protocol.py

Retraces/UkraineBot

3d5d7f8aaa58fa0cb8b98733b8808e5dfbdb8b71

[ "MIT" ]

null

venv/lib/python3.8/site-packages/aiohttp/web_protocol.py

Retraces/UkraineBot

3d5d7f8aaa58fa0cb8b98733b8808e5dfbdb8b71

[ "MIT" ]

null

venv/lib/python3.8/site-packages/aiohttp/web_protocol.py

Retraces/UkraineBot

3d5d7f8aaa58fa0cb8b98733b8808e5dfbdb8b71

[ "MIT" ]

null

/home/runner/.cache/pip/pool/da/71/cd/c7183b31ff193fcf1214fc6704d18bf3398f850a63b59ebc553d0f788a

96

0.895833

9

96

9.555556

1

0

0.40625

0

96

1

96

0.489583

0

1

0

null

0

null

0

1

0

null

0

1

0

1

0

1

0

null

1

0

1

0

6

69c7452188bf1ae7d06aa7f27a1dddb0178c7ffb

393

py

Python

metadata_extractor/builders/rdbms/__init__.py

pongthep/apache-atlas-external-tools

7931375cbd9e4aae1d9f9ced9c77af2e4f9640b4

[ "Apache-2.0", "MIT" ]

1

2021-03-25T04:31:22.000Z

metadata_extractor/builders/rdbms/__init__.py

pongthep/apache-atlas-external-tools

7931375cbd9e4aae1d9f9ced9c77af2e4f9640b4

[ "Apache-2.0", "MIT" ]

1

2021-03-25T09:21:08.000Z

2021-03-25T15:29:08.000Z

metadata_extractor/builders/rdbms/__init__.py

pongthep/apache-atlas-external-tools

7931375cbd9e4aae1d9f9ced9c77af2e4f9640b4

[ "Apache-2.0", "MIT" ]

2

2021-03-25T09:17:03.000Z

2021-12-30T09:52:39.000Z

from metadata_extractor.builders.rdbms.mariadb_builder import MaridbBuilder from metadata_extractor.builders.rdbms.mysql_builder import MysqlBuilder from metadata_extractor.builders.rdbms.postgresql_builder import PostgresqlBuilder from metadata_extractor.builders.rdbms.rdbms_builder_abstract import RDBMSBuilder from metadata_extractor.builders.rdbms.redshift_builder import RedshiftBuilder

65.5

82

0.910941

46

393

7.543478

0.369565

0.172911

0.302594

0.417867

0.489914

0

0.050891

393

5

83

78.6

0.930295

0

1

0

true

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

38a703f3eee746762a322da845e67938f8baebe6

77

py

Python

src/__init__.py

lijiancheng0614/house_finder

1fe5dace1b3f0b03b605eac8097d2be9e62ac6e7

[ "Apache-2.0" ]

1

2021-08-09T06:07:27.000Z

src/__init__.py

lijiancheng0614/house_finder

1fe5dace1b3f0b03b605eac8097d2be9e62ac6e7

[ "Apache-2.0" ]

null

src/__init__.py

lijiancheng0614/house_finder

1fe5dace1b3f0b03b605eac8097d2be9e62ac6e7

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # # @Author: lijiancheng0614 # @Date: 2020-03-06 #

12.833333

26

0.558442

9

77

4.777778

1

0

0.209677

0.194805

77

5

27

15.4

0.483871

0.857143

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

1

0

null

0

1

0

6

38e2625e2f4278c8552f07d98a010105a02fcfd7

210

py

Python

pykotor/resource/formats/ltr/__init__.py

NickHugi/PyKotor

cab1089f8a8a135861bef45340203718d39f5e1f

[ "MIT" ]

1

2022-02-21T15:17:28.000Z

pykotor/resource/formats/ltr/__init__.py

NickHugi/PyKotor

cab1089f8a8a135861bef45340203718d39f5e1f

[ "MIT" ]

1

2022-03-12T16:06:23.000Z

pykotor/resource/formats/ltr/__init__.py

NickHugi/PyKotor

cab1089f8a8a135861bef45340203718d39f5e1f

[ "MIT" ]

null

from pykotor.resource.formats.ltr.data import LTR, LTRBlock from pykotor.resource.formats.ltr.auto import load_ltr, write_ltr from pykotor.resource.formats.ltr.io_binary import LTRBinaryReader, LTRBinaryWriter

52.5

83

0.857143

30

210

5.9

0.5

0.186441

0.322034

0.440678

0.491525

0

0.071429

210

3

84

70

0.907692

0

1

0

true

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

2a075f781ec6737f554732b4173499f8744f5660

30

py

Python

test/test_xndtools.py

xnd-project/xndtools

9478f31954091d861ce538ba278f7f888e23d19b

[ "BSD-3-Clause" ]

3

2019-11-12T16:01:26.000Z

2020-06-27T19:27:27.000Z

test/test_xndtools.py

xnd-project/xndtools

9478f31954091d861ce538ba278f7f888e23d19b

[ "BSD-3-Clause" ]

4

2018-04-25T17:12:43.000Z

2018-08-23T18:17:24.000Z

test/test_xndtools.py

xnd-project/xndtools

9478f31954091d861ce538ba278f7f888e23d19b

[ "BSD-3-Clause" ]

6

2018-05-04T08:10:40.000Z

2019-03-19T10:00:21.000Z

import xndtools # noqa: F401

15

29

0.733333

4

30

5.5

1

0

0.125

0.2

30

1

30

0.791667

0.333333

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

2a112d86cfad35bb4d78b8783af49e868b79699a

3,139

py

Python

ckanext/datajson/test-inventory/test_wrappers.py

OpenGov-OpenData/ckanext-datajson

38af8a777052472d15a2a2a728776d0860eec4bb

[ "CC0-1.0" ]

1

2020-05-13T12:20:17.000Z

ckanext/datajson/test-inventory/test_wrappers.py

OpenGov-OpenData/ckanext-datajson

38af8a777052472d15a2a2a728776d0860eec4bb

[ "CC0-1.0" ]

2

2018-12-12T22:01:53.000Z

2022-02-01T19:48:14.000Z

ckanext/datajson/test-inventory/test_wrappers.py

OpenGov-OpenData/ckanext-datajson

38af8a777052472d15a2a2a728776d0860eec4bb

[ "CC0-1.0" ]

1

2018-07-19T18:06:30.000Z

from ckanext.datajson.package2pod import Wrappers class TestCatalogDateWrapper(object): def test_valid_dcat_issued_date(self): Wrappers.pkg = { "title": "Test Dataset", "name": "test-dataset", "metadata_created": "2021-03-26T00:45:51.542432", "metadata_modified": "2021-03-26T00:45:51.542439", "extras": [ { "key": "dcat_issued", "value": "2019-10-17T23:04:32.000Z" }, { "key": "dcat_modified", "value": "2021-03-20T00:14:12.000Z" } ] } Wrappers.current_field_map = { "field": "metadata_created", "wrapper": "get_catalog_date" } issued_date = Wrappers.get_catalog_date(Wrappers.pkg.get('metadata_created')) assert issued_date == "2019-10-17T23:04:32.000Z" def test_valid_dcat_modified_date(self): Wrappers.pkg = { "title": "Test Dataset", "name": "test-dataset", "metadata_created": "2021-03-26T00:45:51.542432", "metadata_modified": "2021-03-26T00:45:51.542439", "extras": [ { "key": "dcat_issued", "value": "2019-10-17T23:04:32.000Z" }, { "key": "dcat_modified", "value": "2021-03-20T00:14:12.000Z" } ] } Wrappers.current_field_map = { "field": "metadata_modified", "wrapper": "get_catalog_date" } modified_date = Wrappers.get_catalog_date(Wrappers.pkg.get('metadata_modified')) assert modified_date == "2021-03-20T00:14:12.000Z" def test_dcat_modified_only_field(self): Wrappers.pkg = { "title": "Test Dataset", "name": "test-dataset", "metadata_created": "2021-03-26T00:45:51.542432", "metadata_modified": "2021-03-26T00:45:51.542439", "extras": [ { "key": "dcat_modified", "value": "2021-03-20T00:14:12.000Z" } ] } Wrappers.current_field_map = { "field": "metadata_modified", "wrapper": "get_catalog_date" } modified_date = Wrappers.get_catalog_date(Wrappers.pkg.get('metadata_modified')) assert modified_date == "2021-03-26T00:45:51.542439" def test_no_dcat_in_extras(self): Wrappers.pkg = { "title": "Test Dataset", "name": "test-dataset", "metadata_created": "2021-03-26T00:45:51.542432", "metadata_modified": "2021-03-26T00:45:51.542439", "extras": [] } Wrappers.current_field_map = { "field": "metadata_modified", "wrapper": "get_catalog_date" } modified_date = Wrappers.get_catalog_date(Wrappers.pkg.get('metadata_modified')) assert modified_date == "2021-03-26T00:45:51.542439"

34.494505

88

0.510354

311

3,139

4.92926

0.167203

0.054795

0.071755

0.084801

0.85062

0.829746

0.798434

0

0.158181

0.355527

3,139

90

89

34.877778

0.599605

0

0.604938

0

0.338643

0.136349

0

0.049383

1

0.049383

false

0

0.012346

0

0.074074

0

null

0

1

0

null

0

6

2a6c9305c342ccaaed4699ef3ed3bdab4e59313d

35

py

Python

tests/tests/base_tests/__init__.py

vselitsky/aws-device-farm-python-android

86182ec2fae531f7376fc4b7261529700d67eb0f

[ "Apache-2.0" ]

23

2018-02-06T02:08:17.000Z

2022-02-02T08:37:51.000Z

tests/tests/base_tests/__init__.py

vselitsky/aws-device-farm-python-android

86182ec2fae531f7376fc4b7261529700d67eb0f

[ "Apache-2.0" ]

1

2018-06-01T02:31:33.000Z

2021-06-26T11:34:10.000Z

tests/tests/base_tests/__init__.py

vselitsky/aws-device-farm-python-android

86182ec2fae531f7376fc4b7261529700d67eb0f

[ "Apache-2.0" ]

19

2018-02-06T02:08:21.000Z

2022-01-28T02:13:32.000Z

from native_test import NativeTest

17.5

34

0.885714

5

35

6

1

0

0.114286

35

1

35

0.967742

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

2ad1448b3e66b7ea90735e9321d3e391bc32c3b9

536

py

Python

app/src/repositories/categoria_produto_repository.py

Leorfk/gelado-api

23c62598a90229d4b9c0f61562ca71de6e8182a7

[ "MIT" ]

null

app/src/repositories/categoria_produto_repository.py

Leorfk/gelado-api

23c62598a90229d4b9c0f61562ca71de6e8182a7

[ "MIT" ]

null

app/src/repositories/categoria_produto_repository.py

Leorfk/gelado-api

23c62598a90229d4b9c0f61562ca71de6e8182a7

[ "MIT" ]

null

from models.categoria_produto_model import Categoria_Produto class CategoriaProdutoRepository: def __init__(self, database: Categoria_Produto) -> None: self.__categoria_produto_model = database def create_categoria_produto(self): pass def delete_categoria_produto(self, id_categoria_produto: int): pass def update_categoria_produto(self): pass def get_all_categoria_produto(self): pass def get_categoria_produto_by_id(self, id_categoria_produto: int): pass

24.363636

69

0.733209

63

536

5.777778

0.349206

0.483516

0.21978

0.197802

0.398352

0.324176

0

0.210821

536

21

70

25.52381

0.86052

0

0.357143

0

1

0.428571

false

0.357143

0.071429

0

0.571429

0

null

1

0

null

0

1

0

1

0

1

0

6

2ae013924af934718ba99f38e4b14992a674ee8b

25

py

Python

core/game/__init__.py

V1ckeyR/snake_snake

b22fed3ce48319bb8badf91f02ff205e7f853e6d

[ "MIT" ]

1

2021-12-26T21:46:06.000Z

arrow/mappers/fields/__init__.py

effordsbeard/arrowstack

33f2eff3be07cf65e38610f0701743e775c1bbc6

[ "MIT" ]

null

arrow/mappers/fields/__init__.py

effordsbeard/arrowstack

33f2eff3be07cf65e38610f0701743e775c1bbc6

[ "MIT" ]

1

2021-12-11T09:11:14.000Z

from .field import Field

12.5

24

0.8

4

25

5

0.75

0

0.16

25

1

25

0.952381

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

2afc89eedac655f72de7cdc4badf3469a55e3218

23

py

Python

gfapy/line/group/path/__init__.py

ujjwalsh/gfapy

891ef3df695f20c67809e5a54549c876d90690b4

[ "ISC" ]

44

2017-03-18T08:08:04.000Z

2021-11-10T16:11:15.000Z

gfapy/line/group/path/__init__.py

ujjwalsh/gfapy

891ef3df695f20c67809e5a54549c876d90690b4

[ "ISC" ]

22

2017-04-04T21:20:31.000Z

2022-03-09T19:05:30.000Z

gfapy/line/group/path/__init__.py

ujjwalsh/gfapy

891ef3df695f20c67809e5a54549c876d90690b4

[ "ISC" ]

5

2017-07-07T02:56:56.000Z

2020-09-30T20:10:49.000Z

from .path import Path

11.5

22

0.782609

4

23

4.5

0.75

0

0.173913

23

1

23

0.947368

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

2dadd7981d130b906b6d2d6a231d3d193fec5cd1

91

py

Python

Chapter10/word_suffix/main.py

adamvm/Mastering-RUST-Second-Edition

ff65aadd16b3857f0ffe13644ac5906e065fd3e9

[ "MIT" ]

102

2018-10-13T04:52:46.000Z

2022-03-25T05:36:44.000Z

Chapter10/word_suffix/main.py

adamvm/Mastering-RUST-Second-Edition

ff65aadd16b3857f0ffe13644ac5906e065fd3e9

[ "MIT" ]

11

2019-07-27T11:35:35.000Z

2022-02-26T12:37:13.000Z

Chapter10/word_suffix/main.py

adamvm/Mastering-RUST-Second-Edition

ff65aadd16b3857f0ffe13644ac5906e065fd3e9

[ "MIT" ]

37

2018-10-13T04:52:31.000Z

2022-03-15T13:12:33.000Z

import word_suffix print(word_suffix.find_words("Baz, Jazz, Mash, Splash, Squash", "sh"))

22.75

70

0.747253

14

91

4.642857

0.857143

0.307692

0

0.098901

91

3

71

30.333333

0.792683

0

0.362637

0

1

0

true

0

0.5

0

0.5

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

2dd15fda2a3f83b568865ac2b5c22d21d936fa77

44

py

Python

Controllers/__init__.py

Bramsnoek/AutomatedKeggBlast

7458f5be224aac96008a6fc327789b152d49b248

[ "MIT" ]

null

Controllers/__init__.py

Bramsnoek/AutomatedKeggBlast

7458f5be224aac96008a6fc327789b152d49b248

[ "MIT" ]

null

Controllers/__init__.py

Bramsnoek/AutomatedKeggBlast

7458f5be224aac96008a6fc327789b152d49b248

[ "MIT" ]

null

from .blastcontroller import BlastController

44

0.909091

4

44

10

0.75

0

0.068182

44

1

44

0.97561

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6